Journal of Workplace Learning, Volume 17 Number 1 2 2005

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ISBN 1-84544-082-X

ISSN 1366-5626

Volume 17 Number 1/2 2005

Journal of

Workplace Learning Exploring the dynamics of the new generation of corporate universities and enterprise academies Guest Editor: Richard Dealtry

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The Journal of Workplace Learning

ISSN 1366-5626 Volume 17 Number 1/2 2005

Exploring the dynamics of the new generation of corporate universities and enterprise academies Guest Editor Richard Dealtry

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Editorial advisory board ___________________________

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Guest editorial ____________________________________

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Communities of Competence: new resources in the workplace Elizabeth A. Smith ______________________________________________

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Curriculum design and management in the Digital Media U: applying the corporate university concept to a business sub-sector Les Selby and David Russell _______________________________________

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The implementation and use of e-learning in the corporate university Allan Macpherson, Gill Homan and Krystal Wilkinson _________________

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A model of values and actions for personal knowledge management Ortrun Zuber-Skerritt ____________________________________________

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CONTENTS

CONTENTS continued

Achieving integrated performance management with the corporate university Richard Dealtry _________________________________________________

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Designing and managing a strategic academic alliance: an Australian university experience Lindsay Ryan and Ross Morriss ___________________________________

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Managing relationships of the republic of science and the kingdom of industry Jorge F.S. Gomes, Pia Hurmelinna, Virgı´lio Amaral and Kirsimarja Blomqvist ___________________________________________

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Organisational intelligence Maurice Yolles__________________________________________________

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An overview of strategic alliances between universities and corporations Dean Elmuti, Michael Abebe and Marco Nicolosi ______________________

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Corporate universities: driving force of knowledge innovation Martijn Rademakers _____________________________________________

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Note from the publisher ____________________________ 137

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EDITORIAL ADVISORY BOARD Associate Professor Stephen Billet School of Vocational, Technology and Arts Education, Griffith University, Brisbane, Australia Associate Professor John Bratton Faculty of Continuing Education, University of Calgary, Canada Professor Per-Erik Ellstro¨m Center for Studies of Humans, Technology and Organization (CMTO), Linko¨ping University, Sweden Professor John Field Division of Academic Innovation and Continuing Education, University of Stirling, Scotland Professor Rod Gerber Faculty of Education, Health and Professional Studies, University of New England, Australia Professor Zahir Irani Department of Information Systems and Computing, Brunel University, UK Professor Annikki Jarvinen Department of Adult Education, University of Tampere, Finland

The Journal of Workplace Learning Vol. 17 No. 1/2, 2005 p. 4 # Emerald Group Publishing Limited 1366-5626

Dr Tauno Kekðle University of Vasa, Finland Dr Colin Lankshear Centro de Estudios sobre la Universidad, UNAM, Mexico Associate Professor Peter Miller School of Social and Workplace Development, Southern Cross University, Australia Dr Peter Sawchuk Ontario Institute for Studies in Education, The University of Toronto, Ontario, Canada Associate Professor Michelle Wallace Southern Cross University Professor Karen Watkins Department of Adult Education, The University of Georgia, Athens, USA Dr Saundra Wall Williams Division of Administration, North Carolina Community College System, North Carolina, USA

Guest editorial About the Guest Editor Richard Dealtry is the Editor of Professional Practice in The Journal of Workplace Learning. He is essentially from a hands-on business background, having held senior executive positions specialising in corporate development and performance improvement. This work involved significant acquisitions of public companies and programmes of internal restructuring. He was seconded to Government as Under Secretary Industrial Adviser for Scotland and subsequently was Regional Development Director advising seven Middle East Gulf States on diversification strategies. Through his company, Intellectual Partnerships, he has specialised in the design and management of integrated performance improvement processes and intervention impact studies, and he works with companies worldwide.

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Exploring the dynamics of the new generation of corporate universities and enterprise academies The mandate given in the Special Issue Call for Papers implied that authors should have experience in theory and best practice development in learning process design and management: The evolution of the corporate university concept and related management practice as an intervention for strategic business development and organisational innovation is generating new theoretical interpretations and inspiring major developments in professional practice. If you have substantiated leading edge thinking on the design, development and future evolution of these management concepts in large companies or in SMEs present your ideas in this special edition of The Journal of Workplace Learning.

The timing of this call for papers was calculated to capture the co-creative developments taking place as the latest generation of corporate universities approach new levels of sophistication in integrated performance management. From an editorial point of view there is always the temptation in reviewing an innovative management process to wait and wait until the perfect finely tuned solution or product appears. And it never does, as some improvement is always possible – “the perfect solution is the enemy of the good solution”. Both the corporate university concept and practice development fall into this evolutionary never-ending strand of management’s dynamics. Every week someone comes up with a new angle of emphasis or a new innovation in practice, redrawing the landscape of personal, organisational or strategic potential through the application of new or amended corporate university or enterprise academy management ideas. The outlook for the emergence of a specific, all-embracing body of common universal knowledge is, therefore, very dim whilst the outlook for a continuously expanding universe of corporate university knowledge is challengingly inevitable and quite often controversial. What we see is filled with good news and bad news as these individual events live in a corporate education environment that is in itself emotionally charged and churning with change in business and organisational practice. The bad news is that there are very few people who have a clear perspective on the strategic models for managing corporate education effectively, and those that do have perspicacious insights often have their progress blocked due to the inertia of

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out-of-date thinking and bureaucratic administrative departments and systems that are there to serve past management paradigms rather than release the much needed radical solutions that are essential for survival in the age of knowledge innovation. We do, therefore, live in a turbulent age where there are major challenges and policy shifts to be addressed around the subject of integrated management training and performance development. For example, some of the main strands of management development such as those employed in the MBA qualification programmes are being seriously questioned, both with regard to their relevance and also the style of the undergraduate channels of business education that lead to it. These questions relate to both the nature of the learning process itself and the content of the curriculum. There are also serious differences in quality between the good and bad accreditation standards being applied in management education worldwide. There is a critical exposure around the subject of corporate and business education that has not previously been encountered. There is good news, however, and that is that the forward-thinking organisations that invest in corporate education are not content to simply delegate the development task to external providers in the hope that they will get the talented managers they deserve. They want to be involved in formulating the processes of management education in particular and want to see real and tangible benefits. They want much more involvement in the design and ownership of the management development process and want to see payback on their investments in every respect, in the form of organisational effectiveness and business development as well as a real growth in the potential of their cadre of talented managers. And they do not want second-best providers in a globally competitive world! At the same time, those people who participate in management development programmes are becoming more discerning about how they spend their much valued learning time. They know that they have a lifetime of new learning in front of them if they are to be successful, and they want to see direct career-enhancing potential in all their related learning endeavours. It is of considerable interest to see how the authors have managed their developmental projects in these conditions. This Special Issue on the new generation corporate university thinking is an attempt to use a “Managerial Hubble Telescope” and apply its lenses to probe the galaxy of bright new learning innovations, to get first-hand insights into the expanding regime of learning processes and new strategic learning relationships that are taking place around the world. In Issue 1 of this volume, the article authors are the stars that bring new light to some of the key aspects of the subject. In Issue 2, the authors focus more on the strategic relationship issues – those interactive processes between the corporate world of education and institutional providers that are bringing together concepts of business excellence and academic rigour and in new powerful ways. My own contribution in Issue 1 is part reflective and part bridging between Issues 1 and 2, drawing upon some previous experience and writing to provide context for that. The results are articles that make an interim statement of considerable quality about the emerging corporate education mosaic, one that captures each of the authors’ own learning in managing developmental learning. The authors have through their participation made a significant contribution to the creation of that inspirational hub of knowledge that is in itself the corporate university. Richard Dealtry

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Communities of Competence: new resources in the workplace Elizabeth A. Smith

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CRG Medical Foundation for Patient Safety, Houston, Texas, USA Abstract Purpose – This paper examines the ways social networks, socialization, self-organizing systems, and systems thinking have influenced the gradual evolution of communities of practice into Communities of Competence. Design/methodology/approach – Proposes a Community of Competence as a new framework and a methodology to describe, assess, and combine separate strengths and core competencies of individuals, groups, and organizations into a meaningful, goal-oriented whole. Findings – Decisions to assign people to work groups are seldom based on current, valid, reliable assessments of skills, abilities, and knowledge, or overall competence. Productivity, job satisfaction, and work quality can be improved when competencies and job requirements are closely matched to maximize job fit. Individual selection criteria, like self-efficacy, achievement motivation, and emotional intelligence, and group process variables, like workload sharing, can enhance job placement. Members of a flexible, cohesive, goal-driven Community of Competence will likely make better use of their unique and shared competencies, tacit and explicit knowledge, and experience in more effective and efficient ways than traditional forms of groups. Originality/value – Helps in understanding that Communities of Competence, corporate universities, and enterprise academies, as dynamic learning organizations, are positive forces driving systemic organisational change. Keywords Competences, Jobs, Knowledge management, Productivity rate, Quality Paper type Conceptual paper

Introduction In today’s increasingly competitive, profit-centered, electronically based, globally networked world, success means making the best possible use of human and material resources. Reportedly, people function at about half of their true capacity. Possible causes of poor performance could be incorrect job placement, inadequate training, or being placed in a job outside one’s areas of competence. The resulting poor “job fit” wastes valuable human talent and increases the cost of doing business. People who know that their performance level is below standard become dissatisfied with themselves and their jobs. However, when they know their level of competence, they will worry less about what they do not know, and concentrate on learning what they need to know to meet specific job requirements and performance standards. Few managers or leaders have ready access to reliable, valid, documented assessments of their employees’ overall talents and specific competencies. Managers may not know how to restructure work groups to improve ways to use each member’s competencies. Even if complete work histories or performance evaluations are available, many managers lack the experience or training to make informed decisions on initial job placement and on job assignments. Their knowledge of human factors

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and of ways to evaluate and improve work processes in disparate systems may be limited. The concept of “Communities of Competence” (C of Cs) developed by the author is a further extension of communities of practice. It is proposed as a new framework and methodology to describe, assess, and combine separate strengths and core competencies of individuals, groups, and organizations into a meaningful, goal-oriented whole. A major use of C of Cs is to help reduce the gap between what is required at work and what workers know they are competent to do. Job fit can be enhanced by closely matching a person’s competence levels in various work and knowledge areas with specific job requirements. When individuals and groups are assigned to work tasks that each member is capable of doing, tasks can be focused and streamlined to make goals easier to attain. These assignments foster collaborative relationships and result in a disciplined exchange of information and knowledge. Consequently, overall efficiency (do things right), effectiveness (do the right thing), and job satisfaction will be affected in a positive manner. For example, due to close job fit and competence-based assignments, members of a flexible, cohesive, goal-driven C of C can further maximize their unique and shared competencies by applying their tacit (know how) and explicit (know what) knowledge. Members of C of Cs are predicted to outperform other traditional forms of groups by producing higher quality products, being innovative, and completing work projects more quickly. Specifically, use of individualized selection criteria, like self-efficacy, achievement motivation, and emotional intelligence, and group process variables, like workload sharing, enhance the quality of the job placement process. Systems thinking can be used to view, assess, and monitor the skills, abilities, and knowledge, or overall competencies of the workforce. When the entire flow of work processes, from input to outcomes, is monitored closely, changes and improvements made in the entire system optimize the use of human and material resources. A corporate university (CU) and an enterprise academy (EA), like a C of C, differ in vision, strategy, structure, and size, and in many other ways. Each has the same purpose – to maximize strategic fit and strategic alignment to match resources, expertise, and competence with business opportunities and environmental threats. The construction and purpose of these three types of dynamic learning organizations makes them catalysts for systemic change. To illustrate, one innovative person working in the smallest sub-system can be a champion for change and “make things different”. Bringing about change and improvement in work processes, services, and products is everyone’s responsibility. Purpose A major purpose is to describe how driving forces in sociology, socialization, self-organizing systems, and system thinking fostered the author’s creation of a new framework and a methodology to describe, assess, and combine separate strengths and core competencies of individuals, groups, and organizations into a meaningful goal-oriented whole. The resulting concept of C of C meets the worker’s strong need to experience a feeling of “community” or belonging at work. These competency-based communities are a new type of work group that is more dynamic and flexible than high-performance teams or autonomous work groups. C of C members are more skilled

at meeting the steadily growing demands to increase productivity and innovation despite work-related stress, economic cutbacks, and growing marketplace competition. A second purpose is to provide selection criteria that leaders, managers, and group members can use to assign individuals to groups in order to streamline work processes to increase collaboration among members, improve productivity and business competitiveness, and enhance quality of outcomes. A third purpose is to present advantages and practical applications of working in C of Cs. The roles of a CU and an EA, as active catalysts for change, are discussed. Evolution of Communities of Competence Communities of practice, sociology, socialization, self-organizing systems, systems approaches, and systems thinking play unique, overlapping roles in gradually reshaping work groups into Communities of Competence. Communities of practice Communities of practice are in a constant state of evolution. Ancient man formed communities of practice to protect himself from danger and to search for food. In classical Greece, artisans, craftsman, and masons formed “corporations”, or communities of practice, to share expertise (Wenger and Snyder, 2000) and work on a common mission. To illustrate, the 100 worldwide communities of practice at the World Bank are in the continual process of linking together to expand and improve the quality of their knowledge base (Pascarella, 1997). Some communities of practice whose members apply their state-of-the art skills, abilities, and knowledge to come up to speed quickly have evolved into Communities of Competence. Competence, as human capital and strategic readiness, is shown when employees have the right type and level of skills, abilities, knowledge, experience, and proven competence to perform key internal processes or complete a project on time and under budget. Members may come from similar communities, or may be newly hired, loaned, or shared by other communities. Some members move their intact community from job to job or from one organization to another, depending on the availability of work. Talented people whose expertise is in high demand move between communities on an as-needed basis. Sociology and socialization Man has always lived and worked in some type of social network. The forces driving the formation of groups range from safety in primitive, hostile environments to socialization at work. The unique roles sociology (Homans, 1950), social learning theory (Bandura, 1986), and socialization (Hackman, 1992) play in group formation and productivity are rarely considered. Sociology focuses on interpersonal relationships, social networks, task assignment, and the flow and form of communication. Individuals derive information, knowledge, and inspiration from their various social and work-related networks of peers. Members who fail to make the transition from being an outsider to being accepted as active participants may never become effective team members. The need for socialization, or belonging to a valued group, is the second highest need in Maslow’s (1943) five-level need hierarchy.

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Socialization forces may cause members to become alike (Hackman, 1992) and increase group cohesion by reducing the variability of members who work together. Cohesion, or liking for members of one’s group, plays a positive role in group maintenance, satisfaction, and productivity. Self-organizing systems Self-organization is the heart of complexity science (Holland, 1998; Prigogine, 1980; Zimmerman et al., 1998). Self-organizing systems (von Bertalanffy, 1968; Wilson, 1971; Prigogine and Stengers, 1984; Holland, 1995, 1998; Plsek, 2001) and Communities of Competence (Dotan, 2002; Smith, 2006) share similar properties. Both demonstrate disequilibrium, are consistent with themselves, and have the freedom to grow and evolve. Self-organizing systems in an unsteady state use their energy to reconfigure themselves to handle new information. To reconfigure, the system must remain consistent with itself and its past, thereby allowing for creativity and boundaries, for evolution and coherence, and for determinism and free will (Wheatley, 1992). Properties of self-organizing systems useful in designing a template to create, manage, and understand Communities of Competence (Plsek, 2001) are: . adaptive elements – elements of the system can change themselves, primarily through learning; . simple rules – complex outcomes can emerge from a few simple rules that are applied locally, but used universally; . non-linearity – small changes can have large effects; . emergent behavior and novelty – continual creativity is a natural state of the system; . not predictable in detail – forecasting is basically an inexact, bounded art (exhibit-bounded predictability); . inherent order – systems can be orderly without central control; . context and embeddedness – systems exist within systems and each part can be studied independently: context, or how all separate parts fit together, is important; . co-evolution – a system moves forward through constant tension, uncertainty, paradox, and anxiety, toward balance: disequilibrium, an unsteady state, disrupts the status quo, but is healthy; . subsystems exist within larger systems; and . actions are interconnected such that one member’s actions change the context for other members. Systems approaches and systems thinking A “system” is defined as the merging of parts, interconnections, and purposes (von Bertalanffy, 1968; Capra, 1996), and a set of components that work together for the objective of the whole (Haines, 1998). The real power of systems is in the way separate parts, or subsystems, come together and are interconnected to fulfill some purpose (Plsek, 2001). Systems thinking originated in biology and in common laws governing living systems in nature. Likert (1967) pioneered the use of systems thinking in business.

This holistic view, which is rapidly penetrating linear thinking, enables us to see a unit as a whole first and then see how separate parts fit together and how each relates to the other. Feedback loops connect each part of the system with every other part of the system. When the direction and content of feedback are identified, the information obtained can be used to change and improve the system. An example of a complex adaptive system would be the multi-skilled members of a C of C working together on a project. Two major subsytems affect performance: (1) external conditions, like available technology and physical environment that may influence group performance; and (2) the internal environment, or socio-technical aspects of group behavior, like how well people get along and work together (Homans, 1950). Communities of Competence Environmental forces and business trends and fads driving the formation of virtual and boundaryless groups also affect the formation of all types of organizations. Globalization and marketplace competition are inevitable. Complexity, due to advances in technology and acquisition and application of rapidly expanding knowledge bases, supports the creation of a new form of collaborative work group. Major driving forces that can be generalized to C of Cs are: . globalization – companies must operate in countries that differ in culture, professional levels of accomplishment, geography, etc., and operational and leadership structures; . complexity – work groups, products, and services are increasingly complex and pull together people with specific expertise to pool resources to solve problems and develop strategies: co-location is rarely an option; and . growing sophistication of economics – developing and “second tier” countries, like Korea, now have sophisticated manufacturing capabilities. Organizations are becoming increasingly virtual and boundaryless. Much work is done by face-to-face groups and virtual groups distributed across space, time, and/or organizational boundaries. Teams are the most prevalent form of business collaborations (Beyerlein et al., 2003). Self-directed teams, high-performance teams and matrix groups, for instance, require less supervision, as they self-initiate. Also, team members decide how to improve their own work processes. These groups may have traditional leaders who perform established functions, like planning, directing, supervising, and controlling their groups’ activities. Due to the rapid growth of knowledge, few leaders will remain content experts for long. Younger, more experienced members will challenge them. To stay successful, leaders must rely on and solicit the collective competencies of their group members. Traditional leaders, both skilled and experienced at performing standard management functions, may be unable to handle adequately the opportunities and challenges, like facilitating team processes, improving interpersonal relationships, and applying systems thinking. These traditional leaders will have a hard time managing and motivating members of these advanced forms of collaborative, competence-based work systems. Competent individuals who work in a supportive organization culture know what they need to do to perform at the required level and meet deadlines. By its very nature,

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a C of C is made up of competent, goal-driven people who take full responsibility and ownership of their work. Table I compares members of regular work groups and members of C of Cs on specific work-related variables (Smith, 2006). The major ways that these two groups differ are in size, focus, level of work performed, specialization, leadership, values, and synergy. The following descriptions apply to C of Cs. Membership is very selective and incorporates unique competencies. Group size ranges from several key members to thousands of virtual members linked worldwide to work on problems and projects. Focus is intense and entirely goal-directed. The level of work is highly specialized. Leaders facilitate communication, mentor, teach, motivate, and keep their group members inspired. Leaders rotate through various positions and lead only when their expertise is required. Values are consistent with values established in the various work projects. Synergy within the team is created through learning and sharing tacit and explicit knowledge. Work itself, peer recognition, learning from others, and belonging to a cohesive group become strong intrinsic or non-monetary motivators (Smith, 2001). Group members often create their own non-monetary motivators, namely opportunity to mentor or acquire a new skill. Members of C of Cs apply their tacit and explicit knowledge, ideas, creativity, work experience, and “best work processes and practices” from previous communities to their current community. Most members are continually exposed to new problems, challenges, and to new groups and individuals that use an array of work procedures and processes to achieve both team and organisational goals. These cohesive, tightly knit communities foster and reward flexibility and innovation. Combining the mental and physical energy of competent members creates an intellectual catalyst that promotes further achievement and continual learning. Due to synergy, the sum of the group’s achievements exceeds the sum of each individual’s accomplishments. A health-care Community of Competence The goals of an existing health care patient safety C of C are to build a user-friendly, cost-effective, reliable, web-based system to collect, classify, and analyze medical adverse events to determine specific problems and system flaws that cause or contribute to adverse experiences and incidents. For example, a person who is prescribed the wrong medication and takes it may have a life-threatening drug interaction. This adverse event could have been prevented if a system was in place to monitor potential risks, devise action plans, provide early warnings of problems, and prevent reoccurrence of unfortunate events (Dotan, 2002; see also www.communityofcompetence.com[1]). The patient is the center of the healthcare team. The patient’s family provides invaluable support and care. The practice of patient-centered medicine dates back to Sir William Osler, a Canadian born in 1849. He was one of the most esteemed and distinguished physicians in the history of medicine. He was Regius Professor of Medicine at Oxford University from 1905 until his death in 1919. The strengths and competencies of the seven members of the central core surrounding the patient are: patient safety, risk analysis, human factors, information technology, interface between technology and people, training, and compliance with government and accrediting standards. The patient and any of the seven members

When

Accountability Values

Leadership

Duration

How Methods and tools

Size of group

Type of group

Membership selection

Who Membership

Community of Competence

During standard working hours

Any time: 24 hours a day, seven days a week (continued)

Traditional work methods, networking face-to-face, Synergies created due to learning, sharing, electronic, multi-media using existing tools and established redesign/reinvention based on free flow of information, like work methods self-organizing systems. Often reinvent or redesign existing methods, or create state-of-the-art tools to fit needs Use iterative methods, self-learning systems and others to improve individual and group accomplishments Specific roles in serial projects, as needed by the Only when special talents are needed and may cycle in and organization out, as needed The person assigned to lead the group Anyone who has the competence at a time when specific levels of competence or expertise are needed To group/organizational leaders To coordinator outside the community Conform with organizational culture Demonstrate strong values that are consistent with work project, not necessarily where members work

Primary focus of members is on solving specific problems or achieving common goals Work for organization, hired to perform specific jobs Selected on known current competence or on potential to demonstrate competence Set by organization or intact work group. Could be a team, Flexible, highly structured, depending on need. cross-functional group or high-performance team Membership rotates due to work assignments. Highly collaborative Two to 100 people, depending on task Two to thousands of people in standard or virtual groups

Members of groups assigned to do a specific job

Regular work groups

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Table I. Comparison of regular work groups and members of a Community of Competence on specific work-related variables

Table I. Ranges from low to high, depending on economics, task, and leadership

Stability

As assigned or meet group and organizational goals

Goals

Note: Reprinted with permission from Smith (2006)

Complete the current project or as assigned

What Strategies

Where Location

Driving force

Inside the organization, or connected to joint ventures, partners, etc.

As required by organization’s structure. Rarely volunteer information Used as needed to meet goals As designed by organization’s verbal, standard and electronic communication networks Profit motive or meet goals in a specific period of time

Share information

Power Communication

Solve assigned problems using available resources

Focus

As required by the organization to stay in business

Use all expertise for needed time. Achieve goal(s) for which group was formed Achieve specific goal for which group was formed

Inside the organization, but may be connected electronically anywhere in the world – 24 hours a day, seven days a week

Develop state-of-the art solutions that grow and continue to evolve in unexpected ways Intense effort to identify and bring all needed resources from numerous sources to create innovative products and processes Use all channels and networks to exchange explicit and tacit knowledge on a continuous, spontaneous basis Coordination and sharing drive all efforts Anywhere, all channels and all types of verbal, print, and electronic networks Mental and physical energy of members serves as intellectual catalyst to meet goals High and intense while working on project

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Why

Regular work groups

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may work face-to-face or in virtual groups and use a wide range of media and methods to educate and communicate. Members coordinate work they do in their domain(s) and in their community. They report progress to group members and to associates on a regular basis. Tasks are assigned based on proven competencies. Cross-disciplinary knowledge sharing and problem solving occur within our core group and with other C of Cs. Progress, questions, and plans are posted electronically to obtain real-time solutions and to share applications. In the formative stages of our community, problems first considered unique were later discovered to have a common solution. Three years after our health care community and supporting communities were formed, the seven core members have assessed and used resources of members of other local and worldwide communities. Supporting infrastructures are in a continuous state of development. Much effort is currently directed to creating the awareness of the need to foster, document, and reward any small, incremental improvements in the ways work is done. Ultimately, networked health care C of Cs will have thousands of knowledge experts who create and share resources through the socialization processes of face-to-face contact, and through the capture, dissemination, internalization and sharing of tacit and explicit knowledge. If factors directly affecting patient safety and patient-centeredness are to be integrated into health information technology systems, small cultural shifts must lead to major cultural changes in the attitudes, thinking, and behavior of current health care professionals. Patients, their families, and regulatory and compliance agencies must also accept the need to support and expand the practice of patient-centered medicine. Communication, cooperation, coordination, knowledge sharing, and reducing resistance to change are prime concerns. Motivating people to use collaborative technology to collect, document, retrieve, and share their ideas and knowledge requires a new mindset, training, and a supportive infrastructure. This monumental task must begin by educating current and future patients of all ages to ask questions and challenge health care professionals. Factors in selection and placement of people to Communities of Competence Major factors known to affect productivity are seldom used to select and form work groups. Tasks are rarely assigned based on proven individual and group competencies. Prime factors affecting group and individual job satisfaction and productivity are self-efficacy, achievement motivation, productivity, and emotional intelligence. Self-efficacy People who know they have the ability to do a job or reach a specific performance level demonstrate self-efficacy, confidence, or competence. They use their cognitive resources and strategies to exert control over daily events in their lives. Direct experience gained from doing similar tasks and observing others perform increases levels of self-efficacy. There is a positive correlation between self-efficacy and leadership (Bandura, 1997). To illustrate, in difficult situations leaders high in self-efficacy increase their efforts and motivation and those low in self-efficacy reduce their efforts (Robbins, 2003). Encouraging people to see themselves and their abilities in a more positive light can raise their level of self-efficacy.

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Achievement motivation and productivity Achievement motivation (McClelland et al., 1953) and the drive or desire for competence or mastery (White, 1959) form the heart of C of Cs. Galbraith (2000) uses competence, affiliation, and confidence to portray individuals as having and applying their unique talents or capabilities. The motivating power of peer group approval tends to raise job performance and quality, and also create conditions under which conformity or adherence to group standards and norms is increased. Characteristics of groups and teams that demonstrate work group effectiveness, as measured by productivity, satisfaction, and manager judgments, can be generalized to C of Cs (Campion et al., 1993): . job design – self-management, participation, task variety, task significance, and task identity; . interdependence of tasks, goals, feedback and rewards – autonomous or self-managed work groups are participative and can restructure their work so members can work separately or together; . composition – heterogeneity, flexibility, relative size, and preference for group work; . context – training, managerial support, communication, and cooperation between groups; and . process – potency, social support, workload sharing, and communication within groups. Emotional intelligence Emotional intelligence (EQ) is a cluster of skills relating to the emotional side of life. EQ assesses feelings, intuition, relationship skills, motivation, and integrity and personal management and social skills people need to successfully manage themselves and manage their relationships with others (Goleman, 2000, 2004): . Level of EQ helps distinguish star performers from low-level performers (Druskat and Wolff, 2001) and is a prime indicator of personal and professional success (Warner, 2001). Individuals high in EQ are intellectually and emotionally astute in specific situations and in their interrelationships. They are positive influences on their peers, their work groups, and their organisation because they “tune in” or adjust their behavior to fit what they know and perceive. . Pencil-and-paper measures of self-assessed EQ are beginning to be used in job screening, selection, and placement. High EQ, positive attitudes, a strong sense of loyalty, resilience, and trustworthiness add to each individual’s accomplishments and satisfaction levels, for instance. People who have relatively high levels of EQ will be positive influences. Those low in EQ rarely get along well with others and may be unproductive.

Member selection methods Inefficient, poorly planned methods must be replaced by carefully designed methods to improve job fit by maximizing use of individual and group competencies and following

successful management techniques. Many managers forget that the person doing the job knows more about it than anyone else. If asked, workers will tell their managers how to reduce performance gaps and how to increase job satisfaction, efficiency, and effectiveness. The following methods and general guidelines used to gather information, assess competence, and apply information and knowledge apply to numerous types of work groups, not just to C of C members. Gather information .

.

.

Obtain resumes, supervisor and peer evaluations of performance, examples of work, and other job-related information from reliable sources. Examples are performance appraisals, self-assessment, supervisors’ recommendations, and information from human resource departments and company intranets. Incorporate guidelines and definitions from the major profession or discipline of each worker or team member into a taxonomy of key terms. Operationally define each term using clear, precise words and illustrations that indicate how and when each term is to be use and evaluated (Smith, 1997). Identify complementary and conflicting behavioral patterns of people, such as how they deal with different types of information, make decisions, and handle team assignments. The Meyers-Briggs Type Indicatorw is a reliable, valid measure of personality and behavior that has been used successfully for over 50 years.

Assess competence .

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Use job descriptions, job analyses, or other comparable sources to identify and assess specific competencies needed to do each major task or strategic job at or above the expected level. Examine and document each step of major work processes used to perform the job at an acceptable level. Also use job profiling (Kaplan and Norton, 2004). Incorporate feedback from people already doing each job. Develop an overall competency profile for each individual, maintain it, and monitor it at least every two months. Each person should also create a competency profile to maintain, monitor, and document how current competencies are used and how newly acquired competencies will be used. The two profiles can be compared to determine changes, improvements, or deficiencies, particularly at performance appraisal time. Create a leadership competence model for each major leadership position (Kaplan and Norton, 2004). Use job profiles, peer and supervisor recommendations, self-assessment, and other methods and sources to create this model. Group each major job or task into a separate, strategic job family and operationally define key words. A job family is a group of tasks requiring similar knowledge, skills, abilities, and experience. To perform adequately in a communications job family, the person must reach specified levels of competence

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in speaking, listening, clarifying, reflecting, writing, and in other areas vital to job success.

Change words into action: apply information and knowledge

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Discuss with each team member how each of the above steps applies to them, to their group, and to tasks or jobs performed. Ask for their input, and verify how tasks can be changed or assigned to improve job fit. Use feedback on performance to inform workers how their job fits into the overall efforts of their group and organization. Only then will they be more receptive to work and workplace changes. Use input from team members to jointly develop realistic standards or benchmarks for competence and performance. Gather and use information from other comparable groups to make comparisons across disciplines, organizations, CUs, EAs, and C of Cs.

Advantages of creating and working in Communities of Competence .

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The principles, selection criteria, work methods of C of Cs, and collaborative technology, like instant messaging, can help streamline work processes. To restate, productivity improves when there is a close person-job fit. Many concerns and problems arise at the intersection of social systems and technology. Changing technology is easier than changing people. The social system and the people in it must be considered first before changing their technology (Guzzo and Shea, 1992). It is imperative to solicit and heed the input of users of technology before making any changes. Working at one’s level of competence, acquiring new knowledge, and having autonomy, like flexible working hours, are high-level intrinsic motivators. Job satisfaction will be high when group members do challenging, rewarding work in their core areas of expertise, and are recognized by their peers. Resulting increases in job satisfaction and productivity help lower cycle time and cost. Members selected based on their demonstrated competencies can be assigned to groups in which they can share and apply their individual and collective tacit and explicit knowledge. A good idea used only once is a tremendous waste. Tacit knowledge is a vast untapped, poorly documented resource. Metadata, a new source of valuable information, comes from real-time e-mail networking. It can expand existing electronic communication networks and transform information into knowledge for immediate application. Data mining methods that use natural language processing and machine-readable narratives can collect, extract, manipulate, and summarize pertinent information from large databases. C of Cs, as learning organizations of varying sizes, are flexible, dynamic and quick to respond to their changing environments. Their organizational structure enables members to readily acquire and apply new knowledge on a continual

basis. The opportunity to function at a high level of competence is a strong intrinsic motivator. Their highly motivated, knowledgeable members can become recognized leaders in their respective fields, and become champions for change.

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Structure and function of Communities of Competence, corporate universities and enterprise academies The roles of a corporate university (CU), an enterprise academy (EA), and a C of C are similar. A CU is “a process for total developmental integration – a totally inclusive people, learning and business and process idea” (Dealtry, 2000a). A CU aligns or matches its resources, expertise, and competencies with business opportunities and threats in its environment in order to maximize its “strategic fit” and strategic alignment (Dealtry, 2000a). CUs have a dynamic time frame, exhibit multivariate relationships, use specific organizational strategy, and adjust to environmental factors and organisational contingencies. Their “fit” is relatively unique, depending on differences in contingencies (Zajac et al., 2000). These three types of learning organizations demonstrate systems thinking, exist in dynamic equilibrium, and have similar overriding frameworks, support systems, and abilities to solve problems unique to their respective key business processes and strategic intent. Each has unique goals and strategies for continual learning and uses similar methods to maintain competence. All use cross-disciplinary, cross-function communication, and information sharing as major ways to raise professional and organizational competence and more closely align what is learned with the strategic intent of the business. Dealtry (2000a) stresses the importance of building a resource of expertise to manage continuous and fluid real time learning processes. The author believes these three communities meet the need “to raise internal and external partnership platforms of intellectual development that combine with the development of a much enhanced value culture around knowledge and learning in organizisations” (Dealtry, 2000a, p. 222).

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Communities of Competence, corporate universities and enterprise academies as catalysts for change The positive attributes of C of Cs can be used to streamline work processes to improve bottom-line results. These communities, as day-to-day work process-based catalysts for change, can break down barriers that previously prevented the open sharing of knowledge and expertise: . The concept of a Community of Competence, which implies competence, excellence, accomplishment, and teamwork, may help alter how management and employees currently view C of Cs, CUs and EAs. Specifically, as self-managed, independent, multi-disciplinary, cross-disciplinary, cross-functional teams, C of Cs permeate real and virtual organizations. Members of these collaborative work groups can encourage others to redirect their thought processes and behaviors to include thinking, acting, behaving, and evaluating in terms of competence. Also, top management must begin to recognize the potential CUs and EAs have in developing and supporting partnerships with customers to create profitable, innovative services and products.

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C of C members who serve, in turn, as role models, mentors, and teachers, are also active catalysts for change. When C of C are evaluated using Revans’s (1982) criteria, they have a high rate of change and also require a high degree of behavioral change. The assumption is that these communities have low organisational structure, are dynamic, and change focus and direction rapidly, depending on need. They are mini-learning organisations and mini-organisational brains that may be networked together to solve problems. In all three forms of groups, learning interventions, like mentoring and job enrichment, are imbedded or built into daily work process. Business outcomes are improved when an organization’s work processes are aligning with, or linked directly to acceptable performance levels of individuals and groups. Creating collaborative organizations, namely C of Cs, CUs, and EA, is a long-term effort. In most organizations, pockets of excellent collaboration exists in teams, customer groups, or within large project endeavors (Beyerlein et al., 2003). Although few organizations would give their own collaborative efforts a high rating, pockets of excellent collaboration do exist. Collaborative efforts lead by champions for change can grow, one small step at a time. When work processes and associated outcomes are assessed and documented regularly, it is possible to determine what was learned and how new learning will be applied in the future. Results should be reported in a timely, non-threatening manner. Use appreciative inquiry to determine what is right, not what is wrong. Simple, user-friendly information technologies should be used to document the results of evidence-based interventions, like peer-to-peer teaching. Results of interventions that are cycled and recycled throughout all business systems and subsystems will stimulate and reinforce change and improvement.

Practical applications .

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Selection and placement based on a person’s core competencies, work experience, expertise, certifications, and leadership profiles will enhance group satisfaction and productivity and guide career management and training and development. Use of leadership competence models can help reduce leadership burnout. As learning organizations, C of Cs continually evolve into more flexible, efficient, goal-directed systems. They have a competitive edge over regular work groups as they make maximum use of the members’ virtually untapped, undocumented collective tacit knowledge and explicit knowledge. Use of EQ in job assignment and work assessment is starting to pay off. People high in EQ are demonstrated to have positive attitudes and values, are loyal, resilient, compassionate, and trustworthy. They show “positive organizational scholarship”, a new field that uses social contracts, values, and work processes to protect and nurture its employees (Fryer, 2004).

.

.

C of Cs come up to speed quickly, are intensely focused, problem-driven, and rich in knowledge. By themselves, C of Cs fail to meet the governance, marketing, funding, management, regulation, and criteria of Dealtry’s (2000b) 10:10 Blueprint model. However, a supportive organizational infrastructure may minimize some of the deficiencies of these communities. Development of an electronic or print registry of people’s interests, demonstrated competencies, tacit and explicit knowledge, “best practices”, work families, success stories, lessons learned, and other key areas will be extremely useful. Knowledge management systems can be used to assess employees’ overall competence and document each person’s work history and work potential. This valuable data, information, and knowledge can be documented in a registry that has universal, yet protected, access.

In conclusion, making the best possible use of what we already know and use at work can reduce the need to continually seek out new information to further confirm what we know about the basic principles of business. A constant challenge is to get people to really think about what they are doing, share knowledge, and work together cooperatively. Note 1. www.communityofcompetence.com is used to describe and illustrated the steadily growing medical Communities of Competence referenced in this paper. In addition to the core members, current members include health care organizations, universities, government institutions, and numerous people of demonstrated competence. Community of CompetenceTM is trademarked in the USA. References Bandura, A. (1986), Social Foundations of Thought and Action: A Social Cognitive Theory, Prentice-Hall, Englewood Cliffs, NJ. Bandura, A. (1997), Self-Efficacy: The Exercise of Control, Freeman, New York, NY. Beyerlein, M.M., Freedman, S., McGee, C. and Moran, L. (2003), Beyond Teams: Building the Collaborative Organization, Jossey-Bass/Pfeiffer, San Francisco, CA. Campion, M., Medsker, G.J. and Higgs, A.C. (1993), “Relations between work group characteristics and effectiveness: implications for designing effective work groups”, Personnel Psychology, Vol. 46, pp. 823-50. Capra, F. (1996), The Web of Life: The New Scientific Understanding of Living Systems, Anchor Books, New York, NY. Dealtry, R. (2000a), “Establishing a methodology for appraising the strategic potential of the corporate university”, The Journal of Workplace Learning, Vol. 12 No. 5, pp. 217-23. Dealtry, R. (2000b), “Case research into corporate university developments”, The Journal of Workplace Learning, Vol. 12 No. 6, pp. 252-7. Dotan, D.B. (2002), “Communities of Competence: a concept behind implementation of quality improvement and patient safety centers in health care”, American Society for Quality, Health Care Division Newsletter, Spring, pp. 10-11. Druskat, V.U. and Wolff, S.B. (2001), “Building the emotional intelligence of groups”, Harvard Business Review, Vol. 3, pp. 80-90.

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Fryer, B. (2004), “Accentuate the positive”, Harvard Business Review, Vol. 82 No. 2, pp. 22-3. Galbraith, J.R. (2000), Designing the Global Organization, Jossey-Bass, San Francisco, CA. Goleman, D. (2000), “Leadership that gets results”, Harvard Business Review, Vol. 78 No. 2, pp. 78-90.

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Goleman, D. (2004), “What makes a leader?”, Harvard Business Review, Vol. 82 No. 1, pp. 82, 84-91. Guzzo, R.A. and Shea, G.P. (1992), “Group performance and intergroup relations in organizations”, in Dunnett, M.D. and Hough, L.M. (Eds), Handbook of Industrial and Organizational Psychology, 2nd ed., Vol. 3, Consulting Psychologists Press, Palo Alto, CA, pp. 269-313. Hackman, J.R. (1992), “Group influences on individuals in organizations”, in Dunnett, M.D. and Hough, L.M. (Eds), Handbook of Industrial and Organizational Psychology, 2nd ed., Vol. 3, Consulting Psychologists Press, Palo Alto, CA, pp. 199-267. Haines, S. (1998), Systems Thinking and Learning, HRD Press, Amherst, MD. Holland, J.H. (1995), Hidden Order: How Adaptation Builds Complexity, Addison-Wesley, Reading, MA. Holland, J.H. (1998), Emergence: From Chaos to Order, Addison-Wesley, Reading, MA. Homans, G.C. (1950), The Human Group, Harcourt Brace, New York, NY. Kaplan, R.S. and Norton, D.P. (2004), “Measuring the strategic readiness of intangible assets”, Harvard Business Review, Vol. 82 No. 2, pp. 52, 54-63. Likert, R. (1967), The Human Organization, McGraw-Hill, New York, NY. McClelland, D.C., Atkinson, J.W., Clark, R.A. and Lowell, E.L. (1953), The Achievement Motive, Appleton-Century-Crofts, New York, NY. Maslow, A.H. (1943), “A dynamic theory of human motivation”, Psychology Review, Vol. 50, pp. 370-96. Pascarella, P. (1997), “Harnessing knowledge”, Management Review, October, pp. 37-40. Plsek, P. (2001), “Redesigning health care with insights from the science of complex adaptive systems”, in Crossing the Quality Chasm: A New Health System for the 21st Century, National Academy Press, Washington, DC, pp. 309-22. Prigogine, I. (1980), From Being to Becoming, W.H. Freeman, San Francisco, CA. Prigogine, I. and Stengers, L. (1984), Order out of Chaos: Man’s New Dialogue with Nature, Bantam, New York, NY. Revans, R.W. (1982), The Origins and Growth of Action Learning, Chartwell-Bratt, London. Robbins, S.P. (2003), Organizational Behavior, Prentice-Hall, Upper Saddle River, NJ. Smith, E.A. (1997), “Operational definitions: an aid in benchmarking quality”, The 1997 Annual: 1 Consulting, Vol. 2, Pfeiffer & Co., San Francisco, CA, pp. 237-54. Smith, E.A. (2001), “The role of tacit and explicit knowledge in the workplace”, Journal of Knowledge Management, Vol. 5 No. 4, pp. 311-21. Smith, E.A. (2006), “Communities of Competence: a new form of group”, The 2006 Annual: 1 Consulting, Vol. 2, Pfeiffer & Co., San Francisco, CA, forthcoming. von Bertalanffy, L. (1968), General Systems Theory: Foundations, Development, and Applications, rev. ed., George Braziller Publishers, New York, NY. Warner, J. (2001), Emotional Intelligence Style Profile, HRD Press, Amherst, MA.

Wenger, E.C. and Snyder, W.M. (2000), “Communities of practice: the organizational frontier”, Harvard Business Review, Vol. 1, pp. 139-45. Wheatley, M.J. (1992), Leadership and the New Science, Berrett-Koehler Publishers, San Francisco, CA. White, R.W. (1959), “Motivation reconsidered: the concept of competence”, Psychological Review, Vol. 66, pp. 297-334. Wilson, E.O. (1971), The Insect Societies, Harvard University Press, Cambridge, MA. Zajac, E.J., Kraatz, M.S. and Bresser, R.F. (2000), “Modeling the dynamics of strategic fit: a normative approach to strategic change”, Journal of Strategic Management, Vol. 21 No. 4, pp. 429-53. Zimmerman, B.J., Lindberg, C. and Plsek, P.E. (1998), Edgeware: Insights from Complexity Science for Health Care Leaders, VHA Publishing, Dallas, TX.

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The Emerald Research Register for this journal is available at www.emeraldinsight.com/researchregister

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The current issue and full text archive of this journal is available at www.emeraldinsight.com/1366-5626.htm

Curriculum design and management in the Digital Media U Applying the corporate university concept to a business sub-sector Les Selby and David Russell MITER, Manchester Metropolitan University, Manchester, UK Abstract Purpose – To report on the progress of Digital Media U, a tailor-made portal, learning environment and management system. Design/methodology/approach – Discusses the design of the learning content domains, acquisition of the content and the systems for managing the curriculum in the future, including the application of a new model of accreditation. Findings – Digital Media U applies the theories and concept of corporate universities to the digital media business sub-sector, providing continuing professional development that supports regional and individual business strategies. DM-U creates a “sectoral learning community” in the supply chain of project partners, educational institutions, learners and their organisations. DM-U curriculum offers choices of topic, delivery, price and location, accommodating the learning needs of micro and SMEs from “just in time/just enough” through to accreditation. Originality/value – Provides information on a new learning community and network for small and medium-sized enterprises. Keywords Continuing development, Workplace learning, Professional education Paper type Case study

The Journal of Workplace Learning Vol. 17 No. 1/2, 2005 pp. 24-32 q Emerald Group Publishing Limited 1366-5626 DOI 10.1108/13665620510574432

Background to Digital Media U Digital Media U (DM-U) is a “professional development network for people in the digital industries”[1]. For its learning element, it applies the concept of a corporate university (CU) to the digital media business sub-sector. The project is run by MITER at Manchester Metropolitan University (MMU) and is funded by the North West Development Agency and the European Regional Development Fund. Project partners include IBM, FD Learning and Macromedia. Digital Media is considered vital to the North West region’s economy, and DM-U will address the learning needs of the sector by developing a continuous professional development (CPD) service based on corporate university models. DM-U will provide online and instructor-led courses and workshops supported by a range of knowledge resources. DM-U intends to form a “sectoral learning community” with MMU, MITER’s networks, project partners, other educational institutions, DM-U learners and their organisations. Individuals will join DM-U as members and will gain access to the CPD service, knowledge resources, project rooms and other facilities. They will pay at discounted rates for access to learning content as required. At the time of writing, the DM-U portal[2] is built and the first tranche of learning content is being loaded. The principles of a new accreditation model are currently

being explored with project partners. This paper is a work-in-progress report, but we will show that it is more than descriptive: DM-U is informed by current research and responds to market needs. Digital media sector The digital and creative industries have been identified as priority sectors in the North West Development Agency (NWDA) Regional Economic Strategy 2003 and, specifically, the digital media sector is identified as one of the “emerging key determinants of economic prosperity and sustainability”[3]. There is no specific sector committee or professional institution for digital media, which sits across the e-Skills and Skillset sector committees, neither of which offers continuous professional development activities. DM-U intends to fill this gap. For its Strategy Document 2002, Digital Industries North West (DINW) defines the sector as “covering a wide variety of sub-sectors such as web design, multimedia, hardware and software consultancy and supply, networking installation, 3D visualisation and product design, internet service provision, data processing and mining, digital gaming, digital broadcasting and e-learning”. The sector is also difficult to quantify, but DINW estimates that there are around 5,000 businesses in the North West employing 50-60,000 people. Seventy-five per cent employ fewer than ten people, a figure corroborated by the MITER Digital Media-Watch (DM-W) Survey of 2003. Additionally, most businesses are new, with over 70 per cent between two and five years old. These characteristics raise a number of training and skills issues that affect the sector, some of which are specific and others that are “SME generic”. The Strategy for the Digital Industries in the North West (Digital Industries North West, 2002) highlighted a shortage of management and technical skills, as well as “soft skills”. These findings were echoed in the 2003 DM-W survey and many small firms, by necessity, are seeking staff with the ability to work in several areas. The earlier CLIME project[4] workshops evidenced demand among creative businesses for the sort of training and advice common to all micros and SMEs, regardless of sector. Previous MITER research (SMILE[5]) has suggested that many companies rely on sub-contractors or short-term collaborative partners who are sometimes hard to find, and there is also a lack of awareness of support mechanisms. The small size of firms means that the development of the individual becomes synonymous with that of the business: this is a key point for curriculum development. We therefore have a sector with some unique features and a range of requirements that are not being addressed. It also has many of the features of a supply chain, and the NWDA Regional Strategy 2003 applies the “cluster” concept to the sector: we built this into the development of the business plan using the value chain framework (Porter and Millar, 1985), including the concept of the value network (Stabell and Fjeltstad, 1998). The “activity configuration” element of this model includes supply chain management activities and a “partner network”, and Attwell (2003) recommends support networks as a “new developmental paradigm”. Taking this supply chain view, DM-U sees the potential of its members and project partners to form a learning community. We therefore considered that the corporate university model might be appropriate: it provides for “immediate skill development” and it focuses on organisational as well as individual performance (Nixon and Helms,

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2002). There are examples of CUs that have been set up for sectors or groups of professionals, for example real estate brokers in the USA (Nixon and Helms, 2002). CU models

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As we think of it, a corporate university is a portal within a company through which all education takes place. It is an organisation’s strategic hub for educating employees, customers, and suppliers. Corporate universities link an organisation’s strategies to the learning goals of its audiences (Morrison and Meister, 2000).

This definition fits two of the main principles behind DM-U. We need to provide for the many micro businesses where the learning goals of the staff are linked to the strategy of the business (or, in the short term, its tactics). We can also see how CUs have extended into the value chain and the selling of additional services (Clarke and Hermens, 2001). There are some notable themes in the ways in which corporate universities have developed, particularly short, “just in time” courses, work-based learning (WBL) and knowledge capture linked to organisational learning. There are other themes reflected in the CU concept, as exemplified in the Businesslab project[6], that have been carried into the development of DM-U: harnessing the intellectual capital of the workforce, using technology to deliver just-in-time programmes, and using tools to integrate learning with intellectual capital. Accreditation in DM-U The proposed accreditation model is being designed to focus members on gathering evidence of their CPD activities and recording them through the portal as learning events and application of learning in the workplace. The portal would then act as a focus for the organisation and validation of their learning and development, linked with their work practice. Crucial to this approach is work-based learning and allowing members to design their own programmes with reference to their work demands. The accrual of credit-based learning, especially with the support of the reflective and evaluative exercises and a personal e-portfolio, will allow the member to carry credits towards a university award in CPD. The assessment of the members’ work will be conducted through an online “wrap around assessment unit”[7], matched against the appropriate university qualification levels. The emphasis of these assessment units will be to demonstrate how the learning has been applied, reviewed and evaluated within the work/business context. The assessment units will bring together (“wrap around”) learning, application and evaluation and reflection of the members’ practices. In this respect, there is an interesting parallel with Dealtry (2003), who discusses the issue of accreditation in corporate universities: . . . the accredited awards and other forms of recognition must be based on satisfying the developmental needs and career aspirations of individual learners whilst at the same time ensuring that the benefits that flow from these processes are commensurate with the organisation’s strategic learning objectives” (p. 86).

This is what we are looking for from DM-U, but with learners from different organisations.

DM-U would seek to advance upon El-Tannir’s (2002) model in which a “common zone” between academic and corporate universities “is explained in the possible configuration of natural alliances” between the two types (p. 80). DM-U would suggest there are three possible levels of such alliances: . Level 1 – where academic universities (AU) work in partnership to deliver traditional courses but with bespoke CU inputs and content; . Level 2 – where AU accredit CU content into a structured course through the use of accreditation of prior experiential learning; and . Level 3 – where AU accredit work-based learning assessment to provide credits for CPD activities and processes. DM-U seeks also to work beyond the Nottingham Business School model (Prince, 2003), which we would place at our Levels 1 and 2. Design of the curriculum Work-based learning is the main principle behind DM-U’s learning environment and curriculum; learning activity at work can be evidenced and has the potential to be recognised and accredited as part of an award or qualification. DM-U will formalise, record and account for career-long learning that would otherwise have been treated informally. There has been important research into the linking of theory and practice in learning activities (Kolb, 1984) and in practice-based learning (Lave and Wenger, 1990; Brown and Duguid, 1991). Theories and company procedures often differ from actual workplace practice and real competence results from combining them. Staley and MacKenzie (2001) emphasise the value of work-based learning and suggest the question “what opportunities can we create for students to put theory into practice?” (p. 16). They point to the use of “substitute experiences” for students in HE, but, in DM-U, we want the members to build on their real workplace experiences. Moving on from this, there are several further principles underpinning the development of the DM-U curriculum: . The curriculum is based on a continuous professional development (CPD) framework, in which learning content is supplementary to a structured plan, modelled on the practice and procedures of the Chartered Institute of Management, the Institute of IT Training and other professional bodies. CPD is not about attending separate “learning events”, whether or not a “blended learning” approach is used. . Learning and development of the individual would be aimed at “continuous” enhancement of professional practice through development in the workplace. . Activities are designed to improve the individual’s capacity to contribute towards the achievement of the business goals (the small size of the businesses in the sector indicate a close relationship between individual and business development). Learner choices DM-U will offer a choice of: . units of varying lengths; . delivery methods;

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. . . .

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times; locations; prices; and certification and accreditation.

We recognise that some topics are better suited to an instructor-led approach but, where possible, we think the member should be able to choose from face-to-face, online and offline. We also need to accommodate members in remote locations and, of course, to recognise time constraints. Attwell (2003) found that: . . . managers are often looking not for “just in time” but “absolutely-last-minute” training. In order to provide this, they need flexible learning materials and flexible methods of delivery. Current technologies and processes for developing and delivering materials are problematic. SMEs also want to save money – many SMEs say they can no longer afford face-to-face training.

Three domains The learning content was selected and structured around the domains of: (1) i-professional; (2) entrepreneurship; and (3) personal development. These domains are discussed below. i-Professional i-Professional will enable the individual to gather additional technical skills and knowledge in order to offer a broader range of products and services and respond to changing technology in an ever growing and competitive market. i-Professional recognises the importance of “industry standard” tools, and this domain will cover application skills. Recruitment and selection of staff in this sector is often based on market reputation and expertise rather than qualifications. The DINW report cites the influence of technology on the creation of the dynamic digital media market, and DM-U decided that this should be reflected in the main curriculum stream. Feedback from MITER’s Wired City Cheshire meetings shows that freelancers and micros use “vendor specific” certificates and qualifications to inspire confidence in larger clients. Entrepreneurship This aims to develop the individual’s capacity to respond to changing management and business needs. With most businesses in the sector being between two and five years old (MITER, 2003), a critical period for survival, we made this the second major factor and concentrated on developmental rather than start-up issues. These development activities will seek to address current and future business and/or management potential. As the individual moves beyond addressing the needs of the technical development of their work to acquire managerial roles and/ or small business accruement, this element should enable the individual to develop their knowledge and skills to reflect this new potential. The courses might include financial

awareness, project management, sales and client management, the legal environment, management development and consultancy skills.

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Personal development These activities will help the individual to respond to business development needs by offering a broader range of skills within the business organisation to help staff to develop and diversify. Courses will include leadership, coaching, training the trainers, time management, bid writing, business presentations, personal delivery and interviewing skills.

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Selection and acquisition of content All the learning content was selected to reflect the needs of “just in time/just enough” ( JIT/JE), which was seen as crucial in the planning of micros and SMEs. Eventually, we would like to move on to address specific business needs by developing very small, bite-sized units of learning, or learning objects. Importantly, the selection of content sought to avoid the type of provision held within the traditional HE/FE providers and more specifically the technical training providers, where fixed timetables militate against JIT/JE. We argue that DM-U cannot use a standard university course structure or learning units: they are too slow, too long, the timing is inflexible and the mix of theory and practice is wrong. “Companies are going to bypass universities that don’t provide what they want, and they are going to go directly to publishers for training materials” (Morrison and Meister, 2000). We also acknowledge the findings (Owen, 2002) that within the sector most people are graduates and there is little business and professional demand for postgraduate qualifications. Learning content is sourced from suppliers in a number of ways, including commissioning, content acquisition or brokerage, where members are placed on scheduled programmes. Where certificated industry standard products are available, we see no advantage in competing ourselves. However, we have entered into contracts with internationally known providers that will offer to our members discounts only normally available to corporates. This advantage of “consortium purchasing” is enhanced by further discounts for last-minute bookings, which are perfectly suited to small firms with urgent training needs Keeping content up to date Through its agreed objectives, DM-U is charged with creating partnerships and sharing knowledge, and it will deliver value to its members by involving them with the other partners in a “sectoral learning community”, building on theories of communities of practice and applying them in the light of MITER’s work on “Integrated Learning Communities” (Russell et al., 2003). The learning community will be made up of MITER’s existing digital media network, project partners, educational institutions, members and their organisations. DM-U has an SME advisory group drawn from digital media businesses, which will participate in various ways, including focus groups. The principal benefits for DM-U will be the access to a wide range of up to date knowledge and experience that will help to keep us keep close to the market.

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The partners will contribute to the learning element of DM-U in the following ways: . provision of software and courses under contractual agreements; . tutoring for specific courses; . advice on continued development of the service; . provision of experts to a “bank of experts”; and . access to a wider customer base through partners’ channels. One of the future intentions of DM-U is to address the shortage of regionally produced e-learning content (MITER research SMILE[8] and DM-U projects). For the knowledge resources, DM-U fits with the knowledge management model of Earl (1994), in which “for a business to build a strategic capability in knowledge, the proposition is that at least four components are required: Knowledge systems, networks, knowledge workers and learning organisations” (p. 62). DM-U has built knowledge capture systems into its technical architecture, and it has the people and the networks. Partners will contribute by providing research data and other resources, such as white papers. One of the key contributors is Digital Media Watch[9], through its industry research and annual surveys. Members themselves will be encouraged to contribute to the community by generating information and case studies. “Ask the Member” will be a more innovative way of building knowledge. When members join, they complete a kind of “skills audit” and profile of their interests as part of a diagnostic process and preparation for registering for CPD. This profile will also be used for registering with the Ask the Member, service in which natural language questions are submitted. The DM-U search engine recognises key words or phrases and searches the content repository for documents that match them, or synonyms. The question is then directed to the most likely members and, if the enquirer accepts the response, it will join the other material in the system. Conclusions The project’s main objective is “to increase the productivity, creativity and competitive performance of SMEs in the digital media sector, through the provision of support and the creation of a learning community and network”[10]. DM-U is intended to become self-sustaining at the end of the funded period, and the measure of its success is performance increases and specific targets for job creation and turnover. The accreditation model is still being developed, but we remain open as to how much demand there will be: the aim is improvement in business performance as much as individual performance. When fully developed, we believe that DM-U is a model that could be used for any business sub-sector. DM-U will continue to acquire content and we are planning a conference and a series of interactive workshops for potential providers. It is recommended that we continue to work on systems that could use DM-U knowledge resources to create learning objects, and therefore create content ourselves. Notes 1. See the DM-U Welcome page at: www.dm-u.co.uk/public/ 2. See www.dm-u.co.uk

3. The North West England Objective 2 Single Programming Document, available at: www.eurofundingnw.org.uk/DocDetails.asp?DocumentId ¼ 30§ion ¼ documents 4. Creative Leadership in Media Based Enterprises, a one-year European Social Fund project undertaken by MITER, 1999-2000. 5. Report of the Skills for the Missing Industry’s Leaders and Enterprises Project, a one-year European Social Fund/ADAPT project undertaken by MITER, 2000-1. 6. See www.businesslab.co.uk/popup/downloads/CLE_Prospectus.pdf (accessed May 20, 2004). 7. Name and units developed by Les Selby, DM-U Curriculum and Accreditation Manager, 2003. 8. Report of the Skills for the Missing Industry’s Leaders and Enterprises Project, a one-year European Social Fund/ADAPT project undertaken by MITER, 2000-1. 9. An “industry monitoring unit and information hub”. A current MITER project; see www.dm-w.co.uk 10. From the DM-U bid documentation.

References Attwell, G. (2003), “E-learning and small and medium enterprises”, available from: www.elearningeuropa.info/doc.php?lng ¼ 1&id ¼ 4329&doclng ¼ 1 (accessed May 17, 2004). Brown, J.S. and Duguid, P. (1991), “Organisational knowledge and communities of practice”, Organisation Science, Vol. 2 No. 1, pp. 40-57. Clarke, T. and Hermens, A. (2001), “Corporate developments and strategic alliances in e-learning”, Education + Training, Vol. 43 No. 4, pp. 256-67. Dealtry, R. (2003), “Issues relating to learning accreditation in corporate university management”, The Journal of Workplace Learning, Vol. 15 No. 2, pp. 80-6. Digital Industries North West (2002), A Strategy for the Digital Industries in the North West of England: Swot Analysis, Strategic Framework and Action Plan, SQW Ltd for Digital Industries North West, Manchester. Earl, M.J. (1994), “Knowledge as strategy: reflections on Skandia International and Shorko Films”, in Ciborra, C. and Jelassi, T. (Eds), Strategic Information Systems, Wiley, Chichester. El-Tannir, A.A. (2002), “The corporate university model for continuous learning, training and development”, Education + Training, Vol. 44 No. 2, pp. 76-81. Kolb, D. (1984), Experiential Learning – Experience as the Source of Learning and Development, Prentice-Hall, Englewood Cliffs, NJ. Lave, J. and Wenger, E. (1990), Situated Learning: Legitimate Peripheral Participation, Cambridge University Press, Cambridge. MITER (2003), “Digital industries in the North West”, available at: www.dm-w.co.uk/downloads/ industry/research/NWreport311.pdf Morrison, J.L. and Meister, J.C. (2000), “Corporate universities: an interview with Jeanne Meister”, The Technology Source, July/August, available at: http://ts.mivu.org/default.asp?show ¼ article&id ¼ 785 Nixon, J.C. and Helms, M.M. (2002), “Corporate universities vs. higher education institutions”, Industrial and Commercial Training, Vol. 34 No. 4, pp. 144-50.

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Owen, J. (2002), “A report into current and future skills development requirements in the digital content sector”, report for Digital Dynamics, a NWDA Skills Development Fund Project, NWDA, Warrington. Porter, M. and Millar, V. (1985), “How information gives you competitive advantage”, Harvard Business Review, Vol. 63 No. 4, pp. 149-60. Prince, C. (2003), “Corporate education and learning: the accreditation agenda”, The Journal of Workplace Learning, Vol. 15 No. 4, pp. 179-85. Russell, D., Calvey, D. and Banks, M. (2003), “Creating new learning communities: towards effective e-learning production”, The Journal of Workplace Learning, Vol. 15 No. 1, pp. 34-44. Stabell, C.B. and Fjeltstad, O.D. (1998), “Configuring value for competitive advantage: on chains, shops and networks”, Strategic Management Journal, Vol. 19, pp. 413-37. Staley, A. and Mackenzie, N. (2001), Computer Supported Experiential Learning, University of Central England, Birmingham.

The Emerald Research Register for this journal is available at www.emeraldinsight.com/researchregister

The current issue and full text archive of this journal is available at www.emeraldinsight.com/1366-5626.htm

The implementation and use of e-learning in the corporate university Allan Macpherson, Gill Homan and Krystal Wilkinson

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HRM and OB Group, Manchester Metropolitan University Business School, Manchester, UK Abstract Purpose – The use of e-learning in corporate universities enables access and broadens the curriculum. This paper assesses the use and implementation of e-learning through case material, and explores some of the challenges and emerging concerns. Design/methodology/approach – The paper reviews the corporate university concept and considers how an e-learning pedagogy might contribute to its success. Three case reviews of e-learning adoption within corporate universities in the UK are included. Findings – The paper argues that if corporate universities do not incorporate both the pedagogical and learner preferences perspectives into their use of e-learning, this will seriously devalue the training experience. It concludes that the advantages of an online pedagogy are not fully exploited due to limitations in technology and other strategic priorities. In addition, a number of lessons have been learned by the pioneers of corporate e-learning, including the evolutionary nature of the programmes and the need to create “organisational readiness”. Research limitations/implications – Further research into the views of learners in this debate is necessary. Originality/value – Provides evidence of the potential of e-learning as a key learning and development strategy within corporate universities. Keywords Computer based learning, Learning organizations, Workplace learning, Learning methods Paper type Case study

Introduction General Electric’s Management Development Institute was established at Crotonville in New York in 1956. In the last decade, the phenomenon of the corporate university has significantly gathered pace. Dealtry (2001) considers corporate universities to be one of the most significant business interventions in organisational development in the last two decades. These institutions now exist in a variety of formats, with a number of different aims, and cover a broad spread of corporate and public organisations throughout the world. Over 2,400 are said to exist today (Nixon and Helms, 2002). This figure is expected to increase further in future years, with around 37,000 predicted to exist by 2010. Such is the growing influence and respect of these institutions that the UK Government is considering granting award-bearing powers to those that can demonstrate high standards in education (Prince, 2003). This shift to corporate universities is clearly not a passing phenomenon in employee development. These institutions are likely to have a significant impact on the nature and direction of the education of the current and future workforce. It is important, therefore, to examine the purpose of these organisations, the learning paradigms that they incorporate and the educational opportunities that they offer.

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The development of corporate universities is also an attempt to re-engineer business processes for best value and represent not only a renewed corporate appreciation for education but also “a desire to centralize resources to reduce expenses” (Arnone, 1998, p. 200). Consequently, one of the key objectives of the process is cost-effectiveness. In order to maximise training investment, it is essential that the trainees realise that formal training environments are expensive and are not the only opportunity for learning. It is more cost-effective to provide trainees with the tools and technology to continue the learning process in their work and social environments. However, while e-learning may be a route to achieve this, the potentials of technological systems are mediated by the way they are shaped in use as well as by the capabilities and characteristics of the technology (Dawson et al., 2003). Thus, the impact of e-learning will be dependent on how the technology is adopted and used within organisational contexts, and how well the technology supports the objectives, strategies and values of learning within the corporate university framework. This paper reviews the corporate university concept and considers how an e-learning pedagogy might contribute to its success. The paper incorporates lessons learned from three case reviews of e-learning adoption within corporate universities in the UK.

Corporate university models McDonald’s corporate university was one of the earliest, established in 1961. Its aim is to provide a foundation on which to ensure that McDonald’s can operate their business at a consistent level to deliver consistent restaurants across the world (Dalton, 1999). The values being taught in “management” are therefore not primarily focused at the strategic management level, but they accord with the culture of the organisation, focus on standardisation, and seek to perpetuate the current business strategy. Motorola, on the other hand, focused their corporate university on the need to be an agent of change. Indeed, not only were the management training programmes regarded as providing personnel with the skills and knowledge necessary to welcome, seek and implement change, and thus afford the organisation a competitive advantage, but there was a culture change required of senior management to accept education not as a cost, but as an investment (Fulmer and Gibbs, 1998). Alternatively, Finn (1999) notes that several other organisations, and in the UK BAE Systems in particular, have established links with formal educational institutions to underwrite their career development programmes. BAE Systems offers management and technical training in partnership with a number of universities, as well as providing a number of courses through their “virtual university”, open to all employees (BAE Systems, 2004). Indeed, through this programme, BAE Systems’ stated objective is to provide leadership training for its future directors, and it is therefore looking to use the corporate university learning process to drive and shape the future organisational goals and structures. This clear diversity of models and purposes of corporate universities has led Fresina (1997) to categorise them into three types: (1) as reinforcing and perpetuating current cultures and competitiveness; (2) as agents to manage and implement change; and (3) as a force to drive and shape the future strategy of the organisation.

However, Fresina does acknowledge that a particular corporate university is unlikely to fall distinctly into one category, but rather will draw parts from each. This is evident in the multi-layered strategy adopted by BAE Systems, noted above. Barley (1997) argues that this variety is positive and that the corporate university is “a flexible and adaptable vehicle” (p. 1), providing the opportunity to assess the needs of the organisation and model learning accordingly. Whatever the model of corporate university adopted, it could be said that they support the view that learning is fundamental to ensuring the continued effectiveness of the organisation’s human resources, and therefore the organisation itself. Technology and a new generation of corporate universities When considering the nature of the CU concept, while Fresina’s (1997) taxonomy is useful in understanding the various strategic roles which they may fulfil, initiatives also vary considerably in terms of the method of content delivery. An alternative schema for classifying corporate universities is suggested by Walton (1999), incorporating first-, second- and third-generation corporate universities and focusing on both purpose and learning strategy adopted. He uses the Disney University as a typical example of a first-generation type, with a narrow focus on the adoption of organisational culture and values and mainly classroom-based activities. A second-generation university typically offers a wider range of activities, to a range of levels within the organisation, perhaps organised into curriculum areas which address functional skills, cultural issues and remedial learning, and is often characterised by partnerships with other employers, educational institutions and the wider community. Walton gives Motorola as an example of a second-generation corporate university with a wide range of activities delivered by varied means, including the use of technology, but which retains the recognition that its activities must remain relevant to the organisation, albeit with a longer-term perspective. One of the most significant developments in the history of the phenomenon seems to be the emergence of what Walton (1999) terms “third generation” CUs, which move beyond the confines of the “campus”, to become portable, or more significantly, possess a virtual element (Prince and Beaver, 2001). Third-generation corporate universities, Walton argues, are those which seek to make the best use of new technology for learning, and are characterised by process rather than place, adopting the structure of a virtual organisation. Phillips (1999) notes that this is often a feature of corporate universities within the UK: developing rather later than their American counterparts, they are better placed to take advantage of these developments in technology. For many companies, it seems that developments in internet technology had opened up new opportunities for corporate university activity, allowing a move away from a fully pre-scheduled curriculum to provide on demand and open access (El-Tannir, 2002). In a society where knowledge has become a key competitive criterion, e-learning technology has evolved to provide users with the specific tools and information needed at the point of activity, and allows the possibility of investigating a number of solutions in a short time (Lenderman and Sandelands, 2002). Walton (1999) sees the third-generation corporate university is seen as the intellectual engine of the organisation, developing the human capital of all employees, with a focus on developing creativity and innovation and driving strategic change. Third-generation corporate universities represent a philosophy and mission that is

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significantly closer to that of higher education, but differentiated by the maximisation of the use of technology to deliver both the learning and the ethos of the corporate university. This typology has much in common with those presented in the previous section, but the major difference is the extent and purposes for which new technology is seen as not just a means of efficient and cheap delivery, but also as a way of defining the corporate university itself. If the third generation of corporate universities is to rely heavily on the use of technology for its curriculum, it is important to consider both how technology could contribute to the learning environment and to the strategic and cultural goals of the corporate university, and what the barriers might be to effective incorporation. E-learning – possibilities and limitations Much of the content of the available literature concentrates on the advantages of e-learning. Moreover, it tends to be presented with little discussion of possible disadvantages or problems, and under the banner of urging trainers and organisations to join the bandwagon, or be left behind (Rana, 2001; Sloman, 2001; Wilson, 1999). These are based around two main themes: (1) the cost advantages; and (2) flexibility in delivery. Sora (2001) actually refers to e-learning (distance learning) as a force for “profit and efficiency”. Although he uses this term in the context of the traditional university, it is perhaps even more appropriate in the context of the corporate university. The cost advantages centre on reduced training time, the costs saved in travel and time away from the job, and the ability of e-learning to serve large numbers at one time, or over time, with relatively little additional cost (Schriver and Giles, 1999; Warner, 1999; Koprowski, 2000). In addition, the relationship of e-learning and knowledge management is increasingly seen as contributing to the competitive edge of the organisation (Swanson, 2001). This raises expectations in organisations that introduce e-learning in terms of both the extent of the return on investment (ROI), and the period over which the payback will take place. A study of US businesses by Swanson (2001) indicates that 46 per cent of those surveyed are already seeing a return on their investment, whilst 94 per cent are expecting to see returns or further returns within two years. Hammond (2001) also notes that 80 per cent of Fortune 500 companies are using, or intending to use, e-learning, and expect a significant ROI. Discussions on flexibility tend to focus on two main issues: (1) flexibility in delivery; and (2) flexibility in the pace and distribution of learning. The flexibility of delivery offers organisations the ability to deliver consistent learning experiences, independent of time and place. This offers great advantages to a geographically dispersed workforce, those working non-standard hours, and those employees who work from a home base. It also enables learning to be offered easily to those beyond the formal boundaries of the organisation at relatively low cost: this would include customers, suppliers and contractors (Galagan, 2000). Flexibility in the pace of learning is represented largely as an advantage to the learner, in that they can learn at a time and pace to suit their own capability and life circumstances, and enable

their continued marketability through lifelong learning (Sandelands and Wills, 1996; Caudron, 1999). However, it is notable that the issues of flexibility and learner-centeredness fail to address issues of learner styles identified by Honey and Mumford (1992), although it is questionable that any training delivery method could provide the flexibility to address this issue. Nevertheless, it does raise questions about the suitability of e-learning, with its reliance on self-instruction and self motivation, for a broad organisational constituency. The dearth of academic literature available on this subject means that a reasoned debate is lacking, particularly in the areas of quality of content, problems with the technology, learner support and evaluation. There are, however, some authors who do sound a note of caution. Emurian (2001) questions what might be effectively delivered via e-learning, and Angel (2000) suggests that while e-learning is good for communicating facts, areas of complexity and feedback might be better left to human trainers. Dobbs (2000) maintains that much of the “off the shelf material available is poor and lacking in creativity”, whilst Warner (1999) emphasises the importance of tailor-made materials and online help, but acknowledges their cost. This is a significant point that needs to be addressed in the payback debate, and the balance of quality versus the true cost of materials and their support is one that would benefit from further research. It is, however, an area of great complexity as the range of options and capabilities available does not lend itself easily to definition, and this complexity is only likely to increase as technology advances (Barron, 1999). McLennon (2000) provides a clear exposition of the technological complexity of e-learning and the areas in which problems can occur. With regard to the learning experience, Dringus (2000) warns that e-learners may be unable to sustain their momentum unless they have the skills for self-directed learning and technology management, unless they are self motivated, and unless they are prepared for isolation. Indeed, Horwath (1999) recorded anxiety in novice users when the technology failed to respond within 15 seconds. This theme is addressed by Newmann and Smith (1999), who use Lave and Wenger’s (1991) concept, “communities of practice”, to note the significance of a supportive and interactive context of learning, highlighting the danger of the learners’ needs being ignored in the enthusiasm for technology. This point surfaces again in respect to evaluation, and much of the evaluation of e-learning that does take place concentrates on uptake, rather than the comparative effectiveness of online and traditional courses (Horwath, 1999). The exceptions to this include Furnell et al. (1999) and Leins and Orton (2000), who reiterate all of the above concerns and take a stakeholder perspective, and Athanasou (1999) who urges the need for evaluation, and who offers a six-step framework, which includes a range of qualitative issues as well as cost. Hartley (2000) concentrates on the impact of e-learning on the role and skills of the trainer. Moreover, a recent study by Masie (2001) farther reinforces this message, highlighting that “learner acceptance” is not guaranteed and will require firms to address issues of marketing (to encourage participation), support (to aid retention), incentives (to provide validation of the training completed), and technology (to support collaboration and provide blended solutions). These issues seem obvious on reflection, but as Dobbs (2000) and O’Reilly (2000) point out, many trainers responsible for developing and implementing e-learning strategies are struggling within a new field. They possess some of the skills required,

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but lack experience and the “know how” of others, particularly the technical skills. Here again the literature proves less useful than it could in terms of providing guidance across the broad spectrum of issues. Indeed, what is largely ignored in the literature is that e-learning sits within a broader context or agenda of employee development that may be guilty of providing innovative fads at the expense of pedagogically sound training (Beech et al., 2000), where managers retain faith with “traditional” training methods (Sadler-Smith et al., 2000), where there are struggles to balance competing individual and organisational priorities (Antonacopoulou, 2000), and where the language of the democratisation of learning, through employee-led development schemes, is argued to increase motivation (Hamblett and Holden, 2000). These issues will clearly inform the discourse on e-learning and, given that the majority of the literature tends to support a cost-driven and flexibility agenda, the wider context of employee development may also create tensions between employee development practice, the involvement of the line manager, and the needs of the individual. Consequently, new entrants to the field have to piece together the key issues from a range of sources and resolve the tensions that exist within their own organisational context. Moreover, the focus on cost and flexibility may undermine the technical possibilities to create stimulating learning environments – and does not address the issue of providing a unique pedagogy of learning. Indeed, Govindasamy (2001) argues that pedagogy is the most neglected aspect of attempts to implement e-learning. Given these concerns, it is important to consider how e-learning can contribute to the strategic objectives of the third generation of corporate universities, outlined above. Case reviews of e-learning in corporate universities To explore and illustrate the use of e-learning in corporate universities, this research undertook to review the implementation of e-learning in three large organisations in different sectors. Company A is one of the big five high-street banks in the UK; company B is an international engineering and manufacturing concern in the aerospace industry; and company C is a major provider of telecommunications architecture. By investigating the experiences of larger organisations that are implementing e-learning, our aim is to consider the contexts affecting e-learning structures and “success”, to inform the debate on e-learning, and to identify emerging issues that warrant further research. This final point is considered to be particularly important. If e-learning does continue to grow, and become a predominant source of organisational learning, its effective use will have a major impact on employee capability and thus economic performance on an international scale. Consequently, the experiences and problems of those companies leading the implementation of e-learning within corporate universities are likely to be profoundly important for those that follow. Review material was collected through interviews with senior corporate university and e-learning development staff, through seminars involving academics and practitioners within the corporate university, and through practical reviews of the e-learning material available. This triangulation does not establish “the truth”, but allows a variety of perspectives to be considered by the researchers in the construction of their interpretation. Researching with qualitative methodologies creates particular challenges in establishing the “truth” and in the analysis of data (Lincoln and Guba, 2003). Knowledge production clearly relies heavily on the researcher’s lens to make

sense of the data. Consequently, the validation process should also involve recognition that outcomes are ultimately interpretations and as such are fallible and revisable, and that an alternative interpretation or construction may possible (Alvesson and Skoldberg, 2000; Schwandt, 2003). Nevertheless, this approach is particularly useful for developing emerging issues that may warrant further, and more focused, research. Consequently, the research data was analysed in terms of e-learning’s use and contribution to strategic objectives within the corporate university, its pedagogical structure, and issues highlighted in e-learning adoption in the corporate university. Strategic drivers The initial drivers for a move to e-learning were substantially different in each case, but in all cases these initial objectives have evolved over time and with operational experience to present somewhat different aims for the present and future. In company A the most significant factor driving the strategy was cost, both in terms of a reduced headcount within the training function and in the unit cost of delivery. Currently, however, although return on investment is still a major issue, the key drivers are seen as accessibility and flexibility of delivery. In companies B and C the move to e-learning was driven by a strategic review of the training and development function. In company B the aim was to “deliver learning solutions, share best practice and encourage a culture of lifelong learning”. A virtual university was seen as an integral part of this vision. In company C the strategic review focused on performance and the current capability of the training and development function to deliver a consistent standard of face-to-face training, in the quantity and timeframe required, given the rate of technological development in product lines resulting in shorter product lifecycles. E-learning was seen as a means of meeting these requirements for a large audience at an acceptable cost. Company B has developed their ambitious original aim still further, their current aim is to develop an integrated strategy of knowledge management and learning and the virtual university is seen as a key component of this strategy. Company C plans to move further in the direction of e-learning by introducing “a total e-learning solution”. This places both company B and company C within Walton’s (1999) definition of a third-generation corporate university, while the aim of company A sits comfortably in Fresina’s (1997) category of perpetuating and reinforcing the current culture, Company C is using e-learning as “an agent to manage change”, and company B is using e-learning as a “force to drive the future direction of the company”. Furthermore, all three companies reflect the advantages of e-learning reflected in the literature and consider it to provide significant advantages in terms of cost and flexibility in delivery. Integration of e-learning In all three case studies, e-learning was not the sole means of delivering learning and training. Company A still delivers face-to-face training; company B still offers traditional training, placements and a mentoring scheme; and company C still offers face-to-face training and has a college providing technical training to both their own employees and, as an income-generating initiative, to other companies. Delivery of e-learning provided by these institutions varies considerably in terns of both breadth and technical complexity. Company A delivers e-learning through multimedia suites containing stand-alone PCs offering CD-ROMs. These have increased from an initial

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450 to 2,100 and they also have an intranet site which is available to 55,000 of the 78,000 employees, with some 25,000 pages of reference material. Their internet system is also used, in the main, for online testing of re-licensing qualifications required for financial regulation. It does, however, include 247 bulletin boards which build up support via online questions and answers and act effectively as FAQ asynchronous support. In 2001, a total of 250,000 hours were delivered via the CD ROMs, and the intranet was receiving 125,000 hits per month with an average of 6.5 pages being requested. The areas covered include: . training for change initiatives; . generic IT; . interpersonal skills; and . sector-specific skills and qualifications. The courses are 50 per cent off the shelf, mainly IT and interpersonal skills, and 50 per cent tailor-made at the request of faculty heads and project managers focused on company specific initiatives. Company A employs six web designers to create customised training since they found this to be the most cost-effective means of producing this material. They see the main barrier to the development of web-based e-learning as technological limitations such as bandwidth and the need for an effective and compatible online monitoring system. Their plans for future development include introducing online mentoring and the development of more blended learning. A further barrier is seen as the company culture, which is yet to fully accept e-learning. Resistance tends to increase the more senior the grade of employee, and as it is the senior managers and project managers that act as the commissioners of e-learning, this presents a significant issue. At lower levels a recent attitude survey revealed e-learning as one of the two most popular forms of learning. Company B was concerned to ensure that the “backbone” of the virtual university was effective and then to evolve capability, to allow open access and manage by exception. They started with the intranet and provided support through learning resource centres. The intranet now serves over 80 per cent of a workforce of 130,000 based in over 45 countries worldwide. There are approximately 550 online courses, the majority of which are off the shelf. Bespoke online learning is provided for specific business sectors or projects that identify a particular need. There are ambitious plans to develop and integrate the systems with the knowledge management system and to review the use of the learning resource centres. This perspective is more in line with Maule’s view that “[e]ffective use of the collective media is often as much a function of information policies and organisational cultures as it is of technology” (1997, p. 136). Company B also sees technology as one of the main barriers to the development of the e-learning strategy. These are bandwidth, hardware and processing capability issues. These are seen as limiting the use of the latest packages, and the level of interactivity and impact of the material. In the first year there were 16,000 students and 40,000 courses taken. Current take-up is approximately 18,000 students, or 15 per cent of the workforce. Focus groups were conducted after the first year of operation, in 1998, and then again in 2000. The feedback from the first groups indicated that people were confused by the amount of provision on offer. A number of steps were taken to address this problem, including:

the provision of a learning and development guide; searches for course options in a variety of categories, such as future job roles, competencies, career plans, and technical knowledge; and a single point of access in the learning resource centres to the database of options.

E-learning in the corporate university

Despite this, the feedback from the 2000 focus groups was very similar. Further barriers were seen as the difficulty of integrated tracking across both online and offline learning needs and activities together with the perennial problems of motivation to learn and the development of a learning culture. There is a move to counteract this by embedding learning as a key activity in all processes. Company C has a very different approach to delivery, outsourcing its non-core learning provision to a third party and developing e-learning as a part of its overall learning strategy, although this is currently seen as only partially formed. It is company C’s intention to go for total e-learning solution with an integrated learning management system with both company and individual access. Content ranges from technical to soft skills training, which is, in the main, off the shelf. Where bespoke e-learning is provided, this tends to be the most popular. Future development includes both individual and group-based learning activities with digital and video links utilising learning facilitators. Take-up rates have grown ten-fold in less than two years from 300 in 2000 to 3,000 in 2001. However, this must be set in the context of the withdrawal of face-to-face learning opportunities. As befits a company in a high-technology communications business, the capability of the technology was not seen as a barrier. There were, however, a number of other barriers. Culturally, training had previously been viewed as a “reward” with a few days away from the job, and training was not seen as being linked directly to business needs. Thus, the move first to distance learning and then to e-learning was seen by managers as a “cheap” option and, consequently, lacked their support. Additionally, the company had grown through acquisition and merger and retained a number of different sub-cultures, all with their own attitudes to training and technology The biggest barrier, however, was seen as getting people to understand how to e-learn. While the intention was to retain face-to-face courses for technological training, product training and development training was to be fully transferred online.

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Learning the lessons – where to now? All three of these companies have clearly put a great deal of thought and investment into e-learning within their corporate university frameworks. Despite this, there are still barriers to be overcome and issues to be resolved. These provide lessons that might inform future practice in developing and implementing e-learning in a corporate university, and poses a number of questions for future research. Strategic impact and considerations The ability to invest in the infrastructure for e-learning is closely allied to the concentration on the financial benefits of e-learning, particularly the requirement to demonstrate quick returns on any capital invested. As company A stated, “you need to take a long term view of the investment, e-learning was implemented on a zero budget here [. . .] we had to make savings to justify the expenditure. This hampered the speed

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and the effectiveness of the e-learning solutions”. This was echoed by company B, who argued that return on investment was a short-sighted view of e-learning, and more significant was the impact on competitiveness and the development of a learning culture. The evidence from the case studies indicated that, as suggested in the literature (Schriver and Giles 1999, Koprowski, 2000), the key drivers identified were “accessibility and flexibility of delivery” and cost, particularly cheaper delivery through reduced opportunity costs and reduced time away from work. There is a strong suggestion of a shift in emphasis reflected in e-learning to the individual taking responsibility for his or her own learning. More work is needed here to focus on an analysis of learner needs and learner demands for e-learning, which is currently supply-driven rather than demand-driven. There is underlying concern about variability in the quality of learning products amongst users, which would seem to reflect concerns highlighted in the literature, particularly around the level of interactivity of products. The reality is that a considerable amount of learning material is standardised, and consequently not locally sensitive. It is hard to see how such generic material can make a significant strategic impact required in the third generation of corporate universities. Perceptions about the potential benefits of e-learning suggested a lack of clarity or emphasis on how e-learning might contribute to increases in bottom-line performance. This contrasts sharply with Swanson’s (2001) study on US businesses, where it is claimed that real ROI is achieved quickly through e-learning investment. Indeed, the directions being taken by the companies tend to reinforce Newmann and Smith’s (1999) concerns that the emphasis of e-learning is directed towards technological solutions and potential economic efficiencies rather than putting issues of pedagogy and learner experience at the forefront of implementation. Evaluation systems count hits and pages read, with the deeper focus groups not really considering how and why take-up is achieved, or the level of contribution to strategic goals. This warrants careful review in terms of the expectations of e-learning to provide support for the strategies and goals of the corporate university. Concerns remain that the level of personal support available, both online and offline, are not sufficient to achieve the quality learning experiences and outcomes necessary to provide a strategic contribution. Dealtry (2002a, b) considers the corporate university as way of managing performance and potential, and an important part of that process is the release of personal potential. To enable this process some account must surely be taken of individual approaches and preferences in learning, which is at odds with the current menu-driven approach, even if this menu is online and universally accessible. This suggests the need for ongoing research alongside companies to evaluate the impact of e-learning on the various stakeholders, particularly the learner (Roy and Elfner, 2002), to identify key issues of “learner acceptance” (Masie, 2001) and also to consider how and what strategic contribution current e-learning systems provide. Technology First, while technology is the enabler of e-learning it is also in many organisations a barrier to the full realisation of its own potential. E-learning solutions can clearly only progress at the rate of the base technology of the organisation, and this can slow down development, reduce the level of sophistication of the materials used, and create

frustration in users and trainers alike. The technological capability of organisations did not seem to support the level of interactivity or integration necessary to make e-learning sufficiently different from other distance learning material and to provide increased levels of satisfaction. Indeed, as noted earlier, studies by Horwath (1999) found that students became distracted and anxious if the computer did not respond quickly. Thus, the learning experience and technological robustness are clearly linked. Moreover, to achieve the level of virtual interaction that Motiwalla and Tello (2000) highlighted was essential to improved learner satisfaction, technological capability will be fundamental. But, it will be more important to address the pedagogical possibilities in learning programmes design to provide the level of interaction and collaboration that will provide the learner with a stimulating experience (Masie, 2001), and encourage a culture of learning that is so important to drive the organisation forward. This interactivity is crucial to reducing transactional distance and increasing learner autonomy, but was only limited in the development of current programmes, and further strengthens the case for an evaluation of learner experiences within the corporate environment. It is difficult to see how current systems provide the strategic direction desired in the models suggested by both Walton (1999) and Fresina (1997). Issues of pedagogy What is most noticeable by its absence is any reference to the quality of the learning and the impact on the learner that takes place. Company B refers to becoming “learner focused”, but in the sense that the learner is a customer for its products rather than in the nature of the learning experience and the quality of the behavioural outcomes. The emphasis on learning as an outcome expressed in terms of behavioural change and the development of performance through the transfer of knowledge and skills is not new: this has evolved through many years and many pedagogical paradigms such as story-telling, writing and the dissemination of printed material. However, electronic dissemination now requires not only the ability to listen, read and write, but the technical competence and network depth to create a learning community in cyberspace (Horwath, 1999). It is important to consider that e-learning may provide the capability to combine these elements of story-telling, reading, writing and even acting, into a unique and flexible dissemination mechanism. Consequently, serious consideration has to be given to the pedagogical structure of e-learning. Thus, the exploitation of this technical dimension will require consideration both of the possibilities of e-learning and of what is technically possible, as well as the possible loss of what is technically not possible (Campbell and Dealtry, 2003). While there is no reason why e-learning should deliver a less effective alternative to traditional education or existing distance learning (Hodgson, 2002), issues of instructional design, technology and pedagogy (Welle-Strand and Thune, 2003) create tensions between cost and quality that must be balanced if e-learning is to achieve its potential within organisations and to contribute strategically to the corporate university framework. Currently, there is little evidence that e-learning is providing anything more than open and on-time access to a largely generic curriculum. In that sense, it is broadening access to a wider constituency, but how that is influencing behaviour and strategic contribution is not clear.

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The significance of organisational culture The need to manage and prepare the organisation culture is another key learning point, and echoes the findings of Newton et al. (2002). However, this finding has several strands to it. First, there is a need to prepare the organisation for e-learning at all levels. All three companies found that there was more resistance to the introduction, use and development of e-learning solutions from the higher levels of the organisation than the lower levels, with a familiar refrain being that “the more senior the grade of employee, the less likely they are to want to accept e-learning material”. Second, there is the attitude to learning and training in general. E-learning is no more likely to motivate the person who doesn’t want to learn than any other form of learning, and therefore the problem of motivation remains. There is also the importance of building a learning culture within the organisation, which will facilitate and support the transition to e-learning. Organisational readiness involves a number of aspects, but in particular includes managing the change process and managing technology. The complexity of the change requires managing a number of different interfaces involving, for example, senior managers, suppliers, and potential learners. Thus, implementing e-learning requires a comprehensive and effective approach to change management, as advocated in much of the organisational change literature (e.g. Beer et al., 1990; Kotter, 1995). It must be a strategically led and supported initiative that integrates with the overall business strategy and not just a cost-saving and efficiency measure. Moreover, in managing the development of a “learning culture”, it is hard to see how e-learning has contributed to this. Further research is needed to understand how the learner engages with the e-learning material, and whether this encourages a lifelong learning culture expected through the development of strategically orientated third-generation corporate universities. Conclusion Given the investment in e-learning by these case companies, it is clear that it is considered to be a central plank of their learning and development strategy. In terms of the corporate university models reviewed earlier, the investment in technology suggests that these institutions are third-generation corporate universities (Walton, 1999), where technology is used to deliver training and development to a broad organisational constituency. However, while technological support for training and development may provide access for the whole of the workforce, even for suppliers and customers, this review suggest a number of difficulties that must be considered. First, the drive for efficiency tends to override the adoption and inclusion of the full range of technical possibilities of an e-learning pedagogy, and the technology itself may be a significant barrier. While technological solutions to the management and delivery of e-learning are developing at pace, for the full possibilities of e-learning to be realised requires significant investment in technological capability and in pedagogical design. Currently, however, the companies are largely using e-learning to deliver generic “off the shelf” solutions. There is an inherent tension between technological possibilities of an e-learning pedagogy, and the cost of implementation. Second, while the implementation of e-learning may deliver ROI in terms of costs and efficiency savings, the lack of assessment of learners’ experience is a concern. It is difficult to see how organisations can claim a strategic and cultural contribution when

the learner’s voice is almost silent in the assessment of e-learning. Evaluation must be broadened to include behavioral outcomes and learners’ responses to e-learning programmes. Third, there is sufficient evidence from these case companies to suggest that the culture of the companies may play a significant part in the acceptance, or not, of e-learning. Considerable effort will need to be expended in order to create “organisational readiness” for the change to an e-learning strategy. That e-learning has the potential to be a key learning and development strategy within the corporate university is not in doubt. However, the method and design of its adoption will limit its contribution to the organisation. If corporate universities that adopt e-learning are to achieve the strategic and cultural contribution expected in the corporate university models suggested by Nevins (1998) and Fresina (1997), then the implementation of e-learning must address more than the efficiency and flexibility agenda emphasised in these organisations. Moreover, and perhaps most importantly, given that the aim of the sophisticated corporate university is to achieve a strategic and cultural contribution to competitiveness, evaluation of the adoption of e-learning needs to be more sophisticated and to attend to the learners’ experience and behavioral outcomes. References Alvesson, M. and Skoldberg, K. (2000), Reflexive Methodology, Sage, London. Angel, I. (2000), “E-learning”, CIPD Conference Proceedings, Harrogate, October. Antonacopoulou, E. (2000), “Employee development through self-development in three retail banks”, Personnel Review, Vol. 29 No. 4, pp. 491-508. Arnone, M. (1998), “Corporate universities: a viewpoint on the challenges and best practices”, Career Development International, Vol. 3 No. 5, pp. 199-205. Athanasou, J. (1999), “Framework for evaluation of technology assisted learning”, Virtual University Journal, Vol. 2, pp. 13-21. BAE Systems (2004), “Virtual university programmes in leadership and management”, available at: www.baesystems.com/virtualuniversity/programmes.htm (accessed January). Barley, K. (1997), “Process and partnership: focal points for building and growing a corporate university”, Corporate University Review, Vol. 5 No. 1. Barron, T. (1999), “Harnessing online learning”, Training and Development, September, pp. 28-33. Beech, N., Cairns, G. and Robertson, T. (2000), “Transient transfusion; or the wearing-off of the governance of the soul?”, Employee Relations, Vol. 29 No. 4, pp. 460-73. Beer, M., Einstat, R. and Spector, B. (1990), “Why change programs don’t produce change”, Harvard Business Review, November-December, pp. 158-66. Campbell, I. and Dealtry, R. (2003), “The new generation of corporate universities – co-creating sustainable enterprise and development solutions”, Journal of Workplace Learning, Vol. 15 No. 7/8, pp. 368-81. Caudron, S. (1999), “Free agent learner”, Training and Development, August, pp. 27-31. Dalton, R. (1999), “Companies lead university revolution”, The Sunday Times, February 7, p. 4. Dawson, P., Preece, D. and McLoughlin, I. (2003), “From Essex to cyberspace: virtual organisational reality and real organisation virtuality”, Labour and Industry, Vol. 14 No. 1, pp. 73-89.

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Dealtry, R. (2001), “How to configure the corporate university for success”, Journal of Workplace Learning, Vol. 13 No. 2, pp. 73-9. Dealtry, R. (2002a), “Managing the issue of learning relevance in the formulation of corporate learning strategies”, Journal of Workplace Learning, Vol. 14 No. 5, pp. 209-14. Dealtry, R. (2002b), “Establishing a methodology for appraising the strategic potential of the corporate university”, Journal of Workplace Learning, Vol. 12 No. 5, pp. 217-23. Dobbs, K. (2000), “What the online world needs now”, Training, Vol. 37, September, pp. 84-94. Dringus, L. (2000), “Towards active online learning: a dramatic shift in perspectives for learner”, The Internet and Higher Education, Vol. 2 No. 4, pp. 189-95. el Tannir, A.A. (2002), “The corporate university model for continuous learning, training and development”, Education + Training, Vol. 44 No. 2, pp. 76-81. Emurian, H.H. (2001), “The consequences of e-learning”, Information Resources Management, Vol. 14 No. 2, pp. 3-5. Finn, W. (1999), “Virtuality: the new reality in training”, The Times, March 12, p. 4. Fresina, A.J. (1997), “Corporate universities can – and should – be typed according to their missions. Is yours a reinforcer, change manager or shaper?”, Corporate University Review, Vol. 5 No. 1. Fulmer, R. and Gibbs, P. (1998), “Lifelong learning at the corporate university”, Career Development International, Vol. 3 No. 5, pp. 177-85. Furnell, S., Evans, M., Phippen, A. and Abu-Rgheff, M. (1999), “Online distance learning: expectations, requirements and barriers”, Virtual University Journal, No. 2, pp. 34-48. Galagan, P.A. (2000), “E-learning revolution”, Training and Development, Vol. 54 No. 12, pp. 25-30. Govindasamy, T. (2001), “Successful implementation of e-learning: pedagogical considerations”, Internet and Higher Education, Vol. 4 No. 1, pp. 287-99. Hamblett, J. and Holden, R. (2000), “Employee-led development: another piece of left luggage?”, Personnel Review, Vol. 29 No. 4, pp. 509-20. Hammond, D. (2001), “Reality bytes”, People Management, 25 January, pp. 26-31. Hartley, D. (2000), “All aboard the e-learning train”, Training and Development, Vol. 7, pp. 37-42. Hodgson, V. (2002), “The European Union and e-learning: an examination of rhetoric, theory and practice”, Journal of Computer Assisted Learning, Vol. 18 No. 3, pp. 240-52. Honey, P. and Mumford, A. (1992), The Manual of Learning Styles, 3rd ed., Peter Honey, Maidenhead. Horwath, A. (1999), “Novice users’ reaction to a web-enriched classroom”, Virtual University Journal, Vol. 2, pp. 49-57. Koprowski, G. (2000), “Online learning: the competitive edge”, Information Week, August, pp. 124-8. Kotter, J. (1995), “Leading change: why transformation efforts fail”, Harvard Business Review, March-April, pp. 59-67. Lave, J. and Wenger, E. (1991), Situated Learning – Legitimate Peripheral Participation, Cambridge University Press, Cambridge. Leins, N.J. and Orton, P. (2000), “The five attributes of innovative e-learning”, Training and Development, Vol. 54 No. 6, pp. 47-51.

Lenderman, H. and Sandelands, E. (2002), “Learning for a purpose: building a corporate university”, International Journal of Contemporary Hospitality Management, Vol. 14 No. 7, pp. 382-4. McLennon, W. (2000), “Learning online”, Conspectus, pp. 46-8. Masie, E. (2001), “E-learning: ‘If we build it, will they come?’”, Masie Centre and ASTD Report, Alexandria, VA. Motiwalla, L. and Tello, S. (2000), “Distance learning on the internet: an exploratory study”, Internet & Higher Education, Vol. 2 No. 4, pp. 253-64. Nevins, M. (1998), “Teaching to learn and learning to teach: notes toward building a university in a management consulting firm”, Career Development International, Vol. 3 No. 5, pp. 185-93. Newmann, A. and Smith, M. (1999), “How to create a virtual learning community”, Training and Development, July, pp. 44-8. Newton, D., Hase, S. and Ellis, A. (2002), “Effective implementation of online learning: a case study of the Queensland mining industry”, Journal of Workplace Learning, Vol. 14 No. 4, pp. 156-65. Nixon, J.C. and Helms, M.M. (2002), “Corporate universities versus higher education institutions”, Industrial and Commercial Training, Vol. 34 No. 4, pp. 144-50. O’Reilly, S. (2000), “Man and machine in harmony”, Training, September. Phillips, J. (1999), Worldwide Solutions to Competition in a Global Economy, Gulf Publishing, Houston, TX. Prince, C. (2003), “Corporate education and learning: the accreditation agenda”, Journal of Workplace Learning, Vol. 15 No. 4, pp. 179-85. Prince, C. and Beaver, G. (2001), “Facilitating organisational change; the role and development of the corporate university”, Strategic Change, Vol. 10 No. 4, pp. 189-200. Rana, E. (2001), “Take initiative on online learning, trainers urged”, People Management, Vol. 25 January, p. 14. Roy, M.H. and Elfner, E. (2002), “Analysing student satisfaction with instructional technology techniques”, Industrial and Commercial Training, Vol. 34 No. 7, pp. 272-7. Sadler-Smith, E., Down, S. and Lean, J. (2000), “‘Modern’ learning methods: rhetoric and reality”, Personnel Review, Vol. 29 No. 4, pp. 474-90. Sandelands, E. and Wills, M. (1996), “Creating virtual support for lifelong learning”, The Learning Organisation, Vol. 3 No. 5, pp. 26-31. Schriver, R. and Giles, S. (1999), “Real ROI numbers”, Training and Development, August, pp. 51-2. Schwandt, T. (2003), “Three epistemological stances for qualitative human inquiry”, in Denzin, N. and Lincoln, Y. (Eds), The Landscape of Qualitative Research, Sage, London, pp. 292-331. Sloman, M. (2001), “Forewarned is forearmed”, People Management, 5 April, pp. 27-33. Sora, J.W. (2001), “Let’s pretend we’re a corporation: an introduction to the academic/corporate convergence”, Corporate Governance: International Journal of Business in Society, Vol. 1 No. 1, pp. 39-45. Swanson, S. (2001), “E-learning branches out”, Information Week, February, pp. 42-60. Walton, J. (1999), Strategic Human Resource Development, Pearson Education, Harlow. Warner, J. (1999), “Look, no classroom – BAE’s virtual university”, Flexible Working, July, pp. 12-13.

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Welle-Strand, A. and Thune, T. (2003), “E-learning policies, practices and challenges in two Norwegian organizations”, Evaluation and Program Planning, No. 26, pp. 185-92. Wilson, J. (1999), “Internet training: the time is now”, HR Focus, Vol. 76 No. 3, p. 3. Further reading Anderson, L. (2000), “Business education survey – corporate universities”, Financial Times, 23 October. Denzin, N. and Lincoln, Y. (Eds) (1998), The Landscape of Qualitative Research, Sage, London. Lincoln, Y. and Guba, E. (2003), “Paradigmatic controversies, contradictions, and emerging confluences”, in Denzin, N. and Lincoln, Y. (Eds), The Landscape of Qualitative Research, Sage, London, pp. 253-91. Maule, R. (1997), “Adult IT programs: a discourse on pedagogy and strategy on the internet”, Internet Research: Electronic Networking Applications and Policy, Vol. 7 No. 2, pp. 129-52.

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The current issue and full text archive of this journal is available at www.emeraldinsight.com/1366-5626.htm

A model of values and actions for personal knowledge management

Personal knowledge management

Ortrun Zuber-Skerritt Griffith University, Brisbane, Australia

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Abstract Purpose – The purpose of this paper is to present a “soft methodology” model in knowledge management that addresses the problem of accessing and managing one particular type of knowledge: personal (implicit/tacit) knowledge. Design/methodology/approach – The model is based on the theories and methodologies of grounded theory, adult learning, collaborative action learning and action research. These are the approaches advocated and used actively by some international action learning associations and business schools. Discusses their philosophy and explores how the values and actions that they advocate can be used to access personal knowledge for professional and organizational learning. Findings – The model presented consists of seven commonly shared values and principles of an action learning and action research (ALAR) culture, captured in the acronym ACTIONS. The paper demonstrates how these seven principles can actually be translated into concrete actions, giving examples from ALAR programs. The matching actions are captured in another acronym – REFLECT. The resulting model, from which are generated seven kinds of personal knowledge, can be used for knowledge management in management education and the workplace. Originality/value – Provides a model for developing individual knowledge management skills, which is a central concern for corporate universities and business schools. Keywords Action learning, Action research, Process management, Knowledge management Paper type Conceptual paper

Introduction Knowledge is a vital asset. Little wonder then that the “corporate university” (CU) has evolved with rapid socio-economic and technological change in the workplace and in a global environment. Companies need to respond swiftly, to be proactive, to identify and meet the present and future training and development needs of their employees, aligned with corporate mission and goals. Knowledge management (KM) is crucial. KM skills are a central concern for corporate universities and business schools. Knowledge access, acquisition, transmission, creation and development impact on strategies and methods of learning, teaching and research. Yet today, more than the transmission of technical skills and knowledge from expert to novice, we need management development strategies that foster innovative and creative thinking, problem-solving ability, life-long learning, action learning and collaborative inquiry. In this paper we address the challenge of developing generic thinking and people skills through “soft” methodologies useful for managing personal knowledge. Considerable evidence indicates that action learning and action research are effective methods for achieving propitious performance, development, An earlier version of parts of this paper was presented as a Keynote Address to the Sixth World Congress on Action Learning, Action Research and Process Management (ALARPM), Pretoria, South Africa, 21-24 September 2002, and published online in 2003: see www.up.ac.za/academic/ education/alarpm/proceedings.html

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transformational change and success for individuals and organizations, and for personal fulfilment (for example, see the special issue of The Learning Organization, Vol. 9, No. 3/4, 2002; Dotlich and Noel, 1998; Marquardt, 1999; Passfield, 1996; Zuber-Skerritt, 2002a). However, if the potential to achieve these outcomes is neglected, the organization will suffer seriously. Willis (2004) gives many examples of so-called action learning programmes that are closer to traditional training and to what she calls the 3P/intervention form of action learning (with planned, procedural and heavily programmed knowledge, than to Revans’s (1982) original “gold standard” self-organizing/evolutionary form of action learning. Readers of this journal will be familiar with the concept of “action learning” (e.g. Revans, 1982, 1991a, b; Pedler, 1997; Passfield, 1996; Donnenberg, 1999; Zuber-Skerritt, 2002b) and the concept of “action research” (e.g. Carr and Kemmis, 1986; Winter, 1989; Zuber-Skerritt, 1992a, b, 1996; Reason and Bradbury, 2001). This brief definition should suffice here: Action learning means learning from and with each other, from action and concrete experience, as well as taking action as a result of this learning. Similarly, action research is a cyclical iterative process of action and reflection on and in action. There is no separation between, but integration of practice and theory, development and research. The main difference between action learning and action research is the same as that between learning and research generally. Both include learning, searching, problem solving, systematic inquiry and reflection on action. However, action research is more systematic, rigorous, scrutinizable, verifiable, always made public (e.g. in publications, oral or written reports) and represented by a certain methodology and scientific procedure.

These approaches are not only appropriate in KM for professional and organizational development, but they also constitute a “soft methodology” that is largely ignored in the KM literature. Here we discuss the importance of action learning and action research (ALAR) for CU and KM and with a conceptual framework of ALAR values, principles and actions. Most research and development activities in KM have focused on “hard” methodologies, particularly using IT (information technology). However, a central problem in theory and practice has been how to develop in an organization the “creative and innovative capacities of human beings” (see BRINT, below). I present here a “soft methodology” model of ALAR that offers ways to access these “creative and innovative capacities of human beings”, and to develop a type of soft, implicit or tacit know-how that I call “personal knowledge”. The model aims to: . serve as a practical guide for application in situations where personal knowledge can contribute usefully to problem solving and effective organizational management for the organization and all individuals who comprise it; and . contribute to the literature on personal-knowledge creation and development in this neglected field of KM. The final personal-knowledge model is useful for any organizations and groups of people who are interested in understanding the values, world views and actions of a network organization, a learning organization and a knowledge society, as well as for deepening their understanding of the concept and practice of personal knowledge. The paper is organized in three main parts: (1) background: the importance of soft methodologies for the corporate university, knowledge management, and action learning associations;

(2) theoretical framework and value-related actions and applications: a system of values and principles, strategies and practical applications, and a combination of both with actual examples from ALAR programmes; and (3) conclusions: conceptual model of the values/strategies in an ALAR culture and seven types of personal knowledge.

Personal knowledge management

My examples draw in particular on an Australian Government (AusAID)-funded Australia-South Africa Links programme “Leadership Development of Academic Women through Action Learning and Action Research”. This programme exemplifies the utility of the values and actions of action learning for personal and organizational knowledge management.

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Background Here I briefly explain the importance of soft methodologies for corporate universities, knowledge management, and action learning associations. Corporate universities The number of corporate universities has multiplied over the last decade. Jeanne Meister of the Corporate University Xchange (CUX) explains this through a shift in corporations’ focus from employee “training” to employee “education” as a result of “the emergence of the knowledge economy” (Meister, 1998a, p. 52). Corporations believe that they can improve performance and competitiveness and achieve their strategic goals and future business success through continued employee education. The new knowledge economy is strategic in its approach to managing its prime assets, i.e. people, knowledge processes and products. Globally, knowledge assets are considered essential for economic growth, competitive advantage, and quality of human life. Meister (1998b, p. 38) defines a “corporate university” as a: . . . centralized strategic umbrella for the education and development of employees [which] is the chief vehicle for disseminating an organization’s culture and fostering the development of not only job skills, but also such core workplace skills as learning-to-learn, leadership, creative thinking, and problem solving.

However, the shift from “training” to “education” and the development of generic skills requires an understanding of epistemology (theories of knowledge), learning theories and soft methodologies, as well as culture and values. Without this understanding, the vision of a corporate university may remain espoused theory rather than actual practice (Argyris and Scho¨n, 1974). We find the same dilemma in the KM literature. Both CU and KM are marked by great innovations and advances in IT, such as web-based instruction, virtual campuses, and satellite communication. But there is little knowledge about how people learn and how to facilitate their learning-to-learn. Here I hope to contribute to this understanding. Knowledge management The BRINT Institute is arguably the world’s leading institutional resource in KM, providing a global community network for executives, professionals, researchers, and entrepreneurs in business technology, information economy, and KM. The BRINT framework for KM is based on the Institute’s research and practice. It demonstrates

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powerfully the utility of this KM framework in today’s world of unprecedented change and continuous need for creative adaptation: The focus of knowledge management is on the ever-changing environment in which societies, organizations and individuals live, work, learn, adapt, and survive. [. . .] Essentially, knowledge management embodies organizational processes that seek synergistic combination of data and information processing capacity of information technologies, and the creative and innovative capacity of human beings (see www.brint.com/km/).

Similarly, Fink and Roithmayr (2002) argue that the success of an organization depends primarily on “know-how management”, supported by “know-how engineering”. Here the quality of know-how management depends on “know-how architecture” in combination with “know-how software tools”. However, these analysts also point to the need for tools that can actually access and manage the “tacit knowledge” (Polanyi, 1958) and “implicit knowledge” (Nonaka and Takeuchi, 1995) of individuals in an organization, since this is a problem for organizations attempting to maximize the potential of their employees (or “know-how workers”) by drawing on their personal knowledge. In this discussion of KM, we consider accessing and managing tacit, implicit knowledge as personal knowledge. The soft methodologies of action learning and action research are very much consistent with the BRINT Institute’s slogan of KM: “The wise see knowledge and action as one”, and with the Institute’s mission: Educate . . . Enlighten . . . Energize . . . Developing leading edge thinking and practice on contemporary business, information, technology and knowledge management issues to facilitate organizational and individual performance, success and fulfilment.

Action learning associations Here I refer to two associations – IMCA and ALARPM – of which I am a life member, and which aim to use an action learning approach in their programmes, as explained below. . IMCA – the International Management Centres Association, operating since 1984, is based in the UK, and offers ALAR degree programmes in 15 countries on all five continents. Its recently established Revans University is The University of Action Learning in Colorado, USA[1]. . ALARPM – the Action Learning, Action Research and Process Management Association is a network organization, focusing on soft, human and social methodologies[2]. I have been involved since its beginning in 1990 and in organizing six world congresses. Its values and methods have profoundly influenced my modelling of personal-knowledge development. There are now many other network associations using action learning and action research. I offer just a few examples here: . NZARN – the New Zealand Action Research Network; . ALPARSA – Action Learning and Participatory Action Research in South Africa; . CARN – the Collaborative Action Research Network in the UK with international membership; and

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an increasing number of ALAR centres and institutes in universities and business schools, such as The Revans Institute for Action Learning and Research, University of Salford, UK; the Southern Cross University Institute of Action Research in Australia (SCIAR), the Social and Organizational Learning as Action Research team (SOLAR), University of the West of England, Bristol, UK; Business School Netherlands (BSN) in Holland and South Africa; and the SAP Business School in Vienna, where action learning and action research are included in the “Qualitative Research Methods” course.

From this background on CU, KM and action learning associations as world-wide network organizations, let us turn to the values that provide a theoretical framework and then to the value-related actions and applications for the model I propose here for developing personal knowledge. Theoretical framework and value-related actions and applications The theoretical framework for the model I offer in this paper consists of a system of seven values and principles, and seven matching actions (with applications and examples), each followed by advice based on my personal experience. Values and principles Below I outline briefly what I consider to be the most important values and principles in action learning and action research (ALAR). There are many important values and principles in ALAR, and these vary from person to person and from organization to organization. But importantly, there are some core values and principles in ALAR that its advocates and practitioners share. I have reflected on my personal experiences of collaborative AL programmes and AR projects and what might have contributed to their success or otherwise. I produced a long list of influencing factors that include values and principles. In my quest to identify the core of these values and principles I have produced the “Seven Core Values Underpinning Successful Action Learning and Action Research”, and captured these values and principles in what I believe is a powerful, meaningful acronym: ACTIONS. I list these seven points through the ACTIONS acronym below: (1) Advancement of knowledge and learning; (2) Collaboration; (3) Trust, respect and honesty; (4) Imagination and a vision for excellence; (5) Openness; (6) Non-positivist beliefs; and (7) Success. To explain, these values are inherent in an ALAR culture, because: . Advancement of learning and knowledge can be achieved on the basis of concrete experience and reflection on this experience in iterative cycles of reflection and action (or experience) – the essence of action learning. . Collaboration, team spirit and “symmetrical communication” accept that everyone is unique and equal, accepts difference positively, and has capacity to

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contribute as best they can to solving a problem. This leads to systemic development and synergy of results. Synergy is “the value that comes when the whole adds up to more than the sum of its parts” (Kanter, 1990, p. 58). Trust, honesty and respect are preconditions for the search for truth/truths – the heart of action learning and action research. Imagination, intuition, and vision of excellence enrich the pursuit of ideas, possibilities and ultimately knowledge and appreciation, and so lead to high level performance. Openness to criticism and self-criticism fosters the exploration of multiple possibilities, rather than single-minded, black and white solutions. Non-positivist assumptions and beliefs allow grounded theory to be developed, that is, theory based on data collected from multiple sources, including practitioners. Non-positivist assumptions reject the positivist belief that the only valid and legitimate kind of knowledge is scientific in nature – based on descriptions only of what can be observed, to the complete exclusion of what cannot be observed and measured. Non-positivists recognize that knowledge is produced from various sources; it must be practical and integrate both explicit and tacit knowledge, including people’s subjective insights, intuitions and hunches (Nonaka and Takeuchi, 1995). Success here means shared success, accountability, recognition and reward, manifest in learning and productivity outcomes.

Actions, applications, examples Here I outline how the above values and principles that I have acronymed into ACTIONS can actually be translated into actions that we carry out ourselves. Examples from my own experiences indicate how my associates and I have applied each of these seven principles in our action learning and action research programmes. Our strategies can be summarized in another meaningful acronym, REFLECT, as shown below: (1) Reflection on and in action; (2) Effective use of processes and methods; (3) Feedback from “critical friends”; (4) Leadership development; (5) Exploration of new opportunities; (6) Coaching; and (7) Team results. I will briefly explain and exemplify each of these seven strategies for action by relating them to the seven values/principles, as shown in Table I. I do so through examples and advice based on my personal experience. First, Advancement and Reflection: advancement of learning and knowledge has always been the primary goal of our ALAR programmes, as well as improving something in our work practice, for example, learning and teaching, management and

leadership, organizational learning and change, and so forth. Reflection on our actions has been crucial for developing concepts, principles and theories, and for advancing our learning and knowledge in the field. Typical activities are included in the well-known spiral of action research of “plan”, “act”, “observe”, “reflect”. This means in action research we start with: . planning the intervention by identifying the issues, problems or concerns within the immediate and wider contexts, analysing the situation, and considering carefully the most appropriate options for intervention and their likely outcomes; . conducting the intervention (fieldwork); . observing and evaluating the intervention; and . reflecting on the results of the evaluation, trying to understand the change process, and conceptualizing what worked and what did not work and why,

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and then revising the plan and going through the next cycle(s) of planning, acting, observing, and reflecting, etc. until desired changes have been made in the given timeframe. My advice: If you are new to action research, I recommend that you follow this cyclical process and focus on the reflection stage, because this part is often forgotten or cut short, but it is where you can best advance knowledge and your learning, individually and as a team. I also recommend that you use a reflection diary or log book to record the main events, your reflections on and learning from these events, and what action you intend to take as a consequence of your reflection and learning. These are crucial steps in developing and managing personal knowledge, and they can deliver immense personal as well as professional/ organizational reward. Second, Collaboration and Effective use of processes and methods: collaboration and teamwork in action learning and action research require effective use of emancipatory processes and methods. The Australia-South Africa Links programme followed procedures and activities that we include in most programmes, so I shall use the Links Programme to illustrate the generic model presented in Figure 1:

Figure 1. Generic model for designing action learning programmes with action research projects

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After identifying participants’ needs and concerns, we first conducted a so-called Residential Start-up Workshop in a safari resort (away from the office and family obligations). This included, for example, a basic introduction to action learning, action research, leadership, team building, values and worldviews, research paradigms and qualitative research methods, and project design, management and evaluation. In this way, project participants were prepared in how to conduct and lead an action research project. Participants then started a team project in their individual workplaces. They had regular meetings with their teams and monthly meetings of all teams from the six “technikons” (now universities of technology), where each team presented a progress report and shared their problems. The participants came to Australia for a mid-way workshop to receive expert input on matters of particular need or interest to them, including women in leadership, IT and internet searching, and writing and publishing. Each participant was paired with a mentor whom they shadowed at work to experience “a day in the life of a leader” and a night in the mentor’s home. Towards the end of the workshop, they had the opportunity to present their work in progress and to get feedback from the audience in a seminar at Griffith University in Brisbane and at the ALARPM/PAR Fifth World Congress in Ballarat (Victoria, Australia). On return to South Africa they continued their team projects, regular meetings and monthly workshops. Finally, we conducted a one-week Concluding Workshop, again in a safari resort in South Africa, with the ten Australian mentors assisting participants in the preparation for their oral presentations and written papers for publication. The highlight of the Links programme was the Presentation and Celebration Day with the teams’ presentations of their project results to their stakeholders and colleagues, the awarding of certificates from Southern Cross University, and a celebratory dinner party with singing and dancing. After this structured two-year leadership development programme, it was up to each participant to finalize their paper/s for publication. Most of them contributed to the ALAR journal and/or wrote a chapter for a book edited by Speedy (2003). Some women also published in refereed journals and presented a paper or workshop at the Sixth ALARPM/PAR World Congress.

My advice: If you have not already done so, you may try out this generic process model in your next ALAR programme or project. It is described in more detail in Zuber-Skerritt (2002c). Third, Trust and Feedback: mutual trust and honesty are the ethical foundations of collaborative action learning and action research. They need to be established at the very beginning of a programme in the Start-up Workshop since they help to engender mutual respect among participants. On this basis, participants will understand that thoughtful, frank and frequent feedback from critical friends and fellow researchers as “partners in action” and “comrades in adversity” can be extremely helpful for identifying and rectifying problems and shortfalls at an early stage so that participants

can constantly improve their practice. Honesty to oneself and others in the team is the cornerstone of searching for truth, which is the heart of rigorous action research. Building mutual trust, honesty and respect can be achieved through various methods and instruments, such as the team management system (TMS) to establish participants’ management styles and work preferences (see www.tms.com.au/tms03.html) or the “ropes” exercises, i.e. physical outdoor exercises for team building, and many more. In the South Africa Links Programme we used the Myer-Briggs Type Indicator to establish our personality types and how we make decisions and look at the world (see http://dsc.dixie.edu/testingcenter/mbti.htm). We also used AVI (A Values Instrument) to identify our values, worldviews and personal life preferences (see www.minessence.net). All of these activities lead to knowledge of oneself, including strengths and limitations, and respect for others and their differences. The intention of these exercises is for team members not only to understand and respect personal differences, but also to realize that for a “winning team” you need diversity of abilities and work preferences in order to achieve the best results and synergy. Team building enhances trust and respect between members, and these feelings can prevail in a team situation because members focus attention on the shared task and the learning and change processes, rather than on their personal prowess/vanity. These actions therefore help to diminish personal ego trips and the need to act self-defensively. In this environment of trust and respect, feedback from participants can be discussed openly with them and within the project team to make changes and improvements immediately and continuously, rather than at the end of the project. My advice: If you have not used any of these team-building instruments, I recommend you complete the AVI questionnaire on the internet (see www.minessence.net) and receive the computer analysis of your values and brain preferences in a form that is easy to understand. Or you may ask questions by e-mail on how to interpret the results. Once you understand your values, it is generally useful to discuss them with other team members, and this often makes it easier to give and receive constructive feedback. Fourth, Imagination and Leadership Development: imagination, intuition and a vision for excellence are leadership qualities that need to be developed from the very beginning of the project (e.g. in or even before the Start-up Workshop), so that the project results are of the highest quality possible. Leadership development is based on multiple intelligences, IQ as well as EQ (emotional intelligence). An example from our ALAR programmes would be an exercise of vision building where participants are asked, first individually and then in their team, to envisage their project in about three to five years time and to draw a picture, diagram or abstract signs and symbols to express their vision. Each team then explains their picture to the other teams. This is fairly hard in the very beginning, but exciting and fun in the process and in the end. The exercise can provide energy and the driving force for the whole team project work, as well as both accessing personal knowledge in individual participants and sharing it with all in the group. This creative energy can best be released by using art forms, rather than rational thinking, that is, through right-brain, rather than left-brain activities. Here I include a poem written by a doctoral student, Vicky Vaartjes, in such a vision-building workshop, as an example of how creativity, high energy and motivation can be expressed to begin an exciting journey. Vicky had never written a poem before:

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The Journey’s End Five years from now I will reflect and wonder how I felt I knew what there was to know. But now I realize that wasn’t so. This journey has caused my mind to open To new ways, New thoughts, And words to be spoken. And for me my vision has been To create a place we have never seen Where people are proud to fulfil their goals Being happy and effective in their roles And the organization is recognized for success . . . But most important in my mind Is that we create a future for all mankind For which our children’s children will thank us out loud And for which we will be proud. Is this the journey’s end I wonder? No, just part of life’s experience to learn and ponder The journey begins . . . Source: Vaartjes (2003).

My advice: When you have identified the central question and focus of your ALAR team project, I suggest you discuss your vision with your colleagues and present it as creative art through whatever medium best suits your talents and circumstances: painting, poetry, performance, song, or whatever. Even if you believe that you are not an artistic person, give your creative mind a chance to stretch, to imagine, to dare to be bold and to be different. This is a very powerful heuristic tool for building a vision of excellence and creativity since it generates energy, drive and motivation to achieve the highest quality results and to complete the project on time. Fifth, Openness and Exploration of new opportunities: openness means making oneself open to new ways of seeing, questioning, learning, appreciating and valuing. When we are open to others and to ourselves, we can identify and explore new opportunities and assess constructively the critique we receive from others. Openness allows us to admit our ignorance, failure or narrow mindedness, as well as our strengths and abilities, and to constructively use processes of self-reflection and reflection on and in action. For example, Reg Revans (1991) tells us that in 1928 he joined the Cavendish Laboratory at Cambridge University that included ten Nobel Prize winners who met in regular seminars on the condition that they would talk only about what did not go well in their work. I recognize this as true action learning and intellectual modesty, and I think if Nobel Prize winners can be intellectually modest and “share their ignorance”, we can too! Apart from the team-building exercises I have just mentioned, a useful activity for developing openness is the SWOT analysis. “SWOT” stands for “strengths, weaknesses, opportunities and threats”. Here, each team member reflects on his/her strengths and weaknesses and shares these with the other team members. The others

help the person to turn shortcomings or weaknesses into opportunities and to identify possible threats and ways to avert or overcome them. Developing an attitude of openness, and admitting one’s own weaknesses or failures, is essential for effective, transformational learning and knowledge creation and management, for the benefit of both the individual and the organization. My advice: I suggest you try to share some of your ignorance with a couple of friends. Each identify what is not going well in a part of life such as work, study, community group involvement or whatever, discuss each other’s problems and the narrow and broader contexts of these problems, and together conceive of some possible solutions. This is action learning in its original form. Sixth, Non-positivist beliefs and Coaching: positivism, based on the natural sciences, is the belief that human and social sciences, through rigorous scientific methods of observation and statistical analysis, can construct objective knowledge of reality – that is, knowledge of the world as it really is – and that social scientists can be detached observers of objective facts and arrive at “objective truth”. Positivists claim that this kind of knowledge is the only valid and legitimate one. In contrast, non-positivists believe that knowledge in the social sciences must improve practice in order to be valid and useful, integrating (not separating) theory and practice, as well as explicit and tacit knowledge, and that theory can be created through experiential knowledge by following a continuous process of: . having a concrete experience; . observing and reflecting on this experience; . forming general principles and concepts; and . testing these concepts in actual practice and gaining new concrete experience, and so forth (Kolb, 1984). When we have non-positivist assumptions and beliefs, these are often unconscious and unknown to us, but they can be made explicit to ourselves and others through coaching and asking probing questions. This awareness through questioning insight and coaching is important because our philosophical framework influences and usually it ultimately determines our actions and behaviour. I have learned from my experience in many ALAR courses with academics, postgraduates and business executives in various countries that it is possible to change a positivist into a non-positivist disposition by explaining the difference between the two main competing paradigms of positivism and phenomenology, their basic assumptions and applications, and the appropriate use of quantitative and qualitative research methods. However, it is also true that voluntary willingness to enrol and participate in an ALAR or qualitative research methods course indicates a person is willing to pursue change and is open to exploring waters they have not navigated before. As well as explanations of positivist and non-positivist approaches, participants can be introduced to important developmental strategies, which include modelling and demonstrating non-positivist thinking, mentoring people in actual practice, research and writing, and pointing out discrepancies, logical inconsistencies and inappropriate use of language. In this way, participants gradually develop a deeper understanding of epistemology (i.e. the branch of philosophy that deals with the origin and nature of knowledge), of the ALAR paradigm, and of their own values, beliefs and worldviews that are fundamental to personal knowledge.

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My advice: If you are not familiar with epistemology and research paradigms and are interested in learning more about them, I recommend that you engage in some basic reading, such as the classic by Chalmers (1982 1994), What Is this Thing Called Science?, or Moser’s (1999) paper. Seventh, and finally, Success and Team results: success is evident in the results of team projects. The intended outcome of any serious ALAR programme is to achieve success in project work as well as in the learning and development outcomes for individuals, teams and the organizations involved. Success is a likely outcome if we guide participants through the principles and actions discussed above.

Conclusions Most research in knowledge management has focused on “hard” methodologies for developing data and the information processing capacity of information technologies. “Soft” methodologies for developing human and social capabilities, including personal knowledge, have been largely overlooked. My “soft methodology” model of action learning and action research helps us to access, communicate and manage personal knowledge, to develop people’s innovative, and creative capabilities, and to emancipate these people from the shackles of positivism into a non-positivist paradigm of research, development and self-knowledge. Figure 2 is a summary model of the values/strategies in an ALAR culture and of the matching actions/applications/examples of ALAR programmes. Ideally, a holistic approach to knowledge creation and management could be achieved by combining hard and soft methodologies, as Fink and Roithmayr (2002) have argued. However, a combined methodological approach was not the purpose of this paper. Rather, I have focused on developing and managing people’s personal knowledge through soft methodology. I have incorporated constructive feedback and critical comments from many ALAR friends, associates and conference participants to bring the model to its present form in Table I and Figure 2. I invite readers to suggest further improvements, to adopt, adapt or extend the model by further research, for example by integrating the model with IT systems and tools for KM. This may be useful in organizations that are prepared to use a holistic approach to combining soft and hard methodologies – developing human and social capabilities, as well as information technologies. I hope to have shown that action learning and action research are not mere techniques. They are a Weltanschauung, a worldview, a philosophy and methodology in knowledge creation and knowledge management. They have been developed to help

Table I. Matching values and actions

Values/principles

Actions/strategies/examples

Advancement Collaboration Trust Imagination/vision Openness Non-positivist beliefs Success

Reflection Effective use of methods and processes Feedback from “critical friends” Leadership development Exploration of new opportunities Coaching Team results

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Figure 2. A model of ALAR values and actions for personal knowledge management

human beings in their groups, communities, organizations and generally in their lives and workplaces. Major outcomes of ALAR programmes include: .

practical improvement of complex problems in complex situations;

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advancement of knowledge in the particular field of the project; and

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transformational, experiential, existential, life-long learning.

In the third outcome in particular we see development of personal knowledge. If well designed, conducted and evaluated, an ALAR programme can also achieve success in: .

personal growth;

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professional development, and

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organizational and community development and change.

The model presented here is for developing, accessing and making explicit one type of knowledge in KM: the experiential, tacit and implicit knowledge that I call personal knowledge (see Figure 2). This model helps to identify seven types of personal knowledge: (1) knowledge through reflection on action/experience and through developing concepts and personal theories; (2) knowledge through collaboration and effective use of group processes (see Figure 1);

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(3) knowledge of oneself (strengths and weaknesses) and of significant others through feedback, team building, respecting personal differences, and understanding what constitutes a winning team; (4) knowledge of future goals and envisaged high achievements through vision building, creative thinking, right-brain activities and developing energy and motivation for success (see poem by Vicky Vaartjes); (5) knowledge of how to explore new opportunities through self-assessment, self-criticism, and through openness to criticism from others, “sharing ignorance” (like the Cavendish Group of Nobel Prize winners at Cambridge); (6) knowledge of our basic beliefs and of the assumptions underpinning our research and development activities through learning from mentors, coaches and from the literature abot paradigms and epistemology (e.g. Chalmers, 1982; Moser, 1999); and (7) knowledge of our team achievements and success through recognition, reward and celebration. To be competitive we must be collaborative. Some find this slogan a paradox, or a nonsense. I find it spot-on. In times of great evolutionary change, action learning network organization and corporate universities will continue to grow through creative vision, collaboration, synergy, and continuous iteration of reflection and action. The values and practices of action learning, action research and other soft methodologies enable us to develop mutual respect. They help us to connect with people with whom we learn and have fun. Importantly, they build the linkages needed in a knowledge-creating organization and in a knowledge-based society through local, national and international learning partnerships in action. Notes 1. Web sites – IMCA Association: www.imcassociation.org; IMCA Global Campus: www.i-m-c.org; University of Action Learning at Boulder: www.u-a-l.org; Global Action Research Centre: www.i-m-c.pacific-garc.org 2. See www.alarpm.org.au

References Argyris, C. and Scho¨n, D.A. (1974), Theory in Practice: Increasing Personal Effectiveness, Jossey-Bass, San Francisco, CA. Carr, W. and Kemmis, S. (1986), Becoming Critical: Education, Knowledge and Action Research, Deakin University Press, Geelong. Chalmers, A.F. (1982), What Is This Thing Called Science?, Queensland University Press, Brisbane. Chalmers, A.F. (1994), Wege der Wissenschaft: Einfu¨hrung in die Wissenschaftstheorie, 3 auflage, Springer, Berlin. Donnenberg, O. (Ed.) (1999), Action Learning. Ein Handbuch, Klett-Cotta, Stuttgart. Dotlich, D.L. and Noel, J.L. (1998), Action Learning: How the World’s Top Companies Are Re-Creating Their Leaders and Themselves, Jossey-Bass, San Francisco, CA.

Fink, K. and Roithmayr, F. (2002), “Problem solving/problem solving methods ¼ knowhow architecture/knowhow software tools”, in Mathera, W. and Schneider, K. (Eds), Business Information for Management, SAP Business School, Vienna, pp. 195-207. Kanter, R. (1990), When Giants Learn to Dance, Unwin, London. Kolb, D. (1984), Experiential Learning: Experience as the Source of Learning and Development, Prentice-Hall, Englewood Cliffs, NJ. Marquardt, M.J. (1999), Action Learning in Action: Transforming Problems and People for World-Class Organizational Learning, Davis-Black, Palo Alto, CA. Meister, J. (1998a), “Extending the short shelf life of knowledge”, Training and Development, Vol. 52 No. 1, pp. 38-43. Meister, J. (1998b), “Ten steps to creating a corporate university”, Training and Development, Vol. 52 No. 6, pp. 52-3. Moser, H. (1999), “Thick description and abduction: paradigm change in social research”, available at: www.schulnetz.ch/unterrichten/fachbereiche/medienseminar/paradigms.htm Nonaka, I. and Takeuchi, H. (1995), The Knowledge Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press, Oxford. Passfield, R. (1996), “Action learning for professional and organizational development: an action research case study in higher education”, PhD thesis, Griffith University, Brisbane. Pedler, M. (1997), Action Learning in Practice, Gower, Aldershot. Polanyi, M. (1958), Personal Knowledge, Routledge & Kegan Paul, London. Reason, P. and Bradbury, H. (Eds) (2001), Handbook of Action Research: Participatory Inquiry and Practice, Sage, London. Revans, R. (1982), The Origins and Growth of Action Learning, Chartwell-Bratt, Bromley. Revans, R. (1991a), “The concept, origin and growth of action learning”, in Zuber-Skerritt, O. (Ed.), Action Learning for Improved Performance, AEBIS Publishing, Brisbane, pp. 14-25. Revans, R. (1991b), “Reg Revans speaks about action learning”, video programme (now on DVD) produced by Ortrun Zuber-Skerritt, Video Vision, ITS, University of Queensland, Brisbane. Speedy, S. (Ed.) (2003), Women Using Action Learning and Action Research: The South African Context, Southern Cross University Press, Lismore. Vaartjes, V. (2003), “Creating the conditions for sustainable, strategic organizational change: an action research study of an internal laboratory service”, Doctor of Management thesis, International Management Centres, Pacific Region, and Southern Cross University, Lismore. Willis, V.J. (2004), “Inspecting cases against Revans’ ‘gold standard’ of action learning”, Action Learning: Research and Practice, Vol. 1 No. 1, pp. 11-27. Winter, R. (1989), Learning from Experience: Principles and Practice in Action Research, Falmer Press, London. Zuber-Skerritt, O. (1992a), Professional Development in Higher Education – A Theoretical Framework for Action Research, Kogan Page, London. Zuber-Skerritt, O. (1992b), Action Research in Higher Education – Examples and Reflections, Kogan Page, London. Zuber-Skerritt, O. (1996), New Directions in Action Research, Falmer Press, London. Zuber-Skerritt, O. (Ed.) (2002a), “Action learning, action research and process management”, The Learning Organization, Vol. 9 No. 3/4, special double issue. Zuber-Skerritt, O. (2002b), “The concept of action learning”, in Mathera, W. and Schneider, K. (Eds), Business Information for Management, SAP Business School, Vienna, pp. 107-26.

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Further reading Altrichter, H., Kemmis, S., McTaggart, R. and Zuber-Skerritt, O. (2002), “The concept of action research”, The Learning Organization, Vol. 9 No. 3, pp. 125-31. Glasser, W. (1984), Take Effective Control of Your Life, Harper & Row, New York, NY. Limerick, D. and Cunnington, B. (1998), Managing the New Organization: A Blueprint for Networks and Strategic Alliances, Business and Professional Publishing, Chadswood.

The Emerald Research Register for this journal is available at www.emeraldinsight.com/researchregister

The current issue and full text archive of this journal is available at www.emeraldinsight.com/1366-5626.htm

Achieving integrated performance management with the corporate university Richard Dealtry

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Intellectual Partnerships, Birmingham, UK Abstract Purpose – This article aims to deliver synoptic perspectives on the evolution taking place in corporate university management best practice. Design/methodology/approach – The insights are based on the author’s co-creative client experience in the design, management and impact studies of this business and organisation development intervention. The text and narrative imagery provide contextual insights into the many challenges and pivotal issues that have to be addressed and resolved if the potential of the corporate university is to be fully realised as a definitive agency for managing the psyche of the learning organisation in knowledge-intensive environments. Findings – The article articulates the timeline evolution of the new-generation corporate university concept and introduces practical frameworks for configuring and upgrading these platforms through higher levels of value-added design and development. Practical implications – Provides a breakthrough perspective on the real-time co-creative multi-disciplinary environment, infrastructures and transferable skill sets around which viable new generation company solutions are being designed and sustained. Originality/value – The article will be of special interest to those career-minded professional managers and academics who wish to envision the new genre of the corporate university and be well informed about the concepts, practicalities and professionalism required for success. Keywords Performance management, Stakeholder analysis, Training, Value added, Learning curves Paper type Conceptual paper

New generation perspectives Due to the turbulent atmospherics of the knowledge-intensive business environment, it is impractical to attempt to discuss the role of the corporate university other than in the context of satisficing all the firm’s stakeholders. The reason for this is that the corporate university can only serve a really useful purpose as a highly adaptive and integral part of the business entity by being both strategically prescient and practical in character. It has wide-ranging implications for everyone in its role as the inspirational hub for business enterprise, being instrumental in creating new knowledge, harvesting knowledge and disseminating and exploiting new knowledge. Its raison d’eˆtre has emerged after a long period of identity crisis as essentially that of an animator and systemic change agent, introducing and assisting in the evolution and management of new ways of thinking and in the creation of the many processes of continuous adaptation that are necessary for the competitive organisation to thrive and survive the dynamics of the real-time environment. For success the new generation of corporate universities aims to do just that, drawing together all the strands of performance improvement into a coherent whole, or as one CEO put it, filling in the white spaces on the organisational chart.

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The generative formation of their strategic role now commences with an in-depth multi-disciplinary understanding of the nature of the business entity, its diverse relationships – both internally and externally – and its very special individualistic needs. Clear perspectives on the existing intellectual abilities and processes that are available to build and sustain the perceived intrinsic value of the business are essential in order to determine its starting point and potential role. This is a very important point from which to commence the journey into its design and management, as the future viability of the corporate university will depend on the ability of its managers to make a major contribution in the persistent renewal, quality and visibility of the firm’s business-led intellectual capabilities relative to their regional and global competitors. They have to be seen to have the intellectual and robust business integrity and capability that will stimulate and add additional levels of value in every area of the businesses’ activities. They must have those qualities that will satisfy the diverse demands of all the stakeholders, be it in the form of higher levels of equity share value, the firm being the company of first choice to work for, or its reputation as a caring employer in terms of customer service, environmental concern, and many other stakeholder-related issues. Ensuring that the firm has the ability to resource, cohesively handle and canalise these development strands into a coherent unambiguous strategic new learning development programme is the very exacting role of the corporate university manager. It is an ongoing intervention that facilitates the constant improvement and release of the firm’s latent intellectual equity, i.e. the effectiveness with which an organisation utilises the potential of its human capital. How it achieves that in the form of sustainable practice is a unique challenge that demands open minds to all possibilities, proactive flexibility, radical new thinking that challenges current ways of doing things, and a modus operandi that appeals to everyone. This description of the role could imply an overbearing sense of corporatism in its approach which would be very misleading, as the overriding objective of the corporate university intervention is to provide personal development environments and supporting enterprise learning networks that readily connect with the career and lifestyle aspirations of each individual in the organisation, and at the same time provide as many organic development learning opportunities as possible for them to realise their potential and ambitions. As one CEO commented, “this company is essentially a knowledge based enterprise with its own distinctive life-cycle career path and it is only as good as the sum of the individual careers of the people who work here. Personal development plans and arranging opportunities for personal fulfilment at all levels through business-led based projects is the cornerstone of our corporate academy business development planning”. Engendering intellectual equity This type of enlightened knowledge economy corporate university management thinking does, however, benefit from having a contextual base model from which to manage its evolution. The intelligent organisation model approach described below is the home-grown organic model that we apply, and involves a perspective sequence of three co-creative events: (1) the vision of the organisation has to be very clear and top management have to interpret and divine that vision in terms of its intellectual purpose (P1);

(2) the level and mix of intellectual properties (P2) to realise that vision are known and specified; and (3) the intellectual practices (P3) employed in the organisation’s development programmes are timely, relevant and connected to the manifestation of those intellectual properties in P2.

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All three strands – purpose, properties and practice (PPP) – run in a stream iteratively and counterpoint to one-another, first one and then the other taking over as the main subject of attention (see Figure 1). This basic process engages all the potential and capabilities of an organisation as a fully functioning business brain and breaks out of the confines of classic organisational paradigms, mechanistic strategies and planning thinking routines. Each iteration promotes a unique conceptual perspective of the firm’s intellectual promise and what it has to do to develop its people and thereby fully materialise top management’s vision. In the real-world, real-time environment it is a question of judgement as to how sophisticated this notion of the intelligent company is introduced and delivered. At one end of the spectrum we have the ivory tower mental masturbation approach and at the other end an approach that is tailored to the practical passionate short-term thinking that focuses only in the next adrenaline-precipitating crisis. Making the right concept connection with each particular group of managers is an art rather than a science. As a transformational framework of reference for PPP process decision making we introduce the “compass rose” schematic diagram (Figure 2), so that top management can envisage how quickly and how far it will be necessary to adjust their style in order to accelerate organic real-time development capabilities throughout all levels of the organisation. The outcome of the intelligent company PPP blended facilitation process assists in redefining the organisational and people development activities in terms that can be communicated for the mutual benefit of all internal and external stakeholders. In particular, it is important to note the engagement in-depth of top management in P1 – defining the firm’s intellectual purpose. It has often been stressed that a favourable corporate university outcome will not be achieved without top management’s commitment, and this process goes much further than mere words: it gives them full ownership of the corporate university architecture. They recognise the strengths and weaknesses of current capabilities, specialist skills and transferable skill sets that are essential to achieve their envisioned outcomes and also connects with every aspect of their wider responsibilities. The ultimate “gelling effect” is that they are also the people who sanction and provide the money for the subsequent programmes of corporate university investment.

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Figure 1. The intelligent company – PPP Model – intellectual equity generator

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Figure 2. Compass rose: pathways of learning and leadership behaviours

All too often we find, however, that this simple thematic “state-of-flow” does not take place, and consequently there is a lack of relevance in corporate university investments, and that critical discontinuities occur between new informal learning projects, formal training and development programmes and the follow through with executive action. Appraising corporate university performance on the learning curve During the last decade, the corporate university idea as an important strategic development hub has taken a firm hold in the USA in particular, and there are now

more than 2,000 corporate universities listed. In a sample of these, the lead drivers behind these developments are measured in terms of: . globalisation (46 per cent); . creation of new knowledge (77 per cent); . brand management (19 per cent); and . CPD and other (42 per cent). Globally in the other national and regional economies there are an increasing number of corporate universities or academies as they invoke programmes of accelerated catch-up with the iconic organisations based in the more mature economies. For many of these companies the move into a corporate university inspired development mode is a direct proactive response to the continuing escalation of intellectual challenges that they face in competitive business practice, scientific innovation and technological development. These companies are very radical in their approach, involving the introduction of new concepts, more diverse real-time curriculum programmes, and also in nurturing explicit intellectually based social cultures across broad areas of their organisation. Since the introduction of the corporate university or academy concept more than half a century ago, there have been many derivatives in business sectors, government departments, military services and across a wide range of sporting activities. The common bond between them all is the desire to learn about and know how to manage the competitive spur to sustain high standards of excellence in particular areas of individual and collective human endeavor. The outcomes of these interventions have ranged from substantial success to short-term mediocre performances and some well-known and notable failures. The variability of these outcomes has given rise to our undertaking several lines of inquiry which have indicated that the profiles of the under-performers are due mainly to “stuck-in-the-past” shortcomings that are characterised in the corporate university management styles outlined below. All of these inevitably lead to an under-achieving of a satisfactory return on the corporate university enterprise investment. The most common management style contributing to failure or under-performance has been the corporate university “copycat” or “me too-ism” syndrome. This style of corporate university intervention is based upon replicating how other firms or organisations are doing it, “so we will do the same”. In these cases we have found there is little or no innovative interpretation of the concept locally, and de facto that the copy-template firms or organisations are dated in their design or in completely different business sectors, resulting in local intervention becoming quite unsuitable for purpose and out of character. A second problem management style that we have identified is that of the corporate university attempting to mimic the academic university or business school educational paradigm. In such cases, the integrative business case is not well founded. It should also be clear that there is little point in reinventing a mature and highly efficient academic paradigm that is culturally different, has taken years to build and resource and is, in most cases, there to serve a quite different purpose. The third distorted perspective of the corporate university is that it is a glossed-up and re-badged training and development function, i.e. the innovative knowledge economy inspired evolutionary thrust towards a systemic real-time strategic and

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tactical performance improvement intervention is very largely not manifest in these developments. Another – and quite common – corporate university investment is in the style of the ivory tower or magnificent edifice bricks-and-mortar development. These developments usually have all the physical hallmarks of a university campus and corporate status without the accompanying faculty resources and the disciplines we associate with intellectual rigour and best practice processes. In some case studies we have also found little evidence of an in-depth understanding of the potential diversity of the corporate university business development model or of even the most basic rudimentary feasibility studies having been carried out. In the absence of a well managed new-generation corporate university intellectual development framework (e.g. the PPP process or similar), we can now look at the profiles of these interventions from the point of view of their actual performance. Quite often they have been doomed to failure from the outset, having never realised the expectations of top management or their employees, or achieving a satisfactory return on investment. Positioning management styles along the learning curve Figure 3 illustrates the profiles of three lower level “stuck-in-the-past” step-change categories of management styles along the corporate university learning curve relative to the emergent new generation of corporate universities. Clearly management cannot do everything at once, but this reflects a poor quality perspective of the vision and leadership role in engendering a sustainable enterprise-based solution, one that is abreast of current organisation and business realities. Level 3 structure and practice is largely founded on strategic management theory and an objective to align new learning with a highly formalised business strategy. This strand appeared to be growing in credibility at one time but it is so dependent upon the classic strategic management process idea – an idea of business development that lost favour due to its inflexibility in meeting the realities of real-time development – and is only reliable as a basis for corporate university resourcing and evolution if the formulated strategy is totally accurate and do-able, a very rare set of circumstances based upon the fragility of business intelligence, competition and the “shelf-life” of environmental assumptions that make up these scenarios.

Figure 3. Management’s evolutionary steps along the corporate university learning curve

These three lower-level categories of intervention reflect on management’s ability to envision the accelerating trend to the modern, organically driven real-time context of business development and to identify with the need to release higher levels of people potential, make more space for them to think and act freely and to take timely action, i.e. to become world-class talented performers. The importance of the perception of the firm’s stakeholders of the corporate university investment cannot be understated. Its public brand image has far-reaching consequences, and as one senior investment analyst put it, “If you want to know whether or not a company is a good investment for the future, look at how well it manages inter alia the corporate university concept – the strategic development of its intellectual equity!”

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Moving forward – from followers to leaders To be successful in the application of the corporate university concept top management should be instrumental in branding and communicating this development process as a major learning, change event and career development opportunity for everyone (Eccles, 2004). There are, however, no “cookie cutter” solutions, no ready-made answers and “off-the-peg” instant solution management techniques. There are, however, some constructivist facilitating processes that can be usefully employed as a guideline in shaping corporate university best practices. The constructivist configuration methodology revisited here is an enabling approach that we have validated by means of corporate applications (Dealtry, 2002). Constructivist model 3: configuration approach Invoking management’s learner gate Consider Figure 4, a typical learning curve, and consider where you or your organisation is positioned along this educational learning gradient. Positioning on the curve can be denoted by the idea of a ontological frontiers model, in this case a “management learner gate”, beyond which there is a new business model landscape that contains all the properties and realities of the full corporate university concept. The emergent challenge is to characterise that concept comprehensively for your own company and use it as a “mind share” and planning base for your organisation’s further internal and external intellectual partnership development (for full details of the origins of this methodology, see Eccles, 2004).

Figure 4. A typical learning curve

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Figure 5. Thinking in discrete terms about educational models

Figure 5 illustrates that beyond management’s learner-gate there is possibly a third educational model manifesting itself which may contain elements of model 1, the traditional educational model, and model 2, the traditional company training and development model. The objective is to establish a new perspective on model 3. Would it be very similar to models 1 and 2, or alternatively would it be highly differentiated? There are many questions to be addressed concerning the roles of models 1 and 2 in the context of the corporate university intervention, and it is therefore important to raise our level of understanding about possible connections and synergies between them before moving forward through management’s learner-gate. For example, do you consider that models 1 and 2 are competing or complementary ideas? Are there issues of subject relevance to organisational goals in terms of excellence and enterprise? What are their similarities and differences? What is the present state of their relationship? What bridging skills do we have in managing these

two worlds? What is their respective cost effectiveness and return on investment? What are their strengths and weaknesses in relation to model 3? Does model 1 þ model 2 ¼ model 3? Understanding the validity or invalidity of this equation is the first “small step for mankind” along the corporate university learning curve. To what extent will the past inform the future ? Figure 6 illustrates the fields of pulse issues beyond learner-gate in the organisational and business environments that our experience and research has uncovered as being very important areas for success in corporate university decision-making. These clusters of issue dynamics have an important bearing on and a critical influence over the educational properties and intellectual attributes that will optimise model 3 for a given set of strategic conditions. If you can assess their relative importance and blend these strategic fit learning dynamics, you can begin to formulate model 3. To help us do this we have certain management guidelines that can provide a framework for assessing the relevance of model 1 and 2 characteristics and their possible derivatives, and also for innovating new advanced business-led learning paradigms. For example, beyond learner-gate we know that we must achieve strategically balanced ongoing investment thrusts in personal and organisational effectiveness and in capability development (Figure 7).

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Figure 6. A dynamic learning landscape: optimising model 3

Figure 7. Managing performance and potential

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We know that intellectual development practice must be congruent with the intellectual purpose P1 of the business (see Figure 1). In model 3’s environment we are moving on from simple ideas of skills and competencies into a managerial environment where a large part of a businesses strategy is created by chance and opportunity, and will be based on rigorous tactical and strategic learning needs assessments day-by-day and highly adaptive advanced learning capabilities. The task beyond learner-gate is to cherry-pick and innovate to optimise model 3 around a hub of developmental issues facing the business, initially emphasising “defining the menu, not eating the meal”. Table I lists many of the key subject areas that will contribute to the formulation of policy and purpose for the resolution of each of the primary relationship issues identified in Figure 7. All these influences for success have to be appraised and considered beneficially in terms of their possible contribution to model 3 on the other side of management’s learner-gate. A unique combination of these forces will shape the design and effectiveness of model 3 in each organisation. These learning events represent the areas of new learning that have to be accomplished by corporate university management to make good sense of their intended intellectual enterprise and to maximise the inherent benefits of the concept. Figure 8 illustrates the objective order that can be brought to a well-managed portfolio of corporate university development strands. The Corporate University Master Plan Blueprint future-state advantage tool being applied in its developmental mode is the management process used to facilitate mastery over this real-time new learning capability. This concludes the reminder of management’s learner-gate constructivist approach. Whether the corporate university management team use this approach or the “thinking schools” methodology (Dealtry, 2001) is a question of preference. The important factor is that it should be managed as a self-directed learning approach using double-loop learning disciplines to ensure that out-of-date assumptions and performance standards do not creep into the design considerations. Practical outcomes The output from the an organisation’s Intellectual PPP diagnostic process, when combined with the essential core transformational material in the constructivist process model 3 described above, provides a generative configuration forum for comprehensive decision-making about very specific business sector based corporate universities in terms of their priorities, objectives, structure and programmes of curriculum development. In the application sense, however, it is important to note that the corporate university concept, while regularly applied at corporate level, is a also a multi-purpose “moveable feast” that can also be applied differentially at divisional, subsidiary company and strategic business unit (SBU) levels. The same methodologies as those described above can be applied, suitably tailored, to the particular level and area of business. It is also being adopted as a framework model for small and medium enterprise academy developments. The recruitment and briefing of the Design School Group is of course a very important phase in corporate university development, and should be carried out with the same thoroughness as would be applied to any top-level appointments, avoiding any inter-functional rivalries. These appointments should be looked upon as the

Reading and righting issue dynamics

Lifelong Learning alliances learning Learning Performance infrastructure management Learning resources External provision

“Company” School of Management

(Business sector) Academy

Learning organisation/company Corporate university – the “U” Enterprise School of Management

Naming

Model 3

Board of Governors Independent body Intervention strategy

! the BRAND

Internal/external “Industry” university

Organisation

Who should learn, what, how, where and when

Training and development

In real time

Distributed learning

Top management HR function

L,C

Balancing HR Corporate investments , capability Affairs department

Ownership

Strategic fit

E-learning

Classroom teaching Experiential learning Action learning

Learning solutions

Competitive Payback objectives Shareholder Return on expectations investment Premium rate knowledge Knowledge harvesting

Quality assurance

Internal perspective Assessment External criteria/standards perspective Feedback on Differences performance

“Happy” sheets

Intellectual equity

Intellectual leadership in business sectors Competitive Real assignments Intellectual position properties

Needs of the business Needs of the organisation People performance needs People capabilities

Business-led Results-based

Project management

Resourcing

Engagement and communications Time scale

Branding programme

Objectives of model 3 Intervention strategy Define properties

Management

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Table I. Dynamics of pulse issues influencing model 3

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Figure 8. Model 3 integration

opportunity to groom tomorrow’s leaders from the in-house talent pool – people who will gain an understanding of organisational effectiveness and knowledge economy business practice and become tomorrow’s high achievers. Essentially they should be culturally diverse, potentially agents of change and business integrators, and should be capable of: . an ambassadorial role – articulating and communicating the aims and objectives of the corporate university hub as a business development platform; . visualising every aspect of the total corporate university as a strategic learning process from the design school stage through to implementation; . envisioning and professionally managing their own learning as the process unfolds; . understanding the strengths and weaknesses in the level of development and readiness for change in the current organisational set-up; . recruiting key players as and when required on a part-time or secondment basis from across the organisation; . applying with skill and navigating progressively through the CU Project Management Blueprint Master Plan (IPC Ltd, n.d.) development process and delivering feedback; . accurately determining decision resourcing requirements and realistic time scales; . co-ordinating and communicating effectively on every aspect of the brand’s progress across the company; . having a well managed project monitoring and impact feedback system; and . last but by no means least – developing the skills to assess, form and manage strategic alliances with major institutional providers. It is unrealistic to assume that corporate university development will universally attract everyone to participate in its activities and in building the corporate university change agent network. However, it is important to note that there are certain generic

characteristics or outcomes of corporate university activities that will capture the imagination and passion of those people who are inspired to become tomorrow’s corporate entrepreneurs. These are: . the corporate university hub will be largely owned by and based on a strong network of people located in different activities or functions across the organisation; . everyone will have the opportunity to achieve effective skills in “learning to learn” online within the reality of events of their departments or activities; . there will be an established voluntary cadre of people who will provide the supporting infrastructure for learners (e.g. project clients, mentors, coaches, study buddies, etc.); . there will be an efficient, distributed e-learning solution populated with quality content based on the P2 properties; . there will be a CIT infrastructure that is easily accessible to everyone with facilities to form and conduct communities of best practice around the main business disciplines; . the formal P3 programme portfolio of training and development, based on P1 and P2, will be published in detail; . a searchable knowledge harvesting database for lessons learnt will be available in support of communities of best business and management practice; . corporate university activities will become part and parcel of each individual’s career development plan and will be reviewed as a key strand within the job and performance appraisal; . there will be systems of APEL and Career Path Appreciation (CPA) through which each individual can identify and plan their work-based learning; and . both formal and informal learning will be rewarded with career credits that will count in salary and promotion reviews. From these outcomes, it should be clear that the activities of the corporate university are not there to undermine in any way the mainstream functions and activities of organisations. They are entirely complementary. They are essentially facilitative and integral, and bring the real-time learning and transferable knowledge economy skill-sets formally into being, adding depth and quality to an individual’s stream of work experience at the leading edge of the business. Achieving integrated development In good companies, top management recognise the escalating intellectual demands placed on their executives, managers, supervisors and staff in meeting the often crisis-stimulated operational and business environment demands placed upon them. They know that they need to provide rewarding opportunities for further personal development and the acquisition of leading-edge competitive business sector know-how progressively at a level that is well beyond their earlier qualifications. The accredited corporate degree management programmes at PG CMS, DMS and MBA that we have designed and delivered have proved to be immensely powerful, serving well the accelerated development of the individual participants and delivering

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high levels of return-on-investment to the companies. There are, however, many unresolved management issues around the design of real-time academically accredited programmes and the nature of the strategic alliances between the sponsoring companies and external providers. Model 3 provides the forum for that development with checks and balances to ensure that the issues of ownership, learning relevance, assessments and group composition are creatively managed so that everyone profits from these learning experiences. Customisation is the key design consideration. The corporate university, if well founded, is the first step in ensuring that a hub of organic learning corporate degree programmes are timely and fit well into the overall portfolio of corporate provision that will serve the intellectual purpose of the organisation. References Dealtry, R. (2001), “Configuring the corporate university – managing a portfolio of thinking schools”, The Journal of Workplace Learning, Vol. 13 No. 1, pp. 30-8. Dealtry, R. (2002), “Managing the corporate university learning curve”, The Journal of Workplace Learning, Vol. 14 No. 2, pp. 76-81. Eccles, G. (2004), “Marketing the corporate university or enterprise academy”, The Journal of Workplace Learning, Vol. 16 No. 7, pp. 410-18. IPC Ltd (n.d.), “The Corporate University Blueprint Master Plan”, available at: www.ipc-ltd.co.uk Further reading Kitching, A. (n.d.), “International corporate university data and information resource”, available from: [email protected]

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Designing and managing a strategic academic alliance: an Australian university experience Lindsay Ryan and Ross Morriss

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University of South Australia, Adelaide, Australia Abstract Purpose – This article outlines the experience and approach of an Australian university in developing and managing education program partnerships within industry. Design/methodology/approach – Describes how the university has established a specialist Strategic Partnerships unit for managing the customisation and delivery of postgraduate award programs and executive education to industry. Discusses some of the key issues that have contributed to the management of industry partnerships. Findings – Some of the key issues that have contributed to the management of industry partnerships include project management of industry programs and flexibility in developing and delivering education programs to industry from a university perspective. Originality/value – Provides an illustration of the growth of university and corporate education partnerships in an Australian context. Keywords Strategic alliances, Universities, Education Paper type Case study

Introduction The concept of organisations operating in-house educational institutes, academies or universities emerged in the 1960s with early examples including General Motors, McDonalds, Motorola and Disney. Since the 1980s, the concept has expanded rapidly, with one study estimating the number of corporate universities in the United States to have grown from 400 in 1988 to 1,600 in 1998 – a 400 per cent increase in ten years (Labi, 2000). This trend is also reflected in similar growth patterns throughout the United Kingdom and Europe. In Australia, the growth of corporate educational academies commenced in the late 1990s, with the Coles Institute. This academy is a unit within the national Coles Myer retail chain, and has been acknowledged as the first major corporate educational academy in Australia (Healy, 1999). While organisations are developing in-house educational institutions to meet their corporate requirements, many of these organisations are forming partnerships with traditional universities to add objectivity to their programs, enhance the credibility of their programs, and assist their employees to achieve part or full recognition for certain university business qualifications (Arnone, 1998). A worldwide study found that more than 50 per cent of corporate universities surveyed were planning to use existing or future partnerships with accredited universities to enable them to grant degrees in the fields of business and management, engineering/technical, computer science and finance/accounting (Bedar, 1999). While there is a growing source of literature on corporate universities aligning with traditional universities written from the corporate perspective (Arnone, 1998; Bedar,

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1999; Meister, 2000; Dealtry, 2001), there is little research on the alliance from a university perspective. This article discusses the experience of an Australian university, the University of South Australia (UniSA), in responding to growing demand from industry for university-corporate educational program partnerships.

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The University context In 1995, UniSA established a specialist unit called Strategic Partnerships as a means to expand the University’s links with industry to develop and deliver executive education and customised post-graduate management programs linked to recognised University awards. Initially, the Strategic Partnerships unit was located in the University’s Graduate Management School, in response to enquiries from industry for customised deliveries of postgraduate management programs. These programs included the Graduate Certificate in Management, the Graduate Diploma in Management and the Master of Business Administration. In early 2003, Strategic Partnerships expanded to become a unit of the business division/faculty with its portfolio encompassing a broad range of business education programs relevant to industry. As well as management education programs, Strategic Partnerships now has access to education programs in manufacturing, accounting, marketing, engineering, technology and health sciences. The actual programs delivered to each organisation are determined during discussions with potential industry partners. The Strategic Partnerships unit An important aspect of the Strategic Partnerships unit is that it runs as a self-contained business unit of the University. This provides a level of autonomy from schools and an impartial ability to meet the education program requirements of partner organisations. When located in the graduate management school there was a focus on the management education programs provided by this school. By operating as a separate business unit representing a range of schools, there is access to a wide range of business education programs to meet specific partner needs. Additionally, courses can be drawn from across schools to meet partner requirements for executive development programs. Independence of academic rigor in award programs is maintained, with schools controlling content and assessment. This model has not been implemented without some difficulties. Two key areas that have required a focused effort are: (1) income sharing with schools, from revenue derived from industry program deliveries; and (2) inter-school liaison/communication to raise awareness, exchange ideas and share information on courses, programs, expertise and new opportunities amongst representatives of various schools. Information obtained from the schools is used to assist partner organisations in their choice of education programs and the extent to which course content can be customised to suit their requirements. This inter-school liaison also plays a role in promoting awareness and uptake of external education program opportunities from school contacts and communications.

To address the above issues, Strategic Partnerships initiated: . a service agreement that provides explicit details in regard to school and Strategic Partnership responsibilities and details income-sharing arrangements; and . an inter-school liaison group to provide a formal structure and regular forum for communication between schools and Strategic Partnerships. Another important aspect of Strategic Partnerships is its staff. Senior personnel of the unit have university qualifications in business and/or an MBA, and maintain some academic lecturing and research activities. Additionally, these personnel have extensive business experience, with each having worked in senior management positions in private sector organisations prior to joining the University. This combination of academic involvement and business experience equips these personnel with an appreciation of the culture and operating environment of a university as well as knowledge of how business operates, and the demands and expectations of industry. This business experience is reflected in the manner in which the unit operates. Strategic Partnerships has a strategic plan that defines the direction and development strategy of the unit over the forthcoming three years. Drawn from the strategic plan is the business plan that is reviewed and updated on an annual basis. The business plan incorporates the operational plan, marketing activities, income and expenditure budgets. These plans are integrated with the University’s strategic objectives for industry links, and in particular with those of the business division/faculty. Gomes-Casseres (1998) found that effective industry alliances need an underlying business strategy that links the objectives of the alliance to the strategic objectives of the organisation. Key personnel within the Strategic Partnership unit know most partners through a range of inter-related dealings. This ensures that if one staff member is absent, on leave or moved to another position, partnership dealings continue with minimal disruption to the partner organisation. The Strategic Partnerships unit acts as a conduit between industry and the University, which allows academics to focus their skills on research and teaching. For academics, this removes the distraction and pressure to pursue partnerships with industry for education programs, and the need to develop skills in marketing and relationship management. The Strategic Partnerships unit has found relationship management to play a crucial role in maintaining existing partnerships and linking into new opportunities through word-of-mouth referrals. Partnership philosophy A core philosophy of the Strategic Partnerships unit is that it is a partner with its industry clients, not an out-sourced education provider. This philosophy is reflected by the mode in which the unit operates. From the initial meeting, the Strategic Partnerships unit discusses the specific education needs of the potential partner organisation, clarifies the purpose of their education program, defines how their program will link with the organisation’s strategic objectives, and identifies the types of participants in their program and the process for selecting participants in their program. This approach is consistent with an observation by Dealtry that a corporate university education program needs to be an organisation-driven initiative and part of

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an integrated development platform to assist the organisation to achieve its strategic vision (Dealtry, 2000). Additionally, during the initial meeting, the level of senior management involvement in the program is discussed. As education programs play an integral role in change management and organisational development, they need to be driven by senior management. Hence, as well as having senior management endorse the corporate university program, this top-level commitment needs to be underpinned by a team of people committed to the outcome of the program. This ideally includes a project leader/manager reporting directly to the chief executive of the partner organisation (Dealtry, 2001). Strategic Partnerships ensures that it has the involvement of key organisation staff by utilising regular formal and informal meetings agreed to and documented in its contractual agreements. Where applicable, senior managers are also encouraged to assist as mentors to participants during programs delivered within their organisation. The Strategic Partnerships unit works with partner organisations to identify and/or develop the most appropriate program to meet their educational requirements. Program options can range from customised executive education through to in-house delivery of the standard MBA program. Where appropriate, executive education programs are designed to integrate with University award programs, and provide credit towards certain programs with the aim of encouraging life-long learning by participants. University and organisation collaboration in customising industry programs Industry partnerships involving University award programs generally entail some degree of customisation. Partner organisations are able to nominate lecture days and times, program timeframes (within normal University academic restraints), and location of the in-house delivery of lecture sessions. Depending on the organisation and the desired learning outcomes, the customisation may extend to include: . industry-specific case studies; . industry-specific readings; . organisation executives and technical specialists as guest speakers; . industry guest speakers on specialised or technical topics; . assignments that involve participants applying the theory learned in a course to specific organisational issues or opportunities; and . development of industry-specific content to complement the core content of award courses. An example of a program tailored to meet organisational specific objectives is the South Australian Police (SAPOL) Superintendent Qualification program. This program has been developed to meet internal qualification requirements of SAPOL, while also meeting the criteria of a University award program. Strategic Partnerships worked closely with SAPOL to achieve their outcomes, drawing on existing University core program content and tailoring this to meet SAPOL specific requirements. Core theory was maintained and complemented with industry-specific case studies and readings. Additionally, senior SAPOL officers assisted as guest speakers to relate the

theory to the SAPOL context and demonstrate the application of the theory using specific examples within South Australian Police. Matching lecturer experience with partner organisation In consultation with discipline leaders of various University programs, Strategic Partnerships strives to match the skills, knowledge, style and experience of lecturers to suit the context of each partner organisation. In some instances, government agencies may request a lecturer with experience and understanding of public sector administration, while other agencies may request a lecturer with extensive experience in private sector management who is able to provide an alternative perspective for the participants in their programs. Prior to commencing a course with a particular organisation, lecturers are required to contact the organisation to introduce themselves and discuss the aims of the course and learning outcomes. Additionally, organisational expectations are discussed to ensure that the learning outcomes and any industry-specific content or examples can be integrated into the course delivery. From this meeting, partner- and industry-specific issues can be identified and included within the broader context of the course theory or as topics for discussion. The Strategic Partnerships unit also provides lecturers with a brief background profile on partner organisations, so that they have an appreciation of the organisation and its core business, industry position, trends and emerging industry issues. These aspects can be integrated within the context of the course for reference, analysis, and facilitated discussion by the lecturer. Meister’s research on industry education programs found that university lecturers who familiarised themselves with the business of their corporate partner provided an important contribution to the success of the industry program outcomes (Meister, 2000). Project management The Strategic Partnerships unit has evolved from coordinating links between industry and the University to providing a comprehensive project management approach to each of its industry partnerships. This approach involves detailed planning of each industry education program in order to address and coordinate a range of essential factors. These factors include: . specific education program requirements; . contractual arrangements; . university/partner program management; . participant selection and enrolment; . course scheduling; . materials; . venues; and . lecturing staff. A systematic approach is taken to ensure the education program is developed on time, covers all requirements, and is delivered in the most effective manner.

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Prior to any education program proceeding, an income and expenditure template is used to assess a program’s financial viability for Strategic Partnerships. Assessment is based on the agreed course fee per participant, as well as the costs associated with developing, customising and delivering the program. While every partnership program is different, the usual costs include those associated with lecturers, employment on-costs, materials, university levies, project management, venue, facilities hire and catering. Programs that are not financially viable to the Strategic Partnerships unit do not proceed. However, some programs may proceed at a reduced margin, especially where they bring prestige to the University or provide new, long-term business potential. Such examples would include: . potential future links to other similar industries or government cohorts; . an organisation that provides an entry into an industry cluster group; and . the local operation of an organisation with national or multinational operations. From a University perspective, accurate costing and financial viability is important for the sustainability of the Strategic Partnerships unit. Hence, it is essential for the unit to identify the costs and expenses associated with industry delivered programs as well as managing costs of all activities. Financial viability assists in justifying the allocation of appropriate resources to: . . .

effectively deliver customised industry programs; sustain relationships with industry partners; and maintain Strategic Partnerships as a self-contained business unit within the University.

Regular communication with organisations Regular communication between the Strategic Partnerships unit and partner organisations plays a crucial role in managing the expectations and limitations of both organisations. Strategic Partnerships uses both a formal and informal approach to partner communication. Formal communication involves the formation of a steering group with participants including representatives from the partner organisation, the Strategic Partnerships unit and academics from the University school responsible for the partner program. On average, steering groups meet once a month to formally discuss the progress of the program and the progress of participants, to assess feedback from lecturers and participants, and to plan for future intakes and courses. For the latter, this may include discussion on lecture materials, such as industry case studies and readings, guest speakers from outside the partner organisation or internal senior managers of the organisation. During the first year of a new partnership, steering groups are likely to meet frequently, such as fortnightly or even weekly, in order to develop and refine the program to the requirements of the organisation and the University. Once a partnership is well established the steering groups may meet on alternate months. Informal communication occurs on an as-needed basis, and ranges from regular telephone discussions to occasional meetings over coffee. Generally, informal discussions are between one person from the partner organisation and one from the

Strategic Partnerships unit. These informal meetings usually address operational matters as they arise. The issue of communication receives negligible comment in articles on corporate-university partnerships. From experience, Strategic Partnerships has found that regular communication is vital and cannot be assumed. Dialogue between Strategic Partnerships and industry partners plays an essential role in building rapport between the two organisations. This rapport assists in developing an understanding of the requirements, expectations and limitations of each organisation. In turn, this rapport and understanding provides the basis for developing a long-term relationship between the two organisations. Clear and realistic program objectives In order for the Strategic Partnerships unit to develop an appropriate program for a partner organisation, it is important for the organisation to define their expectations in regards to the education program outcomes. Prince (2002) suggests that organisations need to prepare a clear brief of the desired program objectives and learning outcomes. Not only are these objectives important for the Strategic Partnerships unit in developing or identifying an appropriate program, they also assist in determining realistic program outcomes. Strategic Partnerships has found that by supporting the education program with activities drawn from the partner organisation, learning outcomes can be enhanced. The types of support activities provided by partner organisations include: . providing a mentor to each participant in the program – ideally, mentors should be senior or executive managers; . identifying relevant organisation issues or opportunities that students can study as part of program assignments; . extending program participants’ job or task responsibilities where they can demonstrate the application of their learning; and . creating learning teams to work together on organisational specific problems or issues where program participants have the opportunity to develop team leader and project management skills as well as draw on the theory they have learned. This type of organisational support provides a more holistic learning experience for the individual, as it allows them to learn-by-doing within their organisation. Flexibility Embedded in the philosophy of the Strategic Partnerships approach is the need to provide flexibility within partnership arrangements relative to normal academic deliveries. This flexibility extends to: . day, time and location of delivered programs – the majority of Strategic Partnerships programs are conducted in the premises of partner organisations, on days and at times to suit each organisation; and . delivery mode – programs are delivered to suit the requirements and time frames of partner organisations, and the geographical spread of their program participants. The flexibility of program delivery options range from face-to-face to online, and increasingly, to a mixed mode delivery of blended face-to-face and

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online sessions. An example of a blended mode of delivery is face-to-face delivery for the introductory session, online for the structured education program, then one to two face-to-face sessions for guest speakers, workshops, or for participants to present their work-based projects.

Accessibility As Strategic Partnerships acts as a conduit between the University and industry, it operates using contemporary business methods rather than the approach of a traditional university unit. This contemporary business approach is reflected in the types of programs delivered, the costing of each partnership program, the project management of organisational partnerships and the accessibility for partner organisations. The Strategic Partnerships unit provides a prompt response to all requests from partner organisations and enquiries from potential partners. The staff of the Strategic Partnerships unit work closely with external partner organisations and internal university schools so that, where possible, answers to queries can be provided at the time of enquiry. Occasionally, when additional information is required, responses to partner organisations are provided the same day or within 24 hours of receiving the request. Conclusion During its nine years of operation, the Strategic Partnerships unit of the University of South Australia has identified a range of elements that contribute to developing and sustaining its education partnerships with industry. An important issue that has emerged is that universities seeking to develop partnerships with industry need to operate in a manner that is compatible with the expectations and operations of industry. This implies the need for universities to be readily accessible and responsive to enquiries from industry. Additionally, there is a necessity to adopt a flexible approach to the development of content and delivery of programs without compromising academic objectives. From a university perspective, this potentially has structural, staffing and process implications. The University of South Australia has managed this by creating a self-contained unit that has a level of autonomy, although is still closely linked to schools and university policy. Regular communication, both formal and informal, between the university and its industry partners plays an integral role in developing an understanding of the expectations, requirements and limitations of the two organisations. Critically, this also contributes to developing a long-term relationship between the two entities. An aspect universities should consider, using Strategic Partnerships as an example, is a willingness and ability to work with industry, treating the relationship as a partnership rather than as an outsourced education arrangement. References Arnone, M. (1998), “Corporate universities: a viewpoint on the challenges and best practices”, Career Development International, Vol. 3 No. 5, pp. 199-205. Bedar, S. (1999), “Corporate universities – for better or worse?”, Engineers Australia, October, p. 70.

Dealtry, R. (2000), “Strategic directions in the management of the corporate university paradigm”, The Journal of Workplace Learning, Vol. 12 No. 4, pp. 171-5. Dealtry, R. (2001), “Frequently asked questions with reference to the corporate university”, The Journal of Workplace Learning, Vol. 13 No. 5, pp. 254-9. Gomes-Casseres, B. (1998), “Do you really have an alliance strategy?”, Strategy & Leadership, September/October, pp. 6-11, available at: www.alliancestrategy.com.mainpages/ publications/sandl.shtml (accessed 12 December 2002). Healy, G. (1999), “Corporate uni a first for Deakin”, Infonautics: The Australian, 14 April, p. 35, available at: www.elibrary.com/s/edumarkau/getdoc.cgi?id ¼ 217343883 Labi, A. (2000), “Education innovations: Europe’s on-the-job education revolution”, Time International, 5 August, p. 30. Meister, J. (2000), “Corporate universities: market-driven education”, Journal of Business Disciplines, Vol. 1 No. 1, pp. 53-66. Prince, C. (2002), “Developments in the market for client-based management education”, Journal of European Industrial Training, Vol. 26 No. 7, pp. 353-9.

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Managing relationships of the republic of science and the kingdom of industry

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Jorge F.S. Gomes Instituto Superior de Psicologia Aplicada, Lisbon, Portugal

Pia Hurmelinna Department of Business Administration, Lappeenranta University of Technology, Lappeenranta, Finland

Virgı´lio Amaral Instituto Superior de Psicologia Aplicada, Lisbon, Portugal, and

Kirsimarja Blomqvist Telecom Business Research Center, Lappeenranta University of Technology, Lappeenranta, Finland Abstract Purpose – This article investigates the reasons for collaboration and the barriers to cooperation between universities and industry organizations. In an increasingly integrated world, cooperation between universities and companies is likely to grow in forthcoming years. Design/methodology/approach – The approach taken in this article differs from previous works in the sense that it reveals the psychological frameworks that academics and managers hold about collaborating with each other. Data come from a survey of academic and managerial staff working in several universities and companies in Portugal and Finland. Findings – Overall results show that academics still see companies as information sources for their researches, but they are also willing to participate in joint projects in which academic knowledge is not the sole output. Originality/value – Provides information for companies and universities with regard to how to embark on such cooperative endeavors. Keywords Universities, Organizations, Strategic alliances, Communication Paper type Research paper

The Journal of Workplace Learning Vol. 17 No. 1/2, 2005 pp. 88-98 q Emerald Group Publishing Limited 1366-5626 DOI 10.1108/13665620510574487

Introduction As a result of the changes that have emerged in the economy in the last decades, companies face new challenges to maintain their competitive advantages. One sign of such challenges is internal R&D losing its relative importance due to the benefits of facilitated access to external knowledge. R&D is, of course, continuously conducted internally, but there is an ever-increasing emphasis on establishing more contact points with external organizations (Chesbrough, 2003). This “Networked R&D Management” approach emphasizes internal and external collaboration networks as critical for companies facing a dynamic business environment: collaboration is seen as a meta-capability for innovation (Blomqvist et al., 2003). Similarly, universities need collaboration, for example to educate experts for the industry, and to raise external research funds to balance their budgets. While companies increasingly rely on joint R&D projects with other firms, universities are becoming engaged in these activities as

well (Hall et al., 2001; Cyert and Goodman, 1997; Pollitt and Mellors, 1993). Since advances in scientific understanding are important sources of technological opportunity, knowledge generated inside universities is likely to become ever more attractive in future. The basic purposes of universities and companies, however, still remain different from each other. One fundamental purpose of academia is to “produce codified theories and models that explain and predict natural reality”: conversely, business R&D aims to design and develop “produceable and useful artefacts” (Pavitt, 1998, p. 795). Because the aims differ, the means to achieve them are also different. Simplification and reduction of the number of variables to achieve analytical tractability are typical of academic research, whereas industry knowledge is more often gathered through trial and error (Pavitt, 1998). There are positive and negative outcomes from university-industry collaboration. Studies have explored the barriers to cooperation (Lopez-Martinez et al., 1994), and its benefits (e.g. Behrens and Gray, 2001). Academy-industry collaboration can be seen as a typical situation of asymmetric collaboration (Blomqvist et al., 2003), providing a potential for high synergy and collaborative benefits. However, the parties face many challenges due to asymmetric cultures and management. Collaboration between universities and firms has indeed been widely studied, but the issue is so complex that further research is needed (see, for example, Hall et al., 2003). This paper aims to understand the motives for, and barriers to, collaboration from the point of view of those working in each environment. In other words, our goal is to explore the psychological frameworks that both academics and managers hold about collaborating with each other. This study differs from previous ones in the sense that it is neither focused on goals and functions nor on strategies and structures, but on the mental and psychological characteristics of those working in academia and industry.

Why must universities and industries collaborate? Collaboration between universities and companies can lead to several benefits. Nissani (1997) names a few: creative breakthroughs, academic freedom, social change, outsider’s perspective, and flexibility of research. Other reasons include learning from one’s partner, access to knowledge networks, funding (Sa´ez et al., 2002), global improvement of both management research and management practice (Amabile et al., 2001), and blending knowledge as science and knowledge as culture (Delanty, 2001). Access to funding is a central motivation for universities to look for company partners. In fact, universities increasingly face constrained research budgets, and/or pressure towards more useful and readily applicable research (e.g. Hall et al., 2003; Cyert and Goodman, 1997). Other incentives arise as well: through collaboration universities get access to empirical data, which makes research more grounded in “real problems”, and increases the likelihood of publication. It is reasonable to propose that important research questions arise in industries facing pressures for strong technological change and global competition. Academics in active dialogue with industry managers may thus spot emerging research issues earlier than their less active colleagues. Know-how in companies may be diffused to universities not only through research, but also through networking, for example when company representatives act as visiting lecturers. In addition to this, final course assignments

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and training periods may lead to employment opportunities for graduates, and improved reputation and competitiveness of universities (Azaroff, 1982). The increasing number of joint projects makes it reasonable to assume that collaborative projects are economically viable for both universities and companies. Companies’ motivations for collaboration include getting access to scientific frontiers, increasing and using the predictive power of science, delegating selected development activities, and lack of resources (Bonaccorsi and Piccaluga, 1994; Cyert and Goodman, 1997). First (or second) mover advantages may be available to companies performing basic research, and collaborating with universities may facilitate this (Cohen and Levinthal, 1990). If there are considerable uncertainties related to some directions of development and research projects, companies can choose to share risks with universities. Large-scale testing by universities may be very important, especially when collaboration means avoiding further investment in in-house facilities (Bonaccorsi and Piccaluga, 1994). In today’s fast-paced business and lean organizations, there is little opportunity for in-depth analysis: therefore, collaboration with academics may offer well-trained analytical resources to complement corporate business analysts. Notwithstanding all these advantages, the practice shows that the industry and the university often evolve as two distinct worlds, with rare points of contact with each other. Reasons for this separation are explored in the next section. Why is collaboration difficult? Van Dierdonck and Debackere (1988) have identified three categories of barriers to collaboration: (1) cultural; (2) institutional; and (3) operational. Universities and companies have fundamentally different cultures, which are reflected in divergent goals, time orientations, basic assumptions, and languages used. Additionally, universities deal with work that is abstract, complex and ambiguous: much of the knowledge is tacit in nature, and time spans between project initiation and product creation may be very long. Unclear boundaries in interdisciplinary projects and the gap between researchers and industry representatives may lead to conflicts and disappointments because of imprecise expectations (Bruhn, 1995). Another inherent obstacle to collaboration is related to the fact that the corporate world is subject to unexpected radical changes, such as acquisitions, mergers, and bankruptcies: such scenarios are considerably less likely to happen in the academic world (Cyert and Goodman, 1997). The aims and interests of different organizations participating in a cooperation venture may differ significantly from each other. Unlike companies, academic parties do not usually pursue profits – rather, they seek science development. Concealment of information fits poorly into the academic environment, because confidential research results cannot be used as merit (e.g. to apply for a position). Dealing with the results of collaboration and making sure that both parties get them in the first place can be dealt with by contracting, but only if it is handled with care. Similar problems and questions arise with respect to other aspects of formalizing collaboration as well, such as forms of

collaboration (Amabile et al., 2001), scope of collaboration (Sa´ez et al., 2002), or even the quality of research produced (Anderson et al., 2001). Universities may face some serious difficulties related to collaboration with industry. The threat of research concentrating too much on applied research and neglecting basic research and teaching is often an issue when funding comes from companies. Graduating may take more time, and also the quality of results may be affected so that the academic requirements cannot be met without extra work. Restrictions to academic openness, for example in the form of delays in publication or problems related to confidentiality issues, may emerge (Hall et al., 2003). Accepting funding may also lead to disputes over ownership and use of intellectual property rights (Azaroff, 1982). One main worry related to collaboration from the point of view of companies is the outcome of collaboration (Pollitt and Mellors, 1993). The benefits achieved may end up being quite insignificant related to the efforts invested in joint research, and the promised technology transfer may not occur (Cyert and Goodman, 1997). The experience of not receiving enough from collaboration may be further fostered if the “not-invented-here” syndrome affects the attitudes of employees so that they do not even want to see the potential of results produced by researchers. Researchers may be unwilling to let unfinished work out of their hands (even though it might be more than enough for companies): waiting for final reports may be also time-consuming. Another risk emerges because universities collaborate with a number of (competing) companies, and unintended flows of knowledge through the university may occur. Losing proprietary information may be harmful as such, but there also is the possibility that obstacles (e.g. to patentability) arise as a result of lack of awareness or attention to these issues. Research goals The above literature review illustrates that there are many potential gains from collaboration between the academic and industrial worlds. However, it also shows many problems. The literature has been concerned with describing such reasons at the organizational, institutional, cultural and legal levels, but less empirical work exists from the individual level of analysis. Amabile et al. (2001) recognize this when they write: “academics and practitioners may be particularly likely to have different perspectives on research issues” (p. 420). Since collaboration depends on those directly involved in university-company projects, it is important to understand how the views of key actors influence decisions regarding collaboration. Key actors include academics and managers who, for one reason or the other, embark on cross-professional projects. The goal of the current research is to explore the psychological frameworks that academics and managers hold about collaborating with each other. Mental representations of these key actors are likely to influence the process and the outputs of collaboration (de Sa´ et al., 1996). In addition to this, this research also looks at differences between two country settings, i.e. Finland and Portugal. Method Participants Participants were 49 managers and academics from different companies and universities from Finland (11 academics and 13 managers) and Portugal (11 academics

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and 14 managers). The potential respondents were known to have at least some kind of experience or knowledge of university-industry collaboration. The participation rate was 62 percent. Qualifications ranged from bachelor to PhD degrees in various areas from psychology to medicine. Table I breaks down the sample into academic training, country, and academic degree. From Table I it can be seen that the sample is biased towards academic training by country: the Finnish sample is mainly composed of individuals with degrees in economics and engineering/technology, whereas the Portuguese respondents are trained in human and social sciences. The diversity of academic degrees is also wider in the Finnish sample. Tenure also ranges widely, from full professors to company vice-presidents. Variety is extensive as regards previous experience in university-industry collaboration, from being project leader and project manager, to consultancy and training. Differences also exist between countries and origin: in the Portuguese sample, 12 respondents (two of them academics) clearly stated that they did not have previous experience with collaborating with companies or with universities. In the Finnish sample, only one academic revealed a lack of experience with industry. All in all, these differences in sample composition impose caution when interpreting the results, especially those regarding differences between countries. On the other hand, these differences allow comparison between distinct paradigms of thinking, which stimulates benchmarking and gaining cross-knowledge. Instruments A survey was sent via e-mail to several managers and academic staff in Finland and Portugal. The survey consisted of two groups of questions. The first group had three questions and aimed to elicit respondents’ beliefs with regard to the issues under analysis: (1) Why would universities and companies want to cooperate? (2) What are the barriers to collaboration between universities and companies? (3) How are universities and companies dealing with the outputs of joint projects? Each of the three questions produced text material that was subsequently subjected to content analysis (Bardin, 1977). The second group of questions enquired about biographical data, including gender, position, age, tenure, previous experience with collaboration, academic training (economics, law, and so on), and academic degree (PhD, MSc/MBA, and bachelor).

Table I. Sample description by country, academic degree and academic training

Economics Engineering/technology Management/business studies Organizational behaviour/psychology Other (political science, law, medical science, language studies, and theology) Total

PhD

Finland MSc/MBA

Bach

PhD

Portugal MSc/MBA

Bach

4 3 0 0

1 6 0 0

0 3 0 1

0 0 4 2

0 0 1 7

1 1 0 3

0 7

5 12

1 5

0 6

0 8

1 6

Results Why would universities and companies want to cooperate? Table II presents the reasons given by managers and academics from Finland and Portugal to embark on collaborative work. The figures after each sentence represent the number of times the particular reason was mentioned. The most important reason for collaborating is “actualization/competitiveness” (27 hits). This category reflects the acquisition of a competitive edge, or keeping ahead of the competition through collaboration and continuous learning and development. There seems to be no difference between countries or origin (academics and managers). However, there appears to be a difference regarding who is taking more advantage of collaboration: companies seem to have more to gain from collaboration (12 – see bold numbers) than universities (5), as far as actualization/competitiveness is concerned. “Education with meaning” is the second most frequently cited category (19). This is about the need to deliver education that takes a more practical and useful approach and is more adapted to the needs and problems of society and industrial organizations. This need is particularly felt by Portuguese managers, who seem to believe that there is a wide gap between university life and corporate life. Interestingly, academics (6 hits) also believe that education should be given more meaning. As expected, this category refers to the gains that universities make from collaboration. Access to funding/financial resources (16), knowledge (14), information (13) and other resources (8) occupy the next places in the ranking. Access to knowledge, for

University

Finland

Portugal

Access to funding/financial resources: 6 (6) Access to information: 1, 5 (6) Access to knowledge: 2, 5 (7) Access to resources: 1, 3 (4) Actualization/competitiveness: 1, 2, 3 (6) Employment opportunities: 1, 2, (3) Education with meaning: 1, 3 (4) Image: 2 (2) Outsourcing R&D: 2 (2)

Access to funding/financial resources: 4, 1 (5) Access to information: 3, (3) Access to knowledge: 3, (3) Access to resources: 2, (2) Actualization/competitiveness: 3, 1 (4) Employment opportunities: 2, 1 (3) Education with meaning: 1, 1 (2) Image: 1, 1, 3 (5)

Quality of human resources 1, (1) Industry

Access to funding/financial resources: 4, (4) Access to information: 3, (3) Access to knowledge: 1 (1) Access to resources: 2, (2) Actualization/competitiveness: 3, 1, 4 (8) Education with meaning: 4, (4) Employment opportunities: 2 (2) Image: 2, 1 (3) Quality of human resources: 1, 1 (2)

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Individual gains: 1, (1) Quality of human resources: 1, 1, (2) Access to funding/financial resources: 1, (1) Access to information: 1 (1) Access to knowledge: 2, 1 (3) Actualization/competitiveness: 3, 1, 5 (9) Education with meaning: 3, 6, (9) Employment opportunities: 1, 2 (3) Quality of human resources: 1, 2, (3)

Notes: Underlined numbers indicate a reason for both companies and universities to collaborate; italic numbers indicate a reason for universities to collaborate; bold numbers indicate a reason for companies to collaborate; numbers in parentheses indicate the total number of times a particular reason was mentioned

Table II. Reasons for collaboration

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example, reflects the idea that universities and companies are high-intensive knowledge producers, which can benefit from each other greatly. “Image” (10) is worth pointing out: it reflects the organizations’ reputation, visibility, and social status, which can be gained from being on a joint project. What are the barriers to collaboration between universities and companies? Table III presents the main barriers to collaboration pointed out by respondents. The most important obstacles to collaboration were “different needs and objectives” and “attitudes” (15), followed by: “lack of knowledge/experience regarding how to collaborate” and “lack of practical knowledge in universities” (11 each), “different time horizons” (10), and “different language/mental worlds” (9). These key issues are very much in line with earlier research. However, there are differences between the groups. For example, academics feel that the main obstacle to collaboration are the divergences between what universities and companies want and need (11, with the higher contribution from Finnish respondents), whereas managers accuse universities of having knowledge poor in practical and real applications (8). Lack of practical knowledge in universities is shown in text such as: “professors are ignorant as far as solving real problems is concerned”. Finland

Portugal

University Attitudes: 2, 1 (3) Bureaucracy: 2 (2) Confidentiality: 3, 1 (4) Culture: 1, (1) Different language/mental worlds: 3, (1) Different needs and objectives: 7, 1 (8)

Attitudes: 3, 1 (4)

Industry

Attitudes: 2, 1, 2 (5) Bureaucracy: 2 (2) Confidentiality: 1 (1) Culture: 1 (1) Different language/mental worlds: 4, 1 (5) Different needs and objectives: 1, 1 (2) Lack of knowledge/experience: 3 (3) Lack of practical knowledge in universities: 4 (4) Time horizons: 2 (2) Unable to explore knowledge: 2 (2)

Culture: 4, (4) Different language/mental worlds: 1, 1 (2) Different needs and objectives: 1, 1, 1 (3) Fear of challenging mindsets: 1 (1) Lack of knowledge/experience: 2, 1 (3) Lack of knowledge/experience: 1, 1, 1 (3) Lack of practical knowledge in universities: Lack of practical knowledge in universities: 1, (1) 2 (2) Previous bad experiences: 2, (2) Time horizons: 5, (5) Unable to explore knowledge: 1, 2, 1 (4) Unable to explore knowledge: 1, (1)

Table III. Barriers to collaboration

Attitudes: 2, 1 (3) Bureaucracy: 2, 1, (3) Confidentiality: 2, 1 (3) Culture: 1 (1) Different language/mental worlds: 1 (1) Different needs and objectives: 1, 1 (2) Lack of knowledge/experience: 2, (2) Lack of practical knowledge in universities: 4, (4) Time horizons: 2, 1 (3) Unable to explore knowledge: 1, 1 (1)

Notes: Underlined numbers indicate barriers to collaboration (from both universities and companies); italic numbers indicate barriers to collaboration (from universities); bold numbers indicate barriers to collaboration (from companies); numbers in parentheses indicate the total number of times a particular barrier was mentioned

With regard to differences between countries, Finnish respondents give particular attention to time horizons between academics and managers. Bureaucracy is more of an obstacle to the Finnish sample than to the Portuguese sample, and confidentiality is rather neglected in the Portuguese sample, while to the Finnish this looks to be a very important issue.

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How are universities and companies dealing with the outputs of joint projects? The actual question posed to respondents was “How can/are results from collaboration between universities and companies being explored/used?” Table IV shows the main results. The most striking feature in Table IV is the considerable low number of solutions to explore/use results from collaboration, appointed by respondents. This is consistent with the high number of responses in Table III for the category “lack of knowledge/experience”. The Portuguese sample seems to be particularly productive in pointing to ways to explore results from collaboration between universities and companies. However, this may be due to differences between the two academic backgrounds (more “hard-science” oriented in the Finnish sample, and more “soft-science” oriented in the Portuguese sample). For example, using “media” to expose results from collaboration is a popular output among Portuguese respondents (7), but completely absent among Finnish respondents. In the same vein, “internal reflection promoted” (encouragement to organizational change via feedback and critical thinking) and “consultancy/training” are two relatively common outputs in human and social

95

University

Finland

Portugal

Customer orientation: 2

Customer orientation: 1 Company start-ups: 2

Defining risks: 1 Consultancy/training: 1 Defining right of use: 4 Internal reflection promoted/change: 3 Involving other levels in collaboration: 1 Publications: 3 Results agreed upon in the beginning: 2 Industry

Financing: 1 Internal reflection promoted: 7 Involving other levels in collaboration: 3 Media: 3 Publications: 2 Results agreed upon in the beginning: 1

Confidentiality and security: 1 Customer orientation: 1 Defining right of use: 1 Defining risks: 1 Involving other levels in collaboration: 1 Publications: 1 Results agreed upon in the beginning: 3

Company start-ups: 1 Consultancy/training: 2 Customer orientation: 2 Defining right of use: 1 Financing: 2 Internal reflection promoted: 4 Involving other levels in collaboration: 2 Media: 4 Publications: 1 Results agreed upon in the beginning: 1 Training/probation period: 3

Table IV. Exploration/use of results from collaboration

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sciences, whereas in economics, technical and engineering sciences, change comes more from technology and processes. Discussion and conclusions Although the two samples used in this study are not entirely comparable due to differences in academic backgrounds and other attributes of respondents, the results suggest that cultural (both organizational and nationality-based) differences may have an effect on the associations that academics and managers have when talking about universities and companies. When considering motivations for collaboration, many results are expected, such as obtaining funding for university research. Some differences between Finland and Portugal can be seen, however. For example, access to information and knowledge ranks high amongst Finnish academics. This may indicate the general acceptance of “knowledge-based competition” in Finland. An educational system closer to the real world is especially central for Portuguese managers. This may be explained by the fact that in Finland, collaboration between the university and industry has been a desirable goal since the early 2000s, whereas in Portugal these issues have only recently been brought to light. Concerning the barriers to collaboration, confidentiality does not seem to be important at all to Portuguese respondents, whereas the Finnish seem to consider it imperative. This may be due to Finnish respondents thinking more of applied and business-related research, which could alleviate the need for confidentiality. However, since no information was collected on what kind of collaborative university-industry relationship respondents thought about, no definite conclusion can be drawn. Background information about respondents, however, leads to the hypothesis that many of the Finnish respondents had experience of strategic and applied research collaboration related to, for example, partnerships and alliances. In summary, all respondents point out many barriers to collaboration, which reinforces the need to think thoroughly about collaboration if it is to be successful. Many ways of dealing with the results of collaboration can be found in previous research. However, we found an interesting difference between academics and managers: academics believe that companies can benefit from joint projects because of feedback related to research results, whereas among managers this was nearly completely neglected. More important to managers (especially Portuguese managers) is the announcement of joint projects and/or results in the media. Amongst other issues, these results emphasize the need to think about how feedback is being done after research, and suggests that academics may need to be more creative when presenting their work to companies. Improving dialogue between the academic and industrial worlds may prove to be a challenging task. Solutions cannot be restricted to individual differences between academics and managers, since, as suggested by this research’s respondents, many of the problems arise from organizational and institutional sources. Based on the findings reported in this text, and on authors such as Amabile et al. (2001), Azaroff (1982), Ibarra-Colado (2001), and Rynes et al. (2001), Table V puts forward a few guidelines to stimulate collaboration between the “republic of science” and the “kingdom of industry”. A final note: we have argued that there is much to be gained if universities and companies embark on collaboration projects. Several ways to overcome problems have been advanced, so that in the end cooperation is made easier and more productive.

Knowledge transfer (individuals)

Knowledge transfer (organizations)

Internal renewal

Internal renewal of academic curricula

External support to collaboration

Globalization

Investigation based on action research and action learning; Master’s and doctoral programmes directed at company workers; training probation periods and final course assignments; training and consultancy; classes delivered by practitioners and consultants; joint intervention projects Macro-organizational system: science parks, technological centers, regional clusters Meso-organizational system: business incubators, training centers (in universities), corporate universities, conferences, seminars and workshops incorporating cross-presentations Organizational structures and processes which facilitate collaboration (e.g. flat structures and more customer- and client-oriented) Academic curricula in line with the needs and problems of society and the world, emphasis on meta-competences (e.g. learning capability, creativity) Governments, public institutions, and transnational organizations (e.g. Finnish Tekes, Portuguese Fundac¸a˜o para a Cieˆncia e a Tecnologia, or the Cordis Programme) Development of the ability to attract foreign students (e.g. Erasmus programme)

However, this does not mean that universities and companies can substitute each other in the economic and social systems. They are indeed distinct, have different competencies, and fulfill different functions. It is from this difference that synergies are created and more productive results can be achieved. Equating them would be losing the possibility to achieve such outcomes. References Amabile, T.M., Patterson, C., Mueller, J., Wojcik, T., Odomorik, P.W., Marsh, M. and Kramer, S. (2001), “Academic-practitioner collaboration in management research: a case of cross-profession collaboration”, Academy of Management Journal, Vol. 44 No. 2, pp. 418-31. Anderson, N., Herriot, P. and Hodgkinson, G.P. (2001), “The practitioner-researcher divide in industrial, work and organizational (IWO) psychology: where are we now, and where do we go from here?”, Journal of Occupational and Organizational Psychology, Vol. 74, pp. 391-414. Azaroff, L.V. (1982), “Industry-university collaboration: how to make it work?”, Research Management, Vol. 25 No. 3, pp. 31-4. Bardin, L. (1977), L’analyse de contenu, Presses Universitaires de France, Paris. Behrens, T.R. and Gray, D.O. (2001), “Unintended consequences of cooperative research: impact of industry sponsorship of climate for academic freedom and other graduate student outcome”, Research Policy, Vol. 30 No. 2, pp. 179-99. ¨ ijo¨, T. (2003), “Towards networked R&D Blomqvist, K., Koivuniemi, J., Hara, V. and A management: R&D management of Sonera Corporation in a dynamic environment”, paper presented at the 2003 R&D Management Conference, Manchester, 7-9 July.

Managing relationships

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Table V. Guidelines to improve and stimulate collaboration

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Bonaccorsi, A. and Piccaluga, A. (1994), “A theoretical framework for the evaluation of university-industry relationships”, R&D Management, Vol. 24 No. 3, pp. 229-47. Bruhn, J.G. (1995), “Beyond discipline: creating a culture for interdisciplinary research”, Integrative Physiological & Behavioral Science, Vol. 30 No. 4, pp. 331-41. Chesbrough, H. (2003), “The logic of open innovation: managing intellectual property”, California Management Review, Vol. 45 No. 3, pp. 33-58. Cohen, W.M. and Levinthal, D.A. (1990), “Absorptive capacity: a new perspective on learning and innovation”, Administrative Science Quarterly, Vol. 35 No. 1, pp. 128-52. Cyert, R.M. and Goodman, P.S. (1997), “Creating effective university-industry alliances: an organizational learning perspective”, Organizational Dynamics, Vol. 25 No. 4, pp. 45-57. de Sa´, C.P., Souto, S.O. and Mo¨ller, R.C. (1996), “La repre´sentation sociale de la science par des consommateurs et par des non-consommateurs de la vulgarisation scientifique”, Les Cahiers Internationaux de Psychologie Sociale, Vol. 29, pp. 29-38. Delanty, G. (2001), “The university in the knowledge society”, Organization, Vol. 8 No. 2, pp. 149-53. Hall, B.H., Link, A.N. and Scott, J.T. (2001), “Barriers inhibiting industry from partnering with universities: evidence from the advanced technology program”, Journal of Technology Transfer, Vol. 26 No. 1/2, pp. 87-97. Hall, B.H., Link, A.N. and Scott, J.T. (2003), “Universities as research partners”, Review of Economics and Statistics, Vol. 85 No. 2, pp. 485-91. Ibarra-Colado, E. (2001), “Considering ‘new formulas’ for a ‘renewed university’: the Mexican experience”, Organization, Vol. 8 No. 2, pp. 203-17. Lopez-Martinez, R.E., Medellin, E., Scanlon, A.P. and Solleiro, J.L. (1994), “Motivations and obstacles to university industry cooperation (UIC): a Mexican case”, R&D Management, Vol. 24 No. 1, pp. 17-31. Nissani, M. (1997), “Ten cheers for interdisciplinarity: the case for interdisciplinary knowledge and research”, Social Science Journal, Vol. 34, p. 2. Pavitt, K. (1998), “The social shaping of the national science base”, Research Policy, Vol. 27 No. 8, pp. 793-805. Pollitt, D. and Mellors, C. (1993), “Making knowledge work: through closer ties between town and gown”, European Business Review, Vol. 93 No. 4, pp. 36-7. Rynes, S.L., Bartunek, J.M. and Daft, R.L. (2001), “Across the great divide: knowledge creation and transfer between practitioners and academics”, Academy of Management Journal, Vol. 44 No. 2, pp. 340-55. Sa´ez, C.B., Marco, T.G. and Arribas, E.H. (2002), “Collaboration in R&D with universities and research centres: an empirical study of Spanish firms”, R&D Management, Vol. 32 No. 4, pp. 321-41. Van Dierdonck, R. and Debackere, K. (1988), “Academic entrepreneurship at Belgian universities”, R&D Management, Vol. 18 No. 4, pp. 341-53. Further reading Miller, W.L. and Morris, L. (1999), Fourth Generation R&D: Managing Knowledge, Technology, and Innovation, Wiley, New York, NY. Teresko, J. (1997), “Winning by sharing”, Industry Week, Vol. 246 No. 2, pp. 74-9.

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Organisational intelligence

Organisational intelligence

Maurice Yolles Liverpool John Moores University, Liverpool, UK Abstract Purpose – Seeks to explore the notion of organisational intelligence as a simple extension of the notion of the idea of collective intelligence. Design/methodology/approach – Discusses organisational intelligence using previous research, which includes the Purpose, Properties and Practice model of Dealtry, and the Viable Systems model. Findings – The notion of organisational intelligence requires a metaphorically defined psychological frame of reference. In trying to formulate this metaphor, there has been a need to explore the collective from a psychological perspective. Applications of the notion of organisational intelligence operate in a variety of areas, and two of these are in organisational learning and managerial cybernetics. In the latter an interest lies in dealing with organisational pathologies, resulting in viable systems. Originality/value – Addresses the need for developing organisational intelligence.

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Keywords Organizations, Intelligence, Organizational development, Knowledge management Paper type Conceptual paper

1. Introduction The notion of organisational intelligence is an important one, and it subsumes many of the other partial paradigms, which include organisational learning and knowledge management. Taking a general perspective, it will be able deal with a variety of problems, including communications problems and quality issues. The idea that organisations fail because of human error is a defence that does not address the real problem that organisations are just not intelligent. Dealing with inadequate structures and collective processes is part and parcel of addressing the needs of developing that intelligence. There are many approaches in defining organisational intelligence. An interesting one from the perspective of its practical interests is one that has been developed by Dealtry (2005). One of his interests is in knowledge intensification within the context of corporate universities, and the notion of intellectual equity (or the effectiveness with which an organisation utilises the potential of its human capital). Often, it is implied, the potential and capabilities of an organisation operate within the confines of organisational paradigms and routines of mechanistic strategy and planning thinking. To break out of this, the PPP model was proposed. This was used to explain how the organisation might become intelligent by redefining itself and its people development activities in much clearer terms that can be communicated for the mutual benefit of all the internal and external stakeholders. The model derives from the idea that each situation promotes a unique conceptual perspective of the firm’s intellectual promise and what it has to do to develop its people, and thereby fully materialise top management’s vision. The PPP model has three related conceptualisations that connect to this idea of the intelligent organisation. They are: (1) intellectual purpose that is connected with organisational vision (P1); (2) intellectual properties that enable visions to be known and specified (P2); and

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(3) intellectual practices that have phenomenal manifestations in development programmes that are timely and relevant (P3). These three strands that constitute the PPP model are expressed in Figure 1 as purposes, properties and practice. For Dealtry, it engages all the potential and capabilities of an organisation as a fully functioning business brain, and in so doing breaks out of the confines of organisational paradigms and mechanistic strategy and planning thinking routines. Each situation promotes a unique conceptual perspective of the firm’s intellectual promise and what it has to do to develop its people and thereby fully realise top management’s vision. The PPP model is sequential and cyclic. It is sequential in that each of the P phases is activated after its predecessor, and after all have been activated the cycle begins again. Hence, phase P2 will only be activated after phase P1, and this is a prerequisite for the activation of phase P3. One must question, however, whether this neat sequential model is a realistic one, even in ideal conditions. We shall explore this idea a little further, not by centring on the PPP model itself, but rather by generating our own metaphor for the intelligent organisation. To do this we shall eventually need to centre on cybernetic theory, which is embedded in viable systems theory (Yolles, 2001). 2. Viable systems theory The approach adopted here is through viable systems theory as originally proposed by Eric Schwarz (1997). A variation on his model that defines a cybernetic interactive relationship between three ontological domains is illustrated in Figure 2, where autopoiesis is an ontological connecter between the virtual domain ideate and the phenomenal domain of social structure and behaviour. The notion of operative management derives from Schwaninger (2001). It can also be interpreted as operative politics, directly associated with autopoietic processes. Autopoiesis enables images held in the virtual domain by an autonomous actor to self-produce phenomenally, i.e. to give their images a structured related behavioural status. Autogenesis is a second-order form of autopoiesis, and gives the latter guidance through the creation of principles. These ideas are explored more deeply in Yolles (1999), Yolles and Guo (2003) and Yolles (2005). We refer to Figure 2 as the base formulation of the Social Viable System model since, unlike the earlier model of Schwarz, it is principally concerned with autonomous social systems. The approach proposes that adaptive autonomous systems have associated with them not only a phenomenal domain in which structures and behaviours occur, but also a virtual and existential domain. An example of the epistemological content of

Figure 1. PPP model for the intelligent company

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Figure 2. Symbolic ontological relationship between the three domains of Viable System

these domains is given in Figure 3, in which we demonstrate not only the three domains of the Social Viable Systems model, but also the respective systems that correspond to any autonomous social systems that have the potential for being viable. Traditionally, what we call the “phenomenal system” has been referred to simply as “the system” since it is concerned with phenomenal attributes like social structure and behaviour. The notion of the metasystem has a long history of more than a generation, originally proposed in essence by Whitehead and Russell in their book Principia Mathematics at the turn of the nineteenth century. When originally used by Stafford Beer as an applied representation of the organisational processes since the mid-1900s (see Yolles, 2004) it was seen as being essentially responsible for the control functions of the system. However, today it is considered as the seat of organisational culture and paradigm. The virtual system contains the ideate that can be manifested as phenomenal reality in a viable system. 3. Organisational intelligence It is possible to construct a theory of intelligence within the context of viable systems theory, and the details of how this can occur are due to Yolles (2005). The notion of the intelligent organisation is fashionable today, and an interest here is to postulate a set of characteristics as a metaphor that can be used to identify the nature of the intelligent organisation within behavioural and related decision-making contexts. To do this there has been a need to explore some theories of intelligence that relate to both the

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Figure 3. Epistemological content for the three ontological domains of the Social Viable Systems model

organisation and to individuals. Some of these are concerned with the psychological non-conscious, which draws us into the need for a psychological model of the organisation. A Freudian model (Freud, 1962) is chosen for this, but it must be said that what results should necessarily be considered as a detailed metaphor. Having said this, Brown (2003) and others note the importance of metaphors to science in that they enable principles to be articulated: they should not be confused with the simile, which is a simple comparator. Concepts of organisational intelligence also centre on ideas of knowledge, but they extend further than this. Our definition of an actor, a singular individual or a plurality of individuals that make up a collective organisation with intelligence, is as follows: Intelligence is closely linked with the ability of a singular or plural actor to discern attributes of cultural knowledge, and in particular to efficiently and effectively discriminate, relate, manipulate and apply that knowledge in a variety of phenomenal environments. For plural actors this facilitates collective viability.

When an organisation is viable, it has overcome the pathologies that limit its capacity to perform operations and operational processes effectively. Most organisations have some form of pathology, a notion we shall define more fully below. It is pathology that, for instance, drives them to having to develop means of identifying and managing the crises that would not occur in viable organisations. We do not have space here to discuss in depth how the notion of organisational intelligence has arisen, but ultimately it results from the consideration of a variety of conceptualisations that derive from people like Bourdieu, Gardner, Bonnet, Sloman and Schwaninger (Yolles, 2005). These and other authors have been interested in intelligence in one form or another, and here we use a metaphor for organisational intelligence that originally derives from an eclectic analysis of their ideas. Schwaninger is concerned with cybernetic intelligence in the social community, and considers the nature of viability and how it may be achieved. Bonnet and Sloman represent a more traditional information technology

goal-orientated thinking process that is common in artificial intelligence. Bordieu and Gardner were interested in intelligence within the context of child development. The psychological frame of reference (related to that of Freud, 1962) provides a basis from which they can be considered. It extends beyond the purely Freudian notions posited by Kets de Vries (1991) about how organisations can be healed. An important aspect of intelligence is autopoiesis, which in many cases is expressed in terms of political or operative processes. There is another frame of reference that is important – that of politics. Decision-making in organisations may be seen in terms of political processes in which managers and their groups each have their own approaches, wants, styles, interests and views. This idea hinges on seeing actors as pluralistic, where a “host” or “objectivised” culture provides an orientation to many sub-actor cultures (or actor subcultures) that maintain their distinct beliefs, values and attitudes. We say “objectivised” because it is the viewer who determines the cultural commonalities that exist across the subcultures that form the “host” culture in cohesive organisations. The cultural commonalities are culturally homologous, involving elements that are more or less common to all or many of the subcultures within the organisation. The culture and subcultures are jointly responsible for the structure that is ultimately created. When subcultures exist, decision-making managers usually represent them. The subcultures are reflected in the structure because the managers take responsibility for their own areas of interest and try to ensure that these interests are materialised. It is due to cultural pluralism that it is unlikely that only one goal and set of values will arise spontaneously. There will be a multiplicity of them. The creation of multiple goals requires discussion and bargaining, and any conflict that arises because goal differences are contested must be resolved. Schwaninger (2001) suggests that the intelligent organisation is adaptable, effective, virtuous, and sustainable (Table I), and we refer to this form of intelligence as cybernetic. We have already considered adaptability in terms of some of these characteristics. Some of the attributes can be expressed in terms of intrinsic processes – that is, those that occur internally to the organisation. Others are extrinsic since they are outwardly directed.

Characteristic

Intrinsic/extrinsic interests

Adaptability

Both

Extrinsic effectiveness

Extrinsic

Virtuous

Intrinsic

Sustainable

Both

Source: Based on Schwaninger (2001)

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Nature of characteristic The impetus for change comes from extrinsic stimuli to which the organisation responds, and so adaptable organisations must be responsive to change The organisation can effectively influence and shape its environment, and this implies the ability of market organisations to perform well in competitive environments The organisation is virtuous in that it can reconfigure itself in relation to its environment The organisation can make positive net contributions to viability and development of the larger suprasystem (whole) in which it is embedded. It is thus able to sustain itself

Table I. Nature of cybernetic intelligence in organisations

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Expressing the Schwaninger attributes in terms of intrinsic and extrinsic elements enables a linkage to be made to the ideas of Bourdieu about non-conscious processes. For instance, his idea of inculcation occurs through an extrinsic interaction between an actor and its environment. The environment can be seen in terms of physical or psychological structures that can facilitate and constrain extrinsic behaviour, and it is these that inculcate the actor. Actor decision-making may not be limited to making very particular types of decisions that are constrained to a narrow related environment. It is often the case that decision-makers need to achieve a degree of success in searching a wide variety of goals under a wide variety of environments. According to Levine et al. (1986) this constitutes a definition for intelligence. However, if this is the case, then some questions develop about this definition. Thus, what constitutes a “degree of success”, a “variety of goals”, and a “range of environments” is not defined. As a result, comparative evaluation is allowed into the definition of what constitutes intelligent behaviour. Consequently, intelligent behaviour may be seen as a relative concept. However, other areas of work define the nature of intelligence more broadly than simply in terms of decision-making process. Indeed, one interpretation of Gardner’s work that we shall consider below is that it can be explored in terms of culture, structure and behaviour. The concept of the intelligent plural actor is well established in the knowledge management literature (Solesbury, 1994; Quinn, 1992; Quinn, 1993). It also exists in the field of cybernetics, where an intelligent organisational actor can be read into the term complex adaptable system (McMaster, 1997; Schwaninger, 2001). From the above mentioned authors we distinguished between four dimensions of intelligence: (1) non-conscious (Bourdieu, 1984); (2) capability (Gardner, 1985); (3) decision making (Levine et al., 1986); and (4) cybernetic (Schwaninger, 2001). Two of these derive from explorations of the development of children, and the other two are specifically related to the development of human or technological organisations as agents of behaviour. Our interest will be to migrate patterns of conceptualisations from both sets within our paradigm. In this definition, and in line with the arguments about the relationship between individualism and collectivism that explain how the characteristics of the individual can be applied to the collective, we recognise that cultural knowledge relates to the values, attitudes and beliefs that enable primary propositions to develop in the unitary or plural actor, and this may be personal or social. In the latter context of a social community the knowledge is “objectivised” through the formation of normative social knowledge. Since theories about children and organisations are differently posed, contextually distinct, and each has a set of primary propositions, their paradigms are incommensurable. However, the discerning use of principles is a process that enables knowledge embedded in one theory to be migrated into a different frame of reference, a different paradigm, and through this to act catalytically for the development of new hypotheses of social community intelligence. The conceptualisations are qualitative, and may be validated through traditional means.

There is another caveat that we must consider, which comes from diicussions about the creation of a psychological frame of reference for the social. There is a distinction between children as unitary actors and socials as plural actors in that the former can be described in terms of psyche and its associative projection, and the latter is constructed and expressed in terms of the collective psyche. Any intelligence that is attributed to the unitary actor is a function of its individual psyche, while the intelligence that is attributed to the plural actor is a function of its collective psyche. The primary distinction between these two conceptualisations is that the unitary actor operates through a traditional psychological explanation, while intelligence in the plural actor is mediated by cultural structure, rationalised, and then constrained or facilitated through social structure. While there are differences between the social and the individual, it is possible to argue that there is some correspondence between them. We can note further that knowledge about cognitive aspects of organisational theory has already migrated from theories of the individual. Such theory has become important in the human resource management literature (e.g. Nadler, 1993). In the area of child development, people like Piaget (1970, 1977) have produced parallel theory in the same paradigm (Overton and McCarthy Gallagher, 1977), where cybernetic theory is also strongly linked to cognitive (or Gestalt) theory. In developing a model of plural actor intelligence, we relate the four dimensions of intelligence we have referred to and semantically migrate them into the Viable Systems Theory model. We should also note the earlier psychological frame of reference in which the cognitive, virtual and phenomenal domains were directly associated with unconscious, subconscious and conscious dimensions of social (plural actor) awareness. The model of social community intelligence that we postulate is presented in Table II, and was arrived at by exploring and interpreting conceptualisations from other authors provided in the next few subsections, and arguing that they can be represented in the three-domain model. It should be recognised that the psychological frame of reference is very much a “psychic condition”, with distinct ontological representations illustrated by the recursive use of the SVS model, as will be shown in Figure 4. The subconscious and conscious components are, however, manifested in the collective virtual and phenomenal domains respectively through processes that we call “migrations” (Yolles, 2005), which unfortunately we do not have space to discuss. These manifestations are represented in Table II. This leads to some interesting reflections. First, it provides us with an appreciation that the science of conscious intelligence centres on our awareness of extrinsic effectiveness, sustainability and morphogenic transposability. The science of subconscious intelligence involves shared appreciation of rationality, inference, cybernetics, adaptability and intrinsic virtuosity among membership of the social community. It requires that organisations that have subconscious intelligence can access their shared virtual images and modify them communally, and within a critical theory perspective this cannot be achieved through despotic means, but rather requires inclusion of unitary actors in the visualisation process. Finally, the science of unconscious intelligence (which we acquire from Bourdieu’s non-conscious conceptualisation) involves inculcation, generative structure, semantic transposability, worldview, reference, and self-awareness. Organisations that are seen as having unconscious intelligence have the

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Table II. Postulated dimensions of intelligence Attribute

Virtual/subconscious: collective superego operating through norms

Image of intentional behaviour

Inference

Appreciativeness

Rationality

Behaviour

Structure

Morphogenic transposability

(continued)

A response must be appropriate to the situation or events eliciting it. It is not a question of who judges Occurs through reflections of the structures and phenomenal objects that are associated with an organisation. Purposeful reflections centre on the virtual image Gives possible or probable consequences of experience that are logically and information related Occurs through reflections of the structures and objects that are associated with a social community. It is also connected to the facilitation and controls that are exerted by the structures and functions of organisations and the perceived phenomena that are adopted and operate through intentional behaviour

The actor can effectively influence and shape its environment, and this implies an operational ability to perform well in competitive and other situations The actor can make positive net contributions to viability and development to the whole situation in which it is involved. It is thus able to sustain itself Enables an actor’s form or structure to be transposed from one field of activity to another Connected to the facilitation and controls that are exerted by the structures and functions of organisations and the objects that they adopt and operate through In intentional situations that operate within structured environments, behaviour may be legitimate when it conforms (or illegitimate when it does not) to the constraints and facilitation decreed by the norms of the culture in which it develops

Nature

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Phenomenal/conscious: collective ego Extrinsic effectiveness reflected in common behaviours directing interests Sustainability

Domain

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Worldview

Semantic transposability

An actor has experiences that contribute to the generation of dispositional (preconscious) and structured perceptions, attitudes, and beliefs about practices Perceptions, attitudes, and beliefs about practices can be applied from one psychological field for which they were originally acquired to other fields of attention or application. This can also be related to content, and enables a meaning to be transformed from one area of activity to another related one Knowledge is generated and symbols manufactured that can be used in social interactions Enables a position or identity to be made Includes the ability to reflect on and communicate about at least some of one’s own internal processes and explain one’s actions, decisions, or conclusions. Such explanations are often elaborated on with belief-based delusions or myths

An actor is conditioned extrinsically by its environment

The impetus for change comes from extrinsic stimuli to which the actor subjectively responds, and so adaptable actors must be responsive to change. Adaptability is purposeful, it must first be expressed in the form of a virtual image that has within it optional variety. This variety can be enhanced through the creation of new knowledge An actor can reconfigure itself in relation to its environment. However, if virtuosity is to be purposeful, it must be reflected in the virtual image

Adaptability

Intrinsic virtuosity

Nature

Attribute

Existential/unconscious: cultural Inculcation states and dispositions, though likely to be manifested non-collectively through unitary actors Generative structure

Domain

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Table II.

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capacity to access their worldviews and the knowledge associated with them, and to reinvent themselves through the creation of new knowledge. Attributes of the unconscious can also be placed in terms of Wollheim’s (1999) notions about mentality, or metaphorically equivalent within the context of the plural social collective, culture. There are two aspects of this: state and disposition. Cultural state consists of impulses, perceptions, imaginings and drives: it is also transient, relatively brief, and can reoccur frequently to give the impression of a maintaining continuity. Cultural disposition consists of beliefs, knowledge, memories, abilities, phobias and obsessions. Both mental states and dispositions are causally related, cultural state being able to instantiate, terminate reinforce and attenuate cultural disposition. Cultural dispositions can also facilitate cultural states. Three very general properties characterise these two types of cultural phenomena: intentionality, subjectivity and three exclusive grades of consciousness (conscious, preconscious and unconscious). Cultural subjectivity is associated with cultural state, while cultural disposition is experienced through the cultural states in which they are manifest. Emotions also play a part in this structure. Emotions are preconscious cultural dispositions and cannot be directly experienced, while feelings are cultural states (associated with cultural dispositions) that can be experienced. Such a lower focus can include, for instance, a level lower than the unconscious. This would involve non-accessible unitary actor worldviews that are not amenable to reflection and modification for the organisation. They reside at the lower non-accessible focus that belongs to the individual disparate autonomous members of the social community. In the plural actor organisation it is likely the collective preconscious cultural disposition that is defined by the individual and distinct worldviews and associated patterns of knowledge that results in the critical idea of knowledge migration. This cultural disposition will be reflected in the virtual domain as the collective subconscious, and be responsible for differentiation across membership of a social community in the shared images that leads to diverse appreciation of common purpose. It will also be reflected in the conscious domain, resulting in the potential for diverse incoherent behaviour across the organisation. This is addressed by the creation of structures that both facilitate and constrain the behaviour of the membership of a social community, thus more effectively enabling people to work together coherently. It

is through the creation of this facilitation and constraint that the notion of legitimate (and thus illegitimate) behaviour arises. This construction has use, if we are to understand how it is possible to increase the effectiveness of the plural actor, in particular within the context of knowledge management. This may, for instance, indicate a need for plural actors to recognise and address non-conscious and subconscious aspects of their collective psyche.

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109 4. Organisational pathologies Organisations that are intelligent have the capacity to deal with their pathologies. These pathologies have been defined by (Yolles, 2005) in the introduction to his book Organisations as Complex Systems as: . . . interactive phenomenal and existential morphological conditions, which interfere with the capacity of an autonomous social collective to maintain its internal coherence, and its capacity to be viable. The phenomenal morphological interfering condition is normally to do with social structure and process, including communications. In particular they can interfere with the social collective’s capacity to manifest successfully internally constituted imagery intended as new of patterns of phenomena. They may be responsible for the development of social psychological pathologies like attributive projection. The collective judges success subjectively through its proprietary criteria. Unfortunately, such criteria do not always include pointers that provide for the recognition of the potential to introduce new pathologies during this manifestation, which can diminish the collective’s viability. The existential morphological interfering condition may be connected to cognitive structures that include patterns of belief and knowledge, and structures of consciousness that may include patterns of knowledge or processes of thinking, and at a different ontological level of consideration, patterns of cultural and paradigmatic awareness constituted as meanings.

A simpler expression of pathology is that it represents a condition of “ill-health” that inhibits the organisation from performing in a way that enables it to manifest phenomenally (through structures and behaviours) agreed and coherent ideas or purposes. Pathologies can inhibit organisations from performing properly through poor management, poor procedures, poor communications, and so on. This does not refer to individuals who may happen to be incompetent in a particular area, but to structures and processes that inhibit viability. Types of pathology that are capable of being illustrated ontologically are given in Figure 4. The first of the types of pathology (type 11 and 12) that we shall refer to occur when autopoiesis is blocked, and this can result in disassociative behaviour that has little reference to subconscious images. When this occurs, behaviour may be influenced directly by the unconscious. The second type of pathology (including type 21 and 22) that can occur is when autogenesis is blocked, so that normative coherence cannot develop within the cultural fabric of the plural actor, in part because learning is not possible. This has major implication for the way in which patterns of behaviour become manifested. Micro-variations to this can occur by defining two forms of each type of ontological pathology, as illustrated in Table III, as types 11, 12, 21, and 22. An example of the type 11 problem might be when recurrent patterns of behaviour occur independently of subconscious constraint but responsive to the instinctive or emotional unconscious. In the case of social communities that have cultural instability (where there may a be plurality of shifting norms), this non-coherent and perhaps gratuitous/non self-regulated behaviour may simply respond to the instinctive or

Table III. Types of ontological pathology, and possible associative relationships between type combinations Nature

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T22

T21

T12

T11 No phenomenal image projection or feedback resulting in direct link to existential domain No knowledge development/ learning and no phenomenal image projection. Feedback cannot be responded to No phenomenal image projection, and no possibility of coherence through learning capacity

T21

No feedback resulting in regeneration of subconscious image, and no learning process development No regeneration of subconscious image No influence of knowledge or knowledge through experience, and no evaluative development (i.e. no learning or reflection). process deriving from experience Image and phenomenal image projection cannot develop

Associative type combinations T12

1 (11 and 12) Can result in dissociative behaviour that has little reference to subconscious images. When this occurs, behaviour may be influenced directly by the unconscious. Type 11 relates to phenomenal image projection, while type 12 relates to an ability to have a feedback effect 2 (21 and 22) No changes in the normative coherence can develop within the cultural fabric of the plural actor. In type 21 existing knowledge cannot have an impact on the autopoietic loop, while in type 22 learning is not possible. This has major implication for the way in which patterns of behaviour become manifested. An example of the type of pathology might be when patterns of behaviour occur independently of subconscious constraint, but responsive to the instinctive unconscious

Pathology type

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emotional needs of individuals in that community. When type 1 and 2 pathologies occur together, behaviour is purely responsive and determined from structural capacities. Table IV suggests the composite possibilities that can arise with the combination of different microscopic ontological pathologies.

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5. Revising the PPP model Let us now return to the PPP model of Dealtry. At the beginning of this paper we questioned the neatness of the PPP model as a sequential cyclic process. Indeed, by scheduling the sequencing of each P that is required to operate in a given order, we are mechanising a social process. This is not normal, since social systems tend not to conform to mechanistic representations. They tend to be much too complex for this. We are now, therefore, in a position to explore an alternative representation and association between the three Ps. To do this it will be appropriate to establish the model using our cybernetic approach, with each P defined in Table IV, and expressed ontologically in Figure 5. It may be the case that P1, P2 and P3 will occur in a sequential order as the system evolves. This means that changing principles affect changes in the virtual image that

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Type Intellectual P Nature

Ontological connection

P1: Purpose P2: Properties

Connected with organisational vision Enable visions to be known and specified

P3: Practices

Have phenomenal manifestations in programmes of development, these manifestations being timely and relevant

Virtual domain Autopoiesis, in that these practices involve operative management and self-produce phenomena as structures and behaviours Autogenesis that enables principles to be defined and thus facilitate autopoiesis; this has a strategic dimension

Table IV. Representation of the PPP model as a viable system

Figure 5. Proposed relationship between P1, P2 and P3 in a viable system

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are then manifested phenomenally. This is a simple rational sequential argument that is comforting for managers. However, there is never any guarantee that the PPP model will operate in this way. Let us consider that P1, P2, and P3 do not operate together as a sequential and cyclic development. Rather, they have a fundamental cybernetic interconnection and they may “fire” out of sequence or fire simultaneously, resulting in impact delays. Poor sequencing or impact delays may be due to the occurrence of pathologies, or due to external factors that the organisation has not anticipated. There are two forms of anticipation (Yolles and Dubois, 2001) that relate to strategy (autogenesis) and phenomenal organisational structure. Poor anticipation may therefore also be classed as pathology. Practices develop from the current knowledge rich paradigms that the organisation has adopted: this of course assumes that there is a dominant paradigm and that the organisation is therefore not analytically schizophrenic. From this a set of principles develops that, under certain conditions that permit the notion of optimality in relation to certain specific and constrained phenomena, may be called best practices. These principles should emerge from the paradigm, but since they exist in its unconscious, there is not normally an institutional realisation that they exist, even though individual participants in the organisation may recognise it. Whether the use or recognition of these principles is timely, relevant and connected to the manifestation of intellectual properties is determined by whether pathology types 21 or 22 exist. The intellectual properties are an operative management process that enables the phenomenal manifestation of intellectual purposes. Images and purposes may not always be recognised in organisations, since they are part of the subconscious. Social psychiatrists may be needed to help organisations recognise their own images and purposes, and self-reflection through, for instance, action research, may be of value here. Where pathologies type 11 or 12 exist, the capacity to manifest image and purpose becomes seriously incapacitated. Where type 12 operates, the organisation is unable to adapt to change, and finds way of reinforcing the same intellectual properties even though their base intellectual purposes may need to be altered. This representation of the PPP model provides a further insight. Autogenesis and autopoiesis may occur simultaneously or they may not occur at all, even while intellectual purposes are maintained. Different forms of pathology can exist in an organisation so that the relationship between the three Ps is castrated, resulting in severe problems for the organisation and a likely early failure. This, of course, does not mean that the three Ps cannot occur in a sequential and cyclic pattern, but it is likely that this will occur only in very special circumstances. 6. Significance of concept of organisational intelligence The notion of organisational intelligence is best thought of as a metaphor, in particular because it draws on conceptualisations that are normally applied to the individual rather than the collective. However, the metaphor is a powerful tool, and operates to underpin many forms of scientific enquiry. In the picture of organisational intelligence offered here, arrived at by adopting cybernetic principles for the viable system, a new way of exploring the organisation in terms of its intelligence is provided. It incorporates a Freudian/Wollheimian psychological model that offers a powerful way of examining organisational situations and offers a very well developed language to explore its social psychological pathologies. Ontological pathologies also exist that

stand against the organisation’s ability to achieve and maintain its viability, and inhibits its capacity to become competitive, efficient, effective, profitable, or any of the other contextual terms that may be appropriate. There are many applications for the notion of organisational intelligence, and the idea of the intelligent organisation links intimately with that of the learning organisation. However, it is intelligence rather than knowledge management that can effectively deal with the fitness of an organisation. We have shown that the use of the viable systems approach can dig deep into the causes of why certain pathologies exist and how they can be managed. Only one illustration of the cybernetic utility of organisational intelligence has been provided through the PPP model that relates, as indicated by Dealtry, to intellectual equity. However, the idea of the intelligent organisation is broader than this. In the same way as organisational learning and knowledge management paradigms have swept the academic world in the last two decades, the organisational intelligence paradigm that is currently developing and that encompasses these and other attributes will begin to develop and predominate. Just as child intelligence was so important in the time of Piaget and Bourdieu, so the metaphors that enable ideas of collective intelligence to be applied to organisations will be important. The problems of quality that so frequently come up, in some cases dramatically (e.g. from oil tanker disasters to deadly problems in hospital procedures) are all issues, in the end, of organisational intelligence. The notion of the organisation as a psychological entity subject to analysis, as posited for instance by Kets de Vries (1991), is necessarily part of the whole conceptualisation of intelligence. It points to the development of a new status – not only for social psychologists, but also social psychiatrists – that will help diagnose organisational pathologies and help develop viable systems. They will also likely be versed in many of the subsidiary topics that include knowledge processes, organisational learning, change management, and staff inclusion/participation is organisational processes. References Bourdieu, P. (1984), Language and Symbolic Power, Polity Press, Cambridge. Brown, T.L. (2003), Making Truth: Metaphor in Science, University of Illinois Press, Chicago, IL. Dealtry, R. (2005), “Achieving integrated performance management with the corporate university”, The Journal of Workplace Learning, Vol. 16 No. 1, pp. 65-78. Freud, S. (1962), Two Short Accounts of Psycho-Analysis, Penguin, Harmondsworth, originally published in English in 1926 under the title The Problem of Lay-Analyses, Maerker-Branden, New York, NY. Gardner, H. (1985), Frame of Mind, Paladin, London. Kets de Vries, K.M.F.R. (1991), Organisations on the Couch: Clinical Perspectives on Organisational Behavior and Change, Jossey-Bass, San Francisco, CA. Levine, R.I., Drang, D.E. and Edelson, B. (1986), A Comprehensive Guide to AI and Expert Systems, McGraw-Hill, New York, NY. McMaster, M. (1997), “The praxis equation: design principles of intelligent organisation”, Knowledge Based Development Co., available at: www.co-I-l.com/coil/contents Nadler, D.A. (1993), “Concepts for the management of organisational change”, in Mayon-White, B. (Ed.), Planning and Managing Change, Harper & Row, London.

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Overton, W.F. and McCarthy Gallagher, J. (1977), Knowledge and Development: Volume 1, Advances in Research and Theory, Plenum Press, New York, NY. Piaget, J. (1970), Structuralism, Basic Books, New York, NY. Piaget, J. (1977), The Development of Thought: Equilibration of Cognitive Structures, Viking, New York, NY. Quinn, J.B. (1992), “The intelligent enterprise: a new paradigm”, Academy of Management Executive, Vol. 6 No. 4, pp. 48-63. Quinn, J.B. (1993), “Managing the intelligent enterprise: knowledge and service-based strategies”, Planning Review, Vol. 21 No. 5, pp. 13-16. Schwaninger, M. (2001), “Intelligent organisations: an integrative framework”, Systems Research and Behavioural Science, Vol. 18, pp. 137-58. Schwarz, E. (1997), “Towards a holistic cybernetics: from science through epistemology to being”, Cybernetics and Human Knowing, Vol. 4 No. 1, pp. 17-50. Solesbury, W. (1994), “Intelligent organisations: a review of the literature”, ESRC final report, available at: http://sites.netscape.net/mcyrhul/intelligent_organisations.html Wollheim, R. (1999), On the Emotions, Yale University Press, New Haven, CT. Yolles, M.I. (1999), Management Systems: A Viable Approach, Financial Times Pitman, London. Yolles, M.I. (2001), “Viable boundary critique”, Journal of the Operational Research Society, Vol. 51, January, pp. 1-12. Yolles, M.I. (2004), “The system-metasystem dichotomy”, Kybernetes, forthcoming. Yolles, M.I. (2005), Organisations as Complex Systems, forthcoming. Yolles, M.I. and Dubois, D. (2001), “Anticipatory viable systems”, International Journal of Computing Anticipatory Systems, Vol. 9, pp. 3-20. Yolles, M.I. and Guo, K. (2003), “Paradigmatic metamorphosis and organisational development”, Systems Research and Behavioural Science, Vol. 20, pp. 177-99. Further reading Bonnet, A. (1985), Artificial Intelligence, Promise and Performance, Prentice-Hall, Englewood Cliffs, NJ. Sloman, A. (1984), “The structure of the space of possible minds”, in Torrance, S. (Ed.), The Mind in the Machine, Ellis Horwood, Chichester, pp. 35-42. Yolles, M.I. (2000), “From viable systems to surfing the organisation”, Journal of Applied Systems, Vol. 1 No. 1, pp. 127-42. Yolles, M.I. (2002), “Viable boundary critique: a reply to Bryant”, Journal of the Operational Research Society, Vol. 53, pp. 1-3. Yolles, M.I. (2003), “Enhancing competitiveness in European organisation through intelligence and knowledge intensification through a specific targeted research project”, project submitted to the EU Framework 6 Research Initiative.

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An overview of strategic alliances between universities and corporations Dean Elmuti, Michael Abebe and Marco Nicolosi

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Eastern Illinois University, Charleston, Illinois, USA Abstract Purpose – Strategic alliances generally represent inter-firm cooperative agreements aimed at achieving competitive advantage for the partners. In recent years, there has a dramatic increase in strategic alliances by multinational firms.This paper aims to explore the essence of these alliances and why they have become such a growing area of research in business in recent years. Design/methodology/approach – Discusses strategic alliances between corporations and institutions of higher education. The major underlying motives for creating these alliances and the critical success factors are also discussed. The paper also analyzes the success stories. Findings – Highlights the major advantages for the academic community – research funding and practical learning opportunities for students – and for industry – lower research and development costs and technology transfer opportunities that affect competitiveness. The drawbacks may include the partners’ different working cultures and values. Finds that alliances must be supported by continuous learning and restructuring processes to overcome the differences. Originality/value – Extracts the valuable lessons that might help others to effectively utilize strategic alliances between corporations and institutions of higher education. Keywords Strategic alliances, Partnership, Information transfer, Research, Product innovation, Organizational development Paper type General review

Introduction A strategic alliance is “an agreement between firms to do business together in ways that go beyond normal company-to-company dealings, but fall short of a merger or a full partnership” (Wheelen and Hungar, 2000, p. 125). Strategic alliances generally represent inter-firm cooperative agreements aimed at achieving competitive advantage for the partners. These alliances range from informal “hand shake” agreements to formal agreements with lengthy contracts in which the parties may also exchange equity or contribute capital to form joint venture corporations. In recent years there has been a dramatic increase in strategic alliances by multinational firms (Tapsell, 1999; Farris, 1999; Hill, 1999). The last 20 years have witnessed a clear and growing pattern in strategic alliance formation among corporations (Glaister and Buckley, 1996). Even though much of the discussion in current literature regarding strategic alliances has typically focused on alliances between business ventures, strategic alliances between corporations and institutions of higher education have gained momentum in the last couple of years (Saffu and Mamman, 2000). As a result of the challenge brought about by global competition and the changing emphasis on research and development (R&D), institutions of higher education have become important parts of a cooperative agreement that tries to tackle complex, fundamental industrial problems of major business or societal significance (Dismukes and Petkovic, 1997).

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Strategic alliance trends Strategic alliances are becoming more and more prominent in the global economy. Peter Drucker, who has been known as a father of modern management theory states: “the greatest change in corporate culture, and the way business is conducted, may be the accelerating growth of relationships based not on ownership, but on partnership” (Drucker, 1996). The number of strategic alliances has almost doubled in the past ten years, and is expected to increase even more in the future (Booz Allen and Hamilton, 1997). “[M]ore than 20,000 corporate alliances have been formed world wide over the past two years, and the number of alliances in the USA has grown by 25% each year since 1987” (Farris, 1999). A survey published in Electronic Business showed that 80 percent of electronics companies have strategic alliances and most are planning or negotiating additional agreements (Vyas et al., 1995). The potential of strategic alliances is enormous. If implemented correctly, some authors claim it can dramatically improve an organization’s operations and competitiveness. Companies are forming alliances to obtain technology, to gain access specific markets, to reduce financial and political risk, and achieve or ensure competitive advantage (Wheelen and Hungar, 2000). Strategic alliances have been formed more recently between business corporations and universities with the intent of mainly funding joint research programs in exchange for options on the results of the research that might solve their practical business problems (Wheelen and Hunger, 2004, p. 307). A growing number of scientists in public and private universities are involved in close relationships with commercial firms. The relationships offer potential benefits to both parties and are encouraged by many federal and state policies (Ervin et al., 2002). According to Cyert and Goodman (1997), strategic alliances between corporations and academic institutions are worth studying for four key reasons: first, these alliances are growing in significance in terms of producing various patents, prototypes, and licenses through their research projects. They are also important from the standpoint of global competitiveness and increasing demand for innovation in products and processes. The other reason is their increasing vitality in serving as a stepping-stone for a more complex collaboration that involves multiple firms, universities and other research centers. These alliances are also playing an important role in the national R&D policy, and affect the distribution of resources considerably. This study will explore the essence of these alliances and why they have become such a growing area of research in business in recent years. The major underlying motives for creating these alliances and the critical success factors will also be discussed in detail. The study will also analyze the success stories to extract the valuable lessons that might help others to utilize the strategy effectively. The importance of industry-university alliances The primary roles that a contemporary university plays nowadays can be generally classified as the triad of teaching, research and service (Santoro, 2000). University research has traditionally focused on generating and propagating basic forms of knowledge and integrating this knowledge into an overall learning agenda. Moreover, academics concentrate on revealing new scientific knowledge, useful for providing long-term insight on basic and applied research issues which become the foundation for training future scientists, engineers and researchers. On the other hand, firms are

usually interested in utilizing the results of research to resolve current business problems or challenges of immediate concern in order to maximize earnings and stakeholder wealth (Lee, 1998). The increasing complexity of new technologies makes it extremely difficult for any one firm to encompass the necessary resources and capabilities needed for successful technological development and commercialization (Hamel and Prahalad, 1994; Woo, 2003). Consequently, some industrial firms have found that universities can be viable partners. For instance, Mansfield (1991) found a strong linkage between university basic research and the new products and processes introduced by a large number of manufacturing firms. Pisano (1990) also noted that there is strong evidence that university expertise and industrial application were positively linked in the area of biotechnology. Similarly, many pharmaceutical firms have made agreements with various universities for establishing a research project(s) in pharmacology and chemistry to help in creation of new drugs (van Rossum and Cabo, 1995). Moreover, Whirlpool Corporation has worked with a number of university researchers in the areas of computer-integrated manufacturing systems, robotics, and microelectronics to advance several new products (Sparks, 1985; Santoro, 2000). While all these studies indicate the potential power of industry-university alliances, much more evidence shows that the number of industry-university relationships, the intensity of these relationships, and the level of tangible outcomes generated lag behind industry-industry alliances (Betz, 1993). According to Santoro (2000), industry-university strategic alliances usually take place within four important components, including research support, cooperative research, knowledge transfer and technology transfer. Research support focuses more on the “contribution of both money and equipment made to universities by members of corporate community”. More recently, corporate support for university research has been much more concentrated and tied to specific projects from which a high return is expected (Fortune, 1996). Cooperative research, on the other hand, targets close interactions that involve formal institutional agreements, group arrangements and the use of institutional facilities (National Science Foundation, 1982). Santoro (2000) also discussed knowledge transfer that emphasizes n ongoing personal communications, interactive education and personnel exchanges. Knowledge transfer activities are usually seen as a good foundation for future large-scale strategic alliances between parties. Technology transfer programs in such alliances aim to integrate university-driven research into applied initiatives for the development and commercialization of new processes and products. Reasons for creating strategic alliances Strategic alliances between institutions of higher education and corporations have numerous potential benefits. They usually link the intellectual resources of a university with the problem-solving needs of a firm. While universities primarily interact with industrial firms to obtain basic research funding, access to proprietary technology, research tools and an opportunity to develop and bring technologies to the marketplace, they also collaborate to obtain industrial expertise, exposure to practical problems and employment opportunities for university graduates (National Science Foundation, 1982; Ervin et al., 2002).

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Below we discuss some notable reasons why businesses create alliances with academic institutions.

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Rising global competitiveness The speed with which technological changes are taking place, increasingly shorter product life cycles and more intense global competition has radically transformed the current competitive environment for most firms (Bettis and Hitt, 1995). Such a rapid change in the nature of competition has placed ever-increasing demands on corporations to continually create new technologies. Increasing need for innovation in products and processes Businesses nowadays anticipate that more of their revenue and earnings growth must come through the introduction of new technologies (Ali, 1994). New technologies continue to be advanced both intra-and inter-organizationally. While past practices clearly emphasized in-house initiatives, it is becoming increasingly difficult for companies to depend solely on intra-organizational methods (Santoro, 2000). Consequently, inter-organizational collaboration is frequently employed to stimulate the development and commercialization of new technologies (Parkhe, 1993). Lower R&D expenditure According to a recent study conducted by a group of scholars on publicly traded biotechnology firms, companies with university linkages had lower R&D expenses per employee. On the other hand, low R&D spending might lead a firm to further develop relationship with a university to supplement its internal research resources (George et al., 2002). The researchers analyzed 2,500 university alliances formed by 147 publicly traded biotechnological firms. Their goal was to evaluate the effects of business-university alliances on innovative output and financial performance. Based on the result of the survey, the alliances represented a more meaningful expense reduction in in-house R&D, and could lead to beneficial relationships that enhance the long term image of the company (Brennan, 2003). Technological transfer opportunities Technology transfer programs capitalize on joint industry-university research and attempt to integrate university-driven research into applied initiatives for the development and commercialization of new technologies. More specifically, technology transfer usually includes several key activities such as addressing specific research problems, providing technical expertise to companies seeking to develop new products/processes, assisting entrepreneurs in start-ups, and finally providing technology patent or licensing services (Santoro, 2000). The risk and problems facing strategic alliances There are numerous real world situations in which a university and a corporation have formed an alliance to solve a problem that was important to the company and interesting and challenging for the researchers. However, these alliances may not succeed in achieving the desired objectives because of some of the reasons discussed below (Cyert and Goodman, 1997; Kock et al., 2000).

Cultural differences The two partners have essentially different goals, time approaches, languages and assumptions. Institutions of higher education are involved in creating and spreading knowledge, while companies produce products and services in a highly competent business environment (Ervin et al., 2002; Woo, 2003). As Cyert and Goodman (1997) discovered, “[M]ost business entities frame their time in terms of meeting quarterly goals and other short term engagements whereas higher institutions have time frame with longer horizon and relatively not well defined”. In addition, the partners have different organizational cultures, languages and values that pose communication problems (Kock et al., 2000). Companies typically do not comprehend how work is allocated in universities or how university budgets are handled. University partners, on the other hand, do not understand the real market forces, time demands, and the incentive structure of the firm. Differences in objectives The very nature of the final objectives of these two institutions is dissimilar. Most companies insist on applied research that results in a marketable product or service along with new innovative processes or approaches to problem solving. The university or faculty member uses basic research more often to work towards contributions to knowledge in the form of new concepts, models, empirical findings, measurement techniques and other related objectives (Ervin et al., 2002). Even after the successful innovation of a desired output, there is a lack of coordination in taking these findings to the marketplace. According to Cyert and Goodman (1997), “even under successful alliances, there are many obstacles for moving from prototype to commercial product. The possible reasons could be first because university researchers typically lack the motivation and skill to move beyond the prototype and also due to the lack of understanding from the company side [of] the explicit and tacit knowledge inherent in the prototype”. Other external factors Organizations operate under a variety of external unexpected changes (i.e. mergers and acquisitions within the industry, and economic turmoil) and internal readjustments like reorganizations and corporate downsizing. Since many alliances between higher educational institutions and corporations are involved in researching fundamental problems that are expected not to be resolved in the very short term, projects are undertaken within a long-term relationship. Roth and Magee (2002) suggest that a five-year timeframe and commitment is necessary to ensure sufficient stability to support work in long-term research. This kind of external shock is common in industry-university alliances: it hurts both parties and affects future similar strategic alliances. Other problems in strategic alliances Several reasons are also given for the under-performance and failure of strategic alliances. The most common reasons include a break down in trust, a change in strategy, the value did not materialize, the cultures did not mesh very well and the systems were not integrated. According to a study conducted by the Financial Times (1999), the main reason strategic alliances fail to meet expectations is “the failure to

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grasp and articulate their strategic intent”. This reason is generally true for most strategic alliances, including university-industry alliances, which fall in a relatively narrow category and demand rather careful attention due to their nature and complexity. While there are natural synergies in corporate-university alliances, there are also areas of likely conflict. Universities have their own public roles and expectations (i.e. they are mandated to create and disseminate knowledge for the broad benefit of society). They are also expected to make knowledge freely and widely available through education and publication as they enjoy non-profit, tax-free status and receive public funding. On the other hand, corporations survive in contested market places where they compete for customers and investors. They need advantages over their competitors in order to provide growth and profits for their owners and employees. The need for competitive advantage extends to the corporation’s requirement for a relevant return from university research investments that can, for instance, be effectively commercialized in the market place (Kock et al., 2000; Roth and Magee, 2002). The collaboration process The collaboration process between a university and industry often starts with some kind of solicitation from each part. According to a survey conducted by the Software Engineering Institute in March 2000 (see Mead et al., 2000), 44 percent of respondents indicated that they sought out a new contact with industry, university or government to establish the collaboration, while 26 percent expanded an existing relationship to get started. The basic collaboration process between academia and industry usually starts with each party identifying what can possibly acquired from the alliance and the potential needs of the other party. It then proceeds to form a joint working group that basically develops the mission and organization of the strategic alliance. Figure 1 shows the processes of developing the proposal of cooperation and formalizing the alliance. The critical step then will be to implement the collaboration proposal and evaluating the major activities and outputs. Success factors for strategic alliances A recent survey by Technology Associates and Alliances (1999) asked 450 CEOs to rank the importance of certain success factors for strategic alliances. The results of the study indicated that most rate factors like partner selection, senior management commitment, clearly understood roles and communication between partners as “essential” components of alliance success. The commitment of senior management of all parties involved in the strategic alliance is often a critical success factor. Senior management’s commitment to an alliance is important not only to ensure that the alliance receive the necessary resources, but also to convince others throughout the organization of the importance of the alliance. The focus on a close working relationship creates value for both partners (Elmuti and Kathawala, 2001). By dedicating people, time, and attention to the differences within the partnership, the project management tries to balance their values in order to avoid conflicts. Two necessary requirements must be given. First, the research in each field must be truly leading edge, and must be relevant to the company’s competitive advantage. Second, individual matches between university researchers and their corporate counterparts are needed to transfer and apply the research findings into the corporation. The places of knowledge transfer are the people and their interactions (Roth and Magee, 2002).

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Figure 1. Academia and industry collaboration process (adapted From Mead et al., 2000)

Strategic alliance partners should be selected based on their expertise in the operation and their culture fit with the firm. Management of the strategic alliance project should constantly ensure that requirements are met and potential problems are identified early enough to be resolved. The firm must create a management structure that will work with the new organizational arrangement (Elmuti and Kathawala, 2001; Roth and Magee, 2002). Firms tend to consider their strategic alliances to be successful when the benefits generated by the alliances were greater than the costs of developing the required resources and capabilities through internal development or acquisition. Effective and good communications among the partners is also one key area in the effectiveness of strategic alliances. Without effective communication, alliances will inevitably dissolve as a result of the doubt and mistrust which accompany any relationship that does not manifest good communication practices (Hsieh, 1997). In

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order for strategic alliances to succeed, their performance must be continually assessed and evaluated against the short- and long-term goals and objectives. In order for the feedback monitoring system to be successful, it is important that the goals of the alliances are well-defined and measurable. The ultimate mission of any industrial firm is to enhance its competitive position and maximize shareholder wealth (Porter, 1985). As a result, industry-university alliances are valuable to industry if they help the firm achieve these goals. One way these alliances contribute to the attainment of these goals is through advancing new technologies. Thus, many corporations evaluate the success of such alliances largely on the level of technological outcomes generated as a direct result of the partnership (Santoro, 2000). According to Cyert and Goodman (1997), success in university and industry alliances in particular is often measured in terms of the number of new products, publications, patents, students trained, students hired and new enterprises started, as well as some intermediate results (see Figure 2). University-industry strategic alliances: views from academia The last decade in the US has witnessed a significant number of university-industry alliances (more than 1,000) within actual university facilities (Lee, 1998). These alliances have resulted in an increasing number of university patents licensed to industry followed by an increase in royalty income (Association of Technology Managers, 2000). The commercial impact of university licenses was estimated conservatively at $17 billion in 1994 and at over $20 billion in a recent Massachusetts Institute of Technology (Roth and Magee, 2002). According to a nationwide survey in the United States conducted by Lee (1998), while American academics are generally, but cautiously, in favor of close collaboration, they “live with deep tension that is caused by two powerfully competing realities”: the considerable need for industry-solicited funds, and the intrinsic need to preserve

Figure 2. The dynamics of industry-university technology alliances and tangible outcomes (adapted from Santoro, 2000, p. 259)

intellectual freedom. The above survey has also indicated that academics strongly support the policy of university-industry collaboration if they think their collaboration is tied closely tied to regional economic development. Nevertheless, they are much more reluctant about university-industry collaboration if they think the collaboration narrowly would benefit individual firms. The survey generally highlighted that academics prefer more large-scale collaborations that have a considerable impact on the economic development than firm-specific collaborations. One other result from the survey is the observation that the preservation of intellectual freedom is a core value in universities, and the respondents to the survey emphasized their concern about the possible erosion of this core value of the university as a result of close university-industry collaboration. This observation has also been confirmed by other researchers (Ervin et al., 2002; Woo, 2003). Since research funding is the major reason for academics to be involved in R&D collaboration with industry, policy makers could fashion corporate R&D tax structures in such a way that firms are encouraged to increase the “indirect benefits package” for their academic investigators (e.g. investing in laboratory equipment and funding graduate students). Some real world examples of university-industry alliances Universities and industry members are often interested in forming specific alliances due to the perceived technology transfer, R&D cost savings, and more recently because of efficient staff training programs. According to the findings of the Working Group of the Software Engineering Institute’s recent study (see Mead et al., 2000), many US universities have strong collaboration projects with industry members (corporations) in the area of major organization-wide training schemes. Table I gives a brief summary of some successful partnerships. Another real-world example can be mentioned in this regard. Massachusetts Institute of Technology (MIT) also has large scale, multi-year, multi-program alliances with eight corporations (see Table II). Commitment to long-term research is demonstrated by timeframes of at least five years, which also assures the necessary stability of the projects. Experience with multiple corporate alliances helped MIT to learn the management of partnerships. One example of such an experiment is the Ford-MIT alliance, in which Ford has gained immediate access to knowledge. But much more important than the temporal benefit is that Ford people were able to build a lasting competitive advantage by gaining greater depth of knowledge and a better contextual understanding. Ford and MIT already had two decades of experience of one another through research sponsorship, membership of educational and research consortia, hiring graduates and faculty consulting before the alliance officially started with the funding of several new projects in September of 1997. Two years earlier, in 1995, MIT President Charles Vest and Ford CEO Alex Trotman talked for the first time about a deeper partnership. The role of information technology in engineering and education, the globalization of products and the work force, and bringing better science into environmental decisions and policy making were the basis of common interests and led to the program of research areas. The alliance began with six program areas in July of 1997, and was annually funded with $3m over a planned timeframe of five years. Getting the right people involved was the focus: these people were allowed to decide how to best work together. The six program areas include: the development of an environmental research policy consortium,

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AU/CSM

Source: Mead et al., 2000

Professional development

Professional development and graduate program Graduate program

Professional development

Professional development

Professional development

Professional development and graduate program Graduate program

Professional development and graduate program

Professional development

Professional development and graduate program Professional development Professional development

American university

Table I. Major university-industry strategic training partnerships

Center for Systems Management Washington, DC Applied Information Institute (AIM) Creighton University Member companies Omaha, NB Daytona Boston University Corporate ACS (Affiliated Computer Education Center Services), Government Solutions Beach, FL Group Embry-Riddle Aeronautical ACS, Government Solutions Daytona University Group Beach, FL Florida Atlantic University Allied Signal, CITRIX, Encore Boca Raton, FL Computer, IBM, Motorola, Sensomatic, Siemens Telecom, United Technologies University of Akron Lockheed Martin Tactical Akron, OH Defense Systems Idaho Falls, Boise State University (Boise, ID) Lockheed Martin, Idaho ID Idaho State University (Pocatello, Technologies Company ID) University of Idaho (Moscow, ID) Utah State University (Logan, UT) Software Engineering Forum for California State University, Long The Boeing Company, Northrup Costa Mesa, Training (SEFT) Beach, CA Grumman Corp., TRW CA Erickson of Australia Melbourne, Software Engineering Research Royal Melbourne Institute of Australia Center (SERC) Technology, University of Melbourne Software Quality Institute University of Texas at Austin Twenty-eight representatives Austin, TX from industry and government University of California, Santa Santa Cruz operation, Seagate Santa Cruz, Cruz, CA Technologies, Thuridian CA Texas Tech University Raytheon Co. Lubbock, TX Systems Engineering-Master’s Program-with software engineering component University of Maryland, ACS, Government Solutions College Park, University College Group MD

Types of services

Location

University/universities

Industry partner(s)

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Partnership name

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Year started Company 1994 1997 1997 1998 1999 1999 1999 2000

Size

Departments/fields

$30 million in ten years Biology $15 million in five years Biology All MIT – Engineering Product Development and Environmental Policy Ford $18 million in five years and Science Artificial Intelligence and Computer NTT $18 million in five years Science Laboratory Sloan and Engineering – Financial Merrill Lynch $20 million in five years Engineering Chemistry, Biology, Biomedical and Du Pont $35 million in five years Materials Engineering Microsoft $25 million in five years All MIT – educational innovations Hewlett Packard $25 million in five years All MIT – digital libraries, software

Strategic alliances

Amgen Merck

Source: Roth and Magee, 2002

environmental chemical and physical research, virtual engineering (developing and testing new information technology supported approaches for the design and development of products), virtual education (information technology enabled methods for the education of an engineering workforce spread across the globe), and program management and development of needed infrastructure. The alliance set up a three-level collaborative management process that enabled communication to take place on an ongoing basis between the partners. This structure depends on the involvement and commitment of managers at all levels, from senior management to operational staff, which is essential to the success of the venture. The purpose of this alliance is to create new knowledge and unique value from working together to create an advantage in the corporation from the research of the university. The fundamental principles that were generally applied in the partnership were: . engaging interesting and innovative people; . supporting personality matches; . linking projects to company priorities; . aligning with existing organizational resources; . looking beyond costs and focusing on value; . gaining both local and organizational benefits; and . seeking and capturing multiple value streams.

Commercialization of research results in university-industry alliances The commercialization of new technologies gained through university research results has contributed to economic development and growth. In 1980, congress passed the Bayh-Dole Act, which encouraged commercialization of research by permitting universities to patent and exclusively license discoveries supported wholly or in part by federal research funding. Since then, the rate at which universities patent inventions and license them has increased dramatically (Ervin et al., 2002; Elfenbein, 2004).

125

Table II. MIT’s corporate-university alliances

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Technology transfer activities started to become institutionalized in academia and government after World War II, and have also grown significantly in the last two decades, triggered largely by political reforms (Karlesson, 2004; Woo, 2003). According to a recent study by Deborah Woo of the University of California at Santa Cruz, in the year 2001 only, 149 universities reported collecting $827 million from payments derived from licenses on inventions, the top three being Columbia University, MIT, and the University of California (Woo, 2003). In 2001, universities reported a total of 11,259 inventions disclosures in the United States. They filed 9,400 applications for US patents, signed over 3,300 licenses and created 402 start-up companies. Over 3,800 new companies have been formed on the basis of a license from an academic institution since 1980. Of these, 2,100 were still in operation in 2001. After the introduction of the Bayh-Dole Act of 1980, more than 200 US universities are now actively engaged in technology transfer. These technology transfers contributed roughly $40 billion and 260,000 jobs to the US economy in 1999 (Association of University Technology Managers, 2000). The trend is that universities take equity positions with their start-ups (in about 70 percent of companies in 2001). Direct industry sponsorship is the most frequent form of university-industry alliance. There has been a steady trend of increasing industry support during recent years, but its shares of total academic R&D has remained between six and eight percent. Important factors in enabling technology transfer are Industry-University Cooperative Research Centers (IUCRC) and Engineering Research Centers (ERCs). These particular centers are administered nationally by the National Science Foundation (NSF). Currently, there are about 50 IUCRCs and 20 ERCs. The NSF investment is intended to support partnered approaches to new and emerging research areas. The majority of the funding comes from industrial firms. The intention is that the centers will become self-sustaining (usually within a ten-year period). Conclusion Strategic alliances between institutions of higher education and corporations are a relatively recent phenomenon. The concept has received significant attention in recent years mostly because of the changing nature of global business and the subsequent need for overall competitiveness through out many industries. In order to ensure their strong market presence and also effectively address ever-changing customer needs, corporations commit scarce and valuable resources to research and development (R&D). However, the current cost-cutting trend does not embrace expensive R&D programs that are intended to be conducted solely using the internal resources of the organization. Hence, businesses gradually began to shift to partnering with higher education institutions in a bid to cut costs and encourage technology and knowledge transfer efforts. Academic research has played a crucial role in building knowledge, technology, and capital for US economic and social development (Ervin et al., 2002). In this paper, we have tried to highlight the major advantages of such partnerships for both academia and businesses. On one hand, the academic community, among other things, is interested in the significant research funding, the practical learning opportunities for their students, and the real-world experience gained through these strategic alliances. On the other hand, corporations value lower R&D costs and the cutting-edge knowledge and technology transfer opportunities that directly affect their

competitiveness in the market. However, strategic alliances between higher education institutions and corporations have their drawbacks. First, each party has a different working culture and values. Moreover, the perception of goal achievement is not the same with both parties. Universities mainly consider a partnership to be successful when there is a new research finding or when the discovery is published and the innovation is patented. Corporations, however, consider a relationship to be fruitful when the innovation or discovery can go quickly to the market or be commercialized. As long as the value gained from the partnership exceeds the costs of both partners, the basis of the alliance is set. This basis must be supported by continuous learning and restructuring processes to overcome the differences between the partners (Roth and Magee, 2002). Therefore, project management capabilities which take the differences in priorities, cultures, and individual strengths into consideration are necessary. The different points of view are the surplus of such cooperation, when this is accepted and valued, the gain of alliances will follow. References Ali, A. (1994), “Pioneering versus incremental innovation: review and research propositions”, Journal of Product Innovation Management, Vol. 11, pp. 46-61. Association of Technology Managers (2000), “Common questions and answers about technology transfer”, Vol. 12 No. 2, pp. 30-2. Bettis, R. and Hitt, M. (1995), “The new competitive landscape”, Strategic Management Journal, Vol. 16, pp. 7-19. Betz, F. (1993), Strategic Technology Management, McGraw-Hill, New York, NY. Booz Allen and Hamilton (1997), Cross-Border Alliances in the Age of Collaboration, Booz Allen and Hamilton, Los Angeles, CA. Brennan, L. (2003), “The view from the ivory tower: what do university alliances offer technology firms?”, Academy of Management Executive, Vol. 17 No. 1. Cyert, R. and Goodman, P. (1997), “Creating effective university-industry alliances: an organizational learning perspective”, Organizational Dynamics, Vol. 25 No. 4, p. 45. Dismukes, J. and Petkovic, R. (1997), “University based virtual alliances could spur technological innovation”, Research Technology Management, Vol. 40, pp. 10-13. Doz, Y.L. and Hamel, G. (1996), “The evolution of cooperation in strategic alliances: initial conditions or learning processes?”, Strategic Management Journal, pp. 55-83. Drucker, P. (1996), “Non-profit prophet”, The Alliance Analyst, available at: www.allianceanalyst. com Elfenbein, D. (2004), “Contract structure and performance of technology transfer agreements: evidence from university licenses”, available at: www.people.hbs.edu/elfenbein/ contractstructureandperformance.pdf Elmuti, D. and Kathawala, Y. (2001), “An overview of strategic alliances”, Management Decision, Vol. 39 No. 3, pp. 205-17. Ervin, D., Lomax, T., Buccola, S., Kim, K., Minor, E., Yang, H., Glenna, L., Jaeger, E., Biscotti, D., Armbruster, W., Clancy, K., Lacy, W., Welsh, R. and Xia, Y. (2002), “University-industry relationships: framing the issues for academic research in agricultural biotechnology”, available at: www.pewagbiotech.org/research Farris, P. (1999), “Getting together strategic alliances”, CIO Magazine, available at: www.cio.com/ archive/0/0/00_part_content.html

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Financial Times (1999), “Putting the S-word back into alliances”, Financial Times, November 1, p. 3. Fortune (1996), “Will outsourcing save corporate R&D?”, Fortune, January 15. George, G., Zahra, S.A. and Wood, D. Jr (2002), “The effect of business-university alliances on innovative output and financial performance: a case study of publicly traded biotechnology companies”, Journal of Business Venturing, Vol. 17, pp. 577-609. Glaister, K.W. and Buckley, P.J. (1996), “Strategic motives for international alliance formation”, Journal of Management Studies, Vol. 33 No. 3, pp. 301-32. Hamel, G. and Prahalad, C.K. (1994), Competing for the Future, Harvard Business School Press, Cambridge, MA. Hill, W.C. (1999), International Business: Competing in the Global Marketplace, Irwin, Burr Ridge, IL, p. 415. Hsieh, T.Y. (1997), “Prospering through relationships”, Corporate Finance, Vol. 8 No. 3, pp. 21-2. Hutt, M.D., Stafford, E.R., Walker, B.A. and Reingen, P.H. (2000), “Defining the social network of a strategic alliance”, Sloan Management Review, Vol. 41 No. 2, pp. 41-62. Karlesson, M. (2004), Commercialization of Research Results in the United States: An Overview of Federal and Academic Technology Transfer, Swedish Institute for Growth Studies, Stockholm. Kock, N., Auspitz, C. and King, B. (2000), “Using the web to enable industry-university collaboration: an action research study of a course partnership”, Information Science, Vol. 3 No. 3, pp. 157-66. Lee, Y. (1998), “University-industry collaboration on technology transfer: views from the ivory tower”, Policy Study Journal, Vol. 26 No. 1, pp. 69-84. Mansfield, E. (1991), “Academic research and industrial innovation”, Research Policy, Vol. 20, pp. 1-12. Mead, N., Unpingco, P., Beckman, K., Walker, H., Parish, C.L. and O’Mary, G. (2000), “Industry-university collaborations”, Journal of Defense Software Engineering, available at: www.stsc.hill.af.mil/crosstalk/2000/03/mead.html National Science Foundation (1982), University-Industry Research Relationships: Myths, Realities and Potentials, 14th Annual Report, US Government Printing Office, Washington, DC. Parkhe, A. (1993), “Strategic alliance structuring: a game-theoretic and transaction cost examination of inter-firm cooperation”, Academy of Management Journal, Vol. 36 No. 4, pp. 794-829. Pisano, G. (1990), “The R&D boundaries of the firm: an empirical analysis”, Administrative Science Quarterly, Vol. 35, pp. 153-76. Porter, M. (1985), Competitive Advantage: Creating and Sustaining Superior Performance, Free Press, New York, NY, pp. 70-8. Roth, G. and Magee, C. (2002), “Corporate-university alliances and engineering systems research”, Working Paper Series, Massachusetts Institute of Technology, Engineering Division, Cambridge, MA, available at: www.mit.edu/WPS/ESD Saffu, K. and Mamman, A. (1999), “Mechanics, problems and contributions of tertiary strategic alliances: the case of 22 Australian universities”, International Journal of Education Management, Vol. 13 No. 6, p. 281. Santoro, M. (2000), “Success breeds success: the linkage between relationship intensity and tangible outcomes in industry-university collaborative ventures”, The Journal of High Technology Management Research, Vol. 11 No. 2, pp. 255-73.

Sparks, J. (1985), “The creative connection: university-industry relations”, Research Management, pp. 19-21. Tapsell, S. (1999), “Dangerous liaisons”, Management, Vol. 46 No. 10, pp. 28-32. Technology Associates and Alliances (1999), available at: www.taacorp.com/ Tyler, B. and Steensma, H. (1998), “The effects of executives’ experiences and perceptions on their assessment of potential technological alliances”, Strategic Management Journal, Vol. 19 No. 10, pp. 939-65. Van Rossum, W. and Cabo, P. (1995), “The contribution of research institutes in EUREKA projects”, International Journal of Technology Management, Vol. 10, pp. 853-66. Vyas, N.M., Shelbaum, W.L. and Rogers, D.C. (1995), “An analysis of strategic alliances: form, functions and framework”, Journal of Business & Industrial Marketing, Vol. 10 No. 3, pp. 47-60. Wheelen, T.L. and Hungar, D.J. (2000), Strategic Management and Business Policy, 7th ed., Addison-Wesley, Reading, MA, p. 125-34, 314. Woo, D. (2003), “University, industry, and government alliances: escalating conflicts with the public interest”, available at: www.pamij.com/8-3/pam8-3-7-woo.pdf Further reading Das, T.K. and Teng, B.S. (2000), “Instabilities of strategic alliances: an internal tension perspective”, Organization Science, Vol. 11 No. 1, pp. 77-102.

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Martijn Rademakers Strategy Academy, Rotterdam, The Netherlands Abstract Purpose – This paper aims to explain the rapid emergence of corporate universities on the basis of fundamental developments presently shaping the economy and society on a world-wide scale. Design/methodology/approach – Four key forms of innovation are identified and combined with the corporate university concept. The paper explains why corporate universities are emerging world-wide as strategic weapons in the competitive battle among companies, countries and international economic power blocs. Findings – Companies endorsing the importance of knowledge innovation cannot get around the corporate university concept as part of their strategy. Three major corporate university types are identified and linked to their strategic role as the driving force of knowledge innovation. Originality/value – Helps to explain why corporate universities have evolved as an answer to the challenge of competing in a knowledge-driven economy. Keywords Knowledge management, Innovation, Competitive advantage Paper type General review

The Journal of Workplace Learning Vol. 17 No. 1/2, 2005 pp. 130-136 q Emerald Group Publishing Limited 1366-5626 DOI 10.1108/13665620510574513

Introduction The rapid rise of corporate universities since the early 1990s has proved to be more than just another management fashion. Corporate universities have evolved from mere training departments to vehicles of integrated knowledge transfer, exchange and innovation – both within and between organizations. The increasing popularity of corporate universities can be understood by placing them against the backdrop of a broad trend within the economy and society known as the “third wave” (see Toffler, 1980; Naisbitt, 1982). This wave has transformed industrialized economies around the world into knowledge- and information-driven societies, forcing organizations to adjust constantly and renew their knowledge base (cf. Boisot, 1998). The start of the third wave has been traced back to the 1950s, when efficient and effective information processing became an important driver of competitive advantage. Ever since, the influence of this third wave has gained power and emphasis has shifted from information towards knowledge the as key driver of competitive strength. The impact of this development is visible in the management literature, reflecting the insight that excellent quality, productivity, and the exploitation of knowledge and information are important, but no longer sufficient to stay ahead of the competition. Business leaders realize that continuously leveraging and renewing the corporate knowledge base makes the difference between excellent performance or muddling through – or even worse, failure. Moreover, European governments and captains of industry have expressed growing concerns about falling behind with regard to competitors from knowledge-driven economies such as the United States and Japan (see, for example, the Lisbon Summit 2000). In addition, they also feel mounting pressure from new players

such as China, India and Russia. Given these developments, what is the link between the “third wave” and the importance of knowledge? What is knowledge innovation? Why are corporate universities an answer to competitive challenges in knowledge-driven economies? The third wave in the first decade of the twenty-first century The knowledge-driven economy has a history taking us back thousands of years. More than 8,000 years ago, an agricultural revolution started to transform societies formerly based on hunting and gathering. It took until the 18th century before a second wave emerged and changed the face of many societies again. Mechanization and mass production became the driving economic principles, and agriculture became of secondary importance. The third wave arrived halfway through the 20th century, initially driven through information collection, adaptation, and distribution (Toffler, 1980), and later thriving on knowledge transfer, exchange and creation. These three “long waves” have in common that they tend to gain power over time and then gradually make place for the next one, but never entirely disappear. Seen from this perspective, it seems that many national economic systems in Western and East-Asian countries are now riding the third wave, which is still gaining power, while the influence of the previous (second) wave is gradually ebbing away. In such environments, organizations still playing according the old “rules of the game” of industrialization are either slowly or rapidly replaced by companies with new businesses models and organizational systems tuned to the requirements of a knowledge-driven economy. In this light, 21st-century China can be seen as an open-air laboratory where these processes occur within a very short time frame. A key characteristic of “third wave” knowledge-driven economies is the availability of vast quantities of information: there is too much rather than too little of it at hand, and it travels fast and cheaply. In such circumstances, corporate success (and also that of governmental agencies and non-profit organizations) is to a large measure determined by the knowledge resources enabling them to make sense of the information chaos. They need to adapt themselves to a continuously changing and information-loaded environment – or setting/anticipating new rules of the game. Companies able to continuously and rapidly renew their knowledge base (i.e. knowledge innovation) on the one hand and deliberately exploit their existing knowledge resources on the other are well equipped to keep a good fit with their environment. In contrast, organizations lacking these abilities risk strategic drift and obsolescence. Knowledge innovation The view that knowledge is key to organizations pursuing excellent performance and competitive advantage finds broad support in the strategic management literature. Richard Whittington, for example, on the basis of a literature review, states that: In today’s knowledge-based economy, superior knowledge is likely to be the most valuable resource of all (Whittington, 2001).

Whittington hereby refers to the insight shared among leading scholars (including Prahalad, Tsoukas, and Zack) that most actively used knowledge within organizations is dynamic and tacit in nature. This kind of knowledge can hardly (or even can not) be codified, and is therefore difficult to put in databases. As a consequence, this type of

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knowledge is not easily transferable or tradeable, and therefore is also hard for competitors to imitate – which makes it an important source of competitive advantage. Gaining and protecting superior knowledge and also exploiting it, however, requires substantial efforts. Well-known organizations such as a 3M and General Electric, with their extensive organizational systems to maintain the speed of learning and innovation, could testify on that point. After all, the life cycle of competitive knowledge resources in a free market economy tends to be short due to ongoing and international pressures from both innovating and imitating competitors. On top of that, companies constantly need to anticipate and adjust to changing client needs and societal requirements. Hence, one could argue that the ability of organizations to renew their knowledge – in other words, knowledge innovation – is pivotal to achieving corporate excellence and competitive advantage. Although knowledge innovation can be found as an issue at the top of the agendas of both CEOs and politicians, there is still much confusion about its meaning. A major cause is rooted in the many different ways the term “innovation” is used. Sometimes “innovation” is used in a very broad fashion, but mostly it is given a very narrow meaning. The narrow “belief” that innovation is about new products and processes resulting from new scientific insights and technology is dominant. The dominance of this view, however, is slowly but surely crumbling due to increased criticism. Remarkably in this respect, the former CEO of the high-tech Sony Corporation, Mr Akio Morita, noted: Science alone is not technology and technology alone is not innovation (von Stamm, 2003).

This view is supported by Breed et al. (2004b), who argue that innovation of products/services and processes results not only from scientific and technological knowledge (i.e. technology push). New processes and products also can, for example, be based on knowledge about market needs (i.e. market pull). In addition, business model and organizational system innovations can be driven by knowledge and insights derived from social interaction within and between companies. Table I shows an overview of four major types of knowledge-driven innovation. At this point, avoiding the notorious “knowing-doing gap” (Pfeffer and Sutton, 1999) is also relevant. There is a necessity to actually realize new products, services, businesses processes and organizational systems in practice, and not just to figure them out and name them in laboratories and meeting rooms. Obviously, only some of the innovation types in Table I tend to be generated by universities, laboratories and R&D centers. Technological innovation has long been the Innovation type 1 2 3 Table I. Four types of knowledge-driven innovation

4

Characteristics

Product innovations Process innovations

A new product, service or combination of both New methods to perform value-adding activities (e.g. production, distribution) better or cheaper Organizational innovations New methods to organize, coordinate and control employees, tasks, and responsibilities Business innovations New combinations of products, processes and organizational systems (also referred to as business models)

Source: Adapted from Breed et al. (2004b)

traditional domain of universities, but the center of gravity has now shifted more and more towards corporate R&D departments (Leadbeater, 1999). Market-driven innovation and businesses and organizational innovations, however, are increasingly becoming the domain of corporate universities. In other words, the strength of corporate universities is to realize knowledge-driven innovation that is happening all over the place, within and across organizations (cf. Breed et al., 2004a). Because knowledge innovation is dispersed in nature, it happens everywhere and consequently cannot be captured in a single department. In contrast, innovation by R&D departments is usually based on specialist, mono-disciplinary knowledge. Trying to organize innovation by putting it in R&D departments can be seen as products of the “second wave”, and as an example of Taylor’s 19th-century principle of “scientific management”, which seeks optimization by splitting up tasks and separating doing and thinking (Taylor, 1915). There is little doubt that, for many organizations, R&D departments will remain very important. However, are no longer sufficient for businesses and organizations that see themselves confronted with the demands of a knowledge-driven economy, combined with the dispersed nature of innovation. The rise of corporate universities therefore seems to resemble an answer to the pressing question faced by organizations, which emerged in the early 1990s and is still gaining strength today, i.e. how is structural and ongoing knowledge-driven innovation to be realized? Corporate universities and knowledge innovation The development of corporate universities has long been a matter taken care of by scholars and practitioners with a background in human resources development and associated disciplines. This can be explained by the fact that when “knowledge” is identified as an important business issue, this often leads to increased attention for competence development, as employees are seen as both the carriers and users of this precious resource. More often than not, this places corporate university development in the HR domain. Gradually, however, people from other disciplines such as strategy and organization sciences have also become involved in corporate university research and development, making the concept broader and richer in nature – and to some observers more confusing as well. In practice, the term “corporate university” is used to refer to a very broad range of organizational forms and systems, ranging from renamed training departments to institutionalized carriers/drivers of strategic knowledge innovation within and between organizations (Rademakers, 2004). Based on an explorative study of corporate universities (Rademakers and Huizinga, 2000; Rademakers, 2001) this plethora of different forms has been captured within three major corporate university types, referred to as “school”, “college” and “academy”. Table II summarizes their key characteristics. As can be seen from Table II, the “academy” type is the most sophisticated corporate university form and the only one that has knowledge innovation as a top priority. The other two types can be considered as corporate universities at an earlier stage of development, in which knowledge exploitation and leverage is the name of the game. What are the properties of corporate universities acting as a driving force behind knowledge innovation – i.e. the “academy” type? In fact, such corporate universities are nothing more and nothing less than institutionalized forms of ongoing, structural and strategically driven knowledge transfer, exchange and creation processes within and between organizations. In practice, this could take the shape of a separate

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133

Table II. Three major corporate university types The organization Aligning organizational goals and individual competences Deriving training programs from corporate strategy Direct and reactive

The individual Enhancing efficiency of training programs Bundling training activities within the organization Indirect and reactive

Source: Adapted from Rademakers (2001)

Link with strategy

Major activity

(Re)distributor of knowledge Knowledge transfer and exchange

Disseminator of knowledge Knowledge transfer

College

Driver of knowledge innovation Integrated knowledge transfer, exchange and creation Both the individual and organization Gaining competitive advantage through knowledge innovation Forming and realizing strategy through training and exploration Direct and proactive

Academy

134

Corporate university as: The primary process revolves around: Most important “customer” Purpose

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organizational unit with its own “university” or “academy” building. At the other end of the spectrum, however, informal and virtual corporate university forms are present. They are not visible in the organization chart, but are certainly active and effective when it comes to knowledge innovation. For some organizations, for example, regular corporate university meetings organized by self-steering professionals prove effective in this respect. Others rely on a broad arsenal of traditional and modern forms, methods and techniques to make the corporate university work. As far as corporate university forms and systems are concerned, it has become clear over the years that “one size does not fit all”. Every single corporate university is inherently unique, as the company in which it is embedded has its own idiosyncrasies. In other words, different companies require different corporate universities. Hence, the ideal best corporate university does not exist. Many corporate universities have been established, and a large number have also disappeared again. Obviously, some have been more successful than others. The successful ones seem to have at least two particular characteristics in common. First, a direct link between top management and the “dean” (or corporate university manager) has proved to be a key success factor. The reason for this is necessary support from the top enabling the corporate university to win and maintain its “social space” in the organization. Support is needed to be accepted as a serious partner, to shed the “training department image”, and to secure continuity through financial commitments. Moreover, top management support is crucial to the establishment of a direct link between corporate strategy and the corporate university’s activities. This link is of great importance because sustainable success for the corporate university depends on the ability not only to make corporate strategy work through knowledge exploitation but also to “feed” the strategy through knowledge innovation. A second key success factor is largely in the hands of the corporate university itself. Having a sound mission, vision and strategy ensures a good fit with corporate needs and avoids the corporate university being cast adrift. Just like a company operating in a turbulent environment, corporate universities need to build and periodically rethink their purpose, business model and organizational systems to keep a good fit. In short, corporate universities that want to be strategically relevant to their company need to have a sound strategy too. Conclusions When looking back on the past two decades, one could argue that the worldwide rise of corporate universities has been rooted in reactions from corporations to a slow but high-magnitude revolution. This revolution commenced in the second half of the 20th century, gained strength in the decades that followed, and heralded the demise of the industrial era and the rise of the knowledge-driven economy. The requirements to thrive in a knowledge-driven economy differ fundamentally from the requirements that organizations were geared to in the past industrial era. In tune with this, broad consensus exists on the view that in the coming decades knowledge innovation will be an essential weapon in the competitive battle between companies, nations and international economic power blocks. Knowledge innovation, however, has many enemies. Ignorance, silo thinking and holding on to old ideas are perhaps the most dangerous and persistent ones. Some find it hard to endorse that knowledge innovation is not the exclusive domain of scientists, technologists, or human capital developers only. After all, successful knowledge

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innovation depends on a rich spectrum of mutually linked knowledge resources (divisions, disciplines, individuals, peer groups, etc.) within and between organizations. This reasoning is congruent with the philosophy behind the corporate university concept that took shape over the past decades. Advanced corporate universities in the US, Europe and Asia know how to link knowledge transfer, exchange and creation with corporate strategy. They are the key mechanisms to coordinate and drive dispersed knowledge-driven innovation processes within and across organizations. The pressure of an increasingly knowledge-driven economy forces business and political leaders to make strategic choices. Companies, government agencies and non-profit organizations need to adjust themselves to the new rules of the game. Organizations ignoring the importance of knowledge innovation for their competitive position or societal relevance run the risk of becoming the new corporate dinosaurs of this era. By contrast, companies and other organizations that take knowledge innovation seriously cannot get around the corporate university concept as part of their strategy. Thousands of organizations around the world already have engaged in establishing and further developing their corporate universities – with highly competitive companies at the forefront. References Boisot, M.H. (1998), Knowledge Assets: Securing Competitive Advantage in the Information Economy, Oxford University Press, Oxford. Breed, K., Ederer, P. and Meyer, R. (2004a), “Internationale trends in ICT innovatiestrategiee¨n”, Holland Management Review, September (forthcoming). Breed, K., Meyer, R. and Van der Lande, R. (2004b), ICT Innovatie in Nederland: Een Strategische Analyse Van het Nederlandse ICT-Innovatiesysteem, Ministerie Van Economische Zaken, den Haag. Leadbeater, C. (1999), Living on Thin Air: The New Economy, Viking, London. Naisbitt, J. (1982), Megatrends, Warner Books, New York, NY. Pfeffer, J. and Sutton, R.I. (1999), The Knowing-Doing Gap: How Smart Companies Turn Knowledge into Action, Harvard Business School Press, Boston, MA. Rademakers, M.F.L. (2001), “Hoe strategisch is uw corporate university? Drie generieke niveaus Van corporate university ontwikkeling”, Opleiding & Ontwikkeling, Vol. 14 No. 4, pp. 15-20. Rademakers, M.F.L. (2004), “Visies over leren en werken in organisaties”, in Altink, W., Schoonman, W. and Seegers, J. (Eds), Menselijk Kapitaal: de Ontwikkeling Van Mensen in Organisaties, Van Gorcum, Assen, pp. 115-19. Rademakers, M.F.L. and Huizinga, N. (2000), “How strategic is your corporate university? Results from an explorative survey at the Global Corporate University Week 2000”, The New Corporate University Review, Vol. 8 No. 6, pp. 18-23. Taylor, F.W. (1915), The Principles of Scientific Management, Harper & Row, New York, NY. Toffler, A. (1980), The Third Wave, McGraw-Hill, New York, NY. von Stamm, B. (2003), The Innovation Wave: Meeting the Corporate Challenge, Wiley, Chichester. Whittington, R. (2001), What Is Strategy – and Does it Matter?, 2nd ed., Thomson Learning, London.

Note from the publisher Emerald structured abstracts have arrived! Well, it’s finally happened and all the first issues of the 2005 volume of Emerald journals will contain structured abstracts. Have a look at an article title page in this issue of the The Journal of Workplace Learning. That’s how they will look from now on. The look will be slightly different in the electronic version on the web site but this is only a cosmetic variation and takes account of the different media and the way people use the information. The idea took hold at the beginning of 2004 and a small team worked on the design and introduction of structured abstracts throughout the year. Thanks to all the hard work of authors, editors, editorial and production staff at Emerald we can now showcase them for the first time. We believe they provide real benefits to our readers and researchers and that they answer some of the key questions people have about a paper without having to scan or read the entire paper: . “What research has been conducted on this topic?” . “How was the research approached – what methods were used?” . “What were the main findings?” . “Are there any literature reviews on this topic and are they selective or inclusive?” . “So what? They have shown this but what does this mean in my work/organization?” . “I want to conduct research in this area but what questions still need answering?” . “Has this work got any relevance and value for me?” . “What did the writer set out to show?”

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Structured abstracts supply the answers to these types of questions without the researcher having to go any further. Authors can be more confident that their paper will be noticed and read by others with a real interest in the topic or research. As far as possible, all our past authors and editorial team members (editorial advisory boards, etc.) have been alerted to this change. Authors who have been asked to rewrite their abstracts in the new format have readily obliged. The response from all parties has been most gratifying: Structured abstracts are increasing in popularity among the social and behavioral sciences. There’s overwhelming evidence that readers (and indexers) glean more from structured abstracts ( Jonathan Eldredge, MLS, PhD, AHIP, Associate Professor, School of Medicine, Academic & Clinical Services Coordinator and Author, Health Sciences Library and Informatics Center, Health Sciences Center, The University of New Mexico, USA).

To read more on structured abstracts and their value for researchers and writers, read this short paper by Liz Bayley and Jonathan Eldredge outlining their benefits at: http:// research.mlanet.org/structured_abstract.html

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Everyone has problems in the digital environment when weighing up the value of any piece of information – information overload is discussed endlessly in the media. Structured abstracts go some way towards a remedy. Emerald is the very first publisher in the management field to introduce structured abstracts. We know this means change for the author and researcher but the fact that other journals don’t do it shouldn’t stop us from making the advancement. It’s wonderful to be first in the field with a good idea! We have only two regrets! We didn’t think of it before and we are unable to go back through more than 40,000 papers already in the database and change the abstracts into structured ones. Having said that, nearly 5,000 new papers will be deposited in our database this coming year and all will be accompanied by a structured abstract. Let us know what you think. E-mail Sue de Verteuil, Head, Editorial Developments, with your views at: [email protected]