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EDITORIAL 95 Editorial T his June 2008 issue of Creativity and Innovation Management includes five articles and one p...

Author: Petra de Weerd-Nederhof and Olaf Fisscher (Editors)

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EDITORIAL

95

Editorial

T

his June 2008 issue of Creativity and Innovation Management includes five articles and one practitioner’s insight, the announcement of the winners of the 2007 Tudor Rickards Best Paper Award, and a call for submissions for the 18th Doctoral Summer School on Technology Management, this year to be held in Leuven from 22 to 29 August. This year’s theme is ‘Innovation at Crossroads’. Tutors for the Summer School include members of CIM’s editorial board Holger Ernst and Bart Van Looy, and recent CIM authors Abbie Griffin (December 2007) and Wim Vanhaverbeke (co-author of the second article in this June 2008 issue). This should prove a worthwhile event for all PhD students in the area of Innovation Management. Creativity and Innovation Management has supported this event since 2005 by giving one scholarship of 750 euros to a PhD student in the creativity and innovation area. We warmly encourage young researchers from the CIM community to participate! The print version of this issue will come out right on time to mark the 2nd Creativity and Innovation Management Community Meeting in Buffalo. Key Notes will be given by your journal’s founding editor Tudor Rickards, editorial board members Ming Huei Chen and Todd Lubard, Michael Mumford of the University of Oklahoma and by practitioners Mirjam Kelly, vice president Product Design from Fisher Price (subsidiary of Mattel, Inc.) and by Casimer DeCusatis, IBM Distinguished Engineer and Technical Executive. In 2009 we will publish a special based on selected contributions to this conference, but as a prelude to the event in this issue you can already read a practitioner’s insight authored by Casimer DeCusatis based on his Key Note address. His contribution ‘Creating, Growing, and Sustaining Efficient Innovation Teams’, hits the theme of the conference: ‘Integrating Inquiry and Action’. It challenges how we conceive of innovation teams, includes a case study and has clear practical implications. Gerard Puccio and his team have compiled a worthwhile programme for the Buffalo event with some 30 contributions from academics and practitioners in the creativity and innovation field. At the opening banquet on the Wednesday evening, the 2007 Tudor Rickards Best Paper © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

Award will be presented. Hans Georg Gemünden, Sören Salomo and Katharina Hölzle won this award for their paper on role models for radical innovations in times of open innovation. It can be downloaded for free from the journal’s website (www. blackwellpublishing.com/caim). Right after Casimir DeCusatis’ practitioner’s insight you can find more information on this awardwinning paper, the other five nominated papers and the award selection procedure. ‘Opening up the Solution Space: The Role of Analogical Thinking for Breakthrough Product Innovation’ is the title of the article by Oliver Gassmann and Marco Zeschky, which is the first article in this issue. The authors report the first experiences of applying their A4 – Innovation Process, targeting the early innovation challenges in how to find highly novel solutions. Data were taken from projects in four engineering firms where analogical thinking was applied successfully in the development of breakthrough innovations. Among other results, it was shown that abstracting the problem by in-depth technical and contextual analysis is pivotal when searching for analogical solutions. Ying Li, Wim Vanhaverbeke and Wilfred Schoenmakers engage to reframe the interpretation of exploration and exploitation in innovation in the second article in this issue. Some debate on these issues was started off in our special on organizing innovation back in September 2005 (CIM 14.3, pp. 208–98). Now the authors focus in particular on the interpretation of exploration and exploitation of technological innovation. Their first aim is to address the different interpretations of these concepts, and then to set up a framework that reconciles these differences and reduces the ambiguity found in the literature. Their study may not only inspire more research to explore the gaps concerning exploration and exploitation, but may also provide a common ground to encourage the necessary dialogue between academia and practitioners. Next is an article based on a first version presented at the EIASM IPDMC conference in Milan, hosted by Roberto Verganti in 2006. Sihem Jouini and Florence Charue-Duboc present the case of an established firm in the automotive industry to illustrate the

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enhancing of discontinuous innovation through knowledge combination. Their indepth study of an exploratory unit, created in an established multidivisional firm pursuing the development of discontinuous innovation and that generated several breakthroughs, highlights four key factors that enhanced knowledge combination: (i) the definition of the scope of the unit, (ii) the composition of the unit and the dual roles of its members, (iii) the boundary objects that supported the interactions between these members during the creativity process, and (iv) the arenas where new knowledge was further created. Through a case study in an entirely different field, new drug development, Alexander Styhre discusses the element of play in innovation work. The empirical part of the paper reports a study of the work of laboratory scientists in new drug development in a major multinational pharmaceutical company. In the analysis, innovation work is examined as a highly specialized and idiosyncratic form of play wherein the scientists are practising their skills while simultaneously being exposed to serendipity and other residual factors (e.g. luck, chance) outside their control. An article which will broaden our perspective! DEIQ, or Direct Employee Involvement Quality, is the topic of the fifth and final article, written by Nicole Torka, Marianne van Woerkom and Jan-Kees Looise. DEIQ is an aspect of human resource management which is important for innovative employee behaviour, and therefore the reported research and

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its result are very worthwhile for the field of creativity and innovation management. Using a qualitative approach, the authors were able to acquire insight into the ‘black-box’ of wellknown pre-conditions and contribute by identifying additional dimensions. The research supports various pre-conditions critical for successful DEIQ, including the critical role of the HR function and adequate preparation of direct supervisors. Furthermore, an underexplored issue is highlighted: the employment relationship. With the above described content, we hope that you will enjoy yet again a rich and varied issue of Creativity and Innovation Management. Our website includes ‘Online Early’ articles, enabling you to look ahead at subsequent issues, which will feature, among others, a TRIZ special, and contributions based on papers presented at the conferences of our affiliate associations: CINet, EIASM IPDMC and EACI from 2007. Also new calls for specials and conferences will be posted there. An interesting new one is the call for the 2009 World Marketing Congress, where PDMA’s Ken Kahn has invited your CIM editors to co-chair the Innovation and Creativity track. We wish you a good, relaxed and fruitful summer and look forward to your reactions to our current issue as well as new submissions! March 2008 Petra de Weerd-Nederhof Olaf Fisscher Klaasjan Visscher

© 2008 The Authors Journal compilation © 2008 Blackwell Publishing

THE ROLE OF ANALOGICAL THINKING FOR BREAKTHROUGH PRODUCT INNOVATION

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Opening up the Solution Space: The Role of Analogical Thinking for Breakthrough Product Innovation Oliver Gassmann and Marco Zeschky The purpose of this paper is to investigate the approach of analogical thinking for product innovation. We collected data on projects from four engineering firms where analogical thinking was successfully applied for the development of breakthrough innovations. Results show that abstracting the problem by in-depth technical and contextual analysis is pivotal when searching for analogical solutions. Furthermore, the chances of identifying highly novel analogous solutions are increased if the problem is abstracted to the level of its structural similarities to other settings. We also found that the identification of structural similarities is supported when firms not only rely on the cognitive abilities of the individual but also employ an active search based on abstract search terms. Based on these insights, we propose a process model for the development of product innovations by means of analogical thinking.

Introduction

W

hen the BMW Group introduced their path-breaking man-machine interface iDrive in 2001, they took advantage of an analogous solution from a non-automotive domain and integrated it in a single controlling device. The iDrive is a device for controlling a manifold of functions in luxury cars which were until then controlled by up to 200 different knobs and switches. The analogy was found in the joystick as an important device in the video game industry, and the respective knowledge was transferred and adapted to the specific requirements in the course of the development process. Recent studies have emphasized the importance of analogies for radical product innovation (Keane, 1987; Dahl & Moreau, 2002) and increased firm performance (Gavetti, Levinthal & Rivkin, 2005). Analogical thinking, particularly when applied across industry boundaries, may contribute significantly to the development of highly novel innovations (Holyoak & Thagard, 1995) while simultaneously limiting the risks of uncertainty (De Bono, 1990). On the one hand, drawing analogies from an initial problem to distant but similar problem settings may reduce uncertainty as potential solutions have already © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

proved to function in a similar context. On the other hand, non-obvious analogies may entail highly novel solutions because the combination of more distant pieces of knowledge is associated with higher innovative potential (Holyoak & Thagard, 1995; Hargadon & Sutton, 1997). In fact, ‘divergence and lack of shared experiences are critical for developing new ideas’ (Majchrzak, Cooper & Neece, 2004). From a cognitive psychology perspective, analogical thinking entails the transfer of knowledge from one domain that usually already exists in memory to the domain to be explained (Gick & Holyoak, 1983; Vosniadou & Ortony, 1989). Management scholars have argued that the use of analogies typically includes the transfer of knowledge (Majchrzak, Cooper & Neece, 2004), where knowledge acquired in one situation is applied to another (Argote & Ingram, 2000). The ability to combine different pieces of knowledge (‘combinative capability’) for product innovation is a strategically significant resource to a competitive organization (Kogut & Zander, 1992; Grant, 1996). The role and importance of analogies in problem solving has also been widely discussed by creativity researchers (Prince, 1970; Boden, 1990; Rickards, 1990; Csikszentmihalyi, 1996), and analogies are a central mechanism in many creativity

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techniques (Gordon, 1969; De Bono, 1990; Ceserani & Greatwood, 1995). However, there is limited insight into how analogical thinking is enabled and applied at the level of the firm for product innovation. More aptly, the literature lacks empirical insight into the origin of analogies, that is, how analogous problems and solutions are found in the first place (De Bono, 1990). The aim of this paper is to show how analogical thinking is enabled and used for product innovation at the level of the firm. Thus, our research question is: ‘How do firms enable and use analogical thinking, and what are its success factors for new product development?’ We first review the relevant literature on analogical thinking and illustrate four cases where firms have enabled and used analogical thinking for the development of breakthrough product innovations. We then discuss the cases and conclude with managerial implications for how to approach analogical thinking in a systematic manner.

Analogical Thinking in Problem Solving The role and importance of analogies for innovation has mostly been investigated in the product design and psychology literatures (Dahl & Moreau, 2002). However, scholars have recently also started to investigate the role of analogical thinking within the firm for strategy making (Nerkar & Roberts, 2004; Gavetti & Rivkin, 2005; Gavetti, Levinthal & Rivkin, 2005). Analogical thinking is a creative method for a problem that needs a solution. Analogical thinking happens when a familiar problem is used to solve a novel problem of the same type (Reeves & Weisberg, 1994). The literature has argued that the identification of analogies is stimulated by rather specific problems (De Bono, 1990). Analogies can be drawn in different settings and directions. In some cases, a solution is found in one industry and applied to solve a problem in another industry. In other instances, the analogy is drawn from a solution looking for a problem (Gavetti, Levinthal & Rivkin, 2005). In all cases, the search for a solution is stimulated by a rather specific problem. Within this ‘problemistic search’ (Cyert & March, 1992), analogies to settings quite similar to the original problem can be drawn, potentially providing a solution. Cognitive scientists commonly agree that innovation entails reassembling elements from existing knowledge bases in a novel fashion (Gagne & Shoben, 1997; Hampton, 1998). Thus, analogical thinking is a mechanism underlying creative tasks, in which people

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transfer information from a familiar setting and use it for the development of ideas in a new setting (Gentner, Rattermann & Forbus, 1993; Dahl & Moreau, 2002). Similarity of concepts (such as problems or situations) at any level of abstraction is argued to enable analogical thinking (Keane, 1987; Ross, 1989; Reeves & Weisberg, 1994; Holyoak & Thagard, 1997). Thus, similarity of some basic elements between the source where the problem originates (i.e., the problem source) and the source where the analogy is found (i.e., the solution source) is a vital pre-condition for analogies to be identified. Similarity has also been described in a continuum from ‘near’ or ‘surface’ analogies to ‘far’ or ‘structural’ analogies (Dahl & Moreau, 2002). Near analogies are more easily identified than far analogies, as near analogies often entail obvious surface similarities such as similar design, while far analogies typically entail similarities in the structural relationships between source and target attributes. For instance, Dahl and Moreau (2002) illustrate the case of designing a new freeway system. A near analogy would entail looking at an already existing freeway system in another city, whereas a far analogy would entail arriving at a solution by considering the human circulatory system. The distinction is important because near and far analogies require different types of information to be mapped and transferred. At near analogies, both surface-level attributes (e.g., roads) and relations between the attributes (e.g., the flow of cars through the freeway) are mapped and transferred, while the lack of surface-level attributes at far analogies leaves the mapping to occur between common relations (Gentner, 1989; Ward, 1994). The example intuitively shows that far analogies are more difficult to identify and require more cognitive effort. The identification of far analogies requires the identification of similarities in the relational (vs. surface) structure between the problem and the solution source, which is often difficult when surface similarities are completely absent. However, if successfully implemented, far or structural analogies serve as the base for ‘mental leaps’ and can lead to radical innovation (Holyoak & Thagard, 1995). On the other hand, if source and target share the same surface qualities, they often come from the same or close conceptual domain (Ward, 1994), which would lead to incremental innovation. However, surface and structural similarities are two ends of a continuum, and a clear distinction between them is difficult. In this paper, we refer to surface similarities when there are similarities in features such as product design and product features, and to structural similarities when there are similari© 2008 The Authors Journal compilation © 2008 Blackwell Publishing


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Table 1. Types of Product Innovations Customer Need Fulfillment

Newness of Technology

Low High

Low

High

Incremental Innovation Technological Breakthrough

Market Breakthrough Radical Innovation

Source: Chandy and Tellis (1998).

ties in the principal technological function and architecture of the product (Henderson & Clark, 1990). The use of analogies has also been widely discussed as a means of creative thinking for problem solving (Gordon, 1969; Prince, 1970; De Bono, 1990; Ceserani & Greatwood, 1995; Amabile, 1996). Creativity and construction methods such as the ‘theory of inventive problem solving’ (TIPS), first invented by Altshuller and colleagues, lateral thinking (De Bono, 1990), and synectics (Gordon, 1969) all contain notions of analogical thinking as a central mechanism. Altshuller and colleagues found in their analysis of patents that most inventions were based on a rather small number of generally applicable principal solutions (Mann, 2001). Thus, in the case of technical problems, TIPS supports solution finding by systematically pointing out alternative and analogical technical solution principles. A central theme in De Bono’s (1990) lateral thinking is breaking with established thinking patterns of the human mind and exposing the mind to discontinuities. De Bono argues that the deliberate introduction and proper pursuit of an analogue problem not only takes up less time in finding solutions, but will eventually lead to processes, functions and relationships which are then transferred back to the original problem ‘to see if they fit or what ideas they set off’ (De Bono, 1990). Synectics is based on the ‘force-fitting’ or recombination of knowledge that appears to have no relations. However, by recombination, the individual is detached from the problem and forced to relate the new knowledge to the original problem to enable creative solutions (Gordon, 1969).

Research Methodology The purpose of this paper is to show how firms enable and use analogical thinking for product innovation. Because of the lack of empirical insights into how analogical thinking is enabled and applied at the firm level, a qualitative case study approach is employed © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

(Eisenhardt, 1989; Yin, 2003). We employed a multiple case study to obtain richer insight into how different settings may influence the approach and effect of analogical thinking. The case firms were identified in the course of a two-year research project focusing on firms’ use of analogies for radical new product innovation. Out of a sample of 18 companies that participated in the research project, we selected a purposive sample of four companies which met the criteria that they (a) were engineering companies, and (b) had engaged in breakthrough product innovation based on the use of analogies. The case firms are based in Switzerland and Austria where they also develop and manufacture. As is typical for engineering companies, problems mainly concern the technical improvement of existing products. Therefore, the ‘problem’ is typically identical with the product, and we use both terms interchangeably. We adopt Chandy and Tellis’ (1998) taxonomy of innovations along the technology and market domains (see Table 1), which is consistent with many other definitions of the degree of novelty of an innovation (see Garcia & Calantone, 2002). Accordingly, incremental innovations involve relatively minor changes in technology and customer benefit. Market breakthroughs are based on core technologies but provide substantially higher customer benefits. Technological breakthroughs do not provide higher customer benefit, but involve a substantially different technology. Radical innovations involve substantially new technology and provide substantially higher customer benefits (Sorescu, Chandy & Prabhu, 2003). We collected data by means of personal in-depth interviews, archival documents and passive participation in workshops of current development projects with senior managers and R&D employees. We organized the interviews by consistently using the same semistructured interview guide to ensure the reliability of the results (Yin, 2003). The interview guide comprised questions about what actions were taken during the problem-solving

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Table 2. Demographics of the Case Study Firms Case firm

Industry/Sector

AlpineCo

Alpine Sports Equipment Aluminium

AluCo

TextileCo PipesCo

Textile Machinery Piping Systems

Products

No. of employees

Skis, shoes, bindings, poles Automotive crash management systems, cockpit carriers, side-impact carriers, safety system components Household sewing and embroidery machines Fittings, jointing, valves

2,100

process, and thus how the analogy was ultimately identified. The questions concentrated on facts and events within the problem-solving process rather than on respondents’ interpretations (Eisenhardt, 1989). These personal interviews lasted between 60 and 140 minutes and were tape-recorded and transcribed. Table 2 provides a brief overview of the case firms.

Case Studies: Analogical Thinking in Breakthrough Product Innovation Case 1: AlpineCo AlpineCo had the problem that the skis were difficult to control at certain speeds. Analysing the cause, R&D found that the ski was developing a resonance frequency at high speeds which caused the ski to vibrate. During the phase of intense occupation with analysing the problem, the head of R&D and three colleagues were delving into the question how the vibration could be dampened or eliminated. From his background as a mechanical engineer, the head of R&D knew that vibrations were a recurring problem in settings such as machine or building construction. With the terms ‘vibration’, ‘damping’ and ‘cushioning’ unconsciously in mind, the team then decided to search for industries and applications where damping or elimination of vibrations were a problem: ‘we were actively looking for analogous solutions’. However, initial search efforts were in vain because of too large a search scope, as the R&D team was searching for anything that had to with vibrations. The search was only successful when one team member proposed to limit the

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68,000

350 12,000

Project target

Solution for damping vibration problems New technology for crash management system

Solution for gauging cloth displacement Solution for improved joining of interfaces of pipes

search scope to include only frequencies above 1800 Hz, as this was the range of frequency found in the vibrating ski. This frequency is typically found in acoustics, and AlpineCo ultimately found a viable solution at an inventor who had for years researched on the elimination of undesirable frequencies of bowed instruments. Also, the solution proved to be easily transferable, as the material used to filter the undesired frequencies of the bowed instruments could easily be adapted to the skis. ‘It’s a simple idea and easily applicable, and did not require any additional investments’ (head of R&D). AlpineCo then applied the solution to its own demands by developing an extra layer in the ski with similar structure and material like in the bowed instruments and incorporated it into the ski. This technology is termed ‘frequency tuning’ and is today found in virtually every ski.

Case 2: AluCo For a long time, AluCo had been looking for alternative approaches to improve its crash management system (CMS) (consisting of the front beam and two crash-boxes mounted on the longitudinal chassis beams of a car). Somewhat frustrated with the hitherto ‘conventional’ approach, AluCo management realized that mere optimization of materials and tweaking geometric designs would not result in the major advancement that they hoped for: ‘we have been doing this for decades now, and I believe our engineers have become too shortsighted to look beyond their own noses’ (head of future technologies). Before ‘prematurely jumping to solutions’ (head of future technolo© 2008 The Authors Journal compilation © 2008 Blackwell Publishing


gies), a team of four R&D employees engaged in an in-depth investigation of the current crash-box. They focused particularly on gaining a detailed understanding of the product function both from a technological view and customer utility point of view. In subsequent workshops, the team first analysed and described the technological function of the crash-box in terms such as ‘protecting the car’s longitudinal carrier from damage’ and later in terms such as ‘gliding grid structures in the material’. In the course of the analysis, AluCo developed key terms such as ‘energy absorption’ and ‘transformation of kinetic energy’. With these terms in mind, AluCo were able to build associations with different kinds of technologies, applications and industries where the absorption of energy was crucial. AluCo’s R&D then started to search the internet with the focus on the previously developed key terms. In this way, they identified several promising technologies new to their industry, which today are subject for further development.

Case 3: TextileCo TextileCo faced the problem that the speed of the material displacement was different from the speed of the sewing foot, which resulted in inhomogeneous stitch lengths and spaces. Thus, initial activities aimed at synchronizing the speed of the material displacement with the speed of the sewing foot. Analysing how the displacement could be gauged under the given spatial constraints, TextileCo’s R&D concluded that the displacement of the material had to be gauged with high precision because of the high speed of the sewing foot. As gauging was outside their competence, TextileCo agreed to looking for external solutions. A team of five R&D members started looking for solutions that were related to what TextileCo called ‘real-time gauging’. They approached an external technology service provider, who ultimately provided AluCo with the optical sensor of a conventional computer mouse as a solution. The service provider had previously worked on another project where feedback loops played an important role and where a very similar sensor technology was applied. As the R&D leader said, ‘without the service provider we would never have come up with such a brilliant and simple solution, it took us only 18 months from problem formulation to market introduction, which is about half the time we usually need.’ TextileCo adapted the mouse sensor chip to its specific requirements and enhanced it so it would even recognize very smooth or dark fabrics. As a result, because of the auto© 2008 The Authors Journal compilation © 2008 Blackwell Publishing

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mation, even beginners are now able to quilt genuine artwork of high quality. This had previously been a domain only for experienced quilters, and implementing the new technology allowed TextileCo to tap a new and fast growing market.

Case 4: PipesCo The piping division of PipesCo has deep know-how in production techniques such as welding or gluing in combination with material optimization for the joining of pipes. As the industry is characterized by long product life-cycles, the conventional strategy has been the constant improvement of existing technologies and products. One day an R&D employee was watering the flowers in his garden, when he realized that the hose and the sprinkler head were connected via a clicking system: ‘It was a lucky accident. The basic principle is the same, it’s about a medium flowing through a pipe, only the way the pipes are connected is different’ (R&D employee). He introduced the idea into the company, and preliminary assessments convinced the chief technical officer (CTO) to pursue the idea, both because of the simplicity of the technology which would tremendously facilitate the joining of large pipes in construction, and because of the enormous cost savings involved with the new technology. In the eyes of the CTO ‘it was a revolutionary development, but actually we simply incrementally advanced what was already known in another industry. The biggest challenge was to adapt the solution to the existing requirements in terms of pressure, safety and durability.’ Today, the clicking technology has prevailed and led to significant competitive advantage for PipesCo.

Discussion Considering the definition of newness of innovation according to Chandy and Tellis (1998), the use of analogies by the case firms has resulted in the development of technological breakthroughs (AlpineCo, AluCo, PipesCo) and radical innovations (TextileCo). Despite being similar in their highly innovative character, the cases reveal differences in how the analogies were identified, and that analogical thinking is enabled both by pure cognitive abilities (PipesCo) and by systematic effort (AlpineCo, AluCo, TextileCo). In the case of PipesCo, the identification of the analogy might be owed initially to serendipity and then to the ability of the R&D employee to relate the situation to a problem the firm was facing. In contrast, the other cases show that

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the identification of the analogy was only enabled after delving into the problem structure and initiating a deliberate search effort for analogous solutions. Furthermore, we find that mere identification of the analogy is not sufficient but that – particularly in the case of structural analogies – firms need strategic intent (Chandy & Tellis, 1998; Herrmann, Gassmann and Eisert, 2007), that is, the will to question own technologies and the will to adapt new knowledge. Also, the mere identification of the analogous solution is not sufficient, as the transfer of relevant knowledge and its adaptation to the own problem context are vital for the ‘idea’ to become an innovation. The findings are discussed in the following; Table 3 provides an overview of how the analogy was identified and the characteristics of the analogies.

Strategic Intent Analysis of the cases shows that the will to break with conventional boundaries is paramount when searching for solutions that are non-obvious and highly novel. As entirely new technologies often serve as substitutes, existing technologies and competencies might be jeopardized, as is particularly the case at AluCo and PipesCo. Thus, the will to question own products and technologies is vital for successful radical innovations (Herrmann, Gassmann and Eisert, 2007). With respect to analogical thinking, our findings coincide with those of Majchrzak, Cooper and Neece (2004) on knowledge reuse; they found that in order to use analogies, the reusers in the more innovative cases needed to be aware of and open to non-traditional approaches that might lead to

greater levels of innovation. All firms were willing and open to look for solutions that were neither developed internally nor established in their industry. Openness to external developments and innovations has been identified as a critical success factor for firms in technology-intensive industries (Chesbrough, 2003; Gassmann, 2006). Furthermore, TextileCo integrated external experts for the identification of the analogous solution, an approach which is considered highly beneficial for innovation (von Hippel, 1986). Thus, as analogous solutions typically originate outside known environments, an open attitude is pivotal for analogical thinking to be successful (Katz & Allen, 1982).

Cognitive Abilities, Problem Analysis and Deliberate Search The identification of analogies typically depends on the cognitive abilities and the personal experiences of the individual (Gentner, Rattermann & Forbus, 1993; Reeves & Weisberg, 1993; Dahl & Moreau, 2002; Cummings & Teng, 2003), and analogies to some target can only be found if the individual has had prior exposure to at least some elements of the target setting (Gick & Holyoak, 1980; Reeves & Weisberg, 1993). Thus, the successful identification of analogies also depends on the type of analogy, i.e., whether the problem and solution source share surface or structural similarities (Dahl & Moreau, 2002). In the case of PipesCo, the identified analogy might be considered a surface analogy because problem and solution source share very similar physical characteristics. Thus, despite its surface character, the analogy led to a technological

Table 3. Identification and Type of Analogical Solutions Case firm

Type of problem analysis carried out

Elements supporting analogy identification

Type of analogy found

AlpineCo

Technical

Structural

AluCo

Technical + contextual Technical + contextual None

Cognitive ability + deliberate search Deliberate search Deliberate search

Structural

Cognitive ability

Surface

TextileCo PipesCo

Structural

Type of subsequent innovation* Technological breakthrough Technological breakthrough Radical Technological breakthrough

* The type of innovation is defined following Chandy and Tellis (1998) along the market and technology dimension.

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breakthrough innovation for PipesCo as it involved tremendous cost savings and introduced a new technology to the industry (Ward, 1994; Chandy & Tellis, 1998). The example shows that also surface similarities can result in innovations of higher novelty (Holyoak & Thagard, 1995; Hargadon & Sutton, 1997). However, in all the other cases the firms may have required more cognitive abilities to identify the analogy. Here, the analogies were based rather on structural similarities which were not immediately visible as in the case of PipesCo. The firms identified the analogy only by (a) in a first step analysing the problem in detail, and (b) in a second step embarking on a deliberate search effort to find analogous solutions. The detailed problem analysis entailed that these firms carried out an in-depth analysis regarding the technical and contextual functions of the product. The technical analyses included re-building a deep understanding of the technological function and the interrelation between single components of the product. Contextual analysis included building a profound understanding of the true customer benefits, and all firms furthermore ensured they analysed and understood the identified analogy in its original application context (Leonard & Rayport, 1997; Beckman & Barry, 2007). The joint analysis of technical and contextual functions subsequently increased the degree of abstraction from the original problem, as more and more structural elements of the problem were identified. This enabled the firms to identify the underlying mechanisms (i.e., the structure) of the problem, allowing them to look beyond mere superficial similarities (Fernandez & Montes,

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1999). As a consequence, the amount of analogical ideas increased with each additional abstraction from the original problem. This was particularly obvious in the case of AluCo. AluCo first analysed the problem to the point of ‘gliding grid structures in the material’, before arriving at terms such as ‘energy absorption’. The difference between both types of terms is the degree of abstraction, the first one being a quite specific technical description, the latter a description of the underlying purpose of the product. AluCo realized that it did not primarily matter how the kinetic energy was transformed, but that it was turned into another safer type of energy. Thus, the more abstract the description became, the more solutions and also the more structural similarities could be identified (Dahl & Moreau, 2002). When looking at structural analogies, the quality of the information might be more tacit in contrast to superficial similarities where the information is more explicit (Nonaka, 1994). Similarly, abstracting from the problem required the firms to understand how the problem is structured, implying that problem know-how might be more important than mere problem knowledge for the development of more radical innovations (Kogut & Zander, 1992). Thus, by abstracting the original problem to its structural relationships, the space for potential solutions is opened up (Figure 1), and the use of cognitive abilities is enabled or facilitated. Problem abstraction as carried out by the firms might be an effective means for arriving at a proper problem formulation, which has been found vital for successful product innovation (Cooper, 1999; Ward, 2004). In this regard, ‘problems can be defined very con-

Range of analogical solutions

3: Ad ap ta

tio

n

Step 2: Analogy

surface similarity to problem A

St ep

Step 1: Abstraction

Degree of Abstraction

Solution Space

structural similarity to problem A

Problem Source, (Industry A)

Solution Source, (Industry B)

Problem element

Figure 1. Opening up the Solution Space by Abstraction from the Underlying Problem © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

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Creativity & Divergence 0 Phase

Task

1

Strategic Intent Ensure open mindset and allow people to pursue new technologies outside the core

3

2

Abstraction Analyze technical functions Analyze problem context and true customer benefit Define abstract key terms

Rigidity & Convergence

Assessment

Analogy Search for surface and structural similarities regarding technologies and industries

Analyze target source, build understanding Evaluate and filter relevant knowledge

4

Adaptation Transfer and adapt relevant knowledge technology

Figure 2. A4-Innovation Process for New Product Innovation by Analogical Thinking cretely or abstractly, with the former leading to less novelty but more familiarity’ (Ward, 2004).

Knowledge Creation and Adaptation Development Transfer of relevant knowledge to the target problem was in all cases successful. However, the cases of AlpineCo, TextileCo and PipesCo show that further development is necessary to adapt to the original problem’s requirements (Cummings & Teng, 2003). The identified analogies were mostly perceived as simple but powerful solutions, which supports the assumption that analogies entail limited risks while simultaneously having great impact in terms of radical innovations. The case of PipesCo shows that, despite the surface analogy, the target solution had to be adapted to the existing requirements of the source problem, which according to the CTO constituted the biggest problem. In order to arrive at a workable solution, all firms had to analyse and understand the information found in the solution source which included the creation of new knowledge in the analogizing firm. Understanding the information of the solution source subsequently enabled the firms to filter the knowledge pieces most relevant for them. For example, in the case of AlpineCo, the identified analogous solution could not be transferred in its entirety, but relevant knowledge about the grid material was transferred and afterwards applied to the skis with an individually calculated grid structure. The cases show that analogical thinking does not happen merely by accident but is supported by means of a systematic approach. Based on the insights from the cases, we propose a generic process we call A4-Innovation Process (Figure 2). Its purpose is to provide firms with a structured approach

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for how analogical thinking might be enabled and applied for breakthrough product innovation.

Conclusion The aim of this paper is to show how firms enable and use analogical thinking for the development of product innovation. Thus, we aim to extend the literature on analogical thinking by providing empirical insights into how firms enable and use analogies. We found that firms must be open-minded for external solutions and willing to challenge own technologies as a premise for analogical thinking to work. Therefore, top management must foster the search for external solutions and be willing to cannibalize established products and technologies. In this, analogical thinking might be a powerful approach to identify new and non-obvious technological solutions with limited risk and cost. Apart from firms’ strategic decision to be open to external innovations, we found the following aspects to be particularly important: • Firms must establish a deep understanding of the problem and context in which the problem operates. This requires an in-depth analysis of the problem both from a technological and contextual perspective. Such analysis leads to subsequent abstraction from the problem, allowing abstract search terms to be generated. These tasks might be difficult for firms who have established products, as existing technologies, competencies and conventional mindsets are not easily overcome. • Since the identification of both surface and structural analogies between different settings is facilitated when there has been prior exposure to both settings, the firm must © 2008 The Authors Journal compilation © 2008 Blackwell Publishing


establish ways to explore domains which differ from the own application context (Nelson & Winter, 1982; March, 1991; Crossan & Bedrow, 2003). This is particularly true as the cases show that, even without prior exposure, analogies can be found if a deliberate search effort based on abstract search terms is employed. • Firms must understand the context of the analogous solution in order to evaluate what knowledge is valuable and thus is subject for transfer. Failure to do so might lead to the premature identification of a seemingly valuable analogy, leading to the adaptation of useless knowledge. With the A4-Innovation Process we aim to provide a structured approach for the identification of analogical solutions for the development of breakthrough product innovation. The A4-Innovation Process targets the early innovation challenges in how to find highly novel solutions. First experience in applying this process with five engineering firms in different industries has been very positive and encouraging. In particular, these firms found that by applying the approach they arrived sooner at better solutions, compared to their ‘conventional’ problem-solving approach. The outlined process has particular strength in the combination of existing knowledge in the problem source and experience with the solution source for creating new solutions in the own industry. We invite researchers to further test this process with a larger empirical sample across different industries for improved validation and to extend the discussion on analogical thinking for radical new product innovation.

Acknowledgements The authors would like to thank Jeff Butler and the anonymous reviewers for their valuable comments.

References Amabile, T.M. (1996) Creativity in Context. Westview Press, Boulder, CO. Argote, L. and Ingram, P. (2000) Knowledge Transfer: A Basis for Competitive Advantage in Firms. Organizational Behavior and Human Decision Processes, 82, 150–69. Beckman, S.L. and Barry, M. (2007) Innovation as a Learning Process: Embedding Design Thinking. California Management Review, 50, 25–56. Boden, M.A. (1990) The Creative Mind. Myths and Mechanisms. Weidenfeld and Nicholson, London. Ceserani, J. and Greatwood, P. (1995) Innovation & Creativity. Kogan Page, London. © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

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Chandy, R.K. and Tellis, G.J. (1998) Organizing for Radical Product Innovation: The Overlooked Role of Willingness to Cannibalize. Journal of Marketing Research (JMR), 35, 474–87. Chesbrough, H. (2003) Open Innovation. The New Imperative for Creating and Profiting from Technology. Harvard Business School Press, Boston, MA. Cooper, R.G. (1999) From Experience: The Invisible Success Factors in Product Innovation. Journal of Product Innovation Management, 16, 115–33. Crossan, M.M. and Bedrow, I. (2003) Organizational Learning and Strategic Renewal. Strategic Management Journal, 24, 1087–105. Csikszentmihalyi, M. (1996) Creativity. Flow and the Psychology of Discovery and Invention. HarperCollins, New York. Cummings, J.L. and Teng, B.-S. (2003) Transferring R&D Knowledge: The Key Factors Affecting Knowledge Transfer Success. Journal of Engineering & Technology Management, 20, 39–68. Cyert, R. and March, J.G. (1992) A Behavioral Theory of the Firm. Blackwell Publishers, Cambridge, MA. Dahl, D.W. and Moreau, P. (2002) The Influence and Value of Analogical Thinking During New Product Ideation. Journal of Marketing Research, 39, 47–60. De Bono, E. (1990) Lateral Thinking for Management. Penguin Books, London. Eisenhardt, K.M. (1989) Building Theories from Case Study Research. Academy of Management Review, 14, 532–50. Fernandez, E. and Montes, J.M. (1999) Competitive Strategy in Technological Knowledge Imitation. International Journal of Technology Management, 18, 535–48. Gagne, C.L. and Shoben, E.J. (1997) Influence of Thematic Relations on the Comprehension of Modifier-Noun Combinations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 71–87. Garcia, R. and Calantone, R. (2002) A Critical Look at Technological Innovation Typology and Innovativeness Terminology: A Literature Review. Journal of Product Innovation Management, 19, 110– 32. Gassmann, O. (2006) Opening up the Innovation Process: Towards an Agenda. R&D Management, 36, 223–8. Gavetti, G. and Rivkin, J.W. (2005) How Strategists Really Think. Harvard Business Review, 83, 54–63. Gavetti, G., Levinthal, D.A. and Rivkin, J.W. (2005) Strategy Making in Novel and Complex Worlds: The Power of Analogy. Strategic Management Journal, 26, 691–712. Gentner, D. (1989) The Mechanisms of Analogical Transfer. In Vosniadou, S. and Ortony, A. (eds.), Similarity and Analogical Reasoning. Cambridge University Press, Cambridge. Gentner, D., Rattermann, M.J. and Forbus, K. (1993) The Roles of Similarity in Transfer: Separating Retrievability from Inferential Soundness. Cognitive Psychology, 25, 524–75. Gick, M.L. and Holyoak, K.J. (1980) Analogical Problem Solving. Cognitive Psychology, 12, 306–55. Gick, M.L. and Holyoak, K.J. (1983) Schema Induction and Analogical Transfer. Cognitive Psychology, 15, 1–38.

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Gordon, W.J.J. (1969) Synectics – The Development of Creative Capacity. Harper and Row, New York. Grant, R.M. (1996) Prospering in Dynamicallycompetitive Environments: Organizational Capability as Knowledge Integration. Organization Science, 7, 375–87. Hampton, J.A. (1998) Conceptual Combination: Conjunction and Negation of Natural Concepts. Memory & Cognition, 25, 888–909. Hargadon, A. and Sutton, R.I. (1997) Technology Brokering and Innovation in a Product Development Firm. Administrative Science Quarterly, 42, 716–49. Henderson, R.M. and Clark, K.B. (1990) Architectural Innovation: The Reconfiguration of Existing Product Technologies and the Failure of Established Firms. Administrative Science Quarterly, 35, 9–30. Herrmann, A., Gassmann, O. and Eisert, U. (2007) An Empirical Study of the Antecedents for Radical Product Innovations and Capabilities for Transformation. Journal of Engineering & Technology Management, 24, 92–120. von Hippel, E. (1986) Lead Users: A Source of Novel Product Concepts. Management Science, 32, 791– 805. Holyoak, K.J. and Thagard, P. (1995) Mental Leaps: Analogy in Creative Thought. MIT Press, Cambridge, MA. Holyoak, K.J. and Thagard, P. (1997) The Analogical Mind. American Psychologist, 52, 35–44. Katz, R. and Allen, T.J. (1982) Investigating the Not Invented Here (NIH) Syndrome: A Look at the Performance, Tenure, and Communication Patterns of 50 R&D Project Groups. R&D Management, 12, 7–20. Keane, M. (1987) On Retrieving Analogues when Solving Problems. Quarterly Journal of Experimental Psychology Section A, 39, 29–41. Kogut, B. and Zander, U. (1992) Knowledge of the Firm, Combinative Capabilities, and the Replication of Technology. Organization Science, 3, 383– 97. Leonard, D. and Rayport, J.F. (1997) Spark Innovation through Empathic Design. Harvard Business Review, 75, 102–13. Majchrzak, A., Cooper, L.P. and Neece, O.E. (2004) Knowledge Reuse for Innovation. Management Science, 50, 174–88. Mann, D. (2001) An Introduction to TRIZ: The Theory of Inventive Problem Solving. Creativity and Innovation Management, 10, 123–25. March, J.G. (1991) Exploration and Exploitation in Organizational Learning. Organization Science, 2, 71–87. Nelson, R.R. and Winter, S.G. (1982) An Evolutionary Theory of Economic Change. Belknap Press of Harvard University Press, Cambridge, MA. Nerkar, A. and Roberts, P.W. (2004) Technological and Product-Market Experience and the Success of New Product Introductions in the Pharmaceutical Industry. Strategic Management Journal, 25, 779–99. Nonaka, I. (1994) A Dynamic Theory of Organizational Knowledge Creation. Organization Science, 5, 14–37.

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Prince, G.M. (1970) The Practice of Creativity. Macmillan, New York. Reeves, L.M. and Weisberg, R.W. (1993) Abstract Versus Concrete Information as the Basis for Transfer in Problem Solving: Comment on Fong and Nisbett (1991). Journal of Experimental Psychology: General, 122, 125–8. Reeves, L.M. and Weisberg, R.W. (1994) The Role of Content and Abstract Information in Analogical Transfer. Psychological Bulletin, 115, 381–400. Rickards, T. (1990) Creativity and Problem Solving at Work. Gower Publishing, Aldershot. Ross, B.H. (1989) Distinguishing Types of Superficial Similarities: Different Effects on the Access and Use of Earlier Problems. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 456–68. Sorescu, A.B., Chandy, R.K. and Prabhu, J.C. (2003) Sources and Financial Consequences of Radical Innovation: Insights from Pharmaceuticals. Journal of Marketing, 67, 82–102. Vosniadou, S. and Ortony, A. (1989) Similarity and Analogical Reasoning: A Synthesis. In Vosniadou, S. and Ortony, A. (eds.), Similarity and Analogical Reasoning. Cambridge University Press, Cambridge, pp. 1–17. Ward, T.B. (1994) Structured Imagination: The Role of Category Structure in Exemplar Generation. Cognitive Psychology, 27, 1–40. Ward, T.B. (2004) Cognition, Creativity, and Entrepreneurship. Journal of Business Venturing, 19, 173–88. Yin, R.K. (2003) Case Study Research: Design and Methods. Sage Publications, Thousand Oaks, CA.

Prof. Dr Oliver Gassmann (oliver. [email protected]) is Professor of Technology Management at the University of St Gallen and Director at the Institute of Technology Management (ITEM). After obtaining his PhD in 1996, he worked for Schindler Corporation, headquartered in Ebikon (Switzerland). From 1998 to 2002 he was Vice-President Technology Management responsible for corporate research worldwide. In addition, he is a member of several boards, e.g., economiesuisse’s Board for Science and Research and R&D Management’s Editorial Board. He has published 10 books as author, co-author and editor, and over 130 publications in the area of technology and innovation management. Marco Zeschky (marco.zeschky@unisg. ch) is a third-year doctoral candidate and research associate at the University of St. Gallen’s Institute of Technology Management (ITEM). His research focuses on search and exploratory innovation. He has studied industrial engineering with a focus on product development and management at the Darmstadt University of Technology (Germany).


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Exploration and Exploitation in Innovation: Reframing the Interpretation Ying Li, Wim Vanhaverbeke and Wilfred Schoenmakers There has been a burgeoning literature about exploitation and exploration since March’s seminal article in 1991. However, in reviewing the extant literature we find different interpretations of both concepts leading to ambiguity and even some inconsistency. This paper focuses in particular on the interpretation of exploration and exploitation in the literature on technological innovation. It addresses two critical research questions. First, what are the different interpretations of exploitation and exploration? Second, how can we set up a framework that reconciles these differences and reduces the ambiguity that we find in the literature? To answer these two questions, we first explain what the root causes of these different viewpoints are. Second, we provide a theoretical framework that integrates the different perspectives, sets up a new typology to define exploration and exploitation, identifies white spaces in the current research and provides guidance for future research.

Introduction

T

he notion of exploration and exploitation has been widely used in studies on organizational learning, strategic renewal and technological innovation. March (1991) introduced the two concepts as follows: ‘exploration includes things captured by terms such as search, variation, risk taking, experimentation, flexibility, discovery, and innovation. Exploitation includes such things as refinement, choice, production, efficiency, selection, implementation, and execution’ (March, 1991, p. 71). Exploration is variation-seeking, risk-taking and experimentation oriented. Exploitation is variety-reducing and efficiency oriented (March, 1991). These two concepts require different structures, processes, strategies, capabilities and cultures, and may have different impacts on an organization’s performance. Following the seminal work of March in 1991, a great number of researchers have studied the notion of exploitation and exploration from different perspectives. Although the existing literature about exploration and exploitation has greatly contributed to our understanding of technological innovation, © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

organizational learning and strategic renewal, these studies displayed an amount of inconsistency in the interpretation of exploitation and exploration. First, this lack of consistency makes it difficult to compare research findings from different researchers. Second, as a lack of consistency causes greater ambiguity, it may intensify the fuzzy landscape of research on exploitation and exploration, which eventually leads to more problems in future research. Almost two decades after March’s 1991 paper, we believe it is time to look back at what we know so far and find out what the main gaps and challenges are in the research on exploitation and exploration. The large number of studies on exploration and exploitation in different research disciplines make it difficult to review all the articles in all disciplines through a single theoretical review. Therefore, we confine our attention to the literature on one single domain of organizational research – technological innovation – because technological innovation is considered as a critical competitive capability for growth and adaptation (Schumpeter, 1934), and it demonstrates a firm’s capability of effective organizational learning (Von Hippel, 1994).

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The purpose of this paper is two-fold. First, we review the existing literature on technological innovation that has been published since March’s work (1991) and display the different interpretations of exploration and exploitation from different researchers. Second, based on the literature review, we try to reduce the ambiguity in the existing literature by setting up a framework that reconciles these differences. Instead of constructing a universal definition for exploration and exploitation, we argue that it is critical to distinguish two knowledge domains where different types of exploration and exploitation take place. On the one hand, exploration and exploitation may occur within the ‘function domain’ that crosses various functions along the value chain (Lavie & Rosenkopf, 2006). For instance, a manufacturing firm can form an alliance with an R&D institute in order to explore new technological opportunities. On the other hand, within each value chain function, firms may access new knowledge by local search or distant search along different dimensions. The key question is whether the new knowledge is familiar or unfamiliar, compared to a firm’s existing knowledge base. We label this as the ‘knowledge distance domain’. Reframing exploration and exploitation in such a way allows researchers to clearly understand the conceptualization of exploration and exploitation in the literature at different levels of analysis and to identify blind spots for future research. This paper is organized as follows. First, we introduce our research approach and explain how we selected the literature to review. Second, we display the ambiguity and inconsistency in the studies on exploitation and exploration in the existing innovation literature. Third, we propose a framework that may integrate the differences and reduce the conceptual ambiguity in the existing literature. Finally, we sum up with discussions and conclusions.

Research Approach In order to conduct a systematic review of studies on exploration and exploitation in the literature on technological innovation, we carried out a systematic literature search. We focused on articles published in different academic journals since 1991, when March published his seminal work, up to the present (December 2007). We applied three basic selection criteria: first, the article must focus on the notion of exploration and exploitation; second, the theme of the article must be closely related to technological innovation; third, we excluded theoretical review papers.1 A research

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approach similar to Knoben and Oerlemans (2006) has been used for this literature search. We used the WileyInterScience database, the EBSCOHost Research database and the Web of Science database to perform a literature search. Given the differences among the search engines of these databases, we used slightly different search techniques for each of the three databases, though the underlying selection criteria remained the same. In the WileyInterScience database, the search is preliminary based on the keywords: (1) ‘Business’ as journal discipline; (2) ‘March’ in references; and (3) ‘Innovation, technology, exploration, exploitation’ in full text or abstracts.2 This search yielded 91 papers. In the EBSCOHost Research database, the search is preliminary based on the keywords: (1) ‘exploration, exploitation’ in abstracts, and (2) ‘Innovation, technology’ in full text.3 This search yielded 37 papers. In the Web of Science database, the search is based on the keywords: ‘exploration’, ‘exploitation’ and ‘innovation’ in the topic. This search yielded 46 papers. Thus, the first round of selection yielded 174 papers in total. To narrow down this list of articles, we carefully read the abstracts and full text for each paper, and eliminated those papers that only mentioned exploration and exploitation as a relevant theoretical background but did not specifically focus on them. We also eliminated those articles that were not relevant to technological innovation. This manual process reduced the number of articles further to 37. We also realized that such a search method has its disadvantages. Articles that are not listed in these three databases will not be found by this method. Therefore, we also employed a complementary source for the literature search. First, as we have been interested in this research topic for years, we have accumulated a list of papers from various journals that fall within our basic selection criteria. Second, we also consulted many researchers in this research field and asked them to recommend published papers. By carefully reading the articles from this complementary source, we finally agreed to add six papers to the list of articles. Hence, the total number of articles under review for this study is 43. Table 1 lists the selected papers in alphabetical order together with their interpretations of exploration and exploitation.

Different Interpretations of Exploration and Exploitation: A Presentation Theoretical constructs evolve as authors adapt them over time to their research needs. The © 2008 The Authors Journal compilation © 2008 Blackwell Publishing


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Table 1. Alphabetical List of the Selected Literature with the Interpretations of Exploration and Exploitation Article Ahuja & Katila (2004) Ahuja & Lampert (2001)

Argyres (1996) Atuahene-Gima (2005) Audia & Goncalo (2007) Benner & Tushman (2002) Bierly & Daly (2007) Cantwell & Mudambi (2005) Cesaroni, Minin & Piccaluga (2005) Danneels (2002)

Danneels (2007) Dittrich & Duysters (2007) Dittrich, Duysters & de Man (2007) Dowell & Swaminathan (2006) Faems, Van Looy & Debackere (2005) Garcia et al. (2003)

Definition/Interpretation Path-creating search is exploration. Thus, the more diversified the search is, the greater degree of exploration (measure search diversity). Knowledge search in science and across different market dimensions. It defines exploration and exploitation based on knowledge distance, but it goes beyond local vs. distant search. It defines three levels of distant search. Novelty is categorized as ‘new to firm’, ‘new to industry’, and ‘new to world’. It only discusses the knowledge search within the technology function along the technical dimension. Exploration as technological capability broadening; exploitation as technological capability deepening. Exploration is to invest resources to refine and extend its existing product innovation knowledge, skills and processes. Exploitation is to invest resources to acquire entirely new knowledge, skills and processes. Exploration and exploitation as different types of creativity of individuals. It uses number of new subclasses and number of new citations of an inventor as indicators. It only discusses the technology knowledge. Defines exploration and exploitation in terms of technology search activities. Local search is exploitation, distant search is exploration. Exploration is experiment with radical new ideas or ways of doing things. Exploitation involves refining and leveraging existing knowledge and focus on efficiency of current practices. Competence-creating subsidiary as exploration, competence-exploiting subsidiary as exploitation. They have different nature and level of R&D. Investing in a firm’s main operations and establishing alliances to secure complementary assets are exploitation. Investing in R&D in new technology is exploration. Exploration and exploitation are defined by two dimensions of competence used in product innovation: technology and market. Exploration is to develop new technology to serve new customers, and exploitation is to strengthen existing technology to serve existing customers. Same as Danneels (2002). Exploration is non-equity alliances with new partners, who have different technologies. Exploitation is equity alliances with existing partners, who have similar technologies. Same as Dittrich & Duysters (2007).

Exploration is defined as a large variety of technology trajectories ever since a firm’s initial choice of technology. Exploratory collaboration is to create new competences such as those with universities and research institutes, while exploitative collaboration focuses on complementarities between technologies and products, such as those with customers and suppliers. Exploration is to conduct research projects, and exploitation is to conduct product development projects.


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Table 1. continued Article Geiger & Makri (2006) Gilsing & Nooteboom (2006) Greve (2007) Hagedoorn & Duysters (2002) He & Wong (2004) Holmqvist (2004)

Jansen, Van Den Bosch & Volberda (2006) Jayanthi & Sinha (1998) Katila & Ahuja (2002)

Lavie & Rosenkopf (2006)

Lee & Ryu (2002) Lin, Yang & Demirkan (2007) McGrath (2001) Mom, Van Den Bosch & Volberda (2007) Nerkar (2003) Nerkar & Roberts (2004)

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Definition/Interpretation Exploration as science search, exploitation as technology search. Exploration is searching and recombining technology and science; exploitation is search market knowledge. Searching new technology as exploration (explicit), searching new market as exploitation (implicit). Explorative alliances are usually established in order to explore new technological opportunities. Exploitative alliances enable firms to commercialize the technology gained through exploration. Technological innovation activities aimed at emerging new product market is exploration, and those aimed at improving existing product market is exploitation. It examines how exploration and exploitation build upon each other at firm level. The key is dissatisfaction with performance. Firms search for proximate/distant knowledge or do extensive recombination under certain incentives, either from dissatisfaction or from slack. It defines exploration and exploitation with respect to searching new or existing knowledge on the customers/markets.

Exploration as the technology search that aims at meeting future market demand; exploitation as the technology search that aims at meeting current market demand. The degree of exploration is indicated by ‘search scope’, which is how broad knowledge a firm searches. The degree of exploitation is indicated by ‘search depth’, which describes how deeply a firm reuses its existing knowledge. It defines exploration and exploitation in alliances with respect to the ‘function domain’, ‘structure domain’ and ‘attribute domain’. ‘Function domain’ refers to the nature of value chain functions. ‘Structure domain’ refers to whether to ally with a new partner without prior ties. ‘Attribute domain’ describes to what extent the new partner’s organizational attributes are different from those of prior partners. Investment in unknown technological opportunities is exploration, and investment in existing technology is exploitation. Search new knowledge through new alliance partners as exploration, while consolidating a firm’s existing partner networks is exploitation. It uses a multi-item scale to measure exploration and exploitation. It emphasizes the search for new knowledge in technology and market. Managers’ exploration activities include searching for new possibilities with respect to product, service, process or markets, which require learning of new skills and knowledge. Managers’ exploitation activities include serving existing customers with existing products/services, which requires present knowledge and accumulation of experiences. For technology search, ‘temporal recency’ is exploitation, and ‘temporal spread’ is exploration. It defines exploration and exploitation with respect to search technology and market. Distal experience in technology and market is exploration, and proximate experience in technology and market is exploitation. © 2008 The Authors Journal compilation © 2008 Blackwell Publishing


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Table 1. continued Article Perretti & Negro (2007) Phene et al. (2006) Rosenkopf & Nerkar (2001) Rothaermel (2001) Rothaermel & Deeds (2004) Rothaermel, Hagedoorn & Roijakkers (2004) Sidhu, Commandeur & Volberda (2007) Sidhu, Volberda & Commandeur (2004) Vanhaverbeke & Peeters (2005) Van Looy, Martens & Debackere (2005) Vassolo, Anand, & Folta (2004)

Definition/Interpretation Recombining old, reuse existing, leverage prior knowledge is exploitation, and recombining old and new (hiring new employees) is exploration. It defines exploration and exploitation with respect to the combination of local or distant search in technical knowledge dimension and geographic/spatial dimension. It defines exploration and exploitation with respect to the combination of search across the organization boundary (spatial) and the technological boundary (technical). The activities of exploitative alliances include manufacturing, marketing or supply agreements, which are typical product market knowledge. Motivation differs in alliances: R&D alliances is exploration (technology-side), commercialization alliances is exploitation (product market knowledge). Similar to Rothaermel & Deeds (2004)

Three dimensions: 1. technology dimension (supply-side), 2. market dimension (demand-side), 3. spatial side. Supply side may involve both science and technology. It defines exploration in terms of the scope of external information acquisition (thus a search view). External information acquisition is examined through the supply-side, demand-side and geographic side. It mixes up the value chain and knowledge dimensions. Exploration is interpreted as the corporate venturing/NBD, which is related to ‘structural ambidextrous’ organization. Exploitation is to invest in the lucrative part of the technology life cycle, and exploration is to invest in various stages of the technology life cycle.

The alliances set up by big pharmaceutical companies with biotech companies are exploratory alliances, mainly to explore technological advantage.

meaning of theoretical constructs evolves as they circulate. Ambiguity often emerges along with the evolution of constructs. To reduce the ambiguity of theoretical constructs, it is important to trace the source of variation during the evolution. Therefore, we reviewed the selected articles published since 1991 and identified two main sources that cause variation in the interpretation of exploration and exploitation. First, different levels of analysis cause variety. Second, there are substantial differences in the understanding of exploration and exploitation among researchers, which is fundamentally related to what exploration and exploitation is within a particular level of © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

analysis. The purpose of this section is limited to presenting these two sources of inconsistency and ambiguity in the existing literature.

Level of Analysis Scholars interpret the notion of exploration and exploitation differently because they conducted their research at different levels of analysis. These different levels of analysis constrain the focus of researchers and yield great variety in interpretation of constructs. For instance, at the individual level, exploration and exploitation are considered as two different types of creative idea generation (Audia &

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Goncalo, 2007). At the project level, exploration manifests itself in the newness of a project (McGrath, 2001), or the composition of project development teams indicates the degree of exploration (Perretti & Negro, 2007). At the firm level, some scholars interpret exploration as distant knowledge search and exploitation as proximate knowledge search (Benner & Tushman, 2002; Katila & Ahuja, 2002; Nerkar & Roberts, 2004; Sidhu, Commandeur & Volberda, 2007). At the corporate group level, exploration and exploitation are usually considered in terms of corporate strategy for venturing (Cantwell & Mudambi, 2005; Vanhaverbeke & Peeters, 2005). At the alliances level, exploration and exploitation are usually seen as different motivations to enter interfirm collaboration (Hagedoorn & Duysters, 2002; Rothaermel & Deeds, 2004; Rothaermel, Hagedoorn & Roijakkers, 2004). At the industry level, exploitation and exploration build upon each other and form a dynamic ‘cycle of discovery’ (Gilsing & Nooteboom, 2006). Comparison between studies at different levels of analysis is sometimes not easy and straightforward. It, thus, requires a comprehensive understanding of the notion of exploration and exploitation at different levels of analysis. Therefore, it is important to summarize and synthesize the substantial differences in interpreting exploration and exploitation, which will be discussed later. Table 2 summarizes the selected literature according to their level of analysis. Since the majority of papers are studies on exploration and exploitation at the firm level, in the following section we discuss the substantial differences in the definition and interpretation of exploration and exploitation mostly by referring to the articles that are studies at the firm level. Articles at other levels of analysis will be referred to as related issues are raised.

Substantial Differences in Interpretation The second source of variation in the literature comes from the substantial differences in the understanding of exploration and exploitation. Although all authors agree that exploration is the search for new knowledge, technology, competences, markets or relations, and that exploitation is the further development of existing ones, their interpretation of these constructs differs substantially. Although the definitions and interpretations take very different forms, Gupta, Smith and Shalley (2006) suggested analysing the ambiguity in the definition of exploration and exploitation through the lens of the type or amount of learning. Following this suggestion, therefore, we illustrate the substantial differences in the

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interpretation of exploration and exploitation by discussing three distinct topics, from which the ambiguity and inconsistency vividly emerge. First, it is not clear in the existing literature on which function of the value chain (science, technology or product market) learning should be considered as exploration or exploitation. In this case, exploration and exploitation are regarded as dichotomous concepts that are usually linked to a specific pair of functions along the value chain. This issue presents the different interpretations of exploration and exploitation based on the type of learning. Second, exploration and exploitation are sometimes interpreted in terms of the different dimensions of knowledge search. The degree of exploration/exploitation thus depends on how local or distant the knowledge search is along the cognitive, temporal or spatial dimension of the knowledge space. In this case, the interpretation of exploration and exploitation is not only subject to the type of learning on the one hand, because local search represents exploitation and distant search represents exploration, but also to the amount of learning on the other hand, because the degree of local or distant knowledge search can be measured on a continuous scale along different dimensions. Finally, it is also unclear whether exploration and exploitation should be seen as a learning process or as an innovation outcome. Table 3 categorizes the selected literature according to these three topics,4 which are explored further in the next section of this paper.

Learning in Science, Technology and Market along the Value Chain Exploration and exploitation are different types of learning. Exploration is associated with terms such as search, variation, risk taking, experimentation and discovery, while exploitation is associated with refinement, production, efficiency, selection, implementation and execution, for example (March, 1991). These two different types of learning activities are sometimes linked with unique functions in the value chain of a firm, where learning takes place. To simplify the issue, we focus on three main functions along the value chain: science (fundamental research), technology (product development) and product market (manufacturing and marketing). In the existing literature, there is no consensus on which function of the value chain is associated with exploration or exploitation. Hereby, we illustrate the different interpretations in the literature. First, some researchers distinguish exploration from exploitation by highlighting the © 2008 The Authors Journal compilation © 2008 Blackwell Publishing


Industry level

Corporate group level Alliances level

Project/project team level Firm level

Individual level

Level of analysis

Gilsing & Nooteboom (2006)

Rothaermel & Deeds (2004), Lavie & Rosenkopf (2006), Lin, Yang & Demirkan (2007), Dittrich, Duysters & de Man (2007), Dittrich & Duysters (2007)

Rothaermel, Hagedoorn & Roijakkers (2004), Hagedoorn & Duysters (2002), Faems, Van Looy & Debackere (2005)

Vanhaverbeke & Peeters (2005)

Cantwell & Mudambi (2005)

Rothaermel (2001), Vassolo, Anand & Folta (2004)

Sidhu, Volberda & Commandeur (2004), Holmqvist (2004)

Rosenkopf & Nerkar (2001), Greve (2007), Ahuja & Lampert (2001), Benner & Tushman (2002), He & Wong (2004), Katila & Ahuja (2002), Nerkar (2003), Nerkar & Roberts (2004), Garcia et al. (2003) Lee & Ryu (2002), Atuahene-Gima (2005), Bierly & Daly (2007)

Jayanthi & Sinha (1998), Cesaroni, Minin & Piccaluga (2005), Geiger & Makri (2006), Sidhu, Commandeur & Volberda (2007), Jansen, Van Den Bosch & Volberda (2006), Van Looy, Martens & Debackere (2005)

Complementary sources

Argyres (1996), Danneels (2002), Ahuja & Katila (2004), Dowell & Swaminathan (2006), Phene et al. (2006), Danneels (2007)

Mom, Van Den Bosch & Volberda (2007)

Web of Science

McGrath (2001)

Audia & Goncalo (2007)

EBSCOHost Research

Source

Perretti & Negro (2007)

WileyInterScience

Table 2. List of Selected Literature and Level of Analysis

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Table 3. Categorizing Selected Literature According to the Four Discussed Issues Issues Search science, technology and product-market knowledge

Knowledge distance in cognitive, temporal or spatial dimension

Process vs. Outcome

Ahuja & Lampert (2001), Benner & Tushman (2002), Katila & Ahuja (2002), Nerkar (2003), Nerkar & Roberts (2004), Rosenkopf & Nerkar (2001), Sidhu, Volberda & Commandeur (2004), Rothaermel (2001), Ahuja & Katila (2004), Phene et al. (2006), Geiger & Makri (2006), Gilsing & Nooteboom (2006), Greve (2007), Sidhu, Commandeur & Volberda (2007), Audia & Goncalo (2007), Argyres (1996), Danneels (2002), Dowell & Swaminathan (2006), Danneels (2007), Cantwell & Mudambi (2005), Vassolo, Anand & Folta (2004), Jayanthi & Sinha (1998), Jansen, Van Den Bosch & Volberda (2006), McGrath (2001), He & Wong (2004), Hagedoorn & Duysters (2002), Rothaermel & Deeds (2004), Vanhaverbeke & Peeters (2005), Garcia et al. (2003), Van Looy, Martens & Debackere (2005), Faems, Van Looy & Debackere (2005)

Phene et al. (2006), Nerkar (2003), Sidhu, Volberda & Commandeur (2004), Sidhu, Commandeur & Volberda (2007), Rosenkopf & Nerkar (2001), Katila & Ahuja (2002), Ahuja & Katila (2004), Lavie & Rosenkopf (2006) Ahuja & Lampert (2001), Benner & Tushman (2002), Nerkar & Roberts (2004), Rosenkopf & Nerkar (2001), Rothaermel (2001), Geiger & Makri (2006), Gilsing & Nooteboom (2006), Greve (2007), Perretti & Negro (2007), Bierly & Daly (2007), Atuahene-Gima (2005), Mom, Van Den Bosch & Volberda (2007), Dittrich & Duysters (2007), Dittrich, Duysters & de Man (2007), Lin, Yang & Demirkan (2007)

Benner & Tushman (2002), He & Wong (2004), Holmqvist (2004), Katila & Ahuja (2002), Nerkar (2003), Nerkar & Roberts (2004), Argyres (1996), Jayanthi & Sinha (1998), Danneels (2002), Dowell & Swaminathan (2006), Phene et al. (2006), Jansen, Van Den Bosch & Volberda (2006), Geiger & Makri (2006), Gilsing & Nooteboom (2006), Greve (2007), Sidhu, Commandeur & Volberda (2007), Audia & Goncalo (2007), Lavie & Rosenkopf (2006) Van Looy, Martens & Debackere (2005), Lee & Ryu (2002), Faems, Van Looy & Debackere (2005)

distinction between science and technology. Science refers to knowledge concerning general theories about the relationships associated with natural and social phenomena, and technology refers to theoretical and practical knowledge, skills and experiences that are of use to develop products or services (Geiger & Makri, 2006). Science search, thus, is related to fundamental research, which is exploratory and often driven by the researcher’s curiosity, interest or intuition. It is conducted without any practical end in mind, although it may have unexpected results pointing to practical applications. Technology search is related to applied research, which is exploitative and often

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driven by the motivation of solving a particular practical problem. Science and technology are supposed to play different roles in innovation. Science can provide a guide for new technology search (Fleming & Sorenson, 2004). Given their very different natures, the uncertain science search is argued to be exploration, and the technology search is argued to be exploitation (Ahuja & Katila, 2004; Geiger & Makri, 2006). Some examples manifest this interpretation of exploration and exploitation based on the distinction between science and technology. For instance, with respect to R&D projects, some researchers define research projects as exploration and development © 2008 The Authors Journal compilation © 2008 Blackwell Publishing


projects as exploitation (Garcia, Calantone & Levine, 2003). In a recent study on the biotechnology industry, Gilsing and Nooteboom (2006) describe that, for the firms in the biotechnology industry, the collaboration between a biotechnology firm and academic institutes aiming for scientific research is exploration for the biotechnology firm. Nevertheless, the search for science and technology is not sufficient to achieve successful innovations. A successful innovation also requires searching for product market knowledge gained from customers, suppliers and even competitors as a complementary source to scientific and technological knowledge (Von Hippel, 1988; Chesbrough & Rosenbloom, 2002). A commonly accepted product innovation typology defines exploration and exploitation according to the interplay between technology search and product market knowledge search (Danneels, 2002; Nerkar & Roberts, 2004). Sidhu, Volberda and Commandeur (2004) and Sidhu, Commandeur and Volberda (2007) argue that the notion of exploration and exploitation should be understood as external information acquisition. They use the term ‘supply-side’ to label technology search and the term ‘demand-side’ to label the search for product market knowledge. Jayanthi and Sinha (1998) also consider the interplay of technology and product market knowledge, but their definition of exploration and exploitation is different from that of other researchers. They define exploration as the technology search that aims at meeting future market demand, and exploitation as the technology search that aims at meeting current market demand. Several studies at the alliance level also consider technology and product market knowledge in defining exploratory or exploitative alliances (Rothaermel, 2001; Hagedoorn & Duysters, 2002; Rothaermel & Deeds, 2004; Rothaermel, Hagedoorn & Roijakkers, 2004; Lavie & Rosenkopf, 2006). Whether an alliance is exploratory or exploitative depends on the main activities and the motivation to enter an alliance. On the one hand, explorative alliances are usually established in order to explore new technological opportunities (technology search). Therefore, these alliances inevitably have an R&D component (Rothaermel & Deeds, 2004). On the other hand, exploitative alliances are those that leverage complementary competencies across the alliance partners. Exploitative alliances enable firms to commercialize the technology gained through exploration. Therefore, the activities of this type of alliance include manufacturing, marketing or supply agreements, which are typical product market knowledge (Rothaermel, 2001). © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

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The interpretation of exploration and exploitation based on the functions of the value chain has also been recognized by Lavie and Rosenkopf (2006). They argued that the ‘function domain’ is one way to define exploration and exploitation. The rationale here is that the nature of a specific function on the value chain determines ex facto whether the learning is exploratory or exploitative within the function. For each pair of functions, i.e., science vs. technology and technology vs. product market knowledge, the earlier function is exploration, based on which further exploitation takes places in the following function. While this is one way to interpret exploration and exploitation in terms of the type of learning, another track of studies does so based on the amount of learning (knowledge search).

Knowledge Search and its Three Dimensions While March (1991, p. 71) simply associates ‘search’ with exploration, many researchers later extended the idea of knowledge search in explaining exploration and exploitation. Most studies employed the idea of local or distant knowledge search to interpret exploration and exploitation. They interpreted exploitation as activities that search for familiar, mature, current or proximate knowledge; and exploration as consisting of activities that search for unfamiliar, distant and remote knowledge (Ahuja & Lampert, 2001; Rosenkopf & Nerkar, 2001; Benner & Tushman, 2002; Katila & Ahuja, 2002; Nerkar, 2003). Particularly in technological innovation, exploitation involves local search that builds on a firm’s existing technological capabilities, while exploration involves more distant search for new capabilities. Local search provides a firm with advantages in making incremental innovations, while distant search might bring opportunities for a firm to achieve radical innovations (Nerkar & Roberts, 2004). ‘Innovation is increasingly exploratory the more it departs from knowledge used in prior innovation efforts and, conversely, increasingly exploitative the more deeply anchored it is in existing firm knowledge’ (Benner & Tushman, 2002, p. 679). In the existing literature on technological innovation, distant or local knowledge search is a matter of different dimensions. We summarize three independent dimensions that construct the knowledge space. We label the first dimension as the cognitive dimension. It measures the degree of familiarity between the newly searched knowledge and a firm’s existing knowledge base in term of the cognitive distance. It is a matter of substantial content of knowledge. For instance, it is exploration for a firm that is specialized in electronic technology

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to get access to technology from the pharmaceutical industry, and it is exploitation for the firm to search for new technology within the electronics industry. In the literature on technological innovation, a large number of studies define exploration and exploitation from such a perspective (Argyres, 1996; Ahuja & Lampert, 2001; Rosenkopf & Nerkar, 2001; Benner & Tushman, 2002; Katila & Ahuja, 2002; Nerkar, 2003; Dowell & Swaminathan, 2006). Some define the degree of exploration as the variety of technological trajectories developed by a firm since its initial choice of technology (Dowell & Swaminathan, 2006). Some look at the degree of novelty of the technology that a firm searches for. The ‘new to the world’ is most exploratory and ‘new to the firm only’ is least exploratory (Ahuja & Lampert, 2001). Another interpretation is that firms search for new technology within or outside their organizational boundary or technology field. In that case, exploration is considered as technology search in a new technology field outside the firm and exploitation is the search in a firm’s existing technology field within the firm (Rosenkopf & Nerkar, 2001). Most of the studies usually measure how local or distant the knowledge search is along the cognitive dimension by means of patent classification. The patent classes represent the differences between two patents with respect to their cognitive distance. Second, knowledge search also crosses the temporal dimension, which is independent from the cognitive dimension. The temporal dimension of knowledge search examines the role of time and the tension between exploitation and exploration (Katila, 2002; Nerkar, 2003). Temporal exploitation is the creation of new knowledge through searching for recent knowledge (recency) and temporal exploration is the creation of new knowledge through searching for knowledge remote in time (time spread) (Nerkar, 2003). On the one hand, owing to bounded rationality and path dependence, firms tend to look to most recent knowledge to solve current problems. Excessive temporal exploitation may lead to temporal myopia (Miller, 2002). On the other hand, older knowledge is valuable for exploration for two reasons. First, individuals and firms tend to choose the path close to the neighbourhood of their current expertise along the knowledge development path. As a result, some valuable choices might be missed in this process. Second, some of those missed opportunities were not useful because complementary knowledge was not available in the company at a particular point in time. Knowledge that was useless in the past may nevertheless have a high potential in the future because the necessary complementary knowledge and institu-

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tions become available. The time lags between the emerging technological opportunities and complementary markets and technologies require a firm’s capability to explore the storehouse of technology over time (Garud & Nayyar, 1994). For example, large pharmaceutical firms now hire specialty biotech companies to put their previously discarded experimental compounds, some of which failed clinical trails as long as 20 years ago, through series of new tests. They hope that a then useless compound intended for one treatment may be highly useful treating something entirely different nowadays (Simons, 2006). The third dimension is the spatial (geographical) dimension, which refers to the knowledge search crossing physical space. There are different reasons why this spatial dimension matters. First, the availability of common resources within a region is related to agglomeration economies (Saxenian, 1994). Second, since ‘knowledge’ is more tacit than ‘information’, knowledge, and particularly stickyknowledge (Von Hippel, 1994; Szulanski, 1996), is more likely to be transmitted within a small geographical area where organizations have sufficient interactions and joint practices (Asheim & Isaksen, 2002). Third, the geographical dimension is usually tightly related to the institutional and cultural dimension (Knoben & Oerlemans, 2006). Given the differences among countries with respect to culture, customs and regulations, learning tends to be more difficult and the return to learning might be more uncertain across different institutional regimes than within the same institutional regime. At the macro level, the spatial dimension is a matter of national difference because particular countries develop relatively stable and distinct trajectories of technological specialization and display different patterns in R&D (Le Bas & Sierra, 2002). For example, Phene, Fladmoe-Lindquist and Marsh (2006) distinguish knowledge sources between ‘international’ and ‘national’ origin. The search for proximate technology from a national origin is analogous to exploitation, and the search for distant technology from an international origin is analogous to exploration.5 In sum, from the knowledge search perspective, exploration and exploitation are defined according to the knowledge distance between the new knowledge and the existing knowledge along any of the three dimensions of the knowledge space. Search locally is exploitation and search distantly is exploration. Since this perspective concerns the amount of learning, exploration and exploitation can be operationalized as a continuous measure along any of the three dimensions of the knowledge space, while the value chain function perspective © 2008 The Authors Journal compilation © 2008 Blackwell Publishing


usually treats exploration and exploitation as a dichotomous measure. The main challenge in defining exploration and exploitation is how to integrate the value chain function perspective and the knowledge distance perspective, taking the level of analysis into account.

Innovation Process vs. Innovative Outcome The third issue concerning the ambiguity and inconsistency in the interpretation of exploration and exploitation in the literature is the tension in regarding exploration and exploitation as either features of the innovation process or the innovative outcome itself. The differences in the literature on this issue are rather subtle but important. Some researchers investigate exploration and exploitation in terms of the innovation process, which involves learning activities, behaviour, investment and strategies (e.g., Jayanthi & Sinha, 1998; Nerkar, 2003; He & Wong, 2004; Nerkar & Roberts, 2004; Van Looy, Martens & Debackere, 2005; Phene, Fladmoe-Lindquist & Marsh, 2006; Sidhu, Commandeur & Volberda, 2007). These researchers regard exploration and exploitation as different forms of the learning process through which innovations come forth. He and Wong (2004, p. 485) explicitly assert that: We did not use scales related to radical versus incremental innovation because exploration and exploitation should be used with reference to a firm’s ex-ante strategic objectives in pursuing innovation, whereas the radical versus incremental innovation is often used in an ex-post outcome sense. Studies on technology search usually use patent data to measure to what extent the search is distant or local. Patents, here, are considered as indicators of technology search rather than the outcome of innovation (e.g., Argyres, 1996; Katila & Ahuja, 2002; Nerkar & Roberts, 2004). Studies on product market knowledge search usually use multi-item measurement to capture the attributes of the search process (McGrath, 2001, Sidhu, Volberda & Commandeur, 2004; Sidhu, Commandeur & Volberda, 2007). Studies at the alliance level and the industry level also interpret exploration and exploitation as organizational learning from a firm’s upstream or downstream partners (Rothaermel, 2001; Vassolo, Anand & Folta, 2004; Gilsing and Nooteboom, 2006). Others relate exploration and exploitation directly to innovative outcomes, which are the products or services (Dowell & Swaminathan, 2006; Jansen, Van Den Bosch & Volberda, 2006; Greve, 2007). In such case, exploration and exploitation are usually used as synonymous © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

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with ‘radical innovation’ and ‘incremental innovation’, respectively (Benner & Tushman, 2003; Jansen, Van Den Bosch & Volberda, 2006). For example, Greve (2007) measured exploration as the number of innovations that involved the development of new technology that is ‘new to the firm’, and exploitation as all other types of innovations. Similarly, in a study on the bicycle industry, Dowell and Swaminathan (2006) identified four types of bicycles in history. Exploration is measured by the relative numbers of the types of bicycle introduced by a firm before it finally introduces the most modern type. What counts in their study is still the innovative outcome. More interestingly, as a vivid example of how the ambiguity in the literature distorts researchers’ interpretation and makes them take the meaning of the constructs for granted, Jansen, Van Den Bosch and Volberda (2006) in their theoretical argument clearly refer to exploration and exploitation as innovative outcome, but the design of their questionnaire, measures and items take rather a process perspective. To the best of our knowledge, there are only two studies in the selected literature that examine the relationship between exploration and radical innovation, and between exploitation and incremental innovation. First, Atuahene-Gima (2005) found that exploration is positively related to radical innovation and exploitation is positively related to incremental innovation based on a study on 500 firms in a province in China. Second, Faems, Van Looy and Debackere (2005) provided empirical evidence that exploitative collaboration with suppliers and customers has a positive impact on incremental innovation, while explorative collaboration with research institutes has a positive impact on radical innovation. To summarize, the ambiguity in the definition of exploration and exploitation lies mainly between the value chain function perspective and the knowledge distance perspective, and between the innovation process viewpoint and the innovative outcome viewpoint. It seems unlikely that we can achieve a universal definition of exploration and exploitation, but it is worthwhile reconciling the different definitions in the literature within an integrated framework. Such an effort not only reduces the ambiguity in the interpretation of exploration and exploitation, but also provides guidance for future research.

Reconciling the Variety: An Integrated Framework The lack of consistency and its accompanying ambiguity in the interpretation of exploration

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and exploitation call for a framework that reconciles these different perspectives. In the first place, exploration and exploitation comprise various types of organizational learning. Therefore, it is not necessary to find a universal definition for exploration and exploitation. Instead, it is logical to define and interpret the constructs from different perspectives. However, different ways of interpretation need to be organized and synthesized based on the established theories. We suggest that exploration and exploitation can be defined in two different domains. First, the ‘function domain’ defines exploration and exploitation according to the unique nature of a specific value chain function (Lavie & Rosenkopf, 2006). Second, the ‘knowledge distance domain’ takes a knowledge searching perspective and defines exploration and exploitation according to the relative distance between the new knowledge and the existing knowledge base of a firm. These two domains, respectively, correspond to the suggestion by Gupta, Smith and Shalley (2006) that exploration and exploitation should be defined according to the type or amount of learning. Further, these two domains are highly related to and embedded within each other. We propose a framework which does not develop a new theory but clarifies the relationships between these two domains of exploration and exploitation. Such a framework may be applicable to different levels of analysis. In the following, we first introduce the ‘function domain’ and ‘knowledge distance domain’ to define exploration and exploitation. Second, we explain how to link these two domains to study the balance of exploration and exploitation. Finally, by comparing the different perspectives, this framework also identifies the white spaces in the existing literature and provides guidance for future research on this topic.

‘Function Domain’ and ‘Knowledge Distance Domain’ with Three Dimensions To define exploration and exploitation, some researchers examine the search for new scientific knowledge, some focus on technology and others are interested in product market knowledge. Meanwhile, other researchers argue that the local or distant knowledge search is the key to defining exploration and exploitation. They also note that knowledge search takes place along different dimensions. However, a comprehensive picture is missing. That is because these studies define exploration and exploitation within different domains. Our framework explicitly distinguishes two domains to define exploration and exploitation.

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First, the ‘function domain’ regards each function on the value chain as unique in its type of learning. Science, technology and product market knowledge correspond to the sequence along a firm’s value chain. In the early stages, firms invest in fundamental research to gain scientific knowledge. In the middle stages, firms conduct applied research to develop new technology. Finally, firms look for new knowledge that commercializes the products and services. Such a distinction of knowledge along the value chain may also be understood through the lens of the technology life cycle (Van Looy, Martens & Debackere, 2005). At the outset of a technology life cycle is the seed stage, when fundamental research of scientific knowledge is crucial. Activities at this seed stage are highly experimental and exploratory. At the growth stage, applied research and technological knowledge play important roles. Both the seed and growth stages are highly related to creativity. In contrast, at the mature and decline stage of a technology life cycle, productivity and commercialization are more important than creativity, where activities are highly exploitative. In this way, exploration and exploitation must be interpreted as the comparative attributes of different activities along the value chain. The logic is that for each pair of functions, i.e., science vs. technology and technology vs. product market knowledge, the earlier function is comparatively exploratory, which provides input for the next function to exploit. However, the ‘function domain’ is not sufficient to fully comprehend exploration and exploitation. Exploration and exploitation can also be defined within the ‘knowledge distance domain’, which distinguishes exploration from exploitation based on the distance between the new knowledge that a firm searches and its existing knowledge base. Local knowledge search approximates exploitation, while distant knowledge search approximates exploration. The decomposition of knowledge search into a three-dimensional space allows measuring knowledge distance along the cognitive dimension, temporal dimension and spatial dimension. The critical linkage between the ‘function domain’ and the ‘knowledge distance domain’ is that the threedimensional knowledge search may occur at any function of the value chain. Figure 1 depicts the framework that integrates the ‘function domain’ and ‘knowledge distance domain’. At each function of the value chain, knowledge can be further decomposed into three dimensions. The first dimension is the temporal dimension. It holds for the knowledge search at each function along the value chain. © 2008 The Authors Journal compilation © 2008 Blackwell Publishing


Temporal

Temporal Spatial

Science

combine

119

Temporal Spatial

Technology

combine

Spatial

Innovative Outcome

Productmarket

(Products/Services) Incremental or Radical

Organizational boundary Cognitive (Disciplinary)

Cognitive (Technical)

Cognitive (Segment)

Temporal

Familiar (Exploitation)

Spatial

Cognitive Unfamiliar (Exploration)

Figure 1. An Integrated Framework for Studying Exploration and Exploitation from Different Perspectives

No matter what type of knowledge a firm searches, one can always investigate how old the knowledge is and how frequent one uses this knowledge. The second dimension is the spatial dimension, which also holds for all value chain functions because one can usually trace where geographically the knowledge originates, regardless of the scientific, technological and product market knowledge. The introduction of the third dimension is based on the substantial cognitive differences between social actors, which is independent of time and space. At the science (fundamental research) function, it refers to the differences between scientific disciplines: we call it the disciplinary dimension. Scientific knowledge search may cross disciplines such as biology, chemistry, physics, electronics, etc. As to the technology function, the cognitive dimension refers to the difference between technology fields: we call it the technical dimension.6 This dimension involves skills and practices such as chemical compounds development, semiconductor material, software coding, motion engineering, etc. Finally, the cognitive dimension at the product market knowledge function refers to the differences between market segments: we label it the segmental dimension. Examples for this dimension are the different marketing practices across different industries. The best practices, well known in one market, © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

can be surprisingly new in another. In sum, based on the extant literature, we identify that knowledge search differs along the value chain and takes place in different dimensions.7

Definition of Exploration and Exploitation Given the distinction and the linkage between the ‘function domain’ and the ‘knowledge distance domain’, as depicted in Figure 1, exploration and exploitation can be defined by combining both domains and taking into account whether a firm has all value chain functions. On the one hand, within a single function of the value chain, firms exploit by search for knowledge within the organizational boundary and knowledge that is local to their existing knowledge base and explore by searching distant knowledge that is unfamiliar. The local or distant search may occur along cognitive, temporal and spatial dimensions. Thus, at each value chain function, exploration and exploitation can be specified by the type of knowledge based on the ‘knowledge distance domain’. We label them scientific exploration and exploitation, technological exploration and exploitation, and product market exploration and exploitation, which are within-functional. On the other hand, firms may allocate learning activities on different value chain functions. Learning activities at the upstream of the value chain

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Value chain domain

Science

Scientific exploitation

Scientific exploration

Within-functional Cross-functional exploitation

Cross-functional exploration Technological exploitation

Technological exploration

Technology Within-functional Cross-functional exploration

ProductMarket knowledge

Cross-functional exploitation

Product-market exploration

Product-market exploitation

Within-functional

Local

Distant

Knowledge distance domain

Figure 2. The Typology for Defining Within-Functional and Cross-Functional Exploration and Exploitation

are more exploratory than those at the downstream of the value chain. The rationale here is that the upstream value chain activities are more research oriented, and the downstream value chain activities are more commercially oriented with a sharp focus on seeking profitability. We label them as cross-functional exploration and exploitation (see Figure 2). Based on the typology in Figure 2, exploration and exploitation can be clearly defined by taking into account whether or not a firm is involved in all value chain functions. First, for firms that are involved in all value chain functions, exploration and exploitation can be defined either as within-functional or crossfunctional. Second, firms that are not involved in all value chain activities may either search knowledge within the value chain function in which they are active, or search knowledge from the complementary value chain functions externally. The former ones may carry on scientific, technological or product market exploration and exploitation, depending on which value chain functions they belong to. The latter ones may not only conduct withinfunctional but also cross-functional exploration and exploitation. The typology of withinfunctional and cross-functional exploration and exploitation is not only applicable at the firm level of analysis but also at the project team and corporate levels of analysis, because project teams and corporations are also capable of searching knowledge along differ-

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ent dimensions within a single function and across different functions. It is less likely for an individual to explore different types of knowledge. Therefore, the within-functional exploration and exploitation is more suitable for the analysis of individual learning.

Combination of Exploration and Exploitation and Organizational Ambidexterity Recognizing the legitimacy of distinguishing the ‘function domain’ and the ‘knowledge distance domain’ and their linkage provides insights into how knowledge at different functions and along different dimensions can be combined and how organizations can achieve ambidexterity by combining exploration and exploitation. Given the three functions along the value chain and their three unique dimensions, one can identify two different opportunities to combine knowledge (see Figure 1). First, old and new knowledge elements gained from different dimensions can be combined within a single knowledge domain of the value chain. For instance, for technological knowledge, some studies have explored the interplay between the technical dimension and the spatial dimension within the technological domain of knowledge (Rosenkopf & Nerkar, 2001; Phene, Fladmoe-Lindquist & Marsh, 2006). Second, knowledge can also be combined across the functions along the value chain. Marketing literature gives a good © 2008 The Authors Journal compilation © 2008 Blackwell Publishing


example of how technological knowledge and product market knowledge can be recombined across different functions of the value chain. Keegan (1996) argues that in order to introduce a product into a new international market, firms may simply apply the same product technology and same advertising and promotion in all markets. Firms may also either change product technology or transform product image by tailor-made marketing tools. The most expensive strategy is dual adaptation, which means that firms decide to change both technological and product market knowledge in every single market. The possibilities to combine knowledge within and across value chain functions provide different opportunities for organizations to achieve ambidexterity by combining exploration and exploitation. Organizational ambidexterity in the innovation management literature is defined as the ability to simultaneously pursue both incremental and discontinuous innovation and change by exploration and exploitation (Tushman & O’Reilly, 1996). Gibson and Birkinshaw (2004) define two types of organizational ambidexterity. First, structural ambidexterity refers to balancing exploitation and exploration by allocating conflicting activities at different units at various levels within an organization; second, contextual ambidexterity refers to the behavioural capacity to simultaneously demonstrate alignment and adaptability across an entire business unit (Gibson & Birkinshaw, 2004). One way to realize contextual ambidexterity is to combine exploration and exploitation across domains. Lavie and Rosenkopf (2006, p. 804) identified three distinct domains in defining the exploration and exploitation at the alliance level of analysis. They argued that firms can combine exploration and exploitation across the function, structure and attribute domains, so that the tendency to explore (exploit) in one domain will be compensated by the tendency to exploit (explore) in some other domains. Although we introduced two different domains – ‘function domain’ and ‘knowledge distance domain’ – to define exploration and exploitation, the logic of their combination within and across domains is similar to Lavie and Rosenkopf (2006). First, within a single value chain, one can combine the exploration and exploitation from the ‘knowledge distance domain’ perspective by searching distant knowledge in some dimensions and local knowledge in other dimensions. For instance, if a firm searches knowledge from unfamiliar technical fields, it can reduce the risk of exploration by limiting its search scope within a recent time span and within a nearby geo© 2008 The Authors Journal compilation © 2008 Blackwell Publishing

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graphic region. The second way of combining exploration and exploitation might be to search knowledge from upstream and downstream of the value chain functions simultaneously. However, this balancing strategy requires companies to get access to scientific research, manufacturing, and marketing and sales, which sometimes is too expensive to realize. Therefore, the third way of balancing is to combine the ‘function domain’ and the ‘knowledge distance domain’. For instance, one can explore the knowledge from the upstream functions of the value chain, while searching knowledge locally within its own value chain functions, or exploit the knowledge from the downstream functions of the value chain, while searching knowledge distantly within its own value chain functions. Various combinations of exploration and exploitation across the ‘function domain’ and the ‘knowledge distance domain’ are possible. We suggest that future research can examine the notion of organizational ambidexterity through the combination of exploration and exploitation based on the distinction between the ‘function domain’ and the ‘knowledge distance domain’ for exploration and exploitation. Given the ‘function domain’ and the ‘knowledge distance domain’, one can see that some studies in the existing literature either confuse these two domains or focus only on a specific part of one single domain. For instance, Sidhu, Volberda and Commandeur (2004) and Sidhu, Commandeur and Volberda (2007) propose that exploration and exploitation have their ‘supply-side’, ‘demand-side’ and ‘geographic side’, which correspond to the technology function, product market function and the spatial dimension, respectively. They mixed up two functions in the ‘function domain’ with a single dimension in the ‘knowledge distance domain’. Other researchers focus only on one type of knowledge along the value chain and usually only look at one or two dimensions. For instance, Nerkar (2003) focuses only on technological knowledge and its temporal dimension. Benner and Tushman (2002) look only at technological knowledge and its technical dimension. Geiger and Makri (2006) investigate only scientific knowledge and technological knowledge but do not recognize the three dimensions of search. And there are many more such examples. Research in the future should provide more meaningful insight by clearly defining which functions of the value chain and which dimensions are under investigation. A comprehensive investigation of knowledge search along the entire value chain including all the dimensions is a major but interesting challenge for future research.

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Innovation Process vs. Innovative Outcome Finally, the difference in interpretation of exploration and exploitation as an innovation process or an innovative outcome also leaves room for interesting research in the future. Both the process view and the outcome view have their legitimacy and merits. However, we suggest that it is better to define exploration and exploitation from the innovation process perspective. After all, the reason why exploration and exploitation are so important for the adaptation and survival of organizations is because these two distinctive types of learning entail different risk taking, require different investment and resources, and eventually lead to different economic returns. Exploration and exploitation, thus, as learning activities per se, compete for managers’ attention and determine the growth of an organization. The innovative outcomes, i.e., products or services, by no means can represent the complex learning process that a firm goes through. Furthermore, there has been little insight in the existing literature into whether exploratory processes always lead to radical innovation and exploitative processes always lead to incremental innovation. In the extant literature, only a few studies look at the link between process and outcome, and, more importantly, they fail to measure how exploratory or exploitative the outcome is. Researchers usually use a count variable to measure outcome, i.e., the number of new products or innovations (Ahuja & Lampert, 2001; Katila & Ahuja, 2002; Ahuja & Katila, 2004). Sidhu, Commandeur and Volberda (2007) measure innovativeness as the total sales from new products. Nerkar and Roberts (2004) look at the initial sales level as an indicator of new product success. Consequently, readers cannot know how radical the innovations are. For those who do measure the innovativeness of the outcome, they usually pay no attention to knowledge search as a learning process (Jansen, Van Den Bosch & Volberda, 2006). Perhaps the impact of technology on the later knowledge creation can be used as an indicator of innovativeness, but researchers usually limit their focus to the technological knowledge and overlook the product market side (Rosenkopf & Nerkar, 2001; Nerkar, 2003). Hence, there is a lack of comprehensive understanding of how exploratory/exploitative learning processes lead to exploratory/exploitative innovations. Although AtuaheneGima (2005) and Faems, Van Looy and Debackere (2005) found empirical evidences that exploratory activities are associated with radical innovative outcomes and exploitative activities are associated with incremental

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innovative outcomes, it is still questionable whether exploration in some activities may ultimately result in incremental innovations, and whether exploitation in some activities may ultimately surprise a firm with radical innovations. This doubt becomes more vivid if one considers exploration and exploitation as learning processes that occur along the value chain function and its multiple dimensions. Therefore, it might be promising to study the match between exploration and exploitation as an innovation process on the one hand, and the innovativeness of products or services as innovative outcomes on the other.

Conclusion This paper is based on a systematic review of the technological innovation literature on exploration and exploitation since the seminal paper of March (1991). We focus on how different studies define and interpret these constructs. We find that the variety in the literature comes from two sources, namely the level of analysis and the substantial differences in explanation and interpretation of the two concepts. We discuss in detail several issues from which inconsistency and ambiguity emerge in the literature. First, exploration and exploitation are interpreted differently because researchers made their analysis at different levels. There are studies at the individual level, project level, business level, corporate group level, alliance level and industry level. Studies at different levels of analysis focus on different social actors. In this case, the source of variety comes from who explores or exploits. The second source of variation in the literature comes from the substantial differences in the understanding of exploration and exploitation. Some authors define exploration and exploitation from a knowledge search perspective. While most researchers agree that exploration is the search for distant knowledge and exploitation is the search for local knowledge, there is more than one dimension along which knowledge search can take place. Ambiguity also emerges when some define exploration and exploitation based on whether a firm searches for scientific, technological or product-market knowledge within the functions of the value chain. Furthermore, it is unclear how distant and local search can be combined with the knowledge search in science, technology or market applications. Another source of ambiguity comes from whether exploration and exploitation should be interpreted as an innovation process or as the innovative outcomes. There has been little insight into whether © 2008 The Authors Journal compilation © 2008 Blackwell Publishing


exploratory or exploitative knowledge search processes eventually lead to radical or incremental innovative outcomes. Given the ambiguity identified in the extant literature, we propose a framework that aims not to develop new theory but rather clarifies the relationships between the different perspectives and focuses on what has been overlooked in the earlier studies. In so doing, this study contributes to the literature in several ways. First, it clarifies the ambiguity in the existing literature in interpreting exploration and exploitation. Second, it clarifies the distinction between knowledge domains along the value chain and the search dimensions, which have not been clearly defined and are usually operationalized in a deficient way in the literature. The framework points out that those activities at earlier functions of the value chain are by nature more exploratory than those at the later stages of the value chain. Third, based on the ‘function domain’ and the ‘knowledge distance domain’, we propose a typology to define within-functional exploration and exploitation and cross-functional exploration and exploitation. Next, this framework also contributes to explaining how firms can achieve organizational ambidexterity by combining exploration and exploitation within or across domains. Firms can balance exploration and exploitation not only within a value chain function along different dimensions of search but also across value chain functions. Finally, our study indicates some gaps in the current research and provides guidance for future research on exploitation and exploration. For instance, future research should clearly define which functions of the value chain and which dimensions are under investigation. A comprehensive investigation into knowledge search along the whole value chain could be promising too. Similarly, integrating different search dimensions is also highly recommended in future research. Interesting research opportunities may also arise if studies focus on knowledge search both within and across knowledge domains along the value chain. Moreover, studying the match between knowledge search as an innovation process on the one hand, and the innovativeness of products or services on the other, could also be a promising direction for future research. However, our framework was developed based on the existing literature in innovation management with a strong emphasis on the organizational learning perspective. We focused on technology-based innovations. This is reflected in the framework through the links between science and technology on the one hand and between technology and new product development on the other. Future © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

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research should broaden the scope of perspectives from which exploration and exploitation can be examined. For instance, since many technological developments are not necessarily driven by scientific insights, technological developments are not the only source of new businesses opportunities. They can be generated by new business models, customer insights, design and fashion, new social needs, etc. Furthermore, owing to the different natures of learning for exploration and exploitation, they may have different relational features. For instance, exploration and exploitation may differ in ways of building social ties and network configurations and risk taking among organizations. Thus, a relational perspective on exploration and exploitation, complementary to the learning perspective, can be a promising direction for future studies. Finally, exploration and exploitation are shown as multi-level and multi-facet concepts in the framework, which may provide practitioners with several insightful implications for innovation management. First, we have distinguished three types of knowledge along the value chain that are crucial in technological innovations. Scientific knowledge is increasingly important as the source of technological developments, which, in turn, opens opportunities to develop new products or services. Firms that want to develop new products based on science-driven technologies have to understand two processes. First, they need to understand how new technology can be developed successfully from new scientific insights. Second, they need to translate new technologies into new and profitable business models. Scientific research stands on one side of the technological innovation and commercialization stands on the other. The technological innovativeness, which is usually measured by the citation of new patents, is only an intermediate output. What really counts is the successful introduction of new products or services in the markets. Most studies have focused on the intermediate output, i.e., the patents, rather than the products or services as the final innovation outcome. The critical challenge is to manage the three functions as a whole in a coherent way. Exploration and exploitation in science and product market knowledge have different drivers and should be managed differently. Exploration of new scientific disciplines to rejuvenate technological capabilities is a quite different task from the development of new business based on the current technology in the company. The technology base itself should be continuously adapted in conjunction with new societal needs (demand pull) and scientific discoveries (technology push). The integrated framework introduced in this

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study provides managers with a guide to trace the variation in organizational activities in scientific research, technology development and commercialization as a whole. For instance, intensive investment in exploratory scientific research may be best combined with focused product development projects and market plans. Firms that are specialized in one particular function of the value chain may make better use of their expertise by allying with other firms who have complementary functions within the value chain. We hope that our study not only inspires more researchers to explore the gaps concerning exploration and exploitation, but also provides a common ground to encourage the necessary dialogue between academia and practitioners.

Notes 1 We focus on empirical papers to limit the review effort. From empirical papers, we can see directly how researchers interpret exploration and exploitation, while theoretical review papers usually present the reviewer’s subjective interpretation of other authors. We exclude theoretical papers from the literature review, but still refer to them where analysis is necessary. 2 We also used ‘innovation, technology, exploring, exploiting’ or ‘innovation, technology, exploratory, exploitative’ in full text or abstracts. This made no difference to the results. The relationship between these keywords is ‘AND’, which means there is no sequence for the keywords. 3 We also used ‘exploring, exploiting’ or ‘exploratory, exploitative’ in abstracts. This made no difference to the results. The relationship between these keywords is ‘AND’, which means there is no sequence for the keywords. 4 Note that in this section, we only intend to present the substantial differences in the existing literature. Further analysis and integration will follow in the next section. 5 At the micro level, one could consider interorganizational learning as more ‘distant’ or more explorative than intra-firm learning. While reusing a firm’s own knowledge does not necessarily involve spatial distance, acquiring knowledge from other firms inevitably crosses space. For example, Rosenkopf and Nerkar (2001) define the degree of exploration depending on whether knowledge search spans organizational and technological boundaries. Search with the strongest explorative nature is search for distant technology from outside the organization. The least exploratory search is local technology search from within the organization. 6 We use ‘technological’ to label knowledge in the value chain, and ‘technical’ for its content dimension in order to avoid misunderstanding. 7 It is worth noting that studies at the alliance level consider two other domains to define exploratory alliances and exploitative alliances (Lavie & Rosenkopf, 2006). One is the ‘structure domain’,

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which refers to whether or not to form an alliance with a partner with no prior ties. The other is the ‘attribute domain’, which refers to how different a new partner’s organizational attributes are from a firm’s other prior partners. We acknowledge the merits of these two domains. However, these two domains do not go beyond the ‘knowledge distance domain’ as we introduced in our framework. The essence behind whether or not having prior ties and any difference in attributes between old and new partners, eventually turns out to be the knowledge distance between the focal firm and the new partner. And these knowledge differences can, anyhow, be captured by the cognition, temporal and spatial dimensions. For instance, having a new partner with no prior ties implies that the new partner is from a different industry or from a foreign market, which the focal firm is considering tapping into for the first time. Lin, Yang and Demirkan (2007) also recognized that the ‘structure domain’ is highly related to new knowledge searching beyond the local boundary. The ‘attribute domain’ implies that the new partner has a very different knowledge base from other prior partners. Still, it does not go beyond the ‘knowledge distance domain’. For these reasons, we do not incorporate the ‘structure domain’ and ‘attribute domain’ into our framework.

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tation. Academy of Management Journal, 49, 693– 706. Hagedoorn, J. and Duysters, G. (2002) Learning in Dynamic Inter-Firm Networks: The Efficacy of Multiple Contacts. Organization Studies, 23, 525– 48. He, Z.-L. and Wong, P.-K. (2004) Exploration vs. Exploitation: An Empirical Test of the Ambidexterity Hypothesis. Organization Science, 15, 481– 94. Holmqvist, M. (2004) Experiential Learning Process of Exploitation and Exploration within and between Organizations: An Empirical Study of Product Development. Organization Science, 15, 70–81. Jansen, J.J.P., Van Den Bosch, F.A.J. and Volberda, H.W. (2006) Exploratory Innovation, Exploitative Innovation, and Performance: Effects of Organizational Antecedents and Environmental Moderators. Management Science, 52, 1661–74. Jayanthi, S. and Sinha, K.K. (1998) Innovation Implementation in High Technology Manufacturing: A Chaos-Theoretic Empirical Analysis. Journal of Operations Management, 16, 471–94. Katila, R. (2002) New Product Search over Time: Past Ideas in Their Prime? Academy of Management Journal, 45, 995–1010. Katila, R. and Ahuja, G. (2002) Something Old, Something New: A Longitudinal Study of Search Behavior and New Product Introduction. Academy of Management Journal, 45, 1183–94. Keegan, W.J. (1996) Global Marketing Management, 5th edn. Prentice-Hall, Englewood Cliffs, NJ. Knoben, J. and Oerlemans, L.A.G. (2006) Proximity and Inter-Organizational Collaboration: A Literature Review. International Journal of Management Reviews, 8, 71–89. Lavie, D. and Rosenkopf, L. (2006) Balancing Exploration and Exploitation Alliance Formation. Academy of Management Journal, 49, 797–818. Le Bas, C.B. and Sierra, C. (2002) Location versus Home Country Advantages in R&D Activities: Some Further Results on Multinationals Locational Strategies. Research Policy, 31, 589–609. Lee, J. and Ryu, Y.U. (2002) Exploration, Exploitation and Adaptive Rationality: The NeoSchumpeterian Perspective. Simulation Modeling Practice and Theory, 10, 297–321. Lin, Z., Yang, H.B. and Demirkan, I. (2007) The Performance Consequences of Ambidexterity in Strategic Alliance Formations: Empirical Investigation and Computational Theorizing. Management Science, 53, 1645–58. McGrath, R.G. (2001) Exploratory Learning, Innovative Capacity, and Managerial Oversight. Academy of Management Journal, 44, 181–31. March, J.G. (1991) Exploration and Exploitation in Organizational Learning. Organization Science, 2, 71–87. Miller, K. (2002) Knowledge Inventories and Managerial Myopia. Strategic Management Journal, 23, 689–706. Mom, T.J.M., Van den Bosch, F.A.J. and Volberda, H.W. (2007) Investigating Managers’ Exploration and Exploitation Activities: The Influence of TopDown, Bottom-Up, and Horizontal Knowledge Inflows. Journal of Management Studies, 44, 910–31.

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Nerkar, A. (2003) Old is Gold? The Value of Temporal Exploration in the Creation of New Knowledge. Management Science, 49, 211–29. Nerkar, A. and Roberts, P.W. (2004) Technological and Product-Market Experience and the Success of New Product Introductions in the Pharmaceutical Industry. Strategic Management Journal, 25, 779–99. Perretti, F. and Negro, G. (2007) Mixing Genres and Matching People: A Study in Innovation and Team Composition in Hollywood. Journal of Organizational Behavior, 28, 563–86. Phene, A., Fladmoe-Lindquist, K. and Marsh, L. (2006) Breakthrough Innovations in the U.S. Biotechnology Industry: The Effects of Technological Space and Geographic Origin. Strategic Management Journal, 27, 369–88. Rosenkopf, L. and Nerkar, A. (2001) Beyond Local Search: Boundary-Spanning, Exploration, and Impact in the Optical Disk Industry. Strategic Management Journal, 22, 287–306. Rothaermel, F. (2001) Incumbent’s Advantage through Exploiting Complementary Assets via Interfirm Cooperation. Strategic Management Journal, 22, 687–99. Rothaermel, F. and Deeds, D.L. (2004) Exploration and Exploitation Alliances in Biotechnology: A System of New Product Development. Strategic Management Journal, 25, 201–21. Rothaermel, F., Hagedoorn, J. and Roijakkers, N. (2004) Technological Core Transformation through Collaboration: The Role of Exploration and Exploitation Alliances. Academy of Management Special Research Forum: Managing Exploration and Exploitation. Saxenian, A. (1994) Regional Advantage, Culture and Competition in Silicon Valley and Route 128. Harvard University Press, Cambridge, MA. Schumpeter, J. (1934) The Theory of Economic Development. Harvard University Press, Cambridge, MA. Sidhu, J.S., Volberda, H.W. and Commandeur, H.R. (2004) Exploring Exploration Orientation and its Determinants: Some Empirical Evidence. Journal of Management Studies, 41, 913–32. Sidhu, J.S., Commandeur, H.R. and Volberda, H.W. (2007) The Multifaceted Nature of Exploration and Exploitation: Value of Supply, Demand, and Spatial Search for Innovation. Organization Science, 18, 20–38.

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Simons, J. (2006) Big Pharma’s New R&D Center: The Trash Bin. Fortune (European edn), 154, 28. Szulanski, G. (1996) Exploring Internal Stickiness: Impediments to the Transfer of Best Practice within the Firm. Strategic Management Journal, 17, 27–43. Tushman, M.L. and O’Reilly, C.A. III (1996) Ambidextrous Organizations: Managing Evolutionary and Revolutionary Change. California Management Review, 38, 8–30. Vanhaverbeke, W. and Peeters, N. (2005) Embracing Innovation as Strategy: Corporate Venturing, Competence Building and Corporate Strategy Making. Creativity and Innovation Management, 14, 246–57. Van Looy, B., Martens, T. and Debackere, K. (2005) Organizing for Continuous Innovation: On the Sustainability of Ambidextrous Organizations. Creativity and Innovation Management, 14, 208–21. Vassolo, R.S., Anand, J. and Folta, T.B. (2004) Nonadditivity in Portfolios of Exploration Activities: A Real Options-Based Analysis of Equity Alliances in Biotechnology. Strategic Management Journal, 25, 1045–61. Von Hippel, E. (1988) The Source of Innovation. Oxford University Press, New York. Von Hippel, E. (1994) Sticky Information and the Locus of Problem Solving: Implications for Innovation. Management Science, 40, 429–39.

Ying Li ([email protected]) is a researcher and PhD candidate at the Faculty of Applied Economics, Hasselt University, Belgium. Wim Vanhaverbeke is a professor at the Faculty of Applied Economics, Hasselt University, Belgium and visiting research fellow at the Eindhoven University of Technology, the Netherlands. Wilfred Schoenmakers is an assistant professor at the Faculty of Applied Economics, Hasselt University. He holds his PhD from Maastricht University, the Netherlands.


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Enhancing Discontinuous Innovation through Knowledge Combination: The Case of an Exploratory Unit within an Established Automotive Firm Sihem Ben Mahmoud-Jouini and Florence Charue-Duboc The literature on innovation management underlines the necessity to separate the exploratory unit that builds new businesses on the basis of radical innovation from the exploitation unit that emphasizes continuous improvement. However, little research focuses on the exploratory unit in itself: the very nature of its activity, its composition, etc. The aim of this article is to analyse the exploratory unit in mobilizing results highlighted by research on organizational creativity. It is argued that in order to enhance discontinuous innovation, knowledge combination should occur and be facilitated in the exploratory unit. Hence, the research question is what organizational design at a fine-grained level and creativity processes are likely to enhance knowledge combination and thus discontinuous innovation? Based on an in-depth study of an exploratory unit created in an established multidivisional firm pursuing the development of discontinuous innovation and which generated several actual breakthroughs, we highlighted four key factors that enhanced knowledge combination: (i) the definition of the scope of the unit, (ii) the composition of the unit and the dual roles of its members, (iii) the boundary objects that supported the interactions between these members during the creativity process, and (iv) the arenas where new knowledge was further created.

Introduction

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owadays, innovation has become increasingly central to competitiveness. In an innovation-driven environment, firms must combine incremental and discontinuous innovation to ensure their competitive advantage. Several authors have outlined that large multidivisional firms are efficient in exploiting existing product lines and developing incremental innovation but face obstacles when exploring new businesses, technologies and radical innovation (Dougherty, 1992; LeonardBarton, 1995; Christensen, 1998). They have suggested that firms should design different types of organizational units to balance these different kinds of innovation. Following this line of thought, Tushman and O’Reilly (1997) proposed the ambidextrous model character-


ized by the coexistence of two types of organizational units: the productivity-oriented units, ‘exploitation units’, that emphasize continuous improvement, and the entrepreneurial units, ‘exploratory units’, that build new businesses on the basis of radical innovation. However, little research focuses on the exploratory unit and the very nature of its activity. In order to address this gap we conducted an ethnographic study in a large established company, among the ten biggest suppliers in the automotive industry, who created a new unit in order to ensure its growth through discontinuous innovation. This unit can be regarded as an exploratory unit existing alongside the divisions of the company regarded as exploitation units. Within six months, the new unit had identified a portfolio of innovation tracks along which several

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innovative solutions were further developed and represented a potential for growth. Data was collected over this period during which the authors spent one day a week on site. Our aim is to mobilize the organizational creativity literature (Amabile, 1988; Woodman, Sawyer & Griffin, 1993) to enhance the understanding of the exploratory units and of discontinuous innovation development. Exploratory activity can be associated with creativity, defined by Amabile (1988) as ‘the production of novel and useful ideas by an individual or small groups of individuals working together’. The literature on innovation management and organizational creativity share the same conclusions on the conflicted role of knowledge: it is a source of innovation and creativity and a barrier too. The prior related knowledge is crucial for innovation (Cohen & Levinthal, 1990). It represents the raw material for creativity (Sternberg, O’Hara & Lubart, 1997; Amabile, 1998). However, it can impede innovation because core competencies also act as core rigidities (Leonard-Barton, 1995) and highly specialized expertise can hinder the development of creative solutions (Mayer, 1995). In addition, innovative combinations of previous knowledge have been highlighted as key for innovation and creativity (Mayer, 1995; Hargadon & Sutton, 1997). Thus we intend to address more specifically the following research questions: How does knowledge combination occur within the exploratory unit and lead to innovation and value creation? What organizational design and creativity processes are likely to enhance knowledge combination leading to discontinuous innovation? By analysing and focusing on the exploratory unit, we underline four factors that favoured the knowledge combination: (i) the scope of the exploratory unit, (ii) the composition of this unit with the involvement of boundary-spanning individuals, (iii) the boundary objects such as the value axes or the solution concepts that supported the interactions between the unit’s members during the creativity process, and (iv) the arenas where new knowledge was further built such as the exploratory projects. The paper is organized as follows. The next section reviews the existing literature on innovation management and organizational creativity and highlights the main issues regarding the knowledge. Then we present the research setting and our methodology. The exploratory unit and the creativity process studied will be presented in the subsequent section. Based on this case study, we then turn to the discussion and conclusion highlighting the four factors

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that favour the knowledge combination leading to discontinuous innovation.

Theoretical Background and Research Question The innovation and creativity literature shares the same conclusions on the conflicted role of knowledge and highlights the importance of knowledge combination. In the two streams of literature, knowledge is both a source of and a barrier to innovation and creativity. The prior related knowledge of the firm is crucial for innovation (Cohen & Levinthal, 1990). However, it can impede innovation because core competencies can also act as core rigidities (Dougherty, 1992; Leonard-Barton, 1992). Several authors (Dougherty, 1992; Dougherty & Hardy, 1996) identified the challenges that large firms face in developing discontinuous innovation such as the barriers between various functions or thought worlds. These barriers hinder a shared understanding of the problems that must be solved in order to develop the innovation. Core competencies turn out to be core rigidities (Leonard-Barton, 1992). Henderson and Clark (1990) showed that it was difficult for established companies to develop architectural innovation, because the knowledge about the product’s architecture is inscribed into the structure and routines of the organization. In order to address these obstacles, scholars proposed the implementation of independent units such as experimenting units (Leonard-Barton, 1995) outside the firm’s permanent structure with the goal of exploring and generating new ideas. Along the same lines, Tushman and O’Reilly’s (1997) ambidextrous model entails separating exploration from exploitation within the organization’s structure. Benner and Tushman (2003) thus make a distinction between two types of entities: ‘exploiting units’ whose operations follow a certain routine, leaving room mainly for incremental innovation, and ‘exploratory units’, which are less finalized and formalized and dedicated to exploratory activities. The generation of radical innovation is the core mission of the exploratory units. The most recent works on the ambidextrous organizational model (Benner & Tushman, 2003; O’Reilly & Tushman, 2004) pointed out that the processes for generating new ideas in the company must be able to rely on the company’s knowledge and skills without holding back creativity. Such a balance is not easy to manage (Van Looy, Martens & Debackere, 2005) and to the best of our knowledge little © 2008 The Authors Journal compilation © 2008 Blackwell Publishing


research has looked into the exploratory unit at a fine-grained level. Simonton (1999) argues that a large amount of knowledge or discipline-relevant information is a necessary condition for creativity. Sternberg, O’Hara and Lubart (1997) and Amabile (1988, 1998) consider knowledge and relevant skills or expertise as the main components or resources of creativity. Other researchers point out that knowledge can also be an obstacle and impede the development of creative solutions. According to Mayer (1995), knowledge consists of a good understanding of the nature of relationships between different ideas or patterns of associations, and hence knowledgeable people such as experts are efficient in searching for a solution among these existing associations, leading to incremental solutions. In contrast, ‘marginal intellectuals’ (who participate in multiple intellectual networks but are central to none) are more likely to introduce breakthroughs. Many works have related the ability to innovate and the ability to put forward new combinations between fields of knowledge (Schumpeter, 1934; Kogut & Zander, 1992; Nonaka, 1994). Following this line of thought, Hargadon and Sutton (1997) highlighted knowledge brokering as a successful strategy for combining existing knowledge in order to propose new ideas. ‘Knowledge brokers’ are individuals, teams or firms that create new solutions by combining ideas that previously appeared unrelated. Knowledge brokers are trusted third parties that can enhance the communication between disconnected units. Refining the knowledge brokering approach, Hargadon and Sutton (2000) noticed that brokers change the ideas and resources that they transfer: they are not merely conduits that pass along ideas and knowledge. Knowledge is combined and hence it is transformed and new knowledge is created (Hargadon & Fanelli, 2002). Research in innovation management has rarely analysed this process of transforming and combining knowledge at a fine-grained level though it has highlighted its importance. The creativity literature focusing on the factors that favour creative behaviours helps identify and explain factors favouring knowledge combination. We propose to analyse knowledge combination in an exploratory unit as this process has been outlined as crucial for breakthrough innovation which is one of the goals of such units. We explore organizational designs and creativity processes likely to enhance knowledge combination leading to discontinuous innovation and value creation. © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

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Research Setting and Method The automobile industry is characterized by very strong competition. Domauto (a pseudonym) is among the ten largest suppliers in the automobile industry. It is a multidivisional company with autonomous divisions (budget, R&D, product portfolio, etc.) addressing functionally consistent product lines. Domauto is immersed in a highly competitive sector: it must provide automobile manufacturers with a stream of innovative offers in order to stand out and ensure its growth (Fourcade & Midler, 2005). In order to take on this challenge, in 2004 the company launched a creativity process related to a specific feature in the car: the powertrain. The objective was to identify innovations that would radically improve the efficiency of the powertrain and thus to develop new business. The powertrain is a feature transversal to the divisions of the firm. Furthermore, it is composed of components produced by Domauto and other suppliers. The responsibility for this creativity process was held by a specific unit created for this objective and referred to as Innovation Platform (IPL). Several innovative solutions with very different degrees of maturity and feasibility, including some radical solutions that did not exist in the firm’s technical road maps, emerged from this process. IPL can be regarded as an exploratory entity. It is responsible for developing innovation in a new feature for medium- and longterm growth. On the other hand, Domauto’s divisions can be regarded as exploitation units. They are organized into business units, each dedicated to a functionally consistent product line. For example, the engine cooling division handles all components or systems associated with the engine’s cooling loop. They are responsible for maintaining turnover for these products. They have a high level of autonomy (budget, divisional R&D, product portfolio, etc.) and are under great pressure to reduce costs and improve productivity. The divisions’ R&D departments are therefore primarily dedicated to improving components with a lesser focus on developing radical innovations. The creation of the IPL alongside the divisions endowed Domauto with several characteristics of an ambidextrous organization. IPL gives the researchers the opportunity to understand how members of a firm combine previous and generate new knowledge. According to these elements, this research setting is suitable for analysing the organizational design that enhances the creativity process and knowledge combination occurring in an ambidextrous organization. This case was chosen for theoretical rather

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than statistical reasons (Eisenhardt, 1989). This firm provided us with rich data and enabled us to develop ‘a single case that can represent a significant contribution to knowledge and theory building’ (Yin, 1984). An exploratory case study is an appropriate approach for studying organizational processes that cannot easily be quantitatively measured (Elmquist & Segrestin, 2007; Karlsson & Sköld, 2007). Our research is based on an inductive inquiry generating theoretical insights from a single in-depth case. Therefore, the objective is to inductively identify relevant factors associated with the phenomenon under study. Furthermore, such an approach enables ideas to be developed for further study (Yin, 1984; Siggelkow, 2007). Finally, according to Eisenhardt and Graebner (2007), inductive theory is necessary when the research question is tightly constrained within the context of an existing theory, which is the case here. According to the paradigm of grounded research (Glaser & Strauss, 1967; Miles & Huberman, 1984), our analysis drew on detailed field notes, interview notes, transcripts of meetings and company documents. Our orientation with regard to data analysis was inductive and the aim was to generate insights into how this new entity managed the creativity process. Qualitative analysis is an inherently dynamic and ongoing process and we conducted multiple readings of field notes, minutes of meetings and documentation to create categories and identify recurring themes. We proceeded iteratively, with the early stages being more open-ended than the later ones. Bibliographic research on creativity and innovation management was done in order to stimulate theoretical sensitivity and questions (Strauss & Corbin, 1998). By integrating grounded theory with insights from the literature, we develop a fine-grained understanding of creativity processes in ambidextrous organizations. Data was collected over a period of six months: from January 2004 until June 2004, during which the authors spent one day on-site each week. Data was collected from multiple sources: observations, interviews and reviews of documents. Observations consisted of passive participation (sitting in) at each creativity meeting over this period. In total, we attended and documented 22 meetings lasting from 4 to 8 hours each with a mean presence at these meetings of eight company employees. The meetings were our principal area of observation because it provided a valuable database of how new ideas were generated and assessed. Informal interviews were conducted with the creativity team members after the meetings,

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at lunch or during breaks. These lasted from 30 to 90 minutes and were conducted alongside the team meetings. Our aim was to record background information, gain a better understanding of the technological issues discussed during the meetings and gather additional information on events which occurred between meetings. We had hundreds of such informal conversations with team members. During these interviews, we also asked team members about their other activities in the divisions. In addition, we collected data by reviewing the extensive documentation generated by this process, including minutes of meetings, presentations and internal documents and reporting information. We were given every report or sketch exchanged by team members. We received all documents and were included in the e-mail distribution list. We also had access to all preparatory documents for meetings, and all notes and presentations prepared by group members.

Data Analysis In this section, we analyse the organizational design and the creativity process deployed in the exploratory unit IPL. We examine whether the composition and the scope of the unit influenced knowledge combination. In the same way we focus on the characteristics of the creativity process that was adopted and enhanced knowledge combination.

The Organizational Design Unit Composition: The ‘Team’ At the launch of IPL, the top management appointed the former R&D manager of one of the divisions contributing to the powertrain scope to structure the creativity process. The IPL leader built a work group or ‘team’ composed of an R&D and a marketing player from each division involved in the new scope. These individuals were generally one level below the hierarchical level of the IPL leader with whom they had no hierarchical relationship. These members were appointed specifically for their function in their respective division, their domain of expertise, and their past experience and personal skills. Several players therefore had professional experience with automobile manufacturers. These 12 participants devoted 20 per cent of their time (one day a week) to this new activity, which they combined with their other functions. The Creativity Sessions: The ‘Team’ Meetings The ‘team’ assembled for meetings on a weekly basis. There were 22 meetings in six © 2008 The Authors Journal compilation © 2008 Blackwell Publishing


Scope definition

Knowledge sharing on the challenges associated to this scope

Conceptual design

Embodiment design: identification of some technical principles

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Divisions involved Players in the divisions: marketing and R&D External partners

Value axes: reduction of noise, weight, emission, cost, size, consumption and enhancement of power, fun to drive, serviceability safety

Solution concepts that could create value along the axes

Exploratory projects: definition, selection, launch and monitoring

Figure 1. Creativity Process

months. These meetings always took place on Fridays so that the group members could set aside this day for a very long period. The weekend effect helped the participants ease off tensions from the week and put them in a state of mind that was conducive to creativity: one of the members stated that the team meeting was his breath of air during the week. The group established the agenda one session to the next: each member could therefore propose subjects that could contribute to the creativity process, such as sharing a report of technological intelligence or inviting an outside speaker relevant to the subject. The session locations were rotated among the team members’ different divisions. The meetings took place in a relaxed surrounding in a pleasant setting (large conference room): during the sessions, the members were accommodated particularly well by the host division (coffee, breakfast, etc.). At the beginning of each session, the IPL leader informed the members of the interactions that he had had with the company’s management (the CEO or his closest advisors) about the unit. Part of the meetings could be devoted to visiting company sites (test laboratories in particular) or discussing prototypes developed by the divisions. It is significant to point out that no short-term pressure was placed on the teams, which is exceptional in this type of company.

The Creativity Process The creativity process is summarized in Figure 1 and detailed below. © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

Scope Definition and Knowledge Sharing As the objective of the unit was to develop innovation to enhance the efficiency of the powertrain, the first activity of the ‘team’ was to clarify the ‘propulsion efficiency’: what is the scope of the powertrain? In order to specify the powertrain scope, they conducted a functional analysis. The powertrain is composed of components produced by the firm and by others that were not. The members identified all the elements that they would have to consider within the firm or externally. The objective of this approach was to widen the scope in order to be able to consider innovation tracks that were not constrained by the current dominant design. Each member used his knowledge base to formalize his own understanding of the new scope and pointed out the market or the technical challenges associated with it. As the firm is composed of independent divisions developing very different products, the ‘team’ members knew very little about the other divisions. Before moving forward with exploring new ideas in this new scope, they dedicated a period of time to discover the products and the technological road maps of the various divisions involved in the scope, the key skills differentiating the company from the competition, the customers of each division and the position in the industry. The objective of this knowledge sharing was to identify potential synergies between the various divisions on this new scope. Using this approach, however, the team members

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remained anchored in their own division’s products or technical road-map. Their primary motivation was to find out how their division with its technology and products could take part in the new scope. Knowledge Combination and Creation After the knowledge sharing phase, the ‘team’ began to create specific market and technical knowledge on the powertrain scope. The ‘team’ launched a competitive analysis on this scope and collected technical data on the propulsion modes (direct and indirect injection, turbo, hybrid segment, electric vehicle segment). External (suppliers or partners) and internal experts were regularly invited to make presentation to the ‘team’ members about specific issues. Hence, IPL created its own understanding of certain key topics on the basis of a broader perspective than that of the division. Based on their current and past experience, the team members generated a list of areas for improvement of the powertrain scope to which OEMs were likely to bring value. Each member tried to underline from their point of view (relying on their technical and marketing knowledge as well as their experience) the challenges to be addressed on this scope. Unlike the initial analytical activity, this process was not based on formalized and structured knowledge, but on team members’ tacit knowledge. It involved clarifying, formalizing and pooling individual experiences accumulated by the group members from their former and other roles. It resulted in a list of 11 value axes that constituted different areas for improvement likely to be valued by the customer (compactness, reduction of weight, emissions, noise and vibration, etc.). Looking at the emissions’ value axis, for instance, the questions the team tried to answer were: What is the value of a clean powertrain? And for whom? It is important to note that these value axes were not original per se. The originality of the approach came from the idea of taking what is generally considered as constrained by the divisions and transforming it into an innovation track on the new scope. For each value axis, the objective was to find a solution that would improve the powertrain on this dimension. The problem was how to design components leading to a clean powertrain? The exploration of the value axes led to the identification of some recurring concepts of solutions enabling the efficiency of the powertrain, such as the ‘base+boost’ concept for example, which corresponds to delivering two types of functions, one for an average utiliza-

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tion and one that delivers a boost when needed for a specific usage. This concept is appropriate for a reduction of the emissions because the boost operates only when it is needed. These concepts constituted a ‘conceptual design’ according to Pahl and Beitz (1996). The following step was to explore the technical principles that represent the ‘embodiment design’. In order to create value along the 11 axes, the team selected eight solution concepts and explored their technical embodiment. This exploration led to the identification of several exploratory projects in order to build the necessary technical and market knowledge and to assess these concepts. A total of 26 possible exploratory projects were considered. Five exploratory projects were selected because of their high probability of success and their high return on investment from an economic and a knowledge development point of view. These exploratory projects were financed by IPL and developed by project teams comprising the most knowledgeable people from the divisions on the topic. These members did not belong to the IPL ‘team’. However, the project leader reported the project progress during the team meetings. The IPL leader recruited a specific actor in charge of the monitoring of these projects.

Discussion and Conclusion We believe that some important conclusions both for theory and practice can be drawn from this case study. Through the analysis of the creativity process that occurred in the exploration unit, we highlight organizational design and methods that enhance knowledge combination leading to radical innovation. We propose insights into how knowledge can represent the raw material for creativity without impeding the development of discontinuous innovation. As mentioned in the literature review, the literature on innovation management and the literature on creativity both underline the complex role of knowledge in radical innovation. It is both a source of and a barrier to innovation. Knowledge can hinder radical innovation through knowledge boundaries, core rigidities and hyper-specialized experts. At the same time, knowledge is crucial for such innovation and creative processes: it gives the ability to put forward a new design, to formulate a new hypothesis and to solve problems. Knowledge combination has been outlined as a key issue (Schumpeter, 1934; Kogut & Zander, 1992; Nonaka, 1994; Hargadon & Sutton, 1997). Our focus is on how to favour knowledge combination and, © 2008 The Authors Journal compilation © 2008 Blackwell Publishing


more specifically, we aim at providing insights into the organizational design and processes that might facilitate it. Based on our observation and analysis, we would like to underline four key factors that increased the opportunities for combining knowledge and helped overcome the barriers previously mentioned in the literature. The first factor is the scope of the exploratory unit. The scope is broad: it encompasses components of several divisions, and also covers components that are not delivered by the company. In considering innovations on this broad scope, the ‘team’ members had to mobilize their expertise not only on the components they were specialized in but also on adjacent components that were included in the scope. Based on their accumulated experience, they had to articulate propositions and statements relevant for a broader scope. Such knowledge was often less articulated than the knowledge on the components they were specialized in, but it was fruitful for the collective creative process. This was typically what happened in discussing the market and technical trends on the powertrain scope. The various team members were in the same position: none of them had all the knowledge on this new scope for the firm. All of them had some experience that might be useful, and because they had mastered one component of the overall scope, this helped in confronting the various visions and in combining their knowledge. This confrontation was highlighted by Leonard-Barton (1995) as a ‘creative abrasion’, which exploits the differences between the members of the organization. The second factor is the dual position of the individuals involved in the creativity process. With the exception of the leader, these people dedicated one day a week to the exploratory approach, and for the rest of the week they were involved in their division on activities where their expertise was totally relevant, adapted, fully recognized and valued. Furthermore, because of their responsibilities in their division, they knew other experts they were collaborating with on other projects. They could occasionally invite them to take part in the creative process. They could also mobilize their personal network of experts not only within the company but also outside the company. The ability to span boundaries (Allen, 1977) has long been outlined as key in the literature on innovation. This organizational design with dual roles appeared to reinforce opportunities for knowledge combination. The third factor that appeared to favour knowledge combination is the method that was followed during the creativity process. © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

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The identification of value axes was conducive to knowledge combination, as the experience of team members on various specific components as regards a value axis could be confronted with one another. Each team member had had interactions with customers on criteria that were to be improved, for example, the reduction of emissions. Depending on the type of components they were specialized in, they had developed partial understanding of the importance of this dimension for the car manufacturers. For example, one division focused on emissions at high speed and on long distance while another had specialized in emissions when cars stop at traffic lights. The identification of solution concepts was also an opportunity for knowledge combination. Solution concepts could be embodied with various technical principles and could open up various possibilities or applications for the divisions. For example, the team came up with the solution concept called ‘base+boost’: it could be embodied with the turbo for the engine; it also could be applied with a technical principle different from the turbo, such as a boost based on an air pump, or it could be applied to another function such as the air conditioning. These value axes and solution concepts were intermediary objects (Jeantet, 1998) or boundary objects (Star, 1989) supporting knowledge combination. The role of such solution concepts to identify knowledge to be developed was outlined by Hatchuel, Le Masson and Weil (2005) in their theory of modelling design activity, which underlined the back and forth movement between the knowledge space and the concept space. A final factor to stress is the role of the exploratory projects for knowledge creation and combination. As mentioned, the creativity process ended with the selection of five exploratory projects that were considered as the most promising innovation tracks. The exploratory project aimed at reducing technical and market uncertainty and at focusing the innovation process on a new product. One of these projects was aimed at developing a prototype to test the technical feasibility and the functional performance of a solution. These projects were dedicated to knowledge creation and combination on the basis of which development project could be launched in a subsequent step (see also Lenfle & Midler, 2003). The exploratory projects were cross-divisional arenas where knowledge could be combined and created. It relied heavily on the knowledge held in the divisions corresponding to critical competencies for the projects. However, the monitoring of the progress of the projects and their funding at the exploration unit level anchored the exploratory project in this unit.

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The major limitations of the research are related to its richness and to an analysis based on a single case. While providing rich data, this could reduce the generalizability of the conclusions. Concerns of external validity were traded off against opportunities to gain insights into a complex phenomenon (Karlsson & Sköld, 2007). However, we believe that analysing exploratory units from the perspective of knowledge combination opens up a new stream of research in which the factors that we outline could be tested on a broader scale. Further research could also explore in a dynamic way and in the long run whether the outlined factors change over time and the role of actors external to the organization.

References Allen, T. (1977) Managing the Flow of Technology. MIT Press, Cambridge, MA. Amabile, T. (1988) A Model of Creativity and Innovation in Organizations. In Staw, B.M. and Cummings, L.L. (eds.), Research in Organizational Behavior, Vol. 10. JAI Press, Greenwich, CT, pp. 123–67. Amabile, T. (1998) How to Kill Creativity. Harvard Business Review, 76, 76–87. Benner, M.J. and Tushman, M.L. (2003) Exploitation, Exploration and Process Management: The Productivity Dilemma Revisited. Academy of Management Review, 28, 238–56. Christensen, C.M. (1998) The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Harvard Business School Press, Cambridge, MA. Cohen, W.M. and Levinthal, D.A. (1990) Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35, 128–52. Dougherty, D. (1992) Interpretive Barriers to Successful Product Innovation in Large Firms. Organization Science, 3, 179–202. Dougherty, D. and Hardy, C. (1996) Sustained Product Innovation in Large, Mature Organizations: Overcoming Innovation-to-Organization Problems. Academy of Management Review, 39, 1120–53. Eisenhardt, K.M. (1989) Building Theories from Case Study Research. Academy of Management Review, 4, 532–50. Eisenhardt, K.M. and Graebner, M.E. (2007) Theory Building from Cases: Opportunities and Challenges. Academy of Management Journal, 50, 25–32. Elmquist, M. and Segrestin, B. (2007) Towards a New Logic for Front End Management: From Drug Discovery to Drug Design in Pharmaceutical R&D. Creativity and Innovation Management, 16, 106–20. Fourcade, F. and Midler, C. (2005) The Role of 1st Tier Suppliers in Automobile Product Modularisation. The Search for a Coherent Strategy. International Journal of Automotive Technology and Management, 5, 146–65.

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Glaser, B.G. and Strauss, A.L. (1967) The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine Publishing, Chicago, IL. Hargadon, A. and Fanelli, A. (2002) Action and Possibility: Reconciling Dual Perspectives of Knowledge in Organizations. Organization Science, 13, 290–302. Hargadon, A. and Sutton, R. (1997) Technology Brokering and Innovation in a Product Development Firm. Administrative Science Quarterly, 42, 716–49. Hargadon, A. and Sutton, R.I. (2000) Building an Innovation Factory. Harvard Business Review, 78, 157–67. Hatchuel, A., Le Masson, P. and Weil, B. (2005) The Development of Science-Based Products: Managing by Design Spaces. Creativity and Innovation Management, 14, 345–54. Henderson, R.M. and Clark, K.B. (1990) Architectural Innovations: The Reconfiguration of Existing Systems and the Failure of Established Firms. Administrative Science Quarterly, 35, 9–30. Jeantet, A. (1998) Les objets intermédiaires en conception. Sociologie du travail, XL, 291–316. Karlsson, C. and Sköld, M. (2007) Counteracting Forces in Multi-branded Product Platform Development. Creativity and Innovation Management, 16, 133–41. Kogut, B. and Zander, U. (1992) Knowledge of the Firm, Combinative Capabilities and the Replication of Technology. Organization Science, 3, 383– 97. Lenfle, S. and Midler, C. (2003) Innovation-Based Competition and the Dynamics of Design in Upstream Suppliers. Interantional Journal of Automotive Technology and Management, 1, 269–86. Leonard-Barton, D. (1992) Core Capabilities and Core Rigidities: A Paradox on Managing New Product Development. Strategic Management Journal, 13, 111–25. Leonard-Barton, D. (1995) Wellsprings of Knowledge. Harvard Business School Press, Cambridge, MA. Mayer, R.E. (1995) The Search for Insight: Grappling with Gestalt Psychology’s Unanswered Questions. In Sternberg, R.J. and Davidson, J.E. (eds.), The Nature of Insight. MIT Press, Cambridge, MA, pp. 3–32. Miles, M. and Huberman A.M. (1984) Qualitative Data Analysis. Sage Publications, Beverly Hills, CA. Nonaka, I. (1994) A Dynamic Theory of Organizational Knowledge Creation. Organization Science, 5, 14–37. O’Reilly, C. and Tushman, M. (2004) The Ambidextrous Organization. Harvard Business Review, 62, 74–82. Pahl, G. and Beitz, W. (1996) Engineering Design, A Systematic Approach. Springer, Berlin. Schumpeter, J. (1934) The Theory of Economic Development. Harvard University Press, Cambridge, MA. Siggelkow, N. (2007) Persuasion with Case Studies. Academy of Management Journal, 50, 20–24. Simonton, D.K. (1999) Origin of Genius. Oxford University Press, New York. Star, S.L. (1989) The Structure of Ill-Structured Solutions: Boundary Objects and Heterogeneous Distributed Problem Solving. In Huhns, M. and © 2008 The Authors Journal compilation © 2008 Blackwell Publishing


Gasser, L. (eds.), Reading in Distributed Artificial Intelligence. Morgan Kaufman, Menlo Park, CA. Sternberg, R.J., O’Hara, L.A. and Lubart, T.I. (1997) Creativity as Investment. California Management Review, 40, 8–21. Strauss, A. and Corbin, J. (1998) Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory, 2nd edn. Sage, Newbury Park, CA. Tushman, M.L. and O’Reilly, C. (1997) Winning Through Innovation. Harvard Business School Press, Cambridge, MA. Van Looy, B., Martens, T. and Debackere, K. (2005) Organizing for Continuous Innovation: On the Sustainability of Ambidextrous Organizations. Creativity and Innovation Management, 14, 208–21. Woodman, R.W., Sawyer, J.E. and Griffin, R.W. (1993) Toward a Theory of Organizational Creativity. Academy of Management Review, 18, 293– 21. Yin, R. (1984) Case Study Research. Sage Publications, Beverly Hills, CA.


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Sihem Ben Mahmoud-Jouini is Associate Professor at HEC School of Management (France). She holds a PhD from University Paris Dauphine in Project Management and Strategic Management of Innovation. She was visiting professor at Stern Business School (NYU). Her research interests include New Product Development and Strategic Management of Innovation. She has published in JPIM, IJPM and regularly attends IPDMC. In 2007, she obtained an Award at the TIM division at the AOM conference. Florence Charue-Duboc (florence. [email protected]) is researcher at CNRS and professor at Ecole Polytechnique (France). She holds her PhD from Ecole des Mines de Paris in Engineering Management. Her research interests include Strategic Management of Research and Technology and Organizational Change. She has edited a book, Innovation-based Competition and Design System Dynamics, and contributed to several books published by Cambridge University Press, Kluwer and Oxford University Press. She has published in IJIM and regularly attends IPDMC. In 2007, she obtained an Award at the TIM division at the AOM conference.

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The Element of Play in Innovation Work: The Case of New Drug Development Alexander Styhre The innovation literature offers limited social-psychological explanation for why co-workers in organizations are dedicated to innovation work. Instead, innovation work is often portrayed in linear and instrumental terms wherein procedures are structured in accordance with predefined project models. In opposition to this view, innovation work is here examined as a specific form of play. The French social thinker Roger Caillois’ analysis of forms of playing in human culture is introduced as a perspective on innovation work little explored to date. The empirical part of the paper reports a study of the work of laboratory scientists in new drug development activities in a major multinational pharmaceutical company. In the analysis, innovation work is examined as a highly specialized and idiosyncratic form of playing wherein the scientists are practising their skills while simultaneously being exposed to serendipity and other residual factors (e.g., luck, chance) outside their full control. The paper concludes that broadening the theoretical and conceptual perspective on innovation may enrich the innovation literature.

Introduction ‘[T]o declare it once and for all, Man plays only when he is in the full sense of the word a man, and he is only wholly Man when he is playing’. Friedrich Schiller (1795/2004, p. 80)

I

n the substantial literature on new product development and innovation (Dougherty & Heller, 1994; Cheng & van de Ven, 1996; Cardinal, 2001), the development of products or services is generally portrayed as a complex and heterogeneous process including the deployment of a variety of resources. For instance, the sociological view of innovation advocated by Akrich, Callon and Latour (2002) stresses the heterogeneity of innovation processes: ‘An innovation in the making reveals a multiplicity of heterogeneous and often confused decisions made by a large number of different and often conflicting groups, decisions which one is unable to decide a priori as to whether they will be crucial or not’ (Akrich, Callon & Latour, 2002, p. 191). Although it is recognized that new product development and innovation is to some extent a haphazard and a complicated organizational matter, the

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social-psychological and motivational aspects of innovation are undertheorized. Creativity researchers such as Teresa Amabile (Amabile et al., 1996, 2005; Amabile, 1997, 1999) have shown in a series of publications that creativity is a human capacity embedded in emotionality and affects that can be purposefully managed. In order to further advance a theoretical framework taking into account these human factors in innovation work – to conceive of innovation as what Hellström (2004) calls ‘social action’ – new theories of human creativity may be useful. Even though linear and instrumental models of innovation work effectively explain parts of the innovation process, namely the morphology of innovation, its formal structure, toll-gates, and decision-points, such formal models fail to fully account for the serendipity of innovation and what motivates the participants in innovation work. Innovation is, then, not what is of necessity solely emerging in a linear manner on the basis of purposeful, organized activities; innovation is also what derives from the actual engagement with scientific problems in a more playful manner inherently in opposition to instrumental models. Thus, rather than assuming that innovation emerges © 2008 The Author Journal compilation © 2008 Blackwell Publishing

THE ELEMENT OF PLAY IN INNOVATION WORK

along specific patterns, it is necessary to examine how specialists and managers conceive of innovation work, cognitively as well as emotionally (Salaman & Storey, 2002). This article reports a study of new drug development work in a major multinational pharmaceutical company. It suggests that rather than black-boxing innovation in sciencebased industries as the result of the collaboration among skilful and dedicated researchers and managers, innovation must be further problematized as what is partially dependent on formal resources such as skills, technology, management practice, communication, etc. (i.e., endogenous factors), partially the outcome of exogenous factors such as serendipity, luck and chance. In contrast to the linear view of innovation work, innovation is here examined as a particular form of playing, including individual and collective skills, chance and recognition from peers as central components. The association between innovation and play has been emphasized by a number of innovation researchers. Anderson (1994) claims that the notion of play captures the energy and commitment that is demanded in innovation work: ‘Work wears us out, even before we do it. Play energizes us, even after we’re done. Play also gives direction and focus on our activities. In class the mind easily wanders. On the ball field, at the mall, or cruising singles bars, the mind is incredibly focused’ (Anderson, 1994, p. 81). Anderson suggests that innovation work may be regarded as a form of playing. More recently, Dodgson, Gann and Salter (2005) argue that play is an important component in the innovation process mediating ‘thinking’ and ‘doing’, the conception and execution of new product and service innovations. ‘[T]he concept of “play” enables the link between ideas and action. “Play” is the medium between the “thinking” and “doing”’, Dodgson, Gann and Salter (2005, pp. 137–8) claim. In their view, play is defined accordingly: ‘Activities associated with the selection of new ideas to ensure they are practical, economical, targeted, and marketable, including verifying, simulating, extrapolating, interpolating, preparing, testing, validating, transforming, integrating, exploring, and prioritizing’ (Dodgson, Gann & Salter, 2005, p. 242). Dougherty and Takacs (2004) speak of play as what makes multifunctional new product development teams ‘interrelate heedfully’ (Weick & Roberts, 1993) in their work. Just like Anderson (1994), Dougherty and Takacs (2004) contrast work and play (2004, p. 576): ‘Research reports people perceive “work” to be constrictive, structured, tedious, difficult and boring, while © 2008 The Author Journal compilation © 2008 Blackwell Publishing

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“play” is seen as liberating, unstructured, refreshing and emotional, and suggests that the benefits of play can be achieved by framing activities, since relabelling tasks as play instead of work transformed people’s perception, judgements and motivations’. Dougherty and Takacs (2004) found that innovative firms were capable of forming multi-functional teams, perceiving innovation work as a form of play based on heedful interrelating, while noninnovative firms systematically failed to do so. In their analysis of the relations between play and innovation, Anderson (1994), Dodgson, Gann and Salter (2005) and Dougherty and Takacs (2004) do not ground ‘play’ in any specific theoretical framework or discipline but it is used merely as a general metaphor aimed at capturing the creative yet regulated activities in the innovation process. In this article, the concept of play is further explored on the basis of the theoretical writings on play published by the French social theorist Roger Caillois (1961). Drawing on Caillois’ theory of play – a sociological or anthropological rather than psychological theory – innovation is here regarded as what is regulated by both scientific standards and managerial objectives, yet liberating in terms of being the pursuit of what is not yet already fully known. Caillois treats play as something that is substituting for the tedious repetitiveness of everyday life, the existential predicament of being thrown into a life situation with little excitement and surprises, and what provides opportunities for both skilful handling of activities and a meaningful recognition of what is unpredictable in social life. Caillois represents a French tradition of thinking, critical of utilitarian and rationalist forms of thinking. As a member of the ‘Collège de Sociologie’ community in Paris in the 1930s (Pearce, 2003; Richman, 2003), Caillois (1961) and other noted members of the college such as Georges Bataille provided an analysis of how the sacred and the profane are manifested and separated in the modern, de-traditionalized, and increasingly disenchanted society. For Caillois, play is one of the central mechanisms of all higher culture and therefore playing must not be dismissed as some frivolous or peripheral matter. Speaking of science-based innovation as a particular form of playing under determinate conditions is therefore neither a sophism, nor an attempt at trivializing new drug development. On the contrary, it is an attempt at examining new product development work and innovation work as what is mutually dependent on fixed rules and regulations, and the possibility for creativity and novel thinking. Treating science-based innovation as intrinsically embedded in both skills and luck may broaden the perspective on

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innovation work and enable new ways of conceiving innovation work. The article is structured as follows. First, the literature on new product development and innovation is reviewed. Second, Caillois’ theory of play is examined and introduced as part of a broader theoretical project pursued by the Collège de Sociologie. Third, the methodology of the study will be discussed. Fourth, the empirical study of new drug development work will be presented. Finally, some theoretical and practical implications will be discussed.

New Product Development and Innovation as Rule-Governed Activities New product innovation and innovation have become central interests in the management literature. While the literature is diverse and often examines innovation from a systems point of view or explores the actual practice of new product development teams, there is a strong tendency to regard innovation work as a goal-rational and rule-governed process, and as the joint collaboration between groups of individuals representing different know-how and expertise. Andrew Van de Ven (1986, p. 591) defines innovation as ‘[t]he development and implementation of new ideas by people who over time engage in transactions with others in an institutional context’. Moreover, Van de Ven (1986, p. 591) speaks of innovation as a combination of ideas: ‘An innovation is a new idea, which may be a recombination of old ideas, a scheme that challenges the present order, a formula, or a unique approach which is perceived as new by the individuals involved’. Damanpour (1992, p. 376) defines innovation more loosely as ‘[t]he adoption of an idea or behaviour, whether a system, policy, program, device, process, product or service, that is new to the adopting organization’. Van de Ven (1986) points at the managerial consequences of innovation work: ‘From a managerial viewpoint, to understand the process of innovation is to understand the factors that facilitate and inhibit the development of innovations. These factors include ideas, people, transactions, and context over time’ (Van de Ven, 1986, p. 591). Some researchers present taxonomies structuring innovation work into categories. For instance, in her review of the innovation literature, Slappendel (1996) talks about three complementary perspectives on innovation: (1) the individual perspective, emphasizing the individual actors that are the principal agents in innovative work, (2) the structural perspective, ‘assuming that innovation is determined

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by organizational characteristics’ (Slappendel, 1996, p. 113), and (3) the interactive perspective, studying innovation as the outcome of the joint collaboration between groups of individuals, organizations and relevant resources (see also Wolfe, 1994, for a complementary taxonomy). Studies of innovation can take place on many levels and pursue a variety of complementary perspectives. When studying innovation work in practice, it is helpful to distinguish between the generation of a new idea or concept (e.g., the ‘exploration’ of a new product or a service) and the adoption and ‘exploitation’ of a new idea (March, 1991; Levinthal & March, 1993; Holmqvist, 2004; Gupta, Smith & Shalley, 2006). In the former case, ideas are articulated for the first time but are not yet actual; in the latter case an idea is brought forward and turned into, say, a product or a service. In the case of the new drug development activities examined in this article, it is the latter form of innovation that is examined even though both components are included in the new drug development process (see Rothaermel & Deeds, 2004). The explorative innovation work of the early phases is located in the so-called discovery organization in the pharmaceutical company studied. Even though new methods and concepts are part of the new drug development work, in most cases new drugs are the fruits of the systematic endeavour to exploit a certain idea, for instance the idea that a specific molecule is capable of affecting a biological target to accomplish desirable medical effects. Taken together, the field of research on new product development and innovation is diverse and includes a large variety of theoretical perspectives and methodological approaches. The literature has not sufficiently examined and theorized the interaction between formal and systematic approaches and factors such as serendipity and chance, factors that are part of any concept of play. In the following, the new product development and innovation literature will be complemented with Caillois’ writings on play.

Caillois’ Theory of Play Caillois (1961) regards play as an instituted practice enabling an escape from the hegemony of accumulation, goal-rationality and utilitarian thinking. Similar to the Dutch historian Johan Huizinga’s (1949) analysis of play as not only part of culture but actually the driving force behind any advanced culture, Caillois’ emphasis on play as central to human existence is also supported by Winnicott’s (1971) development psychology wherein play is a primary source of creativity. Furthermore, © 2008 The Author Journal compilation © 2008 Blackwell Publishing


anthropologists such as Victor Turner (1982) stress the importance of play in human society. Caillois examines play and games as being at the very centre of human existence. Still, Caillois remains faithful to the critique of utilitarianism of the Collège de Sociologie (see Bataille, 1991; Caillois, 2001, 2003) and defines play as, in essence, a waste, that is, as something that does not aim at fulfilling some external goal or objective. Caillois writes: ‘Play is an occasion of pure waste: waste of time, energy, ingenuity, skill, and often of money for the purchase of gambling equipment or eventually to pay for the establishment’ (Caillois, 1961, pp. 5–6). In addition, play is a ‘free activity’ that must be based on uncertainty: ‘Doubt must remain until the end, and hinges upon the denouement’ (Caillois, 1961, p. 7). Caillois (1961, p. 12) identifies four idealtypes of games that exist in all societies: the first type is called agôn and includes all sorts of games based on competition, body skills and control. Agôn is, then, playing that emphasizes the players’ physical, embodied performance. The second type of game is called alea (from the Latin word for dice) and comprises all games incorporating chance, that is, that cannot be immediately influenced by the players. While agôn is based on skills, alea is based on luck. ‘Agôn is the vindication of personal responsibility; alea is the negation of will, a surrender to destiny’, Caillois writes (1961, p. 18). Third, Caillois speaks of games of mimicry, that is, ‘simulation’ of situations or individuals, historical or fictional. Mimicry is then more closely related to the infant child’s training in what Mead (1934) called ‘taking the role of the other’ than the more advanced rule-governed games of older children. Finally, Caillois examines a group of games that he calls ilinx after the Greek word for vertigo. Ilinx includes all sorts of games wherein the participants are exposed to physical forces and movements that cause a sense of being released from the human body. For instance, merry-go-rounds and roller-coasters are examples of ilinx. All these four ideal-types of games could be combined into hybrid combinations. For instance, agôn is often combined with some component of alea to introduce a component of uncertainty in what otherwise would be strictly a matter of skill. For instance, pinball games may be programmed to offer unexpected and randomly occurring events to surprise and challenge the player. Caillois’ theory of games is sociological in terms of conceptualizing games as being a substitute for the absence of real game-like situations in contemporary life. Instead of merely accepting a predictable life devoid of surprises and sensations, the player is © 2008 The Author Journal compilation © 2008 Blackwell Publishing

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inventing what may be called ‘means of excitement’. Caillois writes: Play, whether agôn or alea, is . . . an attempt to substitute perfect situations for the normal confusion of contemporary life. In games, the role of merit or chance is clear and indisputable. It is also implied that all must play with exactly the same possibility of proving their superiority, or, on another scale, exactly the same chances of winning. In one way or another, one escapes the real world and creates another. (Caillois, 1961, p. 19) This does not mean, however, that all players are indulging in escapist reverie; on the contrary, playing is paradoxically a non-utilitarian and non-rationalist activity latently serving to reproduce a utilitarian and rationalist society through substituting real-life situations with a world that is, in Caillois’ formulation, ‘created’. Caillois (1961) continues to make connections between different types of societies and cultures and games. In the ‘Dionysian society’ (primitive society), rules and organization are eliminated in games and games of mimicry and ilinx are favoured instead. In the ‘rational society’ (including both advanced ancient societies such as the Roman Empire and modern society), rules and organization are of central importance and games of agôn and alea predominate: ‘Vertigo and simulation are in principle and by nature in rebellion against every type of code, rule and organization. Alea, on the contrary, like agôn, calls for calculation and regulation’, Caillois argues (1961, p. 157). Herein lies also one of the most fascinating qualities of games: the simultaneous adherence to rules and the existence of basic liberties within such regime of rules: Rules are inseparable from play as soon as the latter becomes institutionalized. From this moment on they become part of its nature. They transform it into an instrument of fecund and decisive culture. But a basic freedom is central to play in order to stimulate distraction and fantasy. This liberty is its indispensable motive power and is basic to the most complex and carefully organized forms of play. (Caillois, 1961, p. 27) Playing implies rule-following while simultaneously moving beyond these rules. As we will see regarding the case of new drug development, scientific work is substantially rulegoverned, yet such rules must always be put into question in order to be overcome; the laboratory scientist is therefore engaged in a particular form of game wherein novelty is aimed for within what is already, at least partially, known.

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New Drug Development as Play A Note on Methodology and Data Collection The research is based on a single case study methodology. Case studies have been widely debated in organization theory literature (Eisenhardt, 1989; Yin, 1994). While the method is still contested (Eisenhardt & Graebner, 2007), it has been gradually recognized as a method useful for generating new theory (Siggelkow, 2007). The present study is based on a single case study. The reliance on single cases in general limits the ability to make claims on presenting universally valid or broadly applicable theories. However, if the case under study may be considered as being representative of the totality of the object of study (e.g., a population, an industry, a specific field), single cases may provide valuable insights into individual domains of social practice (March, Sproull & Tamuz, 1991). In the present case, the major multinational pharmaceutical company, one of the top five firms in the industry at the time of the study, may be regarded as being representative of the pharmaceutical industry for the following reasons: first, the pharmaceutical industry is highly regulated by standards and is monitored in detail by authorities, thereby being subject to what DiMaggio and Powell (1983) call normative isomorphism, that is, pharmaceutical companies are demanded to develop similar structures to qualify as legitimate players in an industry. Second, there is a significant influence of what DiMaggio and Powell (1983) call mimetic isomorphism, that is, pharmaceutical firms are converging towards a standardized set of practices and operating within specific fields of interests serving underlying markets considered potentially profitable (see, for instance, Angell, 2004). Given these structures in the industry, single cases can provide useful and theoretically interesting insights into the domain of new drug development. Following writers such as Weick (1989, 1999), Whetten (1989), Sutton and Staw (1995) and Siggelkow (2007), a theory needs to be interesting, generally applicable (i.e., make claims of universality), and be capable of providing new perspectives on social life and/or guiding thoughtful action. As Siggelkow (2007, p. 21) emphasizes, a theory needs to (1) be a simplification in order to be useful, and (2) must not over-determine the phenomenon. In the present case, the largely heterogeneous and complex activities in new drug development, including materials, equipment, technology, tacit and explicit knowledge, significant experience and training, etc., are examined as part of the four-category taxonomy in Caillois’

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theory of play. That is, the complexity of new drug development practice is reduced to a more schematic level. Second, the study does not suggest that the four-category taxonomy captures all aspects of new drug development, but rather the model aims at enabling a consideration of new drug development practice in new terms. In other words, Caillois’ theory of play does not, arguably, over-determine the empirical material in terms of suggesting that innovation work is an actual form of play; instead, it is used to indicate some facets of innovation work that are easily overlooked or marginalized. In addition, a theory has to respond to what Siggelkow (2007, p. 23) calls the ‘problem of ex post obviousness’, that is, it needs to present an interesting view or idea regarding a social or organizational condition or event, giving the reader of the paper some valuable insight: ‘A paper should allow the reader to see the world, and just not the literature, in a new way’, Siggelkow suggests (2007, p. 23). In the present single case study, the new drug development work in a pharmaceutical company is examined as a form of play, that is, as what integrates systematic and formal procedures and elements of serendipity and chance. The study thus seeks to present a new perspective on innovation work as benefiting from an open and playful attitude and therefore qualifying as an ‘interesting’ contribution to the innovation literature. The study was conducted at one of the seven sites of a major pharmaceutical company. Twenty researchers were interviewed (on the pros and cons of interviewing, see e.g., Fontana & Frey, 1994; Holstein & Gubrium, 2003) and each interview lasted about one hour. All interviews were conducted by two interviewers, one of whom worked in the company. Interviews were tape-recorded and transcribed by one of the researchers. The interviews were semi-structured in accordance with an interview guide. The interviewees included scientists from medicinal chemistry, two pharmacology departments (integrative and molecular pharmacology), and the drug-metabolism and pharmacokinetics department (DMPK). The interviewees included ten men and ten women. Some of the interviewees had organizational tenure longer than ten years while some had worked in the company for only a few years.

New Drug Development: A Brief Overview The new drug development process in a pharmaceutical company includes the combination of competencies from a series of disciplines such as synthesis chemistry, pharmacology © 2008 The Author Journal compilation © 2008 Blackwell Publishing


(often separated into pharmacokinetics, studies of the absorption, distribution and metabolism of the drug, and pharmacodynamics, the drug’s effect on the body) and biology (in in vitro and in vivo testing). The first stage of new drug development is to identify a target, normally proteins, whose modulation might inhibit or reverse disease progression. Identifying a target requires a detailed knowledge of the etiology of the disease and the biological system associated with it. Once the target is validated, scientists must then find one or more leads (e.g., chemical compounds or molecules) that interact with the therapeutic target so as to induce the desired therapeutic effect. Synthesis chemists create molecules that interact with the therapeutic target and produce the desired effect. To identify a promising molecule, large libraries of substances are explored through the use of techniques such as high-throughput screening. Once a molecule has been identified and validated against a target, it is selected for lead optimization, that is, it is further refined and optimized to qualify for pre-clinical trails. A number of pharmacological analyses examine whether the molecule is standing the tests of stability, metabolism and other qualities that are prescribed in the regulatory demands. Finally, the molecule may be nominated as a new candidate drug that is being further developed prior to the clinical research. The entire process is a highly complex process including the use of advanced scientific practices (for an overview, see Chiesa, 1996; Zucker & Darby, 1997; Abraham & Reed, 2002; Hara, 2003; Hedgecoe & Martin, 2003). The different specialists are also working closely together throughout the process. For instance, the pharmacologists being the experts on the biological system provide the synthesis chemists with information so that they can provide the optimal molecule. Thereafter, the molecule is tested in in vitro or in vivo models and the results are returned to the chemists. When speaking of new drug development in terms of being both the effect of skills and factors of chance, it is important to recognize the conceptual difference between what may be called exogenous factors and endogenous factors. Exogenous factors are ‘pure chance’ and luck that are, by definition, outside the control of the scientists. Endogenous factors are non-deliberate but still not completely random findings, often called serendipities in science work (Roberts, 1989; Eagle, 2004). Serendipities are emergent scientific findings – by-products of the intended scientific route, one may say – that derive from detailed and thoughtful expertise in a domain. It may be debated whether skilful and experienced scientists are capable of producing © 2008 The Author Journal compilation © 2008 Blackwell Publishing

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serendipities or whether they are, in fact, products of genuine luck. Louis Pasteur for instance, claimed that ‘chance favours the prepared mind’, suggesting that what we tend to call serendipities are in fact embedded in scientific know-how and expertise. It is outside the scope of this paper to engage in a more detailed analysis of this problem. However, in the following, the scientific findings discussed by the interviewees are not the outcome of ‘pure chance’, but are dependent on individual and collective scientific expertise within the field. Therefore, when we speak of ‘chance’, it is used to denote a factor in the innovation work that cannot be fully controlled or known ex ante but yet resides in scientific know-how and experience.

New Drug Development: Skills (agôn) and Chance (alea) One of the senior scientists, with an extensive background as researcher at the university, emphasized the impact of chance in all advanced scientific work aimed at discovering new substances. In addition, he thought that the opportunities for exploring unexpected findings would be rather limited in the new regime of management control, emphasizing the output of candidate drugs: Most of the greatest discoveries are random . . . the chances of exploiting serendipities are much lower now, absolutely. That is a dilemma because you cannot say that ‘I’m off to play in the lab for a while. I don’t really know what to do but the outcome may be something amazing’. That would be too ‘anti-industrial’, but it is a fact that it often works that way. The pharmaceutical industry today is forced to optimize its use of available resources to increase output. Thus, identifying the mechanisms inherent in the very process of ‘scientific discovery’ becomes a central managerial objective. However, to date, the attempts at eliminating the factor of chance have not been successful. The senior scientist continued: ‘To be successful here, you need to be competent and have a bit of luck, there’s no doubt about it. We try to do away with this “luck factor” but we will never succeed because it is all about luck as well’. Another scientist, a medical doctor working in the field of pharmacology in a project aimed at exploring inflammatory gastrointestinal diseases, claimed that ‘all the interesting things that I’ve published have been serendipities’. The factor of chance and even luck is therefore regarded as a marginal

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but still significant factor contributing to the discovery process. One of the managers in the medical chemistry department pointed at the indeterminacy of scientific work and the ambiguities that needed to be taken into account when predicting and planning outcomes: After all, we are conducting research, that is, unknown things and we are supposed to do new things. Even if we may think that it will take us a year to go from here to there, it might be that nature is against us and we never reach that point because we cannot predict anything. If we would have been able to say that ‘we need to make 25 syntheses and then we are finished’, then we would have done those 25 or even just the single one we really want, but we cannot foresee where we end up. If scientific work could be predicted in greater detail, scientists would not have been forced to produce the massive amount of data that they regularly provide. The complexity of the undertaking of identifying new chemical substances that affected the target of choice do, however, imply that thousands of substances are ‘screened’, i.e., examined in a pharmacological analysis. Still, the process of identifying promising substances needs to be approached in a somewhat playful manner. One of the synthesis chemists pointed out the importance of being able to exploit individual creativity and curiosity: It is important to have a leader that allows you to be creative. It is really important. I think that in order to conduct research, you need to have a playful attitude. You need to take new routes and that it must be a bit quirky at times. People must be encouraged to present their ideas . . . But at the same time, the leader needs to take charge when needed. She continued, pointing at the explorative nature of the work, where a new substance was tested against the in vitro models employed by pharmacologists: The most exciting is when you developed the idea regarding the substance yourself, and you have a firm belief in it when you submit it. Then you are really waiting for the results to come. You check your computer over and over. It’s really exciting. One of the scientists told a story of how thoughtful reflection, medical expertise and luck intersected in one research project. The idea emerged when the scientist thought of exploring a new substance developed within ‘a completely different therapeutic area’. Here,

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a ‘more or less finished molecule’ was available. The molecule was not synthesized to be tested against the targets of the focal project, but still the scientist thought that there would be some interesting opportunities for new routes of thinking. The scientist said: I was intrigued by this concept and then the question was what receptor should we use? I went down to the coffee shop and discussed the issue with the Disease Area Scientific Leader who was intrigued by the idea. Two weeks later, he happened to come across a publication, showing what receptor to use and now there is a project running. In the stream of different ideas and hypotheses that researchers are navigating, specific ideas are at times brought forward because there happens to be a solution to scientific problems at the right moment in time. For instance, if the journal paper reporting the function of the receptor had been missed by the Disease Area Scientific Leader, the molecule might have been abandoned and some other substance could have been selected. Scientific work under indeterminate and time pressured conditions implies an exposure to similar events and occurrences. The ability to fully exploit one’s scientific skills and know-how was under the constant pressure of the demands of increased performance. Most of the interviewees in the pharmaceutical company were critical of the increased emphasis on output, expressed in terms of the number of synthesized molecules per time unit for the chemists and number of candidate drugs per annum for the whole of the discovery organization. One of the chemists claimed that the lack of time for qualified scientific thinking posed a threat to the longterm innovativeness of the company: To be frank, I think it is tougher already . . . There is a much stronger emphasis on performance: to deliver output and preferably much output. Even if we do discuss the importance of quality, I think we’ve lost what quality means along the way. Quality is also that the researchers are given the opportunity to be engaged in their projects and integrate various threads. I see that opportunity disappearing because they expect us to work in more projects. We ‘speed up’ everything in the laboratory, which of course is a good thing as such, that you don’t need to dose the substances by hand when there are robots capable of doing it. But the risk is that rather than using the time released from the laboratory for letting the researchers engage in the projects, to serve as scientific experts, and © 2008 The Author Journal compilation © 2008 Blackwell Publishing


then we are given additional assignments, and are asked to deliver more data. However, it is also important to take care of data in the right manner. I think we are not really successful there now. Even though top management of the site had declared they did not regarded the implemented performance measures as an optimal choice or thought of them as being by any means flawless, the latent functions of the measures was to increase stress on the laboratory scientists. One of the managers in the chemistry department was critical of the new regime: It is more complicated for all pharmaceutical companies . . . The pressure to deliver results is substantial. Unfortunately, I think that they have defined objectives in terms of number of substances produced. They say that ‘this is not a good performance measure, but it can actually be quantified and therefore we use it; however, we will not care very much about it since it is the quality that counts at the end of the day’. Those are certainly mixed messages and the laboratory scientists are stressed out . . . the objectives are of course determining the actions and it does not add up! I don’t think those are good objectives. Even though all of the scientists thought that it is important to deliver qualitative substances and tests, the one-sided emphasis on quantity was complicated to resist. Consequently, a substantial part of the interviewees argued that they felt their work was increasingly being determined by the ability to deliver a certain amount of substances on time, and that their scientific skills were reduced to the level of a matter of output.

Using Caillois’ Theory of Play in Innovation Studies In the discovery phase in the new drug development project, qualified and effective research were argued to be an outcome of the combination of a number of factors including scientific skills and experience and the time dedicated to fully engage in complex scientific undertakings. In addition, the researchers recognized the influence of chance and luck in the day-to-day laboratory work. The laboratory researchers were highly critical of the present regime wherein they did not have much time to delve into specific research questions, but needed to juggle two or three projects at the same time. In addition, the aggregate of interrelated leadership practices, management control activities and organizational arrange© 2008 The Author Journal compilation © 2008 Blackwell Publishing

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ments were not capable of fully eliminating residual factors such as chance. Such factors are, the interviewees claimed, an inherent and indispensable component in all scientific work. In fact, this ‘openness’ in all scientific work is what motivates the researchers; one cannot fully foresee or anticipate how a molecule interacts with in vitro, in silico or in vivo models. The predominant management regime did not fully recognize residual factors of chance and luck but quite the contrary further structured and streamlined the drug discovery processes into smaller and more closely monitored units and operations. For instance, the interviewees tended to complain that top management favoured what was called ‘frontloading’ in the research project. Frontloading means that as much study and tests as possible should be located in the early phases of the development of a new compound. When successfully eliminating compounds that do not meet quality demands prior to the development phase, and especially the clinical trails, pharmaceutical companies can save a significant amount of resources (Blau et al., 2004). In effect, frontloading implies that new substances are examined more thoroughly, and consequently fewer serendipities are likely to occur when fewer molecules are qualified for further investigation. By and large, the new drug development activities in the discovery phase were becoming increasingly rule-governed, monitored and evaluated by external committees. Notwithstanding all these changes, the influence of chance cannot be wholly eliminated. Even largely determined and thoroughly designed scientific processes such as the discovery phase in a pharmaceutical company cannot eliminate the exposure to residual factors. Seen in this light, Caillois’ (1961) theory of play as the combination of skills (agôn) and luck (alea) is useful when examining innovation work. New drug development work and any other advanced innovation work is ultimately embedded in the intrinsic motivation of highly skilled and specialized scientists (Amabile, 1997; Amabile et al., 2005), that is, a variety of experts making individual contribution to the work. What motivates scientists to work as hard as they do, they strongly emphasized in the study, is not primarily monetary rewards or even career opportunities, but rather the excitement of uncovering previously unknown domains. Seen from this subjectivist viewpoint, notwithstanding the epistemological critique of the use of the term in scientific work (e.g., Rheinberger, 1997, p. 133), the notion of ‘discovery’ in new drug development is not a trite metaphor but actually captures how synthesis chemists and

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pharmacologists perceive events where they gain new insights. The discovery process includes two major factors: first, the skills and know-how of the experienced laboratory scientists. This entire domain of interrelated practices, technologies, routines, inscription procedures, etc., constituting the laboratory researcher’s sleight-of-hand and scientific savoir-faire, corresponds to the concept of agôn in Caillois’ theory; agôn is the totality of embodied and cognitive skills that are mobilized in the activity. Second, all advanced scientific procedures include moments of chance, unexpected outcomes, unpredicted events, surprises, etc., in short a residual component, partially endogenous, partially exogenous, that cannot be fully anticipated, managed or predetermined. These moments of unexpected findings are what all scientific activities must be able to attend to and exploit. Such moments are represented by what Caillois calls alea, the influence of chance in certain forms of playing. It is important to recognize alea as an integral component of scientific practice, not only because it underlines the ability to capture inconsistencies in the research activity, but also because it is an important cultural trait of laboratory work to make use of metaphors and tell stories that exploit the factor of chance in the work. The concepts of agôn and alea underline the need for maintaining a view of science-based innovation work that recognizes both its reliance on scientific skills and know-how and residual events. During the last 15 years, the pharmaceutical industry has dedicated much effort and resources to various forms of automation of drug development work. Techniques such as rapid DNA sequencing, combinatorial chemistry, cell-based assays, and automated high-throughput screening (HTS) and new doctrines in pharmacogenomics, genetics and proteomics and the idea of personalized medicine have been brought into the industry (Hedgecoe & Martin, 2003; Sowa, 2006). However, as Drews (2000) points out, the increase in output has been modest: In this new concept, the critical discourse between chemists and biologists and the quality of scientific reasoning are sometimes replaced by the magic of large numbers . . . So far, this several hundredfold increase in the number of raw data has not yet resulted in a commensurate increase in research productivity. As measured by the number of new compounds entering the market place, the top 50 companies in the pharmaceutical industry collectively have not improved their productivity during the 1990s. (Drews, 2000, p. 1962)

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In addition, only a small proportion of the new registered drugs are based on new compounds. Busfield (2006, p. 302) reports: ‘A study of approval by the US Food and Drug Administration (FDA) between 1989 and 2000 showed that approvals for new drugs consisted of a relatively small proportion of all approvals, with only 35 of applications related to new chemical entities’. Given this limited level of output, it may be that new drug development processes needs to be re-designed to recognize the alea component in the work. Managers have tried to streamline and automate the process through the use of various technologies – using Thrift’s (2005) concept, one may speak of the ‘routinization’ or even ‘bureaucratization’ of innovation – but little of the time released has been invested in more analytical work. Instead, researchers tend to participate in more projects, thereby spreading their time over a series of activities. It may be that it is not just the number of delivered compounds or new candidate drugs that determines long-term competitiveness in the pharmaceutical industry, but that the actual quality of the compounds is what makes a difference. Such qualities may not be accomplished by the use of automated procedures but through the allocation of time to actual research work where agôn and alea are exploited.

Summary and Conclusion In terms of innovation management, this study contributes to the innovation literature suggesting there is an element of play in all innovation work (e.g., Anderson, 1994; Dougherty & Takacs, 2004; Dodgson, Gann & Salter, 2005). Taking Roger Caillois’ (1961) theory of play as a generic model for all human engagement with explorative and creative work, also including scientific social practices, a study of new drug development work in a pharmaceutical company has been examined. Rather than assuming that scientific work operates along linear trajectories, the study suggests that advanced science-based innovation can be explored as being both based on skill and chance, agôn and alea. Several contributions to the innovation and creativity literature (e.g., Amabile, et al., 1996, 2005; Amabile, 1997, 1999) emphasize the influence of motivation. This article contributes to this corpus of literature through the introduction of Caillois’ theory of play, a theory developed as part of a critique of ‘utilitarian thinking’, rendering human lives a strictly purposeful and utilityoriented condition. Caillois’ theory of play may help shed further light on the practice of © 2008 The Author Journal compilation © 2008 Blackwell Publishing


new drug development and other advanced forms of science-based innovation work.

Acknowledgement The author would like to thank the anonymous reviewer for excellent help when revising the paper.

References Abraham, J. and Reed, T. (2002) Progress, Innovation and Regulatory Science in Drug Development: The Politics of International Standardsetting. Social Studies of Science, 32, 337–69. Akrich, M., Callon, M. and Latour, B. (2002) The Key Success in Innovation Part I: The Art of Interessement. International Journal of Innovation Management, 6, 187–206. Amabile, T.M. (1997) Motivating Creativity in Organizations: On Doing What You Love and Loving What You Do. California Management Review, 40, 39–58. Amabile, T.M. (1999) How to Kill Creativity. Harvard Business Review, September–October, 77–87. Amabile, T.M., Conti, R., Coon, H., Lasenby, J. and Herron, M. (1996) Assessing the Work Environment for Creativity. Academy of Management Journal, 39, 1154–84. Amabile, T.M., Barsade, S.G., Mueller, J.S. and Staw, B.M. (2005) Affect and Creativity at Work. Administrative Science Quarterly, 50, 367–403. Anderson, J.V. (1994) Creativity and Play: A Systematic Approach to Managing Innovation. Business Horizons, 37, 80–85. Angell, M. (2004) The Truth about the Drug Companies. Random House, New York. Bataille, G. (1991) The Accursed Share, Vol. 1: Consumption. Zone Books, New York. Blau, G.E, Pekny, J.F., Varma, V.A. and Bunch, P.R. (2004) Managing a Portfolio of Interdependent New Product Candidates in the Pharmaceutical Industry. Journal of Product Innovation Management, 21, 227–45. Busfield, J. (2006) Pills, Power, People. Sociological Understandings of the Pharmaceutical Industry. Sociology, 40, 297–314. Caillois, R. (1961) Man, Play and Games. Trans. by M. Barash. University of Illinois Press, Chicago. Caillois, R. (2001) Man and the Sacred. Trans. by M. Barash. University of Illinois Press, Chicago. Caillois, R. (2003) The Edge of Surrealism: A Roger Caillois Reader. Ed. C. Frank, Trans. by C. Frank and C. Naish. Duke University Press, Durham, NC. Cardinal, L.B. (2001) Technological Innovation in the Pharmaceutical Industry: The Use of Organizational Control in Managing Research and Development. Organization Science, 12, 19–36. Cheng, Y.-T. and Van de Ven, A.H. (1996) Learning the Innovation Journey: Order Out of Chaos? Organization Science, 7, 593–605. © 2008 The Author Journal compilation © 2008 Blackwell Publishing

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Chiesa, V. (1996) Separating Research from Development: Evidence from the Pharmaceutical Industry. European Journal of Management, 14, 638–47. Damanpour, F. (1992) Organizational Size and Innovation. Organization Studies, 13, 375–402. DiMaggio, P. and Powell, W.W. (1983) The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields. American Sociological Review, 48, 147–60. Dodgson, M., Gann, D. and Salter, A. (2005) Think, Play, Do: Technology, Innovation, and Organization. Oxford University Press, Oxford. Dougherty, D. and Heller, T. (1994) The Illegimacy of Successful Product Innovation in Established Firms. Organization Science, 5, 200–18. Dougherty, D. and Takacs, C.H. (2004) Team Play: Heedful Interrelating as the Boundary for Innovation. Long Range Planning, 37, 569–90. Drews, J. (2000) Drug Discovery: A Historical Perspective. Science, 287, 1960–4. Eagle, N. (2004) Can Serendipity be Planned? Sloan Management Review, 46, 10–14. Eisenhardt, K.M. (1989) Building Theories from Case Study Research. Academy of Managament Review, 14, 532–50. Eisenhardt, K.M. and Graebner, M.E. (2007) Theory Building from Cases: Opportunities and Challenges. Academy of Managemernt Journal, 50, 25–32. Fontana, A. and Frey, J.H. (1994) Interviewing. In Denzin, N.K. and Lincoln, Y.S. (eds.), Handbook of Qualitative Research. Sage, Thousand Oaks, CA. Gupta, A.K., Smith, K.G. and Shalley, C.E. (2006) The Interplay between Exploration and Exploitation. Academy of Management Journal, 49, 693– 706. Hara, T. (2003) Innovation in the Pharmaceutical Industry: The Process of Drug Discovery and Development. Edward Elgar, Cheltenham. Hedgecoe, A. and Martin, P. (2003) The Drug Don’t Work: Expectations and the Shaping of Pharmcogenetics. Social Studies of Science, 33, 327–64. Hellström, T. (2004) Innovation as Social Action. Organization, 11, 631–49. Holmqvist, M. (2004) Experiential Learning Processes of Exploitation and Exploration within and between Organizations: An Empirical Study of Product Development. Organization Science, 15, 70–81. Holstein, J.A. and Gubrium, J.F. (eds.) (2003) Inside Interviewing. New Lenses, New Concerns. Sage, Thousand Oaks, CA. Huizinga, J. (1949) Homo Ludens: A Study of the PlayElement in Culture. Routledge & Kegan Paul, London. Levinthal, D.A. and March, J.G. (1993) The Myopia of Learning. Strategic Management Journal, 14, 95–112. March, J.G. (1991) Exploration and Exploitation in Organizational Larning. Organization Science, 2, 71–87. March, J.G, Sproull, L.S. and Tamuz, M. (1991) Learning from Samples of One or Fewer. Organization Science, 2, 1–13. Mead, G.H. (1934) Mind, Self, and Society. The University of Chicago Press, Chicago.

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Pearce, F. (2003) Introduction: The Collège de Sociologie and French Social Thought. Economy & Society, 32, 1–6. Rheinberger, H.-J. (1997) Toward a History of Epistemic Things: Synthesizing Proteins in the Test Tube. Stanford University Press, Stanford, CA. Richman, M. (2003) Myth, Power and the Sacred. Anti-Utilitarianism in the Collège de Sociologie 1937–9. Economy & Society, 32, 29–47. Roberts, R.M. (1989) Serendipity: Accidental Discoveries in Science. Wiley, New York. Rothaermel, F.T. and Deeds, D.L. (2004) Exploration and Exploitation Alliances in Biotechnology: A System of New Product Development. Strategic Management Journal, 25, 201–21. Salaman, G. and Storey, J. (2002) Managers’ Theories about the Process of Innovation. Journal of Management Studies, 39, 147–65. Schiller, F. (1795/2004) On the Aesthetic Education of Man. Dover Publications, Mineola. Siggelkow, N. (2007) Persuasion with Case Studies. Academy of Management Journal, 50, 20–24. Slappendel, C. (1996) Perspectives on Innovation in Organizations. Organization Studies, 17, 107–29. Sowa, Y. (2006) Present State and Advances in Personalized Medicine: Importance of the Development of Information Service Systems for the Public. Quarterly Review, No. 18, National Institute of Science and Technology Policy, Japan [WWW document]. URL http://www.nistep. go.jp/achiev/ftx/eng/stfc/stt018e/qr18pdf/ STTqr1801.pdf [accessed 9 March 2007]. Sutton, R.I. and Staw, B.M. (1995) What Theory Is Not. Administrative Science Quarterly, 40, 371–84. Thrift, N. (2005) Knowing Capitalism. Sage, Thousand Oaks, CA. Turner, V. (1982) From Ritual to Theatre: The Human Seriousness of Play. PAJ Publications, New York. Van de Ven, A. (1986) Central Problems in the Management of Innovation. Management Science, 32, 590–607.

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Weick, K.E. (1989) Theory Construction as Disciplined Imagination. Academy of Management Review, 14, 516–53. Weick, K.E. (1999) Theory Construction as Disciplined Reflexivity: Tradeoffs in the 90s. Academy of Management Review, 24, 797–806. Weick, K.E. and Roberts, K.H. (1993) Collective Mind in Organizations: Heedful Interrelating on Flight Decks. Administrative Science Quarterly, 38, 357–81. Whetten, D.A. (1989) What Constituted a Theoretical Contribution? Academy of Managemernt Review, 14, 490–5. Winnicott, D.W. (1971) Playing and Reality. Tavistock, London. Wolfe, R.A. (1994) Organization Innovation: Review, Critique and Suggested Research Directions. Journal of Management Studies, 31, 405–31. Yin, R.K. (1994) Case Study Research. Design and Methods. Sage, Thousand Oaks, CA. Zucker, L.G. and Darby, M.R. (1997) Present at the Biological Revolution: Transformation of Technological Identity for a Large Incumbent Pharmaceutical Firm. Research Policy, 26, 429–46.

Alexander Styhre (Alexander.Styhre@ chalmers.se) is Professor at Chalmers University of Technology, Gothenburg, Sweden. Alexander is interested in the management of knowledge-intensive organizations and innovation work. He has published widely in organization theory and management studies journals and is the author, co-author and co-editor of ten books including The Innovative Bureaucracy (Routledge, 2007) and Managing Organization Creativity (Palgrave, 2005).

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Direct Employee Involvement Quality (DEIQ) Nicole Torka, Marianne van Woerkom and Jan-Kees Looise This paper focuses on one aspect of human resource management (HRM) that is important for innovative employee behaviour: direct employee involvement quality (DEIQ). However, research has also shown that employee involvement is often in serious need of improvement. This paper presents evidence from three manufacturing companies in the Netherlands. Semistructured interviews were conducted with low- and medium-educated blue-collar workers, top managers (HRM, production and R&D) and direct supervisors. Using a qualitative approach, we were able to acquire insight into the ‘black-box’ of well-known pre-conditions and contribute by identifying additional dimensions. The research supports various preconditions critical for successful DEIQ including the critical role of the HR function and adequate preparation of direct supervisors. Furthermore, an under-explored issue is highlighted: the employment relationship.

Introduction

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esearch supports the idea that employee involvement (EI) influences innovative employee behaviour (e.g., Laursen & Foss, 2003; Dorenbosch, van Engen & Verhagen, 2005). The concept of EI focuses on direct participation, instead of participation through unions or works councils (Marchington & Wilkinson, 2004), and can be defined as ‘a process which allows employees to exert some influence over their work and the conditions under which they work’ (Strauss, 1998, p. 15). However, research has also shown that EI is often in serious need of improvement (e.g., Cunningham & Hyman, 1999). Thus, EI only affects innovative behaviour positively when it meets certain criteria. For this reason, our article focuses specifically on failures and successes concerning Direct Employee Involvement Quality (DEIQ). In doing so, our interest is in the ‘black-box’ of EI: things that can go well and wrong as perceived by the EI participants. Since EI can contribute to the improvement of products and processes, we believe that employers want all individuals to be involved. We therefore decided to conduct semi-structured interviews with 60 low© 2008 The Authors Journal compilation © 2008 Blackwell Publishing

and medium-skilled manufacturing workers (fitters, operators and welders) in three Dutch manufacturing companies. We also interviewed 16 of their managers in order to ‘test’ the views expressed by the workers.

Direct Employee Involvement Quality (DEIQ): Pre-conditions Five critical pre-conditions for EI have been discussed in the literature. We will present each of them in turn in the following sections.

The Role of HR Managers, (Other) Top Managers and Direct Supervisors The HR department is critical in directing EI. The size and status of the HR function in relation to other (top) management areas can seriously undermine EI initiatives (Buyens & de Vos, 2001). A weak contribution from the HR function with respect to EI can also result from time pressure and from a lack of knowledge or skills. In such cases, the HR department will be unable to demand support from top management and unable to support direct supervisors in implementing employee involvement (Cunningham & Hyman, 1999).

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Concerning the latter, the ‘devolution’ of HR practices to direct supervisors, which has been propagated by many, increases the importance of the supervisor for EI even further (e.g., Guest, 1987; Storey, 1992). Therefore, in terms of supporting and activating EI, the role of direct supervisors is indeed crucial. As Bryson, Charlwood and Forth (2006, p. 443) put it: ‘there is a body of evidence which suggests that the attitudes and behaviour of direct supervisors and senior managers in the organization are critical mediating variables in the relationship between direct employee involvement and performance’. However, according to Fenton-O’Creevy (2001, p. 38), blaming direct supervisors for employee participation failure is too easy. Organizations seeking to successfully implement employee involvement should: • treat supervisors as targets as well as implementers of EI initiatives; • ensure the presence of managers with significant experience in participative management and invest in the recruitment and/or development of such experience; • seek to remove constraints on supervisors’ ability to implement EI initiatives; • ensure supervisors have sufficient time, energy, and resources for EI initiatives; • rather than scapegoating EI resistant supervisors, engage in a dialogue with them to reach a common understanding of the barriers to change. All of these issues indicate that the HR function moderates the relationship between the employee involvement quality and its pre-conditions.

Employee Involvement Policies Peirce, Smolinski and Rosen (1998) emphasize the importance of well-written policies and clear reporting procedures for EI. These policies have to be scrutinized with respect to subject matter, degree, level, form (e.g., Ramsay, 1991) and time (e.g., Cotton, Vollrath, Froggatt, Lengnick-Hall & Jennings, 1988). The range of the subject matter in the respective organizational policy can vary from relatively trivial matters to strategic concerns. Most EI literature focuses on job-related issues. The degree refers to the extent to which employees are able to influence decisions on various aspects of management. The degree of EI can vary from simply being informed of changes top-down, to consultation, to actual decision making. Before introducing EI, senior managers should also discuss and define the true position of EI in relation to other organi-

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zational issues. Cunningham and Hyman (1999) found that the demands of the production process can overrule involvement issues. Harlos (2001) describes a case where employee voice was heard, but, because the organization did not want to jeopardize the financing of a project, no action was taken. The level at which EI takes place can vary from the employee level to department, establishment or even corporate level. The ‘employee level’ refers to all of the HRM policies and practices that an employee has to deal with personally, such as task content, rewards and flow policies. The form of EI may vary. Indirect EI is where employees are involved through their representatives, usually elected from the wider group. The focus of this article is not on representative participation, but on direct EI. Direct EI can be broken down into two dimensions: (1) collective vs. individual and (2) formal vs. informal. ‘Collective’ refers to group-based involvement, such as consultation committees, team work, quality circles and team briefings. Since these interventions are planned and obligatory for employees, they can be typified as formal. Individual direct participation can have a more unplanned character, such as spontaneous consultation between a direct supervisor and an individual subordinate. The issue of time should also be a matter for consideration, as it seems to influence the effectiveness of direct participation: short-term direct participation may indicate weak commitment on the part of the organization or a less positive attitude towards direct participation (Cotton, Vollrath, Froggatt, LengnickHall & Jennings, 1988). Furthermore, when responding to employee complaints, suggestions and demands, a timely response is also of importance (Harlos, 2001).

Workforce Characteristics Wilkinson, Dundon, Marchington and Ackers (2004) state that workforce characteristics should also be taken into account when introducing EI. Individuals have different needs (Maslow, 1943). Not every employee seeks to satisfy higher order needs that can be linked to selfactualization and self-esteem. Individuals that participate actively in EI can be seen as ‘acting’ one step beyond the fulfilment of basic, lower order needs. According to Hackman and Oldham (1976, p. 275), the typical high growth need employee is a young and well-educated male. Hackman and Oldham (1975) state that employees’ ‘readiness for change’ plays an important role in responding positively to changes. They advise managers to introduce and implement changes carefully and deliber© 2008 The Authors Journal compilation © 2008 Blackwell Publishing


Top management support

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Unions and Works Councils

Supervisors’ attitudes, knowledge and skills HR function • size • status • time pressure • knowledge • skills

Involvement policies • subject matter • degree • level • form • time

Workforce characteristics

Direct Employee Involvement Quality (DEIQ)

Job characteristics

Figure 1. Conceptual Model of Direct Employee Involvement Quality (DEIQ)

ately when dealing with employees who are relatively low in growth need strength. Porter’s research shows that the higher the organizational position, the greater the importance attached to higher order needs (Porter, 1961, 1962). Furthermore, position level tends to explain more variance in job attitudes than personal variables such as age and education level (Herman & Hulin, 1972). Therefore, before introducing EI, managers should investigate whether such initiatives meet employee needs and should prepare EI with care.

Job Characteristics According to Walton (1985, p. 76), job design is important for EI: ‘workers respond best – and most creatively – not when they are tightly controlled by management, placed in narrowly defined jobs, and treated like an unwelcome necessity, but instead when they are given broader responsibilities, are being encouraged to contribute, and are helped to take satisfaction from their work’. Following Hackman and Oldham, five core job dimensions are needed for employees to become internally motivated and, as a consequence, to perform effectively on their jobs: skill variety, task identity, task significance, autonomy and feedback. However, as has already been stated, employees differ in their needs, and not every worker benefits from the aforementioned job characteristics or job enrichments. Therefore, individual needs should be taken into account when job enrichment is undertaken. © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

Unions and Work Councils Empirical studies have suggested the view that direct EI influences employee attitudes and behaviour more strongly than representative EI through, for example, unions, works councils or union–management co-operation also called ‘workplace partnership’ (e.g., Bryson, 2004). Hence, the focus of this study is on direct EI. Representative EI should, however, also be taken into account since it is not unrelated to direct EI. For example, Drucker (2003, p. 227) concludes that ‘indirect participation [i.e., unions, works councils] is undermined not by direct employee involvement, but by decentralisation without direct participation’. Taking all of the above-mentioned into account, we can present our conceptual model (see Figure 1). We will not test the assumed relationships between several antecedents and direct employee involvement quality (DEIQ). Our aim is to take a look inside the ‘reality’ of EI as perceived by the participants, using a qualitative method of investigation.

Method The data presented in this paper were collected in three different manufacturing firms: one textile printing company and two metal companies. As we were looking for insight into the EI quality perceptions of low- and mediumskilled workers, this criterion was most important for selecting the companies. A total of

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60 interviews with manufacturing workers (operators, fitters and welders) were carried out. Additionally, 16 interviews and informal discussions were held with supervisors, HR managers, production managers and R&D managers. With these additional interviews, we intended to acquire information on the managers’ views and explore whether or not there were additional pre-conditions for the quality of employee involvement. We asked the workers three questions: 1. Do you have any opportunities to raise your voice and/or co-decide? If so, concerning which issues? 2. Are you satisfied with the opportunities given to voice and/or co-decide? 3. Why are you (dis)satisfied with the opportunities given to voice and/or co-decide? We asked the managers two questions: 1. Do you offer the workers any opportunities to raise voice and/or co-decide and, if so, concerning which issues? 2. Are you satisfied with the opportunities given for workers’ voice and/or codecision making? What could be improved?

Company 1: The Textile Printing Factory Some 300 of the 680 employees are production workers, that is, low- and medium-skilled operators. Because managers believed that it would be possible to improve quality and decrease costs, the organization has worked on implementing self-managing teams that have had their own technical service and support functions since 2000. Since then, employees have been expected to participate in weekly team meetings, and supervisors are expected to invite their employees to participate. In addition to the team meetings, workers participate in various project groups or can put their suggestions for improvement in a suggestion box.

Company 2: Metal Company A Company A employs 130 workers with a permanent contract. Eighty of these workers are low- to medium-skilled fitters and welders. The seasonal sensitivity of the product means that the company has to deal with production peaks and troughs. In peak periods, the company hires temporary agency workers. At these times, more than 30 per cent of the manufacturing workers are agency workers. Based on the skills and company-specific experience of the workers, the job complexity from job to job differs. Two rather simple jobs are the fitting of part products and the so-called

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metre welding of vessels. A more complex job is the fitting of the whole recognizable final product inclusive of electrical engineering and necessary hydraulics and pneumatics. Direct employee involvement was introduced for the same reasons as in Company 1. Department meetings take place every two weeks and performance interviews are held once a year. Employees are also invited to contribute on a daily basis. A project-based quality circle also exists: some of the welders are participating in the design of the new welding shop.

Company 3: Metal Company B Metal Company B also employs approximately 130 workers with a permanent contract and the share of manufacturing workers is the same. This company is also a so-called original equipment manufacturer. Production fluctuations also exist here and for the same reason. The amount of agency workers in the peak season is comparable to Company 2, as is the complexity of the work and the reasons for differences in job complexity. In this company, team meetings take place every two weeks and performance interviews are held once a year. Despite the fact that Company 2 and Company 3 share one personnel manager, formal opportunities for employee involvement were introduced later in Company 3 than in company 2. Since the companies have opposite fluctuation periods and both need workers with similar knowledge and skills, they exchange workers on a regular basis.

Empirical Findings Managing Direct Employee Involvement: The Role of HR Managers, (Other) Top Managers and Line Managers In all three companies, the introduction of EI was an HR department idea that received support from top management. Top managers encouraged EI because they believed this could help to improve cost effectiveness and quality. However, in the textile printing company and Metal Company A, the HR department’s power to receive ‘real’ support from top management was limited, as was HR managers’ competency in supporting direct supervisors. Direct supervisors received no support in handling EI before the start up. Supervisors got ‘orders’ to organize team meetings and performance interviews on a regular basis. However, in both companies, none of the supervisors were experienced in directing EI or had ever received training for it. Most supervisors had a technical educational background: they used to be fitters, © 2008 The Authors Journal compilation © 2008 Blackwell Publishing


welders and operators before internal promotion. The technical skills that the supervisors possessed were originally the primary selection criterion. In the past, the supervisors only had to be task-related, determining what had to be done and how. When the companies, and more specifically the HRM department, put EI and other HRM practices such as development issues on the supervisors’ plates, their tasks changed, as did the required skills. According to the supervisors, many felt overloaded in their new roles and alienated from their technical basics. Many employees reported a lack of structure, limits in presentation and listening skills, and poor discussion techniques when describing their supervisors’ behaviour during department meetings. In Company 1, a year after the introduction of team meetings, direct supervisors still had not received training for directing EI. At this point, the HR department was organizing assessments resulting in personal development plans for the next level of management. However, the training of the direct supervisors was still planned for the future. The top management of Metal Company A approved financial investment for the training of direct supervisors only after receiving massive complaints about the team meetings and performance interviews from supervisors and their subordinates. The top management of Metal Company B seemed to learn from the bad experiences of Metal Company A. The HR manager was able to convince top management in Metal Company B to invest in the training of direct supervisors in advance. Metal Company B employees had fewer complaints about the communication skills of their direct supervisors relevant for guiding direct employee involvement than employees from the textile printing company and Metal Company A.

Employee Involvement Policies In all three companies, no written guidelines for direct employee involvement existed. Despite that fact, the range of the subject matter, as well as the level (employee and department level) and the form (team meeting, performance interview, spontaneous face-toface voice), seemed clear to employees. Nevertheless, the degree of employee involvement appeared to be a more problematic issue. The interviews with shop-floor workers from all three companies indicated that it was unclear to them whether they could really participate or whether this was really just a formality. An operator from Company 1 stated: It’s always been the same here that it’s already been agreed upon before they even © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

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ask your opinion. So, actually, it’s virtually all arranged and then you can come along and say what you think. Despite such problems, there are also positive examples. In Metal Company A, management wanted to rebuild the welding shop. Because welders complained about the current design and facilities (e.g., lighting, air conditioning) and because management found this complaint reasonable, a quality circle was introduced, meaning that the workers could ‘really’ co-decide. The involved welders appreciated this opportunity. A timely response to employee complaints, suggestions and demands seems critical in all companies. Many workers who contributed ideas, offered critical remarks or asked questions about different working conditions, such as production process, salary or task content, complained about the lack of feedback. A welder from Metal Company A stated: A few months ago, our supervisor told us that there was a possibility of working at the assembly. I told him of my interest in doing so. He wrote it on a note, but I didn’t receive any information from him when and if I could start to work there. I asked him several times. Supervisors and managers confirm the workers’ perception of a lack of timely feedback. For example, the production manager from the textile printing company stated that it is discouraging when it takes such a long time before employees get any feedback on ideas they have put in the suggestion box. He thought everyone should have some feedback on their ideas. If the idea is not being used – or not being used yet – the reason for this should be made very clear. Within all companies, employees also complained about the time-span between expressing an idea and implementing it. Most of the ideas put forward would not have cost money and would have been relatively easy to implement: a better but cheaper spray-oil for assembling, a new design for the fitting handbook, a better order for assembling, and so on. For this reason, employees who had expressed these ideas became frustrated when the implementation took too long or did not take place at all. Employees from both metal companies talked frequently about the advance of so-called action points. Some of these points surfaced again and again on the team meeting agenda. As a result, participating workers began to suffer from a ‘why-should-I-say-somethingif-they-are-too-lazy-to-implement-it’ attitude. Many times this critique can be linked to ‘limited feedback’. The workers also men-

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tioned that supervisors often cannot explain why relatively simple changes take so long.

Workforce Characteristics In the textile printing company, the ordinary worker had had little opportunity for participation in the past. Various respondents sketched the picture of the worker who had had to hand over his brains at the gate in the morning and pick them up again in the evening. It is hard to say the extent to which this example is, in fact, outdated. A line manager: This company makes a traditional product. The organization is also still traditional. The people on the shop floor, certainly in the past, had to do what they were told. They didn’t have any say in the matter. According to management, the fear of speaking freely is partly based on experiences in the past. Things that happened a long time ago remain vivid in the perceptions of the workers, partly because, in general, the workers have very long contracts of employment (in general much longer than managers). According to an HRM manager of the textile printing company: No, I think they are all very wary here, a bit nervous, a bit mistrustful. Something like: ‘If I say what I think then I’ll first imagine what management thinks about it or what my boss would think, and if he doesn’t agree with me, then I decide to keep it to myself’. Employees support the impression of the managers. One operator was only inclined to give his view if he was explicitly and personally invited to do so: Yeah, well then I do, of course. If they ask me something I’ll really give a good answer. But not on my own initiative, no, I wouldn’t do that. In both metal companies, neither the managers nor the employees referred to the past nor, more specifically, to the difficulties resulting from the changing role of workers. In fact, the reverse seemed to be true. Employees generally appreciated the enhanced opportunities for EI and this was true in particular for workers with a non-permanent contract, especially agency workers. The latter were more satisfied with employee involvement than workers with a permanent contract. According to agency workers, both companies offered more involvement than other organizations they had worked for. According to managers, supervisors and employees, equal practices

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concerning employee influence and daily treatment are ‘active’ within both metal companies. More specifically, agency workers are invited to raise voice and co-decide and, when they have sufficient knowledge and skills, to perform the same jobs as their colleagues with a permanent contract. It is important to know that agency workers have been in both metal companies for at least one season (six months). The companies also ‘employ’ agency workers during the slack season. They do so because of experiences in the past with redundancies: agency workers are seen as a buffer for the permanent staff. Agency work is also used to enhance the trial period (up to three years). Nevertheless, several agency workers have been working for the company for 12 seasons and up to six years continuously. These longterm agency workers share the fact that they have chosen to work through agencies. Through the companies’ lengthy experience with temporary employment (more than 20 years), management has learned that many of these workers build up extensive work experience, skills and tacit knowledge from numerous other companies. Because management has realized that this can help the company enhance quality and save costs, both companies trigger knowledge transfer through influence successfully. In fact, many atypical workers, and especially temporary agency workers, contributed to organizational learning. For example, they helped restructure the assembly guidelines, introduced better and cheaper components and instruments, and, in general, shared their knowledge from other companies.

Job Characteristics One of the textile printing company operators stated that the fact that the work was so routine meant that there was nothing to think about except reaching higher production targets, and that this was the reason why people did not participate in meetings. A colleague operator said: During the work discussions, no one says a word. The boss does all the talking, then he asks if there are any questions and that’s it. Yeah, I don’t say anything either. But that’s OK. I don’t think we’ve got any ideas anyway. No, ‘cos the work is really always the same, isn’t it? The only thing that matters is how you can get more production. The job characteristics did not seem problematic for the workers in the metal companies and also not for those with less complex jobs. Compared to the textile printing company, the © 2008 The Authors Journal compilation © 2008 Blackwell Publishing


job characteristics in both metal companies are enriched. In spite of the fact that the task complexity and variety for welders and fitters assembling product parts is less, the job characteristics seem not to negatively affect employee involvement. In contrast to the textile printing operators, all employees from both metal companies are so-called craftsmen.

Conclusion The aim of this study was to shed light on the pre-conditions for direct employee involvement quality (DEIQ). In doing so, we wanted to acquire insight into the ‘black-box’ of wellknown pre-conditions and contribute by identifying additional dimensions. Previous research shows that DEIQ, as perceived by those that participate, influences innovative behaviour. Therefore, the subject of this article is relevant for the management of creativity and innovation at the workplace. However, this study has limitations. First, given our qualitative approach and the crosssectional design, assumptions about causal relationships cannot be made, nor can premises on the (different) impact of the distinct pre-conditions for DEIQ. Furthermore, we adopted a case study approach, where employees from three manufacturing companies were interviewed. Therefore, the generalizability to other companies, sectors and employees is restricted. Despite these shortcomings, our study contributes to knowledge on how to deal with EI in a way that it is beneficial for employees and companies. First, simple lip service from top management is certainly not enough when introducing EI initiatives. Top management has to invest money and time in advance in those that have to channel these initiatives: the direct supervisors. Second, when introducing EI, direct supervisors and their subordinates should be clear about which issues workers can raise voice or really co-decide on. Furthermore, time issues should be clear ahead of time: the time-span between the expression of an idea and appropriate (interim) feedback on contributions should be set. Third, workforce characteristics should be included in the decision making on EI. Topdown introduction could be disastrous, especially when employees lack the readiness for such changes (see also Hackman & Oldham, 1975). Therefore, managers considering such initiatives should gain insight into the specific needs of their employees before introducing employee involvement. Survey employee research and/or establishing quality circles on © 2008 The Authors Journal compilation © 2008 Blackwell Publishing

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involvement issues beforehand might be a good idea. This caution is particularly necessary for those managers who have to deal with employee groups that lack the desire to satisfy self-actualization needs through work, or employees who have had ‘traumatizing’ company experiences such as mass redundancies and negative experiences with EI. For such employees, rebuilding trust in management and its initiatives has first priority. Fourth, the job characteristics must be taken into account. The findings show that very routine jobs may influence EI negatively. Therefore, managers may want to enrich jobs. However, as mentioned before, managers have to find out if the jobs meet the needs of their employees in advance and whether or not these needs differ from the needs of those in a different organizational position. This calls for so-called differentiated HRM practices: a one-fit-for-all approach does not fit at all. Finally, as shown by previous research, the HR function moderates the relationship between these pre-conditions and DEIQ. HR managers, using their particular knowledge, skills and position, should be able to convince top management of the difficulties and the costs involved in implementing EI and should guide direct supervisors in fulfilling this delegated responsibility successfully. The most important contribution of our research concerns the management of nonpermanent workers such as agency workers and freelancers. The agency workers in our study ‘rewarded’ the direct employee involvement practices with more satisfaction than permanent workers did. The companies had adopted so-called equal practices concerning EI and other relevant HR practices. Since non-permanent employment relationships are increasing in many countries and such workers have worked for many companies, tapping their tangible knowledge can contribute towards creating valuable and innovative knowledge (Matusik & Hill, 1998). For managers, this means that they should think twice before excluding non-permanent workers from direct employee involvement. Our advice to future researchers is to focus on the interrelationships between (changing) employment relationships, employee involvement and innovative behaviour at work.

References Bryson, A. (2004) Managerial Responsiveness to Union and Non-Union Worker Voice in Britain, Industrial Relations, 43, 213–41. Bryson, A., Charlwood, A. and Forth, J. (2006) Worker Voice, Managerial Response and Labour

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Productivity: An Empirical Investigation. Industrial Relations Journal, 37, 438–55. Buyens, D. and de Vos, A. (2001) Perceptions of the Value of the HR Function. Human Resource Management Journal, 11, 70–89. Cotton, J.L., Vollrath, D.A., Froggatt, K.L., Lengnick-Hall, M.L. and Jennings, K.R. (1988) Employee Participation: Diverse Forms and Different Outcomes. Academy of Management Review, 13, 8–22. Cunningham, I. and Hyman, J. (1999) The Poverty of Empowerment? A Critical Case Study. Personnel Review, 28, 192–207. Dorenbosch, L., van Engen, M.L. and Verhagen, M. (2005) On-the-Job Innovation: The Impact of Job Design and Human Resource Management through Production Ownership. Creativity and Innovation Management, 14, 129–41. Drucker, M. (2003) Organisational Renewal and Participation. The Effects of Decentralisation on the Relationship between Works Council and Direct Participation. Twente University Press, Enschede. Fenton-O’Creevy, M. (2001) Employee Involvement and the Middle Manager: Saboteur or Scapegoat? Human Resource Management Journal, 11, 24–40. Guest, D.E. (1987) Human Resource Management and Industrial Relations. Journal of Management Studies, 24, 503–21. Hackman, J.R. and Oldham, G.R. (1975) Development of the Job Diagnostic Survey. Journal of Applied Psychology, 60, 159–70. Hackman, J.R. and Oldham, G.R. (1976) Motivation through the Design of Work: Test of a Theory. Organizational Behavior and Human Performance, 16, 250–79. Harlos, K.P. (2001) When Organizational Voice Systems Fail: More on the Deaf Ear Syndrome and Frustration Effects. Journal of Applied Behavioral Science, 37, 324–42. Herman, J.B. and Hulin, C.L. (1972) Studying Organizational Attitudes from Individual and Organizational Frames of Reference. Organizational Behaviour and Human Performance, 8, 84–108. Laursen, K. and Foss, N.J. (2003) New Human Resource Management Practices, Complementarities and the Impact on Innovation Performance. Cambridge Journal of Economics, 27, 243–63. Marchington, M. and Wilkinson, A. (2004) Participation and Involvement. In Bach, S. (ed.), Personnel Management in Britain, 4th edn. Blackwell, Oxford. Maslow, A.H. (1943) A Theory of Human Motivation. Psychological Review, 50, 370–96. Matusik, S.F. and Hill, C.W. (1998) The Utilization of Contingent Work, Knowledge Creation, and Competitive Advantage. Academy of Management Review, 23, 680–97. Peirce, E.C., Smolinski, C.A. and Rosen, B. (1998) Why Sexual Harassment Complaints Fall on Deaf

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Ears. The Academy of Management Executive, 12, 41–54. Porter, L.W. (1961) A Study of Perceived Need Satisfactions in Bottom and Middle Management Jobs. Journal of Applied Psychology, 45, 1–10. Porter, L.W. (1962) Job Attitudes in Management: 1. Perceived Deficiencies in Need Fulfilment as a Function of Job Level. Journal of Applied Psychology, 46, 375–84. Ramsay, H. (1991) Reinventing the Wheel? A Review of the Development and Performance of Employee Involvement. Human Resource Management Journal, 1, 1–22. Storey, J. (1992) Developments in the Management of Human Resources. Blackwell, Oxford. Strauss, G. (1998) An Overview. In Heller, F., Pusic, E., Strauss, G. and Wilpert, B. (eds.), Organizational Participation: Myth and Reality. Oxford University Press, Oxford, pp. 8–39. Walton, R.E. (1985) From Control to Commitment in the Workplace. Harvard Business Review, 63, 76–81. Wilkinson, A., Dundon, T., Marchington, M. and Ackers, P. (2004) Changing Patterns of Employee Voice: Case Studies from the UK and Republic of Ireland. Journal of Industrial Relations, 46, 298–322.

Dr Nicole Torka ([email protected]) studied work and organizational psychology at the Dutch Open University and received her MA in 1998. Since 1998 she has worked in the Faculty of Management and Governance, University of Twente, the Netherlands. Since 2003 she has been Assistant Professor in Human Resource Management. Her research is focused on employee needs and attitudes. Dr Marianne van Woerkom (m. [email protected]) is Assistant Professor at the Faculty of Social and Behavioural Sciences, Department of Human Resource Studies, University of Tilburg, the Netherlands. Her main interests are human resource development and innovation. Prof. Dr Jan-Kees Looise (j.c.looise@ utwente.nl) is full professor in Human Resource Management, Faculty of Management and Governance, University of Twente, the Netherlands. His main research areas are: HRM and innovation, individualization of employment relations, new forms of work organization and employee participation and E-HRM.


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Creating, Growing and Sustaining Efficient Innovation Teams Casimer DeCusatis Economic forces such as the growing service economy and commoditization of traditional value chains have led many organizations to pursue breakthrough innovations as part of their business strategy. There has been an increased interest in collaboration and teamwork as catalysts of innovation, often without a clear understanding of the different kinds of teams that can be used to foster innovation or the kinds of team building that will be most likely to yield desired results. The author describes a framework for innovation teams, ranging from highly structured to spontaneous, giving examples of how different kinds of teams relate to the characteristics of the next generation of innovators. A case study illustrates how one approach using preference profiling is more likely to yield tangible results from an innovation team.

Introduction: The Changing Nature of Innovation

T

he global innovation – commoditization duality has never been more pronounced than in our current economy. Many organizations are investing in efforts designed to promote innovation, without a clear idea of how these investments translate into business value. Furthermore, organizations are also evolving from an industrial base to a service base. This is driven by several factors, including the removal of barriers to service relationships brought about by the virtualization and dissemination of information. However, value capture in the service market is based on customer perception and utility rather than more traditional metrics such as cost and quality (Ho, 2008). Consequently, the value created through innovative service teams also goes largely unrecognized, making it difficult to assess the impact of teams charged with producing innovative results. There is a great body of literature on the theory of innovation, how people collaborate and the role, structure and types of innovation ecosystems that occur (Rhodes, 1961; Johnson, 1972; Isaksen, Dorval & Treffinger, 1994; Davila, Epstein & Shelton, 2006). While many companies consider themselves innovative, most lack a common lexicon for understanding how their investments in innovation trans© 2008 The Author Journal compilation © 2008 Blackwell Publishing

late into business value. In particular, while it is recognized that collaboration is an important element of innovation, there is a need for better approaches to forming, growing and sustaining teams of innovators. After reviewing the changing nature of innovation and the emerging generation of innovators, we propose a framework for classifying innovation teams. (Note: the pronoun ‘we’ is used throughout the paper to refer to the task force that carried out the internal IBM innovation study that this paper is based on.) This allows us to better match the characteristics of the team with the approach to innovation, making it more likely to achieve the desired results. It is important to first recognize that the fundamental nature of innovation has been changing in recent years. There is a growing emphasis on collaboration as part of the innovation process. There are sound economic reasons why collaborations are growing in importance, including the rising cost of technology development, shortening product lifecycles, and the difficulty in sustaining closed research and development (R&D) models. An increased focus on core competencies at many businesses has provided an opportunity for interdependencies to a much greater degree than at any time previously. As global information networks make knowledge increasingly widespread, social networking tools

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Figure 1. The Changing Nature of Innovation

(Web 2.0 and 3.0) create more opportunities for like-minded parties to find each other and for interdisciplinary teams to form in unexpected ways. In many technology-based industries, the traditional value chain is breaking down; faced with diminishing returns on their R&D or venture capital investments, many companies have begun to emphasize collaborative tools as a catalyst for innovation. We conceptualize this view using the model illustrated in Figure 1, which distinguishes between two types of innovation approaches that we call monolithic and collaborative. The monolithic approach represents the conventional view of innovation, as driven by large organizations that hold an effective monopoly on their markets. Innovations are created by a relatively small group of discipline-specific experts, working under controlled conditions with specialized equipment. The problems they address are typically fairly well defined, and their solutions represent highly valued intellectual capital which is protected by patents. Innovations proceed through the development process in a linear way, eventually reaching a group of passive consumers. Feedback is limited to a sampling of customer opinion in between product development cycles. This approach has held sway in the technology industry for many decades; funding for corporate research and development labs is based on the business value produced by this approach. While we can demonstrate that this approach still works well under some conditions (specifically when there is an effective monopoly), a new approach has emerged within the past decade or so which we call collaborative innovation. In its purest form, this differs significantly from the monolithic model. Collaborative innovation delivers customer value through the creation of relationships and social networks,

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which involve customers early in the development process and maintain their involvement continuously. Valuable ideas can come from anywhere, at any time, and be incorporated into the product based solely on their merit. Such collaboration is interdisciplinary and cuts across organization silos. Intellectual capital is shared freely; indeed, since we may be unable to determine exactly when an idea was first conceived or by whom, the concept of patents breaks down. Some Internet-based companies, universities, and a few others have fully embraced this model. Most organizations, however, fall somewhere in between these two extremes, sharing characteristics of both approaches or changing their focus for different projects. An example from the computer industry helps to illustrate the migration from monolithic to collaborative approaches as an innovation driver in business. Within IBM, consider the mainframe tradition spanning Systems 360, 370, 390, and Z; this began as a monoculture many years ago, and became quite successful, coming to dominate the Fortune 500 market (particularly the financial sector). Starting in the early 1960s, innovation on the mainframe was driven exclusively through corporate R&D, and consisted mainly of delivering anticipated, incremental improvements to the processor speed, memory and other performance benchmarks on a regular basis. Over time, market demand shortened the time between product release cycles, and subsequent advances in basic performance benchmarks became less important. In the early 1990s, recognition that this platform was not leveraging industry standard component development led IBM to transform parts of this business into a more collaborative approach. For example, the input/output (I/O) subsystem, considered to be world class © 2008 The Author Journal compilation © 2008 Blackwell Publishing


in the industry, was able to maintain its leadership while using industry standard rather than proprietary components (fibre optic cable and connectors, optical transceivers, etc.). This led to increased interaction between development and procurement, as well as with technology suppliers outside of IBM. The operating system was another area which was opened to external developers when IBM published many of its interoperability specifications Today, a software development community exists which can port applications to both the Z/OS and Linux operating systems, university courses teach System Z skills, and a fully functional emulator for mainframe application development is available for under $100. This has led to partnerships between hardware, firmware, and software development teams eager to optimize across traditional functional silos and exploit the full value of the server. Similarly, many companies are no longer focused exclusively on the development, manufacture and delivery of information technology, but rather on the application and integration of technology to deliver new and lasting value. The success of an innovating firm thus depends not only on its ability to meet its own innovation challenges but also on the efforts of other innovators in its environment. At the same time that partnerships have become increasingly important to IBM’s business, however, the company continues to generate the largest revenue in the industry from its patent portfolio. The lucrative market for intellectual property is more often associated with the ‘own and protect’ mentality of a monolithic innovation model than a collaborative one. Viewed in this way, the challenge for large companies becomes clearer. Large companies span both pillars of this model, and their business includes many examples of different combinations in between these extremes. Both pillars of this model have their own methodologies and business metrics for success. Tension is created when the conflicting approaches from either extreme overlap, such as when patent rights must be valued in a collaborative partnership. Along with these challenges come new opportunities; if a company is aware of these differences and can successfully balance its business by determining when to apply the proper approach and how to form creative teams, they can succeed where competitors might fail.

Conceptualizing Innovation Teams We note that even within a monolithic culture, innovation cannot exist in a vacuum; research © 2008 The Author Journal compilation © 2008 Blackwell Publishing

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Figure 2. The Context of Innovation scientists must work with each other to build up the necessary insights required for true innovation to occur. In a properly designed framework, this collaboration is increased and can lead to greater innovation (of course, not all partnerships are successful or well developed; today many are formed out of convenience, lacking recognition of how they apply in a broader scale). The impact of teamwork has been studied extensively (Rhodes, 1961; Johnson, 1972; Isaksen, Dorval & Treffinger, 1994; Davila, Epstein & Shelton, 2006); it is the nature of innovation to occur with some context, as illustrated in Figure 2. While the elements, supporting tools, processes or other elements of innovation are the focus of most efforts to enhance innovation, the overreaching context is often neglected. The context includes elements of culture, education and business climate, all of which may vary geographically or over time and are traditionally difficult to quantify. Nevertheless, without giving attention to creating a suitable context, innovation cannot flourish. Contextual measurements are difficult to quantify, though, and it is difficult to manage what you can’t effectively measure. For this reason, poor measurement has been a serious impediment to the effective management of innovation teams. Davila, Epstein and Shelton (2006) cite a recent study in which more than half of the respondents rated their performance measurement system for innovation as poor or less than adequate. This has led to lack of visibility, poor coordination, and enormous waste of money, talent, ideas and other resources. While this is clearly a serious problem with innovation in individual companies, it is even more of a problem in innovation teams, partnerships and alliances, where

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innovation spans organizational boundaries and cultures and the management complexity is truly bewildering. Spitzer (2006) discusses several keys to transforming performance measurement in teams, or in any broader organizational framework. These include a ‘context’ of measurement that encourages people to discover and reward innovative teamwork independent of short-term value capture, rather than use measurement to support existing preconceptions. When measuring the impact of an innovation team, it is therefore essential to legitimize qualitative measurements. It is also important to understand how the context can be improved by building up the characteristics of collaborative innovation, not simply by increasing technical or business knowledge. Our study uncovered a good deal of research into team formation, individual achievement and group dynamics which indirectly supports these assertions. The context is sometimes referred to as the constraints applied to a problem, leading to the observation that innovation proceeds better when it is goal oriented. It has also been observed that collaboration succeeds best when it takes place between peers, with all parties feeling they have a ‘winwin’ situation; context is essential in establishing these roles and relationships. Thus, before we introduce a framework for categorizing different types of innovation teams, we must understand the context which will appeal to the preferences of the next generation of innovators.

Innovation Teams and Generation-Y Each successive generation to enter the workplace bring with them a unique set of expectations and aspirations which have been shaped by their formative environment. Multigenerational workforces thus pose some unique challenges to effective team formation. Until fairly recently, the monolithic form of innovation described in Figure 1 was widely accepted as the only way to achieve significant innovation; this was reflected in the approach taken by more traditional organizations and their employees. This group consequently tended to favour conventional hierarchical learning (classroom or lecture style), and the resulting top-down communication paths were adopted in their approval-based decision making. Management chains of command were strictly observed, reinforcing the specialized role of the innovator as being confined to research or development. Indeed, such specialization was both encouraged and thought to be required, since technology was considered an

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unwieldy tool best left to specialists in the field. As societal norms shifted over time, the term Generation-X was coined to distinguish a new workforce with different expectations, particularly regarding the role of technology and how innovation was created. With a more independent style of learning and problem solving, this generation was also empowered by increasingly easy-to-use technology. These factors contributed to more lateral communication, team building, and mentor or coach relationships in the workplace than previously. However, neither of these two generations had the benefit of being raised in an era surrounded by ubiquitous technology their entire lives; this has been a much more recent occurrence, coincident with the emergence of more collaborative innovation models. The term Generation-Y first appeared in 1993 to describe those children born between 1984 and 1994. The scope of the term has changed since then to include, in many cases, anyone born until 2001 or anyone born until the present day. Numerous alternative terms have arisen that may sometimes be regarded as sub-groups of Generation-Y. These include The Net Generation, Millennials, Second Baby Boom, My Pod generation (from the fusion of Myspace and iPod), and Generation Next. They are rapidly becoming a force for social transformation; as the next generation of innovators, it is important to understand which team-forming strategies will be best suited to Generation-Y. There are several factors which distinguish this generation from previous ones, as noted in Table 1 (Lancaster & Stillman, 2003). Perhaps the most significant distinction is that this is the first generation to grow up surrounded by technology and digital media. Generations are shaped by their childhood experiences and then defined by their early adulthood actions. This is the first generation to have their childhood and early adulthood influenced by trends such as the Internet, graphic interfaces and other non-keyboard access to technology, instant messaging, cell phones, digital cameras, camera-phones, sophisticated computer graphics, portable digital audio players, and robot pets. Accustomed to the strong presence of technology in their lives, immersed in it from early childhood, they are less interested in how technology works and more interested in how it may be applied to solve practical problems. Technology for them is participatory and interactive; they do not wait for anyone else to create new experiences in their technology-enabled lives; instead they create it for themselves. Broadly speaking, they see work as a means of self-actualization, rather than as a means to an end. They value © 2008 The Author Journal compilation © 2008 Blackwell Publishing


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Table 1. Innovation Characteristics of Different Generations Traditionalist Training

The hard way

Learning style

Classroom

Communication style Problem-solving Decision-making Leadership style Feedback Technology use

Top down Hierarchical Seeks approval Command and control No news is good news Uncomfortable

Job changing

Unwise

opportunities to be creative and exciting challenges which can make a difference to the world. They favour immediate and continual feedback, engaging early and often with their peers. The most productive innovation teambuilding strategies for this generation will be tailored to their characteristics and preferences. This makes Generation-Y a particularly fertile ground for developing collaborative innovation teams. It is important to note that every part of the innovation value chain is strongly affected by Generation-Y, whose members have become not merely future innovators but major stakeholders in innovative value creation. Whether or not one is born as Generation-Y, each one of us is affected by their attitudes. When the workforce has an increasing proportion of Generation-Y participants, their approach will tend to influence even those from other generations of thought. The boundaries between three generations seem to be fading as technology becomes more and more user friendly. Therefore, it is becoming increasingly important to align team building to the perspective of Generation-Y, and to form new kinds of innovation teams. We will discuss four types of innovation teams, and how their approaches relate to the characteristics of Generation-Y innovators.

Genius Teams Truly radical innovation is often viewed as coming from great individual thinkers; examples such as Einstein, Da Vinci, Aristotle, and more come to mind. In reality, all of these individuals were at their most innovative when working in a community of like-minded people. We refer to these groups as ‘genius © 2008 The Author Journal compilation © 2008 Blackwell Publishing

Gen X Required to keep me Independent Hub and spoke Independent Team included Coach Weekly/Daily Unable to work without it Necessary

Gen Y Continuous and expected Collaborative and networked Collaborative Collaborative Team decided Partner On demand Unfathomable if not provided Part of my daily routine

teams’. Historically, genius teams are characterized by high levels of ambition and a strong positive outlook. Even if they live in difficult times and take on tough problems, they tend to believe in their ability to make things better despite the odds. Their positive outlook is created through the rules, stated goals and culture of the team. These groups are looking to make a tangible impact, with many of their efforts tied to action and driven by a desire to create change. They focus on pushing their limits in areas of high potential, and individuals in such groups identify strongly with their peers and with the group identity. Typical genius teams are small and highly selective about whom they admit into their ranks. These small inner circles nurture trust; members are highly supportive of each other and have great loyalty to the group. Despite their tendency to be mutual admiration societies, team members also seek to outdo their peers (who are seen as worthy opponents). Members actively seek recognition outside the group, often through tackling big problems and striving for the maximum possible impact. A successful genius team is able to meet frequently in person, socialize and appreciate each other’s contributions. They share and are committed to a common culture, values and rituals. The team is exclusive, tending to be small and having minimal interaction with outside organizations. They are characterized by lofty goals, positive attitude even in the face of difficulty, and a desire for recognition through celebrating goals and sharing success stories outside the team (which also helps to advance their reputation). The genius team appeals to members of Generation-Y as a means of self-actualization and satisfying competitive drives, and because it provides

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immediate feedback on ideas. Because genius teams are less accepting of diversity and collaborative, team-driven decisions, however, they may not be the optimal vehicle for sustainable innovation in a Generation-Y environment.

Improv Teams Members of genius teams are motivated by recognition from their peers and from outside the group; they tend to value ‘star’ performers. A different approach, which can also lead to successful innovation, values the group over the individual. The best examples of this come from improvisational (improv) teams, where all the participants feel as if they are leaders because the focus shifts among members of the group (Parker, 2003; Kail, 2004; Lublin, 2007). Improv teams are characterized by dynamic collaboration, spontaneous creativity and interaction between team members and cues taken from their surroundings. The art of improvisation requires adapting quickly to changing characters and situations based on new (or incomplete) information. Improv teams appeal to the collaborative style and social networking skills of Generation-Y, as well as providing rapid feedback, encouraging diversity among team members and making decisions by group consensus. However, they may not yield the highest impact results, and may also not work well with mixedgenerational teams. Successful improvisation requires being able to accept the contributions of others, even if you do not agree with them. Improv team discussions should never backtrack; they always move forward, or branch off in a new direction not previously explored. Similar to theatrical appearances, this can be frightening to some people; it is vital to trust fellow team members to avoid destructive criticism and share the spotlight. Expressing new ideas and moving into unfamiliar directions can be intimidating or invite ridicule; this must be avoided. Improv teams take advantage of the enthusiasm of the participants; by engaging the team members and their audience, the team becomes bolder and energized. Key enablers include developing mindful presence (awareness of the audience) and willingness to either take or hand off initiative during a meeting. It is beneficial to know the motivations and interests of your team members so you can judge how your actions will affect them and develop more effective strategies for communicating with them. It is important to be aware of what other team members Oare doing to avoid wasteful (and potentially embarrassing) duplication of effort or dropping the train of thought.

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Virtual Teams Global interconnections have made the workforce more tightly integrated, making it possible for people to work from anywhere. This interaction can be encouraged through online experiences such as virtual worlds or the metaverse. This is a new generation of interactive technology, which is perhaps less effective than meeting in person but provides significantly more immersion than conference calls or email at a fraction of the cost of video conferencing or business travel. As interactive technology has become easier to use, there has been an explosive growth in the number of participants in massive online multiplayer games and virtual landscapes. IBM, Google and Linden Labs (creators of Second Life) are only a few of the companies becoming engaged in developing virtual teams for innovation and other purposes. Recent trends suggest these worlds are maturing from novelty games into potentially valuable business tools. Indeed, the user-created landscapes of Second Life serve as examples of how Generation-Y has influenced our culture. Even if those who created Second Life are not part of Generation-Y themselves, they have certainly been influenced by the innovative ideas that arose from this environment. Virtual teams offer several advantages for Generation-Y. These environments meet their desire for social learning and deep collaboration. They also appeal to an acceptance of diversity and a meritocracy of ideas, which may actually be superior to personal interaction since it removes much of the intimidation that Generation-Y associates with the more senior innovation leaders. In the metaverse your avatar can assume any appearance, keeping your real identity anonymous (if a Generation-Y team member happens to be talking with the avatar of a dog, it doesn’t matter that their team member looks like a dog; it only matters what their team member has to contribute to the project). Virtual teams also appeal to Generation-Y’s reliance on technology, and may provide an opportunity to bridge the generational gap among mixed generation teams. Best practice for virtual teams includes regular meetings, supplemented by the occasional meeting in person. As an example, a recent IBM technology conference on innovation included sessions hosted at the IBM Virtual Briefing Center in Second Life. Business conduct guidelines were developed for these teams, just as would apply in any other meeting. The team’s experience in Second Life supports the assertion that virtual worlds enrich the collaborative context. Current versions continue to have significant © 2008 The Author Journal compilation © 2008 Blackwell Publishing


barriers to entry in the form of both technical issues and time investment to learn the metaverse; these are expected to diminish over time. Despite these concerns, feedback on this experiment was very positive. Over 40 avatars attended these sessions, which enabled greater participation across global time zones. We were also able to record and videotape some of the Second Life speakers for later playback (Kristufec, 2007). Meeting content was available both in advance of the scheduled meeting time and for weeks afterwards, so that people who were not able to attend the scheduled talks could still see the material and contact speakers.

FourSight Teams It is fundamental that a successful team will include the required skills and expertise to address the problem at hand. This same approach can be applied to the formation of innovation teams; a more structured approach to building innovation teams involves measuring team members’ preferences and balancing the team accordingly. Because every individual’s personality and temperament differs, their supporting metrics are completely subjective. Instruments such as Myers Briggs Type Indicator (MBTI), Hermann Brain Dominance Instrument (HBDI) and DISC (Dominance, Influence, Steadiness, Conscientiousness) Assessment are some of the most widely known instruments for measuring personality type/temperament and cognitive thinking. The FourSight Breakthrough Thinking Profile discussed in this section differentiates itself from these instruments by building on those that measure thinking skills alone. The FourSight breakthrough thinking process, unlike psychometric instruments, is comprised of a series of discrete, repeatable steps that people regularly engage in a variety of circumstances. This means its measurement is objective. Further, the breakthrough thinking process is validated as a democratic, universal process by over 50 years of study in the field of creativity and creative problem solving (Puccio, Murdock & Mance, 2006; Ackerbauer, 2008). Because it is an objective measure, the breakthrough thinking process is one we can learn and intentionally replicate. If we can replicate it, we have the potential to sustain, and even scale, the results of breakthrough thinking. The more scalable (or the greater the impact of) our breakthrough thinking, the more innovative people and teams can truly be. While the creative process is universal, each step requires unique mental skills, and most individuals prefer some skills above others. Such biases show up as strong points © 2008 The Author Journal compilation © 2008 Blackwell Publishing

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and potential blind spots when solving problems. There are four basic preferences recognized by the breakthrough thinking model, which we will discuss in order. 1. Clarify the situation: John Dewey suggested that a problem well defined is a problem half solved. Clarifying a situation means to bring a problem, challenge or opportunity to its most granular level. If given one hour to save the world, Albert Einstein said he would spend 55 minutes understanding the problem. Clarification requires data gathering, understanding the context of a situation and asking numerous questions. Clarifying a situation can be time-intensive, because it requires a significant level of detail to ensure there are no lingering assumptions that could derail potential solutions. 2. Generate ideas: Linus Pauling said the only way to have good ideas is to have lots of ideas. Generating ideas, or fluid ideating, requires divergent thinking. Divergent thinking is about looking at the big picture, and playing with potentially abstract concepts that stretch our imagination. Because a large quantity of ideas may also breed high quality ideas, we are most effective in generating ideas when we open our minds to new thoughts, and defer judgement long enough to express and capture those ideas. Ideation, therefore, requires a more intuitive approach, whereas clarifying is most effective when employing concrete thinking. 3. Develop a solution: Developing a promising idea or series of ideas into a workable solution is about giving ideas the support required to stand on their own. Developing a solution includes comparing and analysing several noteworthy ideas in order to prioritize and strengthen one or more, then planning for their implementation. Developing a solution is about shaping raw ideas into a workable solution. Successful solution development also requires a contextual understanding of the environment, such as identifying stakeholders who will either assist or resist a solution’s implementation, and taking action to amplify support and mitigate opposition. 4. Implement a plan: Implementing is nothing more than putting the plan into action. While developing a plan details what things need to happen for an idea to solve a problem, implementation is about giving structure to the idea in order for it to become a reality. Being able to successfully implement a solution requires persistence and determination. And because implemen-

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tation generally requires engaging a variety of stakeholders, implementation lends itself to re-iterating the breakthrough thinking process: Is the solution workable? Are we solving the right problem? What do we need to re-think? Who do we need on board to support this effort? Note that qualities from each preference stand in direct contrast to those of the preference immediately preceding it. Specifically, clarification and solution development involve convergent thinking modalities, while ideation and implementation involve divergent thinking. When individuals are not aware of this distinction, conflict may arise as a result of differing approaches to problem solving. When teams are aware of their preferences, conflict can be diffused or leveraged as creative tension, producing a potentially more synergistic result. Table 2 shows the best practices for leveraging each preference when engaging teams in breakthrough thinking.

Table 2. Best Practices for FourSight Team Preferences Actions Clarifier

Ideator

Developer

Case Studies in FourSight Teams Although the FourSight profile does not predict performance, it does provide awareness of how teams would otherwise prefer to perform, if given the appropriate environment. FourSight has been administered to almost 300 people within IBM. Of those surveyed, approximately 75 per cent have been debriefed as to the meaning of their results. Approximately half of those have been debriefed in a formal workshop where the breakthrough thinking process was described and explored at length. In such workshops, participants are taught the breakthrough thinking process and then given their survey results. The remainder of the workshop consists of a detailed breakdown of each of the four elements of breakthrough thinking by preference name (Clarifier, Ideator, Developer and Implementer), and an introduction of critical thinking tools for leveraging that preference. We will describe one example taken from a recent IBM Academy of Technology study on innovation tools, in which the FourSight profile was administered to a self-selected innovation team; results are summarized in Figure 3. The characteristics for each class shown in Figure 3 have been summarized previously in Table 2. Note the high preference for ideation and slight relative preference for clarification, with strong relative non-preferences for solution development and plan implementation. This suggests the team has a propensity for generating relevant ideas, yet may lack energy

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Implementer

• Look at the situation from all angles • Understand the background information and key data • Isolate obstacles that stand in your way • Know what is and is not relevant • List lots of ideas • Look at the problem from a new angle • Use brainstorming to come up with many diverse ideas • Use random associations to think outside the box • Use success criteria to rate competing solutions • Modify solutions to better meet success criteria • Identify sources that may assist and resist implementation • With this in mind, create an action plan • Get into action, realizing that you will learn as you go • ’Test fast. Fail fast. Adjust fast.’ • Ask what’s working well? What should we do differently? What have we learned? • Monitor progress and be prepared to cycle back to other phases

Source: Your Thinking Profile: A Tool for Innovation (THinc Communication, 2002)

for developing and implementing strong solutions. Although the overall team was not debriefed on their preferences, some high level inferences can be drawn from the results. As a statement of preference, this team would likely generate many more ideas than there would be substantive mechanisms developed to help capture, evaluate and refine. With respect to preference, the statistical likelihood of ideas generated by this team becoming © 2008 The Author Journal compilation © 2008 Blackwell Publishing


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viable solutions, or driven to closure, is low. If the team has limited short-term goals and will then be disbanded, homogeneity may be appropriate since other teams may assume the developer and implementer roles. However, if this team were permanent, emphasis would need to be given to complementing the team’s strength with a stronger solutions development focus. If this team were populated by early tenure employees, for instance, retention could be a real threat should a preponderance of ideas generated not be brought to closure. The performance of this team bears out the profile, in that there was a great deal of challenge exploration and a rich forum for sharing thoughts and ideas during the study meetings. However, when it came to taking action and submitting the sub-team findings, there was more emphasis on ensuring ideas were captured in raw form than in a coherent summary of findings and recommendations. A series of late revisions addressed this lack of preference in idea refinement (note this is an implementation statement, proving that preferences do not necessarily predict performance). Educa-

10 8 6 4 2 0 -2

tion on how to prioritize and evaluate ideas, followed by driving them to closure, would have proven valuable to this team. By providing this type of preference list as part of a debriefing session, teams have immediate awareness of collective strengths, and are compelled to engage other team members so as to augment their preference gaps in the breakthrough thinking process. Teams exposed to the breakthrough thinking process have a higher likelihood of approaching problems deliberately. The more conversant teams are in the dynamics of breakthrough thinking, the more confident they are likely to be in compensating for preference gaps in the strengths of their team. The relationship between the different types of innovation teams discussed and the characteristics of next generation innovators is summarized in Table 3. We recognize that hybrid teams incorporating the best aspects of each category might be a beneficial approach in some organizations. Furthermore, this work suggests that multi-generational teams are exposed to potential internal conflicts because of the mismatch between their preferences for different types of team participation; the analysis and treatment of such conflicts has been addressed previously in the literature (Kratzer, Leenders & van Engelen, 2006). Additional taxonomies which extend this work to the classification of different types of innovation relationships are the subject of ongoing research (Ginsberg & DeCusatis, 2008).

-4 -6 -8 Clarifier

Ideator High Preference

Developer

Implementer

Low Preference

Figure 3. FourSight Team Sample Profile

Conclusions With the increasing emphasis on radical innovation as a differentiator, many businesses have begun to invest in building innovation teams without a clear understanding of the

Table 3. Comparison of Innovator Traits and Team Approaches Traits of Gen-Y Innovators

Genius Teams

FourSight Teams

Virtual Teams

Improv Teams

Continuous learning Highly networked, free expression Team decisions/no strong leader Immediate feedback Inherent use of technology Embrace diversity Balance mixed generation team members Achieve self-actualization

High Low Low High Medium Low Medium High

High Medium High Medium Medium Medium High High

Medium High Medium High High High Low Low

Medium High High High Low High Low Medium


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specific strategies which make some types of teams more likely to produce useful innovation. We have investigated different structures for teams charged with producing innovative results, including genius teams, improv teams, virtual teams and FourSight teams. Categorizing these approaches along with the preferences of Generation-Y innovators, we are able to recommend strategies which are more likely to succeed because they appeal to the innovator’s pre-existing motivations. Categorizing these approaches along with the preferences of Generation-Y innovators, we are able to recommend strategies which are more likely to succeed because they appeal to the innovator’s pre-existing motivations. For example, we note that a Generation-Y team is particularly well suited to the characteristics of a FourSight team, and relatively poorly suited to those of a genius team. We further note that preference profiling tools such as FourSight can lead to self-awareness of a team’s relative strengths and weaknesses, and provide opportunities to balance the team membership to increase the prospects for long-term success.

References Ackerbauer, M. (2008) FourSight and the Breakthrough Thinking Process. Proc. 2nd Innovation and Creativity Management Community Meeting, Buffalo, NY. Davila, T., Epstein, M. and Shelton, R. (2006) Making Innovation Work. Wharton School Publishing, Philadelphia, PA. Ginsberg A. and DeCusatis, C. (2008) The Innovation Ecosystem Approach to Open Innovation: Challenges and Solutions. Submitted for publication. Ho, D. (2008) Research, Innovation, and Knowledge Management: The ICT Factor. Submitted to UNESCO (forthcoming). Isaksen, S.G., Dorval, K.B. and Treffinger, D.J. (1994) Creative Approaches to Problem Solving. Kendall/ Hunt Publishing, Dubuque, IA. Johnson, D.M. (1972) Systematic Introduction to the Psychology of Thinking. Harper & Row, New York. Kail, J. (2004) The Improv Model of Organizations [WWW document]. URL http://improvatwork. com/index.php/pages/39 [accessed on 21 June 2007]. Kratzer, J., Leenders, R.Th.A.J. and van Engelen, J.M.L. (2006) Team Polarity and Creative Performance in Innovation Teams, Creativity and Innovation Management, 15, 96–104. Kristufec, J. (2007) Improv for effective collaborative innovation, Nonconfidential session, IBM Academy of Technology conference on Innovation

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Ecosystems [WWW document]. URL http:// www.ustream.tv/BorisFrampton/videos/ nnndssHLIMWqVS,Saq8grg [accessed on 14 March 2008]. Lancaster, L.C. and Stillman, D. (2003) When Generations Collide: Who They Are. Why They Clash. How to Solve the Generational Puzzle at Work. Harper Business, Wheaton, IL. Lublin, J. (2007) Improv Troupe Teaches Managers How to Give Better Presentations. The Wall Street Journal Online [WWW document]. URL http:// online.wsj.com/article/SB117071229131798678 [accessed on 21 June 2007]. Parker, S.G. (2003) Stand Up and Throw Away the Script. Harvard Management Communication Letter, Article Reprint No. C0302A. Puccio, G.J., Murdock, M.C. and Mance, M. (2006) Creative Leadership: Skills that Drive Change. Sage Publications, Thousand Oaks, CA. Rhodes, M. (1961) An Analysis of Creativity, Phi Delta Kappa, 42, 305–11. Spitzer, D. (2006) Transforming Performance Measurement. AMACOM and The American Management Association, New York.

Dr Casimer DeCusatis is an IBM Distinguished Engineer and Technical Executive based in Poughkeepsie, NY. He is an IBM Master Inventor with over 70 patents, and co-leader of the 2007 IBM Academy of Technology study ‘Innovation Ecosystems’. He is the recipient of several industry awards, including the IEEE Kiyo Tomiyasu Award, the EDN Innovator of the Year Award, the Mensa Research Foundation Copper Black Award for Creative Achievement, and the IEEE/HKN Outstanding Young Electrical Engineer award (including a citation from the President of the United States and an American flag flown in his honor over the US Capitol). He is co-author of more than 100 technical papers, book chapters, and encyclopedia articles, and editor of the Handbook of Fiber Optic Data Communication (now in its 3rd edition). Dr DeCusatis received his MS and PhD degrees from Rensselaer Polytechnic Institute (Troy, NY) in 1988 and 1990, respectively, and the BS degree magna cum laude in the Engineering Science Honors Program from the Pennsylvania State University (University Park, PA) in 1986. He is a member of the Order of the Engineer, a Fellow of the IEEE, Optical Society of America, and SPIE (the international optical engineering society), and a member of various other professional organizations and honor societies.


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2007 Tudor Rickards Award for the Best Paper Published in Creativity and Innovation Management On Wednesday 28 May 2008, at the Opening Banquet of the 2nd Creavitity and Innovation Management Community meeting in Buffalo, the 2007 Tudor Rickards Award for the best paper published in Creativity and Innovation Management throughout 2007 will be presented to Hans Georg Gemünden, Sören Salomo and Katharina Hölzle, for their article published in the December 2007 issue (16.4, pp. 408–21, entitled ‘Role Models for Radical Innovations in Times of Open Innovation’. In their prize-winning paper, which can be freely downloaded from CIM’s website (http://www.blackwellpublishing.com/ caim), the authors report on their study of the influence of innovator roles in highly innovative ventures. In order to obtain a differentiated picture, the degree of innovativeness is taken into account as a moderating variable. To test their hypotheses, a sample of 146 highly innovative new product development projects was used. A rigorous sampling design was chosen and state-of-the-art measures for the degree of innovativeness were applied. Furthermore, multi-trait-multi-method methodology (MTMM) was applied to enhance the validity of the study. The results show that innovator roles have a strong influence on innovation success but these influences are positively and negatively moderated by innovativeness. The moderating influences depend on the type of innovativeness. Remarkably, with increasing technological innovativeness, innovator roles which create interorganizational links with the outside world appear to be more important than intraorganizational linker roles, and support from high-ranked organizational members turns out to have a significant negative effect on project success with higher degrees of technological innovativeness. Possible explanations for these findings are discussed and consequences for innovation research and innovation management are shown. This prize-winning article was voted into first place by the members of our editorial board who had to choose their top three © 2008 The Author Journal compilation © 2008 Blackwell Publishing

among the following six high-quality articles selected from the four 2007 issues by the editoral team and the various guest editors of 2007 specials: 1. ‘Innovators and Imitators in Noveltyintensive Markets: A Research Agenda’ by Todd Dewett and Scott David Williams (16.1, pp. 80–92). 2. ‘Clustering: An Essential Step from Diverging to Converging’ by Marc Tassoul and Jan Buijs (16.1, pp. 16–26). 3. ‘Towards a New Logic for Front End Management: From Drug Discovery to Drug Design in Pharmaceutical R&D’ by Maria Elmquist and Blanche Segrestin (16.2, pp. 106–20). 4. ‘Creative Management: A Predicted Development from Reseach into Creativity and Management’ by Fangqi Xu and Tudor Rickards (16.3, pp. 216–28). 5. ‘Convergence or National Specificity? Testing the CI Maturity Model across Multiple Countries’ by Mandar Dabhilkar, Lars Bengtsson and John Bessant (16.4, pp. 348–62). 6. ‘Role Models for Radical Innovations in Times of Open Innovation’ by Hans Georg Gemünden, Sören Salomo and Katharina Hölzle (16.4, pp. 408–21). There are, in fact, two runners-up: ‘Creative Management: A Predicted Development from Reseach into Creativity and Management’ by Fangqi Xu and Tudor Rickards and ‘Convergence or National Specificity? Testing the CI Maturity Model across Multiple Countries’ by Mandar Dabhilkar, Lars Bengtsson and John Bessant received the same number of votes and share second place. They will receive a CIM certificate, and these two papers are also freely available on our website and through Blackwell’s Synergy. In fact, on Wednesday 28 March we will not just be handing out the 2007 Tudor Rickards Award. Creativity and Innovation Management launched the award in 2006 to mark our founding editor Tudor Rickard’s 65th birthday and

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the 15th volume of our Journal. The 2006 award had two prize-winners: Jan Kratzer, Roger Leenders and Jo van Engelen received their award, which was presented at the CIM session during PDMA’s Research Forum in September 2007, for ‘Team Polarity and Creative Performance in Innovation Teams’ (15.1, pp. 96–104); and Gerard Puccio, Roger Firestien, Christina Coyle and Cristina Masucci who also won with their article ‘A Review of

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the Effectiveness of CPS Training: A Focus on Workplace Issues’ (15.1, pp. 19–33). Since Gerrard and his co-authors have not yet received their award, we will also take the opportunity to hand out the 2006 award to the organizers of the Buffalo event during their own Opening Banquet! March 2008 Petra de Weerd-Nederhof


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Reviewers 2007 Reviewers to be thanked who submitted at least a first review between 1 January 2007 and 31 December 2007: Åhlström, Pär Albino, Vito Barczak, Gloria Biemans, Wim Boer, Harry Bondarouk, Tanya Buganza, Tomasso Casimir, Gian Chapman, Ross Chen, Ming-Huei Christiansen, John Corso, Mariano De Cock, Christian Di Benedetto, Anthony Enkel, Ellen Ernst, Holger Faems, Dries Ford, Cameron Gaspersz, Jeff Gertsen, Frank Goffin, Keith Griffin, Abbie Groen, Aard Hauschildt, Juergen Heerkens, Hans Heidemann Lassen, Astrid Hirvensalo, Antero Hsuan Mikkola, Juliana Hultink, Erik Jan Hustad, Thomas Hyland, Paul Isaksen, Scott Jørgensen, Frances Karlsson, Christer Kaufmann, Astrid Kaufmann, Geir Kekäle, Tauno Kim, Jongbae Kobe, Carmen Kratzer, Jan Lakemond, Nicolette Laugen, Bjorge Timenes Looise, Jan-Kees Looy, Bart van Lubart, Todd Lugt, Remko van der


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Manimala, Mathew Marchesi, Alessio Meer, Han van der Moger, Susan Möhrle, Martin Moultrie, James Napier, Nancy Nijhof, André Nosella, Anna O’Quin, Karen Pellegrini, Luisa Piccaluga, Andrea Piller, Frank Pullen, Annemien Ren, Liqin Richtnér, Anders Rickards, Tudor Riemsdijk, Maarten van Rip, Arie Ronchi, Stefano Roosendaal, Hans Rost, Katja Runco, Mark Schoemaker, Michiel Smulders, Frido Soosay, Claudine Stam, Joop Steensma, Herman Strandgaard, Jesper Terlouw, Pieter Tonchia, Stefano Torkelli, Marko Varnes, Claus Velzen, Martijn van Visscher, Klaasjan Von Raesfeld Meijer, Ariane Vrande, Vareska van de Weerd-Nederhof, Petra de

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