Modes of innovation, resources and their influence on product innovation: Empirical evidence from R&D active firms in Norway

Technovation 33 (2013) 225–233

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Technovation journal homepage: www.elsevier.com/locate/technovation

Modes of innovation, resources and their influence on product innovation: Empirical evidence from R&D active firms in Norway Tommy Høyvarde Clausen a,n, Tor Korneliussen b, Einar Lier Madsen a a b

Nordland Research Institute, Bodø 8049, Norway Bodø Graduate School of Business, Bodø, Norway

a r t i c l e i n f o

abstract

Available online 26 March 2013

Evolutionary theory of the firm argues that firms follow different approaches to innovation with implications for their performance. Consistent with evolutionary theory, this paper develops a taxonomy of innovation modes which capture the variation in firms’ approaches to product innovation. The taxonomy is based on the open/closed innovation and exploration/exploitation literatures and identifies the following modes: ‘‘Open exploration’’, ‘‘closed exploration’’ ‘‘open exploitation’’, and ‘‘closed exploitation’’. The paper theorizes that the identified innovation modes influence product innovation through their effect on the firms’ technological and market resources. Using survey data from over 1000 R&D active firms in Norway analyzed with structural equation modelling it is shown how four modes of innovation are related to actual product innovation. & 2013 Elsevier Ltd. All rights reserved.

Keywords: Innovation mode Exploration Exploitation Open innovation Closed innovation Product innovation

1. Introduction It is widely accepted that firms need to develop new product innovations in order to remain competitive in today’s business markets (Teece, 2007). Product innovation can be defined as the market introduction of a product that is new to the firm or its market (OECD, 2005; Hull and Covin, 2010). Evolutionary theorizing on innovation in firms highlights that firms follow different approaches to innovation (Nelson and Winter, 1982). A consequence of heterogeneity across firms in their approach to innovation is that firms persistently differ in their performance (Nelson, 1991). Prior research has focused on how variation in single variables such as size and research and development (R&D) is related to innovation (see Shefer and Frenkel, 2005; Stock et al., 2002; Raymond and St-Pierre, 2010; Sun and Du, 2010 for example studies, Becheikh et al. (2006) for a review). Surprisingly few empirical studies have focused on the variation in firms’ approaches to innovation and ‘‘how firms innovate’’ (Hull and Covin, 2010)—generally referred to as the mode in which innovation is undertaken in the small-but-emerging literature on innovation modes (Hull and Covin, 2010; Clausen et al., 2012; Hollenstein, 2003; Arundel et al., 2007). Still fewer studies have examined variation in innovation modes across firms and

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Corresponding author. Tel.: þ47 7551 7878. E-mail addresses: [email protected], [email protected] (T.H. Clausen). 0166-4972/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.technovation.2013.02.002

analyzed how different modes actually influence the ability of firms to develop product innovations. The purpose of this paper is to examine main differences across firms in how they innovate and analyze the ways in which different innovation modes influence firms’ abilities to develop new product innovations. The following research question is asked: How do firms attempt to develop new product innovations and to what extent and how do ‘‘the way firms innovate’’ influence their abilities to actually develop product innovation? This broad research question is developed into testable hypotheses on the basis of a new taxonomy of innovation modes developed in this paper. This paper makes the following contributions to the literature on innovation modes. First, we develop a taxonomy that distinguishes between four innovation modes. Based on the closed/ open innovation literature (e.g. Chesbrough, 2003; Chesbrough et al., 2006; Laursen and Salter, 2006) and the exploration/ exploitation literature (March, 1991), the following four modes are identified: ‘‘Open exploration’’, ‘‘closed exploration’’, ‘‘open exploitation’’ and ‘‘closed exploitation’’. Our taxonomy of innovation modes differ from the identification of innovation modes in the literature. Existing research has pursued a mainly inductive approach to the identification of innovation modes. An example is the use of the Community Innovation Survey (CIS) and the questions/ items used in this survey to identify innovation modes (e.g. Arundel et al., 2007; Hollenstein, 2003; Clausen et al., 2012). In comparison to such more-or less ad-hoc ways of measuring and defining innovation modes, our paper builds directly on prior theory (e.g. Chesbrough,

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2003; March, 1991) on ‘‘how firms innovate’’. This paper simply combines two well-known theory streams (closed/open innovation and exploration/exploitation) in a new way. We thus extend prior research on innovation modes where it is argued that firms can pursue innovation through an ‘‘internal’’ and/or ‘‘external’’ mode (Hull and Covin, 2010) by adding that an internal/ and or external innovation mode can have an explorative and/or an exploitative character (e.g. March, 1991). A related second contribution is that we analyze how the innovation modes actually influence firms’ resource base and ability to develop new product innovations. This is in line with the conceptualization of innovation in the evolutionary theory of the firm where firms’ ability to innovate is, at least in part, a function of their resource base. The capability to renew the resource base is a source of long term competitive advantage (Nelson and Winter, 1982; Teece et al., 1997). Consistent with this, we examine the relationship between innovation modes and how they influence product innovation through their effect on the firms’ resource base (i.e. technological resources and market resources). A third and related contribution is that we examine whether and to what extent differences in innovation modes across firms can help explain why some firms are better able to develop product innovations. Most of the studies in the innovation mode tradition have measured ‘‘innovation modes’’ on the basis of data of successful innovators only (Hollenstein, 2003; Clausen et al., 2012; Arundel et al., 2007). Firms that have failed to successfully innovate have been excluded from most of the ‘‘mode research’’. Accordingly, we know little about the extent to which some innovation modes are associated with superior product development capabilities. This limitation in research on innovation modes arguably stems from the use of secondary data, such as the CIS, where survey items about innovative activities are only asked to successful innovators. Differing from this approach, our study focuses on firms which has taken the decision to innovate, but where the actual innovation results can vary from noinnovation to many innovations. In our paper four innovation modes are put forth as explanatory variables hypothesized to explain variation in firms’ ability to develop product innovation. The remainder of this article is organized into four sections. In the next section we discuss the proposed taxonomy and formulate a set of hypotheses about how four innovation modes are related to firms’ ability to develop new product innovations. This is followed by a section presenting the research methodology and the empirical data used to test the hypotheses. The analysis is conducted in section four. Finally, the research findings are discussed and implications for the literature on innovation modes and for managers are pointed out.

attempted to unravel the sources of innovation at the firm level. This empirical research has focused on variation across firms on single variables and firm characteristics, for instance analyzing whether it is large or small firms that are most innovative (see Acs and Audretsch, 2003 for a review of this literature). This stream of research is slightly in contrast to evolutionary theorizing which argues that firms differ in their approaches to innovation. Inspired by a small but emerging literature on innovation modes, this paper aims to examine the variation across firms in their approach to innovation and its implications for firms’ ability to develop new product innovations. This is done through the development of a taxonomy of ‘‘how companies attempt to innovate’’ referred to as innovation modes (Hull and Covin, 2010; Clausen et al., 2012). We further examine the various ways that innovation modes influence product innovation. We focus in particular on the relationship between innovation modes and how they may influence product innovation through their effect on the firms technological and market resources. The conceptualization of innovation in this paper follows evolutionary theory and strategic management research where it is argued that a firm attempts to develop new innovations as a response to a problem (Nelson and Winter, 1982). When firms have taken the decision to innovate, they may differ in their approaches to innovation and the strategies used to solve problems. Successful innovation is in this theory conceptualized as an outcome of a search process where firms (re)combine resources in new ways that enable them to discover knowledge that facilitates a solution to the problem in focus. In this theory, firms’ abilities to innovate are, at least in part, a function of the quality of their resource bases and how firms renew their resource base (Nelson and Winter, 1982; Katila, 2002; Stuart and Podolny, 1996; Teece et al., 1997). In line with Schumpeter’s (1934) theorizing, innovation, especially product innovation, is the market commercialization of new products where technological and market resources are key antecedents and the ability to renew those are looked upon as sources of persistent superior performance. Renewal of a firms’ technological and market resources are particularly important and it is hypothesized that firms need well-developed technological resources to develop new products that differ from previously developed innovations, and need superior market resources to introduce new products onto the market successfully. In the following section, we elaborate on how we have proceeded to measure differences across firms in their approach to innovation—referred to as innovation modes. We subsequently put forth hypotheses on the relationship between innovation modes and firms’ technological and market resources.

2. Theoretical framework and hypotheses

2.1. Taxonomy of innovation modes and their relationship with technological and market resources’

Evolutionary theory and strategic management research argue that firms are heterogeneous and that they may follow different approaches to innovation (Nelson and Winter, 1982; Nelson, 1991; Teece et al., 1997). A consequence of heterogeneity in innovation across firms is that firms should differ widely in terms of performance (Nelson, 1991), including financial and innovative performance. A line of empirical research has examined this central tenant in evolutionary theory by comparing the importance of firm level and industry level factors for firm performance (Rumelt, 1991; Schmalensee, 1985; Wernerfelt and Montgomery, 1988). This research concludes that firm factors are the most important source of firm performance, far more important than for instance industry factors. Given the important of firm factors, especially innovation, for firm performance, several studies on Innovation Studies (IS) have

Our taxonomy of innovation modes draws on the exploration/ exploitation reasoning in the organizational learning literature and the open/closed innovation models in the innovation literature (e.g. Chesbrough et al., 2006; Chesbrough, 2003; Lichtenthaler and Lichtenthaler, 2009). The section below briefly reviews this literature, ending with a taxonomy of innovation modes. 2.1.1. Exploration and exploitation When firms have taken the decision to innovate they face the choice of how intensive their focus should be on modification versus renewal of their resource base. This choice can be traced back to Schumpeter (1934) as an enquiry into new possibilities and application of established ideas, and was introduced later into the field of organizational learning by March (1991). According to

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March (1991): ‘‘Exploration includes things captured by terms such as search, variation, risk taking, experimentation, play, flexibility, discovery, innovation. Exploitation includes such things as refinement, choice, production, efficiency, selection, implementation, execution’’ (p. 71). A main challenge for firms is to balance the twin processes of variation (e.g. exploration) and selection (e.g. exploitation) (March, 1991). Although the exploration/exploitation literature has greatly added to organizational theory, less attention has been paid to the internal and the external domain of the exploration and/or exploitation activity (Bierly et al., 2009). Research suggests that firms should search both internally and externally when aiming to modify and renew their resource base in order to enable industrial innovation (Lichtenthaler and Lichtenthaler, 2009; Chesbrough et al., 2006).

2.1.2. Closed and open innovation In the highly influential evolutionary economic theory developed by Nelson and Winter (1982), innovation and superior economic performance was to large extent a function of internal capabilities, most notably internal R&D (Nelson, 1991). Later research, both theoretical and empirical, has confirmed this and shown that sources of innovation and profitability reside at the firm level (Rumelt, 1991; McGahan and Porter, 1997; Schmalensee, 1985; Wernerfelt and Montgomery, 1988; Powell, 1996). Adding to this insight, researchers have since Nelson and Winter (1982) developed their theory, increasingly showed that firms do not innovate in isolation (Von Hippel, 1988). They use information and knowledge from external actors in the innovation process in addition to internally generated knowledge and resources. Adding to the evolutionary theory, Cohen and Levinthal (1989; 1990) and other researchers thus argued that the ability to identify and exploit external knowledge sources – often referred to as absorptive capacity – is both an important capability (Spithoven et al., 2011) and an important antecedent to innovation and economic performance. While absorptive capacity theorizing has had clear orientation towards firms’ internal processes and mechanisms which enable external knowledge sourcing, recent research on open innovation has put more emphasis on external knowledge as a strategic complement to the development of firms’ internal knowledge resources (Chesbrough, 2003; Chesbrough et al., 2006). In one of the clearest definitions of the open innovation paradigm offered so far, open innovation is defined as: ‘‘the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation’’ (Chesbrough, 2006, p. 1). Although there is a discussion about whether open innovation really is a new paradigm (Chesbrough, 2003) – or simply is ‘‘old wine in a new bottle’’ (Trott and Hartmann, 2009) – most innovation research now explicitly acknowledge that firms need to be able to identify, assimilate and use knowledge possessed by external actors in order to enrich firm internal competencies and resources (Enkel et al., 2009; Gassmann, 2006; Hsiehm and Tidd, 2012; Huizingh, 2011). This is important because new and economically valuable economic knowledge is constantly developed by actors and organizations external to the firm in a rapid pace and volume (Vanhaverbeke et al., 2008). Open innovation is in line with previous research which shows that knowledge relevant for industrial innovation is distributed among actors and organizations such as suppliers, users, universities and consultants (Cohen and Levinthal, 1989; 1990; Chesbrough; 2003; Chesbrough et al., 2006; Von Hippel, 1988; Nelson, 1993; Lundvall, 1992; Edquist, 2005). There is less theorizing about the role of ‘‘internal innovation capabilities’’ in the open innovation literature (Keupp and

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Gassmann, 2009). Early contributions to open innovation theory argued – somewhat simplified – that a high reliance upon internal innovation capabilities and internal R&D characterized the ‘‘closed innovation’’ model with little – or no – use of external knowledge sources in the innovation process (Chesbrough, 2003). More recent theorizing on open innovation suggests, however, that ‘‘closed innovation’’ and ‘‘open innovation’’ are not mutually exclusive, at least at the firm level, and that firms can pursue both models of innovation at the same time (Lichtenthaler and Lichtenthaler, 2009). 2.2. Combining exploration/exploitation and open/closed innovation Based on the open/closed innovation and the exploration/ exploitation literatures we propose four ‘‘innovation modes’’ that may be related to technological and market resources. Whereas the literature on open/closed innovation acknowledges that firms can scan both their external environment (e.g. open innovation) and their internal organization for valuable economic knowledge and resources (e.g. closed innovation) (Chesbrough, 2003; Chesbrough et al., 2006), the organizational learning literature distinguishes between the exploratory and exploitative character of innovation search processes (March, 1991). Combining these two literatures allows a theoretical model that spans the open/closed dimension and the exploration/exploitation dimension as shown in Fig. 1: open exploration, closed exploration, open exploitation, closed exploitation. It should be noted that Fig. 1 is mainly illustrative and it is not the intention to suggest that the dimensions proposed in each of the four quadrants are mutually exclusive. Rather, we believe that dimensions of open innovation and closed innovation can be more or less present in each firm. 2.2.1. Open exploration ‘‘Open exploration’’ describes processes that ‘‘open up’’ the firm to radically new technological ideas, insights, and knowledge from the firm’s external environment that subsequently can be used to enrich the firm’s overall set of resources so that they can lead to actual innovation (Cohen and Levinthal, 1990; Lichtenthaler and Lichtenthaler, 2009; Bierly et al., 2009; Lane et al., 2006). The infusion of new ideas and resources from external sources may increase the scope of internal technology variation and experimentation within firms. This may enable subsequent product innovation (Fleming, 2001; Katila, 2002; Katila and Ahuja, 2002; Laursen and Salter, 2006). One reason for this is that ‘‘there is a limit to the number of new ideas that can be created by using the same set of knowledge elements. An increase in scope adds

Fig. 1. Dimensions and generic types of dynamic capabilities.

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new elements to the set, improving the possibilities for finding a new useful combination’’ (Katila and Ahuja, 2002, p. 1185). Similarly, it has been argued that the ability to discover latent customer and market needs, needs that customers and the market have not clearly articulated, is an important driving force behind successful new product development (Narver et al., 2004). Based on the above discussion we put forth the following hypotheses: Hypothesis 1. There is a positive relationship between the open exploration mode and technological resources. Hypothesis 1b. There is a positive relationship between the open exploration mode and market resources. 2.2.2. Open exploitation Open exploitation refers to the management of organizational processes that connects and links the firm with external actors in order to facilitate knowledge access (Lichtenthaler and Lichtenthaler, 2009). Open exploitation captures processes in which firms apply external knowledge in order to enhance existing product technology (Bierly et al., 2009). Innovation studies suggest that external actors, such as suppliers and customers, can add valuably and directly to a firm’s innovation activities (Von Hippel, 1988). The argument is that many external actors possess commercially valuable knowledge that may be directly applicable to firms, and that many firms may benefit from involving external actors in their innovation processes. It is however less clear how these external actors actually add to the firm’s resource base and through which means they may help firms to develop new product innovations. Open innovation is characterized by its clear link to the firm’s business model, the market, and how firms can commercialize external knowledge (Chesbrough et al., 2006). This suggests that open exploitation may be particularly beneficial for the development of a firm’s market resources, but probably less so for the development of the firm’s technological resources. Prior research supports this idea as it has recently been argued that openness in the innovation process increases coordination costs and that firms may lose their freedom to establish the technological trajectory of the product technology when pursuing open innovation (Almirall and Casadesus-Masanell, 2010). The reason is that external actors, such as suppliers and customers, may maximize their own payoffs and not those of the firm (Almirall and Casadesus-Masanell, 2010). This represents a divergence effect of open innovation which may inhibit successful product innovation (Almirall and Casadesus-Masanell, 2010). Hence, there is a trade-off, when pursuing open innovation, between ‘‘the cost of losing control of the product’s technological trajectory and the benefits of aggregating knowledge from other players to promote innovation’’ (Almirall and Casadesus-Masanell, 2010, p. 28). We propose that this trade-off can be seen in how open innovation influences a firm’s technological and market resources. We put forth the following hypotheses: Hypothesis 2a. There is a negative relationship between open exploitation and technological resources. Hypothesis 2b. There is a positive relationship between open exploitation and market resources. 2.2.3. Closed exploration For a rather long time organization theorists have argued that organizations mainly conduct local search, e.g., search in the neighbourhood of current technological practice, knowledge and competence (Nelson and Winter, 1982; March, 1991; Stuart and Podolny, 1996), when they aim to create new product innovations (see Laursen, 2008 for a review). Although local search is an

important element in search behaviour (Katila, 2002), companies are also able to implement radical internal search and undergo radical transformation (Francis et al., 2003). For instance, companies may follow a closed approach to innovation when they pursue complex technological search. Apple, for example, pursued a closed innovation approach with its highly novel product Ipod (Almirall and Casadesus-Masanell, 2010). Closed exploration refers to the management of processes that generate new knowledge inside the firm. This is a key part of the firm’s inventive capacity (Lichtenthaler and Lichtenthaler, 2009). We argue that closed exploration will be more valuable for the development of a firm’s technological resources, but less so for the development of the firm’s market resources. This is so because a firm that pursues a closed approach to innovation will escape the coordination costs and the negative effect of divergence associated with an open approach to innovation (Almirall and CasadesusMasanell, 2010). Hence, a firm that pursue ‘‘closed exploration’’ will be ‘‘freer’’ to pursue technological search without taking into account the technological objectives of external actors. Since the closed model of innovation is heavily oriented towards the generation of new technology in ‘‘isolation’’ from market forces and market input (Chesbrough, 2003), closed exploration may have costs associated with poorly developed market resources. We therefore put forth the following hypotheses: Hypothesis 3a. There is a positive relationship between a closed exploration mode and technological resources. Hypothesis 3b. There is a negative relationship between a closed exploration mode and market resources. 2.2.4. Closed exploitation Local search and the exploitation of current knowledge and resources within the boundaries of the firm is an important element of its ability to incrementally renew its resource base and enhance its performance. In March’s (1991) words: ‘‘Effective selection among forms, routines, or practices is essential to survival’’ (p. 72). Using the same knowledge has obvious benefits in the innovation process (Katila and Ahuja, 2002). It may reduce the likelihood of errors and increase the predictability of search. It can also lead to significantly deeper understanding of knowledge and boost a firm’s ability to identify valuable knowledge elements, to develop connections among them, and to combine them in various ways that are not apparent without experience with the knowledge (Levinthal and March, 1993; Katila and Ahuja, 2002). Enhancing and improving current routines and practices is therefore seen as one of the most efficient ways for organizations to incrementally refine their performance and ability to develop new product innovations. Closed exploitation is thus in line with the management of processes that retain and improve upon internal knowledge and competence and which keep internal knowledge and ‘‘know-how’’ ‘‘alive’’ (Lichtenthaler and Lichtenthaler, 2009). A range of studies have further argued and shown that firms are able to launch product innovations based on local search (Nelson and Winter, 1982; see Laursen, 2008 for a review). We put forth the following hypotheses: Hypothesis 4a. There is a positive relationship between a closed exploitation mode and technological resources. Hypothesis 4b. There is a positive relationship between a closed exploitation mode and market resources. 2.3. Resources and innovation Renewal of a firm’s resource base is deemed to be a key antecedent of innovation (e.g. Nelson and Winter, 1982; Katila, 2002). Without it, the firm would be less and less able to make

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Fig. 2. Research model.

(re)combinations of knowledge and resources that is necessary to develop new innovations. Innovation is thus considered to be, at least in part, a function of the firm’s technological and market resources. Technological and market resources are needed to pursue product innovation successfully (Paladino, 2008; Acur et al., 2010). This is because product innovation is the market introduction and commercialization of new products and technology (Schumpeter, 1934), in which both technological resources (e.g., in order to develop new products and technology) and market resources (in order to commercialize products and technology) are hypothesized to be important (Wiklund and Shepherd, 2003; Abernathy and Utterback, 1978). Technological resources can enhance the discovery and exploitation of opportunities. Such resources can lead to a technological break-through that represents an opportunity despite its market applicability not being readily apparent (e.g. Abernathy and Utterback, 1978). Technological resources can enhance a firm’s ability to effectively exploit an opportunity by, for example, determining the product’s optimal design to optimize functionality, cost, and reliability (Rosenberg, 1990) and ultimately the economic impact of exploiting the opportunity (McEvily and Chakravarthy, 2002). Therefore, technological resources provide a firm with the ability to rapidly exploit opportunities, or to be able to respond quickly when competitors make advancements (Cohen and Levinthal, 1990). Market resources can increase a firm’s ability to discover and exploit opportunities because: (1) Awareness of customer problems may have great generality and thus constitute real market opportunities. (2) It is easier to determine the market value of new scientific discoveries, technological change etc. (3) The locus of innovation often lies with the user of a new technology who cannot easily articulate their needs for not-yet-developed solutions to problems, and therefore the organization must share some of the same tacit knowledge as its users (Cohen and Levinthal, 1990; Shane, 2000; Von Hippel, 1988). In support of this, Shane (2000) found that prior knowledge of customer problems and ways to serve the market influenced the discovery of solutions to customer problems. Those who lack customer familiarity (Shane, 2000; Von Hippel, 1988) and knowledge of ways to serve the market (Shane, 2000) will find it difficult to recognize solutions to customer needs and to formulate an effective marketing strategy to introduce and sell the new product/service. From the above we argue that technological and market resources, will directly influence firms ability to develop product innovations.

Hypothesis 5. There is a positive relationship between technological resources and product innovation. Hypothesis 6. There is a positive relationship between market resources and product innovation. The hypothesised research model is displayed in Fig. 2.

3. Method 3.1. Research design and sample In order to test the relationships between innovation modes, resources and product innovation we collected survey data from Norwegian R&D active firms that had registered for a public R&D tax-credit program. The goal of the R&D tax-credit program is to enable firm innovation. The R&D tax-credit program in Norway is universal. We deem these firms to be suitable for studying whether and to what extent innovation modes influence a firm’s resource base and whether changes in a firm’s resource base influence its ability to develop product innovation. All 1721 enterprises which registered R&D activities between May and December 2005 were approached in the form of a web survey. Of the enterprises approached, 1199 (70%) returned filledin questionnaires.Secondary data regarding each of the firms receiving a tax refund were used to conduct response bias tests as well as data regarding control variables. Response bias tests were conducted related to the firms’ employment size, industry, and geographical location. No serious response bias was detected. 3.2. Self reported data and common method bias In this paper we use self-reported data. There are both benefits and disadvantages with the use of single respondents. One benefit is that: ‘‘For many purposes, the simplest and most accurate way to discover what a person does is to ask him’’ (March and Simon, 1993, p. 163). Thus, the use of perceptual approaches has the advantage that the research may have a relatively high level of validity. The reason is that questions can be posed that directly address the underlying nature of the construct to be measured. Data supplied by a single informant also captures situations and conditions within firms with a high degree of detail and specificity. The use of multi-item scales to measure constructs strengthens this (Lyon et al., 2000). Another obvious benefit is that respondents participating in the research are often the most knowledgeable persons in the organization to provide the information (Lyon et al., 2000).

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Data supplied by single respondents can also be highly problematic as it may introduce the common method bias problem. Common method bias is the potential inflation of correlations between measures accessed via the same method and may be especially problematic in self-reported data. We have attempted to control, at least partly, for this by controlling for the characteristics of the respondents in the econometric analysis, an issue that is related to ‘‘functional bias’’ discussed below. Another disadvantage of self-reported data from a single respondent is what is called functional bias. Different types of respondents may perceive things differently, such as how their firms should score on the dimensions measuring innovation modes (Lyon et al., 2000). To help correct for this we have included data about the role and function of the respondents from a pre-selected list and inserted this information as control variables in our econometric analysis. We distinguished between the following roles and functions: CEO, project leader for the SkatteFUNN project, CFO, R&D manager, accountant or other. Respondents were asked to tick one or more of these roles as respondents in especially small firms can fulfill many roles and functions at the same time. Because these respondent dummy variables were not significant, they were removed from the analysis presented in this paper.

3.3. Measurement 3.3.1. Measuring innovation modes. The open exploration construct includes three items, describing processes related to systematic search, development and identification of external business opportunities and the firm’s ability to absorb these opportunities and external knowledge inputs (Cohen and Levinthal, 1989; 1990; McKelvie and Davidson, 2009). The items are: ‘‘We systematically search for new business concepts through observation of processes in the environment’’, ‘‘The firm is constantly searching for new collaboration partners in order to develop our resource base’’ and ‘‘We systematically bring together creative and knowledgeable persons within the firm to identify new business opportunities’’. Our measurement of the ‘‘open exploration’’ construct contains elements of absorptive capacity (Cohen and Levinthal, 1990), a firm’s ability to use external knowledge to generate new products (Bierly et al., 2009) and to generate ideas (McKelvie and Davidson, 2009). The open exploitation construct includes three items. The items are: ‘‘Compared to our competitors, we search more actively for new partners for competence development’’, ‘‘Compared to our competitors, we cooperate more closely with our customers about innovation and R&D’’, and ‘‘Compared to our competitors, we cooperate more closely with our suppliers about innovation and R&D’’. Our measurement of the ‘‘open exploitation’’ construct describes processes related to identifying and obtaining knowledge from external actors that tend to be incremental in nature. These items reflect the literature on the value of incremental knowledge interactions for firm innovativeness in the Innovation Studies literature (Von Hippel, 1988; Lundvall, 1992). In sum we believe that the construct measures a firm’s focus on exploitation of knowledge possessed by external actors. Closed exploration includes three items and describes processes related to managerial search and involvement in internal R&D and development activities. Items are: ‘‘The board frequently discusses the firm’s R&D policy’’, ‘‘Firm management is involved in R&D processes’’ and ’’Firm management is participating actively in the development processes’’. The items reflect the evolutionary economics literature which has measured strong firm internal capabilities by internal R&D (Nelson, 1991). In sum, we believe that the construct measures a firm’s focus on

exploration for new alternatives within the firm with an emphasis on the role of managerial involvement. Closed exploitation includes three items and describes processes related to competence development of the workforce and the encouragement of ‘‘learning-by-doing’’. The items are: ‘‘The firm allocates resources to increase employees’ competence’’, ‘‘The firm emphasizes the importance of increasing the level of competence among employees’’ and ‘‘Employees are strongly stimulated to learn from their experiences’’. Items reflect the literature on ‘‘learning-by-doing’’ (Arrow, 1962) and by human capital theory which highlight the importance of firm specific training (Becker, 1975). In sum, we believe that the construct measures a firm’s focus on exploitation within the boundaries of the firm. For all items measuring innovation modes we adopted onesided seven point Likert scale where: 1¼strongly disagree and 7¼strongly agree.

3.3.2. Measuring technological and market resources Technological resources include two items and describe the firm’s technical resources. Items are: ‘‘Compared to our competitors the firm has a better technical expertise’’, and ‘‘Compared to our competitors the firm has a better expertise regarding development of products or services’’. Items are adopted from Wiklund and Shepherd (2003) and Madsen et al. (2007). Market resources include two items and describe the firm’s market/marketing resources. Items are: ‘‘Compared to our competitors the firm has a better expertise in marketing’’, and ‘‘Compared to our competitors the firm has a better special expertise regarding customer service’’. Items are adopted from Wiklund and Shepherd (2003).

3.3.3. Measuring product innovation The definition of product innovation follows the OSLO manual (OECD, 2005) and defines product innovation as the market introduction of products that are either ‘‘new to the firm’’ or ‘‘new to the firm’s market’’. Two items are used to measure product innovation: ‘‘During the last three years, did your enterprise introduce onto the market any new or significantly improved products (goods or services) relative to your firms previous products? and ‘‘During the last three years, did your enterprise introduce new or significantly improved products (goods or services) not only new for your enterprise, but also new for your enterprise’s market?’’. We adopted a one-sided seven point Likert scale where: 1 ¼No (or to a very little extent) and 7 ¼To a very large extent. Our definition of innovation has been used in a range of prior studies (e.g., Laursen and Salter, 2006, see Smith, 2005 for a review of studies).

3.3.4. Control variables A number of standard control variables are included in order to control for possible confounding relationships. A traditional control variable is firm size. Research has suggested, based on Schumpeter (1947), that larger firms are more innovative compared to smaller firms. Firm size is measured as (the log of) the number of employees. Recently, firm age has also been included as an important control variable in innovation regressions. Firm age is measured as the number of years since the firm first was established. It has also been customary, especially in the entrepreneurship literature, to control for differences across firms in their environment. The following items measure a latent factor entitled ‘‘environmental turbulence’’: ‘‘The rate at which products/services are becoming obsolete in the industry is very high’’, ‘‘actions of competitors are unpredictable’’ and ‘‘demand

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and tastes are almost unpredictable’’. Items are adopted from Khandwalla (1977).

4. Analysis A confirmatory factor analysis (CFA) is used to verify the unidimensionality, reliability, convergent validity and discriminant validity of the scales used to measure the concepts. Structural ¨ ¨ equation modelling (Joreskog and Sorbom, 1996) is employed to test the hypotheses. The data is analyzed using Mplus 6.0. The data were handled with the full-information MLR estimator to correct for missing data. 4.1. Measure validation The initial measurement model showed an acceptable fit (w2 ¼711.60, d.f. ¼197, p ¼.00, CFI¼.94, TLI¼ .91 and RMSEA¼ .049). Both CFI and TLI are above the recommended level of .90 (Bollen, 1989) and RMSEA suggests a close fit. Convergent validity was assessed using confirmatory factor analysis (Anderson and Gerbing, 1988). All standardized factor loadings are between .51 and .93 on the factor they are assigned to. The composite reliabilites of the constructs in the study varied between .60 and .88. All reliabilities satisfy the recommended level of .60 suggested by Bagozzi and Yi (1988). This is an indication of the convergent validity of the scales (Dabholkar et al., 1996). Discriminant validity was assessed by the correlations between the constructs of this study. These correlations, shown in Table 1, are reasonably low. The only exception is that the correlation between open exploration and closed exploration is .810. All the other correlations are below . 5. Discriminant validity of the constructs was also tested using the approach suggested by by Bagozzi et al. (1991). For all pairs of constructs, a chi-square difference test was used to test whether a two factor solution had statistically significant better fit than a single factor solution. In all cases the two-factor solution was significantly better than the single-factor solution. In sum, the measures of the proposed constructs achieve satisfactory reliability, convergent and discriminant validity.

231

Hypothesis 1b predicts that there is a positive relationship between open exploration and marketing resources. This hypothesis is supported (g ¼.33, t¼1.80) when using a one-tailed significance test. It is deemed appropriate to use a one-tailed test here because of the strong theoretical argument for this relationship (Abelson, 1995). Open exploitation. Hypothesis 2a postulates that there is a positive relationship between open exploitation and technological resources. This hypothesis is rejected (g ¼  .11, t ¼ .64). Hypothesis 2b predicts a positive relationship between open exploitation and marketing resources. This prediction is not supported (g ¼.13, t ¼.76). Closed exploration. Hypothesis 3a states a positive relationship between closed exploration and technological resources. This hypothesis is supported (g ¼.10, t ¼2.12). Hypothesis 3b predicts a negative relationship between closed exploration and marketing resources. This prediction is supported (g ¼  .09, t ¼  1.64) when using a one-tailed significance test. Closed exploitation. Hypothesis 4a postulates that there is a positive relationship between closed exploitation and technological resources. The hypothesis is supported (g ¼.16, t¼3.54). Hypothesis 4b proposes a positive relationship between closed exploitation and marketing resources. This hypothesis is supported (g ¼.15, t ¼2.89). Product innovation. Hypothesis 5 states that technological resources are positively related to product innovation. The hypothesis is supported (b ¼.25, t ¼6.11). Hypothesis 6 proposes Table 2 Standacccs.

4.2. Test of the hypotheses Structural equation modelling was employed to test the hypotheses. The results are shown in Table 2. The goodness-offit tests (w2 ¼772.730, d.f. ¼192, p.¼.00, CFI¼.93, TLI¼.91, RMSEA ¼.051) for the structural models are included in Table 2. The theoretical model fits the data rather well. Open exploration. Hypothesis 1a states that there is a positive relationship between open exploration and technological resources. The hypothesis is supported by the data (g ¼ .44, t¼2.56).

Path

Technological resources

Market resources

Open explorationOpen exploitationClosed explorationClosed exploitationTechnological resourcesMarket resourcesEnvironmental turb.AgeFirm sizew2 (d.f.) p-Value CFI TIL RMSEA R2

.44 (2.56nn)  .11 (  .64) .10 (2.12nn) .16 (3.54nnn)

.33 (1.80n) .13 (.76)  .09 (  1.64n) .15 (2.89nnn)

n

Product innovation Estimate (t-value)

.25 (6.11nnn)

 .01 (  .12) .03 (.83) .02 (.47)

 .17 ( 3.24nn) .04 (.96) .26 (5.59nnn)

.26

.26

.11 (2.34nn) .11 (2.89nnn)  .01 ( .35) .13 (3.73nnn) 772.73 (192) .00 .93 .91 .045 .11

p o0.05 (one-tailed). po 0.05. p o 0.01.

nn

nnn

Table 1 Correlation matrix.

P.Inno (1) O.Expl (2) O.Explt (3) C.Expl (4) C.Explt (5) M.Res (6) T.Res (7) E.Turb (8) Age (9) Size (10)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

1.00 .27 .18 .21 .16 .20 .26 .11 .02 .11

1.00 .51 .81 .52 .30 .44 .31  .10  .236

1.00 .46 .48 .12 .33 .11  .10  .256

1.00 .50 .37 .35 .28  .05  .105

1.00 .29 .37 .19 .04  .078

1.00 .40  .05 .05 .21

1.00 .16  .02  .10

1.00 .00  .19

1.00 .21

1.00

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that marketing resources are positively related to product innovation. This hypothesis is supported (b ¼ .11, t¼2.34). Control variables. Environmental turbulence had no effect on technological resources (g ¼  .01, t ¼ .12), a negative and statistical significant effect on marketing resources (g ¼ .17, t ¼3.25) and a positive and statistical effect on product innovation (g ¼.11, t ¼2.89). Age do neither have a statistically significant effect on technological resources (g ¼.03, t ¼.83), marketing resources (g ¼.04, t ¼.9625), nor on product innovation (g ¼  .01, t¼  .35). Firm size does not have a statistically significant effect on technological resources (g ¼ .02, t ¼.47), but it has a positive and statistically significant effect on marketing resources (g ¼.26, t ¼5.59) and on product innovation (g ¼ .13, t ¼3.73).

5. Discussion and conclusion Given the importance of innovation for firms’ performance and their competitiveness, researchers and practitioners have attempted to better understand that factors that promote innovation within firms. The focus in many of these studies has been on how firms vary in key characteristics and the extent to which these differences have implications for innovation. Inspired by the evolutionary theory of the firm, this paper has argued that firms not only differ in key firm characteristics, but that they also differ in their approach to innovation. These innovation modes have important implications for firms’ abilities to innovate. The paper asked the following research question: How do firms attempt to develop new product innovations and to what extent and how do ‘‘the way firms innovate’’ influence the ability of firms to actually develop product innovation? Integrating research on exploration/exploitation and open/closed innovation, this paper has contributed to theorizing on innovation modes and in particular proposed that firms can differ in the following four modes of innovation: ‘‘open exploration’’, ‘‘open exploitation’’, ‘‘closed exploration’’ and ‘‘closed exploitation’’ with implications for their resource base and ability to develop new product innovations. Using survey data from R&D active firms in Norway, we tested 10 hypotheses as an elaboration of our research question concerning the relationship between innovation modes, resources and product innovation in a SEM model. Table 3 below sums up the hypothesis number, direction and findings. The results show that open exploration and closed exploitation are associated with significantly better technological and market resources, confirming Hypotheses 1a, 1b, 4a and 4b. We also found that while closed exploration has a positive and significant effect on technological resources (confirming Hypothesis 3a), this mode of innovation has a negative and significant influence on market resources (confirming 3b). Although we hypothesised that open exploitation would be negatively associated with Table 3 The effects of antecedents on dependent variables. Antecedents

Technological resources

Market resources

Open exploration Open exploitation Closed exploration Closed exploitation Technological resources Market resources

H1a H2a H3a H4a

H1b H2b H3b H4b

(þ): ( ): (þ): (þ):

s. n.s. s. s.

s. ¼supported, n.s. ¼not supported.

(þ): (þ): ( ): (þ):

Product innovation s. n.s. s. s. H5 ( þ): s. H6 ( þ): s.

technological resources and positively associated with market resources (Hypotheses 2a and 2b), we found no statistical support for this in our data. The estimated SEM model further revealed that firms with better technological and market resource were significantly more likely to develop new product innovations, confirming Hypotheses 5 and 6. The empirical results thus show that open exploration, open exploitation, closed exploration and closed exploitation vary in their influence on a firm’s technological and market resources. Our results show in particular that open exploration and closed exploitation have a strong positive relationship with technological and market resources. This finding sheds some new empirical light on the advantages attributed to pursuing the closed or open approach to innovation (Almirall and Casadesus-Masanell, 2010; Lichtenthaler and Lichtenthaler, 2009). While others have argued that firms should follow the open approach (Chesbrough, 2003), or should sometimes follow the closed approach (Almirall and CasadesusMasanell, 2010: Lichtenthaler and Lichtenthaler, 2009), we have in this paper clarified that this may depend on the extent to which firms pursue exploration or exploitation. Further, our paper has helped to clarify that the choice to pursue open or closed innovation may depend on whether or not the firm aims to develop its technological or market resources. In sum, our paper has contributed to the literature by identifying four modes of innovation that capture (some of) the variance across firms in their approach to innovation. The paper shows how this heterogeneity influences their ability to develop new product innovations. Our research has – like most papers – shortcomings that may represent avenues for further research. One shortcoming is the cross-sectional nature of the data that we use. Although SEM models have been designed to test ‘‘strong theories’’, the model that we estimate can only show associations between concepts. It is difficult to determine causality without longitudinal data. A related shortcoming is that we have little information about how the innovation modes that we measure in this paper first were ‘‘born’’. It is also a shortcoming that the four innovation modes that we have measured in this paper are quite broad in nature. We suspect that several finely-graded measures of innovation modes may be identified and empirically measured within each of the quadrants in Fig. 1. An interesting approach for future research is to examine firms reliance on the modes identified in this paper over time and analyse to what extent a decrease/ increase in their use over time is associated with lower/higher market and technological resources. This would venture deeper into the issue of when firms should pursue open or closed approaches to innovation. This paper has on the other hand offered a theoretical framework and an empirical operationalization of it that we hope can be elaborated and improved upon in future research. References Abelson, R.P, 1995. Statistics as Principled Argument. Lawrence Erlbaum Associates Inc., Hillsdale. Acs, Z., Audretsch, D.B., 2003. Innovation and technological Change. In: Acs, Z, Audretsch, D. (Eds.), Handbook of Entrepreneurship Research. Kluwer Academic Publishers, pp. 55–80. Abernathy, W.J., Utterback, J.M., 1978. Patterns of industrial innovation. Technological Review 80, 40–47. Almirall, E., Casadesus-Masanell, R., 2010. Open versus closed innovation: a model of discovery and divergence. Academy of Management Review 35, 27–47. Acur, N., Kandemir, D., Weerd-Nederhof, P., Song, M., 2010. Exploring the impact of technological competence development on speed and NPD program performance. Journal of Product Innovation Management 27, 915–929. Anderson, J.C., Gerbing, D.W., 1988. Structural equation modeling in practice—a review and recommended two-step approach. Psychological Bulletin 103, 411–423.

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