Absorptive capacity, its determinants, and influence on innovation output: Cross-cultural validation of the structural model

ARTICLE IN PRESS Technovation 29 (2009) 859–872

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

Absorptive capacity, its determinants, and influence on innovation output: Cross-cultural validation of the structural model Nika Murovec a, Igor Prodan b, a b

Institute for Economic Research, Kardeljeva pl. 17, SI-1000 Ljubljana, Slovenia Faculty of Economics, University of Ljubljana, Kardeljeva pl. 17, SI-1000 Ljubljana, Slovenia

a r t i c l e in fo

Keywords: Absorptive capacity Innovation Structural model Cross-cultural study

abstract The main purpose of this study is to provide stronger quantitative evidence in the field of organizational absorptive capacity research by using a more direct measure of absorptive capacity and a wide range of variables in a cross-nationally tested structural model. The results show that there exist two kinds of absorptive capacity: demand-pull and science-push. Their most important determinants proved to be internal R&D, training of personnel, innovation co-operation and attitude toward change. Both kinds of absorptive capacity are positively related to product and process innovation output. Therefore, absorptive capacity is to be given more attention in the future research and innovation policy considerations. & 2009 Elsevier Ltd. All rights reserved.

1. Introduction Modern economies are not based on capital and labour as much as they are based on knowledge, which became the key factor of development (Davenport and Prusak, 1998). The knowledge creation, distribution, diffusion, application and manipulation are crucial in the knowledge society (e.g. Davenport and Prusak, 1998; Shapira et al., 2006; World Bank, 2007). New fields of knowledge progress rapidly and the products’ lifecycles become shorter over time. In order to survive, an organization has to innovate constantly (e.g. Galanakis, 2006). However, innovation is not only crucial for companies, but it also has a great influence on the economy as a whole, since it increases the economic growth, national competitiveness and productivity (Coad and Rao, 2008; Fagerberg, 2001; Freeman and Soete, 1994; Mowery and Nelson, 1999; Porter, 1998). Knowledge and innovation are intertwined; innovation is based on the application of new knowledge and at the same time, the application of new knowledge leads to change and innovation. Therefore, knowledge is the key to innovation (e.g. Jensen et al., 2007; Nonaka and Takeuchi, 1995; World Bank, 2007). However, since the innovations are progressively becoming more complex, the mastering of just one technological field is no longer sufficient. In order for innovation activity to provide a desired output, an organization needs to possess knowledge from many different fields. To be able to set up a broad knowledge base, an organization has to absorb information from all kinds of

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E-mail addresses: [email protected] (N. Murovec), [email protected] (I. Prodan). 0166-4972/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.technovation.2009.05.010

sources—not just internal, but also all available external sources. Extensive acquaintance with the relevant fields is necessary for a company to even be aware of the existing external knowledge and technological development. Furthermore, to understand and implement ideas and concepts of others, organizations must have the competencies that enable them to understand, decodify and utilize these ideas (Grunfeld, 2003). Absorptive capacity represents the link between an organization’s internal capability to develop new products and improve existing ones, and external base of information and opportunities on the other side, and is most commonly defined as the ‘‘ability of an organization to recognize the value of new, external information, assimilate it, and apply it to commercial ends’’ (Cohen and Levinthal, 1990). Despite several theoretical (e.g. Grunfeld, 2003; Lane et al., 2006; Todorova and Durisin, 2007; Zahra and George, 2002) and empirical studies (e.g. Jansen et al., 2005; Stock et al., 2001; Tu et al., 2006; Vinding, 2006) of organizational absorptive capacity, which emerged in the last decade, this field is still underinvestigated. Studies of absorptive capacity usually assume that there exists only one absorptive capacity; however, it seems unlikely that an organization, which has high absorptive capacity, is capable of absorbing information from all available external sources of knowledge. Furthermore, while some studies try to explain the concept of absorptive capacity and its determinants, there is a great lack of quantitative support for their findings. To a large extent this is probably due to the fact that the qualitative nature of the absorptive capacity makes it a very difficult concept to measure quantitatively. This is also the reason that the used approaches in the existing quantitative studies are often argumentative. Commonly, they do not really measure the absorptive capacity but its suggested indicators—in most cases R&D or

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human resources (e.g. Stock et al., 2001; Veugelers, 1997; Vinding, 2006). This approach is also used when examining the influence of absorptive capacity on innovation (e.g. Mancusi, 2004; Stock et al., 2001; Vinding, 2006). Consequently, this leaves the question, whether it is really absorptive capacity that influences innovation or is it the proxies (R&D, human resources, etc.) that influence innovation directly. Due to the great attention placed on the role of knowledge and its relationship to innovation in the current policy discussions in all developed countries, further studies of the issue of absorptive capacity, its determinants and its importance for innovation, are needed. To avoid the above-mentioned weaknesses of the previous research, a more direct measure of absorptive capacity is used in this paper. A contribution is made by statistically investigating whether the absorptive capacity is in fact a one-factor structure or a multiple-factor structure. Furthermore, a conceptual model of absorptive capacity and its influence on innovation output is developed and tested on robust datasets from two different countries to increase its validity and enable a cross-national comparison. This paper is structured as follows: Section 2 provides an overview of the existing research, based on which the hypotheses are postulated, and a conceptual model of absorptive capacity and its influence on innovation output is developed. In Section 3, the research methodology, which was used to test the conceptual model, is described. The estimation results of the empirical analysis in the Czech Republic and Spain are presented in Section 4, followed by their discussion and conclusion in Section 5.

various skills and dimensions. Mowery and Oxley (1995) proposed a definition of absorptive capacity as a broad set of skills needed to deal with the tacit component of transferred knowledge and the need to modify this imported knowledge. Kim (1997, 1998) defines absorptive capacity as the capacity to learn and solve problems. Zahra and George (2002) expanded the most commonly used definition by Cohen and Levinthal (1990) and defined absorptive capacity as ‘‘a set of organizational routines and processes by which organizations acquire, assimilate, transform, and exploit knowledge to produce a dynamic organizational capability’’, which is ‘‘pertaining to knowledge creation and utilization that enhances an organization’s ability to gain and sustain a competitive advantage’’ (Zahra and George, 2002, p. 185). By defining absorptive capacity as a dynamic capability, they emphasized the strategic nature of absorptive capacity. Cohen and Levinthal’s (1990) basic definition is most widely accepted. Since it captures the main idea of the concept very well and is at the same time most simple and comprehensible, we also derive from this definition in this paper. However, based on the distinction between two types of innovation (science-push and demand-pull), we propose two different types of absorptive capacity: (1) science-push absorptive capacity, which is based on scientific information (e.g. universities, non-profit research institutes, commercial R&D enterprises); and (2) demand-pull absorptive capacity, which is based on market information (e.g. customers, suppliers, competitors, professional conferences, fairs). The underlying reasons for this preposition shall be developed further in the next section where a related hypothesis will also be postulated.

2. Theoretical background and hypotheses 2.2. Science-push and demand-pull absorptive capacity In this section, the theoretical background of this paper will be examined. Based on that, several hypotheses will be postulated about the absorptive capacity, its determinants and its relation to innovation. At the end of this section, a conceptual model of absorptive capacity and its influence on innovation output will be presented. 2.1. The concept of absorptive capacity The concept of absorptive capacity originated in macroeconomics, where it refers to the ability of an economy to utilize and absorb external information and resources (Adler, 1965). Cohen and Levinthal (1990) adapted this macroeconomic concept to organizations and described absorptive capacity as ‘‘the ability of an organization to recognize the value of new, external information, assimilate it, and apply it to commercial ends’’. They suggested that absorptive capacity is largely a function of the organization’s level of prior related knowledge and argued that it is critical to the organization’s innovative capabilities. In recent years scholars have used the concept of absorptive capacity in their analyses at different levels: individual (Cohen and Levinthal, 1990), business unit (Szulanski, 1996), organization (Cohen and Levinthal, 1990), dyad (Lane and Lubatkin, 1998), cluster (Giuliani, 2003; Giuliani and Bell, 2005), industrial districts (Aage, 2003a, b) and country (Criscuolo and Narula, 2008; Dahlman and Nelson, 1995). However, very few empirical studies capture the rich theoretical arguments and the multidimensionality of the absorptive capacity construct. The definitions and operationalizations of this construct vary; some scholars have even used the term absorptive capacity without defining it (e.g. Glass and Saggi, 1998; Keller, 1996). Most authors have used different modifications of the concept suggested by Cohen and Levinthal (1990) but still retained the notion that absorptive capacity is not a one-dimensional concept, consisting rather of

Since Schumpeter (1934), the debate has raged about whether it is market demand or technological opportunity that explains inventor’s decisions to bring their ideas to the market. These onesided approaches are designated briefly as ‘‘science-push’’ theories of innovation and ‘‘demand-pull’’ theories of innovation (Freeman and Soete, 1997). Even though Schumpeter (1939) argued strongly against a single factor causation of economic growth or decline, he is nevertheless associated with the sciencepush rather than demand-pull side of the controversy (Walsh, 1993). While Schumpeter argued that entrepreneurs are driven by technological opportunity, early studies indicated that increases in demand preceded increases in inventive activity over the business cycle (Schmookler, 1962). Most scholars confirmed Schmookler’s findings, concluding that user ‘‘need’’ is the most important driver of innovation (for a review see Freeman, 1982). Even though the market demand vs. technological opportunity debate has recently been re-invigorated by research demonstrating support for technological opportunity (Bernstein and Singh, 2006; Goldenberg et al., 2001; Shane, 2001), most researchers do not support such one-sided, but a more integrated approach to innovation (Marsh and Oxley, 2005). Since learning plays a key role in the process of innovation, interaction and information absorption from all available sources are very important. Therefore, in this study a one-sided approach is considered inadequate. Unlike many studies of absorptive capacity, which put emphasis only on science-push side (e.g. Leahy and Neary, 2007; Parisi et al., 2006; Stock et al., 2001), in this research relevant information from market as well as scientific sources is included. Assuming that organizational absorptive capacity also depends upon the characteristics of external knowledge, Nelson and Wolff (1997) pointed out that science-based technological opportunity requires a higher level of absorptive capacity than that generated by other knowledge sources such as customers. Becker and Peters

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(2000) argue that organizations need higher absorptive capacities for scientific knowledge than for other types of knowledge. Based on their findings, Schmidt (2005) assumed (but not empirically supported the assumption) that there are different absorptive capacities for different kinds of knowledge, one distinction being between science-based knowledge and knowledge from the private sector. The market demand vs. technological opportunity debate together with the research indicating that there are different kinds of absorptive capacity regarding the nature of the knowledge, led us to consider whether there exist different kinds of capacities to assimilate external information regarding the nature of this information. We distinguished between the information that enables science-push or demand-pull innovation and formed the following hypotheses: Hypothesis H1. Absorptive capacity is a two-factor structure comprising a science-push component and demand-pull component. 2.3. The determinants of absorptive capacity 2.3.1. Internal R&D and absorptive capacity Cohen and Levinthal (1990) highlighted that organizations must develop absorptive capacity in order to benefit from external knowledge flows. Mowery (1984) has pointed out that an organization is far better equipped to absorb the output of external R&D if it is also performing some amount of R&D internally. Cohen and Levinthal (1990) showed that organizational investment in R&D contributes to organizational absorptive capacity. Since then, R&D has been recognized as a potential determinant in most of the absorptive capacity studies. Many scholars also tried to empirically verify the importance of R&D using different measures (e.g. Escribano et al., 2005; Griffith et al., 2004; Kneller and Stevens, 2006; Mancusi, 2004; Rocha, 1999); however, the overview showed that their results differ and suggest that R&D is not equally important in all circumstances and for all kinds of knowledge (Grunfeld, 2004; Schmidt, 2005). To investigate the influence of internal R&D on science-push absorptive capacity and demand-pull absorptive capacity, the following hypotheses are postulated: Hypothesis H2. The extent of an organization’s internal R&D expenditure will be positively related to the extent of: H2a: science-push absorptive capacity. H2b: demand-pull absorptive capacity. 2.3.2. Extramural R&D and absorptive capacity Cohen and Levinthal (1990) considered the possibility for an organization to buy its absorptive capacity. They suggested that the effectiveness of such options is limited, especially for those bought components of absorptive capacity, which are associated with product and process innovation. This type of information is often organization-specific and thus cannot be bought and quickly integrated into the organization (Cohen and Levinthal, 1990). There are arguments indicating that the acquisition of extramural R&D (bought R&D performed by external organizations) may stimulate rather than substitute internal R&D activities (Braga and Willmore, 1991; Hung and Tang, 2008; Siddharthan, 1992). Veugelers (1997) showed that bought extramural R&D increases organization’s own in-house R&D expenditures, but only if an organization already possesses its own absorptive capacity. Since most research on absorptive capacity showed that in-house R&D expenditures are a determinant of organizational absorptive capacity (e.g. Escribano et al., 2005; Griffith et al., 2004; Kamien

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and Zang, 2000; Mancusi, 2004; Rocha, 1999), it can be inferred that also bought extramural R&D increases organizational absorptive capacity, but again only if an organization has its own prior absorptive capacity. However, most of the scholars focused on internally developed components of absorptive capacity and not on those that can be bought. Therefore, to further investigate the influence of bought components of absorptive capacity, the following hypotheses are postulated: Hypothesis H3. The extent of bought extramural R&D will be positively related to the extent of: H3a: demand-pull absorptive capacity. H3b: science-push absorptive capacity. 2.3.3. Human capital and absorptive capacity Education has long been recognized as significant in improving innovation systems (List, 1841 as reported in Lundvall et al., 2002). Although R&D activities are the most often mentioned determinant of absorptive capacity, lately some studies have moved away from this traditional indicator and have instead focused on the human capital involved in the process (Kneller and Stevens, 2006; Mangematin and Nesta, 1999; Vinding, 2006). Education and training increase the stock of knowledge in the organization (Mangematin and Nesta, 1999). The existing empirical studies mostly examine the importance of highly educated employees for absorptive capacity but neglect the importance of the organization’s later investments in training, which is more focused on an organization’s specific needs. Since the employees’ knowledge is not only the result of their formal education, the following hypotheses were formed: Hypothesis H4. Training of personnel directly related to innovation projects will be positively related to the extent of: H4a: demand-pull absorptive capacity. H4b: science-push absorptive capacity. 2.3.4. Co-operation and absorptive capacity Some research investigating the influence of an organization’s collaboration with different actors on its innovative performance or on certain aspects of its absorptive capacity has been conducted. Belderbos et al. (2004) and Becker and Dietz (2004) showed that R&D co-operation increases organizations’ innovation. Tether’s (2002) logistic regression analysis showed that cooperations are considerably more common when organizations are developing higher level innovations (i.e. more novel and/or more complex innovations). Vinding (2006) argues that the organization’s development of closer external relationships increases the potential effect of transferring information as well as tacit knowledge. His study reveals that organizations that have developed closer relationships to vertically related actors and also to knowledge institutions do significantly better on innovative performance compared to organizations that have only developed a closer relationship to one of the actors, and they do much better as compared to organizations that have not developed closer relationships to their external actors. Cohen and Levinthal (1990) linked together von Hippel’s (1988) findings about the importance of close relationships with buyers and suppliers for innovation, with the concept of absorptive capacity, and concluded that since a broad and active network of organization’s internal and external relationships will strengthen individuals’ awareness of others’ capabilities and knowledge, this will result in leveraging individual absorptive capacities and strengthening of the organization’s absorptive capacity. Fabrizio (2006) suggests that more collaboration with university scientists by an organization is associated with more exploitation of published scientific research (higher

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absorptive capacity) and shorter lag times between existing knowledge and new organization’s inventions exploiting this knowledge. Theory on strategic networks suggests that linkages to many network partners may increase the breadth and variety of information to which an organization has access, while strong linkages to one or few network partners may unproductively limit an organization’s vision of alternatives (Gulati et al., 2000). Based on case studies, Lim (2006) argues that absorptive capacity is primarily a function of connectedness. Most of the existing research (e.g. Cockburn and Henderson, 1998; Fabrizio, 2006; Vinding, 2006), however, only focuses on co-operation with a certain type of actors, and investigates the influence of cooperation on innovation and not on absorptive capacity or is not empirically verified. Therefore, to empirically investigate whether collaboration with different types of actors (not only the actors located in the value chain of the product/service but also public or commercial knowledge institutions and consultants) influences organizational absorptive capacity, the following hypotheses are postulated: Hypothesis H5. The extent of innovation co-operation with different types of partner organizations will be positively related to the extent of: H5a: demand-pull absorptive capacity. H5b: science-push absorptive capacity.

2.3.5. Attitude towards change and absorptive capacity Absorptive capacity is also influenced by organizational factors such as organizational structure, organizational culture and organizational communication (Alvesson, 2002; Cohen and Levinthal, 1990; Van Den Bosch et al., 1999). These factors are intertwined and influence each other. Since they are difficult to measure, they have mostly been studied in qualitative studies. As a consequence, there is a great lack of empirical evidence of their influence on the absorptive capacity. Therefore, in this research, an attempt to quantitatively investigate at least one aspect of this group of factors will be made. Organizational culture has an important influence on an organization’s innovativeness; it is of specific importance whether tradition or continuous change is a value in the organization (Kanter, 1985). People try to adjust to a certain culture and if changes are desired, the individuals will be much more motivated to search information about possible changes and improvements. However, strong cultures tend to be xenophobic and reluctant to everything that is different, hinder the process of change and foster inbreeding (Kotter, 1996). These kinds of cultures are unfavourable to innovation, especially if the ideas for innovation come from outside the organization, which is known as a Not Invented Here (NIH) syndrome (Fagerberg, 2004). Since organizational culture also influences the employees’ perception of the external environment (Oden, 1997), consequently, they are also reluctant to assimilate and use external information because they are incapable of recognizing their value, even though they might be aware of them. Therefore, these kinds of cultures leave little room for the absorption of the external sources of knowledge, especially if they contradict the existing beliefs (Van Den Bosch et al., 1999). Based on this, we can assume that the attitude towards change influences the organizational absorptive capacity. To empirically confirm this assumption, we formed the following hypotheses: Hypothesis H6. The positiveness of the attitude towards change will be positively related to the extent of: H6a: demand-pull absorptive capacity. H6b: science-push absorptive capacity.

2.4. Absorptive capacity and innovation output Schumpeter was the first who included external information alongside internal information in his model of innovation (Freeman, 1982). Nevertheless, for many years, the greater importance was placed on the internal creation of knowledge. Only recently it became apparent that the internal knowledge is not sufficient and that for successful innovation it is crucial that an organization uses the information from all available sources. The closed innovation model has been substituted by the open innovation model (Chesbrough, 2003), which emphasizes the importance of external knowledge. The fact that most innovation results from borrowing rather than invention (Cohen and Levinthal, 1990) further demonstrates the importance of external knowledge. The ability to exploit external information—absorptive capacity—is thus a critical component of innovative capabilities and output. Many researchers investigated the influence of absorptive capacity on various aspects of innovation (e.g. Cohen and Levinthal, 1990; Knudsen et al., 2001; Mancusi, 2004; Vinding, 2006). Most of them, however, did not measure the direct effect of absorptive capacity on innovation, but the effects of certain determinants of absorptive capacity on innovation. Since there is still no consensus regarding which determinants constitute absorptive capacity, their approaches and, consequently, their results, differ. Therefore, we wanted to examine the influence of absorptive capacity on innovation more closely. To avoid the discussions about the operationalisation and measurement issues (e.g. Cormican and O’Sullivan, 2004; Godin, 2003; Kleinknecht et al., 2002; Rejeb et al., 2008; Smith, 2005), instead of the general term ‘‘innovation’’, the more explicit and less complex term ‘‘innovation output’’ is used. Innovation output (e.g. profit, patents, sales, improved quality, cost reduction, etc.) is also what is actually being measured in most of the studies of absorptive capacity and innovation (e.g. Becker and Peters, 2000; Knudsen et al., 2001; Mancusi, 2004; Vinding, 2006). The example of the researchers of innovation, who customarily distinguish between product and process innovation (e.g. Arundel and Kabla, 1998; Gopalakrishnan and Damanpour, 1997; Utterback and Abernathy, 1975) was followed, since the literature on absorptive capacity has dealt primarily with overall innovative activity. Since we also distinguished between demand-pull and science-push absorptive capacity, we wanted to explore the influence that different types of absorptive capacity have on process and product innovation output. Therefore, the following hypotheses are postulated: Hypothesis H7. The extent of demand-pull absorptive capacity will be positively related to the extent of: H7a: product innovation output. H7b: process innovation output. Hypothesis H8. The extent of science-push absorptive capacity will be positively related to the extent of: H8a: product innovation output. H8b: process innovation output. A resulting conceptual model of absorptive capacity and its influence on innovation output that integrates previously developed hypotheses is presented in Fig. 1.

3. Methodology The methodology is discussed in terms of sample and data analysis, and operationalization and measure validation.

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Fig. 1. The conceptual model of absorptive capacity and its influence on innovation output.

3.1. Sample and data analysis The empirical analysis is based on the responses to Spanish and Czech Republic’s third Community Innovation Survey (CIS3). The choice of the countries was dictated by the access to data. A total of 8024 manufacturing firms responded to the Spanish survey and a total of 3300 manufacturing firms responded to the Czech Republic’s survey. Among those, 2422 Spanish and 641 Czech Republic firms were selected for further analysis; these were the firms that engaged in the innovation activities, which means that they did the following: (1) introduced new or significantly improved products or services; or (2) introduced new or significantly improved production methods or service delivery methods; or (3) engaged in the development of new or significantly improved products, services or procedures, which was not yet completed; or (4) engaged in the development of new or significantly improved products, services or procedures, which was abandoned. Based on the low percentage of overall missing data (less than 1%) and no pattern in the missing data spread across variables, the missing data in the dataset can be considered to be missing completely at random (Hair et al., 1998; Rubin, 1976); therefore, different imputation techniques can be applied (Hair et al., 1998). In order to preserve the sample size in each of the two samples, two imputation techniques were used: within case mean (referred to also as person mean) imputation (Bernaards and Sijtsma, 2000; Downey and King, 1998; Roth et al., 1999) and item mean imputation. Within case mean imputation technique was used if there were less than 50% of missing values within a particular construct; otherwise, item mean imputation was used. The rationale behind the multiple imputation approach is that the use of multiple approaches minimizes the specific concerns with any single method (Hair et al., 1998). All variables were standardized using data from the overall sample. Standardizing the data separately for each of the groups would lead to different rescaling of measured variables within each group, destroying the comparability across groups of the common scale for the measured variables and leading to inability to compare parameter estimates across groups (Reise et al., 1993). Construct convergent validity and discriminant validity was assessed using exploratory and confirmatory factor analysis (Floyd and Widaman, 1995). Reliability was assessed using Cronbach’s alpha (Cronbach, 1951) for internal consistency. Exploratory factor analysis was performed, using the maximum likelihood (ML) extraction method and direct oblimin rotation. Reliability and exploratory factor analysis was performed with SPSS, version 13.0. While conducting

the confirmatory factor analysis, the analytical steps suggested by Marsh and Hocevar (1985, 1988) were followed. For confirmatory factor analysis and structural equation modeling, the EQS Multivariate Software version 6.1 (Bentler and Wu, 2006) was used. Since a small amount of non-normality is present in the data, the structural relationships in the model of absorptive capacity were estimated using the elliptical reweighted least squares (ERLS) method. ERLS minimizes the problems deriving from data skewness and kurtosis and is otherwise comparable with the maximum likelihood method (Sharma et al., 1989). In order to identify the determinants of absorptive capacity that are country specific, a multisample analysis was conducted. In the multisample analysis, the model was constrained for equality of factor loadings and for equality of error variances, as suggested by several researchers (e.g. Jo¨reskog and So¨rbom, 1996; Singh, 1995; Vandenberg and Lance, 2000). As recommended by Shook et al. (2004), the fit of the model was assessed with multiple indices: the normed-fit-index (NFI), the non-normed-fit index (NNFI), the comparative fit index (CFI), the goodness-of-fit index (GFI), the standardized root mean square residual (SRMR), and the root mean square error of approximation (RMSEA). Values of NFI, NNFI, CFI and GFI greater than 0.90 indicate a good model fit (Byrne, 2006; Hair et al., 1998). Hu and Bentler (1999) suggest that values of SRMR less than 0.08 indicate an acceptable fit. Values of RMSEA less than 0.05 indicate a good fit, and values as high as 0.08 represent reasonable errors of approximation in the population (Browne and Cudeck, 1992). The w2 is reported, but is not given major consideration, because it is highly sensitive to sample size and the number of items in the model (Bentler and Bonett, 1980). 3.2. Operationalization and measure validation In this study, the variables were measured through scales developed for the Community Innovation Survey. Items with extreme skew and/or extreme kurtosis were transformed by employing the square root transformation (Hair et al., 1998). Measurement items’ descriptive statistics and correlation matrix for both samples are presented in Appendix 1. 3.2.1. Internal R&D Internal R&D was measured with two items. The first item was operationalized as innovative expenditures for intramural research and experimental development. The second item was operationalized as the number of employees involved in intramural R&D activities, including persons both inside and outside

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the R&D department. Cronbach’s alphas of 0.75 for the Czech Republic’s sample and of 0.72 for the Spanish sample were both above the threshold of 0.70 (Hair et al., 1998), indicating strong internal consistency of items operationalized to measure the construct. Internal R&D was entered in the model of absorptive capacity as latent construct. 3.2.2. Extramural R&D Extramural R&D was measured as innovative expenditures for all innovative activities performed by other organizations or other public or private research organizations. 3.2.3. Training of personnel related to innovation projects Training of personnel related to innovation projects was measured as a dichotomous variable, which indicated if the firm engaged in internal or external training of their personnel directly aimed at the development and/or introduction of innovations. 3.2.4. Innovation co-operation Respondents were asked to indicate whether their company was involved in any co-operation arrangements on innovation activities with the following types of organizations: (1) other enterprises within their enterprise group; (2) suppliers of equipment, material, components or software; (3) clients of customers; (4) competitors and other firms from the same industry; (5) consultants; (6) commercial laboratories or R&D enterprises; (7) universities or other higher educational institutes; and (8) government or private non-profit research institutes. Respondents were additionally asked to indicate the location of each type of organization: (1) national; (2) within European Union; (3) within European Union candidate countries; (4) United States; (5) Japan; and (6) other. Accordingly, innovation cooperation was measured as a sum of all co-operation arrangements in the matrix: type of the organization—location of organization. The scale ranged from 0 to 48. 3.2.5. Attitude towards change Respondents were asked to indicate whether their company undertook the following activities related to implementation of: (1) new or significantly changed corporate strategies; (2) advanced management techniques within their enterprise; (3) new or significantly changed organizational structures; and (4) significantly changed enterprise’s marketing concepts or strategies. Cronbach’s alphas of 0.73 for the Czech Republic’s sample and of 0.71 for the Spanish sample were both above the threshold of 0.70 (Hair et al., 1998). The factor analysis indicated that all factor loadings were above 0.4. Attitude towards change was entered in the model of absorptive capacity as latent construct. 3.2.6. Demand-pull and science-push absorptive capacity Most of the researchers, which made an effort to operationalise and quantify the concept of absorptive capacity, measured absorptive capacity with R&D (e.g. Cassiman and Veugelers, 2002; Cockburn and Henderson, 1998; del Carmen Haro-Domı´nguez et al., 2007; Hall et al., 2005; Xia and Roper, 2008). However, this operationalisation is based on an assumption that R&D is an important predictor of the absorptive capacity, which has not been clearly supported by the empirical evidence. Also some other attempts to measure absorptive capacity follow the same logic; only that R&D is substituted by some other assumed absorptive capacity proxy (Keller, 1996; Mowery and Oxley, 1995; Veugelers, 1997). Only very recently some attempts have been made to improve the measurement of absorptive capacity (e.g. Arbussa and Coenders, 2007; Schmidt, 2005). Following these examples, in

this study the absorptive capacity was measured with the use and importance of different sources of information needed for suggesting new innovation projects or contributing to the implementation of existing projects: (1) information from suppliers of equipment, materials, components or software; (2) information from clients or customers; (3) information from competitors within the same industry; (4) information from universities or other higher education institutions; (5) information from government or private non-profit research institutes; and (6) information from fairs and exhibitions (see Appendix 1 for details). This measure is based on an assumption that in order for an organization to be able to use certain external sources of information for its innovation activity, it must possess certain absorptive capacity; an organization that uses more different external sources and considers them to be of greater importance, possesses greater absorptive capacity. It is highly unlikely that there exists no external information relevant for the organization, therefore it can be assumed, that the organization which considers information from all external sources irrelevant for innovation, is in fact just lacking absorptive capacity to detect, acquire or use those information. While this is obviously still not a direct measure of absorptive capacity, the assumptions taken into account seem to be less questionable. The respondents indicated the degree of importance of a specific source of information on a four-level scale. First, an exploratory factor analysis was conducted to determine an initial factor structure of the absorptive capacity construct. The exploratory factor analysis resulted in a two-factor solution in both samples. Factor 1 was interpreted as demand-pull absorptive capacity, whilst factor 2 was interpreted as science-push absorptive capacity. The two dimensions of absorptive capacity (and their six pertaining items with factor loadings) are shown in Table 1. All Cronbach’s alphas were above the threshold of 0.70. In addition, the confirmatory factor analysis was performed to compare the one-factor structure with the two-factor structure. First, the one-factor model was specified. In this model, the six items were modeled to load on one latent, unobserved factor. If that model was supported, that would suggest that one single factor is sufficient to explain the common variance of the six items. In the second model, the items of each scale were constrained to load on factors established with the exploratory factor analysis. Both factors were modeled to correlate with one another. The purpose of this investigation was to test the Hypothesis 1. The fit measures are shown in Table 2. As expected, the one-factor structure showed a poor fit in both samples, and the two-factor structure showed a large and statistically significant improvement over the one-factor structure. Twofactor model goodness-of-fit indices indicated a good model fit; therefore, Hypothesis 1 is supported. Demand-pull and science-push absorptive capacity were entered in the model of absorptive capacity and its influence on innovation output as latent constructs.

3.2.7. Product and process innovation output Product innovation output was measured with two results of innovation activity: (1) increased range of goods or services; and (2) increased market or market share. Process innovations were measured with four results of innovation activity: (1) improved production flexibility; (2) increased production capacity; (3) reduced labour costs per produced unit; and (4) reduced materials and energy per produced unit. The respondents indicated the degree of impact of a specific result of innovation on a four-level scale (see Appendix 1 for details). In order to verify the accuracy of the distinction between product and process innovation, we first performed an exploratory factor analysis. The

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Table 1 Absorptive capacity dimension’s item factor loadings. Dimension/item

Factors

Demand-pull absorptive capacity Information from suppliers of equipment, materials, components or software Information from clients or customers Information from competitors within the same industry Information from fairs and exhibitions

Demand-pull absorptive capacity

Science-push absorptive capacity

Czech republic

Spain

Czech republic

Spain

0.60 0.73 0.72 0.54

0.71 0.56 0.68 0.56 0.85 0.62

0.58 0.98

Science-push absorptive capacity Information from universities or other higher education institutions Information from government or private non-profit research institutes

Extraction method: maximum likelihood. Rotation method: Oblimin with Kaiser Normalization (absolute factor loadings higher than 0.20 displayed). Results vary slightly with Varimax extraction method. Czech Republic: N ¼ 641. Bartlett’s test of sphericity: approx. w2 of 913.11; 15 df; sig. 0.000. Kaiser–Meyer–Olkin measure of sampling adequacy: 0.76. Variance explained: 48.1%. Spain: N ¼ 2422. Bartlett’s test of sphericity: approx. w2 of 3055.10; 15 df; sig. 0.000. Kaiser–Meyer–Olkin measure of sampling adequacy: 0.70. Variance explained: 47.9%.

Table 2 Summary of confirmatory factor analysis fit statistics. Sample/model

Model fit indices

w2a

df

NFI

NNFI

CFI

GFI

SRMR

RMSEA

Czech Republic One-factor absorptive capacity model Two-factor absorptive capacity model

160.45 30.34

9 8

0.86 0.97

0.78 0.96

0.87 0.98

0.92 0.98

0.09 0.03

0.16 0.07

Spain One-factor absorptive capacity model Two-factor absorptive capacity model

735.74 127.76

9 8

0.78 0.96

0.63 0.93

0.78 0.96

0.90 0.98

0.11 0.04

0.18 0.08

a

All w2 significant at 0.001.

Table 3 Innovation dimension’s item factor loadings. Dimension/item

Factors Product innovation output

Product innovation output Increased range of goods or services Increased market or market share

Process innovation output

Czech republic

Spain

0.77 0.91

0.99 0.60

Process innovation output Improved production flexibility Increased production capacity Reduced labor costs per produced unit Reduced materials and energy per produced unit

Czech republic

Spain

0.70 0.70 0.98 0.72

0.73 0.81 0.87 0.68

Extraction method: maximum likelihood. Rotation method: Oblimin with Kaiser Normalization (absolute factor loadings higher than 0.20 displayed). Results vary slightly with Varimax extraction method. Czech Republic: N ¼ 641. Bartlett’s test of sphericity: approx. w2 of 1992.35; 15 df; sig. 0.000. Kaiser–Meyer–Olkin measure of sampling adequacy: 0.81. Variance explained: 65.7%. Spain: N ¼ 2422. Bartlett’s test of sphericity: approx. w2 of 6316.87; 15 df; sig. 0.000. Kaiser–Meyer–Olkin measure of sampling adequacy: 0.78. Variance explained: 64.8%.

results (shown in Table 3) confirmed the existence of two innovation factors. All Cronbach’s alphas were above the threshold of 0.70. To further verify the findings of the exploratory factor analysis, a confirmatory factor analysis was performed. Again, the results (see Table 4) show that a two-factor innovation model is more appropriate. Product and process innovation output were entered in the model of absorptive capacity and its influence on innovation output as latent constructs.

4. Findings The resulting model goodness-of-fit indices for the model of absorptive capacity, which differentiate between demand-pull and science-push absorptive capacity, indicated a good model fit in the multisample analysis (w2 ¼ 2758.140, 358 df, probability 0.000; NFI ¼ 0.91; NNFI ¼ 0.90; CFI ¼ 0.92; GFI ¼ 0.90; SRMR ¼ 0.08; RMSEA ¼ 0.07). EQS reported that parameter estimates appear in order, that no special problems were

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encountered during the optimization, and that all equality constraints were correctly imposed. Examination of the hypotheses related to the model of absorptive capacity, which differentiate between demand-pull and science-push absorptive capacity, is presented in the following paragraphs. The model, which includes hypothesized relationships and results of the model test, is depicted in Fig. 2. Unstandardized coefficients are reported to ensure comparability across Czech Republic’s and Spanish sample (Singh, 1995). Structural equations with standardized and unstandardized coefficients are shown in Table 5 as well. While the proposed model of absorptive capacity, which differentiates between demand-pull and science-push absorptive capacity, provided a good fit to the data, we also considered the model of absorptive capacity, which does not differentiate between demand-pull and science-push absorptive capacity. The results of this additional analysis showed, that the goodnessof-fit indices for the model of absorptive capacity, which does not differentiates between demand-pull and science-push absorptive capacity, indicated only moderate model fit: NFI ¼ 0.87; NNFI ¼ 0.86; CFI ¼ 0.88; GFI ¼ 0.86; SRMR ¼ 0.09; RMSEA ¼ 0.08.

Table 4 Summary of confirmatory factor analysis fit statistics. Sample/model

Model fit indices w2a df NFI NNFI CFI

GFI

SRMR RMSEA

Czech republic One-factor innovation model 275.07 9 Two-factor innovation model 73.09 8

0.88 0.81 0.97 0.95

0.88 0.85 0.09 0.97 0.96 0.04

0.22 0.11

Spain One-factor innovation model 942.27 9 Two-factor innovation model 179.39 8

0.87 0.78 0.97 0.95

0.87 0.87 0.11 0.98 0.97 0.03

0.21 0.09

a

All w2 significant at 0.001.

Thus, results suggest that the proposed model, which differentiates between demand-pull and science-push absorptive capacity, is more appropriate for interpreting the data, than the model which does not differentiate between demand-pull and sciencepush absorptive capacity. These results additionally support hypothesis H1. Hypothesis H2 proposed that the extent of an organization’s internal R&D would be positively related to the extent of demandpull absorptive capacity (H2a) and science-push absorptive capacity (H2b). The results indicate a significant relationship between the extent of an organization’s internal R&D and demand-pull absorptive capacity (a positive, significant standardized coefficient of 0.22 for the Czech Republic’s sample, and of 0.06 for the Spanish sample); therefore, the results support the hypothesis H2a. The results presented in Fig. 2 and Table 5 show that internal R&D also has a significant and positive influence on science-push absorptive capacity (standardized coefficient of 0.27 for the Czech Republic’s sample, and of 0.18 for the Spanish sample), thus hypothesis H2b was supported. Hypothesis H3 examined the relationships between extramural R&D and demand-pull absorptive capacity (H3a) and sciencepush absorptive capacity (H3b). The relationships between extramural R&D and both types of absorptive capacity were not significant in any of the samples, thus hypotheses H3a and H3b were not supported. Hypothesis H4 predicted that training of personnel related to innovation projects would be positively related to the demandpull (H4a) and science-push (H4b) absorptive capacity. Empirical results were in support of hypothesis H4a (a positive and significant standardized coefficient of 0.15 for the Czech Republic’s sample and of 0.10 for the Spanish sample), and in partial support of hypothesis H4b (a positive but non-significant standardized coefficient of 0.05 for the Czech Republic’s sample and a positive and significant standardized coefficient of 0.06 for the Spanish sample) were found.

Fig. 2. The model of absorptive capacity (standardized and unstandardized coefficients; unstandardized coefficients in brackets).

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0.91 0.17

+0.19* +0.14*

0.87 0.25

+0.38* +0.14* +0.54* +0.42* +0.45* +0.17* 0.84 0.30

+0.13* +0.02 +0.05* +0.28* +0.23* +0.18* +0.02 +0.06* +0.35* +0.20*

0.94 0.11

+0.04* 0.03 +0.07* 0.02 +0.30*

0.81 0.35

+0.55* +0.21*

0.85 0.27

Legend: *Sig.o0.05; St. coeff.—standardized coefficient; Unst. coeff—unstandardized coefficient. Note: Czech Republic: N ¼ 641; Spain: N ¼ 2422.

0.78 0.40

+0.49* +0.24* +0.61* +0.21* +0.54* +0.24*

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Hypothesis H5 examined the impact of innovation co-operation on demand-pull absorptive capacity (H5a) and science-push absorptive capacity (H5b). The relationship between innovation co-operation and demand-pull absorptive capacity was not found to be significant in any of the samples, thus hypothesis H5a was not supported. On the other hand, the empirical results were in support of hypothesis H5b for the Czech Republic’s and Spanish sample (positive and significant standardized coefficients of 0.30 and 0.35, respectively). Hypothesis H6 looked at the relationships between the attitude towards change and demand-pull (H6a) and sciencepush (H6b) absorptive capacity. Empirical results were found in support of hypothesis H6a (a positive and significant standardized coefficient of 0.21 for the Czech Republic’s sample and of 0.29 for the Spanish sample), and in partial support of hypothesis H6b (a positive but non-significant standardized coefficient of 0.09 for the Czech Republic’s sample and a positive and significant standardized coefficient of 0.20 for the Spanish sample). Hypothesis H7 looked at the relationships between demandpull (H7a) and science-push (H7b) absorptive capacity, and product innovation output. Based on the empirical results, the hypothesis H7a was supported (positive and significant standardized coefficients of 0.54 and 0.45 for the Czech Republic’s and Spanish sample, respectively). The results also supported hypothesis H7b (positive and significant standardized coefficients of 0.24 and 0.17 for the Czech Republic’s and Spanish sample, respectively). Hypothesis H8 predicted that demand-pull (H8a) and sciencepush (H8b) absorptive capacity would be positively related to process innovation output. Empirical results were found in support of hypothesis H8a (a positive and significant standardized coefficient of 0.49 for Czech Republic’s sample and of 0.38 for the Spanish sample), and in support of hypothesis H8b (a positive and significant standardized coefficient of 0.24 for Czech Republic’s sample and of 0.14 for the Spanish sample).

+0.06* 0.05 +0.10* 0.03 +0.29* +0.36* +0.04 +0.04 +0.22* +0.11 +0.27* +0.02 +0.05 +0.30* +0.09 Internal R&D +0.22* +0.23* Extramural R&D 0.02 0.03 Training of personnel related to innov. projects +0.15* +0.10* Innovation co-operation +0.06 +0.03 Attitude towards change +0.21* +0.20* Demand-pull absorptive capacity Science-push absorptive capacity Error 0.90 R2 0.19

St. coeff. Unst. coeff. St. coeff. Unst. coeff. St. coeff. Unst. coeff. St. coeff. Unst. coeff. St. coeff. Unst. coeff. St. coeff. Unst. coeff. St. coeff. Unst. coeff. St. coeff. Unst. coeff.

Demand-pull absorptive capacity Demand-pull absorptive capacity

Dependent variables

Czech Republic Independent variables

Table 5 Structural equations for the model of absorptive capacity.

Science-push absorptive capacity

Product innovation output

Process innovation output

Dependent variables

Spain

Science-push absorptive capacity

Product innovation output

Process innovation output

N. Murovec, I. Prodan / Technovation 29 (2009) 859–872

5. Discussion, implications and limitations Despite the increase of the absorptive capacity studies in the last years, there is still a great lack of empirical evidence in this field. Therefore, the main purpose of this study was to offer additional quantitative evidence on some of the open issues. Since many of the previous studies raise doubts about the validity of their results by using some very indirect measures of absorptive capacity, in this paper a more direct measure of absorptive capacity was used, as suggested recently also by some other authors (e.g. Arbussa and Coenders, 2007; Schmidt, 2005). While some of the previous studies already tackled the idea of different kinds of absorptive capacities (e.g. Schmidt, 2005) an important contribution is made here by also statistically confirming the hypothesis that the absorptive capacity is not a one-factor structure but rather a two-factor structure. We define the two distinguished dimensions as the demand-pull and the sciencepush absorptive capacity. In order to be able to efficiently assimilate external information from all available sources, both kinds of absorptive capacity are essential. To benefit from information deriving from the market sources of knowledge (such as customers, competition, suppliers, etc.), an organization needs to possess sufficient demand-pull absorptive capacity. Science-push absorptive capacity, on the other hand, enables an organization to benefit from scientific external information (such as information from research institutes or universities). In this paper, a conceptual model of absorptive capacity and its influence on innovation output is also developed, based on an exhaustive literature overview. The conceptual model was tested

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Table 6 Measurement items’ descriptive statistics and correlation matrix for the Spanish sample. Correlations Construct

Demand-pull absorptive capacity

Science-push absorptive capacity Product innovation output Process innovation output

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Max

2583.86 1.00 2.83 3586.69 10.12 1.00 1.00 1.00 1.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00

Mean

71.82 0.38 0.27 261.54 1.16 0.34 0.45 0.54 0.35 1.48 1.39 1.05 1.41 0.48 0.56 1.67 1.59 1.55 1.72 1.36 1.12

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

(19)

(20)

(21)

1.00 0.12 0.25 0.32 0.32 0.12 0.12 0.07 0.02 0.01 0.07 0.02 0.03 0.18 0.13 0.06 0.05 0.02 0.02 0.03 0.04

1.00 0.14 0.18 0.22 0.15 0.23 0.15 0.09 0.11 0.10 0.05 0.12 0.15 0.14 0.14 0.17 0.14 0.12 0.13 0.13

1.00 0.31 0.32 0.13 0.09 0.04 0.03 0.02 0.09 0.00 0.00 0.37 0.31 0.13 0.11 0.03 0.03 0.04 0.06

1.00 0.89 0.18 0.18 0.10 0.06 0.01 0.17 0.07 0.02 0.30 0.23 0.22 0.18 0.05 0.02 0.07 0.11

1.00 0.20 0.20 0.13 0.08 0.04 0.21 0.10 0.07 0.31 0.24 0.27 0.23 0.08 0.01 0.10 0.15

1.00 0.44 0.39 0.35 0.11 0.12 0.08 0.10 0.16 0.14 0.21 0.20 0.13 0.10 0.14 0.14

1.00 0.43 0.30 0.12 0.10 0.07 0.11 0.16 0.16 0.17 0.17 0.19 0.16 0.18 0.18

1.00 0.35 0.11 0.14 0.07 0.09 0.10 0.12 0.14 0.16 0.16 0.13 0.14 0.11

1.00 0.05 0.06 0.09 0.13 0.06 0.09 0.15 0.18 0.09 0.03 0.05 0.09

1.00 0.38 0.43 0.47 0.17 0.18 0.19 0.21 0.23 0.24 0.26 0.23

1.00 0.45 0.25 0.20 0.21 0.29 0.27 0.10 0.10 0.15 0.16

1.00 0.36 0.18 0.15 0.21 0.20 0.13 0.11 0.15 0.14

1.00 0.16 0.21 0.22 0.22 0.19 0.17 0.20 0.16

1.00 0.57 0.16 0.14 0.13 0.07 0.15 0.20

1.00 0.15 0.16 0.14 0.09 0.16 0.18

1.00 0.65 0.28 0.24 0.24 0.27

1.00 0.31 0.34 0.33 0.32

1.00 0.66 0.61 0.50

1.00 0.68 0.51

1.00 0.65

1.00

Legend: (1) Extramural R&D*; (2) training of personnel related to innovation projects; (3) innovation co-operation*; (4) innovative expenditures for intramural research and experimental development*; (5) number of employees involved in intramural R&D activities*; (6) new or significantly changed corporate strategies; (7) advanced management techniques within the enterprise; (8) new or significantly changed organizational structures; (9) significantly changed enterprise’s marketing concepts or strategies; (10) information from suppliers of equipment, materials, components or software; (11) information from clients or customers; (12) Information from competitors within the same industry; (13) information from fairs and exhibitions; (14) information from universities or other higher education institutions; (15) information from government or private non-profit research institutes; (16) increased range of goods or services; (17) increased market or market share; (18) improved production flexibility; (19) increased production capacity; (20) reduced labour costs per produced unit; (21) reduced materials and energy per produced unit. Note: N ¼ 2422. Correlations higher than 0.06 are significant at the 0.01 level. Correlations higher than 0.04 are significant at the 0.05 level. *The following items were transformed by employing the square root transformation because of extreme skew and/or kurtosis: 1, 3, 4, and 5.

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Attitude towards change

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21)

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/ / / Internal R&D

Item

Table 7 Measurement items’ descriptive statistics and correlation matrix for the Czech sample. Correlations Construct

Demand-pull absorptive capacity

Science-push absorptive capacity Product innovation output Process innovation output

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Max

969.88 1.00 2.83 1233.83 11.52 1.00 1.00 1.00 1.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00

Mean

29.25 0.36 0.53 165.04 1.92 0.49 0.40 0.47 0.37 1.53 1.99 1.42 1.54 0.62 0.31 1.86 1.59 1.36 1.35 1.31 1.21

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

(19)

(20)

(21)

1.00 0.18 0.33 0.31 0.25 0.15 0.18 0.12 0.08 0.09 0.08 0.09 0.09 0.21 0.06 0.15 0.13 0.10 0.04 0.08 0.06

1.00 0.18 0.28 0.29 0.11 0.17 0.16 0.13 0.19 0.15 0.07 0.19 0.14 0.12 0.21 0.20 0.23 0.20 0.22 0.22

1.00 0.30 0.33 0.17 0.15 0.13 0.07 0.19 0.12 0.05 0.08 0.34 0.18 0.25 0.19 0.18 0.15 0.18 0.18

1.00 0.71 0.20 0.25 0.14 0.09 0.16 0.20 0.21 0.23 0.33 0.17 0.31 0.30 0.20 0.09 0.21 0.29

1.00 0.23 0.27 0.13 0.10 0.18 0.22 0.25 0.26 0.32 0.14 0.41 0.35 0.28 0.14 0.26 0.33

1.00 0.54 0.35 0.38 0.10 0.09 0.14 0.19 0.14 0.15 0.18 0.23 0.21 0.13 0.22 0.23

1.00 0.42 0.36 0.14 0.09 0.10 0.11 0.12 0.09 0.16 0.16 0.23 0.14 0.23 0.26

1.00 0.34 0.10 0.08 0.06 0.06 0.05 0.04 0.03 0.08 0.09 0.04 0.09 0.19

1.00 0.10 0.09 0.10 0.13 0.04 0.08 0.13 0.14 0.17 0.05 0.19 0.20

1.00 0.43 0.44 0.43 0.30 0.22 0.25 0.28 0.33 0.29 0.28 0.28

1.00 0.51 0.32 0.18 0.15 0.30 0.29 0.26 0.19 0.21 0.25

1.00 0.46 0.33 0.27 0.28 0.29 0.30 0.20 0.26 0.31

1.00 0.29 0.23 0.34 0.32 0.29 0.19 0.27 0.26

1.00 0.54 0.24 0.26 0.28 0.20 0.24 0.26

1.00 0.21 0.23 0.25 0.16 0.22 0.28

1.00 0.71 0.39 0.35 0.40 0.44

1.00 0.47 0.38 0.47 0.52

1.00 0.63 0.66 0.57

1.00 0.62 0.47

1.00 0.74

1.00

Legend: (1) Extramural R&D*; (2) training of personnel related to innovation projects; (3) innovation co-operation*; (4) innovative expenditures for intramural research and experimental development*; (5) number of employees involved in intramural R&D activities*; (6) new or significantly changed corporate strategies; (7) advanced management techniques within the enterprise; (8) new or significantly changed organizational structures; (9) significantly changed enterprise’s marketing concepts or strategies; (10) information from suppliers of equipment, materials, components or software; (11) information from clients or customers; (12) information from competitors within the same industry; (13) information from fairs and exhibitions; (14) information from universities or other higher education institutions; (15) information from government or private non-profit research institutes; (16) increased range of goods or services; (17) increased market or market share; (18) improved production flexibility; (19) increased production capacity; (20) reduced labour costs per produced unit; (21) reduced materials and energy per produced unit. Note: N ¼ 641. Correlations higher than 0.10 are significant at the 0.01 level. Correlations higher than 0.08 are significant at the 0.05 level. *The following items were transformed by employing the square root transformation because of extreme skew and/or kurtosis: 1, 3, 4, and 5.

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Attitude towards change

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21)

Min

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/ / / Internal R&D

Item

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on robust datasets from two different countries, which increases its validity and enables a cross-national comparison. Besides, to our knowledge this is the first study on absorptive capacity to simultaneously test (using structural equation modeling) the influence of several suggested determinants, which not only offers a good foundation for the future research, but also enables a more accurate estimation of the specific determinant’s importance. The results, presented in Section 4, show that the proposed model has a good fit. Internal R&D, training of personnel, innovation cooperation and attitude towards change all proved to be statistically significant determinants of absorptive capacity; however, their importance differs for demand-pull or science-push component and between the two studied countries. Therefore, we can conclude that measuring absorptive capacity should not be simplified into using just one determinant as a proxy. The most important determinants of science-push absorptive capacity are internal R&D and innovation co-operation, while in Spain attitude towards change and training of personnel related to innovation projects are also statistically significant. On the other hand, the most important determinants of demand-pull absorptive capacity in both studied countries are internal R&D, training of personnel and attitude towards change. Despite quite different characteristics of the studied countries—the Czech republic being a post-transition country and one of the new EU members which is steadily growing and belongs to the ‘‘catching up’’ group according to innovativeness; and Spain, an old EU member country, but experiencing slow economic growth and low level of development of the Innovation System—the research results show quite small differences in the model. The aim of this research was however not to study those differences, but to demonstrate a cross-cultural validity of the model, which was also achieved. While we can speculate about the influence of the different innovation systems on the results, the tested model and the available data in fact do not offer any information about the reasons behind those differences; therefore this would make an interesting topic for future research.

Research results also show that the studied determinants explain the variance of demand-pull absorptive capacity to a lower extent than science-push absorptive capacity. Since the studied determinants are based on the literature review this indicates, that the previous research, even though it perceived absorptive capacity as an unidimensional construct, it actually mostly captured the science-push absorptive capacity component. Therefore, the demand-pull absorptive capacity determinants remain an important challenge for the future research. The importance of future demand-pull absorptive capacity research is further demonstrated by the fact that the demand-pull component proved to have a much greater impact on product and process innovation output than the science-push component, even though they are both statistically significant and important. The research results are in favor of those who argue that an organization cannot buy its absorptive capacity (e.g. Cohen and Levinthal, 1990), since the extramural R&D did not prove to be a statistically significant determinant of either demand-pull or science-push absorptive capacity. Due to the cross-culturally confirmed importance of absorptive capacity for innovation, and thus also for the competitiveness and growth of organizations as well as whole economies, this should become an important issue in future innovation policy discussions. Since demand-pull absorptive capacity proved to be even more important than science-push absorptive capacity, this raises the question of the appropriateness of current innovation policies, which usually put emphasis only on the knowledge creation (R&D) side, and practically no emphasis on raising the awareness about the importance of information available on the market, and improving the accessibility of this information. Despite certain limitations of this study (a cross-section analysis, the use of an existing database, not designed specifically for the purpose of absorptive capacity research), the research results show that a greater emphasis should be put on the topic of absorptive capacity not only by policymakers but also by researchers. Future research should, however, acknowledge our

Table 8 Selected Third Community Innovation Survey questionnaire items. 1. Sources of information for innovationa Information source

Importance High

Market sources

Suppliers of equipment, materials, components or software Clients or customers Competitors and other enterprises from the same industry

Institutional sources

Universities or other higher education institutes Government or private non-profit research institutes Fairs, exhibitions

Other sources

Medium

Low

Not used

2. Effects of innovationb Effects of innovation

Degree of impact High

Product-oriented effects

Increased range of goods or services Increased market or market share

Process-oriented effects

Improved production flexibility Increased production capacity Reduced labour costs per produced unit Reduced materials and energy per produced unit

Medium

Low

Not relevant

a The main sources of information needed for suggesting new innovation projects or contributing to the implementation of existing projects are asked in this question. Please indicate the degree of importance attached to various alternative information sources. b The result of innovation activity may have different effects for your enterprise. For the various alternatives, please indicate the degree of impact by innovation activity undertaken by your enterprise.

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finding that absorptive capacity cannot be simply measured based on the implicit assumption that it equals R&D or any other single determinant. Furthermore, the future research should also take into consideration the finding that the absorptive capacity is not a unidimensional concept and that it is thus not adequate to measure it as such.

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