Top PDF The Role of Regional Knowledge Sources for Innovation: An Empirical Assessment

The Role of Regional Knowledge Sources for Innovation: An Empirical Assessment

The Role of Regional Knowledge Sources for Innovation: An Empirical Assessment

12 regions with an average distance up to 50 km, and 0.08 for the districts with an average distance between 50 and 75 km. Private sector R&D activity in more remote areas is not statistically significant or does not lead to plausible estimates. Hence, we conclude that the relevant private sector spillover sources are all located within a radius of about 75 km. Adding up the estimated coefficients for private R&D employment in and around a region, we arrive at an overall effect on patenting of about 0.74. This size of the effect is at the upper limit of results of previous research for other countries (table A1). Also, the spatial pattern of spillovers corresponds quite well to the findings for other countries (Anselin, Varga & Acs, 1997; Anselin, Varga & Acs, 2000). The highly significant positive coefficients for the manufacturing specialization index confirm the expected higher propensity to patent in manufacturing as compared to the service sector (table 2). The positive sign for the industrial diversity index and the negative sign for its squared value indicate a nonlinear relationship with regional patenting that has the shape of an inverse ‘u’. This pattern suggests that a certain degree of specialization may be conducive to innovation activity. If, however, this industry concentration exceeds or falls below a certain level the effect of externalities becomes smaller. Obviously both extremes, broad diversity as well as narrow specialization, are relatively unfavorable for the performance of regional innovation systems. The estimation results suggest that a value of about 15.7 for the inverse Herfindahl index (0.06 of the
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Sources of local knowledge spillover within the Algarve tourism region: evidence to identify  a regional innovation system

Sources of local knowledge spillover within the Algarve tourism region: evidence to identify a regional innovation system

Abstract Tourism sector in Algarve region is the main engine of regional economy. Although frequently, tourism is considered as a low – moderate innovative sector, tourism competitiveness is still highly dependent on specific features of a Regional Innovation Platform, highlighting the crucial importance of knowledge creation and diffusion, learning, cooperative and collaborative interaction that may evolve to a Regional Innovation System (RIS). Studies of Local Knowledge Spillovers have been frequently focused on empirical evidence provided by regions highly related with manufacturing sectors. Considering a case study in Tourism Algarve Region, emphasizing a theoretical character on the analysis of these areas and using a qualitative methodology, the goal of this study was to provide preliminary evidence of the main sources and vehicles of regional knowledge spillovers used by tourism enterprises. Main information has been obtained using primary information collected from 20 interviews over main stakeholders regarding regional private and public sector. Primary information was complemented with secondary information, a deeply and extensive bibliography revision and also statistical information. Results show that, on the one hand, main sources of knowledge used by micro and small tourism enterprises are human resources and formal and informal networks. On the other hand, large tourism companies are weakly related with regional sources using mainly internal company and economic group resources to generate innovation activities. Regional innovation platform shows clear weaknesses on linkages and coordinated initiatives to promote and support innovation performance of firms hampering to increase tourism competitiveness and regional development.
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Measuring the knowledge base of regional innovation systems in Sweden

Measuring the knowledge base of regional innovation systems in Sweden

Abstract: Within the literature on innovation systems, there are a growing number of scholars emphasizing the importance of differentiated knowledge bases underlying innovation activities. The existing work on knowledge bases is largely grounded on in-depth case studies; while surprisingly little effort has been done so far to operationalize the concept in a more systematic manner. In this paper, an attempt is made to develop a scheme of analysis to identify the knowledge base of a regional economy. We suggest using occupation data in association with a location quotient analysis, to assess whether a regional economy has a particular strength in one (or more) knowledge bases. To bring the analytical scheme into practice and assess it, we apply it on the county level in Sweden. The results are explained and contrasted with insights on the regional economies taken from secondary sources. We conclude that the proposed scheme of analysis leads to fairly reliable results, and could stimulate further empirical research on differentiated knowledge bases.
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Business services as actors of knowledge transformation and diffusion: some empirical findings on the role of KIBS in regional and national innovation systems

Business services as actors of knowledge transformation and diffusion: some empirical findings on the role of KIBS in regional and national innovation systems

KIBS can assume for their clients, Gadrey (1994) distinguishes the following three types of functions: (i) the detection and analysis of problems; (ii) the (abstract) estab- lishment of a diagnosis; and (iii) the (concrete) participation to the problem-solving process. Summarising and integrating Teece's views of complementary innovation as- sets KIBS fulfil for their manufacturing clients, it can be concluded that KIBS assume a "bridge" or interface function between the environment and their clients and rein- force or catalyse evolution and innovation capacities of their clients, especially SMEs. Going one step further, it can be suggested that KIBS play a role of co-innovators or even of "midwives" for SMEs (cf. von Einem and Helmstädter 1994, p. 2). Neverthe- less, the impact of KIBS on SMEs' innovation capacities is only one side of the story. In fact, KIBS may also benefit from their interactions with SMEs in terms of ability to innovate. In particular, since the development of KIBS' knowledge base is intimately related to the activity they perform for their clients, it seems logical that their innova- tion capacities are influenced through those interactions. As a consequence, consider- ing SMEs and KIBS together, the hypothesis of a virtuous circle (cf. figure 2), can be expressed. In other words and to summarise: "it can be argued that interacting KIBS
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Are science valleys and clusters panacea for a knowledge economy? An investigation on regional innovation system (RIS) - concepts, theory and empirical analysis

Are science valleys and clusters panacea for a knowledge economy? An investigation on regional innovation system (RIS) - concepts, theory and empirical analysis

question, this paper contributed to the existing literature in two ways. First, we apply a robust non-parametric unconditional order-m partial frontier approach to identify best practice nations in RIS context. It was argued in the paper that a partial frontier such as order-m approach is more applicable for analyzing regional innovation system framework than traditional FDH (Free Disposable Hull) approach due to the advantage of overcoming outliers or extreme points from the sample. We apply a cross-section approach and use latest dataset from World Development Indicators-2011, World Competitiveness Yearbook-2011 and Penn world table for our analysis. We have found that South Korea, Singapore and Malaysia are the best practice countries among most of the emerging and developed knowledge-based countries from our sample. While doing a policy analysis of these three countries, our study reveals that location does matter for successful regional innovation system. Our findings indicate that investing on Techno-parks, Science city or high-tech clusters certainly generate more employment opportunity, build skilled labor force, well-structured financial systems, encourage venture capital in regional locations, and thus ensure a balanced economic development. By combining the strong policy points of each best practice nations (South Korea, Malaysia and Singapore), policy-makers of follower regions could produce an interesting, profitable yet flexible vision of the role regional innovation systems thought which can play significantly in their economic destiny. Hence, in order to transform ideas from classroom education to practical policy implication, we believe, it is essential to investigate regional innovation system and its applications for future knowledge based generations. In future research, we recommend conditional order-m and α (alpha) frontier analysis to observe the comparison of our sample regions with regions having similar values in an external factor z, e.g. the externality variable. In order to achieve (conditional order-m analysis), the m observations are not drawn randomly but conditional on the external factors. We believe, it is worth looking into how results vary when we put condition on the selection of m in order-m frontier analysis.
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Innovation, networking and globalisation:the role of regional innovation systems in the global innovation processes

Innovation, networking and globalisation:the role of regional innovation systems in the global innovation processes

This chapter presents a systematic review of present knowledge about firm networks and their innovative behaviour in the global economic environment. It has been found that globalisation is far from being a mere standardisation of product and services worldwide. Furthermore, when it comes to innovation, local networks are still targeted as the places for knowledge acquisition processes: multinational companies, the most important players in the global markets, need to embed their subsidiaries in local networks in order to enhance market and technology driven innovation. Regional innovation systems are a new network model which seems to well describe those territories with superior innovation performances in the global arena. Still companies are designing and joining global networks with the purpose of enhancing their supply chains and complex products development processes and setting up new standards which represent new barriers for competitors; it has been found that mutual trust (a fundamental condition for knowledge sharing) can be established even globally in the so called spatial innovation systems. Multinational companies can also be considered as global networks of headquarters and subsidiaries and their internal organisational processes are connected to their ability of knowledge acquisition and sharing. This review also identifies some gaps in present literature which might be filled by future research: first there is a need to better clarify the tools and the organisational models that can establish mutual trust in global networks; second it might be better understood if the presence of a multinational company’s subsidiary in one cluster enhances the local innovativeness; last an empirical verification would be needed, checking i) if RIS represent the condition for SMEs and clusters to access and create new knowledge globally and ii) if present innovation policies are effective in supporting RIS.
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A Regional Innovation Impact Assessment Framework for universities

A Regional Innovation Impact Assessment Framework for universities

The proposed approach aims to build on the strengths of the metrics and narrative approaches in order to present illustrative case studies of the impact of universities on their regional innovation ecosystems. In view of the fact that several (potential) impacts can only be captured with qualitative information, rather than indicator-based statistical data, the most appropriate way to describe the outcomes of each RI 2 A exercise of a university is a ‘narrative with numbers’ framework. In other words, as a narrative case study supported by indicators to identify, categorise and explain the (potential) impact they have on their regional innovation ecosystem. This ‘multi-method, multi-sources’ approach has a number of advantages over purely qualitative case studies as they allow for a greater degree of objectivity, comparability and tracking of progress over time. The case studies need to be conducted by qualified experts and reviewed by expert panels. The ‘narrative’ mode comprises a review by a carefully selected group of experts. Adopting the whole university as a unit of analysis, rather than organisational subunits or university programs, requires expert evaluators with sufficient knowledge of the entire university and its region, as well as an appropriate skill set. It is clear that an expert panel should be sufficiently broad and diverse to incorporate the necessary differences in background. Scientific peers are not necessarily good at judging socio-economic impacts (Debackere et al., forthcoming). While academics are conditioned to accept peer review when it comes to the assessment of scientific merit and impact, it is less clear to what extend this acceptation holds when it comes to regional innovation impact – which is a new, uncertain and ambiguous evaluation object. The notion of ‘innovation impact’ is not as well understood as ‘scientific impact’. The fact that key concepts and notions are still in flux, and may not be understood the same by all experts, suggests the application of expert panel reviews, which allows for contesting and conflicting opinions which can be played out and negotiated for consensus seeking (Derrick, forthcoming).
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Challenges to the transformational role of university in regional innovation system

Challenges to the transformational role of university in regional innovation system

Universities and industries collaborate for mutual benefits that also generate opportunities for innovation. Bercovitz and Feldman (2006) provided a conceptual framework to analyze the university–industry relationships (Figure 1). The framework consisted of transactions at the core of the university–industry relationships, an individual researcher, firm characteristics, and university technology transfer strategy and structure. They suggested that the legal, economic, and policy environments that comprise the system of innovation determine the rate and type of university knowledge production and thereby influence the rate of technological change. It is clear from the framework that university-industry collaboration promotes mutual knowledge exchange which forms the basis for new innovations. Empirical investigations suggest that the lack of collaboration between universities and industry has been identified as the main reason for European’s ‘innovation deficit’, comparing with the innovation related performance in USA and Japan. (Tijssen and Van Wijk 1999, Debackere et al., 1999, Porter 2011). Although Europe is active in scientific research, the commercialization of the research output needs further improvement to contribute to the regional innovation system (Debackere et al., 1999).
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Geography of innovation in Europe and Finland : Empirical studies on innovation indicators and regional development

Geography of innovation in Europe and Finland : Empirical studies on innovation indicators and regional development

In light of the emphasis laid on knowledge, learning and innovation in regional development, universities have gained an increasing amount of attention as producers of knowledge (and knowledge workers) and as important economic agents. Traditionally the role of the universities was viewed as the source of scientific knowledge which would gradually spillover, for the benefit of private sector via science publications, conferences and patents, but even more importantly through informal conversations and interaction (tacit knowledge). Based on the early work by Griliches’ (1979) and his knowledge production function, several authors have explored and refined the benefits of university research through impacts of these knowledge spillovers (Jaffe 1989; Acs et al. 1994; Jaffe Trajtenberg 1996). However, the role of the universities has evolved to point where this linear view of them only as source of knowledge to be commercialised by the private sector (Mansfield 1991; Lee 1996) has become outdated (see e.g. Youtie Shapira 2008). In addition, the mere presence of university is not enough to guarantee good regional innovation or economic performance. Therefore, the focus of studies on the impacts of universities has shifted from the warranting of university research’s value to the economy towards exploring the most effective implements of U-I collaboration. In other words successful knowledge transfer mechanisms from the university to industry are complex case- and region-specific processes (Bramwell Wolfe 2008; Uyarra 2010; Hidalgo Albors 2011). Evidently, the educating and training function of the university has been brought forth as the most important channel for knowledge transfer to industry and for its potential to have major impact on the local economy (Schartinger et al. 2001).
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Assessment of Regional Innovation Systems as an Assumption for Innovation Policy Adjustment

Assessment of Regional Innovation Systems as an Assumption for Innovation Policy Adjustment

Doloreux (2002) stated that the regional innovation systems consist of four basic interrelated elements, which are the businesses, institutions, knowledge infrastruc- ture, and the policy. Businesses must be understood as learning organizations which are in interaction with other companies and institutions forming their environment. Institutions are, for example, the governments and other institutions which are key players in the creation and transfer of knowledge. Institutions reduce uncertainty, coordinate the use of knowledge, sett le confl icts, and provide incentives. They can be formal or informal and they are signifi cantly aff ected by the national innovation system. The knowledge infrastructure represents the physical and organizational in- frastructure for the promotion of innovation. It also includes research institutes, labo- ratories, and universities. A policy focused on regional innovation is a policy oriented to the improvement of the interactions among the knowledge infrastructure, busi- nesses, and institutions. Policies are to develop the endogenous potential of regions by encouraging the spread of technologies at the regional level. General regional pol- icy is infl uenced by the idea that innovations play a crucial role within competitive- ness enhancement. Due to that the regional policy is often connected with the innova- tion policy (Klímová and Žítek, 2015).
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How Internal and External Sources of Knowledge Contribute to Firms’ Innovation Performance

How Internal and External Sources of Knowledge Contribute to Firms’ Innovation Performance

Although this study has many strengths, it also has some limitations that need to be acknowledged. Firstly, with regard to local knowledge sources, the problem of knowledge internalization deserves mention; that is, when firms overestimate the role of in-house activities and down- grade the role of the local environment in which they operate. The knowledge exchange between local firms and institutions mainly hap- pens in a socialized way in the form of knowledge spillovers. As soon as a firm acquires this local knowledge, it incorporates it into the existing knowledge base, making it internal to the firm (Henry and Pinch 2000; Cole 2007). Accordingly, firms might underestimate the importance of being located in the local environment, because they take for granted the benefits of the specialized local labor market, the proximity of simi- lar firms, and close linkages with local universities and other knowledge organizations. Secondly, the model of the influence of sources of knowl- edge on innovation performance is not comprehensive (it includes a limited number of elements in order to make the empirical examination feasible) because it ignores some other factors that influence innova- tion performance. Thirdly, although the causal directions hypothesized in the model were suggested by the theory, the cross-sectional nature of this study cannot prove the causation but can only support a set of hypothesized paths (Kline 2005). Therefore, the possibility of reverse causality cannot be eliminated.
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Determinants of the Innovation Propensity in Tunisia: the Central Role of External Knowledge Sources

Determinants of the Innovation Propensity in Tunisia: the Central Role of External Knowledge Sources

The following section discusses the main determinants of innovation that have been emphasized in the theoretical and empirical studies. This discussion allows us to formulate a set of conjectures on the determinants of innovation in a developing country like Tunisia. The third section presents the dataset that we use, as well as the research methodology that we adopt to analyze it. The fourth section is dedicated to our results. We first proceed with a quick analysis of the declared motivations of firms for their R&D activity. The results indicate that product and process innovations do not respond to the same objectives and they should be studied separately. We then present the results of the probit models. These models are formulated in accordance with the conjectures proposed in the second section. The interaction between the determinants of the propensity to innovate is further analyzed using regression trees. The last section concludes the article. Detailed statistical results are provided in the Appendix at the end of the article and in a complementary appendix available on-line.
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How Internal and External Sources of Knowledge Contribute to Firms Innovation Performance

How Internal and External Sources of Knowledge Contribute to Firms Innovation Performance

Although this study has many strengths, it also has some limitations that need to be acknowledged. Firstly, with regard to local knowledge sources, the problem of knowledge internalization deserves mention; that is, when firms overestimate the role of in-house activities and down- grade the role of the local environment in which they operate. The knowledge exchange between local firms and institutions mainly hap- pens in a socialized way in the form of knowledge spillovers. As soon as a firm acquires this local knowledge, it incorporates it into the existing knowledge base, making it internal to the firm (Henry and Pinch 2000; Cole 2007). Accordingly, firms might underestimate the importance of being located in the local environment, because they take for granted the benefits of the specialized local labor market, the proximity of simi- lar firms, and close linkages with local universities and other knowledge organizations. Secondly, the model of the influence of sources of knowl- edge on innovation performance is not comprehensive (it includes a limited number of elements in order to make the empirical examination feasible) because it ignores some other factors that influence innova- tion performance. Thirdly, although the causal directions hypothesized in the model were suggested by the theory, the cross-sectional nature of this study cannot prove the causation but can only support a set of hypothesized paths (Kline 2005). Therefore, the possibility of reverse causality cannot be eliminated.
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The Role of Regional Knowledge for Innovation

The Role of Regional Knowledge for Innovation

2 1. Introduction Empirical research has demonstrated that location matters for innovation activity (Feldman, 1994; Fritsch, 2000, 2002, 2005). Innovation processes have a pronounced regional dimension and conditions for innovative activity differ considerably between geographic areas. A main reason for this impact of location on innovation is the availability of knowledge. A role of location for innovation activity implies that at least part of the relevant knowledge is specific to a certain region and shapes the innovation activities there. Main sources of this knowledge could be private sector firms, universities and public research institutes. The knowledge may stem from inside the respective region, or it may spill over from adjacent regions. The relative importance of these different knowledge sources inside and outside the region is, however, still largely unclear.
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The role of knowledge variety and intensity for regional innovation

The role of knowledge variety and intensity for regional innovation

Since the Maximum Likelihood Estimation (MLE) is used, one way to compare the models with each other is to use Akaike information criterion (AIC) or Bayesian information criterion (BIC). Both criteria get smaller when moving from model (1) to (2) (BIC is not reported in Table 2). This means that by adding the external knowledge variables in model (2), this model is getting better in terms of fitness compared with model (1), which only includes internal knowledge, while there is no evidence of over-fitting. In other words, internal knowledge and external knowledge together can produce the better fit for modelling the patent application compared with including only one of them. Controlling for POPULATION in model (3) further improved the model. While moving to model (4) did not improve the model, model (5), which is the full model, turns out to be the best model in terms of AIC. The same evidence is also obtained by performing the Likelihood Ratio test of restricted vs. unrestricted models, when moving between models 20 . This can be seen as fulfilling the stated aim of the paper, i.e. to empirically test various theoretical conjectures (RKPF and international trade theory) in a common empirical setting.
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Measuring the efficiency of regional innovation systems: an empirical assessment

Measuring the efficiency of regional innovation systems: an empirical assessment

II Abstract We measure the efficiency of regional innovation systems (RIS) in Germany by means of a knowledge production function. This function relates private sector Research and Development (R&D) in a region to the number of inventions that have been registered by residents of that region. Two approaches are followed. First, it is assumed that differences in the productivity of private sector R&D between regions affect the slope of the KPF, which represents the marginal productivity of R&D input. The second approach assesses regional differences within the framework of a stochastic frontier knowledge production function. This approach mainly reveals differences with regard to the intercept of the knowledge production function and, therefore, with regard to the average productivity. We compare the results of both approaches and discuss a number of critical issues such as the properties of the distribution of efficiencies, the appropriate size of RIS, and how to deal with the issue of spatial autocorrelation.
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Knowledge spillovers and sources of knowledge in the manufacturing sector: literature review and empirical evidence for the UK

Knowledge spillovers and sources of knowledge in the manufacturing sector: literature review and empirical evidence for the UK

Endogenous growth theory predicts that growth rates of countries are related through international trade linkages and associated embodied and disembodied knowledge spillovers (Grossman and Helpman 1991). As mentioned earlier, knowledge is inherently non-rival in its use, and hence its creation and diffusion are likely to lead spillovers and increasing returns. It is this non-rival property of knowledge that is at the heart of the theoretical models that predict endogenous growth from research and development (R&D) investments (Aghion and Howitt 1998; Romer 1990; Grossman and Helpman 1991). In this context, the development of a country depends heavily on its knowledge capital, which in turn is determined by the rate of national innovation and international technology diffusion. Three mechanisms have been identified to assess the impact of trade openness on technology diffusion (Redding and Proudman 1998): the degree of international openness can affect the rate of domestic innovation, the quantity of transferred technology or the adoption rate of more advanced countries’ technologies. At the firm level, the empirical literature has long focused on the role of exports, given the exporting firms’ high productivity growth (Aw and Hwang 1995; Clerides, Lach, and
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NEW SOURCES OF GROWTH: THE ROLE OF FRUGAL INNOVATION AND TRANSFORMATIONAL LEADERSHIP

NEW SOURCES OF GROWTH: THE ROLE OF FRUGAL INNOVATION AND TRANSFORMATIONAL LEADERSHIP

Numerous efforts (e.g., Koellinger, 2008; Karadal & Saygin, 2011; Zhao & Lei, 2013) have been made to understand the relation between technological progress and innovation at both the sector and macroeconomic levels. Innovation, we argue, is also linked to research and development (R&D) and leadership (Chersbrough, 2003; García-Morales, et al., 2008). Technological progress has made the world more connected, without rigid borders. In 2013, the Organisation for Economic Co-operation and Development (OECD) realized the potential for developing a knowledge-based economy and became more dependent on knowledge than during the previous era. It was estimated that, from 1995 to 2007, investments in knowledge-based capital (KBC) contributed an average of 23% to manpower productivity gains (Corrado et al., 2012). KBC, together with R&D, software, human capital, and organizational structure, plays an integral role in enhancing productivity and productive efficiency. By investing in KBC, corporations in OECD countries have secured a comparative advantage relative to peer countries. Since then, several developing countries have prioritized KBC investment (OECD, 2013).
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The role of universities in innovation networks: the role of proximity on innovation dynamics in knowledge community precints

The role of universities in innovation networks: the role of proximity on innovation dynamics in knowledge community precints

dimension to proximity – the idea of a ‘click’ between two people – that cannot be explained in terms of the embedding of people (organisations, epistemic communities, cultures) in various contexts but is specific to the people and personalities involved. And finally is the importance of a material basis for collaboration, the underlying innovation projects or activities from which both participants benefit from the creation of a shared knowledge pool (Gertner et al., 2011). It is this last point that we would like to raise in the course of this paper, and in particular to question the assumption that academic scientists automatically have interest in the knowledge being generated within particular KCPs. Although some KCPs have an open innovation ethos bringing together different firms in similar fields with collaboration potential, more typically they attempt to create knowledge concentrations incorporating upstream (academic) and
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From Knowledge to Innovation and Back: Empirical Testing of Knowledge-Intensive Industries in Serbia

From Knowledge to Innovation and Back: Empirical Testing of Knowledge-Intensive Industries in Serbia

The ability to use or share knowledge within an organisation should naturally enhance innovation capabilities. Specifically, a firm practicing inbound activities to strengthen rad- ical innovation is more likely to benefit from knowledge sharing capabilities than from knowledge acquisition capabilities (Cheng, Yang, & Sheu, 2016). Knowledge-sharing cul- ture within organisation has a positive impact on the knowledge flow and innovation (Levi- Jakšić, 1995; Van den Bosch, Volberda, & de Boer, 1999; Tu, Vonderembse, Ragu-Nathan, & Sharkey, 2006). Sharing and dissemination of knowledge are identified as one of the main knowledge management processes (Probst, Raub, & Romhardt, 2002). Some authors suggest that the process of acquiring external knowledge for innovation purposes depends on the degree of similarity of cognitive structures, knowledge base, skills and shared lan- guages between an organisation and the environment (Escribano, Fosfuri, & Tribo, 2006). Prior-related knowledge is one of the suggested components of the organisational absorp- tive capacity (Cohen & Levinthal, 1990; Brown, 1997; Zahra & George, 2002; Tu et al., 2006). It is also the most influential determinant of absorptive capacity of organisations from knowledge-intensive industries (Levi Jakšić, Radovanović, & Radojičić, 2013).
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