Abstract
Innovation districts represent a new path forward for economic growth and business development. In particular, food innovation districts lead to new products, advances in production, safety, nutrition, and other advances that have broad societal and enterprise benefits. However, most innovation does not happen in a vacuum. It takes the cross-breeding of ideas and efforts in a conducive environment – networking. This paper analyzes existing research to determine the appropriate characteristics necessary to optimize food innovation districts’ networking assets. Using a systematic review of 23 scholarly articles, three key themes to consider for optimizing the networking assets of food innovation districts were identified: participants, operational flow, and operational environment (place). This research is helpful to practitioners as it provides insight into enhancing innovative business and economic development. For scholars, this paper advances the literature in innovation, business development, and network design and management.
Introduction
Problem Statement
Innovation districts exist as unique geographic areas that attract skilled artisans and entrepreneurs focused on participating within certain industry clusters. These artisans and entrepreneurs, when co-located with anchor institutions, research institutions, and other contributing assets, can drive economic activity and growth through innovation. Innovation districts can take many forms. For example, Hollywood is an entertainment-focused innovation district, while Wall Street and Silicon Valley are innovation districts centered around other industries. This research will be aimed at food innovation districts (FIDs).
All innovation districts contain economic, physical, and networking assets (see Figure 1). When these three assets combine with a supportive, risk-taking culture, they create an innovation ecosystem – a synergistic relationship between people, firms, and place (the physical geography of the district) that facilitates idea generation and accelerates commercialization (Katz & Wagner, 2014).
Figure 1. General framework of innovation districts (see pdf version for figures)
Networking, as one of the three key assets, allows innovation district members to share information, access resources, face challenges, and improve their competitiveness, along with other benefits (Kuhne, 2014). In the food industry, innovation does not draw purely on firm-level R&D. It involves learning and other processes that benefit from interaction across different enterprises as described in the theory of the New Economy (Avermaete & Viaene, 2002; Weaver, 2008). Yet, the nature of how networks operate within innovation districts is not clearly defined. Through this research, the relationship between innovation districts and business networking will be explored. With a focus on the food industry, the intent is to determine if there are any key themes associated with network optimization within food innovation districts that help accelerate businesses.
Research Question
Research has shown that industrial clusters/innovation districts can have a positive impact on innovation (Marshall, 1920; Arrow, 1962; Romer, 1986; Glaeser et al., 1992; Jacobs, 1969) and that innovation is a primary driver of national or regional economic competitiveness (Soriano et al., 2018; Benneworth & Hospers, 2007; Cooke, 2017; Lee et al., 2014). Structurally, the Brookings Institute identified networking assets as one of the vital elements of innovation districts (Katz & Wagner, 2014, p. 2). What is not obvious is the criteria or factors needed for companies to find positive value from the networking assets. It can be assumed that different industry clusters may require different compositions of the three assets identified by Katz and Wagner (2014). Thus, for the purpose of this research, the food industry will be the primary lens through which networking assets in innovation districts will be assessed. As a result, an initial research question was proposed: What is the relationship between small business acceleration within food innovation districts and the food networking assets of the districts?
This research question was refined using a PICOC (Population, Intervention, Comparison, Outcome, Context) assessment with the results shown below (Barends, Rousseau, & Briner, 2017):
- Population: Businesses within food innovation districts
- Intervention: Networking assets with optimal characteristics
- Comparison: Sub-optimal or non-existent networking assets
- Outcome: Business innovation/development and regional economic growth
- Context: Food companies, non-agriculture (farms)
Based on the PICOC assessment, a final research question was derived: What are some key characteristics of networking assets within food innovation districts that help drive business innovation and growth? This descriptive research question was selected to help food innovation districts’ key influencers, stakeholders, businesses, and policymakers more adequately manage their districts.
Theory
Theoretical Framework
Network Theory was selected to serve as the theoretical framework for this systematic review. Network Theory refers to a group of methods that help understand and interpret systems that consist of relationships between multiple subjects (Kalso, 2018). The focus of this research is on food companies and their networking interactions within innovation districts. Network Theory is being used because it examines the relationship between individual actors as they pertain to a macro-level social environment (Granovetter, 1973).
Networks operate as a connection of nodes or actors (individuals/firms/organizations) and ties (the relationship between the nodes) (see Figure 2). These ties can be strong or weak and the network itself can be broad or very tight. There are beneficial tradeoffs to each combination (Onday, 2016). The importance of the theory, however, is not just the composition of the nodes and ties, but the interplay of the entire system.
Wellman (1988) posits that there are five core principles of networks to be examined. First, the behavior of people is best predicted by examining their relationships. Second, the focus of analysis should be on the relationships between the nodes, not the nodes themselves. Third, nodes should not be analyzed under the assumption of independence – every actor is interrelated. The fourth principle is that the network is more than the aggregation of ties and nodes; a relationship between a tie and nodes also includes the other ties and nodes in the system. The final principle is that nodes or groups of nodes can have strong or weak boundaries and ties. Networking is more of a complex overlay of relationships than neat connections.
Figure 2: A graphic example of Network Theory. In this representation, the dots represent nodes with the lines connecting the dots representing ties. (see pdf version for figures)
This theoretical framework was selected because it allows innovation district networking to be examined beyond the relational bounds of any one individual business or other innovation district actor. It allows for an examination of the entire network. Additionally, Network Theory has been used frequently to examine business activity (Huggins & Thompson, 2015; Jones, 2008) and economic conditions (Soltész & Zilahy, 2020; Lindstrand & Hånell 2017; Blackmond, 2018) with a focus on the actors within the network.
Theoretical Lens
Positioning Wellman’s (1988) five core prinicpals of networks for examination, Onday (2016) suggests that there are three analytical levels from which a network can be viewed, the ego – direct contacts between the nodes/actors, the overall network – all actors and relationships within the network, and network position – an actor’s position within the network. For food innovation districts, the ego represents the individual relationships between the actors. The overall network is the complete collection of actors and relationships within the food innovation district. Finally, the network position of each actor should not be viewed as simply the cluster of nodes to which the actor belongs. It should also include the position of the actor’s relationships across clusters of nodes.
Figure 3: Onday posits that a network can be examined from the perspective of the Ego, Network Position, and Overall Network. (see pdf version for figures)
Additionally, there are key network characteristics that should be examined (Onday 2016): distance between actors; centrality – how important an actor is in the system; clustering – the number of ties among and ego network; structural holes – the gap in additive benefits within a network; equivalency – the similarity of relationship patterns of actors; density – the percentage of possible relations in a network that are observed; centralization – the level at which some actors well connected and others are not
Conceptual Framework
When applying Onday’s three analytical levels of network analysis to the research question, a conceptual framework can be devised as demonstrated in Figure 4. In this conceptual framework, the network exists as an influencer to and is influenced by the district actors and their direct relationships, individual actors’ positions within the network, and the complete composition of the actors and relationships. To answer the research question, what are some key characteristics of networking assets within food innovation districts that help drive business innovation and growth, ego, network position, and the overall network of food innovation district networking is assessed via Onday’s key networking characteristics.
Figure 4. Conceptual framework of Network Theory applied to the examination of food innovation district networking assets.
Alternative Views
Stakeholder Theory was considered as an alternative theoretical framework that could be applied to this systematic review. Stakeholder Theory conceptualizes the firm as a series of groups with different respective relationships to it. Stakeholders consist of internal organizational members, including employees, managers, and board members; external members, such as owners, customers, suppliers, and competitors; and hybrid members engaged in inter-organizational cooperative activity with the firm (Schneider, 2002, p. 211). Additional alternative views considered were Resource-Based View and Resource Dependency Theory. The disadvantage of all three theoretical frameworks as applied to this work is that each of the three theories is focused on the individual actor as it functions within the operating environment, whereas the intent of this research is to understand the operating environment and its impact on the individual actors.
Methodology
Evidence-Based Approach
Evidence-based management encourages the use of the best available evidence and helps organizations to identify evidenced information to keep management informed (Aninag, 2014). The “evidence-based approach to management reduces the potential for irrational thinking, bias, or exhaustion” (Price, 2018; Rynes, Giluk, & Brown, 2007). Evidence-based management is science and data-driven (Price, 2018).
This research began with a search string based on words and associated synonyms from the research question. Inclusion/exclusion criteria were then determined, and literature was selected. A critical appraisal of the articles was also performed to assess the quality, with key data then being extracted for review. Coding was then performed to synthesize the data.
Search Process / Literature Selection
An application of the search strings and cited reference search generated 977 total results for consideration. A broad preliminary search consisting of the string: food n5 innovat* AND district was initially used on the “OneSearch” internet library database advanced search tool. This first search returned 632 results.
Based on the PICOC assessment and refined research question, a librarian-assisted search on “OneSearch” internet library database advanced search tool was completed. This search returned 338 additional results using the following search string: food n5 innovat* n5 AND (expan* OR accelerat* OR invest* OR incubat* OR develop*) AND (anchor* OR institut* OR research* OR universit* OR college* OR corporat* OR partner* or associate* OR foundation* OR investigat*) NOT security.
A Google Scholar and cited reference search was also conducted and yielded 7 additional articles using the following search strings: food AND innovate AND business AND network AND entrepreneur AND district AND growth. The Google Scholar search was not exhaustive as it appeared to duplicate the OneSearch results for the first 15 pages.
After removing duplicates and non-scholarly articles, 291 articles were considered for this research. Screening of titles, abstracts, and publication dates resulted in 268 articles being excluded. A major consideration within the search criteria was the search term “technology” and associated iterations. Most of the articles were focused on food technology and innovation associated with food technology. However, searching with the exclusion of the term “technology” and its iterations severely limited the results and excluded relevant articles. Not excluding the term resulted in a large number of articles that were not relevant. The solution to this dilemma was to allow for technology-related articles in the results and manually filter them out.
As a result, 23 research articles were included in this systematic review. The article selection process is illustrated in Figure 5, which includes a flowchart for article selection using Preferred Reporting Items for Systems Reviews and Meta-Analyses (PRISMA) (Moher, Liberati, Tetzlaff, & Altman, 2010).
Figure 5. The PRISMA for this systematic review resulted in 23 articles for review. (see pdf version for figures)
Inclusion/Exclusion Criteria
For this review, the goal was to ensure that no less than 85% of the articles were published between 2015 and 2020. Articles had to be published in a scholarly journal and had to be published in English. Articles that were identified to not include applicable titles and/or abstracts were disqualified. No constraints were applied related to the location of the research. Additionally, it was important that articles had to be directed toward economic networking, food networking ecosystems, innovation districts or ecosystems, and business growth. To ensure that the research stayed focused, articles centered on food security, food technology, agriculture hubs (often called food hubs), niche food industries, or policy-making without a food business networking context were excluded.
Quality Assessment of the Literature
Transparency, Accuracy, Purposivity, Utility, Propriety, Accessibility, and Specificity (TAPUPAS) was selected as the quality appraisal tool for this systematic review. According to Long, Grayson, & Boaz, (2006), TAPUPAS appraisal provides a standardized framework for constructively assessing the quality of evidence in a body of knowledge. A five-point scale was used to rate each article as either 1 – Very Poor, 2 – Poor, 3 – Average, 4 – Good, or 5 – Excellent. These appraisal ratings were then totaled and presented as scores for each article. Scores were categorized as Low (0 – 11), Medium (12 – 23), and High (24 – 35). The quality appraisal is illustrated in the Quality Appraisal Chart in Appendix A. The appraisal resulted in the 23 articles being rated as High, within the range of 29 and 35.
Data Extraction
Each article offered useful information: sector/population, design and sample size, main findings, effect size, and limitations. The main findings were selected from the findings and discussion sections of each article for synthesis to develop themes. The Data Extraction Chart can be viewed in Appendix B.
Coding, Categorization, and Themes
A thematic synthesis was used in this research in accordance with the research question and the theoretical framework. After completing the quality assessment, the coding of core themes allowed for thematic analysis and synthesis of the data. According to Bowen (2009), organizing data into central themes is a form of pattern recognition, providing an opportunity for integration.
Themes across the 23 studies include horizontal and vertical networking, application of shared knowledge and resources, generation of knowledge and resources, knowledge and resource transfer, the value of an attractive and inviting environment, decentralized and non-competitive interactions, informal management, and clustering. Additionally, there were actors (nodes and egos) who were repeatedly listed in the literature. This list of network actors represents the theme of appropriate participants. The themes were then reviewed and evolved into three overarching findings.
Findings
The systematic review resulted in three key findings. This was based on the emerging themes from the coding synthesis. The first finding is that the appropriate participants should be in the network. The literature frequently cited the following actors as participants within observed food innovation district networks: companies, customers, collaborators, suppliers, competitors, farms, science/technology institutions, public research institutions, workforce development agents, and education assets (Kuhne et al., 2015; Lefebvre et al., 2014; Vaz et al, 2004; Radziwon & Bogers, 2018; McAdam et al, 2015; McKelvey & Ljungberg, 2016; Chojnacki & Creamer, 2019; Pittaway et al., 2004). This finding relates to the Egos within the food innovation district. Additionally, the existence of each actor and their involvement within the district impacts the district’s distance, clustering, and structural holes. The actors’ level of participation impacts the district’s equivalency, density, and centralization.
Beyond participants within the network, the second finding can be called the operational flow of the network. This includes the observation that there are two distinct network positions within the network – knowledge and resource application and knowledge and resource generation. The actors identified in the first finding fall into one of these two network positions (Pittaway et al, 2004; Brink, 2018; Nestle, 2019; Vaz et al., 2004; Huggins & Thompson, 2015; Uhm et al., 2018). Additionally, within this finding, the literature supports the notion that information and resources emanate between these two network positions. The literature showed that, in networking, the transfer of information and resources occurs in two different directions – vertically, between the actors of the two positions, and horizontally, between actors within the knowledge and resource application network position (Radziwon & Bogers, 2018; Sun, et al., 2018; McAdam et al, 2015; Melvey & Ljungberg, 2016; Kuhne et al., 2015; Markarov & Ugnich, 2015; Brink, 2018; Mo et al., 2020; Soriano, et al., 2018). This finding is impacted by the distance between actors, as determined by the network positions of all actors in the district. This finding could also be affected by the density of the network and the centralization of the actors as they participate in information and resource transfer opportunities.
The final finding identified throughout the literature was the emphasis on the operational environment (place) in which the network exists. This includes concepts such as decentralized organizational structures, the informality of participation, organic cultural development, non-competitive interactions and information exchanges, and frequency/availability of information-sharing opportunities (Gellynck et al., 2007; Nestle et al., 2016; Vaz et al., 2004; Huggins & Thompson, 2015; Pannekoek, et al., 2005; Pittaway, et al., 2004; Garcia-Cortijo, et al., 2019; Funk, 2014; Garcia & Chavez, 2014; Soriano, et al., 2018; Mo, et al., 2020; Brink, 2018; Roundy, et al., 2018; Markarov & Ugnich, 2015; Esmaeilpoorarabi et al., 2017; Esmaeilpoorarabi et al., 2020). This finding describes a condition of the overall network. As such, it has an impact on all key network characteristics. The operational environment houses the conditions that host the actors, ties, and overall network.
Discussion
Management Implications
As identified in the second finding, innovation district networking can not and should not be centrally controlled. However, leaders who have a stake in food innovation economies can take some actions to influence networking to encourage the optimality of it as an asset. The most important aspect of the network was shown to be the operational environment of the network (place), finding number three. In economic development, placemaking is the act of creating desired features within a community to make it attractive and more functional. This should be the primary focus of community leaders for network optimization purposes. From a policy perspective, it is important to ensure that policies don’t disrupt the ability to create the inviting and productive operating environment identified in finding three. Such unconstructive policy positions could be those that discriminate or otherwise discourage the participation of actors and egos in the network or tax positions that encourages activity to occur in another location.
Managers should also seek to ensure that there are ample opportunities for informal, non-competitive networking within the community. Since the findings identified the settings for the transfer of information and resources as mostly informal, it can be assumed that operating environments with adequate social settings, e.g. nightlife, well-performing schools, social clubs, and other meeting places should also be a key priority for community leaders. This would also include settings that allowed for the display of new technology and artistic creations. This social setting is important for the transfer of information and resources identified in finding number two.
Limitations
This systematic review is limited in several ways. The scope was limited to include no more than 25 articles in the review. The findings are solid and supported. However, a more exhaustive view may uncover more themes, leading to more findings. Many of the studies were based on food innovation districts based outside of the United States. Additionally, many of the studies were focused on networking for the advancement of food technology and artistic creativity. There may be opportunities to explore networking for financing, marketing, and other food business services. By design, agriculture-based (farming and farming co-op) studies were excluded from this systematic review. This was done to more narrowly focus the research. Finally, this research did not delve into networking within the clusters of key actors. Doing so would have reduced the ability to answer the research question. However, it could be argued that there is variability with the interaction of the egos and as a result, additional or even different dynamics for food innovation district networking at a more micro level.
Scholarly Considerations for Future Research
The research results and limitations of this study demonstrate that there is ample opportunity for future research on this topic. Of the many potential avenues of research, egos within networks are prime targets for further exploration. It would be useful to understand the nature of horizontal networking between actors within each of the egos in food innovation districts. Additionally, for leaders seeking to develop and/or strengthen food innovation district networking, it would be important to understand the value proposition for the knowledge-generation actors necessary for the district. Finally, place appeared to be the most important aspect of the network and the aspect that can be impacted most directly by leaders. As such, exploring the attributes of place that most positively correlate with network optimality would not only add to the scholarly knowledge but could provide significant value at the practitioner level.
Conclusion
This systematic review will help economic development, business, and other leaders improve the functionality of their food innovation districts. Innovation districts, whether they be food-related or other industry-focused, use networking as fuel for innovation and business development. This systematic review was performed with sufficient transparency and rigor using 23 scholarly articles. Those articles were quality appraised using TAPUPAS with all 23 resulting in High-quality ratings. The data extraction and analysis allowed for the evaluation of key data to lead to answering the research question. As a result, the understanding of networking characteristics within food innovation district networks was advanced.
References
Aninag, A. (2014). The advantage of evidence-based management. Retrieved from https://www.usfca.edu/management.news/the-advantage-of-evidence-based-management
Arrow, K. (1962). The economic implications of learning by doing. Review of Economic Studies. 29(3), 155–173.
Avermaete, T., Viaene, J. (2002). On innovation and meeting regulation – the case of the Belgian food industry. DRUID summer conference on industrial dynamics of the new and old economy – who is embracing whom?, Copenhagen /Elsinore.
Avermaete, T., Viaene, J., Morgan, E.J., Pitts, E., Crawford, N., & Mahon, D. (2004b). Determinants of product and process innovation in small food manufacturing firms. Trends in Food Science & Technology, 15, 474–483.
Barends, E., Rousseau, D. M., & Briner, R. B. (Eds.). (2017). CEBMa guideline for rapid evidence assessments in management and organizations, version 1.0. Amsterdam: Center for Evidence-Based Management. Retrieved from https://www.cebma.org/wp-content/uploads/CEBMa-REA-Guideline.pdf
Benneworth, P., Hospers, G.J. (2007). Urban competitiveness in the knowledge economy. Progress in Planning, 67. 105-197.
Blackmond, T. (2018). Does it matter ‘who governs?’ Governance networks, local economic development policies, and the Great Recession. Local Government Studies, 44(5), 624.
Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27-40. doi:10.3316/QRJ0902027
Brink, T. (2018). Organizing of dynamic proximities enables robustness, innovation and growth: The longitudinal case of small and medium-sized enterprises (SMEs) in food producing firm networks. Industrial Marketing Management, 75, 66–79. doi:10.1016/j.indmarman.2018.04.005
Chojnacki, K., & Creamer, N. (2019). Governance and innovations in local food system development. Journal of Agriculture, Food Systems, and Community Development, 9(1), doi:10.5304/jafscd.2019.091.034
Cooke, P. (2017). Complex spaces: Global innovation networks & territorial innovation systems in information & communication technologies. Journal of Open Innovation: Technology, Market, and Complexity, 3(2), 9, doi:10.1186/s40852-017-0060-5
Donaldson, T., & Preston, L. (1995). The stakeholder theory of the corporation: Concepts, evidence, and implications. The Academy of Management Review, 20(1), 65-91.
Esmaeilpoorarabi, N., Yigitcanlar, T., & Guaralda, M. (2018a). Place quality in innovation clusters. Cities, 74, 156–168.
Esmaeilpoorarabi, N., Yigitcanlar, T., Kamruzzaman, M., & Guaralda, M. (2020). How does the public engage with innovation districts? Societal impact assessment of Australian innovation districts. Sustainable Cities and Society, 52, doi:10.1016/j.scs.2019.101813
Funk, R. J. (2014). Making the most of where you are: Geography, networks, and innovation in organizations. Academy of Management Journal, 57(1), 193–222. doi:10.5465/amj.2012.0585
Freeman, R. E. (1984). Strategic management: A stakeholder approach. Boston: Pitman
Garcia, B. C., & Chavez, D. (2014). Network-based innovation systems: A capital base for the Monterrey city-region, Mexico. Expert Systems with Applications, 41(12), 5636–5646. doi:10.1016/j.eswa.2014.02.014
García-Cortijo, M. C., Castillo-Valero, J. S., & Carrasco, I. (2019). Innovation in rural Spain. What drives innovation in the rural-peripheral areas of southern Europe? Journal of Rural Studies, 71, 114–124. doi:10.1016/j.jrurstud.2019.02.027
Gellynck, X., Vermeire, B., & Viaene, J. (2007). Innovation in food firms: Contribution of regional networks within the international business context. Entrepreneurship & Regional Development, 19(3), 209–226. doi:10.1080/08985620701218395
Glaeser, E., Kallal, H., Scheinkman, J., Shleifer, A. (1992). Growth in cities. Journal of Political Economics, 100(6), 1126–1152.
Granovetter, M. (1983). The strength of weak ties: A network theory revisited. Sociological Theory, 1, 201-233. doi: 10.2307/202051
Huggins, R., & Thompson, P. (2015). Entrepreneurship, innovation and regional growth: A network theory. Small Business Economics, 45(1), 103.
Jacobs, J. (1969). The Economy of Cities. Random House, New York.
Jones, A. (2008). Beyond embeddedness: Economic practices and the invisible dimensions of transnational business activity. Progress in Human Geography, 32(1), 71.
Kalso, R. (2018). Network theory. Salem Press Encyclopedia of Science.
Katz, B., & Wagner, J. (2014). The rise of innovation districts. Washington, DC: Brookings Institution. Retrieved from https://www.brookings.edu/essay/riseof-innovation-districts/
Kühne, B., Gellynck, X., & Weaver, R. D. (2015). Enhancing innovation capacity through vertical, horizontal, and third-party networks for traditional foods. Agribusiness, 31(3), 294–313. doi:10.1002/agr.21408
Kühne, B., & Gellynck, X., (2013). Enhancing the innovativeness of food SMEs through the management of strategic network behavior and network learning performance. International Journal on Food System Dynamics, 3(3), 199–200.
Lee, J. H., Hancock, M. G., & Hu, M.-C. (2014). Towards an effective framework for building smart cities: Lessons from Seoul and San Francisco. Technological Forecasting & Social Change, 89, 80–99. doi:10.1016/j.techfore.2013.08.033
Lefebvre, V. M., Raggi, M., Viaggi, D., Sia, L. C., Minarelli, F., Kühne, B., & Gellynck, X. (2014). SMEs’ preference for innovation networks: A choice experimental approach. Creativity & Innovation Management, 23(4), 415.
Lindstrand, A., & Hånell, S. M. (2017). International and market-specific social capital effects on international opportunity exploitation in the internationalization process. Journal of World Business, 52(5), 653–663. doi:10.1016/j.jwb.2017.05.002
Long, A. F., Grayson, L., & Boaz, A. (2006). Assessing the quality of knowledge in social care: Exploring the potential of a set of generic standards. British Journal of Social Work, 36, 207–226. doi:10.1093/bjsw/bch307
Marshall, A. (1920). Principles of Economics. Macmillan, London.
McAdam, M., McAdam, R., Dunn, A., & McCall, C. (2016). Regional horizontal networks within the SME agri-food sector: An innovation and social network perspective. Regional Studies, 50(8), 1316–1329. doi:10.1080/00343404.2015.1007935
Makarov, S., & Ugnich, E. (2015). Business-catalysts as drivers of regional innovation systems. Foresight – Russia, 9(1), 56–67. doi:10.17323/1995-459X.2015.1.56.67
McKelvey, M., & Ljungberg, D. (2017). How public policy can stimulate the capabilities of firms to innovate in a traditional industry through academic engagement: The case of the Swedish food industry. R & D Management, 4, 534. doi:10.1111/radm.12224
Mo, C., He, C., & Yang, L. (2020). Structural characteristics of industrial clusters and regional innovation. Economics Letters, 188, doi: 10.1016/j.econlet.2020.109003
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2010). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. International Journal Of Surgery (London, England), 8(5), 336–341. doi:10.1016/j.ijsu.2010.02.007
Nestle, V., Täube, F. A., Heidenreich, S., & Bogers, M. (2019). Establishing open innovation culture in cluster initiatives: The role of trust and information asymmetry. Technological Forecasting & Social Change, 146, 563–572. doi:10.1016/j.techfore.2018.06.022
Onday, O. (2016). Network theory. Elixir Project Management, 91, 37966-37972.
Pannekoek, L., Van Kooten, O., Kemp, R., & Omta, S.W.F. (2005). Entrepreneurial innovation in chains and networks in Dutch greenhouse horticulture. Journal on Chain and Network Science, 5, 39–50.
Petticrew, R., & Roberts, H. (2012). Systematic review in the social sciences: A practical guide. Malden, MA: Blackwell Publishing.
Pittaway, L., Robertson, M., Munir, K., Denyer, D., & Neely, A. (2004). Networking and innovation: A systematic review of the evidence. International Journal of Management Reviews, 5/6(3/4), 137.
Price, E. (2018). The benefits of evidence-based management in a nutshell. Retrieved from
https://www.ckju.net/en/blog/benefits-evidence-based-management-nutshell/1246
Radziwon, A., & Bogers, M. (2019). Open innovation in SMEs: Exploring inter-organizational relationships in an ecosystem. Technological Forecasting & Social Change. doi:10.1016/j.techfore.2018.04.021
Romer, M. (1986). Increasing returns and long-run growth. Journal of Political Economics, 94(5), 1002–1037.
Roundy, P.T., & Bayer, M.A., (2019). To bridge or buffer? A resource dependence theory of nascent entrepreneurial ecosystems. Journal of Entrepreneurship in Emerging Economies, doi: 10.1108/JEEE-06-2018-0064
Roundy, P., Bradshaw, M. & Brockman, B., (2018). The emergence of entrepreneurial ecosystems: A complex adaptive systems approach. Journal of Business Research, 86(1), 1-10.
Rynes, S. L., Giluk, T. L., & Brown, K. G. (2007). The very separate worlds of academic and practitioner periodicals in human resource management: Implications for evidence-based management. Academy of Management Journal, 50(5), 987-1008.
Schneider, M. (2002). A Stakeholder Model of Organizational Leadership. Organization Science, 13(2), 209.
Soltész, P., & Zilahy, G. (2020). A Network Theory approach to the sharing economy. Periodica Polytechnica: Social & Management Sciences, 28(1), 70.
Soriano, F. A., Villano, R. A., Fleming, E. M., & Battese, G. E. (2019). What’s driving innovation in small businesses in Australia? The case of the food industry. Australian Journal of Agricultural & Resource Economics, 63(1), 39–71. doi:10.1111/14678489.12284
Uhm, C. H., Sung, C.S., & Park, J.Y. (2018). Understanding the accelerator from resources-based perspective. Asia Pacific Journal of Innovation and Entrepreneurship, 12(3), 258–278. doi:10.1108/APJIE-01-2018-0001
Vaz, M. D. N., Cesário, M., & Fernandes, S. (n.d.). Interaction between innovation in small firms and their environments: An exploratory study. European Planning Studies, 14(1), 95–117. doi:10.1080=09654310500339893
Weaver, R.D. (2008). Collaborative pull innovation: Origins and adoption in the new economy. Agribusiness, 24, 388-402.
Wellman, B. (1988). Structural analysis: From method and metaphor to theory and substance. In B. Wellman & S. Berkowitz (Eds.), Social structures: A network approach (pp.19-61). Cambridge, England: Cambridge University Press.
