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Session Overview
20-PM2-10: G8 - Innovation Performance and Policy
Thursday, 20/June/2019:
2:45pm - 4:15pm

Session Chair: Svenja Sommer, HEC Paris
Location: Room PC 18

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Effects of cognitive proximity and collaboration breadth on innovative performance

Muhammad Ismail, Alejandro Bello-Pintado, Teresa García-Marco

Universidad Publica de Navarra, Spain


The aim is to develop a theoretical framework of strategies based on combination of collaboration breadth (number and types of formally engaged partners) and cognitive distance. Study starts by exploring the underlying reasons for choosing a particular combination of these variables and test their combined impact on innovation performance.


Innovation carried out through collaborative efforts is becoming increasingly important within the literature of open innovation (Bogers, 2011; Hottenrott & Lopes-Bento, 2016), as a number of studies suggest that collaboration derives higher gains from joint innovation (Faems et al., 2005, Hottenrott & Lopes-Bento, 2016). The selection of these external actors for collaboration is sometimes influenced by proximities amongst which cognitive dimension holds a special place (Boschma & Lambooy, 1999), being one of the prerequisites for effective coordination amongst firms (Boschma, 2005). Firms try to strengthen their cognitive dimension by investing significant amounts to build social capital (Adler & Kwon, 2002). However, too much similarity in the cognitive dimension of social capital can reduce innovative opportunities and too much distance can limit the understanding and interpretation of external knowledge (Cowan et al., 2007). The work thus studies open innovation with a focus on cognitive and collaborative dimensions.

Literature Gap

Existing studies test effects of collaboration (Belderbos et al., 2004; Nieto & Santamaría, 2007) and cognitive distance (Bertrand & Mol, 2013; Nooteboom et al., 2007) on innovation performance in isolation and there remains a gap regarding the combination of knowledge sources for optimal innovation performance (Criscuolo et al., 2017).

Research Questions

we aim to:

•develop a framework of strategies based on combination of collaboration breadth and cognitive distance & theoretically studying the reasons for choosing a particular combination.

•test the effect of these combination of collaboration breadth and cognitive distance on the innovative performance.


We aim to theoretically differentiate innovation strategies with regard to collaboration depth and cognitive distance, by preparing a 2X2 matrix of four pure and one mixed strategy to show purpose of choosing a particular strategy and its outcomes.

Further perform quantitative analysis for testing the direct and interaction effects of cognitive distance, and collaboration breadth (i.e. the number and types of formally engaged partners) on innovative performance. For purpose of analysis, the study incorporates: the definition of collaborative breadth used by Laursen & Salter, (2014) and scale of cognitive distance established by Criscuolo et al. (2017).

Empirical Material

The study uses panel data that is built on information collected from more than 12,000 Spanish manufacturing and service firms from the year 2003 to 2015. The source is Technological Innovation Panel (PITEC—Panel de Innovación Tecnológica) that is the Spanish contribution to the Community Innovation Survey (CIS) (Garcia Martinez et al., 2017). The data set includes thorough information regarding firms R&D activities and collaborations with different actors. Being panel data, it over comes the limitation faced by previous studies that used CIS data, i.e. simultaneity between innovation input and outputs, by lagging independent variables (Mairesse and Mohnen, 2010) also, this data set allows for reduction of common method bias.


The theoretical part will answer the questions like which combination of cognitive distance and collaboration breadth is most effective for achieving different strategies like exploitation or exploration or other objectives, including minor technical changes or fundamental changes in technology. Since high cognitive distance, as hypothesized is linked to exploration of novel opportunities, and low cognitive distance to exploitation, however when combined with high or low number of partners, the innovation outcome may alter.

The quantitative analysis will test these hypotheses and check the validity of different claims regarding with which combination leads to desired outcome in terms of goals set by firms.

Contribution to Scholarship

The work particularly takes into account the need for development of literature regarding the combination of knowledge sources and their effect on innovation performance (Criscuolo et al.,2017). Both, collaboration and cognitive distance are highly significant to the literature of open innovation, as collaboration derives higher gains from joint innovation (Faems et al.,2005) and cognitive distance enables organizations to engage in dissimilar open innovation practices by altering their choice of external partner in terms of the cognitive distance (Gavetti & Levinthal, 2000).

Despite increase in number of studies carried out to examine the effects of collaboration and cognitive distance on innovation performance, most of these test their effects in isolation. By bringing together these two aspects, the present study aims to advance the understanding of the how the joint effect of these variables of open innovation model can lead to varied performance outcomes and provides relevant recommendations regarding collaborative innovation practices.

Contribution to Practice

Managers can use this study to identify best combination of variables to come up with strategies they need to devise for obtaining firm’s long term goals. Firms which are aiming for varied goals, like product or process innovation require distinct combination of collaboration breadth and cognitive distance. This study will help them in deciding the optimal combination needed to achieve different goals.


The study is in purely carried out in the theme of open innovation. It will add to the literature by evaluating the joint effect of collaboration breadth and cognitive distance on innovation performance. In light of literature, these are highly significant for altering innovation performance (Nooteboom, 2000;Laursen & Salter, 2014)


Adler, P. S., & Kwon, S.W. (2002). Social Capital : Prospects for a New Concept. The Academy of Management Review , 27(1), 17-40.

Belderbos, R., M. Carree, and B. Lokshin (2004). Cooperative R&D and Firm Performance, Research Policy 33(10), 1477–1492.

Bertrand, O., & Mol, M. J. (2013). The antecedents and innovation effects of domestic and offshore R&D outsourcing: the contingent impact of cognitive distance and absorptive capacity. Strategic Management Journal 34, 751–760.

Bogers, M. (2011). The open innovation paradox: Knowledge sharing and protection in R&D collaborations. European Journal of Innovation Management, 14(1).

Boschma, R. A. (2005). Proximity and innovation: A critical assessment. Regional Studies, 39(1), 61–74.

Boschma, R. A., & Lambooy, J. G. (1999). Evolutionary economics and economic geography, Journal of Evolutionary Economics 9, 411–429.

Cowan R., Jonard N. and Zimmermann J. B. (2007) Bilateral collaboration and the emergence of innovation networks, Management Science 53, 1051–1067.

Criscuolo, P., Laursen, K., Reichstein, T., & Salter, A. (2017). Winning combinations: search strategies and innovativeness in the UK. Industry and Innovation 25(2), 115-143.

Faems, D., B. Van Looy, and K. Debackere. (2005) Interorganizational Collaboration and Innovation: Toward a Portfolio Approach. Journal of Product Innovation Management, 22(3), 238–250.

Gavetti, G., & Levinthal, D. (2000). Looking Forward and Looking Backward: Cognitive and Experiential Search. Administrative Science Quarterly, 45(1), 113.

Hottenrott, H., & Lopes-Bento, C. (2016). R&D Partnerships and Innovation Performance: Can There Be too Much of a Good Thing? Journal of Product Innovation Management, 33(6), 773–794.

Laursen, K., & Salter, A. (2014). The paradox of openness: Appropriability, external search and collaboration. Research Policy, 43(5), 867–878.

Mairesse, Jacques and Pierre Mohnen (2010). Using Innovation Surveys for Econometric Analysis. In Handbook of Innovation, Handbook of Economics, 1.

Nieto, M. J., & Santamaría, L. (2007). The importance of diverse collaborative networks for the novelty of product innovation. Technovation, 27(6–7), 367–377.

Nooteboom, B. (2000). Learning by interaction: Absorptive capacity, cognitive distance and governance. Journal of Management and Governance, 4(1–2), 69–92.

Nooteboom, B., Van Haverbeke, W., Duysters, G., Gilsing, V., & van den Oord, A. (2007). Optimal cognitive distance and absorptive capacity. Research Policy, 36(7), 1016–1034.

How do firms adapt their external collaboration vehicles to changing internal organizational attributes? The moderating role of firm age

Farid Mammadaliyev1, Victor Gilsing1,2, Ine Paeleman1

1University of Antwerp, Belgium; 2Free University Amsterdam


Our study employs the behavioral theory of the firm to explain a focal firm's behavior, when it's innovative performance goes below and above innovative aspiration level.


1. The behavioral theory of the firm (Cyert and March, 1963)

2. Performance-induced change (Ocasio, 1995)

3. Performance feedback models (Greve, 2003)

4. Organizational learning (Levit and March, 1988)

5. Environmental uncertainty (Grant and Baden-Fuller, 2004; van de Vrande, Vanhaverbeke, & Duysters, 2009).

6. Transaction cost theory (Williamson, 1975; Geykens, Steenkamp and Kumar, 2006)

Literature Gap

Until now studies have largely focused on how exogenous uncertainty afftects a firm's governance mode choice. However, little attention has been paid to endogenous uncertainty arising from the gap between performance and aspirations set up for this performance influences a firm's governance mode choice.

Research Questions

How does innovative performance relative to aspiration level set up for this performance affect a firm's trade between less (i.e. non-equity strategic alliances) and more integrated governance modes (i.e. joint ventures)


Our study is quantitative (longitudinal panel study). In particular, using data from different sources (SDC Platinum Joint Ventures and Alliances database, Compustat, USPTO, CRSP, NBER's public data) we constructed a unique database for firms from 12 hi-tech industries for the period of 1990-2010.

Empirical Material

We used multiple sources in the data collection. Data on firms’ external knowledge sourcing vehicles were collected from the SDC Platinum Joint Ventures and Alliances database. SDC tracks a very wide range of agreement types including research & development agreements, sales and marketing agreements, banking agreements, manufacturing agreements(Schilling, 2009).

We extracted patent data from The National Bureau of Economic Research (NBER) paper (Kogan, Papanikolaou, Seru and Stoffman, 2017), in which the authors used extensive name-matching tools to assign USPTO patents to focal firms. However, these firms were classified by a “permno” identifier. Thus, we employed a CRSP/Compustat merged database (linking table) to obtain more common identifiers such as gvkey and cusip for each patent. Consequently, we matched our sample firms with their joint venture and alliance data from SDC, patent data and financial data from Compustat using gvkey and/or cusip.e also constructed a large, cross-firm and cross-industry sample of t Compustat firms for the period of 1990 – 2010 in 12 high-tech industries.

We only used agreements whose purposes were developing/improving technoloy (or at least one of the alliance purposes).We ended up with more than 7.000 firm-year observations from more than 800 firms in 12 hi-tech industries.


H1: Firms performing below innovative aspiration level are more likely to source knowledge through less integrated governance modes (i.e. non-equity alliances).

Only historical aspiration model we don’t find a significant relationship between below aspiration and likelihood of undertaking non-equity alliances.

Only social aspiration model we find a positive relationship (at the 10%) between below aspiration and likelihood of undertaking non-equity alliances. This is a partial support for the first hypothesis.

To sum up, the results show that if a firm is below social aspiration, it will more likely tosource technological knowledge through less integrated governance modes (i.e. non-equity alliances).

H2: Firms performing above the aspiration level are more likely to source knowledge through more integrated governance modes (i.e. equity joint ventures).

Only historical aspiration model we find a significant relationship between above aspiration and likelihood of undertaking JVs.

Only social aspiration model we find a positive relationship between above aspiration and likelihood of undertaking JVs. This is a full support for the second hypothesis.

To sum up, the results show that if a firm is above aspiration (both historical and social), it will more likely to source technological knowledge through more integrated governance modes (i.e. JVs).

Contribution to Scholarship

1. Our main contribution is that we find a relation between innovative performamce and a firm's trade off between non-equity strategic alliances and joint ventures. Previous studies have only focused on exogenous factors which can not be decreased by a firm's actions. We conclude that a firm's behavior in governance modes also depends on factors which can be controlled by its actions.

2. We move beyond the focus on collaboration with individual partners (the dyadic perspective) that has been the dominant emphasis in the literature until now. In this study the unit of the analysis is a focal firm.

Dyadic governance mode choice studies assume that a firm has to choose one of potential modes. However, we consider that a firm has 3 options at its disposal :

choosing either one of less and more integrated modes, both of them or neither of them.

Contribution to Practice

Our study makes a distinction between two types of governance mode:

1. non-equity atrategic alliances

2. joint ventures

we find that firms above innovatiove performance prefer joint ventures, because we find such alliances are better equiped to protect technological knowledge. Firms below aspiration prefer non-equity strategic alliances, as they have less to lose.

These findings might give some ideas to top managers how to behave in bad (below aspiration) and good (above aspiration) situations depending on their pupose (to protect technological knowledge or swim in a pool with full of knowledge).


This study only focuses of R&D agreements. In particular, how a firm's behavior in R&D agreements is driven by performance feebacks. Moreover, our focus is a firm's innovative performance, not financial performance. These two factors make this study so relevant to this conference.


Ahuja G. 2000. Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly 45, 425-455.

Audia PG & and Greve HR. 2006. Less likely to fail: low performance, firm size and factory expansion in the shipbuilding industry. Management Science 52 (1), 317-343.

Audia PG, Locke EA & Smith KG. 2000. The paradox of success: an archival and a laboratory study of strategic persistence following radical environmental change. Academy of Management Journal 43 (5), 837–8

Balakrishnan S & Wernerfelt B. 1986. Technical change, competition and vertical integration. Strategic Management Journal 7(4), 347-359.

Baum J, Rowley T & Shipilov A. 2005. Dancing with strangers: aspiration performance and the search for underwriting syndicate partners. Academy of Management Proceedings, 1-6.

Baum JAC & Dahlin KB. 2007. Aspiration performance and railroads’ patterns of learning from train wrecks and crashes. Organization Science 18, 368-385.

Bromiley P. 1991. Testing a causal model of corporate risk taking and performance. Academy of Management Journal 34(1), 37–59.

Bromiley P. 1991. Testing a causal model of corporate risk taking and performance. Academy of Management Journal 34(1), 37–59.

Carayannopoulos S & Auster ER. 2010. External Knowledge in biotecnology through acquisition versus Alliances: A KBV approach. Research Policy 39 (2), 254 - 267.

Chen H & Chen TJ. 2003. Governance structures in strategic alliances: transaction cost versus resource-based perspective. Journal of World Business 38(1), 1-14.

Choi J & Contractor FJ. 2016. Choosing an Appropriate Alliance Governance Mode: The Role of Institutional, Cultural and Geographical Distance in International Research & Development (R&D) Collaborations. Journal of International Business Studies 47(2), 210-232.

Cohen WM, Nelson RR & Walsh JP. 2000. Protecting their intellectual assets: appropriability conditions and why U.S. manufacturing firms patent (or not). NBER working paper no. 7552 National Bureau of Economic Research, Cambridge, MA.

Cyert RM & March JG. 1963. A behavioral theory of the firm. Englewood Cliffs, NJ: Prentice Hall.

Das TK & Rahman N.J. 2010. Determinants of Partner Opportunism in Strategic Alliances:

A Conceptual Framework. Journal Business Psychology 25, 55-74.

Das TK & Rahman N. 2001. Partner misbehavior in strategic alliances: Guidelines for effective deterrence. Journal of General Management, 27(1), 43–70.

Das TK & Teng BS. 1998. Resource and risk management in the strategic alliance making process. Journal of Management 24(1), 21-42.

Denicolai S, Ramirez M & Tidd J. 2014. Creating and Capturing Value from External Knowledge: The Moderating Role of Knowledge Intensity. R&D Management 44(3), 248-264.

Dushnistsky G & Lenox MJ. 2005. When do firms undertake R&D by investing in new ventures? Strategic Management Journal 26 (10), 947-965.

Folta TB & Miller KD. 2002. Real options in equity partnerships. Strategic Management Journal 23, 77-88.

Folta TB. 1998. Governance and uncertainty: the trade‐off between administrative control and commitment. Strategic management journal 19 (11), 1007-1028.

Geykens I, Steenkamp EM & Nirmalya Kumar N. 2006. Make, Buy or Ally: A transaction cost theory meta-analysis. Academy of Management Journal 49(3), 519-543.

Grant RM & Baden-Fuller C. 2004. A Knowledge Accessing Theory of Strategic Alliances. Journal of Management Studies 41 (1), 61-84.

Greve HR. 1998. Performance, aspirations, and risky organizational change. Administrative Science Quarterly 43(1), 58–86

Greve HR. 2003. Organizational learning from performance feedback. A behavioural perspective on innovation and change. Cambridge University Press: New York

Gulati R & Singh H. 1998. The architecture of cooperation: Managing coordination uncertainty and interdependence in strategic alliances. Administrative Science Quarterly 43(4), 781-814.

Gulati, R. 1995. Does familiarity breed trust? The implication of repeated ties for contractual choice in alliances. Academy of Management Journal 38, 85-112.

Hagedoorn J & Cloodt M. 2003. Measuring innovative performance: Is there an advantage in using multiple indicators? Research Policy 32(8), 1365–1379.

Hagedoorn J & Duysters GM. 2002. External sources of innovative capabilities: The preference for strategic alliances or mergers and acquisitions. Journal of Management Studies 39(2), 167-188.

Hagedoorn J. 1993. Understanding the rationale of strategic technology partnering: Interorganizational modes of cooperation and sectoral differences. Strategic Management Journal 14, 371-385.

Hagedoorn J. 1993. Understanding the rationale of strategic technology partnering: Interorganizational modes of coorperation and sectoral differences. Strategic Management Journal 14(5), 371-385.

Hagedoorn, J. 1993. Understanding the rationale of strategic technology partnering: Interorganizational modes of cooperation and sectoral differences. Strategic Management Journal 14, 371-385.

Higgins MJ & Rodriguez D. 2006. The outsourcing of R&D through acquisitions in the pharmaceutical industry. Journal of Financial Economics 80(2), 351–383.

IYin X & Shanley M. 2008. Industry Determinants of the "Merger versus Alliance" Decision. The Academy of Management Review 33(2), 473-491.

Janney JJ & Dess GG. 2004. Can real-options analysis improve decision-making? Promises and pitfalls. Academy of Management Executive 18(4), 60-75.

Joseph J & Gaba V. 2015. The fog of feedback: Ambiguity and firm responses to multiple aspiration levels. Strategic Management Journal 36(13), 1960-1978.

Kameda T & Davis JH. 1990. The function of the reference point in individual and group risk decision making. Organizational Behavior and Human Decision Processes, 46, 55-76.

Keil T, Maula M, Schildt H & Zahra SA. 2008. The effect of governance modes and relatedness of external business development activities on innovative performance. Strategic Management Journal. 29, 895-907.

Kim HJ and Kim BK. 2017. Risk-based perspective on the choice of alliance governance in high tech industries. Journal of Management and Organizaion, 1-18.

Kogan L, Papanikolaou D, Seru A & Stoffman N. 2017. Technological Innovation, Resource Allocation, and Growth. The Quarterly Journal of Economics 132(2), pages 665-712.

Kogut B. 1991. Joint ventures and the option to expand and acquire. Management Science 37, 19- 33.

Kogut, B. 1988. Joint ventures: Theoretical and empirical perspectives. Strategic Management Journal 9, 319-332.

Leiblein MJ and Miller DJ. 2003. An Empirical examination of transaction and firm-level influences on the vertical boundaries of the firm. Strategic Management Journal 24, 839–859.

Leiblein MJ. 2003. The Choice of Organizational Governance Form and Performance: Predictions from Transaction Cost, Resource-based, and Real Options Theories. Journal of Management 29(6), 937–961.

Levitt B & March J. 1988. Organizational learning. Annual Review of Sociology 14(1), 319-338.

Lungeanu R, Stern I & Zajac EJ. 2016. When do firms change technology-sourcing vehicles? The role of poor innovative performance and financial slack. Strategic Management Journal 37, 855–869.

March JG & Shapira Z. 1992. Variable risk preferences and the focus of attention. Psychological review 99(1), 172–183

McGrath R.G. and MacMillan I.C. .2000.. Assessing technology projects using real options reasoning. Research Technology Management 43 (4), 35–49.

Moliterno TP, Beck N, Beckman CM, & Meyer M. 2014. Knowing your place: Social performance feedback in good times and bad times. Organization Science 25, 1684–1702.

Ocasio W. 1995. The enactment of economic adversity-a reconciliation of theories of failure-induced change and threat-rigidity. Research in Organizational Behavior: An Annual Series of Analytical Essays and Critical Reviews 17, 287–331.

Parmigiani A & Rivera-Santos M. 2011. Clearing a path through the forest: A meta-review of international relationships. Journal of Management 37(4), 1108-1136.

Pateli G. 2009. Decision making on governance of strategic technology alliances. Management decision 47(2), 226-270.

Phelps C. 2003. Technological exploration: A longitudinal study of the role of recombinatory search and social capital in alliance networks. Unpublished dissertation, New Yor University, New York.

Pindyck RS. 1991. Irreversibility, uncertainty, and investment. Journal of Economic Literature 29(3), 1110– 1149.

Pisano G. 1990. The R&D boundaries of the firm: an empirical analysis. Administrative Science Quarterly 35, 153–176.

Powell WW, Koput KW & Smith-Doerr L. 1996. Interorganizational collaboration and the locus of innovation: networks of learning in biotechnology. Administrative Science Quarterly 41, 116–145

Powell WW. 1990. Neither market nor hierarchy: Network form of organization. In B. M. Staw and L.L. Cummings, editors. Research in Organizational Behavior, 12: 295-336. Greenwich, Conn.: JAI Press Inc.

Sampson RC. 2007. R&D alliances and firm performance: The impact of technological diversity and alliance organization on innovation. The Academy of Management Journal 50 (2), 364-386.

Santoro MD & McGill JP. 2005. The effect of uncertainty and asset co-specialization on governance in biotechnology alliances. Strategic Management Journal 26 (13), 1261-1269.

Schilling MA & Phelps CC. 2007. Interfirm collaboration networks: The impact of large-scale network structure on firm innovation. Management science 53 (7), 1113-1126.

Schilling MA. 2009. Understanding the alliance data. Strategic Management Journal 30 (3), 233-260.

Stuart TE, Ozdemir SZ & Ding WW. 2007. Vertical alliance networks: The case of university–biotechnology–pharmaceutical alliance chains. Reseacrh Policy 21(4), 477-498.

Sutcliffe KM & Zaheer A. 1998. Uncertainty in the transaction environment: An empirical test. Strategic Management Journal 19(1), 1-23.

Van de Vrande V, Vanhaverbeke W & Duysters G. 2009. External technology sourcing: The effect of uncertainty on governance mode choice. Journal of Business Venturing 24, 62–80

Van de Vrande V. 2013. Balancing your technology- sourcing portfolio: how sourcing mode diversity enhances innovative performance. Strategic Management Journal 34(5), 610–621

Vanhaverbeke W, Duysters G & Noorderhaven N. 2002. External technology sourcing through alliances or acquisitions: an analysis of the application-specific integrated circuits industry. Organization Science 13, 714–733.

Villalonga B & McGahan AM. 2005. The choice among acquisitions, alliances, and divestitures. Strategic Management Journal 26(13), 1183–1208.

Wang L & Zajac EJ. 2007. Alliance or acquisition? A dyadic perspective on interfirm resource combinations. Strategic Management Journal 28(13), 1291–1317.

Wang L & Zajac EJ. 2007. Alliance or Acquisition? A Dyadic Perspective on Interfirm Resource Combinations. Strategic Management Journal 28(13), 1291-1317.

What impacts Corporate Innovation Processes: System-Static Considerations on Interdependent Influence Factors

Kevin Reuther

University of the West of Scotland, United Kingdom


Innovation is perceived as an important factor for organizations to thrive (Chesbrough, 2003; Gemünden et al., 2017). In recent years, researchers studied single factors that impact innovation processes (e.g. Corral et al., 2017; Keum & See, 2017). This paper discusses interdependencies amongst such factors using a systems theory approach.


The origin of the scientific discussion of systems theory goes back to Bertalanffy (1951) and his article on general systems theory. According to Capra (2015), “systems thinking is 'context-related', and […] means that something is placed in the context of a larger whole” or in other words, it shifts the emphasis of analysis from individual elements to their interactions (Jantsch, 1972, 103). It is suggested that systems theory therefore can be related to the study of phenomena in organizations (Skyttner, 2006) and particularly the study of innovation (Colapinto & Porlezza, 2013) that requires researchers to look at individuals’ interaction in organizational processes and the framework and influence factors around them (Vester, 2000).

Literature Gap

This paper argues that the focus of innovation research on a corporate level has been on understanding how single influence factors impact innovation. Little attention has been on the interaction of such influence factors and how an innovation system with a network of interdependent influence factors might behave.

Research Questions

This leads to the research questions a) what influence factors on corporate innovation processes can be identified and b) how are these influence factors interdependent when understood as elements of a corporate innovation system?


Based on the work of Parsons (1970), Luhmann (1984) and Vester (2000), a sensitivity analysis based approach is adopted. It follows six steps: 1) a description of the system, including facts, data, problems, goals and a first system map, 2) the identification of influence factors and indicators to gain a set of variables, 3) the evaluation of these variables’ relevance for the system, 4) questioning the interconnections of influence factors using an influence matrix, 5) the description of the systemic roles of the variables in the system and 6) interlinking the variables as well as examining the overall interconnection.

Empirical Material

As the data collection for this project is still in progress, preliminary results are presented in the course of this conference paper. The data basis for this analysis is initially a systematic literature review to assess the influencing factors on innovation processes discussed in the literature. Building on this, qualitative data is gathered using two focus groups and ten in-depth semi-structured interviews. Following the idea of systems theory that underlying mechanisms of systems exist in different environments, the study focuses on a single industry (Information Technology) in a single country (Germany). The study focuses on companies with a certain size that requires some kind of organisational complexity (e.g. different departments) and that requires some planning of innovation processes and the respective communication.


In the results of the paper, a system description based on the literature and the preliminary data pool is introduced and explained. A draft model describing the corporate innovation system based on the data is set out, distinguishing five categories of influence factors/system elements that are 1) processes, 2) Actors, 3) Traits, 4) Ressources and 5) Internal and External System Environment. These influence factors are critically discussed and single interdependencies that provide first insights on the system behaviour are introduced as 'if-then-relations'. The paper then focuses on explaining the further steps in the course of the project and lays out directions for future research based on the system model, that could include a) simulations and system dynamics, but also b) validation of single interdependencies through quantitative studies.

Contribution to Scholarship

Systems theoretical approaches show new possibilities to investigate innovations beyond traditional, purely empirical methods. They offer a holistic perspective on the field of research and improve the understanding of the relationships between the influencing factors on innovation, as they shift the focus from researching single elements and their impact on innovation towards how such elements interact in something one could refer to as 'corporate innovation system'. Due to its transdisciplinary, the systems approach in general and sensitivity analysis in particular can be used for a variety of related research topics.

Contribution to Practice

Understanding the relationships of factors influencing innovation is not only beneficial from a scientific point of view, but also supports the practical application of the results obtained, since organizations can understand how certain parameters are interdependent and thus need to be aligned with the goal of optimizing innovation conditions.


Systems theory has a wide range of roots in different academic fields (Kleve, 2010) and therefore is transdisciplinary in nature (Pohl & Hirsch Hadorn, 2008). It includes linkages of industry and society in a narrower and wider sense and might be an insightful contribution for this year’s R&D Management Conference.


Avermaete, T., Viaene, J., Morgan, J., & Crawford, N. (2003). Determinants of innovation in small food firms. European Journal of Innovation Management, 6, 8-17.

Bertalanffy, L. (1951). General system theory - A new approach to unity of science. Human Biology, 23, 303-361.

Capra, F. (2015). Lebensnetz: Ein neues Verständnis der lebendigen Welt (1st Edition ed.). Frankfurt am Main: S. Fischer Verlag.

Chesbrough, H. (2003). Open Innovation: The New Imperative for Creating and Profiting from Technology: Harvard Business School Press.

Colapinto, C., & Porlezza, C. (2013). Systems Theory and Innovation. In E. G. Carayannis (Ed.), Encyclopedia of Creativity, Invention, Innovation and Entrepreneurship (pp. 1764-1768). New York, NY: Springer New York.

Corral de Zubielqui, G., Fryges, H., & Jones, J. (2017). Social media, open innovation & HRM: Implications for performance. Technological Forecasting & Social Change. doi:10.1016/j.techfore.2017.07.014

Gemünden, H. G., Lehner, P., & Kock, A. (2017). The project-oriented organization and its contribution to innovation. International Journal of Project Management. doi:10.1016/j.ijproman.2017.07.009

Keum, D. D., & See, K. E. (2017). The Influence of Hierarchy on Idea Generation and Selection in the Innovation Process. Organization Science, 28(4), 653-669. doi:10.1287/orsc.2017.1142

Kleve, H. (2010). Konstruktivismus und Soziale Arbeit. Wiesbaden: VS Verlag für Sozialwissenschaften.

Luhmann, N. (1984). Soziale Systeme: Grundriß einer allgemeinen Theorie. Frankfurt: Suhrkamp.

Parsons, T. (1970). The Social System. London: Routledge & Kegan Paul Ltd. .

Pohl, C., & Hirsch Hadorn, G. (2008). Methodological challenges of transdisciplinary research. Natures Sciences Sociétés, 16(2), 111-121. doi:10.1051/nss:2008035

Ross, A. (2016). Establishing a system for innovation in a professional services firm. Business Horizons, 59(2), 137-147. doi:10.1016/j.bushor.2015.10.002

Schumpeter, J. A. (1910). Über das Wesen der Wirtschaftskrisen. Zeitschrift für Volkswirtschaft, Sozialpolitik und Verwaltung, Organ der Gesellschaft Österreichischer Volkswirte, 19, 271-325.

Schumpeter, J. A. (1912). Theorie der wirtschaftlichen Entwicklung. Berlin: Duncker und Humblot.

Skyttner, L. (2006). General Systems Theory: Problems, Perspectives, Practice. Singapore: World Scientific Publishing.

Stockstrom, C. S., Goduscheit, R. C., Lüthje, C., & Jørgensen, J. H. (2016). Identifying valuable users as informants for innovation processes: Comparing the search efficiency of pyramiding and screening. Research Policy, 45(2), 507-516. doi:10.1016/j.respol.2015.11.002

Vester, F. (2000). Die Kunst vernetzt zu denken (5 ed.). Stuttgart: Deutsche Verlags Anstalt GmbH.