Education, Research

Open Innovation Management on Crowd-based Platforms: An Analysis of Managerial Approaches to Knowledge Sharing, Crowd Control and Intellectual Property Protection

May 8, 2019

Birute Birgelyte, Media Management MA student, Department of Culture and Media, Arcada UAS

Supervisor: Tomas Träskman         Examiner:  Dr. Mats Nylund


In a dynamic business environment, all relevant knowledge can rarely be found in-house. As Felin et al. (2017: 123–124) observe, “[…] the locus of knowledge and innovation increasingly is the network rather than the firm.” Consequently, firms need to think how to utilize knowledge that resides outside their boundaries to enhance their innovative capacity (Lakhani and Panetta, 2007). Firms which do not have sufficient expertise but resist opening up their innovation process will face more challenges with knowledge-intensive tasks. Still, firms which are willing to experiment with ‘hybrid organizational models’ need to address the issues of decentralized problem solving, self-selected participation and self-organization, which characterize distributed innovation systems.

One of the central themes in open innovation management is ‘the emerging phenomenon of creation nets’, where multiple participants collaborate to create and utilize new knowledge (Brown and Hagel III, 2006). Firms still need to learn how to collaborate with communities that cannot be owned nor fully controlled. Results-driven management of ‘creation nets’ (Brown and Hagel III, 2006) requires long-term incentives that motivate project participants to make valuable contributions as well as new managerial approaches. For example, managers need to choose the right work coordination format, balance local innovation with global integration, design effective action points and establish useful performance feedback loops (Brown and Hagel III, 2006). In addition, there is a need to organize knowledge transactions with the environment. Knowledge management is based on 3 core tasks (decisions): (1) ‘knowledge acquisition’ (Make or Buy), (2) ‘knowledge integration’ (Integrate or Relate) and (3) ‘knowledge exploitation’ (Keep or Sell) (Lichtenthaler and Ernst, 2006).

My Master’s thesis examines open innovation (OI) management practices on 3 crowd-based innovation management platforms: Imaginatik, Spigit and 100%Open. The main aim of the study is to analyze managerial approaches to knowledge sharing, crowd control and intellectual property (IP) protection on the platforms in the context of the theories of open innovation, innovation management and collective intelligence (CI). There are 3 research questions (RQ):

  • How to facilitate knowledge sharing / information exchange between firms and external professionals (i.e. the external crowd)?
  • How to strike a balance between crowd control and creative autonomy?
  • How to protect the intellectual property (IP) of firms during their cooperation with the crowd?

The main research method was qualitative content analysis. The non-probability purposive sampling technique was used to highlight the individual character of managerial approaches to knowledge sharing, crowd control and IP protection on the selected platforms. The research data were collected during semi-structured interviews with 4 platform management representatives. The interviews were conducted online, and then transcribed.

The study presents different information management options during platform-based open innovation challenges. Those options could serve as a point of reference for managers of diverse open innovation projects. In addition, the study highlights the complexity of eliciting, processing and utilizing crowdsourced information for business purposes. The observations made in the study could be used as guidelines while designing or implementing one’s managerial strategy. Furthermore, the study could help Media Management specialists better understand open innovation management practices and apply them to relevant knowledge-intensive contexts. Moreover, it could guide them how to better utilize platform technology for managing open innovation projects as well as other complex multi-party projects.

The study has found that the platforms take various approaches to knowledge sharing / information exchange, crowd control and IP protection. There is no one-size-fits-all approach: the network-based business model of the platforms determines the complexity of managing crowd-based innovation projects. To facilitate knowledge sharing / information exchange between firms and the crowd (RQ1), the platforms take various approaches, and structure project-related communications through different mechanisms. Both Imaginatik and Spigit stress the importance of information / communication transparency during open innovation challenges. 100%Open tries to facilitate knowledge sharing / information exchange during co-innovation projects by balancing the needs and interests of all parties involved. 100%Open structures communication between its clients and the crowd mainly through weekly newsletters, while Imaginatik uses various data visualizations. Those mechanisms seem to serve the same purpose: to engage external innovators in co-innovation and to facilitate the exchange of knowledge and information within the network. Both firms thus act as mediators of all interactions and co-innovation efforts on their platforms. By contrast, Spigit’s clients have full managerial control of project-related communications.

To strike a balance between crowd control and creative autonomy (RQ2), the platforms balance their crowd control mechanisms with crowd self-control. If intrinsically motivated external professionals are given enough freedom to explore their creative ideas within the limits of project objectives, balancing crowd control and creative autonomy becomes a viable option as well (RQ2). 100%Open tries to control crowd behaviour through community facilitation. It encourages crowd self-control as long as co-innovation efforts meet project objectives. Imaginatik balances managerial control with crowd self-control through its kudos ranking system: it uses the system to (re)focus the attention of project participants on ideas which receive the highest approval within the group. Spigit tries to control crowd behaviour by assigning moderators of group interactions on the platform as well as through its pairwise voting system.

To protect firms’ IP during their cooperation with the crowd (RQ3), the platforms use different IP protection mechanisms. In each case, firms also need to carefully consider how much they should open up their innovation process to third parties, as recommended by Lee et al. (2010), in order to control access to their IP. In addition, they should allow the platforms to balance their IP protection measures with their efforts to facilitate the exchange of relevant knowledge between all parties involved in co-innovation, to refer to Lakhani and Panetta (2007). If there is a more sensitive commercial or technical issue, 100%Open sets up a so-called innovation airlock: it signs a confidentiality agreement with its client and a separate confidentiality agreement with selected external professionals. To protect user IP from unauthorized use, Imaginatik’s innovation management software provides a lot of data security features. It also gives the option to set up different tiers of IP: project revenue can be put into an escrow account and distributed among co-innovators according to a set-up IP distribution scheme once certain conditions are met. To protect client IP, Spigit’s ideation management software provides a security protocol that clients can audit. It also ensures that client data are processed in accordance with the General Data Protection Regulation (GDPR). These data protection measures grant Spigit’s clients full control of access to their commercially valuable data and IP during open innovation projects.


Brown, J.S. and Hagel III, J. (2006) Creation nets: Getting the most from open innovation. McKinsey Quarterly, No. 2, 40–51.

Felin, T., Lakhani, K.R. and Tushman, M.L. (2017) Firms, crowds, and innovation. Strategic Organization, Vol. 15(2). Thousand Oaks, CA: SAGE Publications, 119–140.

Lakhani, K.R. and Panetta, J.A. (2007) The Principles of Distributed Innovation. Innovations: Technology, Governance, Globalization, Vol. 2(3), 97–112.

Lee, N., Nystén-Haarala, S. and Huhtilainen, L. (2010) Interfacing Intellectual Property Rights and Open Innovation. Lappeenranta University of Technology, Department of Industrial Management Research Report No. 225. Social Science Research Network (SSRN). (accessed on March 26, 2017).

Lichtenthaler, U. and Ernst, H. (2006) Attitudes to externally organising knowledge management tasks: a review, reconsideration and extension of the NIH syndrome. R&D Management, Vol. 36(4), 367–386.


The full thesis may be downloaded from from June 2019.

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