This paper studies how business models can be designed to tap effectively into open innovation labor markets with heterogeneously motivated workers. Using data on open source software, we show that motivations are diverse, and demonstrate how managers can strategically influence the flow of code contributions and their impact on project performance. Unlike previous literature using survey data, we exploit the observed pattern of project membership and code contributions—the “revealed preference” of developers—to infer the motivations driving their decision to contribute. Developers strongly sort along key dimensions of the business model chosen by project managers, especially the degree of openness of the project license. The results indicate an important role for intrinsic motivation, reputation, and labor market signaling, and a more limited role for reciprocity. Copyright © 2014 John Wiley & Sons, Ltd."> This paper studies how business models can be designed to tap effectively into open innovation labor markets with heterogeneously motivated workers. Using data on open source software, we show that motivations are diverse, and demonstrate how managers can strategically influence the flow of code contributions and their impact on project performance. Unlike previous literature using survey data, we exploit the observed pattern of project membership and code contributions—the “revealed preference” of developers—to infer the motivations driving their decision to contribute. Developers strongly sort along key dimensions of the business model chosen by project managers, especially the degree of openness of the project license. The results indicate an important role for intrinsic motivation, reputation, and labor market signaling, and a more limited role for reciprocity. Copyright © 2014 John Wiley & Sons, Ltd."> This paper studies how business models can be designed to tap effectively into open innovation labor markets with heterogeneously motivated ">
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Motivation and sorting of human capital in open innovation

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  • Sharon Belenzon
  • Mark Schankerman
Abstract
type="main" xml:id="smj2284-abs-0001"> This paper studies how business models can be designed to tap effectively into open innovation labor markets with heterogeneously motivated workers. Using data on open source software, we show that motivations are diverse, and demonstrate how managers can strategically influence the flow of code contributions and their impact on project performance. Unlike previous literature using survey data, we exploit the observed pattern of project membership and code contributions—the “revealed preference” of developers—to infer the motivations driving their decision to contribute. Developers strongly sort along key dimensions of the business model chosen by project managers, especially the degree of openness of the project license. The results indicate an important role for intrinsic motivation, reputation, and labor market signaling, and a more limited role for reciprocity. Copyright © 2014 John Wiley & Sons, Ltd.

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  • Sharon Belenzon & Mark Schankerman, 2015. "Motivation and sorting of human capital in open innovation," Strategic Management Journal, Wiley Blackwell, vol. 36(6), pages 795-820, June.
    • Handle: RePEc:bla:stratm:v:36:y:2015:i:6:p:795-820
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    2. Becker, Markus C. & Rullani, Francesco & Zirpoli, Francesco, 2021. "The role of digital artefacts in early stages of distributed innovation processes," Research Policy, Elsevier, vol. 50(10).
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    4. Rhaiem, Khalil & Doloreux, David, 2024. "Inbound open innovation in SMEs: A microfoundations perspective of dynamic capabilities," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    5. Erdem Dogukan Yilmaz & Tim Meyer & Milan Miric, 2023. "Preventing Others from Commercializing Your Innovation: Evidence from Creative Commons Licenses," Papers 2309.00536, arXiv.org.
    6. Johannes Loh & Tobias Kretschmer, 2023. "Online communities on competing platforms: Evidence from game wikis," Strategic Management Journal, Wiley Blackwell, vol. 44(2), pages 441-476, February.
    7. Chiao, Benjamin & MacVaugh, Jason, 2021. "Open innovation and organizational features: An experimental investigation," European Management Journal, Elsevier, vol. 39(3), pages 376-389.
    8. Smirnova, Inna & Reitzig, Markus & Alexy, Oliver, 2022. "What makes the right OSS contributor tick? Treatments to motivate high-skilled developers," Research Policy, Elsevier, vol. 51(1).
    9. Selma Leticia Capinzaiki Ottonicar & Paloma Marin Arraiza & Fabiano Armellini, 2020. "Opening Science and Innovation: Opportunities for Emerging Economies," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 14(4), pages 95-111.
    10. Frank Nagle, 2018. "Learning by Contributing: Gaining Competitive Advantage Through Contribution to Crowdsourced Public Goods," Organization Science, INFORMS, vol. 29(4), pages 569-587, August.
    11. Hurmelinna-Laukkanen, Pia & Yang, Jialei, 2022. "Distinguishing between appropriability and appropriation: A systematic review and a renewed conceptual framing," Research Policy, Elsevier, vol. 51(1).
    12. Vivianna Fang He & Phanish Puranam & Yash Raj Shrestha & Georg von Krogh, 2020. "Resolving governance disputes in communities: A study of software license decisions," Strategic Management Journal, Wiley Blackwell, vol. 41(10), pages 1837-1868, October.
    13. Islam, Mazhar & Miller, Jacob & Park, Haemin Dennis, 2017. "But what will it cost me? How do private costs of participation affect open source software projects?," Research Policy, Elsevier, vol. 46(6), pages 1062-1070.
    14. Colombelli, Alessandra & Belitski, Maksim & D’Amico, Elettra, 2023. "Artificial Intelligence and Firm Innovation: The Resource-Allocation Perspective," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 202304, University of Turin.
    15. Shaphali Gupta, 2020. "Understanding the feasibility and value of grassroots innovation," Journal of the Academy of Marketing Science, Springer, vol. 48(5), pages 941-965, September.
    16. Qinghua Xia & Qinwei Cao & Manqing Tan, 2020. "Basic research intensity and diversified performance: the moderating role of government support intensity," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 577-605, October.
    17. Christoph Riedl & Tom Grad & Christopher Lettl, 2024. "Competition and Collaboration in Crowdsourcing Communities: What happens when peers evaluate each other?," Papers 2404.14141, arXiv.org.
    18. Colombelli, Alessandra & Belitski, Maksim & D’Amico, Elettra, 2023. "Artificial Intelligence and Firm Innovation: The Resource-Allocation Perspective," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202316, University of Turin.
    19. Lena Abou El-Komboz & Thomas Fackler, 2023. "Productivity Spillovers among Knowledge Workers in Agglomerations: Evidence from GitHub," Rationality and Competition Discussion Paper Series 399, CRC TRR 190 Rationality and Competition.
    20. Qinwei Cao & Manqing Tan & Peng Xie & Jian Huang, 2022. "Can emerging economies take advantage of their population size to gain international academic recognition? Evidence from key universities in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 927-957, February.

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