[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v138y2019icp21-28.html
   My bibliography  Save this article

How to facilitate knowledge collaboration in OCs: An integrated perspective of technological and institutional measures

Author

Listed:
  • Yang, Wei
  • Zhou, Qing
  • Yu, Xianyun
  • Wang, Dongpeng
  • Li, Hui
Abstract
Knowledge collaboration in online communities (OCs) is popular and important for companies who are aiming to survive in the digital age, but it has a high failure rate. Most of the existing research and practice have focused only on facilitating knowledge collaboration in OCs through institutional measures, neglecting the role of technological measures. This paper aims to fill this gap by employing a differential equation model as well as case analyses. The research results indicate that increasing knowledge sharing by institutional measures does not lead to knowledge collaboration without continuously advancing technology. It is necessary to simultaneously employ technological and institutional measures. Companies can select a technology-oriented strategy or institution-oriented strategy according to their available resources and strategic positioning. These research results are helpful for remedying the imbalance of existing research and some shortages in practice.

Suggested Citation

  • Yang, Wei & Zhou, Qing & Yu, Xianyun & Wang, Dongpeng & Li, Hui, 2019. "How to facilitate knowledge collaboration in OCs: An integrated perspective of technological and institutional measures," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 21-28.
    • Handle: RePEc:eee:tefoso:v:138:y:2019:i:c:p:21-28
    DOI: 10.1016/j.techfore.2018.10.030
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162518308503
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2018.10.030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stam, Wouter, 2009. "When does community participation enhance the performance of open source software companies?," Research Policy, Elsevier, vol. 38(8), pages 1288-1299, October.
    2. Zeng, Michael A., 2018. "Foresight by online communities – The case of renewable energies," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 27-42.
    3. Samer Faraj & Sirkka L. Jarvenpaa & Ann Majchrzak, 2011. "Knowledge Collaboration in Online Communities," Organization Science, INFORMS, vol. 22(5), pages 1224-1239, October.
    4. Chen, Peng-Ting & Kuo, Shu-Chen, 2017. "Innovation resistance and strategic implications of enterprise social media websites in Taiwan through knowledge sharing perspective," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 55-69.
    5. Bonaccorsi, Andrea & Rossi, Cristina, 2003. "Why Open Source software can succeed," Research Policy, Elsevier, vol. 32(7), pages 1243-1258, July.
    6. Yan Chen & F. Maxwell Harper & Joseph Konstan & Sherry Xin Li, 2010. "Social Comparisons and Contributions to Online Communities: A Field Experiment on MovieLens," American Economic Review, American Economic Association, vol. 100(4), pages 1358-1398, September.
    7. David, Paul A. & Shapiro, Joseph S., 2008. "Community-based production of open-source software: What do we know about the developers who participate?," Information Economics and Policy, Elsevier, vol. 20(4), pages 364-398, December.
    8. Allen, Robert C., 1983. "Collective invention," Journal of Economic Behavior & Organization, Elsevier, vol. 4(1), pages 1-24, March.
    9. Lauto, Giancarlo & Valentin, Finn, 2016. "The knowledge production model of the New Sciences: The case of Translational Medicine," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 12-21.
    10. Olivier Toubia & Andrew T. Stephen, 2013. "Intrinsic vs. Image-Related Utility in Social Media: Why Do People Contribute Content to Twitter?," Marketing Science, INFORMS, vol. 32(3), pages 368-392, May.
    11. Muñoz, Pablo & Cohen, Boyd, 2017. "Mapping out the sharing economy: A configurational approach to sharing business modeling," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 21-37.
    12. Soumya Ray & Sung S. Kim & James G. Morris, 2014. "The Central Role of Engagement in Online Communities," Information Systems Research, INFORMS, vol. 25(3), pages 528-546, September.
    13. Paulo B. Goes & Chenhui Guo & Mingfeng Lin, 2016. "Do Incentive Hierarchies Induce User Effort? Evidence from an Online Knowledge Exchange," Information Systems Research, INFORMS, vol. 27(3), pages 497-516, September.
    14. Sims, Julian M., 2018. "Communities of practice: Telemedicine and online medical communities," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 53-63.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Feng, Yuanyue & Yi, Zihui & Yang, Congcong & Chen, Ruoyi & Feng, Ye, 2022. "How do gamification mechanics drive solvers’ Knowledge contribution? A study of collaborative knowledge crowdsourcing," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    2. Zhang, Xi & Cheng, Yihang & Chen, Aoshuang & Lytras, Miltiadis & de Pablos, Patricia Ordóñez & Zhang, Renyu, 2022. "How rumors diffuse in the infodemic: Evidence from the healthy online social change in China," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    3. Xiao Yu & Yangfeng Dai & Qian Xu & Qilin Ye, 2024. "Knowledge Collaboration and Benefits of Standard Implementation of Enterprise in Technology Standard Alliance," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 8534-8562, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shan, Wei & Qiao, Tong & Zhang, Mingli, 2020. "Getting more resources for better performance: The effect of user-owned resources on the value of user-generated content," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    2. Zaggl, Michael A., 2017. "Manipulation of explicit reputation in innovation and knowledge exchange communities: The example of referencing in science," Research Policy, Elsevier, vol. 46(5), pages 970-983.
    3. F. Rullani & L. Zirulia, 2011. "A supply side story for a threshold model: Endogenous growth of the free and open source community," Working Papers wp781, Dipartimento Scienze Economiche, Universita' di Bologna.
    4. Marchenko, Maria & Sonnabend, Hendrik, 2022. "The Never Ending Book: The role of external stimuli and peer feedback in user-generated content production," Department of Economics Working Paper Series 320, WU Vienna University of Economics and Business.
    5. Hinnosaar, Marit, 2019. "Gender inequality in new media: Evidence from Wikipedia," Journal of Economic Behavior & Organization, Elsevier, vol. 163(C), pages 262-276.
    6. Ni Huang & Gordon Burtch & Bin Gu & Yili Hong & Chen Liang & Kanliang Wang & Dongpu Fu & Bo Yang, 2019. "Motivating User-Generated Content with Performance Feedback: Evidence from Randomized Field Experiments," Management Science, INFORMS, vol. 65(1), pages 327-345, January.
    7. 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).
    8. Samer Faraj & Georg von Krogh & Eric Monteiro & Karim R. Lakhani, 2016. "Special Section Introduction—Online Community as Space for Knowledge Flows," Information Systems Research, INFORMS, vol. 27(4), pages 668-684, December.
    9. Chenhui (Julian) Guo & Tae Hun Kim & Anjana Susarla & Vallabh Sambamurthy, 2020. "Understanding Content Contribution Behavior in a Geosegmented Mobile Virtual Community: The Context of Waze," Information Systems Research, INFORMS, vol. 31(4), pages 1398-1420, December.
    10. Francesco Rullani, 2005. "The Debate and the Community. “Reflexive Identity” in the FLOSS Community," LEM Papers Series 2005/18, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    11. Hu, Xin & He, Liuyi & Liu, Junjun, 2022. "Status reinforcing: Unintended rating bias on online shopping platforms," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    12. Comino, Stefano & Manenti, Fabio M. & Parisi, Maria Laura, 2007. "From planning to mature: On the success of open source projects," Research Policy, Elsevier, vol. 36(10), pages 1575-1586, December.
    13. Liu, Linlin & Lee, Matthew K.O. & Liu, Renjing & Chen, Jiawen, 2018. "Trust transfer in social media brand communities: The role of consumer engagement," International Journal of Information Management, Elsevier, vol. 41(C), pages 1-13.
    14. Blecker, Thorsten & Abdelkafi, Nizar & Raasch, Christina, 2008. "Enabling and Sustaining Collaborative Innovation," MPRA Paper 8964, University Library of Munich, Germany.
    15. Lena Abou El-Komboz & Anna Kerkhof & Johannes Loh, 2023. "Platform Partnership Programs and Content Supply: Evidence from the YouTube “Adpocalypse”," CESifo Working Paper Series 10363, CESifo.
    16. Sheen S. Levine & Michael J. Prietula, 2014. "Open Collaboration for Innovation: Principles and Performance," Organization Science, INFORMS, vol. 25(5), pages 1414-1433, October.
    17. Marios Kokkodis & Theodoros Lappas & Sam Ransbotham, 2020. "From Lurkers to Workers: Predicting Voluntary Contribution and Community Welfare," Information Systems Research, INFORMS, vol. 31(2), pages 607-626, June.
    18. Adrián Kovács & Bart Looy & Bruno Cassiman, 2015. "Exploring the scope of open innovation: a bibliometric review of a decade of research," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 951-983, September.
    19. Zhihong Ke & De Liu & Daniel J. Brass, 2020. "Do Online Friends Bring Out the Best in Us? The Effect of Friend Contributions on Online Review Provision," Information Systems Research, INFORMS, vol. 31(4), pages 1322-1336, December.
    20. Jiangjiang Guo & Ting Ji & Wenqian Zhang & Lingfeng Dong, 2023. "Understanding Sustained Knowledge Contribution from a Motivation Crowding Perspective: A Case Study in a Chinese Q&A Community," Sustainability, MDPI, vol. 15(3), pages 1-13, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:138:y:2019:i:c:p:21-28. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.