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What drives online course sales? Signaling effects of user-generated information in the paid knowledge market

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  • Zhang, Mingli
  • Zhang, Yan
  • Zhao, Lu
  • Li, Xiaoyong

Abstract

The emergence of a paid knowledge market provides knowledge contributors a way to get economic repay from their sharing, but little is known about the determinants of sales of those paid knowledge content. To study this, we identify three user-generated signals: the rating of content product, followers of content producer, and upvotes content producer gains, which may influence consumers' content quality perceptions and purchase decisions. Drawing on a panel data set of 6380 online live courses, we propose a new perspective of signaling effect by distinguishing the influence of the flow and stock of signals. Hypotheses are tested using fixed-effect regression and panel vector autoregression methodology. The result suggests a positive impact of ratings and followers on sales, a negative impact of upvotes on sales, and reveals the dynamic interactivity between the flow of signals and sales. These findings offer insights into signaling theory and knowledge product transactions.

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  • Zhang, Mingli & Zhang, Yan & Zhao, Lu & Li, Xiaoyong, 2020. "What drives online course sales? Signaling effects of user-generated information in the paid knowledge market," Journal of Business Research, Elsevier, vol. 118(C), pages 389-397.
  • Handle: RePEc:eee:jbrese:v:118:y:2020:i:c:p:389-397
    DOI: 10.1016/j.jbusres.2020.07.008
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    as
    1. Oliver Francis Koch & Alexander Benlian, 2017. "The effect of free sampling strategies on freemium conversion rates," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(1), pages 67-76, February.
    2. Binder, Michael & Hsiao, Cheng & Pesaran, M. Hashem, 2005. "Estimation And Inference In Short Panel Vector Autoregressions With Unit Roots And Cointegration," Econometric Theory, Cambridge University Press, vol. 21(4), pages 795-837, August.
    3. Hailiang Chen & Prabuddha De & Yu Jeffrey Hu, 2015. "IT-Enabled Broadcasting in Social Media: An Empirical Study of Artists’ Activities and Music Sales," Information Systems Research, INFORMS, vol. 26(3), pages 513-531, September.
    4. Liu, Zhiwei & Park, Sangwon, 2015. "What makes a useful online review? Implication for travel product websites," Tourism Management, Elsevier, vol. 47(C), pages 140-151.
    5. Girish Punj, 2015. "The relationship between consumer characteristics and willingness to pay for general online content: Implications for content providers considering subscription-based business models," Marketing Letters, Springer, vol. 26(2), pages 175-186, June.
    6. Andreas Ortmann, 1997. "How to Survive in Postindustrial Environments," The Journal of Higher Education, Taylor & Francis Journals, vol. 68(5), pages 483-501, September.
    7. Thies, Ferdinand & Wessel, Michael & Benlian, Alexander, 2016. "Effects of Social Interaction Dynamics on Platforms," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 82420, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    8. Manes, Eran & Tchetchik, Anat, 2018. "The role of electronic word of mouth in reducing information asymmetry: An empirical investigation of online hotel booking," Journal of Business Research, Elsevier, vol. 85(C), pages 185-196.
    9. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    10. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December.
    11. Will Drover & Matthew S. Wood & Andrew C. Corbett, 2018. "Toward a Cognitive View of Signalling Theory: Individual Attention and Signal Set Interpretation," Journal of Management Studies, Wiley Blackwell, vol. 55(2), pages 209-231, March.
    12. Jie Ren & William Yeoh & Mong Shan Ee & AleÅ¡ PopoviÄ, 2018. "Online consumer reviews and sales: Examining the chicken†egg relationships," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(3), pages 449-460, March.
    13. Saboo, Alok R. & Kumar, V. & Ramani, Girish, 2016. "Evaluating the impact of social media activities on human brand sales," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 524-541.
    14. Bart de Langhe & Philip M. Fernbach & Donald R. Lichtenstein, 2016. "Navigating by the Stars: Investigating the Actual and Perceived Validity of Online User Ratings," Journal of Consumer Research, Oxford University Press, vol. 42(6), pages 817-833.
    15. Wang, Feng & Liu, Xuefeng & Fang, Eric (Er), 2015. "User Reviews Variance, Critic Reviews Variance, and Product Sales: An Exploration of Customer Breadth and Depth Effects," Journal of Retailing, Elsevier, vol. 91(3), pages 372-389.
    16. Tingting Song & Jinghua Huang & Yong Tan & Yifan Yu, 2019. "Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms," Service Science, INFORMS, vol. 30(1), pages 191-203, March.
    17. Love, Inessa & Zicchino, Lea, 2006. "Financial development and dynamic investment behavior: Evidence from panel VAR," The Quarterly Review of Economics and Finance, Elsevier, vol. 46(2), pages 190-210, May.
    18. Thies, Ferdinand & Wessel, Michael & Benlian, Alexander, 2016. "Effects of Social Interaction Dynamics on Platforms," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 84688, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    19. Xitong Li, 2018. "Impact of Average Rating on Social Media Endorsement: The Moderating Role of Rating Dispersion and Discount Threshold," Information Systems Research, INFORMS, vol. 29(3), pages 739-754, September.
    20. Kromidha, Endrit & Li, Matthew C., 2019. "Determinants of leadership in online social trading: A signaling theory perspective," Journal of Business Research, Elsevier, vol. 97(C), pages 184-197.
    21. Carsten D. Schultz, 2016. "Insights from consumer interactions on a social networking site: Findings from six apparel retail brands," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(3), pages 203-217, August.
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    3. Qingfeng Zeng & Wei Zhuang & Qian Guo & Weiguo Fan, 2022. "What factors influence grassroots knowledge supplier performance in online knowledge platforms? Evidence from a paid Q&A service," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2507-2523, December.

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