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Towards Sustainable Development of Online Communities in the Big Data Era: A Study of the Causes and Possible Consequence of Voting on User Reviews

Author

Listed:
  • Jie Zhao

    (School of Business, Anhui University, Hefei 230601, China)

  • Jianfei Wang

    (School of Business, Anhui University, Hefei 230601, China)

  • Suping Fang

    (School of Business, Anhui University, Hefei 230601, China)

  • Peiquan Jin

    (School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China)

Abstract

This paper focuses on the review voting in online communities, which allows users to express their own opinions in terms of User-generated Content (UGC). However, the sustainable development of online communities is likely to be affected by the social influence of UGC. In this paper, we study the so-called crowd intelligence paradox of review voting in online communities. The crowd intelligence paradox means that the quality of reviews is not highly connected with the increasing of review votes. This implies that a review with many votes is likely to be of low quality, and a review with few votes is likely to be of high quality. The crowd intelligence paradox existing in online communities inhibits users’ wishes of participating in social networks and may impact the sustainable development of online communities. Aiming to demonstrate the existence of the crowd intelligence paradox in online communities, we first analyzed a large set of reviews crawled from Net Ease Cloud Music, which is one of the most popular online communities in China. The maximum likelihood (ML) and the hierarchical regression approaches are used in this step. Then, we construct a new research model called the Voting Adoption Model (VAM) to study how different factors impact the crowd intelligence paradox in online communities. Particularly, we propose six hypotheses based on the VAM model and conduct experiments based on the measurement model and the structural model to evaluate the hypotheses. The results show that the quality of reviews is not influential to review votes, and the hot-site attribute is a dominant factor influencing review voting. In addition, the variables of the VAM model, including information credibility, perceived ease of use, and social influence have significant impacts on review voting. Finally, based on the empirical study, we present some research implications and suggestions for online communities to realize healthy and sustainable development in the future.

Suggested Citation

  • Jie Zhao & Jianfei Wang & Suping Fang & Peiquan Jin, 2018. "Towards Sustainable Development of Online Communities in the Big Data Era: A Study of the Causes and Possible Consequence of Voting on User Reviews," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:3156-:d:167634
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    References listed on IDEAS

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    1. Stephanie Watts Sussman & Wendy Schneier Siegal, 2003. "Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption," Information Systems Research, INFORMS, vol. 14(1), pages 47-65, March.
    2. Jeff Alstott & Ed Bullmore & Dietmar Plenz, 2014. "powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    3. Editors The, 2008. "From the Editors," Basic Income Studies, De Gruyter, vol. 2(2), pages 1-3, January.
    4. Scott K. Shriver & Harikesh S. Nair & Reto Hofstetter, 2013. "Social Ties and User-Generated Content: Evidence from an Online Social Network," Management Science, INFORMS, vol. 59(6), pages 1425-1443, June.
    5. Editors The, 2008. "Content," Basic Income Studies, De Gruyter, vol. 2(2), pages 1-2, January.
    6. Editors The, 2008. "Content," Basic Income Studies, De Gruyter, vol. 3(3), pages 1-1, December.
    7. Wu, Ye & Zhou, Changsong & Chen, Maoying & Xiao, Jinghua & Kurths, Jürgen, 2010. "Human comment dynamics in on-line social systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5832-5837.
    8. 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.
    9. Editors The, 2008. "Content," Basic Income Studies, De Gruyter, vol. 3(1), pages 1-1, July.
    10. Editors The, 2008. "From the Editors," Basic Income Studies, De Gruyter, vol. 3(1), pages 1-1, July.
    11. Jie Zhao & Suping Fang & Peiquan Jin, 2018. "Modeling and Quantifying User Acceptance of Personalized Business Modes Based on TAM, Trust and Attitude," Sustainability, MDPI, vol. 10(2), pages 1-26, January.
    12. Editors The, 2008. "Content," Basic Income Studies, De Gruyter, vol. 3(2), pages 1-1, November.
    13. Sarah G. Moore, 2015. "Attitude Predictability and Helpfulness in Online Reviews: The Role of Explained Actions and Reactions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 42(1), pages 30-44.
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    Cited by:

    1. Jie Zhao & Can Yan, 2020. "User Acceptance of Information Feed Advertising: A Hybrid Method Based on SEM and QCA," Future Internet, MDPI, vol. 12(12), pages 1-17, November.
    2. Jie Zhao & Chanjuan Zhu & Zhixiang Peng & Xin Xu & Yan Liu, 2018. "User Willingness toward Knowledge Sharing in Social Networks," Sustainability, MDPI, vol. 10(12), pages 1-27, December.
    3. Jie Zhao & Jianfei Wang, 2020. "Health Advertising on Short-Video Social Media: A Study on User Attitudes Based on the Extended Technology Acceptance Model," IJERPH, MDPI, vol. 17(5), pages 1-21, February.
    4. Do-Hyung Park & Sungwook Lee, 2021. "UGC Sharing Motives and Their Effects on UGC Sharing Intention from Quantitative and Qualitative Perspectives: Focusing on Content Creators in South Korea," Sustainability, MDPI, vol. 13(17), pages 1-13, August.
    5. Yang-Chieh Chin & Wen-Zhong Su & Shih-Chih Chen & Jianing Hou & Yu-Chuan Huang, 2018. "Exploring Users’ Self-Disclosure Intention on Social Networking Applying Novel Soft Computing Theories," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    6. Jie Zhao & Zhixiang Peng, 2019. "Shared Short-Term Rentals for Sustainable Tourism in the Social-Network Age: The Impact of Online Reviews on Users’ Purchase Decisions," Sustainability, MDPI, vol. 11(15), pages 1-19, July.
    7. Jiaqi Liu & Zhenping Zhang & Jiayin Qi & Hong Wu & Manyi Chen, 2019. "Understanding the Impact of Opinion Leaders’ Characteristics on Online Group Knowledge-Sharing Engagement from In-Group and Out-Group Perspectives: Evidence from a Chinese Online Knowledge-Sharing Com," Sustainability, MDPI, vol. 11(16), pages 1-28, August.

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