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Study on the Influencing Factors of the Helpfulness of Online Review Based on Commodity Types

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Published:19 March 2020Publication History

ABSTRACT

[Purpose/Significance]The research on the usefulness of online reviews helps to improve consumer welfare, scientific management platform and accurate marketing of goods. It can effectively enhance user participation, enhance user stickiness and promote purchase decision. It is the focus of academics and businesses. [Method/Process] This paper established a hierarchical regression model based on the online negative reviews of search and experience products sold by Taobao, and explored the usefulness of different factors on online negative reviews from two dimensions: Comment content and reviewer characteristics. And ranked the importance factors under the adjustment of commodity type. [Result/Conclusion] The influencing factors of online reviews were regulated by commodity types. For experiential products, additional images and detailed objective descriptions in online negative reviews could promote consumers to make purchases decision. While for the search-type products, comment timeliness, length of comments, and professionalism of reviewers have a greater impact on the usefulness of reviews.

References

  1. Chen Y, Xie J. Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix[J]. Management Science, 2008, 54: 477--491.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Ahluwalia R, Bumkrant R E, Unnava H R. Consumer Response to Negative Publicity: The Moderating Role of Commitment [J]. Journal of Marketing Research, 2000, 37 (2): 203--214.Google ScholarGoogle ScholarCross RefCross Ref
  3. Zeng Wei. ELM-based online review usefulness study: adjustment of product type. Ring [J]. Modern Intelligence, 2014, 34 (12): 148--153.Google ScholarGoogle Scholar
  4. Nelson P. Information and Consumer Behavior [J]. Journal of Political Economy, 1970, 78(2):311--329.Google ScholarGoogle ScholarCross RefCross Ref
  5. Mudambi S M, Schuff D. What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com [J]. MIS Quarterly, 2010, 1(34):185--200.Google ScholarGoogle ScholarCross RefCross Ref
  6. Sarah G, Moore. Attitude Predictability and Helpfulness in Online Reviews: The Role of Explained Actions and Reactions [J]. Journal of Consumer Research, 2015, 42(15):30--44.Google ScholarGoogle Scholar
  7. Zhang Yanhui, Li Zongwei. Study on the Influencing Factors of the Usefulness of Online Comments: The Regulating Effect Based on Product Types [J].Management Review, 2016, 28(10):123--132.Google ScholarGoogle Scholar
  8. Baek H, Ahn J, Choi Y. Helpfulness of Online Consumer Reviews: Readers Objectives and Review Cues[J]. International Journal of Electronic Commerce, 2012, 17(2):99--126.Google ScholarGoogle ScholarCross RefCross Ref
  9. Sussman S W, Siegal W S. Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption [J]. Information Systems Research, 2003, 14(1):47--65.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Cai Shuqin, Qin Zhiyong, Li Cuiping, et al. Research on the Influence of Emotional Intensity on the Usefulness of Negative Online Comments[J].Management Review, 2017, 29(2):79--86.Google ScholarGoogle Scholar
  11. Wang Yang, Wang Weijun, Liu Zhiyu. Study on the Influence of Online Negative Commentary Information on Potential Consumers' Willingness to Buy [J].Information Science, 2018, 36(10):156--163.Google ScholarGoogle Scholar
  12. Wang Xuhui, Nie Kezhen, Chen Rong. "Interpreting behavior" or "interpreting reaction"? What kind of online comment is more useful-the impact of online comments based on interpretation types on consumer purchasing decisions and boundary conditions [J]. Nankai Management Review, 2017, 20(4): 27--37.Google ScholarGoogle Scholar
  13. Albert H, Kuanchin Chen, etal. A Study of Factors that Contribute to Online Review Helpfulness [J]. Computers in Human Behavior, 2015, 48(2):17--27.Google ScholarGoogle Scholar
  14. Zhai Qingfei, Yan Liang, Zhang Keliang. Study on the Factors Affecting the Usefulness of Online Comments [J].Management Review, 2017, 29(10):95--107.Google ScholarGoogle Scholar
  15. Ying Zhang, Zhijie Lin. Predicting the Helpfulness of Online Product Reviews: A Multilingual Approach [J]. Electronic Commerce Research and Applications, 2017, 27(10):1--10.Google ScholarGoogle Scholar
  16. Miao Q, Li Q, Dai R. Amazing: A Sentiment Mining and Retrieval System [J]. Expert Systems with Applications, 2009, 36 (3): 7192--7198.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Zhang K, Cheng Y, Liao W. Mining Millions of Reviews: A Technique to Rank Products Based on Importance of reviews [C]. / /Proceedings of the 13th International Conference on Electronic Commerce. New York: ACM, 2011: 1--8.Google ScholarGoogle Scholar
  18. Wang Cuicui, Gao Hui. Study on the influencing factors of usefulness perception with additional online reviews: based on eye movement experiment [J], Modern Intelligence, 2018, 38 (12): 70--77, 90.Google ScholarGoogle Scholar
  19. Ghose A, Ipeirotis P G. Designing Novel Review Ranking Systems: Predicting Usefulness and Impact of Reviews [C]. / /Proceedings of the Ninth International Conference on Electronic Commerce. New York: ACM, 2007: 303--310.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Other conferences
      EBIMCS '19: Proceedings of the 2019 2nd International Conference on E-Business, Information Management and Computer Science
      August 2019
      175 pages
      ISBN:9781450366496
      DOI:10.1145/3377817

      Copyright © 2019 ACM

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      Publication History

      • Published: 19 March 2020

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      EBIMCS '19 Paper Acceptance Rate31of142submissions,22%Overall Acceptance Rate143of708submissions,20%
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