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The Role of Sentiment Tendency in Affecting Review Helpfulness for Durable Products: Nonlinearity and Complementarity

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  • Jin Li

    (Xi’an Jiaotong University)

  • Yulan Zhang

    (Xidian University)

  • Jianping Li

    (University of Chinese Academy of Sciences)

  • Jiangze Du

    (Jiangxi University of Finance and Economics)

Abstract

The online review has become an important pillar in the decision-making process for purchasing experience products, especially durable goods with relatively high prices. Using a rich data set for automobiles, we quantify the sentiment tendency expressed in textual reviews, and empirically examine the nonlinearly inverted U-shaped relationship between customer satisfaction and sentiment tendency. We then investigate the nonlinear influences of review sentiment and depth on helpfulness. Furthermore, we study the relationship between numerical rating and text contents, i.e., sentiment tendency and review depth, in promoting the review helpfulness, and quantitatively identify the complementary effect of sentiment tendency. Our results indicate that both numerical ratings and sentiments expressed in text contents contribute to an increase in review helpfulness. Compared with polarized reviews, the neutral ones better benefit helpfulness and customer satisfaction. We also find that reviews with moderate depth are more helpful. Based on the empirical findings, we discuss several managerial implications for review system designers and consumers in the durable product market.

Suggested Citation

  • Jin Li & Yulan Zhang & Jianping Li & Jiangze Du, 2023. "The Role of Sentiment Tendency in Affecting Review Helpfulness for Durable Products: Nonlinearity and Complementarity," Information Systems Frontiers, Springer, vol. 25(4), pages 1459-1477, August.
  • Handle: RePEc:spr:infosf:v:25:y:2023:i:4:d:10.1007_s10796-022-10292-3
    DOI: 10.1007/s10796-022-10292-3
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    References listed on IDEAS

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    1. 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.
    2. Geetha, M. & Singha, Pratap & Sinha, Sumedha, 2017. "Relationship between customer sentiment and online customer ratings for hotels - An empirical analysis," Tourism Management, Elsevier, vol. 61(C), pages 43-54.
    3. Xu, Xun, 2020. "Examining an asymmetric effect between online customer reviews emphasis and overall satisfaction determinants," Journal of Business Research, Elsevier, vol. 106(C), pages 196-210.
    4. De Liu & Adib Bagh, 2020. "Preserving Bidder Privacy in Assignment Auctions: Design and Measurement," Management Science, INFORMS, vol. 66(7), pages 3162-3182, July.
    5. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type," Post-Print hal-03511272, HAL.
    6. Jack Belzer, 1973. "Information theory as a measure of information content," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 300-304, July.
    7. Pan, Yue & Zhang, Jason Q., 2011. "Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews," Journal of Retailing, Elsevier, vol. 87(4), pages 598-612.
    8. Engler, Tobias H. & Winter, Patrick & Schulz, Michael, 2015. "Understanding online product ratings: A customer satisfaction model," Journal of Retailing and Consumer Services, Elsevier, vol. 27(C), pages 113-120.
    9. Nanda Kumar & Izak Benbasat, 2006. "Research Note: The Influence of Recommendations and Consumer Reviews on Evaluations of Websites," Information Systems Research, INFORMS, vol. 17(4), pages 425-439, December.
    10. Vicki McKinney & Kanghyun Yoon & Fatemeh “Mariam” Zahedi, 2002. "The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach," Information Systems Research, INFORMS, vol. 13(3), pages 296-315, September.
    11. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type," Grenoble Ecole de Management (Post-Print) halshs-01923243, HAL.
    12. Yalch, Richard F. & Spangenberg, Eric R., 2000. "The Effects of Music in a Retail Setting on Real and Perceived Shopping Times," Journal of Business Research, Elsevier, vol. 49(2), pages 139-147, August.
    13. Wang, Yichuan & Yu, Chiahui, 2017. "Social interaction-based consumer decision-making model in social commerce: The role of word of mouth and observational learning," International Journal of Information Management, Elsevier, vol. 37(3), pages 179-189.
    14. Navid Aghakhani & Onook Oh & Dawn G. Gregg & Jahangir Karimi, 2021. "Online Review Consistency Matters: An Elaboration Likelihood Model Perspective," Information Systems Frontiers, Springer, vol. 23(5), pages 1287-1301, September.
    15. Zhijie Lin & Ying Zhang & Yong Tan, 2019. "An Empirical Study of Free Product Sampling and Rating Bias," Service Science, INFORMS, vol. 30(1), pages 260-275, March.
    16. Wu, Mao-Ying & Pearce, Philip L., 2014. "Chinese recreational vehicle users in Australia: A netnographic study of tourist motivation," Tourism Management, Elsevier, vol. 43(C), pages 22-35.
    17. Ahmad, Shimi Naurin & Laroche, Michel, 2017. "Analyzing electronic word of mouth: A social commerce construct," International Journal of Information Management, Elsevier, vol. 37(3), pages 202-213.
    18. Yani Wang & Jun Wang & Tang Yao, 2019. "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Springer, vol. 19(2), pages 257-284, June.
    19. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type," Post-Print halshs-01923243, HAL.
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