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Exploring Latent Characteristics of Fake Reviews and Their Intermediary Role in Persuading Buying Decisions

Author

Listed:
  • Rahul Kumar

    (Indian Institute of Management Sambalpur)

  • Shubhadeep Mukherjee

    (Aston University)

  • Nripendra P. Rana

    (Qatar University)

Abstract

Online reviews play a significant role in shaping consumer purchase decisions. Accordingly, emergence of fake reviews has proliferated as an instrument to manipulate customers’ buying preferences. Such manifestation, however, lacks theoretical grounding and remains under researched due to two notable challenges: first, absence of conceptual underpinnings between consumers’ writing style and recommendation behavior. Second, little knowledge about the role of product characteristics underlying fake reviews and their influence on nudging product preferences. Through the lens of environmental psychology, this study uses an empirical investigation utilizing natural language processing (NLP) to uncover latent product-specific features underlying customer reviews and their impact on persuading buying preferences. As a major finding, we observe that characteristics underlying fake reviews, as opposed to genuine ones, fail to influence product recommendation or discouragement. Accordingly, we suggest firms permitting fake reviews on their portals to be aware of the limited economic advantages of such practices.

Suggested Citation

  • Rahul Kumar & Shubhadeep Mukherjee & Nripendra P. Rana, 2024. "Exploring Latent Characteristics of Fake Reviews and Their Intermediary Role in Persuading Buying Decisions," Information Systems Frontiers, Springer, vol. 26(3), pages 1091-1108, June.
    • Handle: RePEc:spr:infosf:v:26:y:2024:i:3:d:10.1007_s10796-023-10401-w
    DOI: 10.1007/s10796-023-10401-w
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    as
    1. Plotkina, Daria & Munzel, Andreas, 2016. "Delight the experts, but never dissatisfy your customers! A multi-category study on the effects of online review source on intention to buy a new product," Journal of Retailing and Consumer Services, Elsevier, vol. 29(C), pages 1-11.
    2. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    3. Scott Hendry & Alison Madeley, 2010. "Text Mining and the Information Content of Bank of Canada Communications," Staff Working Papers 10-31, Bank of Canada.
    4. Ben Jabeur, Sami & Ballouk, Hossein & Ben Arfi, Wissal & Sahut, Jean-Michel, 2023. "Artificial intelligence applications in fake review detection: Bibliometric analysis and future avenues for research," Journal of Business Research, Elsevier, vol. 158(C).
    5. Chatterjee, Swagato & Goyal, Divesh & Prakash, Atul & Sharma, Jiwan, 2021. "Exploring healthcare/health-product ecommerce satisfaction: A text mining and machine learning application," Journal of Business Research, Elsevier, vol. 131(C), pages 815-825.
    6. David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
    7. Bitran, Gabriel & Mondschein, Susana, 1997. "A comparative analysis of decision making procedures in the catalog sales industry," European Management Journal, Elsevier, vol. 15(2), pages 105-116, April.
    8. Sambit Tripathi & Amit V. Deokar & Haya Ajjan, 2022. "Understanding the Order Effect of Online Reviews: A Text Mining Perspective," Information Systems Frontiers, Springer, vol. 24(6), pages 1971-1988, December.
    9. Dyer, Travis & Lang, Mark & Stice-Lawrence, Lorien, 2017. "The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation," Journal of Accounting and Economics, Elsevier, vol. 64(2), pages 221-245.
    10. Daria Plotkina & Andreas Munzel, 2016. "Delight the experts, but never dissatisfy your customers! A multi-category study on the effects of online review source on intention to buy a new product," Post-Print halshs-01522518, HAL.
    11. Zhuang, Mengzhou & Cui, Geng & Peng, Ling, 2018. "Manufactured opinions: The effect of manipulating online product reviews," Journal of Business Research, Elsevier, vol. 87(C), pages 24-35.
    12. Kwon, Ohbyung & Lee, Namyeon & Shin, Bongsik, 2014. "Data quality management, data usage experience and acquisition intention of big data analytics," International Journal of Information Management, Elsevier, vol. 34(3), pages 387-394.
    13. Laato, Samuli & Islam, A.K.M. Najmul & Farooq, Ali & Dhir, Amandeep, 2020. "Unusual purchasing behavior during the early stages of the COVID-19 pandemic: The stimulus-organism-response approach," Journal of Retailing and Consumer Services, Elsevier, vol. 57(C).
    14. Ajay Kumar & Ram D. Gopal & Ravi Shankar & Kim Hua Tan, 2022. "Fraudulent review detection model focusing on emotional expressions and explicit aspects : investigating the potential of feature engineering," Post-Print hal-03630420, HAL.
    15. Sharma, Sujeet Kumar & Sharma, Manisha, 2019. "Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation," International Journal of Information Management, Elsevier, vol. 44(C), pages 65-75.
    16. Kuenzel, Johanna & Musters, Pieter, 2007. "Social interaction and low involvement products," Journal of Business Research, Elsevier, vol. 60(8), pages 876-883, August.
    17. Reema Aswani & Arpan Kumar Kar & P. Vigneswara Ilavarasan, 2018. "Detection of Spammers in Twitter marketing: A Hybrid Approach Using Social Media Analytics and Bio Inspired Computing," Information Systems Frontiers, Springer, vol. 20(3), pages 515-530, June.
    18. Kar, Arpan Kumar & Dwivedi, Yogesh K., 2020. "Theory building with big data-driven research – Moving away from the “What” towards the “Why”," International Journal of Information Management, Elsevier, vol. 54(C).
    19. Bang, Chulhwan Chris & Lee, Jaeung & Rao, H. Raghav, 2021. "The Egyptian protest movement in the twittersphere: An investigation of dual sentiment pathways of communication," International Journal of Information Management, Elsevier, vol. 58(C).
    20. Kaushik, Kapil & Mishra, Rajhans & Rana, Nripendra P. & Dwivedi, Yogesh K., 2018. "Exploring reviews and review sequences on e-commerce platform: A study of helpful reviews on Amazon.in," Journal of Retailing and Consumer Services, Elsevier, vol. 45(C), pages 21-32.
    21. Wei-Lun Chang & Yi-Pei Chen, 2019. "Way too sentimental? a credible model for online reviews," Information Systems Frontiers, Springer, vol. 21(2), pages 453-468, April.
    22. Sharma, Anuj & Rana, Nripendra P. & Nunkoo, Robin, 2021. "Fifty years of information management research: A conceptual structure analysis using structural topic modeling," International Journal of Information Management, Elsevier, vol. 58(C).
    23. Cuiqing Jiang & Yao Liu & Yong Ding & Kun Liang & Rui Duan, 2017. "Capturing helpful reviews from social media for product quality improvement: a multi-class classification approach," International Journal of Production Research, Taylor & Francis Journals, vol. 55(12), pages 3528-3541, June.
    24. Xiangbin Yan & Jing Wang & Michael Chau, 2015. "Customer revisit intention to restaurants: Evidence from online reviews," Information Systems Frontiers, Springer, vol. 17(3), pages 645-657, June.
    25. Mohammadreza Mousavizadeh & Mehrdad Koohikamali & Mohammad Salehan & Dam J. Kim, 2022. "An Investigation of Peripheral and Central Cues of Online Customer Review Voting and Helpfulness through the Lens of Elaboration Likelihood Model," Information Systems Frontiers, Springer, vol. 24(1), pages 211-231, February.
    26. Sungha Jang & Ashutosh Prasad & Brian Ratchford, 2012. "How consumers use product reviews in the purchase decision process," Marketing Letters, Springer, vol. 23(3), pages 825-838, September.
    27. Zhu, John Jianjun & Chang, Yung-Chun & Ku, Chih-Hao & Li, Stella Yiyan & Chen, Chi-Jen, 2021. "Online critical review classification in response strategy and service provider rating: Algorithms from heuristic processing, sentiment analysis to deep learning," Journal of Business Research, Elsevier, vol. 129(C), pages 860-877.
    28. Samuel Fosso Wamba & Angappa Gunasekaran & Rameshwar Dubey & Eric W. T. Ngai, 2018. "Big data analytics in operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 1-4, November.
    29. Reimer, Thomas & Benkenstein, Martin, 2016. "When good WOM hurts and bad WOM gains: The effect of untrustworthy online reviews," Journal of Business Research, Elsevier, vol. 69(12), pages 5993-6001.
    30. Zhang, Hong & Zhao, Ling & Gupta, Sumeet, 2018. "The role of online product recommendations on customer decision making and loyalty in social shopping communities," International Journal of Information Management, Elsevier, vol. 38(1), pages 150-166.
    31. Elvira Ismagilova & Emma L. Slade & Nripendra P. Rana & Yogesh K. Dwivedi, 2020. "The Effect of Electronic Word of Mouth Communications on Intention to Buy: A Meta-Analysis," Information Systems Frontiers, Springer, vol. 22(5), pages 1203-1226, October.
    32. Eslami, Seyed Pouyan & Ghasemaghaei, Maryam, 2018. "Effects of online review positiveness and review score inconsistency on sales: A comparison by product involvement," Journal of Retailing and Consumer Services, Elsevier, vol. 45(C), pages 74-80.
    33. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    34. Quan Wang & Beibei Li & Param Vir Singh, 2018. "Copycats vs. Original Mobile Apps: A Machine Learning Copycat-Detection Method and Empirical Analysis," Information Systems Research, INFORMS, vol. 29(2), pages 273-291, June.
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