Benefits or harms? The effect of online review manipulation on sales

Electronic Commerce Research and Applications - Tập 57 - Trang 101224 - 2023
Qiang Wang1, Wen Zhang1, Jian Li1, Zhenzhong Ma2, Jindong Chen3
1College of Economics and Management, Beijing University of Technology, Beijing 100124, China
2Odette School of Business, University of Windsor, Windsor, ON N9E 4Z4, Canada
3College School of Economics and Management, Beijing Information Science & Technology University, Beijing 100192, China

Tài liệu tham khảo

Anderson, 2014, Reviews without a purchase: Low ratings, loyal customers, and deception, J. Mark. Res., 51, 249, 10.1509/jmr.13.0209 Ansari, 2021, Customer perception of the deceptiveness of online product reviews: A speech act theory perspective, Int. J. Inf. Manag., 57, 102286, 10.1016/j.ijinfomgt.2020.102286 Banerjee, 2022, Exaggeration in fake vs. authentic online reviews for luxury and budget hotels, Int. J. Inf. Manag., 62, 10.1016/j.ijinfomgt.2021.102416 Banerjee, 2021, Calling out fake online reviews through robust epistemic belief, Inf. Manag., 58, 103445, 10.1016/j.im.2021.103445 Blundell, 1998, Initial conditions and moment restrictions in dynamic panel data models, J. Econ., 87, 115, 10.1016/S0304-4076(98)00009-8 Bond, 1991, Some tests of specification for panel data: monte carlo evidence and an application to employment equations, Rev. Econ. Stud., 58, 277, 10.2307/2297968 Brynjolfsson, 2003, Consumer surplus in the digital economy: Estimating the value of increased product variety at online booksellers, Manag. Sci., 49, 1580, 10.1287/mnsc.49.11.1580.20580 Campbell, 2015, Latent Dirichlet Allocation: Extracting Topics from Software Engineering Data, Art Sci. Analy. Software Data, 139, 10.1016/B978-0-12-411519-4.00006-9 Cao, 2020, Online review manipulation by asymmetrical firms: Is a firm’s manipulation of online reviews always detrimental to its competitor?, Inf. Manag., 57, 103244, 10.1016/j.im.2019.103244 Chaveesuk, 2021, Digital payment system innovations: A marketing perspective on intention and actual use in the retail sector, Innov. Mark., 17, 109, 10.21511/im.17(3).2021.09 Competition & Markets Authority. (2015). Online reviews and endorsements. https://reputationup.com/wp-content/uploads/2021/10/Online_reviews_and_endorsements.-ReputationUP.PDF.2021-1.pdf. Daryanto, 2019, Avoiding spurious moderation effects: An information-theoretic approach to moderation analysis, J. Bus. Res., 103, 110, 10.1016/j.jbusres.2019.06.012 Fresneda, 2020, Gazing at the stars is not enough, look at the specific word entropy, too!, Inf. Manag., 57, 103388, 10.1016/j.im.2020.103388 Ghose, 2011, Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics, IEEE Trans. Knowl. Data Eng., 23, 1498, 10.1109/TKDE.2010.188 Gössling, 2018, The manager’s dilemma: a conceptualization of online review manipulation strategies, Curr. Issue Tour., 21, 484, 10.1080/13683500.2015.1127337 Grivel, 2021, Kullback-Leibler and Rényi divergence rate for Gaussian stationary ARMA processes comparison, Digital Signal Process.: Rev. J., 116, 10.1016/j.dsp.2021.103089 Gu, 2012, The impact of external word-of-mouth sources on retailer sales of high-involvement products, Inf. Syst. Res., 23, 182, 10.1287/isre.1100.0343 Hansen, 1982, Large sample properties of generalized method of moments estimators, Econometrica, 50, 1029, 10.2307/1912775 Hayes, A. F. (2017). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. Guilford publications. Hu, 2012, Manipulation of online reviews: An analysis of ratings, readability, and sentiments, Decis. Support Syst., 52, 674, 10.1016/j.dss.2011.11.002 Hu, 2014, Ratings lead you to the product, reviews help you clinch it? the mediating role of online review sentiments on product sales, Decis. Support Syst., 57, 42, 10.1016/j.dss.2013.07.009 Hu, 2019, When is enough, enough? Investigating product reviews and information overload from a consumer empowerment perspective, J. Bus. Res., 100, 27, 10.1016/j.jbusres.2019.03.011 Hunter, 2001, Myofascial Manipulation Theory and clinical application, Physiotherapy, 87, 501, 10.1016/S0031-9406(05)60703-1 Jabr, 2014, Know yourself and know your enemy: An analysis of firm recommendations and consumer reviews in a competitive environment, MIS Q.: Manage. Inf. Syst., 38, 635, 10.25300/MISQ/2014/38.3.01 Jensen, 2013, Credibility of anonymous online product reviews: A language expectancy perspective, J. Manag. Inf. Syst., 30, 293, 10.2753/MIS0742-1222300109 Ke, 2020, Do online friends bring out the best in us? The effect of friend contributions on online review provision, Inf. Syst. Res., 31, 1322, 10.1287/isre.2020.0947 Kelly. (2019). Exposed: How rogue firms sell FAKE glowing Amazon reviews to online retailers for £13 each, duping millions of customers into buying shoddy goods. https://www.dailymail.co.uk/news/article-7819903/Millions-families-duped-buying-shoddy-goods-Amazon.html. Kuan, 2015, What makes a review voted? An empirical investigation of review voting in online review systems, J. Assoc. Inf. Syst., 16, 48 Kumar, 2018, Detecting review manipulation on online platforms with hierarchical supervised learning, J. Manag. Inf. Syst., 35, 350, 10.1080/07421222.2018.1440758 Kumar, 2019, Detecting anomalous online reviewers: An unsupervised approach using mixture models, J. Manag. Inf. Syst., 36, 1313, 10.1080/07421222.2019.1661089 Lappas, 2016, The impact of fake reviews on online visibility: A vulnerability assessment of the hotel industry, Inf. Syst. Res., 27, 940, 10.1287/isre.2016.0674 Lee, 2018, Sentiment Manipulation in Online Platforms: An Analysis of Movie Tweets, Prod. Oper. Manag., 27, 393, 10.1111/poms.12805 Lei, 2021, Focus within or on others: The impact of reviewers’ attentional focus on review helpfulness, Inf. Syst. Res., 32, 801, 10.1287/isre.2021.1007 Li, 2021, Strategic manipulation of online information in duopolies: Inducing fight-back?, Electron. Commer. Res. Appl., 47, 101052, 10.1016/j.elerap.2021.101052 Li, 2019, The effect of online reviews on product sales: A joint sentiment-topic analysis, Inf. Manag., 56, 172, 10.1016/j.im.2018.04.007 Liu, 2021, Listening to online reviews: A mixed-methods investigation of customer experience in the sharing economy, Decis. Support Syst., 149, 113609, 10.1016/j.dss.2021.113609 Liu, 2021, Assessing the unacquainted: Inferred reviewer personality and review helpfulness, MIS Quarterly: Management Information Systems, 45, 1113, 10.25300/MISQ/2021/14375 Liu, 2022, Power of information transparency: How online reviews change the effect of agglomeration density on firm revenue, Decis. Support Syst., 153, 113681, 10.1016/j.dss.2021.113681 Luca, 2016, Fake it till you make it: Reputation, competition, and yelp review fraud, Manag. Sci., 62, 3412, 10.1287/mnsc.2015.2304 Ma, 2019, Analyzing dynamic review manipulation and its impact on movie box office revenue, Electron. Commer. Res. Appl., 35, 100840, 10.1016/j.elerap.2019.100840 Ma, 2020, A 2020 perspective on “Analyzing dynamic review manipulation and its impact on movie box office revenue”, Electron. Commer. Res. Appl., 41, 100950, 10.1016/j.elerap.2020.100950 Mayzlin, 2014, Promotional reviews: An empirical investigation of online review manipulation, Am. Econ. Rev., 104, 2421, 10.1257/aer.104.8.2421 McCornack, 1992, Information manipulation theory, Commun. Monogr., 59, 1, 10.1080/03637759209376245 McCornack, 2014, Information Manipulation Theory 2: A Propositional Theory of Deceptive Discourse Production, J. Lang. Soc. Psychol., 33, 348, 10.1177/0261927X14534656 McCornack, S. A. (2014). Information manipulation theory. Engaging Theories in Interpersonal Communication: Multiple Perspectives, 215-298. Nikolay, 2011, Deriving the pricing power of product features by mining consumer reviews, Manag. Sci., 57, 1485 Plotkina, 2020, Illusions of truth—Experimental insights into human and algorithmic detections of fake online reviews, J. Bus. Res., 109, 511, 10.1016/j.jbusres.2018.12.009 Qiao, 2020, Financial incentives dampen altruism in online prosocial contributions: A study of online reviews, Inf. Syst. Res., 31, 1361, 10.1287/isre.2020.0949 Shan, 2021, From conflicts and confusion to doubts: Examining review inconsistency for fake review detection, Decis. Support Syst., 144, 113513, 10.1016/j.dss.2021.113513 Siering, 2019, Information Processing on Online Review Platforms, J. Manag. Inf. Syst., 36, 1347, 10.1080/07421222.2019.1661094 Song, 2020, Effect of online product reviews on third parties’ selling on retail platforms, Electron. Commer. Res. Appl., 39, 100900, 10.1016/j.elerap.2019.100900 Sterling, G. (2018). Study finds 61 percent of electronics reviews on Amazon are ‘fake.’ https://martech.org/study-finds-61-percent-of-electronics-reviews-on-amazon-are-fake/. Ullah, 2018, Dealing with endogeneity bias: The generalized method of moments (GMM) for panel data, Ind. Mark. Manag., 71, 69, 10.1016/j.indmarman.2017.11.010 Wang, 2018, Topic analysis of online reviews for two competitive products using latent Dirichlet allocation, Electron. Commer. Res. Appl., 29, 142, 10.1016/j.elerap.2018.04.003 Wang, 2018, GSLDA: LDA-based group spamming detection in product reviews, Appl. Intell., 48, 3094, 10.1007/s10489-018-1142-1 Wang, 2014, Database submission: Market dynamics and user-generated content about tablet computers, Mark. Sci., 33, 449, 10.1287/mksc.2013.0821 Wang, 2022, Effect of online review sentiment on product sales : The moderating role of review credibility perception, Comput. Hum. Behav., 133, 10.1016/j.chb.2022.107272 Winkler, 2016, Toy safety surveillance from online reviews, Decis. Support Syst., 90, 23, 10.1016/j.dss.2016.06.016 Wu, 2019, Motivation crowding in online product reviewing: A qualitative study of amazon reviewers, Inf. Manag., 56, 103163, 10.1016/j.im.2019.04.006 Wu, 2021, The effect of content depth and deviation on online review helpfulness: Evidence from double-hurdle model, Inf. Manag., 58, 103408, 10.1016/j.im.2020.103408 Wu, 2020, Fake online reviews: Literature review, synthesis, and directions for future research, Decis. Support Syst., 132, 113280, 10.1016/j.dss.2020.113280 Xu, 2020, Effects of online reviews and managerial responses from a review manipulation perspective, Curr. Issue Tour., 23, 2207, 10.1080/13683500.2019.1626814 Zhang, 2017, Welfare economics of review information: Implications for the online selling platform owner, Int. J. Prod. Econ., 184, 69, 10.1016/j.ijpe.2016.10.017 Zhang, 2022, A novel approach for fraudulent reviewer detection based on weighted topic modelling and nearest neighbors with asymmetric Kullback-Leibler divergence, Decis. Support Syst., 157, 113765, 10.1016/j.dss.2022.113765 Zhang, 2016, What online reviewer behaviors really matter? Effects of verbal and nonverbal behaviors on detection of fake online reviews, J. Manag. Inf. Syst., 33, 456, 10.1080/07421222.2016.1205907 Zhuang, 2018, Manufactured opinions: The effect of manipulating online product reviews, J. Bus. Res., 87, 24, 10.1016/j.jbusres.2018.02.016