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