Sincere or Falsehearted? Investigation of Online Reviews and Merchant Response

References

• Abbasi, A.; Li, J.; Adjeroh, D.; Abate, M.; and Zheng, W. Don’t mention it? Analyzing user-generated content signals for early adverse event warnings. Information Systems Research, 30, 3 (2019), 1007–1028.
   Web of Science ®Google Scholar
• Ahmad, S.N.; and Laroche, M. How do expressed emotions affect the helpfulness of a product review? Evidence from reviews using latent semantic analysis. International Journal of Electronic Commerce, 20, 1 (2015), 76–111.
   Web of Science ®Google Scholar
• Basso, K.; and Pizzutti, C. Trust recovery following a double deviation. Journal of Service Research, 19, 2 (2016), 209–223.
   Web of Science ®Google Scholar
• Bitner, M.J.; Booms, B.H.; and Tetreault, M.S. The service encounter: Diagnosing favorable and unfavorable incidents. Journal of Marketing, 54(1990), 71–84.
   Web of Science ®Google Scholar
• Casidy, R.; and Shin, H. The effects of harm directions and service recovery strategies on customer forgiveness and negative word-of-mouth intentions. Journal of Retailing and Consumer Services, 27(2015), 103–112.
   Web of Science ®Google Scholar
• Casado, A.B.; Nicolau, J.L.; and Mas, F.J. The harmful consequences of failed recoveries in the banking industry. International Journal of Bank Marketing, 29, 1 (2011), 32–49.
   Google Scholar
• Chang, H.H.; Tsai, Y.C.; Wong, K.H.; Wang, J.W.; and Cho, F.J. The effects of response strategies and severity of failure on consumer attribution with regard to negative word-of-mouth. Decision Support Systems, 71, Mar. (2015), 48–61.
   Google Scholar
• Chatterjee, S. Explaining customer ratings and recommendations by combining qualitative and quantitative user generated contents. Decision Support Systems, 119, Apr. (2019), 14–22.
   Google Scholar
• Chen, R.; Zheng, Y.; Xu, W.; Liu, M.; and Wang, J. Secondhand seller reputation in online markets: A text analytics framework. Decision Support Systems, 108, Apr. (2018), 96–106.
   Google Scholar
• Chen, Y.; and Xie, J. Online consumer review: Word-of-mouth as a new element of marketing communication mix. Management Science, 54, 3 (2008), 477–491.
   Web of Science ®Google Scholar
• Choi, A.; Cho, D.; Yim, D.; Moon, J.Y.; and Oh, W. When Seeing helps believing: The interactive effects of previews and reviews on e-book purchases. Information Systems Research, 30, 4 (2019), 1164–1183.
   Web of Science ®Google Scholar
• Clémenon, S.; and Vayatis, N. Ranking the best instances. Journal of Machine Learning Research, 8 (2006).
   Web of Science ®Google Scholar
• Cui, G.; Lui, H. K.; and Guo, X. The effect of online consumer reviews on new product sales. International Journal of Electronic Commerce, 17, 1 (2012), 39–58.
   Web of Science ®Google Scholar
• Fernandes, T.; and Fernandes, F. Sharing dissatisfaction online: Analyzing the nature and predictors of hotel guests negative reviews. Journal of Hospitality Marketing & Management, 27, 2 (2017), 127–150.
   Web of Science ®Google Scholar
• Francesco, G.; and Roberta, G. Cross-country analysis of perception and emphasis of hotel attributes. Tourism Management, 74, Oct. (2019), 24–42.
   Google Scholar
• Gensler, S.; Völckner, F.; Egger, M.; Fischbach, K.; and Schoder, D. Listen to your customers: Insights into brand image using online consumer-generated product reviews. International Journal of Electronic Commerce, 20, 1 (2015), 112–141.
   Web of Science ®Google Scholar
• Herhausen, D.; Ludwig, S.; Grewal, D.; Wulf, J.; and Schoegel, M. Detecting, preventing, and mitigating online firestorms in brand communities. Journal of Marketing, 83, 3 (2019), 1–21.
   Web of Science ®Google Scholar
• Hu, F.; Teichert, T.; Liu, Y.; Li, H.; and Gundyreva, E. Evolving customer expectations of hospitality services: Differences in attribute effects on satisfaction and re-patronage. Tourism Management, 74, Oct. (2019), 345–357.
   Google Scholar
• Ibrahim, N.F.; and Wang, X. Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media. Computers in Human Behavior, 96, Jul. (2019), 32–45.
   Google Scholar
• Joireman, J.; Grégoire, Y.; Devezer, B.; and Tripp, T.M. When do customers offer firms a “second chance” following a double deviation? The impact of inferred firm motives on customer revenge and reconciliation. Journal of Retailing, 89, 3, (2013), 315–337.
   Web of Science ®Google Scholar
• Jung, Y.; and Suh, Y. Mining the voice of employees: A text mining approach to identifying and analyzing job satisfaction factors from online employee reviews. Decision Support Systems, 123, Aug. (2019), 1–12.
   Google Scholar
• Kendall, M.G.; and Smith, B.B. The problem of $m$ rankings. Annals of Mathematical Statistics, 10, 3 (1939), 275–287.
   Google Scholar
• Kratzwald, B.; Ilic, S.; Kraus, M.; Feuerriegel, S.; and Prendinger, H. Deep learning for affective computing: Text-based emotion recognition in decision support. Decision Support Systems, 115, Nov. (2018), 24–35.
   Google Scholar
• Krichene, W.; and Rendle, S. On sampled metrics for item recommendation. KDD ‘20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2020).
   Google Scholar
• Lappas, T.; Sabnis, G.; and Valkanas, G. The Impact of fake reviews on online visibility: A vulnerability assessment of the hotel industry. Information Systems Research, 27, 4 (2016).
   Web of Science ®Google Scholar
• Lee, J.S.; Kim, J.; Hwang, J.; and Cui, Y. Does love become hate or forgiveness after a double deviation? The case of hotel loyalty program members. Tourism Management, 84, (2021), 104279.
   Web of Science ®Google Scholar
• Lee, Y.L.; and Song, S. An empirical investigation of electronic word-of-mouth: Informational motive and corporate response strategy. Computers in Human Behavior, 26, 5 (2010), 1073–1080.
   Web of Science ®Google Scholar
• Li, G.; Law, R.; Vu, H.Q.; Rong, J.; and Zhao, X.R. Identifying emerging hotel preferences using emerging pattern mining technique. Tourism Management, 46(2015), 311–321.
   Web of Science ®Google Scholar
• Liang, T. P.; Li, X.; Yang, C.T.; and Wang, M. What in consumer reviews affects the sales of mobile apps: A multifacet sentiment analysis approach. International Journal of Electronic Commerce, 20, 2 (2015), 236–260.
   Web of Science ®Google Scholar
• Liu, Y.; Jiang, C.; and Zhao, H. Assessing product competitive advantages from the perspective of customers by mining user-generated content on social media. Decision Support Systems, 123, Aug. (2019), 113079.113071–113079.113013
   Web of Science ®Google Scholar
• Liu, Y.; Teichert, T.; Rossi, M.; Li, H.; and Hu, F. Big data for big insights: Investigating language-specific drivers of hotel satisfaction with 412,784 user-generated reviews. Tourism Management, 59, Apr. (2017), 554–563.
   Web of Science ®Google Scholar
• Ludwig, S.; Ruyter, K.D.; Friedman, M.; Brueggen, E.C.; Wetzels, M.; and Pfann, G. More than words: The influence of affective content and linguistic style matches in online reviews on conversion rates. Journal of Marketing, 77, 1(2013),87–103.
   Web of Science ®Google Scholar
• Luo, R.; Xu, J.; Zhang, Y.; Ren, X.; and Sun, X. PKUSEG: A toolkit for multi-domain Chinese word segmentation. (2019).
   Google Scholar
• Malik, M.S.I.; and Hussain, A. Helpfulness of product reviews as a function of discrete positive and negative emotions. Computers in Human Behavior, 73(2017), 290–302.
   Web of Science ®Google Scholar
• Manis, K.;Wang, Y.; and Chaudhry, A. 5 Principles for responding to customer reviews. Harvard Business Review (2020).
   Google Scholar
• Manu, C.; and Sreejesh, S. Addressing service failure and initiating service recovery in online platforms: Literature review and research agenda. Journal of Strategic Marketing, 29, 8 (2021), 658–689.
   Web of Science ®Google Scholar
• Maxham, J.G. III; and Netemeyer, R.G. A longitudinal study of complaining customers’ evaluations of multiple service failures and recovery efforts. Journal of Marketing, 66, 4 (2002), 57–71.
   Web of Science ®Google Scholar
• Pacheco, N.A.; Pizzutti, C.; Basso, K.; and Van Vaerenbergh, Y. Trust recovery tactics after double deviation: Better sooner than later? Journal of Service Management, 30, 1 (2019), 2–22.
   Web of Science ®Google Scholar
• Park, S.Y.; and Allen, J.P. Responding to online reviews: Problem solving and engagement in hotels. Cornell Hospitality Quarterly, 54, 1 (2013), 64–73.
   Web of Science ®Google Scholar
• Qiang, J.; Li, Y.; Zhu, Y.; Yuan, Y.; and Wu, X. Lexical simplification with pretrained encoders. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 5 (2020), 8649–8656.
   Google Scholar
• Rhee, H.T.; and Yang, S.B. Does hotel attribute importance differ by hotel? Focusing on hotel star-classifications and customers’ overall ratings. Computers in Human Behavior, 50, Sep. (2015), 576–587.
   Google Scholar
• Sathe, S.; Aggarwal, C.; Kong, X.; and Liu, X. Kernel-based feature extraction for collaborative filtering. IEEE International Conference on Data Mining, (2017), 1057–1062.
   Google Scholar
• Sembada, A.; Tsarenko, Y.; and Tojib, D. The positive effects of customers’ power on their behavioral responses after service failure. Journal of Service Research, 19, 3 (2016), 337–351.
   Web of Science ®Google Scholar
• Sengupta, S.; Ray, D.; Trendel, O.; and Vaerenbergh, Y.V. The effects of apologies for service failures in the global online retail. International Journal of Electronic Commerce, 22, 3 (2018), 419–445.
   Web of Science ®Google Scholar
• Sparks, B.A.; and Bradley, G.L. A “Triple A” typology of responding to negative consumer-generated online reviews. Journal of Hospitality & Tourism Research, 41, 6 (2017), 719–745.
   Web of Science ®Google Scholar
• Sparks, B.A.; So, K.K.F.; and Bradley, G.L. Responding to negative online reviews: The effects of hotel responses on customer inferences of trust and concern. Tourism Management, (2016), 74–85.
   Google Scholar
• Van Vaerenbergh, Y.; Lariviere, B.; and Vermeir, I. The impact of process recovery communication on customer satisfaction, repurchase intentions, and word-of-mouth intentions. Journal of Service Research, 15, 3, (2012), 262–279.
   Web of Science ®Google Scholar
• Wang, S.;Hung, K.; and Li, M. Development of measurement scale for functional congruity in guest houses. Tourism Management, 68(2018), 23–31.
   Web of Science ®Google Scholar
• Wang, Y.; and Chaudhry, A. When and how managers’ responses to online reviews affect subsequent reviews. Journal of Marketing Research, 55, 2 (2018), 163–177.
   Web of Science ®Google Scholar
• Wei, W.; Miao, L.; and Huang, Z. Customer engagement behaviors and hotel responses. International Journal of Hospitality Management, 33(2013), 316–330.
   Web of Science ®Google Scholar
• Xia, L. Effects of companies’ responses to consumer criticism in social media. International Journal of Electronic Commerce, 17, 4 (2013), 73–100.
   Web of Science ®Google Scholar
• Xiang, Z.;Du, Q.;Ma, Y.; and Fan, W. A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, Feb. (2017), 51–65.
   Google Scholar
• Yang, Y.; Duan, Y.; Wang, X.; Zi, H.; Xie, N.; and Tao, S.H. Hierarchical multi-clue modelling for POI popularity prediction with heterogeneous tourist information. IEEE Transactions on Knowledge and Data Engineering (2018), 757–768.
   Web of Science ®Google Scholar
• Ye, Q.; Law, R.; and Gu, B. The impact of online user reviews on hotel room sales. International Journal of Hospitality Management, 28, 1 (2009), 180–182.
   Web of Science ®Google Scholar
• Yi, C.; Jiang, Z.; and Benbasat, I. Designing for diagnosticity and serendipity: An investigation of social product-search mechanisms. Information Systems Research, 28, 2 (2017), 413–429.
   Web of Science ®Google Scholar
• Yin, D.; Bond, S.D.; and Zhang, H. Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Quarterly, 38(2013), 539–560.
   Web of Science ®Google Scholar
• Yin, D.; Bond, S.; and Zhang, H. Anger in consumer reviews: Unhelpful but persuasive. MIS Quarterly, 45, 3 (2021), 1059–1084.
   Web of Science ®Google Scholar
• Yin, D.; Mitra, S.; and Zhang, H. Research note—When do consumers value positive vs. negative reviews? An empirical investigation of confirmation bias in online word of mouth. Information Systems Research, 27, 1 (2016), 131–144.
   Web of Science ®Google Scholar
• Zhang, L.; Yan, Q.; and Zhang, L. A computational framework for understanding antecedents of guests’ perceived trust towards hosts on Airbnb. Decision Support Systems, 115, Nov. (2018), 105–116.
   Google Scholar
• Zhao, W.X.; Chen, J.; Wang, P.; Gu, Q.; and Wen, J.R. Revisiting alternative experimental settings for evaluating top-N item recommendation algorithms. CIKM ’20: The 29th ACM International Conference on Information and Knowledge Management, virtual event, Ireland (2020), 2329–2332.
   Google Scholar