Influencing factors of the persuasiveness of online reviews considering persuasion methods

Abstract Online reviews are important references for purchasing decision. These reviews influence consumer behavior through their awareness and persuasive effects. Persuasion methods used by online reviewers are based on their own expression habits, which have a persuasive effect on other consumers. This study uses theories of linguistics and psychology, and combines with the features of online reviews. At the same time, Text mining techniques are employed to study persuasion methods. We establish a benchmark lexicon for every kind of persuasion method, and also quantify persuasion methods and do quantitative research by scanning every online review and regression analysis. The main conclusions are as follows. Logos, Pathos, and Feature statements have significant roles in improving the persuasiveness of online reviews, while Ethos has less impact in comparison. In addition, as for the characteristics of online review content and reviewers, the number of images, number of videos and member factors also play critical roles.

[1]  Carl I. Hovland,et al.  Personality and Persuasibility. , 1960 .

[2]  Kai H. Lim,et al.  Effects of conflicting aggregated rating on eWOM review credibility and diagnosticity: The moderating role of review valence , 2012, Decis. Support Syst..

[3]  Dimple R. Thadani,et al.  The impact of electronic word-of-mouth communication: A literature analysis and integrative model , 2012, Decis. Support Syst..

[4]  Yuejin Tan,et al.  Impact of product attributes on customer satisfaction: An analysis of online reviews for washing machines , 2018, Electron. Commer. Res. Appl..

[5]  Bin Gu,et al.  Do online reviews matter? - An empirical investigation of panel data , 2008, Decis. Support Syst..

[6]  Fraser McLeay,et al.  Consumer perceptions of information helpfulness and determinants of purchase intention in online consumer reviews of services , 2018, Inf. Manag..

[7]  C. Insko Verbal reinforcement of attitude. , 1965, Journal of personality and social psychology.

[8]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[9]  Qing Cao,et al.  Exploring determinants of voting for the "helpfulness" of online user reviews: A text mining approach , 2011, Decis. Support Syst..

[10]  Panagiotis G. Ipeirotis,et al.  Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics , 2010, IEEE Transactions on Knowledge and Data Engineering.

[11]  Jon D. Morris,et al.  Elaboration likelihood model: A missing intrinsic emotional implication , 2005 .

[12]  Yue Pan,et al.  Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews , 2011 .

[13]  Wenqiang Dai,et al.  Topic analysis of online reviews for two competitive products using latent Dirichlet allocation , 2018, Electron. Commer. Res. Appl..

[14]  B. Sparks,et al.  The impact of online reviews on hotel booking intentions and perception of trust. , 2011 .

[15]  S. Jamar Aristotle Teaches Persuasion: The Psychic Connection , 2008 .

[16]  Hongwei Wang,et al.  Feature–opinion pair identification of product reviews in Chinese: a domain ontology modeling method , 2013, New Rev. Hypermedia Multim..

[17]  JoongHo Ahn,et al.  Helpfulness of Online Consumer Reviews: Readers' Objectives and Review Cues , 2012, Int. J. Electron. Commer..

[18]  Raffaele Filieri What makes an online consumer review trustworthy , 2016 .

[19]  Bing Liu,et al.  Mining Opinion Features in Customer Reviews , 2004, AAAI.

[20]  Juan Luis Nicolau,et al.  Asymmetric effects of online consumer reviews , 2015 .

[21]  Sangwon Park,et al.  What makes a useful online review? Implication for travel product websites. , 2015 .

[22]  Christopher S. Calhoun,et al.  Application of the heuristic-systematic model to computer code trustworthiness: The influence of reputation and transparency , 2017 .

[23]  Robert M. Schindler,et al.  Internet forums as influential sources of consumer information , 2001 .

[24]  Fang Wang,et al.  Online review helpfulness: Impact of reviewer profile image , 2017, Decis. Support Syst..

[25]  Marios D. Sotiriadis,et al.  Electronic word-of-mouth and online reviews in tourism services: the use of twitter by tourists , 2013, Electron. Commer. Res..

[26]  Jiunn-Liang Guo,et al.  AN OPINION FEATURE EXTRACTION APPROACH BASED ON A MULTIDIMENSIONAL SENTENCE ANALYSIS MODEL , 2013, Cybern. Syst..

[27]  J. Tobin Estimation of Relationships for Limited Dependent Variables , 1958 .

[28]  Traci Y. Craig,et al.  Language and Persuasion: Linguistic Extremity Influences Message Processing and Behavioral Intentions , 2011 .

[29]  Michel Laroche,et al.  How Do Expressed Emotions Affect the Helpfulness of a Product Review? Evidence from Reviews Using Latent Semantic Analysis , 2015, Int. J. Electron. Commer..

[30]  C. I. Hovland,et al.  The Influence of Source Credibility on Communication Effectiveness , 1951 .

[31]  Ann E. Schlosser Can including pros and cons increase the helpfulness and persuasiveness of online reviews? The interactive effects of ratings and arguments ☆ , 2011 .

[32]  Stephanie Q. Liu,et al.  Does expressing subjectivity in online reviews enhance persuasion? , 2018, Journal of Consumer Marketing.

[33]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[34]  Yogesh Kumar Dwivedi,et al.  Perceived helpfulness of eWOM: Emotions, fairness and rationality , 2020 .

[35]  Yu-N Cheah,et al.  Aspect extraction in sentiment analysis: comparative analysis and survey , 2016, Artificial Intelligence Review.

[36]  Yang Zhao,et al.  Online Shopping Decision Behavior under Chinese Scene: Retrospect and Review , 2014 .

[37]  Claude H. Miller,et al.  Expanding Language Expectancy Theory: The Suasory Effects of Lexical Complexity and Syntactic Complexity on Effective Message Design , 2014 .

[38]  Ayyaz Hussain,et al.  Exploring the influential reviewer, review and product determinants for review helpfulness , 2018, Artificial Intelligence Review.

[39]  Pradeep Racherla,et al.  Perceived 'usefulness' of online consumer reviews: An exploratory investigation across three services categories , 2012, Electron. Commer. Res. Appl..

[40]  Eojina Kim,et al.  More than words: Do emotional content and linguistic style matching matter on restaurant review helpfulness? , 2019, International Journal of Hospitality Management.

[41]  Choon-Ling Sia,et al.  Is This Review Believable? A Study of Factors Affecting the Credibility of Online Consumer Reviews from an ELM Perspective , 2012, J. Assoc. Inf. Syst..

[42]  Andrew B. Whinston,et al.  Whose and what chatter matters? The effect of tweets on movie sales , 2013, Decis. Support Syst..