Beyond the Lexical Sense of Online Reviews: The Role of Emoticons and Consumer Experience

The present study focuses on the effect of emoticon use in online consumer reviews (OCRs) on consumers’ booking intention and the moderating effect of consumer personal characteristics. Consumers’ prior experience and their reliance on OCRs are embedded in the research model. A 2 × 2 (review valence * emoticon use) experimental study is designed, and an econometric model is used. Results show that the interaction between review valence and emoticons affect booking intention. Consumers with no prior experience are mainly affected by the cognitive aspects of their experience (i.e. review credibility and attitude toward the review) while experienced consumers are affected by the experiential aspects of booking process (i.e. entertainment, satisfaction and social influence). Consumers that rely on OCRs are affected by emoticons while consumers without review reliance are affected by emoticons only in the case of positive reviews. The personalization of websites and the provision of a focused list of emoticons can be adopted by managers to enhance OCRs effectiveness and the online shopping experience as a whole.

[1]  S. Shyam Sundar,et al.  Can synchronicity and visual modality enhance social presence in mobile messaging? , 2015, Comput. Hum. Behav..

[2]  Stellan Ohlsson,et al.  Comparing Multiple Paths to Mastery: What is Learned? , 2005, Cogn. Sci..

[3]  Jay Kandampully,et al.  The influence of eWOM communications: An application of online social network framework , 2018, Comput. Hum. Behav..

[4]  David G. Rand,et al.  The online laboratory: conducting experiments in a real labor market , 2010, ArXiv.

[5]  Naoki Mukawa,et al.  Brain activity when reading sentences and emoticons: an fMRI study of verbal and nonverbal communication , 2011 .

[6]  Jagdip Singh,et al.  Pragmatic Learning Theory: An Inquiry-Action Framework for Distributed Consumer Learning in Online Communities , 2010 .

[7]  Susan R. Fussell,et al.  Figurative Language in Emotional Communication , 1998 .

[8]  Stefan Stieglitz,et al.  Digital nudging and privacy: improving decisions about self-disclosure in social networks , 2019, Behav. Inf. Technol..

[9]  Yi Zhao,et al.  Modeling Consumer Learning from Online Product Reviews , 2012, Mark. Sci..

[10]  Manuel Perea,et al.  ERP correlates of masked affective priming with emoticons 1-s2.0-S0747563212003007-fx1 , 2013, Comput. Hum. Behav..

[11]  Irini Rigopoulou,et al.  Virtual Store Layout Effects on Consumer Behaviour: Applying an Environmental Psychology Approach in the Online Travel Industry , 2011, Internet Res..

[12]  Scott Highhouse.  Designing Experiments That Generalize , 2009 .

[13]  Wei Chen,et al.  The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings , 2011, Comput. Hum. Behav..

[14]  Júlio Cesar dos Reis,et al.  Intenticons: Participatory selection of emoticons for communication of intentions , 2018, Comput. Hum. Behav..

[15]  Mei-Ju Chen,et al.  Examining the Influence of Emotional Expressions in Online Consumer Reviews on Perceived Helpfulness , 2020, Inf. Process. Manag..

[16]  Brent Simpson,et al.  Emotional reactions to losing explain gender differences in entering a risky lottery , 2010, Judgment and Decision Making.

[17]  M. Richard.  Modeling the impact of internet atmospherics on surfer behavior , 2005 .

[18]  Do Personality Traits Affect Productivity? Evidence from the Laboratory , 2016 .

[19]  J. W. Hutchinson,et al.  Action-Based Learning: Goals and Attention in the Acquisition of Market Knowledge , 2006 .

[20]  Efthalia Dimara,et al.  Enhancing the impact of online hotel reviews through the use of emoticons , 2017, Behav. Inf. Technol..

[21]  Tathagata Ghosh.  Predicting hotel book intention: The influential role of helpfulness and advocacy of online reviews , 2018 .

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

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

[24]  Joel B. Cohen,et al.  Affect Monitoring and the Primacy of Feelings in Judgment , 2001 .

[25]  A. Acquisti,et al.  Reputation as a sufficient condition for data quality on Amazon Mechanical Turk , 2013, Behavior Research Methods.

[26]  Júlio Cesar dos Reis,et al.  Design of Interactive Mechanisms to Support the Communication of Users' Intentions , 2018, Interact. Comput..

[27]  Scott Clifford,et al.  Validity and Mechanical Turk: An assessment of exclusion methods and interactive experiments , 2017, Comput. Hum. Behav..

[28]  Madhubalan Viswanathan,et al.  Measurement error and research design , 2005 .

[29]  Claudio Vitari,et al.  Extremely Negative Ratings and Online Consumer Review Helpfulness: The Moderating Role of Product Quality Signals , 2020 .

[30]  Pranjal Gupta,et al.  Emotional expressions in online user reviews: How they influence consumers' product evaluations , 2012 .

[31]  S. Danziger,et al.  “Wii Will Rock You!” The Use and Effect of Figurative Language in Consumer Reviews of Hedonic and Utilitarian Consumption , 2013 .

[32]  Nathalia Purnawirawan,et al.  Balance and Sequence in Online Reviews: How Perceived Usefulness Affects Attitudes and Intentions , 2012 .

[33]  P. Herr,et al.  Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective , 1991 .

[34]  Alex Wang.  The Effects of Expert and Consumer Endorsements on Audience Response , 2005, Journal of Advertising Research.

[35]  Moshe Buchinsky.  Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research , 1998 .

[36]  David C. Yen,et al.  Exploring the potential effects of emoticons , 2008, Inf. Manag..

[37]  Monic Sun,et al.  How Does the Variance of Product Ratings Matter? , 2010, Manag. Sci..

[38]  M. Rosenzweig,et al.  Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture , 1995, Journal of Political Economy.

[39]  Cheng-Chieh Hsiao,et al.  The credibility and attribution of online reviews: Differences between high and low product knowledge consumers , 2018, Online Inf. Rev..

[40]  V. Michael Bove,et al.  Odor emoticon: An olfactory application that conveys emotions , 2016, Int. J. Hum. Comput. Stud..

[41]  Karen M. Lancendorfer,et al.  Antecedents of consumers’ reliance on online product reviews , 2019, Journal of Research in Interactive Marketing.

[42]  Lyn M. Van Swol,et al.  Emoticons' influence on advice taking , 2018, Comput. Hum. Behav..

[43]  Rui Zhang,et al.  Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits , 2012, Comput. Hum. Behav..

[44]  L. T. Le,et al.  Hotel choice: A closer look at demographics and online ratings , 2019, International Journal of Hospitality Management.

[45]  L. Reder,et al.  What determines initial feeling of knowing? Familiarity with question terms, not with the answer , 1992 .

[46]  M. Kesgin,et al.  Consumer engagement: the role of social currency in online reviews , 2018, The Service Industries Journal.

[47]  Xin Chen,et al.  Exploring user behaviour of emoticon use among Chinese youth , 2017, Behav. Inf. Technol..

[48]  David Brinberg,et al.  How Consumer Reviews Persuade Through Narratives , 2015 .

[49]  Sobia Zaman,et al.  Impact of Online Store Atmosphere, Customized Information and Customer Satisfaction on Online Repurchase Intention , 2018 .

[50]  M. Tzagarakis,et al.  Personality traits and performance in online labour markets , 2020, Behav. Inf. Technol..

[51]  S. Fiske.  Social cognition and social perception. , 1993, Annual review of psychology.

[52]  Clayton Shepard,et al.  A longitudinal study of emoticon use in text messaging from smartphones , 2012, Comput. Hum. Behav..

[53]  Kiemute Oyibo,et al.  The relationship between personality traits and susceptibility to social influence , 2019, Comput. Hum. Behav..

[54]  Peter H. Reingen,et al.  Social Ties and Word-of-Mouth Referral Behavior , 1987 .

[55]  Manolis Tzagarakis,et al.  An investigation of factors affecting the visits of online crowdsourcing and labor platforms , 2018, NETNOMICS: Economic Research and Electronic Networking.

[56]  Tao Wang,et al.  The Influence of Social Presence on Consumers’ Perceptions of the Interactivity of Web Sites , 2010 .

[57]  Emi Moriuchi.  IS THAT REALLY AN HONEST ONLINE REVIEW? THE EFFECTIVENESS OF DISCLAIMERS IN ONLINE REVIEWS , 2018, Journal of Marketing Theory and Practice.

[58]  Wen-Chin Tsao,et al.  Compliance with eWOM: the influence of hotel reviews on booking intention from the perspective of consumer conformity. , 2015 .

[59]  Amit M. Joshi,et al.  A Meta-Analysis of Electronic Word-of-Mouth Elasticity , 2015 .

[60]  Donal E. Carlston,et al.  Negativity and extremity biases in impression formation: A review of explanations. , 1989 .

[61]  Karen L. Xie,et al.  The business value of online consumer reviews and management response to hotel performance. , 2014 .

[62]  The Dynamics of Micro-Task Crowdsourcing: The Case of Amazon MTurk , 2015, WWW.

[63]  I. Vermeulen,et al.  Tried and tested: The impact of online hotel reviews on consumer consideration , 2009 .

[64]  Asbjørn Følstad,et al.  How Should Organizations Adapt to Youth Civic Engagement in Social Media? A Lead User Approach , 2016, Interact. Comput..

[65]  R. Petty,et al.  Measuring the Affective and Cognitive Properties of Attitudes: Conceptual and Methodological Issues , 1994 .

[66]  Panagiotis G. Ipeirotis,et al.  Running Experiments on Amazon Mechanical Turk , 2010, Judgment and Decision Making.

[67]  Zoey Chen.  Social Acceptance and Word of Mouth: How the Motive to Belong Leads to Divergent WOM with Strangers and Friends , 2017 .

[68]  Carlos Martin-Rios,et al.  A cross-country comparison of accommodation manager perspectives on online review manipulation , 2019 .