Examining the Impacts of Electronic Word-of-Mouth Message on Consumers’ Attitude

ABSTRACT Electronic word-of-mouth (eWOM) has become one of the most influential communication tools. Few studies have identified what makes certain online reviews more influential than others. The objective of this study is to develop a better understanding of the impact of online reviews on consumer attitude and behavioral intention through a conceptual framework built from a series of theories and models. Combined experiment and survey methods contribute to the literature. Research findings revealed that online review antecedents of eWOM like review quality, valence, credibility, and quantity exerted impact on consumers’ attitude toward products. Personal involvement was a predominant predictor of consumers’ attitude. This study identified emotional strength’s mediating role in enhancing online review credibility and inducing a favorable attitude toward products.

[1]  Brandon Van Der Heide,et al.  Can sharing affect liking? Online taste performances, feedback, and subsequent media preferences , 2015, Comput. Hum. Behav..

[2]  W. Kim,et al.  The effects of perceived justice on recovery satisfaction, trust, word-of-mouth, and revisit intention in upscale hotels. , 2009 .

[3]  J. Cacioppo,et al.  Source factors and the elaboration likelihood model of persuasion , 1984 .

[4]  Jin Li,et al.  Online Persuasion: How the Written Word Drives WOM , 2011, Journal of Advertising Research.

[5]  Silke Bambauer-Sachse,et al.  Brand equity dilution through negative online word-of-mouth communication , 2011 .

[6]  Consumer Retaliation: Confirmation and Extension , 2002 .

[7]  Izak Benbasat,et al.  The Effects of Trust-Assuring Arguments on Consumer Trust in Internet Stores: Application of Toulmin's Model of Argumentation , 2006, Inf. Syst. Res..

[8]  Huaping Chen,et al.  Credibility of Electronic Word-of-Mouth: Informational and Normative Determinants of On-line Consumer Recommendations , 2009, Int. J. Electron. Commer..

[9]  Jing Li,et al.  Information self-efficacy and information channels: Decision quality and online shopping satisfaction , 2013, Online Inf. Rev..

[10]  J. Hair.  Multivariate data analysis : a global perspective , 2010 .

[11]  Mira Lee,et al.  Effects of Valence and Extremity of eWOM on Attitude toward the Brand and Website , 2009 .

[12]  Alex Koohang,et al.  Using Social Technologies for Competitive Advantage: Impact on Organizations and Higher Education , 2014, J. Comput. Inf. Syst..

[13]  R. Mizerski.  An Attribution Explanation of the Disproportionate Influence of Unfavorable Information , 1982 .

[14]  D. A. Kenny,et al.  The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. , 1986, Journal of personality and social psychology.

[15]  J. Cacioppo,et al.  Central and Peripheral Routes to Advertising Effectiveness: The Moderating Role of Involvement , 1983 .

[16]  J. Cacioppo,et al.  Personal involvement as a determinant of argument based persuasion , 1981 .

[17]  J. Chiou,et al.  How Much can you Trust Online Information? Cues for Perceived Trustworthiness of Consumer-generated Online Information , 2011 .

[18]  A. Isen,et al.  Positive Affect Facilitates Integration of Information and Decreases Anchoring in Reasoning among Physicians , 1997 .

[19]  Weng Marc Lim,et al.  Consumer Acceptance and Continuance of Online Group Buying , 2014, J. Comput. Inf. Syst..

[20]  P. Kline.  The handbook of psychological testing, 2nd ed. , 1993 .

[21]  Geng Cui,et al.  Online Reviews as a Driver of New Product Sales , 2010, 2010 International Conference on Management of e-Commerce and e-Government.

[22]  Jang-Sun Hwang,et al.  How Consumers Evaluate eWOM (Electronic Word-of-Mouth) Messages , 2009, Cyberpsychology Behav. Soc. Netw..

[23]  C. Chung,et al.  The effects of single‐message single‐source mixed word‐of‐mouth on product attitude and purchase intention , 2007 .

[24]  Pradeep Racherla,et al.  Factors affecting consumers' trust in online product reviews , 2012 .

[25]  Nada Nasr Bechwati,et al.  Word of Mouse , 2008 .

[26]  Dong-Mo Koo,et al.  Effects of attribute and valence of e-WOM on message adoption: Moderating roles of subjective knowledge and regulatory focus , 2012, Comput. Hum. Behav..

[27]  Franziska Marquart,et al.  Communication and persuasion : central and peripheral routes to attitude change , 1988 .

[28]  Jen-Hung Huang,et al.  Herding in online product choice , 2006 .

[29]  Ingoo Han,et al.  The Effects of Consumer Knowledge on Message Processing of Electronic Word of Mouth via Online Consumer Reviews , 2008, ECIS.

[30]  David Mazursky,et al.  Learning from the ad or relying on related attitudes: The moderating role of involvement , 1992 .

[31]  A. Greenwald,et al.  Twenty years of cognitive dissonance: case study of the evolution of a theory. , 1978, Psychological review.

[32]  Yong Liu.  Word-of-Mouth for Movies: Its Dynamics and Impact on Box Office Revenue , 2006 .

[33]  Wynne W. Chin.  The partial least squares approach for structural equation modeling. , 1998 .

[34]  Bo Sophia Xiao,et al.  Do actions speak louder than voices? The signaling role of social information cues in influencing consumer purchase decisions , 2014, Decis. Support Syst..

[35]  Magnus Söderlund,et al.  Receiving word-of-mouth from the service customer: An emotion-based effectiveness assessment , 2007 .

[36]  Wei Wei Goh,et al.  Conceptualizing Persuasive Messages Using ELM in Social Media , 2014 .

[37]  Dennis A. Pitta,et al.  Online consumer communities and their value to new product developers , 2005 .

[38]  Kathy Hammond,et al.  Measuring the impact of positive and negative word of mouth on brand purchase probability , 2008 .

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

[40]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[41]  Chezy Ofir,et al.  Context Effects on Judgment under Uncertainty , 1984 .

[42]  Chrysanthos Dellarocas,et al.  Exploring the value of online product reviews in forecasting sales: The case of motion pictures , 2007 .

[43]  Cheol Park,et al.  Information direction, website reputation and eWOM effect: A moderating role of product type , 2009 .

[44]  Dong-Mo Koo,et al.  Evaluating right versus just evaluating online consumer reviews , 2015, Comput. Hum. Behav..

[45]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[46]  Seoyeon Hong,et al.  Computer-mediated persuasion in online reviews: Statistical versus narrative evidence , 2012, Comput. Hum. Behav..

[47]  Chao Wen,et al.  An Integrated Model for Customer Online Repurchase Intention , 2011, J. Comput. Inf. Syst..

[48]  Stephanie Watts,et al.  Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption , 2003, Inf. Syst. Res..

[49]  J. Cacioppo,et al.  Attitudes and Persuasion: Classic and Contemporary Approaches , 1981 .

[50]  S. Sen,et al.  Why are you telling me this? An examination into negative consumer reviews on the Web , 2007 .

[51]  Dwayne D. Gremler,et al.  Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? , 2004 .

[52]  Do-Hyung Park,et al.  eWOM overload and its effect on consumer behavioral intention depending on consumer involvement , 2008, Electron. Commer. Res. Appl..

[53]  Alain Yee-Loong Chong,et al.  Examining the antecedents of persuasive eWOM messages in social media , 2014, Online Inf. Rev..

[54]  Chad Edwards,et al.  Computer-Mediated Word-of-Mouth Communication on RateMyProfessors.com: Expectancy Effects on Student Cognitive and Behavioral Learning , 2009, J. Comput. Mediat. Commun..

[55]  J. Zaichkowsky.  The Personal Involvement Inventory: Reduction, Revision, and Application to Advertising , 1994 .