Skip to main content
SpringerLink
Log in

An Investigation of Peripheral and Central Cues of Online Customer Review Voting and Helpfulness through the Lens of Elaboration Likelihood Model

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

Online consumer reviews (OCRs) have become an important part of online consumers’ decision-making to purchse products. Consumers use OCRs not only to get a better understanding of the characteristics of products but also to learn about other customers’ experiences with them. Drawing upon Elaboration Likelihood Model, this research investigates the predictors of popularity and helpfulness of OCRs. The results of the study show that longer reviews, as well as those with extreme star ratings, are more popular. Moreover, the amount of hedonic and utilitarian cues in a review and its sentiment significantly influence perceptions of online consumers regarding its helpfulness. The results also show how product type moderates the effect of utilitarian and hedonic cues on helpfulness. Our results can be used by online review websites to develop more efficient methods for sorting OCRs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. According to Cao et al. [14], some reviews are too long to absorb the attention of customers and may not be read by them. Therefore, online consumers may not read extremely long reviews because it takes too much time and effort to read them. This study examines the quadratic relationship of the review length on review popularity to test whether too short or too long reviews are less likely to be read by the customers.

References

  • Ahluwalia, R. (2000). Examination of psychological processes underlying resistance to persuasion. Journal of Consumer Research, 27(2), 217–232.

    Google Scholar 

  • Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of online consumer reviews: Readers' objectives and review cues. International Journal of Electronic Commerce, 17(2), 99–126.

    Google Scholar 

  • Bailey, A. A. (2005). Consumer awareness and use of product review websites. Journal of Interactive Advertising, 6(1), 68–81.

    Google Scholar 

  • Bansal, G., Zahedi, F., & Gefen, D. (2015). The role of privacy assurance mechanisms in building trust and the moderating role of privacy concern. European Journal of Information Systems, 24(6), 1–21.

    Google Scholar 

  • Basuroy, S., Chatterjee, S., & Ravid, S. A. (2003). How critical are critical reviews? The box office effects of film critics, star power, and budgets. Journal of Marketing, 67(4), 103–117.

    Google Scholar 

  • Baum, D., & Spann, M. (2014). The interplay between online consumer reviews and recommender systems: An experimental analysis. International Journal of Electronic Commerce, 19(1), 129–162.

    Google Scholar 

  • Berry, K. J., & Mielke, P. W. (1988). A generalization of Cohen's kappa agreement measure to interval measurement and multiple raters. Educational and Psychological Measurement, 48(4), 921–933.

    Google Scholar 

  • Burgess, S., Sellitto, C., Cox, C., & Buultjens, J. (2011). Trust perceptions of online travel information by different content creators: Some social and legal implications. Information Systems Frontiers, 13(2), 221–235.

    Google Scholar 

  • Burton, J., & Khammash, M. (2010). Why do people read reviews posted on consumer-opinion portals? Journal of Marketing Management, 26(3–4), 230–255.

    Google Scholar 

  • Cacioppo, J. T., & Petty, R. E. (1984). The elaboration likelihood model of persuasion. ACR North American Advances, 11, 673–675.

    Google Scholar 

  • Cameron, A. C., & Trivedi, P. K. (1990). Regression-based tests for overdispersion in the Poisson model. Journal of Econometrics, 46(3), 347–364.

    Google Scholar 

  • Cameron, A, C., & Trivedi, P, K. (2013). Regression analysis of count data (Vol. 53): Cambridge University Press.

  • Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2), 511–521.

    Google Scholar 

  • Capoccia, C. (2018). Online reviews are the best thing that ever happened to small businesses. Forbes. Retrieved February 2, 2019.

  • Carletta, J. (1996). Assessing agreement on classification tasks: The kappa statistic. Computational Linguistics, 22(2), 249–254.

    Google Scholar 

  • Chang, W. L. (2019). The impact of emotion: A blended model to estimate influence on social media. Information Systems Frontiers, 21(5), 1137–1151.

    Google Scholar 

  • Chatterjee, P. (2001). Online reviews: Do consumers use them? Advances in Consumer Research, 28, 129–133.

    Google Scholar 

  • Chen, C. C., & Tseng, Y.-D. (2011). Quality evaluation of product reviews using an information quality framework. Decision Support Systems, 50(4), 755–768.

    Google Scholar 

  • Chen, P.-Y., Dhanasobhon, S., & Smith, M. D. (2008). All reviews are not created equal: The disaggregate impact of reviews and reviewers at Amazon.com. Available at SSRN: https://ssrn.com/abstract=918083.

  • Cheung, M., Sia, C. L., & Kuan, K. K. (2012). Is this review believable? A study of factors affecting the credibility of online consumer reviews from an ELM perspective. Journal of the Association for Information Systems, 13(8), 618–635.

    Google Scholar 

  • Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345–354.

    Google Scholar 

  • Chitturi, R., Raghunathan, R., & Mahajan, V. (2008). Delight by design: The role of hedonic versus utilitarian benefits. Journal of Marketing, 72(3), 48–63.

    Google Scholar 

  • Connors, L., Mudambi, S, M., & Schuff, D. (2011) Is it the review or the reviewer? A multi-method approach to determine the antecedents of online review helpfulness. In Hawaii International Conference on System Sciences, Kauai, Hawaii, (pp. 1–10): IEEE.

  • Dahlen, M. (2002). Thinking and feeling on the world wide web: The impact of product type and time on world wide web advertising effectiveness. Journal of Marketing Communications, 8(2), 115–125.

    Google Scholar 

  • Dahlen, M., & Bergendahl, J. (2001). Informing and transforming on the web: An empirical study of response to banner ads for functional and expressive products. International Journal of Advertising, 20(2), 189–205.

    Google Scholar 

  • Dahlen, M., Rasch, A., & Rosengren, S. (2003). Love at first site? A study of website advertising effectiveness. Journal of Advertising Research, 43(1), 25–33.

    Google Scholar 

  • Danescu-Niculescu-Mizil, C., Kossinets, G., Kleinberg, J., & Lee, L. (2009). How opinions are received by online communities: A case study on amazon. com helpfulness votes. In International Conference on World Wide Web (pp. 141–150). Madrid, Spain: ACM.

    Google Scholar 

  • Dawson, J. F. (2014). Moderation in management research: What, why, when, and how. Journal of Business and Psychology, 29(1), 1–19.

    Google Scholar 

  • Dhar, R., & Wertenbroch, K. (2000). Consumer choice between hedonic and utilitarian goods. Journal of Marketing Research, 37(1), 60–71.

    Google Scholar 

  • Duric, A., & Song, F. (2012). Feature selection for sentiment analysis based on content and syntax models. Decision Support Systems, 53(4), 704–711.

    Google Scholar 

  • Eastin, M, S. (2001). Credibility assessments of online health information: The effects of source expertise and knowledge of content. Journal of Computer-Mediated Communication, 6(4).

  • Fang, B., Ye, Q., Kucukusta, D., & Law, R. (2016). Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics. Tourism Management, 52, 498–506.

    Google Scholar 

  • Filieri, R., Hofacker, C. F., & Alguezaui, S. (2018). What makes information in online consumer reviews diagnostic over time? The role of review relevancy, factuality, currency, source credibility and ranking score. Computers in Human Behavior, 80, 122–131.

    Google Scholar 

  • Fleiss, J. L., Levin, B., & Paik, M. C. (2013). Statistical methods for rates and proportions. In The measurement of interrater agreement. Wiley.

  • Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291–313.

    Google Scholar 

  • Fox, J. (2015). Applied regression analysis and generalized linear models: Sage Publications.

  • Gershoff, A., Mukherjee, A., & Mukhopadhyay, A. (2003). Consumer acceptance of online agent advice: Extremity and positivity effects. Journal of Consumer Psychology, 13(1&2), 161–170.

    Google Scholar 

  • Ghose, A., & Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10), 1498–1512.

    Google Scholar 

  • Goldsmith, R. E., & Horowitz, D. (2006). Measuring motivations for online opinion seeking. Journal of Interactive Advertising, 6(2), 2–14.

    Google Scholar 

  • Grange, C., & Benbasat, I. (2014). Explaining customers’ utilitarian and hedonic perceptions in the context of product search within social network-enabled shopping websites. In Special Interest Group on Human-Computer Interaction. Auckland, New Zealand.

  • Han, H. J., Mankad, S., Gavirneni, N., & Verma, R. (2016). What guests really think of your hotel: Text analytics of online customer reviews. Cornell Hospitality Report, 16(2), 3–17.

    Google Scholar 

  • Harris, R. B., & Paradice, D. (2007). An investigation of the computer-mediated communication of emotions. Journal of Applied Sciences Research, 3(12), 2081–2090.

    Google Scholar 

  • Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38–52.

    Google Scholar 

  • Hilbe, J. (2011). Negative binomial regression (second ed.). Cambridge University Press.

  • Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consumption: Emerging concepts, methods and propositions. Journal of Marketing, 46(3), 92–101.

    Google Scholar 

  • Hong, H., Xu, D., Wang, G. A., & Fan, W. (2017). Understanding the determinants of online review helpfulness: A meta-analytic investigation. Decision Support Systems, 102, 1–11.

    Google Scholar 

  • Hu, N., Bose, I., Koh, N. S., & Liu, L. (2012). Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision Support Systems, 52(3), 674–684.

    Google Scholar 

  • Hu, N., Koh, N. S., & Reddy, S. K. (2014). Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decision Support Systems, 57, 42–53.

    Google Scholar 

  • Huang, A. H., Chen, K., Yen, D. C., & Tran, T. P. (2015). A study of factors that contribute to online review helpfulness. Computers in Human Behavior, 48, 17–27.

    Google Scholar 

  • Huang, L., Tan, C. H., Ke, W., & Wei, K. K. (2018). Helpfulness of online review content: The moderating effects of temporal and social cues. Journal of the Association for Information Systems, 19(6), 503–522.

    Google Scholar 

  • Hwang, J., Yoon, Y.-S., & Park, N.-H. (2011). Structural effects of cognitive and affective reponses to web advertisements, website and brand attitudes, and purchase intentions: The case of casual-dining restaurants. International Journal of Hospitality Management, 30(4), 897–907.

    Google Scholar 

  • Jiang, Z., Chan, J., Tan, B. C., & Chua, W. S. (2010). Effects of interactivity on website involvement and purchase intention. Journal of the Association for Information Systems, 11(1), 34–59.

    Google Scholar 

  • Johnson, E. J., & Payne, J. W. (1985). Effort and accuracy in choice. Management Science, 31(4), 395–414.

    Google Scholar 

  • Karimi, S., & Wang, F. (2017). Online review helpfulness: Impact of reviewer profile image. Decision Support Systems, 96, 39–48.

    Google Scholar 

  • Khan, F. H., Bashir, S., & Qamar, U. (2014). TOM: Twitter opinion mining framework using hybrid classification scheme. Decision Support Systems, 57, 245–257.

    Google Scholar 

  • Kim, S. M., Pantel, P., Chklovski, T., & Pennacchiotti, M. 2006 Automatically assessing review helpfulness. In Conference on Empirical Methods in Natural Language Processing, Sydney, Australia, (pp. 423–430): Association for Computational Linguistics.

  • Koohikamali, M., & Kim, D. (2015). Does information sensitivity make a difference? Mobile applications’ privacy statements: A text mining approach. In Americas Conference on Information Systems. Puerto Rico.

  • Korfiatis, N., Rodríguez, D., & Sicilia, M.-A. (2008). The impact of readability on the usefulness of online product reviews: A case study on an online bookstore. In Emerging technologies and information systems for the knowledge society (pp. 423–432). Springer.

  • Korfiatis, N., García-Bariocanal, E., & Sánchez-Alonso, S. (2012). Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content. Electronic Commerce Research and Applications, 11(3), 205–217.

    Google Scholar 

  • Kövecses, Z. (1990). Anger. In Emotion concepts (pp. 50–68). Springer.

  • Krippendorff, K. (2012). Content analysis: An introduction to its methodology (Third ed.). London: Sage.

    Google Scholar 

  • Krosnick, J. A., Boninger, D. S., Chuang, Y. C., Berent, M. K., & Carnot, C. G. (1993). Attitude strength: One construct or many related constructs? Journal of Personality and Social Psychology, 65(6), 1132–1151.

    Google Scholar 

  • Kuan, K. K., Hui, K.-L., Prasarnphanich, P., & Lai, H.-Y. (2015). What makes a review voted? An empirical investigation of review voting in online review systems. Journal of the Association for Information Systems, 16(1), 48–71.

    Google Scholar 

  • Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. International Biometric Society, 33(1), 159–174.

    Google Scholar 

  • Lee, S. H. (2009). How do online reviews affect purchasing intention? African Journal of Business Management, 3(10), 581–576.

    Google Scholar 

  • Lee, S., & Choeh, J. Y. (2014). Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Systems with Applications, 41(6), 3041–3046.

    Google Scholar 

  • Li, H., Zhang, Z., Janakiraman, R., & Meng, F. (2016). How review sentiment and readability affect online peer evaluation votes? An examination combining reviewer’s social identity and social network, Tourism Travel and Research Association International Conference. Vail Valley, Colorado.

  • Liu, J., Cao, Y., Lin, C.-Y., Huang, Y., & Zhou, M. (2007). Low-quality product review detection in opinion summarization. In Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Prague, Czech Republic (pp. 334–342).

    Google Scholar 

  • Lombard, M., Snyder-Duch, J., & Bracken, C. C. (2002). Content analysis in mass communication: Assessment and reporting of intercoder reliability. Human Communication Research, 28(4), 587–604.

    Google Scholar 

  • Long, J, S. (1997). Regression Models for Categorical and Limited Dependent Variables (Advanced quantitative techniques in the social sciences): Sage.

  • Mano, H., & Oliver, R. L. (1993). Assessing the dimensionality and structure of the consumption experience: Evaluation, feeling, and satisfaction. Journal of Consumer Research, 20(3), 451–466.

    Google Scholar 

  • Mayzlin, D., Dover, Y., & Chevalier, J. (2014). Promotional reviews: An empirical investigation of online review manipulation. American Economic Review, 104(8), 2421–2455.

    Google Scholar 

  • Mehrabian, A., & Russell, J. A. (1974). The basic emotional impact of environments. Perceptual and Motor Skills, 38(1), 283–301.

    Google Scholar 

  • Mousavizadeh, M., Koohikamali, M., & Salehan, M. Antecedents of online customers reviews’ helpfulness: A support vector machine approach. In Americas Conference on Information Systems, Puerto Rico, 2015: AIS.

  • Mudambi, S. M., & Schuff, D. (2010). What makes a helpful review? A study of customer reviews on Amazon. com. MIS Quarterly, 34(1), 185–200.

    Google Scholar 

  • Murphy, R. (2019). Local consumer review survey. https://www.brightlocal.com/research/local-consumer-review-survey. Accessed Jan 2020.

  • Neuendorf, K. A. (2011). Content analysis-a methodological primer for gender research. Sex Roles, 64(3–4), 276–289.

    Google Scholar 

  • Ngo-Ye, T. L., & Sinha, A. P. (2014). The influence of reviewer engagement characteristics on online review helpfulness: A text regression model. Decision Support Systems, 61, 47–58.

    Google Scholar 

  • Niedenthal, P. M., Winkielman, P., Mondillon, L., & Vermeulen, N. (2009). Embodiment of emotion concepts. Journal of Personality and Social Psychology, 96(6), 1120–1136.

    Google Scholar 

  • Oh, O., Agrawal, M., & Rao, H. R. (2013). Community intelligence and social media services: A rumor theoretic analysis of tweets during social crises. MIS Quarterly, 37(2), 407–426.

    Google Scholar 

  • Overby, J. W., & Lee, E. J. (2006). The effects of utilitarian and hedonic online shopping value on consumer preference and intentions. Journal of Business Research, 59(10), 1160–1166.

    Google Scholar 

  • Palazon, M., & Delgado-Ballester, E. (2013). Hedonic or utilitarian premiums: Does it matter? European Journal of Marketing, 47(8), 1256–1275.

    Google Scholar 

  • Pan, Y., & Zhang, J. Q. (2011). Born unequal: A study of the helpfulness of user-generated product reviews. Journal of Retailing, 87(4), 598–612.

    Google Scholar 

  • Pavlou, P. A., & Dimoka, A. (2006). The nature and role of feedback text comments in online marketplaces: Implications for trust building, price premiums, and seller differentiation. Information Systems Research, 17(4), 392–414.

    Google Scholar 

  • Petty, R. E., & Cacioppo, J. T. (1984). Source factors and the elaboration likelihood model of persuasion. Advances in Consumer Research, 11(1), 668–672.

    Google Scholar 

  • Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology, 19, 123–205.

    Google Scholar 

  • Piccoli, G. (2016). Triggered essential reviewing: The effect of technology affordances on service experience evaluations. European Journal of Information Systems, 25(6), 477–492.

    Google Scholar 

  • Putrevu, S., & Lord, K. R. (1994). Comparative and noncomparative advertising: Attitudinal effects under cognitive and affective involvement conditions. Journal of Advertising, 23(2), 77–91.

    Google Scholar 

  • Racherla, P., & Friske, W. (2012). Perceived ‘usefulness’ of online consumer reviews: An exploratory investigation across three services categories. Electronic Commerce Research and Applications, 11(6), 548–559.

    Google Scholar 

  • Riggio, L., & Kirsner, K. (1997). The relationship between central cues and peripheral cues in covert visual orientation. Perception & Psychophysics, 59(6), 885–899.

    Google Scholar 

  • Riordan, M. A., & Kreuz, R. J. (2010). Emotion encoding and interpretation in computer-mediated communication: Reasons for use. Computers in Human Behavior, 26(6), 1667–1673.

    Google Scholar 

  • Rossiter, J. R., & Percy, L. (1991). Emotions and motivations in advertising. Advances in Consumer Research, 18(1), 100–110.

    Google Scholar 

  • Safi, R., & Yu, Y. (2017). Online product review as an indicator of users’ degree of innovativeness and product adoption time: A longitudinal analysis of text reviews. European Journal of Information Systems, 26(4), 414–431.

    Google Scholar 

  • Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30–40.

    Google Scholar 

  • Salehan, M., Mousavizadeh, M., & Koohikamali, M. (2015). A recommender system for online consumer reviews. In Internation Conference on Information Systems. Fort Worth, TX: AIS.

    Google Scholar 

  • Scherer, K. R. (2005). What are emotions? And how can they be measured? Social Science Information, 44(4), 695–729.

    Google Scholar 

  • Scholz, M., & Dorner, V. (2013). The recipe for the perfect review? Business & Information Systems Engineering, 5(3), 141–151.

    Google Scholar 

  • Schulze, C., Schöler, L., & Skiera, B. (2014). Not all fun and games: Viral marketing for utilitarian products. Journal of Marketing, 78(1), 1–19.

    Google Scholar 

  • Schwenk, C. H. (1986). Information, cognitive biases, and commitment to a course of action. Academy of Management Review, 11(2), 298–310.

    Google Scholar 

  • Skowronski, J. J., & Carlston, D. E. (1987). Social judgment and social memory: The role of cue diagnosticity in negativity, positivity, and extremity biases. Journal of Personality and Social Psychology, 52(4), 689–698.

    Google Scholar 

  • Smith, E, A., & Senter, R, J. (1967). Automated readability index. DTIC Document.

  • Sridhar, S., & Srinivasan, R. (2012). Social influence effects in online product ratings. Journal of Marketing, 76(5), 70–88.

    Google Scholar 

  • Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media-sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217–248.

    Google Scholar 

  • Sundar, S. S., & Kim, J. (2005). Interactivity and persuasion: Influencing attitudes with information and involvement. Journal of Interactive Advertising, 5(2), 5–18.

    Google Scholar 

  • Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544–2558.

    Google Scholar 

  • Treisman, A. M. (1969). Strategies and models of selective attention. Psychological Review, 76(3), 282–299.

    Google Scholar 

  • Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.

    Google Scholar 

  • Van Noort, G., Voorveld, H. A., & van Reijmersdal, E. A. (2012). Interactivity in brand web sites: Cognitive, affective, and behavioral responses explained by consumers' online flow experience. Journal of Interactive Marketing, 26(4), 223–234.

    Google Scholar 

  • Vermeulen, I. E., & Seegers, D. (2009). Tried and tested: The impact of online hotel reviews on consumer consideration. Tourism Management, 30(1), 123–127.

    Google Scholar 

  • Wang, X.-T. (2006). Emotions within reason: Resolving conflicts in risk preference. Cognition and Emotion, 20(8), 1132–1152.

    Google Scholar 

  • Willemsen, L. M., Neijens, P. C., Bronner, F., & de Ridder, J. A. (2011). “Highly recommended!” the content characteristics and perceived usefulness of online consumer reviews. Journal of Computer-Mediated Communication, 17(1), 19–38.

    Google Scholar 

  • Wu, J. (2017). Review popularity and review helpfulness: A model for user review effectiveness. Decision Support Systems, 97, 92–103.

    Google Scholar 

  • Wu, P, F., Van der Heijden, H., & Korfiatis, N. (2011) The influences of negativity and review quality on the helpfulness of online reviews. In International Conference on Information Systems, Shanghai

  • Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 31(1), 137–209.

    Google Scholar 

  • Xu, C., Ryan, S., Prybutok, V., & Wen, C. (2012). It is not for fun: An examination of social network site usage. Information & Management, 49(5), 210–217.

    Google Scholar 

  • Ye, H. J., Chua, C. E. H., & Sun, J. (2019). Enhancing mobile data services performance via online reviews. Information Systems Frontiers, 21(2), 441–452.

    Google Scholar 

  • Yelp (2018). Will Yelp remove a false or defamatory review?

  • Yin, D., Bond, S. D., & Zhang, H. (2014a). Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Quarterly, 38(2), 539–560.

    Google Scholar 

  • Yin, G., Zhang, Q., & Li, Y. (2014b) Effects of emotional valence and arousal on consumer perceptions of online review helpfulness. In Americas Conference on Information Systems, Savannah, US

  • Zhang, J., & Piramuthu, S. (2018). Product recommendation with latent review topics. Information Systems Frontiers, 20(3), 617–625.

    Google Scholar 

  • Zhang, R., & Tran, T. (2011). An information gain-based approach for recommending useful product reviews. Knowledge and Information Systems, 26(3), 419–434.

    Google Scholar 

  • Zhang, P., Lee, H.-M., Zhao, K., & Shah, V. (2019). An empirical investigation of eWOM and used video game trading: The moderation effects of product features. Decision Support Systems, 123, 1–11.

    Google Scholar 

  • Zhiming, L., Li, H., & Lu, L. (2014). An investigation of online review helpfulness based on movie reviews. African Journal of Business Management, 8(12), 441–450.

    Google Scholar 

  • Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing, 74(2), 133–148.

    Google Scholar 

  • Zhu, L., Yin, G., & He, W. (2014). Is this opinion leader's review useful? Peripheral cues for online review helpfulness. Journal of Electronic Commerce Research, 15(4), 267–280.

    Google Scholar 

  • Zhu, Y., Liu, M., Zeng, X., & Huang, P. (2020). The effects of prior reviews on perceived review helpfulness: A configuration perspective. Journal of Business Research, 110, 484–494.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Business Information Systems, Western Michigan University, Kalamazoo, MI, 49008, USA

    Mohammadreza Mousavizadeh

  2. Computer Information Systems, California State Polytechnic University, Pomona, CA, 91768, USA

    Mehrdad Koohikamali & Mohammad Salehan

  3. Department of Information Technology and Decision Sciences, G. Brint Ryan College of Business, University of North Texas, Denton, TX, 76203, USA

    Dam J. Kim

Authors
  1. Mohammadreza Mousavizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehrdad Koohikamali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Salehan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dam J. Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dam J. Kim.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1. Variables studied in previous online consumer review research

Table 7
Full size table

Appendix 2. Coding scheme

Table 8
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mousavizadeh, M., Koohikamali, M., Salehan, M. et al. An Investigation of Peripheral and Central Cues of Online Customer Review Voting and Helpfulness through the Lens of Elaboration Likelihood Model. Inf Syst Front 24, 211–231 (2022). https://doi.org/10.1007/s10796-020-10069-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-020-10069-6

Keywords

Search

Navigation