Skip to main content
SpringerLink
Log in

When to Target Customers for Helpful Reviews: The Evolution of Consumers’ Product Evaluations with Product Exposure

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

Many online sellers send a review request only a few days after product delivery to gather customer reviews. Yet, the value of this strategy is questionable because buyers with short product exposure are unlikely to have enough time to inspect the product thoroughly and thus may not offer valuable evaluations. We address this question by examining the influence of consumers’ product exposure on the helpfulness of their reviews. Our findings suggest that those with a longer product exposure tend to produce more helpful posts. The subsequent topic modeling analyses reveal that reviewers’ assessments of product utilitarian aspects increase with product exposure. Such information is perceived as less subjective and contains more discussions on product functionality. Lastly, we found that users with prior product domain knowledge do not need a long exposure to produce helpful reviews. Businesses with an urgent need to gain reviews may target them as a priority.

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

Similar content being viewed by others

Data Availability

The data supporting this study’s findings are available from a company that signed a non-disclosure agreement with the institution where one of the authors had worked. Therefore, we cannot make the data publicly available. However, the data are available from the author on reasonable request.

Notes

  1. The terms pragmatic and utilitarian are interchangeable according to the definitions in the two fields. Hereafter, we use utilitarian instead of pragmatic because the former term is more widely adopted.

References

  • Aghakhani, N., Oh, O., Gregg, D. G., & Karimi, J. (2021). Online review consistency matters: An elaboration likelihood model perspective. Information Systems Frontiers, 23(5), 1287–1301.

    Article  Google Scholar 

  • Alba, J. W., & Hutchinson, J. W. (1987). Dimensions of consumer expertise. Journal of Consumer Research, 13(4), 411–454.

    Article  Google Scholar 

  • Banerjee, S., Bhattacharyya, S., & Bose, I. (2017). Whose online reviews to trust? Understanding reviewer trustworthiness and its impact on business. Decision Support Systems, 96, 17–26.

    Article  Google Scholar 

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.

    Google Scholar 

  • Botti, S., & McGill, A. L. (2011). The locus of choice: Personal causality and satisfaction with hedonic and utilitarian decisions. Journal of Consumer Research, 37(6), 1065–1078.

    Article  Google Scholar 

  • Brown, D. C. (2003). Functional, behavioral and structural features. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 37017, pp. 895–900).

  • Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81.

    Article  Google Scholar 

  • Chi, M., Glaser, R., Rees, E., & Steinberg, R. J. (1982). Advances in the psychology of human intelligence. Erlbaum.

    Google Scholar 

  • Clemons, E. K., Gao, G. G., & Hitt, L. M. (2006). When online reviews meet hyperdifferentiation: A study of the craft beer industry. Journal of Management Information Systems, 23(2), 149–171.

    Article  Google Scholar 

  • Cordell, V. V. (1997). Consumer knowledge measures as predictors in product evaluation. Psychology & Marketing, 14(3), 241–260.

    Article  Google Scholar 

  • Craciun, G., Zhou, W., & Shan, Z. (2020). Discrete emotions effects on electronic word-of-mouth helpfulness: The moderating role of reviewer gender and contextual emotional tone. Decision Support Systems, 130, 113226.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ghose, A., & Ipeirotis, P. G. (2010). 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.

    Article  Google Scholar 

  • Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Factor analysis. Multivariate data analysis. NJ Prentice-Hall, 3, 98–99.

    Google Scholar 

  • Ham, J., Lee, K., Kim, T., & Koo, C. (2019). Subjective perception patterns of online reviews: A comparison of utilitarian and hedonic values. Information Processing & Management, 56(4), 1439–1456.

    Article  Google Scholar 

  • Hassenzahl, M., Platz, A., Burmester, M., & Lehner, K. (2000). Hedonic and ergonomic quality aspects determine a software’s appeal. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 201–208).

  • Hassenzahl, M. (2008). User experience (UX) towards an experiential perspective on product quality. In Proceedings of the 20th Conference on l’Interaction Homme-Machine (pp. 11–15).

  • Hassenzahl, M. (2001). The effect of perceived hedonic quality on product appealingness. International Journal of Human-Computer Interaction, 13(4), 481–499.

    Article  Google Scholar 

  • Hassenzahl, M. (2004). The interplay of beauty, goodness, and usability in interactive products. Human-Computer Interaction, 19(4), 319–349.

    Article  Google Scholar 

  • Hayes-Roth, B. (1977). Evolution of cognitive structures and processes. Psychological Review, 84(3), 260.

    Article  Google Scholar 

  • Hazari, S., Bergiel, B. J., & Sethna, B. N. (2017). Hedonic and utilitarian use of user-generated content on online shopping websites. Journal of Marketing Communications, 23(6), 572–591.

    Article  Google Scholar 

  • Heng, Y., Gao, Z., Jiang, Y., & Chen, X. (2018). Exploring hidden factors behind online food shopping from Amazon reviews: A topic mining approach. Journal of Retailing and Consumer Services, 42, 161–168.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Holbrook, M. B., & Hirschman, E. C. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of Consumer Research, 9(2), 132–140.

    Article  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.

    Article  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), 3.

    Article  Google Scholar 

  • Jacoby, J., Chestnut, R. W., & Fisher, W. A. (1978). A behavioral process approach to information acquisition in nondurable purchasing. Journal of Marketing Research, 15(4), 532–544.

    Article  Google Scholar 

  • Karapanos, E., Zimmerman, J., Forlizzi, J., & Martens, J. B. (2009). User experience over time: an initial framework. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 729–738).

  • Karapanos, E., Zimmerman, J., Forlizzi, J., & Martens, J. B. (2010). Measuring the dynamics of remembered experience over time. Interacting with Computers, 22(5), 328–335.

    Article  Google Scholar 

  • Krivec, J., Bratko, I., & Guid, M. (2021). Identification and conceptualization of procedural chunks in chess. Cognitive Systems Research, 69, 22–40.

    Article  Google Scholar 

  • Kronrod, A., & Danziger, S. (2013). “Wii will rock you!” The use and effect of figurative language in consumer reviews of hedonic and utilitarian consumption. Journal of Consumer Research, 40(4), 726–739.

    Article  Google Scholar 

  • Kujala, S., Roto, V., Väänänen-Vainio-Mattila, K., Karapanos, E., & Sinnelä, A. (2011). UX Curve: A method for evaluating long-term user experience. Interacting with Computers, 23(5), 473–483.

    Article  Google Scholar 

  • Larkin, J., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208(4450), 1335–1342.

    Article  Google Scholar 

  • Law, E. L. C., Roto, V., Hassenzahl, M., Vermeeren, A. P., & Kort, J. (2009). Understanding, scoping and defining user experience: a survey approach. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 719–728).

  • Li, J., Zhang, Y., Li, J., & Du, J. (2022). The role of sentiment tendency in affecting review helpfulness for durable products: Nonlinearity and complementarity. Information Systems Frontiers, 1–19. https://doi.org/10.1007/s10796-022-10292-3

  • Ludwig, S., De Ruyter, K., Friedman, M., Brüggen, E. C., Wetzels, M., & Pfann, G. (2013). More than words: The influence of affective content and linguistic style matches in online reviews on conversion rates. Journal of Marketing, 77(1), 87–103.

    Article  Google Scholar 

  • Ma, X., Khansa, L., Deng, Y., & Kim, S. S. (2013). Impact of prior reviews on the subsequent review process in reputation systems. Journal of Management Information Systems, 30(3), 279–310.

    Article  Google Scholar 

  • Maheswaran, D. (1994). Country of origin as a stereotype: Effects of consumer expertise and attribute strength on product evaluations. Journal of Consumer Research, 21(2), 354–365.

    Article  Google Scholar 

  • Maheswaran, D., Sternthal, B., & Gürhan, Z. (1996). Acquisition and impact of consumer expertise. Journal of Consumer Psychology, 5(2), 115–133.

    Article  Google Scholar 

  • McCallum, A. K. (2002). Mallet: A machine learning for language toolkit. http://mallet.cs.umass.edu. Accessed 15 May 2023

  • Mousavizadeh, M., Koohikamali, M., Salehan, M., & Kim, D. J. (2022). An investigation of peripheral and central cues of online customer review voting and helpfulness through the lens of elaboration likelihood model. Information Systems Frontiers, 24, 211–231.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Newell, A., & Simon, H. A. (1972). Human Problem Solving (Vol. 104, No. 9). Prentice-hall.

  • 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.

    Article  Google Scholar 

  • Norman, D. A. (2004). Introduction to this special section on beauty, goodness, and usability. Human-Computer Interaction, 19(4), 311–318.

    Article  Google Scholar 

  • O’Connor, F. (2022). Amazon’s request a review: The ultimate guide for 2022. Available from: https://www.edesk.com/blog/amazon-request-review/. Accessed 15 May 2023

  • Oster, E. (2019). Unobservable selection and coefficient stability: Theory and evidence. Journal of Business & Economic Statistics, 37(2), 187–204.

    Article  Google Scholar 

  • Parameswaran, S., Mukherjee, P., & Valecha, R. (2023). I like my anonymity: An empirical investigation of the effect of multidimensional review text and role anonymity on helpfulness of employer reviews. Information Systems Frontiers, 25(2), 853–870. https://doi.org/10.1007/s10796-022-10268-3

    Article  Google Scholar 

  • Park, C. W., & Lessig, V. P. (1981). Familiarity and its impact on consumer decision biases and heuristics. Journal of Consumer Research, 8(2), 223–230.

    Article  Google Scholar 

  • Park, D. H., & Kim, S. (2008). The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews. Electronic Commerce Research and Applications, 7(4), 399–410.

    Article  Google Scholar 

  • Pearl, J. (2001). Direct and indirect effects. In Proceedings of the seventeenth conference on uncertainty in artificial intelligence (pp. 411–420). San Francisco, CA: Morgan Kaufmann.

  • Puranam, D., Narayan, V., & Kadiyali, V. (2017). The effect of calorie posting regulation on consumer opinion: A flexible latent Dirichlet allocation model with informative priors. Marketing Science, 36(5), 726–746.

    Article  Google Scholar 

  • Raju, P. S. (1977). Product familiarity, brand name, and price influences on product evaluation. ACR North American Advances, 4,64–71

  • Raju, P. S., & Reilly, M. D. (1980). Product familiarity and information processing strategies: An exploratory investigation. Journal of Business Research, 8(2), 187–212.

    Article  Google Scholar 

  • Rehurek, R., & Sojka, P. (2011). Gensim–python framework for vector space modelling. NLP Centre, Faculty of Informatics, Masaryk University, Brno, Czech Republic, 3(2), 2.

    Google Scholar 

  • Robins, J. M., & Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3(2), 143–155.

    Article  Google Scholar 

  • Sahoo, N., Dellarocas, C., & Srinivasan, S. (2018). The impact of online product reviews on product returns. Information Systems Research, 29(3), 723–738.

    Article  Google Scholar 

  • Shi, Z., Lee, G. M., & Whinston, A. B. (2016). Toward a better measure of business proximity. MIS Quarterly, 40(4), 1035–1056.

    Article  Google Scholar 

  • Siering, M., Deokar, A. V., & Janze, C. (2018). Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews. Decision Support Systems, 107, 52–63.

    Article  Google Scholar 

  • Stang, D. J. (1975). Effects of “mere exposure” on learning and affect. Journal of Personality and Social Psychology, 31(1), 7.

    Article  Google Scholar 

  • Strahilevitz, M., & Myers, J. G. (1998). Donations to charity as purchase incentives: How well they work may depend on what you are trying to sell. Journal of Consumer Research, 24(4), 434–446.

    Article  Google Scholar 

  • Sun, X., Han, M., & Feng, J. (2019). Helpfulness of online reviews: Examining review informativeness and classification thresholds by search products and experience products. Decision Support Systems, 124, 113099.

    Article  Google Scholar 

  • Tripathi, S., Deokar, A. V., & Ajjan, H. (2022). Understanding the order effect of online reviews: A text mining perspective. Information Systems Frontiers, 24(6), 1971–1988.

    Article  Google Scholar 

  • Valeri, L., & VanderWeele, T. J. (2013). Mediation analysis allowing for exposure–mediator interactions and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros. Psychological Methods, 18(2), 137.

    Article  Google Scholar 

  • Vermeeren, A. P., Law, E. L. C., Roto, V., Obrist, M., Hoonhout, J., & Väänänen-Vainio-Mattila, K. (2010). User experience evaluation methods: current state and development needs. In Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries (pp. 521–530).

  • von Wilamowitz-Moellendorff, M., Hassenzahl, M., & Platz, A. (2006). Dynamics of user experience: How the perceived quality of mobile phones changes over time. In User Experience-Towards a unified view, Workshop at the 4th Nordic Conference on Human-Computer Interaction (pp. 74–78).

  • Wang, Y., Ramachandran, V., & Liu Sheng, O. R. (2021). Do fit opinions matter? The impact of fit context on online product returns. Information Systems Research, 32(1), 268–289.

    Article  Google Scholar 

  • Weathers, D., Swain, S. D., & Grover, V. (2015). Can online product reviews be more helpful? Examining characteristics of information content by product type. Decision Support Systems, 79, 12–23.

    Article  Google Scholar 

  • Yaniv, I. (2004). Receiving other people’s advice: Influence and benefit. Organizational Behavior and Human Decision Processes, 93(1), 1–13.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Yin, D., Bond, S., & Zhang, H. (2021). Anger in consumer reviews: Unhelpful but persuasive? MIS Quarterly, 45(3), 1059–1086.

    Article  Google Scholar 

  • Zajonc, R. B. (1968). Attitudinal effects of mere exposure. Journal of Personality and Social Psychology, 9(2p2), 1.

    Article  Google Scholar 

  • Zajonc, R. B., Markus, H., & Wilson, W. R. (1974). Exposure effects and associative learning. Journal of Experimental Social Psychology, 10(3), 248–263.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Merrick School of Business, University of Baltimore, Baltimore, MD, 21201, USA

    Cong Zhang

  2. Lubar College of Business, University of Wisconsin-Milwaukee, Milwaukee, WI, 53201-0742, USA

    Atish P. Sinha & Yang Wang

Authors
  1. Cong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Atish P. Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. The initial datasets were provided by Yang Wang. Data cleaning and formal analysis were conducted by Cong Zhang. Atish Sinha and Yang Wang commented on analysis. The first draft of the manuscript was written by Cong Zhang and all authors reviewed and edited the previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Cong Zhang.

Ethics declarations

Ethics Approval and Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Competing Interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, C., Sinha, A.P. & Wang, Y. When to Target Customers for Helpful Reviews: The Evolution of Consumers’ Product Evaluations with Product Exposure. Inf Syst Front (2023). https://doi.org/10.1007/s10796-023-10414-5

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10796-023-10414-5

Keywords

Search

Navigation