IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v30y2019i4p1164-1183.html
   My bibliography  Save this article

When Seeing Helps Believing: The Interactive Effects of Previews and Reviews on E-Book Purchases

Author

Listed:
  • Angela Aerry Choi

    (College of Business, Florida State University, Tallahassee, Florida 32306)

  • Daegon Cho

    (College of Business, Korea Advanced Institute of Science and Technology, Seoul 02455, Korea)

  • Dobin Yim

    (School of Business, Loyola University Maryland, Baltimore, Maryland 21210)

  • Jae Yun Moon

    (Business School, Korea University, Seoul 02841, Korea)

  • Wonseok Oh

    (College of Business, Korea Advanced Institute of Science and Technology, Seoul 02455, Korea)

Abstract

Online reviews offer consumers the indirect experience of products through others’ consumption evaluations, whereas previews afford them direct experience through product trials. Although conceptual and empirical studies on the business ramifications of online reviews abound, little is known about how online previews moderate the effects of online reviews on sales. To cast light on this issue, the current research investigated the interactive effects of exposure to online previews and reviews on individual purchase decisions. We analyzed a unique two-month panel data set on 270,260 sessions that comprise clickstream data on consumers’ exposure to previews and reviews and data on their subsequent purchase behaviors. On the basis of analyses underlain by a two-stage hierarchical Bayesian framework, we found that online previews positively influence individual purchase decisions. More importantly, significant interactions exist between previews and reviews, as evidenced by the decreasing positive effect of previews with increasing review volume and average review rating. In addition, previews can complement reviews when a high variance in the latter renders purchase decisions difficult. We further examined the sequence effect of exposure to previews and reviews and discovered that exposure to previews following the experience of reviews may exert a considerable positive influence on individual purchase decisions. The results from an additional field experiment and a text-based sentiment analysis reinforced the validity of our main findings by mitigating concerns regarding the endogeneity and the accuracy of the review quality, respectively. The findings provide practical implications with regard to the design of optimal strategies for releasing preview information to digital platforms.

Suggested Citation

  • Angela Aerry Choi & Daegon Cho & Dobin Yim & Jae Yun Moon & Wonseok Oh, 2019. "When Seeing Helps Believing: The Interactive Effects of Previews and Reviews on E-Book Purchases," Information Systems Research, INFORMS, vol. 30(4), pages 1164-1183, December.
    • Handle: RePEc:inm:orisre:v:30:y:2019:i:4:p:1164-1183
    DOI: 10.1287/isre.2019.0857
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/isre.2019.0857
    Download Restriction: no
    File URL: https://libkey.io/10.1287/isre.2019.0857?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2003. "Instrumental variables and GMM: Estimation and testing," Stata Journal, StataCorp LP, vol. 3(1), pages 1-31, March.
    2. Dina Mayzlin & Yaniv Dover & Judith Chevalier, 2014. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," American Economic Review, American Economic Association, vol. 104(8), pages 2421-2455, August.
    3. Kapil Bawa & Robert Shoemaker, 2004. "The Effects of Free Sample Promotions on Incremental Brand Sales," Marketing Science, INFORMS, vol. 23(3), pages 345-363, November.
    4. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    5. Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
    6. Terza, Joseph V. & Basu, Anirban & Rathouz, Paul J., 2008. "Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling," Journal of Health Economics, Elsevier, vol. 27(3), pages 531-543, May.
    7. Goering, Patricia A, 1985. "Effects of Product Trial on Consumer Expectations, Demand, and Prices," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 12(1), pages 74-82, June.
    8. Hsing Kenneth Cheng & Yipeng Liu, 2012. "Optimal Software Free Trial Strategy: The Impact of Network Externalities and Consumer Uncertainty," Information Systems Research, INFORMS, vol. 23(2), pages 488-504, June.
    9. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    10. Meyers-Levy, Joan & Maheswaran, Durairaj, 1991. "Exploring Differences in Males' and Females' Processing Strategies," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 18(1), pages 63-70, June.
    11. Adaval, Rashmi, 2001. "Sometimes It Just Feels Right: The Differential Weighting of Affect-Consistent and Affect-Inconsistent Product Information," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(1), pages 1-17, June.
    12. Param Vir Singh & Nachiketa Sahoo & Tridas Mukhopadhyay, 2014. "How to Attract and Retain Readers in Enterprise Blogging?," Information Systems Research, INFORMS, vol. 25(1), pages 35-52, March.
    13. Yoon, Carolyn, 1997. "Age Differences in Consumers' Processing Strategies: An Investigation of Moderating Influences," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 24(3), pages 329-342, December.
    14. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    15. Rebecca W. Hamilton & Debora Viana Thompson, 2007. "Is There a Substitute for Direct Experience? Comparing Consumers' Preferences after Direct and Indirect Product Experiences," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(4), pages 546-555, June.
    16. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    17. Xianghua Lu & Sulin Ba & Lihua Huang & Yue Feng, 2013. "Promotional Marketing or Word-of-Mouth? Evidence from Online Restaurant Reviews," Information Systems Research, INFORMS, vol. 24(3), pages 596-612, September.
    18. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    19. Holbrook, Morris B, 1986. "Aims, Concepts, and Methods for the Representation of Individual Differences in Esthetic Responses to Design Features," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 13(3), pages 337-347, December.
    20. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    21. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    22. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    23. McFadden, Daniel, 1974. "The measurement of urban travel demand," Journal of Public Economics, Elsevier, vol. 3(4), pages 303-328, November.
    24. Bram Foubert & Els Gijsbrechts, 2016. "Try It, You’ll Like It—Or Will You? The Perils of Early Free-Trial Promotions for High-Tech Service Adoption," Marketing Science, INFORMS, vol. 35(5), pages 810-826, September.
    25. Wagner, C. & Benlian, Alexander & Hess, Thomas, 2013. "The Advertising Effect of Free - Do Free Basic Versions Promote Premium Versions within the Freemium Business Model of Music Services?," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 58977, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    26. Wagner, Thomas M. & Benlian, Alexander & Hess, Thomas, 2013. "The Advertising Effect of Free - Do Free Basic Versions Promote Premium Versions within the Freemium Business Model of Music Services?," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 66723, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    27. Catherine Tucker & Juanjuan Zhang, 2011. "How Does Popularity Information Affect Choices? A Field Experiment," Management Science, INFORMS, vol. 57(5), pages 828-842, May.
    28. John, Deborah Roedder & Cole, Catherine A, 1986. "Age Differences in Information Processing: Understanding Deficits in Young and Elderly Consumers," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 13(3), pages 297-315, December.
    29. Park, Sangwon & Nicolau, Juan L., 2015. "Asymmetric effects of online consumer reviews," Annals of Tourism Research, Elsevier, vol. 50(C), pages 67-83.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Navid Aghakhani & Onook Oh & Dawn Gregg & Hemant Jain, 2023. "How Review Quality and Source Credibility Interacts to Affect Review Usefulness: An Expansion of the Elaboration Likelihood Model," Information Systems Frontiers, Springer, vol. 25(4), pages 1513-1531, August.
    2. Shengsheng Xiao & Yi‐Chun (Chad) Ho & Hai Che, 2021. "Building the Momentum: Information Disclosure and Herding in Online Crowdfunding," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 3213-3230, September.
    3. Kummer, Michael E. & Laitenberger, Ulrich & Rich, Cyrus E. & Hughes, Danny R. & Ayer, Turgay, 2021. "Healthy reviews! Online physician ratings reduce healthcare interruptions," ZEW Discussion Papers 21-075, ZEW - Leibniz Centre for European Economic Research.
    4. Zhidong Tan & Jianhua Tan & Kam C. Chan, 2021. "Seeing is believing? The impact of air pollution on corporate social responsibility," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 28(1), pages 525-534, January.
    5. Wen Zhang & Qiang Wang & Jian Li & Zhenzhong Ma & Gokul Bhandari & Rui Peng, 2023. "What makes deceptive online reviews? A linguistic analysis perspective," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    6. Hsiu-Li Liao & Su-Houn Liu, 2023. "Integrating Information Technology and Marketing to increase e-Book consumption," Electronic Commerce Research, Springer, vol. 23(1), pages 137-153, March.
    7. Heeseung Andrew Lee & Angela Aerry Choi & Tianshu Sun & Wonseok Oh, 2021. "Reviewing Before Reading? An Empirical Investigation of Book-Consumption Patterns and Their Effects on Reviews and Sales," Information Systems Research, INFORMS, vol. 32(4), pages 1368-1389, December.
    8. Qingfeng Zeng & Wei Zhuang & Qian Guo & Weiguo Fan, 2022. "What factors influence grassroots knowledge supplier performance in online knowledge platforms? Evidence from a paid Q&A service," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2507-2523, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhijie Lin & Ying Zhang & Yong Tan, 2019. "An Empirical Study of Free Product Sampling and Rating Bias," Service Science, INFORMS, vol. 30(1), pages 260-275, March.
    2. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    3. Heeseung Andrew Lee & Angela Aerry Choi & Tianshu Sun & Wonseok Oh, 2021. "Reviewing Before Reading? An Empirical Investigation of Book-Consumption Patterns and Their Effects on Reviews and Sales," Information Systems Research, INFORMS, vol. 32(4), pages 1368-1389, December.
    4. Mochen Yang & Gediminas Adomavicius & Gordon Burtch & Yuqing Rena, 2018. "Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining," Information Systems Research, INFORMS, vol. 29(1), pages 4-24, March.
    5. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    6. Theodoros Lappas & Gaurav Sabnis & Georgios Valkanas, 2016. "The Impact of Fake Reviews on Online Visibility: A Vulnerability Assessment of the Hotel Industry," Information Systems Research, INFORMS, vol. 27(4), pages 940-961, December.
    7. Young Kwark & Gene Moo Lee & Paul A. Pavlou & Liangfei Qiu, 2021. "On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data," Information Systems Research, INFORMS, vol. 32(3), pages 895-913, September.
    8. Kick, Markus, 2015. "Social Media Research: A Narrative Review," EconStor Preprints 182506, ZBW - Leibniz Information Centre for Economics.
    9. Mochen Yang & Yuqing Ren & Gediminas Adomavicius, 2019. "Understanding User-Generated Content and Customer Engagement on Facebook Business Pages," Information Systems Research, INFORMS, vol. 30(3), pages 839-855, September.
    10. Chong (Alex) Wang & Xiaoquan (Michael) Zhang & Il-Horn Hann, 2018. "Socially Nudged: A Quasi-Experimental Study of Friends’ Social Influence in Online Product Ratings," Information Systems Research, INFORMS, vol. 29(3), pages 641-655, September.
    11. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    12. Dominik Gutt & Philipp Herrmann & Mohammad S. Rahman, 2018. "Crowd-Driven Competitive Intelligence: Understanding the Relationship Between Local Market Competition and Online Rating Distributions," Working Papers Dissertations 41, Paderborn University, Faculty of Business Administration and Economics.
    13. Weijia (Daisy) Dai & Ginger Jin & Jungmin Lee & Michael Luca, 2018. "Aggregation of consumer ratings: an application to Yelp.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(3), pages 289-339, September.
    14. Cheng Zhao & Chong Alex Wang, 2023. "A cross-site comparison of online review manipulation using Benford’s law," Electronic Commerce Research, Springer, vol. 23(1), pages 365-406, March.
    15. Dipankar Das, 2022. "Measurement of Trustworthiness of the Online Reviews," Papers 2210.00815, arXiv.org, revised Nov 2023.
    16. Sulin Ba & Yuan Jin & Xinxin Li & Xianghua Lu, 2020. "One Size Fits All? The Differential Impact of Online Reviews and Coupons," Production and Operations Management, Production and Operations Management Society, vol. 29(10), pages 2403-2424, October.
    17. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    18. Christoph Schneider & Markus Weinmann & Peter N.C. Mohr & Jan vom Brocke, 2021. "When the Stars Shine Too Bright: The Influence of Multidimensional Ratings on Online Consumer Ratings," Management Science, INFORMS, vol. 67(6), pages 3871-3898, June.
    19. Jiang, Guoyin & Shang, Jennifer & Liu, Wenping & Feng, Xiaodong & Lei, Junli, 2020. "Modeling the dynamics of online review life cycle: Role of social and economic moderations," European Journal of Operational Research, Elsevier, vol. 285(1), pages 360-379.
    20. Juan Feng & Xin Li & Xiaoquan (Michael) Zhang, 2019. "Online Product Reviews-Triggered Dynamic Pricing: Theory and Evidence," Information Systems Research, INFORMS, vol. 30(4), pages 1107-1123, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orisre:v:30:y:2019:i:4:p:1164-1183. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.