IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v38y2019i5p773-792.html
   My bibliography  Save this article

Creation and Consumption of Mobile Word of Mouth: How Are Mobile Reviews Different?

Author

Listed:
  • Sam Ransbotham

    (Carroll School of Management, Boston College, Chestnut Hill, Massachusetts 02467)

  • Nicholas H. Lurie

    (School of Business, University of Connecticut, Storrs, Connecticut 06269)

  • Hongju Liu

    (Guanghua School of Management, Peking University, Beijing 100871, China)

Abstract

Mobile users can create word of mouth (WOM) wherever they are and whenever they want to do so. This real-time creation process may be associated with differences in the content and consumption value of mobile versus nonmobile word of mouth. We analyze 275,362 reviews from 117,827 reviewers describing their experiences at 134,976 restaurants as well as a dual platform subsample of 21,026 reviews written by 673 reviewers who wrote at least four mobile and four nonmobile reviews. We also examine how the introduction of the mobile platform affected WOM consumption. We find that WOM content is more affective, more concrete, and less extreme when created on mobile devices. These differences in content (more affective, more concrete, and less extreme) vary in their relationships with the perceived consumption value of mobile content. Beyond the indirect relationship between platform and consumption value through content, reviews created on mobile devices are associated with lower consumption value. This direct relationship grows stronger over time. Although consumers initially value both real-time mobile content and nonmobile content, even after controlling for a large set of content and contextual variables, over time consumers value mobile reviews less than they do nonmobile reviews.

Suggested Citation

  • Sam Ransbotham & Nicholas H. Lurie & Hongju Liu, 2019. "Creation and Consumption of Mobile Word of Mouth: How Are Mobile Reviews Different?," Marketing Science, INFORMS, vol. 38(5), pages 773-792, September.
    • Handle: RePEc:inm:ormksc:v:38:y:2019:i:5:p:773-792
    DOI: 10.1287/mksc.2018.1115
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/mksc.2018.1115
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.2018.1115?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. Anindya Ghose & Sang Pil Han, 2011. "An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet," Management Science, INFORMS, vol. 57(9), pages 1671-1691, September.
    2. Kyeongheui Kim & Meng Zhang & Xiuping Li, 2008. "Effects of Temporal and Social Distance on Consumer Evaluations," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 35(4), pages 706-713, August.
    3. Olivier Toubia & Andrew T. Stephen, 2013. "Intrinsic vs. Image-Related Utility in Social Media: Why Do People Contribute Content to Twitter?," Marketing Science, INFORMS, vol. 32(3), pages 368-392, May.
    4. Wang, Rebecca Jen-Hui & Malthouse, Edward C. & Krishnamurthi, Lakshman, 2015. "On the Go: How Mobile Shopping Affects Customer Purchase Behavior," Journal of Retailing, Elsevier, vol. 91(2), pages 217-234.
    5. Unknown, 1967. "Index," 1967 Conference, August 21-30, 1967, Sydney, New South Wales, Australia 209796, International Association of Agricultural Economists.
    6. Inyoung Chae & Andrew T. Stephen & Yakov Bart & Dai Yao, 2017. "Spillover Effects in Seeded Word-of-Mouth Marketing Campaigns," Marketing Science, INFORMS, vol. 36(1), pages 89-104, January.
    7. Duan, Wenjing & Gu, Bin & Whinston, Andrew B., 2008. "The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry," Journal of Retailing, Elsevier, vol. 84(2), pages 233-242.
    8. Purnawirawan, Nathalia & Eisend, Martin & De Pelsmacker, Patrick & Dens, Nathalie, 2015. "A Meta-analytic Investigation of the Role of Valence in Online Reviews," Journal of Interactive Marketing, Elsevier, vol. 31(C), pages 17-27.
    9. Shankar, Venkatesh & Balasubramanian, Sridhar, 2009. "Mobile Marketing: A Synthesis and Prognosis," Journal of Interactive Marketing, Elsevier, vol. 23(2), pages 118-129.
    10. Wendy W. Moe & David A. Schweidel, 2012. "Online Product Opinions: Incidence, Evaluation, and Evolution," Marketing Science, INFORMS, vol. 31(3), pages 372-386, May.
    11. Jonah Berger & Alan T. Sorensen & Scott J. Rasmussen, 2010. "Positive Effects of Negative Publicity: When Negative Reviews Increase Sales," Marketing Science, INFORMS, vol. 29(5), pages 815-827, 09-10.
    12. George Loewenstein, 2000. "Emotions in Economic Theory and Economic Behavior," American Economic Review, American Economic Association, vol. 90(2), pages 426-432, May.
    13. J. J. Po-An Hsieh & Arun Rai & Sean Xin Xu, 2011. "Extracting Business Value from IT: A Sensemaking Perspective of Post-Adoptive Use," Management Science, INFORMS, vol. 57(11), pages 2018-2039, November.
    14. 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.
    15. Petty, Richard E & Cacioppo, John T & Schumann, David, 1983. "Central and Peripheral Routes to Advertising Effectiveness: The Moderating Role of Involvement," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 10(2), pages 135-146, September.
    16. Sekhon, Jasjeet S., 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i07).
    17. 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.
    Full references (including those not matched with items on IDEAS)

    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. Marchand, André & Hennig-Thurau, Thorsten & Wiertz, Caroline, 2017. "Not all digital word of mouth is created equal: Understanding the respective impact of consumer reviews and microblogs on new product success," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 336-354.
    2. Zheng, Lili, 2021. "The classification of online consumer reviews: A systematic literature review and integrative framework," Journal of Business Research, Elsevier, vol. 135(C), pages 226-251.
    3. Roma, Paolo & Aloini, Davide, 2019. "How does brand-related user-generated content differ across social media? Evidence reloaded," Journal of Business Research, Elsevier, vol. 96(C), pages 322-339.
    4. Yuchi Zhang & David Godes, 2018. "Learning from Online Social Ties," Marketing Science, INFORMS, vol. 37(3), pages 425-444, May.
    5. 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.
    6. Mingwen Yang & Zhiqiang (Eric) Zheng & Vijay Mookerjee, 2019. "Prescribing Response Strategies to Manage Customer Opinions: A Stochastic Differential Equation Approach," Information Systems Research, INFORMS, vol. 30(2), pages 351-374, June.
    7. King, Robert Allen & Racherla, Pradeep & Bush, Victoria D., 2014. "What We Know and Don't Know About Online Word-of-Mouth: A Review and Synthesis of the Literature," Journal of Interactive Marketing, Elsevier, vol. 28(3), pages 167-183.
    8. Colmekcioglu, Nazan & Marvi, Reza & Foroudi, Pantea & Okumus, Fevzi, 2022. "Generation, susceptibility, and response regarding negativity: An in-depth analysis on negative online reviews," Journal of Business Research, Elsevier, vol. 153(C), pages 235-250.
    9. Ji Wu & Haichuan Zhao & Haipeng (Allan) Chen, 2021. "Coupons or Free Shipping? Effects of Price Promotion Strategies on Online Review Ratings," Information Systems Research, INFORMS, vol. 32(2), pages 633-652, June.
    10. Peiyu Chen & Lorin M. Hitt & Yili Hong & Shinyi Wu, 2021. "Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application," Information Systems Research, INFORMS, vol. 32(4), pages 1470-1489, December.
    11. Kordrostami, Elika & Rahmani, Vahid, 2020. "Investigating conflicting online review information:evidence from Amazon.com," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    12. Tingting Nian & Yuheng Hu & Cheng Chen, 2021. "Examining the Impact of Television-Program-Induced Emotions on Online Word-of-Mouth Toward Television Advertising," Information Systems Research, INFORMS, vol. 32(2), pages 605-632, June.
    13. Sam Ransbotham & Gerald C. Kane & Nicholas H. Lurie, 2012. "Network Characteristics and the Value of Collaborative User-Generated Content," Marketing Science, INFORMS, vol. 31(3), pages 387-405, May.
    14. Gang Chen & Lihua Huang & Shuaiyong Xiao & Chenghong Zhang & Huimin Zhao, 2024. "Attending to Customer Attention: A Novel Deep Learning Method for Leveraging Multimodal Online Reviews to Enhance Sales Prediction," Information Systems Research, INFORMS, vol. 35(2), pages 829-849, June.
    15. Kunpeng Zhang & Wendy Moe, 2021. "Measuring Brand Favorability Using Large-Scale Social Media Data," Information Systems Research, INFORMS, vol. 32(4), pages 1128-1139, December.
    16. Shan, Wei & Qiao, Tong & Zhang, Mingli, 2020. "Getting more resources for better performance: The effect of user-owned resources on the value of user-generated content," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    17. Chan, Haksin & Yang, Morgan X. & Zeng, Kevin J., 2022. "Bolstering ratings and reviews systems on multi-sided platforms: A co-creation perspective," Journal of Business Research, Elsevier, vol. 139(C), pages 208-217.
    18. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    19. Pradeep Kumar Ponnamma Divakaran & Jie Xiong, 2022. "Eliciting brand association networks: A new method using online community data," Post-Print hal-03700393, HAL.
    20. Qihua Liu & Xiaoyu Zhang & Liyi Zhang & Yang Zhao, 2019. "The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: an empirical investigation," Electronic Commerce Research, Springer, vol. 19(3), pages 521-547, September.

    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:ormksc:v:38:y:2019:i:5:p:773-792. 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.