IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v31y2020i4p1132-1143.html
   My bibliography  Save this article

A Note on the Impact of Daily Deals on Local Retailers’ Online Reputation: Mediation Effects of the Consumer Experience

Author

Listed:
  • Xue Bai

    (Department of Marketing and Supply Chain Management, Department of Management Information Systems, Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122)

  • James R. Marsden

    (Operations and Information Management, School of Business, University of Connecticut, Storrs, Connecticut 06269)

  • William T. Ross

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

  • Gang Wang

    (Accounting and Management Information Systems, Alfred Lerner College of Business and Economics, University of Delaware, Newark, Delaware 19716)

Abstract

This study investigates the impact of daily deals on local retailers’ (restaurants’) online ratings. We collected and utilized a comprehensive panel data set that combines information on restaurants’ deal offerings (Groupon or LivingSocial) with their Yelp review details. Although demonstrating a negative main effect of daily deals on a restaurant’s monthly average ratings, we worked to uncover the underlying mechanisms by focusing on the mediating role of consumers’ postconsumption perception. Our mediation analyses show that daily deals are associated with the reduction of both consumers’ perceived food quality and perceived service quality as revealed in review texts, which leads to subsequent declines in a restaurant’s online ratings. We further noted and studied two types of reviews that existed during the deal-redemption period: (1) reviews that mentioned daily deals ( DD Reviews ) and (2) reviews that did not mention daily deals ( NDD Reviews ). NDD Reviews are usually from regular customers, whereas DD Reviews are likely from a different base of customers, deal users. Our separate analyses demonstrate differential mediation processes between DD Reviews and NDD Reviews . For DD Reviews , both perceived food and service quality had mediation roles, suggesting a mismatch effect. That is, daily deals attract discount-focused consumers, including new customers, who are less likely to appreciate the food or service and, therefore, leave a negative review. In contrast, for NDD Reviews , only perceived service quality had a mediation role, suggesting a negative externality effect. That is, the large volume of deal redemptions by deal users may induce a longer waiting time (to be seated and/or served) and, thus, reduced perceived service quality for regular customers. These results deepen our understanding of the impact of daily deals on business online reputation and provide important practical guidance to both local retailers and daily deal platforms.

Suggested Citation

  • Xue Bai & James R. Marsden & William T. Ross & Gang Wang, 2020. "A Note on the Impact of Daily Deals on Local Retailers’ Online Reputation: Mediation Effects of the Consumer Experience," Information Systems Research, INFORMS, vol. 31(4), pages 1132-1143, December.
    • Handle: RePEc:inm:orisre:v:31:y:2020:i:4:p:1132-1143
    DOI: 10.1287/isre.2020.0935
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/isre.2020.0935
    Download Restriction: no
    File URL: https://libkey.io/10.1287/isre.2020.0935?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. 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.
    2. Kamel Jedidi & Carl F. Mela & Sunil Gupta, 1999. "Managing Advertising and Promotion for Long-Run Profitability," Marketing Science, INFORMS, vol. 18(1), pages 1-22.
    3. Zike Cao & Kai-Lung Hui & Hong Xu, 2018. "When Discounts Hurt Sales: The Case of Daily-Deal Markets," Information Systems Research, INFORMS, vol. 29(3), pages 567-591, September.
    4. Leonard Lee & Claire I. Tsai, 2014. "How Price Promotions Influence Postpurchase Consumption Experience over Time," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 40(5), pages 943-959.
    5. 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.
    6. Michael Anderson & Jeremy Magruder, 2012. "Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database," Economic Journal, Royal Economic Society, vol. 122(563), pages 957-989, September.
    7. Gordon Burtch & Yili Hong & Ravi Bapna & Vladas Griskevicius, 2018. "Stimulating Online Reviews by Combining Financial Incentives and Social Norms," Management Science, INFORMS, vol. 64(5), pages 2065-2082, May.
    8. Wendy W. Moe & David A. Schweidel, 2012. "Online Product Opinions: Incidence, Evaluation, and Evolution," Marketing Science, INFORMS, vol. 31(3), pages 372-386, May.
    9. Chrysanthos Dellarocas & Charles A. Wood, 2008. "The Sound of Silence in Online Feedback: Estimating Trading Risks in the Presence of Reporting Bias," Management Science, INFORMS, vol. 54(3), pages 460-476, March.
    10. 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.
    11. 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.
    12. Xitong Li, 2018. "Impact of Average Rating on Social Media Endorsement: The Moderating Role of Rating Dispersion and Discount Threshold," Information Systems Research, INFORMS, vol. 29(3), pages 739-754, September.
    13. Hui Li & Qiaowei Shen & Yakov Bart, 2018. "Local Market Characteristics and Online-to-Offline Commerce: An Empirical Analysis of Groupon," Management Science, INFORMS, vol. 64(4), pages 1860-1878, April.
    14. 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.
    15. Song, Minjae & Park, Eunho & Yoo, Byungjoon & Jeon, Seongmin, 2016. "Is the Daily Deal Social Shopping?: An Empirical Analysis of Customer Panel Data," Journal of Interactive Marketing, Elsevier, vol. 33(C), pages 57-76.
    16. 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.
    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. Morgan Swink & Kejia Hu & Xiande Zhao, 2022. "Analytics applications, limitations, and opportunities in restaurant supply chains," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3710-3726, October.
    2. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    3. Lei Wang & Ram Gopal & Ramesh Shankar & Joseph Pancras, 2022. "Forecasting venue popularity on location‐based services using interpretable machine learning," Production and Operations Management, Production and Operations Management Society, vol. 31(7), pages 2773-2788, July.
    4. Alantari, Huwail J. & Currim, Imran S. & Deng, Yiting & Singh, Sameer, 2022. "An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews," International Journal of Research in Marketing, Elsevier, vol. 39(1), pages 1-19.
    5. Bei Yan & Feng Mai & Chaojiang Wu & Rui Chen & Xiaolin Li, 2024. "A Computational Framework for Understanding Firm Communication During Disasters," Information Systems Research, INFORMS, vol. 35(2), pages 590-608, June.
    6. Wang, Feng & Zhang, Xueting & Chen, Man & Zeng, Wei & Cao, Rong, 2022. "The influential paradox: Brand and deal content sharing by influencers in friendship networks," Journal of Business Research, Elsevier, vol. 150(C), pages 503-514.

    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. 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.
    2. Foster, Joshua, 2022. "How rating mechanisms shape user search, quality inference and engagement in online platforms: Experimental evidence," Journal of Business Research, Elsevier, vol. 142(C), pages 791-807.
    3. Arslan Aziz & Hui Li & Rahul Telang, 2023. "The Consequences of Rating Inflation on Platforms: Evidence from a Quasi-Experiment," Information Systems Research, INFORMS, vol. 34(2), pages 590-608, June.
    4. 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.
    5. 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.
    6. 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.
    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. Hui, Xiang & Klein, Tobias & Stahl, Konrad, 2022. "Learning from Online Ratings," CEPR Discussion Papers 17006, C.E.P.R. Discussion Papers.
    9. Michael Luca & Georgios Zervas, 2013. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Harvard Business School Working Papers 14-006, Harvard Business School, revised May 2015.
    10. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    11. 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.
    12. Pei-Yu Chen & Yili Hong & Ying Liu, 2018. "The Value of Multidimensional Rating Systems: Evidence from a Natural Experiment and Randomized Experiments," Management Science, INFORMS, vol. 64(10), pages 4629-4647, October.
    13. 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.
    14. Xiang Hui & Tobias J. Klein & Konrad Stahl, 2021. "When and Why Do Buyers Rate in Online Markets?," CRC TR 224 Discussion Paper Series crctr224_2021_267v1, University of Bonn and University of Mannheim, Germany.
    15. Marios Kokkodis & Theodoros Lappas, 2020. "Your Hometown Matters: Popularity-Difference Bias in Online Reputation Platforms," Information Systems Research, INFORMS, vol. 31(2), pages 412-430, June.
    16. Dipankar Das, 2022. "Measurement of Trustworthiness of the Online Reviews," Papers 2210.00815, arXiv.org, revised Nov 2023.
    17. Xitong Li, 2018. "Impact of Average Rating on Social Media Endorsement: The Moderating Role of Rating Dispersion and Discount Threshold," Information Systems Research, INFORMS, vol. 29(3), pages 739-754, September.
    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. 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.

    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:31:y:2020:i:4:p:1132-1143. 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.