Rebate strategy to stimulate online customer reviews

被引:39
|
作者
Yang, Liu [1 ]
Dong, Shaozeng [1 ,2 ]
机构
[1] Univ Int Business & Econ, Business Sch, Beijing 100029, Peoples R China
[2] Harbin Univ Sci & Technol, Rongcheng Campus, Harbin 150080, Heilongjiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Online customer reviews; Rebate strategy; Consumer behavior; e-commerce; Two-period; Online shopping; WORD-OF-MOUTH; CONSUMER REVIEWS; REMANUFACTURED PRODUCTS; SALES; IMPACT; MOTIVATION; SERVICES;
D O I
10.1016/j.ijpe.2018.07.032
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
With the development of e-commerce and communication technology, consumers are heavily relying on the online customer reviews to access to more product information before making purchase decisions online. How to stimulate consumers to provide online customer reviews becomes a critical issue for the online retailers. This paper develops an analytical framework to study the online retailer's optimal rebate strategy and product pricing strategy in a two-period setting. Our analysis shows that the review effort plays a critical role in deterring the retailer's rebate decision and pricing decisions. When the review effort is small, it is efficient for the retailer to set a higher rebate value to persuade consumers to share their opinions online, and charge for a higher product price in the first period to extract more profit. We find that the Rebate strategy expands the market demand in both periods, and earns the retailer more profit. We also examine other influential factors including the unit product cost and the review impact factor.
引用
收藏
页码:99 / 107
页数:9
相关论文
共 50 条
  • [1] Rebate incentive strategy for online reviews
    Zhao, Huan-huan
    Liu, Yong
    Ren, Wen-wen
    MARKETING INTELLIGENCE & PLANNING, 2024, 42 (08) : 1384 - 1406
  • [2] Biases in online reviews: the default positive review rule and the conditional rebate strategy
    An, Haiyuan
    Li, Wenli
    Yu, Yahe
    Wang, Zhen
    INTERNET RESEARCH, 2025,
  • [3] Information disclosing strategy and additional online customer reviews
    Dong, Shaozeng
    Yang, Liu
    Wu, Hsin-Te
    Shi, Baozhen
    Ng, Chi. To.
    Wang, Yifei
    ENTERPRISE INFORMATION SYSTEMS, 2024, 18 (12)
  • [4] Can the Conditional Rebate Strategy Work? Signaling Quality via Induced Online Reviews
    Xiao, Lu
    Qian, Chen
    Wang, Chaojie
    Wang, Jun
    JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH, 2024, 19 (01): : 54 - 72
  • [5] Customer engagement and online reviews
    Thakur, Rakhi
    JOURNAL OF RETAILING AND CONSUMER SERVICES, 2018, 41 : 48 - 59
  • [6] The Value of Online Customer Reviews
    Askalidis, Georgios
    Malthouse, Edward C.
    PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), 2016, : 155 - 158
  • [7] Automatic summarization of online customer reviews
    Zhan, Jiaming
    Loh, Han Tong
    Liu, Ying
    WEBIST 2007: PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND TECHNOLOGIES, VOL SEBEG/EL: SOCIETY, E-BUSINESS AND E-GOVERNMENT, E-LEARNING, 2007, : 5 - +
  • [8] Information multidimensionality in online customer reviews
    Wang, Fang
    Du, Zhao
    Wang, Shan
    JOURNAL OF BUSINESS RESEARCH, 2023, 159
  • [9] Opinion Mining for Online Customer Reviews
    Nanda, Ashok Kumar
    Jalda, Chaitra Sai
    Kumar, V. Pradeep
    Chakali, Venkata Sai Varun
    Munavath, Krishnaveni
    Marukanti, Srihari Prasad Reddy
    Boreda, Divya
    PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON DATA SCIENCE, MACHINE LEARNING AND APPLICATIONS, VOL 1, ICDSMLA 2023, 2025, 1273 : 903 - 910
  • [10] The Impact of the Content of Online Customer Reviews on Customer Satisfaction: Evidence from Yelp Reviews
    Chen, Langtao
    CONFERENCE COMPANION PUBLICATION OF THE 2019 COMPUTER SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING (CSCW'19 COMPANION), 2019, : 171 - 174