Online Product Rollover Strategies Considering Price Anchoring and Online Reviews

被引:2
|
作者
Liu, Xuwang [1 ]
Zhang, Qiannan [2 ]
Qi, Wei [1 ]
Wang, Junwei [3 ]
机构
[1] Henan Univ, Inst Management Sci & Engn, Kaifeng 475000, Peoples R China
[2] Henan Univ, Business Sch, Kaifeng 475000, Peoples R China
[3] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China
基金
中国国家自然科学基金;
关键词
Rollover; Pricing; Reviews; Technological innovation; Costs; Psychology; Optimization; Attribute optimization; dynamic pricing; online reviews; price anchoring; product rollover;
D O I
10.1109/TEM.2024.3418032
中图分类号
F [经济];
学科分类号
02 ;
摘要
Price anchoring and online reviews are prominent features in the customer purchasing process on the platform. These factors adjust customer cognition and significantly impact their choice behavior, ultimately affecting product rollover strategies. Focusing on the product single rollover, this study develops a multistage dynamic pricing and attribute optimization model based on online reviews and price anchoring effect. We explore dynamic pricing, product exit strategies, attributes optimization, and product rollover strategies. The study shows that considering price anchoring effects usually results in firms achieving higher profits compared to when they ignore this factor. As a result, skimming and penetration pricing strategies emerge as preferred strategies for current product dynamic pricing. However, excessively low prices for current products may diminish profits from upgraded products after product rollover, prompting firms to carefully balance current product pricing with upgraded product sales. Additionally, two threshold conditions are obtained, which can determine the priority of attribute improvement and the total quantity of optimized attributes. Counterintuitively, as consumer willingness to pay rises, firms may reduce their innovation efforts. Furthermore, we expanded the product rollover framework and derived sufficient conditions and optimal strategies to adopt dual rollover. The research findings provide theoretical foundations and decision-making support for the product rollover and marketing strategies of innovative enterprises.
引用
收藏
页码:11421 / 11440
页数:20
相关论文
共 50 条
  • [1] Frills and product pricing with online reviews
    Zhang, Yao
    Zhao, Cui
    Liang, Zhe
    COMPUTERS & INDUSTRIAL ENGINEERING, 2021, 159
  • [2] Incentive strategies of an e-tailer considering online reviews: Rebates or services
    Zhang, Qing
    Xiao, Tiaojun
    ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS, 2024, 68
  • [3] Decomposing the effects of online customer reviews on brand, price, and product attributes
    Kostyra, Daniel S.
    Reiner, Jochen
    Natter, Martin
    Klapper, Daniel
    INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING, 2016, 33 (01) : 11 - 26
  • [4] A novel product recommendation model consolidating price, trust and online reviews
    Huang, Ying
    Wang, Nu-nu
    Zhang, Hongyu
    Wang, Jianqiang
    KYBERNETES, 2019, 48 (06) : 1355 - 1372
  • [5] Method for product selection considering consumer's expectations and online reviews
    Li, Ming-Yang
    Zhao, Xiao-Jie
    Zhang, Lei
    Ye, Xin
    Li, Bo
    KYBERNETES, 2021, 50 (09) : 2488 - 2520
  • [6] Two-period decision strategies in a dual-channel supply chain considering reference price and online reviews
    Panda, Srikumar
    Maiti, Tarun
    RAIRO-OPERATIONS RESEARCH, 2023, 57 (06) : 2951 - 2979
  • [7] The impact of online reviews on product returns
    Li, Xiaofei
    Ma, Baolong
    Chu, Hongrui
    ASIA PACIFIC JOURNAL OF MARKETING AND LOGISTICS, 2021, 33 (08) : 1814 - 1828
  • [8] Assertions of Expertise in Online Product Reviews
    Mackiewicz, Jo
    JOURNAL OF BUSINESS AND TECHNICAL COMMUNICATION, 2010, 24 (01) : 3 - 28
  • [9] Data Analysis of online product reviews
    Kamma, Vidya
    Gutta, Sridevi
    Santosh, D. Teja
    JOURNAL OF ALGEBRAIC STATISTICS, 2022, 13 (02) : 1644 - 1654
  • [10] Optimal product rollover strategies
    Lim, Wei Shi
    Tang, Christopher S.
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2006, 174 (02) : 905 - 922