The Relative Effect of the Convergence of Product Recommendations from Various Online Sources

被引:18
作者
Xu, Jingjun [1 ]
Benbasat, Izak [2 ]
Cenfetelli, Ronald T. [2 ]
机构
[1] City Univ Hong Kong, Coll Business, Dept Informat Syst, Kowloon, Hong Kong, Peoples R China
[2] Univ British Columbia, Sauder Sch Business, Accounting & Informat Syst Div, Vancouver, BC, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Online product uncertainty; description uncertainty; fit uncertainty; performance uncertainty; recommendation sources; online recommenders; convergent recommendations; WORD-OF-MOUTH; COMMON METHOD VARIANCE; CONSUMER REVIEWS; TASK COMPLEXITY; 2-STAGE MODEL; FAKE NEWS; INFORMATION; PERCEPTIONS; DECISION; UNCERTAINTY;
D O I
10.1080/07421222.2020.1790192
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Most previous studies about online product recommendation sources (recommendation agents [RAs], consumers, and experts) have been limited to the evaluation by a single source on a website. Thus, the relative influence of convergent recommendations from different sources on consumers' acceptance of the advice remains largely unknown. We draw upon and extend the product uncertainty model to theorize how the convergence of recommendations from various sources differentially influences customers' acceptance of recommendations. Our experiments show that the recommendation convergence between RAs and experts leads to the greater recommendation acceptance of the jointly recommended products than the convergence between experts and consumers or convergence between RAs and consumers. The rationale is that RAs best reduce fit uncertainty, and experts best reduce description and performance uncertainties. Experts and RAs complement each other by reducing all three dimensions of product uncertainty. Online merchants are advised to incorporate multiple sources into their websites, including sources (i.e., RAs and experts) that play complementary roles in reducing product uncertainty.
引用
收藏
页码:788 / 819
页数:32
相关论文
共 94 条
  • [1] Loyalty of young female Arabic customers towards recommendation agents: A new model for B2C E-commerce
    Abumalloh, Rabab Ali
    Ibrahim, Othman
    Nilashi, Mehrbakhsh
    [J]. TECHNOLOGY IN SOCIETY, 2020, 61
  • [2] Adomavicius G., 2019, MIS Quarterly, P19
  • [3] The role of demographic similarity in people's decision to interact with online anthropomorphic recommendation agents: Evidence from a functional magnetic resonance imaging (fMRI) study
    Benbasat, Izak
    Dimoka, Angelika
    Pavlou, Paul A.
    Qiu, Lingyun
    [J]. INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES, 2020, 133 : 56 - 70
  • [4] Differential Effects of Provider Recommendations and Consumer Reviews in E-Commerce Transactions: An Experimental Study
    Benlian, Alexander
    Titah, Ryad
    Hess, Thomas
    [J]. JOURNAL OF MANAGEMENT INFORMATION SYSTEMS, 2012, 29 (01) : 237 - 272
  • [5] Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk
    Berinsky, Adam J.
    Huber, Gregory A.
    Lenz, Gabriel S.
    [J]. POLITICAL ANALYSIS, 2012, 20 (03) : 351 - 368
  • [6] Recommendation Agent Adoption: How Recommendation Presentation Influences Employees' Perceptions, Behaviors, and Decision Quality
    Bigras, Emilie
    Leger, Pierre-Majorique
    Senecal, Sylvain
    [J]. APPLIED SCIENCES-BASEL, 2019, 9 (20):
  • [7] Dynamic Effectiveness of Advertising and Word of Mouth in Sequential Distribution of New Products
    Bruce, Norris I.
    Foutz, Natasha Zhang
    Kolsarici, Ceren
    [J]. JOURNAL OF MARKETING RESEARCH, 2012, 49 (04) : 469 - 486
  • [8] Bui S.N., 2013, P 19 AM C INF SYST C, P15
  • [9] Carrier L.M., 2008, 88 ANN CONV W PSYCH
  • [10] Predicting Intention to Participate in Socially Responsible Collective Action in Social Networking Website Groups
    Chen, Jengchung Victor
    Hiele, Timothy McBush
    Kryszak, Adam
    Ross, William H.
    [J]. JOURNAL OF THE ASSOCIATION FOR INFORMATION SYSTEMS, 2020, 21 (02): : 341 - 363