MIB605-Fall 2021
   Lecture 1
   E-commerce and 
   Social Media Marketing: An Introduction
           Prof. Geng Cui
    My research in e-marketing
     Cui, Geng, Hon-KwongLui, and Xiaoning Guo (2012), "The Effect of Online Consumer Reviews on New 
      Product Sales," International Journal of Electronic Commerce, Vol. 17, No. 1, 39–57.
     Li, Chunyu, Geng Cui, Ling Peng (2017) The Signaling Effect of Management Response In Engaging 
      Customers: A Study of The Hotel Industry. Tourism Management. 62 (October): 42-53.
     MengzhouZhuang, Geng Cui and Ling Peng (2018), "Manufactured Opinions: The Effect of Manipulating 
      Online Product Reviews", Journal of Business Research, 87, 24-35.
     Li, Chunyu, Geng Cui, Ling Peng (2018) Tailoring management response to negative reviews: The 
      effectiveness of accommodative versus defensive responses. Computers in Human Behavior.
     Peng, Ling, Geng Cui, Yuho Chung & Chunyu Li (2019), “A multi-facet item response theory approach to 
      improve customer satisfaction using online product ratings,” Journal of Academy of Marketing Science, 
      47(5), 960–976.
     Li, Chunyu and Geng Cui (2020), “The Role of Explanations and Metadiscourse in Management Responses 
      to Anger-Reviews versus Anxiety-Reviews: The Mediation of Sense-Making,” International Journal of 
      Hospitality Management, 89(8): 1-13
     Peng, Ling, Geng Cui, Yuho Chung and Wanyi Zheng (2020), “The Faces of Success: Beauty and Ugliness 
      Premiums in e-Commerce Platforms,” Journal of Marketing, 84(4), 67-84.
     The Importance of Being Ernest: The Effects of Incentive Disclosures in Online Product Reviews
                                                            A set of 68 landmarks are 
                                                            used for facial feature 
                                                            representation.
                             Attractiveness score     Attractiveness score     Attractiveness score 
                             between 2.0 and 2.5      between 3.0 and 3.5      between 4.0 and 4.5 
                                                                                                  
                                                                                                  
                                                                                                  
                                       Predicted facial attractiveness scores
                                                                                                 3
                                                     0.351**
                                  -0.302**
              Following standard practice (Haans et al. 2016), we use a 3-step procedure 
              to test the U-shaped relationship: 1) direct effect (-, sig.), squared term 
              effect (+, sig.); 2) slopes at both ends are sig. steep and of the expected 
              signs; 3) turning point is sig. and located well within the data range.
                                                                 4