MCPC 2009, Helsinki, Finland

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Merle, A., St-Onge, Anik, and Sénécal, S. (2009), “Do I Recognize Myself in this Avatar? An Exploratory Study of Self-Congruity and Virtual Model Personalization Levels,” 5th World Conference on Mass Customization & Personalization, Helsinki, Finland.

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MCPC 2009, Helsinki, Finland

  1. 1. Aurélie Merle Grenoble Ecole de Management, France aurelie.merle@grenoble-em.com Anik St-Onge University of Quebec in Montreal st-onge.anik@uqam.ca Sylvain Senecal HEC Montreal sylvain.senecal@hec.ca
  2. 2. Advantage of electronic commerce to retailers: capacity to offer a personalized relationships (Wind and Rangaswamy 2001). How much personalization should we offer consumers? Research on personalization extent and how it impacts consumer No research on attitudes and responses “personalized avatars” (e.g., Ansari and Mela 2003, Song and Zinkhan 2008)
  3. 3. Personalized avatar: 2D or 3D virtual image that reflects the consumer’s body Visualize and “try on” clothing on a body similar to her own
  4. 4. Investigate how different levels of personalization of an avatar influence consumers’ perceptions, attitudes, and intentions towards a website
  5. 5. Literature on the influence of avatars on online shopping behavior (e.g., Holzwarth et al. 2006) H1: Consumers using a virtual model will derive more: a) hedonic value, b) utilitarian value, c) satisfaction, and d) have greater purchase intentions than consumers not using a virtual model during their shopping session
  6. 6. H2: The level of the virtual model personalization is positively related to perceived self-congruence Self-concept theory: self image congruence has an influence on the perception of store environment (Sirgy and al. 2000) H3: There is a positive relationship between perceived self- congruence and: a) hedonic value, b) utilitarian value, c) satisfaction, and d) purchase intention
  7. 7. Personalization Outcomes Hedonic No Value Personalization Utilitarian H1 Value Satisfaction Levels of Personalization Self- H3 Purchase congruence Intentions H2
  8. 8.  Experiment conducted with the collaboration of My Virtual Model (mvm.com)  Sample: 166 undergraduate female students
  9. 9. « No virtual model » condition « Basic personalization » condition (gender) Grenoble Ecole de « Medium personalization » condition « High personalization » condition (gender + size) (gender + size + picture)
  10. 10.  Between-subject experimental design (n=166)  Task: Renew you wardrobe using 4 product categories (sweaters, tops, dresses and pants) • Manipulation check: The “gender+size” group did not differ significantly from the two other groups in terms of perceived personalization  Only 2 levels of virtual model personalization used (i.e., basic and high)
  11. 11.  H1: Personalization → Values, satisfaction, and intentions  ANOVA on 3 groups (“no virtual model”, “gender” and “gender+size+picture”)  “Gender+size+picture” group perceived more utilitarian value (p<.05), hedonic value (p<.05) satisfaction (p<.05), and had marginally greater purchase intentions (p<.1) than “no virtual model” group  “Gender” group did not perceive more utilitarian value, hedonic value, satisfaction, and did not have greater purchase intentions than « no virtual model » group H1 partially supported
  12. 12.  H2: Personnalization level → Self-congruence  ANOVA on two groups (“gender” and “gender+size+picture”)  “Gender+size+picture” perceived more self- congruence (M=3.17) than the “gender” group (M=2.36, F(1,76)=7.095, p<0.05). H2 supported
  13. 13.  H3: Self-congruence → Values, satisfaction, and intentions  ANOVA using two self-congruence groups (median-split)  Participants who perceived more self-congruence reported higher utilitarian value, hedonic value, satisfaction, and purchase intentions (F(1,76)=12.45; 10.92; 12.69; 9,02, p-values<0.005). H3 supported
  14. 14.  Highly personalized virtual models  more value, satisfaction, and slightly greater purchase intentions than no virtual models while shopping online for clothes  No difference between the less personalized model and no model at all Retailers should maximize the personalization functionalities of their virtual models in order to fully benefit from their impact on consumers
  15. 15.  Limits & future research - Use of perception data  Addition of behavioral data (in progress) - Which moderators? Variables related to the relationship with the product category (expertise, implication) and related to the perception of her own body.
  16. 16. Aurélie Merle Grenoble Ecole de Management, France aurelie.merle@grenoble-em.com Anik St-Onge University of Quebec in Montreal, st-onge.anik@uqam.ca Sylvain Senecal HEC Montreal, sylvain.senecal@hec.ca

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