Understand Consumer’s Product Decisions when Shopping by Voice

Online shopping is more than moving the mouse and clicking. Using machine learning and voice recognition technologies, many e-merchants nowadays start offering artificially intelligent virtual assistant on their website or through smartphone app (e.g., Amazon Alexa, Domino’s DRU assist), allowing consumers to place an order using their voice. Compared with traditional online shopping that navigated by mouse, shopping by voice resembles two-way communication (between salesperson and consumer), and will be perceived more natural and interactive. In this research, I hypothesize that shopping by voice will (1) enhance mental simulation, making consumers more immersed in the environment and facilitating the choice of hedonic products (e.g., high calorie food); (2) increase the perceived socialness of website, consequently, promoting consumers to apply social norms during the interactions and leading to a higher acceptance of recommended products. I used a real online food ordering website to collect data in the experimental setting. Two studies were conducted by far to test the hypotheses and rule out alternative explanations.

Many thanks to Zhen’s dissertation committee: *Committee Chair - Yanliu Huang - Associate Professor - Drexel University *Committee Member - Chen Wang - Assistant Professor - Drexel University
 *Committee Member - Bert Rosenbloom - Professor - Drexel University
 *Committee Member - Trina Andras - Professor - Drexel University
 *Committee Member - Barbara Kahn - Professor - University of Pennsylvania