Priming Digital Identities for Assortment Recommendation: Development of Web-based Application of Data Collection Tool Capturing Consumer’s Identities against Brand and Influencer in Advertising
(A2023-114099)

AUTHORS

Yilin Feng, Loughborough University London; Jie Meng, Loughborough University

ABSTRACT

Data collection methods in an online retailing environment for massive data mining need an upgrade in capturing consumers’ genuine psychological and behavioural responses for more precise product recommendations. This paper reports a new data collection tool on the webpage to capture and quantify consumers’ projective self-identities and test how the psychological cues in consumers’ self-concepts relate to their decision-making mechanisms in an e-commerce scenario. The focus is on constructing a novel methodology that embraces computer science and analytic psychology that impacts consumers’ engagement and purchase intention. Specifically, we examine the gaps between self-concepts using computer-mediated tools to theorise and testify how they affect each other. A simulated shopping website is built to simulate consumers’ typical shopping experiences. We aim to develop the conceptualisation and measurement of the gaps in self-concepts. The expected results should show that the purchase intention and user engagement are the products of multiple self-images, especially the projective self, which also clarifies the cause of inconsistency of similar research that does not consider the projective self.