Seeking Socially Responsible Consumers: Exploring the Intention-Search-Behaviour Gap

The School of EECS is hosting the following guest seminar:

Seeking Socially Responsible Consumers: Exploring the Intention-Search-Behaviour Gap

Speaker: Dr Leif Azzopardi, Principal Applied Scientist @ Microsoft
Host: Prof Gianluca Demartini

Abstract

The increasing prominence of “Socially Responsible Consumers” has brought about a heightened focus on the ethical, environmental, social, and ideological dimensions influencing product purchasing decisions. Despite this emphasis, studies have consistently revealed a significant gap between individuals’ intentions to be socially responsible and their actual purchasing behaviors: they often choose products that do not align with their values.

We aim to investigate the role of “search” and how it influences this gap. Our investigation involves an online survey of 286 participants, where we inquire about their search behaviors and whether they considered various dimensions—ranging from price and features to environmental, social, and governance issues — in relation to a recent purchase.

Contrary to expectations of a clear intention-behavior gap, our findings suggest most participants exhibited indifference or lack of awareness regarding these “responsible” aspects. While, for those participants who were more ethically minded, they reported difficulties related to searching for and acquiring information regarding such aspects, which contributed to the gap.

Our findings suggests that part of the intention-behaviour gap can be framed as an information seeking problem. Moreover our findings motivate the development of search systems and platforms that better help support consumers make more informed and responsible purchasing decisions.

Bio

Leif Azzopardi, based in Glasgow, UK, is currently a Principal Applied Scientist at Microsoft, and Associate Professor in Artificial Intelligence and Data Science at the University of Strathclyde, where he leads the Interaction Lab (i-lab). He specializes on developing, evaluating and modelling information rich and information intensive applications and agents, and developing novel Recommender Systems and Information Retrieval Systems underpinned by Natural Language Processing, Language Modelling, Machine Learning and Deep Learning.