Prioritizing risk factors in livestream shopping using a fuzzy AHP approach

Abstract

Understanding the risk factors that affect purchase intention in livestream shopping is essential for addressing customer concerns unique to this platform, where risks significantly shape buying decisions. Unaddressed or poorly managed risks can undermine consumer trust, deterring purchases. Existing research on risk identification and information transparency remains limited, presenting obstacles for both academics and industry professionals. This study aims to construct a taxonomy that prioritizes barriers to customer purchasing behavior in livestream environments. By integrating expert surveys with a review of consumer behavior literature, 13 key risk challenges were identified and organized into four practical categories: product transparency risk, post-sales risk, transaction risk, and seller transparency risk. Employing the Fuzzy Analytic Hierarchy Process method, chosen for its strength in processing fuzzy data, this research ensures precise and dependable evaluations in uncertain contexts, outperforming alternative approaches. The resulting multi-criteria decision-making taxonomy ranks these challenges, offering practitioners a framework to refine strategies and innovate solutions that enhance purchase intention in livestream systems by mitigating risks effectively.