How Gig Workers Marketing Communications Shape Customer Perceptions and Repeat Purchase Intentions: A Study of Zomato Delivery Partners in Central Kolkata

Abstract

The rapid expansion of the gig economy has transformed service delivery systems, particularly within platform-based food delivery services in emerging markets. In India, digital platforms such as Zomato increasingly rely on gig workers as key intermediaries between service providers and customers. While existing studies have extensively examined platform strategies and consumer behaviour in online marketplaces, limited attention has been paid to the role of gig workers’ marketing communications in shaping customer perceptions and repeat purchase intentions. This study examines how customers perceive the marketing communications of Zomato delivery partners and assesses their impact on repeat purchase intentions in Central Kolkata, India. Using primary data collected from 120 platform users, the study employs ordinal logistic regression analysis to assess the impact of promotional attitudes, message clarity, perceived professionalism, response time satisfaction and service trust on repeat ordering behaviour. The findings indicate that although customers exhibit generally positive perceptions towards gig workers’ communication efforts, these factors do not exert a statistically significant influence on repeat purchase intentions. The results suggest that platform-level attributes and systemic service quality may play a more dominant role in shaping customer loyalty than individual-level marketing communication. The study contributes to the growing literature on gig economy marketing by highlighting the limited individual agency of gig workers within highly standardised digital platforms. Practical implications are discussed for platform managers and policymakers seeking to enhance service sustainability and worker empowerment.

Keywords: Gig Economy, Marketing Communication, Customer Perception, Repeat Purchase Intention, Food Delivery Platforms, Zomato, India, Ordinal Logistic Regression

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