Nudges in an on-Tipping Culture: A Systematic Literature Review on Consumer Perspectives in Malaysia

In traditionally non-tipping Asian economies like Malaysia, online food delivery (OFD) applications such as GrabFood, Foodpanda, and ShopeeFood have introduced in-app tipping prompts that challenge long-standing social norms and hospitality service-charge practices. As gig-economy platforms increasingly rely on discretionary consumer tips as additional driver income, understanding how digital interface design shapes tipping behavior has become an important research concern. This systematic literature review synthesizes empirical and theoretical research on consumer behavior, digital choice design, and gig-economy dynamics surrounding digital tipping. Following the PRISMA 2020 protocol, a structured search across three major platforms namely Scopus, Web of Science, and Google Scholar yielded an initial pool of 131 records, which was then cut down to a final synthesis corpus of 24 peer-reviewed works by removing duplicates and screenings. There are three main themes that emerge from the analysis: (1) choice architecture and digital nudging mechanisms, such as prompt timing and default-anchoring effects; (2) psychological tensions between feeling sorry for delivery drivers and getting tired of tipping over time because of fee stacking; and (3) platform fee transparency and equity perceptions, where consumers question whether digital tips can replace fair base wages. Drawing on Nudge Theory, the Theory of Planned Behavior, and Equity Theory, the review proposes a conceptual framework linking digital choice architecture to consumer tipping outcomes through empathy, guilt, and fee-transparency pathways, together with five hypotheses for future study. The study concludes with a targeted research agenda for gig-economy platforms, application designers, and consumer policymakers across Southeast Asia, emphasizing the need for ethical, transparent, post-delivery-oriented tipping architectures that balance short-term gratuity conversion against long-term consumer trust.