From algorithms to adoption of AI-based online food delivery platforms: the role of technology fit and emotional trust in adoption

Purpose This study aims to analyze AI-enabled Online Food Delivery Platforms (OFDP) by looking at Task-Technology Fit (TTF) and the Stimulus-Organism-Response (S-O-R) framework as they affect user interaction with the system. It monitors the role of brand love, emotional trust, and perceived privacy risk on consumer adoption of AI-based food delivery service technologies. Design/methodology/approach The study uses an empirical model based on the combination of TTF and S-O-R concepts with a strong focus on privacy risks and brand love in the task, technology, and social paradigms of emotional trust and technology adoption. The study used 502 respondents from India who use OFDPs. Findings The findings reflect that task and social technology fit together positively influence AI adoption, while emotional trust affects users’ perceptions. The study highlights the need for adequate personalization, efficient service, and ethical action to increase consumer trust and engagement. Practical implications The study assists policymakers and practitioners in AI-based OFDP by adopting higher task and social technology fit, emotional trust building, privacy risk mitigation, and increased brand love to boost user engagement and ensure sustained mobility. Originality/value In the context of AI adoption, this research is unique since it combines TTF and S-O-R frameworks to understand consumer behavior in all OFDP driven by AI. It builds on existing work by including social technology fit, emotional trust, brand love, privacy issues, and algorithmic transparency, thus enhancing the understanding of business, policy, and technology issues.