Aligning restaurants and artificial intelligence computing of food delivery service with product development

Abstract

Purpose This study aims to analyze how restaurants' collaboration with mobile food delivery applications (MFDAs) affects product development efficiency and argues that technological capabilities moderate relational ties impact the joint decision-making and development efficiency of restaurant products. Design/methodology/approach A product development efficiency model was formulated using a resource-based view and real options theory. In all, 472 samples were collected from restaurants collaborating with MFDAs, and partial least squares structural equation modeling was applied to the proposed model. Findings The findings of this study indicate three factors are critical to the product development efficiency between restaurants and MFDAs; restaurants must develop a strong connection with the latter to ensure meals are consistently served promptly. Developers of MFDAs should use artificial intelligence analysis, such as order records of different genders and ages or various consumption attributes, to collaborate with restaurants. Originality/value To the best of the authors’ knowledge, this study is one of the few that considers the role of MFDAs as a product strategy for restaurant operations, and the factors the authors found can enhance restaurants’ product development efficiency. Second, as strategic artificial intelligence adaptation changes, collaborating firms and restaurants use such applications for product development to help consumers identify products.