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The theory of consumption values (TCV) has successfully explained much consumer choice behavior, but few studies have investigated the values that drive food-delivery application (FDA) use. This study aims to bridge this gap by extending the TCV to the FDA context to examine food consumption-related values and interpreting and rechristening generic consumption values to adapt the TCV to the FDA context.

An explorative mixed-method research approach was taken to conduct focus group discussions with 20 target users to develop the questionnaire and then administer it for a cross-sectional survey (pen and pencil) to FDA users aged 22–65 years; 423 complete responses so received were analyzed using structural equation modeling.

The findings show that epistemic value (“visibility”) is the chief driver of purchase intentions toward FDAs, followed by conditional (“affordances”), price (part of functional value) and social value (“prestige”). Food-safety concerns and health consciousness (proposed as part of functional value) did not share any statistically significant association with purchase intentions toward FDAs.

The findings of this study are insightful for FDA service providers competing for higher shares in the market by helping them understand ways to influence consumer choices and purchase intentions.

It is the first study that combines FDAs 2014 an online service that it is attracting a lot of investment 2014and TCV which has continued to be one of the most relevant theories of consumer behavior. It extends the TCV by adapting it to the FDA context with food-consumption-related values. Thus, it adds to the relatively scant literature on FDAs on the whole which is essential, as FDAs represent the business model of new economy, i.e. online-to-offline (O2O). Finally, this study formulates a conceptual framework that may serve as the basis of future research.

Demand planning (DP) is a key element of supply chain management (SCM) and is widely regarded as an important catalyst for improving supply chain performance. Regarding the availability of technology to process large amounts of data, artificial intelligence (AI) has received increasing attention in the DP literature in recent years, but there are no reviews of studies on the application of AI in supply chain DP. Given the importance and value of this research area, we aimed to review the current body of knowledge on the application of AI in DP to improve SCM performance.

Using a systematic literature review approach, we identified 141 peer-reviewed articles and conducted content analysis to examine the body of knowledge on AI in DP in the academic literature published from 2012 to 2023.

We found that AI in DP is still in its early stages of development. The literature is dominated by modelling studies. We identified three knowledge clusters for AI in DP: AI tools and techniques, AI applications for supply chain functions and the impact of AI on digital SCM. The three knowledge domains are conceptualised in a framework to demonstrate how AI can be deployed in DP to improve SCM performance. However, challenges remain. We identify gaps in the literature that make suggestions for further research in this area.

This study makes a theoretical contribution by identifying the key elements in applying AI in DP for SCM. The proposed conceptual framework can be used to help guide further empirical research and can help companies to implement AI in DP.