Smart ordering: assessing customer satisfaction towards AI-assisted food ordering and delivery management systems

The research study focuses on the role of artificial intelligence for customer satisfaction in the food service industry. A mixed-method approach was used to gather primary data from 354 users in Punjab, which was derived through a self-structured questionnaire, while also taking secondary data from several sources of academic publishing. The analysis consists of the application of various statistical tools, including MS Excel, SPSS 26 and AMOS 23 to compute various metrics such as mean, standard deviation, regression, and SEM. These tests are used to prove the objectives and hypothesis of the study. The studies key findings are as follows: AI-driven recommendation facilitates further choices for users at the ordering stage in an engaged environment. This positively correlates with overall user satisfaction as they receive the actual results. The research has also brought into the limelight the difficulties users face with AI-supported delivery systems.

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Publication Details

Journal
International Journal of Business Competition and Growth
Published
2025-01-01
DOI
https://doi.org/10.1504/ijbcg.2025.146825
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
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article

Smart ordering: assessing customer satisfaction towards AI-assisted food ordering and delivery management systems

Hardaman Singh Bhinder
International Journal of Business Competition and Growth
Advanced Manufacturing and Logistics Optimization
article

Smart ordering: assessing customer satisfaction towards AI-assisted food ordering and delivery management systems

Hardaman Singh Bhinder
article en

Abstract

The research study focuses on the role of artificial intelligence for customer satisfaction in the food service industry. A mixed-method approach was used to gather primary data from 354 users in Punjab, which was derived through a self-structured questionnaire, while also taking secondary data from several sources of academic publishing. The analysis consists of the application of various statistical tools, including MS Excel, SPSS 26 and AMOS 23 to compute various metrics such as mean, standard deviation, regression, and SEM. These tests are used to prove the objectives and hypothesis of the study. The studies key findings are as follows: AI-driven recommendation facilitates further choices for users at the ordering stage in an engaged environment. This positively correlates with overall user satisfaction as they receive the actual results. The research has also brought into the limelight the difficulties users face with AI-supported delivery systems.

International Journal of Business Competition and GrowthVol. 10(1)
Punjabi University (IN)
Zero hunger
Openalex Percentile: Top 80%
Advanced Manufacturing and Logistics Optimization
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