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

Hardaman Singh Bhinder

International Journal of Business Competition and Growth2025https://doi.org/10.1504/ijbcg.2025.146825article
AJG 1
Weight
0.50

What the paper says

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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Cite this paper

https://doi.org/https://doi.org/10.1504/ijbcg.2025.146825

Or copy a formatted citation

@article{hardaman2025,
  title        = {{Smart ordering: assessing customer satisfaction towards AI-assisted food ordering and delivery management systems}},
  author       = {Hardaman Singh Bhinder},
  journal      = {International Journal of Business Competition and Growth},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijbcg.2025.146825},
}

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

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.