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INSTANT GRATIFICATION: UNDERSTANDING CUSTOMER LOYALTY IN QUICK COMMERCE MOBILE APPS USING A HYBRID SEM-ANN-NCA APPROACH

S. Saibaba

Advances and Applications in Statistics2026https://doi.org/10.17654/0972361726022article
ABDC C
Weight
0.50

What the paper says

This study investigates what drives customer loyalty in the booming fast-moving quick commerce (Q-commerce) mobile app market. A unique mixed-method approach combining partial least squares structural equation modeling (PLS-SEM), artificial neural networks (ANN) and necessary conditional analysis (NCA) using a dataset of 330 Q-commerce users, has shown that service quality is the best predictor of both satisfaction and loyalty. While convenience and utility directly influence loyalty, the perception of risk has a negative impact on both outcomes. ANN confirms that quality of service is dominant and highlights the non-linear effects. The NCA shows that to achieve high levels of satisfaction and loyalty, the basic thresholds of quality, convenience and usefulness of the service must be met. Notably, satisfaction only mediates some paths to loyalty, suggesting that the creation of digital loyalty in Q-commerce is more complex than suggested conventional models. These findings provide theoretical progress as well as practical insights for Q-commerce managers.

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https://doi.org/https://doi.org/10.17654/0972361726022

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@article{s.2026,
  title        = {{INSTANT GRATIFICATION: UNDERSTANDING CUSTOMER LOYALTY IN QUICK COMMERCE MOBILE APPS USING A HYBRID SEM-ANN-NCA APPROACH}},
  author       = {S. Saibaba},
  journal      = {Advances and Applications in Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.17654/0972361726022},
}

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INSTANT GRATIFICATION: UNDERSTANDING CUSTOMER LOYALTY IN QUICK COMMERCE MOBILE APPS USING A HYBRID SEM-ANN-NCA APPROACH

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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.