Investigating and Predicting Intentions to Continue Using Mobile Payment Platforms after the COVID-19 Pandemic: An Empirical Study among Retailers in India
Bibliographic record
Abstract
The Indian retail industry has registered tremendous growth recently. The sudden emergence of Coronavirus Disease 2019 (COVID-19) and the related measures that were taken by the authorities to curb the pandemic have compelled retailers and their consumers to transact using digital platforms. This study investigates the critical precursors to retailers’ behavioral intention to use mobile platforms for their business transactions in the post-pandemic era. This study adopted a framework that combined the theory of planned behavior (TPB) and self-determination theory (SDT) to predict behavioral intentions. A hybrid approach combining partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) techniques was used to test and validate the proposed framework. Four hundred and ninety-six participants from different central Indian cities participated in the study. PLS-SEM results confirmed that the motivational factors (need satisfaction [NS] and need frustration [NF]) significantly influence the attitude (AT), subjective norms (SN), perceived behavioral control (PBC), and behavioral intention (BI). Furthermore, the findings also established the partial mediating effect of AT, SN, and PBC on the relationship between motivational construct (NS and NF) and BI. Finally, the relationship established by SEM was successfully validated by ANN in the existence of a nonlinear relationship in the data. The findings may help retail stakeholders to support retail owners in their pursuit to continue using mobile payment systems in the post-COVID-19 world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".