AI-induced anxiety in the assessment of factors influencing the adoption of mobile payment services in supply chain firms: A mental accounting perspective
Why this work is in the frame
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Bibliographic record
Abstract
This research aims to explore the impact of AI-induced anxiety on the adoption of mobile payment services in supply chain firms, viewed through the lens of Mental Accounting Theory. In an era driven by technological advancement, supply chain companies' use of mobile payment services has arisen as a crucial problem. This study is the first to investigate the complicated links between AI-induced anxiety, perceived utility, and the adoption rate of mobile payment systems using the Mental Accounting Theory as a theoretical framework. The study employs a quantitative research approach, using Smart PLS for regression analysis, and gathers its data from major supply chain business players. Our analysis offered important insights into the many aspects influencing the adoption of mobile payment services in supply chain companies. The acceptance rate was shown to be adversely connected with AI-induced anxiety and integration expenses, posing obstacles for businesses seeking to embrace mobile payment systems. In contrast, characteristics such as perceived utility, usability, confidence in security, and backing from upper management were positively connected with adoption rates. These findings provide not only theoretical contributions to the current research, but also concrete advice for supply chain practitioners seeking to exploit mobile payment systems for operational and strategic advantage.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it