Role of network externalities and trust facilitators in shaping mobile payment application continuance intentions: variations across stages of adoption
Bibliographic record
Abstract
Purpose The study proposes six mobile application attributes that drive pleasurable experiences, perceived usefulness, satisfaction and continuance intentions: number of members, perceived compatibility, perceived complementarity, structural assurance, ubiquitous connection and contextual offerings. Design/methodology/approach The relative effects of these variables on perceived usefulness and pleasurable experiences are evaluated across early and late adopters. The context of the study was m-payment applications and data from 322 early adopters and 321 late adopters were used to validate the framework with structural equation modelling, as well as to measure the intergroup differences with multi-group analysis. Findings The technological and legal backends of the m-payment technology need to be robust such that users derive a meaningful consumption experience, thereby increasing their continued reuse of the m-payment application. Originality/value This work adds theoretically by developing a robust post-adoption model for smartphone applications across different stages of adoption.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".