Exploring Antecedents of Rural Users’ Continuance of Use Intention Toward Mobile Financial Services in Bangladesh: Deployment of Expectation Confirmation Model
Bibliographic record
Abstract
Numerous studies have focused on the phases of technology adoption or acceptance, while little consideration has been given to rural users’ intentions to continue using the technology. Emphasizing this reality, the study has investigated the antecedents that exert ascendancy on rural communities’ inclination to continue using mobile financial services. This paper conceived the theoretical model based on the expectation confirmation model. Participants in this study were 400 Bangladeshi rural users who were continuously using mobile financial services. For the sake of data analysis, utilizing a structural equation modeling approach, R version 4.4.1 software was deployed. The robust findings show that users’ satisfaction with mobile financial services was significantly influenced by perceived value, perceived risk, perceived cost, government support, and perceived trust. Furthermore, satisfaction demonstrated a substantial and positive influence on the continuance of use intention. Theoretically, the study expands on ECM by adapting the concept to the technological and socioeconomic realities of rural Bangladeshi users, developing digital financial inclusion by investigating the crucial antecedents of satisfaction toward continuance of use intention through evaluation. Practically, service providers may yield strategies to increase the users’ satisfaction, which will escalate continuous use intention.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".