Explaining CustomerâÂÂs Continuance Intention to use Mobile Banking Apps with an Integrative Perspective of Expectation Confirmation Theory and Self-determination Theory
Bibliographic record
Abstract
Digital payments evolve as the next generation system to take over the global commerce landscape in the same manner in which the internet and mobile telephony had dominated the traditional communication domains. The use of mobile banking apps has spurred the digital medium across the globe and resulted in a fundamental shift in retailing practices. The purpose of this paper is to comprehend various factors influencing the customer’s continuance intention behavior to use mobile banking apps. In this study, we developed a research model that encompasses the attributes of Expectation confirmation theory (ECT) and Self-determination theory (SDT). The research model was tested using survey data collected from 744 respondents across various demographics and analyzed using SPSS and AMOS software to understand the usage behavior of mobile banking apps in multi-faceted business environment. The various hypothesis of the research model indicate that mobile banking apps continuance intention usage behavior is strongly influenced by the satisfaction, intrinsic and identified regulations, whereas satisfaction is strongly linked with the expectation-confirmation, trust and quality. The research findings revealed that enormous potential available for marketing managers and researchers to tape these opportunities and plan for continual and sustainable growth of mobile banking apps.
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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 | 0.002 | 0.008 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".