Antecedent behaviour and its implication on the intention to reuse the internet banking and mobile services
Bibliographic record
Abstract
The Covid-19 pandemic has forced people to maintain physical distance during everyday contact, known in popular terms as physical distancing, which triggers the banking industry to accelerate its digital transformation to maximize service to customers. It aims to make business processes to work more efficiently and to drive force that will create various business opportunities. This study aims to explain the main factors that contribute and influence the adoption of internet and mobile banking services, which are antecedents of customer attitudes towards the intention to use these services. This study uses primary data and the unit of analysis is 200 respondents of Bank Bukopin customers who are users of its internet and mobile banking services with a replication of the technology acceptance model (TAM) in the context of internet service adoption and mobile banking. This study uses non-probability sampling with purposive sampling technique, and to analyze the research model, the Partial Least Square (PLS) method is used with the SmartPLS 3.0 M3 program as a tool. The research findings indicate that the intention to reuse is directly affected significantly by perceived ease of use, trustworthiness, and attitude to use. Perceived benefits do not have a significant effect on directly affecting intention to use, but have a positive and significant effect if they get a mediating role from the attitude of using. This study proves the existence of a mediating role for perceived benefits and beliefs on attitudes to use. This study also strengthens the evidence that attitudes have a strong and positive and significant effect on intention to reuse, and are able to fully mediate the effect of perceived ease of use, perceived usefulness and trust on intention to reuse. Bank Bukopin management and bankers are also expected to use the results of this study to evaluate and take corrective action on aspects that are deemed incapable of encouraging the adoption of their internet and mobile banking services and creating customer loyalty to continually use the services they provide.
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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 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".