Why consumers are not using internet banking: a qualitative study
Bibliographic record
Abstract
Purpose This paper illustrates why consumers are resistant to using internet banking. Design/methodology/approach A survey was used to acquire data from 127 consumers who were not internet bank users. Findings Using a content analysis procedure, eight factors were identified which explain why consumers are not using internet banking. In order of frequency, the factors are: perceptions about risk; the need; lacking knowledge; inertia; inaccessibility; human touch; pricing and IT fatigue. Research limitations/implications A list of those consumers who were not internet banking users could not be sourced, meaning that a random sample could not be carried out. The factors which emerged, however, appear to provide a comprehensive understanding of why certain consumers are not internet banking users. The factors provide a useful basis for researchers to conduct studies to better understand what influences a consumer decision not to use the internet as a means of sourcing banking services. Practical implications The findings provide a framework for creating a strategy to enhance adoption rates. Originality/value The findings create an awareness of the various reasons explaining why consumers are not becoming internet banking users. The various reasons provide scholars with an opportunity to conduct further research in this area and practitioners with an opportunity to enhance adoption rates.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".