Internet banking in the united kingdom: a customer behaviour perspective
Bibliographic record
Abstract
The primary objective of this thesis is to develop a conceptual model that determines how intentions towards the use of internet banking are formed and to what extent they are related to the actual use of internet banking. The thesis also examines the role of customers' trust in internet banking acceptance and the perceptual differences among customers on the basis of their technology readiness and demographic characteristics of gender and age. The thesis integrates variables associated with behavioural and environmental uncertainty (trust and perceived risk), technology acceptance constructs (perceived usefulness and ease of use), and users' personal characteristics (technology readiness, age and gender) into a coherent and parsimonious model. The structural equation modelling technique is used to rigorously test the validation of measurement models and to examine the extensive set of interrelationships among these variables and their comparative effect on customers' intentions and actual use of internet banking. The data used is collected in collaboration with Halifax Bank of Scotland (HBOS) through postal questionnaire survey and actual internet banking usage logs of the respondents. The empirical results show that: (1) intentions translate over time into actual behaviour; (2) perceived usefulness has a significant effect on intentions; (3) trust and perceived risk are direct antecedents of intention, suggesting uncertainty reduction as a key component in customers' acceptance of internet banking; (4) trust also acts as an indirect antecedent of intention through perceived risk and perceived usefulness; (5) trust is a multidimensional construct: perceived trustworthiness, perceived security, perceived privacy are antecedents of trust; (6) perceived trustworthiness significantly affects perceived security and perceived privacy; (7) different types of customers, defined by their demographic characteristics and technology readiness, develop different perceptions towards the same technology. Overall, the thesis indicates that while Technology Acceptance Model (TAM: Davis 1989) is useful in explaining internet banking acceptance, extending the theory to include the combined effect of new variables and moderators increases our understanding of the underlying phenomenon. User acceptance of technology remains a complex, elusive, yet extremely important phenomenon. Research on the TAM starting from Davis in 1989 has made significant contributions toward unravelling some of its mysteries. The internet banking acceptance model, proposed and validated in this thesis, advances theory and research on this important issue. Keywords. Internet Banking, United Kingdom, Customer Behaviour, Technology Acceptance Model, Trust, Perceived Risk, Perceived Security, Perceived Privacy, Perceived Trustworthiness, Technology Readiness, Structural Equation Modelling.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".