Development and Validation of an Instrument to Measure the Service-Channel Fit of Electronic Banking Services.
Bibliographic record
Abstract
Electronically mediated self-service technologies in the banking industry have impacted the way banks service consumers. Despite a large body of research on electronic banking channels, no study has been undertaken to empirically explore the fit between electronic banking channels and banking services. Therefore, we developed and validated a service-channel fit conceptualization and an associated survey instrument. We initially investigated industry experts' perceptions towards the concept of 'service-channel fit' (SCF). The findings demonstrated that the concept was highly valued by bank managers. Next, we developed a parallel survey instrument to measure the perceived service-channel fit of electronic banking channels. The instrument was developed using expert rounds and two pretest evaluations. Central to the scale development was the measurement of the SCF construct. Drawing on IS strategy and alignment literature, we created a parallel instrument allowing us to calculate the SCF across three unique service-channel fit dimensions, including service complexity, service importance and service routine. To test the research model, data were collected from 340 consumers in New Zealand using Internet banking applications for two different banking tasks. The results have important theoretical and practical implications for how clients should be serviced through electronically mediated electronic banking channels.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".