Toward a measure of service convenience: multiple‐item scale development and empirical test
Bibliographic record
Abstract
Purpose The purpose of this paper is to report on the development and nomological testing of a 17‐item scale measuring the five dimensions of service convenience (decision, access, transaction, benefit, and post‐benefit) as proposed by Berry, Seiders, and Grewal. Design/methodology/approach A cross‐sectional survey methodology was used to collect the data. Findings Reliability and validity assessments provided evidence of the scale's psychometric validity. Service convenience was found to be a significant predictor of overall satisfaction in the context of personal cellular telephone and internet usage. Research limitations/implications This study uses a student sample which may limit its generalizability to other respondents. Also, the cross‐sectional survey methodology does not allow for the investigation of causation. Future research should investigate other contexts outside of the cellular and internet services examined in this study and across a broader sample. Furthermore, the ability to retrospectively rate service convenience, the trade‐off between price and convenience, and the continuum of convenience need to be investigated further. Originality/value This study provides psychometrically valid scales to measure service convenience as conceptualized by Berry et al..
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".