Determinants of adopting eCourier services: the moderating role of resistance to change
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand the determinants affecting the adoption behavior of eCourier services and to establish a moderating role of resistance to change between behavioral intention and actual usage behavior. Design/methodology/approach Data were collected through a survey questionnaire that was distributed to the eCourier users. A total of 260 collected data were analyzed by using structural equation modeling. Findings The results of the study demonstrate that all the factors considered for the study have a significant impact on the adoption of eCourier except social influence and customer service. The results also confirm that there is no moderating effect of resistance to change in the relationship between behavioral intention and actual usage. Practical implications This research provides theoretical contribution by extending the practical knowledge focusing on the adoption intention of eCourier, and actual usage behavior using Unified Theory of Acceptance and Use of Technology model—a pertinent and unresearched topic in the existing literature that presents a number of potential avenues for further study. Originality/value This study provides a general understanding of consumers in underdeveloped nations and advances earlier research on the usage of e-commerce in the courier sector.
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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 | 0.006 | 0.033 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".