Organizational commitment and users’ perception of ease of use: a study among bank managers
Bibliographic record
Abstract
Purpose – The purpose of this paper is to look at the issue of perceived ease of use of a web-based customer relationship management system and consider the role of organizational commitment as a possible antecedent. \n \nDesign/methodology/approach – Data for this study were collected from among managers of a major player in the community banking sector within the EU. A total of 274 valid responses were obtained from 398 managers. \n \nFindings – Results have been mixed and partially conditioned by service providers’ willingness to leverage the possibilities that the technology can provide. \n \nResearch limitations/implications – The study was limited to a single organization and consequently the results should be generalized with caution. Replication studies with improved measures, in other countries and contexts are desirable. \n \nPractical implications – The results can be useful for management, since Web-based customer relationship management systems have been adopted by many service providers in their quest to offer better one-to-one marketing possibilities to their customers. \n \nOriginality/value – This paper demonstrates the importance of fostering a sense of organizational commitment amongst key service providers, as this in turn seems to enable them to overcome many impediments pertaining to technology use.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".