Antecedent factors to end-users' symbolic acceptance of enterprise systems: An analysis in Nigerian organizations
Bibliographic record
Abstract
End-user acceptance of information systems (IS), including enterprise systems (ES) is critically important for the success or effectiveness of such technologies for adopting organizations. While prior research has focused on such issues in the developed West, very little is known about workers' perceptions of ES acceptance or adoption in the developing world. This study was designed to add to the growing body of work in the area by using empirical data collected in Nigeria. We employed symbolic acceptance to enrich insight instead of usage adoption, which is commonly used in the literature. We drew from relevant theoretical frameworks in developing the research model, which was tested with data collected from a survey. Using the partial least square technique (PLS) for data analysis, support was found for the five hypotheses formulated herein. Namely, performance and effort expectancies, social influence, facilitating conditions, and attitude toward ES were found to have significant, positive impacts on ES symbolic acceptance. The implications of the study findings for both research and practice are discussed, and conclusions are drawn.
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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 | 0.001 | 0.006 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".