Interrelated Factors Influencing the Adoption Decision of AIS Applications by SMEs in Jordan
Bibliographic record
Abstract
This study aims to identify the main factors that either facilitating (motivating) or inhabiting the adoption decision of AIS by small –medium sized companies in Jordan. In order to accomplish the research objectives, a conceptual framework was designed. The conceptual framework includes three major interrelated factors: organizational, technological and environmental factors. The data for this research were collected through email survey with 101 respondents. The target respondents were the small-medium sized companies in Jordan and the key respondent approach was used. A group of twenty factors, employed as variables from the previous studies and models of adoption were listed and examined in a neutral manner, without pre-classifying them as barriers or incentives, through email surveys sent to key respondent in the SMEs. Respondents were asked to indicate how these factors influence their AIS adoption decisions. Furthermore, a comparison analysis has conducted to show how these factors are perceived differently among those who have adopted as AIS, those that will not adopt it all and those that might adopt it in the near future. The finding showed that only twelve of these factors were found significant, eight labeled as incentives and four labeled as barriers. However, the set cost factor was the only shared one perceived as a barrier among all groups. The results showed the three groups adopt perceive factors differently. The research has finalized with some theoretical and practical implications and recommendations.
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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 | 0.003 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".