A Benefits Assessment Model of Information Systems for Small Organizations in Developing Countries
Bibliographic record
Abstract
The influence of information systems (IS) on small and medium-sized enterprises (SMEs) has enjoyed much attention by managers and policy makers. Despite the hype and eagerness to commit extensive levels of investment, very little research has focuses on assessing the benefits of IS for SMEs in developing counties. Existing literature has been skewed towards developed countries and large organizations. Consequently, the purpose of this paper is to develop a model for evaluating the benefits of IS for SMEs in Saudi Arabia as a case of a developing country. In order to achieve this, the study builds on and extends past IS-impact literature. Based on quantitative results of 365 responses from SMEs, the model comprises 44 measures across five dimensions: ‘Individual impact’, ‘Organisational impact’, ‘System quality’, ‘Information quality’ and ‘Vendor quality’. Applying confirmatory factor analysis and structural equation model, the validated model contributes to theory development of IS impact within the context of SMEs in developing counties. Additionally, it provides critical insights to policy makers and managers on assessing the benefits of IS for SMEs in developing countries.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".