Small and Medium Enterprises (SMEs) in the Cloud in Developing Countries: A Synthesis of the Literature and Future Research Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research in cloud computing is undergoing rapid growth since its evolution less than a decade ago. This paper contributes to the understanding of this growing research area and by this, considers the potential for cloud computing in small and medium enterprises (SMEs) in developing countries (DCs). The current state of research is assessed in a review of 95 research articles drawn from journals which are both peer-reviewed and academic. To do this, a framework is developed to categorise and analyse the research according to a socio-technical spectrum, identifying levels of analysis and differentiating research activity according to a lifecycle model that incorporates the requirement, needs and desires, adoption, use and adaptation and impact of SMEs in the cloud. The highlights of research in the area to date is an unbalanced use of quantitative approaches and lack of in depth use of case studies to form the basis of theorising in the area. Some gaps are also identified pointing to the fact that issues concerning the extent of impact of cloud computing in SMEs have been ignored, whilst adoption is widely covered. To support in correcting these disparities in the literature, this paper identifies key research gaps relating to conceptual approaches, methodologies, issues addressed and finally provides pointers for future research directions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it