Investigating Factors Influencing the Adoption and Use of Free and Open Source Software (FOSS) in Tanzanian Higher Learning Institutions: Towards an Individual-Technology-Organizational-Environmental (ITOE) Framework
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
This paper is located within the global debates about adoption and use of Free and Open Source Software (FOSS) in developing countries. From the Tanzanian Higher learning Institutions (HLIs), this paper investigates factors influencing the adoption and use of the FOSS. The rationale for the investigation stems from the notion that Tanzanian HLIs is yet to fully adopt and use FOSS, despite huge investments and efforts being made on ground. This is facilitated by the lack of clear FOSS adoption and use framework. The source of this data was a questionnaire which comprised of structured questions, using a five-point Likert Scale. The population sample for the study was all HLIs stakeholders in Tanzania. Participants included both public and private HLIs. The positive factors includes autonomy for code modifications, IT staffs and decision makers, organization awareness, trustworthiness of FOSS, licensing and scalability, collaboration and knowledge sharing, collaboration on international ICT, organization policy and good social economic policy. The negative influences that emerged included, Lack of proper plan, low confidence, lack of expertise, unfit for purpose, difficult to implement, lack of supporting software. Furthermore, this paper motivates other researchers to analyze why the adoption and use of Free and Open source software is still low to higher learning Institutions in East Africa even though there potential benefits that have been advocated in many previous studies. Finally the paper has proposed Individual-Technological-Organizational- Environmental (ITOE) framework for adoption and use of FOSS.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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