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
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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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".