Critical Success Factors Affecting E-Procurement Adoption in Public Sector Organizations in Sri Lanka
Bibliographic record
Abstract
E-procurement is one of the Information Communication Technology (ICT) applications utilized in both public and private sector organizations. Many countries all over the world including Canada, Korea and Philippine benefited through the government procurement using electronic means as it enables the government to offer more convenient and widespread accessible government services in an efficient, cost-effective and participatory manner. The main objectives of the study are (a) to access the level of e-procurement usage in the public-sector in Sri Lanka; (b) to identify the significant factors affecting the public-sector e-procurement adoption in Sri Lanka; and (c) to identify the barriers in adopting e-procurement in those organizations. The population of the study comprises of the national level public sector entities such as; Ministries, Departments, Statutory Boards and Public Companies. A sample of 114 officers who involved in the procurement function was selected as key informants based on stratified random sampling method. 74 officers were responded for the study. Both qualitative and quantitative methods were utilized in this study. Quantitative data were collected through a structured questionnaire to identify the determinants of successful e-procurement adoption and the level of e-procurement usage. The qualitative data were collected through 10 interviews to identify barriers in e-procurement adoption. The study found that Relative Advantage, Compatibility, Complexity, Top Management Support, Employee Knowledge and skills and Supplier Readiness are statistically significant (at p<0.01) determinants of e-procurement adoption. These variables altogether explain 66.2% of variance in e-procurement adoption in the public-sector organizations in Sri Lanka. Among them, Top Management Support and Employee Knowledge are the major determinants of the successful e-procurement adoption. Weak procurement guidelines, Weak Legal framework and ICT infrastructure, and Lack of IT knowledge and experience of employees are the present barriers to e-procurement adoption in public sector organizations in Sri Lanka. Finally, policy recommendations for the e-procurement adoption are proposed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".