Adoption of Human Resource Information Systems in Developing Countries: An Empirical Study
Bibliographic record
Abstract
There is an inadequate understanding of the successful use and effects of a human resource information system (HRIS) in a developing country context. Given this backdrop, this study plans to explore the determinants impact on HRIS adoption in a developing country. A research model was developed after studying the existing literature, and a questionnaire was developed accordingly to collect data through a purposive sampling method.Materials and Methods: To assess adoption of human resource information system, this study applied the Unified Theory of Acceptance and Use of Technology (UTAUT). Data were collected from HR executives and HR professionals of different private and public organizations situated in Dhaka (capital of Bangladesh) and Chittagong (commercial capital of Bangladesh), Bangladesh. To analyze the data, researcher applied partial least square method based on structural equation modeling.Results: The study found that the research factors performance expectancy, effort expectancy, social influence, facilitating condition as well as the extended factors of UTAUT model employee involvement and training support (p < 0.05) had a weighty influence on HRIS adoption.Conclusions: The findings of this study may become beneficial for the human resource department of various organizations (public, private and others) of developing countries like Bangladesh by adopting HRIS.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".