Barriers in Adopting Human Resource Information System (HRIS): An Empirical Study on Selected Bangladeshi Garments Factories
Bibliographic record
Abstract
Garment industry can be considered as the sprinter of the economy in Bangladesh for its significant contribution to the economy. Demand for Bangladeshi garments products are increasing so are the competitions. The garment industry, to keep pace with the increasing competition, needs to adopt Information System (IS) in business functions that help ensure cost management effectively in the labor-intensive garments factory. However, very few garments factories have adopted IS in their operations. This paper tried to identify the factors inhibiting the adoption of HRIS in the garments industry of Bangladesh through a semi-structured questionnaire survey of 150 samples from 25 garment factories in Bangladesh. We have used Exploratory Factor Analysis (EFA) method to identify the factors impeding to adopt HRIS in garments sector of Bangladesh. From the study, we have identified three broad inhibiting factors termed as Financial, Management related, and Organizational; specifically, High investment, Costly maintenance, Long-term benefit, Organizational Structure, Culture of the Organization, Top management support, lack of experts and user, are found as major barriers in this regard. The findings may be useful to both the academicians to explore the factors in their respective countries and the HRIS practitioners in garment sector to emphasize on these areas so that organizations can ensure better HRIS implementation.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".