Determinants that Influencing the Adoption of E-HRM: An Empirical Study on Bangladesh
Bibliographic record
Abstract
Nowadays, information systems (ISs) have tremendous impact on processes and practices of human resource management (HRM). Realizing the effectiveness and efficiency of ISs, now, human resource (HR) managers are reliant on electronic human resource management (E-HRM) – an information system to accomplish HR activities. This system is implemented to reduce the administrative burden for HR professional thus they can deliver better services to firm’s stakeholders (e.g., employees, managers). If the E-HRM is not adopted properly, management of human resource in an organization cannot work smoothly. This paper aims at exploring the determinants that influence the adaptation decision for E-HRM among firms in Bangladesh. This study developed research framework based on the theoretical foundation and previous literature in order to better investigate the relationship between individual, organizational, technological, and environmental determinants, and E-HRM adoption. A total number of 331 respondents were considered from forty six large scale private sector organizations in Bangladesh using stratified random sampling. Employees of the organizations responded a close-ended questionnaire based on a 5-point Likert scale. Here, data was analyzed by statistical tools, for example, descriptive statistics, and factor analysis. They study found top level management support, employee’s individual attributes, system complexity, IT infrastructure, and industry pressure as the most influential determinants that influencing the adoption decision for E-HRM. Limitations and policy implications are discussed at the end of this paper. The scope of future studies is also addressed.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".