Impact of Human Capital Management Information System on Organization Performance: A Case of TRA Head Quarter in Dar Es Salaam
Bibliographic record
Abstract
The study investigated three specific research objective namely to analyses the impact of e-recruitment on the quality performance of TRA Head Quarter; to examine the impact of e-training on customer satisfaction in service delivery system at TRA Head Quarter and to analyze the impact of e-communication system on timely operational performance of TRA Head Quarter. The study employed mixed research strategy based on qualitative and quantitative analysis to investigate human capital management information system on organization performance. The case study research design was used in analyzing the study. Data was collected through questionnaire and interview, and they were analyzed using qualitative and quantitative approaches. The study observed that e-recruitment system has impacts on increases rate of handling customer complaints hence ensures quality of services delivery and increase number of staff employed hence deliver quality works. The finding shows that e-training practices at the office added value of quick response to customer using online services hence impact to customer satisfactions and increases number of staff trained as the results increases professionalism in service delivery hence impacts on customer satisfactions with service delivery. The finding indicates that use of e-communication address operational challenges as the result contribute to operational performance, reduce time taken to respond to work activities that impact of operational performance and lower operational costs in work communication. The study recommends that policy development should be aligned with application of e-human capital management system that contributes toward improving operational performance of an organization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".