A Qualitative Investigation on theImpact of Human Resource Information Systems (HRIS)on the Organisation Performance in theRetail Sector of Canada
Bibliographic record
Abstract
Purpose The primary and the main purpose of the research is analysing and evaluating the impact of the HRIS on organisational performance in the retail sector of Canada. Furthermore, the research emphasises on determining the importance and functions of the HRIS along with ascertaining its impact on the performance of firms and identifying the challenges faced by organisations in implementing the portal of the HRIS, especially in the retail sector of Canada. Overall, the aim and objective of the research study is evaluating the concept of the HRIS and the implication of this software in the HR departments of various organisations. Methods/ Data Collection/ Data Evaluation The research approach or method used in the project is the research onion method that provides different layers for choosing or selecting distinct research methodology components. The research project utilises qualitative methods. The qualitative data is collected in the research by the help of the semi-structured interview with the integration of interpretivism research philosophy. Primary data has been collected to gather real-world data and delve into various unexplored facts of the HRIS through an interview method and secondary data has been derived by going through academic books, latest journal articles and exploring credible websites. The selection of the sample for the research has engaged the non-probability sampling technique. Further, using the purposive sampling technique, 8 HR managers were selected to obtain data. The HR managers from food, apparel, grocery, and drug retail sectors have been selected for an interview and two HR managers are recruited for the interview from each retail sector. Further, the thematic analysis method is used for examining interview data. Findings The primary findings show that the HRIS has profoundly transformed the HRM functions in the retail sector of Canada. Implications The research shows meaningful insight into the HRIS with its features and impact on the overall performance of organisations in the retail sector. Limitations Primary data that was integrated with the help of the semi-structured interview method requires interviewing enough individuals to draw an optimum level of conclusion and make further comparisons. Further, the interview lacks exact facts and figures, and on the other side, the secondary data generally lacks accuracy and originality. Similarly, due to COVID-19, there were various hurdles in measuring primary data through primary resources.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".