Bibliographic record
Abstract
Technology advances have dramatically affected the ways in which we manage and organize work. With new evolutions of technologically mediated systems, the development of electronic human resource management (e-HRM) practices becomes more accepted for many organizations. For example, on the one hand, organizations have been able to extend job searches to attract new employees from around the globe using the World Wide Web. This recruitment feature has provided matches of special skilled workers with employers and has lessened recruitment costs for other searches for less skilled positions by bringing potential candidates directly to the organization. On the other hand, HRIS technologies within operation structures such as call centers have been tightly integrated into e-HRM practices creating heavily defined performance management systems. The developments in the call center area specifically have resulted in an interesting convergence of HRIS and HR architectures to explore lessons learned and future directions in e-HRM. The purpose of this article is to first provide a background of the call center developments over the past 15 years in light of e-HRM. Specifically, a focus on the technological advancements in call center operations will be overlaid with the developments of e-HRM practices to reveal the ways in which both are integrated and implemented to create an end-to-end process. The second focus of this article is on the development of performance management HR practices such as electronic performance monitoring (EPM), e-coaching, and e-learning using HRIS integrations. Although in many ways this integrated e-HRM model has improved organization performance and effectiveness, there have also been other implications resulting in negative affects on performance outcomes such as turnover, job satisfaction, and customer satisfaction. So finally, this article will draw out the lessons learned from the call center model and e-HRM with a focus on the balance between human resource management practices and operational structural design.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".