Bibliographic record
Abstract
Purpose The purpose of this article is to clarify what is meant by talent management and why it is important (particularly with respect to its affect on employee recruitment, retention and engagement), as well as to identify factors that are critical to its effective implementation. Design/methodology/approach This article is based on a review of the academic and popular talent management literatures. Findings Talent management is an espoused and enacted commitment to implementing an integrated, strategic and technology enabled approach to human resource management (HRM). This commitment stems in part from the widely shared belief that human resources are the organization's primary source of competitive advantage; an essential asset that is becoming in increasingly short supply. The benefits of an effectively implemented talent management strategy include improved employee recruitment and retention rates, and enhanced employee engagement. These outcomes in turn have been associated with improved operational and financial performance. The external and internal drivers and restraints for talent management are many. Of particular importance is senior management understanding and commitment. Practical implications Hospitality organizations interested in implementing a talent management strategy would be well advised to: define what is meant by talent management; ensure CEO commitment; align talent management with the strategic goals of the organization; establish talent assessment, data management and analysis systems; ensure clear line management accountability; and conduct an audit of all HRM practices in relation to evidence‐based best practices. Originality/value This article will be of value to anyone seeking to better understand talent management or to improve employee recruitment, retention and engagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".