Training Employees to Be a Source of Sustained Competitive Advantage
Bibliographic record
Abstract
The purpose of this paper is to demonstrate that there is an important role for learning and development professionals in helping the firm achieve Sustained Competitive Advantage (SCA) and, furthermore, to explain how this can be achieved. This hypothesis is developed through an extensive review of the scholarly literature and leads to the formulation of a practitioner's guide for learning and development professionals. The study finds that employees can be a direct source of SCA for their firm by applying Resource-based Theory (RBT) through employee training and development initiatives and programs. The research findings are limited in that the paper is conceptual and while a great deal of evidence exists to support its central premise, the hypothesis is yet to be tested through empirical research. There are important practical implications of the study for learning and development specialists and consultants, for employees, human resource professionals, organizational strategic planners and organizations at large. Specifically, the paper points toward new directions for learning strategy. This paper has originality in that it appears to be the first to explicitly link the practical application of RBT with employee training and development initiatives and to provide examples of how this can be achieved.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".