Functional Flexibility in Human Resource Management Systems: Conceptualization and Measurement
Bibliographic record
Abstract
In spite of its strategic relevance, functional flexibility has still not been clearly defined by the literature. Drawing on different conceptualizations and measurement tools, empirical studies have reached different and even contradictory findings. This paper proposes a construct to define and measure functional flexibility in the field of Human Resource Management. In doing so, we will try to clarify this concept, classifying previous definitions and contributing with an integrative conceptualization. In the first part of this work we discuss and justify the need for certain level of functional flexibility in human resource management systems, which helps the organization to improve its capacity to adaptat to current environments. From this point of view, this capacity is considered as a relevant source of competitive advantage. Drawing on our theoretical analysis, we propose a new functional flexibility construct and a measurement model that could help to develop deeper analysis on this topic. This construct would serve as a starting point in the definition of a measurement scale for functional flexibility. Future research lines derived from this analysis are discussed in the last section of the paper. Specifically, we discuss the need to analyse how exploration and exploitation of human capital can be combined in flexible human resource management strategies.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".