Putting the Behaviors into Family-Supportive Supervision: The Development of a Behavioral Typology
Bibliographic record
Abstract
Widely recognized as an essential management practice that can meaningfully shape key employee attitudes and behaviors, family-supportive supervision (“FSS”) represents a form of managerial support aimed at helping employees effectively navigate the work-family interface. Despite the importance of perceived FSS for both individual and organizational outcomes, questions remain regarding the actual behaviors supervisors engage in to provide this targeted family support (or not). Consequently, substantive conceptual (e.g., ambiguity) and operationalization issues (e.g., confounded variance, abstraction) have stemmed from the lack of clarity and parsimony in the study of FSS. To address these limitations, we conducted a multi-phase mixed methods investigation with three unique samples of full-time employees to develop a behavioral typology of FSS behaviors. The resultant typology is distinguished by two dimensions (i.e., valence and effort) and organized into six categories that together span the full spectrum of unsupportive-supportive supervisor behaviors. This behavioral index and typological structure subsequently help to guide the refinement of the conceptualization of FSS and lays the groundwork for the creation of more objective FSS measures—a foundational step necessary for the advancement FSS research and practice.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".