Interplay between P-O fit, transformational leadership and organizational social capital
Bibliographic record
Abstract
Purpose Using social identity theory, the authors hypothesize that transformational leadership (TL) leads to better person-organization fit (P-O fit), which in turn contributes to the emergence of organizational social capital (i.e. OSC). Furthermore, the authors suggest that the relationship between P-O fit and OSC is contingent upon the level of TL. The paper aims to discuss these issues. Design/methodology/approach Field study data were used to test the hypotheses. In total, 336 employees from eight different service sector organizations in Pakistan participated in this study. Hierarchical linear modeling was used to analyze the data. Findings In support of the hypotheses, the authors found that TL was positively related to both P-O fit and OSC. Also, P-O fit mediated the TL-OSC relationship. Finally, TL moderated the relationship between P-O fit and OSC. Research limitations/implications Cross-sectional data were collected through self-reports, which raises concerns of reporting bias. Practical implications Managers can benefit from the study by focusing on TL as a vehicle for not only achieving change, but also for creating an environment that facilitates better P-O fit and enhanced OSC. Social implications This study provided a rare opportunity to examine the proposed relationships in a developing country. This enhances our insight into the efficacy of theories that have been mainly developed and tested in developed countries. Originality/value Previous research hypothesized P-O fit as a mediator between leadership and performance, yet failed to receive support. The current study is unique by demonstrating that TL, as a relational leadership style, contributes to building an important resource (OSC) through the mediating effect of P-O fit.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".