Person-centered insights into organizational change: Identifying and analyzing profiles using latent profile analysis
Bibliographic record
Abstract
Organizational change is well-studied, yet remains a fragmented field. While existing theory and research identify various organizational change factors, empirical studies tend to examine these factors in isolation. In this respect, the change field has been ineffective in capturing meaningful profiles or configurations informed by the complex interdependencies among these change factors. This limit contributes to a narrower understanding of organizational change phenomena and how change is studied. To address this gap, we propose a person-centered approach as an accessible and effective approach for studying the underlying profiles that characterizes the complexity of organizational change. This article introduces person-centered research and provides a step-by-step guide to latent profile analysis (LPA), a flagship technique used to analyze profiles. After explaining LPA, we outline essential steps for applying this technique in the context of organizational change, illustrating the value of a person-centered approach in conducting this type of analysis. Offering practical insights for researchers and practitioners, we demonstrate how LPA can uncover hidden profiles of subgroups, providing a more nuanced understanding of organizational change. By making person-centered research more accessible, we promote its use to capture the underlying complexity and diversity of organizational change and its impact on the success of change initiatives. JEL Classification: M50
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".