Organizational change capability: a scoping literature review and agenda for future research
Bibliographic record
Abstract
Purpose At a time when organizations are faced with increasing transformations, developing a strong change capability has become crucial to deal with the ever-changing environment. While in recent years, the literature on organizational change capability (OCC) has grown, the understanding of this construct remains overly underdeveloped. Therefore, the purpose of this paper is to provide an in-depth synthesis of the evidence on OCC. Design/methodology/approach A scoping literature review was conducted on peer-reviewed articles published over the past two decades. Findings This review shows that while research largely treats change capacity, change capability and change competency as synonymous, these terms should be interpreted differently since they do not refer to the same organizational phenomenon. Research limitations/implications Although this review focus on the past two decades, this article offers an examination of the latest knowledge on OCC and provides a non-exhaustive set of research avenues. This review also proposes a change maturity framework that can help scholars to conduct more informed investigations. Practical implications The proposed framework can help practitioners to better understand how an organizational potential for change can transform into a change capability, which in turn can evolve into a change competency. Originality/value This review extends prior work by clarifying ambiguities around some constructs in the management field that are fundamental to building sound theories.
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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 | 0.043 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.037 | 0.034 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".