Bibliographic record
Abstract
Purpose This article intends to provide guidance to HR professionals and others involved in the planning and implementation of post‐merger integrations. It seeks to argue that successful integration requires an “art and science” based approach to organizational change, and to illustrate the importance of this approach by drawing on a case study project from the financial services sector. Design/methodology/approach Using a case study approach, the article describes key learnings from a financial services sector post‐merger integration project in which the author was directly involved. The problems and challenges that arose in the case study organization are described, and it is shown how these were addressed using the “Art and Science of Transformation”TM conceptual approach to achieve a successful integration. Findings In the case study project, a lack of detailed integration plans and the absence of integration performance metrics, as well as inadequate understanding of the likely impact of cultural incompatibilities, were identified as representing risks to successful merger. To mitigate these risks, an art‐ and science‐based approach was implemented. This included the development of an integration performance measurement system and a communications strategy, while a phased approach was taken to the integration. Use of the art and science approach to post‐merger integration helped contribute to a financially and operationally successful merger, despite the early risks and the contrasting corporate cultures involved. Research limitations/implications The article is based on a single case study from the Canadian financial services sector, and is written from the perspective of the author, who worked on this project as an external consultant. The specific types of problems and challenges relating to post‐merger integration will vary between organizations and sectors, but the examples discussed in this article are believed to be typical, and of value in demonstrating the importance of an art and science approach to post‐merger integration. Practical implications Post‐merger integration represents just one form of organizational change, and the “Art and Science of Transformation” approach is equally relevant and valuable to other types of projects. The evidence from previous research is that a high percentage of organizational transformations fail to meet their objectives or are abandoned before completion, with project failures most often due to a lack of attention to people‐related factors. By adopting the approach discussed in this article, organizations can help to reduce the risk of failure by achieving a good balance between the art and the science of change. Social implications Unsuccessful organizational change initiatives are wasteful of financial and human capital resources, and may result in demoralized employees – especially if they feel that their experience and skills are not being effectively utilized in the change initiative. The Art and Science of Transformation approach helps ensure that organizational change initiatives build efficiently and effectively on available human and other organizational resources to achieve positive outcomes. Originality/value The Art and Science of Transformation framework was developed by Schroeder & Schroeder Inc. on the basis of its experience of helping organizations achieve successful change. Although other studies have examined the factors associated with successful post‐merger integration using a case study approach, the application of this framework to the post‐merger integration context is unique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.039 |
| Scholarly communication | 0.033 | 0.021 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.010 |
| 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".