Minimising Risk—The Application of Kotter’s Change Management Model on Customer Relationship Management Systems: A Case Study
Bibliographic record
Abstract
Implementing a Customer Relationship Management (CRM) system requires significant consideration with respect to change management and the associated business risks. This paper describes how to best achieve the change goal and minimize these risks. The research question under investigation is: “How can Kotter’s change management model be used effectively to enhance the value and utilisation of a CRM system”. Kotter’s eight-stage change model is the adopted change model used by the organisation under study. As business intelligence (BI) is a growing field within industry and academia alike, limited substantive research has been done regarding how to manage the change process itself within a BI project. Often research either focuses on the technical development (e.g., agile methodology) or the change process from a holistic perspective. However, both are needed to effectively manage the risk of failure. The research design for this study was that of a single organisation case study. The research questions were addressed by using a deductive research style. To allow for multiple perspectives and triangulation of the data, a mixed-methods approach (Quant + QUAL) was used. Outcomes of the research showed that whilst there was some success in the implementation of Kotter’s change model, it could have been significantly improved if the competencies identified in this research were considered and incorporated prior and during the change journey. Building on Kotter’s classic work with change management, this research fills the gap by describing the pertinent competencies required in managing the change process, identifying common pitfalls and investigating the common threads between the ‘data to outcome’ process and the change management process to better mitigate the risk This paper adds value to current change literature/models by defining and describing the importance of these competencies when embarking on a change program related to BI tools and systems and how these competencies are incorporated into Kotter’s model.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".