Managing resistance to change in workplace accommodation projects
Bibliographic record
Abstract
The new management paradigm states that managing people is about managing feelings. For many people, change is a very personal and emotional issue, and can be difficult, especially when it involves their work environment. Employee resistance can pose significant obstacles to the planning and development of an office space relocation, particularly for projects that attempt to change the way in which people work. The relocation of employees is expensive, in terms of both operational costs and investments. This paper deals with both the psychological as well as the economic impacts of introducing a change. It is intended to equip facility managers who are delivering projects to understand not only the change process, but also more importantly, to discern why employees resist change and provide them with a multifaceted approach to facilitating the change process. One key element for managing the resistance to change is the use of effective, ongoing and varied communication vehicles. This paper includes an inventory of recommended communication tools that have proved to be both effective and successful. It will share experiences through ‘lessons learned’, that will demonstrate how ‘skipping steps’ in the process can jeopardise the success of the project. It is hoped to establish that time and resources expended towards the management of the resistance to change equate to time and effort well spent and can make the difference between success and failure.
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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".