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Record W1993649636 · doi:10.1108/14725960310808024

Managing resistance to change in workplace accommodation projects

2002· article· en· W1993649636 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Facilities Management · 2002
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsPublic Works and Government Services Canada
Fundersnot available
KeywordsRelocationProcess (computing)Resistance (ecology)Work (physics)BusinessChange management (ITSM)AccommodationProcess managementFeelingChange orderKey (lock)Space (punctuation)MarketingOperations managementComputer scienceProject managementManagementProject planningEngineeringPsychologyEconomicsComputer security

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.518
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.058
GPT teacher head0.294
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it