Sustainable facilities management: a sociotechnical system perspective and a review of the literature
Bibliographic record
Abstract
Purpose Sustainable facilities management (SFM) research necessitates a sociotechnical system perspective as core organizations and facilities management (FM) suppliers are subject to multiple pressures while participating in buildings’ use, operation, and maintenance. The purpose of this study is to examine the SFM literature and improve understanding of factors that influence SFM practices using a sociotechnical transitions approach. Design/methodology/approach This study, first, examines facility management and sociotechnical system approaches to develop a framework that conceptualizes pressures and management processes that may impact the transition of FM practices to more sustainable ones. In a second step, the framework is combined with a systematic literature review of studies published between 2000 and 2023 to examine pressures applied to FM firms, explore responses and management processes and the evolution of FM practices, and identify research gaps. Findings The review findings indicate that the factors proposed by a sociotechnical system framework are examined in the SFM literature and that FM regimes acknowledged the applied pressures and responded by adapting their strategies, updating technical knowledge and capabilities, establishing new governance mechanisms, and modifying the industry identity and mindset. Research limitations/implications The review is limited to SFM literature between 2000 and 2023. An evolutionary sociotechnical system perspective of SFM practices necessitates context-specific research. Originality/value The study responds to the call for a sociotechnical system view of SFM and adds a sociotechnical transitions perspective to the development and evolution of SFM research. It organizes the current SFM literature, points to the need for context-specific research, and allows for the identification of future SFM research directions.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".