Bibliometric Review of Research Trends in Facilities Management Technologies from 2000 to 2023
Bibliographic record
Abstract
Technologies such as building management systems, building information modelling, computer aided design, virtual reality, internet of things, geographic information systems and robotics have been adopted to achieve effective Facilities Management (FM). However, little attempt has been made to map global research trends in FM technologies. This paper presents a bibliometric review on the application of technology in FM with the aim of determining research trends and gaps in this research area. A total of 107 publications on FM technologies from 2000 to 2023 were reviewed. The analysis was done with the VOSviewer software (version 1.6.13). The analysis focused on bibliographic coupling of countries, co-authorship of authors, citation of documents, and co-occurrence of keywords, which were presented as network visualization maps. The findings revealed that the publication trends in FM technologies have been upwards since 2000, although not completely gradient. Italy, United States, Mainland China, South Korea, Australia, United Kingdom, Sweden, China Taiwan, Canada, and Malaysia are the countries that have had great impact in this research area. Both the keywords and documents citation analysis revealed three distinct phases in the evolution of FM technologies research – energy efficiency technologies, BIM technologies, and advanced BIM technologies/internet of things. It also became evident that Africa lags in this area of research. This study helps to determine the range of technologies used for managing facilities and how such technologies have evolved. This can help facilities managers to become familiar with the current and cutting-edge technologies that could promote effective and efficient FM. Only one data source - Scopus - was used. Therefore, the representation of publications presented in this study are limited. However, the findings contribute to the body of knowledge in FM technologies.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.171 | 0.259 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".