PERFORMANCE MEASUREMENT AND BENCHMARKING IN FACILITIES MANAGEMENT: A SURVEY OF NORTH AMERICAN UNIVERSITIES
Bibliographic record
Abstract
Traditionally facilities management (FM) performance has not been analyzed extensively by FM professionals, especially in educational institutions. Performance management mostly focused on quantitative financial and operational measures. However, given the increased emphasis on customer orientation and optimum utilization of scarce resources it has become necessary to have a balanced view of FM performance.\n\nThis study attempted to examine the performance indicators for FM operation in universities from Balanced Scorecard perspective. It collected data on the key performance indicators tracked by FM in North American (Canada and United States) universities. It also sought organizational issues that hindered benchmarking to other organizations.\n\nA survey was administered via four modes—mail, fax, e-mail and the internet (on-line completion) to FM directors in 200 North American universities seeking information on perceived importance of various performance indicators, and their measurement and usage. A response rate of eighteen percent was attained.\n\nThe results indicated that although benchmarking and performance management is slowly gaining acceptance in universities there was an imbalance in the use of key performance indicators by FM professionals. Most of the indicators perceived important, measured, and/or used, were lag measures. Use of financial and operational indicators was also predominant. The study revealed that the association between measurement of performance indicators and their perceived importance was not always positive as\nanticipated. With respect to financial and customer perspectives there was a statistically significant negative correlation whereas for the other two perspectives the relationship was positive. The study also found no significant relationship between strategic\ndevelopment of FM departments and their drive towards CQI initiative. Relationship between strategy, and measurement and use of performance indicators were also unsubstantiated.\n\nThe results also indicated that the influence of the respondent universities' background on their FM performance could not be properly explained. In future studies, case analysis may be performed to examine the effect of this variable on FM performance. Furthermore, BSC assertion that strategy-driven organizations would have performance indicators in place to track the progress towards the goal—needs to be revisited as well.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".