Enhancing facilities management through generational awareness
Bibliographic record
Abstract
Purpose Facilities management centers on the triad of people, process, and place, but the element of people is incomplete without recognition and consideration of the different generations that make up today's workforce and the differences between these groups. The purpose of this article is to suggest that facilities managers should take advantage of available current information on generational differences, in order to maximize their ability to manage people and knowledge. Design/methodology/approach This article presents pertinent findings from a recent pilot study that surveyed 55 facilities management professionals from the mid‐Western USA, presents a brief overview of current knowledge relating to generational differences, and highlights the relevance of such knowledge to effective facilities management. Findings Almost one quarter of the respondents to the pilot study did not agree that knowledge of generational differences was important, while about half of the respondents only somewhat agreed that it was important. However, a survey of relevant literature suggests that successful management of generational differences in the workplace has the potential to improve the efficiency and viability of an enterprise, including facilitating knowledge management. Research limitations/implications The current study is limited by its small sample size. Additional research is needed to further examine the value facilities managers place on generational knowledge and the relationship between facilities management and knowledge management. Originality/value The current paucity of information regarding the relationship between generational differences, facilities management, and knowledge management makes studies like this one relevant and valuable to facilities managers operating in a workplace with unprecedented generational diversity and an increasingly knowledge‐driven economy.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".