Environmental satisfaction: more than a hygiene factor
Bibliographic record
Abstract
Frederick Herzberg's (1966) two-factor theory of work motivation relegated environmental factors to the realm of 'hygiene' factors, for which motivation would not improve if conditions improved beyond some minimal level. This presentation will summarize field and laboratory research that counters this dismissal. Our research group and others has used laboratory methods to demonstrate that people prefer a mixture of direct and indirect lighting that lights the entire workspace, and individual personal control over the local lighting. Both mediated regression models and path analysis of experimental data have linked people's appraisal of their workstation lighting with the appearance of the office, their mood, and their satisfaction with the work environment overall; one study found that the improved mood positively predicted work engagement. Field investigations have taken this farther, showing that satisfaction with the lit environment is an indirect positive predictor of job satisfaction, organizational commitment, and intent to turnover, and a negative predictor of health problems. These findings are notable because all of the workplace conditions studied is more than merely adequate. Steps taken to improve employees' satisfaction with the working environment do influence their motivation at work, in ways that should benefit both individuals and their employers.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".