Organizational climate, commitment and satisfaction: a case of Universiti Teknologi MARA
Bibliographic record
Abstract
Organizational climate (Field and Abelson, 1982; Guion, 1973; Joyce and Slocum, 1982), organizational commitment (Meyer and Allen, 1997; Steers, 1975; Whitener, 2001) and job satisfaction (Spector, 1988; Lambert, Hogan and Barton, 2001) have been regarded as important constructs in organizational research for many years. Research has directly associated organizational climate with organizational commitment (e.g., Brown and Leigh, 1996; Welsch and LaVan, 1981). The findings of these studies are of significant to employers. For example, a study by Watson (1999) has shown that organizations with highly committed employees experienced greater 3-year returns to shareholders than organizations with low employee commitment (Watson Wyatt, 1999). Another study (c.f. Welsh and LaVan, 1981) found that lack of employees' commitment is related to employer high costs and poor performance. In terms of satisfaction, Lambert, Hogan and Barton (2001) found that job satisfaction acts as a mediating variable between work setting and intention to leave an institution. Literature is proliferated with studies on organizational climate, organizational commitment, and job satisfaction. In spite of the numerous climate, commitment, and satisfaction studies, there appear to be a vacuum in these studies that explain how organizational climate, organizational commitment, and job satisfaction constructs relate (Grant, 2002; McNabb & Sepic, 1995). More specifically, no research exists that examines these constructs in a university environment in countries other than in America or its closest neighbour, Canada. To address this lack of knowledge and to provide viable information to decision-makers within the local universities to make informed decisions, the focus of this study is on university personnel (full-time academics, administrative staff, and supporting staff) perceptions of their organizational climate. Because existing research indicates discrepancies between perceptions of climate by employees at different levels of the organization (James & Jones, 1974; Moran & Volkwein, 1988; Putti, Aryee, & Phua, 1990), administrative, faculty, and staff perceptions will be surveyed in order to examine the university as whole as well as possible sub-climates that might exist in the university environment.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".