The affective revolution in the organizational sciences has yielded a body of theoretical and empirical research examining the relationship between affect and performance. This work has typically advanced affect as a predictor of performance; however, more recent theory suggests that the relationship between affect and performance is reciprocal. Since little empirical work exists supporting reciprocity between affect and performance, the purpose of this dissertation is to test if affect and performance are actually reciprocally related. Importantly, the advent of longitudinal and experiential research designs in the organizational sciences affords empirical opportunities to test such theory. This dissertation examines the temporal patterning of relations between affect and performance using longitudinal meta-analysis. Using longitudinal meta-analysis, this dissertation shows that the relationship between affect and performance is equivalently reciprocal (i.e. performance predicts affect to the same extent that affect predicts performance) and that the relationships between negative affect and performance and positive affect and performance are similar in magnitude (i.e. there is no positive-negative asymmetry). This dissertation also suggested that positive affect and negative affect are compatible with a broad performance construct (i.e. task performance, OCB, CWB and withdrawal). Finally, this dissertation examined important measurement moderators and found: (a) affect is reciprocally related to episodic performance; (b) affect and performance are reciprocally related when time between measurements are longer than a month; and (c) state affect measures and trait affect measures both have reciprocal relationships with performance. This meta-analysis benefits the organizational sciences by providing support for existing theories of affect as a predictor of performance (e.g. "happy-and-productive" theories) and by validating theories which suggest that affect and performance are reciprocally related.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-6923 |
| Date | 01 January 2017 |
| Creators | Lapalme, Matthew |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
Page generated in 0.0264 seconds