Although a large body of research has attempted to answer the question, "Is a happy worker a more productive worker?" by examining the relationship between job attitudes and behaviors, results are often inconsistent. Drawing upon Fishbein and Ajzen's (1974) compatibility principle as well as theory on job attitude change and dynamic performance, the current study sought to answer this question by examining the attitudes-performance relationship at the within-persons level of analysis. Specifically, an Attitude-Engagement Model that specifies a broad conceptualization of job attitudes and behavioral engagement should exhibit the strongest relationship between job attitudes and job behaviors (Harrison, Newman, & Roth, 2006; Newman, Joseph, & Hulin, 2010) within-persons. Although relationships between these two domains have been theorized and examined within a between-subjects framework, no attempts have been made to examine these broad factors at the within-subjects level. Using experience sampling methodology (ESM), job attitudes and job behavior data were collected from 52 hairdressers, cosmetologists, and barbers across 1,438 observations. Using intensive longitudinal methods (Bolger & Laurenceau, 2013), evidence for large within-persons variability in both job attitudes and behavioral engagement was found. Evidence for the Attitude-Engagement model at the within-persons level of analysis was also provided, even after introducing a one "moment" and one "day" time lag. Furthermore, in order to provide evidence for the construct validity of the A-Factor and the E-Factor within-persons, evidence for the within-persons reliability of the assessment of change was established employing a generalizability framework. The findings have both research and practical implications for the study of attitudes and behaviors in the workplace and suggest several interesting avenues for future research.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-5973 |
| Date | 01 January 2016 |
| Creators | Glerum, David |
| Publisher | University of Central Florida |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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