Work ethic and academic performance: Predicting citizenship and counterproductive behavior

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Abstract

In this study, work ethic was examined as a predictor of academic performance, compared with standardized test scores and high school grade point average (GPA). Academic performance was expanded to include student organizational citizenship behavior (OCB) and student counterproductive behavior, comprised of cheating and disengagement, in addition to college GPA. Results indicated that work ethic explained incremental variance in student OCB, cheating and disengagement beyond standardized test scores or high school GPA. However, work ethic did not explain incremental variance in college GPA. Specific work ethic dimensions were related to each outcome. These findings provide support for the importance of non-cognitive variables in academic contexts, particularly when considering an expanded performance domain. In addition, results provide additional validity evidence for the nature of work ethic as a multidimensional construct.

Highlights

► In this study, work ethic was compared with traditional predictors of academic performance. ► Academic performance was expanded to include grade point average, citizenship behavior and counterproductive behavior. ► Work ethic dimensions were differentially related to academic performance outcomes. ► Work ethic explained incremental variance beyond traditional predictors in citizenship and counterproductive behavior.

Section snippets

An expanded academic performance domain

Most studies on academic performance have examined college GPA or graduation rates. Scholars have called for an expanded academic performance domain, where additional information can be measured by looking beyond traditional outcomes (Kaufman & Agars, 2009). Organizational scholars have explored an ‘expanded criterion domain’, capturing information not measured as in-role/task performance (Podsakoff, MacKenzie, Paine, & Bacharach, 2000). Relevant performance outcomes include organizational

Work ethic and academic performance

Originating in the work of Max Weber (1958), work ethic has been conceptualized in several different ways over the past century (Furnham, 1984, Furnham, 1990). Miller et al. (2002) defined work ethic is a multidimensional construct that pertains to work-related activity and generalizes to other domains, such as education. In addition, they developed the multidimensional work ethic profile (MWEP), which has become one of the most widely-used measures of work ethic in recent years. Specifically,

Sample

Participants were 221 undergraduates at a large Midwestern US university. The sample was 80% female, and the mean age was 21.61 years (SD = 6.0). The sample was composed of 4% first-year, 30% second-year, 30% third-year, and 36% fourth-year students. The sample's racial composition was 62% Caucasian, 28% African-American, 6% Asian American, and 1% Latin American.

Work ethic

The MWEP (Miller et al., 2002) is a 65-item scale that measures seven dimensions of the work ethic construct. All items were rated on a

Results

Correlations among the MWEP dimensions, high school GPA, ACT scores, and performance outcomes are presented in Table 1. The pattern of relationships among the MWEP intercorrelations is consistent with previous research (e.g., Miller et al., 2002). In addition, both high school GPA and ACT scores are positively correlated with college GPA, and commensurate with values reported in previous research and technical reports.

Regression coefficients were examined to test Hypothesis 1, Hypothesis 2,

Discussion

These findings support the relevance of work ethic for academic performance, particularly when performance is operationalized as comprising OCB and CPB. Following suggestions to expand the student performance criterion domain (Kaufman & Agars, 2009), these results provide additional support for the use of non-cognitive predictors in high-stakes settings in addition to cognitive predictors, as each explained unique variance in performance outcomes. Specifically, work ethic explained incremental

Acknowledgement

This research was supported by a grant from the University of Missouri Research Board.

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