A Meta-Analysis on the Motivation for Cheating Using a Theory of Planned Behavior

• In Event: The "Dark Side" of Motivation: Unpacking Malleable Personal Factors Associated With Academic Dishonesty

Abstract

To address the rampant prevalence of academic dishonesty, educators and psychologists have turned to student motivation as a possible solution (Whitley, 1998). Associations between achievement motivation variables have been examined in theoretical models (Anderman & Koenka, 2017; Murdock & Anderman, 2006) and meta-analytic reviews (Authors, anonymized); however, less is known about motivations to cheat specifically.
Theory of Planned Behavior (TPB; Ajzen, 1991) provides a framework to investigate cheating-specific motivations as it relates to cheating behaviors. A central motivating factor in TPB is intention to perform a behavior such as cheating (Beck & Ajzen, 1991). Intentions are predicted by three determinants: a) attitude, the degree to which the person has a favorable evaluation of cheating; b) subjective norms, the perceived normative expectations regarding cheating; c) perceived behavioral control, the perceived ease of cheating. In our study, we wanted to extend traditional TPB determinants by also assessing the perceived costs associated with cheating as an additional, complementary determinant. Using meta-analysis, we were guided by the following question: What are the associations between cheating behaviors and students’ attitudes, subjective norms, perceived behavioral control, and costs towards cheating? To explore heterogeneity among these associations, we also assessed moderator variables.
After locating 8,408 potentially relevant reports from an exhaustive search through electronic databases, forward and backward citations, author queries, and listserv requests, we identified 93 studies that met our inclusion criteria of (a) a cheating measure, (b) a measure of cheating-specific attitude, cost, subjective norms, or perceived behavioral control, and (c) an effect size. Coders reliably extracted effect sizes and other study characteristics such as sample demographics (age, gender, academic major) and cheating characteristics (form of academic dishonesty, academic task, domain-specificity). Meta-analytic procedures included random-effects weighting procedures and robust standard variance when synthesizing effect sizes.
First, we examined overall relations between each of the TPB determinants and cheating behaviors (Table 1): attitude (r =.41); subjective norm (r =.31); perceived behavioral control (r =.36); perceived cost (r = -.13). All average weighted correlations were significantly greater than zero and considered small to moderate (Cohen, 1988). Second, we conducted subgroup moderator analyses to examine if study characteristics explained variability among effect sizes (Table 2). Although we found most moderators were not significant or did not have sufficient variability within moderator categories, the relation between perceived behavioral control and cheating was significantly moderated by percentage of STEM majors in their sample (β = -.62). This moderation suggests that for samples with more STEM students, their perceived behavioral control is less associated with cheating.
It is notable that costs or consequences as a result of cheating are less associated than attitude and perceived behavioral control towards cheating. Educators who threaten students with punitive consequences of cheating may wish to minimize perceptions that cheating is viable and favorable. Given the connection between subjective norms and cheating, it is critical to create cultures among peers suggesting cheating is inadvisable, perhaps by creating mastery-oriented environments. Future research should explore whether these associations may differ by domain (STEM vs. non-STEM).

Authors

• Carlton J. Fong, Texas State University

• Megan Krou, The University of Texas

• Meagan Ann Hoff, Collin College