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“Anything worth winning is worth cheating for”? Determinants of cheating behavior among business and theology students

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Abstract

Previous literature discussing driving forces in students’ decisions to cheat in examinations has produced conflicting results. Consensus has emerged however, that in very many social situations including academic settings, in particular business administration and economics students tend to behave less socially-oriented than others. Accordingly, building on the “fraud triangle” and economic reasoning, we study cheating among—allegedly pro-social—students of (Protestant and Catholic) theology as well as—allegedly anti-social—students of business, using a sample of 850 such students from a large German university. Our results show that in fact, significant differences in cheating behavior do not exist across subjects (notably, with Catholic theology and business students displaying equally fraudulent tendencies), but rather, the incentives and opportunity costs driving students to cheat diverge: Whereas business students strongly act on incentives, theologists’ choices are driven by costs in terms of sanctions, especially by avoiding social stigma. The findings explain previous contradictory results in the literature by highlighting the underlying economic reasoning in cheating decisions, and help foster, promote, and fine-tune incentive and control schemes in the context of discouraging cheating strategies in academia.

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Notes

  1. Such cases surrounding high-profile politicians include, e.g. in Germany, Minister of Defense Karl-Theodor zu Guttenberg having to resign from office in 2011, following proof of doctorate plagiarism; Secretary of Education Annette Schavan being stripped of her doctorate as well as her professorship at Berlin University in 2014 for the same reason; likewise in Switzerland, National Councillor Doris Fiala lost her degree in 2013; in Romania, Prime Minister Victor Ponta lost his doctorate due to plagiarism in 2012, announced to resign (which, however, he never put into practise); in Austria, Johannes Hahn, since 2014 Commissioner for European Neighbourhood Policy and Enlargement Negotiations was repeatedly accused of plagiarism, destabilizing his political ambitions; in Hungary, the “Pál Schmitt doctorate plagiarism scandal” led to Schmitt’s resignation from his presidential post in 2012. Examples involving prominent business people, academics and government officials are numerous across nations.

  2. Here, “cheating” refers to copying from neighbors, inadmissibly talking to others, using unauthorized material, making use of smartphones and the like in order to obtain a better grade in an examination.

  3. Whereas most studies from a US-American background look upon students of “economics”, denominations of study subjects vary across literature and context; e.g., Frey and Meier (2002) compare Swiss students of “political economy” (a discipline known to Americans as “economics”) with students of “business economics” (known to Americans as “business administration”) (Bauman and Rose 2011). In the following, we refer to “business administration” and “economics” students as “business students”.

  4. Although one could question the distinction between costs and incentives (“if something is costly, this is an incentive not to do it”), we distinguish between “incentives” and “costs” as the first is a “positive” motivational factor, whereas the other is a “negative” factor, and people might react more strongly to either (e.g., be more oriented towards avoiding “losses” than realizing “gains”).

  5. Other studies come to diverging conclusions, suggesting that economists are more likely to engage in pro-social behavior than others: Laband and Beil (1999) study dues payments to professional associations and find that ‘professional economists are significantly more honest/cooperative than professional political scientists and especially, professional sociologists’. Yezer et al. (1996) attack the notion that the ‘exposure to the self-interest model commonly used in economics alters the extent to which people behave in self-interested ways’ (Frank et al. 1993), arguing instead that different performance in structured games results from a better understanding of the non-cooperative behavior of people in general (Frank and Schulze 2000). For an overview of studies that investigate the determinants of individuals’ self-selection into different fields of studies including business management, see “Appendix 2”.

  6. We check for absence of multicollinearity using Variance Inflation Factors (all below the value of 3.4), heteroscedasticity with Ramsey Tests, and the distribution of disturbance terms with Kolmogorov–Smirnov tests.

  7. When there are three or more groups in a MANOVA-ANOVA analysis, it is customary to carry out simultaneous multiple paired comparisons that contain the experiment-wise error to a pre-specified level (usually, 0.05). Thus, Tukey’s simultaneous multiple paired comparisons were carried out to ferret out the sources of differences across the groups. We use MANOVA in order to detect significant differences among student groups in the coefficients of the variables under study. We could not detect such differences reliably if applying different regression models for the student groups, as the coefficients generated in different samples, for statistical reasons, are not directly comparable.

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Authors and Affiliations

  1. Institute of Strategic Management, Westfälische Wilhelms-Universität Münster, Leonardo-Campus 18, 48149, Münster, Germany

    Brinja Meiseberg & Thomas Ehrmann

  2. Institute of Public Economics II, Westfälische Wilhelms-Universität Münster, Wilmergasse 6-8, 48143, Münster, Germany

    Aloys Prinz

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  1. Brinja Meiseberg
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  2. Thomas Ehrmann
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  3. Aloys Prinz
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Correspondence to Brinja Meiseberg.

Appendices

Appendix 1

See Table 6.

Table 6 Overview of measures
Full size table

Scale reliability was assessed by computation of composite reliabilities (CR). Results indicated scale reliability throughout. Coefficient α values all ranged well above the conventional benchmark of 0.70 (Nunnally et al. 1994). When factor analyzed, all factor loadings were found to be highly significant on their respective constructs and there are no cross-loadings larger than 0.30, which indicates convergent validity (Bagozzi et al. 1991; Homburg et al. 2008). Convergent validity was also assessed by computing the average variance extracted (AVE) estimates. Fornell and Larcker (1981) suggest that AVE estimates of 0.50 or larger are indicative of convergent validity (all estimates are >0.50). Discriminant validity of the constructs was evaluated by comparing AVE with squares of inter-trait correlations (Fornell and Larcker 1981). Our smallest square root of AVE is 0.80 which correctly exceeds all correlation coefficients.

We tested for nonresponse bias by comparing early and late respondents in terms of key study variables, without significant differences. We checked for common method bias using Harman’s single factor test (Podsakoff et al. 2003); as expected, the test yielded more than one factor and no one factor accounted for most of the variance. We also controlled for: gender (Cadsby and Maynes 1998); student’s average grade (Nowell and Laufer 1997); student’s age and employment hours outside the university per month (Nowell and Laufer 1997); family background (Hrabak et al. 2004); moral orientations; religiousness (Freiheit et al. 2006); opinions on (un)fairness of cheating (Bunn et al. 1992; Singhal 1982); and expectations towards “standards” of ethical behavior in the future job environment (Lawson 2004). No significant effects emerged.

Appendix 2

See Table 7.

Table 7 Self-selection into different fields of studies
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Meiseberg, B., Ehrmann, T. & Prinz, A. “Anything worth winning is worth cheating for”? Determinants of cheating behavior among business and theology students. J Bus Econ 87, 985–1016 (2017). https://doi.org/10.1007/s11573-016-0842-4

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