Abstract
Employee reporting dishonesty is a significant area of concern for firms. In this study, we investigate how providing information about their prosocial actions, such as organizational citizenship behaviors, affects the extent of employee reporting dishonesty. We distinguish prosocial actions whose welfare effects are mutually beneficial (i.e., that help others and the employee), which are common in business practice, from those that are selfless in nature (i.e., that help others at a personal cost to the employee). In addition to examining the effect of the type of prosocial action on the extent of employee reporting dishonesty, we also examine the effect of construal (the manner in which individuals perceive and interpret the action). Using an experiment, we find that participants with high moral identity are less dishonest when they describe their selfless prosocial actions than when they describe their mutually beneficial prosocial actions, but only when they abstractly construe this information. However, we do not find evidence that the reporting dishonesty of participants with low moral identity is influenced by the type of prosocial action they provide information about or the construal of that information. We discuss implications of these results for theory and practice.
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Data Availability
The data is available from the authors upon request.
Notes
For brevity, hereafter we refer to employee reporting dishonesty simply as employee dishonesty. An alternative way to frame our paper is to characterize employee dishonesty as employee honesty because the level of employee honesty is inversely related to the level of employee dishonesty.
Mutually beneficial prosocial actions are conceptually consistent with the theory of shared value (Ballou et al., 2012; Porter and Kramer 2006, 2011), which pertains to firms’ CSR activities. Specifically, the theory of shared value outlines that CSR activities can be symbiotic, enhancing social welfare while also creating economic benefits for firms. Similarly, CSR activities that employees conceive, design, and execute that benefit both society and the firm (and by extension the employees) fall under the umbrella of mutually beneficial prosocial actions we discuss in our study.
We acknowledge that prior research provides conflicting evidence about the consistency between past prosocial actions on subsequent behavior (Joosten et al., 2014), which provides tension for our H1 prediction. Namely, moral licensing theory predicts ethically inconsistent or compensatory behavior rather than consistent behavior. That is, moral licensing theory posits that past ethical behavior licenses individuals to subsequently behave unethically (Miller and Effron 2010). While some studies provide evidence consistent with a moral licensing effect, the effect has proven difficult to replicate (e.g., Blanken et al., 2014). This may in part be due to the fact that the “moral licensing effect is small to medium in effect size” (Blanken et al., 2015) in contrast to “decades of research in social psychology [that] support the notion that individuals have a strong drive toward consistency (e.g., Beaman et al. 1983, Burger 1999, Festinger 1954, Gawronski and Strack 2012)” (Mullen and Monin 2016). Thus, we rely on moral identity theory to formulate our predictions rather than moral licensing.
Similarly, accounting research documents evidence of consistency in prosocial behavior by firms’ decision-makers. This research reports that firms’ socially responsible behavior is negatively associated with unethical practices such as aggressive earnings management (Kim et al., 2012), insider trading (Gao et al., 2014), and high-profile misconduct (Christensen 2016). In addition, Hoi et al. (2013) find that firms with better CSR performance engage in less aggressive tax avoidance than those with poorer CSR performance.
For brevity throughout the paper, we discuss the accessibility of individuals’ moral and self-interested identities within their working self-concept simply as the accessibility within their self-concept.
Psychological distance is the subjective perception of something’s distance from the self and can vary along dimensions of time, space, social distance, and hypotheticality (Trope and Liberman 2010).
Recent research investigates the accounting implications of CLT and finds that different levels of construal can affect auditors’ professional skepticism and evidence processing (Rasso 2015), employees’ perception of managers’ evaluations (Choi et al., 2016), and how auditors evaluate management’s assumptions (Backof et al., 2018).
Many prior accounting studies have used the mTurk platform (e.g., Bonner et al., 2014; Grenier et al., 2015; Koonce et al., 2015; Rennekamp 2012; Rennekamp et al., 2015). See Mason and Suri (2012) and Rennekamp (2012) for a detailed overview of this subject pool. Studies conducted on mTurk have produced results consistent with those obtained in formal laboratory settings and have reliably replicated prior research that uses alternative experimental methods (see Farrell et al., 2017 and Prickett and Moreton 2014).
We have participants write about a real past personal action rather than using a hypothetical action because a real personal action is more likely to affect the accessibility of each participant’s moral identity and will therefore provide the best test of our theory. This design choice is also consistent with our setting of interest in which employees provide information about a decision for which they feel personally responsible. It is possible that employees also take ownership of the “firm’s actions” that they are not personally involved with. Consistent with this notion, guidelines such as the Security and Exchange Commission’s plain-English Handbook (SEC 1998) encourage employees to write narrative disclosures to stakeholders using first person personal pronouns to describe the firm’s actions. For example, footnote 4 of Lilly’s 2015 sustainability report states, “By reducing our waste, energy, and water use, we’ve saved more than $200 million over the past 7 years.” Likewise, General Electric’s 2014 report states, “We have generated more than $200 billion in revenue from Ecomagination technologies like these and have seen more than $300 million in savings from reduction in our greenhouse gas emissions and water use.”.
Like previous studies (Brown et al., 2009; Evans et al., 2001; Rankin et al., 2008) our setting does not include penalties for misreporting because we are interested in observing reporting behavior when employees have both the incentive and opportunity to misreport. This experimental design choice models any setting in which employees have an information advantage relative to the individuals who use or rely on their reports.
In a post-experimental question, we ask participants the extent to which they believe the reporting task represents an ethical dilemma. Participants respond on an 11-point scale anchored by “Not at all” (1) and “Completely” (11). Consistent with participants viewing the reporting task as an ethical dilemma, we find that the mean response across all manipulated conditions is 7.29, which is significantly higher than the mid-point of the scale (t762 = 11.06, p < 0.001). Including this variable in our analyses does not change any inferences of our hypotheses tests. This suggests that participants perceive the reporting task as an ethical dilemma and that this perception does not affect the differences in dishonesty we observe.
An additional group of mTurk workers was paid in a separate study based on the reporting decisions of the participants in this study.
Twenty-two participants incorrectly identify the true probability of a good outcome and seventeen participants incorrectly answer the question about the effect of their reporting choice on other mTurk workers. The inferences from our hypotheses tests are unchanged if we exclude participants who incorrectly answer the two comprehension check questions from our analyses.
Aquino and Reed’s (2002) moral identity scale consists of two 5-item subscales. The internalization subscale measures how important an individual’s moral identity is to their self-concept. The symbolization subscale measures the extent to which an individual’s actions attempt to display that they are an ethical person. We collect data for both subscales in our study. A confirmatory factor analysis reports two factors consistent with each of these subscales. The theory and predictions of our study are based on the internalization dimension of moral identity, and we do not find evidence that the symbolization dimension has any influence on our results. Therefore, we do not discuss it further, and throughout the paper we refer only to the internalization dimension when we discuss moral identity.
The inferences from our hypotheses tests are unchanged if we do not include age and gender as covariates in our analyses.
Chi-square tests of differences indicate the number of participants removed across each of our experimental conditions is not statistically significant (\(\chi_{2}^{2}\) = 4.08, p = 0.130).
The statistical inferences from our hypotheses tests are unchanged if we use participants’ assessment of how long ago the event they wrote about happened as the construal variable in our statistical models. Further, as an alternative manipulation check, two coders who are blind to our experimental conditions independently rate the construal level of each participant’s submission from the writing task on a 21-point scale, ranging from low construal level (-10) to high construal level (10) (Gamma et al., 2020; Trope and Liberman 2003). The inter-rater agreement is acceptable (Cronbach’s alpha = 0.755) and as expected, more abstract ratings are marginally significantly positively correlated with the Distant conditions (p = 0.070).
All reported p-values are two-tailed.
Dummy coding is the simplest method of coding a categorical variable with two or more groups when a researcher wants to compare other groups of the predictor variable with one specific group (the reference group) of the predictor variable (Pedhazur, 1997; Starkweather 2010). Without dummy coding, alternatively treating action type as a categorical variable without a reference group, the interaction terms that include action type would not isolate comparisons between the Selfless and Mutually Beneficial conditions; instead, the interaction terms would be jointly testing comparisons across all three action types. Dummy coding allows us to control for the Selfish condition while testing the interactions of interest. Dummy coding is the most common method for handling categorical independent variables in other general linear models such as OLS and logistic regression (Starkweather 2010). We use the Selfless condition as the reference group so that we can isolate comparisons with the Mutually Beneficial condition while controlling for the Selfish condition. Alternatively, using the Mutually Beneficial condition as the reference group has no impact on the interaction terms that include Selfless vs. Mutually Beneficial as these estimates remain exactly the same.
We verify that our trait measure of Moral Identity is independent of our primary manipulations by testing whether our manipulations have a significant effect on our trait measure. We find that neither our construal manipulation (Recent vs. Distant p = 0.590) nor our prosocial action manipulation (Selfless vs. Mutually Beneficial p = 0.752) have a significant effect on our trait measure, which is consistent with prior research that finds trait Moral Identity is relatively stable (Aquino et al., 2009; Blasi 2004).
To rule out the possibility that our results are driven by mood induced by the type of prosocial action participants write about, we ask participants in a post-experimental question to rate their mood on an 11-point scale anchored by “Extremely bad” (− 5) and “Extremely good” (5) with “Neutral” (0) labeled as the mid-point. When included as a covariate in each of our hypotheses tests, a more positive mood is negatively correlated with misreporting (p < 0.001), but the inferences from our predicted interactions for H2 and H3 are unchanged. Therefore, we conclude that our results are not explained by participants’ mood.
Fairness is believed to be an important behavioral motivator in the economics literature when rewards are divided and shared among individuals (see Camerer 2003). We therefore ask participants about fairness to address this as an alternative explanation for our results, but we do not expect fairness concerns to manifest in our study. That is, when our participants make their reporting decision, they know the payoff they will receive and the payoff a group of other mTurk workers will receive based on their decision; however, participants do not know the number of mTurk workers that will share the group payoff. Thus, our participants are unable to calculate an equitable payoff distribution.
If Fairness and Honesty are included, model fit (\(\chi_{3}^{2}\) = 1.39, p = 0.709) and model results are inferentially identical.
We have no a priori expectation that the link between Wealth Maximization and participants’ level of misreporting should differ between the Selfless and Mutually Beneficial conditions. Therefore, we constrain these link coefficient estimates and find that both are positively associated with participants’ misreporting.
In addition, we test for differences between conditional indirect effects (three-way interaction) using the PROCESS macro Model 11 (Hayes, 2013) and the results are consistent with moderated moderated mediation (90% CI, LLCI = − 25.122, ULCI = − 1.100, untabulated; bias-corrected bootstrap sample based on 5000 samples). Tests of conditional indirect effects indicate Wealth Maximization mediates the relationship between action type and misreporting only when participants have high trait moral identity (90% CI, LLCI = − 27.671, ULCI = − 9.937, untabulated) but not when participants have low trait moral identity (90% CI, LLCI = − 13.707, ULCI = 2.282, untabulated).
We did not disclose to participants the number of stakeholders that would split the stakeholder payoff or how the payoff would be split among stakeholders in order to mitigate any tendency among participants to settle on a “default split” (e.g., 50–50), which would decrease our power to detect differences among conditions.
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Acknowledgements
We appreciate helpful comments from Charles Cho (editor) and anonymous reviewers as well as John Anderson (discussant), Scott Asay, Jason Brown, Eddy Cardinaels, Willie Choi, Ted Christensen, Mike Durney, Devon Erickson, Harry Evans, Paul Fischer, Sarah Gochnauer, Jeff Hales, Emily Hornok, Leslie Hodder, Vicky Hoffman, Pat Hopkins, Kathryn Kadous, Khim Kelly, Theresa Libby, Chris Miller, Don Moser, Joel Owens (discussant), Jeff Pickerd, Adam Presslee, Kristi Rennekamp, Lori Shefchik Bhaskar, Greg Stone, Todd Thornock, Mike Tiller, Flora Zhou (discussant) and brownbag and workshop participants at Emory University, Indiana University, the University of Cincinnati, the University of Mississippi, the University of Pittsburgh, the 2015 BYU Accounting Research Symposium, the 2016 Management Accounting Section Meeting, the 2016 Accounting, Behavior and Organizations Section Meeting, and the 2017 Public Interest Section Meeting. We are also grateful to Stefan Hill, Andrew Kim, Jacob Lennard, Aaron McCullough, and Jordan Samet for their research assistance, and to the Scheller College of Business and Dixon School of Accounting for financial support.
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Appendices
Appendix A: Participant Payoffs
We calculated each participant’s payoff as follows:
πi = participant i's payoff; pr = the probability of a good outcome reported by the participant, from a minimum of 20% (the true probability) up to 95% in 5% increments; pt = the true probability of a good outcome (20%).
Thus, participant payoffs increased as they inflated the probability of a good outcome that they report. Participants who reported the true probability of a good outcome (20%) earned $1.50 while those who reported the maximum probability (95%) earned $3.00. Participants knew that although they could increase their own payoff by inflating the reported probability of a good outcome, doing so would decrease the payoff of other participants. Specifically, the payoff for other participants was calculated as:
\({\pi }_{i}^{\text{s}}\) = the payoff for participant i's impacted participants; pr = the probability of a good outcome reported by the participant, from a minimum of 20% (the true probability) up to 95% in 5% increments; pt = the true probability of a good outcome (20%).
Thus, each participant’s group of impacted participants shared between $0.30 (if the participant reported the probability of a good outcome to be 95%) and $6.30 (if the participant reported the true probability of a good outcome of 20%).Footnote 27
Appendix B
See Table 4.
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Johnson, J.A., Martin, P.R., Stikeleather, B. et al. Investigating the Interactive Effects of Prosocial Actions, Construal, and Moral Identity on the Extent of Employee Reporting Dishonesty. J Bus Ethics 181, 721–743 (2022). https://doi.org/10.1007/s10551-021-04915-z
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DOI: https://doi.org/10.1007/s10551-021-04915-z