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Journal of Business Ethics

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18.05.2017 | Original Paper

Deterring Unethical Behavior in Online Labor Markets

verfasst von: William D. Brink, Tim V. Eaton, Jonathan H. Grenier, Andrew Reffett

Erschienen in: Journal of Business Ethics | Ausgabe 1/2019

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Abstract

This study examines how codes of conduct, monitoring, and penalties for dishonest reporting affect reporting honesty in an online labor market setting. Prior research supports the efficacy of codes of conduct in promoting ethical behavior in a variety of contexts. However, the effects of such codes and other methods have not been examined in online labor markets, an increasingly utilized resource that differs from previously examined settings in several key regards (e.g., transient workforce, lack of an established culture). Leveraging social norm activation theory, we predict and find experimental evidence that while codes of conduct and monitoring without economic penalties are ineffective in online settings, monitoring with economic penalties activates social norms for honesty and promotes honest reporting in an online setting. Further, we find that imposing penalties most effectively promotes honest reporting in workers who rate high in Machiavellianism, a trait that is highly correlated with dishonest reporting. In fact, while in the absence of penalties we observe significantly more dishonest reporting from workers who rate high versus low in Machiavellianism, this difference is eliminated in the presence of penalties. Implications of these findings for companies, researchers, online labor market administrators, and educators are discussed.

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In the literature, codes of conduct may be referred to as code of ethics, corporate ethical code, honor code (in education) or other terms. For expositional simplicity, we use the term Code throughout this manuscript.

The recent Wells Fargo scandal, where bank employees allegedly opened bank and credit/debit card accounts for customers without their knowledge or consent, provides an excellent example of how Codes must be paired with a strong ethical culture. These actions stand in stark contrast to the bank’s stated value and Code of Ethics. One of the company’s five shared values in Vision and Values of Wells Fargo is “We value what’s right for our customers in everything we do” and in their Code of Ethics and Business Conduct, they talk about holding themselves to the highest ethical standards (as cited by Verschoor 2016, 20). These Code violations were likely due to the culture created from extremely aggressive sales goals (Verschoor 2016).

We thank an anonymous reviewer for suggesting that Machiavellianism proxies for social norm sensitivity.

See Blay et al. (2016) for a historical prospective on social norm theory, including how it can be traced back to Adam Smith’s (1759/1790) The Theory of Moral Sentiments and an excellent discussion of Bicchieri’s (2006) model and its usefulness in existing and future research.

Codes have existed for decades and are growing in popularity (Kapstein 2004; Erwin 2011). Codes originated in the aftermath of the political scandals (e.g., Watergate) of the 1970s (Stevens 2008). Another major escalation of the use of Codes occurred in the late 1990s and early 2000s in the aftermath of the accounting scandals (e.g., Enron, WorldCom, Tyco). In fact, Sect. 406 of the Sarbanes–Oxley Act of 2002 requires the disclosure of adoption of a corporate code of ethics (or to provide a rationale for not doing so). However, Codes are not only used where mandated but can be an effective instrument in creating an ethical organizational culture for a wide variety of organizations, including nonpublic companies, non-profits, education, and government agencies (cf. Rezaee et al. 2001).

Mechanical Turk workers do have to agree to a worker agreement. We reviewed the agreement (https://www.mturk.com/mturk/conditionsofuse), and it does not directly discuss ethical behavior or culture. As it is primarily legal matters, its intent does not appear to be to communicate the values or culture of Amazon or its online labor market to the workers like a Code does.

We made this design choice to isolate the effects of imposing penalties because without this statement, participants may have assumed there would be penalties, since their performance was being monitored. However, we expect such assumptions to be rarely made by workers as online labor markets, such as MTurk, encourage employers to be extremely detailed in laying out the terms of the employment contract, including anything that could adversely affect the workers’ pay. Nevertheless, future research examining the effectiveness of monitoring without mention of the lack or presence of penalties would be beneficial.

See Wakefield (2008) for an examination of the factors associated with Machiavellianism within the accounting profession.

In addition to the measures described, this study also captured the participants’ authoritarian personalities using a scale develop by Rigby (1984). However, the scale reliability level was not appropriate for further statistical analysis.

We randomly audited 10% of responses within the two monitoring conditions and planned to impose a penalty of 50% of total pay for misreporting for those in the penalty condition. However, of the 12 participants selected for audit, we did not identify any dishonest reporting using this measure.

Participants were presented all twenty matrices in the same order. Matrices 3, 6, 8, 10, and 14 are unsolvable.

We used the same question for Monitoring—No Penalty, and only 36% of participants indicated the statement was false, raising concern about manipulation failure. However, responses to another question give us confidence that this question was simply poorly worded as the participants who failed were likely more focused on the first part of the question about their performance being monitored as opposed to the second part about there being a penalty. Specifically, participants in this condition were significantly less likely to believe their compensation would be penalized for misreporting than participants in Monitoring—Penalty using the Likert scale question discussed below (4.08 vs. 5.72 respectively; t₁₁₇ = 4.71, p_one-tailed < 0.001). Most importantly, a belief that that there would be both monitoring and penalties (i.e., what the “failure’s” responses to this question indicates) biases us against finding results as it would strengthen the Monitoring–No Penalty manipulation, making us more likely to reject H2a (null) and less likely to find support for H3b.

Also presented in the ANOVAs in Tables 1 and 2, Panel B, there is not a significant interaction between the presence or absence of a code and our three levels of monitoring and penalties (p_two-tailed = 0.597 and 0.638, respectively). This suggests that the presence of a code does not moderate the effect of penalties on decreasing unethical behavior.

Robustness checks were performed to ensure that the median split of participants into high/low mach groups were appropriate and did not alter the results discussed in the paper. Separating the participants into high/low mach groups based on mean split (2.71), midpoint of the MACH IV scale (3), or utilizing the MACH IV score as a continuous variable did not affect the significance of the results presented in the body of the paper. .

This contrast coding tests the three main assumptions embedded within the hypothesis. (1) Dishonesty is higher for High Mach relative to Low Mach (evidenced by the High Mach contrast weights being uniformly higher than the Low Mach contrast weights). (2) Monitoring—No Penalty will be ineffective, relative to No Monitoring, for both levels of Mach (evidenced by the contrast weights on No Monitoring and Monitoring—No Penalty being equal). (3) Finally, Monitoring—Penalty will be more effective for High Mach (evidenced by the reduction in dishonesty between Monitoring—Penalty and the other two cells being greater in High Mach than Low Mach). For robustness, we tested three additional sets of contrast weights that model these assumptions (+3, +3, +1, −2.5, −2.5, −2; +1.75, +1.75, +0.5, −2.5, −2.5, −2; and +2.5, +2.5, +0.5, −1.5, −1.5, −2.5) and obtained similar results (all p < 0.001).

The pattern of results is consistent when using Unsolvables as the primary dependent measure, with the exception of the number of Unsolvables reported by Low Mach participants in the penalty imposed condition (M = 0.62) being statistically different than those reported by the Low Mach participants when monitoring was absent condition (M = 1.25, t₅₅ = 1.727, p_one-tailed = 0.045).

For both tests for partial mediation, we combined the no monitoring and monitoring without penalties conditions into a single “no penalties” condition.

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Titel: Deterring Unethical Behavior in Online Labor Markets
verfasst von: William D. Brink
Tim V. Eaton
Jonathan H. Grenier
Andrew Reffett
Publikationsdatum: 18.05.2017
Verlag: Springer Netherlands
Erschienen in: Journal of Business Ethics / Ausgabe 1/2019
Print ISSN: 0167-4544
Elektronische ISSN: 1573-0697
DOI: https://doi.org/10.1007/s10551-017-3570-y

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