Organizations Appear More Unethical than Individuals

Abstract

Both individuals and organizations can (and do) engage in unethical behaviors. Across six experiments, we examine how people’s ethical judgments are affected by whether the agent engaging in unethical action is a person or an organization. People believe organizations are more unethical than individuals, even when both agents engage in identical behaviors (Experiments 1–2). Using both mediation (Experiments 3a–3b) and moderation (Experiment 4) analytical approaches, we find that this effect is explained by people’s beliefs that organizations produce more harm when behaving unethically, even when they do not, as well as people’s perceptions that organizations are relatively more blameworthy agents. We then explore how these judgments manifest across different kinds of organizations (Experiment 5) as well as how they produce discrepant punishments following ethically questionable business activities (Experiment 6). Although society and the law often treat individuals and organizations as equivalent, people believe for-profit organizations’ behaviors are less ethical than identical individual behaviors. We discuss the ethical implications of this discrepancy, as well as additional implications concerning reputation management, punishment, and signaling in organizational contexts.

Ethical judgments—judgments about whether or not some action is an ethical violation, and if so, how severe of a breach it is—form the foundation of much descriptive research (e.g., Haidt 2001). When determining if a behavior is ethically acceptable or not, people rely on a variety of social, situational, and cognitive factors. These include how intentional they believe an action was (Alicke 2000; Cushman 2008; Gray and Wegner 2009), how much harm it produced (Gino et al. 2010), their personal political, cultural, or religious beliefs (Conroy and Emerson 2004; Graham et al. 2009; Haidt et al. 1993; Robertson and Fadil 1999; Van Kenhove et al. 2001), their emotional response to a transgression (Greene and Haidt 2002; Haidt 2001), and their beliefs about the characteristics of a perpetrator (Uhlmann et al. 2015).

Recently, there has been growing interest in one other aspect of situations that affects not only people’s ethical judgments, but also how legal systems operate: whether an agent engaging in a behavior is an individual person or an organized group of people. Court rulings in the USA have affirmed that organizations have the same rights as individual citizens to financially support candidates in elections (Citizens United v. Federal Election Commission 2010) or refuse to provide birth control benefits because of deeply held religious beliefs (Burwell v. Hobby Lobby Stores Inc. 2014). These judgments have reinforced the concept of corporate personhood, an idea with a long legal history (e.g., Santa Clara County v. Southern Pacific Railroad Co. 1886). When US presidential candidate Mitt Romney famously commented that “corporations are people” (Rucker 2011), there was widespread commentary about whether or not corporations—which are indeed legal entities—should be able to hold and exercise rights normally reserved for individuals. Given this equality under the law as well as the fact that both individuals and legal collections of individuals (such as corporations) can commit harm, a fundamental theoretical and practical question emerges: do people make similar ethical judgments of both individuals and organizations that share similar rights, and if not, why not?

Empirical research investigating this question has thus far has reached inconsistent conclusions. Some studies concluded that people judge ethical breaches by corporations less harshly than similar actions individuals engage in. For instance, Haran (2013), in a series of experiments, found that people viewed contracts made by individuals as more analogous with moral promises compared to contracts made by organizations. He went on to argue that “sensitivity to a contract’s moral obligation-related meaning is higher when the signer is an individual” and that therefore “contract breach by an individual would be perceived as a moral transgression…whereas a breach…by an organization would be seen more as a legitimate business decision” (Haran 2013: 2839). Similarly, Tyler and Mentovich (2010) found that when participants were told that either a company or a supervisor systematically discriminated against women in hiring decisions, participants judged discrimination by a single manager as being less ethical compared to the same discrimination committed by an organization.

On the other hand, people believe that for-profit organizations are relatively unethical, which could logically spill over into judgments of specific behaviors (Burson-Marsteller 2014; see Uhlmann et al. 2015). Organizational behaviors often elicit more anger compared to individual behaviors, which also implies that people may ultimately judge organizational behaviors more harshly (Plitt et al. 2015; see Haidt 2001). Jago and Laurin (2017) also found that people believe that corporations, compared to people, are more likely to use rights in ways that create harm, ultimately assigning corporations relatively greater ethical responsibility. Finally, Rai and Diermeier (2015) argued that people see organizations as having agentic mental states, but not experiential ones, ultimately resulting in organizations eliciting anger as perpetrators of unethical behavior but little sympathy when they are victims themselves.

The question of whether people judge individuals or organizations more harshly is important because ethical judgments have consequences. When people believe a specific behavior is unethical, they often attempt to punish it if violators do not utilize a reparative strategy such as justifying the behavior, apologizing for it, or denying responsibility outright (Bradford and Garrett 1995; Carlsmith et al. 2002; Dutta and Pullig 2011; Kim et al. 2004). Moreover, to the extent that people judge organizations or individuals more harshly, social actors can frame communications strategically to emphasize either the personal or the organizational agent behind specific behaviors. For instance, CEOs can personally apologize, as United Airlines’ CEO Oscar Munoz did after a passenger was forcibly removed from an overbooked airplane (McCann 2017), or organizations can emphasize the institutional aspects of behavior, such as when police departments couch actions in the name of the department and do not identify (or downplay the agency of) individual officers. In this article, we propose that organizations of people elicit harsher ethical judgments compared to individual people when behaving unethically. We also explore two mechanisms that might account for this difference: people’s beliefs that organizations harm more people than individuals, even when they do not, as well as people’s perceptions that organizations are relatively more blameworthy for their actions.

This research makes several practical and theoretical contributions to the literature on ethical judgments. First, we identify a descriptive inequality in how people judge organizational and individual transgressions, despite their capacity to engage in similar (or identical) behaviors. We argue that this asymmetry raises an important normative question: is it unethical for organizations to manage impressions when they behave unethically by offering a different framing for misbehavior? Second, we contribute to the understanding of how people form moral opinions of organizations. Specifically, we identify two mechanisms that differentiate moral judgments of organizations compared to other agents, which extends the existing literature on how people tend to interpret organizations’ behaviors or violations (e.g., Rai and Diermeier 2015). Finally, this research speaks to how organizations proactively influence people’s moral opinions following unethical action by scapegoating individuals as responsible for misbehavior to strategically manage impressions.

Background and Hypotheses

People generally understand moral situations as a function of at least two real or imagined actors: moral agents, or perpetrators of harm, and moral patients, or entities that are negatively affected by harm (Gray et al. 2007, 2012; Gray and Wegner 2009). To understand their behaviors, people often endow groups and collective organizations with motivations and intentions superordinate to the individual members that constitute them, and in turn, believe organizations—just like people—are moral agents that can behave unethically (Knobe and Prinz 2008; Rai and Diermeier 2015; Waytz and Young 2012). When people evaluate organizational behaviors, they even utilize similar brain regions as when they evaluate humans’ behaviors (Jenkins et al. 2014; Plitt et al. 2015).

Although some debate exists about whether collective organizations actually exhibit moral agency and to what degree they should be responsible for ethical transgressions (e.g., French 1984), people appear to perceive them as similar to humans—as intentional agents capable of producing harm. Despite these potential similarities, we argue that there are two important ways in which individuals and organizations differ, which ultimately inform how observers evaluate human and organizational transgressions: people’s assumptions about the magnitude of harm these two agents produce as well as their relative blameworthiness.

Magnitudes of Harm

One reason people may judge organizational and human transgressions differently is because they believe organizations tend to produce more harm when engaging in unethical behavior (Gray et al. 2012; Gray and Wegner 2009). Intuitively, organizational entities would seem to have a greater capacity, compared to single individuals, to both positively and negatively influence people around them. This high degree of influence may lead people to heuristically assume that organizational behaviors generally affect more people, compared to a single individual acting in the same way. Importantly, people’s judgments of harm greatly influence their beliefs about whether or not a particular action was unethical (e.g., Gino et al. 2010). If people assume organizations tend to harm many people when transgressing, they logically might also believe those transgressions are more unethical compared to other agents who (seem to) produce less harm.

While this heuristic that organizations tend to produce more harm than people is likely often the case, some ethical violations—such as lying to increase the chance of making a sale or damaging a water source—have objective and identifiable consequences stemming from them that do not differ as a function of perpetrator. For example, either a person or an organization could negligently release fifty pounds of waste into a river, polluting it. If people heuristically assume that organizations generally produce more harm when transgressing, they may insufficiently adjust their perceptions given the specific circumstances of a violation (e.g., Epley and Gilovich 2006). As such, people may believe that organizational transgressions are less ethical compared to human transgressions—even if the actual consequences stemming from both behaviors are otherwise identical—because they believe organizations produced more harm, compared to individuals.

H1

People believe organizational behaviors are more unethical than identical human behaviors.

H2

People believe that organizational transgressions produce more harm, compared to individuals, which explains their perceptions that organizational behaviors are more unethical.

Blameworthiness

Another reason people may believe organizational behaviors are less ethical than identical human behaviors is because they assign organizations greater blame for transgressions. A great deal of research is devoted to the psychology of blame, often focusing on one central question: what facets of actors or situations lead people to ascribe blame to different agents or even intuit blameworthiness in the first place? One of the strongest components of blame that is intentionality: whenever an actor intentionally behaves in an unethical or undesirable way, people tend to assign blame to that actor (Alicke 2000; Coates and Tognazzini 2013). For example, someone who intentionally chooses to cause harm tends to elicit far more blame compared to someone who unintentionally causes the same harm (Alicke et al. 2008; Gray et al. 2012).

In terms of ascribing intentionality to social actors following the occurrence of some unethical behavior, formal organizations often have substantial access to economic, political, and social resources. People also recognize that organizations wield a significant amount of power in modern society (Burson-Marsteller 2014). Because of this power and access to resources, people might believe that organizations are more blameworthy agents, because organizations might appear quite capable of engaging in behaviors they wish to, resisting situational pressures, and intentionally pursuing desired goals. While people generally believe that other people are responsible for their behaviors (e.g., Gray et al. 2007), when other humans transgress, people may assume that there could be exculpating circumstances that explain their behavior, for example, an unknown situational pressure or some sort of legitimate justification.

Although organizations are often greatly influenced by their environment and demands that various stakeholders impose upon them (e.g., Pfeffer and Salancik 1978), most people may not take this into account when evaluating their behaviors and instead rely on the intuition that organizations have a great deal of control over what they do. As such, people may judge organizational violations as particularly unethical, compared to human behaviors, if they believe organizations are relatively more blameworthy agents.

H3

People believe organizations are more blameworthy for their actions, compared to individuals, which explains their perceptions that organizational behaviors are relatively more unethical.

Overview of Studies

Across six experiments, we asked people to judge either human or organizational transgressions. The use of experiments allowed us to causally investigate how people differentiate between human and organizational actors. In each experiment, our goal was to construct situations that were relatively realistic: although participants were never themselves in ethical situations or directly observing transgressions, we asked them about either human or organizational behaviors that were both ostensibly real and the kinds of behaviors both agents can engage in. In addition, we presented most transgressions using news-like briefs (e.g., a headline, a short paragraph summarizing a behavior) in an effort to convey specific agents’ behaviors in the same ways people tend to actually learn about them in the world (e.g., from a physical newspaper or the internet).

We first tested our primary hypothesis that people believe organizational behaviors are more unethical compared to individual behaviors (Experiment 1). Next, we used both mediation (Experiments 2, 3a, and 3b) and moderation (Experiment 4) strategies to assess our proposed mechanisms of perceived magnitudes of harm and blameworthiness. In Experiment 5, we tested whether or not the effect occurs for other types of organizations other than those seeking profits (specifically, nonprofit organizations, government agencies, and family businesses). Finally, in Experiment 6, we investigated how framing ethical violations as coming from single employees as contrasted with entire organizations affects the amount of punishment people desire to exact on those organizations.

Experiment 1

In Experiment 1, our goal was to test our first hypothesis: that people believe organizations are more unethical than humans when both agents engage in identical behaviors. To do this, we used two different samples: Participants from a variety of countries around the world (“Sample A”; Sweden, the UK, India, Australia, South Korea, and the Philippines) and American students who completed the experiment in a behavioral laboratory (“Sample B”). We used a multi-country sample of people of various ages along with a more conventional student sample to see the extent to which our results generalized across different cultures and held for adults as well as students. Participants read about either people or organizations that engaged in ten specific behaviors, after which they assessed how unethical they believed that agent was. We predicted that participants would believe the organizations were more unethical, compared to individuals.

Method

Participants

For Sample A, we used an international survey firm to recruit participants from six different countries: Sweden, the UK, India, Australia, South Korea, and the Philippines. Eight hundred and twenty-five adults across these six countries ultimately completed the experiment for payment. Of these, 90 participants reported either that they did not speak English or did not indicate that they did when asked, yielding a final sample of seven hundred and thirty-five (108 from Sweden, 116 from the UK, 104 from India, 123 from Australia, 116 from South Korea, and 168 from the Philippines; 322 Male, 407 Female, 6 “Other”; M_age = 37.75). All reported results remained statistically significant when including the participants who did not indicate that they spoke English. For Sample B, one hundred and twenty-nine graduate and undergraduate students at a private West-Coast American university (71 Male, M_age = 22.90) completed the experiment in a behavioral laboratory for payment as part of a mass testing session.

Procedure

We randomly assigned participants to answer questions about either people or organizations. We showed each participant a matrix consisting of ten unethical behaviors that people [businesses] had ostensibly engaged in over the past year, for example, breaking import laws or negligently polluting a local stream (see the “Appendix” for full text from each experiment). For each behavior, participants indicated how unethical they believed the person [business] responsible for the action was on a 1 (“Neither Ethical nor Unethical”) to 5 (“Extremely Unethical”) scale.

Results and Discussion

In both samples, we first created a 2 (agent: people vs. organizations, between-subjects) × 10 (moral violation; within-subjects) repeated-measures ANOVA to test if people believed the different agents’ behaviors differed in terms of their unethicality. This particular approach allowed us to account for the fact that each participant in both samples responded to ten different violations in a within-subjects fashion. Results indicated an omnibus effect of our agent manipulation, Sample A: F(1, 728) = 11.87, p = .001; Sample B: F(1, 125) = 11.30, p = .001. The results showed that people believed the organizational behaviors were broadly more unethical (Sample A: M = 3.92, SD = 0.76; Sample B: M = 3.80, SD = 0.67) than the individual behaviors (Sample A: M = 3.72, SD = 0.80; Sample B: M = 3.45, SD = 0.55; ds = .26 and .57, respectively). In both samples, the agent manipulation did not interact with the within-subjects variable representing the different ethical violations (Sample A: F(9, 6552) = 1.48, p = .148; Sample B: F(9, 1125) = 0.70, p = .321), suggesting that this person–organization difference was relatively similar across the different violations. Moreover, although there was a main effect of country on rated unethicality, there was no interaction of country by type of agent engaging in the violation, suggesting that the effect of a violation committed by an organization compared to a person on perceived unethicality did not vary across country.¹ As such, these results supported our first hypothesis: participants believed that organizations were more unethical compared to individual people when both entities engaged in the same behaviors. Moreover, this effect held for different types of samples and for people from a variety of countries.

Experiment 2

In Experiment 2, our intention was twofold. First, we wanted to replicate our results from Experiment 1 using somewhat more detailed descriptions of behaviors. To this end, participants read short paragraphs about different people or businesses that were responsible for five potential ethical violations as though they were news stories reporting recent behaviors. We again predicted that participants would believe the businesses were more unethical compared to the individuals. Second, we asked participants to indicate their liking for people [organizations] in an effort to address the possibility of simple negativity biases against organizations contributing to our observed differences between the two types of agents.

Method

Participants

Two hundred American adults (123 Male, M_age = 32.34) completed the experiment online using Amazon’s Mechanical Turk. Research has suggested that Mechanical Turk samples are approximately as reliable as other laboratory-based approaches, although this particular medium introduces certain risks, such as non-naiveté or inattention given overly complex tasks (Buhrmester et al. 2011; Chandler et al. 2014). Despite these potential issues, we opted to use Mechanical Turk for some experiments in order to ensure adequate statistical power across the many different ethical violations we used (e.g., Wells and Windschitl 1999) as well as to access a relatively diverse geographic sample within the USA (Berinsky et al. 2012).

Procedure

We randomly assigned participants to read about situations involving either people or business organizations. We referred to each person as “Bill,” and every organization as “NatCo.” We told participants that we were using anonymous names to protect the identities of the parties involved in each situation. Importantly, participants also read that the agent in each situation was different, despite having the same anonymous name.

Participants next read about five ostensibly real ethical violations committed by the people or organizations in the form of a short news brief: breaking fruit import laws which led to a parasite being released into a local community, selling a building with a termite infestation without informing the buyer, underpaying a plumber for contract work, saying untrue and negative things about a catering service in an online review, and running loud machinery past quiet hours in a residential area. After reading about each violation, participants indicated their agreement (1 = “Strongly Disagree,” 7 = “Strongly Agree”) with two items: “Bill [NatCo]’s behavior was immoral” and “Bill [NatCo]’s behavior was unethical.” Although these terms refer to normatively different constructs, the two items formed reliable composites of unethicality within each individual vignette, rs > .69, ps < .001, suggesting that people saw them as reasonably similar terms. After responding to the questions about the five violations, we also asked participants to indicate their broad liking of people [businesses] using a 1 (“Strongly Disagree”) to 7 (“Strongly Agree”) scale: “I like people [organizations] “People [Organizations] are okay,” and “I feel positively towards people [organizations].” These items formed a reliable composite of agent liking (α = .94):

Results and Discussion

As in Experiment 1, we first constructed a 2 (agent: people vs. organizations, between-subjects) × 5 (moral violation, within-subjects) mixed-model ANOVA predicting unethicality to test if the effect of our agent manipulation across the different violations. This model indicated an omnibus effect of agent F(1, 198) = 18.89, p < .001, d = .56, broadly replicating Experiment 1. Participants again believed that the organizational behaviors were more unethical (M = 5.79, SD = 0.75) compared to the individual behaviors (M = 5.32, SD = 0.93). As in Experiment 1, our agent manipulation did not interact with the within-subjects variable representing the different violations (F(4, 792) = 0.89, p = .467), suggesting that this person–organization discontinuity was similar across the different ethical situations.

Participants also reported generally liking organizations (M = 3.64, SD = 1.20) less than people (M = 4.74, SD = 1.46). t(198) = 5.70, p < .001, d = .82. However, a regression model predicting unethicality using both the agent manipulation (0 = people, 1 = organizations) as well as participants’ liking of the agents showed that participants’ judgments that the organizational behaviors were less ethical than human behaviors persisted even after statistically controlling for how much participants liked the different agents. Moreover, liking did not predict unethicality in and of itself, (b_agent = .27, p < .001; b_liking = − .006, p = .404). A subsequent 5000-iteration bootstrapped mediation model (Hayes 2013) using agent (0 = people, 1 = organizations) as the independent variable, aggregated unethicality as the dependent variable, and liking as a mediator suggested that liking indeed did not mediate the effect of agent on unethicality, CI₉₅ = [− .06, .14].

Experiment 2 provided additional support for H1. Participants again believed that organizational behaviors were ethically worse compared to identical human behaviors. In Experiment 2, we also assessed participants’ omnibus liking of people or organizations, which, although different, did not explain people’s divergent ethical perceptions of the human and organizational behaviors. While people may tend to broadly like formal organizations less than they like people, these empirical results suggest that different ratings of ethicality are not simply a manifestation of general negativity biases against formal organizations.

Experiment 3a

In Experiment 3a, our goal was to test one proposed mechanism for the difference in ethical judgments people give to individuals versus organizations: that people believe organizational behaviors are less ethical than identical human behaviors in part because they assume organizational behaviors produce more harm (H2). We asked participants about five ethical violations, and again manipulated whether a person or a business was responsible for that violation. In addition, for each violation, we also asked participants to estimate how many individuals the person [organization] harmed by engaging in that behavior. Unlike Experiments 1 and 2, however, we designed each vignette to have precise outcomes, for example, polluting one half acre of land or avoiding $10,000 in taxes. We predicted that participants’ assumptions that organizations produced more harm would mediate their beliefs that organizational behaviors were less ethical than human behaviors, even when those behaviors actually produced otherwise identical consequences.

Method

Participants

Two hundred American adults (116 Male, M_age = 33.67) completed the experiment for payment using Amazon’s Mechanical Turk.

Procedure

We randomly assigned participants to read about either people or business organizations. Participants next read about five situations involving ostensibly real people [businesses] near the university administering the survey: dumping 10 gallons of insecticide into a local lake, improperly installing a single heating unit, experimenting with non-approved pesticides on a half acre of land, reneging on one homeowner’s renovation contract, and avoiding approximately $10,000 in taxes. After reading about each violation, participants responded to two items adapted from Experiment 2: “This person’s [businesses’] action was unethical” and “This person’s [businesses’] action was immoral.” These items again formed reliable composites of unethicality within each vignette (rs > .51, ps < .001). To assess participants’ judgments of how much harm the actions produced, we also asked them to estimate how many people were negatively affected by the action on a “1 or less” (1) to “10 or more” (10) scale.

Results and Discussion

Unethicality

We first created a 2 (agent: people vs. organizations; between-subjects) × 5 (moral violation; within-subjects) mixed-model ANOVA predicting unethicality to test if participants believed the different agents’ behaviors indicated different levels of unethicality. We again observed an omnibus effect of our agent manipulation, F(1, 198) = 4.94, p = .027, d = .31. Consistent with the results from Experiments 1 and 2, participants believed that the actions were more unethical when organizations engaged in them (M = 5.78, SD = 0.83) compared to individual people (M = 5.51, SD = 0.89). We again observed no interaction between our agent manipulation and the within-subjects variable representing the various moral situations, F(4, 792) = 1.02, p = .396. To use in a mediation model, we aggregated participants’ ethical judgments across the vignettes, which formed a composite of unethicality (α = .72).

Perceived Harm

We next constructed an identical repeated-measures ANOVA predicting participants’ estimates of harm in order to test if people believed the organizations produced more harm than individuals across the various situations. Results indicated a significant effect of the agent manipulation, F(1, 198) = 8.25, p = .005, d = .40. Participants, on average, believed that the organizations’ actions negatively affected more people (M = 6.49, SD = 2.10) compared to the individuals’ actions (M = 5.70, SD = 1.82). However, we did observe a significant interaction between the agent manipulation and the within-subjects factor representing the vignettes, F(4, 792) = 4.50, p = .001. Reflecting the interaction, participants assumed the organization harmed relatively more people in the installation, contract, and pesticide vignettes (ts(198) = 1.36, 2.35, and 2.34, respectively) compared to the dumping and tax vignettes (ts(198) = 0.68 and 0.86, respectively). To use a mediation model, we aggregated participants’ estimates of the number of individuals negatively affected by each violation, despite these judgments exhibiting somewhat less reliability given the differential amounts of harm described in each vignette (α = .59).

To test the role of participants’ estimates of harm in explaining their divergent judgments, we constructed a 5000-iteration bootstrapped mediation model with agent (0 = people, 1 = organizations) as the independent variable, unethicality as the dependent variable, and aggregate estimates of harm as the mediator. Results indicated that participants’ beliefs that the organizational behaviors harmed more people mediated their judgments that those activities were less ethical (CI₉₅ = .[05, .27]; c = .27, p = .027; c’ = .13, p = .249; a = .79, p = .005; b = .17, p < .001).

Participants in Experiment 3a believed organizational behaviors were ethically worse compared to identical human behaviors. We found that perceptions of harm explained some of this effect. More specifically, participants believed that the organizational behaviors harmed more people, which accounted for their relative judgments of unethicality. These results are consistent with H2: people’s beliefs that organizations produced more harm than humans explained part of why they believed organizations were more unethical than people, even when they engaged in the exact same behaviors with the exact same consequences.

Experiment 3b

In Experiment 3b, we sought to test H2 and H3—our proposed mechanisms of perceived magnitudes of harm and blameworthiness, respectively—simultaneously using a mediation strategy. We utilized the same materials as Experiment 3a, but in addition, we also asked participants to indicate how blameworthy they believed either people or organizations broadly are for their actions. In line with H2 (and consistent with Experiment 3a), we expected that participants’ increased estimates of harm would mediate their beliefs that organizational behaviors are relatively more unethical than human behaviors. In line with H3, we also predicted that participants’ beliefs that organizations are relatively more blameworthy agents would simultaneously mediate this effect.

Method

Participants

Two hundred American adults (94 Male, M_age = 34.85) completed the experiment online using Amazon’s Mechanical Turk.

Procedure

Experiment 3b was identical to Experiment 3a, but with one change. After answering questions about each of the five different ethical violations (which again formed reliable composites within each vignette, rs > .53, ps < .001), participants responded to six items adapted from Alicke (2000) indicating how blameworthy they believed people [businesses] were for their behaviors. The items were: “People [Businesses] are entirely blameworthy for their behaviors,” “People [Businesses] are entirely in control of their behaviors,” “People’s [Businesses’] unethical behaviors are never excusable,” People’s [Businesses’] unethical behaviors are never justifiable,” “People’s [Businesses’] behaviors are strongly influenced by different situations and pressures” (reverse-scored), and “There are often circumstances where people [businesses] are not blameworthy for their behaviors” (reverse-scored). These items formed a reliable composite of blameworthiness (α = .78).

Results and Discussion

Unethicality

Like in Experiment 3a, we first created a 2 (agent: people vs. organizations; between-subjects) × 5 (moral violation; within-subjects) mixed-model ANOVA predicting unethicality. Results indicated a significant omnibus effect of our agent manipulation, F(1, 198) = 8.31, p = .004, d = .40. As in our other studies, participants believed that the organizational behaviors were more unethical on average (M = 5.82, SD = 0.83), compared to the individual behaviors (M = 5.50, SD = 0.78). We also observed an interaction between our agent manipulation and the different vignettes, F(4, 792) = 4.93, p = .001. While people believed the organization was more unethical in each case, the effect was strongest in the installation (t(198) = 3.95, p < .001) and farmplot (t(198) = 2.58, p < .001) vignettes, weaker in the dumping (t(198) = 1.06, p = .291) and contract (t(198) = 1.30, p = .194) vignettes, and nonexistent in the tax vignette (t(198) = .08, p = .938). Despite these differences, we proceeded to aggregate participants’ ethical judgments across the different situations to use in a subsequent mediation model (α = .66).

Perceived Harm and Blameworthiness

As in Experiment 3a, we also created a mixed-model ANOVA predicting participants’ estimates of harm. Results indicated a main effect of agent condition, F(1, 198) = 3.93, p = .049, d = .27. As in Study 3a, participants again assumed that the organizations harmed more people (M = 6.24, SD = 1.69) compared to humans (M = 5.75, SD = 1.87). Unlike in Experiment 3a, we did not observe an interaction between the agent condition and a factor representing the different vignettes, F(4, 788) = 0.54, p = .704, suggesting that the discontinuity between people and organizations was relatively similar across the distinct transgressions. We proceeded to aggregate participants’ estimates of harm across the five situations to use in a mediation model, despite them again exhibiting somewhat low reliability given their varying assessments of the actual number of people harmed (α = .43). Central to H3, participants also believed organizations were generally more blameworthy (M = 5.06, SD = .90) than people (M = 4.34, SD = .86), t(198) = 5.81, p < .001.

We next constructed a 5000-iteration bootstrapped mediation model using agent (0 = people, 1 = organizations) as the independent variable, unethicality as the dependent variable, and perceived harm and blameworthiness as simultaneous mediators. Consistent with both H2 and H3, participants’ beliefs that organizations produced more harm (CI₉₅ = [.02, .20]) as well as their perceptions that organizations are more blameworthy (CI₉₅ = [.14, .37]) simultaneously mediated their judgments that the organizational behaviors were relatively more unethical (see Fig. 1). Overall, the results from Experiment 3b supported H1-H3. Replicating Experiment 3a, participants believed organizational behaviors were less ethical than identical human behaviors (H1) in part because they believed those behaviors produced more harm (H2). Participants’ perceptions that organizations are generally more blameworthy agents also explained part of this effect (H3).

Fig. 1

Mediation to unethicality through perceived harm and blameworthiness (Experiment 3b)

Experiment 4

In Experiment 4, we sought to test the mechanisms of perceived harm and blameworthiness (H2 and H3) using a moderation strategy. Given that specific ethical judgments, estimates of harm, and broad perceptions of culpability are likely to be correlated with each other as well as concerns about reverse-causality, a moderation analytical approach in which we manipulated the moderators themselves allowed us to test these mechanisms in a more rigorous fashion (Hayes 2013). Specifically, in cases when organizations produce an evaluable amount of harm (H2) or when people can more thoroughly assess an organizations’ culpability in a specific circumstance (H3), our reasoning suggests that people’s discrepant ethical judgments of organizational and individual behaviors should attenuate.

In Experiment 4, we asked participants about one specific organizational or human violation. Crucially, for some participants, we experimentally manipulated either the person’s [organization’s] explicit culpability or the harm he [it] produced, in terms of the number of people the agent negatively affected. While people may spontaneously assume that organizations produce more harm than people and are also relatively more culpable, we predicted that providing reliable information about magnitudes of harm or blame would weaken the effect that we have observed thus far, as people would be less likely to rely on their heuristic stereotypes about organizations in order to inform their ethical judgments.

Method

Participants

Three hundred and one American adults (145 Male, M_age = 34.52) completed the experiment online using Amazon’s Mechanical Turk.

Procedure

We randomly assigned participants to read about either an organization in the USA, Stuza, or a person in the USA, Steve. We told participants that we withheld the real names of the person [organization] to preserve anonymity. We adapted one vignette used in Experiments 3a and 3b to use in Experiment 4: participants read that Steve [Stuza], in the past year, had “…used an organic pesticide on 200 square feet of farmland. This pesticide caused allergic reactions in some nearby residents, including moderate respiratory problems.”

At this point, we randomly assigned participants to one of three conditions, forming a 2 (agent: person vs. organization) × 3 (condition: control vs. low blame vs. low harm) design. Participants in the control condition read only about this pesticide use, and moved on with the survey. Participants in the low harm condition read that “However, further investigation revealed that only two people were negatively impacted by this pesticide, due to the remote location of the farmplot.” Participants in the low blame condition read: “However, further investigation revealed that Stuza [Steve] was given the wrong pesticide by a supplier, and couldn’t have known it would cause these allergic reactions. The supplier claimed responsibility and apologized for the incident.” Following this information, participants responded to two items adapted from previous experiments (explicitly about Steve [Stuza]): “This action was unethical” and “This action was immoral.” These items again formed a reliable composite of unethicality (r = .95, p < .001).

Results and Discussion

We created a 2 (agent: person vs. organization) × 3 (condition: control vs. low blame vs. low harm) ANOVA predicting unethicality. Results indicated a main effect of agent, F(1, 295) = 22.91, p < .001, a main effect of condition, F(2, 295) = 68.51, p < .001, and crucially, a significant interaction between the two, F(2, 295) = 4.53, p = .012 (see Fig. 2). We next conducted simple effects analyses to investigate the differences in perceived unethicality between people and organizations across the three conditions. In the control condition, we observed the same effect as in previous experiments. Participants believed that the organization’s behavior was more unethical (M = 5.30, SD = 1.41) compared to the person’s behavior (M = 3.66, SD = 1.89; F(1, 295) = 27.37, p < .001, d = .98). However, this difference was no longer statistically significant in both the low harm (M_org = 4.36, SD_org = 1.72; M_person = 3.86, SD_person = 1.59; F(1, 295) = 2.43, p = .120, d = .30) and the low blame (M_org = 2.32, SD_org = 1.47; M_person = 1.85, SD_person = 1.29; F(1, 295) = 2.29, p = .131, d = .34) experimental conditions. Although people’s judgments of unethicality still trended in the expected direction—that the organization’s behavior was more unethical than the person’s behavior—this difference diminished when we made explicit how much harm the different entities produced or their relative culpabilities. Simple effects analyses revealed that, for the organization, all three conditions differed significantly from one another, Fs(1, 295) > 8.76, ps < .004. For people, while the control and low harm conditions did not differ significantly (F(1, 295) = 0.39, p = .533), both the control and low harm conditions differed significantly from the low blame condition, Fs(1, 295) > 10.4, ps < .001).

Fig. 2

Unethicality as a function of agent and condition (Experiment 4). Error bars represent 95% confidence intervals of means

Experiment 4 provided additional support for both H2 and H3, this time in the form of moderation. When we manipulated harm and blame experimentally, as opposed to prompting people to infer it from the transgression itself, people no longer believed that the organizational behaviors were significantly more unethical than the human behaviors. This result provides additional confidence that we identified at least two psychological processes responsible for the findings we have obtained. In addition to these mechanisms, Experiment 4 speaks to one strategy that might be able to address people’s spontaneous attributions of harm and blame: to make these aspects of a situation explicit.

Experiment 5

In Experiment 5, our goal was to investigate people’s reactions to different organizations’ behaviors. Although our previous experiments focused on for-profit organizations, many other organizations are capable of unethical behavior. In Experiment 5, we again asked participants about human and for-profit organizations’ behaviors, but also included three additional organizational actors: nonprofit organizations, family businesses, and government agencies. In line with our theorizing, we expected that people might still judge for-profit organizations’ behaviors as more unethical than the others. Given their greater access to resources and potentially confronting fewer constraints than nonprofits or government agencies, people might believe that for-profit organizations both produce more harm and are more culpable compared to other organizational agents that people do not perceive as similarly powerful in modern society.

Method

Participants

Five hundred and six American adults (240 Male, M_age = 35.31) completed the experiment online using Amazon’s Mechanical Turk.

Procedure

At the beginning of the survey, we randomly assigned participants to read about one of five different agents: a person, a for-profit organization, a nonprofit organization, a family business, or a government agency. Although these categories often overlap (e.g., most family businesses are likely for-profit organizations), we presented each agent as a discrete category. Participants next read that this agent had engaged in one of five behaviors utilized in previous experiments: leaving a machine on and accidentally starting a fire, using an pesticide that produced an odor, dumping insecticide into a local lake, running machinery outside of quiet hours, and failing to pay a plumber for contract work. We chose these behaviors because we believed each of the five different agents participants saw could reasonably engage in them. After reading about the behavior, participants responded to the same two items used in Experiment 4 (r = .90, p < .001).

Results and Discussion

To test whether or not there was an omnibus effect of our agent manipulation, we created a 5 (agent) × 5 (behavior) ANOVA predicting participants’ judgments of unethicality. Crucially, this model revealed a significant omnibus effect of agent, F(4, 479) = 3.95, p = .004, suggesting that a difference existed between the agents at some level. To explore participants’ unethicality judgments across the different agents, we conducted a Tukey HSD post hoc test (see Fig. 3). Results indicated that participants believed the for-profit organizational behaviors were broadly more unethical (M = 5.27, SD = 1.68) than the person’s behaviors (M = 4.51, SD = 2.05; p = .001, d = .41), the family business’ behaviors (M = 4.54, SD = 2.27; p = .001, d = .37), and the government agencies’ behaviors (M = 4.60, SD = 1.97; p = .004, d = .37). However, participants did not significantly distinguish between the for-profit and nonprofit organizations (M = 4.88, SD = 2.05, p = .243, d = .21). No other conditions differed significantly, ps > .266. We also observed a significant omnibus effect of behavior, F(4, 479) = 143.30, p < .001, again indicating that participants believed the different behaviors conveyed different levels of unethicality. Finally, we did not observe a significant interaction between the agent and behavior manipulations, F(16, 479) = 1.31, p = .184, suggesting that participants’ comparisons of the different agents did not meaningfully vary across the situations.

Fig. 3

Unethicality as a function of agent (Experiment 5). Error bars represent 95% confidence intervals of means

Experiment 5 investigated how people respond to different kinds of organizations’ ethical violations. Specifically, we found evidence that people believe for-profit organizations’ behaviors are more unethical compared to other types of organizations’ behaviors (family businesses and government agencies), although they did not significantly distinguish between a for-profit organization and a nonprofit organization. People generally believe that for-profit organizations are powerful and have access to many resources in modern society, generally above and beyond other kinds of organizations (Burson-Marsteller 2014). Experiment 5 showcased one potential consequence of these beliefs: perceptions that their behaviors are relatively more unethical, compared to identical behaviors other agents might engage in. As such, although many different kinds of organizations can (and do) engage in unethical activity, Experiment 5 suggests that people judge behaviors as more unethical when for-profit organizations engage in them, compared with other types of organizations.

Experiment 6

In Experiment 6, our goal was to explicitly examine one implication of people’s divergent perceptions of organizational and human ethical violations. When organizations transgress, they can respond in a number of ways, such as apologizing for the unethical behavior or denying it outright (e.g., Kim et al. 2004). Another tactic is to scapegoat specific individuals (or groups) as responsible and punish them accordingly. For example, a powerful CEO might blame financial losses on subordinates and then subsequently replace them in order to evade responsibility (Boeker 1992). In cases where an entire firm would otherwise be punished for unethical behavior by stakeholders or regulatory agencies, businesses that isolate and punish individual people within the organization reduce both perceived collective responsibility for the transgression (see Waytz and Young 2012) as well as subsequent firm-level blame (Douglas 1995; Gangloff et al. 2014).

The results from Experiments 1–5 suggest that framing individuals as responsible for unethical behavior might carry with it an additional consequence above and beyond shifting responsibility. Specifically, framing a single individual as responsible for a violation, as opposed to the superordinate organization, may produce impressions that a specific behavior was actually less unethical. In Experiment 6, we presented either organizations or employees of organizations as responsible for five ethical violations, after which we gave participants the ostensibly real opportunity to punish the businesses. Consistent with research on scapegoating, shifting responsibility from an organization to a single employee likely reduces people’s desire to punish the collective itself. In Experiment 6, we utilized mediation to test if people are more lenient toward organizations that scapegoat because, when organizations frame single individuals as responsible for violations, people believe those violations are intrinsically less unethical.

Method

Participants

A total of 176 students enrolled in a social science course across two American West-Coast community colleges (52 Male, M_age = 23.84) elected to complete the experiment online for course credit in collaboration with a large nearby university.

Procedure

We randomly assigned participants to answer questions about either businesses or individual employees working at businesses for the duration of the survey. Participants read about five ostensibly real violations involving a business [an employee working for a business] in the surrounding area: leaving a machine on that started a fire, losing time cards, improperly storing customer data which resulted in a breach,² claiming food items were organic when they were not, and refusing to refund the purchase of fruit infected with parasites. Following each vignette, participants rated how unethical they felt the action was using the same two-item scale as in Experiments 4 and 5 (rs > .72, ps < .001).

At the end of each vignette, we gave participants the ostensibly real and anonymous opportunity to sign an electronic petition to fine the business in question given the specific transgression that took place. Participants could indicate either that they wanted to sign a petition to fine the business (“Yes, add my anonymous support for the petition”) or indicate that they did not want to sign the petition (“No, I don’t think this business should be fined”). After choosing to whether or not to sign the petition, we asked all participants how large a fine they would recommend the business receive using a 0 (“No Fine”) to 10 (“$1000 or more”) scale.

Results

Unethicality

We first computed a 2 (agent: business vs. employee, between-subjects) × 5 (moral violation; within-subjects) repeated-measures ANOVA predicting unethicality. As in the previous studies, results indicated an omnibus effect of agent across the different violations, F(1, 171) = 15.59, p < .001, d = .59. Overall, participants believed the business’ actions were more unethical (M = 5.72, SD = 0.69) compared to the individual employees’ actions (M = 5.23, SD = 0.95). However, we also observed a significant interaction suggesting that the effect of agent differed across the vignettes, F(4, 684) = 4.65, p = .001. The difference was statistically significant for two vignettes (starting a fire and losing time cards; t(173) = 2.02 and t(173) = 5.20, respectively, ps < .05), marginally significant for two vignettes (improperly storing data and claiming food items were organic; t(173) = 1.87 and t(173) = 1.69, respectively, ps < .10), and statistically insignificant for the fruit refunding vignette, t(174) = 1.40, p = .16. We proceeded to aggregate participants’ responses into a composite of unethicality across the five vignettes to later use in a mediation model (α = .66).

Punishment

To assess participants’ punishment behaviors, we dummy coded their willingness to provide their electronic support to actually fine the business in question (0 = no fine, 1 = fine). Overall, participants were more likely to recommend a fine when we framed the organization as responsible for the violation (77.5%) compared to an individual employee (65.9%). To test the statistical significance of this difference, we created a mixed model using fine recommendation as the dependent variable and our framing manipulation as the independent variable. This model also accounted for a fixed effect representing the different violations as well as random by-participant intercepts (Baayen 2008). Results indicated a significant effect of our agent manipulation, F(1, 656) = 13.77, p < .001, and no significant interaction between our framing manipulation with an index representing the different violations, F(4, 656) = 0.52, p = .995. To use in a mediation model, we summed participants’ willingness to fine the organizations across the five violations into a 0 (no fine) to 5 (five fines) composite (α = .66).

We next computed a 2 (agent: business vs. employee, between-subjects) × 5 (moral violation; within-subjects) repeated-measures ANOVA predicting recommended fine amount. Results indicated a significant effect of our framing manipulation, F(1, 164) = 7.13, p = .008, d = .30. Participants recommended higher fines when we framed the organization as responsible for the violation (M = 6.88, SD = 3.30) compared to a single employee (M = 5.91, SD = 3.27). We also observed a marginally significant interaction between our framing manipulation and an index representing the different moral violations, F(4, 656) = 2.25, p = .063, suggesting that participants proposed larger relative fines for some transgressions compared to others. As with the unethicality and the binary punishment measures, we averaged participants’ suggested fines across the situations to use in a mediation model (α = .62).

Mediation by Unethicality

We next constructed two 5000-iteration bootstrapped mediation models to test if participants’ judgments that the organizations’ actions were more unethical compared to the employees’ actions explained some portion of their subsequent punishment behaviors. We used agent (0 = person, 1 = organization) as the independent variable, unethicality as the mediator, and our two punishment composites as dependent variables. Results indicated that participants’ beliefs that the organizations’ actions were less ethical, compared to the employees’ actions, mediated both their increased support for petitions to fine the businesses, b = .33, CI₉₅ = [.15, .59] as well as larger overall recommended fines for the businesses, b = .47, CI₉₅ = [.21, .85] (see Fig. 4).

Fig. 4

Mediations to petition to fine and fine amount through perceived unethicality (Experiment 6)

In Experiment 6, we not only conceptually found support for H1 using workplace-specific violations, but also demonstrated one implication of people’s perceptions that organizational violations are less ethical compared to human violations. While organizations may often scapegoat individual employees as responsible for ethical transgressions in order to divert collective blame, doing so may improve observers’ judgments of the transgression. Consistent with this logic, participants’ judgments that the violations were less ethical when organizations were responsible for them, compared to people, mediated harsher actual and desired punishment behaviors toward the superordinate collectives. These results thus help explain impression management strategies organizations often use in the world. For example, organizations often frame transgressions as individual (as opposed to institutional) mistakes, and also often invoke individual frames of reference following scandals (e.g., a CEO’s personal apology, compared to one issued by a collective).

General Discussion

Although organizations are different in many ways from individual people, they may sometimes find themselves in similar ethical situations. In this research, however, we found that people’s judgments of organizational and individual behaviors differ in systematic ways. Across six experiments and a variety of transgressions, we found that people believed organizational behaviors were more unethical than the identical behaviors of individual people. We further found that these judgments resulted, at least in part, because observers believed that organizations created more harm from their behaviors (even when they did not) and were more blameworthy. Moreover, our studies suggested that these results were reasonably robust across different populations and over a range of different countries, although people did distinguish between for-profit companies and other organizational categories (e.g., a family business or a government agency). Finally, we found that differences in ethical judgments, not surprisingly, resulted in different degrees of willingness to punish ethical violations, such that organizations that lodged the agency for an unethical act in an individual may confront fewer and smaller sanctions than organizations that do not deflect blame onto one person. These results suggest that organizations can seek to appear less unethical not only by improving their behaviors, but also by framing individual agents as responsible for those actions. Organizations wield a great deal of economic, social, and political power in modern society. Therefore, understanding how organizations can frame behaviors as more or less ethical is of substantial importance not only to understand communication and impression management processes, but also how the public ultimately reacts—or does not react—to unethical business practices.

Implications

Across six experiments, we identified a descriptive inequality: organizations appear more unethical than individuals when both agents engage in identical behaviors. What are some normative implications of this phenomenon? On the one hand, one interpretation of these experiments is that framing individuals as responsible for organizational transgressions is unethical in practice. Instead of managing impressions or stakeholder relations by engaging in less unethical behavior (or more ethical behavior), organizations may be incentivized—at least to some extent—to instead focus their resources toward scapegoating undesirable behaviors by blaming individual actors as opposed to addressing their root causes or preventing bad behaviors from reoccurring at an institutional level. Indeed, the present experiments highlight one reason why organizations might focus on individual behaviors and apologies following unethical behavior (e.g., Oscar Munoz’s apology for United Airlines’ forcible removal of a passenger) as well as why public relations consultants and researchers tend to encourage organizations to focus on individual communications (see Hearit 1994; Kim et al. 2004; Schweitzer et al. 2015). As such, capitalizing on this phenomenon to improve stakeholder impressions might be unethical, compared to trying to address transgressions in the first place or expending resources to prevent them from reoccurring.

However, another interpretation of these results is that people are evaluating for-profit organizations in a biased way, departing from how they “should” be responding to unethical practices. As we reported in Experiment 5, participants believed that other kinds of organizations (government agency, family business) as well as individual people who exhibited similar levels of unethicality were similar; the only significant differences we found were when we compared these agents with for-profit organizations. Because there were no actual differences in the harm caused in the specific scenarios we utilized, one interpretation of these results is that people tend to depart from rationality in a way that disfavors for-profit organizations. The vast literature concerning people’s judgment and decision making (e.g., Tversky and Kahneman 1974) suggests that—while they can err—heuristics are often useful tools for navigating complex social environments. In the case of responding to for-profit organizations’ behaviors, for example, it is almost certainly the case that large firms tend to cause more harm than individuals when transgressing. In situations where they actually do not produce more harm, however, these results suggest that people might indeed “unfairly” evaluate for-profit organizations, thereby inducing potentially unethical impression management strategies that the for-profit entities otherwise would not have to engage in if people evaluated them similarly to other agents. Many different organizational agents can engage in unethical behavior, and the present experiments suggest that people can respond to such behaviors quite differently. We believe that one fruitful avenue of future research is to continue investigating these descriptive inequalities in ethical judgment to continue informing normative approaches to business ethics (and, specific to these experiments, corporate personhood).

Future Directions

Across a variety of participant samples and moral transgressions, we found convergent evidence consistent with a small-to-medium sized effect such that people generally believe organizational behaviors are more unethical compared to individual behaviors. Further research could better identify circumstances in which people view organizational violations more positively than human violations, for example, as a function of how a particular transgression is framed or presented, such as actually seeing a contract in a businesslike context (e.g., Haran (2013)), as compared to simply reading about a situation involving a contract (e.g., in a newspaper). Although we consistently observed omnibus effects, people might also hold humans and organizations to different standards of behavior across different moral domains (see Experiment 3b). In addition to potentially divergent norms of individual and organizational behavior, the mechanisms we identified may also be more or less salient given the specifics of a moral circumstance, for example, the extent to which people can easily parse culpability or harm or the extent to which they can determine which agent (if any) committed a transgression. Further research concerning how people perceive collectives could better explore other dimensions along which people view collectives as similar to individual agents, as well as dimensions along which they separate them, both including and beyond morally relevant characteristics such as estimates of harm and blameworthiness (e.g., Rai and Diermeier 2015; Waytz and Young 2012).

The idea that people judge human and organizational behaviors differently also questions the practical viability of legal systems that treat them as normative equals with identical ethical restrictions. Additional research could better explore the different consequences humans and organizations actually experience (or potentially do not experience) following transgressions, given their structural equality in such systems. As discussed above, any descriptive departure in ethical judgment has clear normative implications for the fair implementation of laws concerning such entities or their rights, as well as potential biases that could influence judicial processes (e.g., associating a particular defendant with a large for-profit corporation). Similarly, these results raise an additional important normative question: Are organizations actually more unethical than individuals when they behave in the same ways? While such a discussion is beyond the scope of this article, the present experiments suggest that people’s descriptive beliefs align with one normative interpretation: that organizations, by virtue of their powerful roles and responsibilities in modern society, are actually more unethical when transgressing compared to individual people. We believe that further research focusing on both people’s descriptive responses to organizations’ behaviors as well as firms’ normative roles in society will more fruitfully answer such questions.

In a similar vein, these findings address a variety of literatures concerning signaling and impression management (e.g., Connelly et al. 2011). If people believe individuals are less unethical than collectives, they may apply these stereotypes to judge organizational behaviors in nonmoral domains as well, for example, an individual leader compared to a collective organization claiming that they did (not) affect many people with specific actions. In addition, organizations can blame many different agents—both internal and external—for unethical behaviors for a variety of reasons, and the ethical implications of these practices necessarily vary. For example, the strategy of scapegoating an individual who was otherwise uninvolved with a transgression involves different—and potentially worse—ethical outcomes compared to the strategy of scapegoating an individual who actually was involved (with the goal of framing a violation as less unethical). While we only investigated internal scapegoating in the present experiments, organizations can also scapegoat third-party entities, such as contractors or competitors, in an effort to avoid negative reputations. Finally, an internal agent intentionally taking on and owning responsibility for unethical behavior might appear more honest to observers, compared to an individual or collective assigning this responsibility to different parties. Future research could investigate people’s responses to these different impression management strategies while also exploring their divergent ethical implications. In a different vein, future research could also investigate potential boundary conditions for the effects we found here. Leaders or people with substantial business experience, for example, might be more aware of the situational pressures that influence organizational behaviors, ultimately treating individuals and collectives as more similar. Following Experiment 5, people’s natural assumptions about different organizations, such as seeing one for-profit company as more akin to a large family business compared to a competitor, could also shift their responses to different unethical behaviors. In addition, the current research necessarily relies on people’s current opinions or perceptions of organizations (e.g., Burson-Marsteller 2014). Substantial changes in public or social policy—for example, restrictions of corporate personhood in important domains, such as political contributions—will necessarily affect people’s stereotypes about businesses and subsequent distinctions between the two agents in both ethical and legal domains (e.g., Jago and Laurin 2017).

Conclusion

Organizations carry a great deal of power in modern society. Across six experiments, we found a descriptive inequality between people and organizations of people: when transgressing, organizations appeared more unethical than people who engaged in similar or identical actions. We believe that understanding people’s asymmetric ethical evaluations of different agents not only speaks to normative legal realities of corporate personhood, but also to broader potential incentive systems whereby organizations can appear more ethical by framing their behaviors strategically, as opposed to minimizing harm or undesirable behaviors in the first place. Although they might be “equals” in many judicial systems, people respond to human and organizational transgressions in very different ways.

Appendix

Experiment 1

Breaking fruit import laws and accidentally importing a parasite.

Misrepresenting the truth in order to sell something.

Refusing to pay a contractor an agreed-upon amount.

Lying about a rival online.

Making noise using machinery after legal quiet hours.

Negligently polluting a local stream.

Avoiding taxes.

Improperly storing toxic chemicals.

Stealing intellectual property.

Accidentally disfiguring a public sidewalk.

Experiment 2

On October 15, 2014, it was found that Bill [NatCo] had broken a number of fruit import laws in California. As a result of breaking these laws, a new strain of parasite that affected fruit trees was released into one local community, endangering plant life and orchards.

On October 2, 2014, Bill [NatCo] sold a property to a small restaurant business in Santa Clara, CA. The couple that purchased the property soon learned that it had a moderate termite problem that would cost a significant amount to fix. No communication from Bill [NatCo] indicated this termite problem to the couple prior to the sale.

On August 29, 2014, Bill [NatCo] hired a plumber—Nancy—to do substantial plumbing work. Upon completion, Bill [NatCo] refused to pay Nancy the agreed-upon price, $1000, instead paying her only $250. Nancy believes that Bill [NatCo] was totally satisfied with the contract work and simply wanted to get away with giving her less money.

On October 8, 2014, it was found that Bill [NatCo] had lied about a catering service, QuickCuisine, on an online review site. Specifically, Bill [NatCo] said that QuickCuisine charged 2 times as much as advertised for a catered event, when in fact, they hadn’t. QuickCuisine said in response that they had many disagreements with Bill [NatCo] in planning the event, and that Bill [NatCo] maybe had lied to make them look worse.

In mid-October, 2014, Bill [NatCo] ran loud machinery near a residential area. Normal hours for work are generally 9AM to 5PM, but Bill [NatCo] ran this loud machinery starting around 7:30AM until 7PM or so every night for approximately two work weeks. A number of residents expressed concerns about this noise pollution, saying that Bill [NatCo] was being negligent toward the community.

Experiments 3a and 3b

On March 14, 2015, a person [business] in Santa Clara County, CA, dumped 10 gallons of crop insecticide into a local lake. Local news agencies speculated that this type of dumping could easily adversely affect both the wildlife in the lake and in the surrounding area.

On March 29, 2015, a person [business] in Santa Clara County, CA, improperly installed a heating unit in a manner that violated local safety laws and ordinances.

On May 17, 2015, a person [business] in Marin County, CA, violated California agriculture laws by experimenting with a non-FDA approved pesticide on approximately one half acre of land.

On June 1, 2015, a person [business] in Santa Clara County, CA, reneged on a contract with a local homeowner. The person [business] previously signed a contract to renovate the homeowner’s kitchen, but broke that contract in order to pursue a more profitable project elsewhere.

On April 4, 2015, a person [business] in Santa Clara County, CA, changed legal residence to another state. However, the person [business] didn’t actually move and stayed in the same place. This reclassification, given many circumstances, allowed the person [business] to avoid approximately $10,000 in taxes.

Experiment 4

On March 14, 2015, Steve [Stuza] used an organic pesticide on 250 square feet of farmland. Local news agencies reported that this pesticide, while harmless to the environment, produced an extremely unpleasant odor for 3–4 days that bothered people.

However, further investigation revealed that Steve [Stuza] was given the wrong pesticide by a supplier, and couldn’t have possibly known it would bother people. The supplier claimed responsibility and apologized for the incident.

However, further investigation revealed that only two people were negatively impacted by this unpleasant odor, due to the extremely remote location of the plot.

Experiment 5

On April 21, 2015, a business [employee working for a business] in San Mateo County, CA, accidentally left a machine on overnight that can normally operate only for an hour or two at a time. Doing so ultimately resulted in a fire, which caused moderate damage to a neighboring property.

On April 04, 2015, a business [employee working for a business] in San Mateo County, CA, lost approximately sixty employee time cards. While repairable, this error resulted in the workers not being paid for their normal work or any overtime for approximately 4 weeks.

On February 11, 2015, a business [employee working for a business] in Marin County, CA, improperly stored customer data, which resulted in approximately fifty customers’ names and email addresses being posted publicly to the internet.

On April 22, 2015, a business [employee working for a business] in Santa Clara County, CA, was found to have been claiming that certain food items on a menu were “organic” when they actually were not, according to USDA standards.

On February 2, 2015, a business [employee working for a business] in San Mateo County, CA, refused to refund a customer for a large purchase of fruit that turned out to be infected by a number of parasites. The customer had no way of knowing that the fruit was infected.