Counterfactual Thinking and Ethical Decision Making

Abstract

The present study extends understanding of the self-regulatory aspects of ethical decision making by integrating and exploring relationships among counterfactual thinking, attribution, anticipatory emotions, and ethical decision-making constructs and processes. Specifically, we examine the effects of a manipulation designed to stimulate a counterfactual mind-set, which is found to significantly affect negative anticipatory emotions and intention associated with unethical conduct. Furthermore, we explore the basis of these effects by delineating and supporting responsibility attributions working through perceived negative consequences to influence both negative anticipatory emotions and intention to perform the unethical behavior. This research addresses both academic and managerial imperatives in the marketing education literature as to how to effectively influence ethical decisions through an understanding of how individuals combine theoretically significant cognitions.

Keywords

ethical decision making counterfactual thinking attribution processes anticipatory emotions

Ethical scandals continue to provide a wealth of material for business professors to use in the curriculum. In classes, we often discuss the behavior of various players implicated in these transgressions as well as the damage done to individuals, companies, industries, and perhaps economies. We can very logically “connect the dots” after the fact. However, more than one professor has, to himself or herself or colleagues, raised the questions, “Does this have any impact on students?” “Can ethics really be taught?” These questions are especially troubling when one considers that the media often highlights the fact that former business students who graduated from prominent programs (undergraduate and MBA) are often implicated in the scandals. If we talk to practitioners, they readily reply that business ethics or organizational risk areas and how to deal with them, most certainly, can be taught.

In response to this state of affairs, business schools among other stakeholders have felt compelled to address the ethics imperative. A prominent accrediting body of business schools globally, the Association to Advance Collegiate Schools of Business International (AACSB), recognizes the importance of ethics and advocates the development of the understanding of ethical issues and their effects on organizations and society (AACSB, 2012). Although many stakeholders would agree with the sentiment expressed by the AACSB regarding ethics, there exists much less clarity with regard to how it can be effectively taught (Beggs, 2011; Roy, 2012). Confounding the matter is the fact that the AACSB has moved from providing ethics programs and workshops to providing sustainability programs and workshops. This move has left a void for academics striving to become more effective educators on the subject.

What are the extant themes in the business literature with respect to teaching ethics? If there is a favored approach to teaching ethics, it falls in the “experiential-learning” category and focuses on building experiential thinking skills (Allan & Wood, 2009; Beggs, 2011; Feldman & Thompson, 1990; Granitz, 2001; Hunt & Laverie, 2004; Loe & Ferrell, 2001; Nill & Schibrowsky, 2005). However, results are mixed with respect to the outcomes of ethical education. Some studies report positive outcomes from ethics training (Lau, 2010; Yoo & Donthu, 2002), whereas others report no improvements (Smith, 1996; Waples, Antes, Murphy, Connelly, & Mumford, 2009). Over the past two decades, a number of studies consistently address the “we need to do better” theme (Allan & Wood, 2009; Beggs, 2011; Dean, Beggs, & Keane, 2010; Mele, 2005; Merritt, 1991; Shannon & Berl, 1997; Yoo & Donthu, 2002). Finally, there have long been calls for theory-driven ethical research (Gibson & Frakes, 1997; Hunt & Vitell, 1986) and, to this end, research that explores, at a more nuanced level, how individuals combine various elements in their ethical thinking and behavior (Anderson, 1997; Celuch & Dill, 2011; Dubinsky & Loken, 1989).

Based on the above review, there is a clearly articulated need to address ethics in our business curriculum. However, exactly how to do this is less clear. Although experiential learning is a favored approach, results have been mixed and at times not tied to strong explanatory theory, rendering our understanding of ethics processes less than complete. The present research aims to address theoretical gaps in the ethics literature by exploring individual-level ethical decision making in an experiential-learning context. We do so by combining theory from counterfactual thinking, the attribution process, and anticipatory emotions to develop and test several hypotheses. This study is unique in that these areas have not been simultaneously explored in business ethics research.

Although each of these areas has a rich history of research, there are also significant gaps in understanding underlying processes. For example, there are calls for better integration of attribution theory across disciplinary domains as well as calls for better recognition of complexity in its application (Gaily & Lee, 2005; Woolfolk, Doris, & Darley, 2006). Furthermore, the cognitive mechanisms that underlie anticipatory emotions and our understanding of their role in goal-directed behavior are not well understood (Baumgartner, Pieters, & Bagozzi, 2008; Harvey & Victoravich, 2009; Tsiros & Mittal, 2000). In merging these domains for the exploration of ethical decision making, we hope to discover insights into self-regulatory behavior that heretofore have been missing due to separate consideration of these areas. As a result, a more nuanced understanding of an individual-level intervention may offer guidance to business educators and corporate trainers in developing curriculum to improve ethical decision making for future as well as novice business professionals.

Counterfactual Thinking Mind-Set

Counterfactual thoughts explore possible alternative realities to events. When an individual questions himself or herself with “what might have happened” with respect to a prior event, he or she is using counterfactual thinking (Kray, Galinsky, & Wong, 2006). A counterfactual thinking mind-set is this type of cognitive orientation activated by a prior context that is then applied in a subsequent context (Gollwitzer, Heckhausen, & Steller, 1990). Mounting evidence suggests that such thinking can play important roles in directing future decision making and behavior (Galinsky, Seiden, Kim, & Medvec, 2002; Kray et al., 2006; Liljenquist, Galinsky, & Kray, 2004; Roese & Olson, 1995). For example, in a series of experiments, Kray et al. (2006) demonstrated that exposure to a scenario with a salient situational element increased the generation of counterfactual thoughts, which induced a counterfactual orientation that was transferred to subsequent problem solving. Furthermore, such mind-sets can lessen the tendency of individuals and groups to employ biased thinking that confirms prior hypotheses by only considering information that is consistent with preexisting viewpoints (Galinsky & Moskowitz, 2000; Kray & Galinsky, 2003).

Exactly how does this sort of mind-set actually work in influencing decision making? Kray et al. (2006) found that the use of counterfactual thoughts is relational in nature such that individuals consider the causal sequence of possible consequences of actions (or inaction). Thus, a salient situational element is one important factor influencing counterfactual thinking because of its mutability and especially when potential alternatives tied to the situational element are associated with outcomes that are undesirable (Kahneman & Miller, 1986). This relational inference process is the basis of forming mental models for using counterfactual thinking in the future (Byrne, 2005; Feeney & Handley, 2006).

In light of the above discussion, the contention that counterfactual thinking can affect aspects of decision making and behavior is well supported. However, questions remain as to exactly when and how specific counterfactual thoughts influence particular types of decision making (Petrocelli, Percy, Sherman, & Tormala, 2011). This is especially so with respect to ethical decision contexts, given the limited application of counterfactual theory to the area as well as the recognized need for theory-based research focused on thinking processes. To our knowledge, this is the first study that includes counterfactual thinking as a basis for a practical intervention to explore its effects on subsequent emotions and intention to perform an unethical behavior, as well as an examination of processes underlying these effects.

In the present study, we employ two ethical scenarios in which the significant mutable situational element is whether the boss (or immediate supervisor) is “hands-on” or “hands-off.” The implication being that the “hands-on” boss situational element is logically tied to a greater likelihood of negative consequences associated with getting caught than is the “hands-off” boss element. In keeping with counterfactual thinking methodology, we manipulate the situational element for one scenario and then have subjects complete relevant measures including manipulation checks and the likelihood of specific negative outcomes associated with the first scenario. Subjects are then exposed to a second scenario that has not been manipulated and complete different measures for the dependent variables (negative anticipatory emotions and intention to perform an unethical act) associated with the second scenario.

Consistent with self-regulatory theory, subjects under the “hands-on” boss manipulation should “connect” stronger likelihoods of negative consequences that, in turn, should prime a prevention-focused counterfactual mind-set that is then carried over to consideration of the second ethical scenario (Higgins, 1997; O’Brien & Oyserman, 2008). This mind-set should influence subsequent thinking about potential negative consequences associated with the second scenario such that the generation of negative anticipatory emotions should be stronger for subjects exposed to the “hands-on” condition (Bagozzi, Baumgartner, & Pieters, 1998). In addition, this mind-set and the associated negative consequences should also negatively affect a subject’s intention to perform the unethical act. Consideration of consequences to acts has been found to be an important determinant of intentions across a range of contexts (Ajzen & Fishbein, 1980; Eagly & Chaiken, 1993; Sheppard, Hartwick, & Warshaw, 1988), including ethical contexts (Buchan, 2005; Carpenter & Reimers, 2005; Celuch & Dill, 2011). In this way, we believe that a counterfactual manipulation associated with a salient situational element (the boss being “hands-on” or “hands-off” and consideration of potential negative consequences) in one scenario should differentially influence negative anticipatory emotions and intention associated with the unethical behavior in a subsequent scenario. Based on the foregoing discussion, we hypothesize the following:

Hypothesis 1: Negative anticipatory emotions associated with an actor’s unethical behavior (for Scenario 2) will be stronger for participants exposed to the “hands-on” manipulation (for Scenario 1) as compared to those exposed to the “hands-off” manipulation (for Scenario 1).

Hypothesis 2: The behavioral intention associated with an actor’s unethical behavior (for Scenario 2) will be weaker for participants exposed to the “hands-on” manipulation (for Scenario 1) as compared to those exposed to the “hands-off” manipulation (for Scenario 1).

Negative Consequences as Mediators of the Influence of Attributions on Negative Anticipatory Emotions and Intentions Associated With Unethical Behavior

Recall that an objective of this study, beyond examining the effectiveness of a counterfactual manipulation, was to explore the nature of effects in order to help explain the when and how gaps in our understanding of counterfactual influence, particularly in an ethical decision context. Appraisal processes have been implicated in both anticipatory emotions and intentions (Bagozzi et al., 1998; Dixon, Spiro, & Jamil, 2001). Indeed, counterfactual thinking has been posited to influence a significant appraisal system—an individual’s attribution processes (Kahneman, 1992; Kahneman & Miller, 1986; Wells & Gavanski, 1989). Attribution theory addresses how people use information to develop explanations for behavior and events (Heider, 1958). Two classes of attributions have been distinguished: causal attributions, which are related to the locus, stability, and global nature of the cause of an event, and the more specific responsibility attributions, which focus only on the locus of cause for the event, such as self, other, or environment (Fincham, Harold, & Gano-Philips, 2000).

Responsibility attributions, an actor’s perceived causal role in an outcome, are likely to be heightened when an outcome is perceived to be in an actor’s control and is therefore brought about intentionally (Alicke, 2000). Stronger affective reactions have been linked to outcomes when an outcome can be tied to an actor (Bagozzi et al., 1998; Weiner, 1982). Furthermore, appraisals of potential outcomes of acts have been linked to anticipatory emotions (Baumgartner et al., 2008; Connelly, Helton-Fauth, & Mumford, 2004; Harvey & Victoravich, 2009; Ortony, Clore, & Collins, 1988) and intentions and behavior (Bagozzi et al., 1998; Dixon et al., 2001). In summary, aspects of events that are mutable, which might influence counterfactual thinking, are also implicated in attribution processes. Individuals infer outcomes to those factors that covary with the outcomes that, in turn, can influence affective and behavioral outcomes.

We now extend thinking from the literature integrated above to the ethical context of the present research to explain the proposed differential effects for the “hands-off” versus “hands-on” manipulation. For the “hands-on” boss manipulation, attribution of actor responsibility should be strongly related to the perceived negative consequences associated with the actor’s behavior in the first scenario. Furthermore, the attribution of actor responsibility should work through the perceived negative consequences associated with the actor’s behavior in the first scenario to influence the negative anticipatory emotions and intention to perform the unethical behavior in the second scenario. In this condition, the effect of the counterfactual mind-set is rooted in the saliency of the negative consequences that should be stronger given the context of the actor’s behavior. That is, the actor chooses to perform the behavior even with a greater likelihood of getting caught because the boss is “hands-on.” In contrast, under the “hands-off” boss manipulation, the negative consequences should not mediate the relationship between actor responsibility and negative anticipatory emotions and intention to perform the unethical behavior given the weakened saliency of the consequences as the actor is not as likely to get caught because the boss is “hands-off.” Thus we hypothesize the following:

Hypothesis 3: For participants in the “hands-on” condition, the attribution of actor responsibility will work through (be mediated by) the perceived negative consequences associated with the actor’s unethical behavior (for Scenario 1) to influence negative anticipatory emotions associated with the actor’s unethical behavior (for Scenario 2). It is expected that actor responsibility should be positively related to negative consequences and negative anticipatory emotions. Mediation is not expected for participants in the “hands-off” condition.

Hypothesis 4: For participants in the “hands-on” condition, the attribution of actor responsibility will work through (be mediated by) the perceived negative consequences associated with the actor’s unethical behavior (for Scenario 1) to influence the behavioral intention associated with the actor’s unethical behavior (for Scenario 2). It is expected that actor responsibility should be positively related to negative consequences and negatively related to behavioral intention. Mediation is not expected for participants in the “hands-off” condition (please refer to Figure 1).

Figure 1.

Hypothesized mediated relationships for the “hands-on” condition.

Method

Procedure and Subjects

In keeping with counterfactual methodology, the study manipulated a salient situational element (“hands-off” or “hands-on” immediate supervisor) for two ethical scenarios in business contexts. Participants were randomly provided a study packet that consisted of, in order, one of two scenarios that included the manipulation, followed by related measures, followed by one of two scenarios that did not include a manipulation and by a second set of measures that were different from those assessed after the first scenario. Participants were provided instructions for completing measures and were then instructed to proceed in order through the packet, reading each scenario and then completing associated measures.

Two phases of data collection were utilized. A total of 124 undergraduate students enrolled in a variety of classes (sections and instructors) ranging from introductory level (e.g., marketing principles) to advanced level (e.g., marketing strategy) at a medium-size Midwestern university participated in the first phase of this study in order to test Hypotheses 1 and 2. Within 2 weeks, an additional 138 undergraduate students enrolled in various classes at the same university were added to the first sample in order to test Hypotheses 3 and 4. This approach was used so as to not overinflate the sample size for mean difference tests for Hypotheses 1 and 2 and to increase the sample in order to ensure the stability of regression equations for testing Hypotheses 3 and 4. The average age of participants in the total sample (n = 262) was 21 years (range = 18-47). Fifty-three percent of the participants were male. Upperclassmen comprised 63% of participants.

Scenarios

Ethical scenarios are used extensively in business ethics research (see Buchan, 2005; Chonko & Hunt 1985; Cohen, Pant, & Sharp, 2001). Scenarios combine the benefits of standardization and mundane realism (Cherry, 2006). Scenarios used in this study were adapted from scenarios used in prior research (Celuch & Dill, 2011; Cohen et al., 2001).

In the first scenario, a male actor is considering copying a copyrighted software package. In the second scenario, a female actor is considering charging some personal items to her company credit card. In each scenario, the actors opt for the unethical action. As previously noted, the manipulation of the situational element involves whether the actor’s immediate supervisor is very “hands-off” or very “hands-on,” as this could affect the chance of getting caught. Approximately half of the participants received the “copying software” scenario first as the manipulation, followed by the “charging personal items” (nonmanipulated) scenario. The other participants received the “charging personal items” scenario first as the manipulation, followed by the “copying software” (nonmanipulated) scenario.

Scenarios used for manipulations were pretested prior to use in the study. The pretest measure consisted of one 7-point item that assessed the likelihood that the actor will experience negative consequences for his or her behavior. Pretesting of scenario manipulations confirm that the “hands-on” manipulations for the “copying software” and “charging personal items” (M = 6.3 and 6.7, respectively) were perceived to be significantly more likely to result in negative consequences than the “hands-off” manipulation for the scenarios: M = 3.3 and 3.6 respectively; t(17) = 7.28, p = .00; and t(18) = 7.78, p < .01, respectively (please see the appendix for example scenarios).

Measures

Measures following the first, manipulated scenario included manipulation checks, attribution of actor responsibility, and perceptions of negative consequences. The manipulation check measure followed the format of the pretest measure and consisted of one 7-point item that assessed a subject’s perception of the likelihood that the actor will experience negative consequences for his or her behavior (adapted from Petrocelli et al., 2011). Attribution of actor responsibility consisted of two 7-point items that assessed the perception that the actor is responsible for and is to blame for the likely outcomes (r = .65; adapted from Fincham et al., 2000; Woolfolk et al., 2006). Perceived negative consequences consisted of three 7-point items that assessed the perception that the actor’s action will hurt his or her relationship with an immediate supervisor, jeopardize his or her job with the company, and hurt his or her reputation in the industry (Cronbach’s α = .91; adapted from Celuch & Dill, 2011).

Measures following the second scenario included negative anticipatory emotions and intention to perform the actor’s behavior. Negative anticipatory emotions consisted of three 7-point items that assessed how a subject would feel if he or she was the actor in the second scenario. Emotions included worry, anxiousness, and discomfort (Cronbach’s α = .88; adapted from Baumgartner et al., 2008). Behavioral intention consisted of one 7-point item that assessed the likelihood that a participant would perform the same unethical behavior as the actor in the second scenario (adapted from Celuch & Dill, 2011). Summated scores of multi-item scales were used to address hypotheses.

Results

The purpose of this study is to test for the effects of the manipulation on (a) negative anticipatory emotions, (b) behavioral intention, (c) the mediational effect of negative consequences on the actor responsibility–negative anticipatory emotions relationship, and (d) the mediational effect of negative consequences on the actor responsibility–behavioral intention relationship.

Manipulation Check and Mean Differences

As previously noted, each participant received two scenarios, with approximately half receiving the “copying software” scenario manipulation followed by the “charging personal items” and the remaining participants receiving the “charging personal items” scenario manipulation followed by the “copying software” scenario. Tests for scenario effects found no differences between the orderings of scenarios for manipulation check and dependent measures. Therefore, the “hands-on” and “hands-off” groups were combined across scenario orderings for purposes of analyses.

In order to perform the manipulation check and test Hypotheses 1 and 2, independent samples t tests were performed with the “hands-on” and hands-off” conditions as the grouping variable and the manipulation check, negative anticipatory emotions, and behavioral intention for the unethical act as dependent variables. As expected, the “hands-on” manipulation (M = 5.9) was perceived to be significantly more likely to result in negative consequences than the “hands-off” manipulation: M = 4.2; t(122) = 9.90, p < .01.

Hypothesis 1 predicts a condition effect for negative anticipatory emotions associated with the actor’s unethical behavior (for Scenario 2) such that he or she should be stronger for the “hands-on” condition as compared to the “hands-off” condition. As predicted, negative anticipatory emotions were significantly stronger for participants in the “hands-on” condition (M = 5.9) in comparison to participants in the “hands-off” condition: M = 5.4; t(120) = 2.15, p < .05.

Hypothesis 2 predicts a condition effect for behavioral intention associated with the actor’s unethical behavior (for Scenario 2) such that it should be weaker in the “hands-on” condition as compared to the “hands-off” condition. As anticipated, intention was significantly weaker for participants in the “hands-on” condition (M = 1.8) than for participants in the “hands-off” condition: M = 2.5; t(120) = 2.67, p < .01.

Regression Analyses Testing for Mediation

As with the first sample, to ensure equivalency, we tested for scenario effects with the larger combined sample and found no differences between the two orderings of scenarios for the manipulation check and dependent measures. Therefore, we combined the groups across scenario orderings for purposes of analyses. Again, in order to check the manipulation, an independent samples t test was performed on the combined sample with the manipulation as the grouping variable. As expected, the “hands-on” manipulation (M = 6.0) was perceived to be significantly more likely to result in negative consequences than the “hands-off” manipulation: M = 4.2; t(260) = 9.97, p < .01.

As a precursor to testing Hypotheses 3 and 4, composite reliability, convergent validity, and discriminant validity were assessed for the multi-item measures with at least three items. Both measures were above recommended thresholds (>.80) for composite reliability (Fornell & Larcker, 1981). Confirmatory factor analysis was used to assess the convergent validity of measures, and observed indicators were all statistically significant (p < .01) for their corresponding factors. Measurement model fit statistics: χ²(8) = 9.48, p = .30, nonnormed fit index (NNFI) = .99, comparative fit index (CFI) = .99, root mean square error of analysis (RMSEA) = .03, suggest that the observed indicators are representative of constructs with the combination of NNFI, CFI, and RMSEA, consistent with fit index standards recommended for good-fitting models (Hair, Black, Babin, Anderson, & Tatham, 2006). Table 1 presents the standardized coefficients for the measures tested with confirmatory factor analysis.

Table 1.

Results of Confirmatory Factor Analysis.

Constructs and items	Standardized coefficient
Negative consequences (in response to the first, manipulation scenario, scaled: strongly disagree/strongly agree): The actor’s action will probably . . .
Hurt her (his) relationship with her (his) manager (supervisor).	.84
Jeopardize her (his) job with the company.	.91
Hurt her (his) reputation in the industry.	.88
Negative anticipatory emotions (in response to the second scenario, scaled: strongly disagree/strongly agree): If I were the actor . . .
I would feel a good deal of worry.	.81
I would feel a good deal of anxiousness.	.84
I would feel a good deal of discomfort.	.88

Note. All standardized coefficients are significant at p < .01.

Pairwise confirmatory factor analysis was conducted to assess discriminant validity of the measures using chi-square difference tests (e.g., Anderson & Gerbing 1988). The chi-square difference test produced a significant result. Therefore, trying to force the measures of different constructs (i.e., negative consequences and negative anticipatory emotions) into a single factor led to a significant deterioration of model fit: χ²(9) = 394.22, p = .00, NFI = .59, CFI = .59, RMSEA = .41) in comparison to the two-factor model. In addition, for these constructs, the amount of variance extracted for each construct is greater than the squared correlation between constructs (Fornell & Larcker, 1981). In sum, these results provide support for the discriminant validity of negative consequences and negative anticipatory emotion construct measures. Tables 2 and 3 provide the means, standard deviations, and correlations for all measures used to test hypotheses 3 and 4.

Table 2.

Descriptive Statistics and Correlations for Constructs in the “Hands-On” Condition.

	M	SD	X1	X2	X3
X1. Actor responsibility	6.4	0.80
X2. Negative consequences	5.9	1.20	.48^**
X3. Negative anticipatory emotions	5.8	1.15	.18^*	.41^**
X4. Behavioral intention	1.5	1.48	−.26^**	−.45^**	−.58^**

p < .05. **p < .01.

Table 3.

Descriptive Statistics and Correlations for Constructs in the “Hands-Off” Condition.

	M	SD	X1	X2	X3
X1. Actor responsibility	6.2	0.99
X2. Negative consequences	4.9	1.51	.24^**
X3. Negative anticipatory emotions	5.5	1.41	.28^**	.16
X4. Behavioral intention	2.4	1.80	−.24^**	−.21^*	−.64^**

p < .05. **p < .01.

In order to test whether the perception of negative consequences mediates the effect of the attribution of actor responsibility on both negative anticipatory emotions and behavioral intention, three conditions must be met:

Attribution of actor responsibility should have a significant effect on the perception of negative consequences.

Attribution of actor responsibility should also have a significant effect on negative anticipatory emotions (or behavioral intention).

As compared to Condition 2, the impact of the attribution of actor responsibility on negative anticipatory emotions (or behavioral intention) should significantly diminish when negative consequences are included in a regression model with actor responsibility as a predictor of negative anticipatory emotions—or behavioral intention (Baron & Kenny, 1986).

The above procedure was utilized for participants in the “hands-on” and “hands-off” conditions, and results are reported in Tables 4 and 5. With respect to the “hands-on” condition, actor responsibility had a significant effect on negative consequences; thus, Condition 1 was met. Furthermore, actor responsibility had a significant effect on negative anticipatory emotions (and behavioral intention); thus, Condition 2 was met. Last, the influence of actor responsibility was diminished (i.e., decreasing from highly significant to not significant) when negative consequences were included in the regression model predicting negative anticipatory emotions (and behavioral intention), meeting Condition 3.

Table 4.

Hierarchical Regression Analyses Testing the Mediating Effect of Negative Consequences on Actor Responsibility Attribution and Negative Anticipatory Emotions and Behavioral Intention for the “Hands-On” Condition.

		Model results
	Adjusted R²	F	Variance inflation factor
Negative consequences = (.48^**) Actor responsibility	.23	38.24^**
Negative anticipatory emotions = (.18^*) Actor responsibility	.02	4.17^*
Negative anticipatory emotions = (.03) Actor responsibility + (.43) Negative consequences	.16	12.88^**	1.30
Negative consequences = (.48^**) Actor responsibility	.23	38.25^**
Behavioral intention = (−.26^**) Actor responsibility	.06	8.84^**
Behavioral intention = (−.05) Actor responsibility + (−.43^**) Negative consequences	.20	16.44^**	1.30

Note. Standardized coefficients appear in parentheses.

p < .05. **p < .01.

Table 5.

		Model results
	Adjusted R²	F	Variance inflation factor
Negative consequences = (.24^**) Actor responsibility	.05	7.92^**
Negative anticipatory emotions = (.28^**) Actor responsibility	.07	11.27^**
Negative anticipatory Emotions = (.26^**) Actor responsibility + (.09) Negative consequences	.07	6.21^**	1.06
Negative consequences = (.24^**) Actor responsibility	.05	7.92^**
Behavioral intention = (−.24^**) Actor responsibility	.05	7.99^**
Behavioral intention = (−.20*) Actor responsibility + (−.16) Negative consequences	.07	5.90^**	1.06

Note. Standardized coefficients appear in parentheses.

p < .05. **p < .01.

Consistent with predictions, after being exposed to a scenario that included a manipulation likely to induce thinking with respect to potential negative outcomes relating to unethical behavior, potential negative consequences related to the manipulation scenario were found to mediate relationships between actor responsibility related to the manipulation scenario and student negative anticipatory emotions and behavioral intention related to a second ethical scenario.

Note that, as expected, no support for mediation is found for participants in the “hands-off” condition, as Condition 3 is not met for negative anticipatory emotions and behavioral intention. Furthermore, as anticipated, note the low explanatory power for the models tested for students in this condition as compared to the models tested for students in the “hands on” condition. As a precaution, variance inflation factors were examined to assess the effects of collinearity among the independent variables for Step 3 models. Note that the variance inflation factors are well below the recommended 10.0 cutoff (Hair et al., 2006). Thus, collinearity between constructs is not indicated.

In summary, the objectives of this study were to test for the effects of the manipulation on negative anticipatory emotions and behavioral intention as well as to test for the mediational effect of negative consequences on the actor responsibility–negative anticipatory emotions and actor responsibility–behavioral intention relationships. Effects were found for stronger negative anticipatory emotions and a weaker behavioral intention associated with the unethical behavior for participants in the “hands-on” compared to the “hand-off” condition thus supporting Hypotheses 1 and 2. Furthermore, the perception of negative consequences was found to mediate the effect of the attribution of actor responsibility on both negative anticipatory emotions and behavioral intention for participants in the “hands-on” condition. No mediation was found for participants in the “hands-off” condition thus supporting Hypotheses 3 and 4.

Discussion

The present study extends understanding of the self-regulatory aspects of ethical decision making by integrating and exploring relationships among counterfactual thinking, attribution, anticipatory emotions, and ethical decision-making constructs and processes. Specifically, we examine the effects of a manipulation designed to stimulate a counterfactual mind-set that was found to significantly affect negative anticipatory emotions and intention associated with unethical conduct. These constructs have both been implicated in the planning and execution of goal-directed behavior (Bagozzi et al., 1998; Fishbein, 1993). Furthermore, we explore the basis of these effects by delineating and supporting responsibility attributions working through perceived negative consequences to influence both negative anticipatory emotions and intention to perform the unethical behavior. To our knowledge, these relationships have not been simultaneously explored in the domain of ethical decision making. As such, we have addressed an academic and managerial imperative in the marketing education literature as to how to effectively influence ethical decisions through an understanding of how individuals combine theoretically significant cognitions.

First, in terms of informing ethics educational interventions, the manipulation of a counterfactual mind-set had significant effects on both affective and behavioral outcomes that have been linked to future goal-directed behavior. Self-regulatory theory posits that one way that individuals pursue goals is with a prevention mind-set (Higgins, 1997). A prevention mind-set is cued to more deliberative processing consisting of attention to detail in order to satisfy duties and avoid failure. Therefore, our reasoning was that participants who were exposed to the “hands-on” manipulation would perceive stronger likelihoods of negative consequences that, in turn, would prime a prevention-focused counterfactual mind-set that would be then carried over to consideration and response to the second ethical scenario. Support was found for this thinking in that observed negative anticipatory emotions for the second scenario were stronger for subjects exposed to the prior “hands-on” scenario. In addition, the mind-set was also found to negatively affect a subject’s intention to perform the unethical act associated with the second scenario.

Marketing educators can use such findings by explicitly highlighting the possibility of a “what if” scenario that includes a “hands-on” boss. Whether it is an ethical scenario or a case related to promotions, marketing research, or strategy, an instructor can ask the question “What if the boss is ‘hands-on’ and decides to carefully review the work of a worker at the center of the scenario, especially if the individual is a new or recent hire for the marketing position?” Although such information may not be explicit in the case, by having students consider the possibility and generate likely negative consequences associated with it, such thinking practiced and reinforced over several scenarios and courses holds the potential to prime not only current but also future ethical decisions through affecting negative anticipatory emotions and intention to perform unethical behavior. In addition, educators can compare marketing positions and the inherent “hands-on” or “hands-off” nature of the profession and speculate as to how to positively influence ethical decision making. For example, salespeople tend to have less strict supervision as compared to those working in advertising. What are the implications for managers in these disciplines?

Thus, consistent with the counterfactual thinking literature, situational cues can make salient particular appraisal processes that can affect key ethical decision-making outcomes. Future research could explore additional mutable aspects of ethical decision scenarios that might influence appraisal processes and subsequent decisions. Extending the thinking of Petrocelli et al. (2011), the alteration of implicit beliefs about the likelihood of outcomes associated with various actions could be a key component of belief change tied to ethical decision contexts.

Marketing educators can use the notion of additional mutable aspects of contexts by explicitly addressing the possibility of a “what if” change that may be unforeseen at the time of the case, such as a surprise external audit or a reorganization that changes reporting relationships and responsibilities. Again, these “morphs” can be used in scenario or case contexts in various marketing classes with the point being to have students generate the possibility of the likely consequences, which have been found to be associated with future anticipatory emotions and intentions for ethical behavior.

In terms of interventions, it is important to highlight that our manipulation effect was associated with making the immediate supervisor “hands-off” or “hands-on” in an ethical context and then having subjects “connect the dots” by assessing potential consequences associated with the same ethical context. Note that this “softer” priming, as opposed to making the negative consequences explicitly associated with the “hands-on” scenario, influenced the emotions and intention associated with a second (nonmanipulated) scenario that immediately followed the first scenario. Such effects are consistent with effects in the counterfactual literature where the effects of seemingly minimal manipulations have been found to persist over different tasks (Gollwitzer et al., 1990). As noted by Liljenquist et al. (2004), “Counterfactual activation represents an organic, unobtrusive way to invoke behavioral norms without undue force or intervention” (p. 276). Furthermore, of relevance to the present study, Kray et al. (2010) found that reflection on counterfactual possibilities increased the perception that an event was meaningful.

Another implication for marketing educators would be to have students combine journal reflections with their generation of possible negative consequences to the boss or other context mutations provided above with particular emphasis on the range of stakeholders’ hurt and the students’ likely emotions. Such self-generated, associational thinking practice could increase not only the depth of student ethical thinking but also the perceived saliency of future ethical decision contexts.

It is acknowledged that the scenario-based approach employed in the present research is not an actual, real-time decision context. However, as with other ethical research, the point is to mimic actual decision contexts. We argue that, although significant, the effects observed in the present study are probably subdued and are likely to be stronger in actual decision making, given the heightened saliency of actual consequences and “hot” emotions. In sum, given the results found for the relatively implied and short-term intervention used in the present study, we believe that systematic and consistent use of counterfactual thinking in the ethical decision domain can be used to develop efficacious individual-level scripts that can positively influence future ethical behavior.

The approach used in the present study also meshes well with helping fill gaps in the business and marketing education literature, which highlighted that although experiential learning is a favored approach, results have been mixed and at times not tied to strong explanatory theory. Indeed, we echo cautionary notes expressed by Beggs (2011) that ethics interventions in the classroom must move beyond ethics sensitization toward modes of ethical decision-making preparation. Also observed by Beggs is that students may learn ethics better under less intrusive approaches. With the approach utilized in the present study, a counterfactual mind-set is more subtly primed and avoids the feel of a “heavy-handed” influence, which might prompt reactance (Kray et al., 2006). Additionally, the systematic use of mind-set activation in the context of scenarios and more extended cases is consistent with recommendations to employ experiential learning in order to engage the “millennial” cohort (Woolridge & Kuntze, 2010) and as a way to seamlessly integrate ethics into the curriculum (Beggs, 2011).

In keeping with the notion that counterfactual thinking primes how we think (Kray et al., 2006), these findings highlight the importance of attribution of actor responsibility as an important antecedent of affective and behavioral reactions in an ethical decision context. Although attributions have been tied to affective reactions (Zeelenberg, van der Pligt, & de Vries, 2000), we extend theory by explicitly connecting them to behavioral intention through perceived outcomes. Findings clearly “speak” to calls for better integration of attribution theory in various domains (Gaily & Lee, 2005; Woolfolk et al., 2006), particularly with respect to ethical decision making, which spans many disciplines. Furthermore, our findings are consistent with thinking that conceives of counterfactual thinking as integral to causal reasoning (Roese & Olson, 1996) in that factors that are imagined to covary with various outcomes become mentally “linked” to the outcomes. Future research in the area can extend the integration of attribution theory by including attributions related to the locus, stability, and globality of the cause of an event rather than the more specific responsibility attribution used in the present study.

Although potential consequences associated with unethical behavior have been incorporated in research examining ethical decision making, they have often been employed as part of the attitude toward the act component of the theory of reasoned action (Buchan, 2005; Cherry, 2006; Dubinsky & Loken, 1989). In the present study, we add to theory in the area by positing and finding support for negative consequences as a key mediator between attribution and affect and attribution and intention. Our findings are consistent with literature highlighting the amplifying effect of personal stakes in decision contexts (Brown, Cron, & Slocum, 1997; Lazarus, 1991). The findings also mesh well with the prominence accorded outcome evaluation in counterfactual thinking as well as the significance of using attributions to process negative outcomes given that the interpretation of potential negative outcomes is more critical for future adaptation than is the interpretation of positive outcomes (Weiner, 1985). Nevertheless, future research could examine the influence of potential positive outcomes associated with the unethical behavior in addition to potential negative outcomes.

Employing anticipatory emotions as a key dependent variable and explaining the basis of counterfactual mind-set effects on the construct is an additional contribution of this research. As noted earlier, the cognitive mechanisms underlying anticipatory emotions are not well understood, particularly, as related to ethical decision making. Yet there is growing recognition of the role that anticipatory emotions can play in goal-directed behavior (Baumgartner et al., 2008; Harvey & Victoravich, 2009; Tsiros & Mittal, 2000). Given the context of this study, we focused on negative anticipatory emotions. Future research could examine positive anticipatory emotions associated with performing future (un)ethical behavior. In addition, research in the area could examine other potentially relevant affective responses, such as the regret that individuals could experience as a result of a decision. As highlighted in the counterfactual literature, counterfactual thinking that leads to the anticipation of future regret might lead to regret-minimizing decisions (Zeelenberg et al., 1998).

As with any study employing cross-sectional, single–time period data collection, results should be interpreted with these limitations in mind. The student sample, although appropriate for inferences regarding students and perhaps entry-level employees, raises issues with respect to generalizing these results to other populations. The key outcome variables, negative anticipatory emotions and intention, are theoretically relevant. However, it is acknowledged that we do not assess actual decisions or behavior. Of course, assessing actual unethical behavior raises alternative issues in the conduct of research in the area.

In conclusion, in the present context of ethical decision making, a counterfactual mind-set not only affected appraisals associated with a scenario about the past but also influenced how individuals approached future intentional behavior. It is for this reason that further research in marketing education employing counterfactual interventions may produce practical findings as well as advance basic understanding of appraisal processes related to ethical decision making.

Footnotes

Appendix

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

Ajzen

Fishbein

(1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall.

Alicke

M. D.

(2000). Culpable control and the psychology of blame. Psychological Bulletin, 126, 556-574.

Allan

Wood

N. T.

(2009). Incorporating ethics into the marketing communications class: A case of old Joe and new Joe Camel. Marketing Education Review, 19, 63-71.

Anderson

(1997). What cognitive science tells us about ethics and the teaching of ethics. Journal of Business Ethics, 16, 279-291.

Anderson

J. C.

Gerbing

D. W.

(1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103, 411-423.

Association to Advance Collegiate Schools of Business International. (2012). Eligibility procedures and accreditation standards for business accreditation. Retrieved from http://www.aacsb.edu/accreditation/standards-busn-jan2012.pdf

Baggozi

R. P.

Baumgartner

Pieters

(1998). Goal-directed emotions. Cognition & Emotion, 12, 1-26.

Baron

R. M.

Kenny

D. A.

(1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.

Baumgartner

Pieters

Bagozzi

R. P.

(2008). Future-oriented emotions: Conceptualization and behavioral effects. European Journal of Social Psychology, 38, 685-696.

10.

Beggs

J. M.

(2011). Seamless integration of ethics. Marketing Education Review, 21, 49-55.

11.

Brown

S. P.

Cron

W. L.

Slocum

J. W.

(1997). Effects of goal-directed emotions on salesperson volitions, behavior, and performance: A longitudinal study. Journal of Marketing, 61, 39-50.

12.

Buchan

H. F.

(2005). Ethical decision making in the public accounting profession: An extension of Azjen’s theory of planned behavior. Journal of Business Ethics, 61, 165-181.

13.

Byrne

R. M.

(2005). The rational imagination: How people create alternatives to reality. Cambridge: MIT Press.

14.

Carpenter

T. C.

Reimers

J. L.

(2005). Unethical and fraudulent financial reporting: Applying the theory of planned behavior. Journal of Business Ethics, 60, 115-129.

15.

Celuch

Dill

(2011). An extension of the theory of reasoned action in ethical decision contexts: The role of normative influence and ethical judgment. Journal of Education for Business, 86, 201-207.

16.

Cherry

(2006). The impact of normative influence and locus of control on ethical judgments and intentions: A cross cultural comparison. Journal of Business Ethics, 69, 113-132.

17.

Chonko

L. B.

Hunt

S. D.

(1985). Ethics and marketing management: An empirical examination. Journal of Business Research, 13, 339-359.

18.

Cohen

J. R.

Pant

L. W.

Sharp

D. J.

(2001). An examination of differences in ethical decision-making between Canadian business students and accounting professionals. Journal of Business Ethics, 30, 319-335.

19.

Connelly

Helton-Fauth

Mumford

M. D.

(2004). A managerial in-basket study of the impact of trait emotions on ethical choice. Journal of Business Ethics, 51, 245-267.

20.

Dean

K. L.

Beggs

J. M.

Keane

T. P.

(2010). Mid-level managers, organizational context, and (un)ethical encounters. Journal of Business Ethics, 97, 51-69.

21.

Dixon

A. L.

Spiro

R. L.

Jamil

(2001). Successful and unsuccessful sales calls: Measuring salesperson attributions and behavioral intentions. Journal of Marketing, 65, 64-78.

22.

Dubinsky

A. J.

Loken

(1989). Analyzing ethical decision making in marketing. Journal of Business Research, 19, 83-107.

23.

Eagly

A. H.

Chaiken

(1993). The psychology of attitudes. Fort Worth, TX: Harcourt Brace.

24.

Feeney

Handley

S. J.

(2006). Mental models, comparisons, and the action effect in judgments of regret. Memory & Cognition, 34, 1422-1430.

25.

Feldman

H. D.

Thompson

R. C.

(1990). Teaching business ethics: A challenge for business ethics in the 1990s. Journal of Marketing Education, 12, 10-22.

26.

Fincham

F. D.

Harold

G. T.

Gano-Philips

(2000). The longitudinal association between attributions and marital satisfaction: Direction of effects and role of efficacy expectations. Journal of Family Psychology, 14, 267-285.

27.

Fishbein

(1993). Introduction. In Terry

D. J.

Gallois

McCamish

(Eds.), The theory of reasoned action: Its application to AIDS-preventive behavior (pp. xv-xxv). Oxford, England: Pergamon.

28.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50.

29.

Gaily

J. A.

Lee

M. T.

(2005). An integrated model of attribution of responsibility for wrongdoing in organizations. Social Psychology Quarterly, 68, 338-358.

30.

Galinsky

A. D.

Moskowitz

G. B.

(2000). Counterfactuals as behavioral primes: Priming the simulation heuristic and consideration of alternatives. Journal of Experimental Social Psychology, 36, 257-383.

31.

Galinsky

A. D.

Seiden

Kim

P. H.

Medvec

V. H.

(2002). The dissatisfaction of having your first offer accepted: The role of counterfactual thinking in negotiations. Personality and Social Psychology Bulletin, 28, 271-283.

32.

Gibson

A. M.

Frakes

A. H.

(1997). Truth or consequences: A study of critical issues and decision making in accounting. Journal of Business Ethics, 16, 161-171.

33.

Gollwitzer

P. M.

Heckhausen

Steller

(1990). Deliberative vs. implemental mind-sets: Cognitive tuning toward congruous thoughts and information. Journal of Personality and Social Psychology, 59, 1119-1127.

34.

Granitz

N. A.

(2001). Active learning and morality: Incorporating greater meaning into marketing education. Marketing Education Review, 11, 25-41.

35.

Hair

J. F.

Jr. Black

W. C.

Babin

B. J.

Anderson

R. E.

Tatham

R. L.

(2006). Multivariate data analysis (6th ed.). Upper Saddle River, NJ: Pearson/Prentice Hall.

36.

Harvey

Victoravich

L. M.

(2009). The influence of forward-looking antecedents, uncertainty, and anticipatory emotions on project escalation. Decision Sciences, 40, 759-782.

37.

Heider

(1958). The psychology of interpersonal relations. New York, NY: Wiley.

38.

Higgins

E. T.

(1997). Beyond pleasure and pain. American Psychologist, 52, 1280-1285.

39.

Hunt

S. D.

Laverie

D. A.

(2004). Experiential learning and the Hunt-Vitell theory of ethics: Teaching marketing ethics by integrating theory and practice. Marketing Education Review, 14, 9-15.

40.

Hunt

S. D.

Vitell

(1986). General theory of marketing ethics. Journal of Macromarketing, 6, 5-16.

41.

Kahneman

(1992). Reference points, anchors, norms, and mixed feelings. Organizational Behavior and Human Decision Processes, 51, 296-312.

42.

Kahneman

Miller

D. T.

(1986). Norm theory: Comparing reality to its alternatives. Psychological Review, 93, 136-153.

43.

Kray

Galinsky

A. D.

(2003). The debiasing effect of counterfactual mind-sets: Increasing the search for disconfirmatory information in group decisions. Organizational Behavior and Human Decision Processes, 91, 69-81.

44.

Kray

Galinsky

A. D.

Wong

E. M.

(2006). Thinking within the box: The relational processing style elicited by counterfactual mind-sets. Journal of Personality and Social Psychology, 91, 33-48.

45.

Kray

George

L. G.

Liljenquist

K. A.

Galinsky

A. D.

Tetlock

P. E.

Roese

N. J.

(2010). From what might have been to what must have been: Counterfactual thinking creates meaning. Journal of Personality and Social Psychology, 98, 106-118.

46.

Lau

C. L. L.

(2010). A step forward: Ethics education matters! Journal of Business Ethics, 92, 565-584.

47.

Lazarus

R. S.

(1991). Emotion and adaptation. New York, NY: Oxford.

48.

Liljenquist

K. A.

Galinsky

A. D.

Kray

L. J.

(2004). Exploring the rabbit hole of possibilities by myself or with my group: The benefits and liabilities of activating counterfactual mind-sets for information sharing and group coordination. Journal of Behavioral Decision Making, 17, 263-279.

49.

Loe

T. W.

Ferrell

(2001). Teaching marketing ethics in the 21st century. Marketing Education Review, 11, 1-16.

50.

Mele

(2005). Ethical education in accounting: integrating rules, values and virtues. Journal of Business Ethics, 57, 97-109.

51.

Merritt

(1991). Marketing ethics and education: Some empirical findings. Journal of Business Ethics, 10, 625-632.

52.

Nill

Schibrowsky

J. A.

(2005). The impact of corporate culture, the reward system, and perceived moral intensity on marketing students’ ethical decision making. Journal of Marketing Education, 27, 68-80.

53.

O’Brien

Oyserman

(2008). It’s not just what you think, but also how you think about it: The effect of situationally primed mindsets on legal judgments and decision making. Marquette Law Review, 92, 149-170.

54.

Ortony

Clore

G. L.

Collins

(1988). The cognitive structure of emotions. New York, NY: Cambridge University Press.

55.

Petrocelli

J. V.

Percy

E. J.

Sherman

S. J.

Tormala

Z. L.

(2011). Counterfactual potency. Journal of Personality and Social Psychology, 100, 30-46.

56.

Roese

N. J.

Olson

J. M.

(1995). Functions of counterfactual thinking. In Roese

N. J.

Olson

J. M.

(Eds.), What might have been: The social psychology of counterfactual thinking (pp. 169-197). Mahwah, NJ: Lawrence Erlbaum.

57.

Roese

N. J.

Olson

J. M.

(1996). Counterfactuals, causal attributions and the hindsight bias: A conceptual integration. Journal of Experimental Social Psychology, 32, 197-227.

58.

Roy

D. P.

(2012). Promoting active learning of ethical issues in marketing communications using debates. Marketing Education Review, 22, 73-76.

59.

Shannon

J. R.

Berl

R. L.

(1997). Are we teaching ethics in marketing? A survey of students’ attitudes and perceptions. Journal of Business Ethics, 16, 1059-1075.

60.

Sheppard

Hartwick

Warshaw

(1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15, 325-343.

61.

Smith

P. L.

(1996). The value of ethics education in business school curriculum. College Student Journal, 30, 274-283

62.

Tsiros

Mittal

(2000). Regret: A model of its antecedents and consequences in consumer decision making. Journal of Consumer Research, 26, 401-417.

63.

Waples

E. P.

Antes

A. L.

Murphy

S. T.

Connelly

Mumford

M. D.

(2009). A meta-analytic investigation of business ethics instruction. Journal of Business Ethics, 87, 133-151.

64.

Weiner

(1982). The emotional consequences of causal attribution. In Clark

M. S.

Fiske

S. T.

(Eds.), Affect and cognition (pp. 185-209). Hillsdale, NJ: Erlbaum.

65.

Weiner

(1985). An attributional theory of achievement motivation and emotion. Psychological Review, 92, 548-573.

66.

Wells

G. L.

Gavanski

(1989). Mental simulation of causality. Journal of Personality and Social Psychology, 58, 161-169.

67.

Woolfolk

R. L.

Doris

J. M.

Darley

J. M.

(2006). Identification, situational constraint, and social cognition: Studies in the attribution of moral responsibility. Cognition, 100, 283-301.

68.

Woolridge

B. R.

Kuntze

(2010). Publish or perish and innovative teaching: Combining the two to explore ethical behaviors. Marketing Education Review, 20, 143-148.

69.

Yoo

Donthu

(2002). The effects of marketing education and individual cultural values on marketing ethics of students. Journal of Marketing Education, 24, 92-103.

70.

Zeelenberg

van der Pligt

de Vries

N. K.

(2000). Attributions of responsibility and affective reactions to decision outcomes. Acta Psychologica, 104, 303-315.

71.

Zeelenberg

van Dijk

W. W.

van der Pligt

Manstead

A. S. R.

van Empelen

Reinderman

(1998). Emotional reactions to the outcomes of decisions: The role of counterfactual thought in the experience of regret and disappointment. Organizational Behavior and Human Decision Processes, 75, 117-141.