When the customer is unethical: the explanatory role of employee emotional exhaustion onto work-family conflict, relationship conflict with coworkers, and job neglect.

Journal of Applied Psychology 2014, Vol. 99, No. 6, 1188 –1203

© 2014 American Psychological Association 0021-9010/14/$12.00 http://dx.doi.org/10.1037/a0037221

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When the Customer Is Unethical: The Explanatory Role of Employee Emotional Exhaustion Onto Work–Family Conflict, Relationship Conflict With Coworkers, and Job Neglect Rebecca L. Greenbaum

Matthew J. Quade

Oklahoma State University

Baylor University

Mary B. Mawritz

Joongseo Kim and Durand Crosby

Drexel University

Oklahoma State University

We integrate deontological ethics (Folger, 1998, 2001; Kant, 1785/1948, 1797/1991) with conservation of resources theory (Hobfoll, 1989) to propose that an employee’s repeated exposure to violations of moral principle can diminish the availability of resources to appropriately attend to other personal and work domains. In particular, we identify customer unethical behavior as a morally charged work demand that leads to a depletion of resources as captured by employee emotional exhaustion. In turn, emotionally exhausted employees experience higher levels of work–family conflict, relationship conflict with coworkers, and job neglect. Employee emotional exhaustion serves as the mediator between customer unethical behavior and such outcomes. To provide further evidence of a deontological effect, we demonstrate the unique effect of customer unethical behavior onto emotional exhaustion beyond perceptions of personal mistreatment and trait negative affectivity. In Study 1, we found support for our theoretical model using multisource field data from customer-service professionals across a variety of industries. In Study 2, we also found support for our theoretical model using multisource, longitudinal field data from service employees in a large government organization. Theoretical and practical implications are discussed. Keywords: customer unethical behavior, deontic justice, emotional exhaustion

customer fraud when members of the general public misuse public assistance programs (Yaniv, 1997). Customers have also been known to cheat by taking advantage of service guarantees (Wirtz & Kum, 2004). For example, some restaurant patrons generate unsubstantiated claims about food or service quality to receive free or discounted meals. Other forms of customer cheating may seem more innocuous, as when a customer lies about a child’s age to receive a discount. The large financial losses associated with customer unethical behavior magnify the seriousness of these behaviors (Hyman, 2001). However, customer unethical behavior may also incite substantial harm to the organization that goes largely unaddressed. Organizations may fail to consider that employees who work with unethical customers may experience unfavorable emotional and behavioral reactions. In a service-based economy, employees are regularly put in the unfortunate position of dealing with customer mistreatment (i.e., customer interpersonal wrongdoing directed toward employees; Gettman & Gelfand, 2007; Harris & Reynolds, 2004; Reynolds & Harris, 2006; Van Kenhove, De Wulf, & Steenhaut, 2003; Vitell & Muncy, 2005). Extant research has primarily focused on customer behavior that directly affects employee well-being, such as customer incivility, aggression, harassment, and/or interpersonal injustice (Gettman & Gelfand, 2007; Grandey, Dickter, & Sin, 2004; Grandey, Kern, & Frone, 2007; Mitchell, Balabanis, Schlegelmilch, & Cornwell, 2009; Rafaeli et al., 2012; Rupp, McCance, Spencer, & Sonntag, 2008;

The Coalition Against Insurance Fraud (2006) has estimated that fraudulent insurance claims account for approximately $80 billion in organizational losses each year. Other estimates have indicated that 10% of healthcare spending in the United States, or approximately $115 billion annually, goes toward fraudulent activity (Hyman, 2001). Furthermore, the National Retail Federation (2011) estimated that retail fraud cost organizations $14.4 billion in 2011. In fact, customer return fraud has become so prevalent that retailers have started using the term “de-shopping” to describe customers who return goods after using them, such as when a person buys expensive clothing to fit the part for an important event and later returns the worn garments, claiming they did not fit (“Return to Vendor,” 2012). Governments, too, can be victims of

This article was published Online First June 23, 2014. Rebecca L. Greenbaum, Department of Management, Oklahoma State University; Matthew J. Quade, Department of Management, Baylor University; Mary B. Mawritz, Department of Management, Drexel University; Joongseo Kim, Department of Management, Oklahoma State University; Durand Crosby, Watson Graduate School of Management, Oklahoma State University. Correspondence concerning this article should be addressed to Rebecca L. Greenbaum, Oklahoma State University, Spears School of Business, Department of Management, 318 Business Building, Stillwater, OK 74078. E-mail: [email protected] 1188


CUSTOMER UNETHICAL BEHAVIOR

Rupp & Spencer, 2006; Skarlicki, Van Jaarsveld, & Walker, 2008; Sliter, Jex, Wolford, & McInnerney, 2010; Sliter, Sliter, & Jex, 2012; Van Jaarsveld, Walker, & Skarlicki, 2010). These customer behaviors are similar to the extent that the behaviors are directed toward the employee in particular, as when the customer sexually harasses an employee, speaks rudely to the employee, or threatens the employee’s physical safety. Research has demonstrated that employees respond to these forms of customer mistreatment by increasing absenteeism, turnover intentions, counterproductive work behaviors, retaliation, sabotage toward customers, and experiencing job and life dissatisfaction (Karatepe, 2011; Skarlicki et al., 2008; Sliter et al., 2012; Wang, Liao, Zhan, & Shi, 2011). The management literature’s recent focus on customer mistreatment is important to understanding the sometimes dysfunctional interplay between customers and employees. However, the literature fails to account for customer unethical behaviors that move beyond the direct mistreatment of employees but still violate generally accepted moral norms (e.g., customer insurance fraud, de-shopping, lying, cheating, stealing). This oversight is unfortunate in that some organizations are particularly fraught with customers who engage in unethical behaviors to get a quick break. Although not employee directed, these customer unethical behaviors may have detrimental effects on employees who are forced to witness and deal with such unethical acts. Yet employee responses to customer unethical behaviors that are not employee directed and the potential implications of these behaviors for relevant stakeholders (e.g., organizations, employees, customers, families) are not fully understood. We aim to advance research on the relationship between customer unethical behaviors and employee reactions by integrating arguments pertaining to deontological ethics (Broad, 1930; Denis, 2010; Folger, 1998, 2001; Kant, 1785/1948) and conservation of resources (COR) theory (Hobfoll, 1989). We offer a moral perspective to the COR stimulus–response process by arguing that customer unethical behavior, as a violation of moral behavioral norms, serves as a job demand that depletes cognitive resources and leads to emotional exhaustion. We then argue that employee emotional exhaustion is positively related to work–family conflict, relationship conflict with coworkers, and job neglect. Thus, organizations may bear the burden of customer unethical behavior in a way that is less obvious than objective financial losses. Employees may feel emotionally drained from working with unethical customers, which then leads to unfavorable outcomes for the employees, their family and coworkers, and the organization.

Theoretical Overview and Hypotheses Customer Unethical Behavior: Why Do Employees Care? We adapt Treviño, Weaver, and Reynolds’s (2006) definition of behavioral ethics to define customer (un)ethical behavior as customer behavior that is subject to or judged according to generally accepted moral norms of behavior. To capture customer unethical behavior that moves beyond the direct mistreatment of employees, we specifically examine customer behaviors that violate moral rules, principles, or standards when purchasing or using goods or services provided by organizations (Muncy & Vitell, 1992; Vitell

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& Muncy, 2005). These behaviors may include (a) reporting a lost item as “stolen” to an insurance company in order to collect money; (b) lying about a child’s age in order to get a lower price (Vitell, Lumpkin, & Rawwas, 1991); (c) wasting, mismanaging, or abusing goods or services provided; and (d) violating service or product terms. The management literature has begun to explore the notion that people care about ethical issues for reasons that move beyond their personal well-being (Cropanzano, Goldman, & Folger 2003; Folger, 1998, 2001; Folger, Cropanzano, & Goldman, 2005; Folger & Salvador, 2008; Folger & Skarlicki, 2008; O’Reilly & Aquino, 2011; Rupp & Bell, 2010; Rupp & Cropanzano, 2002; Thompson & Bunderson, 2003; Turillo, Folger, Lavelle, Umphress, & Gee, 2002; Umphress, Simmons, Folger, Ren, & Bobocel, 2013). Drawing on deontological ethics (Broad, 1934; Kant, 1785/1948, 1797/1991), much of this research contends that people believe in the need to maintain a just social order by upholding and adhering to moral rules and duties (Folger, 1998, 2001; Kant 1785/1948, 1797/1991). People constrain their behavior to live in accordance with social rules that prevent them from infringing on the rights of others (Cropanzano et al., 2003). In return, people expect others to also constrain their behaviors to abide by the moral expectation of doing no harm. The management literature has primarily relied on deontological theorizing to understand observers’ reactions to third-party interpersonal mistreatment (Skarlicki & Kulik, 2005). This form of mistreatment has been captured by examining third-party reactions to one party treating another party in a disrespectful, undignified, or untruthful manner (O’Reilly, Aquino, & Skarlicki, 2012; Skarlicki & Rupp, 2010), or one party withholding resources from the other (Rupp & Bell, 2010; Turillo et al., 2002). Because these studies examine interpersonal unethical behaviors, third-party observers are able to identify the perpetrator and victim of unethical conduct, making it clear that someone is likely to get hurt (Jones, 1991). However, we contend that deontological principles are useful in explaining reactions to the unethical behaviors of others, even when there is not a clear or direct victim of the unethical act or the victim is an organization. In these cases, deontological arguments still apply, as people are likely to care about preserving the moral expectation of abiding by rules and duties and upholding the notion of doing no harm. Thus, we expand the way deontological theorizing has been applied within the management literature by moving beyond a focus on unethical behaviors that occur between persons in an interpersonal sense. Rather, we examine employees’ reactions to violations of moral principles that occur when customers behave unethically and the entity harmed is the organization. Customers and organizations are expected to mutually abide by widely held principles of morality that dictate how each party should behave to keep from infringing on the rights of one another. Kant (1797/1991) argued that moral obligations include respecting the rights of every rational being, defined as a person who can use free will to suppress the inalienable rights of others. Cropanzano, Chrobot-Mason, Rupp, and Prehar (2004) noted that within business, “individuals” are not limited to those with a physical corpus; rather, personhood is constructed by those rights and responsibilities endorsed and upheld by society and recognized by law. Accordingly, organizations can serve as juristic persons and are capable of infringing on the rights of others, as would occur, for

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example, when an organization dumps toxic waste into the environment. However, in a Kantian sense, the notion of the organization embodying a juristic person implies that the organization, too, can fall victim to the injustices of others. For example, customer unethical behavior can constrain the rights of an organization to generate the highest profit possible. In addition, customer unethical behavior may generate indirect harm to organizational members. Employees may respond unfavorably to customer unethical behavior because the behavior violates moral principles, which may lead to additional losses for the organization (i.e., via unfavorable employee outcomes).

The Exhausting Nature of Customer Unethical Behavior COR theory explains that employees are motivated to conserve resources that are needed to effectively manage their work lives (Hobfoll, 1989). Hobfoll (1989) defined resources as “those objects, personal characteristics, conditions, or energies that are valued by the individual” (p. 516). Extant research using COR theory has typically identified work demands as potential sources of job stress that can threaten cognitive, psychological, and emotional resources and lead to emotional exhaustion, defined as “feelings of being emotionally overextended and exhausted by one’s work” (Wright & Cropanzano, 1998, p. 486). For example, work overload, role ambiguity, and role conflict may serve as work demands that threaten and/or deplete work-related resources, which may lead to emotional exhaustion (Ashill, Rod, Thirkell, & Carruthers, 2009; Lee & Ashforth, 1996). In addition, consistent with the contentions of COR theory, past research has demonstrated that other stressful events, such as abusive supervision, customer incivility, and customer aggression, diminish the availability of resources (Rafaeli et al., 2012), leading to emotional exhaustion (Chi & Liang, 2013; Grandey et al., 2004; Van Jaarsveld et al., 2010; Whitman, Halbesleben, & Holmes, 2014; Wu & Hu, 2009). Work demands and stressful events have a direct bearing on employee well-being, making the connection between such antecedents and emotional exhaustion rather evident. However, we argue that similar affects result from an additional, less intuitive work demand. When employees are required to routinely deal with unethical customers, they can experience emotional exhaustion, even when the customer’s unethical behavior does not directly affect the employee. As noted, deontological ethics suggests that people are bothered by violations of moral principles even when they are not directly affected by the violating acts (Cropanzano et al., 2003; Folger, 1998, 2001; Folger et al., 2005; O’Reilly et al., 2012; Rupp & Bell, 2010; Skarlicki & Rupp, 2010; Turillo et al., 2002; Umphress et al., 2013). Kant (1785/1948, 1797/1991) argued that to live peacefully within society, people are expected to abide by universal principles of morality that dictate how people ought to behave to avoid violating the rights of one another. In turn, people conduct themselves accordingly by upholding the duties of mutual benevolence and respect toward the lawful claims of others. A violation of the doctrine of “right,” by creating harm for others, is considered a scandal (Kant, 1797/1991). Those within society are responsible for upholding the law of respect by correcting the behavior of an offending party, even when the observer is not personally affected by the contemptuous act (Folger, 1998, 2001). A lack of

sanctions for the offending party endorses the notion that people can violate moral obligations for their own end, perhaps by lying, cheating, and stealing, creating an undesirable world to live in. Thus, the assumption is that a failure to correct such behavior could create chaos for society. Hence, people who observe the unethical behavior of others are likely to feel a sense of discomfort: Moral principles have been violated, so something should be done to correct the wrongful act (Cropanzano et al., 2003; Folger, 1998, 2001; Folger et al., 2005). Indeed, a key aspect of deontological ethics is that the perpetrator of unjust behavior should be punished in kind (Kant, 1797/1991). An interesting aspect of customer unethical behavior, however, is that employees frequently do not have the ability to correct the customers’ egregious acts. Employees are often instructed to provide superior customer service and to follow the adage that “the customer is always right” (Bitner, Booms, & Tetreault, 1990; Grandey et al., 2004; Yagil, 2008). Rather than questioning customers about their unethical actions, employees may suppress their suspicions (at least while interacting with the customer) and in many cases allow customers to get away with unethical acts. An employee’s failure to act is particularly likely, given that organizations are reluctant to draw attention to customer unethical behavior and it is rare for an organization to legally prosecute a customer (Yagil, 2008). Even if an organization has a fraud department, the evidence necessary to prosecute a customer and the duration of time it takes to launch a full investigation leave employees feeling helpless to correct customer unethical behavior. Importantly, conservation of resources theory posits that unalleviated sources of job stress produce a lasting state of discomfort, captured by an employee’s emotional exhaustion (Wright & Cropanzano, 1998). We argue that customer unethical behavior is an overlooked source of job stress that also produces discomfort. Employees find customer unethical behavior taxing because the behavior violates moral principles, but there is little the employee can do to correct the injustice. In turn, the inability to correct or punish the wrongdoing leaves the employee feeling helpless, yet disturbed by the customers’ unethical acts. Thus, due to its stressful nature, customer unethical behavior consumes a substantial amount of employees’ cognitive, psychological, and emotional resources, as evidenced by emotional exhaustion. In this vein, we predict that customer unethical behavior is positively related to emotional exhaustion. Extant research using COR theory to examine the interface between customers and employees has primarily examined customer interpersonal injustices (i.e., incivility, aggression) aimed at employees directly (Grandey et al., 2004; Karatepe, 2011; Li & Zhou, 2013; Rafaeli et al., 2012; Van Jaarsveld et al., 2010). Employees, as victims of customer interpersonal injustices, are likely to experience emotional exhaustion (Grandey et al., 2004). Therefore, to demonstrate that employees care about the violation of moral principle beyond their direct well-being (Folger, 1998, 2001; Folger et al., 2005), we control for customer interpersonal injustice when testing our theoretical model. By controlling for this alternative explanation, we more accurately capture a deontological– emotional exhaustion effect. Hence, we hypothesize: Hypothesis 1: Customer unethical behavior is positively related to employee emotional exhaustion.


Hypothesis 2: The positive relationship between customer unethical behavior and emotional exhaustion will remain statistically significant when controlling for the effect of customer interpersonal justice.


The Mediating Role of Emotional Exhaustion Extant research has shown that emotional exhaustion results from stressful work demands and is related to a number of unfavorable outcomes (Lee & Ashforth, 1996). Much of this research has relied on COR theory (Hobfoll, 1989; Hobfoll & Freedy, 1993) to explain that emotionally exhausted employees use their cognitive, psychological, and emotional resources to deal with stressful work demands. Hence, these employees lack resources to meet other demands, such as those derived from their interactions with others at home or at work and from their specific job duties. COR theory also implies that an employee’s attempt to alleviate or deal with emotional exhaustion can drain cognitive, psychological, and emotional resources that are normally available to regulate behavior (Hobfoll & Shirom, 1993; Maslach, 1982). In this vein, we expect customer unethical behavior to deplete resources, as captured by employee emotional exhaustion, which then leaves employees with limited resources to attend to family roles. More specifically, these emotionally exhausted employees may experience higher levels of work–family conflict. Work–family conflict represents “a form of inter-role conflict in which the role pressures from the work and family domains are mutually incompatible” (Greenhaus & Beutell, 1985, p. 77). Past research has reported a spillover effect between stressful work events and family problems (Hoobler & Brass, 2006; Rook, Dooley, & Catalano, 1991) and has indicated that emotionally exhausted employees may experience incompatibilities between family and work pressures, resulting in work–family conflict (Kopelman, Greenhaus, & Connolly, 1983). For example, Kopelman et al. (1983) suggested that employees who experience strain use their resources to deal with their work pressures and therefore may find it difficult to also attend to family demands. Similarly, we argue that customer unethical behavior consumes a substantial amount of resources, resulting in emotional exhaustion, which then leaves the employee with limited resources to attend to family pressures. Furthermore, the emotionally exhausted employee’s diminished resources can result in diminished self-control (Muraven & Baumeister, 2000), making it difficult for these employees to remain mindful of their coworkers’ viewpoints, ideas, and opinions (Van Jaarsveld et al., 2010). In particular, employee’s emotional exhaustion may lead to relationship conflict with coworkers. Relationship conflict is defined as “interpersonal incompatibilities among group members, which typically includes tension, animosity, and annoyance among members within a group” (Jehn, 1995, p. 258). Extant research has demonstrated that employee emotional exhaustion is positively related to interpersonal dysfunction in the workplace (e.g., workplace interpersonal deviance, counterproductive work behaviors; Mulki, Jaramillo, & Locander, 2006; Van Jaarsveld et al., 2010). Similarly, we expect that as a result of customer unethical behavior, an employee’s emotional exhaustion will result in higher levels of relationship conflict with coworkers. Finally, COR theory contends that employees respond to sources of job stress and manage emotional exhaustion by adjusting their behaviors to minimize loses and conserve resources

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(Hobfoll, 1989). A common way for employees to cope with emotional exhaustion is to withdraw from the threatening situation (Chi & Liang, 2013; Cole, Bernerth, Walter, & Holt, 2010; Deery, Iverson, & Walsh, 2002; Wright & Cropanzano, 1998). Employee withdrawal may come in the form of job neglect, which refers to “passively allowing [work] conditions to deteriorate through reduced interest or effort, chronic lateness or absences, using company time for personal business, or increased error rate” (Rusbult, Farrell, Rogers, & Mainous, 1988, p. 601). Employees who regularly interact with unethical customers may look for ways to cope with emotional exhaustion and conserve resources by neglecting aspects of their jobs. In addition, as previously argued, employees who are emotionally exhausted as a result of their work with unethical customers lack the resources needed to attend to other job demands. As such, their efforts to conserve resources, coupled with their lack of resources, prompt their job neglect behaviors, which may come in the forms of coming to work late, being absent, or shirking work responsibilities. Overall, customer unethical behavior is expected to produce emotional exhaustion, which then leads to increased work–family conflict, relationship conflict with coworkers, and job neglect. Thus, we hypothesize: Hypothesis 3: Emotional exhaustion is positively related to (a) work–family conflict, (b) relationship conflict with coworkers, and (c) job neglect. To complete our theoretical model, we also predict that employee emotional exhaustion serves as the process through which customer unethical behavior is related to employee work–family conflict, relationship conflict with coworkers, and job neglect. Our integration of COR theory with deontological ethics suggests that violations of moral principle, especially those that cannot be readily corrected, results in prolonged emotional discomfort for an employee that is captured by emotional exhaustion. Emotionally exhausted employees experience reduced cognitive, psychological, and emotional resources, which in turn makes it difficult to deal with other family, relational, and work demands. On the basis of this summary and the theoretical arguments provided in support of the prior hypotheses, we predict: Hypothesis 4: Emotional exhaustion mediates the relationship between customer unethical behavior and (a) work–family conflict, (b) relationship conflict with coworkers, and (c) job neglect.

Study 1 Method Sample and procedure. We collected multisource field data from employees who regularly interact with customers and their supervisors from a variety of organizations in the south central United States. The data collected for Study 1 were part of a larger data collection effort, which includes one prior publication (Greenbaum, Hill, Mawritz, & Quade, in press). The industries represented included retail, medical, insurance, food services, child care, finance, and higher education. The participants’ occupations included but were not limited to sales associate, bank teller, bartender, cashier, disc jockey, realtor, personal trainer, and hostess. Business administration students served as organizational con-


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tacts in exchange for extra credit (Grant & Mayer, 2009; Halbesleben & Bowler, 2007; Mayer, Aquino, Greenbaum, & Kuenzi, 2012; Morgeson & Humphrey, 2006). Students were asked to recruit employees who interact with customers and work at least 20 hr per week to serve as focal employees. The focal employees then asked their supervisors to participate in a separate survey. The surveys were completed via a secure Internet site. To ensure anonymity, participants recorded an 8-digit code, rather than their names, for the purposes of merging the data sets. When the study was introduced, we emphasized the importance of integrity and honesty in scientific research. Students were told that it was essential for the focal employee and supervisor surveys to be completed by the correct participants, and that the researchers would be able to identify suspect cases—in which case the student’s integrity and opportunity for extra credit would be jeopardized. Four hundred twenty-six (426) students were invited to serve as organizational contacts. We received complete responses from 242 focal employees and 239 supervisors. However, 26 of the 242 focal respondents indicated that they did not interact with customers and thus were eliminated from subsequent analyses. Our final sample size with matched employee–supervisor data was 192, for an estimated response rate of 45.1%. The focal employee respondents were 55.5% female, 77.6% Caucasian, 5.7% African American, 5.2% Native American, 4.2% Hispanic/Latino/a, 2.6% Asian American, and 2.1% biracial; 2.6% identified their ethnicity as “other.” Of the focal respondents, 45.0% reported being employed full time. The focal employees’ average age was 28.85 years (SD ⫽ 12.17), with an average organizational tenure of 4.95 years (SD ⫽ 6.80). The supervisor respondents were 43.4% female, 79.1% Caucasian, 3.7% African American, 3.7% Asian American, 3.2% Native American, 3.2% Hispanic/Latino/a, and 3.2% biracial; 3.7% reported their ethnicity as “other.” Among these supervisors, 93.1% were full-time employees. The average age of supervisors was 41.20 years (SD ⫽ 12.69), with an average organizational tenure of 11.54 years (SD ⫽ 9.23). Supervisors also reported having supervised the focal employee for an average of 3.00 years (SD ⫽ 3.74). Measures. The focal employee survey contained measures of customer unethical behavior, emotional exhaustion, work–family conflict, and demographics. The focal employees also responded to items regarding (a) customer injustice and (b) their frequency of interactions with customers (as control variables). The supervisor respondents assessed the degree to which the focal employee experienced relationship conflict with his/her coworkers and also answered demographic questions. Unless noted, all items were measured using a 7-point Likert-type scale (1 ⫽ strongly disagree to 7 ⫽ strongly agree). Customer unethical behavior. Customer unethical behavior (␣ ⫽ .95) was measured using the 21-item updated version of the Consumer Ethics Scale (Vitell & Muncy, 2005). Rather than having customers rate their own behaviors directly (as done with the original scale), we slightly adapted the scale by asking employees to respond to items indicating how much they agreed that the customers they work with are likely to engage in certain behaviors. Sample items include “report a lost item as ‘stolen’ to an insurance company in order to collect the insurance money,” “return damaged goods when the damage was their own fault,” “lie about a child’s age to get a lower price,” and “install software on a computer without buying it.”

Emotional exhaustion. Emotional exhaustion (␣ ⫽ .91) was measured using the nine-item emotional exhaustion portion of the Measurement of Experienced Burnout (Maslach & Jackson, 1981) scale. Sample items include “I feel emotionally drained from my work” and “I feel burned out from my work.” Work–family conflict. Work–family conflict (␣ ⫽ .91) was measured using an eight-item scale (Kopelman et al., 1983). Sample items include, “after work, I come home too tired to do some of the things I’d like to do” and “my family dislikes how often I am preoccupied with my work while I’m at home.” Relationship conflict with coworkers. Supervisors rated the focal employee’s level of relationship conflict with coworkers (␣ ⫽ .93) using Jehn’s (1995) four-item scale. Sample items include “how much friction is there between this person and his/her coworkers?” and “how much are personality conflicts evident between this person and his/her coworkers?” Items were rated using a 7-point Likert-type scale (1 ⫽ none to 7 ⫽ a lot). Control variables. We first controlled for customer mistreatment directed toward employees, operationalized as customer injustice, to rule out the possibility that employees’ emotional exhaustion is due to their own direct mistreatment from customers. Customer injustice was assessed using the four-item measure used by Rupp et al. (2008), which was adapted from Colquitt’s (2001) interpersonal justice scale. Employees rated the level of interpersonal injustice they receive from customers. Sample items include “do your customers treat you with respect” and “do your customers refrain from improper remarks or comments?” The items were rated using a 7-point Likert-type scale (1 ⫽ to a small extent to 7 ⫽ to a large extent; ␣ ⫽ .92). To capture injustice rather than justice, all items were reverse coded. To ensure that customer injustice is distinct from customer unethical behavior, we conducted confirmatory factor analyses (CFA). The two-factor model provided an acceptable fit to the data (␹2 ⫽ 1,178.84, df ⫽ 274, p ⬍ .001; RMSEA ⫽ .13; CFI ⫽ .94; NNFI ⫽ .93; SRMR ⫽ .08; Hu & Bentler, 1999). We compared the two-factor model to a one-factor model in which the indicators for both customer injustice and customer unethical behavior were specified to load onto a single factor. The one-factor model (␹2 ⫽ 1,818.04, df ⫽ 275, p ⬍ .001; RMSEA ⫽ .17; CFI ⫽ .89; NNFI ⫽ .88; SRMR ⫽ .13) provided a significantly worse fit than the two-factor model (⌬␹2 ⴝ 639.2, df ⫽ 1, p ⬍ .001). This evidence of discriminant validity provides support that these two variables are distinct. We also controlled for frequency of customer interactions because the frequency of participants’ interactions with customers could affect their levels of emotional exhaustion (Cordes & Dougherty, 1993; Grandey, 2003; Jackson, Schwab, & Schuler, 1986). Focal employees rated how often they interacted with customers on the job using a 7-point Likert-type scale (1 ⫽ never to 7 ⫽ always).

Results Means, standard deviations, and correlations for Study 1 appear in Table 1. We used structural equation modeling (SEM) with LISREL 8.8 (Jöreskog & Sörbom, 2006) to test our hypotheses. Prior to testing the hypothesized structural model, we tested the fit of the measurement model (Anderson & Gerbing, 1988). There were six latent factors (customer unethical behavior, emotional


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Table 1 Study 1: Descriptive Statistics, Reliability Estimates, and Study Variable Intercorrelations Variables 1. 2. 3. 4. 5. 6.

Customer injustice Frequency of customer interaction Customer unethical behavior Emotional exhaustion Work–family conflict Relationship conflict

M

SD

2.54 6.05 4.33 2.99 3.30 1.79

1.01 1.29 1.20 1.29 1.44 0.99

1 (.92) ⫺.09 .12 .34ⴱⴱ .13 .18ⴱ

2

3

4

5

6

— .13 .04 .01 .01

(.95) .22ⴱⴱ .15ⴱ .08

(.91) .63ⴱⴱ .30ⴱⴱ

(.91) .09

(.93)


Note. N ⫽ 192. Cronbach’s alphas are shown on the diagonal. ⴱ p ⬍ .05 (2-tailed). ⴱⴱ p ⬍ .01 (2-tailed).

exhaustion, work–family conflict, relationship conflict with coworkers, customer injustice, frequency of customer interaction) and 47 indicators (21 items for customer unethical behavior, 9 items for emotional exhaustion, 8 items for work–family conflict, 4 items for relationship conflict with coworkers, 4 items for customer injustice, and 1 item for frequency of customer interaction). The measurement model had acceptable fit (␹2 ⫽ 2,638.27, df ⫽ 1,020, p ⬍ .001; RMSEA ⫽ .09; CFI ⫽ .94; NNFI ⫽ .93; SRMR ⫽ .08; Hu & Bentler, 1999). The factor loadings for all of the indicators were statistically significant (p ⬍ .01). To ensure our measurement model provided the best fit to the data, we conducted a series of CFAs. We tested (a) a five-factor model that combined customer unethical behavior and customer injustice (␹2 ⫽ 3,414.82, df ⫽ 1,025, p ⬍ .001; RMSEA ⫽ .11; CFI ⫽ .90; NNFI ⫽ .90; SRMR ⫽ .10), (b) a five-factor model that combined customer unethical behavior and emotional exhaustion (␹2 ⫽ 4,668.04, df ⫽ 1025, p ⬍ .001; RMSEA ⫽ .14; CFI ⫽ .89; NNFI ⫽ .88; SRMR ⫽ .15), and (c) a five-factor model that combined emotional exhaustion and work–family conflict (␹2 ⫽ 3,201.08, df ⫽ 1025, p ⬍ .001; RMSEA ⫽ .11; CFI ⫽ .92; NNFI ⫽ .92; SRMR ⫽ .08). We also tested a three-factor model that combined customer unethical behavior, customer injustice, emotional exhaustion, and work–family conflict (␹2 ⫽ 8,428.62, df ⫽ 1,032, p ⬍ .001; RMSEA ⫽ .19; CFI ⫽ .81; NNFI ⫽ .80; SRMR ⫽ .18). Chi-square difference tests revealed that our baseline measurement model provided a fit superior to each of the alternative models: (a) ⌬␹2 ⫽ 776.55, df ⫽ 5, p ⬍ .001; (b) ⌬␹2 ⫽ 2,029.77, df ⫽ 5, p ⬍ .001; (c) ⌬␹2 ⫽ 562.81, df ⫽ 5, p ⬍ .001; and (d) ⌬␹2 ⫽ 5,790.35, df ⫽ 12, p ⬍ .001. Next, we tested the hypothesized structural model. Results of the structural analysis indicated that the model had acceptable fit to the data (␹2 ⫽ 2,658.35, df ⫽ 1027, p ⬍ .001; RMSEA ⫽ .09; CFI ⫽ .94; NNFI ⫽ .93; SRMR ⫽ .08; Browne & Cudeck, 1993; Hu & Bentler, 1999). This fully mediated model was compared to a partially mediated model (␹2 ⫽ 2,658.50, df ⫽ 1,025, p ⬍ .001; RMSEA ⫽ .09; CFI ⫽ .93; NNFI ⫽ .93; SRMR ⫽ .08; James, Mulaik, & Brett, 2006). The partially mediated model did not provide better fit (⌬␹2 ⫽ .15, df ⫽ 2, ns), and thus the more parsimonious fully mediated model was better for examining the hypotheses using these data. Hypothesis 1 predicted that customer unethical behavior is positively related to employee emotional exhaustion. This hypothesis was supported (b ⫽ .20, p ⬍ .01, R2 ⫽ .14; see Figure 1). Importantly, an examination of our structural equation model results demonstrated that customer unethical behavior predicted

employee emotional exhaustion above and beyond the effect of customer injustice. Although customer injustice was positively related to employee emotional exhaustion (b ⫽ .25, p ⬍ .01), the relationship between customer unethical behavior and emotional exhaustion remained statistically significant (b ⫽ .20, p ⬍ .01), providing evidence that employees care about customer unethical behavior beyond their own mistreatment from customers (thus providing support for Hypothesis 2 and a possible deontological effect). Hypothesis 3 was also supported, as emotional exhaustion was positively and significantly related to (3a) work–family conflict (b ⫽ .67, p ⬍ .01, R2 ⫽ .47), and (3b) relationship conflict with coworkers (b ⫽ .38, p ⬍ .01, R2 ⫽ .10). Hypothesis 4 predicted that customer unethical behavior mediates the relationship between customer unethical behavior and (4a) work–family conflict and (4b) relationship conflict with coworkers. We followed recommendations of James et al. (2006) to test for mediation. Results for Hypotheses 1 and 3 provided evidence that the first and second steps for mediation were satisfied (i.e., the predictor was significantly related to the mediator and the mediator was significantly related to the outcomes). James et al.’s final step is to demonstrate that the relationship between the predictor and the outcomes occurs through the mediator. To test the goodness of fit of emotional exhaustion as the mediator, we calculated the product of coefficients by using LISREL’s effect decomposition statistics (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). Statistically significant indirect effects provide evidence that the relationship between the predictor and the outcome variables occurred through the mediator. We found support for Hypothesis 4, as the indirect effects were significant for both (4a)

Figure 1. Study 1: Fully mediated structural equation model results. Relationships including control variables are indicated by dashed lines. ⴱ p ⬍ .01.


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work–family conflict (b ⫽ .13, p ⬍ .01) and (4b) relationship conflict with coworkers (b ⫽ .07, p ⬍ .05).

Study 2


Method We collected Study 2 data, in part, to demonstrate that our findings can be replicated using data from a single organization. We also improved upon our Study 1 design in a number of important ways. First, our Study 1 measure of customer unethical behavior, using the Consumer Ethics Scale (Vitell & Muncy, 2005), included rather specific items that may not apply to all customer-oriented professions. To address this limitation, we created a new measure of customer unethical behavior that has good face validity and psychometric properties (please see the validation of our new measure in the Appendix). By using this alternative operationalization of customer unethical behavior, we also provided evidence of the robustness of our model. Second, in addition to controlling for customer interpersonal injustice and frequency of customer interaction as we did in Study 1, we controlled for negative affectivity—a common correlate of emotional exhaustion (Wright & Cropanzano, 1998)—to rule out its potential influence in explaining our study findings. Third, our study design included two time-lagged surveys to account for the potential effects of common method variance (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Participants completed the Time 1 Survey by responding to measures of customer unethical behavior and the control variables; 3 weeks later, they completed the Time 2 Survey, which included measures of employee emotional exhaustion and work– family conflict. Finally, we expanded our theoretical model by testing job neglect as an additional outcome variable. We wanted to see if the wearing nature of customer unethical behavior, as captured by employee emotional exhaustion, is applicable to important work outcomes other than work–family and relationship conflict. Sample and procedure. One thousand one hundred and ninety-eight (1,198) service employees and their supervisors from a large government agency in the south-central United States were invited to participate in a longitudinal study. The participating organization agreed to pull a random sample of all boundaryspanning employees (i.e., those who work directly with customers). Two of the study’s authors met with approximately 15 middle-level managers from the organization to have the study approved and to confirm that customer unethical behavior could pose a problem for their boundary-spanning employees. In Time 1, the employees were asked to rate the extent to which the clients they work with engage in unethical behavior, as well as their own level of negative affect, customer injustice, frequency of customer interactions, and demographics. Three weeks later (Time 2) employees were asked to rate their emotional exhaustion and work– family conflict. Also at Time 2, supervisors were asked to rate the focal employees’ relationship conflict with coworkers and the employees’ job neglect, as well as personal demographics. To ensure anonymity, names were never recorded when completing surveys. Instead, the employees were given the same ID number to use in the Time 1 and Time 2 surveys (transmitted via e-mail using an on-line survey system), and supervisors were given the same ID number to use when answering questions about the

focal employee. The ID numbers were later used to match the data. After completing the anonymous surveys, respondents had the opportunity to place their names in a separate database to enter a drawing for a cash prize of $100. Six names were drawn for each survey, for a total of 18 winners. To further encourage participation, the organization also sent an e-mail endorsing the surveys and encouraging employees to participate. We received survey responses from 800 employees at Time 1, 652 employees at Time 2, and 626 supervisors. After matching the three surveys and accounting for missing data, our final sample size was 496 employee–supervisor dyads across the two time periods, producing a response rate of 41.4%. The focal employee respondents were 82.2% female and 17.8% male. The focal respondents were 71.6% Caucasian, 10.8% African American, 9.6% Native American, 2.7% Hispanic/Latino/a, 1.4% Asian American, and 2.4% biracial; 1.4% identified their ethnicity as “other.” Of the focal respondents, 98.6% reported that they were employed full time, with only 1.4% employed part time. The focal employees’ average age was 40.99 years (SD ⫽ 11.62), with an average organizational tenure of 7.03 years (SD ⫽ 7.32). Employees reported having worked with their supervisor for an average of 2.45 years (SD ⫽ 3.02). The supervisor respondents were 77.3% female and 22.7% male. Of these supervisors, 74.3% were Caucasian, 9.6% Native American, 8.1% African American, 2.5% Hispanic/Latino/a, 1.7% Asian American, and 1.5% biracial; 2.3% reported their ethnicity as “other.” Among these supervisors, 99.4% were full-time employees, while only 0.6% were part-time employees. The average age of supervisors was 46.29 years (SD ⫽ 9.57), with an average organizational tenure of 15.73 years (SD ⫽ 7.74). Measures. Unless noted, all items were measured using a 7-point Likert-type scale (1 ⫽ strongly disagree to 7 ⫽ strongly agree). Customer unethical behavior. Customer unethical behavior (␣ ⫽ .97) was measured using our newly created and validated eight-item measure (see the Appendix). Employee respondents read the following: “Some customers/clients may violate moral rules, principles, or standards when purchasing or using goods or services provided by organizations. To what extent do you believe the customers you work with violate moral rules/principles/standards by engaging in the following behaviors?” Because our sample primarily worked with customers by providing services, rather than goods, we slightly adapted the measure to reflect services only. The item stem read: “When applying for, using, or attempting to retain various assistance programs offered by my organization, the customers I work with . . . .” Sample items include “violate rules or regulations,” “waste, mismanage or abuse the resources or services provided,” and “falsify or manipulate required information.” Items were rated using a 7-point Likert-type scale (1 ⫽ to an extremely small extent to 7 ⫽ to an extremely large extent). Emotional exhaustion. Emotional exhaustion (␣ ⫽ .93) was measured using the same nine-item scale used in Study 1 (Maslach & Jackson, 1981). Work–family conflict. Work–family conflict (␣ ⫽ .84) was assessed using Kopelman et al.’s (1983) measure; however, because of survey space constraints, we used a shortened four-item version of the scale that has been used in a number of studies (Frone, Russell, & Cooper, 1992; Gutek, Searle, & Klepa, 1991;



Judge, Boudreau, & Bretz, 1994). The two sample items provided in Study 1 were included in this shortened four-item scale. Relationship conflict with coworkers. Supervisors rated the focal employees’ relationship conflict with coworkers (␣ ⫽ .96) using the same scale as in Study 1 (Jehn, 1995). Job neglect. Supervisors rated the level of employee job neglect (␣ ⫽ .94) using the six-item scale developed by Rusbult, Farrell, Rogers, and Mainous (1988). Sample items include “sometimes this employee calls in sick or comes in late because he/she doesn’t feel like working on the job” and “this employee has quit caring about his/her job and will allow conditions to get worse and worse.” Control variables. As in Study 1, we controlled for customer mistreatment directed toward employees, operationalized as customer injustice (␣ ⫽ .92). We used the same four-item measure (Rupp et al., 2008) used in Study 1. We conducted CFAs to demonstrate the distinctiveness of customer injustice and customer unethical behavior. The two-factor model provided acceptable fit (␹2 ⫽ 290.67, df ⫽ 53, p ⬍ .001; RMSEA ⫽ .10; CFI ⫽ .95; NNFI ⫽ .97; SRMR ⫽ .03). We compared the two-factor model to a one-factor model in which all of the indicators for both customer injustice and customer unethical behavior were specified to load onto a single factor. The one-factor model (␹2 ⫽ 1,960.04, df ⫽ 54, p ⬍ .001; RMSEA ⫽ .27; CFI ⫽ .83; NNFI ⫽ .79; SRMR ⫽ .20) provided a significantly worse fit than the twofactor model (⌬␹2 ⫽ 1,669.37, df ⫽ 1, p ⬍ .001). Thus, these results provide evidence of discriminant validity between these two measures. We also controlled for frequency of customer interaction using the same item used in Study 1. Finally, we controlled for employee negative affectivity (NA) as those who are predisposed to NA may be more susceptible to emotional exhaustion (Wright & Cropanzano, 1998) and other adverse work outcomes (e.g., Duffy, Ganster, & Pagon, 2002; Greenbaum, Mawritz, & Eissa, 2012; Mitchell & Ambrose, 2007; Thau, Bennett, Mitchell, & Marrs, 2009; Zellars, Tepper, & Duffy, 2002). We measured NA (␣ ⫽ .87) using the negative component of the Positive and Negative Affect Scale (PANAS; Watson, Clark, & Tellegen, 1988). Focal employees rated how often they feel emotions such as “guilty,” “upset,” and “hostile.” This 10-item scale was measured using a 7-point Likert-type scale (1 ⫽ not at all to 7 ⫽ a lot).

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Results Means, standard deviations, and correlations for Study 2 appear in Table 2. As in Study 1, we used SEM with LISREL 8.8 (Jöreskog & Sörbom, 2006) to test our hypotheses. Prior to testing the hypothesized structural model, we tested the fit of the measurement model (Anderson & Gerbing, 1988). The measurement model had eight latent factors (i.e., customer unethical behavior, emotional exhaustion, work–family conflict, relationship conflict with coworkers, job neglect, customer injustice, frequency of customer interaction, negative affect) and 46 indicators (8 items for customer unethical behavior, 9 items for emotional exhaustion, 4 items for work–family conflict, 4 items for relationship conflict with coworkers, 6 items for job neglect, 4 items for customer injustice, 1 item for frequency of customer interaction, and 10 items for negative affect). The measurement model had acceptable fit to the data (␹2 ⫽ 2,888.98, df ⫽ 962, p ⬍ .001; RMSEA ⫽ .06; CFI ⫽ .96; NNFI ⫽ .95; SRMR ⫽ .06; Browne & Cudeck, 1993; Hu & Bentler, 1999). Additionally, the factor loadings for all of the indicators were statistically significant (p ⬍ .01). As in Study 1, we conducted a series of CFAs to compare our baseline measurement model to five alternative models. We compared the measurement model to (a) a seven-factor model that combined customer unethical behavior and customer injustice (␹2 ⫽ 4,687.94, df ⫽ 969, p ⬍ .001; RMSEA ⫽ .09; CFI ⫽ .92; NNFI ⫽ .91; SRMR ⫽ .08); (b) a seven-factor model that combined customer unethical behavior and negative affectivity (␹2 ⫽ 6,473.52, df ⫽ 969, p ⬍ .001; RMSEA ⫽ .11; CFI ⫽ .92; NNFI ⫽ .91; SRMR ⫽ .12); (c) a six-factor model that combined customer unethical behavior, customer injustice, and negative affectivity (␹2 ⫽ 8,315.13, df ⫽ 975, p ⬍ .001; RMSEA ⫽ .12; CFI ⫽ .88; NNFI ⫽ .87; SRMR ⫽ .14); (d) a seven-factor model that combined emotional exhaustion and work–family conflict (␹2 ⫽ 3,317.00, df ⫽ 969, p ⬍ .001; RMSEA ⫽ .07; CFI ⫽ .95; NNFI ⫽ .95; SRMR ⫽ .06); and (e) a seven-factor model that combined relationship conflict with coworkers and job neglect (␹2 ⫽ 6,390.88, df ⫽ 969, p ⬍ .001; RMSEA ⫽ .11; CFI ⫽ .91; NNFI ⫽ .91; SRMR ⫽ .08). The baseline measurement model provided superior fit to the alternative models: (a) ⌬␹2 ⫽ 1,798.96, df ⫽ 7, p ⬍ .001; (b) ⌬␹2 ⫽ 3,584.54, df ⫽ 7, p ⬍ .001; (c) ⌬␹2 ⫽ 5,426.15, df ⫽ 13, p ⬍ .001; (d) ⌬␹2 ⫽ 428.02, df ⫽ 7, p ⬍ .001; (e) ⌬␹2 ⫽ 3,501.90, df ⫽ 7, p ⬍ .001.

Table 2 Study 2: Descriptive Statistics, Reliability Estimates, and Study Variable Intercorrelations Variables 1. 2. 3. 4. 5. 6. 7. 8.

Customer injustice Frequency of customer interaction Negative affect Customer unethical behavior Emotional exhaustion Work–family conflict Relationship conflict Job neglect

M

SD

1

2

3

4

5

6

7

8

3.06 6.22 2.58 3.70 3.75 3.82 1.83 1.97

1.23 1.11 0.73 1.40 1.41 1.57 1.11 1.21

(.92) .03 .14ⴱⴱ .27ⴱⴱ .25ⴱⴱ .21ⴱⴱ .06 .06

— .04 .03 .01 .13ⴱⴱ .03 .00

(.87) .16ⴱⴱ .45ⴱⴱ .40ⴱⴱ .15ⴱⴱ .16ⴱⴱ

(.97) .22ⴱⴱ .11ⴱ .00 .06

(.93) .68ⴱⴱ .13ⴱⴱ .27ⴱⴱ

(.84) .15ⴱⴱ .17ⴱⴱ

(.96) .53ⴱⴱ

(.94)

Note. N ⫽ 496. Cronbach’s alphas are shown on the diagonal. ⴱ p ⬍ .05 (2-tailed). ⴱⴱ p ⬍ .01 (2-tailed).


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Next, we tested the hypothesized structural model. Results of the structural analysis indicated the model had acceptable fit (␹2 ⫽ 3,082.25, df ⫽ 977, p ⬍ .001; RMSEA ⫽ .07; CFI ⫽ .95; NNFI ⫽ .95; SRMR ⫽ .08; Browne & Cudeck, 1993; Hu & Bentler, 1999). This fully mediated model was compared to a partially mediated model (␹2 ⫽ 3,079.44, df ⫽ 974, p ⬍ .001; ␹2/df ⫽ 3.16; RMSEA ⫽ .07; CFI ⫽ .95; NNFI ⫽ .95; SRMR ⫽ .08; James et al., 2006). The partially mediated model did not provide better fit (⌬␹2 ⫽ 2.81, df ⫽ 3, ns). Therefore, the more parsimonious fully mediated model was used to analyze our hypotheses. We found support for Hypothesis 1 as customer unethical behavior was positively and significantly related to employee emotional exhaustion (b ⫽ .09, p ⬍ .05, R2 ⫽ .29; see Figure 2). In support of Hypothesis 2, the relationship between customer unethical behavior and employee emotional exhaustion held (b ⫽ .09, p ⬍ .05), even when controlling for the positive and statistically significant relationship between customer interpersonal injustice and employee emotional exhaustion (b ⫽ .15, p ⬍ .01). This finding provides evidence that employees find it emotionally exhausting to work with unethical customers, even beyond their own mistreatment from the customers, lending support to a deontological effect. Hypothesis 3 predicted that emotional exhaustion would be positively related to (3a) work–family conflict, (3b) relationship conflict with coworkers, and (3c) job neglect. The path coefficients showed support for Hypothesis 3 as emotional exhaustion was positively and significantly related to (3a) work–family conflict (b ⫽ .74, p ⬍ .01, R2 ⫽ .60), (3b) relationship conflict with coworkers (b ⫽ .18, p ⬍ .01, R2 ⫽ .03), and (3c) job neglect (b ⫽ .31, p ⬍ .01, R2 ⫽ .09). Hypothesis 4 predicted that employee emotional exhaustion mediates the relationship between customer unethical behavior and (4a) work–family conflict, (4b) relationship conflict with coworkers, and (4c) job neglect. We found support for Hypotheses 1 and 3, satisfying the first two steps of mediation (James et al., 2006). We used LISREL’s effect decomposition statistics to test the goodness of fit of emotional exhaustion as the mediator. The indirect effects of the relationship between customer unethical behavior and (4a) work–family conflict (b ⫽ .06, p ⬍ .05) and (4c) job neglect (b ⫽ .03, p ⬍ .05) were positive and statistically significant, while the indirect effect of customer unethical behavior onto (4b) relationship conflict with coworkers approached signif-

Figure 2. Study 2: Fully mediated structural equation model results. Relationships including control variables are indicated by dashed lines. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.

icance (b ⫽ .02, p ⬍ .10). Thus, our results lend support to emotional exhaustion serving as a mediator between customer unethical behavior and (4a) work–family conflict and (4c) job neglect, but the mediating role of emotional exhaustion onto relationship conflict with coworkers (4b) did not reach the standard cutoff for statistical significance.

Discussion As our economy becomes increasingly service oriented (Grizzle, Zablah, Brown, Mowen, & Lee, 2009), it becomes important to understand some of the challenges facing employees who interact with customers. We argue that working with unethical customers may serve as a substantial challenge for employees. Our theoretical model and empirical results suggest that employees respond to customer unethical behavior by experiencing emotional exhaustion, which then leads to work–family conflict, relationship conflict with coworkers, and job neglect. Importantly, our theoretical model held even when controlling for employees’ mistreatment from customers (i.e., customer interpersonal injustice), which provides support for the notion that employees react to the violation of moral principles that move beyond their direct exposure to interpersonal transgressions.

Theoretical Implications Our research contributes to the management literature in a number of important ways. First, our research is the first, to our knowledge, to introduce the idea that employees respond unfavorably to customer unethical behavior that moves beyond their own, direct mistreatment from customers. In a service-based economy, it is particularly important to understand the challenges that employees are likely to face when interacting with customers. An initial step in advancing research in this area is to recognize issues that are presumably difficult for these employees to handle. We identify an employee’s exposure to unethical customers as a particularly daunting challenge that may lead to unfavorable employee and organizational outcomes. Second, we contribute to the literature by introducing the idea that employees who regularly deal with immoral events, such as customer unethical behavior, may experience heightened levels of emotional exhaustion. We integrate COR theory with arguments pertaining to deontological ethics to suggest that customer unethical behavior serves as a source of work-related stress. Customer unethical behavior violates moral norms or expectations of the way people ought to behave within society. Although employees may want to address the customer’s unethical behavior, they may also feel ill-equipped to do so. Hence, these employees are likely to experience a lasting state of discomfort that consumes a substantial amount of cognitive, psychological, and emotional resources, leaving the employee emotionally exhausted. The notion that people respond unfavorably to immoral events and subsequently may experience emotional exhaustion when those events are not managed appropriately has yet to be introduced to the management literature, to our knowledge. We find it important to introduce this idea to the management literature because doing so adds additional complexity to research on reactions to the unethical behaviors of others. Our work suggests that observing the immoral events of customers can create negative employee reactions, and this may be especially true when the unethical acts go unpunished.



We provide a third contribution to the literature by arguing that people are bothered by unethical behaviors that move beyond interpersonal exchanges. To date, deontological theorizing has been used to explain third-party reactions to the interpersonal mistreatment of others (Cropanzano et al., 2003; Folger, 1998, 2001; Folger et al., 2005; Folger & Salvador, 2008; Folger & Skarlicki, 2008; O’Reilly & Aquino, 2011; Rupp & Bell, 2010; Rupp & Cropanzano, 2002; Turillo et al., 2002; Umphress et al., 2013). We aim to extend deontological arguments by proposing that people respond unfavorably to immoral events, even when such events lack a clear victim or the victim is an organization. Our theoretical model examines customer behavior that violates moral norms in the sale and use of organizational products or services. When a customer lies about a child’s age to receive a discount, for example, the victim may not immediately come to mind. Employees may not instantly identify the victim as the organization (e.g., due to lost profits) or society (e.g., due to the unfairness bestowed upon members of society that do indeed follow the rules). They may also discount the harm associated with unethical behaviors directed toward organizations that presumably have deep pockets. Regardless, we argue that customer unethical behavior is likely to generate unfavorable reactions on the part of employees primarily because of the violation of moral norms. We also contribute to the literature by being one of the first, to our knowledge, to investigate unethical behaviors directed toward the organization. Research that has examined the role of organizations related to injustices and/or unethical behavior has always implied that the organization is the perpetrator of misconduct (Cropanzano et al., 2004), without considering that the organization can also be a victim. We provide a fourth contribution to the literature by creating and validating a general customer unethical behavior scale. Because customer unethical behavior has not been systematically examined within the management literature, in our first study, we examined customer unethical behavior using the Consumer Ethics Scale that is commonly used in marketing studies (Muncy & Vitell, 1992; Van Kenhove et al., 2003; Vitell & Muncy, 2005). Although the authors of the scale (used in Study 1) created it for the purposes of being applicable across a range of organizations and industries (Vitell & Muncy, 2005), some of the scale items may not seem appropriate for all organizational contexts. To address this limitation of Study 1, we created a measure of customer unethical behavior that has face validity across a variety of organizational contexts. Our customer unethical behavior scale can be used in future management studies that seek to further understand the interesting, yet understudied, interplay between customer unethical behavior and employee outcomes. A final contribution of our research is that we examine the mediating role of employee emotional exhaustion in explaining the relationship between customer unethical behavior and employee work–family conflict, relationship conflict with coworkers, and job neglect. We specifically demonstrate that customer unethical behavior consumes needed cognitive, psychological, and emotional resources, as captured by employee emotional exhaustion, which makes it more difficult for employees to deal with the demands of family and to interact well with coworkers. Emotional exhaustion, as the underlying process, also explains why employees who are exposed to customer unethical behavior may neglect their jobs. They are perhaps worn out by customer unethical

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behavior and thus attempt to conserve resources by withdrawing from their work by increasing job neglect. Importantly, our theoretical model held even when controlling for alternative explanations (i.e., customer interpersonal justice and negative affect). These results provide evidence that customer unethical behavior can indeed lead to unfavorable employee, family, and organizational outcomes via emotional exhaustion—an important finding that has important theoretical and practical implications but has not been adequately studied. It should be noted that Study 1 and Study 2 produced slightly different results regarding the mediating role of emotional exhaustion in explaining the relationship between customer unethical behavior and relationship conflict with coworkers. Study 1 demonstrated support for a mediating effect, whereas Study 2 produced results that only approached significance (p ⬍ .10). We believe these disparate findings may be due to the specific context used for Study 2. Study 1 results were gleaned from a highly diverse sample, whereas Study 2 came from a very specific context that consisted of state employees, most of whom were women. It could be that within this context, emotionally exhausted employees deal with their frustration by relying on each other more— by providing social support—which would thus mitigate the relationship between emotional exhaustion and relationship conflict with coworkers. Overall, we suggest that these slightly different results may be due to the different contexts and perhaps unmeasured moderators.

Practical Implications Unfortunately, some industries and/or companies are particularly fraught with unethical customers. The negative effect of customer unethical behavior may be more detrimental than initially considered. Our research suggests that the ill effects of customer unethical behavior may extend beyond the more obvious financial losses incurred by the organization (e.g., because of fraud). Importantly, employees may respond unfavorably to customer unethical behavior, which could exhaust needed resources to attend to other important work and personal demands. In turn, customer unethical behavior could produce additional secondary losses for organizations because of lost employee productivity and increased conflict within the workplace. Families of employees, too, may indirectly experience unfavorable effects of customer unethical behavior. Organizations may be aware that customer unethical behavior costs millions of dollars annually; however, they may not have considered the substantial hidden costs that occur because of employees’ unfavorable reactions to these customers. As such, organizations should consider ways to manage unethical customers. Organizations may be able to deter customer unethical behavior by having more lenient customer service policies, such that “the customer is not always king” (Gettman & Gelfand, 2007). Many organizations endorse the notion that the customer is always right, which leaves little leeway in terms of correcting customer unethical behavior. Furthermore, organizations may consider giving employees more autonomy in standing up to customers. One important aspect of deontological justice arguments (Folger, 2001) is the notion of retributive justice. Observers of unethical behavior attempt to restore justice by correcting the at-fault party. Thus, within reason, perhaps employees should have the authority to correct or punish customers who clearly violate


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organizational rules (e.g., by refusing service to the customer). Organizations, too, may further encourage employees to establish long-term and caring relationships with customers. If the employee-customer relationship is categorized by a strong socioemotional exchange, then customers may be less likely to engage in behaviors that could harm the employee or organization. Overall, customers may believe it is their right to take advantage of big business by engaging in customer unethical behavior; yet, organizations may be able to more effectively manage such customers by using the above-mentioned practices. Finally, organizations should consider ways to manage employees who regularly interact with potentially unethical customers. Although not a focus of this study, prior research on emotional exhaustion has implied that employees handle emotional exhaustion better if they receive social support (Karatepe & Aga, 2013). Organizations within industries that are perhaps more susceptible to customer unethical behavior (e.g., insurance, social services, retail) may consider providing their employees with heightened levels of social support to overcome the ill effects of customer unethical behavior. Social support may compensate for the lost resources consumed by customer unethical behavior. A supportive boss, for example, may conserve the employee’s emotional resources by listening to the employee and providing reassurance that not all customers are unethical.

Limitations and Future Directions The theoretical and practical implications of our research should be considered in light of its limitations. Although a strength of Study 1 is its generalizability (i.e., the sample consisted of employees from a number of professions and industries), the method used to recruit participants may be called into question. In Study 1, students were asked to serve as organizational contacts. Even though this approach to collecting data has been used before (e.g., Grant & Mayer, 2009; Morgeson & Humphrey, 2006), having students serve as organizational contacts could potentially compromise the validity of the data sources. We attempted to address this limitation by emphasizing to students that the surveys must be completed by the correct participants and that their integrity would be called into question if suspicious behavior was observed. Also, the researchers tracked such data and found potential irregularities in 13 cases. The respective students were contacted, and they confirmed that the surveys were completed by the correct participants using the same computer immediately following one another. As a robustness check, the Study 1 data were analyzed with and without these suspect cases, and the results remained unchanged. The snowball data-collection design also prevents us from drawing firm conclusions regarding response rates. Some students may not have been motivated by extra credit, and thus may not have asked an organizational contact to participate in our study. Furthermore, we have no way of knowing whether students had to ask multiple employees to participate prior to receiving an acceptance. To address some of these limitations, we collected data from a single organization in Study 2. Importantly, we were able to replicate our Study 1 findings with data from a single organization, which provides further confidence in our theoretical predictions and empirical results.

Beyond the recruitment method used in Study 1, the study had additional limitations. First, the Study 1 data were cross-sectional in nature. In Study 2, we attempted to address this limitation by introducing a 3-week time lag between reports of our predictor variables and mediator (Podsakoff et al., 2003). Second, common method variance could be a concern. We should note that we eliminated some common method variance concerns by having supervisors rate the focal employees’ relationship conflict with coworkers (Studies 1 and 2) and job neglect (Study 2; Podsakoff et al., 2003). Furthermore, Study 1 used the Consumer Ethics Scale to capture customer unethical behavior. A limitation of this measure is that not all items may be applicable across a range of customer-service professions. Thus, to address this limitation, we created and validated a general customer unethical behavior measure in Study 2. Interestingly, our results held using our new measure, which provides evidence of the robustness of our predictions. We should also note a limitation of Study 2. The sample was mostly female. However, our study results replicated across two studies, and Study 1 had slightly more male participants than females. Hence, we do not believe our study results are unique to a female sample alone. Future research would benefit from expanding our theoretical model. To provide further evidence of a deontological effect, future research may consider moral moderators that exacerbate the customer unethical behavior to employee emotional exhaustion relationship. For example, employees who are high in moral identity (Aquino & Reed, 2002) or who have strong justice orientations (Liao & Rupp, 2005) may find customer unethical behavior even more offensive and thus may consume higher levels of cognitive, psychological, and emotional resources when dealing with unethical customers and have stronger negative reactions to unethical customers. If highly ethical people experience more hardships when operating in organizations that have high levels of customer unethical behavior, these employees may be more likely to respond to customer unethical behavior by exiting the organization. Thus, organizations may benefit from hiring and retaining employees who do not strongly internalize morality as a central part of their identities, as such employees may more effectively manage their reactions to customer unethical behavior. From a practical standpoint, we also find it important to continue studying customer unethical behavior and how organizations can best manage employees who regularly interact with these customers. It would be interesting to further examine the philosophy that the customer is always right. Organizations that strictly operate with this mentality may suffer not only because it may allow customers to harm the organization but also because employees could potentially blame the organization for allowing customer unethical behavior to occur. Employees may respond unfavorably to customer unethical behavior not only because of the customer’s immorality but also because the organization appears to endorse such conduct, which could lead employees to experience negative reactions such as organizational cynicism and frustration with their organization.

Conclusion As our economy becomes increasingly service oriented, it becomes important to understand the challenges associated with


regularly interacting with unethical customers. We argue that customer unethical behavior may pose a challenge for employees. Employees may find it emotionally exhausting to interact with such customers, which may lead to issues at home and at work. Although it may be difficult to fully prevent customer unethical behavior, we hope that management scholars and practitioners alike will begin to more comprehensively consider the ill effects of customer unethical behavior.


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(Appendix follows)


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Appendix


Customer Unethical Behavior Scale Development Generation of Customer Unethical Behavior Items

5.

Engage in deceptive practices.

We used a deductive approach for scale development (Hinkin, 1995; Hinkin, 1998). We used Muncy and Vitell’s (1992) conceptualization and definition of customer unethical behavior to create our new items. Customer unethical behavior occurs when customers violate moral rules, principles, or standards when purchasing or using goods or services provided by organizations (Muncy & Vitell, 1992; Vitell & Muncy, 2005; Van Kenhove et al., 2003). The original Consumer Ethics Scale, created by Vitell and Muncy (2005) and used in Study 1, has been used in marketing studies to assess customers’ perceptions of the ethicality of a range of customer behaviors and includes rather specific customer behaviors that may not apply to all occupations. Thus, we sought to create a general and widely applicable measure of customer unethical behavior that can be used to assess employees’ reactions to such behaviors. To create general customer unethical behavior scale items, we first consulted the behavioral ethics literature for validated unethical behavior scales. Kaptein (2008) recently created and validated a general measure of organizational unethical conduct. Rebecca L. Greenbaum identified 17 items from Kaptein’s measure that could be adapted to reflect Muncy and Vitell’s (1992) conceptualization and definition of customer unethical behavior. In an effort to create a concise measure, three of us then met to discuss the face validity of each item and to eliminate redundancies. After multiple discussions, we arrived at eight items that could be used to assess customer unethical behavior across a range of occupations. Importantly, one purpose of our research is to understand whether employees respond unfavorably to customer unethical behaviors because of the violation of moral principles. Thus, our measure’s instructions included a brief description of the scale items as violations of moral standards. Our final measure consisted of the following text:

6.

Submit false or misleading documents.

7.

Violate service or product terms.

8.

Purchase or use a product or service under false pretenses.

Some customers/clients may violate moral rules, principles, or standards when purchasing or using goods or services provided by organizations. To what extent do you believe the customers you work with violate moral rules/principles/standards by engaging in the following behaviors? (1 ⫽ to a small extent to 7 ⫽ to a large extent) When purchasing or using goods or services provided by my organization, the customers I work with . . . 1.

Falsify or manipulate required information.

2.

Violate rules or regulations.

3.

Provide inaccurate information.

4.

Waste, mismanage or abuse the goods or services provided.

Scale Development Study We evaluated our customer unethical behavior measure using a sample of working adults who regularly interact with customers. Ninety-two online undergraduate and MBA students at a south central university in the United States were asked to recruit three people who regularly interact with customers as part of their jobs to participate in our study. Students received extra credit for their recruitment efforts. The study participants worked in a variety of industries (e.g., sales and marketing, retail, health care, food, fitness, financial, insurance) and held a variety of occupations (e.g., customer service, front desk clerk, sales associate, account manager, automotive technician). Participants responded to our eight-item measure of customer unethical behavior, the Consumer Ethics Scale (Vitell & Muncy, 2005), customer interpersonal injustice (Rupp et al., 2008), PANAS (Watson et al., 1998), and demographics. We received responses from 176 participants for an estimated response rate of 63.77%. To examine the underlying structure of the customer unethical behavior items, we split the sample and conducted exploratory factor analysis with promax rotation on half of the sample (N ⫽ 88; average age ⫽ 29.7 years; average organizational tenure ⫽ 4 years). One clear factor emerged (an eigenvalue greater than 1), which included all eight items. The factor explained 54% of the variance. All items had standardized factor loadings greater than or equal to .54, and we obtained a Cronbach’s alpha of .90. The measure had a mean of 2.27 and a standard deviation of 1.32 (7-point Likert-type scale). We used the other half of the sample (N ⫽ 88, average age ⫽ 30.1 years, average organizational tenure ⫽ 5.26 years) to perform a CFA using maximum-likelihood estimation on the eight customer unethical behavior items (␹2 ⫽ 83.25, df ⫽ 20, p ⬍ .001, RMSEA ⫽ .19, CFI ⫽ .95, NNFI ⫽ .93, SRMR ⫽ .05). The fit statistics showed that the unidimensional model fit the data well (Browne & Cudeck, 1993). It should be noted, however, that the RMSEA fit statistic was higher than desirable. We did not find this problematic, because Hu and Bentler (1999) found that RMSEA fit statistics tend to be inaccurate when working with smaller sample sizes. The Cronbach’s alpha for the scores in this sample was .95, and the measure had a mean of 2.27 and a standard deviation of 1.32 (7-point Likert-type scale). We also used the same half of the data set to assess the construct validity of our customer unethical behavior measure. In terms of

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convergent validity, we expected our measure of customer unethical behavior to be positively related to the Consumer Ethics Scale (Vitell & Muncy, 2005), customer interpersonal injustice, and negative affectivity, and negatively related to positive affectivity. As expected, results revealed that our measure of customer unethical behavior was positively and significantly related to the Consumer Ethics Scale (r ⫽ .45, p ⬍ .001), customer interpersonal injustice (r ⫽ .50, p ⬍ .001), and negative affectivity (r ⫽ .50, p ⬍ .001), and negatively related to positive affectivity (r ⫽ –.30, p ⬍ .01). In terms of discriminant validity, our customer unethical behavior measure was not significantly correlated with age (r ⫽ –.10, ns), sex (r ⫽ –.10, ns), race/ethnicity (r ⫽ –.06, ns), organizational position (r ⫽ .01, ns), organizational tenure (r ⫽ .11, ns), or full-time/part-time status (r ⫽ –.02, ns). Although we expected our customer unethical behavior measure to converge with the Consumer Ethics Scale and customer interpersonal injustice, we also wanted to ensure that our measure was distinct from these constructs. Theoretically, these three measures are the most similar and thus may be more susceptible capturing the same underlying construct. To rule out this possibility, we conducted CFAs and chi-square difference tests to provide evidence of discriminant validity. First, our customer unethical behavior measure was compared with the Consumer Ethics Scale. We compared a two-factor model

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(with items for each measure loading onto their respective factors; ␹2 ⫽ 908.27, df ⫽ 376, p ⬍ .001, RMSEA ⫽ .13, CFI ⫽ .94, NNFI ⫽ .93, SRMR ⫽ .10) to a single-factor model (␹2 ⫽ 2,156.00, df ⫽ 377, p ⬍ .001, RMSEA ⫽ .23, CFI ⫽ .88, NNFI ⫽ .87, SRMR ⫽ .16). A chi-square difference test indicated that the two-factor model provided a significant improvement in fit over the single-factor model (⌬␹2 ⴝ 1,247.73, df ⫽ 1, p ⬍ .001). Second, for customer interpersonal injustice, we compared a twofactor model (with items for the customer unethical behavior and customer interpersonal injustice measures loading onto their respective factors; ␹2 ⫽ 51.21, df ⫽ 53, p ⬍ .001, RMSEA ⫽ .01, CFI ⫽ 1.00, NNFI ⫽ 1.00, SRMR ⫽ .05) to a single-factor model (␹2 ⫽ 257.56, df ⫽ 54, p ⬍ .001, RMSEA ⫽ .21, CFI ⫽ .87, NNFI ⫽ .84, SRMR ⫽ .14). A chi-square different test indicated that the two-factor model provided a significant improvement in fit over the single-factor model (⌬␹2 ⴝ 206.35, df ⫽ 1, p ⬍ .001). Taken together, these results indicate that our measure of customer unethical behavior is related to, but distinct from, the Consumer Ethics Scale and customer interpersonal injustice. Received December 28, 2012 Revision received April 8, 2014 Accepted May 19, 2014 䡲

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