On the Role of Empathy in Customer-Employee Interactions

Abstract

While the service literature repeatedly emphasizes the role of empathy in service interactions, studies on empathy in customer-employee interactions are nearly absent. This study defines and conceptualizes employee and customer empathy as multidimensional constructs and empirically investigates their impact on customer satisfaction and customer loyalty. A quantitative study based on dyadic data and a multilevel modeling approach finds support for two effects of empathy in service interactions. The study reveals that customer empathy strengthens the positive effect of employee empathy on customer satisfaction, leading to more “symbiotic interactions.” The findings also indicate that empathic customers are more likely to respond to a dissatisfying encounter with “forgiveness,” in the sense that customer empathy is able to mitigate negative effects of customer dissatisfaction on customer loyalty. From these empirical results, the authors derive several implications for service research and the management of service encounters. In particular, the present study provides a valuable basis for strategies of “interaction routing,” that is, matching customers and employees on the basis of their psychological profiles to create smooth and satisfying service interactions. The authors elaborate on approaches to implement this strategy in service organizations.

Keywords

service encounter employee-customer interaction customer empathy employee empathy customer satisfaction customer loyalty

Introduction

Imagine any personal service setting, such as restaurant, hotel, or rental services, where customers and frontline employees interact to produce and deliver a service. The more mutual attentiveness, courtesy, and understanding characterize this interaction, the more likely this interaction is to lead to a satisfying service outcome. Similarly, one could easily imagine that in the same setting a lack of personal connection and an inability to take the perspective of the other could impair the personal interaction, resulting in dissatisfaction and, in worst cases, anger and frustration.

A rich body of literature exists concerning the role frontline employees play in shaping customers' service evaluations (Bitner, Booms, and Tetreault 1990; Hartline and Ferrell 1996; Price, Arnould, and Tierney 1995). In particular, prior research suggests that frontline employees' care for and attention to the customer engender customer satisfaction (Gorry and Westbrook 2011; Tax, Brown, and Chandrashekaran 1998). The display of empathy, or the caring and individualized attention service employees provide their customers, is an important prerequisite for successful service encounters (Parasuraman, Zeithaml, and Berry 1988; Zeithaml, Berry, and Parasuraman 1996).

Similarly, conceptual evidence exists for the importance of customer empathy in service creation and delivery. This article defines customer empathy as a customer’s ability to take the employee’s perspective and to react appropriately to an employee’s thoughts and feelings. Successful service interactions depend on the level of empathy apparent in customer-employee interactions (Gabbott and Hogg 2001). In this vein, customer empathy exerts major influence on customers' perspective-taking, feelings of compassion, motivation toward protection, and perceptions and evaluations of the service encounter (Berry, Seiders, and Grewal 2002). The use of “qualifiers” enhances customers' caring and concern for service employees (Beatty et al. 1996), and empathy can act as a qualifier of social interactions, fostering alignment of feelings and thoughts between people and generating smooth, harmonious interactions (Bernieri 1988; Gremler and Gwinner 2008). Similarly, empathic customers may be more sensitive to a service employee’s working conditions (Bitner, Booms, and Tetreault 1990). In this case, customers may acknowledge the employee’s efforts, which may countervail customers' discomfort and prevent them from switching to another provider in the event of an unsatisfactory outcome (Bitner, Booms, and Tetreault 1990).

However, despite a rich conceptual background in the literature, empirical examinations of the impacts of employee and customer empathy on customer satisfaction and loyalty are scant. More specifically, to the best of our knowledge, empirical investigations of the simultaneous effects of employee and customer empathy in service encounters are absent. Our research therefore aims to contribute to service research by simultaneously investigating employee and customer empathy as important psychological constituents of customer satisfaction and loyalty in service encounters. In contrast to previous studies, we employ a multilevel approach to investigate dyadic interactions between service employees and customers at the individual encounter level.

The article unfolds as follows. We first introduce our theoretical framework and explicate customer and employee empathy as our focal constructs. We then develop hypotheses substantiating our assumptions regarding the effects of customer and employee empathy on customer satisfaction and loyalty intentions. Next, we present methodological aspects of the empirical study, including the development of the measurements, the method of sample selection and data collection, and the approach to estimation. On the basis of an empirical survey, we analyze the hypothesized relationships between the constructs. We conclude with a discussion of implications for service research and management.

Conceptualizing Employee and Customer Empathy

A literature review in the fields of social and clinical psychology as well as sales and marketing reveals considerable ambiguity regarding the nature and conceptualization of empathy (see Table 1 ). Prior investigators have described empathy as a personal trait or stable ability, behavior, experience or interpersonal process, which is either cognitive or affective and either unidimensional or multidimensional (Kerem, Fishman, and Josselson 2001). Examinations of the effects of interpersonal differences in human interactions widely accept the perspective of empathy as an ability or personal trait (Duan and Hill 1996).

Table 1.

Literature Review

Author(s)	Description of Empathy	Conceptualization
Domain: Psychology, psychotherapy
Barrett-Lennard (1981)	Multiphase experiential process, cognitive phenomenon	Process of experiencing the cognitive state of another person
Batson (1987)	Situation-specific state, affective phenomenon	Other-focused, vicarious emotional response congruent with the perceived welfare of another person
Bernstein and Davis (1982)	Situation-specific state, cognitive phenomenon	Cognitive reaction to others' experiences
Coke, Batson, and McDavis (1978)	Situation-specific state, affective phenomenon	Vicarious experience of an emotion that is congruent with, but not identical to, another person’s emotion
Davis (1983, 1996)	Predisposition; comprising cognitive and affective components	Ability to react emotionally and nonemotionally to others; comprising the aspects of perspective-taking, empathic concern, fantasy, and personal distress
Duan and Hill (1996)	Phenomenon containing both cognitive and affective components	Multidimensional construct, comprising empathic emotions and intellectual empathy
Dymond (1949)	Personal trait, cognitive phenomenon	Accurate perception of others
Eisenberg and Strayer (1987)	Ability, affective phenomenon	An emotional response to another’s emotional state or condition that is congruent with the other’s emotional state or situation
Feshbach (1978)	Ability, comprising cognitive and affective components	Ability to discriminate and identify another’s emotions, to take the perspective of another person, and to evoke a shared affective response
Gladstein (1983)	Multistage interpersonal process, containing both cognitive and affective components	A way of knowing and understanding another person as if one were that person through emotional contagion, identification, and role-taking
Hogan (1969)	Ability, cognitive phenomenon	Ability to intellectually or imaginatively apprehend another person’s condition or state of mind; comprising social acuity, social knowledge and social skillfulness
Mehrabian and Epstein (1972)	Ability, affective phenomenon	A person’s ability to imaginatively assume the role of another and to understand and accurately predict that person’s thoughts, feelings, and actions, comprising elements such as “susceptibility to emotional contagion,” “extreme emotional responsiveness,” or “sympathetic tendency”
Domain: Sales and marketing
Aggarwal et al. (2005)	Ability, comprising cognitive and affective components	Ability to feel what others are feeling, to bond with them on an emotional level and respond to their feelings
Ahearne, Jelinek, and Jones (2007)	Individual behavior	A salesperson’s demonstration of interest and concern for the welfare of the customer
Argo, Zhu, and Dahl (2008)	Ability, comprising cognitive and affective components	One’s ability to experience and understand another person’s affective or psychological state
Comer and Drollinger (1999)	Ability, comprising an emotional and a cognitive dimension	Salesperson’s intellectual understanding of their customers' situation (perspective-taking) and their ability to sense, process, and respond to their customers' feelings (empathic concern)
Dawson, Soper, and Pettijohn (1992)	Interpersonal process	Process of understanding the client’s needs
Fisher, Vandenbosch, and Antia (2008)	Experience, emotional response	Emotional response congruent with the perceived welfare of another
Giacobbe et al. (2006)	Ability, comprising cognitive and affective components	Ability of salespeople to spontaneously adopt the perspective of their customers through intellectual apprehension of their mental state or condition and by generating a genuine feeling of concern for the customer
Homburg, Wieseke, and Bornemann (2009)	Ability, cognitive phenomenon	Ability to understand and identify with the perspective of the customer
Lamont and Lundstrom (1977)	Ability, comprising an emotional and a cognitive dimension	A salesperson’s ability to feel as the other person does to enhance the ability to sell a product or service
Pilling and Eroglu (1999)	Ability, comprising an emotional and a cognitive dimension	One’s ability to see from the perspective of the other person

Emphasizing a cognitive view, several scholars refer to empathy as a person’s intellectual understanding of the internal state of another person (Hogan 1969; Lamont and Lundstrom 1977; Pilling and Eroglu 1994), and describe cognitive efforts to recognize and understand someone else’s mind and thoughts as “perspective-taking” (Barrett-Lennard 1981; Bernstein and Davis 1982; Dymond 1949). Perspective-taking enables an individual to understand the role or point of view of another person, to anticipate the reactions of the other, and to address the other’s perceived needs, motivations, or opinions (Devoldre et al. 2010).

In contrast, some scholars advance the notion of empathy as an emotional response to another person’s emotional state or situation (Eisenberg and Strayer 1987; Hoffman 1984; Mehrabian and Epstein 1972). This conception includes facets such as empathic concern and emotional contagion (Coke, Batson, and McDavis 1978). Empathic concern refers to a person responding to another person’s emotions in a given situation without experiencing these emotions. Empathic concern allows individuals to express apprehensiveness for the welfare of others, resulting in altruistic behaviors (i.e., helping others; Batson 1991; Buchheimer 1963). Emotional contagion pertains to the phenomenon in which a person shares another person’s emotions at the moment these emotions occur (Duan and Hill 1996; Gladstein 1983). In contrast to empathic concern, emotional contagion causes transference of emotions between interacting individuals, which may result, for instance, in automatic synchronization of facial expressions or gestures with another person (Davis 1983).

Scholars increasingly agree that empathy is best understood as a multidimensional construct comprising cognitive and emotional constituents (Kerem, Fishman, and Josselson 2001; Smith 2006). Accordingly, we define empathy as a person’s ability to sense another’s thoughts, feelings, and experiences (Davis 1996; Rogers 1959), to share the other’s emotional experience (Duan and Hill 1996; Moore 1990; Redmond 1989), and to react to the observed experiences of another person (Davis 1983). We contend that the empathy construct includes both a cognitive dimension, namely, perspective-taking, and emotional dimensions, namely, empathic concern and emotional contagion.¹

We understand employee empathy as an employee’s ability to sense and react to a customer’s thoughts, feelings, and experiences during a service encounter (Castleberry and Shepherd 1993). We further posit customer empathy to reflect a customer’s ability to take the employee’s perspective, and we propose that customer empathy fosters an increased understanding of the employee’s experience during the service encounter. Thus, empathy incorporates a customer’s apprehension of and reaction to an employee’s thoughts, feelings, and intentions during a service interaction.

Effects of Empathy in Service Encounters

Given that service production and delivery incorporate social interactions (Solomon et al. 1985; Surprenant and Solomon 1987), we contend that empathy is an important mechanism in governing customer-employee interactions. Despite containing a certain ambiguity regarding the nature of empathy, the literature shows substantial agreement on two effects, namely, the facilitation of (1) adaptive and (2) prosocial behaviors.

First, empathy strengthens the ability to competently interact with others and to display behaviors appropriate for a given situation or person (Redmond 1989). By fostering a more complete and accurate understanding of the interaction partner, empathy elevates the ability to predict or anticipate the actions or reactions of others (Hakansson and Montgomery 2003). In addition, adapting behaviors to the other’s thoughts and feelings and acting for the benefit of the other facilitates reciprocal actions (de Waal 2008). Empathy therefore enhances social interactions by eliciting and fortifying mutually supportive attitudes and behaviors (Lazarus 1991).

We conclude that empathy on the part of the employee and the customer enhances mutual adaptation in service encounters, which results in “symbiotic” customer-employee interactions and a satisfying service experience (Varadarajan and Rajaratnam 1986). To illustrate its function in our conceptual framework, we call this interaction effect of customer and employee empathy “symbiosis,” indicating mutual alignment between the actors involved in service encounters.

Second, empathy relates positively to prosocial behaviors, which are actions intended to help others (Batson 1987). Conceptual and empirical evidence indicates that empathy evokes the motivation to help others (Batson 1987, 1990; Lazarus 1991). In particular, sensing and understanding another’s distress increases the inclination to advance the other individual’s welfare (Batson and Shaw 1991). Further, the motivation to help is an integral component of forgiveness (Penner et al. 2005), which social psychology defines as a motivational change in an individual involving a decrease in revenge-seeking and an increase in benevolence (McCullough and Hoyt 2002). Having the ability to understand and relate to another’s thoughts, feelings, and experiences increases the likelihood of forgiving encountered mistakes, since through forgiveness one reframes an unfavorable experience and turns negative emotions, thoughts, and behaviors resulting from a perceived interpersonal harm into more positive emotions, thoughts, and behaviors (Thompson et al. 2005). As previous studies suggest, individuals with greater empathy tend to respond to interpersonal hurt with less anger and greater indulgence than do those with less empathy (Konstam, Chernoff, and Deveney 2001; McCullough and Worthington 1995).

We term this effect of empathy “forgiveness,” referring to the fact that empathy is capable of alleviating dissatisfying experiences in social interactions (Hodgson and Wertheim 2007). We postulate that empathy affects the link between customers' evaluation of service encounters and loyalty intentions in such a way that higher levels of empathy correspond with stronger tendencies to forgive employees for dissatisfactory service encounters.

Model and Hypothesis Development

We introduce a model that proposes a causal chain linking employee empathy, customer satisfaction, and customer loyalty. Customer satisfaction refers to the customer’s satisfaction with the service encounter, and customer loyalty is the customer’s commitment to rebuy or repatronize a product or service in the future. Further, we postulate that customer empathy moderates the relationships between employee empathy, customer satisfaction, and customer loyalty. Figure 1 depicts our conceptual framework.

Figure 1.

Conceptual model.

In employee-customer interactions, employee empathy is vital for identifying and satisfying customer needs (Aggarwal et al. 2005; Giacobbe et al. 2006). The more accurately frontline employees sense how a customer receives the service, the more precisely they can react to these perceptions and target their interaction behavior to customer expectations (Bettencourt and Gwinner 1996; Gwinner et al. 2005). An employee’s ability and willingness to take the perspective of the customer plays a crucial role in the delivery of service quality (Parker and Axtell 2001). Moreover, sensing the positive or negative emotions of customers and responding to them in an appropriate manner enforces the development and coordination of adequate interactive behaviors (Bailey, Gremler, and McCollough 2001). If service employees are able to respond sensitively to their customers, customers will value the interaction and will be more satisfied with the service encounter (Brady and Cronin 2001).

The suggested relationship draws support from the results of previous studies. Empathic employees understand their customers' needs more fully and are therefore able to tailor their interactive behaviors to specific customers (Giacobbe et al. 2006; Pettijohn et al. 2000; Weitz 1978). Additionally, because empathy enhances employees' understanding of customer needs, higher levels of customer satisfaction result (Homburg, Wieseke, and Bornemann 2009). Other studies suggest that satisfying encounters result from positive communication with customers, which can be attributed to higher levels of employee empathy (Boorom, Goolsby, and Ramsey 1998; Comer and Drollinger 1999; Drollinger, Comer, and Warrington 2006; Pilling and Eroglu 1994). Hence, expecting to replicate prior work, we hypothesize:

Hypothesis 1: Employee empathy relates positively to customer satisfaction.

Customers' perceptions of their interactions with frontline employees and their impact on customer satisfaction have received considerable attention in the literature. For example, customers are interested in the feelings and thoughts of contact employees (Beatty et al. 1996; Price, Arnould, and Tierney 1995), and customers' attentiveness toward and interest in the employees with whom they interact can enhance mutual understanding between the interactants (Gremler and Gwinner 2000). By taking the perspective of the employee and trying to comprehend the employee’s feelings and thoughts, customers develop a more accurate view of the employee’s capabilities and, hence, his or her service performance.

Empathy enables customers to recognize the efforts frontline employees make on the customer’s behalf, and in addition, interpersonal sensitivity and concern for the employee relate positively to customer satisfaction (Gremler and Gwinner 2000). The customer’s awareness that an employee is acting in an empathic manner may therefore contribute to a favorable assessment of this employee’s performance and, thus, elevate the level of customer satisfaction, as buyer characteristics moderate the relationship between employees' selling behavior and sales effectiveness (Weitz 1981). On the basis of this rationale, we suggest that customer empathy can leverage the level of customer satisfaction induced by employee empathy. With reference to the relevance of empathy to gaining symbiosis in social interactions, we put forward the following hypothesis:

Hypothesis 2: Customer empathy positively affects the relationship between employee empathy and customer satisfaction.

A rich body of research across industries substantiates the link between a customer’s satisfaction and his or her loyalty intentions toward the service provider (e.g., Boulding et al. 1993; Luo and Homburg 2007; Seiders et al. 2005). Investigations have shown that customer satisfaction results in repurchase intentions and commitment to the service provider. Conversely, previous research has linked customer dissatisfaction with service outcomes to the intention to switch providers (Crosby and Stephens 1987; Kelley, Hoffman, and Davies 1993).

Notably, a single unsatisfactory encounter does not automatically endanger customer loyalty (Oliva, Oliver, and MacMillan 1992). In particular, highly involved customers may not switch to another provider, as they are resistant to an immediate belief change (Laczniak and Muehling 1990). Indeed, studies in financial services support the assumption that, under certain circumstances, customers tend to forgive providers for unsatisfactory service encounters (Davidow and Dacin 1997). This response raises the question of what variables may influence the satisfaction-loyalty link.

Perceiving a firm responsible for unpleasant service encounters has been associated with direct responses such as complaining behavior, perceptions of deserving refunds or apologies, and indirect responses including switching intentions and negative word of mouth (Curren and Folkes 1987; Folkes 1984b). Looking through the lens of empathy research, however, we suggest that customer empathy attenuates negative behavioral intentions after an unsatisfactory service encounter.

According to attribution theory, customers respond to a dissatisfying interaction by seeking reasons for the failure of an interaction (Bettman 1979; Hess, Ganesan, and Klein 2007). In addition to judging the failure per se, customers try to identify what caused the failure (Folkes 1984a). However, the result of the attribution process differs between interacting partners. Whereas an actor (e.g., the frontline employee) tends to attribute interactional failures to situational influences, an observer (e.g., the service customer) is more likely to attribute an unpleasant interaction to the actor’s personal disposition (Jones and Nisbett 1971).

Notably, empathy affects attribution in terms of how the parties make attributions and what information they process (Redmond 1989). Empathizing with an actor results in an alignment of the observer’s assessment of acts with that of the actor (Gould and Sigall 1977; Jones and Nisbett 1971). That is, in situations when an observer empathizes with an actor, both attribute causality for a given phenomenon in a congruent manner (Fiske, Taylor, and Etcoff 1979). Additionally, an individual’s disposition to empathize influences responsibility attributions for negative outcomes (Sulzer and Burglass 1968), as empathic individuals would be able to sense a given situation and to attribute situational factors rather than personal dispositions as causes for an unpleasant encounter. Further, empathic individuals are more apt to make benevolent attributions when an unfavorable event occurs, thus forgiving the actor for the discomfort they have experienced (Takaku 2001).

This research predicts that in customer-employee interactions, empathic customers may be able to develop a deeper understanding of circumstances that have caused an unsatisfactory service encounter. Empathy elicits not only cognitive assessment of the service encounter but also the imagining of how one would feel being in the other’s position (Batson, Early, and Salvarani 1997). Customers who are better able to sense a situation that causes a failure are more inclined to react to an unsatisfactory event in an indulgent, benevolent manner.

This tendency toward benevolence may imply that, in the case of a dissatisfying service experience, these customers are willing to forgive and will refrain from switching. Research on service failure supports this notion. Previous studies have suggested that harmonious and mutually supportive relationships between customers and frontline employees yield satisfaction benefits even if the service is poor (Bitner, Gwinner, and Gremler 1998; Gremler and Gwinner 2000). The fact that empathy is a component of interpersonal interactions between customers and employees (Coan 1984) supports the conclusion that customer empathy mitigates the effect of dissatisfaction on customer loyalty, thus serving as a switching barrier (DeWitt and Brady 2003). Hence, we propose that customer empathy moderates the satisfaction-loyalty link and hypothesize the following:

Hypothesis 3: Customer empathy mitigates the negative effect of customer dissatisfaction on customer loyalty.

Methodology

Collection of Dyadic Data

An appropriate context for examining the role of empathy in customer-employee interactions would be one in which (1) interaction with customers is individual and interpersonal (including verbal and nonverbal exchange) and (2) salespeople may exert a significant influence on customers during the sales presentation. Travel agencies affiliated with large tourism companies meet these criteria, and we therefore chose an integrated German tourism company as a research setting. Integrated tourism companies strive to promote the products and services within their travel agency network (Fyall and Wanhill 2008; Papatheodorou 2006), a practice industry parlance terms “directional selling” (Fyall and Wanhill 2008, p. 385). Furthermore, travel sales require intense collaborative interaction between the travel agent and the customer, and many customers attach high importance to the agent’s recommendations when booking their travel plans (Humphreys 2005).

Data collection occurred in two phases. The first step was a qualitative study with frontline employees and customers to pretest the questionnaire, with the particular aim of clarifying and improving the scales and individual items. The second step comprised contacting the company’s travel agencies by telephone to solicit their participation in the study. When selecting the travel agencies, we took several steps to ensure that the sample was as balanced as possible. In controlling for possible external effects that could systematically bias results in a locally clustered sample, a key issue was to include travel agencies from different locations (e.g., shopping malls, large cities, medium-sized cities, and small cities).

As our scales were measured by self-reports, a social desirability bias may be present. We took a series of measures to address this issue. First, when soliciting the travel agencies for participation in the study, we emphasized that our research institution is independent of the company and that all questionnaires would be sent directly to our university. Second, the research assistants who administered the questionnaire to the frontline employees highlighted the importance of genuine and unbiased answers. Third, we provided participants with a written statutory declaration stating that the study was anonymous and that any sharing of data with the company would occur on an aggregated level only. Fourth, we disseminated to survey participants a written statement from the highest joint works council of the company,² stating that the council supported the survey and assuring the participants of the confidentiality of their individual answers. Given our commitment to anonymity and the strong support of the company’s works council, all frontline employees who were present on the day of the interviews agreed to participate despite our explicit statement that any participation in our study was voluntary. During their visit, research assistants also distributed questionnaires to the customers, who could complete them in a designated area of the store. As with the frontline employees, we assured customers of the confidentiality of their individual answers. We used code numbers to match frontline employees and customer questionnaires.

To achieve the best possible response and matching rates between employees and customers, members of the research team personally administered questionnaires to travel agents. Subsequently, the interviewers spent 1 day in the travel agencies and asked customers for an interview after their interaction with a travel agent. All employees who were at work on the days of the interviews agreed to participate in the study. To trace the employee-customer link, interviewers assigned the customers to the respective employee’s code number. The final matched sample consisted of 214 employees and 752 customers (response rate: 36.9%) in 93 travel agencies.

To test for nonresponse bias in the customer sample, we chose the following approach. All customers visiting on the days of the interviews were offered the chance to participate in a lottery that was independent of their participation in the interviews. To take part in the lottery, customers had to provide their address and telephone number. We then collected additional data from 70 nonrespondents by contacting them via telephone. Concerning the scale means of the customer constructs included in our framework, we found no significant differences between the respondents in our original sample and those in the nonrespondent sample. These results provide evidence that nonresponse bias is not an issue with our data.

Measures

Focal constructs

The measurement scales for our study came from the literature, with only minor modifications on the basis of an extensive qualitative prestudy, which was conducted as required to fit the study’s context. All ratings were on 7-point Likert-type scales, with 1 indicating strong disagreement and 7 indicating strong agreement with each statement. The measurement of customer satisfaction followed the scale proposed by Bettencourt (1997). Customer loyalty was captured by a scale based on the work of Homburg and Giering (2001). We conceptualized empathy as a three-dimensional construct in line with McBane (1995; see Appendix A). Besides cognitive empathy, which was covered by the perspective-taking dimension (3 items), this conceptualization included two emotional aspects of empathy: empathic concern (4 items) and emotional contagion (5 items).

Covariates

We included a number of participant-related covariates in our empirical analyses to test the robustness of the proposed relationships. We controlled for the influences of customer gender, age (in years), and the length of the customer’s relationship with the company (in years). Because these variables are potential correlates with customer loyalty, we included them in our analysis. Furthermore, we controlled for customers' perceived customer orientation, which has been intensively discussed in the service marketing literature and is a potential predictor of customer loyalty. We measured perceived customer orientation with the abbreviated form of the Saxe and Weitz (1982) scale designed by Thomas, Soutar, and Ryan (2001).

Appendix A provides a complete list of the items used in the quantitative study. Table 2 displays the psychometric properties of the measures. Cronbach’s alpha, composite reliability, and average variance extracted for all measurement scales indicate the sufficient reliability and convergent validity of our construct operationalizations. More specifically, no coefficient alpha values and composite reliabilities are lower than .70, thus meeting or exceeding the recommended thresholds (Bagozzi and Yi 1988). We assessed the discriminant validity of the construct measures using the criterion proposed by Fornell and Larcker (1981), which suggests that discriminant validity is supported if the average variance extracted exceeds the squared correlations between all pairs of constructs. All constructs fulfilled this requirement.

Table 2.

Psychometric Properties of Measures

Variables	M	SD	α	CR	AVE
Employees'
Empathy—Perspective-Taking	5.11	1.08	.83	.84	.51
Empathy—Empathic Concern	5.29	1.06	.81	.83	.55
Empathy—Emotional Contagion	3.86	1.32	.77	.78	.52
Customers'
Satisfaction	6.38	.96	.86	.88	.69
Loyalty	6.05	1.24	.89	.90	.74
Empathy—Perspective-Taking	4.98	1.46	.72	.76	.53
Empathy—Empathic Concern	5.34	1.07	.79	.81	.57
Empathy—Emotional Contagion	3.81	2.24	.73	.74	.54

Note: AVE = average variance extracted; CR = composite reliability; SD = standard deviation.

To control for multicollinearity, we inspected the variance inflation factors of the variables and interaction terms. The variables and interactions yielded values between 1.0 and 2.2, indicating the absence of serious multicollinearity problems (Kleinbaum et al. 1998).

Analytical Approach

Because a single frontline employee typically served several customers, those customers' answers might show a greater degree of conformity. Also, because customers are clustered within employees, thus violating the assumption of independence, testing for the suitability of a multilevel analysis was important. To determine whether a two-level approach was warranted, we calculated the intra-class correlation coefficient (ICC), which measures the ratio of variance between groups to variance within groups (Tabachnick and Fidell 2007), and examined the ICC to ascertain the extent of systemic group-level variance (Duncan et al. 1997). The estimated ICC was .25 for customer satisfaction and .21 for customer loyalty. Moreover, the between-group variances in customer satisfaction and customer loyalty were significantly different from zero at p < .05. Importantly, even ICCs as small as .01 can lead to seriously biased analytic results if the multilevel nature of the data has not been taken into account (Raudenbush and Bryk 2002; Tabachnick and Fidell 2007). These results indicate that the variance to be explained in the criterion variables at Level 1 (customer satisfaction and customer loyalty) required another predictor at Level 2, and we proceeded with a two-level model. Consequently, for the full analysis we used a multilevel path model, using MPlus 5 and estimated with full maximum likelihood to take into account the hierarchical structure of the data (Raudenbush and Bryk 2002). In our study, employee empathy varies by employee, and it is therefore modeled as a Level 2 variable. The remaining constructs are modeled as Level 1 variables.

As Figure 1 shows, customer satisfaction is a function of the Level 2 variable employee empathy and the main effect of customer empathy and its cross-level interaction effect with employee empathy. Here, the analysis can be thought of as comprising two steps, although the MPlus two-level modeling technique incorporates these steps into a single model. Step 1 is the regression of customer satisfaction on the Level 1 predictor variable customer empathy, as follows:

{C S}_{i j} = β_{0 j} + β_{1 j} ({C E M P}_{i j}) + r_{i j},

where CS_ij is customer i’s satisfaction when served by employee j, CEMP_ij is the empathy of customer i for employee j, and r_ij is an error term assumed to be distributed as N(0, σ2).

In the second step, the regression parameters (intercept and slope) from Step 1 become the outcome variables and are regressed on employee empathy, as follows:

β_{0 j} = γ_{00} + γ_{01} {E E M P}_{j} + u_{0 j},

β_{1 j} = γ_{10} + γ_{11} {E E M P}_{j} + u_{1 j} .

where EEMP_j represents the empathy for employee j. Thus, these two equations capture the variation present at Level 2. Combining the two sets of equations yields the following:

{CS}_{i j} = γ_{00} + γ_{01} {EEMP}_{j} + γ_{10} ({CEMP}_{i j}) + γ_{11} {EEMP}_{j} ({CEMP}_{i j}) + u_{0 j} + u_{1 j} ({CEMP}_{i j}) + r_{i j} .

Thus, the effects of the employee’s empathy, the customers' empathy, and the cross-level interaction of customer and employee empathy on customer satisfaction are captured by γ₀₁, γ₁₀, and γ₁₁, respectively.

Predicting customer loyalty—that is, the impact of employee empathy, customer satisfaction, customer empathy, and the within-level interaction of customer satisfaction with customer empathy—involves a similar hierarchical approach.³

{C L}_{i j} = β_{0 j} + β_{1 j} {(C S)}_{i j} + β_{2 j} {(C E M P)}_{i j} + β_{3 j} {(C S \times C E M P)}_{i j} + r_{i j},

\begin{aligned} β_{0 j} = γ_{00} + γ_{01} {E E M P}_{j} + u_{0 j}, \\ β_{1 j} = γ_{10} + u_{1 j}, \\ β_{2 j} = γ_{20} + u_{2 j}, \\ β_{3 j} = γ_{30} + u_{3 j} . \end{aligned}

Thus, {CL}_{i j} = γ_{00} + γ_{01} {EEMP}_{j} + γ_{10} {(CS)}_{i j} + γ_{20} {(CEMP)}_{i j} + γ_{30} {(CS \times CEMP)}_{i j} + u_{0 j} + u_{1 j} {(CS)}_{i j} + u_{2 j} ({CEMP}_{i j}) + u_{3 j} {(CS \times CEMP)}_{i j} + r_{i j} .

Results

Because standard fit indices are not available for the numerical integration procedure utilized by MPlus to estimate a multilevel model with cross-level interactions, we employed a log likelihood difference test (−2 * difference in log likelihoods ~ χ², df = # free paths) to compare the fit of evaluated nested models, and we used Akaike’s information criterion (AIC) and the Bayesian information criterion (BIC) to compare the fit of selected non-nested models. To begin, we fitted a model that estimated only direct effects by eliminating the interaction effects as well as the paths to the mediating variable customer satisfaction.

The results indicate positive relationships between customer satisfaction and customer loyalty (γ = .22, p < .01), customer empathy and customer satisfaction (γ = .09, p <.05), and employee empathy and customer loyalty (γ = .14, p < .01), supporting the overall framework of the model (see Table 3 ).

Table 3.

Model Comparison and Effects

Relationships	Model 1 (Direct Effects Only)	Model 2 (Hypothesized Model)
Employee Empathy → Customer Satisfaction (H1)	—	.18^**
Employee Empathy × Customer Empathy → Customer Satisfaction (H2)	—	.16^**
Customer Empathy × Customer Satisfaction → Customer Loyalty (H3)	—	−.21^**
Customer Satisfaction → Customer Loyalty	.22^**	.21^**
Customer Empathy → Customer Satisfaction	.09^*	.10^*
Customer Empathy → Customer Loyalty	.16^**	.20^**
Employee Empathy → Customer Loyalty	.14^**	.15^**
Covariates
Customer’s Age → Customer Loyalty	ns	ns
Customer’s Gender → Customer Loyalty	ns	ns
Perceived Customer Orientation → Customer Loyalty	.13^*	.14^*
Length of Customer Relationship → Customer Loyalty	.15^**	.16^**
Δ df		3
−2^*Log likelihood	539.36	512.20
−2LL change		27.16^**
AIC	88.00	79.06
BIC	89.97	80.37

Note. ns = not significant.

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

Next, we estimated the hypothesized model. A comparison of AIC and BIC values confirms that this less restricted model fits better than the direct-effects only model (8.94 lower AIC, 9.60 lower BIC for the less restricted model). This improved model reflects significant relationships between customer empathy (i.e., the antecedent) and customer satisfaction (i.e., the mediator), fulfilling additional requirements for a mediated structure—that is, significant antecedent–final outcome and mediator–final outcome relationships (Baron and Kenny 1986).

The log likelihood difference test for this hypothesized model confirmed that the inclusion of the effects provides a better fit to the data (Δχ² = 27.16, df = 3, p < .01) than the one obtained with the nested model that did not include these mediating effects.

Table 3 displays the results of our multilevel path analysis. We find support for our predictions that employees' empathy (Hypothesis 1) drives customer satisfaction because the coefficient was positive and highly significant (Hypothesis 1: γ = .18, p < .01). To test the cross-level interaction between customer empathy and employee empathy with customer satisfaction as a criterion variable (Hypothesis 2), we examined the slope of the variable customer empathy at Level 1, which is a function of the employee’s empathy at Level 2. As we predicted in Hypothesis 2, the higher the level of customer empathy, the stronger was the impact of the employee’s empathy (see Figure 2 ) on the customer’s satisfaction. Specifically, the coefficient of the interaction term is positive and significant (Hypothesis 2: γ = .16, p < .01).

Figure 2.

Symbiosis effect: customer satisfaction on customer empathy and their interaction.

The direct impact of customer satisfaction on loyalty is strong and significantly positive (γ = .21, p < .01). Moreover, we find a significant interaction effect between customer empathy and customer satisfaction on customer loyalty. The significantly negative coefficient supports Hypothesis 3 (γ = −.21, p < .01). The interaction plot in Figure 3 shows that the effect size of customer satisfaction on customer loyalty decreases when customer empathy is high. In contrast, when customer empathy is low, the effect size of customer satisfaction on customer loyalty increases. These results provide support for our proposition that customer empathy mitigates the negative effect of customer dissatisfaction on customer loyalty.

Figure 3.

Forgiveness effect: customer loyalty on customer satisfaction and customer empathy.

Prior research reveals considerable differences regarding the conceptualization of empathy and a significant lack of empirical evidence for the conceptualizations preferred. Therefore, to improve our understanding of the multidimensional nature of empathy and the influence of each of these dimensions on the endogenous variables in our framework, we conducted a post hoc analysis. We tested the interaction effects of customer and employee empathy on customer satisfaction and customer loyalty separately. Table 4 presents the results of this analysis.

Table 4.

Additional Analysis Model Comparison and Effects

Relationships	Model 1 (Direct Effects Only)	Model 2 (Hypothesized Model)
Employee Perspective-Taking → Customer Satisfaction	—	.21^**
Employee Empathic Concern → Customer Satisfaction	—	ns
Employee Emotional Contagion → Customer Satisfaction	—	.16^**
Employee Perspective-Taking × Customer Perspective-Taking → Customer Satisfaction	—	.12^*
Employee Empathic Concern × Customer Empathic Concern → Customer Satisfaction	—	.15^**
Employee Emotional Contagion × Customer Emotional Contagion → Customer Satisfaction	—	.19^**
Customer Perspective-Taking × Customer Satisfaction → Customer Loyalty	—	ns
Customer Empathic Concern × Customer Satisfaction → Customer Loyalty	—	−.25^**
Customer Emotional Contagion × Customer Satisfaction → Customer Loyalty	—	−.20^**
Customer Satisfaction → Customer Loyalty	.22^**	.21^**
Customer Perspective-Taking → Customer Satisfaction	ns	ns
Customer Empathic Concern → Customer Satisfaction	.14^**	.15^**
Customer Emotional Contagion → Customer Satisfaction	.09^*	.10^*
Customer Perspective-Taking → Customer Loyalty	ns	ns
Customer Empathic Concern → Customer Loyalty	.22^*	.23^**
Customer Emotional Contagion → Customer Loyalty	.19^**	.19^**
Employee Perspective-Taking → Customer Loyalty	.17^**	.18^**
Employee Empathic Concern → Customer Loyalty	ns	ns
Employee Emotional Contagion → Customer Loyalty	.13^*	.14^**
Covariates
Customer’s Age → Customer Loyalty	ns	ns
Customer’s Gender → Customer Loyalty	ns	ns
Perceived Customer Orientation → Customer Loyalty	.12^*	13^*
Length of Customer Relationship → Customer Loyalty	.15^**	.15^**
Δ df		9
−2*Log likelihood	594.33	554.66
−2LL change		39.67^**
AIC	180,03	163.35
BIC	185.94	168.92

Note. ns = not significant.

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

Pertaining to the interaction effect between employee and customer empathy on customer satisfaction, we find that all three dimensions exert significant effects on customer satisfaction. The coefficient for the interaction of customer and employee perspective-taking on customer satisfaction is positive and significant at p < .05, indicating that the interaction between these dimensions enhances customer satisfaction. Likewise, the coefficients for both the interaction between customer and employee empathic concern and that between customer and employee emotional contagion on customer satisfaction are positive and significant. Interestingly, the interaction between employees' and customers' emotional contagion shows the strongest effect on customer satisfaction (γ = .19, p < .01).

With respect to the interaction effects between customer empathy and customer satisfaction on customer loyalty, our results reveal significant effects only for the affective dimensions of customer empathy. The interaction between customer empathic concern and customer satisfaction has a negative and statistically significant impact on customer loyalty (γ = −.25, p < .01). Additionally, the coefficient for the interaction between customer emotional contagion and customer satisfaction is negative and significant (γ = −.20, p < .01), supporting the assumption that customer empathy mitigates the negative effect of customer dissatisfaction on customer loyalty. The interaction between customer perspective-taking and customer satisfaction on customer loyalty, however, is insignificant. In the following discussion, we provide an interpretation of our results.

Discussion

Research Contributions

Although the marketing literature emphasizes the relevance of empathy in customer-employee interactions (Aggarwal et al. 2005; Giacobbe et al. 2006; Parasuraman, Zeithaml and Berry 1988), systematic investigations on the nature of empathy and its effects remain limited. This study addresses these shortcomings in several ways. First, this work contributes to the literature by conceptualizing customer and employee empathy and by elaborating a framework that explains the various roles of empathy in service encounters. More precisely, this article provides a conceptual model that incorporates two critical functions of empathy in social interactions, namely, fostering successful interactions (symbiosis) and mitigating effects of negative experiences in service interaction (forgiveness). As the empirical study demonstrates, customer empathy can amplify the positive relationship between employee empathy and customer satisfaction (representing symbiosis in a service encounter). Furthermore, the results provide empirical evidence for the role of customer empathy on the satisfaction-loyalty link. We find customer empathy to be one of the parameters explaining the strength of that relationship.

As this study reveals, customer empathy can attenuate negative effects of customer dissatisfaction on customer loyalty. These results further indicate that for empathic customers, satisfaction with the service encounter is less important to customer loyalty than it is for customers who are less empathic.

Second, this work applies a multidimensional conceptualization of empathy. From an extensive literature review and in line with more recent studies on the empathy phenomenon (e.g., Devoldre et al. 2010; Giacobbe et al. 2006; Mencl and May 2009), we understand empathy to be the ability to sense and share another’s thoughts, feelings, and experiences and to react to the observed experiences of another person. Therefore, we posit that empathy involves a cognitive dimension, namely, perspective-taking, and two emotional dimensions, namely, emotional contagion and empathic concern. Given the relative importance of emotions in service encounters (Gabbott, Tsarenko and Wai 2011; Schoefer and Diamantopoulos 2008), we conclude that an extended, multidimensional approach is more capable of reflecting the psychological complexity of service interactions.

As our results demonstrate, the interaction of the cognitive and affective dimensions of empathy determines customer satisfaction. Hence, the ability of interactants to sense each other’s state cognitively and affectively engenders high levels of customer satisfaction. In contrast, regarding the effect of the interaction between customer empathy and customer satisfaction on customer loyalty, our study reveals the dominant effect of the affective dimensions of empathy, namely, emotional contagion and empathic concern. Higher levels of customers' affective response correspond to a decreasing effect of customer satisfaction on customer loyalty. These findings suggest that customers' tendencies to forgive a provider for dissatisfying encounters relate to their ability to sense and share another person’s emotions rather than to embrace the other person’s perspective. Previous research has shown that emotional displays by employees can induce corresponding changes in customers' affective states (Mattila and Enz 2002). The current research suggests that customers who are more empathic than others are more sensitive to emotions displayed by frontline employees, which enhances customer forgiveness after dissatisfying service encounters. To conclude, empathy attenuates the negative effects of dissatisfying service interactions, especially in cases when customers are able to emotionally adapt to rather than cognitively apprehend a given situation.

Third, to the best of our knowledge, this study constitutes one of the few attempts to investigate empathy in service settings. Despite repeated assertions regarding the specific relevance of empathy in service encounters (Parasuraman, Zeithaml, and Berry 1988), empirical evidence is scarce. Previous studies have largely neglected empathy as an explanatory variable in service interactions. Therefore, this work contributes to a better understanding of the mechanisms that govern interactions between customers and employees, particularly in face-to-face service encounters.

Limitations and Future Research Avenues

Like any academic work, this study has several limitations that may stimulate future research. First, this study is anchored in consumer services, namely, in the market for travel services. For the sake of generalizability, future studies should consider investigating these effects in other service industries, such as financial services or consulting services, and in business-to-business markets.

Second, this investigation addresses the level of the individual encounter, investigating specific interactions between customers and frontline employees. An interesting study concerning the management of customer relationships would be to determine whether the effects of customer and employee empathy change over time. Through its focus on forgiveness, this research supports and extends findings from previous studies suggesting that customers temporarily remain loyal even after a dissatisfying encounter (e.g., Davidow and Dacin 1997; Oliva, Oliver and MacMillan 1992). Future research, however, should observe behavioral intentions after the forgiveness effect weakens. Such investigations might provide valuable information regarding, for instance, the time frame within which service recovery should be implemented to keep customers from switching to competitors.

Extensions including further variables could also be valuable. For instance, customer expertise concerning the service may influence how “symbiotic” customers and employees coproduce a service or to what extent forgiveness influences the satisfaction-loyalty link. Research on the impact of customer characteristics such as gender, age, and product knowledge on satisfaction and loyalty (e.g., Cooil et al. 2007; Söderlund 2002) indicates that customer expertise is a crucial factor in determining customer attitudes and behaviors. Also, the importance of the outcome and the financial risk involved in a service purchase may influence a customer’s tendency to respond to a dissatisfying service experience in a forgiving manner. More precisely, we expect that an increase in the importance of an outcome will decrease the customer’s forgiveness, thus impairing his or her willingness to stay with a provider.⁴

Managerial Implications

This study reveals the impact of customer and employee empathy in service encounters, leading to the question of how extensively providers are able to influence the interactants' level of empathy to create successful and valuable service encounters. First, the results of this investigation stress the need to hire service employees capable of sensing customer expectations and fostering symbiotic customer-employee interactions. Candidate profiles, search mechanisms, and recruiting methods should therefore address not only technical skills but also the ability to apprehend and react to customers' thoughts, feelings, and intentions during a service encounter (Schneider and Schechter 1991). More precisely, employees must be able to ascertain the customer’s perspective of a service encounter and the employee’s performance, to sense the customer’s emotions, and to share these emotions during a service interaction. Employees who are capable of and committed to providing symbiotic service interactions with customers are undoubtedly of great value to a service firm.

Second, referring to empathy as an individual ability suggests that empathy can be broadened and deepened by appropriate training measures. Indeed, some scholars emphasize the importance of offering opportunities for frontline employees to learn and develop their abilities to sense customer thoughts and feelings (Peterson and Limbu 2009; Schneider and Bowen 1995). The literature reflects a broad consensus on the benefit of role playing and videotaping (Bateson and Hui 1992) or mystery shopping (Finn and Kayandé 1999; Grove and Fisk 1992) as valuable instruments for training frontline employees. “Walking in the shoes of the customer” can create beneficial experiences for frontline employees, enabling them to better understand how customers perceive the service encounter and how customer interactions with the service provider affect the level of customer satisfaction.

Whereas firms have opportunities to influence their employees' capabilities to empathize with the customer, their hands may be tied with respect to the empathic abilities of their customers. This constraint shifts the focus to approaches of “interaction routing” (van Dolen et al. 2002), through which customers and employees are brought together on the basis of a proven fit of their personalities and their preferred manner of interaction. One way of implementing such a scheme is the use of pre-encounter profiles that allow a matching of customers and employees, enabling the customer to be directed to a frontline employee with whom he or she is most likely to experience mutual understanding and smooth interactions.

Researchers in language and communications have found that language features reveal a great deal about the feelings and assessments people attempt to convey during interactions (Davies and Harré 1990; Mason and Davis 2007). Prior investigators have concluded that analyzing language patterns enables interactants to identify each other’s feelings and thoughts and to tailor their reactions to the other’s personality. The Process Communication Model, which incorporates six personality types and their language styles, enables people to recognize their own personalities, and allows them to modify their communications and interact with others with fewer flaws and misunderstandings (Kahler 1982).

Service providers for call centers such as eLoyalty’s Integrated Contact Solutions (www.teletech.com) employ this approach to steer each caller to an employee who best matches the caller’s personality (Boyd 2010). From the very first contact and during subsequent interactions, the system records and analyzes a caller’s language pattern so as to create and enrich his or her personality profile. In doing so, the company not only leverages the level of each employee’s empathy but also ensures symbiotic interactions by improving communication quality between the interactants.⁵

This recommendation gives rise to the problem of how to successfully implement interaction routing on the basis of customer and employee characteristics. The results from our study offer guidance in two respects. First, we suggest that companies should employ methods to identify customer personalities. eLoyalty’s Integrated Contact Solutions, for instance, employs several linguists, behavioral scientists, and statisticians to elaborate communication patterns and algorithms to predict customer interaction behavior (Boyd 2010). However, since hiring complete research teams can be rather expensive, we suggest asking customers to reveal their communication preferences themselves. Before an interaction takes place, customers could be asked to participate in a survey based on Kahler’s model to help identify personality types and individual abilities. Alternatively, customers' assessments of the interaction and communication quality in service encounters they have just experienced can be very helpful in determining suitable matches between customers and frontline employees.

Second, organizations should foster their employees' abilities to recognize and to respond to different customer characteristics. An effective approach may be to extend employees' knowledge of the impact of personality traits and abilities on behavior and provide alternative routes for responding effectively to these characteristics, especially through knowledge sharing between experienced and inexperienced employees regarding relevant customer characteristics, strategies of recognition, and responses to cope with those characteristics (Gwinner et al. 2005). Similarly important is the coaching of employees in appropriate response skills based on an accurate interpretation of the verbal and nonverbal signals from the customer (Comer and Drollinger 1999).

Training is particularly important regarding the role of emotions in personal service encounters. Customers' adaptation to emotions displayed by employees depends on their individual level of empathy. Thus, employees need strategies to help them manage their own emotions and to respond appropriately to customers, not only verbally but also nonverbally by employing appropriate gestures or facial expressions (Groth, Hennig-Thurau, and Walsh 2008). This requirement holds especially true for dissatisfying encounters, in which employees are challenged to respond to customers' expressions of anger or aggression (Menon and Dubé 2004). Depending on customers' empathic abilities, balancing rational strategies with an increased awareness and purposeful application of affective responses to a customer’s dissatisfying experience may be an important approach for enhancing effective service recovery.

Footnotes

Appendix A

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.

Notes

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