Next Article in Journal
CEO Greed, Corporate Governance, and CSR Performance: Asian Evidence
Previous Article in Journal
Giants with Feet of Clay? An Inquiry into User Payment Patterns for Subscription Video-on-Demand Services
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Administrative Sciences
Volume 13
Issue 5
10.3390/admsci13050123
admsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

An Experimental Study of How Missing Employee Empathy in Failed Service Interactions Affects Empathetic Customers’ EWoM-Giving Behaviour

by
Neele Inken Abend
1,*,
María D. De-Juan-Vigaray
2 and
Mandy Nuszbaum
3
1
Facultad de Economía y Empresa, UCAM Universidad Católica de Murcia, 30107 Murcia, Spain
2
Department of Marketing, University of Alicante, 03080 Alicante, Spain
3
Department of Business Psychology, FOM University of Applied Sciences, 45127 Essen, Germany
*
Author to whom correspondence should be addressed.
Adm. Sci. 2023, 13(5), 123; https://doi.org/10.3390/admsci13050123
Submission received: 8 April 2023 / Revised: 29 April 2023 / Accepted: 1 May 2023 / Published: 4 May 2023
Browse Figures
Versions Notes

Abstract

:
Empathy as an influencing factor of consumer behaviour has mostly been analysed from an empathetic employee’s perspective. Empirical investigations into customer empathy in the context of failed service interactions are still scarce. This study investigates customer–employee reciprocity related to a failed service interaction and its meaning as a predictor of electronic-worth-of-mouth (eWoM)-giving behaviour. The eWoM phenomenon in the context of online purchases is well researched, but the (a) impact of failed service interactions and (b) empathetic customer service agents still needs to be explored. For this purpose, two situational experiments of customer–employee interactions (n = 260) were conducted. Both situations depict disgruntled customers who are looking for help and call the customer support centre after an online purchase. They experience negative customer–employee interaction. The experiments test (a) the impact of employee and customer empathy on eWoM-giving behaviour after failed service interactions and (b) the mediating role of negative emotions. The results show that in service situations, negative emotions fully mediate the relationship between customer empathy and eWoM-behaviour. In addition, empathetic customers seem to be more sensitive to a poorly empathetic employee in comparison to non-empathetic customers. The research enriches the service understanding of empathy in eWoM research and provides practical implications for the management of complaint handling, such as how to consider customer empathy as a complainer’s characteristic to improve the customer service experience, effectiveness, and efficiency.

1. Introduction

The field of purchase behaviour, investigated by numerous scientists, aspires to understand, customers’ reactions, desires, requirements, and decision-making (Zulkarnain et al. 2018). Due to an increase in online purchases, the importance of digital reputation has also increased (Gandini 2020). An attractive digital reputation, which includes digital evaluations of the company, product, or even the company as a place of work, is a key asset for success (Gandini 2020). The digital evaluation of a purchase is a marketing instrument, which can influence the brand image or increase customers’ online purchase intentions (Siddiqui et al. 2021). An attractive digital reputation includes the appropriate handling of online customer complaints, because a positive digital reputation creates trust, which reinforces digital purchases (Gandini 2020).
Companies cannot fully eliminate service failures, at least as long as human interactions are part of the process, but they can minimize and learn how to embrace failures effectively. Suggested approaches are solving failures fairly and reasonably as well as considering an opportunity to implement the customers’ feedback to improve services or products (Wuenschmann 2007). Giving customers the chance to express their dissatisfaction and reacting appropriately to service requests can increase customer satisfaction significantly (Foscht 2011; Wuenschmann 2007). It is unambiguous to reveal the customer’s motives behind complaint behaviour or recovery behaviour to understand the appropriate complaint-handling strategy. In the literature, numerous characteristics of the complaint situation, social demographic characteristics, and personality characteristics of the complainant are already identified as predictors of complaint behaviour (Bodey and Grace 2006; Phau and Baird 2008; Wuenschmann 2007). In the past, empathy was mainly analysed on the company’s side (Clark et al. 2013; Markovic et al. 2015; Simon 2013). So far, scientists have scarcely analysed the perspective of an empathetic customer. Thus, the question arises, what is the effect of customer empathy on post-purchase behaviour, considering the negative eWoM-giving intention after failed service interactions? The present study intends to reveal scientific knowledge and provide insights into complaint behaviour and the reasonable handling of empathetic customers.

2. Theoretical Background and Hypothesis Development

2.1. Service Failure and eWoM-Giving Behaviour

Service failures are “service-related mishaps or problems (real and/or perceived) that occur during a consumer’s experience with a firm” (Maxham 2001, p. 11). This phenomenon of service failure has been a part of the marketing literature for many decades. For instance, Bitner et al. 1990 structured service failures by dividing service failure into three groups: (1) service delivery failures, (2) failures related to customer needs and requests, and (3) failures related to unprompted and unsolicited employee actions (Bitner et al. 1990). Online purchases provide more possibilities for service failure compared to offline purchases, such as: “credit card security, privacy, on-time delivery, and ease of navigation have surfaced as critical elements of e-service quality, and the online environment lacks most of the interactional human elements so vital to the traditional service experience” (Holloway and Beatty 2003, p. 92). Due to these characteristics, the online-retail environment is more likely to leave the customer dissatisfied (Holloway and Beatty 2003), and, due to the fast pace of the online environment, the customer can change the supplier in a few clicks or complain and turn to a competitor (Shankar et al. 2003). Some dissatisfied online customers write negative eWoM reviews about their consumer experience. Major motives for eWoM are a concern for others, self-enhancement, dissonance reduction, advice seeking, and many more (Hennig-Thurau et al. 2004). One main theoretical foundation of eWoM is the theory of social sharing. Individuals encountering stress attempt to reduce their anxiety by verbally interacting with others in the same situation to evaluate their emotional state (Liu et al. 2021). Previous research shows that people tend to talk about the happening immediately when it was an emotional experience—there is evidence that this happens in almost every case, for example (Duprez et al. 2015; Rimé 2009; Rimé et al. 2011).
Emotions strongly impact behaviour: “Emotions also give specific directions to behaviour. What kind of action is desired depends on the goals related to the emotions” (Wetzer et al. 2007, p. 664). Therefore, it is important to examine the prevailing emotion of the complaint situation. Emotions have a complaint-driving or activating effect, which can occur even unconsciously for the customer. Since empathetic humans are more likely to perceive these emotions than those who are less empathetic, it is important to take emotions into account to understand post-purchase behaviour (e.g., Hossain and Rahman 2022; Mano and Oliver 1993; Wetzer et al. 2007). Hence, the study hypothesizes an impact of the perceived emotional intensity on the eWoM-giving intention:
Hypothesis 1. 
The customer’s perceived emotional intensity of failed service interaction positively influences the eWoM-giving intention.
The construct of empathy describes the ability to experience, or at least anticipate, what others feel (Karlstetter 2017; Zaki 2014). Being empathetic delineates the ability to re-feel others’ emotions. Empathy is an extensive multidimensional approach; in general, a cognitive and emotional dimension of empathy is assumed (Ngo et al. 2020; Wieseke et al. 2012). There is no consistent understanding of whether empathy is a human ability or a personality trait (Comer and Drollinger 1999; Duan and Hill 1996; Weißhaar and Huber 2016; Wieseke et al. 2012). Empathy is a social construct because of the involved emotions of the counterpart, which is called shared humanity (Singer 2020). Therefore, empathy is a substantive characteristic of the relationship competence of individuals (Davis and Oathout 1987; Ngo et al. 2020) and a “critical component of successful social interactions […]” (Hossain and Rahman 2022, p. 4). Furthermore, empathy correlates with altruism, helping behaviour, and concern about others’ comfort (Batson and Shaw 1991; Ngo et al. 2020). Research results also imply that customer empathy influences customer forgiveness (Wieseke et al. 2012). This underlines the importance of empathy in the complaint context. Hence, an influence of customer empathy on negative eWoM-giving intention is proposed:
Hypothesis 2. 
Customer empathy influences the eWoM-giving intention after failed service interactions.

2.2. Customer Empathy and Employee Empathy in Service Interaction

Most scientists have focused on the empathetic employee perspective (Clark et al. 2013; Markovic et al. 2015; Bahadur et al. 2018). Prior investigations elucidate that empathy enhances the communication and interaction between customers and employees (Ngo et al. 2020; Comer and Drollinger 1999). Employee empathy, such as individual attention to the customer or employee caring for the customer, is a prerequisite for successful and consumer-satisfying service encounters (for example, Wieseke et al. 2012; Zeithami et al. 1996). In a complaint situation, a missing empathetic approach of the service agent can diminish the positive effects of complaint handling. For example, Ngo et al. 2020 analysed customer- and employee-empathy reciprocity: “[…] customer-oriented behaviour mediates the relationship between employee empathy and customer satisfaction” (Ngo et al. 2020, p. 5). Therefore, the following hypothesis is proposed:
Hypothesis 3. 
Missing employee empathy negatively influences the customers’ eWoM-giving intention after failed service interactions.
Ngo et al. (2020) discovered an invoking effect of employee empathy and customer empathy for customer-oriented behaviour, “such that the positive relationship between employee empathy and customer-oriented behaviour is stronger when the customer empathy is also higher” (Ngo et al. 2020, p. 5). The reciprocity influences customer satisfaction in a way that supportive customer behaviour enhances the likelihood of positive employee behaviour, which in turn yields customer satisfaction (Ngo et al. 2020). Wieseke et al. (2012) elucidate that increasing customer satisfaction and employee empathy are strengthened by customer empathy, explained by the “symbiotic interactions” between customer and employee.
Due to the symbiotic interaction, a more satisfying service experience can be created, and the customer relationship can be positively influenced in the long term—“Customer empathy mitigates the negative effects of customer dissatisfaction on customer loyalty” (Wieseke et al. 2012, p. 320). Customer empathy can amplify the positive relationship between employee empathy and customer satisfaction” (Wieseke et al. 2012, p. 324). Empathetic customers are more sensitive to employees’ displayed emotions than non-empathetic or low-empathetic customers (Wieseke et al. 2012). This higher sensitivity enhances customer forgiveness after dissatisfying service encounters (Wieseke et al. 2012). Therefore, the following hypothesis is postulated:
Hypothesis 4. 
Customer empathy influences the perception of missing employee empathy during a failed service interaction.
Especially empathetic individuals tend to avoid psychological pain (avoid pain theory) (Zaki 2014). Providing an eWoM review means sharing an experience (e.g., experience sharing) (Zaki 2013), which is in turn a way to avoid pain because sharing an experience helps people to feel better and helps them to handle the negative experience (emotional regulation) (Duprez et al. 2015). Central motives explaining why customers engage in eWoM include the enjoyment of helping (altruistic component) or a sense of belonging (Cheung and Lee 2012) as well as protecting others from the same experiences, revenge, harm to the company or giving advice on the product to others, as investigated by Sundaram et al. (1998), who conducted one of the most prominent as well as comprehensive eWoM motive investigations. The altruistic component is a pervasive motive in eWoM. Altruism is a personality trait with a strong correlation to empathy (Aragon 2016; Zaki 2014). That emphasizes the connection of eWoM and empathy and suggests that empathy influences the sharing experience, here in the complaint context.
Negative emotions are an antecedent of overall customer satisfaction (Varela-Neira et al. 2008). The likelihood of eWoM-giving intention increases with an increase in dissatisfaction (Wuenschmann 2007). Jha and Shah (2019) describe eWoM as an emotional regulation mechanism based on one’s own experience. In general, negativity is more impactful than positivity (negative bias) (Kanouse 1984). Research has elucidated that empathy causes a higher emotional response—individuals who score higher on cognitive empathetic understanding obtain a higher level of emotions (Kaplan and Iacoboni 2006). Therefore, the following hypothesis is proposed:
Hypothesis 5. 
Customer empathy positively influences the perception of emotional intensity during failed service interactions.
There is empirical evidence that positive emotions facilitate repeat visiting intentions and recommendations in comparison to negative emotions, which exert complaint behaviours (Umasuthan et al. 2017; Westbrook and Oliver 1991). The customer’s emotional and cognitive experience of service or product purchases determines the decision-making process and the behavioural reaction, such as WoM-giving intention or a repurchase (Decrop 1999; Umasuthan et al. 2017). As explained above, empathy leads to a stronger perception of emotions. Hence, the emotional intensity of a complaint situation is proposed to be a mediator of customer empathy on eWoM-giving intention—thus, a mediation effect of emotional intensity on eWoM-giving intention is proposed:
Hypothesis 6. 
The customer’s perception of emotional intensity mediates the relationship between customer empathy and the eWoM-giving intention.
Figure 1 shows the conceptual model proposed in this research. A mediating effect of emotional intensity on the relationship between customer empathy and the eWoM-giving intention is assumed. In addition, customer empathy is expected to influence perceived employee empathy, which in turn influences eWoM-giving intention. Customer empathy is tested as an interpersonal skill of the customer; all of the other scales are tested in two experimental complaint situations.

3. Materials and Methods

3.1. Sample and Procedures

The study reveals whether there is an impact of customer empathy as a complaint characteristic regarding the willingness to write negative eWoM reviews in consideration of the emotional intensity of the customer–employee interaction. For this purpose, an experimental scenario-based online survey was conducted. The questionnaire was developed in accordance with the relevant literature and contained one sample with two randomized scenarios. In the scenarios, a service interaction between a customer and a customer service agent is described, where a service failure happens. The customer has a problem with the correct handling of a purchased printer and is looking for help on the company’s support hotline. The customer calls because he or she has a question regarding handling a printer. Each scenario starts with the same introduction (ceteris paribus rule). In both scenarios, the customer agent did not meet the customers’ expectations—the customer was rejected without a kind service experience and a solution. In the first scenario, the customer experiences a rude and angry employee in a customer service call. The phone employee is unwilling to solve the customer’s problem. In the second scenario, the employee on the phone is not rude like in the first situation, but the employee is not helpful or customer-orientated at all. The second scenario is described as negative and neutral compared to the first scenario, which is negative and angry. The complete situation description can be found in Appendix A, Table A1. In Table 1 below, an overview of the presumed emotional intensities of the scenario is given.
A pretest (n = 139) confirmed the variation in emotional intensity in the scenarios: the angry formulated scenario scored the highest negative emotional intensity. The conducted pretest also confirmed adequate comprehension (simplicity and translation) and acted as an instrument against method bias. The pretest was executed before the main data collection. The scenario-based approach enabled anyone who was willing to put himself or herself in a complaint situation to participate in the survey. The survey took place at a German university of applied sciences. Subjects were recruited using a student recruiting platform in compensation for course credit. Procedural remedies to prevent method bias were applied to reduce the risks of self-reported studies (Podsakoff et al. 2012). The survey included an introduction part, where the aims were explained and any difficulties were resolved in advance; the survey was assumed to be anonymous, and confidentiality and probands were engaged to facilitate answering spontaneously and without fear of submitting wrong answers. Participation was voluntary. These remedies motivated the participants to answer the questionnaire and prevent method biases (Podsakoff et al. 2012). The adequate sample size of this research objective was evaluated with the software G*Power (Faul et al. 2009). G*Power calculated a sample size of n = 107 to achieve a statistical power of 0.95. The collected data sample exceeded this recommended sample size; therefore, the application for the research objective was sufficient (Bayode and Duarte 2022).

3.2. Measurement Instrument

All constructs were selected from validated scales of the relevant literature. Some scales were adapted to the study’s specific context, with minor modifications, which had been proved with a broad qualitative pretest. All used scales and constructs with the corresponding item text and Cronbach-alpha values are depicted in Table 2. The answer scale for all constructs was a seven-point Likert-type scale. Some answer scales were originally developed based on different scales but were adopted for this specific study to simplify the probands’ understanding and convenience.
The core measurement of empathy was based on the Toronto Empathy Questionnaire (TEQ) (Spreng et al. 2009), which is valid, reliable and widespread in empathy research (Lima and Osório 2021; Spreng et al. 2009). The original TEQ version (Spreng et al. 2009) consists of 16 items. In this study, the validated and shortened version of TEQ (Totan et al. 2012) was used, because it only consists of 13 items and, like the long version, has appropriate validity and reliability (Totan et al. 2012).
Employee empathy was measured by an established scale (Parasuraman et al. 1994), which was validated and adapted from Markovic et al. (2015), who shortened the scale to four items. In this measurement, employee empathy was assessed by customers’ perceptions of employee empathy. The scales had high reliability (0.91).
The measurement of eWoM-giving intention was adapted from a well-established scale in marketing research (e.g., Leung et al. 2015). The original version of the eWoM scale was based on Chiang and Jang (2007). However, Leung et al. (2015) derived a shorter version for eWoM measurement, consisting of three items that measure the customer’s intention of eWoM.
The measurement of emotional intensity was based on the method of Wetzer et al. (2007), which measures the emotions felt after a negative consumer experience. López-López et al. (2014) firstly adapted the scale to measure positive and negative emotions separately as two opposite valences of emotions, as it is broad knowledge in the research of emotions (Umasuthan et al. 2017). Using this two-dimensional framework, positive emotions versus negative emotions is an approved evaluation of emotional service experiences to obtain insights into customers’ overall experiences and their influence on behavioural intention (López-López et al. 2014; Umasuthan et al. 2017). In addition to the prior described scales, demographic and socio-economic variables were parts of the survey, such as age, gender, country, and income. Income is a typical status variable, like education and occupation (Hoffmeyer-Zlotnik 2016). In the absence of a translated German version, the following scales were translated from English into German with the back-translation approach originally based on Brislin (1970): eWoM, emotional intensity, employee empathy, and customer empathy. The former back-translation method of Brislin (1970) was extended to the recommendations of Jones et al. (2001), who adapted Brislin’s methods to a more efficient and valid approach. Table 2 presents the described constructs with the item text and the authors’ Cronbach reliability; furthermore, reverse coded item texts are marked with (-).

4. Results

4.1. Descriptive Statistics

Overall data from 40 participants were removed because they disagreed with the scientific use of their data, or they were distracted while answering the questionnaire (“Were you distracted while answering the questions?”). Further cases were erased because there were too many missing values in their data sets. The demographic profile of the probands is depicted in the table below. After data reduction, a dataset of n = 260 remained for analysis. Table 3 presents the demographic profile of the respondents.
Table 4 shows an overview of Cronbach-alpha values, the number of scale items, and mean values. Cronbach-alpha values were analysed for reliability analysis and yield, finding that the internal consistency of the study was satisfactory. The scales were found to be reliable in both scenarios; Cronbach-alpha values were in the range between 0.62 and 0.93, which indicated predominantly sufficient reliability. The emotional intensity scale (negative and positive continuum variable) was on a non-acceptable level (α = 0.62–0.67); therefore, this scale was excluded from following analysis. The mean value of the emotional intensity in scenario 1 (negative angry) was higher compared to that in scenario 2 (negative neutral). In addition, scenario 1 was characterized by decreasing employee empathy (negative angry) and increasing eWoM-giving intention compared to scenario 2 (negative neutral). The customer’s empathy was on an equal level because customer empathy was measured as a general interpersonal skill, with no dependence on the scenario.
Before testing the hypotheses, Pearson’s correlation coefficients were monitored (Table 5). The results indicate that there was a correlation between customers’ perceptions of negative emotions and eWoM-giving-intention (S1: r = 0.39, p < 0.001 and S2: r = 0.35, p < 0.001), indicating that higher levels of negative emotions are related to higher eWoM-giving intention. The results also indicate that there was a positive correlation between customer empathy and customers’ perceptions of negative emotions (S1: r = 0.24, p < 0.01, S2: r = 0.31, p < 0.001), indicating that higher levels of customer empathy are related to stronger levels of negative emotional intensity. Customer empathy and eWoM-giving intention were also significantly related in scenario 2 (negative neutral) (r = 0.25, p < 0.01), and a negative correlation was observed between customer empathy and employee empathy in scenario 1 (r = −0.39, p < 0.001). As the correlation was negative, higher levels of customer empathy corresponded with lower levels of perceived employee empathy.

4.2. Hypothesis Testing

Regression analysis was used to test if negative emotional intensity significantly predicted the eWoM-giving-intention (Table 6). The results of the regression analysis of scenario 1 (negative angry) indicated that this predictor explained 15.5% of the variance (R2 = 0.16, F [1,127] = 23.33, p < 0.01). It was found that the emotional intensity significantly predicted the negative eWoM-giving-intention (β = 0.63, p < 0.001), as proposed by Hypothesis 2. The R2 for the overall model was 0.16 (adjusted R2 = 0.15), indicative of a moderate goodness-of-fit according to Cohen (1988). The results of the regression analysis of scenario 2 (negative neutral) indicated that this predictor explained 12.6% of the variance (R2 = 0.13, F [1,129] = 18.53, p < 0.01). It was found that the emotional intensity significantly predicted the eWoM-giving-intention (β = 0.59, p < 0.001). The R2 for the overall model was 0.13 (adjusted R2 = 0.12), indicative of a moderate goodness-of-fit according to Cohen (1988). These results support the first alternative hypothesis that the customer’s perceived emotional intensity positively influences the eWoM-giving intention.
Simple regression was used to predict the eWoM-giving-intention based on customer empathy, as proposed by Hypothesis 1 (Table 7). In scenario 1 (negative angry), customer empathy did not explain a significant amount of the variance in negative eWoM-giving intention (F (1,127) = 0.09 p = 0.76, R2 = 0.00, R2adjusted = −0.01). In scenario 2 (negative neutral): customer empathy explained a significant amount of the variance in eWoM-giving intention (F (1,129) = 8.56 p = 0.004, R2 = 0.06, R2adjusted = 0.06). R2 was indicative of a low goodness-of-fit according to Cohen (1988). These results support the second hypothesis, namely, that customer empathy influenced the eWoM-giving intention in scenario 2 (negative neutral).
To test the third hypothesis, namely, that missing employee empathy influences the customers’ eWoM-giving intention, another regression analysis was performed (Table 8). For scenario 1 (negative angry), the predictor explained 5.3% of the variance (R2 = 0.05, F [1,127] = 7.12, p = 0.009). It was found that employee empathy significantly predicted the customers’ eWoM-giving-intention (β = −0.57, p = 0.009). The R2 for the overall model was 0.05 (adjusted R2 = 0.05), indicative of a low goodness-of-fit according to Cohen (1988). For scenario 2 (negative neutral), the predictor explained 3.6% of the variance (R2 = 0.04, F [1,129] = 4.87, p = 0.029). It was found that employee empathy significantly predicted the customers’ negative eWoM-giving-intention (β = −0.25, p = 0.029). The R2 for the overall model was 0.04 (adjusted R2 = 0.03), indicative of a low goodness-of-fit according to Cohen (1988). These results support the third alternative hypothesis, i.e., that missing employee empathy negatively influences the customers’ eWoM-giving intention in a complaint situation.
Simple regression was used to predict the perception of missing employee empathy due to customer empathy, as proposed by Hypothesis 4 (Table 9). In scenario 1 (negative angry), customer empathy explained a significant amount of the variance in employee empathy (F (1,127) = 23.03 p < 0.001, R2 = 0.15, R2 adjusted = 0.15). The regression coefficient (β = −0.41, p < 0.001) indicated that an increase in customer empathy corresponded, on average, with a decrease in perception of employee empathy, by −0.44 points. The R2 for the overall model was 0.15 (adjusted R2 = 0.15), indicative of a moderate goodness-of-fit according to Cohen (1988). This result was only found in scenario 1 (negative angry). In scenario 2 (negative neutral), a different effect was found: customer empathy did not explain a significant amount of the variance in employee empathy (F (1,129) = 1.41 p = 0.238, R2 = 0.01, R2 adjusted = 0.00). These results only support the fourth hypothesis, namely, that customer empathy influences the customer’s perception of missing employee empathy in scenario 1 (negative angry).
The regression analysis of scenario 1 (negative angry) indicated that the predictor customer empathy explained 5.6% of the variance (R2 = 0.06, F (1,127) = 7.49, p = 0.007) of the perception of emotional intensities (Table 10). It was found that customer empathy significantly predicted the perception of emotional intensity (β = 0.38, p = 0.007). The R2 for the overall model was 0.06 (adjusted R2 = 0.05), indicative of a low goodness-of-fit according to Cohen (1988). The regression analysis results for scenario 2 (negative neutral) indicated that this predictor explained 9.5% of the variance (R2 = 0.10, F (1,129) = 13.60, p < 0.01). It was found that customer empathy significantly predicted the perception of emotional intensity (β = 0.53, p < 0.001). The R2 for the overall model was 0.10 (adjusted R2 = 0.09), indicative of a low to moderate goodness-of-fit according to Cohen (1988). These results support the fifth alternative hypothesis: Customer empathy positively influences the customer’s perception of the emotional intensity during failed service interactions.
Mediation analyses were performed using the SPSS PROCESS Macro of Hayes model 4 (Hayes 2022). It uses ordinary least-squares regression and yields non-standard path coefficients for total, direct, and indirect effects. The mediation analyses bootstrapped with 5000 samples and heteroscedasticity-consistent inference standard errors were conducted to yield the confidence intervals and inferential statistics. An effect was adopted as significant when the confidence interval (95.0% CI) did not include zero. Before starting the mediation analysis, a multicollinearity test was performed, and a variance inflation factor < 1.2 was obtained, indicating no multicollinearity in the data (Daoud 2018).
A first mediation analysis of scenario 1 (negative angry) was performed to analyse whether customer empathy predicted eWoM-giving intention and whether the direct path would be mediated by emotional intensity. No total effect of customer empathy on eWoM-giving intention was observed (B = 0.07; 95% CI = (−0.58, 0.72); p = 0.84), nor was a direct effect of customer empathy on eWoM-giving intention observed (B = −0.18; CI = (−0.69, 0.33); p = 0.483). After entering the mediator emotional intensity into the model, customer empathy predicted this mediator significantly (B = 0.38, p = 0.047), which in turn predicted eWoM-giving intention significantly (B = 0.63, p < 0.001). Therefore, it was found that the relationship between customer empathy and eWoM-giving intention is fully mediated by negative emotional intensity (indirect effect = 0.25, 95% CI [0.00, 0.56]). Figure 2 depicts the research model of scenario 1 (negative angry) supported by the results.
Outdated literature (Baron and Kenny 1986), described a total effect as an obligatory requirement for mediation analysis, but in current research, it is no longer a requirement, and research even reports that a total effect might lead to wrong conclusions (Rucker et al. 2011; Zhao et al. 2010). The current literature argues that this is the most important criterion for conducting mediation analysis, regardless of other requirements, because the pure effect of mediation is described by the indirect effect (Rucker et al. 2011; Zhao et al. 2010).
A second mediation analysis of scenario 2 (negative neutral) was performed to analyse whether customer empathy predicts eWoM-giving intention and whether the direct path would be mediated by emotional intensity. A total effect of customer empathy on eWoM-giving intention was observed (B = 0.71; 95% CI = (0.31, 1.11); p = 0.001), but no direct effect was observed from customer empathy on eWoM-giving intention (B = 0.44; 95% CI = (−0.02, 0.90); p = 0.06). After entering the mediator emotional intensity into the model, customer empathy predicted this mediator significantly (B = 0.53, p = 0.005), which in turn predicted eWoM-giving intention significantly (B = 0.59, p = 0.000). It was found that the relationship between customer empathy and eWoM-giving intention is fully mediated by the negative emotional intensity (indirect effect = 0.27, 95% CI [0.06, 0.57]). Figure 3 depicts the research model of scenario 2 (negative neutral) supported by the result.

5. Discussion and Conclusions

5.1. Main Findings

Hereinafter, a recapitulation of scenarios is given to contextualise the finding in the scenarios. Both scenarios contained the same setting—the customer has a problem with the correct handling of a purchased printer and is looking for help on the company support hotline. In the first scenario, the customer experiences a rude and angry employee in a customer service call. The phone employee is unwilling to solve the customer’s problem. In the second scenario, the employee on the phone is not rude like in the first situation, but the employee is not helpful or customer-orientated at all. The first scenario is described as negative and angry compared to the second scenario, which is negative and neutral. Table 11 provides an overview of the hypotheses testing and depicts the results. Not every hypothesis is supported in both scenarios; therefore, the supported scenarios are marked with “x”.
In scenario 1, the study revealed no influence of customer empathy on eWoM (Hypothesis 2). Only scenario 2 (negative neutral) demonstrated that customer empathy is a predictor of the customer’s eWoM-giving intention. Hence, the different sets of scenarios influenced the effect of customer empathy on eWoM-giving behaviour. This influence can be explained by Hypothesis 1, which indicates the impact of emotional intensity on eWoM-giving intention (supported in both scenarios). The more negative the emotion, the more likely the customer is to write an eWoM review. These two hypotheses in combination advocate performing the mediation analysis (Hypothesis 6): The mediation analysis reveals a complete mediation in both scenarios; therefore, the effect of customer empathy on eWoM is completely mediated by negative emotional intensity.
Another key result of the study is the influence of customer empathy on the perception of emotional intensity, which means a higher tendency toward empathy evinces a more sensitive perception of emotional intensity (Hypothesis 5). Both scenarios revealed that customer empathy positively influences the perceived emotional intensity of the complaint situation. Subsequently, empathetic customers perceive the negative emotional intensity more strongly. This effect, again, influences the eWoM-giving intention positively. Therefore, the likelihood of giving an eWoM review depends on the current emotions of the complaint situation. The more negative the emotions, the higher the likelihood of writing an eWoM review. Similar effects were found in both scenarios, even though the negative emotional intensity of scenario 2 (negative neutral) was weaker compared to that of the first scenario (mean value 5.24 versus 4.59). These findings confirm the existing research of Wetzer et al. (2007), where specific emotions as motivation for negative eWoM were investigated. Wetzer et al. (2007) identified that different emotions yield different motives for eWoM, but in general, they provide evidence that negative emotions predict negative eWoM behaviour.
In the first scenario, customer empathy influenced the customers’ perception of employee empathy (Hypothesis 4). However, in the context of scenario 1, there was a weak empathetic employee approach. In consequence, the negative relationship can be explained as follows: an empathetic customer does notice missing employee empathy in comparison to a non-empathetic customer. The recognized reciprocity of empathy between employee and customer, which was found in scenario 1 (negative angry), concurs with the results of Ngo et al. (2020). In addition, the perceived employee empathy in scenario 1 (negative angry) was lower compared to that in scenario 2 (negative neutral); therefore, the employee seemed less empathetic in scenario 1. In scenario 2 (negative neutral), this effect was not found—subsequently, there was no influence of customer empathy on the perception of employee empathy in scenario 2 (negative neutral). This missing effect could be caused by the lower emotional intensity of the complaint situation in scenario 2; therefore, the customer was not as emotional as in the first scenario. However, even if customer empathy did not influence the perception of employee empathy in scenario 2 (negative neutral), employee empathy, in turn, influenced the eWoM-giving intention (Hypothesis 3). An impact of employee empathy on customers’ eWoM-giving intention was found in both scenarios. These results of employee empathy as an influencing factor of consumer behaviour are related to the research findings of Umasuthan et al. (2017) and Simon (2013). Simon (2013) analysed how empathy can increase transactional satisfaction, trust, commitment, and the customer’s repurchase intention. The influence of employee empathy on the customer’s eWoM-giving behaviour, which is supported by underlying research, derives from these results and establishes a fundament aspect of complaint research.

5.2. Theoretical Contributions and Implications for Management

This article focused on customer behaviour in complaint situations after a failed employee–customer service interaction. Previous research mainly focused on the eWoM after a product purchase. The approach in this article broadens the scope of research by adding a new perspective to eWoM research. Furthermore, previous complaint research often focused on employee empathy, while the perspective of customer empathy was underrepresented. Few research studies exist on this reciprocity-based approach to both perspectives (Ngo et al. 2020). The mediating effect of emotions in the relationship between customer empathy and eWoM is a novelty, especially in the context of failed service interactions. The focus on a missing empathetic employee approach reveals a new perspective. Previous research mainly focused on empathetic approaches and not on missing empathetic approaches. This new combined approach of missing employee empathy enriches complaint research and provides novel perspectives on failed service interactions. The theoretical contribution of this work is the validation of customer empathy as a part of eWoM-giving behaviour after negative service interactions. Customer empathy is revealed as an additional predictor of eWoM in the context of failed service interactions. The study extends the current literature by revealing the indirect effect of customer empathy on the eWoM-giving intention, mediated by negative emotional intensity. Hence, among consumer characteristics that reinforce eWoM-giving behaviour, customer empathy seems to be one more influential factor for eWoM. The research contribution consists of a more detailed and deeper understanding of consumer behaviour and elicits an additional understanding of which customer service skills influence the eWoM-giving intention.
In addition, the results of this study can be used for future analysis of post-purchase behaviour considering the effect of self-assessments versus external assessments of empathy and emotional intensity to build a benchmark of the described effect.
Emotional intensity as an influence on complaint behaviour is also a key element in the context of empathy and service interactions supported by this study. Numerous studies reveal the importance of emotions in complaint research (Hossain and Rahman 2022; Mano and Oliver 1993; Wetzer et al. 2007). This study extends the emotional complaint research by identifying negative emotional intensity as an eWoM predictor after service interactions.
Processual, administrative, and customer satisfaction-oriented implications for management can be derived from the current research: The study evidenced how important the emotional intensity of the service failure situation is for the eWoM-giving intention. This shows companies clear possibilities for exerting influence, which means that the situation in which the failure occurs should be as unemotional as possible, or should not evoke any additional negative emotions for the customer. Therefore, service employees need to be trained in the best possible way to remain professionally calm and handle customers’ emotions, even in critical situations (Shooshtari et al. 2012). Companies that gain a deeper understanding of pre- and post-purchase behaviour will be empowered to tailor their customer service to a customer-centric, proactive approach to differentiate their products and services from those of their competitors. It is important to understand how customers’ emotions and customer characteristics influence their negative eWoM behaviour. Due to these insights, the customer-service employee can be trained to act or actually be more empathetic in a specific service failure situation. It is expedient to customize complaint management—each customer has a different expectation of an appropriate complaint response. A standard response procedure is not evaluated as customer-oriented and does not provide the desired customer satisfaction. A customized recovery process helps companies to decide how high investments in complaint management should be, for example, how high financial compensation or financial goodwill should be. A customer who is more likely to provide a negative eWoM review should receive more extensive financial goodwill in the first place.
Through customer analysis, companies can route customers’ inquiries to the employee with the most appropriate skill set according to the specific customer needs. That means an emotional customer will only be routed to an employee with high empathy scores. This leads to an increase in complaint satisfaction in an efficient way. Employees with high empathy scores are not wasting internal resources on customer interaction, which should be unemotional standard inquiries that can be solved without advanced emotional skills. It is suggested to route less empathetic customers to artificial intelligence technology solutions in customer service (such as chatbots) instead of highly empathetic customers, who should be prioritised for routing to an empathetic and personal employee. These empathetic skill sets of employees are inevitable for human resources departments, especially recruitment teams, since they need to look into the characteristics and skill sets of potential applicants to match these with the job requirements. When empathetic employees leave companies, they need to be replaced by successors with similar characteristics, in accordance with the current business requirements. In addition, if employees are deployed according to their strengths and preferences and predominantly interact with customers in complex interactions, this could also increase job satisfaction, which can be another economic advantage in the current labour market.

5.3. Limitations and Future Research

A limitation of the study is the non-representative study design. Due to economic reasons, this setting was used; therefore, future research could conduct a representative approach, hence the risk of missing representativeness is removed, and further research could investigate whether these effects also exist in real complaint situations and in broader sample sizes. The results of this study are limited given the used scenarios. Accordingly, the scope can be extended. The study was limited to service failure interactions in the eCommerce market. Prior research has found similar effects in the tourism sector or retail (Hossain and Rahman 2022; Ngo et al. 2020; Umasuthan et al. 2017).
The sample was based on an unbalanced gender representation; more than 75% of participants were female, which could have led to biases since women are, in general, more empathetic than men (Toussaint and Webb 2005). In addition, the sample’s average age was relatively young (27 years), which could reveal biases because of existing empathy differences attributable to age (Wieck 2015). Therefore, representations of the population need to avoid these unbalanced gender and age relationships. Even though it is, in general, gender-neutral if someone can easily put themself in the position of someone else, gender differences can be critical regarding empathy tendencies because, as some scientific studies have shown, women tend to be more empathetic than men, which could impact the underlying results. Future research needs to ensure a balanced relationship between the gender and age of study participants.
The measurement of empathy was based on a self-reported approach. Further research could test customer empathy with a different assessment, for example, with an objective assessment by other people.
This research identified customer empathy as an indicator of eWoM-giving intentions and provided findings that strengthen the meaning of empathy in post-purchase behaviour research. The findings need to be studied in further research. In order to fully elucidate human behaviour, it is recommended to develop supplementary qualitative research. In this study, the phenomenon of eWoM was the main focus, but there are numerous phenomena in post-purchase consumer behaviour where empathy seems to be impacted. Future research could investigate the relationship between customer empathy, complaint recovery and customer forgiveness in the context of failed service interactions.

Author Contributions

Conceptualization, N.I.A., M.D.D.-J.-V. and M.N.; Methodology, N.I.A., M.D.D.-J.-V. and M.N.; Software, N.I.A.; Validation, N.I.A., M.D.D.-J.-V. and M.N.; Formal analysis, N.I.A.; Investigation, N.I.A.; Writing—original draft, N.I.A.; Writing—review & editing, N.I.A., M.D.D.-J.-V. and M.N.; Visualization, N.I.A.; Supervision, M.D.D.-J.-V. and M.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all participants involved in the study.

Data Availability Statement

The data will be made available on a reasonable request by contacting the corresponding author.

Acknowledgments

The authors wish to thank the participants, without whom this study would not have been possible.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Overview of the Scenarios.
Table A1. Overview of the Scenarios.
Text of Scenarios
Introduction
passage of scenarios		You purchased a new printer and already used basic functions for several months. Before the purchase you spent several weeks researching, comparing prices and functions, and finally decided on a printer from a leading manufacturer. Although the printer was a little more expensive, you were convinced that you purchased a high-quality product—also because there was always talk of excellent customer service during the selection process. You already experienced this personally, as you used other products from the manufacturer. However, since the last time you used the printer, it no longer works properly, which you cannot explain. You, therefore, pick up the phone to ask for support from customer service.
Scenario 1
Negative and angry 		You describe your situation and expect a competent solution to your problem. The customer service employee seems very annoyed right from the start. He is unfriendly and snippy in his language, for example, he says “what do I know what you’ve done to your printer?”. Instead of providing help, he refers to the “idiot-proof” manual, which “should even help you”. After some back and forth, the customer service employee recommends that you visit a local copy shop and then hangs up. At the last moment, you hear a rude “idiot”. Your problem is not solved—instead, you wasted a lot of time being treated rudely!
Scenario 2
Negative and neutral		You describe your situation and expect a competent solution to your problem. The customer service employee puts you on hold to find out about possible solutions. After some time on hold, he gets back to you but does not have a solution to your problem. The customer service employee tells you to read the instructions again. Finally, you say goodbye, and the phone call is ended without an answer or solution to your problem. For the very high price, you would have hoped for more. Unfortunately, you are not satisfied with the printer you bought and the customer service. Your problem has not been solved!

References

  1. Aragon, Patrick. 2016. Empathy-Motivated Forgiveness: The Influence of Empathy, Prior Experience, and Contextual Factors in route to Forgiveness of a Transgressor. International Journal of Psychology & Behavior Analysis 2: 114–221. [Google Scholar] [CrossRef]
  2. Bahadur, Waseem, Saira Aziz, and Salman Zulfiqar. 2018. Effect of employee empathy on customer satisfaction and loyalty during employee–customer interactions: The mediating role of customer affective commitment and perceived service quality. Cogent Business and Management 5: 1491780. [Google Scholar] [CrossRef]
  3. Baron, Reuben M., and David A. Kenny. 1986. The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations. Journal of Personality and Social Psychology 51: 1173–82. [Google Scholar] [CrossRef] [PubMed]
  4. Batson, C. Daniel, and Laura L. Shaw. 1991. Evidence for Altruism: Toward a Pluralism of Prosocial Motives. Psychological Inquiry 2: 107–22. [Google Scholar] [CrossRef]
  5. Bayode, Olubanke Tolulope, and Ana Patrícia Duarte. 2022. Examining the Mediating Role of Work Engagement in the Relationship between Corporate Social Responsibility and Turnover Intention: Evidence from Nigeria. Administrative Sciences 12: 150. [Google Scholar] [CrossRef]
  6. Bitner, Mary Jo, Bernard H. Booms, and Mary Stanfield Tetreault. 1990. The Service Encounter Diagnosing Favorable and unfavorable incidents. Journal of Marketing 54: 71–84. [Google Scholar] [CrossRef]
  7. Bodey, Kelli, and Debra Grace. 2006. Segmenting service ‘complainers’ and ‘non-complainers’ on the basis of consumer characteristics. Journal of Services Marketing 20: 178–87. [Google Scholar] [CrossRef]
  8. Brislin, Richard W. 1970. Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology 1: 185–216. [Google Scholar] [CrossRef]
  9. Cheung, Christy M. K., and Matthew K. O. Lee. 2012. What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. Decision Support Systems 53: 218–25. [Google Scholar] [CrossRef]
  10. Chiang, Chun-Fang, and SooCheong Shawn Jang. 2007. The effects of perceived price and brand image on value and purchase intention: Leisure travelers? attitudes toward online hotel booking. Journal of Hospitality and Leisure Marketing 15: 49–69. [Google Scholar] [CrossRef]
  11. Clark, Colin Mackinnon, Ulrike Marianne Murfett, Priscilla S. Rogers, and Soon Ang. 2013. Is Empathy Effective for Customer Service? Evidence From Call Center Interactions. Journal of Business and Technical Communication 27: 123–53. [Google Scholar] [CrossRef]
  12. Cohen, Jacob. 1988. Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale: L. Erlbaum Associates. [Google Scholar]
  13. Comer, Lucette B., and Tanya Drollinger. 1999. Active Empathetic Listening and Selling Success: A Conceptual Framework. Journal of Personal Selling and Sales Management 19: 15–29. [Google Scholar]
  14. Daoud, Jamal I. 2018. Multicollinearity and Regression Analysis. Journal of Physics: Conference Series 949: 012009. [Google Scholar] [CrossRef]
  15. Davis, Mark H., and H. Alan Oathout. 1987. Maintenance of Satisfaction in Romantic Relationships: Empathy and Relational Competence. Journal of Personality and Social Psychology 53: 397–410. [Google Scholar] [CrossRef]
  16. Decrop, Alain. 1999. Tourists’ decision-making and behavior processes. In Consumer Behavior in Travel and Tourism. New York: Routledge, pp. 103–33. [Google Scholar]
  17. Duan, Changming, and Clara E. Hill. 1996. The Current State of Empathy Research. Journal of Counseling Psychology 43: 261–74. [Google Scholar] [CrossRef]
  18. Duprez, Christelle, Véronique Christophe, Bernard Rimé, Anne Congard, and Pascal Antoine. 2015. Motives for the social sharing of an emotional experience. Journal of Social and Personal Relationships 32: 757–87. [Google Scholar] [CrossRef]
  19. Faul, Franz, Edgar Erdfelder, Axel Buchner, and Albert-Georg Lang. 2009. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods 41: 1149–60. [Google Scholar] [CrossRef]
  20. Foscht, Thomas. 2011. Käuferverhalten- Grundlagen- Perspektiven– Anwendungen, 4th ed. Wiesbaden: Gabler. [Google Scholar]
  21. Gandini, Alessandro. 2020. Reaching for the Stars: The Role and Value of Digital Reputation. NIM Marketing Intelligence Review 12: 18–21. [Google Scholar] [CrossRef]
  22. Hayes, Andrew F. 2022. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach, 3rd ed. New York: The Guilford Press. [Google Scholar]
  23. Hennig-Thurau, Thorsten, Kevin P. Gwinner, Gianfranco Walsh, and Dwayne D. Gremler. 2004. Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing 18: 38–52. [Google Scholar] [CrossRef]
  24. Hoffmeyer-Zlotnik, Jürgen H. P. 2016. GESIS Survey Guidelines Standardisation and Harmonisation of Socio-Demographic Variables. Mannheim: GESIS—Leibniz Institute for the Social Sciences. [Google Scholar] [CrossRef]
  25. Holloway, Betsy B., and Sharon E. Beatty. 2003. Service Failure in Online Retailing: A Recovery Opportunity. Journal of Service Research 6: 92–105. [Google Scholar] [CrossRef]
  26. Hossain, Md Shamim, and Mst Farjana Rahman. 2022. Detection of potential customers’ empathy behavior towards customers’ reviews. Journal of Retailing and Consumer Services 65: 102881. [Google Scholar] [CrossRef]
  27. Jha, Ashish Kumar, and Snehal Shah. 2019. Social Influence on Future Review Sentiments: An Appraisal-Theoretic View. Journal of Management Information Systems 36: 610–38. [Google Scholar] [CrossRef]
  28. Jones, Patricia S., Jerry W. Lee, Linda R. Phillips, Xinwei E. Zhang, and Karen B. Jaceldo. 2001. An Adaptation of Brislin’s Translation Model for Cross-cultural Research. Nursing Research 50: 300–4. [Google Scholar] [CrossRef]
  29. Kanouse, David E. 1984. Explaining Negativity Biases in Evaluation and Choice Behavior: Theory and Research. Consumer Research 11: 703–8. [Google Scholar]
  30. Kaplan, Jonas T., and Marco Iacoboni. 2006. Getting a grip on other minds: Mirror neurons, intention understanding, and cognitive empathy. Social Neuroscience 1: 175–83. [Google Scholar] [CrossRef]
  31. Karlstetter, Wolfgang. 2017. The Emotional Empathic Drive Short Scale (EED). Zusammenstellung sozialwissenschaftlicher Items und Skalen (ZIS). Mannheim: GESIS. [Google Scholar] [CrossRef]
  32. Leung, Xi Y., Billy Bai, and Kurt A. Stahura. 2015. The Marketing Effectiveness of Social Media in the Hotel Industry: A Comparison of Facebook and Twitter. Journal of Hospitality and Tourism Research 39: 147–69. [Google Scholar] [CrossRef]
  33. Lima, Felipe Fernandes de, and Flavia de Lima Osório. 2021. Empathy: Assessment Instruments and Psychometric Quality—A Systematic Literature Review with a Meta-Analysis of the Past Ten Years. Frontiers in Psychology 12: 781346. [Google Scholar] [CrossRef]
  34. Liu, Hongfei, Chanaka Jayawardhena, Victoria-Sophie Osburg, Vignesh Yoganathan, and Severina Cartwright. 2021. Social sharing of consumption emotion in electronic word of mouth (eWOM): A cross-media perspective. Journal of Business Research 132: 208–20. [Google Scholar] [CrossRef]
  35. López-López, Inés, Salvador Ruiz-de-Maya, and Luk Warlop. 2014. When Sharing Consumption Emotions With Strangers Is More Satisfying Than Sharing Them With Friends. Journal of Service Research 17: 475–88. [Google Scholar] [CrossRef]
  36. Mano, Haim, and Richard L. Oliver. 1993. Assessing the Dimensionality and Structure of the Consumption Experience: Evaluation, Feeling, and Satisfaction. Journal of Consumer Research 20: 451–66. [Google Scholar] [CrossRef]
  37. Markovic, Stefan, Oriol Iglesias, Jatinder Jit Singh, and Vicenta Sierra. 2015. How does the Perceived Ethicality of Corporate Services Brands Influence Loyalty and Positive Word-of-Mouth? Analyzing the Roles of Empathy, Affective Commitment, and Perceived Quality. Journal of Business Ethics 148: 721–40. [Google Scholar] [CrossRef]
  38. Maxham, James G. 2001. Service recovery’s influence on consumer satisfaction, positive word-of-mouth, and purchase intentions. Journal of Business Research 54: 11–24. [Google Scholar] [CrossRef]
  39. Ngo, Liem Viet, Thi Nguyet Que Nguyen, Nam The Tran, and Widya Paramita. 2020. It takes two to tango: The role of customer empathy and resources to improve the efficacy of frontline employee empathy. Journal of Retailing and Consumer Services 56: 102141. [Google Scholar] [CrossRef]
  40. Parasuraman, Arun, Valarie A. Zeithaml, and Leonard L. Berry. 1994. Alternative scales for measuring service quality: A comparative assessment based on psychometric and diagnostic criteria. Journal of Retailing 70: 201–30. [Google Scholar] [CrossRef]
  41. Phau, Ian, and Michael Baird. 2008. Complainers versus non-complainers retaliatory responses towards service dissatisfactions. Marketing Intelligence and Planning 26: 587–604. [Google Scholar] [CrossRef]
  42. Podsakoff, Philip M., Scott B. MacKenzie, and Nathan P. Podsakoff. 2012. Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology 63: 539–69. [Google Scholar] [CrossRef]
  43. Rimé, Bernard. 2009. Emotion Elicits the Social Sharing of Emotion: Theory and Empirical Review. Emotion Review 1: 1–90. [Google Scholar] [CrossRef]
  44. Rimé, Bernard, Dario Paez, Patrick Kanyangara, and Vincent Yzerbyt. 2011. The Social Sharing of Emotions in Interpersonal and in Collective Situations: Common Psychosocial Consequences. In Emotion Regulation and Well-Being. New York: Springer. [Google Scholar]
  45. Rucker, Derek D., Kristopher J. Preacher, Zakary L. Tormala, and Richard E. Petty. 2011. Mediation Analysis in Social Psychology: Current Practices and New Recommendations. Social and Personality Psychology Compass 5: 359–71. [Google Scholar] [CrossRef]
  46. Shankar, Venkatesh, Amy K. Smith, and Arvind Rangaswamy. 2003. Customer satisfaction and loyalty in online and offline environments. International Journal of Research in Marketing 20: 153–75. [Google Scholar] [CrossRef]
  47. Shooshtari, Nader, Shawn Clouse, and Simona Stan. 2012. Customer complaint handling: The importance of employee training for customer service. Journal of Applied Marketing Theory 3: 20–37. [Google Scholar] [CrossRef]
  48. Siddiqui, Mohd Shuaib, Urooj Ahmad Siddiqui, Mohammed Arshad Khan, Ibrahim Ghazi Alkandi, Anoop Krishna Saxena, and Jaziba Haroon Siddiqui. 2021. Creating electronic word of mouth credibility through social networking sites and determining its impact on brand image and online purchase intentions in India. Journal of Theoretical and Applied Electronic Commerce Research 16: 1008–24. [Google Scholar] [CrossRef]
  49. Simon, Françoise. 2013. The influence of empathy in complaint handling: Evidence of gratitudinal and transactional routes to loyalty. Journal of Retailing and Consumer Services 20: 599–608. [Google Scholar] [CrossRef]
  50. Singer, Tania. 2020. Die Neurobiologie von Empathie und Mitgefühl—10. Empathie-Konferenz. Heidelberg: DAI Heidelberg. [Google Scholar]
  51. Spreng, R. Nathan, Margaret C. McKinnon, Raymond A. Mar, and Brian Levine. 2009. The Toronto empathy questionnaire: Scale development and initial validation of a factor-analytic solution to multiple empathy measures. Journal of Personality Assessment 91: 62–71. [Google Scholar] [CrossRef]
  52. Sundaram, Dinesh S., Kaushik Mitra, and Cynthia Webster. 1998. Word-of-Mouth Communications: A Motivational Analysis. In Advances in Consumer Research Volume. Edited by Joseph W. Alba and J. Wesley Hutchinson. Provo: Association for Consumer Research, vol. 25, pp. 527–31. [Google Scholar]
  53. Totan, Tarik, Tayfun Dogan, and Fatma Sapmaz. 2012. The Toronto Empathy Questionnaire: Evaluation of Psychometric Properties among Turkish University Students. Eurasian Journal of Educational Research 46: 179–98. [Google Scholar]
  54. Toussaint, Loren, and Jon R. Webb. 2005. Gender Differences in the Relationship Between Empathy and Forgiveness. The Journal of Social Psychology 145: 673–85. [Google Scholar] [CrossRef]
  55. Umasuthan, Hamsanandini, Oun-Joung Park, and Jong-Hyun Ryu. 2017. Influence of empathy on hotel guests’ emotional service experience. Journal of Services Marketing 31: 618–35. [Google Scholar] [CrossRef]
  56. Varela-Neira, Concepción, Rodolfo Vázquez-Casielles, and Víctor Iglesias-Argüelles. 2008. The influence of emotions on customer’s cognitive evaluations and satisfaction in a service failure and recovery context. Service Industries Journal 28: 497–512. [Google Scholar] [CrossRef]
  57. Weißhaar, Isabelle, and Frank Huber. 2016. Empathic Relationships in Professional Services and the Moderating Role of Relationship Age. Psychology and Marketing 33: 525–41. [Google Scholar] [CrossRef]
  58. Westbrook, Robert A., and Richard L. Oliver. 1991. The Dimensionality of Consumption Emotion Patterns and Consumer Satisfaction. Journal of Consumer Research 18: 84–91. [Google Scholar] [CrossRef]
  59. Wetzer, Inge M., Marcel Zeelenberg, and Rik Pieters. 2007. ‘Never eat in that restaurant, I did!’: Exploring why people engage in negative word-of-mouth communication. Psychology and Marketing 24: 661–80. [Google Scholar] [CrossRef]
  60. Wieck, Cornelia. 2015. Altersunterschiede in der Empathie: Multidirektional und eine Frage des Kontexts? Ph.D. dissertation, University of Leipzig, Leipzig, Germany. [Google Scholar]
  61. Wieseke, Jan, Anja Geigenmüller, and Florian Kraus. 2012. On the Role of Empathy in Customer-Employee Interactions. Journal of Service Research 15: 316–31. [Google Scholar] [CrossRef]
  62. Wuenschmann, Stefan. 2007. Beschwerdeverhalten und Kundenwert, 1st ed. Wiesbaden: Deutscher Universitäts-Verlag. [Google Scholar]
  63. Zaki, Jamil. 2013. Cue Integration: A Common Framework for Social Cognition and Physical Perception. Perspectives on Psychological Science 8: 296–312. [Google Scholar] [CrossRef]
  64. Zaki, Jamil. 2014. Empathy: A motivated account. Psychological Bulletin 140: 1608–47. [Google Scholar] [CrossRef]
  65. Zeithami, Valarie A., Leonard L. Berry, and Ananthanarayanan Parasuraman. 1996. The Behavioral Consequences of Service Quality. Journal of Marketing 60: 31–46. [Google Scholar] [CrossRef]
  66. Zhao, Xinshu, Jr., John G. Lynch, and Qimei Chen. 2010. Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research 37: 197–206. [Google Scholar] [CrossRef]
  67. Zulkarnain, Ferry, Siti Zahreni, and Lila M. Iskandar. 2018. Consumer purchase regret: How personality influences outcome regret and process regret. Journal of Business & Retail Management Research 13: 100–7. [Google Scholar] [CrossRef]
Admsci 13 00123 g001 550
Figure 1. The proposed research model and hypotheses.
Figure 1. The proposed research model and hypotheses.
Admsci 13 00123 g001
Admsci 13 00123 g002 550
Figure 2. Research model of scenario 1 (negative angry). Indicators of the significance level: *** indicates p < 0.001, and * indicates 0.01 ≤ p < 0.05.
Figure 2. Research model of scenario 1 (negative angry). Indicators of the significance level: *** indicates p < 0.001, and * indicates 0.01 ≤ p < 0.05.
Admsci 13 00123 g002
Admsci 13 00123 g003 550
Figure 3. Research model of scenario 2 (negative neutral). Indicators of the significance level: *** indicates p < 0.001, ** indicates 0.001 ≤ p < 0.01.
Figure 3. Research model of scenario 2 (negative neutral). Indicators of the significance level: *** indicates p < 0.001, ** indicates 0.001 ≤ p < 0.01.
Admsci 13 00123 g003
Table
Table 1. Emotional intensities of scenarios and description.
Table 1. Emotional intensities of scenarios and description.
ScenarioEmotional State of EmployeeEmotional IntensityScenario Description
1Negative angryNegativeRude and offending employee
2Negative neutralNegativeNeutral and objective employee
Table
Table 2. Used scales and constructs with original Cronbach-alpha values.
Table 2. Used scales and constructs with original Cronbach-alpha values.
Construct and Item Text CR
eWoM-giving intention adapted to the specific situation from Leung et al. (2015) 0.97–0.98
1: My willingness of writing a review about this consumer experience is very high.
2: The probability that I would consider writing a review about this consumer experience is very high.
3: The likelihood of writing a review about this consumer experience to others is very high.
Negative and Positive Emotional Intensity (López-López et al. 2014) adapted from Wetzer et al. (2007)
1: Anger9: Enjoyment0.95–0.96
2: Sadness10: Pleasantness
3: Irritation11: Euphoria
4: Disappointment12: Fun
5: Frustration13: Entertainment
6: Resentment14: Happiness
7: Indignation15: Enthusiasm
8: Disgust16: Fascination
Customer Empathy (Spreng et al. 2009; Totan et al. 2012)
2: Other people’s misfortunes do not disturb me a great deal (-). 0.79–0.85
3: It upsets me to see someone being treated disrespectfully.
4: I remain unaffected when someone close to me is happy (-).
5: I enjoy making other people feel better.
7: When a friend starts to talk about his\her problems, I try to steer the conversation towards something else (-).
8: I can tell when others are sad even when they do not say anything.
10: I do not feel sympathy for people who cause their own serious illnesses (-).
11: I become irritated when someone cries (-).
12: I am not really interested in how other people feel (-).
13: I get a strong urge to help when I see someone who is upset.
14: When I see someone being treated unfairly, I do not feel very much pity for them (-).
15: I find it silly for people to cry out of happiness (-).
16: When I see someone being taken advantage of, I feel kind of protective towards him\her.
Employee Empathy (Markovic et al. 2015; Parasuraman et al. 1994)
The brand employees give customers individual attention. 0.91
The brand employees deal with customers in a caring fashion.
The brand employees have the customer’s best interest at heart.
The brand employees understand the needs of their customers.
Notes. CR = Cronbach reliability, (-) = reverse-coded items.
Table
Table 3. Demographic profile of respondents (n = 260).
Table 3. Demographic profile of respondents (n = 260).
Scenario 1Scenario 2 Scenario 1Scenario 2
Negative AngryNegative Neutral Negative AngryNegative Neutral
Descriptionn = 129n = 131Descriptionn = 129n = 131
Male2830Income group in €
Female99101250–50004
Others20501–1.000105
Total1291311.001–1.5002524
Age (Mean)27.0626.941.501–2.0002629
Occupation 2.001–3.0005248
Student28263.001–4.0001214
Employee93934.001–5.00023
Civil servant24≥5.00102
Self-employed32no answer22
Seeking work01
Others35
Table
Table 4. Scale, number of items, Cronbach-alpha, mean and standard deviation.
Table 4. Scale, number of items, Cronbach-alpha, mean and standard deviation.
ScaleNo. ItemsCRMSD
S1S2S1S2S1S2
Customer Empathy (TEQ)130.780.795.825.880.600.64
Employee Empathy40.850.921.352.870.631.36
Emotional Intensity (Scenario)160.620.673.343.090.510.57
  Negative Emotional Intensity80.770.815.244.590.971.08
  Positive Emotional Intensity80.810.841.441.590.640.72
eWoM-giving Intention30.880.935.374.281.561.82
Notes. CR = Cronbach-alpha Reliability, M = Mean, S1 = Scenario 1, S2 = Scenario 2, SD = Standard Deviation.
Table
Table 5. Correlation table of two scenarios.
Table 5. Correlation table of two scenarios.
Customer EmpathyNegative EmotionsEmployee EmpathyeWoM
S1S2S1S2S1S2S1S2
Customer EmpathyS1 0.24 ** −0.39 *** 0.03
S2 0.31 *** −0.10 0.25 **
Negative EmotionsS1 −0.26 ** 0.39 ***
S2 −0.27 ** 0.35 ***
Employee EmpathyS1 −0.23 **
S2 −0.19 *
eWoMS1
S2
Notes. Indicators of the significance level: *** indicates p < 0.001, ** indicates 0.001 ≤ p < 0.01, and * indicates 0.01 ≤ p < 0.05. Scenario 1 negative angry = S1 and scenario 2 negative neutral = S2.
Table
Table 6. Regression analysis of scenarios 1 and 2 for dependent variable, eWoM-giving intention.
Table 6. Regression analysis of scenarios 1 and 2 for dependent variable, eWoM-giving intention.
95.0% CI
Predictor BSEβtpLLUL
(Constant)2.050.70 2.940.0040.673.43
Negative Emotional Intensity (S1)0.630.130.394.830.0000.370.89
(Constant)1.560.65 2.390.0180.272.84
Negative Emotional Intensity (S2)0.590.140.354.310.0000.320.87
Notes. S1: n = 129; R2 = 0.16; R2 adjusted = 0.15; F (1,127) = 23.33 p < 0.01. S2: n = 131; R2 = 0.126; R2 adjusted = 0.12; F (1,129) = 18.53 p < 0.001. LL and UL indicate the lower and upper limits of confidence interval (CI).
Table
Table 7. Regression analysis of scenarios 1 and 2 for dependent variable, eWoM-giving intention.
Table 7. Regression analysis of scenarios 1 and 2 for dependent variable, eWoM-giving intention.
95.0% CI
Predictor BSEβtpLLUL
(Constant)4.971.34 3.700.0002.317.62
Customer Empathy (S1)0.070.230.030.300.764−0.390.52
(Constant)0.111.44 0.070.942−2.732.94
Customer Empathy (S2)0.710.240.252.930.0040.231.19
Notes: S1: n = 129; R2 = 0.00; R2 adjusted = 0.01; F (1,127) = 0.09 p = 0.76. S2: n = 131; R2 = 0.06; R2 adjusted = 0.06; F (1,129) = 8.56 p < 0.004. LL and UL indicate the lower and upper limits of confidence interval (CI).
Table
Table 8. Regression analysis of scenarios 1 and 2 for dependent variable, eWoM-giving intention.
Table 8. Regression analysis of scenarios 1 and 2 for dependent variable, eWoM-giving intention.
95.0% CI
Predictor BSEβtpLLUL
(Constant)6.130.32 19.390.0005.516.76
Employee Empathy (S1)−0.570.21−0.23−2.670.009−0.99−0.147
(Constant)5.010.37 13.700.0004.285.73
Employee Empathy (S2)−0.250.12−0.19−2.210.029−0.48−0.03
Notes. S1: n = 129; R2 = 0.05; R2 adjusted = 0.05; F (1,127) = 7.12 p = 0.009. S2: n = 131; R2 = 0.04; R2 adjusted = 0.03; F (1,129) = 4.87 p = 0.029. LL and UL indicate the lower and upper limits of confidence interval (CI).
Table
Table 9. Regression analysis of scenarios 1 and 2 for dependent variable, employee empathy.
Table 9. Regression analysis of scenarios 1 and 2 for dependent variable, employee empathy.
95.0% CI
Predictor BSEβtpLLUL
(Constant)3.740.50 7.460.0002.754.73
Customer Empathy (S1)−0.410.09−0.39−4.800.000−0.58−0.24
(Constant)4.171.11 3.770.0001.986.36
Customer Empathy (S2)−0.220.19−0.10−1.190.238−0.590.15
Notes. S1: n = 129; R2 = 0.15; R2 adjusted = 0.15; F (1,127) = 23.03 p < 0.001. S2: n = 131; R2= 0.11; R2 adjusted = 0.03; F (1,129) = 1.41 p < 0.238. LL and UL indicate the lower and upper limits of confidence interval (CI).
Table
Table 10. Regression analysis of scenarios 1 and 2 for dependent variable, negative emotional intensity.
Table 10. Regression analysis of scenarios 1 and 2 for dependent variable, negative emotional intensity.
95.0% CI
Predictor BSEβtpLLUL
(Constant)3.030.81 3.730.0001.424.64
Customer Empathy (S1)0.380.140.242.740.0070.110.65
(Constant)1.500.84 1.780.077−0.163.17
Customer Empathy (S2)0.530.140.313.690.0000.240.81
Notes. S1: n = 129; R2 = 0.06; R2 adjusted = 0.05; F (1,127) = 7.49 p = 0.007. S2: n = 131; R2 = 0.10; R2 adjusted = 0.09; F (1,129) = 13.60 p < 0.01. LL and UL indicate the lower and upper limits of confidence interval (CI).
Table
Table 11. Results of Hypotheses Testing.
Table 11. Results of Hypotheses Testing.
Hypotheses Hypotheses Testing
S1S2
H1: The customer’s perceived emotional intensity of failed service interaction positively influences the eWoM-giving intention.xx
H2: Customer empathy influences the eWoM-giving intention after failed service interactions. x
H3: Missing employee empathy negatively influences the customers’ eWoM-giving intention after failed service interactions.xx
H4: Customer empathy influences the perception of missing employee empathy during a failed service interaction.x
H5: Customer empathy positively influences the perception of emotional intensity during failed service interactions.xx
H6: The customer’s perception of emotional intensity mediates the relationship between customer empathy and the eWoM-giving intention.xx
Notes. S1 = Scenario 1 (negative angry) and S2 = Scenario 2 (negative neutral); the “x” indicates the supported hypothesis.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Abend, N.I.; De-Juan-Vigaray, M.D.; Nuszbaum, M. An Experimental Study of How Missing Employee Empathy in Failed Service Interactions Affects Empathetic Customers’ EWoM-Giving Behaviour. Adm. Sci. 2023, 13, 123. https://doi.org/10.3390/admsci13050123

AMA Style

Abend NI, De-Juan-Vigaray MD, Nuszbaum M. An Experimental Study of How Missing Employee Empathy in Failed Service Interactions Affects Empathetic Customers’ EWoM-Giving Behaviour. Administrative Sciences. 2023; 13(5):123. https://doi.org/10.3390/admsci13050123

Chicago/Turabian Style

Abend, Neele Inken, María D. De-Juan-Vigaray, and Mandy Nuszbaum. 2023. "An Experimental Study of How Missing Employee Empathy in Failed Service Interactions Affects Empathetic Customers’ EWoM-Giving Behaviour" Administrative Sciences 13, no. 5: 123. https://doi.org/10.3390/admsci13050123

APA Style

Abend, N. I., De-Juan-Vigaray, M. D., & Nuszbaum, M. (2023). An Experimental Study of How Missing Employee Empathy in Failed Service Interactions Affects Empathetic Customers’ EWoM-Giving Behaviour. Administrative Sciences, 13(5), 123. https://doi.org/10.3390/admsci13050123

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop