1 Introduction
Interactions between customers and employees are vital for business success in a variety of industries. This is true for retailers, such as hardware stores (Albrecht et al.
2016) and pharmacies (Olk et al.
2021), or for the service sector, such as hotels (Lechner and Mathmann
2020) and restaurants (Chi et al.
2011). One way to positively affect the outcomes of customer-employee interactions is the use of emotional labor. Hochschild (
2003) describes emotional labor as control over emotions to generate a desired facial and bodily display. Therefore, employees are expected to exhibit positive emotions when dealing with customers (i.e., smiling). Two techniques are available for generating desired emotional displays.
Deep acting enables an individual to experience a desired emotion by placing himself or herself in a situation that elicits the actual emotion (i.e., authentic displays).
Surface acting is a technique in which the desired emotion is displayed but not experienced (i.e., inauthentic displays) (Grandey
2000).
However, research on the effects of emotional labor strategies has led to inconsistent findings (Chi et al.
2011). Thus, discussion has arisen regarding the effects of authentic and inauthentic emotional labor strategies (Houston III et al.
2018). While some studies have attempted to explain these inconsistencies based on the characteristics of employees and service encounters (Chi et al.
2011), there is a lack of research that considers customer-related factors (Lechner and Mathmann
2020).
This study contributes to research by considering customer expectations regarding emotional facial displays and involvement. As suggested by Golder et al. (
2012), customer expectations are critical to both the quality experience and the quality evaluation process. Our study shows that customer involvement influences expectations of employees’ emotional displays. In addition to customers’ cognitive reaction to displayed emotions (van Kleef
2014), customers also experience an affective reaction. We show that affective processing is also contingent on involvement. Moreover, we demonstrate that short-term changes in involvement can influence customer processing. Finally, we classify the effects of emotional labor into further emotional displays.
Practitioners can benefit from our findings in several ways. We show when authenticity is important in service encounters and how emotional labor strategies’ effectiveness can be increased. Furthermore, we incorporate our findings into the stages of the customer decision process and develop recommendations for service managers to help them tailor emotional labor strategies to low- vs. high-involvement customers. Moreover, we present recommendations to companies that provide service training programs for service employees.
4 Study 2
In Study 2, we tested our hypotheses that the authenticity of emotional displays affects both cognitive (H2) and affective (H3) processing contingent on involvement.
4.1 Method
By using a 2 (inauthentic vs. authentic positive emotional displays) × 2 (low vs. high involvement) video-stimulated online experiment, we ensured internal validity. We recruited a female actor and recorded two videos that showed a service encounter in a supermarket. The filming took place in a real supermarket, and the videos represented a typical checkout situation. Each showed the scene from a first-person perspective and took the same amount of time (approximately 30 s; see Web
Appendix). All dialogue was the same (see Appendix
2). In line with prior research (Lechner and Mathmann
2020), only the facial expression of the employee was manipulated (i.e., inauthentic vs. authentic), and body characteristics were held constant. For this manipulation, we instructed the actor to use
surface acting or
deep acting (Wang et al.
2017) and ensured that marks of authenticity matched (i.e., inauthentic (authentic): without (with) wrinkles around eyes; Houston III et al.
2018).
To manipulate involvement, we used two different scenarios. To account for the multidimensionality of involvement (Zaichkowsky
1985), we manipulated the reason for purchase (i.e.,
personal relevance), the products (i.e.,
physical relevance), and the relevance of the product to the reason for purchase (i.e.,
situational relevance) (see Appendix
3). The results of manipulation checks for perceived facial displays of the employee and participants’ involvement are provided in the Web
Appendix.
The participants were recruited via Qualtrics. The sample included data from 431 subjects (Mage = 38.2, SD = 14.9; 48.3% female).
4.2 Measures
The following explanations are presented in the same order as in the experiment. In Study 1, participants reported their involvement using a scale developed by De Wulf et al. (
2001). However, this scale does not fully capture the affective and cognitive involvement components. To address this issue, we used the Personal Involvement Inventory (α = .94) developed by Zaichkowsky (
1994). To confirm our authenticity manipulation, the participants responded to three items (“The smile of the employee was natural,” “The smile of the employee was genuine,” and “The smile of the employee was authentic”; 1 = strongly disagree, 7 = strongly agree; α = .93; Houston III et al.
2018). To measure
exceeded expectations, we used two items (“The service encounter was…” and “The service employee was…”; 1 = much poorer than expected, 7 = much better than expected; α = .78; Oliver and Burke
1999). To measure customers’
positive affect, we used the original valence dimension of the Self-Assessment Manikin Scale (1 = negative, 5 = positive; Kulczynski et al.
2016). Finally, to measure
loyalty intention, we used three items (“I will gladly visit this retailer in the future,” “I will say positive things about this retailer,” and “I can recommend this retailer without hesitation”; 1 = strongly disagree, 7 = strongly agree; α = .9; Hennig-Thurau et al.
2006).
4.3 Results
To test our hypotheses, we conducted a regression-based analysis using bootstrap resampling in AMOS 25 that considers all variables simultaneously. The results revealed significant interaction effects of emotional displays and scenarios on exceeded expectations (b = .52, SE = .16, p < .01) and positive affect (b = .51, SE = .17, p < .01). In accordance with H2a, simple slope analysis showed that in the high-involvement condition, inauthentic emotional displays had a more negative effect on exceeded expectations (b = −1.04, SE = .11, p < .01) than the positive effect of authentic emotional displays in the low-involvement condition (b = .52, SE = .11, p < .01). Furthermore, and in line with H3a, simple slope analysis showed that authentic emotional displays had a stronger effect on positive affect in the high-involvement condition (b = 1.06, SE = .12, p < .01) than in the low-involvement condition (b = .55, SE = .12, p < .01).
To test H2b, we calculated the indirect effects of emotional displays on loyalty intention via exceeded expectations. The results showed a stronger indirect effect in the high-involvement condition (b = .49, SE = .09, p < .01) than in the low-involvement condition (b = .24, SE = .06, p < .01). Calculating the index of moderated mediation supported H2b (b = .24, SE = .08, p < .01). To test H3b, we calculated the indirect effects of emotional displays on loyalty intention via positive affect. The results showed a stronger indirect effect in the high-involvement condition (b = .38, SE = .07, p < .01) than in the low-involvement condition (b = .2, SE = .05, p < .01). Calculating the index of moderated mediation supported H3b (b = .18, SE = .07, p < .01).
5 Study 3
In Study 3, we validated the results from Study 2 in a different scenario. In addition, we considered a short-term change in involvement. While customer involvement was determined before the service encounter in Study 2, in Study 3, we investigated whether the change in involvement during the interaction had an effect on customer processing. Furthermore, we broadened the set of emotions considered. Since research shows that emotional labor can lead to emotional exhaustion (Gaucher and Chebat
2019), we included frustrated negative emotional displays. As a baseline, we also considered a neutral expression.
5.1 Method
We used a 4 (inauthentic positive vs. authentic positive vs. frustrated negative vs. neutral emotional displays) × 2 (constant vs. increased involvement) video-stimulated online experiment. The methodological procedure was the same as in Study 2. This time, the videos (approximately 35 s; see Web
Appendix) showed a bank service encounter in which the customer wanted to invest a certain amount of money and was served by a male employee (see Appendix
4).
To manipulate emotional expression, the actor again applied corresponding emotional labor techniques (see Study 2). To manipulate the participants’ involvement, we followed Shao et al. (
2004) and used two different investment options (see Appendix
5). Initially, the customer is interested in a low-involvement product. During the service encounter, he or she either maintains his or her original decision (i.e., constant involvement) or chooses a high-involvement product (i.e., increased involvement). The results of manipulation checks for the perceived emotional expression of the employee and participants’ involvement are provided in the Web
Appendix.
The participants were acquired via Qualtrics. The sample included data from 806 subjects (Mage = 46, SD = 15.3; 47.1% female).
5.2 Measures
To confirm our manipulation of an increase in involvement, we used the same 10-item scale as in Study 2. We measured once based on the customer’s original intention (α = .94) and once based on the customer’s final decision (α = .96). To calculate the increase in involvement, we subtracted the second value from the first (Hennig-Thurau et al.
2006). To confirm our authenticity manipulation, the participants responded to the same items as in Study 2 (α = .9). To measure emotional exhaustion, we used 2 items (“The employee felt emotionally drained by his job,” “The employee felt frustrated by his job”; 1 = strongly disagree, 7 = strongly agree; α = .86; Gaucher and Chebat
2019). To measure customers’
positive affect, we used 3 items from Hennig-Thurau et al. (
2006) (α = .84). Finally, to measure
exceeded expectations (α = .94) and
loyalty intention (α = .97), we used the same scales as in Study 2.
5.3 Results
To validate our findings from Study 2, we conducted a regression-based analysis using bootstrap resampling in AMOS 25, including the positive emotional displays condition. The results revealed positive interaction effects of emotional displays and scenarios on exceeded expectations (b = .46, SE = .16, p < .01) and positive affect (b = .46, SE = .23, p < .05), confirming H2a and H3a.
To validate H2b, we calculated the indirect effects of emotional displays on loyalty intention via exceeded expectations. The results showed a significant indirect effect in the increased-involvement condition (b = .34, SE = .08, p < .01) and a non-significant effect in the constant-involvement condition (p > .1). Calculating the index of moderated mediation confirmed H2b (b = .25, SE = .09, p < .05). To test H3b, we calculated the indirect effects of emotional displays on loyalty intention via positive affect. The results showed a significant indirect effect in the increased-involvement condition (b = .41, SE = .12, p < .01) and a non-significant effect in the constant-involvement condition (p > .1). Calculating the index of moderated mediation confirmed H3b (b = .29, SE = .15, p < .05).
A four-by-two ANOVA (emotional displays condition by scenario condition) on loyalty intention revealed a significant group difference between positive emotional displays (Minauthentic positive = 3.36, Mauthentic positive = 3.87) and frustrated negative and neutral emotional displays (Mfrustrated negative = 2, Mneutral = 1.81) of 1.71 (SE = .09, p < .01). However, there was neither a significant main effect of emotional displays (p > .1) nor a significant interaction effect of emotional displays and scenarios (p > .1) when comparing frustrated negative and neutral emotional displays. In addition to classifying the effects of emotional labor into further emotional displays, the results show that H2b and H3b cannot be transferred to negative and neutral emotional displays.
6 Discussion
Our research provides a deeper understanding of the effects of emotional labor strategies on customer loyalty intention. We find that involvement influences customers’ expectations of the authenticity of employees’ emotional displays. More precisely, high-involvement (low-involvement) customers expect authentic (inauthentic) emotional displays. Thus, we demonstrate from the perspective of cognitive processing the importance of expectation (dis)confirmation with regard to the authenticity of emotional displays. We show that even inauthentic emotional displays can meet customers’ expectations depending on customers’ involvement. Thus, our results show that loyalty intention depends more strongly on employees’ authentic behavior for high-involvement customers than for low-involvement customers. From the perspective of affective processing, we deepen the understanding by identifying the reinforcing effect of involvement. Finally, we show that the identified relationships apply not only to service encounters in which involvement is determined in advance but also to dynamic encounters in which involvement increases during the interaction.
The findings of this study provide implications for service management. At first glance, it appears that companies would be well advised to train their employees in deep acting techniques (Hennig-Thurau et al.
2006). However, when opportunity costs are taken into account, the question arises of whether this recommendation is always applicable. The present research provides guidelines for (1) when the authenticity of employee behavior should be enhanced and (2) how the effectiveness of authentic emotional displays can be increased.
We provide three different approaches to the first question. First, managerial action should depend on the context of the service. Thus, managers should pay more attention to the use of
deep acting in situations with rather high levels of involvement (e.g., high prices, high risk). Second, involvement can also vary depending on the personal situation of the customer (Zaichkowsky
1994). To identify high-involvement customers, employees could offer customers additional background knowledge or highlight additional services or products. Customers who respond to this strategy are likely to be more involved, and
deep acting should be used carefully. Third, managers could observe the customer decision-making process. Research has shown that involvement in certain stages (i.e.,
need recognition,
information search, and
evaluation) has different origins and consequences (Puccinelli et al.
2009). Depending on the stage, employees can ask specific questions to identify the level of customer involvement. In the
need recognition stage, involvement depends on the customer’s goals (Celsi and Olson
1988). Customers who are seeking a superior service or product are usually highly involved (classifying question: “Are you looking for an ideal solution or a quick one?”). In the
information search stage, involvement determines the extent and depth of customers’ information search (Zaichkowsky
1985) (classifying question: “Do you know of the various available opportunities?”). Finally, customers make a judgment during the
evaluation stage based on sufficient certainty (Suh and Yi
2006). Therefore, high-involvement customers will have several arguments that justify their decision (classifying question: “How did you come to this decision?”).
Training companies that teach deep acting techniques would be well advised to pay attention to endurance training because our findings show that the level of authenticity that is sufficient at the beginning of a service encounter may not be sufficient at the end of the encounter. When involvement increases during the encounter, employees must be able to respond with positive emotional displays that are more authentic. Conversely, employees could increase the effect of authentic emotional displays by increasing customer involvement (e.g., using in-store demonstration; Grewal et al.
2009).
Given the potential risks of surface acting (i.e., emotional exhaustion), our results show that in the case of frustrated negative emotional displays, loyalty intentions are at the same level as in the case of neutral emotional displays regardless of customer involvement. This finding supports the position of continuing to smile even if it is not authentic.
This study also yields future research avenues. Although we considered several different service contexts in Study 1, we used only two service contexts in Study 2 and Study 3. Therefore, it may be premature to generalize the results to all service contexts, and future research should validate our findings in other contexts. For instance, we expect different effects in the area of luxury providers, whose customers have a strong desire to belong to a social group (Amaral and Loken
2016). Luxury brands often promote a “superior mood” in high-end retail stores to differentiate themselves from other brands. Ward and Dahl (
2014) demonstrated that the rejection of customers in such a context increases customers’ desire to belong to the group to reduce the feeling of exclusion. Thus, inauthentic positive emotional displays could also induce a perception of rejection and could strengthen the desire to belong to the exclusive group. Furthermore, our results show that low-involvement customers expect inauthentic rather than authentic emotional displays. Based on their own low involvement, customers may assume the same for employee and therefore consider employees’ inauthentic positive behavior more likely than authentic behavior. This involves “a psychological projection whereby one’s own feelings or actions are attributed to others” (Wood and Essien-Wood
2012, p. 985). Future research should elaborate this process more broadly by considering when and with regard to which emotional states projection takes place and how it influences the relationship between customers and employees.
Finally, the present study did not consider the effects of authenticity of emotions displayed in repeated service encounters. In these cases, the relationship between customers and employees could continuously improve. Chi and Chen (
2019) demonstrated that in repeated service interactions, relationship strength enhances the positive effect of authentic emotional displays because customers expect higher emotional effort. Therefore, in repeated service encounters, even low-involvement customers could expect authentic employee behavior due to a stronger relationship. Future research should examine the effects of emotional labor strategies in repeated service encounters by considering customer involvement and relationship strength.
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