Integrating Emotion Recognition

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I Know How You Feel, but It Does Not Always Help: Integrating Emotion Recognition,. Agreeableness, and Cognitive Ability in a Compensatory Model of Service ...

EMOTION RECOGNITION AND SERVICE PERFORMANCE I Know How You Feel, but It Does Not Always Help: Integrating Emotion Recognition, Agreeableness, and Cognitive Ability in a Compensatory Model of Service Performance

Lorna Doucet School of Management Fudan University Room 403, Siyuan Building, 670 Guoshun Road, Shanghai, China Phone: +86 21 2501 1154 Fax: +86 21 6564 3920 Email: [email protected] Bo Shao UNSW Business School University of New South Wales Kensington, NSW, Australia Email: [email protected] Lu Wang UNSW Business School University of New South Wales Kensington, NSW, Australia Phone: +61 2 9358 6886 Fax: +61 2 9662 8531 Email: [email protected]

Greg R. Oldham A. B. Freeman School of Business Tulane University 7 McAlister Dr., New Orleans, LA 70118 U.S.A Phone: 504 865 5558 Fax: 504 865 5491 Email: [email protected]

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Acknowledgement

We would like to thank the retail bank that provided access, data and financial support for this research project, as well as the Wharton Financial Institutions Center for its support.

Author Biographies

Lorna Doucet is an Associate Professor in the School of Management, Fudan University, China. She received her Ph.D. in Management from the Wharton School, University of Pennsylvania. Dr. Doucet’s research focuses on emotions, emotional intelligence and cross-cultural interactions in the workplace. Her work has appeared in such journals as Academy of Management Journal, Journal of Service Research, Journal of Organizational Behavior, and Industrial and Labor Relations Review. Dr. Doucet is a Fellow of the Wharton Financial Institutions Center.

Bo (Jeff) Shao is a PhD candidate in the School of Management, UNSW Business School at University of New South Wales, Australia. His research examines workplace emotions and emotional intelligence. His work has been published in such journals as Journal of CrossCultural Psychology and Journal of Business and Psychology. Bo (Jeff) Shao is the corresponding author and can be contacted at: [email protected]

Lu (Nick) Wang is a Senior Lecturer in the School of Management, UNSW Business School at University of New South Wales, Australia. He received his MBA and PhD degrees from University of Illinois Urbana Champaign in the United States. Dr. Wang’s research focuses on emotions and emotion capabilities in work-related context. His work has appeared in journals such as Organizational Behavior and Human Decision Processes, Journal of Management, Management and Organization Review, Personality and Individual Differences, and Journal of Research on Personality. Dr. Wang has won research awards at the Australian and New Zealand Academy of Management Conference and is a recipient of UNSW’s Goldstar Research Award.

Greg R. Oldham is the J. F., Jr. and Jesse Lee Seinsheimer Chair in the A. B. Freeman School of Business at Tulane University. He received his Ph.D. in Organizational Behavior from Yale

EMOTION RECOGNITION AND SERVICE PERFORMANCE University. Professor Oldham’s research focuses on the contextual and personal conditions that prompt the creativity of individuals and teams in organizations. His work has appeared in many leading journals including, Academy of Management Journal, Journal of Applied Psychology, and Organization Science. He is a Fellow of the Academy of Management and the American Psychological Association. Professor Oldham received the 2004 Distinguished Educator Award from the Academy of Management.

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I Know How You Feel, but It Does Not Always Help: Integrating Emotion Recognition, Agreeableness, and Cognitive Ability in a Compensatory Model of Service Performance

Abstract Purpose – Previous research has demonstrated the importance of emotion recognition ability in negotiations and leadership, but scant research has investigated the role of emotion recognition ability in service contexts. This study proposes and tests a compensatory model in which service employees’ emotion recognition ability helps enhance their job performance, particularly when employees score low on the agreeableness personality dimension or have low cognitive ability. Design/methodology/approach – With a two-wave multisource dataset collected from a service center of a large retail bank, multiple regression analysis was used to test the moderating roles of agreeableness and cognitive ability on the relationship between service employees’ emotion recognition ability and their performance. Findings – Service employees’ emotion recognition ability helped enhance their job performance. However, the positive effect of emotion recognition ability on job performance was only statistically significant when employees’ agreeableness or cognitive ability was low. Practical implications – Findings have important implications for how service organizations select and recruit employees. In particular, service employees with low agreeableness or cognitive ability may still be able to perform well when possessing high emotion recognition ability. Therefore, emotion recognition ability should be considered in the selection and recruitment process. Originality/value – Going beyond self-report measures of emotion recognition and using a performance measure from organizational records, this study is one of the first to examine how emotion recognition ability interacts with personality and cognitive ability in predicting service employees’ effectiveness in a service organization. Keywords – Emotion recognition ability, Service performance, Agreeableness, Cognitive ability Research type – Research paper

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Philosophers and scholars have pondered the value of emotion recognition for centuries (see Pound, 1951). In recent years, both researchers and practitioners have begun to explore the benefits of being able to “read” others’ emotional cues in the workplace (Elfenbein et al., 2007; Rubin et al., 2005). For example, anecdotal evidence suggests that those who are skilled in emotion recognition tend to be better executives, diplomats, and teachers (Goleman, 1995). Research on leadership and negotiations has largely supported this view by showing that emotion recognition accuracy enhances leadership and negotiation effectiveness (Elfenbein et al., 2007; Rubin et al., 2005). Early research suggests that customers’ emotions play an important role in service encounters (Mattila and Enz, 2002; Pugh, 2001), but surprisingly, the importance of emotion recognition in the service context has been relatively neglected. This study aims to address this research gap by examining the effects of service employees’ emotion recognition ability on their service performance. Previous research on service performance has produced strong evidence on the impact of service employees’ emotional labor (e.g., Groth et al., 2009; Hulsheger and Schewe, 2011). Emotional labor research focuses primarily on how service employees regulate their own emotions when interacting with customers (e.g., surface acting or deep acting). Understandably, this research has not paid much attention to the performance impact of service employees’ ability to recognize customers’ emotions (for an exception see Mesmer-Magnus et al., 2012). Conceptualized as a unique dimension of emotional intelligence (Joseph and Newman, 2010; Mayer and Salovey, 1997), emotion recognition concerns an individual’s ability to accurately decode others’ expressions of emotions through nonverbal channels (Elfenbein and Ambady, 2002; Mayer and Salovey, 1997; Rubin et al., 2005). Although the cascading model of emotional intelligence (Joseph and Newman, 2010) suggests that emotion recognition ability represents a

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more fundamental aspect of an individual’s emotional intelligence as it precedes emotion understanding and emotion regulation ability, few studies have focused on whether this aspect of emotional intelligence enhances performance in the workplace. Emotion-related abilities should play a pronounced role in contexts that require interpersonal interactions and emotional labor, such as in service encounters (e.g., Joseph and Newman, 2010). Therefore, in the present study, we investigate whether service employees’ emotion recognition ability enhances their job performance. Although service effectiveness can be influenced by many factors (Subramony and Pugh, 2015), a large body of research has demonstrated the importance of individual differences such as personality and cognitive ability in affecting service employees’ performance (Brown et al., 2002; Hunter, 1986; Liao and Chuang, 2004). One goal of this study is to extend this perspective by examining whether service effectiveness is associated with an employee’s emotion recognition ability. In addition, we examine whether the relationship between emotion recognition ability and service effectiveness is influenced by one dimension of employees’ personality (i.e., agreeableness) and their cognitive ability. We focus on the personality dimension of agreeableness because it is widely considered as a service disposition (Sawyerr et al., 2009; Teng and Hsu, 2012). Individuals with low agreeableness have often been considered as less suitable for service jobs (Sawyerr et al., 2009; Teng and Hsu, 2012). Therefore, it is valuable to investigate whether emotion recognition ability compensates for deficiencies in agreeableness in service interactions. Furthermore, while cognitive ability has been shown to be one of the most robust determinants of job performance (Hunter, 1986; Ree et al., 1994), some research now suggests that emotion-related abilities may provide an alternative pathway to effective performance in the workplace (see Côte and Miners, 2006). We investigate this

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possibility in the customer service context by examining whether emotion recognition ability of service employees compensates for deficiencies in cognitive ability. Conceptual Framework: Emotion Recognition and Service Performance

Previous research has demonstrated that customers attend to two aspects of service interactions (Grönroos, 1983; Iacobucci and Ostrom, 1993; Swartz and Brown, 1989): (a) a relational or “human contact” aspect (Goodwin and Smith, 1990; Gutek, 1995), which concerns service employees’ ability to create a pleasant and friendly interaction; and (b) a technical or “core” aspect (Iacobucci and Ostrom, 1993), which concerns service employees’ ability to solve customer problems. Stated differently, customers are likely to be satisfied when they are treated well and when their problems are solved. For example, a customer who needs to open an online bank account would evaluate the quality of the service encounter based on whether the service employee is considerate and caring during the service (i.e., the relational aspect), and whether the online bank account is created successfully and efficiently (i.e., the technical aspect). Therefore, effective service delivery, which is often referred to as overall excellence or superiority of a service encounter (Parasuraman et al., 1988), generally encompasses both aspects. To achieve overall service excellence, service employees need to excel in both the relational (e.g., develop good relationships with customers) and the technical aspects of a service encounter (e.g., solve customer problems) (Grönroos, 1983; Iacobucci and Ostrom, 1993; Swartz and Brown, 1989). In the next section, we review evidence concerning how emotion recognition ability may influence both aspects of service performance. Service employees communicate with customers primarily through verbal channels. But emotions are often present during customer service interactions. More than a read-out of one’s private feelings, emotion expressions communicate important information about the expresser’s

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intentions and attitudes (Frank, 1988; Keltner and Haidt, 1999). Particularly, the emotion as social information model (EASI; Van Kleef, 2009; Van Kleef et al., 2012) states that other’s emotions provide important social information based on which observers make inferences and adjust their behaviors. Accordingly, customers’ emotions can reveal what they need and value in the service encounter. Customer emotions are often expressed through nonverbal channels such as voice tones, facial expressions, body movements, or a combination of them (Wallbott and Scherer, 1986; Zuckerman et al., 1975). While strong and intense emotions expressed through these channels are easily recognizable (e.g., most employees would have no trouble recognizing a customer’s anger when the customer yells and shouts his frustration on the phone), less intense emotion expressions are harder to detect accurately. Concerning these more subtle emotion expressions, research shows that there are stable differences in individuals’ accuracy in emotion recognition (Hall and Bernieri, 2001; Nowicki and Duke, 1994; Rubin et al., 2005). Emotion recognition ability has been shown to be integral to the development of positive social relationships because it helps people recognize and understand social cues (Brackett et al., 2006; Feldman et al., 1991; Nowicki and Duke, 1994). Those who can accurately recognize others’ emotional expressions tend to be more effective in social situations (Elfenbein et al., 2007). By contrast, individuals who lack this ability tend to experience more difficulties in social interactions and are less capable of forging and maintaining strong interpersonal relationships, because they often lack sensitivity in social situations and are less responsive to social cues (Sabatelli et al., 1983). The relational benefits associated with emotion recognition ability can have important implications in the workplace. For example, Caruso et al. (2002) argued that accurately recognizing others’ emotions is critical to a leader’s capacity to inspire and build a strong

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relationship with followers. Momm, Blickle, Liu, Wihler, Kholin, and Menges (2015) also found that people who had higher emotion recognition ability earned more annual income due to their interpersonal skills. In service interactions, more accurate emotion recognition can translate into stronger relationships with customers. For example, accurate recognition of a customer’s emotions may give employees a sense of whether customers are interested in engaging in small talk during the service encounter. This information should help service employees approach customers more appropriately, enhancing their ability to develop rapport with customers. By contrast, the failure to read the emotional cues of customers can cause service employees to adopt inappropriate strategies to connect with customers. The above considerations suggest that customers should appreciate and value those employees who can recognize their feelings. Therefore, emotion recognition ability should enhance the relational aspect of service performance by facilitating relationship-building between service employees and customers. Going beyond the relational aspect of service performance, research suggests that emotion recognition ability may also help individuals make decisions and solve problems in social situations. For example, Day and Carroll (2004) demonstrated that those who are more accurate at recognizing emotions performed better on a managerial decision task. Elfenbein and colleagues (2007) showed that negotiators with high emotion recognition ability were more likely to discover optimal solutions to a negotiation problem. Effective problem-solving requires accurate understanding of the problem (Newell and Simon, 1972; Stevenson and Gilly, 1991). In the service context, this means employees must accurately understand the problem and challenge faced by customers (Bitner et al., 1997). Emotion recognition ability increases individuals’ empathy and perspective taking (Blair, 2005). By sensing how customers feel and seeing a problem from a customer’s perspective, service employees are more likely to discover solutions

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to address customers’ needs. These considerations suggest that emotion recognition ability may also enhance the technical aspect of service performance. In summary, customers’ emotions carry important information relevant to service effectiveness. Drawing on research on emotion recognition, we propose that service employees with high emotion recognition ability should be more effective in connecting with customers as well as in solving the customers’ problems, compared with service employees with low emotion recognition ability. Therefore, we expect a positive relationship between service employees’ emotion recognition ability and their overall service effectiveness.

Hypothesis 1: Emotion recognition ability is positively related to service performance.

Two Moderators: Agreeableness and Cognitive Ability As proposed above, emotion recognition ability should enhance service performance because it helps employees build relationships with customers and solve customer problems. But what is the role of emotion recognition ability vis-a-vis the personality dimension of agreeableness and the individual’s cognitive ability? Below we review evidence and develop a compensatory model where emotion recognition ability is most valuable to service employees with a low level of agreeableness or low cognitive ability.

The Moderating Role of Agreeableness Agreeableness refers to an individual’s general tendency to be helpful, friendly, and cooperative with others (Costa and McCrae, 1992). People who are high on agreeableness tend to be friendly and amicable by nature. In the workplace, agreeableness is mostly associated with employees’ accommodating and prosocial behaviors (Graziano et al., 2007; Graziano and Tobin, 2002; Jensen-Campbell et al., 2002). For example, individuals high on agreeableness have been found

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to focus more on social relationships and engage in more cooperative behaviors when working with others (LePine and Van Dyne, 2001). While the relationship between agreeableness and job performance is complex (Judge et al., 2012), there is consensus that agreeableness is positively associated with the interpersonal dimension of job performance (Hurtz and Donovan, 2000). This is because courtesy and affability are important ingredients to developing positive relationships with others (Parasuraman et al., 1988). Indeed, a meta-analysis by Mount and colleagues (1998) indicates that agreeableness plays a significant role in predicting job performance in serviceoriented contexts because the compliance and tender-mindedness associated with agreeableness are important predictors of service effectiveness. Compared to highly agreeable people, individuals with a less agreeable personality do not value affiliation as an important goal in social interactions (Costa and McCrae, 1992). Research has shown that disagreeable individuals are less interested in being courteous, considerate, and accommodating in social interactions (Graziano and Eisenberg, 1997); they are more comfortable with behaving disagreeably and confrontationally in order to advance their own interests (Colbert et al., 2004; Skarlicki et al., 1999). Therefore, employees with a disagreeable personality often do not perform well in service jobs because they are not always motivated to build positive relationships with customers (Ilies et al., 2006; Witt et al., 2002). Because employees with a disagreeable personality are less “relational” by nature (Ilies et al., 2006; Witt et al., 2002), their emotion recognition ability should exert a stronger impact on their ability to develop positive relationships with customers. Recognizing how customers feel can help disagreeable employees become more attuned to the relational needs of the customers, enhancing their social effectiveness. For example, the accurate reading of a customer’s frustration may prompt a disagreeable employee to act more courteously in the service

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encounter. By contrast, when agreeable employees lack emotional recognition ability, customers’ emotional reactions may bring very little change to how these employees approach the customers, because they are friendly and considerate by nature regardless of the situation. Based on the preceding arguments, emotion recognition ability may not play a central role in how agreeable employees forge positive relationships with customers. Rather, emotion recognition ability should make a bigger difference in the relational aspect of service performance for employees who are low on agreeableness, compared to those who are high on agreeableness. We therefore expect a stronger relationship between emotion recognition ability and service effectiveness when employees are low versus high on agreeableness.

Hypothesis 2: The positive relationship between emotion recognition ability and service performance is moderated by agreeableness such that when agreeableness is low, emotion recognition ability has a stronger relationship with service performance than when agreeableness is high.

The Moderating Role of Cognitive Ability Cognitive ability refers to “the resultant of the processes of acquiring, storing in memory, retrieving, combining, comparing, and using in new contexts information and conceptual skills; it is an abstraction” (Humphreys, 1979, p. 115). Stated simply, cognitive ability represents the capacity of an individual’s cognitive functioning, and is separate and distinct from emotion recognition ability (Ciarrochi et al., 2000; Lopes et al., 2003). According to the literature on intelligence, cognitive ability is positively related to job performance in most, if not all, jobs (Chan and Schmitt, 2002; Motowidlo and Vanscotter, 1994; Schmidt and Hunter, 1998). Greater cognitive ability is associated with proficiency in job-relevant knowledge and increased

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analytical and information-processing skills, both of which enhance job performance (Motowidlo et al., 1997). Accordingly, cognitive ability should be positively associated with the technical aspect of service performance. For example, a service representative with high cognitive ability can effectively solve customer problems by leveraging their extensive knowledge of products and services, as well as the associated procedures and rules for effectively dealing with customer needs and the problems they have (Doucet, 2004). Although employees who are low in cognitive ability may have less job knowledge or skills to rely on when solving customer problems, they may take advantage of their emotion recognition ability when addressing customers’ needs. Consistent with the preceding arguments, emotion recognition enhances problem solving in service interactions through empathy and perspective taking. This view suggests emotion recognition ability may be more important in enhancing the technical aspect of service performance when service employees’ cognitive ability is low. By contrast, because employees with high cognitive ability can solve customer problems easily with their job related knowledge and skills, we expect that emotion recognition ability would render less benefit in improving service employees’ performance.

Hypothesis 3: The positive relationship between emotion recognition ability and service performance is moderated by cognitive ability such that when cognitive ability is low, emotion recognition ability has a stronger relationship with service performance than when cognitive ability is high.

Figure 1 depicts the hypothesized theoretical model.

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-----------------------------Insert Figure 1 about here ------------------------------

Methods Research Setting and Participants This study was conducted at a telephone service center of a large retail bank located in the northeastern United States. Typical service interactions in this setting consist of bank customers contacting the service center via telephone for a variety of services, the most common of which included account balance inquiries, account transaction inquiries, and change-of-address requests. Service interactions typically lasted a few minutes, and ranged from under one minute to over 20 minutes in duration, depending on the complexity of the service request. There were three separate data sources: (a) employee questionnaires, (b) employee standardized tests, and (c) organizational records. A total of 132 service providers working at the telephone service center agreed to participate in the study. Service providers were paid overtime for the questionnaire and testing portions of the study. Complete data were obtained from 70 providers and it was this sample that was used to test our hypotheses. The average age of the participants in the final sample was 33 years, and 81.3% had at least some college education. To ensure that our data were not biased, we first compared the final sample used in our analysis (N = 70) with those excluded due to missing data (N = 62). We found no statistically significant differences (ps > .05) in the age or education levels. Second, we discussed our final sample with a manager of the service center. She indicated that the

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demographic profile of our participants was comparable to that of all service providers in the center. Procedure Employees completed a questionnaire at home that included questions assessing their agreeableness and demographic characteristics. They then completed standardized tests of emotion recognition and cognitive ability at the workplace in groups of 15–20. Employees used names and identification numbers on questionnaires and tests. The center provided performance records with names and identification numbers. The first author administered all standardized tests, and matched the data from different sources. Measures Emotion Recognition Ability Employees’ emotion recognition ability was measured using the Profile of Nonverbal Sensitivity, typically called the PONS test (Rosenthal et al., 1979). This test consists of 220 two-second audio clips, video clips, or combined audio-video clips, and a printed answer sheet on which 220 pairs of brief verbal descriptions are presented in multiple-choice style. The test has been extensively validated (Hall, 2001). Possible scores range from 0 to 220. Agreeableness1 This personality dimension was measured using 22 items from the NEO Five-Factor Inventory (Costa and McCrae, 1992). Items were rated on a scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”) and were averaged to form an index. The reliability of the measure in the present study was  = .80. Cognitive Ability

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We used the Wonderlic Personnel Test (Wonderlic, 1992) to measure employees’ cognitive ability. This is a 12-minute, 50-question paper-and-pencil test of general cognitive ability that has been shown to be highly consistent with other cognitive ability tests (Wonderlic, 1992). Possible scores range from 0 to 50. Performance We measured performance using the organization’s standard performance score one month after we collected data on the independent variables. Throughout the month, members of the organization’s quality control (QC) department remotely monitored 5–10 service interactions for each employee without the employee being aware of the timing. Members of the QC department indicated that these interactions were not selected using a purely random sample strategy. Instead, individual QC employees used their own informal strategies for selecting interactions to record and evaluate. The QC department evaluated each service interaction on a variety of dimensions covering both relational (e.g. did employee end the call using a friendly tone?) and technical aspects of performance (e.g. did employee provide accurate information?) and then aggregated these evaluations into an overall rating of perfect, pass, or fail for each interaction. At the end of the month, the department computed a performance score for each employee by calculating the ratio of service interactions with perfect scores to total service interactions monitored. Possible scores ranged from 0.0, indicating no service interactions rated as perfect, to 1.0 indicating all service interactions rated as perfect. We did not have access to ratings for specific performance dimensions and it was not possible to create separate indices for relational and technical performance. Controls

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To reduce the likelihood of employees’ demographic characteristics confounding the relationships examined, we controlled for both employee age and education.

Results Table Ι displays the means, standard deviations, and correlations among all variables. As shown in the table, age was positively related to agreeableness (r = .28, p < .05), cognitive ability was positively related to both emotion recognition ability (r = .38, p < .01) and performance (r = .33, p < .01), and emotion recognition ability was positively related to performance (r = .31, p < .01).

-----------------------------Insert Table Ι about here -----------------------------We tested our hypotheses using moderated hierarchical regression analyses. In order to assess whether OLS regression assumptions held for these analyses, we examined residual plots for homogeneity of variance, VIF scores for problems with collinearity, and case-wise diagnoses for problematic outliers. We found no major violations. In line with Aiken and West (1991) guidelines for moderated regression, the predictor and moderator variables were mean-centered before creating interaction terms. The control variables (i.e., age and education) and the moderators (i.e., agreeableness and cognitive ability) were entered in step 1, and the independent variable (i.e., emotion recognition ability) was added in step 2. Finally, the two-way interaction terms were entered in step 3. Results of these analyses are displayed in Table ΙΙ. -----------------------------Insert Table ΙΙ about here ------------------------------

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Results in Table II show that cognitive ability was positively related to service performance (β = .32, p .10). These results indicate that cognitive ability was more important than agreeableness in predicting service performance in this sample. Hypothesis 1 predicted that emotion recognition ability was positively related to service employees’ job performance. Results reported in Table ΙΙ provided support for Hypotheses 1. As shown in the Table, after controlling for employees’ agreeableness and cognitive ability, emotion recognition ability was found to be positively related to employee performance ( = .25, p .10). Together, these results provide support for Hypothesis 2.

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-----------------------------Insert Figure 2 about here ------------------------------

Hypothesis 3 predicted that cognitive ability would moderate the relationship between emotion recognition ability and performance. Consistent with this hypothesis, results in Table 2 show a statistically significant emotion recognition × cognitive ability interaction (β = -.31, p < .01). To interpret the nature of the interaction, we plotted the relationship between emotion recognition ability and performance at different levels of cognitive ability (i.e., 1 SD above/below the mean). The interaction is displayed in Figure 3 and shows that the relationship between emotion recognition ability and performance is more positive if cognitive ability is low than if it is high. Further, results indicated that the simple slope of the regression line had a positive, significant value for employees with low cognitive ability (β = .12, p < .01), but did not differ from zero for those with high cognitive ability (β = -.01, p > .10). In total, these results support Hypothesis 3.

-----------------------------Insert Figure 3 about here ------------------------------

Discussion It has long been recognized that people differ in their ability to detect others’ emotions (Hall and Bernieri, 2001; Nowicki and Duke, 2001; Rubin et al., 2005), and more research has started to examine the implications of this difference in the workplace (Elfenbein et al., 2007; Rubin et al., 2005). Extending this research to the service domain, this study examines the relationship

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between service employees’ emotion recognition ability and their service performance. Seventy full-time service employees in a large retail bank completed the PONS test (Rosenthal et al., 1979) which measured their ability to accurately detect others’ emotional expressions. A month later, performance data for each employee were collected from the company’s QC department. Results showed that emotion recognition ability had incremental validity in predicting service performance, beyond the personality dimension of agreeableness and cognitive ability. Results also showed that the relationship between emotion recognition ability and service performance was affected by service employees’ agreeableness and cognitive ability. The positive relationship was only statistically significant for service employees with low agreeableness or with low cognitive ability. Together, these results provide evidence for the value of emotion recognition ability in work settings (Elfenbein et al., 2007; Rubin et al., 2005). The current study has several important methodological strengths. We collected data from full-time employees in a financial organization. Field data help demonstrate the external validity of previous laboratory studies on the benefits of emotion recognition ability. Also, we used strong and robust measures for the key variables. Emotion recognition ability was gauged using an ability-based performance measure as opposed to self-report, which helps reduce the social desirability response bias (Arnold and Feldman, 1981; Brackett et al., 2006). Furthermore, we measured service performance using organizational records one month after we collected data on the predictors, which helps alleviate the potential bias from common method variance (Lindell and Whitney, 2001; Podsakoff et al., 2003). Theoretical Contributions and Practical Implications The findings of the present study contribute to the literature on emotions and service effectiveness in several ways. First, research on service effectiveness or job performance in

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general has traditionally emphasized cognitive ability or personality as important causes of employees’ performance differences in the workplace (Brown et al., 2002; Liao and Chuang, 2004; Motowidlo et al., 1997; Rosse et al., 1991; Stewart and Carson, 1995). For example, in the theory of job performance proposed by Motowidlo and colleagues (1997), personality and cognitive ability are featured as two primary determinants of job performance. While recent research has started to show the importance of emotion-related capabilities and behaviors in shaping service performance (Groth et al., 2009; Kernbach and Schutte, 2005; Mayer et al., 2008; Wang and Groth, 2014), this research mostly focuses on employees’ ability and strategy to regulate their own emotions. We extend this work by demonstrating that employees’ ability to read others’ emotions also affects service effectiveness. Therefore, individual differences in emotion recognition ability help explain why employees have variations in performance in service jobs. Second, we develop and test a more nuanced model of when emotion recognition ability affects service effectiveness by examining its interactions with agreeableness and cognitive ability. Particularly, we found that the positive relationship between emotion recognition ability and service effectiveness was statistically significant only when an employee’s agreeableness or cognitive ability was low. This is not to say that emotion recognition and other aspects of emotional intelligence are less important, but that their potential contribution to workplace outcomes is more complex than originally argued (Goleman, 1995). Therefore, consistent with scholars who suggest that emotional intelligence research needs to go beyond sweeping claims, such as that emotional intelligence is always positively related to performance, future research should pay more attention to boundary conditions of emotion-related abilities (e.g., Joseph and Newman 2010).

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Third, the compensatory model we propose provides a new perspective in looking at how emotion recognition ability interacts with personal traits to affect work outcomes. This is different from previous research in which emotion recognition ability complements other desirable traits in producing positive outcomes. For example, Rubin et al. (2005) found that as extraversion became higher, the positive relationship between leader emotion recognition ability and transformational leadership behavior was stronger. However, we contend that this is not contradictory with our findings. One explanation is that how emotion recognition ability interacts with traits depends on the work outcome under investigation. In our study, we examine service performance. It is plausible that employees can increase service performance by developing positive relationship with customers in different ways, such as by having a friendly disposition (i.e., agreeableness) or by being sensitive to customers’ emotions (i.e., emotion recognition ability). Stated differently, as long as an employee acts friendly, lacking sensitivity to customers’ emotional cues may not have many detrimental effects on the relational aspect of service performance. This is unlikely to be true when it comes to transformational leadership behaviors. To be a transformational leader, an outgoing disposition (i.e., extraversion) may not be enough. Rather, transformational leadership behavior may require a leader to display more complex responses which may require leaders to accurately read followers’ emotional expressions. Therefore, an extraverted leader is more likely to benefit from having greater emotional recognition ability. In summary, the different findings suggest that emotion recognition ability may interact with other traits in different ways, depending on work outcomes being examined. This cautions against sweeping claims that emotion recognition always compensates or complements certain traits in affecting work outcomes.

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Our findings have important practical implications. Although some human resource practitioners have already embraced emotional intelligence as a useful tool for personnel hiring and training (Fineman, 2004), the current study provides evidence for the validity of using emotion recognition ability as an additional criterion in the selection and recruitment of service employees (Fineman, 2004). In particular, employees who are traditionally believed to be ineffective in service jobs may nevertheless perform well when they have emotion recognition ability. Therefore, our findings imply that overreliance on a single selection criterion when recruiting service employees should be avoided, as there are different employee characteristics which can enhance service performance in the similar fashion. Organizations should take into consideration job candidates’ emotion recognition ability, personality, and cognitive ability in recruiting employees for service jobs. This approach may result in improvements in the match between employees and their job roles in the organization. In addition, our findings suggest organizations can benefit from training and developing service employees’ emotion recognition abilities. There exists evidence that emotional intelligence, including emotion recognition, can be improved through training. For example, Boyatzis, Stubbs, and Taylor (2002) found that emotional intelligence can be developed in MBA students. Groves, McEnrue, and Shen (2008) showed in an experiment that the emotional intelligence of leaders can be deliberately developed. Focusing on emotional recognition, Matsumoto and Hwang (2011) presented evidence that the ability to read micro expressions among department store employees can be trained. These findings suggest that organizations aiming to increase their service performance may utilize training, such as the micro expression training tool and subtle expression training tool (Ekman, 2003), to build their employees’ emotion recognition ability.

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Limitations and Future Research Our study has a few limitations. First, the small sample size raises concerns about potential nonresponse bias. Future research might attempt to replicate our findings using a larger sample from different settings. Second, we theorize that service employees who are high in emotion recognition ability should be more effective because they are more capable of building relationships and solving customer problems. However, we did not examine these employee behaviors, nor did we measure the two aspects of service performance (i.e., technical and relational) separately. Therefore, future studies might focus on uncovering the psychological and behavioral mechanisms that translate emotion recognition ability into different aspects of service performance. In particular, given the importance of emotion regulation in service interactions (Grandey, 2000; Groth et al., 2009), emotion regulation strategies may be an important mediator between emotion recognition ability and aspects of service performance. For example, service employees who are better at recognizing customers’ emotions (e.g., anger) may be more likely to engage in the regulation of their own emotions (e.g., showing a smile), which in turn, helps them build good relationships with the customers, leading to good performance. Future research might examine an extended model with mediating factors included based on research in emotion regulation (Gross, 1998; Gross, 1999; Gross and John, 2003) and emotional labor (Grandey, 2000; Groth et al., 2009; Hennig-Thurau et al., 2006). Third, the present study examined moderators of cognitive ability and the personality dimension of agreeableness on the relationship between emotion recognition ability and job performance. Future research is needed to investigate other factors that may serve as moderators but were not included in this research, such as a service employee’s positive affect (Watson et al.,

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1988, Kammeyer-Mueller et al., 2013). Those with high positive affect may benefit little from emotion recognition ability because they tend to be happy and cheerful by nature. Therefore, positive affect is likely to be another factor that interacts with emotion recognition ability in a compensatory model of service performance. Finally, service employees in our sample only had voice-to-voice interactions with customers as opposed to face-to-face. This sample is appropriate, because in many industries such as banking and telecommunication, service is provided through phones (Dean, 2007; Malhotra and Mukherjee, 2004). Therefore, it is important to examine how emotion recognition affects service performance in this context. Based on our theoretical model, emotion recognition ability affects service performance through customer relationship building and customer problem solving, particularly when agreeableness or cognitive ability is low. We have no reason to believe that these mechanisms would have worked differently in a face-to-face interaction context. In both voice-to-voice interactions and face-to-face interactions, emotion recognition is critical to service performance. It is just that the ways of recognizing emotions are different— one is focusing on voice, whereas the other one is focusing on facial expressions. Nevertheless, in future research, data from service employees involved in face-to-face interactions with customers may be collected to test the generalizability of the proposed model in the present research. Conclusion The importance of service employees’ emotion-related abilities in service encounters has been highlighted in recent years. In the present study, we propose and find support for a compensatory model where emotion recognition ability is positively associated with service performance, and this positive relationship is moderated by the agreeableness personality dimension and cognitive

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ability. The results supported our model, and suggest that emotion recognition ability is helpful in enhancing service performance, and in particular when service employees are low on agreeableness or cognitive ability. Therefore, to enhance job performance of service employees, personality factors, cognitive ability, and emotional abilities should be taken into consideration.

Endnote 1. In an effort to explore how other personality dimensions relate to emotion recognition ability and service performance, our study included measures of two additional dimensions of personality from the NEO Five-Factor Inventory (Costa and McCrae, 1992): extraversion and openness to experience. Extraversion is associated with being gregarious, sociable, assertive and talkative (Costa and McCrae, 1992). Openness to experience is associated with being imaginative, curious, original and broad-minded (Costa and McCrae, 1992). Although we present no formal hypotheses involving these dimensions, we performed supplementary analyses and examine whether they directly relate to service performance or interact with emotion recognition ability to affect service performance. Results of analyses indicated that the two dimensions and their interactions with emotion recognition ability made statistically nonsignificant contributions (ps > .05) to service performance. Moreover, when these terms were included in the regression equation along with agreeableness and cognitive ability, the pattern of results did not change. Specifically, the interactions involving emotion recognition, agreeableness and cognitive ability remained statistically significant. Details of these results are available from the authors on request.

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Figure 1

Agreeableness

Emotion recognition ability

Service performance

Cognitive ability

Figure 1. The hypothesized theoretical model

EMOTION RECOGNITION AND SERVICE PERFORMANCE Figure 2

Figure 2. Interaction of emotion recognition ability and agreeableness on performance

39

EMOTION RECOGNITION AND SERVICE PERFORMANCE Figure 3

Figure 3. Interaction of emotion recognition ability and cognitive ability on performance

40

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Table 1 Table Ι. Means, standard deviations, and correlations

Variable 1. Age 2. Education 3. Agreeableness 4. Cognitive Ability 5. Emotion Recognition 6. Performance

M 33.41 4.40 3.61 22.49 171.19 0.83

Notes: N = 70. *p < .05; **p < .01

SD 10.52 1.15 0.45 5.21 8.47 0.23

1 0.08 0.28* -0.11 -0.22 0.20

2

3

-0.07 0.16 -0.03 -0.12

-0.18 -0.02 0.12

4

.38** .33**

5

.31**

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Table 2 Table II. Hierarchical multiple regression of service performance Step 1 Variables Entered Age Education Agreeableness (A) Cognitive ability (CA) Emotion recognition (ER) ER*A ER*CA ∆R2 ∆F d.f.

β .23 -.19 .11 .41

t 2.03* -1.73 .98 3.64**

Step 2 β .28 -.18 .09 .32 .25

t 2.46* -1.62 .78 2.66* 2.09*

.05* 4.35* 1, 64

Step 3 β .34 -.09 .08 .25 .24 -.24 -.31

t 3.08** -.81 -.74 2.20* 2.15* -2.11* -2.94**

.11** 5.37** 2, 62

Notes: N = 70. β, standardized coefficients. * p < .05; ** p < .01. The inclusion or exclusion of control variables did not significantly alter the results reported above.