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trait ei, customer orientation and service performance

Research Article ºÚÁÏÍø
Catherine Prentice*
Lecturer Marketing, Swinburne University, Australia
*Corresponding author: Catherine Prentice
Lecturer Marketing, Swinburne University, Australia
Tel: +61406-627622
E-mail: cathyjournalarticles@gmail.com
 
Received March 03, 2012; Published September 29, 2012
 
Citation: Prentice C (2012) Trait EI, Customer Orientation and Service Performance. 1:369. doi:10.4172/scientificreports.3691
 
Copyright: © 2012 Prentice C. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
 
Abstract
 
In assessing the relationship between personality traits and job performance, researchers have noted that though significant, the former only account for a relatively small portion of the evident variance. In addressing this deficient explanation, it has been suggested that the inclusion of surface traits as a mediator will enhance the predictive capacity. The present research focuses whether there is an evidence for the existence of a mediation relationship between trait emotional intelligence (as a personality trait), customer orientation (as a surface trait) and service performance. To test the proposed relationship, a self-administered survey was administered to frontline personnel within a large service organisation located in the Asia-Pacific region. A mediation regression analysis was conducted on the 174 valid responses to test the hypotheses. The results confirmed the validity of the proposed mediation relationship, leading to a range of conclusions about the applicability of the findings in service settings, including suggestions about the merit of incorporating emotional intelligence and customer orientation into employee training and development programs.
 
Keywords
 
Emotional intelligence; Customer orientation; Basic trait; Surface trait; Service performance; Frontline employees
 
Introduction
 
Personality traits have been commonly recognised as important predictors of job performance [1]. Although significant, these traits have generally explained only a small portion of the variance in job performance [2]. The weak relationship between personality traits and performance suggests that it may be worth exploring the influence of other factors. Surface traits are believed to mediate between basic traits and performance evaluation, and may enhance the outcome variable. In their empirical testing of the mediation relationship, [1] concluded that the incorporation of a surface trait as the mediator enhanced the evaluation of performance.
 
Since its first appearance, the concept of emotional intelligence has been widely discussed in the relevant literature. It is commonly acknowledged as offering predictive validity in various contexts, and as meriting further research [3]. Despite its undoubted importance, the construct presents a number of questions about its conceptualisation, measurement and sometimes inconsistent findings. The current study employs the concept trait EI proposed by Petrides [4] to investigate its role in a hierarchical relationship between basic personality trait, surface trait and performance evaluation. The present study focuses on service aspects of performance ratings. From relationship marketing perspective, employee service performance has implication for customer loyalty and repetitive patronage, which in turn leads to company profitability [5-7]. The following literature review examines the operationalisation of emotional intelligence as a trait, and reveals connections with customer orientation as a surface trait in a performance context. Relevant propositions are formed on this basis.
 
Emotional intelligence
 
Emotional intelligence may be defined as the ability to perceive, respond and manipulate emotional information without necessarily understanding it and the ability to understand and manage emotions without necessarily perceiving feelings well or fully experiencing them [ 8]. There are two main conceptualisations of emotional intelligence within the literature. One approaches emotional intelligence from the perspective of pure intelligence, indicative of cognitive ability [8]. Such ability includes a capacity to engage in abstract reasoning about emotional signals that convey regular and discernable meanings about relationships and about a number of universal basic emotions [9].This approach provides a representation of the potential to master specific abilities, incorporating emotional abilities which are viewed as being congruent with emotional intelligence and as integrating the domains of intelligence and emotion. Since the conceptualisation emphasises the cognitive components of emotional intelligence, it is generally regarded as an ability model.
 
The alternative paradigm embraces a mixed approach to intelligence which combines cognitive ability and personality, referred to as mixed models, such as those of Bar-On [10-12]. Mixed models included not only emotion and intelligence, but also motivation, non-ability dispositions and traits, and global personal and social functioning. Goleman [11] describes emotional intelligence as the ability to motivate oneself and overcome frustrations; to control impulses and delay gratification; to regulate emotions to facilitate thinking; to empathize and hope. Bar-On’s [10] model of emotional intelligence includes the ability to be aware of, to understand, and to express oneself; the ability to be aware of, to understand and relate to others; the ability to deal with strong emotions and control one’s impulses; and the ability to adapt to change and to solve problems of a personal or social nature. They are more closely related to personality traits [13].
 
Although the multiplicity of theoretical perspectives within the emotional intelligence paradigm has been claimed to be indicative of strength rather than weakness [14,15], such diversity of conceptualisation has inevitably created some confusion around this psychological construct, leading to apparently contradictory findings. Nevertheless, the classification of trait EI and ability EI proposed by Petrides [4] has shed light on the lack of a coherent operational framework for emotional intelligence.
 
Ability EI and trait EI are conceptualised on the basis of measurement approaches to emotional intelligence. Measuring emotional intelligence through the use of self-report questionnaires leads to operationalisation of the construct as a personality trait and to be classified as trait EI, because self-reporting scales generally include items encompassing “behavioural dispositions and selfperceived abilities”, which are embedded in a personality framework [ 4]; In contrast, measurement by the testing of maximum performance leads to the operationalisation of the construct as a cognitive ability, and to be classified as ability EI, because the objective performance measurement indicates actually abilities, and is concerned with psychometric intelligence. Despite various arguments for performancebased EI measures such as MSCEIT developed by Mayer [9], Van Rooy [3] meta-analysis has shown that self-reporting EI measures are more frequently used and demonstrate good validity and reliability. Induction of the concept trait EI is not only conducive to clarifying diversified conceptualisation but also to the construct development.
 
Within this classification context, trait EI draws heavily on personality variables such as empathy, optimism and impulsiveness, and is evidently embedded within the personality framework [12,16]. Considering the five-factor model of personality, substantial and significant correlations are often evident between trait EI measures, Extraversion and Neuroticism. Smaller though still significant positive correlations have been found with Openness, Agreeableness and Conscientiousness [4,17]. On this basis, trait EI is expected to study within personality domain, and it is conceptualised on the basis of the measurement but not on the elements that the various models are hypothesised to encompass [4]. The classification should not affect EI predictability that has been proposed and testified.
 
Van Rooy [3] have indicated that all emotional intelligence measures demonstrate predictive validity, although the criterion variables exhibit quite different characteristics. Emmerling [15] indicated that the predictive validity of emotional intelligence may be quite variable depending on the settings within which the study occurs, the criterion that has been selected and the model that has been used. It has been claimed that in work settings, emotional intelligence affects a wide range of behaviours, including employee commitment [12]; job satisfaction [10]; teamwork [8,18] and leadership [19]. Some of these claims have not however been substantiated and should be viewed with caution [20]. The extent to which affects may be explained on the basis of the criterion variable will depend on the characteristics of the job [ 21]. Studies have shown that emotional intelligence is particularly effective in professions that demand high emotional labour and logically require high level of emotional intelligence, such as the job of customer service representative [21]. Various researchers [21,22] have confirmed the positive influence of emotional intelligence on behaviours associated with emotional labour in service settings. For the purposes of the present study a relationship is proposed between trait EI and employee service performance in service contexts, leading to the following hypothesis:
 
H1: Trait EI is positively related to employee service performance.
 
Basic trait and surface trait
 
Surface trait was introduced into the literature as contextual behaviours opposing to “focal” behaviours such as number of calls taken, frequency of smiling and response time. Such behaviours are classified as traits because they indicate a persistent tendency [23]. Surface traits occur in particular contextual settings and manifest themselves as “dispositions, inclinations or tendencies to behave in certain ways in certain situations and are more abstract than concrete behaviours” [2]. Compared with surface traits, basic personality traits are enduring dispositions which are indicative of behaviour in diverse situations. Surface traits are context specific and result from interactions between basic traits and situational contexts.
 
According to Brown [2], personality traits may be too far removed from actual focal behaviours to provide an effective prediction of employee performance effectively, reflective of the fact that basic personality traits account for only a small proportion of variance when rating employee performance using a direct model. When compared with basic personality traits, surface traits are closer to the specific behaviours that determine performance ratings, and may enhance ultimate performance. Surface traits “surface” from the time that basic personality traits operate to influence the performance result and impact directly on performance. The effect of personality factors on performance may be viewed as indirect and as occurring through surface traits. Surface traits may be viewed as mediating between personality factors and the evaluation of performance. According to Sekaran [24], a mediation model occurs when a variable surfaces between the time the independent variables operate to influence the dependent variable and their impact on the dependent variable. The variable may be referred to as a mediator, acting as a function of the independent variable, and helping to conceptualise and explain the influence of the independent variable(s) on the dependent variable [24]. Brown [2] tested the mediation relationship within a service context, and the results confirmed their proposition. As discussed previously, trait EI is classified as a personality trait. On the basis of Brown’s study and the above discussion, it is anticipated that trait EI may predict employee performance through the mediation effect of a surface trait and incorporating a surface trait may enhance the predictability.
 
Customer orientation as a surface trait
 
Customer orientation is evidently prevalent in the service context, especially in the case of service interactions [25,26]. Referred to as “the ability of the service provider to adjust to his or her service to take account of the circumstances of the customer” [27], customer orientation involves a desire to help customers in a variety of ways [ 28]. Such assistance may include making purchasing decisions, assessing customer needs, offering products which address such needs, providing adequate product and service descriptions, avoiding deceptive or manipulative tactics, or high pressure selling. These behaviours have a strong tendency to achieve customer satisfaction and loyalty, and encompass dispositions of developing long-term buyerseller relationships [26]. The tendency and disposition are ultimately reflective of employee performance. Numerous studies have provided evidence of the close relationship between customer orientation and customer satisfaction, which in turn leads to customer loyalty and retention [29-31]. These characteristics of customer orientation are consistent with the conceptualisation of surface trait.
 
In the relevant literature, customer orientation has been viewed as a surface trait mediating between basic traits and performance outcomes. For example, McIntyre [31] found that a person’s cognitive style, such as information intake by intuiting and information processing by thinking, is a predicator of his or her customer orientation and influences self-perceived selling performance. Brown [2] proposed a mediation model and tested the relationship between the personality factor (basic personality trait), customer orientation (surface trait), and service performance. Their study confirmed the mediation model and concluded that incorporating the surface trait as a mediator enhanced the evaluation of performance. As the present research has exploratory nature, consistent with Brown et al.’s study, customer orientation was incorporated as a surface trait to test the mediation relationship between trait emotional intelligence and the evaluation of service performance. On this basis, the following hypothesis is made:
 
H2: Customer orientation mediates the relationship between trait EI and employee service performance.
 
Research Design
 
Participants
 
To test the proposed relationship, a self-administered survey was conducted to frontline personnel within a large service organisation with hotel and casino operations located in the Asia-Pacific region. As requested by the management, the company name remains anonymous. The respondent sample was drawn from key account service employees who took care of the key accounts of the organisation. This approach was adopted because the firm is large enough to provide a sufficient sample size. Second, emotional intelligence is claimed to be effective in contexts where emotions are prevalent and emotional work is required from customer-contact employees [22]. Third, a single entity was chosen because of the need to control for a variety of extraneous, uncontrollable variables, such as different corporate values, cultural values, market performance, and geographic location.
 
From a total of 300 surveys distributed to prospective respondents, 174 usable responses were returned (61%). Of the total usable sample, 76 were male, and 98 were female. The age of the participants ranged from 18 to 55, and the majority was in the 18 to 35 age group. Almost 24.3%) had completed their studies at secondary school level, with the rest possessing a diploma or university degree. This is indicative of a fairly educated group of respondents.
 
Measures and procedure
 
Emotional intelligence: Consistent with the conceptualization of trait EI by Petrides [32], the present study uses the Self-Report EI Test (SREIT) designed by Schutte [32]. This test is based on Salovey [33] ability model. It is a 33-item self-report measure that includes items such as “By looking at their facial expression, I recognize the emotions people are experiencing” and “I easily recognize my emotions as I experience them.” According to Schutte [17], the scale generated correlations with theoretically related constructs, such as alexithymia, attention to feelings, clarity of feelings, mood repair, optimism and impulse control. It exhibited good internal consistency and test– retest reliability, predictive validity, and discriminant validity with strong results for each analysis [17]. These positive attributes led to its adoption in the current study. Data were collected on a five-point Likert scale, with 1 representing strongly disagree and 5 strongly agree. These labels indicate the extent to which each item describes the respondent view. Higher total scores are reflective of greater self-report emotional intelligence. The Cronbach alpha coefficient reported for this scale was 0.91.
 
Customer orientation: To measure the customer orientation of service providers, a 13-item Customer Orientation Scale (COS) developed by Daniel [27] was employed. This scale drew upon the original 24-item selling orientation (12 items) and customer orientation (12 items) scale (SOCO) designed by Saxe [34]. Daniel and Darby adopted and modified the 12 items of customer orientation from the SOCO scale, and supplemented this with a single item from the selling orientation of the SOCO scale, which relates specifically to customer orientation. They reported a standardized Cronbach alpha of 0.86 for COS, and suggested that the modified COS is suitable for measuring the customer orientation of service providers. Prospective survey respondents were required to indicate their agreement with each item on a five-point Likert-type scale ranging from “strongly disagree” to “strongly agree.” Higher scores are a reflection of higher level employee customer orientation. The Cronbach alpha coefficient for this scale was 0.86.
 
Performance: The performance measure employed was consistent with the approach to staff performance appraisal used by the organisation from which the sample was drawn. This approach involves measuring the service performance of employees and their contributions measured against the requirements and standards applicable to the relevant job and against their peers. The items selected to measure employ service performance were recommended by Senior Vice President of Human Resource Department in the organisation. The study relied on respondent self-reporting, an approach which has been common in previous research on performance measurement [2,35,36]. In their meta-analysis, Churchill [37] concluded that self-reporting does not generally lead to biased outcomes or inflated assessments. Each item was assessed using a five-point scale, ranging from 1 (lowest) to 5 (highest). The applicable Cronbach alpha coefficient for this scale in the case of the present study was 0.78.
 
The questionnaire consisted of a paper-pencil test and aimed to collect information about emotional intelligence, customer orientation and service performance. Respondents were assured of anonymity in the instructions which accompanied each of the documents. The survey packets included a cover letter, which provided an introduction and explanation of the significance and objectives of the research, an expression of thanks, a 20-dollar dining voucher for use by prospective respondents, a consent form, a questionnaire, and a pre-paid envelope. Detailed instructions were provided to guide respondent participation. The questionnaire was distributed to prospective respondents when undertaking a work shift, and could be completed at home or at offpeak times during weekday work shifts. Responses were required within two months of receipt.
 
Results
 
Multiple Regression analyses were conducted to test the hypotheses using the most recent version of SPSS. The emotional intelligence scale was factor analysed prior to entering the regression equation. Although Schutte [17] argued for a single-factor structure for the Self-Report Emotional Intelligence Scale (SREIS), other researchers [32,38] have derived a four-factor solution. The authors warned that data obtained with the SREIT should undergo factor analysis to confirm the fourfactor structure found in their analysis, as they were unsure of the stability of their solution.
 
Principal Components Analysis (PCA) was employed for the purposes of factor analysis. Prior to performing PCA, the suitability of the data for factor analysis was assessed and supported. Principal components analysis revealed the presence of nine components with eigenvalues exceeding 1. An inspection of the Screeplot revealed the possibility of one explaining 28.73 percent of the total variance. A parallel analysis in which the actual eigenvalues were compared to average eigenvalues derived from a series of randomly generated data sets suggested the presence of three factors explaining 40.44 percent of the variance. This finding confirms the cautionary note that Petrides [ 4] made: “we may have overestimated the number of factors, which means that some of them (especially the fourth) might not emerge in other data sets”. However, for the sake of interpretability as Petrides and Furnham recommended, four components were retained for further investigation. A varimax rotation was conducted to identify the meaningful subscales. Based on items with loading of 0.40 or above, the four sub-components that were present were labeled “Mood Regulation”, “Appraisal of Emotions” “Social Skills” and “Utilisation of Emotions” with Cronbach alpha coefficients of 0.88, 0.72, 0.71, and 0.66 respectively. The results in terms of the four factors and item loading were consistent with the finding reported by Petrides [4]. The coefficient for the last component was lower than 0.70, also consistent with the finding by Petrides and Furnham who indicated they might have overestimated the number of factors.
 
The testing of Hypothesis 1 involved investigating the relationship between trait EI as a predictor and employee service performance as the dependent variable. Based on the finding that four factors could be identified from the principal component analysis of trait EI, the four dimensions were entered into the regression equation to investigate the unique variance in the dependent variable explained by each of the dimensions. The results from the multiple regression analysis are presented in table 1.
 
 
Table 1: Trait EI as a Predictor of Service Performance - Multiple Regression Analysis.
 
Table 1 demonstrates that the four factors of trait EI explained 35 percent of the variance in service performance (R2=0.35, F(4,169)=39.04, p<0.0005). These indicate that a statistically significant result was obtained from the regression model. On the basis of these results, hypothesis 1 was supported. A review of the coefficients table shows that only two factors of trait EI made statistically significant contributions to service performance. These are respectively Component 1, Mood Regulation and Component 2, Appraisal of Emotions, with Beta=0.34, t=3.51, p<0.001 and Beta=0.41, t=4.47, p<0.0005 respectively.
 
The second hypothesis involves testing a mediation relationship. The most commonly used mediation test technique is the four step approach recommended by Baron and Kenny in 1986. To apply the four-step mediation model theory in the current study, the first step is to show that independent variable trait EI is related to the dependent variable service performance. This relationship has been confirmed in the above test. The second step is to show that trait EI is related to the presumed mediator customer orientation. The third step is to show that customer orientation affects the evaluation of service performance. The last step is to establish that customer orientation mediates trait EI and service performance. By controlling customer orientation, the effect of trait EI on service performance should be reduced or zero.
 
The next step of the investigation involved testing the relationship between trait EI and customer orientation. Multiple regression was undertaken and the results are displayed in table 2. This shows that trait EI explained 24 percent of the variance in customer orientation, F(4,169)=31.20, p<0.0005. A review of the coefficients reveals that only Mood Regulation and Social Skills made statistically significant contributions to customer orientation. The Beta for Mood Regulation=0.55, t=3.86, p<0.0005 and for Social Skills was =-0.22, t=- 2.66, p<0.01. Since the full model reached statistical significance, Step 2 was confirmed (Table 2).
 
Table 2: Trait EI as a Predictor of Customer Orientation Scale - Multiple Regression Analysis.
 
The third step investigated whether the customer orientation scale, as the presumed mediator, explained variance in service performance (the dependent variable). This condition was tested by conducting a simple regression analysis, with the total score of customer orientation scale used in the regression equation. The results indicated that customer orientation made a statistically significant contribution to the dependent variable. The beta value for customer orientation was 0.41 (p<0.0005).
 
The fourth step assessed whether the effect of trait EI on service performance became zero or is reduced after controlling for COS. A review of the results shows that the variance explained by the whole model reduced from 51.3 percent to 18 percent. The beta weight for Mood Regulation reduced from 0.34 to 0.16 (p< 0.05), for Appraisal of Emotions from 0.07 to 0.02, for Social Skills from 0.11 to 0.05, and for Utilisation of Emotions from 0.41 to 0.28 (p<0.0005). The results indicate that COS did mediate between trait EI and service performance, though the mediation effects were not reduced to zero. It appears as if the indirect effects were partially mediated by COS. The relevant findings are presented in table 3.
 
Table 3: Mediation Regression Analysis for Customer Orientation Scale as Mediator between Trait EI and Service Performance.
 
Conclusion and Discussion
 
The present study has argued that EI is a trait from the perspective of construct operationalisation, and examined its relationship with employee service performance in a mediation model. Hypothesis 1 tested whether trait EI is related positively with service performance. The results confirmed the hypothesis and supported the claim [39] that emotional intelligence is positively related to performance in the case of jobs involving a high level of emotional intelligence and demand high emotional labour, such as that of customer service representative. As indicated previously, service employees are positioned in the boundary-spanning interface with customers and emotional skills affect their capacity to deal with their own emotions as well as those of customers. The application and demonstration of emotional skills can prospectively help to settle emotional customers. It can also assist employees to regulate their own emotions and assist them to deal with unreasonable requests.
 
The coefficient analysis has shown that, of the four identified factors of trait EI, Mood Regulation (managing emotions) and Appraisal of Emotions made statistically significant contributions to service performance. This seems to be plausible. Frontline employees deal with customer emotions and occupy organisation boundary spanning roles. Customers are not always right, but they are always emotional during purchasing or engaging in transactions. Hartel, Barker [40] noted that emotional management skills can affect a customer’s formation of emotions and appraisal process and, in turn, attitudes and behaviours. A positive attitude and strong behavioural intention reflects the quality of the firm’s customer-contact employees. Those who manage emotions effectively have indicated that they can avoid the detrimental effects of emotional dissonance, such as burnout. This is directly associated with job satisfaction and job performance. To be able to manage emotions, one has to understand and appraise emotions effectively. These two concepts are connected logically, consistent with Daus [41] finding that those who are better able to appraise emotions feel less of an emotional load from work. Less emotional load means less emotional dissonance, which implies less likelihood of burnout. Performance can be enhanced.
 
The mediation test has indicated that customer orientation provided a partial mediation between trait EI and the evaluation of service performance. This finding has confirmed the significance of the mediation model theory initiated by Brown [2]. It also supports Petrides [ 32] distinction between trait EI and ability EI who noted that trait EI is embedded within the personality framework. The confirmation of partial mediation reveals that measuring surface traits accounts for more variance than an exclusive focus on basic personality traits. By using mediation analysis, it has been found that the mediation model with the mediator accounted for a greater proportion of variance in the dependent variable than without the mediator. This is consistent with the conclusions of Brown [2]. The present research has confirmed that within the personality trait hierarchy customer orientation can be considered as a surface trait, closer to the evaluation of service performance.
 
Testing of the mediation of customer orientation between trait EI and the evaluation of performance concluded that trait EI has a statistically significant relationship with customer orientation. This is an important finding since customer satisfaction is a fundamental goal of marketing and customer emotions play an important role in the formulation of customer satisfaction. Liljander [42] have indicated that negative emotions have a greater influence on satisfaction than positive emotions. Whilst a strongly positive feeling does not explain satisfaction, a strongly negative feeling does explain dissatisfaction. Westbrook [43] noted that negative emotions have a significant influence on customer satisfaction. Emotional intelligence clearly has connotations for managing emotions and particularly for those that are negative. Customer satisfaction will be more attainable once negative emotions have been brought under control. This is achieved through the more direct influence of emotional intelligence as a basic trait on customer orientation as a surface trait.
 
Implications of the Study
 
This study has implications for the relevant literature and for human resource practitioners by providing insights into EI construct development, and by extending its implication into an organizational context through the connection with a mediation model. The significant relationship identified between emotional intelligence and service performance indicates the importance of incorporating this psychological construct into staff recruitment and training, particularly in the case of frontline employees. Their performance over service encounters has implications for customer satisfaction and loyalty. Emotional intelligence is trainable and developable. The longitudinal studies conducted by Boyatzis [44] provide the most persuasive evidence that emotional intelligence can also be developed and improved. Therefore, emotional intelligence training would be a worthwhile initiative for existing employees to adopt. Furthermore, the greater proportion of variance in the performance ratings explained by the combination of basic personality trait and mediator indicates that management needs to identify both factors during the process of staff recruitment and training process. This finding provides a means of identifying the underlying dispositions associated with higher levels of performance.
 
Results of this study also indicate that the future research should focus on identifying other service traits. It is worth comparing the effects of different surface traits in the mediation relationships, and exploring the unique variance that the surface trait contributes to the criterion variable. Furthermore, extending the sample frame to a broader context would increase the generalizability of the findings.
 
 
References