pk jbr 12

Journal of Business Research xxx (2013) xxx–xxx

JBR-07678; No of Pages 7

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Journal of Business Research

Impacts of personality, emotional intelligence and adaptiveness on serviceperformance of casino hosts: A hierarchical approach

Catherine Prentice a,⁎, Brian E.M. King b

a Marketing, Operations and Management, Faculty of Business & Enterprise, Swinburne University of Technology, 91 Lancaster Drive, Point Cook, 3030, Victoria, Australiab School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Hong Kong

⁎ Corresponding author.E-mail address: [email protected] (C.

0148-2963/$ – see front matter © 2012 Elsevier Inc. Allhttp://dx.doi.org/10.1016/j.jbusres.2012.12.009

Please cite this article as: Prentice, C., & Kinof casino hosts: A hierarchical approach, Jo

a b s t r a c t
a r t i c l e i n f o
Article history:Received 1 December 2011Received in revised form 1 April 2012Accepted 1 September 2012Available online xxxx

Keywords:Casino premium playerCasino hostsThe FFM of personalityEmotional intelligenceAdaptivenessService performance

The premium player segment is a major source of revenues and profit for the casino sector. In their rolesas providers of personalised services casino hosts are an important determinant in attracting and retainingthis market segment. The service performance of such hosts impacts on both player retention and casinoprofitability. In seeking to explain these relationships, the present study identifies the antecedents ofhost service performance by using the five factor model of personality (FFM) and the concepts of emotionalintelligence and adaptiveness. The researchers test the proposed relationships by adopting a hierarchicalapproach to FFM and emotional intelligence as basic personality traits or independent variables, adaptivenessas a surface trait or mediator, and host performance as the dependent variable. A sample of casino hosts at alarge Australasia-based casino responded to a questionnaire-based survey which considered the five factorsof personality, emotional intelligence, adaptiveness and service performance ratings. The results indicate thatthe FFM, emotional intelligence and adaptiveness have a significant influence on host performance. Structuralequationmodelling confirmed the existence of a hierarchical relationship between the basic personality traits,adaptiveness and performance outcomes and demonstrates that the inclusion of a mediator contributes toan enhanced evaluation of service performance. These findings enrich the literature by identifying new traitsand provide insights that will support practitioners with their selection and training-related activities.

© 2012 Elsevier Inc. All rights reserved.

1. Introduction

The premium player segment generates the bulk of casino reve-nues and profits (see Hannum & Kale, 2004; Kale, 2003; Manthorpe,2012). Marketing to this segment involves a combination of customeracquisition and retention. Initiallymarketers will offer incentives suchas free coupons and accommodation to lure players, and then nurturerelationship with them in the hope of securing return business. Kilby,Fox, and Lucas (2005) propose a three dimensional marketing tool totarget this segment, consisting of: casino amenities; the value of theincentives offered to players; and casino hosts operating as primaryservice providers who are in direct contact with premium players.The casino hosts are an important channel of communication betweenmanagement and premium players (Kilby et al., 2005).

As competition intensifies, the first two dimensions are insufficientfor securing competitive advantage since they are practiced in aggre-gate bymost casinos (Johnson, 2002). Casino hosts have become a cru-cial element in attracting and retaining premium players (Kale, 2005a,2005b). Their customer interactions and encounters play a critical role

Prentice).

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g, B.E.M., Impacts of personalurnal of Business Research (20

in shaping player satisfaction and their perceptions of casino servicequality. These outcomes, in turn, lead to player retention and casinoprofitability (Kale & Klugsberger, 2007). Interactions between hostsand clients represent antecedent to client evaluations of service per-formance, and the performance relates directly to assessments of casi-no service quality and ultimately to casino revenues. Thus,understanding the factors influencing the service performance ofhosts has implications for casino profitability.

The role of basic personality traits as “antecedents” of serviceperformance has been widely discussed in the literature (e.g. Brown,Mowen, Donovan, & Licata, 2002; Hurley, 1998). Whilst some studieshave tested for statistical significance, they have been unsuccessful inattributing substantial variation in performance ratings to these traits(see Hurley, 1998). Incorporating surface traits into performance-related research offers the prospect of expanding the trait domainand enhancing performance outcome. Researchers including Licata,Mowen, Harris, and Brown (2003); Moven and Spears (1999) andBrown et al. (2002) argue that traits function hierarchically, whereasbasic personality traits operate at a deeper level and provide a founda-tion for surface traits which in turn function as mediators and relatemore closely to individual behaviours and performance.

In testing this hierarchical/mediation model, Brown et al. reportthat the inclusion of surface traits explains a greater proportion of

ity, emotional intelligence and adaptiveness on service performance13), http://dx.doi.org/10.1016/j.jbusres.2012.12.009

http://dx.doi.org/10.1016/j.jbusres.2012.12.009

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2 C. Prentice, B.E.M. King / Journal of Business Research xxx (2013) xxx–xxx

the variance in service performance than when using a direct modelwhich excludes any consideration of such traits. Drawing on theBrown et al. study, the present research incorporates two additionaltraits into the personality-performance relationship, namely emo-tional intelligence and adaptiveness and tests the hierarchical modelin the casino context.

Although Moven and Spears (1999) indicate that no specificcriteria are identified for the purposes of categorising different traits,these authors and Brown et al. (2002) provide sufficient definitionand description to distinguish between personality traits and surfacetraits. Drawing upon their definitions and with a view to investigatingwhether there is a relationship with host service performance, thepresent research conceptualises emotional intelligence and adaptive-ness as a basic personality trait and as a surface trait respectively.Choosing these variables has particular relevance to the presentstudy because service encounters between casino hosts and premiumplayers are variable and involve a substantial emotional dimension(see Prentice & King, 2011). Consistent with the above discussion,the present investigation has the two following aims: identify theantecedents of host service performance and investigate their respec-tive relationships; and test the hierarchical model with proposed traits.The following section reviews the relevant literature and providesbackground based on the applicable theory.

2. Basic personality traits and surface traits

Researchers such as Allport (1961) argue that personality traitsexist at different levels and distinguish between psychological andsurface traits. The former are described as “the basic, underlyingpredispositions of individuals that arise from genetics and theirearly learning history” (Moven & Spears, 1999, p410). The five fac-tor model of personality (FFM), namely, Extraversion, Neuroticism,Openness to experience, Agreeableness and Conscientiousness, hasbeen approached from different perspectives including as a series ofpsychological traits (Moven & Spears, 1999), as cardinal traits (Allport,1961) or as basic personality traits (Brown et al., 2002). For the pur-poses of the present investigation the researchers use the expression“basic personality traits”.

Surface traits are referred to as surface behaviours, described as“individual differences in tendencies to behave within specific situa-tional contexts” (Moven & Spears, 1999, p 409). Surface traits referto dispositions, inclinations or tendencies to behaviours in certainsituations and are more abstract than concrete behaviours whichinvolve measures such as the number of calls taken, the incidenceof smiling and response times (cf. Brown et al., 2002). Comparedwith surface traits, basic personality traits are enduring dispositionswhich are indicative of prevailing behaviours in a range of situations.Surface traits are context specific and result from interactions betweenbasic traits and situational contexts. On the basis of the foregoingdiscussion, the researchers regard emotional intelligence as a basicpersonality trait and adaptiveness as a surface trait.

2.1. Emotional intelligence—personality trait

Emotional intelligence (EI) is defined as the capacity to perceiveand manipulate emotional information without necessarily under-standing it, and to understand and manage emotions without neces-sarily perceiving feelings well or fully experiencing them (Mayer &Salovey, 1997). It is variously conceptualised as a pure intelligencemodel (e.g. Salovey & Mayer, 1990), or as a mixed model comprisingof cognitive abilities and traits (e.g. Bar-On, 1997; Goleman, 1995).The former is measured using objective performance scales or abilitytests such as Mayer, Salovey, and Caruso (2002), whereas the latter ismeasured using a self-reporting method such as Bar-On (1997).

The use of different conceptualisations and measurements to asingle construct prompts debate and a degree of confusion amongst

Please cite this article as: Prentice, C., & King, B.E.M., Impacts of personalof casino hosts: A hierarchical approach, Journal of Business Research (20

EI researchers (see Emmerling & Goleman, 2003). In seeking tooperationalise the construct, Petrides and Furnham (2001) suggestusing the concepts “ability EI” and “trait EI” to distinguish betweenperformance-based measures and self-reporting scales. They arguethat measuring emotional intelligence using performance testsoperationalises the construct as a cognitive ability (described as abilityEI), whereas using self-report questionnaires operationalises the con-struct as a personality trait (described as trait EI).

The ability EI classification is more akin to traditional intelligence,whereas trait EI is more closely associated with consistency in cross-situational behaviours, and operates as a personality trait within thebroad personality domain. On this basis one might anticipate evidenceof a specific correlation with personality traits. In their use of the FFMof personality, various researchers (e.g. Petrides & Furnham, 2001;Schutte et al., 1998) report significant correlations between trait EIand the five dimensions of FFM. Furthermore, several empirical studieshave investigated self-reporting EI as a personality trait to predict indi-vidual behaviours and performance (e.g. Petrides, Perez-Conzalez, &Furnham, 2007; Prentice & King, 2011).

2.2. Adaptiveness—surface trait

Although the concept of surface traits first appeared in the litera-ture over 60 years ago (Allport, 1961), it has only attracted the atten-tion of researchers relatively recently. The concept is applied in theconsumer behaviour literature (Moven & Spears, 1999), and alsoin service related settings (Brown et al., 2002; Licata et al., 2003). Therelevant studies confirm that surface traits have significant behaviouralconsequences. Mowen and Spears stress the urgent need to identifynew surface traits with a view to enriching the relevant literature andsupporting practitioner efforts to enhance employee performance.

Adaptiveness is the ability of service employees to adjust theirbehaviours to the interpersonal demands of service encounters, andas a continuum ranging from conformity to service personalisation(Hartline & Ferrell, 1996). This definition is consistent with the adap-tive selling approach prevalent in the sales management literature.The adaptive selling approach refers to the alteration and adjustmentof selling behaviours during customer interactions or across customerinteractions based on perceived information and on the relevant sellingsituation (Spiro & Weitz, 1990). It involves developing impressions,formulating strategies, transmitting messages, evaluating reactions,and making appropriate adjustments (Spiro & Weitz, 1990). Thesedimensions imply that adaptiveness involves a tendency to makebehavioural adjustments during service encounters (the situationalcontext). It is plausible to classify adaptiveness as a surface trait onthe basis of this conceptualisation.

3. A hierarchical approach to the service performance ofcasino hosts

The rationale for including surface traits within the personality–performance relationship is that basic traits are remote from theactual behaviours that form a basis for performance evaluations,whereas surface traits are closer to these behaviours and thereforemore accurate predictors of performance (see Brown et al., 2002).Surface traits “surface” between basic personality traits and perfor-mance on a hierarchical basis, and function asmediators in influencingand probably enhancing performance evaluation. Brown et al. (2002)conceptualise customer orientation as a surface trait mediating be-tween personality and service performance and report that includingthe surface trait enhances performance evaluation. Drawing uponthe Brown et al. study and consistent with the foregoing discussion,the present investigation tests the hierarchical (mediation) relation-ship in the casino context by incorporating EI (in its capacity as atrait) into the domain of basic personality traits, and by introducingadaptiveness as a new surface trait. It is intended that this approach



3C. Prentice, B.E.M. King / Journal of Business Research xxx (2013) xxx–xxx

will help to investigate the antecedents of service performance bycasino hosts who are the primary providers of service to premiumplayers.

Within the domain of basic personality traits in the proposed hier-archical relationship, the FFM of personality is opted for this testing.Although numerous personality theories are cited within the relevantliterature, the FFM of personality is the most prevalent over the pasttwo decades. Mount, Barrick, and Stewart (1998) apply FFM of per-sonality to analyse employee behaviours and performance in variouscontexts including in service settings and in jobs requiring interper-sonal interactions. Researchers view extraversion as a valid predictorof service performance; whereas conscientiousness, agreeablenessand neuroticism associate with service orientation (see Hogan, Hogan,& Busch, 1984; Mount et al., 1998). The present study evaluates theservice performance of casino hosts on the basis of their interactionswith casino premium players. In light of this consideration it is appro-priate to adopt the FFM of personality for the purposes of the presentinvestigation. Fig. 1 shows the model.

4. Method

4.1. Participants

Since the present study focuses on casino hosts who provide ser-vice to premium players, it makes sense to draw up the sample incasinos where there are gaming facilities catering specially to suchplayers. Most casinos describe such facilities as VIP gaming rooms.Although the hierarchical trait model has been discussed in a coupleof previous studies, the inclusion of new constructs (namely trait EIand adaptiveness) and their exploration in a vastly different setting(casinos that are frequented by premium players) is indicative of anexploratory investigation.

To control for extraneous and uncontrollable variables, such asdifferent corporate and cultural values, market performance, andgeographic location, the researchers administered the survey at asingle site. The setting is one of the world's largest casinos, locatedwithin the Asia Pacific region. The respondent sample was drawnfrom all hosts who were working within the premium-player gamingareas of the relevant casino. From a total of 300 surveys distributedto prospective respondents, 167 usable responses were returned(56 percent). Of the total usable sample, 75 were male, and 92 werefemale. The age of the participants ranged from 18 to 55, and 82%were in the 18 to 35 age group. The majority of respondents (74%)possessed a diploma or university degree and the remainder hadcompleted secondary level studies.

Fig. 1. The proposed model for this study—a hierarchical relationship between basicpersonality traits (the FFM of personality and trait EI), surface trait (Adaptiveness) andservice performance of casino hosts.


5. Measures

5.1. The FFM of personality

The present study draws upon the 44-item Big Five Inventory(BFI) by John, Donahue, and Kentle (1991) to measure personality.The BFI uses short phrases based on trait adjectives that are reflectiveof the lexical tradition attributable to personality researchers such asGoldberg (1990) and Digman (1990). These phrases function as pro-totypical markers of the FFM of personality factors (John & Srivastava,1999). The use of a short inventory allows for the five dimensions tobe assessed efficiently and flexibly and avoids the need for a more dif-ferentiated measurement of the various individual facets. Shortscales are particularly helpful because they reduce the time requiredfor testing, and minimise respondent boredom and fatigue. Burisch(1984) has argued that the poorest response rates occur when a testlooks too long. In the current study the Cronbach alpha valuesreported for the five personality factors were respectively: Agreeable-ness .80, Conscientiousness .73, Extraversion .72, Neuroticism .62, andOpenness .72. Although Neuroticism showed an internal consistencymeasure of less than .70, the inter-item correlation fell within therange of .2 to .4, which is considered optimal (see Briggs & Cheek, 1986).

5.2. Emotional intelligence

Consistent with the approach that was first adopted in Petridesand Furnham's (2000) conceptualization of trait EI, the present re-search uses the self-report EI test (SREIT) designed by Schutte et al.(1998). This test is based on the ability model first developed bySalovey and Mayer (1990). This 33-item self-report measure includesitems such as, “By looking at their facial expression, I recognize theemotions people are experiencing” and “I easily recognize my emo-tions as I experience them.” According to Schutte et al. (1998), thescale generates significant correlations with theoretically related con-structs such as alexithymia, attention to feelings, clarity of feelings,mood repair, optimism and impulse control. The scale exhibits goodinternal consistency and test–retest reliability, predictive validity,and discriminant validity with strong results for each analysis(Schutte et al., 1998). These positive attributes led to the adoptionof the scale for the purposes of the current study. Data were collectedon a five-point Likert scale, with 1 representing strongly disagree and5 representing strongly agree, indicative of the extent to which eachitem provides an accurate description. Higher total scores are reflec-tive of greater self-report emotional intelligence. The next sectionreports its Cronbach alpha coefficients for the present study.

5.3. Adaptiveness

A 7-item scale was adapted from Spiro and Weitz's (1990)ADAPTSwhichmeasures the level of adaptiveness exhibited by casinohosts. ADAPTS comprises of 16 items and involves two underlyingdimensions: adaptive belief and adaptive behaviour (see Marks,Vorhies, & Badovick, 1996). In their respective studies, Mark etal. and, more recently Park and Holloway (2003) report that onlythe adaptive behaviour dimension explains significant variance inperformance.

Consistent with their finding, the researchers selected the 7 itemsthat measure adaptive behaviour explicitly and reworded them tosuit the context of the present study. These items assess the abilityof casino hosts to adapt their approach to premium players duringthe course of service encounters. Participants were asked to indicatetheir level of agreement with each item, using a five-point Likertscale ranging from “strongly disagree” to “strongly agree.” Higherscores are reflective of greater adaptiveness. The applicable Cronbachalpha coefficient was found to be .83.



Table 1Results of factor analysis for SREIT.

F1 F2 F3 F4

1. I know when to speak about my personalproblems to others

.56

2. When I am faced with obstacles, I remembertimes I faced similar obstacles and overcomethem

.52

3. I expect that I will do well on most things I try .536. Some of the major events of my life have ledme to re-evaluate what is important and notimportant

.64

9. I am aware of my emotions as I experience them .6612. When I experience a positive emotion, I knowhow to make it last

.55

16. I present myself in a way that makes a goodimpression on others

.68

19. I know why my emotions change .7221. I have control over my emotions .5822. I easily recognise my emotions as I experiencethem

.67

24. I compliment others when they have donesomething well

.54

28. When I am faced with a challenge, I give upbecause I believe I will fail

.65

31. I use good moods to help myself keep tryingin the face of obstacles

.56

4. Other people find it easy to confide in me .535. I find it hard to understand the non-verbalmessages of other people

.59

8. Emotions are one of the things that make mylife worth living

− .62

18. By looking at their facial expressions, I recognisethe emotions people are experiencing

.50

29. I know what other people are feeling just bylooking at them

.54

30. I help other people feel better when theyare down

.62

32. I can tell how people are feeling by listeningto the tone of their voice

.51

33. It is difficult for me to understand why peoplefeel the way they do

.58

11. I like to sharemy emotions as I experience them .6713. I arrange events others enjoy .6214. I seek out activities that make me happy .6215. I am aware of the non-verbal messages thatI send to others

.52

25. I am aware of the non-verbal messages otherpeople send

.69

10. I expect good things to happen .7617.When I am in a positivemood, solving problemsis easy for me

.52

20. When I am in a positive mood, I am able tocome up with new ideas

.76

23. I motivate myself by imaging a good outcometo tasks I take on

.67

27. When I feel a change in emotions, I tend tocome up with new ideas

.70

variance explained 28.73% 6.37% 5.34% 4.85%Eigenvalue 9.48 2.1 1.76 1.6randomly generated average eigenvalues 2.01 1.87 1.75 1.66SD (100 replications) .08 .06 .06 .05Cronbach alpha coefficients .88 .82 .71 .66Cronbach alpha reported by Petrides andFurnham (2000)

.90 .93 .73 .55


5.4. Service performance

Following discussions with casino management, the researchersdecided to deploy a scale that was identical to the one used by thesurvey casino to measure the service performance of casino hosts.This scale consists of the following dimensions: Punctuality andattendance, job knowledge, quantity of work, quality of work, humanrelations and customer relations, dependability, interest, initiative, dili-gence, and appearance. The casino uses a combination of self-reportingand supervisor-rating approaches for the purposes of host performanceappraisal.

Since the researchers did not have permission to make use ofthe existing performance evaluation, they relied on respondent self-reporting, which has been a commonplace approach in previous re-search conducted on the measurement of performance (e.g., Brownet al., 2002; Busch & Bush, 1978; Sujan, Weitz, & Kumar 1994).Churchill, Ford, Hartley, & Walker (1985) have noted that self-reportingdoes not lead to biased outcomes or inflated assessments. In the contextof the present study each item was assessed using a five-point scale,ranging from 1 (lowest) to 5 (highest). The applicable Cronbachalpha coefficient for this scale was .78.

5.5. Procedure

A self-report questionnaire was developed using a paper-penciltest to collect information about the FFM of personality, emotionalintelligence, adaptiveness and service performance. Respondentswere assured of their anonymity in the instructions which accompa-nied each of the documents. The survey packets included a coverletter providing an introduction and explanation of the significanceand objectives of the research, an expression of thanks to prospectiverespondents, a consent form, a questionnaire, and a pre-paid enve-lope. Detailed instructions were provided to guide participation byprospective respondents. The questionnaires were distributed duringtheir work shifts, and could be completed at home or at off-peak timesduring their weekday work shifts. Responses were required withintwo months of receipt.

6. Analyses and results

6.1. Factor analysis

6.1.1. Emotional intelligencePrior to testing the model, SREIT was factor analysed to identify

its underlying dimensions. Although a number of researchers haveanalysed its factor structure, the dimensionality and labels of factorsreported in those studies are inconsistent (see Petrides & Furnham,2000). This inconsistency may be attributable to the use of differentsamples; and factor analyses are recommended before applying thisscale (see Petrides & Furnham, 2000).

The present study makes use of principal components analysis toidentify the SREIT factor structure. This approach has been advocatedby Petrides and Furnham (2000) and by Saklofske, Austin, and Minski(2003). A four-factor structure was identified for the emotional intel-ligence scale, consistent with the results that were reported by thepreviously noted authors and the results are displayed in Table 1.The four factors include “Mood Regulation”, “Appraisal of Emotions”“Social Skills” and “Utilization of Emotions”. The respective Cronbachalpha coefficients were .88, .82, .71, and .66. In the case of “utilizationof emotion” the Cronbach's alpha was less than .70, consistent withPetrides and Furnham's concern about the risk of overestimatingthe number of factors. To facilitate interpretation, the researchersused the four factors for the purposes of further analysis. Table 1shows the results of the factor analysis for the emotional intelli-gence scale.


Structural equation modelling was conducted to test the proposedhierarchical relationship. Two absolute close-fit indices (SRMR andRMSEA) and two incremental close-fit indices were chosen (TLI andCFI) to evaluate the fit of the model, with SRMR and RMSEAb .06and TLI and CFI>.95 considered to be good fitting (see Hu & Bentler,1999). The results show that the model produced χ2=796.51, df=656, pb .001, SRMR=.05 and RMSEA=.05, indicative of a good modelfit; whereas the incremental close-fit index values (CFI=.97 andTLI=.99) were also acceptable. The results of standardised residual




co-variances and modification index values indicate that no con-spicuously significant changes to this model are needed.

Table 2 presents the means, standard deviations and Pearson'scorrelation coefficients which apply to the study variables. The resultsshow that the correlations between the proposed independent vari-ables (the FFM of personality factors and the four dimensions identi-fied from SREIT) were less than .70, indicating that multicollinearity isabsent. The shared variance between the various study variables doesnot exceed 40%, which indicates that they are empirically distinctconstructs. Collinearity is not violated because the tolerance levelswere found to be above .10 and the variance inflation factors wereless than 10.

Consistent with the approach that was recommended by Singh,Goolsby, and Rhoads (1994) for testing hierarchical/mediation rela-tionships, the researchers estimated two competing models. In thefirst model (direct effects), the effects of the personality traits wereestimated including the FFM of personality and trait EI on host serviceperformance. The second model—the mediation model in the currentstudy—involved estimating the effects of (1) personality traits onadaptiveness; (2) personality traits on service performance; and(3) adaptiveness on service performance. The mediation effects ofadaptiveness are established in cases where the mediation modelyields; (1) higher variances, (2) a significant relationship betweenpersonality traits on adaptiveness, (3) substantially reduced or in-significant effects of personality traits on service performance, and(4) adaptiveness has a significant effects on service performance.

Drawing upon the model fit index values, both models appear tofit the data reasonably well. In particular, testing of the relationshipbetween the FFM of personality and adaptiveness indicates thatthe former explained 35% variance in host adaptiveness, R2=.35, F(5, 166)=25.42, pb .0005. This effect was statistically significant,although only openness to experience (β=.19, t=2.37, pb .05) andextraversion (β=.19, t=2.09, pb .05) had a significant influence onhost adaptiveness. Results from testing of the relationship betweenthe FFM and the service performance of casino hosts indicates thatthe predictors explained 28 percent of variance in host service perfor-mance, R2=.28, F (5, 166)=22.83, pb .0005), although only extra-version (β=.20, t=2.25, pb .05) and conscientiousness (β=.29,t=2.92, pb .01) made a statistically significant and unique contribu-tion to performance evaluation.

The results arising from testing the four factors of trait EI withadaptiveness and host service performance show that emotionalintelligence explained 48% of the variance in adaptiveness, R 2=.48,F (4, 166)=33.47, pb .0005, and 25% of the variance in service perfor-mance, R2=.32, F (4, 167)=21.76, pb .0005. Of the four factors,mood regulation and appraisal of emotions significantly influencedhost performance ratings (β=.24, t=3.18, pb .001; β=.31, t=3.77,pb .0005 respectively) and host adaptiveness (β=.51, t=5.83, pb .001

Table 2Correlation matrix for the study variables.

Variables M SD MR AE SS UE

MR 51.21 6.4AE 18.25 2.88 .60⁎⁎SS 18.8 2.67 .55⁎⁎ .42⁎⁎UE 31.57 3.75 .63⁎⁎ .54⁎⁎ .51⁎⁎E 27.15 3.97 .52⁎⁎ .40⁎⁎ .20⁎ .44⁎A 35.16 4.76 .61⁎⁎ .42⁎⁎ .39⁎⁎ .48⁎C 33.35 4.19 .59⁎⁎ .39⁎⁎ .34⁎⁎ .54⁎N 21.3 3.81 − .40⁎⁎ − .12 − .09 − .29⁎O 34.53 4.29 .53⁎⁎ .39⁎⁎ .42⁎⁎ .44⁎Adapt 18.21 2.8 .64⁎⁎ .37⁎⁎ .33⁎⁎ .56⁎Perf. 20.75 3.34 .51⁎⁎ .32⁎⁎ .28⁎⁎ .54⁎

MR=Mood Regulation, AE=Appraisal of Emotions, SS= Social Skills, UE=Utilisation of EmO = Openness to Experience, Adapt = adaptiveness, Perf. = the service performance of cas⁎⁎ Correlation is significant at the .01 level (2-tailed).⁎ . Correlation is significant at the .05 level (2-tailed).


and β=.27, t=3.28, pb .001 respectively). The path between adaptive-ness and host service performance is also significant (β=.61, pb .0005).These results are displayed in Table 3.

Table 4 summarises the results from the structural equation analy-ses for the direct and mediation effect models. The analyses showa clear mediating effect, and the two models differ significantly inexplaining variations in host service performance. The mediationmodel which incorporates adaptiveness accounts for 65% in theservice performance evaluation, compared with 43% in the case ofthe direct model. Personality traits, combining the FFM of personalityand trait EI had a significant effect on adaptiveness. As evident inTable 4, the effects of personality traits generally and of each factorof these personality traits on service performance are reduced aftercontrolling adaptiveness. This finding is indicative of a partial media-tion model. The evidence suggests that the mediation model is con-firmed and that adaptiveness partially mediates between personalitytraits and host service performance.

7. Discussion and conclusions

This study investigates the antecedents of host service perfor-mance from a casino profitability perspective and tests a hierarchicalmodel incorporating personality traits, surface traits and performanceratings. The results of the structural equation modelling confirm thata hierarchical relationship exists between basic personality traits,adaptiveness, and host service performance, and supports the propo-sition that incorporating a surface trait increases variance in serviceperformance—performance ratings increase from 43 to 65% whenadaptiveness is included in the analysis.

The partial mediation effect indicates that although personalitytraits (the FFM of personality and trait EI) have a significant influenceon host service performance, the outcome is enhanced by includingadaptiveness as a mediator. This finding is consistent with Brownet al.'s (2002) study which supports the hierarchical approach ofbasic personality traits and surface traits as a prediction of perfor-mance outcome, as initiated by Moven and Spears (1999).

This testing also generates the following findings. In the FFM—

service performance/adaptiveness relationship, extroversion relatessignificantly to both host adaptiveness and to service performance.This finding conforms with Barrick, Stewart, and Piotrowski (2002)assertion that extraverts are better at jobs involving social interactionssuch as customer-contact, since extraversion is characterised as beingsociable and initiative (Hogan, 1986). These characteristics likely af-fect employee encounter behaviours, such as adaptiveness in meetingplayer needs and demands. The significant relationship betweenconscientiousness and host service performance is consistent withMount et al.'s (1998) research. As indicated in Zeithaml, Berry, andParasuraman's (1986) service quality research, more conscientious

E A C N O Adapt

⁎

⁎ .45⁎⁎⁎ .57⁎⁎ .66⁎⁎⁎ − .52⁎⁎ − .41⁎⁎ − .51⁎⁎⁎ .53⁎⁎ .36⁎⁎ .49⁎⁎ − .37⁎⁎⁎ .53⁎⁎ .39⁎⁎ .48⁎⁎ − .43⁎⁎ .44⁎⁎⁎ .51⁎⁎ .42⁎⁎ .55⁎⁎ − .41⁎⁎ .44⁎⁎ .64⁎⁎

otions, E=Extraversion, A=Agreeableness, C= Conscientiousness, N=Neuroticism,ino hosts.



Table 3Results for the FFM of personality, SREIT as predictors of host service performance andadaptiveness, and adaptiveness as a predictor of host service performance.

Predictors Service performance Adaptiveness

β t β t

Extraversion .20⁎ 2.25 .19⁎ 2.09Agreeableness .05⁎⁎ .55 .14 1.60Conscientiousness .29⁎⁎⁎ 2.92 .16 1.57Neuroticism − .09 −1.09 − .07 .08Openness to experience .14 1.73 .19⁎ 2.37F (5, 166)=22.83, Sig. F=.00, R2=.28 (service performance)F(5, 166)=25.42, Sig. F=.00, R2=.35(adaptiveness)Mood regulation .24⁎⁎⁎ 3.18 .51⁎⁎⁎ 5.83Social skills − .07 − .80 − .03 − .44Utilisation of Emotions − .09 −1.04 − .00 .03Appraisal of emotions .31⁎⁎⁎ 3.77 .27⁎⁎⁎ 3.28F (4, 167)=21.76, Sig. F=.00, R2=.32 (service performance)F (4, 166)=33.47, Sig. F=.00, R2=.48 (adaptiveness)Adaptiveness .61⁎⁎⁎ 8.29F (1, 167)=19.76, Sig. F=.00, R2=.41

⁎ Pb .05.⁎⁎ pb .01.

⁎⁎⁎ pb .0005.


employees tend to provide better and higher quality service. Serviceproviders who are accurate, dependable, responsive, and timely areinclined to perform their jobs more effectively. It is plausible thatopenness to experience has a significant influence on host adaptive-ness. As the personality literature indicates, traits associating withopenness to experience include imagination, being cultured, curiosity,originality, broad-mindedness, intelligence, artistic sensitivity, andthe need for variety. The practice of adaptiveness requires casinohosts to identify customer needs, and assess interactions based onperceived information in order to find the most appropriate strategyfor achieving customer satisfaction. These requirements imply thatemployees should be intelligent, imaginative, and broad-minded. InBarrick and Mount (1991) meta-analysis, openness to experienceis found to be a valid predictor of training proficiency. Marketing re-searchers (e.g., Park & Holloway, 2003) consistently argue that trainingis critical for the efficient implementation of adaptiveness.

Table 4Results of mediation model testing: adaptiveness as the mediator between personalitytraits and the service performance of casino hosts.

IV Mediator DE IE

Extraversion .19⁎ .11Agreeableness .05 .01Conscientiousness .27⁎⁎ .19⁎

Neuroticism AT − .09 − .05Openness to experience .13 .07Mood regulation .34⁎⁎⁎ .16Appraisal of emotions − .10 − .06Social skills − .09 − .09Utilisation of emotions .41⁎⁎⁎ .22⁎

Goodness of fit statistics Direct effects model Mediation modelχ2 796.51 637.21df 656 535RMSEA .05 .03NNFI .96 .97CFI .97 .99R2 .43 .65

The direct effects model includes only the direct effects between personality traits andthe host service performance.Notes: RMSEA = root mean square error of approximation; NNFI = non-normed fitindex; CFI = comparative fit index; AT = adaptiveness, DE = direct effect of indepen-dent variables on the dependent variable, IE = indirect effect after the inclusion ofadaptability as a control.

⁎ pb .05.⁎⁎ pb .01.

⁎⁎⁎ pb .0005.


When measured using SREIT it is found that Trait EI is signifi-cantly related to both host adaptiveness and service performance.This finding confirms that emotional intelligence is an effective predic-tor of behaviours and performance in the case of jobs such as casinohost which involve interpersonal interactions and emotion work (seeAshkanasy & Daus, 2005).

Of the four factors, mood regulation (managing emotions) andappraisal of emotions are found to have a particularly significant in-fluence on the service performance of hosts. Emotional managementskills can influence a customer's formation of emotions and theirappraisal process which in turn affects their attitudes and behaviours.A positive attitude and strong behavioural intentions reflect theappropriateness of the contact employee's approach to dealing withcustomers. Tomanage one's emotions, appraising emotions effectivelyis necessary. These two concepts are connected logically and also havea significant relationshipwith host adaptiveness. The practice of adap-tiveness requires frontline employees to adjust their behaviours tomeet the interpersonal demands of service encounters based on per-ceived information. The process of perception suggests that employeeappraisals of customer emotions will help then to adjust encounterbehaviours, whereas managing emotions facilitates the interactions.

The findings of this investigation have a number of implications.First, measures of personality and emotional intelligence can be use-fully incorporated into the processes of personnel recruitment andselection in cases where adaptiveness is the applicable practice,particularly in the casino context. Second, the significant influenceof casino host adaptiveness on service performance suggests thattraining is necessary to build adaptive capacity. Such training in turnoffers the prospect of improving employee performance and enhancescasino profitability, consistent with the concepts of the service profitchain and relationship marketing. Integrating emotional intelligencewithin training programs may be advisable with a view to facilitatingthe adaptiveness of casino employees in adjusting their behavioursduring interactions with casino players. This study adds knowledge toperformance-related research and identifying additional traits enrichesthe literature on surface traits.

8. Limitations and prospects for future research

Although the contexts of this study is in the Asia Pacific region, thefindings may be applicable to casinos in other prominent localeswhich cater to the premium player segment, most notably Las Vegasin the USA. Since the present research was administered to a singlecohort, broad application of the findings should be approached withcaution.

For the purposes of future investigations, researchers may benefitfrom endeavouring to access casinos in diverse locations with a viewto generalising the findings. The researchers acknowledge that despitethe widespread use of self-rating performance within the literature,obtaining ratings from other parties could reinforce understandingof the traits–performance relationship from an additional perspective.As Moven and Spears (1999) suggest, new surface traits are identifi-able, offering the capacity of enhancing the evaluation of employeeperformance as well as cross-validating the hierarchical approach tostudying individual traits. Incorporating player perceptions of casinohosts from a service quality perspective is also worth exploring.

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