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Journal of Hospitality Marketing & Management
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Service Orientation, Service Quality, Customer Satisfaction, and Customer Loyalty: Testing a Structural Model
Hyun Jeong Kim a a

School of Hospitality Business Management, Washington State University, Pullman, Washington, USA Published online: 30 Jun 2011.

To cite this article: Hyun Jeong Kim (2011): Service Orientation, Service Quality, Customer Satisfaction, and Customer Loyalty: Testing a Structural Model, Journal of Hospitality Marketing & Management, 20:6, 619-637 To link to this article: http://dx.doi.org/10.1080/19368623.2011.577698

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Journal of Hospitality Marketing & Management, 20:619–637, 2011 Copyright © Taylor & Francis Group, LLC ISSN: 1936-8623 print/1936-8631 online DOI: 10.1080/19368623.2011.577698

Service Orientation, Service Quality, Customer Satisfaction, and Customer Loyalty: Testing a Structural Model
HYUN JEONG KIM
School of Hospitality Business Management, Washington State University, Pullman, Washington, USA

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The objective of this study is to develop and validate a conceptual model that incorporates the relationships among service orientation, service quality, customer satisfaction, and customer loyalty. Data were collected from a chain of casual dining restaurants located in Seoul, Korea. Frontline employees completed a questionnaire measuring service orientation while customers completed a questionnaire measuring perceived service quality, satisfaction, and loyalty. The questionnaires from customers then were paired with the questionnaires from employees who had attended to the customers. The proposed model indicated that customers’ perceptions of service quality fully mediate the effect of employees’ service orientation on customer satisfaction and that customer satisfaction fully mediates the relationship between customers’ perceptions of service quality and customers’ decision to remain loyal. KEYWORDS Service orientation, customer satisfaction, loyalty perceived service quality,

INTRODUCTION
The restaurant industry in Korea has expanded dramatically. In 13 years (1991–2004), the number of restaurants rose from 298,196 to 600,233; this is an average annual growth rate of 7.5% (Statistics Korea, 2006). One of the major forces behind this significant growth is the introduction of many

Address correspondence to Hyun Jeong Kim, PhD, School of Hospitality Business Management, 471 Todd Hall, Washington State University, Pullman, WA 99164, USA. E-mail: Jennykim@wsu.edu 619

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chain restaurants from other countries, particularly from the United States. For example, quick-service restaurants like McDonald’s, Wendy’s, and Pizza Hut appeared in Korea in the mid- and late-1980s while mega casual-dining chains such as T.G.I. Friday’s, Bennigan’s, Outback Steakhouse, and Chili’s penetrated the Korean market in the 1990s (Park, 2009). Korean operators also became sophisticated, developing their own chains or specialty restaurants to draw diners away from foreign-brand chain restaurants (Park, 2009). Taken together, these phenomena have intensified the competition in the restaurant industry. As the market became saturated and the global economic recession, which started in the late 2000s, dragged on, the Korean restaurant industry ceased to enjoy its traditional influx of customers. Within the hospitality industry, the competition has led many organizations to look for profitable ways of differentiating themselves. One such strategy is the delivery of high service quality (Zemke & Algright, 1985; Stevens, Knutson, & Patton, 1995). High service quality increases customer satisfaction and produces measurable long-term benefits in market share and profitability (Anderson, Fornell, & Lehmann, 1994). From the managerial point of view, the ultimate goal is to attract and maintain customers. Hospitality operations have learned that attracting new clients takes four to five times as much money as maintaining existing ones (Bowen & Basch, 1994); therefore, customer retention is a key to survival in the hospitality industry. Numerous studies have indicated that customer satisfaction, service quality perceptions, and customers’ decision to remain loyal or switch service providers are significantly affected by the customer-oriented attitude or behaviors of contact employees. (Bitner, 1990; Crosby & Stephens, 1987; Ekiz, 2009; Schneider, Parkington, & Buxton, 1980; Surprenant & Soloman, 1987). However, empirical research to find the relationships among these constructs is limited. The objective of this study is to develop and validate a conceptual model that integrates the relationships among service orientation, service quality, customer satisfaction, and customer loyalty in the context of casual dining restaurants in Korea.

HYPOTHESES AND RESEARCH MODEL Service Orientation
Service orientation has been viewed as a blend of certain dimensions of personality (Cran, 1994; Herley, 1998; Hogan, Hogan, & Busch, 1984). Hogan et al. (1984) defined service orientation as a “disposition to be helpful, thoughtful, considerate, and cooperative” (p. 167). Other researchers have emphasized the impact of situational and environmental variables on service-oriented behaviors (Soloman, Surprenant, Czepiel, & Gutman, 1985). Donavan (1999) defined service orientation as an

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interaction between individual employee’s personality traits and the service environment. The first researchers to assess service orientation in the restaurant industry were Dienhart, Gregoire, and Downey (1991). Groves (1992) expanded Dienhart et al.’s (1991) nine-item service orientation scale to 34. Both the original and expanded versions comprised three constructs: customer focus, organizational support, and service under pressure. The researchers argued that the first view (service orientation as an innate personality trait) is embedded in the customer focus construct, while the second view (service orientation focusing on situational or environmental elements) is embedded in organizational support and service under pressure. This study followed Dienhart et al.’s (1999) integrative approach and defined service orientation as a function of individual traits and environmental or situational factors.
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Service Quality
In the early 1980s, the manufacturing industry implemented total quality management (TQM) and similar approaches. As service did not resemble physical goods due to its intangibility, heterogeneity, and inseparability of production and consumption, it was difficult to define and measure the concept of service quality (Parasuraman, Zeithaml, & Berry, 1985). Unlike quality concepts from the manufacturing sector, service quality experts in the service industry developed a unique service quality concept using consumer behavior models. With the emphasis on the voice of the customer, service quality was defined as the difference between customer expectations of service and the perceptions of the actual service received (Parasuraman, Zeithaml, & Berry, 1988). This popular concept resulted in the creation of the SERVQUAL instrument (Parasuraman et al., 1988). Originally, SERVQUAL was proposed as a generic measure that could be applied to any service. However, Carman (1990) indicated that SERVQUAL must be customized to the service in question. In the hospitality industry, a modified version of SERVQUAL, DINESERV was developed to measure service quality in restaurants (Stevens et al., 1995). Researchers have disagreed about the best way to operationalize the SERVQUAL instrument (Cronin & Taylor, 1992; Parasuraman, Berry, & Zeithaml, 1993). Cronin and Taylor (1992) demonstrated that the perceptions battery alone explains more variances in the structural model. However, Parasuraman et al. (1993) argued that the decision depends on the objective of the study; difference scores are useful for the purpose of diagnosing service shortfalls, whereas perception ratings alone are useful when explaining the variance in some dependent variables. Because the objective of this study is to investigate the interrelationships (or causal relationships) among the four constructs (service orientation, service quality, customer satisfaction,

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and customer loyalty) in the structural model, it is deemed appropriate to utilize the perceptions score of service quality rather than the gap score (difference between expectations and perceptions of service performance).

Customer Satisfaction and Loyalty
This study adopted Oliver’s (1981) concept of customer satisfaction. Oliver (1981) defined customer satisfaction as the “summary psychological state resulting when the emotion surrounding confirmed or disconfirmed expectation is coupled with the consumer’s prior feelings about the consumption experience” (p. 27). The term disconfirmation in this context relates to the fulfillment of expectation, and may be positive (where product performance exceeds expectations), negative (where product performance falls below expectations), or zero (where performance equals expectations). More specifically, an individual’s expectations are (a) confirmed when a product performs as expected, (b) negatively disconfirmed when the product performs more poorly than expected, and (c) positively disconfirmed when the product performs better than expected. This paradigm, known as confirmation/disconfirmation, leads to an emotional reaction called satisfaction or dissatisfaction. Customer loyalty is defined as “the feeling of attachment to or affection for a company’s people, products or services” (T. O. Jones & Sasser, 1995, p. 94). Marketing researchers have long favored behavioral scales to assess customer loyalty in the belief that the share of purchase ultimately represents the level of loyalty. Although customer loyalty and repeat purchase behaviors are closely associated, there is criticism of the exclusive use of behavioral scales as a loyalty measure (Jacoby, Chestnut, & Fisher 1978). For example, behavioral loyalty can be influenced by a variety of circumstantial constraints such as accessibility of services or products (Dick & Basu, 1994). In this situation, behavioral scales are likely to fail to distinguish spurious loyalty from true loyalty. As an alternative, attitudinal scales highlighting trust or emotional attachment have been proposed (Baloglu, 2002). Ponnavolu (2000) argued that both behavioral and attitudinal measures are necessary to appreciate the full picture of customer loyalty because they are two integral dimensions of loyalty. This study utilized the concept of customer loyalty that bears both behavioral and attitudinal aspects.

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Relationships Among Service Orientation, Perceived Service Quality, and Customer Satisfaction
The relationship between service orientation and perceived service quality was first suggested by Schneider et al. (1980). In a study of 23 banks, they discovered that the climate for service in a bank was correlated to customers’

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attitudes about service quality. Furthermore, there was a strong correlation between employees’ perceptions of service orientation and customers’ perceptions of overall service quality. Additional studies found a substantial correlation between employees’ service attitudes and customers’ perceptions of service quality (Schneider & Bowen, 1985). C. Jones and DeCotiis (1986) stated “service quality in a hotel or restaurant depends absolutely on the ability of an operation’s employees to deal graciously with guests in all situations” (p. 68). Similarly, other hospitality scholars asserted that hospitality business organizations should have employees who make customers feel special, have a positive attitude, and work well under pressure, thereby providing excellent customer service (Kim, McCahon, & Miller, 2003; Marsh, 1994). Therefore, the first hypothesis of the proposed model is as follows:
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H1: A high degree of service orientation for the contact employee has a positive and significant effect on the customer’s perception of service quality.

Research has shown the possibility of a direct link between customer satisfaction and the contact employee’s service orientation. Westbrook (1981) identified the eight factors that could influence customer satisfaction in a retail setting (store salespeople, store environment, merchandising policies, store service orientation, product, clientele, value/price relationship, and special sales). Factor scores of these eight components then were used as predictor variables for multiple regression analysis. The results indicated that satisfaction with store salespeople (encompassing the four variables of helpfulness, friendliness, politeness, and number of salespeople) had the most influence on customer satisfaction. Other scholars have reported similar findings that customer satisfaction depends directly on particular behaviors of contact employees (Bitner, 1990; Donavan & Hocutt, 2001; Surprenant & Soloman, 1987). These conclusions suggest the following hypothesis:
H2: A high degree of service orientation by the contact employee has a positive and significant effect on customer satisfaction.

Relationships Among Service Quality, Customer Satisfaction, and Customer Loyalty
Literature has suggested contradictory views of whether customer satisfaction or service quality directly affects customer loyalty (Bitner, 1990; Cronin & Taylor, 1992). This conceptual conflict has resulted from a definition of service quality; “a form of attitude, which is related but not equivalent to customer satisfaction, and results from a comparison of expectations with perceptions of performance” (Parasurman et al., 1988, p. 15). Bitner (1990)

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supported this definition and proposed that satisfaction derived from individual transactions (Oliver, 1981), leads to a more general construct, service quality (or attitude), which in turn leads to customer loyalty. Cronin and Taylor (1992) hypothesized that service quality mediates customer satisfaction and future purchase intentions; that is, customer satisfaction is an antecedent of service quality. However, the empirical result in this nonrecursive LISREL model suggested that service quality is an antecedent of customer satisfaction. Accordingly, the relationship between service quality and customer satisfaction is hypothesized as follows:
H3: Favorably perceived service quality has a positive and significant effect on customer satisfaction.

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Although Cronin and Taylor (1992) demonstrated that customer satisfaction has a more significant effect on customer loyalty than service quality, it seems feasible that both factors could influence customer loyalty significantly. In fact, when Heung, Mok, and Kwan (1996) examined the degree of hotel brand loyalty in the free independent travelers market for the Hong Kong hotel industry, their results revealed that the quality of hotel services is critical for hotel brand loyalty. Placing equal weights on the findings by Cronin and Taylor (1992) and Heung, Mok, and Kwan (1996), the following two hypotheses are proposed:
H4: Favorably perceived service quality exerts a positive and significant effect on customer loyalty. H5: Customer satisfaction exerts a positive and significant effect on customer loyalty.

After the earlier proposed paths (H1 through H5) were put together, two mediating relationships became apparent: service quality as a mediator between service orientation and customer satisfaction (Service Orientation → Perceived Service Quality → Customer Satisfaction); and customer satisfaction as a mediator between service quality and customer loyalty (Perceived Service Quality → Customer Satisfaction → Customer Loyalty). Therefore, the following additional hypotheses are put forward:
H6: Perceived service quality mediates the effect of contact employees’ service orientation on customer satisfaction. H7: Customer satisfaction mediates the effect of service quality on customer loyalty.

Figure 1 depicts a full model with hypothesized relationships.

Service Orientation, Service Quality, Satisfaction and Loyalty
Employee Differences: Customer Outcomes:

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Service Quality

Service Orientation

Customer Loyalty

Customer Satisfaction

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FIGURE 1 Proposed model.

METHOD Sample and Procedure
Data were collected from one chain of casual dining restaurants located in Seoul, Korea. A total of seven restaurants in the chain participated in this study. Frontline employees who made frequent face-to-face contacts with diners (i.e., waitpersons and bartenders) were invited to this study. A total of 169 usable questionnaires assessing service orientation were gathered from contact employees, while 508 usable questionnaires measuring service quality, customer satisfaction, and customer loyalty were gathered from diners. Questionnaires for customers were distributed by participating employees during the lunch (noon to 3:00 p.m.) and dinner (5:00 p.m. to 8:00 p.m.) periods. The employees were instructed to solicit male and female customers of various age groups at the time of the survey distribution and to recruit a minimum of three customers. Customer participants completed the questionnaire after receiving service from the employee who attended to them. The researcher coded employee surveys numerically and assigned one specific number to each contact employee. Customer questionnaires were coded with the same number assigned to the particular server or bartender so that the researcher could match employee questionnaires with customer evaluations. Customers placed the completed questionnaire in an envelope, sealed it, and handed it to the employee. The employee kept customers’ questionnaires, along with their own questionnaire, in a large envelope until all questionnaires in the unit had been completed. Then, all the questionnaires were collected and handed to the researcher. As a token

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of appreciation, a cash incentive (equivalent to US$200) was offered to each unit to be used for employee socials.

Survey Instrument
To assess employee service orientation, Groves’ (1992) scale was selected. Groves’ (1992) scale, which originated in the United States, was subject to factor analysis in order to assess its validity in Korean culture (Kim et al., 2003). The factor analysis by Kim et al. (2003) indicated that four factors (customer focus, prior customer relationship, service under pressure, and organizational support) were more appropriate for Groves’ (1992) measure and several items were eliminated because of low factor loadings. Therefore, the shortened version with four factors (after the elimination of low-factorloading items) was used to assess contact employees’ service orientation in this study. The sample items for the four factors are: “I will go out of my way to provide good service to customers” (Customer Focus); “People I have served before ask for me” (Prior Customer Relationship); “Our service procedures make it easy for me to give excellent customer service” (Organizational Support); and “Sometimes I forget to smile when the restaurant is really busy” (Service Under Pressure). The four factors each yielded a coefficient alpha of >.70, with an overall coefficient alpha of .78. All of the statements were rated on a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). To evaluate service quality of the participating restaurants, the perceptions battery of DINESERV was utilized. DINESERV consists of 29 items with five service quality dimensions (tangibles, assurance, reliability, responsiveness, and empathy), which originated from the SERVQUAL measure of Parasuraman et al. (1988). Five dimensions of DINESERV were not confirmed using the customer data of this study. Previous studies have also reported inconsistent outcomes on the five-factor structure of SERVQUAL (Carman, 1990; Babakus & Boller, 1992). The results of this study suggested the existence of three subdimensions in tangibles and indistinguishability between the responsiveness and assurance dimensions. The first tangibles dimension focused on appearance of physical facilities and staff; the second pertained to menu of the restaurant; and the third emphasized comfort and cleanliness of facilities. Consequently, six factors (Tangibles I, Tangibles II, Tangibles III, Reliability, Combination of Responsiveness and Assurance, and Empathy) were used for this study. The alpha values for the six constructs of DINESERV ranged from .78 to .93, with an overall coefficient alpha of .96. All DINESERV items used a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). Customer satisfaction was assessed with two items (α = .76). The first item assessed the “summary psychological state” derived from a consumer’s dining experience. The item was stated as “Overall, I am satisfied with this

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restaurant” and rated using a seven-point scale from 1 (strongly disagree) to 7 (strongly agree). The second item was a modification of the Circles Scales (Andrews & Withey, 1976); it has a rectangular shape with a 7-point graphic rating. The first rectangle containing all minuses represents the worst dining experience; the seventh rectangle containing all plusses represents the most pleasant. Customer loyalty was also measured with two items (α = .83). The first item pertained to behavioral loyalty (“I would come back to this restaurant to dine”) and the second item related to attitudinal loyalty (“I feel attached to this restaurant”). Both items were rated on a seven-point scale ranging from 1 (strongly disagree) to 7 (strongly agree).

Data Analysis
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Prior to data analysis, the questionnaires from customers, collected by each employee, were averaged and then paired with the questionnaire from the employee who attended to the customers. Hypotheses were tested using a structural equation modeling (SEM) method with LISREL 8.71 (Joreskog & Sorbom, 2004). Input for the LISREL 8.71 program consisted of a 14 × 14 covariance matrix. Four latent variables and 14 indicators were constructed for the model (see Figure 2). The four latent variables included service orientation, service quality, customer satisfaction, and customer loyalty. The 14 indicators included: (a) four from employee service orientation (Customer
T1 0.60 CF 0.47 PCR 0.52 0.61 OS 0.49 SUP Service Orientation 0.08 0.29* 0.73** 0.08 0.90 Customer Loyalty 0.78 Customer Satisfaction 0.75 S1 0.89 S2 0.84** EA RI T2 0.58 T3 0.67 0.81 R 0.85 R/A 0.84 E

Service Quality

FIGURE 2 Service orientation–customer loyalty model. Note. Dotted lines indicate insignificant relationships. For Service Quality, T1 through T3 = tangibles; R = reliability; R/A = responsiveness/assurance; and E = empathy. For Service Orientation, CF = customer focus; PCR = prior customer relationship; OS = organizational support; and SUP = service under pressure. For Customer Satisfaction, S1 = satisfaction 1 and S2 = satisfaction 2. For Customer Loyalty, RI = revisit intention and EA = emotional attachment. ∗ p < .05. ∗∗ p < .00.

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Focus, Prior Customer Relationship, Organizational Support, and Service Under Pressure); (b) six from service quality (Tangibles I, Tangibles II, Tangibles III, Reliability, Combination of Responsiveness and Assurance, and Empathy); (c) two from customer satisfaction (two satisfaction items); and (d) two from customer loyalty (Future Buying Intentions and Emotional Attachment). The value of each indicator (subfactor) for the two constructs (Service Quality and Service Orientation) was a composite score obtained by averaging all variables in its respective subdimension; the indicators for the two remaining constructs (Customer Satisfaction and Customer Loyalty) used raw scores. The paths connecting latent variables and their respective indicators are called the measurement model, and the paths connecting the sets of latent variables are called the structural model. As recommended by SEM researchers (Hair, Anderson, Tatham, & Black, 1998; Joreskog, 1993), two steps were used to test the proposed model. The first step involved confirmatory factor analysis to assess the validity of the measurement model. The second step involved the generation of a structural model that tests the research hypotheses. To evaluate fits of both the measurement and structural model, several fit indices were used including the chi-square statistic; the goodness-of-fit index (GFI: a measure of the correspondence of the actual covariance matrix with that predicted from the proposed model); the adjusted GFI index (AGFI: a GFI index adjusted for sample size); the standardized root mean square residual (standardized RMSR: average standardized residual value derived from the fitting of the covariance matrix for the proposed model); and the normed fit index (NFI: a measure that provides the incremental improvement of the fit of the proposed model from a baseline model). After the evaluation of the fit indices, the parameter estimates with their associated significance levels using t values (parameter estimate or standard error) were reported for the proposed model.

RESULTS Model Fit
The overall chi-square for the measurement model was 107.12 with 71 df and a p value less than .0057. When the chi-square is not significant (p > .05), the model fit is appropriate; that is, there is no significant difference between the actual matrix and the predicted matrix (Loehlin, 1992). The small p value of the model indicated a significant difference between the actual matrix and the predicted matrix. The chi-square statistic is known to be very sensitive to sample size and the number of parameters estimated; thus, using the normed chi-square (χ 2/df ) is appropriate (Hair et al., 1998; Wheaton, Muthen, Alvin, & Summers, 1977). The normed chi-square had a value of 1.51 (107.12/71) for the measurement model. This falls well

Service Orientation, Service Quality, Satisfaction and Loyalty TABLE 1 Goodness-of-Fit Indices for Measurement and Structural Models Model Measurement model Structural model χ2 107.12 108.51 df 71 72 χ 2/df 1.51 1.50 GFI .93 .93 AGFI .88 .88 NFI .91 .91

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Std. RMSR .052 .050

Note. GFI = goodness-of-fit index; AGFI = adjusted goodness-of-fit index; NFI = normed fit index; Std. RMSR = standardized root mean square residual.

within the recommended levels of 1.0 to 2.0 (Hair et al., 1998), indicating that the model fit is acceptable. In addition to the normed chi-square, other fit indices (GFI = .93; AGFI = .88; NFI = .91) fell around the desired level of .90, revealing that the model is representative of the observed data (Bentler & Bonett, 1980; Hair et al., 1998). Lastly, the standardized RMSR (.05) suggested that the magnitude of the differences between the actual and predicted covariance matrices are relatively small (Brown & Chdeck, 1993). The overall chi-square for the structural model was 108.51 with 72 df (p < .0055). Table 1 shows no significant difference between the structural model and the measurement model (chi-square difference = 1.4, df = 1). Because the measurement model allows all latent constructs to covary freely, a comparison of the conceptual model to the measurement model is one indication of adequate model fit. A lack of significant difference between the two models implied that the data supported the theory. Other fit values for the structural model were almost identical to those of the measurement model (Normed chi-square = 1.51; GFI = .93; AGFI = .88; NFI = .91; standardized RMSR = .05), satisfying the acceptable fit criteria mentioned earlier. Another method of evaluating the model fit is to examine the modification indices (chi-square reduction computed for each nonestimated relationship). The modification indices suggested that the proposed model fit could be improved by freeing additional correlations among measurement errors. However, because those relationships could not be justified theoretically, no changes were made to the model (Joreskog, 1993).

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Parameter Estimates
The significance of the parameter estimates was judged using t values. The critical t values are 1.96 for the 0.05 significance level and 2.58 for the 0.01 significance level. Table 2 presents the summary statistics of the measurement model with LISREL estimates (factor loadings) and Table 3 presents the path coefficients for the structural portion of the proposed model. An examination of Table 2 reveals that each relationship between the latent variables and their respective indicators are large, and all are statistically significant (t > 2.58, p < .01). All latent variables displayed acceptable

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TABLE 2 Summary Statistics and Measurement Model Factor loadings .67 .52 .61 .49 .60 .58 .67 .81 .85 .84 .75 .89 .90 .78 Composite reliability 77 Average variance extracted .51

Variables Service Orientation Customer focus Prior customer relationship Organizational support Service under pressurea Service Quality Tangible I Tangible II Tangible III Reliability Responsiveness & assurance empathy Customer Satisfaction Satisfaction I Satisfaction II Customer Loyalty Future buying intensions Emotional attachment

M 4.65 3.92 4.34 1.81 5.37 5.16 5.87 6.14 5.89 5.60 4.72 5.83 6.08 5.61

SD .33 .92 .47 .77 .79 .87 .66 .57 .60 .80 .61 .76 .77 .90

t value — 3.03∗ 3.20∗ 3.27∗

α .78

.96 — 6.05∗ 7.28∗ 8.10∗ 8.25∗ 8.19∗ .76 — 10.27∗ .83 — 11.56∗

.96

.87

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.75

.82

.85

.84

Note. Factor loading values are based upon a completely standardized solution. Dashes indicate t values were not computed as the parameters fixed to 1.00 during estimation. a Mean of the service under pressure variable is reverse coded. ∗ p < .01.

TABLE 3 Standardized Path Coefficients for Structural Equation Model Proposed model relations Service Orientation → Perceived Service Quality (γ 11 ) Service Orientation → Satisfaction (γ 21 ) Perceived Service Quality → Satisfaction (β 21 ) Perceived Service Quality → Customer Loyalty (β 31 ) Satisfaction → Customer Loyalty (β 32 )
∗

LISREL estimate .29 .08 .73 −.07 .84

t value 2.35∗ 1.01 5.87∗∗ −0.09 7.42∗∗

Indirect effect .21 (Satisfaction)

t value 2.01∗

.61 (Customer Loyalty)

4.93∗∗

p < .05.

∗∗

p < .01.

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composite construct reliability ranging from .77 to .96. Average variance extracted ranged from .51 to .87, all above the standard of .50 recommended by Fornell and Larcker (1981), suggesting adequate convergent validity. In Table 3, the standardized path coefficient between service orientation and service quality supported the first hypothesis (H1); a high degree of service orientation for the contact employee significantly affected the customer’s perception of service quality (γ 11 = .29, t = 2.05, p < .05). On the other hand, the second hypothesis (H2) regarding the causal relationship between service orientation and customer satisfaction was rejected. The third hypothesis (H3) stating the positive relationship between perceived service quality and satisfaction was supported; the perceived service quality significantly influenced customer satisfaction (β 21 = .73, t = 5.87, p < .01), confirming the previous empirical result (Cronin & Taylor, 1992) that service quality is an antecedent of customer satisfaction. The sixth hypothesis (H6) regarding the mediating role of service quality between service orientation and customer satisfaction was supported; service orientation showed a significant indirect effect on customer satisfaction via service quality (γ 11 × β 21 = .21, t = 2.50, p > .05). In other words, contact employees’ high degree of service orientation was conveyed via their service performance, which in turn led to high levels of customer satisfaction. Further, the insignificant direct path from service orientation to customer satisfaction suggested that service quality plays a role as a full (rather than a partial) mediator between employee service orientation and diners’ satisfaction in the restaurant setting (for the distinction between full and partial mediators, see Baron & Kenny, 1986). The fourth (H4) and fifth (H5) hypotheses focused on the effect of service quality on customer loyalty and the effect of customer satisfaction on customer loyalty, respectively. H4 was rejected, as service quality did not have a significant, direct effect on customer loyalty. H5 was supported; the result suggested that customer satisfaction has a significant influence on customer loyalty (β 32 = .84, t = 7.42, p < .01). The last hypothesis (H7) regarding customer satisfaction as a mediator between service quality and customer loyalty was supported as there was a significant indirect effect on customer loyalty via customer satisfaction (β 21× β 32 = .61, t = 4.93, p < .01). Similar to H6, no significant path from service quality to customer loyalty suggested the possibility of customer satisfaction as a full mediator between perceived service quality and customer loyalty.

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DISCUSSION AND MANAGERIAL IMPLICATIONS
This study contributes to the existing body of literature by its unique research design. Previous studies that argue the positive associations between frontline employees’ service orientation and customer outcome variables

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such as satisfaction often gathered the ratings of all study variables from customers (e.g., see Donavan & Hocutt, 2001; Brady & Cronin, 2001). However, the present study depends on two data sources: Service oriented behaviors were reported by frontline employees themselves (i.e., servers and bartenders) and dining customers provided their perceptions of service quality, satisfaction, and loyalty to the service company. This research design reduces the impact of common method variance (i.e., single source) and therefore the results are more likely to present reliable, true effects of service orientation (predictor) on customer perceptions of a service company (outcomes). This study supports the prevalent theory or common belief that customer-oriented companies benefit at multiple levels. Specifically, it shows that frontline employees (i.e., servers and bartenders) with a high degree of service orientation influence restaurant diners’ perceptions of service quality and ultimately lead to diners’ satisfaction and loyalty. It sends a crucial message to restaurateurs as to how important it is to have customer-oriented staff members. This study adopted the approach of service orientation as a function of innate traits and situational or environmental factors. Following this approach, restaurant operators should come up with a proper personality trait profile and hire people that fit that profile. Some recent studies in the hospitality field have pointed out the fundamental role of the individual employee’s personality traits in work engagement and burnout (Kim, Shin, & Swanger, 2009; Kim, Shin, & Umbreit, 2007). Despite such recruitment efforts to select the applicants with suitable personality traits, companies may find that employees, who are hired, have different levels of customer focus. This situation indicates the significance of the other variable in the service orientation equation—a situational or environmental factor. Practical and useful environmental or situational factors to enhance employee service orientation may include: offering ongoing training and rewarding good performance of employees. The benefits of rewards and proper training in the hospitality industry have been well documented (Kim, Tavitiyaman, & Kim, 2009). Another key issue addressed in this study is the relationships among service quality, customer satisfaction, and customer loyalty. The results confirmed that customers’ decisions to remain loyal depend directly on their satisfaction (Cronin & Taylor, 1992) and found that customers’ perceptions of service quality have an indirect influence on customer loyalty via customer satisfaction (Perceived Service Quality → Customer Satisfaction → Customer Loyalty). Rust and Oliver (1994) explained the relationship between service quality and customer satisfaction best. They suggested that quality is one of the many potential service dimensions that are factored into customer satisfaction. Similarly, Westbrook (1981) indicated that satisfaction comes from multiple sources, and a higher level of satisfaction with certain sources might compensate for lower levels of satisfaction with others. Rust and

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Oliver’s (1994) and Westbrook’s (1981) arguments, in a sense, justify the current finding (service quality as an antecedent of customer satisfaction) and help support the mediating effect of customer satisfaction: Perceived Service Quality → Customer Satisfaction → Customer Loyalty. In addition, Rust and Oliver’s (1994) and Westbrook’s (1981) arguments imply that it may be wise to conduct a further investigation on how vital it really is to provide a high level of service to customers in different dining segments. For instance, high levels of service quality may be the most significant predictor of customer satisfaction in fine dining restaurants, but price or promotion may be more essential for customers at quick-service restaurants. This kind of detailed information is more likely to be useful for practitioners than the simple managerial implication that service quality has a positive effect on customer satisfaction. Restaurant operators should also note that satisfaction results from both current and past experiences (Oliver, 1981). Customers are becoming more demanding as they acquire a greater range of dining experiences and have more choices in all food service segments. The operators who can ensure that their customers are always satisfied are more likely to enjoy repeat business from their customers.

Limitations and Future Research
First, although this study reduced the common method variance to some extent by relying on two data sources (employees and customers), interrelationships among service quality, satisfaction, and loyalty are still subject to common rater bias since these constructs are all assessed by customers. The much stronger relationships are shown among customer outcome variables (service quality to satisfaction and satisfaction to loyalty) than the path from employee service orientation to service quality. Perhaps this may reflect the common rater bias and therefore the relationships among service quality, satisfaction, and loyalty should be attenuated and interpreted with some caution. Next, each participating employee was paired with a rather small number of customers. Although there is no concrete criterion about how many customer responses are appropriate, the aggregated responses from three or four customers may not be sufficient to make an objective, accurate judgment about the quality of service provided by the individual employee. The overall sample size of this research (n = 169) is also quite small although it falls into the acceptable range (100–200; Hair et al., 1998) to employ the SEM method. These circumstances could contribute to resulting in weaker associations (than expected) between service orientation and service quality. This study shows, although significant at the 0.05 level, the path from service orientation to service quality was not significant at the conservative 0.01 level.

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Third, Groves’ (1992) instrument used for this study is designed for the restaurant business and the population of this study comprises employees in casual dining restaurants in Korea. Therefore, generalization of this study’s findings to other service industries (e.g., airlines, travel agencies, hotels) and other cultures is limited. To validate the result of this study in different hospitality or service segments, it is necessary to choose a service-orientation measure that may be applicable to a broad spectrum of service jobs to assess frontline employees’ service orientation. One exemplary measure is the service-orientation scale recently developed by Donavan et al. (2004). Their scale items are created using diverse service settings (e.g., travel agency, financial services, food service) and the measure includes the four service orientation components (pamper, read, deliver, and personal relationship). Finally, the proposed model in this study is rather simple because it investigates the impact of service orientation as a whole on customer satisfaction and service quality. In the future, it is feasible to build more complex service orientation models. For example, it would be interesting to see the influence of service orientation on customer outcomes such as perceived service quality, customer satisfaction, and revisit intention after splitting service orientation into innate personality traits and environmental variables. In this scenario, researchers could compare the two distinctive service orientation components (personality vs. environmental factor) and assess which component is more influential on customer outcome variables. The results of these kinds of models may assist industry practitioners in terms of how to prioritize the use of their financial resources in the human resource areas (e.g., recruitment vs. service training).

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