Service quality, customer satisfaction, and behavioral

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Service quality, customer satisfaction, and behavioral intentions in fast-food restaurants

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Hong Qin and Victor R. Prybutok Information Technology and Decision Sciences Department, College of Business Administration, University of North Texas, Denton, Texas, USA Abstract Purpose – This study aims to explore the potential dimensions of service quality, and examine the relationship among service quality, food quality, perceived value, customer satisfaction and behavioral intentions in fast-food restaurants (FFRs). Design/methodology/approach – The construct reliability and validity was assessed using exploratory factor analysis and confirmatory factor analysis. Structural equation modeling was employed to estimate the relationship among service quality, customer satisfaction, and behavioral intentions. Findings – Results indicated that five dimensions were significant: tangibles, reliability/ responsiveness, recovery, assurance, and empathy. Service quality and food quality were two main determinants of customer satisfaction. The insignificance of perceived value is potentially due to the homogeneous nature of the construct within the FFR group rather than the importance of the perceived value construct within food service. Originality/value – The FFR success model, using the original five in the SERVPERF scale and another new dimension “recovery” to measure service quality, was empirically examined in the fast food industry. Several potential antecedents of satisfaction, including service quality, food quality and perceived value were also tested. Keywords Consumer behaviour, Customer satisfaction, Fast foods, Customer services quality Paper type Research paper

International Journal of Quality and Service Sciences Vol. 1 No. 1, 2009 pp. 78-95 q Emerald Group Publishing Limited 1756-669X DOI 10.1108/17566690910945886

1. Introduction Customers’ evaluations of the service quality are critical to service firms that aim to improve their marketing strategies (Cronin and Taylor, 1992; Jain and Gupta, 2004; Ofir and Simonson, 2001). Firms that provide superior service quality also have a more satisfied customer base (Aaker and Jacobson, 1994; Gilbert et al., 2004; Gilbert and Veloutsou, 2006). Customer satisfaction is viewed as influencing repurchase intentions and behavior, which, in turn, leads to an organization’s future revenue and profits. As a result of the direct link with profits, the issue of service quality and customer satisfaction has become a focus of the hospitality industries. More and more companies are compelled to assess and improve their service quality in an effort to attract customers (Gilbert and Veloutsou, 2006). There are some academic studies to address the service quality and customer satisfaction in fast-food restaurants (FFRs) (Brady et al., 2001; Gilbert et al., 2004; Kara et al., 1995; Lee and Ulgado, 1997; Qin and Prybutok, 2008); however, most of the studies are limited to the relationship between customer satisfaction and service quality.

Some other potential determinants of customer satisfaction such as food quality and perceived value are ignored. Furthermore, to the best of our knowledge, very few studies have examined the recovery ability of FFRs, much less of its effect on the perceived service quality or customer satisfaction. Understanding the interplay between the recovery mechanism and customer behavioral intentions is important, because better recoveries increase the customer’s propensity to return to the same service provider whereas ineffective service recovery may reinforce the customer’s dissatisfaction with the service (Harris et al., 2006). However, service recovery is not considered in the well-known SERVPERF model even though some findings suggest that recovery dominates formation of customer satisfaction and behavioral intentions (Spreng et al., 1995). This study contributes to the investigation of the above issues. First, we seek to develop a FFR success model by examining the key dimensions of service quality in the fast food industry. Specifically, another potential dimension, recovery, is incorporated into the SERVPERF instrument. After establishing sufficient reliability and validity of this instrument, we proceed with the second objective – to examine the relationship among service quality, food quality, perceived value, customer satisfaction, and behavioral intentions. The organization of this paper includes another five sections. The theoretical foundation of perceived service quality and its dimensions are reviewed in the next section, followed by the research methodology including the development of the instrument. Then, the data analysis and findings of this study are presented, followed by the conclusions and managerial implications. The paper concludes with a section on the limitations of this work and potential future research. 2. Theoretical foundation The importance of service quality is substantially addressed in the fast-food management literature. Superior service leads to satisfied and loyal customers whose continued patronage is essential to the success of FFRs. Conversely, poor service quality increases customer dissatisfaction and the likelihood that customers dine at a competitor’s FFR and/or become an active champion in persuading others to go elsewhere (Gilbert et al., 2004). Hence, it is crucial for service managers to understand how customers perceive the service they provide, and what components might determine the nature of the perceived service quality in FFRs. 2.1 Measurement of service quality Over the past two decades, the research related to perceived service quality has swelled enormously. An important contribution to that research stream is Parasuraman et al.’s (1988) 22-item SERVQUAL scale. This scale measures service quality by the degree of discrepancy between customers’ normative expectations for the service and their perceptions of the providers’ actual performances (Parasuraman et al., 1985, 1988). Five dimensions are unsheathed as the main attributes of service quality across a variety of services. These dimensions include tangibles, reliability, responsiveness, assurance, and empathy. Subsequent empirical works have applied the SERVQUAL instrument to measure service quality in a variety of business settings (Bojanic and Rosen, 1994; Fu and Parks, 2001; Furrer et al., 2000; Gounaris, 2005; Heung et al., 2000; Lassar et al., 2000; Lee and Ulgado, 1997).

Service quality in restaurants

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Although the SERVQUAL instrument is employed enthusiastically, it has received heavy criticism from both a theoretical and practical perspective. The issues questioned include the use of gap scores, the overlap among five dimensions, poor predictive and convergent validity, the ambiguous definition of the “expectation” construct, and unstable dimensionality (Babakus and Boller, 1992; Carman, 1990; Peter et al., 1993; van Dyke et al., 1999). By discarding the expectation portion in the SERVQUAL model, Cronin and Taylor (1992) justify their SERVPERF or performance-only instrument in place of the gap measurement approach. In addition, they provide empirical evidence that the SERVPERF instrument outperforms the SERVQUAL scale across four industries: fast food, dry cleaning, banks and pest control. The performance-only measures are used and suggested by many scholars in various industries (Gilbert et al., 2004; Keillor et al., 2004; Law et al., 2004; Parasuraman et al., 1994; van Dyke et al., 1997). In addition to the research of Cronin and Taylor (1992) in fast food industry, Jain and Gupta (2004) compare weighted and un-weighted versions of the SERVQUAL and SERVPERF instruments by conducting a survey of FFR customers in India. They find that the SERVPERF scale is more effective in explaining the service quality constructs and variations in service quality scores within the restaurant industry. For the purpose of this study, we are following Cronin and Taylor’s conceptual model and use performance only to measure service quality. Several previous studies suggest that modification of SERVPERF is necessary for application to different service industries (Andaleeb and Conway, 2006; Carman, 1990; Olorunniwo et al., 2006). This served as an impetus to investigate other influential components of service quality within the fast-food industry. Specifically, we investigated 60 customer reviews of FFR service at: www.my3cents.com. From those reviews, we gleaned that most of the customers experiencing dissatisfactory service were complaining about the poor resolution of their negative experience rather than the service incidence itself. This supports the contention that most customers can accept that service is not flawless and mistakes are tolerated if they believe that the restaurant is concerned about resolution of the service problem. This is consistent with prior findings in the literature (Bitner et al., 1990; Heskett et al., 1994; McColl et al., 2005). Failure itself does not necessarily lead to customer dissatisfaction; however, failure to handle recoveries effectively can lead to lost customers and negative word-of-mouth (Heskett et al., 1994; McColl et al., 2005). Moreover, the complete resolution of a critical service failure can even provide positive word-of-mouth endorsements and future repeat patronage (Leong and Kim, 2002). Therefore, in addition to the five dimensions employed in the SERVPERF instrument, recovery was included in this study as one of the potential dimensions of the perceived service quality. It is defined as the ability to actively take responsive actions when the service delivery goes wrong. Four items of recovery, as shown in Table I, were modified from the mass service environment (Olorunniwo et al., 2006) to create our items for use in the fast-food industry. 2.2 Interrelationship among service quality, customer satisfaction and behavioral intentions The relationship among service quality, customer satisfaction and behavioral intensions has received considerable attention in the marketing literature (Brady et al., 2001, 2002;

Constructs

Indicators

Tangibles

Clean dining area Well-dressed employees Using disposable gloves and hair net Seating availability Parking availability Providing service as promised Sympathetic and reassuring Dependable On-schedule service Accurate charge Trust employees Feel safe for financial transactions Friendly employees Knowledgeable employees Telling exact service time Employees available to requests Prompt service Employees willing to help Availability of utensils, etc. Convenient operating hours Convenient locations Completely packaged food Employees quickly apologize for mistakes Cares about customer’s complaints Skills and ability to deal with complains Employees empowered to provide compensation Fresh Presentation Well cooked A variety of food and beverage Competitive price for food Competitive price for beverage Value worthy of price Satisfied with dining Wise choice Right thing Enjoyable experience Recommendation Intention to dine here again Saying good things about the FFR

Reliability

Assurance

Responsiveness

Empathy

Recovery

Food quality

Perceived value Customer satisfaction

Behavioral intentions

Sources

Service quality in restaurants

Cronin and Taylor (1992), Johns and Howard (1998) and Kara et al. (1995)

81 Cronin and Taylor (1992)

Cronin and Taylor (1992)

Cronin and Taylor (1992)

Cronin and Taylor (1992) and Johns and Howard (1998) Olorunniwo et al. (2006)

Johns and Howard (1998) and Kivela et al. (1999) Kim and Kim (2004) and Kara et al. (1995) Olorunniwo et al. (2006)

Boulding et al. (1993) and Keillor et al. (2004)

Cronin and Taylor, 1992; Meuter et al., 2000; Oliva et al., 1992; Olorunniwo and Hsu, 2006; Olorunniwo et al., 2006; Zeithaml et al., 1996). Within this research area, numerous empirical studies have reported the positive relationship between customer satisfaction and behavioral intentions (Cronin et al., 2000; Kivela et al., 1999; Olorunniwo et al., 2006). Consistent with the prior research, the first hypothesis in this study is that:

Table I. Sources of questionnaire items

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H1. Customer satisfaction directly and positively influences behavioral intentions. An ongoing debate in the marketing literature relates to the direction of the quality/ satisfaction causal relationship – whether customer satisfaction is an antecedent or consequence of service quality (Andaleeb and Conway, 2006). One group of researchers refers to service quality as a global evaluation of a particular service setting and consistent with this theory service quality is the consequence of satisfaction incidents over time (Parasuraman et al., 1985, 1988). The European and American customer satisfaction indices models, however, suggest that service quality is a component of satisfaction (Fornell et al., 1996). Bagozzi (1992) proposes that service quality evaluation of a product or a service encounter leads to an emotive satisfaction assessment that in turn drives behavioral intentions. Although there is no consensus in the literature on the causal order of these two constructs, the converging opinion is that service quality perceptions lead to customer satisfaction (Cronin et al., 2000). Building upon these findings, we posit that service quality is the antecedent of customer satisfaction. As a result, our second research hypothesis is: H2. Service quality directly and positively influences consumer satisfaction. 2.3 Other factors of customer satisfaction in FFRs In addition to service quality, several other determinants of customer satisfaction in FFRs were examined because service is only one of the components of the offering for FFRs (Andaleeb and Conway, 2006). Food quality is viewed related to satisfaction within FFRs (Kivela et al., 1999; Law et al., 2004; Johns and Howard, 1998) and is tested in this study. As a result, the third hypothesis is: H3. Food quality directly and positively influences customer satisfaction. The perceived value can also greatly influence customer satisfaction owing to its ability to attract or repel customers (Fornell et al., 1996; Gilbert et al., 2004; Kara et al., 1995; Lee and Ulgado, 1997; Monroe, 1989) and, as a consequence, it is viewed as another determinant of customer satisfaction. The next hypothesis is: H4. Perceived value directly and positively influences customer satisfaction. All these hypotheses lead to the research model posited in this study and shown in Figure 1. 3. Research methodology and data analysis The FFR success model includes service quality, food quality, perceived value, customer satisfaction and behavioral intensions. Most of 22 original items in the SERVPERF scale were preserved and the other items used were all selected from prior marketing and food studies (Table I). The items that measure empathy was modified based on the prior findings (Andaleeb and Conway, 2006; Lee and Ulgado, 1997). Minor customization in the wording was conducted for the other items in an effort to better fit the FFRs context. All items were rated by respondents on a seven-point Likert scale. Each item was scaled from number 1 with the verbal statement “Strongly Disagree” to number 7 with the verbal statement “Strongly Agree”. Each subject was asked to evaluate the FFR they most recently visited. The proposed questionnaire was first reviewed by several

Service Quality (SQ)

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H2

Food Quality (FQ)

H3

Perceived Value (PV)

H4

Customer Satisfaction (CS)

H1

Behavioral Intentions (BI)

knowledgeable faculty and experts in the field of service quality management. The next step was to administer a pilot test to 30 doctoral students. Based on all the feedback, several modifications were made to the items so that they better fit the FFR context. Following that effort, the FFR success instrument with 40 conceptual items and several demographic questions was administered via an online survey. The respondents were college students in a large southwestern university in the USA. The online survey format was used instead of the traditional paper survey because it is cheaper, faster, and offers a flexible format. College students were selected as subjects because of the ease in accessing them and because they dine at FFRs frequently, and possess an intuitive understanding of service quality in FFRs. A total of 305 responses were received, and 23 of them were determined to be unusable. Of the 282 usable responses, 45.7 percent were completed by male respondents. More than 55.7 percent of the respondents were between 21- and 25-years old. This is in consistence with our use of college students as the sampling frame. All the respondents surveyed had dined at a FFR in the last month, and around 60 percent of respondents had dined in a FFR more than five times within about one month. Over 45 percent of the respondents have a monthly income less than $800. The detailed demographic information is provided in Table II. 3.1 Reliability and validity assessment Principle component analysis with a varimax rotation was employed to test the discriminant and convergent validity of the instrument. Factor analysis was used for each construct and then for the six dimensions of service quality together. All the items with a loading less than 0.55 on any latent variable were deleted. The results of principal components factor analysis in Table III present that most of the factor loadings are above 0.6 with cross loadings less than 0.4 after rotation. The 22 remaining items loaded into five latent variables. The items that measure reliability and responsiveness were loaded together and were named reliability/responsiveness in this research. All the four items to measure the dimension recovery loaded together but there were some cross-loadings. Overall, the exploratory factor analysis supports the validity of the service quality instrument in the proposed FFR success model. Some of the discrepancies that we experience were also consistent with previous criticisms of the SERVPERF scale such as items not loading on their theoretical factors and items that cross-loading (Buttle, 1996; van Dyke et al., 1999).

83 Figure 1. FFR success model

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Table II. Demographics of sample

Characteristic Dining frequency 0-5 times 6-10 times 11-15 times More than 15 times Gender Male Female Age (years) 18-20 21-25 26-30 31-35 Over 35 Monthly income Less than $400 $400 to less than $800 $800 to less than $1,200 $1,200 to less than $1,600 $1,600 to less than $2,000 $2,000 or more

Percentage 41.1 31.6 13.8 13.5 45.7 54.3 26.6 55.7 10.7 3.5 3.5 22.7 23.0 25.5 9.2 6.4 13.1

Confirmatory factor analysis (CFA) was performed to test unidimensionality (Gerbing and Anderson, 1988). The LISREL 8.54 structural equation analysis package (Jo¨reskog and So¨rbom, 2003) was utilized to conduct the CFA. The primary outputs (Table IV) show the relationships between the five main dimensions of service quality and their associated items (Model A in Figure 3). The standardized loadings are higher than 0.80, and the t-values are higher than 1.96, which supports the convergent validity of the item measures (Olorunniwo et al., 2006). The goodness-of-fit indices of Model A, shown in Table VII, support the acceptability of the measurement model (see the Appendix for details). Composite reliability rather than Cronbach’s a was used to assess reliability because the latter may over- or underestimate the reliability coefficient especially when multidimensional measures or pre-specified sets of items are used (Brunner and SU¨ß, 2005; Raykov and Grayson, 2003). The composite reliability scores for five service quality dimensions, shown as diagonal elements in Table V, range from 0.80 to 0.94 and are all higher than the benchmark value of 0.70 (Olorunniwo et al., 2006). The means and standard deviations of all latent variables were summarized for the four major FFR chains in this study. Table VI shows the actual performance of these FFRs in terms of service quality, food quality, customer satisfaction, etc. These results are shown for the chains that had at least 30 respondents, and they indicate the relative strength of each construct both within and between the four FFRs. Discriminant validity is supported because all the composite reliability scores on diagonal are higher than the off-diagonal correlation coefficients in Table V. In addition, the 95 percent confidence interval of the inter-factor correlation between two latent variables was further applied to assess the discriminant validity. Although some of the correlation coefficients are high, their confidence intervals do not include the absolute

F2 F1 Reliability/ F3 F4 F5 Recovery responsiveness Assurance Empathy Tangibles CareComplnt RecoverySkills RecoveryCompensation Apology PrmptMtgProms TimelyService Promptness Dependability EmplyAvailability AccurateCharge EmplyWillingness Trust SafeTransaction EmplyFriendly EmplyKnowledgeable ConvenientLocation ConvenientHours UtensilsAvailability Parking Seating ClnDiningArea WellDressedEmly Mean Standard deviation Percentage of explained variance (post-rotation total ¼ 77.64 percent)

0.825 0.809 0.793 0.748

0.429 0.409 0.412 0.439

Service quality in restaurants

85 0.732 0.720 0.689 0.642 0.614 0.606 0.605

0.437 0.405

0.435 0.421 0.726 0.695 0.660 0.599 0.828 0.801 0.595

4.69 1.45

5.17 1.24

19.02

18.86

0.431 0.525 4.79 1.35 14.98

5.65 1.32 13.19

0.417 0.718 0.709 0.621 0.563 5.16 1.23 11.58

Notes: Extraction method: principal component analysis. Rotation method: varimax with Kaiser normalization. Rotation converged in six iterations

value of 1.0, which supports the discriminant validity of these constructs as well (Anderson and Gerbing, 1988). Given their good measurement properties (reliability and validity), the average score for each latent construct that comprised service quality was employed in the hypothesis testing procedures. This is identical to the technique used by Paswan et al. (1998) where the scale items were averaged to establish composite scores for the purposes of hypotheses testing. A second-order CFA was employed to examine the relationship among service quality, its five main dimensions and all indicator variables. From the standardized factor loadings shown in Figure 2, we could see that all the dimensions have a significant and positive relationship with the latent variable service quality. Specifically, the path coefficient of 0.83 between the added dimension recovery and service quality is significant, which supports our modification of the SERVPERF instrument. All the previously selected fit indices presented in Table VII (Model B) indicate that the measurement model of service quality is deemed acceptable.

Table III. Factor loadings for service quality in FFR success model

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Table IV. First-order CFA for the FFR success model – Model A

Table V. Correlation matrix of perceived service quality-FFR success model

Construct and items

t-valuea

Standardized solution

Recovery Apology CareComplnt RecoverySkills RecoveryCompensation Reliability/responsiveness PrmptMtgProms Dependability TimelyService AccurateCharge EmplyAvailability Promptness EmplyWillingness Assurance Trust SafeTransaction EmplyFriendly EmplyKnowledgeable Empathy UtensilsAvailability ConvenientHours ConvenientLocation Tangibles ClnDiningArea WellDressedEmly Seating Parking

0.87 0.92 0.92 0.80

22.36 _b 25.45 18.55

0.82 0.82 0.84 0.76 0.88 0.87 0.85

18.65 18.69 19.17 16.05 _b 20.94 19.66

0.83 0.82 0.88 0.82

18.61 17.99 _b 17.98

0.71 0.89 0.86

13.58 _b 17.69

0.82 0.76 0.68 0.57

_b 13.33 11.65 9.66

Notes: at-values are from unstandardized solution. bt-values are unavailable because the loadings are fixed for scaling purposes

Reli/Res Recovery Empathy Assurance Tangibles

Reli/Res

Recovery

Empathy

Assurance

Tangibles

0.94 a 0.73 * 0.68 * 0.80 * 0.74 *

0.93 a 0.53 * 0.75 * 0.59 *

0.86 a 0.56 * 0.65 *

0.90 a 0.64 *

0.80 a

Notes: aThe diagonal elements are composite reliability scores. Significance at *p , 0.01 level

3.2 Model fit assessment Given the support of strong internal and external validity in both first- and second-order measurement models, structural modeling analysis enables us to examine the hypothesized relationships. Structural equation modeling (SEM) was employed because it is generally considered more suitable for the mathematical modeling that involves complicated variable relationships. SEM allows analysis of both the measurement

Variables Tangibles Recovery Reliability/responsiveness Empathy Assurance Food quality Perceived value Service quality Customer satisfaction Behavioral intentions

All FFRs Mean SD 5.16 4.69 5.17 5.65 4.79 5.28 5.21 5.09 5.20 5.26

1.23 1.45 1.24 1.32 1.35 1.31 1.38 1.13 1.48 1.22

FFR1 Mean SD 5.03 4.82 5.25 5.61 4.96 5.34 5.12 5.13 5.32 5.27

1.37 1.53 1.40 1.41 1.41 1.42 1.42 1.27 1.55 1.34

FFR2 Mean SD 5.01 4.83 5.11 5.93 4.86 5.48 5.37 5.15 5.21 5.40

1.27 1.31 1.22 1.23 1.28 1.29 1.23 1.10 1.42 1.19

FFR3 Mean SD 5.35 4.73 5.20 5.70 4.92 5.27 5.42 5.18 5.56 5.26

0.88 1.18 0.99 1.44 1.20 1.20 1.15 0.88 1.16 1.04

FFR4 Mean SD 5.10 4.43 4.65 5.56 4.02 4.63 4.86 4.75 4.72 4.77

1.18 1.26 1.05 1.23 1.15 1.14 1.31 0.96 1.53 0.99

model and the structural model. It cannot only address measurement errors but also allows the examination of the factor analysis and hypothesis testing together (Gefen et al., 2000). Model C in Figure 3 includes all the proposed relationships in this study. For model evaluation emphasis was placed on x 2/df, standardized root mean square residual (SRMR), adjusted goodness of fit index (AGFI), normed fit index (NFI), non-normed fit index (NNFI), CFI and parsimonious normed fit index (PNFI), reported in Table VII (Model C). The x 2/df is 3.03, slightly higher than the cutoff value of 3.0. The other indices support the model fit (AGFI higher than 0.80; PNFI higher than 0.75; SRMR lower than 0.10; NFI, NNFI, CFI higher than 0.90). These emphasized indices indicate the acceptability of this structural model. All the parameter estimates between items and their associated latent variable, as shown in Table VIII, are significant. H1 posits a direct and positive relationship between customer satisfaction and behavioral intentions and is supported by a significant path coefficient. H2, positing a direct and positive relationship between perceived service quality and customer satisfaction, is statistically significant. H3 investigates a direct and positive relationship between food quality and customer satisfaction, and is statistically significant and supported. This result shows that the improvement of food quality might yield higher level of customer satisfaction. H4, modeling perceived value as one of the antecedents of customer satisfaction, is rejected because of the non-significant t-value. 4. Conclusion and implications This study posits and develops an instrument of service quality in the context of FFRs, and examines the relationship among service quality, food quality, perceived value, customer satisfaction, and behavioral intentions. The proposed FFR success model is then calibrated using the data from an online survey completed by college students that eat at FFRs. Five significant dimensions of service quality identified are: tangibles, reliability/responsiveness, empathy, assurance, and recovery. The results support our modification of the SERVPERF model, with the addition of the “recovery” dimension in the service quality instrument for FFRs. This is also consistent with prior work that shows it is necessary to modify the SERVPERF model for a particular industry (Carman, 1990; Olorunniwo et al., 2006).

Service quality in restaurants

87 Table VI. Summary statistics for latent variables

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ClnDiningArea 0.82* WellDressedEml 0.77*

88

Tangible

Seating

0.66* 0.56*

Parking PrmptMtgProms

0.87*

0.83* Dependability 0.83* TimelyService 0.83* Reli/Resa

0.75*

AccurateCharge

0.88*

EmplyAvailibility

0.86* Promptness

0.96*

0.83* EmplyWillingness Trust

SQ

0.83* 0.91*

SafeTransaction

0.83* Assurance

0.88* 0.82*

EmplyFriendly EmplyKnledgeable

0.72* EssentialAvail 0.71* Empathy

ConvenientHours

0.88* 0.87*

0.83*

CvntLocation

Apology

0.87* Recovery

0.92*

CareComplnt

0.92* 0.80*

RecoverySkills Compensation

Figure 2. Second-order CFA for the FFR success model (Model B)

a

Notes: Parameter estimates between five dimensions of service quality are omitted. Reli/Res = Reliability/ Responsiveness. * Indicates significance at p < 0.01 level

Tangibles

4 items

Tangibles

4 items

Reli/Resa

7 items

Reli/Resa

7 items

Assurance

4 items

Assurance

4 items

Empathy

3 items

Empathy

3 items

Recovery

4 items

Recovery

4 items

Serv Qualb

Model A-First-order CFA for the FFR Success Model

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Model B-Second-Order CFA for the FFR Success Model

Serv Qualb with Recovery

Food Quality

Customer Satisfaction

Behavioral Intentions

PVc

Figure 3. Models

Model C-Structural Model for the FFR Success Model Notes: a Reli/Res – Reliability/Responsiveness. b Serv Qual – Service Quality. c PV – Perceived Value

Model A Model B Model C

x2

df

x 2/df

p-value

AGFI

SRMR

NFI

NNFI

CFI

PNFI

505.97 511.63 436.86

199 204 145

2.54 2.51 3.03

0.00 0.00 0.00

0.80 0.80 0.82

0.058 0.061 0.063

0.98 0.97 0.98

0.98 0.98 0.98

0.98 0.98 0.98

0.84 0.84 0.83

Notes: Model A: first-order CFA for the FFR Success model; Model B: second-order CFA for the FFR success model; Model C: structural model for the FFR Success model. Detailed information about these models is shown in Figure 3

Among these five dimensions, reliability/responsiveness, tangibles, assurance, and recovery are slightly more important than empathy with standardized loadings higher than 0.80. As part of the work, we also looked at the model fit with and without recovery. The importance of recovery in FFRs was supported by the better model fit obtained with its inclusion. This supports the importance of building a reliable customer relationship, and responding to customer requests or complaints in a prompt manner. Our results also suggest that FFRs should train employees in service recovery standards and guidelines. It is also important to empower employees so that they can compensate customers for service failure. For example, employees might be

Table VII. Comparative goodness-of-fit indices among models

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Table VIII. Structural modeling for the FFR success model – Model C

Item Tangible ! ServQual Reli/Res ! ServQual Assurance ! ServQual Empathy ! ServQual Recovery ! ServQual Food1 ! FoodQual Food2 ! FoodQual Food3 ! FoodQual Food4 ! FoodQual PV1 ! Perceived value PV2 ! Perceived value PV3 ! Perceived value CS1 ! CustomerSat CS2 ! CustomerSat CS3 ! CustomerSat CS4 ! CustomerSat BI1 ! BehavioralInt BI2 ! BehavioralInt BI3 ! BehavioralInt ServQual ! CustomerSat FoodQual ! CustomerSat Perceived value ! CustomerSat CustomerSat ! BehavioralInt

Standardized solution 0.78 0.91 0.87 0.74 0.80 0.82 0.84 0.85 0.69 0.93 0.86 0.80 0.93 0.96 0.94 0.91 0.89 0.92 0.95 0.15 0.64 0.01 1.25

t-valuea 16.50 22.03 _b 14.91 16.96 _b 17.03 17.24 12.97 _b 19.95 17.44 32.25 _b 34.14 29.40 26.20 30.36 _b 2.53 9.60 0.43 17.34

Notes: at-values are from unstandardized solution. bt-values are unavailable because the loadings are fixed for scaling purposes

empowered to provide a price discount or coupons for a future visit to compensate customers in the event of a service failure. The effective resolution of service failure can improve customers’ perceived service quality that, in turn, can positively influence word-of-mouth and customer loyalty. Moreover, our findings show that service quality is an important antecedent of customer satisfaction. This finding reinforces the need for FFR owners and managers to place an emphasis on the underlying dimensions of service quality, especially on responsiveness/reliability and recovery strategies. The overwhelmingly direct effect of customer satisfaction on behavioral intentions substantiates the need to constantly monitor customers’ responses. Practically, FFRs could collect customer feedback about the service they received in real time; furthermore, they could trace the customers in accordance with their evaluations, particularly those complaining about the service quality. This strategy enables FFRs to identify the issues they are confronting and take corrective actions. Two other hypothesized factors of customer satisfaction are examined. The significant relationship between food quality and satisfaction supports the contention that food characteristics are still influential factors when customers make their decisions about the selection of FFRs. Consistent with this finding providing fresh, tasty, and a variety of food and beverages remains an important criterion for satisfying customers. This finding provides support for FFR managers to develop better strategies to differentiate their services. For example, FFRs might find that offering

specialty market segment options such as low fat, low calorie, or low-carbohydrate items increases their market appeal. Additionally, FFRs could prominently display calorie information for their menu items inside of their restaurants and/or on their web site, to better address the concerns of the portion of FFR customers concerned with these aspects of food quality. Another proposed factor of customer satisfaction, perceived value, is not significant in this study. This does not suggest perceived value is not important for satisfaction. Rather, we believe that this characteristic is relatively homogenous among the restaurants in this study and as a result perceived value does not exhibit significant variation relative to the other factors. The FFRs in this study have comparatively inexpensive food options and other factors such as quick response and recoverability are more critical to the respondents’ decision process. This is not a critical issue in evaluating FFRs service quality because an individual selecting a FFR is doing so in comparison to other FFRs and not because they are deciding between a FFR and a fine dining establishment. 5. Limitations and future research It is essential to acknowledge the limitations of this study. First of all, the findings of this research should be used cautiously in attempting to make generalizations. Our research is based on the use of college students as our sample pool but this group is relatively homogeneous. While students do constitute a large market for FFRs, broader generalizability is desirable. A more varied set of respondents could be obtained via on-site surveys. Despite the homogeneity of our subjects we still think they are representative of an important FFR market segment and qualified to provide feedback about their perception of FFR performance. In addition, it is worthwhile to investigate the determinants of service quality in FFRs across multi-national settings (e.g. different countries). Many FFRs have extended their business reach to include global markets and the factors identified in this study are potentially inconsistent across cultures even with college students. As a result, a direct comparison of the service quality factors across different countries such as the USA and China is of significant value to FFRs. References Aaker, D.A. and Jacobson, R. (1994), “The financial information content of perceived quality”, Journal of Marketing Research, Vol. 31 No. 2, pp. 191-201. Andaleeb, S.S. and Conway, C. (2006), “Customer satisfaction in the restaurant industry: an examination of the transaction-specific model”, The Journal of Services Marketing, Vol. 20 No. 1, pp. 3-11. Anderson, J.C. and Gerbing, D.W. (1988), “Structural equation modeling in practice: a review of the two-step approach”, Psychological Bulletin, Vol. 103 No. 3, pp. 411-23. Babakus, E. and Boller, G.W. (1992), “An empirical assessment of the SERVQUAL scale”, Journal of Business Research, Vol. 24 No. 3, pp. 253-68. Bagozzi, R.P. (1992), “The self regulation of attitudes, intentions, and behavior”, Social Psychology Quarterly, Vol. 55, pp. 178-204. Bitner, M.J., Booms, B.H. and Tetreault, M.S. (1990), “The service encounter: favorable and unfavorable incidents”, Journal of Marketing, Vol. 54, pp. 71-84.

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Appendix. Reliability and validity assessment In Table VII, several goodness-of-fit indices for this measurement model (Model A) are reported. The x 2/df is 2.54 for Model A and supports the model fit because the value is lower than the suggested cutoff value 3.0 (Simon and Paper, 2007). An absolute fit index – SRMR was also examined, and its value of 0.058 is lower than the cutoff value 0.10 that is required to conclude a reasonable model fit when sample size is around 250 (Sivo et al., 2006). Although Chin and Todd (1995) and Segars and Grover (1993) suggest that SRMR is preferably below a 0.05 cutoff, Sivo et al. (2006) recommend this number be moderately increased with smaller sample sizes. The AGFI of this model is right at the cutoff value of 0.80 (Gefen et al., 2000). The PNFI is 0.84, higher than the benchmark 0.75 (Sivo et al., 2006). Other fit indices included for this measurement model are the NFI, NNFI, and the comparative fit index. All their values are higher than the benchmark 0.90 (Anderson and Gerbing, 1988; Mulaik et al., 1989). Therefore, the fit indices support the acceptability of the measurement model.

About the authors Hong Qin is a doctoral candidate in Management Science in the Information Technology and Decision Sciences Department, College of Business Administration, University of North Texas. She has published in Quality Management Journal and the Decision Sciences Institute conference proceedings, and has presented her research at the 2007 INFORMS Annual Meeting and the 2007 DSI Annual Meeting. Her research interests include service operations, quality control, product line design, and production technology. She has taught the two course sequence in undergraduate business statistics at the University of North Texas’ College of Business Administration. The topics she has taught include descriptive analysis, hypothesis testing, multiple regression, and quality control. Hong Qin is the corresponding author and can be contacted at: [email protected] Victor R. Prybutok is a Regents Professor of Decision Sciences in the Information Technology and Decision Sciences Department and Director of the Center for Quality and Productivity in the College of Business Administration at the University of North Texas. He received, from Drexel University, his BS with High Honors in 1974, an MS in Bio-Mathematics in 1976, an MS in Environmental Health in 1980, and a PhD in Environmental Analysis and Applied Statistics in 1984. He is an ASQ certified quality engineer, certified quality auditor, certified quality manager, and served as a Texas Quality Award Examiner in 1993. He has authored over 90 journal articles, several book chapters, and more than 70 conference presentations in information systems measurement, quality control, risk assessment, and applied statistics.

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