204 Eppley Center, The School of Hospitality Business, Eli Broad College of Business, Michigan State University, East Lansing, MI 48824, United States234 Eppley Center, The School of Hospitality Business, Eli Broad College of Business, Michigan State University, East Lansing, MI 48824, United States242 Eppley Center, The School of Hospitality Business, Eli Broad College of Business, Michigan State University, East Lansing, MI 48824, United States235 Eppley Center, The School of Hospitality Business, Eli Broad College of Business, Michigan State University, East Lansing, MI 48824, United States
This study was designed to test the validity of the American Customer Satisfaction Index (ACSI) modelspecifically for the hotel industry. The main objective of this study was to determine consistency of theACSI hotel model over three periods: 1994, 2001, and 2009. The model was tested using the StructuralEquation Modeling (SEM) technique using a two stage data analysis procedure. The findings from multi-
group structural equation modeling showed that the model fit of ACSI lodging is consistent, regardlessof economic conditions, demonstrating the validity of that model. Several plausible interpretations arepresented in explaining meaningful patterns of path coefficients, for each of the study test periods. Fur-thermore, as the model tests validate the model fit of the hotel ACSI, academicians studying customersatisfaction may use the theoretical underpinnings and conceptual foundation of the model as a basis fortheir research.
. Introduction
Research on customer satisfaction is not new. It has receivedignificant attention in service marketing literature, as well as inospitality literature. Customer satisfaction is known as a primaryource of a company’s customer relationship, and a significant indi-ator of that relationship. Built on the five models of satisfactionliver proposed in 1989, he (1993) included an extensive review,
ocusing on a traditional view of customer satisfaction and explain-ng how customer satisfaction determines customer intentions orctual behaviors. Anderson et al. (2004, p. 174) argued that cus-omer satisfaction provides “a valuable, forward-looking indicatorf future net cash flows.” Satisfied customers can be consideredan asset to the firm and should be acknowledged as such onhe balance sheet” (Anderson and Fornell, 2000, p. 871). Customeratisfaction also was shown to influence on consumer spendingrowth (Fornell et al., 2010). In the hotel industry context, cus-omer satisfaction especially provides an important measurement
f a hotel brand’s most fundamental revenue-generating assets: itsustomers (Singh et al., 2011). With a proliferation of hotel brands,uests now have many choices, so satisfying guests and retaining
loyal guests is important for maintaining and increasing marketshare.
As the review of the relevant literature indicates, understand-ing key drivers and consequences of customer satisfaction is a coreconcept in the hotel industry. This is indicated in wide-rangingresearch from the 1990s to the present. For example, Dev et al.(2010) conducted an analysis of hospitality marketing literatureover the past 50 years to identify themes of hospitality research.Their analysis indicates that the most cited research studies duringthe 1990s and early 2000s included topics relating to customer sat-isfaction, measuring customer satisfaction, and building customerloyalty through increasing customer satisfaction.
Fornell et al. (1996) introduced the American Customer Satis-faction Index (ACSI) to provide a comprehensive view of customersatisfaction for each of the twelve major economic sectors, ran-ging over 47 industries, the hotel industry being one. Establishedin 1994, the American Customer Satisfaction Index (ACSI) repre-sents an economic indicator that measures the satisfaction of thecustomers with the quality of goods and services available to them.The consumers who actually experience consuming goods andservices evaluate them. This measurement system evaluates andenhances the performance of firms, industries, economic sectors,and national economies (Fornell et al., 1996).
ACSI’s full or partial framework has been widely used andapplied to federal government or local agencies (Morgeson andPetrescu, 2011; Morgeson et al., 2011; Van Ryzin et al., 2004), onlinestores (Hsu, 2008; Mithas et al., 2007), manufacturing industries
Gustaffson, 2002), and service industries (Knutson et al., 2003;ingh et al., 2011). The ACSI model has been tested to provide valu-ble benchmarks for satisfaction and related constructs includinguality, value, and loyalty (Johnson et al., 2001).
The relevant literature review indicates that previousesearchers focused on identifying key drivers and consequences ofustomer satisfaction, using cross-sectional data. This is importantn understanding the effect and influence of customer satisfaction,ut longitudinal data examining customer satisfaction have longeen advocated by academics and practitioners. Szymanski andenard (2001), who conducted a meta-analysis of customer
atisfaction, using 50 empirical studies, called for longitudinalesearch on the satisfaction process. Bernhardt et al. (2000)rgued that relationships influencing and determining customeratisfaction need to be examined via the longitudinal approach,o fully appreciate the real importance of customer satisfaction.nutson et al. (2003) initially examined the ACSI model for theotel industry, and found that the hotel industry scores mirroredhe national score, but were slightly higher than the overall serviceector’s ACSI score. Their 2003 study, however, analyzed ACSIodel for only a single year, providing just a snapshot. Singh et al.
2011) extended this ACSI study by evaluating the hotel ACSI overime (1994–2009). This study, Singh et al. (2011) was primarily
descriptive analysis of trends in customer satisfaction, driversf satisfaction and outcomes in the U.S. hotel industry over aixteen year period. The research described, analyzed, and graphedhanges in the various indices of the ACSI satisfaction model. It didot however, validate the various constructs in the ACSI modelver time.
Following the logic of construct validation, it may be neces-ary to validate the ACSI hotel model, using multiple time periods.here have been arguments and evidence about how economicnd financial factors (reactions to market) may influence customervaluations of service. An important question is whether the ACSIotel model would fit the data, regardless of economic conditions.espite the importance of this issue (and/or despite a wide vari-ty of ACSI published studies), a comprehensive model test of ACSIotel and other service industries has not been conducted. Recently,here have been significant developments in using the structuralquation model (SEM) to test the model fit, dealing with multi-le time-frames, by employing multiple group analyses. However,ue to the complexity of SEM, its application to the longitudinalata with multiple time-frames is rare in hospitality literature. Inther words, the hotel industry has not been validated and tested toxamine hypothesized relationships within the longitudinal ACSIramework. Therefore, this study focuses on the testing and vali-ation of this framework under three diverse economic conditions
n the hotel industry: 1994, 2001, and 2009. Both 2001 and 2009re characterized by economic downturns, while the 1994 year isharacterized by an economic boom. As customer travel demandnd patterns were different during these three time frames weeel that they represent a logical framework to test the stabilityf ACSI. In particular, two thousand one (2001) and 2009 werehosen for this research because the customer travel demand wasmpacted by different market conditions. The negative impact onemand in 2001 was caused by an unexpected, catastrophic event,he September 11 attacks, and therefore categorized as an unpre-ictable event. On the other hand, the 2009 financial crisis, whichegan in 2008 was caused by global economic recession, result-
ng from a structural breakdown of financial markets, financialnstitutions, and consumer confidence. As an objective measure,he Smith Travel Research data (2011) were used: Both 2001 and
009 are characterized by economic downturns, while the 1994ear is characterized by an economic boom, as indicated by theotal U.S. supply and demand percentage change. Given this con-ext, the study’s focus is an analysis of the ACSI model for the hotel
Fig. 1. ACSI framework proposed by Fornell et al. (1996).
industry, using longitudinal data to examine how associated driversof satisfaction and their outcomes may have changed, dependingon different economic conditions as measured by hotel demand.
1.1. Study objectives
There were two primary objectives for this study. First was toconduct an extensive empirical examination of the ACSI modelas applicable to the hotel industry. The second objective was toexamine hypothesized linkages in that model using multiple timeframes, because, to date, model validation of hotel time-series hasnot been conducted. In other words, by conducting a multi-groupstructural equation modeling analysis, this study was designed todetermine consistency and validity of the ACSI hotel model overtime. Additionally, this study attempted to explore how associ-ated drivers of customer satisfaction, and their outcomes, mayhave changed depending on significant economic downturns andupturns.
2. Theoretical background
ACSI was originally developed in 1994 by Fornell and hiscolleagues (1996). Its objective was to measure overall customersatisfaction within a broad range of consumer goods and services.The ACSI model provided a comprehensive view of customer sat-isfaction for each of the 12 major economic sectors. The hotelindustry is part of the Accommodations and Food Services sector,comprising full service restaurants, limited service restaurants, andhotels. ACSI is differentiated from other frameworks of customersatisfaction. While traditional research of customer satisfactionfocuses on transaction-specific satisfaction, or a customer’s expe-rience with a particular episode or a service encounter (Johnsonet al., 2001), the ACSI framework emphasizes overall evaluationof the total purchase and consumption experience, covering boththe actual and the anticipated (Anderson and Fornell, 2000). ACSIwas built on the well-established theory of the quality, satis-faction, and performance (QSP) paradigm (Oliver, 1989) and onHirschman’s (1970) exit-voice theory, as extensively explained inHsu (2008).
The ACSI model shows the cause-and-effect relationship linkfrom the antecedents of customer satisfaction to its consequences.As shown in Fig. 1, overall satisfaction has three antecedents:perceived quality, customer expectations, and perceived value.Two consequences of the customer satisfaction exist, namely cus-tomer complaints and customer loyalty. More specifically, Fornellet al. (1996) emphasized two main components underlying theACSI model. First, constructs of the model represent differenttypes of customer evaluations that can be measured only indi-
rectly. Thus, ACSI uses a multiple indicator approach to measurethe latent construct of overall customer satisfaction. Fornell et al.(1996) explained that the latent variable score on a scale of0–100 can be general enough to compare and evaluate across
Hospi
fioespileneiF
2
p(aecetnriohciwte
etaraoraptapdtt1c(pirhL
2
iid(a
pared to respondents in 1994 as more than half the sample had
S.H. Kim et al. / International Journal of
rms, industries, sectors, specific brands, and even nations. Sec-nd, ACSI represents a series of cause and effect relationshipsxamining antecedents and consequences of overall customeratisfaction. Identified antecedents of customer satisfaction areerceived quality, customer expectations, and perceived value, and
ts consequences include voice (complaint behavior) and customeroyalty. Fornell et al. (1996, p. 8) used the term “forward-looking” tomphasize that a primary objective in evaluating the ACSI model isot simply an accounting of the consumption experience, but alsoxplaining customer loyalty. That is, the primary goal in evaluat-ng the ACSI model is to explain customer loyalty (Anderson andornell, 2000).
.1. Predictors of customer satisfaction
The ACSI model predicts that as both perceived quality anderceived value increases, customer satisfaction should increaseFornell et al., 1996). More specifically, perceived quality is defineds the served market’s evaluation of a recent consumption experi-nce (Oliver, 1997), consisting of the overall evaluation of quality,ustomization, and reliability in an experience. In particular, Fornellt al. (1996, p. 9) defined customization as “the degree to whichhe form’s offering is customized to meet heterogeneous customereeds,” and reliability as “the degree to which the firm’s offering iseliable, standardized, and free from deficiencies.” Perceived qual-ty is expected to have a direct and positive and positive influencen customer satisfaction. Whereas there is an argument addressingighly inter-correlated relationship between perceived quality andustomer satisfaction (Cronin et al., 2000), an overall arguments that perceived quality leads to customer satisfaction, especially
hen quality is framed as a specific belief evaluation and satisfac-ion as a more general evaluative construct (Olsen, 2002; Johnsont al., 2001).
Customer expectations are included in the ACSI model as anxogenous influence on both overall quality and customer satisfac-ion. That is, the model assumes that customers have expectationsbout service quality that are formed by prior experience or by theeputation of the service, as noted by Oliver (1997). Expectationsre the level of quality a customer expects to receive and are basedn both prior exposure to product or service—including past expe-iences, recommendations from others, and corporate promotionalctivities such as advertising, online reviews, public relations, andublicity. Expectations serve as an anchor in the evaluation process,hereby allowing comparisons of high- and low-priced productsnd services (Oliver, 1980). Expectations capture the customer’srior knowledge of the product or service and are adjusted up andown in light of his or her more recent purchase and consump-ion experience. Thus, expectations capture the customer’s abilityo learn from experience and predict quality and value (Howard,977). These expectations are assumed to be positively related tourrent perceptions of service quality and customer satisfactionFornell et al., 1996). Perceived value, defined as perceived level ofroduct quality relative to price paid, also is expected to positively
nfluence customer satisfaction (Fornell et al., 1996). A positiveelationship between perceived value and customer satisfactionas been documented frequently in the literature (McDougall andevesque, 1987; Ryu et al., 2007).
.2. Consequences of customer satisfaction
The ACSI model includes two consequences of customer sat-sfaction, namely complaint behaviors and customer loyalty,
ndicating that an increase in customer satisfaction results inecreased customer complaints and increased customer loyaltyrefer to Fig. 1). Customer loyalty is the ultimate dependent vari-ble in the model because of its value as a proxy for actual customer
tality Management 35 (2013) 193– 202 195
retention and subsequent profitability (Johnson et al., 2001). Cus-tomer satisfaction meta-analysis conducted by Szymanski andHenard (2001) shows that this relationship is stronger in a servicesetting than in a consumable goods context. The post-complaintbehavior topic has received considerable attention in the litera-ture, in particular in the context of hospitality industry (Reynoldsand Harris, 2005; Heung, 2003; Osman, 2006).
These consequences of satisfaction in the original ACSI modelderive from Hirschman’s (1970) exit-voice theory. Based onHirschman’s (1970) exit-voice theory, Fornell and Wernerfelt(1988) offered a detailed explanation of why increased customersatisfaction may result in decreased customer complaints, and inincreased customer loyalty. That theory is that an unsatisfied cus-tomer is most likely to choose another competitor by exiting thecurrent company, and tends to voice his or her dissatisfaction inan effort to release tension, obtain sympathy from others, andreceive restitution (Szymanski and Henard, 2001). Oliver (1980,1989) earlier explained that complaining is used as a mechanism forrelieving cognitive dissonance when the consumption experienceis dissatisfied. Accordingly, immediate consequences of increasedsatisfaction are decreased customer complaints and increased cus-tomer loyalty (Bloemer and Kasper, 1995), which is a customer’spsychological predisposition to repeat-purchase or revisit the sameorganization.
3. Methods
3.1. Sample and data collection
The National Quality Research Center at the University of Michi-gan collected ACSI hotel data from ACSI national data. ACSI datahave been collected from random telephone surveys of customers(ages 18–84), using a computer-assisted telephone interview sys-tem. Six hotel companies representing 52 brands were includedin the hotel ACSI data set. These six hotel companies—Hilton,InterContinental (formerly Holiday Inn), Global Hyatt, Marriott,Wyndham (formerly Ramada), and Starwood—reflect a broadcross-section of market segments, location, price tiers, brandaffiliations, and amenities and service offered. Together, they rep-resent 2,049,089 guest rooms, i.e., 43% of the total U.S. hotel roominventory (American Hotel and Lodging Association, 2010). It isimportant to note that the research included respondents as “qual-ified participants” in the study if they had stayed at any one ofthe six hotel companies’ 52 brands sometime within three yearspreceding each data-collecting period. Hotel ACSIs from three setsof data—1994, 2001, 2009—were selected for data analysis. Datacollected from 1994 represent an economic boom period, while2001 and 2009 data represent economic downturn periods. Sam-ple sizes are 1585, 1499, and 1955 for 1994, 2001, and 2009 yearsrespectively. Table 1 outlines the demographic characteristics ofrespondents to the hotel ACSI questionnaire for the study periods,1994, 2001 and 2009. A review of the table provides some salientcharacteristics of the sample. There were more women respondentscompared to men across all three years, with the highest percent-age (60.7%) of women respondents in 1994. The highest percentageof respondents (70% on average) was the Gen X and baby boomerage group segments. In particular, for years 1994 and 2001 theGen X group had the highest percentage of respondents and for2009, 48% of the respondents were baby boomers. Respondents in2001 and 2009 had slightly higher levels of formal education com-
college or post graduate credentials. Across all three years a largepercentage of the respondents reported earnings of over $40,000with 62% of the respondents in 2009, with reported income of over$60,000.
196 S.H. Kim et al. / International Journal of Hospitality Management 35 (2013) 193– 202
Table 1Comparison of demographic characteristics of sample across three years.
Gender Male 39.3% 46.4% 40.8%Female 60.7% 53.6% 59.2%
Age 18–29 (The Y gen) 17.3% 19.6% 6.5%30–45 (The X gen) 42.0% 37.4% 23.6%46–64 (Baby boomer) 27.6% 33.9% 48.1%65 or older (Silent) 13.1% 8.9% 21.8%
Income Under $20k 9.2% 6.7% 6.4%$20k but less than $30k 14.7% 9.1% 6.6%$30k but less than $40k 15.6% 10.9% 9.0%$40k but less than $60k 29.8% 21.1% 16.1%$60k or more 30.7% 52.2% 62.0%
Education Less than high school 2.8% 2.4% 2.0%High school graduate 24.1% 15.5% 13.9%
N
3
a(Awci
qqrcdcafsrqtq
tde1sg
tmcartorcsfi
3
m
Some college or associate degree
College graduate
ote: Income is not applicable due to inflation change.
.2. Measures
A detailed description of ACSI model measurement proceduresnd items can be found in Fornell et al. (1996) and Bryant et al.2008). Fifteen survey questions measured six constructs in theCSI model. Survey questions are all rated on a 1–10-point scale,ith the exception of price tolerance (component of measuring
ustomer loyalty) and complaint behavior (a dichotomous variablendicating whether the customer complained or not).
Three antecedents of overall customer satisfaction are perceiveduality, customer expectations, and perceived value. Perceiveduality refers to evaluating a customer’s post-purchasing expe-ience, with three components: overall evaluation of quality,ustomization, and reliability experience. Fornell et al. (1996, p. 9)efined customization as “the degree to which the firm’s offering isustomized to meet heterogeneous customer needs,” and reliabilitys “degree to which the firm’s offering is reliable, standardized, andree from deficiencies.” Customer expectations also comprise theame three components but in the customer’s pre-purchase expe-ience. Perceived value measures the perceived level of productuality relative to price paid. Perceived value construct is opera-ionalized using two survey items, namely rating prices paid foruality received and rating price given quality.
Customer satisfaction is measured as a latent variable usinghree components, namely overall satisfaction, expectancy-isconfirmation, and comparison to an ideal, with efforts tovaluate customers’ overall consumption experience (Fornell et al.,996). Customer satisfaction measured from ACSI studies repre-ents cumulative satisfaction, and evaluating the organization ineneral (Gelbrich and Roschk, 2011).
Two consequences of overall customer satisfaction are cus-omer complaints and customer loyalty. Customer complaints were
easured using a dichotomous variable indicating whether theustomer has complained formally or informally. Customer loy-lty was measured by three items, one dealing with expectedepurchase frequency, and two dealing with price sensitivity orolerance. In sum, Anderson and Fornell (2000) discussed rigor-us characteristics of ACSI data, in terms of precision, validity,eliability, predictive power, coverage, simplicity, diagnostics, andomparability. They concluded that ACSI “represents a significanttep forward in the measurement of performance for nations andrms” (p. 878).
.3. Statistical analysis
Over more than 25 years, structural equationodeling—SEM—has emerged as a popular statistical method
30.3% 30.2% 29.6%26.8% 29.2% 28.6%
for testing conceptual models with latent variables and to evaluatecausal or hypothesized relationships. Measurement issues canbe central to research evaluating the validity of a conceptualmodel, but SEM has the advantage of adding this issue prior toexamining hypothesized relationships (Kline, 2010). Evidenceof psychometric properties (reliability and validity) of measuresemployed from ACSI models was achieved by employing SEM’sconfirmatory factor analysis—CFA (Olsen, 2002). Calculating allparameters in the model simultaneously is another well-knownSEM advantage over other popular methods such as multipleregression analysis. Another important SEM strength is to examinemodel consistency across different groups of subjects or differenttime frames. To achieve this, multiple-group analyses were usedto determine the extent to which a hotel ACSI model is consistentacross different time frames. As with all structural equationmodeling data, including both cross-sectional and longitudinalstudies, how to treat missing data is a critical issue. The casewisedeletion was selected for the missing data. A detailed presentationof the conduct of SEM, using multiple group analysis, is beyondthe scope of our study focus, but this information can be found inother literature (e.g., Kline, 2010; Schumacker and Lomax, 2010).
4. Analysis and results
This research employed SEM to test and validate the ACSI modelin the hotel sector using data from 1994, 2001, and 2009. It useda two-stage data analysis procedure for model testing. The firststage involved evaluating the measurement model. CFA was usedto test construct validity of the proposed ACSI (American Index)measurement model within the sample (i.e., year). Stage two usedmultiple group analysis to evaluate the whole model across threeindependent samples (year) and compare the construct means andstructural relationships across year samples. The AMOS 19.0 sta-tistical package program was used for SEM. Maximum Likelihoodestimation procedure was used as a parameter estimation methodin SEM.
4.1. Measurement model evaluation
In evaluating the measurement model, CFA findings show thatthe final measurement model fits relatively well in all three sampleyears. All model fit indices are within the range of recommended
values across all samples, as shown in Tables 1 and 2.
Convergent validity is used to determine whether differentobserved variables used to measure the same factor are highly cor-related. In SEM, convergent validity can be assessed by reviewing
S.H. Kim et al. / International Journal of Hospitality Management 35 (2013) 193– 202 197
Table 2Confirmatory factor analysis results: full measurement model.
a Standardized factor loadings were all significant at p < .001.b
f(vsrtc(tl
tsnvvidB
Composite reliability.c Average variance extracted.
actor loadings (Hatcher, 1994) and Average Variances ExtractedAVE). As can be seen in Table 1, all factor loadings for observedariables measuring the same construct are statistically significant,howing that all observed variables effectively measure their cor-esponding factors, thereby supporting the convergent validity ofhe results. All observed variables specified to measure each of theonstructs in the measurement model have relatively high loadingsstatistically significant at p < .05), ranging from .62 to .93. Addi-ional testing showed that AVEs in all constructs exceed the criticalevel of 0.5. Both indicators are evidences of convergent validity.
Discriminant validity was assessed in two ways. First, inspec-ion of correlations among constructs was conducted. As can beeen in Table 2, estimated correlations between constructs wereot excessively high, and no pairs for the 95% confidence inter-al approach 1.00. These results provide support for discriminant
alidity (Anderson and Gerbing, 1988). The second method useds a stronger test of discriminant validity. A series of chi-squareifference tests were conducted (Anderson and Gerbing, 1988;agozzi and Phillips, 1982). Chi-square difference test can be
used to assess discriminant validity of two constructs by calculat-ing the difference of the chi-square statistics for the constrainedand unconstrained measurement model (Hatcher, 1994). The con-strained model is identical to the unconstrained model, in which allfactors are allowed to co-vary, except that the correlation betweenthe two constructs of interest is fixed at one. A significant chi-square difference indicates discriminant validity between the pairof constructs (Anderson and Gerbing, 1988). Discriminant validityis demonstrated if the chi-square difference (with 1df) is signif-icant, indicating that the chi-square of the constrained model issignificantly lower than that of the unconstrained model, implyingthat the model in which the two factors are viewed as distinct (butcorrelated) factors is superior. As shown in Table 2, all chi-squaredifferences ranged from 4.4 to 738.5 which all exceed 3.84 (p < .05)benchmark, demonstrating adequate discriminant validity for all
factors.
Reliability of measures was also evaluated by estimating Cron-bach’s alpha. Reliability scores range from ̨ = .692 to ̨ = .906, i.e.,well above the recommended level ( ̨ ≥ .70).
198 S.H. Kim et al. / International Journal of Hospitality Management 35 (2013) 193– 202
Table 3Chi-square difference test for discriminant validity.
Years 1994 2001 2009
Constructs r �2 (free r)a �2 (r = 1)b ��2 c r �2 (free r) �2 (r = 1) ��2 r �2 (free r) �2 (r = 1) ��2
Note: r = correlation; ��2 (difference in �2) > 3.84 at p < .05 or 6.64 at p < .01 demonstrates strong discriminant validity.a Model 3.b Constrained model having correlation between two factors that set to 1.0.c �2 difference between unconstrained model and constrained model.
* Significant at the .05 overall significant level. A significant �2 difference (��2) indicates discriminant validity between the pair of factors.
Table 4Multiple group CFA analysis: measurement invariance test and construct mean difference test.
4.2.3. Structural relationship across groupsThe purpose of this analysis was to see whether differences exist
in paths in the tested model across groups by three years. Multi-ple group analysis was used to fit the proposed structural model
-0.20
-0.10
0.00
0.10
0.20
0.30
2001
2009
1994
Const
ruct
mea
n
PQ CE PV SAT CL CC
C: invariant factor loadings and intercepts 495.6(177)
D: invariant factor loadings, intercepts, andconstruct means (fully constrained)
506.8(181)
.2. Multi-group analysis
.2.1. Measurement invariance testPrior to group comparisons of construct means and construct
elationships, measurement invariance test in multiple group anal-sis was conducted (e.g., Byrne and Watkins, 2003) to determinehether obtained factor loadings in the measurement model are
ndifferent across particular year groups. Results are displayed inable 4.
Three models (labeled A, B, C in Table 4) were tested by compar-ng nested models (Model A vs. Model B and Model A vs. Model C).
odel A was unconstrained model. Model B (as shown in Table 4s “invariant factor loadings”) was estimated by constraining theactor loadings while allowing the construct means to be freelystimated within each group. Model C (as shown in Table 3 asinvariant factor loadings and intercepts) was estimated by con-training the factor loadings and intercepts of the indicators toe equal across groups. Three series of CFI difference tests rec-mmended by Cheung and Rensvold (2002) were done separatelyo see whether factor loadings are invariant across three groups.ccording to Cheung and Rensvold (2002)’s conclusion, change of.01 in CFI, or less indicates that the invariance hypothesis shouldot be rejected. As shown in Table 4, all three models were foundo have good overall fit to the data. More importantly, the CFIemained unchanged across models, proving support of factor load-ngs and construct mean invariance (Cheung and Rensvold, 2002).
.2.2. Comparisons of construct means across groupsModels C and D in Table 4 were employed to evaluate differ-
nces in construct means across the group. Model D was estimatedy fully constraining all factor loadings, intercepts, and constructeans while Model C allowed the construct means. Between Model
and Model D, chi-square value increased (�2 difference = 45.64)
2.8 0.027 0.98 0.98 0.992.8 0.027 0.98 0.98 0.98
and CFI decreased by .01 indicating that difference exists in con-struct means among the three groups. To compare the constructmeans, the economic boom year, 1994, was used as a basis for com-parisons to the 2001 and 2009 sample years. Table 4 and Fig. 2show that the means of the constructs of perceived quality (PQ),customer expectations (CE), perceived value (PV), and customerloyalty (CL) in 2001 are statistically significantly lower (p < .05) thanthose in 1994. Among construct means in 2009, PV, customer sat-isfaction (SAT), and CL are statistically significantly (p < .05) higherthan those in 1994. Across the three years, the 2009 mean of CL andPV are highest while the 2001 mean of CL and PV are lowest.
Note: The mean is th e re lativ e dif ferenc e to the 1994 mean
-0.30
Fig. 2. Multiple group CFA analysis: construct mean comparisons.
S.H. Kim et al. / International Journal of Hospitality Management 35 (2013) 193– 202 199
SAT → CC 381.0 383.5 2.5SAT → CL 381.0 404.4 23.4*
CC → CL 381.0 384.4 3.4
a The unconstrained model was estimated, with path coefficients allowed to varyacross the cross-group datasets (1994 vs.2001 vs. 2009).
b Partially constrained means that only the target path coefficients were set to beequal for cross-group datasets.
c �2 difference between the unconstrained models and partially constrained mod-
* p<.05
Fig. 3. Multiple group path analysis re
or three year groups. Fig. 3 shows multiple group path analysisesults. All model fit indices shown in Fig. 3 fall within the rec-mmended range. Also, all hypothesized effects are statisticallyignificant, except for the paths from CE to PV in 1994 and 2001,nd the path from CC to CL in 1994. All statistically significant rela-ionships are in the hypothesized direction. SAT has 94%, 94%, and1% of its variance explained in 1994, 2001, and 2009, respectively.xplained variances of CL are 45% in 1994, 44% in 2001, and 53% in009.
The �2 difference test was used to test group difference onndividual paths. Table 5 presents chi-square difference resultsor path coefficients across three years. Chi-square differenceests showed that the paths of PQ → PV, PQ → SAT, PV → SAT, andAT → CL, were statistically significant as indicated in the follow-ng: ��2(2) = 8.6, p < .05, ��2(2) = 6.4, p < .05, ��2(2) = 10.2, p < .01,nd ��2(2) = 23.4, p < .01. These results mean that differences existn four paths across samples. But no statistically significant differ-nce could be found for paths from other links.
As shown in Fig. 3, several significant changes exist in structuralelationship from 1994 to 2009. Results indicate that three pathoefficients, PQ → PV, PV → SAT, and SAT → CL, increased from 1994o 2001: .80, .81, .89 in PQ → PV, .32, .35, .42 in PV → SAT, and .66,67, .74 in SAT → CL. On the contrary, the path coefficient betweenQ and SAT decreased from .72, in 1994, then .69 in 2001, and .61n 2009.
Table 6 summarizes the indirect, direct and total effects of theQ, CE, and PV constructs on SAT and CL based on test results of theroposed structural model presented in Fig. 3. Total effects were
stimated by summing indirect and direct effects. Total effect of PQn SAT was highest across all years (.98 in 1994, .97 in 2001, .96 in009). From 1994 to 2009, total effect of PV on SAT increased (.32o 35, then to .42). This could be interpreted as the hotel companies
able 5ultiple group CFA analysis: comparison of construct means.
ote: 1994 is the reference group. In each cell, the first value is the estimated relativeean difference (relative to the 1994 mean) derived from Multiple group CFA in
EM; The t-test statistics is the italicized value.* p < .05.
els.* The significant difference (at p < .05) indicates a difference in path coefficient
across three years.
providing better value to their customers in 2009 versus 1994.Hence from the perspective of hotel companies, the implicationsof these results is that it is more important to focus on value versusonly quality, where the direct impact (PQ to SAT) went from .72 in1994 to .63 in 2009. CE is also shown to be an important determi-nant of SAT due to strong indirect effects through PQ and PV acrossall years (.60 in 1994, .65 in 2001, .63 in 2009). More important,SAT had the strongest total effect on CL. Specifically, in 2009, totaleffect of SAT on CL significantly increased (.64 to .65, then to .71)because indirect effect of PQ on SAT increased (.26–.33) while thedirect effect of PQ on SAT decreased (.72–.63). During the economicdownturn, PV had stronger effect on SAT, which had highest directeffect on CL. SAT also played a significant role of mediation inthe model between each of three explanatory variables (CE, PQ,PV) and CL. CC had strongest indirect effect on CL through SAT(Table 7).
5. Discussion
ACSI has advantages over other customer satisfaction models.One major such advantage is the capability of comparing ACSIscores among individual industries and sectors. It also allows theseindividual industries and sectors to benchmark, over both time and
200 S.H. Kim et al. / International Journal of Hospi
Table 7Summary of indirect and direct effects on satisfaction (SAT) and customer loyalty(CL) in multiple group path analysis.
Paths Direct effects Indirect effects Total effects
CE → SAT 0.07 0.53 0.600.06 0.58 0.640.04 0.59 0.63
PQ → SAT 0.72 0.26 0.980.69 0.28 0.970.61 0.33 0.94
PV → SAT 0.32 – 0.320.35 – 0.350.42 – 0.42
CE → CL – 0.39 0.39– 0.44 0.44– 0.48 0.48
PQ → CL – 0.18 0.18– 0.21 0.21– 0.27 0.27
PV → CL – 0.21 0.21– 0.24 0.24– 0.30 0.30
SAT → CL 0.66 – 0.660.66 −0.02 0.640.74 −0.03 0.71
CC → CL – – –
ctstsotpAcatotoM
towWtsiym
CtwAwpqohi
0.06 – 0.060.08 – 0.08
ontext, as initially argued by Fornell et al. (1996). Given that cus-omer satisfaction is crucial to hotel (or any other) organizations’uccess, this research makes several important contributions tohe current literature on SAT and related constructs for the lodgingector. First, this research demonstrates nominological validityf the constructs as applied to ACSI hotel data. More specifically,he ACSI model is embedded in a set of causal equations that linkerceived quality, customer expectations, and perceived value toCSI, and customer satisfaction, to outcome variables, includingomplaint behavior and customer loyalty. Second, this researchssesses and compares model fit, depending on economic situa-ions. To achieve this goal, this study, unlike other model testingr ACSI models in other sectors, adopted the approach of testinghe consistency and validity of the ACSI hotel model, dependingn economic conditions, by using a rigorous statistical method:ultiple group SEM analysis.In summarizing findings of the present study, the most impor-
ant result is that the model fit of ACSI hotel is consistent, regardlessf economic conditions, supporting the original theoretical frame-ork of general ACSI model, suggested by Fornell et al. (1996).hereas model fit is stable and valid across three selected years,
his study found several interesting patterns, depending on the yearelected. Overall results show that the relationship between sat-sfaction and loyalty is also significant and positive across threeears, but the following describes possible interpretations of someeaningful patterns and findings.As shown in Table 4 and Fig. 2, the means of the constructs of PQ,
E, PV, and CL in 2001 are statistically significantly lower (p < .05)han those in 1994. Among construct means in 2009, PV, SAT and CLere statistically significantly (p < .05) higher than those in 1994.cross the three years, the 2009 mean of CL and PV are highesthile the 2001 mean of CL and PV are lowest. There are severallausible explanations derived from these findings. The level of
uality a CE to receive is an exogenous induced construct basedn the customer’s prior exposure to and/or experience with theotel’s services. The period from 1994 to 2000 was one of growth
n the hotel industry and the general economic fundamentals were
tality Management 35 (2013) 193– 202
strong. With the technological boom (1995–2000), the hotel busi-ness and leisure travel industry was increasingly fueled by young,upwardly mobile, and affluent customer segments. This is reflecteddemographically in the rise in median family income of 25% from1995 to 2000 (US Census Bureau, 2012). This is also reflected ina 23% growth of the Revenue per Available Room for U.S. hotelsfor the same period (Singh et al., 2011). The period also reflected agrowth of new hotel brands which is a direct reflection of chang-ing profile of customer demographics (Singh, 1999). Furthermore,advances in information technology during this period resulted inthe generation of large customer databases, and the development ofconcepts, such as customer relationship management, to advanceand customize the guest’s travel experience. As such, leading upto 2000, it could be expected that customers would have higherexpectations for their hotel experiences. In 2001, multiple eventscould possibly explain the lower CE, PQ and CL.
The technology bust in 2000, leading to a stock market crashand ensuing reduction of overall business travel during that period,generally considered to be the more sophisticated market seg-ment with a higher level of customer expectation. As hotels facedan uncertain future due to the weakened economic climate andculminating with the catastrophic event on September 11, 2001,they may have chosen to restrict expenditures on capital improve-ments and other service and facility related expenditures, whichwould have impacted perceived quality. Finally by 1998, third partyintermediaries, widely available to travelers via the Internet, begancontrolling a larger portion of the room inventory. Their businessmodel was based primarily on price differentiation and gave birthto the concept of “lodging commoditization” which had a directimpact on customer loyalty as customers shifted their loyalty basedon the company which offered the best price as compared to thecustomer experience (Pine and Gilmore, 1999). As the hotel indus-try began emerging from the depths of the global economic crisis of2007–2008, declining hotel demand and its inability to raise roomrates, resulted in many brands shifting their focus to value, whichrepresents quality per dollar spent by the customer. Based on theresults of this research, it appears that the value driven strategies(ACSI Antecedent) resulted in higher SAT and CL in 2009.
In addition to differences in means among these three years, thefindings of multiple group analysis in SEM clearly show some signif-icant changes occurred in the path coefficients between variablesduring the three years studied. In particular, relationships between(1) PQ and PV, (2) PV and SAT, and (3) SAT and loyalty were upwardin direction, indicating that the relationship strengthened over theyears. These are of particular interests because there appears to bea noteworthy changes between the second (2001) and third (2009)points in time for each of the four relationships. While the ACSI datado not directly offer a reason for these shifts, a case can be madefor several interlinking explanations. First, business, in general, andthe hotel sector, in particular, is moving more into the ExperienceEconomy. From themed properties in Las Vegas to Boutique hotelsin South Beach, there is evidence that the lodging industry is tryingto provide a compelling guest experience. The notion of an expe-rience economy was initially described by Toffler (1970) in FutureShock. He speculated about a future experiential industry wherepeople would be willing pay more for amazing experiences. Theconcept was later popularized by Pine and Gilmore (1999). Theyargue that the experience is the next evolutionary economy follow-ing the agrarian, industrial and more recently, the service economy.They further contend that the experience is a natural progressionfor a business to add value for the consumer. Second, value maybe thought of as the difference between the guest experience and
what it takes to have that experience. An experience embraces morethan just the product and service quality (Pine and Gilmore, 1999)and therefore, might relate differently to satisfaction, an in turn,brand loyalty. Third, guests are increasingly more sophisticated,
Hospi
einnltfaftmdnptptqoeytsdfitaeo
5
Stkeirtioern2TbafiammraI
tota(p
mf
S.H. Kim et al. / International Journal of
xperienced, savvy, knowledgeable, and demanding. To illustrate,n one poll by NFI research analyzing trends and attitudes in busi-ess, organizational management, and organizational behaviors,early 90% of business leaders believe customers are more chal-
enging than they were a few years earlier. Another study foundhat two-thirds of organizations are seeing an increase in demandor customer relations or customer service training. As one man-ger remarked: “With new technologies, everything can be doneaster, so expectations rise for speed and effectiveness from the cus-omer’s standpoint, but one still only has two hands to accomplish
ore work faster” (Paton, 2007). Thus, at a time of high consumeremand, hotel providers need to quickly understand customers’eeds and respond to their changing demands. Hotels often com-ete on loyalty programs. Guests want to be rewarded for how oftenhey stay, how much they spend, and even if they recommend theroperty to others. Such expectations raise the question of whetherhese guests are truly loyal or just in it for the rewards. Again, suchuestions indicate a changing relationship between loyalty and thether variables measured in the ACSI. Finally, there was a seriousconomic downturn between the second (2001) and third (2009)ears used in this study. This downturn spread globally in 2007 andook a sharp turn downward in September 2008 when the globaltock markets had their largest fall since September 11, 2001. Theecline was reflected in consumers’ confidence. The Consumer Con-dence Index, a barometer of the health of the U.S. economy fromhe consumers’ perspective, fell from just over 140 in 2001 to justbove 20 in 2009 (Trading Economics, 2012). Such a slump would bexpected to affect guests’ travel patterns, but also their perceptionsf quality, satisfaction, value, and loyalty.
.1. Limitations and future research direction
Whereas analysis of the findings can advance understanding ofAT effects in general, and of ACSI hotel data and model in par-icular, several issues still need to be addressed to expand ournowledge. While the ACSI model includes most critical and rel-vant predictors and consequences of SAT, the ACSI model wasnitially developed in 1994, and has not incorporated the mostecent research works to increase explanatory powers. Logically,hen, an important issue is whether critical variables are miss-ng in ACSI’s theoretical framework. For example, an affectiver emotional component has been a recent focus of research inxplaining SAT and loyalty. Several researchers argue that affectepresents a potentially important construct different from cog-itive approaches to examine SAT and loyalty (e.g., Berry et al.,002; Chebat and Slusarczyk, 2005; Gountas and Gountas, 2007).hese empirical studies imply or show a positive relationshipetween affect and satisfaction, as also confirmed by Szymanskind Henard’s (2001) meta-analysis of SAT. More specifically, thesendings endorse the stability of the ACSI model in general as wells specifically the ACSI hotel model to confirm consistency of theodel regardless of economic situations. Still, given that the ACSIodel was originally developed in 1994, there is a clear need to
eview the ACSI model to determine whether newly learnt vari-bles affecting SAT should be incorporated into the American SATndex.
Another direction for the research is to evaluate an alterna-ive or competing model of ACSI, addressing casual order effectf hypothesized relationships. For example, the structural rela-ionship between quality and satisfaction (Dabholkar et al., 2000),nd possible relationship toward loyalty, have been questionedJohnson et al., 2001). Perhaps future research should test and com-
are different causal orders of ACSI models to such relationships.
While the purpose of this article is to test the validity of the ACSIodel over years, depending on economic situations, there are a
ew limitations that are associated with this point. For example,
tality Management 35 (2013) 193– 202 201
the PV may not be an important factor among business travelers,since they do not pay for the room themselves. It would be inter-esting to investigate the hypothesized relationships in the ACSImodel, comparing the purposes of staying in the hotel (e.g., busi-ness travel vs. leisure travel). In addition, a future study can bedesigned to examine differences of paths, depending on marketsegments. For example, the relative influence of PE, PQ, and PV onSAT may be somewhat different between Gen X and baby boomerage groups. That is, incorporating other moderating variables canadvance knowledge and understanding of ACSI model.
References
American Hotel & Lodging Association, 2010. AH&LA Lodging Profile, Available fromhttp://www.ahla.com
Anderson, E., Fornell, C., 2000. Foundations of the American SAT index. Total QualityManagement 11 (7), 869–882.
Anderson, J., Gerbing, D., 1988. Structural equation modeling in practice: a reviewand recommended two-step approach. Psychological Bulletin 103 (3), 411–423.
Anderson, E., Fornell, C., Mazvancheryl, S., 2004. Customer satisfaction and share-holder value. Journal of Marketing 68 (4), 172–185.
Bagozzi, R.P., Phillips, L.W., 1982. Representing and testing organizational theories:a holistic construal. Administrative Service Quarterly 27, 459–489.
Bernhardt, K.L., Donthu, N., Kennett, P., 2000. A longitudinal analysis of satisfactionand profitability. Journal of Business Research 47 (2), 161–171.
Berry, L., Carobone, L., Haeckel, S., 2002. Managing the total customer experience.MIT Sloan Management Review 43 (3), 1–6.
Bloemer, J., Kasper, H., 1995. The complex relationship between consumer satisfac-tion and brand loyalty. Journal of Economic Psychology 16, 311–329.
Bryant, B., Fornell, C., Morgeson, F.V., 2008. American Customer Satisfaction IndexMethodology Report. American Society for Quality, Milwaukee, WI, Retrievedfrom http://www.theacsi.org
Byrne, B.M., Watkins, D., 2003. The issue of measurement invariance revisited. Jour-nal of Cross-Cultural Psychology 34, 155–175.
Chebat, J.C., Slusarczyk, W., 2005. How emotions mediate the effects of perceivedjustice on loyalty in service recovery situations: an empirical study. Journal ofBusiness Research 5, 664–673.
Cronin, J.J., Brady, M.K., Hult, G.T., 2000. Assessing the effects of quality, value,and SAT on consumer behavioral intentions in service environments. Journalof Retailing 76 (2), 193–218.
Dabholkar, P.A., Shepherd, C.D., Thorpe, D.I., 2000. A comprehensive framework forservice quality: an investigation of critical conceptual and measurement issuesthrough a longitudinal study. Journal of Retailing 76 (2), 131–139.
Fornell, C., Johnson, M.D., Anderson, E.W., Cha, J., Bryant, B.E., 1996. American cus-tomer satisfaction index: nature, purpose, and findings. Journal of Marketing 60(4), 7–18.
Fornell, C., Rust, R., Dekimpe, M., 2010. The effect of customer satisfaction on con-sumer spending growth. Journal of Marketing Research 47 (1), 28–35.
Fornell, C., Wernerfelt, B., 1988. A model for customer complaint management.Marketing Science 7 (3), 287–298.
Gelbrich, K., Roschk, H., 2011. A meta-analysis of organizational complaint handlingand customer responses. Journal of Services Research 14 (1), 24–43.
Gountas, J., Gountas, S., 2007. Personality orientations, emotional states, SAT, andintention to repurchase. Journal of Business Research 60 (1), 72–75.
Gustaffson, A., 2002. Measuring and Managing the satisfaction-loyalty-performancelinks at Volvo. Journal of Targeting, Measurement and Analysis for Marketing 10(3), 249–258.
Hatcher, L., 1994. A Step-by-Step Approach to Using the SAS System for FactorAnalysis and Structural Equation Modeling. The SAS Institute, Cary, NC.
Heung, V., 2003. Customer complaint behavior towards hotel restaurant ser-vices. International Journal of Contemporary Hospitality Management 15 (5),283–289.
Hirschman, A.O., 1970. Exit, Voice, and Loyalty: Responses to Decline in Firms, Orga-nizations, and States. Harvard University Press, Cambridge, MA.
Hsu, S.H., 2008. Developing an index for online SAT: adaptation of American SATindex. Expert Systems with Applications 34, 3033–3042.
Johnson, M.D., Gustafsson, A., Andressen, T.W., Lervik, L., Cha, J., 2001. The evolutionand future of national SAT index models. Journal of Economic Psychology 22 (2),217–245.
Kline, R.B., 2010. Principles and Practice of Structural Equation Modeling. In: 3rd ed.Guilford Press, New York.
Knutson, B.J., Singh, A.J., Yen, H., Bryant, B.E., 2003. Guest satisfaction in the U.S.lodging industry: using the ACSI model as a service quality scoreboard. Journalof Quality Assurance in Hospitality and Tourism 4 (3/4), 97–118.
cDougall, G., Levesque, T., 1987. SAT with services: putting perceived value intothe equation. Journal of Services Marketing 14 (5), 392–410.
ithas, S., Ramasubbu, N., Krishnan, M.S., Fornell, C., 2007. Designing web sitesfor customer loyalty across business domains: a multilevel analysis. Journal ofManagement Information Systems 23 (3), 97–127.
orgeson, F.V., Petrescu, C., 2011. Do they all perform alike? An examination ofperceived performance, citizen satisfaction and trust with U.S. federal agencies.International Review of Administrative Science 77 (3), 451–457.
orgeson, F.V., VanAmburg, D., Mithas, S., 2011. Misplaced trust? Exploring thestructure of the E-government–citizen trust relationship. Journal of PublicAdministration Research and Theory 21 (2), 257–283.
liver, R.L., 1980. A cognitive model of the antecedents and consequences of satis-faction decisions. Journal of Marketing Research 17 (4), 460–469.
liver, R.L., 1989. Processing of the satisfaction response in consumption: a sug-gested framework and research propositions. Journal of Consumer Satisfaction,Dissatisfaction and Complaining Behavior 2, 1–16.
liver, R.L., 1993. Cognitive, affective, and attribute bases of the satisfactionresponse. Journal of Consumer Research 20, 418–430.
liver, R.L., 1997. Satisfaction: A Behavioral Perspective on the Consumer.Irwin/McGraw-Hill, New York.
lsen, S.O., 2002. Comparative evaluation and the relationship between quality, sat-isfaction, and repurchase loyalty. Journal of the Academy of Marketing Science30, 240–249.
sman, M., 2006. Customer complaints and organizational responses: the effectsof complaints’ perceptions of justice on satisfaction and loyalty. InternationalJournal of Hospitality Management 25 (1), 69–90.
aton, N., 2007. Demanding Customers Bring Headaches for Managers.Management-Issues, Retrieved from http://www.management-issues.com/
ine II., B., Gilmore, J., 1999a. The Experience Economy. Harvard Business SchoolPress, Boston, MA.
ine, J.B., Gilmore, J.H., 1999b. The Experience Economy: Work is Theatre & EveryBusiness a Stage. Harvard Business School Press, Boston, MA, pp. 6–11.
tality Management 35 (2013) 193– 202
Reynolds, K.L., Harris, L.C., 2005. When service failure is not service failure: an explo-ration of the forms and motives of “illegitimate” customer complaining. Journalof Services Marketing 19 (5), 321–335.
Ryu, S., Park, J.E., Min, S., 2007. Factors of determining long-term orien-tation in interfirm relationships. Journal of Business Research 60 (12),12–25.
Schumacker, R., Lomax, R., 2010. A Beginner’s Guide to Structural Equation modeling,3rd ed. Lawrence Erlbaum Associates, New Jersey.
Singh, A.J., 1999. A 20th Century History and Delphi Study to Predict Changes in theLodging Industry Structure, Performance and Capital Sources. Michigan StateUniversity, East Lansing, Unpublished doctoral dissertation.
Singh, A.J., Knutson, B., Cha, J., Kim, S., 2011. Trends in guest satisfaction from1994–2009 in the U.S. hotel industry. Interpretation and analysis of the Ameri-can SAT index (ACSI) Model. In: Paper Presented at the 7th Annual InternationalConference on Tourism. Athens Institute of Education and Research (ATINER),Athens, Greece, June 2011.
Smith Travel Research, 2011. Total US Room Demand and Supply PercentChange in Global Hotel Industry Outlook, Retrieved from http://www.scps.nyu.edu/export/sites/scps/areas-of-study/tisch/hospitalityconference/presentations-industry-update.pdf
Szymanski, D.M., Henard, D.H., 2001. SAT: a meta-analysis of the empirical evidence.Academy of Marketing Science Journal 29 (1), 16–35.
Toffler, A., 1970. Future Shock. Random House, New York.Trading Economics, 2012. United States consumer confidence: 1994–2012,
Retrieved from http://www.tradingeconomics.com/united-states/consumer-confidence
US Census Bureau, 2012. Statistical Abstract of United States: 2010. US Census
Van Ryzin, G.G., Muzzio, D., Immerwahr, S., Gulick, L., Martinez, E., 2004. Driversand consequences of citizen satisfaction: an application of the AmericanSAT index model to New York City. Public Administration Review 64 (3),286–296.
