THE EMPIRICAL STUDY OF PUBLIC TRANSPORT PASSENGERS .
International Journal for Traffic and Transport Engineering, 2012, 2(1): 83 – 97UDC: 656.121.072(594)THE EMPIRICAL STUDY OF PUBLIC TRANSPORT PASSENGERS’BEHAVIORAL INTENTIONS: THE ROLES OF SERVICE QUALITY,PERCEIVED SACRIFICE, PERCEIVED VALUE, AND SATISFACTION(CASE STUDY: PARATRANSIT PASSENGERS IN JAKARTA, INDONESIA)Sik Sumaedi1, I Gede Mahatma Yuda Bakti2, Medi Yarmen31, 2, 3Research Center for Quality System and Testing Technology, Indonesian Institute of ScienceReceived 27 November 2011; accepted 19 January 2012Abstract: Behavioral intentions have been much discussed in marketing literature. Surprisingly,except for airline industry, few studies have been found in transportation industries. Thispaper studies the public transport passengers’ behavioral intentions in Jakarta city, especiallyparatransit’s passengers. This paper explores the relationship between passengers’ behavioralintentions and other latent factors, including satisfaction, perceived value, perceived sacrifice,and service quality. The empirical data were drawn from 339 paratransit’s passengers usingquestionnaire method. Structural Equation Modeling technique is used to analyze theconceptualized relationship model. The empirical results reveal that perceived value andservice quality significantly affect passengers’ behavioral intention. The results also show thatperceived value is significantly affected by service quality and perceived sacrifice. From theempirical results, managerial implications are discussed.Keywords: behavioral intention, public transportation, service quality, perceived value,sacrifice, satisfaction.1. IntroductionDominance of motor vehicles in landtransportation has resulted in a lot of seriousenvironmental and social problems, i.e.traffic congestion, air pollution, noise andclimate change (Cox, 2010). This highlightsthe importance of private transport use’sreduction and public transport use’senhancement. To respond these signs,“Public transport systems need to becomemore market oriented and competitive, asthey tend to be viewed as service product”(Lai and Chen, 2010). Understanding thebehavioral intention of public transport1passenger is important, because favorablebehavioral intention lead to customer loyaltywhich plays a crucial role for success andsurvival of the service firm (Wen et al. 2005;Lai and Chen, 2010). For public transportoperators, especially paratransit that wasstudied in this paper, “understandingpassengers’ behavior intentions afterexperiencing the public transit [paratransit]services is also an essential [important]task” since it can help them “design effectivestrategies to meet passengers’ needs, andthus retain existing passengers’ as well asattract new ones from other modes” (Laiand Chen, 2010).Corresponding author: sik s 01@yahoo.com83
Sumaedi S. The Empirical Study of Public Transport Passengers’ Behavioral Intentions: The Roles of Service Quality, Perceived Sacrifice, Perceived Value, and SatisfactionBehavioral intentions have been much discussedin marketing literature. Surprisingly, exceptfor airline industry, few empirical studies havebeen found in transportation industries (Wenet al. 2005; Joewono, 2007). Three studies,carried out by Jen and Hu (2003), Wen et al.(2005), and Lai and Chen (2010) investigatedpassengers’ behavioral intentions in publictransport context. All of those studies wereperformed in Taipei. Therefore, extensiveresearch on countries other than Taipei, suchas Indonesia, seems essential to improve theunderstanding of the state of different field.To address this gap in the current literature,this paper aims to study public transportpassengers’ behavioral intentions in Jakarta city,especially paratransit’s passengers. This paperexplores the relationship between passengers’behavioral intentions and other latent factorthat affect them through Structural EquationModeling (SEM). The remainder of this paperis organized as follows. The first section reviewsthe theoretical background and hypothesesof the proposed model. The second sectiondescribes the research methodology. The thirdsection provides the empirical results of factoranalysis and path analysis are provided. Finally,managerial implications are presented andfuture research directions are proposed.2. Theoretical Background andHypothesesBehavioral intentions have become populartopic in marketing literature nowadays.According to the theory of planed behavior(TPB), behavior intentions are the mostimportant determinant of a person’s futurebehaviors (Ajzen, 1991). Most academicianshave viewed behavior intentions as measuresfor representing customer loyalty (Yangand Peterson, 2004). Therefore, behavioralintentions encompass repurchase and84recommendation intention (Fornell, 1992;Zeithaml et al. 1996; Clemes, 2008; Laiand Chen, 2010). This paper thus employsbehavioral intentions concept, which includesrepurchase and recommendation intention.Previous studies found that behavioralintentions were affected by some antecedentvariables. From the existing literature, this paperaddresses satisfaction, service value, servicequality, and perceived sacrifice as variablesaffecting the paratransit passengers’ behavioralintentions. The meaning of those variablesand the proposed hypotheses are explainedbelow. The proposed conceptual model isshown in Fig. 1.2.1. SatisfactionSatisfaction is “a cumulative constructthat is affected by market expectations andperformance perceptions in any given period,and is also affected by past satisfaction fromperiod to period” ( Johnson et al. 1995). Fromthe definition, customer satisfaction lies inthe disconfirmation of customer expectationparadigm, whereas a positive disconfirmationleads to customer satisfaction and negativesatisfaction leads to customer dissatisfaction(Oliver, 1980; Tse and Wilton, 1988; Yi, 1990;Jamali, 2005; Ismail et al. 2006). Furthermore,it “pertains to a holistic evaluation after aservice delivery experience, and acts as aconsequence of satisfaction with individualattributes (i.e. service quality)” (Lai andChen, 2010).In services industry, since satisfaction bringsa lot of benefits to organizations, it has beenwidely identified as a key intermediaryobjective (Ranaweera and Prabhu, 2003).One of its benefits is that satisfactionis generally seen as the main driver ofcustomers’ favorable behavioral intentions
International Journal for Traffic and Transport Engineering, 2012, 2(1): 83 – 97(Ranaweera and Prabhu, 2003; Clemes, 2008;Lai and Chen, 2010). Customers’ favorablebehavioral responses, such as repurchase andpositive word of mouth, will be obtainedby the organization if the customers weresatisfied (Cronin and Taylor, 1992; Fornell,1992; Swanson and Kelly, 2001; Wen et al.2005; Clemes et al. 2008). Empirically,previous studies have confirmed a directpositive relationship between satisfactionand behavioral intention in various industries(Oliver, 1980; Cronin and Taylor, 1992;Fornell, 1992; Anderson and Sulivan, 1993;Petrick and Backman, 2002; Hellier et al.2003; Liu et al. 2005; Meng et al. 2010),including public land transport services (Wenet al. 2005; Joewono and Kubota, 2007; Laiand Chen, 2010). Thus, the first hypothesisin this paper is proposed as follows:H1: Satisfaction has a positive effect onbehavioral intention2.2. Service Quality“Service quality is a measure of how wellthe service level that is delivered matchescustomer expectations, while a firm deliveringquality service means conforming to customerexpectations on a consistent basis ( Joewonoand Kubota, 2007; Transportation ResearchBoard, 1999, 2004)” (Lai and Chen, 2010).Service quality is an abstract concept thatis hard to be defined, and in practice, oftenused interchangeably with satisfaction (Lienand Yu, 2001; Lagrosen et al. 2004; Lai andChen, 2010; Sumaedi et al. 2011). However,the differences between both variables havebeen clarified in the literature. Oliver (1997)explains that service quality is more specificand related to cognitive judgments whilesatisfaction is more holistic and associatedwith affective judgments. Furthermore, otherresearchers (Parasuraman et al. 1994; Zeithamland Bitner, 1996; Lien and Yu, 2001) stated thatFig. 1.The Proposed Conceptual Model85
Sumaedi S. The Empirical Study of Public Transport Passengers’ Behavioral Intentions: The Roles of Service Quality, Perceived Sacrifice, Perceived Value, and Satisfactionsatisfaction judgments include many factors, i.e.product quality, price, situation and personalattributes, not to mention service quality.In the existing literature, service quality isregarded as the determinant of behavioralintention (Cronin et al. 2000; Liu et al. 2005;Huang, 2009). Previous studies have shown thatservice quality may affect behavioral intentiondirectly (Lien and Yu, 2001; Al-Rousan etal. 2010) or influence behavioral intentionindirectly via satisfaction (Cronin and Taylor,1992; Ostrowski et al. 1993; Stank et al. 1999;Butcher et al. 2001; Hellier et al. 2003) andperceived value (Zeithaml, 1988; Dodds etal. 1991). In the public transport context, adirect positive relationship between servicequality and behavioral intention has beenconfirmed by Lai and Chen (2010)’s research.In addition, other researchers found that servicequality influence behavioral intention indirectlythrough satisfaction (Wen et al. 2005; Jowoenoand Kubota, 2007) and perceived value (Wenet al. 2005; Jen and Hu, 2003). Hence, theproposed second, third, and fourth hypothesesare stated as follows:H2: Service quality has a positive effect onbehavioral intentionH3: Service quality has a positive effect onsatisfactionH4: Service quality has a positive effect onperceived value2.3. Perceived ValuePerceived value is defined as “consumer’soverall assessment of the utility of a product[or service] based on perceptions of what isreceived and what is given” (Zeithaml, 1988).In other words, perceived value representsthe discrepancies between perceived benefitand perceived cost (Lee and Cunningham,2001; Wen et al. 2005; Lai and Chen, 2010).86The perceived value is likely to be high if theperceived benefit is higher than the perceivedcost and vice versa (Doods et al. 1991; Wenet al. 2005).According to means-end model of Zeithaml(1988), the perceived value affects thebehavioral intention. Past studies have shownevidence that perceived value significantlyaffect the behavioral intention (Petrick andBackman, 2002; Petrick, 2004; Chen and Sai,2008; Meng et al. 2011). In addition, perceivedvalue has also been identified as the driver ofsatisfaction (Andersen and Lindestead, 1998;Hellier et al. 2003). Perceived value is suggestedas better predictor of behavioral intention thaneither satisfaction or service quality (Cronin etal. 2000; Petrick and Backman, 2002; Petrick,2004). In the public transport study, Lai andChen (2010) investigate the effect of perceivedvalue on behavioral intentions in mass rapidtransport services. Their results revealed thatperceived value has a direct positive effect onbehavioral intention. Moreover, the researchalso found that behavioral intentions areindirectly influenced by perceived value viasatisfaction. In another study conductedby Wen et al. (2005), the authors showedthat the perceived value has a direct positiveeffect on customer loyalty and in-direct effecton customer loyalty through satisfaction.Therefore, this paper’s fifth and sixth hypothesesare formulated as follows:H5: Perceived value has a positive effect onbehavioral intentionH6: Perceived value has a positive effect onsatisfaction2.4. Perceived SacrificePerceived Sacrifice pertains to “what is given upor sacrificed to obtain a product [or a service]”(Zeithaml, 1988). It encompasses not only the
International Journal for Traffic and Transport Engineering, 2012, 2(1): 83 – 97monetary price but also non monetary price,i.e. time, effort, search, and psychic (Zeithaml,1988; Wen et al. 2005). “If consumers cannotfind products by themselves, or if they musttravel distances to buy them, a sacrifice hasbeen made. If consumers must expend effort toassemble durable products or time to preparepackaged goods, and if this time and effort doesnot provide satisfaction to the consumer inthe form of recreation or a hobby, a sacrificehas been made” (Zeithaml, 1988).In Zeithaml (1988)’s means-end model,perceived sacrifice has been identified aspredictor of the perceived value. In transportliterature, a direct negative relationship betweenperceived sacrifice and perceived value has beenconfirmed. For example, the study conductedby Wen et al. (2005) showed that perceivedsacrifice is statistically significant and thushas a negative direct effect on perceivedvalue. This means low perceived value maybe resulted from the high perceived sacrificeand conversely, the low perceived sacrificemay result in high perceived value (Wen et al.2005). This paper thus proposed the seventhhypothesis as follows:H7: Perceived sacrifice has a negative effecton Perceived value3. Research MethodologyThe research adopted quantitative approachsince this approach: (1) “allowed the researcherto search for truths of the observation byempirical evidence via the hypothetic-deductivemethod” (Ling et al. 2010); and (2) the extantliterature that is relevant to this research waswell developed. This research involves fivevariables, i.e. passengers’ behavioral intentions,satisfaction, perceived value, perceived sacrifice,and service quality. The measures of thosevariables are explained below.3.1. MeasuresBehavioral intentions, satisfaction, servicequality, perceived value and perceived sacrificeconstruct cannot be measured directly.Normally, those constructs were measured bymultiple indicators (Wen et al. 2005). In thiscurrent research, the measures of dependentvariable as well as the independent variableswere adopted from the existing literature.Table 1 below indicates each variable’s numberof indicators and the source that were usedas input in order to generate the indicatorsfor measuring constructs in this research.All of those construct were measured using aseven-point Likert scale ranging from ‘stronglydisagree (1)’ to “strongly agree (7)’.Table 1Number of Indicators and Sources for ConstructMeasuresConstructsNumber eived value3Service n et al.(2005)Wen et al.(2005)TransportationResearch Board(1999), Wen etal. (2005)Wen et al.(2005)3.2. Data CollectionThe population under research, which became theunit analysis for this research, includes paratransitpassengers in Jakarta city, Indonesia. The datacollection was carried out through survey withquestionnaire in August, 2011. Actual data ofthe research population’s profile, such as thecomposition of paratransit passengers’ gender, age,or frequency in using public transport that could87
Sumaedi S. The Empirical Study of Public Transport Passengers’ Behavioral Intentions: The Roles of Service Quality, Perceived Sacrifice, Perceived Value, and Satisfactionbe used as the basis for determining the sampleto represent the population were not known. Thisis because both the paratransit operators and theparatransit operators’ association in Jakarta didnot collect and publish those data. On the otherhand, there are operational limitations for usto contact all paratransit passengers in Jakartato know the population profile. Therefore, thesampling technique that could truly representthe research population becomes difficult to beemployed because the profile of the researchpopulation itself is not known. Under theseconditions, following what is suggested by Laiand Chen (2010), we employed a conveniencesampling technique. Although conveniencesampling technique was employed, all of thesurvey participants are paratransit passengersin Jakarta. In addition, we also spread thequestionnaires in places where paratransit wasin operation, i.e paratransit stops and terminals. Atotal of 339 questionnaires were obtained in thesurvey. This sample size is considered adequateand satisfactory in conducting SEM since theprerequisite sample size is 100 to 150 observations(Hair et al. 1998; Lee et al. 2010). Half of therespondents (50%) were male and 47% werestudents. Nearly half of the respondents (49%)were below than 20 years old. In terms of thereason in using paratransit services, vast majorityof the respondents’ stated that their reason issimplicity of using paratransit services (28%)and not having a private vehicle (27%). 40% ofrespondents are daily paratransit users and 60%of respondents are non daily paratransit users.3.3. Testing the Assumption ofMultivariate AnalysisBefore multivariate data analysis could beperformed, some assumptions of multivariateanalysis should be tested. The size of sample,scale of variables, their multicollinearity,their multivariate normal distribution andoutliers are the assumptions that need to be88fulfilled (Hair et al. 1998; Fotopoulos andPsomas, 2009; Lee et al. 2010). Regardingthe sample size, Hair et al. (1998) stated that,in conducting SEM, a sample size of 100 to200 observations is adequate and satisfactory.In this research, the sample size (n 339),a seven point Likert scale, and the skewnessand kurtosis of variables ( 1) is withinthe acceptable limits, indicating distributionsymmetry (Fotopoulos and Psomas, 2009;Lee et al. 2010). The correlation among theindependent variables is less than 0.9, a factthat suggest no multicolinearity problem (Hairet al. 1998; Fotopoulos and Psomas, 2009;Lee et al. 2010). As far as multivariate normaldistribution is concerned, the skewness andkurtosis ( 1) and the standardized residual( 2.5) indicated that there are no seriousindications that it is violated (Hair et al. 1998;Fotopoulos and Psomas, 2009). Therefore, itcan be suggested that the basic assumptionsof multivariate analysis are fulfilled.3.4. Data AnalysisThe Structural Equation Modeling (SEM)was used to test the causal relationshipsbetween constructs that are shown in Fig. 1.The researchers employ two stage modelingprocess in conducting SEM, as proposed bya number of researchers (e.g. Lin and Lee,2004; 2005; Hair et al. 1998; Sit et al. 2009),that is, before testing the structure model,the measurement model should be examinedusing Confirmatory Factor Analysis (CFA)(Lee et al. 2010).4. Empirical Results4.1. Measurement ModelThe confirmatory factor analysis (CFA) wasused to assess the measurement model. Theratio of the chi-square value to degrees of
International Journal for Traffic and Transport Engineering, 2012, 2(1): 83 – 97freedom (x2/d.f.), root mean square error ofapproximation (RMSEA), normed fit index(NFI), non-normed fit index (NNFI), and thecomparative fit index (CFI) are five measures thatwere used to estimate the measurement modelfit. The goodness of model fit to this researchmeasurement model is shown in Table 2 andthey are within the acceptable limits. Therefore,it can be suggested that the measurement modelhas a good fit with the data collected.The measurement model’s validity was assessedempirically by examining its convergent validitywhile the internal reliability of the measurementmodel was examined by performing thealpha cronbach analysis (Meng et al. 2011;Hair et al. 1998; Lai and Chen, 2010; Liuet al. 2005; Foutopolos and Psomas, 2009;2010). The results are shown in Table 4. Theconvergent validity was confirmed becauseall factor loadings are equal or larger than 0.5and statistically significant (Hair et al. 1998).The internal consistency of the constructs inthe measurement model or the measurementmodel reliability was tested by employingCronbach’s alpha analysis. Most authorsproposed that an alpha of 0.60 or greater isconsidered to be reliable (Churchill, 1977;Hair et al. 1998). This research Cronbachanalysis results indicated that all constructs’alpha values were well above 0.60. The alphavalue ranged from the lowest of perceivedvalue (0.642) to service quality (0.863). Theresults are show
The perceived value is likely to be high if the perceived benefit is higher than the perceived cost and vice versa (Doods et al. 1991; Wen et al. 2005). According to means-end model of Zeithaml (1988), the perceived value affects the behavioral intention. Past studies have shown evidence that perceived value significantly
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Le genou de Lucy. Odile Jacob. 1999. Coppens Y. Pré-textes. L’homme préhistorique en morceaux. Eds Odile Jacob. 2011. Costentin J., Delaveau P. Café, thé, chocolat, les bons effets sur le cerveau et pour le corps. Editions Odile Jacob. 2010. Crawford M., Marsh D. The driving force : food in human evolution and the future.
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