154846966 Contrasting Reflective and Formative Model of E Service

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Contrasting Reflective and Formative Model on Airline E-Service Quality:

an Empirical Study

Sabrina O. Sihombing

Universitas Pelita Harapan

1. Introduction

In this competitive environment, businesses are in a condition where technology and

information are rapidly developing. For instance, service businesses are competing to achieve

high service quality to satisfy customers with any form of techniques. As the technology isemerging, marketers are shifting their businesses to the networks (Kotler & Armstrong,

2011). Everyone could access business in networks. Internet users, whom have the chance toaccess the network, are increasing in numbers. Retailers have realized that doing business in

the Internet creates efficiency for the customers to purchase products or services. E-retailerwebsites are the place for them to sell goods and services for customers via the Internet

(Dennis et al., 2004). All transactions in the Internet are done in e-retail websites. Moreover,e-retailers have to have a specific quality measurement that could be used to improve e-

service quality (Rossiter, 2009; OCass & Carlson, 2012; Sadeh et al. 2011; Batagan et al.

2009; Huang, 2009).

Although research into measuring e-service model has significant development (Collier &

Bienstock, 2009), it needs more attention on the modeling of the right measurement for e-

service quality (OCass & Carlson, 2012). E-service quality is usually judged from reflective

measurement indicators (OCass & Carlson, 2012). However, formative measurement model

could be an alternative to reflective model (OCass & Carlson, 2012). Consequently, there is

an uncertainty in deciding which model should be used. This is caused by the inadequacy of

justifying the difference of reflective and formative constructs and came up with model

misspecification.

Model misspecification is any model that is close to the approximation to the result (Raoet al., 2008). A research might mislead its result in interpreting information. According to

Hoyle (2012), model misspecification is the usage of information that creates a slippery slopefor a research. The research has to be completed to obtain the conventional result. In general,

the lack of knowledge on certain research model could create wrong interpretation is called asmodel misspecification (Rao et al., 2008; Hoyle, 2012).

According to Jarvis et al. (2003), there are two potential reasons that modelmisspecification present. Firstly, marketing researchers might not simply think that

measurement model has little devotion or no attention to the issue of describing the modelspecification (Jarvis et al., 2003). Secondly, researchers are unaware of the conceptual

distinctions between formative and reflective measurement models (Jarvis et al., 2003). In

reverse to that, it has been distinctively made in the field over 30 years ago (Fornell &

Bookstein, 1982).

In fact, academic literatures concerning formative indicators are still sparse (Collier &

Bienstock, 2009). Nevertheless, there is lack of attention regarding to this subject on

formative and reflective indicators (Collier & Bienstock, 2009). Thereby, indicators for each

model are the differential for these two conceptual models. Reflective model is manifesting

the indicators. In this case, indicators have to be correlated. Contrasting to that, formative

model has indicators that manifested the indicators and how it can be viewed to a specific

construct (Hair et al. 2010). Importantly, this issue should be of high concern to e-service

Jessica Adelaide Gusti


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researchers and practitioners since failure to specify a model properly is still occurring

(OCass & Carlson, 2012). This present study focuses on providing proofs of results in

contrasting a structural equation model using reflective indicators with the same model on

formative indicators.

2. Literature ReviewE-service is all cues and encounters that occur before, during, and after a consumer interacts

with a user interface (Kim & Lennon, 2012). E-service that is exists today are banks online

account services, ATMs, packaging tracking services, travel online purchasing option and

many more. Today, e-services have become more commonly known as self-service

technologies (Hoffman & Bateson, 2010). Self-service technologies (SSTs) are

technologically based services that help customers help themselves (Hoffman & Bateson,2010). Consumers often enjoyed the convenience, speed, and ease of using self-service

technologies as compared to traditional assisted services (Hoffman & Bateson, 2010).Technology in fact would not exist without innovation and e-services (Bataga et al., 2009).

E-services have two main characteristics (Bataga et al., 2009). The first characteristic isthat the service is accessible with electronic networks. The second characteristic is that a

person via the Internet consumes the service. Accessing e-service by the Internet involves thecustomer to have an online transaction. Online transaction is a complex process that can be

divided into various sub-processes such as navigation, information searching, negotiation,

online payment, delivery and after-sales service (Yen & Lu, 2008). E-service quality has two

attributes that reflect its measurement, such as system attributes and service attributes (Yen &

Lu, 2008). System attributes emphasize on the efficiency, speed and security of the service

(Yen & Lu, 2008). Reverse to that, service attributes relates to the customer service as in

order delivery and after-sales service (Yen & Lu, 2008). Referring to Hoyer and MacInnis

(2008), assessing e-service quality by the customers is related to how they would intend to re-

purchase the e-service quality and theory is called Theory of Reasoned Action (TRA).

2.1 Behavioral intention

Behavioral intention is defined, as a measure of the likelihood that a person will engage ingiven behavior (Ajzen & Fishbein, 1980). Moreover, it is the feeling and thoughts

experienced by consumers and the behavior during their buying process (Huang, 2009).Behavioral intention involved the environment, which affect consumers emotion, cognition

and behavior (Huang, 2009). Behavioral intention model is referred to as the theory ofreasoned action (Babin & Harris, 2011).

The first determination of the TRA model is attitude toward act. Attitude is how we feelabout doing something (Hoyer & McInnis, 2008). Attitude influenced by their belief about

the consequence of their action (Hoyer & McInnis, 2008). The second determination of theTRA model is subjective norms. Subjective norm is how other feels about the action of the

individuals action (Hoyer & McInnis, 2008). Social pressure is the determination to do or

not to do certain behavior. Social environment may influence consumer behavior and it is

affected by normative influences. Normative influences is how other people influence the

behavior of the consumer self through social pressure (Hoyer & McInnis, 2008). Other than

normative belief, subjective norms are affected by motivation to comply with (Hoyer &

McInnis, 2008). As the result of attitude and subjective norms, it influenced the behavioral

intention of customer to any product or services.

According to Saha and Theingi (2009), two behaviors that are associated with

behavioral intention are word of mouth and repurchase intention. First, whenever the service

provider has delivered a satisfying service for the customer, then repurchase intention arouses


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(Saha & Theingi, 2009). It arouses because customer have experience the benefit from the

service provider. Moreover, word of mouth acts as the same as repurchase intention. It is

related to the flow of information of services from one customer to another. It includes on

how the source of information is related to the service evaluation from the customer. The

primary factor which influences behavioral intention on e-retail website is process quality.

2.2 Process quality

Process quality is referring to the interactivity between a customer and an online retailers

Web site (Collier & Bienstock, 2009). The evaluation of quality is a heterogeneous process

that varies with the information seeking functions of the consumer (Spink & Zimmer, 2008).

It is also stated that different consumer with different decision in functioning the Internet,

differs on the Web site evaluation (Spink & Zimmer, 2008). According to Collier andBienstock (2009), process quality involves several dimensions such as design, information

accuracy, ease of use, functionality and privacy.Privacy is referring to the company that is not sharing any private information to the third

parties unless customer has given the permission (Collier & Bienstock, 2009). Design is thevisual appearance and audible applications of a site, which includes uses of color, animation,

pictures, text, format, and sound (Collier & Bienstock, 2009). Information accuracy involvespresenting about the product or service in a clear and concise manner (Collier & Bienstock,

2009). Ease of use refers to a customers ability to find information or enact a transaction

with the least amount of effort (Collier & Bienstock, 2009). Functionality refers to the

manner in which an online retailers Web site actually operates and executes the commands

and wishes of a customer (Collier & Bienstock, 2009). Process quality has the impact on

outcome quality on the airline service quality. Moreover, it affects the behavioral intention

and satisfaction towards the provided service. Therefore, it is proposed that:

H1: There is a positive relationship between process quality and outcome quality.

H2: There is a positive relationship between process quality and customer satisfaction.

H3: There is a positive relationship between process quality and customer satisfaction.

2.3 Outcome quality

Outcome quality defines as the conclusion of a transaction-that is, whether or not a customerreceives his or her order (Collier & Bienstock, 2009). It is also stated by (Chen & Kao, 2009)

that outcome quality is what a customer is left with at the end of the transaction and it playsan incredibly influential role in the evaluation of overall service quality. It is the outcome of

the website that a customer has the eagerness to go to the website. Meanwhile, certain studiesabout e-service quality dimensions (Boshoff, 2007; Kannan & Saravanan, 2012) contradict

with the study of Collier and Bienstock (2009). This research refers to Collier and Bienstock(2009) dimensions of outcome quality that is measured by order condition, order accuracy

and order timeliness.

Order timeliness the receipt of a production that was ordered within an expected amount

of time (Collier & Bienstock, 2009). Order accuracy entails receiving a product ordered from

the service provider for the accurate receipt, quantity and agreed price (Collier & Bienstock,

2009; Chen & Kao, 2010). Order condition is when the products are received with no damage

and meet the customers specification (Collier & Bienstock, 2009). Hence, this study focuses

on travel services that have specified airline service quality, order condition is substituted by

the tangible factors and order timeliness substituted by flight schedule. Based on the review

of outcome quality, it also affects the behavioral intention and satisfaction towards the

service. Therefore, it is proposed that:


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H4: There is a positive relationship between outcome quality and customer

satisfaction

H5: There is a positive relationship between outcome quality and behavioral

intention.

2.4 Recovery qualityRecovery quality refers to the manner in which a service failure (if one occurs) is resolved

(Collier & Bienstock, 2009). It is determined by which how the service productivity andquality is right in the first time (Lovelock & Wirtz, 2007). Till then, businesses cannot ignore

the fact that failures continue to occur, sometimes it is out from the organizations control

(Lovelock & Wirtz, 2007). Several forms of customer complaints, which can be present after

the services, have occurred. It could be by taking some form of public action (including

complaining to the firm or to a third party), take some form of private action (including

abandoning the supplier) or take no action (Lovelock & Wirtz, 2007). Despite that,

businesses are able to learn the customer feedback.

The three dimensions are included for recovery quality that is interactive fairness,procedural fairness and distributive fairness. Stated by Lai et al. (2010), customers

evaluations are centered by the outcome fairness and interactive fairness which interactive

fairness is included in this study. Interactive fairness is the customers ability to locate and

interact with technology support on a retailers Web site (Collier & Bienstock, 2009).

Other than that, procedural fairness is involving companys policies, procedures, and

responsiveness how the problem is solved. The last dimension is distributive fairness is whencompensation is given by the company to customer for service failures. Therefore, recovery

quality affects the behavioral intention and satisfaction upon the e-service quality ofAirAsia.com. Therefore, it is proposed that:

H6: There is a positive relationship between recovery quality and customer

satisfaction.H7: There is a positive relationship between recovery quality and behavioral

intention.

2.5 Satisfaction

Satisfaction is related to the customers evaluation of a product or service that determined

whether it meets the customers expectancies (Zeithaml et al., 2009). Having purchased a

product previously, the consumer has more than likely developed an attitude toward it

(Oliver, 2010). It is likely that the attitude is tied fairly strongly to the consumers intention to

repurchase the product or patronize the service in the future. Satisfaction can be defined as

judgment made on the basis of a specific service encounter (Huang, 2009). Whenever

satisfaction fails, dissatisfaction is the result when the needs and expectations are in failure.From the mentioned definitions it can be concluded that satisfaction is related on how

customer had the feeling to re-purchase certain products or services.

There are several determination of customer satisfaction such as service features,perceptions of service quality and price (Zeithaml et al., 2009). In addition, personal factors

and situational factors may influence customer satisfaction as well. Feeling of satisfaction oncertain products or services related to other types of feelings. Satisfactions can be viewed as

contentment. More than that, satisfaction can be also being viewed as feelings of pleasure(Zeithaml et al., 2009). Satisfaction is the important factor to attract more customers.


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It is possible for customers to be loyal without experiencing any satisfaction or to be

highly satisfied but not yet loyal (Huang, 2009). Customer might feel unsatisfied but they

could be loyal because there a little alternative for them to choose while, in the other hand,

many alternatives are available. In terms of online environment, satisfaction of customers is

related on how they would feel a highly satisfied on online websites quality (Huang, 2009.

Therefore, it is proposed that:

H8: There is a positive relationship between customer satisfaction and behavioral

intention

2.6 Reflective Model

A reflective construct should be highly correlated among themselves since they manifest or

represent phenomenon associated with the same construct (Roy et al., 2012). Reflectivemodel has its latent constructs that cause the measured variables (Hair et al., 2010). Hereafter,

the error results in an inability of the construct to fully explain these measured variables (Hairet al., 2012). The direction of the arrow of reflective model is from the latent construct to the

measured variables and error terms associated with the each measured variable (Hair et al.,2010).

Fig. 1 Research Model (Reflective Model)

Source: Collier & Bienstock (2009, p.287)

2.7 Formative ModelIn formative construct, the causality flows from the indicators to the construct, that is, the

indicators cause the construct (Roy et al, 2012). Moreover, agreeing with Roy et al. (2012), aformative construct is the result of the corresponding variables indicators. Formative model

is not simply by removing the arrows of the constructs to the variables but it is about how itcan change the view of the construct (Hair et al., 2010). Formative construct are better

viewed as indices where each indicator is a potential contributing cause (Hair et al., 2010).


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Fig. 2 Research Model

Source: Collier & Bienstock (2009, p. 287)

From the review of reflective and formative model, it is proposed that:

H9: Reflective model is better than formative model in measuring e-service quality.

3. Methodology

3.1 Research sample and object

The researcher conducted a survey on what e-retail websites that is frequently accessed by

students in a private university in Tangerang. AirAsia was chosen as the object of this

research because the results of exploratory study which found AirAsia as the most frequently

used for online retailer. Judgmental sampling was applied as sampling design for this study.

Furthermore, we determined 350 respondents which they were selected based on their

experience in accessing AirAsia.com, as the research object. Self-administered questions

were used as the method in giving out questionnaires.

3.2 Measurement

This study draws the construct based on the literature of this research. Items were formulated

for the variables with statements in a Likert scale. Every variable are explained clearly by aconceptual definition and operational. This research consists of 5 variables with 69 items. In

accessing formative model, there are 8 additional items that is added in measuring formativemodel.

3.3 Pilot Study

The researched conducted a pretesting on questionnaires to indicate the efficiency of thevalidity and reliability of the research questionnaires. Pretesting or pilot study refers to a

tentative, small-scale study done to pretest and modify study design and procedures


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(McBurney & White, 2009). The intention for the researcher to do pre-test is to examine the

validity and reliability for each indicator, distinguish the obvious pattern from the respondent

and able to do revision in due to a better research result. Samples that cooperated in this pre-

test were 101 respondents.

3.4 Actual Study

Referring to the conducted pilot study, the questionnaires were given to different 350

respondents. Data were analyzed repeatedly from the validity and reliability test and followed

by the hypotheses testing. The test involved confirmatory factor analysis, hypotheses analysis

and model comparison for reflective and formative models.

4. Results

Questionnaires were distributed to 350 respondents. Out of 350 questionnaires, the returnedquestionnaires were 327 questionnaires. The response rate from the returned questionnaires is

93.43%. From the 327 returned questionnaires, there were some answers that were notcompleted. As a result, there were 293 out of 327 questionnaires that are qualified for the data

analysis. The response rate from the usable questionnaires is 83.71%. Table 1 summarizes thedemographic information involved in this research.

Table 1 Respondents Profile

Demographic

VariableCategory Total Percentage (%)

Gender MaleFemale

122171

41.60%58.40%

Age 21 years old

1918094

6.50%61.40%32.10%

Residence Tangerang

North JakartaEast JakartaWest JakartaSouth JakartaBekasiDepokBogorLain-lain

123

27144050201360

42.00%

9.20%4.80%13.70%17.10%6.80%4.40%2.00%0.00%

Amount of

money spentRp 100.000 Rp 500.000Rp 500.001 Rp 1.000.000Rp 1.000.001 Rp 5.000.000>Rp 5.000.001

611288519

20.80%43.70%29.00%6.50%

Source: Data analysis for 293 respondents (2012)

4.1 Validity and reliability

4.1.1 Reflective Model

In this research, reflective model were assessed based on the traditional reliability testing

while formative does not need reliability testing (Sekaran & Bougie, 2010). Validity result

test is based on Hair et al. (2010), which stated the minimum value of factor loading for this

research is 0.35 and this research uses 0.50 for the factor loading. Variables in reflective


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model were tested by validity testing by exploratory factor analysis (EFA). The item

consistency in reflective model was observed based on the value of Cronbachs Alpha and the

corrected item-total correlation. According to Hair et al. (2010), the range of 0.70 and 0.80

are acceptable values for determining the Cronbachs Alpha and 0.30 as the minimum value

of corrected item-total correlation. Validity is examined by an exploratory factor analysis

(EFA). The results show that all indicators are valid and reliable for reflective model.

4.1.2 Formative model

In the contrary, formative model is test by four types of validation and ends with determining

the goodness of model fit (Dimantopolous & Winklhofer, 2001; Collier & Bienstock, 2009).

Diamantopolous and Winklhofer (2001) imply four criteria to achieve the model validation.

Based on previous research (Engelen & Brettel, 2011; Collier & Bienstock, 2009; Collier &Bienstock, 2006; Reinartz et al., 2004), it indicates that formative models are not appropriate

to be tested with traditional techniques of reliability and validity. It is not suggested that totest the inter-item consistency of formative model because items are not homogeneous and

not to be correlate (Sekaran & Bougie, 2010). The criteria are content specification, indicatorspecification, indicator collinearity and external validity.

First, content specification is the assumption in which the researcher is sure about theindicators that determine and define the construct. Second, indicator specification is the

condition in which listed indicators are appropriate and described the content. Third, indicator

collinearity is presence in the formative measurement and multicollinearity as a result of this

test. Last, external validity is assessed by the additional of reflective indicators to three

variables of process, recovery and outcome quality. The additions of these indicators are

called MIMIC (multiple indicators and multiple cause) framework. As a result, indicators

have to be correlated. Other assessing the external validity is estimating non-centrality

parameter (NCP), scaled non-centrality parameter (SNCP), goodness of fit (GFI), adjusted

goodness of fit (AGFI), the goodness model fit of root mean square error of approximation

(RMSEA), expected cross validation (ECVI), normed fit index (NFI) and Tucker Lewis

Index (TLI).

4.2 Confirmatory Factor Analysis

A 63 items is employed by confirmatory factor analysis. Structural equation modeling isapplied by using AMOS 18. Both of the reflective and formative models is assess according

to the first and the second order. For both models, all of the first-order indicators and secondorder indicators were found significantly related.

4.3 Structural Equation Model

Amos 18 was used to confirm the relationship between all of variables in the model (Table2). The overall fit statistics for reflective and formative models is shown in table 3.

Table 2. Structural Model Relationship

Hypotheses Path StandardizationRegressionWeights

C.R. Hypothesis

Analysis

H1 Outcome QualityProcess Quality

0.878 3.698 Substantiated

H2 SatisfactionProcess Quality


H3 Behavioral -0.559 -1.053 Not


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IntentionProcess Quality

Substantiated

H4 SatisfactionOutcome Quality

-0.100 -0.341NotSubstantiated

H5 Behavioral

IntentionOutcome Quality

0.835 1.556 Not

Substantiated

H6 SatisfactionRecovery Quality


H7 BehavioralIntentionRecovery Quality

-0.111 -0.854 Not

Substantiated

H8

H9

Behavioral

IntentionSatisfaction

Reflective model isbetter than formative

model in measuringe-service quality.

0.660

(Table 3)

3.824 Substantiated

Substantiated


Table 3 Model Fit Statistics Reflective and Formative Model

Absolute Fit Measures

Noncentrality parameter(NCP)

Scaled noncentralityparameter (SNCP)

Goodness-of-fit (GFI)

Root mean square error

of approximation(RMSEA)

Expected cross-validation

(ECVI)

IncrementalFit Measures

Adjusted goodness-of-fit

(AGFI)

Tucker-Lewis index

(TLI)

Normed Fit Index (NFI)

Reflective Model

2281.006

0.959

0.880

0.075

2.014

0.847

0.879

0.841

Formative Model

914.294

3.120

0.746

0.096

5.123

0.689

0.720

0.693


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Parsimonious Fit

Measures

Parsimonious normed fit

index (PNFI)

Parsimonious goodness-

of-fit index (PGFI)

Normed chi-square

(CMIN/DF)

Akaike information

criterion (AIC)

0.725

0.690

2.553

562.006

0.606

0.609

3.575

1429.294


4.4 Results and ComparisonBased on the hypotheses testing analysis, there are four out of nine hypotheses that wererejected. Hypothesis 1 proposed that there is a positive relationship between process quality

and outcome quality. The result supports the hypothesis (CR = 3.698). The second hypothesisproposed that there is a positive relationship between process quality and satisfaction. Thee

results also supports the hypothesis (CR= 2.119). Hypothesis 3 proposed that there is apositive relationship between process quality and behavioral intention. However, the

hypothesis is not supported (CR = -1.053). Fourth, the hypothesis is proposed that there is a

positive relationship between outcome quality and satisfaction. Again, the result of the

hypothesis is rejected (CR = -0.341).

The fifth hypothesis proposed that there is a positive relationship between outcome

quality and behavioral intention and the result is rejected (CR =1.556). The sixth hypothesis

proposed that there is a positive relationship between recovery quality and satisfaction. Theresult of the hypothesis (CR = 7.051) is supported. The seventh hypothesis proposed that

there is a positive relationship between recovery quality and behavioral intention. The

hypothesis is not supported (CR = -0.854). The eighth hypothesis of this study proposed that

there is positive relationship between satisfaction and behavioral intention. The result of the

hypothesis (CR = 3.824) is supported.

The last hypothesis proposed that reflective model is better than formative model in

measuring e-service quality. The proposed hypothesis is supported based on the modelcomparison of its absolute fit measures (NCP, SNCP, GFI, RMSEA and ECVI), incremental

fit measures (AGFI, TLI and NFI) and parsimonious fit measures (PNFI, PGFI, CMIN/DFand AIC).

Based on the comparing model result, reflective model is shown as the suitable model

for measuring e-service quality. First, the result from absolute fit measures indicates goodfit values. The NCP and SNCP for reflective model show smaller value than formativemodel. The GFI of reflective model got higher values than formative model. The value of

RMSEA is under 0.08 and ECVI in reflective model has lower values than the formative

model. Second, the result incremental fit measures similarly show the good fit model. The

value of AGFI is close to 0.90 and the value in reflective model is greater than the value of

formative model. The TLI and NFI value shows greater value in reflective model than in

formative model. Thirdly, parsimonious fit measures, PNFI, PGFI, CMIN/DF and AIC are

showing relevant values on reflective model in measuring e-service quality.


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5. Discussion and Managerial Implications

5.1 Discussion

The objective of this study was to contrast reflective and formative model on measuringairline e-service quality. The study gives the result that reflective model is shown as the

suitable model for measuring e-service quality. The research result shows that process and

outcome has positive relationship. It shows that AirAsia has a well done in maintaining their

process and outcome quality on the e-retailer websites through times. According to Spink and

Zimmer (2008), customers evaluated good process quality based on their needs. Air Asia has

shown that it fulfills customers needs in searching for flight tickets. Customer who gotsatisfying information will likely to pay more attention on the websites (Spink & Zimmer,

2008). They will pay more attention on how all the website elements are visually pleasing,accurate and have complete information.

Hypothesis 2 proposed that there is a positive relationship between process quality andsatisfaction. Denotes from Huang (2009), it is said that satisfaction is achieved whenever

specific service is encounter. Most of the respondents are quite satisfied with AirAsia.com. Itmeans that AirAsia created a specific service encounter with high levels of ease of use,

elegant designs, accurate information, controlled privacy and good functionality. According

to the respondents profile for this research, most of the respondents are between 18-21 years

old. Teenagers who are in this age group are an important segment. Teenagers have their

spending power from their family money and have great influence to their parents spending

(Dines, 2012).

Hypothesis 3 proposed that there is a positive relationship between process quality and

behavioral intention. The result of this hypothesis is not substantiated. There are two reasons

that influenced this unsubstantiated hypothesis. It shown that there are 58.40% of females

who gave responses and more than male respondents. According to the research of Bray

(2007, p. 38), it is said that male are viewed as having natural ability regarding to technology

where female are having fear or dislike it. The research is related to this hypothesis thatprocess quality does not have any relationship to behavioral intention. The female

respondents might face difficulties in operating and figuring out on AirAsias website.Moreover, it also shows that the relationship is not significant to the future intention for the

respondents to have the intention to get involve with AirAsia.com. Costumers are not havingtheir intention in re-purchasing or to make AirAsia website as their first choice. More choices

on airlines such as Garuda Airlines, Lion Air, and other local airlines would becomealternatives for customers.

Hypothesis 4 proposed that there is a positive relationship between outcome quality andsatisfaction. The result of this hypothesis is not substantiated. It means that good outcome

quality will not lead to satisfying service. The tangible features of Air Asia influence the

rejected hypothesis. Tangible features have high influenced to customer satisfaction (Dias,

2011). Customers are giving attention on the inside flight cleanliness and comfort, positive

quality interactions by the in-flight crew. As a low cost airline, customers are used to

experience flight delays and uncomfortable seats by purchasing cheap tickets. When service

performances are poor, it needs greater improvement to reach customer satisfaction. Low cost

airlines are eliminating comfort and services that is usually promised by the airlines

(Malighetti, 2009).

The fifth hypothesis is predicting that there is no relationship between outcome quality

and behavioral intention. Based on the respondents profile, Females give most of the


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obtained result for these study responses. All outcome quality is being answered as neutral

from the descriptive statistics. From this result, it is said that females are giving more concern

on safety for airplanes (Pappas, 2010). Stated by Liou and Tzeng (2007), from other

attributes of other airline features, flight safety is found to be the most important factor. As

safety is the most important factor, it might be that AirAsias outcome quality is not that

reliable and behavioral intentions are not occurring thus, satisfaction is not achieved too.The sixth relationship between variables was examined between recovery quality and

satisfaction. The use of technology overcomes customer difficulties upon problems and as a

result faced problems are limited (Sousa & Voss, 2009). It shows the positive relationship

between recovery quality and satisfaction as in previous researches (Vlachos &

Vrechopoulos, 2008; Sousa & Voss, 2009; Wu, 2011; Ha & Stoel, 2012; Gounaris, 2010;

Pollack, 2009).Hypothesis 7 proposed that there is positive relationship between recovery quality and

behavioral intention. The result of this hypothesis is not substantiated. First, according to therespondents profile, more females are involved to this research and as a result females have

higher expectations on service recovery (Hess et al., 2003). It also said that females wanttheir views to be heard by the service provider than male (Mattila & Ro, 2008). It requires

more effort for AirAsia to build their customer behavioral intention. Second, this hypothesisis consistent with the findings of Collier and Bienstock (2009). It was stated that the influence

of recovery quality were not significantly related because it is mediated through customer

satisfaction.

Hypothesis 8 proposed that there is a positive relationship between behavioral intention

and satisfaction. There are two reasons that this hypothesis is supported. First, as there are

293 respondents answered the questions upon their satisfaction and behavioral intention, it

shows that AirAsia.com is considered as satisfying website because the responds are mostly

Agree. Second, it is supported by previous researches (e.g., Huang, 2009; Saha & Theingi,

2009; Chen and Kao, 2010; Carlson and OCass, 2010; Liang & Zhang, 2011; Ladhari, 2009;

Jewanto, 2012; Kuo et al., 2009; Meng et al., 2011, Ha & Stoel, 2012).

Hypothesis 9 proposed that reflective model is better than formative model in measuring

e-service quality. The result of this hypothesis is substantiated based on the statistical analysisand based on competing model result. It is supported by several researches that focus on

measuring e-service quality with reflective indicators (e.g., Collier & Bienstock, 2006; Luo &Lee, 2011; Sohn & Tadisina, 2008; Sahadev & Purani, 2008; Sabiote et al., 2011; Gounaris et

al., 2010). From the listed researches, reflective model is having the characteristic in whichthe construct is acting as the independent to the measures. As the independent construct,

adding or dropping an item does not change the conceptual context of the construct. It doesnot change the context because the construct is the basis for the measures used (Jarvis et al.,

2003).

5.2 Managerial Implications

The findings showed strong managerial implications for AirAsia. From the outcome of this

research, there are four out of eight hypotheses that were rejected. The result of this research

provides insights towards the e-service quality of AirAsia. The result from this research

shows that customer satisfaction is not qualified yet by the customers. Till then, behavioral

intention is not arrays by Air Asias customers. To get a scope for customer satisfaction to get

behavioral intention, the researcher is giving out suggestions on three implications that could

be done by the managers.

Firstly, marketing managers should be able to pay attention on Air Asia website quality

from its process. Second, despite from website quality, outcome quality is also important to


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be improved. The result of this research clearly shows that AirAsia facilities are still

moderate and unsatisfactory. This is happening because customers are not realizing the price

they pay is what they got. For marketers, it could be improved by giving additional values

other than putting more cost to change aircraft features. Adding value could be achieved by

giving warm towel, light snacks and others. This value would create an ambience for the

customer to experienced high class flights and comfortableThe third implications that is beneficial for Air Asia is to entail recovery quality

whenever problems occur. This aspect presents the capability of AirAsia in handling

complaints from customers. In serving customers for complaints, AirAsia has to give

alternatives whenever their customer service is not available, such as online messenger or 24

hours email conversation.

Regarding three important aspects that have been explained, it might improve therelationship of AirAsias customer satisfaction and the intention will arouse. Behavioral

intention is the path by which customers will have AirAsia.com as their first choice in havingonline ticketing retailer.

6. Limitations and Suggestions for Future Research

There were some limitations happened during the process of this research. First, this researchis using judgmental sampling as one of the type of non-probability sampling. Second, the unit

of analysis in this research was the undergraduates students of Pelita Harapan University.

Thirdly, this research object for this study is about AirAsia and its website. AirAsia has the

existing airline websites while some other airlines do not. It limits the capability of this

research to be applied to other services such as restaurants and clothing store. This research is

focusing on airlines services and it might gives different result for other types of services.

Researcher has to be aware in determining the specific service type in applying services for

reflective and formative models.

The three limitations can be handled with several suggestions. First, the use of non-

probability design sampling in this study limits the generalization of the findings. Second, it

is suggested that the future research might as well tested other types of services upon the

consecutive research models, reflective and formative model. Other types of services that canbe used to generalize this research are restaurants, clothing line and other transportation

services. The third suggestion is to enlarge the respondents scope other than Pelita HarapanUniversity students. Enhancing sample scope could be with travellers, whom might travel a

lot with AirAsia and samples from Indonesians citizens.

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