Inflight Service Quality of Malaysia Airlines: Validation Using SEM

International Journal of Academic Research in Business and Social Sciences

2017, Vol. 7, No. 10

ISSN: 2222-6990

478

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Inflight Service Quality of Malaysia Airlines: Validation

Using SEM and AMOS

*Ibrahim Rose, Zainudin Awang, and Shukri Yazid

Faculty of Economics & Management Sciences, Universiti Sultan Zainal Abidin (UniSZA)

Malaysia

DOI: 10.6007/IJARBSS/v7-i10/3395 URL: http://dx.doi.org/10.6007/IJARBSS/v7-i10/3395

ABSTRACT

This study analysed Malaysia Airlines’ inflight service quality (IFSQUAL) from the perception of

passenger satisfaction because it was important to know passenger’s quality perception

regarding the airline’s quality improvement. A total of 2,000 complete questionnaires were

successfully compiled to build a sampling frame, and a total of 282 questionnaires were

selected using a simple random sampling technique, which was one of the probability sampling

methods. The data were analysed using the IBM-SPSS Amos 23.0 software. The latent construct

measurement model had been validated through the Confirmatory Factor Analysis (CFA)

procedure, and developed 30-item scale based on 4 distinct dimensions: Personal Attributes,

Flight Safety, Inflight Service, and Passenger Satisfaction. The finding of Structural Equation

Modelling (SEM) showed that approximately 93% of the variance in Passenger Satisfaction was

accounted for with the predictors (R

=0.930). The direct and indirect (mediation) hypothesis

testing had been verified with bootstrapping with 1000 samples, and 95% confidence level.

Results revealed; five hypotheses were significant on the direct effect, and two mediation

effects were not significant. We were able to identify the gap of this study; inflight service

quality was not a ‘quick-fix’, and thus had to be approached from a long-term perspective.

Keywords: Airline, Inflight Services Quality, Passenger Satisfaction, SEM, AMOS

INTRODUCTION

Air transportation plays an important role in moving people, or products fast from one place to

another either domestically or internationally. Airline industry is also at the heart of the travel

and tourism industry, and is the main contributor to many countries’ overall economy through

international tourist arrivals (Oyewole et al., 2007; Zahari et al., 2011; Norazah, 2014; Rahim,

2016; Rose et al., 2016). The positive development of the travel, and tourism industry has

created great competition among the large and small airline companies for passenger

satisfaction (Pincus 2001; Jankalová, 2016; Rose et al., 2016). Broad marketing with a full range

of innovative strategies can exploit to the fullest advantage through its quality inflight service in

inflight entertainment, cabin facilities, flight safety policy and its competent flight attendant.

This study aims to introduce the system of measuring the inflight service quality (IFSQUAL) from

passenger’s perspective; hence it is imperative to deal with this issue for passenger satisfaction.

The IFSQUAL falls under the airline’s product and inflight service excellence. To measure it is

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more often being mentioned as part of the corporate practice, but it also being done at the

theoretical level (Jankalová, 2014; Giovanis, et al., 2015; Ranaweera & Sigala, 2015). The

situation in the area of measuring it is quite different from multinational institutions, non-

profit, and public organisations. We assume that passenger’s perception of IFSQUAL includes

more than mere satisfaction from the provided service as derive from the passenger’s

perspective on their disadvantages or inconveniences (Zeithaml et al., 2013; Jankalová, 2016;

Rose et al., 2016), which perceive that passenger’s opinion is affected also by the way her/his

request is received, method of timing for the need of satisfaction, clarity and willingness,

accuracy, and punctuality of dealing with the requests.

Juran (1974) coins quality as ‘fitness for use’ in user-based approach. Crosby (1979) interprets

quality as ‘conformance to requirements’ in manufacturing-based approach. There are five

main approaches that identify the definition of quality (Garvin, 1984; Yarimoglu, 2014;

Jankalová, 2016):

1. The transcendent approach of philosophy; according to the transcendent view, quality

means ‘innate excellence’. It is a mark of uncompromising standards and high attainment,

which can be recognised only through experience.

2. The product-based approach of economics, which quality is perceived as ‘a precise and

measurable variable’ and variances in quality reflect differences in the quantity element, or

attribute, so that better quality can only be obtained at a higher cost.

3. The user-based approach of economics, marketing, and operations management; quality is

associated with the satisfaction. The supreme quality means the best satisfaction of

consumers’ preferences.

4. The manufacturing-based; defined quality as ‘making it right the first time’. This is a supply

based and concerned with engineering and manufacturing practice. The airline is also

involved in engineering to ensure its aircrafts are airworthy.

5. Value-based approaches of operation management; defined quality in terms of cost and

price. Usually perceived as a function of price.

Another categorisation of approaches to defining inflight service quality:

1. Perceived quality vs. objective quality:

• Passenger does not use the term of quality in the same way as researchers and marketers

do; they define it conceptually (the conceptual means distinguishes between mechanistic

and humanistic quality) (Garvin, 1983; Dodds & Monroe, 1984; Holbrook & Corfman,

1985; Jacoby & Olson, 1985; Zeithaml, 1987; Rose et al., 2016).

• Mechanistic quality involves an objective aspect or feature of a thing or event, humanistic

quality involves the subjective response of flight attendant towards objects, and is

therefore a highly relativistic phenomenon that differs between judges (Holbrook &

Corfman, 1985; Jankalová, 2016).

2. Quality as attitude: The importance of the inflight service quality as an overall evaluation is

similar to attitude (Olshavsky, 1985; Parasuraman et al., 1985; Jankalová, 2016).

3. Quality vs. satisfaction: Perceived inflight service quality is a global judgment, or attitude,

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relating to the superiority of the inflight service, whereas satisfaction is related to a specific

transaction (Howard & Sheth, 1969; Hunt, 1979; Oliver, 1981; Jankalová, 2016; Rose et al.,

2016).

4. Expectations compared to perceptions: Perceived inflight service quality is therefore viewed

as the degree and direction of discrepancy between passenger’s perceptions and

expectations (expectations can be viewed as passenger’s desires/wants, merely what s/he

feels the airline should offer rather than would offer (Sasser et al., 1978; Grönroos, 1982;

Lehtinen & Lehtinen, 1982; Parasuraman et al., 1985; Jankalová, 2016; Rose et al, 2016).

In the universal quality methods; present competitive environment can aid measuring systems

taking into account the environment of inflight service provision and individual quality of the

flight attendant (Zeithaml et al., 2013; Yarimoglu, 2014; Rose et al., 2016), which both the co-

existence and inconsistency of individual approaches to defining the concept of IFSQUAL

gradually can bring the need to determine the quality dimensions of inflight service.

There are some major differences about inflight service, flight safety policy, and product. The

nature of inflight service is intangible whereas product is tangible, and policy of flight safety is

tangible (Edkins & Coakes, 2007; Sengupta, 2011; Yang & Chang, 2012; Oster et al., 2013; Rose

et al., 2016). Since inflight service is intangible, measurement of IFSQUAL can be more

complicated because IFSQUAL is measuring all at the same time (Zeithaml et al., 2013; Rahim,

2016; Rose et al., 2016). IFSQUAL measures how much the inflight service being rendered

meets the passenger satisfaction. In order to measure the intangible quality of inflight service;

the term ‘perceived’ is commonly used by researchers (Parasuraman et al., 1985; Yarimoglu,

2014; Rose et al., 2016). Perceived IFSQUAL is a result of the comparison of perceptions about

inflight service delivery process and the actual outcome of inflight service (Grönroos, 1984;

Wirtz et al., 2012; Jankalová, 2016; Rose et al., 2016). Sweeney et al. (1997), Jankalová (2016),

Rose et al. (2016) analysed whether service quality in service encounter stage affects perceived

value and consumer willingness to buy; as a result of the study, they found that service quality

perceptions in service encounter stage affects consumers more than product quality. Rahim

(2016), Rose et al. (2016), and Sandada and Matibiri (2016) mention, due to increasing

competition in the market has led many airlines to consider quality as a strategic tool. IFSQUAL

is becoming more important and the airline should improve its inflight service to gain

sustainable competitive advantage, passenger satisfaction, and loyalty (Rahim, 2016; Rose et

al., 2016; Sandada & Matibiri, 2016). In extant literature shows that passengers who are

dissatisfied with inflight service spread their experiences to more than three other people

(Chinunda, 2013; Jankalová, 2016; Rose et al., 2016). Not everyone will identify with that kind

of perception, but airline should realise that it will not achieve business excellence without the

constant cycle of measuring the quality of its own inflight service (Shewhart, 1931; Vincoli,

2014).

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METHOD

Malaysia Airlines was the unit of analysis, and passengers were the survey respondents of this

study (Awang, 2014; Trochim et al., 2015). We selected Malaysia as the country to be

investigated, and the airline industry as the organisation to be examined. This airline had

registered with Department of Civil Aviation Malaysia for air/ground operator certification. It

comprised of international passengers who had at least travelled once in the last 12 months

arrived at Kuala Lumpur International Airport (KLIA), which meant the participants had a clear

view about airline(s) inflight service. We chose KLIA because it was the main international

airport that handled international flights in Malaysia. Observed the precise and specific scope of

the above, the target population of the study were: (1) All international flights above 6 hours

only; as these flights served more than one meal service per flight. Those flights were from

London, Melbourne, Sydney, Adelaide, Jeddah, Narita, Incheon, and Beijing; because

passengers were able to experience more from these flights; (2) Arrived at KLIA on Malaysia

Airlines only (not from all airlines in the world); (3) At KLIA only (not at any other airports).

Population and Sampling

The average number of total passengers travelling with this airline was about 50,000 on 360

flights a day (Malaysia Airlines, n.d.). The next level was to select the group of international

passengers from which the sample was actually selected, and termed as the sampling frame

(Awang, 2014; Trochim et al., 2015). The sampling frame was identical to the target populating

since it was desirable that all passengers of the target population were potential passengers, or

the sample. The sampling frame for this research comprised all passengers travelling with

Malaysia Airlines and arriving from international flights during two months’ period of February

and March 2015. We expected approximately 30% of the distributed questionnaire to be

completed, and returned within four months after the survey distributions were completed.

Questionnaire

We distributed 2,000 questionnaires on 40 selected flights at a rate of 50 questionnaires for

each flight directly to passengers who agreed to contribute in the study (refer to Table 1).

Though questionnaires were distributed to those passengers who agreed to participate, only

915 questionnaires were returned giving the response rate of 45.75%. After careful scrutiny of

the data, the completed questionnaires were coded and statistically analysed. Sample size of

900 (45%) was retained for further analysis on the random sampling by using SPSS 23.0. The

excluded questionnaires were either inaccurate or incomplete responses.

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Table 1: Collecting Data on Selected Flights of the Eight Weeks

Flight

1st

2nd

3rd

4th

5th

6th

7th

8th

London

Melbourne

Sydney

Adelaide

Jeddah

Narita

Incheon

Beijing

Sampling

In the case of this study, because of the mobile and polarised passengers on the jet plane there

was no proper sampling frame for the specific available passengers, hence the study had to

develop the sampling frame for this purposes. We distributed questionnaire randomly to 50

incoming passengers per flight at KLIA. The obtained responses of 2,000 samples were listed

into a grand list of passengers, and then the study employed the probability random sampling

procedure to obtain a random sample of 282 passengers from the sampling frame for this study

(Awang, 2014; Hair et al., 2015; Trochim et al., 2015), sampling design helped this study to

understand easily the research process, and to analyse data.

Sampling Design

Sampling was the selection of a subset of cases of the total number of units in order to be

able to draw general conclusions about the entire body of units (Babbie, 2013; Awang, 2014;

Trochim et al., 2015). We selected an appropriate method of sampling to generalise results,

especially when the population was very large (Babbie 2013; Awang, 2014; Hair et al. (2015). It

was considered unusual if this study were to survey a big total of population because this

research type was cross-sectional; as it had to comply with airline’s policy; had to comply with

KLIA’s policy; had financial constraints and time limit.

Sample for heterogeneities were to include all opinions, or views (Takeuchi, 2008; Tashakkori &

Teddlie, 2010; Awang, 2014; Trochim et al., 2015). Awang (2014), and Trochim et al. (2015)

mentioned that in many brainstorming, or nominal group processes (including concept

mapping), heterogeneity sampling were used because the primary interest was in getting broad

spectrum of ideas, not identifying the ‘average’ or ‘modal instance’ ones, in fact, the sampling

was not about people, but ideas. Indeed and undoubtedly, in order to get all of the ideas, and

especially the outlier or unusual ones, broad and diverse ranges of participants were included

(Hair et al., 2015; Trochim et al., 2015; Bakar & Afthanorhan, 2016). That was the reason 2,000

questionnaires were distributed to eight international flights.

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Sample Size for Structural Equation Modelling (SEM)

There were many approaches, including a number of different formulas, for calculating sample

size. To employ SEM in this study there was no clear-cut answer of how many number of

respondents should be obtained because every research differs (among other things) in terms

of the population characteristics, and the number of constructs that were employed in a model

(Tanaka, 1993; Awang, 2014; 2015; Hair et al., 2014; 2015).

Research Instruments (Questionnaire Design)

This study’s questionnaire comprised of two main sections and took approximately eight

minutes to complete. In answering the questions, respondents were required to circle the most

suitable answer on the scale. The questionnaire was in English because it was an international

language, using simple, and direct question. The intention was to keep the questionnaire

simple, so that it would not take too much of the respondent’s time (Parasuraman et al., 1988;

Awang, 2014; Trochim et al., 2015). It was a 2-page questionnaire to keep in environmentally

responsible and user friendly way. In the questionnaire survey the 7-point Interval scale was

employed, which was possible to be quantified in the research, and to see two different

contraries (Likert, 1932; Parasuraman et al., 1985; 1988; Pitt et al., 1995; Johns, 2010; Losby &

Wetmore, 2012; Sekaran & Bougie, 2013; Awang, 2014; Trochim et al., 2015).

Section A: Focused on respondent’s profile, there were seven questions.

Section B: Refer to Appendix A

a) This section focused on personal attributes as an independent variable. Initially, there were

10 questions before exploratory factor analysis (EFA); the questions were measuring the

respondent’s acknowledgement of the personal attributes aspects of the flight attendant,

which they observed and experienced during their journey. Those characteristics were obvious

in IFSQUAL because the flight attendant had attended various training programmes as their on-

going personal development. This section measured the respondent’s agreement towards flight

attendant’s personal attributes throughout the flight.

b) The questionnaire was measuring flight safety as another independent variable. Initially,

there were 10 questions; the questions were measuring respondents’ understanding of the

existence of the flight safety as it was considered to be important to the extreme of humans,

things or situations in the form of policies. This section measured the respondent’s

understanding and awareness of the flight safety during their journey.

c) This questionnaire focused on inflight service, as the mediator. Initially, there were 10

questions; the instruments for this section were created from a comprehensive literature

review and training manuals, hypotheses, and researcher’s working experience as a flight

attendant. This section measured the respondent’s perception of the inflight service offered by

the airline.

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d) The questionnaire focused on passenger satisfaction as dependent variable towards the

overall quality from passenger expectation and experience. Initially, there were 10 questions;

the questions were influencing respondent’s knowledge. The instruments for this section were

created from a comprehensive literature review, from researcher’s working experience as she

received face-to-face feedback from passengers during flight, and her observation when

travelled with other airlines. This section influenced, and measured the respondent’s feedback

about the airline’s products, inflight service and their awareness of flight safety.

Measurement of Construct

Essentially, too few items would not capture the construct, but too many items would tire the

subject, who would either not answer the items or would not answer them carefully (Pett et al.,

2003; Sekaran & Bougie, 2013; Awang, 2014; Trochim et al., 2015). Babbie (2013), Sekaran &

Bougie (2013), and Trochim et al. (2015) mentioned that most researchers made the mistake of

asking too many questions, which was the greatest enemy in survey research that caused poor

response rate. They suggested clear and concise questionnaires to get the best response. They

continued to explain that in determining the number of items that was initially needed to be

included in an instrument, researchers must consider the format of the item, time availability of

the subject, and the characteristics of the population from the data to be gathered. This study

employed its survey instruments designed by extant researchers. They were the prominent

researchers in service quality, and had designed instruments to measure items associated with

personal attributes, flight safety, inflight service, and passenger satisfaction. Hence, we adapted

and customised their items below to suit with our study, which were verified and validated by

two experts on the content for the content validity (Awang, 2014).

Parasuraman et al. (1985) analysed the dimensions of service quality, which offered an

important framework for defining and measuring service quality. Parasuraman et al. (1985)

developed the GAP Service Quality Model through the findings from exploratory research. The

GAP relations and names were shown below (Parasuraman et al., 1985; Wirtz et al., 2012;

Saglik et al., 2014; Yarimoglu, 2014):

GAP 1: Customer expectation-management perceptions gap (the Knowledge Gap).

GAP 2: Management perception-service quality specifications gap (the Policy Gap).

GAP 3: Service quality specifications-service delivery gap (the Delivery Gap).

GAP 4: Service delivery-external communications gap (the Communications Gap).

GAP 5: Expected service-perceived service gap (the Service Quality Gap).

Haywood-Farmer (1988) discussed his service quality model comprising of three basic

attributes, which the model associated with Parasuraman et al.’s Service Quality Determinants

(1985). Parasuraman et al. (1988) develop simplified SERVQUAL, which was an advanced model

for measuring service quality. In SERVQUAL model there were 5 dimensions and 22 items

presented in 7-point Likert scale. SERVQUAL measured especially functional service quality

through empirical studies in banking, credit card, repair and maintenance, and long-distance

telephone services, which had been adopted/adapted by other researchers for other types of

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studies (Haywood-Farmer, 1988; Bari et al., 2001; Saglik et al., 2014; Yarimoglu, 2014; Debasish

& Dey, 2015).

Cronin and Taylor (1992) developed SERVPERF, which was a performance-only model for

measuring service quality with empirical studies in banking, pest control, dry-cleaning, and fast

food sectors. They developed a service quality scale dimensions of expectation (22 items-same

as SERVQUAL), performance (22 items-same as SERVQUAL), importance (22 items-same as

SERVQUAL), future purchase behaviour (1 item), overall quality (1 item), and satisfaction (1

item), which were measured by 7-point semantic differential scale. Performance-based

SERVPERF scale and the gap-based SERVQUAL scale could measure service quality

(Parasuraman et al., 1988; Cronin & Taylor, 1992; Saglik et al., 2014; Yarimoglu, 2014; Alotaibi,

2015; Debasish & Dey, 2015).

Bari et al. (2001) discussed airline service quality (AIRQUAL) model including five basic

attributes. To achieve their goal they followed two important methods; the first method was

the sequence of 8 steps presented by Churchill (1999). Secondly, the AIRQUAL was also

associated with SERVQUAL instrument revealed by Parasuraman et al. (1988) that were based

on Perceptions–Expectations, which was known as a disconfirmation Paradigm (Alotaibi, 2015).

Table 2 was analysed to adapt and customise the items in our study.

Table 2: Dimensions of Service Quality Models

Study

Model

Dimension

Parasuraman et al.,

1985

GAP Model

Reliability, Responsiveness, Competence, Access,

Courtesy, Communication, Credibility, Security,

Understanding/Knowing the Customer, Tangibles

Haywood-Farmer,

1988

Service

Quality

Attributes

Physical facilities, processes and procedures; People

behaviour and conviviality; Professional judgment

Parasuraman et al.,

1988

SERVQUAL

Tangibles, Reliability, Responsiveness, Assurance,

Empathy

Cronin & Taylor,

1992

SERVPERF

Same as SERVQUAL but with performance only

statements

Bari et al., 2001

AIRQUAL

Airline tangibles, Terminal tangibles, Personnel,

Empathy, Image

Rahim, 2016

Service

Quality

Reliability, Responsiveness, Assurance, Customisation,

Employees, Facilities, Flight patterns, Passenger

satisfaction, Customer loyalty

Rose et al., 2016

Inflight

Service

Quality

Personal Attributes, Inflight Service, Flight Safety,

Customer Satisfaction

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RESULTS & DISCUSSION

This study applied the two-steps approach of modelling and analysing the structural model

namely Confirmatory Factor Analysis (CFA) and SEM. According to Hair et al. (2014), Awang

(2015), and Byrne (2016) the measurement model of latent constructs must pass three types of

validity: (1) Construct Validity was assessed through Fitness Indexes of the Measurement

Model; (2) Convergent Validity was assessed through Average Variance Extracted (AVE); (3)

Discriminant Validity was assessed through the Discriminant Validity Index Summary. As for the

reliability, it was assessed though the Composite Reliability (CR). The CR replaced the Internal

Reliability measurement using Cronbach’s Alpha as this study was analysing using SEM, and the

latent construct was considered valid when fitness indexes achieved the three Model Fit

categories (see Table 3) (Awang, 2014; 2015; Hair et al., 2014; 2015; Bakar & Afthanorhan,

2016; Byrne, 2016; Hoque et al, 2017).

We simplified the analyses by converting the second order construct into first order by taking

the composite mean for every sub-construct. Afthanorhan et al. (2014), Hair et al. (2014),

Awang (2015), Byrne (2016), and Hoque et al. (2017) mentioned that prior to modelling the

structural model and executing SEM, researcher must prove that all constructs involved in the

model were discriminant of each other, or they were not highly correlated especially between

the exogenous constructs; if the two exogenous constructs were highly correlated (correlation

coefficient greater than 0.85), then a serious problem called Multi-collinearity occurred.

Following the above theory by them, the two exogenous (Personal Attributes and Flight Safety),

mediation (Inflight Service), and endogenous (Passenger Satisfaction) constructs in the model

became second-order constructs with certain number of sub-constructs and every sub-

construct was measured using certain number of items from the questionnaire.

Pooled Measurement Model for All Constructs

For this procedure, all constructs were combined together and executed the Pooled-CFA;

the conversion was carried out by computing a single composite mean for items in every sub-

construct of the measurement model (Afthanorhan et al., 2014; Awang, 2015; Byrne, 2016;

Bakar & Afthanorhan, 2016; Hoque et al, 2017). Figure 1 demonstrated the initial measurement

model for each construct in the Pooled Measurement model.

In Figure 1, the fitness indexes did not meet the required level as proposed by Afthanorhan et

al. (2014), Hair et al. (2014), Awang (2015), Byrne (2016), and Hoque et al. (2017); in order to

remedy this problem, they suggested researcher must inspect the poor factor loading items,

and remove them from the model (one item at a time from each sub-construct and re-analyse

the CFA); the process continued until the measurement model achieved the threshold values.

We identified eight poor factor loading items less than 0.6 namely IFSQ1 (0.17), IFSQ5 (0.20),

IFSQ6 (0.23), IFSQ10 (0.33), PAX7 (0.37), PAX1 (0.53), PAX2 (0.54), and PAX6 (0.57). These poor

items had caused the model to be unfit. In Figure 2, the Fitness Indexes readings were good and

fit after several procedures, and the significance level for coefficients was p<0.001, see Table 3.

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Figure 1: The Initial Measurement Model

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Figure 2: The Final Measurement Model after PAX6 was removed

Assessment for Validity and Reliability

After few CFA procedures, the measurement model results were as follows:

a) Construct validity (Table 3). The fitness indexes as the constructs had achieved the

required level (Afthanorhan et al., 2014; Awang et al., 2015; Byrne, 2016; Hoque et al.,

2017).

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Table 3: Construct Validity

Category

Model Fit

Result

Fit Criteria

Reference

Acceptable

Absolute Fit

Index

RMSEA

0.06

Range 0.05 to 0.1

Hu & Bentler, 1999;

Awang, 2015; Byrne, 2016

Yes

GFI

0.86

Close to 0.95

Jöreskog & Sörbom, 1996;

Awang, 2015; Byrne, 2016

Yes

Incremental

Fit Index

AGFI

0.83

Close to 0.95

Jöreskog & Sörbom, 1996;

Awang, 2015, Byrne, 2016

Yes

CFI

0.94

Close to 0.95

Hu & Bentler, 1999;

Awang, 2015, Byrne, 2016

Yes

NFI

0.89

Close to 0.95

Hu & Bentler, 1999;

Awang, 2015, Byrne, 2016

Yes

TLI

0.94

Close to 0.95

Hu & Bentler, 1999;

Awang, 2015, Byrne, 2016

Yes

Parsimonious

Fit Index

ChiSq/

1.885

Below 5.00

Hair et al., 2014;

Awang, 2015, Byrne, 2016

Yes

NB: The indexes in bold were recommended since they were frequently reported in literature (Awang, 2015).

b) Convergent validity. All items in measurement model were statistically significant. The

convergent validity was also verified by computing AVE and CR (Table 4) for every construct.

Afthanorhan et al. (2014), Hair et al. (2014), Awang (2015), Byrne (2016), and Hoque et al.

(2017) agreed that the values of AVE should not less than 0.5, and CR should not less than 0.6;

low result could affect low AVE and CR; as both were computed based on the factor loading.

Table 4: AVE and CR for the main constructs

Construct

Component

Factor Loading

AVE

Personal Attributes

Competency

0.97

0.87

0.95

Approachable

0.97

Communication

Skills

0.85

Flight Safety

Reliable

0.96

0.86

0.95

Credible

0.95

Compliance

0.89

Inflight Service

Consistency

0.95

0.66

0.78

Convenience

0.63

Passenger

Satisfaction

Service

Satisfaction

0.83

0.70

0.84

Safety Satisfaction

0.87

C) Discriminant validity (Table 5). This study model was free from redundant items. The

diagonal values in bold were the square root of AVE, which was higher than the values in its

row and column, thus the discriminant validity had achieved the required level (Afthanorhan et

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al., 2014; Hair et al., 2014; Awang, 2015; Awang et al., 2015; Byrne, 2016). While other values

were the correlation between the respective constructs.

Table 5: Discriminant Validity Index Summary

Constructs

Personal

Attribute

Flight

Safety

Inflight

Service

Passenger

Satisfactio

Personal Attributes

0.93

Flight Safety

0.65

0.93

Inflight Service

0.65

0.60

0.81

Passenger Satisfaction

0.87

0.85

0.79

0.84

Table 6 was the hypotheses results of the direct effects between the constructs (see Figure 3).

Table 6: Regression Weights and Its Significance

Test

Construct

Direc

Effec

Construct

Estimate

Std.

Error

Critical

Region

P-

Valu

Supporte

Inflight Service



Personal Attributes

0.46

0.096

4.825

0.001

Yes

Passenger

Satisfaction



Personal Attributes

0.38

0.068

5.487

0.001

Yes

Inflight Service



Flight Safety

0.29

0.084

3.498

0.001

Yes

Passenger

Satisfaction



Flight Safety

0.35

0.060

5.814

0.001

Yes

Passenger

Satisfaction



Inflight Service

0.22

0.065

3.377

0.001

Yes

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Figure 3: Regression Weights

Table 7 was the mediation hypotheses results; the analyses were computed from Figure 3,

using AMOS:

Table 7: Bootstrapping Summary of Mediation Effect (H6 & H7)

Path

Indirect Effect

Direct Effect

Personal Attributes

Bootstrapping Results

0.118

0.435

Passenger

Satisfaction

Bootstrapping P-Value

0.003

0.004

(H6)

Result

Not Supported

Supported

Type of Mediation

No Mediation (Not Supported)

Flight Safety to

Bootstrapping Results

0.077

0.415

Passenger

Satisfaction

Bootstrapping P-Value

0.007

0.001

(H7)

Result

Not Supported

Supported

Type of Mediation

No Mediation (Not Supported)

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H6: Inflight Service mediates the relationship between Personal Attributes and Passenger

Satisfaction – Not Supported (refer Table 7). Results indicated that Inflight Service was not a

significant mediation predictor of Personal Attributes, = 0.376, SE = 0.068, p<0.05; but Inflight

Service was a direct predictor of Passenger Satisfaction, = 0.221, SE = 0.065, p<0.05. The

result was not significant; thus it did not support the mediation hypothesis. Personal Attributes

was still a direct, and significant predictor of Passenger Satisfaction after it was controlled by

the mediator (Inflight Service), = 0.118, SE = 0.060, consistent with No Mediation (Kafaji,

2013; Osman & Sentosa, 2013; Hair et al, 2014; Awang, 2015; Byrne, 2016; Rahim, 2016).

H7: Inflight Service mediates the relationship between Flight Safety and Passenger Satisfaction

– Not Supported (refer Table 7). Results indicated that Inflight Service was not a significant

mediation predictor of Flight Safety, = 0.345, SE = 0.060, p<0.05. The result was not

significant; hence the result did not support the mediation hypothesis. Flight Safety was still a

direct, and significant predictor of Passenger Satisfaction after it was controlled by the

mediator (Inflight Service), = 0.077, SE = 0.045, consistent with No Mediation (Kafaji, 2013;

Osman & Sentosa, 2013; Awang, 2015; Byrne, 2016; Rahim, 2016).

CONCLUSION

Approximately 93% of the variance in Passenger Satisfaction was accounted for by the

predictors; the coefficient of determination, or R-Square (R

) for the model was 0.93.

=0.930); the direct and indirect effects were tested using bootstrap estimation approach

with 2,000 samples, and 95% of confidence level (Hair et al., 2014; Awang, 2015; Byrne, 2016).

Hence, the value implied in the model, which comprised of two exogenous constructs and one

mediator namely Personal Attributes, Flight Safety, and Inflight Service managed to estimate

93% of the information in Passenger Satisfaction (Hair et al., 2014; Awang, 2015; Byrne, 2016).

‘Supported’ and ‘not supported’ assumption results: (1) Personal Attributes dimension was the

flight attendant’s characteristics – her/his soft skills and technical skills in IFSQUAL were built

from the sequence of training programmes that s/he had been attended, and also the

knowledge and experience from day-to-day work. Though flight attendant was the airline

product but her/his appearance, personality, knowledge, dedication, decision-making, and

leadership skills in delivering IFSQUAL might not be similar to her/his peers, hence this Personal

Attributes dimension could only be a direct effect to the passengers who recognised and

understand the ‘transcendent approach’, which will definitely mark their satisfaction level

according to how they consumed the IFSQUAL (Garvin, 1984; Zeithaml et al., 2013; Jankalová,

2016; Rose et al., 2016). Consequently, an Inflight Service could not mediate this human skills

and tacit knowledge. (2) Flight safety dimension was a policy, thus it could not be mediated by

Inflight Service, because the policy could not be adjusted simply to suit the passenger

emotional needs during her/his journey. Policy was a principle of action; it was implemented

and approved by the Department of Civil Aviation Malaysia, airline’s own policy, international

association such as International Air Transport Association (IATA), and International Civil

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Aviation Organisation (ICAO) for the safety of the people on board, and the aircraft (Crosby,

1979; Yang & Chang, 2012; Baker, 2013; Jankalová, 2016; Rose et al., 2016; Sandada & Matibiri,

2016).

We had discussed in detail the statistical analysis of the findings generated from the passenger

survey at KLIA. From the demographic analysis, we would like to give an advice that these

results were not being fully generalisable to the population of all air travellers globally. This

quantitative research had produced hypotheses, and developed understandings about

particular groups through sampling. This sampling involved in making a series of decisions not

only about how many individuals to include in a study and how to select these individuals, but

also about the conditions under which the selection was done; and the story was from the

participant’s standpoint (Kafaji, 2013; Awang, 2014; Hair et al., 2015; Al Zefeiti & Mohamad,

2015; Ngo & Nguyen, 2016; Rahim, 2016).

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APPENDIX A

Types of Statistical Analyses of the Study

Construct

Item

Scale

Section A

Respondents’

profile

Gender, age, education level, occupation, number of times

travelling with any airlines, travelling class, and reason for

choosing Malaysia Airlines

Descriptive

Analysis

Section B

Personal

Attributes

1. Flight attendant is efficient

7-point Interval

Scale

2. Flight attendant is competent

3. Flight attendant is confident

4. Flight attendant is approachable

5. Flight attendant smiles at me

6. Flight attendant always pleasant

7. Flight attendant is friendly

8. Flight attendant communicates well

9. PA announcement is clear

10. Flight attendant is courteous

Flight Safety

1. Highly safe air transportation experience

7-point Interval

Scale

2. Reliable air transportation service

3. I am confident to fly with this airline

4. Flight attendant checks cabin for take-off

5. Flight attendant checks cabin for landing

6. Flight attendant checks cabin during bad weather

7. Flight attendant complies with safety

8. Flight attendant is conversant with safety

9. Flight attendant is well trained in safety

10. Aircraft is new

Inflight Service

Quality

1. Adequate seat facilities

7-point Interval

Scale

2. Comfortable seat

3. My seat is clean when I boarded

4. Consistent inflight service delivery

5. Completed meal service at the right time

6. Cabin temperature is satisfactory

7. Cabin ambience is satisfactory

8. Variety choice of food

9. Variety choice of beverages

10. Inflight entertainment is easy to use

Passenger

Satisfaction

1. Airline should improve on seat quality

7-point Interval

Scale

2. Airline should improve on food

3. Airline should improve on safety

4. Satisfied with inflight service

5. Satisfied with on board food

6. Inflight service value for money

7. Satisfactory inflight entertainment

8. Satisfied with current inflight service provision

9. Fly with this airline again

10. Recommend this airline to friend