2011 Ninth International Conference on ICT and Knowledge Engineering

Factors affecting the Passengers’ Intention toward “Airline Electronic Ticketing”

In Thailand Wichian Premchaiswadi1, Parham Porouhan2

Graduate School of Information Technology in Business Siam University

Bangkok, Thailand wichian@siam.edu1, pporouhan@yahoo.com2

Abstract— This study uses an ‘adoption model’ to assess airline passengers’ intention to use an online e-ticketing system in Thailand. The study also integrates constructs from “United Theory of Acceptance”, “Use of Technology model”, “Transaction Cost Saving Model”, “Perceived Security Model” and “Perceived Enjoyment Theory”. The “survey” was conducted at Suvarnabhumi International Airport in Thailand (Domestic Flights Terminal). The study aims to focus on those AirAsia’s passengers who have ever used online e-ticketing at least for once in their life (experienced users). SPSS Program (Version 17.0) was used for data mining and survey authoring (Linear regression and Correlation analysis). Consequently, the results indicate that “Perceived Security”, “Perceived Enjoyment”, “Price Saving”, “Effort Expectancy” and “Facilitating Conditions” -in sequence- have significant positive effects on airlines passengers’ intention to use online e-ticketing systems in Thailand, whereas “Performance Expectancy” did not have any significant effect on passengers’ intention. After collecting the data; “Time Saving” and “Social Influence” hypotheses (H3 & H5) were not supported by Inter-Item Correlation Matrix’ results and thus were ‘eliminated’ from the initial conceptual framework model. The model finally explains 71.6% percent of the variance in airlines passengers’ intention to use an online e-ticketing system in Thailand.

Keywords—Ticketing, Thai, Airline, Online, Internet, Use

I. INTRODUCTION An electronic ticket or e-ticket is used to represent the

purchase of a seat on a passenger airline, usually through a website or by telephone. Once a reservation is made, an e-ticket exists only as a digital record in the airline computers. Customers usually print out a copy of their receipt which contains the record locator or reservation number and the e-ticket number. This form of airline ticket has rapidly replaced the old multi-layered paper tickets (from close to zero to 100% in about 10 years) and became mandatory for IATA members as of June 1, 2008. During the last few years, where paper tickets were still available, airlines frequently charged extra for issuing them. E-tickets are also available for certain entertainment venues. While e-ticket itinerary receipts may at

first glance look like a basic itinerary, they contain a number of other features that distinguish them.

* E-tickets, like their paper counterparts, will contain an official ticket number (including the airline's 3-digit code, a 4-digit form number, a 6-digit serial number, and sometimes a check digit).

* Carriage terms and conditions, (or at least a reference to them)

* Fare and tax details, including fare calculation details and some additional data such as tour codes. The exact cost might not be stated, but a "fare basis" code will always identify the fare used.

* A short summary of fare restrictions, usually specifying only whether change or refund are permitted but not the penalties to which they are subject.

* Form of payment. * Issuing office. * Baggage allowance.

According to critical acclaim, Joel R. Goheen is recognized as the Inventor of Electronic Ticketing in the Airline Industry, an industry where global electronic ticket sales (the industry standard) accounts for over $400 Billion (US) a year (2007).

Background of “Internet” in Thailand

As of 2004, there are 11,900,000 internet users in Thailand, of whom 570,000 have broadband access. Broadband Internet is readily available in major cities and towns, but is still to be sought after in smaller villages and in the countryside. As the statistics have shown, the majority of internet users in Thailand still rely on dial-up access. TOT operates a nationwide local rate number, 1222, allowing dialing to most Internet service providers. Dial-up prepaid internet packs can be readily bought in convenience stores such as 7-11, Family Mart and Tesco Lotus Express for approximately 150-400 Baht/month for unlimited access depending on the provider. Subscribers of fixed telephone lines by True Corporation have access to dial up internet simply by dialing up to a certain

177 978-1-4577-2162-5/11/$26.00 ©2011 IEEE

number then being billed along with the telephone bill at the end of every billing cycle.

The majority of broadband Internet access uses Asymmetric Digital Subscriber Line (ADSL). Some areas are covered by Cable Modems and G.shdsl. Prices for consumer broadband internet access varies from 99 Baht/month to 3800 Baht/month, with the speed ranging from 128 kbit/s to 8 Mbit/s. Medium and large businesses use Leased Lines or Ethernet Internet/MPLS where fiber optic cables link many office buildings in the central business district areas such as Sukhumvit, Silom and Sathorn areas to the Thailand Internet backbone. Universities have access to fast internet access, including the Trans-Eurasia Information Network (TEIN2) research network.

From 2008, AIS has launched Thailand's first 3GSM wireless broadband internet in Chiang Mai, Thailand. There are initiatives to offer FTTH (Fiber to the Home) providing connectivities of up to 100 Mbit/s bundled with IPTV and VoIP, but this has yet to materialize.

Thailand saw a rapid growth in the number of broadband users in 2005 with the initiation of unmetered broadband in 2004. There are 1,116,000 (2008) Internet hosts in Thailand being the highest in South East Asia.

Lufthansa was the first European airline to launch an Internet auction of air tickets in Thailand in January 1999. Air Asia has been the first Asian airline to launch electronic ticketing system in October 2005.

II. BACKGROUND & THEORY

A. Literature Review

Taylor and Todd in 1995, Conducted a study to assess the role of prior “experience” in assessing IT usage. They tested the predictive ability of the Augmented TAM model based upon the data gathered from two distinct groups of experienced and inexperienced users of the computer resource center separately and compared the results to assess the role of experience. ** The following variables were used in previous e-commerce adoption researches in a Likert five-point scale:

TABLE I. RELATED WORKS

B. Theories

(1) Unified Theory of Acceptance and Use of Technology: The UTAUT aims to explain user intentions to use an IS and subsequent usage behavior as shown in Fig. 1. The theory holds that four key constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions) are direct determinants of usage intention and behavior (Venkatesh et. al., 2003).

Figure 1. Unified Theory of Acceptance and Use of Technology

(2) Transaction Cost Analysis” Theory (TCA):

The three dimensions of transaction costs are Perceived Ease of Use, time efficiency, and price saving measure different aspects of the efficiency of retail transactions.

(3) Perceived Enjoyment: Perceived Enjoyment refers to the extent to which the

activity of using the computer is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated (Teo 2001). The importance of enjoyment in online shopping has been challenged in the past.

(4) Perceived Security: Perceived Security is defined as a person’s perception of the

possibility of having positive outcome despite of suffering harm or losses associated with E-commerce issues.

C. Conceptual Framework

This research applies the “Conceptual Framework” as a combination of Behavioral Intention Theories (as shown in Fig. 2) including: “Theory of Reasoned Action” (TRA), “Theory of Planned Behavior” (TPB), “Technology acceptance model” (TAM)—“Theory of Acceptance and Use of Technology (UTAUT)”-- and some new links like “Transaction Cost Analysis” (TCA) Model.

Figure 2. Conceptual Framework (TRA, TPB, TAM, UTAUT & TCA)

178

Thus, Proposed Conceptual Framework = (Theory of Reasoned Action + Theory of Planned Behavior + Technology Acceptance Model + Theory of Acceptance and Use of Technology + Transaction Cost Analysis Model)

Eight independent variables and their activities as following:

� Performance Expectancy is defined as the degree to which an individual believes that using the system will help him or her to attain gains in job performance.

� Effort Expectancy is defined as the degree of ease associated with the use of the system.

� Social Influence is defined as the degree to which an individual perceives that important others believe he or she should use the new system.

� Facilitating Conditions are defined as the degree to which an Individual believes that an organizational and technical infrastructure exists to support use of the system.

� Perceived Enjoyment refers to the extent to which the activity of using the computer is perceived to be enjoyable in its own right, apart from any performance consequences.

� Perceived Security refers to perception of the possibility of “NOT” having negative outcome or suffering harm or losses associated with E-commerce.

� Price Saving is a measure of online or conventional store transaction efficiency which will cause in saving money.

� Time Saving refers to time efficiency is a measure of online or conventional store time saving process.

TABLE II. INDEPENDENT VARIABLES

H1: “Performance Expectancy” has a significant and positive effect on Thai airline passengers’ intention to use e-ticketing. H2: “Effort Expectancy” has a significant and positive effect on Thai airline passengers’ intention to use e-ticketing systems.

H3: “Social Influence” has a significant and positive effect on Thai airline passengers’ intention to use e-ticketing. H4: “Facilitating Conditions” has a significant and positive effect on Thai airline passengers’ intention to use e-ticketing. H5: “Time Saving” has a significant and positive effect on Thai airline passengers’ intention to use e-ticketing. H6: “Price Saving” has a significant and positive effect on Thai airline passengers’ intention. H7: “Perceived enjoyment” has a significant and positive effect on Thai airline passengers’ intention to use e-ticketing. H8: “Perceived Security” has a significant and positive effect on Thai airline passengers’ intention to use e-ticketing.

Major Purposes/ Objectives of this Research:

1) To identify the main factors affecting Thai passengers’ “Intention” towards airline e-ticketing in Thai. 2) To recommend the strategies and solutions to the online ticket policy makers and electronic-commerce marketing management practices for the development of the online e-ticketing systems.

Problem Statement:

As mentioned earlier, “critical understanding of passenger’s behavior” in cyberspace towards airline ticket purchasing, cannot be achieved without a good appreciation of the factors Thus the main problem is; “still so many of Thai Passengers are Not using online E-ticketing for their flights”. Research Question:

“What are the main factors that influence Thai airline passenger’s “intention” to purchase tickets through online e-ticketing systems?” “Validity” of Research’s Survey (Questionnaire):

Survey questions were made based on literature review to ensure the validity of the results. To further “validate” the questionnaire one “Interview” was conducted with Dr. Wichian Premchaiswadi at “siam university” to make sure the questions were compatible with the Thai real context before its vast distribution process. Minor changes were suggested by him during this stage except his recommendations in adding “Perceived Security” to conceptual framework model as one of the independent variables. On the other hand, a “Pilot test” on Sunday 1st of June, was done to assure questions were fully understood by the subjects.

179

“Reliability” of Research’s Survey (Questionnaire):

One way to think of reliability is that other things being equal, a person should get the same score on a questionnaire if they complete it at two different points in time test-retest reliability. The average of the values is equivalent to “Cronbach’s alpha”,�, which is the most common measure of “scale reliability” and I also used it in this research.

Scale: Reliability Testing

[DataSet1] C:\Documents and Settings\Parham Porouhan

\Desktop\Wichian Premchaiswadi3.sav

TABLE III. RELIABILITY TESTING RESULTS

Using SPSS for windows version 17, all the 30 items and indicators (from questions#7-36) were tested which the results displayed in Table III. Often in books and Journal Articles are said that a value above 0.704 is an acceptable value for Cronbach’s Alpha; values substantially lower indicate and unreliable scale.

Thus, a value of 0.704 shows a “Fair Extend” of ‘Reliability’ for our Research’s Questionnaire (70.4%).

Cronbach’s Alpha Reliability Test also was conducted for constructs “Independently” as well, which also indicates high reliability (as displayed in Fig. 3) by going to Transform> Compute Variables in SPSS 17. You will see the final results here:

Figure 3. A Screenshot from Cronbach’s Alpha Reliability Testing in SPSS

TABLE IV. CRONBACH’S ALPHA RELIABILITY TEST

As you consider, Table IV shows a high reliability within research constructs. “Performance Expectancy” with 95.3% reliability is the maximum and “Perceived Security” with 69% is the minimum reliability in this “independently reliability testing results”.

Scope of the Research:

(a) The research applies the Theoretical Conceptual Framework as a “Combination” of Behavioral Intention Theories including: “Theory of Reasoned Action (TRA)”, “Theory of Planned Behavior (TPB)”, “Technology Acceptance Model (TAM)”, “Theory of Acceptance and Use of Technology (UTAUT)”, and some new links like “Transaction Cost Analysis (TCA) Model”.

-- Research s Conceptual Framework = (Theory of Reasoned Action + Theory of Planned Behavior + Technology Acceptance Model + Theory of Acceptance and Use of Technology + Transaction Cost Analysis Model)

(b) “Eight independent variables” of Thai Passengers “Intention” include: • Performance Expectancy (PE) (Questions#8-12) • Effort Expectancy (EE) (Questions#16-19) • Social Influence (SI) (Questions#27-30) • Facilitating Conditions (FC) (Questions#23-26) • Time saving (TS) (Questions#20-21) • Price saving (PS) (Question#22) • Perceived enjoyment (PEJ) (Questions#13-15) • Perceived Security (PS) (Qyestions#31-36)

(c) “One Dependant Variable” as ‘Thai Passenger’s “Intention” towards E-Ticketing’ which is going to be measured by “Number of Tickets been bought by passengers within the last year”. (Question#7)

III. METHODOLOGY “Quantitative” versus “Qualitative” Method:

“Quantitative Approach” is suitable for this research, because the main objective for this research is testing a model for e-ticketing intention in Thailand.

180

“Inductive & Deductive” Methods:

In this study both of “Inductive & deductive approaches” were applied, since the research starts with a literature review which later on is compared with the empirical findings some elimination of unnecessary factors will be done (deductive). In addition, in this study, “Security Theory” was chosen and added (inductive) to our final conceptual framework model, based on the observations and “Interview” made with Dr Wichian Premchaiswadi. Adding “Perceived Security” to the rest of other available independent variables, was the innovative part of our research and can be considered as the major purpose of an inductive approach.

Defining the “Target Population” Method :

Considering that everyday, a lot of “domestic flights” will be done from “Suvarnabhumi International Airport” by “Air Asia” airline, in Thailand and also AirAsia has been the first airline company in Asia which has provided e-ticketing facilities, so we decided to: • “Target only those Thai passengers who had ever used any e-ticketing system in before” (experienced users). Since we were interested in the concept of “intention”, the fact that the respondents are non-experienced users of the online e-ticketing systems could not affect the result of this study. Testing the acceptance models based on the data gathered from experienced users is not something unusual and has been studied many times in previous literatures.

Based on above mentioned explanations “Target Population” of our study is defined as:

(a) Elements: “Experienced Users” of the e-ticketing system who have

minimum internet abilities (i.e., those who previously have used any e-ticketing system for online reservation, at least once)

(b) Sampling Units: “AirAsia Airline Passengers” who are waiting in the

transit area of “Domestic Flights” at Suvarnabhumi Airport to get on board the aircraft for traveling another city in Thai from Bangkok (24th to 26th of September 2010).

(c) Extent: “Suvarnabhumi International Airport”, terminal

“Domestic Departures”.

“Sample Selection” Method:

In “probability sampling”, sampling units are selected by chance. In this study “Probability” or representative sampling has been used to allow us to make inferences or projections about the target population. The subjects were chosen randomly from the transit area of domestic departures terminal of “Suvarnabhumi Airport” traveling to different cities in Thailand. The respondents throughout the airport firstly will be questioned about their “experience” in using e-ticketing plus also their “ability” of using internet and then the if they have had an experience of using online airline e-ticketing

systems with a minimum knowledge of internet and computer, then the questionnaires will be given to them as “experienced passengers” who are familiar and acquainted with the online e-ticketing system. “Data Collection” Method:

• Secondary data was used in this research for collecting the information from former existing studies and literatures and theories and theoretical framework • Collecting data (by questionnaire) and investigating data results (by SPSS Software version 17) to check the hypotheses in this study, no secondary data was available before, so a Quantitative type of Survey was used as the primary data source • Descriptive statistics and demographic results are presented finally by mean, standard deviation, frequency and percentages to describe the survey results. For inferential statistics, correlations and ridge regressions are employed by SPSS version17 for our model hypotheses testing. “Questionnaire Development” Method:

Generally, two types of Questionnaires have been provided in this research; English Version and Thai Version of questionnaires. As mentioned above, our target would be the passengers of AirAsia airline in Suvarnabhumi airport, domestic flights terminal. The “English version” of questionnaire was handed to those kinds of passengers who were foreigners or as tourists who indented to travel to another city in Thai for leisure or short-time periods, but in advance I asked them about; have they ever used any online e-ticketing website and system in Thai for their flight ticket reservation or purchasing? If their answer was “Yes”, then I gave them the English version of questionnaire to know what they think about e-ticketing system in Thai and what has made their intention to use and try it in Thai. Another “Thai Version” of questionnaire, were handed to 100% natively Thai and local passengers at airport and that was the reason I provided them the same questionnaire but in Thai Language for making their feedback and comprehension time more convenient and faster. Questionnaire in this research consists of 36 questions that relate to possible factors affecting “Intention” to use online e-ticketing systems. “Likert five point scales” ranging from “strongly agree” to “strongly disagree” were used as a basis of questions. To facilitate the interpreting and processing of SPSS data analysis results, a 5-point Likert Five-Point Scale has been chosen for Questionnaire’ design which commences with; “Strongly disagree= 1, Disagree=2, Neutral=3, Agree=4, strongly agree=5” Altogether there will be 36 questions, which the first 6 questions are Demographic and General questions. Question #7 is related to measuring our dependant variable as Intention of passengers to use e-ticketing systems, which is going to be

181

measured by the “# Number of the Tickets” that passengers and users have bought during the last year. Furthermore, the questions after #8 onward (Questions#8-36) will be related to our eight independent variables as illustrated and elaborated in chapter III. *the questionnaires as mentioned previously, have been distributed only amongst those kinds of passengers who they’ve had any background or experience in using e-ticketing systems before.*

Figure 4. Summery” of the Research Methodology

As you consider in Fig. 4, the research follows a descriptive method and quantitative approach followed by survey to collect primary data using a questionnaire and which a probability sample selection was conducted.

IV. FACTS AND FINDINGS

Data collection took place at “Suvarnabhumi International Airport”, terminal “Domestic departures”. 100 questionnaires were distributed among AirAsia Airline passengers in “terminal four” which is used for “domestic departures". Out of the “100 questionnaires” -which were distributed- all of them (100 questionnaires) were completed and collected. To exclude incomplete and inappropriate questionnaires a simple cleansing method was used which reduced the sample size to 86 which then was used for Data presentation and analysis, so the “Response Rate” of research is 86%.

As was mentioned earlier, our subjects and target audience are “experienced” AirAsia Airline passengers who have purchased and traveled with an Airline by using of an online E-Ticketing system-at least once before. Demographic statistics are provided in the below diagrams which describe Gender, educational level and Age of respondents respectively besides city of residence, purpose of their traveling and finally the number of hours that they spend on the internet.

Gender Diagram

Women, 51%

Men, 49% Men

women

Figure 5. Gender Diagram

As displayed in Fig. 5, 49% (42 out of 86)of the respondents are male and (44 out of 86) 51% of the respondents are females.

Diploma 23

Bachelors 47

Masters 14

PHD 2

0

5

10

15

20

25

30

35

40

45

50

Series1

Series1 23 47 14 2

Diploma Bachelors Masters PHD

Figure 6. Education Background

Fig. 6 shows the Education level of the respondents. Majority of the respondents have above high school education. 54.65% of the respondents have a B.A degree which was no surprise considering Thai government’s policies towards increasing capacity for undergraduates in recent years.

�

12

43

20

11

0

5

10

15

20

25

30

35

40

45

50

Series1

Series1 12 43 20 11

below 20 20 to 30 30 to 40 40 & above

Figure 7. Age Diagram

The majority of the respondents were between 20 to 30 years old which represents 50% of the total sample as illustrated in Fig. 7. Youngsters below 20 years of age represented only 13.95%. Considering demographical statistics of the population in Thai, the results had no surprise.

�Bangkok, 18

Chiang Mai, 17

Chiang Rai, 6

Hua Hin, 12

Koh Samui, 2Khon Kaen, 1

Phuket, 13

Lampang, 5

Melbourne, 3

Sydney, 1Gotenburg, 2Dartford, 2

Moscow, 4

0

2

4

6

8

10

12

14

16

18

20

Series1

Series1 18 17 6 12 2 1 13 5 3 1 2 2 4

Ban

gko

Chi

ang

Chi

ang

Hua

Hin

Koh

Sa

Kho

n

Phu

ket

Lam

pan

Mel

bour

Syd

ney

Got

enb

Dart

ford

Mos

cow

Figure 8. City of Residence

Fig. 8 categorizes respondents by their city of residence. Citizens of 100% thai and local passengers and also cities of foreigners who stayed in Thai for leisure or short-time, have participated in the survey which justifies the sample of

182

AisAsia Airline passengers regardless of the questionnaires being distributed in Suvarnabhumi airport.

It worth to mention that before distributing the questionnaires to AirAsia domestic passengers, a precondition question was asked as: “Have you ever used any online E-ticketing system in Thailand for booking and reservation of your flight tickets?” If their answer was “Yes” and positive then the questionnaire was distributed to them. That was because; I meant to focus only on those kinds of Experienced Users who have tried any online e-ticketing system in Thai at least for once. As it is illustrated in Fig. 9, 100% of the respondents spend some time on the Internet during the week. 54.65% of them spend between 5 to 10 hours on the internet during the week.

Figure 9. Internet Usage Diagram

As you consider in Fig. 10, 28% of respondents are going to take domestic AirAsia flights to visit their family in our research, and 25% for business issues and 21% for leisure.

Figure 10. Purpose of Travel

“Average Mean” of eight independent variables affecting passenger’s Intention to use e-ticketing systems (Fig. 11).

Figure 11. Mean of 8 Independent Variables

TABLE V. MEAN OF EIGHT VARIABLES (NOT AVERAGE)

�

0

13

47

26

0

5

10

15

20

25

30

35

40

45

50

Series1

Series1 0 13 47 26

Zero Less than 5 5 to 10 More than 10

�

17

24

2018

7

0

5

10

15

20

25

30

Series1

Series1 17 24 20 18 7

Visit

FriendsFamily Business Leisure Student

0.000.501.001.502.002.503.003.504.004.505.00

Per

form

_Expec

t_X1

Per

ceived_

Enjoy

_X2

Effo

rt_Exp

ect_X3

Time_

Sav

ing_

X4

Pric

e_Sav

ing_X

5

Facilia

t_Con

dition

s_X6

Soc

ial_In

fluenc

e_X7

Per

ceived_

Sec

urity

_X8

Series1

M

In sum, the Mean and Standard Deviation for all the Dependent and Independent variables are as shown in Table VI as follows:

[DataSet1] C:\Documents and Settings\Parham Porouhan\Desktop\Wichian Premchaiswadi3.sav

183

TABLE VI. MEAN AND STANDARD DEVIATION RESULTS

Table VI illustrates the correlation between 8 independent variables and 1 dependent variable (intention of users) in detail. Using SPSS 17.0 helped us to test the hypotheses and correlation in a 2-tailed Pearson Correlation method illustrated in Table VII:

TABLE VII. 2-TAILED PEARSON CORRELATION RESULTS

Considering the results of “Pearson Correlation” and “Hypothesis Testing”, Hypotheses 3 and 5 were eliminated. Therefore the number of independent variables reduces from eight to six. Below figure illustrates our new hypothesis model.

Figure 12. New hypothesis model after elimination of time-saving and

social-influence factors

(B) Analysis of Data: TABLE VIII. ANOVA TABLE

Based on the following ridge regression analysis (shown in Table IX), we can discuss and interpret each of the above variables results individually and then we compare them with the previous studies done in this field by Manzari and Taylor&Tod. Consequently, having analyzed the findings, we can decide to “Reject” or “Accept” the hypotheses.

TABLE IX. RIDGE REGRESSION ANALYSIS

� Performance Expectancy (rejected)

Intention to use online e-ticketing systems is found not to be significantly affected by “performance expectancy” (Sig=0.986>0.05 and t-value=0.018<2.0), thereby rejecting hypothesis one. This finding contradicts the previous results done by Manzari and Taylor-Todd.

� Perceived Enjoyment Hypothesis (accepted)

Intention to use online e-ticketing systems is positively affected by perceived enjoyment (Sig=0.0<0.05, t-value=4.972>2.0), thereby supporting hypothesis seven. This indicates that Thai airline passenger’s intentions to use an online e-ticketing system will increase if they perceive using

184

the service to purchasing a ticket will cause more joyful activity than going to physical travel agency. This is compatible with previous research by Manzari and Todd-Taylor.

� Effort Expectancy Hypothesis (accepted)

Intention to use online e-ticketing systems is positively affected by effort expectancy (Sig=0.0, t-value=4.143>2.0) thereby supporting hypothesis two. This indicates that Thai airline passenger’s intentions to use an online e-ticketing system will increase if they expect the service make it easier for them to purchasing a ticket. This is compatible with previous research by Manzari and Todd-Taylor.

� Price Saving Hypothesis (accepted)

Intention to use online e-ticketing systems is positively affected by transaction “price saving” (Sig. = 0.0<0.05, t-value=4.205>2.0) thereby supporting hypothesis six. This indicates that Thai airline passenger’s intentions to use an online e-ticketing system will increase if they perceive using the service to purchase tickets will allow them to save money. This is compatible with previous research by Manzari and Todd-Taylor.

� Facilitating Conditions Hypothesis(accepted)

Intention to use online e-ticketing systems is positively affected by “Facilitating conditions” (Sig. = 0.001<0.05, t-value=3.551>2.0) thereby supporting hypothesis four. This indicates that Thai airline passenger’s intentions to use an online e-ticketing system will increase if they easily have access to internet connection or computers equipments. This is compatible with previous research by Manzari and Todd-Taylor.

� Perceived Security Hypothesis(accepted)

Intention to use online e-ticketing systems is found to be positively and strongly affected by “perceived security” (Sig. =0.0, t-value=6.282>2) thereby supporting hypothesis eight. This indicates that Thai airline passenger’s intention to use online e-ticketing systems will increase if they perceive using the service to purchase tickets will not be associated with risk. This finding contradicts former research done by Manzari and Todd-Taylor.

Finally, we can sort up the latest significant factors (or variables) in Table X as the following:

TABLE X. SIGNIFICANT FACTORS (SORTED UP)

As you consider, ‘Intention To Use E-ticketing’ systems is being affected very strongly by ‘Perceived Security’ which indicates how important the security issues could be for Thai passengers and users. The next important factors are ‘Perceived Enjoyment’ and ‘Price Saving’.

TABLE XI. REJECTING THE NON-SIGNIFICANT FACTOR

As you see in Table XI, “Intention” to use online e-ticketing

systems is found not to be significantly affected by “performance expectancy” (Sig=0.986>0.05 and t-value=0.018<2.0), thereby rejecting hypothesis one.

TABLE XII. REJECT OR ACCEPTANCE OF HYPOTHESES

Figure 13. FINAL CONCEPTUAL FRAMEWORK

Based on the ridge regression analysis, the most significant factors/variables -affecting the Thai passenger’s intention to use electronic ticketing systems- can be formulated in a Linear Regression model (function) as follows:

Y (Intention-to-Use) = -18.725 + .336 Perceived_Enjoy_X2 + .327 Effort_Expect_X3 + .360 Price_Saving_X5 + .239 Faciliat_Conditions_X6 + .426 Perceived_Security_X8

Where: Perceived_Enjoy_X2 = Perceived Enjoyment; Effort_Expect_X3 = Effort Expectancy; Price_Saving_X5 = Price Saving; Faciliat_Conditions_X6 = Facilitating Conditions; Perceived_Security_X8 = Perceived Security

185

The coefficients provided in the above linear regression model are interpreted as following:

A) For each additional score increase in “Perceived Enjoy”, Intention -to use e-ticketing systems by Thai passengers- increases by 33.6%.

B) For each additional score increase in “Effort Expectancy”, Intention -to use e-ticketing systems by Thai passengers- increases by 32.7%.

C) For each unit added to “Price Saving”, Intention -to use e-ticketing systems by Thai passengers- increases by 36%.

D) For each unit added to “Facilitating Conditions”, Intention -to use e-ticketing systems by Thai passengers- increases by 23.9%.

186

E) Each incremental score increase in “Perceived Security”, leads to 42.6% increase in Thai passengers’ Intention to use e-ticketing systems.

Moreover, R-Squared is most often used in linear regression. Given a set of data points, linear regression gives a formula for the line most closely matching those points. It also gives an R-Squared value to say how well the resulting line matches the original data points. In fact, R-Squared is a statistical term saying how good one term is at predicting another. If R-Squared is 1.0 then given the value of one term, you can perfectly predict the value of another term. If R-Squared is 0.0, then knowing one term doesn't not help you know the other term at all. More generally, a higher value of R-Squared means that you can better predict one term from another.

TABLE XIII. R-SQUARE IN LINEAR REGRESSION MODEL

R square= 0.716 (71.6% of final linear regression model is good at predicting) Adjusted R square= 0.695 (69.5%)

V. CONCLUSION AND FUTURE WORK

This study validates three constructs out of the four constructs proposed by Venkatesh (2003) model of user acceptance of information technology (UTAT) in a different context, intention to use online e-ticketing systems. Perceived Security was replaced by perceived risk which is defined as the degree of which an individual believes that the system would be free of harm, damage or insecure issues. “Perceived Security”, “Perceived Enjoyment”, “Price Saving”, “Effort

Expectancy” and “Facilitating Conditions” -in sequence- had the most significant effects on Thai airlines passengers’ intention to use online e-ticketing systems, where “Performance Expectancy” did Not have any significant affect on their intention. After collecting the data; “Time Saving” and “Social Influence” hypothesis (H3 & H5) were Not been supported by Inter-Item Correlation Matrix’ results and were eliminated from the initial conceptual framework model. Compatible with previous Todd and Taylor and Manzari research; “perceived enjoyment” and “price saving” were proven to have significant and positive effects on intention of passengers to use e-ticketing systems. The model finally explains 71.6% percent of the variance.

The study only focuses on experienced users (passengers) with having a minimum knowledge in computer and internet, whereas future research can be conducted on inexperienced users with having no background in using any e-ticketing system yet. Additionally, UTAT and TCA can be replaced by other Technology Acceptance or Technology Adoption related models and theories.

REFERENCES [1] Venkatesh, V and Davis, F. (2000), “A theoretical extension of the technology acceptance model” page 186-204. (siam university central library) [2] Venkatesh, V. , et al. (2003), “user acceptance of information technology: toward a unified view”, 27 (3), page 425-78. (siam university central library) [3] William G. Zikmund. (2003), “Business Research Methods”, page 472-534 [4] http://www.amadeus.com/amadeus/x133405.html [5] http://www.asiatraveltips.com/news06/57-ThaiAirways.shtml [6] http://www.thaiairways.com.au/vwm/upload/fabpics/Agt_Adv_1508_ THAI%27s_E-ticket_policy_-update_iv_08.pdf [7] http://www.airasia.com/site/in/en/page.jsp?reference=xpressboarding [8] http://www.thaipr.net/nc/readnews.aspx?newsid=465BA697E3DFEB9 120D33A7A67A3527E [9]http://www.hanoihoteltravel.com/?A=X&C=58&N=909&T=0&2N=12&L=Home.hnt [10] www.thailandqa.com/forum/showthread.php [11] http://en.wikipedia.org/wiki/Air_Asia#Thai_AirAsia [12] http://www.itu.int/ITU-D/ict/papers/ecom/18Marweb.pdf