factors affecting on iranian passengers' acceptance ...

ISBN: 978-972-8924-35-5 © 2007 IADIS

FACTORS AFFECTING ON IRANIAN PASSENGERS' ACCEPTANCE TOWARDS ELECTRONIC TICKETING PROVIDED BY AIRLINES Shima Dehbashi, Msc in marketing and E-commerce, Joint program between Luleå University of Technology and Tarbiat Modares University

Nasim Nahavandi, Assistant Prof. of Industrial Engineering Department, Tarbiat Modares University

ABSTRACT

Using internet as a new channel for providing different services is growing rapidly in Iran, but it’s clear that focusing only on technology can’t lead the business to be successful. The key point of success business is still focusing on the customers and using new opportunities of rapid technological changes. Since the anxiety of customers' adoption / rejection of offering old services based on new technology is considerable, understanding what factors are important for customers to adopt these new services is the challenge for researchers and service providers. The objective of this research is to gain better understanding of the factors affecting on e-ticketing adoption of Iranian customers. Since the adoption of e-ticketing provided by airlines can be explained with an intention based model, we developed the model based on Theory of Planned Behavior to explain the effect of different factors on e-ticketing intention. Data collected from 133 passengers indicates that Attitude, Subjective norms and perceived usefulness significantly affect on Iranian customers’ intention to purchase e-ticket and Perceived risk has negative significant effect on intention to use e-ticketing. Also the result provides support for the effect of Trust, Perceived risk and perceived usefulness on attitude toward using e-ticketing. In this study we worked not only on evaluating a model and the relationship between constructs, but also we tried to find specific factors that drive people to use e-ticketing. KEYWORDS Adoption, Theory of Planned behavior, e-ticketing, airlines, Trust, perceived usefulness

1. INTRODUCTION A recent reports show that in 20th century, the basis of economic successfulness is introduction of self service which affected all area of our lives. (Butler Group www.iwks.com) Technology-based-self-service as a part of e-service now play an important role in changing the way that firms, manufacturers and customers interact.In fact traditional e-commerce is replacing with new paradigm known as e-service (Rust and Lemon Spring 2001) and many assumptions about using online environment for doing business has been changed. Using e-service in airline industry –known as e-ticketing (using internet) or self check-in (using ATM) has changed the airline industry and behavior of consumers with reducing costs and providing new channel for communication and support. E-ticketing is one of the most rapidly growth services which has been provided in internet (Pew 2002). Online airline ticket sales reached approximately $14.2 billion in 2002 (Foss, 2003). The researches in different countries shows that people use internet to search travel but they are not so quick to make online travel purchase (TNS Survey,2005). On the other hand, airline industry is facing with the IATA deadline about 100% e-ticket at the end of 2007(IATA 2006). It means that not only airlines must be ready for this new concept but also customers have to accept this new technology with all its advantage and

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disadvantages. When airlines shift from paper ticket to e-ticket, customers’ acceptance will become important for both sides. Airlines try to offer services through electronic infrastructures, especially through the web, to decrease their costs, expand revenue, creating reliable database of customers for future customer relationship management plans. A critical understanding of expected behavior cannot be achieved without a good appreciation of the factors affecting the purchase decision. If decision makers of service providers realize how consumers make decisions to adopt or reject their provided services, they can set suitable strategies to persuade current and potential customers of traditional services to accept new service. Similarly, web site designers and web site developers can find important points about customers’ behavior that help them to design website interface and functionality in a way that not only be easy to use, but also meet all customers’ need and increase sales. The objective of this study is to find the factors affecting on customers’ adoption of e-ticketing. To achieve this objective we need to test the validity of proposed model. The result can provide good understanding about customers’ priorities on intention to use e-ticketing.

2. THEORETICAL BACKGROUND AND RESEARCH MODEL There are different behavioral theories that the adoption can be explained by using them such as the theory of reasoned action (TRA) proposed by Fishbein and Ajzen (Fishbein and Ajzen 1975), Triandis model (Triandis 1980), the theory of planned behavior (TPB) proposed by Ajzen (Ajzen 1991) and Information system theories such Technology acceptance model (TAM) (Davis 1989). TRA suggests that a person’s behavior is determined by his/her intention to perform the behavior and this intention is a function of his/her attitude (the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior (Ajzen1991)) toward the behavior and the effect of subjective norms on his/her decision (i.e., social influence). Limitations of TRA on predicting behavior where individuals do not have complete control over their behavior become the main reason for proposing TPB the model which suggested that although subjective norms and attitude affect on intention to perform behavior, perceived behavioral controls (individual’s perceptions of her/his ability to perform the behavior) can be considered as a new concept that affecting both attitude and intention. Davis (Davis 1989) with TAM asserted that perceived usefulness and ease of use represent the beliefs that lead to adoption of new ideas. In this model intention determined by attitude and perceived usefulness. Attitude determined by perceived ease of use and perceived usefulness. A limitation of TAM was that it assumes usage is volitional and there are no barriers that would prevent an individual from using an IS if he or she decides to do so. Although there are many factors preventing an individual from using an application (IS application) such as perceived user resources (Kieran Mathieson, Eileen Peacock et al. 2001) and perceived behavior control. Triandis' model is similar to the TPB in modeling intentions and facilitating conditions as direct antecedents of behavior. In addition, Triandis’ model suggests that behavior is affected by habits and intention and facilitating conditions. Although Triandis’ model is more comprehensive than the TPB, several of its constructs are difficult to operation (Limayem et, al. 2003).We developed our research model based on TPB and it helps us to study mainly individual’s characteristics and behavior and their effect on intention to use new ideas. In fact, by using TPB we tried to look at the problem from customers’ point of view. We also augment TPB by adding four new constructs: Trust on organization, Trust on website, Perceived risk and perceived usefulness. Intention is defined as a person’s motivation in the senses of his or her conscious plan to exert effort to carry out a behavior (Fishbein and Ajzen 1975) . As mentioned already, intention could be a function of different factors such as attitude, subjective norms and perceived behavioral control. To improve the prediction power of our intention-based research model we proposed 11 hypotheses Figure1. In this study, attitude represents the degree to which a passenger has a favorable or unfavorable evaluation of purchasing e-ticket, for this context we propose: H1: Attitude toward using e-ticket will increase intention of purchasing ticket online Subjective norm is the second TPB construct and in this study is defined as perceived social pressure that influences on passengers’ decision to purchase e-ticket, for this context we propose: H2: Subjective norms have positive impact on passengers’ intention to use e-ticket. In this study, perceived behavioral control is the ability of using internet and facilitating conditions in order to buy ticket online.

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ISBN: 978-972-8924-35-5 © 2007 IADIS

H3:

There is a positive link between Perceived behavioral control and intention to use e-ticket.

TrustW = Trust on Website, TrustB : Trust on Brand ,PBC : Perceived behavioral Control, ATT: Attitude INT: Intention,SN : Subjective Norm ; PR : Perceived Risk, PC: Perceived usefulness

Figure 1. Research Model

Trust is necessary part of a relationship and since the objective of e-service is creating long term relationship with customers, we included trust as a construct of our research model .Trust, defined as the willingness to rely on an exchanging partner in whom one has confidence (Moorman, 1993) and considered as one of the most important variables in the relations between human and computer (Fogg et al 1999). The importance of trust has been especially emphasized in the context of electronic commerce (Handy 1995), and is basically seen as a common way for reducing social complexity and perceived risk of transaction through increasing the expectation of a positive outcome and perceived certainty regarding the expected behavior of trust (Gefen 2004). Researches show that trust on e-vendors can be viewed as an important factor that directly affect on customers’ attitude toward using e-vendors services. When the customers find the evendors trustworthy they will be more interested in adoption of using e-vendors’ services. It possibly decreases the risk of adoption (McKnight and Chervany 2002). The level of perceived risk decreases when the individual trusts others that are involved in the transaction (Featherman & Pavlou, 2003; Gefen et al., 2002). Because trust is an abstract concept, it is difficult to define trust and to identify the elements that construct it (Wang & Emurian, 2005). (Warkentin et al. 2002) found that trust in the organization using the technology and trust in government (in case of e-government) as responsible for the electronic services are important factor of the adoption of the service. In this study we tried to show the affect of trust on both attitude and intention. (Gefen et al., 2003) proposed model for online shopping and found the relationship between trust and intention to purchase online. We will have two types of trust, trust to the e-service provider and trust to the organization that e-service will be offered under its name. In this study, Trust to the organization is the passengers’ willingness to rely on a trust to the brand name or organization image, and Trust to the e-service provider website is the passengers’ willingness to rely on a trust to the website who offers e-service. For this context we propose: H4: Trust to the organization will increase customers’ willingness in order to trust website H5: Trust to the website will increase attitude toward buying ticket online. H6: Trust to the website will increase intention to buying ticket online. Despite many researches worked on constructs which have positive effect on adoption of e-service, we decided to add perceived risk to the model in order to study the negative factors that can reduce e-ticketing adoption. Perceived risk (PR) is commonly defined as uncertainties of possible negative consequences of

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using a product or service. In this study, Perceived risk is the uncertainties regarding possible negative consequences of using e-ticketing. These uncertainties consist of risk of e-payment, website malfunctions, privacy and policy. For this context we proposed two hypotheses about direct effect of perceived risk on intention and direct effect of Perceived Risk on attitude and the same time we tried to find the effect of perceived risk on trust on e-ticketing provider website: H7: Perceived Risk has negative impact on intention to purchase ticket online. H8: Perceived Risk has negative impact on trust to the e-ticket provider website. H9: Perceived Risk decreases attitude toward purchasing e-ticket. Based on TAM model, potential utilities of a service can be explained as perceived usefulness (PU). We decided to add this new construct to the model because we are interested in finding positive factors that motivate people to use online ticketing. TAM modeled perceived usefulness as a direct antecedent of attitude. In this study, Perceived usefulness is the positive outcomes for passengers’ when they purchase e-ticket and not only can indirectly affect on intention, but also perceived usefulness can influence passengers intention to use e-ticketing service. For this context we propose: H10: Perceived usefulness have positive impact on intention to purchase ticket online. H11: Perceived usefulness have positive impact on attitude toward purchasing ticket online.

2.1 Survey Working on e-service as a new technology in Iran and new technology for Iranians is useful when it’s limited to a single technology and single industry. The numbers of provided e-services in Iran are so limited; therefore the scope of the field study is limited to the number of technologies such as ATMs or e-services which have been provided through the web. On the other hand, where the customer interacts with the eservice is the most interesting step and also suitable opportunity for direct research at service site (Interviewing with customers who use ATM) or at customers’ site (sending questionnaire to the customers who use technology from home/work to perform service) (Wang et al,2004). In this research we selected customers who are using services at Customers’ site. To do so, we distributed questionnaire in airport and received the responses. The Summery of data gathering has been presented in Table1. And table 2 describes demographic profiles of respondents. Table 1. Overview

Total 164

Received 144

Completed 104

Acceptable 133

Response rate 81.1

2.2 Measures The measures of this research have been adopted from previous studies to ensure the reliability. For finding measures we used previous studies attitude and intention (Limayem et, al. 2003) perceived risk (Featherman, M. S. and P. A. Pavlou 2003), Perceived usefulness (Davis 1989, McIvor et al, 2003), Trus (Egger 1999, Lee etal 2000), subjective norm and ,Perceived behavioral Control (Limayem et, al. 2003) which has been divided into 4 formative measures for "facilitating conditions” and one formative measure for "self-efficacy". Self-efficacy is evaluating with the ability of an individual to navigate on the Web and facilitating conditions were measured with 5 other formative measures. We should mention that 5 constructs of model have formative items, because we want to evaluate the model and the same time we wanted to find specific factors that form important constructs for the customers.

3. DATA ANALYZING Due to the complexity of research model, the Structural Equation Modeling (SEM) approach was used to evaluate the model. Recently many researchers (Gefen et al 2000) were interested in using PLS. In addition, Although PLS can be used for theory confirmation; it can also be used to suggest where relationships might or might not exist and to suggest propositions for later testing (Chin 1998). In this survey we used a two-step

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ISBN: 978-972-8924-35-5 © 2007 IADIS

model testing approach to ensure that the constructs are meaningful and suitable for estimating study. The PLS measurement(outer) model for reflective measure is evaluated by examining the convergent and discriminate validity of the indicators, and the composite reliability of a block of indicators. On the other hand, the formative measures are evaluated on their substantive content, by comparing the relative size of their estimated weights, and by examining the statistical significance of their measure weight (Chin 1998). The convergent validity of the measurement model of reflective measure is assed by the examining the correlation between the component/item measure scores and their construct scores in PLS (Chin 1998). Acceptable loading is 0.707 for reflective construct. Moreover Chin (1998) suggested the internal composite reliability(ICR) score as another indicator to estimate the reliability of the reflective measurement. Average variance extracted (AVE) is another item to check reliability of a reflective measure and is the amount of variance captured by the indicators of a construct versus the amount of variance caused by the measurement error(Yao 2004) and must be higher than 0.5 for all reflective measures. For discriminate validity items should have a higher correlation with the construct that they are supposed to measure than with any other constructs in the model (Chin 1998) and the square root of AVE of each construct should be larger than the correlation of the two constructs (Staples, et al., 1999) (Chin 1998) .The structural(inner) model is evaluated by R-square for the dependent latent construct. The stability of the estimates is examined by using the tstatistics obtained from the bootstrap resampling procedure (Chin 1998) with 300 resampling. After passing all steps of validity and reliability of instrument (formative and reflective measures) results show that the instrument is reliable and all reflective items have acceptable loading. Similarly AVE and ICR of constructs with reflective measures meet the needs of reliable and valid construct. Since the loadings and cross-loadings cannot be used to assess the reliability and validity of the formative indicators, these majors are evaluated on the basis of their substantive content, and by comparing relative size and statistical significance of their estimated weights (Chin 1998). The results are presented in Table2. All formative measures, significant and non-significant, will remain in model for estimating model. Table 2. Measure model - result formative measures PBCAcce PBCDesc PBCHelp PBCNav PBCSpee PUAvail PUCost PUCust PUPack PUReal PUSave PUUp PRHack PRInf PRLose PRPlan PRServ PRTime SNFamil SNMass SNRep TBrand TCert TCrm TForum TInfor TLocal TPriva TTech

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Weight T Statistics 0.019604 0.080 0.489599 1.574 0.078216 0.345 -0.025859 0.119 0.733788 2.855 0.322217 1.766 0.456483 2.760 0.168426 1.112 -0.061979 -0.491 0.193731 1.151 0.406537 2.220 0.054381 0.420 -0.118279 -0.374 -0.197654 -0.740 0.887755 2.813 0.343630 1.695 -0.027327 -0.135 0.408610 1.946 0.544548 2.552 0.293625 1.313 0.468452 1.876 0.593386 2.479 0.613286 2.848 0.137116 0.813 0.082946 0.558 0.042126 0.253 0.564722 2.773 0.410459 1.806 0.327637 1.804 NS: Not significant, P-values : *