An empirical examination of the acceptance ...

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Tourism Management 29 (2008) 500–513 www.elsevier.com/locate/tourman

An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model Tae Goo Kima, Jae Hyoung Leeb,, Rob Lawc a

BK21 Institute for HRD in Tourism Research for Jeju International Free City, Cheju National University, Jeju City, Jeju Special Self-Governing Province, Republic of Korea b Department of Tourism Management, Sangji University, Woosan-dong, Wonju City, Kangwon Province, Republic of Korea c Department of Hotel and Tourism Management, Hong Kong Polytechnic University, Hung Hom, Kowloon, China Received 5 June 2006; received in revised form 8 March 2007; accepted 22 May 2007

Abstract Information technology (IT) is an important strategic asset for hospitality organisations to improve organisational performance and strategic competitiveness. This paper makes an attempt to investigate the relationship between antecedents including information system quality, perceived value, and users’ acceptance of hotel front office systems (HFOSs) by adopting an extended technology acceptance model (TAM). Perceived ease of use, perceived usefulness, attitude towards use, and actual use were investigated including information system quality and perceived value. Empirical findings indicate that the significance of all but two new variables. As a result, the study is able to find the acceptance of HFOSs from the perspective of hotel frontline employees through the external variables of information system quality and perceived value in order to enhance the model. Additionally, the paper presents a progressive theory and a practical contribution to increase the acceptance in order to provide useful suggestions for hotel managers and hotel information system (HIS) practitioners. r 2007 Elsevier Ltd. All rights reserved. Keywords: Hotel front office system (HFOS); Technology acceptance model (TAM); Information technology acceptance; External variables; Information system quality; Perceived value

1. Introduction Organisations have gradually increased their investment in information technology (IT) for planning in order to increase the efficiency of their business processes, support management decision-making, and improve productivity. Thus, IT has become a strategic tool for attaining competitive advantages in organisations. Accordingly, the hotel industry also extensively relies on IT to improve employees’ productivity and efficiency, as well as to improve customer satisfaction, since IT has been perceived to have notable advantages in competition (Ham, Kim, & Jeong, 2005; Lam, Cho, & Qu, 2007).

Corresponding author. Tel.: +8233 738 7538; fax: +8233 730 0128.

E-mail addresses: [email protected] (T.G. Kim), [email protected] (J.H. Lee), [email protected] (R. Law). 0261-5177/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.tourman.2007.05.016

For this reason, the study related to technology adoption and diffusion has been largely performed by researchers and practitioners in the hospitality industry (Siguaw, Enz, & Namasivayam, 2000). Also, many studies have found that there is a positive relationship between IT investment and organisation productivity and performance (Byrd & Turner, 2001; Powell & Dent-Micallef, 1997; Rai, Patnayakuni, & Patnayakuni, 1997). However, researchers have stated that even though there are positive effects and benefits, new IT would not be fully accepted if barriers of external factors influenced the acceptance of IT (Davis, 1989; Davis, Bagozzi, & Warshaw, 1992). Based on the theory of reasoned action (TRA), the proposed technology acceptance model (TAM) can explain the process of the acceptance of IT on an individual level (Davis, 1989). Therefore in this study, Davis’s (1989) TAM is utilised as a theoretical background as it is regarded as one of the most influential research models in explaining the users’ IT usage

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or acceptance behaviour in various contexts (Bruner & Kumar, 2005; Chen, Gillenson, & Sherrell, 2002; Davis, Bagozzi, & Warshaw, 1989; Hong, Thong, Wong, & Tam, 2002; Lee, Kim, & Lee, 2006). One thing to note is that the studies of the extended TAM have focused on the external variables’ effects on perceived usefulness and perceived ease of use, and the antecedents of these two beliefs have been chosen for investigation (Agarwal & Prasad, 1999; AmoakoGyampah & Salam, 2004; Bruner & Kumar, 2005; Jackson, Chow, & Leitch, 1997; Lee, Kim, Rhee, & Trimi, 2006; Shang, Chen, & Shen, 2005; Venkatesh, 2000; Venkatesh & Brown, 2001; Venkatesh & Davis, 2000). Prior studies on the extended TAM have been done after Davis’s (1989) study and included antecedents, mediating, and moderating variables to explain IT acceptance behaviour (Chen & Tan, 2004; Davis et al., 1992; Paul, John, & Pierre, 2003; Shin, 2004). Moreover, technology adoption in hotel organisations is a complex process, and it has demonstrated unique characteristics, thus calling for distinctive approaches in examining technology adoption behaviour such as organisation technology climate and technology characteristics (Wang & Qualls, 2007). Law and Jogaratnam (2005) stated that hotels have widely adopted technologies to improve operational efficiency, enhance service quality, and lessen costs. However, despite the increasing use of technology in the hospitality industry, few studies have been conducted to investigate the relationship between the external variables and the TAM framework to explain the acceptance behaviour of technology in tourism and hospitality organisations (e.g., Lam et al., 2007; Lee et al., 2006; Wo¨ber & Gretzel, 2000). Information system quality is represented by three dimensions of information, system, and service quality (Eldon, 1997; Pitt, Watson, & Kavan, 1995). Also, hotel employees must use HFOSs regardless of their personal desires. It is important to determine how relevant a HFOS is to perceived value because it is not a voluntary environment (e.g., Internet banking and online shopping mall) but a mandatory environment. Thus, the more benefits (e.g., increasing efficiency, improving productivity, and reducing completion time for a task) that can be foreseen from the use of HFOSs, the more likely hotels are to adopt the technology. Accordingly, the perceived value is applied as an external variable. Hotel information system (HIS) is the most typical IT tool in hotel organisations. HIS is divided into four categories of front office system, back office system, restaurant and banquet management system, and guestrelated interface (Ham et al., 2005). Among these, the HFOS is the most important system, which operates 24 h a day, 365 days a year. This system is used by service employees at the point of contact with the customer. Accordingly, this study has selected the employees who use the HFOSs as the users of IT. The purpose of this study is to investigate the relationship between antecedents and users’ acceptance of a HFOS

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through the TAM framework, which is based on the survey of hotel frontline employees. Utilising the result, this study can demonstrate the acceptance of HFOSs from the perspective of hotel frontline employees through the information system quality and the perceived value as the external variables. Specifically, the objectives of this study are: (1) to investigate how information system quality can lead to the formation of perceived ease of use and perceived usefulness of HFOSs; (2) to explore the impact of perceived value on attitude towards use of HFOSs; (3) to assess the impact of perceived ease of use on perceived usefulness and attitude towards use of HFOSs; (4) to examine the impact of perceived usefulness on attitude towards use and on actual use of HFOSs and (5) to explore the impact of attitude towards use on actual use of HFOSs. This study would contribute to the theoretical development of behaviour formation regarding HFOS acceptance in the hotel industry. Results of the study can also provide practical implications for hotel managers and HIS practitioners to plan strategically and implement effective tools to motivate frontline employees towards actual use and acceptance of HFOSs. 2. Literature review 2.1. TAM and related theories New IT is rapidly replacing old applications by providing more powerful tools and faster speeds for users. This adoption can be successful, however, only when employees accept and effectively use technology. Therefore, an organisation should understand that the acceptance process is essential in making the process effective. At present, this is particularly true for object-oriented technology that endeavours to maximise its effectiveness (Lee et al., 2006). There are four leading theories that explain the acceptance process (Venkatesh & Brown, 2001). These theories are: (1) the TRA; (2) the theory of planned behaviour (TPB); (3) the innovation diffusion theory (IDT) and (4) the TAM. The main concept of TRA is that a person’s actions are determined by behavioural intention, and behavioural intention is influenced by attitude and the subjective norm. While on one side, attitude is primarily affected by the factors of belief and evaluation, it is shown that the subjective norm is influenced by norm belief and the motivation to comply (Fishbein & Ajzen, 1975). The TPB, as an extended model of TRA, which includes the primary factor of perceived behavioural control, states that an individual’s actions are influenced by both interior and exterior control factors (Ajzen, 1991). The TAM has been widely applied to studies of technology acceptance and usage behaviour (Bruner & Kumar, 2005; Davis et al., 1989; Hong et al., 2002; Lee et al., 2006). TAM adapts Fishbein and Ajzen’s (1975) TRA as a basis for specifying the casual linkages flow in

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a sequence from beliefs, attitudes, and intentions to behaviours. The TAM, which was introduced by Davis (1989), modified TRA to predict computer adoption by replacing the belief determinants of TRA with two key beliefs (i.e., perceived ease of use and perceived usefulness). Davis (1989, p. 320) defined perceived ease of use as ‘‘the degree to which a person believes that use of a particular system would be free of effort’’; in contrast, perceived usefulness is ‘‘the degree to which a person believes that use of a particular system would enhance his or her job performance’’. In a TAM, technology acceptance or use is determined by behavioural intention. Behavioural intention, in turn, is affected by attitude towards use, as well as the direct and indirect effects of perceived ease of use and perceived usefulness. Both perceived ease of use and perceived usefulness jointly affect attitude towards use, whilst perceived ease of use has a direct impact on perceived usefulness. 2.2. Extended TAM and external variables In the TAM, through perceived ease of use and perceived usefulness, external variables such as personal features (e.g., computer self-efficacy, innovativeness, and past adoption behaviour), system features (e.g., design and functionality), and organisational features (e.g., top management support and training) can affect attitude and behaviour. Therefore, Davis et al. (1989) proposed that the external variables of a TAM can affect the beliefs of perceived ease of use and perceived usefulness. In this way, much research has verified the external variables of a TAM (e.g., Amoako-Gyampah & Salam, 2004; Burton-Jones & Hubona, 2006; Hong et al., 2002; Hu, Clark, & Ma, 2003; Shang et al., 2005; Venkatesh & Davis, 2000). To predict technology acceptance, these studies found external variables such as personal features, system features, and organisational features were the determining factors. Since the acceptance of a HFOS must be approached from the standpoint of mandatory acceptance, the perceived value of a system is judged to play an important role in the positive attitude towards usage. In this study, information system quality and perceived value as external variables of TAM are examined. The following subsections provide further discussion on these two external variables. 2.2.1. Information system quality In relation to the key factors of success in an information system, the quality of an information system must be examined in three dimensions of information, system, and service quality (Eldon, 1997; Pitt et al., 1995). When information quality is measured, the standard should be adjusted towards currency, accuracy, relevancy, and efficiency of the yielded product of the information system. Because information quality is very much subjective from the user’s perspective, it is also factored in as part of user satisfaction (Bailey & Pearson, 1983). A system’s quality

signifies the operational efficiency in the function of an information system. In prior studies, the measure of system quality was often reflected by a system’s technical characteristics, including solubility, dependability, response, and other related factors (Bailey & Pearson, 1983; DeLone & McLean, 1992). Moreover, due to the correlation between system quality and the presence of potential errors in the system, the consistency of user interaction, response rate, documentation, program code quality, and maintenance were all included (Seddon, 1997). Also, since service quality can signify the overall quality of information system service, it is analogous to the departmental staff of the information system (Baroudi & Orlikowski, 1988). Following the studies conducted by Palmer (2002) as well as Ranganathan and Ganapathy (2002), Ahn, Ryu, and Han (2004) developed an instrument for measuring the quality of online properties. Similar to the claim made by Pitt et al. (1995), they utilised SERVQUAL in service quality as the component factor, and they subsequently emphasised the importance of SERVQUAL as the foundation for service quality. Such a statement was made because from the aspect of websites, there can be no faceto-face personal contact. In an Internet shopping mall that offers information about placing orders and delivery, higher quality of information is needed which describes the applications of service quality. In the original research, however, users of a HFOS were the employees who were associated with HFOS uses. As such, the degree of applications, training, attitude, supply of IT, and problem solving by the electronic data processing (EDP) department for HFOS users is extremely important. 2.2.2. Perceived value Depending on the perceptions of what consumers give and receive, the overall assessment of a product’s use shows that there is a trade-off between giving and receiving (Lee, Yoon, & Lee, 2007; McDougall & Levesque, 2000; Sanchez, Callarisa, Rodriguez, & Moliner, 2006). If the cost of service is too expensive, no matter how high the quality is, it will be considered as not worth the cost. In this way, users would consider the gain and loss. Furthermore, the opinion of users is of great importance. In studies involving marketing, either the consumer products or the reasons for purchase are also important factors, and these have been found to be crucial in the perceived value of consumers (Cronin, Brady, & Hult, 2000; Dodds, Monroe, & Grewal, 1991; McDougall & Levesque, 2000; Petrick & Backman, 2002). In the literature of marketing and quality, the concepts of perceived value are considered proportional to cost (e.g., price, time, and distance) and profit (e.g., quality) (Gallarza & Saura, 2006; Sweeney, Soutar, & Johnson, 1996; Woodruff, 1997; Woodruff & Gardial, 1996). So far, no study has been performed on the model for assessment of user satisfaction in an information system where the concept of cost to users is defined specifically or


classified by assessment. However, the concepts of net benefits, usefulness, and user satisfaction have been evaluated. From the users’ point of view, perceived value is a net benefit (Seddon, 1997). The net benefit is a comprehensive measure of the expected profits of the past and the future taking out the expected expense, which coincides with the definition found in marketing and quality based literature, and somewhat concurs with personal effectiveness as a factor for a successful information system (Eldon, 1997; Seddon, 1997). Net benefit is the practical application in the use of IT. Establishing an information system, studying usage, system usage, and all other useful resources including time can be considered as expenses. To measure net benefit, it must be decided whether it is worthwhile to the users. 3. Hypotheses 3.1. Information system quality, perceived ease of use, and perceived usefulness According to DeLone and McLean (1992), Rai, Lang, and Weiker (2002), as well as Teo and Choo (2001), information quality plays a dominant role in the success of an information system. Agarwal and Prasad (1999), Igbaria, Guimaraes, and Davis (1995), Seddon (1997), and Venkatesh and Davis (1996) showed that information quality had an effect on perceived ease of use and perceived usefulness. Also, system quality is important in user beliefs (Hong et al., 2002; Lederer, Maupin, Senza, & Zhuang, 2000; Ruth, 2000). Likewise, in the study conducted by Ruth (2000) on the effect of system quality on Internet shopping and perceived ease of use, results showed that system quality had no direct effect on Internet shopping behaviour, but perceived ease of use and perceived usefulness were relatively strong and positive, and they are expected to have a constant and direct effect. Ahn et al. (2004) classified the quality of online properties for an Internet shopping mall into system quality, information, system, and service quality. The actual use of the Internet shopping mall was analysed by a TAM. It was found that quality affects perceived ease of use and perceived usefulness. Thus, the following hypotheses are proposed:

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3.2. Perceived ease of use, perceived usefulness, and attitude towards use Most studies on technology acceptance showed that perceived ease of use directly influenced perceived usefulness and attitude towards use (e.g., Ahn et al., 2004; Bruner & Kumar, 2005; Chen et al., 2002; Davis et al., 1989). In particular, Davis (1989) stated that through perceived usefulness, perceived ease of use indirectly influences attitude towards use and acceptance intention, which in turn clearly shows that perceived ease of use is the antecedent of perceived usefulness. Also, perceived ease of use does not directly influence acceptance or actual using, but the model assumes that through the medium of technology acceptance behaviour, there is in an indirect relationship (Davis, 1989). Thus, the following hypotheses are proposed: Hypothesis 3: Perceived ease of use positively affects perceived usefulness. Hypothesis 4: Perceived ease of use positively affects attitude towards use. 3.3. Perceived usefulness, attitude towards use, and actual use Previous studies on technology acceptance pertaining to various fields showed perceived usefulness has a positive effect on attitude towards expected use and actual use (e.g., Adams, Nelson, & Todd, 1992; Agarwal & Prasad, 1999, 1997; Davis, 1989; Shin, 2004). In the study conducted by Lederer et al. (2000) on web usage, it was found that perceived ease of use and perceived usefulness of web use have positive influences on attitude towards using the web. Also, in Jeong and Lambert’s (2001) study, perceived usefulness is shown to have an effect on actual use of information. Thus, the following hypotheses are proposed: Hypothesis 5: Perceived usefulness positively affects attitude towards use. Hypothesis 6: Perceived usefulness positively affects actual use. 3.4. Perceived value and attitude towards use

Hypothesis 1a: Information quality positively affects perceived ease of use. Hypothesis 1b: System quality positively affects perceived ease of use. Hypothesis 1c: Service quality positively affects perceived ease of use. Hypothesis 2a: Information quality positively affects perceived usefulness. Hypothesis 2b: System quality positively affects perceived usefulness. Hypothesis 2c: Service quality positively affects perceived usefulness.

Most research on an individual’s perceived value has been done in marketing research. Marketing studies show that if a consumer perceives benefit or value in the quality of the product or service, satisfaction in that product or service will increase (Cronin et al., 2000; McDougall & Levesque, 2000; Parasuraman & Grewal, 2000; Sanchez et al., 2006). This, in turn, increases purchase intention and ultimately results in the purchase of that product or service (Dodds et al., 1991). If a consumer perceives value in the purchased product or service, the perceived value is believed to be the underlying variable that gives rise to



behaviour (Flint, Woodruff, & Gardial, 1997; Woodruff & Gardial, 1996). When similar marketing studies have investigated and applied these ideas, it was found that in the preceding factor of user satisfaction found in management information system (MIS) studies, a higher perceived value and a higher satisfaction level often related to a more positive attitude towards technology use. In this sense, this study posits the following hypothesis: Hypothesis 7: Perceived value positively affects attitude towards use. 3.5. Attitude towards use and actual use Davis (1989) showed that attitude towards use had a direct effect on actual use. Further studies on TAM have also demonstrated strong empirical support for a positive relationship between attitude towards use and actual use (Adams et al., 1992; Davis et al., 1989; Mathieson, 1991). In addition, attitude towards the use of a specific system has a direct effect on the intention to use the specific system in the future (Davis et al., 1989), as well as the actual use of related systems (Bajaj & Nidumolu, 1998). Thus, the following hypothesis is proposed: Hypothesis 8: Attitude towards use positively affects actual use.

4. Methodology 4.1. Measurements In this study, responses to the items in information system quality dimensions (i.e., information, system, and service quality), perceived value, perceived ease of use, perceived usefulness, attitude towards use, and actual use were measured on a 5-point Likert scale from 1 ( ¼ ‘‘strongly disagree’’) to 5 ( ¼ ‘‘strongly agree’’). In addition, the fitness of the measurement variables for HFOSs was validated through interviews with employees who were at a managerial or higher position and hotel employees in the departments that used HFOSs. As shown in Table 1, the Information quality construct was measured by seven items (Ahn et al., 2004; Bailey & Pearson, 1983; Eldon, 1997). The System quality construct was composed of six items (Ahn et al., 2004; Bailey & Pearson, 1983; Eldon, 1997), and the Service quality construct comprised six items (Bailey & Pearson, 1983; Baroudi & Orlikowski, 1988; Eldon, 1997). Similarly, the Perceived value construct was measured by three items (Dodds et al., 1991), whereas the Perceived ease of use construct was measured by three items (Davis, 1989; Venkatesh & Davis, 2000), and the Perceived usefulness construct had four items (Davis, 1989; Venkatesh & Davis, 2000). Lastly, the Attitude towards use construct was

composed of five items (Davis et al., 1992), and the Actual use construct had only one item (Davis et al., 1992). 4.2. Sample and data collection The research sample consisted of employees from eight major deluxe hotels in Seoul, Korea. These hotels are wellknown international chain hotels, which included the Radisson Plaza Seoul, Millennium Hilton, Novotel Ambassador (Kangnam), Novotel Ambassador (Doksan), Sofitel Ambassador Seoul, Shilla Seoul, Ramada Renaissance Seoul, and J.W. Marriot Seoul. In addition, the research sample of hotel frontline employees that used front office systems comprised front desk, housekeeping, room reservations, sales and marketing, bell desk, guest relation officer (GRO), and executive floor lounge (EFL). A total of 320 questionnaires were distributed, and 253 (79.1%) questionnaires were received. Fourteen of these received questionnaires were discarded due to large portions of missing values. Finally, 239 (74.7%) questionnaires were analysed in this study. 5. Findings and discussions 5.1. Descriptive characteristics of the respondents Descriptive characteristics of the respondents are summarised in Table 2. Among the 239 respondents, 125 respondents (52.3%) were males, and 114 respondents (47.7%) were females. Approximately one-half of the respondents were in the age group 25–29, followed by the age groups: 30–34 (22.6%), 35–39 (9.6%), under 24 (7.1%), and 40 or above (3.8%). Educational levels were generally high. Respondents who had completed high school accounted for 0.8%, respondents who had completed college numbered 8.8%, while respondents who had graduated from university comprised 77.0% of the survey samples, with the remaining 32 respondents (13.4%) graduating from graduate school. Additionally, 55.7% of the respondents worked at the front desk, 30.5% of respondents worked in housekeeping, and the remaining 13.8% worked in other departments (e.g., room reservations, sales and marketing, bell desk, GRO, and EFL). 5.2. Dimensionality, convergent, and discriminant validity of the scales In this study, to evaluate the structural equation modelling (SEM), AMOS 4.0 (Arbuckle, 1999) was used. The measures were subject to confirmatory factor analysis (CFA) to provide support for issues of dimensionality, convergent, and discriminant validity (Anderson & Gerbing, 1988; Fornell & Larcker, 1981). In this study, first of all, to analyse the internal consistency of the constructs, the Cronbach’s alpha was calculated and its reliability was investigated. As reported in Table 3, reliability coefficients of all constructs (0.89 for information quality, 0.83 for


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Table 1 Construct measurement Variables

Measures

Supporting literatures

Information quality

HFOS HFOS HFOS HFOS HFOS HFOS HFOS

Bailey and Pearson (1983) Eldon (1997) Ahn et al. (2004)

System quality

HFOS reacts and responds quickly to the user’s entry The language and terminology of HFOS are easy to understand User can easily obtain necessary information from HFOS HFOS can be a guide to errors HFOS can be flexible and changeable against the user’s requirement and new working condition HFOS can exchange information easily with other systems

Bailey and Pearson (1983) Eldon (1997) Ahn et al. (2004)

Service quality

The information systems department allows use of HFOS to run smoothly The information systems department is always helpful when there is a problem with HFOS The information systems department is very cooperative User training on using HFOS has been well established The information systems department is skilled and knowledgeable about the technical aspects of computers The information systems department provides new and practical applications for the IT

Bailey and Pearson (1983) Baroudi and Orlikowski (1988) Eldon (1997)

Perceived value

Investment of time and effort in HFOS use can be profitable to my business There is benefit in using HFOS There is no loss in using HFOS

Dodds et al. (1991)

Perceived ease of use

It is easy to learn to use HFOS It is easy to become proficient in using HFOS

Davis (1989) Venkatesh and Davis (2000)

Perceived usefulness

offers information in a useful format offers clear information supplies accurate information supplies sufficient information offers up-to-date information offers relevant and necessary information offers information that satisfies my need

It is easy to remember how to use HFOS HFOS improves the outcome of work HFOS increases work productivity HFOS increases the effectiveness of work HFOS is valuable to work

Davis (1989) Venkatesh and Davis (2000)

Attitude towards use

Using HFOS is a good idea Using HFOS is advisable Using HFOS is a pleasant idea I enjoy using HFOS I am satisfied in using HFOS

Davis et al. (1992)

Actual use

I use HFOS so that my work is swift and efficient

Davis et al. (1992)

Note: HFOS ¼ Hotel front office system.

system quality, 0.85 for service quality, 0.70 for perceived value, 0.72 for perceived ease of use, 0.88 for perceived usefulness, and 0.86 for attitude towards use) exceeded the 0.7 cut-off value as recommended by Hair, Anderson, Taltam, and Black (1998) and Nunnally (1978). Therefore, all constructs in this study demonstrated acceptable reliability. As can be seen in Table 3, the results of CFA demonstrated an excellent fit with w2 ¼ 857.99, df ¼ 502, normed-w2 ¼ 1.71, goodness-of-fit index (GFI) ¼ 0.93, adjusted goodness-of-fit index (AGFI) ¼ 0.91, root-mean-

square error of approximation (RMSEA) ¼ 0.05, and comparative fit index (CFI) ¼ 0.97. In the SEM procedure, the CFA showed that the indices were over their respective common acceptance levels as suggested by prior research (Bollen, 1989; Browne & Cudeck, 1993; Hair et al., 1998; Joreskog & Sorbom, 1993; Lam et al., 2007). Thus, the proposed model generally fits the sample data well. The standardised loadings and the squared multiple correlation (SMC) for the measurement variables and the constructs were examined as evidence of convergent validity (Bollen, 1989). Since the t-value for the standardised


506 Table 2 Respondents’ profile (N ¼ 239) Variables

Frequency

Percentage (%)

Gender Male Female

125 114

52.3 47.7

Age Under 24 25–29 30–34 35–39 40 or above

17 136 54 23 9

7.1 56.9 22.6 9.6 3.8

Education level High school College University Graduate school

2 21 184 32

0.8 8.8 77.0 13.4

Department Front desk Housekeeping Others

133 73 33

55.7 30.5 13.8

Position Rank and file staff Assistant manager Manager

159 57 23

66.5 23.9 9.6

Note: Others are room reservations, sales and marketing, bell desk, guest relation officer (GRO), and executive floor lounge (EFL).

loadings in the measurement variables for the constructs all exceeded 1.96 (po0.05), because the SMC must be greater than 0.4 (Bollen, 1989) in order for the construct to well interpret the variance of related measurement variables, the convergent validity was established. In addition, to assess whether the measurement variable is representative of the related construct composite reliability (CCR) and average variance extracted (AVE) presented by Fornell and Larcker (1981) was calculated. The CCR of all constructs (0.92 for information quality, 0.88 for system quality, 0.91 for service quality, 0.79 for perceived value, 0.84 for perceived ease of use, 0.92 for perceived usefulness, and 0.90 for attitude towards use) exceeded the 0.7 cut-off value (Fornell & Larcker, 1981). Also, the AVE of all constructs (0.63 for information quality, 0.56 for system quality, 0.65 for service quality, 0.55 for perceived value, 0.65 for perceived ease of use, 0.73 for perceived usefulness, and 0.65 for attitude towards use) exceeded the 0.5 cut-off value (Fornell & Larcker, 1981; Hair et al., 1998). Therefore, these results support convergent validity for each construct. The correlations among the constructs were calculated using composite scores. Specifically, composite scores for each construct were computed by averaging scores across items representing that construct. Table 4 indicates the correlations among the composite scores representing the constructs range from 0.19 (information quality and perceived usefulness, system quality, and actual use) to 0.64 (system quality and service quality). The structural part

of the model with all structural parameters specified as correlated (i.e., f), while none of the structural correlations contained the value of 1.0 in their confidence interval (Anderson & Gerbing, 1988; Bagozzi, 1995). In addition, a similar approach also confirmed that the plus or minus two standard errors of each correlation coefficient did not include the value of 1.0. Therefore, the discriminant validity was also acceptable. Means and standard deviations of composite scores of constructs are presented in Table 4. 5.3. Structural equation model analysis and hypotheses testing results SEM was performed to investigate the relationship between criterion variables of actual use of HFOSs and the respective predictor variables of information system quality (i.e., information, system, and service quality), perceived value, perceived ease of use, perceived usefulness, and attitude towards use. In this study, the goodness-of-fit and the parameter of the SEM were estimated using the maximum likelihood estimation (MLE), and the results are depicted in Fig. 1. The overall fit of the model was sound with w2 ¼ 895.04, df ¼ 537, normed-w2 ¼ 1.67, GFI ¼ 0.92, AGFI ¼ 0.90, RMSEA ¼ 0.06, CFI ¼ 0.97, indicating that the proposed model achieved a strong predictive validity (Joreskog & Sorbom, 1993). The signs of all structural paths were also consistent with the hypothesised relationships among the latent constructs. Moreover, the model accounted for a substantial proportion of the variance (R2) in four endogenous variables: 38% of variance in perceived usefulness, 33% of variance in perceived ease of use, 76% of variance in attitude towards use, and 46% of variance in actual use (Fig. 1). As shown in Fig. 1 and Table 5, the correlations between perceived ease of use and system quality, and service quality were positive and significant. Thus, Hypotheses H1b and H1c are supported as shown by the path coefficients. However, the correlation between perceived ease of use and information quality was positive but not significant. Therefore, Hypothesis H1a is not supported as shown by the path coefficient. In this way, the results of this study are contrary to the results of previous studies (e.g., Agarwal & Prasad, 1999; Lucas & Spitler, 1999; Rai et al., 2002; Seddon, 1997). Although the information is accurate and efficient, it is unrelated to the belief that the information is easy to use. The absolute magnitude of the estimated standardised path coefficients showed that service quality had the greatest impact on perceived ease of use for HFOSs. The correlations between perceived usefulness and information quality, and system quality were positive and significant at 0.32 and 0.19, respectively. Thus, Hypotheses H2a and H2b are supported in this study. The correlation between perceived usefulness and service quality was positive but not significant at 0.02. Therefore, Hypothesis H2c is not supported. The absolute magnitude of the estimated standardised path coefficients showed that


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Table 3 Measurement model assessment results Scale items

Information quality (a ¼ 0.89) HFOS offers information in a useful format HFOS offers clear information HFOS supplies accurate information HFOS supplies sufficient information HFOS offers up-to-date information HFOS offers relevant and necessary information HFOS offers information that satisfies my need System quality (a ¼ 0.83) HFOS reacts and responds quickly to the user’s entry The language and terminology of HFOS are easy to understand User can easily obtain necessary information from HFOS HFOS can be a guide to errors HFOS can be flexible and changeable against the user’s requirement and new working condition HFOS can exchange information easily with other systems Service quality (a ¼ 0.85) The information systems department allows use of HFOS to run smoothly The information systems department is always helpful when there is a problem with HFOS The information systems department is very cooperative User training on using HFOS has been well established The information systems department is skilled and knowledgeable about technical aspects of computers The information systems department provides new and practical applications for the IT

Standardised loadings

t-values

SMCs

0.73 0.87 0.81 0.69 0.71 0.54 0.71

13.72 –a 16.34 10.47 11.19 9.44 13.29

0.53 0.76 0.66 0.48 0.50 0.41 0.50

0.87 0.63 0.51 0.63 0.65

–a 8.34 7.35 8.41 8.81

0.76 0.48 0.42 0.44 0.54

0.75

8.10

0.57

0.69 0.71

10.03 12.83

0.48 0.51

0.79 0.57 0.74

–a 9.49 13.68

0.63 0.40 0.54

0.74

12.62

0.54

0.65

–a

Perceived value (a ¼ 0.70) Investment of time and effort in HFOS use can be profitable to my business There is benefit in using HFOS There is no loss in using HFOS

0.58 0.48

8.81 7.74

Perceived ease of use (a ¼ 0.72) It is easy to learn to use HFOS It is easy to become proficient in using HFOS It is easy to remember how to use HFOS

0.53 0.64 0.81

7.07 9.01 –a

0.41 0.41 0.66

Perceived usefulness (a ¼ 0.88) HFOS improves the outcome of work HFOS increases work productivity HFOS increases the effectiveness of work HFOS is valuable to work

0.79 0.86 0.76 0.82

14.93 –a 15.10 13.43

0.62 0.74 0.57 0.68

Attitude towards use (a ¼ 0.86) Using HFOS is a good idea Using HFOS is advisable Using HFOS is a pleasant idea I enjoy using HFOS I am satisfied in using HFOS

0.83 0.86 0.73 0.61 0.68

17.12 –a 13.43 10.68 12.87

0.69 0.74 0.53 0.41 0.47

Actual use I use HFOS so that my work is swift and efficient

0.81

–a

0.64

AVE

CCR

0.63

0.92

0.56

0.88

0.65

0.91

0.55

0.79

0.65

0.84

0.73

0.92

0.65

0.90

NA

NA

Model fit statistics: w2 ¼ 857.99, df ¼ 502, normed-w2 ¼ 1.71, GFI ¼ 0.93, AGFI ¼ 0.91, RMSEA ¼ 0.05, CFI ¼ 0.97. Note: All items were measured on a 5-point Likert-type scale ranging from ‘‘1 ( ¼ strongly disagree)’’ to ‘‘5 ( ¼ strongly agree)’’. Because actual use construct was measured as one measurement variable, it was excluded from the reliability assessment. All reliability coefficients of all constructs exceeded the 0.7 cut-off value (Hair et al., 1998; Nunnally, 1978). All standardised loadings are significant at the 0.01 level or better. All composite reliability estimates are above the 0.70 cut-off value (Fornell & Larcker, 1981), and all AVEs are above the 0.50 cut-off value (Fornell & Larcker, 1981; Hair et al., 1998). Composite reliability ¼ (S standardised loadings)2/(S standardised loadings)2+(S indicator measurement error), AVE ¼ (S squared standardised loadings)/(S squared standardised loadings)+(S indicator measurement error). HFOS ¼ Hotel front office system, NA ¼ not available, GFI ¼ goodnessof-fit index, AGFI ¼ adjusted goodness-of-fit index, RMSEA ¼ root-mean-square-error of approximation, CFI ¼ comparative fit index, SMC ¼ squared multiple correlation, AVE ¼ average variance extracted, CCR ¼ construct composite reliability. a This path was fixed to one to identify the corresponding parameters.


508

Table 4 Descriptive statistics, and correlation of composite scores of constructs Constructs

1

2

1. 2. 3. 4. 5. 6. 7. 8.

1.00 0.56 0.51 0.37 0.19 0.46 0.34 0.21

1.00 0.64 0.39 0.28 0.40 0.39 0.19

Information quality System quality Service quality Perceived ease of use Perceived usefulness Perceived value Attitude towards use Actual use

3

4

1.00 0.41 0.27 0.36 0.47 0.22

5

1.00 0.44 0.38 0.56 0.47

6

1.00 0.36 0.47 0.43

1.00 0.48 0.38

7

Mean

SD

1.00 0.54

3.66 3.40 3.46 3.74 3.70 3.72 3.68 3.93

0.61 0.62 0.56 0.55 0.56 0.60 0.59 0.74

Note: All items were measured on a 5-point Likert-type scale ranging from ‘‘1 ( ¼ strongly disagree)’’ to ‘‘5 ( ¼ strongly agree)’’. Composite scores were calculated by averaging scores across items representing that construct. The scores ranged from 1 to 5. A higher score indicated a more favourable response. All correlations are significant at the 0.01 level (2-tailed).

0.32 (4.61**)

IQ (ξ1)

0.01 (0.12)

PU (η2) R2 = 0.38

0.19 (2.34*) 0.20 (3.41**)

SQ (ξ2) 0.18 (2.04*)

0.29 (4.92**)

0.02 (0.19) SVQ (ξ3)

0.24

0.26 (4.74**)

PEOU (η1) R2 = 0.33

(3.91**)

0.15 (2.59*)

AU (η4)

ATU (η3) R2 = 0.76

0.46 (7.57**)

R2 = 0.46

0.47 (7.71**)

PV (ξ4)

Fig. 1. Structural equation model analysis and hypotheses testing results. Model fit statistics: w2 ¼ 895.04, df ¼ 537, normed-w2 ¼ 1.67, GFI ¼ 0.92, AGFI ¼ 0.90, RMSEA ¼ 0.06, CFI ¼ 0.97. Note: IQ ¼ Information quality, SQ ¼ system quality, SVQ ¼ service quality, PV ¼ perceived value, PEOU ¼ perceived ease of use, PU ¼ perceived usefulness, ATU ¼ attitude towards use, AU ¼ actual use, GFI ¼ goodness-of-fit index, AGFI ¼ adjusted goodness-of-fit index, RMSEA ¼ root mean square error of approximation, CFI ¼ comparative fit index. Solid line: significant paths, Dotted line: non-significant paths. *Significant at po0.05. **Significant at po0.01.

information quality had the greatest impact on perceived usefulness of HFOSs. Previous studies supported the concept that information quality plays a dominant role in information system success (e.g., DeLone & McLean, 1992; Lederer et al., 2000; Liu & Arnett, 2000; Rai et al., 2002; Seddon, 1997; Skok, Andrew, & Ian, 2001; Teo & Choo, 2001). In this study, information quality acts as the most important determinant of perceived usefulness. In an involuntary environment, service quality must be efficiently supported in order to lead to the voluntary use of IT for the improvement in productivity at work. However, if service quality, such as systematic support, is offered in an HIS related department, users will perceive the use of the front office systems as easy, but will not perceive it as useful.

Significant and positive relationships were found between perceived ease of use and perceived usefulness. Therefore, Hypothesis H3 receives support given the significant and positive path coefficients. This result implies that the easier the use of front office systems is preceived to be, the more likely employees will perceive their usefulness. This result corroborates with the results of the study performed by Agarwal and Prasad (1997), Burton-Jones and Hubona (2006), Davis et al. (1992), and Shang et al. (2005). Perceived ease of use and perceived usefulness were positively associated with attitude towards use. Therefore, Hypotheses H4 and H5 were supported. As such, hotel frontline employees have a positive attitude towards


509

Table 5 Structural equation model analysis and hypotheses testing results Hypothesised paths

H1a H1b H1c H2a H2b H2c H3 H4 H5 H6 H7 H8

R2 R2 R2 R2

Information quality-Perceived ease of use System quality-Perceived ease of use Service quality-Perceived ease of use Information quality-Perceived usefulness System quality-Perceived usefulness Service quality-Perceived usefulness Perceived ease of use-Perceived usefulness Perceived ease of use-Attitude towards use Perceived usefulness-Attitude towards use Perceived usefulness-Actual use Perceived value-Attitude towards use Attitude towards use-Actual use Information quality-Attitude towards use System quality-Attitude towards use Service quality-Attitude towards use Information quality-Actual use System quality-Actual use Service quality-Actual use Perceived ease of use-Actual use Perceived value-Actual use (perceived ease of use) (perceived usefulness) (attitude towards use) (actual use)

Direct effects

Indirect effects

Path coefficients

t-values

0.01 0.18 0.24 0.32 0.19 0.02 0.20 0.29 0.26 0.15 0.47 0.46

0.12 2.04 3.91 4.61 2.34 0.19 3.41 4.92 4.74 2.59 7.71 7.57

0.01 0.04 0.03 0.05 0.12

0.09 0.10 0.04 0.09 0.08 0.02 0.16 0.18

Total effects

Remarks

0.01 0.18 0.24 0.33 0.23 0.05 0.20 0.34 0.26 0.27 0.47 0.46 0.09 0.10 0.04 0.09 0.08 0.02 0.16 0.18

Not supported Supported Supported Supported Supported Not supported Supported Supported Supported Supported Supported Supported

0.33 0.38 0.76 0.46

Goodness fit statistics: w2 ¼ 895.04, df ¼ 537, normed-w2 ¼ 1.67, GFI ¼ 0.92, AGFI ¼ 0.90, RMSEA ¼ 0.06, CFI ¼ 0.97. Note: Total effect ¼ Direct effect+indirect effect: for example, the total effect of information quality on perceived usefulness is 0.33, which can be calculated from adding the direct effect (0.32) to the indirect effect (0.01). GFI ¼ Goodness-of-fit index, AGFI ¼ adjusted goodness-of-fit index, RMSEA ¼ root-mean-square-error of approximation, CFI ¼ comparative fit index. Significant at po0.05. Significant at po0.01.

a particular technology to the degree that they believe the use of the technology will be relatively free of additional effort. These results contradict the findings claimed by some researchers (e.g., Adams et al., 1992; Agarwal & Prasad, 1997) that perceived ease of use did not have a direct effect on attitude towards use. However, findings of this research were in accordance with the results by Ahn et al. (2004), which showed perceived ease of use and perceived usefulness had a direct effect on attitude towards use. Perceived ease of use has an indirect impact on hotel frontline employees’ attitude towards the use of a HFOS through its relationship to perceived usefulness. As hotel frontline employees perceive the HFOS as being free of added effort or that it reduces effort, frontline employees may take the opportunity to redirect the unused effort towards other tasks. This will allow for accomplishment of more work for the same effort, hence greater productivity. Ease of use will have a direct effort on usefulness to the extent that the increased ease of use contributes to working more efficiently and improving performance. Furthermore, perceived value had a significant and positive effect on attitude towards use. Hence, Hypothesis H7 was supported. Among the antecedents of attitude

towards use (i.e., perceived ease of use, perceived usefulness, and perceived value), the absolute magnitude of the estimated standardised path coefficients showed that perceived value of the HFOS had the greatest impact on attitude of hotel employees towards use of HFOS. Among the variables within the TAM, perceived ease of use, rather than perceived usefulness, had a greater effect on actual use. This result shows that this is because in an involuntary environment, users need an IT system that is easy to use. When considering the effect that the antecedents of perceived usefulness and attitude towards use have on actual use, perceived usefulness and attitude towards use had a significantly positive effect on actual use. Thus, Hypotheses H6 and H8 were supported as shown by the path coefficients. These results showed that perceived usefulness had a significantly positive effect on actual use that corresponds to technology acceptance as advocated by Adams et al. (1992), Agarwal and Prasad (1997), Davis (1989), and Shin (2004). The results for attitude towards using a system had a direct effect on actual use of computers, web browsers, and other related technologies, and were similar to the findings found in Adams et al.



(1992), Davis (1989), Davis et al. (1989) and Mathieson (1991). In particular, among the determinants of actual use, attitude towards use was found to have the greatest effect and was found to be the most important determinant.

no direct effect, and only indirect effects were taken into consideration, perceived value was found to have the greatest effect on actual use.

5.4. Direct and indirect effects

6. Conclusions

We further examined the direct and indirect effects subsumed in the proposed model in an effort to gain further insights into the actual use process. Information (0.01), system (0.04), and service quality (0.03) affected perceived usefulness positively via perceived ease of use. Table 5 presents the mediating role of perceived usefulness between perceived ease of use (0.05) and actual use. The direct effect of perceived usefulness on actual use appeared to be significant (0.15), and the indirect effect via attitude towards use was found to be apparent (0.12). Therefore, attitude towards use served as an important mediating variable between perceived usefulness and actual use. The empirical results further suggested that information (0.09), system (0.10), and service quality (0.04) exerted positive effects on attitude towards use via perceived ease of use and usefulness. Specifically, system quality had the largest effect on attitude towards use via perceived ease of use and perceived usefulness. Therefore, the two constructs of perceived ease of use and perceived usefulness mediated the external variables to influence HFOS users’ attitudes towards using the system. In addition, the data further suggested that information quality (0.09), system quality (0.08), and service quality (0.02) exerted positive effects on actual use via perceived ease of use, usefulness, and attitude towards use. In particular, information quality had the largest effect on actual use via perceived ease of use, usefulness, and attitude towards use. Perceived ease of use (0.16) affected actual use positively via perceived usefulness and attitude towards use. Furthermore, perceived value (0.18) affected actual use positively via attitude towards use. Perceived ease of use and perceived usefulness are concrete personal beliefs that have effect on attitude towards using technology. Perceived usefulness is affected through the perceived ease of use. In this study, these results showed that perceived ease of use through perceived usefulness had an indirect effect on attitude towards use and actual use, and this supported the findings of Davis (1989) that claimed perceived ease of use is an antecedent of perceived usefulness. From the view of the total effects, which took into consideration both direct and indirect effects, these variables were found to have the greatest effect compared to other antecedents: (1) service quality in the case of perceived ease of use; (2) information quality in the case of perceived usefulness; (3) perceived value in the case of attitude towards use; (4) attitude towards use in the case of actual use and (5) attitude towards use was found to be an important intervening variable in the perceived usefulness effect on actual use. In the case where there was

Technology is gradually becoming a critical source of sustainable competitive advantages in the hospitality industry. In view of the benefits that IT provides to the hospitality industry, the extensive use of technology would appear to be an inevitable trend (Ham et al., 2005; Siguaw et al., 2000). However, despite the increasing use of technology in the hospitality industry, few studies have been conducted to investigate the relationship between the external variables and the TAM framework to explain the acceptance behaviour of hospitality organisations. Moreover, technology acceptance in hotel organisations needs distinctive approaches in investigating technology adoption behaviour because of the complex process affecting both internal and external variables and their unique characteristics (Wang & Qualls, 2007). On the basis of empirical results, this study would offer a practical direction to increase frontline employees’ acceptance of the HFOS. The details of managerial implications are presented as follows. First, the HFOS must offer a variety of information about the job to frontline employees. In order for service employees to offer professional services to customers, a system must be installed where customer information is offered once, and the hotel customer information can be shared without having to re-offer the data. In other words, all departments must be able to continuously share and update the information. Second, the HFOS must provide a safe and swift transaction time so that frontline employees may decrease the time spent on the system and increase the time spent on customer service. Also, the HFOS must be designed in such a way that the language and technology are easy to understand. Lastly, in order to facilitate information sharing, the exchange with other systems should be provided with restricted access, and the HFOS should be flexible to changes. Third, the helpful support from managers and HIS practitioners is important for frontline employees to use the HFOS more easily. In addition, HIS practitioners must administrate the HFOS to provide swift support in case a problem occurs (e.g., system error or limited technological skill), and must provide sufficient training in order for frontline employees to understand and utilise the HFOS without difficulty. The shorter the time needed for hotel employees to master the skills of IT, the greater their motivation to accept IT will be (Lam et al., 2007). Fourth, in the case where frontline employees need to use HFOSs for obtaining a large quantity and variety of information, convenience and ease of system use should be given higher priority because they are the point of contact for guests. Thus, frontline employees will perceive the


HFOS as useful when they can obtain timely information in a convenient and easy way. Finally, frontline service employees must realise that the benefits (e.g., increasing efficiency, improving productivity, and reducing the time to complete a task) of using HFOSs would lead to better job performance. This, in turn, will improve customer satisfaction and operational efficiency. This is particularly important when a HFOS is implemented, as employees can observe and realise the benefits of using a new HFOS and how it can help improve their performance and enhance guest satisfaction (Lam et al., 2007). If the benefits outweigh the losses in job progress when using the HFOS, this will result in a more positive attitude towards using the system. According to the results, when frontline employees use a HFOS and perceive that through its use, the efficiency, productivity, and outcome of their work would be improved, their motivation will noticeably increase. With this in mind, they recognise the value of the HFOS to their job. Also, when it is believed that job efficiency, productivity, and outcome can be increased, it is determined that the HFOS use will become voluntary. Through this study, hotel managers can consider how to best apply their HFOS in their front office, and to convey the opinion of HFOS users to HIS practitioners. There are some limitations in this study. First, even though different issues have been investigated, a few areas can be further examined in future studies. In hotels as well as in other organisations, IT acceptance appears as rather optional or voluntary. The IT acceptance presented in studies that applied TAM has primarily focused on a voluntary environment, where the individual’s situation and external variables had a great effect. However, unlike studies of a voluntary environment, there does not seem to be enough studies conducted on a mandatory environment. In future research, different studies can be performed on IT, targeting other technology systems instead of HFOSs. Also, it is important to find factors other than perceived ease of use, perceived usefulness, and perceived value that can affect the attitude towards technology acceptance. In the mandatory environment as delineated in this study, perceived value was applied as an external variable of TAM to determine the attitude towards technology acceptance. The factor played an important role in attitude towards expected use and actual use. A study to determine whether or not these characteristics appear in voluntary IT acceptance would also be valuable. Second, future studies can examine whether perceived value, an external variable of this study, can be applied to a general environment, or if it can only be applied to special information systems such as HFOSs. Perceived value adapted from marketing and quality studies (Gallarza & Saura, 2006; Sweeney et al., 1996; Woodruff, 1997; Woodruff & Gardial, 1996) was applied to this study, and empirical results show that there is a positive effect on the actual use of the HFOS, as stated in Section 5. Perceived value is considered a general variable even for

511

different targets (e.g., products, services, and brands). The perceived value dealt with in this study can be considered a general variable. Hence, in future studies, it will be necessary to reconfirm whether this variable is still reliable and valid. Third, although the results show information system quality affects users’ beliefs in HFOSs, it is important to realise that other factors may also play an important role in user beliefs. Examples of these factors include computer use experience (Yang, 2005; Zain, Rose, Abdullah, & Masrom, 2005), computer self-efficacy (Hu et al., 2003; Ong & Lai, 2006; Pituch & Lee, 2004), job relevance (Hu et al., 2003), and innovativeness (Lu, Yao, & Yu, 2005). Future research should enhance the search for antecedents affecting user beliefs. Finally, because this study enforced the cross-sectional study, the effects of the time variables cannot be estimated. Presently, there is a study that the societal effects and the effects of ease of use are influenced by the period of system use (e.g., Hu et al., 2003; Venkatesh & Morris, 2000). Therefore, it would be desirable to conduct a longitudinal study by taking into consideration the time periods of system use.

Acknowledgment The authors would like to thank the three anonymous reviewers for providing constructive comments to improve an earlier draft of this paper.

References Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16(2), 227–247. Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28(3), 557–582. Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361–391. Ahn, T., Ryu, S., & Han, I. (2004). The impact of online and offline features on the user acceptance of Internet shopping malls. Electronic Commerce Research and Applications, 3(4), 405–420. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. Amoako-Gyampah, K., & Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information & Management, 41(6), 731–745. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. Arbuckle, J.L. (1999). AMOS 4.0: User’s guide 4. Chicago: SPSS. Bagozzi, R. P. (1995). Reflections on relationship marketing in consumer markets. Journal of the Academy of Marketing Science, 23(4), 272–277. Bailey, J. E., & Pearson, S. W. (1983). Development of a tool measuring and analyzing computer user satisfaction. Management Sciences, 29(5), 530–545. Bajaj, A., & Nidumolu, S. R. (1998). A feedback model to understand to information system usage. Information & Management, 33(4), 213–224.



Baroudi, J. J., & Orlikowski, W. J. (1988). A shot form measure of user satisfaction and notes on use. Journal of Management Information Systems, 4(1), 44–59. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. Bollen, & S. Long (Eds.), Testing structural equation models. Newbury Park, NJ: Sage. Bruner, G. C., & Kumar, A. (2005). Explaining consumer acceptance of handheld Internet devices. Journal of Business Research, 58(5), 553–558. Burton-Jones, A., & Hubona, G. S. (2006). The Mediation of external variables in the technology acceptance model. Information & Management, 43(6), 706–717. Byrd, T. A., & Turner, E. T. (2001). An exploratory examination of the relationship between flexible IT infrastructure and competitive advantage. Information & Management, 39(1), 41–52. Chen, L., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information & Management, 39(8), 705–719. Chen, L. D., & Tan, J. (2004). Technology adaptation in e-commerce: Key determinants of virtual stores acceptance. European Management Journal, 22(1), 74–86. Cronin, J. J., Brady, M. K., & Hult, G. T. (2000). Assessing the effects of quality, value, customer satisfaction on consumer behavioral intention in service environments. Journal of Retailing, 76(2), 193–218. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(1), 1111–1132. DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95. Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effect of price, brand and store information on buyer’s product evaluations. Journal of Marketing Research, 28(3), 307–319. Eldon, Y. L. (1997). Perceived importance of information system success factors: A meta analysis of group difference. Information & Management, 32(1), 15–28. Fishbein, M., & Ajzen, I. (1975). Beliefs, attitude, intention and behavior: An introduction to theory and research. Boston: Addison-Wesley. Flint, D. J., Woodruff, R. B., & Gardial, S. F. (1997). Customer value change in industrial marketing relationships: A call for new strategies and research. Industrial Marketing Management, 26(2), 163–175. Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable and measurement error. Journal of Marketing Research, 18(1), 39–50. Gallarza, M. G., & Saura, I. G. (2006). Value dimensions, perceived value, satisfaction and loyalty: An investigation of university students’ travel behavior. Tourism Management, 27(3), 437–452. Hair, J. F., Anderson, R. E., Taltam, R. L., & Black, W. C. (1998). Multivariate data analysis with readings (Fifth ed.). New York: Prentice-Hall. Ham, S., Kim, W. G., & Jeong, S. (2005). Effects of information technology on performance in upscale hotels. International Journal of Hospitality Management, 24(2), 281–294. Hong, W., Thong, J. Y. L., Wong, W. M., & Tam, K. Y. (2002). Determinants of user acceptance of digital libraries: An empirical examination of individual differences and system characteristics. Journal of Management Information Systems, 18(3), 97–124. Hu, P. J. H., Clark, T. H. K., & Ma, W. W. (2003). Examining technology acceptance by school teachers: A longitudinal study. Information & Management, 41(2), 227–241.

Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of Management Information Systems, 11(4), 87–114. Jackson, C. M., Chow, S., & Leitch, R. A. (1997). Toward an understanding of the behavioral intention to use an information system. Decision Sciences, 28(2), 357–387. Jeong, M., & Lambert, C. U. (2001). Adaptation of an information quality framework to measure customers’ behavioral intentions to use lodging Web sites. International Journal of Hospitality Management, 20(2), 129–146. Joreskog, K. G., & Sorbom, D. (1993). LISREL 8 User’s reference guide. Chicago, IL: Scientific Software International Inc. Lam, T., Cho, V., & Qu, H. (2007). A study of hotel employee behavioral intentions towards adoption of information technology. International Journal of Hospitality Management, 26(1), 49–65. Law, R., & Jogaratnam, G. (2005). A study of hotel and information technology applications. International Journal of Contemporary Hospitality Management, 17(2), 170–180. Lederer, A., Maupin, D. J., Senza, M. P., & Zhuang, Y. (2000). The technology acceptance model and the World Wide Web. Decision Support Systems, 29(3), 269–282. Lee, C. K., Yoon, Y. S., & Lee, S. K. (2007). Investigation the relationships among perceived value, satisfaction, and recommendations: The case of the Korean DMZ. Tourism Management, 28(1), 204–214. Lee, H. Y., Kim, W. G., & Lee, Y. K. (2006). Testing the determinants of computerized reservation system users’ intention to use via a structural equation model. Journal of Hospitality & Tourism Research, 30(2), 246–266. Lee, S. M., Kim, I., Rhee, S., & Trimi, S. (2006). The role of exogenous factors in technology acceptance: The case of object-oriented technology. Information & Management, 43(4), 469–480. Liu, C., & Arnett, K. P. (2000). Exploring the factors associated with Web site success in the context of electronic commerce. Information & Management, 38(1), 23–33. Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. Journal of Strategic Information Systems, 14(3), 245–268. Lucas, H. C., & Spitler, V. K. (1999). Technology use and performance: A field study of broker workstations. Decision Sciences, 30(2), 291–311. Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191. McDougall, G. H. G., & Levesque, T. (2000). Customer satisfaction with services: Putting perceived value into the equation. Journal of Service Marketing, 14(5), 392–410. Nunnally, J. C. (1978). Psychometric theory (Second ed.). New York: McGraw-Hill. Ong, C. S., & Lai, J. U. (2006). Gender difference in perceptions and relationships among dominants of e-learning acceptance. Computer in Human Behavior, 22(5), 816–829. Palmer, J. W. (2002). Web site usability, design and performance metrics. Information Systems Research, 13(2), 151–167. Parasuraman, A., & Grewal, D. (2000). The impact of technology on the quality-value-loyalty chain: A research agenda. Journal of the Academy of Marketing Science, 28(1), 168–174. Paul, L., John, I., & Pierre, C. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191–204. Petrick, J. F., & Backman, S. J. (2002). An examination of the construct of perceived value for the prediction of golf travelers’ intentions to revisit. Journal of Travel Research, 41(1), 38–45. Pitt, F. L., Watson, T. R., & Kavan, C. B. (1995). Service quality: A measure of information system effectiveness. MIS Quarterly, 19(2), 173–187. Pituch, K. A., & Lee, Y. (2004). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222–244.

ARTICLE IN PRESS T.G. Kim et al. / Tourism Management 29 (2008) 500–513 Powell, T. C., & Dent-Micallef, A. (1997). Information technology as competitive advantage: The role of human, business and technology resource. Strategic Management Journal, 18(5), 375–405. Rai, A., Lang, S. S., & Weiker, R. B. (2002). Assessing the validity of IS success model: An empirical test and theoretical analysis. Information Systems Research, 13(1), 50–69. Rai, A., Patnayakuni, R., & Patnayakuni, N. (1997). Technology investment and business performance. Communication of the ACM, 40(7), 89–97. Ranganathan, C., & Ganapathy, S. (2002). Key dimensions of business-toconsumer web sites. Information & Management, 39(6), 457–465. Ruth, C. (2000). Applying a modified technology acceptance model to determine factors affecting behavioral intention to adopt electronic shopping on the world wide web: A structural equation modeling approach. Doctoral dissertation, Drexel University. Sanchez, J., Callarisa, L., Rodriguez, R. M., & Moliner, M. A. (2006). Perceived value of the purchase of a tourism product. Tourism Management, 27(3), 394–409. Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS Success. Information Systems Research, 8(3), 240–253. Shang, R. A., Chen, Y. C., & Shen, L. (2005). Extrinsic versus intrinsic motivations for consumers to shop on-line. Information & Management, 42(3), 401–413. Shin, H. P. (2004). An empirical study on predicting user acceptance of e-shopping on the web. Information & Management, 41(3), 351–368. Siguaw, J., Enz, C., & Namasivayam, K. (2000). Adaptation of information technology in US Hotels: strategically driven objectives. Journal of Travel Research, 39(2), 192–201. Skok, W., Andrew, K., & Ian, R. (2001). Diagnosing information system success: Importance-performance maps in the health club industry. Information & Management, 38(7), 409–419. Sweeney, J. C., Soutar, G. N., & Johnson, L. W. (1996). Retail service quality and perceived value: A comparison of two models. Journal of Retailing and Consumer Services, 4(1), 39–48.

513

Teo, T. S. H., & Choo, W. Y. (2001). Assessing the impact of using the Internet for competitive intelligence. Information & Management, 39(1), 67–83. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information System Research, 11(4), 342–365. Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challengers. MIS Quarterly, 25(1), 71–102. Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115–139. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451–481. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. Wang, Y., & Qualls, W. (2007). Towards a theoretical model of technology adoption in hospitality organizations. International Journal of Hospitality Management, 26(3), 560–573. Wo¨ber, K., & Gretzel, U. (2000). Tourism managers’ adoption of marketing decision support systems. Journal of Travel Research, 39(2), 172–181. Woodruff, R. B. (1997). Customer value: The next source for competitive edge. Journal of the Academy of Marketing Science, 25(2), 139–153. Woodruff, R. B., & Gardial, S. F. (1996). Know your customer: New approach to understanding customer value and satisfaction. Blackwell Business. Yang, K. C. C. (2005). Exploring factors affecting the adoption of mobile commerce in Singapore. Telematics & Informatics, 22(3), 257–277. Zain, M., Rose, R. C., Abdullah, I., & Masrom, M. (2005). The relationship between information technology acceptance and organizational agility in Malaysia. Information & Management, 42(6), 829–839.