A study of hotel employee behavioral intentions ...

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Vincent Cho b .... influences on the behavior of technology adoption (e.g., Davis, 1989; Moore and ... Davis (1989) stated that within an organizational context,.
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Hospitality Management 26 (2007) 49–65 www.elsevier.com/locate/ijhosman

A study of hotel employee behavioral intentions towards adoption of information technology Terry Lama,, Vincent Chob, Hailin Quc a

School of Hotel & Tourism Management, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, SAR, PRC b Department of Management and Marketing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, SAR, PRC c School of Hotel and Restaurant Administration, Oklahoma State University, 210 HESW, Stillwater, OK 74078-6173,USA

Abstract The purpose of this study was to explore the influence of perceived IT beliefs, task-technology fit, attitude, self-efficacy, and subjective norm on behavioral intention of adopting information technology in hotels in Hangzhou, China. A number of 458 usable self-administered questionnaires were collected. Results show that attitude, self-efficacy, and subjective norm are positively related to behavioral intention. Perceived IT beliefs had influence on the intention through attitude formation. Task-technology fit appears to interact with perceived IT beliefs towards attitude formation. Suggestions were provide for hotel practitioners to enhance employees’ intention of adopting new information technologies. r 2005 Elsevier Ltd. All rights reserved. Keyword: Information technologies; TRA; Self-efficacy; Task-technology fit; China

1. Introduction In such a highly competitive business environment, information technology has become an essential source of sustainable competitive advantage and a strategic weapon for an organization. The ability to harness the technologies to improve the efficiency of hotel Corresponding author. Tel.:+852 2766 6370; fax: +852 2362 9362.

E-mail addresses: [email protected] (T. Lam), [email protected] (V. Cho), [email protected] (H. Qu). 0278-4319/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhm.2005.09.002

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operations and service to guests will be the key to future success in the hotel industry (Siguaw and Enz, 1999). Using information technologies can result in having advantages in competition, decreasing costs, gaining time, and acquiring and sharing information. Thus, information technology has profound impacts on hotels, as a large amount of information has to be processed and communicated among internal and external customers. The hotel industry extensively relies on information technology to improve employees’ productivity and efficiency, accordingly to improve customer satisfaction. The Internet, voice-mail, e-mail, Internet automated room reservation, computerized accounting and financial reporting systems, computerized food and beverage ordering, teleconferencing, interactive guides for guests, cell phones, electronic credit-card authorization, and graphic reporting are some examples of information technologies (IT) utilized in hotels. Previous studies have shown that information technologies play an important role in improving effectiveness of the business operations and enhancing customer satisfaction (e.g., Zahra and Covin, 1993; Hoof et al, 1995; Powell and DentMicallef, 1997; Byrd and Turner, 2001). For example, in a study of information technology in small Scottish hotels, Buick (2003) depicts that almost 80% of them used computers to market their business through the Internet. Paraskevas and Buahlis (2003) also found out that small independent hoteliers agreed a number of benefits gained from using information technologies in hotels. In terms of benefits, many studies have concluded that IT could improve work processes, productivity, profitability, and customer satisfaction. However, researchers have indicated that new information technologies would not be fully accepted if barriers of human factors are overlooked (e.g., Ross et al., 1996; Lee and Miller, 1999; Roepke et al., 2000; Hasan, 2003). Such barriers hindering successful implementation of information technology in an organization include employees’ willingness, ability, and managers’ support. Thompson and Richardson (1996) also lamented that technologies are designed, developed, and implemented with little or no attention either to the needs of employees or to the impact that the technologies might have on the workforce. It appears that the impact of technological change on human behavior has not been extensively studied, and has not received sufficient attention in academic literature (Baker and Riley, 1994). Furthermore, researchers examined the nature and knowledge of information technology as a construct in the manufacturing, and service industries (e.g. banks). However, few studies have been conducted in the context of the hotel environment. Although the hotel industry is a labor-intensive sector, hotel managers are willing to increase technology investment to enhance their business thrust on employee productivity. Yet, disregarding human aspects will affect effective use of information technology (Hoof et al, 1995). Therefore, this study fills the gap by investigating employee factors based on the theory of planned behavior developed by Fishbein and Ajzen (1975), and other social cognitive theories in the literature (e.g., Davis, 1989; Igbaria and Iivari, 1995; Hu et al, 1999; Karahanna et al., 1999; Liao and Landry, 2000; Heine et al., 2003), towards adoption of information technology in hotels. Overall, the purpose of the study was to investigate the relationship between attitude, self-efficacy, and subjective norm and behavioral intention towards perceptions of adoption of information technology by hotel employees. Specifically, the objectives of this study were: 1. to investigate how perceived IT beliefs lead to the formation of attitude towards perceptions of adoption of information technology;

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2. to explore the impact of task-technology fit of employees on attitude towards perceptions of adoption of information technology; 3. to assess the impacts of self-efficacy on attitude; and 4. to examine the impacts of attitude, self-efficacy, and subjective norm on behavioral intention of IT adoption. This study would contribute to theoretical development of behavior formation about IT adoption in the context of the hotel industry. Results of the study can also provide practical implications for hotel practitioners to think strategically and implement effective tools to motivate employees towards adopting new information technology. 2. Literature review 2.1. Theory of reasoned action Previous studies have used behavioral intention models or behavioral decision theories to explain usage of information system (e.g., Davis, 1989; Agarwal and Prasad, 1997), and results further show that behavioral intentions are significantly and positively correlated with actual behavior. The findings imply that an effective use of information technology relies on positive intention towards adoption of IT. In the context of social science study, Fishbein and Ajzen (1975) developed and refined the theory of reasoned action (TRA) over time (Ajzen and Fishbein, 1980; Fishbein and Ajzen, 1975). This theory has been utilized by researchers to investigate human behavior in the disciplines of social psychology (Conner et al., 2001; Buttle and Bok, 1996), and has found support in the prediction of various social behaviors in the literature (e.g., Sheppard et al., 1988; van den Putte, 1991). According to Fishbein and Ajzen (1975), an individual’s intention to perform a specific act or behavioral intention with respect to a given stimulus object, in a given situation is a function of the individual’s attitude toward the behavior and his/her subjective norm. Subjective norm (SN), as another determinant of attitude, is perception of general social pressures to perform or not to perform a particular act. Underlying subjective norm are normative beliefs that consist of two components of multiplicatively combined (Fishbein, 1967). Therefore, individuals are more likely to perform an act if they perceive the existence of greater social pressure from salient referents to perform that act. In hotels, social pressure for operative employees is likely to come from managers. That is, managers’ perspectives generally affect adoption and application of IT in hotels. Hotel’s senior executives are aware of the importance of IT in replacing existing paper systems, improving customer services, enhancing operational effectiveness (Law and Jogaratnam, 2005), and improving guest satisfaction (Van Hoof et al., 1996). Positive hotel managers’ perspectives about operational benefits by adopting IT have extended an unseen pressure on operational employees to make use of IT. 2.2. Perceived IT beliefs Management information system (MIS) researchers have identified perceived beliefs in usefulness, ease of use, compatibility, image, trialability of an innovation are the key influences on the behavior of technology adoption (e.g., Davis, 1989; Moore and Benbasat, 1991; Rogers, 1995; Agarwal and Prasad, 1997; Agarwal and Karahanna, 2000; Liao and

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Landry, 2000). Perceived usefulness is a user’s perception of usefulness of a specific application system. Davis (1989) stated that within an organizational context, employees are generally reinforced for good performance by pay rise, promotions, bonuses, and other rewards. Yet, high-perceived usefulness of a system can help reinforcement of employees and gain their high performance. Liao and Landry (2000) suggested that short-term satisfaction with information systems is related to employees’ perception of short-term usefulness of that system in facilitating their daily tasks. Longterm satisfaction is also incurred when employees perceived long-term usefulness with respect to future promotion opportunity and better career prospects. Liao and Landry indicated that provision of training on system functionality of a technology might lead to users’ perception of its usefulness and enhance their willingness to manage difficulties in using the technology. Perceived ease of use encapsulates the degree to which a potential adopter views usage of an information technology to be relatively free of effort (Rogers, 1995). That is, the more complex the innovation of an information technology, the lower the probability of its adoption will be. In this connection, Davis (1989) identified a positive correlation between perceived ease of use and behavioral intentions. Compatibility is the degree to which an innovation is perceived as being consistent with the individual values, needs, and past experiences of potential adopters. Agarwal and Prasad (1997) found that perception of compatibility appears to be the most important predictor of usage of an innovation. Agarwal and Karahanna (2000) provided a comprehensive conceptual definition that views compatibility as a multi-dimensional construct; the decomposition of compatibility into its constituent dimensions specifies precisely the paths through which compatibility influences attitude. Agarwal and Karahanna further argue that technologies perceived to be compatible with various aspects of an individual’s experiences (e.g., compatibility with prior experience) and work styles (e.g., compatibility with current working practices and compatibility with preferred work style) are likely to induce feelings of familiarity and positive affect on behavior. On the contrary, strong past negative individual values about the role of technology in work, even in the presence of apparent benefits from technology use, may result in less positive attitudes being formed (i.e., non-compatibility with individual values). Moore and Benbasat (1991) depicted that image is referred to the degree to which an innovation is perceived to enhance one’s status in a social system. People often respond to social normative influences to establish or to maintain a favorable image within a reference group. Thus, positive perceived image as a result of using technologies generally leads to a favorable behavioral intention of adoption. Last, but not least, the perceived belief of trialability connotes a risk-free exploration of the technology. That is trialability measures the extent to which potential adopters perceive that they have an opportunity to experiment with the innovation prior to the usage. Agarwal and Prasad (1997) stated that the more adopters experiment with a new technology and explore its ramifications, the greater the likelihood that the innovation will be used during early stages of adoption. Thus, based on the literature reviews, a hypothesis was suggested: H1. More positive perceived IT beliefs lead to a more positive attitude towards IT adoption.

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2.3. Technology acceptance model (TAM) versus task-technology fit (TTF) model Two significant models have emerged that provide a strong theoretical base for studies of IT utilization behavior. The first is the technology acceptance model (TAM). The goal of TAM is to provide an explanation of the determinants of computer acceptance that in general is capable of explaining user behavior across a broad range of end-user computing technologies and user populations, while at the same time being both parsimonious and theoretically justified (Davis et al., 1989). The major determinants of TAM include perceived usefulness, and perceived ease of use (Davis, 1989) that have been shown to be related to attitude formation (Davis, 1989; Moon and Kim, 2001; Venkatesh, 2000; Venkatesh and Davis, 2000; Agarwal and Prasad, 1999). TAM was developed to explain and predict computer-usage behavior. It has its theoretical grounding in Fishbein and Ajzen’s (1975) theory of reasoned action (TRA). The TAM adapts the TRA theory of belief, attitude, intention, and behavior into an IT acceptance model. For TAM, the subjective norms construct is not included. One of the possible explanations is the use of students in many of the tests of TAM (Dishaw and Strong, 1999). However, subjective norms may be more important in an organizational setting as in a hotel in which users may feel some social pressure to use the IT (Taylor and Todd, 1995). Thus, the significance of subjective norms was tested in the study. The second is the task-technology fit (TTF) model. Goodhue and Thompson (1995) proposed the TTF model that extends the TAM by considering how the task affects use. More specifically, the TTF model depicts that a technology will have a positive impact on individual performance if it is well utilized, and technology adoption depends in part on how well the new technology fits with the task it supports. Goodhue and Thompson further explained that technology is viewed as a tool by individuals when they carry out their tasks with it. Tasks are the actions carried out by individuals in turning inputs into outputs. Task characteristics in terms of variety, difficulty, and interdependence may link to an individual’s reliance on using technologies, and if the individuals find that the technology can help them perform well, they will perceive it as useful and important to them. Goodhue et al. (2000) supported the argument that TTF is applicable in both mandatory and voluntary use situations. Palmer et al. (1993) studied an order entry system in restaurants, and argued that a major challenge for kitchen workers was to read orders from a remote display screen or from a workstation printer. Having to stop and read can slow down the workers’ efficiency, cause them to lose their concentration or increase errors on the job. Palmer et al., concluded that once the technology does not fit the task, the corresponding system cannot be implemented successfully (Morris and Turner, 2001). In the context of hotels, task-technology fit is the degree to which a technology can assist an employee in performing his/her portfolio of services or tasks on the job. The higher the degree of the fit, the better performance may result. Specifically, TTF corresponds to the relationship of matching among task characteristics, employee abilities and functionalities of technology in hotels. Thus, two further hypotheses were suggested in this study: H2. A better task technology fit will lead to a more positive attitude towards IT adoption. H3. A better task-technology fit and a more positive perceived IT belief are positively correlated with each other.

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2.4. Attitude Attitude is an individual’s feeling of the favorableness or unfavorableness of his/her performance of the behavior. According to Fishbein and Ajzen (1975), an attitude is the function of behavioral beliefs and evaluation of outcomes. Behavioral belief is one’s belief in performing a specific behavior that will lead to a specific consequence, and evaluation of outcome is one’s assessment of that specific consequence. An attitude is an individual’s disposition to respond favorably or unfavorably to an object, person, institution, or event (Ajzen, 1989, p. 241). A hierarchical model, which includes cognition (beliefs), affect (feelings), and conation (intentions) as first-order factors and attitude as a single second-order factor, has served as a starting point for many attitude-behavior theories. Attitude towards information system is an aggregate belief among other factors such as TTF, self-efficacy and IT beliefs in our model. It somehow reflects the internal tendency towards technology acceptance. Few managers would be happy if their employees were simply using a system because of their obligation but who privately felt very negative about the system. So it is interesting to study attitude as it reflects the underlying mental acceptance towards an information system. Attitudes towards information systems have been extensively studied in the past. For instance, Liao and Landry (2000) argued that employee’s attitude towards the acceptance of IT would affect the intention of IT adoption. Thus, a hypothesis was proposed: H4. A more positive attitude will lead to a higher degree of intentions towards IT. 2.5. Self-efficacy Self-efficacy is the belief in one’s capabilities to mobilize the motivation, cognitive resources, and courses of action needed to meet given situational demands (Bandura, 1986). Self-efficacy also refers to one’s interest and willingness to use and interact with information technology (Hasan, 2003). Perceived self-efficacy plays an important role in affecting an individual’s motivation and behavior. That is, individuals’ self-perception of their own capabilities to attain the standards that they have been pursuing has an impact on individual cognitive and behavioral reactions. Those individuals who lose their confidence about their capabilities are easily discouraged and fail, whereas those who are highly assured of their efficacy for goal attainment will intensify their efforts when their performances fall short and persevere until they succeed (Igbaria and Iivari, 1995). Among the various individual factors examined in the literature, computer self-efficacy has been identified as a key determinant of computer usage. Derived from the general concept of self-efficacy, computer self-efficacy refers to an individual’s perceptions about his or her ability to use a computer to perform a computing task successfully (Bandura, 1986). And self-efficacy has been shown to be associated with an individual’s performance in computer training and technology acceptance (Compeau and Higgins, 1995). A similar argument was suggested by Marakas et al. (1998) that computer self-efficacy affects not only an individual’s perceptions of his or her ability to perform a computing task, but also his or her intentions toward future use of computers. However, in

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a study of electronic learning programs in information technology, Hayashi et al. (2004) found that computer self-efficacy, as a moderating factor, appeared to be a less salient factor to improve end-user satisfaction with e-learning systems, and this factor might also not directly affect end-user intention to continue using e-learning system. Thus, based on previous studies on self-efficacy, two relevant hypotheses were further suggested for the study: H5. The higher the level of self-efficacy, the higher the degree of positive attitude towards IT adoption. H6. The higher the level of self-efficacy, the higher the degree of intentions towards IT adoption. 2.6. Subjective norm Subjective norm is important in an organizational setting (Taylor and Todd 1995). According to Fishbein and Ajzen (1975), subjective norm refers to perceived pressures on a person to perform a given behavior and the person’s motivation to comply with those pressures, and a person’s behavioral intentions were found to correlate with a subjective norm. A number of studies have supported such correlation in the context of social psychology (e.g. Conner et al., 2001; Buttle and Bok, 1996). Fishbein and Ajzen further state that subjective norm is related to the normative beliefs that a person complies with the expectation from other people, such as a person’s family or friends, supervisor or society at large. Social norms represent perceived external pressures to use (or not to use) the system. Individuals who perceive that others expect that they should use the system will have a high score on intentions to use the system, even when they may personally not feel positive about the system. Lucas and Spitler (1999) and Venkatesh and Davis (2000) also reported that organizational variables such as social norms are more important than user’s perceptions of the information technology in predicting system usage and acceptance. Thus, the social normative component captures the collective effect of these influences on behavioral intention. A hypothesis related to subjective norm was suggested: H7. A higher level of subjective norms will cause a higher degree of intentions towards IT adoption.

3. Research model Based on the literature review, a research model was developed for this study that incorporates relevant constructs developed in prior studies into a comprehensive model as shown in Fig. 1. On one hand, the model is composed of the external determinant factors that include perceived beliefs in using IT, task-technology fit. These factors were hypothesized to have impact on attitude towards IT adoption. On the other hand, attitude, subjective norm, and self-efficacy were expected to influence behavioral intention of using IT. Seven hypotheses based on the literature reviews were developed and shown in Fig. 1.

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H1 Perceived IT Beliefs H2

H3

Attitude H4

H5 H6

Task– technology Fit

Behavioral Intention

Self-efficacy

H7 Subjective Norm Fig. 1. Proposed model and hypotheses.

4. Methodology 4.1. Sampling procedure An empirical study of employee behavioral intentions of IT adoption was undertaken in hotels China and Hong Kong. The sampling population of the study consisted of five- and four-star hotel employees in Hangzhou and Hong Kong who used IT equipment or systems to support their daily work. Respondents were selected by means of a convenience sampling method. They were called through their departmental manager to the human resources office on an individual or a small group basis during slack hours. Once they arrived in the office, they were briefed about the survey before completing the questionnaires. For the hotel sample in Hangzhou, all five five-star hotels in Hangzhou were selected. In Hong Kong, three four- and three five-star hotels were randomly selected. These hotels were invited to participate in the survey. However, one hotel in Hangzhou and two in Hong Kong declined the invitation. As a result, eight hotels participated in the survey. Furthermore, these two cities were chosen because of the well-developed hotel industry, and the target hotels were well equipped with information technologies. Thus the employees in these two cities were expected to provide pragmatic judgments and opinions in the survey, as their actual experience of using IT was high. A self-administered questionnaire was used to collect data. The questionnaires were distributed to respondents via their human resource managers. The human resource managers were briefed about the purpose of the study, selection of respondents and collection method, and to safeguard the integrity of the survey, two approaches were adopted. First, human resources managers were invited to complete the self-administered questionnaires. If they had problems in filling in the questionnaires, the researchers could answer and clarify immediately. It was assumed that these problems might occur when their employees were invited to complete the questionnaires at a later stage, and a human resource manager, as a coordinator, would be able to handle a similar problem. Second, respondents were asked to complete the questionnaire immediately upon receipt in the human resources office. Researchers were allowed to stay in the office to administer data collection for a few hours on the day of data collection. This gave the impression to respondents that the survey was conducted by a third party, and not the hotel itself.

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4.2. Questionnaire development Based on a comprehensive literature review, a questionnaire was developed to investigate the relationship between attitude, self-efficacy, and subjective norm and behavioral intention towards adoption of information technologies in hotels. The specific research instrument was comprised of the following constructs:













Perceived Beliefs (PB)—The instrument was adopted from previous studies by Davis (1989) and Moore and Benbasat (1991). Five variables were used to measure perceived beliefs that included: (i) perceived usefulness (e.g., ‘‘Adopting IT in my job enables me to accomplish tasks more quickly.’’); (ii) perceived ease of use (e.g., ‘‘I found IT easy to use in hotels.’’); (iii) perceived compatibility (e.g., ‘‘Adopting IT is a new experience for me.’’); (iv) perceived image (e.g., ‘‘People who adopt IT have more prestige than those who do not.’’); and, (v) perceived trialability (e.g., ‘‘Before deciding to use IT, I would be able to properly try it out.’’). A five-point Likert scale was used ranging from strongly agree (5) to strongly disagree (1). Task-technology Fit (TTF)—The instrument was adopted from Goodhue and Thompson (1995) with some minor modifications based on focus group interviews in the pilot test. It assessed how close the information systems and services were related to job needs of hotel employees. A 10-item questionnaire was developed with a 5-point Likert scale ranging from strongly agree (5), to strongly disagree (1). Respondents were asked to indicate their agreement to importance level for each item. Two representative statements of the TTF were: ‘‘The data is presented in a readable format’’, and ‘‘The information system is timely in providing the data’’. Attitude—Based on Ajzen and Fishbein (1980), attitude was measured by five statements using a 5-point semantic differential scale: ‘‘All things considered, I think using IT on my job would bey’’ important–unimportant, relevant–irrelevant, trifle–basic, boring–interesting, and unattractive–attractive. Self-efficacy—Adopted from Hill et al. (1987) and Igbaria and Iivari (1995), the instrument measured self-efficacy by three statements: ‘‘I will understand the working principal of a new information system’’; ‘‘I will be able to learn the application of a new information system’’; and ‘‘The information system varies a lot and it is not easy to operate’’. A 5-point Likert scale ranging from strongly agree (5) to strongly disagree (1) was used. Subjective norm—According to Ajzen and Fishbein (1980), four statements were used to evaluate subjective norm: ‘‘My supervisor always encourages me to use information systems’’; ‘‘My colleagues think that I should use information systems’’; ‘‘My guests perceive using information systems to be useful in a hotel’’, and ‘‘My hotel manager believes that there are advantages of using information systems’’ with 5 ¼ strongly agree and 1 ¼ strongly disagree;. Behavioral intention—Behavioral intention was measured by three items with a 5-point Likert scale, ranging from strongly agree (5) to strongly disagree (1). The items include: ‘‘I intend to work with IT more increasingly in the future’’; ‘‘I want to use IT for my work’’; and ‘‘It is likely that I will use IT for my future work’’.

Since the target sample comprised of Chinese employees working in hotels in China, the research instrument was translated into Chinese using a blind translation-back-translation

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method as described by Brislin (1976). A pilot test was conducted to test the validity and reliability of the questionnaire with 33 hotel employees randomly selected from hotels in Hong Kong. Through hotel human resource managers, the proposed questionnaires were distributed to staff in the hotel staff canteens. Feedback was obtained about the length of the instrument, the format of the scales, content validity, and question clarity. For example, one of the items, ‘‘Use of IT can increase my job efficiency,’’ which was not clear to respondents was revised as ‘‘Use of IT can decrease the time needed for accomplishing my work.’’ The instrument was revised and further administered to another group of 20 hotel employees in Hong Kong. This group of employees in Hong Kong, who were working in front office, housekeeping, and restaurants, was randomly selected. In the pilot test, the clarity was confirmed, and no change was made to the items of the questionnaire. The reliability coefficients (Cronbach’s alpha) for the questionnaire constructs ranged from 0.83 to 0.95, which exceeded the recommended satisfactory level of 0.70 (Nunnally and Bernstein, 1994). 4.3. Analytical method Scale reliability analysis was used to measure the internal consistency of each construct, and the generally agreed upon lower limit for the Cronbach’s alpha was set at 0.70 (Hair et al., 2002). Structural Equation Modelling (SEM) and measures of correlations were applied to test causal relationships in the model as shown in Fig. 1. By using SEM, important latent constructs can be modelled, while taking account of the unreliability of the indicators. Further, the SEM considers unknown reliability of the measures and ranks the measures in terms of their importance (Bacon et al., 1998). 5. Findings and discussion Of the 788 questionnaires distributed, 678 were received, and among 678 completed questionnaires, 458 were usable. About one-third of respondents were from Front Office (30.4%), followed by Sales and Marketing (24.5%), Human Resources (14.7%), and Finance office (14.4%). A few respondents were from Housekeeping (7.5%), Food and Beverage (3.5%), others (4.2%). Female employees dominated the sample (66.9%). The majority of respondents were operational employees (58.3%), followed by supervisors (36.1%) and assistant managers or above (4.6%). Most respondents were aged 29 or below (77.9%) followed by respondents aged 30–39 (19.4%) and 49 or above (2.7%). Half of respondents were university graduates (50%), and almost half of them were graduates from high schools or vocational training schools (48%). In order to reduce the number of variables in the measurement of TTF and to group these variables into key categories, a factor analysis was employed using the principal component method with VARIMAX orthogonal rotation. Factor loadings greater than .50 were considered acceptable in the study. Data was first tested to ensure its adequacy for the application of factor analysis. The overall significance of the correlation matrix was 0.000. Bartlett’s test of Sphericity was 1205.26, which is very significant (p ¼ 0:000) in rejecting the hypothesis that the correlation matrix is an identity. The value for the KMO (Kaiser–Meyer–Olkin) model, which tests for the adequacy of the sample, was 0.824. Results indicate that data were significantly correlated and suitable for factor analysis. Using the principle component analysis, three factors with an eigenvalue greater than one

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Table 1 Measures of task-technology fit Item description

IT Dependence The information system is available when needed IT is important to my job Information dependence The data is displayed in a readable and understandable form when needed Accessible data from the information system are convenient and easy to use The information system is timely The data generated from the information system is accurate Decision making dependence The information system is able to integrate information across multiple departments The information system can help me to deal with unexpected affair The information system can enable me to make good decision Improves quality of decision

Factor loadings

Eigenvalues

% of Variance

Cumulative %

Reliability

3.737

37.372

37.372

0.65

1.405

14.054

51.426

0.63

1.014

10.139

61.565

0.80

0.811 0.824 0.519

0.435

0.811 0.532

0.580

0.761 0.848 0.853

Total scale reliability 0.70. KMO ¼ 0.824. Bartlett’s test ¼ 1205.260 with significance ¼ 0.000.

were extracted from the ten attributes. Altogether, these four factors explained 61.57% of the total variance. As shown in Table 1, the three factors were IT Dependence, Information Dependence, and Decision-Making Dependence. A clean factor structure with relative higher loadings on the appropriate factors was produced. As explained above, this indicated that only minimal overlap existed among these factors and that all factors were independently structured. Discriminate validity was tested in the study to examine the degree to which the attributes differentiate among the constructs (Hair et al., 1995). All attributes were fed into the factor analysis to assess whether they were loaded across the constructs; some were eliminated since they were not factorially pure. For example, the attributes of ‘‘perceived ease of use’’, and ‘‘perceived image’’ of the IT perceived belief construct, and those of ‘‘rapid change of information system’’, and ‘‘difficult to operate’’ of the self-efficacy construct were discarded because of their low factor loadings for maintaining the reliability and validity. As a result, there were altogether 20 attributes. As shown in Table 2, the reliability coefficients (Cronbach’s alpha) of the constructs ranged from 0.777 to 0.910, which exceeded the recommended acceptable level of 0.70 (Nunnally and Bernstein, 1994).

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Table 2 Reliability analysis of observed variables

Perceived belief Perceived usefulness Perceived compatibility Perceived trial ability Task-technology fit IT dependence Information dependence Decision making dependence Self-efficacy I will understand the working principal of a new information system I will be able to learn the application of a new information system Attitude IT is important to my job, IT is relevant to my job IT is trifle IT is interesting IT is attractive Subjective norm My supervisor always encourages me to use information systems My colleagues think that I should use information systems My guests perceive using information systems to be useful in a hotel My hotel manager believes that there are advantages of using information systems Behavioral intention I intend to work with IT more increasingly in the future I want to use IT for my work It is likely that I will use IT for my future work

Mean

Standard Deviation

Factor loadings

3.77 3.79 4.10

0.68 0.67 0.73

0.702 0.675 0.411

3.29 4.13 3.59

0.67 0.61 0.54

0.603 0.560 0.696

3.81

0.65

0.73

4.00

0.61

0.82

4.10 4.12 4.12 3.83 3.91

0.92 0.89 0.97 0.93 0.96

0.717 0.791 0.764 0.711 0.738

3.89

0.70

0.605

3.79

0.61

0.518

3.76

0.71

0.523

3.90

0.79

0.704

3.61

0.74

0.656

3.54 3.92

0.77 0.69

0.494 0.747

Cronbach’s alpha 0.777

0.833

0.882

0.876

0.813

0.910

6. Model testing structural equation model SEM was performed to investigate relationships between criterion variable of behavioral intention of IT adoption and the respective predictor variables of perceived IT beliefs, task-technology fit, attitude, subjective norm, and self-efficacy. Results of SEM and those of the causal path testing are shown in Fig. 2, respectively. Five common model-fit measures were used to assess the model’s overall goodness of fit: Goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), norm fit index (NFI), comparative fit index (CFI), and root-mean-square error of approximation (RMSEA). The path coefficients were reviewed to see if any of the paths in the initial model should be deleted; if an

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Perceived IT Beliefs

0.352**

Attitude 0.36**

-0.232** 0.415**

Tasktechnology Fit

61

0.239** 0.40** Self-efficacy

Behavioral Intention

0.21** Subjective Norm

Fig. 2. Path diagram with standardized estimates of proposed model. Note: all stated estimated standardized regression coefficients were significant at p ¼ 0:05,  po0:01.

attribute loaded with an absolute t value of lower than 1.96 (p4:05), it would be eliminated from the model (Hatcher, 1994). Furthermore, if the fit of the initial model was not adequate, modification indices for each fixed parameter were used as indicators for model modification, by adding parameters to improve the fit. The value of a given modification index was the minimum amount that the chi-square statistic was expected to decrease if the corresponding parameter was freed. When a parameter was freed at each step, it produced the largest improvement in fit; this process was continued until an adequate fit was reached. In the first SEM procedure, the initial hypothetical model showed that the four indices (GFI ¼ 0.85, AGFI ¼ 0.81, NFI ¼ 0.97, and CFI ¼ 0.98) were close to or over their respective common acceptance levels as suggested by previous research (e.g., Browne and Cudeck, 1993). This indicated that the model fitted fairly well. The RMSEA reached .0887, which was less than 0.1, and thus was acceptable. As shown in Fig. 2, the correlations between attitude and respective perceived IT beliefs and self-efficacy were positive. Thus, Hypotheses H1 and H5 are supported as shown by the path coefficients. The absolute magnitude of the estimated standardized path coefficients showed that perceived IT beliefs had the greatest impact on attitude of hotel employees towards behavioral intention of adopting IT. This finding is consistent with previous studies (e.g., Davis, 1989), that when employees perceive stronger beliefs of IT in usefulness, ease of use, compatibility, image, and trialability, they would have a more positive perspective towards an innovation. Moreover, an employee’s capability of using IT will influence his/her attitude to adopting it on the job directly and indirectly through attitude. However, task-technology fit was negatively related to attitude. The results show that Hypothesis H2 is not supported. Such a finding is contradictory to previous studies (e.g., Goodhue and Thompson, 1995; Dishaw and Strong, 1999) which found that tasktechnology fit had positive impact on people attitude towards IT. One of the possible reasons was that even though hotel employees perceived a high degree of good fit between task requirement and technology application, lack of know-how and mastering skill might result in a non-positive attitude. The best result should be a good fit between IT

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application and task requirement, plus provision of training to equip employees with necessary knowledge and skills so that they can perform well. Under these circumstances, employees should have a positive attitude towards IT adoption. Thus, training appears to be a possible moderator in this causal relationship. Given the inconsistent result with previous studies, future studies are suggested to further explore the relationship. A significant covariance relationship was found between perceived IT beliefs and tasktechnology fit indicating that there was an interaction between these predictors. Hypothesis H3 receives support given the significant path coefficient. The correlations between behavioral intention and attitude, self-efficacy, and subjective norm were significant at .36, .40, and .21, respectively. Thus, Hypotheses H4, H6 and H7 are supported in this study. The magnitude of the estimated standardized path coefficients revealed that self-efficacy and attitude had a greater impact than subjective norm in determining behavioral intention of hotel employees in adopting IT in China. Subjective norm was associated with behavioral intentions of using IT. That is, social pressure from the referent group to the employees did have an influence on their behavioral intention. This referent group might include immediate supervisors, team members, colleagues, and family members. On the other hand, it appears that the employees were keen on adopting new technologies, and believed that IT would be important to help them on job performance. Importantly, they were confident in using new technologies. A possible reason for such a phenomenon may be because the hotel industry is a booming industry in China and many new hotels are being built. A huge demand for labor leads to the industry recruiting a large number of new young and well-educated employees, as found in the sample of this study in Hangzhou. This group of employees might be familiar with functions and usage of information technologies as they grew up in the world of rapidly developed Internet and world-wide information web. They generally accepted new technologies and considered that technologies were important to help enhance their job performance.

7. Conclusion and implications Based on the literature review, this study integrated the constructs of theory of reasoned action, self-efficacy, and task-technology fit into a comprehensive research framework. Overall, the hypothesized research model explained behavioral intention of adopting IT by hotel employees in China moderately well. On one hand, perceived IT beliefs were found to have positive impacts on attitude. On the other hand, attitude, self-efficacy and subjective norm were related to behavioral intention of adopting IT. Congruent with this, analysis of the standardized path coefficients indicated that self-efficacy was the most important factor affecting behavioral intention. The study has provided some preliminary evidence concerning employees’ psychological factors to IT adoption. Based on these findings, a number of salient implications were suggested for hotel managers to consider. First, relevant training should be provided for employees to improve their competency of using IT, especially during the early stage of implementation of new IT in hotels. Hotels should work closely with trainers from IT suppliers to provide on-the-job and off-the-job training. The shorter the time for hotel employees to master the skills of information technology, the higher the motivation of the employees to adopt new IT will be.

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Second, the significant effect of subjective norm on employees’ behavioral intentions reveals that those persons who are considered by the employees as most important should be encouraged to provide support and motivation during the early stage of IT implementation. During this stage, the employees will likely encounter problems as well as exhibit resistance to using IT. Normatively, it is reasonably assumed the most important persons for the employees are their managers in the hotel. Thus, hotel managers should counsel their employees as soon as they have problems with IT adoption. They should also provide continuous feedback, support, and encouragement for employees so that they can master the technological skills within a short period of time. Third, employees should be aware of their performance progress as a result of adopting information technology. This is particularly important when an information system is implemented as they can observe and realize the benefits of using a new system that can help improve their performance and enhance guest satisfaction in hotels. Their motivation level will also noticeably increase. Managers should try to make known any improvement in the areas of sales, guest satisfaction, service quality, and productivity and even reduction of work-related accidents as a result of implementing new technology. Relevant indexes, if they exist, should be publicized to employees, and it can help improve their belief in IT usage. Limitations of the study included the limited sampling frame. Another limitation included the convenience sampling method used to select hotel samples and sampling population. Given the methodology constraints, results of the study might not be representative of the general hotel employees in China and Hong Kong.

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