Proceedings of 21st International Business Research Conference 10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2 Applying the Extended Technology Acceptance Model to Understand Online Purchase Behavior of Travelers Robin Nunkoo*, T. D. Juwaheer** and Tekranee Rambhunjun*** This paper seeks to establish the relationships between travelers’ attitude, perceived ease of use, perceived usefulness, trust, perceived risk and online purchase intention of tourism and travel related products by using an extended technology acceptance model. Primary data were collected using a self- structured questionnaire from 150 travelers visiting Mauritius in 2012. Seven out of eight hypotheses tested were supported. Perceived ease of use was not a significant predictor of attitude towards online purchasing. The model can assist destination marketing practitioners to better understand the online purchasing behavior of travelers. Field: Marketing 1. Introduction The travel sector is rated among the top three product/ service categories purchased via the Internet (Eric et.al, 2006). Use of the Internet is becoming a positive trend and an important core competency for hospitality businesses due to the fact that travel related services are among the fastest emergent areas for e-commerce (Yeh et al., 2005). However, the dramatic development of commerce through the Internet has also brought forth many new challenges. Risks arising from lack of human contact, lack of previous experience could also impede on consumers’ trust in e-commerce. Trust is recognized as a vital key for an e-vendor for customer retention (Reichheld and Schefter 2000). The full potential of tourism e-commerce can only be achieved if the determinants of travelers’ online purchase intent are known to researchers and practitioners. This research extends the use of technology acceptance model (TAM) to investigate the online purchase behavior of travelers by including perceived risks and trusts in the model. 2. Literature Review: Technology Acceptance Model According to Pavlou (2003), online purchase intention is defined as a situation where a consumer is willing and intends to make online transactions. Online purchase intentions are explained in part by the TAM (Davis 1989). The TAM model (Figure 1) constitutes of two belief factors namely: perceived usefulness and perceived ease of use, which are predictors of user attitude towards using technology. In turn, the latter influences behavioral intentions. Perceived ease of use also influences perceived usefulness of technology. ______________________________________________________________________ *Dr. Robin Nunkoo, Department of Management, University of Mauritius, E-mail: email@example.com ** Dr T.D Juwaheer, Department of Management, University of Mauritius, E-mail: firstname.lastname@example.org *** Miss Tekranee Rambhunjun, MSc Marketing Management student, University of Mauritius, E-mail: email@example.com Proceedings of 21st International Business Research Conference 10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2 Figure 1: Technology Acceptance Model Perceived Usefulness Attitudes towards using Behavioural intention to use Actual Usage Perceived Ease of Use Source: Original Technology Acceptance Model (Davis 1989) 2.1 Perceived Usefulness According to Davis et al., (1989), perceived usefulness (PU) can be explained as the extent to which the consumer believes that using the system can increase his/ her performance. From a perspective of e-commerce in tourism, perceived usefulness may be defined as the prospective traveler’s subjective probability that using the Internet will efficiently facilitate his/her purchasing tourism and travel service/product. Chen et al., (2002), Childers et al., (2001) and Heijden et al., (2001) found that perceived usefulness affects attitudes towards online shopping. Other researchers (Heijden et al., 2001; Koufaris, 2002; Pavlou, 2001) have also found that PU influences the purchase intention of potential Internet shoppers. Thus: H1: There is a direct positive relationship between perceived usefulness and attitude towards online purchasing. H2: There is a direct positive relationship between perceived usefulness and intention towards online purchasing of tourism and travel related products. 2.2. Perceived Ease of Use Perceived ease-of-use (PEOU) is defined as the degree to which a person believes that using a particular system would be free from effort (Davis, 1989). Many researchers (e.g. Aladwani, 2002; Moon & Kim, 2001) have studied the relationship between perceived ease of use and perceived usefulness. Nonetheless the relationship remains contradictory. Yet, past research on consumer adoption of online services found that PEOU has been found to be an important antecedent of user’s adoption of new Web technology in several studies (e.g., Chen, et al. 2002; Hong et al., 2008; Morris and Dhillon, 1997; Pikkarainen, Pikkarainen, Karjaluoto, & Pahnila, 2004; Shin et al., 2009; Taylor and Todd, 1995). Thus: H3: There is a direct positive relationship between perceived ease of use of tourism and travel related websites and perceived usefulness of the websites. H4: There is a direct positive relationship between the perceived ease of use of tourism and travel related websites and attitudes towards online purchasing. Proceedings of 21st International Business Research Conference 10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2 2.3. Trust in Travel Related Websites According to Kolsaker and Payne (2002), trust is defined as the dimension of a business relationship that determines the level to which each party feels they can rely on the integrity of the promise offered by the other. Chen et al. (2002) hypothesized that a consumer’s trust in a virtual store positively affects his/ her attitude toward using the e-store. Jarvenpaa et al. (2000) also found that trust in the online store directly influence attitude. Supported by the above literature, it can be hypothesized that: H5: There is a direct positive relationship between the trust on tourism and travelrelated websites and attitudes towards online purchasing. 2.4. Perceived Risks Perceived risk is defined as a consumer’s subjective perception of the possibility to reach unexpected consequences (Dowling and Staelin, 1994). In this study, perceived risk refers to the uncertainties associated with possible negative consequences of using e-commerce for tourism and travel products. Trust and risk are essential in explaining ecommerce adoption, as uncertainty is present in the technology-driven environment (Lee and Turban 2001; Pavlou 2003). Several studies show that perceived risk negatively influences trust in a given website (e.g. Featherman, 2001; Pavlou, 2001). Thus, the following hypothesis is proposed: H6: There is a direct negative relationship between perceived risk and trust in tourism and travel websites. Perceived risks also impact on attitude towards online purchasing. Jarvenpaa et al. (2000) showed attitude and risk perception affected consumer's attitudes toward an online-store. However, a study of Brown et al., (2007) noted that even though perceived risk is noted as an important barrier to e-commerce usage, it does not seem to stop people from buying online travel services. Yet, Wu and Wang’s (2005) study incorporated perceived risk into the TAM to evaluate consumers’ adoption of mobile commerce, and revealed that potential risks of online transaction in process affect consumers’ attitudes. Thus, H7: There is a direct negative relationship between perceived risk and attitudes towards online purchasing of tourism and travel related products. 2.5. Attitudes toward Online Purchasing Attitude is defined as a consumer’s positive or negative feelings related to accomplishing the purchasing behavior on the internet (Chiu et al., 2005). In the context of this study, attitude is a traveler’s salient belief of whether the outcome of his/ her use of the internet for purchasing tourism and travel related products will be positive or negative. Attitude is said to have a direct impact on intention. The original TAM model (Davis, 1986), and the models of Taylor and Todd (1995) and Morris and Dillon (1997) indicate that attitude exerts a positive effect on behavioral intention. This relationship Proceedings of 21st International Business Research Conference 10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2 has also been proven in general e-commerce studies (Bruner and Kumar, 2005). Hence, H8: There is a direct positive relationship between the attitudes and online purchase intention of tourism and travel related products 3. Methodology The survey research was based on a self- administered questionnaire originally designed in English and translated in French. The study measured TAM construct variables by using an adapted five-point Likert-type scale (1= strongly disagree to 5= strongly agree) devised by Davis (1989) and Taylor and Todd (1995). The questionnaire entailed items borrowed from literature which were slightly modified to suit the tourism and travel context. The demographic profile of the respondents was also captured. Data were collected from 150 travelers at different strategic tourism locations of Mauritius during the months of June and July 2012. To ensure the reliability of the scales, a reliability test was carried out and the Cronbach’s Alpha values (> 0.70) were verified. Multiple regression analysis was used to evaluate the relationships between the variables. 4. Findings 4.1 Sample Profile The sample results revealed a well- balanced gender distribution with 76 males (50.7%) and 74 females (49.3%). The age distribution was as follows: 53 respondents (35.3%) were between 18- 27 years, 30 respondents (20.0%) were between 28-37 years, 32 respondents (21.3%) were between 38-47 years, 29 respondents (19.3%) were between 48-57 years and 6 respondents (4.0%) were 58 years and above. As for the marital status, the majority of the respondents were married accounting for 48.0% while 41.3% of the respondents were single and 10.7% of the respondents were divorced/ widowed. The nationality composition was as follows: 74 French (49.3%), 38 British (25.3%), 11 Indian (7.3%), 10 German (6.7%), 5 South Africans (3.3%), 4 Australian (2.7%), 3 Swiss (2.0%) and 5 were from other nationalities (3.5%). In terms of frequency of visit, the majority was repeated visitors (60.0%) while 40.0% were first- time visitors. 4.2 Results of the Multiple Regression Analysis Four multiple regression analyses were performed to test the hypotheses. Results from Table 1 shows that Model 1 was a significant predictor of attitude towards online purchase intention of tourism and travel products (F = 32.92; p < 0.001) and explained 48 % (R2 = 0.48) of variance in the dependent variable. This relatively moderate percentage of explained variance is not surprising in tourism research given that attitude is affected by a multiplicity of factors, not all accounted in this model. PEOU variable did not significantly predict the dependent variable (β = - 0.066; t = - 0.863; p > 0.05). The other variables entered, namely PU (β = 0.554; t = 7.654; p < 0.001); TRUST (β = 0.173; t = 2.347; p < 0.05); RISK (β = - 0.221; t = - 3.276; p < 0.001) showed Proceedings of 21st International Business Research Conference 10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2 independent predictive effects on attitude towards online purchasing of tourism and travel products. Table 1: Multiple Regression Analysis Regression Models b t p Collinearity diagnostic VIF Tolerance Model Summary Model 1: Predicting attitude towards online purchasing PEOU -0.066 -0.863 Not sig. PU 0.554 7.654 < 0.001 TRUST 0.173 2.347 < 0.05 RISK -0.221 -3.276 < 0.001 1.629 1.448 1.504 1.257 0.614 0.691 0.665 0.796 R = 0.48; adj. R = 0.46; F = 32.92; p < 0.001 Model 2: Predicting online purchase intent PU 0.284 4.295 ATTITUDE 0.568 8.580 < 0.001 < 0.001 1.622 1.622 0.617 0.617 R = .60; adj. R = .60; F = 111.661; p < 0.001 < 0.001 1.000 1.000 R = .54; adj. R = .29; F = 60.50; p < .001 Model 3: Predicting perceived usefulness PEOU 0.539 7.778 -0.436 2 2 Model 4: Predicting perceived risks TRUST 2 2 -5.900 < 0.001 1.000 1.000 2 2 2 2 R = .19; adj. R = .19; F = 34.81; p < .001 Model 2 was a significant predictor of online purchase intent of tourism and travel products (F = 111.661; p < 0.001) and accounted for 60 % (R2 = 0.60) of variance in the dependent variable. Results suggest that PU significantly predicted online purchase intent of tourism and travel products (β = 0.284; t = 4.295; p < 0.001). Thus, there was a significant positive relationship between PU and online purchase intent. Attitude also significantly predicted online purchase intent of tourism and travel products (β = 0.568; t = 8.580; p < 0.001). Hence, there was a significant positive relationship between attitude towards tourism and travel related websites and online purchase intention of tourism and travel products. Results from the multiple regression analysis suggested that Model 3 was a significant predictor of perceived usefulness of tourism and travel websites (F = 60.50; p < 0.001). The model explained 29 % of variance in the dependent variable (R² = 0.290). PEOU significantly predicted perceived usefulness of tourism and travel websites (β = 0.539; t = 7.778; p < 0.001). Model 4 significantly predicted the perceived risks associated with tourism and travel websites (F = 34.81; p < 0.001) and explained around 19 % of variance in the dependent variable (R² = 0.190). The results confirm the significant negative relationship between perceived risks and trust associated with tourism and travel websites (β = - 0.436; t = - 5.900; p < 0.001). Proceedings of 21st International Business Research Conference 10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2 5. Discussion H1 which proposed a direct positive relationship between PU and attitude towards online purchasing of tourism and travel products was supported. This indicated that the more the travelers perceived the Internet to facilitate his/her purchase of tourism and travel products, the more positive was their attitude towards these websites. These findings are in line with the study of Chau and Hu (2001) who found perceived usefulness to be a significant determinant of attitude. Carey and Day (2005) also found a strong relationship between perceived usefulness and attitude. Based on the finding, PU was the strongest predictor of attitude. This outcome yields the implication that PU factor is of more concern to the travelers. H2, postulating a positive direct relationship between PU and online purchase intention of travelers was supported. This further explains that the more travelers perceived his/her online purchase of tourism and travel products useful, the more likely he/she will engage in online purchasing. This is echoed in the findings of Tan and Teo (2000) who applied the TAM in e- banking and found that the greater the perceived usefulness of using electronic banking services, the more likely electronic banking will be adopted. H3 which proposed a direct positive relationship between PEOU and PU was supported implying that tourism enterprises should make their websites more useful and usable. To travelers, the degree to which the tourism and travel website is perceived to be easy to use strongly influences their perceived usefulness of online purchasing. The causal relationship of PEOU to PU is corroborated by Hubona and Blanton (1996). Morosan and Jeong’s (2008) study findings also revealed that PEOU of hotel reservation websites had a significant influence PU of the websites. Based on the result, both constructs are concluded to be closely linked in part, this is due to the fact that a traveler would inherently try to mould his/her perception of online purchasing based on his/her experiences in engaging in online purchasing and the ease in which the task was executed. H4 which proposed a direct positive relationship between PEOU and attitude was not supported. This finding corroborates the study of Chong et al. (2010) where it was found that contrary to TAM, perceived ease of use was not significant. Although PEOU was second to PU in importance concerning consumer attitude as per previously documented studies, the results of the multiple regression revealed that it was not a significant predictor of consumer attitude. This due to the fact that online shopping systems have become easier to use and users have become more technically savvy, variations in the perceived ease of use dimension are reduced. Likewise, the study was conducted with travelers already had the online purchase experience. Therefore, perceived ease of use was found to have no effect on their attitude. H5 which predicted a direct relationship between trust and attitude towards online purchasing was supported. Previous research has consistently argued that there is a positive relationship between trust and attitude (e.g. Grazioli and Jarvenpaa 2000; Macintosh and Lockshin 1997; Suh and Han 2003). Similarly, empirical evidence has underlined the direct positive influence of trust on shopping attitude (Wu and Chen, 2005). This further explains that trust in tourism and travel website enables favorable Proceedings of 21st International Business Research Conference 10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2 attitudes since the website is perceived to be reliable and that no harmful consequences will occur if travelers use it to carry out online purchasing of tourism and travel products. H6 which proposed a direct negative relationship between trust and perceived risk was supported implying that the more travelers perceive the websites to be trustworthy, the lesser he/she will perceive the risk associated with online purchasing. The inverse relationship between trust and perceived risk has been discussed by Kim et al. (2007) who argue that trust negatively affects a consumer’s perceived risk in the online transaction. However, Morgan and Hunt (1994) asserted that consumer’s perceived risk is not related to trust. Yet, other scholars argue that consumer’s perceived risk in the online marketplace can be reduced by trust or by raising security of web sites (Pavlou, 2003). The result of this current study support H7 where perceived risk was found to have a direct negative influence on attitudes towards online purchasing of tourism and travel products. The online purchase of travel products has high levels of perceived risk, due both to the shopping channel being used (Internet) (Cunnigham et al., 2005). Thus, the risk factor associated with the tourism and travel websites can have an impact on attitudes of travelers. The result also coincides with the studies of Shih (2004) and Van der Heijden et al., (2003), who stated that prior research has shown that perceived risk in e-commerce has a negative effect on attitude towards the behavior. Potential risks of online transaction in process will affect travelers’ attitude of adopting it for instance, finance loss, divulgence of personal information or lack of product quality warranty. H8 which predicts a positive direct relationship between attitude of travelers towards tourism and travel websites and online purchasing intention was accepted. This result suggests that if the travelers’ attitude towards the website is positive, his/her behavior is more likely to be optimistic. This finding is in line with that of Shu and Han (2003) who empirically validated that behavioral intention is determined by the individual’s attitude. Past research has also examined purchase intent and attitudes to purchasing online (van der Heijden et al., 2003). According to the result, attitude would be favorable if appropriate strategies are adopted to increase perceived usefulness and trust and reduce perceived risks. 6. Conclusion This particular study provides further evidence on the appropriateness of TAM to measure the different dimensions of online purchase intent of travelers. The results confirmed seven out of eight proposed hypotheses among which, perceived usefulness, trusts and perceived risks have been found to be significant in influencing attitude to online purchase of tourism and travel products. On the other hand, the result suggests that perceived ease of use is not a significant predictor of attitude. Yet, perceived usefulness impacts positively on online purchase intent of travelers and perceived ease of use significantly influences perceived usefulness while trusts negatively impacts on perceived risks associated with tourism and travel websites. This study has its limitations since it does not cover potential travelers, who have never conducted any online transaction but have the intention to engage in online purchase of tourism and travel products. The study was also conducted in off- peak period. It is possible that Proceedings of 21st International Business Research Conference 10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2 different sample profile would be derived in summer (peak period). Despite these limitations, this study is the first attempt in Mauritius to examine factors influencing online purchase intent of travelers. Therefore, these limitations should be viewed as opportunities for future research. References Aladwani, A. M. 2002. “The development of two tools for measuring the easiness and usefulness of transactional Web sites”, European Journal of Information systems, Vol. 11. No. 3, pp 223-234. Brown M. R., R. Muchira and Gottlieb, U. 2007. “Privacy concerns and the purchasing of travel services online” Information Technology & Tourism, Vol. 9, pp. 15 – 25. Bruner II, G. C. and Kumar A. 2005. “Applying T.A.M. to consumer usage of handheld Internet devices”, Journal of Business Research, Vol. 58, pp. 553 – 558. Carey, J. M. and Day, D. 2005. “Cultural aspects for technology acceptance: Asian perspectives and research techniques”, Americas Conference on Information Systems, Omaha, NE. Chau P. Y. K. and Hu P. J. 2001. “Information technology acceptance by individual professionals: A model comparison approach”, Decision Sciences, Vol. 32, No. 4, pp. 699 – 719. Chen L. D., Gillenson M. L. and Sherrell D. L. 2002. “Enticing online consumers: An extended technology acceptance perspective”, Information and Management, Vol. 39, No. 8, pp. 705 – 719. Chen, C. (2006), “Identifying significant factors influencing trust in an online travel website”. Information Technology and Tourism, Vol. 8, pp. 197-214. Childers T. L., Christopher L. C., Peck, J., and Carson, S. 2001. “Hedonic and utilitarian motivations for online retail shopping behaviour”, Journal of Retailing, Vol. 77, pp. 511 – 535. Chiu, Y. B., Lin, C. P., and Tang L. L. 2005. “Gender differs: Assessing a model of online purchase intentions in e-tail service”, International Journal of Service Industry Management, Vol. 16, No. 5, pp. 416-435. Chong, A.Y., Ooi, K., Lin, B and Tan, B. 2010. “Online banking adoption: an empirical analysis”, International Journal of Bank Marketing, Vol. 28, No. 4, pp. 267-287. Cunningham L., Gerlach J., Harper M. and Young C. 2005. “Perceived risk and the consumer buying process: Internet airline reservations”, International Journal of Service Industry Management, Vol. 16, No. 4, pp. 357 – 372. Davis F. D. 1989. “Perceived usefulness, perceived ease of use and user acceptance of information technology”, MIS Quarterly, Vol. 13, No. 3, pp. 319 – 340. Davis, F. D. Bagozzi, R., Warshaw, P. R. 1989. “User acceptance of computer technology: A comparison of two theoretical models”, Management Science, Vol. 35, No. 8, pp. 982–1003. Dowling, G. R. and Staelin, R. 1994. “A model of perceived risk and intended riskhandling activity”, Journal of Consumer Research”, Vol. 21, pp. 119-134. Eric, N. and Cassidy, F. Brown L. 2006. “Exploring the major factors influencing consumer selection of travel agencies in a regional setting”, Journal of Hospitality and Tourism Management, Vol. 13, No.1, pp 26-42. Proceedings of 21st International Business Research Conference 10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2 Featherman M. 2001. “Is perceived risk germane to technology acceptance research?” AMCIS Proceedings, Boston, MA. Grazioli, S., and Jarvenpaa, S. L. 2000. “Perils of internet fraud: An empirical investigation of deception and trust with experienced Internet consumers”, IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans”, Vol. 39, No. 4: pp 395-410. Heijden V. H., Verhagen T. and Creemers M. 2001. “Predicting online purchase behavior: replications and test of competing models”, Paper presented at the Proceedings of the 34th international conference on system sciences. Hong, S. J, Thong, Y. L, Moon, J. Y and Tam, K. Y. 2008. “Understanding the behavior of mobile data services consumers”, Information Systems Frontiers, Vol. 10, pp. 431 – 445. Hubona, G. S., & Blanton, J. E. 1996. “Evaluating system design features” International Journal of Human-Computer Studies, Vol. 44, pp 93-118. Jarvenpaa S. L., Tractinsky N., and Vitale, M. 1999, “Consumer trust in an Internet store”, Information Technology and Management, Vol. 1, No. 12, pp. 45 – 71. Kim D. J., Ferrin, D. L. and Rao, H. R. 2007, “A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents”, Decision Support System, Vol. 44, pp. 544 – 564. Kolsaker, A., and Payne, C. 2002. “Engendering trust in e-commerce: a study of gender-based concerns”, Marketing Intelligence & Planning, Vol. 20, No. 4, pp. 206 – 214. Koufaris, M. (2002), “Applying the technology acceptance model and flow theory to online consumer behavior”, Information Systems Research, Vol. 13, pp 205-223. Lee M. K. O., and Turban, E. 2001. “A trust model for consumer internet shopping”, International Journal of Electronic Commerce, Vol. 6, No. 1, pp. 75 – 91. Macintosh, G. and L.S. Lockshin. 1997. “Retail relationships and store loyalty: A multilevel perspective”, International Journal of Research in Marketing, Vol. 14, No. 5, pp. 487 – 497. Moon J. and Kim Y. 2001. “Extending the TAM for a World Wide Web context”, Information and Management, Vol. 38, pp. 217 – 230. Morosan C. and Jeong M. 2008. “User’s perceptions of two types of hotel reservation Web sites”, International Journal of Hospitality Management, Vol. 27, pp. 284 – 292. Morris, M. G and Dillon, A. 1997. “How user perceptions influence software use”, IEEE software, Vol. 14, No. 4, pp. 58 – 65. Pavlou P. A. 2001. “Integrating trust in electronic commerce with the technology acceptance model: Model development and validation”, Proceedings of the Seventh Americas Conference in Information Systems”, Boston, MA. Pavlou P. A. 2003. “Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model”, International Journal of Electronic Commerce, Vol. 7, No. 3, pp. 101 – 134. Pikkarainen T., Pikkarainen K., Karjaluoto H. and Pahnila S. 2004. “Consumer acceptance of online banking: an extension of the technology acceptance model”, Internet Research, Vol. 14, No. 3, pp. 224 – 235. Reichheld, F. F. and Schefter, P. 2000. “E-loyalty: your secret weapon on the web”, Harvard Business Review, Vol. 78 No. 4, pp. 105-13. Proceedings of 21st International Business Research Conference 10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2 Shih H. (2004), “An empirical study on predicting user acceptance of e-shopping on the Web”, Information Management, Vol. 41, No. 3, pp. 351 – 368. Shin, H. K., Kim, K. K., and Lee, K. W. 2009. “Understanding the user acceptance of Multimedia messaging services”, Proceedings of the 11th international conference on Advanced Communication Technology, GangwonDo, South Korea”, pp. 1382 – 1385. Suh, B. and I. Han, 2003, “The impact of customer trust and perception of security, control on the acceptance of electronic commerce”, International Journal of Electronic Commerce, Vol. 7, No. 3, pp. 135 – 161. Tan, M., and Teo T. S. H. 2000. Factors influencing the adoption of internet banking”, Journal of the Association for Information Systems, Vol. 1, No. 5, pp. 22 – 38. Taylor, S., and Todd, P. 1995. “Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions”, International Journal of Research in Marketing”, Vol. 12, pp. 137 – 155. Van der Heijden H. 2003. “Factors influencing the usage of websites: The case of a generic portal in the Netherlands”, Information & Management, Vol. 40, No. 6, pp. 541 – 549. Wu J. H., and Wang S. C. 2005, “What drives mobile commerce? An empirical evaluation of the revised technology acceptance model”, Information & Management, Vol. 42, No. 5, pp. 719 – 729. Wu, I. L., and Chen, J. L. 2005. “An extension of Trust and TAM model with TPB in the initial adoption of online tax: an empirical study”, Int. J. Hum. Comp. Stud, Vol. 62, No. 6, pp. 784-808. Yeh R. J., Leong J. K., Blecher L., and Lai H. S. H. 2005. “Analysis of hoteliers’ Ecommerce and information technology applications: Business travellers’ perceptions and needs”, International Journal of Hospitality and Tourism Administration”, Vol. 6, No. 2, pp. 29 – 62.