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TOJET: The Turkish Online Journal of Educational Technology – October 2012, volume 11 Issue 4 Conclusions on the integration of cultural dimensions in UTAUT. This study shows once again that different definitions of culture apply in the context of ETA, and have different impacts on acceptance. Besides national culture, professional culture such as STEM vs. non-STEM can impact acceptance profiles. Several publications (Li et al., 2010; Li & Kirkup, 2007; Nistor et al., in press; Teo et al., 2008; Veltri & Elgarah, 2009; Venkatesh & Zhang, 2010; Zakour, 2007) are available suggesting how cultural dimensions can be integrated in acceptance models such as UTAUT. However, the present findings are still insufficient for proposing an acceptable model. Comparing acceptance profiles across national and professional cultures, as the study at hand does, may result in the progress of this research line. In this study, progress in integrating culture in ETA models was done with respect to the cultural dimension of uncertainty avoidance. Both in this study and in Nistor et al. (in press), STEM professionals appear less uncertainty avoidant; in the Turkish, as opposite to the measurements in Germany and Romania, STEM professions are associated with significant higher computer anxiety and with a stronger influence of computer anxiety on use behavior. This reinforces the assumption of a correlation between UAI and computer anxiety, but also suggests that additional variables may be involved here in a more complex relationship that should be further explored. An additional variable that may be highly relevant for acceptance models together with culture may be the availability of the technological infrastructure. The particularly strong correlation between technology use, computer literacy, computer anxiety and performance/effort expectancy, on the one hand, and the non-significant cultural differences between regions of Turkey, on the other, suggest regional differences in the technological and educational infrastructure availability. Hence, future acceptance research should also consider infrastructure availability along with cultural differences. Conclusions for educational practice. As a general conclusion, which is not new at all, this study evidentiates the importance of computer literacy, which is tightly intertwined with the acceptance and use of educational technology. This may be directly supported by computer skills training, and indirectly by supporting selfdirected experiential learning (Çağlar & Demirok, 2010). With respect to culture, this study suggests that acceptance and Hofstede’s culture dimensions are appropriate starting points when dealing with cultural discrepancies in the context of educational technology. The use of educational technology requires taking into consideration the individual differences in ETA, especially when members of different – national and professional – cultures are involved. Different learners may have different expectations with respect to the design and outcome of computer-enhanced learning environments, and need different support, e.g. in order to successfully deal with computer anxiety. In mixed, STEM and non-STEM learner groups, probably STEM professionals will initially expect more performance. However, the influence of performance expectations will be similar for both STEM and non-STEM. Educational technology designers should provide means of communicating these expectations, as well as possibilities to fulfill them. In general, forced use of educational technology should be avoided (Liu, 2012), no matter what the profession of the learners is. Notably, technology use may implicitly become compulsory, if there is no alternative in using technology in order to reach an educational goal. Forced technology use contradicts personal attitudes and intentions, and may thus increase comptuer anxiety and impair learning motivation (Deci & Ryan, 2000). Therefore, learning environments should be provided along with technology-free alternatives, or at least include face-to-face components. Individual learners’ characteristics, from which acceptance profiles are most important, should be considered in association with design elements of learning environments. Relying on the paradigm of mass-customization (Nistor, Dehne & Drews, 2010), technology-enhanced learning environments may be designed for specific groups of users defined by national and professional culture. Limitations of the study and open research questions. While this study, corroborated with former studies, provides robust evidence of UTAUT and its associated measure instrument’s validity across cultures, the findings still have some limitations. One of them is due to the subjective character of the data, hence future research should also include objective data gained by methods such as observation or artifact analysis. As already mentioned, the comparative study of ETA across cultures should be continued in order to provide deeper insight in the complex relationship between ETA and culture. Copyright © The Turkish Online Journal of Educational Technology 405
TOJET: The Turkish Online Journal of Educational Technology – October 2012, volume 11 Issue 4 ACKNOWLEDGEMENTS We would like to express our thanks to help Gurol Zırhlıoğlu for calculating the composite reliability for the UTAUT variables. We also are grateful to Dr. Aytac Gogus’ students at Project 102 course during Spring and Fall 2011 semesters for their help during the data collection in Turkey. REFERENCES Ajzen, I. & Fishbein, M. (2000). Attitudes and the attitude-behaviour relation: Reasoned and automatic processes. European Review of Social Psychology, 11, 1-33. Aydin, C. H. & McIsaac, M. S. (2004). The Impact of Instructional Technology in Turkey. Educational Technology Research & Development, 52(1), 105-112. Bagozzi, R. P. (2007). The legacy of the Technology Acceptance Model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 4 (3), 244-254. Barnett, G. A. & Sung, E. (2006). Culture and the structure of the international hyperlink network. Journal of Computer-Mediated Communication, 11 (2), 217-238. Barton, S. (2010). Social and cultural factors that influence the uptake of e-learning: case studies in Malaysia, Indonesia, Turkey, Singapore and Australia. PhD Thesis. Melbourne: School of Business Information Technology, RMIT University. Beaudry, A. & Pinsonneault, A. (2010). The other side of acceptance: Studying the direct and indirect effects of emotions on information technology use. MIS Quarterly, 34 (4), 689-710. Bentler, P. M. & Weeks, D. G. (1980). Linear structural equations with latent variables. Psychometrika, 45 (3), 289-308. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley. Bonnett, A. (2002). Makers of the West: national identity and occidentalism in the work of Fukuzawa Yukichi and Ziya Gökalp. Scottish Geographical Magazine, 118 (3), 165-182. Çağlar, M. & Demirok, M. S. (2010). Students’ computer skills in faculty of education. Cypriot Journal of Educational Sciences, 5 (3), 203-211. Conti-Ramsden, G., Durkin, K. & Walker, A. (2010). Computer anxiety: A comparison of adolescents with and without a history of specific language impairment (SLI). Computers & Education, 54 (1), 136-145. Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319-340. Deci, E. L. & Ryan, R. M. (2000). The „what?“ and „why?“ of goal pursuits: Human needs and the selfdetermination of behavior. Psychological Inquiry, 11 (4), 227-268. Göğüş, A. & Nistor, N. (in press). A validation of the UTAUT measure instrument for educational technology acceptance in Turkey. In International Educational Technology Conference IETC 2012. Procedia Social and Behavioral Sciences. New York, NY: Elsevier. Gök, B. & Erdoğan, T. (2010). Investigation of pre-sercive teachers’ perceptions about concept of technology through metaphor analysis. Turkish Online Journal of Educational Technology, 9 (2), 145-160. Hofstede, G. (2001). Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations. Thousand Oaks, CA: Sage. Hofstede, G. (2012). VSM 94. Online available at http://www.geerthofstede.nl/vsm-94 (last download: September 20, 2012) Hu, L. & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6 (1), 1-55. Im, I., Kim, Y. & Han, H. J. (2008). The effects of perceived risk and technology type on users’ acceptance of technologies. Information & Management, 45, 1-9. Leidner, D. E. & Kayworth, T. (2006). A review of culture in information systems research: Toward a theory of information technology culture conflict. MIS Quarterly, 30 (2), 357-399. Lerche, T. & Kiel, E. (under review). Signifikanzaussagen für Kulturdimensionen nach Geert Hofstede. [Assessing the statistical significance of Hofstede’s cultural data.] Statistika. Li, D., Chau, P. Y. K. & Van Slyke, C. (2010). A comparative study of individual acceptance of instant messaging in the US and China: A structural equation modeling approach. Communications of the Association for Information Systems, 26 (5), 85-106. Li, N. & Kirkup, G. (2007). Gender and cultural differences in Internet use: A study of China and the UK. Computers & Education, 48, 301-317. Liu, S. (2012). The impact of forced use on customer adoption of self-service technologies. Computers in Human Behavior, 28 (4), 1194-1201. Mulaik, S. & Millsap, R. E. (2000). Doing the four-step right. Structural Equation Modeling, 7 (1), 36-73. Nistor, N. (under review). The evolution of acceptance and participation in online university courses: Changing perspectives from Information Systems towards Instructional Design. Copyright © The Turkish Online Journal of Educational Technology 406
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