Year 2019, Volume 5 , Issue 15, Pages 300 - 320 2020-01-07

THE INFLUENCE OF PERCEIVED USEFULNESS, SOCIAL INFLUENCE, INTERNET SELF-EFFICACY AND COMPATIBILITY ON USERS’ INTENTIONS TO ADOPT E-LEARNING: INVESTIGATING THE MODERATING EFFECTS OF CULTURE

Khaled M S FAQİH [1]


The current study has been inspired by two significant issues: (1) The proliferation of e-technologies such as e-learning have dramatically motivated global research intended to advance our knowledge of the dynamics of these technologies in varying environmental contexts and settings, and (2) the importance of cultural values at individual-level analysis in technology adoption merits greater level of attention and interests from researchers and practitioners, particularly in relation to developing country contexts. This study intends to investigate the significance of highly influential adoption factors acknowledged as relevant in prior literature in predicting user’s behavioral intention to adopt new technologies. These potentially important factors were drawn from highly popular technology adoption and social theories including perceived usefulness (Technology Acceptance Model), social influence (Theory of Planned Behavior), Internet self-efficacy (Social Cognitive Theory) and perceived compatibility (Innovation Diffusion Theory). Further, the present study examines the moderating impact of both individualism-collectivism and uncertainty avoidance cultural dimensions at individual-level on the hypothesized relationships linking these highly influential adoption factors with behavioral intention to adopt e-learning environment in order to facilitate and enhance learning processes and in an effort to achieve value maximization and waste minimization requirements in the context of e-learning technology. The empirical data which consists of 262 valid datasets was collected from undergraduate university students in Jordan via self-administered paper-based questionnaire. The questionnaire was developed from previously accepted and validated a set of measurements items. The empirical data was numerically assessed and analyzed with the help of WarpPLS 5.0. The findings of this study demonstrate that perceived usefulness, social influence, Internet self-efficacy and perceived compatibility are important predictors of individuals’ behavioral intention to adopt e-learning technology. Further, the current findings provide adequate empirical evidence to support all hypotheses involving moderating effects with one exception whereby both individualism-collectivism and uncertainty avoidance cultural values have little statistical significance on the relationship linking perceived usefulness with behavioral intention to adopt e-learning technologies. Interestingly, the proposed model explains a substantial amount of variance (63%) which signifies that the model fits the data well. Research findings are discussed and contribution to theory and practice are presented.

adoption, e-learning, culture, WarpPLS, Jordan
  • Aghaei, M., & Rezagholizadeh, M. (2017). The impact of information and communication technology (ICT) on economic growth in the OIC Countries. Economic and Environmental Studies, 17(42), 255-276. Ahmad, W., Attiq, S., Ahmad, A., Ilyas, A., & Kulsoom, K. (2019). Investigating the impact of Consumer’s Involvement, Risk-taking Personality, Internet Self-Efficacy, Life Style and Privacy Concern on Online Purchase Intention and Shopping Adoption. Pakistan Business Review, 20(3), 582-599. Ajzen, I., Fishbein, M., 1980. Understanding Attitudes and Predicting Social Behavior. Prentice-Hall Inc., Englewood Cliffs, NJ. Akhtar, S., Irfan, M., Sarwar, A., & Rashid, Q. U. A. (2019). Factors influencing individuals’ intention to adopt mobile banking in China and Pakistan: The moderating role of cultural values. Journal of Public Affairs, 19(1), e1884. Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27-50. Alqurashi, E. (2016). Self-efficacy in online learning environments: A literature review. Contemporary Issues in Education Research (Online), 9(1), 45. Arshad, A. M., & Su, Q. (2015). Interlinking service delivery innovation and service quality: a conceptual framework. Journal of Applied Business Research (JABR), 31(5), 1807-1822. Belkhamza, Z., & Wafa, S. A. (2014). The role of uncertainty avoidance on e-commerce acceptance across cultures. International Business Research, 7(5), 166. Buckenmeyer, J. A., Barczyk, C., Hixon, E., Zamojski, H., & Tomory, A. (2016). Technology’s role in learning at a commuter campus: The student perspective. Journal of Further and Higher Education, 40(3), 412-431. Caporarello, L., & Sarchioni, G. (2014 E-learning: the recipe for success. Journal of e-learning and Knowledge Society, 10(1) Chen, Y., Li, X., Liu, J., & Ying, Z. (2018). Recommendation system for adaptive learning. Applied Psychological Measurement, 42( 1), 24– 41. Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & education, 63, 160-175. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336. Choi, J., & Geistfeld, L. V. (2004). A cross-cultural investigation of consumer e-shopping adoption. Journal of Economic Psychology, 25(6), 821-838. Cowie, N., & Sakui, K. (2015). Assessment and e-learning: Current issues and future trends. JALT CALL Journal, 11(3), 271-281. De Mooij, M. (2019). Consumer behavior and culture: Consequences for global marketing and advertising. SAGE Publications Limited. Dinev, T., Goo, J., Hu, Q., & Nam, K. (2009). User behavior towards protective information technologies: the role of national cultural differences. Information Systems Journal, 19(4), 391-412. Duan, Y., He, Q., Feng, W., Li, D., & Fu, Z. (2010). A study on e-learning take-up intention from an innovation adoption perspective: A case in China. Computers & Education, 55(1), 237-246. Eastin, M. S., & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide. Journal of computer-mediated communication, 6(1), JCMC611. Fang, T. Yin Yang, 2012. A new perspective on culture. Manage. Organ. Rev. 8 (1), 25–50. Faqih, K. M. (2011). Integrating perceived risk and trust with technology Acceptance model: An empirical assessment of customers’ acceptance of online shopping in Jordan. 2011 International Conference on Research and Innovation in Information Systems (ICRIIS 2011), Kuala Lumpur, Malaysia. Faqih, K. M. (2013). Exploring the influence of perceived risk and internet self-efficacy on consumer online shopping intentions: Perspective of technology acceptance model. International Management Review, 9(1), 67-77. Faqih, K. M. (2016a). An empirical analysis of factors predicting the behavioral intention to adopt Internet shopping technology among non-shoppers in a developing country context: Does gender matter?. Journal of Retailing and Consumer Services, 30, 140-164. Faqih, K. M. (2016b). Which is more important in e-learning adoption, perceived value or perceived usefulness? Examining the moderating influence of perceived compatibility. 4th global summit on education. GSE. Faqih, K. M., & Jaradat, M. I. R. M. (2015). Assessing the moderating effect of gender differences and individualism-collectivism at individual-level on the adoption of mobile commerce technology: TAM3 perspective. Journal of Retailing and Consumer Services, 22, 37-52. Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley. Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable and measurement error. Journal of Marketing Research, 18 (1), 39-50. Hair, J. F., Black, W. C., Balin, B. j., & Anderson, R. E. (2010). Multivariate data analysis: Maxwell Macmillan International Editions. Hofstede, G. (1980). Motivation, leadership, and organization: do American theories apply abroad?. Organizational dynamics, 9(1), 42-63. Hofstede, G. (1991). Organizations and cultures: Software of the mind. McGrawHill, New York. Hofstede, G. (2001). Culture’s Consequences: Comparing Values, Behaviors. Institutions and Organizations across Nations. Sage Publication, 2nd edition. Hussein, Z. (2018). Subjective norm and perceived enjoyment among students in influencing the intention to use e-learning. International Journal of Civil Engineering and Technology (IJCIET), 9(13), 852-858. Jaradat, M. I. R. M., & Faqih, K. M. (2014). Investigating the moderating effects of gender and self-efficacy in the context of mobile payment adoption: A developing country perspective. International Journal of Business and Management, 9(11), 147. Johannisson, B. (2017). Networking and entrepreneurial growth. The Blackwell handbook of entrepreneurship, 368-386. Kimiloglu, H., Ozturan, M., & Kutlu, B. (2017). Perceptions about and attitude toward the usage of e-learning in corporate training. Computers in Human Behavior, 72, 339-349. Kock, N. (2012). WarpPLS 5.0 user manual. Laredo, TX: ScriptWarp Systems. Lee, Y. H., Hsieh, Y. C., & Hsu, C. N. (2011). Adding innovation diffusion theory to the technology acceptance model: Supporting employees’ intentions to use e-learning systems. Journal of Educational Technology & Society, 14(4), 124-137. Liang, J. C., Wu, S. H., & Tsai, C. C. (2011). Nurses’ Internet self-efficacy and attitudes toward web-based continuing learning. Nurse Education Today, 31(8), 768-773. Lorenz, G. V., & Buhtz, K. (2017). Social influence in technology adoption research: a literature review and research agenda. In Proceedings of the 25th European Conf. Info. Systems (ECIS), 2017, pp. 2331-2351. McCoy, S., Galletta, D. F., & King, W. R. (2005). Integrating national culture into IS research: The need for current individual level measures. Communications of the Association for Information Systems, 15(1), 12. McCoy, S., Galletta, D. F., & King, W. R. (2007). Applying TAM across cultures: the need for caution. European Journal of Information Systems, 16(1), 81-90. Mohammadi, H. (2015). Factors affecting the e-learning outcomes: An integration of TAM and IS success model. Telematics and Informatics, 32(4), 701-719. Mohammadyari, S., & Singh, H. (2015). Understanding the effect of e-learning on individual performance: The role of digital literacy. Computers & Education, 82, 11-25. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222. Morgan, N. A., & Vorhies, D. W. (2018). The Business Performance Outcomes of Market Orientation Culture and Behaviors, in (ed.) Innovation and Strategy. Naveed, Q. N., Muhammed, A., Sanober, S., Qureshi, M. R. N., & Shah, A. (2017). Barriers Effecting Successful Implementation of E-Learning in Saudi Arabian Universities. International Journal of Emerging Technologies in Learning, 12(6). Pavlou, P. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7, 101–134. Reddick, C. G. (Ed.). (2010). Citizens and e-government: Evaluating policy and management: Evaluating policy and Management. IGI Global. Retnawati, H., Munadi, S., Arlinwibowo, J., Wulandari, N. F., & Sulistyaningsih, E. (2017). Teachers’ difficulties in implementing thematic teaching and learning in elementary schools. The New Educational Review, 48, 201-212. Rufín, R., Bélanger, F., Molina, C. M., Carter, L., & Figueroa, J. C. S. (2014). A cross-cultural comparison of electronic government adoption in Spain and the USA. International Journal of Electronic Government Research (IJEGR), 10(2), 43-59. Salehi, H., Shojaee, M., & Sattar, S. (2015). Using E-Learning and ICT Courses in Educational Environment: A Review. English Language Teaching, 8(1), 63-70. Sanchez-Franco, M. J., Martínez-López, F. J., & Martín-Velicia, F. A. (2009). Exploring the impact of individualism and uncertainty avoidance in Web-based electronic learning: An empirical analysis in European higher education. Computers & Education, 52(3), 588-598. Sangrà, A., Vlachopoulos, D., & Cabrera, N. (2012). Building an inclusive definition of e-learning: An approach to the conceptual framework. The International Review of Research in Open and Distributed Learning, 13(2), 145-159. Solomon, M. R., Bamossy, G. J., Askegaard, S. T. & Hogg, M. K., 2013. Consumer behaviour: A European perspective. 5th ed. Essex: Pearson Education. Srite, M., Karahanna, E., 2006. The influence of national culture on the acceptance of information technologies: an empirical study. MIS Q. 30 (3), 679–704. Straub, D., Boudreau, M. C., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information systems, 13(1), 24. Tam, C., & Oliveira, T. (2017). Understanding mobile banking individual performance: the DeLone & McLean model and the moderating effects of individual culture. Internet Research, 27(3), 538-562. Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: a structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306-328. Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational statistics & data analysis, 48(1), 159-205. Teo, T., & Huang, F. (2019). Investigating the influence of individually espoused cultural values on teachers’ intentions to use educational technologies in Chinese universities. Interactive Learning Environments, 27(5-6), 813-829. Tian, M., Deng, P., Zhang, Y., & Salmador, M. P. (2018). How does culture influence innovation? A systematic literature review. Management Decision, 56(5), 1088-1107. Tigre, P. B., and Dedrick, J. (2004). E-commerce in Brazil: Local Adaptation of a Global Technology. Electronic Markets; London, 14.1: 36-47. Tsai, C. C., Chuang, S. C., Liang, J. C., & Tsai, M. J. (2011). Self-efficacy in internet-based learning environments: A literature review. Educational Technology & Society, 14(4), 222–240. Uppal, M. A., Ali, S., & Gulliver, S. R. (2018). Factors determining e‐learning service quality. British Journal of Educational Technology, 49(3), 412-426. Vandenhouten, C., Gallagher-Lepak, S., Reilly, J., & Ralston-Berg, P. (2014). Collaboration in E-Learning: A Study Using the Flexible E-Learning Framework. Online Learning, 18(3), n3. Vey, J. F. (2017). Does Innovation Equal Gentrification. Brookings Institution. Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365. Weinstein, A. (2016). Superior customer value: Strategies for winning and retaining customers. CRC Press. World Economic Forum’s Global IT Report. (2016), http://www3.weforum.org/docs/GITR2016/chapter1 Wu, J. H., Tennyson, R. D., & Hsia, T. L. (2010). A study of student satisfaction in a blended e-learning system environment. Computers & Education, 55(1), 155-164. Yates, J. F., & de Oliveira, S. (2016). Culture and decision making. Organizational Behavior and Human Decision Processes, 136, 106-118. Zakour, A. B. (2004). Cultural differences and information technology acceptance. In Proceedings of the 7th annual conference of the Southern association for information systems (pp. 156-161).
Primary Language en
Subjects Education and Educational Research
Journal Section Articles
Authors

Author: Khaled M S FAQİH
Country: Jordan


Dates

Publication Date : January 7, 2020

EndNote %0 International E-Journal of Advances in Education THE INFLUENCE OF PERCEIVED USEFULNESS, SOCIAL INFLUENCE, INTERNET SELF-EFFICACY AND COMPATIBILITY ON USERS’ INTENTIONS TO ADOPT E-LEARNING: INVESTIGATING THE MODERATING EFFECTS OF CULTURE %A Khaled M S FAQİH %T THE INFLUENCE OF PERCEIVED USEFULNESS, SOCIAL INFLUENCE, INTERNET SELF-EFFICACY AND COMPATIBILITY ON USERS’ INTENTIONS TO ADOPT E-LEARNING: INVESTIGATING THE MODERATING EFFECTS OF CULTURE %D 2020 %J IJAEDU- International E-Journal of Advances in Education %P 2411-1821-2411-1821 %V 5 %N 15 %R doi: 10.18768/ijaedu.593878 %U 10.18768/ijaedu.593878