Factors contributing pre-school trainees teachers adoption of virtual learning environment: Malaysian evidence
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| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 |
| date | 2015-10-07 08:59:31 |
| format | Restricted Document |
| id | 12388 |
| institution | UniSZA |
| internalnotes | Bakar, A. A., Razak, F. Z. A., & Abdullah, W. S. W. (2013). Assessing the Effects of UTAUT and SelfDetermination Predictor on Students Continuance Intention to Use Student Portal. World Applied Sciences Journal, 21(10), 1484-1489. Casey, T., & Wilson-Evered, E. (2012). Predicting uptake of technology innovations in online family dispute resolution services: An application and extension of the UTAUT. Computers in Human Behavior, 28(6), 2034-2045. Chang, M. K., & Cheung, W. (2001). Determinants of the intention to use Internet/WWW at work: a confirmatory study. Information & Management, 39(1), 1-14. Chen, C.-F., & Chao, W.-H. (2011). Habitual or reasoned? Using the theory of planned behavior, technology acceptance model, and habit to examine switching intentions toward public transit. Transportation Research Part F: Traffic Psychology and Behaviour, 14(2), 128-137. Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63(0), 160-175. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Mahwah NJ: Lawrence Erlbaum Associates. Chiu, C.-M., & Wang, E. T. G. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194-201. Chong, A. Y.-L. (2013). Predicting m-commerce adoption determinants: A neural network approach. Expert Systems with Applications, 40(2), 523-530. Chow, M., Herold, D. K., Choo, T.-M., & Chan, K. (2012). Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education. Computers & Education, 59(4), 1136-1144. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340. Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 440-452. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. Gruzd, A., Staves, K., & Wilk, A. (2012). Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior, 28(6), 2340-2350. Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis Sixth Edition Pearson Education. New Jersey. Hernandez, B., Jimenez, J., & Martin, M. J. (2009). Future use intentions versus intensity of use: An analysis of corporate technology acceptance. Industrial Marketing Management, 38(3), 338-354. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic management journal, 20(2), 195-204. Jeong, H. (2011). An investigation of user perceptions and behavioral intentions towards the e-library. Library Collections, Acquisitions, and Technical Services, 35(2–3), 45-60. Jou, M., & Wang, J. (2013). Investigation of effects of virtual reality environments on learning performance of technical skills. Computers in Human Behavior, 29(2), 433-438. Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand's community health centers: Applying the UTAUT model. international journal of medical informatics, 78(6), 404-416. Kuo, Y.-F., & Yen, S.-N. (2009). Towards an understanding of the behavioral intention to use 3G mobile valueadded services. Computers in Human Behavior, 25(1), 103-110. Lai, J.-Y., Wang, C.-T., & Chou, C.-Y. (2009). How knowledge map fit and personalization affect success of KMS in high-tech firms. Technovation, 29(4), 313-324. Lee, W., Xiong, L., & Hu, C. (2012). The effect of Facebook users’ arousal and valence on intention to go to the festival: Applying an extension of the technology acceptance model. International Journal of Hospitality Management, 31(3), 819-827. Lee, Y.-C., Li, M.-L., Yen, T.-M., & Huang, T.-H. (2010). Analysis of adopting an integrated decision making trial and evaluation laboratory on a technology acceptance model. Expert Systems with Applications, 37(2), 1745-1754. Liao, H.-L., & Lu, H.-P. (2008). The role of experience and innovation characteristics in the adoption and continued use of e-learning websites. Computers & Education, 51(4), 1405-1416. Lin, F., Fofanah, S. S., & Liang, D. (2011). Assessing citizen adoption of e-Government initiatives in Gambia: A validation of the technology acceptance model in information systems success. Government Information Quarterly, 28(2), 271-279. Lin, W.-S. (2012). Perceived fit and satisfaction on web learning performance: IS continuance intention and task-technology fit perspectives. International Journal of Human-Computer Studies, 70(7), 498-507. McGill, T. J., & Klobas, J. E. (2009). A task–technology fit view of learning management system impact. Computers & Education, 52(2), 496-508. Min, Q., Ji, S., & Qu, G. (2008). Mobile Commerce User Acceptance Study in China: A Revised UTAUT Model. Tsinghua Science & Technology, 13(3), 257-264. Ministry of Education Malaysia. (2012). Malaysia Education Blueprint 2013-2025. Putrajaya: Ministry of Education Malaysia. Motaghian, H., Hassanzadeh, A., & Moghadam, D. K. (2013). Factors affecting university instructors' adoption of web-based learning systems: Case study of Iran. Computers & Education, 61(0), 158-167. Nunally, J. C., & Bernstein, I. H. (1994). Psychometric Theory. New York: McGraww-Hill. Ong, C.-S., & Lai, J.-Y. (2007). Measuring user satisfaction with knowledge management systems: scale development, purification, and initial test. Computers in Human Behavior, 23(3), 1329-1346. Ong, C.-S., Lai, J.-Y., & Wang, Y.-S. (2004). Factors affecting engineers’ acceptance of asynchronous elearning systems in high-tech companies. Information & Management, 41(6), 795-804. Pan, S., & Jordan-Marsh, M. (2010). Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Computers in Human Behavior, 26(5), 1111-1119. Roca, J. C., Chiu, C.-M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683-696. Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A selfdetermination theory perspective. Computers in Human Behavior, 24(4), 1585-1604. San Martín, H., & Herrero, Á. (2012). Influence of the user’s psychological factors on the online purchase intention in rural tourism: Integrating innovativeness to the UTAUT framework. Tourism Management, 33(2), 341-350. Sanchez-Franco, M. J. (2010). WebCT – The quasimoderating effect of perceived affective quality on an extending Technology Acceptance Model. Computers & Education, 54(1), 37-46. Shin, D.-H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior, 25(6), 1343-1354. Shyu, S. H.-P., & Huang, J.-H. (2011). Elucidating usage of e-government learning: A perspective of the extended technology acceptance model. Government Information Quarterly, 28(4), 491-502. Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432-2440. Teo, T., Lee, C. B., Chai, C. S., & Wong, S. L. (2009). Assessing the intention to use technology among preservice teachers in Singapore and Malaysia: A multigroup invariance analysis of the Technology Acceptance Model (TAM). Computers & Education, 53(3), 1000-1009. Terzis, V., & Economides, A. A. (2011). The acceptance and use of computer based assessment. Computers & Education, 56(4), 1032-1044. Terzis, V., Moridis, C. N., & Economides, A. A. (2012). The effect of emotional feedback on behavioral intention to use computer based assessment. Computers & Education, 59(2), 710-721. Tung, F.-C., & Chang, S.-C. (2008). Nursing students’ behavioral intention to use online courses: A questionnaire survey. International Journal of Nursing Studies, 45(9), 1299-1309. Venkatesh, V., Morris, M. G., & Davis, G. B. (2003). User Acceptance of Information Technology: Toward A Unified View. MIS Quarterly, 27(3), 425-478. Wang, W.-T., & Wang, C.-C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761-774. Wang, Y.-S., & Shih, Y.-W. (2009). Why do people use information kiosks? A validation of the Unified Theory of Acceptance and Use of Technology. Government Information Quarterly, 26(1), 158-165. Weerakkody, V., El-Haddadeh, R., Al-Sobhi, F., Shareef, M. A., & Dwivedi, Y. K. (2013). Examining the influence of intermediaries in facilitating e-government adoption: An empirical investigation. International Journal of Information Management, 33(5), 716-725. Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior, 28(1), 129-142. Yen, D. C., Wu, C.-S., Cheng, F.-F., & Huang, Y.-W. (2010). Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906-915. Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767. |
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| person | UniSZA Unisza unisza |
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| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12388 |
| spelling | 12388 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12388 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal UniSZA Unisza unisza image/jpeg inches 96 96 14 14 1419 799 2015-10-07 08:59:31 1419x799 6688-01-FH02-FSSG-15-03878.jpg UniSZA Private Access Factors contributing pre-school trainees teachers adoption of virtual learning environment: Malaysian evidence Turkish Online Journal of Educational Technology Virtual Learning Environment (VLE) has become the main mechanism in supporting on-line education either in primary or secondary school. Although VLE efforts are considered to be a significant corporate investment, many surveys indicate high drop-out rates or failures. This research uses an integrated model in order to assessing the influence of IS-oriented, psychological and behavioral factors on instructors’ adoption of virtual learning systems. Survey data collected from 76 pre-school teachers were analyzed using structural equation modeling to examine the theoretical model. The research results show that, perceived ease of use and compatibility increase pre-school teachers intention to use virtual learning systems; however, perceived ease of use is the most important factor affecting on intention and actual use of the system (adoption). 14 2 Sakarya University Sakarya University 73-79 Bakar, A. A., Razak, F. Z. A., & Abdullah, W. S. W. (2013). Assessing the Effects of UTAUT and SelfDetermination Predictor on Students Continuance Intention to Use Student Portal. World Applied Sciences Journal, 21(10), 1484-1489. Casey, T., & Wilson-Evered, E. (2012). Predicting uptake of technology innovations in online family dispute resolution services: An application and extension of the UTAUT. Computers in Human Behavior, 28(6), 2034-2045. Chang, M. K., & Cheung, W. (2001). Determinants of the intention to use Internet/WWW at work: a confirmatory study. Information & Management, 39(1), 1-14. Chen, C.-F., & Chao, W.-H. (2011). Habitual or reasoned? Using the theory of planned behavior, technology acceptance model, and habit to examine switching intentions toward public transit. Transportation Research Part F: Traffic Psychology and Behaviour, 14(2), 128-137. Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63(0), 160-175. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Mahwah NJ: Lawrence Erlbaum Associates. Chiu, C.-M., & Wang, E. T. G. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194-201. Chong, A. Y.-L. (2013). Predicting m-commerce adoption determinants: A neural network approach. Expert Systems with Applications, 40(2), 523-530. Chow, M., Herold, D. K., Choo, T.-M., & Chan, K. (2012). Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education. Computers & Education, 59(4), 1136-1144. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340. Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 440-452. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. Gruzd, A., Staves, K., & Wilk, A. (2012). Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior, 28(6), 2340-2350. Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis Sixth Edition Pearson Education. New Jersey. Hernandez, B., Jimenez, J., & Martin, M. J. (2009). Future use intentions versus intensity of use: An analysis of corporate technology acceptance. Industrial Marketing Management, 38(3), 338-354. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic management journal, 20(2), 195-204. Jeong, H. (2011). An investigation of user perceptions and behavioral intentions towards the e-library. Library Collections, Acquisitions, and Technical Services, 35(2–3), 45-60. Jou, M., & Wang, J. (2013). Investigation of effects of virtual reality environments on learning performance of technical skills. Computers in Human Behavior, 29(2), 433-438. Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand's community health centers: Applying the UTAUT model. international journal of medical informatics, 78(6), 404-416. Kuo, Y.-F., & Yen, S.-N. (2009). Towards an understanding of the behavioral intention to use 3G mobile valueadded services. Computers in Human Behavior, 25(1), 103-110. Lai, J.-Y., Wang, C.-T., & Chou, C.-Y. (2009). How knowledge map fit and personalization affect success of KMS in high-tech firms. Technovation, 29(4), 313-324. Lee, W., Xiong, L., & Hu, C. (2012). The effect of Facebook users’ arousal and valence on intention to go to the festival: Applying an extension of the technology acceptance model. International Journal of Hospitality Management, 31(3), 819-827. Lee, Y.-C., Li, M.-L., Yen, T.-M., & Huang, T.-H. (2010). Analysis of adopting an integrated decision making trial and evaluation laboratory on a technology acceptance model. Expert Systems with Applications, 37(2), 1745-1754. Liao, H.-L., & Lu, H.-P. (2008). The role of experience and innovation characteristics in the adoption and continued use of e-learning websites. Computers & Education, 51(4), 1405-1416. Lin, F., Fofanah, S. S., & Liang, D. (2011). Assessing citizen adoption of e-Government initiatives in Gambia: A validation of the technology acceptance model in information systems success. Government Information Quarterly, 28(2), 271-279. Lin, W.-S. (2012). Perceived fit and satisfaction on web learning performance: IS continuance intention and task-technology fit perspectives. International Journal of Human-Computer Studies, 70(7), 498-507. McGill, T. J., & Klobas, J. E. (2009). A task–technology fit view of learning management system impact. Computers & Education, 52(2), 496-508. Min, Q., Ji, S., & Qu, G. (2008). Mobile Commerce User Acceptance Study in China: A Revised UTAUT Model. Tsinghua Science & Technology, 13(3), 257-264. Ministry of Education Malaysia. (2012). Malaysia Education Blueprint 2013-2025. Putrajaya: Ministry of Education Malaysia. Motaghian, H., Hassanzadeh, A., & Moghadam, D. K. (2013). Factors affecting university instructors' adoption of web-based learning systems: Case study of Iran. Computers & Education, 61(0), 158-167. Nunally, J. C., & Bernstein, I. H. (1994). Psychometric Theory. New York: McGraww-Hill. Ong, C.-S., & Lai, J.-Y. (2007). Measuring user satisfaction with knowledge management systems: scale development, purification, and initial test. Computers in Human Behavior, 23(3), 1329-1346. Ong, C.-S., Lai, J.-Y., & Wang, Y.-S. (2004). Factors affecting engineers’ acceptance of asynchronous elearning systems in high-tech companies. Information & Management, 41(6), 795-804. Pan, S., & Jordan-Marsh, M. (2010). Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Computers in Human Behavior, 26(5), 1111-1119. Roca, J. C., Chiu, C.-M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683-696. Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A selfdetermination theory perspective. Computers in Human Behavior, 24(4), 1585-1604. San Martín, H., & Herrero, Á. (2012). Influence of the user’s psychological factors on the online purchase intention in rural tourism: Integrating innovativeness to the UTAUT framework. Tourism Management, 33(2), 341-350. Sanchez-Franco, M. J. (2010). WebCT – The quasimoderating effect of perceived affective quality on an extending Technology Acceptance Model. Computers & Education, 54(1), 37-46. Shin, D.-H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior, 25(6), 1343-1354. Shyu, S. H.-P., & Huang, J.-H. (2011). Elucidating usage of e-government learning: A perspective of the extended technology acceptance model. Government Information Quarterly, 28(4), 491-502. Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432-2440. Teo, T., Lee, C. B., Chai, C. S., & Wong, S. L. (2009). Assessing the intention to use technology among preservice teachers in Singapore and Malaysia: A multigroup invariance analysis of the Technology Acceptance Model (TAM). Computers & Education, 53(3), 1000-1009. Terzis, V., & Economides, A. A. (2011). The acceptance and use of computer based assessment. Computers & Education, 56(4), 1032-1044. Terzis, V., Moridis, C. N., & Economides, A. A. (2012). The effect of emotional feedback on behavioral intention to use computer based assessment. Computers & Education, 59(2), 710-721. Tung, F.-C., & Chang, S.-C. (2008). Nursing students’ behavioral intention to use online courses: A questionnaire survey. International Journal of Nursing Studies, 45(9), 1299-1309. Venkatesh, V., Morris, M. G., & Davis, G. B. (2003). User Acceptance of Information Technology: Toward A Unified View. MIS Quarterly, 27(3), 425-478. Wang, W.-T., & Wang, C.-C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761-774. Wang, Y.-S., & Shih, Y.-W. (2009). Why do people use information kiosks? A validation of the Unified Theory of Acceptance and Use of Technology. Government Information Quarterly, 26(1), 158-165. Weerakkody, V., El-Haddadeh, R., Al-Sobhi, F., Shareef, M. A., & Dwivedi, Y. K. (2013). Examining the influence of intermediaries in facilitating e-government adoption: An empirical investigation. International Journal of Information Management, 33(5), 716-725. Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior, 28(1), 129-142. Yen, D. C., Wu, C.-S., Cheng, F.-F., & Huang, Y.-W. (2010). Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906-915. Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767. |
| spellingShingle | Factors contributing pre-school trainees teachers adoption of virtual learning environment: Malaysian evidence |
| summary | Virtual Learning Environment (VLE) has become the main mechanism in supporting on-line education either in primary or secondary school. Although VLE efforts are considered to be a significant corporate investment, many surveys indicate high drop-out rates or failures. This research uses an integrated model in order to assessing the influence of IS-oriented, psychological and behavioral factors on instructors’ adoption of virtual learning systems. Survey data collected from 76 pre-school teachers were analyzed using structural equation modeling to examine the theoretical model. The research results show that, perceived ease of use and compatibility increase pre-school teachers intention to use virtual learning systems; however, perceived ease of use is the most important factor affecting on intention and actual use of the system (adoption). |
| title | Factors contributing pre-school trainees teachers adoption of virtual learning environment: Malaysian evidence |
| title_full | Factors contributing pre-school trainees teachers adoption of virtual learning environment: Malaysian evidence |
| title_fullStr | Factors contributing pre-school trainees teachers adoption of virtual learning environment: Malaysian evidence |
| title_full_unstemmed | Factors contributing pre-school trainees teachers adoption of virtual learning environment: Malaysian evidence |
| title_short | Factors contributing pre-school trainees teachers adoption of virtual learning environment: Malaysian evidence |
| title_sort | factors contributing pre-school trainees teachers adoption of virtual learning environment: malaysian evidence |