The Impact of Mobile Learning Application Through Intention to Use on Employees Skill Usage

Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS quarterly, 227-247.
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology Acceptance Model in M-learning context: A systematic review. Computers & education, 125, 389-412.
Alzaza, N. S. (2013). Mobile learning services acceptance model among higher education students.
Arpaci, I. (2019 ). A hybrid modeling approach for predicting the educational use of mobile cloud computing services in higher education. chb.2018.09.005. Computers in Human behavior, 90, 181 – 187. https://doi.org/https://doi.org/10.1016/j
Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to casual modeling: personal computer adoption ans use as an Illustration.
Barkhi, R., Belanger, F., & Hicks, J. (2008). A model of the determinants of purchasing from virtual stores. Journal of Organizational Computing and Electronic Commerce, 18(3), 177-196.
Brown, I., Cajee, Z., Davies, D., & Stroebel, S. (2003). Cell phone banking: predictors of adoption in South Africa—an exploratory study. International journal of information management, 23(5), 381-394.
Cheng, M., & Yuen, A. H. K. (2018). Student continuance of learning management system use: A longitudinal exploration. Computers & education, 120, 241-253.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
Chiou, W.-C., Lin, C.-C., Perng, C., & Tsai, J.-T. (2009). E-Learning usability measurement-using technology acceptance model and usability test. 10th Asia Pacific Industrial Engineering & Management Systems Conference, Kitakyushu, Japan. Retrieved from https://pdfs. semanticscholar. org/51b2/ace32d3b3025f0d7a348d085c7ae4154c461. pdf,
Cohen, J. (1992). Statistical power analysis. Current directions in psychological science, 1(3), 98-101.
Cook, J., Bradley, C., Lance, J., Smith, C., & Haynes, R. (2007). Generating learning contexts with mobile devices. In N. Pachler (Eds.), Mobile learning: Towards a research agenda. WLE Centre Occasional Papers in Work-based Learning 1 (pp.
55–74), WLE Centre, London.
Cummings, J., Merrill, A., & Borrelli, S. (2010). The use of handheld mobile devices: their impact and implications for library services. Library Hi Tech.
Davis, D., & Cosenza, R. (1993). Business Research for Decision Making. Wadsworth Publishing Company.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior research methods, 41(4), 1149-1160.
Fetaji, M., & Fetaji, B. (2008). Universities go mobile—Case study experiment in using mobile devices. ITI 2008-30th International Conference on Information Technology Interfaces,
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research, Reading, Mass: Addision-Wesley. Fransson, N., and Garling, 369-382.
Fishbein, M., & Ajzen, I. (1979). ‘A theory of reason action: some application sand implications’ (H. Howe and M. Page (Eds.) ed.). University of Nebraska Press, .
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. In: Sage Publications Sage CA: Los Angeles, CA.
García, M. V., Blasco López, M. F., & Sastre Castillo, M. Á. (2019). Determinants of the acceptance of mobile learning as an element of human capital training in organisations. Technological Forecasting and Social Change, 149, 119783. https://doi.org/https://doi.org/10.1016/j.techfore.2019.119783
Gibson, S. G., Harris, M. L., & Colaric, S. M. (2008). Technology acceptance in an academic context: Faculty acceptance of online education. Journal of Education for Business, 83(6), 355-359.
Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does PLS have advantages for small sample size or non-normal data? MIS quarterly, 981-1001.
GSMA. (2018). The mobile economy. Retrieved from https://www.gsma.com/mobileeconomy/wp-content/uploads/2018/02/The-Mobile-Economy-Global-2018.pdf.
Hahn, J. (2008). Mobile learning for the twenty?first century librarian. Reference Services Review.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2016). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd Ed. ed.). Sage.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd Ed. ed.). Sage.
Hsu, T. Y., Ke, H. R., & Yang, W. P. (2006). Knowledge?based mobile learning framework for museums. The electronic library.
Hwang, G.-J., Wu, P.-H., Zhuang, Y.-Y., Kuo, W.-L., & Huang, Y.-M. (2010). An investigation on students' cognitive load and learning achievements for participating in a local culture mobile learning activity. 2010 6th IEEE International Conference on Wireless, Mobile, and Ubiquitous Technologies in Education,
Ibrahim, M. A., Kim-Soon, N., Ahmad, A. R., & Sirisa, N. M. X. (2017). Mediating roles of attitude on intention to use M-learning among students at Malaysian technical universities. Advanced Science Letters, 23(4), 2795-2798.
Igbaria, M., Iivari, J., & Maragahh, H. (1995). Why do individuals use computer technology? A Finnish case study. Information & management, 29(5), 227-238.
Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. (1997). Personal computing acceptance factors in small firms: a structural equation model. MIS quarterly, 279-305.
Islam, A. (2011a). Online database adoption and satisfaction model. Saarbrücken: Lambert Academic Publishing, 50.
Islam, A. Y. M. A. (2011b). Viability of the extended technology acceptance model: An empirical study. Journal of Information and Communication Technology, 10, 85-98.
Izuagbe, R., Ibrahim, N., Lilofa, O., Olawoyin, O., Nwokeoma, N., Promise, I., & Osayande, O. (2019). Effect of perceived ease of use on librarians' e-skills: Basis for library technology acceptance intention. Library & Information Science Research, 100969. https://doi.org/10.1016/j.lisr.2019.100969
Jahangir, N., & Begum, N. (2008). The role of perceived usefulness, perceived ease of use, security and privacy, and customer attitude to engender customer adaptation in the context of electronic banking. African journal of business management, 2(2), 032-040.
Joo, Y. J., Kim, N., & Kim, N. H. (2016). Factors predicting online university students’ use of a mobile learning management system (m-LMS). Educational Technology Research and Development, 64(4), 611-630.
Karim, N. S. A., Darus, S. H., & Hussin, R. (2006). Mobile phone applications in academic library services: a students' feedback survey. Campus-Wide Information Systems.
Kim, B. (2010). An empirical investigation of mobile data service continuance: Incorporating the theory of planned behavior into the expectation–confirmation model. Expert Systems with Applications, 37(10), 7033-7039.
Kim, J. (2009). A Comprehensive Structural Model of Factors Influencing Customers' Intention to Use Biometrics in the Hospitality Industry. ERIC.
Kirkpatrick, D., & Kirkpatrick, J. (2006). Evaluating training programs: The four levels. Berrett-Koehler Publishers.
Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications. https://doi.org/https://doi.org/10.1038/156278a0
Liu, Y., Li, H., & Carlsson, C. (2010). Factors driving the adoption of m-learning: An empirical study. Computers & education, 55(3), 1211-1219.
Lu, J., Yao, J. E., & Yu, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245-268.
Lu, X., & Viehland, D. (2008). Factors influencing the adoption of mobile learning.
Luarn, P., & Lin, H.-H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human behavior, 21(6), 873-891.
Mac Callum, K., & Jeffrey, L. (2013). The influence of students' ICT skills and their adoption of mobile learning. Australasian Journal of Educational Technology, 29(3).
Mac Callum, K., & Jeffrey, L. (2014). Factors impacting teachers’ adoption of mobile learning. Journal of Information Technology Education, 13.
Magni, M., Taylor, M. S., & Venkatesh, V. (2010). ‘To play or not to play’: A cross-temporal investigation using hedonic and instrumental perspectives to explain user intentions to explore a technology. International journal of human-computer studies, 68(9), 572-588.
Malhotra, Y., & Galletta, D. F. (1999). Extending the technology acceptance model to account for social influence: Theoretical bases and empirical validation. Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers,
Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research, 2(3), 173-191.
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human behavior, 45, 359-374.
Muchran, M., & Ahmar, A. S. (2019). Application of TAM model to the use of information technology. arXiv preprint arXiv:1901.11358.
Ndubisi, N. O., Jantan, M., & Richardson, S. (2001). Is the technology acceptance model valid for entrepreneurs? Model testing and examining usage determinants. Asian Academy of Management Journal, 6(2), 31-54.
Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & education, 109, 56-73.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. McGraw-Hill. https://doi.org/https://doi.org/10.1037/018882
Oghuma, A. P., Chang, Y., Libaque-Saenz, C. F., Park, M.-C., & Rho, J. J. (2015). Benefit-confirmation model for post-adoption behavior of mobile instant messaging applications: A comparative analysis of KakaoTalk and Joyn in Korea. Telecommunications Policy, 39(8), 658-677.
Ong, C.-S., Lai, J.-Y., & Wang, Y.-S. (2004). Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information & management, 41(6), 795-804.
Ramayah, T., & Aafaqi, B. (2004). Role of self-efficacy in e-library usage among students of a public university in Malaysia. Malaysian Journal of Library & Information Science, 9(1), 39-57.
Ramayah, T., Aafaqi, B., & Ignatius, J. (2004). E-library usage among students of a public University in Malaysia. The Proceedings of the International Conference on Management Education,
Ramayah, T., Jantan, M., & Aafaqi, B. (2003). Internet usage among students of institutions of higher learning: The role of motivational variables. The proceedings of The 1st International conference on Asian Academy of Applied Business conference,
Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH. http://www.smartpls.com
Rogoski, R. R. (2005). Wireless by design. Health Management Technology, 26(1), 1–7.
Sabah, N. (2016). Exploring students’ awareness and perceptions. . Comput. Human Behav., 65, 522–533.
Savill-Smith, C., Attewell, J., & Stead, G. (2006). Mobile Learning in practice: Piloting a Mobile Learning teacher's toolkit in further education colleges. Learning and Skills Network.
Segars, A. H., & Grover, V. (1993). Re-examining perceived ease of use and usefulness: A confirmatory factor analysis. MIS quarterly, 517-525.
Tan, G. W.-H., Ooi, K.-B., Leong, L.-Y., & Lin, B. (2014). Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach. Computers in Human behavior, 36, 198-213.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176.
Telekom Malaysia Corporate Profile. Telekom Malaysia Corporate Profile. www.tm.com.my
The European Qualifications Framework (EQF). (2008). The European Qualifications Framework (EQF), Commission of the European Communities. Retrieved from http://ec.europa.eu/education/policies/educ/eqf/rec08_en.pdf
Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information technology theory and application, 11(2), 5-40.
Vate-U-Lan, P. (2008). Mobile learning: Major challenges for engineering education. 2008 38th Annual Frontiers in Education Conference,
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), 342-365.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. https://doi.org/doi:10.1287/mnsc.46.2.186.11926
Walsh, A. (2009). Text messaging (SMS) and libraries. Library Hi Tech News.
Wang, Y. S., Lin, H. H., & Luarn, P. (2006). Predicting consumer intention to use mobile service. Information systems journal, 16(2), 157-179.
Yadegaridehkordi, E., Iahad, N. A., & Baloch, H. Z. (2013). Success factors influencing the adoption of M-learning. International Journal of Continuing Engineering Education and Life Long Learning, 23(2), 167-178.
Yoon, C., & Kim, S. (2007). Convenience and TAM in a ubiquitous computing environment: The case of wireless LAN. Electronic Commerce Research and Applications, 6(1), 102-112.
Yusoff, Y. M., Muhammad, Z., Zahari, M. S. M., Pasah, E. S., & Robert, E. (2009). Individual differences, perceived ease of use, and perceived usefulness in the e-library usage. Computer and Information Science, 2(1), 76-83.

In-Text Citation: (Noor et al., 2021)
To Cite this Article: Noor, A. M., Mahmood, N. H. N., & Zakaria, W. N. W. (2021). The Impact of Mobile Learning Application Through Intention to Use on Employees Skill Usage. International Journal of Academic Research in Business and Social Sciences, 11(11), 342 – 361.

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