مدل علّی پذیرش و به کارگیری یادگیری مجازی در کارکنان: نقش تناسب فناوری – شغل، خودکارآمدی و هنجار ذهنی
محورهای موضوعی :
1 - دانشگاه ميبد يزد
2 - پيام نور استان فارس
کلید واژه: مدل پذیرش فناوری, تناسب فناوری- شغل, هنجار ذهنی, خودکارآمدی رایانه.,
چکیده مقاله :
هدف پژوهش حاضر ارائه مدل علی پذیرش و به کارگیری یادگیری مجازی در کارکنان با تأکید بر نقش تناسب فناوری – شغل، خودکارآمدی و هنجار ذهنی در بین کارکنان صنعت نفت میباشد و تلاش شده است با بررسی عواملی مانند هنجارذهنی، خودکارآمدی رایانه، تناسب فناوری– شغل و اثرات آن ها با متغیرهای مدل پذیرش فناوری یعنی سودمندی ادراک شده، سهولت استفاده ادراک شده و تمایل و به کارگیری آموزش مجازی را مورد بررسی قرار دهد. پژوهش حاضر از نظر هدف کاربردی است و از نظر نحوه گردآوری و تحلیل داده ها از نوع توصیفی – همبستگی می باشد. جامعه آماری پژوهش حاضر کارکنان شرکت نفت که با آموزش مجازی آشنایی دارند (330 نفر) را شامل می‌شود. شرکت کنندگان چندین پرسشنامه مثل هنجارذهنی از پرسشنامه آژان و هارتشورن (2008)، فناوری - شغل از پرسشنامه واتاناساک داکول و همکاران(2010)، سهولت استفاده ادراک شده از پرسش نامه مون و کیم (2001)، سودمندی ادراک شده از پرسشنامه کیم و همکاران (2007)، تمایل استفاده یادگیری مجازی از پرسشنامه سامی ینتو (2009)، خودکارآمدی رایانه از پرسشنامه والترز، و داگرتی (2007) تکمیل نمودند. برای بررسی فرضیه های تحقیق از روش تحلیل مسیر استفاده شده است. نتایج نشان داد که متغیرهای تناسب فناوری – شغل، هنجارذهنی، خودکارآمدی رایانه به صورت مستقیم و غیرمستقیم از طریق متغیرهای واسطه ای سهولت استفاده ادراک شده و سودمندی ادراک شده بر متغیر تمایل و به کارگیری یادگیری مجازی دربین کارکنان اثر معناداردارند. بیشترین اثر کل مربوط به متغیر تناسب فناوری – شغل و سهولت استفاده ادراک شده و کم ترین اثر کل مربوط به متغیر هنجار ذهنی بر تمایل و به کارگیری یادگیری مجازی میباشد.
The purpose of this study was to present a causal model of acceptance and utilization in the usage of virtual learning among oil industrial staffs. It has been tried to investigate the factors such as subjective norm, computer self- efficacy, task-technology fit and their relations to variables associated with the technology acceptance model, perceived usefulness, perceived ease of use and intention to use of virtual learning among staffs. In terms of objectives, this study was an applied research and in terms of the method of collecting and analyzing data, it was a descriptive and correlational research. The populations of this study were Oil Company employees who are familiar with virtual learning (330) included. The sample were 172(based on Morgan table), who, were selected through the simple-random sampling methods and Several questionnaires were completed by the participants, such as subjective norm Ajjan & Hartshorn (2008), task-technology fit Vatanasakdakul & et al (2010), perceived ease of use Moon & kim (2001), perceived usefulness Kim & et al (2007), and intention to use Samiento (2009) and computer self-efficacy Wolters & Daugherty (2007.To examine the research hypotheses, the path analysis was used. Findings indicated that computer self- efficacy, task-technology fit and Subjective norm have a significant effect on intention to use of virtual learning directly or indirectly, through the intermediate role of the variables perceived ease of use and perceived usefulness. The greatest total effect on intention to use of virtual learning were related to the Task-technology fit and perceived ease of use and the least total effect was related to the subjective norm.
Abbasi, M. Sh., Irani, Z., Chandio, F. H. (2010). Departments of Social and Institutional Beliefs about Internet Acceptance within Developing Country's Context: A Structural Evaluation of Higher Education Systems in Pakistan, European, Mediterranean & Middle Eastern conference on Information systems. April 12-13, Abu Dhabi, UAE.
Agglidis,V.P., & Chatzoglou, P.D. (2009). Using a modified technology acceptance model in hospitals. International¬ Journal of Medical Informatics, 78, 115-126.
Ajjan, H., Hartshorn, R. (2008). Investigating Faculty Dicisions to Adopt Web 2.0 Technologies Theory and Emperical Tests, Internet and Higher Education, 11, 71-80.
Al-Harbi,K.A.(2011). E-learning in Saudi tertiary education: Potential and challenges. Computing and Informatics, 9, 31-46.
Alshibly, H.(2014). An Emprical Investigation into Factors Influencing the Intention to E-earning System: An Extended Technology Acceptance Model. British Journal of Applied Science & Technology, 4(17), 2440-2457.
Andrews, L., Drenan, J., Tossan, V., Cacho, E. S. (2010). Using TAM to Examine Consumer Acceptance of a Mobile Phone Assisted Smoking Program in Australia, 39th conference European Marketing Academy Conference, 1-4 June, Copenhagen, Denmark.
Bertta, S., Kristina, t., & Anssi, O. (2009). The role of training in decreasing anxiety among experienced computer user. 17th European conference on Information system.
Chang, H. H. (2010). Task-technology fit and user acceptance of online auction. International Journal of Huma-Computer Studies, 68, 69-89.
Chatzoglou, P. D.,Vraimaki, E., Diamantidis, A., & Sarigiannidis, L. (2010). Computer acceptance in Greek SMEs. Journal of Small Business and Enterprise Development, 17(1), 78-101.
Chatzoglou, P.D., Sarigiannidis, L., Vraimaki, E., & Diamantidis, A. (2009). Investigating Greek employees ‘intention to use web-based training. Journal of Computers & Education, 53, 877-889.
Chau, V.S., & Ngai, L.W.L.C. (2010). The youth market for internet banking services: Perceptions, attitude and behaviour. Journal of Services Marketing, 24(1), 42-60.
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. Journal of MIS Quarterly, 2(19), 213-236.
Hashim, J. (2008). Factors influencing the acceptance of web-based training in Malaysia: Applying the technology acceptance model. International Journal of training and Development, 12(4), 253-264.
Hsia, J. (2007). An enhanced technology acceptance model for e-learning systems in high-tech companies, proceedings of the WSEAS international conference on Distance learning and web Engineering.
Karaali, D., Gumussoy, C. A., Calisir, F. (2011). Factors affecting the intention to use a web-based learning system among blue-collar workers in the automotive industry. Computers in Human Behaviour, 27, 343-354.
Kim, B. G., Park, S. C., Lee, K. J. (2007). A structural Equation Modelling of the Internet Acceptance in Korea, Electronic commerce Research and Application, 6, 425-432.
Klopping, L. M., & McKinney, E. (2004). Extending the technology acceptance model and the task-technology fit model to consumers-commerce. Information Technology, Learning, and Performance Journal, 1(22), 35-48.
Komar Sharma, S., & Komar Chandel, J.(2013).Technology Acceptance Model for the use of learning Through Websites Among Students in Oman .International Arab Journal of Technology,3,44-49.
Lau. S., & woods, P.C. (2009). Understanding learner acceptance of learning objects: the roles of learning object characteristics and individual differences.British Journal of Educational Technology, 40(6), 1059-1075.
Long, L .K. (2005). The role of trainee reactions in online training. School of Management. Ph.D Dissertation, Kent State University.
Macharia, J., Nyakwende, E. (2010). The influence of e-mail on students’ learning in higher education: An extension to the technology acceptance model (TAM). Asian Journal of Information Technology, 9(3), 123-132.
Minotti, J., Giguere, P. (2003). The realities of web-based training. T.H.E Journal, 30(11), 41-44.
Min Ma,Ch., Min Chao,Ch., & Cheng, B.W.(2013). Integrating Technology Acceptance Model and Task-technology fit into Blended E learning System. Journal of Applied Sciences, 13, 736-742.
Moon, J., kim, Yo. (2001). Extending the TAM for a world- wide- web context. Information and Management, 38, 217-230.
Ozer, G., Yilmaz, E. (2011). Comparison of the theory of reasoned action and the theory of planned behaviour: An adoption on accountants information technology usage, African Journal of business Management, 5(1), 50-58.
Park, S. K. (2009). An analysis of the technology acceptance model in understanding university students’ behavioural intention to use e-learning. Educational Technology & Society, 12(3), 150-162.
Peker, Can(2010). An analysis of the main critical factors that affect the acceptance of technology in hospital management systems, Master Thesis, The Middle East Technical University.
Rice, C.C. (2005). Comparing the comprehension of employees at Hewlett-Packard who have participated in interactive Web-based training and the comprehension of employees at Hewlett-Packard who have participated in statics Web-based training, Ph.D. Dissertation, Houston University.
Samiento, Pablo Manual Cardenas. (2009). The Study on Behavioural Intention of Use Towards a Clinical Decision Support Systems: A Casa in CNS La Paz- Bolivia, Master thesis, National Chang Kung University.
Sang, S. Lee, J. D., & Lee, J. (2009). E-government adoption in ASEAN: the case of Cambodia. Journal of Internet Research, 19(5), 517-534.
Siang, J.J. (2015). Students Perspective of learning Management System: An Empirical Evidence of Technology Acceptance Model in Emerging Countries. Journal of Art, Science & Commerce.
Scott, J. E., Walczak, S. (2009). Cognitive engagement with a multimedia EPR training tool: Assessing computer self-efficacy and technology acceptance. Journal of Information and Management, 46, 221-232.
Shen,J.,& Eder,L.B.(2009).Exploring intentions to use virtual worlds for business. Journal of Electronic Commerce Research, 10(2), 94-103.
Terzis,V. Econmides, A. A. (2011). The acceptance and use of computer based assessment. Computers & Education, 56, 1032-1044.
Theng, Y. L., Tan, K. L., Lim, E. P., Zhang, J., Goh, D.H., Chatterjea, K., et al. (2007). Mobile G-portal supporting collaborative sharing and learning in geography fieldwork: An empirical study. Proceedings from the ACM+IEEE Joint Conference on Digital Libraries JCDL. Vancouver, British Columbia, Canada.
Thompson, T. (2010). Assessing the determinations of information technology adoption in Jamaica’s public sector using the technology acceptance model, Ph.D. Dissertation, Prescott Valley, Arizona.
Usoro, A., Shoyelu, S., & Kuofie, M. (2010). Task-technology fit and technology acceptance models applicability to e-tourism. Journal of Economic Development, Management, IT, Finance and Marketing, 2(1), 1-32.
Vatanasakdakul, S., Dambra, J., & Ramburuth, P. (2010). IT Doesn't Fit! The Influence of Culture on B2B in Thailand. Journal of Global Information Technology Management, 13(3), 10-38.
Venkatesh, V., Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.
Wolters CA, Daugherty SG. (2007). Goal structures and teachers’ sense of efficacy: Their relation and association to teaching experience and academic level. J Educ Psychol; 99, 181–193.