Technology Acceptance Model (TAM) As a Predictor Model for Explaining Agricultural Experts Behavior in Acceptance of ICT
محورهای موضوعی : Information and communication technology in agricultureامیر علم بیگی 1 , اسماعیل آهنگری 2
1 - استادیار گروه ترویج و آموزش کشاورزی، دانشگاه تهران، دانشکده اقتصاد و توسعه کشاورزی
2 - دانشجوی دکتری آموزش کشاورزی، گروه ترویج و آموزش کشاورزی، دانشگاه تهران، دانشکده اقتصاد و توسعه کشاورزی
کلید واژه: IT, Technology acceptance model, Job relevance, partial least squares,
چکیده مقاله :
This study aimed to develop TAM model to explain adoption of information technologies process .Descriptive – correlation study was conducted and data were collected through a survey. Statistical population was West Azerbaijan Agricultural extension agents who 120 of them were selected randomly using the krejcie and Morgan table. A questionnaire was employed to measure the variables in the model. Its validity was confirmed by a panel of experts. The Cronbach's alpha coefficient ranged between from 0.704 to 0.816 show satisfied reliability. For data processing, partial least squares (PLS) method as a new approach to structural equation modeling was used. The results showed that among three variables for development of technology acceptance model including Job relevance, experience and organization willingness to invest, the first and second show significant effects. Thus Job relevance and experience as an external variable was added to the basic TAM. Other relations between variables in basic technology acceptance model in current study were also seen significant. Our developed TAM can explain 64% of the actual behavior of employee in information technology utilization. TAM is one of the most influential extensions of Ajzen and Fishbein’s theory of reasoned action (TRA) in the literature. The theories behind it assume that when a person forms an intention to act, that s/he will be free to act without limitation. While In the real world there will be many constraints, such as limited freedom to act. For example, people in organized working environments are forced to use most of the relevant applications irrespective of their opinion or attitude. In this research mentioned model was used as a strong model to predict actual use behavior that affected by three variables namely Job relevance, experience and organization willingness to invest.
هدف مطالعه حاضر توسعه مدل پذیرش فناوری جهت بیان چگونگی فؤیند پذیرش فناوری اطلاعات است. در این راستا یک مطالعه توصیفی همبستگی که داده های ان از طریق پیمایش جمع آوری گردید انجام شد. جامعه آماری پژوهش مروجان کشاورززی استان آذربایجان غربی بود که 120 نفر از آنها به صورت تصادفی و با استفاده از جدول کرجسی و مورگان انتخاب شدند. جهت اندازه گیری متغیرها از ابزار پرسشنامه استفاده شد. روایی صوری پرسشنامه به تایید متخصصان (اساتید و دانشجویان دکتری دانشگاه تهران) رسید. پایایی پرسشنامه نیز از طریق مقدار آلفای کرونباخ سنجیده شد که مقدار آن بین 704/0 الی 816/0 بدست آمد. جهت پردازش داده ها، روش حداقل مربعات جزئی، به عنوان یک رویکرد جدید به مدل معادلات ساختاری استفاده شد. نتایج نشان داد که در میان سه متغیر برای توسعه مدل پذیرش فناوری از جمله ارتباط شغلی، تجربه و تمایل سازمان به سرمایه گذاری، نشان می دهد اثرات متغیرهای اول و دوم قابل توجه است. بنابراین ارتباط شغلی و تجربه به عنوان یک متغیر بیرونی به مدل اولیه پذیرش فناوری اضافه شده است. بر اساس نتایج تحقیق، در مطالعه حاضر روابط بین سایر متغیرها در مدل اولیه پذیرش فناوری نیز قابل توجه است. مدل پذیرش فناوری توسعه داده شده در تحقیق حاضر قادر است 64٪ از رفتار واقعی کارکنان در استفاده از فن آوری اطلاعات را تبیین و تشریح نماید. بر اساس مرور ادبیات مدل پذیرش فناوری یکی از تاثیرگذارترین مدل های اضافه شده به تئوری عمل منطقی آجزن و فیش بین است. در تحقیق حاضر، مدل پذیرش فناوری که تحت تاثیر سه متغیر ارتباط شغلی، تجربه و تمایل سازمان به سرمایه گذاری می باشد پیش بینی کننده رفتار واقعی استفاده از فناوری اطلاعات و ارتباطات است.
1- Ahangari, E. (2014). Providing a model of Environmental Friendly technologies acceptance
among the West Azerbaijan province’s wheat farmers. Unpublished thesis, University of Tehran, Iran. (In Persian)
2- Charfeddine, L., & Nasri, W. (2012). Factors affecting the adoption of Internet banking in Tunisia: An integration theory of acceptance model and theory of planned behavior. Journal of High Technology Management Research, 23, 1-14.
3- Chen, C., Fan, Y., & Farn, C. (2007). Predicting electronic toll collection service adoption: An integration of the technology acceptance model and the theory of planned behavior. Emerging Technologies, 15(5), 300-311.
4- Cothran, T. (2011). Google scholar acceptance and use among graduate students: A quantitative study. Journal of Library and Information Science Research, 33(4), 293–301.
5- Dabholkar, P. A. (1996). Consumer evaluations of new technology-based self-service options: An investigation of alternative models of service quality. International Journal of Research in Marketing, 13 (1), 29-51.
6- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Journal, 13, 319- 339.
7- 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.
8- Dillon, A., & Morris, M. G. (1996). User acceptance of information technology: Theories and models. In M. E. Williams (Ed.), Annual Review of Information Science and Technology (ARIST): Vol. 31 (pp. 3-32).
9- Eccles, J. S. (1990). Predictors of math anxiety and its influence on young adolescents, course enrollment intention and performance in mathematics. Journal of Educational Psychology, 82, 60-70.
10- Elahi M. M. Cagampang, F. R. Mukhtar, D. Anthony F.W. Ohri, S. K & Hanson, M. A. (2009). Long-term maternal high-fat feeding from weaning through pregnancy and lactation predisposes offspring to hypertension, raised plasma lipids and fatty liver in mice. British. Journal of Nutrition, 10 (2), 514–519.
11- Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley
12- Gyampah, K. A., & Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information & management, 41 (6), 731-745.
13- Haenlein, M., & Kaplan, A. (2004). A Beginner’s Guide to Partial Least Squares Analysis. Understanding Statistics, 3, 283-297.
14- Hair, J.F. Jr., Anderson, R.E., Tatham, R.L., & Black, W.C. (1998). Multivariate Data Analysis, (5th Edition). Upper Saddle River, NJ: Prentice Hall.
15- Holden, R. J., & Karsh, B. T. (2010). The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics, 43, 159–172.
16- Hu, P.J.H., Clark, T.H.A., & Ma, W.W. (2003). Examining technology acceptance by school teachers: a longitudinal study. Information and Management, 41, 227–241.
17- Kim, S.H. (2008). Moderating effects of job relevance and experience on mobile wireless technology acceptance: Adoption of a smartphone by individuals. Information and Management, 45, 387–393.
18- Kim, H.J., Mannino, M., & Nieschwietz, R.J. (2009). Information technology acceptance in the internal audit profession: Impact of technology features and complexity. International Journal of Accounting Information Systems, 10, 214–228.
19- Kulviwat, S., Bruner, C.G., & Al-Shuridah, O. (2009). The role of social influence on adoption of high tech innovations: The moderating effect of public/private consumption. Journal of Business Research, 62, 706–712.
20- Kuttschreuter, M., Horst, M., & Gutteling, J. M. (2007). Perceived usefulness, personal experiences, risk perception and trust as determinants of adoption of e-government services in The Netherlands. Computers in Human Behavior, 23, 1838–1852.
21- Lee, F.H., & Wu, W.Y. (2011). Moderating effects of technology acceptance perspectives on eservice quality formation: Evidence from airline websites in Taiwan. Journal of Expert Systems with Applications, 38, 7766–7773.
22- Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information and Management, 40(3), 191–204.
23- Lehto, M. R., & Lee, D. U. (2013). User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Computers and Education, 61, 193-208.
24- Lin, F., Fofana, 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 Journal, 28, 271-279.
25- Lucas Jr., H. C., & Spitler, V. (2000). Implementation in a world of workstation and networks. Information and Management, 38, 119- 128.
26- Majchrzak, A., Lim, R., & Chin, W. W. (2005). Managing Client Dialogues during Information Systems Design to Facilitate Client Learning. MIS Journal, 29(4), 653-672.
27- Melas, C. D., Zampetakis, L. A., Dimopoulou, A., & Moustakis, V. (2011). Modeling the acceptance of clinical information systems among hospital medical staff: An extended TAM model. Journal of Biomedical Informatics, 44, 553–564.
28- Morris, M.G., & Dillon, A. (1997). How user perceptions influence software use. IEEE Software, 14(4), 58-65.
29- Nasri, W., & Charfeddine, L. (2012) Factors Affecting the Adoption of Internet Banking in Tunisia: An Integration Theory of Acceptance Model and Theory of Planned Behavior. The Journal of High Technology Management Research, 23, 1-14.
30- Newsted, P. R., Chin, W. W., & Marcolin, B. L. (1996). A Partial Least Squares Latent Variable Modeling Aproach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and Voice Mail Emotion/Adoption Study. Proceedings of the seventeenth international conference on information systems, Cleveland, Ohio, December 16- 18, 1996.
31- Ozkan, S., & Kanat, I.E. (2011). E-Government adoption model based on theory of planned behavior: Empirical validation. Government Information Journal, 28, 503–513.
32- Yaghoobi, N.M., & Shakeri, R. (2010). Comparative analysis of the technology acceptance model.