عوامل موثر بر پذیرش فناوری اطلاعات و ارتباطات توسط آموزشگران کشاورزی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری آموزش کشاورزی پایدار و محیط زیست، گروه ترویج و آموزش کشاورزی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، ایران

2 استاد گروه ترویج و آموزش کشاورزی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، ایران

3 استادیار گروه ترویج و آموزش کشاورزی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، ایران

4 پژوهشگر گروه همکاری و تحول، موسسه بین المللی تحلیل سیستم های کاربردی ) IIASA (، لاگزنبورگ، اتریش

5 استاد گروه رهبری کشاورزی، آموزش و ارتباطات، دانشگاه جورجیا، آتن، ایالات متحده آمریکا

10.22092/jaear.2023.363174.1967

چکیده

در حال حاضر، فناوری اطلاعات و ارتباطات تأثیری شگرف بر کارایی نظام آموزشی دارد و استفاده از آن با هدف آموزش مستمر بهره‌برداران در برنامه های آموزشی قرار گرفته است. تدوام این آموزش به تمایل آموزشگران به ادامه استفاده از فناوری اطلاعات و ارتباطات بستگی دارد، لذا پژوهش حاضر با هدف بررسی عوامل موثر بر پذیرش فناوری اطلاعات و ارتباطات توسط آموزشگران در زمستان 1401 انجام شده است. برای این منظور از مدل عمومی توسعه یافته پذیرش فناوری برای تبیین عوامل پیش بینی کننده رفتار آموزشگران سازمان جهاد کشاورزی استفاده شد. در این مدل سازه های لذت، هنجارذهنی، خوداثربخشی و اضطراب به عنوان عوامل خارجی موثر بر سازه های سودمندی درک شده و سهولت درک شده از نظریه پذیرش فناوری هستند. جامعه آماری این پژوهش کارشناسان جهاد کشاورزی استان فارس که آموزشگران دوره های آموزشی بودند و با استفاده از روش نمونه گیری روش چند مرحله ای خوشه ای تصادفی بود. ابزار گردآوری اطلاعات، پرسشنامه ای بود که روایی محتوایی آن توسط پنل متخصصان، پایایی آن به وسیله ضریب تتا ترتیبی تأیید شد (θ˃0.8 ). داده ها با استفاده از فراوانی، میانگین، مدل بندی معادلات ساختاری و نرم‌افزارهای SPSS24 و Smart-PLS3 مورد تجریه و تحلیل قرار گرفتند. مدل عمومی توسعه یافته پذیرش فناوری 64 درصد از واریانس رفتار استفاده از فناوری اطلاعات و ارتباطات را پیش بینی کرده است. علاوه برآن، یافته ها نشان داد که سودمندی درک شده اثری مستقیم بر خود اثربخشی، لذت و هنجارذهنی دارد، سهولت درک شده نیز اثری مستقیم بر هنجارذهنی و اثری معکوس بر اضطراب دارد. یافته های این پژوهش می تواند برای تدوین سیاست هایی برای حفظ انگیزه آموزشگران برای استفاده از فناوری اطلاعات و ارتباطات در آموزش کشاورزی ارزشمند باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Factors affecting the Acceptance of Information and Communication Technology by Agricultural Educators

نویسندگان [English]

  • Seyedeh Bahar Homayoon 1
  • Masoud Yazdanpanah 2
  • saeed Mohammadzadeh 3
  • Tahereh Zobeidi 4
  • Alexa J. Lamm 5
1 Ph.D. student, Department of Agricultural Extension and Education, Agricultural Sciences and Natural Resources University of Khuzestan, Ahvaz, Iran
2 Professor, Agricultural Extension and Education Department, Khuzestan University of Agricultural Sciences and Natural Resources, Iran
3 Assistant Professor, Agricultural Extension and Education Department, Khuzestan University of Agricultural Sciences and Natural Resources, Iran
4 Researcher, Cooperation and Transformative Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
5 Professor, Department of Agricultural Leadership, Education, and Communication, University of Georgia, Athens, GA 30602, USA
چکیده [English]

Currently, ICT has a tremendous impact on the efficiency of the educational system, and its usage is aimed at continuously training users in educational programs. The continuity of this education depends on the teachers' willingness to continue using ICT. Therefore, the present research was conducted with the aim of investigating the factors that affect the utilization of ICT by educators in the winter of2022. To achieve this objective, the GETAMEL model was utilized to explain the factors that predict Jihad Agricultural Organization teachers' behavior. Within this model, the constructs of enjoyment, subjective norms, self-efficacy, and anxiety are external factors that influence the constructs of perceived usefulness and perceived ease of the TAM theory. The statistical population of this research was the experts of agricultural jihad of Fars province who were the instructors of training courses and were selected using the multi-stage random cluster sampling method. The data collection tool was a questionnaire which content validity was confirmed by a panel of experts and its reliability was confirmed by the Ordinal theta coefficient (θ˃0.8). The data were analyzed using frequency, mean, SEM and, SPSS24 and, Smart-PLS3 software. The GETAMEL model successfully predicted 64% of the variance of ICT usage behavior. Additionally, the results indicated that perceived usefulness had a direct effect on self-efficacy, enjoyment, and subjective norms. Moreover, perceived ease of use directly influences subjective norms and inversely affects anxiety. The results of this research can be valuable in formulating policies to sustain educator's motivation to use ICT within agricultural education.

کلیدواژه‌ها [English]

  • Agricultural educators
  • Adoption
  • Information and Communication Technology
  • Continuous training
  • Agricultural experts
حافظی‌زاده، ن. و موحدی، م. م. (1401). نقش مدیریت ریسک پروژه بر عملکرد با نقش میانجی سرمایه فکری. مدیریت عملیات, 2(7), 9-26.‎ سازمان جهاد کشاورزی استان فارس. (1402). قابل دسترس در : http://fajo.ir/site/index.php/samaneh
فارس نیوز. (1398). قابل دسترس در : https://www.farsnews.ir/fars/news/13980524000681
فارس نیوز. (1401). قابل دسترس در : https://www.farsnews.ir/fars/news/14010620000090
Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in human behavior, 56, 238-256. Abuta, C. M. A., & Agumagu, A. C. (2021). Social Media Used by Arable Crop Farmers for Communicating Climate Change Adaptation Strategies in Imo State, Nigeria. Journal of Agricultural Extension, 25(1), 68-77. Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological bulletin, 82(2), 261 Alfadda, H. A., & Mahdi, H. S. (2021). Measuring students’ use of zoom application in language course based on the technology acceptance model (TAM). Journal of Psycholinguistic Research, 50(4), 883-900. Al-Fraihat, D., Joy, M., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in human behavior, 102, 67-86. Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301-314. Alkamel, M. A. A., & Chouthaiwale, S. S. (2018). The use of ICT tools in English language teaching and learning: A literature review. Journal of English language and literature (JOELL), 5(2), 29-33. Alkhwaldi, A. F. A., & Kamala, M. A. (2017). Why do users accept innovative technologies? A critical review of models and theories of technology acceptance in the information system literature. Al-Maroof, R. S., Salloum, S. A., Hassanien, A. E., & Shaalan, K. (2023). Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic. Interactive Learning Environments, 31(3), 1293-1308. Al-Rahmi, W. M., Yahaya, N., Aldraiweesh, A. A., Alamri, M. M., Aljarboa, N. A., Alturki, U., & Aljeraiwi, A. A. (2019). Integrating technology acceptance model with innovation diffusion theory: An empirical investigation on students’ intention to use E-learning systems. Ieee Access, 7, 26797-26809. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411. Anh, T. V., Nguyen, H. T. T., & Linh, N. T. M. (2019, September). Digital transformation: A digital learning case study. In Proceedings of the 1st World Symposium on Software Engineering (pp. 119-124). Ayre, M., Mc Collum, V., Waters, W., Samson, P., Curro, A., Nettle, R., ... & Reichelt, N. (2019). Supporting and practising digital innovation with advisers in smart farming. NJAS-Wageningen Journal of Life Sciences, 90, 100302. Baumüller, H. (2018). The little we know: an exploratory literature review on the utility of mobile phone‐enabled services for smallholder farmers. Journal of International Development, 30(1), 134-154. Bell, M. (2013, November). e-Afghan Ag. In Content is just part of the story. Presentation at CTA’s ICT4Ag International Conference, Kigali, Rwanda. Bhatiasevi, V., & Yoopetch, C. (2015). The determinants of intention to use electronic booking among young users in Thailand. Journal of Hospitality and Tourism Management, 23, 1-11. Bhutani, S., & Paliwal, Y. (2015). Digitalization: a step towards sustainable development. OIDA International Journal of Sustainable Development, 8(12), 11-24. Briz-Ponce, L., & García-Peñalvo, F. J. (2015). An empirical assessment of a technology acceptance model for apps in medical education. Journal of medical systems, 39, 1-5. Buabeng-Andoh, C. (2018). Predicting students’ intention to adopt mobile learning: A combination of theory of reasoned action and technology acceptance model. Journal of Research in Innovative Teaching & Learning, 11(2), 178-191. Calandro, E., Chavula, J., & Phokeer, A. (2019). Internet development in Africa: a content use, hosting and distribution perspective. In e-Infrastructure and e-Services for Developing Countries: 10th EAI International Conference, AFRICOMM 2018, Dakar, Senegal, November 29-30, 2019, Proceedings 10 (pp. 131-141). Springer International Publishing. Calisir, F., Altin Gumussoy, C., Bayraktaroglu, A. E., & Karaali, D. (2014). Predicting the intention to use a web‐based learning system: Perceived content quality, anxiety, perceived system quality, image, and the technology acceptance model. Human Factors and Ergonomics in Manufacturing & Service Industries, 24(5), 515-531. Capalbo, S. M., Antle, J. M., & Seavert, C. (2017). Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making. Agricultural systems, 155, 191-199. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336. Chen, T., Li, G., Feng, Q., Liu, J., Wang, P., & Luo, H. (2021, August). What drives college teachers’ behavioral intention to teach online? A structural equation modelling approach. In 2021 International Symposium on Educational Technology (ISET) (pp. 106-111). IEEE. Churi, A. J., Mlozi, M. R., Tumbo, S. D., & Casmir, R. (2012). Understanding farmers information communication strategies for managing climate risks in rural semi-arid areas, Tanzania. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. Daum, T., Buchwald, H., Gerlicher, A., & Birner, R. (2018). Smartphone apps as a new method to collect data on smallholder farming systems in the digital age: A case study from Zambia. Computers and electronics in agriculture, 153, 144-150. Ebrahimi, S., Moeinikia, M., & Babelan, A. Z. (2018). Simple and Multiple Relationships among Perceived Ease of Use and Perceived Usefulness with E-Learning Acceptance in Universities’ Instructors. Quarterly Journal of Iranian Distance Education (IDEJ), 1(2). Fielke, S., Taylor, B., & Jakku, E. (2020). Digitalisation of agricultural knowledge and advice networks: A state-of-the-art review. Agricultural Systems, 180, 102763. Findik-Coşkunçay, D., Alkiş, N., & Özkan-Yildirim, S. (2018). A structural model for students' adoption of learning management systems: An empirical investigation in the higher education context. Journal of Educational Technology & Society, 21(2), 13-27. Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. Gupta, C., Gupta, V., & Stachowiak, A. (2021). Adoption of ICT-Based Teaching in engineering: An extended technology acceptance model perspective. IEEE Access, 9, 58652-58666. Hamid, A. A., Razak, F. Z. A., Bakar, A. A., & Abdullah, W. S. W. (2016). The effects of perceived usefulness and perceived ease of use on continuance intention to use e-government. Procedia economics and finance, 35, 644-649. Hasanah, R. L., Wati, F. F., & Riana, D. (2019). TAM Analysis on the Factors Affecting Admission of Students for Ruangguru Application. Jurnal Sistem Informasi, 15(2), 1-14. Hatlevik, O. E. (2017). Examining the relationship between teachers’ self-efficacy, their digital competence, strategies to evaluate information, and use of ICT at school. Scandinavian Journal of Educational Research, 61(5), 555-567. Hernández García, Á. (2012). Desarrollo de un modelo unificado de adopción del comercio electrónico entre empresas y consumidores finales. Aplicación al mercado español (Doctoral dissertation, Telecomunicacion). Hove, C., & Osunkunle, O. O. (2020). Social Media Use for Water Conservation Education in South Africa: Perceptions of Raymond Mhlaba Local Municipality’s Residents. Journal of Asian and African Studies, 55(3), 351-369. Hung Anh, N., Bokelmann, W., Thi Thuan, N., Do Nga, T., & Van Minh, N. (2019). Smallholders’ preferences for different contract farming models: Empirical evidence from sustainable certified coffee production in Vietnam. Sustainability, 11(14), 3799. Huang, F., & Teo, T. (2020). Influence of teacher-perceived organisational culture and school policy on Chinese teachers’ intention to use technology: An extension of technology acceptance model. Educational Technology Research and Development, 68(3), 1547-1567. Ibrahim, R., Leng, N. S., Yusoff, R. C. M., Samy, G. N., Masrom, S., & Rizman, Z. I. (2017). E-learning acceptance based on technology acceptance model (TAM). Journal of Fundamental and Applied Sciences, 9(4S), 871-889. Inwood, S. E. E., & Dale, V. H. (2019). State of apps targeting management for sustainability of agricultural landscapes. A review. Agronomy for sustainable development, 39(1), 8. Isaac, O., Abdullah, Z., Ramayah, T., Mutahar, A. M., & Alrajawy, I. (2016, December). Perceived Usefulness, Perceived Ease of Use, Perceived Compatibility, and Net Benefits: an empirical study of internet usage among employees in Yemen. In The 7th International Conference Postgraduate Education (ICPE7) (pp. 899-919). Selangor: Universiti Teknologi MARA (UiTM). Isaac, O., Abdullah, Z., Ramayah, T., & Mutahar, A. M. (2017). Internet usage within government institutions in Yemen: An extended technology acceptance model (TAM) with internet self-efficacy and performance impact. Science International, 29(4), 737-747. Jiang, M. Y. C., Jong, M. S. Y., Lau, W. W. F., Meng, Y. L., Chai, C. S., & Chen, M. (2021). Validating the general extended technology acceptance model for e-learning: Evidence from an online English as a foreign language course amid COVID-19. Frontiers in Psychology, 12, 671615. K’adamawe, K. (2012). Evaluation of RADA Text Messages on Agricultural Disaster Risk Management (ADRM). Report. Youth Crime Watch/Office of Social Entrepreneurship, University of the West Indies, Jamaica. Kankanhalli, A., Tan, B. C., & Wei, K. K. (2005). Contributing knowledge to electronic knowledge repositories: An empirical investigation. MIS quarterly, 113-143. Kim, S. H. (2014). A study on adoption factors of Korean smartphone users: A focus on TAM (Technology Acceptance Model) and UTAUT (Unified Theory of Acceptance and Use of Technology). Advanced Science and Technology Letters, 57(1), 27-30. Krivtsov, A. I., Polinova, L. V., & Chupina, I. P. (2016). Managing change in the holding company as a factor in solving strategic problems of the region. International Journal of Environmental and Science Education, 11(15), 7754-7762. Kumar, J. A., Bervell, B., Annamalai, N., & Osman, S. (2020). Behavioral intention to use mobile learning: Evaluating the role of self-efficacy, subjective norm, and WhatsApp use habit. IEEE Access, 8, 208058-208074. Landrum, B. (2020). Examining Students' Confidence to Learn Online, Self-Regulation Skills and Perceptions of Satisfaction and Usefulness of Online Classes. Online Learning, 24(3), 128-146. Li, X. Z., Chen, C. C., Kang, X., & Kang, J. (2022). Research on relevant dimensions of tourism experience of intangible cultural heritage lantern festival: Integrating generic learning outcomes with the technology acceptance model. Frontiers in Psychology, 13, 943277. Liao, C. H., & Tsou, C. W. (2009). User acceptance of computer-mediated communication: The SkypeOut case. Expert Systems with Applications, 36(3), 4595-4603. Mailizar, M., Almanthari, A., & Maulina, S. (2021). Examining teachers’ behavioral intention to use E-learning in teaching of mathematics: An extended TAM model. Contemporary educational technology, 13(2), ep298. Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society, 14, 81-95. Materia, V. C., Giarè, F., & Klerkx, L. (2015). Increasing knowledge flows between the agricultural research and advisory system in Italy: combining virtual and non-virtual interaction in communities of practice. The Journal of Agricultural Education and Extension, 21(3), 203-218. McConnell, D. (2018). E-learning in Chinese higher education: the view from inside. Higher Education, 75(6), 1031-1045. Mokhtar, S. A., Katan, H., & Hidayat-ur-Rehman, I. (2018). Instructors' Behavioural Intention to Use Learning Management System: An Integrated TAM Perspective. TEM Journal, 7(3). Moghadami, M., Mantegh, H., & Malekolkalami, M. (2021). Challenges of Creating and Operating Digital Libraries in the Digital Age in Iran. International Journal of Digital Content Management, 2(3), 115-130. Muhaimin, H., Mukminin, A., Pratama, R., & Asrial, H. (2019). Predicting factors affecting intention to use Web 2.0 in learning: evidence from science education. Journal of Baltic Science Education, 18(4), 595. Napitupulu, D., Kadar, J. A., & Jati, R. K. (2017). Validity testing of technology acceptance model based on factor analysis approach. Indonesian Journal of Electrical Engineering and Computer Science, 5(3), 697-704. Nettle, R., Crawford, A., & Brightling, P. (2018). How private-sector farm advisors change their practices: an Australian case study. Journal of Rural Studies, 58, 20-27. (OLC) Online Learning Consortium. (2017). Our quality framework. Retrieved from: https:// onlinelearningconsortium.org/about/quality-framework-five-pillars/ Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Electronic journal of information systems evaluation, 14(1), pp110-121. Park, Yoora, Hyojoo Son, and Changwan Kim. "Investigating the determinants of construction professionals' acceptance of web-based training: An extension of the technology acceptance model." Automation in construction 22 (2012a): 377-386. Park, S. Y., Nam, M. W., & Cha, S. B. (2012b). University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model. British journal of educational technology, 43(4), 592-605. Pizzi, G., & Scarpi, D. (2020). Privacy threats with retail technologies: A consumer perspective. Journal of Retailing and Consumer Services, 56, 102160. Ploj Virtic, M., Dolenc, K., & Šorgo, A. (2021). Changes in Online Distance Learning Behaviour of University Students during the Coronavirus Disease 2019 Outbreak, and Development of the Model of Forced Distance Online Learning Preferences. European Journal of Educational Research, 10(1), 393-411. Racero, F. J., Bueno, S., & Gallego, M. D. (2020). Predicting students’ behavioral intention to use open source software: A combined view of the technology acceptance model and self-determination theory. Applied Sciences, 10(8), 2711. Rejón-Guardia, F., Polo-Peña, A. I., & Maraver-Tarifa, G. (2020). The acceptance of a personal learning environment based on Google apps: The role of subjective norms and social image. Journal of Computing in Higher Education, 32, 203-233. Salau, E. S., & Saingbe, N. D. (2008). Access and utilization of information and communication technologies (ICTs) among agricultural researchers and extension workers in selected institutions in Nasarawa State of Nigeria. Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2017). MLearning and pre-service teachers: An assessment of the behavioral intention using an expanded TAM model. Computers in human behavior, 72, 644-654. SARKER, M. A. (2017). PROFESSIONALS’INTENTION TO USE SOCIAL MEDIA FOR SHARING AGRICULTURAL KNOWLEDGE (Doctoral dissertation, DEPT. OF AGRICULTURAL EXTENSION & INFORMATION SYSTEM). Sewell, A. M., Hartnett, M. K., Gray, D. I., Blair, H. T., Kemp, P. D., Kenyon, P. R., ... & Wood, B. A. (2017). Using educational theory and research to refine agricultural extension: affordances and barriers for farmers’ learning and practice change. The Journal of Agricultural Education and Extension, 23(4), 313-333. Shepherd, M., Turner, J. A., Small, B., & Wheeler, D. (2020). Priorities for science to overcome hurdles thwarting the full promise of the ‘digital agriculture’revolution. Journal of the Science of Food and Agriculture, 100(14), 5083-5092. Singh, H. K., Joshi, A., Malepati, R. N., Najeeb, S., Balakrishna, P., Pannerselvam, N. K., ... & Ganne, P. (2021). A survey of E-learning methods in nursing and medical education during COVID-19 pandemic in India. Nurse education today, 99, 104796. Sprenger, D. A., & Schwaninger, A. (2023). Video demonstrations can predict the intention to use digital learning technologies. British Journal of Educational Technology, 54(4), 857-877. Songkram, N., & Osuwan, H. (2022). Applying the technology acceptance model to elucidate k-12 teachers’ use of digital learning platforms in Thailand during the COVID-19 pandemic. Sustainability, 14(10), 6027. Sulaiman V, R., Hall, A., Kalaivani, N. J., Dorai, K., & Reddy, T. V. (2012). Necessary, but not sufficient: Critiquing the role of information and communication technology in putting knowledge into use. The Journal of Agricultural Education and Extension, 18(4), 331-346. 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. Tarutė, A., & Gatautis, R. (2014). ICT impact on SMEs performance. Procedia-social and behavioral Sciences, 110, 1218-1225. Teo, T., Faruk Ursavaş, Ö., & Bahçekapili, E. (2011). Efficiency of the technology acceptance model to explain pre‐service teachers' intention to use technology: A Turkish study. Campus-Wide Information Systems, 28(2), 93-101. Teo, T., Sang, G., Mei, B., & Hoi, C. K. W. (2018). Investigating pre-service teachers’ acceptance of Web 2.0 technologies in their future teaching: a Chinese perspective. Interactive Learning Environments, 27(4), 530-546. Tran, H. T. T., Nguyen, N. T., & Tang, T. T. (2023). Influences of subjective norms on teachers’ intention to use social media in working. Contemporary Educational Technology, 15(1), ep400. Tsai, T. H., Lin, W. Y., Chang, Y. S., Chang, P. C., & Lee, M. Y. (2020). Technology anxiety and resistance to change behavioral study of a wearable cardiac warming system using an extended TAM for older adults. PloS one, 15(1), e0227270. Unal, E., & Uzun, A. M. (2021). Understanding university students’ behavioral intention to use Edmodo through the lens of an extended technology acceptance model. British Journal of Educational Technology, 52(2), 619-637. Uzun, A. M., & Kilis, S. (2020). Investigating antecedents of plagiarism using extended theory of planned behavior. Computers & Education, 144, 103700. Van Acker, F., Van Buuren, H., Kreijns, K., & Vermeulen, M. (2013). Why teachers use digital learning materials: The role of self-efficacy, subjective norm and attitude. Education and Information Technologies, 18, 495-514. Vangrieken, K., Dochy, F., Raes, E., & Kyndt, E. (2015). Teacher collaboration: A systematic review. Educational Research Review, 15, 17-40. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. Vodafone Foundation. (2016). Connected Farming in India. How Mobile can support farmers Livelihoods, A report. Wang, Y. S., Wang, Y. M., Lin, H. H., & Tang, T. I. (2003). Determinants of user acceptance of Internet banking: an empirical study. International journal of service industry management, 14(5), 501-519. Wittek, D., Wiesche, M., Goffart, K., & Krcmar, H. (2019). Theory-Based Affordances of Utilitarian, Hedonic and Dual-Purposed Technologies: A Literature Review. World Health Organization. Coronavirus. World Health Organization, cited January 19, (2020). Available: https://www.who.int/health-topics/coronavirus). Yakubu, D. H., Abubakar, B. Z., Atala, T. K., & Muhammed, A. (2013). Use of information and communication technologies among extension agents in Kano State, Nigeria. Journal of Agricultural Extension, 17(1), 162-173. Zare, M., Seyed Mohammad, H., & Ahangar, A. (2020). A comparative study on BIM (building information modeling) implementation and maturity across different countries with a review on Iran. In Proceeding of 3rd International Conference of Building Information Modelling (pp. 1-12).