Have a personal or library account? Click to login
Factors Affecting the Adoption of Agricultural Automation Using Davis’s Acceptance Model (Case Study: Ardabil) Cover

Factors Affecting the Adoption of Agricultural Automation Using Davis’s Acceptance Model (Case Study: Ardabil)

Open Access
|Feb 2020

References

  1. ADEBIYI, S. – EKONOLA, J. 2010. Factors affecting adoption of cocoa rehabilitation techniques in Oyo State of Nigeria. In World Journal of Agricultural Sciences, vol. 9, no. 3, pp. 258–265.
  2. AKUDUGU, M. – GUO, E. – DADZIE, S. 2012. Adoption of modern agricultural production technologies by farm households in Ghana: What factors influence their decisions? In Journal of Biology, Agriculture and Healthcare, vol. 2, no. 3.
  3. AMEN, U. 2010. Consumer attitude towards mobile advertising. In Interdisciplinary Journal of Contemporary Research in Business, vol. 70, no. 3, pp. 75–104.
  4. BELOEV, I. H. 2016. A review on current and emerging application possibilities for unmanned aerial vehicle. In Acta Technologica Agriculturae, vol. 19, no. 3, pp. 70–76.
  5. BIAŁY, W. – ŽARNOVSKÝ, J. 2017. Acquiring EU funds for the development of research potential of enterprises as a method for developing smart specialisations. In Acta Technologica Agriculturae, vol. 20, no. 2, pp. 46–51.
  6. BONABANA-WABBI, J. 2002. Assessing Factors Affecting Adoption of Agricultural Technologies: The Case of Integrated Pest Management (IPM) in Kumi District. MSc. Thesis, Eastern Uganda. Available at: http://hdl.handle.net/10919/36266
  7. CALLUM, K. M. – JEFFREY, L. – KINSHUK. 2014. Comparing the role of ICT literacy and anxiety in the adoption of mobile learning. In Computers in Human Behavior, vol. 39, pp. 8–19.
  8. CHONG, A. – OOI, K. – TAN, B. 2010. Online banking adoption: an empirical analysis. In International Journal of Bank Marketing, vol. 28, pp. 267–287.
  9. DAVIS, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. In MIS Quarterly, vol. 13, no. 3, pp. 319–340.
  10. DOSS, C. R. 2003. Understanding Farm Level Technology Adoption: Lessons Learned from CIMMYT’s Microsurveysin Eastern Africa. CIMMYT Economics Working Paper 03–07. Mexico, D.F.: CIMMYT.
  11. EBRAHIMI, A. – BIZHNI, M. – SEDIGHI, H. 2017. Acceptance of nuclear technology in the field of agriculture: Application of DTBP and UTAUT theories. In The Second National Congress on the Development and Promotion of Iranian Agricultural Engineering and Soil Science, May 27, Tehran, Iran. (In Farsi)
  12. GOODARZI, A – SADEGHI FARD, S. 2014. Intelligence and the place of industrial automation in agriculture. In The Second National Agricultural and Sustainable Natural Resources Conference, July, Tehran, Iran. (In Farsi)
  13. KARIYASA, K. – DEWI, A. 2011. Analysis of factors affecting adoption of integrated crop management farmer. In International Journal of Food and Agricultural Economics, vol. 1, no. 2, pp. 29–38.
  14. KARUGIA, S. – BALTENWECK, I. – WAITHAKA, M. – MIANO, M. – NYIKAL, R. – ROMNEY, D. 2004. Perception of technology and its impact on technology uptake: The case of fodder legume in central Kenya highlands. In The Role of Social Scientists Proceedings of the Inaugural Symposium, 6–8 December, Grand Regency Hotel, Nairobi, Kenya.
  15. KEELAN, C. – THORNE, F. – FLANAFAN, P. – NEWMAN, C. 2014. Predicted willingness of Irish farmers to adopt GM. In Journal of Economics and Sustainable Development, vol. 12, pp. 208–216.
  16. KREJCIE, R. V. – MORGAN, D. W. 1970. Determining sample size for research activities. In Educational and Psychological Measurement, vol. 30, pp. 607–610. Available at: https://doi.org/10.1177/00131644700300030810.1177/001316447003000308
  17. KIM, H. W. – CHAN, H. C. – GUPTA, S. 2007. Value-based adoption of mobile internet: an empirical investigation. In Decision Support System, vol. 43, pp. 111–126.
  18. LOTFI, A. – BAKHSHAYESHI, M. 2010. Investigating the factors affecting e-library acceptance. In Fifth National Conference and Second International Conference on E-Learning and Education, Tehran.
  19. LIU, S. – LIAO, H. – PRATT, J. A. 2009. Impact of media richness and flow on e-learning technology acceptance. In Computers & Education, vol. 52, pp. 599–607.
  20. MALTE, N. – ROSSI, M. – TUUNIAINEN, V. – OORNI, A. 2008. An empirical investigation of mobile ticketing service adoption in public transportation. In Personal and Ubiquitous Computing, vol. 12, pp. 57–65.
  21. MANSOUR, I. H. – ELJELLY, A. M. – ABDULLAH, A. M., 2017. Consumers‘ attitude towards e-banking services in Islamic banks: the case of Sudan. In Review of International Business and Strategy, vol. 26, no. 2, pp. 244–260.10.1108/RIBS-02-2014-0024
  22. MASOUDI, H. 2016. A new ground for innovating and developing entrepreneurship in the animal husbandry. In Entrepreneurship Journal in Agriculture, vol. 2, no. 3, pp. 256–273. (In Farsi).
  23. MIGNOUNA, B. – MANYONG, M. – RUSIKE, J. – MUTABAZI, S. – SENKONDO, M. 2011. Determinants of adopting imazapyr-resistant maize technology and its impact on household income in western Kenya. In AgBioforum, vol. 14, no. 3, pp. 158–163.
  24. MOMENI, M. – QIYOMI, A. 2017. Statistical Analysis Using SPSS. Negarandedanesh publication.
  25. OKUNLOLA, O. – OLUDARO, O. – AKINWAERE, B. 2011. Adoption of new technologies by fish farmers in Akure, Ondo. In Journal of Agricultural Technology, vol. 7, no. 6, pp. 1539–1548.
  26. PARK, Y. – CHEN, J. V. 2007. Acceptance and adoption of the innovative use of smartphone. In Industrial Management & Data Systems Information, vol. 107, no. 9, pp. 1349–1365.
  27. PUTLER, D. S. – ZILBERMAN, D. 1988. Computer Use in Agriculture: Evidence from Tulare County, California.10.2307/1241920
  28. ROBERTS, K. – ENGLISH, B. – LARSON, J. – COCHRAN, R. – GOODMAN, W. – LARKIN, S. – MARRA, M. – MARTIN, S. – SHURLEY, W. – REEVES, M. 2004. Adoption of site-specific information and variable-rate technologies in cotton precision farming. In Journal of Agricultural and Applied Economics, vol. 36. no. 1, pp. 143–158.
  29. SAGHAFI, F. – MOGHADDAM, E. N. – ASLANI, A. 2017. Examining effective factors in initial acceptance of high-tech localized technologies: Xamin, Iranian localized operating system. In Technological Forecasting and Social Change, vol. 122, pp. 275–288.
  30. SCHUELLER, J. K. 2006. Applied machine vision of plants: a review with implications for field deployment in automated farming operations. In Intelligent Service Robotics, vol. 3, no. 4, pp. 209–217.
  31. SHIRMOHAMMADI, M. 2004. Development of Technology Acceptance Model (TAM) and its Testing at the Ministry of the Interior. MSc. thesis, Tehran University (In Farsi).
  32. SIREGAR, J. J. – PUSPOKUSUMO, R. A. W. – RAHAYU, A. 2017. Analysis of affecting factors technology acceptance model in the application of knowledge management for small medium enterprises in industry creative. In Procedia Computer Science, vol. 116, pp. 500–508.
  33. SREEKANTHA, D. K. 2016. Automation in agriculture. In International Journal of Engineering Science Invention Research & Development, vol. 2, no. 6, pp. 458–472.
  34. SSERUNKUUMA, D. 2005. The adoption and impact of improved maize and land management technologies in Uganda. In The Electronic Journal of Agricultural and Development Economics, Food and Agriculture Organization of the United Nations, vol. 2, no. 1, pp. 67–84.
  35. TORABI, A. – MALEKI, A. – ISHAG BEIGI, A. 2014. The importance and position of the robot in the modern agriculture. In The Second National Conference on Agricultural Engineering, Environment and Sustainable Natural Resources, March 20, Tehran, Iran (In Farsi).
  36. TURNER, M. – KICHENHAM, B. – BRERETON, P. – CHARTERS, S. – BUDGEN, D. 2010. Does the technology acceptance model predict actual use? A systematic literature review. In Information and Software Technology, vol. 52, no. 5, pp. 463–479.
  37. VENKATESH, V. – DAVIS, F. D. 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. In Management Science, vol. 46, no. 2, pp. 186–204.
  38. VERKANTESH, V. – MORRIS, M. G. – DAVIS, G. B. – DAVIS, F. D. 2003. User acceptance of information technology: Toward a unified view. In MIS Quarterly, vol. 27, no. 3, pp. 425–478.
  39. VERMA, P. – SINHA, N. 2018. Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service. In Technological Forecasting and Social Change, vol. 126, pp. 207–216.
  40. WAFAEI, N. 2009. Identifying and Prioritizing Factors Affecting Adoption of Mobile Banking from the Point of View of Customers (Case Study: National Bank of Iran Branches in Tehran). MSc. thesis, Tarbiat Modares University, Tehran (In Farsi).
  41. YADAV, R. – PATHAK, G. 2016. Determinants of consumers‘ green purchase behavior in a developing nation: Applying and extending the theory of planned behavior. In Ecological Economics, vol. 134. pp. 114–122.
Language: English
Page range: 30 - 39
Published on: Feb 20, 2020
Published by: Slovak University of Agriculture in Nitra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2020 Mahdi Salimi, Razieh Pourdarbani, Bagher Asgarnezhad Nouri, published by Slovak University of Agriculture in Nitra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.