Applying an integrated acceptance model and using technology for accepting innovations among farmers in Delfan County)

Document Type : Research Paper

Authors

1 Associate professor in rural development, Faculty of Agriculture, Bu-Ali Sina University, Hamedan

2 MSc in rural development . Department of Agricultural Extension and Education, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

Abstract

Achieving sustainability, profitability and productivity in the agricultural sector requires the development and exploitation of appropriate technology resulting from agricultural research and the promotion of innovation in this sector. The decision - making process for the adoption of innovation for farmers in developing countries is complicated. Individual characteristics, perceptions, beliefs, attitudes are intriduced as important factors influencing the acceptance of innovations. Given the importance of the innovation acceptance process in recent decades, many theories and models have been proposed regarding acceptance process. One of the notable models for decision-making and acceptance of innovation is the integrated theory of acceptance and use of technology. In terms of technology acceptance, this model demonstrates the intertwining of the main structures of several well-known models of variance with the intention of using behavior. The total population of farmers in Delfan city was 18,000 households according to the statistical yearbook of 2016, and considering its extent, using the Cochran's formula, a statistical sample consisting of 250 heads of households was assigned proportionally and then randomly selected. Accordingly, the present paper examines the use of an integrated acceptance model and the use of technology to adopt innovations among farmers. The results showed that among the dimensions expressed for the tendency to accept innovation, performance expectation, hope for effort and intention to use are more important and the lowest score recorded by farmers are social conditions, facilitating conditions and practical use, respectively

Keywords

References

  • Adnan, N., Nordin, S. M., & bin Abu Bakar, Z. (2017). Understanding and facilitating sustainable agricultural practice: a comprehensive analysis of adoption behaviour among Malaysian paddy farmers. Land Use Policy, 68, 372-382.
  • Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision sciences, 30(2), 361-391.
  • Agwu, A. E., Dimelu, M. U., & Madukwe, M. C. (2008). Innovation system approach to agricultural development: Policy implications for agricultural extension delivery in Nigeria. African Journal of Biotechnology, 7(11).‏
  • Alfred, S. Y., & Fagbenro, O. A. (2006). Perception of tilapia farmers on information sources in the coastal region of Ondo State, Nigeria. Age, 97(23), 80-84.‏
  • Ashoogh, M., Aghamolaei, T., Ghanbarnejad, A., & Tajvar, A. (2013). Utilizing the theory of planned behavior to Prediction the safety driving behaviors in truck drivers in Bandar Abbas 1392. Iranian Journal of Health Education and Health Promotion, 1(3), 5-14.‏
  • Chang, M. K., & Cheung, W. (2001). Determinants of the intention to use Internet/WWW at work: a confirmatory study. Information & Management, 39(1), 1-14.‏
  • Dixon, J., Nalley, L., Kosina, P., La Rovere, R., Hellin, J., & Aquino, P. (2006). Adoption and economic impact of improved wheat varieties in the developing world. The Journal of Agricultural Science, 144(6), 489-502.
  • Downs, D. S., & Hausenblas, H. A. (2005). Elicitation studies and the theory of planned behavior: a systematic review of exercise beliefs. Psychology of sport and exercise, 6(1), 1-31
  • Hailu, B. K., Abrha, B. K., & Weldegiorgis, K. A. (2014). Adoption and impact of agricultural technologies on farm income: Evidence from Southern Tigray, Northern Ethiopia. International Journal of Food and Agricultural Economics (IJFAEC), 2(1128-2016-92058), 91.
  • Hudson, J., & Minea, A. (2013). Innovation, intellectual property rights, and economic development: a unified empirical investigation. World Development, 46, 66-78.‏
  • Karnowski, V., Leonhard, L., & Kümpel, A. S. (2018). Why users share the news: A theory of reasoned action-based study on the antecedents of news-sharing behavior. Communication Research Reports, 35(2), 91-100.
  • Keršulienė, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of Rational Dispute Resolution Method by Applying New Step-wise Weight Assessment Ratio Analysis (SWARA). Journal of Business Economics and Management, 11(2), 243-258.
  • Kumar, D. S. (2012). Adoption of drip irrigation system in India: Some experience and evidence. The Bangladesh Development Studies, 61-78.‏
  • Lai, V. S., & Li, H. (2005). Technology acceptance model for internet banking: an invariance analysis. Information & management, 42(2), 373-386.‏
  • Läpple, D., Renwick, A., Cullinan, J., & Thorne, F. (2016). What drives innovation in the agricultural sector? A spatial analysis of knowledge spillovers. Land use policy, 56, 238-250.‏
  • Leeuwis, C., Leeuwis, C., & Ban, A. (2004). Communication for rural innovation. Blackwell publishers.
  • Martin, M. H. (2003). Factors influencing faculty adoption of web-based courses in teacher education programs within the State University of New York (Doctoral dissertation, Virginia Tech).‏
  • Ohlmer, B., Olson, K., & Brehmer, B. (1998). Understanding farmers' decision making processes and improving managerial assistance. Agricultural Economics, 18(3), 273-290.
  • Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404-414.‏
  • Park, D. H., & Lee, J. (2008). eWOM overload and its effect on consumer behavioral intention depending on consumer involvement. Electronic Commerce Research and Applications, 7(4), 386-398.‏
  • Park, Y., & Chen, J. V. (2007). Acceptance and adoption of the innovative use of smartphone. Industrial Management & Data Systems, 107(9), 1349-1365.‏
  • Robinson, L. (2009). A summary of diffusion of innovations. Enabling change, 5(10).‏
  • Rogers, E. M. (2003). Elements of diffusion. Diffusion of innovations, 5(1.38).‏
  • Sambodo, L. A. 2009. The Decision Making Processes Of Semi-Commercial Farmers: A Case Study Of Technology Adoption In Indonesia. Ph. D. Thesis. Univ. of Lincoln, St Lucia, QLD, Australia.
  • Shiferaw, B. A., Okello, J., & Reddy, R. V. (2009). Adoption and adaptation of natural resource management innovations in smallholder agriculture: reflections on key lessons and best practices. Environment, development and sustainability, 11(3), 601-619.
  • Tzeng, G. H., Chiang, C. H., & Li, C. W. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert systems with Applications, 32(4), 1028-1044.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.‏
  • 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.
  • Zhou, T. (2012). Examining location-based services usage from the perspectives of unified theory of acceptance and use of technology and privacy risk. Journal of Electronic Commerce Research, 13(2), 135.‏
  • Zolfani, S., Aghdaie, M., Derakhti, A., Zavadskas, K., & Varzandeh, M. (2013). Decision makingon business issues with foresight perspective; an application of new hybrid MCDM modelin shopping mall locating. Expert Systems with Applications, 40(17), 7111–7121.

Files

Share

How to cite

Statistics