Analyzing Rice Farmers’ Intention to Adopt Modern Rice Technologies Using Technology Acceptance Model (TAM)

Main Article Content

Ryan Mark A. Ambong
Maybelle A. Paulino


This paper analyzes rice farmers’ intention to adopt modern rice technologies using the Technology Acceptance Model (TAM). Quantitative data were gathered through a survey among 404 rice farmers selected using three-stage sampling design. The empirical analysis was done using Partial Least Squares Structural Equation Modeling (PLS-SEM) via WARP PLS software version 3.0. The outcome of the hypothesized framework shows that perceived usefulness and relative advantage have a direct and significant influence on farmers’ attitude towards modern rice technologies. This implies that the perceived usefulness and relative advantage of the technology influences the positive or negative attitude of the farmers toward the technology. On the other hand, the model suggests that perceived convenience of the technology does not influence farmers’ attitude. Nevertheless, the hypothesized model demonstrates that farmers’ intention influences their decision to adopt modern rice technologies. The paper suggests that further studies be conducted to incorporate external variables in TAM.

PLS-SEM, rice technologies, TAM, technology adoption, technology transfer.

Article Details

How to Cite
Ambong, R. M. A., & Paulino, M. A. (2020). Analyzing Rice Farmers’ Intention to Adopt Modern Rice Technologies Using Technology Acceptance Model (TAM). Asian Research Journal of Agriculture, 13(1), 21-30.
Original Research Article


FAO. Rice Post-harvest conference draft. (D. de Padua, Ed.) Rome, Italy: Publications Division, Food and Agriculture Organization of the United Nations; 1988.

Mariano MJ, Villano R, Fleming E. Factors influencing farmers’ adoption of modern rice technologies and good management practices in the Philippines. Agricultural Systems. 2012;105(1):41-53.

Tey YS, Brindal M. Factors Influencing the Adoption of Precision Agricultural Technologies: A Review for Policy Implications. Precision Agriculture. 2012;13:713-730.


Tondo DT. Comparative economic analysis of rice processing methods in Benue State, Nigeria. International Journal of Environment, Agriculture and Biotechnology (IJEAB). 2017;II(6):2776-2782.

Galina CS, Turnbull F, Noguez-Ortiz A. Factors affecting technology adoption in small community farmers in Relation to Reproductive Events in Tropical Cattle Raised under Dual Purpose Systems. Open Journal of Veterinary Medicine. 2016;VI(1).

Paulino MA, Amora JT. Towards the development of crop productivity model. Review of Integrative Business and Economics Research. 2019;8(4): 412-422.

Amin MK, Li J. Applying farmer technology acceptance model to understand farmer’s behavior intention to use ICT based microfinance platform: A comparative analysis between Bangladesh and China". WHICEB 2014 Proceedings. Paper 31; 2014.


Fishbein M, Ajzen I. Belief, attitude, intention and behavior: An introduction to theory and research; 1975.

Ajzen I, Madden TJ. Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology. 1986;22:453-474.

Venkatesh V, Davis F. A theoretical extension of the technology acceptance model: Four longitudinal field studies, Management Science. 2000;46(2):186-204.

Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Quarterly. 2003;425-478.

Khaled AM, Li J. Applying farmer technology acceptance model to understand farmer’s behavior intention to use ICT based microfinance platform: A comparative analysis between Bangladesh and China. WHICEB 2014 Proceedings. Wuhan International Conference on e-Business at AIS Electronic Library (AISeL). 2014;31.

Rogers E. Diffusion of Innovations; 2003.

Wang Y, Meister D, Wang Y. Relative advantage and perceived usefulness: The adoption of competing ICTs. DIGIT 2008 Proceedings. 2008;6.

Asaduzzaman M, Hossain T. Adoption of selected homestead agricultural technologies by the rural women in Madhupur Upazila under Tangail District. Japanese Institutional Repositories Online; 2015.

CIMMYT Economics Program. The Adoption of Agricultural Technology: A Guide for Survey Design. Mexico DF, CIMMYT; 1993.

Chang C, Yan C, Tseng J. Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, [S.l.]. 2012;28(5).

[ISSN: 1449-5554]

Far ST, Rezaei-Moghaddam K. Determinants of Iranian agricultural consultants’ intention towards precision agriculture: Integrating innovativeness to the technology acceptance model. Journal of the Saudi Society of Agricultural Sciences. 2015;280-286.

Liu S, Liao H, Peng C. Applying the technology acceptance model and flow theory to on-line e-learning users’ acceptance behavior. Issues Inform. Syst. 2005;6(2):175–181.

Wu J, Wang S. What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Inform. Manage. 2005;42(5):719– 729.

OECD. Adoption of technologies for sustainable farming systems. Wageningen Workshop Proceedings (pp. 3-149). Paris: OECD Publications, 2, rue André-Pascal, 75775 Paris Cedex 16, France; 2001.

Issa FO, Kagbu JH. Institutional Factors Influencing Crop Farmers Adoption of Recommended Agrochemical Practices in Nigeria. Journal of Agricultural Extension. 2017;21(1):187-203.

Langyintuo S, Mungoma C. The effect of household wealth on the adoption of improved maize varieties in Zambia," Food Policy, Elsevier. 2008;33(6):550-559.

Ndagi AH, Kolo IN, Ybagi AA, Garba Y. Adoption of production technologies by lowland rice farmers in Lavun Local Government Areas of Niger State, Nigeria. International Journal of Agricultural Extension. 2016;4(1):49-56.

Kinyangi AA. Factors influencing the adoption of agricultural technology among smallholder farmers in Kakamega North Sub-County, Kenya. University of Nairobi. 2014;1-55.

Mottaleb KA. Perception and adoption of a new agricultural technology: Evidence from a developing country. Technology in Society. 2018;126-135.

Thi Ngoc Chi T, Yamada R. Factors affecting farmers’ adoption of technologies in farming system: A case study in Omon district, can Tho Province, Mekong Delta. Omonrice. 2002; 94-100.