Factors Influencing the Intentions to Adopt Technology of the Broiler Farmer in Livestock Region 3, Thailand
DOI:
https://doi.org/10.48048/tis.2022.1707Keywords:
Mindsets, Growth mindset, Technology adoption, Poultry farm management system, Smart farmAbstract
This research aimed to study the types and influence of mindsets, performance expectancy, effort expectancy, social influence and facilitating conditions on farmers' intention to adopt a technology. The research participants were 110 broiler farmers in livestock region 3. The research used a questionnaire to collect quantitative data and analyse the data using frequency, percentage, mean, standard deviation, correlation coefficient and structural equation modeling with maximum likelihood estimation to analyse path coefficienct and structural relationships. The result showed that most of the participants have a growth mindset more than a fixed mindset. Performance expectancy, effort expectancy, social influence and facilitating conditions have directly affected boiler farmers' intention to adopt the technology. Effort expectancy has a total effect on attitude toward using technology. Interestingly, facilitating conditions have shown the most considerable influence on attitude toward adopting the technology. Mindsets have an influence on effort expectancy and facilitating conditions.
HIGHLIGHTS
- Most broiler farmers in livestock region 3 possess a growth mindset than a fixed mindset
- Performance expectancy, effort expectancy, social influence and facilitating conditions have direct effect on boiler farmers' intention to adopt the technology
- Mindsets influence farmers' perception of effort expectancy and facilitating conditions of adopting the technology
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References
Office of the National Broadcasting and Telecommunications Commission, Available at: http://www.nbtc.go.th, accessed July 2018.
T Goodwin. Digital darwinism: Survival of the fittest in the age of business disruption. Kogan Page, New York, 2018, p. 11-2.
P Booasang. Digital Transformation (in Thai), Available at: https://www.scbeic.com/th/detail/product/4327, accessed July 2018.
R Agarwal, S Chandrasekaran and M Sridhar. Imagining construction's digital future, Available at: https://www.mckinsey.com/industries/capital-projects-and-infrastructure/our-insights/imagining-constructions-digital-future, accessed July 2018.
W Petchseechoung. Frozen & processed chicken industry, Available at: https://www.krungsri.com/bank/getmedia/547be15f-6d5d-44c3-ace7-996f3d03e4dc/IO_Chicken_2016_EN.aspx, accessed November 2017.
W Petchseechoung. Frozen & processed chicken industry, Available at: https://www.krungsri.com/bank/getmedia/81639011-fd0b-4e89-8a38-64402b37ccce/IO_Chicken_171019_EN_EX.aspx, accessed July 2018.
G Bucci, D Bentivoglio and A Finco. Factors affecting ICT adoption in agriculture: A case study in Italy. Qual. Access Success 2019; 20, 122-9.
I Ajzen. The theory of planned behavior. In: PAM Van Lange, AW Kruglanski and ET Higgins (Eds.). Handbook of theories of social psychology. Sage, London, 2012, p. 438-57.
V Venkatesh, MG Morris, GB Davis and FD Davis. User acceptance of information technology: Toward a unified view. MIS Q. 2003; 27, 425-78.
M Sharifzadeh, GH Zamani, D Khalili and E Karami. Agricultural climate information use: An application of the planned behaviour theory. J. Agr. Sci. Tech. 2012; 14, 479-92.
I Artikov, SJ Hoffman, GD Lynne, LM PytlikZillig, Q Hu, A Tomkins, KG Hubbard, MJ Hayes and WJ Waltman. Understanding the influence of climate forecasts on farmer decisions as planned behavior. J. Appl. Meteorol. Climatol. 2006; 45, 1202-14.
M Ali, N Man, FM Muharam and SZ Omar. Factors influencing behavioral intention of farmers to use ICTs for agricultural risk management in Malaysia. Pakistan J. Agr. Res. 2020; 33, 295-302.
P Watcharaanantapong, RK Roberts, DM Lambert, JA Larson, M Velandia, BC English, RM Rejesus and C Wang. Timing of precision agriculture technology adoption in U.S. cotton production. Precis. Agr. 2014; 15, 427-46.
S Zheng, Z Wang and CJ Wachenheim. Technology adoption among farmers in Jilin Province, China. China Agr. Econ. Rev. 2019; 11, 206-16.
J Lee, K Kim, H Shin and J Hwang. Acceptance factors of appropriate technology: Case of water purification systems in Binh Dinh, Vietnam. Sustainability 2018; 10, 2255.
M Michels, V Bonke and M Oliver. Understanding the adoption of crop protection smartphone apps: An application of the unified theory of acceptance and use of technology. Diskussionsbeitrag 2019; 2019, 1905.
N Nejadrezaei, MS Allahyari, M Sadeghzadeh, A Michailidis and HE Bilali. Factors affecting adoption of pressurised irrigation technology among olive farmers in Northern Iran. Appl. Water Sci. 2018; 8, 190.
J Molina-Maturano, N Verhulst, J Tur-Cardona, DT Güereña, A Gardeazábal-Monsalve, B Govaerts and S Speelman. Understanding smallholder farmers’ intention to adopt agricultural apps: The role of mastery approach and innovation hubs in Mexico. Agronomy 2021; 11, 194.
J Krümpel. 2019, Factors influencing the use of mobile applications by farmers for data and information management. M. Sc. Thesis. Wageningen University & Research, Wageningen, Netherlands.
S Bates, B Brunsman and P Crozier. Available at: https://assets.kpmg/content/dam/kpmg/xx/pdf/ 2019/03/market-speed-it-operating-models-in-the-age-of-the-connected-enterprise.pdf, accessed April 2021.
GC Kane, D Palmer, AN Phiilps, D Kinron and N Buckley. Available at: https://www2.deloitte.com/us/en/insights/focus/digital-maturity/coming-of-age-digitally-learning-leadership-legacy.html, accessed April 2021.
A Derler. Available at: https://www.researchgate.net/publication/341219768, accessed April 2021.
CS Dweck and DC Molden. Mindsets: Their impact on competence motivation and acquisition. In: AJ Elliot, CS Dweck and DS Yeager (Eds.). Handbook of competence and motivation: Theory and application. 2nd ed. Guilford Press, New York, 2017, p. 135-54.
CS Dweck. Mindset: The new psychology of success. Ballantine Books, New York, 2008, p. 3-11.
MC Murphy and CS Dweck. Mindsets shape consumer behavior. J. Consum. Psychol. 2015; 26, 127-36.
MA Martinez. 2018, Mindset as it relates to implementation of mobile devices for instruction. Ph. D. Dissertation. The University of Texas at Arlington, Texas, United States.
KL Stewart. 2018, The role of growth mindset and efficacy in teachers as change agents. Ed. D. Dissertation. Saint Mary's College of California, Ann Arbor, United States.
E Diehl, Available at: http://www.classroom20.com/forum/topics/motivating-students-with, accessed August 2018.
R Likert. The method of constructing an attitude scale. In: GM Maranell (Ed.). Scaling: A sourcebook for behavioral scientists. Aldine Transaction, New Jersey, United States, 2007, p. 233-43.
B Srisatidnarakul. Development and validation of research instruments: Psychometric properties (in Thai). Chulalongkorn University Printing House, Bangkok, Thailand, 2012.
S Prasitratthasin. Social science research methodology (in Thai). Fueang Fa Printing, Bangkok, Thailand, 2003.
N Songsrirote, Available at: http://www.nitiphong.com/paper_pdf/phd/FactorAnalysis_concept.pdf, accessed August 2018.
JC Nunnally. Psychometric theory. Vol II. McGraw-Hill, New York, 1978.
JF Hair, WC Black, BJ Babin and RE Anderson. Multivariate data analysis. Pearson Prentice Hall, New Jersey, United States, 2010.
M Bunnag. Statistical analysis for decision making (in Thai). Chulalongkorn University, Bangkok, Thailand, 1994.
T Silpcharu. Research and data analysis by SPSS and AMOS (in Thai). Business R and D, Bangkok, Thailand, 2012.
R Songsraboon. Structural equation modelling for prediction (in Thai). Silpakorn Univ. J. 2018; 38, 185-205.
Bureau of Livestock Standards and Certifaication, Available at: https://certify.dld.go.th/certify/index.php/th/2016-05-01-14-50-24, accessed May 2020.
Plant Standard and Certification Division, Available at: http://gap.doa.go.th, accessed May 2020.
Fisheries Commodity Standard System and Traceability Division, Available at: http://thacert. fisheries. go.th/wscert/site/certificate_list.jsp, accessed May 2020.
DW Kahenya, M Sakawa and M Iravo. Assessing use of information communication technologies among agricultural extension workers in Kenya using modified UTAUT model. Int. J. Sci. Basic Appl. Res. 2014; 16, 11-22.
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