The Development of a Model for Farmers’ Intention to Use Low-Carbon Agricultural Technology in Mianyang City, Sichuan Province, People’s Republic of China
Article Sidebar
Main Article Content
บทคัดย่อ
This study employed a mixed-methods research design with the following objectives: (1) To study the level of importance of subjective norms, information and access to information, perceived ease of use, perceived benefits of using technology, and the intention to adopt environmentally friendly technology. (2) To examine the relationships between subjective norms, information and access to information, perceived ease of use, perceived benefits of using technology, and their impact on the intention to adopt environmentally friendly technology. And (3) To explore the model of intention to use environmentally friendly technology. For the quantitative phase, the sample consisted of 500 farmers selected through accidental sampling from five districts of Mianyang: (1) Peicheng District, (2) Anzhou District, (3) Santai County, (4) Yanting County, and (5) Pingwu County. Data were collected using questionnaires and analyzed through descriptive statistics, inferential statistics, and structural equation modeling. For the qualitative phase, the participants included 25 farmers drawn from the same five districts, selected through snowball sampling. Data were collected using semi-structured interviews and analyzed through content analysis.
The findings revealed that: (1) the latent variable with the highest level of importance was the intention to adopt low-carbon technologies, followed by perceived usefulness, perceived ease of use, information and access to information, and subjective norms; (2) the factor most strongly associated with the model of farmers’ intention to adopt low-carbon technologies in Mianyang was subjective norms, followed by information and access to information, perceived ease of use, and perceived usefulness; and (3) the newly developed causal structural model influencing farmers’ intention to adopt low-carbon technologies in Mianyang comprised five components, with the number of observed variables increased from 12 to 15 by adding items consistent with theoretical criteria. Organize activities to enhance knowledge and support from community leaders. The government should create learning centers and a digital platform with incentives.
Article Details
อนุญาตภายใต้เงื่อนไข Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
ทัศนะและความคิดเห็นที่ปรากฏในบทความในวารสาร ถือเป็นความรับผิดชอบของผู้เขียนบทความนั้น และไม่ถือเป็นทัศนะและความรับผิดชอบของกองบรรณาธิการ ยินยอมว่าบทความเป็นลิขสิทธิ์ของวารสาร
เอกสารอ้างอิง
Agarwal, R., & Prasad, J. (1999). Are Individual Differences Germane to the Acceptance of New Information Technologies? Decision Sciences, 30(2), 361–391.
Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Hoboken, NJ: Prentice-Hall.
Bhosale, G., & Kadam, R. P. (2022). Attitude of Farmers towards the Use of Information and Communication Technology for Seeking Agricultural Information. International Journal of Agricultural Sciences, 18(1), 88–92.
Byrne, B. M. (2010). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. (2nd ed.). New York: Routledge.
Creswell, J. W. (2013). Qualitative Inquiry & Research Design: Choosing among Five Approaches (3rd ed.). Thousand Oaks, CA: SAGE.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13, 319–340.
Haseeb, M. et al. (2023). Challenges Encountered in the Provision of Enteral Nutrition in Pediatric Intensive Care Unit: An Observational Study. Cureus, 15(11), 1–6.
Kumar, S. et al. (2023). Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis. Information Systems Frontiers, 25, 871–896.
Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2004). How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance. MIS Quarterly, 28(4), 705–737.
Morse, J. M., & Field, P. (1996). Nursing Research: The Application of Qualitative Approaches. (2nd ed.). London: Chapman & Hall.
Nyagango, A. I., Sife, A. S., & Kanzungu, I. (2023). Use of Mobile Phone Technologies for Accessing Agricultural Marketing Information by Grape Smallholder Farmers: A Technological Acceptance Model (TAM) Perspective. Technological Sustainability, 2(3), 320–336.
Purvis, B., Mao, Y., & Robinson, D. (2019). Three Pillars of Sustainability: In Search of Conceptual Origins. Sustainability Science, 14(3), 681–695.
Savari, M. et al. (2023). Promotion of Adopting Preventive Behavioral Intention toward Biodiversity Degradation among Iranian Farmers. Global Ecology and Conservation, 43, 1–13.
Schwartz, S. H. (1973). Normative Explanations of Helping Behavior: A Critique, Proposal, and Empirical Test. Journal of Experimental Social Psychology, 9(4), 349–364.
Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics (5th ed.). Boston, MA: Allyn & Bacon.
Thanaphon, P. (2024). Factors Influencing the Adoption of Agricultural Innovations for Sustainability: A Case Study of the Smile Innovation Project. (Master’s Thesis). Thammasat University, Bangkok.
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204.
Xueying, Y., Guojun, S., Di, S., & Rui, H. (2024). Effect of Digital Multimedia on the Adoption of Agricultural Green Production Technology among Farmers in Liaoning Province, China. Scientific Reports, 14, 13092.
Zimbardo, P. G., & Ebbesen, E. B. (1970). Influence, Attitudes, and Behavior. New York: Wadsworth.
