Factors Affecting Community's Intention to Grow Bamboo as Bioenergy Crop in Khun Tat Wai Subdistrict, Chana District, Songkhla Province

Main Article Content

Nattawut Yingthavorn
Snitnuth Niyomsin

Abstract

The objective of this research was to examine factors influencing community intention to cultivate bamboo as a bioenergy crop by utilizing an extended framework of the Theory of Planned Behavior (TPB), incorporating Perceived Organizational Support as an additional variable. Data were collected via questionnaires from 322 residents (representing 322 households) in Khun Tat Wai Subdistrict, Chana District, Songkhla Province. Using convenience sampling, paper-based questionnaires were distributed across all nine villages. Data was analyzed using descriptive statistics, One-way ANOVA, and Multiple Regression Analysis. The results revealed that Perceived Behavioral Control was the strongest significant predictor of intention (β = 0.474, p < .001), followed by Subjective Norm (β = 0.216, p < .001). Conversely, Perceived Organizational Support had a significant negative effect on intention (β = -0.118, p = .019), while Attitude showed no significant effect. Collectively, the independent variables accounted for 40.20% of the variance in intention. Furthermore, findings indicated a relationship between educational level, Perceived Organizational Support, and intention; specifically, the group with lower secondary education or below reported the highest level of Perceived Organizational Support but the lowest intention to cultivate. The study suggests that policy implementation for bamboo as an energy crop should prioritize enhancing ease of management and leveraging community leaders as communication channels. Providing clear instructions and practical tools to simplify the cultivation process could help ensure that educational levels are no longer a barrier and could potentially shift the impact of Perceived Organizational Support in a positive direction.

Article Details

Section
Research Article

References

นพดล สุทธินนท์. (2561). โอกาสทางการตลาดและปัจจัยแห่งความสำเร็จของธุรกิจผลิตไฟฟ้าจากพลังงาน

ชีวมวล. วารสารวิชาการมหาวิทยาลัยธนบุรี, 12(28), 260–272.

รัญญพิสิษฐ์ พวงจิก. (2557). ไผ่: พืชพลังงานแห่งอนาคต?. วารสารวิทยาศาสตร์และเทคโนโลยี, 22(1),

–136.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision

Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. In D. Albarracín, B. T.

Johnson, & M. P. Zanna (Eds.), The handbook of attitudes (pp. 173–221). Lawrence

Erlbaum Associates Publishers.

Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2),

–147. https://doi.org/10.1037/0003-066X.37.2.122

Belsley, D. A. (1991). Conditioning Diagnostics: Collinearity and Weak Data in Regression. John

Wiley & Sons.

Chen, X. X., & Slade, E. (2025). Theory of planned behaviour: A review. In S. Papagiannidis

(Ed.), TheoryHub Book. OpenNCL. https://open.ncl.ac.uk/theories/18/theory-of-planned-behaviour/

Cherry, K. (2024, May 5). The components of attitude. Verywell Mind.

https://www.verywellmind.com/attitudes-how-they-form-change-shape-behavior-2795897

Green, S. B. (1991). How many subjects does it take to do a regression analysis? Multivariate

Behavioral Research, 26(3), 499–510. https://doi.org/10.1207/s15327906mbr2603_7

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018). Multivariate data analysis

(8th ed.). Cengage Learning EMEA.

Hou, J., & Hou, B. (2019). Farmers’ adoption of low-carbon agriculture in China: An extended

theory of the planned behavior model. Sustainability, 11(5), 1500. https://doi.org/10.3390/su11051500

Huang, Y., Hou, Y., Ren, J., Yang, J., & Wen, Y. (2024). How to promote sustainable bamboo

forest management: An empirical study from small-scale farmers in China. Forests, 15(1),

https://doi.org/10.3390/f15010012

Li, X., Dai, J., Zhu, X., Liu, J., He, J., Huang, Y., Liu, X., & Shen, Q. (2023). Mechanism of

attitude, subjective norms, and perceived behavioral control influence the green development behavior of construction enterprises. Humanities & Social Sciences Communications, 10(1), 1–16.

Marafon, A. C., Amaral, A. F. C., & Lemos, E. E. P. (2019). Characterization of bamboo species

and other biomasses with potential for thermal energy generation. Pesquisa

Agropecuária Tropical, 49, e55282. https://doi.org/10.1590/1983-40632019v4955282

Pahuriray, A. V. & Algara, R. O. (2021). Mobile-based PhilNITS reviewer design: Its functionality,

reliability, usability and efficiency. International Research Journal of Science, Technology,

Education, and Management, 1(2), 184-196. https://doi.org/10.5281/zenodo.5726596

Puiu, S., Yilmaz, S. E., Udrioiu, M. T., Raganova, J., Raykova, Z., Yildizhan, H., & Ameen, A.

(2023). The expanded theory of planned behavior for energy saving among academics

in Romania, Bulgaria, Turkey, and Slovakia. Scientific Reports, 13, 21350. https://doi.org/10.1038/s41598-023-48866-9

Savari, M., Damaneh, H. E., Damaneh, H. E., & Cotton, M. (2023). Integrating the norm activation

model and theory of planned behaviour to investigate farmer pro-environmental

behavioral intention. Scientific Reports, 13(1), 1–14. https://doi.org/10.1038/s41598-023-32342-w

Waiswa, D., Muriithi, B. W., Murage, A. W., Ireri, D. M., Maina, F., Chidawanyika, F., & Yavuz, F.

(2025). The role of social-psychological factors in the adoption of push-pull technology

by small-scale farmers in East Africa: Application of the theory of planned behavior.

Heliyon, 11(1), e14149. https://doi.org/10.1016/j.heliyon.2024.e14149

Watson Todd, R. (2018). Analyzing and interpreting rating scale data from questionnaires.

rEFLections, 25(1), 69-77.