FACTORS INFLUENCING RICE FARMERS' INTENTION OF SMART FARMING TECHNOLOGY IN KANIGORO VILLAGE, MALANG REGENCY

Syifa Aulia; Tri Wahyu Nugroho; Deny Meitasari; Fitria Dina Riana; Moh. Shadiqur Rahman

doi:10.14710/agrisocionomics.v10i2.28572

DOI: https://doi.org/10.14710/agrisocionomics.v10i2.28572

FACTORS INFLUENCING RICE FARMERS' INTENTION OF SMART FARMING TECHNOLOGY IN KANIGORO VILLAGE, MALANG REGENCY

*Syifa Aulia

- Department of Socio-Economics, Faculty of Agriculture, University of Brawijaya, Malang, Indonesia 65145, Indonesia

Tri Wahyu Nugroho

- Department of Socio-Economics, Faculty of Agriculture, University of Brawijaya, Malang, Indonesia 65145, Indonesia

Deny Meitasari

- Department of Socio-Economics, Faculty of Agriculture, University of Brawijaya, Malang, Indonesia 65145, Indonesia

Fitria Dina Riana

- Department of Socio-Economics, Faculty of Agriculture, University of Brawijaya, Malang, Indonesia 65145, Indonesia

Moh. Shadiqur Rahman

- Department of Socio-Economics, Faculty of Agriculture, University of Brawijaya, Malang, Indonesia 65145, Indonesia

under http://creativecommons.org/licenses/by-sa/4.0.

BibTex Citation Data :

@article{Agrisocionomics28572,
    author = {Syifa Aulia and Tri Nugroho and Deny Meitasari and Fitria Riana and Moh. Rahman},
    title = {FACTORS INFLUENCING RICE FARMERS' INTENTION OF SMART FARMING TECHNOLOGY IN KANIGORO VILLAGE, MALANG REGENCY},
    journal = {Agrisocionomics: Jurnal Sosial Ekonomi Pertanian},
  volume = {10},
    number = {2},
    year = {2026},
    keywords = {attitude, intention, perceived behavioral control, smart farming, subjective norm},
    abstract = { The adoption of smart farming technology among rice farmers in Kanigoro Village, Pagelaran Subdistrict, remains low despite the availability of tools designed to enhance agricultural efficiency. Technologies such as the Smart Soil Sensor and Bird Control Sound System are still underutilized, reflecting a gap between technological availability and farmer adoption. This study analyzes the influence of attitude, subjective norm, and perceived behavioral control on farmers’ intention to adopt smart farming technology, using the Theory of Planned Behavior (TPB) as the analytical framework. A total of 100 farmers were surveyed through structured questionnaires and direct interviews, with data analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that all three variables significantly affect intention: attitude (β = 0.19, p = 0.023), subjective norm (β = 0.33, p < 0.001), and perceived behavioral control (β = 0.42, p < 0.001). Perceived behavioral control emerged as the strongest predictor, followed by subjective norm and attitude. These findings highlight that enhancing adoption requires not only promoting positive attitudes but also strengthening social support and improving farmers’ confidence in their ability to access and operate smart farming technologies },
   issn = {2621-9778},  doi = {10.14710/agrisocionomics.v10i2.28572},
    url = {https://ejournal2.undip.ac.id/index.php/agrisocionomics/article/view/28572}
}

Citation Format:

Abstract

The adoption of smart farming technology among rice farmers in Kanigoro Village, Pagelaran Subdistrict, remains low despite the availability of tools designed to enhance agricultural efficiency. Technologies such as the Smart Soil Sensor and Bird Control Sound System are still underutilized, reflecting a gap between technological availability and farmer adoption. This study analyzes the influence of attitude, subjective norm, and perceived behavioral control on farmers’ intention to adopt smart farming technology, using the Theory of Planned Behavior (TPB) as the analytical framework. A total of 100 farmers were surveyed through structured questionnaires and direct interviews, with data analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that all three variables significantly affect intention: attitude (β = 0.19, p = 0.023), subjective norm (β = 0.33, p < 0.001), and perceived behavioral control (β = 0.42, p < 0.001). Perceived behavioral control emerged as the strongest predictor, followed by subjective norm and attitude. These findings highlight that enhancing adoption requires not only promoting positive attitudes but also strengthening social support and improving farmers’ confidence in their ability to access and operate smart farming technologies

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Keywords: attitude, intention, perceived behavioral control, smart farming, subjective norm

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In Vol 10, No 2 (2026): June 2026

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