Evidence map›Paper›PMID 42151279›Full record

ArticleScientific reports2026

Factors affecting Iranian farmers' intention to adopt digital agriculture: an integrated TPB-TAM approach.

Amirhossein Alibaygi, Masoumeh Taghibaygi

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Amirhossein AlibaygiDepartment of Agricultural Extension and Education, Faculty of Agriculture, Razi University, Kermanshah, Iran.ORCID http://orcid.org/0000-0001-5776-1116
Masoumeh TaghibaygiDepartment of Agricultural Extension and Education, Faculty of Agriculture, Razi University, Kermanshah, Iran. resina2011@gmail.com.ORCID http://orcid.org/0000-0002-0979-1692

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital agricultural technologies, with their proven potential to enhance productivity, resilience, and resource efficiency, play a key role in achieving food security and sustainable development. However, the adoption of these technologies in developing countries like Iran faces a significant limitation. Focusing on the existing knowledge gap. This study investigates the individual and social determinants of digital technology adoption among Iranian farmers by integrating the Theory of Planned Behavior and the Technology Acceptance Model. We apply this framework to targeted research with farmers of Kermanshah Province in western Iran, using survey data (sample n = 384) analyzed through structural equation modeling. Based on the results, the integrated model demonstrated strong explanatory power. Attitude and perceived behavioral control emerged as the strongest predictors of adoption intention, with attitude itself being largely shaped by perceived usefulness and ease of use. Contrary to expectations, subjective norm exerted no significant direct effect on intention, nor was it influenced by perceived usefulness. These findings reveal that the adoption process in this context is driven primarily by individual assessments of utility and feasibility rather than social pressures. This suggests a critical need to adapt classic adoption frameworks when applied to settings with weaker social capital or less cohesive institutional structures. Consequently, extension strategies should pivot from normative campaigns to approaches that emphasize personalized financial incentives, risk mitigation guarantees, and user-centric technology design.

Indexed as

AgricultureDigital TechnologyFarmersIntentionHumansIranSurveys and QuestionnairesTheory of Planned BehaviorBehavioral intentionsDigital agricultureFood securitySustainable developmentTechnology adoption

Identifiers

PMID42151279
PMCPMC13377014

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.