Behavioral and cognitive factors affecting farmer uptake of agricultural technologies: Implications for advisory service design

Introduction

Why do farmers reject innovations that researchers and extension agents believe would benefit them? This question has driven agricultural adoption research for decades, yet most answers remain focused on economic and structural barriers land size, credit access, input costs while overlooking the psychological factors that shape how farmers perceive, evaluate, and respond to new practices [1,2] . Extension programs that address only material constraints still face low adoption when farmers lack confidence in their ability to manage new technologies or perceive risks that outweigh expected benefits [3] . Psychological models of behavior particularly Bandura's self-efficacy theory [4] and the Technology Acceptance Model [5] have been applied successfully in health and education contexts but remain underutilized in agricultural extension research [6] . Indonesia, with its diverse farming systems and active extension network, provided an appropriate setting for testing these psychological pathways [7] . This research measured five behavioral and cognitive factors and modeled their relative influence on adoption behavior among smallholder rice and horticultural farmers in Java [8] .

Theoretical Framework

The research integrated Bandura's Social Cognitive Theory [4] , which positions self-efficacy as the central driver of behavioral change, with the Technology Acceptance Model (TAM) [5] , which emphasizes perceived usefulness and ease of use as adoption predictors. Rogers' theory [9] contributed the concepts of innovation attributes and social system influence. The combined framework hypothesized that adoption behavior is driven by self-efficacy and perceived usefulness (direct paths), moderated by risk perception (negative path), and facilitated by innovation attitude and social influence (indirect paths through behavioral intention).

Material and Methods

Research Area

The research was conducted in Magelang and Klaten districts (Central Java) and Malang and Jombang districts (East Java), Indonesia. These districts represent intensive rice-horticultural farming zones with 1,800-2,400 mm annual rainfall. The research period spanned April 2021 to June 2022.

Material

Instrument Development and Validation

The 47-item instrument was developed from established psychological scales adapted for agricultural contexts. Selfefficacy items drew from Bandura's General Self-Efficacy Scale, risk perception from Weber et al.'s domain-specific risk scale, and perceived usefulness from Davis's TAM scale. Content validation by eight experts retained 47 of 58 initial items (CVI > 0.80). Pilot testing with 40 farmers in Sleman district yielded Cronbach's alpha values of 0.81-0.89 across constructs. Confirmatory factor analysis verified the five-factor structure (CFI = 0.94, RMSEA = 0.047).

Methods

Structural equation modeling (SEM) using AMOS 26.0 tested the hypothesized relationships among psychological constructs and adoption behavior. Model fit was assessed through multiple indices (Chi-square/df, CFI, TLI, RMSEA, SRMR). Pearson correlations examined bivariate relationships. Mediation effects were tested through bootstrapping with 5,000 resamples [10] . Perceived usefulness showed the strongest positive correlation with adoption (r = 0.67, p< 0.001), while risk perception had the strongest negative association (r = -0.47, p< 0.001). All correlations were significant at p< 0.05. *Note: p< 0.05 for all reported correlations. 2) confirmed all five hypothesized paths. The model demonstrated good fit (Chi-square/df = 1.87, CFI = 0.96, TLI = 0.95, RMSEA = 0.043, SRMR = 0.038) and explained 63.7% of variance in adoption behavior. Perceived usefulness had the strongest direct effect (Beta = 0.33), followed by self-efficacy (Beta = 0.29). Risk perception's negative path (Beta = -0.24) confirmed its inhibiting role.

Results

Discussion

Perceived usefulness as the strongest predictor confirms TAM principles applied to agricultural settings [5] , and aligns with Meijer et al. [11] who found that farmers' perception of tangible benefits matters more than technical knowledge for adoption. Self-efficacy's strong contribution supports Bandura's theory [4] and echoes findings from Nyang et al. [12] in East African extension contexts. The negative effect of risk perception (Beta = -0.24) was expected but its magnitude was smaller than reported by Feder et al. [13] in earlier Asian research, possibly reflecting improved safety-net programs in contemporary Indonesia [14] . Social influence showed the weakest but still significant path, suggesting that peer effects operate indirectly through attitude formation rather than directly driving adoption decisions. This partial mediation pattern was confirmed through bootstrapping analysis and is consistent with Bandura's concept of vicarious learning [15,16] .

Limitations

The cross-sectional design limits causal inference longitudinal data would better capture how psychological states evolve during the adoption process. Self-reported adoption may overestimate actual behavior. The Javafocused sample may not represent outer island farming contexts in Indonesia [17] .

Conclusion

Behavioral and cognitive factors explained a substantial portion (63.7%) of farmer uptake behavior. Perceived usefulness and self-efficacy emerged as the strongest drivers, while risk perception acted as a significant barrier.

Extension strategies should shift from information-heavy approaches toward confidence-building activitiesincluding demonstration experiences, peer success stories, and graduated risk exposure. Programs that help farmers see tangible results and believe in their own capacity to manage innovations are likely to achieve higher adoption than those focused solely on knowledge transfer.

References

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