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<p>Aquaculture underpins global food security; its digitalization is vital for resilience and high-quality growth, with smallholders’ digital adoption as the key foundation. This study examines the factors associated with adoption of smart aquaculture digital technologies (ASDT) among smallholder fish farmers within China’s freshwater fish sector. First, drawing on survey data, the ASDT and relevant characteristics of smallholder fish farmers were identified. Efficiency scores were calculated using a Slack-Based Measure Data Envelopment Analysis (SBM-DEA) model. By integrating efficiency levels with digital adoption intentions, farmers were segmented into eight distinct “efficiency–adoption” scenarios. Second, applying the Unified Theory of Acceptance and Use of Technology (UTAUT), survey data were analyzed using principal component analysis (PCA) and ordinary least squares (OLS) regression to examine the economic and behavioral factors associated with ASDT. Finally, interpretable machine learning methods were then employed to explain the each “efficiency–adoption” scenarios, incorporating transportation, climate, geographic, and economic–psychological factors. Findings indicate that: (1) The UTAUT exhibits strong explanatory relevance. performance expectancy, effort expectancy, social influence, and facilitating conditions are significantly associated with adoption intentions, while technology exposure, risk perception, and perceived resilience serve as important moderating variables. (2) For farmers with high technical but low scale efficiency, those who exposed to climatic risks are more likely to adopt digital technology. (3) For farmers with high scale but low technical efficiency, effective digital technology adoption requires high individual exposure to digital technologies and well-developed rural digital infrastructure. (4) Low-efficiency farmers with high technical exposure may attempt to improve performance through digital technology adoption, when perceived risks increase decline the likelihood of such attempts. (5) Reaching the production frontier is related to technology exposure, while variation in geography and climate shapes ASDT beyond that point. In conclusion, this study validates the applicability of UTAUT framework in aquaculture, clarifies the factors shaping ASDT across efficiency scenarios, and provides policy insights to guide efficiency-oriented digital transformation in the sector.</p>

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Why do smallholder fish farmer go smart? Insights from a UTAUT framework combined with interpretable machine learning of freshwater aquaculture in China

  • 期刊论文

  • 发表日期：

    2025年11月22日

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  • Yixin Qian,
  • Li Liu,
  • Xueming Wang,
  • Jianming Zheng

摘要

Aquaculture underpins global food security; its digitalization is vital for resilience and high-quality growth, with smallholders’ digital adoption as the key foundation. This study examines the factors associated with adoption of smart aquaculture digital technologies (ASDT) among smallholder fish farmers within China’s freshwater fish sector. First, drawing on survey data, the ASDT and relevant characteristics of smallholder fish farmers were identified. Efficiency scores were calculated using a Slack-Based Measure Data Envelopment Analysis (SBM-DEA) model. By integrating efficiency levels with digital adoption intentions, farmers were segmented into eight distinct “efficiency–adoption” scenarios. Second, applying the Unified Theory of Acceptance and Use of Technology (UTAUT), survey data were analyzed using principal component analysis (PCA) and ordinary least squares (OLS) regression to examine the economic and behavioral factors associated with ASDT. Finally, interpretable machine learning methods were then employed to explain the each “efficiency–adoption” scenarios, incorporating transportation, climate, geographic, and economic–psychological factors. Findings indicate that: (1) The UTAUT exhibits strong explanatory relevance. performance expectancy, effort expectancy, social influence, and facilitating conditions are significantly associated with adoption intentions, while technology exposure, risk perception, and perceived resilience serve as important moderating variables. (2) For farmers with high technical but low scale efficiency, those who exposed to climatic risks are more likely to adopt digital technology. (3) For farmers with high scale but low technical efficiency, effective digital technology adoption requires high individual exposure to digital technologies and well-developed rural digital infrastructure. (4) Low-efficiency farmers with high technical exposure may attempt to improve performance through digital technology adoption, when perceived risks increase decline the likelihood of such attempts. (5) Reaching the production frontier is related to technology exposure, while variation in geography and climate shapes ASDT beyond that point. In conclusion, this study validates the applicability of UTAUT framework in aquaculture, clarifies the factors shaping ASDT across efficiency scenarios, and provides policy insights to guide efficiency-oriented digital transformation in the sector.