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<p>The use of solar energy technologies has been on the rise as a sustainable measure to climate change and energy security issues. This paper discusses how technological, socio-economical, cognitive, and value-based determinants affect the adoption of solar energy at the household level through the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB). Data has been collected on 385 households in Bihar with the help of a structured questionnaire. A two-step analytical approach, a combination of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN) has been used to analyze both linear and non-linear relationships. The results of PLS-SEM show that the attitude (ATT) is the most predictive variable on behavioral intention (BIAGE), followed by awareness (AW), perceived behavioral control (PBC), subjective norms (SN), and spiritual inclination (SI). Attitude is greatly affected by perceived usefulness (SPU), perception of ease of use (PEU) and biodiversity concern (BD). The ANN analysis also confirms the model, with attitude being the most significant factor, followed by awareness. These results provide practical implications to policymakers to develop more effective interventions that can help to transition to renewable energy in India, which would guarantee environmental sustainability and energy security.</p>

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Attitudinal and behavioural determinants of solar energy adoption among households in Bihar using a two stage SEM ANN approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月5日

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  • Shampy Dubey,
  • Rupinder Katoch,
  • Anand Kumar,
  • Hassan Wali,
  • Ahmed Ali

摘要

The use of solar energy technologies has been on the rise as a sustainable measure to climate change and energy security issues. This paper discusses how technological, socio-economical, cognitive, and value-based determinants affect the adoption of solar energy at the household level through the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB). Data has been collected on 385 households in Bihar with the help of a structured questionnaire. A two-step analytical approach, a combination of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN) has been used to analyze both linear and non-linear relationships. The results of PLS-SEM show that the attitude (ATT) is the most predictive variable on behavioral intention (BIAGE), followed by awareness (AW), perceived behavioral control (PBC), subjective norms (SN), and spiritual inclination (SI). Attitude is greatly affected by perceived usefulness (SPU), perception of ease of use (PEU) and biodiversity concern (BD). The ANN analysis also confirms the model, with attitude being the most significant factor, followed by awareness. These results provide practical implications to policymakers to develop more effective interventions that can help to transition to renewable energy in India, which would guarantee environmental sustainability and energy security.