Perception towards rooftop solar PV in India: comparison between adopters and non-adopters

Satapathy, Amitabh; Jain, Arvind Kumar; Barthwal, S.

doi:10.1504/ijret.2021.114609

Cited by 3 publications

(2 citation statements)

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“…Researchers Abu-Elsamen et al (2019), Irfan et al (2020); Reyes-Mercado and Rajagopal (2017), Sahu et al (2021); Srivastava and Mahendar (2018) discovered a positive correlation between subjective norm and purchase intention for renewable energyefficient products. Satapathy et al's (2021) study based in India found social image factor impacting heavily on rooftop solar adoption, and in a similar study in Pakistan, Liang et al (2021) observed that social influence has a significant impact on solar PV panel buying intentions. Recent research in American and Indonesian households on purchase intention of solar system indicated social effect to be positive and significant, and particularly neighbor installation of solar household system influenced households intention to purchase (Loveldy et al, 2021;Mabile, 2021).…”

Section: Subjective Normmentioning

confidence: 87%

“…Among different renewable energy products, SEPs are very feasible to use in the Indian context as most of the places in India receive sunlight. Literature suggests that former studies had been conducted by taking the different geographical areas such as Punjab, Bangalore, Kerala and Telangana concerning SEPs (Aggarwal et al , 2019a; Kumar et al , 2019; Kumar et al , 2020b; Parsad et al , 2020; Satapathy et al , 2021; Srivastava and Mahendar, 2018). Whereas present research study has chosen the Tamil Nadu state as very few studies have been conducted in Tamil Nadu on SEPs (Ananthanarayana et al , 2015; Gowrishanker and Mohanasundaram, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Factors affecting consumer purchase intention for buying solar energy products

M.S

Batcha

Alam

2022

IJESM

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Purpose The purpose of this paper is to identify the factors influencing consumers’ intention to purchase solar energy products (SEPs). Design/methodology/approach The research is based on primary data that has been collected from a total of 351 non-users of SEPs from different cities of Tamil Nadu state (India). Confirmatory factor analysis and structural equation modelling have been used to examine the collected data. Findings Attitude, perceived behavioral control and energy concern variables are significant towards consumers’ purchase intention. However, other variables such as subjective norms and energy awareness were found insignificant. Research limitations/implications The sample size was chosen based on convenience sampling, and the sample was collected from the different cities in Tamil Nadu. As a result, the outcome may not be representative of the whole population. Respondents’ perspectives in one section of India may differ from those in another. Practical implications The findings of this study help policymakers, business experts, marketers, social marketers and energy conservation organizations in gaining a better understanding of consumer behavior. Social implications The outcome of this study will be effective in developing action plans to improve environmental quality through solar products. Originality/value This study has targeted the potential consumers and identified factors that are influencing consumers’ intention to purchase SEP. Therefore, the study’s findings will add value to SEP companies, government and non-government organizations, marketers, academicians and the research community.

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Section: Subjective Normmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Factors affecting consumer purchase intention for buying solar energy products

M.S

Batcha

Alam

2022

IJESM

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Purchase intention for energy-efficient equipment appliances: extending TPB with eco-labels, green trust, and environmental concern

et al. 2023

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Machine learning reduces soft costs for residential solar photovoltaics

Dong

Nemet

Gao

et al. 2023

Sci Rep

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Further deployment of rooftop solar photovoltaics (PV) hinges on the reduction of soft (non-hardware) costs—now larger and more resistant to reductions than hardware costs. The largest portion of these soft costs is the expenses solar companies incur to acquire new customers. In this study, we demonstrate the value of a shift from significance-based methodologies to prediction-oriented models to better identify PV adopters and reduce soft costs. We employ machine learning to predict PV adopters and non-adopters, and compare its prediction performance with logistic regression, the dominant significance-based method in technology adoption studies. Our results show that machine learning substantially enhances adoption prediction performance: The true positive rate of predicting adopters increased from 66 to 87%, and the true negative rate of predicting non-adopters increased from 75 to 88%. We attribute the enhanced performance to complex variable interactions and nonlinear effects incorporated by machine learning. With more accurate predictions, machine learning is able to reduce customer acquisition costs by 15% ($0.07/Watt) and identify new market opportunities for solar companies to expand and diversify their customer bases. Our research methods and findings provide broader implications for the adoption of similar clean energy technologies and related policy challenges such as market growth and energy inequality.

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Perception towards rooftop solar PV in India: comparison between adopters and non-adopters

Cited by 3 publications

References 0 publications

Factors affecting consumer purchase intention for buying solar energy products

Factors affecting consumer purchase intention for buying solar energy products

Purchase intention for energy-efficient equipment appliances: extending TPB with eco-labels, green trust, and environmental concern

Machine learning reduces soft costs for residential solar photovoltaics

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