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PurposeAdoption of solar energy plays an important role in the growth of a country. There are many factors which influence the adoption of solar energy in India. The study is designed to identify factors that determine the acceptance or rejection of solar energy systems in India.Design/methodology/approachRelationship among identified variables is established through interpretive structural modelling (ISM) and thus a conceptually validated model is evolved. Further, MICMAC analysis is conducted to understand the driving power and dependence of these variables.FindingsIt is revealed that experience and habit, awareness and social influence are the intermediary variables. MICMAC Analysis shows that no variable is disconnected from the system and all the variables influence the adoption of solar energy in India.Practical implicationsThe present study is expected to be useful to decision makers, end users and research organisations related to solar energy adoption.Originality/valueVarious intentional factors influencing solar energy systems adoption have been acknowledged in the present study, thus making it useful for formulation of action plans and enhance the usage of solar energy systems to improve environment quality.

Purpose – Due to country-wise lockdown and state-wise curfews in COVID-19, people were not able to make offline payments (i.e. cash payments) during purchases in India. So, people are switching their payment behavior from offline to online mode. But, as per the central bank report, the rate of adoption through mobile payments is still slow. The paper focuses on identifying critical barriers to mobile payment systems (MPSs) adoption in India. Innovation resistance theory (IRT) has been used as a base model for barriers, despite the wide range of choices of barriers available in the MPSs context. Additionally, three external variables which are out of the wider coverage of IRT constructs were incorporated in this paper. The study, on the other hand, adds to innovation resistance theory in the frame of reference of MPSs from a theoretical perspective. Interpretive structural modeling (ISM), together with MICMAC analysis is brought into play to analyse the direct and indirect relationship amongst the barriers. Research methodology – ISM approach has been used to establish the relationship among the eight (08) identified barriers, through literature and expert opinions. The key barriers to high driving power are then identified with the help of MICMAC analysis. Findings – The results reveal that value barrier (b2), image barrier (b5) and visibility barrier (b7) are the most significant variables. Interestingly, IRTs’ risk barrier (b3) and privacy barrier (b6) from the literature fall in the lowest level of the ISM model. The majority of the barriers fall under quadrant III of MICMAC analysis, indicating the high driving and dependence power. Research limitations – The developed ISM model is based on the sentiments of five (05) experts, which could be biased and influence the structural model’s final output. Due to COVID-19, data has been collected through online video conferencing mode, this may vary if data will be collected through an offline or face-to-face interview. The proposed model’s key findings aim to assist in explaining the barriers that exist during MPS adoption. Originality/Value – This study is the first attempt to use the ISM approach in conjunction with IRT to detect barriers within MPSs. The result of this paper will guide and motivate the researcher to analyse more critical barriers with IRT to contribute to the theoretical development.

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