PurposeThe growing pressure on businesses to balance environmental sustainability with profit maximisation has led to the development of green entrepreneurial orientation (GEO), which proactively integrates green practices into core business operations. Grounded in the ecological modernisation theory, GEO acts as a green management practice which helps in achieving competitiveness through environmental innovation. However, despite the growing emphasis on GEO, there remains a gap in understanding how specific drivers influence the organisational structures and processes that lead to GEO. Therefore, the study addresses this gap by analysing the key drivers of GEO using an integrated approach.Design/methodology/approachIn this study, total interpretive structural modelling (TISM) and cross-impact matrix multiplication applied to classification (MICMAC) were used to identify and interpret the interrelationship between key drivers of GEO. Here, TISM technique aided in constructing a contextual relationship-based structural model of drivers, whereas MICMAC assisted in categorising the drivers based on their driving and dependence power. A case evaluation was also carried out in the Indian textile industry to validate the TISM model.FindingsThe result indicates that institutional pressure, managerial environment concern, organisational resilience and big data analytical capabilities are the most influential drivers of GEO at organisational level, and other drivers act as secondary and linked variables in this process. The MICMAC analysis further supports the results of TISM. In addition, the overall TISM model is validated in the Indian textile sector.Practical implicationsThe study findings will assist researchers and policymakers in adopting a systematic approach to prioritise GEO in pollution intensive industries. Moreover, it will help managers in leveraging GEO to achieve strategic advantages amid environmental challenges.Originality/valueThis study is amongst the first to employ an integrated qualitative approach to analyse drivers of GEO.
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