ABSTRACT This paper examines the drivers of radical green innovation (RGI) in Pakistan's textile manufacturing sector, with reference to environmental regulatory pressure (ERP), green market pressure (GMP), exploratory green learning (EGL), and big data analytics capability (BDAC). A quantitative research design was utilized, employing a self‐administered questionnaire to collect data from 313 employees across 20 textile manufacturing companies in Punjab, Pakistan. The descriptive statistics were done by SPSS 30.0, whereas structural equation modeling (SEM) was done by SmartPLS 4.1 to test the relationships between the key constructs. The analysis shows that GMP had a direct impact on EGL and RGI. ERP had no direct impact on EGL and RGI, indicating that regulatory compliance itself does not support radical green innovation. It is notable that BDAC had a negative moderating effect on the relationship between EGL and RGI, indicating that excessive use of data analytics may suppress the creative thinking required for radical green innovation. The study is the first to examine how the interaction of environmental regulatory pressure, green market pressure, exploratory green learning, and big data analytics capability affects radical green innovation within the textile industry of an emerging economy. To promote radical innovations, policymakers, managers, and decision‐makers should pay attention to the balance between data analytics and exploratory green learning. This balance can enable companies to be more aligned with market needs and regulatory conditions.
Song et al. (Mon,) studied this question.