Big Data Analytics-Artificial Intelligence Capability and Circular Economy Adoption: Evidence from Pakistan's Automobile Sector

Abstract

This study examines whether big data analytics-artificial intelligence (BDA-AI) significantly affects circular economy (CE) adoption in Pakistan's automobile sector. A single hypothesis was tested using a cross-sectional survey design. Data were collected through a structured, adapted questionnaire from 396 valid respondents drawn from 19 automobile-sector firms operating in Pakistan. CE adoption was measured through a five-item scale reflecting reduce, reuse, recycle, recover, and regenerate practices, while BDA-AI capability was measured using items adapted from prior literature. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Reliability and validity criteria were broadly satisfied, with a combined Cronbach's alpha of 0.92, composite reliability values of 0.727 (BDA-AI) and 0.701 (CE), and average variance extracted values of 0.620 (BDA-AI) and 0.743 (CE). The hypothesized path from BDA-AI to CE was statistically significant (β = 0.014, t = 2.745, p = 0.006), supporting the hypothesis, although the path coefficient itself was small in magnitude. The findings suggest that BDA-AI capability is positively associated with CE adoption among automobile-sector firms in Pakistan, but the modest effect size indicates that technology adoption alone is unlikely to drive substantial circularity gains without complementary organizational and contextual support. This is among the first studies to empirically test this relationship within Pakistan's automobile sector.