Interrogating the Economic, Environmental, and Social Impact of Artificial Intelligence and Big Data in Sustainable Entrepreneurship

Abstract

Artificial intelligence and big data are increasingly being integrated into sustainable entrepreneurship practices. Yet, conventional literature often neglects to critically examine their economic, environmental, and social implications. We conducted a systematic literature review to understand when, how, and for whom artificial intelligence and big data in sustainable entrepreneurship generate value. Our findings suggest that the three dimensions of sustainability—economic, environmental, and social—should be examined through a tri‐level impact prism: the immediate efficiency or transparency gains firms report; the hidden or temporally deferred costs that accumulate; and—notably—the distributional consequences that determine who reaps the benefits and who inherits the burdens. Direct benefits can evolve into costs over time and, if neglected, may reinforce injustices that rebound and erode future gains. Whether the broader trajectory settles on the virtuous or vicious side of that loop depends on five boundary conditions: organizational capabilities, technological maturity, socio‐cultural values, sectoral and regulatory context, and temporal dynamics. Our study advances theory by extending the triple‐bottom‐line lens into a reflexive impact‐by‐cost framework—one that foregrounds rebound effects and justice considerations, injects power, path dependency, and distributional conflict into socio‐technical transition debates, and recasts contingency and dynamic capabilities theories around shifting cost and justice configurations.