Beyond more papers: Reinvesting AI's research efficiency dividend in international management scholarship

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Artificial intelligence is expanding research capacity by making many scholarly tasks faster, more scalable, and more accessible. For international management, the central question is no longer simply how AI should be used, but how the capacity it releases should be deployed. We conceptualize this gain as an AI-enabled research efficiency dividend and identify a corresponding publication productivity trap, in which efficiency gains are absorbed into higher output expectations and escalating requirements for each paper rather than stronger scholarship. We distinguish two pathways: publication amplification, where AI accelerates familiar research routines, and capability amplification, where released capacity is reinvested in more important global problems, novel cross-border evidence, stronger contextual and multilevel explanation, research orchestration and validation, and impact beyond publication. We argue that the distinctive opportunity for international management is to use AI to pursue questions that were previously too difficult, costly, multilingual, longitudinal, or institutionally complex to study well. Realizing this opportunity requires journals, universities, funders, and scholars to reward the conversion of greater research capacity into greater originality, credibility, explanatory reach, and intellectual and societal value.