Generative AI Adoption in B2B Firms: Ethical Governance, Innovation Capabilities, and Long-Term Competitive Performance
The essential argument, in the authors’ own words.
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The rapid diffusion of generative artificial intelligence (GenAI) is reshaping organisational systems and digital transformation strategies, yet it remains unclear which organisational conditions are associated with long-term competitive performance in business-to-business (B2B) contexts. This study adopts a systems-informed perspective and examines how ethical governance, environmental dynamism, exploratory and exploitative innovation, and GenAI adoption are associated with long-term competitive performance in B2B firms. Using survey data from 104 Portuguese B2B managers and Partial Least Squares Struc- tural Equation Modelling (PLS-SEM), the findings show that ethical governance is the strongest organisational correlate of long-term competitive performance, underscoring the central role of governance structures in responsible GenAI use. GenAI adoption is positively associated with performance, but its role is complementary rather than dominant. Exploratory innovation does not show a significant direct association with performance; instead, its association with performance operates through GenAI adoption in the esti- mated model, suggesting that experimentation becomes more performance-relevant when translated into digitally enabled routines. In contrast, exploitative innovation is directly associated with performance through incremental efficiency mechanisms. These findings challenge technology-deterministic assumptions and suggest that long-term competitive performance in B2B firms is more closely associated with the organisational alignment of governance structures, innovation capabilities, and GenAI adoption than with technology adoption alone.
Authors: Michele Alves, Domingos Martinho, Ricardo Marcão, Pedro Sobreiro
Published in: Systems
Publication date: 2026-04-08
Read the paper: https://doi.org/10.3390/systems14040410
Source license: Creative Commons Attribution 4.0 International — https://creativecommons.org/licenses/by/4.0/
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