This study aims to examine how organizational factors arising from isomorphic pressures – and individual perception factors of perceived ease of use and perceived usefulness – influence the adoption of artificial intelligence (AI) in management accounting. By exploring cross-country cases from the United States, Germany and Austria, it seeks to uncover the mechanisms through which forces shape firms’ decisions, providing empirical insights into organizational responses and contextual variations in AI implementation.
Findings are based on an exploratory, qualitative research design using semistructured interviews. Data were analyzed through within- and cross-case analysis, applying deductive coding.
Adoption is driven by distinct isomorphic pressures across the USA, Germany and Austria. Mimetic pressures emerge from competitive necessity and leadership vision, while coercive pressures are exerted through regulatory compliance and client return on investment demands. Normative pressures focus on professional standards and data security. Internal strategic goals moderate responses, highlighting cross-national differences. While institutional pressures initiate adoption, the long-term integration of AI is contingent upon high levels of perceived usefulness and ease of use. At the same time, adoption is shaped by organizational frictions, validation burdens and the risk of ceremonial compliance, dynamics that are constitutive of the adoption process rather than merely incidental to it.
Understanding mimetic, coercive and normative forces helps organizations anticipate external expectations, align strategies and address barriers such as data security, skill shortages and resistance to change, fostering effective AI integration. Particular attention should be paid to governance and oversight mechanisms as preconditions for substantive adoption, and to the risk that formal compliance with institutional pressures may produce ceremonial rather than genuine integration.
To the best of the authors’ knowledge, this study is among the first to combine institutional theory and technology acceptance model with empirical evidence, it provides novel cross-national insights and expands understanding of organizational responses to technological transformation. It further challenges predominantly efficiency-oriented accounts by demonstrating that organizational frictions and ceremonial adoption are analytically co-equal dimensions of AI-related change.
