Examination of expert attitudes toward AI finds a stable normative position of “trust but verify” emerges across professional contexts and proposes the framework of “exploratory qualitative research with quantitative validation” as a practical alternative to strict mixed-methods designs in limited-sample research situations.
Abstract
The article examines how professionals from diverse occupational contexts — banking, IT, academia, business analytics, and small entrepreneurship — construct trust in artificial intelligence (AI), interpret its institutional legitimacy, and articulate ethical boundaries of automation in organizational practices. The empirical basis includes six semi-structured expert interviews conducted in 2026, with a structured author-developed guide of eight thematic blocks; the qualitative material is contextualized by author's survey data (n = 448, Saint Petersburg, 2024–2026) and by all-Russian polling data from VCIOM, Levada Center, and HSE. The research design is framed as an exploratory qualitative study with quantitative validation: interviews reconstruct experts' meaning constructs, while the survey captures the prevalence of corresponding attitudes in a broader population. Transcripts were analyzed using thematic analysis in the Braun & Clarke tradition. Three profiles of expert attitudes toward AI are identified — operational, techno-critical, and entrepreneurially-adaptive; a stable normative position of “trust but verify” emerges across professional contexts; a regulatory gap is documented between strong public demand for state oversight of AI (80%) and very low awareness of existing legal norms (16%). The article contributes to the journal's methodological debate on qualitative approaches to studying AI and proposes the framework of “exploratory qualitative research with quantitative validation” as a practical alternative to strict mixed-methods designs in limited-sample research situations.
Investigating ethical AI practices and its impact on Customer Retention and its influence from the one-sided effect shows that ethical AI is not only a compliance obligation, but a strategic lever for building trust and long-term customer relationships.
Herbert Kimura, Alaaeddine Ramadan, Najla A. Al-Thani et al.· Frontiers in Artificial Inte...· 0 citations
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.
Fares Getzin, T. Henschel, M. Kuttner et al.· Journal of Accounting &...· 0 citations
A strong positive relationship was found between the use of AI and the four dimensions of corporate administration that were investigated, thereby suggesting that the proposed integrated framework could serve as a basis for developing a theory and organizational practice for the future.
Tulika Dutta Roy· International Journal of Mod...· 0 citations
Generative artificial intelligence (GenAI) holds transformative potential for small and medium-sized enterprises (SMEs). Yet, despite increasing access to GenAI tool use, many SMEs face significant challenges in moving beyond experimentation toward sustained adoption. While extant literature covers the strategic benefits, ethical considerations and performance implications, limited insight exists into how adoption is enacted across individual and organizational levels. This study, therefore, adopts an enactment perspective to examine how SMEs adopt GenAI, and how enablers and barriers across technological, organizational and environmental (TOE) dimensions interact to shape this process.
This exploratory, qualitative study draws on 31 semi-structured interviews with SME decision-makers across European–Mediterranean contexts (Germany and France), supplemented by data from South Africa and Vietnam to enrich analytical depth across varying levels of digital maturity and institutional contexts. Data were analyzed inductively, using the Gioia methodology.
The study develops a three-phase process model of GenAI adoption enactment in SMEs: (1) activation of individual trust and engagement, (2) legitimizing and direction setting and (3) embedding and sustained value realization. GenAI unfolds through the interplay of bottom-up individual experimentation and top-down organizational legitimization, with distinct TOE dimensions dominating each phase. Sustained adoption emerges when these dynamics are deliberately coordinated.
The study offers two theoretical contributions. First, it extends the TOE framework from a static-factor model to a dynamic, processual account of GenAI adoption enactment in SMEs. Second, it complements TOE with bricolage to explain how structural conditions are operationalized through micro-level “making do” practices in resource-constrained contexts.
Justin Siljeur, Niklas Schulte, Dominik K. Kanbach· EuroMed Journal of Business· 0 citations
AI does not replace the role of executive leaders; instead, it serves as a cognitive aid that frees up a leader's capacity from routine operational tasks, yet it still requires the contextual intuition and ethical governance of human leaders.
Alifah Widya Rachmawati, Syamsul Hadi, Eni Purnasari et al.· INTERNATIONAL JOURNAL OF ECO...· 0 citations
The study concludes that cultural transformation is not a by-product but a prerequisite of successful AI integration, and that change capacity, trust, and inclusive leadership are decisive.
Sadik M. Amr· World Journal of Advanced Re...· 0 citations
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