Drivers and Disparities: Artificial Intelligence Adoption among Greek Marketing Executives

Efthimios Dragotis (), Despina A. Karayanni, Yannis Stamatiou and Irene Anagnostopoulou
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Efthimios Dragotis: University of Patras
Despina A. Karayanni: University of Patras
Yannis Stamatiou: University of Patras
Irene Anagnostopoulou: University of Patras

A chapter in Strategic Innovative Marketing and Tourism, 2026, pp 635-643 from Springer

Abstract: Abstract The global diffusion of artificial intelligence (AI) in business environments exhibits significant variability across national contexts and organizational types. In Greece, where digital transformation progresses unevenly, understanding the drivers of AI adoption is critical for shaping policy and managerial strategies. This exploratory quantitative study investigates these factors through a survey of a convenience sample of 100 Greek marketing executives from unique respective participant companies, across diverse sectors selected through the LinkedIn business platform. Using regression and ANOVA analyses in SPSS, the study investigates the perceptual and structural factors influencing AI adoption among marketing executives in Greece. Specifically, the research examines how perceived ease of use, perceived efficiency, attitudes toward AI, and future budgeting intentions interact to shape adoption behavior. Moreover, firm size moderates adoption behavior, with larger firms reporting consistently higher levels of readiness across all variables. By integrating cognitive, affective, financial, and structural dimensions, this study proposes a multidimensional framework for understanding AI diffusion in marketing. Practical implications include the need for user-centric AI design, evidence-based ROI communication, and targeted support for SMEs. Findings offer actionable insights for marketing leaders and policymakers, emphasizing the need to address not only perceptual barriers but also structural inequities in adoption capacity, particularly for smaller firms.

Keywords: AI diffusion; Budget; Technology acceptance (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-12968-0_69

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DOI: 10.1007/978-3-032-12968-0_69

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