Flávio Tiago, António Almeida
This study examined the determinants of artificial intelligence (AI) adoption in small- and medium-sized enterprises (SMEs) through a comprehensive dual-methodology approach. Drawing on the Country-Human resources-Adoption/technological-INdividual AI adoption (CHAIN-AI) framework, we integrated different theoretical currents, namely, the Technology-Organization-Environment (TOE) framework, the Technology Acceptance Model (TAM), and Regret Theory (RT), to analyze adoption patterns across multiple levels. Our research methodology encompassed two distinct phases. First, we conducted a macro-level analysis using Eurobarometer (2023) survey data to examine AI adoption trends across European SMEs. Second, we administered an online survey to 186 digital marketing professionals to investigate micro-level adoption behaviors and attitudes. Study 1 revealed significant correlations between AI adoption rates and organizational characteristics, including firm size, sector, and geographic location. Larger and service-oriented firms demonstrated higher adoption propensities. Macroeconomic indicators such as gross domestic product (GDP) and innovation capacity were positively correlated with adoption rates, whereas cultural dimensions, particularly long-term orientation (LTO), exhibited negative correlations. Study 2 illustrated the psychological and organizational mechanisms underlying AI adoption, with perceived usefulness (PU) emerging as the primary predictor of behavioral intention. In contrast, perceived ease of use (PEOU) showed non-significant effects on adoption outcomes. Post-adoption regret (R) negatively influenced both attitudes and behavioral intentions toward AI implementation. This study contributes to the literature on technology adoption by providing an empirically grounded framework for understanding the dynamics of AI adoption in SMEs. This model offers evidence-based recommendations for policy development and organizational implementation strategies.