Introduction: Artificial Intelligence (AI) is a transformative technology with
increasing impact across social and economic domains. Understanding the
factors that influence its adoption is essential for both research and practice.
The most widely used model in this field is the Technology Acceptance Model
(TAM; Davis, 1989), which identifies perceived usefulness (PU) and perceived
ease of use (PEOU) as predictors of behavioral intention to use (INT).
Objectives: To date, the literature has predominantly focused on variable
centered approaches, often neglecting individual differences. This study adopts
a person-centered approach to identify unobserved subgroups of workers and
to explore the role of personal and organizational resources in AI acceptance
processes.
Method: A total of 653 employees from an Italian company participated in the
study (73% male; 52.3% aged between 51-60), completing an online survey.
Latent Profile Analysis (LPA) was used to identify profiles based on individual
innovativeness, openness to change, digital skills, organizational support for
innovation, and training opportunities. These profiles were then compared on
TAM dimensions (PU, PEOU, INT), as well as on technology anxiety, job
insecurity, and work engagement.
Results: Four distinct profiles emerged: the Uninspired (n=213), the
Misunderstood Innovators (n=70), the Change Champions (n=360), and the
Disillusioned (n=66). Change Champions reported the highest levels of AI
acceptance and work engagement, along with the lowest levels of technology
anxiety and job insecurity. Conversely, the Disillusioned exhibited the most
negative scores across all indicators.
Conclusions: The findings provide new insights into the role of personal and
organizational resources in AI acceptance processes, offering practical
implications for fostering more informed and positive attitudes toward the use of
artificial intelligence. The cross-sectional design prevents causal inferences.
Additionally, the relatively high average age of the sample may have influenced

the results, limiting their generalizability to younger populations.