Publicação
Adoption and Impact of Artificial Intelligence in Organizational Decision-Making
| Resumo: | Artificial intelligence (AI) is becoming a critical enabler of data-driven business decisionmaking, yet the factors driving its successful adoption and organizational impact remain insufficiently explored. This study proposes and validates an integrated model combining the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) with the DeLone & McLean Information Systems Success Model to explain AI adoption in professional contexts. Structural Equation Modeling (PLS-SEM) was applied to data collected from 391 respondents, primarily students and professionals with an active interest and background in AI. The findings confirm that performance expectancy, hedonic motivation, social influence, and habit significantly influence behavioral intention and use behavior about these tools. These, in turn, enhance the perception of net organizational benefits. The model also incorporates age as a moderating factor, revealing generational differences in how users respond to key adoption drivers. The results provide both theoretical contributions to the refinement technology acceptance models and practical implications for the effective implementation of AI in decision-making environments. |
|---|---|
| Autores principais: | Aguilera, Javier Montilla |
| Assunto: | Artificial intelligence Decision-making Generational moderation Information Success Model UTAUT2 Technology adoption SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Artificial intelligence (AI) is becoming a critical enabler of data-driven business decisionmaking, yet the factors driving its successful adoption and organizational impact remain insufficiently explored. This study proposes and validates an integrated model combining the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) with the DeLone & McLean Information Systems Success Model to explain AI adoption in professional contexts. Structural Equation Modeling (PLS-SEM) was applied to data collected from 391 respondents, primarily students and professionals with an active interest and background in AI. The findings confirm that performance expectancy, hedonic motivation, social influence, and habit significantly influence behavioral intention and use behavior about these tools. These, in turn, enhance the perception of net organizational benefits. The model also incorporates age as a moderating factor, revealing generational differences in how users respond to key adoption drivers. The results provide both theoretical contributions to the refinement technology acceptance models and practical implications for the effective implementation of AI in decision-making environments. |
|---|