From technology to artificial intelligence: crafting individual adoption and use

This PhD dissertation examines the adoption and use of technology in organizational settings, with particular attention to the individual characteristics and behaviors that can lead to positive or negative outcomes. Across three interconnected studies, it traces a path from traditional workplace technologies to artificial intelligence, and from adoption to actual use, seeking to better understand the value that technology generates through human choices. The first study investigates the role of individual characteristic, such that age, in the adoption of technology at work. Through a systematic literature review, 51 papers were identified and analyzed. The findings were organized into five dimensions that emerged as relevant for addressing technology adoption from an age-sensitive perspective. Building on this, the second study shifts the focus from adoption to use. It examines the different forms of technology use behavior that have been studied in organizational contexts. Using a Bibliographic Systematic Literature Review approach, 122 articles were identified and clustered. On this basis an integrative model was proposed to guide individual responsibility for the ethical use of technology in the workplace. Grounded in the insights of the first two studies, the third study integrates job crafting theory with the augmentation potential of new AI tools. It introduces the concept of AI Augmented Crafting, defined as workers’ enhancement of their own capabilities through the proactive and goal-directed use of AI tools to alter the work. The construct, along with its corresponding measurement, was developed and validated through five different field studies, following established guidelines for the development and validation of formative constructs. Taken together, these three studies deepen our understanding of technology adoption and technology use in the workplace. This dissertation aims to support both theory and practice in approaching human–technology interaction in a more informed, human-centered way.