The adoption of artificial intelligence (AI) is reshaping organizational practices, yet workers’ attitudes remain crucial for its successful integration. This study examines how perceived organizational ethical culture, organizational innovativeness, and job performance influence workers’ attitudes towards AI. A survey was administered to 356 workers across diverse sectors, with analyses focusing on 154 participants who reported prior AI use. Measures included the Attitudes Towards Artificial Intelligence at Work (AAAW), Corporate Ethical Virtues (CEV), Inventory of Organizational Innovativeness (IOI), and an adapted version of the In-Role Behaviour Scale. Hierarchical regression analyses revealed that ethical culture dimensions, particularly Clarity and Feasibility, significantly predicted attitudes towards AI, such as anxiety and job insecurity, with Feasibility also associated with the attribution of human-like traits to AI. Supportability, reflecting a cooperative work environment, was linked to lower perceptions of AI human-likeness and adaptability. Among innovation dimensions, only Raising Projects, the active encouragement of employees’ ideas, was positively related to perceptions of AI adaptability, highlighting the importance of participatory innovation practices over abstract signals. Most importantly, perceived job performance improvements through AI predicted more positive attitudes, including greater perceived quality, utility, and reduced anxiety. Overall, this study contributes to the growing literature on AI in organizations by offering an exploratory yet integrative framework that captures the multifaceted nature of AI acceptance in the workplace.

Attitudes toward artificial intelligence in organizational contexts / Marocco, Silvia; Bellini, Diego; Barbieri, Barbara; Presaghi, Fabio; Grossi, Elena; Talamo, Alessandra. - In: AI. - ISSN 2673-2688. - 6:11(2025). [10.3390/ai6110292]

Attitudes toward artificial intelligence in organizational contexts

Marocco, Silvia
Primo
;
Barbieri, Barbara;Presaghi, Fabio;Grossi, Elena
Penultimo
;
Talamo, Alessandra
Ultimo
2025

Abstract

The adoption of artificial intelligence (AI) is reshaping organizational practices, yet workers’ attitudes remain crucial for its successful integration. This study examines how perceived organizational ethical culture, organizational innovativeness, and job performance influence workers’ attitudes towards AI. A survey was administered to 356 workers across diverse sectors, with analyses focusing on 154 participants who reported prior AI use. Measures included the Attitudes Towards Artificial Intelligence at Work (AAAW), Corporate Ethical Virtues (CEV), Inventory of Organizational Innovativeness (IOI), and an adapted version of the In-Role Behaviour Scale. Hierarchical regression analyses revealed that ethical culture dimensions, particularly Clarity and Feasibility, significantly predicted attitudes towards AI, such as anxiety and job insecurity, with Feasibility also associated with the attribution of human-like traits to AI. Supportability, reflecting a cooperative work environment, was linked to lower perceptions of AI human-likeness and adaptability. Among innovation dimensions, only Raising Projects, the active encouragement of employees’ ideas, was positively related to perceptions of AI adaptability, highlighting the importance of participatory innovation practices over abstract signals. Most importantly, perceived job performance improvements through AI predicted more positive attitudes, including greater perceived quality, utility, and reduced anxiety. Overall, this study contributes to the growing literature on AI in organizations by offering an exploratory yet integrative framework that captures the multifaceted nature of AI acceptance in the workplace.
2025
artificial intelligence; workers’ attitudes; organizational innovativeness; organizational ethical climate; job performance; AI adoption
01 Pubblicazione su rivista::01a Articolo in rivista
Attitudes toward artificial intelligence in organizational contexts / Marocco, Silvia; Bellini, Diego; Barbieri, Barbara; Presaghi, Fabio; Grossi, Elena; Talamo, Alessandra. - In: AI. - ISSN 2673-2688. - 6:11(2025). [10.3390/ai6110292]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1755852
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