Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
Artificial intelligence (AI) is gaining importance across various industries, enabling resource-efficient production and the sustainable management of business processes. With its capacity to analyze vast, interconnected databases, the adoption of green AI with in organizations can support the development of specific actions aimed at preserving the environment. In order to examine relevant influencing factors, we conducted a qualitative study using the established technology-organization-environment framework and institutional theory as analytical lenses. Through interviews with 21 experts representing diverse industries, we identified 17 factors that play a pivotal role in determining the adoption of green AI. Our key findings show that the adoption of green AI is mainly driven by its performance coupled with coercive pressure. Furthermore, the results reveal eight propositions regarding the effects of the identified factors and provide a basis for further research and guidance for practitioners.
Recommended Citation
Fecho, Mariska; Wahl, Nihal; Von Ahsen, Anette; and Vetter, Oliver, "Paving the Way to a Green Future With Artificial Intelligence – Exploring Organizational Adoption Factors" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 3.
https://aisel.aisnet.org/hicss-57/da/analytics_for_green_is/3
Paving the Way to a Green Future With Artificial Intelligence – Exploring Organizational Adoption Factors
Hilton Hawaiian Village, Honolulu, Hawaii
Artificial intelligence (AI) is gaining importance across various industries, enabling resource-efficient production and the sustainable management of business processes. With its capacity to analyze vast, interconnected databases, the adoption of green AI with in organizations can support the development of specific actions aimed at preserving the environment. In order to examine relevant influencing factors, we conducted a qualitative study using the established technology-organization-environment framework and institutional theory as analytical lenses. Through interviews with 21 experts representing diverse industries, we identified 17 factors that play a pivotal role in determining the adoption of green AI. Our key findings show that the adoption of green AI is mainly driven by its performance coupled with coercive pressure. Furthermore, the results reveal eight propositions regarding the effects of the identified factors and provide a basis for further research and guidance for practitioners.
https://aisel.aisnet.org/hicss-57/da/analytics_for_green_is/3