Analytics and Decision Support for Green IS and Sustainability Applications

Permanent URI for this collectionhttps://hdl.handle.net/10125/107418

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    Paving the Way to a Green Future With Artificial Intelligence – Exploring Organizational Adoption Factors
    (2024-01-03) Fecho, Mariska; Wahl, Nihal; Von Ahsen, Anette; Vetter, Oliver
    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.
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    Introduction to the Minitrack on Analytics and Decision Support for Green IS and Sustainability Applications
    (2024-01-03) El-Gayar, Omar; Leung, Pingsun; Wahbeh, Abdullah; Scharl, Arno
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    Delivering Green Persuasion Strategies with a Conversational Agent: a Pilot Study
    (2024-01-03) Giudici, Mathyas; Abbo, Giulio Antonio; Crovari, Pietro; Garzotto, Franca
    Climate change is undeniable. The drastic consequences it may have on our lives make a collective effort crucial. Our research explores how Conversational Agents (CAs) can persuade people into environmentally sustainable behaviors, particularly in domestic spaces where these technologies are becoming increasingly popular. In this research work, we conducted an empirical evaluation (N=29) exploring the effectiveness and stance towards the adoption of different persuasive strategies compared to a CA delivering messages referring to just one persuasion strategy. Furthermore, this contribution reports on a custom dialogue manager's implementation, designed to enable the execution of the experiment. Although study results suggested no significant difference in persuasion effectiveness and usability of the conversational agents, participants reported a significant difference in the perceptions of parasocial interactions and dialogue with the CA, preferring the one delivering multiple persuasive strategies.