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1 Introduction Recent artificial intelligence (AI) research has focused on the power of AI to support human agents' decision-making, rather than replace or automate it; a term referred to as ‘augmented intelligence’ (Jain et al. 2021). In augmented intelligence systems, an AI application provides human agents with a recommendation to help those human agents make decisions under uncertain and complex conditions (Bansal et al. 2019a).
Introduction Discontinuance of information systems (IS) use is a prevalent phenomenon (Soliman and Rinta-Kahila, 2020). Understanding the decision-making process and psychophysiological mechanisms underlying both the intention to discontinue IS use and actual discontinuing behavior is thus critical, particularly within the digital financial technology usage context, where continuance rates remain low despite increased adoption (e.g., Koghut and Ai-Tabbaa, 2021).
Introduction Artificial intelligence (AI) and machine learning algorithms are increasing in sophistication and accuracy to automate an ever-increasing array of tasks. However, these algorithms’ rapid growth in complexity and performance makes them more opaque and less interpretable. Thus, the decisions taken and processes used by these AI systems to make said decisions are more challenging to understand and explain for its end users (Rudin, 2019).
New emotion-sensing technologies can help employees make better decisions, improve concentration, and adopt healthier and more productive work styles. But companies must address important privacy issues. advertisement As companies search for new ways to improve performance, some executives have begun paying attention to developments in emotion-sensing technologies (ESTs) and software fueled by artificial emotional intelligence.
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