There is increasing interest in the use of artificial intelligence (AI) to improve organizational decision-making. However, research indicates that people’s trust in and choice to rely on “AI decision aids” can be tenuous. In the present paper, we connect research on trust in AI with Mayer, Davis, and Schoorman’s (1995) model of organizational trust to elaborate a conceptual model of trust, perceived risk, and reliance on AI decision aids at work. Drawing from the trust in technology, trust in automation, and decision support systems literatures, we redefine central concepts in Mayer et al.’s (1995) model, expand the model to include new, relevant constructs (like perceived control over an AI decision aid), and refine propositions about the relationships expected in this context. The conceptual model put forward presents a framework that can help researchers studying trust in and reliance on AI decision aids develop their research models, build systematically on each other’s research, and contribute to a more cohesive understanding of the phenomenon. Our paper concludes with five next steps to take research on the topic forward.

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This paper describes a data collection method for obtaining high volumes of performance data from a short testing session. The method is aimed at documenting the performance benefits and drawbacks of innovative interface features in terms of identification times and error rate on a component and system level. Higherlevel aspects of control room operation, such as teamwork, communication, context and situation awareness, are not currently within the scope of this method (we consider this methodology as one element of a larger evaluation process where these higher-level aspects are tested separately). The benefits of this methodology are economy of data collection (amount of data points per unit of time), economy of analysis, objectivity of measures, and clarity of results.

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Truck platooning can potentially make road freight transportation safer and greener. Technological readiness, business opportunities, and acceptance of truck platooning have mainly been studied for multilane highways with ample truck volumes. Less is known about truck platooning in areas with low traffic volumes and challenging roads and weather conditions. This paper investigates the opportunities and barriers for truck platooning on Norwegian rural freight routes, through stakeholder interviews and realistic case examples. Given modest freight volumes, dispersed industry clusters, and challenging road conditions, this study identified several prerequisites to deploying platooning and achieving economic savings. The paper discusses the future steps required to organize platooning across carriers, ensure appropriate infrastructure, and gain acceptance among truck drivers, motorists, and other road users.