Exploring the AI use effects on information evaluation tasks for new product development decision-making. A case study at manufacturing firms that explores the use of AI for decision maker’s information evaluation tasks at PD evaluation gates to improve decision-making.
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2020-09-25
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en
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This study explores in what way information derived from AI use does improve decision-making for new product development (NPD). Based on the bounded rationality principles, we designed a multiple case study that explored NPD decision-making improvements through AI use at manufacturing firms. We found two types of AI uses incorporated in NPD decision-making. First, in-machine AI that is an application within the manufacturing machine that create deep learning performance information under real-time customers circumstances. Second, AI analytics that use deep learning on extensive amounts of online customer behaviour data to create future customer preferences of a new market. Research results revealed how these two different AI uses changed the decision-makers information evaluation tasks differently, although both leading to decision-making effectiveness. Still, the effect is conditional as it depends on conditions regarding NPD evaluation gates, decision-making efficiency, and NPD customer orientation. Our study contributes to the bounded rationality theory by demonstrating that both AI uses have different influence on bounded rationality principles. We further contribute by proposing that different types of evaluation gates moderate the AI-related changes leading to decision-making effectiveness. The use of AI analytics further seems to provide more decision-making effectiveness as it supports NPD decision-makers for more focused orientation towards future customers. We offer NPD decision-makers a guide with three steps of consideration to improve NPD decision-making through AI use.
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Faculteit der Managementwetenschappen