首页|Findings from Justus-Liebig-University Giessen in the Area of Machine Learning R eported (Consumer Decisions In Virtual Commerce: Predict Good Help-timing Based On Cognitive Load)
Findings from Justus-Liebig-University Giessen in the Area of Machine Learning R eported (Consumer Decisions In Virtual Commerce: Predict Good Help-timing Based On Cognitive Load)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news originating from Giessen, Germany, by NewsRx corres pondents, research stated, "The retail sector is steadily moving toward virtual commerce (v-commerce), and the process has recently gained momentum. With the la test developments in headset technology and the rise of artificial intelligence, virtual shopping has become relevant for an increasing number of products." Financial support for this research came from German Research Foundation (DFG). Our news journalists obtained a quote from the research from Justus-Liebig-Unive rsity Giessen, "In this article, we present a study that combines consumer behav ior research, eye tracking, electrocardiography, machine learning, and the appli cation of virtual reality. Fifty participants were invited to experience a virtu al scenario, perform multiple mentally demanding tasks, and make a purchase deci sion for a product from one of two different product categories. In a post hoc v ideo analysis based on the first-person view, participants determined different points in time when they would have appreciated help from an algorithmic user as sistance system or a digital human agent. Our statistical analysis suggests that the desired help-timing depends on the product category. For fast-moving consum er goods, algorithmic help was requested particularly early. Furthermore, we col lected eye-tracking and electrocardiographic data to build and evaluate a predic tive classification model that differentiates between three levels of cognitive load. The trained machine learning algorithm aims to classify cognitive load dur ing decision making, which may indicate the right time to offer help."
GiessenGermanyEuropeCyborgsEmerg ing TechnologiesMachine LearningJustus-Liebig-University Giessen
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