MultiMotion ARC Accelerate

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MultiMotion is aimed at market researchers who need to understand consumers' emotional responses to marketing stimuli. A well-known phenomenon in consumer research is the intention-behaviour gap where research participants tend to report high intentions to engage in conscious (e.g., healthy or sustainable) consumption behaviours but then do not. Research has shown that emotions mediate the intention-behaviour relationship. Therefore, a better understanding of consumers' emotional responses can help create better interventions to encourage conscious consumption behaviours.Current methods of predicting consumers' emotional responses tend to be inaccurate, inconsistent, subjective, and very time-consuming. This is because existing analysis tools require researchers to analyse biosignals manually and separately of one another, expecting researchers to make sense of and integrate huge amounts of data while not providing a consistent framework for deducing emotional states from that data. MultiMotion, however, will analyse and integrate the data automatically and systematically to provide accurate predictions in real-time.MultiMotion is being developed by an interdisciplinary team of academics from social science (marketing and psychology) as well as computer science to address pertinent marketing challenges, especially those pertaining to conscious consumption. Decades of research in emotion science suggests emotional responses to not have a one-to-one mapping with individual biological signals (such as heart rate or sweating). Instead, the fingerprints of an emotional experience depend on multiple converging signals, that, taken together, can be used to decode an emotional response without asking people how they are feeling. MultiMotion, then, is based on our research into current multidimensional theoretical models of emotional state representation, which we use to develop a comprehensive integrated model capable of representing emotional states holistically. Furthermore, we are developing specific methods to predict emotions from facial micro expressions, electroencephalographic signals, heart rate, and galvanic skin resistance. We then draw on our research into consumer behaviour, especially research into how emotions shape motivation and mediate the intention-behaviour relationship. Here, we specifically build on our research into how information technologies influence people's intentions to consume more sustainably.