MultiMotion ARC Accelerate

MultiMotion面向需要了解消费者对营销刺激的情绪反应的市场研究人员。消费者研究中的一个众所周知的现象是意图-行为差距，其中研究参与者倾向于报告参与有意识行为的高意图（例如，健康的或可持续的消费行为，但不是。研究表明，情绪调节意图-行为关系。因此，更好地了解消费者的情绪反应有助于创造更好的干预措施，以鼓励有意识的消费行为。目前预测消费者情绪反应的方法往往不准确，不一致，主观，而且非常耗时。这是因为现有的分析工具需要研究人员手动分析生物信号，并相互独立，期望研究人员理解和整合大量数据，同时不能提供一个一致的框架来从数据中推断情绪状态。MultiMotion将自动系统地分析和整合数据，实时提供准确的预测。MultiMotion由社会科学（营销和心理学）以及计算机科学的跨学科学者团队开发，以解决相关的营销挑战，特别是与有意识消费有关的挑战。几十年来的情绪科学研究表明，情绪反应与个体生物信号（如心率或出汗）没有一一对应的关系。相反，情感体验的指纹取决于多个会聚信号，这些信号结合在一起，可以用来解码情感反应，而无需询问人们的感受。MultiMotion是基于我们对当前情绪状态表征的多维理论模型的研究，我们使用它来开发一个全面的集成模型，能够整体地表示情绪状态。此外，我们正在开发特定的方法来预测面部微表情，脑电图信号，心率和皮肤电阻的情绪。然后，我们利用我们对消费者行为的研究，特别是对情绪如何塑造动机和调解意图-行为关系的研究。在这里，我们特别建立在我们的研究如何信息技术影响人们的意图，更可持续地消费。

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.