Consumers have rapidly adopted touch-sensitive devices, which they now use for everything from social interactions to online shopping. Many firms are also replacing human customer service employees with robots, in both physical and digital form. We study human-computer interaction in marketing and the role of computing interfaces as a source of malleability in consumer decision-making.
Our studies are part of a burgeoning field of research seeking to develop systems and devices that can recognize, interpret, process, and simulate human emotion. We are developing a hybrid machine learning approach to improve emotion recognition, which we hope can then be leveraged in a both research and industry contexts.
Recent industry reports indicate that conversational interfaces will be integrated by at least 25% of all customer service and support operations by 2020. Robo-advisors are also increasingly being used in the financial sector. Building on prior work in human-to-human communication and interpersonal psychology, we are studying how and why different conversational robo-advisors evoke differential emotional responses along core dimensions such as trust and benevolence, as well as their affect on personal finance management. We are also interested in how the proliferation of AI-enabled technologies that appear increasingly more human-like impact mind perception and self-expression in the consumer technology landscape.
The use of natural language and voice-based interfaces is gradually transforming how consumers search, shop, and express their preferences. Our research explores the potential of feature extraction in human voice as a novel data format linking consumers’ vocal features during speech formation and subjective task experiences. We also aim to provide direction on how to effectively employ voice and sound analytics in business research while being mindful of the ethical implications of building multi-modal databases for business and society.
Mobile phones contain hundreds of sensors, some of which are conspicuous such as microphones, cameras and GPS. Others are less well-known and include accelerometers, gyroscopes, proximity sensors, and ambient light detectors. Together these sensors can create highly unique “mobility traces” that are increasingly being used to identify individuals. We are currently studying how mobile sensing can reveal an array of user characteristics ranging from personality traits to managerial decision making ability.