Association for Consumer Research (ACR) Virtual Conference 2020
William Hampton, Francesc Busquet, Anouk Bergner, Anna Bouwer, Ilaria Querci, and Christian Hildebrand presented their latest research on mobile interface haptics, emotion detection algorithms, deep learning language models, and resistance to AI at the first virtual Association for Consumer Research Conference on October 1st – 3rd 2020. Additionally, Christian Hildebrand was a special guest at a roundtable discussion of senior academics sharing insights from consumer research on technology.
This roundtable brings together a diverse group of senior academics to offer guidance to early-career researchers who are interested in studying technology in consumer research. Discussants will talk about theoretical, methodological, empirical and substantive (e.g., publication process, career management) challenges in this research area. (see also additional file)
Today many people spend a large portion of their day interacting with vibrating mobile devices, yet how we perceive the vibrotactile sensations emitted by these devices, and their effect on consumer choice is largely unknown. Building on both classical conditioning theory and recent work on haptic sensory processing, the current research examines the functional relationship between vibration duration and perception, the role of vibrational stimuli as rewards and modifiers of choice, as well as the underlying mechanism of this relationship. We examine mobile vibrations in a variety of experimental settings, drawing on a diverse participant pool, leveraging both within- and between-subject experimental designs to assess theoretically and practically important boundary conditions. We find that certain mobile vibrations are perceived as rewarding, contingent upon prior exposure, and can boost purchasing in ecological online shopping environments, whereas short or long durations are perceived as neutral or punishing, respectively. We further show that these effects are amplified for impulsive consumers, relate to a range of demographic and psychological trait variable, and provide evidence that associative learning may underpin mobile vibration reward perception. Our findings have important implications for the effective design of haptic human-machine interfaces in marketing and the role of vibrotactile stimuli as a novel form of reward.
“Conferring Minds to Machines: A Deep Learning Approach to Mind Perception, Technology Attachment, and Trust” provides a novel approach to understand and model consumer smart-object relationships from customer reviews. We develop a state-of-the-art deep learning model to classify mind perception from unstructured text and demonstrate that the extent of mind perception successfully predicts consumers’ attachment to, trust in, and evaluation of smart objects.
“A Contingency Theory of Artificial Intelligence: Consumer Beliefs, Value Creation, And Resistance to Creative AI” provides novel insights for consumers’ resistance to creative AI, the use of deep-learning models as an unexplored method in the study of consumer psychology, and the interplay of time and effort in the evaluation of aesthetic quality.
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The current research demonstrates considerable variability in predictive accuracy across major emotion detection systems (such as Google ML or Microsoft Cognitive Services) with lower (higher) classification accuracy for negative (positive) discrete emotions. We provide two modeling strategies to improve prediction accuracy by either combining feature sets or using ensemble methods. |
“Consumers’ Resistance to the Adoption of Smart Objects: A Relational Perspective”aims at explaining consumer resistance to smart objects by adopting a relational perspective. Pivoting on smart objects’ social presence and social roles, and borrowing the fear of intimacy construct from the literature on interpersonal relationships, this work identifies four fears that consumers anticipate when they think about the possibility of entering a future relationship with a smart object.
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