MindMiner: Uncovering linguistic markers of mind perception as a new lens to understand consumer–smart object relationships

Abstract

Prior research revealed a striking heterogeneity of how consumers view smart objects, from seeing them as helpful partners to merely a useful tool. We draw on mind perception theory to assess whether the attribution of mental states to smart objects reveals differences in consumer–smart object relationships and device usage. We train a language model to unobtrusively predict mind perception in smart objects from consumer-generated text. We provide a rich set of interpretable linguistic markers for mind perception, drawing on a diverse collection of text-mining techniques, and demonstrate that greater mind perception is associated with expressing a more communal (vs. instrumental) relationship with the device and using it more expansively. We find converging evidence for these associations using over 20,000 real-world customer reviews and also provide causal evidence that inducing a more communal (vs. instrumental) relationship with a smart object enhances mind perception and in turn increases the number of tasks consumers engage in with the device. These findings have important implications for the role of mind perception as a novel lens to study consumer–smart object relationships. We offer an easy-to-use web interface to access our language model using researchers own data or to fine-tune the model to entirely new domains.