Cited by 42AI in Service InteractionsConsumer Behavior in Brand Consumption and IdentificationInnovative Human-Technology Interaction

MindMiner:Uncovering Linguistic Markers of Mind Perception as a New Lens to Understand Consumer‐Smart Object Relationships

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Hartmann, Jochen, Bergner, Anouk, Hildebrand, Christian·2023·Journal of Consumer Psychology

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.

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References 91 · Cited by 42

Research works printed but not linked: 42

  1. S Abele, A. E., & Bruckmüller, S. (2013). The big two of agency and communion in language and communication. In J. P. Forgas, O. Vincze, & J. László (Eds.), Social cognition and communication (pp. 173-184). Pschology Press.
  2. Abele, A. E., Uchronski, M., Suitner, C., & Wojciszke, B. (2008). Towards an operationalization of the fundamental dimensions of agency and communion: Trait content ratings in five coun- tries considering valence and frequency of word occurrence. European Journal of Social Psychology, 38, 1202-1217.
  3. Abele, A. E., & Wojciszke, B. (2014). Communal and agentic content in social cognition: A dual perspective model, 50 advances in ex- perimental social psychology (1st ed.). Elsevier Inc. https://doi. org/10.1016/B978-0-12-80028 4-1.00004 -7 · DOI · Paper page
  4. Aggarwal, P. (2004). The effects of brand relationship norms on con- sumer attitudes and behavior. Journal of Consumer Research, 31(1), 87-101.
  5. Aggarwal, P., & Law, S. (2005). Role of relationship norms in pro- cessing brand information. Journal of Consumer Research, 32, 453-464.
  6. Aggarwal, P., & Mcgill, A. L. (2012). When brands seem human, do humans act like brands? Automatic behavioral priming effects of brand anthropomorphism. Journal of Consumer Research, 39(2), 307-323.
  7. Astington, J. W., & Baird, J. A. (2005). Why language matters for the- ory of mind. Oxford University Press.
  8. Barasch, A., & Berger, J. (2014). Broadcasting and narrowcasting: How audience size affects what people share. Journal of Marketing Research, 51(3), 286-299.
  9. Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeabil- ity, perceived intelligence, and perceived safety of robots. International Journal of Social Robotics, 1(1), 71-81.
  10. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet alloca- tion. Journal of Machine Learning Research, 3, 993-1022.
  11. Bolton, L. E., Reed, A., Volpp, K. G., & Armstrong, K. (2008). How does drug and supplement marketing affect a healthy lifestyle? Journal of Consumer Research, 34(5), 713-726.
  12. Bretherton, I., & Beeghly, M. (1982). Talking about internal states: The acquisition of an explicit theory of mind. Developmental Psychology, 18(6), 906-921.
  13. Bretherton, I., McNew, S., & Beeghly-Smith, M. (1981). Early per- son knowledge as expressed in gestural and verbal communi- cation: When do infants acquire a 'theory of mind'? In M. E. Lamb & L. R. Sherrod (Eds.), Infant social cognition (pp. 333- 373). Erlbaum.
  14. Castelo, N., Schmitt, B., & Sarvary, M. (2019). Human or robot? Consumer responses to radical cognitive enhancement products. Journal of the Association for Consumer Research, 4(3), 217-230.
  15. Crego, C., Oltmanns, J. R., & Widiger, T. A. (2018). FFMPD scales: Comparisons with the FFM, PID-5, and CAT-PD-SF. Psychological Assessment, 30(1), 62-73.
  16. Cuddy, A. J. C., Fiske, S. T., & Glick, P. (2008). Warmth and compe- tence as universal dimensions of social perception: The stereo- type content model and the BIAS map. Advances in Experimental Social Psychology, 40(7), 61-149.
  17. de Bellis, E., & Johar, G. V. (2020). Autonomous shopping systems: Identifying and overcoming barriers to consumer adoption. Journal of Retailing, 96(1), 74-87.
  18. Kurt, D., & Frimer, J. (2015). Agency and communion as a framework to understand consumer behavior. In M. Norton, D. Rucker, & C. Lamberton (Eds.), The Cambridge handbook of consumer psychology (pp. 446-475). Cambridge University Press.
  19. Liu, L., Dzyabura, D., & Mizik, N. (2020). Visual listening in: Extracting brand image portrayed on social media. Marketing Science, 39(4), 669-686.
  20. Malle, B. F., & Knobe, J. (1997). The folk concept of intentionality. Journal of Experimental Social Psychology, 33(2), 101-121.
  21. Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press.
  22. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their com- positionality. Journal of Machine Learning Research, 3(4-5), 951-991. https://doi.org/10.1162/jmlr.2003.3.4-5.951 · DOI
  23. Novak, T., & Hoffman, D. L. (2023). Automation assemblages in the internet of things: Discovering qualitative practices at the boundaries of quantitative change. Journal of Consumer Research, 49, 811-837.
  24. Ordenes, F. V., Grewal, D., Ludwig, S., De Ruyter, K., Mahr, D., & Wetzels, M. (2019). Cutting through content clutter: How speech and image acts drive consumer sharing of social media brand messages. Journal of Consumer Research, 45(5), 988-1012.
  25. Packard, G., & Berger, J. (2019). How concrete language shapes customer satisfaction. In R. Bagchi, L. Block, & L. Lee (Eds.), Advances in consumer research (pp. 29-34). Association for Consumer Research.
  26. Packard, G., & Berger, J. (2020). Thinking of you: How second-person pronouns shape cultural success. Psychological Science, 31(4), 397-407.
  27. Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015. University of Texas at Austin. http://hdl.handle.net/2152/31333
  28. Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003). Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology, 54, 547-577.
  29. Perner, J., Ruffman, T., & Leekam, S. R. (1994). Theory of mind is contagious: You catch it from your sibs. Child Development, 65(4), 1228-1238.
  30. Pitardi, V., & Marriott, H. R. (2021). Alexa, She's not human but… un- veiling the drivers of Consumers' Trust in voice-based artificial intelligence. Psychology and Marketing, 38(4), 626-642.
  31. Pryzant, R., Chung, Y. J., & Jurafsky, D. (2017). Predicting sales from the language of product descriptions. CEUR Workshop Proceedings, 2311.
  32. Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137-141.
  33. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). 'Why Should I Trust You?' Explaining the predictions of any classifier. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-Augu, 1135-1144.
  34. Schmitt, B. (2019). From atoms to bits and Back: A research curation on digital technology and agenda for future research. Journal of Consumer Research, 46(4), 825-832.
  35. Schweitzer, S., & Waytz, A. (2021). Language as a window into mind perception: How mental state language differentiates body and mind, human and nonhuman, and the self from others. Journal of Experimental Psychology: General, 150(8), 1642-1672.
  36. Sirdeshmukh, D., Singh, J., & Sabol, B. (2002). Consumer trust, value, and loyalty in relational exchanges. Journal of Marketing, 66(1), 15-37.
  37. Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society, Series B (Methodological), 58(1), 267-288.
  38. Trope, Y., Liberman, N., & Wakslak, C. (2007). Construal levels and psychological distance: Effects on representation, prediction, evaluation, and behavior. Journal of Consumer Psychology, 17(2), 83-95.
  39. Waytz, A., Morewedge, C. K., Epley, N., Monteleone, G., Gao, J.-h., Cacioppo, J. T., Diana Greene, J. S., Irick, M. K., & Kwong, J. (2010). Making sense by making sentient: Effectance motivation increases anthropomorphism. Journal of Personality and Social Psychology, 99(3), 410-435.
  40. Wellman, H. M., Cross, D., & Watson, J. (2001). Meta-analysis of theory-of-mind development: The truth about false belief. Child Development, 72(3), 655-684.
  41. Wilson, A. T., Chew, P. A., & Llp, M. A. (2010). Term weighting schemes for latent Dirichlet allocation. International Journal of High Performance Systems Architecture, 8(1-2), 465-473.
  42. Yang, L. W., Aggarwal, P., & McGill, A. L. (2020). The 3 C's of anthro- pomorphism: Connection, comprehension, and competition. Consumer Psychology Review, 3(1), 3-19.

Authors
Hartmann, J., Bergner, A., & Hildebrand, C.
Affiliations
  • Technical University of Munich (DE)
  • University of St.Gallen (CH)
Corresponding author
Hildebrand, Christian
Venue
Journal of Consumer Psychology
Publisher
Wiley
Published
2023-08-09
Volume
33
Issue
4
Pages
645-667
ISSN
  • 1057-7408 (ISSN-L)
  • 1532-7663
OpenAlex
W4385347961
Cited by
42
References
91
Citations by year
2026: 9 · 2025: 15 · 2024: 10 · 2023: 8
FWCI
3.96
Type
journal article
Language
en
Open access
Locations
  • Journal of Consumer Psychology (publishedVersion, other-oa)
  • Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior in Brand Consumption and Identification
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
Keywords
mind perception, smart objects, linguistic markers, customer reviews, device utilization, text mining

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