Making conversations with chatbots more personalized

By Michael Shumanov, Lester W. Johnson. Computers in Human Behavior, 2020

313 citations recorded by the source corpus.

Abstract Many of the world’s leading brands and increasingly government agencies are using intelligent agent technologies, also known as chatbots to interact with consumers. However, consumer satisfaction with chatbots is mixed. Consumers report frustration with chatbots arising from misunderstood questions, irrelevant responses, and poor integration with human service agents. This study examines whether human-computer interactions can be more personalized by matching consumer personality with congruent machine personality using language. Although the idea that personality is manifested through language, and that people are more likely to be responsive to others with the same personality is well known, there is a dearth of research that examines whether this is consistent for human-computer interactions. Based on a sample of over 57,000 chatbot interactions, this study demonstrates that consumer personality can be predicted during contextual interactions, and that chatbots can be manipulated to ‘assume a personality’ using response language. Matching consumer personality with congruent chatbot personality had a positive impact on consumer engagement with chatbots and purchasing outcomes for interactions involving social gain.

Subfields: Artificial Intelligence, Clinical Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/14a5f0a0fc1f81f16808936ec88dc3a410413c63

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In Clinical Psychology

  1. A. Parks
  2. D. Chan

In Social Psychology

  1. A. Parks

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