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Mitigating Information Overload in e-Commerce Interactions with Conversational Agents

In: Information Systems and Neuroscience

Author

Listed:
  • Maria del Carmen Ocón Palma

    (Camelot ITLab GmbH)

  • Anna-Maria Seeger

    (University of Mannheim)

  • Armin Heinzl

    (University of Mannheim)

Abstract

Information overload influences users’ satisfaction and performance when completing a complex task. In e-commerce interactions, this has the effect that customers’ decision making becomes confused, less accurate and less effective. For websites, numerous countermeasures to mitigate information overload have been presented, whereas not many attempts have been made to reduce cognitive load when conversational agents are used instead. Conversational agents are expected to increase the perceived overload due to the voice interface characteristics. In this pilot study, the cognitive load of subjects was measured during an online shopping task which required different custom shopping skills for Amazon Alexa. It was tested if the countermeasure filtered repetition can reduce subjects’ perceived overload when using the voice assistant and which load differences can be found in comparison to a shopping website. To measure the mental load, the skin conductance level was recorded.

Suggested Citation

  • Maria del Carmen Ocón Palma & Anna-Maria Seeger & Armin Heinzl, 2020. "Mitigating Information Overload in e-Commerce Interactions with Conversational Agents," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane Randolph & Thomas Fis (ed.), Information Systems and Neuroscience, pages 221-228, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-28144-1_24
    DOI: 10.1007/978-3-030-28144-1_24
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