Data and analysis underlying the research into the Artificial-Social-Agent Questionnaire: Establishing the long and short questionnaire versions

Datacite citation style:
Fitrianie, Siska; Merijn Bruijnes; Fengxiang Li; Amal Abdulrahman; Brinkman, Willem-Paul (2023): Data and analysis underlying the research into the Artificial-Social-Agent Questionnaire: Establishing the long and short questionnaire versions. Version 5. 4TU.ResearchData. dataset. https://doi.org/10.4121/19758436.v5
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We present a technical report, data and analysis underlying the study on establishing the Artificial Social Agent (ASA) Questionnaire. The ASA Questionnaire is an instrument for evaluating human interaction with an ASA, resulted from multi-year efforts involving more than 100 Intelligent Virtual Agent (IVA) researchers worldwide. It has 19 measurement constructs constituted by 90 items, which capture more than 80% of the constructs identified in empirical studies published in the IVA conference 2013-2018. This paper reports on construct validity analysis, specifically convergent and discriminant validity of initial 131 instrument items that involved 532 crowd-workers who were asked to rate human interaction with 14 different ASAs. The analysis included several factor analysis models, and resulted in the selection of 90 items for inclusion of the long version of the ASA questionnaire. In addition, a representative item of each construct or dimension was select to create a 24-item short version of the ASA questionnaire. Whereas the long version is suitable for a comprehensive evaluation of human-ASA interaction, the short version allows quick analysis and description of the interaction with the ASA. To support reporting ASA questionnaire results, we also put forward an ASA chart. The chart provides a quick overview of agent profile. 

history
  • 2022-07-22 first online
  • 2023-10-11 published, posted
publisher
4TU.ResearchData
format
.zip, .html, .png, .xlsx, .R, .pdf, .txt and .csv
funding
  • The Dutch 4TU - Humans and Technology, Pride and Prejudice project.
organizations
Department of Intelligent Systems, Delft University of Technology, Delft, the Netherlands;
Utrecht University School of Governance, Utrecht University, Utrecht, the Netherlands;
Department of Management Science and Engineering, School of Business Administration, Northestern University, Shenyang, China;

DATA

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