Appel, J., Puetten, A., Kramer, C., & Gratch, J. (2012) Does humanity matter? Analyzing the importance of social cues and perceived agency of a computer system for the emergence of social reactions during human-computer interaction. Advances in Human-Computer Interaction, 2012. Doi: 10.1155/2012/324694
Purpose of the research
This research had two main hypotheses:
(H1) The social effects will be higher in the conditions with a presented virtual character as interaction partner (high number of social cues) than in the conditions with a presented text-based interface as interaction partner (low number of social cues). (The effect of social cues).
(H2) The social effects will be higher in the conditions with an assumed avatar as interaction partner (high agency) than in the conditions with an assumed agent as interaction partner (low agency). (The effect of agency).
Their overall purpose was to see what factors led for the emergence of social behavior in human-computer interaction.
The authors tested two competing explanations against one another, the agency and the number of social cues approach. They did this by having the participant tell three stories about their life to either a computer text program or animated character
Agency assumes there will be a difference between the assumed interaction with a computer or a human being, suggesting that real humans as interlocutor will evoke stronger social reactions than computers.
In order to test the factor agency, the instructed participants they would communicate with an artificial intelligence or another real participant in the next room. The number of social cues factor was varied by using either a text-based interface of animated character.
The authors used a scale called the Social Presence scale to generate quantitative data. They also applied qualitative analysis to the participant’s answers to the computer.
For agency, there was no strong support found. They only noted that agency was found for the feeling of social presence which was more intense after communicating with the “other subject” via avatar or text chat than after communicating with the computer, however the social presence scale could have provoked stronger reactions that usual due to its wording. The author’s advice the rewording of the scale in order to eliminate this problem in future studies.
For the social cues factor, they found several results supporting the assumption that the number of social cues displayed influences the strength of social reactions. This suggests that a human likely virtual character triggers stronger social reactions than a text based interface. The authors did not find this data to be supported extremely well and so can only suggest that the assumption that the more computes present characteristics that are associated with humans, the more likely they are to elicit social behavior.
I think this is the first article I have read that basically had no strong findings. They can make a suggestion that the more social characteristics that a computer elicits, the more likely the user will elicit social behavior with the computer but they admit their data doesn’t strongly support this. They note that different scales of measurement should have been used as well as the fact that they only measured two levels of social cues. This article, for all that it defended its methods well enough at the beginning seemed to almost lose steam at the end when the authors had to write about how they didn’t really find out much. While one could say that any knowledge is useful, I am used to stronger results. I find their premise interesting and I was hoping for a lot stronger of a statement at the end. It’s a pity this study didn’t support them and so many limitations.
If you are interested in this kind of topic, see the book The Media Equation by Nass and Reeves. Oldie, but goodie, and the claims are based on many studies. The book is very entertaining and well written, if I remember well.
In future RAAs, please explain the purpose of the research more clearly. Provide a bit of context before jumping into hypotheses.
Nice work, your points on Bb.