Welcome to our presentation about chatbot-based emotion
management for distributed teams--
a participatory design study.
My name is Ivo Benke.
And on behalf of my coauthors, Michael Knierim and Alexander
Maedche, I'm happy to share with you our experiences.
This paper focuses on remote work and distributed teams
which determine our everyday life and our working
environment.
Especially remote workers and distributed teams
are heavily relying on modern collaboration technologies
like Microsoft Teams, Slack, or Zoom.
Especially when using text-based communication technologies,
management of emotions is difficult.
Management of emotions or the ability
to reason about or use emotions to enhance thought
through perception, understanding,
and regulation of emotions is highly important for team
collaboration.
In turn, chatbots as a novel opportunity
can serve as the emotion managers for teams.
Through their specific capabilities,
like natural language understanding,
anthropomorphic appearance, as well as emotion recognition,
they potentially serve as team emotion managers very well.
This motivation leads to the following research problem
on how to design chatbot-based emotion management
for distributed teams based on end-user involvement
into the design process.
In order to tackle the presented research problem,
we have pursued the following research approach.
First of all, we have conducted three design workshops
where we used two participatory design methods in order
to involve end users into the design process.
Second, we have distilled three chatbot design prototypes,
which we developed and finally evaluated them
in a laboratory group experiment with 27 participants.
Based on the three design workshops,
we were able to develop three system prototypes.
We received 153 design sketches.
And for a user-informed selection process,
we were able to distill three design types--
first of all, the NeutralBot, which is very lean;
second, the SocialBot, which shows
human anthropomorphic appearances;
and third, the ActionBot, which was guided
by interactive design features.
The three chatbot designs should serve
in the following scenario.
A group of team members is chatting
via text-based communication.
The chatbot is listening and simultaneously recognizing
the emotions.
In times of conflict or in cases of negative emotions,
the chatbot intervenes and tries to help the team
to get into a better situation again.
On the right side here now, we brought a very small example
from an ActionBot.
This is a chat example.
A three-team group is chatting.
And suddenly, the ActionBot is intervening, which happens now.
And this is the second escalation level.
It deletes the chat and, afterwards, start it again.
The three prototypes were evaluated
in a between-design laboratory experiment
with nine three-member groups.
The analysis took place in a quantitative and qualitative
fashion.
The descriptive statistics showed
that there was an emotional competence
decrease in NeutralBot treatment and a maintenance in SocialBot
and ActionBot treatments.
On a qualitative side, we saw seven themes
ranging from a thematic inductive analysis.
Key results here are emotional perception and regulation,
especially in social and interactive design features,
as well as compromised facilitation and communication
efficiency.
In conclusion, we want to present
three takeaways of our study.
First of all, chatbot-based emotion management works.
We have shown that for all three chatbot designs
there was an increased emotion perception.
Second, for our interactive and social design features,
we were able to emphasize these effects
by stimulating emotional regulation and communication
efficiency.
Finally, however, we saw some downsides
which represent threats for chatbot-based emotion
management, especially surveillance
and loss of control reported by the participants.
Finally, we believe that our results
are especially interesting in times of COVID-19
and the consequences we have to live with.
So having chatbot-based emotion management
might be a very interesting asset in the future,
and it's worth investigating beyond our explorative results.
Thank you very much for your attention.