Hello, everyone.
This is Hancheng from Stanford University.
Today, I'm happy to share with you our work,
my team will go on.
Differentiating high and low viability teams
through team interaction.
I would like to start this talk by posing two questions on you.
Have you ever experienced a collaboration that felt toxic
and fell apart?
How quickly did you know that's how
this collaboration would end?
In this paper, we set out to classify and predict
teams by viability to shed light on this common phenomena.
As pointed out by prior literature,
team viability is one fundamental attribute of team.
Team viability capture the team's interest
in continuing to work together and reflects team members
satisfaction towards each other.
Low team viability could lead to team falling apart.
However, despite its importance, past research mostly
focused on team performance.
What is lacking here is a systematic understanding
of mechanisms behind team viability.
In this work, we aim at filling this research gap
by introducing a mechanism for classifying
high and low viability teams using only team chat
interactions.
Specifically, in this talk, we seek to address three research
questions.
First of all, can we differentiate high and low
viability teams by analyzing team chat interactions?
Secondly, which behaviors are most strongly
associated with high versus low viability teams?
And thirdly, how much time must the team
interact before we can make accurate classifications
of team viability?
We based all our analysis on unique data
set from our prior work.
The data set includes complete chat history of 669 newly
convened online teams from Amazon Mechanical
Turk collaborating on multiple task over 10 minutes.
The data set also includes viability surveys associated
with each interaction.
After the completion of each task,
team members were asked to complete 14 item viability
instrument on a five point scale.
Here are some of the survey statement.
For example, the member of this team could work for a long time
together.
After we get the survey response,
we sum each collaborator's 14 viability measures
into a single individual score with a range of 14 to 70.
And calculated the mean of all of the individual scores
to determine a team viability score.
Here's the distribution of team viability
score in our data set.
Here comes our task we divide all teams into two groups.
Either high viability or low viability
based on their viability score.
We set a cutoff viability score as a varying decision
threshold.
For example, classifying the top 10s, 50s, or 90s percentile
teams by mean viability.
We tested multiple models in the paper.
But here, we primarily focused on logistic regression.
We extracted those computational derived features
and human labor features.
Including, work pattern features.
Linguistic features.
Communication features.
And behavioral features.
Here comes our findings.
First of all, team viability can be
classified with high accuracy.
Especially, for the most viable teams
indicating that the most viable teams may share very
similar behavioral patterns.
Through a systematic feature analysis,
we find that active response and engagement is
an indicator of high a teams.
Here's a transcript from a high viability team.
We can see that they are using lots of exclusive language.
Furthermore, our analysis show that classification
of viability is feasible after only 70 seconds of the team
interaction.
Which is consistent across different viability splits.
This finding points to the fact that there's enough viability
signals embedded in interactions of team members
within short time span.
In summary, our work not only enables a better understanding
of team viability, but also points
to the exciting possibility of early detection of no viability
teams and corresponding recommendations.
We're also building online team viability classification tool
at viability.stanford.edu.
Thank you very much for your attention.
Be free to reach out to us if you have any any questions
or comments.