Summary
The authors mention that the previous HCI research focused on ideal team structures and how roles, norms, and interaction patterns are influenced by systems. The state of research directed teams towards those structures by increasing shared awareness, adding channels of communications, convening effective collaborators. Yet organizational behavior research denies the existence of universally ideal team structures. And believes that structural contingency theory has demonstrated that the best team structures depend on the task, the members, and some other factors. The authors introduce Dream Team, a system that identifies effective team structures for each team by adapting teams to different structures and evaluating each fit. Dream Team explores over time, experimenting with values along many dimensions of team structures such as hierarchy, interaction patterns, and norms. The system utilizes feedback, such as team performance or satisfaction, to iteratively identify the team structures that best fit each team. It helps teams in identifying the structures that are most effective for them by experimenting with different structures over time on multi-armed bandits.
Reflection
The paper presented a system that focuses on virtual teams. In my opinion, the presented system is a very specific application to a very specific problem. The authors address their long list of limitations, including how they don’t believe their system generalizes to other problems easily. I also believe that the way they utilize feedback in the system is complex and unclear. Their reward function did not explain how qualitative factors were taken into consideration. The authors mention that high variance tasks would require more time for DreamTeam to converge.
Which means more time to get a response from the system, and I don’t know how that would be useful if it slows teams down? Also, when looking at the snapshot of the slack integration, it seems that they handle team satisfaction based on users response to a task, which is not always the case when it comes to collaboration on slack. The enthusiasm of the responses just seems out of the norm. The authors did not address how would their system address “team satisfaction” when there’s little to no response? Would that be counted as a negative response? Or would it be neutral? And even though their system worked well for the very specific task they chose, it’s also a virtual team. Which raises questions about how would this method be applicable for in person teams or hybrid teams? It seems that their controlled environment was very controlled. Even though they presented a good idea, I doubt how applicable it is to real life situations.
Discussion
- In your opinion, what makes a dream team?
- Are you pro or against ideal team structures? Why?
- What were the qualities of collaborators in the best group project/research you had?
- What makes the “chemistry” between team members?
- What does a successful collaborative team project look like during a cycle?
- What tools do you use in project management?
- Would you use DreamTeam in your project?
- What would you change in DreamTeam to make it work better for you?
I have the same concern about the experiment presented in the paper. The only concept I am agreed to is that there is no universal ideal team structure. I think it is a straightforward conclusion since even the same team would use a different team structure to tackle different tasks. Besides, I think since the paper is trying to explore the best structure for a team, he should find a real team in which the team member is familiar with each other. I think a DreamTeam should be formed by a group of people who knows their strength and weakness clearly, and they know how to cooperate based on each team member’s strength. A team should have a clear goal, each person should have a clear clue of what they should do and a responsible team leader is needed. A good tool that I would recommend for the division of labor in a group is Trello. I used this it quite often when assigning tasks to each team member.
Hi Lulwah! That’s a very interesting point that you make about the non-generalizability of this application. I completely agree, it’s focused on a very specific application and am not sure how it would apply to other areas. How would you work on making the solution more generalizable? Based on prior work, I think all solutions are highly contextual and need to be adapted as such.
Regarding your question about whether I’m for or against the idea of a dream team…that’s an interesting thought. I certainly think that some teams function better than others, and it’s often due to luck that I’m in a great team. But I wonder if by optimizing for some teams to be great, other teams might become not-so-great because of one low performer?