Doctoral Dissertation Defense: Joohee Choi’s
Event Start Date:
Thursday, November 5, 2020
- 4:00 pm
Event End Date:
Thursday, November 5, 2020 - 6:00 pm
Add to Calendar
Thursday, November 5, 2020 4:00 pm
Thursday, November 5, 2020 6:00 pm
America/New York
Doctoral Dissertation Defense: Joohee Choi’s
Please join us for Joohee Choi’s doctoral dissertation defense.
Date: Thursday, November 5, 2020
Time: 4:00 PM eastern time
Title: The Effect of Role Specialization And Transactive Memory Systems On Performance in Data Science Teams
Abstract:
Teamwork is an integral part of data science work. Data science work requires knowledge from many different disciplines including statistics, information visualization, programming, and subject matter knowledge related to a given set of data sets (e.g., politics, education). Data science teams are often formed by individuals who have different areas of knowledge and expertise and, as a result, may take on different functional roles within a team. Due to their distinctive expertise, members in data science teams may take on specialized task roles matching their expertise, and such division of labor could increase coordination cost among team members. As data science work is often open-ended and dynamic by nature, high coordination costs could deteriorate performance in data science teams. In this research, I argued that developing shared cognition on who-knows-what (i.e., transactive memory system) in data science teams would be beneficial for team performance, especially when the members have specialized roles. I conducted two studies to understand the effect of role specialization and transactive memory systems on team performance with a goal to identify and test a lever to facilitate transactive memory system in data science teams. I collected data from two consecutive Data Challenge events; Data Challenge is an week-long data science competition hosted annually as a university-wide event. In Study 1, I conducted an observational study by collecting survey data from 74 individuals in 36 teams in Data Challenge 2019. In Study 2, I conducted a field experiment to examine the effectiveness of an experimental intervention designed to facilitate transactive memory system in data science teams by highlighting any inaccuracies in the perceived expertise between members. Major findings include: 1) Non-technical roles such as project manager and communicator were prevalent in the teams. 2) Assuming technical roles was about the presence of skills whereas taking on non-technical roles was about the absence of technical skills. 3) Teams who were given an experimental intervention to highlight any inaccuracies in the perceived expertise scored higher in the quality of coordination and team performance, but not as much to be statistically significant.
Committee:
Dr. Yla Tausczik, Chair
Dr. Brian Butler
Dr. Wayne Lutters
Dr. Joel Chan
Dr. Kathryn Bartol, Dean’s Representative
Join Zoom Meeting
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Meeting ID: 770 341 5816
Passcode: 98033
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Meeting ID: 770 341 5816
Passcode: 98033
Please join us for Joohee Choi’s doctoral dissertation defense.
Date: Thursday, November 5, 2020
Time: 4:00 PM eastern time
Title: The Effect of Role Specialization And Transactive Memory Systems On Performance in Data Science Teams
Abstract:
Teamwork is an integral part of data science work. Data science work requires knowledge from many different disciplines including statistics, information visualization, programming, and subject matter knowledge related to a given set of data sets (e.g., politics, education). Data science teams are often formed by individuals who have different areas of knowledge and expertise and, as a result, may take on different functional roles within a team. Due to their distinctive expertise, members in data science teams may take on specialized task roles matching their expertise, and such division of labor could increase coordination cost among team members. As data science work is often open-ended and dynamic by nature, high coordination costs could deteriorate performance in data science teams. In this research, I argued that developing shared cognition on who-knows-what (i.e., transactive memory system) in data science teams would be beneficial for team performance, especially when the members have specialized roles. I conducted two studies to understand the effect of role specialization and transactive memory systems on team performance with a goal to identify and test a lever to facilitate transactive memory system in data science teams. I collected data from two consecutive Data Challenge events; Data Challenge is an week-long data science competition hosted annually as a university-wide event. In Study 1, I conducted an observational study by collecting survey data from 74 individuals in 36 teams in Data Challenge 2019. In Study 2, I conducted a field experiment to examine the effectiveness of an experimental intervention designed to facilitate transactive memory system in data science teams by highlighting any inaccuracies in the perceived expertise between members. Major findings include: 1) Non-technical roles such as project manager and communicator were prevalent in the teams. 2) Assuming technical roles was about the presence of skills whereas taking on non-technical roles was about the absence of technical skills. 3) Teams who were given an experimental intervention to highlight any inaccuracies in the perceived expertise scored higher in the quality of coordination and team performance, but not as much to be statistically significant.
Committee:
Dr. Yla Tausczik, Chair
Dr. Brian Butler
Dr. Wayne Lutters
Dr. Joel Chan
Dr. Kathryn Bartol, Dean’s Representative
Join Zoom Meeting
https://umd.zoom.us/j/7703415816?pwd=eG51bWllNEpCYnNSWmZlNlBTSk1QUT09
Meeting ID: 770 341 5816
Passcode: 98033
One tap mobile
+13017158592,,7703415816# US (Germantown)
+13126266799,,7703415816# US (Chicago)
Dial by your location
+1 301 715 8592 US (Germantown)
+1 312 626 6799 US (Chicago)
+1 929 436 2866 US (New York)
+1 253 215 8782 US (Tacoma)
+1 346 248 7799 US (Houston)
+1 669 900 6833 US (San Jose)
Meeting ID: 770 341 5816
Find your local number: https://umd.zoom.us/u/abBuYhcUNY
Join by SIP
7703415816@zoomcrc.com
Join by H.323
162.255.37.11 (US West)
162.255.36.11 (US East)
115.114.131.7 (India Mumbai)
115.114.115.7 (India Hyderabad)
213.19.144.110 (Amsterdam Netherlands)
213.244.140.110 (Germany)
103.122.166.55 (Australia)
149.137.40.110 (Singapore)
64.211.144.160 (Brazil)
69.174.57.160 (Canada)
207.226.132.110 (Japan)
Meeting ID: 770 341 5816
Passcode: 98033