| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | August 3, 2018 |
| Latest Amendment Date: | August 3, 2018 |
| Award Number: | 1841374 |
| Award Instrument: | Standard Grant |
| Program Manager: |
William Bainbridge
IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | August 15, 2018 |
| End Date: | July 31, 2021 (Estimated) |
| Total Intended Award Amount: | $199,864.00 |
| Total Awarded Amount to Date: | $199,864.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
3100 MARINE ST Boulder CO US 80309-0001 (303)492-6221 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
3100 Marine Street, Room 481 Boulder CO US 80303-1058 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | HCC-Human-Centered Computing |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
This research leverages the large-scale, detailed, international, and unobtrusive data logged by online electronic sports (e-sports) to understand the organizational behavior of temporary virtual teams. Teamwork and collaboration are essential for success in contemporary organizations, and as teams become increasingly distributed, virtual, self-assembled, cross-functional, and temporary, existing frameworks for supporting effective teamwork need to be revised. Multiplayer online e-sports are models for developing new and more effective frameworks for technologically-mediated teamwork: clear and consistent performance metrics, detailed and public behavioral data for quantitative analysis, a large and international user base, and extensive randomization, repeated observations, and matching for making strong causal inferences. The inferences made from detailed behavioral records of high-tempo, naturalistic decision making can be extended to many other settings such as disaster response or breaking news. The findings from this research could provide the empirical basis for identifying under-utilized expertise in noisy social systems, optimizing team assembly algorithms to improve performance, improving decision making in temporary virtual teams, and using online e-sports as diagnostic tools for existing teams. The findings from this project will also inform the design and governance of e-sports that already attract tens of millions of users around the world.
The project pursues two initiatives to understand how to improve the performance of temporary virtual teams. The first initiative examines how team assembly decision-making in high-tempo contexts influences team performance. The second initiative examines how software and database patches disrupt mental models and decision-making. This research will triangulate between (1) existing organizational theories and constructs about team processes, (2) quantitative methods from data mining, machine learning, and econometrics for analyzing unobtrusive observational data about user behavior, and (3) the unique affordances of several popular e-sports to examine the variables and mechanisms that influence team performance in a naturalistic setting. The results of this research project will (1) provide comparative empirical insights into a rapidly growing cultural and economic phenomenon; (2) develop frameworks and models to increase engagement in sociotechnical systems; and (3) provide generalizable recommendations for improving the performance of temporary virtual teams. This project will create free software libraries, data collections, and supporting tutorials and documentation enabling other researchers to develop and evaluate organizational theories using data drawn from e-sports.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Software patches are a ubiquitous part of maintaining the infrastructure of an information society, but they happen in the background and with little disruption for most applications. However, software patches are highly disruptive within multiplayer online gaming communities because their changes alter the incentives and balances in unpredictable ways. Improving our understanding how globally distributed, computer-mediated social organizations respond to sudden disruptions has only become more important through the COVID-19 pandemic.
Using publicly-available large-scale digital trace data, we analyzed the effects of software patches across multiple social platforms. First, we developed a method for translating the natural language of the patch notes into a quantitative measure of the patch's severity. Second, we validated the relationship between patch severity and behavior change by surveying users. Third, we analyzed the relationship between patch severity and behavioral change across hundreds of millions of matches and found significant relationships. Finally, we examined the effects of patches on community discussions on Reddit and found patch severity correlated with volume of posts and comments.
The project concluded in summer 2021 after extensions due to the COVID-19 pandemic. It funded a PhD graduate research assistant for four semesters and a faculty member for a total of three summer months. The graduate student successfully defended their dissertation in 2021 using data and methods developed with this grant. The manuscripts developed using the code, data, and results from this project are currently under preparation and review. This project's findings about behavior change patterns following disruptions involving millions of users around the globe has implications for understanding decision-making and information-seeking in the aftermath of other shocks.
Last Modified: 11/29/2021
Modified by: Brian C Keegan
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