Advancing Methodology for Social Science Research Using Alternate Reality Games: Proof-of-Concept Through Measuring Individual Differences and Adaptability and their impact on Team Performance

TL;DR

This paper introduces a novel methodology using Alternate Reality Games to quantify individual differences and adaptability, assessing their influence on team performance through behavioral data analysis.

Contribution

It presents new methods that combine survey and behavioral data to evaluate team adaptation and performance in controlled ARG environments.

Findings

Behavioral data enhances understanding of team dynamics.

ARGs provide a controllable environment for studying team processes.

The proposed methods effectively measure adaptation and performance.

Abstract

While work in fields of CSCW (Computer Supported Collaborative Work), Psychology and Social Sciences have progressed our understanding of team processes and their effect performance and effectiveness, current methods rely on observations or self-report, with little work directed towards studying team processes with quantifiable measures based on behavioral data. In this report we discuss work tackling this open problem with a focus on understanding individual differences and its effect on team adaptation, and further explore the effect of these factors on team performance as both an outcome and a process. We specifically discuss our contribution in terms of methods that augment survey data and behavioral data that allow us to gain more insight on team performance as well as develop a method to evaluate adaptation and performance across and within a group. To make this problem more tractable we chose to focus on specific types of environments, Alternate Reality Games (ARGs), and for several reasons. First, these types of games involve setups that are similar to a real-world setup, e.g., communication through slack or email. Second, they are more controllable than real environments allowing us to embed stimuli if needed. Lastly, they allow us to collect data needed to understand decisions and communications made through the entire duration of the experience, which makes team processes more transparent than otherwise possible. In this report we discuss the work we did so far and demonstrate the efficacy of the approach.