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1. From Individual Change Agents to ‘Revolutionary’ Teams: The Search and Selection Process of Team Formation within a Community of Practice

   https://doi.org/10.1007/s10755-024-09729-1  

   Margherio, Cara; Swan, Anna L; Güler, Selen (August 2024, Innovative Higher Education)

   While the role of teams in leading transformations within academia is increasingly recognized, few studies have analyzed how teams form. Understanding the processes of interdisciplinary team formation within higher education will allow leaders to intentionally bring together individuals and form teams with higher likelihoods of success. In this study, we examine the early stages of change team formation within higher education, specifically looking at the two interconnected processes of search and selection, and we explore how a community of practice influences these processes through situated learning. Our longitudinal qualitative analysis demonstrates how teams form and transform over time, from the initial search process for team members to the factors that informed the initial and ongoing selection of team members. We find that a community of practice influenced these processes by shaping how teams understood their instrumental needs and how members understood their role within interdisciplinary teams. Finally, we examine a correlation between leadership structure and team member turnover, finding that a centralized leadership structure that lacks a vision for change shared among team members may drive turnover. The results provide insights into the dynamic nature of change team formation within academia. 

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2. Towards Real Time Team Optimization

   https://doi.org/10.1109/BigData47090.2019.9006078  

   Zhou, Qinghai; Li, Liangyue; Tong, Hanghang (December 2019, IEEE BigData)

   Teams can be often viewed as a dynamic system where the team configuration evolves over time (e.g., new members join the team; existing members leave the team; the skills of the members improve over time). Consequently, the performance of the team might be changing due to such team dynamics. A natural question is how to plan the (re-)staffing actions (e.g., recruiting a new team member) at each time step so as to maximize the expected cumulative performance of the team. In this paper, we address the problem of real-time team optimization by intelligently selecting the best candidates towards increasing the similarity between the current team and the high-performance teams according to the team configuration at each time-step. The key idea is to formulate it as a Markov Decision process (MDP) problem and leverage recent advances in reinforcement learning to optimize the team dynamically. The proposed method bears two main advantages, including (1) dynamics, being able to model the dynamics of the team to optimize the initial team towards the direction of a high-performance team via performance feedback; (2) efficacy, being able to handle the large state/action space via deep reinforcement learning based value estimation. We demonstrate the effectiveness of the proposed method through extensive empirical evaluations. 

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3. It Depends on the Timing: The Ripple Effect of AI on Team Decision-Making

   Yan, Bei; Gurkan, Necdet (January 2023, Proceedings of the Hawaii International Conference on System Sciences)

   Whereas artificial intelligence (AI) is increasingly used to facilitate team decision-making, little is known about how the timing of AI assistance may impact team performance. The study investigates this question with an online experiment in which teams completed a new product development task with assistance from a chatbot. Information needed for making the decision was distributed among the team members. The chatbot shared information critical to the decision in either the first half or second half of team interaction. The results suggest that teams assisted by the chatbot in the first half of the decision-making task made better decisions than those assisted by the chatbot in the second half. Analysis of team member perceptions and interaction processes suggests that having a chatbot at the beginning of team interaction may have generated a ripple effect in the team that promoted information sharing among team members. 

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4. On Team Decision Problems with Nonclassical Information Structures

   https://doi.org/10.1109/TAC.2022.3195126  

   Malikopoulos, Andreas A (January 2023, IEEE Transactions on Automatic Control)

   In this article, we consider sequential dynamic team decision problems with nonclassical information structures. First, we address the problem from the point of view of a “manager” who seeks to derive the optimal strategy of the team in a centralized process. We derive structural results that yield an information state for the team, which does not depend on the control strategy, and thus, it can lead to a dynamic programming decomposition where the optimization problem is over the space of the team’s decisions. We, then, derive structural results for each team member that yield an information state which does not depend on their control strategy, and thus, it can lead to a dynamic programming decomposition where the optimization problem for each team member is over the space of their decisions. Finally, we show that the solution of each team member is the same as the one derived by the manager. We present an illustrative example of a dynamic team with a delayed sharing information structure. 

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5. Privacy-Preserving and Diversity-Aware Trust-based Team Formation in Online Social Networks

   https://doi.org/10.1145/3670411  

   Mahajan, Yash; Cho, Jin-Hee; Chen, Ing-Ray (June 2024, ACM Transactions on Intelligent Systems and Technology)

   As online social networks (OSNs) become more prevalent, a new paradigm for problem-solving through crowd-sourcing has emerged. By leveraging the OSN platforms, users can post a problem to be solved and then form a team to collaborate and solve the problem. A common concern in OSNs is how to form effective collaborative teams, as various tasks are completed through online collaborative networks. A team’s diversity in expertise has received high attention to producing high team performance in developing team formation (TF) algorithms. However, the effect of team diversity on performance under different types of tasks has not been extensively studied. Another important issue is how to balance the need to preserve individuals’ privacy with the need to maximize performance through active collaboration, as these two goals may conflict with each other. This research has not been actively studied in the literature. In this work, we develop a team formation (TF) algorithm in the context of OSNs that can maximize team performance and preserve team members’ privacy under different types of tasks. Our proposedPRivAcy-Diversity-AwareTeamFormation framework, calledPRADA-TF, is based on trust relationships between users in OSNs where trust is measured based on a user’s expertise and privacy preference levels. The PRADA-TF algorithm considers the team members’ domain expertise, privacy preferences, and the team’s expertise diversity in the process of team formation. Our approach employs game-theoretic principlesMechanism Designto motivate self-interested individuals within a team formation context, positioning the mechanism designer as the pivotal team leader responsible for assembling the team. We use two real-world datasets (i.e., Netscience and IMDb) to generate different semi-synthetic datasets for constructing trust networks using a belief model (i.e., Subjective Logic) and identifying trustworthy users as candidate team members. We evaluate the effectiveness of our proposedPRADA-TFscheme in four variants against three baseline methods in the literature. Our analysis focuses on three performance metrics for studying OSNs: social welfare, privacy loss, and team diversity. 

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