Sensing spatial and temporal coordination in teams using the smartphone

Author:

Feese Sebastian,Burscher Michael Joseph,Jonas Klaus,Tröster Gerhard

Abstract

AbstractTeams are at the heart of today’s organizations and their performance is crucial for organizational success. It is therefore important to understand and monitor team processes. Traditional approaches employ questionnaires, which have low temporal resolution or manual behavior observation, which is labor intensive and thus costly. In this work, we propose to apply mobile behavior sensing to capture team coordination processes in an automatic manner, thereby enabling cost-effective and real-time monitoring of teams. In particular, we use the built-in sensors of smartphones to sense interpersonal body movement alignment and to detect moving sub-groups. We aggregate the data on team level in form of networks that capture a) how long team members are together in a sub-group and b) how synchronized team members move. Density and centralization metrics extract team coordination indicators from the team networks. We demonstrate the validity of our approach in firefighting teams performing a realistic training scenario and investigate the link between the coordination indicators and team performance as well as experienced team coordination. Our method enables researchers and practitioners alike to capture temporal and spatial team coordination automatically and objectively in real-time.

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Reference37 articles.

1. Salas E, Cooke NJ, Rosen MA: On teams, teamwork, and team performance: Discoveries and developments. Hum Factors 2008, 50(3):540–547. 10.1518/001872008X288457

2. Cohen SG, Bailey DE: What makes team work: group effectiveness from the shop floor to the executive suite. J Manag 1997, 23(3):239–290.

3. Moreland RL, Fetterman JD, Flagg JJ, Swanenburg K: Behavioral assessment practices among social psychologists who study small groups. In Then A Miracle Occurs: Focusing on Behavior in Social Psychological Theory and Research. Oxford University Press, New York; 2010:28–53.

4. Rosen MA, Bedwell WL, Wildman JL, Fritzsche BA, Salas E, Burke CS: Managing adaptive performance in teams: guiding principles and behavioral markers for measurement. Hum Res Manag Rev 2011, 21(2):107–122.

5. Brannick MT, Prince C: An overview of team performance measurement. In Team Performance Assessment and Measurement: Theory, Methods, and Applications. Lawrence Erlbaum Associates, London; 1997:3–16.

Cited by 18 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Clustering spatio-temporal bi-partite graphs for finding crowdsourcing communities in IoMT networks;Big Earth Data;2021-01-02

2. Toward Wearable Devices for Multiteam Systems Learning;Perspectives on Wearable Enhanced Learning (WELL);2019

3. Introduction;Human–Computer Interaction Series;2018

4. Predicting the next turn at road junction from big traffic data;The Journal of Supercomputing;2017-03-21

5. Social identification-issuing system (SIIS) using micro-movement in social lifelogging;The Journal of Supercomputing;2017-03-11

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3