869,055 research outputs found

    The effects of team-skills training on transactive memory and performance

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    The existence of effective Transactive Memory Systems (TMS) in teams has been found to enhance task performance. Methods of developing Transactive Memory (TM) are therefore an important focus of research. This study aimed to explore one such method, the use of a generic team-skills training programme to develop TM and subsequent task performance. Sixteen three-member teams were all trained to complete a complex collaborative task, prior to which half the teams (n=8), completed a team-skills training programme. Results confirmed that those teams who had been trained to develop a range of team skills such as problem-solving, interpersonal relationships, goal setting and role allocation, evidenced significantly higher team skill, TM and performance than those who were not trained in such skills. Results are discussed with reference to the wider TM literature and the mechanisms through which team-skills training could facilitate the more rapid development of TM

    When AI joins the Team: A Literature Review on Intragroup Processes and their Effect on Team Performance in Team-AI Collaboration

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    Although systems based on artificial intelligence (AI) can collaborate with humans on various complex tasks, little is known about how AI systems can successfully collaborate with human teams (team-AI collaboration). Team performance research states that team composition and intragroup processes are important predictors of team performance. However, it is not clear how intragroup processes differ in team-AI collaboration from human teams and if this is reflected in differences in team performance. To answer these questions, we synthesize evidence from 18 empirical articles. Results indicate that intragroup processes like communication and coordination are less effective in team-AI collaboration. Moreover, whether team cognition and trust are higher in team-AI collaboration compared to human teams is not clear, since studies find conflicting results. Likewise, the results on team performance differences between team-AI collaboration and human teams are inconsistent. With this article we offer a foundation for future research on team-AI collaboration

    Complexity-based learning and teaching: a case study in higher education

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    This paper presents a learning and teaching strategy based on complexity science and explores its impacts on a higher education game design course. The strategy aimed at generating conditions fostering individual and collective learning in educational complex adaptive systems, and led the design of the course through an iterative and adaptive process informed by evidence emerging from course dynamics. The data collected indicate that collaboration was initially challenging for students, but collective learning emerged as the course developed, positively affecting individual and team performance. Even though challenged, students felt highly motivated and enjoyed working on course activities. Their perception of progress and expertise were always high, and the academic performance was on average very good. The strategy fostered collaboration and allowed students and tutors to deal with complex situations requiring adaptation

    Culture and the performance of teams in complex systems

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    The practice of systems engineering is becoming more formalized. However, this formalization is aimed primarily at the technical and process components of complex systems. National cultural variations in the human components of such systems (typically functioning as groups or teams) are not typically included in the formal specifications and, as a result, the technical end-products do not fully compensate for these variations. This paper provides an introduction to culture, its measurement and its effects on team performance. The paper then describes a methodology and software tool for the assessment of the cultural traits of team members and the estimation of the effects of team culture on task or mission performance. The paper concludes that, despite some disparities in the results of research studies, sufficiently strong relationships between culture and team performance have been established to justify the representation of user culture in systems engineering toolsets

    The Role of Hypernetworks as a Multilevel Methodology for Modelling and Understanding Dynamics of Team Sports Performance.

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    Despite its importance in many academic fields, traditional scientific methodologies struggle to cope with analysis of interactions in many complex adaptive systems, including team sports. Inherent features of such systems (e.g. emergent behaviours) require a more holistic approach to measurement and analysis for understanding system properties. Complexity sciences encompass a holistic approach to research on collective adaptive systems, which integrates concepts and tools from other theories and methods (e.g. ecological dynamics and social network analysis) to explain functioning of such systems in their natural environments. Multilevel networks and hypernetworks comprise novel and potent methodological tools for assessing team dynamics at more sophisticated levels of analysis, increasing their potential to impact on competitive performance in team sports. Here, we discuss how concepts and tools derived from studies of multilevel networks and hypernetworks have the potential for revealing key properties of sports teams as complex, adaptive social systems. This type of analysis can provide valuable information on team performance, which can be used by coaches, sport scientists and performance analysts for enhancing practice and training. We examine the relevance of network sciences, as a sub-discipline of complexity sciences, for studying the dynamics of relational structures of sports teams during practice and competition. Specifically, we explore the benefits of implementing multilevel networks, in contrast to traditional network techniques, highlighting future research possibilities. We conclude by recommending methods for enhancing the applicability of hypernetworks in analysing team dynamics at multiple levels

    Identity asymmetries:An experimental investigation of social identity and information exchange in multiteam systems

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    Many complex organizational tasks are performed by networks of teams, or “multiteam systems.” A critical challenge in multiteam systems is how to promote information exchange across teams. In three studies, we investigate how identity “asymmetries”—differences between teams in terms of whether the team or overarching system constitutes their primary focus of identification—affect interteam information sharing and performance. In Study 1, we manipulate teams’ foci of identification (team vs. system focused) in a sample of 84 five-member teams working in one of 21 four-team multiteam systems performing a computer strategy simulation. We find that, while system-focused teams shared information equally with all teams, team-focused teams shared less information with system-focused teams than they did with other team-focused teams. Interteam information sharing positively predicted interteam performance. In Study 2, we test the assumptions underlying our theory in a vignette experiment, demonstrating that team-focused individuals adopt instrumental motives toward interteam interaction. Finally, in Study 3, we investigate the implications of system composition in terms of team identity foci by means of a simulation study based on the empirical results of Study 1. The results of the simulation yield novel propositions about the nonlinear effects of social identity in multiteam systems

    How Agile Practices Influence the Performance of Software Development Teams: The Role of Shared Mental Models and Backup

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    This study draws on team adaptation theory to examine how agile behavior within Information Systems development (ISD) teams influences team performance. We conceptualize agile behavior as the degree to which ISD teams use agile practices and test a theoretical model that links agile practice use to two key components of team adaptation—shared mental models and backup behavior. Moreover, in line with team adaption theory, shared mental models among team members are hypothesized to increase backup behavior, which in turn is suggested to lead to higher levels of ISD team performance in complex environments. To test our hypotheses, we collected data from Scrum masters, project leaders and more than 490 professional software engineers of a global enterprise software development company. Our findings broadly confirm our theoretical model linking agility, adaptation, and ISD team performance, leading to several theoretical and practical contributions

    Analysing the behaviour of robot teams through relational sequential pattern mining

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    This report outlines the use of a relational representation in a Multi-Agent domain to model the behaviour of the whole system. A desired property in this systems is the ability of the team members to work together to achieve a common goal in a cooperative manner. The aim is to define a systematic method to verify the effective collaboration among the members of a team and comparing the different multi-agent behaviours. Using external observations of a Multi-Agent System to analyse, model, recognize agent behaviour could be very useful to direct team actions. In particular, this report focuses on the challenge of autonomous unsupervised sequential learning of the team's behaviour from observations. Our approach allows to learn a symbolic sequence (a relational representation) to translate raw multi-agent, multi-variate observations of a dynamic, complex environment, into a set of sequential behaviours that are characteristic of the team in question, represented by a set of sequences expressed in first-order logic atoms. We propose to use a relational learning algorithm to mine meaningful frequent patterns among the relational sequences to characterise team behaviours. We compared the performance of two teams in the RoboCup four-legged league environment, that have a very different approach to the game. One uses a Case Based Reasoning approach, the other uses a pure reactive behaviour.Comment: 25 page

    A Complexity-Based Approach to Intra-Organizational Team Selection

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    Early studies recognized the significance of team's work capacity and suggested the selection of team members based on individual skills and performance in alignment with task characteristics. The equitable team selection method, for example, assigns people to different tasks with even skill distributions for the best overall performance. Recent advancement in organization science also identifies the importance of contextual skills. However, work teams are complex adaptive systems with interdependence between workers and social environment, and exhibit surprising, nonlinear behavior. Optimizing individual stages without taking organizational complexity into account is unlikely to yield a high performing new combination of teams. The objectives of this study can be stated as: a) Utilizing complex system theory to better understand the processes of team selection including forming teams with considering worker's interdependence and replacing the unsuitable members through a time frame; b) Comparing different team selection methods, including random selection, equity method, using knowledge of interdependence in different economic conditions through simulation; c) Comparing different policies of replacing members of teams. This study utilizes a computational model to understand the complexity of project team selection and to examine how diversity of capability and interdependence between workers to effect team performance in different economic conditions. The NK model, a widely used theory for complex systems is utilized here to illustrate the worker's interdependence and fed into an Agent-Based Model. This study uses a small design firm as a case implementation to examine the performance of a variety of team selection approaches and replacement policies. Project data, task assignment, and individual and team performance information were collected for the period of 2009-2011. The simulation results show that while the equity selection method can increase the diversity of capabilities of teams, the net performance is often worse than optimizing worker interdependencies. This study suggests that managers should protect their higher-performing workers with minimal interdependence disruption when they considered team selection. Thus taking the advantages and disadvantages of all three policies into account, transferring low contributors or least supported members are recommended to be enacted before hiring new workers to avoid this last policy's especially large additional costs

    An integrated framework to assess financial reward systems in construction projects

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    Motivation is a major driver of project performance. Despite team member ability to deliver successful project outcomes if they are not positively motivated to pursue joint project goals, then performance will be constrained. One approach to improving the motivation of project organizations is by offering a financial reward for the achievement of set performance standards above a minimum required level. However, little investigation has been undertaken into the features of successful incentive systems as a part of an overall delivery strategy. With input from organizational management literature, and drawing on the literature covering psychological and economic theories of motivation, this paper presents an integrated framework that can be used by project organizations to assess the impact of financial reward systems on motivation in construction projects. The integrated framework offers four motivation indicators which reflect key theoretical concepts across both psychological and economic disciplines. The indicators are: (1) Goal Commitment, (2) Distributive Justice, (3) Procedural Justice, and (4) Reciprocity. The paper also interprets the integrated framework against the results of a successful Australian social infrastructure project case study and identifies key learning’s for project organizations to consider when designing financial reward systems. Case study results suggest that motivation directed towards the achievement of incentive goals is influenced not only by the value placed on the financial reward for commercial benefit, but also driven by the strength of the project initiatives that encourage just and fair dealings, supporting the establishment of trust and positive reciprocal behavior across a project team. The strength of the project relationships was found to be influenced by how attractive the achievement of the goal is to the incentive recipient and how likely they were to push for the achievement of the goal. Interestingly, findings also suggested that contractor motivation is also influenced by the fairness of the performance measurement process and their perception of the trustworthiness and transparency of their client. These findings provide the basis for future research on the impact of financial reward systems on motivation in construction projects. It is anticipated that such research will shed new light on this complex topic and further define how reward systems should be designed to promote project team motivation. Due to the unique nature of construction projects with high levels of task complexity and interdependence, results are expected to vary in comparison to previous studies based on individuals or single-entity organizations
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