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This paper studies competitions with rank-based reward among a large number of teams. Within each sizable team, we consider a mean-field contribution game in which each team member contributes to the jump intensity of a common Poisson project process; across all teams, a mean field competition game is formulated on the rank of the completion time, namely the jump time of Poisson project process, and the reward to each team is paid based on its ranking. On the layer of teamwise competition game, three optimization problems are introduced when the team size is determined by: (i) the team manager; (ii) the central planner; (iii) the team members’ voting as partnership. We propose a relative performance criteria for each team member to share the team’s reward and formulate some special cases of mean field games of mean field games, which are new to the literature. In all problems with homogeneous parameters, the equilibrium control of each worker and the equilibrium or optimal team size can be computed in an explicit manner, allowing us to analytically examine the impacts of some model parameters and discuss their economic implications. Two numerical examples are also presented to illustrate the parameter dependence and comparison between different team size decision making.

Agile software development includes a group of software development methodologies based on iterative development, where requirements and solutions evolve through collaboration between cross-functional self-organizing teams. Different software houses were visited in a developing country to determine the experiences faced by people working on a real world projects using Agile software development methodology following different variants in different team sizes to determine the preference of using Agile software development methodology in larger team sizes. Several people were surveyed out of which few responded with an opinion of not to use agile development in a team sizes exceeding 25 members. According to the experience of people the ideal team size was 5 to maximum 10. Because according to the survey increase in the number of individuals create issues of communication as it is not possible to keep everyone on the same track with larger teams especially in case of scrum meetings which usually held on daily basis, taking responsibilities as everyone becomes reluctant in taking responsibilities believing someone else will take it, sub teams because the more the number of individuals the more will be the sub teams which indirectly increases the dependency among the teams by breaking the tasks into much smaller chunks. The findings also suggest that customer feedback would increase if the team size is less than 25 which in turn says that the Quality of Software is increased. As this study had only focused on the software companies of a developing country it is recommended that further studies should be carried out by surveying the people of other different developed countries.

With teams growing in all areas of scientific and scholarly research, we explore the relationship between team structure and the character of knowledge they produce. Drawing on 89,575 self-reports of team member research activity underlying scientific publications, we show how individual activities cohere into broad roles of 1) leadership through the direction and presentation of research and 2) support through data collection, analysis, and discussion. The hidden hierarchy of a scientific team is characterized by its lead (or L) ratio of members playing leadership roles to total team size. The L ratio is validated through correlation with imputed contributions to the specific paper and to science as a whole, which we use to effectively extrapolate the L ratio for 16,397,750 papers where roles are not explicit. We find that, relative to flat, egalitarian teams, tall, hierarchical teams produce less novelty and more often develop existing ideas, increase productivity for those on top and decrease it for those beneath, and increase short-term citations but decrease long-term influence. These effects hold within person—the same person on the same-sized team produces science much more likely to disruptively innovate if they work on a flat, high-L-ratio team. These results suggest the critical role flat teams play for sustainable scientific advance and the training and advancement of scientists.

One of the most universal trends in science and technology today is the growth of large teams in all areas, as solitary researchers and small teams diminish in prevalence 1 – 3 . Increases in team size have been attributed to the specialization of scientific activities 3 , improvements in communication technology 4 , 5 , or the complexity of modern problems that require interdisciplinary solutions 6 – 8 . This shift in team size raises the question of whether and how the character of the science and technology produced by large teams differs from that of small teams. Here we analyse more than 65 million papers, patents and software products that span the period 1954–2014, and demonstrate that across this period smaller teams have tended to disrupt science and technology with new ideas and opportunities, whereas larger teams have tended to develop existing ones. Work from larger teams builds on more-recent and popular developments, and attention to their work comes immediately. By contrast, contributions by smaller teams search more deeply into the past, are viewed as disruptive to science and technology and succeed further into the future—if at all. Observed differences between small and large teams are magnified for higher-impact work, with small teams known for disruptive work and large teams for developing work. Differences in topic and research design account for a small part of the relationship between team size and disruption; most of the effect occurs at the level of the individual, as people move between smaller and larger teams. These results demonstrate that both small and large teams are essential to a flourishing ecology of science and technology, and suggest that, to achieve this, science policies should aim to support a diversity of team sizes. Analyses of the output produced by large versus small teams of researchers and innovators demonstrate that their work differs systematically in the extent to which it disrupts or develops existing science and technology.

Science is undergoing rapid change with the movement to improve science focused largely on reproducibility/replicability and open science practices. This moment of change—in which science turns inward to examine its methods and practices—provides an opportunity to address its historic lack of diversity and noninclusive culture. Through network modeling and semantic analysis, we provide an initial exploration of the structure, cultural frames, and women’s participation in the open science and reproducibility literatures (n = 2,926 articles and conference proceedings). Network analyses suggest that the open science and reproducibility literatures are emerging relatively independently of each other, sharing few common papers or authors. We next examine whether the literatures differentially incorporate collaborative, prosocial ideals that are known to engage members of underrepresented groups more than independent, winner-takes-all approaches. We find that open science has a more connected, collaborative structure than does reproducibility. Semantic analyses of paper abstracts reveal that these literatures have adopted different cultural frames: open science includes more explicitly communal and prosocial language than does reproducibility. Finally, consistent with literature suggesting the diversity benefits of communal and prosocial purposes, we find that women publish more frequently in high-status author positions (first or last) within open science (vs. reproducibility). Furthermore, this finding is further patterned by team size and time. Women are more represented in larger teams within reproducibility, and women’s participation is increasing in open science over time and decreasing in reproducibility. We conclude with actionable suggestions for cultivating a more prosocial and diverse culture of science.

The relationship between team size and productivity is a question of broad relevance across economics, psychology, and management science. For complex tasks, however, where both the potential benefits and costs of coordinated work increase with the number of workers, neither theoretical arguments nor empirical evidence consistently favor larger vs. smaller teams. Experimental findings, meanwhile, have relied on small groups and highly stylized tasks, hence are hard to generalize to realistic settings. Here we narrow the gap between real-world task complexity and experimental control, reporting results from an online experiment in which 47 teams of size ranging from n = 1 to 32 collaborated on a realistic crisis mapping task. We find that individuals in teams exerted lower overall effort than independent workers, in part by allocating their effort to less demanding (and less productive) sub-tasks; however, we also find that individuals in teams collaborated more with increasing team size. Directly comparing these competing effects, we find that the largest teams outperformed an equivalent number of independent workers, suggesting that gains to collaboration dominated losses to effort. Importantly, these teams also performed comparably to a field deployment of crisis mappers, suggesting that experiments of the type described here can help solve practical problems as well as advancing the science of collective intelligence.

The rise in team size in academic science has generated an unintended side effect: junior scientists are less likely to secure research funding or obtain tenure and are more likely to leave academia.

Purpose The purpose of this study is two-fold. First, the nature of the relationship between team trust and team performance in the business context is determined. Second, both team design (team size and team type) and methodological moderators (source of criterion measure and study date) of the relationship are assessed. Design/methodology/approach A random-effects meta-analysis was performed on published and unpublished empirical studies. Subgroup moderator analyses were conducted using Cochran’s Q. Continuous moderator analyses were conducted using meta-regression. Findings Data from 55 independent studies (3,671 teams) were pooled. Results indicated a large, positive relationship between team trust and team performance in real business teams. Further analyses indicated that the relationship was significantly moderated by business team type, team size and source of criterion measure. Research limitations/implications Results indicate that different team types, sizes and performance criteria should not be treated as equivalent. Results are based on cross-sectional research and can only be generalized to business teams. Practical implications Managers should be attentive to trust issues in work teams, as they may portend future performance problems or mirror other organizational issues that affect team performance. Team function and size predict how team trust is related to team performance. Originality/value The present study answers a call by Costa et al. (2018) for additional investigation of moderators of the trust-performance relationship in teams using a quantitative review of studies.

Team reflexivity (TR)–defined as a team's conscious reflection on their objectives, strategies, and processes—is an important team process that fosters adaptation and information processing. However, traditional conceptualizations frame TR as a process that occurs in periods of downtime to reflect on past, terminated performance, largely ignoring reflective team processes occurring during intense performance events of action teams. To address this gap, we conceptualize TR as a team process that occurs not only during periods of downtime after the action but also during performance events as brief TR moments. We elaborate on the concept of in-action TR and explore it by delineating its relationship to task type and timing during a performance event. Further, we test a team level contingency model of in-action TR, namely, team size and performance. Using behavior observation, we test our hypothesis with 70 medical teams responding to simulated in-hospital emergencies. Task type is related to in-action TR and reflection tends to increase as action progresses. Further, in-action TR is related to team performance and is especially important for larger teams. Our study is the first to investigate in-action TR and provides theoretical and practical implications on how in-action TR operates in extreme action teams.

Since the advent of start-ups and old companies starting to shift to e-business, it has become troublesome to choose the right methods for projects that’s where extreme programming came to use. In this paper, we tried to compile different applications of the same. Extreme programming is a well-known agile method for software development. Extreme programming ensures customer satisfaction, better software quality and efficient project management. The team size is usually small but the group is team-oriented. It is a dynamic software development model, i.e., continuous discussion and integration of new features and ideas is the cornerstone of this model.