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Assessing the dependency of teamwork dynamics to cultural differences essay

This article has been cited by other articles in PMC. Abstract Complex problems often require coordinated group effort and can consume significant resources, yet our understanding of how teams form and succeed has been limited by a lack of large-scale, quantitative data.

  • While larger teams tend to be more successful, workload is highly focused across the team, with only a few members performing most work;
  • Instead this is a measure of popularity as would be other statistics such as web traffic, number of downloads and so forth [ 47 ];
  • After, clearly communicate expectations, roles and responsibilities — a good way of doing this is by asking team members what they feel they can contribute to the project and having them picking their own responsibilities;
  • Make sure everyone is heard;
  • The comparison of this predictive IPO model organizational culture I , interprofessional teamwork P , job satisfaction O and the predictive IO model organizational culture I , job satisfaction O showed that the effect of organizational culture is completely mediated by interprofessional teamwork;
  • The comparison of this predictive IPO model organizational culture I , interprofessional teamwork P , job satisfaction O and the predictive IO model organizational culture I , job satisfaction O showed that the effect of organizational culture is completely mediated by interprofessional teamwork.

We analyse activity traces and success levels for approximately 150 000 self-organized, online team projects. While larger teams tend to be more successful, workload is highly focused across the team, with only a few members performing most work.

The relations between team success and size, focus and especially team experience cannot be explained by confounding factors such as team age, external contributions from non-team members, nor by group mechanisms such as social loafing.

Taken together, these features point to organizational principles that may maximize the success of collaborative endeavours. Introduction Massive datasets describing the activity patterns of large human populations now provide researchers with rich opportunities to quantitatively study human dynamics [ 12 ], including the activities of groups or teams [ 34 ].

New tools, including electronic sensor systems, can quantify team activity and performance [ 54 ]. With the rise in prominence of network science [ 67 ], much effort has gone into discovering meaningful groups within social networks [ 8 — 15 ] and quantifying their evolution [ 1516 ]. Teams are increasingly important in research and industrial efforts [ 3417 — 21 ], and small, coordinated groups are a significant component of modern human conflict [ 2223 ]. There are many important dimensions along which teams should be studied, including their size, how work is distributed among their members, and the differences and similarities in the experiences and backgrounds of those team members.

Scholars of science have noted for decades that collaborative research teams have been growing in size and importance [ 2028 — 30 ]. At the same time, however, assessing the dependency of teamwork dynamics to cultural differences essay loafing, where individuals apply less effort to a task when they are in a group than when they are alone, may counterbalance the effectiveness of larger teams [ 31 — 33 ].

Meanwhile, case studies show that leadership [ 334 — 36 ] and experience [ 3738 ] are key components of successful team outcomes, while specialization and multitasking are important but potentially error-prone mechanisms for dealing with complexity and cognitive overload [ 3940 ].

In all of these areas, large-scale, quantitative data can push the study of teams forward. Teams are important for modern software engineering tasks, and researchers have long studied the digital traces of open source software projects to better quantify and understand how teams work on software projects [ 4142 ]. Researchers have investigated estimators of work activity or effort based on edit volume, such as different ways to count the number of changes made to a software's source code [ 43 — 46 ].

Relationship of organizational culture, teamwork and job satisfaction in interprofessional teams

Various dimensions of success of software projects such as popularity, timeliness of bug fixes or other quality measures have been studied [ 47 — 49 ]. Successful open source software projects show a layered structure of primary or core contributors surrounded by lesser, secondary contributors [ 50 ]. At the same time, much work is focused on case studies [ 4551 ] of small numbers of highly successful, large projects [ 41 ]. Considering these studies alone runs the risk of survivorship bias or other selection biases, so large-scale studies of large quantities of teams are important complements to these works.

Users of the GitHub web platform can form teams to work on real-world projects, primarily software development but also music, literature, design work and more. A number of important scientific computing resources are now developed through GitHub, including astronomical software, genetic sequencing tools and key components of the Compact Muon Solenoid experiment's data pipeline.

1. Introduction

GitHub provides rich public data on team activities, including when new teams form, when members join existing teams and when a team's project is updated. GitHub also provides social media tools for the discovery of interesting projects. Of course, as with bibliometric impact, one should be cautious and not consider success to be a perfectly accurate measure of quality, something that is far more difficult to objectively quantify.

Instead this is a measure of popularity as would be other statistics such as web traffic, number of downloads and so forth [ 47 ]. In this study, we analyse the memberships and activities of approximately 150 000 teams, as they perform real-world tasks, to uncover the blend of features that relate to success. To the best of our knowledge this is the largest study of real-world team success to date.

Storming stage

We present results that demonstrate i how teams distribute or focus work activity across their members, ii the mixture of experiential diversity and collective leadership roles in teams, and iii how successful teams are different from other teams while accounting for confounds such as team size.

The rest of this paper is organized as follows: Material and methods 2. Dataset and team selection Public GitHub data covering 1 January 2013 to 1 April 2014 was collected from githubarchive. These activity traces contain approximately 110M unique events, assessing the dependency of teamwork dynamics to cultural differences essay when users create, join, or update projects.

For this work, we define a team as the set of users who can directly update push to a repository. These users constitute the primary team members as they have either created the project or been granted autonomy to work on the project. The number of team members was denoted by M.

Activity or workload W was estimated from the number of pushes. A push is a bundle of code updates known as commitshowever most pushes contain only a single commit electronic supplementary material; see also [ 46 ]. As with all studies measuring worker effort from lines-of-code metrics, this is an imperfect measure as the complexity of a unit of work does not generally map to the quantity of edits.

Users on GitHub can bookmark projects they find interesting. We take the maximum number of stargazers for a team as its measure of success S. This is a popularity measure of success; however, the choice to bookmark a project does imply it offers some value to the user.

We also collect the time of creation on GitHub for each team project. This is useful for measuring confounds: Of the teams studied, 67. Beyond considering team age as a potential confounder, we do not study temporal dynamics such as team formation in this work.

A small number of studied teams 1. Lastly, to ensure our results are not due to outliers, in some analyses we excluded teams above the 99th percentile of S.

Despite a strong skew in the distribution of S, these highly popular teams account for only 2. Secondary team GitHub provides a mechanism for external, non-team contributors to propose work that team members can then choose to use or not.

Forming, Storming, Norming and Performing: The Stages of Team Formation

These proposals are called pull requests. Other mechanisms, such as discussions about issues, are also available to non-team contributors. These secondary or external team contributors are not the focus of this work and have already been well studied by OSS researchers [ 41 ]. However, it is important to ensure that they do not act as confounding factors for our results, as more successful teams will tend to have more secondary contributions than other teams.

So we measure for each team Mext, the number of unique users who submit at least one pull request, and Wext, the number of pull requests. We will include these measures in our combined regression models. Despite their visibility in GitHub, pull requests are rare [ 53 ]; in our data, 57.

Effective team size The number of team members, M, does not fully represent the size of a team as the distribution of work may be highly skewed across team members.