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The purpose of this paper is to investigate the potential influence of virtual team size on team performance by examining group processes in the context of information technology (IT) service provisioning. This paper proposes a theoretical model of the relationships and presents an empirical study to verify the model.

Utilizing a survey questionnaire as the data collection instrument, this study focuses on IT service provisioning professionals who are actively engaged in virtual work contexts to test the relationships proposed by the theoretical model. A consistent version of the partial least square structural equation modeling (PLS-SEM) approach is used to assess the proposed hypotheses.

Although the statistical analyses did not provide support for the hypothesized effects of team size on virtual team performance, the results provide novel insights that may help teams overcome the functioning challenges that they face, as reported in the previous literature on virtual team size. In addition, the results highlight the importance of specific group processes for obtaining superior team performance.

Currently, virtual teams are a reality in several organizations, especially in the IT service provisioning industry. However, despite its importance, the literature suggests that virtual team size has not yet been fully explored as a possible means of enhancing group collaboration in such contexts. This paper attempts to provide an empirical contribution to this field using the latest developments in PLS-SEM.

Scientific collaboration is becoming a common pattern in the social organization of knowledge production. The paper tries to figure out the relationship between scientific collaboration team size and scientific output.

Based on ESI database from year 2009–2019, the paper describes changes of collaboration team size from one author to more than 10 authors in 22 disciplines. Kernel density estimation and multidimensional kernel density estimation method are used to calculate optimal collaboration team size and appropriate collaboration team size in 22 disciplines. As bandwidth is one of the major issues in construction of kernel density estimation, the paper uses five different algorithms to calculate bandwidth. The method with the lowest mean absolute percentage error is chosen. Robustness test is conducted based on different sets of data.

The results show that scientific collaboration becomes more widely and deeply. As time goes by, collaboration team size is becoming larger and larger. Natural science disciplines have larger collaboration team size and faster growth rate than social science disciplines. Considering both qualitative and quantitative measures, the paper proves the universality of optimal and appropriate scientific collaboration team size among 22 disciplines and calculates the specific number.

The paper tries to investigate the law of scientific collaboration team size variation and provide a full picture of evolution of collaboration team size among 22 disciplines in 10 years. The paper first applies distribution method to figure out the relationship between scientific collaboration team size and scientific output and provides optimal collaboration team size and appropriate collaboration team size.

The research studies the role of contextual moderating variables on the relationship between person-oriented leadership behaviors (POLBs) and team performance. The authors claim that the varying effect sizes between POLBs and team performance are large because of the context the team is functioning in. Therefore, based on the framework of Johns (2006), this paper aims to investigate the moderating role of the relevant demographic (leader gender), social (in-group collectivism and team size), task (skill differentiation) and methodological (common method bias and the rater of the team performance) contextual variables in the study.

The authors accumulated evidence from 48 independent primary studies (N team = 4,276) to run the meta-analytic analyses. The authors followed the procedures described by Schmidt and Hunter (2015). For the categorical moderators, the analyzes were aided by the Hunter–Schmidt meta-analysis programs (2.0) (Schmidt and Le, 2014), which is an interactive software using a random-effects model. In the analyzes for the continuous moderators, the authors used Lipsey and Wilson’s (2001) statistical package for the social sciences macros and run meta-regressions using a random-effects model with unrestricted maximum likelihood.

The results indicate that the relationship weakens when female leaders exhibit these behaviors and when the team size increases. On the other hand, in-group collectivism strengthens the relationship. The study also found that the common method bias and the assessment method of the team performance are significant moderators altering the relationship.

The study highlights the perceptual differences and biases based on leader gender. Acknowledging these biases may help practitioners to appreciate the female qualities in leadership and decrease the undervaluation of female effectiveness. To create high-performing teams, leaders in high in-group collectivist countries are expected to develop a family feeling in the team by showing their concern for personal issues and build close interpersonal relationships. Researchers should use multiple sources to assess the predictor and criterion variables and also opt for more objective assessment methods for team performance.

With this study, the authors follow a substantively different perspective compared to the past meta-analytic reviews on this relationship. Rather than testing the inquiry whether there is a relationship between the two variables, the authors specifically focus on the role of contextual moderating variables. Several researchers have acknowledged that contextual considerations are critical in leadership-team performance research. Nevertheless, the body of research remains to be not cohesive. Thus, the study answers a call in the leadership area for a more context-based and cohesive understanding of the effects of leadership on team performance.

Team social capital (TSC) has been attracting increasing research attention aiming to explore team effectiveness through within- and cross-team resource conduits. This study bridges two disconnected theories – TSC and evolutionary theory – to examine gaming clans and analyzes mechanisms of the clans' TSC building from an evolutionary perspective.

This research draws longitudinal data from a sample of gaming teams (N = 1,267) from anonymized player data from the game World of Tanks spanning 32 months. The authors explored teams' evolutionary patterns using hidden Markov models and applied longitudinal multilevel modeling to test hypotheses.

The results showed that teams of different sizes and levels of evolutionary fitness vary in team closure and bridging social capital. The authors also found that larger teams are more effective than smaller ones. The positive association between team-bridging social capital and effectiveness is more substantial for smaller teams.

This research advances the theoretical development of TSC by including the constructs of teams' evolutionary status when analyzing strategic social capital building. Adding to existing literature studying the outcome of TSC, this research also found a moderating effect of team size between TSC and effectiveness. Finally, this study also contributes to a longitudinal view of TSC and found significant evolutionary patterns of teams' membership, TSC, and effectiveness.

The purpose of this paper is to describe, illustrate and provide a deeper understanding of team composition and labor allocation in audit teams by quantifying the exact value of resources at different levels of the audit production. Audit teams have been considered as a black box in audit research. Therefore, this paper reports descriptive statistics on (levels and proportions of) hours and costs allocated to auditor ranks (and the number and value, i.e. billing rates, of auditors for different ranks and the entire team) to shed new light on audit teams.

This study uses a proprietary data set containing disaggregated information on hours, costs and billing rates for each team member in each of 908 audit engagements. The data are provided by a Swedish Big 4 audit firm. The study uses a purely descriptive approach and categorizes auditors into seven ranks. As size and the publicly listed status are crucial determinants of audit production, the paper splits engagements in public and private companies and reports statistics for size quartiles of both public and private clients.

The paper provides descriptive statistics for (1) client size, (2) audit team members, (3) audit hours, (4) audit costs, (5) proportion of audit hours, (6) proportion of audit costs, (7) billing rates and (8) variation of billing rates. Results show that compared to private clients, the audit firm allocates higher effort from auditors in higher ranks and lower effort from auditors in lower ranks to public clients. Another finding is that allocation varies with client size for private clients, but less so for public clients.

In an area with sparse literature, this descriptive study serves as a first step to improve our understanding and guide future research. It provides concrete support for previously known theory.

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.

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.

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.

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.

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.

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.

The research on people and project factors is found extensively in general but not specific to software engineering. Secondly, the existing research has not concentrated on the communication and time complexity of the teams on software economics. The purpose this paper is to develop a model to investigate and quantify the impact of time pressure (TP) on software economics through the communication influence of software team sizes (TS).

A research model and five hypotheses are developed based on the gaps in the literature. The data set from International Software Benchmarking Standards Group repository is used for testing the hypotheses.

Important findings include: smaller TS tends to exert less TP on average; TP is directly proportional to software economics, however; and TP does not affect the productivity required for the software.

The study has the following implications: Selection of an appropriate TS for project completion that ensures minimum pressure on team members; and maximize software outcomes in stress-free environment.

This work is useful for organizations carrying out software projects with teamwork. The project managers can benefit from the results while planning the team factors for achieving the project goals.

The results uphold not to exert pressure on the team as it will not only affect the duly completion of the project but also the well-being of employees.

The paper is the first one where the proposition of TP estimation is done using TS and communication complexity, and empirically evaluating the impact of TP on four major software economics are the major key contributions of this research work.

Often, it is assumed that teams are better at solving problems than individuals working independently. However, recent work in engineering, design and psychology contradicts this assumption. This study aims to examine the behavior of teams engaged in data science competitions. Crowdsourced competitions have seen increased use for software development and data science, and platforms often encourage teamwork between participants.

We specifically examine the teams participating in data science competitions hosted by Kaggle. We analyze the data provided by Kaggle to compare the effect of team size and interaction frequency on team performance. We also contextualize these results through a semantic analysis.

This work demonstrates that groups of individuals working independently may outperform interacting teams on average, but that small, interacting teams are more likely to win competitions. The semantic analysis revealed differences in forum participation, verb usage and pronoun usage when comparing top- and bottom-performing teams.

These results reveal a perplexing tension that must be explored further: true teams may experience better performance with higher cohesion, but nominal teams may perform even better on average with essentially no cohesion. Limitations of this research include not factoring in team member experience level and reliance on extant data.

These results are potentially of use to designers of crowdsourced data science competitions as well as managers and contributors to distributed software development projects.

The purpose of this paper is to examine the relationships between managers’ and supervisors’ trust in subordinates and team cooperation and to suggest that the downward flow of trust affects team employees.

Data were collected from supervisor-employee dyads from a multisource field study.

Feeling trusted by managers has an indirect effect on team cooperation through feeling trusted by supervisors. In addition, there was a strong positive relation between feeling trusted by supervisors and team cooperation when team size was smaller, but a weak positive relation when team size was larger.

In order for subordinates to feel trusted, management leaders must implement actions that include: delegation and empowerment, participative decision-making and listening with respect and full attention. It is also suggested that the team size should not be too large.

We integrate theories of social exchange, social information processing, social learning and attraction-selection-attrition to test a trickle-down model of how trust in subordinates cascades down through management levels and ultimately affects team cooperation.

This paper aims to explore the design of management teams when the critical task facing individual managers is monitoring the performance of worker teams and producing performance measures under uncertain information environments.

The authors use a multi-agent LEN framework – linear contract, exponential utility and normal density – to model the incentive provision and organizational design.

The main lesson is that the use of performance measures under uncertainty is greatly affected by the potential for free-riding in the very monitoring activities which generate the measures to begin with. Accordingly, the value of having a management team, that is the incremental benefit of having a second manager, depends on the monitoring technology. Of particular importance are the potential free-riding in monitoring effort among multiple managers and synergies gained from having more than one manager, such as correlation among the performance measures produced or improvement due to splitting workers pool into separate groups for each manager to monitor separately.

The paper pushes this line of research further by explicitly modeling the endogenous process of signal generation within a rich economic environment. In this environment, number of workers being evaluated and number of managers who produce the signals are both endogenous. Furthermore, both workers and managers are subject to moral hazard problem. In particular, the managers suffer from potential free-riding problems but may benefit from synergistic forces due to team monitoring.