Advancing Teams Research: What, When, and How to Measure Team Dynamics Over Time

We report a within-teams experiment testing the effects of fit between team structure and regulatory task demands on task performance and satisfaction through average team member positive affect and helping behaviors. We used a completely crossed repeated-observations design in which 21 teams enacted 2 tasks with different regulatory focus characteristics (prevention and promotion) in 2 organizational structures (functional and divisional), resulting in 84 observations. Results suggested that salient regulatory demands inherent in the task interacted with structure to determine objective and subjective team-level outcomes, such that functional structures were best suited to (i.e., had best fit with) tasks with a prevention regulatory focus and divisional structures were best suited to tasks with a promotion regulatory focus. This contingency finding integrates regulatory focus and structural contingency theories, and extends them to the team level with implications for models of performance, satisfaction, and team dynamics.

Many new product innovations are attributed to new venture creation. Although there are many success cases, many new ventures quit before they have fulfilled their greatest potential mainly because the team quits their effort. This research tries to delve into several factors that affect the dynamics of an entrepreneurial team along the different stages of the new venture creation process. Given such direction, primary data from 12 entrepreneurial teams are undertaken within an incubator environment, which is an underresearched context. An in-depth study of these entrepreneurial teams suggests numerous determinants (variation in the intentions and expectations, improper leadership, ineffective communication, engagement, team structure) to have a serious effect on team dynamics and team cohesiveness. Following a grounded theory approach, specific research propositions are formulated and theoretical insights are drawn. Moreover, the results of this research assist nascent entrepreneurs in assessing their potential, recognizing critical team dynamics, and basing their choice on moving to a sustainable new venture creation.

The influence of software team dynamics on well-organized software development knowledge process could prevent software development organizations from suffering from the knowledge atrophy problem. To explore this, we have studied several team dynamics factors that influence the Knowledge Management Processes (KMP) in Very Small Entities (VSEs) [1]. A survey was conducted in a variety of VSEs and through statistical and qualitative content analysis for the research data, results indicate that small teams, informal team process and structure have an important influence on the level of team dynamics in the software development process.

Research suggests that humans and autonomous agents can be more effective when working together as a combined unit rather than as individual entities. However, most research has focused on autonomous agent design characteristics while ignoring the importance of social interactions and team dynamics. Two experiments examined how the perception of teamwork among human–human and human–autonomous agents and the application of team building interventions could enhance teamwork outcomes. Participants collaborated with either a human or an autonomous agent. In the first experiment, it was revealed that manipulating team structure by considering your human and autonomous partner as a teammate rather than a tool can increase affect and behavior, but does not benefit performance. In the second experiment, participants completed goal setting and role clarification (team building) with their teammate prior to task performance. Team building interventions led to significant improvements for all teamwork outcomes, including performance. Across both studies, participants communicated more substantially with human partners than they did with autonomous partners. Taken together, these findings suggest that social interactions between humans and autonomous teammates should be an important design consideration and that particular attention should be given to team building interventions to improve affect, behavior, and performance.

This scientific paper aims to explore the identification and quantification of the knowledge level within a team working on a software engineering project. The knowledge level of a team plays a crucial role in the success and efficiency of project outcomes. To achieve this objective, a comprehensive review of existing literature on knowledge management, team dynamics, and software engineering practices was conducted. This article reveals insights into the knowledge dynamics within software engineering teams. The research methodology consists of different quantitative and qualitative methods for data collection and analysis. The identified knowledge gaps and strengths can inform strategies for improving team performance, such as targeted training programs or knowledge sharing initiatives. Furthermore, the quantification of knowledge levels can serve as a benchmark for future projects, allowing for better resource allocation and team composition. Overall, this research is bringing benefits to the field of software engineering by providing a systematic approach to identify and quantify the knowledge level of teams, enabling organizations to optimize their team structures and enhance project outcomes.

Background : While early interprofessional teamwork is of uncertain benefit to patients, it has value for changing attitudes towards interprofessionalism and for developing skills and traits relevant for healthcare students. Exploring and evaluating changes in these skills and attitudes is under-researched, especially in early healthcare education. Reflective assessment, while frequently used to gauge clinical students’ professional skills and attitudes, is hampered by issues related to authenticity and reliability. A potentially more productive use of reflection is for developing skills and attitudes such as self-awareness, communication and respect in early interprofessional teamwork. The aim of the study was to collect and analyse first-year non-clinical students’ reflections in which they specifically evaluated their own and their team’s success and collaboration on an interprofessional teamwork project. Methods : Phenomenographic analysis of summative participant reflections was conducted to demonstrate variations in factors and elements that influenced interprofessional teamwork function and success. Findings: Four factors were perceived to influence team function: individual characteristics, team dynamics, team structure and external factors. From participant responses, four critical elements of successful teamwork emerged: time management, communication, cooperation and leadership. The phenomenographic outcome space shows how variations in the presence of and relationships between these elements produced differences in team function and success. Variation in individual self- awareness and reflective capacity was also identified. Conclusions: This study identified factors and elements that students believe affect collaboration, and how these influence team success. The findings confirm the value of formal reflection in helping students to develop self-awareness in teamwork settings.

The project described in this paper is part of a cohort of projects funded to revolutionize engineering and computer science departments (National Science Foundation RED initiative). The focus of our work is to take a systems-level perspective regarding how change can be facilitated within an organization. Our efforts aim to empower faculty to be agents of change and to cultivate a culture that recognizes and rewards risk-taking in the classroom. We approach our work by attending to the larger ecosystem of people and organizations within which change happens. Our evidence-based methods are intended to foster revolutionary advances within our engineering program. We are in the third year of our five-year project and have recognized that part of the revolution entails how our team adapts internally and externally through the different stages of the project. From an internal perspective, we have been intentional in reflecting on team process, team dynamics, and team structure so that we modify and adapt as necessary to maximize performance. From an external perspective, we are intentional to recognize and be responsive to changes that happen in the larger ecosystem, i.e., our institution and professional community, within which we are situated. We are nearing completion of foundational aspects of the project and are transitioning during the pivotal year three to new focus areas and different phases of work. This paper provides a summary of our progress to date regarding meeting the project objectives, in addition to recent adjustments made to support our next steps. These adjustments are driven by our reflection of what, precisely, is our revolution, and how our project team is adapting to accelerate progress on the pathway to reach the revolution.

The structure and dynamics of software development project team have the potential to influence and impact the knowledge sharing process or mechanism within the project team that may affect the timely and successful completion of the project milestones. In order to confirm and validate this hypothesis, we carried out a comprehensive study in which 271 employees of 47 software development companies of Pakistan have been interviewed to share their experiences and views on the impact of project team structure and dynamics on the Knowledge Management Process (KMP) of their respective organizations. The included organizations have come under the category of Very Small Entities (VSEs). The results of the study revealed that team size, commitment, time constraint, level of motivation, communication, trust, reward system, language, multiple roles of team members and complex hierarchical organizational structure possess an enormous potential in disturbing the KMP within the software development team. Based on these factors, a knowledge sharing framework within software development project teams has been proposed.

PurposeThe objective of this research was to compare the behavior of two methods of managing an engineering change request (ECR) process, namely, perform changes as they occur or in a batch.Design/methodology/approachThis comparison was accomplished by creating a computer model of a new product development (NPD) process and simulating ECR management. The model connects process design and process characteristics (teamwork, parallel activities) to process outcomes (development time, effort). The first method executes the ECR promptly and the rework is done as soon as the ECR is initiated. In the second method, ECRs are batched; in other words, a number of them are accumulated, and processing of the ECRs takes place when a batch of a certain size has accumulated. Thus, the change requests are grouped into a batch, and then, the section(s) of the process to effect the change(s) is (are) reworked.FindingsBatching ECRs was found to be superior to doing them one at a time.Research limitations/implicationsFuture work should focus on refining the computer model and differentiating ECRs by assigning priorities to incoming ECRs.Practical implicationsFor product development managers, processing ECRs in batches is preferable than attending to them on an individual basis. Nevertheless, in some situations ECRs require immediate attention. A mechanism will always be needed to deal with situations directly. Also, in terms of batching, ECRs could be processed in groups on a periodic basis. Periodically performing ECRs due to new design versions or prototypes in a timely manner is a good compromise between a random batch mode and doing them individually.Originality/valueThe paper shows that batch processing is superior to executing ECRs promptly as they are received. This result has been shown through the use of a computer model of NPD. To the authors' knowledge, no other studies have used computer modeling to study this problem.

During the past three decades a number of theories have been proposed to explain the idiosyncrasies of software development as a team activity. The paper compares and combines these theories into a coherent model of software development that links software coupling and dependency management with team productivity. As a practical test of this model, the paper then investigates, the effects of coupling in two large commercial systems. It achieves this by using the VCML Views visualisation technique, developed by the authors, to expose the system wide coupling found in the code and how this coupling develops during the lifetime of a project. It then compares the resultant VCML views with simple attributes of the two projects to derive a set of important conclusions. In particular, it finds that unmanaged coupling within the code is a good indicator of potential productivity bottlenecks; that the number of programmers on a project is not necessarily a good indicator of programmer productivity; and that the architecture of a software system can radically alter the number of programmers that can effectively work together on a system.

A Petri Net-based approach for ergonomic task analysis and modeling with emphasis on adaptation to system changes

В статті визначено сутність групової динаміки та її різні аспекти в команді проєкту та креативних групах, систематизовано моделі розвитку команд з врахуванням різних стилів комунікації та співпраці, технологій забезпечення згуртованості команди та адекватної поведінки на різних етапах співпраці. Встановлено, що групова динаміка може включати взаємодію, комунікацію, конфлікти, співпрацю, координацію та інші аспекти, що виникають під час спільної діяльності. В статті з’ясовано сутність різних моделей, що визначають ролі членів команди, стилі керівництва, типи взаємодії та конфліктів між учасниками команди. Доведено, що вивчення та використання сучасних моделей налагодження взаємодії, управління комунікаціями та конфліктами може бути корисне для розуміння ролей, які виконують учасники команди проєкту, а також (що більш важливо) як ці ролі членів команди можуть впливати на загальну ефективність команди, як враховувати той факт, що кожен учасник команди унікальний та може мати свої власні особливості, як краще зрозуміти специфіку бізнес-середовища та потреби конкретної команди. Встановлено, що врахування культурних відмінностей, поведінкових та інших індивідуальних характеристик різних психологічних типів учасників команди може допомогти забезпечити ефективну взаємодію та досягнення успіху проєкту. В статті обґрунтовано, що керівнику креативної команди та/або проджект менеджеру проєкту варто використовувати різні ефективні методи управління командою, залежно від потреб та характеру проєкту та бізнес-середовища, використовуючи різні моделі взаємодії, співпраці та обміну знаннями, згуртованості команди, лідерства, управління комунікаціями та конфліктами. В статті також аргументовано, що врахування цих аспектів може забезпечити ефективну роботу команди проєкту та / або креативних груп буде сприяти досягненню поставлених цілей. В статті запропоновано використовувати методологію «командних будинків», яка базується на ідеї, що для досягнення максимального результату в проєктній роботі необхідна гармонійна робота всіх членів команди, а також на уявленні про команду як будинок.

PurposeFrontline teams are at the centre of lean transformations, but the teams also transform as they implement lean. This study examines these changes and seeks to understand how lean relates to team psychological safety and learning.Design/methodology/approachThis research setting is the Romanian division of a leading European energy company. The authors collected team-level audit and survey data, which the authors used to test the effect of lean implementation on team psychological safety and learning. The authors’ team-level data are complemented with qualitative interviews conducted with team members and headquarters leaders.FindingsThe results of the regression analyses show that leanness is positively associated with team psychological safety, which is in turn positively associated with learning. Thus, this research provides evidence that leanness – mediated by team psychological safety – increases team learning.Practical implicationsLean changes team dynamics and learning positively by ensuring and promoting an emotionally sound work environment with clear team structures, an appropriate level of autonomy, and strong leadership.Originality/valueThis paper contributes evidence of important psychological mechanisms that characterise team-level lean implementation. Particularly, the authors highlight how team psychological safety mediates the relationship between leanness and team learning.

Introduction . Current economic situation is transforming the management of the national enterprises. This outlines the need for new management approaches focused more on modern technologies which optimize management, economic growth and enhance competitive strengths at the national and international markets. The development of innovative management with its subprocesses is getting more and more relevant. E fficient innovative performance of the economic units and high target indicators mainly depend on the subprocesses’ adjustment to the global economic changes. Purpose . The paper aims to prepare scientific and practical recommendations for designing the tec hnologies for the innovation management implementation, management typology and unification , and the development of a balanced innovation management system for the national enterprises. This system could ensure their timely adjustment to the constant changes. Materials and Methods . The author applies the methods of structural analysis and synthesis, generalization, analogy, system analysis, and optimization. Results. The paper describes the management technology models for the enterprise’s inn ovative processes. Unlike the existing models, the offered ones could visualize their coherent structure, organize end -to-end innovation management, and evaluate the dynamics and quality of inter -process interactions. This could algorithmize the activities aimed to boost the innovative performance of the national enterprises. Scientific findings and suggestions are likely to be useful to adjust the innovation policy, determine the prospects and the fundamental direction of economic growth for nati onal enterprises at the time of global changes. Conclusion . The scientific recommendations and findings from this work can become methodological tools for the industrial enterprises’ managers to solve the economic growth tasks and to strengthen their compe titiveness. Strategically, further research could develop the existing approaches to enterprises’ innovation management, unify methodological tools of innovation management with modern digital technologies.

ABSTRACTIntroduction: There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches.Areas covered: This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials.Expert opinion: Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.