Selection Of Team Members Research Articles

A Systematic Literature Review of Machine Learning Applications for Team Formation Problems

With the development of technology, the variety and number of data held for any process has increased exponentially. By processing and analyzing this data, it is possible to solve many problems. Selection of the most appropriate team member and correct team formation in the activities carried out by the team are the factors that affect the success and result of teamwork. For this reason, the problem of team member selection and team formation has become one of the increasing research topics in recent years. Researchers from different disciplines are trying to develop tools, techniques and methodologies to ensure a successful team building process. Machine Learning (ML) methods have become one of the methods that have started to be used in team formation and team member selection problems in recent years. The successful outcome of this problem depends on the correct collection and processing of data and the selection of appropriate machine learning methods. The aim of this article is to present a systematic literature review of machine learning methods applied in team formation and team member selection problems, and to show which machine learning methods are applied in this field and their performance. Articles on the subject were searched in six scientific databases. In addition to providing fundamental information about ML methods, this review also supports new research efforts on team formation problems.

Factors Affecting Success or Failure in Software Projects in the Kingdom of Saudi Arabia

This study aimed to identify factors affecting the success or failure of software projects in the Kingdom of Saudi Arabia. The study used the descriptive method as a study approach and employed the questionnaire as a study tool. The study included (111) employees in some software projects in administrative, medical, educational, military and commercial facilities of the public and private sectors in the Kingdom of Saudi Arabia. The study findings indicated that administrative projects were the most common type of project, with government projects having a higher percentage. Teams with one to ten members were the most typical size, and most projects were completed within six months to a year. Project success depended on an appropriate timeline, teamwork, and thorough task schedules. Project failures were often caused by delays, lack of teamwork, and legal permission requirements. Public opinion on project success was not significantly impacted by project type or team size. The study recommended the need to analyze and estimate the project, identify potential risks, conduct training for team members, develop a clear task schedule, and focus on selecting team members to achieve project objectives and ensure adaptability and collaboration.

Selecting workers like expert for crowdsourcing by integration evaluation of individual and collaborative abilities

Team-based worker selection has been extensively studied for Mobile Crowdsourcing (MCS), in which a set of workers are recruited to form a team to complete complex tasks collaboratively. However, existing studies face two typical challenges: 1) how to dynamically evaluate workers’ individual abilities and collaborative contributions to the team; 2) how to select unknown workers to form a team with high quality at low cost. To tackle the above challenges, this paper proposes an Integration of Individual and Collaborative Abilities based Dynamic Worker Selection (IICA-DWS) algorithm to recruit excellent workers as a team in a high-quality and low-cost style. In the IICA-DWS algorithm, each worker’s individual ability and collaborative contribution to the team are evaluated more accurately using the Approximate Shapley Value (ASV). In addition, a high-quality team formation method is established to complete complex tasks at low cost. This involves the selection of both team leaders and team members. In this process, the Multi-Armed Bandit (MAB) model is adopted to dynamically select excellent workers using exploration and exploitation phases. Lastly, the IICA-DWS algorithm is evaluated through theoretical analysis and experimental results. The results show that the IICA-DWS algorithm can improve the data quality of tasks by 47.3% and reduce the cost by 61.7% on average. Moreover, the IICA-DWS algorithm has a high probability of approximating optimal results, which shows the best performance among the comparative algorithms.

Communication dynamics of a two-agent interaction model with applications to human-autonomy teaming

ABSTRACT Communication is pivotal for the emergence of coordination and cooperation within teams. As a result, communication plays an important role in team dynamics as better communication between teammates can lead to more efficient and successful teams. To better understand communication dynamics and their impact on team performance, we develop a modelling framework of two-agent interaction dynamics in a discrete-time fashion. Our proposed model considers the communications between two agents based on each agent's personality and their individual impact on any received communication. Combined with data, we perform mathematical analysis and bifurcation diagrams to study how agents' personality and training may impact the quality of the team's communications and therefore their performance in task completion. The validations and parameter estimations of our model from data could potentially help us to select team members that could work together efficiently, and train members in the established team to collaborate better.

Precision Sports Science: What Is Next for Data Analytics for Athlete Performance and Well-Being Optimization?

In elite sports, athletic excellence demands meticulous performance preparation and a sound health status. This paper overviews the current propositions and applications of pervasive computing and data analytics and our vision on how they should be used in future frameworks to contribute to the optimal balance of athletes’ performance and health requirements. Two main areas will be discussed. The first area is Sports Performance Optimization, in which we consider interesting recent advancements in data analytics for performance improvement, equipment design, and team member recruitment and selection. We will also briefly discuss how the betting industry has been relaying and developing sports analytics. The second area is Athlete’s Wellness and Wellbeing, which will discuss how wearables and data analytics have been used to assess physical activity and sedentary behavior profiles, sleep and circadian rhythm, nutrition and eating behavior, menstrual cycles, and training/performance readiness. In the final part of this paper, we argue that a critical issue for managers to enhance their decision making is the standardization of acquired information and decision-making processes, while introducing an adaptable, personalized approach. Thus, we present and discuss new theoretical and practical approaches that could potentially address this problem and identify precision medicine as a recommended methodology. This conceptualization involves the integration of pervasive computing and data analytics by employing predictive models that are constantly updated with the outcomes from monitoring tools and athletes’ feedback interventions. This framework has the potential to revolutionize how athletes’ performance and well-being are monitored, assessed, and optimized, contributing to a new era of precision in sports science and medicine.

An Application of Innovative Algorithm of Integrated Social Network Analysis with Statistical Lstm Chain Network Analysis (Slstm-Cna) for Entrepreneurial Team Member Selection

Selecting the right members for an entrepreneurial team is a critical step in ensuring the success and viability of a startup venture. Each team member brings unique skills, experiences, and perspectives that contribute to the overall vision and execution of the business idea. The factors such as cultural fit, adaptability, and a shared commitment to the venture's mission and values can foster cohesion and synergy within the team. This paper presents an innovative algorithm for the selection of entrepreneurial team members, Leveraging insights from social network dynamics and LSTM-based predictive modeling, the algorithm aims to identify individuals with the highest potential for contributing to the success of a startup venture. By analyzing social network structures, the algorithm identifies key influencers and connectors within professional networks, thereby facilitating the identification of candidates who possess valuable connections and collaborative capacities. Furthermore, SLSTM-CNA enables the algorithm to forecast future network trends and dynamics, aiding in the selection of team members who can adapt and thrive in evolving entrepreneurial ecosystems. simulated entrepreneurial ecosystem, the algorithm identified individuals with the highest potential for contributing to startup success based on various criteria, including network centrality, connectivity, and predictive network dynamics. For instance, individuals with a centrality score exceeding 0.7 demonstrated a 25% higher likelihood of facilitating valuable connections critical for business growth. Additionally, candidates identified through SLSTM-CNA exhibited a 30% increase in adaptability to changing network structures compared to those selected through conventional methods

Optimization Practice of University Innovation and Entrepreneurship Education Based on the Perspective of OBE

Innovation and entrepreneurship and education have become an important topic in China’s higher education. Based on pedagogy theory, this paper divides innovation and entrepreneurship education in universities into three levels: ideological education, innovative education and entrepreneurial education. Innovation is the content of higher education, and it is also the ability that contemporary college students must have. Only with education can there be innovation, only with innovation can there be entrepreneurship, and only with entrepreneurship can there be innovation. This is of great significance to the development of multi-level education, universal education, innovation and entrepreneurship education, and the improvement of education, teaching and child-rearing levels. In order to promote the optimization practice of college students’ innovation and entrepreneurship education, this paper designs a software system which is convenient for college students’ project application, project implementation, data verification and progress report. At the same time, it can help people review and select team members, thus greatly improving management efficiency.

A Genetic Algorithm-based Group Formation to Assign Student with Academic Advisor: A Study on User Acceptance using UTAUT

Group formation to assign students with academic advisors based on student demography can be exhaustive as various possibilities and combinations can be formed. Hence, this paper proposed a genetic algorithm-based approach to automate group formation based on student demography to assign students to their academic advisors. The genetic algorithm (GA) will optimize the group formation of students with a balanced number of nationalities, races, and genders. Also, this paper examines the user acceptance of the proposed genetic algorithm-based application to automate group formation using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The survey aims to study the impact of independent and moderating variables on dependent variables. The result proved that all the independent variables, Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Condition (FC), have a positive impact on the dependent variable, Behavioral Intention (BI). In contrast, the moderating variable Experience (EX) and Voluntariness of Use (VU) have a negative impact on Behavioral Intention (BI). Thus, this paper concludes that the proposed application can increase the performance and efficiency of group formation and automatically assign students to academic advisors. However, respondents are reluctant and not ready to use the system. Thus, training and workshops can be conducted to introduce and train the users to utilize the system. Future works can be done where the application of the proposed genetic algorithm-based system can be further expanded to different academic purposes such as team formation for group assignment and team member selection for competition.

Critical Success Factors for Applying Six Sigma in Transformative Industries in Iraq

Purpose: the article aims to measuring Critical Success Factors (CSFs) for Applying Six Sigma (SS) in Iraq Transformative Industries (TI). Theoretical framework: The theoretical framework included a conceptual presentation of organizational success factors according to the Six Sigma tool in a sample of Iraqi companies. Design/methodology/approach: To ensure six sigma is successful, twenty-two CSFs have been found in the literature. These include managerial involvement and commitment, alignment with institutional strategy; communication; and team member selection. Surveys of Iraqi TI verified the instrument, resulting in (97) results. Only experienced project managers were asked to take the test. Exploratory factor analysis were performed on the collected data using the SPSS software package. Findings: TI can use the proposed instrument to evaluate how Six Sigma's important success characteristics are controlled throughout the construction of improvement projects (IPs), according to our findings. Research, Practical & Social implications: the survey can help industries discover areas for development when adopting the six-sigma technique and fit models to evaluate how key success factors combine to achieve the desired results of improvement projects. Originality/value: A project's success is dependent on the achievement of its crucial success criteria. Based on these findings, a six-sigma critical success element implementation assessment instrument will be developed and tested.

Bringing strengths and differences to the table - challenges and opportunities in practitioner-researcher collaborations

ABSTRACT In this paper, we (two researchers and three practitioners from Singapore) reflect on the challenges and opportunities of practice-research collaborations. While researchers and practitioners share a common goal to improve services, challenges can arise due to differing perspectives and priorities and power dynamics between team members. However, diversity of perspectives can enrich the research process when strengths of practitioners and researchers are utilised. Collaborative relationships can be optimised with careful selection of the appropriate team members, effective communication, and by practicing reflexivity. Our reflections highlight the benefits of practice-research collaborations and the importance of navigating partnerships to achieve shared outcomes.

Selection of virtual team members for smart port development projects through the application of the direct and indirect uncertain TOPSIS method

The virtual team member selection problem has a significant impact on the execution efficiency of smart port development projects, especially in this temporarily constructed team environment and in the era of information and communication technology development. On the other hand, selecting the optimal virtual team members will directly affect the harmony of the remote team working environment. Apart from that, the process of selecting virtual team members from all candidates is a multicriteria decision-making (MCDM) problem. This paper develops a direct and indirect uncertain MCDM methodology (called DI uncertain TOPSIS) based on failure mode and effect analysis (FMEA), decision-making trial and evaluation laboratory (DEMATEL), and a technique for order performance by similarity to ideal solution (TOPSIS) to help port companies select the optimal team members in a virtual environment where smart port development projects execute more efficiently, safely, and remotely working environments harmoniously. The FMEA and DEMATEL methodologies are combined with the TOPSIS method to analyze the uncertainty of the virtual team member selection problem and determine the Risk Priority Number (RPN) for the MCDM criteria. The TOPSIS method approaches the MCDM problem and obtains the final virtual team member combinations.

ТИМБІЛДИНГ НА ПІДПРИЄМСТВІ: СУТНІСТЬ І ПРИЗНАЧЕННЯ

The article examines the issue of increasing team spirit at the enterprise, increasing the level of trust and mutual assistance in the team, increasing the level of initiative of employees, increasing the level of personal responsibility for the team's performance, creating friendly relations in the team, reducing conflict situations, and others. Modern managers at the enterprise strive not just to form a team, but to build a cohesive team. In this regard, questions concerning the features of the team that distinguish it from the usual collective of employees, as well as the issue of developing a team building strategy, become relevant. The peculiarities of the selection of members of the future team are largely determined by the leader's subjective ideas about the meaning of the concept of "team" and his leading orientation in the selection of team members, which, as research shows, are in turn interconnected with the leading values of the manager as an organizational leader. If the leader seeks to optimize the team building process, he can rely on a comprehensive approach in his work aimed at forming a common vision and goal setting in the team. Team building is considered as a cyclical process consisting of combinations of action and reflection (analysis of action) aimed at realizing the strategic goals of the team, as well as at identifying and solving problems. Thus, creating an effective team is an integral part of being a leader. Leadership varies depending on the task and situation according to the decision of the team. At the current stage of the development of science, there is a tangible need to modernize the content and structure of leadership. To improve the professional level of an individual, it is necessary to consider the concepts of leader and leadership through the prism of team building. There is a potential opportunity in the leadership of a new model. One of the functions of team building is to identify incompatible people and determine ways of their non-conflict interaction and effective work. This is especially important at the first stage – the stage of team formation. Team building is especially important at the stages of formation and normalization, since the experience of Ukrainian and foreign institutions that have achieved significant success in organizational and innovative activities confirms that the main role in them is not played by individuals, but by work groups, the highest form of which are teams.

Analysis of the Training Paths for Foreign-Related Legal Talents: From the Perspective of International Humanitarian Law Moot Court

The Department of International Law of China Foreign Affairs University has established a relatively effective moot court training model and system in the long-term practice of the excellent foreign-related legal talent training project. As one of the distinctive and influential competitions, the International Humanitarian Law (IHL) Moot Court has been proven to be an effective education method for cultivating foreign language-savvy, high-quality compound and application-oriented legal talents for international organizations. Based on years of experience, the IHL Moot Court of China Foreign Affairs University emphasizes the ability and potential of the students in English, law, and teamwork when selecting team members, and respects student self-governance and teamwork during preparation and competition. There is a relatively mature moot court talent training model in the IHL Moot Court.

Construction and Application of Cloud Computing Model for Reciprocal and燙ollaborative Knowledge Management

Promoting the co-constructing and sharing of organizational knowledge and improving organizational performance have always been the core research subject of knowledge management. Existing research focuses on the construction of knowledge management systems and knowledge sharing and transfer mechanisms. With the rapid development and application of cloud computing and big data technology, knowledge management is faced with many problems, such as how to combine with the new generation of information technology, how to achieve integration with organizational business processes, and so on. To solve such problems, this paper proposes a reciprocal collaborative knowledge management model (RCKM model) based on cloud computing technology, reciprocity theory, and collaboration technology. RCKM model includes project group management and cloud computing technology, which can realize management, finance, communication, and quality assurance of multiple projects and solve the problem of business integration with knowledge management. This paper designs evaluation methods of tacit knowledge and reciprocity preference based on the Bayesian formula and analyzes their effect with simulation data. The methods can provide quantitative support for the integration of knowledge management and business management to realize reciprocity and collaboration in the RCKM model. The research found that RCKM model can fully use cloud computing technology to promote the integration of knowledge management and organizational business, and the evaluation method based on the Bayesian formula can provide relatively accurate data support for the evaluation and selection of project team members.

The role of affect in the selection of nonfamily top management team members in family businesses

Utilizing a qualitative research design based on 53 interviews with 19 Swiss family businesses, supplemented by 14 expert interviews, this study demonstrates that different family firm-specific elements of the process of selecting top management team (TMT) members alter affect infusion in family firms. These are the informal selection context, the involvement of informal advisors, and relationship-related evaluation criteria. The study moreover shows that the context-specific attitude (openness, defensiveness, readiness to delegate) of the family business decision-maker regulates affect infusion. Lastly, the study demonstrates that sabotage in the selection process can occur in high-affect infusion scenarios. Contributions and implications for future research are discussed.

Swiftly judging whom to bring on board: How person perception (accurate or not) influences selection of prospective team members

We develop and test a holistic model of how team members’ swift judgments about a prospective team member impact their selection decisions and how accurate those judgments are in predicting the prospective member’s performance. Applying the social psychology literature on person perception to the organizational literature on team member selection, we argue that team members’ perceptions of the prospective member’s competence primarily shape their predictions about the prospective member’s task-related performance in the team, whereas perceptions of warmth primarily shape predictions about the prospective member’s interpersonal contextual performance in the team. We further propose that, although team members rely on both performance predictions when choosing a prospective member, predicted task-related performance receives more weight than predicted interpersonal contextual performance, and that the importance of predicted interpersonal contextual performance is elevated when team task interdependence is high. Importantly, we theorize that the predictions about task-related performance show good accuracy, whereas the predictions about interpersonal contextual performance do not, which makes the reliance on the latter erroneous. Across two studies utilizing prospective members’ actual task-related and interpersonal contextual performance (objective and peer-rated), as well as team members’ predictions about such performances, we found support for our predictions. Our research resolves several outstanding puzzles in the literature on person perception, integrates it into organizational research, and offers novel and actionable insights for selecting prospective team members.

TMT experience and corporate social (ir)responsibility: the moderating effects of faultlines

PurposeThe purpose of this study is to examine how the experience of the top management team (TMT), such as industrial experience and functional experience heterogeneity, affect corporate social performance (CSP) and whether TMT faultlines act as a moderator.Design/methodology/approachTo examine the effect of TMT experience on CSP, this study uses upper echelons theory as theoretical background, and data are selected from 212 Chinese high-polluting companies with A-shares from 2012 to 2016. The dependent variable is lagged by one year from 2013 to 2017.FindingsIndustrial experience both positively influenced CSR and negatively influenced corporate social irresponsibility. Functional experience heterogeneity had an inverted U-shaped effect on responsible behaviors and a U-shaped effect on irresponsible behaviors. Meanwhile, TMT faultlines played a moderating roles in the relationship between TMT experience and CSP, in which faultlines reinforces the non-linear relationship between functional experience heterogeneity and CSP.Research limitations/implicationsThe existence of impact paths between TMT experience and corporate social performances must still be examined. Other moderators need to be verified.Practical implicationsThe important ways to promote more corporate responsible behavior and reduce irresponsible corporate behavior is to choose the right team members. During team formation, it is important to have experience in related industries and select team members with different functional experiences. Companies can consider hiring executives who tend to work together and have relevant experience, which can reduce the time cost of unnecessary conflicts.Originality/valueThis study combined the upper echelons theory with some attention perspectives to study the impacts of TMT experience on CSP.

Knowledge complexity and team information processing: the mediating role of team learning goal orientation

PurposeDrawing on job demands and resources theory and the challenge-hindrance stressor framework, this study aims to investigate the effect of team knowledge complexity on team information sharing and information searching and examine whether team learning goal orientation mediates these effects.Design/methodology/approachThe authors conducted two studies. Study 1 used a field survey study conducted among 374 employees positioned in 68 new product teams. Study 2 used a three-wave online survey study conducted among 208 leaders to investigate the teams they managed.FindingsThe findings of the two studies reveal that team knowledge complexity has a positive direct effect on team information sharing and information searching. Furthermore, team learning goal orientation mediates these two relationships.Practical implicationsThe findings indicate that team knowledge complexity is generally beneficial for the team information process. Therefore, instead of fearing an increase in the knowledge complexity of the projects, organizations should dare to present challenge demands to team members to enhance their engagement in information processing. Organizations could also pay attention to team member selection during team composition processes. For example, selecting team members with a high level of learning goal orientation is helpful in facilitating team information processing.Originality/valueAlthough previous studies have found that knowledge complexity is beneficial for team output, less is known about how knowledge complexity influences team processes. This study clarifies the relationships between team knowledge complexity, information sharing and information searching and examines team learning goal orientation as a vital mediator.

The impact of graduate student goal orientation on design projects

Many universities offer courses in which students from various disciplines form teams to work on complex design challenges. Despite the importance of such teams for engineering design education, very little is known about them. This study explores the impact of graduate student goal orientations (mastery, performance-proof, and performance-avoidance) on course performance (team performance and peer assessment) via functional team member selection. The study examined 122 teams and found that students with performance-proof and performance-avoidance orientations are more likely to form functionally homogenous teams. However, there is no significant relationship between a mastery goal orientation and functional team diversity. Furthermore, the results indicate that functional team diversity is a double-edged sword for course performance, insofar as it has a positive impact on team performance, but also a negative effect on peer assessment. In addition, both performance goal orientations (performance-proof and performance-avoidance) have a negative effect via functional diversity on team performance and a positive effect on peer assessment. The findings advance our understanding of team member selection by graduate students and the effect thereof on course performance. Hence they may assist in developing future tools and processes to enhance learning by engineering design students in interdisciplinary design projects.

Implementing collaborative care for major depression in a cancer center: An observational study using mixed-methods

ObjectivesTo describe the implementation of a collaborative care (CC) screening and treatment program for major depression in people with cancer, found to be effective in clinical trials, into routine outpatient care of a cancer center. MethodA mixed-methods observational study guided by the RE-AIM implementation framework using quantitative and qualitative data collected over five years. ResultsProgram set-up took three years and required more involvement of CC experts than anticipated. Barriers to implementation were uncertainty about whether oncology or psychiatry owned the program and the hospital's organizational complexity. Selecting and training CC team members was a major task. 90% (14,412/16,074) of patients participated in depression screening and 61% (136/224) of those offered treatment attended at least one session. Depression outcomes were similar to trial benchmarks (61%; 78/127 patients had a treatment response). After two years the program obtained long-term funding. Facilitators of implementation were strong trial evidence, effective integration into cancer care and ongoing clinical and managerial support. ConclusionA CC program for major depression, designed for the cancer setting, can be successfully implemented into routine care, but requires time, persistence and involvement of CC experts. Once operating it can be an effective and valued component of medical care.