Int. Journal of Business Science and Applied Management, Volume 18, Issue 2, 2023
Team Formation: A Systematic Literature Review
Georgios Stavrou
Business Administration, University of Macedonia
Egnatias 156, 54636, Thessaloniki, Greece
Tel: +302310744728
Panagiotis Adamidis
Department of Information and Electronic Engi-neering, International Hellenic University
Alexander Campus, Sindos, Thessaloniki, 57400, Greece
Tel: +302310013985
Jason Papathanasiou
Business Administration, University of Macedonia
Egnatias 156, 54636, Thessaloniki, Greece
Tel: 00302310891571
Konstantinos Tarabanis
Business Administration Department, University of Macedonia
156, Egnatia street, 54636, Thessaloniki, Greece
Tel: +30 2310 891 578
Collaborative activities require the formation of team(s), whose members may have weak or strong ties to each
other, depending on the task to be completed. Team formation is a time consuming, complex, critical and
essential process in the life cycle and development of the team, especially when the list of participants is
disputed. To enhance learning and intellectual development in a team, it is important to select individuals based
on specific prerequisites and create an environment that fosters genuine and effective interactions.
However, the success of the team is not always guaranteed, and inappropriate team formation can lead to
discouragement and hindered learning. As a result, researchers are exploring various clustering models and
methods using best practices and other approaches to improve team formation and overcome these challenges.
The process and the techniques used to form a team can vary depending on various factors, such as member
characteristics, attributes, personalities, task, or context. However, despite the importance of team formation,
there is no up-to-date comprehensive and systematic study to investigate and analyze its important techniques.
Therefore, the main objective of this paper is to provide a comprehensive, detailed, systematic study, and to
survey the most recent literature on team formation algorithms/techniques/mechanisms. This paper presents a
systematic literature review (SLR) of recently published work investigating the team formation process,
attributes, and techniques. Out of the 640 papers selected for review, 103 pass initial screening and accepted as
papers related to team formation, and after a careful analysis, 30 papers meet the mandatory
requirements/criteria defined in our protocol, using a specific selection and quality assessment method. The
review reveals the current state of the art in the team formation literature and also sheds light on prospective
topics for additional study. This systematic literature review can immediately assist academics and working
professionals in understanding the evolving mechanisms and tactics of team formation by providing up to date
Keywords: team formation, team formation algorithms/methods/techniques, Systematic Literature Review
Int. Journal of Business Science and Applied Management /
Team formation is crucial because team success depends largely on the appropriate assignment of team
members to the teams. Therefore, it is important to implement and apply an effective technique to ensure (to
some extent) the optimal team composition. No teams are identical nor do they operate according to the same
processes with the same size, purpose, and common characteristics. Distinctions between size, types, styles of
the teams, etc. are identified (Anderson et al., 2001; Borman et al., 2003; Handbook of Aviation Human Factors,
1999). In addition, attributes such as communication skills, teamwork experience, and personality traits, are
criteria that affect team effectiveness and have an impact on team collaboration, efficiency and productivity
(Bales & Strodtbeck, n.d.; Schermerhorn, 1948; Wieringa et al., 2006). Researchers from different disciplines,
using many types of data, are trying to develop tools, techniques, and methodologies in order to facilitate the
process of a successful team composition. Different goals, different teaming criteria and technologies make the
task hard and complicated. Forming appropriate teams is also a challenge for most decision-makers (Mathieu &
Rapp, 2009; Paris et al., 2000; Salas et al., 2008; Teamwork: Emerging Principles - Salas - 2000 - International
Journal of Management Reviews - Wiley Online Library, n.d.). Traditionally formed teams tend to be a non-
automated, human-dependent and error-prone process due to complexity, limited time and many other issues.
Team formation strategies offer a growing range of tools and practices that increase the probability of creating
an ideal team.
Studies have revealed relevant attributes in team formation. Knowledge, technical expertise,
communication skills, and motivation are the main characteristics of a team and are mentioned in many studies
(Blackwell, 1955; Silva et al., 2011), (Clark & Wheelwright, 1992), (Converse et al., 1991), (Haque et al.,
2000), (Thamhain, 2003). Other studies combine these attributes or report the impact they have on team
performance when one is missing or lacking (Lappas et al., 2009), (Campion et al., 1993b), (Allen, 1986) (Smart
& Barnum, 2000), (Prasad, 1998) (Blackwell, 1955; Campion et al., 1993a; Logan, 1993; Taylor, 1975).
Personality type is another attribute that many studies have emphasized and applied, (Trower & Moore, 1996),
(Prince & Brannick, 1992), (Gilal et al., 2016b), (Gardner & Martinko, 1996), (Zakarian, 1999), These studies
consider it as part of the solution to the problem of team formation among many other factors also identified
(Sundstrom et al., 1990).
As noted, there are different characteristics of team formation. Depending on the grouping context, team
members are selected in various ways and the process of team formation is computerized using various
methodologies that are considered in particular teaming settings with various methodologies and techniques
considered in specific grouping settings. The methodologies and techniques used to form a team may vary
depending on various factors, such as member characteristics, attributes, personalities, task, or context. Search
based techniques (Penta et al., 2011), genetic algorithm methods (Costa et al., 2018) [12], mathematical models
(Graphs) (Lappas et al., 2009) or fuzzy genetic algorithms (Strnad & Guid, 2010) are some of them.
Despite the valuable contributions made so far, a comprehensive review of the team formation process,
including all its useful elements, such as attributes and techniques, is still lacking. Furthermore, there has been
no recent research on the method of team formation. The goal of this systematic review is to provide an
overview and classify computational techniques, particularly algorithms/techniques/methods and attributes, that
have been used to assist team formation in collaborative or cooperative environments from previous research
work in the field. This paper will provide several categorized viewpoints on the methods and characteristics of
team formation. This paper aims to review the latest developments in team formation and provide insights into
the computational approaches that support it. We will summarize the findings and identify the knowledge gaps,
challenges, and opportunities related to group formation.
This study aims to contribute to the knowledge of the field and achieve its objectives by addressing specific
research questions (RQ) through the use of a systematic literature mapping method. The systematic review
focuses on providing an overview and classification of computational techniques, including algorithms,
methods, and attributes, used in team formation within collaborative or cooperative environments. The primary
objectives are to identify the most effective attributes in the team formation process, uncover knowledge gaps,
challenges, and opportunities extending our previous research (Stavrou et al., 2018).
To answer these research questions, the study conducted a systematic mapping of the literature using the
method proposed by Petersen et al. (2008, 2015), as described in Section 2. The research protocol was carefully
defined, and the most relevant digital libraries in the computing and educational technologies field were
selected. Through a meticulous screening process, the study identified 30 research papers that align with the
research objectives and meet the defined inclusion and exclusion criteria. These papers were thoroughly
analyzed and categorized, as presented in Section 3, to address the following research questions: RQ-1) What
are the most common research types and objectives? RQ-2) What are the most common computational
techniques or methods used in team formation? RQ-3) What are the frequently measured team formation
characteristics? RQ-4) Do the studies compare their results with other studies or approaches?
Georgios Stavrou, Panagiotis Adamidis, Jason Papathanasiou and Konstantinos Tarabanis
Section 4 further analyzes the results of our literature review, providing additional insights into the features
and techniques found in the literature. Section 5 discusses the opportunities and challenges in group formation
research, while Section 6 presents the conclusions and limitations regarding our research.
By conducting this comprehensive review, the study aims to contribute to the existing body of knowledge
in the field, define clear research objectives, and identify primary studies on algorithms for team formation.
Systematic mapping is a research assessment method that guides literature reviews to address a specific
issue (Petersen et al., 2015). It involves methodical steps to search, analyze and evaluate literature using specific
criteria. This review method aims to provide an overview of the field of interest, reduce systematic errors, and
enhance the legitimacy of analyzed data for more reliable results (Buller & McEvoy, 2012; Charband & Jafari
Navimipour, 2016; Petersen et al., 2008, 2015).
In this paper we followed the five-step process (Fig 1) proposed by Petersen (Petersen et al., 2008, 2015)
with, additionally, snowball, back-tracing approaches, and additional searches that included “team OR group”,
“project OR task”, “multifunctional OR role OR allocation”, “knowledge OR expertise, which were used to
supplement our searches.
Figure 1. Sequential steps of Petersen
2.1 Search Strategy
To define the search terms, we first defined the search string considering the most relevant terms related to
previously known papers related to our research topic. To conduct our research, we selected relevant online
databases. To create the search string, we used frequent keywords contained in the research questions extracted
from highly cited papers (Table 1). Three main keywords have been defined: “team formation”, “team
collaboration” and “algorithms. Other very common terms were often used in the related literature, such as
‘team allocation’, ‘team selection’, ‘team composition’ etc. Each keyword forms a category that contains their
respective synonyms. Paper searches based on category keywords C1, C2, and C3 are merged based on the
Boolean operator “AND”, and for each keyword synonym and category using the “OR” Boolean operator.
Table 1. Category, Synonyms and Search String
Search String
Team formation
team creation,
group creation,
team formation,
group formation,
team design,
group design,
team forming,
group forming,
(group OR team OR team creation OR group creation OR team
formation OR group formation OR team design OR group
design OR team forming OR group forming)
(Methods OR approach OR technique OR model OR framework
OR tool)
(Knowledge OR collaboration OR coordination OR cooperation
OR team learning OR team coordination)
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Team collaboration
team learning,
team coordination
Since each online database uses different mechanisms and standards, the developed search string was
adapted to each database to work properly and conduct our search. Our literature search using the keywords
identified relevant papers from various databases, including ACM Digital Library (8), IEEE Xplore Digital
Library (23), Elsevier - Science Direct (23), SpringerLink (15), Google Scholar (26), Microsoft Academic (2),
and Scopus (6), resulting in a total of 103 papers.
2.2 Screening of Papers for Inclusion & Exclusion
The final analysis included only 30 papers in the systematic literature review, after examining more than
103 publications using a scientific approach known as systematic literature review. Many steps were taken to
gradually filter the papers and determine which were more relevant to the research objectives. Returned papers
were screened using multi-level criteria, which were either for inclusion or exclusion (Table 2). The process is
described below:
Practical Screening: This is the screening in which we narrow down the range of published papers, by reading
each paper’s title and abstract (and sometimes the introduction) to verify close relevance to the reviews goals
and questions. The relevant keywords of each paper were also examined.
Table 2. Inclusion & Exclusion Criteria
If several papers are related to the same
paper, only the most recent
paper is
Papers that present studies relating to education environments only
(meaning classrooms)
If the paper describes more than one study,
each study is assessed individually.
Papers that do not present studies relating to team formation algorithms or
If there are versions of the same study, a
short and a full one, the full version must
be included.
Papers in languages other than English
Include Papers from 2010.
Not relevant to the research questions
Include only published papers.
Papers that lack original contribution or novelty in research. Similar
research has already been published.
Include Papers related to Team/Group
Quality Appraisal: To ensure the quality of strongly related papers, the remaining papers were assessed after
full reading. This task applies the exclusion filter to selected papers, answering the question using a Boolean
scale (0 or 1) (Table 3).
Table 3. Quality Appraisal Criteria
Does this study follow a scientific methodology in order to prove its contributions?
Are the research objectives clear?
Was the research design appropriate to address the aims of the research?
Does this study identify a task or a project?
Does the study clearly explain or present the methods with equations or techniques?
Does the study identify specific member attributes or criteria?
Is the technique used clear and supported through experiments?
Does the study have clear tests, experiments, or case studies that support their research?
Does the study have comparison results with other studies or techniques?
Georgios Stavrou, Panagiotis Adamidis, Jason Papathanasiou and Konstantinos Tarabanis
2.3 Classification Scheme
For each paper, we used the categories proposed by Wieringa (Wieringa et al., 2006) to analyze, classify
and categorize the types of studies described in them. The categories of study types are:
Validation Research: novel, unimplemented techniques commonly used in experimental settings.
Evaluation Research: techniques that include implementation, advantages and disadvantages and have
been applied in practice.
Solution Proposal: Proposed solutions, which may be new or an extension of others, with potential
benefits of using case studies or comparative results to improve their arguments.
Philosophical Papers: papers that often use conceptual frameworks or taxonomies that present a new
direction or point of view to the field.
Opinion Papers: papers expressing personal opinions about whether an approach is good or bad with their
Experience Papers: personal experience of the author explaining how and what has been applied in
2.4 Data Extraction & Mapping
We followed the steps and categories outlined in the previous sections to identify the necessary data to
extract elements from the included papers for our team formation review. We extracted the following
information from each paper: Author, Year, Title, Keywords, Findings of the proposed solution, Computational
techniques, Publication type (Journal, Conference, etc.), Team formation characteristics, Study parameters
(communication cost, skill grading, etc.), Digital library, and Comparison results with other studies. We stored
and analyzed these data qualitatively and/or quantitatively to answer the research questions presented in
previous sections, and also performed statistical analysis, such as the number of publications per year, their type,
venue and so on.
During the systematic literature review, 103 papers were reviewed, and only 30 relevant papers, published
in the period 2010-2020, were included for final evaluation. The selected papers were published in various
outlets, from conferences and workshops to journals (Fig 2).
Figure 2. Distribution of papers according to their type over the years
2010 2011 2012
2014 2015 2016 2017 2018 2019
The necessary data required in order to create the review of team formation include publication type,
research type, research objective (the classification of the research objectives is discussed in section 3.1), the
technique or method used, and whether each paper compares its results with other studies or other
techniques/methods. This information was collected from 30 papers in a tabular form to help us explore the
team formation process (Tables 4). The findings of the team formation studies are organized in a structural
manner by presenting appropriate answers to the study's research questions (VS = has comparison).
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Table 4. Information about relevant studies
Techniques or Methods
(Feng et al.,
A method for member selection of
cross-functional teams
using the
ual and collaborative
Multi-objective genetic
(André et
al., 2011b)
Formal model for assigning human
resources to teams in software
Delphi algorithm &
(Farhadi et
al., 2011)
An effective expert team formation
in social networks, based on skil
Approximation algorithms
(Mazur &
Chen, 2011)
A task-member assignment model
for complex engineering projects
Genetic algorithm
Blas et al.,
Team formation based on group
technology: A h
ybrid grouping
genetic algorithm approach
Genetic algorithm
(Ren et al.,
Cooperative Co-evolutionary
Optimization of Software
Staff A
ssignments and Job
Genetic algorithm
(Gajewar &
Multi-skill Collaborative Teams
based on Densest Subgraphs
Approximation algorithms
(Farhadi et
al., 2012)
Teamfinder: A co-clustering-based
for finding an effective
team of experts in social networks
Data mining (Clustering
(Britto et
al., 2012a)
A hybrid approach to solving the
agile team allocation problem
Multi-objective & Fuzzy
(Zhang &
Multi-objective team formation
optimization for new prod
Multi-objective Particle
Swarm Optimization
(Tavana et
al., 2013)
A fuzzy inference system with
application to player se
lection and
team formation in multi-pla
Fuzzy systems
(Neshati et
al., 2014)
Expert group formation using
facility location analysis
Greedy algorithm
(Kamel et
al., 2014)
Realistic team formation using
navigation and homophily
Approximation algorithm
Domingo et
al., 2014a)
A multi-objective genetic algorithm
for software personnel
staffing for
HCIM solutions
Multi-objective genetic
(Wang &
A win win team formation problem
based on negotiation
Agent-based negotiation
algorithm for team formation
(Huang, Lv,
et al., 2017)
Forming Grouped Teams with
collaboration in social
Heuristic algorithm
(J. Yang et
al., 2016)
Forming a research team of experts
in expert-skill co-oc
network of research news
Approximation algorithms
(Akhavan et
al., 2016)
Selecting new product development
team members with knowledge
sharing approach
Fuzzy multi-objective integer
nonlinear programming
& Andreou,
A Multi-objective Genetic
Algorithm for Soft
ment Team Staffing Based
on Personality Types
Multi -objective genetic
m et al.,
Resolving team selection in agile
development using NSGA-
Multi -objective genetic
Sun, et al.,
A team formation model with
personnel work hours and project
2 Approximation algorithms
Georgios Stavrou, Panagiotis Adamidis, Jason Papathanasiou and Konstantinos Tarabanis
workload quantified
(Ding et al.,
Online formation of large tree-
structured team
Competitive algorithm
(Arias et al.,
A Multi-criteria Approach for
Team Recommendation
(X. Yang et
al., 2018)
Team Formation with Relationship
ngth Based on Meta Path in
Heterogeneous Network
Approximation Algorithms
Valverde et
al., 2018b)
An ontology-based approach with
which to assign human resources to
software projects
et al., 2019)
Optimal Learning Group
Formation: A Multi-
Search Strategy for Enhancing
Inter-group Homogeneity and
Intra-group Heterogeneity
Multi -objective genetic
ay et al.,
A multi-objective multi-stage
stochastic mo
del for project team
formation under uncertainty in time
Heuristic algorithm
(Miranda et
al., 2020b)
A multi-objective optimization
approach for the group formation
Multi objective genetic
(Putro et al.,
Intelligent Agent to Form
Heterogeneous Group Based On
ity Traits with Genetic
Genetic Algorithm
(Krouska &
An Enhanced Genetic Algorithm
for Heterogeneous Group
Formation based on Multi-
Characteristics in Social
Networking-based Learning
Genetic Algorithm
3.1 Most common research types and objectives (RQ-1)
To address RQ1 (Table 1), we conducted a classification of the selected papers based on their research
objectives. Upon thorough reading, we propose four major categories for classifying the papers. Technique/
Procedure: Papers that focus on implementing specific techniques or procedures for team formation.
Tool/Notation: Papers that provide mechanisms or procedures to support team formation. Improvement/
Extension: Papers that present extensions or improvements to existing algorithms or procedures for team
formation. Framework: Papers that propose technical solutions in the form of team formation algorithms. In
addition, as discussed in Section 2.3, we adopted the classification categories proposed by Wieringa (Wieringa
et al., 2006), which group studies based on their study type. Table 4 provides the classification and study types
of the included papers, offering insights into the research data.
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Figure 3. Distribution of papers in each category
As shown in Fig 3, the most explored category in the literature is the “Techniquescategory with 19 papers. In
this category, according to their research objective, nine of these studies propose a solution to a problem
(‘Solution Proposals’). The proposed solution is either new or an extension of an existing technique, providing
potential benefits over the existing solution, using examples or other arguments. The potential benefit of the
solution is presented using case studies (small examples) or other arguments. The remaining nine techniques
(“Evaluation Research) are implemented in practice and evaluated. This includes analysis of their
implementation, benefits and drawbacks. Next is theFrameworkcategory, with 3 studies, which proposes a
model (technical foundations) that supports the creation or facilitates the use of team formation algorithms, and
finally the ‘Tools & Improvements’, category with 2 papers, in which the main objective was to develop or
extend a tool that implements a specific algorithm for team formation.
3.2 Most common computational techniques or methods (RQ-2)
To address research question 2, we thoroughly analyze each of the selected articles, focusing on the
computational techniques proposed as solutions to the team building problem. We identified more than 18
different techniques, which we divide into four main categories (Fig. 4).
Figure 4. Categories of the techniques
The most prevalent category, comprising 53% of the contributions, is "Search and Optimization". This
category includes a number of algorithms, including genetic algorithms (GAs), heuristic and greedy algorithms,
dynamic programming, and backtracking. GA is the most commonly used technique, accounting for nearly 20%
of the papers studied. GAs are guided by natural selection and use mechanisms such as crossover and mutation
to effectively explore the solution space.
Georgios Stavrou, Panagiotis Adamidis, Jason Papathanasiou and Konstantinos Tarabanis
The second largest category, accounting for 27% of the papers, is "Statistics and Mathematics". In this
category, researchers use regression models, approximation algorithms, distance measures, and statistical
methods to accurately quantify and evaluate team formation criteria. These techniques use statistical analysis
and mathematical models to provide a solid foundation for decision making.
The "data mining" category, which represents 7% of the contributions, includes techniques such as
clustering, association, and classification. These data mining techniques aim to extract meaningful patterns and
relationships from large data sets to provide insights for the team building process.
The remaining 13% of studies fall under the "other" category, which includes various computing
techniques. In particular, fuzzy logic and Bayesian networks are commonly used to deal with the uncertainties
associated with individual profiles and to allow decision makers to take subjective factors into account. In
addition, Gray decision theory, the Analytic Hierarchy Process, and ontology techniques play a supporting role
by helping decision makers make informed judgments rather than automating the team-building process.
By explaining each of the computational techniques, we aim to provide a comprehensive understanding of
the specific approaches used in the work discussed and their relevance to the team building problem. This
expanded explanation will enhance the reader's understanding of the various computational tools used in the
field and facilitate further exploration and analysis.
3.3 Most commonly measured team formation characteristics (RQ-3)
The reviewed papers report that certain characteristics are critical for effectively assigning team members
to tasks during team formation. Knowledge expertise was found to be the most widely used characteristic in the
majority of primary papers reviewed, accounting for 57% of the analyzed papers. Technical knowledge, which
refers to specific skills and expertise in a particular field or industry, such as programming, finance, or
mechanics, was the most common type of knowledge expertise used in these studies. The level of technical
knowledge was often described as a percentage or with predefined values such as "bad," "average," "good," or
"very good," depending on the criteria being assessed (Akhavan et al., 2016; Bellhäuser et al., 2018; Farhadi et
al., 2011; Feng et al., 2010; Ren et al., 2011b; Zhang & Zhang, 2013).
Figure 5. Team Formation Characteristics
Communication -
Learning &
Usually, more than one type of characteristic is used in the team formation process. Figure 5 shows the
main category types considered in the selected papers. After the knowledge characteristic, the second most
frequent type of characteristic was personality traits (20% of the papers). This characteristic was reported in
many studies in our review process and is derived from psychometric instruments such as Belbin's Team Roles
and the Myers-Briggs Type Indicator (Aritzeta et al., 2007; Myers-Briggs Type Indicator (MBTI) | SpringerLink,
n.d.). Communication and Collaboration characteristics such as communication, motivation, leadership,
preference and other metrics that can establish connections between members are used in 16% of the papers.
‘Learning & Knowledge Sharing”, which reflects individuals’ skills in acquiring useful information from others,
and the ability to share knowledge among team members, is used in 7% of the papers.
3.4 Comparative Analysis: Papers Assessing Results in Comparison to Other Approaches (RQ-4)
Although many studies implement team formation algorithms, the lack of source code or pseudocode to
reproduce and reuse the team formation algorithm and the different approaches in each study make it difficult to
find or categorize studies with comparison results. When a study or research has comparative results with other
studies or research, it means that the findings of the current study or research have been compared and
contrasted with the findings of previous studies or research on the same or similar topic. This type of
comparison can provide valuable context and perspective for the current study or research, and can help validate
or invalidate the findings. It can also help identify gaps in previous research and suggest areas for further study.
Int. Journal of Business Science and Applied Management /
By comparing and contrasting the results of different studies or research, scientists and researchers can gain a
deeper understanding of the subject and ultimately make more informed decisions and recommendations based
on the available evidence. Additionally, comparative research helps identify similarities and differences between
studies and can help understand the bigger picture of the subject or problem. It also helps identify strengths and
weaknesses of past and current research.
In our review we categorized studies which show or present benefits, drawbacks and comparison results
with other studies. Unfortunately, most studies (63%) do not compare their results with others (Fig 6). On the
other hand, 37% of the studies which have comparative results are mostly extensions of previous studies, such
as (Farhadi et al., 2011, 2012; Feng et al., 2010; Gajewar & Sarma, 2011; Huang et al., 2016; Kamel et al.,
2014; Neshati et al., 2014; Ren et al., 2011b; J. Yang et al., 2016; X. Yang et al., 2018). This means that the
present study builds on the findings of earlier research and aims to further explore or expand the topic. In these
cases, the present study is often designed to address limitations or gaps identified in previous research. By
comparing and contrasting the results of the current study with those of previous studies, researchers are able to
gain a more complete understanding of the topic and make more informed conclusions and recommendations.
Additionally, this type of research often helps to confirm or disprove previous findings and identify new areas
for future study. In many cases, researchers will use results from previous studies as a starting point for their
own research and build on it by adding new data, new methods, new perspectives or new hypotheses. This type
of research is called "extension research" and it is valuable because it helps to strengthen the evidence base and
to provide more robust and reliable conclusions.
Figure 6. Papers with comparison examples
To accomplish the research goals of this SLR, 30 studies in the field of team formation have been reviewed
and evaluated. The objectives were to identify recent advances in team formation across contexts, investigate
useful characteristics and grouping process methodologies/techniques, summarize and depict findings in a
structural way to highlight knowledge gaps, challenges, and opportunities.
It is clear that the team formation process has been examined from two critical angles. The first concerns
the characteristics that influence team formation, and the second concerns the methods applied to certain
situations. Sections 4.1 and 4.2 provide additional information on these viewpoints. The results of the analysis
of the study allowed the classification of the contributions into different categories. Finally, based on the
knowledge gaps identified in the relevant literature, constraints, limitations and possibilities are described.
4.1 Characteristics of Team Formation
There is a substantial body of research on how teams are formed. The papers cover a wide range of topics,
such as compiling information about team members while using various models of the team formation process
in different circumstances. The team formation process depends on the parameters selected for each study, as
shown in Figure 5. It is evident that the research varied in terms of the selected attributes and their number.
In this context, some research studies (10 papers) focused on the use of a particular attribute to form
productive teams (Agustín-Blas et al., 2011; Ding et al., 2017; Farhadi et al., 2011; Gajewar & Sarma, 2011;
Georgios Stavrou, Panagiotis Adamidis, Jason Papathanasiou and Konstantinos Tarabanis
Huang et al., 2016; Neshati et al., 2014; Paredes-Valverde et al., 2018a; Ren et al., 2011b; Wang & Zhang,
2015; J. Yang et al., 2016). That is, these studies emphasized a particular attribute, such as technical
expertise/knowledge in a particular domain, as the primary criterion for team building. On the other hand,
another group of studies (6 papers) considered two attributes simultaneously (André et al., 2011a; Arunachalam
et al., 2016a; Farhadi et al., 2012; Feng et al., 2010; Jiménez-Domingo et al., 2014b; Paredes-Valverde et al.,
2018a). These studies aimed to examine the effects of combining two specific attributes, such as technical
expertise and cooperation/collaboration, on team building outcomes.
In addition, a smaller group of studies (4 out of 30 papers) examined the process of team formation with
three specific attributes (Arias et al., 2017b; Feng et al., 2010; Huang, Sun, et al., 2017; Mazur & Chen, 2011).
These studies aimed to explore the possibilities of multicriteria team formation under increasingly complex
conditions and recognized that the inclusion of more team member attributes would lead to a more complicated
team formation process.
Other studies in our review used different attributes based on the available datasets and their specific
research objectives (Arias et al., 2017b; Arunachalam et al., 2016a; Britto et al., 2012b; Farhadi et al., 2012;
Garshasbi et al., 2019; Kamel et al., 2014; Miranda et al., 2020a; Putro et al., 2020; Tavana et al., 2013; Zhang
& Zhang, 2013). These attributes included factors such as cooperation/collaboration, personality traits, roles
within teams, and connections among members.
Technical expertise/knowledge in a particular field emerged as the most commonly used attribute in the
work reviewed. This attribute was consistently used in most studies due to its significant influence on team
outcomes (Agustín-Blas et al., 2011; André et al., 2011c; Arias et al., 2017b; Britto et al., 2012b, 2012b; Ding et
al., 2017; Farhadi et al., 2011; Feng et al., 2010; Gajewar & Sarma, 2011; Huang, Sun, et al., 2017; Huang, Lv,
et al., 2017; Jiménez-Domingo et al., 2014b; Mazur & Chen, 2011; Neshati et al., 2014; Paredes-Valverde et al.,
2018a; Ren et al., 2011a; Stylianou & Andreou, 2013; X. Yang et al., 2018).
Cooperation/collaboration characteristics have also been discussed extensively in the literature
(Anagnostopoulos et al., 2010; Feng et al., 2010; Gajewar & Sarma, 2011; Huang et al., 2016; Largillier &
Vassileva, 2012; Magnisalis et al., 2011; Mazur & Chen, 2011; Wi et al., 2009; Zhang & Zhang, 2013). These
traits capture team members' willingness and ability to work together effectively. Similarly, personality traits
have been considered in team formation (André et al., 2011a; Bellhäuser et al., 2018; Gilal et al., 2015, 2016a;
Licorish et al., 2009; Putro et al., 2020; Stylianou et al., 2012; Stylianou & Andreou, 2012b; Zhang & Zhang,
2013) because they contribute to team cohesion and are easy to identify for research and experimental purposes.
More recent studies have introduced additional attributes that focus on team members' roles and
connections within the team. Three studies explicitly included these attributes in the team-building process
(André et al., 2011a; Farhadi et al., 2012; Huang et al., 2016). By considering these attributes, researchers
sought to examine how the roles and relationships among team members influence overall team dynamics and
It is important to note that the selection of team formation criteria, although seemingly simple, requires
significant effort on the part of researchers to collect and record data as objectively as possible. Attribute
selection plays a critical role in determining the effectiveness of team-building strategies and must be carefully
considered in the research process.
4.2 Techniques Used in Team Formation
It is evident that the use of evolutionary algorithms is one of the main methods in the team formation
process (Fig 4). Approximately 50% of the reviewed papers used these evolutionary methods (Agustín-Blas et
al., 2011; Akhavan et al., 2016; Arias et al., 2017b; Arunachalam et al., 2016a; Huang et al., 2016; Jiménez-
Domingo et al., 2014b; Mazur & Chen, 2011; Neshati et al., 2014; Putro et al., 2020; Rahmanniyay et al., 2019;
Ren et al., 2011b; Stylianou & Andreou, 2012b, 2013; Zhang & Zhang, 2013) or a combination of an
evolutionary method with another technique, for example fuzzy with genetic algorithms (Britto et al., 2012b;
Tavana et al., 2013). Τhe most widely used method was the genetic algorithm. This was applied in more than 10
studies (Agustín-Blas et al., 2011; Mazur & Chen, 2011; Ren et al., 2011b). Data mining, which consists of
techniques based on clustering and association rules, were also used, particularly for homogeneous grouping
(Farhadi et al., 2012; X. Yang et al., 2018). Other studies used a variety of methods to achieve their goals, such
as Approximation algorithms (Farhadi et al., 2011; Ren et al., 2011b), mathematics and statistics, which is
composed of regression models, distance measures, statistical methods, and others (Farhadi et al., 2011;
Gajewar & Sarma, 2011; Kamel et al., 2014)). Other papers employed multi-agent systems
(Soh, 2004; Wang &
Zhang, 2015), or decision making approaches using the Analytic Hierarchy Process, and semantic ontologies
(Paredes-Valverde et al., 2018a). In general, studies using these techniques do not try to automate tasks
associated with forming teams but rather suggest the team.
It was interesting to discover that the majority of solutions try to maximize the project requirements.
Finding a team whose members' talents align with the needs of the intended project is the goal here (Akhavan et
al., 2016; Arias et al., 2017b; Feng et al., 2010; Huang, Sun, et al., 2017; Jiménez-Domingo et al., 2014b; Kamel
Int. Journal of Business Science and Applied Management /
et al., 2014; Wang & Zhang, 2015). Typically, dozens or even hundreds of potential team members with a
variety of abilities are considered for the position. Therefore, it is difficult to assemble the best team without
computer support. Other solutions try to improve working relationships, which entails creating a cohesive team
where members may easily commit to each other (Acuña & Juristo, 2004; André et al., 2011a; Chiang & Lin,
2020; Colomo-Palacios et al., 2012; Gilal et al., 2016a; Martínez et al., 2010). This goal often appears in
solutions based on psychometric instruments. The major objective of some of the studies is to identify a team
that can reduce project or communication costs by selecting team members based on their individual costs
(Farhadi et al., 2012; Gajewar & Sarma, 2011; Neshati et al., 2014; X. Yang et al., 2018). Other options, which
focus on delivering the final product as quickly as possible so that team members can move on to other projects,
aim to assemble a team that can minimize project delivery time (Arunachalam et al., 2016a; Rahmanniyay et al.,
The present literature review makes it clear that there are current trends in the field of team formation. It is
also evident that the existing literature mainly focuses on the automated team formation process from a broader
perspective, possibly overlooking the specificities of particular environments such as education, sports,
business, etc. It would be valuable to consider re-evaluating the process incorporating these perspectives as well.
However, there are still many weaknesses and gaps in the current research. They serve as a basis for defining the
following directions for future research:
To effectively form teams in various contexts with diverse criteria, a well-defined model that includes all
aspects of the team formation process should be created in collaboration with other fields such as education,
training, and psychology. This model can then be used as a standard in all team formation initiatives.
There is a lack of sufficient solutions to address team formation in different contexts. What is needed is the
creation of a computational system that will provide assistance in the main grouping functions and learning
The majority of solutions are tested using hypothetical or simple cases. These techniques may not be
equally effective or scalable in real-world settings, that is, they may not adequately address complex situations.
In order to create teams that meet the needs of projects, the majority of papers define a set of attributes that
represent the knowledge and skills of prospective team members. However, the way that these attributes are
specified is often rather vague. For instance, when quantifying programming language experience, the
considered attribute simply contains a score indicating the degree of the attribute and does not identify the
languages. As a result, it is impossible to determine the exact extent of the person's experience.
The specific context of the team formation problem determines the selection of the appropriate technique to
be used. However, there are many suitable methods that can be applied in the team building process depending
on the situation, which prompts researchers to question the reasoning behind the chosen technique.
Project managers and potential team members often raise concerns about the assessment of attributes. This
approach can lead to subjective and unreliable results, as the data generated may not accurately reflect the actual
situation. The literature review indicates that this gap exists due to the fact that most solutions focus on the
allocation phase without providing a systematic way to monitor the knowledge and skills of the staff.
Lack of community access to information and solutions. The reviewed papers cannot be evaluated in other
contexts because they lack the tools required for other researchers to reproduce and use their findings. As a
result, there are not many studies that compare their findings with similar ones in the literature.
Poor contributions are readily apparent in the realm of quality measures that assess the effectiveness of
team formation from multiple perspectives. Because of this, using quality of service (QoS) as an evaluation
framework for team formation is a fruitful way to determine whether the process of forming the teams has been
successful. Furthermore, regarding the methodology of the studies, it is evident that there are no ready-made
solutions available for use, nor the data collected during their studies. Consequently, replication of the studies
becomes unfeasible, and the cost of (i) conducting comparative studies with the available tools and (ii)
transferring the technology to industry is high.
5.1 Implications
Several trends in the field of team formation are observed based on the literature reviewed. The present
literature review places a central emphasis on the automated process of team formation from a general
perspective, potentially overlooking the view of educational contexts and cooperative learning often applied in
educational institutions. It would be beneficial to enrich it by incorporating these topics as well, where
collaboration with others is important. However, there are still many issues that are not properly handled during
team formation. These problems include weaknesses and gaps in the reviewed literature. They are used to
emphasize the conclusions drawn from the examination of team formation in many studies. These implications
relate to research perspectives: implications for current research as well as practical applications.
Georgios Stavrou, Panagiotis Adamidis, Jason Papathanasiou and Konstantinos Tarabanis
5.1.1 Relevance to Future Research
The implications of our review for the field of team formation are significant, as it highlights the need for a
comprehensive approach that considers both technical and non-technical characteristics. Traditionally, team
formation has focused primarily on technical skills, but our review challenges this perspective. By comparing
computer-based and pedagogical outcomes, we identified strengths, weaknesses, and gaps in current research
that suggest team-building strategies may not be generalizable when based only on local data sets. To address
these limitations, we propose the development of a comprehensive paradigm that captures the specifics of team
formation in different settings. This paradigm should be developed in collaboration with fields such as
education, training, and psychology to ensure a holistic approach.
Our review not only evaluates team building effectiveness from multiple perspectives, including qualitative
measures that reveal inadequate contributions, but it also highlights the increasing importance of non-technical
characteristics or soft skills, such as personality traits and social attributes, in effective team building. However,
there is a significant lack of research regarding the costs and benefits associated with the use of soft skills in
team building, which presents a valuable direction for future research.
By challenging the prevailing notion that team building focuses on technical characteristics, we call for a
more comprehensive approach that considers both technical and non-technical aspects. We urge researchers to
consider contextual factors that influence the selection of team-building techniques, aiming to better understand
the circumstances in which certain approaches are more effective than others.
Overall, our review provides a roadmap for future research in team building that identifies areas for further
investigation and argues for a shift in the prevailing perspective.
Team formation has emerged as a prominent area of research in recent decades, and our systematic review
provides a comprehensive overview of the current state of team formation from various perspectives. This
review is beneficial for researchers seeking to understand team formation or learn about existing solutions,
methods, or techniques. Most of the reviewed papers effectively achieve their objectives by optimizing project
requirements, selecting team members based on their individual attributes, or improving working relationships
through team composition that aligns with project requirements. The presented techniques and methods aim to
facilitate successful team formation based on each study's objective or to achieve superior performance
compared to other or previous studies. Additionally, we found that more than six different types of
characteristics were measured, each having a different effect on team formation.
Despite the growing number of publications and the variety of computational approaches supporting team
formation, we identified a lack of comparability among the results across studies, as discussed in Section 3.4.
Most studies only incorporate a limited number of fixed sets of characteristics, typically focusing on just two:
technical expertise and personality traits. This limitation restricts the diversity of team compositions, probably
due to the complexities that arise when increasing the set of parameters for selecting suitable team members.
Moreover, many proposed approaches primarily focus on validating algorithm efficiency rather than
evaluating the impact on team performance after formation. This highlights the need for further improvements
in team formation to optimize its functionality. An important aspect to consider is the development of integrated
solutions that offer superior performance. In future research, we plan to explore some of the directions presented
in this review. This includes investigating the most relevant technical and non-technical attributes for team
formation, providing a concrete attribute list, and conducting tests to implement and benchmark existing
algorithms for performance and efficiency.
It is essential to acknowledge the limitations of this study. Firstly, despite conducting searches across major
online databases, there is a possibility of additional scientific publications that could contribute to a more
comprehensive understanding of the field and available solutions. Lastly, this literature review did not include
non-English publications, thus excluding team formation approaches explored in other languages.
In summary, this comprehensive review highlights the importance of improving team formation processes
and sets the stage for future research endeavours.
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