3.1.1 Capacity: individual and social capabilities of agents

In many domains, a capability is defined as a particular skill required to perform an action. The capacity dimension has been exploited by numerous previous works, like Robust Team Formation (Okimoto et al., Reference Okimoto, Schwind, Clement, Ribeiro, Inoue and Marquis2015; Crawford et al., Reference Crawford, Rahaman and Sen2016) or Online Team Formation (Anagnostopoulos et al., Reference Anagnostopoulos, Becchetti, Castillo, Gionis and Leonardi2012). In these works, agents are assumed to have multiple binary skills (i.e. the agent either has a required skill or not). This is a simplistic way to model an agent’s capabilities since it ignores any skill degree. In real life, capabilities are not binary since every individual (e.g. human or robot) shows different action performance. This is why some works propose a more realistic approach by defining graded agent capabilities, for instance, by defining skill levels (Chalkiadakis & Boutilier, Reference Chalkiadakis and Boutilier2012).

On a different vein, Rangapuram et al. (Reference Rangapuram, Biihler and Hein2015) builds a weighted, undirected graph where the weight between each pair of agents reflects their degree of compatibility to jointly solve tasks. These weights are updated along multiple encounters between agents. On a somehow related vein, Peleteiro et al. (Reference Peleteiro, Burguillo-Rial, Luck, Arcos and Rodriguez-Aguilar2015) try to capture the quality of the solutions of team tasks via a model that besides using skills and compatibility between agents (called the strength of collaboration synergies within coalitions), calculates the reputation of teams (coalitions) as a whole and of single agents. These reputation values are used by the team composition process.

Typically, the capabilities of agents are assumed to be known, though there exist models that consider that an agent can learn the capability levels of other agents. For instance, Liemhetcharat and Veloso (Reference Liemhetcharat and Veloso2014) had the insight that repeated interactions allow to discover the capabilities of other agents. Agents learn a model of synergy via repeated interactions. Such synergy values are then used by individual agents to learn the capabilities of others, and hence to subsequently compose teams with improved performance. However, in open environments (i.e. when new agents and tasks are dynamically introduced), agents need more sophisticated procedures to decide which team to join. For instance, Chen et al. (Reference Chen, Chen, Timsina and Soh2015) propose an ad hoc team formation framework that considers learning other agents’ capabilities in the context of unknown tasks. In order to solve a new task, agents would prefer to team up with unknown agents instead of with agents whose known capabilities do not adjust to the task. They observe that learning the capabilities of others in the context of agent and task openness improves team composition and task resolution.

3.1.2 Personality: individual behaviour models

Personality is key to understand people’s behaviour, cognition, and emotion. The use of personality models in agents helps to create more realistic complex scenarios. Indeed, autonomy is related to how individuals behave and what makes them behave differently, even when facing the very same situation. Personality provides a mechanism for behaviour selection that is independent of social background (such as beliefs or morality). Very few MAS contributions considered the notion of personality, that is, individual behaviour model, to compose heterogeneous teams. For instance, Hanna and Richards (Reference Hanna and Richards2015) study the influence of two agent personality traits: extraversion and agreeableness, both expressed as verbal and non-verbal communication skills. They construct pairs of human users and Intelligent Virtual Agents (IVAs) and analyze how the personality traits influence the development and maintenance of a Shared Mental Model (SMM). The results confirm the importance of providing IVAs with these personality traits to succeed in jointly solving tasks. On a different vein, Andrejczuk et al. (Reference Andrejczuk, Rodriguez-Aguilar and Sierra2016) use personality traits to partition a group of humans into psychologically balanced and gender-balanced heterogeneous teams with the purpose of increasing the overall performance of the resulting teams. In the other paper, Andrejczuk et al. (Reference Andrejczuk, Rodriguez-Aguilar, Roig and Sierra2017) use personality as well as competences to partition agents into competent, personality, and gender balanced teams. The personality traits are measured by the Post-Jungian Personality Theory, which is a modified version of the Myers–Briggs Type Indicator (MBTI) (Myers et al., Reference Myers, McCaulley, Quenk and Hammer1998; Wilde, Reference Wilde2013).

Marcolino et al. (Marcolino et al., Reference Marcolino, Jiang and Tambe2013, Reference Marcolino, Xu, Gerber, Kolev, Price, Pantazis and Tambe2016; Nagarajan et al., Reference Nagarajan, Marcolino and Tambe2015) propose a new approach for action selection. A task is a sequence of actions to be decided at execution time. To choose which action to execute next, every heterogeneous agent within a team votes for its preferred candidate action. Agents vote according to a probability distribution over actions that varies for each agent. This can be understood as a way of modelling an agent’s personality, motivations, and beliefs (causing him to behave in a certain way).

In a series of papers, Farhangian et al. (Reference Farhangian, Purvis, Purvis and Savarimuthu2015a, Reference Farhangian, Purvis, Purvis and Savarimuthu2015b) use the MBTI scheme to model different agent personality types. Farhangian et al. (Reference Farhangian, Purvis, Purvis and Savarimuthu2015a) use both individuals’ skills and personality types (measured by MBTI and Belbin, Reference Belbin1993, personality tests) to compose teams. These two dimensions are used to simulate human team composition in a business environment.

Another aspect covered by the existing literature is the individual agent knowledge about the other team members’ personalities, that is, about their behaviour models. These works go beyond many ‘ad hoc’ team composition systems where information details about the behaviour of individual agents is absent. Barrett et al. (Reference Barrett, Stone, Kraus and Rosenfeld2013) focus on how a new member in a team behaves in order to co-operate well with the other team members whose behaviours are unknown. Each agent is endowed with a learning mechanism for building models of the behaviours of many distinct types of other agents via repeated interactions. A similar setting is presented by Agmon et al. (Reference Agmon, Barrett and Stone2014), though they consider that there are only two types of agents: a best response agent (choosing his action based on the current state of the world), and an ad hoc agent (has a better awareness of the team’s possible actions and the resulting joint utility). There is no a priori model, hence, similarly to Barrett et al. (Reference Barrett, Stone, Kraus and Rosenfeld2013), an ad hoc agent needs to decide his behaviour by observing his peers.

3.2.1 Individual-based approaches

Sometimes teams work less effectively than initially expected due to several reasons: a bad balance of their capacities, bad personal relations, or difficult social situations. Hence, in order to make sure a task is performed the most effectively, the large body of literature defines the action plan of the task as a set of requirements for agent individual characteristics. It is assumed that the task can be fulfilled if the task requirements are a subset of the capabilities of team members. We categorize existing work on team composition with the purpose to solve a task into two categories of individual properties: capacity and personality.

3.2.2 Plan-based approaches

The notion of task in plan-based approaches is normally understood either as a set of actions or as a sequence of actions. Well-organized teamwork can shorten the time required for completing a particular task by distributing a set of actions across team members. Both Barrett et al. (Reference Barrett, Stone, Kraus and Rosenfeld2013) and Agmon et al. (Reference Agmon, Barrett and Stone2014) employ an indirect planning method driven by the most informed agents to solve a set of actions. Barrett et al. (Reference Barrett, Stone, Kraus and Rosenfeld2013) introduce an ad hoc team agent that learns its teammates’ models (i.e. their predictable action selection) and chooses its own actions so that they collectively maximize the likelihood of success. In detail, they use Monte Carlo sampling to simulate the long term effects of collective actions. As an extension to the previous work, in Agmon et al. (Reference Agmon, Barrett and Stone2014) the actions selected by ad hoc agents influence the actions that the other team members will choose. Each agent has a set of possible actions that it may choose in order to solve each subtask. The ad hoc agents need to predict the actions of its teammates (conditioned in this case to its own actions) and behave based on these predictions with the purpose of influencing the collective selection of actions in the team to reach a joint optimal solution.

Among the approaches considering a task as a sequence of actions, in Marcolino et al. (Reference Marcolino, Jiang and Tambe2013) a team of agents jointly playing the computer game Go plan which action to take next by voting on the possible alternatives from a discrete set of possible actions. Authors prove that under certain conditions of opinion diversity, aggregating the decisions of a team of heterogeneous agents is a better planning strategy than the decision of a team built with copies of the most competent agent (called the strongest agent). This shows that diversity improves the planning capacity of a team solving a complex task like Go. In Marcolino et al. (Reference Marcolino, Xu, Gerber, Kolev, Price, Pantazis and Tambe2016), the authors use the same technique to suggest a user a number of optimal solutions for their next action decision. The application domain of their algorithm is house design. Various design alternatives are proposed to the user in order to select one for further study.

Similarly, in Moon et al. (Reference Moon, Carley, Schneider and Shigiltchoff2005) the plan is created by team members during a game. The domain used for this study is an on-line multi-player computer game called America’s Army, which is a first-person shooting game. The game is the duel of two teams, usually an assault team and a defense team. A team consists of one to fourteen players. Every game starts with a new set of players that need to co-ordinate their activities during the game in order to win. Players are allowed to communicate in a team chat. A team wins the game either by killing all of its opposing players, or by accomplishing the goal for that mission (for instance, securing an oil pipeline or crossing a bridge).

Finally, Rochlin et al. (Reference Rochlin, Aumann, Sarne and Golosman2016) deal with self-interested agents in a team that select one agent to accomplish the task of purchasing a jointly desired item with the lowest possible cost. By doing so, the team assigns the execution of the plan to a single member of the team, becoming the buyer. The buyer’s strategy decides whether to maintain the search looking for better deals (search for a further action), or stop looking and buy at the lowest price found so far, bearing the incurred buyer’s overhead. This strategy balances the expectation of finding a better price (considering the price distribution built during the search) and the team policy to reimburse the cost of the task solution finding to the buyer.

3.3.1 Minimizing overall cost

Team cost efficiency has received some attention in the literature. There are various costs associated with team composition and formation problems (e.g. communication costs or agent service costs). For instance, some results balancing cost and quality were obtained by Kargar et al. (Reference Kargar, An and Zihayat2012). They propose algorithms for composing a competent team in a social network. When composing a team, those algorithms minimize team members’ costs and communication costs within the team. Kargar et al. (Reference Kargar, An and Zihayat2012) require that agents have the necessary competences to perform a task, but do not require any specific motivation from them.

A similar approach is presented in Crawford et al. (Reference Crawford, Rahaman and Sen2016) and Okimoto et al. (Reference Okimoto, Schwind, Clement, Ribeiro, Inoue and Marquis2015). These works propose a model for robust team composition and go a step further with respect to Kargar et al. (Reference Kargar, An and Zihayat2012) since they minimize the overall cost among k-robust teams (see Section 3.4.1 for a definition of a k-robust team). That is, this model assumes that up to k agents within a team may eventually fail without affecting the achievement of the task. Thus, it assumes more realistic conditions than Kargar et al. (Reference Kargar, An and Zihayat2012). However, likewise Kargar et al. (Reference Kargar, An and Zihayat2012), agents’ motivations to work together in a team are not considered. Finally, Anagnostopoulos et al. (Reference Anagnostopoulos, Becchetti, Castillo, Gionis and Leonardi2012) propose approximation algorithms to compose teams minimizing simultaneously low co-ordination costs and agent workload.

3.3.2 Maximizing social welfare

A second objective considered in the team composition and formation literature is maximizing social welfare. That is, maximizing the utility function of a team, as a whole, while performing a task. The utility obtained is then allocated to the individual members of the team. For instance, Chalkiadakis and Boutilier (Reference Chalkiadakis and Boutilier2012) propose a Bayesian Reinforcement Learning framework where agents learn from iterated coalition compositions. Agents can choose between exploration (select coalitions to learn more about new agent types) and exploitation (rely on known agents). Exploitation enables agents to maximize their utility function by performing tasks with reliable agents (discovered during the exploration phase).

Paradoxically, the agent motivation to maximize its individual welfare may reduce the overall team cost and additionally increase the overall quality of the performed task. For instance, in Rokicki et al. (Reference Rokicki, Zerr and Siersdorfer2015) a human team competition mechanism improves cost efficiency and the quality of a solution in a team-based crowdsourcing scenario. In conventional crowdsourcing reward schemes, the payment of online workers is proportional to the number of accomplished tasks (pay-per-task). Rokicki et al. examine the possibility of getting much higher rewards by introducing strategies (e.g. random or self-organized) for team composition. Their mechanism triggers the competition among human teams as the reward is only given to the top-5 performing teams or individuals. Their evaluation shows substantial performance boosts (30% in the best scenario) for team-based settings without decreasing the quality of the outcome.

The objective of maximizing social welfare is also considered in many ad hoc settings, like the one proposed by Agmon et al. (Reference Agmon, Barrett and Stone2014). Agmon et al. consider a framework with two types of agents: best-response and ad hoc agents forming teams. On the one hand, best-response agents have limited knowledge and assume that the environment and their teammates will behave as observed in the past. On the other hand, ad hoc agents have a more complete view of a team actions, agents’ joint utilities and their action costs. Using such information, ad hoc agents try to influence joint decisions. In Agmon et al. (Reference Agmon, Barrett and Stone2014) the authors consider that ad hoc agents know with uncertainty their teammates’ behaviour. The paper analyzes the impact on optimal solutions of ad hoc agents misidentifying their teammates’ types.

The study of self-interested agents that co-operate in a team has also attracted the interest of researchers in MAS. An interesting example of this approach is presented in Farhangian et al. (Reference Farhangian, Purvis, Purvis and Savarimuthu2015b), where self-interested agents need to maximize the welfare of all team members in order to maximize their own benefit. Hence, they indirectly aim at maximizing the utility of the team. Similarly, in Chen et al. (Reference Chen, Chen, Timsina and Soh2015) agents repetitively decide which team to join by balancing both rewards from completing tasks and learning opportunities from more qualified agents. That is, each agent consider whether to sacrifice short-term rewards to acquire new knowledge that benefits himself and the whole community in the long run.

3.3.3 Maximizing quality

The last range of models propose a number of methods where agents try to maximize the quality of solutions whilst minimizing the time to achieve them, namely to maximize team performance.

Recent OP studies show that team members’ diversity is a key factor to increase team performance Wilde (Reference Wilde2009). As mentioned in Section 3.1 Marcolino et al. (Reference Marcolino, Jiang and Tambe2013) present a setting where agents in a team vote together to decide on the next joint action to execute that maximizes the team’s solution quality. The authors prove that a diverse team can overcome a stronger team (i.e. a team built of copies of the strongest agent) if at least one agent has a higher probability of taking the best action in at least one world state than the probability that the best agent has of taking that action in that state. The attempt of capturing heterogeneity is also used by Andrejczuk et al. (Reference Andrejczuk, Rodriguez-Aguilar, Roig and Sierra2017). There, instead of looking for a single heterogeneous team, Andrejczuk et al. partition a group of agents into psychologically balanced, gender-balanced, and competent heterogeneous teams with the purpose of increasing the overall performance of the resulting teams.

Hanna and Richards (Reference Hanna and Richards2015) also use personality to investigate the influence of IVAs on team collaboration. Their findings reveal that team performance boosts when the human and the IVA in a team have a SMM. Building a SMM is directly related to the psychological traits of IVA.

Carley et al. (Reference Carley, Moon, Schneider and Shigiltchoff2005) found that the most favourable size of a team is 10 because of the relatively higher survival ratio. Also, frequent usage of the weapon, precision of the weapon used, and frequency of communication, can be the distinctions between winning teams and losing teams. Moreover, frequent communication increases a team’s situation awareness, that is, gives information about where other team members are and how they can be supported.

Peleteiro et al. (Reference Peleteiro, Burguillo-Rial, Luck, Arcos and Rodriguez-Aguilar2015) introduce a decision-making mechanism that on top of improving the quality, aims at increasing the quantity of completed tasks. It uses reputation and adaptation mechanisms to allow agents in a competitive environment to autonomously join and preserve coalitions (teams). In terms of team performance, they show that coalitions keep a high percentage of tasks serviced on time despite a high percentage of unreliable workers. Moreover, coalitions and agents demonstrate that they successfully adapt to a varying distribution of incoming tasks.

Liemhetcharat and Veloso (Reference Liemhetcharat and Veloso2012) developed a model to learn and analyze capabilities of agents and synergies among them to solve the team composition problem using previous joint experiences. They define a synergy model as a graph where the distance between agents is an indicator of how well they work together. Their main contribution is that their algorithm learns from only a partial set of agent interactions in order to learn the complete synergy model. In a subsequent article (Liemhetcharat & Veloso, Reference Liemhetcharat and Veloso2014), the authors study the learning agent team formation problem with the goal of maximizing the mean performance of a team after K learning instances. There, learning agent pairs have heterogeneous rates of co-ordination improvement, and hence the allocation of training instances has a larger impact on the performance of the final team.

The notion of fairness is also considered in the context of team performance. An example of this approach is given in Liemhetcharat and Veloso (Reference Liemhetcharat and Veloso2014). Rochlin et al. analyze the correlation between efficiency and fairness in teams consisting of self-interested agents. They prove that the more fair the team the more efficient its members are.

3.3.4 Validation methods

Finally, it is worth discussing how researchers in CS evaluate and monitor the achievement of the objectives mentioned above. Omitting this information can create a false equivalence between the findings of research studies conducted in very different conditions. We distinguish among three main data sources, that is:

• ∙ existing databases available online containing real data;

• ∙ data simulation;

• ∙ empirical data.

3.4.1 Team composition

Although team composition in MAS has mainly focussed on building teams of software agents, that is agent teams, there is a growing number of works considering either mixed teams (Hanna & Richards, Reference Hanna and Richards2015), where agents and humans co-operate to achieve common goals (Ramchurn et al., Reference Ramchurn, Wu, Fischer, Reece, Roberts, Rodden and Jennings2016), or human environments, where people are supported by software (Jennings et al., Reference Jennings, Moreau, Nicholson, Ramchurn, Roberts, Rodden and Rogers2014). In MAS, we distinguish between two groups of methods (or processes) to compose team(s), namely:

1. 1. Exogenous team composition: there is an algorithm external to the agents that determines the composition of teams.

2. 2. Endogenous team composition: agents themselves decide in a distributed manner the composition of a team.

3.4.2 Team formation

We identify two main team organization structures to build effective teams:

1. 1. Hierarchical.

2. 2. Egalitarian.

We describe each team organization structure in the following sub-sections.

3.5.1 Non-successive and non-simultaneous tasks

In this case we face a one-shot task resolution. This is the simplest case for the team composition and formation problems. There is no long-term strategy used to compose and form teams. Thus, agents do not learn from past experiences and we cannot talk about the notion of community in this set-up.

3.5.2 Non-successive and simultaneous tasks

In non-successive and simultaneous tasks, the composition and formation problem becomes more complex as it now considers a set of one-shot tasks. There is still no use of the past experiences as the tasks are non-successive.

3.5.3 Successive and non-simultaneous tasks

When tasks are successive and non-simultaneous, the algorithms for team composition and formation deal with a task that has to be assigned to a team, and in many cases solved, before new tasks arrive. A successive setting can discover phenomena which we believe are important, but which are not captured when the attention is limited to static, non-successive tasks. If in the system of the same set of agents, teams are created and dismantled depending on the task, the agents may behave very differently than in a non-successive settings. For instance, a person will behave in a different manner if she repeatedly borrows a car from her friends, than when she simply rents a car. The successive setting has its advantages: it lets agents learn from the past experiences and build their beliefs based on this knowledge.

3.5.4 Successive and simultaneous tasks

When tasks are successive and simultaneous, the algorithms for team composition and formation deal with a set of tasks arriving, possibly overlapping in time that have to be assigned to newly composed teams.

3.6.1 Analysis

To the best of our knowledge, there are only few works in MAS literature that recognize the context as an important variable. Besides Anagnostopoulos et al. (Reference Anagnostopoulos, Becchetti, Castillo, Gionis and Leonardi2012), which considers both social and geographical contexts, the methods in the literature only consider the social context (if analyzed at all).

4.1.1 Capacity

In OP, the most important capacity of team members that is related to team performance is their cognitive ability. Hence, the main goal is to study how cognitive abilities influence team performance. Cognitive ability refers to the ‘capacity to understand complex ideas, learn from experience, reason, solve problems, and adapt’ (Devine & Philips, Reference Devine and Philips2001: 507). Hence, cognitive ability in OP is a much wider concept than capacity in MAS as on top of skills widely used in MAS systems, it contains many other properties such as experience, competences, age, or even gender.

Moreover, in contrast to CS, where capabilities are static, psychologists deal with the dynamism of human capacity. Humans learn new capabilities and increase their level every day for whole live (Laal and Salamati, Reference Laal and Salamati2012: 399–403] for the concept of the lifelong learning). There are diverse tests and methods to examine humans capacity, such as: intelligence or cognitive competences tests, assessment centres or social and behavioural competence tests. Regarding team composition, on the one hand, Bell and Devine and Philips (Reference Devine and Philips2001) found that mean team values of cognitive ability are correlated with team performance. Moreover, she also found that the lowest and the highest team members’ cognitive abilities are correlated with team performance in lab and field settings. In addition, Devine and Philips (Reference Devine and Philips2001) found that the variance of team members’ cognitive ability did not help predict team performance. These authors also found that the mean value is twice more informative in predicting than the lowest and the highest member’s scores. On the other hand, Devine and Philips (Reference Devine and Philips2001) found that cognitive ability influences team performance differently depending on contextual variables (such as working normative procedures or human resources policies). These findings suggest that, when composing a team, organizations and managers should not only take into account the members’ cognitive ability, but also the context in which the team will operate. This will be further discussed in Section 4.6.

Woolley et al. (Reference Woolley, Aggarwal and Malone2015) discuss the existence of a measurable collective intelligence in teams that is analogous to individual intelligence. Authors suggest the existence of a general collective intelligence factor that explains a team performance on a wide variety of tasks. Woolley et al. (Reference Woolley, Chabris, Pentland, Hashmi and Malone2010) show that collective intelligence is correlated with the average social sensitivity of group members, the equality in distribution of conversational turn-taking, and the proportion of females in the group. In STEM (Science, Technology, Engineering and Math) teams, gender diversity can enhance group processes, which are increasingly important as collaboration becomes a centre piece in the production of science. The enhancement of group processes and higher levels of collective intelligence can, in turn, lead to greater innovation and scientific discovery (Bear & Woolley, Reference Bear and Woolley2011). Finally, similarly to findings in the CS literature, the concept of team properties is normally understood as a sum of humans’ individual properties.

4.1.2 Personality

In addition to the before-mentioned individual properties, the literature has examined the role of personality. The most prominent approaches have been the ‘Big Five’ personality traits theory (Mount et al., Reference Mount, Barrick and Stewart1998), Schutz’s theory of fundamental interpersonal relations orientation (FIRO) (Schutz, Reference Schutz1958) and the MBTI method (White, Reference White1984). They have been used to find the personality traits and types associated with team performance. Regarding the ‘Big Five’ theory, meta-analytic research has found that certain levels of conscientiousness, openness to experience, and agreeableness are good performance predictors (Mount et al., Reference Mount, Barrick and Stewart1998).

Another approach is that of the theory of FIRO (Schutz, Reference Schutz1958). The idea is that humans have several needs (i.e. need for inclusion, control, and affection) and that groups with team members that have compatible needs will perform better than those with incompatible ones. Nevertheless, research has found mixed support for this theory (West, Reference West2012b).

Some companies have also tried to base their team formation on cognitive styles of the members, by using the MBTI assessment instrument—(Myers et al., Reference Myers, McCaulley, Quenk and Hammer1998), which is a questionnaire that measures cognitive styles along four dimensions: Extraversion–Introversion, Sensing–Intuition, Thinking–Feeling, and Judging–Perceiving. Nevertheless, there is not enough rigorous research evidence showing its relationship with team performance (West, Reference West2012b).

There are also novel approaches created with the purpose of team composition and formation. For instance, the Post-Jungian Personality Theory, which is a modified version of MBTI (Wilde, Reference Wilde2013). It operates on the same dimensions as MBTI. The main novelty of this approach is its use of the numerical data generated by the instrument (Wilde, Reference Wilde2011). The results of this method seem promising as within a decade this novel approach tripled the fraction of Stanford teams awarded national prizes by the Lincoln Foundation (Wilde, Reference Wilde2009). However, the method is not properly validated and tested, which makes it disregarded by psychologists.

4.1.3 Analysis

Several correlations have been found between cognitive ability and team performance. The personality is also present while composing teams, although the correlation between personality and team performance is not clearly explained. The most widely used test to measure personality is the ‘Big Five’. OP studies show that besides cognitive ability and personality, experience, and gender are further properties to consider for team composition (West, Reference West2012a). Indeed, research findings on this topic suggest that diversity in those characteristics can have an effect on team performance and innovation (West, Reference West2012a). Additionally, some further research has also paid attention to values and has found collectivism and teamwork preferencesFootnote ² to be additional good team performance predictors (Bell, Reference Bell2007).

4.2.1 Analysis

The OP literature provides many different classifications of task types, where the most important are the classifications based on the motivation of individuals, the cognitive abilities and the task context. Provided the amount of classifications and the apparent lack of consensus among them, we believe that choosing among the several classifications previously presented in order to apply them to the study of team composition is a hard decision. Nevertheless, such decision must be made in order to move forward with the understanding of how a task type can influence team composition. In an attempt to advice researchers, notice that the research show that the classification based on the motivation characteristics predicts more accurately team performance.

From OP perspective team performance cannot be assessed by simply measuring how long it takes for the group to finish a certain task or by counting the number of right answers to predefined and clear questions, which is a common approach in CS. OP rather analyzes joint team objectives and the team composition and formation setting (such as not realistic deadlines, a number of individuals in a team, the level of stress in a team, or the quality of the outcome).

The current research on OP focus has moved from task analysis so not many results are present. Although task types are defined, different task instances constantly appear because of technological development. That makes it very difficult to keep the pace. That is why the focus on OP moved to competences (understood as cognitive ability, see Section skills2). This is why not much work has appeared after defining task taxonomy. At the same time task complexity increased and hence, teams are getting more and more important. Moreover, a clear mapping between cognitive ability of individuals and task types is needed. As a major benefit such mapping would ease team composition.

4.3.1 Analysis

An important difference with respect to the CS literature is that team performance is considered from two perspectives: objective and subjective. Objective and subjective team performance are significantly and directly correlated. Therefore, and not surprisingly, the OP literature considers both types of performances when tackling team composition and team formation. The CS literature can benefit from the concept of subjective team performance that currently disregarded. Therefore, current team composition models, which mainly focus on the objective team performance, need to be extended.

4.4.1 Team composition

The OP research on team composition has been very influenced by task classification. For several authors, there is a relationship between task type and team type (structure). For example, according to Hackman (Reference Hackman1990), there are seven team types based on the task type to perform:

1. 1. top management;

2. 2. task force;

3. 3. professional support;

4. 4. performing groups;

5. 5. human service;

6. 6. customer service;

7. 7. production teams.

Devine (Reference Devine2002) and Delgado Piña et al. (Reference Delgado Pña, Romero Martinez and Gomez Martinez2008) highlighted that team performance depends on a good matching between team type and task type.

On the other hand, there are multiple team type classifications in the literature based on other criteria (Gibson and Kirkman, Reference Gibson and Kirkman1999; Marks et al., Reference Marks, Mathieu and Zaccaro2001; Devine, Reference Devine2002): motivation-based, cognitive-based, or context-based (see Section 4.2), though none of them has been widely used or accepted. Also, there is agreement that team diversity must be exploited while composing teams. Diversity refers to the degree or level to which the members of a group differ or contrast in one or more properties. Diversity has been shown to have an impact on team performance (Mathieu et al., Reference Mathieu, Maynard, Rapp and Gilson2008). In their review, Mathieu et al. (Reference Mathieu, Maynard, Rapp and Gilson2008) point out the vastness of the literature featuring team diversity and draw attention to four main diversity dimensions: demographic, personality, functional background, and attitudes and values.

Horwitz and Horwitz (Reference Horwitz and Horwitz2007) conducted a meta-analysis to understand the relationships between team diversity and team performance. For this, they differentiated between two classes of diversity: bio-demographic and task-related. The former refers to diversity in individual properties that are observable and not learned (e.g. personality, gender, age, ethnicity), whereas the latter regards diversity in acquired capabilities, such as education or expertise. Using meta-analytic techniques, they found task-related diversity to be positively correlated to both qualitative and quantitative measures of team performance. However, they did not find a clear relationship between bio-demographic diversity and team performance. Although pointing out the small number of studies supporting these latest findings, their preliminary results seem to give more importance to the diversity of acquired team member properties, such as the type of education or knowledge expertise.

Finally, another factor influencing team performance is team size. The relationship between team size and productivity is a question of broad relevance across economics, psychology, and management science. Hence, the size of a team is one of the most frequently studied parameter when analyzing team performance. There is a disparity in the literature due to the fact that appropriate team size is dependent on the task and the social context in which the team operates. When it comes to athletics, sport teams have a defined number of team players: a football team needs 11, the Standard Platform League in RoboCup five players per team, and baseball teams require nine players. But when it comes to organizations, it is hard to find a golden rule to determine the optimal number of team members. For complex tasks, however, where both the potential profits and risks of teamwork increase with the number of team members, neither theoretical studies nor empirical evaluations consistently favour larger vs. smaller teams (Mao et al., Reference Mao, Mason, Suri and Watts2016). Regarding established theories, psychology (Steiner, Reference Steiner2007), economics (Holmstrom, Reference Holmstrom1982), and management (Malone & Crowston, Reference Malone and Crowston1994) studies suggest that increasing team size can be harmful to team performance. This happens because: individuals find it tempting to free ride on the efforts of teammates (Holmstrom, Reference Holmstrom1982; Steiner, Reference Steiner2007); the overhead associated with communication increases with team size (Steiner, Reference Steiner2007); and communication among team members causes partitioning into sub-teams (Lorenz et al., Reference Lorenz, Rauhut, Schweitzer and Helbing2011) and chitchat (Tetlock et al., Reference Tetlock, Peterson, McGuire, Chang and Feld1992). Therefore, in complex tasks, where all these reasons may exist simultaneously, the relationship between team size and performance is not well described by existing theories. Mao et al. (Reference Mao, Mason, Suri and Watts2016) performed a study of the dynamics of team performance and its relationship with team size in the digital volunteer setting of crisis mapping. Their findings show that although social loafing and co-ordination costs result in reduced contribution from individuals in larger teams, the potential benefits of co-ordination can outweigh this loss in performance.

However, other studies show that there is an inverse relationship between the size of the team and its performance (Bartol, Reference Bartol1977; Oyster, Reference Oyster1999). Oyster (Reference Oyster1999) and Bartol (Reference Bartol1977) show that team size is important when analyzing team performance. Yet, they have offered different recommendations concerning the best size for various types of tasks to achieve acceptable performance. Oyster (Reference Oyster1999) states that the right number of people in a team depends on the kind of tasks team members need to perform. They believe that for teams ranging from four to six, all the team members’ competences can be fully used, but for larger teams some members’ competences are under-used and this provokes that teams split up. According to the studies of Bartol (Reference Bartol1977), the optimal number of members for problem-solving tasks is five. He states that there is a limit to the team size, which, if exceeded, causes a drop in the performance of the team. Bartol (Reference Bartol1977) says that in the case of a team containing more than six people there is a tendency to split the team into two, which brings about negative effects. The cause is twofold: high co-ordination cost and loss of motivation by team members Oyster (Reference Oyster1999).

Finally, some studies have found team size to be unrelated to performance Martz et al. (Reference Martz, Vogel and Nunamaker1992) or that increasing team size actually improves performance without limit Campion et al. (Reference Campion, Medsker and Higgs1993).

4.4.2 Team formation

Once a team has been composed, there are different processes that the team carries out to execute the task and achieve the collective goal. Several classifications of team processes have been proposed in the literature, from which, the most recent and overarching one is the one proposed by Marks et al. (Reference Marks, Mathieu and Zaccaro2001) and Salas et al. (Reference Salas, Sims and Burke2005), Salas et al. (2009). Typically the research investigated the ways of implementing team processes and of measuring how well teams perform. To begin with, Marks et al. (Reference Marks, Mathieu and Zaccaro2001) distinguish between three broad types of processes: action-orientated, transition-orientated, and interpersonal. The first ones refer to actions that team members undertake to accomplish goals, namely team monitoring, systems monitoring, monitoring progress towards goals, and co-ordinating activities. Regarding transition-orientated processes, these are actions related to planning and/or evaluating in order to guide in attaining team goals, that is goal specification, mission analysis, formulation and planning, and strategy formulation. Finally, interpersonal processes are those intended to manage interpersonal relationships. They comprise motivating/confidence building, conflict management and affect management (Marks et al., Reference Marks, Mathieu and Zaccaro2001). On the other hand, Salas et al. (Reference Salas, Sims and Burke2005) built upon previous research and narrowed down the main processes into ‘Big Five’ team processes: team orientation, backup behaviour, team leadership, adaptability, and mutual performance monitoring.

Another important aspect is that team climate influences the effectiveness of processes. A team climate is defined by the degree to which a team of people possesses certain core properties that are needed for the team to work effectively. These properties include the interrelationship among team members, the identification of each person with the team and its social values, the co-ordination of team resources, behaviours and technologies, as well as the desire of each team member to achieve the objectives of the team (Meneses & Navarro, Reference Meneses and Navarro2015). A good climate assures the sharing of resources, mutual rewards, and information exchange. It promotes a high level of openness, safety, and a mix of upward, downward, and horizontal communication processes that help to increase team performance (Kozlowski and Ilgen; Mathieu et al., Reference Mathieu, Maynard, Taylor, Gilson and Rudy2007; Knapp, Reference Knapp2010; Rico et al., Reference Rico, Alcover and Tabernero2010).

A team climate that is conductive to learning requires shared perceptions of work settings (Brodbeck, Reference Brodbeck2003; James et al., Reference James, Choi, Ko, McNeil, Minton and Wright2008; Ramirez-Heller et al., Reference Ramirez-Heller, Berger and Brodbeck2014). According to Brodbeck (Reference Brodbeck2003) and Ramirez-Heller et al. (Reference Ramirez-Heller, Berger and Brodbeck2014), a team climate conductive to learning is characterized as one in which:

1. 1. There is empathy, support, as well as a common understanding among its members, conveying an atmosphere of mutual trust.

2. 2. There is a regular contact as well as informal and formal communication processes among its members.

3. 3. There exists a common agreement with the goals and objectives to be achieved, and these shared goals are clear, realistic, and feasible.

4. 4. There is a prevailing notion of democracy and equality among its members, with no one having particular control over the others.

5. 5. Members perceive a personal development as the team enhances their creativity and provides general support in fulfilling their individual plans.

Finally, there are various studies in OP analyzing the effect of communication and network structure on team performance. Typically, teams in organizations are strategically composed by heterogeneous individuals (Osatuyi, Reference Osatuyi2012). This is based on the assumption that once team members share their information, the team as a whole can access a larger pool of information, knowledge and expertise. However, studies have shown that teams, unlike individuals, sometimes do not effectively share and use the unique information available to them. This leads to poorer decision making. Informational influence theory holds that the subjective importance of information may affect if information is shared or not. Henceforth, an important factor for performance improvement is the proactive communication of information about team members’ goals (Butchibabu et al., Reference Butchibabu, Sparano-Huiban, Sonenberg and Shah2016). It is also found that task complexity is negatively correlated with information exchange. Surprisingly, teams tend to share less information when working on complex tasks, compared to when working on simple tasks (Osatuyi, Reference Osatuyi2012). Also in an online game domain communication plays an important role on the performance of virtual team members (Leavitt et al., Reference Leavitt, Keegan and Clark2016). For instance, League of Legends enables non-verbal communication through ‘pings’, alerts that are easy to activate and provide auditory and visual hints for teammates. Leavitt et al. (Reference Leavitt, Keegan and Clark2016) analyze 10.293 matches in this popular game and test the impact of ping actions on team performance. They show that pings by players have a positive but concave relationship with player performance. That is, teams sending more pings have higher levels of performance up to a point after which sending more pings leads to lower levels of performance.

Another important factor influencing team performance is team shared belief in their collective power to produce desired results (Yildir, Reference Yildir2005). In Yildir (Reference Yildir2005), the team shared beliefs of computer game players were measured as 126 teams competed in a highly interdependent, online role-playing team game. Structural equation modelling results indicated that for all interdependent teams, as team shared belief increased, both team persistence and performance also increased positively and linearly.

4.5.1 Analysis

Unlike CS, the reviewed OP literature does not study simple scenarios such as non-successive and non-simultaneous tasks. Typically, OP analyzes complex and realistic scenarios as human learning capabilities need to be considered. Moreover, on top of including the social network and memory about the outcomes of past experiences, the researchers in OP deal with the dynamics of individuals’ capabilities (as humans learn new capabilities and forget not used ones).

4.6.1 Analysis

In contrast with CS approaches, the context where teams solve tasks plays an important role in the OP literature. The context is understood as internal and external factors influencing teamwork. The internal context can be characterized as dimensions of the organization, such as vision and mission, values, policies and strategies, or organizational benefit system. The external context can be characterized as dimensions of the environment in which the organization operates, that is the culture, the available resources, and the uncertainty about other players behaviour.