DOES SIZE MATTER? A MULTILEVEL MODEL OF THE RELATIONSHIP BETWEEN TEAM SIZE AND TEAM AND INDIVIDUAL PERFORMANCE,
MODERATED BY TIME ALLOCATION
Master thesis, MSc, specialization Human Resource Management University of Groningen, Faculty of Economics and Business
January 8, 2020
GIJSBERT JOZUA VAN HUNNIK MSc S2735873
Evert Harm Woltersweg 35B 9831 TG Aduard Tel.: +31 (0)6-83347011 G.J.van.Hunnik@student.rug.nl
Supervisor
Dr. JOOST VAN DE BRAKE H.J.van.de.Brake@rug.nl
Word count: 11647
ABSTRACT
Despite more than fifty years of team research, it still remains unclear which team size is best for team and individual performance. Although the bulk of the team size literature hints at smaller teams being more effective for performance, some studies have found positive effects of larger team sizes. These conflicting findings highlight the need to research moderating variables, such as the time employees spent in their focal team. Based on theories on communication, social loafing and time allocation, we construed a multilevel model in which we test the effect of team size on team and individual performance, moderated by time allocation. We test these predictions based on team (N = 59) and individual level data (N = 293) from Dutch organizations operating in knowledge intensive sectors. Our results show that team size is negatively related to performance on the individual level even after controlling for gender, age, tenure, educational level and time allocation. As time allocation has not been found to moderate this relationship, we encourage scholars to research other moderating variables by using our multilevel model. Our findings have direct implications for HR personnel and managers, as they can focus on creating smaller teams to increase their employees’ individual performance.
DOES SIZE MATTER? A MULTILEVEL MODEL OF THE RELATIONSHIP BETWEEN TEAM SIZE AND TEAM AND INDIVIDUAL PERFORMANCE,
MODERATED BY TIME ALLOCATION
Over the last century, teamwork has been widely regarded as a means to reach higher performance than can be done by working individually. Indeed, many organizations increasingly structure their work around teams (Stewart, 1999). Consequently, a considerable amount of research is aimed at the optimal design of teams (Hollenbeck, Beersma & Schouten, 2012) for example research on expertise diversity (Bunderson & Sutcliffe, 2002; Littlepage, Robison & Reddington, 1997; Shin, Kim, Lee & Bian, 2012), authority differentiation (Stewart & Barrick, 2000), hierarchy within the team (Greer, De Jong, Schouten & Dannals, 2018) and temporal stability (Gersick, 1988). However, one of the most basic, essential parts of team design is the respective team size (LePine, Piccolo, Jackson, Mathieu & Saul, 2008; Gooding & Wagner III, 1985).
Team size is typically defined as the number of members that work in a focal team (Gooding & Wagner III, 1985). A work team can be defined as “a collection of individuals who (1) are interdependent in their tasks, (2) share responsibility for outcomes, (3) see themselves and who are seen by others as an intact social entity embedded in one or more larger social systems and (4) who manage their relationships across organizational boundaries”1 (Cohen & Bailey, 1997: 241). We adopt this definition in our current research on teams. To be more precise, the target of our research are work teams in organizations, rather than sports teams, student teams or groups in other social contexts (Mathieu, Maynard, Rapp & Gilson, 2008). It is important to note that these work teams differ in their respective sizes (Gooding & Wagner, 1985). It is sometimes reasoned that sizes of teams differ because teams work in different environments and configurations (Mathieu et al., 2008). A complex environment might require more differing
expertise and, thus, more team members compared to a more stable environment (Cohen & Bailey, 1997; Heneman III, Judge & Kammeyer-Mueller, 2015). However, the exact reason why an organization uses smaller or larger teams is often unknown (Gooding & Wagner III, 1985). Yet, organizations aim to be as efficient, effective and productive as possible (Huselid, 1997). Naturally, they want their teams to reach their optimal performance. This leads us to wonder, what is the perfect size of a team in order to achieve the best performance?
Most of the research on team size focusses specifically on the relationship between team size and team performance. However, in their meta-review on team characteristics research Mathieu et al. (2008: 419) state that “historically, team research were concerned with aggregation of data from the individual- to the team-level”, instead emphasizing the need for multilevel research that studies the impact of team level data on the individual level. In the current study we develop such a multilevel framework, in which we investigate both the effects of team size on team performance as well as on individual performance.
There is conflicting evidence in the team size literature about the size of a team and the respective team performance. The bulk of the team size literature hints at smaller teams being more effective to reach optimal team performance than larger teams (Gooding & Wagner III, 1985; Ingham, Levinger, Graves, & Peckham, 1974; Kravitz & Martin, 1986; Ringelmann, 1913). In contrast, some studies have shown that in certain situations larger teams can be more productive than smaller teams (Mao, Mason, Suri & Watts, 2016; Mason & Watts, 2012). Therefore, the question what team size is best is ever more relevant.
and it differs significantly between teams (Cummings & Haas, 2012; Mathieu et al., 2008). Namely, the time allocated to a team is dependent on the type of job and also on the number of team memberships (Mortensen, Woolley & O’Leary, 2007). For example, an Administrative Assistant usually works in one team whilst an Organization Advisor might work in multiple teams or project groups. Unfortunately for organizations, an individual can only pay attention to the work within one team at a time.
The time that is allocated to a team can be of great importance for team processes such as communication, team cohesion, information elaboration and social loafing (Urban, Bowers & Monday, 1995; Cummings & Haas, 2012; Rhodes, 1991; Marlow, Lacerenza, Paoletti, Burke, & Salas, 2018). When members spend little time in their team, they might benefit from smaller team sizes because it is easier to communicate and coordinate in smaller teams (Marlow et al., 2018). On the same token, larger teams will require a higher average percentage of time allocation in order to create effective team work and team cohesion (Goodman, Raflink & Schminke, 1987). In the current study we examine this moderating role of time allocation. By making a distinction between the team and individual level, we try to answer the following research question: How is team size related to team- and individual performance and how does time allocation moderate these relationships?
2010). However, as individuals spend more time in a team they may have more opportunities to control and monitor one another, subsequently reducing social loafing (Petty, Harkins, Williams & Latane, 1977). Additionally, commitment to the team goals may grow, which leads to more organizational citizenship behaviour and consequently to less free-riding (Bishop, Scott & Burroughs, 2000). Therefore, time allocation on the individual level is expected to weaken the negative relationship between team size and individual performance (H4). To empirically test these predictions, this paper presents quantitative survey data consisting of a multitude of participating teams (N = 59) and individuals working in those teams (N = 293) from Dutch organizations operating in knowledge intensive sectors.
We contribute to the current scientific knowledge of team size effects by examining the impact of team size on both the individual and team performance (Mathieu et al., 2008). Moreover, we introduce time allocation as a key moderator of size and performance relationships (Cummings & Haas, 2012) thereby shedding a light on the conflicting evidence in the team size debate thus far (Cohen & Bailey, 1997). By doing so, we create a novel multilevel framework (summarized in Figure 1) that can account for differences in the relationship between team size and performance indicators.
study generates will not only advance our scholarly understanding of teams, it will also serve as a guideline for Human Resource professionals in their quest to optimize their team design.
Figure 1. Conceptual model Team level
Individual level
Figure 1. Team size is negatively related to individual and team performance, but this relationship
weakens when individuals allocate a high percentage of time to the team.
Team size: amount of team members Team performance Individual performance Individual percentage of time spent in focal team Team average percentage of time spent in focal team
H 1 (-)
H 3 (-)
H 2 (+)
THEORY AND HYPOTHESES
Teams are a collection of individuals, who share responsibilities for outcomes (Cohen & Bailey, 1997). These outcomes are ought to be tailored to the organization’s objectives (Mathieu et al., 2008; Huselid, 1997). In the definition of Cohen and Bailey (1997) teams are seen as a part of a larger social system who manage their relationships across organizational boundaries. Within those boundaries, individuals have to work together toward a certain goal or outcome, and in doing so they create relationships, communication patterns and shared knowledge (Bavelas, 1950). If teams manage to cooperate efficiently and effectively, then they can create value for their organization (Bates, 1990; Stewart, 1999).
Team size is an important variable of team design as organizations differ in how many team members they assign to each of their teams (Mathieu et al., 2008). But why do teams differ in size? Haleblein and Finkelstein (1993) argue that the size of a team depends on their environment. Specifically, teams in more stable environments are often smaller, whilst in turbulent environments differing expertise is needed. Therefore, in a turbulent environment the teams are often larger in size (Haleblein & Finkelstein, 1993). However, Heneman et al. (2015) argue that it is not just the environment that is important for staffing decisions, but rather the complexity of the team’s tasks and the knowledge, skills and abilities (KSAOs) needed. Subsequently, the size of a team can have important consequences for team performance (Heneman et al., 2015; Cohen & Bailey, 1997).
team performance (Bates, 1990). Moreover, in a seminal article, Huselid (1997) showed that having the right kind of HR practices, policies and team design can lead to a competitive advantage. Naturally, performance is thus a topic of interest for many organizations (Mathieu et al., 2008).
However, scholars have focussed mostly, up until now, on researching team performance rather than individual level performance (Mathieu et al., 2008; Gooding & Wagner III, 1985). One might argue that team performance is the sum of the individual performances. However, this argument is invalid as research has shown that the group outcome is often less than the expected individual outcome (Steiner, 1972; Stewart, 1999; Ringelmann, 1913). Furthermore, several meta reviews point at the lack of multilevel studies done on team characteristics and individual characteristics (Cohen & Bailey, 1997; Mathieu et al., 2008). There is thus a gap in the team size literature. Keeping this gap in mind, we will firstly start our theory section off with a review of what is known of the relationship between team size and team performance (Gooding & Walker, 1985). Secondly, we will dive into what is known of the relationship between team size and individual performance (Lepine et al., 2008; Chidambaram & Tung, 2005). Taken together, we create a novel multilevel framework of team size and performance indicators that has not been researched before.
Team Size and Team Performance
(Ingham, Levinger, Graves, & Peckham, 1974; Kravitz & Martin, 1986). Moreover, research on more complex tasks regarding innovation and problem solving also found that team size is negatively related to team performance (Curral, Forrester, Dawson & West, 2010). One explanation given by Curral et al. (2010) is that larger teams have poorer team processes, leading to worse performance. These processes are, for example, communication of team objectives, participation in team work and support for innovation. Lastly, a meta-analysis of 31 studies on team size and team performance concludes that smaller teams perform better on a range of tasks than larger teams (Gooding & Wagner III, 1985). According to the authors, this is mainly because of effective communication and collaboration in smaller teams.
Despite this overwhelming evidence for the positive effects of smaller team sizes, there are also authors who claim otherwise (e.g. Mao et al., 2016); Mason & Watts, 2012). For example, Mason & Watts (2012) found that teams with more members learned more from one another, leading to higher team performance. Furthermore, some authors suggests a curvilinear relationship between team size and performance in an inverted U-shape (Littlepage, 1991; Wheelan, 2009). Therefore, meta-analyses on team performance highlight that the findings of team size on performance are still inconclusive (Cohen & Bailey, 1997; Mathieu et al., 2008). In light of these differing findings we turn to communication theory in teams (Bavelas, 1950), to hypothesize the relationship between team size and team performance.
as the exchange of information is often needed to divide the workload and solve complex problems. In Bavelas (1950: 725) words: “for entire classes of tasks any hope for success depends upon an effective flow of information”. A more recent meta-analysis (i.e., Marlow, Lacarenza, Paoletti, Burke & Salas, 2018) shows that team communication is significantly related to team performance. Especially the quality of communication is important for performance, for example the extent to which information was contributed during group meetings (Marlow et al., 2018; Urban, Bowers & Monday, 1995). Thus, effective communication within a team is an important underlying factor for achieving good team performance.
Communication issues may arise more often in larger teams compared to smaller teams (Smart & Barnum, 2000). For example, research by Riopelle et al. (2003) showed that larger team sizes lead to inefficient and impractical communication. When groups become larger, information becomes harder to transfer and may even become distorted. Failure to establish effective communication leads to conflict, disrupts relationships and lowers productivity (Rhodes, 1991 in Smart & Barnum, 2000). Therefore, poor communication within large teams diminishes the respective team performance.
which tasks there are and how to divide them is more accessible when there are a low amount of team members (Mathieu et al., 2008). Therefore, smaller teams lead to more efficient task division (Marlow et al., 2018). Lastly, it may well be easier for members of small sized teams to coordinate and to monitor task progress (Crawford & LePine, 2013). Monitoring task progress means staying up to date about each team member their tasks and responsibilities, which prevents misunderstandings and work conflicts consequently resulting in better team performance (Smart & Barnum, 2000).
Summarizing this section, we argue that communication is related to team performance. Through increased information elaboration, task division and task monitoring, smaller teams have better communication than larger teams. Consequently, we expect a negative relationship between team size and team performance. The following hypothesis is construed:
Hypothesis 1. Team size is negatively related to team performance.
The Moderating Role of Time Allocation
The research on teams has been extensive and some mixed evidence on team size has been found. Contrary to our first hypothesis, there are a few authors who argue that larger team sizes can actually lead to better team performance. Mao et al. (2016) examined the effect of team size on group performance when dealing with complex tasks such as emergency planning. The participants spent one day of collaborating together, and the performance was better in the larger teams (Mao et al., 2016). Other research argues that large teams can be better than small teams because there are more learning opportunities that can lead to better team outcomes (Mason & Watts, 2012). This evidence is in direct contrast with the previous literature (i.e., Gooding & Walker, 1985) by stating that larger teams lead to better performance.
(Cohen & Bailey, 1997; Gooding & Wagner III, 1985; Mathieu et al., 2008). One of the variables that has received scant attention so far in the literature is the percentage of time that people allocate to their focal team (Cummings & Haas, 2012).
According to the psychological theories on attention individuals have limited time and attention to divide (Ocasio, 2011). This is especially prevalent for employees working in multiple teams (Cummings & Haas, 2012; Mortensen et al., 2007). Furthermore, employees may work part time, may have multiple employers, care duties or other reasons to divide their time between working in one team and focussing on other tasks. The way employees divide their time between teams is called time allocation (Cummings & Haas, 2012). On a team level, time allocation can be defined as the average percentage of time team members spend in their focal team. As indicated earlier, we hypothesize that larger team sizes are negatively related to team performance due to worse communication processes. However, when it comes to these relationships, the time allocation of team members to the team can be important.
Specifically, we argue that the negative effect of team size on team performance can be reduced by spending a higher percentage of time in the team. As larger teams deal with communication problems, spending more time in the team naturally leads to more opportunities for team members to communicate with one another (Marlow et al., 2018). One of the reasons communicating within large teams is harder, is because there are more team members one needs to communicate and deal with on a daily basis (Riopelle et al., 2003). By creating more opportunities to communicate, possible miscommunication and work conflicts can be prevented (Zahn, 1991). When time allocation to the team work is low, by contrast, information elaboration decreases, communication becomes more difficult and, therefore, work conflicts are more prevalent (Dahm, Glomb, Manchester & Leroy, 2015; Zahn, 1991).
Crawford & LePine, 2013). As we have noted earlier, in larger teams it is usually harder to keep oversight of what each member is doing. This is especially hard when the amount of time that team members on average spend in their team is low. However, higher levels of time allocation indicate that people spend more time in their team, naturally creating more opportunities for coordination and task monitoring (Crawford & LePine, 2013). Task monitoring may further lead to better collegial relationships and less misunderstandings (Rhodes, 1991; Zahn, 1991). This is especially relevant for larger teams rather than smaller teams, as they struggle more with these kind of communication issues (Marlow et al., 2018). Thus, we argue that on the team level, a high average of time allocation can lead to improved task monitoring subsequently weakening the negative effect of team size on team performance. The aforementioned arguments combined lead to the following hypothesis:
Hypothesis 2. Time allocation moderates the negative relationship between team size and team performance. This relationship is weaker when team members’ average time allocation is higher.
Team Size and Individual Performance
According to the theories of social loafing, people are prone to exert less effort on group tasks than they would on individual tasks (Albanese & Van Fleet, 1985; Karau & Williams, 1993; Latané et al., 1979). A key reason for exerting less effort, is the expectation that other group members can compensate for their lack of effort. When it is expected that others will do the work, an individual is less inclined to put in effort. This effect is especially relevant when there is low social control or a lack of monitoring of individual efforts, such that it is relatively unknown what an individual is contributing (Petty, Harkins, Williams & Latane, 1977).
Team size is a primary driver of social loafing (Alnuaimi, Robert, Maruping, 2010). In larger teams, individuals have to deal and communicate with more members than in smaller teams. This means that on the individual level, there is less time and opportunity to control the effort each member puts in their work. In addition, in large teams the tasks are more likely to be divided rather than shared (Mathieu et al., 2008). This results in even less monitoring possibilities to see the amount of effort extended by others. Moreover, individuals in large teams might be more inclined to free-ride, as there are a higher amount of other team members that can compensate for their lack of effort (Albanese & Van Fleet, 1985). Thus, when the size of a team is large and social control is low, people have a higher tendency to be socially loafing.
In smaller teams, on the other hand, the tendency to be socially loafing is weaker. This again can be explained by the conditions of social control and direct supervision. In teams with fewer members, it is easier to see and notice individual efforts (Petty et al., 1977). Therefore, if one member shirks in their duties others will notice and will punish or reprimand the respective member. Most people seek to avoid negative interactions or punishments (Albanese & Van Vleet, 1985). Thus, social loafing is less prone to happen in smaller teams.
lower performance (Weingart, 1992; Chidambaram & Tung, 2005). As reasoned above, social loafing is more prone in larger teams compared to smaller teams. Following this line of reasoning, the individual effort and the resulting performance is expected to be lower for individual members of a large team compared to a small team. Therefore, we hypothesize that a larger team size has a negative effect on individual performance, meaning that if the team size increases individual performance is likely to become less. This leads to the following hypothesis:
Hypothesis 3. Team size is negatively related to individual performance.
Time Allocation on the Individual Level
In the previous sections, we defined time allocation as the average percentage of time the team members spend in their focal team. On an individual level however, time allocation is the percentage of time one individual spends in their focal team. This distinction is important. First of all, differences in time allocation between individuals may be larger than differences between team averages. Secondly, the research on individual time allocation and team size requires a multilevel approach that has not yet been investigated. As the multilevel research on team variables is scarce, we view it as a chance to make a relevant addition to the team size literature.
who spend much time in a team are more easily found out when they shirk in their duties. Consequently, individuals who spend more of their time in the team are more motivated to put in effort and reach a good performance. In contrast, spending a low amount of time in a team leads to less opportunities for social control. When there are few opportunities to see each other, meet or share information about progress then people are prone to be socially loafing (Karau & Williams, 1993). Therefore, time allocation positively moderates the negative effect between team size and individual performance.
Another theoretical explanation is that individuals might become more committed to teams in which they spent a lot of time (Bishop, Scott & Burroughs, 2000). Commitment means that employees are dedicated to a team and it’s goals. In order to become committed, one has to identify with the team or bond with their colleagues. In larger teams, it may be harder to get to know your colleagues and commit to shared values. However, spending more time in a team can accelerate these processes, in turn leading to more commitment. Research shows that being more committed to a team reduces the tendency to free-ride as it leads to more citizenship behaviour (Pearce & Herbik, 2004). Consequently, we argue that allocating a higher percentage of time to the focal team increases commitment, which in turn leads to less free-riding, subsequently weakening the negative impact of team size on individual performance. Moreover, we have argued that by spending a higher percentage of time in a team increases the social control and monitoring which motivates individuals to put in more effort. Time allocation is thus a positive moderator of the relationship between team size and individual performance. This leads to the following hypothesis:
METHODS
Data and Study Design
In order to test our hypotheses, we conducted quantitative surveys which were administered to knowledge workers in a wide variety of industries (e.g., consultancy, governmental sector, IT sector). The work in these fields was structured in teams. Furthermore, within these teams it was not uncommon for employees to work in multiple teams at any given point in time and, as such, to spend less than 100% of their time in one particular team (Cummings & Haas, 2012; O’Leary et al., 2011). Therefore, these teams provided a viable context to research the relationship between team size, performance and the moderating role of time allocation.
We drew our data from two surveys, one of which was administered to team members and one of which was administered to the respective team supervisor. Both surveys were tailored to measure team demographics, processes and performance indicators (cf. Oedzes, Rink, Walter, Van der Vegt, 2019). Our surveys also measured team concepts and characteristics beyond the scope of the current study, but which enabled us to provide the participating teams with feedback as a reward for their participation. The surveys were conducted by bachelor- and master students from the Faculty of Economic and Business at the University of Groningen. Additional to the surveys, we drew from available organizational data to determine demographic information (e.g. organizational industry, organization size). The supervisor of each participating team provided us with information about their team members (e.g. their names and e-mail addresses).
English speaking, (2) consisting of at least three team members and a maximum of fifteen members, (3) interdependent and thus working toward a shared goal, (4) been having team meetings on a somewhat regular basis (5) and lastly they had to be work teams rather than student or sport teams (Oedzes, Rink, Walter, Van der Vegt, 2019).
During the period of 17th January 2019 until 15 November 2019 the surveys were administered. Participants were notified by e-mail. Reminders were sent twice, both to the supervisors and team members. After this period the data was anonymized and combined into one dataset.
Sample
The response rate within each team was high, namely 77%, because our participants were approached by students who used their personal network to gain permission to administer the survey. The total amount of teams in our team analysis was N = 61, but we omitted two teams with respective sizes of 23 and 27 because they do not qualify as teams but rather as a combination of several project groups (Carton & Cummings, 2012). Furthermore, these teams did not meet our research criteria of size (i.e. only teams consisting of 3-15 team members could participate). Therefore, our final team sample consisted of N = 59 teams. The individual sample size was N = 335, but after omitting participants that had missing data for our research purposes we retained a usable sample of N = 293.
approximately 72% (i.e. on average, participants spent 28% of their time on work outside the focal team).
Team Level Measures
Team size. Similar to Goodman and Wagner (1985) team size has been measured by asking the team supervisors to indicate how many team members were currently working in their focal team.
Team Performance. On the team level, performance must be seen as the perceived effectiveness and efficiency of the team (Cohen & Bailey, 1997; Mathieu et al., 2008). We asked team supervisors to indicate their team’ performance. We used five statements on a five point scale (1=“totally disagree”, 5=“totally agree”) to determine the team’s performance, which was in line with research by Hoegl and Gemuenden (2001). Examples of statements are “At this moment the team is successful” or “The product and services delivered by this team are of high quality”. The respective Cronbach’s alpha of the team performance scale was .88 indicating that the scale is highly reliable.
Time allocation on the team level. Time allocation was measured by asking participants how many of their working hours they spent on average in their focal team. This was then turned into a percentage for clarity, by diving the work hours in the focal team by the total amount of work hours that individuals work in their organization. On the team level, time allocation was operationalized as the aggregate of average percentages of time spent by the individuals within one team (Cummings & Haas, 2012).
as performance ratings can be age related, such that older employees receive lower scores (Krueger & Rouse, 1998). Thirdly, we controlled for organizational tenure in months, as performance ratings are usually higher for employees with more experience and job related knowledge in their organization (Ng & Feldman, 2010). Fourthly, we included the educational level (low, middle, high) as a control variable because higher education levels lead to higher performance ratings (Ng & Feldman, 2009; Krueger & Rouse, 1998). Lastly, we controlled for the three team characteristics (temporal stability, authority differentiation and skill differentiation) that have been found to be significantly related to team performance in past research (Lee, Koopman, Hollenbeck, Wang & Lanaj, 2015; Gersick, 1988).
Individual Level Measures
Individual performance. We used supervisor rated performance indicator to determine individual performance based on three dimensions (1) whether an individual works very efficiently, (2) delivers high quality work and (3) is generally performing well (see also Janssen & Van Yperen, 2004). This approach to measure individual performance is also in line with prior research (e.g. Ittner and Larcker, 1998) that suggests that efficiency and quality are important indicators for individual performance. The Cronbachs Alpha of our scale was .89, indicating high reliability.
Time allocation on the individual level. Time allocation was measured by asking respondents how many hours they spent in their focal team, and dividing that number by the total amount of hours they work in their organization. This proportion is then turned into a percentage (Cummings & Haas, 2012).
Statistical Approach
In order to test our hypotheses, we utilized two distinct regression analyses in which we measured the impact of team size on team performance moderated by team average time allocation (1), and team size on individual performance, moderated by individual time allocation (2). This required a multilevel approach. Namely, individuals are nested within teams which influences their pattern of data because scores of members in the same team are more alike (Kreft & De Leeuw, 1998; Grewal, Cote & Baumgartner, 2004). A nested structure therefore decreases the statistical validity by inflating standard errors, making it easier to find significant results when really there are none (Maas & Hox, 2005). Furthermore, the observations of team size are alike for each individual within a team which leads to an error term that is highly correlated (Kreft & De Leeuw, 1998), therefore requiring a multilevel analysis (Cummings & Haas, 2012). One important condition for this multilevel analysis is that one uses a large enough sample size above 50 teams (Maas & Hox, 2005), which is met in the current study (N = 59).
Secondly, in our individual level analysis, we used a multilevel approach estimate the impact of team size on individual performance (N = 293). We used individual time allocation as our moderator variable, and we also controlled for gender, age, tenure and educational level. Instead of centring our data by their mean we centred the data by their respective group mean (see Kreft & de Leeuw, 1995). By doing so, we look at how the scores of an individual differ compared to their group mean, which is common in multilevel research (Grewal, Cote & Baumgartner, 2004; Kreft & de Leeuw, 1995). This also is an extra measure that enables us to account for the multicollinearity between team scores (Grewal, Cote & Baumgartner, 2004; Kreft & de Leeuw, 1995). Again, we standardized the variables which eases the interpretation of our findings (as suggested by Hayes, 2014).
RESULTS
Descriptive Statistics
Team level descriptives. Our team sample (N = 59) included team sizes ranging from 2 to 15. The average team size was 8.03 (SD = 3.33) members per team. The average team performance in our sample was 3.76 (SD = .71), meaning that the team supervisors were reasonably satisfied with their team members performance. The average time spent on the team level was 72.84% (SD = 28.74), indicating that team members on average spent a relatively large amount of their time in their focal team.
Individual level descriptives. The mean individual performance was 3.98 (SD = .81), meaning that team supervisors were satisfied with the efficiency, quality and overall performance of their individual members. The individual time our participants spent in their focal team was 72.88% (SD = 35.46). Note that this is very similar to the average team time allocation, but the standard deviation is higher as on the individual level participants differ more strongly in their time allocation.
Additionally, team size was positively correlated to skill differentiation (r = .43, p < .01), indicating that employees in larger teams differed more in their respective knowledge and skills. In a similar vein, skill differentiation was positively correlated to time allocation on the team level (r = .40, p < .01) and on the individual level (r = .32, p < .01) meaning that in teams in which employees have a heterogeneous skillset, the time allocated to said team was relatively higher.
TABLE 1
Means, Standard Deviations, and Pearson Correlation Coefficients
Hypotheses Testing
Team level analysis. Table 2 below presents an Ordinary Least Squares regression analysis on the team level with team performance as the independent variable (N = 59). Model 1A shows the regression model including only our control variables. As can be seen, education was positively associated to team performance (B = .24, SE = 0.34; p < .05). Interestingly enough, age was positively associated to team performance (i.e. rather than negatively, Krueger & Rouse, 1998), but the relationship was only marginally significant (B = .27, SE = .16; p < .10). On the contrary, gender and tenure were not significant predictors for team performance.
In model 1B team size (B = -.11, SE = .11; p > .05) was added as our main predictor for team performance. In line with Hypothesis 1 the respective slope of team size and team performance was negative, however the relationship was not significant. Furthermore, team average time allocation (B = .03, SE = .12; p > .05) was added, but it did not predict team performance either.
TABLE 2
Moderated results for the relationship between Team Size, Time Allocation and Team Performance
Model 1A Model 1B Model 1C
Predictors B SE B SE BE SE Constant 3.75** .10 3.75** .10 3.75** .10 Gender .00 .10 -.02 .11 -.02 .11 Age .27† .16 .27 .16 .26 .17 Tenure -.07 .15 -.06 .15 -.06 .16 Educational level .24* .10 .24* .10 .24* .11 Temporal stability .03 .11 .04 .11 .04 .13 Authority differentiation .05 .11 .04 .12 .04 .12 Skill differentiation .02 .11 .04 .11 .04 .12 Team Size -.11 .11 -.05 .12
Team average time allocation .03 .12 .10 .14
Moderator Team size *Time allocation
-.13 .13 Dependent variable: Team performance. N = 59 Teams. Predictors were grand mean centred and standardized.
† p < .10 * p < .05. ** p < .01.
Individual level analysis. Table 3 below represents the results for our multilevel regression analysis of team size on individual performance. Model 2A contained only the control variables as predictors. As expected, age was negatively associated to individual performance (B = -.11, SE = .01; p < .05) such that older employees received lower performance ratings. Gender, tenure and educational level, however, were not significantly related to individual performance.
In our final model 2C our moderator was introduced. Hypothesis 4 assumed a positive moderating effect of time allocation on the relationship between team size and individual performance. Although as expected the slope of our moderator was positive, it was not significant (B = .06, SE = .07; p > .05). Hence, Hypothesis 4 is not corroborated.
However, as predicted by Hypothesis 3, in our final model team size was indeed significantly negatively associated with individual performance (B = -.15, SE = .08; p < .05), even after controlling for gender, age, tenure, educational level and time allocation. This means that for each standard deviation in team size under the mean of 8.03 (SD = 3.33) our model predicts an increase of +.15 in individual performance, and any standard deviation above the mean team size predicts a decrease of -.15 of individual performance (starting at intercept B = 3.91). Thus, we found strong evidence for a negative association between team size and individual performance, meaning employees in larger teams score significantly lower on individual performance than employees in smaller teams.
TABLE 3
Moderated results for the relationship between Team Size, Time Allocation and Individual Performance
Model 2A Model 2B Model 2C
Predictors B SE B SE B SE Constant 4.00** .08 3.97** .08 3.91** .11 Gender .01 .04 .01 .04 .01 .04 Age -.11* .05 -.11* .05 -.11* .05 Tenure .03 .05 .04 .05 .03 .05 Educational level .01 .04 .02 .04 .02 .04 Team Size -.15† .08 -.15* .08
Individual time allocation .03 .04 .03 .04
Moderator Team size *Time allocation
.06 .07 Dependent variable: Individual performance. N = 293 Individuals. Predictors were group mean centred and standardized.
DISCUSSION
The goal of this research was to specifically identify the relationships between team size and performance indicators, and whether time allocation moderates these relationships. Our results indicate a significant negative correlation between team size and team and individual performance which is in line with prior research on team size (cf. Gooding & Wagner II, 1985; Curral et al. 2010). Furthermore, our multilevel regression analysis has shown that larger team sizes indeed predict a lower individual performance, even controlling for gender, age, tenure, educational level and time allocation. Subsequently, we reviewed a possible moderating effect of time allocation. However, in our sample, time allocation has not been found to moderate the negative relationship between team size and performance.
Theoretical Implications
Together these findings make several important contributions to the literature on team size, performance and time allocation in organizations. First, the reason for our current research is the conflicting evidence found in earlier scholarly work. Specifically, many studies found smaller teams to be related to better performance (e.g. Gooding & Wagner III, 1985; Ingham, Levinger, Graves, & Peckham, 1974; Kravitz & Martin, 1986; Ringelmann, 1913), yet some studies found that larger teams predict higher performance (e.g. Mao et al., 2016; Mason & Watts, 2012). We answer this discrepancy in the team size literature by reaffirming that, in our sample, smaller team sizes predict better individual performance.
2012). Our sample was suitable for our research purposes because our team members divided their time over multiple teams and project groups (Cummings & Haas, 2012; Oedzes, Rink, Walter, Van der Vegt, 2019). However, based on our results we cannot conclude that time allocation moderates the relationship between team size and performance. These findings directly challenge earlier research on time allocation and multiple team memberships (Cummings & Haas, 2012).
Finally, our research used the latest insights in team research and time allocation to create an integrative multilevel perspective on how team size affects performance. By doing so, we followed up on recommendations by Mathieu et al. (2008) to create a multilevel approach to study teams and their performance. Furthermore, we used a multisource dataset using team and supervisor ratings, enabling us to properly research the aforementioned relationships (Gooding & Wagner, 1985). By creating an integrative multilevel model, we provide future scholars with a tool which they can use for their research design (i.e. by adding new team and individual variables to the model and studying their impact). We hope future scholarly work can build on our multilevel-approach to unraffle the contextual factors that play a key role in the relationship between team size and performance indicators.
Limitations and Further Research Directions
mediate the impact of team size on team performance (Mesmer-Magnus & DeChurch, 2009). This can explain the lack of significant results in our analysis of team size and team performance. Therefore, we strongly encourage other scholars to do take communication and social loafing into account, for example by creating an IMOI model (i.e. Input-Mediator- Outcome-Input model) in which team size is the input variable, communication and social loafing are mediators, and performance is the outcome (cf. Ilgen, Hollenbeck, Johnson & Jundt, 2005). Future scholarly work can then use our multilevel framework to build upon and add mediating and moderating processes that drive the team size- performance relationship. Clearly, more research is needed to further improve our knowledge of teams.
Secondly, our study is limited by the relatively short time frame in which it was performed (i.e. a master thesis of less than six months) and consequently the small sample size of teams on which we based our analysis (Maas & Hox, 2005). The latter can explain our lack of significant results for the team level analysis. Specifically, scholars in statistics and mathematics have shown that team research requires at least fifty participating teams, and preferably many more, to obtain statistically valid results (Maas & Hox, 2005). At the same time it is crucial to increase the sample sizes in team research, not only to increase statistical validity but also to research teams from different work fields and industries. Thus, we strongly encourage scholars to try and use larger team data sets (Kreft & De Leeuw, 1998; Maas & Hox, 2005).
supervisors are not seen as a full-fledged member of their team (Gooding & Wagner, 1985). They are seen as a leader who is higher up the hierarchy (Greer et al., 2018). In contrast, some of our participating teams noted that their team supervisor is working closely with their team, and can thus also be seen as a part of the team (according to the definition by Gooding & Wagner, 1985). Furthermore, over the decades, self-organizing teams without clear supervisors have become increasingly more common (Balkema & Molleman, 1999), therefore challenging the assumption of a team leader working separately from their team. Subsequent research could be devoted to tackling this assumption of team hierarchy, or use other objective performance criteria such as 360 degree feedback or client ratings (Pollack & Pollack, 1996). Yet, for our specific research purposes the supervisor ratings were rather suitable because the vast majority of our teams had a clear hierarchy and the supervisors thus provided us with valid indicators of actual team performance.
Practical Implications
Our research has brought about multiple practical implications. As stated in our introduction, most organizations aim to be as efficient, effective and productive as possible. Naturally, they want their teams to reach their optimal performance, because this is vital for the continuity and profitability of their organization (Bates, 1990; Held et al., 2018). Our results have direct implications for HR personnel and managers, because we have shown that creating smaller teams or project groups can increase their employees’ individual performance (in line with recommendations by Gooding & Wagner III, 1985; Littlepage, 1991; Wheelan, 2009; Cohen and Bailey, 1997). Thus, in order to increase individual performance, organizations should consider these results in their respective team design.
associated with performance indicators. This is interesting, because prior research has hinted at the positive effects of higher time allocation for performance (Cummings & Haas, 2012). On the other hand, research on multiple team membership has shown that only under certain circumstances (i.e. after longer time periods) working in multiple teams or project groups (and thus dividing your time allocation) can lead to performance improvements (Van de Brake, Walter, Rink, Essens & Van der Vegt, 2018). Therefore, our recommendation is that in practice managers have to take into account the specific circumstances of their workplace and environment (Mathieu et al., 2008), and adjust the time spent in teams based on the strain and workload of their employees such that they only spent the time necessary in their focal team in order to reach the best performance (Hollenbeck, Beersma & Schouten, 2012). More research on this topic is needed.
CONCLUSION
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