team power and hierarchy-final

TEAMPOWERANDHIERARCHY:UNDERSTANDINGTEAMSUCCESS

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Team Power and Hierarchy: Understanding Team Success

Huimin Xu

School of Journalism and Communication, Nanjing University, Nanjing 210023, China

Yi Bu Department of Information Management, Peking University, Beijing 100871, China

Meijun Liu Institute for Global Public Policy, Fudan University, Shanghai 200433, China

Chenwei Zhang Faculty of Education, The University of Hong Kong, Hong Kong 999077, China

Mengyi Sun Department of Ecology and Evolutionary Biology, University of Michigan, MI 48109,

USA

Yi Zhang Faculty of Engineering and IT, University of Technology Sydney, NSW 2007, Australia

Eric Meyer School of Information, University of Texas at Austin, Austin, TX 78701, USA

Eduardo Salas Department of Psychological Science, Rice University, Houston, TX 77005, USA

Ying Ding School of Information, University of Texas at Austin, Austin, TX 78701, USA

Corresponding author: Ying Ding ([email protected]).


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Team Power and Hierarchy: Understanding Team Success

Abstract: Teamwork is cooperative, participative and power sharing. In science of

science, few studies have looked at the impact of team collaboration from the

perspective of team power and hierarchy. This research examines in depth the

relationships between team power and team success in the field of Computer Science

(CS) using the DBLP dataset. Team power and hierarchy are measured using

academic age and team success is quantified by citation. By analyzing 4,106,995 CS

teams, we find that high power teams with flat structure have the best performance.

On the contrary, low-power teams with hierarchical structure is a facilitator of team

performance. These results are consistent across different time periods and team sizes.

Keywords: Team Power; Power Hierarchy; Power level; Team Success; Scientific

Collaboration; Artificial Intelligence

INTRODUCTION

To understand teamwork and its success, it is imperfect if we ignore the notion of

power. As the prominent social scientist Bertrand Russell highlighted in his book that

power is a fundamental concept in social science “in the same sense that energy is the

fundamental concept in physics” (Russell, 1938, p.10). Power is manifested through

cooperation (Blau, 1964, 1977). As long as more than one person works on a given

task, power starts to influence every step of the process and the ultimate performance.

Power is pervasive and often camouflaged in our society (French & Raven, 1959).

But power is an essential, unavoidable, yet unrecognized element of collaboration

(Galinsky et al., 2008).

Scientific teams have increasingly dominated the process of knowledge production

(Wuchty et al., 2007). Individuals with different skills and diverse levels of


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experience need to form a team to tackle complex problems and stay competitive

(Becker & Murphy, 1992). When collaboration is increasingly common, power

dynamics are consistently present in team activities (Dahl, 1957). Researchers are

eager to find out the best way to form teams and improve team effectiveness in

scientific settings (for review, Hall et al., 2018), but few pay attention to the role of

power dynamics in scientific endeavors. John Heider once emphasized the importance

of power in collaboration, saying, “Since all creation is a whole, separateness is an

illusion. Like it or not, we are team players. Power comes through cooperation,

independence through service, and a greater self through selflessness” (Heider 1997,

p.77). For effective and successful scientific collaborations, all types of power should

be recognized, acknowledged, and balanced to avoid power struggles and achieve

optimal performance.

Greer et al. (2017) conceptualized team power as the combination of two variables:

power hierarchy and power level. Hierarchy can help or hurt team effectiveness in

different contexts and task types (Anderson & Brown, 2010; Bunderson et al., 2016).

Hierarchy can improve cooperation, coordination, and role clarity, but it can also

breed inequality and resentment. Greer and van Kleef (2010) found that power level

moderates the effect of power hierarchy on team performance in their study of a

single multinational financial company. When team power is at its lowest, some group

members have substantially more power than others have, power hierarchy may

facilitate conflict resolution by creating order and improving coordination. On the

other hand, power hierarchy can create a perception of inequality and injustice

between high-power individuals, heightening intragroup competition. Whether the

findings on the complex relationships between team power and success from

organizational science and behavioral science can be generalized to teams in science

is still unknown.


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This paper takes Computer Science (CS) as the test field to study the relationships

between team power and team success. CS, especially data science and Artificial

Intelligence (AI), has been widely applied in various domains, ranging from drug

discovery (Gao et al., 2019), medical imaging diagnosis (Han et al, 2021), to fraud

detection (Dunis et al., 2016). CS has been developed by teams of scientists, thus

team power dynamics are inevitable. Power can play a crucial role to empower or

disarm team members. Hierarchy can improve the coordination efficiency of a team,

but it can also diminish the capability for solving conflicts, making the

communication from bottom to top ineffective (Leavitt, 2003). Both highly flexible

coordination and communication are key to building confidence when facing the

uncertainty of CS research (Fountaine et al., 2019; Thamhain et al. 2004). Team

power and hierarchy are highly sensitive to the size of a team, which is shaped by the

tradeoff between benefits and risks (Fortunato et al., 2018). An interview with

Andrew Ng, founder and CEO of Landing AI and former lead of the Google Brain AI

research team, shows that a small team with AI experts and domain specialists to act

quickly and fail fast is the good way to start (Insights Team, 2019). Fierce competition

in CS also demands the quick turnover of tested experiments and requires adaptability

to unknown future directions. However, big teams are more capable of solving “big

problems.” For example, the breakthrough discovery of Higgs boson relied on CERN,

the largest particle physics research center hosting thousands of scientists from all

over the world. AI research is experiencing a rapid shift (Tang et al., 2020), reaching

the stage of deep learning (Tang et al., 2020), and becomes more method-oriented

(Frank et al., 2019b) and application-driven (Yuan et al., 2020). Thus, CS teams need

to adapt to the changes of topics and directions over time.

Practicing modern science requires an unprecedented level of teamwork (Bozeman &

Youtie, 2017). This project is among the first to use large-scale datasets to study the

dynamics of team power from millions of scientific teams. This paper aims to


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understand the complex relationships between team power and success in the field of

Computer Science from the following three perspectives: (1) power and hierarchy in

teams, (2) power and hierarchy in teams of different sizes, and (3) power and

hierarchy in different time stages of CS. This paper starts with the introduction and

definition of team power. The literature review section outlines related work from

behavioral science, management science, philosophy in science, and science of

science. The methodology section details data collection and measures for team power

and hierarchy. The result section illustrates patterns of combinations of team power

and hierarchy, as well as their relationships with team success. The conclusion section

summarizes the findings and highlights future directions.

THE DEFINITION OF TEAM POWER

Team power can be defined as whether one team member will obey/agree or

resist/disagree with the thoughts/activities of others in the same team (Magee &

Galinsky, 2008). There are three types of team power: power over, power with/to, and

power for (Boulding, 1989). Power over is to control over resources for collaboration

using force, coercion or threat, this is adversarial rather than collaborative. Power

over is a tool to benefit the needs of a smaller group rather than the whole. Power

with/to is to draw the collective capabilities of members to accomplish things together.

It can be enacted by building and strengthening shared power for a team. Power for

extends the collaborative membership to stakeholders who are not team members. It

builds capacity for underpowered stakeholders. These three types of power exist in

almost all collaborative endeavors which means that most collaborations use some

force, some mutual exchange or negotiation, or some additional inclusion. There are

two additional types of power that can affect team work: personal and positional

(French & Raven, 1959). Personal power includes expertise (e.g., expert power when

a person has knowledge or expertise which is valued by organizations or individuals),


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referent authority (e.g., referent power when an individual can generate admiration

and loyalty of others, such as charismatic leaders), and rights to access resources (e.g.,

informational power that a person has control and access to important resources,

connections and information). Positional power (e.g., leaders) is acquired by a person

through a position within an organization which can be used to punish or reward team

members.

In team collaboration, there are two primary types of team power structures: team

power hierarchy and team power level (Greer et al., 2017). Hierarchy is defined as an

implicit or explicit rank order of individuals or groups with respect to a valued social

dimension (Magee & Galinsky, 2008) or as vertical differences between team

members in their possession of team resources and their levels of authority for

decision making (Greer et al., 2018). Team power hierarchy can be hierarchical (e.g.,

top down or concentrated power) or flat (e.g., everyone holds similar levels of power).

Power level is conceptualized as the average control of resources (e.g., important

decision-making), average expertise level, or average position (Greer et al., 2011;

Groysberg et al., 2011; Greer & Van Kleef, 2010). Team power level can be high (e.g.,

manager teams) or low (e.g., entry-level employee teams).

RELATED WORK

Power and Hierarchy in Teams

Despite strongly expressed beliefs in many societies that emphasize equality of

opportunity, hierarchy is a persistent and possibly unavoidable part of social life

(Bunderson et al., 2016). The extent to which hierarchy is beneficial or harmful for

teams is an unsettled debate in current literature. The functionalist view (for reviews,

see Anderson & Brown, 2010; Greer et al., 2018) highlights the functional benefits of

hierarchy as: 1) creating a psychologically safe environment so that lower power


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members have less responsibility; 2) motivating performance through

hierarchy-related incentives (e.g., promotions, economic benefits, admiration); and 3)

supporting division of labor and coordination (e.g., people with higher or lower

powers work on different tasks). Meta-analytic evidence shows that through

coordination-enabling processes, hierarchy can be beneficial to team performance.

For example, in a lab experiment, when members had collaborative planning, teams

with a clear expertise hierarchy could integrate information more effectively to

analyze problems in a terrorist plot (Woolley et al., 2008). Also, age-diverse teams

were shown to have in-depth discussions and share task-relevant information when

team need for cognition was high (Kearney et al., 2009).

However, these coordination-enabling results from controlled contexts do not

necessarily exist in complex and dynamic real-world settings. The negative sides of

the hierarchy are based on: 1) conflict theory, which argues that inequality over

limited resources is more likely to trigger struggles in teams (for review, see Greer et

al., 2017); and 2) the salience of power, which suggests that subordinates might not

want to voice their opinions even though hierarchy can give people safety and

certainty. Especially when leaders’ power over others is salient, members can be

afraid to speak up and thus risk inhibiting the collective learning and information

sharing needed to make adaptive changes (Edmondson, 2002). Indeed, equal and

turn-taking communication can contribute to collective intelligence because people

can take full advantage of their skills and knowledge (Woolley et al, 2010).

Whether hierarchy is beneficial to team performance depends, in part, on task type.

Greer et al. (2018) reviewed three aspects of team tasks which would influence the

function of hierarchy: task ambiguity, task complexity, and task interdependence.

When tasks are more predictable, routine, and stable, teams prefer hierarchical

structures (Anderson & Brown, 2010). In contrast, if tasks are ambiguous, requiring


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more flexibility and creativity, flatter structures are better. Ronay et al. (2012) showed

that hierarchy can improve team performance if team members deal with

interdependent tasks (e.g., basketball), but can decrease team performance if team

members perform independent tasks (e.g., baseball). Compared with baseball

(Simmons, 2012), basketball requires more coordination and cooperation among team

members. In professional basketball teams from the NBA, differentiation of players’

pay and participation (e.g., playing time and starting lineup), a proxy of power

hierarchy, can increase team performance by enhancing team processes (Halevy et al.,

2012).

Whether developing a new algorithm, or applying existing algorithms to a specific

domain, scientific research is non-routine and complex, and it requires demonstrating

novelty by advancing the state of the art. In such scientific tasks, member voice and

participation are critical, and power dispersion could be flat to allow members with

complementary skills to work together. But, the situation is not fully true in academia

because power hierarchy is pervasive in academic activities. Merton (1973) claimed

that scientific community is controlled by gerontocracy, where the elderly sets the

rules in scientific domains. As scientists become more senior, their positions in the

hierarchy tends to rise, they gain access to more resources, and thus can increase their

productivity and impact, following the principles of the Matthew effect (Merton,

1973). Researchers of different ages have different roles in the division of labor, with

younger scholars often making more “technical” contributions while senior ones focus

on “conceptual” tasks (Larivière et al., 2016). In some scientific fields, junior

researchers appear in the first position whereas veteran or highly ranked ones tend to

appear in the last, usually playing the supervisory roles (Costas & Bordons, 2011).

Another key to reconciling the two different perspectives on the effect of power

hierarchy is power level. By considering power level and power hierarchy together,


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Greer and Van Kleef (2010) found that hierarchy was useful for teams with low power,

but problematic for teams with high power. In the low-power teams, individuals are

more likely to accept hierarchy because clear roles could help them better know what

to do. For low-power individuals, a lack of hierarchy creates a power vacuum that

gives rise to conflicts and power struggles. However, in high-power teams, members

prefer balanced power without hierarchy because members with high power are more

prone to be competitive, aggressive, jealous, and sensitive when power differences

exist within the team (Greer et al., 2011). For example, in science, if a senior full-time

professor is paired with several junior students, then the paper is more likely to be in

the high-impact publications (Bales et al., 2014), which stresses the importance of

mentorship. Mentoring in academia is dominated by the intergenerational relationship

that mentor and mentee are from different generations with a wide age gap (Satterly et

al., 2018). In the organizational behavior field, Greer et al. (2011) did field studies

within a financial company to test the effect of power level on team performance.

They found that a team made up of high power people (e.g., a management team),

performs worse than a team consisting of low position power people (e.g., secretarial

teams) while collaborating in a logic puzzle game. This is because, when people with

high power work together, they are more likely to have intragroup conflicts. For lower

power level teams, like aircrews or rifle squads, a highly hierarchical setting with

military leaders and commands is necessary (Goodwin et al., 2018). To avoid the

disadvantages of power centralization, they design effective training interventions to

improve coordination and performance across units (Salas et al., 1995).

Power and Hierarchy in Large Teams and Small Teams

Team size plays an important role in team performance. As a parameter in a model for

the assembly of creative teams, team size could determine team outcomes (Guimera et

al., 2015). As team size becomes larger (Wuchty et al., 2007), there is a trade-off


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between solving big problems and having practical burdens (Fortunato et al., 2018).

Large teams enable clear division of labor and specialization in complex tasks

particularly when there is a high degree of certainty about the tasks to be

accomplished (Meyer & Schroeder, 2015; Whitley, 2000). Small teams are more agile,

which can lead to more effective communication and adaptability (Katzenback &

Smith, 1993). Large teams tend to have more citations (Wuchty et al., 2007; Larivière

et al., 2014), earn prestigious awards, and receive more funding (Adams et al., 2005).

Even though large teams may receive more attention, Wu et al. (2019) showed that

innovation does not scale up with large teams. Hall et al., (2018) reviewed that the

ideal team size is six to nine members, which is a manageable size.

Large teams have extra cognitive resources because they can be more diverse in skills

and knowledge than small teams (Bantel & Jackson, 1989). However, large teams

likely produce additional affective conflicts (Amason & Sapienza, 1997) and process

conflicts (Behfar et al., 2011) due to increased communication costs (Staats et al.,

2012) and decreased relational support (Mueller, 2012). Conflicts and struggles

mediate the interactive effects of team power level and hierarchy on team

performance (Greer & Van Kleef, 2010). Therefore, we need to exclude the effect of

team size to study the relationship between hierarchy and team performance. For

example, Woolley et al. (2010) found that collective intelligence is strongly correlated

with equity rather than hierarchy in their experiments. They also replicated the same

result in teams consisting of two to five people. Similarly, when examining the role of

team power and hierarchy amidst team conflicts, Greer et al. (2011) and Bunderson et

al. (2016) controlled team size. Team performance might be influenced by other

factors besides the team power indicators proposed in this paper, and here we control

team size as a possible influential factor of papers’ citations.

Power and Hierarchy over Time


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According to Yin & Wang (2017), time helps us understand paradigm shifts, identify

breakthroughs and sleeping beauties, understand resource allocation, and predict

future citation impact. In the AI field, by analyzing AI research from arXiv between

1993 and 2019, Tang et al. (2020) observed the pace of AI innovations from four

stages: embryo, stable, machine-learning and deep-learning stage. Before 1980, AI

research typically made references not only to computer science, mathematics, but

also to social sciences, including philosophy, geography and arts. However, after that

time, AI has relied more heavily on mathematics and computer science, indicating

more emphasis on developing methods. Other social science disciplines find that it is

difficult to track AI papers that are increasingly published in highly specialized outlets

(Frank et al., 2019b). Therefore, we need to consider the temporality of CS teams, i.e.,

how teams evolve over time.

As McGrath et al. argued, “Rather than static, groups are inherently dynamic systems,

operating via processes that unfold over time”. (McGrath et al., 2000, p.98).

Accordingly, a holistic and longitudinal design may best capture group process and

performance. As time goes by, the effect of surface-level diversity (e.g., demographic

variables) on team outcomes would diminish compared with deep-level diversity (e.g.,

psychological variables) (Harrison et al., 2002). Since we measure team power and

hierarchy via career age, we need to consider how this demographic characteristic

affects team performance over time. Laboratory studies have several constraints,

which are different from “real-world” team processes over time. Even though

researchers design games to illustrate how equal or hierarchical team power

influences the process of cooperation in lab experiments (Woolley et al., 2008; Greer

et al., 2011), limited time is available to observe their long-term performance.

However, time might influence the performance of power hierarchy on team function

(Martínez-Córcoles et al., 2021). At an early stage, teams might need a hierarchical

structure, like a powerful leader, for close guidance. But the process is gradual, since


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team members need some time to adapt to each other. In later stages, members could

be involved naturally in communication, sharing decision-making under the guidance

of a leader. Therefore, a long-term experiment observation could provide us more

fine-grained metadata about user behaviors and far more valid results. In the

regression analyses, we overcome time effects to capture more accurate associations

between team power indicators and team success.

Team Success in Computer Science

Many studies have examined the success of Computer Science teams from the

perspective of the individual, publication, and institution (Fiala & Tutoky, 2017).

Yuan et al. (2020) found that CS, especially AI, is experiencing a shift from

theory-oriented to application-driven. Also, distinguished CS scientists have high

mobility in job positions which spark their productivity in publications after changing

institutions. In addition to talents, Tang et al. (2020) analyzed the rapid pace of AI

innovation using another two indicators: AI publication number and publication

updates. Firstly, as more newcomers join the AI community, they breed more

innovative ideas. Secondly, the number of AI preprints in arXiv has increased

dramatically over time. Thirdly, the acceleration of refining, improving, and updating

processes suggests that AI researchers have frequent trail-and-error lifecycles. Frank

et al. (2019b) calculated the Gini coefficient of AI papers, authors, and citations

across institutions. They found that the diversity of AI research decreases based on

these metrics. This reflects the power concentration of AI, which is caused by

preferential attachment between privileged institutions and conferences. The CS field

is becoming exceedingly hierarchical and is dominated by a few organizations.

CS is a knowledge intensive field and is fully driven by innovations (Zhai et al., 2021).

AI is one critical subfield of CS. AI teams focus on AI+X, where X is a domain or an

application which AI will be applied to. AI teams typically contain data scientists,


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data engineers, and applied scientists; sometimes even product managers and end

users are included (Insights Team, 2019). How to incorporate these members with

diverse skills and backgrounds into CS teams deserves our attention. Previous

research focuses on the patterns of scientific collaboration in computer science teams

from different perspectives. For example, Bu et al. (2018a) concluded that

heterogeneous teams (e.g., diverse career age) benefit more from persistent

collaboration than homogenous teams. By tracing the entire career stage of

researchers in the field of computer science, Bu et al. (2018b) concluded that young

scholars could maximize their success if they can accumulate some experience before

working with ‘giants’. The most famous AI scientists are clustered in the same

community in a cooperation network (Yuan et al., 2020). Meanwhile, collaboration

between industry and academia in teams is becoming more frequent (Frank et al.,

2019b).

For CS teams in tech companies, the working environment is shifting towards

self-driven and self-managed rather than a traditional hierarchical team structure.

Thamhain et al. (2004) surveyed 80 teams in 27 technology companies over five years.

These teams were made up of managers, scientists, engineers, and technicians. They

found that team structure, like power sharing among team members, is important to

team effectiveness. The authors suggest that workers in tech teams prefer autonomy

and freedom compared with the formal command, control and managerial power

(Thamhain et al., 2003). CS teams should have the test-and-learn mentality that can

turn mistakes into new discoveries. Therefore, CS teams should be agile and ready to

fail (Fountaine et al., 2019). Adaptability and flexibility of teams would enable CS

teams, especially AI teams, to possess a competitive edge. Just as we have

summarized in the section Power and Hierarchy in Teams, researchers have conducted

experiments, surveys, and observations to unpack the complex relationships between

team power and success in science, sports, healthcare, organizations, and the military.


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However, the patterns of team power and hierarchy for successful CS teams are not

yet explored using a large-scale quantitative lens.

METHODS

Data

We use DBLP April 2020 version (https://dblp.org/), which contains journal and

conference papers in computer science (Tang et al., 2008). The citation data is

extracted by Tang et al. (2008) from DBLP, ACM, and MAG (i.e., Microsoft

Academic Graph). First, Tang et al. (2012) disambiguated the author names in the

DBLP dataset, reaching a precision rate of 83.01%. Given there still exists limitations

in the name disambiguation, we delete those papers with authors’ career age above 80

based on the fact that individual scientists do not have active careers longer than most

people’s lifespan. Then, we choose papers with complete information, including

4,894,063 unique paper IDs, 4,398,138 unique author IDs, and 82,226,114 total

citations ranging from 1800 to 2020. Finally, by excluding solo-authored papers, we

get a final dataset, providing 4,106,995 co-authored papers, 4,099,577 unique authors,

68,965,173 citations from 1800 to 2020.

Measures

The Conceptualization of Power and Hierarchy

Power can be conceptualized as job position, expertise, age, wealth, gender, race or

ethnicity, ability, physical stature, and perceived intelligence (Greer & Van Kleef,

2010; Groysberg et al., 2011; Piercy, 2019). Although power can be rooted in various

sources, this paper primarily focuses on age differences as a demonstration of power

hierarchy in academia. Age has been historically emphasized as the basis of

hierarchical power (Linton, 1936, p.118). Members may be presumably conferred


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power based on age (Piercy, 2019). When people of different ages work together, old

people would be given higher power or status in the hierarchy because they might

have more experience, knowledge and wisdom (Harrison & Klein, 2007; Gingras et al.

2008). The gap between mentor and mentee in academia can be further enlarged by

the personal power of mentors, such as knowledge, expertise, collaboration networks,

access to resources, and reputation/authority (Anderson et al., 2011).

Harrison and Klein (2007) suggested that the variable age distributed within a unit,

might indicate different diversities in different contexts. Prior research on age effects

has considered either variety-based or separation-based diversity, but it has ignored

disparity diversity. For example, age-diverse teams consist of team members who can

exchange and integrate diverse task-relevant information, knowledge, or experience to

enhance performance (Kearney et al., 2009). In this case, they categorize participants

into different age groups and measure diversity using Blau’s (1977) index of

heterogeneity. However, the isolation of some age groups from others might

discourage the exchange of information and cause conflicts (Bayazit & Mannix, 2003).

The separation of opinions regarding a particular event is thus measured as the

standard deviation. Compared with variety-based or separation-based diversity,

disparity diversity (hierarchy) emphasizes more vertical differences, which means

higher-level people might have more influence on lower-level ones.

In science, we focus more on career age rather than chronological age (Amjad et al.,

2017; Bu et al., 2018b). On the one hand, it is easier to measure scientists’ career age

in the large-scale publication datasets compared to asking their ages directly. On the

other hand, compared with physical age, career age is a better indicator for seniority

since the age of earning a terminal degree varies significantly by field and by personal

circumstances. In this research, we conceptualize career age differences as evidence

of a power hierarchy and examine how seniority-based hierarchy affects team success.


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The Operationalization of Power, Hierarchy and Team Success

Hierarchy also has other synonyms, such as disparity, dispersion, inequality,

centralization, steepness, inequality, and asymmetry. Even though all of these

concepts concern vertical differences within teams, the operationalization is quite

different (Bunderson et al., 2016). The most common approach measuring hierarchy

is centralization, which views hierarchy as the concentration of valued social assets or

sources in a small group. Harrison and Klein (2007) suggested that power hierarchy

belongs to disparity and should be operationalized with the Gini coefficient (Gini,

1936) or the coefficient of variation (Bedeian & Mossholder, 2000) to represent the

concentration degree and vertical differences. He and Huang (2011) used the Gini

coefficient to represent age inequality. The second common approach is steepness.

Steepness is referred to as separation by Harrison and Klein (2007), which represents

horizontal differences in positions or opinions. For example, Greer and Van Kleef

(2010) measured power dispersion with the standard deviation of members’ positions

in the organizational hierarchy. The difference between these two measures is that

centralization focuses on the concentration of valued assets in a few members,

whereas steepness is the difference aggregated across all members (Anderson &

Brown, 2010). Based on the definition of hierarchy (Bunderson et al., 2016; Harrison

& Klein, 2007), hierarchy not only stresses vertical difference but also the control of

resources in the minority relative to the majority. Therefore, we choose to

operationalize hierarchy with the Gini coefficient since this measure meets these two

requirements.

In sum, we use career age (from the researcher’s first paper until the focal one) to

represent his or her power in a team. The average career age of team members can

serve as a proxy to measure team power. The power hierarchy is quantified by the

Gini coefficient of career age of team members. Team performance is measured by the


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average citations per year since the paper was published, which is a normalization of

citations based on time (Abbasi & Jaafari, 2013; Zhang et al., 2019). The descriptive

statistics of these variables are shown in Table 1.

Table 1. Descriptive statistics of team performance, power level and power hierarchy

Variable Mean Std. Dev. Min Max Q1 Q2 Q3

Team size 3.44 1.79 2.00 398.00 2.00 3.00 4.00

Year 2010 8.00 1800 2020 2006 2012 2016

Annual average citation 1.47 7.82 0.00 3104.70 0.00 0.38 1.25

Mean career age/power

level 7.38 5.06 1.00 79.00 3.40 6.86 10.50

Gini coefficient of career

age/power hierarchy 0.30 0.17 0.00 0.80 0.17 0.33 0.43

Note. Q1 is 1st Quartile, 25% of the numbers in the dataset; Q2 is 2nd Quartile, 50% of the numbers in

the dataset; Q3 is 3rd Quartile, 75% of the numbers in the dataset.

RESULTS

Team Power, Hierarchy and Team Success

Figure 1 presents the average number of citations per year for a paper conditional on

the combinations of its power level and hierarchy of team members. Since 26.16% of

papers do not have any citation, we consider all papers including those without

citations (Figure 1A) and papers with citations (Figure 1B) separately. The horizontal

axis in Figure 1A-B shows 2×2 categories indicating the paper’s power level (i.e.,

whether this paper’s mean career age of authors is higher or lower than the median

value for all papers’ mean career age) and power hierarchy (i.e., whether this paper’s

Gini coefficient of career ages of its authors is higher or lower than the median value


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for all papers’ Gini coefficient). Figure 1 shows that teams with high power level

outperform those with low power level. The average annual citations of higher power

teams (blue and orange) are larger than those of lower power ones (green and red). In

Figure 1, when team power is high (blue and orange), power hierarchy is negatively

associated with citations, which indicates that teams with high hierarchy will get

lower citations than those with low hierarchy given all teams have high team power.

The differences between mean citations are statistically significant through the

independent-sample t-test for two groups in Figure 1A (t = 35.34, p < 0.001) and

Figure 1B (t = 30.12, p < 0.001). The absolute value of t score represents the

difference between two groups (the larger, the more difference). A positive t-score

indicates the mean value of the first team is larger than that of the second team, and p

value tells how significant the difference between two groups is. It indicates that

power equity is a better facilitator in high-level teams. When the team power level is

low (green and red), power hierarchy is positively associated with citations. The

citation number of low hierarchy teams is significantly smaller than that of high

hierarchy teams given all teams have low team power. The differences of mean

citations are statistically significant through t-test results in Figure 1A (t = -42.23, p <

0.001) and Figure 1B (t = -25.95, p < 0.001). Since Figure 1 A and B have no

significant difference, we choose all paper (i.e., papers having zero or more citations)

as our dataset for the following experiments.


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Note. * P < 0.05; ** P < 0.01; *** P < 0.001.

Figure 1. Team power level and hierarchy and their relationships with team success

In Figure 1, we divide high/low team power and hierarchy using the 50th percentile. In

Figure 2, we show the cutoff for high and low at different percentiles of all papers’

mean career age and Gini index: the 20th, 40th, 60th, 80th, and 100th. As the power level

increases and the hierarchy decreases, the surface becomes dark red and the average

annual citation number becomes larger. As the power level decreases and the

hierarchy decreases, the surface becomes dark blue and the average annual citation

number becomes smaller. Therefore, hierarchy empowers low power teams but

disarms high power teams. These patterns suggest that the association is not sensitive

to the percentile of cutoff.

Figure 2. The relationship between team success and power according to different percentile cutoffs

Results Considering Control Variables: Time and Team Size


20

Further analyses verify the universality of these associations for scientific teams

across different time periods and team sizes. We bin the time periods with 10-year

windows and merge the scientific papers before 1990 since these papers only occupy

1.96% of all papers. Figure 3 indicates that the results in Figure 1 still exist across all

time periods. The citation of high power low hierarchy group is significantly higher

than that of high power high hierarchy group at four stages using t-test, before 1990 (t

= 3.57, p < 0.001), 1990-2000 (t = 9.55, p < 0.001), 2000-2010 (t = 19.55, p < 0.001),

2010-2020 (t = 26.25, p < 0.001). By contrast, the citation of low power low hierarchy

teams is significantly lower than that of low power high hierarchy teams in four

different time periods using t-test, before 1990 (t = -8.86, p < 0.001), 1990-2000 (t =

-13.57, p < 0.001), 2000-2010 (t = -24.25, p < 0.001), 2010-2020 (t = -32.59, p <

0.001). We can see that the absolute value of t becomes larger with time, which

suggests that hierarchy brings larger differences to teams’ citations over time.

Especially when CS enters the 2010s, hierarchical structure produces dramatic

increases of citations for low power teams, whereas flat structure leads to the highest

citations for high power teams.

Note. * P < 0.05; ** P < 0.01; *** P < 0.001.

Figure 3. The relationship between team success and power across time periods


21

Regardless of the team size, the findings in Figure 1 are consistent in Figure 4 as well.

For papers with more than seven authors (2.38% of all papers), we merge them into

one category. The impact of high power low hierarchy group is significantly higher

than that of high power high hierarchy group across different team sizes: two

members (t = 26.50, p < 0.001), three members (t = 20.69, p < 0.001), four members

(t = 19.40, p < 0.001), five members (t = 16.12, p < 0.001), six members (t = 9.52, p <

0.001), seven members (t = 5.91, p < 0.001), and more than eight members (t = 4.38,

p < 0.001). On the contrary, the impact of low power low hierarchy teams is

significantly lower than that of low power high hierarchy teams in both small and

large teams: two members (t = -16.81, p < 0.001), three members (t = -19.82, p <

0.001), four members (t = -18.17, p < 0.001), five members (t = -13.97, p < 0.001),

six members (t = -8.93, p < 0.001), seven members (t = -5.53, p < 0.001), and more

than eight members (t = -7.89, p < 0.001). We can see that as the size of the team

grows, the t score becomes smaller for teams with high power. The results show that

teams consisting of two members most need equal communication and teams with

three to four scholars especially need hierarchy in order to achieve success.

Note. * P < 0.05; ** P < 0.01; *** P < 0.001.

Figure 4. The relationship between team success and power across different team sizes


22

In the previous analysis, we consider each variable: percentile cutoffs, time, and team

size separately. To check the robustness of our results, we run a regression model with

all variables considered in Table 2. Power level (t = 0.11, p < 0.001) and hierarchy (t =

0.51, p < 0.001) are positively related to team success. When we do not consider the

cutoff of power level and hierarchy, the coefficient of the interaction effect between

these two variables is significant (t = -0.13, p < 0.001). The negative coefficient

suggests that in teams with relatively high power, hierarchy is negatively related to

citation, whereas in teams with low power, hierarchy is positively related to citation.

The effects of hierarchy on team success is contingent on the level of team power.

Larger teams have more citations (t = 0.23, p < 0.001). Even when we include fixed

effects regarding year, the results are consistent with those previously reported.

Table 2. Linear regression results for team success based on team power and hierarchy

Variable Coef. Std. Dev.

Power level (Mean) 0.11*** 0.001

Hierarchy (Gini) 0.51*** 0.037

Power level * Hierarchy -0.13*** 0.005

Team size 0.23*** 0.001

Fixed effect: Year Yes

R2 0.01

N 4,106,991

Note. * P < 0.05; ** P < 0.01; *** P < 0.001.

DISCUSSION AND CONCLUSION

Science is no longer an individual endeavor. Countless scientific adventures and

breakthroughs have been achieved by teams of scientists. Power is pervasive in all

kinds of team collaboration and is absolutely critical for effective collaboration. Even


23

though team power has developed a rich literature in other domains, such as

healthcare (Arslanian-Engoren, 1995; Lockhart-Wood, 2000), sports (Halevy et al.,

2012; Ronay et al., 2012), the military (Goodwin et al., 2018; Salas et al., 1995), and

business management (Greer et al., 2011, 2017, 2018), understanding team power

dynamics in team collaboration is relatively new in science of science, especially on

the side of scientific collaboration (Chi & Glänzel, 2017; Wagner et al., 2011; Wang

& Wei, 2016; Zhang et al., 2018). Power in scientific teams can be manifested by

seniority, scientific status, and scholarly impact. The imbalance in power is an

intrinsic feature of scientific teams. The dispersion of team power forms hierarchies,

which can be viewed as vertical differences between team members in their

possession of team resources and their levels of authority for decision making.

Hierarchy in science is prevalent given the pervasiveness of mentoring in our

education systems. Supervisors (e.g., faculty) and subordinates (e.g., graduate

students) have significant differences in personal power in academia (Green and

Bauer, 1995). Even though a growing literature focuses on power differences from the

single dyadic relationship between mentor and mentee, it does not take the whole

team into account, like the average team power level and power hierarchy within

teams. In this paper, we clarify these concepts of team power and hierarchy with

authors’ career age. This paper can help us understand the characteristics of team

power in scientific teams and provide guidance for building successful teams.

There are two different accounts about the effects of power hierarchy: the conflict

(Anderson & Brown, 2010) and functional perspectives (Greer et al., 2017).

Hierarchy provides the mechanism to facilitate team member interactions by

clarifying expectations, roles, and members’ placement in the ranks as well as by

guiding resource allocation. However, it can also create a negative pathway to team

effectiveness by heightening conflict-enabling states, such as highlighting differences

between members, opposing interests among leadership, and provoking contests and


24

conflicts within the pecking order of the team. This paper is among the first to use a

large-scale dataset to reconcile these two opposing lines of thoughts from millions of

scientific teams. The rapid rise of Computer Science has been heavily driven by

research teams in academia and industry. How to form effective AI teams is now one

of the most critical questions that executives, administrators, and leaders need urgent

answers. Therefore, it is critical to understand the recipe of success for Computer

Science teams, especially AI teams, from the power perspective. We unpack the

complex relationships between team power and success by introducing power-level as

the key moderator that determines whether hierarchy enhances or inhibits team

outcomes, inspired by Greer & Van Kleef (2010). They show that power level

moderates the effect of power dispersion on conflict resolution in business

management. In low-power groups, members accept hierarchy, thereby reducing

power struggles. When multiple high-power team members share similar resources,

egos and power competition can create conflicts and harm team function (Greer et al.,

2011). Prior research primarily relies on survey or experimental methods to explore

team hierarchy and performance. As a result, the sample size, space and time are often

limited. It is also problematic to rely on these methods since it is difficult to simulate

real-life task settings. With millions of CS papers in the DBLP dataset, we find that

power hierarchy is a two-sided sword. In different power-level teams, hierarchy plays

different roles. Compared with low-level teams, power equity is a better facilitator in

high-level teams. Our findings are consistent with the results from the study

conducted by Greer and van Kleef (2010), whose results were based on only one

international company. We also test the universality of these results in small and large

teams and in different time spans. The results tell us that a team consisting of young

students especially needs a powerful supervisor to lead to success. However, in the

teams with all-star researchers, they would need equal cooperation relationships.

These findings suggest important consequences of hierarchy in academia and point

out directions of future CS collaboration.


25

There are several limitations in this research. First, career age is a simple proxy of

power in scientific teams. We must admit there are few cases that people who are

young in the academia are more senior than the elderly in terms of knowledge and

creativity. As we have reviewed in the conceptualization of power, power can be

represented with position, expertise, ability, and perceived intelligence (Greer & Van

Kleef, 2010; Groysberg et al., 2011; Piercy, 2019). In the operationalization part, we

can consider using other variables, like h-index (Egghe & Rousseau, 2008) to measure

team power level and hierarchy. All other alternative measures could provide a

baseline for the power measurement based on career age. Second, the measure of

citation is just one dimension to evaluate scientific success. It would be interesting to

measure success using disruptive innovation (e.g., new knowledge overshadowing

past ideas) (Wu et al., 2019) or combinational novelty (e.g., atypical combination of

pre-existing knowledge components) (Uzzi et al., 2013; Liu et al., 2020). Thirdly, we

only discuss the power dynamics in CS teams. In the future, we would test the

relationship between team power and success in other domains in order to see whether

this pattern is still consistent in different disciplines.

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