empirical essays on the role of stars in collaborative organizations · 2019-06-13 · empirical...

Empirical Essays on the Role of Starsin Collaborative Organizations

vorgelegt vonArne Thomas, M.Sc.

(ORCID 0000-0002-8515-6340)

von der Fakultat VII – Wirtschaft und Managementder Technischen Universitat Berlin

zur Erlangung des akademischen GradesDoktor der Wirtschaftswissenschaften

– Dr. rer. oec. –

genehmigte Dissertation

Promotionsausschuss:

Vorsitzender: Prof. Dr. Søren Salomo (TU Berlin)

Gutachter: Prof. Axel Werwatz, Ph.D. (TU Berlin)Prof. Linus Dahlander, Ph.D. (ESMT Berlin)

Tag der wissenschaftlichen Aussprache: 10. April 2019

Berlin 2019

To Simone.

Abstract

Inspired by the challenges modern organizations face when integrating the

talent of their “best and brightest” into increasingly interdependent processes,

this dissertation analyzes the role of stars in collaborative organizations. In

three independent essays, I investigate (1) how stars affect team performance

and what mechanisms are at play, (2) how temporary star absence provides

new opportunities for non-star colleagues, and (3) how personal rivalry among

stars impacts their individual output and contribution to organizational per-

formance. I address these questions quantitatively by applying microecono-

metric and quasi-experimental techniques to data from the National Basketball

Association (NBA). Rich individual and team-level data enables me to identify

stars and link them to the performance of their colleagues and organizations.

The core findings of this thesis suggest that the impact of stars is not unidirec-

tional, but ambiguous: On the one hand, stars are vital for team performance

and the success of organizations because they possess unique skills that are

hard to replace. On the other hand, the dominant role of stars limits the

performance opportunities for non-star colleagues and constrains their pro-

fessional development. Finally, personal motives like trumping a rival induce

stars to prioritize individual output, which distinctly reduces their positive

impact on organizations. Taken together, the three essays of this thesis thus

paint a comprehensive and nuanced picture of stardom in collaborative orga-

nizations.

v

Zusammenfassung

Moderne Organisationen stehen vor der Herausforderung, die Fahigkeiten

ihrer Mitarbeiter in zunehmend interdependente Arbeitsablaufe zu integrieren.

Die herausragenden Fahigkeiten einiger weniger Stars sind dabei von beson-

derer Bedeutung. Diese Dissertation untersucht deshalb die Rolle von Stars in

kollaborativen Organisationen. In drei eigenstandigen Essays analysiere ich,

(1) wie Stars die Leistung von Teams beeinflussen und welche Mechanismen

dabei wirken, (2) wie die vorubergehende Abwesenheit von Stars ihren Kolle-

gen neue Moglichkeiten eroffnet und (3) wie personliche Rivalitaten zwischen

Stars ihre individuelle Leistung und ihren Beitrag zur Leistung der Organisa-

tion beeinflussen. Ich untersuche diese Fragen quantitativ auf der Grundlage

von Daten aus der National Basketball Association (NBA), die umfangreiche

Informationen auf Individual- und Teamebene enthalten. Auf dieser Basis ist

es moglich, Stars zu identifizieren und mit der Leistung ihrer Kollegen und

Organisationen zu verknupfen. Dazu verwende ich mikrookonometrische und

quasi-experimentelle Methoden. Die zentralen Ergebnisse dieser Dissertation

legen nahe, dass der Einfluss von Stars nicht nur in eine Richtung wirkt, son-

dern ambivalent ist. Einerseits sind Stars zentral fur die Leistung von Teams

und den Erfolg von Organisationen, weil sie uber einzigartige Fahigkeiten

verfugen, die sich kaum ersetzen lassen. Andererseits nehmen sie herausra-

gende Stellungen innerhalb von Organisationen ein und beschranken so die

Entfaltungs- und Entwicklungsmoglichkeiten ihrer Kollegen. Zuletzt spielen

die personlichen Motive von Stars eine wichtige Rolle: Personliche Rivalitaten

verleiten Stars dazu, ihre individuelle Leistung in den Vordergrund zu stellen,

was ihren positiven Einfluss auf Organisationen deutlich verringert. Zusam-

mengenommen vermitteln die drei Essays dieser Dissertation ein umfassendes

und differenziertes Bild uber die Rolle von Stars in kollaborativen Organisa-

tionen.

vii

Acknowledgements

While working on this thesis, I have received help and support from many

different people, for which I am thankful.

First and foremost, I would like to thank my supervisors, Axel Werwatz

and Linus Dahlander, both of whom have taught me so much about research,

and have helped me to grow as a person. I want to thank Axel Werwatz for

his prudent guidance, constructive encouragement and insightful advice. Your

support has been invaluable to me. I would like to thank Linus Dahlander for

his enthusiastic support, creative thinking and inspiring collaboration. Work-

ing with you has made this thesis so much more fun.

I am grateful to my “Kuhbrucke Fellows” Friederike Lenel, Hannah Liep-

mann and Niko de Silva for all the valuable feedback, lively discussions and

great support. Founding our research group was probably the best idea I had

during my time as a PhD student. Niko de Silva deserves special thanks for

providing great copy editing.

I am indebted to many scholars in Berlin and around the world for their

valuable feedback at conferences, in workshops, and in other discussions. In

particular, I would like to thank Matthew Bothner, Gianluca Carnabuci, Xu

Li, Henning Piezunka and Martin Schweinsberg for their detailed feedback and

insightful comments. I am also thankful to my colleagues at ESMT Berlin,

which has been such an appreciative and inspiring place to conduct my re-

search. I would like to thank my colleagues at TU Berlin, who always made

me feel welcome though I seldom made the trip to Charlottenburg.

Frank Eckert helped me greatly in gathering the NBA data, for which I am

thankful. I would also like to thank Benjamin Vahle for connecting us (and for

being a great friend). Moreover, I am thankful to Rita Alvarez Martinez and

Nina Xue who provided remarkable assistence in complementing the dataset.

Financial support from the Einstein Stiftung and the state of Berlin (through

the Elsa-Neumann Stipendium) is gratefully acknowledged.

ix

Contents

Abstract v

Zusammenfassung vii

Acknowledgements ix

Preface 1

I When the Magic’s Gone:

How Key Player Absence Affects Team Performance 3

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Key Players in Teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.1 Teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Key Players . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3 The Absence of Key Players . . . . . . . . . . . . . . . . . . . . . . . . 7

3 Research Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.1 The National Basketball Association . . . . . . . . . . . . . . . . . . . . 9

3.2 Key Players in NBA Teams . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.3 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4 Econometric Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.1 Identification Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.2 Key Player Injuries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4.3 Randomization Check . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

5.1 Cross-Section Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 18

5.2 Before-After Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

5.3 Difference-In-Differences Approach . . . . . . . . . . . . . . . . . . . . . 20

6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

6.1 The Key Player Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

6.2 Underlying Mechanisms of the Key Player Effect . . . . . . . . . . . . . 24

6.3 Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

7 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

II Out of the Shade, into the Light:

Star Absence as an Opportunity for Non-Stars 43

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

2 Stars in Organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

x

3 Star Absence and Opportunity . . . . . . . . . . . . . . . . . . . . . . . . . 47

4 Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.1 Research Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.2 Sample and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

4.3 Econometric Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

4.4 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

4.5 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

5.1 Immediate and Long-Term Effects of Star Absence . . . . . . . . . . . . 58

5.2 Star Absence and Performance Opportunities . . . . . . . . . . . . . . . 60

5.3 Star Absence and Junior Employees . . . . . . . . . . . . . . . . . . . . 62

6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

III Me over We: Personal Rivalry

Increases Individual Output, but Crowds Out Organizational Interests 73

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

2 Theory and Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

2.1 Personal Rivalry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

2.2 Personal Rivalry and Individual Performance . . . . . . . . . . . . . . . . 77

3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

3.1 Empirical Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

3.2 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

3.3 Estimation Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

4.1 Personal Rivalry and Individual Performance . . . . . . . . . . . . . . . . 85

4.2 Additional Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

4.3 Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

Concluding Remarks 111

xi

Preface

I have been playing football since I was five years old. In my first team, our best player

was a striker named Andre. He was as fast as he was technically adept and I remember

that he once scored nine goals in a single game. Although our coach prized sportsmanship

and team spirit above everything else, it sometimes felt as if there were different rules for

Andre. Us other boys would let him get away with antics that we would not have tolerated

had he not scored all those goals. Andre was the first “star” I knew. The challenging

endeavor of integrating exceptional individual talent into a collaborative group is of course

not specific to junior football teams. Quite the contrary, it is ubiquitous in modern

organizations, where tasks have become increasingly cooperative and interdependent and

the inputs of the “best and brightest” are considered essential. Thus, this thesis studies

the role of stars in collaborative organizations.

For this purpose, I gathered data from the National Basketball Association (NBA).

Over the course of the years, I have come to realize that I have learned a lot more by

playing football than just how to shoot or pass the ball. For example, I have learned to

get along with easy and not so easy teammates, and how to be a good teammate myself

(at least I hope so). The things I have learned also helped me outside of sports, where I

have found that study groups at university or project teams at internships followed similar

principles. Because of these experiences, it felt natural to harness the potential of sports

data when I decided to study stars and their role in organizational settings. The NBA

offers unique research opportunities, as it provides rich data at the individual and team

level. As such, it makes for an ideal “laboratory” for empirical microfoundational work

that seeks to understand how stars affect organizations. In three independent essays, I

use it to illuminate different facets of stardom within organizations.

The first chapter (co-authored by Linus Dahlander) addresses the foundational question

of how stars affect team performance. One objective of this chapter is to present causal

evidence on the effect of stars. In general, disentangling the causal effect of individuals

on team performance is complicated, because individuals often self-select into teams. To

address this challenge, we exploit quasi-experiments induced by injuries of stars in the

NBA and estimate the effect of star absence. Across different econometric approaches

and specifications, we consistently find that star absence reduces team performance. This

effect is economically important and lasting. Exploiting rich variation in team and star

characteristics, we further investigate three mechanisms that potentially underlie the ef-

fect: (1) imperfect skill substitution, (2) the loss of complementarities within the team,

and (3) demotivation and reduced effort by the remaining team members. Results suggest

2 Preface

that the effect is driven by imperfect skill substitution, where teams cannot replace the

unique skills of the star.

In the second chapter, I focus on the relationship between stars and their colleagues.

Specifically, I analyze how the temporary absence of stars affects the productivity of the

non-stars surrounding them. Adopting a quasi-experimental approach that is similar to

the first chapter, I consider changes in the productivity of non-stars while the star is

injured and after he has come back. Results suggest that non-stars increase their output

while the star is absent. In the aftermath of long absences, they can sustain an increased

level of production even after the star has come back. Besides differentiating the imme-

diate and long-term effects of star absence, this chapter proposes that dominant stars

constrain non-star colleagues because they limit their opportunities to perform. In line

with this, I find that increased performance opportunities are indeed the key mechanism

underlying the positive effect of star absence on non-star productivity. Results also in-

dicate that junior employees, especially those in the middle of the talent distribution,

particularly benefit from these opportunities.

The third chapter turns to the personal motives of stars and how they affect stars’

behavior. In particular, I examine how the presence of an external personal rival affects

stars’ individual output and their contribution to organizational performance. Utilizing

supplementary information on personal rivalries among NBA stars, I compare star per-

formance in games where they compete with a personal rival to games where no rival is

present. Based on theories of social comparison and self-maintenance, this chapter sug-

gests that stars focus on highly visible and self-relevant individual output in their pursuit

to outperform their personal rivals. They thereby neglect other activities that are impor-

tant for collaborative organizations and they hurt organizational efficiency. Supporting

this, results suggest an adverse effect of competing with a personal rival: Stars increase

their individual output, but they contribute less to organizational performance.

While the three essays share the overarching goal of analyzing the role of stars in

collaborative organizations, the definition of a star varies slightly among them. The first

chapter uses the most comprehensive star definition because it goes beyond individual

productivity to determine stars, and also considers helpfulness. It also stands out in

terms of terminology as we speak of key players in this chapter. We make this distinction

to underline that we identify “stars” within each team, but one might as well think of

key players as local stars. In the second chapter, I seize upon the notion of local stars,

but take a more traditional approach to identify them, determining stars just based on

individual productivity. The third chapter finally relies on an implicit star definition,

because information about personal rivalries is only available for a group of elite NBA

players. These players are characterized by exceptional individual productivity making

the star definition fairly consistent across the three essays. In the end, they all study

key personnel who stand out at their organizations, thereby advancing our knowledge of

stardom in collaborative organizations.

When the Magic’s Gone:

How Key Player Absence Affects

Team Performance

Abstract. A fundamental question in strategy research is how individual be-

haviors aggregate into organizational performance. However, disentangling

the causal effect of individuals on team performance is complicated as indi-

viduals self-select into teams. To address this challenge, we exploit quasi-

experiments induced by injuries of key players in the National Basketball

Association (NBA). Across different models, we find a lasting and econom-

ically important negative effect of key player absence on teams’ chances of

winning. We further investigate three mechanisms that potentially underlie

the key player effect: (1) imperfect skill substitution, (2) the loss of comple-

mentarities within the team, and (3) demotivation and reduced effort by the

remaining team members.

1 Introduction

In 1989, the reigning two-time champion Los Angeles Lakers faced their rival, the Detroit

Pistons, in the National Basketball Association (NBA) Finals. The championship round

was anxiously awaited, as it was not only a match-up between the best teams of the regular

season, but also a clash of different playing philosophies: the finesse, offensively-minded

“Showtime” Lakers against the Pistons’ rough and physical “Bad Boys.” In the second

game, the Lakers lost Earvin “Magic” Johnson due to a pulled hamstring. Johnson was

the Most Valuable Player (MVP) of the regular season and the undisputed superstar of

the team. With their best player out, the Lakers ended up losing the series 0-4. In the

post game analysis, sports pundits attributed the Pistons’ triumph to the Lakers’ injury

list: the four-game sweep was seen as a direct result of Magic Johnson’s absence.

The 1989 NBA Finals exemplify how key players in teams attract special attention –

prominent athletes often outshine their teams. This is evident in many other settings,

such as the academic or business worlds, where single prominent scientists or managers

attract immense attention (Azoulay et al., 2010; Groysberg et al., 2008; Malmendier and

Tate, 2009). While research has suggested that individual ability is an important input

to team production, it is also widely accepted that the right mixture of individual talent

4 Chapter 1

and experience maximizes team performance (Hoisl et al., 2016). Interdependent teams

are more than the mere sum of their parts, such that it is not clear how individual

excellence carries over to the team-level. We seek to shed new light on the complex

relationship between prominent individuals and the “soul of teamwork” by investigating

the importance of key players in teams, and studying three different mechanisms by which

they can impact team performance. How individual contributions aggregate into team and

organizational performance is a question of fundamental importance for strategy research

– for example, Foss and Pedersen (2016) recently highlighted that greater attention needs

to be paid to the micro-foundations of strategy.

We base our empirical analysis on the absence of key players from teams (hereafter,

‘key player absence’). Obtaining empirical evidence on the effect of key player absence

on team performance is challenging for two reasons. First, it requires data on individual

contributions to team outcomes that is typically unavailable. Second, the self-selection

of players into teams complicates the disentangling of causal effects. Key player absence

is often endogenous, because key players frequently leave their team for reasons that are

also tied to team performance. To tackle these challenges, we harness NBA data that

includes a broad range of individual- and team-level metrics. We establish a causal effect

and deal with endogeneity using a quasi-experimental approach that leverages injuries of

key NBA players.

Studying top performers has a rich tradition in organizational research. Classical work

on superstars has established the antecedents and effects of superstardom (e.g., Adler,

1985; Hausman and Leonard, 1997; Rosen, 1981). However, it routinely viewed superstars

in isolation. At the same time, the incidence and importance of teamwork in modern,

knowledge-based organizations is well-documented (e.g., Wuchty et al., 2007). Recently,

the work of Azoulay et al. (2010) and Oettl (2012) has broadened the perspective by

evaluating the impact of superstars in collaborative environments. This paper builds

upon the work of Azoulay et al. (2010) and Oettl (2012) and complements their research.

While they focus on individual-level spillovers among peers, we analyze the effect of key

player absence on the joint output of the team and the underlying mechanisms.

Following Oettl (2012), we determine key players on the basis of productivity and help-

fulness. We focus on offensive output as a distinct part of NBA team performance and

measure individual productivity and helpfulness by the players’ points and assists, respec-

tively. Assists are an appropriate measure of helpfulness because they are only awarded

when the pass directly contributes to the basket made. We observed NBA teams during

the seasons 1998-2013, corresponding to 474 team-seasons.1 For these teams, we identified

a sample of 231 key players (corresponding to 907 key player-team-season combinations)

and examined how the teams’ probability of winning changed when the key player was ab-

1 We observed six seasons with 29 teams (1998-2003) and ten seasons with 30 teams (2004-2013). Webase our analyses on team-seasons, because NBA teams face significant turnover between seasons andmajor roster changes are common.

When the Magic’s Gone 5

sent with an injury. Across different econometric approaches and specifications, we found

a negative effect of key player absence on team performance. On average, the teams’ prob-

abilities of winning were decreased by 6.4 percentage points when they played without

their injured key player. This is an economically meaningful effect that can be decisive

when competing for the league’s prestigious playoff spots. 29.5 percent of the teams in

our sample finished within a margin of 6.4 percentage points of an even win-loss record,

which is generally seen as an indicator for a playoff team.2 For these teams, playing the

campaign with or without their key player would make or break a season. We extend

our analysis to evaluate three mechanisms that potentially underlie the key player effect:

(1) imperfect skill substitution, (2) the loss of complementarities within the team, and

(3) demotivation and reduced effort by remaining team members. Team performance can

suffer from key player absence because the key players possess unique skills that the team

cannot substitute for (imperfect skill substitution). Alternatively, team performance may

decrease because key players and their team-mates have complementary skills and those

productivity-enhancing pairings are lost in the absence of a key player (loss of comple-

mentarities). Finally, losing a key player can psychologically affect the team members

“left behind” who exert less effort as a result (demotivation and reduced effort). Exploit-

ing rich variation in team and key player characteristics as well as in the dynamics of the

effect, we found that imperfect skill substitution rather than the loss of complementarities

or reduced effort is the dominant mechanism behind the key player effect.

Our paper makes three contributions to the literature. First, we add to the teams

literature by showing how key players matter for team-level outcomes. Whereas earlier

literature has shown that losing stars has a negative effect on peers (Azoulay et al., 2010;

Oettl, 2012), we show that the absence of key players has a negative effect on the whole

team. Even in situations where substitutes were trained and ready to step in, losing a

key player decreased team performance. Second, we establish a causal effect by using

a quasi-experimental approach induced by injuries. This methodological advancement

has important implications because, without accounting for endogeneity, it would be

easy to overestimate the effect of key players (Hamilton and Nickerson, 2003). Third,

we examined three mechanisms potentially underlying the key player effect, and found

that teams suffer due to imperfect skill substitution. While earlier literature has argued

that complementarities in teams are important (Hamilton et al., 2003; Lazear and Shaw,

2007) and network structures matter (Reagans et al., 2004), we blend these insights with

evidence that outstanding skills are hard to replace.

2 In our data, only 5.3 percent of the teams with a winning percentage below 50 percent made theplayoffs and only 8.2 percent of the teams with a winning percentage above 50 percent did not make theplayoffs. Although not perfect, an even record is thus a useful heuristic.

6 Chapter 1

2 Key Players in Teams

2.1 Teams

Teams have become a basic building block of most contemporary business organizations,

a development which is reflected in the sizeable stream of team-related research (Balkundi

and Harrison, 2006). As this research spans different disciplines, the definition of a team

varies across much of the prior work. In general, teamwork can be characterized as “people

working together to achieve something beyond the capabilities of individuals working

alone” (Marks et al., 2001, 356). We define a team as a group of two or more persons

working toward a common goal. Each team member contributes specific expertise and

performs a specific function (Zaccaro et al., 2001). However, teams differ in their degree of

interdependence, which can be defined as “the extent to which team members cooperate

and work interactively to complete tasks” (Stewart and Barrick, 2000, 137). We focus

on teams with a high degree of interdependence, where team members must provide a

coordinated effort to achieve the team’s goal (Landis, 2001). As such, team output is not

just the simple sum of individual inputs. In interdependent teams the role of key players is

particularly interesting because motivation, coordination and cooperation play important

roles (Wageman, 1995). In other words, “team chemistry” is important in interdependent

teams.

Organizational scholars have examined very different determinants of team performance

such as reward schemes (e.g., Beersma et al., 2003; Boning et al., 2007), leadership (e.g.,

Dirks, 2000; Goodall et al., 2011), trust (e.g., De Jong and Elfring, 2010; Dirks and

Ferrin, 2002), and team composition (e.g. Jehn et al., 1999; Reagans et al., 2004). Yet

the influence of key players remains understudied.

2.2 Key Players

Analyzing the effect of key player absence on team performance inevitably raises the ques-

tion of who is key within a team. The notion of a key player is a prominent concept in

social network analysis (e.g., Ballester et al., 2006; Borgatti, 2006; Liu et al., 2012). This

literature does not offer a consistent definition of a key player – rather, the appropriate

definition is thought to hinge on the research question (Borgatti, 2006). In general, re-

search on key players in social networks is concerned with the question of which individuals

are important for the network (Borgatti, 2006). Correspondingly, team research on key

players revolves around the individuals who are important for the focal team. Intuitively,

a key player is a team member who makes a major contribution to team performance.

This intuitive definition of a key player has two important components that distinguish a

key player from similar concepts. The first component is hierarchical: the key player is a

normal team member rather than a team leader. While team leaders can rely on formal

power sources (Balkundi and Harrison, 2006), key players are co-equal to their teammates


Table 1: Taxonomy of Key Players

High Average or lowproductivity productivity

High All-star Helpfulhelpfulness key player key player

Average or low Productive Nohelpfulness key player key player

from a hierarchical standpoint. The second component is relational: key player status

is related to the team. This distinguishes the key player from a superstar. While su-

perstars are usually defined within broader fields of activity, such as in a scientific field

(e.g., Azoulay et al., 2010), music genre (e.g., Krueger, 2005), or occupational group (e.g.,

Malmendier and Tate, 2009), key players are defined in comparison to their teammates.

Obviously, a team member can make a major contribution to team performance by

being very productive. However, those who have worked in teams know that this is only

half the story. Besides the gifted crackerjack, there is usually another team member who

is vital for team performance because he or she helps others and brings out the best in

team members. Thus, team members can make a major contribution to team performance

either directly or indirectly, i.e. by being highly productive, highly helpful, or both.

Oettl (2012) reconceptualized scientific stars along two dimensions: productivity and

helpfulness. In collaborative environments, stars are not only defined by what they pro-

duce themselves but also by how they help others (Oettl, 2012). This reasoning also

applies to key players in teams. Building on Oettl (2012), we therefore differentiate three

key player types: productive, helpful, and all-star. Productive key players are highly

productive but average or below-average on helpfulness. Helpful key players are highly

helpful but average or below-average on productivity. All-star key players are both highly

productive and highly helpful. The taxonomy of key players is summarized in Table 1.

It accounts for the fact that team members can contribute to team performance in more

than one way.

2.3 The Absence of Key Players

When a team happens to lose a key player, it appears intuitive that team performance

decreases. However, the dynamics involved are not as straightforward as they may seem

at first glance. Key players often play a dominant role within a team, usually make more

money than their teammates, and receive most of the attention. This can be detrimental

to team chemistry. Everybody has heard the often-told story of the egocentric star who

demoralizes his or her own team. Supporting this notion, Groysberg et al. (2008) found

that newly hired star analysts underperform in their new working environments. One

explanation they emphasize is the ramifications for collegial relationships, which may in-

clude interpersonal conflicts. Resentful veteran employees who envy a star’s compensation

8 Chapter 1

and status may react with a lack of cooperativeness to the new hire. Similarly, the special

treatment for the key player may cause demoralization. If that is the case, the absence of a

key player can ease tensions and boost morale within the team. Additionally, the absence

of a key player may improve coordination within the team. Swaab et al. (2014) found that

team coordination and performance suffer when too many talented individuals are on the

team. They attributed the too-much-talent effect to status competition within the team.

Status conflicts can harm team performance because team members are overly concerned

with their own standing within the team (Groysberg et al., 2011). Fighting for pecking

order can go as far as undermining other team member’s efforts (Greer et al., 2011). The

absence of a key player may cure dysfunctional teams plagued by status competition and

a lack of coordination. Under these circumstances, it may decrease the team’s overall tal-

ent, but counterintuitively, increase its performance. Finally, the absence of a key player

necessarily leaves a void that presents an opportunity for the remaining team members.

Resources, responsibilities, and tasks are reallocated and the new constellation may help

remaining team members reach their full potential. In sum, these arguments suggest that

the absence of a key player will not necessarily be detrimental to team performance.

Still, there are good reasons to presume that the absence of a key player has a negative

effect on team performance. There are three distinct mechanisms that we distinguish in

the paper: (1) imperfect skill substitution, (2) loss of complementarities between the key

player and his or her teammates, and (3) demotivation and reduced effort of the team

members left behind.

First, imperfect skill substitution implies that key players contribute essential inputs to

the team output that cannot be substituted for (Azoulay et al., 2010). Teams therefore

struggle to replace the skills that have gone missing. Key NBA players possess unique skills

within their teams, as is the case in many other settings (Goodall et al., 2011). As such, a

hallmark of team play in the NBA is imperfect skill substitution, which is one of the key

characteristics of working with superstars (Rosen, 1981). Hamilton et al. (2003) suggested

that imperfect skill substitution goes beyond technical skills. High-ability workers are

more influential for team productivity than low-ability workers because they can enforce

more rigorous work norms and teach less-able workers to be more productive (Hamilton

et al., 2003). This implies that teams lose something beyond pure technical abilities when

key players are absent.

Second, the loss of complementarities from a key player’s absence can cause team per-

formance to decrease. Complementarities within a team imply that the team members’

productivity is enhanced by combining their effort with teammates who have different

skills (Lazear and Shaw, 2007). Complementarities can arise either through matching or

by investing in special skills (Hayes et al., 2006). Given the immense scouting and training

efforts of NBA teams, strong complementarities between team members accrue over time.

Indeed, recent research has provided evidence of significant productivity spillovers among

players in NBA teams (Arcidiacono et al., 2017; Kendall, 2003). However, the mere ex-


istence of complementarities is not enough. Rather, a negative net effect requires that

complementarities between the key players and their teammates are higher than those

between the key players’ substitutes and the team. Again, this is plausible since teams

are often built around key players, a fact that has found expression in the NBA jargon

of “franchise player.” Teammates are frequently selected according to their fit with the

skills and playing style of the key player, i.e. when complementarities between the key

player and the other teammates are maximized.

Third, the absence of a key player can change the motivation among the remaining

team members. One reasoning suggests that the absence of a big ego key player can boost

morale, which shows that the effect on motivation is not clear a priori. The competing

argument is that players left behind might get discouraged by the loss of the key player,

and reduce their effort as a result. Empirical evidence supports the idea that highly

productive personnel increases the effort of coworkers (Mas and Moretti, 2009). High-

ability workers are also associated with more rigorous work norms (Hamilton et al., 2003).

Consequently, the remaining team members may exert less effort after losing a key player.

This also seems plausible for NBA teams. NBA coverage routinely stresses teams “playing

hard” and since 2016 the NBA reports “hustle stats,” both suggesting that teams (and

players) vary in their effort despite the high level of professionalism and visibility in the

NBA. Given the small team size (just five players on the court), injuries of key players

are perceived as particularly severe in NBA teams and can therefore reduce motivation.

Besides demotivation, NBA teams may also strategically reduce effort after key player

injuries. Seeing their chances slip away without their key player, teams may decide to

reduce effort to improve their position in the upcoming rookie draft (Taylor and Trogdon,

2002).

3 Research Context

3.1 The National Basketball Association

The National Basketball Association is one of the four major sports leagues in North

America. It is generally considered the world’s leading league in men’s professional bas-

ketball, and has been used to study organizational phenomena in the management litera-

ture (e.g., Berger and Pope, 2011; Ertug and Castellucci, 2013; Fonti and Maoret, 2016).

The league’s athletic importance is also mirrored economically. In 2015, Forbes estimated

the NBA’s total revenue to be 4.8 billion USD (Badenhausen, 2015). The NBA consists of

30 teams (29 teams until 2004) divided into two conferences (East and West) with three

divisions each (Atlantic, Central, Southeast in the East and Northwest, Pacific, Southwest

in the West). A NBA season consists of two phases: the regular season (82 games per

team) and the playoffs (between 4 and 28 games per team). The eight best teams of each

conference advance to the playoffs, a tournament consisting of four best-of-seven rounds

10 Chapter 1

to determine the NBA champion.

The NBA is an attractive empirical setting for both conceptual and empirical reasons.

Conceptually, NBA teams incorporate essential features for studying teams, and key play-

ers within them. In terms of teams, roster regulations ensure that all teams operate under

equal conditions and are thus comparable (Dirks, 2000). NBA teams exist over decades,

pursue identical goals, and are governed by the same rules. They are also characterized by

a high degree of interdependence among team members, which forces them to provide a

coordinated effort (Berman et al., 2002). In terms of key players, differences in individual

performance are well-known and easily observable. With only five players on the court,

individual players can make a difference in basketball. Outstanding players like Michael

Jordan or LeBron James have turned losing franchises into championship teams.

Empirically, NBA data offers unique opportunities for team research. For our purposes,

it has two distinct advantages. First, it enables us to link individual efforts to team

outcomes, thus relaxing the data constraint normally associated with the analysis of

individual behavior in teams. Second, using data from the NBA allows us to pursue

an identification strategy that sheds light on the causal relationship between key player

absence and team performance. Our identification strategy relies on quasi-experiments

induced by injuries of key players. We are able to distinguish injuries from other reasons

for player absence, which is vital for our empirical strategy. It also differentiates our

dataset from those NBA datasets that have been employed in earlier studies (e.g., Price

et al., 2010; Taylor and Trogdon, 2002).

To create a dataset that fulfills the requirements of our research question, we compiled

data from the NBA’s official website, www.nba.com. We selected nba.com because it is

the premier source for NBA data with regards to both volume and depth. Due to the

league’s efforts toward data accuracy, the official statistics are very credible and reliable.

The official NBA statistics are recorded courtside and reviewed by league officials to

make sure they are credited properly (Biderman, 2009). Hence, the measures should not

be greatly affected by measurement error. We complemented the official statistics by

information on player characteristics, individual awards and salaries, which we retrieved

from www.basketball-reference.com. Our dataset includes longitudinal information about

NBA teams and players from the 1998-99 season to the 2013-14 season, i.e. for 16 seasons

in total. This corresponds to 20,026 games, 40,052 team-game observations (two teams

per game), and 624,411 player-game observations (between fifteen and sixteen players per

team). In some rare cases, nba.com did not list all players that competed in a game.

201 team-game observations (0.5%) (corresponding to 2,236 player-game observations

(0.36%)) were excluded because of incomplete information.


3.2 Key Players in NBA Teams

Consistent with the key player definition, we utilize intrateam rankings to identify key

players. Characterizing individuals by their relative position in a distribution is a common

approach for identifying superstars (e.g., Azoulay et al., 2010; Krueger, 2005; Oettl, 2012).

We followed this path by identifying key players based on their relative position within

their team. The different key player types were delineated using points per game and

assists per game as measures for individual productivity and helpfulness, respectively.

Points per game and assists per game are averages calculated on the basis of all games

the player competed in during the season.

Team members were classified as key players if (1) they led their team in points per

game (productivity) or assists per game (helpfulness), and (2) their individual statistics

exceeded the team’s average by at least 1.5 standard deviations. Accompanying the rank

criteria with a threshold ensures that the identified key players were indeed vital to their

team.3 Players who led their team in points per game and passed the threshold were

classified as productive key players. Players who led their team in assists per game and

passed the threshold were classified as helpful key players. In cases where one player led

the team both in points and in assists and passed the thresholds, he was classified as an

all-star key player. Revisiting the introductory example, Magic Johnson led the 1988-89

Lakers in both categories and would have thus been identified as an all-star key player.

NBA teams can trade their players until a deadline, which is roughly after two thirds

of the regular season. In case a key player changed teams, we decomposed the season into

two parts delimited by the date of the key player trade. For the period in which the key

player was not with the team (because he was traded away or arrived later), we identified

a new key player. We observed 57 key player trades.4

This procedure yielded a key player sample of 907 key player-team-season combinations

consisting of 386 productive key players (42.6%), 403 helpful key players (44.4%), and 118

all-star key players (13.0%). We identified at least one key player for all team-seasons.

On average, 1.7 key players were identified per team-season. As one NBA player could be

the key player for multiple teams or seasons, 231 NBA players (14.8% of the entire player

sample) constituted the key player sample.

Table 2 reports individual statistics and awards of NBA players by key player type.

Unsurprisingly, Panel A reveals that key players scored more points and gave more assists

than non-key players. They also played more minutes. One potential objection to the

key player sample is that it was determined solely on offensive contribution. However,

3 We also ran a robustness check with a higher threshold of two standard deviations. The results stayedqualitatively unaltered, but the effects were quantitatively stronger. This is unsurprising because thekey players identified with the higher threshold were more outstanding than the original ones. Detailedresults are available upon request.4 We conducted a robustness check where we excluded all teams that were involved in a key player trade.Although the exclusion affected 5,111 team-game observations, it did not change the results significantlyin substance or magnitude. The results are available upon request.

12 Chapter 1

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Panel A shows that key players also excelled on the defensive side as they made more

defensive rebounds and steals compared to non-key players. The positive overall impact

of key players is also reflected in their plus-minus statistic, which records the team’s

point differential when the player is on the court, and the player impact estimate, which

measures the player’s overall statistical contribution. Salary data shows the high market

value of key players: All-star and productive key players earned roughly three times more

and helpful key players almost twice as much as non-key players. Panel B shows that

key players also received more awards than non-key players. This is particularly striking

for the Most Valuable Player award, the highest individual honor for NBA players.5 The

nominations to the All NBA Teams and to the All-Star Game honor the season’s best

players. Over 90 percent of the All NBA First Team and over 85 percent of the All NBA

Second Team were composed of key players. Key players also dominated the nominations

(made by NBA head coaches) and starting spots (determined by a fan ballot) of the

All-Star Game. Overall, Table 2 provides confidence that our key player sample contains

players that were indeed key.

3.3 Variables

Dependent variable. A natural way to consider team performance at the game-level is

to evaluate the game outcome, i.e. whether the team won or lost a game. Winning can

be considered the single most important indicator of team performance in professional

sports and in the NBA. The number of wins determines both the regular season ranking

and advancement in the playoffs. It also takes the relative nature of competition in

professional basketball into account. Alternative measures of team performance are the

offensive output (points scored) or the point differential (also known as plus-minus in

the NBA). The latter seems particularly interesting, because it determines winning and

losing and quantifies the game outcome in more detail. However, unlike in other sports,

the score differential does not matter for the team’s standing in the NBA. That is why

scores are regularly inflated or compressed in the so called “garbage time,” the period

of an already decided game where substitutes come in and the best players are rested.

Therefore, our dependent variable is an indicator Win that becomes 1 when the focal team

won the game. This binary variable exhibited a correlation of 0.81 with point differential

and displayed a correlation of 0.44 with points. In our robustness checks, we show that

our results are consistent with these alternative measures.

Independent variable. To follow the identification strategy, it is essential to distin-

guish injuries from other reasons for key player absence. We undertook two steps to

5 The NBA has more awards than listed in Table 2. With exception of the Sixth Man of the Year award,an honor specially designed for substitute players, they were also dominated by key players. Althoughwe obtained key players based on their offensive contribution, the title of Defensive Player of the Yearwas awarded to key players in almost 40 percent of the cases.

14 Chapter 1

construct a dummy variable that recorded key player injuries reliably. First, we eval-

uated official NBA box scores. The box score summarizes a NBA game and lists the

names, positions, and statistics of the players. More importantly, it also lists players

who did not play and explicitly states whether the player was missing due to an in-

jury or another reason, e.g. the coach’s decision. Unfortunately, the box score did not

clarify all key player absences. In those cases, we researched key player absences man-

ually and analyzed game reports and daily NBA news. The game reports were from

ESPN (http://espn.go.com/nba/scoreboard). The daily news reports were from Patricia

Bender’s NBA archive (https://www.eskimo.com/ pbender/), which has been utilized by

previous researchers (e.g., Ertug and Castellucci, 2013). We coded Injured Key Player as

1 if the box score or the game reports indicated a medical reason for key player absence

and 0 otherwise.

Control variables. In our estimations we also controlled for other factors that might

influence team performance. Three aspects seem particularly important. First, playing at

home is generally seen as an advantage in professional sports, so we controlled for location

of the game. Home is a dummy variable that indicates whether a team played at home.

Second, performance capability is affected by the physical condition of the team. Games

on two consecutive days are called back to back games and are considered particularly

energy-sapping in the NBA. To account for potential exhaustion, Back to Back indicates

whether the team has played the night before. Finally, NBA team performance is also

affected by the opponent. To account for the opponent’s quality, we included Opponent

Win Percentage. It records the proportion of games the opposing team has won during

the season. It is important to note that adding control variables will not change the

estimate of the treatment effect if key player absences are indeed exogenous, which we

will show in the results. We included the control variables to reduce residual variance and

lower the standard errors of the estimates (Angrist and Pischke, 2009).

4 Econometric Considerations

4.1 Identification Strategy

The aim of this paper is to estimate the causal effect of key player absence on team

performance. Ideally, in order to separate the effect of the treatment from other factors

influencing the outcome, one would randomly allocate the treatment across teams, i.e.

randomly withdraw key players. Such a randomized experiment would ensure that the

treatment is exogenous. For obvious reasons, the experimental approach is not feasible.

A natural starting point for a non-experimental approach is to look for teams that

at some point lose their key player and see how their performance changes after the

key player has left the team. However, this approach is problematic because key player


absence is potentially endogenous: key players often leave their team for reasons that are

tied to team performance. Just as the best scientists often choose to join research teams

of other highly productive scientists, expert physicians locate at the same hospitals, or

top lawyers form cooperative teams in law firms, key players in the NBA will tend to leave

weaker teams to join better ones. This exposes weaker teams to the loss of a key player

more frequently, leading to a systematic downward bias in the treatment group and to an

overstatement of the treatment effect.

To tackle this problem, we focus on key player absences due to injuries. While injuries

are probably not completely random, they have a significant random component. This

is especially true in the NBA as many other important factors are relatively constant

across the teams. For example, the intensity of training, the physical effort in the games

and the quality of the medical staff are comparable across the league. Hence, injuries

can be regarded “as good as randomly assigned” in the empirical context. They induce a

quasi-experimental variation in the treatment allocation across teams. With an exogenous

treatment, the treatment group no longer differs systematically from the control group.

Exploiting key player injuries thus eliminates sample selection bias. Relying on injuries

as an exogenous source of variation is a novel empirical strategy in teams research that

has begun to get some traction (see e.g., Chen and Garg, 2018; Stuart, 2017). A similar

strategy was used by Azoulay et al. (2010), Jones and Olken (2005) and Oettl (2012),

who exploited the deaths of prominent individuals as an exogenous source of treatment

variation.

4.2 Key Player Injuries

In every natural experiment, it is essential to understand the exogenous source that de-

termines treatment assignment (Meyer, 1995). In our case, that corresponds to injuries of

key NBA players. Most of the teams experienced at least one key player injury over the

course of a season, i.e. they were treated at some point. Only 63 of the 474 team-seasons

went by without any key player injury. In turn, almost 87 percent of the teams were

treated at least once.

Fortunately, injuries are not permanent and the players recover and come back at

some point in time. This has two important implications. First, teams were observed in

different treatment statuses: pre-treatment, treatment, and post-treatment.6 This implies

that teams potentially switch back and forth between treatment state and no-treatment

state. A typical pattern of key player injuries can be represented schematically as in

Figure 1.

Second, teams can be treated multiple times. As soon as the key player comes back, he

is at risk of injuring himself again. Indeed, most teams lost their key player more than

6 It is also possible that key players are injured at the beginning of the season and return to the teamlater. 32 team-seasons (7.8% of the treated teams) began with an injured key player.

16 Chapter 1

Figure 1: Schematic Representation of Typical Treatment Pattern

Yes

Key Player Injury

Nopost-

treatmentpre-

treatmenttreatment post-

treatmenttreatmentPhase

Game Number

once during the long NBA season, i.e. they were treated multiple times. Around 82.5

percent of the treated teams lost their key player due to an injury at least twice. Of the

treated teams, 9.7 percent experienced six or more key player injuries (with a maximum

of 11).

Finally, key player injuries can happen at any time so that teams were treated at

different points in time. Usually, natural experiments exploit situations where treatment is

“switched on” at a certain point in time and the investigation period can be distinguished

clearly in a treatment and no-treatment phase.

The treatment assignment mechanism here differs in three important ways from text-

book treatment-control studies: (1) teams can be treated at different points in time, (2)

teams can be treated multiple times, and (3) teams can switch back and forth between

the treatment and no-treatment states. We accounted for these peculiarities with the

before-after approach and a series of robustness checks.

4.3 Randomization Check

One important step in evaluating quasi-experimental data is to check whether random-

ization has worked as intended (Angrist and Pischke, 2009). The central idea of our

identification strategy is that key player injuries have a considerable random component

and that treatment is therefore “as good as randomly assigned” to teams. Hence, we ex-

pect key player injuries to balance the characteristics of the treatment and control teams.

To assess this empirically, we compared characteristics for treated and control teams; re-

sults are shown in Table 3. We only included team characteristics that were constant over

a season and independent of the treatment.

Table 3 shows good balance between the treated teams and control teams. In general,

the differences across both groups were small and most of them were not significantly

different from zero, as indicated by the t-statistic in the last column. The only dimension

where treated and control teams differed was in salaries. On average, treated teams paid

more than control teams. As long as higher paying teams exhibit higher quality and are

therefore more adept to playing without their key player, this difference should make the

cross-section approach more conservative. Generally, the differences between the treated

and control teams were too small to support the concern that selection systematically bi-


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18 Chapter 1

ased our results. Thus, injuries appear to be random across teams and potential outcomes

should be independent from treatment status.

5 Estimation

To build robust evidence about the effect of key player absence on team performance, we

estimated a series of regressions. We decomposed the variation available in the data and

used different estimation approaches. The models differ in their approach to constructing

the required counterfactual, i.e. the outcome of the treated teams had they not been

treated. The analysis proceeded as follows: First, we followed a cross-section approach

and compared treated and untreated teams at a fixed time. Second, we employed a

before-after approach and compared treated teams to themselves. Third and lastly, we

utilized a difference-in-differences approach and compared treated teams to themselves

incorporating a counterfactual time trend. The difference-in-differences approach plays a

central role in the empirical strategy because it exploits all variation available in the data.

We thus use this as the preferred approach to evaluate the role of key players in teams.

Since we are interested in marginal effects, we estimated linear probability models for all

three approaches. When it comes to marginal effects, the linear probability model is as

good as nonlinear models that explicitly account for the binary nature of the dependent

variable, such as logit or probit models (Angrist and Pischke, 2009).

5.1 Cross-Section Approach

The cross-section approach compares outcomes of treated and control teams at a specific

point in time. It thus uses the outcome of the untreated teams at the same point in

time to approximate the counterfactual situation. The cross-section estimator disregards

variation over time (within variation) and exploits variation between teams (between

variation).

The key identifying assumption of the cross-section approach is that treated and control

teams on average have the same no-treatment outcome (Heckman et al., 1999). The

randomization check revealed that treated teams did not differ systematically from control

teams. Thus, the cross-section estimator does not suffer from selection bias. Another

concern with the cross-section approach in this empirical setting is that teams which serve

as the control group at one point in time could have been treated before (as they switch

between treatment states). To address this issue, we assessed how far prior treatment

“contaminated” the teams as controls at later stages in a robustness check.

One way to implement the cross-section approach econometrically is to “slice” the

sample into 82 cross-sections, one for each regular season match day. The treatment

group then consists of teams with a key player injury at that match day. Teams that

did not suffer from a key player injury at the same match day form the control group.


To obtain the cross-sections, we successively restricted the sample to one point in time

and performed an ordinary least squares (OLS) estimation with the following estimating

equation:

Wini = β0 + β1InjuredKeyP layeri + β2Homei + β3BacktoBacki

+ β4OpponentWinPercentagej + εk + θl(1)

We also incorporated franchise and season fixed effects, εk and θl. They capture ef-

fects specific to one franchise (e.g., training facilities) or one season (e.g., schedule or rule

changes). Note that the cross-sections were compiled of games from different seasons be-

cause we pooled games by match days, not by calendar date. The underlying assumption

is that important dynamics operate identically within the seasons. The variable of interest

is InjuredKeyP layeri, which serves as a treatment indicator giving a causal estimate for

the effect of key player absence on team performance.

5.2 Before-After Approach

The before-after approach focuses on treated teams, i.e. teams that at some point experi-

enced a key player injury. For those teams, it compares the outcomes of the pre-treatment

period to the outcomes of the (subsequent) treatment period.7 In other words, the treated

teams are compared to themselves. This is intuitively appealing because many factors

that potentially influence team performance are held constant in the comparison.

The key identifying assumption is that, among treated teams, the mean outcome in the

no-treatment state is the same in both periods (Heckman et al., 1999). This assumption

would be problematic if there existed a time trend in the outcome or a transitory shock

prior to the treatment, commonly referred to as Ashenfelter’s dip (Ashenfelter, 1978). We

conducted a robustness check that included team-specific time trends to address these

issues.

To implement the before-after approach econometrically, we restricted the sample to

treated teams and censored the data when the injured key player returned from his first

injury, i.e. after the last game of the first treatment spell. Because we exclude teams

that never experienced a key player injury and censor the data, the control group consists

exclusively of the treated teams before their first key player injury. The treatment group

consists of the treated teams while their key player was absent with his first injury.

Censoring after the end of the first treatment spell also resolves problems associated

with multiple treatments and switching between treatment states. It provides a clear

comparison of pre-treatment and treatment outcomes, which ensures that the identified

7 We call this approach before-after because it is an established term in the literature on empiricalprogram evaluation (see e.g., Heckman et al., 1999). Note, however, that teams were in the treatmentstate during the second period – the “after” period.

20 Chapter 1

effect was not “contaminated” by prior treatment. On the modified sample, we performed

an OLS estimation. The estimating equation for the before-after approach was:

Winit = β0 + β1InjuredKeyP layerit + β2Homeit + β3BacktoBackit

+ β4OpponentWinPercentagej + γi + δt(2)

The structure of the model is similar to Equation 1. Instead of franchise and season

fixed effects, now team-season and match day fixed effects were included (γi and δt).

The team-season fixed effects absorb time-invariant team and season characteristics. The

match day fixed effects account for match day differences in how far the season had

progressed. Controlling for time effects is important because of the incentive structure in

the NBA. Some teams may strategically rest important players to preserve their fitness

for the playoffs (Price et al., 2010). Others may even try to lose intentionally at the end

of the regular season to improve their chances in the next rookie draft, also known as

“tanking” (Taylor and Trogdon, 2002). On the other hand, competing for the last playoff

spots can increase morale, motivation, and ultimately performance.

5.3 Difference-In-Differences Approach

The difference-in-differences approach can be seen as an extension of the before-after

approach. The counterfactual situation is approximated by evaluating the change in

performance of the treated teams around the treatment and subtracting the change in

performance of the control teams over the same period. The intuition behind this approach

is that team performance might have changed in the particular time window even if the

key player had not been injured. Therefore, the counterfactual time trend is subtracted

to ensure that the observed change in performance can be attributed to the treatment,

i.e. the injured key player.

The key identifying assumption of the difference-in-differences approach is that the

mean change in the outcome is the same in the treatment and control group in the absence

of the treatment (Heckman et al., 1999). This assumption is also known as “common

trends.” If it is valid, the change in performance among the teams of the control group

serves as a benchmark for common time effects (Heckman et al., 1999). To address the

issue of preexisting and potentially deviating time trends, we estimated a specification

that allows for team-specific time trends as a robustness check.

The difference-in-differences approach takes advantage of the longitudinal nature of

the data and uses both variation over time (within variation) and variation over teams

(between variation). Therefore, we did not restrict the sample and performed an OLS

estimation with the following estimating equation:


Table 4: Descriptive Statistics

Descriptive statistics Correlations(39,851 Team-game obs.) (39,851 Team-game obs.)Mean SD Min Max 1 2 3 4

1. Win 0.50 0.50 0 12. Injured key player 0.15 0.36 0 1 -0.153. Home 0.50 0.50 0 1 0.33 -0.004. Back to back 0.23 0.42 0 1 -0.12 0.07 -0.335. Opponent winning percentage 0.51 0.15 0.11 0.82 -0.29 -0.00 -0.00 -0.05

Notes. This table reports descriptive statistics and correlations for all variables in the model. Betweenthe binary variables (variables 1 to 4) tetrachoric correlations are reported.

Winit = β0 + β1InjuredKeyP layerit + β2Homeit + β3BacktoBackit

+ β4OpponentWinPercentagej + γi + δt(3)

The difference-in-differences approach implies an additive structure of potential out-

comes in the no-treatment state, which is composed of a time-invariant team-season effect

and a general time effect. This is also reflected in Equation 3. The team-season fixed

effects (γi) account for time-invariant team characteristics (first difference). The time

fixed effects (δt) account for the general time trend (second difference). The difference-in-

differences approach extends the before-after approach as it neither restricts the sample

nor censors the data. Equation 2 and Equation 3 are identical and the two approaches dif-

fer in the sample restrictions in the before-after approach. As before, InjuredKeyP layerit

is the main variable of interest.

6 Results

Table 4 reports the descriptive statistics and pairwise correlations of all variables. In 15

percent of our observations teams were playing without their key player due to injury.

In general, the pairwise correlations between the variables used in the models were small

and give no reason for concerns about multicollinearity.

6.1 The Key Player Effect

Cross-section approach. A first approximation of the cross-sectional approach is achieved

by comparing the win percentages of the treated and control teams at each match day.

A simple mean comparison revealed that teams that lost their key player to an injury

won only 40.77 percent of their games while control teams won 51.77 percent, yielding

a difference of 11 percentage points. While comparing win percentages for treated and

control teams surely helps to get a first idea about the effect of key player absence on

22 Chapter 1

Figure 2: Key Player Effect – Results from the 82 Match Day Cross-Sections

0.10

-0.05

-0.10

0.05

0

-0.15

-0.20

-0.25

00.2 0.10.30.40.50.9 0.8 0.7 0.6

p-value

Coe

ffici

ent

estim

ate

Notes. This figure presents the plot of the coefficient estimates from the cross-sectional approach.Each dot represents the estimate of the treatment effect from one of the 82 match day regressions andis placed according to its effect size (vertical axis) and significance (horizontal axis). P-values werecalculated based on robust standard errors, clustered at the team-level. The dashed line represents the5% significance level. The horizontal line represents the mean point estimate.

team performance, it is a somewhat naive assessment.

A more sophisticated version of the mean comparison is achieved by splitting the sample

by the 82 regular-season match days, adding control variables, and running match day

regressions as outlined in Equation 1. The results of the match day regressions are depicted

in Figure 2. The 82 dots in the figure represent the estimates of the treatment effect. They

are placed according to their effect size and significance level. Figure 2 shows that in 27

of the 82 cross-sections, there was a significant negative effect of key player absence on

team performance (at the 5% level; 41 at the 10% level). In 47 regressions, the effect was

negative but not significant. Only in eight match day regressions did we find a positive

effect from key player absence; however, the effect was never significant. The estimates

fluctuated due to rather modest sample size as each cross-section utilized between 410

and 474 observations. On average, the coefficient for the indicator variable Injured Key

Player was -0.0965 (horizontal line).

The results of the match day regressions thus conform to the first impression given

by the naive mean comparison and consistently suggest a negative effect of key player

absence on team performance.

Before-after approach. In the before-after approach, we test the effect of key player

absence on team performance by comparing the treated teams to themselves. The esti-


Table 5: Key Player Effect – Before-After Estimation and Difference-In-DifferencesEstimations

Before-after Difference-in-differences(DV: win) (DV: win)

(1) (2) (3)

Injured key player -0.0496** -0.0683*** -0.0641***(0.0177) (0.0086) (0.0081)

Home 0.2019*** 0.2029***(0.0086) (0.0050)

Back to back -0.0393*** -0.0375***(0.0095) (0.0054)

Opponent win percentage -0.9722*** -0.9738***(0.0249) (0.0141)

Team-season fixed effects Yes Yes YesMatch day fixed effects Yes Yes YesR2 0.1439 0.0037 0.1347Obs. 11,181 39,851 39,851

Notes. This table reports estimation results from the before-after regression and the difference-in-differences re-gressions. The dependent variable in all models is a dummy variable for winning the game. Column (1) reportsthe results of the before-after regression (Equation 2). Column (2) reports the results of the difference-in-difference regression without controls. Column (3) reports the results of the difference-in-differences regressionwith controls (Equation 3). Robust standard errors clustered at the team level are in parentheses. Significancelevels: * p<0.05; ** p<0.01; *** p<0.001.

mating equation of the before-after approach is given in Equation 2. The first column

of Table 5 presents the corresponding results. We found a significant negative effect of

key player absence on team performance, where the probability of winning fell by 4.96

percentage points when a key player was injured. This is in line with the results from

the cross-section approach, although the effect size was greater. Compared to the cross-

section approach, the sample size in the before-after approach was bigger, which increased

the precision of the estimates. However, the before-after approach utilized only one fourth

of the observations due to the imposed sample restrictions.

Difference-in-differences approach. For the last step, we estimated the difference-in-

differences model. It utilized the full wealth of the longitudinal data and is therefore

critical in evaluating the effect of key player absence on team performance. The results

of the difference-in-differences approach are given in the second and third column of

Table 5. The second column reports the results from the difference-in-differences model

estimated without controls. The third column reports the results from the model with

a full set of controls, as outlined in Equation 3. We again found a significant negative

effect of key player absence on team performance. With their key players out the teams’

probability of winning decreased on average by 6.83 percentage points (estimation without

controls) and 6.41 percentage points (estimation with controls). Adding controls to the

estimation changed the estimate of the treatment effect only slightly. Instead, the controls

24 Chapter 1

increased the precision of the estimate and the explanatory power of the model. Utilizing

all variation in the data, the standard errors were roughly halved (compared to the before-

after estimates). The negligible change in the treatment effect supports the idea that key

player injuries are not correlated with other explanatory variables, i.e. that the treatment

is indeed exogenous.8 This also implies that adding more control variables would not

change the estimate. It is noteworthy that not only the treatment effect but also the

coefficients of the control variables were estimated consistently across all models. Overall,

the results of the different approaches paint a consistent picture: Irrespectively of how we

modeled the counterfactual situation, we found a significant negative effect of key player

absence on the teams’ probability of winning.

6.2 Underlying Mechanisms of the Key Player Effect

We advanced three potential mechanisms that underlie the negative effect of key player

absence on team performance: (1) imperfect skill substitution, (2) loss of complementar-

ities between the key players and their teammates, and (3) reduced effort by the players

left behind. While it is hard to come up with one conclusive test to assess the relative

importance of the mechanisms, we exploit the rich detail in the data and provide evidence

from several analyses.

First insights can be obtained by analyzing if and how the effect of key player absence

differs by key player type. To evaluate this we differentiated the general treatment in-

dicator by three dummy variables that indicate the injuries of helpful, productive, and

all-star key players. Additionally, we included an indicator for the injury of a regular

player, which is a player that ranks among the top five in average minutes played (up

to the focal game) and is not a key player. The results are given in the first column of

Table 6. The baseline is a situation where all regular players are available, i.e. neither

a key nor a regular player is injured. Compared to this baseline, the injury of a helpful

key player decreased the teams’ chances of winning by 3.96 percentage points, the injury

of a productive key player by 7.71 percentage points, and the injury of an all-star key

player by 8.15 percentage points (the injury of a regular player by 2.29 percentage points).

Although the absence of all key player types had a negative effect on team performance,

it makes a difference what type of key player a team is missing. The key player effect is

largely driven by the productive key player types, which is a first indication that imperfect

skill substitution drives the main effect. Of course, individual productivity in a team does

not have to be the result of irreplaceable skills, but can arise from complementarities with

other team members, too. However, the absence of a helpful key player is directly linked

to the loss of complementarities, because helpful key players enhance the productivity of

8 We also estimated a specification where we excluded the 63 control team-seasons. Naturally, this didnot change the estimate of the treatment effect. More importantly, the other point estimates remainedlargely unchanged supporting again the exogeneity of injuries to the other variables in the model. Detailedresults are available upon request.


Ta

ble

6:

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der

lyin

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ech

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Diff

eren

ce-I

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iffer

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s(D

V:

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26 Chapter 1

(Table

6C

ontinued)

Diff

erence-in

-diff

erences

(DV

:W

in)

(DV

:E

ffort)

Key

player

Inju

ryK

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efensive

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erteam

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oun

ds

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(3)(4)

(5)(6)

Hom

e0.2026***

0.2029***0.2039***

0.1969***0.1968***

0.0133***(0.0050)

(0.0050)(0.0059)

(0.0051)(0.0051)

(0.0008)B

ackto

back

-0.0368***-0.0375***

-0.0358***-0.0363***

-0.0363***-0.0016

+

(0.0054)(0.0054)

(0.0059)(0.0053)

(0.0053)(0.0009)

Op

pon

ent

win

percen

tage-0.9770***

-0.9741***-0.9769***

-0.9778***-0.9777***

(0.0142)(0.0141)

(0.0158)(0.0140)

(0.0140)O

pp

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ensive

-0.9972***reb

oun

dp

ercentage

(0.0145)A

ttend

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20.1370

0.13450.1505

0.14260.1427

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bs.

39,29739,851

30,82539,851

39,85139,660

Notes.

Th

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ationresu

ltsfrom

diff

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-diff

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regressions.

Th

ed

epen

den

tvariab

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to(5)

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teractionterm

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levels:+

p<

0.10;*

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0.01;***

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Figure 3: Underlying Mechanisms – Dynamics of the Treatment Effect

0

0.04

-0.04

-0.08

-0.12

-0.16

-0.20

Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7or longer

Tre

atm

ent

effe

ct

Time of absence

Notes. This figure presents the plot of the coefficients of the difference-in-differences regres-sion where the treatment indicator variable was replaced by a set of interaction terms thatindicate the duration of key player absence. The dots represent the estimates of the interac-tion between the treatment effect and seven indicator variables corresponding to the weeks ofabsence (counted from the date of the first missed game). The whiskers represent the 95%confidence interval (corresponding to robust standard errors, clustered at the team-level).

their team-mates by assisting them. Therefore, we would expect a strong absence effect

of helpful key players if the loss of complementarities or demotivation of effort were the

dominant mechanisms underlying the key player effect. Later, we return to exploring

these mechanisms in greater depth.

Another informative aspect is the dynamics of the treatment effect. Depending on

which mechanism dominates, one should expect different dynamics. If the effect is driven

by imperfect skill substitution, it would remain largely unchanged over time and team

performance should exhibit no significant signs of recovery until the key player returns

and brings back the unique skills. In contrast, if the effect is primarily caused by lost

complementarities between the key players and their teammates or reduced effort by the

remaining team members, it would reduce over time and eventually vanish. This, of

course, assumes that new complementarities between the key player’s substitute and the

rest of the team arise over time (due to training effort and match practice) and that

demotivation is temporary and teams accept playing without their key players after a

while.

To assess what mechanism is salient, we estimated a specification in which the treatment

indicator is interacted with a set of variables that indicate the duration of key player

absence in weeks. Figure 3 depicts the dynamics of the treatment effect by graphing the

28 Chapter 1

estimates with the 95 percent confidence intervals around them. The graph shows an

immediate and persistent decrease in the chances of winning in the aftermath of a key

player injury. The treatment effect ranged between a 4.28 and 9.80 percentage points

decrease. Naturally, longer absence durations were estimated with fewer teams, which is

also reflected in broader confidence intervals. Overall, Figure 3 shows no signs of recovery

– the key player effect was permanent and relatively stable over time. This suggests that

teams miss the skills embodied in the key player, and indicates that the key player effect

is largely driven by imperfect skill substitution and less so by lost complementarities or

reduced effort.

Analyzing the chronology of the key player injuries provides a slightly different version of

this dynamic argument. We therefore differentiated key player injuries by their temporal

occurrence, numbered them and replaced the general treatment indicator with a set of

dummy variables for the first key player injury, the second key player injury, etc. The

second column of Table 6 reports the results. They show that the effect of key player

absence was relatively stable across the number of injuries. This again implies that the

teams do not learn to live without their key player. Rather, they miss their key player

again and again, presumably due to his irreplaceable skills. Note that these results can

also be interpreted as a robustness check. Viewed from this perspective, the second column

of Table 6 shows that the key player effect is robust to differentiating key player injuries

by their temporal occurrence.

A more direct test for the loss of complementarities is to evaluate the level of comple-

mentarities within the team and analyze whether they moderate the key player effect.

We measured complementarities between the key players and their team-mates by the

amount of time they have played together. The degree of dyad-level experience can be

employed as a measure for coworker complementarity for two reasons (Hayes et al., 2006):

First, investments in coworker specific skills accrue over time. Second, matches with low

complementarities are dissolved over time, while highly complementary dyads persist.

Therefore, we calculated how many games the key player has played with each of the

team-mates up to the focal game (at the dyad-level). To arrive at a team-level measure,

we computed the average for all team members that have played in the focal game. We

estimated a specification where we interacted this variable with the treatment indicator.

To ensure that the estimates did not suffer from left-censoring, we excluded the first four

years and restricted the sample to the seasons 2002 to 2013 (the average career of a NBA

player is between four and five years).9 The results are reported in the third column of

Table 6. They indicate that the dyad-level experience between the key players and their

team-mates positively affects team performance, but does not alter the key player effect

significantly. This again suggests that the loss of complementarities is not the dominant

9 To make sure that the sample restriction did not drive the results, we ran a robustness check with thefull sample. The moderating effect was the same in the full sample. Detailed results are available uponrequest.


Table 7: Underlying Mechanisms – Split Sample Estimations

Difference-in-differences(DV: Win)

Split sample by: Team’s productivity Team’s helpfulnessdistribution distribution

Low inequality High inequality Low inequality High inequality(1) (2) (3) (4)

Injured productive -0.0190 -0.1152*** -0.0809** -0.1067***key player (0.0244) (0.0290) (0.0239) (0.0247)Injured helpful -0.0112 -0.0869*** -0.0199 -0.0751**key player (0.0207) (0.0243) (0.0204) (0.0227)Controls Yes Yes Yes YesTeam-season fixed effects Yes Yes Yes YesMatch day fixed effects Yes Yes Yes YesR2 0.1502 0.1495 0.1457 0.1545Obs. 7,015 6,688 6,972 7,727

Notes. This table reports estimation results from split sample difference-in-differences regressions. The depen-dent variable in all models is a dummy variable for winning the game. Controls include Home, Back to Back,and Opponent Win Percentage. Column (1) reports the results for teams with low inequality in individualproductivity. Column (2) reports the results for teams with high inequality in individual productivity. Column(3) reports the results for teams with low inequality in individual helpfulness. Column (4) reports the resultsfor teams with high inequality in individual helpfulness. Inequality was measured by the team’s Gini coefficientof individual points (productivity) and assists (helpfulness), respectively. Calculations of the Gini coefficientwere based on games with the key player. Teams in the bottom quartile were classified as low inequality.Teams in the top quartile were classified as high inequality. Robust standard errors clustered at the team levelare in parentheses. Significance levels: * p<0.05; ** p<0.01; *** p<0.001.

mechanism underlying the key player effect.

Similarly, we used defensive rebounds as a measure for effort to directly test the third

mechanism of demotivation and reduced effort. Specifically, we employed Defensive Re-

bound Percentage which records the team’s defensive rebounds divided by the rebound

chances, i.e. the opponent’s missed shots. Defensive rebounds are a good measure for

effort for several reasons. First, rebounding is considered to be less skill-driven than other

aspects of basketball, because it is mostly about getting in a good position and defend-

ing it. This exercise is known as “boxing out” in the NBA, which stresses its physical

and occasionally painful nature. Still, a lot of the sweat is in vain due to the random

nature of the ball bounces. Moreover, rebounds are not as vivid as as other (offensive)

aspects of the game and are rewarded less (Wang, 2009). As a first step in analyzing the

relationship between key player absence and effort, we included Defensive Rebound Per-

centage as an independent variable in the estimation. The results are given in the fourth

column of Table 6. As could be expected, effort had a positive and significant effect on

teams’ chances of winning. More importantly, the inclusion did not change the estimate

of the treatment effect, which suggests that the key player effect does not operate via

effort. To investigate this further, we estimated an interaction model between key player

injuries and defensive rebounds. The results are reported in the fifth column of Table 6.

30 Chapter 1

The negative and significant coefficient for the interaction term indicates that effort has

a smaller effect on the winning probability in the case of a key player injury, suggesting

that teams cannot compensate for the loss of a key player by increasing defensive effort.

Finally, we specified a model that directly related key player absences to effort by em-

ploying Defensive Rebound Percentage as the dependent and Injured Key Player as the

independent variable. We also changed the set of control variables and included variables

for the opponent’s offensive rebounding quality and attendance. The results are presented

in the sixth column of Table 6. Key player absence did not change defensive rebounding

significantly, suggesting that teams do not exert less effort after losing their key player.

Overall, the results thus consistently suggest that demotivation and reduced effort is not

the mechanism underlying the key player effect.

Finally, the imperfect skill substitution mechanism can be tested more directly by

exploring how scarce the key players’ skills are in the teams. If imperfect skill substitution

drives the effect, we should see a stronger key player effect in teams where the key player’s

skills are scarcer. To assess the scarcity of the key player’s skill, we measured the team’s

inequality in individual productivity and helpfulness by the Gini coefficient of individual

points and assists, respectively. We then split the sample by the degree of inequality,

where we classified teams in the top quartile as teams with high inequality and teams in

the bottom quartile as teams with low inequality. On those subsamples we estimated the

effect of missing a productive and helpful key player. The intuition behind this procedure

is that the skills that make a key player productive are scarcer in teams with only a few

productive team members. Allstar key players and their teams were excluded from this

analysis. The results are presented in Table 7. The first and second columns of Table

7 reveal that the injury of a productive key player decreased the probability of winning

by 11.52 percentage points for teams with high inequality in productivity. In contrast,

the effect was not significant in teams where individual productivity was more balanced.

Similarly, the third and fourth columns of Table 7 show that the absence of a helpful

key player decreased the chances of winning by 7.51 percentage points for teams with

high inequality in individual helpfulness and had no significant effect on teams with low

inequality. Overall, Table 7 thus supports the idea that key players are missed more,

the scarcer their skills are. This lends further credence to the imperfect skill substitution

story.

6.3 Robustness Checks

We estimated a series of specifications to examine the robustness of our results.

Heterogeneity in injuries. One potential concern is the considerable heterogeneity in

key player injuries. Teams can be treated multiple times and switch between treatment

and no-treatment states. Most models averaged over all key player injuries. A potential


problem with this procedure is that we might have mingled very different situations that

should be analyzed separately. By focusing on the first key player injury and the period

before that, the before-after approach was a first step in reducing treatment heterogeneity.

We explored treatment heterogeneity further with another robustness check.

Specifically, we added an indicator variable for the post-treatment period to the esti-

mation. The variable becomes one when the team played with its key player but had

experienced a key player injury before (see Figure 1). The first column of Table 8 shows

that the results are robust to this inclusion. Compared to the third column of Table 5,

the effect of key player absence was slightly higher. More importantly, the coefficient

for the post-treatment phase was insignificant, implying that team performance did not

differ significantly between the pre- and post-treatment period. Having experienced a key

player injury before does not have persistent effects on team performance. Thus, prior

treatments do not “contaminate” teams used as controls at later stages.

Specific time trends. For internal validity, the difference-in-differences approach re-

quires treated and control units to follow a parallel time trend. Our research setting

involves multiple treated and control units that are treated at different points in time.

Therefore, evaluating the common trends assumption graphically is rather difficult. An

alternative is to include unit-specific trends in the estimation (see e.g., Besley and Burgess,

2004). To alleviate concerns about potential time trends, we thus estimated a model that

included (linear) team-specific trends. The second column of Table 8 reveals that the re-

sults are robust to including team-specific time trends. The treatment effect was slightly

reduced but remained stable, at around six percentage points. Thus, the key player effect

is not driven by preexisting team-specific time trends.

Non-injury absences. To ensure that the treatment is exogenous, we focus on key player

absences due to injuries. The dummy Injured Key Player is coded as zero in cases where

the key player was available from a medical standpoint. In the vast majority of these

cases, the key player played. In some cases, however, the key player was fit but still did

not play. For example, coaches may have decided to strategically rest their key players

or there may have been family matters, e.g. births or bereavements. To ensure that the

coding of the treatment indicator did not drive our results, we estimated a specification

where we included an additional indicator variable for key player absences due to reasons

other than injuries. The third column of Table 8 reveals that the results are robust

to this inclusion. The coefficient of the newly defined variable Absent Key Player was

significantly negative and greater than for injury absences. This is not surprising, as non-

injury absences are planned or at least approved by the team manager. Resting a key

player is a signal to the team, which affects team performance and is presumably only

sent in less important games. Thus, the new variable not only reflected the effect of key

player absence but also other factors tied to performance, e.g. reduced motivation and

32 Chapter 1

Ta

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8:

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-0.0756***-0.0598***

-0.0672***-0.0640***

-0.0643***-1.7459***

-2.2572***(0.0111)

(0.0087)(0.0081)

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(0.2043)(0.1981)

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sent

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0.1977***6.2988***

3.1541***(0.0050)

(0.0051)(0.0050)

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(0.1138)B

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-0.0375***-0.0378***

-0.0371***-0.0382***

-0.0369***-0.0390***

-1.2953***-0.6358***

(0.0054)(0.0054)

(0.0054)(0.0054)

(0.0054)(0.0054)

(0.1389)(0.1239)

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pon

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percen

tage-0.9740***

-0.9740***-0.9742***

-0.9773***-0.9767***

-0.9704***-27.8826***

(0.0141)(0.0143)

(0.0141)(0.0142)

(0.0142)(0.0144)

(0.3741)O

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tp

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allowed

0.9930***(0.0117)

Min

utes

1.8159***(0.0335)

Team

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0.13170.1365

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39,85139,851

39,85139,297

39,29737,269

39,85139,680

Notes.

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effort.

Absences of other team members. Another possible objection to the results presented

thus far is that they be might driven by simply losing any player and hence do not

necessarily reflect a key player effect. To address these concerns, we conducted two

robustness checks that investigate the injuries of non-key players. First, we estimated

the effect of missing an important team member, who is not a key player. Therefore, we

constructed an indicator variable Injured Regular Player that switches to one whenever

the team lost, due to an injury, a team member that ranked within the top five of average

minutes played (up to the focal game) and was not a key player. The fourth column of

Table 8 shows that missing a regular player significantly decreased the team’s probability

of winning, although at a distinctly lower rate than for missing a key player (2.30 vs 6.41

percentage points). More importantly, the fifth column of Table 8 shows that the key

player effect is robust to including injuries of regular players. This dispels concerns about

absences of other important players or injury crises driving the key player effect.

Panel attrition. Furthermore, we conducted a robustness check that addresses potential

panel attrition bias. Bias from panel attrition can arise when the dropout probability is

correlated with the random term in the model (Hausman and Wise, 1979). In this empir-

ical setting, including playoff games could introduce problems similar to panel attrition.

We observed playoff teams more frequently than non-playoff teams and it is clearly not

random which teams make it to the playoffs. Therefore, we conducted a robustness check

where the sample was restricted to regular season games. These results are presented in

the sixth column of Table 8. The sample was reduced by 2,582 team-game observations

but the results stayed virtually the same.

Alternative team performance measures. Finally, we conducted two robustness checks

with alternative measures of team performance. First, we employed point differential as

the dependent variable. The point differential captures the margin by how many points

the team won or lost the game. The results are reported in the seventh column of Table 8.

Like in the main analysis, the key player effect was significant and negative. On average,

the teams’ point differential was decreased by 1.75 points when they played without their

injured key player.

Second, we evaluated team performance by points scored. As we identified key play-

ers based on their offensive contribution, it seems natural to focus on a measure that

also resides on the offensive side of the game (as points). To account for the specificity

of the dependent variable, we altered the model slightly. In particular, we added the

variable Minutes, which records the length of a game in minutes and captures scoring

opportunities.10 We also replaced the variable Opponent Win Percentage (reflecting op-

10 Games that are tied at the end of the regular playing time go into overtime. The overtime consists of

34 Chapter 1

ponent quality in general) by Opponent Points Allowed, which records the average of

points per game the opponent team has conceded during the season and hence captures

the opponent’s defensive quality more specifically. The results are reported in the eighth

column of Table 8. Again, we found a significant negative effect of key player absence on

team performance. With their key players out, the teams scored on average 2.26 points

less. Overall, Table 8 shows that the effect of key player absence on team performance is

remarkably robust.

7 Discussion and Conclusion

Our results provide clear evidence for the importance of outstanding individual talent in

team settings. While research has begun to recognize the importance of individuals and

how their efforts aggregate into team performance, our research provides causal evidence

of the effect and highlights the mechanism at work. Utilizing injuries of key NBA players

as a quasi-experiment allows us to examine the causal effect of key player absence on team

performance. In different econometric approaches, we found that losing a key player had

a robust and economically meaningful negative effect on a team’s winning probability.

These findings are relevant for strategy scholars as it is important to anchor higher-

order constructs such as team or organizational performance on lower levels, meaning the

individual who works alone and jointly to achieve an outcome (Foss and Pedersen, 2016).

We proposed and tested three mechanisms that potentially drive the key player effect:

(1) imperfect skill substitution, (2) the loss of complementarities, and (3) reduced effort

by remaining players. Our results suggest that imperfect skill substitution is the dominant

mechanism underlying the performance effect of losing a key player. It is the unique skills

embodied in the key player that the team is missing when playing without him. The

minor role of effort, team fit, and complementarities is surprising given that the literature

routinely stresses the importance of team familiarity, work routines, and team composition

(e.g., Bantel and Jackson, 1989; Huckman et al., 2009; Weick and Roberts, 1993). This

literature would suggest that teams learn how to act in the absence of a key player,

develop new routines, and perhaps ultimately raise the performance to the same level as

before. Our results, however, suggest that living without a key player is more difficult than

commonly assumed: special skills of individual team members play a deciding role for team

performance. Consequently, teamwork does not dissolve the organizational dependence on

outstanding individuals. Instead, our results suggest an interesting trade-off between team

efficiency and team resilience. Maximizing complementary skill sets might be optimal

from an efficiency standpoint (Hamilton et al., 2003; Lazear, 1999; Milgrom and Roberts,

1990). However, it comes at a cost because unique skills make it particularly difficult to

overcome the unexpected loss of key personnel.

extra periods of five minutes that are added to the game until a winner is determined.


Our findings speak to the resource-based view of the firm, which has emphasized the

importance of tacit knowledge at the organizational and team level rather than the indi-

vidual excellence of star employees (Groysberg et al., 2008). For example, Berman et al.

(2002) suggest that tacit knowledge is a source of competitive advantage and question

the strategy of hiring top talent from outside the organization. Our findings suggest that

the individual skills of key players matter for team performance. Therefore, focusing uni-

laterally on tacit knowledge and neglecting individual skills can hurt team performance.

While prior research has documented that colleague relationships and the tacit knowl-

edge embedded in them is important for individual performance (Groysberg et al., 2008),

our results show that there is reciprocal dependency: the performance of the team also

depends on the key players. The extent to which single outstanding individuals can be a

source of a sustainable competitive advantage depends on the nature of their human cap-

ital. General human capital is portable across organizations, whereas firm-specific human

capital is tied to one specific organization (Becker, 1962). Other research has examined

the interorganizational portability of individual performance and stressed the importance

of firm-specific human capital (Groysberg et al., 2008; Huckman and Pisano, 2006). The

minor role of complementarities documented in this paper suggests a substantial compo-

nent of general human capital residing in the key players. Interpreted with due care, our

results thus seem to re-shift some bargaining power towards individual top performers in

teams.

In the bigger picture, our research connects to the question of how outstanding in-

dividuals shape and influence their environment and organizations. At the peer level,

Azoulay et al. (2010) and Oettl (2012) have shown that the death of academic superstars

adversely affects the scientific output of their colleagues. Assessing potential mechanisms

behind this, Azoulay et al. (2010) interestingly found that imperfect skill substitution

alone cannot explain the extinction effect. Rather, spillovers in the “idea space” play

an important role. Similarly, Oettl (2012) demonstrated the importance of helpful scien-

tists. Both findings contrast with our results that emphasize unique individual skills and

individual productivity. There are two possible explanations for this divergence. First,

whereas Azoulay et al. (2010) and Oettl (2012) have studied the effect on direct peers, we

turned attention to the consequences for the joint output of the team. Team outcomes

are directly tied to the contribution of each individual team member. Compared to that,

spillovers among peers are more indirect. Therefore, it is intuitive that individual skills

and productivity matter more for team outcomes than for the individual outcomes of

peers. Second, while Azoulay et al. (2010) and Oettl (2012) study knowledge production,

we focused on teams of professional athletes. This illuminates that the importance of key

players and the mechanism through which they affect their surroundings depends at least

partially on the specific context. At the organizational level, Agrawal et al. (2017) have

suggested that stars can positively affect recruiting and increase the quality of new hires.

We complement the positive findings at the peer and organizational level by showing that

36 Chapter 1

outstanding individuals are also important for teamwork.

As with any research, our results should be interpreted against the backdrop of our

empirical setting. One obvious issue with evidence from professional sports is the ap-

plicability of the findings to other contexts. Studying an environment where millionaire

players face billionaire owners almost inevitably evokes the question of external validity.

At the same time, evidence should not be discounted a priori just because it comes from

high-profile environments (Kahn, 2000). The NBA is peculiar not only because of its

high-profile incentives; the single-gender composition, the constant public exposure, the

immense training effort, the highly specialized skill sets of players, the work under time

pressure, and the associated stress are all exceptional traits. Then again, many teams

work under similar circumstances. Consider, for example, physicians in surgical teams,

lawyers in law firms, general partners in venture capital firms, consultants in management

consulting firms, or engineers in technical emergency units. All of these teams are com-

posed of highly-trained specialists who work interdependently and under time pressure.

The team members also earn considerably more than the average due to their high degree

of specialization. While surgical teams may carry out their operations without direct

monitoring, consulting teams (both in law and management) as well as technical support

teams usually face a significant amount of observation (and pressure) by their customers

and in some cases even by the public. Altogether, these occupations share many charac-

teristics with the professional athletes analyzed in this study. Therefore, our results are

likely to hold in the presence of the following boundary conditions: (1) business organi-

zations where highly qualified specialists work in interdependent teams and (2) carry out

operational tasks under time pressure.

Our research also raises questions for future investigations. One important question is

how the organizational setting determines the relative importance of helpful and produc-

tive key players and how the two dimensions interrelate in other settings. Similarly, there

is some ambiguity in the eminence of individual skills that merits further attention. More

generally, the absence of key players may be associated with manifold changes at very

different levels. Particularly interesting is whether organizations can offset their loss by

hiring new employees. The possibility of responding with a new hire is very limited for

NBA teams, because top talent is scarce and the league’s labor market regulations are

severe. Thus, research in more flexible settings might help to clarify how external hires

can compensate for key player absence. Additionally, losing a key member of the team

has implications for the workflow. It would therefore be interesting to investigate how

teams adapt their processes and routines without their key player. In general, different

strategies in replacing the key player and what distinguishes successful ones from less

successful ones merit attention from future research.

In closing, how do our findings relate to the 1989 NBA Finals? Was the media right

that the Lakers loss was entirely due to Magic Johnson’s absence? Given that average

effects cannot explain each individual case, our results are by no means conclusive. Still,


they suggest that losing Magic was indeed a major reason for the Lakers’ upset. After

all, Magic is considered one of the truly singular players in the game’s history. His unique

skill set allowed him to play multiple positions and to become the tallest point guard in

league history. Unsurprisingly, Magic was an all-star key player – one of the key player

types that this paper has shown are of exceptional importance. Replacing Magic’s skills

appears virtually impossible. It seems like the star-centered sports coverage might have

been right this time, and that it was indeed the absent key player that inked the Lakers’

destiny.

38 Chapter 1

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Out of the Shade, into the Light:

Star Absence as an

Opportunity for Non-Stars

Abstract. To develop a deeper understanding of how star employees affect

their colleagues, this paper investigates temporary star absence and its effect

on the productivity of non-stars. Exploiting quasi-experiments induced by

injuries of star players in the National Basketball Association (NBA), I analyze

changes in the offensive output of non-star players while the star is absent

and after he has come back. Compared to the pre-absence phase, non-stars

increase their offensive output while the star is absent. After long absences,

this effect becomes lasting and non-stars can sustain an increased level of

production even when the star has come back. Further analyses reveal that

junior employees particularly benefit from star absence, especially those in the

middle of the talent distribution. The key mechanism underlying these effects

is that non-stars get more opportunities to perform in the absence of the star.

Star absence thus provides new development possibilities for employees who

are usually overshadowed by the dominant star.

1 Introduction

Many organizations deem the exceptional talent of a few “high potentials” to be essential

to innovation and success. This is particularly salient in high-tech companies, where

the role of individual star employees is cultivated and legendized in stories of innovative

genius and intellectual brilliance. Take Cisco and their former employees Mario Mazzola,

Prem Jain, Luca Cafiero and Soni Jiandani. The four enigneers were an integral part of

Cisco’s spin-in strategy and helped the company to develop several of its most-lucrative

businesses (Bort, 2016). Their success gave them legendary status at and around Cisco

that is reflected by the fact that they were simply known by an acronym of their first

initials – MPLS. They were even described as the “heart, soul, brains, and mouth of Cisco”

(Bort, 2016). Unsurprisingly, it made massive waves when MPLS publicly left Cisco, with

the Business Insider writing: “Their departure is a major event for Cisco and the tech

industry segment, networking, that Cisco has ruled for more than 20 years.” Indeed,

evidence from the biotechnology sector has demonstrated that stars play an essential

44 Chapter 2

role in developing and growing new ventures and industries (Rothaermel and Hess, 2007;

Zucker et al., 1998).

At the same time, organizations face difficulties when integrating star employees (Groys-

berg et al., 2008). Too many stars can hinder group effectiveness (Groysberg et al., 2011;

Swaab et al., 2014), presumably because they fight over hierarchy and power. Consistent

with this, rumor has it that not everyone within Cisco was unhappy about the departure

of MPLS (Bort, 2016). This illuminates a tension that surrounds stars. On the one hand,

stars are treasured by organizations because they bring exceptional value. On the other

hand, they can become so dominant that they overshadow their non-star colleagues.

To shed light on the impact of stars on their colleagues, this paper investigates tempo-

rary star absence and its immediate and long-term effect on the productivity of non-star

employees. I propose that star absence opens new possibilities for the star’s colleagues.

Due to their status, visibility and social capital, stars possess power and resources (Zucker

et al., 2002), which provide them with superior performance opportunities in comparison

to their non-star colleagues (Kehoe and Tzabbar, 2015). In the absence of the star, re-

sources that were previously controlled by the star become available and are redistributed

among non-star employees. Beyond resources, star absence also affects work norms, pro-

cedures and hierarchies. The absent star leaves a void, which provides non-star employees

with new opportunities to perform. As an immediate effect of star absence the star’s

colleagues therefore increase their productivity. Moreover, the increased performance op-

portuntities trigger two-sided learning. On the one side, non-star employees engage in

learning by doing as they take on new tasks and responsibilities. On the other side, man-

agers learn about the non-stars’ abilities and how to deploy them more efficiently. This

leads to a lasting effect of star absence, where non-stars can sustain an increased level of

output even after the star has come back. To test the proposed immediate and long-term

effects of star absence, I analyze changes in the productivity of non-star employees during

and after star absence.

Simply examining changes in non-star productivity following star absence is problematic

because star absence can be endogenous in cases where managers deliberately choose to sit

stars out. To overcome endogeneity in star absence, I exploit quasi-experiments induced by

injuries of star players in the National Basketball Association (NBA). Using injuries as an

exogenous source of variation is a novel empirical strategy for analyzing the effect of team

member change (see e.g., Chen and Garg, 2018; Stuart, 2017; Thomas and Dahlander,

2017).

Professional sports data has been frequently used to study team and organizational

phenomena (e.g., Bloom, 1999; Stuart, 2017). For my purposes, utilizing NBA data has

four distinct advantages. First, NBA teams are characterized by high interdependence

(Swaab et al., 2014) mirroring many collaborative working environments in the modern

economy. Second, detailed performance data over a long season enables me not only to

distinguish star and non-star players reliably, but also to draw inference about the long-

Out of the Shade, into the Light 45

term effects of star absence. Third, the dataset provides a rare measure for each player’s

opportunity to perform, namely field goal attempts. Finally, injuries of top players can

be exploited to overcome the endogeneity in star absence allowing for causal inference.

Following prior research (e.g., Kehoe and Tzabbar, 2015; Rothaermel and Hess, 2007;

Zucker et al., 1998), I determine stars by individual productivity and define the leading

scorer of each team as a star. Consequently, I focus on injuries of teams’ top scorers and

analyze how the offensive output of their non-star colleagues changes while the star is

absent and after he has come back (for a similar empirical strategy, see Chen and Garg,

2018). Employing a difference-in-differences framework, I compare the number of points

of non-star players in three phases: pre-absence, absence and post absence. The pre-

absence phase serves as a baseline and the absence phase pins down the immediate effect

of star absence. The post absence phase is used to evaluate the long-term effect of star

absence, i.e. whether the productivity change is temporary or lasting.

Examining more than 350,000 player-game observations from the NBA seasons 1998-99

to 2013-14, I find that star absence indeed has a positive immediate impact on non-star

productivity. Non-star players increase their offensive output by 1.59 points, or 19%, while

the star is absent. Differentiating long-term effects by the length of the preceding star

absence shows that non-stars can sustain an increased level of output of 0.61 points, or

7.3%, after long absences. Mediation analyses reveal that star absence provides non-stars

with more opportunities to perform and that the increased opportunities fully mediate

the effect of star absence on non-star productivity. The opportunity mechanism explains

both the immediate increase in non-star productivity and the lasting effect of long star

absences. This suggests that opportunities play a central role for individual performance

and that long absences sustainably alter the allocation of performance opportunities.

Further analyses demonstrate that junior employees particularly benefit from star absence,

both immediately and in the long term. Interestingly, it is not the most talented juniors

that profit the most, but rookies in the middle of the talent distribution.

These findings advance our understanding of stars and their impact in four respects.

First, they demonstrate that the absence of stars can affect organizations and colleagues

very differently. While prior research has shown that star absence is detrimental to orga-

nizations and collaborative teams (e.g., Azoulay et al., 2010; Oettl, 2012; Stuart, 2017),

I establish that it can be beneficial for the star’s colleagues. Second, this paper extends

recent findings that suggest that star absence leads to improved routines (Chen and Garg,

2018) by highlighting learning processes that lead to an improved performance of non-

stars. Third, I identify performance opportunities as a key mechanism through which

stars affect their colleagues. Directly investigating the role of opportunity complements

earlier research that has suggested that stars dominate resources and thereby constrain

their colleagues (Kehoe and Tzabbar, 2015; Tzabbar, 2009). Finally, my findings call at-

tention to the characteristics of non-stars, which tend to get overlooked when explaining

the peer effects of stars. Whereas prior research is strongly centered around stars and how

46 Chapter 2

their characteristics affect non-stars (e.g., Grigoriou and Rothaermel, 2014; Oettl, 2012), I

demonstrate that non-star characteristics play an important role for the interplay between

stars and their colleagues.

2 Stars in Organizations

Stars receive special attention in public life and the business world, which is usually justi-

fied by the disproportionate value they bring to organizations. Indeed, prior research has

shown that a small group of stars produces disproportionately large amounts of scientific

discoveries (Cole and Cole, 1972), patented inventions (Ernst et al., 2000) and revenues

in creative industries (Ravid, 1999). Findings from the biotechnology sector even suggest

that whole companies and eventually industries are built around stars’ unique expertise

(Zucker et al., 1998, 2002). Beyond individual output, stars can enhance their organiza-

tions by improving recruiting (Agrawal et al., 2017) and directing research efforts towards

promising areas (Rothaermel and Hess, 2007).

Due to their disproportionate value, stars not only enjoy special social status within

firms, but also superior visibility in the external labor market (Groysberg et al., 2008).

Stars possess lucrative outside employment options as competing firms may try to lure

them away. While the extent is contingent on the portability of the star’s skills (Groysberg

et al., 2008; Huckman and Pisano, 2006), companies thus face a constant risk that their

stars could leave. Taken together, their exceptional value and the credible threat of leaving

provide stars with considerable internal bargaining power, which they can utilize to accrue

resources within the organization. Zucker et al. (2002) document that star scientists in

the biotechnology industry attract and control key resources within and between firm

boundaries, which they use to promote their research programs. How internal status

translates into resources is exemplified by MPLS, who were believed to have their very

own strategy to attract additional funding for their spin-ins: they just called Cisco’s CEO

directly (Bort, 2016). Besides absorbing formal resources, stars can also exercise their

influence informally since they possess central positions in intrafirm networks (Paruchuri,

2010). Supporting this, Tzabbar (2009) demonstrates that influential star scientists exert

influence on the technological positioning of their firms.

In general, stars will use their power to sustain their unique, high-status positions that

are financially lucrative (Rosen, 1981) and allow them to pursue their personal agenda

without being contested (Overbeck et al., 2005). They have little incentive to use their

tangible resources and informal power to develop new stars within the organization (Kehoe

and Tzabbar, 2015). As a result, stars may strategically withhold resources from up-and-

coming colleagues. Similarly, their tendency to share knowledge and engage in cooperative

behaviors may be limited (Groysberg et al., 2011). Again, MPLS provide an example for

this: colleagues suspected that the four engineers made use of their internal influence to

criticize and even derail projects that competed with their own interests (Bort, 2016).


The concentration of power and resources on star employees has implications for the

colleagues surrounding them. Resources are scarce and even capable and motivated em-

ployees will struggle to perform well without them (Call et al., 2015; Ericsson et al., 1993).

As stars absorb scarce resources, they thus limit performance opportunities for non-stars.

In line with this, Kehoe and Tzabbar (2015) demonstrate that stars constrain the emer-

gence of new innovative leaders in biotechnology firms: As stars dominate their firms’

research efforts, few resources are available for non-star scientists to develop their own re-

search agenda. Similarly, Tzabbar (2009) shows that stars limit the technological impact

of newly-hired scientists because they dominate corporate knowledge and resources.

3 Star Absence and Opportunity

The dominant role of stars within organizations suggests that non-star colleagues can

benefit from star absence. The absence of stars disrupts organizational structures, work-

flows and routines (Chen and Garg, 2018; Stuart, 2017). In particular, resources which

were previously controlled by the star are now redistributed among non-star colleagues.

Supporting this idea, Tzabbar and Kehoe (2014) demonstrate that the departure of highly

involved star scientists unlocks resources and creates new opportunities for exploration

and technological renewal in biotechnology firms. Similarly, Azoulay et al. (2018) show

that deaths of star scientists induce new scientists to enter their scientific fields and evolve

them into novel directions. While the star was alive, outsiders’ entry was hindered by

resource constraints and by intellectual and social barriers.

This illuminates that star absence alters more than just resource allocation. As highly

able and productive employees, stars are influential in shaping work norms (Hamilton

et al., 2003) and can exert social pressure on their colleagues (Mas and Moretti, 2009).

For instance, MPLS’ Luca Cafiero was feared for “grilling” his employees in meetings that

could last all day (Bort, 2016). Moreover, stars influence how things are done as they

encourage their colleagues to adopt specific methods (Lacetera et al., 2004) or technologies

(Burke et al., 2007). Lastly, stars often head hierarchies and possess influential positions

within companies (Zucker et al., 2002). Consequently, star absence may change work

norms, alter procedures and shake up rigid hierarchies. Beyond the allocation of tangible

resources, the absence of a star thus also affects intangible factors. Naturally, absent

stars also leave a void in terms of tasks and responsibilities. Equipped with additional

resources and the freedom to do things their way, non-stars can fill this void. As such, star

absence provides new performance opportunities for non-star employees. Capitalizing on

the expanded opportunities, non-stars can increase their productivity while the star is

absent (the immediate effect of star absence).

But can temporary star absence also have a lasting effect, changing the productivity

of non-star employees even after the star has come back? Skeptics may speculate that

performance opportunities will be completely reallocated to stars once they come back,

48 Chapter 2

which would help them to regain their dominant positions. However, this static perspec-

tive may underestimate corporate dynamics where interruptive events have been shown

to trigger a search for new routines and knowledge (Chen and Garg, 2018; Okhuysen and

Eisenhardt, 2002; Zellmer-Bruhn, 2003).

Specifically, the increased performance opportunities for non-stars can trigger a two-

sided learning process. On the one side of this process are the non-star employees. While

non-stars can learn from stars by observing them in everyday work (Hamilton et al.,

2003), the absence of a star provides a particularly valuable learning opportunity. Taking

advantage of their increased opportunities to perform, non-stars engage in learning by

doing and develop or improve important management, leadership and decision-making

skills. Due to their increased role, non-stars also face higher performance expectations and

learn to cope with them. On the other side of the learning process are managers, who gain

valuable information about their non-star employees. Observing how non-stars utilize the

performance opportunities while the star is absent helps them to evaluate the potential of

their employees. Furthermore, monitoring how non-stars tackle unfamiliar tasks, harness

newly available resources and cope with additional pressure provides managers with novel

information about the strengths and weaknesses of each employee. This enables them to

better leverage the employees’ skill sets and it also builds trust. In short, non-stars improve

and earn the trust of managers during star absence. As a result, some performance

opportunities remain with non-star employees even after the star has come back. This

enables them to sustain an increased level of production (the long-term effect of star

absence).

While increased opportunities to perform can be beneficial for non-stars in general,

they may be particularly favorable for employees at early career stages. Typically, em-

ployees are granted less responsibility and have only limited resources at the beginning

of their careers. Normally they would gradually receive more performance opportunities,

but star absence can accelerate this process, along with their learning and professional

development. Junior employees particularly benefit from additional opportunities because

they possess only limited professional experience and have more room for improvement

(Becker, 1962). At the same time, managers typically know little about junior employees

(Ferguson and Hasan, 2013). While managers can gain an accurate impression of senior

employees over time, they are likely less certain about their junior staff’s abilities, traits

and skill sets. Managers may be reluctant to rely on junior employees because they have

only noisy signals about their capabilities. This is particularly distinct in settings where

talent is not directly apparent or ambiguous (Tervio, 2009). As outlined, star absence can

accelerate the process of resolving managerial uncertainty, which is particularly important

for junior employees. Thus, employees in early career stages are likely to benefit more

from the performance opportunities that star absence brings. This is why they have a

higher chance of sustaining a lasting productivity increase after the temporary absence of

a star.


In sum, the theoretical considerations have four testable implications that guide the

empirical analyses: (1) Star absence increases non-star productivity while the star is

absent (the immediate effect of star absence), (2) non-stars can sustain an increased level

of output even after the star has come back (the long-term effect of star absence), (3)

these effects are driven by increased opportunities to perform and (4) these effects are

particularly strong for junior employees.

4 Data and Methods

4.1 Research Setting

The empirical setting for this study is the National Basketball Association, the world’s

leading league in mens’ professional basketball. Providing a wealth of detailed data,

professional sports has frequently been used to study organizational phenomena (e.g.,

Bloom, 1999; Kim and King, 2014; Stuart, 2017). The NBA is no exception and has

lately served as a research site to analyze the role of complementarities (Ethiraj and

Garg, 2012), competitive dynamics (Berger and Pope, 2011) and racial biases (Zhang,

2017). For this inquiry, the NBA offers exceptional data in four respects.

First, NBA seasons are relatively long compared to other leagues and sports. The

regular season comprises 82 games per team and is followed by the playoffs, in which

teams can play up to 28 additional games.1 The long observation period allows me to

distinguish stars and non-stars reliably. Equally important, it enables me to observe

non-stars prior to, during and after star absence, which is neccessary to pin down the

immediate and long-term effects of star absence.

Second, the NBA records information on how many points a player scored in a game,

along with data on how many shots he took. Accordingly, I observe not only the mere

offensive output (points) but also how it was achieved (field goal attempts). Field goal

attempts provide a rare measure of individual performance opportunities because they

are (a) a neccessity to acquire a valued resource, i.e. points, and (b) largely result from

a planned process, in which the coaching staff designs plays for specific players. This

is a valuable feature because data on unsuccessful attempts is often unavailable leading

to success bias in organizational research (Denrell and Kovacs, 2008). Even in settings

where detailed microdata on individual output is available, we usually cannot observe the

process that has generated the output. Having this information enables me to test the

extent to which the absence of a star provides new performance opportunities for non-star

employees.

Third, the NBA’s arduous schedule and the athletically demanding nature of basketball

1 In the sample period, the seasons 1998-99 and 2011-12 were shortened due to lockouts to 50 and 66games per team, respectively. The playoffs are a best-of-seven elimination tournament over four roundsto determine the NBA champion.

50 Chapter 2

pose a constant risk of injury to the players. While tragic for those affected, this is an

attractive empirical feature from a researcher’s perspective because it introduces exoge-

nous variation in star absence. This can be exploited to estimate the causal effect of star

absence on the productvity of non-star colleagues.

Finally, NBA players constantly interact and coordinate their positions as well as move-

ments to attack the opponent’s basket and defend their own. For example, one of the

most common offensive actions is to “set a screen,” where one player blocks an opposing

defender by standing in his way to free a team-mate for a pass or shot. Consequently,

basketball players are reliant on their team-mates and basketball teams are characterized

by high interdependence. Basketball thus resembles a lot of modern work environments

where tasks have become increasingly complex and are carried out collaboratively (Wuchty

et al., 2007).

4.2 Sample and Data

The dataset underlying the empirical analysis contains longitudinal information about

NBA games from the 1998-99 season to the 2013-14 season. This corresponds to 16

seasons, 20,026 games, 40,052 team-game observations (two teams per game), and 624,411

player-game observations (between fifteen and sixteen players per team). I retrieved this

data from the NBA’s offical website, www.nba.com. Since the official statistics mainly

provide information at the game-level, I complemented them with additional data on the

NBA draft, player characteristics, individual awards and salaries from www.basketball-

reference.com. Both data sources provide reliable statistics and have been used in prior

research (e.g., Arcidiacono et al., 2017; Ertug and Castellucci, 2013; Zhang, 2017). The

official NBA data, for example, is recorded courtside and subsequently reviewed by league

officials to ensure their accuracy (Biderman, 2009).

It is essential for my empirical strategy to identify injuries reliably and precisely distin-

guish absences due to injury from other absences. Information about injuries was retrieved

from official NBA box scores that summarize player statistics for each game. They also

contain information about players who did not play and state the reason, e.g. an injury. In

cases where the box score did not explicitly clarify the reason for the absence of a player,

I examined game reports from ESPN, browsed daily NBA news from Patricia Bender’s

NBA archive, and searched the Pro Sports Transactions database.2 Again, these sources

have been utilized in prior research (e.g., Chen and Garg, 2018; Ertug and Castellucci,

2013).

As my interest lies in the impact of star absence on non-star productivity, I focus on a

sample of non-star players. I define the top scorer of each team as a star and the other

team members as non-stars. To construct an appropriate sample, I first preclude games

2 The respective websites are: http://espn.go.com/nba/scoreboard, https://www.eskimo.com/pbender,and http://www.prosportstransactions.com.


for which nba.com did not list all players that competed in a game. This concerns only

2,236 player-game observations (0.36%) in 201 team-game observations (0.5%). Second,

I exclude star players. Third, I eliminate observations from the first game of each team-

season, because I identify top scorers based on prior data from the team-season, which is

unavailable for the first game. Finally, I exclude non-star players who have played less

than five percent of all possible minutes in a team-season. Some marginal NBA players

constantly fluctuate between NBA teams and their respective development teams or are

signed to short-term 10-day contracts. Imposing a playing time threshold ensures that

only regular team-members are included in the analysis. After these steps, the final sample

consists of 355,317 player-game observations, which is my unit of analysis.3

4.3 Econometric Strategy

The goal of this study is to analyze the immediate effect of star absence on the produc-

tivity of non-star employees and to determine whether this effect is temporary or lasting.

One straightforward approach would be to simply compare games where non-stars play

alongside the star to games where they play without the star. However, the decision to

rest a star player is clearly not random. Coaches are more likely to sit their stars out

in games that are less important or where other team members can compensate for the

missing star, for example against weaker opponents. As the choice to sit stars out is

related to the expected performance of their colleagues, star absence is potentially en-

dogenous, which would bias estimates. To overcome endogeneity in star absence and

receive unbiased estimates, I focus on injuries of NBA star players.

Exploiting injuries as an exogenous shock to team membership is a novel empirical

strategy in organizational research (see Chen and Garg, 2018; Stuart, 2017; Thomas and

Dahlander, 2017). Injuries are unexpected and largely independent of managerial decisions

and individual or team performance (Stuart, 2017). Although injuries in the NBA are

probably not completely random, they have a substantial random component as many

factors influencing their likelihood are comparable across NBA teams. For example, the

quality of the medical staff, training effort or competition intensity should be similar and

should not cause a systematic bias in who gets injured.

As my interest also lies in the question of whether temporary star absence has a long-

term effect, I examine both the absence and post absence phase. While the injury is

forced upon the team and therefore exogenous, the comeback is at least in part subject to

managerial decision making. A potential risk is that teams that do well without their star

delay the comeback and prolong the absence phase. If that was the case, it would bias

coefficients for the absence phase upwards. However, given the importance of stars for

team performance in the NBA (Chen and Garg, 2018; Thomas and Dahlander, 2017) and

3 The full dataset contains all players on the team’s roster, corresponding to 624,411 player-gameobservations. In contrast, my analysis relies on performance data that is only available if players playedin a game.

52 Chapter 2

the pressure from media and fans, it seems unlikely that teams would routinely prolong

their star’s absence. Still, one has to acknowledge that the start and duration of the post

absence phase is less obviously exogenous than the absence itself.

I use three phases to pin down the immediate and long-term effects of star absence:

the pre-absence, absence and post absence phase. I employ two indicators, Star absence

and Post absence, to delineate the absence and post absence phases and compare the

productivity of non-star players in games of these phases to games of the pre-absence

phase. To analyze the changes in productivity of non-star players, I estimate a fixed

effects panel data model by ordinary least squares (OLS).

The baseline estimating equation relates the offensive output of non-star player i in

game t to the absence and return of the star, and characteristics of the focal player i,

game t, opponent j and season T :

PointsijtT = β0 + β1Star absenceit + β2Post absenceit + β3X′jt + γi + δT ,

where Star absence and Post absence are the independent variables indicating the absence

and post absence phase, respectively. β1 captures the immediate effect of star absence

and β2 indicates whether it is temporary or lasting. The pre-absence phase is the omit-

ted category and serves as a baseline. As such, the coefficients of β1 and β2 should be

interpreted as changes in productivity relative to the pre-absence phase.

X ′jt represents the matrix of control variables. γi are player fixed effects consistent

with the objective to estimate changes in i’s offensive output in the aftermath of star

absence. δT stands for a set of season indicator variables capturing effects specific to

individual seasons. Combined with the dummy variables for season progress, Mid season,

Late season and Playoff, these variables pin down effects specific to different times in

a player’s career. Structurally, the model resembles a difference-in-differences approach

with the player fixed effects capturing time-invariant individual effects and the temporal

variables accounting for the general time trend.

As observations for each player are clearly interdependent, I cluster standard errors

at the player level. Allowing for correlation among the idiosyncratic individual errors

and heteroskedasticity, clustered standard errors provide a more conservative base for

inference.

4.4 Variables

Dependent Variable

Points. I measure a player’s offensive output by Points, the number of points a player

scored in a game. Points are crucial for the success of basketball teams because games are

decided by points. Points are also important for the individual player as they are related

to players’ salaries and popularity (Wang, 2009). As such, they are the most dominant


individual-level statistic in the coverage of NBA games.

Independent Variables

Star absence. I focus on star injuries as an exogenous source of star absence. Consistent

with my approach to measure individual output, I use points to identify star players. Prior

to each game, I determine the leading scorer of a team based on the average points in

all prior games of the focal season. The team’s top scorer is defined as a star player and

a star injury occurs when the top scorer missed a game due to medical reasons. Thus,

Star absenceit is an indicator variable that switches to one when the star colleague of

focal non-star player i misses game t due to an injury. By defining stardom based on

a comparison among teammates, I identify local stars. Being a star by this definition is

related to other constructs such as being a key player (Thomas and Dahlander, 2017).

Post absence. Stars recover from their injuries and eventually come back. To investigate

whether star absence has a lasting effect, Post absence indicates games after the star has

come back from injury. Since the long-term effects of star absence likely depend on absence

duration, I differentiate post absence phases by the length of the preceding star absences

in some analyses. There, I replace the general indicator Post absence with three mutually

exclusive dummy variables: Post absence (short absence), Post absence (medium absence)

and Post absence (long absence) that indicate post absence phases after preceding star

absences of less than 5 games, between 5 and 14 games, and at least 15 games, respectively.

If the star is injured multiple times within one season, the games of different absences are

accumulated. Figure 1 illustrates the coding of the different phases. As an alternative

measure for absence length, I use Absence experience that counts all games of the focal

season prior to game t, in which the star was injured and the focal player i played. Again,

games of multiple absences are added.

Field goal attempts. To investigate the role of performance opportunities, Field goal

attempts measures the number of field goals a player attempted in a game. It includes

both two and three point attempts. Field goal attempts are a good measure of opportunity

because they allow players to score points, a valuable resource for NBA players. Moreover,

field goal attempts mostly emerge from purposeful planning by the coaching staff implying

that players are granted opportunities based on their internal standing.

Moderator Variables

Rookie. To test the effect of star absence on junior players, I identify players in early

career stages by rookie status. Rookies are players that have never played a game in the

NBA until the focal season. Rookie status is defined for the whole season implying that

54 Chapter 2

Figure 1: Schematic Representation of Pre-Absence, Absence and Post AbsencePhases

(4 games) (7 games)

Pre absence

Star absence

Post absence

Star absence

Post absence

(short) (medium)

Phase

Topscorerinjury

Yes

No

Game

players do not lose it after their first game, but after their first season in the NBA. Rookie

is thus an indicator variable for players in their first NBA year.

Non-established player. Extending the definition of early career stages, I employ the

indicator variable Non-established player for all players that are in their first three years in

the NBA. After three years, NBA teams are eligible to offer contract extensions to young

players, who are on four-year rookie deals. Hence, three years seem to be an appropriate

time frame for NBA players to establish themselves in the league.

Control Variables

I control for other factors that may influence the individual productivity of non-star

players. Since the independent variables Star absence and Post absence are exogenous,

including control variables should not change the estimate of the independent variables,

which I will also demonstrate. Therefore, the role of control variables is somewhat limited

in my empirical strategy. Nonetheless, I include control variables to increase the precision

of the estimates and the explanatory power of the models. The control variables can be

partitioned into three groups. First, there are controls pertaining to the characteristics of

the game. Home is an indicator variable for playing at home. Back to back indicates so

called back to back games, where the team has also played the night before. Attendance

is a count variable for the number of spectators and Overtime is a dummy variable that


switches to one if the game was tied at the end of regular playing time and went to

overtime. Second, two variables control for the quality of the opponent. Opponent points

allowed records the average number of points the opposing team has conceded in all

prior games of the focal season and captures defensive quality. Similarly, Opponent win

percentage measures the proportion of prior games in the focal season the opponent has

won, reflecting overall quality. Third, I capture time effects with a set of dummy variables.

Mid season and Late season indicate games in the second and final third of the regular

season, respectively. Playoff is an indicator for playoff games. Finally, season fixed effects

are employed to control for factors specific to one season.

4.5 Descriptive Statistics

Table 1 presents descriptive statistics of the variables and correlations among them. The

non-star players in my sample score on average 8.51 points (stars: 21.4 points). Ten

percent of the games are in the absence phase and 40 percent fall in the post absence phase,

which implies that half of the games belong to the pre-absence phase. Differentiating the

post absence phases, 26, 11 and three percent of all games are in the post absence phase

after short, medium and long star absences, respectively. Most of the post absence games

are thus after short or medium absences (the median length of absences is 2 games, mean:

4.03; not in the table). The short absences are also reflected in the average absence

experience of 1.97 games. In general, correlations among variables used in one model are

low (highest correlation being -0.45). In particular, the two independent variables Star

absence and Post absence are not strongly correlated with the control variables signaling

exogeneity and mitigating concerns about imprecise estimates due to multicollinearity

(highest correlation being 0.22).

Table 2 provides further insight into the effectiveness of my quasi-experimental ap-

proach. It compares means for various characteristics of non-star players to assess whether

non-star players affected by star absence (treatment group) differ systematically from play-

ers who do not experience star absence (control group). In general, the differences across

the two groups are small and most of them do not differ significantly from zero. There are

two exceptions: treated non-stars are less experienced and more likely to be undrafted

than control non-star players. Given that experience enhances productivity and being

undrafted signals lower talent, this suggests that the control group might be of slightly

higher quality, which would compress the estimated effect of star absence. Overall, Table

2 shows that there is good balance between the two groups indicating that treated and

control non-stars are comparable.

56 Chapter 2

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58 Chapter 2

Table 2: Randomization Check – Comparison of Treatment and Control Group

Control group Treatment group(1,566 Player-seasons) (5,230 Player-seasons)

Mean SD Mean SD Diff. t-Stat.Age 27.45 4.55 27.22 4.32 0.23 1.86Experience 5.02 4.13 4.73 4.03 0.29 2.48*Rookie 0.13 0.34 0.15 0.35 -0.01 -1.26Height 200.88 9.44 200.74 9.29 0.14 0.51Weight 220.28 28.38 220.31 28.02 -0.04 -0.04Guard 0.38 0.49 0.40 0.49 -0.01 -1.02Forward 0.40 0.49 0.40 0.49 0.00 0.26Center 0.22 0.41 0.22 0.41 0.00 0.41Draft pick 21.25 16.76 21.69 16.22 -0.44 -0.87Undrafted 0.13 0.33 0.17 0.37 -0.04 -3.58**Salary 3,864,330 4,051,472 3,866,587 4,094,155 -2,257 -0.02

Notes. All reported statistics do not vary within the player-season (age is fixed at the beginning of theseason). The last column reports the t-statistic of the difference. Significance levels: * p<0.05; **p<0.01; *** p<0.001.

5 Results

5.1 Immediate and Long-Term Effects of Star Absence

Before estimating the baseline specification as presented in the estimating equation, I

test the exogeneity of star injuries in a regression set-up. Therefore, I first estimate a

parsimonious specification that only contains player and season fixed effects plus temporal

control variables, but leaves out all other controls. Then, I include the control variables in

the estimation. The results in Table 3 show that non-star players score more points when

the star is absent (Model 1). Including control variables does not change the coefficient

of Star absence (Model 2), which supports the idea that star absence due to injuries is

exogenous to the other variables in the model.

To investigate the immediate and long-term effects of star absence, I include the indica-

tor variable for the post-absence phase and estimate the baseline specification (Model 3).

When the star is absent, non-star players increase their scoring by 1.59 points. This cor-

responds to a considerable 19% growth in offensive output as non-stars score on average

8.38 points in the pre-absence phase. At the same time, the offensive output of non-stars

in the post absence phase is statistically indistinguishable from the pre-absence phase.

The results thus suggest that star absence has only a temporary effect, where non-stars

cannot sustain an increased productivity level after the star has come back. However,

injury absences in the NBA are short: the median length of absences is two games (mean:

4.03) and 90 percent of all absences last nine games or less. Considering this, it is less

surprising that star absence does not have a lasting effect on average.

I therefore differentiate post absence phases by the length of the preceding star absences

(Model 4). This reveals that long absences do have a lasting effect on non-star productiv-


Table 3: Effects of Star Absence – Results from Fixed Effects Estimations (DV:Points)

Model 1 Model 2 Model 3 Model 4 Model 5Star absence 1.57*** 1.57*** 1.59*** 1.61*** 1.62***

(0.08) (0.08) (0.10) (0.10) (0.10)Post absence 0.05 -0.07

(0.08) (0.09)Post absence (short absence) -0.02

(0.08)Post absence (medium absence) 0.12

(0.12)Post absence (long absence) 0.61***

(0.17)Post absence × Absence experience 0.03***

(0.01)Home 0.29*** 0.29*** 0.29*** 0.29***

(0.02) (0.02) (0.02) (0.02)Back to back 0.04 0.04 0.04 0.04

(0.02) (0.02) (0.02) (0.02)Attendance -0.00*** -0.00*** -0.00*** -0.00***

(0.00) (0.00) (0.00) (0.00)Overtime 0.89*** 0.89*** 0.89*** 0.89***

(0.06) (0.06) (0.06) (0.06)Opponent points allowed 0.07*** 0.07*** 0.07*** 0.07***

(0.01) (0.01) (0.01) (0.01)Opponent win percentage -0.61*** -0.61*** -0.61*** -0.61***

(0.07) (0.07) (0.07) (0.07)Mid season 0.18*** 0.16*** 0.15*** 0.14** 0.14**

(0.04) (0.04) (0.04) (0.04) (0.04)Late season 0.37*** 0.36*** 0.34*** 0.31*** 0.31***

(0.05) (0.05) (0.06) (0.06) (0.06)Playoff -0.90*** -0.62*** -0.64*** -0.68*** -0.68***

(0.09) (0.09) (0.09) (0.10) (0.10)Constant 10.10*** 4.65*** 4.64*** 4.68*** 4.66***

(0.28) (0.99) (0.99) (0.98) (0.98)Season fixed effects Yes Yes Yes Yes YesPlayer fixed effects Yes Yes Yes Yes Yes

R2 0.019 0.024 0.024 0.025 0.025Player-game obs. 356,263 355,317 355,317 355,317 355,317

Notes. Coefficients from OLS estimations. Robust standard errors clustered at the player level are in paren-theses. Significance levels: * p<0.05; ** p<0.01; *** p<0.001.

60 Chapter 2

ity. After long star absences (of at least 15 games), non-stars can sustain an enhanced level

of productivity even after the star has come back. Compared to the pre-absence phase,

they increase their offensive output in the post absence phase by 0.61 points or 7.3%.

After short and medium absences, non-star players cannot sustain their increased level of

output.4 An alternative way to analyze how absence length affects non-star productivity

in the long term is to interact the post absence indicator with Absence experience, a count

variable that records all games of the focal season where the star was injured and the focal

player played (Model 5). Results indicate that non-star output in the post absence phase

increases the longer the preceding star absence is, thus corroborating the importance of

absence length. Whereas star absences do not have a lasting effect on average, non-stars

can sustain an increased level of production after long absences. Given that learning does

not happen instantaneously, this finding supports the idea of a two-sided learning process

that takes some time to unfold.

5.2 Star Absence and Performance Opportunities

A natural explanation for the immediate and long-term effects of star absence is that

non-star players get more opportunities to perform when star players are absent and that

they can retain some of these opportunities after long absences. To test this mechanism

and examine whether an increase in opportunities indeed drives the results, I conduct a

three-step mediation analysis as outlined by Baron and Kenny (1986). At this, I leverage

the richness of the NBA data and measure opportunity by field goal attempts. The results

are presented in Table 4.

The first step of a mediation analysis is to establish that there is a relationship that can

be mediated. The baseline estimation has shown that there is a positive relationship be-

tween the absence of star players and the productivity of their non-star colleagues, which

I replicate here (Model 6). In the second step, the independent variable, star absence,

is related to the proposed mediating factor, the opportunity to perform (Model 7). The

results show that star absence indeed leads to more opportunities for non-star players. In

the third step, the potential mediator is added to the baseline estimation to test whether

opportunity mediates the relationship between star absence and non-star productivity

(Model 8). The results are striking: After controlling for opportunity, the effect of star

absence turns negative indicating competitive mediation (Zhao et al., 2010) or full me-

diation (Baron and Kenny, 1986). This suggests that the positive relationship between

star absence and non-star productivity stems completely from increased opportunities.

Applied to the empirical context this implies that non-star players only score more points

in the absence of the star because they are able to attempt more field goals. Holding the

4 Assessing the robustness of this finding, I estimate additional specifications in which I distinguishbetween short and long absences with different thresholds of 5, 10 and 15 games. The results consistentlyshow that long absences lead to a lasting increase in non-star productivity. Detailed results are availableupon request.


Table 4: Mediation Analysis – Results from Fixed Effects Estimations (Various DVs)

Model 6 Model 7 Model 8 Model 9 Model 10 Model 11(Points) (FGA) (Points) (Points) (FGA) (Points)

Star absence 1.59*** 1.44*** -0.13*** 1.61*** 1.45*** -0.13***(0.10) (0.08) (0.03) (0.10) (0.08) (0.03)

Post absence 0.05 0.04 0.00(0.08) (0.06) (0.02)

Post absence (short) -0.02 -0.02 -0.00(0.08) (0.06) (0.02)

Post absence (medium) 0.12 0.09 0.02(0.12) (0.09) (0.03)

Post absence (long) 0.61*** 0.54*** -0.04(0.17) (0.13) (0.05)

Field goal attempts 1.20*** 1.20***(0.00) (0.00)

Control variables Yes Yes Yes Yes Yes YesConstant Yes Yes Yes Yes Yes YesSeason fixed effects Yes Yes Yes Yes Yes YesPlayer fixed effects Yes Yes Yes Yes Yes Yes

R2 0.024 0.033 0.666 0.025 0.034 0.666Player-game obs. 355,317 355,317 355,317 355,317 355,317 355,317

Notes. Coefficients from OLS estimations. Dependent variables are Points and Field goal attempts (FGA).Robust standard errors clustered at the player level are in parentheses. Significance levels: * p<0.05; **p<0.01; *** p<0.001.

number of field goal attempts constant, non-star players even score less points indicat-

ing that they are less efficient without the star. This is in line with previous findings of

offensive spillovers in basketball (see Kendall, 2003).

To investigate whether the opportunity mechanism can also explain the lasting effect of

long star absences, I run another mediation analysis that distinguishes the post absence

phases by the length of the preceding injury. The first step establishes that non-stars

can sustain an increased level of output after long star absences, thereby replicating

earlier results (Model 9). The second step demonstrates that non-star players retain

some opportunities to perform after long star absences (Model 10). Finally, the third

step reveals that the lasting productivity increase after long absences also results from

increased opportunities, as indicated by the negative and insignificant coefficient on Post

absence (long absence) when controlling for Field goal attempts (Model 11).

Taken together, the mediation analyses thus support the idea that the positive effect

of star absence on non-star productivity can be explained by the non-stars’ increased

opportunities to perform. In the absence of the star, the remaining colleagues receive more

opportunities and therefore increase their productivity. Similarly, long star absences lead

to lasting performance increases because some opportunities remain with the non-stars

and are not completely reallocated to the star.

62 Chapter 2

Table 5: Career Stage Analysis – Results from Fixed Effects Estimations (DV:Points)

Model 12 Model 13 Model 14Star absence 1.59*** 1.56*** 1.42***

(0.10) (0.10) (0.13)Post absence 0.05 -0.04 -0.13

(0.08) (0.08) (0.09)Rookie -3.79***

(0.19)Star absence × Rookie 0.71**

(0.24)Post absence × Rookie 0.78***

(0.20)Non-established player -2.76***

(0.21)Star absence × Non-established player 0.49**

(0.18)Post absence × Non-established player 0.51***

(0.13)Control variables Yes Yes YesConstant Yes Yes YesSeason fixed effects Yes Yes YesPlayer fixed effects Yes Yes Yes

R2 0.024 0.046 0.037Player-game obs. 355,317 355,144 355,317

Notes. Coefficients from OLS estimations. Robust standard errors clustered at the player level are in paren-theses. Significance levels: * p<0.05; ** p<0.01; *** p<0.001.

5.3 Star Absence and Junior Employees

The analyses thus far have demonstrated that star absence opens up new possibilities for

non-stars because they receive more performance opportunities. It seems plausible that

some non-stars need these additional opportunities more than others. Junior employees

are natural candidates because they typically receive less performance opportunities than

their senior colleagues, who are already established. To test how non-stars at early career

stages are affected, I employ two early career stage indicators, Rookie and Non-established

player, and interact them with the indicator variables for the absence and post absence

phase. The results are presented in Table 5, which also reprints the baseline estimation

for comparison (Model 12). First, I concentrate on rookies, i.e. players in their first

NBA year (Model 13). The results show that rookies are clearly less productive than

senior players. On average, they score 3.79 points less in the pre-absence phase. More

importantly, the results highlight that rookies do indeed particularly benefit from star

absence. Compared to senior players, they increase their offensive output by additional

0.71 points while the star is absent. After the star has come back, rookies can sustain an

increased level of productivity of 0.78 points (compared to non-rookie players). Second,

I extend the definition of early career stages by focusing on players in their first three


Table 6: Draft Position Analysis – Results from Fixed Effects Estimations (DV:Points)

Model 15 Model 16 Model 17 Model 18 Model 19(Undrafted) (Second round) (First round) (Lottery) (Top 5)

Star absence 1.94*** 1.57*** 2.17*** 1.83*** 0.72(0.40) (0.25) (0.32) (0.49) (0.48)

Post absence 0.35 -0.09 0.77** 0.54 0.12(0.38) (0.23) (0.27) (0.43) (0.37)

Control variables Yes Yes Yes Yes YesConstant Yes Yes Yes Yes YesSeason fixed effects No No No No NoPlayer fixed effects Yes Yes Yes Yes Yes

R2 0.025 0.032 0.028 0.039 0.027Player-game obs. 7,322 11,229 12,034 7,731 4,753

Notes. Coefficients from OLS estimations. Sample is restricted to rookies and split into five groups by theirdraft position: undrafted, second round (positions 60-31), first round (positions 30-15), lottery (positions 14-6)and top 5 (positions 5-1). Robust standard errors clustered at the player level are in parentheses. Significancelevels: * p<0.05; ** p<0.01; *** p<0.001.

NBA years, whom I consider as non-established (Model 14). The results support the

findings of Model 13, although with smaller effect sizes. While non-established players

score 2.76 points less than established players in the pre-absence phase, they increase

their offensive output in the absence of the star by an additional 0.49 points. Compared

to their established counterparts, they also have a significantly higher offensive output

after the star has come back (by 0.51 points). Overall, the results in Table 5 suggest that

non-stars in early career stages particularly benefit from star absence.

Refining the findings further, I analyze what types of rookies benefit the most from

star absence. To this end, I classify rookies by their draft position. The NBA draft is an

annual event through which new players enter the league. In the draft, teams take turns

selecting a prospect from a pool of eligible players, with the best team of the previous

season picking last. The NBA draft involves sixty players, selected over two rounds.

Naturally, the most promising prospects are selected early in the draft. Consequently, the

draft position can be used to distinguish rookies by their talent.

To analyze the impact of star absence on different groups of junior players, I restrict my

sample to rookies and assign them to five mutually exclusive groups: undrafted, second

round (draft positions 60-31), first round (30-15), lottery (14-6), and top 5 (5-1).5 I

split the sample by these categories and separately estimate the effect of star absence

on non-star productivity. The results are given in Table 6 and offer interesting insights.

First, rookies drafted in the top 5 do not benefit significantly from star absence, neither

5 Players can also enter the NBA without being drafted, but they had to be eligible for at least onedraft before. The distinction of draft picks into second round, first round, lottery and top 5 reflects thedisproportionate value of top talent and is frequently made within the NBA. The first 14 picks are calledlottery picks since the 14 worst teams of the previous season take part in a lottery for the first threepicks.

64 Chapter 2

immediately nor in the long term (Model 19). Instead, rookies drafted in the late first

round are the main beneficiaries of star absence (Model 17). They increase their scoring

substantially while the star is absent (by 2.17 points) and are the only group that can

sustain a significant increase in offensive output after the star has come back (of 0.77

points). Similarly, undrafted rookies and rookies drafted in the lottery show an immediate

and long-term increase in productivity (Models 15 and 18). However, their productivity

after the star has come back is statistically not distinguishable from their productivity in

the pre-absence phase. It is thus not the most talented rookies, but rookies in the middle

of the talent distribution that benefit the most from star absence.

6 Discussion

Examining how the temporary absence of star employees affects the productivity of those

surrounding them, I find that non-stars’ output increases while the star is absent. This

effect is not only temporary: long star absences have a lasting effect on non-star produc-

tivity and the star’s colleagues can sustain an increased level of production even after the

star has come back. Mediation analyses reveal that non-stars receive more performance

opportunities in the absence of the star, which explains both the immediate effect of star

absence and the lasting effect after long absences. Finally, my results suggest that em-

ployees in early career stages benefit particularly from star absence. Refining this further,

I interestingly find that it is not the most talented junior employees that profit the most

from star absence, but juniors in the middle of the talent distribution.

These findings make several contributions to the literature. First, they highlight that

the absence of stars has very different consequences for the star’s organization and his

or her colleagues. Recent research on star absence has demonstrated that organizations

and collaborative teams are generally worse off without their star (e.g., Azoulay et al.,

2010; Oettl, 2012; Stuart, 2017). In particular, Thomas and Dahlander (2017) show that

the performance of NBA teams is significantly reduced when they lose a key player. At

the same time, my results show that non-star players get more opportunities to perform

and increase their offensive output when the team’s star is absent. While star absence

is detrimental to the organization as a whole, this paper thus highlights that it can be

beneficial for non-star colleagues surrounding the star.

The results from this paper also speak to recent findings by Chen and Garg (2018), who

show that the temporary absence of NBA stars has a negative effect on team performance

in the short run, but can improve it in the long run. They argue that the temporary

absence of a star triggers a search for new routines, which improve team performance in

the long term. However, it is also possible that non-stars receive more opportunities and

improve their skills during star absence, as the authors themselves acknowledge (Chen

and Garg, 2018, 1257). Indeed, my findings indicate that non-stars receive more perfor-

mance opportunities in the absence of the star and that this leads to a lasting increase


in productivity. As such, the results here extend the work of Chen and Garg (2018) by

highlighting that routines alone may not tell the full story. Instead, a combination of im-

proved routines and improved performance of non-stars seems to be behind the improved

long-term team performance. That said, my results do not contradict the findings of Chen

and Garg (2018). In fact, the finding that some opportunities remain with non-stars after

long star absences can be interpreted as the change in routines they suggest.

More generally, this paper highlights the importance of opportunities for individual

performance. Naturally, even able and motivated employees need to get opportunities to

perform well (Call et al., 2015; Ericsson et al., 1993). Yet, it remains hard to study the

role of opportunity for professional development empirically because information about

individual performance opportunities is usually unavailable. Exploiting a rare measure

of opportunity in the NBA data, I identify the allocation of performance opportunities

as a key mechanism through which star employees affect non-stars. In the absence of

dominating stars, non-star employees increase their productivity because they receive

more opportunities to perform. Prior research has suggested that stars constrain the

development of their colleagues because they dominate resources (Kehoe and Tzabbar,

2015; Tzabbar, 2009). I extend these findings by providing direct evidence that the

absence of stars provides new opportunities for non-star employees, which can trigger a

two-sided learning process and turn into sustainable increased output. This indicates that

influential stars may indeed constrain the professional development of employees around

them.

The degree to which non-stars benefit from star absence, however, varies. Prior re-

search has found that stars can affect their colleagues both positively and negatively

(Call et al., 2015). One possible explanation for the diverging findings are characteristics

of the star. Indeed, earlier work has highlighted the importance of specific star types for

their colleagues, such as relational stars (Grigoriou and Rothaermel, 2014) or helpful stars

(Oettl, 2012). My results offer another perspective to reconcile the diverging findings on

stars’ peer effects: the characteristics of non-stars. While non-stars are often treated as

homogenous, I find that employees in early career stages particularly benefit from star ab-

sence. This suggests that non-star characteristics play an important role for the interplay

between stars and their colleagues. Neglecting them might cause us to miss important

nuances of these interactions.

The individual development of junior employees can be boosted by star absence, pre-

sumably because they particularly benefit from the increased opportunities. For non-stars

in early career stages, the presence of stars might thus be a double-edged sword: While

stars give them the chance to learn effective techniques (Burke et al., 2007) and can serve

as a role model (Lockwood and Kunda, 1997), they can also hinder their professional

development because they constrain the opportunity to perform that junior employees

especially need. The importance of providing employees in early career stages with per-

formance opportunities is underlined by the finding that the positive effect of star absence

66 Chapter 2

is particularly salient for rookies in the middle of the talent distribution. Whereas the

most talented rookies presumably do not need star absence to get enough opportunities,

the extra opportunities are integral for the development of less talented junior employees.

Beyond these theoretical considerations, the results of this paper also have practical

implications. First, this paper highlights that opportunities are of paramount importance

for the professional development of employees. Managers should therefore ensure that they

grant non-stars enough opportunities to perform, especially junior employees. Somewhat

counterintuitively, my results suggest that managers should focus these efforts on junior

employees who are talented, but not among the very best of their cohort. Although this

strategy obviously does not guarantee that junior employees will develop into stars, the

lack of opportunity will impede all efforts to grow stars internally.

Second, managers should be aware that there is, quite literally, an opportunity cost

associated with focusing their operations on a few dominating stars. While it may be

beneficial from a performance standpoint, it can be an impediment to the development of

other employees. Managers interested in long-term planning should therefore be cautious

of relying too heavily on stars. They may even want to sit out their stars occasionally,

for example by sending them to training programs (Chen and Garg, 2018). This can be

seen as an investment in both the star, who gets additional training, and the non-stars,

who profit from new opportunities during the star’s absence.

Finally, my findings suggest that there is a certain value to disruptive events, such as

star absence. In particular, these events may help to identify undiscovered talent and

utilize untapped potential. Managers may thus want to reconsider their tendency to view

change as threatening and instead actively embrace the chances it brings.

While the NBA context has several important advantages for the purpose of this study,

employing data from professional sports always implies a trade-off between data richness

and generalizability. Over the past years, sports data has become increasingly popular

in organizational research indicating a general consensus about its usefulness. Still, it is

a valid concern whether the findings of this paper generalize to other settings. A cen-

tral aspects of my theorizing is that stars use their internal power to become dominant

forces within the organization, thereby constraining the performance opportunities for

non-stars. Regarding the empirical setting, NBA teams are characterized by high inter-

dependence and NBA players are equipped with highly specialized human capital. As

such, my findings might be representative for other settings that exhibit high levels of

interdependence and specialization, such as surgical units, technical emergency services,

and teams of management consultants or lawyers. The opportunities to perform are of-

ten limited in these professions making it likely that stars overshadow their colleagues.

Whether it is the important client presentation, the pivotal court hearing or the critical

surgical intervention – very much like in basketball teams, stars often take over in these

situations.

Besides the question of generalizability, this study has a number of other limitations.


First, my measures for individual productivity and opportunity, points and field goal

attempts, are based on offense. This has practical reasons – field goal attempts provide

a formidable measure of opportunity – as well as theoretical reasons – offensive output

matters for players’ popularity and salaries (Wang, 2009). Yet, both measures focus on a

specific part of the game and do not capture the overall performance of a player. Second,

stars are solely defined in terms of productivity in this paper. While this approach has a

rich tradition in prior research, it neglects other star types that have been shown to impact

their colleagues (see e.g., Grigoriou and Rothaermel, 2014; Oettl, 2012). Moreover, the

star definition relies on a simple dichotomy of stars and non-stars, which is not nuanced

enough to cover all gray areas inbetween. It is also based on a within team comparison

and thus identifies local stars, whereas other studies often use industry-wide benchmarks

(e.g., Azoulay et al., 2010; Krueger, 2005; Zucker et al., 2002). Third and lastly, the

start and duration of the post absence phase is potentially endogenous. While injuries

provide an exogenous shock to star absence, the comeback of a star is at least partly

a managerial decision. Although this issue should not invalidate my empirical strategy

given the incentives in the NBA, it deserves mention.

In the end, this paper points to several avenues for future research. First, it would be

interesting to examine further how non-star characteristics affect the interplay between

stars and their colleagues. In particular, investigating the non-stars’ skill sets and com-

paring them to the skills of the star seems promising. Similarly, the extent to which

non-stars are specialized deserves attention because it affects how they can capitalize on

new opportunities. Second, future work could also expand on the long-term effects of star

absence. The opportunities provided by star absence increase the non-stars’ visibility

in the labor market and it would be interesting to analyze whether this leads to higher

wages or more successful career paths. Finally, organizational features like hierarchy or

task allocation certainly influence how stars impact other employees and exploring these

contingencies merits attention.

7 Conclusion

When MPLS left Cisco, management and media were alarmed but the former colleagues of

the four star engineers did not neccessarily share that grief. By investigating the effects

of star absence on non-star productivity, this paper provides an explanation for their

opposite reactions: While star absence is detrimental to the organization as a whole, it

provides new opportunities for employees that were overshadowed by the star. This study

thus highlights that the impact of stars on their environment is not unidirectional, but

can be ambiguous. Stars are undeniably valuable for organizations, but managers should

recognize that relying on them comes at a price as it constrains development opportunities

for those around them.

68 Chapter 2

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Me over We:

Personal Rivalry Increases

Individual Output, but Crowds Out

Organizational Interests

Abstract. Acknowledging the relational nature of competition, this paper

investigates the effects of personal rivalry on individual behavior in collabora-

tive and interdependent working environments. Specifically, I analyze how the

presence of an external personal rival affects star employees’ individual output

and their contribution to organizational performance. Drawing on theories of

social comparison and self-maintenance, I propose that personal rivals invoke

strong social comparisons and threaten the star’s self-esteem. In their desire to

outperform their rivals and avert damage to their ego, stars focus on individual

output that is both highly visible and highly self-relevant – the “me.” At the

same time, they neglect other activities that are less visible and self-relevant,

but still important for organizational performance – the “we.” Competing with

a personal rival therefore has an adverse effect: Stars increase their individual

output, but they contribute less to organizational performance. I test these

arguments on a sample of stars in the National Basketball Association. Com-

paring star performance in games where they compete with a personal rival

to games where no rival is present provides strong support for the proposed

ideas. The findings underline the importance of personal motives in orga-

nizational contexts where strong feelings like personal rivalry can crowd out

organizational interests.

1 Introduction

Rivalry is more than just competition. While the desire to win is inherent in competition,

rivalry amplifies the urge to beat the opponent. Recent research has established that

rivalry can increase motivation and performance (Kilduff et al., 2010; Kilduff, 2014). At

the same time, it has been shown that rivalry has a “dark side” that can hurt organizations

(Kilduff et al., 2016). Employees in modern organizations are embedded in organizational

structures with manifold interrelations and conflicting interests. In this environment,

74 Chapter 3

personal rivalry is particularly delicate because the individual’s desire to outperform a

rival might not be in the best interest of the organization. This raises the question of how

personal rivalries affect individual behavior and organizational outcomes.

I address this question by analyzing how personal rivalry influences the individual

behavior of star employees in collaborative and interdependent working environments.

I focus on stars because they have a big impact on their peers (Azoulay et al., 2010),

teams (Thomas and Dahlander, 2017) and organizations (Zucker et al., 1998). Since stars

are defined by disproportionately high output and visibility (Call et al., 2015) and elicit

particularly strong feelings of rivalry (Kilduff et al., 2010), rivalries among them attract

special public interest and are more likely to carry over to the organizational level. In

short, star rivalries are particularly consequential for organizations. Therefore, this paper

asks how stars’ individual output and their contribution to organizational performance

change when they compete with an external personal rival, an opponent who makes them

feel heightened stakes of competition (Kilduff et al., 2010).

To answer this question, I draw on theories of social comparison and self-maintenance.

I propose that personal rivalry has an adverse effect on star performance: Stars increase

their individual output when facing a personal rival, but at the same time they contribute

less to organizational performance. Researchers across different fields have long acknowl-

edged that individual and organizational interests are not always perfectly aligned (e.g.,

DeShon et al., 2004; Holmstrom, 1982; Van Der Vegt and Bunderson, 2005). For example,

helping others might be important for collaborative outcomes, but often is not adequately

rewarded (Oettl, 2012). Thus, employees have to constantly balance how much time, at-

tention and resources they devote to actions that do not increase their individual output,

but do help the organization. I suggest that competing with a personal rival upsets this

balance because it shifts the stars’ interests towards their individual output. Personal

rivals invoke social comparisons that threaten the self, including one’s status and sense of

competence (Kilduff et al., 2016; Menon et al., 2006). To maintain self-esteem, stars strive

to outperform their personal rivals and focus on highly visible and self-relevant individual

output. They thereby neglect less visible but still important parts of their performance

and hurt organizational efficiency. This reduces their contribution to organizational per-

formance. In other words, personal rivalry crowds out organizational interests.

I tested these hypotheses on a dataset of star basketball players in the National Bas-

ketball Association (NBA) by comparing their individual output and their contribution

to organizational performance in games where they compete with a personal rival to

games where no rival is present. My dataset contains comprehensive information on all

NBA games of the seasons 1998-99 to 2013-14 and offers detailed data to quantify each

player’s individual output as well as his contribution to team performance. Combining

this data with external information on personal rivalries among NBA stars enables me to

distinguish between rival and non-rival competition. The final sample consisted of 25,293

player-game observations with 1,678 player-game observations where a star played against

Me over We 75

his personal rival (6.6%). Consistent with my approach of analyzing differences between

rival and non-rival competition, I estimated fixed effects models and controlled for a wide

range of game, team, opponent and individual characteristics.

My results support the adverse effect of personal rivalry on stars’ individual output and

their contribution to organizational performance. In line with the hypotheses, I find that

stars do score more points when facing a personal rival. Points are particularly visible and

highly self-relevant for the stars’ identities. At the same time, I find that stars contribute

less to the performance of their organizations when they compete with a personal rival.

This is indicated by significantly lower net ratings, which essentially measure how well

the team is doing when the star is on the court. Additional analyses provide evidence

that stars indeed use individual output as a benchmark for social comparison. I also

consider and rule out three alternate explanations for the adverse effect of competing

with a personal rival: opposing star talent, financial and non-pecuniary incentives and

organizational rivalries. Finally, I examine two channels through which the star’s reduced

contribution can be explained: (1) neglecting less visible but still important activities,

and (2) decreasing organizational efficiency.

This paper makes three contributions, to the literatures on individual and organizational

performance, stars, and rivalry. First, I contribute to the literature by underlining the

importance of personal motives for individual and organizational performance. While

much of the literature has focused on the effects of different kinds of human capital (e.g.,

Becker, 1962; Groysberg et al., 2008; Huckman and Pisano, 2006; Oldroyd and Morris,

2012), my paper highlights that individual performance is strongly determined by the

competitive environment under which the skills are put to use. By investigating the

personal relationships of star employees, I comply with the call to focus more strongly on

the micro-foundations of organizational performance (Foss and Pedersen, 2016). Indeed,

my results show that personal motives directly affect organizations, because they alter how

much employees contribute to organizational performance. Second, this paper extends

the literature on stars by providing an important boundary condition for their positive

impact. While prior research has found that organizations generally benefit from stars

(e.g., Agrawal et al., 2017; Kehoe and Tzabbar, 2015; Zucker et al., 1998), this paper

shows that this effect is distinctly reduced when the stars face a personal rival. As such,

this study extends our understanding of star behavior by identifying personal rivalry as a

strong, previously unexplored driver. Finally, I advance the relatively novel literature on

rivalry by highlighting an important behavioral consequence that has not been explored

thus far. Lately, the “dark side” of rivalry has gained attention and rivalry has been

linked to unethical behavior (Kilduff et al., 2016). This paper illuminates the dark side

of rivalry further by presenting a more subtle version of it: Even when individuals do not

commit misconduct, personal rivalry can be harmful because it crowds out organizational

interests.

76 Chapter 3

2 Theory and Hypotheses

2.1 Personal Rivalry

Rivalry is a subjective competitive relationship, in which an actor shows increased psy-

chological involvement and perceives higher stakes of competition, independently of the

objective characteristics of the competition (Kilduff et al., 2010). It is thus not the po-

tential rewards of the contest that cause extra motivation. Instead, rivals place greater

emphasis on their competitive comparisons because of their special relationship. This

definition of rivalry differs from other research that has used rivalry simply as a synonym

for competition (e.g., Levine and Thompson, 1996). Following Kilduff et al. (2010), I view

rivalry as an explictly distinct construct.

Vital to rivalry is that the competitors’ relationship heigthens the stakes of competition.

But how does competition turn into rivalry? Kilduff et al. (2010) have identified three

antecedents of rivalry: similarity, repeated competition and competitiveness. Rivalry

thus develops when similar actors compete repeatedly in evenly-matched contests. At the

same time, rivalry is subjective and depends on perception. As such, it does not have

to be reciprocal and indeed high-status actors elicit strong feelings of rivalry they do not

neccessarily reciprocate (Kilduff et al., 2010).

Rivalry can arise both between individuals and between organizations. At the organiza-

tional level, firms may define their identity, performance and goals in contrast to their long

standing industry competitor (Kilduff et al., 2010). Corporate rivalries such as Microsoft

vs. Apple, Coke vs. Pepsi or Adidas vs. Nike elucidate that rivalry can permeate whole

organizations. At the individual level, rivalry may arise between colleagues competing

for promotions, athletes competing for roster spots or highschool students competing for

popularity. I focus on rivalries between individuals and refer to them as personal rivalries.

Personal rivalries can occur both within and between firms. Rivals at the same organiza-

tion can be regarded as internal rivals and rivals at competing organizations as external

rivals (Menon et al., 2006). I concentrate on external personal rivals, more specifically on

personal rivalries between stars of opposing NBA teams.

Star rivalries are particularly interesting for several reasons. First, stars have a big

impact on the performance of their peers (Azoulay et al., 2010), teams (Thomas and

Dahlander, 2017) and organizations (Zucker et al., 1998). Changes in star performance are

therefore especially consequential to organizations. Second, star rivalries are more likely

to carry over to the organizational level due to the stars’ high status and influence within

organizations. As such, star rivalries are oftentimes emblematic for rivalries between

organizations. Consider Bill Gates and Steve Jobs; beyond all personal competition and

animosity, the star CEOs also personified the organizational rivalry between Microsoft

and Apple. Third, high rankings intensify competition (Garcia et al., 2006) and rivalry

is felt more strongly towards high-status actors (Kilduff et al., 2010). Thus, stars elicit

Me over We 77

stronger feelings of rivalry. Finally, there is a special public interest in star rivalries. In

professional sports, these rivalries are often the focus of media coverage and the hook used

to market games.

2.2 Personal Rivalry and Individual Performance

Personal rivalry is a powerful psychological phenomenon with substantial behavioral im-

plications. Recent research has found that rivalry can increase motivation and perfor-

mance. Kilduff et al. (2010) showed that rivalry is associated with increased performance

in effort-based tasks, namely defense in college basketball. At the individual level, Kilduff

(2014) provided evidence that rivalry increases motivation and performance by showing

that long-distance runners ran significantly faster when they competed with a rival. These

empirical findings support theoretical considerations, where rivalry is conceptualized as a

relationship that increases the subjective valence of the competitive results (Kilduff et al.,

2010). But why does rivalry increase motivation and performance?

According to social comparison theory (Festinger, 1954), people have an inherent drive

to evaluate themselves, their opinions and abilities. As objective means for comparison are

usually unavailable, they rely on social comparisons. One reason why individuals engage

in social comparison is self-enhancement, i.e. to maintain or increase their self-esteem

(Wood, 1989). To that end, individuals aim to achieve favorable comparisons, for example

by making downward comparisons (Wills, 1981). When competing with a personal rival,

however, individuals naturally compare themselves to their rivals. Personal rivals prevent

self-enhancing comparisons and are therefore perceived as an ego threat. Losing to a

personal rival not only threatens self-esteem, but can also involve loss of social status and

economic costs (Menon et al., 2006). To avoid these consequences, people are particularly

motivated against personal rivals and adopt performance goals (Kilduff et al., 2016).

While learning goals are focused on increasing own skills and competence, performance

goals are characterized by the desire to outperform others and demonstrate superior ability

(Dweck and Leggett, 1988). As personal rivals invoke strong social comparisons, stars thus

primarily focus on their relative performance to their rival. Striving to outperform their

rivals, they concentrate on individual output, and not organizational outcomes, for two

reasons.

First, individual output is highly visible. It can be measured with relative ease, is reg-

ularly used to evaluate employees and often determines part of their pay, for example

in piece rate pay schemes (Lazear, 1986). As work in modern organizations has become

increasingly collaborative and interdependent (Wuchty et al., 2007), stars affect organiza-

tional outcomes in more ways than only by what they produce themselves. For instance,

it also matters how they help others (Oettl, 2012). However, indirect contributions like

helping often happen informally and are therefore more opaque. Whereas individuals

often fail to get proper recognition for their indirect contributions, employees do receive

78 Chapter 3

credit for individual output. As such, focusing on individual output can boost self-esteem

and is a response to the self-maintenance needs invoked by the personal rival.

Second, individual output is highly self-relevant. Following self-evaluation maintenance

theory, the evaluation of another’s performance depends critically on whether the domain

is self-relevant (Tesser, 1988): If a close other performs well in a domain that is self-

relevant, it threatens the individual and his or her self-esteem. This implies that rival

performance is particularly motivating in domains that are relevant to the star’s self-

definition. Since individual output can be clearly attributed to the employee, it is more

self-relevant than organizational performance, which is more distant and can only partly

be controlled by the individual. For stars, individual output is particularly self-relevant

because extraordinary individual performance is a hallmark of stardom (Call et al., 2015).

Focusing on individual output is therefore a self-serving reaction to the ego threat imposed

by a personal rival.

Overall, personal rivals invoke a psychological threat to the star’s self-esteem and social

status. To outperform their personal rivals and avert damage to their self-esteem, stars

focus on individual output that is both highly visible and self-relevant. I therefore predict:

Hypothesis 1. When competing with a personal rival, stars increase their

individual output compared to non-rival competition.

In principle, increased individual output by the star is also advantageous for organiza-

tions. At the same time, it has already been noted that it is not the only way that stars

contribute to organizational performance. In fact, stars already produce disproportion-

ately high levels of individual output so that a further increase might not be optimal from

an organizational standpoint. In particular, the stars’ focus on individual output can

hurt their organizations for two reasons: (1) Stars neglect less visible but still important

activities and (2) stars behave selfishly at the cost of organizational efficiency.

First, focusing on individual output implies neglecting other activities that are less

visible but make an indirect contribution to organizational performance, such as helping,

mentoring or coordinating. In collaborative and interdependent working environments,

employees face a fundamental trade-off between individual output and these indirect con-

tributions. While they engage in one, they cannot engage in the other. Consequently,

they have to constantly balance how much time, attention and resources they devote to

each of the actions. As indirect contributions are less visible and less self-relevant, outper-

forming the personal rival along these dimensions would not satisfy the self maintenance

needs of the ego-threatened star. This is problematic because these activities are vital for

organizations where people work collaboratively and interdependently. High interdepen-

dence requires close coordination of the employees’ actions (Wageman, 1995). Similarly,

mentoring facilitates knowledge transfer that is crucial for organizations (Argote and In-

gram, 2000). Lastly, helping is at the very heart of teamwork where people achieve more

Me over We 79

together than they could have done individually (Marks et al., 2001). Neglecting these

activities thus hurts organizational performance.

Second, it is possible that stars increase their individual output at the cost of efficiency.

Star research has repeatedly shown that organizations face difficulties in integrating star

talent (e.g., Chen and Garg, 2018; Groysberg et al., 2008, 2011). A basic theme in this

context is that stars behave too selfishly. For example, Groysberg et al. (2011) have

argued that too many stars hurt organizational performance because they are unwilling

to share information, cooperate, make joint decisions, and engage in related integrative

behaviors. Apparently, stars prioritize individual performance. As they already produce a

disproportionately high amount of individual output under normal conditions (Call et al.,

2015), producing even more to outshine their rival is only feasible with great effort. As

such, the star’s focus on individual output might come at the expense of organizational

efficiency. As a byproduct, the star’s behavior might have adverse effects on his or her

colleagues. Highly talented employees are particularly influential in organizations (Hamil-

ton et al., 2003). Furthermore, the prominent role of stars can cause envy and make their

colleagues particularly sensitive to the star’s behavior (Groysberg et al., 2008). A star

focusing on individual output may therefore cause other employees to do the same and

withhold cooperation, which could decrease organizational efficiency further.

Altogether, the stars’ increasing individual output might not be as beneficial to orga-

nizations as one might initially think. Focusing on individual output comes at the price

of neglecting less visible activties that are important for interdependent and collaborative

organizations. Further, increasing an already high level of individual output can hurt

organizational efficiency. I therefore hypothesize:

Hypothesis 2. When competing with a personal rival, stars contribute less to

organizational performance compared to non-rival competition.

3 Methods

3.1 Empirical Setting

Research Context

Analyzing the effect of personal rivalry on star performance requires detailed information

about individual performance and personal relationships. The NBA offers both: The

league records a wide range of performance-related statistics at the individual and or-

ganizational level. This enables me to measure individual output in a collaborative and

interdependent setting, which is critical to test the hypotheses. Equally important, data

on team outcomes render it possible to quantify the star’s contribution to organizational

performance. Additionally, the broad media coverage provides information about personal

rivalries (as detailed below).

80 Chapter 3

The NBA is an interesting setting to study star rivalries for other reasons than just data

availability. The NBA is the leading league in mens’ professional basketball and highly

competitive. A rich competitive history can turn competition into rivalry (Kilduff et al.,

2010) and NBA teams play each other between two and four times in the regular season

and up to seven times in the playoffs. NBA stars compete against each other frequently

and intensively. Within each game, they play against each other on most possessions, often

guarding each other. Add athletes’ notoriously big egos and the infamous “trash talk”

that is common to some competitive sports, and the contests quickly become personal.

Off the court, the coverage and marketing of NBA games often draws on star comparisons

(“marquee matchups”) amplifying personal rivalries. Considering these factors, it does

not come as a surprise that one of the most iconic rivalries in professional sports stems

from the NBA – the legendary feud between Larry Bird and Magic Johnson.

Rival Identification

Personal rivalry is an intimate feeling that people might be reluctant to report. Therefore,

a major obstacle in rivalry research is to get information about rivalrous relationships,

i.e. who feels rivalry towards whom. Luckily, my setting provides such information. On

the occasion of its 70th birthday, the NBA published a list of the “70 greatest player

rivalries in NBA history” (Cohen, 2016). The list featured 70 pairs of NBA stars that

constituted the greatest player rivalries in the league. The following six criteria were used

to determine the ranking (Cohen, 2016):

1) What is the historical significance between the two players?

2) Did the two players meet often in the playoffs and if so, how competitive

were they against one another?

3) Are the players attached in some way (same draft year, traded for each

other, former teammates)?

4) Were the two players often the “marquee matchup” of the night when

their teams played against each other?

5) Was there an incident or altercation that created bad blood between the

two players?

6) Do media and fans tend to compare the two players and create debate as

to who was the better of the two?

These criteria capture the antecedents of rivalry well: similarity (criterion 3), repeated

competition (criteria 2 and 4) and competitiveness (criteria 2 and 6) all play an important

role in identifying the star rivalries. This validates the list as a legitimate and reliable

source for star rivalries in the NBA. As an aside, it also shows that the (academically

identified) antecedents of rivalry are in harmony with the public perception of what drives

Me over We 81

rivalry. Another advantage is that the list utilizes journalistic background information

(criteria 5 and 6) and expertise (criterion 1). As such, it reflects public perception which

seems appropriate, as star rivalries in the NBA are fueled and enhanced by the media

and public. Finally, the ranking provides an exogenous rival definition which is desirable

from a methodological standpoint.

Sample and Data

I consider the star pairs listed in the ranking as rivals and assume that both stars feel

rivalry towards one another, i.e. that rivalry is reciprocal. While rivalry does not neces-

sarily have to be symmetric (Kilduff et al., 2010), reciprocity is a reasonable assumption

for the highly recognized star rivalries considered here. The list included 68 NBA stars in

70 rival pairs. Out of these, 39 stars (57.4%) and 45 rival pairs (64.3%) were still active

in the seasons 1998-99 to 2013-14 that constitute my sample. The full ranking as well as

information about the rival pairs in my sample is given in Table A1 in the appendix.

The analysis is based on a comprehensive dataset of 20,026 NBA games during the

seasons 1998-99 to 2013-14, which I retrieved from the league’s official site, www.nba.com,

and complemented with additional information on player characteristics, individual awards

and salaries from www.basketball-reference.com.1 Both data sources provide reliable

statistics and have been used in recent empirical research in economics and manage-

ment (e.g., Arcidiacono et al., 2017; Ertug and Castellucci, 2013; Zhang, 2017). Based on

this dataset, I constructed my sample by selecting all player-game observations of stars

that were (1) listed in the ranking and (2) active in that time period. I included all

player-seasons in which both the focal star and his rival were active in the NBA. This

means that all player-seasons were included in which a star could potentially have faced

a rival. It does not mean, however, that the stars have played a rival in all those seasons

because either of the rivals can miss the pairing, e.g. due to an injury. The final sample

consisted of 25,293 player-game observations with 1,678 player-game observations, where

a star played against his personal rival (6.6%).

Some of the NBA’s greatest players like Michael Jordan, Kobe Bryant or LeBron James

appear in my sample. Overall, the players in the sample averaged 35.39 minutes per game

(22.98 for the other players active between 1998 and 2014), 20.11 points per game (other

players: 8.84) and an individual plus-minus statistic of 3.08 (other players: -0.21).2 On

average, the players in my sample made 11.75 million USD per year, more than three

times as much as players that were not included (3.54 million USD per year). These

numbers illustrate that the player sample is indeed composed of NBA stars.

1 The data was collected in 2014 and was not available at the required level of granularity before theseason 1998-99.2 The plus-minus statistic is an individual point differential which records the team’s point differentialwhen a player is on the court.

82 Chapter 3

3.2 Variables

Dependent Variables

H1: Individual output. I employed Points as a measure for individual output. Points

are critical for basketball teams as games are decided by the number of points. At the

same time, scoring is a particularly visible element of individual performance in the NBA

(Wang, 2009): It is easily observed, attracts more attention than other actions and is

positively related to players’ salaries and popularity. Usually it is also the most dominant

statistic in the coverage of NBA games.

H2: Contribution to organizational performance. I measured the star’s contribution

to organizational performance with the Net rating. It measures the team’s point differen-

tial per 100 possessions while a player is on the court. It is important that the net rating

is an individual-level statistic. It captures how well the team is doing while a player is

on the court and varies from player to player.3 It reflects not only the direct individual

output of the players, but also their indirect contributions. As such, it is a good measure

for the star’s overall contribution to organizational performance. The net rating is closely

related to the plus-minus statistic (r = 0.93), but is adjusted for the number of possessions

a player plays. As it accounts for differences in playing time and game pace, it is more

comparable. Another advantage is that the NBA provides a decomposition into offensive

and defensive ratings, which I used in additional analyses.

Independent Variable

Rival present. The presence of a personal rival was recorded by an indicator variable

that switches to one whenever two stars that were listed as rivals competed against each

other. I additionally required that the two players must have played against each other at

least four times in their careers. This accounts for the fact that some rivalries may have

only developed over time and may have not existed in earlier phases of the stars’ careers.

More technically, Rival present became one whenever (i) star i competed against one of

his listed rivals in game t and (ii) they had competed in at least four games prior to t

during their careers.4

Control Variables

There are a number of factors that could potentially affect the relationship between per-

sonal rivalry and the outcomes. To ensure that these factors do not bias the results, I

controlled for them in the estimation. The control variables can be grouped into four

sets: game, team, opponent and individual controls. Table 1 provides an overview of the

3 The net rating of two players would only be identical if they were always on the court simultaneously.4 In the robustness checks, I imposed different thresholds for the rivalry definition to ensure that theresults are not driven by a specific cut-off point.

Me over We 83

control variables and their definitions. Some of the included variables are important for

individual output or organizational contribution, but are not systematically related to

the presence of a personal rival. Missing to control for them would therefore not bias the

estimates. I still included them to improve model fit.

3.3 Estimation Strategy

To investigate the effects of personal rivalry and test my hypotheses, I compared the

performance of stars in games in which they face a personal rival to games in which

they do not. Hence, the basic unit of observation is the player-game. Since I observe

all NBA players in all games of the seasons 1998-99 to 2013-14, I am able to trace the

stars over multiple games. The longitudinal data structure also enabled me to employ

individual fixed effects to account for time-invariant unobservable characteristics of the

stars, such as talent and competitiveness. While this is critical for any study analyzing

individual performance, it seems particularly important for my research question because

these characteristics very likely affect both individual performance and feelings of personal

rivalry. Failing to account for such time-invariant unobservables would therefore introduce

endogeneity and bias the estimates.

To circumvent this problem, I estimated fixed effects panel data models by ordinary

least squares (OLS). The estimating equations related star i’s individual output (H1) and

contribution to organizational performance (H2) in game t to the presence of a personal

rival while controlling for characteristics of the game t, the focal team j, the opponent k

and the focal star i:

Yijkt = β0 + β1RivalPresentit + β2X′ijkt + γi + δT ,

where Yijkt denotes the outcome of interest, i.e. Points for H1 and Net rating for H2.

Rival presentit is the independent variable indicating whether star i faced a rival in game t.

X ′ijkt represents the matrix of control variables that change slightly between the different

models as outlined in Table 1. The γi’s are individual level fixed effects consistent with my

approach of focusing on changes in i’s outcome when competing against a rival. Finally,

the δT ’s stand for a set of season indicator variables capturing effects specific to individual

seasons. Together with the dummy variables for season progress, Mid season, Late season

and Playoff, these variables pin down effects specific to different time frames in a star’s

career.

Due to the interdependence of the observations for each star, I clustered standard

errors at the individual level. Clustered standard errors allow for correlation among the

idiosyncratic individual errors and heteroskedasticity and provide a more conservative

test of the hypotheses. The asymptotic approximation of clustered standard errors relies

on a large number of clusters and the 39 clusters formed by the stars in my sample

seem to be just enough (Angrist and Pischke, 2009). I ran the estimation in steps to

84 Chapter 3

Table 1: Description of Control Variables

Variable DescriptionGame controls

Home To account for home-court advantage, Home is an indicatorvariable for games played at home.

Back to back Back to back indicates whether the player has played thenight before to control for physical exhaustion.

Mid/Late season Accounting for season progress, Mid season and Late seasonindicate games in the second and final third of the regularseason, respectively.

Playoff Playoff is a dummy variable for playoff games, the tourna-ment to determine the NBA champion.

Attendance To control for public attention, Attendance contains thenumber of spectators of game t.

Overtime NBA games that are tied at the end of the fourth quarter gointo overtime, a series of additional five minutes to decidethe game. Overtime 1, Overtime 2 and Overtime 3 areindicator variables for the respective overtime.

Team controlsWin percentage To control for general quality of the focal team, Win per-

centage captures the team’s winning percentage. It is cal-culated based on all games of the focal season that wereplayed prior to game t.

H1: Team points per game Team points per game measures the focal team’s offensivequality as the average number of points the team has scoredin all games of the focal season prior to game t.

H2: Team net rating Team net rating is calculated as the focal team’s averagenet rating in all games of the focal season up to gamet. Analogously to the player level, it measures the team’spoint differential per 100 possessions and reflects overallteam quality. To avoid multicollinearity, I omitted Win per-centage when testing H2.

Opponent controlsOpp. win percentage Capturing overall quality of the opponent team, Opponent

win percentage is calculated analogous to the focal team’swinning percentage.

H1: Opp. points allowed per game To capture the defensive quality of the opposition, Oppo-nent points allowed per game records the average pointsconceded in all games of the focal season prior to game t.

H2: Opp. net rating Opponent net rating is calculated analogously to the focalteam’s average net rating and accounts for opponent qual-ity. Again, I excluded the opposing team’s win percentagewhen testing H2 for multicollinearity concerns.

Individual controlsAge Age captures the player’s age (to the day).

Experience To account for experience, Experience counts the star’sNBA games up to game t.

Tenure Tenure counts the star’s games for his team up to game t.

Me over We 85

make the modelling process as transparent as possible. I first estimated a model that

included only the control variables. Analyzing the effect of competing with a personal

rival, I started with a parsimonious model that contained only the independent variable,

individual fixed effects and season dummies. Then, I successively added the different sets

of control variables: game, team, opponent and individual controls.

4 Results

4.1 Personal Rivalry and Individual Performance

Table 2 reports descriptive statistics and pairwise correlations of all variables. In 7%

of the observations a star faced a personal rival. On average, the stars scored 20.12

points per game and achieved a net rating of 4.44. Generally, the correlations among

the variables that were included in one model are modest and provide little concern of

multicollinearity (excluding individual controls which are highly correlated, the highest

correlation is 0.44).5

Hypothesis 1

The first hypothesis suggests that stars increase their individual output when they com-

pete with a personal rival. I tested this hypothesis by comparing the stars’ scoring in

games where they faced a personal rival to games where they did not compete with a

rival. Table 3 presents the results. Model 1 reports a specification including only the

controls as a baseline. Without including any controls, the presence of a personal rival

was associated with an increase in star scoring of 1.42 points (Model 2). This effect was

reduced by successively adding the different sets of control variables, but remained posi-

tive and significant. In the full model the point estimate was exactly 1 (Model 6). When

competing with a personal rival, stars increase their individual output by one point. At

first glance, the effect size may not seem huge given that the stars scored roughly 20 points

per game on average (corresponding to a 5% increase in individual scoring). However,

the coefficient is more than twice as big as that of playing at home, which is generally

considered a big advantage in professional sports. Thus, competing with a personal rival

has a significant and considerable effect on individual output, even after controlling for a

wide range of game, team, opponent and individual characteristics. Hence, I find support

for Hypothesis 1.

5 There are differences between the statistics for the focal and opponent teams because not all teamshave a star. Opponent statistics are based on all teams while focal team statistics are only based on theteams of the stars studied.

86 Chapter 3

Ta

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0.151

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0.500.50

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0.460

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0.450

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0.310

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3.225.10

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Me over We 87

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88 Chapter 3

Table 3: Hypothesis 1 – Results from Fixed Effects Estimations

Model Model Model Model Model Model1 2 3 4 5 6

(DV: Points)

Rival present 1.42*** 1.20** 1.20** 1.25*** 1.00**(0.33) (0.34) (0.34) (0.34) (0.33)

Game controlsHome 0.42** 0.41** 0.44** 0.44** 0.42**

(0.14) (0.13) (0.13) (0.13) (0.14)Back to back 0.00 0.02 0.02 0.04 0.02

(0.14) (0.13) (0.14) (0.14) (0.14)Mid season 0.51+ 0.57*** 0.59*** 0.52** 0.50+

(0.29) (0.16) (0.16) (0.15) (0.29)Late season 0.25 0.34 0.35 0.20 0.24

(0.49) (0.21) (0.22) (0.22) (0.48)Playoff 1.41+ 1.33*** 1.18*** 1.14*** 1.30+

(0.73) (0.26) (0.26) (0.25) (0.74)Attendance -0.00 -0.00 -0.00 -0.00 -0.00

(0.00) (0.00) (0.00) (0.00) (0.00)Overtime 1 3.48*** 3.42*** 3.48*** 3.48*** 3.47***

(0.37) (0.35) (0.35) (0.35) (0.37)Overtime 2 6.57*** 6.42*** 6.58*** 6.64*** 6.54***

(0.78) (0.82) (0.83) (0.84) (0.77)Overtime 3 9.27*** 8.90*** 9.27*** 9.09*** 9.33***

(1.80) (1.75) (1.75) (1.80) (1.79)Team controls

Win percentage 3.65* 3.99* 4.12* 3.66*(1.39) (1.59) (1.64) (1.38)

Team points per game -0.00 -0.01 -0.01 -0.00(0.05) (0.05) (0.05) (0.05)

Opponent controlsOpp. win percentage 1.63*** 1.49*** 1.48***

(0.34) (0.36) (0.34)Opp. points allowed per game 0.13*** 0.13*** 0.13***

(0.01) (0.01) (0.01)Individual controls

Age -6.70* -6.61*(2.71) (2.72)

Experience 0.04** 0.04***(0.01) (0.01)

Tenure 0.01** 0.01**(0.00) (0.00)

Constant Yes Yes Yes Yes Yes YesSeason dummies Yes Yes Yes Yes Yes YesIndividual fixed effects Yes Yes Yes Yes Yes Yes

R2 0.1028 0.0026 0.0513 0.0565 0.0609 0.1037Player-game observations 25,293 25,706 25,691 25,368 25,293 25,293

Notes. Coefficients from OLS estimations. Robust standard errors clustered at the star level are inparentheses. Significance levels: + p<0.10; * p<0.05; ** p<0.01; *** p<0.001.

Me over We 89

Hypothesis 2

My second hypothesis is that stars contribute less to organizational performance when

competing with a personal rival. I measured the star’s contribution to organizational

performance by how well his team does when he is on the court, as captured by the

net rating. Table 4 reports the results. Model 7 contains only the control variables

as a baseline. In the parsimonious model without controls the presence of a rival was

associated with a decrease in the net rating of 4.84 points (Model 8). Again, including

the different sets of control variables reduced the effect with opponent controls having the

biggest impact on the point estimate and increasing model fit substantially (Model 11).

The full model indicated a significant net rating reduction of 1.62 points when stars face a

personal rival (Model 12). This is a sizable effect as the stars’ average net rating was 4.44.

Consequently, the presence of a rival is associated with an average net rating reduction

of 36.5%. While stars still have a positive impact on their teams’ point differentials,

their contribution is reduced by more than one third in games against personal rivals.

Hypothesis 2 is thus supported.

Illustration of Results

Figure 1 illustrates the marginal effects of competing with a personal rival on the stars’ in-

dividual output (vertical axis) and contribution to organizational performance (horizontal

axis). Competing with personal rivals moves the points-net rating coordinate northwest

indicating the adverse effect of personal rivalry: It increases individual output, but reduces

the contribution to organizational performance.

4.2 Additional Analyses

To further analyze the proposed theoretical ideas and rule out alternative explanations, I

conducted several additional analyses. First, I tested the assumption that stars compare

each other based on individual output. Subsequently, I considered three alternate expla-

nations for the adverse effect of competing with a personal rival: opposing star talent,

financial and non-pecuniary incentives and organizational rivalries. Finally, I examined

the channels through which the star’s reduced contribution can be explained.

Individual Output as a Benchmark

A central idea in my theorizing is that stars respond to the ego-threat evoked by their

personal rivals by striving to outperform them on highly visible and self-relevant individual

output. This reasoning assumes that rival stars compare themselves based on individual

output. Therefore, more individual output by the personal rival should encourage the

focal star to also produce more individual output. To test this presumption, I replaced

the general indicator Rival present by a measure of the rival’s points, Rival points, and

90 Chapter 3

Table 4: Hypothesis 2 – Results from Fixed Effects Estimations

Model Model Model Model Model Model7 8 9 10 11 12

(DV: Net rating)

Rival present -4.84*** -4.06*** -4.09*** -1.58** -1.62**(0.62) (0.60) (0.62) (0.54) (0.51)

Game controlsHome 8.93*** 8.92*** 9.07*** 8.92*** 8.92***

(0.23) (0.24) (0.26) (0.23) (0.23)Back to back -1.55*** -1.35*** -1.31*** -1.57*** -1.57***

(0.30) (0.33) (0.33) (0.29) (0.30)Mid season -0.53 -0.01 0.06 -0.08 -0.51

(0.56) (0.46) (0.41) (0.38) (0.56)Late season -0.06 1.00* 1.01* 0.82* -0.04

(0.83) (0.41) (0.38) (0.38) (0.83)Playoff -1.86 -2.54*** -3.21*** -0.49 -1.69

(1.14) (0.45) (0.44) (0.45) (1.13)Attendance -0.00 -0.00*** -0.00*** -0.00 -0.00

(0.00) (0.00) (0.00) (0.00) (0.00)Overtime 1 -2.55*** -2.77*** -2.57*** -2.53*** -2.53***

(0.46) (0.48) (0.48) (0.47) (0.46)Overtime 2 -3.47** -3.53*** -3.08** -3.42** -3.44**

(1.12) (0.97) (0.97) (1.12) (1.12)Overtime 3 0.76 -0.14 0.06 0.61 0.65

(1.71) (1.75) (1.69) (1.68) (1.72)Team controls

Team net rating 0.52*** 0.53*** 0.52*** 0.52***(0.05) (0.05) (0.05) (0.05)

Opponent controlsOpp. net rating -0.84*** -0.83*** -0.83***

(0.02) (0.02) (0.02)Individual controls

Age 1.93 1.78(2.84) (2.82)

Experience 0.01 0.01(0.01) (0.01)

Tenure 0.00 0.00(0.00) (0.00)

Constant Yes Yes Yes Yes Yes YesSeason dummies Yes Yes Yes Yes Yes YesIndividual fixed effects Yes Yes Yes Yes Yes Yes

R2 0.1169 0.0046 0.0574 0.0706 0.1171 0.1172Player-game observations 25,293 25,706 25,691 25,377 25,293 25,293

Notes. Coefficients from OLS estimations. Robust standard errors clustered at the star level are inparentheses. Significance levels: + p<0.10; * p<0.05; ** p<0.01; *** p<0.001.

Me over We 91

Figure 1: Hypotheses 1 and 2 – Illustration of Marginal Effects

22

21

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Points

Net rating

Rival present

Notes. This figure plots the marginal effects of competing with a personal rival onpoints (vertical axis) and net rating (horizontal axis), evaluated at the means ofthe other covariates in the model (marginal effect at the mean). The coordinaterepresents the combination of estimated points and net rating, and the whiskersprovide the corresponding 95% confidence intervals.

regressed it on Points (Model 13) and Net rating (Model 14). The set of control variables

paralleled those in Models 6 and 12, respectively. Since there is no proper equivalent to

the rival’s points in non-rival games, I restricted the sample to games of rival competition.

The results are reported in Table 5.6 They provide support for the theorizing as rival’s

points were associated with a significant increase in the focal star’s points and a significant

decrease in the focal star’s net rating. The adverse effect of competing with a personal

rival thus increases with increasing individual output of the personal rival. This lends

credence to the proposed theoretical mechanism that rival stars focus on individual output

to outperform their personal rivals.

Opposing Star Talent

A first alternate explanation that may account for the adverse effect of competing with

a personal rival is the star talent on the opposing team. One may speculate that the

focal star contributes less to organizational performance because there is another star on

the court whose outstanding talent limits the star’s positive influence. It is possible that

the games where stars compete with a personal rival are also the games where they face

exceptional individual talent, and that precisely this is what reduces their organizational

contribution. To mitigate this concern, I estimated two models where I explicitly con-

6 I do not report the coefficients of control variables in Tables 5 to 7. Generally, these coefficientsstrongly resemble those in Models 6 and 12. Full results for all models are available upon request.

92 Chapter 3

Table 5: Additional Analyses – Results from Fixed Effects Estimations

Model 13 Model 14 Model 15 Model 16(Points) (Net rating) (Points) (Net rating)

Rival present 1.01** -1.25*(0.33) (0.51)

Rival points 0.09** -0.27***(0.03) (0.05)

Opposing star -0.06 -3.08***(0.13) (0.23)

Control variables Yes Yes Yes YesConstant Yes Yes Yes YesSeason dummies Yes Yes Yes YesIndividual fixed effects Yes Yes Yes Yes

R2 0.1458 0.1079 0.1037 0.1208Player-game observations 1,648 1,648 25,293 25,293

Notes. Coefficients from OLS estimations. Dependent variables are in column headers.Robust standard errors clustered at the star level are in parentheses. Significance levels:+ p<0.10; * p<0.05; ** p<0.01; *** p<0.001.

trolled for stars on the opposing team. Therefore, I constructed an indicator variable

Opposing star that became one when the opposing team had at least one All Star or

All NBA Player.7 The results are reported in Table 5 (Model 15 and 16). I find con-

sistent support for the main results. While a star on the opposing team had indeed a

strong negative effect on the focal star’s net rating, the negative effect of competing with

a personal rival persisted (Model 16). Interestingly, playing against a non-rival star has

no significant effect on the focal star’s points (Model 15). This supports the idea that

personal rivals, and not simply other stars, evoke social comparisons that induce stars to

focus on individual output. Consequently, the observed relationships cannot be explained

by competition from opposing stars, but are indeed rivalry effects.

Financial and Non-Pecuniary Incentives

Another potential reason why stars might focus on individual output and neglect other

activities are individual incentives. Individual scoring is connected to higher popularity

and salaries in the NBA (Wang, 2009). Hence, stars have an additional incentive to

focus on individual output when they compete for a new contract. This phenomenon is

also known as the contract year syndrome (White and Sheldon, 2014). To rule out this

competing explanation, I included a dummy variable Contract year indicating seasons

in which the focal star had an expiring contract and had not yet signed a new contract.

Models 17 and 18 in Table 6 show that my findings are not driven by the contract year

syndrome. Interestingly, the coefficients for Contract year were insignificant and negative.

7 The best players of each season are nominated for the NBA All Star Game (24 players; after twothirds of the regular season) and the All NBA Teams (15 players; end of the season). Besides individualawards, these nominations are the highest individual honors and hence a good indicator for star status.

Me over We 93

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94 Chapter 3

A possible explanation for this is that the stars in my sample typically extended their

contracts before the last contract year. Only in later stages of their careers, did they

go into seasons with an expiring contract. Besides financial incentives, stars can also

be motivated to focus on individual output by public recognition. A common theme in

NBA coverage are so called career milestones, where players pass a certain threshold in

points (typically in steps of thousands). To control for this temporary extra incentive, I

added the indicator variable Milestone that became one when the focal star was within

50 points of the next 1,000-increment in career points. Models 19 and 20 in Table 6 show

that being close to a career milestone did not significantly change star’s behavior and,

more importantly, left the coefficients of Rival present unchanged. Neither financial nor

non-pecuniary incentives can thus explain the adverse effect of competing with a personal

rival.

Organizational Rivalries

Lastly, stars might not react to personal rivalries but to rivalries at the organizational level.

They might increase their individual output not to outshine their personal rival, but to

gain popularity by standing out against a rival franchise. Disentangling these two levels of

rivalry, I constructed a dummy variable Team rivalry that indicated franchise rivalries and

included it in the estimation. This variable switched to one for 23 well-known franchise

rivalries in the NBA.8 Table 6 presents the results. Playing against a rival team has a

similar effect as competing with a personal rival. It increased individual output (Model

21), but reduced the contribution to organizational performance (Model 22). Controlling

for organizational rivalries also reduced the estimated effects of competing with a personal

rival, but both coefficients remained significant. The adverse effect of personal rivalry thus

persists over and above rivalries at the organizational level. However, the reduced effect

sizes suggest that personal and organizational rivalries are correlated and intertwined.

While the two levels of rivalry cannot always be cleanly distinguished, organizational

rivalries alone cannot explain my findings.

Mechanisms Underlying the Star’s Reduced Contribution

After ruling out alternate explanations, I extended the analysis to shed light on the chan-

nels through which the star’s reduced contribution to organizational performance can be

explained. In my theorizing, I propose two main mechanisms: (1) Stars neglect less visible

but still important activities and (2) stars behave selfishly at the cost of organizational

efficiency. To test the presence and relative importance of these two mechanisms, I scru-

tinized the star’s contribution to organizational performance. To this end, I decomposed

8 Information about franchise rivalries were taken from Wikipedia and knowrivalry.com, a website thatsurveys fans about their feelings towards other teams. I treat franchise pairs as rivals that either have aWikipedia article dedicated to their rivalry or are listed among the top ten rivalries on knowrivalry.com.The list is given in Table A2 in the appendix.

Me over We 95

the net rating and estimated two models where I used Offensive rating (Model 23) and

Defensive rating (Model 24) as the dependent variables. Offensive rating (Defensive rat-

ing) measures the team’s points scored (allowed) per 100 possessions while the player is

on the court. As such, it captures how well the team is doing offensively (defensively)

when the player is on the court. The net rating is the difference between the offensive and

defensive rating. Table 6 reports the results. Models 23 and 24 parallelled Model 12, but

contained a set of control variables specific to the respective dependent variables.9 When

competing with a personal rival, stars made a smaller offensive contribution as indicated

by a significant decrease in their offensive rating (Model 23). Given that the presence of a

personal rival increased individual scoring (Hypothesis 1), a decrease in the offensive rat-

ing suggests that the stars increased their individual output at the cost of the team. This

indicates that the star’s focus on scoring decreased organizational efficiency. Competing

with a personal rival was also associated with a significant increase in the defensive rating

(Model 24), corresponding to a worse defensive performance. This suggests that the stars

neglect defensive responsibilities in order to score. Playing good defense is considered

less visible, but vital for success in the NBA. It seems that both proposed mechanisms

are at play. Moreover, the statistical significance and the effect sizes were very similar in

Model 23 and 24 indicating that both channels account for roughly half of the reduced

organizational contribution.

4.3 Robustness Checks

To assess the robustness of my results, I conducted two robustness checks, where I em-

ployed different thresholds for the rivalry indicator and an alternative measure for the

star’s contribution to organizational performance. Table 7 displays the results of the

robustness checks.

Thresholds for Rivalry Definition

First, I imposed different thresholds for the rivalry indicator to ensure that results were

not driven by the choice of a specific cut-off point. In the original operationalization,

rivals were required to have played against each other at least four times. I conducted

estimations where no threshold (Models 25 and 26) and a higher threshold of ten games

were imposed (Models 27 and 28). Without a threshold, the stars are treated as rivals

as soon as the play against each other in the NBA for the first time. I use this oper-

ationalization to safeguard against the possibility that the players have developed their

rivalry before arriving in the NBA, for example in highschool or college. Not imposing

a threshold increased the number of player-game observations in which a star competes

with a rival to 1,858 (7.3%). The higher threshold of ten games is a more conservative

9 In particular, I added the team’s offensive rating and the opponent’s defensive rating for Model 23and vice versa for Model 24.

96 Chapter 3

Table 7: Robustness Checks – Results from Fixed Effects Estimations

Model 25 Model 26 Model 27 Model 28 Model 29(Points) (Net rating) (Points) (Net rating) (Plus-minus)

Rival present -0.87*(0.33)

Rival present 0.71* -1.73**(No threshold) (0.29) (0.50)Rival present 1.12** -1.77**(10 games threshold) (0.37) (0.57)Control variables Yes Yes Yes Yes YesConstant Yes Yes Yes Yes YesSeason dummies Yes Yes Yes Yes YesIndividual fixed effects Yes Yes Yes Yes Yes

R2 0.1033 0.1173 0.1037 0.1172 0.1162Player-game obs. 25,293 25,293 25,293 25,293 25,293

Notes. Coefficients from OLS estimations. Dependent variables are in column headers. Robust standarderrors clustered at the star level are in parentheses. Significance levels: + p<0.10; * p<0.05; ** p<0.01;*** p<0.001.

approach that ensures that the stars have competed against each other over multiple

years. Using a more strict threshold reduced the number of “treated” observations to

1,358 (5.4%). The results in Table 7 confirm the conclusions from the original Models 6

and 12. While the negative effect on the net rating was slightly higher with alternative

thresholds, the results on individual output reveal an interesting dynamic: The higher

the imposed threshold, the more pronounced was the positive impact of rival presence

on the star’s points (effect sizes increased from 0.71 without a threshold to 1.12 with a

ten-games threshold). Over and above showing the robustness of the results, this enriches

my findings because it shows that rivalries evolve over time. A long competitive history

seems to intensify the urge to outperform the personal rival.10

Alternative Measure for Contribution to Organizational Performance

Second, I employed an alternative statistic to measure the star’s contribution to organi-

zational performance. One potential concern with the net rating is that it is adjusted to

100 possessions. While the adjustment makes the net rating more comparable across stars

with differences in playing time and game paces, it might be informative to have a look

at the “raw” plus-minus statistic that is not adjusted and simply records the team’s point

differential while a player is on the court. I estimated a model where I used the plus-minus

statistic as the dependent variable and replaced Team net rating and Opponent net rating

with corresponding variables that utilize team-level plus-minus statistics. Table 7 shows

10 A natural way to investigate this further would be to interact the rivalry indicator with the numberof games the rivals have played against each other. In my setting, this corresponds to restricting thesample to rival games because there is no equivalent to rivalry experience in non-rival games. Such aninteraction analysis did not yield statistically significant results.

Me over We 97

that the findings were robust to using the alternative measure (Model 29).

5 Discussion

Following recent literature that has argued that relationships matter for competitive be-

havior and outcomes (e.g., Grohsjean et al., 2016; Kilduff et al., 2010; Piezunka et al.,

2018), this paper concentrates on the effects of personal rivalry in collaborative and in-

terdependent settings. Specifically, I analyze how the presence of an external personal

rival affects star employees’ individual output and their contribution to organizational

performance. Drawing on theories of social comparison and self-maintenance, I propose

that the star’s desire to outperform the personal rival has an adverse effect: Stars in-

crease their individual output, but they contribute less to organizational performance. In

a dataset of NBA stars, I found support for the hypotheses. Stars do score more points

when facing a personal rival. At the same time, they contribute less to organizational

performance as indicated by lower net ratings. These findings cannot be explained by

opposing star talent, financial and non-pecuniary incentives or organizational rivalries.

Instead, additional analyses provide support for my theorizing that rival stars focus on

individual output and thereby neglect important, but less visible activities and hurt or-

ganizational efficiency. This has important implications for the literatures on individual

and organizational performance, stars, and rivalry.

Implications for Research on Individual and Organizational Performance

Following human capital theory, the classical starting point to understand, explain and

predict individual performance of employees are their skills (Becker, 1962). Since the in-

troduction of social capital theory (Coleman, 1988), multiple studies have acknowledged

that employees bond with their colleagues and that the resulting relationships are conse-

quential for individual performance (e.g., Groysberg et al., 2008; Huckman and Pisano,

2006) and organizational outcomes (e.g., Nahapiet and Ghoshal, 1998; Oldroyd and Mor-

ris, 2012; Reagans and Zuckerman, 2001). Less appreciated is the fact that individuals

also form relationships with their competitors. Only recently, studies have begun to link

relationships among competitors to competitive behavior and outcomes (e.g., Grohsjean

et al., 2016; Kilduff, 2014; Piezunka et al., 2018). This paper extends these efforts by

considering rivalrous relationships and demonstrating that they impact individual be-

havior and performance. While research has commonly investigated relationships among

colleagues, this paper shows that external relationships can be just as consequential. To

fully understand the personal motives of employees, it is not enough to just look inside

the firm.

Earlier research has studied the effects of competitive relationships in individual compe-

titions, such as golf (Flynn and Amanatullah, 2012), running (Kilduff, 2014), and racing

98 Chapter 3

(Piezunka et al., 2018). In contrast, I analyze the effect of personal rivalry in an or-

ganizational setting that is characterized by interdependence and collaboration. In this

context, I explicitly link personal motives of stars not only to their individual output,

but also to their contribution to organizational performance. In line with the recent call

to pay more attention to the micro-foundations of organizational performance (Foss and

Pedersen, 2016), this paper enhances organizational research by demonstrating that the

personal motives of employees directly affect the performance of their organizations. As

such, it complements human capital theory and provides new avenues for understanding

and assessing individual and organizational performance. It is not just the individual skills

that determine performance, but also the competitive environment under which they are

put to use.

Beyond that, this paper demonstrates that personal rivalry is an important factor in

determining what employees focus on. Grohsjean et al. (2016) have shown that personal

relationships among competitors influence how competitive individuals behave. I blend

this insight with the finding that employees focus on specific activities that are in line

with their personal motives. Personal motives thus determine what individuals are willing

to do and how they prioritize different tasks. This is particularly delicate for modern

organizations, where employees usually have considerable leeway in allocating their time.

Implications for Star Research

The observation that a few high-performing employees add disproportionate value to or-

ganizations has stimulated a considerable body of research on stars (for an overview, see

Call et al., 2015). Although not completely univocal, this research has repeatedly shown

that organizations benefit from stars (e.g., Agrawal et al., 2017; Kehoe and Tzabbar, 2015;

Zucker et al., 1998). My paper generally supports this notion and refines it by providing

an important boundary condition for the positive impact of stars. When stars compete

with a personal rival, they contribute distinctly less to organizational performance. This

study thus enhances our understanding of star behavior by identifying personal rivalry as

a strong, previously unexplored driver. It is interesting that competitiveness seems like a

double-edged sword in this context. On the one hand, it is usually assumed that stars are

not only more talented, but also more competitive and motivated than their peers (Duck-

worth et al., 2007). On the other hand, it is exactly the drive and competitiveness that

backfire when competing with a personal rival. The stars’ determination to outperform

others can thus hurt organizations in certain situations.

This finding resonates well with a more pessimistic view of stars. Earlier research

has shown that organizations can have difficulties integrating stars and utilizing their full

potential (e.g., Groysberg et al., 2008, 2011; Swaab et al., 2014). One common explanation

for these problems is the selfish and uncooperative behavior of the stars (e.g., Groysberg

et al., 2011; Swaab et al., 2014). My core finding that stars focus on highly visible and

Me over We 99

self-relevant individual output when competing with a personal rival supports this notion.

I also provide evidence that stars hurt organizational efficiency and neglect less visible

activities in their pursuit of individual output. Interpreted with due care, this paper

thus underpins what others have speculated, namely that stars follow their own interests

at the expense of others. A related issue is that more star talent is not always better

(Groysberg et al., 2011), also known as the too-much-talent effect (Swaab et al., 2014).

This finding is usually attributed to a lack of cooperation among prominent employees

(Groysberg et al., 2011; Swaab et al., 2014). My paper offers a profound explanation

for this effect: Personal rivalry could induce the stars to focus on individual output and

neglect collaborative activities.

Implications for Rivalry Research

Finally, I contribute to recent studies that consider rivalry as a relational construct which

is explicitly distinct from competition. This research has established rivalry as a powerful

psychological phenomenon that can have different behavioral implications. Rivalry can

increase motivation and performance (Kilduff, 2014; Kilduff et al., 2010), but can also

have consequences that are not always desirable, such as increased risk-taking (To et al.,

2018). Kilduff et al. (2016) have even demonstrated that rivalry increases unethical be-

havior. Extending our understanding of the “dark side” of rivalry, this paper reveals that

personal rivalry crowds out organizational interests. Rivalry can hurt organizations not

only because employees bend the law or ethical guidelines, but also because it motivates

them to prioritize personal motives. This paper thus calls attention to an undesirable

behavioral consequence of rivalry that has not been explored before.

I find that the adverse effect of competing with a rival increases with the rival’s points.

Evidently, star rivals compare each other based on individual output and “overbid” each

other. In this regard, my results resemble earlier research on auction fever and overbidding

(Ku et al., 2005; Malhotra, 2010). At the same time, they enrich the literature on rivalry

because they show that individuals react offensively to the presence of a personal rival.

Former research on knowledge sharing and information use has shown that rivals can

trigger a defensive pattern of response, where employees avoid the knowledge of a rival

(Menon et al., 2006). In contrast, this study shows that stars actively respond to their

rivals – which can be just as detrimental to organizational interests.

Lastly, I contribute to the literature on rivalry by providing first evidence on the dy-

namics of rivalries. Although not the focus of the paper, applying different thresholds to

the rival definition highlights that personal rivalries evolve over time. My results indicate

that longer rivalries increase the stars’ desire to trump their personal rivals. Similarly, this

paper suggests that personal and organizational rivalries are correlated and intertwined,

which provides first evidence for potential carry-over effects between the different levels of

rivalry. Investigating these interdependencies further seems a promising route for future

100 Chapter 3

research.

Scope Conditions, Generalizability and Limitations

While this paper provides important insights on individual and organizational perfor-

mance, star employees and personal rivalry, it certainly has limitations. To start with,

there are three characteristics of my conceptual framework that define important scope

conditions for my findings: (1) Employees must work interdependently and collaboratively

with their colleagues, (2) they must engage in competitive situations in which they can

observe the individual output of employees from competing organizations and (3) they

must encounter these situations often enough so that personal rivalry can develop.

While these scope conditions seem specific at first, there are multiple environments

where they are met and my findings should apply. Examples include teams in man-

agement consulting or law firms, research groups and sales teams. Employees in these

professions work interdependently and collaboratively, experience head-to-head compe-

tition with observable individual output and regularly meet the same competitors with

whom they develop relationships. Think of lawyers in law firms. Within their company,

they work interdependently and collaboratively on legal cases. Outside the company, they

regularly compete with lawyers from other offices; be it in legal negotiations, in court or

when pitching for potential clients. In some aspects, these situations resemble NBA games

as the lawyers receive immediate feedback, react to an opposing party and are required

to make fast decisions. And just like NBA teams practice, teams of lawyers prepare for

their meetings. Due to their high level of specialization and the limited number of com-

peting firms, the lawyers repeatedly compete with the same firms and personnel. This is

essential because it lays the ground for personal rivalry. A similar reasoning applies to

management consultants who regularly face the same professionals when acquiring clients,

researchers who repeatedly compete with the same scholars for funding or salespeople who

constantly fight for customers with the same agents. From an organizational standpoint,

the NBA has a franchise structure with the teams as franchises. As such, it is akin to

large enterprises with multiple offices and business units. Thus, the findings of this study

might generalize to between-unit or between-office rivalries in such companies.

Central characteristics of the conceptual framework and empirical setting are thus ap-

plicable to a variety of modern industries, but questions about the generalizability of my

findings remain. Two aspects seem especially noteworthy. First, the inferences are drawn

from a small group of star athletes. At the level of the empirical analysis, my dataset pro-

vides enough information to produce statistically significant conclusions. Still, one has to

acknowledge that the underlying sample of stars and rivalries is small. The limiting factor

in this regard is information on rivalries among NBA players, which is only available for

the most prominent players. Having said that, there is no obvious reason why the group

of stars studied here should not be representative for the broader population of NBA stars

Me over We 101

and star athletes in general. Infamous rivalries between star CEOs (Bill Gates vs. Steve

Jobs), star inventors (Thomas Edinson vs. Nikola Tesla) or star scientists (Isaac Newton

vs. Gottfried Leibniz) suggest that star rivalries also loom large outside the sports world.

More open is whether the results generalize to non-star employees. While cornerstones of

my theorizing like social comparisons or self-maintenance needs are universally valid, some

aspects might be specific to star employees. For example, the distinctive competitiveness

of stars might be especially conducive to rivalry. It is thus up to future research to verify

my results in samples of non-star employees. Second, like in many studies utilizing data

from professional sports, my sample is composed exclusively of male employees. This ob-

viously limits generalizability as it is conceivable that women perceive personal rivalries

differently and therefore also react differently to them. Moreover, same-sex environments

may generate special types of personal rivalries and promote rivalries in general.

Besides issues of generalizability, this study is of course also subject to other limitations.

Two issues deserve special attention. First, the identification strategy used in this paper

does not enable causal inference about the effect of personal rivalry on individual output

and organizational contributions. Shedding light on causal relationships would require

some exogenous variation in the presence of a personal rival. Since this is not the case

in my empirical setting, this paper makes no claim to causality. Second, I only indirectly

test the channels through which the star’s reduced organizational contribution can be

explained. In particular, I am not able to pin down the exact activities that stars neglect

when they focus on individual output. My theorizing suggests the nature of these activities

and the empirics provide support for the considerations, which is a valuable first step. Still,

more direct empirical tests would be desirable to define the underlying mechanism more

precisely. Acknowledging these limitations, I believe that this paper makes an important

contribution to our understanding of personal rivalries and how they affect individuals

and organizations.

102 Chapter 3

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106 Chapter 3

Appendix

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110 Chapter 3

Table A2: List of Franchise Rivalries

Franchise A Franchise B Listed as RivalsWikipedia Knowrivalry

Boston Celtics Detroit Pistons YesBoston Celtics Los Angeles Lakers Yes YesBoston Celtics New York Knicks YesBoston Celtics Philadelphia 76ers YesBrooklyn Nets New York Knicks Yes YesBrooklyn Nets Toronto Raptors YesChicago Bulls Cleveland Cavaliers YesChicago Bulls Detroit Pistons Yes YesChicago Bulls Miami Heat YesChicago Bulls Milwaukee Bucks YesChicago Bulls New York Knicks YesCleveland Cavaliers Golden State Warriors Yes YesDallas Mavericks Houston Rockets Yes YesDallas Mavericks San Antonio Spurs YesDetroit Pistons Los Angeles Lakers YesHouston Rockets San Antonio Spurs YesHouston Rockets Utah Jazz YesIndiana Pacers New York Knicks YesLos Angeles Lakers Los Angeles Clippers YesLos Angeles Lakers Sacramento Kings YesLos Angeles Lakers San Antonio Spurs YesMiami Heat New York Knicks YesPhoenix Suns San Antonio Spurs Yes Yes

Notes. The list displays 23 franchise rivalries in the NBA that ei-ther have an own wikipedia-article (https://en.wikipedia.org/wiki/List of National Basketball Association rivalries) or are listed among thetop ten rivalries on knowrivalry.com (https://knowrivalry.com/category/nba).The Brooklyn Nets are the former New Jersey Nets, the franchise relocated in2012. Franchises are listed alphabetically.

Concluding Remarks

Investigating the role of stars in collaborative organizations, this thesis contributes to

recent efforts in organizational research to better understand the microfoundations of

organizational performance. Anchoring higher-level outcomes – like the performance of

organizations – on the individual level rests on the fundamental insight that these out-

comes emerge because individuals act and interact. Identifying micro-level mechanisms

and discriminating between alternative explanations is also crucial if one hopes to inform

managerial decision-making. Embracing the explanatory power of the micro level, this

thesis focuses on stars’ actions, their interactions with colleagues and competitors, and the

dynamics that drive these actions and interactions. The NBA is an ideal research setting

for this purpose as it provides data on the individual and organizational level, which is es-

sential for empirical microfoundational work. In this sense, microeconometric techniques

are a natural ally for the microfoundational approach. Analyzing individual-level data

from an pronounced organizational perspective, each essay in this thesis illuminated a

different facet of stardom within organizations: the first chapter adressed how stars affect

team performance, the second chapter focused on the peer effects of stars and the third

chapter examined stars’ personal motives and how they affect behavior. The microfoun-

dational approach might be most notable in the first chapter, but it is inherent in all three

essays as the studied individual-level phenomena are continuously linked to the interests

of organizations. The essays also comply with the microfoundations agenda because they

try to pin down the mechanisms underlying their core findings. Taken together, the re-

sults of the three essays suggest that the effect of stars in collaborative organizations is

not unidirectional, but ambiguous.

On the one hand, stars are vital for the success of organizations because they possess

unique skills that are hard to replace. The first chapter shows that star absence distinctly

reduces team performance and that imperfect skill substitution is the key mechanism

underlying this effect. Neither the loss of complementarities between stars and their col-

leagues, nor the demotivation and reduced effort of the remaining team members can

explain the decrease in team performance. Instead, stars possess unique skills that the

other team members cannot replace. Consequently, teamwork does not dissolve the orga-

nization’s dependency on star talent. As such, the findings of the first chapter indicate

that the individual excellence of stars is crucial for the performance of teams and organi-

zations.

On the other hand, stars limit performance opportunities for their colleagues and con-

strain their professional development. The second chapter demonstrates that non-star

colleagues benefit from temporary star absence, both immediately and in the long term.

112 Concluding Remarks

The absence of the star provides them with increased opportunities to perform, which

they convert into higher productivity while the star is absent. After long star absences,

non-stars retain some of the performance opportunities, enabling them to sustain an in-

creased level of output even after the star has come back. Performance opportunities are

thus a key mechanism behind non-star productivity. The presence of influential stars,

who dominate performance opportunities, thus impedes the professional development of

their colleagues and is therefore detrimental to the long-term interests of organizations.

Supporting this, I find that junior employees particularly benefit from star absence, es-

pecially those in the middle of the talent distribution. Apparently, this group tends to

be especially marginalized alongside dominant stars. The findings of the second chap-

ter therefore highlight a potential drawback of stars, namely that they overshadow their

colleagues.

Finally, the organizational impact of stars is contigent upon their personal motives. The

third chapter establishes that competing with a personal rival has an adverse effect on star

performance: stars increase their individual output but contribute less to organizational

performance. Alternate explanations such as opposing star talent, financial and non-

pecuniary incentives or organizational rivalries cannot explain the finding. The mechanism

underlying the adverse effect of rival competition is that stars focus on highly visible

and self-relevant individual output in their attempts to outperform their personal rival.

Doing so, they neglect other activities that are important for collaborative organizations

and they hurt organizational efficiency. In this way, the positive organizational impact

of stars is distinctly reduced when they compete with a personal rival. Results from the

third chapter thus suggest that personal motives of stars directly affect organizational

interests.

Taken together, the three essays of this thesis paint a comprehensive and nuanced pic-

ture of the role of stars in collaborative organizations. This thesis investigates stardom

within organizations at different levels of analysis – at the organizational level, at the

individual level and at an inner, motivational level. The findings reveal differential star

effects, which are partly contradictory but have important consequences for organizations.

As such, it is not easy to assess the value of stars for collaborative organizations conclu-

sively. My findings suggest that stars do bring exceptional value to organizations. At the

same time, they also indicate trade-offs between utilizing the stars’ outstanding individual

talent to maximize short-term success, and hedging their influence to ensure long-term

development. Since the organizational impact of stars is ambiguous and depends on their

personal motives, organizations should monitor stars’ interactions with their colleagues

and competitors. Stars need autonomy to put their unique skills to good use, but they

also need oversight to ensure that they do not overshadow non-star colleagues or prioritize

their personal agenda. Illuminating such micro-level contingencies, this thesis hopefully

helps organizations to get the most out of their stars.

empirical essays on the role of stars in collaborative organizations · 2019-06-13 · empirical...

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