The Influence of Formal and Informal Controls on Employee

The Influence of Formal and Informal Controls on Employee

Performance: Three Essays

Ruidi Shang

Department of Accounting

The University of Melbourne

ORCID identifier: 0000-0002-6671-0764

Doctor of Philosophy

July 2017

Submitted in total fulfilment of the requirements of the degree of Doctor of

Philosophy

Produced on archival quality paper

ii

DECLARATION

This is to certify that:

(1) This thesis compromises only my original work;

(2) Due acknowledgment has been made to in the text to all other material used;

(3) This thesis is less than 100,000 words in length, exclusive of tables, figures,

bibliographic references and appendices.

Ruidi Shang

03 July 2017

iii

ACKNOWLEDGEMENTS

The past four years have been a wonderful journey for me. I could explore the area that I am

interested in, build up my ability and skills in doing research, and share ideas with wonderful

people from all over the world. None of these could happen without the support from my

supervisors, colleagues, friends, and family. I am glad to express my deep and sincere gratitude

to them.

First and most, I would like to thank my supervisor, Prof. Maggie Abernethy, for her enormous

help, great guidance, and critical evaluation of my work throughout the last four years. When I

joined the Ph.D. program in 2013, I was inexperienced, depressed, and not sure about what I

should do in the future. Maggie found me and kindly offered to be my supervisor. In the past

four years, she helped me find myself and grow up into a mature, responsible person and an

enthusiastic accounting researcher. I cannot remember how many times she read my thesis and

how many comments she has given me (all of which are fast and constructive)! Her kind and

sharp feedback not only helped me develop this thesis but also helped me find my interests in

management accounting research. I really appreciate the effort that she put into my thesis and

the Ph.D. program, and I really enjoy working with her. Maggie is the best role model that I

can even imagine. She works so efficiently and effectively, which changed my view about

lifestyle and helped me become an efficient worker. As an academic, she cares for people in

both academia and practice. Her attitude and vision amazed me and changed the way that I

think of my responsibility in the society. She has demonstrated what is an academic, a mentor,

and a real woman. I have learned a lot from her, and I will keep learning.

I would also like to thank Dr. Chung-Yu Hung, who has been my co-supervisor since 2015.

Chung-Yu gave me wonderful comments and suggestions in the last two years. When I was

frustrated about the framework of my thesis, Chung-Yu gave me enormous help so that I can

eventually develop the three papers in this thesis. Chung-Yu had numerous chats with me and

I really appreciate her effort, patience, and advice. I am thankful for having her as my co-

supervisor. I will remember all the fun we had together and look forward to having more in the

future!

I was fortunate to be part of the Department of Accounting at the University of Melbourne. It

is a wonderful department and I received tremendous support from its faculty members and

Ph.D. students. I would like to give my special thanks to Prof. Greg Clinch, who recruited me

into the Ph.D. program and opened the gate of a wonderful world for me. I give my sincere

thanks to Prof. Naomi Soderstrom and Prof. Anne Lillis, who gave me constant support,

extremely useful feedback, and wise advice in the last four years and in the job market. I thank

Prof. Floral Kuang and Bo Qin for their useful feedback and kind encouragement. I also

appreciate that they shared their life experiences in the Netherlands with me, which prepared

me for the next journey! I thank Dr. Gladys Lee for all the thoughts and experiences she shared

with me, and all the good time we spent together. I also thank Prof. Michael Davern, Prof.

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Jennifer Grafton, and Dr. Stephan Schantl for their support and feedback. I also thank the Ph.D.

fellows in the department. They provide me with constant help and make the last four years

more tasteful. I thank the Department for funding me and supporting me in attending various

conferences.

I am highly indebted to the faculty members in Harvard Business School (HBS) for their

support and feedback. They made my visit in HBS enjoyable, fruitful, and memorable. I

especially thank Prof. Dennis Campbell, who not only helped me improve my paper and

prepared me for the job market but also shared many inspiring ideas (and the best coffee in

Boston) with me. I truly appreciate his effort and the wonderful time we spent together. I would

like to thank Wei Cai (now a Ph.D. student at HBS). Without her, I would not be able to

overcome the pressure on the job market. We spent so much wonderful time working and eating

together. I also thank Carolyn, Jee Eun, Jihwon, Paul, and the other PhD fellows for their help

and feedback. I thank Ms. Ellen Willemin for helping arrange my visit.

I was fortunate enough to receive tremendous help from academics around the world. I would

like to thank the participants in the 2016 EAA Doctoral Symposium and the 2017 GMARS

conference, as well as the faculty members at Tilburg University, the University of Amsterdam,

and the University of New South Wales, for their kind and constructive comments on my papers.

I give my special thanks to Prof. Shannon Anderson, for her comments on my paper and her

wonderful classes. My sincere thanks also go to Prof. Michael Williamson, who has been

helping me since 2014. Michael gave me wonderful life and career suggestions, as well as sharp

and detailed feedback on my job market paper. I give great thanks to Prof. Stephan Hollander,

for his critical feedback, the opportunities he kindly offered me, and all the generous help I

received from him. I thank Prof. Eddy Cardinaels for his sharp and thorough comments. I also

thank Prof. Laurance ven Lent, Shane Dikolli, Eva Labro, Karen Sedatole, Jeroen Suijs, Jan

Bowens, Victor Maas, Henri Dekker, Frank Moers, and Gavin Cassar, for their help and

feedback. I also thank my colleagues and friends Lu Yang, Nan Jiang, Wenjiao Cao, Yusiyu

Wang, and Ties de Kok for their support and all the fun we had together.

Finally, I would like to thank my parents for their life-long support. I thank my father for his

unconditional love and support. My deepest thanks go to my mother, an incredible woman who

came from a small village but used all her effort and recourses to help her daughter explore the

big world. Without her vision, support, and hard work in the last 26 years, I would not be able

to have so many wonderful experiences. I hope this thesis makes her proud.

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TABLE OF CONTENTS

Chapter 1 Thesis Outline…………………………………………………………………… 1

1.1 Overview……………………………………………………………………... 1

1.2 Outline of the Three Essays........…………………………………………...... 3

1.3 Conclusion...…………………………………………………………………. 6

Chapter 2 Performance Reporting Transparency, Group Identity, and Employee

Performance………………………………………………………………….... 10

2.1 Introduction...…………………………………………………………………. 11

2.2 Literature and Hypothesis Development…………………………………….... 16

2.3 Research Site…………………………………………………………….…..... 22

2.4 Method…………………………………….………………………………...... 27

2.5 Results…………………………………..…………………………………….. 34

2.6 Concluding Remarks………………………………………………………….. 57

Chapter 3 Work Norms as a Control Mechanism: Implications for Employee

Performance…………………………………………………………………...… 67

3.1 Introduction...…………………………………………………………………. 68

3.2 Literature and Hypothesis Development…………………………………….... 72

3.3 Research Site…………………………………………………………….…..... 77

3.4 Method…………………………………….………………………………...... 80

3.5 Results…………………………………..…………………………………….. 89

3.6 Concluding Remarks………………………………………………………… 102

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Chapter 4 Internal Reporting, Personal Connections, and Employee Performance… 110

4.1 Introduction...……………………………………………………………….. 111

4.2 Literature and Hypothesis Development…………………………………….. 114

4.3 Research Site…………………………………………………………….…... 121

4.4 Method…………………………………….……………………………….... 127

4.5 Results…………………………………..…………………………………… 137

4.6 Concluding Remarks………………………………………………………… 149

Chapter 5 Conclusion…………………………………………………………………….. 156

5.1 Summary……………………………………………………………………. 156

5.2 Contributions……………….…………………………………..…………… 156

5.3 Limitations and Future Research…………………………………….……… 157

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LIST OF FIGURES


Performance

Figure 1 Performance Reporting at the Research Site ...……...…………………. 26

Figure 2 Employee Performance in the Group that Changed from Private Reporting

to Public Reporting at the End of 2013……………………………….... 50


Performance

Figure 1 Motivation and Research Question……………………………………… 76

Figure 2 Theoretical Model……………….…………………………………….... 77

Figure 3 Timeline and Groups in the Research Site.………………….……........... 81

Figure 4 Empirical Model……………….………………………………...…….... 89

Figure 5 Promotion of Work Norms and Employee Performance …...………….. 92

Figure 6 Removal of Work Norms and Employee Performance………………… 101

Chapter 4 Internal Reporting, Personal Connections, and Employee Performance

Figure 1 Theoretical and Empirical Framework…………………………………….. 118

Figure 2 Performance Reporting at the Research Site....……...…….………..…. 125

Figure 3 Personal Connections at the Research Site..……………………….…... 126

Figure 4 Theoretical and Empirical Framework………….…………………….... 135

Figure 5 Timeline and Groups………………………..………………….……… 136

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LIST OF TABLES


Performance

Table 1 Sample Selection ...………………………………………………………. 29

Table 2 Survey Instruments ...……………………………………………………. 36

Table 3 Descriptive Statistics ...……………………………………………..……. 39

Table 4 Pearson Correlations ...……………………………………………..……. 41

Table 5 Performance Reporting Transparency, Group Identity, and Employee

Performance …………..…………………………………….…………….. 44

Table 6 Group Identity and Employee Performance under Public Reporting..….. 47

Table 7 Group Identity and Employee Performance in a Department with Public

Reporting………...…..…………………………………….…………….. 54

Table 8 Determinants of Group Identity…………………………………..…..…. 56


Performance

Table 1 Sample Selection and Structure ...………………………………………. 84

Table 2 Descriptive Statistics of Employee Performance...………………….……. 91

Table 3 Pearson Correlations ...……………………………………………..……. 94

Table 4 Managers’ Promotion of Work Norms and Employee Performance…..... 96

Table 5 Managers’ Promotion of Work Norms and Employee Performance:

Estimations by Demographic Groups ………………...………………….. 98

Table 6 Removal of Work Norms and Employee Performance ………………… 100

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Chapter 4 Internal Reporting, Personal Connections, and Employee Performance

Table 1 Sample Selection ...………………………………………………..……. 128

Table 2 Descriptive Statistics…………………………...………………………. 138

Table 3 Pearson Correlations ...…………………………………………………. 139

Table 4 Cross-Sectional Analysis……………………………………………….. 141

Table 5 Change Analysis………………………………………………………... 143

Table 6 High and Low Performers………………………………………………. 146

Table 7 Types of Connection…………………………………………………..... 148

CHAPTER 1: THESIS OUTLINE

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CHAPTER I

THESIS OUTLINE

1.1 Overview

This thesis includes three essays that examine how management control systems (MCSs) affect

the behavior of lower-level employees. MCSs play an important role in directing the task

performance of lower-level employees. Managers can align employees’ interests with the

organization’s goals and strategic priorities, and motivate employee actions that contribute to

the desired organizational outcomes through the design of MCSs. Management controls

include both formal and informal controls (Gibbson and Kaplan 2015; Langfield-Smith 1997).

Formal controls—such as financial incentives, performance reporting, and employee

selection—are commonly used by organizations to motivate employee performance. However,

the effectiveness of these formal controls is conditional upon the informal controls, which work

through the cultures and norms that develop within organizations, or by encouraging employees’

identification with their organizations or workgroups. Little research has been conducted on

the role of informal controls, or whether they are effective in motivating employee performance.

The three essays in this thesis add to the existing literature by documenting how formal and

informal controls jointly affect employee performance.

I draw on psychology, management, and economic literature to inform my empirical models.

The development of social identity theory (Akerlof and Kranton 2000, 2003, 2010; Ashforth

and Mael 1989; Hogg 2006; Hogg and Terry 2000; Turner 1991), social comparison theory

(Festinger 1954; Suls and Wheeler 2000), and theories on social and work norms (Cialdini,

Kallgren, and Reno 1990, 1991; Cialdini and Goldstein 2004; Cialdini and Trost 1998) provide


2

theoretical foundations for the three studies relating to formal and informal controls. Drawing

on these theories, I examine how employee performance is affected by internal performance

reporting and group identity (essay 1), managers’ promotion of work norms (essay 2), and the

personal connections that employees have in the workplace (essay 3).

I conducted the three studies in a state-owned enterprise (SOE) in China. The SOE provides a

rich dataset of employee performance throughout multiple periods. A range of different formal

controls—including performance measurement and financial incentives, performance

reporting, and employee selection—are adopted in this organization. Additionally, employees’

group identity, work norms, and personal connections are salient in this setting, which allows

the informal norms and beliefs of employees to function as part of the organization’s control

system. These features make this organization an ideal setting for the studies in this thesis.

Specifically, I conduct the first and the third studies in a department with 146 employees. This

department has variation in performance reporting transparency. It allows me to examine how

employee performance is related employees’ group identity and personal connections, and

whether these relations are conditional upon the performance reporting choices made by

managers. I conduct the second study in a different department with 169 employees. Two out

of the five group leaders in this department have taken actions to promote work norms within

their groups. This allows me to examine whether managers’ promotion of work norms is

effective in motivating employee performance in a setting where weak financial incentives are

adopted.


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1.2 Outline of the Three Essays

Essay 1: Performance Reporting Transparency, Group Identity and Employee

Performance

The first essay examines the relation between group identity and employee performance in

groups with private reporting (i.e., employees receive information on their own performance

only) and groups with public reporting (i.e., employees receive information on their own and

their peers’ performance). Using laboratory settings, previous studies find that compared with

private reporting, public reporting is more effective in motivating employee performance

(Hannan et al. 2013; Tafkov 2012). Public reporting makes employees concerned about their

self-image and motivates employees to demonstrate higher performance to “look good” in front

of their peers (Luft, 2016). However, in practice, how employees behave under private and

public reporting may depend on their group identity. “Group identity” is the extent to which

employees identify with their workgroups. Economics, management, and psychology theories

suggest that group identity plays an important role in directing individual behaviors (Akerlof

and Kranton 2000, 2003, 2010; Ashforth and Mael 1989; Hogg and Terry 2000; Terry, Hogg,

and White 1999; Turner 1991). In this study, I examine how group identity is related to

employee performance, and whether the relation between the two variables varies across

workgroups with private and public reporting.

I examine the research question in a department with 146 employees and five workgroups,

three of which adopt private reporting and the other two adopt public reporting. The results

indicate that the relation between group identity and employee performance is conditional upon

managers’ choice regarding performance reporting transparency. Under private reporting,

group identity motivates employees to improve their performance; while under public reporting,

group identity motivates employees with high (low) ability to suppress (improve) their


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performance to look more similar to their peers. These findings add to the literature on internal

reporting (Azmat and Iriberri 2010; Frederickson 1992; Hannan, Krishnan, and Newman 2008;

Hannan McPhee, Newman, and Tafkov 2013; Maas and Van Rinsum 2013; Tafkov 2012), as

well as the growing literature on employees’ group identity (Abernethy, Bouwens, and Kroos

2017; Boivie, Lange, McDonald, and Westphal 2011; Towry 2003). This study also has

practical implications, as the findings suggest that when making decisions about performance

reporting transparency, managers need to consider employees’ group identity and ability, as

well as the performance distribution in the workplace.

Essay 2: Work Norms as a Control Mechanism—Implications for Employee Performance

The second essay examines one mechanism that can be used as part of an organization’s

management control system: manager’s promotion of work norms. In laboratory settings,

psychology studies find that focusing individuals’ attention on a particular norm leads to

individuals’ adoption of the norm (Cialdini et al. 1990, 1991). In organizations, it is an

empirical question whether managers can motivate employee performance through deliberately

promoting the work norms that contribute to the desired organization outcomes. On the one

hand, the formal control mechanisms used in organizations, such as financial incentives, may

overrule the effect of the work norms (Ariely, Loewenstein, and Prelec 2006; Gneezy and

Rustichini 2000; Taylor and Bloomfield 2010). On the other hand, work norms may

complement financial incentives by guiding and motivating employees to demonstrate high

performance (Akerlof and Kranton 2000, 2003; Ashforth and Meal 1989; Hogg and Terry

2000). This study examines this question in a multi-task setting, where the control problem is

to motivate desirable employee actions that can be precisely measured and desirable employee

actions that cannot be precisely measured.


5

I conducted this study in a department with 169 employees and five workgroups, where the

leaders of two of the groups take procedures to promote work norms within their groups. The

promoted work norms require employees to engage in desirable actions that can be precisely

measured as well as desirable actions that cannot be precisely measured. The procedures that

the two group leaders take include directly communicating the work norms to employees, as

well as using feedback and recognition to focus employees’ attention on the work norms. The

performance measurement and reward system used by the SOE allowed me to not only measure

employee actions that can be precisely measured, but also construct a proxy for employee

actions that cannot be precisely measured. The results indicate that the group leaders’

promotion of work norms is related to an increase in both types of action. After removing the

work norms, employee performance on tasks that can be precisely measured declined, while

employee performance on tasks that cannot be precisely measured did not change significantly.

These findings contribute to the literature on management controls and employee performance.

The existing literature suggests that the use of financial incentives is subject to high monetary

costs and potential unintended consequences (e.g., employees will pay less attention to tasks

that cannot be measured precisely). This study finds that managers’ promotion of work norms

can complement financial incentives by motivating the desirable actions of employees,

especially for tasks that cannot be precisely measured. The findings also help managers to

better understand how their practices affect the way that employees perform their tasks.

Essay 3: Internal Reporting, Personal Connections and Employee Performance

The third essay examines how personal connections affect the relation between performance

reporting transparency and employee performance. Drawing on social comparison theory

(Festinger 1954; Suls and Wheeler 2000), existing studies experimentally find that reporting


6

employee performance publicly in the workplace increases employees’ concern for their self-

image, and motivates employees to demonstrate high performance to “look good” in front of

their peers (Hannan et al. 2013; Tafkov 2012). However, in real-world organizations, different

types of employees may have different image concerns and react differently toward public

reporting. This study examines whether the performance impact of public reporting varies

across employees with and without personal connections in the workplace.

This study focuses on two types of personal connection: the referrer–referral connection and

the family connection. Both types of connections are common in organizations, and can be

important to managers’ decision-making regarding employee selection. Based on social

psychology literature, both types of connection are likely to increase employees’ image concern

(Cupach and Metts 1994; Goffman 1979; Tedeschi 2013). The employee selection channels

and criteria of the SOE allow me to classify employees into two groups: those with and those

without personal connections in the SOE. By comparing the two types of employees, I find that

public reporting has a significant and positive performance effect on those with personal

connections. However, it has no significant effect on the performance of employees who have

no personal connections. These findings contribute to the literature on internal performance

reporting (Hannan et al. 2013; Luft 2016; Maas and Rinsum 2013; Tafkov 2013) and employee

selection (Abernethy, Dekker, and Schultz 2015; Campbell 2012), and suggest that managers

need to consider internal performance reporting and employee selection as integrated control

choices.

1.3 Conclusion

The three essays in this thesis examine how formal and informal controls jointly affect the task

performance of lower-level employees. Drawing on behavioral theories in the social


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psychology, management, and economic literature, and using archival and survey data from a

field site, I find that formal and informal controls function as an integrated system. First, in

settings where group identity is salient, the relation between group identity and employee

performance is conditional on managers’ decisions regarding performance reporting

transparency. Second, in multi-tasking settings where employee performance in some tasks

cannot be precisely measured, managers can combine the use of financial incentives with the

promotion of work norms. Third, the relation between performance reporting transparency and

employee performance is conditional upon employees’ personal connections, which can be

controlled by employee selection channel and criteria. These findings not only add to the

management accounting literature, but also have important implications for the design and

implementation of management controls in organizations.

The remainder of this thesis is structured as follows. Each chapter describes one of the three

essays in detail. The structure of each chapter allows the three essays to be read separately and

in a different order than that presented here. Chapter 2 presents the study that examines the

relation between group identity and employee performance under private and public reporting.

Chapter 3 presents the study on managers’ promotion of work norms. Chapter 4 presents the

study on how personal connections in the workplace affect the performance impact of public

reporting. Chapter 5 concludes the thesis by summarizing the findings, contributions, and

limitations.


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CHAPTER 2: PERFORMANCE REPORTING TRANSPARENCY, GROUP IDENTITY AND

EMPLOYEE PERFORMANCE

CHAPTER II

PERFORMANCE REPORTING TRANSPARENCY, GROUP IDENTITY

AND EMPLOYEE PERFORMANCE

ABSTRACT

Experimental evidence indicates that compared with private reporting (i.e., employees receive

information on their own performance only), public reporting (i.e., employees receive

information on their own and their peers’ performance) is more effective in motivating

employee performance, as public reporting increases employees’ concern for their self-image.

However, in practice, employees’ image concern may depend on their group identity—the

extent to which employees identify with their workgroups. This study investigates the relation

between group identity and employee performance under private and public reporting. Using

archival and survey data from a Chinese organization, I find that in work groups with private

reporting, group identity is positively related to employee performance. However, in work

groups with public reporting, group identity is negatively related to employee performance. I

explore this further and find that, under public reporting, group identity is negatively (positively)

related to the performance of employees with high (low) ability. This study extends prior

research on the influence of performance reporting transparency on employee performance. It

also highlights the importance for managers to consider the group identity and ability of their

subordinates when making internal reporting choices.



11

2.1 Introduction

Social comparison theories in psychology suggest that individuals tend to compare themselves

with others. Doing so allows individuals to evaluate their ability, and adjust their behaviors to

maintain a positive self-image in their social or work group (Festinger 1954; Suls and Wheeler

2000). In organizations, social comparisons make internal performance reporting an important

control mechanism. The way that employee performance is reported in organizations affects

the information that employees receive, the benchmark that they choose to evaluate themselves,

and their subsequent performance. Experimental evidence indicates that compared with private

reporting (i.e., employees receive information on their own performance only), public reporting

(i.e., employees receive information on their own and their peers’ performance) is more

effective in motivating employee performance, as public reporting increases employees’

concern for their self-image (Hannan, McPhee, Newman, and Tafkov 2013; Maas and Rinsum,

2013; Tafkov, 2012). However, in practice, employees’ concern for their self-image may

depend on the extent to which employees perceive themselves as a part of their work group, or

in other words, employees’ group identity.

“Group identity” refers to the extent to which employees identify with their work group

(Akerlof and Kranton 2000, 2003, 2010; Ashforth and Mael 1989). Employees with high group

identity have strong motivation to adjust their performance to enhance their self-image as

members of their group (Akerlof and Kranton 2000, 2003, 2010; Ashforth and Mael 1989;

Hogg and Terry 2000; Terry, Hogg, and White 1999; Turner 1991; Van Knippenberg 2000; Yun,

Takeuchi, and Liu 2007). Performance reporting transparency (private vs. public) affects the

information environment within work groups and the performance benchmark that employees

can choose to evaluate their performance. Therefore, managers’ choice of reporting

transparency is likely to affect the relation between group identity and employee performance.



12

This study investigates the relation between group identity and employee performance in

groups with private reporting and groups with public reporting. Examining this question

extends prior research on the influence of performance reporting transparency on employee

performance. This study also highlights the importance for managers to consider the group

identity of their subordinates when making internal reporting choices.

Under private reporting, managers provide employees with information on their own

performance, but not the information on their peers’ performance. Employees do not receive

information on the descriptive norms (i.e., the performance of other employees) of their group.

Therefore, under private reporting, the performance requirements set by the organization tend

to be a more salient performance benchmark than the descriptive norms. Based on the existing

theories, group identity may motivate employees to increase their compliance with the

performance requirements, to reinforce their self-image as organizational participants and

members of their workgroups (Akerlof and Kranton 2000, 2003; Ashforth and Mael 1989;

Hoagg and Terry 2000). Increasing compliance with the performance requirements set by the

organization allows employees to demonstrate higher performance. Therefore, I expect that

group identity is positively related to employee performance in workgroups with private

reporting,

Under public reporting, employees not only can observe the descriptive norms of their groups,

but also are aware that their own performance can be observed by others in their group. It is

unclear how group identity is related to employee performance under public reporting. On the

one hand, employees with high group identity may still be motivated to demonstrate high

performance, as what they would do under private reporting. On the other hand, employees

with high group identity may be motivated to adjust their performance to look more similar to



13

others in their group (or in other words, conforming to the group descriptive norms).

Conforming to the group descriptive norms can reinforce employees’ self-image as group

members and prevent them from receiving social punishment from other group members (e.g.,

contempt for low performers, envy for high performers, or even social isolation) (Akerlof and

Kranton 2000, 2010; Charness, Masclet, and Villeval 2013; Smith 2000; Vecchio 2000).

Employees with high group identity may be motivated to increase or decrease their

performance to conform to the group descriptive norms. A potential downside of public

reporting is that some employees may decrease their performance to conform to look more

similar to others in their group. If this is the case, the relation between group identity and

employee performance under public reporting will not be as positive as under private reporting.

Therefore, I hypothesize that there is a positive relation between group identity and employee

performance; and this relation is more positive under private reporting than under public

reporting.

I examine the hypothesis using archival and survey data from a large department within a

Chinese state-owned enterprise (SOE). Employees in this department are responsible for

equipment inspection, operation and maintenance. The department head divides employees

into five workgroups to improve the efficiency of the management. Three features of this

department makes it an ideal setting for this study. First, three of the five workgroups in this

department adopt private reporting, and the other two adopt public reporting. In the three

groups with private reporting, the group leaders provide each group member with a note with

his or her own name and performance on it at the end of every month. In the two groups with

public reporting, the group leaders print the names and performance of all group members in a

table, and distribute this performance table within their groups at the end of every month. The

variation in the performance reporting practices provides an opportunity to examine the role of



14

performance reporting transparency with the contextual factors controlled. Second, employees’

group identity is salient in this setting, as employees usually work in the same workgroups for

years and engage various activities with the other members of their workgroups. This feature

provides an opportunity to examine the relation between group identity and employee

performance. Third, the operating environment of this setting is stable. Because of several

features of the SOE (explained in detail in the research site section), most employees work in

the organization and the same workgroups for many years. It allows the examination of

employee performance in multiple periods, which helps mitigate potential biases caused by

unobservable factors in certain time period(s).

I measure employee performance using the archival data of the monthly performance of

employees, and measure employees’ group identity using the survey instruments designed and

validated by prior research (Bartel 2001; Bergami and Bagozzi 2000; Boivie et al. 2011; Mael

and Ashforth 1992; Dukerich et al. 2002; Johnson et al. 2006; Shamir and Kark 2004). The

results indicate that group identity is positively related to employee performance in the groups

with private reporting. Surprisingly, in the groups with public reporting, group identity is

negatively related to employee performance. The unexpected findings for public reporting may

be the result of employees’ conformity. Under public reporting, it is possible that those with

high ability are motivated to suppress their performance and those with low ability are

motivated to improve their performance to conform to the group descriptive norms. To better

interpret the findings of public reporting, I divide employees under public reporting into high

and low ability. I find that, under public reporting, group identity is negatively (positively)

related to the performance of employees with high (low) ability. Additional analyses indicate

that the results are not driven by unobservable group feature(s), and employees’ group identity

is not significantly affected by performance reporting transparency. Overall, the results



15

demonstrate that the relation between group identity and employee performance is conditional

on the reporting choices made by the group leaders. Under private reporting, group identity

motivates employees to improve their performance; while under public reporting, group

identity motivates employees with high (low) ability to suppress (improve) their performance

to conform to the descriptive norms (i.e., the performance of others) of their group.

These findings contribute to the literature on internal performance reporting. Using

experimental settings, previous studies demonstrate that public reporting leads to higher

employee performance than private reporting does (Hannan et al. 2013; Tafkov 2012). This

study finds that performance reporting transparency affects employees’ choice of performance

benchmark and their task performance, and suggests that the performance effect of public

reporting is not always positive. Specifically, under private reporting, the salient performance

benchmark is the performance requirements set by the organization. Employees with high

group identity tend to exhibit high compliance toward the performance requirements, which

leads to high task performance. In comparison, the salient performance benchmark under

public reporting is the group descriptive norms. Employees with high group identity exhibit

high conformity toward the group descriptive norms, which means those with high (low) ability

tend to suppress (improve) their task performance.

This study also contributes to the growing literature on employees’ group identity. In recent

years, several studies examine the role of group identity in the accounting literature. Previous

studies find that group identity is negatively related to accounting manipulation (Abernethy et

al. 2017) and agency costs (Boivie et al. 2011). Additionally, experimental evidence indicates

that group identity increases the effectiveness of horizontal monitoring (i.e., employees control

the actions of each other), but decreases the effectiveness of vertical monitoring (i.e.,



16

employees report observations of their peers’ actions to managers) (Towry 2003). There is little

evidence on how group identity is related to employee performance, and how this relation is

affected by management control mechanisms such as internal performance reporting. This

study adds to the literature by documenting that the relation between group identity and

employee performance is conditional on the managers’ choice over performance reporting

transparency. When performance reporting transparency is low, group identity is positively

related to employee performance; when it is high, group identity is negatively (positively)

related to the performance of employees with high (low) ability.

This study also has practical implications. The findings suggest that when making decisions

about performance reporting, managers need to consider employees’ group identity and ability,

as well as descriptive norms (or performance distribution) in the workplace. If most employees

in a group demonstrate satisfactory performance, public reporting is likely to lead those with

high group identity and low ability to improve their performance. However, if most employees

fail to demonstrate satisfactory performance, public reporting may lead those with high group

identity and high ability to decrease their performance. Further, management theories posit that

group identity is determined by the in-group and out-group structure, the similarity between

employees and others in their group, as well as group outcome and prestige (Ashforth and Mael

1989; Hogg and Terry 2000). Therefore, managers may need to consider and design internal

performance reporting, employee selection, and group design as an integrated control system.

The remainder of this paper is organized as follows. The next section explains the theoretical

constructs and develops the hypotheses. I then describe the research site and the methodology

of the study, before presenting the results. The final section provides the concluding comments.



17

2.2 Literature and Hypothesis Development

Performance Reporting Transparency

Social comparison theories in psychology suggest that individuals have an inherent desire to

compare themselves with others in order to evaluate themselves (Festinger 1954; Suls and

Wheeler 2000). The social comparison process motivates individuals to adjust their behavior

to maintain a positive self-image (i.e., how individuals believe others think of them) in their

social or work groups. Drawing on the social comparison theories, prior studies demonstrate

that internal performance reporting is an effective control mechanism in motivating employee

performance. Specifically, Frederickson (1992) experimentally find that providing employees

with information on their own performance relative to the performance of their peers (i.e.,

relative performance information, or RPI) motivates employees to exhibit high effort. This

finding is consistent with the findings obtained by Azmat and Iriberri (2010) using a natural

experiment in a high school. Further, Hannan et al. (2008) find that the performance effect of

RPI is conditional on the incentive contract (i.e., tournament or individual compensation) as

well as the precision of the RPI information. Later studies also find that providing employees

with RPI may motivate undesirable behaviors, such as sabotaging the work of peers (Charness,

Masclet, and Villeval 2013) or misreporting the budget (Brown, Fisher, Sooy, and Sprinkle

2014).

Previous studies have not only examined the content of internal performance reporting, but

also examined the transparency of internal performance reporting. Using laboratory settings,

recent studies have examined how making performance information public in the workplace

affects employee behaviors. Specifically, Tafkov (2012) find that public RPI (i.e., the

performance ranking of each employee in a group is provided to all group members) is more

effective in motivating employee performance than private RPI (i.e., each employee knows his



18

or her own rank only). Hannan et al. (2013) also find that compared to private RPI, public PRI

is more effective in motivating employees to exert high effort in a multi-task setting.

Additionally, Maas and Van Rinsum (2013) find that publicly disclosing managers’ self-

reported performance in the workplace motivates them to report their performance honestly, in

order to maintain a positive self-image in front of their peers. These studies suggest that public

reporting increases employees’ concern for their self-image. Demonstrating a positive (e.g.,

competent or honest) self-image enhances employees’ self-esteem and improves their feelings

about self (Beach and Tesser 2000). It also prevents employees from being looked down on by

their peers, and increases employees’ status in the workplace (Smith 2000). To create and/or

maintain a positive self-image, employees are motivated to adjust their behavior, such as

demonstrating higher performance (Hannan et al. 2013; Tafkov 2012) or reporting their

performance honestly (Maas and Van Rinsum 2013).

Group Identity

However, in practice, employees’ concern for their self-image may depend on the extent to

which they perceive themselves as a part of their work group, or in other words, their group

identity. The concept of “group identity” originates from the social identity theory developed

by Tajfel (1974, 1982) and Turner (1975, 1991). They define group identity as the extent to

which individuals identify with their social or work groups. That is, group identity refers to

individuals’ self-concept which derives from their group membership (Tajfel 1974, 1982;

Turner 1975, 1991). In management literature, Ashforth and Mael (1989) discuss employees’

group identity in organizations and how it differs from other related constructs. They suggest

that one’s group identity refers to the self in terms of one’s group (“I am”). It is a perceptual

cognitive construct that describes the extent to which an employee defines herself in terms of

the group she works for. Conceptually, group identity differs from any specific behaviors (e.g.,



19

effort), affective states (e.g., loyalty), or internalization of the goals or values of one’s group

(e.g., commitment). In practice, group identity may affect or be affected by these factors. It

may also be affected by group structure, the similarity and shared goal(s) between group

members, and other individual or group features (Ashforth and Mael 1989; Bergami and

Bagozzi 2000).

The psychology, management, and economics literatures on group identity suggest that group

identity plays an important role in directing individual behaviors. The psychology theories posit

that individuals with higher group identity are more likely to engage in actions that contribute

to group outcomes and the interests of other group members (Haslam and Ellemers 2005; Tajfel

1982; Turner 1975). The management theories posit that group identity motivates employees

to engage in actions that are aligned with the values and norms of their group (Ashforth and

Mael 1989; Hogg and Terry 2000). These propositions are supported by empirical evidence.

For example, psychology studies find that group identity motivates employees to exhibit high

job performance (Van Knippenberg 2000; Yun et al. 2007) and engage in organization

citizenship behaviors and cooperative behaviors (Bergami and Bagozzi 2000; Haslam and

Ellemers 2005; Van Dick, Van Knippenberg, Kerschreiter, Hertel, and Wieseke 2008).

Additionally, Terry et al. (1999) find that group identity leads individuals to conform to the

norms of their group. In the management literature, Boivie et al. (2011) find that chief executive

officers (CEOs) with higher organization identity tend to avoid pursuit of personal gains that

harm the value and image of their firms.

Akerlof and Kranton (2000, 2003, 2008, 2010) consider group identity from an economic

perspective. They suggest that employees with high group identity derive utility by adopting

the norms of their groups (Akerlof and Kranton 2000, 2003). Deviating from group norms not



20

only undermines one’s self-image as a group member, but also increases the risk of being

marginalized or isolated by other group members. Employees with high group identity have

strong motivation to adopt the norms of their groups to avoid such negative consequences

(Akerlof and Kranton 2000, 2003). In the accounting literature, Towry (2003) experimentally

finds that group identity increases the effectiveness of horizontal monitoring (i.e., employees

control the actions of each other), but decreases the effectiveness of vertical monitoring (i.e.,

employees report observations of their peers' efforts to managers). Abernethy et al. (2017) find

that managers with incentive-based compensation engage in less opportunistic earnings

manipulation if they identify with their organization. Overall, previous studies indicate that

group identity has an important behavioral effect in organizations.

In real-world organizations, employees may identify with their organizations as well as

subunits (such as divisions, departments, and workgroups) within their organizations.

(Ashforth and Mael 1989; Hogg and Terry 2000). Among units in different organizational

levels, workgroups are most relevant to lower-level employees in their daily work; and

employees’ workgroup identity is usually more salient than their identities derived from units

higher up in the organization (Van Dick et al. 2008). Therefore, this study focuses on employees’

workgroup identity (hereafter, “group” and “workgroup” are interchangeable).

In this study, I investigate the relation between group identity and employee performance under

private and public reporting. Performance reporting transparency (private vs. public) affects

the information environment in the workplace and the performance benchmark that employees

can choose to evaluate their performance. Therefore, the choice of reporting format is likely to

affect the relation between group identity and employee performance. I first consider groups

with private reporting. Under private reporting, managers provide employees with information



21

of their own performance, but not the information of their peers’ performance. That is,

employees do not receive information on the descriptive norms (i.e., the common practice of

other employees) of their group. Therefore, the performance requirements set by the

organization tend to be a more salient performance benchmark than the descriptive norms under

private reporting. Based on the existing theories, group identity may motivate employees to

increase their compliance with the performances requirements in order to reinforce their self-

image as organizational participants and members of their workgroups (Akerlof and Kranton

2000, 2003; Ashforth and Mael 1989; Hogg and Terry 2000). Following the performance

requirements allows employees to demonstrate high performance, as organizations usually

measure employee performance based on the extent to which employees meet the performances

requirements. Therefore, I expect that group identity is positively related to employee

performance under private reporting.

I then consider groups with public reporting. Under public reporting, managers provide

employees with the information on their own and their peers’ performance. That is, employees

can observe the descriptive norms (i.e., common practices of others) of their groups, and are

aware that their own performance can be observed by others in their group. It is unclear how

group identity is related to employee performance under public reporting. On the one hand,

employees with high group identity may still tend to demonstrate high performance, as what

they would do under private reporting. On the other hand, based on management and economic

theories, employees with high group identity may be motivated to conform to the descriptive

norms of their groups. Conforming to the descriptive norms of one’s group increases one’s

similarity with other group members, and reinforces one’s self-image as part of the group

(Akerlof and Kranton 2000, 2003, 2010; Ashforth and Mael 1989; Hogg and Terry 2000).

Further, individuals whose behaviors deviate from the group descriptive norms may receive



22

social punishment from other group members (e.g., contempt for low performers, envy for high

performers, social isolation), because such deviation threatens the self-image of other group

members (Akerlof and Kranton 2000; Ashforth and Mael 1989; Charness et al. 2013; Hogg

and Terry 2000; Smith 2000; Vecchio 2000). Employees with high group identity may have

strong motivation to reinforce their self-image as group members and avoid being punished by

others in their group. Therefore, employees with high group identity may choose the group

descriptive norms as the performance benchmark, and adjust their behaviors to become more

similar to others in their group. In other words, under public reporting, employees with high

group identity may increase or decrease their performance to conform to the group descriptive

norms.

Overall, based on group identity theories, I expect that group identity is positively related to

employee performance. However, under public reporting, group identity may motivate some

employees to decrease their performance to conform to the group descriptive norms. Because

of this potential downside of public reporting, I expect that relation between group identity and

employee is more positive under private reporting than under public reporting.

Hypothesis: There is a positive relation between group identity and employee performance;

and the relation is more positive under private reporting than under public

reporting.

2.3 Research Site

Overview

The research site of this study is a large department within a Chinese SOE. The SOE has been

the principal driver of the local economy, and has its own schools, hospitals, media, and



23

communities. It comprises 34 factories, plants, and institutions. This study focuses on one

department in a power plant in the SOE. Employees in the department are responsible for

equipment inspection, operation, and maintenance. Tasks include inspecting and operating

equipment, identifying and solving hidden issues, and keeping records of the conditions of the

equipment. The purpose of these tasks is to ensure that the equipment functions normally and

safely, prolonging the usable life of the equipment and reducing the risk of operational disasters.

Each employee’s task and responsibility is clearly specified by the SOE.

As the equipment functions 24/7, employees take shifts. The head of the department divides

employees into five workgroups to make the shifts more manageable. Employees in the same

group work on the same shifts. During each shift, employees work individually in different

areas of the workshops. Once allocated into a group, employees usually stay in the same group,

and a change of groups rarely happens. Each group is managed by a group leader and has 18–

25 group members.

Performance Measurement and Reward System

The SOE uses performance measures and financial incentives to motivate employee

performance. Specifically, employees receive performance scores based on the operational

actions they undertake and the operational outcomes they achieve. The operational actions refer

to the procedures and steps that employees take to inspect, operate, and maintain the equipment.

The operational outcomes include a range of parameters on the equipment, such as temperature

and pressure, which reflect the conditions of the equipment. The organization has specified a

series of performance requirements for operational actions and outcomes. It has also specified

rules about how to allocate performance scores to employees based on the extent to which their

operational actions and outcomes meet the performance requirements. Taking a certain set of



24

operational actions that can keep the equipment functioning normally allows employees to earn

a reasonable score. In comparison, inspecting the equipment more carefully, identifying and

solving more hidden issues, and choosing optimal operational actions based on the exact

conditions of the equipment helps employees earn higher scores.

The department manager measures employee performance objectively. In particular, the

department manager assesses employee performance by checking the operational records and

the conditions of the equipment. The operational records are kept by the machines and the

computer system employed by the organization. When taking actions to inspect, operate, and

maintain the equipment, employees must tap their ID cards on the machines in the workshops.

The system then records that the employees have taken certain actions in certain areas of the

workshop. The department manager measures employees’ operational actions by checking the

records kept by the system. Additionally, the department manager measures employees’

operational outcomes by checking the parameters on the equipment during each shift, which

are captured by specialized machines and kept in the system. By the end of every month, each

employee receives performance scores based on the extent to which their operational actions

and outcomes meet the performance requirements specified by the organization.

At the end of every month, employees receive a fixed payment and financial incentives. The

financial incentives are calculated based on the performance scores that employees received

for the month, and account for approximately 0–10% of the overall compensation that they

receive. The performance measurement, score allocation, and compensation structure are pre-

specified by the organization and require little judgment of the manager or group leaders.



25


The SOE requires managers to report employee performance so that employees can access the

feedback on their own performance. However, the SOE does not impose strict rules on how

employee performance should be reported in each department or workgroup. In the department

of this study, two types of performance reporting practices have coexisted for years. In one of

the five workgroups (hereafter, Group 5), the group leader prints the names and performance

of all group members on a table, and distributes this performance table to the group members

at the end of every month. Each employee in the group can see their own performance as well

as the performance of their peers (i.e., public reporting). In the other four groups, the group

leaders prepare the same information but print the information on each employee’s

performance in a separate note and only provide each employee with the note that presents his

or her own performance. Employees can only see their own performance and not the

performance of their peers (i.e., private reporting).1

One of the groups (Group 3) switched from private reporting to public reporting at the end of

2013, because the group leader retired and the SOE appointed a new group leader.2 The old

group leader used to print employee performance on A4 paper, cut the paper into pieces, and

gave each employee the piece that contained his or her performance only. The new group leader

asks employees to pass the paper around the group without cutting it.

To sum up, three of the five groups (Groups 1, 2 and 4) in the department adopt private

1 In groups with private reporting, employees in the same group may exchange performance information privately.

When employees have questions or concerns about their performance, they may also request to see the

performance of their peers, in the office of their group leader. Although employees may obtain some information

on their peers’ performance through these channels, the performance transparency in these groups is still lower

than in the group that publicly discloses every employee’s performance. 2 The new group leader was one of the group members in Group 3. The SOE appointed him based on his seniority

(the length of time that he had worked in the SOE) and his leadership potential (subjectively assessed by the

SOE’s upper-level management).



26

reporting, one (Group 5) adopts public reporting, and one (Group 3) switched from private

reporting to public reporting. Figure 1 visually illustrates the two types of performance

reporting practice in this department, and workgroups with difference reporting practices.

FIGURE 1

Performance Reporting at the Research Site

Private Reporting

Public Reporting

Groups with Different Reporting Practices


2011 2012 2013 2014 2015 Overall

Group 1 private private private private private Private


Group 3 private private private public public Switched


Group 5 public public public public public Public

Each employee receives a note with

his/her performance only. Employees can

see their own names on the note.

This approach was adopted by Group 1,

Group 2, and Group 4.

Employees pass around the performance table

that presents everyone’s name and performance.

This approach was adopted by Group 5. Group

3 switched from private to public reporting at

the end of 2013.



27

Rationale for the Choice of Research Site

This department is a suitable research site for the following reasons. First, the two types of

performance reporting in the department (public vs. private) provide an opportunity to examine

the research question with the contextual factors controlled. Second, once allocated into a group,

employees usually stay in the same group for years. Employees in the same group work during

the same shift and conduct various activities together (have lunch, take buses, and attend

group/department meetings, etc.). Therefore, employees’ group identity is likely to be salient.

As financial incentives are relatively weak in this setting, group identity is likely to play a

significant role in affecting employee performance. Third, the operating environment in the

department is stable. Because of the rules imposed by the government, the SOE cannot fire its

employees for poor performance. The compensation and other benefits of this SOE are better

than that of other organizations in the local area. Therefore, most employees choose to stay in

the organization and the same workgroup until retirement. The incentive of promotion is low,

as the operating environment is very stable and the group leaders and department managers are

usually appointed directly by the upper-level management team. The stable operating

environment provides an opportunity to examine the same employees’ performance over

multiple periods. It helps mitigate the effects of unobservable factors or other incentives (such

as career concern) that may affect employee performance in certain period(s).

2.4 Method

Data and Sample

In order to examine the research questions, I collected archival and survey data from the

research site. First, I extracted the archival performance data from the database of the SOE.

The SOE measures the individual performance of employees on a monthly basis. I obtained

the performance data from January 2011 to July 2015. The original dataset includes 146



28

employees and 5,422 performance observations. Second, I collected the demographic

information of the 146 employees from the personnel files of the SOE, including the employees’

gender, birthday, hometown, education, political affiliation, and recruitment date. Employees’

demographic characteristics are time-invariant. Third, I administered a survey on May 2015 to

measure employees’ perception on their group and the organization, as well as the personal

connections they have in the workplace. I sent the survey to 445 employees in four different

departments of the SOE, including the department of this study. There are 290 employees who

responded the survey (response rate = 65%). In the department of this study, 79 of the 146

employees responded (response rate = 54%).

In the sample selection process, only Group 5 consistently uses public reporting during the

sample period. Therefore, Group 5 is included in the sample to examine the relation between

group identity and performance under public reporting. It means that the groups chosen to

examine this relation under private reporting should be comparable to Group 5, in terms of data

availability and demographic characteristics. Because there was a breakdown in the computer

system of the SOE in early 2015, most observations in Group 1 and 3 are missing. Specifically,

most data for Group 1 (private reporting) is missing, except for that from January to August

2012 and July to October 2014. In Group 3 (switched reporting), the data from August to

December 2013 and the data from June to December 2014 are missing. To minimize any bias

introduced by unobservable factors in particular years or months and make sure data of private

reporting is comparable to the data of public reporting, I exclude these two groups in the

estimation. I include Group 2 and Group 4 in the sample of estimation, as these two groups

consistently use private reporting during the sample period and are not subject to the data

availability issue. The sample selection is described in Table 1.



29

Panel A of Table 1 shows the number of employees and performance observations. The original

dataset on employee performance is from January 2011 to July 2015, and includes all five

workgroups in the department. There are 146 employees and 5,422 performance observations

in total. After excluding the groups with data availability issue, there are 93 employees and

4,253 performance observations left. In the remaining three groups, Group 2 and 4 adopt

private reporting while Group 5 adopt public reporting. After excluding the non-respondents

and one employee whose demographic information is missing in the personnel file, the final

sample includes 51 employees and 2,471 performance observations. Panel B compares

respondents and non-respondents. It shows that respondents are not significantly different with

non-respondents in terms of demographic characteristics. Panel C compares the groups with

private reporting and the group with public reporting. It shows that respondents under private

reporting are not significantly different from those under public reporting in terms of

demographic characteristics, except for the level of education (edu). Specifically, edu is slightly

higher under private reporting than under public reporting (difference = 0.39). This is because

there are five respondents who have master degree in Group 2 (private reporting). In

comparison, Group 4 (private reporting) and Group 5 (public reporting) each has three

respondents with master degree. Given the relatively small size of each group, this small

difference is statistically significant. However, I believe it is unlikely to affect the results

significantly, especially after controlling for employees’ demographic characteristics and the

group fixed effects in the estimation.



30

TABLE 1

Sample Selection

Panel A: Sample Selection Process

Workgroups Employees Observations

Original performance data

(Jan. 2011–July 2015) 1, 2, 3, 4, 5 146 5,422

Workgroups with missing

observations (1, 3) (53) (1,169)

2, 4, 5 93 4,253

Non-respondents (41) (1,736)

Missing demographic information (1) (46)

Final sample 2, 4, 5 51 2,471

Panel B: Respondents and Non-respondents

Non-

respondents Respondents Difference t-statistics

age 42.95 42.92 0.03*** 0.02

tenure 23.78 23.70 0.08*** 0.05

gender 0.32 0.24 0.08*** 0.84

home 0.53 0.57 −0.04*** −0.39

edu 0.92 0.98 −0.06*** −0.42

CCP 0.11 0.18 −0.07*** −0.93

Panel C: Private vs. Public Reporting

Private

(Group 2&4)

Public

(Group 5) Difference t-statistics

group size* 32 19 N/A N/A

age 42.55 43.44 −0.89 −0.52

tenure 23.34 24.19 −0.85 −0.47

gender 0.25 0.21 0.04 0.32

home 0.53 0.63 −0.10 −0.69

edu 1.13 0.74 0.39 2.04

CCP 0.13 0.26 −1.34 −1.25

Panel A illustrates the sample selection process. The department used in the main analyses of this study have five workgroups.

Two (Groups 1 and 3) lost most performance observations because of a computer system breakdown. In the remaining three

groups, one (Group 5) adopts public reporting and the other two (Groups 2 and 4) adopt private reporting. After excluding the

non-respondents and the one employee whose demographic information is missing from the system, there are 51 employees

and 2,471 performance observations in the final sample. Panel B compares respondents and non-respondents in the three

groups in the final sample, and shows that respondents are not significantly different with non-respondents. Panel C compares

the group size and demographic characteristics of groups with private reporting and public reporting, respectively.

*Among the 32 respondents under private reporting, 15 were from Group 2 (which has 29 employees total) and 17 were from

Group 4 (which has 31 employees in total). Under public reporting, all the 19 respondents were from the group with public

reporting, Group 5 (which has 33 employees in total).

Empirical Model

I examine the association between group identity and employee performance in groups with

different levels of performance reporting transparency using the following equation:



31

Performanceit = β1group_identityi + β2publicit + β3(group_identityi×publicit) + β4leveli

+ β5tenureit + β6horizonit + β7genderi + β8homei + β9edui + β10CCPi

+ β11familyi + β12referrali + β13supporti + β14systemi + εit (1)

Specifically, I examine the association between employees’ performance (Performance) and

group identity (group_identity) under private reporting and public reporting (public). The

coefficient on group identity (β1) captures the relation between group identity and employee

performance under private reporting. The results would be consistent with the hypothesis if β1

is significantly positive. The coefficient on the indictor for public reporting (β2) captures the

effect of public reporting on employee performance. Prior studies find that public reporting is

positively related to employee performance (Hannan et al. 2013; Tafkov 2012). The results

would be consistent with the prior findings if β2 is significantly positive. The coefficient on the

interaction between group identity and public reporting (β3) captures the effect of group identity

under public reporting, incremental to the effect under private reporting (β1). As the hypothesis

expects that the relation between group identity and employee performance is less positive

under public reporting, I expect β3 to be negative. To better interpret the findings, I also divide

the sample based on performance reporting transparency and estimate the following model in

the subsamples:

Performanceit = β1group_identityi + β2leveli + β3tenureit + β4horizonit + β5genderi + β6homei

+ β7edui + β8CCPi + β9familyi + β10referrali + β11supporti + β12systemi

+ εit (2)



32

Under both private and public reporting, the results would be consistent with the hypothesis if

the coefficient on group identity (β1) is significantly positive, and β1 is larger under private

reporting than under public reporting. It means that there is a positive association between

employee performance and group identity under private reporting, and the relation is more

positive under private reporting than under public reporting. The details of the variables in the

model are presented as follow.

Variables

Dependent Variable: The dependent variable, Performance, is a measure for employee

performance. As explained in the research site section, the department head evaluates

employees’ actions and outcomes by checking the operational records and equipment

conditions. At the end of every month, the department manager allocates employees with

performance scores by comparing their actions and outcomes with the pre-specified

performance requirements. Performance is the overall performance score each employee

receives at the end of every month. It captures the judgment that employees make when

choosing their operational actions, as well as the effort that employees exert when engaging in

their chosen actions. Based on the rules of the SOE, the financial incentives received by

employees by the end of every month are calculated as (10×Performance). I extract the data

of Performance from the database of the SOE. The data of Performance is on a monthly basis,

and the sample period is from January 2013 to July 2015. Because the highest (lowest)

performance score that an employee can received is 130 (−160), the estimation is censored at

these levels.

Independent Variable: The first independent variable, group_identity, is constructed by nine

survey items designed and validated in previous studies. In particular, Mael and Ashforth (1992)



33

develop a six-item measure for individuals’ group identity in an organizational context.

Bergami and Bagozzi (2000) add another two questions about the overlap between one’s self-

image and the image of one’s group. This measurement has been used and validated extensively

by studies in management and organization behaviors (Bartel 2001; Dukerich et al. 2002;

Johnson et al. 2006; Shamir and Kark 2004). Additionally, Boivie et al. (2011) incorporate one

more question on one’s sense of self derived from one’s group membership. In this study, I

adjust the wording of the questions to match with research site. I administered the survey to

445 employees in four departments of the SOE on May 2015, and 290 employees responded

(response rate = 65%). At the department of this study, 79 of the 146 employees responded

(response rate = 54%). The original survey is in Chinese. The English version of the survey is

presented in Appendix A.

The second independent variable, public, is an indicator for workgroups with public reporting.

In the sample, Group 5 adopts public reporting while Group 2 and 4 adopt private reporting.

Public is one for performance observations under public reporting, or zero for performance

observations under private reporting.

Control Variables: To minimize any bias in estimates as a result of omitted variables, I include

three sets of control variables in the estimations. The first set includes employees’ ability and

demographic characteristics. Specifically, level is a proxy for employee ability. Every three

years, the SOE holds an exam to assess employees’ operational skills, and assigns skill levels

to employees based on their exam results. There are four skill levels: 0 (none), 1 (low), 2

(medium), and 3 (high). To achieve a particular level, employees must reach a certain score in

the exam. In this study, the variable level is the skill level assigned by the SOE at the end of



34

2012.3 As for demographic characteristics, I control for the number of years that employees

have been working in the SOE (tenure), the indicator for female employees (gender), the

indicator for employees who come from the city where the SOE is located (home), employees’

education level (edu), and the indicator for Chinese Communist Party member (CCP).4

The second set of control variables includes employees’ social and family connections in the

workplace: referral and family. These connections may affect employees’ group identity and

their performance (Beaman and Magruder 2012; Burks, Cowgill, Hoffman, and Housman 2015;

Brown, Setren, and Topa 2016; Hensvik and Skans 2016). Both variables are measured using

survey questions. Referral is constructed using two questions: “How did you enter into the

SOE? (1 = through government allocation = 1; 2 = through another channel)” and “Did you

know any existing employee(s) in the SOE before your entry? (1 = Yes; 2 = No)”. Referral is

an indicator for respondents who were not allocated by the government, and who had personal

connections with existing employees prior to their entry. It is a noisy proxy for the referral–

referrer connection.5 Family is an indicator for employees with family members who also work

3 Due to the data availability constraint, employees’ skill level assessed at the end of 2015 is not available.

However, the skill level assessed in 2012 still constitutes a valid proxy for employee ability between January

2011 and July 2015. 4 Tenure is the number of years that employees have worked in the SOE. Previous research finds that tenure may

affect employee performance through work experiences and commitment to organization (Wright and Bonett

2002). Gender is an indicator for female employees. As the tasks in the research site involve operating heavy

machinery and most employees are male, gender may affect employee performance both physically and

psychologically (Gardiner and Tiggemann 1999). Home is 1 for employees who came from the city where the

SOE is located, or 0 for those who came from other regions of China. As the SOE is the principal driver of the

local economy, employees from the local area may be motivated to work hard and contribute to the economy in

their hometown. Further, I control for the level of education that employees received (edu). Employees with

higher education levels may have received more training and have higher ability to perform their tasks. The next

control variable is CCP, which is a dummy variable indicating employees who have joined the Chinese

Communist Party. Previous research indicates that when the goals and beliefs of employees are congruent with

those of their organizations, employees tend to have higher motivation and better performance (Rich et al. 2010).

As the research site is state owned, the political affiliation of employees may affect their motivation and

performance. The data on these variables is from the personnel files of the SOE. Employees’ age (age) is also

available in the personnel files of the SOE. However, as age is highly correlated with tenure and introduces a

multicollinearity problem, I present age in the descriptive statistics but remove it from the regression estimations. 5 I measure this variable indirectly using the two questions because according to the managers of the SOE, it is

too sensitive to ask employees whether they were introduced to the organization by existing employees. Because

of Chinese social norms (Luo 2007), referrals usually give their referrers material gifts to return the favor and

show their thanks. The referrals may be reluctant to directly tell a third party that they entered into the

organization through personal connections to avoid being perceived as unethical and/or incompetent. To



35

in the SOE. It is measured by the following question: “Do you have family members who also

work in the SOE?” The questions used to measure referral and family are presented in

Appendix A.

The third set of control variables includes employees’ perception of the support they received

in their workgroup (support), the shared goals with other members in their group (shared_goal),

and the performance measurement and reward system (PMRS) adopted by the SOE (system).

These perceptions may affect employees’ group identity and performance. These perceptions

are measured by survey questions (Hogg 2006). Following previous research (Eisenberger,

Fasolo, and Davis-LaMastro 1990; Hogg 2006; Rich, Lepine, and Crawford 2010), I include

three questions about employees’ perceived support from their group, and two on the shared

goals between employees and their peers in the survey questionnaire (Eisenberger et al. 1990).

I also include 12 questions about the perceived information and motivational effects of the

PMRS adopted by the SOE (Steelman and Rutkowski 2004; Taylor and Pierce 1999). These

questions are presented in Appendix A. When estimating the empirical models, I also control

for time and group fixed effects. The detailed variable definition is presented in Appendix B.

2.5 Results

Survey Instruments

Table 2 presents the descriptive statistics of the survey instruments. I administered the survey

to 445 employees in four departments of the SOE on May 2015, and 290 employees responded

(response rate = 65%). When answering the questions about group identity, approximately half

of respondents indicated that they agree or highly agree with the statements, suggesting that

increase the response rate and accuracy of the responses, I use the two questions to indirectly capture the

referral–referrer connection of the respondents.



36

they highly identify with their groups. Forty percent of respondents indicate that they somewhat

agree with the statements, suggesting that these employees moderately identify with their

groups. The answers of the remaining 10% of the respondents fall between “neutral” and

“strongly disagree”, indicating lower group identity. As Table 2 shows, the mean of employees’

answers is around 5 and 6 for the instruments of group identity (1 = strongly disagree; 7 =

strongly agree). These instruments load into the same factor (α = 0.94).

Besides group identity, the survey also asks about employees’ perception of the support they

received in their workgroup, and the shared goals with other employees in the same workgroup.

For both variables, the instruments load into the same factor (α = 0.95 and 0.91 for perceived

support and perceived shared goals, respectively). The survey also includes questions about

employees’ identification with the SOE as a whole (i.e., organization identity), as well as their

perception of the PMRS adopted by the SOE. The questions on organization identity are similar

to the questions of group identity, with some words adjusted (α = 0.91). As for employees’

perception of the PMRS, the survey includes six questions on the perceived information effects

and six on the perceived motivational effects of the financial incentives (α = 0.96). Similar to

group identity, most answers for the questions on employees’ perception and organization

identity are between 5 (somewhat agree) and 7 (strongly agree). The mean is between 5 and 6,

and the median is 6 for these questions. The final three questions are about employees’ social

and family connections in the workplace. The answers indicate that about 53% of respondents

were allocated into the SOE by the government, while the rest entered through other channels.

About 38% of respondents had connections with the existing employees before they entered

the SOE. Most employees have family connections in the SOE.



37

TABLE 2

Survey Instruments (N = 290)

Mean Median s.d. Min Max

Factor

loadings

Group identity (α = 0.94)

Reaction to criticism about group 5.77 6 1.32 1 7 0.85

Interest in others’ opinion about group 5.72 6 1.29 1 7 0.79

Term used to refer to group 6.02 6 0.96 1 7 0.78

Group’s success 5.64 6 1.33 1 7 0.85

Reaction to praise about group 5.79 6 1.20 1 7 0.91

Embarrassment 5.73 6 1.23 1 7 0.90

Meaning of group membership 5.89 6 1.03 1 7 0.89

Image overlap 5.73 6 1.26 1 7 0.81

Image overlap (in visual expression) 6.37 7 1.70 1 8 0.63

Goodness of fit statistics: χ2 = 2450.58 (df = 36; p < 0.00)

Perceived support from workgroup (α= 0.95)

Contribution 5.64 6 1.25 1 7 0.92

Extra effort 5.62 6 1.30 1 7 0.93

Wellbeing 5.69 6 1.28 1 7 0.90

Goodness of fit statistics: χ2 = 843.64 (df = 3; p < 0.00)

Shared goals with other group members (α= 0.91)

Agreement 5.76 6 1.11 1 7 0.88

Ambitions and vision 5.57 6 1.22 1 7 0.88

Goodness of fit statistics: Chi-square = 358.93 (df = 1; p < 0.00)

Organization identity (α = 0.91)

Reaction to criticism about

organization 5.38 6 1.52 1 7 0.72

Interest in others’ opinion about

organization 5.52 6 1.32 1 7 0.70

Term used to refer to organization 6.06 6 0.92 1 7 0.74

Organization’s success 5.59 6 1.46 1 7 0.79

Reaction to praise about organization 5.65 6 1.32 1 7 0.89

Embarrassment 5.67 6 1.22 1 7 0.77

Meaning of organization membership 5.84 6 1.10 1 7 0.81

Image overlap 5.52 6 1.32 1 7 0.74

Image overlap (in visual expression) 5.73 7 1.83 1 8 0.55

Goodness of fit statistics: χ2 = 1684.45 (df = 36; p < 0.00) (continued.)



38

TABLE 2 (continued.)

Mean Median s.d. Min Max

Factor

loadings

Perception of the PMRS (α = 0.96)

Bonus and work knowledge 5.53 6 1.41 1 7 0.86

Bonus and superior’s expectation 5.49 6 1.37 1 7 0.84

Bonus and learning 5.59 6 1.26 1 7 0.85

Bonus and intention to improve 5.56 6 1.31 1 7 0.82

Bonus and effort 5.69 6 1.14 1 7 0.86

Bonus and motivation 5.73 6 1.13 1 7 0.85

Penalty and work knowledge 5.33 6 1.40 1 7 0.86

Penalty and superiors’ expectations 5.20 6 1.45 1 7 0.85

Penalty and learning 5.27 6 1.35 1 7 0.85

Penalty and intention to improve 5.21 6 1.43 1 7 0.90

Penalty and effort 5.23 6 1.44 1 7 0.88

Penalty and motivation 5.27 6 1.41 1 7 0.88

Goodness of fit statistics: Chi-square = 4889.15 (df = 66; p < 0.00)

Connections

Recruitment channel 1.47 1 0.50 1 2 N/A

Connections before recruitment 1.38 1 0.49 1 2 N/A

Family in SOE 1.74 1 0.88 1 3 N/A This table presents information on the survey instruments used to capture employees’ workgroup identity, perceived support

from workgroup, perceived shared goals with other group members, organization identity, perception of the PMRS, and social

connections in the workplace. The survey was sent to 445 employees in four departments of the SOE in May 2015; 290

employees responded (response rate = 65%). Cronbach’s alpha, denoted by α, indicates the internal reliability of multi-item

scales. The details of the survey questions are presented in Appendix A. All items on identity and perception are measured on

a seven-point Likert scale (1 = strongly disagree; 7 = strongly agree), unless stated otherwise. Items on social connections are

measured using categorical variables.

Descriptive Statistics

Table 3 presents the descriptive statistics of employee performance and demographic

characteristics. Specifically, the mean of Performance is 25.61. It means that on average, the

performance score received by employees is 25.61, and the average financial incentive they

received is 256.1 Chinese yuan. The maximum (minimum) score that employees received

during the sample period is 115.5 (−111.90), and the standard deviation is 20.49. Employees’

group identity (group_identity) is a latent variable generated using nine survey questions

(Bergami and Bagozzi 2000; Boivie et al. 2011; Mael and Ashforth 1992). The mean (median)

of identity is 0.10 (0.17). The standard deviation is 0.51, and the minimum (maximum) is −1.50

(1.28).



39

The average skill level of employees is 1.69, which is between the low (= 1) and medium (= 2)

level set by the SOE. By July 2015, the average age of the employees was 42.92 years old, and

the average tenure was 23.70 years. The age and the tenure of employees indicate that most

employees entered the SOE during the late 1980s and early 1990s. The difference between the

average age and the average tenure is only 19 years. This is because most employees entered

the organization immediately after graduating from high school or vocational school, and chose

to stay in the organization for a long time. Twenty-two percent of the employees have five

years, or less than five years, until retirement. Female employees account for 24%; and 57%

of employees came from the city where the SOE is located. Most employees graduated from

senior high school or vocational school, and 18% have joined the Chinese Communist Party.

Thirty-three percent of employees entered the SOE through referral, and 47% have family

connections in the SOE.

The three variables on employee perception are latent variables constructed using the survey

questions. For employees’ perceived support from their workgroups, the mean is 0.15 and the

standard deviation is 0.68. In comparison, employees’ perceptions on shared goals with their

peers and the PMRS adopted by the SOE have larger variation. The mean of these two variables

is 0.07 and 0.20, respectively. The standard deviation of both variables is about 0.80, which is

larger than the standard deviation of the perceived support (0.68). The minimum values of these

two variables are between −2 and −3, which are smaller than the minimum value of the

perceived support (−1.34). The maximum values of all three variables are between 1.00 and

1.50. The final variable is employees’ organization identity. Compared to group_identity,

organization_identity has smaller mean (0.04) and larger standard deviation (0.81).



40

TABLE 3


N Mean Median s.d. Min Max

Dependent Variable

Performance 2,471 25.61 23.10 20.49 −111.90 115.5

Independent Variable

group_identity 51 0.10 0.17 0.51 −1.50 1.28

Demographic Characteristics

level 51 1.69 2.00 0.65 0.00 3.00

age 51 42.92 44.03 5.86 26.02 55.50

tenure 51 23.70 26.06 6.26 2.25 30.64

horizon 51 0.22 0.00 0.42 0.00 1.00

gender 51 0.24 0.00 0.43 0.00 1.00

home 51 0.57 1.00 0.50 0.00 1.00

edu 51 0.98 1.00 0.68 0.00 2.00

CCP 51 0.18 0.00 0.39 0.00 1.00

Connection

family 51 0.47 0.00 0.50 0.00 1.00

referral 51 0.33 0.00 0.48 0.00 1.00

Perception

support 51 0.15 0.26 0.68 −1.34 1.06

shared_goal 51 0.07 0.26 0.80 −2.24 1.09

system 51 0.20 0.46 0.81 −2.83 1.32

Organization Identity

organization_identity 51 0.04 0.18 0.81 −3.58 1.03 This table presents the descriptive statistics of employee performance, group identity, and the control variables. The data of

employee performance, Performance, is on a monthly basis. The sample includes 51 employees and 2,471 performance

observations from January 2011 to July 2015. The performance and demographic data is extracted from the database of the

research site. Group identity and other control variables are measured using survey instruments that are presented in Appendix

A. For the definitions of variables, see Appendix B.

Univariate Correlations

Table 4 provides details on Pearson correlations among the variables used in the analysis. It

shows that Performance is negatively correlated with the indicator for public reporting (public)

as well as group_identity. Further, Performance is positively correlated with level, which is

consistent with the fact that employees with higher skill levels tend to have higher performance.

Performance is also positively correlated with home and edu and negatively correlated with

the other control variables. As for the independent variable, group_identity is positively



41

correlated with the variables of employee perception and organization identity, which is

consistent with the propositions in the management theories (Ashforth and Mael 1989; Hogg

and Terry 2000). Group_identity is also negatively correlated with level, gender, edu, and

referral. This may be because the tasks in the research site require heavy manual labor.

Employees with high skill and/or education levels, as well as female workers, may thus identify

less with their workgroup. It is unclear whether group_identity and referral are negatively

correlated in the research site. Further, group_identity is positively correlated with age, tenure,

and CCP, which suggests that those who have worked in the organization for a longer time and

those with political affiliation tend to have higher group identity. Overall, the Pearson

correlations indicate that the relation between employee performance, group identity, and

performance reporting transparency is not straightforward and requires further analysis.

Most independent and control variables are significantly correlated with each other. I use

variance inflation factor tests to quantify the severity of multicollinearity, making sure the

results are not affected by the multicollinearity problem. Age, organization_identity, and

shared_goal are not included in the regression estimations because of the multicollinearity

problem. The other variables are not subject to the severe multicollinearity issue.

CHAPTER 2: PERFORMANCE REPORTING TRANSPARENCY, GROUP IDENTITY AND EMPLOYEE PERFORMANCE

42

TABLE 4

Pearson Correlation

Performance public group_identity level age tenure horizon

public −0.15***

group_identity −0.14*** 0.27***

level 0.41*** −0.04*** −0.06***

age −0.14*** 0.13*** 0.15*** −0.16***

tenure −0.13*** 0.14*** 0.20*** −0.08*** 0.94***

horizon −0.16*** 0.08*** 0.03*** −0.24*** 0.49*** 0.34***

gender −0.25*** −0.12*** −0.04*** −0.50*** −0.03*** 0.02*** 0.15***

home 0.04*** 0.16*** −0.01*** 0.13*** 0.32*** 0.38*** 0.12***

edu 0.06*** −0.09*** −0.14*** 0.11*** −0.42*** −0.38*** −0.19***

CCP −0.05*** 0.21*** 0.28*** 0.00*** 0.03*** 0.01*** 0.05***

family −0.14*** 0.19*** 0.01*** −0.25*** 0.21*** 0.21*** 0.13***

referral −0.04*** −0.10*** −0.06*** −0.05*** 0.37*** 0.36*** 0.06***

support −0.12*** 0.22*** 0.79*** 0.00*** 0.03*** 0.08*** 0.03***

shared_goal −0.23*** 0.35*** 0.84*** −0.13*** 0.13*** 0.17*** 0.08***

system −0.23*** 0.28*** 0.69*** −0.19*** 0.18*** 0.18*** 0.11***

organization_identity −0.16*** 0.28*** 0.53*** −0.01*** 0.36*** 0.39*** 0.09***

(continued.)

CHAPTER 2: PERFORMANCE REPORTING TRANSPARENCY, GROUP IDENTITY AND EMPLOYEE PERFORMANCE

43


gender home edu CCP family referral support shared_goal system

home −0.12***

edu 0.01*** −0.29***

CCP −0.25*** 0.24*** 0.13***

family −0.01*** 0.19*** −0.22*** 0.14***

referral −0.10*** 0.10*** −0.07*** 0.05*** 0.04***

support 0.02*** −0.12*** −0.06*** 0.05*** 0.01*** −0.14***

shared_goal 0.11*** −0.07*** −0.20*** 0.07*** −0.04*** −0.15*** 0.82***

system 0.06*** −0.09*** −0.25*** 0.10*** −0.03*** −0.03*** 0.70*** 0.77***

organization_identity −0.14*** 0.17*** −0.25*** 0.06*** 0.12*** 0.04*** 0.26*** 0.54*** 0.32***

This table presents the pairwise correlation coefficients between the variables. *, **, *** indicates that the correlation coefficient is significantly different from zero at the 10%, 5% and 1%

levels, respectively (two-tailed). For definitions of variables, see Appendix B.



44

Hypothesis Testing

Table 5 presents the results of the regression estimations. I first estimate the association

between employee performance and group identity in the full sample. The results are presented

in column (1). This shows that group identity is positively associated with employee

performance (β = 6.50, t = 3.98). The indicator for public reporting, public, is also positively

associated with employee performance (β = 3.72, t = 3.98). This is consistent with the prior

finding that public reporting leads to higher employee performance than private reporting does.

However, employee performance is negatively related to the interaction between group identity

and public (β = −10.84, t = −5.23). These coefficients indicate that, under private (public)

reporting, as group identity increases by 1, employee performance increases (decreases) by

6.50 (4.34). This finding is consistent with the expectation that group identity is positively

related to employee performance under private reporting. Surprisingly, it also indicates that in

the group with public reporting, group identity is negatively related to employee performance.

To better understand the association between employee performance and group identity, I

divide the sample based on performance reporting transparency. The results are presented in

columns (2) and (3), respectively. Column (2) shows that in groups with private reporting,

employee performance is positively associated with group identity (β = 11.17, t = 6.13). This

further supports the expectation that group identity and employee performance are positively

related under private reporting. In comparison, column (3) shows that in the group with public

reporting there is a negative association between group identity and employee performance (β

= −5.13, t = 1.73), which is consistent with the interaction coefficient in the full sample.



45

TABLE 5

Performance Reporting Transparency, Group Identity, and Employee Performance DV: Performance (1) (2) (3)

Full Sample

(Groups 2, 4, 5) Private Reporting

(Groups 2 and 4) Public Reporting

(Group 5)

Variables of Interest

group_identity 6.50*** 11.17*** −5.13*

(3.98) (6.13) (−1.73)

public 3.72***

(3.98)

group_identity×public −10.84***

(−5.23)


level 10.36*** 13.10*** 8.99***

(14.48) (15.41) (7.68)

tenure 0.16** −0.02 0.48**

(2.12) (−0.25) (2.47)

horizon −0.82 −1.80 5.93**

(−0.57) (−0.99) (2.11)

gender −5.68*** −0.35 −19.32***

(−6.03) (−0.33) (−6.89)

home −0.71 −0.68 −4.81***

(−0.89) (−0.65) (−2.76)

edu 0.90 2.84*** −4.77***

(1.57) (3.60) (−4.04)

CCP −2.74** −3.45** −3.55

(−2.27) (−2.52) (−1.29)

Connection

family −3.39*** −2.60*** −10.41***

(−4.58) (−2.83) (−5.24)

referral −2.56*** −0.02 −6.67***

(−3.12) (−0.03) (−3.45)

Perception

support −0.58 −7.46*** −1.04

(−0.59) (−5.46) (−0.50)

system −0.75 1.47* −1.72

(−1.03) (1.70) (−1.12)

(continued.)



46

TABLE 5 - (continued.)

(1) (2) (3)

Fixed Effects

Time fixed effect Controlled

Controlled

Controlled

Group fixed effects* Controlled

Controlled

N/A

Log-likelihood −10438.39 −6548.69 −3715.54

N 2,471 1,616 855

Column (1) presents the estimated relation between group identity and employee performance using the full sample:

Performanceit = β1group_identityi + β2publicit + β3(group_identityi×publicit) + β4leveli + β5tenureit + β6horizonit + β7genderi

+ β8homei + β9edui + β10CCPi + β11familyi + β12referrali + β13supporti + β14systemi + εit (1)

The full sample includes the performance of employees under private and public reporting. The sample period is from January

2011 to July 2015. The dependent variable is the monthly performance of employees.

Column (2) and (3) present the estimated relation between group identity and employee performance under private and public

reporting respectively:

Performanceit = β1group_identityi + β2leveli + β3tenureit + β4horizonit + β5genderi + β6homei + β7edui + β8CCPi


Specifically, the results of groups with private reporting are presented in column (2), and the results of groups with public

reporting are presented in column (3). The sample period is from January 2011 to July 2015. The dependent variable is the

monthly performance of employees.

All estimations are truncated at −160 and 130, because of the nature of the tasks and the performance measurement used by

the company. *, **, *** indicate that the correlation coefficient is significantly different from zero at the 10%, 5% and 1%

levels, respectively (two-tailed). For definitions of variables, see Appendix B.

* The group indicator for group 5 (with public reporting) is dropped because of collinearity, as the models has already included

the indicator for public reporting (i.e., public).

The negative relation between group identity and employee performance under public reporting

is unexpected. It may be the outcome of employees’ conformity under public reporting. As

group identity theories suggest, employees with high group identity may be motivated to

increase or decrease their performance to conform to the descriptive norms of their group

(Akerlof and Kranton 2000, 2003, 2010). It is reasonable to assume that the direction of

employees’ conformity depends on their ability. “Ability” is defined as the possession of the

power or skill to do something (Cambridge University Press 2017). In the sociology literature,

Smith and Arnkelsson (2000) refer to ability as a personal disposition that describes a typical

level of performance that one can achieve. In this study employee ability includes their skills

to perform their tasks. When exerting the same level of effort, those with higher ability (skill)



47

can achieve better task performance than those with lower ability. That is, to achieve a certain

level of performance, those with low ability must exert higher effort than those with high ability.

To conform to the group descriptive norms (i.e., adjust performance to “look” more similar to

others in one’s group), employees with high (low) ability are likely to suppress (improve) their

performance. If this is the case, group identify would be negatively (positively) related to the

performance of employees with high (low) ability.

To better understand how group identity and employee performance are related under public

reporting, I divide employees in the group with public reporting into two categories based on

their ability. In the research site, the level skills (level) that the SOE assigns to employees based

on their performance in the regular exams reflect employees’ ability in analyzing the equipment

conditions and choosing the appropriate actions. I thus use level to proxy employees’ ability.

The results are presented in Table 6. The results show that group identity is positively related

to the performance of employees with low ability (β = 15.70, t = 1.81), but negatively related

to the performance of employees with high ability (β = −4.52, t = −1.68). These results are

consistent with the argument that group identity motivates employees with high (low) ability

to conform to the descriptive norms in their workgroup by suppressing (improving) their

performance.



48

TABLE 6

Group Identity and Employee Performance under Public Reporting DV: Performance (1) (2) (3)

All Employees under

Public Reporting Low Ability

(level = 0 or 1) High Ability

(level = 2 or 3)


group_identity 6.29 15.70* −4.52*

(1.03) (1.81) (−1.68)

group_identity×level −5.92**

(−2.09)

level 11.45*** −2.41 12.14***

(7.35) (−0.25) (5.43)

Demographic

Characteristics Controlled

Controlled

Controlled

Connection Controlled Controlled Controlled

Perception Controlled Controlled Controlled

Fixed Effects

Time fixed effect Controlled Controlled Controlled

Group fixed effects N/A N/A N/A

Log-likelihood −3713.43 −1666.51 −1964.54

N 855 382 473

This table presents the estimated relation between group identity and employee performance in the group with public reporting

(Group 5):



The results of all employees under public reporting are presented in column (1). The results of low- and high-ability employees

are presented in column (2) and (3), respectively. Employee ability is proxied by employees’ skill levels, which are assigned

by the department manager based on the scores that employees earned in the skill exam held regularly in the organization.

The sample period is from January 2011 to July 2015. The dependent variable is the monthly performance of employees. The

estimations are truncated at −160 and 130, because of the nature of the tasks and the performance measurement used by the

company. *, **, *** indicates that the correlation coefficient is significantly different from zero at the 10%, 5% and 1% levels,

respectively (two-tailed). For definitions of variables, see Appendix B.

Overall, the results presented in Tables 5 and 6 document a positive association between group

identity and employee performance under private reporting. The results also indicate that under

public reporting, group identity is positively (negatively) associated with the performance of

employees with low (high) ability.



49

Additional Analyses

The results of the main findings are subject to two potential issues. First, there is only one

group with public reporting (i.e., Group 5) in the sample. It is thus important to demonstrate

whether the findings about public reporting are driven by performance reporting transparency

or any unobservable group feature(s). Second, it is unclear whether group identity is affected

by performance reporting transparency. If so, the effects of the performance reporting

transparency and group identity would be confounding, and this would complicate the

interpretation of the findings. I conduct three sets of additional analyses to address these issues.

Change of Performance Reporting Transparency

I use two methods to rule out the alternative explanation that the findings about public reporting

are driven by unobservable group feature(s). The first method is to examine employee

performance in a group that changed its performance reporting transparency. Specifically, one

of the groups in the department (Group 3) switched from private to public reporting by the end

of 2013. As mentioned in the research site section, Group 3 experienced a change in group

leader. The old group leader used private reporting, while the new group leader started to use

public reporting since December, 2013. This provides an opportunity to examine whether

employees with different levels of group identity react differently toward a change in

performance reporting transparency.

Because of a computer breakdown in early 2015, the switched group (Group 3) lost its

performance observations for August to December 2013, June to December 2014, and January

to July 2015. Because most data after the change is missing from the switched group, it is

difficult to obtain difference-in-difference estimations. Therefore, I compare employee

performance before and after the change using the available data for the switched group. I also



50

use employee performance in the groups with private reporting (Groups 2 and 4) as a reference

for the general patterns in employee performance. Figure 2 presents the results with tables and

figures.

I divide employees into high and low group identity based on the median of group_identity.

Within the subsamples of high or low group identity, I further divide employees into high and

low ability based on their skill levels assessed by the SOE (i.e., level). I first look at employees

with high group identity. Figure 2 shows that after the switched group changed from private to

public reporting, those with high group identity and low ability increased their performance

from 17.42 to 21.25 (p < 0.10). In comparison, those with high group identity and high ability

decreased their performance from 33.40 to 28.82 (p < 0.05). I then look at employees with low

group identity. Figure 2 shows that in the subsample of low group identity, the performance of

employees with low (high) ability increased (decreased) after the change. However, these

changes are not statistically significant. The overall employee performance of the switched

group increased after the change from private to public reporting. Taken together, the patterns

demonstrated in Figure 2 are consistent with the results of the main analysis, which suggest

that group identity is positively (negatively) related to the performance of employees with low

(high) ability under public reporting.

CHAPTER 2: PERFORMANCE REPORTING TRANSPARENCY, GROUP IDENTITY, AND EMPLOYEE PERFORMANCE

51

FIGURE 2

Employee Performance in the Group that Changed from Private Reporting to Public Reporting at the End of 2013

High and low identity is divided based on the median of group_identity. High and low ability is divided based on level (low ability if level = 0 or 1; high ability if level = 2 or

3)

(continued.)

High identity & High ability

2011–2013 2014 Difference

Groups with private

reporting from 2011–

2014

27.78 30.80 3.03

Group changed from

private to public

reporting in 2014

33.40 28.82 −4.59**

Difference 5.64*** −1.98 −7.62*

High identity & Low ability


Groups with private


2014

19.95 13.63 −6.32**

Group changed from

private to public

reporting in 2014

17.42 21.25 3.83*

Difference −2.54* 7.62** 10.16***


52

FIGURE 2 (continued.)

Low identity & Low ability


Groups with private


2014

23.38 18.64 −4.74**

Group changed from

private to public

reporting in 2014

8.98 11.33 2.35

Difference −14.40*** −7.31 7.09

(continued.)

Low identity & High ability


Groups with private


2014

37.28 39.18 −1.90

Group changed from

private to public

reporting in 2014

32.72 30.29 −2.43

Difference −4.56* −8.89* −4.33


53

FIGURE 2 (continued.)

All Employees


Groups with private

reporting from 2011–2014 26.39 23.93 −2.46*

Group changed from

private to public reporting

in 2014

20.90 24.47 3.57*

Difference −5.48*** 0.54 6.02***

CHAPTER 2: PERFORMANCE REPORTING TRANSPARENCY, GROUP IDENTITY, AND


54

Group Identity and Employee Performance in a Department with Public Reporting

The second method to rule out the alternative explanation that the main findings are driven by

unobservable group feature(s) is to examine group identity and employee performance in a

different department. This department also has five workgroups, all of which adopt public

reporting. Specifically, in all five workgroups, the group leaders post employee performance

in their workshops at the end of every month. The tasks in this department involve equipment

operation and maintenance. The nature of the task is similar as that of the department used in

the main analysis, but the operating procedures and performance measurement are different6. I

examine how employee performance is related to group identity in this department. The results

are presented in Table 7. The results indicate that in this department with five workgroups and

public reporting, there is a positive relation between group identity and the performance of

employees with low ability (t = 3.58). In comparison, for employees with high ability, the

relation between the two variables is negative (t = −5.42). These results indicate that group

identity leads to conformity under public reporting, and further supports the results of main

analyses.

6 Because of the different tasks, operating procedures and performance measurement, it is difficult to compare

this department with the one used in the main analysis. Within each department, the tasks, operating procedures,

and performance measurement are the same across different workgroups. Therefore, different workgroups

within each department are more comparable than different departments.



55

TABLE 7

Group Identity and Employee Performance in a Department with Public Reporting DV: Performance

(1) (2) (3)

All Employees Low ability

(level = 0 or 1) High Ability

(level = 2 or 3)


group_identity −0.24 3.96*** −2.47***

(−0.38) (3.58) (−5.42)

group_identity×level −0.99***

(−2.72)

level 3.15*** 4.40** −3.23**

(8.12) (2.55) (−2.37)

Demographic

Characteristics Controlled

Controlled

Controlled

Connection Controlled Controlled Controlled

Perception Controlled Controlled Controlled

Fixed Effects


Group fixed effects Controlled Controlled Controlled

Log-likelihood −32407.32 −7471.22 −24692.90

N 7,602 1,831 5,771

This table presents the estimated relation between group identity and employee performance in another department with

public reporting:



This department has five workgroups, all of which adopt public reporting. The results of all employees in this department are

presented in column (1). The results of low- and high-ability employees are presented in column (2) and (3), respectively.

Employee ability is proxied by employees’ skill levels, which are assigned by the department manager based on the scores

that employees earned in the skill exam held regularly in the organization. The sample period is from January 2008 to July

2015. The dependent variable is the monthly performance of employees The estimations are truncated at −160 and 100,

because of the nature of the tasks and the performance measurement used by the company. *, **, *** indicate that the

correlation coefficient is significantly different from zero at the 10%, 5% and 1% levels, respectively (two-tailed). For

definitions of variables, see Appendix B.



56

Determinants of Group Identity

The second potential issue of the main results is that group identity may be affected by public

reporting. If this is the case, the effects of group identity and performance reporting would

confound, and it would be difficult to interpret how these variables are related to employee

performance. To find out whether employees’ group identity is affected by performance

reporting transparency, I examine how group identity is associated with the indicator for public

reporting and a range of different variables. I examine both the 290 respondents from four

different departments of the SOE, as well as the 79 respondents from the department used in

the main analysis. The results are presented in Table 8.

Public reporting is adopted in two of the four departments: the department of the main analysis

(Groups 3 and 5), and the department examined in Table 7 (all of its five workgroups). The

other departments and groups adopt private reporting. The variable public is an indicator for

workgroups with public reporting. In addition to public, I also examine other variables,

including group size, employees’ demographic characteristics, connections, perception, and

organization identity. Table 8 shows shows that group identity is positively associated with

employees’ perceived support from their workgroup, which is consistent with previous studies

(Ashforth and Mael 1989; Eisenberger et al. 1990; Hogg 2006; Hogg and Terry 2000). It is also

positively associated with employees’ organization identity, which indicates that employees’

identification with their workgroup overlaps with their identification with the organization as

a whole. However, group identity is not significantly associated with performance reporting

transparency or the other variables. In other words, the confounding issue is not salient in the

research site.



57

TABLE 8

Determinants of Group Identity DV: group_identity

All Respondents Respondents from the Department

used in the Main Analysis (1) (2) (3) (4) (5) (6)

Group Feature

public −0.10 0.07 −0.05 0.09 0.14 0.07

(−0.59) (0.79) (−0.24) (0.61) (1.29) (0.47)

size_large −0.12 −0.03 −0.28

(−0.52) (−0.41) (−1.24)


level 0.04 0.04 0.06

(0.49) (0.58) (0.66)

tenure 0.01 0.00 0.01 −0.00 0.00 −0.00

(0.77) (0.73) (0.89) (−0.12) (0.05) (−0.32)

horizon 0.03 0.02 −0.01 0.04 0.04 0.05

(0.32) (0.27) (−0.09) (0.31) (0.33) (0.38)

gender 0.06 0.07 0.03 −0.02 −0.01 −0.07

(0.81) (1.00) (0.40) (−0.24) (−0.07) (−0.78)

home 0.02 −0.01 0.07 −0.06 −0.07 −0.05

(0.25) (−0.14) (0.92) (−0.89) (−1.01) (−0.70)

edu −0.02 −0.09* −0.01 −0.05 −0.05 −0.05

(−0.46) (−2.04) (−0.30) (−0.99) (−0.97) (−1.02)

CCP 0.06 0.07 0.02 0.14 0.12 0.14

(0.74) (0.87) (0.27) (0.97) (0.90) (0.94)

Connection

family −0.01 −0.02 −0.00 −0.14 −0.13 −0.08

(−0.12) (−0.26) (−0.03) (−1.13) (−1.12) (−0.68)

referral 0.04 0.07 0.07 0.11 0.13 0.14*

(0.53) (0.83) (0.83) (1.37) (1.54) (1.74)

Perception

support 0.46*** 0.49*** 0.54*** 0.19 0.21 0.09

(5.09) (4.80) (5.45) (1.29) (1.49) (0.69)

shared_goal 0.03 0.01 0.07 0.18 0.18 0.35**

(0.28) (0.12) (0.67) (1.07) (1.07) (2.23)

system −0.00 −0.02 0.05 0.20 0.17 0.21

(−0.01) (−0.33) (0.62) (1.33) (1.22) (1.36)

Organization Identity

organization_identity 0.30*** 0.30*** 0.18*** 0.15**

(4.39) (4.60)

(3.02) (2.65)

(continued.)



58


(1) (2) (3) (4) (5) (6)

Group fixed

effects Controlled

No

Controlled

Controlled

No

Controlled

Adjusted R2 58.68% 58.36% 53.44% 60.21% 60.32% 57.12%

N 286 286 286 76 76 76 This table presents the estimated relations between employees’ group identity and other variables. Results of all respondents

(from four different departments of the organization) are presented in column (1) – (3), and the results of the respondents from

the department used in the main analysis (i.e., Tables 5 and 6) are presented in column (4) – (6). *, **, *** indicate that the

correlation coefficient is significantly different from zero at the 10%, 5% and 1% levels, respectively (two-tailed). For definitions

of variables, see Appendix B.

Robustness Checks

In the robustness tests, I first winsorize Performance at 5% and 1% to ensure the results are

not distorted by any extreme values in employee performance. Second, as the dataset in this

study involves multiple periods, I include time-series variables into the models to check the

robustness of the results. After including the lag of Performance in the estimations, the

coefficients on some demographic variables lose significance. The coefficients on the lag

Performance are significantly positive, which suggests employee performance is persistent

over time. The conclusions remain the same. Further, because some employees took sick leave

in certain months and the function of the equipment at times was affected by external factors

like extreme weather, the performance information of some employees is missing in some

months. Excluding the months with missing observations does not change the conclusions.

Additionally, although the main tests exclude two groups to make the private and public

conditions more comparable, including these two groups in the analysis does not change the

conclusion. Finally, I also use OLS instead of the censored model. The adjusted R2 of the

estimations are around 60%, and the conclusions remain the same.

2.6 Concluding Remarks

Using archival and survey data from a field site, I find that the relation between group identity

and employee performance is conditional on the performance reporting transparency in the



59

workplace. Specifically, in workgroups with private reporting, higher group identity motivates

employees to improve their performance by following the performance requirements set by the

organization. However, in workgroups with public reporting, higher group identity motivates

employees with high (low) ability to suppress (improve) their performance to conform to the

group descriptive norms.

This study examines how performance reporting transparency affects employees’ performance

through their group identity. However, I do not attempt to make a conclusion about how

performance reporting transparency affects overall employee performance within a group. The

answer to this question is based on the specific conditions of different workgroups. As the

results indicate, the overall performance impact of performance reporting transparency

depends on the group identity and ability of employees, as well as the performance distribution

within a group.

Furthermore, this study is different from the prior research studying the relation between

information environment, employee ability and performance. In particular, Casas-Arce and

Asís Martínez-Jerez (2009) find that providing relative performance information in

tournaments leads high performers to decrease their effort. They suggest that relative

performance information reveals the ability gap between high performers and their peers, so

high performers realize that they can still win the tournaments by exerting lower effort. This

study differs from Casas-Arce and Asís Martínez-Jerez (2009) for three reasons. First, there is

no tournament, formal competition or performance ranking in the research site of this study.

Therefore, employees in this study are less likely to have strong motivation to “win” or “lead”

in their workgroup. Second, when high-ability employees lower their performance, they forgoe

some financial rewards. Such financial sacrifice indicates that high-ability employees are less



60

likely to lower their performance just because they realize less effort is needed to demonstrate

higher performance than their peers. Third, the results indicate that it is only the high-ability

employees with higher group identity that are more likely to lower their performance and

sacrifice their financial rewards. In other words, group identity is likely to be the factor that

motivates high-ability employees to lower their performance and sacrifice financial rewards.

Overall, the findings of this study differ from the existing evidence, and highlights the

importance of group identity in driving employee behaviors.

This study contributes to the literature on internal performance reporting (Azmat and Iriberri

2010; Frederickson 1992; Hannan et al. 2008, 2013; Maas and Rinsum 2013; Tafkov 2012).

Previous research finds that public reporting leads to higher employee performance than private

reporting does (Tafkov 2012). However, this study shows that public reporting may lead

employees with high group identity and ability to suppress their performance to conform to

group descriptive norms. This study also adds to the growing literature on group identity

(Abernethy et al. 2016; Empson 2004; Heinle, Hoffman, and Kunz 2012; Towry 2003), by

documenting the relation between group identity and employee performance, and how this

relation is affected by the performance reporting choices made by managers. Further, this study

has practical implications. It suggests that managers need to consider employees’ group identity

and ability, as well as the group descriptive norms when making reporting choices. As group

identity may be affected by such control mechanisms as employee selection and the design of

group structure (Ahsforth and Mael 1989; Hogg and Terry 2000), managers may also need to

consider different management controls as an integrated system.

This study has several limitations. First, it is an open question as to what extent we can

generalize the findings from a single research site. To examine the research question, this study



61

used a field site where employees’ group identity is salient and there is a variation in

performance reporting transparency across different workgroups. The findings might not

generalize directly to other firms with different tasks, incentives, and employees with different

backgrounds and personalities. Second, although this study documents the relation between

group identity and employee performance in groups with different performance transparency,

I cannot examine which underlying mechanism(s) are driving these relations, and thus do not

attempt to demonstrate the causal chain that explains these relations. Finally, this study employs

long time-series data from a real-world setting, which makes it difficult to rule out alternative

explanations for the findings. However, given that the context is controlled for and the research

subjects have long tenures in this setting, this does not pose a significant threat. Future research

could attempt to replicate and extend this study in a setting that controls for these limitations.



62

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66

APPENDIX A Sample of the Survey Questionnaire

Group Identity7

All multi-item measures used 7-point Likert-type scales ranging from 1 (strongly disagree) to 7 (strongly

agree), unless stated otherwise.

1. When someone criticizes my group, it feels like a personal insult.

2. I am very interested in what others think about my group.

3. When I talk about my group, I usually say ‘we’ rather than ‘they’.

4. My group’s successes are my successes.

5. When someone praises my group, it feels like a personal compliment.

6. If my group was criticized by someone outside my group, I would feel embarrassed.

7. Being a member of my group is a major part of who I am.

8. Please indicate to what degree your self-image overlaps with your group’s image. (Answers

ranging from 1 [does not overlap at all] to 7 [fully overlaps])

9. Imagine that one of the circles on the left represents you and the circle on the right represents your

group. Please indicate which case best describes the level of overlap between you and your group.

me my group

Perceived support from workgroup 1. My group values my contributions.

2. My group appreciates any extra effort from me.

3. My group really cares about my wellbeing.

Shared goal with other group members 1. My group members and I always agree on what is important at work.

2. My group members and I always share the same ambitions and vision at work.

(continued.)

7 The survey questionnaires sent to the employees did not include the subtitles. The subtitles are used here to

clearly present and distinguish different instruments.



67

APPENDIX A (continued)

Organization identity

1. When someone criticizes my organization, it feels like a personal insult.

2. I am very interested in what others think about my organization.

3. When I talk about my organization, I usually say ‘we’ rather than ‘they’.

4. My organization’s successes are my successes.

5. When someone praises my organization, it feels like a personal compliment.

6. If my organization was criticized by someone outside my organization, I would feel embarrassed.

7. Being a member of my organization is a major part of who I am.

8. Please indicate to what degree your self-image overlaps with your organization’s image. (Answers

ranging from 1 [does not overlap at all] to 7 [fully overlaps])

9. Imagine that one of the circles on the left represents you and the circle on the right represents your

organization. Please indicate which case best describes the level of overlap between you and your

organization (followed by the figure of image overlap)

Perception of the PMRS

1. The performance bonus helps me understand how I can do my job better.

2. The performance bonus helps me better understand the expectations of my superiors.

3. I learn a lot from the performance bonus I receive.

4. The performance bonus I receive makes me want to improve my performance.

5. After receiving performance bonuses, I tend to work harder.

6. The performance bonus I receive motivates me to do my best at work.

7. The performance penalty helps me understand how I can do my job better.

8. The performance penalty helps me better understand the expectations of my superiors.

9. I learn a lot from the performance penalty I receive.

10. The performance penalty I receive makes me want to improve my performance.

11. After receiving the performance penalty, I tend to work harder.

12. The performance penalty I receive motivates me to do my best at work.

Connection

1. How did you enter the SOE? (1 = allocated by the government; 2 = through another channel)

2. Before entering the SOE, did you have any relative/friend/acquaintance in the SOE? (1 = Yes; 2 =

No)

3. Do you have any family (including spouse, parent[s], offspring[s] and/or other family members)

who also work in the SOE? (1 = Yes, I have family who also work[s] in the SOE; 2 = I used to

have but they left the SOE; 3 = No, I never had any family working in the SOE)



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APPENDIX B Variable Definitions

Performance

Performance scores that employees receive at the end of every month for their

operational actions and outcomes; objectively measured by department

managers through checking the system records and the status of the

equipment, and recorded in the computer system of the SOE. Because of the

nature of the task and the performance measurement designed by the SOE, the

highest (lowest) performance score that an employee can receive in a month

is 130 (−160).

group_identity Employees’ workgroup identity, measured by nine survey questions

(presented in Appendix A).

public

1 for observations in groups with public reporting (i.e., employees can see

their own performance scores and those of others in their workgroup), or 0 for

observations in groups with private reporting (i.e., employees can only see

their own, but not their peers’, performance scores).

level

Employees’ skill level; assessed by the SOE through exams. Every three

years, the SOE holds an exam to assess employees’ operational skills, and

assigns skill levels to employees based on their exam results. There are four

skill levels: 0 (none), 1 (low), 2 (medium), and 3 (high). To achieve a

particular level, employees must achieve a certain score in the exam. In this

study, the variable level is the skill level assigned by the SOE at the end of

2012.

age Employee i’s age; calculated by employee i’s date of birth and the dates that i

received his or her performance score.

tenure Number of years that employee i has been working in the SOE, calculated by

the dates that i received his or her performance score.

horizon Equal to 1 for male workers over 49, and female workers over 44 (i.e., 5 years

before retirement), or 0 otherwise.

gender 1 for employees who are female, 0 otherwise.

home 1 for employees recruited from the local area, 0 for employees recruited from

other regions of China.

edu

Education level of an employee, taking the value 0 for junior high school or

equivalent, 1 for senior high school or equivalent, 2 for undergraduate degree,

or 3 for postgraduate degree and higher.

CCP 1 for employees who are members of the Chinese Communist Party, 0

otherwise.

family 1 for employees with family members who also work in the SOE, 0 otherwise;

measured by one survey question presented in Appendix A.

referral

The proxy for employees who entered into the SOE through the

recommendation of existing employees. Measured by two survey questions

presented in Appendix A. Equal to 1 for employees who were not allocated

by the government and had connections with existing employees before

entering the SOE, 0 otherwise.

support Employees’ perception of the support they received in their workgroup,

measured by three survey questions, presented in Appendix A.

shared_goal Employees’ perception of the shared goals with other employees in their

workgroup, measured by two survey questions, presented Appendix A.

system Employees’ perception of the PMRS, measured by two survey questions,


organization_identity Employees’ organization identity, measured by nine survey questions,


size_large 1 for groups with more than 19 employees, and 0 for the groups with 10–15 employees. The SOE only has these two types of groups.

CHAPTER 3: WORK NORMS AS A CONTROL MECHANISM – IMPLICATIONS FOR


CHAPTER III

WORK NORMS AS A CONTROL MECHANISM: IMPLICATIONS

FOR EMPLOYEE PERFORMANCE

ABSTRACT

This study examines one mechanism that can be used as part of an organization’s management

control system, namely managers’ promotion of work norms. I investigate whether managers

can motivate employees by promoting work norms that contribute to the desired organizational

outcomes. On the one hand, the effect of work norms may be overruled by the financial

incentives used in organizations. On the other hand, work norms may complement financial

incentives by guiding and motivating employees to perform their tasks in the right ways. I

conducted this study using performance and personnel data from a large department within a

Chinese state-owned enterprise, where employees are responsible for multiple tasks and are

divided into five workgroups. Group leaders in two of the five workgroups have taken actions

to promote the desired work norms within their groups. The results indicate that managers’

promotion of work norms has a significant and positive effect on employee performance on

both tasks that can be precisely measured and tasks that cannot be precisely measured by the

organization. These findings contribute to the accounting literature and managerial practice by

demonstrating that work norms can operate as an effective control mechanism.



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3.1 Introduction

Management controls play an important role in motivating employee performance. To achieve

desired organizational outcomes, it is important for managers to motivate employees to perform

their tasks in the right ways (i.e., the ways aligned with the goal and strategy of the

organization). Financial incentives are one commonly used control mechanism for achieving

this. Although previous research suggests that financial incentives can be effective in

motivating employee performance (Bonner and Sprinkle 2002; Sprinkle 2000), the use of

financial incentives is subject to the following limitations. First, it leads to high monetary costs

and is subject to the budget of organizations. Second, when employee performance on some

tasks cannot be precisely measured, financial incentives can motivate employees to exert less

effort on these tasks (Holmstrom and Milgrom 1991; Roberts 2010). This study investigates

whether a control mechanism—namely managers’ promotion of work norms—can

complement financial incentives by motivating employee performance across different tasks.

Norms are the informal rules or standards that guide the behaviors of group members without

the force of laws (Cialdini and Trost 1989). In social or work groups, norms may be specified

by group leaders or develop from the practices of group members (Cialdini and Trost 1998;

Cialdini, Kallgren, and Reno 1990, 1991). Psychology studies find that focusing individuals’

attention on a social norm (such as “do not litter”) leads to individuals’ adoption of the norm

(Cialdini et al. 1990, 1991). This study examines whether managers can motivate employee

performance by deliberately promoting the work norms that contribute to the desired

organizational outcomes. What types of work norms are desirable to organizations depends on

different settings. In order to examine the research question, I focus on a multi-task setting,

where managers can precisely measure employees’ performance on some tasks, but cannot

precisely measure their performance on the other tasks. Managers in a multi-task setting need



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to solve a basic control problem—motivating employee performance on both types of tasks. A

solution to this problem is to cultivate a work norm of exhibiting high performance on both

types of tasks. Using financial incentives only is often not sufficient to achieve this goal, as

financial incentives can be costly, and it is difficult for managers to incentivize employees

based on their performance on tasks that cannot be precisely measured (Brüggen and Moers

2007; Roberts 2010). This study examines whether managers can motivate employee

performance on both types of tasks by deliberately promoting the work norm. Managers can

promote the work norm by directly communicating this work norm to employees, and focusing

employees on the work norm using feedback and recognition.

Whether mangers’ promotion of work norms is effective in motivating employee performance

is an empirical question. On the one hand, experimental evidence indicates that other control

mechanisms used in organizations, such as financial incentives, may overrule the effect of

work norms (Ariely et al. 2006; Gneezy and Rustichini 2000; Taylor and Bloomfield 2010).

On the other hand, in a multi-task setting where the use of financial incentives is subject to

monetary costs and imprecise performance measurement, work norms may guide and motivate

employees to work hard on both types of tasks. Adopting the work norms not only helps

employees earn higher financial incentives from the tasks that can be precisely measured, but

also promotes their self-image as group members (Akerlof and Kranton 2000, 2003; Ashforth

and Meal 1989; Hogg and Terry 2000). Therefore, I expect that managers’ promotion of work

norms is positively associated with employees’ performance on tasks that can be precisely

measured and tasks that cannot be precisely measured.

I conducted this study using performance and personnel data from a large department within a

state-owned enterprise (SOE) in China. Employees in the department choose their actions to



72

inspect, operate, and maintain the equipment. This is a useful setting to examine the research

question, as some actions of employees can be precisely measured by machines and the

organization’s computer system, while other actions of employees are unobservable to

managers. Both measured and unmeasured actions of employees affect the status of the

equipment. The control problem in this setting is how to motivate employees to work hard and

choose the appropriate actions. Using financial incentives is not enough to alleviate this

problem, as the upper level management team strictly controls the amount of the financial

incentives, and some actions of employees cannot be precisely measured.

From 2008 to 2014, three events occurred in this department provide an opportunity to

investigate whether managers’ promotion of work norms can function as an effective control

mechanism. In particular, the department manager divides employees into five workgroups to

make the daily management more efficient. Group leaders in two of the five workgroups

(Groups 3 and 4) have taken actions to promote the work norms within their groups. In

particular, the two group leaders have posted a range of behavioral principles within their

workgroups since 2009 and 2011, respectively. The behavioral principles suggest that it is

employees’ responsibility to exert high effort on all of their tasks; and employees who have

worked hard on both measured and unmeasured tasks shall be recognized by their group leader,

even though their actions may not be recognized by the performance measurement system

adopted by the organization. In addition to posting the behavioral principles publicly in their

workshops, the two group leaders tried to focus employee attentions on these principles by

using informal feedback and symbolic rewards. These behavioral principles were aimed to

guide employee to perform their tasks in “the right ways” without implementing formal rules

or regulations. Therefore, the group leaders’ promotion of these behavioral principles can be

used as a proxy for the promotion of work norms. Comparing the groups with and without



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these principles allows me to examine how employees react to managers’ promotion of the

work norms. Additionally, by the end of 2011, the leader of Group 3 retired. The new group

leader arrived on January 2012 and has strictly followed the formal rules and requirements

specified by the SOE, without promoting the behavioral principles. This event provides an

opportunity to examine how employees react to the removal of the work norms.

I examine how the promotion and removal of the work norms affects employee performance.

The SOE uses electronic tools to objectively measure employees’ operational procedures and

outcomes. I extracted the monthly performance data from the database of the SOE, and

examine how it changed after the introduction and the removal of the work norms. The results

indicate that the group leaders’ promotion of work norms functions as an effective control

mechanism. It is associated with a significant increase in desirable employee actions that can

be precisely measured, as well as those that cannot be precisely measured in both Group 3 and

4. Additionally, after the new group leader arrived in Group 3 and stopped promoting the work

norms, employee performance on actions that can be precisely measured declined. However,

employee performance on actions that cannot be precisely measured did not change

significantly. This may be because for tasks that cannot be precisely measured, work norms

provide employees with the guidelines, and frame their beliefs about how they should perform

these tasks. Overall, the results suggest that the promotion of work norms is effective in

motivating employee performance, especially for tasks that cannot be precisely measured.

This study contributes to the literature on management control mechanisms and employee

performance. The existing literature suggests that financial incentives are effective in

motivating employee performance (Bonner and Sprinkle 2002; Kachelmeier, Reichert, and

Williamson 2008; Sprinkle 2000); but the use of financial incentives is subject to high



74

monetary costs and potential unintended consequences (Holmstrom and Milgrom 1991;

Roberts 2010). This study finds that an informal control mechanism—namely managers’

promotion of work norms—can complement financial incentives by motivating desirable

employee actions. Using performance and personnel data from a field site, this study finds a

significant increase in employee performance across different tasks in the groups where

managers have taken action to promote the work norms that require employees to work hard

on all of their tasks.

This study also contributes to a broader literature on the role of work norms as an integral part

of an organization’s control system. Recent studies find that work norms in organizations can

be shaped by control mechanisms such as employee selection (Abernethy, Dekker, and Schultz

2015; Campbell 2012), performance reporting (Maas and Van Rinsum 2013), and performance

measures (Gibbons and Kaplan 2015; Kachelmeier et al. 2008). Psychology studies find that

focusing individuals’ attention on a particular norm leads to compliance toward the norm. This

study shows that this approach functions as an effective control mechanism in organizations.

In other words, managers can shape work norms in organizations by deliberately promoting

the work norms that contribute to the desired organization outcomes.

Finally, this study has practical implications. Corporate culture is critical to organization

success, and one of the most important functions of corporate culture is to encourage employee

actions that are aligned with the strategy and goal of the organization (Coleman 2013). Using

multi-period data from a field site, this study shows how managers’ promotion of work norms

helps encourage desirable employee actions. The findings help managers understand how their

practices affect the way that employees perform their tasks.



75

The remainder of this paper is organized as follows. The next section discusses the relevant

literature and develops the hypotheses. I then describe the setting and research method, before

presenting and discussing the results. The final section provides the concluding remarks.


“Norms” are the informal rules or standards that guide and/or constrain the behavior of group

members without the force of laws (Cialdini and Trost 1989). Norms can be specified by the

leader(s) of a group or develop out of the practices of group members (Cialdini and Trost 1998;

Cialdini et al. 1990, 1991). Psychology studies find that individuals are motivated to follow the

norms of their social or work group in order to maintain positive self-image (Cialdini et al.

1991; Cialdini and Goldstein 2004; Cialdini and Trost 1998). Using laboratory settings,

Cialdini et al. (1990, 1991) find that taking procedures that focus individuals on the norm

against littering can significantly reduce littering rates. The procedures they took include

sending flyers to individuals and making them read a relevant article. This study examines

whether this approach can function as an effective control mechanism in a multi-task

organizational setting. In other words, this study investigates whether managers can motivate

employee performance by deliberately promoting the work norms that contribute to the

organizational outcomes.

In organizations with multiple tasks, managers can focus employee attentions on the work

norms (i.e., working hard on both tasks that can be precisely measured and tasks cannot be

precisely measured) by explicitly communicating the work norms to employees. Managers can

also provide employees with feedback and/or recognitions to emphasize the importance of the

work norms. Whether such procedures are effective in motivating employee performance is an

empirical question. On the one hand, the use of financial incentives may “crowd out” the effects



76

of the work norms, because financial incentives lead employees to frame their behaviors as a

way of maximizing their wealth and motivating employees to act in their self-interest (Ariely

et al. 2006; Gneezy and Rustichini 2000; Taylor and Bloomfield 2010). It may thus overrule

employees’ intrinsic motivation to follow the work norms promoted by their managers. In the

economic literature, Gneezy and Rustichini (2000) find that, in a day-care center, financial

penalties lead parents to frame the pickup of their children in a strategic way, thus encouraging

late pickups. Ariely et al. (2006) find that financial incentives lead individuals to frame an

activity (e.g., listening to their professor reading poetry) as one that they would need to be paid

to suffer through, and thus overrules their intrinsic motivation to engage in the activity. In the

accounting literature, Taylor and Bloomfield (2010) experimentally find that formal controls,

including monitoring and financial penalties, lead individuals to frame their contribution to a

public good as a mean to maximize their own interests, and thus crowd out their intrinsic

motivation to contribute. Other studies also find that financial incentives lead employees to

frame their relationship with their employers in a strategic way and overrule the effects of

reciprocity and trust (e.g., Chen and Sandino 2012; Christ, Sedatole, and Towry 2012; Hannan,

Hoffman, and Moser 2005). Based on these studies, the use of financial incentives may crowd

out the performance effects of the work norms promoted by managers.

On the other hand, work norms may complement financial incentives in motivating employee

performance. In a multi-task setting where the use of financial incentives is subject to monetary

costs and imprecise performance measurement, work norms provide employees with the

guidelines about how they should perform their tasks. By promoting the work norms that

employees should work hard on both tasks that can be precisely measured and tasks that cannot

be precisely measured, managers communicate the desired behavioral principle to employees

and motivate employees to follow the principle. Specifically, economic theory suggests that



77

employees tend to lose utility if they fail to follow the norms in their workgroup (Akerlof and

Kranton 2000, 2003, 2010). Management theories also posit that following the norms of one’s

organization or workgroup enhances the employee’s self-concept as a participant of the

organization or workgroup (Ashforth and Meal 1989; Hogg and Terry 2000). Based on

economic and management theories, managers’ promotion of the work norms may function as

an effective control mechanism by motivating employee performance on both types of tasks in

a multi-task setting. Following the work norms not only helps employees earn higher financial

incentives from the tasks that can be precisely measured, but also promote their self-image as

group members. In other words, the effects of work norms may not be overruled by financial

incentives. Therefore, I expect that managers’ promotion of work norms is positively related

to employee performance on tasks that can be precisely measured and tasks that cannot be

precisely measured. Figure 1 illustrates the motivation and research question of this study,

while Figure 2 presents the theoretical links that this study aims to examine.

H1: Managers’ promotion of work norms is positively associated with employee performance

on tasks that can be precisely measured by the organization.

H2: Managers’ promotion of work norms is positively associated with employee performance

on tasks that cannot be precisely measured by the organization.



78

FIGURE 1

Motivation and Research Question

Research Question: Can managers’ promotion of the work norms complement financial incentives by

motivating desirable employee actions in a multi-task setting?

Psychology, management,

and economics literature on

norm adoption:

Employees derive utility

from adopting the norms of

their organization.

Economics and accounting

literature on multi-task:

In a multi-task setting,

employees tend to ignore

tasks that cannot be

measured precisely by the

organization.

Economics and accounting

literature on “crowding out” effect:

Formal controls, such as financial

incentives, are likely to overrule

employees’ intrinsic motivation to

adopt the norms in their

organization.

Research

Question

CHAPTER 3: WORK NORMS AS A CONTROL MECHANISM: IMPLICATIONS FOR


79

FIGURE 2

Theoretical Model

3.3 Research Site

Overview

I examine the hypotheses using performance and personnel data from a large department within

a SOE in China. The manager of the department divides employees into five workgroups to

make daily management more efficient. Each group is managed by one group leader and has

29–35 group members. Employees in the department perform multiple tasks individually to

keep the equipment in the workshops functioning safely and efficiently. Specifically,

employees are required to arrive at and leave the workshops at specified times, and choose their

actions to inspect, operate, and maintain the equipment.


The SOE implements a strict and objective performance measurement and reward system

(PMRS). In particular, the SOE uses a computer system to measure employee actions. Every

time employees arrive at/leave the workshop, inspect certain areas of the workshop, or take

Promotion of the

work norms

Performance on tasks

that can be precisely

measured by the

organization

Performance on tasks

that cannot be

precisely measured

by the organization

Performance

outcome

Link 3 (+)

Link 4 (+)

Link 1 (H1)

Link 2 (H2)



80

certain actions to operate the equipment, they must tap their identification card on the machines

located in different areas of the workshops. The system then makes a record of their actions.

The department manager assesses employee performance by checking the records kept by the

system, and then assigns employees with performance scores based on their actions. By the

end of every month, each employee receives a fixed payment and financial incentive. The

amount of the financial incentive is calculated based on the performance scores that employees

received in the past month. Financial incentives make a relatively small proportion of the

overall compensation received by employees (approximately 0–8%). The performance

measurement, performance score allocation, and compensation method are pre-specified by the

upper-level management of the SOE, and requires little judgment from the department manager

or group leaders.

Control Problem

The system adopted by the SOE can only capture some key actions of employees, but cannot

capture all the actions taken by employees. For example, the computer system can record

whether employees have conducted routine inspections in certain areas as required. However,

it cannot measure the exact actions that employees engage in when inspecting the equipment.

Therefore, employees may tap their cards without inspecting the equipment carefully.

As the compensation structure is strictly specified by the upper-level management, the

department manager and group leaders cannot manipulate compensation to motivate employee

performance. Employees tend to have low motivation because of small financial incentives and

the imprecise measurement of their performance. That is, the department faces a problem

typical to multi-task settings—motivating employees to engage in desirable actions that can be

precisely measured and those that cannot be precisely measured by the organization.



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Promotion of Work Norms

From 2008 to 2014, two events that occurred at the research site provide an opportunity to

investigate how managers’ promotion of work norms affects employee performance.

Specifically, leaders of Groups 3 and 4 have taken actions to promote work norms within their

groups since 2009 and 2011, respectively. The work norms that the two managers have

promoted require their group members to “work hard and responsibly, focus on the details

without overlooking any steps or tiny issues in the daily operation, try to avoid any mistake

and detect any hidden risk”. The aim is to “improve employees’ sense of responsibility and

working quality, improve the safety and efficiency of the operation, and create a good working

environment for everyone”. Employees who have conducted these work norms “will be

recognized, even when their actions are not recognized by the PMRS of the organization”1.

Group leaders at the research site post performance requirements in their workshops. The

performance requirements are pre-specified by the SOE, and illustrate the actions that

employees should take and the performance score linked to each of the actions. The two group

leaders (Groups 3 and 4) add the work norms to the performance requirements disclosed within

their workgroups, so everyone in their groups can see these norms. They also focus employees

on these norms by providing informal feedback and recognition. An example of the

performance requirements disclosed in different workgroups is illustrated in Appendix A.

In addition to the promotion of work norms, at the end of 2011, the leader of Group 3 retired.

The new group leader arrived on January 2012, and strictly followed the performance

requirements specified by the SOE without promoting the work norms. This event provides an

1 These quotes were extracted directly from the performance requirements disclosed by the group leaders in

their workshops, and were translated from Chinese into English.



82

opportunity to investigate how employees in Group 3 reacted when work norms were removed.

According to the group leaders and the documents in the department, there was no other major

event which occurred during the periods that work norms were introduced or removed.

3.4 Method

Quasi-natural Experiments

Based on the events at the research site, I construct quasi-natural experiments to examine the

hypotheses. The term “quasi-natural experiment” describes a naturally-occurring contrast

between a treatment and a control condition, where the assignment of the condition is not

random (Shadish, Cook, and Cambell 2002). In the research site, the change (i.e., promotion

or removal of work norms) occurs naturally in some workgroups (i.e., Groups 3 and 4), but not

others. The five workgroups were divided to make the shifts and the daily operation more

manageable, so theoretically the allocation of employees should be random. However, in a field

site, the assumption of random allocation may not hold strictly. Therefore, the events in the

research site satisfy the definition of quasi-natural experiment.

I construct two quasi-natural experiments to examine how employees reacted to the group

leaders’ promotion of work norms. Specifically, the treatment in quasi-natural experiment 1 (2)

is Group 3 (Group 4), the leader of which started to promote work norms since January 2009

(January 2011). I examine how employees reacted to the promotion of work norms, using those

from the other three groups (Group 1, 2, and 5) as control. Figure 3 shows the timeline of the

events and the groups in the research site.

CHAPTER 3: WORK NORMS AS A CONTROL MECHANISM: IMPLICATIONS FOR EMPLOYEE PERFORMANCE

83

FIGURE 3

Timeline and Groups in the Research Site

Managers’ Promotion of Work Norms

2008 2009 2010 2011 2012 2013 2014 2015 Overall

Group 1 No No No No No No No No Control


Group 3 No Yes Yes Yes No No No No Treatment for Exp 1

Group 4 No No No Yes Yes Yes Yes Yes Treatment for Exp 2


Jan. 2008

Sample period begins

Dec. 2014

Sample period ends

New group leader arrived

in Group 3 and stopped

promoting work norms

Jan. 2012

Jan. 2011

Group leader in Group 4

started to promote work

norms

Group leader in Group 3

started to promote work

norms

Jan. 2009



84

Data and Sample

I collected performance and personnel data to examine the hypotheses. I first extracted

performance data from the database of the SOE. In the research site, individual performance is

measured by the department manager at a monthly basis. The original sample includes

performance observations from January 2008 to December 2014 (i.e., 92 months). I then

obtained the information of employees’ ability and demographic information from the

personnel files of the SOE. Employees’ ability and demographic characteristic are time-

invariant.

Panel A of Table 1 reports the sample selection process. During the sample periods of quasi-

natural experiment 1 and 2, some employees were absent from their groups for one or more

months, for such reasons as sick leave. Some employees left their group permanently during

the sample periods because of retirement. I excluded these employees from the sample and

only kept those who had worked in their group for the whole sample period. Specifically, the

sample period of quasi-natural experiment 1 is from January 2008 to December 2011, as the

treatment (Group 3) started to promote the work norms since January 2009, and removed the

work norms at the end of 2011. The purpose of quasi-natural experiment 1 is to examine how

employees in the treatment (Group 3) reacted to the promotion of the work norms. There are

97 employees who worked for all the months during the sample period (72 from control and

25 from treatment). The 97 employees correspond with 4,656 performance observations in the

sample period (3,456 from control and 1,200 from treatment). For experiment 2, the sample

period is from January 2008 to December 2014, as the treatment (Group 4) started to promote

work norms since January 2011. The purpose of quasi-natural experiment 2 is to examine how

employees in the treatment reacted to the promotion of the work norms. After excluding those

who did not work in their group for the whole sample period, there are 72 employees (52 from



85

control and 20 from treatment) and 6,048 performance observations (4,368 from control and

1,860 from treatment) in the final sample.

Panel B of Table 1 presents the demographic characteristics of employees, and compares the

treatment and control conditions. For experiment 1, the average age of employees in the

treatment condition is 40.38 years old by the end of 2011. The average tenure of employees is

21.63 years by that time. It means that most employees in this department joined the company

during the late 1980s and early 1990s. The small difference between their age and tenure (about

19 years) suggests that most employees entered the company immediately after they graduated

from high school or vocational school, and chose to stay in the company until retirement. Four

percent of employees had a short horizon (i.e., five years or less to retire) by the end of 2011.

The average skill level of employees is about 1.8, which is between 1 (low) and 2 (medium).

In the treatment condition, 28% of the employees are female, and 84% of the employees came

from the city where the SOE is located. Most employees graduated from senior high school or

vocational school. In the treatment condition, 20% of the employees have joined the Chinese

Communist Party. The sample structure of experiment 2 is similar to the structure of

experiment 1. As the end of the sample period of experiment 2 is December 2014, the average

age and tenure of employees increases by four years and the percentage of employees with

short horizon increases to 25%. In both experiments, employees in the treatment condition are

not significantly different from those in the control condition. The variables are explained in

detail in the following subsections and in Appendix B.


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TABLE 1

Sample Selection and Structure

Panel A: Sample Selection Process Quasi-Natural Experiment 1 Quasi-Natural Experiment 2 Original Final Sample Original Final Sample

Employees Employees Observations Employees Employees Observations

Group 1 34 24 1,152 41 17 1,428

Group 2 34 23 1,104 40 19 1,596

Group 5 31 25 1,200 35 16 1,344

Control-Total 99 72 3,456 116 52 4,368

Treatment-Total 33 25 1,200 36 20 1,680

Total 132 97 4,656 152 72 6,048

Panel B: Sample Structure

Quasi-Natural Experiment 1 Quasi-Natural Experiment 2

Control

(N = 72) Treatment

(N = 25) Difference

(T-C) Control

(N = 52) Treatment

(N = 20) Difference

(T-C)

age 41.22 40.38 0.84 44.03 44.13 −0.10

tenure 22.26 21.63 0.64 25.19 25.06 −0.10

horizon 0.04 0.04 0.00 0.15 0.25 −0.10

level 1.79 1.80 −0.01 1.83 1.65 0.18

gender 0.18 0.28 −0.10 0.19 0.15 0.04

home 0.69 0.84 −0.15 0.67 0.85 −0.18

edu 0.71 0.76 −0.05 0.65 0.60 0.05

CCP 0.13 0.20 −0.08 0.12 0.10 0.02 This table presents the sample selection process and sample structure. See variable definitions in Appendix B. Panel A presents the number of employees and observations used in the

analysis. I exclude employees who left their group or took leave during the sample period. The sample period is from January 2008 to December 2011 for experiment 1, and from

January 2008 to December 2014 for experiment 2. The control group includes Groups 1, 2, and 5, which followed the performance measurement and reward rules set by the SOE

without promoting work norms. The treatment group is Group 3 for experiment 1 and Group 4 for experiment 2, the group leaders of which started to promote work norms from January

2009 and January 2011, respectively. Panel B compares the mean of the demographic characteristics of employees in the control and treatment conditions. Employee age, tenure, and

horizon are calculated by December 2011 for experiment 1, and by December 2014 for experiment 2. None of the difference in Panel B is statistically significant.



87

Empirical Models

In quasi-natural experiment 1, Group 3 started to promote work norms from January 2009; in

quasi-natural experiment 2, Group 4 started to promote work norms from January 2011. The

control condition includes the other three groups (Groups 1, 2, and 5), which did not promote

work norms from 2008 to 2014. I examine how the promotion of work norms affects employee

performance using the following equations:

Measured = β1Postit + β2Treatmenti + β3(Postit × Treatmenti) + β4tenureit + β5horizoni

+ β6leveli + β7genderi + β8homei + β9CCPi + εit (1)

Unmeasured = β1Postit + β2Treatmenti + β3(Postit × Treatmenti) + β4tenureit + β5horizoni

+ β6leveli + β7genderi + β8homei + β9CCPi + εit (2)

Equation (1) examines how the promotion of work norms is related to employee performance

that can be precisely measured (Measured); while equation (2) examines how the promotion

of work norms is related to employee performance that cannot be precisely measured

(Unmeasured). The coefficient (β1) on the indicator for the post-change period (Post) captures

the general trend experienced by both the treatment and the control, after the treatment started

to promote work norms. The coefficient (β2) on the indicator for the treatment condition

(Treatment) captures any group feature(s) of the treatment. According to the group leaders and

the documents, there was no other major event occurring when group leaders in the treatment

conditions started to promote the work norms. Therefore, the coefficient (β3) on the interaction

of the two indicators captures how employees in the treatment reacted to the promotion of the

work norms. The hypotheses expect β3 to be significantly positive in both equations, which

means that in the treatment conditions, employees’ performance on both types of tasks



88

increased significantly after the promotion of work norms. The details of the variables are

presented as follow.

Variables

Dependent variables: The first dependent variable (Measured) is employee actions which are

measured by the organization on a monthly basis. Measured is the performance score that

employees received for their attendance and operational actions, which are measured by the

organization. In terms of attendance, employees lose scores if they are absent from the

workshop during their shift, including coming later and leaving earlier than the specified time.

The lowest score that an employee can earn for attendance is −160, which means the employee

is absent for the whole month and lost all of his/her financial incentives. Employees earn scores

if they come earlier than the specified time of their shift. The highest score that an employee

can earn for attendance is 10.

In terms of operational actions, employees’ tasks include inspecting, operating, and

maintaining equipment. Employees lose scores if they fail to follow certain pre-specified

operational procedures, and earn scores if they follow certain pre-specified procedures.

Employees can lose scores for some procedures and earn scores for others. The lowest score

that an employee can earn for operational actions is −10, which means the employee fails to

follow any of the pre-specified procedures. The highest score that an employee can earn for

operational actions is 90. This means that in the past month, the employee has followed all pre-

specified procedures that are optimal to the conditions of the equipment without overlooking

any step or making any mistake. Employees need to use both their effort and judgment to

analyze the exact condition of the equipment and take the appropriate actions.



89

The second dependent variable, Unmeasured, is a noisy measure for employee actions that are

not directly measured by the organization, which is constructed using the following equation:

Figureit = β0 + β1Measuredit + εit

Figure is the performance score that employees receive for their operational outcomes.

Employees’ operational actions (both measured and unmeasured actions) affect the status and

efficiency of the equipment, including temperature and pressure, the electricity consumed by

the equipment, and the waste generated by the equipment. These figures are captured by

specialized machines and recorded in the organization’s computer system. The organization

assigns performance scores to employees by comparing the figures in employees’ shifts with

the optimal ranges of these figures. The highest (lowest) score that employees can earn for

these figures is 40 (−15). However, the performance scores usually earned by employees for

these figures is between −2 and +2, as the fluctuations of the figures are usually small. Figure

is affected by both the measured and unmeasured actions of employees, as well as external

factors such as the weather and the quality of fuel. The residual (ε) of the equation thus captures

the unmeasured actions of employees and the random errors. I use the residual as a noisy proxy

for employees’ unmeasured actions, which is denoted by Unmeasured.

Independent variables: To examine how the group leader’s promotion of work norms affects

employee performance, I construct a difference-in-difference design. The treatment indicator

is Treatment, which is 1 for observations in the group that promoted work norms, and 0 for

observation in the control condition. For experiment 1, Treatment is 1 for observations in

Group 3, which started to promote work norms since January 2009; and 0 for observations in

Groups 1, 2, 5, which did not promote work norms. For experiment 2, Treatment is 1 for



90

observations in Group 4, which started to promote work norms since January 2011; and 0 for

observations in Groups 1, 2, 5. The time indicator is Post. For experiment 1, the sample period

is from 2008 to 2011. Post indicates periods (i.e., months) in and after 2009, as the treatment

(i.e., Group 3) started to promote work norms since January 2009. For experiment 2, the sample

period is from 2008 to 2014. Post indicates periods in and after 2011, as the treatment (i.e.,

Group 4) started to promote work norms since January 2011. The coefficient on the interaction

between Post and Treatment thus captures how employees in the treatment condition react

toward the promotion of the work norms.

Control variables: To minimize any bias in estimates as a result of omitted variables, I

controlled for employees’ skill level and demographic characteristics. Employees’ skill level

(level), is assessed by the SOE through exams. Every three years, the SOE holds an exam to

assess employees’ ability in analyzing the equipment condition and choosing the appropriate

actions. The SOE assign skill levels to employees based on their examine results. The skill

levels include 0 (none), 1 (low), 2 (medium), and 3 (high). The demographic characteristics

include employees’ tenure (tenure) and horizon (horizon), gender (gender), whether they came

from the city where the SOE is located or other regions of China (home), level of education

(edu), and whether they are members of the Chinese Communist Party (CCP). 2 When

estimating the empirical models, I also control for time (month) and group fixed effects, and

2 I controlled tenure and gender as the tasks involve operating heavy machinery, and employees’ physical

conditions may significantly affect their performance. I controlled horizon as employees who approach

retirement may have less concern for their reputation in the company, and thus have lower motivation. I

controlled for home, as the SOE is the principal driver of the local economy and employees from the local area

may be motivated to work hard and contribute to the economy in their hometown. Further, I controlled for edu,

as employees with higher education levels may have received more training and have higher ability to perform

their tasks. CCP is controlled as previous research indicates that when the goals and beliefs of employees are

congruent with those of their organizations, employees tend to have higher motivation and better performance

(Rich et al. 2010). As the research site is state-owned, the political beliefs of employees may affect their

motivation and performance. I did not control employees’ age (age), as it is highly correlated with tenure.



91

correct the standard errors for clustering within employees. The detailed variable definition is

presented in Appendix B. Figure 4 visually presents the empirical model of this study.

FIGURE 4

Empirical Model

3.5 Results


Table 2 presents the mean of employee performance in control and treatment conditions. In

quasi-natural experiment 1, Measured was small in both conditions in the pre-change period.

Specifically, the mean of Measured was 5.91 (6.24) in the control (treatment) condition in 2008.

After the group leader started to promote work norms since January 2009, Measured increased

significantly in both conditions. In the control condition, Measured increased to 9.76 (p < 0.01);

while in the treatment condition, it increased to 17.83 (p < 0.01). The increase in the treatment

condition is significantly larger than the increase in the control condition (p < 0.01). The

changes of Unmeasured exhibit a similar pattern. In the control condition, Unmeasured

Group leader’s

promotion of work norms

(Post × Treatment)

Attendance and operational

actions precisely measured

by the organization

(Measured)

Operational actions not

precisely measured by the

organization (Unmeasured)

Equipment

efficiency and

safety (Figure)

(+)

(+)

(?)

(?)



92

increased from −0.47 to 0.42 (p < 0.01); whereas in the treatment condition, it increased from

−0.65 to 1.21 (p < 0.01). For quasi-natural experiment 2, Measured increased in both conditions

after 2011, and the increase in the treatment is significantly larger (p < 0.01). Unmeasured

decreased from 0.22 to 0.09 (p < 0.10) in the control condition, but increased from −0.67 to

−0.40 (p < 0.05) in the treatment condition. Overall, the changes in employee performance in

the two experiments are consistent with the argument that managers’ promotion of work norms

encourage desirable employee actions. Figure 5 visually illustrates these changes.


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TABLE 2

Descriptive Statistics of Employee Performance

Panel A: Quasi-Natural Experiment 1

Controla

(Group 3) Treatmentb

(Group 1, 2, 5) Treatment - Control

Measured Performance:

Measured

Pre: Jan to Dec 2008 5.91 6.24 0.33***

Post: Jan 2009 to Dec 2010 9.76 17.83 8.07***

Post - Pre 3.85*** 11.59*** 7.74***

Unmeasured Performance:

Unmeasured

Pre: Jan to Dec 2008 −0.47 −0.65 −0.19***

Post: Jan 2009 to Dec 2010 0.42 1.21 0.79***

Post - Pre 0.89*** 1.86*** 0.98***

Panel B: Quasi-Natural Experiment 2

Controlc

(Group 4)

Treatmentd

(Group 1, 2, 5) Treatment - Control

Measured Performance:

Measured

Pre: Jan 2008 to Dec 2010 8.00 9.93 1.93***

Post: Jan 2011 to Dec 2014 14.22 21.26 7.04***

Post - Pre 6.22*** 11.33*** 5.12***

Unmeasured Performance:

Unmeasured

Pre: Jan 2008 to Dec 2010 0.22 −0.67 −0.90***

Post: Jan 2011 to Dec 2014 0.09 −0.40 −0.50***

Post - Pre −0.12* 0.27** 0.40***

This table compares the mean of employee performance in the control and treatment conditions, before and after the treatment group (i.e., Group 3 for experiment 1 and Group 4 for

experiment 2) started to promote work norms in January 2009 and January 2011, respectively. See variable definitions in Appendix B. ∗, ∗∗, and ∗∗∗ are significant at 10%, 5%, and

1% levels, respectively. a Control condition for experiment 1 includes observations from Groups 1, 2, and 5, which followed the performance measurement and reward rules set by the SOE without promoting

work norms. Before and after the change, there were 864 and 2,592 observations in the control condition, respectively. b Treatment condition for experiment 1 includes observations from Group 3, the group leader of which started to promote work norms in January 2009. Before and after the change,

there were 300 and 900 observations in the treatment condition, respectively. c Control condition for experiment 2 includes observations from Groups 1, 2, and 5, which followed the performance measurement and reward rules set by the SOE without promoting

work norms. Before and after the change, there were 1,872 and 2,496 observations in the control group, respectively. d Treatment condition for experiment 2 includes observations from Group 4, the group leader of which started to promote work norms in January 2011. Before and after the change,

there were 720 and 960 observations in the control group, respectively.


94

FIGURE 5

Promotion of Work Norms and Employee Performance

Quasi-Natural Experiment 1 Quasi-Natural Experiment 2

Control: Groups 1, 2, 5, which did not promote work norms; Control: Group 1, 2, 5, which did not promote work norms;

Treatment: Group 3, which started to promote work norms in 2009 Treatment: Group 4, which started to promote work norms since 2011



95

Correlations among Variables

Table 3 presents the Pearson correlations for both experiments. Panel A shows the correlations

for experiment 1. Specifically, both Measured and Unmeasured are positively correlated with

age, level, and home. It indicates that older employees, employees with high skill level, and

those recruited from the local area tend to perform better than the other employees. Both

Measured and Unmeasured are negatively related to horizon, which is consistent with the

expectation that employees who approach retirement tend to have lower motivation.

Additionally, Measured is also positively related to tenure, edu, and CCP, which suggests that

employees with longer tenure, high education, and who joined the Communist Party tend to

engage in more desirable actions that can be precisely measured by the organizations. Panel B

presents the correlations for experiment 2. It shows that Measured and Unmeasured are

negatively correlated. This may be because if employees exert higher effort in one type of

action, they would give less effort for another type of action. Similar to experiment 1, Measured

is positively related to age, tenure, level, and home. It is also negatively related to edu and

gender. As the tasks in the research site require manual labor, it may limit the performance of

female employees. As for the negative correlation with edu, this may be because employees

with higher education (especially an undergraduate degree or higher) tend to be younger and

less experienced than other employees. Similar to experiment 1, Unmeasured is positively

related to home and level, and negatively related to horizon. In both experiments, most of the

control variables are significantly correlated with each other. I use variance inflation tests (VIFs)

to ensure the estimations are not subject to a multicollinearity problem.


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TABLE 3

Pearson Correlations

Panel A: Quasi-Natural Experiment 1 (N = 4,656)

Measured Unmeasured age tenure horizon level gender home edu

Unmeasured 0.00***

age 0.04*** 0.03***

tenure 0.04*** 0.02*** 0.92***

horizon −0.03*** −0.04*** 0.36*** 0.35***

level 0.12*** 0.04*** −0.01*** −0.06*** −0.20***

gender −0.02*** −0.03*** −0.04*** −0.03*** 0.05*** −0.04***

home 0.08*** 0.06*** 0.04*** 0.07*** −0.10*** 0.12*** 0.14***

edu 0.03*** 0.00*** −0.44*** −0.55*** −0.16*** 0.08*** 0.07*** −0.05***

CCP 0.06*** 0.02*** 0.03*** 0.05*** −0.05*** −0.12*** −0.21*** 0.05*** 0.09***

Panel B: Quasi-Natural Experiment 2 (N = 6,048)

Measured Unmeasured age tenure horizon level gender home edu

Unmeasured −0.04***

age 0.11*** −0.01***

tenure 0.11*** 0.00*** 0.89***

horizon 0.00*** −0.05*** 0.37*** 0.26***

level 0.09*** 0.02*** 0.02*** −0.05*** −0.12***

gender −0.07*** −0.01*** −0.04*** −0.03*** 0.15*** −0.15***

home 0.08*** 0.02*** −0.01*** 0.07*** 0.10*** −0.09*** 0.13***

edu −0.03*** −0.01*** −0.36*** −0.52*** −0.06*** 0.21*** 0.11*** −0.12***

CCP −0.02*** −0.01*** 0.05*** 0.01*** −0.01*** −0.02*** −0.17*** 0.12*** −0.01*** This table presents the Pearson correlations for quasi-natural experiment 1 (Panel A) and quasi-natural experiment 2 (Panel B). For quasi-natural experiment 1, the sample period is

from January 2008 to December 2011, as the treatment (Group 3) started to promote the work norms since January 2009, and removed the work norms at the end of 2011. The purpose

of quasi-natural experiment 1 is to examine how employees in the treatment (Group 3) reacted to the promotion of the work norms. There are 97 employees and 4,656 performance

observations in the sample. For experiment 2, the sample period is from January 2008 to December 2014, as the treatment (Group 4) started to promote work norms since January 2011.

The purpose of quasi-natural experiment 2 is to examine how employees in the treatment reacted to the promotion of the work norms. There are 72 employees and 6,048 performance

observations in the sample. See variable definitions in Appendix B. ∗, ∗∗, and ∗∗∗ are significant at 10%, 5%, and 1% levels, respectively.



97

Promotion of Work Norms and Employee Performance

Table 4 presents the regression estimations. Columns (1) and (2) present the results for quasi-

natural experiment 1. Column (1) shows that since the group leader in Group 3 started to

promote the work norms from January 2009, Measured increased significantly (β = 2.18, t =

2.77). Column (2) indicates that Unmeasured also experienced a significant increase after the

change (β = 1.05, t = 4.05). Columns (3) and (4) suggest that similar patterns exist in quasi-

natural experiment 2, as both Measured and Unmeasured increased significantly in Group 4

after the group leader started to promote the work norms in January 2011. Overall, these results

are consistent with the hypotheses, by showing that the promotion of work norms motivate

desirable employee actions that can be precisely measured and those that cannot be precisely

measured.

As for the other variables in the model, Post is positively related to Unmeasured in experiment

1, and positively (negatively) related to Measured (Unmeasured) in experiment 2. Treatment

is positively related to Unmeasured (Measured) in experiment 1 (2). These results are

consistent with the patterns shown in the descriptive statistics. In terms of control variables,

level is positively related to Measured in both experiments. This is consistent with the fact that

employees with higher skill level tend to have higher ability in analyzing the equipment

conditions and choosing the appropriate actions. The coefficients of the other control variables

are not consistently significant across the estimations.



98

Table 4

Managers’ Promotion of Work Norms and Employee Performance

Quasi-Natural Experiment 1a Quasi-Natural Experiment 2b

Measured Unmeasured Measured Unmeasured

(1) (2) (3) (4)

Post 1.72 0.88* 9.59* −1.60***

(0.41) (1.86) (1.94) (−4.12)

Treatment 1.00 1.14*** 5.52*** 0.18

(0.40) (6.70) (3.46) (1.03)

Post × Treatment 8.18*** 1.05*** 5.13*** 0.42*

(2.77) (4.05) (4.50) (1.92)

tenure −0.06 −0.02 −0.18 0.00

(−0.35) (−1.12) (−1.07) (0.09)

horizon −0.01 −0.22 −2.69 −0.15

(−0.00) (−0.55) (−1.59) (−0.61)

level 3.98*** 0.13 3.59*** −0.02

(2.71) (0.80) (2.67) (−0.12)

gender −0.97 −0.49* −1.29 −0.23

(−0.53) (−1.70) (−0.97) (−1.14)

home 1.80 −0.10 2.26 −0.05

(0.98) (−0.51) (1.56) (−0.37)

edu −0.03 −0.28 −1.66 −0.21

(−0.02) (−1.59) (−1.59) (−1.37)

CCP 3.50 −0.00 −0.76 −0.42**

(1.57) (−0.00) (−0.38) (−2.50)

Time fixed effect Controlled Controlled Controlled Controlled

Group fixed effects Controlled Controlled Controlled Controlled

Log-likelihood/

Adjusted R2 −20108.22 17.79% −24742.38 16.22%

N 4,656 4,656 6,048 6,048

This table presents the results of the regression estimations. The empirical models are presented as follow:

Measured = β1Postit + β2Treatmenti + β3(Postit × Treatmenti) + β4tenureit + β5horizoni + β6leveli + β7genderi + β8homei

+ β9CCPi + εit (1)

Unmeasured = β1Postit + β2Treatmenti + β3(Postit × Treatmenti) + β4tenureit + β5horizoni + β6leveli + β7genderi

+ β8homei + β9CCPi + εit (2)

See variable definitions in Appendix B. ∗, ∗∗, and ∗∗∗ are significant at 10%, 5%, and 1% levels, respectively. Standard

errors are corrected for clustering within employees. Because of the nature of the tasks and the performance

measurement used in the research site, the highest (lowest) measured performance (i.e., Measured) that employees can

achieve is 100 (−160). The estimations for Measured are thus censored at −160 and 100. a Columns (1) and (2) present the results for experiment 1. The sample includes 97 employees and 4,656 performance

observations from January 2008 to December 2010. The control condition includes observations in Groups 1, 2, and 5;

these groups strictly followed the PMRS imposed by the organization and did not promote work norms. The treatment

condition includes observations in Group 3, which started to promote work norms since January 2009 and removed the

work norms at the end of 2011. b Columns (1) and (2) present the results for experiment 2. The sample includes 72 employees and 6,048 performance

observations from January 2008 to December 2014. The control condition includes observations in Groups 1, 2, and 5;

these groups strictly followed the PMRS imposed by the organization and did not promote work norms. The treatment

condition includes observations in Group 4, which started to promote work norms since January 2011.



99

Further, I check whether the association between work norms and employee performance

varies across different types of employee. I divide employees into different subsamples based

on their demographic characteristics. The coefficients on the interaction between Post and

Treatment estimated using different demographic groups are presented in Table 5. In both

experiments, female employees, employees with lower education level, and Communist Party

members tend to be less reactive toward the promotion of work norms. As the tasks in the

research site require judgment, it may limit the performance of those with lower education level.

Additionally, the tasks in the research site also require manual labor, which may limit the

performance of female employees. That party members tend to have insignificant reactions

may be because the small number of party members in the research site reduces the significance

of the results, or because political affiliation plays some role in moderating employee reactions.



100

Table 5

Managers’ Promotion of Work Norms and Employee Performance:

Estimations by Demographic Groups

Quasi-Natural Experiment 1a Quasi-Natural Experiment 2b

Measured Unmeasured Measured Unmeasured

tenure

short 10.04** 1.00** 6.14*** 0.77*

(2.09) (2.64) (3.17) (1.96)

long 6.56* 1.07*** 4.44*** 0.19

(1.90) (2.99) (3.17) (0.69)

level

low 9.44*** 1.27** 6.03** 0.67

(3.95) (2.50) (2.50) (1.57)

high 7.28* 0.93*** 4.66*** 0.28

(1.94) (3.05) (3.77) (1.06)

gender

female 8.17 0.75 4.81*** 0.42

(1.50) (1.62) (2.63) (0.94)

male 8.02** 1.16*** 4.98*** 0.44*

(2.31) (3.77) (3.80) (1.87)

edu

low 9.62 0.79 5.52*** 0.11

(1.51) (1.59) (3.66) (0.31)

high 8.19** 1.22*** 5.00*** 0.62**

(2.55) (4.03) (3.10) (2.32)

home

local 6.61** 0.93*** 4.10*** 0.42

(2.04) (3.34) (3.51) (1.62)

foreign 10.46** 1.01 9.40*** 0.30

(2.43) (1.14) (4.85) (0.65)

CCP

yes 5.23 1.05 2.25 0.99

(1.51) (1.21) (0.66) (1.15)

no 8.90** 1.03*** 5.48*** 0.35

(2.50) (3.76) (4.63) (1.58)

This table presents the coefficients of Post × Treatment (i.e., the estimated treatment effect) of different demographic

groups. The empirical models are presented as follow:





See variable definitions in Appendix B. ∗, ∗∗, and ∗∗∗ are significant at 10%, 5%, and 1% levels, respectively. Standard

errors are corrected for clustering within employees. Because of the nature of the tasks and the performance

measurement used in the research site, the highest (lowest) measured performance (i.e., Measured) that employees can

achieve is 100 (−160). The estimations for Measured are thus censored at −160 and 100. a The sample of experiment 1 includes 97 employees and 4,656 performance observations from January 2008 to December

2010. The control condition includes observations in Groups 1, 2, and 5; these groups strictly followed the PMRS

imposed by the organization and did not promote work norms. The treatment condition includes observations in Group

3, which started to promote work norms in January 2009. b The sample of experiment 2 includes 72 employees and 6,048 performance observations from January 2008 to

December 2014.. The control condition includes observations in Groups 1, 2, and 5; these groups strictly followed the

PMRS imposed by the organization and did not promote work norms. The treatment condition includes observations in

Group 4, which started to promote work norms in January 2011.



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Removal of Work Norms and Employee Performance

The third event in the research site is that the group leader in Group 3 retired in December 2011.

The new group leader arrived in January 2012 and did not continue to promote the work norms.

I use employee performance data from 2009 to 2014 (i.e., 72 months) to examine how

employees in Group 3 reacted to the removal of the work norms, with employees from Groups

1, 2, and 5 as control. Table 6 presents the results. Panel A shows how employee performance

changed in both the control and the treatment conditions (Figure 6 visually illustrates these

changes). After 2012, Measured increased from 10.97 to 14.30 in the control condition, and

decreased from 17.90 to 12.41 in the treatment condition. The difference between the treatment

and the control changed from 6.92 (p < 0.01) to −1.89 (p < 0.01). Unmeasured decreased in

both conditions, and the difference between the decreases in the two conditions is not

significant. Panel B of Table 6 presents the regression estimations, which indicate that after the

removal of the work norms, Measured in the treatment condition decreased significantly (t =

−6.15). Unmeasured also decreased, but the decrease is not statistically significant (t = −0.45).

Overall, the results presented in Table 6 indicate that, for tasks that can be precisely measured,

the performance impact of work norms only persists when managers keep taking actions to

promote them. In comparison, for tasks that cannot be precisely measured, the performance

impact of work norms does not decline even when managers stop taking actions to promote

these norms. This may be because for tasks that cannot be precisely measured, work norms

provide employees with the guidelines, and frame their beliefs about how they should perform

these tasks. These results further demonstrate that managers’ promotion of work norms can

function as an effective control mechanism, especially for tasks that cannot be precisely

measured.



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Table 6


Panel A: Change of Employee Performance

Control (Group 3)

Treatment (Group 1, 2, 5) T - C

Measured

Pre: Jan 2009–Dec 2011 10.97 17.90 6.92***

Post: Jan 2012–Dec 2014 14.30 12.41 −1.89***

Post - Pre 3.33*** −5.49*** 8.81***

Unmeasured

Pre: Jan 2009–Dec 2011 0.48 1.38 0.90***

Post: Jan 2012–Dec 2014 0.02 0.75 0.74***

Post - Pre −0.46*** −0.62** −0.16***

Panel B: Regression Estimations

Measured Unmeasured

(1) (2)

Post 10.53* −1.76***

(1.79) (−4.04)

Treatment 10.47*** 2.09***

(3.24) (8.23)

Post × Treatment −8.71*** −0.12

(−6.15) (−0.45)

Control variable Controlled Controlled

Time fixed effect Controlled Controlled

Group fixed effects

Controlled Controlled

Log-likelihood/

Adjusted R2 −20225.14

19.98%

N 4,757 4,757

This table presents how employees in Group 3 reacted to the new group leader stopping promoting work norms since

January 2012, with employees in Groups 1, 2 and 5 as control. Sample period is from January 2009 to December 2014.

The empirical models are presented as follow:





Panel A presents the mean of employee performance before and after this change; while Panel B presents the regression

estimations. See variable definitions in Appendix B. ∗, ∗∗, and ∗∗∗ are significant at 10%, 5%, and 1% levels,

respectively. Standard errors are corrected for clustering within employees. Because of the task nature and the

performance measurement used in the research site, the highest (lowest) measured performance (i.e., Measured) that

employees can achieve is 100 (−160). The estimations for Measured are thus censored at −160 and 100.



103

FIGURE 6


Control: Groups 1, 2, 5, which did not promote work norms

Treatment: Group 3, which removed work norms since 2012



104

Robustness Checks

In the robustness tests, I first winsorize Performance at 5% and 1% to ensure that the results

are not distorted by the extreme values in employee performance. Further, as the dataset in this

study involves multiple periods, I include time-series variables in the models to check the

robustness of the results. After including the lag of Performance in the estimations, the


Performance are significantly positive, suggesting that employee performance persists over

time. Some control variables lose significance, while conclusions remain the same. Moreover,

I include control variables in the model used to estimating Unmeasured. The control variables

include employees’ skill level, demographic characteristics, as well as group and time fixed

effects. Controlling these variables in the estimation of Unmeasured does not change the

conclusions.


This study investigates whether managers’ promotion of work norms functions as an effective

control mechanism in a multi-task setting. Psychology studies find that focusing individuals on

a particular norm motivate individuals to follow the norm (Cialdini et al. 1990, 1991). This

study examines whether this approach can motivate employee performance in a multi-task

setting, where the use of financial incentives is subject to high monetary costs and imprecise

performance measurement. On the one hand, the use of financial incentives may overrule

employees’ motivation to adopt work norms (Gneezy and Rustichini 2000; Taylor and

Bloomfield 2010). On the other hand, work norms may complement financial incentives by

guiding and motivating employees to perform their tasks in the right ways. Following work

norms promoted by managers not only help employees earn higher financial incentives from

the tasks that can be precisely measured, but also promotes their self-image as group



105

participants (Akerlof and Kranton 2000, 2003; Ashforth and Meal 1989; Hogg and Terry 2000).

Therefore, it is an empirical question whether managers can motivate employee performance

by deliberately promoting work norms that contribute to the desired organizational outcome.

This study examines this question using performance and personnel data from a large

department within a Chinese SOE. The department has five workgroups and faces a typical,

salient control problem, which is motivating employee performance across multiple tasks. The

use of financial incentives is not sufficient to alleviate this problem, as the upper-level

management strictly controls the amount of financial incentive and some employee actions

cannot be precisely measured. Leaders in two of the five workgroups have taken actions to

promote the work norms, which require employees to engage in the desirable actions that can

be precisely measured by the organization, as well as those that cannot. I compare employee

performance before and after the promotion of the works norms, with employees in the other

three groups as control. The results indicate that the promotion of work norms motivates high

employee performance in both tasks that can and cannot be precisely measured by

organizations. One of the groups stopped promoting the work norms in 2012 because of a

change in leader. Employees’ performance on tasks that can be precisely measured dropped

significantly, while their performance on tasks that cannot be precisely measured does not

changed significantly afterwards. These findings indicate that managers’ promotion of work

norms functions as an effective control mechanism, especially for tasks that cannot be precisely

measured.

The contribution of this study is threefold. First, it contributes to the literature on management

controls and employee performance by documenting managers’ promotion of work norms as

an effective control mechanism. The existing literature suggests that financial incentives are



106

effective in motivating employee performance (Bonner and Sprinkle 2002; Kachelmeier et al.

2008; Sprinkle 2000). However, the use of financial incentives is subject to high monetary cost

and potential unintended consequences (Holmstrom and Milgrom 1991; Roberts 2010). This

study documents that managers’ promotion of work norms can complement financial

incentives by motivating employees’ desirable actions. Further, this study also contributes to

the broader literature on work norms (Abernethy et al. 2015; Campbell 2012; Gibbons and

Kaplan 2015; Kachelmeier et al. 2008; Maas and Van Rinsum 2013) by showing that managers

can shape work norms in organizations by focusing employees’ attention on the behavioral

principles that contribute to desired organizational outcomes. Finally, this study has practical

implications. It helps managers better understand how to encourage employees to perform their

tasks in ways that are aligned with the strategy and goal of their organizations.

As for the limitations of this study, it is an open question to what extent we can generalize the

findings from a single research site. This study adopted a field site where motivating employee

performance across different tasks is a salient problem, and the promotion of work norms is

used by managers as a control mechanism. The findings might not generalize directly to other

firms with different tasks, incentives, and employees. Future research could replicate and

extend this study in different settings. Further, using archival and personnel data from a field

site, this study cannot, and does not, attempt to demonstrate the causal chain underlying the

association between work norms and employee behavior. Future studies may extend this study

by exploring the mechanisms that drive the association.



107

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109

APPENDIX A

Performance Requirements Disclosed in Different Workgroups

Without Work Norms With Work Norms

1. Attendance

1.1 Being late

1.1.1 Within 30 minutes: −2

1.1.2 30–60 minutes: −4

… …

1.2 Working overtime: Coming earlier

… …

1.3 Working overtime: Leaving late

… …

2. Operation

2.1 Code of conduct

2.2 Operational procedures

2.3 Record keeping

… …

3. Outcomes

3.1 Carbon in the waste

3.2 Temperature of the vapour

3.3 Pressure of the vapour

3.4 Electricity consumed

… …

4. Others

4.1 Uniform and helmet

4.2 Cleanness of the workshops

… …

(This example omitted clauses and sub-clauses under each section)

1. Attendance

1.1 Being late

1.2 Working overtime: Coming earlier

1.3 Working overtime: Leaving late

… …

2. Operation

2.1 Code of conduct

2.2 Operational procedures

… …

2.6 It is important for employees to improve their sense of

responsibility and work quality, improve the safety and

efficiency of their operation, and create a good working

environment for everyone.

2.6.1 Employees should work hard and responsibly, focus on

details without overlooking any steps or tiny issues in the

daily operation, try to avoid any mistakes and detect any

hidden risks.

2.6.2 To encourage these behaviours, employees who

conducted these principles will be recognized by the

group leader, even though their actions are not

recognized by the company.

3. Outcomes

3.1 Carbon in the waste

3.2 Temperature of the vapour

3.3 Pressure of the vapour

3.4 Electricity consumed

… …

4. Others

4.1 Uniform and helmet

4.2 Cleanness of the workshops

… …

(This example omitted clauses and sub-clauses under each section)



110

APPENDIX B

Variable Definitions

Measured

Measured is the performance score that employees receive for their attendance

and operational actions that are measured by the organization. Because of the

task nature and the performance measurement system designed by the

organization, the highest (lowest) performance score that an employee can earn

is 100 (−160). Specifically, Measured is constructed is as following:

In terms of attendance, employees lose marks if they are absent from the

workshop during their shift, including coming later and leaving earlier than the

specified time. The lowest score that an employee can earn for attendance is

−160, which means they are absent for the whole month and lost all of his/her

financial incentives. Employees earn marks if they come earlier or leave later

than the specified time of their shift. The highest score that an employee can

earn for attendance is 10.

In terms of operational actions, employees’ tasks include inspecting, operating,

and maintaining the equipment. Employees lose marks if they fail to follow

certain pre-specified operational procedures, and earn marks if they followed

certain pre-specified procedures. Employees can lose marks for some

procedures and earn them for other procedures. The lowest score that an

employee can earn for operational actions is −10, which means the employee

fails to follow any of the pre-specified procedures. The highest score that an

employee can earn for operational actions is 90. This means that during the past

month, the employee has followed all the pre-specified procedures that are

optimal to the conditions of the equipment without overlooking any step or

making any mistake. Employees must use both their effort and judgment to

choose the right procedures based on the exact conditions of the equipment, in

order to ensure the equipment functions efficiently and safely.

Figure

Figure is the performance score that employees receive for their operational

outcomes. Employees’ operational actions (both measured and unmeasured

actions) affect the status and efficiency of the equipment, such as the

temperature and pressure in the equipment, the electricity consumed by the

equipment, the waste generated by the equipment, etc. These figures are

captured by specialized machines and recorded in the computer system used

by the organization. The organization assigns performance scores to employees

based on the difference between the optimal ranges of the figures and the

figures in employees’ shifts. The highest (lowest) score that employees can

earn for operational outcomes is 40 (−15). However, most of the time, Figure

earned by employees is between −2 and +2, as the fluctuation in figures is

small. Figure is affected by employees’ measured and unmeasured operational

actions, as well as external factors such as the weather and quality of the fuel.

Unmeasured

Unmeasured is a proxy for employees’ operational actions that are not directly

measured by the organization. Besides the measured operational actions,

employees can also engage in other actions that contribute to the safe and/or

efficient function of the equipment. Unmeasured is a noisy measure for these

actions. It is constructed using the following function:

Figure = β0 + β1Measured + ε

The residual (ε) captures both the unmeasured actions of employees and the

random errors. I thus use the residual as a noisy measure for employees’

unmeasured actions.

(continued.)



111

APPENDIX B (continued.)

Treatment

1 for observations in the group that started or stopped promoting work norms,

and 0 for observation in the group of the control condition.

For experiment 1, Treatment is 1 for observations in Group 3, which started to

promote work norms since 2009; and 0 for observations in Groups 1, 2, 5,

which did not promote work norms;

For experiment 2, Treatment is 1 for observations in Group 4, which started to

promote work norms since 2011; and 0 for observations in Groups 1, 2, 5,

which did not promote work norms;

For Table 6, Treatment is 1 for observations in Group 3, which stopped

promoting work norms since 2012; and 0 for observations in Groups 1, 2, 5.

Post

1 for observations in and after the year that the treatment group started to

promote or remove the work norms, or 0 otherwise.

For experiment 1, Post is 1 for periods in and after 2009, as the treatment (i.e.,

Group 3) started to promote work norms after 2009; and 0 for other periods;

For experiment 2, Post is 1 for periods in and after 2011, as the treatment (i.e.,

Group 4) started to promote work norms since 2011; and 0 for other periods;

For Table 6, Post is 1 for periods in and after 2012, as the treatment (i.e., Group

3) removed work norms since 2012; and 0 for other periods.

age Employees’ years of age. Calculated based on employees’ date of birth and the

end date of the sample period.

tenure Number of years that employee i has been working in the SOE. Calculated

based on employees’ date of entry and the end date of the sample period.

horizon

1 for male workers over 49 years and female workers over 44 years, or 0

otherwise. The policy of the SOE specifies that male (female) workers can

retire at 50 (45) years old.

level

Employees’ skill level measured by the SOE; spans from 0 to 3. Every three

years, employees can choose to take the exams held within the SOE. Their skill

level is determined by their exam results.



elsewhere.

edu

Level of education of an employee, taking the value 0 for junior high school or

equivalence, 1 for senior high school or equivalence, 2 for undergraduate

degree, or 3 for postgraduate degree and higher.


otherwise.

CHAPTER 4: INTERNAL REPORTING, PERSONAL CONNECTIONS AND


CHAPTER IV

INTERNAL REPORTING, PERSONAL CONNECTIONS, AND


ABSTRACT

This study examines how personal connections affect the relation between internal reporting

and employee performance. Previous studies find that public reporting (i.e., reporting employee

performance publicly within organizations) motivates employees to demonstrate high

performance to maintain a positive self-image in the workplace. In this study, I examine

whether the personal connections that employees have in the workplace influence the

performance effect of public reporting. I focus on the referrer–referral connection and the

family connection, which are common in organizations and can be important to managers’

decisions about employee selection. Based on the social psychology literature, personal

connections increase employees’ concern for their own image and that of their referrers or

family members, and are thus likely to enhance the positive effect of public reporting. Using

archival and survey data from five workgroups in a Chinese state-owned enterprise, I find that

public reporting has a significant and positive effect on the performance of employees with

personal connections. However, it has no significant performance effect on employees without

personal connections. These findings not only contribute to the literature on internal

performance reporting and employee selection, but also have important practical implications.



113

4.1 Introduction

This study examines how internal reporting and personal connections jointly affect employee

performance. Previous studies have documented a positive relation between public reporting

(i.e., reporting employee performance publicly in the workplace) and employee performance

(Hannan, McPhee, Newman, and Tafkov 2013; Tafkov 2012). In this study, I extend the

existing literature by examining whether the relation between public reporting and employee

performance is affected by the personal connections that employees have in the workplace.

Social comparison theory (Festinger 1954; Suls and Wheeler 2000) posits that individuals have

an innate drive to compare themselves with others in order to evaluate their own abilities. The

social comparison process motivates individuals to adjust their behavior to maintain a positive

self-image (i.e., how individuals believe others think of them) in their social or work group.

Existing studies find that internal reporting facilitates social comparison in the workplace and

functions as an effective control mechanism in motivating employee performance (Azmat and

Iriberri 2010; Frederickson 1992; Hannan, Krishnan, and Newman 2008; Hannan et al. 2013;

Maas and Van Rinsum 2013; Tafkov 2012). Using controlled experiments, previous studies

find that reporting employee performance publicly in the workplace increases employees’

concern for their self-image and motivates them to demonstrate high performance to “look

good” in front of their peers (Hannan et al. 2013; Tafkov 2012). However, in real-world

organizations, different types of employees may have different image concerns and different

reactions to public reporting. This study examines whether the performance effect of public

reporting varies between employees with and without personal connections in the workplace.

I refer to “personal connections” as the connections between employees that are outside the

formal working relations that co-workers have as a part of their job. Personal connections in



114

organizations vary from friendships to family connections to intimate relations. In this study, I

focus on two types of personal connections: the referrer–referral connection and the family

connection. Both types of connections are common in organizations and are important to

managers’ decision-making regarding employee selection. As for the referrer–referral

connection, there is widespread use of referrals in the labor market (Brown, Setren, and Topa

2016; LinkedIn 2015). Academics and business practitioners are interested in how the referrer–

referral connection affects employee performance and organizational outcomes (Beaman and

Magruder 2012; Brown et al. 2016; Campbell 2012). As for family connections, whether

organizations should hire the family members of existing employees receives extensive

attention, and constitutes an important part of the hiring policy of many organizations

(Encyclopedia of Management 2009; Stinson and Wignall 2014).

Based on the social psychology literature, both types of connections are likely to increase

employees’ image concern and affect their reactions to public reporting. Specifically, public

reporting increases performance transparency in the workplace and motivates employees to

increase their performance in order to be perceived positively by their peers (Hannan et al.

2013; Tafkov 2012). Employees with referrer–referral and/or family connections may have

higher concern for their image, as they tend to consider not only how their peers perceive them,

but also how their referrers and/or family members do. Further, the social psychology literature

indicates that one’s behavior not only affects one’s own image, but also affects the image of

those with whom one is connected (Cupach and Metts 1994; Goffman 1979; Tedeschi 2013).

Therefore, public reporting may also lead employees with personal connections to consider

how their performance affects the image of their referrers and/or family members in the

workplace. Overall, personal connections in the workplace may enhance the positive relation

between public reporting and employee performance.



115

Given the importance and the potential influence of personal connections, this study

empirically examines whether these connections affect employees’ reaction to public reporting.

I conduct this study using performance and personnel data from a large department within a

state-owned enterprise (SOE) in China. In this department, employee tasks include operating

and maintaining equipment. The department manager divides employees into five workgroups

to improve the efficiency of daily management. Several features of this department make it an

ideal setting for this study. First, although the SOE has implemented a strict and objective

performance measurement and reward system, group leaders have discretion to make internal

reporting choices. Two of the five group leaders choose to report employees’ individual

performance publicly within their workshops at the end of every month, while the other three

choose to communicate each employee’s performance to him or her privately. Second,

referrer–referral and family connections are common in this department. Employees with

family members and/or referrers tend to have stronger personal connections than those without

these relationships. It thus provides me with an opportunity to examine how personal

connections may affect employee performance in the organization. Third, the operating

environment of the organization is stable and most employees stay in the same department and

workgroup for years. Therefore, employees are likely to have a salient concern for their own

image and those of their colleagues.

Using archival data on employee performance and survey data on personal connections, I find

that personal connections enhance the positive performance effect of public reporting. The

results suggest that public reporting has a significant and positive performance effect on

employees with personal connections. However, public reporting has no significant effect on

the performance of employees who have no personal connections in the organization. The

contribution of these findings is threefold. First, it contributes to the literature on internal



116

performance reporting (Hannan et al. 2013; Luft 2016; Maas and Van Rinsum 2013; Tafkov

2013), by demonstrating that the performance effect of public reporting is contingent on the

saliency of employees’ image concern. The findings indicate that personal connections in the

workplace, as a factor that is likely to increase employees’ image concern, significantly

enhances the performance effect of public reporting. Second, this study adds to the literature

on employee selection. Existing studies in accounting and economics suggest that employee

selection functions as an effective control mechanism (Abernethy, Dekker, and Schultz 2015;

Campbell 2012). Examining the role of personal connections provides insight into how

employment channels (i.e., referrals and non-referrals) and criteria (i.e., with or without family

connections) affect the performance effect of public reporting. Finally, this study has practical

implications. The findings could help managers better understand the performance effect of

internal performance reporting, and suggest that managers need to consider internal

performance reporting and employee selection as an integrated system.

This paper is organized as follows. I review the existing literature and develop hypotheses in

the next section. I then describe the research site, before explaining the method and discussing

the results. Concluding remarks are offered in the final section.


Internal Reporting and Employee Performance

Social comparison theory posits that individuals have an innate desire to compare themselves

with others in order to evaluate their own abilities (Festinger 1954; Suls and Wheeler 2000).

The social comparison process motivates individuals to adjust their behavior to maintain a

positive self-image (i.e., how individuals believe others think of them). Individuals prefer to

“look better”, or at least not “look worse”, than others in their social or work groups. Drawing



117

on the social comparison theory, previous studies find that employee performance is affected

by whether and how performance information is reported in the workplace. Specifically,

Frederickson (1992) experimentally find that providing employees with information on their

own performance relative to the performance of their peers (i.e., relative performance

information, or RPI) motivates them to exhibit higher effort. This finding is consistent with the

findings obtained by Azmat and Iriberri (2010) using a natural experiment in a high school.

Further, Hannan et al. (2008) find that the performance impact of RPI is conditional on the

incentive contract (i.e., tournament or individual compensation) as well as the precision of the

RPI information. Later studies also find that providing employees with RPI may motivate

undesirable behaviors in employees, such as sabotaging the work of peers (Charness, Masclet,

and Villeval 2013) or misreporting the budget (Brown, Fisher, Sooy, and Sprinkle 2014).

Recent studies have examined whether making performance information public in the

workplace affects employee behaviors. In particular, Tafkov (2012) experimentally find that

public RPI (i.e., when the performance ranking of each employee in a group is provided to all

group members) is more effective in motivating employee performance than private RPI (i.e.,

each employee knows his or her own rank only). Hannan et al. (2013) also find that compared

to private RPI, public PRI is more effective in motivating employees to exert high effort in a

multi-task setting. Additionally, Maas and Van Rinsum (2013) find that publicly disclosing

managers’ self-reported performance in the workplace motivates them to report their

performance honestly, in order to maintain a positive self-image in front of their peers. These

studies suggest that public reporting increases employees’ concern for their self-image.

Demonstrating a positive (e.g., competent or honest) self-image enhances employees’ self-

esteem and improves their feelings about self (Beach and Tesser 2000). It also prevents

employees from being looked down on by their peers, and increases employees’ status in the



118

workplace (Smith 2000). To create and/or maintain a positive self-image, employees are

motivated to adjust their behavior, such as demonstrating higher performance (Hannan et al.

2013; Tafkov 2012) or reporting their performance honestly (Maas and Van Rinsum 2013).

Personal Connections in the Workplace

In real-world organizations, different employees may have different image concerns and react

differently to public reporting. In this study, I examine whether the relation between public

reporting and employee performance is affected by personal connections, a factor that is likely

to affect employees’ image concern. I refer to personal connections as the connections between

employees that are outside formal working relationships that co-workers have as a part of their

job. Personal connections in organizations vary from friendships to family connections to

intimate relations. In this study, I focus on two types of personal connections: the connection

between referrer and referral, and the connection between family members.

Both types of connection are common in organizations and are important to managers’

decisions regarding employee selection. Specifically, referrals made by existing employees is

one of the most important employment channels in the labor market (Brown et al. 2016;

LinkedIn 2015). Whether and how the referrer–referral connection affects employee

performance is important to managers’ decisions regarding employee selection channels

(Beaman and Magruder 2012; Brown et al. 2016; Campbell 2012). Whether organizations

should hire family members of existing employees is also important to managers, and

constitutes an important part of hiring rules in many organizations (Encyclopedia of

Management 2009; Stinson and Wignall 2014). Based on the social psychological literature,

both types of connections are likely to increase employees’ image concern and affect the way

that employees react to public reporting. Drawing on the social psychological theories, I



119

develop a theoretical framework predicting that the positive effect of public reporting is

enhanced by the personal connections of employees. The theoretical framework is presented in

Figure 11.

1 This figure has been inspired by the figure used to describe the theoretical framework in Tafkov (2012).

CHAPTER 4: INTERNAL REPORTING, PERSONAL CONNECTIONS AND EMPLOYEE PERFORMANCE

120

FIGURE 1

Theoretical and Empirical Framework

Public performance reporting

Social comparison and

self-evaluation

Higher effort Higher performance

Employee selection channel and criteria

Personal connections in the workplace

Management Controls Psychological Activities Behavioural Outcomes

Operating environment, task nature, personal ability, etc.

Contextual Factors

Link 1 (+)

Link 2 (+) Link 3 (+)

Link 4 (+)



121

The framework first describes how public reporting affect employees’ task performance. Social

comparison theory posits that individuals have an innate desire to compare themselves with

others and adjust their behaviors to maintain a positive self-image (Festinger 1954; Suls and

Wheeler 2000). Public reporting provides employees with information on their own and their

peers’ performance, and exposes employees’ performance to their peers. It enables employees

to compare themselves with their peers (Link 1), and motivates employees to exert higher effort

(Link 2) to demonstrate higher performance (Link 3) so that they will look better, or at least

not worse, than their peers (Hannan et al. 2013; Luft 2016; Tafkov 2012). The relation between

motivation and effort (Link 2) and between effort and performance (Link 3) may be moderated

by contextual factors, including but not limited to task nature, employees’ abilities, and the

operating environment.

The framework next describes how personal connections interact with public reporting,

increasing employees’ image concern and ultimately affecting their effort and performance.

Personal connections affect employees’ concern for their own image, as well as their concern

for the image of their referrers or family members. Specifically, public reporting increases

performance transparency within organizations. Employees without personal connections only

need to consider how their peers think of them, and this concern affects the way they evaluate

themselves and their subsequent performance. In comparison, employees with personal

connections may consider not only how their peers perceive them, but also how their referrers

or family members perceive them. Therefore, under public reporting, employees with personal

connections may have a stronger motivation to demonstrate high performance in order to be

perceived positively by their referrers or family members.



122

Further, one’s behavior not only affects one’s own image, but also affects the image of those

with whom one is connected (Cupach and Metts 1994; Goffman 1979; Giacalone and

Rosenfeld 2013; Tedeschi 2013). Based on psychology theory, if observers evaluate individual

A positively and perceive a positive connection between individual A and individual B, then

in order to keep their cognitive systems in balance, observers would have to positively evaluate

individual B as well (Cialdini 2013). If a referrer–referral connection exists and the referral

demonstrates low performance, it may negatively affect the image of the referrer, as other

employees may believe that the referrer has made an unfavorable recommendation to the

organization. Similarly, the social psychology literature suggests that individuals in a close

relationship present a “unified” image in public. When one individual in the relationship

engages in behavior that negatively affects his/her image, these behaviors also affect the image

of the other individual negatively (Cupach and Metts 1994; Goffman 1979). That is, if an

employee has a family member(s) in the workplace and demonstrates low performance, others

may perceive the family member(s) negatively. Therefore, under public reporting, employees

with personal connections may have a stronger motivation to demonstrate high performance in

order to protect the image of their referrers or family members.

Public reporting increases performance transparency in the workplace, and motivates

employees to demonstrate high performance to maintain their self-image. Personal connections

increase employees’ concern for their own image as well as the image of their referrers or

family members, and motivates employees to demonstrate higher performance under public

reporting. Therefore, I hypothesize that the positive relation between public reporting and

employee performance is enhanced by the personal connections that employees have in the

workplace:



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Hypothesis: Personal connections enhance the positive relation between public reporting and

employee performance.

The referrer–referral connection and family connection may overlap with each other. For

example, it is possible that an employee is recommended by a family member who also works

in the same organization; it is also possible that an employee is recommended by a family

member or friend, and has another family member(s) working in the same organization. In the

first case, the employee has two types of connections with the same person; in the second case,

the employee has two types of connections with two or more different people. In either case,

the employee has stronger personal connections in the workplace than those with only one type

of connection and those without any connection. Previous studies do not posit the performance

effect of the type or strength of personal connections. This study explores this question in the

additional analyses.

4.3 Research Site

Overview

The research site is a department within a Chinese state-owned enterprise (SOE). The SOE is

the largest organization in the city where it is located. It has been the principal driver of the

local economy, and has its own schools, hospitals, media, and communities. It comprises 34

factories, plants, and institutions. This study focuses on one department in a power plant in the

SOE. Employees in the department are responsible for equipment operation, inspection, and

maintenance. Employees’ tasks include inspecting and operating the equipment, identifying

and solving hidden issues, and keeping records of the condition of the equipment. The purpose

of these tasks is to ensure the equipment functions normally and safely, to prolong the usable

life of the equipment, and reduce the risk of operational disasters. Employees perform their



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tasks individually, and each employee’s tasks and responsibilities are clearly specified by the

SOE.

As the equipment functions 24/7, employees take shifts. The department head divides

employees into five workgroups, to make the shifts more manageable. Employees in the same

group work on the same shifts. During each shift, employees work individually in different

areas of the workshop. Employees cannot directly observe the operational actions of their peers.

Once allocated in a group, employees usually stay in the same group; changing groups rarely

happens. Each group is managed by a group leader and has 18–25 group members.


The SOE uses performance measures and financial incentives to motivate employee effort. By

the end of every month, employees receive performance scores based on the operational actions

that they took and the performance outcomes that they achieved over the past month. Employee

actions include the procedures and steps that employees take to inspect, operate, and maintain

the equipment. Performance outcomes include a range of parameters on the equipment, such

as temperature and pressure, which reflect the status of the equipment. The organization has

specified a series of performance requirements for the actions and outcomes that employees

should take and achieve. It has also specified rules about how to allocate performance scores

to employees based on the extent to which their actions and outcomes meet performance

requirements. Engaging in some of the actions and achieving some of the outcomes allows

employees to earn performance scores. In comparison, failing to engaging in some of the

actions or achieving some of the outcomes causes employees to lose performance scores.

Employees may earn scores for some actions and/or outcomes and lose scores for others.

Taking a certain set of actions that keep the equipment functioning normally allows employees



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to earn a reasonable overall performance score. In comparison, inspecting the equipment more

carefully, identifying and solving more hidden issues, or choosing the optimal operational

actions based on the exact conditions helps employees earn higher performance scores.

The department manager measures employee performance objectively. In particular, the

department manager assesses employee performance by checking the operational records and

the status of the equipment. The operational records are kept by the machines and the computer

system employed by the organization. When taking actions to inspect, operate, and maintain

the equipment, employees need to tap their identification cards on the machines located in

different areas of the workshops. The system then records that the employees have taken certain

actions in certain areas of the workshop. The department manager measures employee actions

using the information kept by the system. Additionally, the department manager measures

employees’ performance outcomes by checking the parameters on the equipment during each

shift, which is also kept by the system. At the end of every month, each employee receives

performance scores based on the objective measurement of their operational actions and

performance outcomes.

At the end of every month, employees receive a fixed payment and financial incentives. The

financial incentives are calculated as 10 times the total performance scores that each employee

received for the month. The financial incentives account for approximately 0–10% of the

overall salary received by each employee. The performance measurement, score allocation, and

compensation structure are pre-specified by the organization and require little judgment from

the department manager or the group leader.



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Internal Reporting Structure

The SOE requires each of its subunits to provide employees with the feedback on their

performance. However, it does not impose any rules on the performance reporting practices of

the department manager or group leaders. In the department of this study, two types of

performance reporting practices have coexisted for years. In one of the five workgroups (Group

5), the group leader prints the names and the performance of all group members on a table, and

distributes this performance table to the group members at the end of every month. Each

employee in the group can see their own performance, as well as the performance of their peers

(i.e., public reporting). In the other four groups, the group leaders prepare the same information

but print the information of each employee’s performance on a separate note, and provide each

employee only with the note regarding his or her own performance. Employees can only see

their own performance, but cannot see the performance of their peers (i.e., private reporting).

One of the four groups (Group 3) switched from private to public reporting by the end of 2013,

because the group leader retired and the SOE appointed a new group leader. The old group

leader used to print employee performance on A4 paper, cut the paper into pieces, and provide

each employee with the piece containing his or her performance only. The new group leader

asks employees to pass the paper around the group without cutting it. To sum up, three of the

five groups (Groups 1, 2, and 4) in the department have adopted private reporting, one (Group

5) has adopted public reporting, and one (Group 3) has switched from private reporting to

public reporting2. Figure 2 illustrates the two types of performance reporting practices.

2 Most employees in the department entered into the organization in the late 1980s or early 1990s. Employees

were allocated into different groups by the department head when they first entered into the organization. The

reporting choice was made by the group leaders in later years. Therefore, the possibility that employees choose

to be allocated into certain reporting structure is very low.



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FIGURE 2

Performance Reporting at the Research Site

Private Reporting

Public Reporting

Personal Connections

The two types of personal connection examined in this study are common in the research site.

First, some employees entered the SOE through the referrals of existing employees. The SOE

recruits employees through three channels: government allocation, normal recruiting processes,

and referrals made by existing employees. In the late 1980s and early 1990s, the government

allocated college and high school graduates to SOEs based on the needs of the SOEs. In the

research site, about 50% of the employees were allocated by the government. The second

channel is the normal recruiting process. Like most other organizations, the SOE recruits

employees by advertising the available positions, screening applicants, and hiring the most

suitable ones. Approximately 20% of the employees were recruited via this channel. The third

Each employee receives a note with his

or her performance only. Employees

can see their names on the note.

This approach has been adopted by

Groups 1, 2, and 4.

Employees pass around the performance

table that presents everyone’s name and

performance.

This approach has been adopted by Group 5.

Group 3 switched from private to public

reporting at the end of 2013.



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employment channel, which is the focus of this study, is recruiting employees through referrals.

Individuals who have relatives or friends who work in the SOE may join the organization

through the recommendation of their relatives or friends. Chinese SOEs usually have limited

human resource functions and financial resources to design and implement sophisticated

recruitment process, so hiring referrals made by existing employees is an inexpensive and

convenient method of recruiting new employees (Han and Han 2009). Approximately 30% of

the employees at the research site were recruited through referrals made by existing employees.

Some employees have family members who also work in the SOE. The SOE does not impose

any rules forbidding family connections in the workplace. Given the large size of the SOE, it

is common for employees to marry other employees and/or have other family members

working in the SOE. At the research site, approximately 50% of the employees have family

members who work in the SOE. The two types of personal connections overlap, but do not

fully overlap. Based on the survey used to collect information on personal connections, about

11% of respondents only have referrer–referral connections, and about 35% only have family

connections. Approximately 15% of respondents have both types of connection. Figure 3

illustrates the personal connections of the survey respondents. Details of the survey are

provided in the next section.

FIGURE 3

Personal Connections at the Research Site

Missing Demographic Information = 1

Non-Respondents = 24

Respondents = 79

Referral =1 Referral = 0 Total

Family = 1 12 28 40

Family = 0 9 30 39

Total 21 58 79



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4.4 Method

Data and Sample

I collected archival and survey data to examine the research question. First, I extracted the

performance data from the database of the SOE. The SOE measures the individual performance

of employees at a monthly basis. The sample includes 146 employees and 5,422 performance

observations from January 2011 to July 2015. Second, I collected the demographic information

of the 146 employees from the personnel files of the SOE, including the employees’ gender,

birthday, hometown, education, political affiliation, and recruitment date. Employees’

demographic characteristics are time-invariant. Third, I administered a survey on May 2015 to

measure employees’ personal connections in the workplace. Panel A of Table 1 presents the

sample selection process. Among the 146 employees in the department, 79 responded to the

survey (54%), corresponding with 3,069 performance observations. Panel B compares the

demographic characteristics of respondents and non-respondents, and shows that respondents

and non-respondents are not significantly different.



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TABLE 1

Sample Selection

Panel A: Sample Selection

Employees Observations

Total employees/observations 146 5,422

Non-response (66) (2,307)

Missing demographic information (1) (46)

Final sample 79 3,069

Panel B: Respondents and Non-respondents

Non-

respondents Respondents Difference t-statistics

age 41.34 43.13 −1.79 −1.61

tenure 22.44 23.86 −1.42 −1.20

gender 0.29 0.24 0.05 0.50

home 0.58 0.58 0.00 0.01

edu 1.04 1.01 0.03 0.18

CCP 0.17 0.16 0.00 0.02 This table presents the sample selection process. Panel A displays the number of employees and observations used

in this study. Among the 146 employees at the research site, 79 responded to the survey. The 79 respondents

correspond with 3,069 performance observations from January 2011 to July 2015. Panel B compares the

performance and demographic characteristics of respondents (N = 3,069 for performance and N = 79 for

demographic characteristics) and non-respondents (N = 2,307 for performance and N = 66 for demographic

characteristics). See variable definitions in Appendix B.

Variables

Employee Performance

The dependent variable, performance, measures employee performance at the research site.

Performance is individual performance measured and recorded by the SOE’s computer system.

At the end of every month, the department head measures employee performance by checking

the operational records and equipment status, and allocates employees with performance scores

based on the measurement. Performance is the overall performance score each employee

receives at the end of the month. The financial incentives received by employees at the end of

every month are calculated as (10×performance) at the research site. I obtained the monthly

data of employee performance from January 2011 to July 2015. Because of the nature of the

task and the performance measurement system adopted by the SOE, the highest (lowest)

performance score that employees in this department can earn in one month is 130 (−160).

Personal Connections



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I use survey questions to capture the personal connections in the research site. The survey

collects information of employees’ group identity as well as background information, such as

how they entered into the SOE and whether they have any personal connections in the SOE.

The survey questions are presented in Appendix A.

I construct an indicator, connection, to indicate employees who have personal connections in

the SOE. Connection equals 1 for employees who have referrals or/and family members in the

SOE, and 0 for employees without any of these connections. The referrer–referral connection,

referral, is measured using two questions in the survey: “How did you enter into the SOE? (1

= through government allocation = 1; 2 = through another channel)” and “Did you know any

existing employee(s) in the SOE before your entry? (1 = Yes; 2 = No)”. Referral equals 1 if

employees’ answer to the first question is 2 and their answer to the second question is 1; and 0

otherwise. In other words, referral indicates respondents who were not allocated by the

government and had connections with existing employees prior to their entry. It is a noisy proxy

for the referral–referrer connection, as some employees might have personal connections but

entered the SOE through the normal recruitment process instead of their personal connections.

The reason I measure this variable indirectly using the two questions is because, according to

the managers of the SOE, it is too sensitive to ask employees whether they were introduced

into the organization by existing employees. Because of the Chinese social norms (Luo 2007),

referrals usually give their referrers material gifts to return the favor and show their thanks.

The referrals may be reluctant to directly tell a third party that they entered the organization

through personal connections, to avoid being perceived as unethical and/or incompetent. To

increase the response rate and the accuracy of the responses, I use the two questions to

indirectly capture the referral–referrer connection of the respondents.



132

The family connection, family, is an indicator for employees with family members who also

worked in the SOE at the time the survey was administered. Information for this variable is

captured by one question in the survey: “Do you have family members who also work in the

SOE (1 = YES, I do have a family member[s] who also works in the SOE; 2 = I used to have a

family member[s] who worked in the SOE, but they have retired or left; 3 = I have never had

any family member who works in the SOE)”. Family equals 1 for respondents whose answer

is 1, or 0 otherwise.

Internal Performance Reporting

I first examine how public reporting is related to the performance of employees with and

without personal connections using cross-sectional analysis. I measure the internal

performance reporting choice in the research site using reporting. Reporting is an indicator for

observations in groups with public reporting. That is, reporting equals 1 for observations in

Group 3 after 2013 and all observations in Group 5, and 0 for all other observations. Previous

studies find that public reporting motivates employees to demonstrate high performance to

maintain a positive self-image, which means a positive association between reporting and

performance (Hannan et al. 2013; Tafkov 2012). I examine whether this link is enhanced by

employees’ personal connections.

I also conduct change analysis by examining how employees in Group 3 react to the change

from private to public reporting. This change occurred at the end of 2013. Therefore, I use

post2013 to indicate observations after the change, and use Group3 to indicate employees in

Group 3. The interaction between the two indicators (post2013×Group3) thus captures any

change experienced by employees in Group 3, with employees in the other groups as control.



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I expect that, after controlling for the group fixed effects and time fixed effects, the relation

between performance and post2013×Group3 will be more positive for employees with personal

connections than for those without.

Control Variables

To minimize any bias in estimates as a result of omitted variables, I control for the following

variables that may affect employee performance. The first control variable is level, which is a

proxy for employee ability. Every three years, the SOE holds an exam testing employees’

operational skills. The exam is compulsory for employees, and their exam results are used by

the SOE to determine their skill level (0 = none, 1 = low, 2 = medium, 3 = high). I also control

for tenure, which is the number of years that employees have been working in the SOE.

Previous research finds that tenure may affect employee performance through work

experiences and organization commitment (Wright and Bonett 2002). 3 The next control

variable is horizon, which equals 1 for male workers over 49 and female workers over 44. The

SOE’s policy specifies that male (female) workers can retire at 50 (45). Employees may have

lower motivation when approaching retirement. Further, I also control for gender, an indicator

for female employees. As the tasks at the research site involve operating heavy machine and

most employees in the research site are male, gender may affect employee performance both

physically and psychologically (Gardiner and Tiggemann 1999). The next control variable is

home, which is 1 for employees who came from the city where the SOE is located, or 0 for

those who came from elsewhere. As the SOE is the principal driver of the local economy,

employees from the local area may be motivated to work hard and contribute to the economy

of their hometown. I also control for the level of education that employees received (edu).

3 The Variance Inflation Factor (VIF) test indicates that the time that employees have been working in the SOE

(tenure) is highly correlated with their age (age) and is likely to introduce the multicollinearity problem into the

regression estimations. Therefore, I use Age to describe the sample but drop it in the empirical models.



134

Employees with a higher education level may have received more training and have a higher

ability to perform their tasks. The next control variable is CCP, which is a dummy variable

indicating employees who have joined the Chinese Communist Party. Previous research

indicates that when the goals and beliefs of employees are congruent with those of their

organizations, employees tend to have higher motivation and better performance (Rich, Lepine,

and Crawford 2010). As the research site is state-owned, the political affiliation of employees

may affect their motivation and performance. Finally, I control for employees’ group identity

(identity), the extent to which employees consider themselves part of their group (Akerlof and

Kranton 2003, 2010; Bergami and Bagozzi 2000; Boivie, Lange, McDonald, and Westphal

2011; Mael and Ashforth 1992). As found in the first essay of this dissertation, group identity

affects the way employees evaluate themselves and their reaction to internal performance

reporting. Appendix B provides definitions for the variables.

When estimating the empirical models, I also control for the time fixed effects and the group

fixed effects. The time fixed effects captured any unobservable effects that influence all

employees during the sample period, and the group fixed effects capture the effects of any

unobservable group features. I also correct standard errors for clustering at employee level in

all estimations.

Empirical Models

Cross-sectional Analysis

As explained in the research site section, three of the groups adopt private reporting while the

other two adopt public reporting. The cross-sectional analysis uses performance observations

of all five groups, during the period from January 2011 to July 2015. I examine the joint effects

of reporting and connection on employee performance using the following equation:



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Performanceit = α0 + α1reportingit + α2connectionit+ α3reportingit×connectionit

+ α4levelit + α5tenurei + α6horizonit + α7genderi + α8homei + α9edui

+ α10CCPi + α11identityi + εit (1)

In equation (1), the coefficient on reporting (α1) captures the performance effect of public

reporting on employees who have no personal connections in the SOE. The coefficient on

connection (α2) captures the relation between personal connection and employee performance

under private performance reporting. The coefficient on the interaction (α3) captures the effect

of public reporting on the employees who have personal connections in the workplace,

incremental to the effect captured by α2. Based on the social psychology literature, I expect α3

to be positive. To better understand whether and how personal connections affect the

performance impact of public reporting, I also divide the full sample into subsamples based on

connection, and estimate the performance impact of reporting within each subsample.

Change Analysis

Among the five workgroups in the research site, one of groups (i.e., Group 3) switched from

private reporting to public reporting at the end of 2013. In the change analysis, I consider

switched group as the treatment and the groups with private reporting (i.e., Group 1, 2, and 4)

as the control. Because of a technical problem that happened in early 2015, performance data

of 2015 is missing from Group 3. Therefore, I exclude 2015 from the sample of the change

analysis. The sample period of the change analysis is from January 2011 to December 2014. I

examine how employees in the switched group reacted to the change from private to public

reporting using the following equation:



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Performanceit = α1post2013it + α2group3it + α3post2013it×group3it + α4levelit + α5tenurei

+ α6horizonit + α7genderi + α8homei + α9edui + α10CCPi + α10identityi

+ εit (2)

The coefficient on the time indicator, post2013, captures any unobservable factors that affect

employee performance in all the groups after the change. The coefficient on the treatment

indicator, group3, captures any unobservable factors that affect the performance of employees

in Group 3 (i.e., the group that changed from private to public reporting). The coefficient on

the interaction between the time indicator and the treatment indicator (i.e., α3) represents the

effect of the change on the performance of employees in Group 3. I estimate equation (2) in

the subsamples of different levels of connection. I expect α3 to be positive and has larger

magnitude in the subsample with higher connection.

As explained in the research site section, because of the nature of the task (i.e., operating and

maintaining equipment) as well as the performance measurement system adopted by the SOE,

the highest (lowest) performance score that employees can earn in each month is 130 (−160).

Therefore, I estimate the empirical models using Tobit (censored) regressions. The left (lower)

limit for the estimations is −160, while the right (higher) limit is 130. I controlled time (month)

fixed effects and group fixed effects in all estimations. To control for error dependence of

individual observations, the standard errors are clustered at individual levels in all regressions.

Figure 4 visually displays the empirical model. Figure 5 presents the timeline and the groups

in the research site.


137

FIGURE 4

Theoretical and Empirical Framework

Cross-sectional Analyses: Indicator for public reporting (reporting); Change Analyses: Indicator for Group 3’s change from private to public reporting (post2013×group3)

Unobservable social comparison and self-evaluation

processes

Unobservable employee

effort

Employee performance

measured and recorded by the SOE (DV: Performance)

Employee selection channel (government allocation,

referral, normal recruitment

process) and loose policy on family connections

Referrer-referral connections and

family connections in the SOE (connection)

Control Variables: Employee age, tenure, gender, horizon, hometown, education, party member, group identity, time and

group fixed effects.

Contextual Factors

Management Controls Psychological Activities Behavioural Outcomes


138

FIGURE 5

Timeline and Groups

Groups in the Research Site


Department of this

study

(146 employees, 79

responded the survey)

2011 2012 2013 2014 2015 Overall



Group 3 private private private public public Switched


Group 5 public public public public public Public

Jan. 2011

Sample period begins

Private reporting used in

Groups 1, 2, 3, and 4;

Public reporting used in

Group 5.

Jul. 2015

Sample period ends

Private reporting used in

Groups 1, 2, and 4;

Public reporting used in

Groups 3 and 5.

Deadline of Survey

Response

01-Jun-2015

1 May 2015

Survey sent

Dec. 2013

Group 3 changed from private

to public reporting



139

4.5 Results


Table 2 presents the descriptive statistics of employee performance and demographic

characteristics. Specifically, the mean of performance is 25.70, meaning that, on average, the

performance score received by employees is 25.70 and the financial incentives they received

is 257.0 Chinese yuan. The maximum (minimum) score that employees received during the

sample period is 115.50 (−111.90), and the standard deviation is 19.90. The mean of connection

is 0.62. Based on Figure 3, 38% of employees (N = 30) have no referrers or family members

in the SOE; 47% of employees have either referrers (N = 9) or family members (N = 28) in the

SOE. The remaining 15% (N = 12) have both referrers and family members.

By July 2015, the average skill level of employees is 1.68, which is between the low (= 1) and

medium (= 2) levels set by the SOE. The mean (median) age of the employees is 43.13 (43.51)

years old, and the mean (median) of tenure is 23.86 (25.39) years. The age and tenure indicate

that most employees entered the SOE during the late 1980s and early 1990s. The difference

between the average age and the average tenure is only 19 years, because most employees

entered the company immediately after graduating from high school or vocational school, and

they choose to stay in the company until retirement. In the sample, 24% of the employees are

female, and 58% came from the city in which the SOE is located. Most employees graduated

from senior high school or vocational school, and 16% have joined the Chinese Communist

Party. Twenty-seven percent of employees entered the SOE through their personal connections,

and 51% have family connections in the SOE. Employees’ group identity (i.e. identity) is a

latent variable generated using nine survey questions (Bergami and Bagozzi 2000; Boivie et al.

2011; Mael and Ashforth 1992). The mean (median) of identity is 0.10, while the minimum

(maximum) is −1.51 (1.54).



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TABLE 2


N Mean Median s.d. Min Max

Performance 3,069 25.70 22.50 19.90 −111.90 115.50

connection 79 0.62 1 0.49 0 1

level 79 1.68 2 0.63 0 3

age 79 43.13 43.51 5.54 29.02 59.08

tenure 79 23.86 25.39 5.85 5.25 42.82

gender 79 0.24 0 0.43 0 1

home 79 0.58 1 0.50 0 1

edu 79 1.01 1 0.67 0 2

CCP 79 0.16 0 0.37 0 1

identity 79 0.10 0.17 0.58 −1.51 1.54

This table presents the descriptive statistics of employee performance and demographic characteristics. The sample

period for Performance is from January 2011 to December 2015. The other variables are time-invariant. Age and

tenure are calculated as of July 2015, based on employees’ birthday and the date they entered the organization. See

variable definitions in Appendix B.

Correlations between Variables

Table 3 provides details on Pearson correlations among the variables used in the analyses. It

shows that performance is negatively correlated with both reporting and connection. This

differs from the findings of previous studies, and suggests that the association between

performance, reporting, and connection is not straightforward and requires further analysis.

Performance is also negatively correlated with connection, horizon, tenure, gender, and

identity, and positively correlated with home and edu. Most independent and control variables

are significantly correlated with each other. I use VIF tests to quantify the severity of

multicollinearity for each regression, making sure the results are not affected by the

multicollinearity problem. None of the variables in the estimation have a VIF that is higher

than 5, which means there is no severe multicollinearity problem in the estimations.


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TABLE 3

Pearson Correlations

Performance reporting connection level tenure horizon gender home edu CCP

reporting −0.14***

connection −0.21*** 0.14***

level 0.40*** 0.03*** −0.22***

tenure −0.12*** 0.09*** 0.27*** −0.07***

horizon −0.15*** 0.05*** 0.17*** −0.23*** 0.39***

gender −0.25*** −0.09*** 0.00*** −0.43*** −0.02*** 0.20***

home 0.05*** 0.10*** 0.04*** 0.16*** 0.37*** 0.04*** −0.18***

edu 0.08*** −0.06*** −0.13*** 0.09*** −0.44*** −0.23*** −0.02*** −0.31***

CCP −0.01*** 0.20*** 0.03*** 0.07*** −0.02*** 0.00*** −0.23*** 0.18*** 0.15***

identity −0.13*** 0.23*** 0.14*** −0.03*** 0.11*** 0.06*** 0.01*** −0.06*** −0.09*** 0.18***

This table presents the pairwise correlation coefficients between the variables used in the empirical analyses. The sample include 79 employees and 3,069 from January 2011 to December

2015. *, **, *** indicates that the correlation coefficient is significantly different from zero at the 10%, 5%, and 1% levels, respectively (two-tailed). For the definitions of variables, see

Appendix B.



142

Cross-sectional Analysis

Table 4 presents the results of the cross-sectional analysis. I first estimate equation (1) in the

full sample. The results, which are presented in column (1), indicate that reporting is not

significantly related to performance (t = 1.46), and connection is negatively related to

performance (t = −2.15). The relation between reporting×connection and performance is,

however, not significant (t = −0.68). To further examine the effect of public reporting and the

role of personal connections, I divide the sample based on connection and examine the relation

between performance and reporting within subsamples. The results indicate that the relation

between reporting and performance varies across subsamples with and without personal

connection. Specifically, the relation between the two variables is not significant among

employees without any personal connection in the SOE (t = −0.61). In comparison, for

employees with personal connections, public reporting is positively related to their task

performance (t = 6.03). This is consistent with the hypothesis, that personal connections

enhance the relation between public reporting and employee performance.

As for control variables, level is positively related to employee performance in full sample as

well as the subsamples. It is consistent with the fact that employees with higher skill level tend

to exhibit higher performance. Gender is negatively related to employee performance, and this

negative relation is significant in the full sample and the subsample with personal connections.

As the tasks in the research site requires operating heavy machinery, it may limit the

performance of female employees. The coefficients on other control variables are not

statistically significant, except for identity, which is positively related to employee performance

in the subsample without connections.



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TABLE 4

Cross-Sectional Analysis (1) (2) (3)

Full Sample connection = 0 connection = 1

reporting 8.73 −5.17 14.42*** (1.46) (−0.61) (6.03)

connection −4.74** (−2.15)

reporting×connection −3.51 (−0.68)

level 9.97*** 11.94*** 7.21*** (5.29) −4.25 (2.75)

tenure 0.13 0.08 0.24 (0.66) −0.4 (0.74)

horizon −1.33 −8.71 −0.91 (−0.46) (−1.37) (−0.29)

gender −5.47** −5.33 −6.41** (−2.12) (−1.62) (−2.01)

home −1.86 −2.7 −2.68 (−0.66) (−0.67) (−0.75)

edu 1.15 2.16 1.00 (0.76) (0.86) (0.55)

CCP −1.45 −2.36 −2.36 (−0.53) (−0.47) (−0.72)

identity 1.10 5.88** −2.17

(0.54) (2.58) (−0.89)

Time fixed effects Controlled Controlled Controlled


Log-likelihood −13068.91 −5039.69 −7940.68

N 3,069 1,181 1,888

This table presents the results of the cross-sectional analysis. The empirical model is presented as follow:

Performanceit = α0 + α1reportingit + α2connectionit+ α3reportingit×connectioni + α4levelit + α5tenurei

+ α6horizonit + α7genderi + α8homei + α9edui + α10CCPi + α11identityi + εit (1)

The sample includes 79 employees from the five groups of the department, and 3,069 performance observations

from January 2011 to July 2015. The sample of column (1) include all employees in the five groups; the sample of

column (2) includes employees without personal connections; and the sample of column (3) include employees

with personal connections. The dependent variable is the monthly performance of employees (performance). The

estimations are censored at −160 and 130. Because of the task’s nature and the performance measurement used by

the organization, −160 and 130 are the lowest and highest limits for performance. Standard errors are corrected for

clustering at individual level. *, **, *** indicates that the correlation coefficient is significantly different from zero

at the 10%, 5%, and 1% levels, respectively (two-tailed). For the definitions of variables, see Appendix B.



144

Change Analysis

Table 5 presents the results of the change analysis, which are consistent with the results of the

cross-sectional analysis. Specifically, Group 3’s change from private reporting to public

reporting has no significant effect on employee performance in the full sample (t = 0.92). After

dividing the sample based on connection, I find that the change has a significant and positive

effect on employees with personal connections (t = 4.44). However, it has no significant effect

on employees without personal connections (t = −0.57).

In terms of control variables, level is positively related to employee performance in full sample

as well as the subsamples. It is consistent with the fact that employees with higher skill level

tend to exhibit higher performance. Gender is negatively related to employee performance, and

this negative relation is significant in the full sample and the subsample with personal

connections. As the tasks in the research site requires operating heavy machinery, it may limit

the performance of female employees. The coefficients on the other control variables are not

consistently significant across the estimations.



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TABLE 5

Change Analysis

(1) (2) (3)

Full Sample connection=0 connection=1

post2013 5.70* 6.51 4.77

(1.72) (1.01) (1.40)

group3 −1.00 4.96 −0.08

(−0.19) (0.38) (−0.01)

post2013×group3 4.04 −6.30 10.89*** (0.92) (−0.57) (4.44)

level 12.10*** 14.27*** 5.99** (4.86) (5.50) (2.07)

tenure −0.05 0.25 −0.12

(−0.24) (1.15) (−0.41)

horizon −3.55 −17.34 −0.04 (−0.97) (−1.41) (−0.01)

gender −1.96 −2.32 −3.55 (−0.78) (−0.83) (−1.12)

home −2.96 −7.66* 0.98 (−0.91) (−1.91) (0.31)

edu 0.52 −0.02 2.54 (0.32) (−0.01) (1.21)

CCP 1.92 2.15 0.69 (0.56) (0.22) (0.30)

identity 1.51 9.16** 0.65

(0.54) (2.15) (0.25)

Time fixed effects* Controlled Controlled Controlled

Group fixed effects* Controlled Controlled Controlled

Log-likelihood −7914.97 −3312.01 −4471.92

N 1,896 782 1,114

This table presents the results of the change analysis. The empirical model is presented as follow:

Performanceit = α1post2013it + α2group3it + α3post2013it×group3it + α4levelit + α5tenurei + α6horizonit

+ α7genderi + α8homei + α9edui + α10CCPi + α10identityi + εit (2)

The sample period is from January 2011 to December 2014, as the performance data of 2015 is missing in Group

3. Group 3 changed from private to public reporting at the end of 2013. The sample includes observations of

Group 3 (i.e., treatment) and observations of Groups 1, 2, and 4 (i.e., control). The sample of column (1) include

all employees in control and treatment; the sample of column (2) includes employees without personal

connections; and the sample of column (3) include employees with personal connections. The dependent variable

is the monthly performance of employees (i.e., performance). The estimations are censored at −160 and 130, as a

result of the task nature and the performance measurement used by the organization. Standard errors are clustered

at individual level. *, **, *** indicates that the correlation coefficient is significantly different from zero at the

10%, 5%, and 1% levels, respectively (two-tailed). For the definitions of variables, see Appendix B.

* Some of the time and group indicators were dropped because of collinearity, as the model has already included

the indictor for the post-change period (post2013) and the indicator for the change group (group3).



146

Overall, the results presented in Tables 4 and 5 indicate that public reporting positively affects

the performance of employees with personal connections in the workplace. However, it has no

significant effect on employees without personal connections. These results are consistent with

the argument that personal connections enhance the performance effect of public reporting. To

better interpret these findings, I conduct additional analyses on high and low performers, as

well as different types of connection.

Additional Analyses

High and Low Performers

In the additional analyses, I first examine whether public reporting affects high and low

performers differently. High and low performers may have different concerns for their self-

image: low performers may be more concerned about their self-image and work harder to avoid

“looking worse” than their peers. Low performers also have more “room” to improve their

performance than high performers do and may thus react more positively to public reporting,

which may confound with the effect of personal connections. As connection is negatively

related to performance in the Pearson correlations and the main tests, employees with personal

connections are more likely to be low performers. The reason that they react more positively

to public reporting may be because they are low performers, but not because they have personal

connections. Examining high and low performers separately helps clarify this issue and

facilitates better understanding of the main results.

I classify employees as high and low performers based on the skill level of employees. The

SOE holds a skill exam every three years to assess the operational skills of employees, and

assigns each employee a skill level based on their exam results. The skill level spans from 0 to



147

3 (0 = none, 1 = low, 2 = medium, 3 = high). The skill level reflects employees’ ability in

choosing the appropriate actions in different conditions, and is positively related to employee

performance (as shown in Tables 4 and 5). In this study, I classify employees as low performers

if their skill level equals 0 or 1, and as high performers if their skill level equals 2 or 3.

The results of the cross-sectional analysis are presented in Panel A of Table 6. It shows that

public reporting positively affects low performers, regardless of their personal connections.

Specifically, public reporting has a significant and positive performance effect on low

performers without personal connections (t = 3.90), as well as low performers with personal

connections (t = 5.29). As for high performers, public reporting has no significant effect on

those without personal connections (t = −0.37). However, it positively affects high performers

with personal connections (t = 4.29).

The change analysis demonstrates similar results. Panel B of Table 6 indicates that the change

from private to public reporting in Group 3 is positively associated with the performance of

low performers, regardless of their personal connections. In comparison, for high performers,

the change is only positively associated with the performance of those with personal

connections (t = 2.60), but not significantly related to the performance of those without

personal connections (t = −0.37). Overall, the analyses of high and low performers indicate that

public reporting is positively associated with the performance of employees who have personal

connections in the workplace, regardless of whether they are low or high performers. These

results further support the hypothesis that personal connections enhance the performance effect

of public reporting.



148

TABLE 6

High and Low Performers

Panel A: Cross-sectional Analysis Low Performers (level = 0 or 1) High Performers(level = 2 or 3)

(1) (2) (3) (4)

connection=0 connection=1 connection=0 connection=1

reporting 34.54*** 16.23*** −4.2 0 12.57*** (3.90) (5.29) (−0.37) (4.29)

Control variables Included Included Included Included

Controlled time

fixed effects YES YES YES

YES

Controlled group

fixed effects YES YES YES

YES

Log-likelihood −1692.37 −3863.00 −3265.27 −3978.95

N 425 926 756 962

Panel B: Change Analysis

(1) (2) (3) (4)

connection=0 connection=1 connection=0 connection=1

post2013 13.35*** 0.14 4.55 −0.60

(2.68) (0.06) (0.46) (−0.13)

group3 −18.10* 3.90 1.62 −0.63

(−1.93) (0.55) (0.10) (−0.12)

post2013×group3 18.90*** 11.48*** −4.59 9.50** (5.34) (7.18) (−0.37) (2.60)

Control Variables Included Included Included Included

Time fixed effects* Controlled Controlled Controlled Controlled

Group fixed effects* Controlled Controlled Controlled Controlled

Log-likelihood −1056.21 −2103.52 −2161.63 −2260.64

N 285 554 497 560 This table presents the results of the cross-sectional and the change analyses, using subsamples of high and low performers.

High and low performers are divided based on employees’ skill level assessed by the SOE (i.e., level). Panel A presents the

results for the cross-sectional analysis. The sample includes all the five groups in the research site. The sample period is

from January 2011 to July 2015. The empirical model is presented as follow:

Performanceit = α0 + α1reportingit + α2connectionit+ α3reportingit×connectioni + α4levelit + α5tenurei + α6horizonit


Penal B presents the results for change analysis. The sample period is from January 2011 to December 2014, as the

performance data of 2015 is missing in Group 3. Group 3 switched from private to public reporting at the end of 2013. The

sample includes observations of Group 3 (i.e., treatment) and observations of Groups 1, 2, and 4 (i.e., control). The empirical

model is presented as follow:

Performanceit = α1post2013it + α2group3it + α3post2013it×group3it + α4levelit + α5tenurei + α6horizonit + α7genderi

+ α8homei + α9edui + α10CCPi + α10identityi + εit (2)

The dependent variable for both models is the monthly performance of employees (i.e., performance). The estimations are

censored at −160 and 130, as a result of the task nature and the performance measurement used by the organization. Standard

errors are clustered at individual level. *, **, *** indicates that the correlation coefficient is significantly different from

zero at the 10%, 5% and 1% levels, respectively (two-tailed). For the definitions of variables, see Appendix B.



149

* Some of the time and group indicators were dropped because of collinearity, as the model has already included the indictor

for the post-change period (post2013) and the indicator for the change group (group3).

Types of Connections

In Table 7, I examine the referrer–referral connection and the family connection separately. In

particular, I examine the relation between public reporting and employee performance in three

subsamples: employees with referrer–referral connection only, employees with family

connection only, and employees with both types of connections. Panel A presents the results

of the cross-sectional analysis. Columns (1)–(3) indicate that the relation between public

reporting and employee performance is significantly positive in all three subsamples. However,

this relation is less positive in the subsample with only family connection than in the other two

subsamples. The change analysis documents similar results, which are presented in Panel B. It

shows that the change from private to public reporting is positively associated with the

performance of those with referrer–referral connections and with both types of connections.

However, it is not significantly associated with the performance of the employees with family

connections only. Overall, the results presented in Table 7 indicate that the referrer–referral

connection is more effective in enhancing the relation between public reporting and

performance than the family connection.



150

TABLE 7

Types of Connection

Panel A: Cross-sectional Analysis (1) (2) (3)

referral=1&

family=0 referral=0&

family=1

referral=1&

family=1

reporting 18.22*** 12.26*** 16.86*** (6.96) (3.16) (6.20)

Control variables Included Included Included



Log-likelihood −1778.84 −4479.61 −1641.50

N 459 1,058 422

Panel B: Change Analysis (1) (2) (3)

referral=1&

family=0 referral=0&

family=1

referral=1&

family=1

post2013 25.72*** 1.95 −1.16

(6.43) −0.72 (−0.44)

group3 −4.82*** 2.70** 2.07

(−3.35) −3.21 (1.27)

post2013×group3 17.65*** 3.14 13.69*** (7.34) −1.07 (8.29)

Control variables No No No

Time fixed effects* Controlled Controlled Controlled

Group fixed effects* Controlled Controlled Controlled

Individual fixed effectsa Controlled Controlled Controlled

Log-likelihood −1044.01 −2121.60 −1207.28

N 270 549 318

This table compares the relation between public reporting and employee performance across subsamples with different

types of personal connections. Panel A presents the results for cross-sectional analysis. The sample period is from

January 2011 to July 2015. The sample include all the five groups in the research site. The empirical model mode is

presented as follow:

Performanceit = α0 + α1reportingit + α2connectionit+ α3reportingit×connectioni + α4levelit + α5tenurei + α6horizonit


Panel B presents the results for the change analysis. The sample period is from January 2011 to December 2014, as

the performance data of 2015 is missing in Group 3. Group 3 switched from private to public reporting at the end of

2013. The sample includes observations of Group 3 (i.e., treatment) and observations of Groups 1, 2, and 4 (i.e.,

control). The empirical model is presented as follow:

Performanceit = α1post2013it + α2group3it + α3post2013it×group3it + α4levelit + α5tenurei + α6horizonit + α7genderi

+ α8homei + α9edui + α10CCPi + α10identityi + εit (2)

(continued.)



151

TABLE 7 (cont.)

The dependent variable for both models is the monthly performance of employees (i.e., performance). The

estimations are censored at −160 and 130, as a result of the task nature and the performance measurement used by

the organization. Standard errors are clustered at individual level. *, **, *** indicates that the correlation coefficient

is significantly different from zero at the 10%, 5% and 1% levels, respectively (two-tailed). For the definitions of

variables, see Appendix B.

a Including control variables leads to a multicollinearity problem in the change analysis. I therefore control for the

individual fixed effects and exclude the control variables.

* Some of the time and group indicators were dropped because of collinearity, as the model has already included the

indictor for the post-change period (post2013) and the indicator for the change group (group3).

Robustness Checks

In the robustness tests, I first winsorize performance at 5% and 1% to ensure the results are not

distorted by the extreme values in employee performance. Second, as the dataset in this study

involves multiple periods, I include time-series variables into the models to check the

robustness of the results. After including the lag of performance in the estimations, the


performance are significantly positive, suggesting employee performance is persistent

throughout time. The conclusions remain the same. Further, performance information of some

employees is missing from certain months because of external factors (such as extreme

weather), technical problems (such as system breakdown), or sick leave. Excluding the months

with missing observations does not change the conclusion. Finally, I also use ordinary least

square (OLS) instead of the censored model. The adjusted R2 of the estimations are around 60%

and the conclusions remain the same.


This study examines how personal connections, including referral–referrer connection and

family connection, affect the relation between public reporting and employee performance.

Drawing on the social psychology literature, I expect that personal connections in the

workplace enhance the positive relation between public reporting and employee performance.

Using archival and survey data from a Chinese SOE, I find that public reporting is positively



152

associated with the performance of employees with personal connections, but not significantly

associated with the performance of those without personal connections. Based on the social

psychology literature, employees’ image concern is increased by the personal connections they

have in the workplace (Cupach and Metts 1994; Goffman 1979; Festinger 1954; Suls and

Wheeler 2000; Tedeschi 2013). The findings of this study suggest that the positive relation

between public reporting and employee performance is only significant when employees’

image concern is salient.

This study contributes to the accounting literature in two ways. First, previous studies

experimentally find that public reporting is positively related to employee performance

(Hannan et al. 2013; Maas and Van Rinsum 2013; Tafkov 2012). This study adds to the existing

literature by documenting that the performance effect of public reporting is conditional on

employees’ image concern. Using data from a field site, this study shows that personal

connections, as a factor that increases employees’ image concern, significantly enhances the

performance impact of public reporting. This study also contributes to the literature on

employee selection (Abernethy et al. 2015; Campbell 2012), by showing that employee

selection channel (i.e., referral) and criteria (i.e., family connections) can affect the

performance effect of the internal reporting choices made by managers. Finally, the findings

of this study have practical implications by showing that managers should consider internal

reporting and employee selection as an integrated system.

This study is not without limitations. First, it is an open question that to what extent we can

generalize the findings from a single company to other settings. The primary aim of this study

is to examine whether the performance effect of public reporting is affected by personal

connections. Examining this research question requires a setting with private and public



153

reporting, personal connections of employees, and detailed data on employee performance and

background. Future studies may examine whether the findings of this study can be replicated

in other countries and/or other types of organizations. Further, using archival and survey data,

this study cannot and does not attempt to demonstrate the causal chain underlying the

associations between performance reporting, personal connections, and employee performance.

Future studies may extend this study by exploring the mechanisms that drive these associations.



154

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APPENDIX A

Sample of the Survey Questionnaire Workgroup identity4

All multi-item measures used 7-point Likert-type scales ranging from 1 (strongly disagree) to 7

(strongly agree), unless stated otherwise. 1. When someone criticizes my group, it feels like a personal insult.

2. I am very interested in what others think about my group.

3. When I talk about my group, I usually say ‘we’ rather than ‘they’.

4. My group’s successes are my successes.

5. When someone praises my group, it feels like a personal compliment.

6. If my group was criticized by someone outside my group, I would feel embarrassed.

7. Being a member of my group is a major part of who I am.

8. Please indicate to what degree your self-image overlaps with your group’s image. (Answers

ranging from 1 [no overlap at all] to 7 [fully overlaps]).

9. Imagine that one of the circles on the left represents you and the circle on the right represents

your group. Please indicate which case best describes the level of overlap between you and

your group.

me my group

Construct validity tests for work group identity

Perceived support from workgroup

1. My group values my contributions.

2. My group appreciates any extra effort from me.

3. My group really cares about my wellbeing.

Shared goals with other group members

1. My group members and I always agree on what is important at work.

2. My group members and I always share the same ambitions and vision at work.

Employee backgrounds

Recruitment channel

1. How did you enter into the SOE?

1) Through government allocation.

2) Through another channel.

2. Did you have any existing employee(s) in the SOE before your entry?

1) Yes

2) No

(continued.)

4 The survey questionnaires sent to the employees did not include the subtitles. The subtitles are used here to

clearly present and distinguish different instruments.



157

APPENDIX A (continued)

Family connection

Do you have family members who also work in the SOE?

1) YES, I do have family member(s) who also work in the SOE.

2) I used to have family members who worked in the SOE, but they have retired or left.

3) No, I have never had any family member who also works in the SOE.



158

APPENDIX B

Variable Definitions

Performance

Performance scores that employees receive at the end of every month for their

operational actions and outcomes; objectively measured by department

managers through checking the system records and the status of the equipment,

and recorded in the SOE’s computer system. Because of the nature of the task

and the performance measurement designed by the SOE, the highest (lowest)

performance score that an employee can receive in a month is 130 (–160).

connection

The personal connection(s) that employees have in the SOE; equals 0 for

employees without a referrer or any family members in the SOE; equal to 1 for

employees with a referrer or family member in the SOE. Constructed using two

survey questions on the channel that employees entered into the SOE and one

survey question on whether employees have family members in the SOE.

Details of the questions are presented in Appendix A.

referral

The proxy for employees who entered the SOE through the recommendation

of existing employees. Measured by two survey questions presented in

Appendix A. Equal to 1 for employees who were not allocated by the

government and had connections with existing employees before entering the

SOE, 0 otherwise.

family 1 for employees with family members who also work in the SOE; measured by

one survey question presented in Appendix A.

reporting

An indicator for observations in groups with public reporting. Equal to 1 for

observations in Group 3 after 2013 and all the observations in Group 5; and 0

for all other observations.

post2013 1 for observations after 2013, 0 otherwise.

group3 1 for observations in Group 3, 0 otherwise.

age Employees’ age. Calculated based on employees’ date of birth.

tenure Number of years that employee i has been working in the SOE.


horizon Equal to 1 for male workers over 49 and female workers over 44 (i.e., 5 years

before retirement), 0 otherwise.


other regions.

edu

Level of education of an employee, taking the value 0 for junior high school or

equivalence, 1 for senior high school or equivalence, 2 for undergraduate

degree, or 3 for postgraduate degree and higher.


otherwise.

identity Employees’ group identity, measured by nine survey questions (see Appendix

A).

CHAPTER 5: CONCLUSION

159

CHAPTER V

CONCLUSION

5.1 Summary

The three essays in this thesis examine how formal and informal controls jointly affect the task

performance of lower-level employees. The first essay finds that under private reporting, group

identity is positively related to employee performance. In comparison, under public reporting,

group identity is negatively (positively) related to the performance of employees with high

(low) ability. These findings suggest that the relation between group identity and employee

performance is conditional on managers’ choice regarding performance reporting transparency.

The second essay suggests that in multi-task settings where employee performance in some

task(s) cannot be precisely measured, managers can motivate employee performance through

promoting work norms that contribute to the desired organizational outcomes. However, the

effects of the work norms only persist if managers keep taking actions to promote them. The

third essay documents a positive relation between public reporting and employee performance.

However, this relation is only salient among employees who have personal connections in the

workplace.

5.2 Contributions

All three essays contribute to the literature on management controls and employee performance.

In particular, the first and the third essay contribute to the literature on internal performance

reporting (Azmat and Iriberri 2010; Frederickson 1992; Hannan, Krishnan, and Newman 2008;

Hannan, McPhee, Newman, and Tafkov 2013; Maas and Van Rinsum 2013; Tafkov 2012), by

demonstrating how public reporting affects employee performance through group identity and


160

employees’ image concern. Further, the first essay also contributes to the growing literature on

employees’ group identity (Abernethy, Bouwens, and Kroos 2017; Empson 2004; Heinle,

Hofmann, and Kunz 2012; Towry 2003), by documenting that the behavioral effect of group

identity is conditional on the information environment within groups as well as the ability of

employees. Additionally, the second essay contributes to the broader literature on the role of

work norms as an integral part of an organization’s control system (Abernethy, Dekker, and

Schultz 2015; Campbell 2012; Gibbons and Kaplan 2015; Kachelmeier, Reichert, and

Williamson 2008; Maas and Van Rinsum 2013), by showing that managers can shape the work

norms in organizations by deliberately promoting the work norms that contribute to the desired

organizational outcomes. Moreover, the third essay contributes to the growing literature on

employee selection (Abernethy et al. 2015; Campbell 2012) by documenting how employment

channels (i.e., referrals and non-referrals) and criteria (i.e., with or without family connections)

affect the performance impact of internal performance reporting.

Taken together, the three essays in this thesis add to the management accounting literature by

demonstrating how different formal and informal controls jointly affect the performance of

lower-level employees. The findings also suggest that managers need to consider different

controls as an integrated system when designing and implementing management control

systems (MCSs).

5.3 Limitations and Future Research

As discussed in each of the three essays, the findings of this thesis are subject to several

limitations. First, to examine the research questions, I used data from a field site where there

are variations in the formal control mechanisms, and where the effects of the informal controls

are salient. It is an open question to what extent we can generalize the findings from a single


161

research site. Future research could replicate and extend the studies in this thesis in different

settings. Second, although this study documents the relation between employee performance

and different management controls, I cannot examine which underlying mechanisms are

driving these relations, and thus do not attempt to demonstrate the causal chain that explains

these relations. Future research may examine the mechanisms underlying the findings of this

thesis using controlled experiments. Finally, this study employs long time-series data from a

real-world setting, which makes it difficult to rule out alternative explanations for the findings.

However, given that the context is controlled for and the research subjects have long tenures

in this setting, this does not pose a significant threat.


162

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Public Economics 94(7): 435–452


50(4): 931–966.

Empson, L. 2004. Organizational identity change: Managerial regulation and member

identification in an accounting firm acquisition. Accounting, Organizations and Society

29(8): 759–781.





Proceedings 105(5): 447–451.







Heinle, M. S., C. Hofmann, and A. H. Kunz. 2012. Identity, incentives, and the value of

information. The Accounting Review 87(4): 1309–1334.









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SHANG, RUIDI

Title:

The influence of formal and informal controls on employee performance: three essays

Date:

2017

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The Influence of Formal and Informal Controls on Employee Performance: Three Essays

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