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Impact of performance appraisals on double-hatting employees

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Impact of performance appraisals on double-hatting employees

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Content Impact of Performance Appraisals on Double-Hatting Employees
By
Nicholas Joo
Rossier School of Education
University of Southern California
A dissertation submitted to the faculty
in partial fulfillment of the requirements for the degree of
Doctor of Education
August 2023

© Copyright by Nicholas Joo 2023
All Rights Reserved

The Committee for Nicholas Joo certifies the approval of this Dissertation

Lawrence Picus
Marcus Pritchard
Monique Datta, Committee Chair

Rossier School of Education
University of Southern California
2023
iv
Abstract
Anecdotal evidence suggests double-hatting roles in organizations have increased since 2015.
However, there is limited literature on double-hatting in a corporate setting. In addition, there is
limited literature on performance appraisals (PA) in Singapore since 2017. No study has
investigated the impact of PAs on double-hatting employee outcomes globally. The current study
investigates the effect of PA on double-hatting employees through the lens of organizational
justice with the Burke-Litwin Organizational Model as its theoretical framework. The study
surveys 400 double-hatting employees based in Singapore on their perceptions of their
organization’s PA, fairness with PA, satisfaction, motivation, and organizational commitment.
The dissertation uses a structural equation model to ascertain the impact of PA on employee
satisfaction, motivation, and organizational commitment. The results show fairness in PA is
critical and significantly impacts employee satisfaction, motivation, and organizational
commitment. The author also discusses practical suggestions for organizations and future
research to investigate the different aspects of double-hatting roles in organizations.

Keywords: performance appraisal, double-hatting, organizational justice, Singapore,
satisfaction, motivation, organizational commitment

v
Dedication
To my mom and wife: Thank you both for your endless support. This dissertation could not have
happened without either of you. You have kept me grounded from the beginning, and your
sacrifices have allowed me to complete this study.
vi
Acknowledgments
To my dissertation committee: Dr. Monique Datta, I could not have asked for a better
chair. Your patience, support, and steer throughout this process have been invaluable. Your
invaluable advice made this dissertation come to life on a white-space topic. Dr. Marcus
Pritchard, I am grateful for instilling the formulaic nature of academic writing in me and for your
quick turnarounds for feedback. Dr. Lawrence Picus, thank you for your wise counsel in
identifying the small areas of improvement to elevate this study further.
To my Adaptive Group, Adriana Merlo, Francesca Munda, and Janine Lee: Working with
each of you in the leadership course has been one of the best things in OCL. I appreciate your
support throughout this journey. Your constant words of encouragement and sharing of resources
have been beneficial in allowing me to complete this study. Also, to everyone else in Cohort 19,
thank you for being on this journey with me.
There are no conflicts of interest to disclose.
You can address any questions or comments about this dissertation to Nicholas Joo.
Email: njoo@usc.edu

vii
Table of Contents
Abstract .................................................................................................................................... iv
Dedication .................................................................................................................................. v
Acknowledgments ..................................................................................................................... vi
List of Tables ............................................................................................................................ ix
List of Figures ............................................................................................................................ x
Chapter One: Overview of the Study .......................................................................................... 1
Context and Background of the Problem ......................................................................... 1
Purpose of the Study ....................................................................................................... 2
Significance of the Study ................................................................................................ 3
Overview of Theoretical Framework .............................................................................. 4
Definition of Terms ........................................................................................................ 5
Organization of the Study ............................................................................................... 6
Chapter Two: Review of the Literature....................................................................................... 7
History of Performance Appraisals ................................................................................. 7
Modern Day Performance Appraisals ............................................................................10
Performance Appraisals in Singapore ............................................................................18
Organizational Justice ....................................................................................................19
Impact of Organizational Justice on Performance Appraisals .........................................26
Double-Hatting Employees ............................................................................................29
Conceptual Framework ..................................................................................................30
Chapter Three: Methodology .....................................................................................................35
Research Questions .......................................................................................................35
Research Design ............................................................................................................35
The Researcher ..............................................................................................................36
viii
Data Sources..................................................................................................................37
Ethics ............................................................................................................................42
Chapter Four: Results ................................................................................................................44
Survey Results ...............................................................................................................44
Summary .......................................................................................................................61
Chapter Five: Discussion ...........................................................................................................62
Recommendation 1: Use Multiple PA Systems ..............................................................62
Recommendation 2: Ensure Fairness with PAs ..............................................................64
Recommendation 3: Understand Specific Drivers of the Key Employee Outcomes ........65
Limitations and Delimitations ........................................................................................66
Future Research .............................................................................................................68
Conclusion ....................................................................................................................69
References ................................................................................................................................70
Appendix A: Survey Questions .................................................................................................91

ix
List of Tables
Table 1 Data Sources ................................................................................................................. 36
Table 2 Inferential Analysis ....................................................................................................... 40
Table 3 Participants' Demographics ........................................................................................... 45
Table 4 Number of PA Systems Used by an Organization (n = 258) ........................................... 47
Table 5 Incidence of PA Systems Used in an Organization (n=400) ........................................... 48
Table 6 Descriptive Statistics, Sample, and Independent T-Test Comparison – PA Validity ........ 50
Table 7 Descriptive Statistics, Sample, and Independent T-Test Comparison – DJ ratings .......... 51
Table 8 Descriptive Statistics, Sample, and Independent T-Test Comparison – DJ rewards ........ 52
Table 9 Descriptive Statistics, Sample, and Independent T-Test Comparison – PJ ...................... 53
Table 10 Descriptive Statistics, Sample, and Independent T-Test Comparison – IJ ..................... 54
Table 11 Pearson’s Correlation Coefficients for PA Components and Employee Outcomes ........ 55
Table 12 Indicator Loadings, Construct Composite Reliability, and VIF Scores ......................... 58
Table 13 Regression Analysis of Latent Constructs.................................................................... 59
Table 14 SEM Pathway Coefficient, T Scores, and p Values ...................................................... 60

x
List of Figures
Figure 1 Burke-Litwin Model for Organizational Change .......................................................... 32
Figure 2 Conceptual Framework ............................................................................................... 33
Figure 3 Comparison of Means by Number of PA Systems ........................................................ 49
Figure 4 Structural Equation Model of PA Components and Employee Outcomes ..................... 56

1

Chapter One: Overview of the Study
Inaccurate performance appraisals (PAs) of employees, who hold multiple organizational
roles (double-hat), lead to lower satisfaction, motivation, and commitment levels. When PAs do
not capture what an employee has done over a year accurately, it affects employee performance.
Ojokuku (2013) determined inaccurate performance evaluation negatively influenced employee
motivation and job performance. Capturing accurate performance evaluations is harder in
organizations when employees report to multiple managers and they require multiple raters.
Appelbaum et al. (2008) posit that poorly designed performance appraisals in matrix
organizations (where individuals report to more than one manager) lead to lower employee
morale. Fairness in PAs is a critical component in ensuring employees are satisfied. Researchers
in Asia showed biases and a lack of transparency negatively affected employee satisfaction in
organizations (Yamazaki & Yoon, 2016). The issue of inaccurate PAs among employees who
report to multiple managers is critical to resolve as lower levels of satisfaction, motivation, and
commitment result in higher organizational turnover, which is a high cost for organizations
(Zhao et al., 2019).
Context and Background of the Problem
PAs in organizations incur significant investments financially and employee hours, yet
still have issues fairly assessing employees. Corporate Executive Board’s (CEB) study (2012, as
cited in Pulakos et al., 2019) estimated organizations with at least 10,000 employees incur $35
million annually to conduct annual performance evaluations, while the average manager spends
about 210 hours on administrative tasks relating to PAs. Yet, with these financial and human
capital investments, organizations cannot ascertain performance accurately. Studies across
industries since 2020 still demonstrate accuracy issues with different PA systems in various
2

organizations (Dangol, 2021; Elangovan & Rajendran, 2020; Na-Nan et al., 2022). The inability
to accurately and fairly assess employees leads to adverse outcomes for these employees.
Researchers have determined unfair performance evaluations lead to low levels of employee
satisfaction, motivation, and commitment (Ahuja et al., 2018; Elangovan & Rajendran, 2020;
Islami et al., 2018; Kampkötter, 2017). Employees who require multisource feedback face
additional issues beyond additional cost and adverse employee outcomes.
Employees who double-hat regularly require input into their appraisals from two or more
managers they work with, which creates its own set of issues. With many more appraisal reports
to fill in for their direct reports, but also those with a dotted reporting line, survey fatigue may set
in for some of these managers, resulting in less accurate reviews as managers look to get through
the process quickly as possible (McCarthy & Garavan, 2001). Second, in multisource feedback
environments, it introduces additional rater biases due to the presence of more evaluators in the
PA process. Bernardin et al. (2016) demonstrated rater bias across various situations and argued
that personality impacted the type of bias introduced when rating these subordinates.
Furthermore, different raters would have differing expectations, resulting in vastly different
ratings, and creating discrepancies. A study showed differing groups of raters had come into the
evaluation with a distinct emphasis on what was essential to the rater. While some of the ratings
were consistent, they also had meaningful differences among the rater groups (Tariq et al., 2020).
The complexity of rating double-hat employees creates issues with fairness in the PA process
and affects employee satisfaction, motivation, and commitment.
Purpose of the Study
The purpose of the study is to understand how employees, who double-hat, perceive PAs
in their respective organizations and how it affects their satisfaction, motivation, and
3

commitment to their organization. Additionally, the study addresses the different PAs and their
components to ascertain how they impact employee outcomes. These comparisons feed into
recommendations on appropriate PA solutions for double-hatting employees. The study uses a
quantitative methodology to survey employees in Singapore to address the research questions. A
large sample (n = 400) allows for correlation and a structural equation model of PAs and their
components against satisfaction, motivation, and commitment. The following research questions
will guide the study:
1. How do employees perceive performance evaluation systems?
2. What is the relationship of inaccurate performance evaluation with employee satisfaction,
motivation, and commitment?
3. How do different components of the performance evaluation process impact employee
satisfaction, motivation, and commitment?
Significance of the Study
The reason for the study is two-fold. First, it addresses a gap in the literature, which
identifies the impact of PAs on employees with two or more formal roles in an organization. The
literature to date has investigated the influence of PA on employee or organizational outcomes.
However, there has been no study on how it impacts employees who double-hat in organizations.
Second, there is limited literature on understanding PAs impact on employees in Singapore. The
most recent studies conducted were in the late 2000s and early 2010s (Chiang & Birtch, 2010;
Kelly et al., 2008; Poocharoen & Lee, 2013). The current study will address two key gaps in the
literature for double-hatting employees and provides an updated view of PA impact in Singapore.
The study ultimately aims to provide senior human capital leaders in corporations and
business owners an overview of the importance of fair and accurate PAs for double-hatting
4

employees. Organizations consider PAs as an essential strategic tool to drive organizational
performance. Bayo-Moriones et al. (2021) posit PAs when used appropriately to match the
organization’s strategy, improve financial performance. Based on the analyses and insights, the
study proposes practical recommendations to allow key organization decision-makers to design a
PA process to ensure accurate appraisals for double-hatting employees.
Overview of Theoretical Framework
The study will use Burke-Litwin’s model (BLM) for organizational change as its
theoretical framework. According to Burke and Litwin (1992), there are 12 different and
interconnected elements of organizational change. A change in one element impacts one or more
other element. BLM categorizes all 12 elements into transformational, transactional, or
individual factors. The transformational factors include mission & strategy, leadership,
organizational culture, and individual and organizational performance. The external environment
impacts transformational factors, which BLM considers to be long-term drivers of change.
Transactional factors include structure, management practices, and systems, while individual
factors include work unit climate, tasks and individual skills, motivation, and individual needs
and values. BLM considers transactional factors to be operational levers of change, which are
short-term.
BLM is appropriate because this study’s purpose is to understand the impact of
performance appraisals on double-hatting employee satisfaction, motivation, and commitment,
which transactional factors cover. As defined by Burke and Litwin (1992), systems encompass
performance appraisals, while organizational commitment, motivation, and satisfaction would
fall under the motivation driver. Structure will cover the concept of double-hatting employees.
5

Structure, systems, and motivation are interconnected elements and impact one another. BLM
connects the three key elements of the study, making it the most appropriate framework to use.
Definition of Terms
This section will define the key terms used in the study:
• Commitment describes organizational commitment, which refers to the degree to which
an individual feels loyal to the organization and intends to remain with the organization
(J. P. Meyer & Allen, 1991). Meyer and Allen (1991) further defined organizational
commitment and identified potential job loss as another aspect of organizational
commitment, which the study does not analyze.
• Double-hat or double-hatting refers to employees who take on two formal organizational
roles (Mohamed Hoosen Carrim, 2020).
• Motivation refers to an individual's determination to achieve their personal and
organizational goals (Lindner, 1998).
• Performance appraisal (PA) is the human resource practice of evaluating employees'
work performance against a fixed set of criteria or agreed-upon goals (Daoanis, 2012).
• Satisfaction relates to employee satisfaction, which is the perception of how well the
organization has met the employees’ needs and desires (Küskü, 2001). Scholars have
measured satisfaction with a single question or a multi-dimensional battery of questions
to parse out individual aspects of satisfaction (Koustelios & Bagiatis, 1997; Küskü,
2001).
• PA Validity refers to the construct of questions that measure whether a PA is addressing
the key objectives of what a PA is meant to do (Gabris & Ihrke, 2001).
6

• Distributive Justice (ratings) is a component of organizational justice and consists of a
battery of questions to understand if ratings are fairly allocated during a PA (Tang &
Sarsfield-Baldwin, 1996).
• Distributive Justice (rewards) measures whether the allocation of rewards during a PA is
attributed fairly to each individual (Tang & Sarsfield-Baldwin, 1996).
• Procedural Justice consists of five questions which determine if processes during the PA
have been applied fairly (Tang & Sarsfield-Baldwin, 1996).
• Interactional Justice refers to how the manager treats employees during the PA process
and measured using five questions (Tang & Sarsfield-Baldwin, 1996).
Organization of the Study
Chapter One provides an overview of the problem of practice. Chapter Two reviews the
existing literature regarding the history of PA, the common types of PAs, their impact on
organizations and employees, and types of studies involving double-hatting employees. Chapter
Three explains the quantitative methodology used in this study and the data analysis approach.
Chapter Four details the descriptive and inferential statistics and analyses from the quantitative
survey. Finally, Chapter Five discusses the results and provides recommendations on how
organizations should approach PAs for double-hatting employees to ensure high levels of
employee satisfaction, motivation, and commitment. In addition, Chapter Five will discuss the
potential direction of future research.

7

Chapter Two: Review of the Literature
The literature review aims to provide an understanding of performance appraisals and
their impact on employees through the lens of organizational justice concepts. In Chapter Two,
there are three principal areas. In the first area, the history and evolution of PAs will provide an
overview of how it originated and its purpose. Second, the review will explore how PAs have
evolved and how corporations today use them. In addition, the literature examines the most
popular PA methods used in corporations and investigates the benefits and limitations of each.
Finally, the first section will conclude with an overview of the existing PA studies in Singapore.
The second area of the literature review explores the origins of organizational justice and
its related concepts: distributive, procedural, and interactional justice. The next section will cover
the impact of performance appraisals through the lens of organizational justice. Specifically, this
section will investigate the impact on an employee's motivation, satisfaction, and organizational
commitment.
The third area of the literature review covers the remaining areas related to the study. The
first section begins with examining the limited literature on double-hatting employees. Finally,
the review examines BLM, its appropriateness, and how the framework influences the
conceptual framework.
History of Performance Appraisals
Performance appraisals began as a simple evaluation tool to measure employee
performance before evolving. Several authors cite China and its emperors as the first users of PA
to identify scholars (Denisi & Murphy, 2017; Wiese & Buckley, 1998). Its modern roots,
however, date back to the 1800s (Pulakos et al., 2019). The U.S. Federal Civil Service was the
first large organization to employ a PA system. In 1883, the U.S. Congress passed the Pendleton
8

Act to allow for the neutral examination of candidates for jobs in the Federal Civil Service
(Milkovich & Wigdor, 1991). In World War I, the U.S. military used ratings to identify poor
performers and dismissed them as part of their evaluation process (Cappelli & Tavis, 2016;
Wiese & Buckley, 1998). In World War II, the U.S. military continued with evaluation systems,
however, this time they had applied a forced ranking to classify every soldier and identified the
top 5-10% for promotion to officers or soldiers with the potential to become officers (Sisson,
1948). In this same period, PA became a key focus for corporations. Spriegel (1962), in a
landmark paper, showed an estimated 60% of U.S. organizations began to incorporate some form
of an evaluation process to measure the performance of their employees and used those scores
for compensation, rewards, and dismissal decisions. However, it was not till the 1950s that PAs
began to change to give employees an opportunity for input (Cappelli & Tavis, 2016).
The turning point for PAs came in 1957 with the publication of McGregor’s Theory X
and Theory Y concepts. In his seminal paper, McGregor (1957) countered conventional thinking
by arguing that employees were not naturally motivated and needed instructions for their tasks.
Employees could achieve their goals and fulfill their potential with the right guidance. With the
advent of McGregor’s paper, researchers and organizations began testing new methods of
appraisals and allowed employees to have a say in goal setting and their assessments (Wiese &
Buckley, 1998). PAs to measure performance continued until the 1960s when General Electric
(GE) became the first organization to split appraisals into two topics: performance and
development. GE’s appraisal method allowed for clearer accountability of each employee and
yet at the same time provided employees with opportunities for learning and development. GE
continued to be a pioneer in evaluating employees in the 1960s (Meyer et al., 1965) and the
1980s.
9

In 1980, Jack Welch, then-CEO of GE, instituted what was known as forced distribution
rankings to classify employees into the top 20%, middle 70%, and bottom 10% (Moon et al.,
2016; Welch & Byrne, 2003). GE would fire the bottom 10% and replace the fired employees in
the organization. The fire and hire cycle would repeat annually. Welsh believed ranking
employees and firing the bottom 10% would ensure he always had the best employees in the
organization. In addition, firing the bottom 10% would motivate employees to work hard to not
fall into the bottom 10% (Welch & Byrne, 2003). Instead, it promoted a cut-throat environment.
Many organizations followed, but there were issues with such a system regarding fairness and
how it worked (Chattopadhyay, 2019; Giumetti et al., 2015; Loberg et al., 2021; Moon et al.,
2016). Forced distribution rankings continued to remain popular, and organizations still use
them. A study in 2011 estimated about 15% of organizations were still using this form of
appraisal to evaluate employees (Olson, 2013). Over the next 20 years, organizations introduced
new appraisal methods; however, the next major shift in appraisals took place during the 2000s
(Cappelli & Tavis, 2016).
In the 2000s, as organizations got larger and the cost of appraisals continued to grow,
organizations explored more effective ways of evaluating employees and reducing costs. In
2011, the cost-cutting approach led Kelly Services to become the first professional services firm
to drop appraisals (Cappelli & Tavis, 2016). Other major organizations, including Accenture,
Deloitte, and Adobe, also followed suit (Lawler et al., 2016). These organizations moved away
from formal appraisals to frequent, but informal feedback sessions with managers to ensure
employees were tracking against goals and received the necessary training without the formal
constraints of rating scales or paperwork (Lawler et al., 2016; Stone et al., 2019). Lawler et al.’s
(2016) study of 244 U.S. organizations showed 70% of firms use at least some form of informal
10

feedback mechanism to facilitate evaluations and performance accountability. An estimated 10%
of organizations have eliminated formal appraisals and moved entirely to informal feedback
systems.
Formal appraisal systems continue to be a mainstay for many organizations, and several
tools, such as 360 feedback, management by objectives, and forced rankings, remain popular in
evaluating employees (Sahadi, 2015; Zenger, 2016). Some organizations have transitioned from
one to another, while others have used multiple methods to evaluate employees (Lawler et al.,
2016; Stone et al., 2019). These popular appraisal methods have a mixed record of success. The
subsequent sections explore the details of the most popular appraisal methods.
Modern Day Performance Appraisals
Organizations use PAs as a key strategic human resource tool to guide key decision-
making. In addition to providing simple rating information for administrative decisions,
companies now use PAs to make developmental and strategic decisions. Chiang and Birtch
(2010) claimed firms initially used PAs to help make simple decisions regarding promotions and
salary increments, but later, organizations used PAs to include developmental aspects.
Organizations used the identified developmental elements to feed into training and development
programs (Thuy & Trinh, 2020). When organizations implement and execute PAs properly, there
is an improvement in the organization’s overall financial performance. Baird et al.’s (2020) study
of 203 Australian companies showed the importance of high-quality PAs. High levels of trust,
fairness, transparent communication, and clarity on measurements in the entire PA process
resulted in significantly higher financial performance for the firm. As a result of the potential
impact of performance appraisals on organizations and their employees, companies invested
11

heavily in PA systems and training (Kampkötter, 2017). These investments led to more
sophisticated PA systems.
To deal with the complex needs of organizations today, PAs in the past, which were
Likert Scale-type questions, have become more sophisticated. This complexity has also resulted
in high costs for organizations, and as such, organizations have looked to simplify their PAs and
still achieve similar outcomes (Stone et al., 2019). Modern appraisals now include aspects of
goal setting, informal feedback, feedback from peers and subordinates, and tracking against these
goals (Lawler et al., 2016). Using the information provided by appraisals, organizations
determine remuneration, training, promotions, and redundancies (Brefo-Manuh et al., 2016). The
evolution of PAs has popularized certain appraisal methods: 360 feedback, management by
objectives, forced distribution rankings, and informal feedback. These appraisal methods'
popularity has led some organizations to use a combination of appraisal methods to evaluate their
employees (Lawler et al., 2016). The subsequent sections of the literature will explore the
popular appraisal methods in detail.
360 Feedback
Multi-rater feedback began in the 1900s; however, it was not until 1996 that the term 360
feedback became popularized. The terms 360 feedback, multi-source feedback, and multi-rater
feedback all refer to similar concepts. Today, the term 360 feedback is most used, especially
after the publication of Edward and Ewen’s book: 360° Feedback: The Powerful New Model for
Employee Assessment & Performance Improvement (Bracken et al., 2016). A benchmark study
conducted among 84 organizations with an average annual turnover of 45 billion by Church and
Rotolo (2013) determined almost 70% of these organizations used 360 feedback as an appraisal
tool. A later article by Zenger (2016) estimated 85% of Fortune 500 organizations used 360
12

feedback to enable leadership development in their organizations. The growth of 360 feedback as
a PA tool was due to its perceived benefits.
360 feedback was born out of the need to improve the accuracy of single-rater appraisals
and for developmental areas. Church et al. (2019) explained 360 feedback allowed organizations
to form a more holistic and accurate view of the individual by having peers, subordinates, and
superiors provide input on the individual’s performance. Obtaining multiple views would thereby
overcome any potential prejudice managers may have against the individual and allow managers
also to understand other areas of performance, which the immediate managers may not get to
observe on a day-to-day basis. A study of 400 employees in the electronics sector showed
employees and their managers perceived the 360 feedback tool to provide a fair assessment of
their performance (Karkoulian et al., 2016). Furthermore, several studies have argued a holistic
overview of these individuals would allow organizations to craft better developmental plans for
these individuals by understanding their areas of improvement (Bracken et al., 2016; Church et
al., 2019; Church & Rotolo, 2013). Additional benefits of the 360 feedback tool included more
open communication and trust between colleagues (McCarthy & Garavan, 2001; Waldman et al.,
1998). While studies have shown 360 feedback to be a more accurate method to assess
employees, the system has drawbacks.
Studies over the last 2 decades have highlighted issues with biases and the accuracy of
360 feedback. A study conducted with 125 master of business administration (MBA) students at
a Southwestern university in the United States concluded any negative comments received
during 360 feedback required careful management. Students perceived the feedback to be less
accurate as compared to positive feedback. To cope with negative feedback, companies must be
prepared to invest in coaches or facilitators to teach receivers how to deal with negative feedback
13

(Brett & Atwater, 2001). Becton and Schraeder (2004) also determined ratees commonly
selected raters who liked them or were close to them, further positively skewing scores.
Furthermore, with multiple raters involved, there is no standard for rating the individual. Every
rater had their own expectation, resulting in significant discrepancies in the ratings (Tornow,
1993). Addressing the issues of accuracy and biases in 360 feedback, Brown et al. (2016)
proposed forcing raters to choose from given options, rather than allowing free rating, could
mitigate these biases and inaccuracies in 360 feedback systems. Forcing evaluators to rank or
prioritize would reduce the amount of rating inflation. Thus, firms could potentially minimize
issues with 360 feedback.
Forced Distribution Rankings
The U.S. military was the first organization to use forced distribution rankings (FDR) to
classify employees but made popular by Jack Welch, then-CEO of GE. FDR’s purpose was to
rank individuals and differentiate individuals in an organization (Dominick, 2009). After GE
implemented FDR, other major companies, for example, Microsoft, Goldman Sachs, and
American Express followed suit to try and improve employee performance through a similar
system (Guralnik et al., 2004). In 2015, a study showed an estimated 20% of organizations used
some variation of FDRs in their organizations (Sahadi, 2015). FDR has proponents given its
ability to increase organizational performance in the early stages of implementation.
Organizations have demonstrated FDRs to have positive outcomes in their first few years.
As an appraisal system that weeds out low performers, it attracts potential high performers. A
study of 148 graduate business students showed they preferred organizations with an FDR
(Thomason et al., 2018). A similar study among 143 graduate students argued students perceived
these organizations as valuing high achievers (Blume et al., 2013). These graduate students
14

wanted to work for organizations that looked only to retain high performers. There are also
arguments FDR reduces rating inflation. Cao and Drasgow's (2019) meta-analysis of forced-
choice studies showed minimal rating inflation across the studies when raters had to rank. In
addition, before implementing FDR at Ford, managers rated more than 90% of individuals as
meeting expectations. FDR allowed organizations to segregate their top and worst performers
compared to an absolute rating scale. By separating individuals, organizations reserve their
rewards for the top performers and ensure they do not reward poor performers (Dominick, 2009).
However, the positive effects of FDR are short-lived, as the negative effects of FDR begin to
show within a few years.
Despite the positive intent of FDR as a performance appraisal system, FDR grapples with
numerous issues. First, FDR fosters unhealthy competition, leading to employees sabotaging one
another to get ahead. Studies showed employees under an FDR were less likely to collaborate as
a team. If easy enough, employees may even sabotage their colleagues to secure a higher ranking
(Berger et al., 2012; Malhotra & Mukherjee, 2013). On an individual level, FDRs eventually
reduce employee motivation and satisfaction as they cannot attain similar scores or rankings
despite achieving the same or greater results in subsequent appraisal cycles. Stewart et al. (2010)
posited while the initial impact of FDRs tends to be positive, as the years go by and when
employees are forced into a category below average to meet the requirements of the FDRs,
despite performing well, employees will not be motivated to do their best and report lower
employee satisfaction and motivation scores. In addition, employees feel FDRs are unfair and do
not accurately represent their job performance. Researchers demonstrated that raters found
forcing fit ratees into FDR categories difficult and unfair, impacting their confidence in
managing and dealing with their ratees (Schleicher et al., 2009). Eventually, the impact of FDR
15

is detrimental to employees’ performance (Moon et al., 2016). The negative aspects of FDR
outweigh any positive gains in the early days of its implementation.
Management by Objectives (MBO)
Proposed by Peter Drucker in 1954, MBO was meant to create goals for an organization
and cascaded them throughout so everybody would work together in a concerted effort. As a PA
tool, MBO consists of three broad steps: goal setting, execution, and feedback (Wu, 2005). In the
ideal situation, the individual goals crafted should align with the broader organizational goals.
Robbins and Coulter (2017) proposed companies should first formulate goals and cascade them
to the different departments and teams, where managers can translate them into individual goals.
Scholars have argued MBO as a PA tool is easy to implement and use in organizations due to its
three simple steps (Shaout & Yousif, 2014).
Researchers have agreed MBO has considerable benefits, with ease of use as its most
significant benefit. Shaout and Yousif (2014) determined beyond its ease of implementation,
MBO also allowed employees to understand their expectations clearly and provided direction on
what they had to do. Their analysis determined MBO to have the highest overall score over other
PA methods (360 feedback and FDR) when looking at accuracy, cost, training needs, and ability
to compare employees. Hoffmann-Burdzińska and Flak (2016) added MBO had the ability to
align employees towards a common goal. MBO was an objective performance measure because
it focused on outcomes rather than individual traits or personalities. MBO also allowed
employees to take control of their careers as they would have a say in the objective setting
exercise and execution of the actions towards their goals. However, despite its ease of use, MBO
also has its own disadvantages.
16

Scholars have identified MBO to have issues around the goal-setting process. Studies
have shown agreement on goals may not be a smooth sailing process. MBO can be time-
consuming for managers and their employees to agree on the right goals to track (Aggarwal &
Sundar, 2013; Certo & Certo, 2020; Shaout & Yousif, 2014). In addition, given the nature of
PAs, which organizations typically conduct once a year, MBO goals are also short-term. The
short-term focus of goals does not benefit the organization in the longer term as the goals are
generally tactical, and employees tend to propose goals they will likely achieve within the span
of one year (Koontz, 1977). Dagar (2014) also argued MBO is more suitable for managers than
every employee in an organization. Organizations did not need to measure employees on a
factory line performing the same action daily with multiple objectives. In summary, MBO has
issues with goal setting and its appropriateness for employees in the organization, which is the
cornerstone of the tool.
Leading Performance Appraisal Practices
Organizations are now pivoting to PAs, which are simpler to administer administratively
and can provide more accurate feedback about an individual’s performance. Since 2015, several
large organizations have moved away from formal PA systems and favor simpler evaluations.
For example, Deloitte announced in 2015 it was doing away with its PA system and evaluating
employees using only four questions, significantly reducing the time each manager had to spend
on the PA process (Buckingham & Goodall, 2015). Other companies, including GE, Gap, and
Accenture, have tried simplifying their PAs (Cappelli & Tavis, 2016). These companies have
pivoted to cutting-edge practices known as ongoing feedback, crowdsourced feedback, and
ratingless reviews (Lawler et al., 2016). According to Stone et al. (2019) ongoing feedback refers
to a system of frequent and informal feedback given by managers to their subordinates.
17

Crowdsourced feedback usually leverages an internal platform for colleagues to share feedback
with one another. The key difference between crowdsourced feedback and 360 feedback is
crowdsourced feedback is usually informal and immediate. The last practice, ratingless review,
refers to evaluations without a quantitative metric tagged to the evaluation (Stone et al., 2019).
Organizations have begun to implement these cutting-edge practices since 2010.
In 2012, when researchers surveyed organizations on their use of cutting-edge practices,
less than 10% were using these practices (Lawler et al., 2016). However, when Lawler et al.
(2016) replicated the study in 2015, 97% of companies used some form of cutting-edge practice
in their organization. A more recent survey by Bersin-Deloitte in 2018 showed that high-
performing organizations were more likely to use innovative PA practices than low-performing
organizations (Enderes & Deruntz, 2018 as cited in Stone et al., 2019). The growth of these
leading PA tools will continue in the near future. A survey by Gartner (2020) among human
resource leaders showed 87% of organizations were looking to change how they reviewed
employees' performance. Gartner (2020) further argued as part of these changes, appraisals need
to be business-oriented, employee-driven, and focused on work. New practices will only grow as
more organizations pivot to simpler methods to evaluate employees while ensuring optimal
outcomes.
Innovative practices in PA are more effective at ensuring the right employee outcomes
and focused on development. Among 244 managers surveyed, one key reason for moving
towards these cutting-edge practices was its ability to provide useful feedback to employees and
motivate them (Lawler et al., 2016). The survey also found applying a combination of these
innovative practices versus using one was more effective in the organization. When comparing
ongoing feedback versus all three practices combined, managers rated the effectiveness of their
18

PA process as more effective (6-10% better) compared to using only ongoing feedback (Lawler
et al., 2016). However, it is clear many organizations are also not ready to transition to leading
practices fully. Lawler et al. (2016) showed firms were using new practices in addition to the
traditional PA systems (e.g., 360 feedback). Bersin-Deloitte also shared few organizations were
ready to implement ratingless reviews (Enderes & Deruntz, 2018 as cited in Stone et al., 2019).
In surveys conducted by Gartner in 2016 and 2018, companies that transitioned to a ratingless
review saw their employee performance drop by 10% and 4%, respectively (Wiles, 2019).
Hence, despite the positives of simpler PA methods, there are still concerns about their
effectiveness and ability to regulate employee performance.
Performance Appraisals in Singapore
There is limited research on the impact of PAs in a corporate setting in Singapore. There
is also a limited number of recent studies after 2016. Much of the literature about PAs in
Singapore was related to the civil service or teacher evaluations or appraisals (Chew & Tan,
1999; Chew, 2002; Kelly et al., 2008; Vallance, 1999). Chew (2002) interviewed school
principals in Singapore and observed principals were unhappy with the appraisal system used
because of how the system ranked individuals, and organizations prioritized the estimated
potential of the individual rather than current work performance. A survey by Kelly et al. (2008)
of 85 elementary school teachers in Singapore on the fairness of PAs and its impact on employee
motivation and satisfaction showed fairness of PAs significantly contributed to employee levels
of satisfaction and motivation (R
2
= 0.41, p < 0.01). Kelly et al.’s study is the closest study
available to what this study aims to achieve, albeit with a different target audience. Additional
studies relating to PAs in Singapore corporations date back to the early 2010s.
19

Two studies in the early 2010s are related to PAs in corporations. Stanton and Nankervis
(2011) recognized a gap in the literature regarding PAs in Singapore and studied how managers
perceived PAs to drive organizational effectiveness and key human resource practices. The
study, however, did not explore the importance of organizational justice or PAs impact on
satisfaction, motivation, or organizational commitment. A second study published in 2010
investigated the influence of culture on PA practices across multiple countries, of which
Singapore was one of them. The study explored Hofstede’s cultural dimensions and how they
influenced different components of the PA process among bank employees across the globe
(Chiang & Birtch, 2010). Like Stanton and Nankervis, Chiang and Birtch did not look into the
fairness of PAs or their impact on employees. This study hopes to enrich the existing literature
with more recent research on corporations.
Organizational Justice
Greenberg developed the concept of organizational justice in 1987 to address
organizational fairness, which prior studies did not. Greenberg (1987), in his seminal paper,
argued existing theories and research could only address one aspect of organizational fairness,
and there was a growing need to address workplace fairness and consideration, which Greenberg
termed organizational justice. Greenberg defined organizational justice as an employee’s
perception of fairness in the organization, which could be related to processes, policies, or
outcomes (Greenberg, 1987). In the same paper, Greenberg classified and proposed how scholars
could break down organizational justice based on the literature available before 1987. Using
existing research, Greenberg classified organizational justice into four areas: reactive content,
proactive content, reactive process, and proactive process. Greenberg used reactive and proactive
terms to describe employee behaviors, while he used content and process terms to describe
20

outcomes and processes. For instance, Greenberg defined how an employee would react to unfair
policies as a reactive process, while he referred to employees creating equitable compensation
structures as proactive content. Within 3 years of Greenberg’s seminal paper, he published
another landmark paper building on the four areas.
In 1990, Greenberg published a paper with a visual taxonomy expanding on the four
areas he initially proposed in his first paper on organizational justice. Greenberg (1990)
mentioned there was a shift from content related studies to process related studies and began
using the terms distributive justice (content) and procedural justice (process). While Greenberg
did not coin the terms distributive and procedural justice and different scholars had developed
the terms prior, Greenberg began using the terms distributive justice and procedural justice as
subsets of organizational justice. Greenberg referred to distributive justice as fairness in
outcomes and procedural justice as fairness in procedures. In a similar vein, Greenberg classified
another concept of interactional justice, which had appeared as a separate topic previously, as a
subset of procedural justice. Interactional justice is the perception of fairness in how individuals
interact with one another (Bies & Moag, 1986). As the growth of organizational justice research
became mainstream and researchers focused on the topic in the 1990s to 2000s, interactional
justice became independent from procedural justice and became known as one of the three
components of organizational justice (Colquitt & Greenberg, 2003). The topic of organizational
justice has since been a key focus for various aspects of organizational behavior and human
resource management.
Researchers have used organizational justice as a lens for many organizational behavior
studies and its impact since the 1990s. According to Cropanzano et al. (2007), organizations
cannot understate the importance of organizational justice. Organizational justice has immense
21

benefits for an organization, which improves trust between the organization and its employees,
individual job performance, and overall organizational financial performance. Scholars have
studied fairness across various human resource management applications, including performance
appraisals (Birecikli et al., 2016; Karkoulian et al., 2016; Roch et al., 2007). The importance of
organizational justice has not diminished over the years. Recent research in the past 3 years has
continued to test organizational justice theory in Asian countries and specific occupations. These
recent studies have shown similar outcomes to what Greenberg had initially shown in his
research (Akram et al., 2020; Boateng & Hsieh, 2019; Khan et al., 2020; Wolfe & Lawson,
2020). The subsequent section of the literature will explore the three components of
organizational justice in greater detail.
Distributive Justice
In its simplest terms, distributive justice refers to the fairness of outcomes. Distributive
justice originated from multiple theories (Knight, 2014), of which this section will explore the
three most popular theories. The earliest of them began with Rawls (1971) and his seminal book
A Theory of Justice. Rawls argued there were two key principles relating to fairness: (a) Each
individual has an entitlement to equal rights and freedom, (b) Inequalities regarding social and
economic areas can exist provided they benefit the least advantaged as well, and all individuals
have equal opportunity for offices and positions. Rawl’s position on fairness differs from the
theories of utilitarianism and egalitarianism. The second theory related to distributive justice is
the theory of utilitarianism. Bentham (1970), the first scholar to pen the theory of utilitarianism,
asserted that society deems any action taken by an individual to increase its welfare as good and
views any action worsening its welfare as bad. The outcomes of these actions thereby equate to
distributive justice with how well society fares. The third theory relating to distributive justice is
22

luck egalitarianism. Luck egalitarianism asserts that the process yielding the outcomes holds as
much importance as the outcomes themselves (Dworkin, 2000). In luck egalitarianism, an
individual, who does not wish to put in the effort and obtains a worse outcome compared to
another individual who works hard and achieves a better outcome, would have been considered
to have achieved distributive justice given the process by which the outcomes are derived is key
(Dworkin, 2000). While the three theories apply different lenses to distributive justice, the
outcomes should be fair and equal. Scholars in different fields have tested this concept of equal
distribution.
Distributive justice has a wide range of applications beyond organizational behavior.
Scholars have used distributive justice as a concept to inform policymaking. For example,
researchers proposed the importance of fair access to transportation and how governments need
to give marginalized groups special consideration when making transport investments (Pereira et
al., 2016). On another topic regarding environmental law, Kaswan (2002) argued about the
different models of land use and considered the models as distributive justice or injustice
depending on perspective. Scholars have also investigated the issue of cost and benefits to
address environmental problems (e.g., climate change) and how governments fairly distribute the
cost and ensure benefits among the population (Johansson-Stenman & Konow, 2010).
Researchers have also applied the idea of cost and benefits to taxation arguments. Farrelly (2004)
examined different arguments for taxation models and argued it was critical to begin with
societal fairness in any tax reduction or increase. Various studies have shown distributive justice
and its applicability across multiple topics. However, despite its influence on policymaking,
distributive justice does not appear to impact performance appraisals significantly.
23

Unlike the other aspects of organizational justice, distributive justice does not impact
performance appraisals and their outcomes. Studies have shown distributive justice to have a
weak relationship with organizational commitment. Krishnan et al. (2018) determined in a
survey of 259 oil and gas non-executives that distributive justice was an insignificant predictor of
organizational commitment. The findings were similar to a landmark study by Konovsky and
Cropanzano (1991), who posited distributive justice had minimal effect on employee trust and
commitment in an organization. In Asia, a field study conducted in Vietnam among 546
employees showed distributive justice in PAs did not impact intention to quit or job
performance, while procedural justice did (Phuong, 2018). The results of a neighboring country
in southeast Asia may yield similar findings in Singapore.
Procedural Justice
Procedural justice is concerned with the fairness of processes or procedures in
organizations. Thibaut and Walker (1975) conceived the theory of procedural justice. In their
seminal book, Thibaut and Walker conducted a social experiment to understand the implications
of participants' control over certain procedures and outcomes. The authors showed when
participants had control and input over the process, participants deemed the outcomes to be fairer
than when they had not. With the publication of the theory, other researchers began to evaluate it
in other fields and found it applicable. Greenberg (1986) was one of the first scholars to suggest
procedural justice could also apply to performance appraisals. In a study of 217 managers,
Greenberg showed worker inputs and how consistent application of the appraisal processes was
critical in how employees viewed the fairness of the appraisals. Next, Levanthal (1980), on the
back of the publication of distributive justice, argued scholars were reviewing fairness only from
one angle, which were outcomes, but the steps to arrive at the outcomes were also important.
24

Levanthal further added beyond the ability to influence the process, there were seven
components to procedural justice (identifying the decision makers, laying down the ground rules,
collecting data, identifying the decision-making structure, process for appeals, policies, and
procedures to ensure integrity in the decision-making process, and options for change). The
research by Thibaut and Walker, Levanthal, and especially, Greenberg, has led to more research
on the importance of procedural justice in appraisals.
Procedural justice is a key component of fairness in PAs. When employees have a say
over the design and process of PAs, they perceive PAs to be fairer. An experiment conducted
among 1,400 government employees in northeastern United States divided the employees into
two groups. While one group served as a control, the other group had the opportunity to shape a
new PA in the agency. At the end of the PA cycle, the group which had input on the PA process
scored significantly higher in how they perceived fairness, satisfaction, and accuracy of the
appraisal (Taylor et al., 1995). Procedural justice is also a key moderating variable on employee
outcomes (e.g., satisfaction, motivation, and turnover intention). In a survey of 105 white-collar
employees, Birecikli et al. (2016) identified procedural justice as a key moderating factor in
determining an employee’s turnover intentions and commitment to the organization. A study of
267 managers in southeastern Asian countries, Malaysia and Thailand, indicated procedural
justice was critical to employee satisfaction (Yamazaki & Yoon, 2016). Thus, procedural justice
is key in performance appraisals and impacts employee outcomes globally.
Interactional Justice
Interactional justice is the third component of organizational justice and relates to how
people interact in an organizational setting. Before 1986, scholars viewed interactional justice as
a component of procedural justice. It was not until Bies and Moag’s (1986) seminal paper on
25

interactional justice that Bies and Moag argued interactional justice was a distinct component of
organizational justice. Bies and Moag claimed procedures led to interactions, which then led to
outcomes (distributive justice), thereby providing a diagrammatic flow of how each component
of organizational justice related to one another. Building on the work by Bies and Moag,
Greenberg (1993) further classified interactional justice into two sub-categories: informational
and interpersonal justice. Informational justice relates to the amount and timeliness of data,
information, and knowledge the organization shares with employees, while interpersonal justice
refers to how the organization treats its employees (e.g., with dignity and respect). In addition,
Greenberg (2009) argued interactional justice was the only one of the three components an
individual had full control over. In comparison, an organization’s policies or systems may bind
distributive justice and procedural justice, how and what an individual chooses to communicate
or interact with a colleague is entirely within the individual’s control. A study of 467 nurses in
the United States demonstrated the impact of interactional justice; the study showed interactional
justice was a mitigating factor in overcoming injustice in distributive and procedural areas
(Greenberg, 2006). Thus, interactional justice is critical for organizations beyond its use in
performance appraisals.
Interactional justice is critical for a well-functioning organization, including effective
performance appraisals. First, interactional justice ensures employees take a client-centric
approach to interacting with clients. A study of 312 Vietnamese wholesale bank employees
demonstrated employees treated positively with interactional justice internally were more likely
to engage in customer-centric behavior, where employees considered customers' interests first
(Dang & Pham, 2020). Interactional justice is key to building trust among employees, and trust is
a critical component of well-functioning organizations (Sharkie, 2009; Sousa-Lima et al., 2013).
26

Researchers in Spain surveyed approximately 850 managers and their team members in disability
services centers and determined mutual trust between managers and their subordinates was
critical to achieving organizational goals. Finally, interactional justice critically impacts
performance appraisal perceptions and outcomes. A study of 270 telecommunications employees
in Indonesia showed when interactional justice was present, satisfaction with performance
appraisals increased, which correlated with better job performance (Miharja et al., 2020).
Organizations cannot understate the importance of interactional justice as it has far-reaching
implications.
Impact of Organizational Justice on Performance Appraisals
Researchers have shown organizational justice has a profound impact on performance
appraisals. When fairness is not present, it affects employee outcomes. In this next section of the
literature review, the study explores the impact of organizational justice on PAs across three
employee outcomes: satisfaction, motivation, and organizational commitment.
Impact on Employee Satisfaction
Performance appraisals play a key role in ensuring employees are satisfied in an
organization. Eskildsen and Nussler (2000) surveyed 215 Danish human resource managers,
which showed components of performance management (e.g., setting goals, receiving
performance feedback, and rewards) were critical to driving employee satisfaction in an
organization. While PAs are critical, organizational justice plays a moderating role in employee
satisfaction levels during the PA process. Employees who perceive PAs to be fair improve their
satisfaction levels. Umair et al. (2016) investigated the impact of fairness in PAs in India and
showed distributive, procedural, and interactional justice could explain 75% of the variation in
employee satisfaction. Organizational justice's impact on satisfaction appears consistent across
27

the western and eastern hemispheres. In southeast Asia, researchers showed similar outcomes
where organizational justice had a moderating impact on employee satisfaction in PAs. A survey
of 99 employees in a Malaysian university determined procedural justice was a mediating factor
between PAs and employee satisfaction (Ismail et al., 2016). However, a study also
demonstrated organizational justice had no impact on satisfaction. A study of 128 workers in the
southern United States contended while the purpose of performance appraisals had a significant
impact on employee satisfaction, organizational justice itself did not influence the satisfaction
levels (Boswell & Boudreau, 2000). Hence, while studies generally confirm the impact of
organizational justice on employee satisfaction, there may be exceptions in a limited number of
cases. Ensuring high levels of employee satisfaction is ultimately critical as it affects motivation
and organizational commitment (Ismail & Razak, 2016; Jalagat, 2016).
Impact on Motivation
Studies have shown PAs can be a strong motivator or demotivator in organizations. Some
aspects of the PA process are more important than others on motivation. Aydin and Tiryaki
(2018) divided PA into different components (purpose, measurement criteria, practices, and
feedback) and measured its impact on employee motivation. The authors surveyed 432
employees in the forestry industry and showed all PA components significantly impacted
employee motivation except for the purpose of PA. However, the importance of each component
on motivation in the PA can also differ by industry and organization. A qualitative study of
pharmaceutical researchers in Europe revealed the procedures in the PA and following through
with actions after communication was essential to motivation. However, goal setting did not
impact motivation (Pogrebnyakov et al., 2017). Furthermore, organizations whose PAs have
subjective criteria and conduct PAs on an infrequent basis reduce motivation. Interviews with
28

healthcare workers in Mali demonstrated the staff was unaware of what the organization was
measuring them on and claimed the criteria depended on which manager was appraising them.
Also, the infrequent nature, where the organization evaluated only 48% of employees annually,
affected their motivation to work (Dieleman et al., 2006). The impact of PAs on employee
motivation is clear. However, what is less certain is the moderating impact of organizational
justice on PAs and motivation.
There are limited studies that understand the moderating effect of organizational justice
on PAs and employee motivation. While studies have measured the impact of fair PAs on
motivation (Dangol, 2021), none have explicitly investigated the moderating effects of
organizational justice on PAs and employee motivation. Scholars have instead examined the link
between organizational justice and motivation and have seen a clear and positive correlation. A
study of over 400 American and Mexican employees showed all components of organizational
justice to be significantly positively correlated with employee motivation (Selvarajan et al.,
2018). Scholars have observed similar results in southeast Asia. For example, a survey conducted
among 100 employees in Indonesia found fairness in the workplace significantly impacted
employee motivation (Sutanto et al., 2018). The lack of literature on the moderating effects of
organizational justice on PAs and motivation is a gap the current study will aim to address.
Impact on Organizational Commitment
Fair PAs are essential to an employee’s commitment to an organization. Scholars have
defined organizational commitment as an individual’s willingness to work hard on behalf of the
organization and their alignment with the organization’s goals and values (Mowday et al., 1979).
Perceptions of PA affect an employee’s affective commitment to an organization. Husain (2017)
surveyed 101 employees from five banks in Pakistan and showed fair PAs significantly
29

contributed to the employees’ willingness to maintain their association with the organization. In
addition, a survey of 150 nurses in Bangladesh showed fair PAs significantly affected
organizational commitment, which in turn had a moderating impact on turnover intention (Rubel
& Kee, 2015). While several studies have shown a direct impact on organizational commitment,
a moderating variable may affect organizational commitment. In another study in Malaysia,
researchers showed while fair PAs did not have a direct impact on organizational commitment, it
was directly impacting satisfaction with PAs, and the satisfaction levels played a moderating
effect on organizational commitment (Salleh et al., 2013). Regardless of the role of
organizational justice, it is clear it directly and indirectly impacts organizational commitment.
Double-Hatting Employees
There are limited studies on double-hatting corporate employees in the literature. Articles
related to double-hatting include an article by Barnes (2014), who argued certain military roles
should not be double-hatted due to the responsibilities needed to execute the role. Other military
articles use the term to describe how the military combines certain roles with others (Dumais,
2007; Efjestad, 2017). The legal fraternity similarly applies the concept of double-hatting among
lawyers who also function as international arbitrators (Langford et al., 2017). Researchers have
also used double-hatting to describe individuals executing and living through organizational
changes (Teerikangas & Birollo, 2018). However, this definition differs from an individual
taking on two or more formal roles in a company (Mohamed Hoosen Carrim, 2020). Double-
hatting roles are permanent for the duration of the individual’s tenure with the organization until
the individual leaves or applies for a new role in the organization or the organization formally
changes the role. The next closest term to double-hatting is multi-team membership (MTM).
MTM refers to individuals who work in two or more project teams simultaneously (Mortensen et
30

al., 2007). These project teams are based on the project tenure and requirements and may change
from quarter to quarter or year to year, which would not be the case in double-hatting.
Depending on the project requirements, these individuals can be part of more than four to five
different teams. In addition, another term, matrix organizations, is different from double-hatting
or MTM. Matrix organizations simply refer to two or more managers one reports to (Sy et al.,
2005), but one may still perform only one role and reside in a single team. While there are
studies from MTM or matrix organizations this study could use as proxies for double-hatting
employees, the actual findings specific to double-hatting employees may be different. The study
will address the gap in the literature, specifically looking at PA's impact on double-hatting
employees.
Conceptual Framework
The study uses Burke-Litwin’s model (BLM) for organizational change as the starting
point for developing the conceptual framework. The BLM is an evolution of what Litwin and his
peers had previously researched (Burke & Litwin, 1992). In addition, Burke and Litwin did not
develop BLM in isolation, but incorporated other organizational frameworks (e.g., McKinsey’s
7S), thereby relying heavily on prior research (Burke & Litwin, 1992). Since 2018, studies have
continued using BLM as a framework for analyzing organizations. Singh and Ramdeo (2020)
suggested BLM as one of the most notable diagnostic models for organizational development
and change. Singh and Ramdeo cited Leavitt’s model, open-systems theory, Weisbord’s six-box
model, Tichy’s model, and congruence model by Nadler-Tushman as alternatives. The BLM,
like others, uses the open-systems model as an underlying assumption. Given the unpredictable
and ever-changing world, there is a preference for open-systems that offer a flexible model to
accommodate these changes (Singh & Ramdeo, 2020). Researchers argued BLM was a practical
31

framework that enabled companies to diagnose change issues (Coleman, 2018; Coruzzi, 2020).
In addition, BLM has inspired the creation of survey instruments for diagnostics. Olivier (2018)
constructed a survey instrument to evaluate organizational performance in South Africa with its
metrics based on the various BLM elements. Scholars have also used BLM to analyze PA
changes in an organization.
Church and Burke (2019) used BLM to study the implications of implementing a 360
feedback PA system in a company, thereby making BLM a suitable choice of framework for this
study. Church and Burke argued implementing a new PA system had implications across all 12
elements of the BLM. The scholars identified repercussions on each element to showcase
whether the elements aligned with the new PA system. They further asserted the BLM
framework was a suitable approach to identifying potential organizational challenges. A prior
study leveraging the BLM framework lends credence to the appropriateness of the BLM
framework for this study. Furthermore, BLM has strong validity and reliability as a model.
Multiple studies have shown evidence for the model’s reliability and validity. While there
has not been a single study that measured the relationships of every single variable, Burke (2017)
argued the numerous studies to have assessed different factors of the model had demonstrated the
strong validity of the BLM. In addition, Stone's (2015) meta-analysis of the BLM statistically
showed other than the external environment, which had the weakest validity in the model, the
rest of the factors had strong content validity. Similarly, for BLM’s reliability, all factors had
strong internal reliability except for the external environment (Stone, 2015). This dissertation,
however, is not studying the external environment as a factor.

32

Figure 1
Burke-Litwin Model for Organizational Change

Note. Burke-Litwin Model for Organizational Change. Reprinted from “A Causal Model of
Organizational Performance and Change,” by W. W. Burke and G. H. Litwin, 1992, Journal of
Management, 18(3), 523-545. Copyright 1992 by SAGE Publications. Reprinted with
permission.

While BLM has 12 elements, this dissertation study will only focus on three (see red
highlights in Figure 1) and overlays an organizational justice lens as a mediating factor to study
33

the implications of PA (systems, policies, and procedures) on motivation. Structure will cover
the aspect of double-hatting employees. Researchers have shown fairness (organizational justice)
to be an important aspect of PAs across multiple studies. However, Spangenberg and Theron
(2013) argued BLM is not relevant as a framework because organizations have not used the
framework for major organizational changes since 1992. The second point raised by
Spangenberg and Theron was that the factors were not mutually exclusive and had overlapped.
For instance, performance appraisals could also fall under organizational culture. As such, this
dissertation uses BLM as a starting point and improves the model by adding employee
satisfaction and organizational commitment as additional impact factors. Scholars have also
argued employee satisfaction and organizational commitment are key to an organization’s
performance (Baird et al., 2019; Nanu et al., 2020). Figure 2 depicts the study’s conceptual
framework with organizational justice as a mediating factor on employee satisfaction,
motivation, and organizational commitment.

Figure 2
Conceptual Framework

Conclusion
The literature review covered three key areas of the impact of PA on double-hatting
employees. First, a review of PAs historically and how it has evolved over the decades. While
34

organizations today are transitioning to less formal systems, many organizations are still using
systems of the past (360 feedback, FDR, MBO). In addition, the literature review identified a
literature gap with PA-related studies in Singapore. The second area explored organizational
justice and its impact on employee motivation, satisfaction, and organizational commitment.
Scholars have demonstrated organizational justice to be a critical component in organizations,
and fairness for PAs is fundamental in leading to positive employee outcomes. Third, the
literature explored the topic of double-hatting. There is currently limited academic research in
this area, and there is an opportunity to address the white space. To conclude Chapter Two, the
literature review explored BLM and its relevance for this dissertation. As a result of prior
studies, this dissertation has used BLM as a starting point and adapted it accordingly to be
relevant for the study.

35

Chapter Three: Methodology
The study aims to understand the impact of performance appraisals (PA) on double-
hatting employee outcomes. Chapter Three begins with an overview of the research questions.
The study’s research design and researcher's positionality follow the overview. Next, Chapter
Three concludes with data sources and ethical considerations.
Research Questions
1. How do employees perceive performance evaluation systems?
2. What is the relationship of inaccurate performance evaluation with employee motivation,
commitment, and satisfaction?
3. How do different components of the performance evaluation process impact employee
satisfaction, motivation, and commitment?
Research Design
The study used a quantitative survey to address the research questions. A survey enables
researchers to gather numerical data in a structured and organized manner for analysis (Robinson
& Leonard, 2018). The study sought to study the perceptions of PAs and its impact on employee
satisfaction, motivation, and organizational commitment. To address the research questions, the
study required a large dataset with numerical data across multiple questions. While qualitative
methodologies may achieve the same objective, qualitative studies are too time-consuming to
obtain a sufficient sample size for analysis (Merriam & Tisdell, 2015). To ensure the survey’s
reliability and validity, the survey adopted questions relating to organizational justice and
employee outcomes from prior studies.

36

Table 1
Data Sources
Research Question Survey
RQ1. How do employees perceive different
performance evaluation systems?
X
RQ2. How do perceptions of unfair
performance evaluation affect employee
motivation, commitment, and satisfaction?
X
RQ3. How do different components of the
performance evaluation process impact
employee satisfaction, motivation, and
commitment?
X

The Researcher
My background has led to the dissertation topic. As a Psychology major and having spent
my entire career in a corporate setting in Asia, I wanted to combine my interest in human
behavior with my professional experience. Furthermore, my professional experience has mostly
been in roles where I was heavily involved in quantitative analyses of data sets in market
research, strategy consulting, and now leading organization design in a global bank. The unique
set of experiences has led me to study the impact of performance appraisals (human resource
practice) on employee outcomes (human behavior) in Singapore.
My quantitative background may bias my current thinking on the study's survey design or
data analysis. Therefore, I intend to adapt most of my survey questions from existing studies to
ensure survey reliability and validity. With prior studies testing many of the survey questions, it
reduces the risk of any biases I might introduce to the survey construct. In addition, to mitigate
the risk of not employing the correct quantitative analysis, I took guidance from my dissertation
37

committee members and reviewed what analyses scholars in other studies have undertaken.
Finally, additional guard rails will include checking with my peers to ensure sound analysis.
Data Sources
The study used a quantitative survey to collect data. Surveys, easily administered online,
are ideal for capturing information that is otherwise unavailable (Robinson & Leonard, 2018).
The following sections detail the participants, survey questions, data collection, data analysis,
and the reliability and validity of the survey.
Participants
The target population for the survey was double-hatting employees working in
corporations in Singapore. At the time of the survey, these participants physically resided and
worked in Singapore and held at least two formal roles (double-hatting) in their organization. In
addition to the above, participants had undergone at least one annual cycle of PA in their
organization. Going through one PA cycle would likely mean participants would need to have
been in the organization for at least 1 year. Since this was a field study, the principal investigator
did not set any quotas for age, gender, ethnicity, or other demographics. There were no
constraints on the organizational type (public vs. private) or organizational size.
The target sample the study achieved was n = 400. A sample of at least 200 to 500 is
sufficient for quantitative analysis (Israel, 1992). A sample of 400 resulted in a margin of error of
± 4.85% (Raosoft, 2004). The researcher selected the identified sample size to balance the need
for accuracy, data collection time, and budget. The study employed a purposive sampling method
to achieve the sample size. Purposive sampling targets participants who fit the study’s profile and
is a nonrandom sampling technique (Etikan et al., 2016). The researcher considered the
38

purposive sampling method as the most appropriate to address the study’s objective and research
questions.
Instrumentation
The study used an online survey. Online surveys can be the most time-efficient (Robinson
& Leonard, 2018). The survey had five sections, which took approximately 10-15 minutes to
complete. Keeping the online survey short kept participants engaged (Robinson & Leonard,
2018). Appendix A depicts the survey instrument. The survey began with three screening
questions to ensure participants qualified for the study. The following section sought to
understand the type of PA in the participant’s organization. Subsequently, the next section was
the participant’s perception of fairness of the PA in their organization. The fairness questions
covered the components of organizational justice. The penultimate section of the survey
addressed the participant’s current feelings toward their satisfaction, motivation, and
commitment to the organization. Lastly, the survey captured demographic information about the
participant to allow for sub-segment analyses, if required.
Data Collection Procedures
The dissertation collected the necessary data from an online market research panel.
Online market research panels consist of pre-recruited participants who agree to complete
surveys regularly (Dennis, 2001). These pre-recruited participants come from all backgrounds
and allow researchers to target participants that meet their study criteria. Online panels have
advantages with faster completion rates and enable scholars to easily reach their target
population (McDevitt & Small, 2002). This dissertation used Pollfish as its market research
panel. Pollfish is a do-it-yourself (DIY) market research panel, meaning users must design and
upload their own survey onto their platform. Pollfish subsequently sends the survey to their pre-
39

recruited participants to complete, assuming they pass the screening criteria. The panel charges a
fee per completed sample. The lower the incidence of eligible participants for the survey, the
more expensive the cost per sample. The final cost of each completed sample was $4 based on
the incidence rate of participants who qualified for the survey. Pollfish sent the surveys to
participants via email containing the survey link. The platform captured the participants’
responses, which the researcher could monitor for live updates. For data security, only the
researcher could access the results. The downloaded raw data from the platform was password
protected as an additional layer of security. However, the survey did not collect any personal
identifiable information (e.g., name, address, email, cell phone numbers, or social security
numbers) from the participants. To incentivize participants to complete the survey, the panel
provided a small incentive to each participant that completed the survey. Pollfish included the
incentive in the overall fee of $4 (the panel did not provide an exact cost breakdown). The panel
automatically sent reminders to participants, and a dashboard allowed the researcher to monitor
the results in real-time. The data collection took approximately 4 days to complete.
Data Analysis
The study undertook three types of quantitative analyses. The first of the three was
descriptive statistics. Descriptive statistics provide an overview of means, standard deviations,
and percentages of counts for each question (Fisher & Marshall, 2009). Descriptive statistics
addressed RQ1. Next, correlation analysis addressed RQ2. Correlation analysis improves the
understanding of relationships between variables but does not determine causality (Schober et
al., 2018). Structural equation modeling (SEM) was the third analysis and addressed RQ3 (see
Table 2 for details). SEM considers every pathway depicted in the conceptual framework and
determines which pathways are significant. Rather than perform a regression analysis on each
40

pathway individually, SEM performed all the regressions at once and evaluated the model's fit in
its entirety. It combined factor analysis of the constructs (e.g., procedural justice, satisfaction,
motivation, etc.) and could simultaneously evaluate the organizational justice constructs as an
independent and dependent variable (Gefen et al., 2000). SEM is the right approach to
understanding organizational justice as a mediating factor. Several authors have applied a similar
analysis to understand organizational justice's impact on employee outcomes (Aydın & Tiryaki,
2018; Dang & Pham, 2020). A regression analysis requires multiple individual runs of each
pathway, which is more time-consuming and does not report an overall score for the conceptual
framework (Gefen et al., 2000).
Table 2
Inferential Analysis
Research
Question
Independent Variable / Level
of Measurement
Dependent Variable / Level
of Measurement
Test
How do
perceptions of
unfair
performance
evaluation
affect
employee
motivation,
commitment,
and
satisfaction?
Performance
Appraisal
Validity
Ordinal
(Likert)
Employee
Satisfaction

Motivation

Organization
Commitment
All
Ordinal
(Likert)
Correlation
How do
different
components
of the
performance
evaluation
process
impact
employee
satisfaction,
Performance
Appraisal
Validity

Distributive
Justice

Procedural
Justice

All
Ordinal
(Likert)
Distributive
Justice

Procedural
Justice

Interactional
Justice

All
Ordinal
(Likert)
Structural
Equation
Modeling
41

Research
Question
Independent Variable / Level
of Measurement
Dependent Variable / Level
of Measurement
Test
motivation,
and
commitment?
Interactional
Justice
Employee
Satisfaction

Motivation

Organization
Commitment

Reliability and Validity
Reliability is concerned with the survey’s consistency of results. Consistency is vital as
the survey results should not change significantly if the participant takes the same survey twice
in a short period. The survey adapts questions from multiple sources to develop the various
constructs to improve its reliability. Robinson and Leonard (2018) argue adapting survey
questions from prior research, which had undergone reliability and validity checks, can be
valuable. The survey consists of five sections, measuring different conceptual framework
constructs. Three sections have questions adapted from prior studies (see Appendix A for details).
The Cronbach alpha for each adapted section ranged from .71-.95. The Cronbach alpha
determines how consistent each variable is in measuring the construct of the respective section. A
Cronbach alpha of at least 0.70 means the survey questions have strong internal reliability
(Mohajan, 2017). For sections with no survey to adapt from, I solicited feedback from peers and
colleagues to ensure the definitions were clear and there was no confusion. The survey is
available to other researchers to use and replicate the findings in Singapore or other countries for
external reliability.
Validity refers to whether the survey measures what it purports to measure. Researchers
must consider both internal and external validity; validity is important because the findings are
42

invalid if it does not measure what the researcher has in mind (Mohajan, 2017). For internal
validity, the survey must first be reliable. To further increase internal validity, each section
contained multiple questions to test for a similar concept (e.g., procedural justice). Obtaining
peer or domain expert review is also an additional way of increasing internal validity (Sekaran &
Bougie, 2016). The study collected its results via a field study of double-hatting employees in
Singapore rather than a specific organization to improve external validity.
Ethics
I guarantee the study complies with ethical guidelines and will conduct the survey
lawfully in the United States and Singapore. Multiple safety measures were in place to ensure all
ethics concerns were complied with. First, I submitted the study, which the Institutional Review
Board (IRB) vetted to verify the study met the requirements to protect its participants from harm.
Second, the survey began with an explanation of the study's purpose in an information sheet
before allowing the participant to proceed with the survey. The information sheet plays a crucial
role, as it provides the participant with an opportunity to reject participation in the study.
(Robinson & Leonard, 2018). Third, the survey was voluntary for the pre-recruited participants
on the market research panel. Participants could choose not to start the survey. In addition, at any
point during the survey, the participant could drop out or stop answering the questions. There
were no penalties for not completing the survey. Fourth, the survey did not capture any
personally identifiable information to protect the identities of every participant. This approach
ensured the anonymity of every participant, which was critical for honest responses (van Selm &
Jankowski, 2006). Lastly, the study complied with the guidelines of ICC/ESOMAR
(International Chamber of Commerce & ESOMAR, 2016), the governing agency for market
43

research studies. This study did not harm any of its participants and took the necessary steps to
ensure the well-being of every participant.
44

Chapter Four: Results
The study aimed to ascertain the impact of fair performance appraisals on double-hatting
employees’ satisfaction, motivation, and organizational commitment. The study used three
research questions to address the study’s objective.
1. How do employees perceive performance evaluation systems?
2. What is the relationship of inaccurate performance evaluation with employee
motivation, commitment, and satisfaction?
3. How do different components of the performance evaluation process impact
employee satisfaction, motivation, and commitment?
Survey Results
A DIY online market research platform, Pollfish, recruited all participants for the study.
The survey recorded a total of 400 responses. Pollfish collected all responses within a week. At
the time of the survey, all participants worked in Singapore, had undergone at least one complete
performance appraisal cycle with their current organization, and were double-hatting (holding
two formal roles in their respective organizations). The survey employed three screening
questions to ensure participants met the qualifying criteria. Table A1 in the appendix details the
type of questions and
Quantitative Overview
The overview describes the data transformation, statistical methodology, and software the
study used. Except for the demographics and screening questions, which were nominal data, the
remaining survey sections collected ordinal data across PA validity, organizational justice,
employee satisfaction, motivation, and organizational commitment questions. The ordinal
responses collected were on a scale from strongly disagree to strongly agree (one to five). Two
45

questions from the employee satisfaction section (S3 and S4) were reverse-coded to prepare the
data for analysis. While for the data analysis, the study used SPSS Version 28 and SmartPLS 4.
RQ1 utilized descriptive statistics and independent samples t-test, and RQ2 applied Pearson’s
correlation from SPSS Version 28. SmartPLS 4 addressed RQ3 with the structural equation
model (SEM) analysis.
Participants’ Demographics
Participants in the survey came from a variety of backgrounds. Participants aged 25 and
up responded to the survey, with 62% falling between 25 and 44 years old. Participants were an
even split between 45 and 54 years old (19%) or 55 years old and above (19%). Gender was
almost equal, with males at 52% and females at 48%. As a field study, participants came from
many industries; no industry comprised more than 6% of the sample. The top three sectors were
hotel and F&B (6%), consulting (5%), followed by healthcare (5%). The top ten industries
formed 45% of the total sample. The majority of double-hatting employees (53%) held roles in
middle and senior management. Individual contributors or individuals without managerial
responsibilities comprised 37% of the sample, with the remaining in junior management (7%) or
prefer not to disclose (4%). Table 3 provides a summary of the participants’ demographics.

Table 3
Participants' Demographics
Characteristics Category %
Age group
25-34 years old
35-44 years old
45-54 years old
55 years old and above
27%
36%
19%
19%
46

Characteristics Category %
Gender
Male
Female
52%
48%
Industry*
Hotel and F&B
Consulting
Healthcare
Transportation and Warehousing
Finance and Insurance
Security Services
Automotive
Education
Manufacturing
Retail
Others
6%
5%
5%
5%
5%
4%
4%
4%
4%
4%
55%
Seniority in Organization
Senior Management
Middle Management
Junior Management
Individual Contributor / No
Management Responsibilities
Prefer not to say
29%
24%
7%
37%

4%

Note. *Only the top ten industries are shown due to the number of industries.

Perceptions of Performance Appraisal Systems (RQ1)
Organizations in Singapore typically use more than one performance appraisal system.
On average, an organization uses 2.5 types of PAs to assess employees. An estimated 70% of
companies use two or more PA systems. The availability of different PAs and the potential ease
of implementation for certain PAs (e.g., informal feedback) enable companies to use multiple
systems to assess employees. While the current perception of the number of PA systems used is
from double-hatting employees, there is no indication organizations utilize different PA systems
47

compared to regular employees. Table 4 provides a percentage count of the number of PA
systems participants‘ firms use.

Table 4
Number of PA Systems Used by an Organization (n = 258)
No. of PA systems %
1
2
3
4 or more
30%
22%
28%
20%

Note. Table 4 excludes participants who answered “Don’t know” or “No formal appraisal
system.”

There are no preferred PA systems firms use in their organization. The highest incidence
of 360 feedback (27%) is consistent with the existing literature, with it being the most popular
PA system utilized in the last decade (Zenger, 2016). Participants also experienced new PA
methods, such as ratingless reviews and crowdsourced feedback in their organizations. In
addition, 16% of participants claimed their companies had no formal appraisal system used to
evaluate employees, thereby highlighting an opportunity for companies to consider
implementing a formal system to ensure consistency in evaluating double-hatting employees.
Table 5 depicts the incidence of PA systems in the participants’ respective organizations.

48

Table 5
Incidence of PA Systems Used in an Organization (n=400)
Type of PA systems %
360 feedback
Management by objectives
Behaviorally anchored scales
Ratingless reviews
Crowdsourced feedback
Forced distribution rankings
Informal feedback
No formal system
27%
27%
25%
22%
21%
21%
20%
16%

Participants perceive PAs to be fairer in organizations that use more PAs. In the survey,
participants evaluated PA components on a scale ranging from one (strongly disagree) to five
(strongly agree). A higher score indicated a stronger perception of fairness for that particular
question. As each component of PA consisted of a battery of questions, scores across all
questions were summed up and averaged to obtain a mean score for the respective PA component
(see Appendix A for questions). The mean scores suggest double-hatting employees perceive
greater validity and fairness with their PAs when companies use multiple PA systems. There is a
clear trend of mean scores being significantly higher in organizations that use more than one type
of PA to assess employees. Mean scores between one versus four or more PA systems for PA
validity saw the largest difference (one PA system M = 3.07, four or more PA systems M = 4.07).
Across the organizational justice components, the smallest absolute difference in perception of
fairness was in interactional justice (one PA system M = 3.19, four or more PA systems M =
3.95). Figure 3 shows the mean scores across the multiple PA components based on the number
of PA systems used.
49

Figure 3
Comparison of Mean Scores by Number of PA Systems

Note. PA Validity – Performance Appraisal Validity, DJ Ratings – Distributive Justice Ratings,
DJ Rewards – Distributive Justice Rewards, PJ – Procedural Justice, IJ – Interactional Justice.

The study used an independent samples t-test to ascertain if there was a significant
difference in having more PA systems in an organization. In PA validity, Table 6 shows there is
no significant difference in adding one additional PA to one existing PA system (Group 1 M =
3.07, Group 2 M = 3.33, p > .05). However, in other groups, there is a significant difference in
how participants perceive the validity of PA at the 95% CI. The most notable difference is
between one and four or more PA systems (Group 1 M = 3.07, Group 4 M = 4.07, p < .01). Any
3.07
2.96
3.03
3.48
3.19
3.33
3.18
3.39
3.75
3.36
3.64
3.6
3.63
3.93
3.61
4.07
3.91
3.87
4.25
3.95
2.5
3
3.5
4
4.5
PA Validity DJ Ratings DJ Rewards PJ IJ
Mean Score
PA Components
One PA system Two PA systems Three PA systems Four or more PA systems
50

other incremental PA systems added beyond two PA systems saw a significant difference. Table 6
displays the subgroup comparisons for PA validity with different PA systems.

Table 6
Descriptive Statistics, Sample, and Independent T-Test Comparison – PA Validity
Comparison n M SD t df p
Group 1
Group 2
76
57
3.07
3.33
0.53
0.73
-2.40 131 .18
Group 1
Group 3
76
73
3.07
3.64
0.53
0.73
-5.56 147 <.01**
Group 1
Group 4
76
52
3.07
4.07
0.53
0.77
-8.77 126 <.01**
Group 2
Group 3
57
73
3.33
3.64
0.73
0.73
-2.45 128 .02*
Group 2
Group 4
57
52
3.33
4.07
0.73
0.77
-5.19 107 <.01**
Group 3
Group 4
73
52
3.64
4.07
0.73
0.77
-3.19 123 <.01**

Note. Group 1 – One PA system, Group 2 – Two PA systems, Group 3 – Three PA systems,
Group 4 – Four or more PA systems.
*p < .05. **p < .01.

In DJ ratings, the independent t-test shows no significant difference in adding one PA
system to a single PA system (Group 1 M = 2.96, Group 2 M = 3.18, p > .05) in an organization.
However, there is a significant difference across all other subgroup comparisons at 95% CI. The
largest difference is between having one and four or more PA systems (Group 1 M = 2.96, Group
4 M = 3.91, p < .01) in an organization. The data indicates the number of PA systems in an
51

organization affects employees’ perceptions of DJ ratings; the more systems an organization
employs, the likelihood of a more positive perception. Table 7 displays the comparison of
subgroups with different PA systems.

Table 7
Descriptive Statistics, Sample, and Independent T-Test Comparison – DJ ratings
Comparison n M SD t df p
Group 1
Group 2
76
57
2.96
3.18
0.72
0.87
-1.64 131 .10
Group 1
Group 3
76
73
2.96
3.60
0.72
0.85
-4.96 147 <.01**
Group 1
Group 4
76
52
2.96
3.91
0.72
0.85
-6.83 126 <.01**
Group 2
Group 3
57
73
3.18
3.60
0.87
0.85
-2.72 128 .01**
Group 2
Group 4
57
52
3.18
3.91
0.87
0.85
-4.41 107 <.01**
Group 3
Group 4
73
52
3.60
3.91
0.85
0.85
-2.06 123 .04*

Note. Group 1 – One PA system, Group 2 – Two PA systems, Group 3 – Three PA systems,
Group 4 – Four or more PA systems.
*p < .05. **p < .01.

In DJ rewards, the independent t-test shows having an additional PA system from one
makes a difference. However, any incremental gains from two or three existing PA systems will
likely be minimal for DJ rewards. The comparison between Groups 2 and 3 and Groups 3 and 4
52

showed no significant difference between the mean scores. Table 8 displays the comparison of
subgroups with different PA systems.

Table 8
Descriptive Statistics, Sample, and Independent T-Test Comparison – DJ rewards
Comparison n M SD t df p
Group 1
Group 2
76
57
3.03
3.39
0.75
0.81
-2.65 131 .01**
Group 1
Group 3
76
73
3.03
3.63
0.75
0.86
-4.50 147 <.01**
Group 1
Group 4
76
52
3.03
3.87
0.75
0.85
-5.87 126 <.01**
Group 2
Group 3
57
73
3.39
3.63
0.81
0.86
-1.58 128 .12
Group 2
Group 4
57
52
3.39
3.87
0.81
0.85
-3.00 107 <.01**
Group 3
Group 4
73
52
3.63
3.87
0.86
0.85
-1.56 123 .12

Note. Group 1 – One PA system, Group 2 – Two PA systems, Group 3 – Three PA systems,
Group 4 – Four or more PA systems.
*p < .05. **p < .01.

There is a significant difference for PJ when adding at least two PA systems. Adding one
system from a current one or two PA systems is unlikely to make a significant impact. Mean
scores for Groups 1 and 2 and Groups 2 and 3 displayed no significant difference. However,
implementing more than one PA system will likely result in more positive perceptions of PJ in an
organization. Table 9 displays the comparison of subgroups with different PA systems.
53

Table 9
Descriptive Statistics, Sample, and Independent T-Test Comparison – PJ
Comparison n M SD t df p
Group 1
Group 2
76
57
3.48
3.75
0.53
0.58
-1.93 131 .06
Group 1
Group 3
76
73
3.48
3.93
0.53
0.59
-3.58 147 <.01**
Group 1
Group 4
76
52
3.48
4.25
0.53
0.51
-6.24 126 <.01**
Group 2
Group 3
57
73
3.75
3.93
0.58
0.59
-1.41 128 .16
Group 2
Group 4
57
52
3.75
4.25
0.58
0.51
-3.86 107 <.01**
Group 3
Group 4
73
52
3.93
4.25
0.59
0.51
-2.69 123 .01**

Note. Group 1 – One PA system, Group 2 – Two PA systems, Group 3 – Three PA systems,
Group 4 – Four or more PA systems.
*p < .05. **p < .01.

In IJ, the independent t-test suggests no significant difference in adding one PA system.
Similar to PJ, key differences exist when adding more than one PA system. The comparison
between Groups 1 and 2 and Groups 2 and 3 resulted in no significant difference. However, there
was a significant difference at the 95% CI across all other subgroup comparisons. Companies
looking to improve fairness in IJ, should add at least two systems. Table 10 displays the
comparison of subgroups with different PA systems.

54

Table 10
Descriptive Statistics, Sample, and Independent T-Test Comparison – IJ
Comparison n M SD t df p
Group 1
Group 2
76
57
3.19
3.36
0.61
0.74
-1.45 131 .15
Group 1
Group 3
76
73
3.19
3.61
0.61
0.84
-3.48 147 <.01**
Group 1
Group 4
76
52
3.19
3.95
0.61
0.82
-6.00 126 <.01**
Group 2
Group 3
57
73
3.36
3.61
0.74
0.84
-1.74 128 .08
Group 2
Group 4
57
52
3.36
3.95
0.74
0.82
-3.93 107 <.01**
Group 3
Group 4
73
52
3.61
3.95
0.84
0.82
-2.28 123 .03*

Note. Group 1 – One PA system, Group 2 – Two PA systems, Group 3 – Three PA systems,
Group 4 – Four or more PA systems.
*p < .05. **p < .01.

Relationship between PA Components and Employee Outcomes (RQ2)
There is a positive correlation between fairness in PAs and employee outcomes.
Pearson’s correlation coefficients suggest three key findings. First, across the different
components, procedural justice has the strongest relationship with employee satisfaction (r
= .45), motivation (r = .55), and organizational commitment (r = .58). Next, all components of
PA are also positively correlated, which indicate a PA which has one component right, will likely
also have fair components across the board. Finally, employee satisfaction correlation, while
significant, its coefficient values with PA validity (r = .38), DJ ratings (r = .31), DJ rewards (r
55

= .35), PJ (r = .45), and IJ (r = .36) are lower than motivation and organizational commitment,
which suggests other factors may influence employee satisfaction more than motivation and
organizational commitment. Table 11 shows the relationship between the different PA
components and employee outcomes. Chapter 5 explores other potential factors that may relate
to employee satisfaction.

Table 11
Pearson’s Correlation Coefficients for PA Components and Employee Outcomes
Measure 1 2 3 4 5 6 7 8
1. PA Validity - .53* .49* .59* .48* .38* .52* .47*
2. Distributive Justice (ratings) - .48* .64* .46* .31* .49* .45*
3. Distributive Justice (rewards) - .56* .49* .35* .53* .44*
4. Procedural Justice - .57* .45* .55* .58*
5. Interactional Justice - .36* .53* .45*
6. Satisfaction - .31* .35*
7. Motivation - .44*
8. Organizational Commitment -

Note. *p < .01.

Impact of PA Components on Employee Outcomes (RQ3)
Organizational justice significantly impacts employee satisfaction, motivation, and
organizational commitment. The study employed a reflective SEM using SmartPLS 4 to assess
the impact of various organizational justice constructs on three key employee outcomes:
satisfaction, motivation, and organizational commitment. The SEM is a good fit based on
56

existing benchmarks (SRMR = .079; Hair et al., 2019). The positive relationships between the
variables also support the original BLM framework, in which Burke and Litwin (1992) theorized
how the different elements related to one another. Burke and Litwin suggested systems, policies,
and processes impacted work unit climate, which impacted motivation. The SEM demonstrated
how PAs affect different employee outcomes, including motivation. A visual of the SEM is
displayed in Figure 4 to showcase the indicators, latent constructs, and pathways. Additional
results on the reliability, validity, multicollinearity, and relationship between variables are
covered below.

Figure 4
Structural Equation Model of PA Components and Employee Outcomes

57

Note. PA – Performance Appraisal Validity, DJ (ratings) – Distributive Justice (ratings), DJ
(rewards) – Distributive Justice (rewards), PJ – Procedural Justice, IJ – Interactional Justice), S –
Satisfaction, M – Motivation, OC – Organizational Commitment. See Appendix A for reference
to respective indicators.

Latent constructs in the SEM had an acceptable composite reliability score. The study
used rhoc to calculate the composite reliability scores. All latent constructs had a composite
reliability that met acceptable benchmarks (rhoc > .70; Hair et al., 2019), except for employee
satisfaction (rhoc = .66). The composite reliability scores demonstrate the indicators are likely to
be measuring a similar construct and not something entirely different. However, the loadings of
each indicator were generally below acceptable benchmarks ( < .70), suggesting that the
construct could only explain less than 50% of the variance in the respective indicators (Hair et
al., 2019). Table 12 shows the respective indicator loadings and the construct’s composite
reliability.
The indicators display no multicollinearity for each latent construct. Multicollinearity can
result in a large R
2
, even when the variables are insignificant; hence, it is important to ascertain if
the indicators are highly correlated through variance inflation factors (VIF) scores (O’Brien,
2007). Table 14 shows the VIF scores for each indicator within their respective constructs to be
less than five, demonstrating the indicators are likely to be independent of one another with a
minimal likelihood of a change in one indicator affecting another (Hair et al., 2019).

58

Table 12
Indicator Loadings, Construct Composite Reliability, and VIF Scores
Construct and Indicators Loadings VIF
PA Validity
PA1
PA2
PA3
PA4
PA5
PA6
PA7
PA8
.75*
.42
.57
.55
.52
.53
.53
.55
.52

1.08
1.15
1.15
1.13
1.13
1.13
1.20
1.13
Distributive Justice (ratings)
DJR1
DJR2
DJR3
DJR4
.72*
.63
.64
.65
.59

1.10
1.10
1.10
1.08
Distributive Justice (rewards)
DJRW1
DJRW2
DJRW3
DJRW4
DJRW5
.74*
.59
.64
.57
.58
.63

1.13
1.15
1.11
1.13
1.13
Procedural Justice
PJ1
PJ2
PJ3
PJ4
PJ5
.72*
.60
.65
.57
.46
.63

1.09
1.16
1.09
1.05
1.19
Interactional Justice
IJ1
IJ2
IJ3
IJ4
.73*
.61
.64
.65
.65

1.08
1.10
1.11
1.12
Employee Satisfaction
S1
S2
S3**
S4**
.66*
.70
.71
.46
.39

1.03
1.06
1.10
1.07
Motivation
M1
M2
M3
M4
.73*
.57
.65
.66
.58

1.10
1.13
1.19
1.13
59

Construct and Indicators Loadings VIF
M5 .50 1.06
Organizational Commitment
OC1
OC2
OC3
OC4
OC5
.71*
.60
.55
.59
.54
.60

1.13
1.05
1.10
1.13
1.10

Note. * rhoc. **Reverse coded.

Organizational justice components could best explain motivation and organizational
commitment. The study employed regression analysis from SmartPLS 4 to ascertain the impact
of the exogenous constructs. All endogenous latent constructs were significant at the 99% CI.
Organizational justice components had the weakest explanatory power for employee satisfaction
(S R
2
= .23). The R
2
scores indicate there are likely to be other variables, which the study has not
measured, which could lead to a better explanation of the variance within each latent construct.
Table 13 displays the R
2
and p values.

Table 13
Regression Analysis of Latent Constructs
Latent Construct R
2
p
DJ (ratings)
DJ (rewards)
PJ
IJ
S
M
OC
.28
.25
.27
.20
.23
.41
.42
.00**
.00**
.00**
.00**
.00**
.00**
.00**

Note. *p < .05. **p < .01.
60

The SEM model pathways demonstrate the latent construct significantly impact one
another. PA validity significantly impacts all components of organizational justice, which in turn
positively impacts employee outcomes. Perceived fairness in the different components of PA is
critical in ensuring positive employee outcomes. The coefficients indicate how the PAs are set up
(PA validity) is crucial as it impacts the organizational justice components. Organizational justice
then significantly affects employee satisfaction, motivation, and organizational commitment.
The statistically insignificant relationship between DJ ratings -> S (.11) suggests organizations
determining areas of focus for driving employee satisfaction can redirect investments into DJ
rewards, PJ, and IJ instead, where there is a significant impact.

Table 14
SEM Pathway Coefficient, T Scores, and p Values
Pathway Coefficient t p
PA validity-> DJ (ratings)
PA validity -> DJ (rewards)
PA validity -> PJ
PA validity -> IJ
DJ (ratings) -> S
DJ (ratings) -> M
DJ (ratings) -> OC
DJ (rewards) -> S
DJ (rewards) -> M
DJ (rewards) -> OC
PJ -> S
PJ -> M
PJ -> OC
IJ -> S
IJ -> M
IJ -> OC
.53
.50
.52
.44
.11
.20
.17
.17
.27
.17
.23
.12
.29
.19
.24
.20
13.82
11.88
12.64
10.03
1.86
3.57
3.31
3.29
5.57
3.03
4.19
2.30
5.79
4.77
3.69
2.43
.00**
.00**
.00**
.00**
.06
.00**
.00**
.00**
.00**
.00**
.00**
.00**
.02*
.02*
.00**
.00**

Note. *p < .05. **p < .01.
61

Summary
The results indicate fairness in PAs significantly impacts employee outcomes.
Independent sample t-tests comparisons showed the more PAs an organization used, the
likelihood of double-hatting employees perceiving PAs as fair increased. While the overall SEM
fit is considered good according to benchmarks, the relationships between the indicators and
latent constructs were weak to moderate (Hair et al., 2019). Latent construct correlations had
mixed results. PJ had the strongest correlations to employee outcomes, while DJ (ratings) had the
lowest correlation scores. All organizational justice constructs also had weaker coefficients
against employee satisfaction than motivation and organizational commitment. The indicator
loadings for each latent construct were below acceptable benchmarks (Hair et al., 2019), which
suggests the indicators in an Asian context may not be as relevant as the survey adopted the
indicators from Western studies. The regression output and pathway coefficients were all
significant except for one pathway. However, the R
2
indicated a weak to moderate relationship
between the latent constructs, thereby suggesting additional factors could be at play in driving
organizational justice and employee outcomes. Despite the weak to moderate relationships
between the latent constructs, the SEM supports the BLM framework and its connected
elements, where the current model has shown how an organization’s process, policy, or system
impacts individual outcomes (e.g., motivation). Nonetheless, the overall results support the
importance of PA and organizational justice on employee outcomes. Chapter Five discusses these
results in greater detail and the implications for organizations.

62

Chapter Five: Discussion
This study explored the impact of PAs on double-hatting employees in Singapore. The
aim was to ascertain how double-hatting employees perceived fairness with the PAs in their
current organization and its implications for their satisfaction, motivation, and organizational
commitment. The descriptive statistics, correlations, and SEM analysis allowed the study to
understand the relationships between PA components and employee outcomes. The results
showed fairness in PAs for double-hatting employees is critical to their satisfaction, motivation,
and organizational commitment.
Chapter Five offers three recommendations for firms to consider with their PAs,
especially if they have a large population of double-hatting employees. The recommendations
provide practical ways for companies to improve fairness in their PA process and improve the
likelihood of PAs impacting their employees positively. The chapter closes with the limitations
and delimitations of the study, followed by options for future research, given the limited existing
literature surrounding double-hatting employees and PA research in Singapore.
Recommendation 1: Use Multiple PA Systems
The more PAs an organization uses for double-hatting employees, the more positively
these employees perceive fairness with the PA. Mean scores comparing one PA system against
three and even four or more revealed significantly higher mean scores. The results are congruent
with prior studies that have looked at assessing PA systems. Scholars evaluating the efficacy of
different PAs have argued that each PA has pros and cons and no system is perfect (Shaout &
Yousif, 2014). A combination of PAs allows the different PA systems to mitigate each PA
system's adverse effects and strengthens the evaluation process. Lawler et al. (2016) showed
using a combination of evaluation practices (e.g., ongoing feedback and ratingless reviews) was
63

more effective than just ongoing feedback alone. The analysis and existing literature support
organizations to use a combination of PAs rather than just one. Employing multiple systems
guarantees a holistic performance capture, ensuring fairness during the PA process.
Organizations employing only one PA system should invest in at least one or two other
PA systems. These PA systems do not necessarily need to be formal IT systems but can be policy
or process changes in evaluating employees. For instance, companies can combine 360 feedback
with regular ongoing feedback. Heller (2017) argued implementing ongoing feedback in an
organization is not as complicated as it sounds. Managers can provide feedback regularly based
on deliverables or at critical deadlines rather than waiting for the evaluation cycle. Firms that
currently use two or more traditional PA systems and have not implemented innovative ones can
consider doing so. Specifically for ongoing feedback, scholars have demonstrated employees
perceive it as highly effective (Lawler et al., 2016; Ledford Jr & Benson, 2019). However,
organizations should note the current study did not measure the fairness of each specific PA
system or the various combinations of PAs. Decision-makers should fully understand the
limitations of each system and look to complement their existing PA rather than undertake a
costly overhaul.
Companies should be mindful not to go overboard with the administration of PAs to drive
a perception of fairness and create undue admin work for managers. With double-hatting
employees having at least two managers they report to (Burton et al., 2015), it is essential to look
at PAs for double-hatting employees as a holistic process and not allow each manager to run their
own separate and independent PA process and label it as two systems appraising these
employees. Separate processes create unnecessary admin and still do not consolidate feedback or
assess the performance of the individual in totality. PAs must be conducted in an integrated
64

fashion and provide meaningful feedback and outcomes to drive individual and organizational
performance.
Recommendation 2: Ensure Fairness with PAs
Fairness in PAs significantly impacts employee outcomes. Organizational justice in PA is
critical to ensuring positive employee satisfaction, motivation, and organizational commitment.
While the study surveyed the Singapore population, the results of the SEM are consistent with
multiple studies which looked at the impact of fair PAs on similar employee outcomes (Aydın &
Tiryaki, 2018; Rubel & Kee, 2015; Umair et al., 2016). However, the strength of the relationship
between the latent construct pathways differed, possibly due to the cultural context and employee
population. Furthermore, the study focused on double-hatting employees only, while prior
studies assessing the impact of PAs looked at the general employee population. For instance,
Umair et al.'s (2016) study on satisfaction had higher pathway coefficients for IJ, DJ, and PJ than
the current study. While the current study showed DJ (ratings) had no significant effect on
employee satisfaction, given DJ (rewards), PJ, and IJ still impact employee outcomes, firms
should ensure fairness with their PAs and direct investments accordingly, depending on the
employee outcome they wish to drive.
Companies can ensure fairness with PAs by applying the organizational justice
framework. By looking at organizational justice through its three components: distributive,
procedural, and interactional, firms can tailor intervention strategies to correct fairness for
different aspects of their PA. First, moving away from unfair PA systems (e.g., FDR) would be a
starting point in distributive justice. In addition, companies should also ensure there are
transparent and equitable pay-for-performance policies to ensure employees are rewarded fairly
for their work. Second, De Clercq et al. (2021) suggested transparency throughout the decision-
65

making process and to ensure all parties are informed. Firms can communicate policies and
procedures through official channels or verbal conversations. To ensure interactional justice in
PAs, organizations must conduct the appropriate training to ensure managers know their
expected behaviors when interacting with employees (Palaiologos et al., 2011). The varied
approach to address each component of organizational justice will require firms to identify the
low-hanging fruits and address priority areas of concern first.
Recommendation 3: Understand Specific Drivers of the Key Employee Outcomes
Drivers are likely to be unique to each organization. Each latent construct's indicators
(drivers) were weaker than the accepted benchmarks. Hair et al. (2019) advocated for a loading
of .708 for each indicator, considering it preferable to explain the variance in the latent construct.
The low loadings for each indicator are likely due to the cultural context in which the study
conducted the survey. In this study, the indicator loadings for employee satisfaction were lower
than the survey the questions were adopted from (Chi & Gursoy, 2009). A multi-country study
investigating employee satisfaction demonstrated vital differences in the importance of the
drivers across Asia, North America, Europe, and Latin America regions (Andreassi et al., 2014).
Thus, the lower indicator scores observed are not a surprise. While the indicator loadings for
employee outcome constructs were considered low, they were still significant at the 95% CI. In
particular, double-hatting employees differ from the general employee population in that they
hold at least two formal roles and may have other considerations in a PA that affect their
satisfaction, motivation, or commitment. These findings suggest companies should not apply
existing frameworks wholesale in their organizations. Respective firms should tailor the
framework for their use, similar to how this study tailored the BLM to inform the conceptual
framework.
66

The dissertation adapted BLM for its use and demonstrated the relationships between
selected elements of the BLM. However, this study did not investigate all the BLM elements.
While it is clear that structure, systems, policies, and processes impact individual employee
outcomes, the study did not look into other elements (e.g., strategy or leadership), which could
have also impacted PAs and employee outcomes. A complete understanding of the different
elements within the organization’s context before enacting change is critical for successful
change efforts. As such, companies need to undertake their own studies.
Organizations looking to improve key employee outcomes via PAs should undertake a
study internally to understand their key drivers. Each firm is unique in its organizational culture
and requires an understanding of its double-hatting employees to design the most effective
interventions. Bercea et al. (2019) analyzed a non-profit and for-profit organization and
determined significant cultural differences between the two types of firms. In addition, Denisi
and Murphy (2017) argued most PA research conducted had not considered the organizational
culture and climate in which they operate. Cookie-cutter PAs are unlikely to lead to positive and
improved employee outcomes. To ensure a PA leads to positive results, PAs are becoming more
tailored toward the needs and culture of the organization (Schrage et al., 2019). To implement
the most effective solutions, firms must understand their employees first.
Limitations and Delimitations
The study has several limitations in its current form. First, the study’s participants come
from Singapore. The participants’ location limits the generalizability of the survey results to a
broader population. The study's results may not apply to the rest of Asia and the western
hemisphere. Prior studies exploring PAs and comparing across cultures found differences
67

between western and eastern countries and intra-eastern countries (Chong, 2008; Yamazaki &
Yoon, 2016).
Another limitation is the source of the samples the study used. There may be double-
hatting employees outside the online panel (Pollfish), and the study would have no way of
reaching out to them based on the current design. As a result, the source of samples also limits
the generalizability to the double-hatting population in Singapore. In addition, the study results
are only as good as the participants' responses.
Participants may misunderstand questions, click on wrong responses, or straight-line their
answers to complete the online survey as quickly as possible. The survey platform built data
quality checks to discard responses that had completed the survey quickly (e.g., in under 2
minutes) or straight-lined their answers. To further strengthen the quality of responses, one or
two questions, the survey included questions, which forced the participant to select a particular
answer, to have the participant demonstrate they were reading the questions and not clicking
random responses. The survey included clear definitions and instructions to minimize the
possibility of misinterpretation of questions and terms, especially on double-hatting. The
researcher undertook a peer review before launching the survey to ensure the survey flowed
logically. The limitations are a result of the researcher’s choices.
The study has several delimitations of note based on its research design and objectives.
Delimitations define the boundaries the researcher has purposefully set for the study
(Theofanidis & Fountouki, 2018). First, the study sets out to study the impact of PAs on double-
hatting employees in Singapore. There is extensive literature on PAs' impact on regular
employees; thus, the study’s target population excluded non-double-hatting employees. Second,
the investigator resides in Singapore and has also chosen to focus the study on the Singapore
68

working population only. The scope limits the participants who can qualify and complete the
survey. Finally, the survey filtered out double-hatting employees who had not been with their
respective organizations for at least 1 year, as they were required to have gone through at least
one annual PA cycle to be able to assess their organization’s PA.
Future Research
The current study will likely be one of the first academic studies looking at double-
hatting employees in a corporate setting. Anecdotal evidence indicates that double-hatting has
increased since 2015 for HR professionals (Hakikat, 2015). However, the trend is likely to be
across all functions, especially in smaller firms with limited resources. Tov and Chan (2012)
suggested employees in small-medium enterprises (SMEs) normally undertake multiple roles due
to resource constraints. The growing trend of double-hatting and its incidence for SMEs will
significantly affect how organizations structure roles. There are still many aspects of double-
hatting that are unknown. While this study focused on the impact of PAs on double-hatting
employee outcomes, additional elements could include the incidence of double-hatting, job
performance, training and development needs, operating models, stress levels, successes, and
challenges of double-hatting employees. The different areas of further research will provide
additional data points to either further support the concept of double-hatting or allow firms to
find new solutions to work around double-hatting. Beyond double-hatting, PAs in Singapore is
also worth exploring.
Singapore is home to many Fortune 500 regional headquarters and SMEs. The Singapore
Economic Development Board (EDB) cites the country as having the most regional headquarters
for international companies in the past 10 years in Asia Pacific (EDB, 2023). In addition,
Singapore is also home to an estimated 298,000 SMEs (SingStat, 2023). With the limited number
69

of PA studies focusing on Singapore and its location as a business hub for companies, firms must
understand the impact of their PA systems on employees. Further investigation, particularly into
the impact of PAs on Singapore’s workforce, will enable companies to implement fair PAs,
which positively influence employee outcomes, making Singapore a more appealing workplace.
Conclusion
The study is one of the first looking at double-hatting employees as its target population.
While explicitly focused on PA and its impact on double-hatting employees, the study has shown
how critical it is to be fair in PAs. Employees are an organization’s most important asset and will
only stay loyal to an organization if they are satisfied, motivated, and committed (Gabčanová,
2011). The study has demonstrated how PAs significantly impacts employee satisfaction,
motivation, and organizational commitment, so organizations should take heed and review their
current PAs to ensure fairness. Organizational justice applies to all. The results are similar to
prior studies investigating PA's impact on regular employees. The dissertation has proposed
practical suggestions for organizations that do not require significant investments yet can
increase fairness with their PAs. These recommendations range from simple adjustments of
reviewing existing processes for fairness and incorporating additional feedback channels to
implementing new PA systems.
I hope this study will be a catalyst for more studies on double-hatting corporate
employees in the future. There are too many unknowns with double-hatting employees, which
require further investigation. In addition, with multiple industries laying off a significant portion
of their employees in 2023 (Q.ai, 2023; Stringer, 2023), companies will expect more employees
to double-hat to save costs. The time is ripe to fully understand the implications of double-
hatting.
70

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91

Appendix A: Survey Questions
Dear Participant,
This survey aims to understand the impact of human resource management practices on
employee attitudes and behaviors. The survey should take no more than 10-15 minutes to
complete. All results will be anonymously aggregated for analysis, and the survey will not collect
any personally identifiable information. Your participation in this survey is voluntary.
If you have any questions or concerns, do not hesitate to contact the principal
investigator, Nick Joo, at njoo@usc.edu.
Thank you for your participation!

Table A1
Survey Questions
Question
Open
or
closed
Level of
Measurement
Response options RQ
Concept
Measured
Screener
S1. Are you
currently a
double-hatting
employee?

Double-hatting
refers to you
taking on two or
more formal
roles in your
organization
(e.g., head of
sales and
finance
manager, HR,
IT, etc.)
Closed Nominal 1.Yes
2.No
3.Maybe
4.Don’t know
Demographic
S2. How many
years have you
Closed Nominal 1. Less than 1 year
2. 1-2 years
Demographic
92

Question
Open
or
closed
Level of
Measurement
Response options RQ
Concept
Measured
been double-
hatting in your
current roles?
3. 2-4 years
4. 4 or more years
S3. How many
annual cycles of
performance
appraisals have
you undergone
in your current
organization?
Closed Nominal 1. 0
2. 1
3. 2
4. 3
5. 4
6. More than 4

Performance Appraisal Type
Q1. Please
select the
performance
appraisal
systems that
your
organization
uses. You may
select more than
one type (e.g.,
360 feedback
and forced
rankings)
Closed Nominal 1. 360 Feedback
2. Management by
objectives
3. No formal
system
4. Behaviorally
anchored scales
5. Informal
feedback
6. Forced rankings
7. Ratingless
reviews
8. Crowdsourced
feedback
8. Don’t know

RQ1 Performance
Appraisal
Performance Appraisal Validity Questions
PA1. The level
of involvement
in my
performance
appraisal has
been adequate
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ3
Performance
Appraisal
PA2. The
performance
appraisal is
accurate and
clear in its
standards and
measures
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ3
Performance
Appraisal
PA3. Feedback
regarding my
Closed Ordinal 1. Strongly disagree
2. Disagree
RQ1,
RQ3
Performance
Appraisal
93

Question
Open
or
closed
Level of
Measurement
Response options RQ
Concept
Measured
performance is
clear and
helpful for
improving
3. Neutral
4. Agree
5. Strongly agree
PA4. My
manager takes
the performance
appraisal
process
seriously
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ3
Performance
Appraisal
PA5. The goals
developed for
my annual
performance
period are
meaningful
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ3
Performance
Appraisal
PA6. My
individual
performance
factors on my
appraisal are
clear and related
to my roles
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ3
Performance
Appraisal
PA7. I am given
the opportunity
to identify the
appropriate
goals for my
roles
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ3
Performance
Appraisal
PA8. The
performance
appraisal for my
position
accurately
measures what I
do on the job
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ3
Performance
Appraisal
Organizational Justice Questions
Distributive Justice (rating)
DJR1. The last
performance
rating I received
truly
represented how
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Distributive
Justice
94

Question
Open
or
closed
Level of
Measurement
Response options RQ
Concept
Measured
well I
performed in
my roles
DJR2. The last
performance
appraisal was
conducted fairly
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Distributive
Justice
DJR3. My
performance
was accurately
evaluated
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Distributive
Justice
DJR4. My
manager was
justified with
their rating of
my performance
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Distributive
Justice
DJR5. My last
performance
rating was free
from bias
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Distributive
Justice
Distributive Justice (rewards)
DJRW1. The
organization has
been fair in
rewarding me
when
considering the
amount of effort
I have put forth
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Distributive
Justice
DJRW2. The
organization has
been fair in
rewarding me
when
considering the
responsibilities
I have
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Distributive
Justice
DJRW3. The
organization has
been fair in
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
RQ1,
RQ2
Distributive
Justice
95

Question
Open
or
closed
Level of
Measurement
Response options RQ
Concept
Measured
rewarding me
when
considering the
stresses and
strains of my
roles
4. Agree
5. Strongly agree
DJRW4. The
organization has
been fair in
rewarding me
when
considering the
amount of
education and
training that I
have
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Distributive
Justice
DJRW5. The
organization has
been fair in
rewarding me
when
considering the
work I have
done well
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Distributive
Justice
Procedural Justice
PJ1. The
performance
appraisal
process was
clearly
communicated
to me
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Procedural
Justice
PJ2. My
manager
possessed
adequate
knowledge and
training to
properly
implement my
performance
appraisal
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Procedural
Justice
96

Question
Open
or
closed
Level of
Measurement
Response options RQ
Concept
Measured
PJ3. My
manager is able
to objectively
assess my
performance
based on the
given criteria
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Procedural
Justice
PJ5. The
organization has
provided a clear
channel for me
to voice my
concerns about
the appraisal
process
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Procedural
Justice
PJ6. The
organization has
provided a clear
channel for me
to voice my
concerns about
my performance
evaluation
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Procedural
Justice
Interactional Justice
My manager
has regular
conversations
with me to
review goals set
for the year
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Interactional
Justice
My manager
treats me with
respect and
dignity during
our appraisal
conversations
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Interactional
Justice
My manager
sits and
discusses the
results of my
performance
appraisal with
me
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Interactional
Justice
97

Question
Open
or
closed
Level of
Measurement
Response options RQ
Concept
Measured
My manager
gives me the
opportunity to
share my
feedback /
feelings about
my performance
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Interactional
Justice
My manager
provides
guidance on
how to improve
my performance
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ1,
RQ2
Interactional
Justice
Employee Satisfaction
S1. Overall, I
am satisfied
with my roles in
the current
organization
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ2,
RQ3
Employee
Satisfaction
S2. I intend to
keep working at
my current
organization
long into the
future
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ2,
RQ3
Employee
Satisfaction
S3. I often think
about quitting
my job
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ2,
RQ3
Employee
Satisfaction
S4. As soon as I
can find another
job, I am going
to leave
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ2,
RQ3
Employee
Satisfaction
Motivation
M1. I take pride
in doing my
jobs as well as I
can
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ2,
RQ3
Motivation
M2. I try to
think of ways of
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
RQ2,
RQ3
Motivation
98

Question
Open
or
closed
Level of
Measurement
Response options RQ
Concept
Measured
doing my jobs
effectively
4. Agree
5. Strongly agree
M3. I feel a
sense of
personal
satisfaction
when I do this
job well
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ2,
RQ3
Motivation
M4. I like to
look back on
the day’s work
with a sense of
a job well done
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ2,
RQ3
Motivation
M5. I feel
unhappy when
my work is not
up to my usual
standard
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ2,
RQ3
Motivation
Organizational Commitment
OC1. I am
willing to put in
a great deal of
effort beyond
that is normally
expected to help
this
organization be
successful
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ2,
RQ3
Organizational
Commitment
OC2. I find that
my values and
the
organization's
values are very
similar
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ2,
RQ3
Organizational
Commitment
OC3. I really
care about the
fate of this
organization
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ2,
RQ3
Organizational
Commitment
OC4. I talk up
this
organization to
my friends as a
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
RQ2,
RQ3
Organizational
Commitment
99

Question
Open
or
closed
Level of
Measurement
Response options RQ
Concept
Measured
great
organization to
work for
5. Strongly agree
OC5. I would
accept almost
any type of job
assignment to
keep working
for this
organization
Closed Ordinal 1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
RQ2,
RQ3
Organizational
Commitment
Demographics
Gender Closed Nominal 1. Male
2. Female
3. Non-binary
4. Prefer not to say
Demographics
Age Closed Nominal 1. 21-25
2. 26-30
3. 31-35
4. 36-40
5. 41-45
6. 46-50
7. 50 and up
Demographics
Highest
education
attained
Closed Nominal 1. High school /
Secondary school
and below
2. Diploma / ITE
3. Bachelor's degree
3. Post-graduate
degree
Demographics
Estimated
number of
employees in
your current
organization
Closed Nominal 1. Less than 50
employees
2. Between 50 –
500 employees
3. Between 501 –
1,000 employees
4. Between 1001 –
5,000 employees
5. Between 5001 –
10,000 employees
6. 10,001 and more
employees
Demographics
100

Question
Open
or
closed
Level of
Measurement
Response options RQ
Concept
Measured
Years of
working
experience
Closed Nominal 1. 1 – 2 years
2. 3-5 years
3. 6-10 years
4. 11 – 15 years
5. 15 or more years
Demographics
Industry your
organization is
in
Closed
Nominal
1. Agriculture and
fishing
2. Mining and
quarrying
3. Manufacturing
4. Construction
5. Wholesale and
retail trade
6. Transportation
and storage
7. Hospitality and
tourism
8. Information and
communications
9. Financial
services and
banking
10. Real estate
11. Professional,
scientific, and
technical activities
12. Public
administration and
defense
13. Education
14. Healthcare and
social services
15. Arts,
entertainment,
recreation
16. Technology
17. Oil and gas
18. Food and
beverage
99. Others: Please
specify
Demographics

101

Survey questions adapted from:
Chi, C. G., & Gursoy, D. (2009). Employee satisfaction, customer satisfaction, and financial
performance: An empirical examination. International Journal of Hospitality
Management, 28(2), 245-253.
Gabris, G. T., & Ihrke, D. M. (2001). Does performance appraisal contribute to heightened levels
of employee burnout? The results of one study. Public Personnel Management, 30(2),
157-172.
Mowday, R. T., Steers, R. M., & Porter, L. W. (1979). The measurement of organizational
commitment. Journal of Vocational Behavior, 14(2), 224-247.
Tang, & Sarsfield-Baldwin, L. J. (1996). Distributive and procedural justice as related to
satisfaction and commitment. S.A.M. Advanced Management Journal, 61(3), 25–31.
Warr, P ., Cook, J., & Wall, T. (1979). Scales for the measurement of some work attitudes and
aspects of psychological well-being. Journal of Occupational Psychology, 52(2), 129-
148.

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Asset Metadata

Creator Joo, Nicholas (author)

Core Title Impact of performance appraisals on double-hatting employees

School Rossier School of Education

Degree Doctor of Education

Degree Program Organizational Change and Leadership (On Line)

Degree Conferral Date 2023-08

Publication Date 07/21/2023

Defense Date 07/12/2023

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag correlation,Motivation,OAI-PMH Harvest,organizational commitment,performance appraisal,quantitative,Satisfaction,Singapore,structural equation model

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Datta, Monique (committee chair), Picus, Lawrence (committee member), Pritchard, Marcus (committee member)

Creator Email njoo@usc.edu,njoozw@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113282101

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