HOW MONETARY INCENTIVES AND LOYALTY AFFECT GOAL REALIZATION
A Dissertation
Submitted to the Graduate Faculty of the
University of South Alabama
in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
in
Business Administration
by
Lawrence E. Goehrig
MBA, Nova Southeastern University, 2013
BS, University of Florida, 1986
May 2023
To My Loving Wife, Carmen, For Whom I Am So Grateful.
Thank you for your support and patience in my journey.
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ACKNOWLEDGEMENTS
To my committee, thank you so much for your insights and encouragement. Joe,
your scholarship and forward thinking which created the program that enabled me to
continue my academic pursuits. Bill, your tireless work ethic, precise writing, and high
expectations have helped push me to the finish line. Regina, you have always had
guidance for me over the past ten years and constantly mentioned the benefits of a PhD
and Joe Hair. Ben, thank you so much for your time, efforts and feedback which greatly
helped accomplish my dissertation.
To my wife, Carmen, thank you for being by my side and encouraging me to keep
pushing during the tough times of these studies. Without your support, this paper would
not have been possible. The hours, days, weeks, and months spent reading and writing
could not have happened without you being the rock that you have always been.
To the members of Cohort VI and all the PhD faculty, thank you for your
friendship and support. Full weekends and late study sessions were bearable with the
great friendships we developed through our time together. I look forward to the future
with such a remarkable group in my corner. I cherish the times we had together while
looking forward to future endeavors and successes.
A special thank you to Britton for your great friendship and help over the years on
this journey. You definitely are a great asset, and I am truly grateful for your help and
guidance. Thank you to Dr John Riggs for your referral to Joe Hair and our meetings to
let me know what this journey would be like and to stick it out.
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Thank you to Dr Matt Howard for your patience and time spent helping me
understand research methods.
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TABLE OF CONTENTS
Page
LIST OF TABLES ........................................................................................................... viii
LIST OF FIGURES ............................................................................................................ x
LIST OF ABBREVIATIONS ............................................................................................ xi
ABSTRACT...................................................................................................................... xii
CHAPTER I INTRODUCTION ......................................................................................... 1
1.1 Statement of the Problem....................................................................................... 6
1.2 Purpose .................................................................................................................. 9
1.3 Contribution ........................................................................................................... 9
1.4 Summary of Remaining Chapters ........................................................................ 11
CHAPTER II LITERATURE REVIEW .......................................................................... 13
2.1 Goal Attainment Theories.................................................................................... 14
2.2 Monetary Incentives ............................................................................................ 19
2.3 Locus of Control .................................................................................................. 23
2.4 Dishonest Behavior .............................................................................................. 27
2.5 Employee Loyalty ................................................................................................ 34
Chapter III MODEL DEVELOPMENT AND HYPOTHESES ....................................... 40
3.1 Locus of Control .................................................................................................. 42
3.2 Monetary Incentives ............................................................................................ 42
3.3 Organization Employee Loyalty .......................................................................... 43
3.4 Dishonest Behavior .............................................................................................. 44
CHAPTER IV METHODS ............................................................................................... 49
4.1 Sample Criteria and Data Sources ....................................................................... 49
4.1.1 Analytical Process ..................................................................................... 49
4.1.2 Data Sources .............................................................................................. 50
4.1.3 Criteria ....................................................................................................... 50
4.2 Survey Instrument ................................................................................................ 51
4.3 Sample ................................................................................................................. 52
4.4 Scales ................................................................................................................... 54
4.4.1 Locus of Control ........................................................................................ 54
4.4.2 Employee Loyalty ..................................................................................... 54
4.4.3 Monetary Incentives .................................................................................. 55
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4.4.4 Dishonest Behavior ................................................................................... 55
4.4.5 Goal Attainment ........................................................................................ 55
4.5 Bias, Validity, and Analysis ................................................................................ 56
4.5.1 Common Method Bias ............................................................................... 56
4.5.2 Analysis ..................................................................................................... 57
4.5.3 Pilot Testing............................................................................................... 57
CHAPTER V RESULTS .................................................................................................. 58
5.1 Analytical Process ............................................................................................... 58
5.2 Data Analysis and Findings ................................................................................. 58
5.2.1 Partial Least Squares Structural Equation Modeling................................. 58
5.3 Assessment of Measurement Model .................................................................... 59
5.3.1 Data Distribution ....................................................................................... 59
5.3.2 Common Method Variance ....................................................................... 60
5.3.3 Estimate of Loadings, Significance, and Item Reliability ......................... 60
5.3.4 Composite Reliability ................................................................................ 63
5.3.5 Convergent Validity .................................................................................. 63
5.3.6 Discriminant Validity ................................................................................ 64
5.3.7 Nomological Validity ................................................................................ 66
5.4 Evaluation of the Structural Model...................................................................... 67
5.4.1 Assessment of Collinearity ........................................................................ 67
5.4.2 Path Coefficients and Significance ............................................................ 68
5.4.3 Coefficients of Determination (R2) ............................................................ 69
5.4.4 Effect Sizes (ƒ2) ......................................................................................... 70
5.4.5 PLSpredict ................................................................................................. 71
5.5 Overview of Hypotheses Results ......................................................................... 73
5.5.1 Assessment – Moderation and Mediation ................................................. 74
CHAPTER VI DISCUSSION AND CONCLUSIONS .................................................... 78
6.1 Discussion ............................................................................................................ 78
6.1.1 Theoretical Implications ............................................................................ 80
6.1.2 Managerial Implications ............................................................................ 80
6.2 Conclusions.......................................................................................................... 81
6.3 Limitations ........................................................................................................... 81
6.4 Future Research ................................................................................................... 82
6.5 Research Conclusion and Summary .................................................................... 83
REFERENCES ................................................................................................................. 85
vi
APPENDICES .................................................................................................................. 98
Appendix A Original Item Factor Loadings .............................................................. 98
Appendix B Invitation to Participate ....................................................................... 100
Appendix C Survey Statements ............................................................................... 102
Appendix D IRB Approval ...................................................................................... 105
BIOGRAPHICAL SKETCH .......................................................................................... 106
vii
LIST OF TABLES
Table
Page
3.1. Hypothesized Path Relationships in the Study for the Constructs, Mediation and
Moderation .........................................................................................................................48
4.1. Demographics of Survey Respondents– Financial Sales Employees ........................53
5.1. Factor Loadings of the Constructs in the Study ..........................................................61
5.2. Descriptive Statistics of Variables ..............................................................................62
5.3. Correlation Values of the Variables Showing Significance Level .............................62
5.4. Internal Consistency Assessments for each Construct to Show Items Within the
Construct are Similar .........................................................................................................63
5.5. Average Variance Extracted for Convergent Validity for Each Construct to Show
Shared Variance Between the Construct and Each Item for That Construct .....................64
5.6. Fornell-Larcker Criterion for Discriminant Validity for Each Construct ...................65
5.7. Heterotrait-Monotrait Discriminant Validity for the Measurement Model ................66
5.8. Variance Inflation Factor for each Latent Variable to Show There are no Collinearity
Issues ..................................................................................................................................67
5.9. Size and Significance of Path Coefficients .................................................................68
5.10. Coefficients of Determination for the Dependent Variables to Show the Predictive
Power of the DV ................................................................................................................70
5.11. Effect Sizes of Constructs for the Structural Equation Model ..................................70
5.12. PLSpredict Summary of Items to Show Comparison of the Error Measurement in
the Predictive Power ..........................................................................................................72
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5.13. PLSpredict Summary of Constructs..........................................................................72
5.14. Path Relationship, Betas, and Significance for the Proposed Hypotheses in This
Study ..................................................................................................................................74
5.15. Outcomes of Mediation and Moderation on Path Relationship Hypotheses ............75
Appendix Table
A1. Original Items – Factor Loadings for all Constructs...................................................98
ix
LIST OF FIGURES
Figure
Page
1. Research Model of Hypothesized Relationships Showing the Influences of the
Variables on Goal Attainment...................................................................................... 41
2. Structural Model of One-Tail Test Results Showing the Strength of the Relationships
and Their Significance ................................................................................................. 69
3. Graph A Showing the Moderating Effects of Employee Loyalty on the Relationship
of Monetary Incentive to Goal Attainment .................................................................. 76
4. Graph B Showing the Moderating Effects of Employee Loyalty on the Relationship
of Monetary Incentive to Dishonest Behavior ............................................................. 77
x
LIST OF ABBREVIATIONS
AVE
Average Variance Extracted
CCA
Confirmatory Composite Analysis
DB
Dishonest Behavior
DM
Decision Maker
DWB
Deviant Workplace Behavior
EL
Employee Loyalty
EUT
Expected Utility Theory
GA
Goal Attainment
HTMT
Heterotrait-Monotrait Ratio
LM
Linear Model
LOC
Locus of Control
MI
Monetary Incentives
PLS-SEM
Partial Least Squares-Structural Equation Modeling
SET
Social Exchange Theory
VIF
Variance Inflation Factor
xi
ABSTRACT
Lawrence E. Goehrig, Ph.D., University of South Alabama, May 2023. How Monetary
Incentives and Loyalty Affect Goal Realization. Chair of Committee: William Gillis,
Ph.D.
Monetary incentives are widely known to help motivate employees. Mostly these
monetary incentives are used to increase the effort of the employees to attain the goals of
the firm. Attaining firm goals will increase revenues, profits, market share, and similarly
valued outcomes. These monetary incentives are often a gamble for the firm as there is no
direct method to show the impact monetary incentives will have on goal attainment.
Other variables come into play when monetary incentives are used. Employee loyalty,
locus of control, and dishonest behavior are three that were researched.
Employee loyalty can help the firm attain goals when employees feel a connection
to the firm and value the firm for their own wants and achievements. Employee loyalty
was investigated to show if employee loyalty influences goal attainment when monetary
incentives are involved. The research showed that monetary incentives are not as
important to loyal employees. Locus of control also influences goal attainment. This
effect is solely based on the level of locus of control of the employee and was related
directly to goal attainment. Employee dishonesty was investigated to see the influence
monetary incentives have on dishonest behavior and how that dishonest behavior affects
goal attainment. All three of these variables were examined to see how much effect they
have on goal attainment and, more importantly, how valuable employee loyalty is to the
firm.
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Keywords: monetary incentives, locus of control, dishonest behavior, employee loyalty,
goal attainment, expected utility theory, social exchange theory
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CHAPTER I
INTRODUCTION
Monetary incentives are frequently suggested as a method for motivating and
improving the performance of people (Bonner & Sprinkle, 2002). Monetary incentives
may also motivate people to invest effort to acquire the skills needed to perform a task so
that future performance and rewards will be higher than they otherwise would be (Bonner
& Sprinkle, 2002). This increased effort can be an asset for the firm as employees may
seek additional training, work longer hours, increase contacts, etc., which will both help
the employee increase performance and help to attain the firm’s goals. Overall, then,
individual incentives are thought to promote effort directed toward strategic development
when more standard and predetermined conditions are not sufficient to attain desired
performance and reward levels (Locke & Latham, 1990). However, employers have no
way of knowing if they overpaid with the incentive, resulting in wasteful spending while
meeting company objectives, or if the level of bonus is so high that employees are
incentivized to commit dishonest behaviors. From the employees’ standpoint, thus, a
dilemma arises because dishonesty often serves their self-interest at the expense of their
morality and may even cause material loss to others (Graham et al., 2016).
From the employers’ standpoint, goal setting is important in the work
environment (Locke, 2004). Compared to vague easy goals, specific challenging goals
boost performance, so long as the person is committed to the goal, has the requisite
ability to attain it, and does not have conflicting goals. When these conditions are met,
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there is a positive linear relationship between goal difficulty and task performance (Locke
& Latham, 2006).
Locke (2004) researched goals using bonuses to incentivize effort. Although this
method provided a strong incentive to reach the goal, it was also found that there was
considerable temptation for the participants to consider the short term only, and therefore,
create short cuts, cheat, and fake results in order to fraudulently attain the incentive.
Locke (2004) concluded that it is better to have no bonus system at all, other than simple
merit pay, than to have a bad bonus system.
Employees are considered the core of an organization and the success or failure of
the organization is attributed to the performance of the employees (Murali et al., 2017). It
is of prime importance that the employees are loyal to the organization and don’t actively
search for other alternative opportunities. Loyalty, as a general term, can be defined as a
person’s devotion or sentiment of attachment to a particular object, which may be another
person or a group of persons, an idea, a duty, or a cause (Murali et al., 2017). Employee
loyalty “is a deliberate commitment to further the best interests of one’s employer, even
when doing so, might demand sacrificing some aspect of one’s self-interest beyond what
would be required by one’s legal and other moral duties” (Elegido, 2013, p. 496).
A loyal employee might work harder and increase effort to attain the goal. Bonner
and Sprinkle (2002) found a positive relationship between employee loyalty and job
performance. The relationship between employee loyalty and job performance shows that
loyal and committed employees perform their duties better than disloyal ones (Khan et
al., 2020). Loyalty acts as a key success factor in performance evaluations. Loyalty to
one’s employer makes the employee more trustworthy and therefore more valuable as an
2
employee (Elegido, 2013). Older employees generally consider themselves more loyal to
an organization than younger employees. It appears that as employees age and remain
with an organization, they become more loyal and more satisfied with their job (Murali et
al., 2017).
The underpinning theory for studying the relationship between employee loyalty
and dishonesty in the workplace is social exchange theory. According to Nawaz et al.
(2014), the theories of social exchange (SET) provide the theoretical basis for employees’
creative and destructive behaviors. SET also provides a conceptual paradigm for
understanding behavior at the workplace. So, it is supported by research, based on SET,
that employee loyalty is negatively associated with workplace deviance (Bilal et al.,
2020). This research explored the connection between employee loyalty and deviant
workplace behavior (DWB) in the banking sector. The findings of the regression analysis
revealed that employee’s loyalty had a significant negative impact on DWB, which
confirmed Rishipal’s (2019) findings, which found a significant negative association in
the hospitality sector between loyalty and counterproductive work behavior.
Locus of control is another factor that might enhance goal attainment without the
motivation of a monetary incentive. Locus of control has been conceptualized as “the
extent to which people believe that the rewards they receive in life can be controlled by
their own personal actions” (Wang et al., 2010, p. 761) and captures whether individuals
“attribute the cause or control of events either to themselves or to the external
environment” (Spector, 1982, p. 483). Locus of control relates to one’s generalized
expectations about the ability to take responsibility for what happens.
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This study explores the effect monetary incentives have on goal attainment when
accounting for other directly related factors such as locus of control and other indirectly
related factors such as employee loyalty. This study also shows the impact employee
loyalty has in moderating the monetary incentive to dishonest behavior relationship.
Also, this study shows the impact that employee loyalty has on moderating the monetary
incentive to goal attainment relationship.
I posit that the moderating relationships with employee loyalty will show that
monetary incentive will have less of an impact on goal attainment when employee loyalty
is high. I also posit that monetary incentive will have less of an impact on the dishonest
behavior relationship when moderated by employee loyalty.
This study shows the importance of high employee loyalty to an organization.
Employee loyalty was researched to help show the effect that the monetary incentive can
contribute to the employee behaving in a dishonest manner to accomplish the goal and
attain the monetary incentive. Employee deviance, or unethical behavior, or dishonesty,
is defined here as voluntary behavior that violates significant organizational norms and in
so doing threatens the well-being of an organization, its members, or both (Robinson &
Bennett, 1995). Finally, I will investigate whether employee loyalty mitigates this
dishonest behavior.
The three theories that detail the mechanisms through which monetary incentives
increase goal attainment is expected utility theory, agency theory and expectancy theory.
Social exchange theory is also used to show how locus of control and employee loyalty
are involved in goal attainment.
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In expected utility theory, Mongin (1997) states that the decision maker (DM)
chooses between risky or uncertain prospects by comparing expected utility values. In
expected utility theory (EUT), it is assumed that individuals attempt to maximize the
amount of a reward that they desire. When the reward is a monetary incentive, this theory
helps explain the level of effort the employee may use to attain that monetary incentive.
Expected utility theory (EUT) posits that the employee chooses between risky or
uncertain prospects by comparing expected utility or outcomes (Mongin, 1997). A
fundamental assumption is that individuals are completely rationale and have welldefined preferences (Bonner & Sprinkle, 2002). Thus, according to Lengwiler (2009),
EUT posits that people use or should use the most rational choice with the highest
expected value of the utility of different possible outcomes for their choices as a guide for
making decisions.
From an economic perspective, agency theory assumes that agents are selfinterested and risk averse. Agency theory recommends monetary incentives as a means of
motivating increased effort and improvements in performance (Eisenhardt, 1989).
Agency theory views the firm as a complex nexus of contracts where authority to perform
specialized tasks is delegated by the principal (typically managers) to agents (the
employees) with the expectation that the agent will act in the best interest of the firm
(Eisenhardt, 1989).
Expectancy theory posits that financial incentives will illicit increased effort only
if the individual believes that there is a high probability of receiving increased results
from expending more effort (Vroom, 1964). Lawler and Suttle (1973) further expanded
on Vroom’s expectancy theory by explaining in their research that the expectancy model
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of effort leading to successful performance and the expectancy that this action of
increased effort will produce outcomes.
Overall, all three theories examine how monetary incentives work by increasing
effort to attain a goal, which, in turn, leads to increases in performance and a monetary
reward (Bonner & Sprinkle, 2002).
Locus of control emerged from Rotter’s (1954, 1966) seminal work on social
learning theory. Social learning theory posits that individuals learn by observing the
events that occur around them in a manner that ultimately influences behavior (Rotter,
1954). In this learning process, individuals develop expectancies that specific behaviors
will result in reinforced results (Rotter, 1966). According to Rotter (1966), a relatively
stable individual difference (i.e., locus of control) emerges over time that pertains to the
extent to which individuals perceive a causal relationship between behavior and rewards.
Although some individuals perceive that their behavior and personal attributes drive
outcomes (i.e., an internal locus of control), others develop a general sense that external
forces govern outcomes (Galvin et al., 2018). While locus of control has been studied in
several contexts, this research is unique in examining how this variable may affect or
may not affect goal attainment.
1.1 Statement of the Problem
According to Nnubia (2020), there have been several issues associated with
financial incentives on workers’ performance on the part of workers and managers in
various business organizations. Three of these are (a) a poor incentive package which is a
major factor affecting employees’ commitment and productivity, (b) the employees’
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unwillingness to increase their performance because they feel that their contributions are
not well recognized by their organizations, and (c) the shortage of necessary management
skills that could help in the formulation of a good monetary incentive policy (Nnubia,
2020). The issue of employee performance cannot be over emphasized. Employees want
to achieve their own personal goals and employers want them to attain the organizational
goals. These objectives should be reasonably aligned. Managers need to recognize that
employee loyalty is a core component of an employee’s conduct and needs to be assessed
from time to time (Bilal et al., 2020). Rewards are highly used to overcome
dissatisfaction and to increase performance of employees (Bonner & Sprinkle, 2002).
Another issue is that monetary incentives can create the opportunity for employees to act
in a dishonest or unethical way to benefit themselves to attain the monetary incentive.
Successful service firms, such as banking, will invest resources or maintain the
long-term relationships in the programs in order to increase job satisfaction and
employees’ performance (Nnubia, 2020). In turn, the increased effort increases
performance, and that increased performance will make the goal attainment more
possible or more likely and subsequently, the monetary incentive will be attained. Thus,
my research question concerns how much employee loyalty influences the relationship
between monetary incentive and goal attainment and how monetary incentives and
employee loyalty affect goal realization. Also, how do monetary incentives really help
goal attainment when measured against loyal employees and locus of control? This will
help determine whether to emphasize employee loyalty within the organization, to hire
employees with a high locus of control, or to primarily offer monetary incentives.
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According to Locke and Latham (2002), specific and difficult goals (but those
perceived as attainable), lead to greater performance than vague and easy goals. They
also found workers are more motivated or more committed to attain the organizational
goals when they perceive the goals as being relevant and difficult or challenging to attain.
Finally, they found that goals can increase workers’ persistence to exert effort. These
results suggest that goal setting may be an effective tool to boost a worker’s motivation
and effort (Corgnet et al., 2015).
The ability to set reasonable and attainable goals by the firm enhances employee
effort and that increased employee effort will then transfer into increased production for
the firm and in turn create an increase in output which will increase revenues and
ultimately increase net profit to the firm (Locke & Latham, 2004).
According to expected utility theory (EUT), workers expect compensation or a
benefit for their work. One possible issue pointed out by EUT is that if the monetary
incentive is not enough to motivate the workers in the firm, the workers will not put forth
effort to attain the goal. Thus, firms will not be getting effort from their workers to attain
the goals and increase profits. Therefore, one goal for the firm is to determine what level
of monetary incentive is needed to motivate employees to perform better and increase
effort to accomplish firm goals.
The setting of these goals and the attachment of monetary incentives to attain the
goals must be at a level that the employees trust that the firm is operating in a manner that
the employees are cared for, and the goals are attainable (Bonner & Sprinkle, 2002).
Otherwise, the employees may put forth minimal effort for their pay without considering
the goals attached to the monetary incentives. Therefore, the employer must know how
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loyal their employees are to determine how much a monetary incentive will really
increase performance to attain the goal. For example, if a monetary incentive is given but
the employees are highly loyal, then it is possible that monetary incentive was not needed
to attain the goal and would have increased the net profit.
1.2 Purpose
This study focuses on the motivational process of monetary incentives to help
increase goal attainment. Therefore, this study will help obtain knowledge that can be
used when setting goals and attaching monetary incentives for attaining those goals.
Employee loyalty is researched in this study to help determine the impact employee
loyalty has on goal attainment and the effect employee loyalty has on the monetary
incentive to goal attainment relationship to help determine if employee loyalty can be
increased and monetary incentives can be decreased.
1.3 Contribution
The main contribution is attempting to find how much monetary incentives help
goal attainment when controlling for other variables including locus of control and
employee loyalty. This study is researched in the financial services sector.
In economics literature, monetary incentives are considered to be the most
effective way to induce workers to exert effort (Corgnet et al., 2015). Money is a symbol
of power, status, and respect; it plays a crucial role in meeting the social, security and
physiological needs of a person (Daramola & Daramola, 2019). Employee motivation is
linked closely to employee performance, the performance of employees will make or
9
break a company; therefore, it is important to find a method to motivate employees
(Daramola & Daramola, 2019). The most obvious form of motivation for an employee is
money (Daramola & Daramola, 2019) but other factors such as work life balance, and
external circumstances may influence motivation.
A second contribution will be for the financial services sector to help it determine
whether to put resources into keeping, maintaining, and enhancing employee loyalty or
whether to primarily focus on monetary incentives. Employees are the most important
factor of organizations because the success of the organization depends upon the
performance of employees (Khan et al., 2020). Employee loyalty increases profit,
improves quality, reduces turnover, and increases the reputation of the organization
(Khan et al., 2020). Therefore, it is imperative that the organization finds a way to
maintain and attract loyal employees.
A third contribution will be to show if high locus of control employees performs
better for goal attainment without relying much on a monetary incentive as motivation.
Managers may then be able to set better goals with the possibility of less monetary
incentives if they have loyal employees with high locus of control. This study can see if
and how much loyalty to the firm contributes to an increase in effort to attain the goal. A
more precise area is the financial services industry. In that sector this study will show if
monetary incentives really have a strong effect on goal attainment with loyal employees
as well as the affect locus of control has on goal attainment. From the social learning
theory, a contribution will be to show if the more loyal employees attain the goal with
less motivation from the monetary incentive. This can show that newer employees
becoming more loyal by observing that the more loyal employees attain the goals with
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less motivation from the monetary incentive can help an organization by implementing
methods to enhance the loyalty of their employees.
1.4 Summary of Remaining Chapters
This chapter addresses the importance of determining the need to find if monetary
incentives really affect goal attainment with loyal employees. The remainder of this
dissertation outlines how this question will be researched.
Chapter II, the literature review, concentrates on how the relevant literature has
addressed monetary incentives. The three major research streams expected utility theory,
agency theory and expectancy theory will be shown to influence worker performance to
accomplish goals and attain the incentives. The fourth theory of social exchange theory
will show how employees with locus of control and are loyal to the firm will work hard
for the goal and not rely on a monetary incentive to motivate them. Included in this
literature review will also be significant literature defending the need for this research
contribution.
Chapter III will develop the model and hypotheses which depict how monetary
incentives contributes to increased effort and performance to accomplish the goal and
attain the monetary incentive. The model will also measure how locus of control can
increase goal attainment and that employee loyalty to the firm can act as motivation to
attain the goal without any monetary incentive. Another measurement will be to show
how dishonest behavior influences the monetary incentive to goal attainment relationship
while also showing the effects employee loyalty has on the dishonest behavior variable
on goal attainment.
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Chapter IV will present the method used and the analysis of the data. Chapter V
will be the results and show the results of the hypothesis tests. Chapter VI will be the
discussion and conclusion to restate the problem and summarize the findings. Chapter VI
will also state what these findings contribute to theory, method, and practice.
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CHAPTER II
LITERATURE REVIEW
A focal contribution of this study, noted in Chapter I, is to apply three theories to
understand the effects of monetary incentives on goal attainment. Those three theories are
expected utility theory, agency theory and expectancy theory. An additional related
theory, social exchange theory, is examined to understand the effects of locus of control
on goal attainment. A contribution of this study is a better understanding of the effect
employee loyalty has on the monetary incentive – goal attainment relationship. Another
contribution will be to study the effect employee loyalty has on the monetary incentive dishonest behavior relationship. This chapter reviews the previous research literature that
has investigated monetary incentives, goal attainment, employee loyalty, locus of control,
and dishonest behavior. The first section reviews theories used in monetary incentive
research, such as agency theory, and expected utility theory as well as expectancy theory.
The purpose of this section is to bring to light the accumulated knowledge found in
monetary incentive research and show why there is a need to extend the research on
monetary incentives.
How valuable are monetary incentives to enhance goal realization? This
question’s answer is expanded on by measuring the locus of control of employees to the
goal attainment relationship. In addition, measuring employee loyalty to see the effect
that employee loyalty has on moderating the goal attainment and dishonest behavior
relationships with monetary incentives. These measurements will help analyze the impact
that monetary incentives really have on goal attainment. The analysis will help determine
13
how valuable employee loyalty can be and the effect employee loyalty has on goal
attainment when measured with the monetary incentives and dishonest behavior
relationships.
The second section reviews monetary incentive literature as it relates to goal
attainment. The purpose of this section is to bring to light the limited research and studies
we have on understanding the impact of locus of control and loyal employees on goal
attainment. Overall, this review shows a need to further understand and study the effects
of monetary incentives on goal attainment and the effects of employee loyalty.
2.1 Goal Attainment Theories
For monetary incentives to be accepted as a positive force in goal attainment,
performance improvement must be measured by results or outcomes instead of a process
or an input. The industry must be one in which there are outputs or deliverables that
increase firm profit, such as the financial industry. For example, industries that have
resistance to change or has constraints to change like government entities, monetary
incentives or performance related pay can fail (Spano & Monfardini, 2018).
Employees perform better if they possess the necessary knowledge and skills,
desire to do the work, and are adequately motivated (Ponta et al., 2020). The aspect that
makes it hard to interpret the relationship between incentive and performance is linked to
the identification of the contribution of individual employees (Ponta et al., 2020).
According to Ponta et al., (2020), it is known that economic motivation is one of
the most used stimuli to improve the firm’s performance. Focusing on the relationship
between monetary incentives and employees’ performance, one of the most important
14
aspects is performance related pay. Employees work harder if they value the monetary
rewards and trust that those awards are related to their increased efforts.
Expected Utility Theory (EUT). Expected utility theory (EUT) states that
decision makers (DM) choose between risky or uncertain prospects by comparing their
expected utility values (Mongin, 1997). Early theoretical developments in economics
advocated that individuals make decisions by ranking the choices based on perceived
utility and select the one that provides the most usefulness (Stigler, 1950). Researchers
went back to the 1700s and found from that era that individuals place subjective values
(utilities) on monetary outcomes and the value of a gamble is the expectations of these
utilities (Yaqub et al., 2009).
Expected utility theory (EUT) generalized this concept of utility maximization
under the microeconomic paradigm of rationality and served as a foundation to model
decision-making with risky choices or uncertain outcomes (Mongin, 1997). Apart from
the assumption of a rational economic agent who makes consistent decisions by
objectively valuing the expected utility of probable outcomes, EUT has been
academically evaluated as both a “positive” and a “normative” theory (Mongin, 1997).
Therefore, even if it is primarily known as a descriptive theory based on observations –
“How do individuals actually make decisions,” and consequently applied to make
predictions; economists have appraised the EUT as prescriptive, “How should individuals
ideally make decisions” (Sapre, 2021).
There is a longstanding debate regarding the effectiveness of financial incentives
in improving work performance (Robinson & Farkas, 2021). Robinson and Farkas (2021)
continued to find in their study that task attributes and personal preferences moderate the
15
effects of monetary incentives on performance. Thus, monetary incentives may have a
positive effect on goal attainment, but the incentive-performance relationship may have a
bigger impact on job tasks, compensation and hiring ability.
Yaqub et al. (2009) explains the origin and history of the EUT very well. In the
17th century, the efforts to describe rational behavior in mathematical terms ran into
problems with the discovery of the St. Petersburg paradox. Daniel Bernoulli and Gabriel
Cramer were the first to solve and explain the St. Petersburg paradox in 18th century. The
solution to this paradox revealed that the value of a gamble is not, in general, equal to its
expected value. Individuals place subjective values (utilities) on monetary outcomes and
the value of a gamble is the expectations of these utilities (Bernoulli, 1738/1954). Since
Bernoulli’s statement (or theory) was based on a cardinal (interval) utility scale, it failed
to attract much attention from economists until the 1950s when economists were more
interested in developing ordinal theories. John von Neumann, a German-Hungarian
mathematician, and Oskar Morgenstern, an Austrian economist, were the first scientists
who formally proved that EUT was a viable theory for rational decision making and that
it can be derived from certain axioms. They propounded that any “normal” preference
relation over a finite set of states can be written as an expected utility. That is why it is
also called the von Neumann-Morgenstern utility. The theory became important as it was
developed shortly after the Hicks-Allen “ordinal revolution” of the 1930s; and it revived
the idea of cardinal utility in economic theory. Although the economic and simple
tractability of EUT cannot be criticized, a crucial question is whether it provides a
sufficiently accurate representation of actual choice behavior.
16
Agency Theory. In the economic perspective, agency theory assumes that agents
are self-interested and risk averse and recommends monetary incentives as a means of
motivating increased effort and improvements in performance (Eisenhardt, 1989).
Agency theory views the firm as a complex nexus of contracts where authority to perform
specialized tasks is delegated by the principal (typically managers) to agents (the
employees) with the expectation that the agent will act in the best interest of the firm
(Eisenhardt, 1989). However, there are numerous studies where the principals (managers)
may be acting in their own personal best interest (i.e., to get a monetary incentive for
themselves) and thus can affect the agent’s (employee) performance and their actions
may not be in the best interest of the firm. For agency theory to be shown as a factor in
monetary incentives, both parties must be acting in the best interest of the firm.
Expectancy Theory. Expectancy theory posits that financial incentives will illicit
increased effort only if the individual believes that there is a high probability of receiving
increased results from expending more effort (Vroom, 1964). Vroom’s (1964)
instrumentality theory represents the first attempt to use cognitively oriented assumptions
as the basis for a general theory of work motivation. Vroom defines motivation as the
“force” impelling a person to perform a particular action, as determined by the interaction
of (a) the person’s expectancy that the act will be followed by a particular outcome, and
(b) the valence of that outcome whereas an outcome is positively valent when the person
prefers attaining it to not attaining it. Khan et al. (2020) expanded on Vroom’s
expectancy theory to closely relate to modern era and suggests that the behavior will
develop certain attitudes among employees and that will lead to actions. Lawler and
Suttle (1973) further expanded on Vroom by explaining in their research that the
17
expectancy model of effort leading to successful performance and the expectancy that
this action will produce outcomes or results. Their expectancy model defines motivation
as a function of the combination of the following variables: the perceived likelihood that
effort toward a behavioral or task goal will lead to the successful accomplishment of that
goal, the likelihood that the successful accomplishment of the goal will result in securing
outcomes or rewards, and the valence of these outcomes.
Lawler and Suttle (1973) further explain in their research that job behavior is a
joint function of ability, role perceptions and motivation. Also, that expectancy theory
proposes a causal relationship between expectancy attitudes and motivation. Further
explained by Lawler and Suttle (1973) is that there needs to be better measures for
motivation.
Theoretically, monetary incentives work by increasing effort, which, in turn, leads
to increases in performance. It is critical to determine how to maximize the effectiveness
of monetary incentives. Expectancy theory describes in detail the mechanism through
which monetary incentives are presumed to lead to increases in effort (Bonner &
Sprinkle, 2002). In expected utility theory, the employee will receive a reward for
accomplishing the task. In expectancy theory the employee will see an increased result
for their increased performance (Bonner & Sprinkle, 2002).
Based on expectancy theory, Robinson and Farkas (2021) concluded that
individual perceptions of job characteristics and performance feedback jointly affect
effort and performance and they provided evidence in their study that decline in
performance is associated with dissatisfaction with prior performance. An easier
explanation for expectancy theory is that the employee will be more motivated to perform
18
and accomplish the goal and therefore attain the monetary incentive if there is a positive
relationship between their effort and their performance to attain that monetary incentive.
Decline in performance can be attributed to their personal perception. If one
attains the monetary incentive but the performance was tedious or viewed by the
employee as dissatisfactory, then the continuance of that performance to attain further
monetary incentives may be viewed as not worth the effort based on their personal
perception.
The use of financial incentives and, in particular, monetary incentives, is based
primarily on the theoretical propositions of expectancy theory, whereas expectancy
theory is an analytical tool to study how a reward system motivates the employees (Ponta
et al., 2020). Employees modify their behavior based on their estimation of anticipated
outcomes and the motivation for work is related to a series of causes and effects: first,
persons should trust that their increased efforts will translate into a better performance;
second, a satisfactory performance should lead to a wanted reward; third, the reward
should fulfill a significant need for the individual. Finally, the desire to fulfill this need
should be strong enough to make the initial efforts worthwhile (Ponta et al., 2020).
2.2 Monetary Incentives
Monetary incentives are rewards given to employees or individuals for excellent
performance or attaining their goal. They are a management tool to motivate employees
and increase performance levels (Khan et al., 2020). Examples include a bonus, salary
increase, commission or paid time off. These monetary incentives are used to motivate
workers to increase performance. Motivating workers is a crucial dimension of labor
19
relationships that has been studied at length (Corgnet et al., 2015). In the economic
literature, the principal-agent paradigm has emphasized the importance of monetary
incentives as the most effective way to induce workers to exert effort (Corgnet et al.,
2015).
Corgnet et al. (2015) explain in their literature review that specific and difficult
goals, perceived as attainable, lead to greater performance than vague and easy goals.
Second, workers are more motivated or more committed to attain goals when they
perceive their goals as being relevant and difficult to attain. Finally, goals are shown to
increase workers’ persistence to exert effort. Corgnet et al. (2015) references Locke and
Latham’s (2006) research on goal setting and expand on the incentives used to attain
those goals. If the goals are relevant to the employees, the employees will increase effort
to attain the goals and incentives (Corgnet et al., 2015).
Gneezy and Rustichini (2000) found in their research that although performance
increases with significant monetary compensation, small monetary incentives may
undermine performance compared to a situation with no compensation at all. Pokorny
(2008) reported experimental evidence that very high monetary incentives can also
decrease performance as that research showed an increase in lying to attain the goal.
Thus, it is not only the case that low rewards can do worse than no rewards at all; very
high incentives may also have a detrimental effect on workers’ motivation (Corgnet et al.,
2015).
Robinson and Farkas (2021) found that motivation and performance are also
grounded in economic agency theory, which assumes that agents are self-interested and
risk adverse; they recommend monetary incentives as a means of motivating increased
20
effort and improvements in performance. They continued their research to show that the
expectancy theory posits that financial incentives will elicit increased effort only if the
individual believes that there is a high probability of receiving rewards from expending
more effort.
Every experienced executive knows the importance of rewarding good
performance and how difficult it is to design an incentive system that works. (Locke,
2004). Locke further explains that the original idea of monetary incentives, or pay for
performance, is to foster performance by making rewards contingent on employee
performance (Locke, 2004). In order to provide a significant monetary incentive to
accomplish the goals, there must be a difference between the reward for attaining the goal
and not attaining the goal. This leaves no ambiguity about what is required of the person
to receive the significant bonus (Locke, 2004). In Nnubia’s (2020) work, research data
demonstrated that it is obvious that monetary incentives play a vital role in increasing
employee performance. This is in line with the view of Jack Welch who stated, “If you
pick the right people and give them the opportunity to spread their wings – and put
compensation and incentive as a carrier behind it – you almost don’t have to manage
them” (Nnubia, 2020, p. 15).
However a 2003 Wall Street Journal article stated that in 83 percent of companies
with a monetary incentive system, the monetary incentive system did not work at all or
was only somewhat successful (Locke, 2004). Locke (2004) researched stretch goals with
a strong incentive to reach a goal. However, he also found that there was considerable
temptation to think short term, and therefore create short cuts. It has been said that it is
better to have no incentive system at all, than to have a bad one (Locke, 2004). Bad
21
incentive plans encourage people to do wrong things in the wrong way and goals should
be set for the desired outcomes (Locke, 2004).
Another issue with the level of monetary incentive attributed to the goal is that the
level of incentive can create and increase competition. This competition can significantly
increase dishonest behaviors as employees strive to attain the monetary incentive and
recognition. Recent studies and reviews on the topic of monetary incentives for goal
attainment have concluded that financial incentives are positively related to performance
(Glaser et al., 2017). These studies have also noted that the underlying belief of these
monetary incentive programs is that they only produce positive effects.
In Glaser et al. (2017), researchers studied the size of the incentive and whether
the size or amount of the incentive spurred more intense aggression to attain the
incentive. Their study found that the incentive is related to more negative interpersonal
behavior and aggression. They concluded that pay for performance, or monetary
incentives, increases competitiveness and therefore increases the likelihood of unhealthy
competitive behavior.
Research also found that in the accounting field, monetary incentives positively
affected motivation and performance for those that did not lack the skill needed (Bonner
& Sprinkle, 2002).
In their findings, Daramola and Daramola (2019), found that monetary incentives
were a motivational factor for performance. Their study showed the employees should be
included in the discussion of what the motivating incentives should be. Nnubia (2020)
conducted their research on a manufacturing firm and the study revealed a positive
association between salary, wage, commission, and job performance.
22
Ponta et al., (2020) conducted their research on a public administration company
and determined that monetary incentives have a positive impact on employee
performance. Khan et al. (2020) found in their study of hospital staff that monetary
incentives and job performance are positively related to each other. Furthermore, their
findings suggest that there is a positive association between monetary incentives and
employee loyalty which indicates that an increase in the monetary incentives, increases
the motivation level of the employees, and makes them loyal to their organization (Khan
et al., 2020).
Al-Belushi and Khan (2017) suggest that organizations need to provide the right
kind of monetary incentive to their employees because it will increase their satisfaction
level and enhance their loyalty towards the organization. Idowu et al. (2019) concluded
that a sufficient incentive package is required to maintain the loyalty of employees and
increase job performance.
2.3 Locus of Control
Locus of control emerged from Rotter’s (1954, 1966) seminal work on social
learning theory. Social learning theory posits that individuals learn by observing the
events that occur around them in a manner that ultimately influences behavior (Rotter,
1954). In this learning process, individuals develop expectancies that specific behaviors
will create particular results (Rotter, 1966). Reinforcement acts to strengthen an
expectancy that a behavior or outcome will be followed by more reinforcement in the
future (Rotter, 1966). According to Rotter (1966), a relatively stable individual difference
(i.e., locus of control) emerges over time that pertains to the extent to which individuals
23
perceive a causal relationship between behavior and rewards. Although some individuals
perceive that their behavior and personal attributes drive outcomes (i.e., an internal locus
of control), others develop a general sense that external forces govern outcomes (Galvin
et al., 2018). Specifically, these unique characteristics shape the extent to which
individuals attribute their ability to achieve future success or failure to their own actions
(Galvin et al., 2018).
Locus of control involves an assessment of the environment and external rewards
(Rotter, 1966; Spector, 1982). In fact, Johnson et al. (2015) concluded that locus of
control may be a fundamental evaluation of the environment rather than the self. As
social learning theory would suggest, a locus of control orientation is acquired and
strongly influenced by childhood experiences (Galvin et al., 2018). Locus of control is a
personality trait that represents the extent to which people believe that the rewards they
receive in life can be controlled by their own personal actions (Wang et al., 2010).
Antecedents of locus of control are thought to revolve around early life
influences, including parenting style, socioeconomic circumstances, and childhood
experiences. Social learning theory, which provides the theoretical foundation for locus
of control, asserts that reinforcement alters (i.e., strengthens or weakens) expectancy of
certain behaviors or events (Rotter, 1966; Spector, 1982). Although the exact genesis of
locus of control is not precisely understood, research suggests children begin to interpret
control over their experiences as they consider the source of reinforcement (Galvin et al.,
2018). As social learning theory would suggest, a locus of control orientation is acquired
and strongly influenced by childhood experiences. However, little is known about how
physiological or social factors experienced in one’s youth contribute to the loci
24
experiences as an adult (Galvin et al., 2018). These perceptions result in a generalized
belief regarding “the causal relationship between one’s own behavior and its
consequences,” which ultimately influences the actions of the individual (Rotter, 1966, p.
2). In essence, social learning theory suggests that over time individuals conclude what
type of behavior will lead to desired or undesired results, and develop expectations of
future exchanges (Galvin et al., 2018).
Locus of control, a concept that has long played a role in psychology, is among
the fundamental personality characteristics economists examine (Heywood et al., 2017).
Heywood et al. (2017) further expands on locus of control by stating those individuals
with more focus believe that investing in human capital has a higher return. Thus, such
individuals perform better in school and are more likely to complete high school and
attend college. They are also more likely to make long-term investments in personal
health (Heywood et al., 2017).
Motivation largely depends on this perception of the extent of control. If
individuals believe that they cannot produce the desired effects, they have virtually no
motivation to put forth effort (Bandura, 1991). Thus, as important as incentives can be,
they need not be synonymous with motivation (Cobb-Clark, 2015).
From a psychological viewpoint, workers’ decisions reflect more than the
extrinsic motivation to earn more money (Heywood et al., 2017). Workers are also
intrinsically motivated by the need to feel competent. Workers derive feelings of pride,
self-worth, and self-esteem from successfully accomplishing tasks and achieving goals
even if wage goals are irrelevant (Heywood et al., 2017). Heywood et al.’s (2017) study
that recognized basic personality traits as drivers of economic choices has been hailed as
25
an important addition to the perspective of labor economists and others interested in
worker behavior. Among these traits, seen as largely fixed in grown adults, locus of
control seems central for understanding the sorting of workers across jobs.
Early scholarship demonstrated that locus of control was a key predictor of
various work‐related outcomes, ranging from job attitudes and affect to motivation and
behavior (Galvin et al., 2018). In organizational settings, rewards or outcomes include
promotions, favorable circumstances, salary increases and general career advancement
(Spector, 1988).
In addition to its role as an antecedent, research literature also posits locus of
control as a moderator. Extant work reveals that one’s locus orientation can alter the
effects of both personal and environ-mental factors (Galvin et al., 2018). For instance, the
influence of workload on stress and organizational constraints on counterproductive
behavior is higher among individuals with high locus of control (Galvin et al., 2018).
Strong intrinsic motivation characterizes those with internal locus of control, and
reinforcement is central to the effort–outcome link in expectancy theory (Galvin et al.,
2018).
Expectancy value theory (Fishbein & Ajzen, 1975) posits that motivation to
engage in behavior is a function of expectations and valences. Individuals with high locus
of control expect success and that, in turn, motivates their behavior (Eccles & Wigfield,
2002). Locus of control also has a strong, positive relationship with other work‐related
attitudes, such as organizational satisfaction and affective commitment (Wang et al.,
2010). This construct captures an individuals’ sense of control over work‐related
26
outcomes. Work locus of control generally predicts organizational‐related outcomes more
effectively than general locus of control (Wang et al., 2010).
Locus of control positively relates to motivation-related variables, including
psychological empowerment, self-efficacy, self-esteem, and intrinsic task motivation
(Johnson et al., 2008). This relates to expectancy theory in goal attainment. Whereas the
motivation to work harder and attain the goals and engage in behaviors to attain the goals
is also a function of the expectancy theory. Johnson et al. (2015), also found that locus of
control has shown relationships with behavioral outcomes such as job performance, in
addition to positively relating to volitional behaviors (i.e., the act of making your own
decisions without others’ influence). This suggests that locus of control tends to be
associated with positive attitudes, behavior, and well-being in the workplace (Galvin et
al., 2018).
Padmanabhan (2021) conducted a study on locus of control and job satisfaction.
That research was designed to determine if there was a difference between males and
females in addition to other measures, such as absenteeism and the negative effects of
people that do not like their job. There was no significant difference between males and
females concerning locus of control (Padmanabhan, 2021). There was also no significant
difference between males and females for job satisfaction (Padmanabhan, 2021).
2.4 Dishonest Behavior
Dishonest behavior in this study refers to the acts of the employee for their own
benefit. Deviant behaviors are more severe and intentional to harm the organization. That
27
area is not studied in this research. I am only researching the employee that acts
dishonestly for their own benefit to attain the monetary incentive.
Employees are the strength of any organization and organizations with employees
that have dishonest behaviors never last long (Bilal et al., 2020). It is necessary for
management to identify and reduce dishonest behaviors to achieve the goals of the
organization. According to Bennet and Robinson (2000), workplace dishonest behavior
accounts for corporation losses in the billions. Almost daily there are media reports of
dishonest behavior in the workplace. This dishonest behavior may harm the organization
or its members or both.
Individuals tend to overlook morally questionable aspects of goal-directed
unethical behaviors or see them in a positive light when a goal is being pursued
(Melnikoff & Bailey, 2018). Therefore, individuals might be somewhat “immune” to the
negative impacts of these behaviors during the pursuit of a goal (Zhang et al., 2020). The
conventional economic model assumes that people are willing to misreport private
information if the material incentives of acting dishonestly outweigh those of acting
honestly (Gerlach et al., 2019).
The seminal work of Gneezy (2005) reports that people exhibit an aversion to lie,
and that lying comes at a moral cost. The decision to lie is sensitive to a variety of
factors, such as monetary consequences and strength of incentives and intrinsic cost
(Mitra & Shahriar, 2020). Mitra and Shahriar’s (2020) study demonstrate how an
unethical act or dishonest behavior is governed by the interplay between norm and
benefit. Their findings showed that changes in the perception of a descriptive norm of
28
lying counteracts the opposing impact of changes in the benefit from lying (Mitra &
Shahriar, 2020).
Opportunism corresponds to the frailty of motive ‘which requires a certain degree
of circumspection and distrust’ in the transaction cost economics scheme of things
(Williamson, 1993, p. 97). Williamson’s (1993) insistence that opportunism be accorded
co-equal status with bounded rationality does not imply that most economic agents are
engaged in dishonest behavior practices most of the time. Rather, most economic agents
are engaged in business-as-usual, with little or no thought to opportunism for dishonest
behavior, most of the time (Williamson, 1993).
Bradach and Eccles (1989), contend that mutual dependence between exchange
partners promotes trust, which contrasts sharply with the argument central to transaction
cost economics that dependence fosters opportunistic behavior. What transaction cost
economics says, however, is that because opportunistic agents will not self-enforce openended promises to behave responsibly, efficient exchange will be realized only if
dependencies are supported by credible commitments (Bradach & Eccles, 1989).
A traditional incentive scheme works well if the only agency problem comes from
the fact that management dislikes working hard (Williamson, 1993). The reason incentive
schemes may be less effective under these conditions is that a very large incentive
payment may be required to induce management to give up control (Williamson, 1993).
The conventional economic model assumes that people are willing to misreport
private information if the material incentives of acting dishonestly outweigh those of
acting honestly (Gerlach et al., 2019). Also, the majority of those who behave dishonestly
do so only to the extent that they can appear honest (Gerlach et al., 2019). The degree to
29
which individuals engage in dishonest behavior largely depends on personal and
situational factors (Gerlach et al., 2019). Gerlach et al. (2019) showed that the degree and
direction of dishonest behavior depends on situational factors and on personal factors.
Examples of those factors can be personal medical expenses, holidays, or wanting a
luxury item.
In Pascual-Ezama et al. (2013), found that to reduce the cost of supervision and
encourage workers to actively contribute, organizations often distribute incentives.
Financial incentives are commonly used in the labor market, and the effect of those
incentives varies depending on the type of task (Pascual-Ezama et al., 2013). However,
incentives not only have effects on subjects’ motivation and performance but also on
cheating (Pascual-Ezama et al., 2013). According to Nagin et al. (2002) employees are
“rational cheaters.” They anticipate the consequences and firms respond with monitoring
and incentive systems. Employees have many incentives for being dishonest. For
example, bank sales employees should advise customers how to best invest their money.
However, bank employees have sales targets that will affect the quality of their advice to
increase sales and, in turn, their salary (Pascual-Ezama et al., 2013).
According to Pascual-Ezama et al. (2013), with respect to the level of cheating
and considering that there are different types of cheaters, the Theory of Self-Concept
Maintenance suggests that workers typically solve this motivational dilemma adaptively
by finding a balance or equilibrium between two motivating forces: financial incentive
and positive self-concept. In this equilibrium, workers can derive some financial benefit
from behaving dishonestly (but not too dishonestly) and still maintain their positive selfconcept in terms of being honest individuals. Pascual-Ezama et al. (2013) also found in
30
their study that the lack of supervision contributed to an increase in dishonesty as did the
lack of motivation in workers. They continued to explain that when there were highly
supervised workers, the dishonesty was very low. They also discovered that workers who
had a lack of motivation in their job were highly dishonest.
In Pascual-Ezama et al.’s (2013) conclusion, their results show the importance of
a firm’s hiring procedure. Companies should place a high value on hiring employees who
display a preference for the tasks they would need to complete during their job duties.
They also concluded that because the performance of motivated employees ultimately
benefits the organization, human resource professionals should understand and support
antecedents that empower their most capable employees. In ending their conclusion, they
surmised that monetary incentives could modulate the amount of cheating and in some
cases can increase dishonesty.
Performance or ability is rewarded by companies, the means of achieving these
targets are often imperfectly monitored for the employees and provide a scope for
dishonest acts on the part of the employee (Kaushik et al., 2022). A rational individual
would commit a crime as long as the marginal benefit exceeds the marginal cost of
committing that crime and will cheat to the maximum extent possible (Kaushik et al.,
2022).
Kaushik et al. (2022) found in their study that participants who were paid
monetary incentives on average cheated by an amount significantly different from zero.
Participants in their study were overall more dishonest when there was an independent
payment involved. Also in their study, they found that individuals were not motivated to
cheat when there was no financial reward for the goal. Mazar et al. (2008) found that an
31
individual’s desire to not appear unintelligent compared to their co-workers also drove
dishonest behavior.
Increasing economic satisfaction increased the magnitude of dishonesty in a study
by Kaushik et al. (2022). Different levels of economic satisfaction showed a change in
the magnitude of dishonesty; the level of dishonesty increased when increasing the level
of economic satisfaction (Kaushik et al., 2022). Based on their study, that result will
relate to an increase in goal attainment based on an increase in opportunity for dishonest
behavior as the monetary incentive increases.
Kaushik et al. (2022) concluded that when the threat of detection was zero (or
very marginal) and there was an incentive, the level of cheating increased higher than the
level of no incentive. They also concluded that decreasing the probability of detection of
cheating to zero significantly increases the magnitude of cheating for the individuals that
are paid the financial incentive.
In Le Maux et al. (2021), their study analyzed both theoretically and empirically
how monetary incentives affect dishonesty. Dishonesty is a major concern in modern
societies. Dishonesty is at the heart of the principal-agent problem, and can cause
substantial damage by eroding trust, creating uncertainty, reducing efficiency, and being
harmful for collaboration (Le Maux et al., 2021). A study by Kajackaite and Gneezy
(2017) showed that dishonesty increases with payoffs (incentives) increasing further
increases when the concern about being exposed as a liar or cheat are removed. Le Maux
et al. (2021) varied the amount of incentive in their study. They also observed and
measured the dishonesty on an individual level. They concluded from their study that
dishonesty rises over time in all payoff amounts (Le Maux et al., 2021).
32
Honesty is a moral virtue that is consistently endorsed as desirable across
societies, yet we choose not to be honest from time to time (Mitra & Shahriar, 2020). The
dilemma arises because dishonesty often serves our self-interest at the expense of our
morality and may even cause material loss to others and our decisions that involve moral
judgement are greatly influenced by social norms (Mitra & Shahriar, 2020).
In Mitra and Shahriar (2020), they studied the decision-maker’s propensity to lie
and the attributes that influenced that decision. They studied how monetary incentives
influenced lying and the perception of the descriptive norm of lying (i.e., the belief about
how likely others are to lie in that same situation). Specifically, they studied how lying
behavior is influenced by two simultaneous and potentially counteracting changes: the
benefit from lying is lowered (raised), and the decision-maker is intervened to believe in
a higher (lower) propensity to lie among peers (Mitra & Shahriar, 2020). Mitra and
Shahriar (2020) indicated from their study that a law-and-order policy aimed at curbing
dishonest behavior via reducing incentives may not be sufficient when a society or
organization operates under the norm of a high level of dishonesty.
Kajackaite and Gneezy (2017) did a study on how cheating behavior is affected
by incentives. The economic consequences of cheating are large, and hence,
understanding the factors that influence the decision to lie is important in order to
understand many economic behaviors (Kajackaite & Gneezy, 2017). Their study was to
better understand how the size of the incentive affects behavior. As incentives were
increased, the lying increased as it became more beneficial (Kajackaite & Gneezy, 2017).
In Kajackaite and Gneezy (2017), they showed that when they modified the
standard to eliminate the concern about being exposed as a liar, the participants react to
33
the incentives to lie. Their results showed that incentives increase cheating and more so
where the concerns about being exposed are eliminated. When benefit from lying
increases, more people choose to lie (Kajackaite & Gneezy, 2017). Their findings have
important implications for constructing policies to reduce cheating, such as when people
are less concerned about the chance of being exposed, it might make sense to reduce the
incentives to cheat (Kajackaite & Gneezy, 2017).
2.5 Employee Loyalty
Employees are considered the core of an organization and the success or failure of
the organization is attributed to the performance of the employees (Murali et al., 2017).
Loyalty can be defined as “a strong tie that binds an employee to his/her company even
when it may not be economically sound for him/her to stay there” (Logan, 1984).
Therefore, it is important for the organization to attract and retain their talented
employees as talented employees influence the performance of the organization in a
positive manner (Khan et al., 2020).
In this study, employee loyalty is being researched as an individual construct for
the sake of the employee being loyal to the company. Organizational commitment is a
separate construct and not being researched in this study. This study is researching the
individual employee’s loyalty to the firm and other employees for the sake of their
motivation to attain the goal. This study is not using organizational loyalty as a construct
either. This study is only researching the employee’s individual loyalty to the firm as
their motivation to attain the goal.
34
The economic performance of organizations is becoming ever more dependent on
the participation, commitment, and, more generally, loyalty of their employees (Murali et
al., 2017). Loyalty has become one of the vital concerns for organizations, especially in
the context of the economic tensions related to the “psychological contract” between
employers and employees (Murali et al., 2017). Employee loyalty is a deliberate
commitment to further the best interest of one’s employer, even when doing so may
demand sacrificing some aspect of one’s self-interest beyond what would be required by
one’s legal and other moral duties (Elegido, 2013). An employee’s loyalty to the
occupation, his/her emotional investment, and the regularity of his/her commitment to the
organization, are key factors that determine the longevity and the performance of
organizations (Bakker & Schaufeli, 2008). The quality of employees, their competencies,
loyalty, and commitment are extremely important for business performance achievement
(Antoncic & Antoncic, 2011).
According to Antoncic and Antoncic (2011), long-term business objectives of the
company can be achieved when employee loyalty can be established. In practice, it often
happens that the employer expects or requires its employees to be loyal but fails to
provide a positive atmosphere at work; such attempts to obtain loyalty can be almost
always far from successful. The main objective of creating the environment of employee
loyalty is to achieve a situation in which employees will knowingly and without coercion
become committed, accept responsibilities, and pursue them at their own best efforts. To
achieve employee loyalty in the company, a company must take good care of employees
(Antoncic & Antoncic, 2011). A sense of belonging is associated with confidence and
accepting the objectives and values of the firm and it is accompanied by the employees’
35
willingness and commitment to the efforts (Antoncic & Antoncic, 2011). Employee
loyalty can contribute to greater efficiency, better business results, firm growth, reduced
employee turnover, etc. Loyal employees also contribute to the creation of the image that
the company has toward its environment and outside stakeholders, such as customers.
The trust of employees in the company very importantly defines the employee welfare at
work and satisfaction with work. Internal service quality can influence employee
satisfaction, employee loyalty and productivity. Employees are key to achieving the
internal quality of service in the company and hence for the business results of enterprises
(Antoncic & Antoncic, 2011).
Hart and Thompson (2007) elaborate in their study that in the workplace,
individuals must adopt a fluid approach to assessing the demands to be loyal as contexts
and relationships change. Without addressing this fluidity, it is difficult if not impossible
to predict behavioral or attitudinal outcomes of people’s perceptions that others have
been loyal or disloyal (Hart & Thompson, 2007).
Kyle LaMalfa (2007) pointed out that employers need to understand why
employees are emotionally connected to the business. That connection is usually much
more than salaries, training, or benefits. Research has shown that emotionally connected
employees are the best employees as they are engaged and productive, and they feel
authorized and appreciated (Murali et al., 2017). Employee loyalty is critical for
organizations as continuous turnover can be very expensive (Reichheld, 2006). If
employees feel like the organization is listening to them, recognizing them for their
contributions, they will more likely be loyal to the company (Murali et al., 2017).
36
In Murali et al.’s (2017) study, they were able to expand on how loyalty acts as a
key factor in performance evaluation of an employee and sees loyalty from an
employee’s perspective. Employees in general with increasing age consider themselves
loyal to their organization. Compared to the employees in the lower age band those in the
higher age band are more satisfied working for their current company.
Murali et al.’s (2017) concluded that organizations that have long time employees
understand the need for employee loyalty to the organization. Examples they found in
organizations showed that individuals who are single or are not settled with their family
in a particular city would be more likely to change their current job when they get better
job prospects elsewhere. This was rarely seen in the employees who are married and are
settled in a city (Murali et al., 2017). They further elaborated that from the employee’s
perspective, loyalty is seen as a factor which may not necessarily bring them monetary
benefits but would make the drawbacks in them (Murali et al., 2017).
Pandey and Khare (2012) found in their study that employees are a vital resource
for nearly all organizations, especially since they represent a significant investment in
terms of locating, recruiting, and training let alone salaries, healthcare plans, bonuses, etc.
The longer an employee works for a company the more valuable they become. They
continued in their findings that employee loyalty to the organization has sometimes been
viewed as an attitude. However, it is not so much an attitude (or thought component) that
is important in organizations, but rather it is the bottom-line action component (Pandey &
Khare, 2012).
Thus, organizations need to invest in employees and enhance their performance
through higher level of motivation and commitment (Khan et al., 2020). Organizations
37
need to include financial incentives and rewards such as profit sharing, bonus, promotion,
and stock ownership in their strategy to maintain the relationship and increase their
employee’s loyalty (Saleem, 2011).
Employee loyalty is the relative strength of an individual’s identification with and
involvement in a particular organization (Mowday et al., 1982). According to Mowday et
al. (1979), this behavior can be characterized by three related factors — strong belief and
acceptance of the organization’s goals and values, a willingness to exert considerable
effort on behalf of the organization, and a strong desire to maintain membership in the
organization.
Satisfied employees will become loyal when they perceive their organization as
offering the opportunities to learn, grow, and at the same time providing a clear
established career path they can pursue in the organization (Pandey & Khare, 2012).
Training and development are the biggest factors that lead to employee loyalty and to be
committed employees look forward to the opportunities of continuous learning to
improve their skills and knowledge (Pandey & Khare, 2012).
Benefits of employee loyalty include trust, identification, commitment,
participation, and attachment. Other benefits recently have included increasing profit,
improving quality, reducing turnover, and increasing reputation of the organization (Khan
et al., 2020).
Today employers are more inclined towards employee loyalty because of its
linkages with attendance and organizational citizenship and are frequently searching for
new methods of promoting loyalty and as a result, employee loyalty decreases the
turnover and old approaches are no longer efficient (Khan et al., 2020). Loyalty is
38
dependent upon the satisfaction of the employee which means that satisfied employees
are more loyal than dissatisfied employees and loyalty is about showing pride in an
organization and not complaining about the organization (Khan et al., 2020).
39
CHAPTER III
MODEL DEVELOPMENT AND HYPOTHESES
In the previous chapters, I discussed the theories that affect goal attainment. The
four antecedents discussed were monetary incentives, locus of control, dishonest
behavior, and employee loyalty. Goal attainment is affected by these four antecedents. I
posit that once a goal is in place, monetary incentives increase goal attainment. My study
researches the effect of employee loyalty and locus of control on goal attainment. My
study addresses how monetary incentive increases goal attainment and by showing how
employee loyalty affects that monetary incentive to goal attainment relationship. Finally,
I investigate how employee dishonest behavior impacts goal attainment. My research
posits that dishonest behavior positively mediates the monetary incentive to goal
attainment relationship.
Monetary incentives increase goal attainment based on the research in the
literature review. I argue that a monetary incentive increases goal attainment but that this
effect will be reduced when employees are highly loyal. Thus, I hope to show how
employee loyalty negatively affects the monetary incentive to goal attainment
relationship. Monetary incentives may also increase the likelihood that employees will be
dishonest. This dishonest behavior could result in fraudulently attaining the goal.
However, I posit that employee loyalty attenuates the monetary incentive to dishonest
behavior relationship to. This study hopes to show how employee loyalty positively
affects goal attainment and negatively affects dishonest behavior as well as how
important locus of control is for goal attainment.
40
My model, as shown in Figure 1, posits that adding a monetary incentive
increases the likelihood of goal attainment. My model also shows that locus of control
has a positive effect on goal attainment directly without the influence of a monetary
incentive. My model also shows that a monetary incentive has less of an effect on goal
attainment with loyal employees. Additionally, the monetary incentive has a positive
influence on dishonest behavior to attain the goal and therefore that dishonest behavior
has a positive effect on goal attainment. Lastly monetary incentives have a minimal effect
on dishonest behavior when employee loyalty is high. Hypotheses are developed to show
these variables influences on the goal attainment relationship. My variables and the
contribution of these variables are explained below.
Figure 1. Research model of hypothesized relationships showing the influences of the
variables on goal attainment.
41
3.1 Locus of Control
Locus of Control stems from the social learning theory (Rotter, 1954). Social
learning theory posits that individuals learn by observing the events that occur around
them in a manner that ultimately influences behavior (Rotter, 1954). Locus of control is a
fundamental individual difference variable that reflects individuals’ beliefs about the
degree of control they have over events in their lives (Galvin et al., 2018). Locus of
control has been conceptualized as “the extent to which people believe that the rewards
they receive in life can be controlled by their own personal actions” (Wang et al., 2010, p.
761) and captures whether individuals “attribute the cause or control of events either to
themselves or to the external environment” (Spector, 1982, p. 482).
Using this concept of locus of control, people with high locus of control work
hard enough on their own to attain the goals set by management. According to Galvin et
al. (2018), locus of control positively relates to motivation. These employees with high
locus of control want to be successful and are self-motivated and work harder to attain
the goals. Thus:
Hypothesis 1: Locus of control is positively associated with goal attainment.
3.2 Monetary Incentives
As previously mentioned, monetary incentives are rewards in the form of bonus,
raise, commission or financial gain given to individual employees for excellent
performance or for attaining their goal through money (Ponta et al., 2020). Thus,
monetary incentives have value to increase performance and effort to attain the goal.
Monetary incentives for employees are studied under expected utility theory (Bonner &
42
Sprinkle, 2002). Expected utility theory (EUT) posits that the employee chooses between
risky or uncertain prospects by comparing expected utility or outcomes (Mongin, 1997).
These monetary incentives are used to motivate workers to increase effort. Motivating
workers is a crucial dimension of labor relationships that have been studied at length
(Corgnet et al., 2015). The prime purpose of a monetary incentive is to motivate the
employees and encourage them to excel in their job performances (Al-Belushi & Khan,
2017).
According to Nnubia (2020), modern corporate organizations have deemed it
imperative to incorporate an effective monetary incentive plan for workers as part of their
corporate goals and objectives. This is believed to shape a work force focused on
strategic performance goals with the capability of achieving them. This positively and
significantly influences the overall corporate performance. The prime purpose of a
monetary incentive towards successful accomplishment is to motivate employees and
encourage them to excel in their job performances (Al-Belushi & Khan, 2017). Thus:
Hypothesis 2: Monetary incentive is positively associated with goal attainment.
3.3 Organization Employee Loyalty
Employees are considered the core of an organization and the success or failure of
the organization is attributed to the performance of the employees (Murali et al., 2017). It
is of prime importance that the employees are loyal to the organization and don’t actively
search for other alternative opportunities (Pandey & Khare, 2012). Loyalty, as a general
term can be defined as a person’s devotion or sentiment of attachment to a particular
43
object, which may be another person or a group of persons, an ideal, a duty, or a cause
(Murali et al., 2017).
A loyal employee works hard for the company to attain the company goals.
Employee loyalty “is a deliberate commitment to further the best interests of one’s
employer, even when doing so may demand sacrificing some aspect of one’s self-interest
beyond what would be required by one’s legal and other moral duties” (Elegido, 2013, p.
497). The more loyal an employee is, the more that employee believes in the vision,
culture and values of the company and continues to work hard and attain the goals.
Loyalty towards one’s employer can increase an employee’s motivation to work
(Elegido, 2013). I argue that employee loyalty moderates the monetary incentive to goal
attainment relationship, such that monetary incentive will not mean as much when
employees are loyal simply because the loyalty towards the organization will replace the
need for the monetary incentive. This would be because the monetary incentive does not
have a significantly increased effect on goal attainment for loyal employees. Using
motivational techniques is common practice and this study posits that the more loyal the
employee the less the monetary incentives matter to attain the goal. Thus:
Hypothesis 3: Employee loyalty negatively moderates the monetary incentive to
goal attainment relationship such that when employee loyalty is high, monetary
incentives have less of an impact on goal attainment.
3.4 Dishonest Behavior
Dishonest behavior is a major concern in society (Le Maux et al., 2021).
Dishonest behaviors include cheating, fare-dodging, questionable research practices, tax
44
evasion, misrepresentation of output at work, and misuse of public funds amongst other
fraudulent activities (Le Maux et al., 2021). The economic consequences of cheating are
large, and hence, understanding the factors that influence the decision to lie is important
to understand many economic behaviors (Kajackaite & Gneezy, 2017). People increase
their level of acceptable dishonest behavior if presented with the right incentive (PascualEzama et al., 2013).
Regarding incentives, both economic and social incentives motivate employees
who enjoy their job and are more efficient, while at the same time, it seems that
employees increase their band of acceptable dishonest behavior if presented with the right
incentive (Pascual-Ezama et al., 2013). In short, this shows that people are more likely to
do something dishonest if money is involved and the higher the amount of money, the
more likely this type of behavior becomes. Thus:
Hypothesis 4: Monetary incentive is positively associated with dishonest
behavior.
However, this dishonest behavior is not without purpose. Its purpose is to attain
the goal in order to receive the monetary incentive. When an individual is determined to
achieve a certain goal, the motivation to act morally may be temporarily inhibited as
mental resources are diverted from moral standards to goal attainment (Barsky, 2008).
When the benefit from dishonest behavior increases, more people choose to act
dishonestly (Kajackaite & Gneezy, 2017).
A person commits an offense if the expected utility to him exceeds the utility he
could get by using his time and other resources at other activities. Some persons become
45
“criminals,” therefore, not because their basic motivation differs from that of other
persons, but because their benefits and costs differ (Becker, 1968).
A rational individual would commit a crime as long as the benefit exceeds the
cost of committing the crime (Kaushik et al., 2022). People are willing to act dishonest if
the incentives to act dishonest outweigh those of acting honestly (Gerlach et al., 2019).
Therefore, when a monetary incentive is added to help motivate employees to attain the
goal, there will be dishonest behaviors. Thus:
Hypothesis 5: Dishonest behavior increases goal attainment.
Individuals tend to overlook morally questionable aspects of goal-directed
unethical behaviors or see them in a positive light when a goal is being pursued
(Melnikoff & Bailey, 2018). Therefore, they may be somewhat “immune” to the negative
impacts of these behaviors during the pursuit of a goal (Zhang et al., 2020). Thus, it
seems clear that while performance is rewarded, the means of achieving those
productivity targets are imperfectly monitored and provides an opportunity for dishonest
acts (Kaushik et al., 2022).
Performance pressure creates an expectation to perform at a certain level, and
when this level cannot be met, it produces a conflict between what one is able to do
(performance) and what one is expected to do. This conflict can be resolved by lying
(Grover, 1993) or other dishonest behaviors. It seems reasonable that performance
pressure and reward should support one another. Reward or performance pressure alone
might be enough to lead some people to lie, and when combined, they provide even
greater impetus to lie (Grover, 1993). Thus, the higher the monetary incentive as a
46
reward, the greater the pressure to achieve it. If dishonest behavior can attain that reward,
then:
Hypothesis 6: Dishonest behavior mediates the monetary incentive to goal
attainment relationship.
However, when employees are very loyal, dishonest behavior may not be as
prevalent. People view deceit as unethical and morally wrong, especially when it benefits
the liar at the expense of others (Hildreth & Anderson, 2018). Employee loyalty is a
significant attribute to gain a competitive advantage in any organization (Dutta & Dhir,
2021).
Employee loyalty should also moderate the monetary incentive to opportunity for
dishonesty relationship negatively. High employee loyalty acts like an ethical brake and
as a monetary incentive is used to attain the goal, employee loyalty decreases the
monetary incentive to opportunity for dishonest behavior relationship.
A loyal employee acts and behaves for the greater good of the organization and
they do not want the organization to look bad or fail (Hildreth & Anderson, 2018). The
greater the loyalty of the employee the less likely that employee is to act dishonestly to
attain the goal. Thus:
Hypothesis 7: Employee loyalty negatively moderates the monetary incentive to
dishonest behavior relationship, such that, when employee loyalty is high, there is less
dishonest behavior.
Below is Table 3.1 which lists the seven hypotheses and the path relationships.
47
Table 3.1. Hypothesized Path Relationships in the Study for the Constructs, Mediation
and Moderation
Hypothesis
Path Relationship
Hypothesis 1
Hypothesis 2
Hypothesis 3
Hypothesis 4
Hypothesis 5
Hypothesis 6
Hypothesis 7
LOC → GA
MI → GA
EL → moderates MI → GA
MI → DB
DB → GA
DB → mediates MI → GA
EL → moderates MI → DB
Note. LOC = locus of control; GA = goal attainment; MI = monetary incentive; DB =
dishonest behavior; EL = employee loyalty; N = 342.
This table outlines the seven hypotheses used for this study. LOC and MI are
measured to GA to show the impact that each construct has on GA. EL is used to see the
impact employee loyalty has on moderating the GA and DB relationships with MI. DB is
used to see the impact dishonest behavior has on GA and the moderating effect of DB on
the MI to GA relationship.
48
CHAPTER IV
METHODS
This chapter sets forth the procedure used for evaluating the hypotheses. In the
first section, the criteria used to select the participants is outlined. The data source is then
explained followed by the sample size criteria and minimums. The next section describes
the strategy used for the data collection followed by the survey development, and an
overview of the sample. The fourth section explains the scales and how the validity of the
scales is determined. The last section elaborates on the procedures used to determine the
measurement of variables, establish reliability, validity, and determine the effect of
biases. Finally, the hypotheses are evaluated.
The study uses a survey randomly distributed using a Qualtrics survey and
distributed by Prolific as a means make sure that the participants fit the criteria for the
study. This is the best way to express a statement facing the participants in the financial
industry to help determine the value they place on monetary incentives and researching
their loyalty and locus of control.
4.1 Sample Criteria and Data Sources
4.1.1 Analytical Process
Partial Least Squares-Structural Equation Modeling (PLS-SEM) is the analytical
modeling technique used in this study. First, unlike other frequently used analytical
techniques, PLS-SEM focuses on predicting the variance of the dependent variables when
49
assessing the entire model (Hair et al., 2011; Hair et al., 2021). Second, PLS-SEM
enables researchers to assess multiple dependent variables simultaneously. Third, PLSSEM is the technique of choice when conducting exploratory research, which is the focus
of this research. Finally, PLS-SEM is a non-parametric statistical method not requiring
normally distributed data and therefore provide both flexibility of analysis and the ability
to assess overly complex models (Hair, Black, et al., 2019; Hair et al., 2017).
The survey was sent to 350 respondents. Using the pool of subjects from Prolific,
data cleaning was very minimal. Only eight subjects were removed from the data pool.
The final qualified sample consisted of 342 participants.
4.1.2 Data Sources
The respondents received a Qualtrics survey from Prolific with statements asking
about their salary, monetary incentive structure, loyalty, locus of control and dishonest
behavior. The object is to see if their loyalty, locus of control and monetary incentive
affects goal attainment. There are also questions pertaining to their years in the financial
industry and with their current employer. There will be statements about their loyalty to
their current company. There will also be statements to determine their locus of control
and dishonest behavior.
4.1.3 Criteria
The survey was developed using Qualtrics. The participant eligibility required
they be over the age of twenty-five and understand the English language. The participants
must be in the finance or financial sales sector where they have a base salary and attain
monetary incentives for accomplishing their goal. Participation was voluntary and the
participants were compensated for their time. All information provided is anonymous,
50
including the decisions to participate or not participate. There was no identifying
information collected. To make sure the participants are engaged in the survey, there was
several attention checks. The attention checks were in a statement that directs the
respondent to “Please select Very Likely for this question” or some similar type of
statement.
4.2 Survey Instrument
Several scales developed in prior research were updated and adapted for
clarification and a more concise outcome. The survey was pre-tested, and feedback was
obtained. The pretest consisted of ten PhD students with a minimum “all but dissertation
(ABD)” status, and five faculty members familiar with research design and the theoretical
foundation of the research. I then tested with an online pre-test to obtain feedback. This
enhanced the clarity of the survey and made sure it was no longer than 11 minutes.
The survey included general demographic questions such as tenure at the
company, tenure in the industry, gender, and age for all participants. There were several
salary and length of employment categories. This method captures if their effort to attain
the goal will increase when a monetary incentive is attached to that goal. Goal attainment
scales were used from previous research.
Locus of control scales and employee loyalty scales were used from previous
research to help determine the level of employee loyalty and locus of control when
measuring the effect of a monetary incentive attached to the goal. Dishonest behavior
scales were used from previous research to evaluate the potential influence a monetary
51
incentive may have on dishonest behavior and if employee loyalty influences dishonest
behavior.
4.3 Sample
The sample for this research is United States employees in the financial sales or
insurance sales industry. The website, Prolific, was utilized to pre-screen the sample and
recruit respondents. The Prolific online platform maintains a panel with a total population
of 123,645 eligible participants for this type of research. After inputting the control
variables for age and industry, the total population of eligible respondents became 946.
To ensure an appropriately sized sample, Hair et al.’s (2017) rule of ten observations for
one variable was initially considered. The method recommends that a theoretical model
with five variables requires a minimum of fifty observations. Similar recommended
sample size guidelines based on the concept of power (Cohen, 1992) require a minimum
of eighty-five observations to achieve a statistical power of 80% for the number of paths
for 1% significance and a minimum R2 of 0.10. Both sample size guidelines were met by
the sample of 342 used in this study. The final sample of financial sales employees who
completed an online survey was delivered through Qualtrics. Table 4.1 provides the
demographics of the sample.
52
Table 4.1. Demographics of Survey Respondents who are Financial Sales Employees
Demographic Field
Number
Gender
Female
Male
Other
Age
25-29
30-39
40-49
50-59
60+
Ethnicity
African American
Asian
Caucasian
Hispanic
Other
Salary
Less than 30K
31K-60K
61K-90K
91K-120K
121K-150K
151K-180K
181K-210K
Greater than 210K
Education Level
High School Graduate
Some College
2-year Degree
4-year Degree
Professional Degree (MBA)
Doctorate
Demographic Field
Education Level
Mean
3.79
Percent
127
213
2
37
62
1
81
127
81
36
17
24
37
24
10
5
26
33
272
9
2
7.5
9.5
80
2.5
0.5
24
105
96
58
28
18
7
7
7
30.5
28
17
8
5.5
2
2
12
39
30
191
69
2
3.5
11
9
56
20
0.5
Std. Deviation
1.02
53
Variance
1.04
Count
342
4.4 Scales
4.4.1 Locus of Control
The statements for this construct come from Spector (1988). The purpose of this
construct is to get a more precise idea of the employee’s work ethic and behavior as an
independent variable to goal attainment. Sample questions include “To make a lot of
money you have to know the right people,” “A job is what you make of it,” and “Most
people are capable of doing their jobs well if they make the effort.” These questions are
measured on a six-point scale ranging from 1 = Disagree Very Much to 6 = Agree Very
Much.
4.4.2 Employee Loyalty
The statements for this construct are from Pandey and Khare (2012) and Dutta
and Dhir (2021). The purpose of this construct is to determine the loyalty of the
employees. This employee loyalty will be broken down by salary, length of time in
industry, length of time at their current company and age of employee. This construct will
help show as an independent variable how valuable loyalty to the company is when
looking at monetary incentives as a motivator for goal attainment and to see how loyalty
deters dishonest behavior to attain the goal. Sample questions include “I speak positively
about my company to friends and relatives,” “I would like to stay with this company for
the future,” and “I would not change companies if I got an offer.” These questions are
measured on a five-point scale ranging from 1 = Strongly Agree to 5 =Strongly Disagree.
54
4.4.3 Monetary Incentives
The statements for this construct are from Al-Belushi and Khan (2017). The
purpose of this construct is to see how important monetary incentives are as a motivating
factor to attain the goal. Sample questions are “Monetary incentives are important to me,”
“Financial incentives encourage employees to produce more at my company,” and
“Monetary incentives have a positive effect on my motivation at work.” These questions
are measured on a five-point scale ranging from 1 = Strongly Agree to 5 =Strongly
Disagree.
4.4.4 Dishonest Behavior
The statements for this construct are from Kaptein (2008) and Bennett and
Robinson (2000). The purpose of this construct is to help determine how dishonest
behavior’s influence on goal attainment as an independent variable when monetary
incentives are involved. This construct will show how employees become dishonest to
obtain the monetary incentive and help show how employee loyalty helps deter dishonest
behavior. Sample questions are “I would falsify or manipulate financial reporting
information to attain the goal,” “I would engage in false or deceptive sales and marketing
practices (e.g., creating unrealistic expectations) to gain the sale,” and “I accept
inappropriate gifts, favors, entertainment, or kickbacks from customers.” These questions
are measured on a five-point scale ranging from 1 = Never to 5 = Almost Always.
4.4.5 Goal Attainment
The statements for this construct are from Khazanov et al. (2020). The purpose of
this construct is the dependent variable. The questions in the survey will help show how
the employees feel about attaining their workplace goal. Sample questions are “I expect
55
to master the tasks I undertake to reach my company goal,” “I acquire the ability to
perform the tasks to reach my goal,” and “I achieve my personal goals.” These questions
are measured on a five-point scale ranging from 1 = Not At All to 5 = Totally.
4.5 Bias, Validity, and Analysis
4.5.1 Common Method Bias
When multiple constructs are measured by the same method, such as a survey,
part of the covariance among the constructs may be attributed to the fact that the same
method is used. Common method variance creates a divergence between the observed
and true relationships (Doty & Glick, 1998) that can imply significant relationships that
do not exist (Williams & Brown, 1994).
One means of preventing common method bias is to use different response
formats for the measurement of dependent and independent variables (Podsakoff et al.,
2003). This study employs this technique. Some of the constructs are on a five-point scale
and some on a six-point scale. There are different range answers on some of the fivepoint scale variables in order to prevent identical options with all the five-point variables.
Other techniques for controlling common method bias include protecting respondent
anonymity, counterbalancing question order, and improving scale items. All these design
measures are incorporated into the current study. However, constructs measured by the
survey may still be vulnerable to common method variance, which is a possible limitation
of the current study.
56
4.5.2 Analysis
To test the hypotheses, Partial Least Squared Structural Equation Modeling PLSSEM was utilized. SMART PLS software was used to test the measurement and
structural model.
4.5.3 Pilot Testing
To ensure the quality of survey responses, a pilot test was administered to a
convenience sample (Hair et al., 2015). The pilot study targeted several professors at a
university, sales associates at DHL Aviation, and sales associates at PNC Bank. While
the sample size was small (n = 20), problematic items on the survey were identified and
corrected. Once the pilot test was analyzed, the final survey instrument was more precise
and yielded accurate results (Perneger et al., 2015).
57
CHAPTER V
RESULTS
5.1 Analytical Process
In this study, Partial Least Squares-Structural Equation Modeling (PLS-SEM) was
the analytical modeling technique used. PLS-SEM focuses on predicting the variance of
the dependent variables when assessing the entire structural model (Hair et al., 2011; Hair
et al., 2021). PLS-SEM is the technique of choice when conducting exploratory research,
which is the focus of this research, and when prediction is the primary statistical objective
of the analytical process. Finally, PLS-SEM is a non-parametric statistical method not
requiring normally distributed data and therefore provides both flexibility of analysis and
the ability to assess highly complex models (Hair, Black, et al., 2019; Hair et al., 2017;
Hair et al., 2021).
The survey was sent to 360 respondents. Using the pool of subjects from Prolific,
data cleaning was very minimal. Only four subjects failed the attention checks. Fourteen
participants failed to answer enough questions or did not complete the survey and were
eliminated. The final qualified sample consisted of 342 participants.
5.2 Data Analysis and Findings
5.2.1 Partial Least Squares Structural Equation Modeling
The theoretical research model examined in this research is complex and involves
mediation and moderation. The application of Partial Least Squares Structural Equation
58
Modeling (PLS-SEM), therefore, facilitates a better understanding of the relationships
proposed in the research (Hair & Sarstedt, 2020). The following sections outline the
procedures followed.
5.3 Assessment of Measurement Model
As described in Hair, Howard, and Nitzl (2020), PLS-SEM involves a two-step
process. The first step is referred to as confirmatory composite analysis (CCA) and
explores and confirms the measurement model. The second step examines the structural
relationships and predictive ability of the theoretical model. The formative measurement
model is evaluated per the CCA guidelines for the following criteria: Item loadings,
composite reliability, average variance extracted, discriminant validity, nomological
validity and predictive validity.
5.3.1 Data Distribution
Each item in the study has a varying degree of departure from normality. There
were 20 of the original 45 items that were considered highly skewed, falling outside the
range -1 to +1 (Bulmer, 1979). All other items were moderately skewed or approximately
symmetric. Whether positively or negatively skewed, every item fit within the acceptable
range of -2 to +2 for structural equation modeling (Kline, 2011). The data is moderately
leptokurtic or platykurtic for each item and well within the acceptable range of -7 to +7
(Hair, Black, et al., 2019). Slightly non-normal data distribution will not affect the study
due to this research’s use of PLS-SEM (Hair, Howard, & Nitzl, 2020).
59
5.3.2 Common Method Variance
This study uses contextual data from a single time period and is a cross-sectional
design (Hair, Page, & Brunsveld, 2020). To reduce the likelihood of common method
variance, the research design and questionnaire were executed through the application of a
variety of scaling methods and sequencing based on guidelines by Podsakoff et al. (2003;
2012).
5.3.3 Estimate of Loadings, Significance, and Item Reliability
Indicator validity was assessed by evaluating the size of the factor loadings. To
capture sufficient variance from each item within the construct, Hair, Sarstedt, and Ringle
(2019) suggest removing any items below the .708. There were five items that were
removed but were very close as they were over .670 but below .708. Removing them
increased the factor loadings of the remaining items and all constructs had at least three
items that were greater than .708 (Hair, Risher, et al., 2019).
The PLS algorithm was executed followed by initially screening to identify the
items below .708. Indicator loadings met or exceeded .708 (Hair et al., 2021). The result
was a total of 18 items being removed. After removal of these items, all outer loadings
met recommended guidelines and were highly significant (p values < .05). By squaring the
loadings, item reliability measures display the amount of variance shared by each item to
the construct (Hair, Black, et al., 2019). The factor loadings are displayed in Table 5.1
60
Table 5.1. Factor Loadings of the Constructs in the Study
Variable
DB
DB1
DB5
DB6
DB7
DB8
EL1
EL10
0.730
0.889
0.821
0.767
0.852
EL
GA
LOC
MI
0.741
0.827
EL2
EL3
0.886
0.782
EL4
EL5
EL6
EL7
EL8
GA1
GA2
0.833
0.747
0.792
0.870
0.845
0.789
0.728
GA6
GA7
LOC2
LOC3
LOC4
LOC8
MI6
MI7
MI8
0.820
0.800
0.792
0.869
0.770
0.778
0.878
0.735
0.928
Note. LOC = locus of control; GA = goal attainment; MI = monetary incentive; DB =
dishonest behavior; EL = employee loyalty; N = 342.
61
Table 5.2. Descriptive Statistics of the Variables
Variable
Monetary Incentive
Dishonest Behavior
Goal Attainment
Employee Loyalty
Locus of Control
Minimum
Maximum
Mean
Std Deviation
1
1
1
1
1
5
5
5
5
6
4.287
4.869
4.087
3.862
2.435
.764
.440
.769
.988
1.079
Note. N = 342.
Table 5.3. Correlation Values of the Variables Showing Significance Level
Variable
DB
EL
GA
LOC
MI
DB
EL
GA
LOC
MI
EL x MI
0.235
0.292
-0.235
0.158
-0.082
0.524
-0.423
0.057
0.134
-0.467
0.189
-0.079
-0.072
-0.054
-0.119
Note. LOC = locus of control; GA = goal attainment; MI = monetary incentive; DB =
dishonest behavior; EL = employee loyalty; N = 342.
All correlations above .102 are significant at p < .05 and all correlations above
.162 are correlated at p < .01. The highest correlations are those variables with employee
loyalty. The highest correlation is between employee loyalty and goal attainment. There
were some correlations that were the lowest, but still significant. The MI correlations were
the ones on the lowest end. The moderating relationship correlation of EL moderating the
MI to DB relationship was low and not significant. None of the effect VIFs were above
62
1.5, well within acceptable tolerances for ordinary least squares regression (Hair et al.,
2021).
5.3.4 Composite Reliability
The measure of composite reliability was evaluated next. The measures of both
Cronbach’s alpha and composite reliability criteria minimums (> 0.70) were met for all
constructs (Hair, Risher, et al., 2019). Cronbach’s alpha applies to reflective measurement
models like this one (Hair et al., 2021). Averaging the correlations of the items within the
construct, the most similar items were identified. There were no constructs that violated
the composite reliability maximum of 0.95 that would indicate redundancy with the items
of those constructs (Hair, Risher, et al., 2019). Table 5.4 outlines the internal consistency
reliability measures for each construct.
Table 5.4. Internal Consistency Assessments for each Construct to Show Items Within the
Construct are Similar
Variable
DB
EL
GA
LOC
MI
Cronbach’s alpha
Composite reliability
0.871
0.936
0.796
0.816
0.829
0.885
0.940
0.809
0.823
0.947
Note. LOC = locus of control; GA = goal attainment; MI = monetary incentive; DB =
dishonest behavior; EL = employee loyalty; N = 342.
5.3.5 Convergent Validity
Convergent validity tests to make sure all of the items used for the construct are
valid for that construct and correlate to the other items testing for that construct. Average
63
variance extracted (AVE) is the measure used to evaluate convergent validity (Hair et al.,
2021). All constructs meet the AVE criterion of 0.5 or greater. These measures indicated
the shared variance between the construct and their items. The AVE values for each
construct are outlined in Table 5.5 below.
Table 5.5. Average Variance Extracted for Convergent Validity for Each Construct to
Show Shared Variance Between the Construct and Each Item for That Construct
Variable
AVE
DB
EL
GA
LOC
MI
0.662
0.665
0.616
0.645
0.724
Note. LOC = locus of control; GA = goal attainment; MI = monetary incentive; DB =
dishonest behavior; EL = employee loyalty; N = 342.
5.3.6 Discriminant Validity
Discriminant validity measures the extent to which the constructs are representing
and measuring distinctly different concepts (Hair, Risher, et al., 2019). For example, when
measuring abstract constructs such as employee loyalty, it is important to determine that
all constructs measure a different concept (Hair & Sarstedt, 2021; Henseler et al., 2015).
Measuring individual items cross loadings is one way to assess the uniqueness of the
construct. The Fornell-Larcker criterion is another recommended measurement of
discriminant validity. The Fornell-Larcker criterion takes the square root of the average
variance extracted (AVE) for each construct and should exceed the correlation to other
64
constructs when measuring distinct constructs (Fornell & Larcker, 1981). The variance
from each item should be more highly contributed to the construct being measured (Chin,
1998). All items’ variances are highest on the intended constructs. The Fornell-Larcker
criterion values are outlined in Table 5.6 below.
Table 5.6. Fornell-Larcker Criterion for Discriminant Validity for Each Construct
Variable
DB
EL
GA
LOC
MI
DB
EL
GA
LOC
MI
0.814
0.235
0.292
-0.235
0.158
0.815
0.524
-0.423
0.057
0.785
-0.467
0.189
0.803
-0.072
0.851
Note. LOC = locus of control; GA = goal attainment; MI = monetary incentive; DB =
dishonest behavior; EL = employee loyalty; N = 342.
The table above shows the off-diagonal values are all lower compared to the ondiagonal (bolded) values. The Fornell-Larcker criterion for discriminant validity is
therefore met.
Heterotrait-monotrait ratio (HTMT) is another measurement of discriminant
validity. HTMT is a more rigorous metric than the Fornell-Larcker criterion to ensure that
each construct uniquely captures the phenomenon. The HTMT criterion confidence levels
were assessed after bootstrapping 5000 subsamples (Hair et al., 2021). All measurements
met the rule of thumb below 0.900 (Hair, Risher, et al., 2019). The Heterotrait-Monotrait
discriminant validity measurements are in Table 5.7.
65
Table 5.7. Heterotrait-Monotrait Discriminant Validity for the Measurement Model
Variable
DB
EL
GA
LOC
MI
EL x DB
DB
EL
GA
LOC
MI
EL x DB
EL x MI
0.256
0.335
0.273
0.156
0.763
0.087
0.583
0.484
0.071
0.133
0.147
0.570
0.189
0.137
0.091
0.118
0.068
0.063
0.076
0.121
0.051
Note. LOC = locus of control; GA = goal attainment; MI = monetary incentive; DB =
dishonest behavior; EL = employee loyalty; N = 342.
HTMT metrics are evaluated based on two recommended guidelines; 0.85 for
concepts considered to be measuring diverse constructs and 0.90 for similar constructs
(Hair, Sarstedt, & Ringle, 2019). Discriminant validity measurements are affected by the
removal of items to meet the composite reliability maximum cutoffs. Since extensive
literature review is used, this research will use the less conservative HTMT value of < 1
when considering discriminant validity (Franke & Sarstedt, 2019). All HTMT values are
acceptable (< 1.0; (Franke & Sarstedt, 2019).
5.3.7 Nomological Validity
Another assessment of construct validity is nomological validity (Hair et al.,
2021). Correlation of construct scores should be consistent with the theoretical direction,
size, and significance of the correlations. Reviewing the latent variable correlations, all
nomological relationships are consistent with theory as supported by the literature review
in Chapter III.
66
5.4 Evaluation of the Structural Model
The following steps, consistent with the second step of the CCA process (Hair et
al., 2021), will be used to evaluate the structural model. Evaluation of collinearity,
examination of size and significance of path coefficient, R2 of endogenous variables, ƒ2
effect size, predictive relevance Q2, and PLSpredict prediction errors.
5.4.1 Assessment of Collinearity
Variance inflation factor (VIF) is used to assess collinearity issues. The rule of
thumb for variance inflation factor (VIF) is constructs have collinearity issues above the
cut-off of 3.00 (Hair et al., 2021). The VIF statistics for each latent variable are below in
Table 5.8.
Table 5.8. Variance Inflation Factor for each Latent Variable to Show There are no
Collinearity Issues
Variable
DB
EL
GA
LOC
MI
EL x MI
DB
EL
GA
1.024
1.118
1.274
1.020
1.035
1.249
1.040
1.046
Note. LOC = locus of control; GA = goal attainment; MI = monetary incentive; DB =
dishonest behavior; EL = employee loyalty; N = 342.
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5.4.2 Path Coefficients and Significance
The path coefficients were calculated using the SmartPLS algorithm. Through
bootstrapping 5,000 subsamples and one-tail test, path statistical significance was
ascertained (Hair et al., 2021). Path coefficients and significance (p values) are labeled in
Figure 2. All path coefficients were statistically significant. The path coefficient for the
moderating effect of EL on the MI → DB was not significant as it had a p value of 0.158.
The path coefficient for the moderating effect of EL on the MI → GA was significant (p =
0.005). Table 5.9 and Figure 2 display the strength of the relationships and their
significance for this one-tail test.
Table 5.9. Size and Significance of the Path Coefficients
Relationship
DB → GA
EL → DB
EL → GA
LOC → GA
MI → DB
MI → GA
EL x MI → DB
EL x MI → GA
Original
Sample
(O)
0.106
0.241
0.419
-0.274
0.133
0.129
-0.090
-0.123
Sample
Mean
(M)
0.105
0.244
0.422
-0.270
0.142
0.133
-0.103
-0.125
Standard
Deviation
(STDEV)
0.053
0.068
0.048
0.058
0.077
0.045
0.089
0.047
T Statistics P Values
(|O/STDEV|)
2.005
3.519
8.649
4.740
1.731
2.901
1.002
2.601
0.023
0.000
0.000
0.000
0.042
0.002
0.158
0.005
Note. LOC = locus of control; GA = goal attainment; MI = monetary incentive; DB =
dishonest behavior; EL = employee loyalty; N = 342.
68
Figure 2. Structural model of one-tail test results showing the strength of the relationships
and their significance.
5.4.3 Coefficients of Determination (R2)
To assess the in-sample predictive power of the structural model, the coefficients
of determination (R2) are determined after reliability and validity (Hair & Sarstedt, 2021).
The larger the R2, the more variation of the endogenous variable is explained respectively
to the independent variables. Measures closer to 1 have higher predictive power while
those closer to 0 have lower. Table 5.10 below outlines both measures, R2 and R2 adjusted
for the dependent variables for this one-tail test.
69
Table 5.10. Coefficients of Determination for the Dependent Variables to Show the
Predictive Power of the DV
Variable
R-square
R-square adjusted
DB
GA
0.086
0.397
0.077
0.388
Note. DB = dishonest behavior; GA = goal attainment; N = 342.
This table shows that DB is relatively weak when measuring predictive power. GA
is low moderate for the measurement of predictive power (Hair & Sarstedt, 2021).
5.4.4 Effect Sizes (ƒ2)
The ƒ2 statistic measures the model with exogenous variables included than
excluded to determine their effect sizes upon the model (Hair, Risher, et al., 2019). Less
than .02 indicates no effect, between .02 and 0.15 are small effects, between 0.15 and 0.35
are medium effects, and greater than 0.35 are large effects (Cohen, 1988). Table 5.11
illustrates the effect sizes.
Table 5.11. Effect Sizes of Constructs for the Structural Equation Model to Show the
Effect Each Construct as on the Model
Variable
DB
EL
GA
LOC
MI
EL x MI
DB
EL
GA
0.062
0.017
0.201
0.019
0.010
0.100
0.021
0.024
Note. LOC = locus of control; GA = goal attainment; MI = monetary incentive; DB =
dishonest behavior; EL = employee loyalty; N = 342.
70
These results show that the path LOC → GA has a moderate effect. The paths DB
→ GA and MI → GA have small effects. The moderation path of EL to MI → DB has a
small moderation effect. The moderation path of EL to MI → GA has a moderate effect.
5.4.5 PLSpredict
This study posits that in-sample prediction is adequate. Future studies in goal
attainment and dishonest behavior may enhance a model assessment metric. SmartPLS
software does apply the PLSpredict to help researchers calculate an out of sample
prediction by training the model on a selected part of the sample and predicting the other
data on a second holdout sample (Shmueli et al., 2019). Following recommendations on
holdout sample size to be equal to or greater than 30, PLSpredict folds were set to 20
ensuring that holdout samples of the 342 respondents would be adequate (Hair, Black, et
al., 2019).
The mean absolute error (MAE) measures the average magnitude of the errors in a
set of predictions without considering their direction (over or under). The MAE is thus the
average absolute differences between the predictions and the actual observations, with all
the individual differences having equal weight. Another popular prediction metric is the
root mean squared error (RMSE), which is defined as the square root of the average of the
squared differences between the predictions and the actual observations. As the RMSE
squares the errors before averaging, the statistic assigns a greater weight to larger errors,
which makes it particularly useful when large errors are undesirable – as is typically the
case in business research applications (Hair, Sarstedt, & Ringle, 2019).
Using Shmueli et al.’s (2019) evaluation guidelines, all Q2 metrics were above
zero as shown in Table 5.12. Since the prediction errors are highly symmetrically
71
distributed, the root mean squared error (RMSE) metrics for the PLS-SEM model and the
naïve (linear) model were compared. The PLS-SEM model has a medium predictive
power since a majority of indicators for the endogenous variables have less error
compared to the naïve LM model (Manley et al., 2021; Shmueli et al., 2019).
Table 5.12. PLSpredict Summary of Items to Show Comparison of the Error Measurement
in the Predictive Power
Item
Q² predict PLS-SEM_RMSE
PLS-SEM_MAE LM_RMSE LM_MAE
DB1
DB5
DB6
DB7
DB8
GA1
GA2
GA6
0.012
0.046
0.024
0.012
0.015
0.192
0.115
0.27
0.622
0.389
0.522
0.326
0.324
0.678
0.71
0.663
0.433
0.151
0.285
0.126
0.134
0.519
0.54
0.535
0.631
0.391
0.525
0.335
0.332
0.693
0.726
0.672
0.43
0.2
0.306
0.159
0.165
0.538
0.563
0.525
GA7
0.277
0.682
0.529
0.683
0.523
Note. LM = linear model; RMSE = root mean squared error; MAE = mean absolute error;
DB = dishonest behavior; GA = goal attainment; N = 342.
Table 5.13. PLSpredict Summary of Constructs
Variable
Q²predict
RMSE
MAE
DB
GA
0.039
0.359
1.037
0.807
0.499
0.646
Note. DB = dishonest behavior; GA = goal attainment; RMSE = root mean squared error;
MAE = mean absolute error; N = 342.
72
5.5 Overview of Hypotheses Results
Hypothesis one posited that locus of control is positively associated with goal
attainment. While the coefficient was significant, the sign was in the opposite direction
(negative). Therefore, H1 was not supported. Hypothesis 2 posited that monetary incentive
is positively associated with goal attainment. This hypothesis was supported. Hypothesis 3
posited that employee loyalty negatively moderates the monetary incentive to goal
attainment relationship such that, when employee loyalty is high, monetary incentives
have less of an impact on goal attainment. This hypothesis was supported. Hypothesis 4
posited that monetary incentive is positively associated with dishonest behavior. This
hypothesis was supported. Hypothesis 5 posited that dishonest behavior increases goal
attainment. This hypothesis was supported. Hypothesis 6 posited dishonest behavior
mediates the monetary incentive to goal attainment relationship. This hypothesis was
supported. Hypothesis seven posited that employee loyalty negatively moderates the
monetary incentive to dishonest behavior relationship, such that, when employee loyalty is
high, there is less dishonest behavior. This hypothesis was not supported as the path
coefficient is negative the p-value is 0.158 (> 0.05) and therefore not significant.
The path coefficients and the effect sizes for the proposed hypotheses were
assessed. In addition, mediation was examined following the PLS-SEM procedure, which
is superior to the PROCESS method (Sarstedt et al., 2020). The path beta coefficients and
significance are shown in Table 5.14 below.
73
Table 5.14. Path Relationship, Betas, and Significance for the Proposed Hypotheses in
This Study
Path Relationship
Beta
T statistics
P values
DB → GA
EL → DB
EL → GA
LOC → GA
MI → DB
MI → GA
0.106
0.241
0.393
-0.274
0.133
0.115
2.005
3.519
7.946
4.740
1.731
2.643
0.023
0.000
0.000
0.000
0.042
0.004
EL x MI → DB
EL x MI → GA
-0.090
-0.113
1.002
2.461
0.158
0.007
Note. LOC = locus of control; GA = goal attainment; MI = monetary incentive; DB =
dishonest behavior; EL = employee loyalty; N = 342.
5.5.1 Assessment – Moderation and Mediation
In the model, LOC was hypothesized to have a positive effect on goal attainment
(GA). Results show LOC does not have a positive effect on GA and that relationship is
significant (t = 4.740, p < 0.05). Therefore, these results show that LOC does not
contribute positively to attaining the goal. Monetary incentive (MI) was hypothesized to
have a positive effect on GA and in this study, MI does have a positive effect on GA (t =
2.643, p < 0.05). Therefore, these results show that MI does contribute positively to
attaining the goal. Employee loyalty (EL) was hypothesized to have a negative moderating
effect on the MI to GA relationship, the findings indicate moderation does exist and EL
does reduce the MI to GA relationship and is significant (t = 2.461, p < 0.05) as shown by
Graph A in Figure 3 below. These results show that EL does contribute to reducing the
monetary incentive to attain the goal. MI was hypothesized to have a positive effect on
74
dishonest behavior (DB). In this study, MI does have a positive effect on DB but slightly
and barely significant as the p value < 0.05 at 0.042 and the t = 1.731. Similarly, DB was
hypothesized to have a positive effect on GA, and it does have a positive effect on GA and
is significant (t = 2.005, p < 0.05). In addition, DB was hypothesized to mediate the
relationship of MI to GA, and does mediate that relationship as MI has a positive effect on
DB and in turn, DB has a positive effect on GA. Both of those effects are significant. EL
was hypothesized to have a negative moderating effect on the MI to DB relationship.
Findings indicate MI is negatively associated related in the MI to DB relationship, but that
effect is not significant as the p value is 0.158. This is demonstrated by Graph B in Figure
4 below. These results show that EL does have an impact on reducing dishonest behavior
when a monetary incentive is involved but that moderating relationship is not significant.
Table 5.15 displays the results of the mediation and moderation outcomes on the path
relationships of all hypotheses
Table 5.15. Outcomes of Mediation and Moderation on Path Relationship Hypotheses
Hypothesis
Path Relationship
Outcome
Hypothesis 1
Hypothesis 2
Hypothesis 3
Hypothesis 4
LOC → GA
MI → GA
EL → moderates MI → GA
MI → DB
Not Supported
Supported
Supported
Supported
Hypothesis 5
Hypothesis 6
Hypothesis 7
DB → GA
DB → mediates MI → GA
EL → moderates MI → DB
Supported
Supported
Not Supported
Note. LOC = locus of control; GA = goal attainment; MI = monetary incentive; DB =
dishonest behavior; EL = employee loyalty; N = 342.
75
Figure 3. Graph A showing the moderating effects of employee loyalty on the relationship
of monetary incentive to goal attainment.
Above, the graph also shows the moderating effect EL has on the MI to GA relationship.
This graphs the effect EL has on reducing the impact of MI on GA relationship and the
effect is significant. This graph shows the positive effect EL has on reducing the need for
monetary incentive to attain the goal.
76
Figure 4. Graph B showing the moderating effects of employee loyalty on the relationship
of monetary incentive to dishonest behavior.
Above is the simple slope analysis of the moderating effect of EL on the MI to DB
relationship. The graph displays the slight effect EL has on moderating the MI to DB
relationship. However, this is not significant and shows that EL has minimal effect
reducing the MI to DB relationship.
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CHAPTER VI
DISCUSSION AND CONCLUSIONS
6.1 Discussion
In this research, it was shown that monetary incentives do have a positive effect
on goal realization. This research also found that there are other factors or byproducts of
monetary incentives when used for goal attainment. Monetary incentive was found to
have a positive effect on dishonest behavior which in turn positively contributed to goal
attainment. Employee loyalty was found to moderate the relationships that involved
monetary incentives. This research found that employee loyalty negatively impacted the
relationships that involved monetary incentives to dishonest behavior, although not
significant, and the monetary incentives to goal attainment. This can be used to show that
employee loyalty can decrease dishonest behavior and help decrease the need for
monetary incentives for goal realization in the organization.
In previous research, Corgnet et al. (2015), found that monetary incentives had a
detrimental effect on workers’ motivation. Gneezy and Rustichini (2000) found as well
that small monetary incentives undermined employee performance and did not enhance
performance. Glaser et al. (2017) found that high incentives spurred intense aggression
with employees and negatively impacted the firm. Alternatively, Khan et al. (2020) found
that monetary incentives increase job performance and motivation. In their study,
monetary incentives are shown to increase goal attainment and increase dishonest
behavior which also enhance goal attainment. This study used dishonest behavior as a
mediator for the monetary incentive to goal attainment relationship and found that
78
dishonest behavior increases goal attainment and is increased by monetary incentives.
This means that management should be careful with monetary incentives and be aware
that dishonest behavior can be increased by the monetary incentive and create goal
realization that may not be accurate.
Murali et al. (2017) found in their study that loyalty increases performance.
Pandey and Khare (2012) found that employee loyalty decreases organizational costs.
Mowday et al. (1979) found in their study that employee loyalty creates an increased
acceptance of organizational goals and values. In this study, employee loyalty was used
as a moderator to the monetary incentive to goal attainment relationship. This study
found that as shown in Figure 2B, employee loyalty does decrease that relationship and
shows that employee loyalty would allow for goal attainment with fewer monetary
incentives. This means that management should enhance employee loyalty and that
would decrease the amount of monetary incentive needed for goal realization. This study
also measured the moderation effect employee loyalty has on the monetary incentive to
dishonest behavior relationship. The results found employee loyalty does moderate that
relationship and decreases the effect monetary incentive has on dishonest behavior. This
means that management should enhance employee loyalty and the result would be a
decrease in the dishonest behavior.
This study enhances the previous studies and finds that employee loyalty not only
reduces dishonest behavior but also decreases the need or amount of monetary incentive
for goal attainment. These results can help show how employee loyalty can be used in
organizations to help enhance goal attainment and realization. Organizations can
concentrate on increasing the loyalty of employees and use in turn fewer monetary
79
incentives to help realize the organization’s goals. This study also found that monetary
incentive does increase goal attainment and organizations can use monetary incentives to
help attain the goals of the organization.
6.1.1 Theoretical Implications
The implications of this research can show that expected utility theory is still a
factor for goal attainment when monetary incentives are added. As shown in this
research, as monetary incentive is added for goal attainment there is a positive effect on
that relationship. This would still defend the EUT theory that employees will perform the
needed tasks for the goal if there is a benefit for the employees. Employee loyalty in this
research shows an impact on the relationships of MI to GA and MI to DB. This relates to
the agency theory where the employee is the agent and acts for the benefit of themselves
while at the same time performing for the benefit of the organization. As the employee
becomes more loyal, they buy into the goals and culture of the organization and in turn
can attain the goals and benefit from the monetary incentives.
6.1.2 Managerial Implications
This research shows that MI impacts GA in a positive manner. This can help
managers and organization stakeholders understand the need to incentivize employees to
help goal realization. Employee loyalty is also shown to have an impact on the MI to GA
and the MI to DB relationships in a positive manner. This shows that managers and
stakeholders need to help employees become loyal so that goal realization of the firm can
be accomplished with less MI and possibly less DB within the firm.
80
6.2 Conclusions
The purpose of this study is to research the effects monetary incentives have on
the final realization of the goal. The research seeks out to see the effects that locus of
control has on goal attainment. Other factors studied in this research are employee loyalty
and the effect that employee loyalty has on goal attainment when measured against the
monetary incentive to goal attainment relationship and the monetary incentive to
dishonest behavior relationship. The idea is to see if highly loyal employees still regard a
monetary incentive as a major driving force for goal realization. Also researching the
effect employee loyalty has on dishonest behavior and if highly loyal employees are less
likely to partake in dishonest behaviors to attain the goal. An additional factor affecting
goal realization is studied as well to see the effect that dishonest behavior has on goal
attainment when monetary incentives are involved.
One hypothesis is not supported, and those findings should lead to future research
to breakdown the employees by demographics and possibly identify groups that do
support the hypothesis even though the overall respondent’s data does not. Having 342
respondents allowed this study to have enough respondents to separate out the data for
future research.
6.3 Limitations
There are several limitations with this study. The respondents are from several
different companies in the financial sales industry. This gives a generic perception and
not very specific to one firm. Management has several different styles and, since this
study includes numerous employees from different firms, the results are generic in nature
81
and not very specific to a single firm. Using this model and scales in a specific firm will
be much more precise to help management make better decisions for goal realization.
A second limitation is that most of the scales used for these constructs are very
dated. Due to our economic changes over the past twenty years, some of the scales could
be updated and help be more precise in research. Employees change over time as new
hires enter the workforce and different generations of employees affect the firm.
The third limitation of this study is it is primarily quantitative. Interviews with
employees at a specific company could help have a better understanding of how
monetary incentives affect employees and their work behaviors and intentions. This
research is left to rely on previous literature and scales to construct models and interpret
results. Although this research supported most of the hypotheses, exploring more
possibilities with employees and updating the scales to narrow the constructs could help
the understanding of the concepts that play a part in employees work ethics and wants or
needs that are external to the firm that affect the employees’ actions for goal realization.
6.4 Future Research
Since locus of control was not supported when measured to increase goal
attainment, that would be a significant area to further the research and look at
demographics or length of employment to see if there is a factor that does positively
increase GA. For example, looking at the data set and one LOC question, it was noticed
that a majority of respondents with high school and associates degree rated luck high
when asked about promotions whereas, the majority of respondents with Master and
higher degrees rated hard work high for that question.
82
There are several areas where the constructs could be more precise. Monetary
incentives can be researched in the future to see if there is a level of that incentive which
influences employees to work harder to attain the goal.
Another area for future research is similar to LOC with employee loyalty.
Employee loyalty can be researched to see how much of an impact long term employees
have on GA as well as breaking out the demographics of EL to see if there is a group that
measures more positively to GA or more negatively to DB for example.
As for dishonest behavior, there needs to be future research for that construct.
Based on literature, it is stated that respondents are not likely to admit their dishonesty
even in anonymous surveys. Future research can possibly test better scales to make the
DB construct a little narrower and possibly get a better measure of how DB affects goal
realization in the workplace.
Additional future research can be done on the employee loyalty construct.
Employee loyalty should be researched to see how much of an impact that has on goal
realization and then use the demographics of that construct to look at the highly loyal
employees. If it can be determined the most significant positive effect EL has on goal
realization, then firms can look at their workforce and spend resources helping their
workforce become much more loyal and use less MI to enhance goal realization.
6.5 Research Conclusion and Summary
The summary of this research shows that, based on the 342 respondents from the
financial sales industry, monetary incentives positively impact goal realization. Within
that model, factors such as employee loyalty enhances goal attainment. Employee loyalty
83
moderates the monetary incentive to goal attainment relationship and moderates the
monetary incentive to dishonest behavior relationship. This research also shows that
monetary incentives positively impact dishonest behavior and in turn that dishonest
behavior positively impacts goal attainment. To summarize, monetary incentive and
employee loyalty positively impacts goal realization.
84
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APPENDICES
Appendix A Original Item Factor Loadings
Table A1. Original Items – Factor Loadings for all Constructs
Item
DB
DB1
0.744
DB2
DB3
0.653*
0.532*
DB4
DB5
DB6
DB7
DB8
EL1
EL10
0.688*
0.868
0.812
0.724
0.799
EL
LOC
0.740
0.827
EL2
EL3
EL4
EL5
EL6
EL7
EL8
0.867
0.770
0.818
0.736
0.775
0.846
0.857
EL9
GA1
GA2
0.674*
GA3
GA4
GA5
GA6
GA7
GA8
GA
0.782
0.742
0.555*
0.692*
0.644*
0.752
0.728
0.639
98
MI
EL x MI
Table A1 cont.
LOC1
LOC2
LOC3
LOC4
LOC5
LOC6
LOC7
LOC8
0.317*
0.748
0.822
0.743
0.662*
0.589*
0.650*
0.756
LOC9
MI1
MI2
MI3
MI4
MI5
MI6
MI7
MI8
MI9
0.592*
0.418*
0.393*
0.431*
0.502*
0.559*
0.796
0.711
0.828
0.642*
Note. LOC = locus of control; GA = goal attainment; MI = monetary incentive; DB =
dishonest behavior; EL = employee loyalty; N = 342. Items marked with asterisk (*) were
removed after initial screening.
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Appendix B Invitation to Participate
You are invited to voluntarily participate in a research project on goal attainment
and loyalty in your workplace. The purpose of the study is to better understand the
measurement of goals and loyalty in the workplace.
This will take no longer than 8 to 11 minutes to complete. Participation will
remain anonymous, and no identifying data will be collected. You will be given a survey
of about 60 items, most of which will be on a 1 (Strongly Disagree) to 5 (Strongly Agree)
answer format.
You have the right to refuse to answer any questions that you do not wish to
complete and/or answer.
The results of this study may be potentially advantageous in future society as a
result of information gathered in the study.
While unlikely, it is possible that a loss of confidentiality may occur. Be aware,
however, that no identifying information is recorded for the current study, and all
responses will be saved on a password protected online account. Even if a loss of
confidentiality may occur, the data will not have indicators to track responses to
individual participants. Also, if a publication is produced from the data obtained in the
current study, all results will be presented as overall findings – no direct information
about particular responses will be provided.
All answers will be deleted after all data has been collected and three years have
passed after any eventual publication. All information will be used for research purposes
only.
100
If you agree to participate, you must be at least 25 years of age and proficient in
the English language. You can withdraw at any time without consequence.
Please contact me at G1822@Jagmail.SouthAlabama.edu or the Institutional
Review Board at the University of South Alabama at 251-460-6308 if you have questions
about your rights as a research subject.
101
Appendix C Survey Statements
Goal Attainment: 5 points
1 = Not At All; 2 = Rarely; 3 = Sometimes; 4 = Usually; 5 = Totally
I have made considerable progress toward attaining my goal for the company.
I accomplished what I set out to do for my goals for the company.
I achieve my personal goals.
I regularly achieve goals set by my company.
I acquire the ability to perform the tasks to reach my goal for the company.
I want to accomplish the goals set for myself by my company.
I feel pleased when I reach the goal set by my company.
I expect to master the tasks I undertake to reach my company goal.
Dishonest Behavior: 5 points
1 = Never; 2 = Rarely; 3 = Sometimes; 4 = Often; 5 = Almost Always
I would falsify work data to benefit myself.
I would make false or misleading claims to the customer to attain the sale.
Please select often for this question.
I accept inappropriate gifts, favors, entertainment, or kickbacks from customers.
I would engage in false or deceptive sales and marketing practices (i.e., creating
unrealistic expectations) to gain the sale.
I would falsify or manipulate financial reporting information to attain the goal.
I would falsify time and expense reports to attain more money.
I would breach computer, network, or database controls to obtain private information.
I would steal or misappropriate assets to benefit me financially (e.g., money, equipment,
materials).
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Monetary Incentive: 5 points
1 = Strongly Agree; 2 = Agree; 3 = Neutral; 4 = Disagree; 5 = Strongly Disagree
Monetary incentives are important to me.
I place importance on monetary incentives.
Monetary incentives are of no value to me.
Monetary incentives on my job match my work effort.
Monetary incentives at my company are not up to my expectations.
Financial incentives increase employee job satisfaction.
Monetary incentives increase employee loyalty at my company.
Monetary incentives help to develop employee attitudes towards organizational success at
my company.
If I attain the goal for the monetary incentive, I know I will receive it from my company.
Please select Agree for this question.
Employee Loyalty: 5 points
1 = Strongly Agree; 2= Agree; 3 = Undecided; 4 = Disagree; 5 = Strongly Disagree
I speak positively about my company when talking to customers.
I speak positively about my company when talking to friends and relatives.
I can recommend the products and services of my company to others.
I would like to stay with this company in the future.
I would not change immediately to another company if I got an offer.
I enjoy discussing my organization to others.
I would recommend this company to friends.
I am committed to the company’s success.
I take pride in my work.
I have a strong belonging to my organization.
103
Locus of Control: 6 points
1 = Disagree Very Much; 2 = Disagree Moderately; 3 = Disagree Slightly;
4 = Agree Slightly; 5 = Agree Moderately; 6 = Agree Very Much
Promotions are usually a matter of good fortune.
How my life takes course is dependent on me.
Success is gained through hard work.
If you know what you want out of a job, you can find a job that gives it to you.
Getting the job you want is mostly a matter of luck.
Making money is primarily a matter of good fortune.
In order to get a really good job, you need to have family members or friends in high
places.
People who perform their jobs well generally get rewarded for it.
I have little control over things that happen in my life.
It takes a lot of luck to be an outstanding employee on most jobs.
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Appendix D IRB Approval
105
BIOGRAPHICAL SKETCH
Name of Author: Lawrence E Goehrig
Graduate and Undergraduate School Attended:
University of South Alabama, Mobile, Alabama
Nova Southeastern University, Fort Lauderdale, Florida
University of Florida, Gainesville, Florida
Degrees Awarded:
Doctor of Philosophy in Business Administration, 2022, Mobile, Alabama
Master of Business Administration Certificate, 2014, Fort Lauderdale, Florida
Master of Business Administration, 2013, Fort Lauderdale, Florida
Bachelor of Science, 1986, Gainesville, Florida
Publications:
Merkle, A. C., Hessick, C., Leggett, B. R., Goehrig, L., & O’Connor, K. (2020).
Exploring the components of brand equity amid declining ticket sales in
Major League Baseball. Journal of Marketing Analytics, 8(3), 149–164.
Hair, J. F., Anderson, R. E., Mehta, R., & Babin, B. J. (2021). Sales force
management: Building customer relationships and partnerships (2nd ed.).
Wiley. [Contributor, Case Study: Midwest Auto Parts, February 2021, pp.
283-284].
106
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