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Safety ratings and their effect on operating costs in the trucking Safety ratings and their effect on operating costs in the trucking
industry: a data-driven analysis industry: a data-driven analysis
Teresa L. Smith
smith3290@marshall.edu
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SAFETY RATINGS AND THEIR EFFECT ON OPERATING COSTS IN THE
TRUCKING INDUSTRY: A DATA-DRIVEN ANALYSIS
A dissertation submitted to
Marshall University
in partial fulfillment of
the requirements for the degree of
Doctor of Business Administration
by
Teresa L. Smith
Approved by
Dr. Marc Sollosy, Committee Chairperson
Dr. Deepak Subedi, Committee Member
Mr. Paul Davis, Committee Member
Marshall University
May 2025
ii
Approval of Dissertation
We, the faculty supervising the work of Teresa L. Smith, affirm that the dissertation, Safety
Ratings and Their Effect on Operating Costs in the Trucking Industry: A Data-Driven Analysis,
meets the high academic standards for original scholarship and creative work established by the
Lewis College of Business. The work also conforms to the requirements and formatting
guidelines of Marshall University. With our signatures, we approve the manuscript for
publication.
iii
© 2025
Teresa L. Smith
ALL RIGHTS RESERVED
iv
Dedication
This dissertation is dedicated to the incredible people who have walked this journey with me,
offering their unwavering love, encouragement, and belief in my success.
To my partner, Reece, your steadfast support, encouragement, and love have been my
anchor throughout this journey. Your belief in me never wavered, even when mine did, and for
that, I am eternally grateful. Your support allows me to chase dreams I once thought were
impossible, and I am forever thankful for the strength and confidence you give me.
To my children, Nikolas and Makenzie, you are my greatest inspirations. Your love and
encouragement reminded me why I embarked on this path, and your presence in my life
continues to be my driving force. Through this journey, I hope to show you that it is never too
late to change course, chase a new dream, and achieve what once seemed impossible. However,
no accomplishment—academic or otherwise—will ever compare to the privilege of being your
mother. You are the greatest achievement I will ever have.
To my mother, Donna, and my sister, Debbie, your unwavering encouragement and belief
in me have meant more than words can express. Your constant support has been invaluable,
whether through words of wisdom, reassurance, or simply being there when I needed strength. I
am deeply grateful for your presence in my life and your role in helping me reach this milestone.
In memory of my father, Bobby, we never talked about dreams because, to you, my
sister, my children, and I were the dream. Though you are no longer here, your love and
guidance continue to shape my path. I carry your memory in all I do, and I know you would be
proud of this achievement. I wish I could share this moment with you, but I find comfort in
knowing that your influence and legacy live on.
This journey has been one of determination, sacrifice, and resilience. Through every
challenge and triumph, I have been lifted by the unwavering support of my loved ones. Tim
Cahill once said, "A journey is best measured in the people who walk it with you." This
achievement is a testament to perseverance—not just my own but also of those who stood by me,
encouraging me to press forward when the road seemed long. To them, I am forever grateful.
v
Acknowledgments
I would like to express my deepest gratitude to those who have supported and guided me
throughout this doctoral journey.
To my Committee Chair, Dr. Marc Sollosy, and my Committee Members, Dr. Deepak Subedi
and Mr. Paul Davis, thank you for your invaluable guidance, expertise, and encouragement. Your
thoughtful feedback, unwavering support, and ability to keep me sane throughout this process
have all been instrumental in my success. I am truly grateful for your dedication and
commitment to my growth.
To Dr. James Kirby Easterling, my (former) professor, mentor, colleague, and friend, your
mentorship and personal encouragement have profoundly impacted me. Your belief in my
abilities and willingness to guide me along this path have been a source of strength, and I deeply
appreciate your support.
To the professors in the Marshall University DBA program, thank you for your teaching,
guidance, and for continuously pushing us beyond what we thought was possible. Your
expertise and dedication have shaped me into a better scholar and professional, and I am forever
grateful for the knowledge and insight you have shared.
To my cohort members, this journey would not have been the same without you. Your
camaraderie, shared struggles, and unwavering support made the challenges easier to bear and
the victories even sweeter. I am honored to have walked this path with such an incredible group
of individuals: Clint B.A. Arnold, Elizabeth Arthur, Matt Carroll, Brian Cox, Eddie Fuller,
Myesha Holmes, Beth Houran, Christin Kooti, Lisa Nash, Kevin Noe, Eric Pulice, and Brittney
Theirman. The journey does not end here, my friends; it is only beginning.
While the challenges were many, they were made easier by the support, wisdom, and
encouragement of those who walked with me. I am deeply grateful for the guidance of my
mentors, the camaraderie of my peers, and the unwavering belief of those who pushed me
beyond what I thought was possible. This accomplishment reflects the knowledge you have
shared, the expectations you have set, and the support you have so generously given. I am
forever grateful.
vi
Table of Contents
List of Tables ................................................................................................................................. xi
List of Figures ............................................................................................................................... xii
Abstract ........................................................................................................................................ xiii
Chapter 1: Introduction ................................................................................................................... 1
1.1 Research Problem ............................................................................................................. 1
1.2 Motivation ......................................................................................................................... 1
1.3 Background ....................................................................................................................... 2
1.4 Research Objective ........................................................................................................... 5
Chapter 2: Literature Review .......................................................................................................... 6
2.1 Evolution of Legislation ................................................................................................... 6
2.2 Impact on the Economy .................................................................................................... 7
2.3 CSA Safety Ratings ........................................................................................................ 11
Safety Measurement System ............................................................................................. 11
BASICs ............................................................................................................................. 12
2.4 Human Factor Analysis and Classification System ........................................................ 21
Automotive ....................................................................................................................... 23
Aviation............................................................................................................................. 24
Maritime ............................................................................................................................ 26
Rail .................................................................................................................................... 28
vii
2.5 Operating Costs in the Trucking Industry ....................................................................... 29
Typical Components of Operating Costs in the Trucking Industry .................................. 30
Conclusion ........................................................................................................................ 33
2.6 Theoretical Framework ................................................................................................... 33
Chapter 3: Research Hypotheses .................................................................................................. 39
3.1 Introduction ..................................................................................................................... 39
3.2 Theoretical Framework ................................................................................................... 39
3.3 Hypothesis Development ................................................................................................ 40
3.4 Conclusion ...................................................................................................................... 43
Chapter 4: Methodology ............................................................................................................... 44
4.1 Introduction ..................................................................................................................... 44
4.2 Research Design............................................................................................................. 44
4.3 Sample Selection ............................................................................................................ 44
4.4 Data Collection ............................................................................................................... 46
Data Cleaning.................................................................................................................... 47
4.5 Data Analysis .................................................................................................................. 50
Descriptive Statistics ......................................................................................................... 50
Multiple Linear Regression (MLR) .................................................................................. 50
The general form of the multiple linear regression used in this study is: ......................... 51
Conclusion ........................................................................................................................ 52
viii
4.6 Measures ......................................................................................................................... 53
Unsafe Driving .................................................................................................................. 53
Hours-of-Service (HOS) ................................................................................................... 53
Vehicle Maintenance ........................................................................................................ 54
Controlled Substances/Alcohol ......................................................................................... 54
Driver Fitness .................................................................................................................... 54
Operating Expenses .......................................................................................................... 54
Conclusion ........................................................................................................................ 55
4.7 Ethical Considerations .................................................................................................... 55
Ethical Considerations and Guidelines ............................................................................. 55
Data Integrity and Accuracy ............................................................................................. 55
Confidentiality and Anonymity ........................................................................................ 56
Ethical Use of Secondary Data ......................................................................................... 56
Independence and Conflict of Interest .............................................................................. 56
Conclusion ........................................................................................................................ 56
Chapter 5: Results ......................................................................................................................... 58
5.1 Introduction ..................................................................................................................... 58
5.2 Descriptive Statistics ....................................................................................................... 58
5.3 Reliability Analysis ......................................................................................................... 60
5.4 Correlation Analysis ....................................................................................................... 61
ix
Pearson Correlation Analysis ............................................................................................ 61
5.5 Multicollinearity Assessment.......................................................................................... 62
5.6 Regression Analysis and Hypothesis Testing ................................................................. 64
Model Fit ........................................................................................................................... 64
Regression Analysis and Hypothesis Testing ................................................................... 66
Summary of Hypothesis Testing ....................................................................................... 69
5.7 Effect Size: Partial Eta Squared ...................................................................................... 69
5.8 Residual Analysis and Assumption Checks .................................................................... 71
Residual Normality ........................................................................................................... 71
Homoscedasticity .............................................................................................................. 73
5.9 Summary of Key Findings .............................................................................................. 75
Chapter 6: Discussion ................................................................................................................... 77
6.1 Introduction ..................................................................................................................... 77
6.2 Interpretation of Key Findings ........................................................................................ 78
Unsafe Driving and Operating Expenses .......................................................................... 79
Hours-Of-Service and Operating Expenses ...................................................................... 81
Vehicle Maintenance and Operating Expenses................................................................. 83
Controlled Substances and Alcohol and Operating Expenses .......................................... 86
Driver Fitness and Operating Expenses ............................................................................ 88
6.3 Theoretical Implications ................................................................................................. 90
x
6.4 Practical and Managerial Implications ............................................................................ 92
Implications for Fleet Management and Operational Decision-Making .......................... 92
Regulatory and Policy Implications .................................................................................. 93
Financial and Risk Management ....................................................................................... 94
6.5 Limitations of the Study.................................................................................................. 95
Data Limitations................................................................................................................ 95
Methodology Limitations.................................................................................................. 96
Industry-Specific Limitations ........................................................................................... 97
6.6 Directions for Future Research ....................................................................................... 98
Expanding the Scope of Safety Violations and Financial Analysis .................................. 98
Studying Small and Mid-Sized Trucking Firms ............................................................... 99
Assessing Regulatory Effectiveness and Policy Implications ........................................ 100
Exploring Firm-Level Strategies for Risk Management ................................................. 100
6.7 Conclusion .................................................................................................................... 101
Chapter 7: Conclusions ............................................................................................................... 102
References ................................................................................................................................... 109
Appendix A: IRB Approval Letter ............................................................................................. 129
Appendix B: FAST Act (2015) ................................................................................................... 130
xi
List of Tables
Table 1 Driver Fitness Violation Groups and Severity Weights ........................................19
Table 2 Typical Financial Quarters and SMS Scores by Adjusted Quarters ......................49
Table 3 Descriptive Statistics ..............................................................................................59
Table 4 Pearson Correlation Table ......................................................................................61
Table 5 Variance Inflation Factor (VIF) and Tolerance Values .........................................63
Table 6 Condition Index Values and Variance Proportions ...............................................64
Table 7 Model Summary.....................................................................................................65
Table 8 Regression Results for H1 .....................................................................................66
Table 9 Regression Results for H2 .....................................................................................67
Table 10 Regression Results for H3 .....................................................................................68
Table 11 Regression Results for H4 .....................................................................................68
Table 12 Regression Results for H5 .....................................................................................69
Table 13 Effect Size (Partial Eta Squared) for Predictors ....................................................70
Table 14 Tests of Normality for Residuals ...........................................................................73
Table 15 Breusch-Pagan Test for Heteroscedasticity ...........................................................74
xii
List of Figures
Figure 1 2023 Freight Volume by Transportation Mode in Billions of Tons ........................9
Figure 2 HFACS Model Level 1 Unsafe Acts .....................................................................35
Figure 3 HFACS Model with CSA Categories ....................................................................40
Figure 4 GICS Code Map of the Trucking Industry ............................................................45
Figure 5 Conceptual Framework ..........................................................................................51
Figure 6 Histogram of Unstandardized Residuals ................................................................71
Figure 7 Normal Q-Q Plot of Unstandardized Residuals .....................................................72
Figure 8 Scatterplot of Residuals .........................................................................................75
xiii
Abstract
This dissertation explored the interplay between safety ratings and financial performance
within the transportation sector, with a specific focus on the trucking industry. In an era where
safety standards are paramount, the primary objective of the study was to determine the impact
of safety ratings on the operating costs of trucking firms. Using a robust methodology grounded
in quantitative analysis and industry-specific secondary data, the study established empirical
connections between safety ratings and operating costs. This included a focused effort to
identify direct effects, highlighting the potential influence of lower safety ratings on economic
outcomes within the trucking industry.
Furthermore, the dissertation provided insights for industry stakeholders, policymakers,
and business leaders by offering a nuanced understanding of the relationship between prioritizing
safety and achieving financial performance. By examining these dynamics, the study contributed
to the broader discourse on sustainable business practices and the economic implications of
safety prioritization in the trucking sector.
1
Chapter 1: Introduction
1.1 Research Problem
The transportation industry faces the persistent challenge of mitigating accidents and the
consequential financial burden they impose (Kaewunruen et al., 2016). This challenge is
compounded by the need to ensure compliance with safety standards and regulations under the
Federal Motor Carrier Safety Administration (FMCSA), which poses a threat to the economic
viability of companies within the trucking sector of the transportation industry (Miller &
Saldanha, 2016). While these safety regulations are in place to address these perils, adherence to
such standards can pose formidable challenges for transportation companies (Awaysheh, 2024).
A critical examination of the impact of safety ratings and operating costs is crucial for devising
effective risk management strategies that enhance economic resilience while ensuring regulatory
compliance (Miller & Saldanha, 2016).
1.2 Motivation
The proposed research stems from the critical need to address the intertwined challenges
of safety management and financial sustainability faced by transportation industry members,
specifically trucking (Goel, 2014; Miller & Saldanha, 2016; Guntuka et al., 2019). The trucking
industry is a cornerstone of global commerce, facilitating the movement of goods across vast
distances, including last-mile deliveries (Douglas et al., 2019). However, the inherent risks of
these activities, including accidents and their financial repercussions, necessitate concerted
efforts to safeguard human lives and economic interests.
Accidents and safety violations pose a significant threat to the well-being of all parties
involved, from drivers and passengers to pedestrians, other road users, and property owners
(Wang et al., 2021; Haq et al., 2020; Bureau of Transportation Statistics, 2023). The
2
consequences of these incidents extend beyond the human toll, inflicting substantial financial
damage on transportation companies (Miller & Saldanha, 2016; LeMay & Keller, 2018; Haq et
al., 2020). These impacts can range from property damage (Bureau of Transportation Statistics,
2023) to legal liabilities (Thron et al., 2024) and increased insurance premiums (Chen & Jiang,
2019), which can significantly affect a company's financial sustainability.
Moreover, trucking companies operate within a strict regulatory framework with high
safety standards to ensure public safety and minimize adverse impacts (FMCSA, 2013).
Compliance with these regulations is a legal requirement and strategic imperative for companies
seeking to maintain their operational licensing and protect their brand reputation (Miller, 2020).
Navigating the complex regulatory landscape poses challenges for trucking companies,
particularly when allocating scarce resources and prioritizing compliance.
Given the dynamic nature of their environment, shaped by consistently evolving
technological (LeMay & Keller, 2019), economic (LeMay & Keller, 2019), and regulatory
factors (Williams et al., 2017; Johnston, 2019), understanding the implications of these
developments on safety ratings and financial performance is essential for maintaining
competitiveness and resilience in an ever-changing market landscape. By investigating the
nexus between safety ratings and financial performance in the trucking industry, this research
seeks to fill the gap between theory and practice by showing the correlation that better safety
scores result in improved financial performance.
1.3 Background
The trucking industry has undergone significant transformations since its inception in the
early 1900s, evolving from localized owner-operators to a cornerstone of global commerce
(LeMay & Keller, 2019; Schuster et al., 2023). The industry is pivotal in the movement of
3
goods and in shaping economic landscapes through its evolution from a heavily regulated to a
more liberalized sector. The Motor Carrier Act of 1980 marked a significant shift by
deregulating the industry, eliminating entry controls, and allowing carriers to set their rates,
leading to an influx of new entrants, increased competition, and set the stage for today’s modern
landscape of the industry (H.R.6418 - 96th Congress, 1979-1980). The deregulation aimed to
boost efficiency and lower costs, but it also introduced new safety and operational standards
challenges.
In response to these challenges, the Commercial Motor Vehicle Safety Act of 1986 was
enacted to address the growing safety concerns in the industry (S.1903 - 99th Congress, 1985-
1986). This legislation introduced comprehensive regulations focused on the licensing, testing,
and qualifications of commercial motor vehicle (CMV) operators (S.1903 - 99th Congress, 1985-
1986). This was further strengthened by the creation of the Federal Motor Carrier Safety
Administration (FMCSA) under the Motor Carrier Safety Act of 1999, tasked with overseeing
motor carrier safety programs and ensuring compliance with federal safety standards (H.R.2679
– 106th Congress, 1999-2000).
While the regulatory framework has undeniably improved safety standards (Miller &
Saldanha, 2016; Green & Blower, 2011), it has also imposed economic burdens on trucking
companies (Williams et al., 2017; Johnston, 2019). Compliance with safety regulations often
necessitates substantial financial investments. For example, adherence to Hours-of-Service
(HOS) regulations requires sophisticated electronic logging devices (ELDs) to monitor drivers’
work hours, which involves significant upfront and ongoing costs (Johnston, 2019; Miller, 2020).
Additionally, regular vehicle maintenance and mandatory safety training programs add to the
operational expenses.
4
These financial implications extend beyond direct costs. Compliance with safety
standards also affects insurance premiums (Chen & Jiang, 2019), legal liabilities (Thron et al.,
2024), and the overall financial stability of trucking firms. Companies with poor safety ratings
often face higher insurance premiums (Chen & Jiang, 2019) and legal costs (Thron et al., 2024),
which can erode profit margins. Conversely, firms with excellent safety records may benefit
from lower insurance premiums (Elias & Grekin, 2021) and enhanced market reputation,
attracting more business.
These factors make the relationship between safety ratings and financial performance
complex and multifaceted. Existing research indicates that investing in safety can improve CSA
ratings and enhance economic outcomes. Studies have shown that firms that allocate resources
to safety measures, such as advanced operator training and preventative maintenance, tend to
achieve better safety scores during periods of economic stability (Miller & Saldanha, 2016;
Miller et al., 2018).
However, the scenario often reverses during times of financial constraints. When
resources are scarce, companies may be tempted to cut corners on safety measures, leading to
deteriorating safety ratings and increased risk of accidents (Miller & Saldanha, 2016; Miller,
2020). This cyclical relationship underscores the necessity of a balanced approach to safety
management, ensuring that short-term financial pressures do not undermine long-term safety and
operational integrity. The challenge for trucking companies is managing these competing
priorities effectively to maintain safety and profitability.
5
1.4 Research Objective
Academic research has extensively examined the relationship between firm financial
performance and safety (Britto et al., 2010; Miller & Saldanha, 2016; Miller, 2020; Soro et al.,
2023). This research demonstrates that investing additional resources in safety can improve CSA
safety ratings, particularly in times of financial abundance. However, a critical question remains:
What happens when resources are scarce? This scenario frequently occurs in the trucking
industry. During financial constraints, trucking companies often allocate fewer resources to
safety measures, training, and protocols (Soro et al., 2023; Miller & Saldanha, 2016).
Specifically, this research seeks to answer the question: How do safety ratings impact operating
costs in the trucking industry?
Therefore, this research aims to determine the direct impact of safety ratings on operating
costs within the transportation sector, specifically focusing on the trucking industry. This study
aims to quantify the direct impact of safety ratings on firms' operating costs by comprehensively
analyzing industry-specific secondary data. This research uses robust quantitative methods to
provide a nuanced understanding of how safety ratings directly influence operating costs.
Ultimately, this study offers practical recommendations for enhancing compliance with
safety regulations, improving financial performance, and promoting sustainable business
practices within the trucking industry, particularly when resources are limited. By addressing
these critical issues, the research will provide valuable insights to industry stakeholders,
policymakers, and business leaders, aiding them in navigating the complexities of maintaining
safety and profitability in a challenging regulatory and economic environment.
6
Chapter 2: Literature Review
2.1 Evolution of Legislation
The evolution of legislation within the transportation industry reflects a dynamic
interaction between societal, technological, and regulatory factors that shape the landscape of
today’s management safety practices and FMCSA compliance obligations. The Motor Carrier
Act of 1980 launched the trucking industry observed today by deregulating it, abolishing entry
controls, and allowing carriers to set their rates. This led to an explosion of new carriers entering
the industry as they were no longer required to obtain government approval before starting
operations.
Following the deregulation, Congress enacted the Commercial Motor Vehicle Safety Act
in 1986, introducing comprehensive regulations to enhance safety standards within the
transportation industry. Specifically, the act focused on the licensing, testing, and qualifications
of commercial motor vehicle (CMV) drivers by establishing Federal minimum standards and
prohibiting an operator from holding multiple operator’s licenses, thus launching the
Commercial Driver’s License (CDL) system (S.1903 - 99th Congress, 1985-1986).
The Motor Carrier Safety Act of 1999 represents a pivotal legislative framework focused
on CMVs to improve safety standards within the transportation industry further. One of the key
provisions of this law is the formation of the Federal Motor Carrier Safety Administration
(FMCSA). The FMCSA operates under the umbrella of the U.S. Department of Transportation
(DOT) and oversees motor carrier safety programs and research initiatives. Additionally, the Act
institutes strict measures to ensure the competence and compliance of CMV operators, including
provisions to disqualify individuals with revoked, suspended, or canceled CDLs and enhancing
7
enforcement mechanisms by requiring states to comply with the regulations set forth (H.R.2679 -
106th Congress, 1999-2000).
Introduced in 2010, the FMCSA introduced the Compliance, Safety, Accountability
(CSA) program in its continued effort to improve safety in the CMV industry (Miller, 2017). The
data-driven CSA program is designed to improve safety and prevent crashes, injuries, and
fatalities. It aims to target high-risk carriers and drivers more effectively using a data-centric
methodology.
The CSA program consists of several databases, such as the Safety Measurement System
(SMS) and Behavior Analysis and Safety Improvement Categories (BASICs), targeting multiple
dimensions of safety that provide carriers with monthly updated information on their safety
performance (Miller, 2017). Data is collected from roadside inspections and utilized to generate
a CSA score for each motor carrier company in the U.S. It has undergone multiple updates since
inception, with each update focused on improving safety outcomes within the industry. In an
environment where safety needs and challenges are consistently evolving, the updates to the
program are essential to maintaining and enhancing the program’s effectiveness.
Former President Obama signed the Fixing America’s Surface Transportation Act (FAST
Act) into law in December 2015. This Act provided updates and introduced vital legislation
regarding the operation of CMVs in the United States. Six (6) key areas are identified in the Act
that directly impact the operation of CMVs, ranging from insurance regulations, safety standards,
and driver regulations to technology and data.
2.2 Impact on the Economy
The transportation industry plays a critical role in the regional, national, and global
economic framework, with the trucking industry being a significant contributor. Numerous
8
studies underscore this industry's importance, highlighting its pervasive influence across
different scales of economic activities (Shin et al., 2019; Hamid et al., 2020; Lemke et al., 2022;
Schuster et al., 2023; Modjadji et al., 2022; Coiquaud, 2016; Hamid et al., 2020).
The American Trucking Associations (ATA), the largest national trade association for the
trucking industry in the United States, reported that the trucking industry generated $940.8
billion in gross freight revenues in 2022. This substantial revenue is derived from primary
shipments, which are shipments transported from the original location to the next, not the final
destination or last-mile delivery, representing 80.7% of the US freight bill for the year. This
figure highlights the trucking sector’s dominance and pivotal role in logistics and supply chains
(Economics & Industry Data, n.d.).
Furthermore, data from the U.S. Department of Transportation’s Transportation Statistics
Annual Report for 2023 revealed that the trucking industry maintained its position as the leading
mode of freight transportation in the U.S. During 2023, trucks moved 12.6 billion tons of cargo,
valued at over $13.6 trillion. This represented 64.5 percent of the total freight weight and
72.5 percent of the total freight value (Bureau of Transportation Statistics, 2023). These
statistics underscore the trucking industry’s dominance over other modes of freight
transportation, such as rail, air, and maritime, as shown in Figure 1.
Additionally, the trucking industry also significantly impacts employment within the U.S.
economy. In 2022, the ATA reported that 8.4 million people, excluding those who are self-
employed, are employed in the trucking industry in some capacity. Among these, truck drivers
alone accounted for 3.54 million, marking an increase of 1.5 percent from 2021 (Economics &
Industry Data, n.d.). This increase in employment demonstrates the industry’s growth and its
critical role in providing jobs and supporting livelihoods.
9
Figure 1
2023 Freight Volume by Transportation Mode in Billions of Tons
Note. 2023 Freight volume by transportation mode in billions of tons. *Air statistics contain air
freight and air/truck combination freight. Statistics from “Transportation Statistics Annual
Report 2023,” published by the U.S. Department of Transportation, 2023.
The vast employment in the trucking sector reflects its extensive reach and various roles,
from drivers to logistics planners, mechanics, and administrative staff. This broad employment
base contributes significantly to the overall economic stability and growth by providing income
and sustaining consumer spending (Black et al., 2017; Miller, 2018).
At the regional level, the trucking industry facilitates commerce and trade by ensuring the
smooth movement of goods across different areas, thereby supporting local economies. Studies
have shown that efficient freight transportation systems are crucial for regional economic
development (Shin et al., 2019; Hamid et al., 2020). The trucking industry's ability to deliver
10
goods promptly and reliably enhances the competitiveness of regional businesses and can attract
investment and industry to these areas.
On a national scale, the trucking industry is integral to the supply chain, linking
producers with consumers and businesses with markets. Reliable means of transportation helps
stabilize prices and ensure the availability of goods, thereby contributing to economic stability
and growth (Lemke et al., 2022; Schuster et al., 2023). The industry's capacity to handle a vast
majority of freight transport needs underscores its critical role in maintaining economic
continuity and resilience.
Internationally, the trucking industry supports global trade by facilitating the
transportation of goods across borders and to ports for export. This function is vital for countries
engaged in international trade, as it ensures that products can reach global markets efficiently
(Modjadji et al., 2022; Coiquaud, 2016). Integrating trucking services with other modes of
transportation, such as maritime and air freight, creates a cohesive logistics network that supports
global commerce.
The synergy between trucking and international trade also underscores the industry's
adaptability and importance in global supply chains. As economies become more
interconnected, the demand for efficient freight transportation grows, further highlighting the
trucking industry's pivotal role in supporting global economic activities.
The trucking industry is undeniably a cornerstone of the regional, national, and
international economies. Its significant contribution to freight revenue, dominant role in freight
transportation, substantial employment provision, and critical support for regional and global
trade underscores its multifaceted economic impact. The industry's ongoing growth and ability
to adapt to changing economic conditions ensure that it will remain a vital component of the
11
economic infrastructure, driving commerce and trade into the future. The comprehensive
influence of the trucking industry, as evidenced by its revenue generation and employment
capabilities, highlights its indispensable role in supporting and sustaining economic activity
across various levels.
2.3 CSA Safety Ratings
The FMCSA aims to improve the trucking industry's safety by “reducing crashes,
injuries, and fatalities (Our Mission, 2013).” One critical program utilized for this effort is the
CSA program, launched in 2010. The program has four goals: identifying carriers that pose a
safety hazard, intervening with those carriers, assigning CSA scores, and prohibiting carriers
with an “unsatisfactory” rating from operating CMVs (3 How FMCSA Monitors Motor Carrier
Safety). Using data collected from roadside inspections, the FMCSA generates a CSA score for
every commercial carrier from the company’s registration throughout the business's life (3.2
Compliance, Safety, Accountability (CSA) program). The agency accomplishes this with three
(3) core components: the Safety Measurement System, the Safety Ratings Process, and the
Intervention Process (3.2 Compliance, Safety, Accountability (CSA) program).
Safety Measurement System
The Safety Measurement System is designed to integrate safety regulations regarding
CMVs and operators. It evaluates compliance with current regulations and prioritizes carriers for
interventions based on their on-road performance and roadside inspection results. The
evaluation encompasses seven (7) Behavior Analysis and Safety Improvement Categories
(BASICs). These categories are Unsafe Driving, Crash Indicator, Hours-of-Service (HOS)
Compliance, Vehicle Maintenance, Controlled Substances/Alcohol, Hazardous Materials (HM)
Compliance, and Driver Fitness. Each BASICs category generates a quantifiable measure of a
12
carrier’s performance using the data gathered from roadside inspections. The derived scores
highlight carriers at substantial risk for crashes and, therefore, are subject to FMCSA
interventions.
The efficacy of CSA scores and the Safety Measurement System (SMS) has been a
subject of considerable debate within academic literature. Numerous studies have presented
varied perspectives and insights on the subject. While several studies have raised concerns about
their effectiveness (Mitra, 2016; Miller et al., 2018; Henrio, 2018), others have countered with
evidence supporting their utility (LeMay & Keller, 2019; Forlines et al., 2019). In fact, an
FMCSA-funded report was released in February 2014 stating that the CSA program is more
effective than its predecessor, SafeStat. However, that same week, an audit released by the
Government Accountability Office questioned whether CSA’s Safety Measurement System
assigned scores fairly (Bukowski, 2014). Nevertheless, regardless of the ongoing debate and
contrasting viewpoints, it remains imperative to acknowledge that carriers are obligated to
operate within the system's established parameters.
BASICs
The seven (7) BASICs categories are integral to the CSA scores and the overall safety of
the public, operators, pedestrians, property owners, shippers, consignees, and other supply chain
businesses (Miller, 2017). It is important to note that data from all BASICs categories and CSA
scores for companies operating in the trucking industry were available to the public until 2015,
when Congress passed the Fixing America’s Surface Transportation (FAST) Act. During that
period, companies and trucking industry associations contended that public awareness of these
scores could adversely impact the profitability of firms with poorer scores compared to
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counterparts (Zhang & Zhou, 2023). Currently, the data is publicly available for five (5)
categories, which are discussed in further detail below.
Unsafe Driving
In the realm of BASICs, the Unsafe Driving category stands out as a pivotal element, focusing
on carriers and drivers operating a Commercial Motor Vehicle (CMV) in a manner deemed
unsafe or hazardous, thereby increasing the risk of accidents and endangering public safety. This
category assesses behaviors that pose significant risks, such as speeding, reckless driving,
improper lane changes, and failure to obey traffic control devices. These behaviors jeopardize
CMV operators' safety and endanger other road users, pedestrians, and property within the
transportation route.
Speeding is a leading cause of CMV accidents, contributing to a disproportionate number
of fatalities and serious injuries on the country’s roadways (Haq et al., 2020). The strict
regulations set forth by the FMCSA not only enforce the posted speed limits but also add
additional restrictions when there are adverse weather conditions and in congested traffic areas.
In addition, CMV operators are to be in compliance with the laws, regulations, and rules of the
jurisdiction where the CMV is being operated unless the FMCSA sets forth a more stringent
standard, in which case, adherence to the FMCSA regulations becomes mandatory (Driving of
Commercial Motor Vehicles, 2024).
Hours-of-Service Compliance
The Hours-Of-Service (HOS) category, as implemented by the FMCSA, is a significant
metric in evaluating the safety performance of motor carriers within the framework of the CSA
program. This category serves as a pivotal tool for assessing violations related to records of duty
status (RODS) and driver fatigue management, which are vital components of the Federal Motor
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Carrier Safety Regulations (FMCSRs) (CSA, 2013b). The HOS category is instrumental in
identifying carriers and drivers at higher risk of fatigue-related incidents due to non-compliance
with HOS regulations (CSA, 2013b). It evaluates the adherence of carriers and drivers to a range
of regulations, including the 11-hour driving limit within a 14-hour duty period, mandatory rest
breaks, sleeper berth provisions, on-duty/not driving periods, maximum weekly driving limits,
and the 34-hour restart provision (Goel, 2014).
Moreover, the HOS category has become a focal point of extensive research and
discourse within BASICs. The significance of this category is underscored by its significant
revisions in 2004 and 2013, which prompted heightened scrutiny regarding their impact on both
safety outcomes and the operational dynamics of motor carriers. Scholarly investigations have
explored the multifaceted implications of these regulatory changes, mainly focusing on the
effects on firm profitability. Studies by Goel (2014) and Johnston (2014) have notably suggested
that the negative economic impact of reducing the daily and weekly allowable driving time is
offset by potential safety and driver well-being benefits.
Vehicle Maintenance
Vehicle maintenance is another vital component within the CSA program, serving as a
cornerstone for ensuring safety, efficiency, and regulatory compliance. Effective maintenance
protocols focus on the systematic upkeep and repair of CMVs to prevent mechanical failures that
could lead to accidents or operational inefficiencies (Škerlič et al., 2020; Federal Motor Carrier
Safety Administration, 2012c). This area of CSA assesses the thoroughness and regularity of
inspections, repairs, and servicing routines, highlighting the importance of proactive measures to
mitigate risks associated with vehicle malfunctions (Federal Motor Carrier Safety
Administration, 2012c).
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Regular maintenance is essential to the safe operation of CMVs, as mechanical failures
can result in more severe consequences compared to other vehicles, including accidents, injuries,
and fatalities (Škerlič et al., 2020). Key focus areas include addressing mechanical defects such
as an inoperative braking system or lights and proper maintenance of tires and steering
mechanisms (Federal Motor Carrier Safety Administration, 2012c). For instance, in a 2022
study completed in China, brake system failures were found to be the major contributor to 45
percent of CMV accidents (Wang, F. et al., 2022). Most of these braking system failures were
due to inadequate maintenance or inspection.
The FMCSA mandates strict adherence to vehicle maintenance requirements yet does not
explicitly define the time frame or frequency for the maintenance performance. The regulations
specify that carriers “must systematically inspect, repair, and maintain, or cause to be
systematically inspected, repaired, and maintained, all motor vehicles and intermodal equipment
subject to its control” (Federal Motor Carrier Safety Administration, 2024b, p. 2). However,
there is a requirement for a comprehensive annual inspection to be completed by a certified
inspector or mechanic, as well as pre-trip and post-trip inspections that are to be completed by
the driver before and upon completion of any travel that moves the CMV (Federal Motor Carrier
Safety Administration, 2024b, pp. 7-8).
Proper vehicle maintenance prevents accidents, contributes to operational efficiency, and
saves money (Škerlič et al., 2020), making it a highly relevant component of current research.
Conducting systematic maintenance on CMVs can prevent unexpected breakdowns, which not
only delay deliveries but can also damage cargo and improve the availability and utilization of
the unit, all of which contribute to cost savings (Škerlič et al., 2020). Moreover, regular
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preventative maintenance can extend the lifespan of the unit, optimizing the return on investment
for carriers (Škerlič et al., 2020).
The FMCSA’s regulatory framework underscores the importance of vehicle maintenance
through stringent guidelines and periodic audits. Non-compliance with maintenance
requirements can result in significant penalties, including safety interventions, fines, and the
suspension of operating privileges (Federal Motor Carrier Safety Administration, 2012c). By
investing in regular and systematic preventative maintenance, carriers and drivers can minimize
risks, enhance operational efficiencies, and uphold safety and reliability standards in the
industry.
Controlled Substances/Alcohol
The alcohol and controlled substances category of the CSA program aims to address the
significant risks associated with impaired driving among CMV operators. This category
evaluates the prevalence and effects of alcohol and controlled substances on CMV operations,
highlighting the severe consequences for road safety, public health, and regulatory compliance
(Federal Motor Carrier Safety Administration, 2012a). According to the National Highway
Traffic Safety Administration (NHTSA), in 2022, an accident involving impaired driving with a
fatality occurred, on average, every 39 minutes in the United States (Drunk Driving, n.d.). While
the NHTSA reports that only 3 percent of these accidents involved a truck with a gross vehicle
weight of ten thousand pounds or more, research shows alcohol and controlled substances are an
issue for CMV operators in the U.S. (Haq et al., 2020).
The abuse of alcohol and drugs by CMV operators is a leading cause of impaired driving
incidents, contributing to a disproportionate number of accidents, injuries, and fatalities on the
highways of the U.S. and nations around the world (Bragazzi et al., 2018; Haq et al., 2020;
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Wang et al., 2022; Batson et al., 2022). CMV operators may have a propensity to engage in
unhealthy behaviors, such as binge drinking, potentially due to the nature of long-hauls. A
worldwide meta-analysis showed that 19 percent of truck drivers partake in regular binge
drinking, while an additional 9.4 percent have a regular pattern of everyday drinking (Baston et
al., 2022).
With patterns like these showing themselves around the globe, the FMCSA enforces
strict regulations to prevent alcohol and substance abuse among CMV operators. These include
mandatory testing protocols and stringent penalties (Federal Register, 2009) for violations,
including fines and potential loss of the operator’s license (Federal Motor Carrier Safety
Administration, 2024a). The testing regulations mandate pre-employment, random, post-
accident, and reasonable suspicion testing to ensure operators are not under the influence of
impairing substances while operating CMVs (Federal Motor Carrier Safety Administration,
2024a). The potential penalties for operators found to be in violation of the regulations include
suspension of up to a year or revocation of commercial driving privileges, substantial monetary
fines, and potential criminal charges. Penalties can also be assessed to the motor carrier in the
form of out-of-service orders, fines, and criminal charges. Motor carriers have three (3) days
from the date of knowledge of the violation to report it to FMCSA, or they risk additional
penalties (Federal Register, 2009).
In addition to regulatory measures, the CSA program encourages carriers to implement
comprehensive substance abuse prevention programs (CSA, 2013a). These programs typically
include education and training initiatives designed to raise awareness regarding the dangers of
alcohol and drug use, provide support systems for drivers struggling with substance abuse, and
promote a culture of safety and well-being (CSA, 2013a). In fostering a proactive approach to
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substance abuse prevention, carriers can reduce the incidence of impaired driving and enhance
overall safety on highways (CSA, 2013a).
The impact of alcohol and controlled substance use and abuse extends beyond the
immediate risk of accidents. Impaired drivers compromise the reputation of carriers, leading to
increased scrutiny from regulatory bodies and the public. Additionally, substance abuse issues
can result in higher insurance premiums (Chen & Jiang, 2019), legal costs (Thron et al., 2024),
and a loss of trust and revenue from clients and stakeholders (Ellis & Grekin, 2021), causing a
direct impact on operating expenses, as suggested by the current study. Thus, addressing this
abuse is a regulatory requirement and a critical component of risk management and operational
integrity (Bamberger & Cohen, 2015).
Driver Fitness
Driver fitness is arguably one of the least researched and discussed categories within the
CSA BASICs measures. However, it is essential to ensure that CMV operators are qualified
medically, have the proper training and experience to operate a CMV, and perform their duties
safely (Federal Motor Carrier Safety Administration, 2012b). This category evaluates operators'
health and physical qualifications, addressing medical conditions and certification processes
crucial for safe CMV operations (Federal Motor Carrier Safety Administration, 2012b). Within
this BASICs category, there are seven (7) violation groups, as shown in Table 1.
Maintaining driver fitness is imperative for preventing accidents caused by medical
conditions that can impair an operator’s ability to operate a CMV safely. Medical conditions
such as sleep apnea, cardiovascular diseases, and diabetes can significantly affect a driver’s
alertness and response times, increasing the risk of accidents (Batson et al., 2022).
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Table 1.
Driver Fitness Violation Groups and Severity Weights.
Note. Adapted from “The Driver Fitness Violations Basic” by Riegel, M. (2022), Blue Ink Tech
Blog.
The FMCSA requires drivers to undergo regular medical examinations to ensure they
meet the physical qualifications necessary for the safe operation of a CMV. These examinations
are typically required every two years. However, some waivers require every-year examinations,
and they must be conducted by certified medical examiners listed in the National Registry of
Certified Medical Examiners (Federal Motor Carrier Safety Administration, 2024a).
The regulations stipulated by the FMCSA require operators to meet specific medical
standards to obtain and maintain their CDL licenses. These standards are designed to identify
and mitigate the risks associated with health conditions that could impair a driver’s ability to
operate a CMV safely. For instance, drivers must have adequate vision and hearing, be free from
epilepsy, and effectively manage chronic conditions such as diabetes and hypertension.
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In fact, before 2003, being an Insulin-Treated Diabetes Mellitus (ITDM) patient, which
has a higher prevalence rate among CMV operators than the general population (Batson et al.,
2022), had been a disqualifying condition since 1970 (Federal Register, 2018). With rulings in
2003 and 2005 granting a waiver for ITDM patients (Federal Register, 2018), most remained out
of the industry due to the considerable time and financial resources required to obtain the
necessary waiver. A final ruling in November 2018 removed the conditions to obtain the waiver.
It allowed those affected by ITDM to return or remain in the industry with medical certification
every twelve (12) months (Federal Register, 2018).
The research underscores the crucial role of driver fitness standards in reducing accidents
caused by medical issues. For instance, strict adherence to these standards, such as treating
Obstructive Sleep Apnea (OSA) and other chronic conditions (Garbarino et al., 2016; N, E. et
al., 2019), can significantly reduce the incidence of such accidents. A 2016 study by Garbarino
et al. found that professional operators have almost double the risk of injuries when OSA is
present and have a higher odds ratio (OR) for accidents than other occupational categories
(Garbarino et al., 2016). Similarly, studies have shown that drivers with poorly controlled
diabetes or hypertension are at a higher risk of experiencing medical emergencies while driving,
which can lead to catastrophic outcomes (Cox et al., 2017; Yosef, 2020).
The driver fitness category also evaluates motor carriers' compliance with these medical
certification requirements. Carriers are responsible for ensuring that their operators meet the
FMCSA’s medical standards and maintain valid medical certificates. Failure to comply with
these requirements can result in significant penalties, including fines and out-of-service orders
(FMCSA, 2024c). Additionally, this non-compliance can adversely affect a carrier’s CSA
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scores, leading to additional scrutiny from regulatory bodies (FMCSA, 2024c) and potentially
increased insurance premiums (Chen & Jiang, 2019).
While driver fitness is often under-researched within the BASICs measures, it is vital to
ensure CMV operations' safety and efficiency. This focus on medical qualifications and the
management of chronic health conditions directly impacts accident prevention, overall road
safety, and, thereby, operating costs incurred by carriers. Ensuring compliance with FMCSA’s
stringent medical standards helps mitigate risks associated with impaired driving due to health
issues.
2.4 Human Factor Analysis and Classification System
The Human Factor Analysis and Classification System, a pioneering work by Dr. Scott
Shapell and Dr. Douglas Wiegmann in 2000, is a comprehensive framework aimed at
understanding, categorizing, and analyzing the human factors that impact a system’s
performance and safety. Derived from James Reason’s seminal Swiss Cheese model of human
failure, HFACS is a robust tool for investigating any underlying human errors that may
contribute to workplace safety incidents. It delineates these errors across four levels: unsafe acts,
which will be the focus of the present research; preconditions for unsafe acts; unsafe supervision;
and organizational influences (Shapell & Wiegmann, 2000).
Initially tailored for the aviation sector, the HFACS framework has witnessed widespread
adoption across various industries in the past two decades. Its applicability has extended beyond
aviation to encompass automotive (Zhang et al., 2018; Wang, 2021), rail (Madigan et al., 2016;
Ebrahimi et al., 2021), maritime (Kaptan et al., 2021; Kahn, 2022; Li et al., 2021), chemical
(Theophilus, 2017; Nwankwo et al., 2021; Zarei et al., 2019), healthcare (Jalali et al., 2024;
Cohen et al., 2018; Diller et al., 2014), and construction (Tang et al., 2022; Wang et al., 2024)
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sectors. This adaptability underscores its versatility and effectiveness in addressing safety
concerns across diverse operational landscapes.
By utilizing the HFACS framework, organizations across different industries can gain
valuable insights into the factors contributing to human errors and related safety incidents.
Organizations can then implement targeted interventions, strategies, and protocols to mitigate
risks and enhance and improve overall safety (Manjunath & Bharath, 2023). In addition to being
utilized across vast industries, the framework has undergone many modifications for specific
industries (Nwankwo et al., 2021). These modifications have allowed for a more customized
and tailored approach to analyzing human factors and improving safety within each specific
industry (Wang et al., 2020; Manjunath & Bharath, 2023).
For instance, in the coal mining industry, a tailored version of the framework, HFACS-
CM, has been implemented with five (5) specific categories, nineteen subcategories, and forty-
two unsafe factors (Fa et al., 2021). Similarly, within the oil and gas industry, the HFACS-OGI
framework has highlighted the pivotal role of individual and team capacity as preconditions for
unsafe acts, emphasizing the need for and importance of integrating organizational and
environmental factors into accident analysis (Theophilus et al., 2017; Nwankwo et al., 2021).
Moreover, the healthcare domain has also implemented a modified framework. The HFACS-
MES has included a fifth causal level, extra-organizational issues, while also incorporating the
management of change, patient safety culture, patient-related factors, and task elements as causal
categories across various levels of the model (Jalali et al., 2024).
The extensive utilization and continual adaptation of the HFACS framework across
diverse industries is compelling evidence of its adaptability and resilience. Initially crafted for
the aviation sector, an industry synonymous with intricate systems and high-stakes operations,
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HFACS has transcended its original application and emerged as a cornerstone in safety
management across a myriad of industries. While its inception is rooted in aviation, the
framework has seamlessly integrated into other transportation modes, including automotive, rail,
and maritime sectors.
The use of HFACS in these transportation modes is particularly significant for this study,
as the automotive industry operates in the same physical environment as the trucking industry.
In contrast, aviation, rail, and maritime industries share similarly stringent government-imposed
regulatory environments. Understanding how HFACS has been applied and its impact on safety
in these industries will provide valuable insights that can be adapted for the trucking industry.
Automotive
The use of HFACS in the automotive industry has been limited. Still, Zhang et al. (2018)
introduced an innovative method integrating the HFACS and the Contributory Factor
Interactions Model (CFIM) to analyze 396 road traffic accidents in China. The study revealed
"unsafe behaviors" as the most frequent contributory factor, with "violations" being the highest
at the subcategory level (Zhang et al., 2018). This approach demonstrated that latent failures,
external factors, organizational influences, unsafe supervision, and preconditions for unsafe
behaviors could influence active failures and each other, highlighting the complex interplay of
contributory factors in road traffic accidents. Despite some limitations, the findings emphasize
the importance of adopting a systems-based approach to analyze road traffic accidents (Zhang et
al, 2018).
Wang et al. (2021) presented a comprehensive framework for incorporating accident
liability into crash risk analysis using HFACS to enhance the framework. The study identified
risk factors contributing to accidents using a multidimensional risk source approach and
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developed an Accident Risk Quantify Model (ARQM) to measure the impact of these risk
factors. Descriptive statistics revealed that human-related risk sources were predominant, with
law and regulation violations as prominent risk factors. Analyzing 364 fatal accidents, the study
provided insights into the causes of accidents, guiding safety interventions and traffic
management strategies, and contributing to global road safety efforts.
The studies demonstrate the effectiveness of incorporating the HFACS in analyzing road
traffic accidents. Integrating HFACS with other frameworks provided a comprehensive
understanding of the contributory factors and risk sources. The findings emphasize the
importance of adopting a systems-based approach to identify and mitigate the complex interplay
of factors contributing to incidents. These insights are valuable for developing interventions and
traffic management strategies.
This dissertation will extend this perspective by investigating how the interplay of
various safety ratings affects operating costs in trucking firms. Identifying the key drivers of
operating costs linked to safety ratings will provide valuable insights for developing cost-
effective safety interventions for trucking companies.
Aviation
The prevalence of academic literature employing the HFACS model in aviation surpasses
that in other industries, primarily because the model was initially developed for the aviation
sector. The aviation industry comprises three (3) distinct sectors: commercial aviation, general
aviation, and military aviation. Commercial aviation includes flights for passengers and cargo.
General aviation encompasses those that operate private, business, or recreational aircraft.
Military aviation covers any aircraft operated by armed forces. The HFACS model has been
utilized in academic research across all three sectors of aviation (Yang & Mott, 2020; Illankoon
25
et al., 2019; Hulme et al., 2019) and in various countries (Hulme et al., 2019; Chi et al., 2022)
around the globe.
A literature review conducted by Hulme et al. in 2019 included fifteen aviation studies
using the HFACS or modified versions from 2000 to 2018. This review classified human errors
into skill-based errors, decision errors, perceptual errors, and violations, helping identify specific
areas for effective interventions. It also noted the significant role of organizational factors, such
as inadequate supervision, organizational climate, and resource management, highlighting the
need for systematic changes to improve safety (Hulme et al., 2019).
Despite its original design for this sector, numerous modified versions of HFACS have
been developed for various industries, including aviation. Some studies included in Hulme et
al.’s review incorporated an additional fifth level to include internal and external factors. These
studies emphasized that considering industry-wide influences and the regulatory environment is
crucial for developing effective safety interventions that address broader systematic issues
(Hulme et al., 2019).
Lyu et al. (2019) introduced a modified HFACS-BN model, integrating subjective expert
information with objective accident data to evaluate Air Traffic Control (ATC) performance.
Analyzing 142 aviation accidents, the study identified twenty-five key human factors
emphasizing unsafe acts and supervision as significant safety impacts. The model was validated
through rigorous methods, demonstrating its effectiveness in identifying and analyzing human
factors, thereby enhancing safety management and accident prevention strategies. Key findings
included high instances of procedural noncompliance and ineffective monitoring. These findings
are relevant to the current study regarding HOS compliance. With insufficient monitoring, HOS
compliance can quickly lapse into non-compliance.
26
In recent years, HFACS use in aviation has continued. Vempati et al. (2023) used the
model to identify key indicators of non-fatal accidents or incidents in general aviation by
analyzing safety reports from the Aviation Safety Reporting System (ASRS) (Vempati et al.,
2023). This study continued using HFACS as a precursor to actual accidents, mirroring the
current study’s approach in the trucking industry.
Recent literature consensus is that while human error types may differ across the three
aviation sectors, common factors include crew resource management, situational awareness,
adherence to procedures, and comprehensive training and safety management practices (Hulme
et al., 2019; Lyu et al., 2019; Vempati et al., 2023). This alignment underscores the potential for
applying similar HFACA principles and methodologies to enhance safety management practices
in the trucking industry.
Maritime
HFACS has substantial applicability within the maritime industry, serving as a critical
tool for analyzing and improving safety. The maritime sector, characterized by complex
operations and high-risk environments, benefits significantly from HFACS’s structured approach
to dissecting human errors and their underlying causes. Studies by Kaptan et al. (2021), Kahn
(2022), and Li et al. (2021) underscore the framework’s effectiveness in identifying the human
factors contributing to incidents and accidents at sea.
Kaptan et al. (2021) reviewed five modified HFACS models developed for use in
maritime accidents, addressing unique challenges, such as different ship types and accident
scenarios. Applying HFACS to 230 accident reports, the study found that skill-based errors and
violations were the most frequent contributors to unsafe acts and 949 non-conformities (Kaptan
27
et al., 2021). The research highlighted the necessity for enhanced training programs and stricter
adherence to safety protocols to mitigate these errors (Kaptan et al., 2021).
Li et al. (2022) employs the HFACS model alongside Bayesian network methodologies
to investigate human and organizational factors in ship collision accidents on the Yangtze River
(Li et al., 2022). Analyzing data from 191 ship collision incidents, the study identified key
factors such as adverse meteorological and hydrological conditions, perception failures, decision-
making failures, and execution failures (Li et al., 2022). The findings underscore the significant
impact of human factors on ship collisions and suggest that mitigating these factors can
substantially enhance maritime safety (Li et al., 2022).
Kahn et al. (2022) analyzed 352 hazardous cargo accidents at ports from 1960 to 2018
using the modified HFACS-PEHCA framework. The study identified key factors, including
violations, limited intellect, inappropriate supervision, and inadequate safety culture (Kahn et al.,
2022). They highlighted five causal paths leading to accidents, emphasizing the need for direct
safety procedures, such as enhanced training and supervision, and indirect measures, like
fostering a robust safety culture (Kahn et al., 2022). Violations were the predominant cause of
incidents, highlighting the importance of addressing human factors to prevent accidents while
handling hazardous cargo.
In summary, these studies illustrate HFACS’s application within the maritime industry,
highlighting the model’s adaptability and effectiveness in addressing human factors and
enhancing safety. Extending the HFACS model into the trucking industry utilizing the CSA
BASICs categories at the level of unsafe acts will further demonstrate the model’s versatility and
provide valuable insights for developing effective safety interventions in this new context.
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Rail
The implementation of the HFACS within the rail industry represents a transformative
leap in safety management and accident analysis (Madigan et al., 2016; Bhuiyan et al., 2023).
HFACS has been tailored to address the unique challenges of the rail sector, where human error
is a significant factor in safety incidents (Zhan et al., 2017; Bhuiyan et al., 2023). Its application
has provided valuable insights into the complex nature of human factors influencing rail
accidents and paved the way for targeted interventions to enhance safety (Ebrahimi et al., 2021;
Bhuiyan et al., 2023).
Madigan et al. (2016) conducted a seminal study utilizing seventy-four investigation
reports, applying HFACS to rail accidents. Their findings revealed that skill-based errors related
to work distractions and environmental factors were predominant contributors to unsafe acts
(Madigan et al., 2016). This study underscored the importance of enhancing training programs
and implementing advanced simulation-based training to prepare rail personnel for real-world
scenarios (Madigan et al., 2016).
Building on this, Ebrahimi et al. (2021) explored forty-two incidents from 2007 to 2018
in Canada, focusing on organizational factors in rail accidents involving dangerous goods
(Ebrahimi et al., 2021). Key factors identified included skill-based errors, violations, inadequate
supervision, and organizational influences (Ebrahimi et al., 2021). The findings emphasized the
need for systemic organizational changes to foster a proactive safety culture and ensure adequate
resource allocation for safety measures (Ebrahimi et al., 2021). Ebrahimi et al. advocated for
regular safety audits and continuous improvement initiatives to maintain high safety standards in
rail operations (Ebrahimi et al., 2021).
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The adaptation of HFACS in the rail industry marks a significant advancement in safety
management (Madigan et al., 2016; Ebrahimi et al., 2021; Bhuiyan et al., 2023). Tailored to
address rail-specific challenges, HFACS has provided insights into human factors influencing
rail accidents, paving the way for targeted safety interventions (Zhan et al., 2017; Bhuiyan et al.,
2023). Studies by Madigan et al. (2016) and Ebrahimi et al. (2021) highlight its efficacy in
identifying skill-based errors, violations, and organizational issues.
The framework's adaptation, including HFACS-RA (Zhan et al., 2017), has identified
critical areas for improvement, such as maintenance protocols and communication systems. By
analyzing human errors and contributing factors, HFACS empowers rail organizations to
enhance training, supervision, and overall safety, ensuring its continued relevance and
effectiveness (Zhan et al., 2017; Ebrahimi et al., 2021; Bhuiyan et al., 2023). The success of
HFACS in the rail industry suggests it could similarly revolutionize safety management in the
trucking industry by addressing human factors and organizational influences to prevent
accidents.
2.5 Operating Costs in the Trucking Industry
The trucking industry is a vital component of the global supply chain, operating in a
highly competitive environment where managing operating costs is crucial for maintaining
viability and profitability. Operating costs encompass various expenses, including fuel,
maintenance, labor, insurance, and regulatory compliance. Understanding these expenses and
their drivers is essential for industry stakeholders to develop effective strategies and optimize
their operational efficiency, ensuring the industry’s competitiveness and sustainability. By
focusing on these factors, trucking companies can better navigate the challenges they face and
improve their overall financial performance.
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Typical Components of Operating Costs in the Trucking Industry
Fuel
Fuel costs represent one of the largest and most volatile expenses for trucking companies.
According to the American Transportation Research Institute (ATRI), fuel costs accounted for
approximately 28% of total operating expenses for motor carriers in 2022 (ATRI, 2023). The
volatility of fuel prices, influenced by global oil markets, geopolitical tensions, and supply chain
disruptions, pose a significant challenge for trucking firms (Miller, 2018; Chang et al., 2019;
Okogwu et al., 2023). Companies often employ fuel surcharges and hedging strategies to
mitigate the impact of fluctuating fuel prices, but these measures can only partially offset the
inherent volatility (Gu et al., 2018; Ding et al., 2019).
In recent years, advancements in fuel-efficient technologies and alternative fuels have
gained traction as potential solutions to manage fuel costs. Adopting technologies such as
aerodynamic enhancements, low rolling resistance tires, and advanced engine technologies can
significantly improve fuel efficiency (ATRI, 2023). Additionally, the exploration of alternative
fuels like compressed natural gas (CNG) and electric vehicles (EVs) presents promising avenues
for reducing dependency on traditional diesel fuel and mitigating cost volatility (ATRI, 2023).
Maintenance and Repairs
Maintenance and repair costs are critical components of operating expenses in the
trucking industry. They encompass routine preventative maintenance, unexpected repairs, and
the necessity of complying with safety regulations. ATRI’s 2023 report highlights that
maintenance and repair costs increased 12 percent in 2022 to an average of $0.196 per mile
(ATRI, 2023). Regular maintenance is essential to ensure the safe and efficient operation of
CMVs, to comply with FMCSA regulations, and to prevent costly breakdowns and accidents
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(Miller, 2020; Škerlič et al., 2020). Moreover, as a category of the BASICs, when tickets and
citations are issued to trucking companies for maintenance-related violations, they not only result
in fines, which impact operating costs, but also negatively impact the overall CSA safety score.
Effective maintenance protocols focus on the systematic upkeep and repair of CMVs to
prevent mechanical failures that could lead to accidents or operational inefficiencies (FMCSA,
2012c; Škerlič et al., 2020; Zhu et al., 2024). Key focus areas include addressing mechanical
defects, such as inoperative braking systems or lights, and proper maintenance of tires and
steering mechanisms. For instance, a 2022 study in China found that brake system failures were
a major contributor to 45 percent of CMV accidents (Wang et al., 2022). Additionally,
unexpected breakdowns are becoming increasingly problematic and costly in the post-COVID
era due to supply chain disruptions affecting the availability of parts. Prolonged truck
downtimes, resulting from these delays, lead to higher expenses as companies incur additional
costs for renting equipment to cover the breakdowns.
Labor Costs
Labor costs, including wages, benefits, and training expenses, constitute a significant
portion, on average 36 percent, of trucking companies' operating expenses. According to the
ATRI, labor costs increased 6.2 percent in 2022, averaging 90.7 cents per mile for combined
labor and benefits (ATRI, 2023). The industry also faces challenges related to driver shortages,
high turnover rates, and the need for ongoing training and certification to comply with regulatory
requirements (LeMay & Keller, 2019; Schuster et al., 2023).
Investing in driver training and retention programs can improve safety ratings and reduce
turnover, but these initiatives also add to operating expenses (LeMay & Keller, 2019). Enhanced
training programs that focus on safety, compliance, and efficient driving practices can lead to
32
better safety ratings, which in turn can reduce insurance premiums (Elias & Grekin, 2021) and
accident-related costs (LeMay & Keller, 2018). However, the initial investment in these
programs can be substantial, necessitating a careful balance between immediate costs and long-
term benefits.
Insurance Premiums
Insurance premiums are a substantial and growing expense for trucking companies,
increasing 7.2 percent in the latter part of 2022 after rising nearly 50 percent in the 2010s (ATRI,
2023). Insurance companies operate the same way for trucking as with automobiles: Firms with
poor safety ratings often face higher premiums due to the increased risk of accidents and
liabilities (Chen & Jiang, 2019). Conversely, companies with excellent safety records may
benefit from lower premiums (Elias & Grekin, 2021) and enhanced market reputation, attracting
more business.
Regulatory Compliance
Compliance with safety regulations, such as HOS rules and ELD mandates, imposes
additional costs on trucking companies (Johnston, 2019). These costs include purchasing and
maintaining ELDs, regular training, and ensuring vehicles and drivers meet stringent safety
standards (McLean, 2016). While compliance is essential for legal and operational reasons, it
also contributes to higher operating costs. Companies must allocate resources to ensure
compliance, which can strain financial resources, particularly for smaller companies.
Maintaining regulatory compliance is a significant expense for a trucking company;
however, these costs can exceed the initial outlay for proper equipment and training (Miller et
al., 2018). Each category of BASICs encompasses numerous tickets and citations, each with
corresponding fines and penalties that can accumulate rapidly. In 2023, the FMCSA reported
33
nearly three million inspections, an increase from previous years (Pocket Guide, 2023), resulting
in over four and a half million violations (A&I Online). These violations incur substantial fines
and negatively impact a company’s safety ratings for twenty-four months (FMCSA, 2012a).
Nearly 900,000 CMVs were put out of service (OOS), causing the companies to endure more
operating costs (A&I Online).
Conclusion
The analysis of operating costs in the trucking industry underscores the intricate balance
required to maintain financial viability and regulatory compliance. Fuel, maintenance, labor,
insurance, and regulatory compliance pose significant challenges, with costs influenced by
market volatility, supply chain disruptions, and regulatory demands. Effective cost management
through advanced technologies, rigorous maintenance protocols, comprehensive training
programs, and proactive compliance measures can enhance safety ratings and reduce financial
burdens.
2.6 Theoretical Framework
Selecting an appropriate theoretical framework is crucial to studying the relationship
between the BASICs categories and their impact on operating expenses. While the primary
focus of this research is not on the relationship between actual accidents and their causes,
theories from safety and accident literature can offer valuable insights. Several relevant theories
from recent literature have been considered and will be briefly discussed.
Heinrich’s Accident Theory Pyramid, also known as Heinrich’s Safety Pyramid, is a
foundational concept in industrial safety management proposed by H.W. Heinrich in the 1930s
(Marshall et al., 2018). The theory posits that for every major accident, there is a multitude of
minor incidents and near-misses (Iwashita et al., 2019). The theory suggests that by addressing
34
minor incidents and near-misses, organizations can prevent major accidents from occurring (Li et
al., 2024). While this theory has been utilized in transportation literature (Bitinš et al., 2021;
Majumdar et al., 2021; Kuşkapan et al., 2022), it gives no way for the categories of the BASICs
scores, where all data points would be considered near-misses, to be utilized or to understand the
interplay between the categories.
Likewise, Normal Accident Theory (NAT), developed by Charles Perrow in 1984, posits
that in complex, tightly coupled systems, accidents are inevitable due to the inherent interactions
between the system's components (Murata et al., 2021; Muecklich et al., 2023). NAT suggests
these accidents result from unexpected interactions of multiple failures, often undetectable until
they converge to cause a significant event (Murata et al., 2021; Muecklich et al., 2023). NAT
has been applied in high-risk industries such as nuclear power (Murata et al., 2021) and modes of
transportation (Scheibe & Blackhurst, 2017; Muecklich et al., 2023) to understand how system
complexity and interdependencies contribute to accidents. Despite the relevance to safety in
complex systems, NAT focuses mainly on accidents. In contrast, the current study will focus on
those factors the FMCSA has deemed as contributing factors to accidents.
High-Reliability Organizations (HRO) and its offshoot, High-Reliability Theory (HRT),
evolved as a countermeasure to Perrow’s NAT theory (Scott et al., 2022). The foundational
work by Weick and Sutcliffe in 2001, rooted in grounded theory (Cantu et al., 2020), identified
five principles that have become defining characteristics of HRO and HRT. These five
principles are a preoccupation with failure, reluctance to simplify explanations, sensitivity to
operations, deference to expertise, and commitment to resilience (Espinoza-Gala et al., 2021;
Scott et al., 2022; Amici & Farnese, 2023). While there is the relevance of HRO and HRT in
high-risk industries, these theories are not being utilized in this research due to the difficulty of
35
operationalizing and due to it being centered on the collective commitment to values and shared
assumptions (Scott et al., 2022).
The theoretical framework chosen for the current study is James Reason’s Theory of
Active and Latent Failures, often called the “Swiss Cheese Model.” This model has become a
fundamental framework for studying human error and organizational accidents and is the
foundation of the HFACS model (Shappell & Wiegmann, 2000), as shown in Figure 2.
First articulated in the late 1980s and early 1990s, Reason’s model has significantly
impacted numerous high-risk industries, including healthcare (Jalali et al., 2024; Cohen et al.,
2018; Diller et al., 2014), aviation (Yang & Mott, 2020; Illankoon et al., 2019; Hulme et al.,
2019), and maritime (Kaptan et al., 2021; Kahn, 2022; Li et al., 2021). The theory provides
Figure 2
HFACS Model Level 1 Unsafe Acts
comprehensive and nuanced understanding of how errors and failures occur and propagate within
complex systems, highlighting the interplay between human actions and systematic
vulnerabilities (Wiegmann et al., 2022).
36
Central to Reason’s theory is the distinction between active and latent failures. Active
failures are those direct and often immediate observable failures or errors made by individuals at
the operational level, such as pilots, surgeons, or plant operators (Wiegmann et al., 2022; Keers
et al., 2015; Hulme et al., 2019). These errors manifest as mental slips or lapses and mistakes or
violations, each representing different facets of human error (Cohen et al., 2015; Duarte et al.,
2018). For instance, a pilot might misinterpret an instrument reading, leading to an incorrect
flight maneuver, or a surgeon might accidentally cut a vital artery during surgery (Shappell &
Wiegmann, 2000; Cohen et al., 2015).
Conversely, latent failures are hidden within the system and can lie dormant for extended
periods (Wiegmann et al., 2022). These failures originate from systemic issues, such as
organizational decisions, flawed designs, inadequate training, and ineffective communication
(Wiegmann et al., 2022; Chi et al., 2022). Latent failure conditions set the stage for active
failures by creating an environment conducive to errors. For instance, insufficient training
programs or inadequate maintenance of equipment can predispose individuals to make mistakes
(Liu et al., 2018; Ebrahimi et al., 2021). When latent conditions align with active failures, they
can culminate into accidents or near misses (Keers et al., 2015; Duarte et al., 2018).
Reason’s theory has been instrumental in advancing safety practices across various
industries (Miranda, 2018; Kilic & Gumus, 2020). In healthcare, for instance, it has been
instrumental in understanding medical errors and improving patient safety (Cohen et al., 2018;
Wiegmann et al., 2022). Researchers have used Reason’s framework to analyze adverse events
in hospitals, highlighting how latent conditions such as understaffing (Duarte et al., 2018), poor
communication (Mushtaq et al., 2018), and inadequate training (Duarte et al., 2018) contribute to
medical errors.
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This theory has bolstered the industry’s long-standing commitment to safety in aviation.
It has been instrumental in understanding the complex interplay of factors leading to aviation
accidents. For example, a two-day workshop was convened at the Eurocontrol Experimental
Centre (EEC) in Bretigny, France, to review the final report from the Uberlingen mid-air
collision on July 1, 2002, and to consider new safety recommendations for European air traffic
(Reason et al., 2006). The resulting article revisits James Reason’s Swiss Cheese Model (SCM)
of accidents and the components and distinctions of the Theory of Active and Latent Failures,
highlighting their utility and limitations in safety-critical domains like air traffic management.
The implications of Reason’s theory for enhancing safety and reliability across many
industries are profound. It encourages organizations to adopt a systemic perspective, recognizing
that frontline workers are often the last line of defense against errors (Ebrahimi et al., 2021;
Miranda, 2018; Duarte et al., 2018). The shift from blaming individuals to understanding and
addressing systemic conditions fosters an organization’s safety culture (Ebrahimi et al., 2021).
Organizations can create environments that reduce the likelihood of failures by identifying and
mitigating latent failures (Miranda, 2018).
A key implication of Reason’s theory is proactive risk management (Duarte et al., 2018).
The original SCM and, thereby, the HFACS model promotes identifying and rectifying latent
conditions before they lead to near-misses, incidents, or accidents (Dönmez & Uslu, 2020). The
proactive approach involves utilizing regular safety audits, hazard analyses, and continuous
monitoring of organizational processes (Haas & Yorio, 2016; Chatterjee & Mitra, 2019). In the
trucking industry, for example, proactive risk management might include comprehensive driver
training programs and advanced safety technologies such as collision avoidance systems and lane
departure warning systems (Douglas et al., 2019; Škerlič et al., 2020). These measures would
38
address latent conditions such as driver fatigue, inadequate training, and equipment failure,
thereby reducing the likelihood of accidents (Nasr et al., 2021).
While various safety theories provide valuable insights into accident prevention and
management, James Reason’s Theory of Active and Latent Failures offers the most robust
framework for analyzing the relationship between BASICs categories and their impact on
operating expenses. With its focus on both active and latent failures, Reason's model allows for
a comprehensive understanding of how errors propagate within complex systems, such as the
trucking industry. This dual perspective is crucial for identifying the specific systemic
vulnerabilities contributing to operational inefficiencies. By emphasizing the need for proactive
risk management and systemic interventions, Reason’s theory aligns well with the goals of this
research, facilitating a nuanced analysis of how organizational and operational factors influence
safety outcomes and operating costs. Thus, Reason’s framework provides a solid foundation for
developing targeted strategies to enhance safety and efficiency in the trucking industry.
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Chapter 3: Research Hypotheses
3.1 Introduction
This chapter focuses on developing research hypotheses that investigate the direct impact
of safety compliance measures on operating expenses within the trucking industry. Drawing
from the CSA program, which assesses safety performance through various BASICs, this study
intends to identify the key factors contributing to operating expenses. The analysis seeks to
understand how different aspects of safety compliance affect the economic burden on trucking
firms. By formulating clear and testable hypotheses, this study aims to provide a deeper
understanding of the financial implications of maintaining high safety standards within the
industry. These hypotheses will guide the empirical investigation of the direct impact of safety
ratings on operating costs, providing a focused framework for the analysis and helping
systematically address the research questions.
3.2 Theoretical Framework
The theoretical framework for this study is grounded in James Reason’s Theory of Active
and Latent Failures, which highlights the importance of understanding how systemic issues and
human errors contribute to safety outcomes. Building upon this theory, the HFACS model
provides a comprehensive framework for analyzing the human and organizational factors
contributing to safety performance.
By integrating the unsafe acts level of the HFACS model with the CSA program’s
BASICs categories, as shown in Figure 3, this study adopts a unique approach to examine how
the safety scores of those categories influence operating costs. This combined methodology
offers a nuanced understanding of how systemic issues and human errors impact safety
compliance and financial performance in the trucking industry. This structured approach will
40
guide the reader through the study, ensuring a clear understanding of the research process and its
implications.
Figure 3
HFACS Model with CSA Categories
3.3 Hypothesis Development
Unsafe Driving
The Unsafe Driving score reflects the number and severity of violations related to
dangerous driving behaviors, such as speeding, reckless driving, improper lane changes, and
failure to obey traffic control devices. These behaviors significantly increase the risk of
accidents, endangering public safety. Speeding, a leading cause of CMV accidents, contributes
to numerous fatalities and serious injuries. Violations in this category result in fines, elevated
accident risks, and potentially the loss of a driver due to the revocation of the CDL license,
which further increases operating costs through higher insurance premiums (Chen & Jiang,
41
2019), potential legal liabilities (Thron et al., 2024), and driver turnover. Therefore, the first
hypothesis is:
H1: An increase in the Unsafe Driving score will result in an increase in operating
expenses, and a decrease in the Unsafe Driving score will result in a decrease in operating
expenses.
Hours-of-Service
As implemented by the FMCSA, the HOS score evaluates violations related to RODS
and driver fatigue management, which are crucial for safety performance within the CSA
framework. This category assesses adherence to regulations such as the 11-hour driving limit
within a 14-hour duty period, mandatory rest breaks, and the 34-hour restart provision. Non-
compliance with HOS regulations identifies carriers and drivers at higher risk of fatigue-related
incidents, which can lead to fines, increased accidents, and trucks and drivers being placed in
OOS for a mandatory 10 hours, thus raising operating costs. Extensive research highlights that
while reducing allowable driving time impacts firm profitability negatively, it also enhances
safety and driver well-being. Therefore, the second hypothesis is:
H2: An increase in the Hours-of-Service score will result in an increase in operating
expenses, and a decrease in the Hours-of-Service score will result in a decrease in operating
expenses.
Vehicle Maintenance
Vehicle maintenance ensures safety, efficiency, and regulatory compliance within the
CSA program. Effective maintenance protocols prevent mechanical failures that could lead to
accidents or operational inefficiencies. This area of CSA assesses the thoroughness and
regularity of inspections, repairs, and servicing routines, highlighting the importance of proactive
42
measures to mitigate risks associated with vehicle malfunctions. Non-compliance with
maintenance requirements can result in significant penalties, including fines and suspension of
operating privileges, which can significantly increase operating costs. Therefore, the third
hypothesis is:
H3: An increase in the Vehicle Maintenance score will result in an increase in
operating expenses, and a decrease in the Vehicle Maintenance score will result in a decrease
in operating expenses.
Controlled Substances/Alcohol
The Controlled Substances/Alcohol score addresses the significant risks associated with
impaired driving among CMV operators. This category evaluates the prevalence and effects of
alcohol and controlled substances, highlighting severe consequences for road safety and
regulatory compliance (FMCSA, 2012a). Despite only three percent of impaired driving
fatalities involving trucks, substance abuse remains a critical issue (Haq et al., 2022). Strict
FMCSA regulations mandate pre-employment, random, post-accident, and reasonable suspicion
testing, with severe penalties for violations, including fines and license revocation (FMCSA,
2024a). Substance abuse issues can lead to higher insurance premiums (Chen & Jiang, 2019),
legal costs (Thron et al., 2024), loss of client trust, and driver retention issues, increasing
operating costs (Ellis & Grekin, 2021). Therefore, the fourth hypothesis is:
H4: An increase in the Controlled Substances/Alcohol score will result in an increase
in operating expenses, and a decrease in the Controlled Substances/Alcohol score will result
in a decrease in operating expenses.
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Driver Fitness
The Driver Fitness score ensures that CMV operators are medically qualified, adequately
trained, and licensed to operate safely. This category evaluates drivers' health and physical
qualifications, addressing medical conditions like sleep apnea, cardiovascular diseases, and
diabetes, which can impair driving ability (FMCSA, 2012b). Regular medical examinations are
required to meet FMCSA standards, with non-compliance resulting in penalties and increased
operating costs (FMCSA, 2024a). Ensuring driver fitness reduces the risk of accidents caused by
medical issues, thus impacting operational expenses. Therefore, the fifth hypothesis is:
H5: An increase in the Driver Fitness score will result in an increase in operating
expenses, and a decrease in the Driver Fitness score will result in a decrease in operating
expenses.
3.4 Conclusion
This chapter has developed five research hypotheses to investigate the direct impact of
various safety compliance measures on operating expenses within the trucking industry.
Grounded in James Reason’s Theory of Active and Latent Failures and the HFACS model, these
hypotheses provide a structured framework for examining how the CSA program’s BASICs
categories individually and collectively influence firm operating costs. By exploring these
relationships, this study aims to offer a deeper understanding of the financial implications of
maintaining high safety standards and compliance within the trucking industry. The next chapter
will outline the methodology to test these hypotheses, detailing the data collection and analysis
techniques necessary to evaluate the proposed relationships rigorously.
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Chapter 4: Methodology
4.1 Introduction
This chapter details the methodology employed to examine the direct impact of safety
compliance measures on operating expenses within the trucking industry. The primary objective
of this study was to investigate how publicly available Compliance, Safety, and Accountability
(CSA) categories—Unsafe Driving, Controlled Substances/Alcohol, Driver Fitness, Vehicle
Maintenance, and Hours-of-Service—affect financial performance. The study utilized a multiple
linear regression model to analyze the relationship between the CSA’s five (5) publicly available
BASICs categories (Unsafe Driving, Controlled Substances/Alcohol, Driver Fitness, Vehicle
Maintenance, and Hours-of-Service) and quarterly operating expenses among publicly traded
trucking companies. This chapter details the research design, sample selection, data collection
process, analytical approach, measures used, and ethical considerations to ensure the reliability
and validity of the findings.
4.2 Research Design
This study employs a quantitative, correlational research design to examine the
relationship between safety compliance measures and operating expenses in the trucking
industry. The research is based on secondary data analysis utilizing publicly available CSA
violation scores from the FMCSA and financial data from the Wharton Research Data Services
(WRDS) CompuStat database. The unit of analysis is at the company level, focusing exclusively
on publicly traded trucking companies due to the availability of financial records.
4.3 Sample Selection
The dataset was compiled from two primary sources: WRDS CompuStat for obtaining
quarterly operating expenses and the FMCSA’s SMS for safety violation data. The first criterion
45
for study participant inclusion was based on the availability of public-domain financial
information from the WRDS CompuStat database. Therefore, all participants are publicly traded
companies, limited to those within the United States, listed on US stock exchanges such as the
New York Stock Exchange, Nasdaq, or NYSE American.
The second selection criterion is the Global Industry Classification Standard (GICS)
code. The GICS codes are broken down into eleven (11) sectors, twenty-five (25) industry
groups, seventy-four (74) industries, and 163 sub-industries. As of May 31, 2023, the previous
code for Trucking companies has been discontinued. The new trucking industry code is listed as
Cargo Ground Transportation. GICS defines the companies listed in this category as providing
transportation services for goods and freight (GICS, 2023). The code for utilizing the GICS map
is shown in Figure 4. Following the GICS map for transportation, specifically the trucking
industry, the participants will be limited to those companies included in the code for Cargo
Ground Transportation (20304030).
Figure 4
GICS Code Map of the Trucking Industry
Note: Adapted from https://www.msci.com/our-solutions/indexes/gics.
The dependent variable for this study is the total operating expense per quarter. Violation
fines are not singled out or readily available. Therefore, the total operating expenses per quarter
will serve as a proxy for violation fines since these penalties are included in each participant's
46
operating expenses. The five (5) independent variables are the CSA BASICs, publicly available
violation points, or scores. The FMCSA utilizes these categories to calculate each trucking
company's CSA score.
The timeframe for selection was dependent upon the information available from the
FMCSA database (Safety measurement system—downloads). The second timeframe
consideration was the availability of the most recent quarterly financial data from CompuStat.
4.4 Data Collection
The data collection for this study involved gathering historical secondary data from two
primary sources: the FMCSA SMS website and the WRDS CompuStat database. The FMCSA
SMS data includes all violations issued during a 24-month rolling period. The financial data,
retrieved from the WRDS CompuStat database, focuses on operating expenses during the same
time frame as the SMS data.
The GICS code (20304030) was utilized in the CompuStat database to obtain only the
financial data of companies whose GICS code matched the Cargo Ground Transportation code
and was further limited to those companies registered in the United States. This initial query
produced 29 potential participant companies. Of these companies, ten (10) were shown to be
holding companies. Lacking a way to confirm the isolation of the operating expenses to only the
specific GICS code and its subsidiaries that operate only in the cargo ground transportation
sector, these companies were excluded from the dataset. Two additional companies were
removed due to acquisition by another firm or business closures, which resulted in incomplete
financial data for the study’s time frame. One company, based in Canada, was removed as well.
While this company operates within the United States, it has multiple subsidiaries that do not.
47
Therefore, the financial data could not be confirmed only to contain subsidiaries within the
United States.
The SMS database is set up as a rolling 24-month timeframe and is available via public
access. The database was accessed in May and October 2024, each resulting in a data download.
This first download produced 6.35 million lines of violation data from May 31, 2022, to May 30,
2024. The second access was limited to dates between October 25, 2022, and October 24, 2024,
and produced an additional 6.31 million lines of data. It is important to note that the database
contains every violation, and oftentimes multiple violations, resulting from one traffic or
inspection stop, for every active US DOT number legally authorized to operate within the United
States.
Access to the WRDS CompuStat database was gained through Marshall University.
Utilizing the Compustat – Capital IQ database, the search was limited to North America. The
CompuStat - Fundamentals Quarterly database was accessed in July 2024 and January and
February 2025. The first query produced 200 lines of data representing 29 companies and was
limited to Q2 2022 through the current date at the time of access and the GICS code of
20304030. The original data was utilized for the study except for one update identified in the
subsequent confirmation queries.
Data Cleaning
Data cleaning began with eliminating the companies from the CompuStat data that were
not registered in the US, found to be holding companies, or ceased operations during the study's
timeframe. As a result, the initial 29 companies were narrowed down to 16 companies to serve
as participants. The following cleaning step required obtaining subsidiary information for each
of the 16 participants.
48
This process began by searching the Securities and Exchange Commission’s (SEC)
Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system for each participant’s
Exhibit 21 form filed within the 10-k Annual Report (Edgar Full Text Search, nd.). Exhibit 21 is
utilized to disclose subsidiary information to the SEC. While research has shown that companies
are guilty of nondisclosure (Dyreng, et al., 2018), most companies do comply. Due to the
potential for nondisclosure, a second search was conducted on the participant's parent company
websites. The websites were scoured for listings of additional companies owned and operated in
the trucking industry.
The resulting list of participant parent companies and their subsidiaries was utilized in the
FMCSA’s SMS website database to locate all US DOT numbers associated with each
participant. This resulted in a list of 54 US DOT numbers, one of which was excluded from the
study due to the FMCSA's declaration of being inactive. After eliminating the overlapping
timeframe, matching the timeframe of the CompuStat data to the SMS data, and utilizing the list
of US DOT numbers attached to the participants, the final dataset contained 91,815 observations.
At this point, each US DOT number remaining in the study was mapped to a Group ID column
added to the dataset. This combined each participant's US DOT numbers with their assigned
Group ID, thereby creating a company-level view of the violations received by each company.
In regard to received violations, trucking companies have 30 days to pay a fine before
late penalties, interest, and administrative fees are added to the original fine (Federal Motor
Carrier Safety Administration Fine Payment, nd.). Due to this, the violation dates in the SMS
data need to be adjusted to match the financial quarter where they would most likely have
impacted the participant's operating expenses. With the CompuStat data being aligned with
typical financial quarters of Q1, being January, February, and March, the SMS data needed to be
49
placed into quarters that were offset to the preceding month. As such, a new column was added
to the dataset to show the violation-adjusted quarter. The resulting adjustment is shown below in
Table 2.
Table 2.
Typical Financial Quarters and SMS Scores by Adjusted Quarters.
The SMS database assigns a time weight to every violation in the 24-month rolling
period. Violations within the previous six (6) months are given a time weight of three (3), six (6)
to twelve (12) months a weight of two (2), and older than twelve (12) months but within the past
24 months a weight of one (1). This places more emphasis on the newest violations in relation to
the older violations. Each score, or severity weight of the violation, is multiplied by the time
weight factor to arrive at the total score for a violation. Violations in the SMS database will
range between one (1) and 30 points per violation depending upon the severity weight and the
time weight. Since this study focuses on the financial impact of the scores utilizing a quarterly
timeframe, all original severity weight scores were multiplied by three (3) since the timeframe
50
falls within the six (6) month window of the time weight scale. Thus, a new score was generated
inside the dataset for the violation value times three (3).
Again, since the financial data is in quarters, an adjustment was needed to group the
individual violation scores into quarters. To accomplish this, it was determined that the most
logical process was to create a sum of all violation scores received during the adjusted quarterly
timeframe. Once this was completed, all data was aggregated to the quarterly timeframe,
producing a final dataset of 144 observations representing 16 companies and nine (9) financial
quarters.
4.5 Data Analysis
This section will detail the statistical methods and procedures employed to examine the
direct impact of safety compliance measures on operating expenses within the trucking industry.
By applying a range of statistical techniques, this analysis ensures a robust and comprehensive
understanding of the data, allowing for the validation of the research hypotheses.
Descriptive Statistics
Descriptive statistics were utilized to summarize the basic features of the dataset,
providing an overview of the sample and measures. This includes calculating each variable's
mean, median, standard deviation, minimum, and maximum values. These statistics help
understand the data's distribution and central tendencies, setting the stage for more complex
analyses.
Multiple Linear Regression (MLR)
Multiple Linear Regression (MLR), employing IBM’s SPSS Statistics (version 30.0.0.0),
was chosen to test the relationship between the independent and dependent variables to
51
determine whether the dataset supports the hypotheses. The conceptual framework for the MLR
is shown in Figure 5 below.
Figure 5
Conceptual Framework
The general form of the multiple linear regression used in this study is:
OperatingExpenseTotal = β0 + β1(DRI_FIT_sum) + β2(CON_SUB_sum) +
β3(HOS_COMP_sum) + β4(UNS_DRIV_sum) + β5(VEH_MAIN_sum) + ε
Where:
β0 = Intercept (constant term)
β1, β2, β3, β4, β5 = Regression coefficients for each predictor
ε = Error term
A Pearson correlation analysis was also conducted to evaluate construct validity. The
Pearson correlation coefficient is a fundamental statistical method that measures the strength and
direction of linear relationships between two continuous variables, ranging from -1 to 1. Positive
52
correlations indicate that as one variable increases, the other tends to increase, while a negative
correlation suggests an inverse relationship. The p-values in the table indicate whether a
relationship is statistically significant at the 95% confidence level. This provides evidence of an
independent variable impacting the dependent variable in the regression model.
Model fit was assessed using R2 and adjusted R2, which quantify the proportion of
variance in the dependent variable explained by the predictor or independent variables. Adjusted
R2 accounts for model complexity and ensures that additional predictors contribute meaningfully
to model improvement. An ANOVA F-test was conducted to assess the statistical significance
of the overall regression model. A significant F-statistic indicates that the model collectively
explains a meaningful proportion of the variance in the dependent variable.
Multicollinearity was assessed using the Variance inflation factor (VIF), which quantifies
how much a predictor is correlated with other independent variables. The standard VIF
threshold in most academic studies is five (5), which detects excessive multicollinearity and
ensures model stability. Residual normality was examined using histograms and Q-Q plots to
validate model assumptions.
To assess the effect size of the MLR, partial eta squared was calculated. This is a
commonly used measure in ANOVA and regression analysis that quantifies the proportion of
variance explained by the independent variables while accounting for other factors. This
measure provides insight into the overall strength of the model beyond statistical significance,
providing practical relevance.
Conclusion
Several diagnostic tests were conducted to ensure the multiple linear regression model's
robustness, reliability, and validity. Model fit was assessed using R2, Adjusted R2, and an
53
ANOVA F-test, confirming the model’s ability to explain variations in the dependent variable.
The Variance Inflation Factor (VIF) was used to check for multicollinearity, while residual
diagnostics, including normality plots, tested compliance with regression assumptions.
The reliability of the independent variables was examined using Cronbach’s Alpha,
ensuring consistency among related measures. Finally, predictive validity was evaluated by
comparing model predictions to actual observed values. Collectively, these tests confirm the
robustness of the regression model, ensuring its appropriateness for addressing the research
questions.
4.6 Measures
This section details the specific variables and metrics utilized to analyze the impact of
safety compliance measures on operating expenses within the trucking industry. The measures
are designed to capture the compliance levels with the CSA’s BASICs categories and the
companies' financial performance. Each measure will be defined and explained to ensure clarity
and facilitate accurate data collection and analysis.
Unsafe Driving
The Unsafe Driving measure captures violations related to dangerous driving behaviors,
such as speeding, reckless driving, improper lane changes, and failure to obey traffic control
devices. Data for this measure will be sourced from the FMCSA SMS website, reflecting daily
violation counts for each company.
Hours-of-Service (HOS)
The HOS measure assesses compliance with regulations related to driver fatigue
management, including adherence to the 11-hour driving limit within a 14-hour duty period,
54
mandatory rest breaks, and the 34-hour restart provision. Daily violation data will be collected
from the FMCSA SMS website to evaluate this measure.
Vehicle Maintenance
The Vehicle Maintenance measure includes data on violations related to the upkeep and
repair of commercial motor vehicles. This measure will focus on the thoroughness and
regularity of inspections, repairs, and servicing routines. Daily data on maintenance violations
will be retrieved from the FMCSA SMS website.
Controlled Substances/Alcohol
This measure tracks violations related to the use of controlled substances and alcohol
among CMV operators. It will include data on pre-employment, random, post-accident, and
reasonable suspicion testing violations. The FMCSA SMS website will provide daily violation
data for this measure.
Driver Fitness
The Driver Fitness measure assesses compliance with medical and physical qualification
requirements for CMV operators. It will include violations related to proper training, medical
examinations, and certifications. The FMCSA SMS website provides data gathered daily to
evaluate this measure.
Operating Expenses
Operating expenses serve as the proxy for the financial performance measure used to
evaluate the economic impact of safety compliance. This data was sourced from the CompuStat
database and includes fuel, maintenance, labor, insurance, and regulatory compliance expenses.
The specific financial metric utilized is the total quarterly operating expenses.
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Conclusion
The measures outlined in this section provide a detailed framework for assessing the
impact of safety compliance on operating expenses within the trucking industry. By capturing
data from the FMCSA SMS and CompuStat databases, these measures comprehensively analyze
how adherence to safety standards influences financial performance. This structured approach
ensures that all relevant aspects of safety compliance and operating costs are systematically
evaluated, facilitating robust and insightful research findings.
4.7 Ethical Considerations
This section addresses the ethical considerations of using secondary data in this research.
The ethical risks are minimal since the study relies solely on publicly available secondary data.
However, it is essential to ensure that data is used responsibly and transparently.
Ethical Considerations and Guidelines
This study's research design exclusively relies on secondary data sources, and no direct
engagement or surveying of participants will be conducted. As such, this study poses minimal to
no risk to participants. The researcher affirms adherence to ethical considerations and guidelines
throughout the research process, ensuring the responsible and confidential use of existing data.
Consequently, based on the nature of the study, formal IRB approval is not deemed necessary for
this research endeavor, as evidenced in Appendix A.
Data Integrity and Accuracy
Ensuring the integrity and accuracy of the data is paramount. The data collected from the
FMCSA SMS and CompuStat databases will be verified and validated to maintain high-
reliability standards. Proper data handling procedures will be followed to avoid manipulation or
misrepresentation of the data.
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Confidentiality and Anonymity
As the data is publicly available and pertains to publicly traded companies, there are no
confidentiality or anonymity concerns related to individual participants. However, the study will
ensure that the data is used to respect the companies' privacy and proprietary information,
avoiding any unauthorized disclosure of sensitive data. The researcher has purposefully only
identified the companies in the final dataset by creating a variable listed as Group_ID to protect
the confidentiality of the individual companies.
Ethical Use of Secondary Data
The research will adhere to all ethical guidelines for the use of secondary data. This
includes accurately citing data sources and acknowledging any limitations associated with the
use of secondary data. The research will also ensure that the data is used solely for academic
purposes and in a manner consistent with the original intent of the data collection.
Independence and Conflict of Interest
This research project lacks ethical concerns attributable to external funding or direct
interaction with participants. It exclusively utilizes secondary data, so no direct involvement
with individuals exists, thus eliminating any potential risks or ethical dilemmas accompanying
participant engagement. Additionally, the absence of external funding ensures autonomy and
independence in the research process, mitigating any conflicts of interest while leveraging
existing data sources to contribute to scholarly inquiry.
Conclusion
This research maintains a high standard of integrity and responsibility by focusing on the
ethical use of secondary data. Using publicly available data minimizes ethical risks, and careful
attention to data integrity, accuracy, and proper citation ensures the research is conducted
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ethically and transparently. The research process's autonomy and independence further mitigate
potential conflicts of interest, ensuring unbiased and credible findings.
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Chapter 5: Results
5.1 Introduction
This chapter presents the findings of multiple tests examining the relationship between
safety compliance violations and operating expenses in the trucking industry. This quantitative
correlation study aimed to find direct links between safety compliance categories used by the
FMCSA to generate CSA scores for the trucking industry and how those scores and violations
impact financial performance, proxied by operating expenses. By identifying significant
predictors, the study provides insights into cost implications associated with the different
categories set forth by the FMCSA and those associated with overall safety compliance or
noncompliance.
5.2 Descriptive Statistics
Before performing the regression analysis, descriptive statistics were generated for the
key variables to provide an overview of their distribution, central tendencies, and variability.
Table 3 presents the summary statistics, including the number of observations, mean, standard
deviation, skewness, and kurtosis.
Multiple key observations emerge from the descriptive statistics. First, the dataset's mean
operating expenses for trucking companies amount to approximately $1.03 billion with a
standard deviation of $762.54 million. The significant standard deviation suggests considerable
variability in operating expenses across companies. This is due, in part, to the differing and
widely varying number of power units operated by each company.
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Table 3.
Descriptive Statistics
Second, in all independent variables (IVs), the standard deviation is higher than the mean,
indicating a high degree of dispersion in the number of violations received by trucking
companies. This suggests that most companies have relatively low rates of violations. In
contrast, a few companies have exceptionally high violations, resulting in a highly skewed
distribution, which is also evident in the skewness and kurtosis values. All IVs exhibit positive
skewness, or greater than 0, further indicating that a few firms have extremely high violations
compared to the majority. Results of kurtosis show that Driver Fitness (DRI_FIT) and Hours-
Of-Service (HOS_COMP), 5.861 and 6.863, respectively, have heavy tails and extreme outliers.
The high skewness and kurtosis indicate that violations are not evenly distributed across
firms, with a few firms having disproportionately high counts. While the non-normal
distribution of the IVs may impact linear regression assumptions, this study acknowledges them
as an integral part of the trucking industry’s safety compliance variability and chooses not to
transform or remove outliers, as this would degrade the real-world applicability of the findings.
Removing or transforming these extreme values could artificially distort the true nature of safety
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violations in the trucking industry, where large firms or those with poor compliance histories
naturally accumulate more violations.
The high standard deviations of the IVs indicate significant variations in safety violations
among publicly traded trucking firms, with a small subset of companies accounting for
excessively high violations. This variability is a natural consequence of the industry’s diverse
structure and regulatory landscape. The implications of these findings will be considered in the
regression analysis and interpretation of results.
5.3 Reliability Analysis
A Cronbach's Alpha reliability test was conducted to assess the internal reliability of the
five (5) safety violation categories. The results indicate a Cronbach’s Alpha value of 0.615 for
the five-item scale. Cronbach’s Alpha is typically considered questionable at the 0.6 – 0.7 level
(Tavakol & Dennick, 2011). However, a growing body of literature supports that lower levels
are acceptable when considering the study context (Ursachi et al., 2015; Taber, 2017).
While higher Cronbach’s Alpha values are typically preferred to ensure strong internal
consistency, it is important to recognize that the five (5) safety violation categories inherently
represent distinct aspects of regulatory compliance rather than a single underlying construct.
Their moderate internal consistency may reflect meaningful differences in their impact on
Operating Expenses rather than measurement error. Therefore, considering this study relies on
secondary data capturing real-world safety violations in the trucking industry, a Cronbach’s
Alpha > .60 is acceptable.
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5.4 Correlation Analysis
Pearson Correlation Analysis
A Pearson correlation analysis evaluated the relationships between the independent
variables (IVs) and the dependent variable (DV). This analysis provides insight into the strength
and direction of the relationships between the IVs and DV. The results in Table 4 illustrate the
dataset’s correlation coefficients and significance levels.
The correlation analysis indicates three (3) safety violation categories: Driver Fitness (r =
0.571, p < 0.001), Unsafe Driving (r = 0.576, p < 0.001), and Vehicle Maintenance (r = 0.529, p
< 0.001), exhibit moderate to strong positive correlations with Operating Expenses (Masha et al.,
2019). All three (3) correlations are statistically significant at the 99% confidence level. Hours-
Of-Service Compliance also shows a statistically significant correlation with Operating Expense
(r = 0.360, p < 0.001), although the strength of this relationship is weaker than the previously
mentioned categories.
Table 4. Pearson Correlation Table
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The Controlled Substance and Alcohol category demonstrates the weakest correlation
with Operating Expenses (r = 0.128, p = 0.063), insignificant at the 95% confidence level. This
suggests that violations in this category may not have a meaningful association with operating
costs.
Additionally, several IVs demonstrate high intercorrelations, raising concerns about
multicollinearity. The strongest correlations include the relationships between Hours-Of-Service
Compliance and Vehicle Maintenance (r = 0.949), Unsafe Driving and Vehicle Maintenance (r =
0.939), and Driver Fitness and Unsafe Driving (r = 0.886). These high correlations suggest some
violation categories are closely related and may share significant variance. Multicollinearity will
be assessed further using Variance Inflation Factor (VIF).
5.5 Multicollinearity Assessment
Multicollinearity occurs when independent variables in a regression model are highly
correlated. This can result in distorted estimates and reduce the reliability of statistical
inferences. This study utilizes two diagnostic measures, Variance Inflation Factor (VIF) and
Tolerance values, as well as Collinearity Diagnostics, including Condition Index and Variance
proportions, to assess the presence of multicollinearity.
The VIF and Tolerance values provide insight into the extent of multicollinearity among
independent variables. Tolerance is the inverse of VIF, where low tolerance values, typically
below 0.1, suggest a high degree of shared variance with other predictors, which indicates
multicollinearity. Similarly, VIF values that exceed ten (10) are generally considered
problematic and suggest that a predictor is highly collinear with others in the model (Haq et al.,
2020).
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Table 5.
Variance Inflation Factor (VIF) and Tolerance Values
Table 5 shows that Vehicle Maintenance violations exhibit the highest level of
multicollinearity, with a VIF value of 21.446 and a Tolerance value of 0.047. Hours-Of-Service
and Unsafe Driving violations also demonstrate concerns, with VIF values of 11.811 and 11.109,
respectively, and corresponding Tolerance values of .085 and .090. Driver Fitness and
Controlled Substances and Alcohol violations do not appear to contribute significantly to
multicollinearity, as their values remain within an acceptable range at 5.398 and 1.336,
respectively.
To examine multicollinearity further, Condition Index (CI) values and Variance
Proportions (VP) were assessed, as shown in Table 6. A CI below 10 implies no collinearity,
values between 10 and 30 suggest moderate collinearity, while values greater than 30 indicate
severe multicollinearity concerns (Dar et al., 2023). The CI for Dimension 6 is 17.378, which,
while not exceeding the severe threshold, suggests moderate multicollinearity. The Variance
proportions, which are considered high at >.50 (Brown et al., 2013), further support this
conclusion, with HOS_COMP, UNS_DRIV, and VEC_MAIN contributing substantially to
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variance in Dimension 6, with proportions of 0.61, 0.60, and 0.95, respectively. These findings
align with the VIF results, reinforcing that these variables share a substantial portion of variance,
making it difficult to isolate their individual effects in the regression model.
Table 6.
Condition Index Values and Variance Proportions.
5.6 Regression Analysis and Hypothesis Testing
Model Fit
The model summary, shown in Table 7, examines the explanatory strength of the
relationship between the independent and dependent variables. The regression model
demonstrates a strong relationship between independent and dependent variables, with an R-
value of 0.767, indicating a high degree of correlation. The R2 value of 0.588 suggests that
approximately 58.8% of the variance in Operating Expenses can be explained by the CSA safety
violation categories included in the model. This level of explanatory power is considered
moderate (Pratama et al., 2023; Hair et al., 2019), suggesting that while safety violations
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significantly impact operating expenses, additional unmeasured factors may contribute to cost
variations across firms.
Table 7.
Model Summary.
The adjusted R2 value of 0.573 accounts for the number of predictors in the model and
provides a more refined estimate of model fit considering model complexity. The slight variance
between R2 and adjusted R2 indicates that the model does not suffer significantly from
overfitting, and the independent variables explain the changes in operating expenses
meaningfully. The standard error of the estimate (498.51 million) suggests a moderate level of
prediction accuracy, given the scale of operating costs in the dataset.
The F-test (F = 39.317, p < .001), shown in the model summary and confirmed by
ANOVA, validates that the overall regression model is statistically significant. This confirms
that the independent variables collectively explain a substantial proportion of variance in
operating expenses. The Durbin-Watson statistic (0.560) suggests a positive autocorrelation in
the residuals, indicating there may be a pattern in the error terms that violates the assumption of
independence (Kong et al., 2019). This issue will be examined further in the regression
diagnostics.
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Regression Analysis and Hypothesis Testing
Using multiple linear regression, the regression analysis examines the relationship
between safety violations and Operating Expenses. The results of regression analysis,
coefficients, standard errors, t-values, and significance levels for each of the predictor variables
determine whether the study’s hypotheses are supported based on the statistical significance (p-
value < 0.05) and whether the observed direction of the relationship (positive or negative) aligns
with the expected effect.
This study hypothesized that higher levels of safety violations would be positively
associated with increased Operating Expenses due to regulatory penalties, insurance costs, and
operational inefficiencies, and that, in turn, lower levels would be associated with lower
Operating Expenses, would also be true.
H1: An increase in the Unsafe Driving score will result in an increase in operating
expenses, and a decrease in the Unsafe Driving score will result in a decrease in operating
expenses.
Unsafe Driving violations do not exhibit a statistically significant relationship with
Operating Expenses (β = 0.017, p = 0.767). The high p-value suggests these violations do not
meaningfully contribute to Operating Expense variations, leading to H1 being unsupported.
Table 8
Regression Results for H1
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H2: An increase in the Hours-of-Service score will increase operating expenses, and a
decrease in the Hours-of-Service score will result in a decrease in operating expenses.
Hours-Of-Service violations exhibit a statistically significant negative relationship with
Operating Expenses (β = -1.016, p < 0.001). This finding is counterintuitive and contradicts the
hypothesis, as the expected relationship was positive. The negative coefficient suggests that
firms with more Hours-Of-Service violations tend to have lower operating expenses, which leads
to H2 not being supported.
Table 9
Regression Results for H2
H3: An increase in the Vehicle Maintenance score will result in an increase in
operating expenses, and a decrease in the Vehicle Maintenance score will result in a decrease
in operating expenses.
Vehicle Maintenance violations demonstrate a statistically significant positive relationship with
Operating Expenses (β = 0.152, p < 0.001). The positive coefficient confirms that firms with
more violations in this CSA category tend to have higher operating expenses, thus H3 is
supported.
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Table 10
Regression Results for H3
H4: An increase in the Controlled Substances and Alcohol score will result in an
increase in operating expenses, and a decrease in the Controlled Substances and Alcohol
score will result in a decrease in operating expenses
The regression results indicate that Controlled Substances and Alcohol violations are not
statistically significant predictors of Operating Expenses (β = -1.940, p = .206). Due to the p-
value exceeding 0.05 and the negative coefficient contradicting the hypothesized positive
relationship, the findings indicate that H4 is not supported.
Table 11
Regression Results for H4
H5: An increase in the Driver Fitness score will result in an increase in operating
expenses, and a decrease in the Driver Fitness score will result in a decrease in operating
expenses
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Driver Fitness violations demonstrate a statistically significant positive relationship with
Operating Expenses (β = 2.142, p < .001). The positive coefficient confirms that firms with
more Driver Fitness violations tend to have higher Operating Expenses. Additionally, the
standardized coefficient (Β = 0.636) indicates that Driver Fitness violations have a substantial
impact on Operating Expenses compared to other predictors. The result is that H5 is supported.
Table 12
Regression Results for H5
Summary of Hypothesis Testing
The regression analysis results support H3 and H5, confirming that Driver Fitness and
Vehicle Maintenance violations are positively associated with Operating Expenses. However,
H1, H2, and H4 are not supported. Controlled Substances and Alcohol and Unsafe Driving
violations do not exhibit statistically significant relationships with Operating Expenses. In
addition, Hours-Of-Service violations exhibit a statistically significant inverse (negative)
relationship, contradicting the original hypothesis, which will be discussed more in-depth later in
the discussion section.
5.7 Effect Size: Partial Eta Squared
Effect size measures the practical significance of predictor variables beyond their
statistical significance. While p-values indicate if relationships exist, effect size measures the
strength of those relationships by quantifying the proportion of the variance in Operating
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Expenses explained by each independent variable. To measure effect size, this study utilizes
Partial Eta Squared (η2) to assess the contribution of each safety violation category to the model.
Table 13 presents the Partial Eta Squared values for each predictor variable.
Table 13
Effect Size (Partial Eta Squared) for Predictors
The results indicate that Hours-Of-Service, Driver Fitness, and Vehicle Maintenance
violation exhibit large (> .14) effect sizes, suggesting these variables explain a substantial
portion of the variance in Operating Expenses (Hausberg et al., 2012; Cohen, 2013). Controlled
Substances and Alcohol demonstrate only a tiny effect (> .01) (Hausberg et al., 2012; Cohen,
2013). Unsafe Driving violations exhibit a negligible effect (0.001), further confirming their
lack of statistical significance in the regression analysis (Hausberg et al., 2012; Cohen, 2013).
These findings highlight the relative influence of the different safety violation categories on
Operating Expenses.
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5.8 Residual Analysis and Assumption Checks
Residual Normality
A key assumption of multiple linear regression is that residuals are normally distributed
(Mousaei & Naderi, 2023). This assumption was evaluated using a Histogram and Normal Q-Q
Plot of Unstandardized Residuals, as well as Kolmogorov-Smirnov (KS) and Shapiro-Wilk Tests
of Normality.
Histogram of Unstandardized Residuals
The histogram of unstandardized residuals (Figure 6) compares the observed residuals
against a theoretical normal distribution. The histogram suggests that residuals are
approximately normal, with a peak near zero (0). However, some deviations from perfect
normality are visible, particularly in the slight skewness and heavy tails, which may indicate the
presence of outliers or heterogeneity in variance. The overlaid normal distribution curve
suggests that while the distribution is not perfectly normal, it is reasonably symmetrical.
Figure 6
Histogram of Unstandardized Residuals
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Normal Q-Q Plot
The Q-Q Plot (Figure 7) compares the observed residuals against a theoretical normal
distribution. Most data points closely follow the diagonal reference line, which indicates that the
residuals are approximately normal. The slight deviations at the extreme ends suggest a
departure from normality in the tails, likely due to outliers. Despite the minor deviations, the
overall pattern supports the assumption of normality.
Figure 7
Normal Q-Q Plot of Unstandardized Residuals
Kolmogorov-Smirnov and Shapiro-Wilk Tests
Statistical normality tests were conducted using the KS and Shapiro-Wilk tests, the
results of which are shown in Table 14. These tests assess whether the residuals significantly
deviate from a normal distribution. Both tests provide a statistically significant result (p < 0.05),
indicating that the residuals deviate from a perfectly normal distribution. Statistical tests for
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normality are often highly sensitive in large samples, meaning minor deviations can produce
significant results. Considering that the histogram and Q-Q plot suggest only minor deviations
from normality and that MLR is generally robust to slight violations, no corrective
transformations are deemed necessary.
Table 14
Tests of Normality for Residuals
Homoscedasticity
Homoscedasticity, or constant variance of residuals across predicted values, is another
key assumption of MLR. If residuals exhibit heteroscedasticity or non-constant variance,
standard errors may be biased, leading to unreliable hypothesis tests and confidence intervals.
This assumption was evaluated using the Breusch-Pagan Test for Heteroscedasticity and a
Residual Scatterplot.
Breusch-Pagan Test for Heteroscedasticity
The Breusch-Pagan test was conducted to determine whether the variance of residuals
depends on the independent variables. The null hypothesis states that residuals exhibit
homoscedasticity, meaning their variance remains consistent across dependent variable levels. A
significant result (p < 0.05) would indicate a violation of this assumption (Breusch & Pagan,
1979).
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As evidenced in Table 15, the p-value of 0.538 is not statistically significant, which
suggests there is no firm evidence of heteroscedasticity in the model. This finding indicates that
residual variance does not systematically increase or decrease with predicted values, thus
supporting the assumption of homoscedasticity.
Table 15
Breusch-Pagan Test for Heteroscedasticity
Residual Scatterplot
A residual scatterplot was analyzed to assess the distribution of residuals across predicted
values visually. In an ideal regression model, residuals should exhibit random dispersion around
zero (0) with no apparent pattern or structure. Figure 8 shows that the scatterplot reveals
residuals appear randomly distributed, with no strong pattern indicating increasing or decreasing
variance. While some clustering is visible, no funnel shape or other systematic structure is
observed. This visual confirmation aligns with the Breusch-Pagan test, further supporting the
assumption of homoscedasticity.
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Figure 8
Scatterplot of Residuals
5.9 Summary of Key Findings
This chapter presented the statistical analysis results to examine the relationship between
the FMCSA’s CSA BASICs categories and operating expenses. Multiple linear regression was
utilized to determine whether specific categories of safety violations significantly impacted
operating expenses. The model demonstrated an R2 value of 0.588, indicating that the
independent variables could explain 58.8% of the variance in Operating Expenses. The adjusted
R2 value of 0.573 suggested that the model did not suffer significantly from overfitting. The F-
test result (F = 39.317, p < 0.001) confirmed the statistical significance of the overall model.
The hypothesis testing results confirmed that Driver Fitness and Vehicle Maintenance
violations were statistically significant positive predictors of Operating Expenses. Hours-Of-
Service violations showed a statistically significant negative relationship with Operating
Expenses, while Controlled Substances and Unsafe Driving were not statistically significant
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predictors. The effect size analysis used Partial Eta Squared and indicated that Hours-Of-
Service, Driver Fitness, and Vehicle Maintenance violations had the largest effect sizes.
The assessment of multicollinearity identified moderate to high collinearity among
Vehicle Maintenance, Hours-Of-Service, and Unsafe Driving violations, with high VIF values.
The normality of residuals was evaluated using a histogram, Q-Q plot, and statistical tests.
While minor deviations from normality were observed, the overall distribution of the residuals
was approximately normal. The Breusch-Pagan test and residual scatterplot were used to assess
homoscedasticity, with results indicating that residual variance remained stable across predicted
values. These findings establish the statistical relationships between safety violations and
Operating Expenses, providing the basis for further discussion in Chapter 6.
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Chapter 6: Discussion
6.1 Introduction
This study examined the relationship between safety violations and Operating Expenses
in the trucking industry utilizing the Federal Motor Carrier Safety Administration’s (FMCSA)
Compliance, Safety, and Accountability (CSA) Behavior Analysis and Safety Improvement
Categories (BASICs) (3.2 Compliance, Safety, Accountability (CSA) program). Safety
compliance is critical in transportation management, influencing regulatory standing and
financial performance. This study utilized multiple linear regression to analyze publicly
available safety violation data and its association with operating expenses.
The results presented in Chapter 5 indicate that not all safety violations contribute equally
to variations in operating expenses. Driver Fitness and Vehicle Maintenance violations were the
most statistically significant positive predictors, suggesting deficiencies in driver qualifications
and vehicle upkeep are associated with increased costs. Conversely, Hours-Of-Service
violations exhibited a statistically significant negative relationship with Operating Expenses, a
finding that deviates from expectations. Controlled Substances and Alcohol and Unsafe Driving
violations were not statistically significant predictors, indicating their direct financial impact may
be less pronounced.
This chapter provides an in-depth discussion of these findings, interpreting their
implications within the broader context of transportation safety and financial management. The
discussion connects the results to the theoretical frameworks outlined in Chapter 2, including
James Reason’s Theory of Active and Latent Failures (Reason et al., 2006) and the Human
Factors Analysis and Classification System (HFACS) (Shappell & Wiegmann, 2000).
Additionally, the chapter explores the practical significance of the findings for industry
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stakeholders, addressing potential managerial strategies, policy considerations, and regulatory
implications.
This chapter will also consider the study's limitations and offer recommendations for
future research. While the study provides valuable insights into the financial consequences of
safety violations, certain methodological constraints must be acknowledged, including
multicollinearity and the use of cross-sectional data. The chapter concludes with suggestions for
future research avenues that could further refine the understanding of the relationship between
regulatory compliance and financial performance in the trucking industry.
6.2 Interpretation of Key Findings
The statistical analyses revealed important insights regarding the relationship between
safety violations and Operating Expenses in the trucking industry. While the multiple linear
regression model demonstrated moderate explanatory power (Pratama et al., 2023; Hair et al.,
2019), the findings indicate that not all safety violations contribute equally to variations in
operating costs. This section provides a deeper interpretation of the key findings, considering
their theoretical, practical, and industry-specific implications. Each predictor is discussed
individually, with attention to whether the observed relationships align with prior expectations
and existing research.
This section also clarifies the meaning of a one (1)-unit increase in violations to ensure
consistency in interpretation. Since the dataset captures the quarterly totals of raw CSA violation
scores across all power units within a firm, a one (1)-unit increase does not represent a single
additional violation but rather an increase in total CSA points equal to the mean number of points
recorded in that category across all firms in the dataset. These CSA points include FMCSA’s
severity weighting (1-10 scale) and the 3x time-weighting factor, meaning a single violation may
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contribute anywhere from three (3) to 30 CSA points. As a result, when the regression model
estimates the financial impact of a one (1)-unit increase, it reflects the expected change in
Operating Expenses when a firm’s total CSA points in a specific category increase by the
industry average for that quarter.
This interpretation provides a real-world perspective on how safety violations accumulate
within a trucking company’s operations and how firms with higher compliance failures, reflected
in increased CSA points, experience more significant financial consequences.
Unsafe Driving and Operating Expenses
As reported in Chapter 5, Unsafe Driving violations were not found to be a statistically
significant predictor of Operating Expenses (β = 0.017, p = 0.767). This suggests that variations
in Unsafe Driving violations across companies do not contribute meaningfully to differences in
operating costs.
One possible explanation for this finding is that unsafe driving behaviors may not lead to
immediate financial consequences for firms. Unlike other violations that directly lead to
maintenance costs, fines, or regulatory penalties, Unsafe Driving violations, such as speeding,
improper lane changes, or reckless driving, are often issued directly to drivers rather than the
trucking companies. While these infractions do negatively affect a firm’s CSA score, they may
not immediately translate into increased Operating Expenses because the fines and penalties are
often the responsibility of the driver rather than the company.
Additionally, the financial impact of Unsafe Driving violations may be more indirect and
long-term. Repeated violations could lead to higher insurance premiums (Chen & Jiang, 2019),
increased legal liability (Thron et al., 2024), or reputational damage (Miller, 2020), but these
costs may not be immediately reflected in the financial data analyzed in this study. Some firms
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also mitigate risks associated with unsafe driving through safety incentive programs, training
interventions, telematics monitoring, or other proactive measures, which may help prevent
operational cost increases even when violations occur (Federal Motor Carrier Safety
Administration, 2012c).
Understanding a One-Unit Increase in Unsafe Driving Violations
A one (1)-unit increase in Unsafe Driving violations represents an increase in total CSA
point equivalent to the mean number of Unsafe Driving points recorded across all firms in the
dataset for a quarter. Due to the FMCSA’s severity-weightings of 1-10 points per violation, with
a time-weighting factor of up to 3x, the total CSA points accumulated by a firm may stem from
multiple violations of varying severity. Based on the descriptive statistics from Chapter 5, the
mean CSA points for Unsafe Driving violation points per quarter is 2,345 (rounded up), meaning
that a one (1)-unit increase could result in anywhere between 78 to 782 additional violations.
Since Unsafe Driving violations were not statistically significant predictors of Operating
Expenses, an increase of one unit in this category does not yield a meaningful change in
operating costs based on the regression results. This further reinforces that, while Unsafe
Driving violations are serious from a safety perspective, they do not impose substantial direct
financial consequences on trucking firms within the timeframe analyzed.
Financial and Operational Implications
Although this study did not find a statistically significant relationship between Unsafe
Driving violations and Operating Expenses, firms should not overlook the long-term risks
associated with repeated violations. While they may not immediately impact Operating
Expenses, persistent violations could still lead to higher insurance costs (Chen & Jiang, 2019),
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legal actions (Thron et al., 2024), or FMCSA interventions, which may carry financial
consequences beyond the scope of this study.
Hours-Of-Service and Operating Expenses
A notable and unexpected finding was the statistically significant negative relationship
between Hours-Of-Service violations and Operating Expenses (β = -1.016, p < 0.001). This
result suggests, counterintuitively, that firms with more recorded HOS violations tend to have
lower Operating Expenses, contradicting the initial hypothesis that an increase in violations
would correspond to higher operating expenses.
One possible explanation is that firms with frequent HOS violations may prioritize
delivery efficiency over compliance, reducing costs associated with driver downtime. By
exceeding legal driving limits, firms may reduce the need for additional drivers, avoid delays
associated with compliance stops, or optimize their delivery schedules at the expense of
regulatory compliance (Chen et al., 2021), contributing to lower operating costs despite non-
compliance risks. This interpretation aligns with industry concerns about HOS regulations
creating cost pressures for carriers (Miller, 2020), leading some to engage in strategic violations
to maintain profitability.
Again, this result must be interpreted with caution due to the presence of
multicollinearity. The VIF value for Hours-Of-Service violations was 11.811, indicating a
strong correlation with other predictors. This suggests that high correlations between vehicle
maintenance and unsafe driving violations could influence the observed negative relationship
rather than reflect a genuine causal link between HOS violations and lower expenses.
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Understanding a One-Unit Increase in Hours-Of-Service Violations
A one (1)-unit increase in HOS violations corresponds to a rise in total CSA points
equivalent to the average number of HOS points recorded across all firms in the dataset for a
quarter. Unlike a single additional violation, this increase represents the cumulative effect of
multiple infractions issued to a company’s fleet, with each violation contributing a different
number of points based on the FMCSA’s severity scale and time-weighting factors.
Since HOS violations were found to be a statistically significant predictor of Operating
Expenses, it is important to understand the range of violations that would result in a one (1)-unit
increase and its corresponding financial impact. A one (1)-unit increase corresponds to
approximately 757 (756.81 per quarter, as reported in Chapter 5) additional CSA points per
quarter using the mean CSA points for HOS violations.
Applying the regression coefficient (β = - 1.016), this increase translates to an estimated
decrease in Operating Expenses of $1,016,000 per quarter. Since individual violations range
from three (3) to 30 CSA points, this increase could represent between 25 and 252 additional
violations per quarter, depending upon the severity of the violation.
Financial and Operational Implications
While the statistical model suggests that firms with higher HOS violations experience
lower Operating Expenses, this result does not imply a sustainable or risk-free strategy. The
short-term cost reductions associated with exceeding legal driving limits may be offset by more
severe long-term consequences, including higher regulatory scrutiny (National Academies of
Sciences, Engineering, and Medicine, 2017), driver fatigue-related accidents (Zhang et al.,
2018), increased insurance premiums (Chen & Jiang, 2019), and potentially costly litigation
(Thron et al., 2024).
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These primary concerns consist of multiple ways the persistent and habitual non-
compliance with HOS regulations can lead to more significant long-term impacts. FMCSA
interventions, including heightened monitoring, compliance reviews, and more frequent roadside
inspections, can disrupt operations and increase administrative costs (National Academies of
Sciences, Engineering, and Medicine, 2017). Additionally, companies repeatedly exceeding
legal driving limits expose themselves to higher accident risks due to driver fatigue (Zhang et al.,
2018). Fatigue-related crashes can lead to substantial financial losses through legal settlements
(Thron et al., 2024), liability claims, and reputational damage, particularly for firms with a
pattern of non-compliance.
Beyond regulatory concerns, insurance premiums are another financial risk associated
with habitual HOS violations. Insurers assess risk based on compliance records, and companies
with poor safety performance may face rising insurance costs (Chen & Jiang, 2019), which could
erode any short-term cost savings achieved through non-compliance.
Although the data suggests a negative relationship between HOS violations and
Operating Expenses in the short term, the broader financial and safety implications of repeated
violations should not be overlooked. Firms that rely on excessive driving hours as a cost-saving
measure may find that the long-term consequences, including heightened regulatory attention,
increased accident liability, and rising insurance costs, outweigh any temporary financial
benefits.
Vehicle Maintenance and Operating Expenses
The regression results indicate that Vehicle Maintenance Violations were a statistically
significant positive predictor of Operating Expenses (β = 0.152, p < 0.001), meaning that
trucking companies with higher violations in this category tend to experience increased
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Operating Expenses. This finding aligns with expectations, as poor vehicle maintenance can
lead to direct and indirect financial costs, including higher repair expenses, regulatory penalties
(Federal Register, 2009), and operational inefficiencies due to equipment downtime. Given that
Operating Expenses are recorded in millions, the coefficient suggests that for each additional
recorded Vehicle Maintenance Violation, Operating Expenses increase, reinforcing the financial
burden of inadequate fleet management.
One explanation for this relationship is that Vehicle Maintenance violations are weighted
less than other categories in the FMCSA methodology of calculating CSA scores. Thus, this has
adversely caused trucking companies to focus less on vehicle maintenance than other violation
categories. A second explanation is that Vehicle Maintenance violations directly reflect
deficiencies in fleet upkeep and safety compliance, leading to higher repair and replacement
costs, increased roadside inspection failures, and additional regulatory fines. Common violations
in this category, such as brake failures, work tires, and lighting defects, can result in unplanned
vehicle downtime, delays in service delivery, and costly out-of-service orders. Additionally,
persistent maintenance violations can negatively impact a company’s CSA score, leading to
increased insurance premiums (Chen & Jiang, 2019) and reduced business opportunities due to
reputational damage.
Once again, these results must be interpreted with caution due to the presence of
multicollinearity. The VIF value for Vehicle Maintenance violations was 21.446, indicating a
high correlation with other predictor variables, particularly HOS and Unsafe Driving. This
suggests that while Vehicle Maintenance violations directly impact Operating Expenses, part of
their variance is shared with other compliance-related deficiencies. Thus, the financial burden
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associated with vehicle maintenance issues may overlap with broader safety and regulatory
compliance challenges within firms.
Understanding a One-Unit Increase in Vehicle Maintenance Violations
A one (1) unit increase in Vehicle Maintenance violations reflects the accumulation of
additional violations with a cumulative value equal to the mean, or industry average, for this
category. Again, this represents not a single additional violation but the combined impact of
multiple infractions, each contributing varying points based on the severity and time weighting
penalty application according to the FMCSA regulations. Based on the descriptive statistics
from Chapter 5, the mean CSA points for Vehicle Maintenance violations per quarter is 6,802
(rounded up), meaning a one (1)-unit increase reflects this industry-average level of violations.
Applying the regression coefficient (β = 0.152), this increase translates to an estimated
rise in Operating Expenses of approximately $152,000 per quarter. Since individual violations
range from three (3) to 30 CSA points, this increase could represent 227 and 2,267 additional
violations per quarter, depending on the severity weight of the violations.
Financial and Operational Implications
The findings reinforce that Vehicle Maintenance violations impose a substantial financial
burden on trucking companies through immediate repair and compliance costs and affect long-
term operational efficiency. Firms with higher maintenance violations are more likely to
experience frequent roadside inspections, increased downtime, and greater regulatory scrutiny,
further driving up costs. Additionally, the high multicollinearity of Vehicle Maintenance with
other violation categories suggests that companies struggling with maintenance compliance often
face broader safety deficiencies, intensifying their financial challenges.
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These results emphasize the importance of proactive fleet maintenance strategies.
Companies that invest in preventative maintenance, real-time diagnostics, and compliance
training may mitigate many of these costs, ensuring violations do not escalate into significant
financial burdens.
Controlled Substances and Alcohol and Operating Expenses
The regression results indicate that Controlled Substances and Alcohol violations were
not statistically significant predictors of Operating Expenses (β = 0.128, p = 0.063). This
suggests that variations in these violations across firms do not contribute meaningfully to
differences in operating expenses.
One possible explanation for this finding is that Controlled Substances and Alcohol
violations are relatively infrequent compared to the other safety violations, as evidenced by their
low mean count (21.15 violations per company) and high standard deviation (31.58). While
drivers may face the most severe individual consequences for Controlled Substances and Alcohol
violations, companies are also held accountable for ensuring compliance. Firms face fines, legal
liability (Thron et al., 2024), and increased insurance premiums (Chen & Jiang, 2019) if they fail
to enforce proper substance testing policies or allow non-compliant drivers to operate under their
authority. However, the financial penalties imposed on companies may not be immediately
reflected in Operating Expenses in the same way as more frequent violations such as Vehicle
Maintenance and Hours-Of-Service.
Another factor that could influence the lack of statistical significance is the nature of
enforcement for drug and alcohol violations. Unlike Vehicle Maintenance or Hours-Of-Service
violations, which are often identified through routine roadside inspections, Controlled
Substances and Alcohol violations typically arise from targeted enforcement actions, random
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screenings, or post-accident investigations. This could result in a lower overall violation count
and less direct impact on day-to-day operational costs.
Understanding a 1-Unit Increase in Controlled Substances and Alcohol Violations
A one (1)-unit increase in Controlled Substances and Alcohol violations reflects an
increase in total CSA points that aligns with the industry average for this category per quarter.
Given the low frequency of these violations, a firm experiencing a one (1)-unit increase is likely
facing a small number of severe infractions rather than a widespread compliance issue. Since
each violation carries a severity weighting between one (1) and 10 and is subject to FMCSA’s
time-weighting factor, up to 3x, the exact number of violations contributing to a one-unit
increase depends on their severity and recency.
Since the regression coefficient (β = 0.128) was not statistically significant (p = 0.063),
there is insufficient evidence to conclude that an increase in violations in this category has a
meaningful impact on Operating Expenses. However, this relationship was not statistically
significant in this study, and it does not imply that drug and alcohol violations have no financial
consequences. Companies found in violation of FMCSA drug and alcohol testing requirements
may still face fines, legal costs (Thron et al., 2024), reputational damage, and increased
insurance premiums (Chen & Jiang, 2019). However, these costs may not be systematically
reflected in the quarterly operating expenses data analyzed in this study.
Financial and Operational Implications
Although this study does not find a statistically significant relationship between
Controlled Substances and Alcohol violations and Operating Expenses, it remains a critical
compliance area for trucking companies. Violations in this category can lead to severe legal
(Thron et al., 2024) and regulatory repercussions, including suspensions, license revocations, and
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increased FMCSA monitoring. Additionally, a company’s CSA score may be negatively
impacted, influencing customer contracts, insurance rates (Chen & Jiang, 2019), and hiring
practices.
Companies can mitigate risks related to Controlled Substances and Alcohol violations by
implementing strict pre-employment and random drug testing policies, driver education
programs, and intervention strategies for at-risk employees (FMCSA, 2024b). While the
immediate financial impact of these violations may not be evident in quarterly operating
expenses, the long-term consequences for companies failing to address drug and alcohol
compliance can be severe.
Driver Fitness and Operating Expenses
The regression results indicate that Driver Fitness violations were a statistically
significant positive predictor of Operating Expenses (β = 2.142, p < 0.001), suggesting that
trucking companies with higher violations in this category tend to experience an increase in
Operating Expenses. This finding aligns with expectations, as non-compliance in driver
qualification standards introduces financial burdens in the form of higher insurance premiums
(Chen & Jiang, 2019), legal liabilities (Thron et al., 2024), and administrative penalties (Federal
Register, 2009). The coefficient estimate suggests that for each additional recorded Driver
Fitness Violation, Operating Expenses increase by a measurable amount, reflecting the financial
burden associated with non-compliance. Given that Operating Expenses are recorded in
millions, even small changes in violation counts can translate into significant cost implications
for trucking firms.
One explanation for this relationship is that Driver Fitness violations reflect systemic
hiring, training, and driver compliance issues, leading to increased costs. Violations in this
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category, which include failure to meet medical qualifications, improper licensing, and
inadequate training, can result in higher driver turnover, increased recruitment and training costs,
and regulatory penalties (Federal Register, 2009). As with other categories, repeated Driver
Fitness violations may cause an increase in insurance premiums (Chen & Jiang, 2019) and legal
exposure (Thron et al., 2024), further contributing to higher Operating Expenses.
Again, this result must be interpreted with caution due to the multicollinearity inherent to
the dataset. The VIF value for Driver Fitness violations was 5.398, indicating that collinearity
concerns are less severe than other violation categories, such as Vehicle Maintenance and Hour-
Of-Service violations. This suggests that while Driver Fitness violations independently affect
Operating Expenses, some variance is still shared with other compliance-related factors.
Understanding a One-Unit Increase in Driver Fitness Violations
A one (1)-unit increase in Driver Fitness Violations represents the total CSA points
accumulated by a firm in a quarter, equal to the industry average for this category. Unlike
categories such as Vehicle Maintenance, which reflect fleet condition, Driver Fitness violations
primarily indicate deficiencies in driver qualification and regulatory compliance. Firms
experiencing a one (1)-unit increase are likely dealing with multiple compliance failures rather
than an isolated violation. Based on Chapter 5 results, the one (1)-unit increase translates to an
estimated 181 CSA points per quarter.
Applying the regression coefficient (β = 2.142), this increase translates to an estimated
rise in Operating Expenses of $2,142,000 per quarter. Since individual violations can range from
3 to 30 CSA points, this increase could represent anywhere from 6 to 61 additional violations per
quarter, depending upon the severity of the violation.
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Financial and Operational Implications
The findings of this study suggest that Driver Fitness violations represent a significant
financial burden for trucking companies, as firms with more violations in this category tend to
incur substantially higher Operating Expenses. This is likely due to the cost associated with
hiring, training (McKenzie et al., 2018), regulatory penalties (Federal Register, 2009), and
potential legal risks (Thron et al., 2024) arising from non-compliant driver management
practices.
Companies with high Driver Fitness violations may also face increased regulatory
oversight, leading to additional administrative burdens and potential FMCSA intervention (3.2
Compliance, Safety, Accountability (CSA) program). Firms with poor driver qualification
practices may also struggle with workforce retention, as drivers who do not meet compliance
requirements may be terminated, forcing companies to invest in recruiting and onboarding new
personnel at a higher frequency (LeMay & Keller, 2019).
These results emphasize the need for strong compliance programs in driver hiring and
training. Companies that implement rigorous background checks, medical fitness evaluations,
and ongoing training initiatives can potentially mitigate these financial burdens and reduce the
risk of regulatory penalties (Federal Register, 2009) and increased insurance costs (LeMay &
Keller, 2019).
6.3 Theoretical Implications
This study was grounded in James Reason’s Theory of Active and Latent Failures and the
Human Factors Analysis and Classification System (HFACS), providing a framework for
understanding how regulatory non-compliance manifests in the trucking industry and the
financial consequences. Reason’s theory distinguishes between active failures, which are direct,
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observable violations such as Unsafe Driving behaviors, and latent failures, which stem from
systematic issues like inadequate training, poor vehicle maintenance, or deficient company
policies (Reason et al., 2006). Similarly, HFACS categorizes violations within a hierarchical
structure that includes organizational influences, unsafe supervision, preconditions for unsafe
acts, and unsafe actions, reinforcing that safety violations do not occur in isolation but instead
reflect more significant systemic weaknesses (Shapell & Wiegmann, 2000).
The results of this study align with these theoretical models in several ways. First,
violations with a direct operational component, such as Unsafe Driving, were not statistically
significant predictors of Operating Expenses. This suggests that active failures alone may not
have an immediate financial impact on firms. Instead, violations that reflect latent system
failures, such as Vehicle Maintenance and Driver Fitness, were the strongest predictors of
increased Operating Expenses, reinforcing that organizational deficiencies contribute more
substantially to financial burdens than individual driver actions. This finding aligns with
Reason’s claim that latent failures are often more significant than active failures because they
weaken the overall safety framework of an organization, leading to increased financial and
operational risks (Reason et al., 2006).
In addition, the multicollinearity among specific violation categories, particularly Vehicle
Maintenance, Hours-Of-Service, and Unsafe Driving violations, further supports the systemic
nature of safety violations. The fact that these violations frequently happen concurrently
suggests that firms with poor regulatory compliance in one area are likely to have deficiencies in
multiple categories, reinforcing HFACS’s assertion that violations at the operational level often
originate from more profound organizational and supervisory insufficiencies (Shapell &
Wiegmann, 2000). This highlights the interconnected nature of safety failures, suggesting that
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safety-related costs are not driven by a single violation type but rather by the cumulative effect of
multiple non-compliance issues.
Furthermore, this study expands upon existing applications of Reason’s Theory and
HFACS in transportation safety research by integrating a financial perspective into theoretical
models of compliance and risk. While prior research has primarily focused on accident
prevention and regulatory enforcement, this study demonstrates that latent system failures have
tangible financial consequences, suggesting that regulatory frameworks should consider safety
and cost implications when assessing compliance risks. These findings contribute to the broader
understanding of how regulatory violations influence not only safety outcomes but also the
economic viability of trucking industry firms.
6.4 Practical and Managerial Implications
The findings of this study have significant practical and managerial implications for
trucking firms, regulatory agencies, and industry stakeholders. Understanding the financial
consequences of safety violations, or lack thereof, allows fleet managers, compliance officers,
and policymakers to make more informed decisions regarding safety enforcement, operational
improvements, and regulatory compliance strategies. This study highlights that not all violations
have the same financial impact, with Vehicle Maintenance and Driver Fitness violations
emerging as the costliest predictors of Operating Expenses. This suggests that firms should
prioritize preventative maintenance programs, driver training, and hiring standards to mitigate
long-term financial risks associated with regulatory non-compliance.
Implications for Fleet Management and Operational Decision-Making
This study’s findings suggest that trucking firms should reallocate safety and compliance
resources toward preventing violations with the highest financial impact. Preventative
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maintenance programs should be prioritized due to the strong association of Vehicle
Maintenance violations with increased operating expenses. Regular fleet inspections, predictive
maintenance technologies, and stricter internal compliance procedures could reduce costly
violations, unplanned downtime, and regulatory fines.
Driver Fitness violations indicate systemic workforce deficiencies, such as poor hiring
practices, inadequate training, or medical qualification issues. Implementing more rigorous
driver screening, continuous training, and wellness programs could significantly reduce legal
risks (Thron et al., 2024), compliance-related costs, and turnover (LeMay & Keller, 2019), an
industry-wide problem.
Finally, this study reinforces that violations often occur in clusters, as seen in the high
multicollinearity among Vehicle Maintenance, Hours-Of-Service, and Unsafe Driving violations.
This suggests that firms should adopt a holistic safety approach rather than addressing
compliance issues in silos. For example, a firm with frequent Hours-Of-Service violations will
likely have Vehicle Maintenance issues, indicating that poor operation management contributes
to violations across multiple categories. Developing comprehensive safety programs that address
multiple violation types simultaneously can lead to more significant long-term cost savings and
improved regulatory compliance.
Regulatory and Policy Implications
From a regulatory perspective, these findings suggest that current FMCSA enforcement
efforts may benefit from shifting greater focus toward systemic organizational failures rather
than isolated driver infractions. While Unsafe Driving violations were not statistically
significant predictors of Operating Expenses, violations related to fleet maintenance and driver
qualifications had a much more substantial financial impact. This underscores the importance of
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increased targeting enforcement efforts toward firms with chronic systemic failures rather than
only penalizing individual driver infractions.
Hours-Of-Service violations have a statistically significant negative relationship,
suggesting that timely deliveries are more valuable to firms than maintaining regulatory
compliance with Hours-Of-Service rules. This suggests that regulators may consider increasing
Hours-Of-Service violation fines to encourage adherence to regulations over the financial benefit
of delivering products on time.
Additionally, the high correlation between multiple violation categories suggests that
current CSA scoring methods may not fully capture the interconnected nature of compliance
failures. Regulators may consider refining CSA metrics to weigh systemic violations more
heavily, encouraging firms to address the root causes of safety weaknesses rather than merely
reacting to individual infractions.
Financial and Risk Management
Trucking firms' financial risks associated with regulatory non-compliance extend beyond
direct fines and penalties. Violations can increase insurance premiums (Chen & Jiang, 2019),
disrupt operations due to vehicle downtime, and lead to reputational harm (Miller, 2020), all of
which impact long-term profitability. Given these risks, firms should view compliance
investments as risk mitigation rather than an operational burden.
Furthermore, insurance providers, investors, and financial institutions may leverage CSA
violation data as a financial risk assessment tool. Firms with high levels of Vehicle Maintenance
or Driver Fitness violations may be perceived as higher-risk investments, affecting their
creditworthiness, insurance costs, and ability to secure contracts. By proactively addressing
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safety compliance issues, firms can improve their operational efficiency and strengthen their
financial standing within the industry.
6.5 Limitations of the Study
While this study provides valuable insights into the relationship between safety violations
and Operating Expenses in the trucking industry, several limitations should be acknowledged.
These limitations pertain to the data composition, methodology constraints, and industry-specific
considerations, all of which should be scrutinized when interpreting the findings.
Data Limitations
One key limitation of this study is that the dataset focuses on publicly traded trucking
companies, representing only a fraction of the overall industry. Most trucking companies
operate small fleets of one (1) to 50 (fifty) trucks, whereas the firms in this study are
significantly larger and publicly held. Smaller firms may face different financial constraints,
regulatory compliance challenges, and operational cost structures than their larger counterparts.
As a result, the findings of this study may not generalize to most trucking firms, especially small
carriers that operate on thinner profit margins and may experience different financial
consequences from safety violations.
Also, while this study spans nine (9) financial quarters, which offers a broader
perspective than a purely cross-sectional dataset, it does not capture long-term trends. A longer
time-series dataset would be necessary to understand better how changes in safety compliance
influence financial performance over extended periods. Although the study provides evidence of
relationships between safety violations and Operating Expenses, longer-term financial effects,
such as changes in insurance rates, contact, retention, and operation adjustments, remain outside
the scope of this analysis.
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Finally, an additional limitation of this study is that Operating Expenses are a broad
financial measure that includes many costs beyond safety-related violations. While violations
contribute to increased expenses, firms’ cost structures may also be influenced by external
factors such as fuel prices, labor costs, and economic conditions, which are not directly
accounted for in this model. This means that while the study identifies significant relationships
between certain safety violations and Operating Expenses, it cannot fully isolate the financial
impact of violations from other cost drivers.
Methodology Limitations
A primary methodology limitation is the presence of multicollinearity among several
independent variables, particularly Vehicle Maintenance, Hours-Of-Service, and Unsafe Driving
violations. Since these violation categories often coincide, isolating their individual effects on
Operating Expenses is challenging. While Variance Inflation Factor (VIF) tests and collinearity
diagnostics were conducted, the high correlation among some predictors may inflate standard
errors and reduce the precision of coefficient estimates.
Furthermore, this study employs multiple linear regression (MLR) as the primary
analytical method. While MLR is a widely used statistical approach, it assumes linear
relationships between predictors and the dependent variable. However, real-world financial
impacts may not always follow a perfectly linear pattern, meaning that nonlinear effects or
interaction terms could further refine the understanding of these relationships. Additionally,
MLR does not account for unobserved variables that could influence Operating Expenses, such
as company-specific risk management strategies or geographical operating regions.
While the regression analysis identifies statistically significant relationships between
safety violations and Operating Expenses, the degree to which violations cause cost increases
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varies by category. Certain violations, such as Vehicle Maintenance and Hours of Service
Violations, impose immediate financial burdens through fines, required repairs, and equipment
downtime, suggesting a direct causal link to increased Operating Expenses.
However, other violations, such as Unsafe Driving and Driver Fitness Violations, may
have indirect or delayed financial consequences, which could be influenced by additional factors
such as insurance adjustments, accident liabilities, or workforce management strategies. While
this study provides strong evidence of an association between safety violations and operating
costs, long-term financial impacts and causal mechanisms may require further investigation
using longitudinal data.
Industry-Specific Limitations
Another important consideration is that these findings may not generalize beyond the
trucking industry. The study focuses on FMCSA-regulated violations specific to commercial
motor carriers, meaning its conclusions may not apply to industries with different regulatory
structures. For example, while Vehicle Maintenance violations strongly impact operating costs
in trucking, similar violations may have less financial impact in industries where transportation is
not a primary business function.
Furthermore, company-specific strategies and risk tolerance levels vary widely across the
industry, meaning firms may experience different financial impacts from similar violations.
Some companies may proactively invest in safety compliance and maintenance, while others
may absorb short-term costs to prioritize operational efficiency. These variations in strategic
decision-making could influence the degree to which violations translate into higher expenses.
Despite these limitations, this study provides meaningful insights into the financial
impact of safety violations in the trucking industry. Identifying which violations contribute most
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to increased operating costs highlights the importance of preventative maintenance, driver fitness
programs, and compliance-focused operational strategies. Although certain methodological and
industry-specific constraints must be acknowledged, the study offers a robust foundation for
understanding how safety violations affect financial performance in large trucking firms.
6.6 Directions for Future Research
While this study provides meaningful insights into the relationship between safety
violations and Operating Expenses, several areas remain for further investigation. Future
research could expand upon these findings by incorporating additional financial metrics,
exploring firm size variations, assessing regulatory effectiveness, and evaluating industry risk
management strategies.
Expanding the Scope of Safety Violations and Financial Analysis
One potential avenue for future research is broadening the financial metrics used to
assess the economic impact of safety violations. This study focuses on Operating Expenses, a
broad measure of financial performance, but future studies could explore how violations impact
specific cost components such as insurance premiums (Chen & Jiang, 2019), legal fees, fuel
efficiency, and revenue loss from downtime. A more detailed financial analysis could help
distinguish between direct compliance costs (e.g., fines, repairs) and indirect financial
consequences (e.g., lost contracts, reputational damage).
Furthermore, although this study covers nine financial quarters, future research may gain
from longitudinal analyses extending over several years. Investigating how ongoing safety
violations affect financial performance over a longer time frame would offer deeper insights into
the cumulative financial impacts of non-compliance. Such studies could examine whether
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chronic offenders face increasing financial penalties or if improvements in compliance result in
measurable cost savings over time.
Another area for improvement involves exploring alternative and more sophisticated
modeling techniques. First, log transformations could help alleviate the multicollinearity issues
inherent within the data. Also, while this study employs MLR, future research could extend
beyond traditional regression methods to incorporate more advanced econometric models and
machine learning approaches. Time-series analysis could provide deeper insights into how
safety violations impact Operating Expenses over time, particularly when considering seasonal
patterns, regulatory changes, or external economic factors.
Structural Equation Modeling (SEM) could allow for examining complex relationships,
including mediating and moderating effects between violations, risk management strategies, and
financial outcomes. Additionally, machine learning models could be used to identify hidden
patterns and nonlinear relationships that traditional regression approaches may not fully capture.
These techniques could enhance predictive accuracy and improve the understanding of how
regulatory compliance influences financial performance in the trucking industry.
Studying Small and Mid-Sized Trucking Firms
Due to time constraints and the availability of secondary data, this study focuses on
publicly traded, large motor carriers, which do not represent the majority of the trucking
industry. Most trucking firms operate small fleets (1-50 trucks) (ATRI, 2023), and their
financial constraints, regulatory burdens, and risk exposure differ significantly from large
carriers. Future research should examine how safety violations affect small trucking companies,
as they may have fewer resources to absorb compliance-related costs. Understanding whether
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smaller firms experience disproportionate financial consequences due to violations could provide
valuable insights for industry stakeholders and policymakers.
Assessing Regulatory Effectiveness and Policy Implications
Future research could investigate whether current FMCSA enforcement strategies
effectively mitigate financial and operational risks for carriers. While this study demonstrates
that some safety violations have significant cost implications, further investigation is needed to
determine whether regulatory policies effectively incentivize compliance. Research could
evaluate whether safety incentive programs, targeted enforcement, or alternative penalty
structures influence firms’ regulatory behavior and financial health.
In addition, policymakers may benefit from research that examines whether CSA scoring
methods accurately capture systemic compliance failures. Since this study found high
multicollinearity between multiple violation categories, future studies could investigate whether
CSA scores should weigh systemic violations more heavily, encouraging firms to address root
causes rather than isolated incidents.
Exploring Firm-Level Strategies for Risk Management
A promising area for future research involves assessing how firms proactively manage
compliance-related risks. While this study identifies which violations have the highest financial
impact, future research could examine how different fleet management strategies influence these
outcomes. For example, studies could examine whether proactive safety investments, such as
AI-enabled cameras and predictive maintenance, mitigate the financial consequences of
violations. Additionally, future research could investigate how insurance companies, lenders,
and investors utilize CSA scores to assess financial risk in the trucking industry, as firms with
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higher CSA scores may face higher insurance premiums (Chen & Jiang, 2019), increased
borrowing costs, or reduced business opportunities.
This study provides a strong foundation for understanding the financial consequences of
safety violations in the trucking industry, but multiple avenues remain for further exploration.
Future research can build upon these findings by expanding financial metrics, incorporating
longitudinal designs, analyzing small carriers, assessing regulatory effectiveness, and evaluating
risk management strategies to provide deeper insight into the intersection of regulatory
compliance and financial performance.
6.7 Conclusion
This study examined the financial implications of safety violations in the trucking
industry, identifying which violations contribute most significantly to increased Operating
Expenses. These findings suggest that Vehicle Maintenance and Driver Fitness Violations
impose the most significant financial burden on firms, reinforcing that latent system failures are
more costly than isolated infractions. In contrast, Unsafe Driving violations were not significant
predictors of cost, and Hours-Of-Service violations showed an inverse relationship with
Operating Expenses, raising questions about compliance trade-offs in the industry.
These results underscore the importance of proactive fleet management, comprehensive
safety programs, and a holistic approach to regulatory compliance. Firms prioritizing
preventative maintenance and driver training may be better positioned to reduce long-term
compliance costs and improve financial sustainability. Additionally, the interconnected nature of
violations suggests that compliance challenges should be addressed systemically rather than in
isolation.
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Chapter 7: Conclusions
7.1 Introduction
This dissertation examined the relationship between safety violation categories and
Operating Expenses in the trucking industry. It utilized FMCSA compliance data and financial
performance metrics to evaluate the impact of these different categories of violations on costs.
The study employed multiple linear regression modeling to investigate whether safety violations
significantly predict financial performance, offering insights into which compliance failures
impose the most substantial financial burden on firms.
The central research question guiding this study was:
“How do safety ratings impact operating costs in the trucking industry?”
By addressing this question, the study aimed to quantify the financial consequences of
non-compliance, identify which safety violations have the most significant financial impact, and
provide insights for fleet managers, regulators, and policymakers. The findings contribute to a
deeper understanding of how safety performance impacts financial sustainability in the trucking
industry, providing both theoretical and practical implications.
This chapter presents the research's conclusions, summarizing key findings,
contributions to academic literature, implications for industry and policy, and recommendations
for future research.
7.2 Summary of Key Findings
The results of this study indicate that not all safety violations have the same financial
impact on trucking firms. The analysis identified Vehicle Maintenance and Driver Fitness
violations as the strongest predictors of increased Operating Expenses, reinforcing that latent
system failures impose more significant financial burdens than isolated infractions. These
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violations reflect deficiencies in fleet upkeep, driver qualifications, and compliance with
regulatory standards, leading to higher repair costs, regulatory penalties (Federal Register, 2009),
and potential disruptions to operations.
In contrast, Unsafe Driving violations were not statistically significant, suggesting that
momentary unsafe behaviors, while serious for safety outcomes, do not translate directly into
higher Operating Expenses within this study’s timeframe. Additionally, Hours-Of-Service
violations exhibited an unexpected inverse relationship with Operating Expenses, raising
questions about how firms may strategically balance regulatory adherence with operational
efficiency.
Furthermore, the study found strong correlations between multiple violation categories,
particularly Vehicle Maintenance, Hours-Of-Service, and Unsafe Driving violations. This
suggests that firms with deficiencies in one compliance area often exhibit non-compliance in
another, reinforcing that safety and regulatory issues are often systemic rather than isolated.
These findings contribute to a better understanding of how safety performance influences
financial sustainability in the trucking industry, highlighting the need for a more strategic
approach to safety management that goes beyond reactive regulatory compliance.
7.3 Contributions to Research and Theory
This study contributes to the transportation safety and financial management literature by
providing empirical evidence on the relationship between safety violations and Operating
Expenses in the trucking industry. By integrating financial data with safety compliance metrics,
this research expands the understanding of how regulatory non-compliance affects firm-level
financial performance, an area that has been largely underexplored in previous studies.
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A key theoretical contribution is the application of James Reason’s Theory of Active and
Latent Failures. The findings support Reason’s assertion that latent failures, such as fleet
maintenance and driver fitness deficiencies, impose more significant organizational
consequences than active failures, such as individual unsafe driving behaviors. The study
demonstrates that Vehicle Maintenance and Driver Fitness violations have the most substantial
financial impact. In contrast, Unsafe Driving violations were not statistically significant,
reinforcing the need for a systemic approach to safety compliance rather than focusing solely on
driver infractions.
This research also builds upon the Human Factors Analysis and Classification System
(HFACS) by highlighting the interconnected nature of safety violations. High multicollinearity
among multiple violation categories suggests that firms with deficiencies in one compliance area
often struggle in others, reinforcing HFACS’s emphasis on organizational and supervisory
influences on safety performance. These findings suggest that compliance issues should be
addressed holistically rather than as isolated regulatory infractions.
Additionally, this study expands existing research on FMCSA compliance by
incorporating firm-level financial data. While previous research has focused on safety violations
in relation to accident risk or regulatory enforcement, this study provides a new perspective by
demonstrating that compliance failures also carry measurable financial consequences. This
improves the broader understanding of the economic impact of safety compliance and its role in
industry sustainability.
7.4 Implications for Practice and Policy
The findings of this study have significant implications for trucking firms, regulators, and
policymakers, particularly regarding the impact of safety compliance on financial sustainability.
105
The results demonstrate that Vehicle Maintenance and Driver Fitness violations impose the most
significant financial burden on firms, emphasizing the need for proactive fleet management and
driver qualification strategies.
For trucking firms and fleet managers, prioritizing preventative maintenance, predictive
safety technologies, and comprehensive driver training may help reduce violations and lower
long-term costs. Additionally, the high correlation between certain violations suggests that
compliance failures are often systemic rather than isolated, reinforcing the need for a holistic
approach to regulatory adherence. Firms that accumulate excessive violations risk falling under
increased FMCSA oversight, including interventions and additional roadside inspections, which
could further escalate operating costs and impact fleet efficiency.
For regulators and policymakers, these findings suggest that while FMCSA’s existing
Compliance, Safety, and Accountability (CSA) program already identifies high-risk carriers for
intervention, there may be opportunities to enhance targeted enforcement strategies. Given that
Vehicle Maintenance and Driver Fitness violations have the most substantial financial impact,
regulators could consider refining risk-weighting systems or expanding incentive-based
programs that encourage preventative maintenance and workforce training to improve
compliance without increasing penalties.
Beyond regulatory enforcement, insurance companies and financial institutions may use
these findings to refine risk assessment models, adjusting premium structures to reward firms
with strong compliance records. By quantifying the financial impact of safety violations, this
study underscores the importance of integrating safety management into long-term business
strategies to enhance compliance and financial performance while mitigating the risk of
heightened regulatory scrutiny.
106
7.5 Limitations and Directions for Future Research
While this study provides valuable insights into the financial impact of safety violations
in the trucking industry, certain limitations should be acknowledged. The dataset focuses on
publicly traded large trucking firms, which may not fully represent small and mid-sized carriers
that dominate the industry. Given that smaller firms operate under different financial constraints
and regulatory pressures, future research should examine whether safety violations impose a
more significant financial burden on small carriers with fewer resources to absorb compliance
costs.
Additionally, while this study spans nine (9) financial quarters, it does not assess long-
term financial trends. A longitudinal study spanning several years could provide deeper insights
into how sustained compliance improvements or continued violations impact financial
performance over time.
Methodologically, this study employs multiple linear regression (MLR), which, while
robust, assumes linear relationships between safety violations and operating expenses. Future
research could explore alternative econometric techniques, such as time-series modeling or
causal inference approaches, to capture more complex relationships and control for additional
firm-level factors that may influence financial performance.
Finally, additional research could examine the effectiveness of FMCSA’s current
enforcement mechanisms in shaping safety outcomes and financial stability. While this study
highlights which violations contribute most to increased costs, further investigation is needed to
determine whether current penalties, compliance programs, and intervention strategies
effectively deter repeat violations. Understanding how firms respond to FMCSA oversight could
107
help regulators refine policy interventions to balance safety enforcement with industry
sustainability.
7.6 Final Thoughts
This study provides quantifiable evidence that safety violations in the trucking industry
have measurable financial consequences. It reinforces the importance of regulatory compliance
in ensuring both safety and economic sustainability by demonstrating that Vehicle Maintenance
and Driver Fitness violations impose the most significant financial burden. In contrast, other
violations show weaker or unexpected relationships. This research offers fleet managers,
policymakers, and industry stakeholders valuable insights.
The findings emphasize that compliance failures are rarely isolated incidents but are often
part of broader systemic issues within firms, underscoring the need for holistic safety
management strategies rather than reactive corrective measures. Additionally, given the high
costs associated with non-compliance, firms prioritizing proactive maintenance, driver training,
and compliance initiatives may experience long-term financial benefits and reduced regulatory
scrutiny.
Understanding the financial impact of safety compliance will remain critical as the
trucking industry continues to evolve in response to regulatory changes, technological
advancements, and economic pressures. Future research should continue to explore how safety
violations influence firm performance over extended periods, how small and mid-sized carriers
experience financial consequences differently, and whether current regulatory enforcement
strategies are effectively driving compliance improvements.
This study bridges the gap between regulatory compliance and financial performance,
contributing to academic literature and industry practice and providing a foundation for future
108
exploration of safety, risk management, and economic sustainability in commercial
transportation.
109
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Appendix A: IRB Approval Letter
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Appendix B: FAST Act (2015)
Summary of Rules and Guidelines from the FAST Act (2015)
Category
Summary of Rules and Guidelines from FAST Act
Insurance
Requirements
Minimum Financial Responsibility
Motor carriers are required to maintain minimum levels of
financial responsibility to cover potential liabilities from
accidents, including medical care, compensation, and other
costs.
Impact on Safety and Industry
Regulations must consider the potential impact on the safety
of motor vehicle transportation and the industry. This
includes assessing how insurance affects motor carriers’
behavior and safety record, particularly regarding crash
reduction.
Adequacy of Current Insurance Levels
Studies and analyses must be conducted to determine
whether current minimum levels of financial responsibility
adequately cover the costs associated with accidents,
including reviewing the frequency with which insurance
claims exceed current minimum levels in fatalities.
Safety Standards
Traffic Law Enforcement & Vehicle Inspections
States must adopt and enforce regulations compatible with
federal standards.
Regular inspections are mandated; States must maintain a
level of expenditure on safety programs.
Performance-Based Activities
States must implement performance-based activities,
including deployment and maintenance of technology to
enhance the efficiency and effectiveness of CMV safety
programs.
These activities are designed to promote safe CMV
transportation, including passengers and hazardous
materials.
Certification Standards for Roadside Inspection Inspectors
The FMCSA must incorporate certification standards for
roadside inspectors, ensuring inspections are conducted
uniformly and effectively.
Public Awareness and Education
Efforts to increase public awareness and education on CMV
safety, including targeting unsafe driving behaviors in high-
risk crash corridors and demonstrating new technologies to
improve safety.
Reciprocity for Inspections
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States are encouraged to grant maximum reciprocity for
inspections conducted under the North American Inspection
Standards, which would facilitate the identification of
previously inspected CMVs and reduce redundant
inspections.
Coordination with State Highway Safety Programs
State CMV safety agencies must coordinate their plans, data
collection, and information systems with state highway
safety improvement programs to ensure a cohesive
approach to road safety.
Driver Regulations
Driver Qualifications
Drivers must meet specific qualifications to operate CMVs:
o Hold a valid CDL
o Meet age requirements
o Meet medical requirements
Hours of Service (HOS):
Regulate the number of hours a driver can operate a CMV
to prevent fatigue-related accidents.
Maximum driving time
Mandatory rest breaks
Off-duty requirements
Drug and Alcohol Testing:
CMV drivers are subject to drug and alcohol testing to
ensure they are not impaired while operating vehicles
Testing is required:
o Pre-employment
o Post-accident
o Randomly
o Upon reasonable suspicion
o As part of return-to-duty
o Follow-up testing
Use of controlled substances and alcohol is strictly
regulated, with specific thresholds for blood alcohol
concentration and prohibitions on the use of certain drugs
Medical Certification
Drivers must undergo regular (yearly or biennial) medical
examinations to ensure they are physically capable of
operating a CMV
Medical certification process includes assessments of:
o Vision
o Hearing
o Diabetes management
o Cardiovascular health
o Other conditions that could impair driving ability
Training and
Endorsements
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Specific training and endorsements are required for drivers
operating specialized types of CMVs
Endorsements vary from state to state. The most common
six (6) are:
o H – Hazardous Materials
o N – Tanker (Non-hazardous material)
o P – People (16 or more seats)
o S – School Buses
o T – Double or Triple trailers
o X – Tanker & Hazardous Materials (combo)
Endorsements are added to the CDL license after passing
the required testing and/or meeting other requirements
specific to the type of vehicle or cargo
Record-Keeping and Reporting
Drivers and carriers must maintain accurate records of duty
status, vehicle inspections, and other relevant information.
This information is subject to review during inspections and
audits to ensure compliance
Electronic Logging Devices (ELDs) are mandated for most
CMV drivers to record driving hours to ensure adherence to
HOS regulations
Technology & Data
Intelligent Transportation Systems (ITS)
ITS technologies are deployed to improve the safety and
efficiency of CMV operations. These include:
o advanced traveler information systems
o transportation management technologies
o infrastructure maintenance and monitoring systems
Examples of ITS applications include:
o vehicle-to-vehicle (V2V)
o vehicle-to-infrastructure (V2I) communications
o both of which help prevent collisions and improve
traffic flow
Electronic Logging Devices (ELDs)
ELDs automatically track:
o Driving time
o Engine hours
o Vehicle movement
o Vehicle miles
o Location
Vehicle Safety Technologies
Allows various safety technologies to be integrated into
CMVs to reduce accidents and improve driver performance.
These include:
o forward collision warning systems
o lane departure warning systems
o
active cruise control systems
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The Secretary of Transportation has the authority to exempt
certain safety technologies from regulations that might
otherwise restrict their use, provided they achieve a level of
safety equivalent to or greater than existing standards
Operational
Exemptions
Operational Exemptions have been granted to certain vehicles to
allow them to operate in a less stringent regulatory environment:
Covered Farm Vehicles
Hi-Rail Vehicles
Automobile Transporters
Ready Mix Concrete Delivery Vehicles
Transportation of Construction Materials and Equipment
Commercial delivery of light- and medium-duty trailers
Certain Welding Trucks used in pipeline operations
Regulatory Reform
Notice of Cancellation of Insurance
Mandates carriers must be notified promptly if their
insurance is canceled
Regulations and Guidance
Requires FMCSA to review and update regulations and
guidance to ensure they are current and relevant, including
simplifying and clarifying existing regulations to reduce the
burden on carriers and drivers
Petitions
Provides a mechanism for stakeholders to petition the
FMCSA for regulatory changes, allowing for more
responsive and adaptive regulatory processes
Inspector Standards
Sets standards for inspectors to ensure consistency and
accuracy in the enforcement of commercial vehicle
regulations, which helps in maintaining fair and uniform
enforcement across different jurisdictions
Applications
Streamlines the application process for carriers and drivers,
reducing paperwork and making it easier to comply with
regulatory requirements
