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Received: 17 January 2025
Revised: 27 February 2025
Accepted: 6 March 2025
Published: 8 March 2025
Citation: Gumasing, M.J.J.
Determinants of Behavioral Intention
and Compliance Behavior Among
Transportation Network Vehicle
Service Drivers During the COVID-19
Pandemic. COVID 2025,5, 38.
https://doi.org/10.3390/
covid5030038
Copyright: © 2025 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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Article
Determinants of Behavioral Intention and Compliance Behavior
Among Transportation Network Vehicle Service Drivers During
the COVID-19 Pandemic
Ma. Janice J. Gumasing
Department of Industrial and Systems Engineering, De La Salle University, 2401 Taft Ave.,
Manila 1004, Philippines; ma.janice.gumasing@dlsu.edu.ph
Abstract: This study examines the factors influencing the behavioral intention and com-
pliance behavior of Transportation Network Vehicle Service (TNVS) drivers during the
COVID-19 pandemic. Grounded in the Theory of Planned Behavior (TPB) and the Health
Belief Model (HBM), the study integrates psychological, environmental, and organizational
factors to explain TNVS drivers’ adherence to safety protocols. Data were collected from
342 TNVS drivers in the National Capital Region (NCR) and CALABARZON through
a structured survey. Structural Equation Modeling (SEM) was employed to analyze the
relationships among variables and assess the determinants of compliance behavior. The
results indicate that attitude toward compliance (
β
= 0.453, p< 0.001), risk perception
(
β
= 0.289, p= 0.001), availability of personal protective equipment (PPE) (
β
= 0.341,
p< 0.001), passenger compliance (
β
= 0.293, p= 0.002), company policies (
β
= 0.336,
p= 0.001
), and organizational support systems (
β
= 0.433, p< 0.001) significantly influence
behavioral intention. In turn, behavioral intention strongly predicts compliance behavior
(β= 0.643, p< 0.001), confirming its mediating role in linking influencing factors to actual
adherence. However, stress and fatigue (
β
= 0.131, p= 0.211), ride conditions (
β
= 0.198,
p= 0.241
), and communication and training (
β
= 0.211, p= 0.058) showed non-significant
relationships, suggesting that their direct effects on behavioral intention are limited. The
model explains 69.1% of the variance in compliance behavior, demonstrating its robust-
ness. These findings highlight the importance of fostering positive attitudes, ensuring
adequate resource availability, and reinforcing organizational support to improve TNVS
drivers’ compliance with safety measures. Practical recommendations include implement-
ing educational campaigns, ensuring PPE access, strengthening company policies, and
promoting passenger adherence to safety protocols. The study contributes to the broader
understanding of health behavior in the ride-hailing sector, offering actionable insights for
policymakers, ride-hailing platforms, and public health authorities. Future research should
explore additional contextual factors, gender-based differences, and regional variations, as
well as assess long-term compliance behaviors beyond the pandemic context.
Keywords: COVID-19; partial least square structural equation modeling (PLS-SEM);
behavioral intention; compliance behavior; TNVS
1. Introduction
The COVID-19 pandemic has significantly reshaped various industries, including
the transportation sector, where maintaining safety and hygiene has become a paramount
concern [
1
]. Transportation Network Vehicle Services (TNVS), such as ride-hailing plat-
forms, have played a critical role in sustaining mobility during the pandemic, providing
COVID 2025,5, 38 https://doi.org/10.3390/covid5030038
COVID 2025,5, 38 2 of 25
essential services to commuters while ensuring public safety [
2
]. However, TNVS drivers
face unique challenges in adhering to health protocols, including limited access to personal
protective equipment (PPE) [
3
], passenger non-compliance [
4
], and heightened exposure to
infection risks due to frequent interactions with passengers in enclosed spaces [5].
Recent research has examined compliance behaviors across different transportation
sectors, including public transport drivers [
6
], taxi operators [
7
], and TNVS drivers [
8
],
highlighting sector-specific differences in adherence to COVID-19 safety measures. Public
transport drivers, such as bus and train operators, were generally required to follow strict
regulations, including mask mandates, passenger capacity limits, and frequent vehicle
sanitization [
9
,
10
]. However, studies have found that enforcement challenges and passenger
resistance reduced the overall effectiveness of compliance efforts [11,12].
Taxi drivers, operating within more informal and flexible work environments, reported
lower levels of compliance, often driven by economic pressures and lack of enforcement
mechanisms [
13
]. Research from countries like China, the United Kingdom, and the United
States indicates that self-employed drivers, particularly those in the TNVS sector, faced
greater autonomy in decision-making regarding COVID-19 safety behaviors, leading to
variations in compliance based on individual risk perception, local health policies, and
passenger expectations [14–16].
Studies on TNVS drivers in different regions have revealed behavioral disparities in
compliance, often shaped by socioeconomic conditions, governmental regulations, and
cultural norms. For instance, research in European countries found that TNVS drivers
exhibited higher adherence to safety measures due to government-mandated health proto-
cols and financial support [
17
]. In contrast, studies in low- and middle-income countries
(LMICs), such as Rwanda and Nigeria, report that economic necessity and weak regulatory
enforcement led to lower compliance rates [11,18].
Despite the growing body of literature on compliance behaviors in the transportation
sector, limited research has focused on TNVS drivers, leaving critical gaps in understanding
the specific psychological, environmental, and organizational factors influencing their
behavioral intentions and compliance behaviors during a health crisis.
Theoretical frameworks like the Theory of Planned Behavior (TPB) and the Health
Belief Model (HBM) are foundational tools for understanding health-related behaviors.
Both frameworks have been extensively used to study individuals’ decision-making pro-
cesses, particularly in the context of public health [
19
–
21
]. The TPB emphasizes three
key determinants of behavior: attitudes (personal evaluation of the behavior), subjective
norms (perceived social pressure to perform or not perform the behavior), and perceived
behavioral control (belief in one’s ability to carry out the behavior) [
22
]. On the other hand,
the HBM focuses on health-specific factors such as perceived susceptibility (belief in the
likelihood of contracting a disease), perceived severity (seriousness of the consequences),
perceived barriers (obstacles to performing a behavior), and perceived benefits (advantages
of taking preventive action), as well as cues to action that trigger behavior [23].
During the COVID-19 pandemic, empirical studies have applied TPB and HBM to
examine compliance with preventive behaviors, such as mask-wearing, social distancing,
and vaccination uptake [
24
,
25
]. Studies confirm that higher risk perception (HBM) and
stronger perceived social norms (TPB) significantly predict compliance behaviors, while
perceived barriers (e.g., cost, inconvenience) negatively impact adherence [
26
,
27
]. However,
research has primarily focused on healthcare workers and general populations, with limited
application of these models in the transportation sector, particularly among TNVS drivers.
While these frameworks have proven valuable in various contexts, they have limita-
tions when applied to multifaceted environments like those experienced by TNVS drivers
during the COVID-19 pandemic. First, both TPB and HBM primarily emphasize individual
COVID 2025,5, 38 3 of 25
perceptions and decision-making processes, often overlooking the broader environmental
and organizational contexts. For TNVS drivers, factors such as the availability of personal
protective equipment (PPE), vehicle conditions, and passenger compliance are critical
determinants of behavior that are not adequately addressed by these models. Second, in
the pandemic context, external conditions such as physical workspace design (e.g., vehicle
ventilation), external pressures from passengers or customers, and the overall health in-
frastructure play a significant role. TPB and HBM do not comprehensively integrate these
situational variables into their predictive models. Lastly, the role of companies or ride-
hailing platforms in shaping compliance behaviors, through policies, training programs, or
resource provision, is a key driver of TNVS drivers’ adherence to safety protocols. These
organizational factors are largely absent in traditional frameworks.
While there is substantial research on public transportation safety and health protocols,
few studies have addressed the behavioral determinants of TNVS drivers’ compliance
during the COVID-19 pandemic. Specifically, there is limited understanding of how
psychological factors (e.g., attitudes, risk perception, and stress) influence behavioral
intentions in this unique occupational setting. Also, the role of environmental factors (e.g.,
availability of PPE, passenger compliance, and vehicle conditions) in shaping safety-related
behaviors remains underexplored. And lastly, the influence of organizational support,
such as company policies, communication, and training, on drivers’ adherence to health
protocols is not well documented.
Given this, this study aims to fill these gaps by proposing and empirically testing an
Integrated COVID-19 Behavior Framework that incorporates psychological, environmental,
and organizational factors to explain and predict TNVS drivers’ behavioral intentions and
compliance behaviors. By addressing these unexplored areas, the research seeks to provide
actionable insights for policymakers, ride-hailing platforms, and public health authorities
to enhance safety practices in TNVS operations during ongoing and future health crises.
2. Conceptual Framework
The conceptual framework for this study, as shown in Figure 1, integrates psychologi-
cal, environmental, and organizational factors to comprehensively assess the behavioral
intention and compliance behaviors of TNVS drivers during the COVID-19 pandemic.
It is rooted in well-established theoretical foundations, such as the Theory of Planned
Behavior (TPB) and the Health Belief Model (HBM), while addressing the limitations
of these traditional models by incorporating context-specific variables relevant to the
TNVS environment.
Psychological factors in the framework emphasize the individual perceptions and
cognitive processes that influence a driver’s behavioral intention. These include the at-
titude toward compliance, risk perception, and stress and fatigue. By examining these
psychological dimensions, the framework captures the cognitive and emotional factors
behind a TNVS driver’s intention to comply with safety protocols.
On the other hand, environmental factors represent the external, situational elements
that affect a driver’s ability and motivation to comply with safety protocols. These in-
clude the availability of PPE, passenger compliance, and ride conditions. These environ-
mental factors highlight the importance of external supports and constraints in shaping
compliance behaviors, recognizing that individual intentions are often moderated by
situational realities.
Lastly, organizational factors address the role of ride-hailing companies and regula-
tory bodies in enabling and reinforcing compliance among TNVS drivers. These factors
include the company policies, support systems, and communication and training. These
COVID 2025,5, 38 4 of 25
organizational factors serve as critical enablers of compliance, addressing systemic barriers
and providing the structural support needed for sustained behavioral change.
COVID 2025, 5, 38 4 of 25
Lastly, organizational factors address the role of ride-hailing companies and regula-
tory bodies in enabling and reinforcing compliance among TNVS drivers. These factors
include the company policies, support systems, and communication and training. These
organizational factors serve as critical enablers of compliance, addressing systemic barri-
ers and providing the structural support needed for sustained behavioral change.
The framework hypothesizes that behavioral intention acts as a mediating variable
between the three core factors (psychological, environmental, and organizational) and
compliance behavior. Behavioral intention reflects the driver’s determination and readi-
ness to perform a specific behavior, such as wearing masks, sanitizing vehicles, or ensur-
ing passenger compliance.
The actual compliance behavior represents the observable adherence to COVID-19
safety protocols. The framework suggests that a strong behavioral intention, when sup-
ported by favorable environmental and organizational conditions, is more likely to trans-
late into consistent compliance behavior.
Figure 1. Integrated COVID-19 Behavior Framework.
Determinants of Behavioral Intention and Compliance Behavior
Psychological factors, which include aitude toward compliance, risk perception,
and stress and fatigue, play a pivotal role in shaping the behavioral intention of Transpor-
tation Network Vehicle Services (TNVS) drivers during the COVID-19 pandemic. These
factors represent the cognitive and emotional processes that drive a person’s willingness
and determination to perform specific actions, such as adhering to safety protocols [28].
Aitude refers to a driver’s positive or negative evaluation of complying with safety
protocols, such as wearing masks, sanitizing vehicles, and ensuring passenger adherence
to health measures. Research suggests that when drivers perceive these actions as benefi-
cial, effective, and worth the effort, they are more likely to develop a strong intention to
comply [29]. Additionally, Akamangwa [30] highlights that drivers who believe adhering
to safety protocols protects their health and ensures passenger safety are more inclined to
Figure 1. Integrated COVID-19 Behavior Framework.
The framework hypothesizes that behavioral intention acts as a mediating variable
between the three core factors (psychological, environmental, and organizational) and
compliance behavior. Behavioral intention reflects the driver’s determination and readiness
to perform a specific behavior, such as wearing masks, sanitizing vehicles, or ensuring
passenger compliance.
The actual compliance behavior represents the observable adherence to COVID-19
safety protocols. The framework suggests that a strong behavioral intention, when sup-
ported by favorable environmental and organizational conditions, is more likely to translate
into consistent compliance behavior.
Determinants of Behavioral Intention and Compliance Behavior
Psychological factors, which include attitude toward compliance, risk perception, and
stress and fatigue, play a pivotal role in shaping the behavioral intention of Transportation
Network Vehicle Services (TNVS) drivers during the COVID-19 pandemic. These factors
represent the cognitive and emotional processes that drive a person’s willingness and
determination to perform specific actions, such as adhering to safety protocols [28].
Attitude refers to a driver’s positive or negative evaluation of complying with safety
protocols, such as wearing masks, sanitizing vehicles, and ensuring passenger adherence
to health measures. Research suggests that when drivers perceive these actions as benefi-
cial, effective, and worth the effort, they are more likely to develop a strong intention to
COVID 2025,5, 38 5 of 25
comply [29]. Additionally, Akamangwa [30] highlights that drivers who believe adhering
to safety protocols protects their health and ensures passenger safety are more inclined
to comply. For example, a driver who perceives regular sanitization as reducing the risk
of infection is more likely to commit to this behavior [
31
]. Based on this, the following
hypothesis was proposed:
H1. Attitude toward compliance significantly and positively influences the behavioral intention of
TNVS drivers.
Risk perception refers to a driver’s belief in their vulnerability to COVID-19 (perceived
susceptibility) and the potential severity of the disease’s impact. Previous studies have
shown that higher levels of risk perception are associated with stronger behavioral inten-
tions to adopt preventive measures [
32
,
33
]. Beckman et al. [
14
] indicate that drivers who
perceive themselves at high risk of contracting COVID-19 due to frequent interactions with
passengers are more likely to take precautions. For instance, recognizing that working in
an enclosed space increases exposure can motivate drivers to adhere to health guidelines.
Based on this, the following hypothesis was proposed:
H2. Risk perception significantly and positively impacts the behavioral intention of TNVS drivers.
Stress and fatigue are psychological states that can either enhance or hinder behavioral
intention, depending on how they are managed. TNVS drivers experience significant stress
due to the fear of infection, long working hours, and the financial pressures brought about
by the pandemic [
14
]. Previous studies have shown that chronic stress and fatigue can lead
to cognitive overload [
34
], reducing a driver’s ability to focus on compliance behaviors.
For instance, Mtetwa [
35
] found that a fatigued driver might forget to sanitize the vehicle
after each ride or feel less motivated to enforce passenger mask-wearing. Based on this, the
following hypothesis was proposed:
H3. Stress and fatigue significantly and positively influence the behavioral intention of TNVS drivers.
Environmental factors significantly shape the behavioral intention of Transportation
Network Vehicle Services (TNVS) drivers to adhere to COVID-19 safety protocols. These
factors refer to the external conditions and situational influences that either enable or
hinder compliance. In the context of the COVID-19 pandemic, key environmental factors
include availability of personal protective equipment (PPE), passenger compliance, and
ride conditions.
Access to adequate personal protective equipment (PPE), such as masks, gloves, and
sanitizers, is a critical factor influencing a driver’s intention to comply with safety proto-
cols [
36
]. Without the necessary tools, drivers may feel less capable of performing protective
behaviors, even if they have strong intentions, as demonstrated in Rezaei et al.’s [
37
] study.
Previous research has shown that the availability of PPE reduces logistical and financial
barriers, making it easier for drivers to adopt safety measures [
38
]. For instance, Bloomfield
et al. [
39
] highlighted that drivers who are regularly supplied with sanitizers are more
likely to sanitize their vehicles after each ride. Based on this, the following hypothesis
was proposed:
H4. The availability of PPE significantly and positively influences the behavioral intention of
TNVS drivers.
COVID 2025,5, 38 6 of 25
Passenger adherence to COVID-19 safety protocols, such as wearing masks and prac-
ticing social distancing, directly impacts drivers’ intentions to comply with their own
protective behaviors [
40
]. As noted by Chuenyindee et al. [
3
], drivers operate in shared
spaces where passenger behavior can either support or undermine health measures. When
passengers follow safety guidelines, drivers are more motivated to maintain their own
adherence. For example, Yu and Dahai [
41
] found that seeing passengers wear masks rein-
forces the perceived importance of mask-wearing. Based on this, the following hypothesis
was proposed:
H5. Passenger compliance significantly and positively influences the behavioral intention of
TNVS drivers.
The physical and operational conditions of a driver’s vehicle play a crucial role in
shaping their behavioral intentions. For example, a well-maintained and sanitized vehicle
not only protects both drivers and passengers but also reinforces the driver’s belief in
the value of these practices, thereby strengthening their intention to comply [
3
]. A study
by Sun and Zhai [
42
] also highlights that good ventilation reduces the risk of airborne
transmission of COVID-19, increasing drivers’ confidence in their ability to provide a
safe environment, which, in turn, motivates compliance. Additionally, features such as
partitions or space modifications enhance drivers’ perception of safety and lower perceived
barriers to compliance, as noted in Beckman et al.’s [
14
] study. Based on this, the following
hypothesis was proposed:
H6. Ride conditions significantly and positively influence the behavioral intention of TNVS drivers.
Organizational factors encompass the policies, support systems, and communication
strategies provided by ride-hailing platforms and regulatory agencies to promote adherence
to safety protocols among Transportation Network Vehicle Services (TNVS) drivers. These
factors play a crucial role in shaping drivers’ behavioral intention, or their willingness and
commitment to comply with COVID-19 safety measures.
Clear and enforceable policies set by ride-hailing platforms establish expectations for
TNVS drivers’ behavior and foster an organizational culture that prioritizes safety [
43
].
Previous research shows that policies mandating mask-wearing, vehicle sanitization, and
passenger screening emphasize the importance of adhering to safety protocols [
44
]. More-
over, Na and Lee [
45
] demonstrate that individuals who perceive these policies as well-
structured and essential are more likely to comply. Based on this, the following hypothesis
was proposed:
H7. Company policies significantly and positively influence the behavioral intention of TNVS drivers.
Support systems refer to the tangible and intangible resources provided by organi-
zations to facilitate compliance, such as access to PPE, financial assistance, and health
programs. Studies indicate that individuals who receive regular supplies of PPE, sanitizers,
and cleaning materials are more likely to develop a strong intention to comply with safety
protocols [
46
,
47
]. Furthermore, NeJhaddadgar et al. [
48
] demonstrate that companies pro-
viding free masks eliminate financial barriers to compliance. Based on this, the following
hypothesis was proposed:
H8. Support systems significantly and positively influence the behavioral intention of TNVS drivers.
COVID 2025,5, 38 7 of 25
Effective communication and training programs ensure that drivers are well-informed
about safety guidelines and equipped to implement them effectively [
49
]. Research shows
that frequent and concise updates about COVID-19 safety measures, delivered via mobile
apps, text messages, or newsletters, reinforce compliance intentions by keeping individuals
informed and engaged [50]. Additionally, transparent communication during crises, such
as a pandemic, helps people understand the importance of adhering to protocols and
reduces uncertainty, as highlighted in Enria et al.’s [
51
] study. Based on this, the following
hypothesis was proposed:
H9. Communication and training significantly and positively influence the behavioral intention of
TNVS drivers.
Behavioral intention refers to a driver’s motivation, commitment, and readiness to
perform a specific behavior, such as adhering to COVID-19 safety protocols [
52
]. It is
regarded as the strongest predictor of actual compliance behavior in many behavioral
models, including the Theory of Planned Behavior (TPB) [
53
]. For Transportation Network
Vehicle Services (TNVS) drivers, behavioral intention manifests in observable actions such
as wearing masks, sanitizing vehicles, and ensuring passenger compliance with health
guidelines. Research indicates that individuals with strong behavioral intentions are more
likely to consistently adhere to COVID-19 safety protocols [
54
,
55
]. Intention reflects a
psychological commitment to specific actions, forming the foundation for actual behavior.
For example, a driver with a strong intention to sanitize their vehicle after every ride is more
likely to follow through, especially when supported by positive attitudes and perceived
control [56]. Based on this, the following hypothesis was proposed:
H10. Behavioral intention to follow COVID-19 protocols significantly and positively influences
the compliance behavior of TNVS drivers during the COVID-19 pandemic.
3. Methodology
3.1. Respondents of the Study
The target participants of this study were Filipino Transportation Network Vehicle
Service (TNVS) drivers residing in the National Capital Region (NCR) and CALABARZON
(Region IV-A), areas with high TNVS activity and significant urban mobility demands.
These areas were chosen due to their dense urban environments, where TNVS services play
a crucial role in daily commuting, and where drivers face high exposure to passengers and
potential infection risks. Purposive sampling, a non-probabilistic sampling method, was
employed to ensure that respondents met specific inclusion criteria: (1) actively working as
TNVS drivers during the COVID-19 pandemic, (2) operating under ride-hailing platforms
such as Grab, Angkas, or JoyRide, and (3) experiencing direct passenger interactions
requiring adherence to COVID-19 safety protocols.
Data collection was carried out using a digital survey questionnaire designed to
evaluate key factors influencing TNVS drivers’ behavioral intentions and compliance with
COVID-19 safety protocols over a three-month period, from October to December 2021. The
questionnaire was distributed using Google Forms and shared across various social media
groups, forums, and platforms frequently accessed by TNVS drivers, such as Facebook
groups dedicated to ride-hailing communities and messaging apps.
The study initially set a target of gathering responses from at least 300 participants, as
justified by Kyriazos’ [
57
] formula. A total of 342 responses were successfully collected,
exceeding the minimum target sample size. The larger sample enhances the robustness
of the study by providing greater statistical power and reducing the margin of error. The
COVID 2025,5, 38 8 of 25
participants’ diversity, in terms of geographic location within NCR and CALABARZON,
work schedules, and exposure to passengers, allows the study to capture a comprehensive
understanding of the factors influencing TNVS drivers’ behaviors during the pandemic.
3.2. Instrumentation
Data collection was carried out using a digital survey questionnaire designed to eval-
uate key factors influencing TNVS drivers’ behavioral intentions and compliance with
COVID-19 safety protocols. The questionnaire consisted of two main sections: (1) demo-
graphic information, including age, gender, location of residence (NCR or CALABARZON),
years of experience as a TNVS driver, type of ride-hailing platform used (e.g., Grab, Angkas,
etc.), average number of daily trips, and access to personal protective equipment (PPE),
and (2) validated measures assessing psychological, environmental, and organizational
factors influencing compliance behavior.
To ensure the clarity, reliability, and validity of the questionnaire, a pretest and pilot
study were conducted before full-scale data collection. Pretesting involved a review by
three subject matter experts in transportation research, occupational safety, and behavioral
science, who evaluated the content validity, wording clarity, and relevance of the survey
items. Based on expert feedback, minor revisions were made to improve question phrasing
and response format clarity.
Following pretesting, a pilot study was conducted with a small sample of 30 TNVS
drivers from NCR and CALABARZON. Participants were asked to complete the survey
and provide feedback on the questionnaire’s clarity, length, and ease of comprehension.
Additionally, Cronbach’s alpha was computed for each latent variable to assess internal
consistency and reliability, with all constructs meeting the recommended threshold of
0.70 [
58
]. No major modifications were necessary after the pilot study, confirming the
questionnaire’s suitability for full deployment.
The survey items were designed based on existing theoretical frameworks, including
the Theory of Planned Behavior (TPB) and the Health Belief Model (HBM), to ensure their
relevance and validity. Each item was carefully phrased to capture specific aspects of the
latent variables. The summary of measurement items is presented in Table 1. Pretest-
ing and piloting were conducted to refine the instrument, ensuring clarity, consistency,
and reliability.
Table 1. Indicators for measurement.
Construct Item Measure Supporting
References
Attitude Towards
Compliance
AC1 I believe following COVID-19 safety protocols (e.g., wearing masks)
protects me and my passengers.
[59–61]
AC2 Sanitizing my vehicle regularly is worth the time and effort.
AC3 Adhering to COVID-19 safety measures ensures safer
working conditions.
AC4 I feel a sense of responsibility to follow health guidelines for the
safety of others.
Risk Perception
RP1 I am at high risk of contracting COVID-19 due to frequent
interactions with passengers.
[62,63]
RP2 Driving during the pandemic exposes me to significant health risks.
RP3 COVID-19 poses a serious threat to my overall well-being.
RP4 I feel vulnerable to COVID-19 because of my job as a TNVS driver.
COVID 2025,5, 38 9 of 25
Table 1. Cont.
Construct Item Measure Supporting
References
Stress and Fatigue
SF1
I feel stressed about the possibility of getting infected while working.
[64,65]
SF2 Long working hours reduce my ability to follow safety
protocols effectively.
SF3 I experience mental exhaustion from balancing passenger
interactions and safety measures.
SF4
Thinking about the risks of COVID-19 adds to my daily stress levels.
Availability of PPE
AV1
I have easy access to personal protective equipment (PPE) like masks
and sanitizers.
[66,67]
AV2 My ride-hailing platform provides adequate resources to
maintain hygiene.
AV3 I never run out of necessary PPE while working.
AV4 I can afford to replenish my PPE supplies regularly.
Passenger
Compliance
PC1 Most of my passengers comply with wearing masks during rides.
[68,69]
PC2 I rarely encounter passengers who refuse to follow
COVID-19 protocols.
PC3 Passengers respect social distancing guidelines inside my vehicle.
PC4
My passengers willingly follow hygiene protocols, such as sanitizing
their hands.
Ride Conditions
RC1 My vehicle is well-ventilated, minimizing the risk of
COVID-19 transmission.
[56,70]
RC2 I frequently clean and sanitize my vehicle to maintain hygiene.
RC3 The physical layout of my vehicle supports safe interactions
with passengers.
RC4 I use dividers or barriers to separate myself from passengers.
Company Policies
CP1
My ride-hailing platform enforces strict COVID-19 safety guidelines.
[71,72]
CP2 The company monitors drivers’ compliance with health protocols.
CP3 I am aware of the company’s policies regarding COVID-19
safety measures.
CP4 The company takes passenger non-compliance seriously and
provides support to drivers.
Support Systems
SS1
My ride-hailing platform provides free or subsidized PPE for drivers.
[73,74]
SS2 The company offers regular COVID-19 testing for its drivers.
SS3 I receive financial support or incentives for following
safety protocols.
SS4
The company provides resources to address drivers’ health concerns.
Communication
and Training
CT1 My ride-hailing platform regularly communicates updates about
COVID-19 protocols.
[75,76]
CT2 I have received training on how to implement safety
measures effectively.
CT3 I am informed about the latest COVID-19 guidelines from
the company.
CT4 The company provides clear instructions on managing
non-compliant passengers.
Behavioral Intention
BI1 I intend to sanitize my vehicle after every ride.
[77–79]
BI2
I plan to ensure passengers follow COVID-19 protocols during rides.
BI3 I am committed to wearing a mask while driving.
BI4 I will take all necessary precautions to minimize the risk of
COVID-19 transmission.
COVID 2025,5, 38 10 of 25
Table 1. Cont.
Construct Item Measure Supporting
References
Compliance
Behavior
CB1 I sanitize my vehicle after every ride.
[61,62,80]
CB2 I always wear a mask while working.
CB3 I ensure passengers comply with health protocols, such as wearing
masks.
CB4
I use dividers or barriers to maintain social distancing in my vehicle.
3.3. Structural Equation Modeling
Before conducting the analysis, several data preprocessing steps were implemented
to ensure the accuracy and reliability of the dataset. Data cleaning was performed by
screening all collected responses for missing values, incomplete submissions, and response
inconsistencies. Cases with significant missing data exceeding 10% were removed to main-
tain data integrity. Additionally, outlier detection was conducted to identify and address
potential anomalies in the dataset. Univariate and multivariate outliers were assessed using
box plots and Mahalanobis distance, ensuring that response patterns remained consistent
and normally distributed.
To further enhance data validity, Common Method Bias (CMB) testing was conducted
using Harman’s single-factor test. The results confirmed that no single factor accounted
for the majority of variance, indicating that method bias was not a major concern in the
study. These data preprocessing steps ensured that the final dataset was clean, reliable, and
suitable for structural equation modeling (SEM) analysis, providing a robust foundation
for examining the relationships between psychological, environmental, and organizational
factors influencing TNVS drivers’ compliance behavior.
Structural Equation Modeling (SEM) is a robust statistical method widely used to
analyze and determine the causal relationships among variables, providing a deeper
understanding of the theoretical constructs underlying complex frameworks [
81
]. Similarly
to regression-based analyses (e.g., multiple regression) and analysis of variance, SEM
allows for the simultaneous examination of multiple relationships, making it particularly
suitable for behavioral research [
82
]. In this study, SEM was employed to investigate the
relationships between psychological, environmental, and organizational factors; behavioral
intention; and compliance behavior among TNVS drivers during the COVID-19 pandemic.
Partial Least Squares SEM (PLS-SEM) was chosen for its “causal-predictive” approach,
which is effective for estimating relationships among latent variables, indicator variables,
and paths, especially in exploratory models with complex constructs and minimal sta-
tistical assumptions [
83
]. Using SmartPLS 4.1.0.0, the method provided a means to ana-
lyze the strength and significance of relationships between constructs derived from the
theoretical framework.
4. Results
4.1. Respondent’s Profile
The survey gathered responses from a total of 342 TNVS drivers, providing insights
into their demographic profiles. The respondents ranged in age from 21 to 55 years,
with the majority falling within the 31–40 age group (42.7%), followed by the 21–30 age
group (28.9%) and the 41–55 age group (28.4%). Male drivers comprised a significant
majority of the respondents (87.4%), while female drivers accounted for 12.6%, reflecting
the predominantly male workforce in the TNVS sector.
In terms of geographic distribution, 62.3% of the respondents resided in the National
Capital Region (NCR), while 37.7% were from CALABARZON (Region IV-A). This distribu-
COVID 2025,5, 38 11 of 25
tion highlights the concentration of TNVS operations in urbanized and densely populated
areas. Regarding professional experience, 41.8% of the drivers reported having 1–3 years of
experience as a TNVS driver, 32.2% had 4–6 years of experience, and 26.0% had been in the
industry for over six years.
The respondents utilized various ride-hailing platforms, with Grab being the most
frequently used (64.9%), followed by Angkas (19.3%) and other platforms, including
Lalamove and JoyRide (15.8%). On average, drivers completed 8–12 trips per day, with
48.8% of the respondents reporting this range. Meanwhile, 28.7% completed 4–7 trips
daily, and 22.5% managed over 12 trips daily, indicating a high level of activity among
many drivers.
Access to personal protective equipment (PPE) was reported by 85.4% of respondents,
who confirmed having regular access to masks, sanitizers, and other necessary equip-
ment. However, 14.6% indicated challenges in consistently accessing PPE, often citing
financial constraints or limited distribution from ride-hailing companies. This disparity
highlights the importance of organizational support in ensuring safety compliance among
TNVS drivers.
4.2. Result of Initial SEM
The initial model used to examine the factors influencing the compliance behavior
of TNVS drivers during the COVID-19 pandemic is presented in Figure 2. The model
comprises eleven latent variables and forty-four indicators, aligned with the theoretical
framework integrating psychological, environmental, and organizational dimensions. Be-
fore data collection, the model underwent rigorous reliability and validity testing, adhering
to the recommendations of Chan and Lay [
84
]. These steps ensured that the measurement
instrument was statistically sound and suitable for capturing the constructs under study.
COVID 2025, 5, 38 12 of 25
Figure 2. Initial COVID-19 Behavior Framework.
Table 2. Consistency reliability and convergent validity.
Construct Item Mean S.D. F.L. (≥0.7) α (≥0.7) C.R. (≥0.7) A.V.E.
(≥0.5)
Attitude Towards
Compliance
AC1 3.51 1.02 0.78
0.876 0.853 0.672
AC2 3.40 1.09 0.71
AC3 3.29 1.06 0.83
AC4 3.62 0.98 0.72
Risk Perception
RP1 3.54 1.02 0.79
0.892 0.881 0.658
RP2 3.45 1.01 0.88
RP3 3.47 0.96 0.73
RP4 3.62 1.02 0.73
Stress and Fatigue
SF1 3.54 1.07 0.74
0.923 0.890 0.781
SF2 3.53 1.12 0.77
SF3 3.84 1.11 0.78
SF4 3.58 1.03 0.77
Availability of PPE
AV1 3.49 1.01 0.80
0.821 0.804 0.762
AV2 3.39 0.94 0.88
AV3 3.46 0.96 0.78
AV4 3.42 1.08 0.82
Passenger Compliance
PC1 3.56 1.03 0.88
0.890 0.870 0.769
PC2 4.01 1.22 0.78
PC3 3.60 1.03 0.76
PC4 3.63 1.04 0.70
Ride Conditions
RC1 3.62 1.09 0.71
0.925 0.891 0.792
RC2 3.65 1.05 0.78
RC3 3.57 1.06 0.70
Figure 2. Initial COVID-19 Behavior Framework.
COVID 2025,5, 38 12 of 25
Reliability was assessed using Cronbach’s alpha and composite reliability, while
validity was evaluated through the average variance extracted (AVE). As outlined by Haji-
Othman and Yusuff [
85
], a composite reliability and Cronbach’s alpha value of at least
0.70 and an AVE value of at least 0.50 are required to confirm the internal consistency
and convergent validity of the constructs. The results of these tests, presented in Table 2,
show that all constructs exceeded the recommended thresholds, establishing the model’s
reliability and validity.
Table 2. Consistency reliability and convergent validity.
Construct Item Mean S.D. F.L. (≥0.7) α(≥0.7) C.R. (≥0.7) A.V.E. (≥0.5)
Attitude
Towards
Compliance
AC1 3.51 1.02 0.78
0.876 0.853 0.672
AC2 3.40 1.09 0.71
AC3 3.29 1.06 0.83
AC4 3.62 0.98 0.72
Risk Perception
RP1 3.54 1.02 0.79
0.892 0.881 0.658
RP2 3.45 1.01 0.88
RP3 3.47 0.96 0.73
RP4 3.62 1.02 0.73
Stress and
Fatigue
SF1 3.54 1.07 0.74
0.923 0.890 0.781
SF2 3.53 1.12 0.77
SF3 3.84 1.11 0.78
SF4 3.58 1.03 0.77
Availability
of PPE
AV1 3.49 1.01 0.80
0.821 0.804 0.762
AV2 3.39 0.94 0.88
AV3 3.46 0.96 0.78
AV4 3.42 1.08 0.82
Passenger
Compliance
PC1 3.56 1.03 0.88
0.890 0.870 0.769
PC2 4.01 1.22 0.78
PC3 3.60 1.03 0.76
PC4 3.63 1.04 0.70
Ride Conditions
RC1 3.62 1.09 0.71
0.925 0.891 0.792
RC2 3.65 1.05 0.78
RC3 3.57 1.06 0.70
RC4 3.51 1.03 0.70
Company
Policies
CP1 3.43 1.07 0.90
0.890 0.850 0.791
CP2 3.21 1.07 0.71
CP3 3.60 0.99 0.76
CP4 3.56 1.03 0.78
Support Systems
SS1 3.45 1.02 0.81
0.924 0.911 0.781
SS2 3.48 0.98 0.80
SS3 3.68 1.04 0.76
SS4 3.54 1.09 0.72
Communication
and Training
CT1 3.56 1.11 0.73
0.858 0.842 0.722
CT2 3.85 1.11 0.77
CT3 3.57 1.02 0.83
CT4 3.47 1.00 0.85
Behavioral
Intention
BI1 3.51 1.03 0.73
0.871 0.859 0.676
BI2 3.41 1.07 0.77
BI3 3.24 1.07 0.73
BI4 3.69 0.99 0.84
COVID 2025,5, 38 13 of 25
Table 2. Cont.
Construct Item Mean S.D. F.L. (≥0.7) α(≥0.7) C.R. (≥0.7) A.V.E. (≥0.5)
Compliance
Behavior
CB1 3.51 1.03 0.91
0.892 0.889 0.652
CB2 3.40 1.02 0.88
CB3 3.49 0.98 0.89
CB4 3.66 1.04 0.90
These results confirm that the latent variables—covering psychological factors (e.g.,
attitudes, risk perception), environmental factors (e.g., PPE availability, passenger compli-
ance), organizational factors (e.g., company policies, support systems), behavioral intention,
and compliance behavior—were measured accurately and consistently. The robust statis-
tical foundation of the model ensures that the subsequent analysis reliably captures the
relationships between these variables, providing meaningful insights into the determinants
of TNVS drivers’ compliance behavior.
Following the recommendations of Henseler et al. [
86
], this study employed the
Fornell–Larcker criterion and the heterotrait–monotrait (HTMT) ratio of correlation to
analyze the relationships between the variables influencing the compliance behavior of
TNVS drivers. These methods were used to assess discriminant validity, which ensures
that the constructs in the model are sufficiently distinct from one another [
87
]. Establishing
discriminant validity is crucial for confirming that each latent variable—such as psycho-
logical, environmental, and organizational factors—captures unique aspects of the drivers’
behavioral intention and compliance behavior.
The HTMT ratio was particularly effective in achieving higher specificity and sensi-
tivity in identifying potential issues with discriminant validity, serving as a complement
to the Fornell–Larcker criterion. This dual approach ensures a more robust evaluation of
the constructs. The results, presented in Tables 3and 4, indicate that all values fell within
the acceptable range, confirming that the constructs were distinct and the model met the
required standards for discriminant validity.
Table 3. Discriminant validity: Fornell–Larcker criterion.
AC AV B1 CB CP CT PC RC RP SF SS
AC 0.897
AV 0.587 0.768
BI 0.600 0.710 0.751
CB 0.663 0.623 0.654 0.825
CP 0.437 0.531 0.565 0.653 0.708
CT 0.667 0.656 0.640 0.673 0.497 0.727
PC 0.448 0.611 0.527 0.446 0.329 0.585 0.752
RC 0.716 0.720 0.608 0.698 0.526 0.676 0.575 0.853
RP 0.487 0.690 0.681 0.675 0.600 0.657 0.473 0.600 0.817
SF 0.350 0.761 0.561 0.671 0.450 0.711 0.651 0.661 0.771 0.881
SS 0.671 0.451 0.661 0.541 0.659 0.606 0.671 0.561 0.712 0.701 0.761
These findings validate the overall results of the study by confirming that the rela-
tionships between latent variables, such as psychological factors (e.g., attitudes and risk
perception), environmental factors (e.g., PPE availability and passenger compliance), and
organizational factors (e.g., company policies and support systems), are appropriately
COVID 2025,5, 38 14 of 25
measured and interpreted. The robust discriminant validity of the model enhances confi-
dence in the theoretical framework and its ability to accurately capture the determinants of
compliance behavior among TNVS drivers.
Table 4. Discriminant validity: heterotrait–monotrait (HTMT) ratio.
AC AV B1 CB CP CT PC RC RP SF SS
AC
AV 0.623
BI 0.648 0.771
CB 0.723 0.679 0.726
CP 0.496 0.591 0.652 0.762
CT 0.733 0.723 0.720 0.759 0.579
PC 0.426 0.752 0.558 0.446 0.347 0.663
RC 0.770 0.769 0.661 0.758 0.595 0.747 0.588
RP 0.551 0.792 0.791 0.789 0.738 0.676 0.533 0.690
SF 0.231 0.342 0.459 0.698 0.761 0.651 0.761 0.661 0.541
SS 0.623 0.561 0.716 0.459 0.566 0.551 0.671 0.653 0.551 0.655
The hypothesis testing results shown in Table 5highlight the key factors influencing
behavioral intention and compliance behavior among TNVS drivers during the COVID-19
pandemic. Seven out of ten relationships were statistically significant, indicating that atti-
tude toward compliance, risk perception, PPE availability, passenger compliance, company
policies, and support systems positively impact drivers’ behavioral intention. In turn,
behavioral intention strongly predicts compliance behavior (
β
= 0.643, p< 0.001, f² = 0.413),
confirming its critical role in ensuring adherence to safety protocols.
Table 5. Hypothesis test.
No
Relationship
Beta
Coefficient p-Value Result Significance Hypothesis Effect Size
(f2)
1 AC→BI 0.453 <0.001 Positive Significant Do not reject 0.268
2 RP→BI 0.289 0.001 Positive Significant Do not reject 0.153
3 SF→BI 0.131 0.211 Positive Not Significant Reject 0.012
4 AV→BI 0.341 <0.001 Positive Significant Do not reject 0.204
5 PC→BI 0.293 0.002 Positive Significant Do not reject 0.167
6 RC→BI 0.198 0.241 Positive Not Significant Reject 0.031
7 CP→BI 0.336 0.001 Positive Significant Do not reject 0.178
8 SS→BI 0.433 <0.001 Positive Significant Do not reject 0.242
9 CT→BI 0.211 0.058 Positive Not Significant Reject 0.045
10 BI→CB 0.643 <0.001 Positive Significant Do not reject 0.413
Additionally, effect size was measured to evaluate the strength of relationships be-
tween variables, providing insights beyond statistical significance. While p-values indicate
whether a relationship exists, effect size quantifies its practical impact [
88
]. In Structural
Equation Modeling (SEM), Cohen’s f² is commonly used to assess predictor strength, with
values of 0.02 considered small, 0.15 moderate, and 0.35 or higher large. Understand-
COVID 2025,5, 38 15 of 25
ing effect size helps researchers and policymakers prioritize interventions based on their
real-world impact.
4.3. Results of Final SEM
The study’s final SEM model, based on the proposed framework for understanding
behavioral intention and compliance behavior of TNVS drivers, is presented in Figure 3.
Solid lines in the model represent significant positive relationships between constructs. The
model explains 62.1% of the variance in compliance behavior, demonstrating its robustness
in identifying key factors influencing drivers’ adherence to COVID-19 safety protocols.
This emphasizes the critical role of constructs such as attitude toward compliance, risk
perception, PPE availability, organizational support systems, and behavioral intention in
promoting compliance behavior among TNVS drivers during the pandemic.
COVID 2025, 5, 38 15 of 25
4.3. Results of Final SEM
The study’s final SEM model, based on the proposed framework for understanding
behavioral intention and compliance behavior of TNVS drivers, is presented in Figure 3.
Solid lines in the model represent significant positive relationships between constructs.
The model explains 62.1% of the variance in compliance behavior, demonstrating its ro-
bustness in identifying key factors influencing drivers’ adherence to COVID-19 safety pro-
tocols. This emphasizes the critical role of constructs such as aitude toward compliance,
risk perception, PPE availability, organizational support systems, and behavioral inten-
tion in promoting compliance behavior among TNVS drivers during the pandemic.
Figure 3. Final COVID-19 Behavior Framework.
To validate the proposed model, a model fit analysis was conducted using key indi-
ces such as the Standardized Root Mean Square Residual (SRMR), chi-square, and
Normed Fit Index (NFI), following established benchmarks from prior research. As pre-
sented in Table 6, the results indicate that all parameter estimates meet the minimum
thresholds, with SRMR and chi-square/dF values within acceptable limits and NFI exceed-
ing the required benchmark. These findings confirm that the model is a valid fit for ex-
plaining the compliance behavior of TNVS drivers, supporting its applicability in under-
standing the factors influencing their adherence to COVID-19 safety protocols.
Table 6. Model fit.
Model Fit for SEM Parameter
Estimates
Minimum
Cut-Off Recommended by
SRMR 0.072 <0.08 [89,90]
(Adjusted) Chi-square/dF 4.21 <5.0 [89,90]
Normal Fit Index (NFI) 0.918 >0.90 [89,90]
5. Discussion
The present study provides a comprehensive understanding of the relationships be-
tween psychological, environmental, and organizational factors influencing the behav-
ioral intention (BI) and compliance behavior (CB) of TNVS drivers during the COVID-19
pandemic using Structural Equation Modeling (SEM). Each hypothesized relationship
Figure 3. Final COVID-19 Behavior Framework.
To validate the proposed model, a model fit analysis was conducted using key indices
such as the Standardized Root Mean Square Residual (SRMR), chi-square, and Normed
Fit Index (NFI), following established benchmarks from prior research. As presented in
Table 6, the results indicate that all parameter estimates meet the minimum thresholds,
with SRMR and chi-square/dF values within acceptable limits and NFI exceeding the
required benchmark. These findings confirm that the model is a valid fit for explaining the
compliance behavior of TNVS drivers, supporting its applicability in understanding the
factors influencing their adherence to COVID-19 safety protocols.
Table 6. Model fit.
Model Fit for SEM Parameter Estimates Minimum
Cut-Off Recommended by
SRMR 0.072 <0.08 [89,90]
(Adjusted) Chi-square/dF 4.21 <5.0 [89,90]
Normal Fit Index (NFI) 0.918 >0.90 [89,90]
COVID 2025,5, 38 16 of 25
5. Discussion
The present study provides a comprehensive understanding of the relationships be-
tween psychological, environmental, and organizational factors influencing the behavioral
intention (BI) and compliance behavior (CB) of TNVS drivers during the COVID-19 pan-
demic using Structural Equation Modeling (SEM). Each hypothesized relationship was
examined to identify significant predictors of compliance behavior, offering insights into
the key determinants and mechanisms driving adherence to safety protocols.
The results indicate a strong positive and significant relationship between attitude
toward compliance (AC) and behavioral intention (BI) (
β
= 0.453, p< 0.001), suggesting that
drivers with favorable attitudes toward COVID-19 safety protocols are more likely to form
a strong intention to comply. According to Tweneboah-Koduah and Coffie [
91
], individuals
who recognize the importance and benefits of adhering to protocols, such as wearing
masks and sanitizing vehicles, are more motivated to engage in compliance behaviors. This
highlights the critical role of fostering positive attitudes in promoting adherence to safety
measures. Ajzen’s [
22
] Theory of Planned Behavior highlights the importance of attitude in
shaping behavioral intention, a finding further supported by studies during the pandemic,
which demonstrate that positive attitudes significantly predict compliance with health
protocols [
92
]. To strengthen drivers’ attitudes toward compliance, ride-hailing companies
should implement educational campaigns that emphasize the benefits of adherence, such
as reduced health risks for both drivers and passengers. Testimonials from other drivers
who successfully follow protocols can further reinforce positive attitudes and encourage
widespread adoption of safety measures.
The results also reveal a positive and significant relationship between risk perception
(RP) and behavioral intention (BI) (
β
= 0.289, p= 0.001), indicating that drivers who
have a higher awareness of COVID-19 risks are more likely to intend to comply with
safety measures. Perceiving oneself at risk of infection serves as a strong motivator for
adopting preventive behaviors, such as consistent mask-wearing and regular vehicle
sanitization [
93
]. This aligns with the Health Belief Model [
94
], which identifies perceived
susceptibility and severity as key drivers of health-related behaviors. Supporting evidence
from studies during the COVID-19 pandemic further demonstrates that heightened risk
perception correlates with increased adherence to safety protocols [
95
,
96
]. To capitalize
on this relationship, ride-hailing platforms and public health authorities should enhance
risk communication by sharing relevant data on infection rates among TNVS drivers and
emphasizing the protective benefits of compliance. These efforts can help reinforce drivers’
awareness and commitment to adhering to safety guidelines.
The results also show a significant positive relationship between company policies (CP)
and behavioral intention (BI) (
β
= 0.336, p= 0.001), demonstrating that clear and consistently
enforced company policies have a strong influence on drivers’ intention to comply with
COVID-19 safety protocols. According to prior studies, individuals are more likely to
adopt compliance behaviors when they perceive policies as fair [
97
], clearly articulated [
98
],
and consistently applied [
99
], highlighting the critical role of organizational governance
in shaping behavioral intention. Organizational behavior research supports this, showing
that clear policies help establish expectations, reduce ambiguity, and promote adherence to
organizational standards [
100
]. To maximize compliance, ride-hailing platforms should
ensure their policies are effectively communicated to drivers through multiple channels and
consistently enforced. Additionally, providing drivers with incentives, such as recognition
or financial rewards for adherence, can further strengthen their motivation to comply with
safety measures.
The results further demonstrate a strong positive relationship between support sys-
tems (SS) and behavioral intention (BI) (
β
= 0.433, p< 0.001), indicating that organizational
COVID 2025,5, 38 17 of 25
support plays a significant role in boosting drivers’ intention to comply with COVID-19
safety protocols. Prior studies reveal that individuals who have access to support systems,
such as financial aid, health programs, and resource assistance, feel more empowered and
motivated to adhere to safety measures [
101
]. This emphasizes the importance of organi-
zations providing robust support to their workforce. Studies confirm that organizational
support is a critical determinant of employee compliance with health protocols, as it re-
duces barriers and fosters a sense of care and accountability [
102
,
103
]. To further strengthen
drivers’ compliance intentions, ride-hailing platforms should expand their support systems
by offering subsidized PPE, free COVID-19 testing, and financial incentives for adherence
to safety protocols. Such measures will not only improve compliance but also enhance
drivers’ trust and loyalty to the platform.
Furthermore, the results indicate that behavioral intention (BI) is a strong predictor
of compliance behavior (CB) (
β
= 0.643, p< 0.001), showing that drivers with higher
intentions are significantly more likely to adhere to COVID-19 safety protocols. Behavioral
intention serves as a critical mediator, effectively translating psychological, environmental,
and organizational factors into observable compliance behaviors, such as wearing masks,
sanitizing vehicles, and ensuring passenger adherence to health measures [
104
]. Ajzen [
22
]
emphasizes the pivotal role of intention in predicting behavior, a finding supported by
numerous studies during the pandemic that highlight the importance of fostering strong
intentions to drive adherence to health protocols [
105
,
106
]. To enhance compliance behavior
among TNVS drivers, interventions should focus on strengthening behavioral intention.
This can be achieved by addressing key factors such as promoting positive attitudes,
ensuring access to necessary resources like PPE, and reinforcing organizational support
systems, ultimately creating an environment conducive to consistent compliance.
On the other hand, the analysis reveals a positive but non-significant relationship
between ride conditions (RC) and behavioral intention (BI) (
β
= 0.198, p= 0.241), suggesting
that factors such as vehicle ventilation and cleanliness have minimal direct influence on
drivers’ motivation to comply with safety protocols. While ride conditions are essential
for reducing the risk of disease transmission, their impact on drivers’ intention to adopt
safety behaviors appears to be more situationally dependent and indirect. The supporting
literature highlights the role of proper ventilation and hygiene in mitigating COVID-19
transmission risks but does not strongly associate these factors with behavioral inten-
tion [
107
]. To address this, ride-hailing companies should promote vehicle modifications,
including improved ventilation systems and routine cleaning, as part of broader safety
initiatives. These efforts should be coupled with monitoring to evaluate their indirect
contributions to enhancing compliance behaviors.
The analysis also indicates a positive but non-significant relationship between stress
and fatigue (SF) and behavioral intention (BI) (
β
= 0.131, p= 0.211), suggesting that these
factors do not significantly influence drivers’ intentions to comply with COVID-19 safety
protocols. While stress and fatigue may indirectly affect compliance behavior, they do
not appear to play a direct role in shaping drivers’ behavioral intentions. This highlights
the need for further research into their potential indirect effects on compliance behavior.
Previous studies suggest that chronic stress and fatigue can reduce motivation and lead
to decreased adherence to safety protocols [
108
,
109
]. However, some evidence indicates
that acute stress may temporarily heighten compliance with safety measures [
110
]. To
address these challenges, ride-hailing companies should implement wellness programs
aimed at reducing driver fatigue and stress. Initiatives such as flexible scheduling, access to
mental health resources, and support for better work–life balance can help improve overall
well-being and indirectly enhance compliance behavior.
COVID 2025,5, 38 18 of 25
The results also show a positive but non-significant relationship between communica-
tion and training (CT) and behavioral intention (BI) (
β
= 0.211, p= 0.058), suggesting that
while communication and training programs may influence drivers’ behavioral intentions,
their direct impact is limited. This finding implies that while drivers may benefit from
receiving information and training about COVID-19 safety protocols, these efforts alone
are insufficient to strongly drive their intention to comply. Other factors, such as personal
attitudes, perceived risks, and organizational support, likely play a more dominant role
in shaping behavioral intention. The supporting literature highlights the importance of
effective communication and training in ensuring knowledge dissemination and fostering
compliance behaviors. For example, Bandura’s [
111
] Social Learning Theory emphasizes
the role of observational learning and information sharing in shaping individual behaviors.
However, studies have shown that communication and training are often more effective
when combined with other motivational factors, such as incentives, practical demonstra-
tions, and continuous support [
112
,
113
]. To maximize the impact of communication and
training programs on behavioral intention, ride-hailing platforms should adopt a more
integrated approach. This includes tailoring training content to drivers’ specific chal-
lenges, incorporating practical demonstrations of safety measures, and ensuring consistent
follow-up to reinforce key messages.
In summary, the SEM results provide a comprehensive understanding of the factors
influencing TNVS drivers’ compliance behavior. Key determinants include attitude, risk
perception, availability of PPE, passenger compliance, company policies, and support
systems. Strengthening these factors through targeted interventions will enhance drivers’
compliance with COVID-19 safety protocols, ultimately ensuring a safer environment for
both drivers and passengers.
6. Conclusions
The novel This study explored the factors influencing the behavioral intention (BI)
and compliance behavior (CB) of Transportation Network Vehicle Service (TNVS) drivers
during the COVID-19 pandemic, using Structural Equation Modeling (SEM) to analyze
the relationships among psychological, environmental, and organizational factors. The
findings reveal that drivers’ attitudes toward compliance, risk perception, availability of
PPE, passenger compliance, company policies, and support systems significantly influence
their behavioral intentions. Behavioral intention, in turn, emerged as a strong predictor of
compliance behavior, highlighting its critical role as a mediator in translating these factors
into adherence to safety protocols.
The study proves the importance of fostering positive attitudes, enhancing access to
resources like PPE, and reinforcing organizational support to strengthen drivers’ behavioral
intentions and compliance. While factors such as stress, fatigue, ride conditions, and
communication and training showed limited direct influence on behavioral intention,
their indirect or situational impacts warrant further investigation. These insights provide
actionable recommendations for ride-hailing platforms and public health authorities to
design targeted interventions that promote safety and well-being among TNVS drivers and
their passengers.
Overall, the study contributes to the growing body of research on behavioral com-
pliance during health crises, emphasizing the need for a holistic approach that addresses
individual, environmental, and organizational dimensions. By leveraging these findings,
stakeholders can enhance compliance behaviors, ensuring safer ride-hailing operations
during and beyond the COVID-19 pandemic.
COVID 2025,5, 38 19 of 25
6.1. Theoretical and Practical Implications
The findings of this study have significant theoretical and practical implications for
understanding and improving the compliance behavior of TNVS drivers during health
crises like the COVID-19 pandemic. Theoretically, the study extends the application of
established frameworks such as the Theory of Planned Behavior (TPB) and the Health Belief
Model (HBM) by integrating psychological, environmental, and organizational factors into
a comprehensive model. It highlights the critical role of behavioral intention as a mediator,
translating attitudes, perceptions, and organizational influences into observable compliance
behaviors. The study also contributes to the literature on workplace safety and health
behavior by identifying unique factors, such as passenger compliance and support systems,
which are particularly relevant in the ride-hailing context.
Practically, the study provides actionable insights for ride-hailing platforms, policy-
makers, and public health authorities. Key recommendations include implementing educa-
tional campaigns to foster positive attitudes toward compliance, ensuring consistent access
to PPE, enforcing passenger adherence to safety protocols, and offering organizational
support systems such as financial incentives and health programs. These interventions
can enhance drivers’ behavioral intentions and compliance, creating a safer environment
for both drivers and passengers. Additionally, by addressing situational factors such as
stress and fatigue through wellness initiatives, ride-hailing companies can further support
their workforce and improve long-term adherence to safety measures. Overall, the study
emphasizes the need for a multi-dimensional approach to promote health and safety in the
TNVS sector.
6.2. Limitations and Future Research
This study has several limitations that should be addressed in future research. First,
while this study explains 69.1% of the variance in compliance behavior, 30.9% remains
unexplained, suggesting that other factors may also influence TNVS drivers’ adherence
to safety protocols. One possible limitation is the exclusion of individual traits, such
as personality, motivation, and self-efficacy, which may impact compliance. Financial
pressures may also play a role, as drivers balancing income concerns with health risks might
prioritize earnings over strict protocol adherence. Additionally, regulatory enforcement
and company-imposed penalties may affect compliance but were not explicitly examined.
Moreover, peer influence and social norms within the TNVS community could shape
compliance behavior, as drivers often observe and discuss safety practices with colleagues.
Future studies should explore economic, social, and regulatory factors to improve model
accuracy. Research using qualitative methods or behavioral economics frameworks could
provide deeper insights into TNVS drivers’ decision-making regarding safety protocols.
By acknowledging these limitations, this study highlights the need for a broader approach
to understanding compliance behavior, particularly in ride-hailing services where safety
practices are essential for public health.
Second, while the study focused on NCR and CALABARZON, which represent the
largest TNVS markets in the Philippines, the findings may not fully capture regional varia-
tions in driver compliance behavior across rural or less urbanized areas. Additionally, the
sample was predominantly male, reflecting the gender distribution of the TNVS workforce,
where male drivers outnumber female drivers due to industry norms and safety concerns.
Although the study’s sampling strategy successfully captured a diverse range of drivers in
terms of work experience, platform usage, and passenger exposure, future studies should
consider expanding the geographic scope and employing stratified sampling techniques to
further enhance sample representativeness across different driver demographics.
COVID 2025,5, 38 20 of 25
Third, the data collection relied on self-reported responses from TNVS drivers, which
may be subject to social desirability bias, potentially leading to overestimation of compli-
ance behavior. Future studies could incorporate observational methods or secondary data
from ride-hailing platforms to validate self-reported findings.
Finally, as the study was conducted during the COVID-19 pandemic, the findings
may not fully generalize to other public health crises or normal operational conditions.
Longitudinal studies could explore how compliance behaviors evolve over time and across
different contexts to enhance the robustness and applicability of the model.
Funding: This research was funded by Mapua University Directed Research for Innovation and
Value Enhancement (DRIVE).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Informed consent was obtained from all subjects involved in this
study (FM-RC-21-54).
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Acknowledgments: The author would like to thank all the respondents who answered our
online questionnaire.
Conflicts of Interest: The author declares no conflicts of interest.
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