Determinants of Behavioral Intention and Compliance Behavior Among Transportation Network Vehicle Service Drivers During the COVID-19 Pandemic

Abstract

This study examines the factors influencing the behavioral intention and compliance 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 robustness. 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 implementing 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.

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 platforms, have played a critical role in sustaining mobility during the pandemic, providing 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 [14,15,16].

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 protocols 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 processes, particularly in the context of public health [19,20,21]. 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 limitations when applied to multifaceted environments like those experienced by TNVS drivers during the COVID-19 pandemic. First, both TPB and HBM primarily emphasize individual 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 infrastructure 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 psychological, 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 attitude 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 include the availability of PPE, passenger compliance, and ride conditions. These environmental 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 regulatory 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 barriers 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 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 supported 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 beneficial, 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 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 intentions 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 protocols [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.

Passenger adherence to COVID-19 safety protocols, such as wearing masks and practicing 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 reinforces 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]. Moreover, 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 organizations 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 providing 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.

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 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 evaluate key factors influencing TNVS drivers’ behavioral intentions and compliance with COVID-19 safety protocols. The questionnaire consisted of two main sections: (1) demographic 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. 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.	[59,60,61]
	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.
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.	[77,78,79]
	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.
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.

. Pretesting and piloting were conducted to refine the instrument, ensuring clarity, consistency, and reliability.

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 maintain 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 statistical assumptions [83]. Using SmartPLS 4.1.0.0, the method provided a means to analyze 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 distribution 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 equipment. 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. Before 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.

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. 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
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

, show that all constructs exceeded the recommended thresholds, establishing the model’s reliability and validity.

These results confirm that the latent variables—covering psychological factors (e.g., attitudes, risk perception), environmental factors (e.g., PPE availability, passenger compliance), organizational factors (e.g., company policies, support systems), behavioral intention, and compliance behavior—were measured accurately and consistently. The robust statistical 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 psychological, 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 sensitivity 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

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

and

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

, 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.

These findings validate the overall results of the study by confirming that the relationships 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 measured and interpreted. The robust discriminant validity of the model enhances confidence in the theoretical framework and its ability to accurately capture the determinants of compliance behavior among TNVS drivers.

The hypothesis testing results shown in

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

highlight 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 attitude 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.

Additionally, effect size was measured to evaluate the strength of relationships between 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. Understanding 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.

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. 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]

, 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.

5. Discussion

The present study provides a comprehensive understanding of the relationships between psychological, environmental, and organizational factors influencing the behavioral intention (BI) and compliance behavior (CB) of TNVS drivers during the COVID-19 pandemic 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 systems (SS) and behavioral intention (BI) (β = 0.433, p < 0.001), indicating that organizational 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 organizations providing robust support to their workforce. Studies confirm that organizational support is a critical determinant of employee compliance with health protocols, as it reduces 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 intention [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.

The results also show a positive but non-significant relationship between communication 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 demonstrations, 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 challenges, 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 compliance 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.

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, policymakers, and public health authorities. Key recommendations include implementing educational 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 variations 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.

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 compliance 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.