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Article

How Trust Affects Hazardous Chemicals Logistics Enterprises’ Sustainable Safety Behavior: The Moderating Role of Government Governance

by
Li Hou
1,2,
Bin Yao
1,
Yibo Hu
1,
Keyi Yu
1 and
Kebiao Yuan
1,*
1
School of Economics and Management, Ningbo University of Technology, Ningbo 315211, China
2
School of Economics and Management, Dalian University of Technology, Dalian 116024, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(8), 3577; https://doi.org/10.3390/su17083577
Submission received: 5 March 2025 / Revised: 9 April 2025 / Accepted: 10 April 2025 / Published: 16 April 2025
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Abstract

:
Hazardous chemicals logistics (HCL) management improves safety and operational efficiency; however, the management process faces challenges, including safety hazards. Trust in the government is critical for shaping the sustainable and safe behavior of hazardous chemicals logistics enterprises (HCLEs). However, its interaction with governance mechanisms remains unclear, and a systematic categorization of governmental regulatory methods is lacking. To improve the relationship between HCLEs and the government, this study employs structural equation modeling to examine the effects of trust and governance mechanisms on HCLEs’ sustainable safety behavior. Trust is categorized into cognitive trust and affective trust based on trust and reinforcement theories, whereas government governance is divided into motivational and punitive governance. A conceptual trust–government governance–HCLEs’ behavior model that introduces government governance as a moderating variable is formulated. The results show that trust significantly improved HCLEs’ behavior; motivational governance positively moderated the impact on the relationship between trust and HCLEs’ behavior; and punitive governance failed. These results emphasize the importance of trust-based partnerships between governments and HCLEs, as well as motivational governance, in building compliance and improving safety. Moreover, this study expands our understanding of the interrelationships among trust, government governance, and HCLEs’ sustainable safety behavior in the HCL industry.

1. Introduction

Sustainable safety management in HCL is a critical component of the logistics industry. However, the industry faces persistent challenges, including limited adaptability to national development goals and safety needs, which has led to a high number of accidents involving hazardous chemicals. This highlights the need for a comprehensive regulatory framework and innovative safety-management techniques. The demand for transportation of hazardous chemicals by road, water, rail, and air is steadily increasing. This indicates that the demand for hazardous materials is rising in many industries, including industrial manufacturing, energy production, gas, flammable liquids, toxic and infectious substances, and radioactive materials, all of which have high logistical demand. In this context, an in-depth understanding of the determinants influencing HCLEs’ sustainable safety behavior (hereinafter referred to as HCLEs’ behavior) is important to reduce risks, ensure safe operations, and improve the sustainability of the HCL industry.
Despite the current importance of these shipments, they can pose serious risks to humans and the environment in the event of accidents or leakage from a container [1]. From 2021 to 2023, an average of 82 accidents occurred per day, resulting in 89 fatalities and 249 injuries. Bakshi and Peura suggested that the primary causes of most accidents in the HCL industry are inadequate safety training and insufficient supervision. Chang and Zhang noted that hazardous chemical fires and explosions are primarily caused by human factors [2]. The HCL industry is a technology-intensive and high-risk industry involving high temperatures, high pressures, flammability, explosives, and toxic substances. Therefore, safety management is critical for the success of HCLEs. The HCL industry also grapples with conflicting stakeholder interests, a shortage of specialized talent, and inconsistent industry standards [3]. Taken together, these factors make HCL one of the riskiest industries. A high level of trust in the government can encourage HCLEs to bolster their knowledge, build safety management systems, obtain safety assurance, improve their safety management, and enhance the industry’s competitive advantage [4].
Some countries have adopted new strategies to improve HCL safety. For example, India has focused on providing technical and financial support to promote the logistics industry [5]. Serbia has advanced software to address safety concerns during hazardous goods transport. In 2020, the Chinese government introduced the “Measures for the Safe Management of Road Transportation of Dangerous Goods”, in 2020, to strengthen HCL safety. Canada enacted the Canada Dangerous Goods Transport Act. However, the effectiveness of these measures has been hampered by industry regulations, competitive pricing pressures, and a lack of safety infrastructure and operations [6]. Considering these challenges, trust is crucial for establishing robust safety management mechanisms for HCLEs [7]. Empirical evidence has substantiated the importance of trust in fostering safety advancement. Failure to prioritize HCL often leads to frequent hazardous chemical accidents, resulting in potentially catastrophic losses. Despite various regulatory efforts worldwide, gaps remain in the understanding of how trust dynamics shape HCLEs’ behavior, particularly in relation to government governance mechanisms.
HCL is characterized by the following three distinctive attributes: high operational risk, a sophisticated technological threshold, and profound social implications. Therefore, HCLEs are strongly dependent on the government. The trust of enterprises in the government’s standard-setting, infrastructure construction, and establishment of policy tools, as well as the guidance of the government’s management style (e.g., international low-carbon mandates, under the Paris Accord, increasingly shape domestic governance strategies, compelling firms to adopt stricter safety protocols and decarbonization measures) to enterprises, are important for the safety management and sustainable development of the HCL industry. Moreover, trust has been widely used in the field of social relations, tourism, etc., but has rarely been applied to the field of HCL in conjunction with government governance. Previous research on HCL has mainly focused on technical solutions (e.g., route optimization and risk assessment models) and internal organizational factors (e.g., employee training) instead of trust [8,9,10]. Meanwhile, social exchange theory and the theory of planned behavior indicate that high trust promotes positive behavior in subjects [11,12,13]. To fill this research gap, this study considers trust as an independent variable and introduces government governance as a moderating variable to explore the effect of trust on HCLEs’ behavior.
To verify the interaction between trust, government governance, and HCLEs’ behavior, we conducted a survey targeting professionals and stakeholders actively engaged in the HCL industry across diverse sectors and regions. They were drawn from governmental organizations, logistics enterprises, and other relevant entities in countries with high HCL demand, including China, the United States, and Germany. We studied these participants using a questionnaire and obtained 205 valid responses. Specifically, we categorize trust into cognitive and affective trust [14,15,16]. Based on reinforcement theory, we defined government governance as a dual mechanism containing punitive and motivational governance. Building on this empirical foundation, we conducted an in-depth analysis to unravel the complex relationships between trust, government governance, and HCLEs’ behavior. The results show that even when combined with deterrence theory, punitive governance was found to have no moderating effect, which means that the role of punitive governance is very limited in the HCL industry; motivational governance can positively moderate the relationship between trust and HCLEs’ behavior, and both cognitive trust and affective trust play a key role in shaping HCLEs’ behavior. This study advances the theory of safety management models while offering policymakers, regulators, and business leaders practical insights into promoting safe and efficient practices within the HCL industry.

2. Literature Review and Theoretical Background

2.1. Trust in Government

Trust in government refers to the level of confidence in government institutions, encompassing the honesty, reliability, operational effectiveness, and commitment to public interest inherent in their policies and actions [17]. In HCLEs, trust in the government’s ability to formulate policy, enforce policies, and the inclusiveness of policy negotiations are crucial. Most previous studies have focused on technical aspects or within enterprises and neglected to examine the impact of trust on behavior [8,9,10]. Additionally, safety failures can trigger catastrophic consequences, and trust in the government is critical. Enterprises operating under high-risk conditions require the confidence that regulators balance strict enforcement with technical support, thus enabling proactive risk control rather than mere compliance [18]. According to social exchange theory, trust fosters reciprocal obligations. HCLEs perceive regulators as competent and benevolent partners (cognitive/affective trust), they are more likely to exceed compliance through voluntary safety investments, viewing such actions as mutually beneficial [11,12]. Trust in government promotes policy adoption and reduces the cost of policy implementation [17]. In this context, trust not only encourages enterprises to comply with safety and environmental standards but also promotes cooperative and information-sharing behaviors between enterprises and regulators. This increases transparency, reduces information asymmetry, and further contributes to the industry’s sustainable development [19].
Many studies have categorized trust into the following two types: cognitive and affective [14,15,16]. Cognitive trust arises from enterprises’ confidence in the government’s competence, managerial fairness, and professionalism in all aspects, which motivates firms to proactively comply with government regulations, enhance security measures, and strive to align themselves with government policies [20,21]. Conversely, affective trust, which is rooted in interpersonal relationships, shared values, and emotional ties, boosts businesses’ confidence in the government’s long-term goals and intentions [22]. Although cognitive and affective trust have been extensively studied, their interactions and effects in the specific context of HCLEs’ behavior remain unclear. Therefore, an exploration of trust in the government is important to deepen our understanding of how cognitive and affective trust affect HCLEs’ behavior.

2.2. Safety Behavior in HCLEs

Most proactive safety behaviors are influenced by trust; however, research on the HCL industry has overlooked it. The existing literature has focused extensively on the response behaviors and remedial measures of HCLEs after accidents, but only a limited number of studies have been conducted on prospective measures to prevent and avoid such incidents [23,24]. To address this gap, this study proposes a theoretical framework for HCLEs’ behavior that emphasizes feed-forward control strategies to reduce risks before an incident occurs and maximize the protection of human safety and the environment [25]. The framework categorizes HCLEs’ behavior into the following three key dimensions: timely policy adjustment and compliance with laws and regulations, corporate safety management practices, and reinforcement of safety mechanisms and active communication [26]. Specifically, HCLEs are expected to adopt a range of measures to enhance safety management, meet government regulations, raise operational standards, and reduce safety risks. These measures include prioritizing investment in logistics infrastructure construction, introducing advanced technologies, and implementing preventive measures to reduce risks in hazardous chemical logistics. HCLEs strive to align with government policy requirements to ensure that their operations comply with the regulatory framework while contributing to broader safety and environmental goals.

2.3. Multifaceted Role of Government Governance in HCL

Government governance refers to the management of national resources and public affairs for the purpose of realizing the public interest as a core objective [27,28]. HCL industry is characterized by high risk and high technological thresholds. The role of government governance in guiding HCLEs is important. While prior studies extensively explore the direct impacts of government governance (e.g., promotion of science and technology innovation) [29], they largely overlook its moderating role in influencing the link between organizational psychological factors (e.g., trust) and behavior outcomes [30,31,32]. This paper will fill this gap by revealing how government governance moderates the relationship between trust and security behaviors.
Furthermore, previous research has not provided many empirical assessments or systematic classifications of this government governance. The mechanisms of motivational and punitive governance operate through distinct pathways. Punitive governance relies on deterrence effects, where actors comply with rules to avoid sanctions (e.g., fines and reputational damage) [33]. This approach is rooted in deterrence theory, emphasizing monitoring and enforcement. In contrast, motivational governance is based on self-determination theory, which promotes intrinsic and extrinsic incentives, such as performance-based rewards or participatory decision making, which align individual goals with collective interests [34]. By examining the moderating effects of motivational and punitive governance, this study seeks to close these research gaps and provide a more thorough understanding of how these practices contribute to industrial safety and sustainable development.

3. Hypotheses Development

3.1. Influence of Trust on HCLEs’ Behavior

According to social exchange theory, trust strengthens public–private partnerships and supports the achievement of shared goals in hazardous materials logistics management. However, empirical evidence indicates that both high and low levels of trust may encourage positive enterprise behaviors [28,29,30], leaving the overall impact of trust on enterprise behavior unclear. While prior studies have used trust as a dependent variable [11,12], this study examined the effect of trust on HCLEs’ behavior [35]. Between HCLEs and the government, trust can be categorized into cognitive trust and affective trust, both of which complement each other by fostering reliability and emotional consistency for lasting cooperation [14,15,16]. Cognitive trust refers to a rational judgment formed through objective evidence, assessment of competence, and prior performance. In contrast, affective trust arises from emotional bonds, build through interpersonal care, emotional resonance, and value recognition [36]. Some studies suggest that cognitive and affective trust play different roles in various domains, including customer cooperative behavior. While both forms of trust influence customers’ willingness to disclose information, affective trust may also prompt customers to provide false information, motivated by relationship maintenance [37]. In this case, affective trust contributes to negative behavior. Similar dynamics have been observed in business relationships, human–computer interactions, and entrepreneurship [14,38,39,40]. Thus, both cognitive and affective trust can exert positive and negative effects on enterprise behavior. Based on the above analysis, we propose the following hypotheses:
H1a. 
Cognitive trust positively influences HCLEs’ behavior.
H1b. 
Affective trust negatively influences HCLEs’ behavior.

3.2. Moderating Effect of Government Governance

Government governance increases the cost of violations, reduces noncompliance, and encourages self-regulation among HCLEs [41]. The government’s management style plays a crucial role in moderating the relationship between trust and firm behavior. Based on reinforcement theory, government governance is typically classified into punitive and motivational governance [42,43]. According to deterrence theory, punitive measures function as exogenous regulatory mechanisms that impose behavioral constraints, whereas incentive-based governance, aligned with self-determination theory, harnesses endogenous motivation by aligning individual goals with systemic safety objectives [33,34]. In the process of building trust between the government and enterprises, the government’s management style may influence HCLEs’ behavior to varying degrees. Motivational governance fosters intrinsic motivation by aligning enterprise actions with societal expectations, encouraging technological innovation, sustainable practices, and long-term compliance. This approach strengthens collaboration between the government and HCLEs, promoting self-regulation and continuous improvement. However, excessive reliance on incentives may lead to imbalances in resource allocation and reduced motivation over time [44,45]. In contrast, punitive governance plays a critical role in ensuring immediate accountability by reinforcing governmental authority and regulatory supervision. This compels enterprises to adopt risk-averse behaviors, ensuring short-term compliance to avoid legal consequences and financial losses. Moreover, punitive measures create a deterrent effect, discourage violations, enhance industry-wide adherence to regulations, and reduce the risk of accidents at HCLEs, to some extent. However, an overreliance on punitive mechanisms may result in compliance driven primarily by external pressures, potentially limiting enterprises’ proactive engagement in innovation and sustainable development. Punitive governance may cause psychological stress, damage trust, hinder teamwork, and inhibit innovation [46,47].
Following organizational behavior modification theory, this study hypothesizes that motivational and punitive governance moderate the relationship between trust and HCLEs’ behavior [48]. Accordingly, we formulated the following hypotheses:
H2a. 
Motivational governance positively moderates the relationship between cognitive trust and HCLEs’ behavior.
H2b. 
Motivational governance positively moderates the relationship between affective trust and HCLEs’ behavior.
H2c. 
Punitive governance positively moderates the relationship between cognitive trust and HCLEs’ behavior.
H2d. 
Punitive governance positively moderates the relationship between affective trust and HCLEs’ behavior.
Figure 1 describes the study design. This study investigates the impact of trust on HCLEs’ behavior and the underlying mechanisms of this effect. The independent variable was trust, which was categorized into cognitive and affective trust. The dependent variable was the behavior of HCLEs. The moderating variable was government governance, which was categorized as motivational and punitive governance.
Trust in government is essential for companies in high-risk industries to participate effectively in sharing programs. Such trust helps prevent regulatory avoidance, declining compliance, and information asymmetry, where enterprises might withhold or misrepresent their intentions, leading to disruptive behaviors. Establishing trust in advance motivates HCLEs to improve safety management, mitigate risks, and actively engage in governmental initiatives.

4. Methodology

4.1. Sampling and Data Collection

The study data were collected through a questionnaire administered to stakeholders in HCLEs and researchers involved in HCL projects globally. The respondents included representatives from financial institutions, universities or research institutions, logistics enterprises, consulting companies, and industry associations, particularly in countries with a high demand for HCL, such as China, the United States, and Germany. These three countries served as key cases, representing diverse stages in hazardous chemical logistics development owing to their distinct regulatory environments and industry practices. Most of the HCLEs surveyed in this study were located in East and South China, the Gulf Coast and California in the United States, and North Rhine-Westphalia in Germany.
We followed several key steps in designing the questionnaire. It was developed to align with the study’s objectives, with careful attention to the question type, complexity, and completion time to ensure respondent engagement and data reliability. To ensure quality and comprehensiveness, a preliminary version was tested through interviews with HCLE representatives in Ningbo, China. In addition, during an academic conference, industry practitioners from the United States, Germany, and the United Kingdom were invited to complete preliminary questionnaires. Their feedback was used to refine and finalize the questionnaire. The final version targeted professionals specializing in HCL.
Our survey involved human subject research conducted through a questionnaire that began on 20 March 2024 and ended on 20 June 2024. All participants were adults and their personal information was fully safeguarded. They received detailed information regarding the purpose of the study and the process of obtaining informed consent. Before administering the questionnaire, we briefly notified the respondents of its content and purpose, assured them that their personal information would be kept confidential, and explained how to complete the questionnaire to ensure its validity. Simultaneously, we indicated that if the respondent felt that they were not the most suitable person to complete the questionnaire, they could find and ask a more appropriate person to answer the questions [49].
We distributed questionnaires to representatives from logistics enterprises and related industry personnel through logistics industry conferences, including Sinotrans, Zhenhai Petrochemical Logistics, and many other enterprises, as well as to hazardous goods road transport experts from the Ministry of Communications and members of the Highway Research Institute from different countries. As some practitioners were unable to attend the conference as scheduled, we distributed questionnaires through online channels during the research period. A total of 117 and 138 questionnaires were collected offline and online, respectively. Moreover, 205 of the 255 questionnaires were used after applying the exclusion criteria. Specifically, 50 responses were excluded because of four primary issues, as follows: (1) took less than two minutes to complete the questionnaire (n = 27), (2) incomplete submissions (n = 8), (3) systematic straight-lining patterns (identical selections across 90%+ Likert-scale items [50]) (n = 8), and (4) failure to pass attention checks designed to ensure respondent engagement (n = 7) [50]. Among the 205 respondents who completed valid questionnaires, 30 worked in governmental organizations, 42 in universities and research institutes, 43 in logistics enterprises, 28 in consulting firms, 42 in industry associations, and 20 in other organizations. In addition, 82 respondents had been employed in their organizations for one to three years, 103 had been employed in their organizations for three to five years, and 20 had been employed in their organizations for five to ten years. From the perspective of special vehicles owned by HCLEs, large, medium, and small enterprises accounted for 2%, 40%, and 58%, respectively.

4.2. Measures

All variables in the questionnaire were modified or adopted from previous research and developed based on a comprehensive review of the relevant literature. This study included five variables across the following three categories: trust (cognitive trust and affective trust), HCLEs’ behavior, and government governance (motivational governance and punitive governance). The questionnaire was designed to gather data on these variables, and respondents answered questions using five-point Likert scales (e.g., “1” for no trust to “5” for very strong trust).

4.2.1. Trust

Trust is used as an independent variable. Based on the aforementioned classification, this study measured the HCLEs’ trust level in the government using the following two dimensions: cognitive and affective trust [14,15,16]. Specifically, cognitive trust includes the following three measurement indicators: government’s professional expertise (e.g., policy effectiveness and regulatory capacity), perception of the government’s fairness and transparency (e.g., consistent and open decision making), and belief in the government’s efficiency (e.g., problem-solving speed and resource allocation) [14]. Affective trust was measured using the following three indicators: perceived government care and support (e.g., attention to business needs), recognition of emotional ties in the relationship (e.g., mutual respect and trust), and perceived emotional investment in the government (e.g., a friendly and collaborative approach to communication) [51]. The items were scored on a five-point Likert scale (1 = not at all, 2 = very weak, 3 = moderate, 4 = strong, and 5 = very strong).

4.2.2. HCLEs’ Behavior

As specialized logistics entities, HCLEs play an irreplaceable role in ensuring safe transportation of chemicals and maintaining social stability. To gain a deeper understanding of HCLEs’ contribution to safety management, this study systematically analyzes their behavior across multiple dimensions. We measured HCLEs’ behavior based on the following eight aspects: engagement in safety management, cooperation with government policies, participation in the construction of regulatory platforms, proposing and designing operational solutions, striving to meet assessment requirements, improving safety knowledge systems, raising internal management standards, and establishing communication mechanisms. The items were assessed using a five-point Likert scale (1 = not at all, 2 = very weak, 3 = moderate, 4 = strong, and 5 = very strong).

4.2.3. Government Governance

Government regulation was used as a moderating variable in this study and was classified into punitive and motivational governance. Punitive governance of HCLEs included the following four primary measurement indicators: emergency response capability (the government assesses the emergency response capacity of HCLEs and imposes penalties, such as issuing notifications on enterprises with inadequate emergency response capabilities), professional capability (which means that the government certifies the skill levels of employees within HCLEs and imposes certain penalties on enterprises that fail to meet required standards), safety system construction (the government supervises and manages hazardous chemical logistics enterprises, conducts regular safety inspections, and imposes penalties on enterprises that do not meet the required standards), and the reputation of HCLEs (the government provides safety ratings for HCLEs based on various criteria and publicly announces those that fail to meet the required standards). Conversely, motivational governance includes the following six measurement indicators: support for auxiliary infrastructure (the government actively invests in and improves infrastructure related to HCL to encourage HCLEs to enhance their safety management capabilities, such as the construction of dedicated parking areas for hazardous chemical vehicles); information technology (the government promotes technological innovation, advances digitalization efforts, provides an information-based regulatory platform, and encourages enterprises to adopt advanced technologies in the field of hazardous chemical logistics); restricting the number of competitors (within a certain timeframe or geographic area, the government restricts the approval and establishment of new HCLEs with similar competitive capabilities or provides local protection to existing hazardous chemical logistics enterprises); tax incentives (the government implements existing logistics streamlining and cost-reduction policies to ease the burden on HCLEs effectively); price protection (the government regulates pricing in the business environment of HCLEs, resists unfair competition, and provides compensation); and legal and regulatory guarantees (when changes in laws, regulations, taxes, policies, standards, or other factors cause losses to HCLEs, the government provides corresponding compensation) [26,52]. All items were evaluated on a five-point Likert scale (1 = no corresponding guarantee, 2 = very weak, 3 = moderate, 4 = strong, and 5 = very strong).

4.3. Reliability and Validity

Table 1 presents the reliability (Cronbach’s α) and validity (Kaiser–Meyer–Olkin [KMO]) indicators for the five dimensions.
All dimensions had Cronbach’s α values exceeding 0.900, indicating excellent internal consistency. Additionally, the KMO values were all greater than 0.696, demonstrating the suitability of the data for factor analysis. In particular, the HCLEs’ behavior dimension, with its high Cronbach’s α and KMO values, further confirms its strong reliability and suitability for analysis.

4.4. Descriptive Statistical Analysis

Table 2 presents the mean and standard deviation (SD) of the following five dimensions: motivational governance (mean = 3.444, SD = 1.195), punitive governance (mean = 3.502, SD = 1.189), cognitive trust (mean = 3.180, SD = 1.355), affective trust (mean = 2.982, SD = 1.325), and HCLE behavior (mean = 3.096, standard deviation = 1.339). In terms of the means, the punitive governance scores were slightly higher than the motivational governance scores, while the cognitive trust, affective trust, and HCLE’s behavior scores were relatively low, which may reflect respondents’ more conservative perceptions or attitudes toward these dimensions. The standard deviation of all dimensions ranged from 1.189 to 1.355, indicating a certain degree of dispersion in the data distribution of the dimensions, with cognitive trust having the largest standard deviation, indicating that the respondents’ opinions on this dimension differed significantly.

4.5. Correlation Analysis

Table 3 presents the correlation coefficients among the following five scale dimensions: motivational governance, punitive governance, cognitive trust, affective trust, and HCLEs’ behavior. Motivational governance is significantly and positively correlated with the other four dimensions, with a correlation coefficient of 0.453 ** for punitive governance, 0.404 ** for cognitive trust, 0.466 ** for affective trust, and 0.402 ** for HCLE’s behavior. The correlation coefficient between punitive governance and affective trust is 0.154 *, indicating a weak positive correlation. Cognitive trust was moderately and strongly positively correlated with affective trust (0.485 **) and HCLEs’ behavior (0.478 **). The highest correlation coefficient, 0.608 **, was found between affective trust and HCLE’s behavior, showing a strong correlation. Asterisks (* and **) in the notes indicate statistical significance at the 0.05 and 0.01 levels, respectively. These findings imply that while researching structural equation modeling, the impact of the links among these variables on model construction should be considered.

4.6. Validated Factor Analysis

The average variance extracted (AVE), composite reliability (CR), standardized factor loadings, and square root of AVE for each of the five scale dimensions are presented in Table 4.
Overall, all dimensions had AVE values greater than 0.5 and CR values close to or exceeding 0.9, indicating good convergent validity and internal consistency. Furthermore, the square root of the AVE for each dimension is greater than the correlation coefficients between that dimension and others, supporting discriminant validity among the dimensions.

4.7. Results

Table 5 presents the results of the structural path analysis, which explored the direct effects of affective trust, cognitive trust, motivational governance, and punitive governance on HCLEs’ behavior and their interactions. The results of the analysis show that affective trust has a direct and significant positive effect on HCLEs’ behavior, highlighting its significant role in shaping HCLEs’ behavior. Similarly, cognitive trust also has a significantly positive effect on HCLEs’ behavior, although its influence is weaker than that of affective trust. In addition, motivational governance has a significant positive effect on HCLEs’ behavior. The interaction between motivational governance and affective trust significantly enhances the positive behaviors of HCLEs. Notably, the interaction between motivational governance and affective trust significantly enhances the positive behaviors of HCLEs, suggesting that motivational governance is more effective in promoting the optimization of corporate behaviors when the level of affective trust is high. Meanwhile, the interaction between incentive governance and cognitive trust also has a significant positive effect on HCLEs’ behavior, suggesting that high levels of cognitive trust can further strengthen the positive effect of incentive governance on HCLEs’ behavior and that affective trust exhibits a similar reinforcing effect. In contrast, the interactions between punitive governance and cognitive trust and between punitive governance and affective trust are either non-significant or only marginally significant. These findings highlight the critical role of positive management strategies and trust mechanisms in shaping HCLEs’ behavior.
Figure 2 depicts the standardized coefficients for selected paths.
The significantly positive path coefficient from cognitive trust to HCLEs’ behavior suggests that cognitive trust has a significant and positive influence on HCLEs’ behavior, thereby supporting H1a. Although affective trust also exhibited significant influence, its negative direction contradicted the proposed positive effect, leading to the rejection of H1b. These results indicate that both cognitive and affective trust can positively influence HCLEs’ behavior.
We used IBM® SPSS® Statistics Version 27.0 (IBM Corp, Armonk, NY, USA) to examine the moderating roles of different kinds of governance to test H2a–H2b and H2c–H2d. Motivational governance demonstrated a significant moderating effect on the relationship between affective trust and HCLEs’ behavior (p = 0.006; Table 3). Therefore, motivational governance can positively modulate the impact of affective trust on HCLEs’ behavior. H2a is supported. Additionally, motivational governance significantly positively impacts the relationship between cognitive trust and HCLEs’ behavior (B = 0.129, SD = 0.063, t = 2.053, p < 0.01). Therefore, motivational governance can moderate the impact of cognitive trust on HCLEs’ behavior, supporting H2b.
The analysis indicates that the interaction between cognitive trust and punitive governance in influencing HCLEs’ behavior is not statistically significant. With a p-value of 0.763, it concludes that punitive governance does not play a significant role in moderating the relationship between cognitive trust and HCLEs’ behavior. Thus, H2c is rejected. Additionally, the results show that the interaction term between affective trust and motivational governance has a marginally significant impact on HCLEs’ behavior, with a p-value of 0.059. This suggests that evidence for the impact of punitive governance on the relationship between affective trust and HCLEs’ behavior is relatively weak. Consequently, H2d is rejected.

5. Discussion and Implication

Trust is widely recognized for its positive influence on corporate behavior. Our research further reveals that when trust is disaggregated into cognitive and affective dimensions, both exhibit a favorable impact on HCLEs’ behavior. Safety in high-risk industries such as HCL is vital for sustainable development and economic growth. This study found that punitive governance did not significantly influence the relationship between trust and HCLEs’ behavior. While conventional wisdom suggests that incentives and punishments can enhance corporate behavior, deterrence theory suggests that showing the determination to act causes the other party to abandon the behavior out of fear of consequences [53]. Penalties have had a significant effect in areas such as the financial and healthcare industries [54,55]. This is because fines can amount to several times the proceeds of an offense and can have a significant impact on the reputation of an individual or firm. However, our findings indicate that punitive governance is less effective in the context of HCL, as the severe consequences of accidents (e.g., loss of life, environmental damage, and financial instability) serve as powerful deterrents. The hazardous materials logistics industry is different from other industries (e.g., financial and medical industries, which can generate huge profits if not penalized by the government). However, even without a government punishment, the consequences of accidents in the dangerous goods logistics industry are unbearable. Thus, punitive governance is not found to have a significant moderating effect on the hazardous materials logistics industry, for instance, the Chernobyl explosion, where the entire city was destroyed and punishment was meaningless. Furthermore, in February 2023, a train derailment in Ohio led to a vinyl chloride spill that contaminated water with 1500 tons of carcinogens, leading to an ecological crisis in the Mississippi River Basin, where penalties could not act as a moderating factor for the incalculable damage [56]. Conversely, motivational governance positively regulates the trust–behavior relationship by fostering intrinsic motivation, encouraging proactive safety practices, and enhancing long-term compliance. Few studies have explored the moderating role of government governance on HCLEs’ behavior, particularly in distinguishing between punitive and motivational governance. To address this gap, this study introduces a model that incorporates government governance as a moderating variable and applies social exchange and reinforcement theories to provide a nuanced understanding of the interactions between trust, governance, and HCLEs’ behavior.

5.1. Theoretical Contribution

Our study contributes to the literature by empirically examining the relationship between trust and HCLEs’ behavior, with government governance serving as a moderating variable, which can be specified in three aspects. First, this study advances the understanding of trust among organizations, extending beyond traditional contexts such as information systems and supply chain management [57,58]. Specifically, in the theoretical framework of this study, trust is used as the independent variable and categorized to specifically examine the different roles of cognitive trust and affective trust in HCLEs and to explore how enterprises’ trust in the government influences and shapes the HCLEs’ behavior. The dual-path mechanism of trust is validated, with cognitive trust promoting compliance behavior through institutional and competence recognition and affective trust promoting proactive safety management through emotional commitment [38]. This framework provides novel insights that address the previously underexplored mechanisms underlying government–enterprise trust dynamics in existing literature.
Second, this study extends the application of social exchange theory to explore the influence of trust on HCLEs’ behavior, whereas previous research has primarily focused on fields such as supply chain management, psychology, and tourism [11,51,58,59]. Reinforcement theory has been used in the fields of artificial intelligence, optimization algorithms, and education previously [60,61,62]. We innovatively apply reinforcement theory to classify government governance, proposing a binary regulation model of motivational governance (i.e., positive reinforcement) and punitive governance (i.e., negative reinforcement), which is a theoretical extension of the original literature [42,43]. Meanwhile, the differing moderating effects of various governance styles on the trust–behavior relationship were revealed.
Finally, this study introduced government governance as a moderating variable. It reveals the ineffectiveness of punitive governance in the sustainable safety management of the HCL industry, thereby advancing the theoretical framework. Much of the existing literature focuses on the direct relationship between trust and HCLEs’ behavior. Our study finds that punitive governance fails to enhance the impact of trust on HCLEs’ behavior, which contradicts deterrence theory and reveals its limitations of deterrence theory in the HCL industry. Simultaneously, this dual perspective offers a nuanced understanding of how external regulatory mechanisms interact with internal organizational dynamics. These insights encourage future research to investigate the interplay between regulatory frameworks and organizational behavior across various contexts.

5.2. Managerial Implications

These findings have several practical implications for industrial practitioners. First, trust has an important and direct impact on HCLEs’ behavior [63]. This study finds that both cognitive and affective trust can positively influence HCLEs’ behavior. Therefore, government authorities should strengthen enterprises’ professional capacity building and promote technical standardization, thereby enhancing their cognitive trust in the government. Simultaneously, the government can guide enterprises to fulfill their social responsibility or support enterprises in building a good social image as a way to enhance affective trust. By enhancing these two dimensions of trust, the government can work with HCLEs to create a more collaborative environment that incentivizes them to adopt safer practices and align their operations with regulatory expectations.
The classification of government governance has often been neglected in previous studies on the safety management of HCL [7,10]. This study verifies the necessity of categorizing government governance through reinforcement theory. Therefore, governmental authorities (including but not limited to the HCL management department) should consciously categorize government governance into motivational and punitive governance when formulating policies. In addition, categorizing existing governance policies with reference to the definition of different types of management styles using reinforcement theory can also improve management efficiency [25].
Finally, the most notable inference is that punitive governance has a limited impact on HCLEs’ behavior. This study identifies the limitations of the deterrence theory in the HCL industry. Therefore, the government needs to redefine the role of punitive governance, which should be used as a backstop, primarily to deter major violations, rather than as a primary tool for day-to-day management. Additionally, motivational governance can promote corporate compliance and trust-based cooperation through positive reinforcement incentives [34]. For HCLEs, the results of this study emphasize the importance of a mindset shift, where governments tend to be regulators in past experiences, and it is much more efficient to shift the government’s role to that of a partner and build mutual trust.

6. Conclusions, Limitations, and Directions for Future Research

In this study, a moderated structural equation model is developed and tested using data collected from 205 practitioners in the global HCL industry. This study primarily investigated the roles of trust and government governance in shaping the behavior of HCLEs. Specifically, this study first explored the influence of trust, revealing that both cognitive trust and competence trust significantly and positively affect HCLEs’ behavior. This highlights the critical role that trust plays in fostering desirable HCLEs’ behavior. Furthermore, this study examined the moderating role of government governance. The findings indicate that motivational governance positively enhances the impact of trust on HCLEs’ behavior, suggesting that supportive and motivational regulatory strategies can strengthen trust-driven outcomes. By contrast, punitive governance was found to be ineffective in moderating the trust–behavior relationship, highlighting the limited role of punitive measures in influencing HCLEs. These findings provide actionable insights for enhancing safety management capabilities and promoting sustainable development in the HCL industry.
This study provides valuable insights into the effects of two types of trust and two types of government governance on HCLEs’ behavior; however, the findings also have some limitations, and further research needs to be conducted. First, considering factors such as cost and time, the sample of this study only covered China, the United States, and Germany, and there may have been sampling bias. In future studies, samples from more countries and regions should be considered when studying safety management in the HCL industry. Second, this study only focuses on one kind of trust—business-to-government trust—and ignores public and inter-organizational trust. Hence, future research could consider other variables, such as inter-organizational trust, to study the effects of multiple types of trust on HCLEs’ behavior [62]. Third, we used a cross-sectional research design that failed to reveal the long-term dynamic effects of trust and government governance on HCLEs’ behavior (e.g., policy iterations). Hence, future studies could adopt a longitudinal approach to explore the long-term interactive effects of cumulative trust and government governance strategies on the behavior of HCLEs.

Author Contributions

Conceptualization, L.H., B.Y. and Y.H.; methodology, L.H. and B.Y.; software, L.H., Y.H. and K.Y. (Kebiao Yuan); validation, L.H. and B.Y.; formal analysis, K.Y. (Kebiao Yuan); investigation, B.Y. and K.Y. (Kebiao Yuan); resources, L.H., Y.H. and K.Y. (Kebiao Yuan); data curation, L.H., B.Y. and K.Y. (Kebiao Yuan); writing—original draft preparation, L.H. and B.Y.; writing—review and editing, Y.H., K.Y. (Keyi Yu) and K.Y. (Kebiao Yuan); supervision, L.H. and Y.H.; project administration, L.H. and K.Y. (Kebiao Yuan); funding acquisition, L.H. and Y.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Philosophy and Social Science Research Base of Ningbo, (grant no. JD6-394), Research Launch Fund Project of Ningbo University of Technology (grant no. 2023KQ096) and Research on the Digital Transformation of Manufacturing Supply Chain in Ningbo (grant no. JD6-026).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of School of Economics and Management, Ningbo University of Technology.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
HCLHazardous Chemical Logistics
HCLEsHazardous Chemical Logistics Enterprises

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Sustainability 17 03577 g001
Figure 1. Proposed model.
Figure 1. Proposed model.
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Figure 2. Model with the results.
Figure 2. Model with the results.
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Table 1. Analysis of the reliability (Cronbach’s α) and validity (Kaiser–Meyer–Olkin) indicators.
Table 1. Analysis of the reliability (Cronbach’s α) and validity (Kaiser–Meyer–Olkin) indicators.
Scale DimensionCronbach’s αKaiser–Meyer–Olkin
motivational governance0.9160.895
punitive governance0.9120.834
cognitive trust0.9000.713
affective trust0.9070.696
HCLEs’ behavior0.9540.940
Table 2. Descriptive statistics: mean and standard deviation of key variables.
Table 2. Descriptive statistics: mean and standard deviation of key variables.
Scale DimensionsMeanSD
motivational governance3.4441.195
punitive governance3.5021.189
cognitive trust3.1801.355
affective trust2.9821.325
HCLEs’ behavior3.0961.339
Table 3. Correlation coefficients among the five scale dimensions.
Table 3. Correlation coefficients among the five scale dimensions.
Scale DimensionMotivational GovernancePunitive GovernanceCognitive TrustAffective TrustHCLEs’ Behavior
motivational governance1.000
punitive governance0.453 **1.000
cognitive trust0.404 **0.212 *1.000
affective trust0.466 **0.154 *0.485 **1.000
HCLEs behavior0.402 **0.0970.478 **0.608 **1.000
* p < 0.05 and ** p < 0.01.
Table 4. Results of the confirmatory factor analysis.
Table 4. Results of the confirmatory factor analysis.
Scale DimensionItemStandardized Factor LoadingAVECRSquare Root of AVE
motivational governancemotivational governance10.9740.6510.9170.807
motivational governance20.766
motivational governance30.803
motivational governance40.732
motivational governance50.746
motivational governance60.795
punitive governancepunitive governance70.9510.7260.9130.852
punitive governance80.836
punitive governance90.792
punitive governance100.819
cognitive trustcognitive trust10.9810.7550.9020.869
cognitive trust20.810
cognitive trust30.804
affective trustaffective trust11.0000.7740.9110.880
affective trust20.784
affective trust30.841
HCLEs’ behaviorHCLEs’ behavior10.9910.7240.9540.851
HCLEs’ behavior20.841
HCLEs’ behavior30.818
HCLEs’ behavior40.826
HCLEs’ behavior50.813
HCLEs’ behavior60.833
HCLEs’ behavior70.847
HCLEs’ behavior80.824
Table 5. Structural path analysis: the effects of trust and governance on HCLEs’ behavior.
Table 5. Structural path analysis: the effects of trust and governance on HCLEs’ behavior.
Structural PathBSDtp
Affective trust → HCLEs’ behavior0.3760.0715.288<0.001
Cognitive trust → HCLEs’ behavior0.2000.0623.2200.001
Motivational governance → HCLEs’ behavior0.1980.0752.6440.008
Punitive governance → HCLEs’ behavior−0.1000.0651.5490.121
Motivational governance × Affective trust → HCLEs’ behavior0.1840.0672.7390.006
Punitive governance × Cognitive trust → HCLEs’ behavior−0.0200.0650.3020.763
Punitive governance × Affective trust → HCLEs’ behavior0.1310.0701.8850.059
Motivational governance × Cognitive trust → HCLEs’ behavior0.1290.0632.0530.040
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Hou, L.; Yao, B.; Hu, Y.; Yu, K.; Yuan, K. How Trust Affects Hazardous Chemicals Logistics Enterprises’ Sustainable Safety Behavior: The Moderating Role of Government Governance. Sustainability 2025, 17, 3577. https://doi.org/10.3390/su17083577

AMA Style

Hou L, Yao B, Hu Y, Yu K, Yuan K. How Trust Affects Hazardous Chemicals Logistics Enterprises’ Sustainable Safety Behavior: The Moderating Role of Government Governance. Sustainability. 2025; 17(8):3577. https://doi.org/10.3390/su17083577

Chicago/Turabian Style

Hou, Li, Bin Yao, Yibo Hu, Keyi Yu, and Kebiao Yuan. 2025. "How Trust Affects Hazardous Chemicals Logistics Enterprises’ Sustainable Safety Behavior: The Moderating Role of Government Governance" Sustainability 17, no. 8: 3577. https://doi.org/10.3390/su17083577

APA Style

Hou, L., Yao, B., Hu, Y., Yu, K., & Yuan, K. (2025). How Trust Affects Hazardous Chemicals Logistics Enterprises’ Sustainable Safety Behavior: The Moderating Role of Government Governance. Sustainability, 17(8), 3577. https://doi.org/10.3390/su17083577

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