Sewage Treatment Equipment Supply Chain Collaboration and Resilience Improvement Path Analysis: Collaborative Decision-Making, Information Sharing, Risk Management

Abstract

Since the dawn of the 21st century, supply chains (SCs) have faced an array of unprecedented challenges, encompassing economic volatility, escalating geopolitical conflicts, intensified trade disputes, and persistent environmental degradation. These challenges have imposed immense pressures on SCs, amplifying both the risks and occurrences of disruptions. In response to these critical demands, there has been a substantial increase in academic, industrial, and governmental focus on the resilience and recovery capabilities of SCs. In this context, the supply chain (SC) for sewage treatment equipment has been significantly affected. This study aims to systematically investigate collaborative strategies and mechanisms for bolstering the resilience of manufacturing SCs within the sewage treatment sector, with particular attention to collaborative decision-making, information sharing, and risk mitigation throughout the SC lifecycle. Following a comparative analysis of various methodologies, this paper employed the structural equation model (SEM) and subsequently conducted a comprehensive survey of manufacturers specializing in glass fiber sewage treatment systems, yielding 385 valid responses. Through this data, a structural equation model was developed to analyze pathways for resilience enhancement. A thorough analysis of the results indicates that Supply Chain Collaboration (SCC) and collaborative decision-making are instrumental in strengthening Glass Fiber Sewage Treatment Supply Chain Resilience (SCR) through the implementation of effective information sharing and risk management practices. These findings contribute to the theoretical framework of SCC and SCR by clarifying the influence of collaborative practices while also providing practical guidance for the governance and strategic management of sewage treatment equipment enterprises and their associated SCs. The conclusions drawn from this study can guide us in establishing a more resilient and sustainable SC ecosystem, making it more directive and capable of mitigating complex disruptions.

1. Introduction

The contemporary global landscape is marked by unprecedented transformative forces, representing a distinctive moment in recent history. The rapid and widespread impact of the COVID-19 pandemic, coupled with disruptions in global supply chains and shifts in international trade, has accelerated significant changes in various sectors of the economy. Furthermore, the emergence of trade protectionism, escalating geopolitical tensions, such as the Russia-Ukraine conflict, and the intensifying Israel-Palestine situation, as well as significant reforms in global governance, have further complicated matters. The convergence of these elements has markedly heightened global uncertainty, resulting in instability across various industries and sectors worldwide.

Against this backdrop of turmoil, SCs are particularly vulnerable to disruptions, facing a range of challenges, including severe shortages of raw materials, unforeseen production halts, interruptions in product availability, and severe logistics bottlenecks. These obstacles collectively hinder the smooth transport of crucial resources such as labor, capital, and goods, undermining the efficiency and reliability of global commerce. The inherent interconnectedness and interdependence of security systems mean that these challenges are not isolated; instead, they can spread throughout the global economic system, triggering a domino effect with broad and cascading impacts. To address these complexities, the United States released the “National Strategy for Global Supply Chain Security” white paper in 2012, introducing the concept of “resilient supply chains”. Subsequently, in 2019, the U.S. collaborated with four other nations to enhance the “Quad” mechanism and proposed the idea of “short supply chains” as a means to strengthen the system’s resilience. The outbreak of the COVID-19 pandemic in 2020 prompted the European Union Council to advocate for a “regional supply chain” strategy aimed at localizing and regionalizing key materials to mitigate the risks of disruptions in SCs critical to essential materials. Establishing a secure, stable, flexible, and manageable SC has become an urgent priority. This paradigm shift underscores the necessity for a strategic reassessment of SC dynamics to ensure robustness in the face of an increasingly unpredictable global environment.

Collaboration is increasingly recognized as a key strategy for enhancing SCR. The integration of information sharing, risk management, and coordinated decision-making plays a pivotal role in strengthening SCR. As uncertainties and challenges grow, organizations across industries are actively exploring how SCC can improve operational efficiency, reduce costs, and enhance resilience. Simultaneously, governments and international institutions are promoting SCC as a driver of global commerce and economic progress. In the knowledge economy, the competitive advantage of modern enterprises depends on their ability to form strategic partnerships across the SC. This collaboration allows companies to combine resources and leverage competitive advantages for mutual benefit. Fostering SCR through collaboration helps organizations respond effectively to market volatility, natural disasters, and other disruptions, while also promoting environmental consciousness in production processes. This not only reduces negative environmental impacts but also highlights the critical link between corporate collaboration and the adoption of sustainable, efficient technologies. By building such resilience, companies are better equipped to navigate complex global SCs and contribute to a more sustainable and cooperative economic ecosystem.

The integration and sustainability of fiberglass sewage treatment systems within the SC emphasize the company’s commitment to environmental management, sustainable development, and innovation. These advanced systems utilize corrosion-resistant fiberglass materials to treat both industrial and domestic sewage, significantly reducing pollutant emissions while enhancing water recycling efforts. For example, China’s Pris, a manufacturer of sewage treatment equipment, has successfully applied fiberglass-reinforced polymer (FRP) materials in both municipal and industrial sewage treatment solutions. However, the industry faces several challenges, including unstable raw material supplies, inefficient logistics, limited information sharing, and weak risk management capabilities. In response, manufacturers have adopted a range of strategies: forming strategic partnerships with resin and glass fiber suppliers, optimizing logistics and transportation solutions, establishing information-sharing platforms, enhancing risk management, and promoting collaborative innovation across the SC. These initiatives aim to mitigate risks associated with raw material shortages, strengthen SCR, ensure production stability, and improve customer satisfaction—all of which contribute to a stronger competitive position in the market. By implementing these solutions, manufacturers can better navigate market fluctuations, improve delivery efficiency, and promote long-term sustainable development. The inclusion of FRP sewage treatment enterprises within the SC sets a benchmark for environmental performance and encourages partners to embrace eco-friendly practices, fostering positive environmental synergies. Furthermore, the strategic integration of FRP sewage treatment technologies within SC management underscores the importance of optimizing processes to minimize resource waste, reduce emissions, and enhance production efficiency, providing a solid framework for achieving sustainable development goals.

The sustainability of fiberglass sewage treatment systems often generates limited economic returns, which frequently leads to insufficient attention and research. In the article A Mechanistic Study of Enterprise Digital Intelligence Transformation, Innovation Resilience, and Firm Performance, it is argued that organizational innovation resilience serves as a mediating factor between digital transformation and service process upgrades, ultimately influencing firm performance. Similarly, SCR should play a bridging role between sustainability and economic returns. But stakeholders often prioritize market profitability and stable development, inadvertently overlooking critical environmental impacts.

This paper employs the SC of the sewage treatment equipment at Jiangsu Integrated Technology Environmental Co., Ltd. as a case study to delve into the operational mechanics of a sewage treatment firm’s SC. Figure 1 illustrates the comprehensive SC journey, from the initial procurement of raw materials to the ultimate delivery of the finished product. The author has personally crafted Figure 1 to provide a clear visual representation of the distinct phases within a sewage treatment enterprise’s SC. As depicted in Figure 1, the SC for an FRP sewage treatment enterprise starts when a construction company issues a purchase order detailing equipment requirements. In response, FRP sewage treatment companies relay these instructions to FRP pipe manufacturers and logistics providers. The transportation companies, in turn, collaborate with valve and pump manufacturers to procure the necessary components. Meanwhile, FRP pipe manufacturers work with fiber and resin producers to source essential raw materials. Once the materials are procured, processed, and integrated, the logistics providers deliver the components to the FRP sewage treatment company, which handles the production and assembly processes to meet the construction company’s specifications efficiently and sustainably. This coordinated approach not only optimizes the SC for FRP sewage treatment systems but also enhances the sustainability of the construction and manufacturing sectors. It highlights the critical role of stakeholder collaboration in achieving environmental and operational efficiency. Through this model, manufacturers can better align with global sustainability targets while simultaneously boosting market competitiveness and building resilience against external disruptions.

Figure 1 not only presents the main stakeholders involved in the upstream and downstream processes of fiberglass-reinforced sewage treatment machinery but also depicts the collaborative ecosystem required to achieve a resilient and sustainable SC. The strategic integration of FRP sewage treatment machines with SC management demonstrates the importance of optimizing SCR to reduce resource waste, reduce pollution emissions, and improve production efficiency, providing a valuable framework for achieving sustainable development goals.

The architectural structure of this scholarly investigation is outlined as follows: Initially, an extensive synthesis of extant literature was conducted to lay the theoretical foundation and delineate the research objectives. However, it was noted that the available literature on the sustainability of sewage treatment equipment SC is rather sparse. The subsequent literature review, detailed in Section 2, serves to pinpoint knowledge gaps and rationalize the chosen research design. Thereafter, Section 3 delineates the employment of SEM as a sophisticated analytical instrument for probing intricate relationships within the study’s framework. After articulating the methodology, the empirical results are explained, grounded in the analysis of the gathered data. In Section 5 of this manuscript, the implications of the study’s findings for academic theory and management practice concerning the sustainability of the sewage treatment equipment SCs are discussed for the first time. The theoretical contribution section underscores how this research extends current knowledge and offers a more profound comprehension of the investigated phenomenon. The practical implications serve as a guide for managers and decision-makers in the sewage treatment equipment SC, enabling them to leverage the findings to refine operational strategies and decision-making processes. The framework of this paper is shown in Figure 2 below.

2. Literature Review

2.1. Supply Chain Resilience

SCR is an emerging field in the academic corpus, tracing its origins back to pioneering scholarly research in response to fuel price protests in the United Kingdom in the year 2000. The concept of resilience is interdisciplinary and multifaceted, leading to the absence of a unified consensus among scholars regarding its definition. Existing research primarily focuses on three aspects: the exploration of resilience connotations, the study of influencing factors, and resilience assessment. Academic interpretations of SCR are varied, reflecting the diverse epistemological and methodological approaches adopted by researchers.

In the study of resilience connotations, Dulude [1], from the perspective of recovery attributes, defines resilience as a form of recovery capability, emphasizing that this ability is a critical component of SC resilience. On this basis, Christopher [2] believes that SCR is the ability to respond to emergencies and return to normal functioning. Furthermore, the ability of businesses to swiftly adjust organizational structures, flexibly coordinate resources in response to environmental changes, rapidly meet customer demands in new settings, restore supply conditions, ensure continuous supply, and even benefit from crises [3] is crucial.

In the study of influencing factors, Wieland [4], from the perspective of SC relationships, explored the positive impact of integration capabilities, collaborative cooperation, and close communication on SCR. Brandon [5], through an empirical investigation of actual data from UK manufacturers, found that connectivity and resource information sharing significantly enhance SC visibility, thereby effectively improving SCR. Mandal [6] embedded big data analytics capabilities into various aspects of the SC, including collaboration, control, and planning, and pointed out that big data analytics play a crucial role in SC forecasting, flexibility, and responsiveness.

In the area of resilience assessment, Hohenstein [7], through a comprehensive review of existing literature on SCR, highlighted that current research is predominantly qualitative, lacking sufficient studies on the evaluation and measurement of resilience. Building on this, Sahu [8] proposed a resilience assessment framework based on resilience thinking, which evaluates SCR in terms of time, impact, and recovery degree. They also provided detailed explanations on quantifying and improving resilience through practical case studies. Rajesh [9] developed a decision support model for managers to measure and enhance SCR using a novel grey relational analysis method, which was validated through case examples, laying a theoretical foundation for future research.

The theory of dynamic capabilities focuses on the dynamism of resource capabilities, rooted in the concept of dynamic capabilities [10]. It demands that companies identify external opportunities and threats, adapt their dynamic capabilities, and maintain a synchronous response to the ever-changing external environment. SCR requires firms to recognize external opportunities and threats, adjust their dynamic capabilities, and keep pace with changes in the external environment. This includes quantitative research methods to establish mathematical models for assessing SCR or flexibility [11]. Focusing on the practice path of environmental strategy of SC enterprises, from the perspective of Chinese SC enterprises, combined with classical theories, IT is discussed from a dynamic perspective [12]. Based on dynamic capability theory, IT explores the impact of integration between emerging and traditional IT technologies and integration between emerging IT technologies on SCR and corporate sustainability performance through IT-enabled organizational transformation and digital transformation [13].

2.2. Supply Chain Collaboration

Collaboration, by definition, refers to two or more enterprises working together to achieve common strategic goals, thereby establishing a network-based SCC. Since their inception, industrial enterprises have sought competitive advantages, from the 1980s when they improved production efficiency through human-machine interaction to the end of the century when they simplified, reorganized, and integrated production with Just-in-Time (JIT) and lean thinking. Now, companies aim to enhance their overall competitiveness through SCC, and the concept of SCC emerged in this context.

Hauert formally introduced the term “supply chain collaboration” in 2001, which sparked significant academic interest in the field. Quick response, supplier-managed inventory, collaborative planning, forecasting, and replenishment are all encompassed within SCC and represent its main forms and content [14]. Companies jointly address market changes and risks [15], aligning with the resource-based view of competitive advantage. Alzoubi [16] investigated the relationship between sustainable SC strategies and SCC, emphasizing the importance of achieving sustainability standards while maintaining competitive priorities. Similarly, Yang [17] focused on the effects of SCC on green innovation performance, highlighting the role collaboration plays in driving innovation within SCs. Hofman [18] explored the impact of SCC on eco-innovations in Chinese manufacturing supplier firms, highlighting the role collaboration plays in driving eco-innovations. Furthermore, Sudusinghe [19] conducted a systematic literature review on SCC and sustainability performance in the circular economy, emphasizing the importance of collaboration in achieving sustainability goals. Lastly, Ali [20] focused on fusion-based SCC using machine learning techniques, highlighting the potential of advanced technologies in enhancing collaboration within SCs.

The research methods for SCC primarily involve establishing mathematical models to describe and analyze the distribution of interests within the SC, thereby promoting the realization of SC logistics information collaboration [21]. Case study methods were employed to conduct comprehensive surveys and analyses of cross-border SC enterprises in the agriculture and pastoral sectors of China and Mongolia, aiming to construct an analytical framework for the SC triangle theory [22]. By collecting empirical data, the impact of business collaboration, strategic collaboration, and product modularization on SC flexibility was studied [23]. These scholars have explored and analyzed the concept of SCC, interest distribution, and influencing factors from various perspectives.

The resource-based view (RBV) of competitive advantage posits that a company’s competitive advantage stems from unique resources and capabilities that are difficult for competitors to imitate. In the context of SCC, companies use their collaborative relationships and shared resources to create sustainable competitive advantages. This aligns with the RBV’s emphasis on the strategic role of resources and capabilities in determining a company’s competitive position.

2.3. Collaborative Decision-Making

Collaborative decision-making in SCs refers to the process where different enterprises or departments within a SCC coordinate to make decisions collectively, with the aim of enhancing the efficiency and competitiveness of the entire SC. This process involves sharing critical information, co-planning resources, synchronizing operational activities, and jointly addressing market changes and external risks. The goal is to maximize individual and organizational objectives through collective action. Collaborative decision-making in SCs has been the focus of research in recent years.

In the realm of collaborative decision-making, Allaoui [24] introduced a comprehensive decision support framework tailored for sustainable SC collaborative planning, with a specific emphasis on agricultural and food SCs within a significant European research endeavor. Concurrently, in Canada, an empirically grounded framework was meticulously crafted through a collaborative knowledge co-production process that engaged businesses, immigrant government agencies, and Indigenous communities. This framework delineates the values, principles, and best practices that serve as guiding principles for effective environmental decision-making in collaborative settings [25]. Furthermore, Gemma [26] proposes a framework that empowers scientific teams to circumvent common pitfalls by operationalizing collaborative leadership as an iterative loop encompassing distributed sense-making, decision-making, and action-taking.

2.4. Information Sharing

The academic and industrial research on SC management has consistently highlighted the criticality of information sharing for enhancing efficiency, transparency, and overall performance within SCs. Guan [27] delved into the intricacies of demand information sharing in service-providing, competitive SCs, using a multi-stage game theoretical framework to analyze the effects on pricing and service decisions, emphasizing its importance for individual SC performance improvement. Similarly, Tang [28] focused on the reduction of the bullwhip effect and the enhancement of SC robustness through demand information sharing, employing an extended Taguchi design for multi-response issues. Yu [29] investigated the role of information sharing in carbon emission reduction across the SC, emphasizing its significance in environmental sustainability.

In parallel, Xue [30] introduced a blockchain-driven decentralized operations model for SCs, aiming to reconstruct the information-sharing architecture for collaborative operations. Wei [31] conducted a differential game analysis on green technology innovation, examining the impact of dynamic demand information sharing on profitability and environmental initiatives. Kasparis [32] further contributed by evaluating the effect of information sharing on improving timeliness in SCs for neglected tropical disease prevention.

The literature by Xu [33] expands this field by studying how logistics center decisions in information sharing affect logistics model selection, while Cynthia [34] addresses municipal service delivery challenges in South Africa, advocating for a strategic approach to information sharing and collaboration for SC performance enhancement. These studies collectively underscore the multifaceted benefits of information sharing, including demand visibility, carbon footprint reduction, innovation, and responsiveness, thus establishing information sharing as a pivotal element in modern SC management strategies.

2.5. Risk Management

SC risk management is a complex process that involves the identification, evaluation, monitoring, and mitigation of potential risks within the SC ecosystem. As a crucial element in ensuring operational performance and sustainability, risk management has emerged as a significant area of scholarly inquiry and practical application. Munir [35] emphasizes the facilitative role of SC integration in enhancing risk management and operational performance, highlighting the necessity of integrating various components of the SC to effectively manage risks. The discipline of risk management necessitates a clear conceptual framework and functional definition that serves as a foundational reference for subsequent research, as underscored by Jüttner [36]. Marbini [37] contributes to this field by proposing an innovative risk management framework based on Data Envelopment Analysis (DEA), specifically designed for the oil SC, thereby providing an advanced tool for proactive risk management. Abdel-Basset [38] introduces a lithogenic TOPSIS-CRITIC model aimed at sustainable risk management, illustrating the need for innovative methodologies to address risks. Proactive risk mitigation strategies significantly influence risk management performance; Saglam [39] demonstrates positive correlations between SCR and responsiveness. However, further investigation is warranted regarding the relationship between SC flexibility and risk management performance. Pournader [40] conducts a comprehensive review of existing literature on risk management trends and emerging topics, while Manhart [41] performs a meta-analytic assessment of buffering and bridging strategies along with their impact on firm performance. Baz [42] examines how risk management practices mitigate disruption impacts on SCR and robustness—particularly during the COVID-19 pandemic. Jacobus [43] analyzes strategies employed to mitigate SC disruptions amid the pandemic.

In summary, current literature underscores essential themes such as proactive risk mitigation strategies, integrated approaches across diverse elements, and innovative solutions necessary for effective governance over risks. These efforts are vital not only for maintaining uninterrupted operations but also for bolstering overall resiliency alongside enhanced performances across various facets inherent within modern-day SCs.

3. Materials and Methods

3.1. Structural Equation Model

This paper employs SEM for its examination of SCR and collaboration, favoring it over Ordinary Least Squares (OLS) and Generalized Structured Component Analysis (GSCA) for its superior handling of complex relationships. SEM’s capability to analyze multiple dependent and independent variables concurrently is crucial for understanding the multifaceted nature of SCR and collaboration. It adeptly estimates the interplay among latent constructs through observed indicators, a feature beyond the reach of other methods. While OLS is simple and accessible, its assumption of variable independence is unrealistic in SC research, where variable correlations are common. OLS also falls short in addressing latent variables, a significant drawback in this context. GSCA, similar to SEM in some respects, prioritizes component extraction, whereas SEM’s synthesis of factors and path analysis allows for comprehensive evaluation of both measurement and structural models. SEM also offers a richer suite of statistical tests, supports hypothesis testing, and is adaptable to complex frameworks, including mediation and moderation. Thus, SEM stands out as the optimal approach for investigating SCR and collaboration.

SEM is a robust statistical tool that melds multiple regression, factor analysis, and path analysis to explore the intricate web of direct and indirect effects among variables. Its key strengths include the ability to manage multiple dependent variables, accommodate latent variable correlations, estimate measurement errors, and visually depict variable interrelationships through path diagrams. These features make SEM a versatile and invaluable technique across disciplines such as social sciences, management, and psychology.

3.2. Research Hypotheses

Many scholars have conducted in-depth research on the relationship between SCC and SCR. The earliest suggestion that collaboration impacts SCR can be traced back to Rice and Sheff [44], who analyzed SCR and discussed the role of collaboration in it. Digitalization has a direct impact on SCR, and SCC can directly enhance the resilience and robustness of the SC [45]. IT capabilities are positively correlated with external SCR, and SCC is positively correlated with internal SCR [46].

It is evident from the aforementioned that SCs are better equipped to handle uncertainty, risk, and contingencies through enhanced collaboration, thus improving their overall resilience. It is therefore assumed that H1 is as follows:

H1. 

Collaboration is positively related to SCR.

Information sharing, risk management, and collaborative decision-making are the key topics discussed in SCC. Based on the information processing theory, Mandal [47] demonstrated through empirical research that SC visibility promotes information sharing among enterprises and mitigates the bullwhip effect, thereby positively impacting SCR. SCC is not only cooperation between enterprises but also encompasses collaboration with suppliers, distributors, retailers, and end consumers. Therefore, this paper subdivides the SCC framework into information sharing, risk management, and collaborative decision-making. It is therefore assumed that H2 is as follows:

H2. 

Information Sharing, risk management, and collaborative decision-making have positive effects on SCR.

Information is an important resource in contemporary society, with business transactions between enterprises being completed through the exchange of information. However, information sharing is not merely the exchange of information but also the sharing of critical information with other enterprises, thereby fostering trust relationships between enterprises [48]. SC management based on this effectively reduces SC costs and enhances efficient SC management. Most scholars argue that information sharing is a crucial characteristic of an effective SC, which can mitigate the negative impact of the bullwhip effect and assist node enterprises in establishing an organic link between supply and demand, thereby improving the rapid response capability of enterprises. Evidence suggests that the effectiveness of information sharing affects SCR. Information sharing effectiveness acts as a mediator between information leakage and SCR, and information leakage is influenced by information security culture [49]. In contrast to high levels of information sharing, where variations in the severity of SCR at different levels of technical uncertainty are reduced [50], low levels of information sharing create such variations. It is therefore assumed that H2a is as follows:

H2a. 

Information sharing positively affects SCR.

Risk management refers to the process of project or business management that aims to minimize the negative effects of risk, which encompasses risk identification, assessment, prevention, and control measures. Enterprises will encounter various risks during the course of production and management. As an effective means to resist adverse factors, risk management is highlighted by scholars such as Juttner [36], Cai Shilong [51], Fan [52], and others. They have discussed how SC partners can collaborate to mitigate losses or reduce the vulnerability of the SC. Chi [53] posits that SC risk management involves the integration of SC risk characteristics, which can be identified and analyzed through scientific methods to guide management activities. As SC becomes increasingly important to enterprises, their ability to manage SC risks effectively will determine whether they can survive and thrive in SC risk events [54]. It is therefore assumed that H2b is as follows:

H2b. 

Risk management positively affects SCR.

Collaborative decision-making is proposed on the basis of the SCC and represents a management approach adopted by the SC system to achieve its overall objectives. It involves the economic management and control of logistics, capital flow, and information flow within the SCS. The fundamental purpose of this decision-making management system is to enhance the operational efficiency of the SCS and reduce its costs. Juttner [36] and other scholars suggest that SC collaborative decision-making can help enterprises better understand and adapt to the complex SC environment and improve the ability to identify and respond to potential risks. Collaborative decision-making in SC is very important for solving the problem of SC risk. Through active partnership and an effective collaborative decision-making mechanism, enterprises can better control and deal with various risks, enhance the risk-resistance ability of the SC, and ensure continuous and stable operation and development [55]. It is therefore assumed that H2c is as follows:

H2c. 

Collaborative decision-making positively affects SCR.

Partnership plays a pivotal role in SC management, which entails trust, cooperation, and knowledge sharing among enterprises. Therefore, it is imperative to investigate the relationship between SCR and SCC as an intermediary variable to elucidate the impact of SC management strategies. Robust partnerships foster an environment of enhanced trust and communication between suppliers and buyers, thereby bolstering the resilience and collaborative capabilities of the SC. Enterprises are increasingly acknowledging that synchronization and collaboration among SC partners represent a crucial avenue for enhancing their competitive edge and risk management prowess [56]. SC partners can exchange information more efficiently and effectively, mitigate communication barriers, and enhance data precision, enabling SC partners to access and analyze real-time information, which aids in comprehending and responding to fluctuations in demand, supply, and other market dynamics [57]. It is therefore assumed that H3 is as follows:

H3. 

Partnerships play an intermediary role in SCC and SCR.

The hypothetical model in this paper is shown in Figure 3.

3.3. Data Collection

To ensure the objectivity and fairness of the data collected, this study aims to investigate the correlation between SCC and SCR. The research sample for data collection was selected from the relevant SC companies shown in Figure 1, with the questionnaire serving as the primary data collection tool. To guarantee the reliability and validity of the questionnaire, all items were measured using a Likert five-point scale, ranging from “1” for “strongly disagree” to “5” for “strongly agree”, with the degree of each variable increasing proportionally with the scale value (In order to measure the effectiveness of SCC, we designed questions aimed at assessing various aspects of partnership between companies and their SC partners. These questions included: ‘Our company works closely with SC partners to make joint decisions’ and ‘Information sharing between our SC partners is timely and transparent’, and so on).

Study Overview and Data Collection. This study focuses on the sewage treatment equipment industry in China, encompassing a range of products and services including resins, valves, fiberglass, water pumps, transportation companies, and pipeline companies. The sample for this research was predominantly centered in Jiangsu Province, renowned for its robust industrial base and being a hub for many such companies. To ensure the relevance and representativeness of the sample, the research team applied multiple filtering criteria in the selection process: companies had to be engaged in the production, distribution, or service of wastewater treatment equipment, have been in operation for at least five years, and employ a minimum of 50 staff members. Data collection commenced in September 2023 and concluded in June 2024. To maximize the number of survey participants and ensure the universality and authenticity of the survey, a total of 500 questionnaires were distributed through various channels, including face-to-face interviews, Questionnaire Star links, and emails. Ultimately, 385 valid questionnaires were obtained, yielding an effective response rate of 77%, which demonstrates the significant participation of the selected sample and enhances the representativeness and credibility of the data.

Geographical Distribution In terms of geographical distribution, the companies participating in this study are spread across various regions in China, with a notable concentration in Jiangsu Province. Specifically, approximately 68% of the surveyed companies are located in Jiangsu, reflecting the province’s pivotal role in the wastewater treatment equipment industry. The remaining companies are distributed across other provinces as follows: Northern China: Comprising 10% of the total, with major cities including Beijing and Tianjin. Eastern China (excluding Jiangsu Province): Making up 8% of the total, with major cities such as Shanghai and Hangzhou in Zhejiang Province. Southern China: constituting 7% of the total, with major cities including Guangzhou and Shenzhen. Southwest China: accounting for 4% of the total, with major cities such as Chengdu and Chongqing. Other regions: comprising 3% of the total, scattered across other key industrial cities. For instance, companies in Beijing and Tianjin constitute 8% of the total surveyed companies, while those in Shanghai and Hangzhou account for another 8%.

An in-depth analysis of the collected questionnaire data were conducted using SPSS software. The survey, designed to gather comprehensive insights, consisted of 44 questions: 43 single-choice questions and 1 control question designed to assess respondent attentiveness. The average completion time was approximately 15 min. The questionnaire covered a broad range of topics, including SC risk management practices, the company’s products and services, management’s views on SC disruptions, strategies and measures to address SC disruptions, and individuals’ roles and experiences in SC management.

To mitigate the limitations of face-to-face questionnaire completion, anonymity was assured, and clear instructions were provided to enable respondents to complete the questionnaire independently, without additional explanation from surveyors. Additionally, to address potential drawbacks of online survey completion, the questionnaire was designed to be concise and clear, reducing respondent fatigue and distraction. A reliable online survey platform was utilized, and a misleading question was included to ensure data accuracy and respondent engagement. Technical support was also available to address any issues encountered during the online completion process.

During the data processing phase, a rigorous data cleaning process was implemented to ensure the integrity and accuracy of the survey responses. This process included several steps to identify and handle missing, inconsistent, or outlier data. Firstly, all completed questionnaires were screened for logical consistency and completeness. Respondents who failed to complete essential questions or whose responses indicated a lack of attention—such as selecting the same answer for all questions or choosing the clearly invalid option in the control question—were excluded from the analysis.

Secondly, a thorough examination of the data were conducted to identify outliers and errors. This involved checking for responses that fell outside the expected range for each question and for any unusual patterns that could indicate misunderstanding or misreporting. When outliers were identified, they were reviewed in the context of the full dataset to determine their validity. If an outlier was deemed to be a data entry error or a misunderstanding, it was corrected if possible or excluded if it could not be verified.

Lastly, missing data were addressed using appropriate imputation methods where necessary, or the cases with missing data were excluded from the analysis if they did not constitute a significant portion of the dataset. The final dataset used for analysis was free from logical inconsistencies, outliers, and incomplete responses, ensuring that the results reflect a high degree of data quality and reliability.

3.4. Descriptive Analysis of Samples

According to the results of the descriptive analysis shown in

Table 1. Sample descriptive analysis.

Project	Description	Percentage
Gender	Female	46%
	Male	54%
Products	Resin	7%
	Fiberglass	12%
	Fiberglass pipes	37%
	Sewage Treatment Plant	26%
	Others (valves, pumps, etc.)	18%
Persons under investigation	General Staff	6%
	Managers at the grass-roots level	8%
	Middle Management	52%
	Senior Management	34%
Years of service	1 year and below	5%
	One to three years	17%
	Three to six years	47%
	6 years and above	31%

, several key data points strongly reflect the characteristics of the survey sample and the basic information of the FRP sewage treatment equipment enterprises.

3.4.1. Gender and Equality

To ensure fairness and equity, the survey also captures the gender distribution of the respondents, demonstrating a balanced representation of both male and female managers and employees. This focus on gender equality underscores the commitment to inclusive and fair data collection practices, aligning with modern corporate governance standards.

3.4.2. Product Categories and Company Distribution

In terms of product categories, the surveyed companies are predominantly engaged in the production of glass-reinforced plastic (GRP) pipes, which account for 37% of the total, significantly higher than other product categories. This reflects the centrality of GRP pipes in the SC and the heightened impact of disruptions on these products. Resin and glass fiber, the main raw materials for GRP pipes, account for 7% and 12%, respectively, while sewage processors and other products (such as valves and water pumps) make up 26% and 18% of the sample. The geographical distribution of these companies spans various regions, ensuring an equitable representation of different markets and highlighting the diversity within the SC. This diversity strengthens the robustness of our conclusions about SC resilience across the industry.

3.4.3. Management Level of Respondents

Regarding the distribution of roles among the survey participants, middle and senior management personnel constitute the majority, with middle managers accounting for 52% and senior managers for 34%. This indicates that the questionnaire data primarily captures the perspectives and insights of corporate decision-makers. The proportions of junior managers and general employees are 8% and 6%, respectively, suggesting that the survey focuses on higher management levels, ensuring that the responses reflect informed strategic decision-making processes within the companies.

3.4.4. Experience and Expertise

The work experience of respondents further supports the depth of the data. Nearly one-third of respondents (31%) have been employed in their companies for more than six years, while those with three to six years of experience make up 47% of the sample. Respondents with one to three years of experience and those with one year or less account for 17% and 5%, respectively. This distribution underscores that most respondents possess significant expertise and practical experience in SC operations, enhancing the credibility and reliability of the data for analyzing SC disruption risk management.

In summary, the sample for this survey is characterized by a predominance of GRP pipe manufacturing companies, a high proportion of management-level participants, and respondents with substantial work experience. This comprehensive structure ensures that the questionnaire data provide deep insights into SC disruption risk management with a balanced representation of gender and geographical distribution, enhancing the overall quality and applicability of the research findings.

3.5. Related Data Processing and Testing

To ensure the reliability and stability of the questionnaire, conducting a reliability test is of paramount importance, as it forms the foundation for subsequent difference tests, correlation analysis, and regression analysis. Therefore, assessing the reliability of the questionnaire is a critical step.

Typically, Cronbach’s α coefficient is employed to evaluate the reliability of the questionnaire. A higher α coefficient indicates greater reliability and stability of the questionnaire. As evidenced in

Table 2. Reliability and Ave Test.

Fit Index	Collaboration X	Information Sharing X1	Risk Management X2	Collaborative Decision Making X3	Flexibility Y1	Adaptability Y2	Recovery Y3	Compressive Ability Y4	Partnership M
Reliability value	0.876	0.799	0.809	0.787	0.939	0.916	0.930	0.922	0.900

The reliability values of each dimension are above 0.7, indicating the questionnaire has a good reliability value.

, all the coefficients are above 0.7, which suggests that our questionnaire design is robust and suitable for subsequent data analysis.

Factor analysis is a statistical technique that identifies and extracts common underlying factors from a set of observed variables. These factors represent the shared variance among the variables and provide a more succinct and insightful description of the dataset. By employing factor analysis, we can transform a multitude of variables into a smaller set of meaningful factors, thereby facilitating a deeper understanding of the data’s underlying structure and relationships.

Confirmatory factor analysis (CFA) was conducted using the software Amos 26.1.0. Prior to this, exploratory factor analysis (EFA) was performed using SPSS 26.1.0 to assess the suitability of the data for factor analysis. The results, presented in

Table 3. KMO and Bartlett test.

KMO Sampling Appropriateness Quantity		0.935
Bartlett sphericity test	Approximately chi-squared	9886.289
	df	703
	p-value	0.000

, indicate a Kaiser–Meyer–Olkin (KMO) value of 0.935, which exceeds the commonly accepted threshold of 0.50. A KMO value above 0.50 suggests that the variables are correlated and suitable for factor analysis. Additionally, the significance of Bartlett’s test of sphericity (p = 0.000, less than 0.05) further confirms that the data are appropriate for factor analysis.

The purpose of conducting both EFA and CFA is to ensure the reliability and validity of the factor analysis process. EFA helps in determining whether the data are suitable for factor analysis, while CFA confirms the structure of the factors extracted. This dual approach enhances confidence in the factor analysis results and ensures that the subsequent interpretation of the factors is accurate and meaningful.

Table 4. Analysis of variance diagram.

Explanation of Total Variance
Ingredients	Extract the Sum of Squares of Loads			Sum of Squares of Rotational Loads
	Total	Variance %	Cumulative %	Total	Variance %	Cumulative %
X	12.632	33.243	33.243	5.073	13.351	13.351
X1	3.338	8.783	42.026	3.483	9.166	22.517
X2	2.698	7.099	49.125	3.224	8.483	31.000
X3	2.390	6.291	55.415	3.175	8.354	39.354
Y1	2.014	5.300	60.716	3.032	7.980	47.334
Y2	1.493	3.929	64.644	2.781	7.317	54.651
Y3	1.375	3.619	68.263	2.659	6.997	61.649
Y4	1.224	3.222	71.485	2.657	6.993	68.642
M	1.105	2.909	74.393	2.186	5.752	74.393

Extraction method: principal component analysis.

below provides the analysis of variance (ANOVA) results for each factor. A total of +9 factors were extracted, with an extraction rate of 74%. This extraction rate indicates the effectiveness of the factor analysis in capturing the majority of the variance in the data. By identifying these key factors, we can gain a more comprehensive understanding of the relationships and patterns within the dataset, enabling more informed decision-making and strategic planning.

The analysis method employed in this study utilizes principal component analysis (PCA) as the primary technique for factor extraction. PCA is a statistical procedure that identifies the underlying structure of a set of correlated variables and transforms them into a new set of uncorrelated variables known as principal components. This method effectively reduces the dimensionality of the data while retaining as much of the original information as possible.

To enhance the interpretability of the factors, the Kaiser normalization maximum variance rotation method was applied. This rotation method improves the factor pattern by aligning the factors to maximize the variance between them and to minimize the variance within each factor. This alignment aids in the interpretation of the factors by ensuring that each factor is as unique as possible and that the factors are clearly distinguishable from one another.

Table 5. Rotation matrix table.

The Component Matrix after Rotation a
	Ingredients
	1	2	3	4	5	6	7	8	9
Qcol-6		0.754
Qcol-7		0.801
Qcol-8		0.794
Qcol-9		0.797
Qcol-10		0.794
Qimf-11								0.768
Qimf-12								0.782
Qimf-13								0.764
Qimf-14								0.752
Qris-15							0.781
Qris-16							0.75
Qris-17							0.782
Qris-18							0.802
Qdec-20									0.794
Qdec-21									0.834
Qdec-22									0.796
Qpar-39	0.782
Qpar-40	0.78
Qpar-41	0.803
Qpar-42	0.775
Qpar-43	0.769
Qpar-44	0.796
Qfex-23					0.748
Qfex-24					0.767
Qfex-25					0.75
Qfex-26					0.762
Qada-27						0.725
Qada-28						0.706
Qada-29						0.738
Qada-30						0.757
Qrec-31			0.787
Qrec-32			0.76
Qrec-33			0.802
Qrec-34			0.791
Qcom-35				0.814
Qcom-36				0.834
Qcom-37				0.833
Qcom-38				0.815

presents the loading values of each factor following the rotation process. These loading values indicate the degree to which each observed variable is associated with each factor. High loading values suggest a strong relationship between the observed variable and the corresponding factor, while low loading values indicate a weaker association. By examining the loading values, researchers can assess the relevance and significance of each factor in relation to the observed variables and make informed decisions regarding their interpretation and application in subsequent analyses.

The load values for each factor are above 0.7, and they are clearly different from each other. This means that these factors have a strong impact on the variables we’re studying, and there’s a strong connection between them. These high load values tell us that the relationship between each factor and the variables we’re interested in is consistent and dependable. The factors were named as follows: Collaboration X, Information Sharing X1, Risk Management X2, Collaborative Decision X3, Flexibility Y1, Adaptability Y2, Resilience Y3, Resilience Y4, and Partnership M.

Next, we used correlation analysis to look at how two or more variables are related to each other. This helps us find out if there’s a straight-line connection between the variables and how strong and in what direction this connection is.

Table 6. Correlation analysis (mean (M) and standard deviation (SD)).

Variable	M	SD
X	0.136	0.683	—
X1	0.129	0.733	0.180	—
X2	0.962	0.731	0.242	−0.064	—
X3	0.063	0.772	0.139	−0.031	−0.014	—
Y1	0.982	0.759	0.300	0.270	0.222	0.314	—
Y2	0.833	0.812	0.303	0.297	0.244	0.246	0.628	—
Y3	0.176	0.869	0.310	0.287	0.264	0.273	0.667	0.654	—
Y4	0.919	0.795	0.273	0.256	0.198	0.203	0.626	0.612	0.607	—
M	0.879	0.832	0.386	0.250	0.214	0.274	0.344	0.329	0.418	0.288

shows the relationships between these elements and uses the Pearson correlation coefficient to show us these relationships.

Through correlation analysis, we examined the relationships between the variables Y1, Y2, Y3, Y4, M, X, X1, X2, and X3. Pearson’s correlation coefficient was used to quantify these relationships, providing a numerical measure of the strength and direction of the linear association between each pair of variables.

The table indicates that there is a positive correlation between these variables. This means that as one variable increases, the others tend to increase as well. For instance, Y1 is positively correlated with Y2, Y3, Y4, and M, suggesting that an increase in Y1 is likely to be accompanied by an increase in Y2, Y3, Y4, and M. Similarly, X2 is positively correlated with Y1, Y2, Y3, and Y4, indicating that an increase in X2 is associated with an increase in these other variables.

The correlations are expressed as numerical values, with higher absolute values indicating stronger correlations. For example, the correlation between X2 and Y3 is 0.287, indicating a moderate positive correlation, while the correlation between X2 and Y4 is 0.203, suggesting a weaker positive correlation.

In summary, the positive correlations observed in the table indicate that there is a tendency for these variables to move together in a positive direction, with increases in one variable often accompanied by increases in the others. This understanding of the relationships between these variables can help inform further analysis and interpretation of the data.

Given the observed positive correlations, it is suitable to proceed with an SEM analysis to further investigate the relationships between these variables. SEM allows for the estimation of relationships between latent and observed variables, which can help in testing the hypothesized relationships and providing a more comprehensive understanding of the complex interactions within the model. By employing SEM, we can assess the direct and indirect effects of the variables, as well as the overall fit of the model to the data, thereby enhancing the robustness of our findings.

4. Results

4.1. Hypothesis Testing

Having conducted a comprehensive analysis of the reliability and validity of the data, including the KMO (Kaiser-Meyer-Olkin) test and Bartlett’s test of sphericity to assess the suitability for factor analysis and subsequent correlation analysis to examine the relationships between the variables, this study proceeds to test the SEM using AMOS 28 statistical software. The choice of AMOS 28 is based on its ability to provide a better fit to the model, ensuring the reliability and accuracy of the subsequent analysis.

Table 7. Table of path coefficients.

Path			Hypothesis	Estimate	S.E.	C.R.	p	Hypothesis Testing
Resilience	<---	Collaboration	H1	0.427	0.05	5.644	***	Set up

Note: *** denotes significance at p < 0.001, indicating a highly reliable result unlikely to be random.

presents the path coefficients from the SEM analysis. The table includes hypotheses, estimates, standard errors, confidence intervals (C.I.), and p-values for each path.

From Table 7 and Figure 4 (which is based on the output from Amos 28 software and was subsequently hand-drawn by the author), the standardized coefficient for the path from SCC to SCR is 0.427. This coefficient suggests a positive relationship between collaboration (as measured by SCC) and SCR. Consequently, it can be inferred that Hypothesis 1 (H1) is supported by the data. This finding indicates that enhancing collaboration within the SC can lead to an improvement in the level of SCR. Consequently, a more robust and resilient SC can be developed to effectively manage uncertainty and mitigate potential risks.

From

Table 8. Table of path coefficients.

Path			Hypothesis	Estimate	S.E.	C.R.	p	Hypothesis Testing
Resilience	<---	Information Sharing; Risk Management; Collaborative decision-making	H2	0.350	0.02	5.687	***	Set up

Note: *** denotes significance at p < 0.001, indicating a highly reliable result unlikely to be random.

and Figure 5 (which is based on the output from Amos 28 software and was subsequently hand-drawn by the author), the standardized coefficient for the path from information sharing, risk management, and collaborative decision-making to SCR is 0.350. This coefficient indicates a significant positive effect of these collaborative aspects on SCR. It suggests that when organizations effectively implement and apply information sharing, risk management, and collaborative decision-making, SCR is better equipped to respond to risks and changes, ensuring continued operations and business success. Consequently, Hypothesis 2 (H2) is supported by the data.

In summary, the findings from

Table 9. Table of path coefficients.

Path			Hypothesis	Estimate	S.E.	C.R.	p	Hypothesis Testing
Resilience	<---	Information Sharing	H2a	0.458	0.062	7.310	***	Set up
Resilience	<---	Risk management	H2b	0.385	0.054	6.577	***	Set up
Resilience	<---	Collaborative Decision-making	H2c	0.394	0.056	6.584	***	Set up

Note: *** denotes significance at p < 0.001, indicating a highly reliable result unlikely to be random.

demonstrate that the collaborative aspects of information sharing, risk management, and collaborative decision-making have a significant and positive impact on SCR, supporting the notion that these practices enhance the ability of the SC to adapt and thrive in the face of challenges.

From Table 9 and Figure 6 (which is based on the output from Amos 28 software and was subsequently hand-drawn by the author), the standardized coefficients for the paths from information sharing, risk management, and collaborative decision-making to SCR are 0.458, 0.385, and 0.394, respectively. These coefficients indicate that each of these factors has a significant positive impact on enhancing the resilience of the SC. Specifically, the high standardization coefficient for information sharing suggests that when organizations actively engage in information sharing, the SC is better equipped to adapt and respond to uncertainty and change. Similarly, the high standardization coefficients for risk management and collaborative decision-making indicate that effective risk management and collaborative decision-making can significantly enhance the SC’s ability to cope with risks and improve its overall resilience.

In conclusion, Table 9 supports the hypothesis that information sharing, risk management, and collaborative decision-making each contribute to the improvement of SCR. This finding underscores the importance of these collaborative practices in building a robust and adaptable SC that can effectively navigate and overcome challenges.

4.2. Mediation Model Analysis

From

Table 10. Table of path coefficients.

Path			Hypothesis	Estimate	S.E.	C.R.	p	Hypothesis Testing
Partnership	<---	Collaboration	H3	0.360	0.080	6.278	***	Set up
Resilience	<---	Partnership		0.767	0.049	13.182	***

Note: *** denotes significance at p < 0.001, indicating a highly reliable result unlikely to be random.

and Figure 7 (which is based on the output from Amos 28 software and was subsequently hand-drawn by the author), we observe that collaboration has a positive relationship with partnership, as evidenced by a standardized coefficient of 0.360 and a non-standardized coefficient of 0.505, with a p-value less than 0.001. This indicates that as the level of collaboration within an organization increases, the quality of partnerships also tends to improve. This finding underscores the significance of collaboration in fostering strong partnerships.

Furthermore, partnerships are positively related to SCR, as evidenced by a standardized coefficient of 0.767 and a non-standardized coefficient of 0.643, with a p-value less than 0.001. This suggests that strong partnerships can enhance the resilience of SCs. It implies that establishing close, trusting relationships with SC partners can improve the ability to cope with risks and changes, thereby enhancing the overall level of resilience.

In summary, the results from

Table 11. Intermediate effect test table.

Parameter	Estimate	Low Level	High Level	p
Mesomeric Effect	0.325	0.234	0.431	0.006
Direct Effect	0.219	0.139	0.313	0.007
Total Effect	0.544	0.427	0.667	0.006

indicate that collaboration is positively related to both partnerships and SCR and that partnerships are also positively related to SCR. These findings emphasize the importance of collaboration in building resilient SCs and the role of partnerships in strengthening the ability to respond to uncertainties and disruptions.

Table 11 presents the results of an intermediate effect test, which assesses the mediating role of partnerships between SCC and SCR. The table includes estimates for the mesomeric effect, direct effect, and total effect, along with their respective confidence intervals and p-values.

From Table 11, it is evident that partnerships act as a mediating variable between SCC and SCR. The mediating effect of partnerships is quantified at 0.325, indicating that 32.5% of the total impact of SCC on SCR is mediated through partnerships. This suggests that partnerships play a significant role in the relationship between SCC and SCR, with a portion of the impact being realized directly through these collaborative relationships.

Furthermore, the direct effect value of 0.219 is observed alongside a total effect value of 0.544, indicating that partnerships are not the sole mechanism by which SCC influences SCR. This implies the presence of additional factors influencing SCR, although partnerships remain a crucial component.

Through rigorous statistical analysis, it is confirmed that both the mediating and direct effects are statistically significant, with p-values of 0.006 and 0.007, respectively. This statistical robustness provides strong evidence to support our hypothesis H3, which posits the mediating role of partnerships in the relationship between SCC and SCR.

In conclusion, the results of Table 11 unequivocally demonstrate the validity of the mediating effect between SCC and SCR. This finding underscores the pivotal role of partnerships in enhancing SCR, emphasizing the significance of fostering collaborative relationships, building trust, and sharing resources. Partnerships serve as a robust and resilient foundation for SCs to effectively respond to challenges and uncertainties, further bolstering their overall resilience.

5. Conclusions

Firstly, from a theoretical perspective, our findings offer significant insights into the sewage treatment equipment manufacturing industry, underscoring the pivotal role of SCC in enhancing SCR. Our study fills a critical research gap by demonstrating that robust collaboration is essential for constructing a resilient SC network capable of enduring environmental fluctuations and external shocks. The SEM analysis reveals that information sharing, collaborative decision-making, and risk management are key factors that synergistically contribute to SCR, providing a theoretical framework for future research in SC dynamics.

At the practical level, our SEM analysis illuminates the positive correlation between a high degree of collaboration and a firm’s capacity to respond to demand changes, unforeseen events, and policy shifts, culminating in superior performance. Our research underscores that building a resilient SC network—one that can withstand environmental changes and external disturbances—requires actionable strategies and strong collaborative efforts. The practical implications of our findings suggest that organizations should focus on the following:

Collaborative Decision-Making: The importance of collaborative decision-making in SCC is paramount, as it facilitates coordination and teamwork among various SC entities. The implementation of collaborative platforms and frameworks significantly reduces information delays and decision-making errors, enhancing the agility and adaptability of the SC.

Information Sharing: Information sharing is a critical component of SCC, with our SEM analysis confirming its positive impact on SCR. This finding advocates for the establishment of robust information-sharing mechanisms that enable stakeholders to recognize risks, predict market trends, and execute countermeasures effectively.

Risk Management: Effective risk management, encompassing identification, assessment, and mitigation, constitutes a crucial element of SCC. Our research substantiates the perspective that collaborative methodologies are efficacious in identifying risk factors and formulating coping strategies. The collective endeavors of SC participants in dealing with risks have a positive contribution to the SCR. Risk management tactics, including preventive measures, as well as rapid response and recovery plans, are indispensable for maintaining SC stability.

To enhance SC performance, it is recommended that companies regularly assess their collaboration practices. This can be done by utilizing questions such as ’We regularly communicate and provide feedback to our SC partners to improve collaboration’ and ‘We work with our SC partners to set long-term strategic goals. Such assessments can provide valuable insights into areas of improvement and help build stronger, more resilient partnerships.

Our findings in the Sewage Treatment Equipment Manufacturing sector reinforce the central role of SCC in bolstering SCR. The SEM analysis reveals that high levels of collaboration are positively associated with an enterprise’s ability to navigate demand fluctuations, unexpected events, and policy changes, leading to enhanced performance.

Although our study provides valuable insights into the advancement of SCC within the sewage treatment equipment industry, it is not without limitations. The industry-specific focus on FRP sewage treatment may restrict the generalizability of our findings. Additionally, the self-reported nature of the survey data could introduce bias. Future research should aim to replicate these findings across different industries and incorporate more objective measures of SCC and SCR. Furthermore, investigating the role of technology in facilitating SCC and the impact of cultural differences on collaborative practices can offer new insights and avenues for research.