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Article

Investigating the Role of Supply Chain Environmental Risk in Shaping the Nexus of Supply Chain Agility, Resilience, and Performance

by
Chia-Chun Hsieh
,
Shieh-Liang Chen
* and
Chun-Chen Huang
Department of Business Administration, Asia University, Taichung 413305, Taiwan
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(20), 15003; https://doi.org/10.3390/su152015003
Submission received: 5 September 2023 / Revised: 5 October 2023 / Accepted: 16 October 2023 / Published: 18 October 2023
(This article belongs to the Special Issue Advances in Supply Chain Resilience and Extended Issues of Operations Management)
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Abstract

:
Supply chain environmental risks are pivotal situational factors that significantly influence the intricate relationship between a business’s supply chain agility, supply chain resilience, and its ultimate supply chain performance. This study aims to explore the interplay between supply chain agility, supply chain resilience, and supply chain performance, while also investigating the moderating effect of supply chain environmental risks. Data analysis was conducted using hierarchical regression based on a questionnaire survey involving 416 companies in Taiwan’s manufacturing supply chain. The findings reveal several key insights. Firstly, supply chain agility has a positive influence on supply chain resilience, highlighting the importance of a flexible and responsive supply chain to handle challenges effectively. Secondly, supply chain resilience plays a vital role in determining supply chain performance, underscoring its significance in maintaining operational efficiency and effectiveness. Furthermore, the study identifies that supply chain environmental risks can act as a positive moderator in the relationship between supply chain agility and supply chain resilience. In other words, when faced with environmental risks, companies with higher supply chain agility can leverage this capability to reinforce their supply chain resilience, leading to improved supply chain performance. Additionally, the results shed light on the mediating role of supply chain resilience between supply chain agility and supply chain performance. This suggests that a resilient supply chain acts as an intermediary mechanism through which the positive effects of supply chain agility translate into enhanced overall performance. Given the uncertain and turbulent market environment today, these findings emphasize the importance of adopting supply chain agility and supply chain resilience as indispensable business strategies. Therefore, enterprise leaders and managers should proactively implement measures to enhance these aspects of their supply chain to effectively navigate and overcome environmental risks, ultimately driving supply chain performance.

1. Introduction

In today’s dynamic and interconnected global business landscape, supply chains play a pivotal role in driving the success and sustainability of enterprises. However, these intricate networks are vulnerable to various risks that can profoundly impact business operations and continuity. Christopher and Peck [1] categorize risks faced by enterprise supply chains into five types: supply, process, demand, control, and environmental risks. Environmental risks are defined in our article as supply environmental chain risks. It is different from other risk types in that the probability of this risk occurring is not high, but it has a great impact on business performance. Among these, supply chain environmental risks emerge as significant concerns, leading to troublesome supply chain disruptions [2]. These supply chain environmental risks encompass a range of factors, including climate change, political turmoil, and epidemics, which have been occurring with greater frequency and intensity in recent years [3,4,5,6].
One such significant event that exemplifies the impact of supply chain environmental risks is the global outbreak of the novel coronavirus (COVID-19) in early 2020, which resulted in widespread supply chain interruptions, affecting over 94% of the top 1000 U.S. companies [7]. This outbreak brought to light the increasing complexity and unpredictability of uncertainties in supply chain management [8]. The reverberating effects of such events across the global economy highlight the need for businesses to understand and effectively manage these supply chain environmental risks to thrive in a rapidly changing and unpredictable business environment.
The ability of enterprises to maintain supply chain resilience in the aftermath of disruptions becomes a critical determinant of their competitive advantage and overall performance [9]. Supply chain disruptions can lead to challenges in maintaining continuity, disrupting production schedules, and causing sudden surges in demand. Enterprises that exhibit robust supply chain resilience demonstrate a capacity to anticipate and effectively respond to these disruptions, minimizing their adverse effects. By doing so, resilient businesses are better positioned to mitigate financial losses, maintain customer satisfaction, and sustain market share during challenging times.
For instance, consider a global manufacturing company operating in the automobile industry. During a severe natural disaster that affected a major supplier in their supply chain, several critical components became unavailable, disrupting the company’s production schedules. In such a scenario, an enterprise with a resilient supply chain would have developed alternative sourcing strategies, identified backup suppliers, and maintained higher levels of safety stock. These proactive measures would enable the company to quickly adapt to the disruption, continue production, and fulfill customer orders without significant delays. As a result, the resilient company would maintain a competitive edge over its competitors, safeguarding its reputation and customer loyalty [10].
Furthermore, supply chain resilience can have a cascading effect on the entire supply chain ecosystem. When a business demonstrates resilience, it instills confidence in its suppliers and customers alike. Suppliers are more likely to view resilient enterprises as dependable partners, leading to stronger supplier relationships and preferential treatment in securing critical resources. Similarly, customers are likely to trust and maintain loyalty towards resilient companies, leading to increased customer retention and a positive brand reputation. Such positive impacts on supply chain relationships and customer perception contribute to enhanced business performance and long-term sustainability [11].
One of the key antecedents that significantly affect the resilience of an enterprise’s supply chain is supply chain agility. Supply chain agility refers to the ability to quickly address issues in response to various risks and disturbances. For example, even in the face of a supply chain interruption due to a natural disaster, a company with an agile supply chain would have an effective strategy to deal with the interruption. In a globalized environment characterized by constant changes, intense competition, unprecedented levels of outsourcing, and a growing need for customized products and services, companies are finding it increasingly difficult to improve their performance and gain a competitive advantage. In such a situation, supply chain agility is an important capability that can help a business outperform others [12]. Characterized by its adaptability and responsiveness, supply chain agility plays a vital role in mitigating the effects of disruptions caused by environmental risks. Past supply chain strategies have included the lean supply chain strategy and the agile supply chain strategy. The latter, particularly suited for companies operating in changing and volatile environments, has garnered increasing attention in recent times.
In this paper, we aim to explore the impact of supply chain agility on supply chain resilience and its subsequent influence on business performance, particularly when faced with supply chain environmental risks. By synthesizing insights from previous articles, we aim to provide valuable and actionable strategies for businesses seeking to foster supply chain resilience and agility in the face of environmental uncertainties. Based on the above discussion, the following research questions are proposed:
RQ1: How does supply chain agility impact supply chain resilience and supply chain performance, with a focus on complex and unpredictable supply chain environmental risks?
RQ2: Does supply chain resilience act as a mediator between supply chain agility and supply chain performance?
RQ3: Do supply chain environmental risks moderate the effect of supply chain agility on supply chain resilience, and, consequently, supply chain performance?
The theoretical framework for this research draws from the contingency theory, which allows us to comprehensively analyze the interactions between supply chain agility, resilience, and business performance under the influence of supply chain environmental risks.
Through a combination of theoretical constructs and empirical evidence, this research contributes to a comprehensive understanding of how supply chain environmental risks shape the nexus of supply chain agility, resilience, and performance. We aim to explore the impact of supply chain agility on performance while considering the moderating effect of supply chain environmental risks. Additionally, we seek to assess the role of supply chain resilience as a mediator in the relationship between supply chain agility and performance, particularly when confronted with supply chain environmental risks.
Three main contributions of this paper become evident through this lens. Firstly, this research stands as the first to delve into supply chain environmental risks as a moderator influencing the relationship between supply chain agility and supply chain resilience. Secondly, it fills a critical gap in the literature by discussing the impact of supply chain risks on both supply chain resilience and overall supply chain performance, providing valuable insights for enterprises facing such challenges. Lastly, this study highlights the role of supply chain resilience as a mediator that enables supply chain agility to drive superior business performance in the face of supply chain environmental risks.
The remainder of this paper is organized as follows. We first review the literature on supply chain risk management, supply chain resilience, supply chain agility, and supply chain environmental risks. We then provide our theoretical framework that relates supply chain agility and a firm’s resilience to supply chain environmental risk, and we develop our hypotheses. Subsequently, we describe our research methodology, the sample, and the estimation procedure to examine our research questions. Finally, we discuss our results, address the relevance of our findings to the theory, and discuss managerial implications and conclusions.

2. Review of the Literature and Hypothesis Development

2.1. Supply Chain Risk Management (SCRM)

Supply chain risk management is a strategic discipline that involves identifying, assessing, and mitigating potential disruptions that could adversely impact the flow of goods, services, information, or finances within a supply chain network [1]. Effective risk management strategies encompass both proactive measures, such as risk identification and assessment, and reactive measures, including contingency planning and risk mitigation techniques. Firms that are adept at supply chain risk management can anticipate, respond to, and recover from disruptions more efficiently, thereby maintaining operational continuity and customer satisfaction [13]. The relationship between supply chain risk management and resilience is symbiotic. Resilience refers to the supply chain’s ability to absorb shocks, adapt to changes, and quickly recover from disruptions while minimizing impact [14]. Effective risk management strategies enhance resilience by reducing the probability and severity of disruptions. Concurrently, resilient supply chains possess attributes that bolster risk management, as their adaptable structures and contingency plans enable more effective responses to unforeseen events [10]. Supply chain agility entails the capacity to swiftly and efficiently adjust operations in response to changing market conditions, customer demands, or disruptions [15]. While risk management aims to minimize the occurrence of disruptions, agility focuses on minimizing the impact of disruptions that do occur. Effective risk management enhances supply chain agility by reducing the magnitude of potential disruptions, enabling organizations to better navigate unexpected changes and capitalize on emerging opportunities. Sustainability has emerged as a key consideration in supply chain management, encompassing environmental, social, and economic dimensions [16]. Supply chain risk management and sustainability are interconnected through their shared goal of long-term viability. By incorporating sustainable practices, such as responsible sourcing and reduced waste, risk management strategies can mitigate vulnerabilities arising from regulatory changes, resource scarcity, and reputational risks, thus, fostering a more sustainable supply chain [17].
In the past, the focus was primarily on combining lean production and agile responses to form a lean synthesis [18]. However, since 2005, risk management has gained popularity, aiming to mitigate uncertainties caused by man-made disasters and emphasizing supply chain resilience. Furthermore, environmental awareness and corporate social responsibility have led to an upgraded focus on sustainable development within the supply chain.
Supply chain risk management involves identifying potential sources of risk and implementing strategies to reduce vulnerability through coordinated efforts among supply chain members [19]. The growing emphasis on risk management is a response to the uncertainty and unexpected challenges faced in managing sustainable supply chains. Organizations that solely focus on supply chain results and effectiveness while neglecting risks jeopardize their sustainability and face business instability [20,21]. Supply chain disruptions can lead to significant economic losses, damage corporate reputation, and impact safety and health [19]. It is evident that prioritizing risk management is crucial for the smooth functioning and performance of the supply chain. Several companies have incurred substantial daily losses, reaching up to USD 5.01 billion, underscoring the critical consequences of ineffective supply chain risk management [22].

2.2. Supply Chain Resilience (SCR)

In today’s competitive landscape, companies are increasingly focusing on building supply chain resilience (SCR) to minimize the negative impacts of disruptions. Supply chain resilience represents a supply network’s capability to anticipate, adapt to, and recover from disruptions while maintaining essential functions and performance [14]. It involves various dimensions, including redundancy, flexibility, risk management, collaboration, and robustness. Organizations with resilient supply chains can minimize the impact of disruptions, thereby enhancing their overall survivability. SCR is regarded as a dynamic capability that empowers firms to perform effectively during crises and in unpredictable environments [23,24]. A firm’s capacity for innovation plays a vital role in adapting to environmental changes and devising effective strategies for emerging challenges. Research indicates that innovative firms demonstrate greater resilience [25], excel in delivering customer satisfaction, efficiently address environmental uncertainties [26], and are better equipped to handle demand fluctuations. Emphasizing the development of SCR and fostering innovation can position companies to thrive amidst disruptions while ensuring their long-term success. The relationship between supply chain resilience and performance is nuanced. Resilient supply chains often exhibit enhanced operational efficiency, reduced lead times, improved customer service, and better risk management, all of which contribute to improved supply chain performance [10]. The ability to quickly recover from disruptions reduces downtime, minimizing financial losses and preserving customer satisfaction. The intersection of supply chain resilience and sustainability underscores the long-term orientation of both concepts. Resilient supply chains integrate sustainable practices such as responsible sourcing, reduced waste, and energy-efficient transportation [17]. By minimizing environmental impact and ensuring resource efficiency, resilient supply chains contribute to the organization’s sustainability goals.

2.3. Supply Chain Agility (SCAG)

Resource-based theory, as proposed by Wernerfelt [27] and expanded upon by Barney [28], provides a robust theoretical framework for understanding the relationship between supply chain agility and resilience. RBT asserts that a firm’s competitive advantage stems from its unique and valuable resources. In the context of supply chain management, these resources include tangible assets, human capital, and organizational capabilities. One way to conceptualize supply chain agility is as a valuable resource within the RBT framework. Firms that cultivate agility as a core competency can leverage it to respond quickly to disruptions and changing market conditions. As such, supply chain agility can be considered a strategic resource that enhances a firm’s competitive advantage. Naylor and Naim [29] emphasize that competent supply chain systems should strike a balance between fast configuration and stability. On the other hand, van Hoek [30] stresses the criticality of agility in ensuring the success of supply chain operations. According to Lee’s definition, agility refers to the capacity to swiftly respond to shifts in demand, supply, and external disruptions. This involves enhancing information exchanges with customers, establishing cooperative relationships with suppliers, and maintaining reliable logistics or partnerships [18]. Incorporating agility within the supply chain entails several key aspects, including the ability of supply chain elements to collaborate effectively and respond promptly to customer changes [31]. It also involves maintaining flexibility in the face of potential fluctuations in customer demand and promptly addressing short-term problems and disruptions [32]. While agile supply chains can effectively reduce overall supply chain costs, it is essential to exercise caution in both stable and competitive environments. As Lee suggests, companies with efficient supply chains cannot remain competitive in dynamic environments unless their supply chains possess the traits of agility, adaptability, and collaboration [18]. Sreedevi and Saranga [33] further explore the moderating role of supply chain flexibility in mitigating risk and uncertainty arising from environmental factors, thereby influencing the development of supply chain strategies. A company’s ability to respond swiftly to external changes enhances its resilience and capacity to recover from short-term setbacks [34]. The agility of a company in adapting and responding quickly plays a crucial role in determining supply chain resilience, enabling rapid recovery and subsequent growth [35]. The integration of agility and sustainability aligns with the principles of responsible business practices. Agile supply chains can adapt to incorporate sustainable strategies, such as leaner production processes, reduced waste, and eco-friendly transportation [36]. These practices contribute to the organization’s sustainability goals by minimizing environmental impact and enhancing resource efficiency, all while maintaining competitive agility. Building on these insights, the study presents the following hypothesis:
H1. 
There exists a positive association between supply chain agility and supply chain resilience.

2.4. Supply Chain Environmental Risk (SCER)

Contingency theory posits that organizational effectiveness depends on aligning internal structures and processes with the external environment. The contingency-based view (CBV) extends this concept by emphasizing the need to tailor strategies and practices to specific contingencies, such as environmental factors. In the context of supply chain management, this theory offers valuable insights into how organizations should adapt to environmental risks. Supply chain environmental risk encompasses the potential for adverse environmental impacts arising from supply chain operations, which can lead to disruptions, regulatory challenges, reputational damage, and increased costs. These risks are diverse and include factors such as natural resource scarcity, pollution, regulatory changes, and climate-related disruptions. The landscape of business operations has been characterized by an increasingly turbulent and complex supply chain environment. The escalating intricacy arises from global sourcing practices and the prevailing trend towards streamlining operations, which, in turn, has led to a significant rise in supply chain risks. Managing and mitigating these risks have become the primary challenges faced by businesses today, necessitating the development of more resilient supply chains [1]. A notable subset of these risks is associated with supply chain environmental factors, such as climate change, political turmoil, and infectious diseases, which have been occurring with greater frequency and intensity. These events have reverberated across the global economy, causing substantial negative impacts on supply chain vulnerability and disrupting business operations. Their research identifies four primary capabilities for developing resilience: supply chain engineering; collaboration; agility; and risk awareness. Some researchers have explored the interplay between supply chain strategy and the external environment [37]. Additionally, several studies have demonstrated that environmental uncertainty significantly influences the development of supply chain strategies [38,39]. Events that disrupt the supply chain and affect its normal operation, such as natural disasters, human factors, system failures, etc., will have a negative impact on the cost, service, quality, and reputation of the supply chain. Using more data and technology can enhance the competitiveness of the supply chain [2]. Supply chain environmental risk plays a crucial role in moderating the relationships between key supply chain factors. It interacts with supply chain agility, resilience, and performance, shaping how these dimensions influence one another. The degree of an organization’s exposure to environmental risk can impact its agility, as firms need to adapt to changing environmental regulations and resource availability. Similarly, the ability to manage environmental risk can influence the resilience of a supply chain by determining its capacity to bounce back from disruptions and adapt to changing environmental conditions [40]. Based on these findings, the study presents the following hypothesis:
H2. 
Supply chain environmental risk positively moderates the relationship between supply chain agility and supply chain resilience.

2.5. Supply Chain Performance (SCP)

Dynamic capabilities theory, initially proposed by Teece, Pisano, and Shuen (1997) [41], focuses on a firm’s ability to integrate, build, and reconfigure internal and external competencies in response to rapidly changing environments. DCT is particularly relevant when examining how firms can adapt their supply chain strategies to enhance resilience and subsequently impact supply chain performance. Within the DCT framework, supply chain resilience can be viewed as a dynamic capability. Firms with strong dynamic capabilities can identify disruptions, rapidly reconfigure their supply chain processes, and leverage external relationships to adapt to changing circumstances. This adaptability enhances their ability to maintain or even improve supply chain performance under challenging conditions.
Given the significant impact of environmental uncertainties on enterprise performance [42], measuring performance has become widely accepted as a crucial indicator of success and corporate well-being for business owners, managers, and researchers. Superior performance is attained when a company establishes a sustainable competitive advantage by delivering high-quality products and commanding premium prices [43]. In this context, performance is defined as the extent to which customer needs are met and how well the company achieves availability and on-time delivery [44]. Building on the above findings, the study proposes the following hypotheses:
H3. 
There exists a positive association between supply chain resilience and supply chain performance.
H4. 
Supply chain environmental risks positively moderate the relationship between supply chain resilience and supply chain performance.
H5. 
Supply chain resilience positively mediates the relationship between supply chain agility and supply chain performance.
To examine the relationships between supply chain agility, supply chain resilience, and supply chain performance, the theoretical model presented in Figure 1 is utilized.

3. Research Methodology

3.1. Data Collection

The data collection and validation methods employed SPSS 25 statistical software tool, and the hypotheses (H1 to H5) were derived from existing theories on supply chain risk management. A web-based database platform was utilized to distribute the questionnaire to 416 companies in Taiwan, aiming to understand the level of supply chain resilience concerning supply chain environmental risks in the context of Taiwan.
The questionnaire was designed based on a review of the literature, and the survey respondents consisted of individuals connected to the supply chain, including junior, middle, and senior managers as the research participants. The questionnaire was pre-tested and refined based on feedback received. Non-response bias was also checked, and common method variation (CMV) was assessed to identify any significant differences among the respondents.

3.2. Dependent, Independent, and Control Variables

Supply chain resilience (SCR) serves as a dependent variable in this study, wherein innovative companies exhibit better performance in terms of customer satisfaction, navigate environmental uncertainties more efficiently [26], and cope with demand uncertainties, indicating higher resilience [25]. Another dependent variable is supply chain performance (SCP), which refers to the primary objective of the supply chain, focusing on the overall ex ante performance of the supply chain and each link, particularly the operational status of the core business and operational relationships between the links.
Supply chain agility (SCAG) is identified as the independent variable, representing the enterprise’s ability to respond swiftly to market changes, manage a diverse range of products, and introduce new products to the market [45].
To ensure that survey results are not biased by specific company characteristics, the age of the company, the number of employees, and the industry to which the company belongs are used as control variables. Company age and the number of employees serve as indicators of company size, while the industry type is used as an indicator of technical strength.

4. Results

4.1. Descriptive Statistics of the Sample

The study analyzed a total of 416 valid samples for data analysis. The demographic characteristics of the respondents are presented in Table 1, while Table 2 provides information on the years of establishment, number of employees, and industry type of the companies.

4.2. Non-Response Bias and Common Method Variance (CMV)

Data collection in this study involved surveys administered to 416 companies in Taiwan’s manufacturing chain. To ensure the responses were unbiased, the researchers referred to the study conducted by Malhotra and Grover [46] to determine the distribution of the year of establishment, number of employees, and industry category among the respondents. The results of the chi-square test reveal no significant difference between the distributions of the respondents (p > 0.05), leading to the acceptance of the null hypothesis. This finding indicates that there is no substantial discrepancy between the characteristics of the respondents who participated in the survey and those who did not respond.
Furthermore, the study compared the responses obtained during both the pre- and post-recovery periods, and no significant difference was observed between the two sets of responses. These results, presented in Table 3, affirm that the survey data were not influenced by non-response bias and can be considered reliable for subsequent analysis.
Common method variance (CMV) refers to the spurious correlation that can arise between variables due to using the same measurement method. CMV can lead to erroneous conclusions about relationships between variables, either inflating or deflating findings. To mitigate CMV, the questionnaire used in this study underwent pre-processing to minimize respondent bias. Only respondents holding management roles were included in the survey to reduce common method differences and encourage honest responses. Harman’s one-factor test method was utilized in the questionnaire to detect CMV and conduct exploratory factor analysis (EFA) [47]. The number of factors before rotation and the amount of explained variation are considered to assess the degree of CMV. The EFA results show more than one factor, and the explained variation of the first factor is 38.805%, which does not exceed 50% of the total variation, indicating that CMV is not significant in this study.

4.3. Reliability and Validity Analysis

The questionnaire used in this study has undergone modifications, drawing from past theories, and its content validity has been verified by academics and industry professionals, ensuring that the questionnaire items and grammar are appropriate.
To assess the reliability of the questionnaire, Cronbach’s α is employed as the judgment index. Hee [48] recommends a standard value of α above 0.7 for satisfactory reliability. As shown in Table 4, the Cronbach’s α values for each dimension are as follows: 0.910 for supply chain agility (SCAG), 0.912 for supply chain resilience (SCR), 0.862 for Supply chain environmental risks (SCER), and 0.937 for supply chain performance (SCP). All Cronbach’s α values exceed the recommended threshold of 0.7, indicating a strong degree of correlation between the observed scores and the actual scores. Given that the observed scores have a higher correlation with the actual scores than the recommended value, the questionnaire used in this study has demonstrated a satisfactory level of reliability.

Convergent Validity

Composite reliability (CR) measures the internal consistency of variables. As per Fornell and Larcker (1981) [49], a CR value above the recommended threshold of 0.6 is considered acceptable. The CR values listed in Table 4 are as follows: 0.902 for supply chain agility (SCAG), 0.911 for supply chain resilience (SCR), 0.878 for supply chain environmental risks (SCER), and 0.937 for supply chain performance (SCP), indicating CR values higher than 0.6.
Average variance extracted (AVE). According to Fornell and Larcker (1981), the average variance extracted should exceed 0.5 for satisfactory validity. In Table 4, the AVE values are as follows: 0.698 for SCAG, 0.720 for SCR, 0.594 for ER, and 0.789 for SCP, all of which surpass the threshold of 0.5, confirming acceptable AVE.
CR and AVE are used as judgment indicators, supporting the notion that the research dimensions exhibit acceptable convergent validity.
Discriminant validity. To ensure good discriminant validity, the highest correlation coefficient between each construct should be less than the square root of the corresponding AVE [49]. In this study, the correlation coefficients of each construct, presented in Table 4, range between 0.771 and 0.888. These values are higher than the correlation coefficients between the dimensions, indicating good discriminant validity among the studied dimensions.

4.4. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA)

Exploratory factor analysis was conducted using maximum likelihood estimation (MLE). The validity and reliability of the items were evaluated through Bartlett’s test and Kaiser–Meyer–Olkin test, which yielded satisfactory results (KMO = 0.887, χ2 = 3498.6416, df = 78). To retain the most significant factors, the Kaiser–Guttman criterion of normalized maximum variation was applied. Since the cumulative sum of squared loadings explained 71.006% of the variance, surpassing the standard value of 50%, the presence of random errors affecting the correlations is unlikely. As a result, all eigenvalue facets in this analysis were retained. The factor loading and rotation component matrices in this analysis are presented in Table 5.
Confirmatory factor analysis (CFA) was conducted using AMOS 24.0 software, retaining the entire model architecture. The results of the CFA, presented in Table 5, demonstrate good consistency, as all factor loadings for the aspects exceed 0.5 [50], thus, confirming the reliability of the facets. The results of the structural model, as shown in χ2 = 855.279; χ2/df = 1.670; df = 512; CFI = 0.978; GFI = 0.902; TLI = 0.972, fall within an acceptable range for the model evaluation.
Table 5. Construct items and factor loadings.
Table 5. Construct items and factor loadings.
Construct ItemsFactor Loadings
Supply chain environmental risks (Parast 2020) [25]Political instability, war, civil unrest, or other socio-political crises.0.571
Disease or epidemics (e.g., SARS, foot and mouth disease, Ebola, COVID-19).0.586
Natural disasters (e.g., earthquake, flooding, extreme climate, tsunami)0.695
Changes in the political environment due to the introduction of new laws, stipulations, etc.0.658
Administrative barriers for the setup or operation of supply chains (e.g., authorizations).0.72
Supply chain agility (Aslam et al. 2018) [51]Adapting services and/or products to new customer requirements quickly.0.768
Reacting to new market developments quickly.0.796
Reacting to significant increases and decreases in demand quickly.0.849
Adjusting product portfolio as per market requirements.0.822
Supply chain resilience
(Baz and Ruel 2021) [52]		We are able to cope with changes brought by the supply chain disruption.0.781
We are able to adapt to the supply chain disruption easily.0.827
We are able to provide a quick response to the supply chain disruption.0.822
We are able to maintain high situational awareness at all times.0.785
Supply chain performance
(Ambulkar, Blackhurst, and Grawe 2015) [53]		In the past three years, the company’s supply chain delivery capacity is better than its peers.0.891
In the past three years, the reliability of the company’s supply chain delivery is better than its peers.0.9
In the past three years, the company’s supply chain customer satisfaction is better than its peers.0.842
In the past three years, the company’s supply chain delivery speed is better than its peers.0.91

4.5. Hierarchical Regression Analysis

In this study, hierarchical regression analysis was employed to test the research hypotheses, and the results of the hypothesis test are presented in Table 6.
Model 1 examines the relationship between supply chain agility (SCAG) and supply chain resilience (SCR), and the results show a significant effect (β = 0.694, p < 0.001). This confirms hypothesis 1 (H1), which proposed a positive impact of SCAG on SCR.
Model 2 tests the moderated effect of supply chain environmental risks (SCER) on the relationship between SCAG and SCR, and the results also reveal a significant effect (β = 0.064, p < 0.10). Hypothesis 2 (H2) suggested that under the influence of SCER interference, SCAG would have a positive effect on SCR, and the findings support this proposed hypothesis. The results imply that firms with high supply chain agility can enhance their supply chain resilience when facing supply chain environmental risks.
Model 3 investigates the effect of SCR on supply chain performance (SCP), and the result demonstrates a significant impact (β = 0.269, p < 0.001). Therefore, hypothesis 3 (H3), proposing a positive relationship between SCR and SCP, is validated.
Model 4 examines the moderated effect of supply chain environmental risks (SCER) on the relationship between SCR and SCP, but the result is not significant (β= 0.004, p > 0.10). As a result, hypothesis 4 (H4), suggesting that SCER interference leads to a positive effect of SCR on SCP, is not supported.
Model 5 investigates the direct effect of SCAG on SCP, and the result is not significant (β = 0.030, p > 0.10), aligning with hypothesis 5 (H5) regarding the mediating effect of SCR between SCAG and SCP.

4.6. Robustness Test

To validate the findings, AMOS software was utilized to construct the model, and structural equation modeling (SEM) was employed to assess the consistency of the above regression model. The results are displayed in Figure 2.
H6. 
The positive effect of supply chain agility (SCAG) on supply chain resilience (SCR) is confirmed (β = 0.784 ***, p < 0.001);
H7. 
The positive impact of SCAG on SCR under the influence of supply chain environmental risks (SCER) interference in the multiplicative variable dimension is supported (β = 0.102 **, p < 0.01);
H8. 
The positive effect of SCR on supply chain performance (SCP) is confirmed (β = 0.245 **, p < 0.01);
H9. 
The positive impact of SCR on SCP under the moderation of SCER does not align with the proposed hypothesis (β = 0.003, p > 0.1);
H10. 
SCAG has no significant effect on SCP (β = −0.071, p > 0.1), which is consistent with the hypothesized fully mediating effect between SCAG and SCP.
By employing empirical analysis through the structural equation method, we have ascertained that the outcomes align consistently with those derived from the hierarchical regression method discussed in the preceding section. The above findings suggest that supply chain resilience plays a mediating role between supply chain agility and supply chain performance, while supply chain environmental risk is an important moderating variable between supply chain agility and supply chain resilience.

4.7. Discussion

In recent times, businesses have been strategically adapting their production, manufacturing, and supply chain capacities to effectively navigate the dynamic market landscape. The significance of supply chain agility in facilitating rapid responses to unforeseen shifts, fostering improved communication with customers, cultivating collaborative partnerships with suppliers and dependable associates, and swiftly recalibrating strategies cannot be overstated. Particularly in the face of volatile market conditions, the intricate interplay among supply chain agility, resilience, and performance gains paramount importance, with the added dimension of supply chain environmental risks exerting influence [54].
This research endeavors to delve into the intricate dynamics of how supply chain environmental risks impact the trio of supply chain agility, resilience, and performance, all while examining how organizations strategically recalibrate in response to unpredictable market dynamics. The intricate link between supply chain agility, which reflects an organization’s adeptness at adapting to external shifts, and its resilience has been substantiated in the literature [31]. The capacity of an organization to swiftly respond to alterations defines its supply chain agility, affording it the ability to rapidly recover and attain swift progress. The outcomes of this study corroborate the positive influence of supply chain agility on the resilience of manufacturing firms [32]. Further reinforcing this relationship, regression analysis underscores that supply chain agility indeed has a notable and constructive impact on supply chain resilience, which subsequently interfaces significantly with supply chain performance. Moreover, the encroachment of supply chain environmental risks upon the alignment between supply chain strategy and external circumstances has been well-documented [34]. This study not only substantiates Christopher’s advocacy for agile supply chains in inherently uncertain settings [35], but also echoes Sun’s findings on the interconnectedness of supply chain strategy and environmental unpredictability [36]. As such, enterprises must adeptly equip their supply chains to mitigate the ramifications of supply chain environmental risks, a challenge further underscored by the ongoing COVID-19 pandemic and global geopolitical tensions [25].
Intriguingly, the regression analysis goes on to unveil that supply chain environmental risks wield a noteworthy and affirmative influence on both supply chain agility and resilience. When these risks intersect with supply chain agility, the intermediary role of supply chain resilience in shaping the nexus between agility and performance comes to light. Alongside fortifying the management of internal risk factors, the formulation of strategies to effectively counterbalance unpredictable external supply chain environmental risks emerges as a compelling imperative [35]. Looking ahead, businesses must prioritize the reinforcement of supply chain resilience, recognizing its vulnerability to the impacts of supply chain environmental risks, the neglect of which could precipitate substantial organizational losses.

5. Conclusions

The main objective of this study is to explore the impact of supply chain agility and supply chain resilience on supply chain performance when firms are confronted with supply chain risks. Drawing upon the literature of supply chain theory, this paper presents five hypotheses. Through empirical data analysis, utilizing hierarchical regression analysis and structural equation modeling, the research results endorse the assertions in this paper, with the exception of one hypothesis (H4). The study reveals that a critical antecedent of supply chain resilience is supply chain agility. In other words, as businesses become more agile in dynamic environments, their resilience also strengthens. Furthermore, the study finds that in the face of significant supply chain environmental risks, both supply chain resilience and supply chain agility significantly impact supply chain performance for businesses.
This study constitutes a pivotal juncture of theoretical and practical significance, yielding multifaceted contributions. The discerned outcomes forge substantive connections among supply chain environmental risks, supply chain agility, supply chain resilience, and the ultimate yardstick of supply chain performance. Such findings furnish invaluable discernment into the avenues wherein companies can amplify their supply chain agility and resilience, thus, orchestrating resilient supply chain risk management strategies to adroitly counterbalance the repercussions of supply chain environmental risks.
In the present landscape of cut-throat competition and unwavering uncertainty underpinning supply chain performance, the attainment of optimal supply chain performance has transmuted into an indispensably sought-after enterprise capability. The specter of climate change and the imperative of carbon neutrality, wielding a palpable sway, interweave with the tapestry of supply chain agility and resilience for businesses. The tenor of the study’s outcomes underscores the pivotal role of supply chain agility in diverse facets of a firm’s performance, further spotlighting the affirmative influence of environmental risks on both supply chain agility and resilience. Within the milieu of heightened supply chain environmental risks, the tandem of supply chain agility and resilience galvanizes companies to seamlessly interlace market orientation with supply chain orientation, culminating in a notable uptick in supply chain performance. With the escalating requisition for heightened supply chain agility and resilience to usher in market and supply chain undertakings, this study artfully amalgamates insights from preceding research with fresh revelations.
In this intricate interplay, supply chain environmental risks emerge as pivotal contextual catalysts propelling the dynamism of supply chain agility, cascading their influence through supply chain resilience and culminating in the mosaic of supply chain performance. Thus, it behooves managers to ascend the prioritization ladder, championing the implementation of supply chain agility and resilience as quintessential strategies and indispensable competencies for enterprises. As the omnipresence of supply chain environmental risks presents an impending crucible for corporate entities, forthcoming research avenues beckon, beckoning exploration into the intricate nexus whereby climate change, a harbinger of environmental risk, exercises potent impact upon supply chain agility and supply chain resilience, with a particular lens on the canvas of carbon neutrality. This trajectory of inquiry holds the promise of unearthing further profundities, navigating the labyrinth of sustainable supply chain management with enhanced sagacity.

Author Contributions

Conceptualization, C.-C.H. (Chia-Chun Hsieh); Methodology, S.-L.C. (Shieh-Liang Chen) and C.-C.H. (Chun-Chen Huang); Validation, S.-L.C. and C.-C.H. (Chun-Chen Huang); Formal analysis, C.-C.H. (Chia-Chun Hsieh); Investigation, C.-C.H. (Chia-Chun Hsieh); Supervision, S.-L.C.; Project administration, C.-C.H. (Chun-Chen Huang). All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data that support the findings of this study are openly available in [datadryad.org] 22 September 2022 at https://datadryad.org/stash/share/T4n0joJjZ3WxdtQ6mbg-c-LWzpMtBlF2L0hZsTJIPbk.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Christopher, M.; Peck, H. Building the Resilient Supply Chain. Int. J. Logist. Manag. 2004, 15, 1–14. [Google Scholar] [CrossRef]
  2. Katsaliaki, K.; Galetsi, P.; Kumar, S. Supply chain disruptions and resilience: A major review and future research agenda. Ann. Oper. Res. 2022, 319, 965–1002. [Google Scholar] [CrossRef] [PubMed]
  3. Araz, O.M.; Choi, T.M.; Olson, D.L.; Salman, F.S. Data Analytics for Operational Risk Management. Decis. Sci. 2020, 51, 1316–1319. [Google Scholar] [CrossRef]
  4. Guha-Sapir, D.; Hoyois, P.; Below, R. Annual Disaster Statistical Review 2014: The numbers and trends. Rev.—Lit. Arts Am. 2015, 1–50. Available online: https://reliefweb.int/report/world/annual-disaster-statistical-review-2014-numbers-and-trends (accessed on 22 September 2022).
  5. Scholten, K.; Stevenson, M.; van Donk, D.P. Dealing with the Unpredictable: Supply Chain Resilience. Int. J. Oper. Prod. Manag. 2019, 40, 1–10. [Google Scholar] [CrossRef]
  6. Karmaker, C.L.; Aziz, R.A.; Palit, T.; Bari, A.B.M.M. Analyzing supply chain risk factors in the small and medium enterprises under fuzzy environment: Implications towards sustainability for emerging economies. Sustain. Technol. Entrep. 2023, 2, 100032. [Google Scholar] [CrossRef]
  7. Sherman, E. 94% of the Fortune 1000 Are Seeing Coronavirus Supply Chain Disruptions: Report; Fortune: New York, NY, USA, 2020. [Google Scholar]
  8. Gligor, D.; Gligor, N.; Holcomb, M.; Bozkurt, S. Distinguishing between the Concepts of Supply Chain Agility and Resilience: A Multidisciplinary Literature Review. Int. J. Logist. Manag. 2019, 30, 467–487. [Google Scholar] [CrossRef]
  9. Manuj, I.; Mentzer, J.T. Global supply chain risk management strategies. Int. J. Phy. Distrib. Logist. Manag. 2008, 38, 192–223. [Google Scholar] [CrossRef]
  10. Ponomarov, S.Y.; Holcomb, M.C. Understanding the concept of supply chain resilience. Int. J. Phy. Distrib. Logist. Manag. 2009, 20, 124–143. [Google Scholar] [CrossRef]
  11. Fawcett, S.E.; Wallin, C.; Allred, C.; Fawcett, A.M.; Magnan, G.M. Information technology as an enabler of supply chain collaboration: A dynamic capabilities perspective. J. Supply Chain Manag. 2011, 47, 38–59. [Google Scholar] [CrossRef]
  12. Eckstein, D.; Goellner, M.; Blome, C.; Henke, M. The Performance Impact of Supply Chain Agility and Supply Chain Adaptability: The Moderating Effect of Product Complexity. Int. J. Prod. Res. 2015, 53, 3028–3046. [Google Scholar] [CrossRef]
  13. Chopra, S.; Sodhi, M. Reducing the risk of supply chain disruptions. MIT Sloan Manag. Rev. 2014, 55, 72–80. [Google Scholar]
  14. Pettit, T.J.; Croxton, K.L.; Fiksel, J. Ensuring Supply Chain Resilience: Development of a Conceptual Framework. J. Bus. Logist. 2010, 31, 1–21. [Google Scholar] [CrossRef]
  15. Christopher, M.; Ryals, L. Supply Chain Strategy: Its Impact on Shareholder Value. Eletronic Libr. 2010, 34, 1–5. [Google Scholar] [CrossRef]
  16. Seuring, S.; Müller, M. From a literature review to a conceptual framework for sustainable supply chain management. J. Clean. Prod. 2008, 16, 1699–1710. [Google Scholar] [CrossRef]
  17. Carter, C.R.; Rogers, D.S. A framework of sustainable supply chain management: Moving toward new theory. Int. J. Phys. Distrib. Logist. Manag. 2008, 38, 360–387. [Google Scholar] [CrossRef]
  18. Lee, H.L. The Triple-A Supply Chain. Hepatobiliary Pancreat. Dis. Int. 2004, 2, 391–396. [Google Scholar]
  19. Jüttner, U.; Peck, H.; Christopher, M. Supply Chain Risk Management: Outlining an Agenda for Future Research. Int. J. Logist. Res. Appl. 2003, 6, 197–210. [Google Scholar] [CrossRef]
  20. Abdel-Basset, M.; Mohamed, R. A Novel Pathogenic TOPSIS- CRITIC Model for Sustainable Supply Chain Risk Management. J. Clean. Prod. 2020, 247, 119586. [Google Scholar] [CrossRef]
  21. Dong, Q.; Cooper, O. An Orders-of-Magnitude AHP Supply Chain Risk Assessment Framework. Int. J. Prod. Econ. 2016, 182, 144–156. [Google Scholar] [CrossRef]
  22. Thun, J.H.; Hoenig, D. An Empirical Analysis of Supply Chain Risk Management in the German Automotive Industry. Int. J. Prod. Econ. 2011, 131, 242–249. [Google Scholar] [CrossRef]
  23. Blome, C.; Schoenherr, T.; Rexhausen, D. Antecedents and Enablers of Supply Chain Agility and Its Effect on Performance: A Dynamic Capabilities Perspective. Int. J. Prod. Res. 2013, 51, 1295–1318. [Google Scholar] [CrossRef]
  24. Gunasekaran, A.; Papadopoulos, T.; Dubey, R.; Wamba, S.F.; Childe, S.J.; Hazen, B.; Akter, S. Big Data and Predictive Analytics for Supply Chain and Organizational Performance. J. Bus. Res. 2017, 70, 308–317. [Google Scholar] [CrossRef]
  25. Parast, M.M. The Impact of R&D Investment on Mitigating Supply Chain Disruptions: Empirical Evidence from U.S. Firms. Int. J. Prod. Econ. 2020, 227, 107671. [Google Scholar] [CrossRef]
  26. Stevens, E.; Dimitriadis, S. New Service Development through the Lens of Organisational Learning: Evidence from Longitudinal Case Studies. J. Bus. Res. 2004, 57, 1074–1084. [Google Scholar] [CrossRef]
  27. Wernerfelt, B. A resource-based view of the firm. Strateg. Manag. J. 1984, 5, 171–180. [Google Scholar] [CrossRef]
  28. Barney, J. Firm Resources and Sustained Competitive Advantage. J. Manag. 1991, 17, 99–120. [Google Scholar] [CrossRef]
  29. BenNaylor, J.; Naim, M.M.; Berry, D. Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain. Int. J. Prod. Econ. 1999, 62, 107–118. [Google Scholar] [CrossRef]
  30. van Hoek, R.I. Moving Forward with Agility—A Little Faster. Int. J. Phys. Distrib. Logist. Manag. 2006, 36. [Google Scholar] [CrossRef]
  31. Baker, P. The design and operation of distribution centres within agile supply chains. Int. J. Prod. Econ. 2008, 111, 27–41. [Google Scholar] [CrossRef]
  32. Tang, C.; Tomlin, B. The Power of Flexibility for Mitigating Supply Chain Risks. Int. J. Prod. Econ. 2008, 116, 12–27. [Google Scholar] [CrossRef]
  33. Sreedevi, R.; Saranga, H. Uncertainty and Supply Chain Risk: The Moderating Role of Supply Chain Flexibility in Risk Mitigation. Int. J. Prod. Econ. 2017, 193, 332–342. [Google Scholar] [CrossRef]
  34. Fayezi, S.; Zutshi, A.; O’Loughlin, A. How Australian Manufacturing Firms Perceive and Understand the Concepts of Agility and Flexibility in the Supply Chain. Eletronic Libr. 2018, 34, 1–5. [Google Scholar] [CrossRef]
  35. Tarigan, Z.J.H.; Siagian, H.; Jie, F. Impact of Internal Integration, Supply Chain Partnership, Supply Chain Agility, and Supply Chain Resilience on Sustainable Advantage. Sustainability 2021, 13, 5460. [Google Scholar] [CrossRef]
  36. Sodhi, C.; Tang, M. Researchers’ Perspectives on Supply Chain Risk Management. Prod. Oper. Manag. 2011, 21, 1–13. [Google Scholar] [CrossRef]
  37. Qi, Y.; Zahao, X.; Sheu, C. The impact of competitive strategy and supply chain strategy on business performance: The role of environmental uncertainty. Decis. Sci. 2011, 42, 371–389. [Google Scholar] [CrossRef]
  38. Christopher, M. The Agile Supply Chain. Ind. Mark. Manag. 2000, 29, 37–44. [Google Scholar] [CrossRef]
  39. Sun, S.Y.; Hsu, M.H.; Hwang, W.J. The Impact of Alignment between Supply Chain Strategy and Environmental Uncertainty on SCM Performance. Supply Chain Manag. Int. J. 2009, 14, 201–212. [Google Scholar] [CrossRef]
  40. Pagell, M.; Wu, Z. Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars. J. Supply Chain Manag. 2009, 45, 37–56. [Google Scholar] [CrossRef]
  41. Teece, D.J.; Pisano, G.; Shuen, A. Dynamic Capabilities and Strategic Management. Strateg. Manag. J. 1997, 18, 509–533. [Google Scholar] [CrossRef]
  42. Mukherji, A.; Mukherji, J. Environmental Uncertainty and Positive Performance of Small Firms: The Roles of Key Mediators. Acad. Manag. Proc. 2016, 2016, 10437. [Google Scholar] [CrossRef]
  43. Porter, M.E. Towards à Dynamic Theory of Strategy. Strateg. Manag. J. 1991, 12, 95–117. [Google Scholar]
  44. Tarafdar, M.; Qrunfleh, S. Agile Supply Chain Strategy and Supply Chain Performance: Complementary Roles of Supply Chain Practices and Information Systems Capability for Agility. Int. J. Prod. Res. 2017, 55, 925–938. [Google Scholar] [CrossRef]
  45. Al-Shboul, M.A. Infrastructure Framework and Manufacturing Supply Chain Agility: The Role of Delivery Dependability and Time to Market. Supply Chain Manag. Int. J. 2017, 22, 172–185. [Google Scholar] [CrossRef]
  46. Madhavan, R.; Grover, R. From Embedded Knowledge to Embodied Knowledge: New Product Development as Knowledge Management. J. Mark. 1998, 62, 1–12. [Google Scholar] [CrossRef]
  47. Podsakoff, P.M.; Organ, D.W. Self-Reports in Organizational Research: Problems and Prospects. J. Manag. 1986, 12, 531–544. [Google Scholar] [CrossRef]
  48. Hee, O.C. Validity and Reliability of the Customer-Oriented Behaviour Scale in the Health Tourism Hospitals in Malaysia. Int. J. Caring Sci. 2014, 7, 771–775. [Google Scholar]
  49. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Market. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  50. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis, 7th ed.; Pearson: New York, NY, USA, 2010. [Google Scholar]
  51. Aslam, B.; Wang, W.; Arshad, M.I.; Khurshid, M.; Muzammil, S.; Rasool, M.H.; Nisar, M.A.; Alvi, R.F.; Aslam, M.A.; Qamar, M.U.; et al. Antibiotic resistance: A rundown of a global crisis. Infect. Drug Resist. 2018, 1645–1658. [Google Scholar] [CrossRef]
  52. El Baz, J.; Ruel, S. Can supply chain risk management practices mitigate the disruption impacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era. Int. J. Prod. Econ. 2021, 233, 107972. [Google Scholar] [CrossRef]
  53. Ambulkar, S.; Blackhurst, J.; Grawe, S. Firm’s resilience to supply chain disruptions: Scale development and empirical examination. J. Oper. Manag. 2015, 33, 111–122. [Google Scholar] [CrossRef]
  54. Inman, R.A.; Green, K.W. Environmental Uncertainty and Supply Chain Performance: The Effect of Agility. J. Manuf. Technol. Manag. 2022, 33, 239–258. [Google Scholar] [CrossRef]
Sustainability 15 15003 g001
Figure 1. Theoretical model.
Figure 1. Theoretical model.
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Figure 2. Structural equation modeling.
Figure 2. Structural equation modeling.
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Table 1. Basic information of respondents (N = 416).
Table 1. Basic information of respondents (N = 416).
VariablesGroupFrequency%
Job positionEngineers17642.3
Production supervisors10124.3
Logistics supervisors7117.1
Supply chain managers4410.6
Company managers245.8
Table 2. Basic information of respondents’ companies (N = 416).
Table 2. Basic information of respondents’ companies (N = 416).
VariablesGroup%
Year of establishment3–5 years10.3
6–9 years14.4
9–12 years14.2
12 years or more61.1
Number of employees5–10 people13.7
10–20 people11.8
20–30 people10.8
30–40 people9.6
40–50 people9.4
50 people or more44.7
Industry typeFinancial and insurance services5.5
Electronic information15.1
Traditional manufacturing35.8
High-tech manufacturing20.2
Medical services4.8
Communication services4.1
Distribution and retail11.8
Tourism and travel2.6
Table 3. Non-response bias check.
Table 3. Non-response bias check.
Non-Response Bias check
χ2dfp Value
Year of establishment3.63230.304
Number of employees9.07850.106
Industry type2.05770.957
Note: The p-values for all chi-square tests were greater than 0.05, so the null hypothesis was accepted, indicating no significant difference between the responses in the pre-recovery and post-recovery periods.
Table 4. Composite reliability, average variance extracted, maximum shared variance, and average shared variance.
Table 4. Composite reliability, average variance extracted, maximum shared variance, and average shared variance.
MeanSDCRAVEMSVCronbach’s
α		SCRSCAGSCERSCP
SCR5.1701.0000.9110.7200.5730.9120.848 *
SCAG5.1901.0020.9020.6980.5730.9100.7570.835 *
SCER4.2111.2550.8780.5940.5520.8620.1350.1790.771 *
SCP4.4681.2750.9370.7890.5520.9370.2390.2330.7430.888 *
Mean value, standard deviations (SD), composite reliability (CR), average variance extracted (AVE), maximum shared variance (MSV), Cronbach’s (α), and correlation variables. * The diagonal value is the square root of the AVE of the latent variables, which should be greater than the off-diagonal value.
Table 6. Regression coefficients and prediction estimates.
Table 6. Regression coefficients and prediction estimates.
Independent VariableDependent Variable SCRDependent Variable SCP Dependent Variable SCP
Model 1Model 2Model 3Model 4Model 5
Constant1.263 ***2.659 ***3.629 ***1.220 ***1.966 *
Year of establishment−0.035−0.028−0.0430.0060.007
Number of employees0.0390.0390.010−0.007−0.006
Financial and insurance services0.3140.306−0.431−0.486−0.486
Electronic information0.2250.200−0.656−0.647−0.657
Traditional manufacturing0.3100.278−0.484−0.551−0.563
High-tech manufacturing0.2180.186−0.474−0.635−0.648
Medical0.2570.224−0.266−0.614−0.625
Communication services0.3450.323−0.213−0.775−0.780
Distribution and retailing0.1540.142−0.366−0.528−0.536
SCAG 0.694 ***0.714 ***
SCER −0.357
SCER*SCAG 0.064 *
SCR 0.269 ***0.163 ***
SCER 0.708 ***
SCER*SCR 0.004
SCAG 0.030
SCR 0.148 *
SCER 0.514 **
SCER*SCAG 0.035
R20.4760.4810.054 0.5100.525
Note: * p < 0.1; ** p < 0.05; *** p < 0.01.
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Hsieh, C.-C.; Chen, S.-L.; Huang, C.-C. Investigating the Role of Supply Chain Environmental Risk in Shaping the Nexus of Supply Chain Agility, Resilience, and Performance. Sustainability 2023, 15, 15003. https://doi.org/10.3390/su152015003

AMA Style

Hsieh C-C, Chen S-L, Huang C-C. Investigating the Role of Supply Chain Environmental Risk in Shaping the Nexus of Supply Chain Agility, Resilience, and Performance. Sustainability. 2023; 15(20):15003. https://doi.org/10.3390/su152015003

Chicago/Turabian Style

Hsieh, Chia-Chun, Shieh-Liang Chen, and Chun-Chen Huang. 2023. "Investigating the Role of Supply Chain Environmental Risk in Shaping the Nexus of Supply Chain Agility, Resilience, and Performance" Sustainability 15, no. 20: 15003. https://doi.org/10.3390/su152015003

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