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Article

An Empirical Study on the Mechanism of Dynamic Capacity Formation in the Supply Chain

by
Chun-Yan Zhu
and
Dong-Liang Zhang
*
School of Management, Xi’an University of Science and Technology, Xi’an 710054, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(22), 15044; https://doi.org/10.3390/su142215044
Submission received: 11 October 2022 / Revised: 9 November 2022 / Accepted: 11 November 2022 / Published: 15 November 2022
(This article belongs to the Special Issue Business Process Improvement for Sustainable Supply Chain)
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Abstract

:
Supply chain dynamic capability is becoming an indispensable core competency for organizations. However, it is still unknown how dynamic capability functions during actual use. Based on this reason, this study compiles research on supply chain dynamic capability, proposes the formation mechanism of supply chain dynamic capability, and validates it using data from Chinese companies. The results of structural equation modeling based on a sample of 162 Chinese companies show that environmental turbulence profoundly affects supply chain dynamic capabilities through direct and indirect effects. The dimensions of supply chain synergy have an important mediating effect on the dynamic capability of supply chains. Supply chain flexibility and agility, as the external manifestation of supply chain dynamic capability, are not only influenced by environmental turbulence and supply chain synergy, but also, the supply chain flexibility affects the supply chain agility. Based on these results, the formation mechanism of supply chain dynamic capability was further proposed.

1. Introduction

Economic globalization has created a more stable supply chain system in which companies focus on their core products, giving them an advantage in their industry segments [1]. However, the uneven development eventually affected the trend of economic globalization, and a series of crises led to the contraction of global value chains, and “localization” became the trend as regions began to expand regional partners, hoping to take the initiative in the supply chain [2]. Previously, stable supply chain systems have been challenged by changes in consumer demand, international political uncertainty, and global emergencies on the supply and demand side, which can lead to huge fluctuations in production and sales [3,4,5]. Technological turbulence brings even greater uncertainty, with technological blockades, technological changes, and even the choice of technological routes affecting the supply chain system in the long term [6]. In this context, enterprises and supply chains must cope with possible turbulence at any time, and innovation, supply chain integration, and supply chain risk management have successively become hot topics of research [7,8]. These studies have made significant progress, and in these studies, resources are very important factors, and the resources that firms and supply chains have and the resources they can use effectively are key to building core competencies. Managing resources in a turbulent environment is what supply chain dynamic capabilities focus on, and some scholars define supply chain dynamic capabilities as “the ability of an organization to intentionally create, expand, and modify its resource base” [9,10], a definition that emphasizes the utilization of resources. Therefore, in a turbulent environment, it is necessary to analyze firms and supply chains from the perspective of dynamic capabilities. Since various scholars have different definitions of supply chain dynamic capabilities and emphases, research on supply chain dynamic capabilities is scattered. Therefore, it is necessary to conduct a systematic study on the dynamic capability of supply chains.
The current research on supply chain dynamic capabilities focuses on its relationship with the supply chain performance. The knowledge reserve and learning ability [11], supply chain synergy [12], and supply chain flexibility and agility [13] are considered representative supply chain dynamic capabilities. However, there are few systematic studies on these aspects in the literature, and it is important to study the interplay between these factors and how they contribute to the formation of dynamic supply chain capabilities in turbulent environments in terms of theory and reality. Based on the above analysis, this study explores the following issues: (1) Definition and composition of supply chain dynamic capability. (2) How do supply chain dynamic capability factors interact with each other? (3) Formation mechanism of supply chain dynamic capability. This study summarizes the theories related to supply chain dynamic capability, uses survey data from Chinese enterprises to empirically study the relationship between the elements of supply chain dynamic capability, and finally, arrives at a preliminary conjecture of the formation mechanism of supply chain dynamic capability.
Based on the above analysis, this study attempts to achieve the following objectives: (1) construct the influence path of each element of dynamic supply chain capability, (2) to demonstrate the influence of relationships among the elements using a survey of Chinese firms, (3) investigate the effect of environmental turbulence on the performance of supply chain dynamic capabilities, and (4) to propose a preliminary conjecture regarding the formation mechanism of dynamic supply chain capabilities. In terms of theory, our research will systematically study the elements of supply chain dynamic capability and use survey data to derive the interrelationship among the elements and further propose the formation mechanism of the supply chain dynamic capability. In practice, our research will provide a reference for decision-making in supply chain systems and improve the resource utilization level of supply chain systems.
The next part of this article will be distributed according to the following. Section 2 introduces the theoretical basis of this study and presents the hypotheses in relation to existing studies to propose the conceptual model of this paper. Section 3 describes the methodology, the design of the questionnaire, and the data collection process of this study and analyzes the reliability and validity of the data and provides a preliminary validation of the correlations of the variables. Section 4 tests and updates the model using the data and explains the results. Section 5 presents the study of this paper with existing studies in the same field. Section 6 draws conclusions, analyses the shortcomings of the study, and points out directions for future research.

2. Theoretical Background and Assumptions

2.1. Supply Chain Dynamic Capabilities

The concept of dynamic capabilities originated from firms, and Teece (1994) first suggested that firms that have been able to maintain a competitive advantage in the global marketplace tend to have timely responsiveness and rapid product innovation, and dynamic capabilities create this competitive advantage [14]. Beske viewed the supply chain as a complex system and dynamic capabilities as a system of capabilities that can handle complex internal relationships in a turbulent environment and have the ability to change themselves [15]. This concept is seen as a further development of the resource-based view of the firm (RBV), which assumes that a firm consists of a set of resources and that its sustained competitive advantage is derived from a portion of its rare, valuable, and hard-to-imitate resources. Supply chain dynamic capabilities can be defined as “the ability of an organization to actively build, expand, and adjust its resource base” by further coordinating the resources possessed by the supply chain [16].
Scholars have made various claims regarding what constitutes the dynamic supply chain capability. Defee and Brian-S proposed a holistic framework of dynamic supply chain capability with knowledge acquisition and coevolution as the foundational capabilities [13]. More scholars consider the supply chain dynamic capability as a higher-level capability, while flexibility and agility are second-level capabilities derived from the supply chain dynamic capability [11]. Although scholars do not agree on this, there is a proliferation of literature on dynamic capabilities based on flexibility and agility. The knowledge base and organizational learning theory have also been the focus of continuous research on supply chain dynamic capabilities. In distinguishing the concept of dynamic capabilities from supply chain dynamic capabilities, Defee and Fugate pointed out that supply chain dynamic capabilities should be jointly formed by supply chain members. Therefore, supply chain dynamic capabilities must allow the entry of new knowledge and generate new capabilities based on the newly entered knowledge [11]. Beske added to Defee and Fugate’s research by arguing that supply chain dynamic capabilities should also have the ability to bring new partners within the supply chain’s knowledge system and reconceptualize them [17]. In addition, collaborative learning and collaborative organizational learning capabilities have been studied by scholars based on the knowledge and learning perspectives on supply chain dynamic capabilities [18]. Supply chain synergy is the third most important capability that supply chain dynamic capabilities focus on. Allred et al. argued that supply chain synergy is an inimitable, scarce, and valuable dynamic capability, and the process of synergy is the process by which the supply chain expands its dynamic capabilities [19]. Supply chain synergy has been recognized by many scholars as a component of supply chain dynamic capability [20] and has gradually developed into a comprehensive collaboration capability consisting of three dimensions: information sharing, incentive alliance, and synchronous decision-making.
Synthesizing the aforementioned studies on dynamic supply chain capabilities, this study considers the following factors when defining the concept of dynamic supply chain capabilities. (1) The context of dynamic supply chain capabilities is that of environmental turbulence. (2) The dynamic capability of the supply chain is manifested by its flexibility and agility of the supply chain. (3) Supply chain dynamic capability is the process of forming new capabilities in a continuous cycle of supply chain synergy. (4) Knowledgebase and organizational learning influence the dynamic capability of the supply chain. Therefore, this study defines supply chain dynamic capability as the process of the supply chain perceiving changes, updating and accumulating knowledge in a turbulent environment under the direction of supply chain synergy theory, continuously integrating and reconfiguring internal and external resources, and developing new capabilities through organizational learning. It is comprised of the following elements: environmental turbulence, supply chain flexibility and agility, and the community of information sharing, incentive alliances, and synchronized decision-making–supply chain synergy.

2.2. Environmental Turbulence

The concept of environmental turbulence is mainly used to study the unpredictable external environmental changes faced by firms [9], but it is rarely applied to supply chains. Unlike firms, supply chains face not only the unpredictability of the external environment but also serious uncertainty in internal relationships. The risks of supply chain disruption [21], uncertainty in demand [22], and uncertainty in delivery time [23] are among the turbulent environments faced by supply chains. As a complex adaptive system [24], the supply chain can be understood as a complex community that is constantly changing, and the complexity of internal operations and unpredictability of the external environment are objective uncertainty factors. Reviewing the studies of scholars on the turbulent environment faced by supply chains, it can be found that there are risks associated with supply and demand, trade, finance, and technology [25]. Under the requirement of sustainable supply chain development, the green supply chain contributes to supporting low carbon and other aspects but faces complex and hidden risks due to too many uncertainties [26]. Manufacturing industries that depend on high-end technologies and core components still face technological blockades [27].
In short, the supply chain is facing an uncertain and unpredictable dynamic environment, and the factors affecting its stable operation of the supply chain come from various aspects. The turbulent environment faced by the supply chain profoundly affects its operations in the supply chain. For enterprises and the supply chain, turbulence in the external environment is both an opportunity and a threat. In a review of the supply chain dynamic capability theory, environmental turbulence affects all aspects of the dynamic capabilities of the supply chain, as described below.
According to scholars’ definitions of different dimensions of the supply chain dynamic capabilities, we can define supply chain flexibility and agility as the manifestation of supply chain dynamic capabilities. The turbulent environment drives companies to innovate, and this innovation is not only for the development of new products but also on business processes, forms of organizational learning between supply chains, and knowledge exchanges [9]. Gomezel found that technological turbulence can seriously affect product development [28]. Through an empirical study of 553 Japanese development projects, Song et al. found that technological turbulence has a significant impact on the ability to sustain innovation, such as new product development [29]. A study in China found that technological turbulence significantly affects the positive relationship between technology integration and continuous innovation [30]. Rojo et al. emphasized supply chain flexibility as a response to customer needs in an uncertain environment [31]. Naylor et al. argued that supply chain flexibility enables organizations to be profitable in turbulent environments [32].
When examining the relationship between environmental turbulence and the performance of supply chain dynamic capabilities from the perspective of knowledge accumulation and organizational learning, it can be found that, in a turbulent environment, companies in the supply chain will strengthen the exchange of experience and knowledge, and this accumulation will continue to innovate and remain competitive in terms of knowledge stock, product power, service quality, and governance level; improve the flexibility and agility of the supply chain; and create benefits for the supply chain in terms of cost, efficiency, and innovation. It creates benefits for the supply chain in terms of cost, efficiency, and innovation [33]. In the new round of industrial change, technology and capital are no longer enough to build the core competitiveness of enterprises and supply chains, and only through learning can raw data such as information, experience, and communication be condensed into knowledge that can empower organizations, and the accumulation of knowledge can help organizations improve their performance and innovation [34]. Some scholars have suggested that dynamic capability, as an important ability to cope with turbulent environments, relies heavily on the continuous learning ability of organizations [35]. Empirical studies have also demonstrated that organizational learning has a significant positive impact on the performance of R&D alliances [36], and based on this, we propose the following hypotheses.
Hypothesis 1a (H1a).
Environmental turbulence has a significant positive relationship with supply chain flexibility.
Hypothesis 1b (H1b).
Environmental turbulence has a significant positive relationship with supply chain agility.
Environmental turbulence also has a huge impact on supply chain operations. We can see that information sharing plays an important role in turbulent environments. According to Fawcett, firms actively exchange information with firms in their supply chains in turbulent environments to avoid the adverse effects of turbulent environments. At the same time, this information exchange strengthens the relationship between supply chain firms [37]. The collaboration resulting from this information sharing can effectively provide competitive advantages to the supply chain, which further forms a more stable supply chain alliance [19]. Information sharing and joint learning among alliance members can simultaneously strengthen the supply chain alliance. In the process of studying organizational learning at the supply chain level, scholars have found that the complexity of the supply chain environment makes the organizational learning capability very demanding on the cognitive ability of the firms within the supply chain. For firms within the supply chain, the complexity within the firm and the complexity within the supply chain make the organization invest more effort into organizational learning [36]. In the knowledge economy, the information sources involved in the supply chain are extensive, and the inability to handle the massive amount of information makes the supply chain unable to gain greater benefits in organizational learning. At the same time, with the development of information technology, diverse information exchanges make supply chain knowledge scattered in various stages, which makes supply chain learning more difficult. This suggests that environmental volatility profoundly affects the exchange of information between supply chains while testing the stability of alliances between supply chain firms. Recent studies have shown that the integration and configuration capabilities of platforms have a significant positive impact on supply chain collaboration in cloud manufacturing environments, enabled by digital technologies, and environmental turbulence plays a positive moderating role in this influence relationship [36]. In the new manufacturing scenario, supply chain companies with high environmental turbulence will improve their resilience through active information relationships and resource integration, while synchronized decision consistency can be achieved [38]. It has been pointed out that, when market volatility is high, supply chain companies will be more active in inventory planning, demand forecasting, inventory management, and customer relationship maintenance, and during this process, they will act jointly, share risks and benefits, and achieve a higher level of synergy [39]. These studies show that environmental turbulence can profoundly affect all the dimensions of supply chain synergy, so we propose the following hypotheses.
Hypothesis 2a (H2a).
Environmental turbulence has a significant positive relationship with information sharing.
Hypothesis 2b (H2b).
Environmental turbulence has a significant positive relationship with incentive alliances.
Hypothesis 2c (H2c).
Environmental turbulence has a significant positive relationship with synchronization decisions.

2.3. Supply Chain Synergy

Synergy in management originated in Ansoff’s 1965 book Corporate Strategy, which defined synergy as the value bigger than the sum of its parts in the whole. Then, Herman Hacken proposed the theoretical framework of “synergy”, which is the synergistic effect of the collaboration of all parts of a system, emphasizing the effect of “1 + 1 > 2”, and the study of synergy has been put on the right track since then. Simatupang et al. defined supply chain synergy in terms of three dimensions: information sharing, synchronized decision-making, and incentive alliance, and it has been widely adopted [40]. Information sharing refers to the degree of information sharing among firms in the supply chain, synchronized decision-making refers to the degree of dynamic control of business processes among firms, and incentive alliance refers to the degree of importance that firms attach to the supply chain at the strategic level. Numerous studies have investigated the impact of supply chain synergy on supply chains’ dynamic capabilities from different dimensions. Supply chain synergy can promote a strategic consensus among supply chain firms, and the degree of strategic consensus is positively related to the stability of supply chain alliances [41,42]. In a well-stabilized supply chain alliance, firms tend to focus on long-term rewards, trust each other, and form long-term commitments. If the degree of supply chain synergy is low, the synergy of information, technology, and capital among supply chain links is insufficient; much of the knowledge cannot be clearly expressed; and major breakthroughs in technological innovation [43], strategic consensus, and resource integration cannot be achieved, making the organization less flexible [44]. Some scholars describe supply chain flexibility as a continuous innovation capability [45]. From this perspective, we find that all three dimensions of supply chain synergy have a significant impact on supply chain flexibility. Some scholars consider virtual organizations as an innovation platform for information sharing and knowledge integration among enterprises and proposed the influencing factors of the continuous innovation capability of virtual organizations from several perspectives, which indirectly indicates that information sharing and incentive alliances help improve supply chain flexibility [46]. It has been proposed that information sharing and synchronized decision-making among member firms can promote knowledge interaction and generate innovative ideas. At the same time, firms are able to continuously innovate products, management, processes, and production techniques to quickly meet potential demands in accordance with the environment’s dynamic changes, further indicating that different dimensions of supply chain synergy contribute significantly to the dynamic capabilities of the supply chain [47]. Based on the above analysis, this study proposes the following hypotheses:
Hypothesis 3a (H3a).
Information sharing has a significant positive relationship with supply chain flexibility.
Hypothesis 3b (H3b).
Incentive alliance has a significant positive relationship with supply chain flexibility.
Hypothesis 3c (H3c).
Synchronization decision has a significant positive relationship with supply chain flexibility.
The supply chain agility is also deeply influenced by supply chain collaboration. Supply chain agility involves three key processes: procurement, manufacturing and logistics [48]. Market sensitivity, network status, process integration, and virtual capabilities have an impact on supply chain agility. Studies of supply chain agility have found that the level of information technology, the level of information sharing, and the consistency of supply chain decisions all affect the supply chain agility to a greater or lesser extent. There is an “IT agility trap” debate on the effect of the information sharing level on supply chain agility [49]. IT can achieve agility by accelerating information sharing capabilities, facilitating the synchronization of decisions, and responding quickly to changing conditions [50]. Similarly, supply chain alliances have a significant impact on the level of information technology in the supply chain, and information sharing among supply chains requires consistency and sophistication in information exchange technology in the supply chain [51]. Supply chain agility also requires the supply chain to have the ability to deal with emergencies. Considering the huge impact of COVID-19 on the global supply chain, the supply chain faces the risk of disruption at any time, and the chaos of the supply chain operation under emergencies exposes the problems when the supply chain works together. The lack of timely and adequate information sharing led to increased supply chain costs and wasted resources. The long-term disruption of logistics in some regions exposes the supply chain alliance to the risk of adjustment at any time [52]. Long-term disruptions in logistics in some regions expose supply chain alliances to the risk of adjustment at any time [53]. The ability to synchronize decision-making within the supply chain is also challenged under the influence of unexpected events. All these phenomena suggest that supply chain synergy has a significant impact on supply chain agility. Therefore, we propose the following hypotheses.
Hypothesis 4a (H4a).
Information sharing has a significant positive relationship with supply chain agility.
Hypothesis 4b (H4b).
Incentive alliance has a significant positive relationship with supply chain agility.
Hypothesis 4c (H4c).
Synchronization decisions have a significant positive relationship with supply chain agility.

2.4. Supply Chain Flexibility and Agility

Flexibility in the supply chain, which was first proposed in 2005, is the capacity to quickly satisfy customer demand [52]. Subsequently, it was gradually combined with enterprise competitiveness and environmental uncertainty. Wang et al. believed that supply chain flexibility could indirectly improve the competitiveness of enterprises by affecting the supply chain resilience [53]. Some scholars define supply chain flexibility based on the enterprise resource view, and Voudouris used the margin of resources as a measure of supply chain flexibility [54]. In defining supply chain flexibility, Ma Shihua also emphasized the sharing of resources among firms within the supply chain network [55]. Under the requirements of supply chain flexibility, flexible manufacturing systems and other methods that can meet the changes in customer demand have been proposed [56]. Scholars have differing opinions on the composition of supply chain flexibility. Ji et al. argued that supply chain flexibility is developed based on manufacturing flexibility and therefore focuses more on the ability to offer customers products and services in a turbulent environment rather than just referring to production capacity [57]. Scholars generally agree that supply chain restructuring is an important component of flexibility. For example, Lummus et al. proposed a conceptual model of supply chain flexibility that defines logistics, information, supply network reorganization, organizational operations, and organizational design as the five components of supply chain flexibility [58]. Li et al. [59] argued that supply chain flexibility consists of information, operations, robustness, and reconfiguration capabilities. Regardless of the perspective from which supply chain flexibility is classified, the ability to respond to demand, efficiency of information exchange, and speed of meeting the demand for products and services are always the core capabilities of supply chain flexibility.
The idea of agility also originated from flexible manufacturing, and the definition of agility by the American Society of Supply Chain Management Professionals (CSCMP) has always emphasized quality supply chain management on two successive occasions. As globalization intensified and the global division of labor led to the inability of a single company to cope with the turbulent environment alone, the idea of agility was gradually incorporated into the supply chain, and the concept of an agile supply chain was proposed [60]. Scholars’ definitions of supply chain agility emphasize organizational responses to turbulent environments with a focus on processes. Blome et al. argued that supply chain agility is a series of processes internalized in the management of both supply and demand, which can be planned in advance or summarized as “improvisation” in different problem scenarios [13]. Since the concept of supply chain agility is embedded in supply chain flexibility, supply chain agility can be understood as a manifestation of supply chain flexibility. The following hypothesis is proposed in this paper:
Hypothesis 5 (H5).
Supply chain flexibility has a significant positive impact on supply chain agility.

2.5. Conceptual Model

In the highly unstable climate of the current global supply chains, companies need more supply chain resources. Beske argued that the supply chain dynamic capability is a collection of constantly integrated resources requiring constant identification for supply chain information and resources, the coordination of internal and external members of the supply chain alliance, and response to a constantly changing and volatile environment. From a resource perspective, the difference between the resources available to the supply chain and those utilized by the supply chain is the most direct manifestation of supply chain dynamic capability. Failure to properly utilize the resources available to the supply chain results in lost opportunities for innovation in a turbulent environment, leading to a loss of competitive advantage for the organization. Therefore, a turbulent environment prompts the formation of dynamic supply chain capabilities. Through supply chain synergy, consolidating supply chain alliances, and enhancing knowledge accumulation and technology upgrading, organizations can obtain more information and resources, reduce the negative impact of information deficiency, and avoid the risk of supply chain disruption. The conceptual model of this study is suggested in Figure 1 and is based on the literature review and research hypotheses mentioned above.

3. Research Methodology

3.1. Questionnaire Design

To ensure the comprehensiveness and representativeness of the research scale, after reading the relevant literature, the views of different scholars were summarized and compared, and items that were consistent with the theory and background of this study were selected, appropriately modified, and adjusted. To ensure the reliability and validity of the survey, all items were measured on a 7-point Likert Scale, where 7 indicated perfect agreement, 6 indicated agreement, 5 indicated slight agreement, 4 indicated uncertainty, 3 indicated slight inconsistency, 2 indicated inconsistency, and 1 indicated complete inconsistency.
For the design of the questionnaire items, we referred to more widely used measures across dimensions that have been repeatedly used by scholars and shown to be valid. The examination of environmental turbulence was based on the studies of Haleblian (1993) [61] and Miller (2010) [62], and these measures have been adopted by numerous scholars since they were first proposed and have been continuously updated as time progresses. For the measurement of supply chain synergy, Simatupang et al. (2018) conducted a more systematic study on supply chain synergy and defined it in three dimensions: synchronous decision-making, information sharing, and incentive alliance [63]. This definition has also been recognized by domestic scholars, and its dimensional division has been adopted to measure supply chain synergy [64]. Therefore, the measurement scale in this study adopts this dimensional division to measure the supply chain synergy. To measure the supply chain flexibility and agility, which are manifestations of dynamic supply chain capabilities, we borrowed the scales of Sreedevi et al. [65] and Wenfang et al. [66] to measure supply chain flexibility and Feng et al. [67] and Tarafdar et al. [68] to measure supply chain agility. In addition, considering the impact of the firm’s nature on innovation capability, this study uses the type of firm ownership, number of employees, and product type as control variables. The measured items are shown in Appendix A.

3.2. Data Collection

In this study, a questionnaire was used to obtain relevant survey data on the resources of partners, a MSc in engineering management, and a MBA. Fifteen companies were randomly selected for a presurvey, which was amended to a final questionnaire based on feedback from the survey results. We limited the sample to the top 500 companies in China. Owing to the vast territory of China and the uneven development of each province, we used a stratified sampling method similar to stratified sampling to make the sample selection representative, with equal proportional distributions by province. To make the study generalizable, the sample was selected to cover most regions in China, and representative manufacturing companies in each province were selected. Due to unbalanced regional development in China, there are more representative enterprises in the eastern coastal regions than in the mainland and more there in number than in the central and western regions. Therefore, this survey mainly focused on Beijing, Guangdong, Shandong, and the provinces and cities of Jiangsu and Zhejiang. A wide geographical distribution can effectively eliminate the influence of the economic level and geographical development. The sample was selected mainly based on the top 500 Chinese enterprises, which were all established more than 15 years ago, and intermediate and senior managers who had some knowledge of the company and its supply chain participated in the survey. The data of this sample were collected from August 2021 to June 2022. During this time period, countries around the world faced an extremely volatile environment due to the ongoing trade war between China and the U.S. and the rampant COVID-19, and the repetitive and highly volatile environment made the vulnerability of the supply chain visible, so the data of this time point made the study of the dynamic capacity of the supply chain more representative. Two survey methods were used: online Q&A and field research. A total of 169 questionnaires were returned, and seven questionnaires with incomplete data were deleted, leaving 162 valid questionnaires with an effective rate of 96%.
In the final 162 questionnaires, the sample enterprises included numerous categories specified in the National Economic Classification to ensure the representativeness of the selected enterprises; the samples were all leading enterprises in the industry, and the sample enterprises were all at the core of the supply chain, including head enterprises such as SF, Haier, and Foxconn. All companies have been running smoothly for more than 15 years, and 119 companies have been operating for more than 20 years, indicating that the studied enterprises are in good operating conditions. A survey of the number of employees in the enterprises revealed that 64.7% of the enterprises had more than 300 employees, and 47.4% had more than 1000 employees, indicating that the studied enterprises were representative. The sample characteristics are listed in Table 1.

3.3. Reliability and Validity Analysis

First, before analyzing the questionnaire for reliability, we conducted a normality test on the collected data, and the results showed that the collected data conformed to a normal distribution. For the reliability test of the questionnaire, we used Cronbach’s alpha coefficients and analyzed them using SPSS 28.0 (IBM, Armonk, NY, USA). Table 2 shows the results of the reliability analysis. Cronbach’s alpha coefficients for environmental turbulence, information sharing, incentive alliance, simultaneous decision-making, supply chain flexibility, and agility were 0.828, 0.870, 0.917, 0.933,0.833, and 0.821, respectively. The overall Cronbach’s alpha coefficient of the questionnaire was 0.928. Good stability and internal consistency of the measurements are sufficient to demonstrate the reliability of the total scale and subscales used.
Second, despite the use of well-established scales, studies by different scholars were selected for comparison in this study and modified to consider the actual situation and the development of the study. The structural validity of the questionnaire was inevitably tested because of its poor consideration. An exploratory factor analysis was conducted using SPSS 25.0, and the results supported the factor analysis of the above question items (23 items with a KMO value of 0.882, Bartlett’s spherical test X2 value of 2585.021, and degrees of freedom of 253, significant at the p < 0.001 level). The factors were extracted using the principal component method and the maximum variance rotation method, and the standard loadings of the factors were above 0.6 after rotation, the cumulative explained variance of the six factors was 75.535%, and the test results of the EFA are shown in Figure 2. According to the validation results, the number of factors was more appropriate between five and seven, which was consistent with the design of the questionnaire.
Finally, the data were subjected to a confirmatory factor analysis (CFA) in Amos 28.0 software, and the model fit well, with the values of each indicator shown in Table 2. In addition, the composite reliability (CR) was higher than 0.7; therefore, the reliability of the selected variables in this study was considered satisfactory. Meanwhile, the validity test involves structural validity, including convergent validity and discriminant validity. Convergent validity is measured by three main aspects: factor loadings, average variance extracted (AVE), and CR. As can be seen in Table 3, the factor loadings, CR values, and AVE values for each measured variable were in accordance with the requirements, with values greater than 0.5 (the AVE value of SCA is 497, which is in the acceptable range), such results show that the model proposed in this paper has good convergence. We tested the data for discriminant validity using the method proposed by Bagozzi and Yi [69]. According to the correlation analysis of the variables shown in Table 4, positive correlations were found between the SCF, SCA, IS, IA, SMD, and ET. The correlation coefficients between the variables were below 0.6, which indicated that there was no multicollinearity between the variables. In addition, the quadratic roots of AVE for each latent variable were significantly larger than the correlation coefficients between the latent variables, and these values were higher than 0.7. Therefore, the discriminant validity of the model was satisfactory. As can be seen from Table 4, both supply chain flexibility and agility are significantly and positively correlated with the environmental turbulence, and the dimensions of the supply chain synergy are significantly and positively correlated with each of the different variables, and the above results tentatively prove the hypotheses of this study.
Additionally, it is necessary to investigate the presence of common method bias (CMB). Harman’s one-way test was used. The results showed that the variance explained by a single common factor was 14.704%, which was below the suggested threshold of 40% [70]. Therefore, our data do not contain obvious errors in CMB.

4. Results

4.1. Hypothesis Test

The results of testing the model using the maximum likelihood estimation are shown in Table 5. p-values for H1a, H3b, H4a, and H4c are greater than 0.05, and these data suggest that the above hypotheses are not supported in the empirical evidence. The p-values for H1b, H3a, and H4b are less than 0.01, so the estimates of the path coefficients are significant at the 95% confidence level. The remaining five hypotheses have p-values less than 0.001; therefore, the estimated values of the path coefficients are significant at the 99% confidence level. The model was adjusted according to the test results to remove unsupported hypothetical paths and to fit the structural model. The results of the model fit are shown in Table 6, the results of all the indicators are in line with the requirements, and the model has a good fit.
Figure 3 shows the updated model. The structural figure and the validation results show that both environmental turbulence and incentive alliance have a positive relationship with the supply chain agility, while environmental turbulence has a greater positive relationship with all three dimensions of supply chain synergy. The standardized coefficients of H2a, H2b, and H2c are 0.375, 0.407, and 0.341, respectively, indicating that the positive impact of environmental turbulence on the incentive alliance is greater than that on information sharing and simultaneous decision-making. Meanwhile, environmental turbulence does not positively affect the supply chain flexibility, and both information sharing and synchronous decision-making positively stimulate supply chain flexibility to some extent. The normalized coefficient of H5 is 0.474, indicating that supply chain flexibility positively affects the supply chain agility, to some extent. We further infer that, although environmental turbulence has no direct positive effect on supply chain flexibility, it has an indirect effect on supply chain flexibility under the influence of information sharing and synchronous decision-making as mediators. Similarly, based on the structural model, we propose the following hypotheses:
Hypothesis 6a (H6a).
Environmental turbulence has a positive impact on supply chain flexibility through information sharing.
Hypothesis 6b (H6b).
Environmental turbulence has a positive impact on supply chain flexibility through synchronous decision-making.
Hypothesis 7a (H7a).
Environmental turbulence has a positive impact on supply chain agility through information sharing and flexibility.
Hypothesis 7b (H7b).
Environmental turbulence has a positive impact on supply chain agility by incentivizing alliances.
Hypothesis 7c (H7c).
Environmental turbulence has a positive impact on supply chain agility through synchronous decision-making and flexibility.

4.2. Mediation Effects Test

To test the chain mediation effect proposed in Section 4.1, this study used the bootstrap test with 5000 replicate samples and obtained the results in Table 7 at a 90% confidence level. p-values for both H6b and H7c were less than 0.001, and p-values for H7b were less than 0.01, and the mediation effect was significant. In the path of the effect of environmental turbulence on supply chain flexibility, the p-value of the synchronous decision was less than 0.001. Since environmental turbulence has no direct effect on supply chain flexibility, synchronous decisions play a fully mediating role. In the path of environmental turbulence on supply chain agility, the effect of incentive alliances is 0.036 with a p-value less than 0.001; thus, incentive alliances play a significant mediating role. In addition, environmental turbulence can positively affect supply chain agility through the chain effect of synchronous decision-making and supply chain flexibility, with an effect value of 0.053 and a p-value less than 0.01.

5. Comparison

In a turbulent international environment, supply chains face serious challenges, and the development of dynamic capabilities is gradually shifting from firms to supply chains [71]. In this study, we propose and verify the formation mechanism of supply chain dynamic capabilities, which is an important addition to the current research on supply chain dynamic capabilities. Unlike studies conducted on supply chains from the perspective of dynamic capabilities, this study summarizes the dimensions included in the dynamic capabilities of supply chains and investigates the relationship between them. We found that environmental turbulence profoundly affects the elements of dynamic supply chain capabilities and impacts supply chain performances through different impact paths. Ju et al. explored the impact of dynamic supply chain capabilities on performance and explored the role played by technological innovation in this context, finding empirically that implementing dynamic capabilities in a sustainable supply chain in a dynamic and ever-changing environment is important for innovation in technology and improving the operational performance [72]. Similar studies that include supply chain dynamic capabilities as an overall variable are numerous. Budhiartini et al. explored the impact of supply chain dynamic capabilities on sustainable supply chains [73]. These studies emphasize the external manifestations of supply chain dynamic capabilities and do not explore how supply chain dynamic capabilities work. Our study looks at the internal logic of supply chain dynamic capabilities and further explores the mechanisms by which supply chain dynamic capabilities impact organizations by internalizing the concepts of performance, technological innovation, and sustainable supply chains in supply chain dynamic capabilities. Another mainstream research on supply chain dynamic capability is to study the supply chain from the perspective of dynamic capability, and this type of research usually selects certain elements of supply chain dynamic capability to study the supply chain. Eslami et al. used supply chain integration and supply chain agility as proxies for dynamic supply chain capabilities and explored the impact of Industry 4.0 on the relationship between supply chain integration, supply chain agility, and financial performance [73]. This study proves the significance of our research, which explores the various components and interaction mechanisms of supply chain dynamic capabilities as an amalgamation of various factors with complex internal relationships and interactions of various variables that are important for the refined management of supply chains.

6. Conclusions, Limitations and Prospects

This study concludes that, first, environmental turbulence requires supply chains to have certain dynamic capabilities, which is an important basic variable for studying the supply chain dynamic capabilities. Supply chain collaboration, supply chain flexibility, and agility are all important components of the supply chain dynamics. Second, turbulent environments affect all elements of supply chain dynamics, manifested in the flexibility and agility of supply chains. A volatile environment not only directly affects the agility of the supply chain but also indirectly affects the flexibility of the supply chain through synchronous decision-making. Finally, each dimension of supply chain coordination plays different intermediary roles in the supply chain dynamic capabilities, and information sharing, synchronous decision-making, and incentive alliances directly or indirectly affect the performance of the supply chain dynamic capabilities in turbulent environments. This shows that stable supply chain alliances and the consistency of decision-making are important in a volatile environment.
This study has certain theoretical and practical significance for modern supply chain management. In theory, firstly, the environmental turbulence faced by the supply chain is included in the basic variables of the dynamic capability of the supply chain, and the impact of environmental turbulence on the supply chain coordination, supply chain flexibility, and agility is discussed from the theoretical perspective of dynamic capability. Previous studies have often focused on environmental turbulence and the impact of a certain link in the supply chain, resulting in problems that are not thoroughly studied. This study uses the structural equation model to incorporate environmental turbulence into the overall framework, and the variables discussed are closely related to many variables in supply chain management, which can provide a new theoretical perspective for future supply chain innovation, green, and sustainability. Secondly, different dimensions of supply chain collaboration are incorporated into the overall study of supply chain dynamic capabilities, and the role of different dimensions of supply chain collaboration in supply chain dynamic capabilities is explored, and the influence path of different dimensions on supply chain management is clearer. Through the data obtained by empirical research, we can find that environmental turbulence has a more significant impact on incentive alliances, indicating that supply chain alliances are more stable in a turbulent environment. At the same time, it can be seen that, in the turbulent environment of the supply chain, synchronous decision-making on the flexibility of the supply chain is significant, and this positive impact will be transmitted to the performance of supply chain agility. This chain reaction deserves our attention; previous studies often took supply chain synchronous decision-making as a kind of business process collaboration and did not explore the impact of more supply chain synchronous decision-making. However, the empirical results showed that, in a turbulent environment, this kind of business process-level coordination is very important for supply chain management. It is worth noting that environmental turbulence does not directly affect the supply chain flexibility and will not have an impact on supply chain flexibility through supply chain information sharing but has an impact on supply chain flexibility through supply chain synchronous decision-making, which shows that the ability of the supply chain itself will be affected by an uncertain environment; this influence path is complex, and the theoretical study of subsequent supply chain dynamic capabilities can consider the interactions between different dimensions of supply chain coordination at the same time. At the same time, the formation mechanism of supply chain dynamic capability is proposed and tested, which is of great significance for the theoretical study of supply chain dynamic capability. Finally, the two similar concepts of supply chain flexibility and supply chain agility are distinguished, and incorporated into the performance of supply chain dynamic capabilities, the results of empirical research show that there is a certain difference between supply chain flexibility and agility, and information sharing within the supply chain alliance can effectively affect the supply chain flexibility. Relative to supply chain agility, supply chain flexibility can be regarded as the internal capability of the supply chain, and supply chain agility is the external manifestation of the supply chain. Corresponding to the different dimensions of supply chain collaboration, we can find that the relationship between supply chain flexibility and agility is similar to the relationship between supply chain information sharing and synchronous decision-making, and the synchronous decision-making ability of the supply chain and the agility of the supply chain are due to the information sharing ability of the supply chain and the flexibility of the supply chain.
The more important significance of this study lies in its impact on supply chain management practice, and the issues discussed in this study are of great practical significance. Today’s world is facing huge environmental turbulence, the supply chain pattern formed under the trend of economic globalization is being rewritten, and the uncertain environment makes the supply chain face the trends of localization and regionalization. In order to achieve economic development and social stability, different countries and regions have made certain development plans for the local and surrounding areas, which makes the supply chain face the trend of reorganization. High-end manufacturing, advanced manufacturing, etc. have become the key areas of all countries. International industrial transfer, trade protectionism, a new round of scientific and technological revolution, the upgrading of the consumer market, and the rise of the digital economy have adjusted the global supply chain pattern, and these trends have brought great risks and challenges to the global supply chain system. The supply chain dynamic capability must play a role as a product of environmental turbulence, so it is necessary to explore the formation mechanism and influence path of supply chain dynamic capability. In order to effectively manage supply chain risks, new logistics and supply chain scenarios are being created, such as e-commerce and on-demand purchases, due to the danger of disruption in global and regional supply chains [74]. Supply chain risks that can occur anytime and anywhere have led scholars to explore a variety of different countermeasures, for example, in order to mitigate the risks that may occur at the end of the supply chain and improve the “last mile delivery level”, the 15-min city circle is being implemented in different countries around the world, in order to solve various urban problems in the 15-min living circle, the research on the supply chain is more rigorous, and coordinated policies are very important to help this digital transformation, and it is also an effective measure to deal with the adverse conditions of the supply chain. This puts forward huge requirements for the dynamic capabilities of regional supply chains, and a clearer path of influence of the supply chain dynamic capabilities will also be able to reduce the waste of resources when it plays a huge role. At the same time, we should realize that, although the huge environmental turbulence puts forward higher requirements for the dynamic capabilities of the supply chain, it also makes the management of the supply chain more efficient and also promotes future research such as smart cities [75,76]. The sample of this study comes from 162 enterprises in China, so it has more obvious reference significance for the development of China’s supply chain, the planning of a “national unified market” puts forward higher requirements for the logistics and supply chain, modern circulation network, market information interaction channels, and platform optimization and upgrading. While building a high-standard market, it is necessary to cultivate a number of digital technologies, platform enterprises, and supply chain enterprises with global influence. These policies promote the all-around upgrading of the supply chain and also put forward higher requirements for the dynamic capabilities of the supply chain, so this study has greater reference significance for Chinese enterprises. China has the world’s largest market and the most complete industrial system, which also has certain reference significance for the development of other countries.
Although this study appropriately adds to the related research, it still has significant limitations. First, the internal influence of supply chain synergy is not considered. Numerous studies have shown that information sharing has an important influence on supply chain management, so we speculate that information sharing can have an important influence on the dynamic capacity of the supply chain through another path that we have not yet verified. Therefore, subsequent studies should consider the internal influence of supply chain synergy as an important factor. Second, it has been suggested that the knowledgebase and learning ability of an organization have a significant impact on the supply chain dynamic capability; however, we did not include them in the supply chain dynamic capability. Therefore, it is necessary to study the influence of external factors on the dynamic capability of supply chains. Finally, this study used Chinese enterprises as the research object, which lack an international perspective, and it is worthwhile to explore supply chain dynamic capabilities in different contexts.

Author Contributions

Questionnaire design and data collection: C.-Y.Z.; methods and software: D.-L.Z.; data analysis: D.-L.Z.; writing—original draft preparation: D.-L.Z.; and writing—review and editing: C.-Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This study received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

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

Data Availability Statement

If required, the corresponding author can be contacted to provide it.

Acknowledgments

We acknowledge the support provided by Chun-Yan Zhu.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Questionnaire items
Environmental volatility (ET)
(a)
Key suppliers are always able to meet our needs.
(b)
Our master production schedule adjusts to changes in demand.
(c)
Technology in our industry is changing at a very fast pace.
Information Sharing (IS)
(a)
We share sensitive information about our company’s product design, R&D, production, and financial and competitive strategies with key suppliers.
(b)
We share sensitive information about our company’s product design, R&D, production, and financial and competitive strategies with the key customers.
(c)
We always inform our suppliers and customers of events or changes that may affect them in a timely manner.
(d)
We have good information exchange with our key suppliers and customers, and we know everything we need to know and say everything we need to say.
Incentive Alliance (IA)
(a)
We collaborate with key suppliers to continuously improve the quality of their products.
(b)
Customers are often involved in setting the standards. (e.g., product reliability and order response time).
(c)
Key suppliers view our mutual relationship as a long-lasting alliance.
(d)
Collaborative activities between us, key suppliers and customers follow a standardized workflow.
Simultaneous Decision-Making (SDM)
(a)
We have established an efficient ordering system with key suppliers and customers.
(b)
We often work together with key suppliers to solve problems.
(c)
We involve key customers in the design and development of products.
(d)
We and our suppliers can readily adjust production schedules and deliveries in response to changes in customer demands.
Supply Chain Flexibility (SCF)
(a)
Information sharing among supply chain enterprises can accurately and quickly grasp trends in external environmental changes.
(b)
Supply chain companies can quickly respond to the changing market demand and reallocate resources.
(c)
Supply chain companies maintain the stability of network structures when affected by internal and external environmental disturbances.
(d)
Supply chain enterprises can reconfigure or reorganize a supply chain’s functional network.
Supply Chain Agility (SCA)
(a)
Companies in the supply chain can share infrastructure, R&D and financial resources.
(b)
Firms in the supply chain can complement each other in their core competencies.
(c)
Firms in the supply chain can leverage productivity capabilities.
(d)
Companies in the supply chain improve product customization.

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Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Figure 2. Scree test.
Figure 2. Scree test.
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Figure 3. Structural model. Note: *** indicates significance at the 0.001 level. ** indicates significance at the 0.01 level.
Figure 3. Structural model. Note: *** indicates significance at the 0.001 level. ** indicates significance at the 0.01 level.
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Table
Table 1. Sample descriptions (N = 162).
Table 1. Sample descriptions (N = 162).
Demography FactorFrequencyPercentageDemography FactorFrequencyPercentage
Position of respondents Type of ownership
Chairman148.70State-owned enterprise3722.90
General manager148.70Collective enterprises74.30
Senior manager4729.00Private enterprise6540.10
Middle manager4729.00Sino-foreign joint venture1710.50
Others4024.60Foreign-owned enterprises3622.20
Education level Number of employees
Primary school21.201–49159.30
Junior high school21.2050–9995.60
High school74.30100–2993320.40
Junior college3119.20300–9992817.30
Undergraduate8250.601000–19992515.40
Postgraduate3823.502000–49993219.70
Total assets (CNY) 5000 and above2012.30
Less than 5 million159.20Type of product
5–10 million169.90Raw materials169.90
10–20 million1710.50Components3521.60
20–50 million1710.50Single machine equipment2414.80
50–100 million159.30Complete equipment3421.00
More than 100 million8250.60Others5332.70
Table
Table 2. Overall fitting coefficient.
Table 2. Overall fitting coefficient.
IndexX2/dfRMSEAIFITLICFI
Value1.5610.0590.9530.9430.952
Criterion<3<0.08>0.9>0.9>0.9
Table
Table 3. Reliability and convergence validity.
Table 3. Reliability and convergence validity.
Latent VariablesItemsLoad CoefficientAVECRCronbach’s Alpha
ETET 10.6070.6490.8430.828
ET 20.938
ET 30.835
ISIS 10.8570.6120.8620.870
IS 20.838
IS 30.709
IS 40.713
IAIA 10.8740.7400.9190.917
IA 20.796
IA 30.873
IA 40.895
SDMSDM 10.8810.7850.9360.933
SDM 20.943
SDM 30.884
SDM 40.832
SCFSCF 10.7170.5640.8380.833
SCF 20.74
SCF 30.808
SCF 40.735
SCASCA 10.6480.4970.7970.821
SCA 20.723
SCA 30.769
SCA 40.674
Table
Table 4. Discriminant validity.
Table 4. Discriminant validity.
Latent Variables123456
1 ET0.806
2 IS0.283 ***0.782
3 IA0.316 ***0.579 ***0.860
4 SDM0.224 **0.544 ***0.544 ***0.886
5 SCF0.159 **0.354 ***0.316 ***0.373 ***0.750
6 SCA0.265 ***0.43 ***0.42 ***0.389 ***0.353 ***0.704
Note: *** means significance at the 0.001 level, ** means significance at the 0.01 level.
Table
Table 5. Hypotheses tests.
Table 5. Hypotheses tests.
PathsHypothesisStandardized
Coefficient		S.E.C.R.pResults
ET-SCFH1a0.0660.0820.7070.48Not Supported
ET-SCAH1b0.170.081.932**Supported
ET-ISH2a0.3750.1123.878***Supported
ET-IAH2b0.4070.1184.373***Supported
ET-SDMH2c0.3410.0953.762***Supported
IS-SCFH3a0.150.0891.288**Supported
IA-SCFH3b0.1410.0711.3770.169Not Supported
SDM-SCFH3c0.4260.0973.704***Supported
IS-SCAH4a0.1350.0851.2430.214Not Supported
IS-SCAH4b0.1860.0681.968**Supported
IS-SCAH4c0.0450.0960.4030.687Not Supported
SCF-SCAH50.4740.1144.291***Supported
Note: *** indicates significance at the 0.001 level. ** indicates significance at the 0.01 level.
Table
Table 6. Updated overall fit coefficient.
Table 6. Updated overall fit coefficient.
IndexX2/dfRMSEAIFITLICFI
Value1.9280.0570.9560.9610.966
Criterion<3<0.08>0.9>0.9>0.9
Table
Table 7. Mediation effect test.
Table 7. Mediation effect test.
Bias-Corrected 90% CI
PathsHypothesisSEEstimateLowerUpperpResults
ET → IS → SCFH6a0.1080.056−0.0290.1580.205Not Supported
ET → SDM → SCFH6b0.060.1450.0740.262***Supported
ET → IS → SCF → SCAH7a0.0610.027−0.0080.0840.153Not Supported
ET → SDM → SCF → SCAH7b0.0530.0760.0040.171**Supported
ET → IA → SCAH7c0.0360.0690.0330.153***Supported
Note: *** indicates significance at the 0.001 level. ** indicates significance at the 0.01 level.
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Zhu, C.-Y.; Zhang, D.-L. An Empirical Study on the Mechanism of Dynamic Capacity Formation in the Supply Chain. Sustainability 2022, 14, 15044. https://doi.org/10.3390/su142215044

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Zhu C-Y, Zhang D-L. An Empirical Study on the Mechanism of Dynamic Capacity Formation in the Supply Chain. Sustainability. 2022; 14(22):15044. https://doi.org/10.3390/su142215044

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Zhu, Chun-Yan, and Dong-Liang Zhang. 2022. "An Empirical Study on the Mechanism of Dynamic Capacity Formation in the Supply Chain" Sustainability 14, no. 22: 15044. https://doi.org/10.3390/su142215044

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