Exploring the Influence of Cloud Computing on Supply Chain Performance: The Mediating Role of Supply Chain Governance

Abstract

Cloud computing represents a groundbreaking technological change that transforms traditional IT operational paradigms, driving significant improvements in supply chain efficiency and unlocking new value through digital capabilities. Despite its growing influence, empirical research on this subject remains limited, with unclear explanations of the specific ways cloud computing enhances supply chain operations. The precise mechanisms through which it influences supply chain dynamics are yet to be fully explored. This study employs survey data from Chinese enterprises utilizing cloud computing, applying Smart PLS 3.0 for partial least squares structural equation modeling (PLS-SEM) to assess how cloud-based technical competencies affect supply chain outcomes. Grounded in IT capability theory, we conceptualize cloud computing’s technical dimensions as Flexible IT Infrastructure and Cloud/Business Synergy while incorporating supply chain governance as a mediator and market uncertainty as a moderator to clarify the relationship between cloud capabilities and performance. Our findings advance both scholarly and managerial perspectives on cloud computing’s role in modern supply chains.

1. Introduction

As a cutting-edge technology, cloud computing provides substantial potential to support enterprises in achieving business integration and enabling the transformation of supply chains [1,2]. Contemporary enterprises require next-gen digital solutions to build agile supply chain ecosystems that sustain long-term market leadership. However, disruptive technologies like cloud platforms have introduced paradigm shifts that challenge legacy supply chain information systems.

This technological breakthrough has redefined digital service provisioning through its pay-per-use model. The framework distributes computational assets—including processing infrastructure and application suites—via configurable, needs-based allocation. Authorized users can self-provision services through web portals, minimizing administrative overhead [2,3]. Characteristic advantages like universal access, adaptive capacity, resource pooling, and architectural flexibility are revolutionizing conventional IT deployment approaches. These systemic innovations are consequently necessitating comprehensive reforms in supply chain oversight methodologies, with measurable impacts on supply chain performance.

The academic exploration of cloud computing has yielded substantial advancements across multiple research domains. Significant scholarly contributions have emerged in several key areas, including comprehensive literature reviews and theoretical frameworks [2], technology adoption patterns and organizational assimilation processes [4], as well as analyses of enterprise-level impacts and transformational outcomes [5]. However, extant research exhibits three principal limitations: predominance of conceptual over empirical methodologies in supply chain applications, insufficient operational guidance for implementing technical features, and inadequate explanation of causal mechanisms linking cloud competencies to operational outcomes. Prior studies have not systematically investigated how cloud capabilities interact with governance structures or their performance implications in diverse market contexts.

Building upon IT capability theory, this study proposes a novel construct—cloud computing technology capabilities comprising two constitutive elements: flexible IT infrastructure and cloud/business synergy. We further examine supply chain governance as a mediating mechanism and market uncertainty as a contextual moderator. Our investigation addresses three core questions:

(1)

How should cloud computing technology capabilities be theoretically defined and empirically operationalized?

(2)

Through what mediating pathways do these computing technology capabilities enhance supply chain performance?

(3)

How does market uncertainty condition these influence mechanisms?

This study outlines several key criteria. To begin with, the diverse approaches for implementing cloud-based systems are divided into four primary types: public, private, hybrid, and community clouds [2]. Among these, public clouds deliver a wider range of functionalities, offering greater cost efficiency and academic value [6]. Consequently, the focus of this investigation on cloud computing is restricted to public cloud solutions, as demonstrated by platforms such as Amazon Web Services, Google Cloud, and Salesforce. Additionally, the analysis concentrates solely on the influence of cloud computing’s technical features on supply chain efficiency. Nevertheless, factors like organizational scale, maturity in cloud adoption, and deployment strategies may also shape supply chain outcomes. Such constraints could marginally limit the generalizability of the results.

The study offers notable theoretical and practical insights. First, it introduces the construct of “cloud computing technical capabilities”, operationalized through two core aspects: Flexible IT Infrastructure and cloud–business synergy, thereby presenting a novel lens for understanding cloud advancements. Next, it evaluates how these technological competencies enhance supply chain performance, addressing prior gaps in assessing cloud computing’s supply chain implications. Finally, by integrating market uncertainty, the research probes how firms operating in dynamic markets modulate the aforementioned relationship. These findings empower enterprises to harness cloud technologies for optimizing supply chain operations.

2. Literature Search/Review

2.1. Cloud Computing

Cloud computing represents a transformative IT paradigm that provides on-demand access to computing resources through networked systems [2]. Compared to traditional IT architectures, this technology offers several defining attributes [6]:

(1)

Universal accessibility: users can connect to cloud-based services from any location using internet-enabled devices like smartphones or laptops, allowing instant resource utilization and service provisioning;

(2)

Pooled infrastructure: computational and storage assets are virtualized and collectively managed, enabling dynamic allocation across multiple users;

(3)

Adaptive capacity: organizations can flexibly adjust technical resources (including processing power, memory allocation, network throughput, and application instances) to meet fluctuating operational demands;

(4)

Rapid implementation: new software solutions can be deployed within minutes, significantly accelerating business process innovation and service expansion;

(5)

Usage-based pricing: customers pay only for consumed resources, with providers employing granular metering systems for accurate billing.

While substantial research exists regarding cloud computing’s organizational impacts, most studies concentrate on single-enterprise contexts. The technology’s inherent characteristics—particularly its capacity to facilitate cross-organizational data exchange, collaborative workflows, and process synchronization [7]—make it particularly valuable for supply chain applications [8]. By enhancing system interoperability, cloud adoption can substantially improve supply chain efficiency [9,10]. Although prior work has examined factors influencing cloud adoption in supply chains [4,11], comprehensive investigations into its performance-enhancing mechanisms remain limited in top-tier publications.

The IS and SCM research communities currently lack robust empirical evidence regarding cloud computing’s supply chain impacts. Our study addresses this gap by analyzing real-world implementation scenarios, thereby advancing theoretical understanding while contributing practical insights for cloud-enabled supply chain optimization.

2.2. Cloud Computing Technology Capabilities—Theoretical Foundations

Information technology constitutes a critical component of modern enterprise resource systems. The concept of IT capability was first conceptualized by Ross et al. [12] as an organization’s ability to manage IT expenditures while leveraging technology to achieve strategic objectives. Bharadwaj [13] expanded this framework, emphasizing the importance of resource integration and alignment with core business competencies.

Contemporary IT systems exhibit two fundamental characteristics: flexibility and synergy, which represent primary focus areas in current research.

Table 1. Key dimensions of IT technology capabilities.

Studies	Dimensions of IT Technology Capabilities
	Flexibility	Synergy
Ray et al. [14]	Rapid development and deployment of IT applications for hardware and operations systems	Standardization and compatibility of hardware, operating systems, networks, data, and applications
Fink and Neumann [15]	Modularity	Connectivity; compatibility
Bhatt et al. [16]	Scalability; modularity; system can deal with the extent of different applications	Compatibility
Tallon and Pinsonneault [17]	Modularity of the software: hardware capabilities can be easily and quickly added and subtracted.	Hardware compatibility; fast transmission, access, and sharing of data
Lu and Ramamurthy [18]	Availability and timeliness of data; software modularity	Data accessibility and sharing; connectivity and compatibility
Liu et al. [19]	Modularity	Connectivity; compatibility
Liu et al. [20]	Elasticity, scalability, ubiquitous access, and pay-per-use model	Data centralization and resource-sharing environment
Cai et al. [21]	Leapfrogging IT capabilities	Inside-out IT capabilities; outside-in IT capabilities
Liu et al. [22]	The extent to which organizations can effectively and rapidly deploy cloud-based IT solutions to support their business.	The ability of organizations to integrate data and applications
Li and Chan [23]	IT infrastructure flexibility	IT integration capabilities
Zeng and Lu [24]	IT talent capacity; IT infrastructure capacity	IT internal and external communication skills

outlines the main elements of existing research on IT technology characteristics, including key features/dimensions.

Table 1 shows that numerous studies have addressed both the flexibility and synergy aspects of IT technology characteristics. The majority of studies have identified the flexibility of the IT infrastructure as a crucial factor in the influence of IT on value creation. The flexibility of IT infrastructure is driven by its scalability, modularity, compatibility, and capacity to accommodate multiple applications [16]. These attributes facilitate the exchange of corporate knowledge, promote process synergy, and enhance process agility [19]. Additionally, IT technologies’ synergistic features, such as connectivity, shared information, data consistency, and application integration, are also essential for understanding the influence of IT infrastructure on value creation [14]. The synergy of IT technology is the technical support that enables enterprises and their partner companies to achieve real-time information communication and maintain consistency.

Expanding upon prior studies concerning IT infrastructure, the cloud-based variant has evolved to incorporate a multitude of unique functions. The existence of these functions makes it possible for IT to achieve flexibility and synergy, which is illustrated in

Table 2. The functions of cloud computing technology capabilities.

Studies	Dimensions of IT Technology Capabilities
	Flexibility					Synergy
	Ubiquitous Access	Elasticity	Scalability	Pay Per Use	Low Cost	Data Concentration	Sharing Environment	Resource Pool
Hewitt [25]	√	√	√	√			√
Buyya et al. [26]	√	√		√		√	√
Armbrust et al. [6]		√	√	√	√	√	√	√
Marston et al. [2]		√	√	√			√	√
Sultan [27]		√	√	√	√		√	√
Xu [28]		√	√	√		√	√
Demirkan and Delen [29]	√	√	√	√			√	√
Liu et al. [20]	√	√		√		√	√	√
Liu et al. [22]		√	√	√	√	√	√	√
Liu et al. [30]		√	√	√			√	√
Chen et al. [5]		√	√	√	√	√	√

.

The technical capacities of cloud computing, examined through the lens of IT capability theory and its inherent attributes, manifest in two domains: the flexibility of IT infrastructure and cloud–business synergy.

Drawing on the explanation provided by Ravichandran and Lertwongsatien [31], Flexible IT Infrastructure is defined as the ability of an enterprise to utilize cloud computing technology without being constrained by the limitations of IT hardware capacity and software architecture. This flexibility is not found in traditional IT models, which have elasticity, scalability, and ubiquity.

Drawing on the explanation of cross-organizational IT compatibility by Rajaguru and Matanda [32], Cloud/Business Synergyis defined as the necessity for enterprises to effectively integrate IT and business processes from both technical and managerial perspectives when applying cloud computing. This includes features such as resource pooling, shared environments, and data clustering.

2.3. Supply Chain Performance

Supply chain performance refers to the outcomes generated by the collective actions of all participants within the supply chain, serving as a measure of the efficiency and effectiveness of supply chain operations. A universal measure of supply chain performance can be obtained through synthetic process indicators, including financial and non-financial indicators [33].

Many organizations usually use financial performance indicators to monitor their activities and processes. A number of research works have clearly correlated the improved financial outcomes of supply chains with the capital expenditures undertaken by their partners [34,35]. Weaker partners may be compelled to seek external financing, thereby assuming greater risk and incurring higher costs of capital [36]. Some research has shown the factors that influence financial performance, such as the operational scale and bargaining power of core enterprises.

The growing focus on green supply chains has led to an expansion of supply chain performance metrics, now encompassing social and ecological dimensions for a holistic evaluation [37]. Roberts and Grover [38] indicated that enterprises’ adaptive capabilities play a crucial role in attaining sustainable outcomes. Wang et al. [39] explored how sustainable capabilities can positively influence a firm’s performance by leveraging prior successes and managing existing capabilities while continuously adapting to environmental changes. Additionally, supply chain sustainability could significantly influence supply chain performance, with the components of sustainability facilitating effective risk management throughout the supply chain [40].

Existing scholars have examined various factors influencing supply chain performance, including information technology [41], additive manufacturing [40], digital business transformation [42], big data predictive analytics [43], supply chain integration [44], supply chain digitization [45], and Industry 4.0 [46]. However, no research has been found that combines cloud computing technology with supply chain performance.

2.4. Supply Chain Governance

Supply chain governance involves a collection of institutional standards and tools aimed at harmonizing the interests and objectives of supply chain participants, thereby ensuring the continuous and stable functioning and expansion of the supply chain [47,48,49,50]. In contrast to supply chain management, which mainly concentrates on operational tasks like resource sharing, cost reduction, and boosting competitiveness, supply chain governance emphasizes the significance of institutional structures and regulations to curb opportunistic behavior, lessen uncertainty, and align the interests of those involved in the supply chain [50,51].

Li Wei-an et al. [50] reviewed the theoretical frameworks in supply chain governance research and categorized them into three major schools of thought widely accepted by scholars: the transaction cost school, the resource-based school, and the social relationships school. These schools highlight the relationships between supply chain governance structures and transaction costs, resource management, and social structures, respectively. Ref. [49] clarified that transaction cost theory serves as the theoretical foundation for supply chain governance. Resource-based theory defines its scope, and social embeddedness theory adds a social dimension to the governance process.

Table 3. Representative studies of supply chain governance.

Studies	Methods	Theories	Dimensions of Supply Chain Governance
Blome et al. [52]	Questionnaire survey	Organizational duality	Contractual, Relational governance
Tachizawa and Wong [53]	Theoretical deduction	Social network theory	Formal, Informal governance
Ran Jiaseng et al. [54]	Case study	Dualist governance	Contractual, Relational governance
Ghozzi et al. [55]	Case study	Transaction costs and resource-based view	Formation, Operation, and Monitoring
Dolci et al. [47]	Case study and questionnaire survey	Transaction cost theory	Contractual, Relational, and Transactional Governance
Um and Oh [56]	Questionnaire survey	Transaction cost and relationship theory	Contractual, Relational governance
Feng Hua et al. [48]	Questionnaire survey	Governance theory	Social, Formal control
Li Jingjing et al. [57]	Case study	Resource-based view	Resource reorganization, Capacity reconfiguration
Paolucci et al. [58]	Questionnaire survey	Transaction cost theory	Contractual, Relational governance
Bonatto et al. [51]	Theoretical deduction	Transaction cost and social exchange theory	Contractual, Relational governance

summarizes some representative studies on supply chain governance in recent years.

The literature review and the table above clearly indicate that the academic community has reached a consensus regarding the categorization of supply chain governance into two main types: contractual governance and relational governance. Contractual governance refers to the relatively formal and strict written governance measures and instruments that supply chain members implement to cooperate [49,51]. Contractual governance defines the responsibilities, obligations, rewards, and penalties of supply chain members through explicit contracts and close control [48]. Contractual governance emphasizes the importance of formal contracts between supply chain members and rules to prevent opportunism and conflict [51]. However, contractual governance in supply chains also suffers from limited capacity and scope of protection, poor adaptability, and impediments to cooperative intimacy [49,50,51,55,59]. Successful supply chain governance necessitates the combination of formal and informal governance methods, where relational governance enhances contractual governance. This type of governance focuses on fostering relationships among participants through informal means. Trust, in particular, plays a crucial role in governance by reducing conflicts among members [49]. It helps mitigate opportunistic behavior by aligning the values and culture of supply chain members, fostering cooperative problem solving, and facilitating the coordination of interests [48,60,61].

Building on this analysis, the present paper applies the two dimensions—contractual and relational governance—to assess supply chain governance and examine the impact of cloud computing on existing governance structures. Contractual governance focuses on the importance of formal agreements and rules to prevent opportunism and conflict, whereas relational governance emphasizes the management of relationships through informal means.

2.5. Market Uncertainty

Market uncertainty represents a specific aspect of environmental uncertainty at the market level. Environmental uncertainty refers to the condition in which enterprises make decisions with insufficient information, thereby increasing the risk of strategic failure. In contrast, market uncertainty arises from unpredictable shifts in customer preferences, attitudes, and competitive dynamics, which enterprises struggle to anticipate effectively. Elevated market uncertainty can erode firms’ confidence in collaboration and discourage investment in innovation, particularly in radical innovation, where firms are more likely to adopt conservative strategies [62,63].

Within dynamic market environments, this study analyzes the conditional effects whereby cloud infrastructure competencies and supply governance protocols interact to determine supply chain performance. While research on supply chain governance provides theoretical insights into how cloud computing influences supply chains, the extent of its impact on supply chain performance remains contingent upon the specific market conditions in which enterprises operate. As a key variable in information systems research, market uncertainty encapsulates the dynamic nature of the market environment. Therefore, this paper explores how cloud computing technology differentially affects supply chain governance and performance under varying levels of market uncertainty, revealing how its role fluctuates with market dynamics. This analysis offers more context-specific theoretical support for supply chain management practices.

Based on the above literature review and theoretical background analysis, we provide a concise overview of the dimensions and descriptions of each variable in

Table 4. Description of the variables in this study.

Variables	Definitions	Studies
Flexible IT Infrastructure	The ability of an enterprise to utilize cloud computing technology without being constrained by the limitations of IT hardware capacity and software architecture.	Ravichandran and Lertwongsatien [31]
Cloud/Business Synergy	The necessity for enterprises to effectively integrate IT and business processes from both technical and managerial perspectives when applying cloud computing.	Rajaguru and Matanda [32]
Supply Chain Performance	The outcomes generated by the collective actions of all participants within the supply chain, serving as a measure of the efficiency and effectiveness of supply chain operations.	Maetrini et al. [33]
Contractual Governance	The relatively formal and strict written governance measures and instruments that supply chain members implement to cooperate.	Hua Lian-lian et al. [49], Bonatto et al. [51]
Relational Governance	Supply chain members manage their relationships through informal mechanisms such as trust, solidarity, and fairness.	Hua Lian-lian et al. [49]
Market Uncertainty	It is characterized by unpredictable changes in customer preferences, attitudes, and competitor dynamics.	Zhang et al. [62]

.

3. Research Model and Hypotheses

In an era of rapidly advancing digital technologies, cloud computing has become a fundamental component, attracting significant attention from both academia and industry. However, research in supply chain management has yet to clearly define how cloud computing influences supply chain performance. This research investigates the underlying processes by which cloud-based technological competencies influence supply chain performance. As a critical aspect of supply chain integration, supply chain governance encompasses both contractual and relational governance, both of which play a key role in enhancing supply chain performance. The intermediary function of supply chain governance mechanisms in translating cloud infrastructure capabilities into enhanced supply system performance is systematically examined in this investigation.

Moreover, enterprises operate in dynamic market environments where external factors inevitably affect the effectiveness of cloud computing technology. Therefore, this study explores whether the impact of cloud computing technology capabilities on supply chain performance varies under different market conditions, offering deeper insights into this relationship, and this analysis provides a theoretical foundation for enterprises to optimize cloud computing adoption in complex market environments.

Building upon the theoretical foundations discussed, this study conceptualizes “cloud computing technology capabilities” and develops an integrated research framework. As illustrated in Figure 1, the framework examines the capability–performance relationship in uncertain market environments, with particular emphasis on supply chain governance as the mediating mechanism.

3.1. Cloud Computing Technical Capabilities and Supply Chain Performance

Organizations can leverage cloud computing to allocate and decommission resources dynamically in accordance with real-time demand, thereby obviating the prerequisite to procure hardware beforehand. This flexibility enables businesses to more effectively accommodate supply chain volatility and to reallocate resources in reaction to demand shifts, which in turn enhances resource efficiency and economic viability [64]. During peak demand periods, organizations can quickly scale their computing and storage resources to ensure that supply chain systems can manage more data and transactions at critical times. This capability helps to avoid bottlenecks and performance issues, enhancing supply chain responsiveness. Meanwhile, cloud computing service providers usually have a global network of data centers, enabling enterprises to deploy and manage their supply chain systems globally, which could help them to establish a global supply chain network, and enhance their responsiveness to international markets and the ability to provide localized services. Therefore, the following hypothesis is proposed:

H1a. 

Flexible IT Infrastructure exerts a positive influence on supply chain performance.

Secondly, robust connectivity on cloud computing platforms leads to easier collaborative decision making and planning among all stakeholders. Real-time sharing of data and information provides a more comprehensive and accurate information base for decision makers, which helps to jointly develop more rational supply chain strategies and plans. Meanwhile, by leveraging cloud computing, businesses can implement digital supply chain management, covering processes such as digital order processing, inventory management, and logistics tracking. These digital processes enhance the visibility and transparency of operations, and these benefits will help to optimize the overall performance of the supply chain. Therefore, the following hypothesis is proposed:

H1b. 

Cloud/Business Synergy demonstrates a significant positive relationship with supply chain effectiveness.

3.2. The Mediating Role of Supply Chain Governance

In terms of Flexible IT Infrastructure, cloud computing enables organizations to swiftly deploy new applications and services using an out-of-the-box approach. This capability allows organizations to capitalize on emerging business opportunities, thereby enhancing supply chain agility and fostering innovation. IT infrastructure agility provides the necessary tools and platforms for effective supply chain collaboration, enabling coordination among various participants, including suppliers, manufacturers, distributors, and retailers. This, in turn, leads to improved customer satisfaction and greater supply chain efficiency.

Through cloud computing technologies, companies can also digitize contract management [52], making contracts more accessible, easier to store, and simpler to manage. This capability strengthens relational governance with partners by improving transparency, consistency, and the execution of contracts through more detailed management practices [41]. Supply chain governance can leverage this flexibility to better adjust to evolving market demands and challenges, ultimately driving higher levels of supply chain performance [38]. Therefore, the following hypothesis is proposed:

H2a. 

Contractual governance mediates the impact of Flexible IT Infrastructure on supply chain performance.

Secondly, a flexible IT infrastructure can enhance the efficiency of information exchange and communication, minimizing delays and distortions in the information transfer process, which is crucial for effective relational governance. Flexible IT Infrastructure is the capacity of IT systems to swiftly adjust to alterations such as technological updates, shifts in business requirements, or market fluctuations. This flexibility will play a significant role in supporting relational governance and improving supply chain performance. The flexible IT systems enable better information sharing and communication, which are essential components of relational governance [52]. By providing the necessary technical support, flexible IT infrastructures facilitate closer collaboration and smoother information sharing with partners. For instance, through cloud services and virtualization technologies, organizations can swiftly adjust their resources to meet changing business demands, thereby strengthening collaborative relationships and increasing transparency and synergy across the supply chain. The flexible IT infrastructure directly impacts supply chain performance by allowing organizations to adapt more swiftly to changes in the market., enhancing supply chain responsiveness and agility. With advanced IT technologies, firms can perform real-time data analysis and optimize processes, leading to better inventory management, production scheduling, and delivery efficiency—ultimately improving overall supply chain performance. Therefore, the following hypothesis is proposed:

H2b. 

Relational governance mediates the impact of Flexible IT Infrastructure on supply chain performance.

Regarding cloud and business synergies, these synergies create convenience for supply chain members by allowing partners to work together in creating new products, services, and solutions that address market needs [41]. Cloud computing technology provides a platform for collaborative work and decision making, allowing departments and teams to engage in discussions and strategic planning within the same cloud environment. This fosters more effective relational governance, ensuring alignment in decision making and strategic direction across all parties. Cloud computing enhances organizational agility and flexibility, enabling faster adaptation to market changes. For instance, Haier facilitates communication between business and IT managers to unlock greater IT value. Strong relational governance ensures appropriate trading behavior and supports joint planning between the enterprise and its partners, allowing for timely adjustments and quick responses in governance. This adaptability ensures that relationships with partners can evolve with the changing business environment. Supply chain governance is essential for fostering and enhancing collaborative innovation within the supply chain [51], which in turn leads to better overall performance of the supply chain. Therefore, the following hypothesis is proposed:

H2c. 

Contractual governance mediates the impact of Cloud/Business Synergy on supply chain performance.

Secondly, cloud technology offers a centralized platform that enables partners to exchange information in real time. This level of transparency minimizes information gaps, improves supply chain predictability, and helps address uncertainties. By reducing the costs associated with coordination and communication among companies, cloud services lower market transaction costs, and this will enhance the efficiency of relational governance [51]. Additionally, the combination of big data analytics and artificial intelligence with cloud platforms allows businesses to make better-informed decisions, thereby increasing the efficiency and effectiveness of relational governance. Furthermore, cloud technology boosts supply chain agility, enabling companies to swiftly respond to market changes and demand fluctuations. This heightened agility contributes to the adaptability and flexibility of relational governance [51]. Therefore, the following hypothesis is proposed:

H2d. 

Relational governance mediates the impact of Cloud/Business Synergy on supply chain performance.

3.3. The Moderating Role of Market Uncertainty

Market uncertainty can manifest in uncertainties related to demand, prices, and competition. For the research to be all-encompassing, it is imperative to account for market uncertainty when investigating the mechanisms through which cloud computing affects supply chain performance. This inclusion is crucial to determine if the interplay between cloud computing and supply chain performance varies across different scenarios.

Uncertainty is often accompanied by new market opportunities. A flexible IT infrastructure can support rapid innovation, enabling businesses to develop new products, seize market demands, and create new growth opportunities. The sheer scale of market demand helps the reduction of uncertainty and safeguards the potential benefits of research and development (R&D) activities. Enterprises that adopt cloud computing technology can overcome the limitations of traditional IT models, improve infrastructure flexibility, and enhance the ability to adapt to market uncertainty. Cloud computing technology allows enterprises to quickly implement new applications and services, thereby enhancing the efficiency and resilience of the supply chain [65]. At the same time, cloud computing platforms typically provide stable data storage and processing capabilities, reducing the problem of data uncertainty and enabling organizations to reliably manage and share data more and, in this way, supports real-time decision making [66]. In an uncertain market environment, organizations need to allocate resources effectively to remain competitive. Flexible IT infrastructures can help companies allocate and utilize resources and improve operational efficiency. Therefore, the following hypothesis is proposed:

H3a. 

The performance benefits of Flexible IT Infrastructure are amplified under conditions of heightened market uncertainty.

Second, Cloud/Business Synergyoffers a more effective platform for information sharing, enabling supply chain members to exchange crucial data in real time. This includes updates on demand fluctuations, market trends, inventory levels, and more, which is essential for answering the rapid changes induced by environmental uncertainty. Such capabilities enable the supply chain to adjust its strategies more flexibly [45]. Market uncertainty demands that organizations have the ability to rapidly adapt their operational scale and reallocate resources as needed [62]. Cloud technology provides flexible resource acquisition and scalability, allowing organizations to quickly adapt to market changes. Cloud/Business Synergywill also contribute to the establishment of closer partnerships [66]. In uncertain environments, strengthened partnerships can enhance trust, increase mutual dependence, and encourage supply chain participants to collaborate more effectively in managing uncertainty and sharing risks. Therefore, the following hypothesis is proposed:

H3b. 

Increased market uncertainty strengthens the positive impact of Cloud/Business Synergyon supply chain performance.

4. Method

This paper primarily employs an empirical research methodology. The research methods include literature analysis, case analysis, questionnaire survey, and empirical analysis based on structural equation modeling, specifically through partial least squares (PLS-SEM).

4.1. Design and Measurement

The measurement instruments used in this investigation were derived from established scales, with appropriate adjustments implemented to ensure contextual relevance. To establish instrument validity, a dual-phase validation approach was adopted. During the preliminary evaluation phase, eight domain specialists (comprising four IT/SCM scholars and four industry practitioners) were engaged to examine the survey design, providing suggestions to enhance item comprehensibility and conciseness. Subsequently, a pilot study was administered to a sample of 30 graduate students (both doctoral and master’s candidates), whose responses facilitated final instrument calibration. All constructs were evaluated using a 7-point Likert-type scale (1 = “completely disagree” to 7 = “completely agree”) to quantify the target variables.

Operationalization of cloud computing technical capabilities incorporated two constituent factors: Flexible IT infrastructure and Cloud/Business Synergy, utilizing the scales developed by Bhatt et al. [16], Saraf et al. [67], Son et al. [68], as well as Bhattacherjee and Park [69]. For assessing supply chain performance, the primary reference is the scale proposed by Delic and Eyers [40], which evaluates performance across five dimensions. In terms of supply chain governance, the main reference is the scale of [70,71] to measure contractual governance and the scale of [70] to measure relational governance. In terms of market uncertainty, the scale referenced by Darvishmotevali et al. [72] and DeSarbo et al. [73] was primarily used to measure the extent of change and unpredictability in customer preferences, attitudes, and tastes across each of the six dimensions. The specific measurement items are shown in Appendix A.

4.2. Sample Selection and Data Collection

This study utilizes a questionnaire survey as the primary data collection method, a widely recognized and frequently employed approach in academic research due to its efficiency in gathering large datasets for quantitative analysis (e.g., Lin and Chen [74] and Zailani et al. [75]). To enhance data confidentiality, we rigorously scrutinized the questionnaire during its design phase to ensure that no item solicited personally sensitive information. Prior to commencing the survey, all participants were explicitly assured of anonymity and data protection, with a clear delineation of the study’s objectives. Questionnaire submission was contingent upon respondents’ affirmative consent after reviewing our confidentiality disclosure protocol. Throughout the entire survey process, we implemented multiple technical safeguards to ensure participant anonymity. During the questionnaire design phase, we conducted rigorous item screening to eliminate all potentially identifiable information, retaining only de-identified demographic options. Furthermore, during data collection, we utilized the anonymous response mode of professional survey platforms, explicitly informing participants that no digital identifiers (e.g., IP addresses) would be recorded. This study was conducted in accordance with the Declaration of Helsinki and approved on 30 May 2022 by the Yunnan University of Finance and Economics, China (Reference No. 20220537). This approval reinforces this study’s commitment to ethical standards, safeguarding participant confidentiality, informed consent, and voluntary participation. To ensure data integrity, we collaborated closely with partner organizations. Following the methodology of Han et al. [76], online and offline methods—email and field surveys—were used to collect questionnaire data. Firstly, cloud computing is extensively adopted in industries such as manufacturing and information services, particularly in developed regions like Beijing, Shanghai, and Guangdong. Targeting enterprises from these regions where cloud computing adoption is more advanced enhances the representativeness of this study. According to the statistics of the 2023 Intelligence on the Cloud White Paper, we obtained the provinces and cities that are more widely applying cloud computing technology, specifically Beijing, Shanghai, Guangdong, Zhejiang, Hubei, and Jiangsu. By reviewing industry reports and conducting market research, we assessed the application of platformed supply chains in these regions, and then, we selected the provinces and cities with higher levels of platform supply chain implementation as the target areas for distributing the questionnaires. Through strategic partnerships with the China Cloud Computing Industry Alliance and relevant trade associations, we acquired a comprehensive roster of enterprises actively employing cloud computing technologies. The study investigates factors such as the enterprise’s nature, industry type, years of operation, and the number of platforms involved in the supply chains of these listed companies. Additionally, we gathered insights and recommendations from professionals, consultants, and analysts in the supply chain field through our collaborations.

In the initial phase, to ensure both the quantity and quality of questionnaire responses, we contacted target enterprises through industry associations to assess their willingness to participate. Regarding enterprise participation willingness, we contacted target firms through the official channels of industry associations, significantly enhancing engagement by leveraging their established cooperative relationships. To address firms’ concerns about data sensitivity, we implemented dual safeguards: (1) utilizing industry associations as trusted third parties to uniformly distribute and collect questionnaires, ensuring standardized data handling and (2) explicitly stating the study’s academic value and data usage scope in the survey instructions, emphasizing that findings would contribute to industry development. This approach—relying on industry association endorsement and restricting usage to academic purposes—effectively alleviated firms’ data-sharing concerns and ultimately facilitated collaboration.

Given the pre-existing institutional affiliations between target enterprises and industry associations, leveraging these professional networks significantly enhanced organizational participation willingness. As a result, approximately 500 enterprises agreed to take part in the survey. From November 2022 to May 2023, we employed a randomized email-based questionnaire distribution protocol through accredited industry associations to the selected enterprises, thereby ensuring robust response rates through institutional credibility. In the second stage, reminder emails were sent to firms that had not responded, and field questionnaires were sent to the relevant firms.

To ensure the authenticity, reliability, and usability of the survey responses, the target group for this study was defined as enterprises that utilize cloud computing and cloud services and are actively involved with supply chain platforms. Respondents were specifically selected from middle and senior management, individuals with expertise in both supply chain operations and information technology within their organizations. A total of 478 questionnaires were distributed, and after removing 149 invalid responses, the study ultimately collected 329 valid questionnaires, resulting in a valid response rate of 68.83%.

Table 5. Sample descriptive statistics.

Variables	Frequency (N = 329)	Percent
Size (the number of employees)
1–50	35	10.64%
51–100	52	15.81%
101–500	124	37.69%
501–1000	59	17.93%
>1000	59	17.93%
Industry Group
Information services	169	51.37%
Wholesale and retail trade	30	9.12%
Manufacturing	88	26.75%
Finance	27	8.21%
Others	15	4.55%
Stage of the Firm
Start-up	104	31.61%
Growth	133	49.43%
Maturity	56	17.02%
Decline	23	6.99%
Second start-up	13	3.92%
Firm Age
1–5 years	96	29.18%
5–10 years	122	37.08%
10–20 years	71	21.55%
>20 years	40	12.16%

summarizes the descriptive statistics of the surveyed firms. Key control variables encompass firm age, size (measured by employee count), industry classification (information services, wholesale/retail trade, manufacturing, finance, and others), and business lifecycle stage (start-up, growth, maturity, decline, or second start-up) [30]. Given the study’s focus on cloud computing and supply chain applications, information services and manufacturing firms constituted the majority of the sample. Firm age distribution revealed over 50% of respondents fall within the 1–10 year range, categorized into four tiers: 1–5, 5–10, 10–20, and 20+ years.

4.3. Reliability and Validity

Firstly, this study measured the values of loadings, Cronbach’s alpha, composite reliability, average variance extracted (AVE), Fornell–Larcker, cross-loadings, and the heterogeneity/monogeneity ratio (HTMT) of the constructs. These were judged according to the following criteria: single-item loadings, Cronbach’s alpha, and composite reliability must each be greater than 0.7; the average variance extracted must exceed 0.5; the HTMT value must be less than 0.85; cross-loadings must indicate that only the loadings for the specific construct exceed 0.7, while all other loadings are below 0.7; and the Fornell–Larcker criterion requires that the square root of the AVE is greater than the correlation coefficients between the construct and other constructs.

Table 6. Measurement model results.

Constructs	Loadings
Flexible IT Infrastructure (ITIF) (α = 0.782, CR = 0.874, AVE = 0.751)
ITIF1	0.852
ITIF2	0.881
ITIF3	0.860
ITIF4	0.893
ITIF5	0.886
ITIF6	0.863
Cloud/Business Synergy (CBS) (α = 0.908, CR = 0.936, AVE = 0.794)
CBS1	0.904
CBS2	0.940
CBS3	0.922
CBS4	0.917
Contractual Governance (CG) (α = 0.915, CR = 0.934, AVE = 0.711)
CG1	0.923
CG2	0.908
CG3	0.897
CG4	0.911
Relational Governance (RG) (α = 0.910, CR = 0.941, AVE = 0.793)
RG1	0.907
RG2	0.907
RG3	0.914
RG4	0.910
Supply Chain Performance (SCP) (α = 0.765, CR = 0.831, AVE = 0.583)
SCP1	0.825
SCP2	0.742
SCP3	0.824
SCP4	0.767
SCP5	0.772
SCP6	0.760
SCP7	0.810
Market Uncertainty (MU) (α = 0.792, CR = 0.844, AVE = 0.695)
MU1	0.874
MU2	0.877
MU3	0.892
MU4	0.814
MU5	0.855
MU6	0.900

demonstrates that all item loadings exceed 0.7. Four constructs exhibit Cronbach’s alpha coefficients greater than 0.8, while three constructs fell between 0.7 and 0.8. The majority of the composite reliability is higher than 0.8, with three constructs exceeding 0.9, indicating that the data presented in this paper possess strong reliability. Furthermore, all average variance extracted (AVE) values greater than 0.5. As shown in

Table 7. Fornell–Larcker.

	ITIF	CBS	CG	RG	SCP	MU
ITIF	0.884
CBS	0.512	0.953
CG	0.304	0.104	0.957
RG	0.217	0.177	0.440	0.954
SCP	0.482	0.520	0.372	0.414	0.875
MU	0.355	0.414	0.347	0.218	0.512	0.890

, the square root of each AVE is greater than the correlation coefficients among the constructs. Additionally, the topics listed in

Table 8. Loadings, cross-loadings.

	ITIF	CBS	CG	RG	SCP	MU
ITIF1	0.852	0.402	0.364	0.476	0.583	0.307
ITIF2	0.881	0.417	0.408	0.498	0.507	0.440
ITIF3	0.860	0.384	0.388	0.483	0.516	0.416
ITIF4	0.893	0.399	0.380	0.387	0.432	0.460
ITIF5	0.886	0.442	0.319	0.439	0.446	0.451
ITIF6	0.863	0.467	0.438	0.327	0.448	0.495
CBS1	0.352	0.904	0.402	0.318	0.516	0.455
CBS2	0.404	0.940	0.340	0.363	0.580	0.390
CBS3	0.401	0.922	0.371	0.457	0.574	0.430
CBS4	0.449	0.917	0.345	0.356	0.563	0.349
CG1	0.444	0.412	0.923	0.321	0.470	0.456
CG2	0.379	0.389	0.908	0.485	0.369	0.483
CG3	0.425	0.355	0.897	0.385	0.377	0.493
CG4	0.376	0.334	0.911	0.465	0.463	0.383
RG1	0.421	0.367	0.302	0.907	0.483	0.379
RG2	0.375	0.312	0.368	0.907	0.316	0.310
RG3	0.316	0.489	0.335	0.914	0.476	0.316
RG4	0.281	0.477	0.478	0.910	0.337	0.386
SCP1	0.514	0.522	0.407	0.348	0.825	0.410
SCP2	0.592	0.477	0.390	0.385	0.742	0.440
SCP3	0.444	0.505	0.364	0.445	0.824	0.369
SCP4	0.489	0.457	0.372	0.406	0.767	0.413
SCP5	0.412	0.467	0.417	0.330	0.772	0.377
SCP6	0.547	0.444	0.400	0.442	0.760	0.314
SCP7	0.551	0.522	0.500	0.369	0.810	0.499
MU1	0.351	0.361	0.332	0.479	0.361	0.874
MU2	0.356	0.441	0.423	0.438	0.368	0.877
MU3	0.377	0.342	0.378	0.346	0.481	0.892
MU4	0.331	0.333	0.333	0.416	0.428	0.814
MU5	0.332	0.356	0.343	0.467	0.341	0.855
MU6	0.331	0.412	0.359	0.405	0.439	0.900

demonstrate the highest loadings on their respective factors, and all HTMT values in

Table 9. HTMT.

	ITIF	CBS	CG	RG	SCP	MU
ITIF
CBS	0.747
CG	0.672	0.822
RG	0.714	0.764	0.629
SCP	0.819	0.800	0.645	0.708
MU	0.742	0.624	0.595	0.606	0.800

are below 0.85. This further supports the conclusion that the validity of the data in this paper is also credible.

4.4. No-Response Bias and Common Method Bias

Many studies have used the delayed response method to detect no-response bias [22]. To assess whether no-response bias exists in this study, we utilized a late regression method in our study, which included 268 early samples and 61 late samples. Regarding employee size, a t-test revealed no statistically significant difference between the early and late samples (p = 0.514). Similarly, there was no statistically significant difference in annual sales (p = 0.355). Consequently, we conclude that there is no non-response bias in our model.

Then, we applied methods proposed by previous scholars to assess common method bias. Firstly, to avoid the singularity of data sources, we ensured that different constructed questions were completed by leaders from various departments; for instance, the flexible IT infrastructure and cloud/business synergy scales were filled out by leaders of the IT department, while the supply chain performance section was completed by leaders of the supply chain department [77,78]. This approach was adopted to relieve common method bias from a research design perspective. Secondly, we analyzed the correlation matrix. As shown in Table 7, the highest correlation between variables is 0.520 (CBS vs. SCP), significantly lower than the threshold of 0.9 [79]. Thirdly, to examine all individual factors, we employed Harman’s single-factor test. The results show that the maximum covariance is 36%, which is well below 50%. Hence, we can conclude that there is no common method bias in this study.

5. Data Analysis and Results

5.1. SEM Model Evaluation

This paper first presents the goodness-of-fit indices for the structural equation model. Specifically, the chi-square degrees of freedom ratio (χ²/df) is 2.672, the CFI is 0.911, and the RMSE is 0.056. Therefore, the model proposed in this paper has good model adaptability.

To evaluate the explanatory power of the endogenous variable (supply chain performance),

Table 10. Model Predictive Relevance.

Variables	R2	Q2
CG	0.503	0.396
RG	0.418	0.300
SCP	0.447	0.362

reports R-squared values of 0.503, 0.418, and 0.447, indicating robust predictive validity. Additionally, the Q-squared value of the endogenous variables are 0.396, 0.300, and 0.362, indicating that the path model has good predictive relevance.

5.2. Direct Effect Analysis

The proposed causal relationships were empirically validated using the bootstrapping technique in Smart-PLS 3.0, with direct effect testing results detailed in

Table 11. Main effects test results.

Path	Path Coefficient	Standard Deviation	T Statistics	p Statistics
ITIF → SCP	0.259	0.046	5.580	0.000
CBS → SCP	0.343	0.076	4.489	0.000
ITIF → CG	0.217	0.032	6.781	0.000
ITIF → RG	0.402	0.122	3.300	0.001
CBS → CG	0.273	0.139	1.994	0.047
CBS → RG	0.369	0.142	2.599	0.009
CG → SCP	0.410	0.204	2.010	0.045
RG → SCP	0.354	0.141	2.511	0.012

.

Table 11 demonstrates statistically significant support for both hypotheses. H1a reveals a substantial positive relationship (β = 0.259, p < 0.001), confirming that Flexible IT Infrastructure significantly enhances supply chain performance. Similarly, H1b shows stronger effects (β = 0.343, p < 0.001), validating that Cloud/Business Synergy positively impacts operational outcomes. These results provide empirical evidence for both hypothesized relationships.

5.3. Mediating Effect Analysis

In this research, the bootstrap technique was employed to determine both the direct and indirect impacts of the mediating variables, along with the t-values, which are deemed significant if they exceed 1.96. The total impact is derived from the combination of direct and indirect impacts, and the variance accounted for (VAF) is calculated by dividing the indirect effect by the total effect. The VAF thresholds are established at 20% and 80%. A VAF exceeding 20% indicates partial mediation, whereas a VAF above 80% signifies full mediation [80].

Table 12. Mediation effect test results.

Path	Direct Impact	Indirect Impact	Total Impact	T Statistics	VAF	Significance
ITIF → CG → SCP	0.149	0.110	0.259	2.451	42.47%	Yes
ITIF → RG → SCP	0.102	0.157	0.259	2.083	60.62%	Yes
CBS → CG → SCP	0.447	−0.104	0.343	1.662	−30.32%	No
CBS → RG → SCP	0.345	−0.002	0.343	0.747	−0.58%	No

presents the results of the analysis of the four mediated paths. Two of these paths showed significant results, while the other two did not. The analysis identified two significant mediation effects, in line with the previous hypotheses. Specifically, the supply chain contractual governance component mediates the relationship between Flexible IT Infrastructure and supply chain performance, with a VAF of 42.47%. Similarly, the supply chain relational governance component mediates the relationship between Flexible IT Infrastructure and supply chain performance, with a VAF of 60.62%.

The two non-significant paths are related to the mediating roles of supply chain contractual governance and supply chain relational governance between cloud–business synergies and supply chain performance. The VAF values for these paths are −30.32% and −0.58%, respectively, with t-values of 1.662 and 0.747, both below the threshold of 1.96. Although a factor seems to cause Cloud/Business Synergyto reduce supply chain performance, it does not reach the significance threshold, as indicated by the indirect effect of −0.104. These two paths do not support the original hypotheses.

Therefore, the results indicate that the majority of mediation hypotheses (H2a and H2b) are supported, while hypotheses H2c and H2d are not. Figure 2 and Figure 3 illustrate the results of the mediation effect test for IT infrastructure and Cloud/Business Synergywith respect to supply chain performance for the two governance models.

5.4. Moderating Effect Analysis

Table 13. Moderating effect test results.

Variables	Coefficient	p Statistics	Variables	Coefficient	p Statistics
ITIF	0.174	0.000	ITIF	0.160	0.000
MU	0.164	0.012	MU	0.112	0.034
			ITIF × MU	0.124	0.040
CBS	0.245	0.000	CBS	0.193	0.000
MU	0.201	0.002	MU	0.122	0.012
			CBS × MU	0.089	0.031

displays hierarchical regression analyses examining moderation effects. The initial models regressed (a) Flexible IT Infrastructure (ITIF) and market uncertainty (MU) on supply chain performance (SCP) and (b) Cloud/Business Synergy(CBS) and MU on SCP. Subsequent models incorporated interaction terms (ITIF × MU and CBS × MU) to test moderation. The results demonstrate significant interaction effects, namely ITIF × MU (β = 0.124, p = 0.040) and CBS × MU (β = 0.089, p = 0.031), confirming that market uncertainty strengthens both relationships. Specifically, higher uncertainty amplifies the positive impact of IT flexibility (supporting H3a) and Cloud/Business Synergy(supporting H3b) on performance. The complete moderation analysis and hypothesis testing results are presented in

Table 14. Results of hypothesis testing.

	Hypotheses	Results
H1a	Flexible IT Infrastructure exerts a positive influence on supply chain performance.	Supported
H1b	Cloud/Business Synergy demonstrates a significant positive relationship with supply chain effectiveness.	Supported
H2a	Contractual governance mediates the effect of Flexible IT Infrastructure on supply chain performance.	Supported
H2b	Relational governance mediates the impact of IT infrastructure agility on supply chain performance.	Supported
H2c	Contractual governance mediates the effect of Cloud/Business Synergy on supply chain performance.	Non-supported
H2d	Relational governance mediates the effect of Cloud/Business Synergy on supply chain performance.	Non-supported
H3a	The performance benefits of Flexible IT Infrastructure are amplified under conditions of heightened market uncertainty.	Supported
H3b	Increased market uncertainty strengthens the positive impact of Cloud/Business Synergyon supply chain performance.	Supported

and Figure 4.

6. Discussion

This research adopted a quantitative methodology involving survey data collected from 329 enterprises. Through rigorous reliability/validity assessments and PLS-SEM analysis, we empirically investigated (1) the influence of cloud-based technological capabilities on supply chain performance, (2) the contingent role of market uncertainty, and (3) the intermediary function of supply chain governance. The key empirical findings corroborating our theoretical propositions include the following:

First, our results demonstrate statistically significant positive relationships between both technological dimensions and supply chain performance. These findings substantiate the core proposition that cloud-enabled capabilities drive operational enhancements. Specifically, cloud adoption enables dynamic resource reconfiguration, which fosters cross-organizational coordination and ultimately elevates supply chain efficacy. Moreover, cloud computing adoption fosters cloud–business synergy, which strengthens supply chain collaboration through platform integration and contributes to overall performance enhancement. This result aligns with the resource-based view (RBV) theory, which asserts that firms can gain competitive advantages by leveraging unique, valuable, and inimitable resources [81,82]. As a technological resource, cloud computing improves Flexible IT Infrastructure and fosters cloud–business synergy, allowing firms to optimize supply chain operations, enhance collaboration, and better respond to dynamic market conditions. These capabilities are essential for strengthening supply chain performance, as they help enterprises align their products and markets strategically. By facilitating efficient resource allocation, reducing operational costs, and enhancing responsiveness, cloud computing enables firms to achieve competitive advantages and adapt more effectively to market fluctuations [83].

Theoretical implications emerge from testing the mediated relationship. Our analysis confirms that supply chain governance partially mediates the technology–performance linkage, explaining why cloud capabilities yield differential performance outcomes across organizational contexts. While most mediation hypotheses were supported, two pathways did not yield significant results. On the positive side, cloud computing enhances IT flexibility, which improves coordination among supply chain participants, leading to better governance and overall performance. Additionally, flexible IT facilitates the development and adjustment of contract systems among supply chain members, enhancing information sharing and, consequently, improving supply chain performance. Moreover, the adaptable IT infrastructure enabled by cloud computing reduces the cost of inter-organizational information exchange, fostering stronger collaboration, creating new opportunities, and enhancing supply chain efficiency. The mediating role of supply chain governance can be understood through transaction cost economics (TCE) theory. According to Grover and Malhotra [84], transaction costs in supply chain management consist of coordination costs and transaction risks. Coordination costs refer to expenses incurred when exchanging and processing information for decision making. For instance, in manufacturer–supplier collaborations, these costs may arise from sharing product specifications, pricing, and demand forecasts as well as handling design modifications. Transaction risks, on the other hand, stem from the possibility that parties involved in the transaction may fail to fulfill contractual obligations. The findings suggest that effective governance mechanisms, such as contractual agreements and relational norms, reduce transaction costs and improve coordination among supply chain partners. Cloud computing enhances IT infrastructure agility, which, in turn, facilitates contract design and enforcement while fostering trust and collaboration. These governance improvements contribute to better supply chain performance. However, the relationship between Cloud/Business Synergyand supply chain performance, mediated by contractual and relational governance, is not statistically significant. This outcome may be due to the fact that contractual and relational governance primarily address business-specific factors, making it difficult to evaluate Cloud/Business Synergywithin the framework of supply chain governance. Additionally, differences in cloud computing adoption across organizations, coupled with compatibility challenges, may have contributed to this result.

Finally, the results support the moderating role of market uncertainty, showing amplified performance benefits from cloud capabilities in uncertain conditions. The adaptive nature of cloud-based IT infrastructure proves particularly valuable for operational scaling and partner coordination when market stability decreases. This flexibility reduces supply chain costs and improves overall performance. Growing market instability prompts more active partner engagement, making cloud–business alignment particularly impactful for supply chain performance. This moderating effect can be explained through contingency theory, which posits that the effectiveness of organizational strategies and technologies depends on external environmental conditions [85]. In highly uncertain markets, firms face greater variability in demand, supply, and competitive dynamics. The flexibility and scalability of cloud computing enable firms to adapt more quickly to these changes, thereby enhancing supply chain performance. [86]. For example, the ability to rapidly scale IT resources and leverage real-time data analytics allows firms to deploy and manage IT resources more efficiently [87].

6.1. Theoretical Implication

This research introduces an original conceptual framework grounded in IT capability theory. To operationalize this construct, we delineated it into two measurable dimensions: Flexible IT Infrastructure and Cloud/Business Synergy, building upon established theoretical foundations. The investigation further elucidated value creation pathways through these dual service capabilities, presenting innovative insights into cloud technology evolution.

A second contribution lies in examining the mediating role of supply chain governance in translating cloud capabilities into performance outcomes. While prior industry reports acknowledge cloud computing’s business impact, and the academic literature identifies its value-generating technical attributes, empirical evidence specifically linking cloud adoption to supply chain performance remains scarce. Our study addresses this critical gap through rigorous contextual analysis.

The research framework additionally incorporates market uncertainty as a moderating factor, analyzing its conditioning effects on the cloud capability–performance relationship across diverse market scenarios. Cloud-enabled enterprises demonstrate enhanced capacity for inter-organizational coordination, facilitating robust information exchange and collaborative adaptation—key advantages for maintaining supply chain resilience amid market fluctuations. These findings advocate for strategic cloud adoption as a catalyst for supply chain, whether through business model transformation or operational process optimization.

6.2. Managerial Implications

Contemporary enterprises increasingly prioritize cloud technology integration within strategic planning, yet many struggle to maximize its potential benefits. Our findings yield three actionable recommendations:

First, organizations must develop holistic comprehension of cloud computing’s distinctive attributes. Beyond basic adoption, supply chain entities should strategically leverage elasticity, scalable architecture, and ubiquitous access features. Investment in adaptable IT infrastructure and flexible cloud–partner architectures enables synchronized response to demand variability and evolving partner business models, ultimately strengthening governance efficacy and operational performance.

Second, enterprises should capitalize on cloud computing’s inherent resource consolidation and collaborative potentials. Developing unified cloud platforms with standardized interfaces facilitates seamless integration of disparate IT systems across supply chain networks. This convergence achieves synchronization of critical flows (information, materials, and finances) while reducing inter-organizational data barriers, thereby optimizing IT governance efficiency. Centralized cloud repositories further enable shared resource utilization across the supply ecosystem.

Empirical evidence from our enterprise survey demonstrates that strategic utilization of cloud flexibility and synergy effectively mitigates structural rigidities in partner IT systems. Such adaptive capacity proves instrumental in responding to dynamic supply chain demands, yielding measurable performance improvements across operational metrics.

6.3. Limitations and Future Research

This research identifies several noteworthy limitations that provide valuable directions for future scholarly inquiry. Most fundamentally, while our analysis thoroughly examines the performance implications stemming from cloud computing’s technical capabilities, it does not account for potentially moderating organizational factors such as firm size, varying stages of digital transformation maturity, or distinct implementation strategies. Subsequent investigations would benefit from conducting comparative analyses across different cloud deployment models (public, private, and hybrid) to determine their respective influences on supply chain efficiency and resilience.

Secondly, the current study’s performance evaluation framework, while methodologically sound, could be enhanced through more comprehensive measurement approaches. Future research should incorporate a balanced scorecard perspective encompassing financial performance indicators (ROI and cost savings), environmental sustainability metrics (carbon footprint reduction and energy efficiency), and operational excellence benchmarks (lead time reduction and inventory turnover) to provide a more holistic assessment of cloud computing’s supply chain impacts.

Thirdly, regarding methodological considerations, our sample composition was geographically skewed toward regions with mature cloud computing ecosystems. To strengthen the external validity of findings, subsequent studies should adopt more inclusive sampling strategies that incorporate enterprises from developing markets where cloud adoption is in earlier stages. Notwithstanding the methodological rigor implemented in this study, several limitations pertaining to the questionnaire data collection process warrant acknowledgment. Primarily, while comprehensive measures were instituted to safeguard respondent anonymity, absolute confidentiality could not be fully guaranteed due to inherent traceability in survey administration protocols, which may have inadvertently constrained participant responses. Secondly, although leveraging industry associations as institutional intermediaries significantly enhanced enterprise participation willingness, the current collaboration framework exhibits discernible limitations. Future research could benefit from establishing more robust public–private partnerships to facilitate deeper organizational engagement, thereby yielding higher-fidelity datasets.

Finally, the cross-sectional nature of our data, while providing valuable snapshots of current cloud implementation effects, cannot capture longitudinal developments. Given that the surveyed enterprises had heterogeneous cloud adoption timelines ranging from initial implementation to mature utilization, future longitudinal studies employing panel data methodologies would better elucidate the evolutionary trajectory of cloud computing’s supply chain benefits.

7. Conclusions

Cloud computing has emerged as a paradigmatic digital innovation that continues to transform supply chain management practices globally. The extant literature has well documented its role in facilitating supply chain integration, as evidenced by seminal works from Bruque et al. [9,10]. However, our systematic review revealed a conspicuous gap in rigorous qualitative examinations of cloud computing’s enabling mechanisms—a critical oversight that has constrained managerial understanding of practical implementation strategies. Our research directly addresses this gap through the development and empirical validation of the multidimensional construct, which we operationalized through two theoretically grounded dimensions: dynamic IT infrastructure adaptability and synergistic cloud–business integration.

The empirical results yielded three substantive contributions to both academic discourse and managerial practice: First, our mediation analysis revealed that supply chain governance mechanisms fully account for the relationship between IT infrastructure adaptability and performance outcomes, whereas their mediating role in translating cloud–business integration benefits proves statistically non-significant. Second, our contingency analysis demonstrated that environmental volatility serves as a positive moderator, with cloud computing’s performance advantages becoming exponentially more pronounced under conditions of heightened market turbulence and uncertainty. Third, we provide empirically validated implementation guidelines suggesting that enterprises should strategically prioritize investments in modular, scalable IT architectures while simultaneously cultivating deep organizational alignment between cloud service functionalities and core business processes to maximize supply chain performance gains.

By systematically elucidating the boundary conditions and value creation pathways through which cloud computing enhances supply chain performance in dynamic market environments, this study makes significant theoretical contributions to the information systems and operations management literature while offering actionable, evidence-based recommendations for supply chain practitioners navigating digital transformation initiatives.