The Impact of Digital Technology Characteristics on Operational Decision Optimization: The Mediating Role of Information System Agility in Healthcare Supply Chains

Abstract

Healthcare supply chains operate in highly dynamic environments characterized by fluctuating demand. Digitalization, by enhancing the agility of information systems, can shorten information processing cycles, strengthen environmental sensing, and improve rapid response capabilities, thereby becoming a critical pathway and urgent requirement for achieving decision optimization in supply chains. The objective of this study is to analyze the mediating role of information system agility between digital technology characteristics and the optimization of operational decisions in healthcare supply chains in China. A questionnaire survey was conducted, yielding 360 valid responses from managers in Chinese healthcare supply chain enterprises, representing a response rate of 94.48%. The hypothesized paths were analyzed through the application of structural equation modeling. The results reveal that compatibility and visibility significantly promote operational decision optimization both directly and indirectly through improved information system agility, whereas reliability influences decision optimization only indirectly through the full mediating role of agility. This study provides a robust theoretical model that extends organizational information processing theory to the healthcare supply chain while offering practical insights into digital transformation and decision optimization.

1. Introduction

The global environment in which the Healthcare Supply Chain (HSC) operates is increasingly unstable. This instability is driven in part by repeated public health crises, rapid technological advancements, and shifting policy landscapes. Factors such as pandemics, AI-enabled healthcare services, and geopolitical conflicts have contributed to an unprecedented level of complexity, intensifying task uncertainty and operational challenges across the HSC [1,2,3]. In such a dynamic and high-risk external environment, organizations are required not only to maintain operational efficiency but also to make timely and optimal decisions under uncertainty [4]. The HSC typically involves highly sensitive, strictly regulated, and time-critical products and services [5]. It integrates multiple stakeholders across the value chain, including manufacturers, distributors, logistics providers, storage facilities, hospitals, and patients [6]. As a result, HSCs are distinguished by a high level of intricacy, multi-actor involvement, and low tolerance for risk [5]. In the context of China, the sector faces a range of structural challenges, such as lengthy management chains, information overload, fragmented coordination mechanisms, and poor system interoperability. These limitations severely constrain response agility and hinder the fulfillment of modern healthcare’s demands for flexibility [7,8].

To achieve their intended performance goals, firms must have the capacity to effectively respond to uncertainty. According to Organizational Information Processing Theory (OIPT), the core function of a firm is to coordinate the acquisition, transmission, and utilization of information in order to cope with task-related uncertainty [9]. As uncertainty limits the organization’s capacity to make decisions through pre-established plans, enhancing information processing capability becomes critical for enabling high-quality decision-making. OIPT posits two strategic pathways for managing uncertainty: the first involves reducing information processing requirements, such as creating slack resources or decoupling tasks to minimize coordination costs; the second focuses on enhancing information processing capacity, through investments in vertical information systems and the development of lateral coordination mechanisms to improve sensing, communication, and response capabilities [10]. In today’s highly interconnected and fast-paced supply chain environments, the former strategy often proves insufficient to keep pace with rapid change, whereas the latter is increasingly viewed as a more strategically viable option.

In the digital era, organizations are increasingly leveraging advanced technologies to build high-performance information systems tailored for dynamic environments [11]. For instance, Internet of Things (IoT) technologies have enhanced supply chain visibility by enabling cross-level and cross-organizational information transparency [12]. Blockchain technologies, through smart contracts and immutable ledgers, have improved the security and reliability of information systems [13,14]. Furthermore, enterprise platforms such as Enterprise Resource Planning (ERP), Health Information Exchange (HIE), and Manufacturing Execution Systems (MES) have significantly enhanced system compatibility and cross-organizational data integration [15,16,17]. These digital characteristics serve as essential enablers of information processing capability and form the technical foundation for developing agile information system competencies [18]. Agility, considered an adaptive capability, provides entities with the means to swiftly perceive environmental variations, process information, and respond effectively, ultimately supporting the optimization of operational decisions [19]. In highly uncertain and time-sensitive domains such as the pharmaceutical supply chain, establishing an agile information infrastructure powered by digital technologies is critical to strengthening risk responsiveness and decision-making efficiency.

While prior research has largely examined the direct benefits of IT investment and system deployment, such as Vishwakarma et al. [20], who examined the impact of blockchain technology on HSC performance, most of this literature tends to analyze the effect of specific drivers on firm on performance outcomes. For instance, Kumar [21] investigated how flexibility affects the sustainable development of healthcare organizations. However, in the HSC, an area characterized by high dynamism and risk, technologically enabled information decision systems play an especially critical role, yet empirical studies exploring this mechanism remain limited. To address this research gap, the current study builds on OIPT to develop an integrated framework. It posits that three key Digital Technology Characteristic (DTC)s, including compatibility, reliability, and visibility, can enhance supply chain Information System Agility (ISA), thereby improving Operational Decision Optimization (ODO).

This study focuses on the HSC, which is characterized by high complexity, information intensity, and time sensitivity. Grounded in OIPT, it investigates how DTCs influence the optimization of operational decisions through the mediating role of supply chain ISA. The primary aim is to extend the theoretical and empirical applicability of OIPT within the HSC context. By developing an integrated conceptual framework and validating it through empirical analysis, this research offers a novel perspective that reframes digital transformation not merely as an upgrade of technological infrastructure but as a strategic pathway for building agile decision-making systems tailored to high-risk and highly dynamic healthcare environments.

The rest of this article is organized as follows. Section 2 provides a review of the relevant literature on DTCs (i.e., compatibility, reliability, and visibility), ISA, and ODO in the context of HSCs, while identifying key research gaps. Section 3 presents the proposed research model and hypotheses, and outlines the research design and data collection procedures. Section 4 reports the empirical results, including the validation of the structural model, path analysis, and mediation effect testing. Section 5 examines the results with respect to their theoretical and practical significance and underscores the contributions of the study, and acknowledges its limitations along with suggestions for future research.

The objective of this study is to analyze the mediating role of ISA between DTCs and the optimization of operational decisions in HSCs in China. Although prior studies have examined digitalization in supply chains, little is known about how specific technological features are transformed into decision-making advantages through enhanced information processing capabilities. By adopting a Structural Equation Modeling (SEM) approach, this study not only addresses this gap but also provides a robust theoretical model that extends OIPT to the HSC while offering practical insights into digital transformation and decision optimization.

2. Literature Review

2.1. Digital Technology Characteristics

DTCs refer to the inherent attributes of digital tools, systems, or infrastructures that determine their potential to support organizational processes and decision-making [22]. These characteristics are grounded in classical theories such as the Diffusion of Innovations Theory and the Resource-Based View [23,24]. For instance, the Information System Success Model highlights key dimensions of system quality, including compatibility, reliability, usability, and flexibility [25,26]. Similarly, the Technology Acceptance Model underscores technology attributes such as compatibility, visibility, and complexity as critical determinants of adoption intention [27]. These foundational perspectives offer a theoretical lens for evaluating the value and applicability of digital solutions in HSC management.

During the digital transformation of HSC, deploying digital technologies requires addressing challenges associated with data-intensive and time-sensitive operations [28]. Drawing on OIPT [10], this study focuses on three interdependent DTCs, including compatibility, reliability, and visibility, as core enablers of information processing capabilities. Compatibility ensures seamless system integration and workflow coordination [29]; reliability provides secure, stable, and consistent information flows [30]; and visibility enables timely access to and monitoring of supply chain status [31]. These characteristics form a mutually reinforcing structure that supports the input, processing, and output of organizational information.

By integrating these three characteristics into a unified framework, this study explores how digital technologies can support agile decision-making in complex and high-risk HSCs. This approach not only addresses the fragmented treatment of digital capabilities in the extant literature but also offers a solid basis for analyzing the digital prerequisites of ODO.

2.1.1. Compatibility

From a technological systems perspective, Serôdio et al. [32] conceptualize compatibility as the structural adaptability between new systems and existing technological infrastructures, emphasizing interconnectivity and standardized interfaces. Within the framework of supply chain digital transformation, compatibility is widely recognized as a critical factor influencing inter-system collaboration, platform integration efficiency, and data-sharing capabilities [33]. This is particularly salient in the healthcare and pharmaceutical supply chain sectors, where compatibility presents more complex challenges.

On one hand, the pharmaceutical industry imposes stringent requirements on system standardization, security, and regulatory compliance. Inconsistent data interfaces among heterogeneous systems often result in information silos and operational risks [34]. On the other hand, HSCs involve multiple stakeholders, including patients, hospitals, pharmaceutical suppliers, logistics platforms, and regulatory bodies, each utilizing distinct system architectures, process rules, and technological languages, thereby exacerbating the difficulty of system integration [35].

Against this backdrop, compatibility not only determines the feasibility of digital system deployment but also deeply influences the level of inter-organizational collaboration and the general operational effectiveness of the supply chain. Accordingly, this study defines compatibility as the integrative adaptability of digital technologies with existing information platforms, business processes, and regulatory standards during digital transformation initiatives in healthcare organizations. Anchored in the framework of OIPT, this research analyzes how compatibility influences ISA and decision optimization, thereby addressing critical gaps in the extant literature.

2.1.2. Reliability

According to the classical ERVQUAL framework proposed by Parasuraman et al. [36], reliability was initially defined as the capacity to deliver the committed service reliably and precisely, highlighting the predictability and consistency of service outputs. From an organizational operations perspective, Skowron-Grabowska et al. [37] argue that reliability should also encompass the effectiveness and continuity of system operations in meeting user requirements, particularly relevant in highly sensitive sectors such as healthcare and emergency logistics.

Over time, the concept of reliability has evolved beyond service-level trustworthiness and process safety to encompass a broader set of performance attributes, including system stability, functional consistency, and operational robustness [38,39]. In this study, reliability refers to the extent to which digital technology systems in the HSC can ensure stable, secure, and consistent performance during information processing, command transmission, and task execution [40].

Particular emphasis is placed on whether digital systems can continuously generate accurate, complete, and trustworthy data and operational outcomes in high-frequency, high-risk medical environments. This reliability is essential for ensuring effective pharmaceutical distribution, inventory coordination, and collaborative decision-making, thereby minimizing the risks posed by system failures or information errors to patient safety and organizational performance [41,42]. However, despite its importance, current research on digital technology reliability in enhancing supply chain agility and decision efficiency in healthcare remains limited. Most existing studies focus narrowly on specific technologies such as blockchain, offering insufficient insight into internal operational management mechanisms.

2.1.3. Visibility

In digital supply chain research, visibility is widely recognized as a key technological characteristic for assessing the transparency, monitorability, and traceability of HSC operations [43,44]. As HSC networks grow increasingly complex, the ability of organizations to dynamically access cross-departmental and cross-node data has become fundamental to achieving coordinated and efficient operations [45]. According to Fabbe-Costes et al. [46], visibility enables end-to-end traceability across supply chain entities through digital technologies, thereby enhancing the transparency of process information.

In the HSC, visibility plays an even more critical role due to its reliance on stringent requirements such as regulatory compliance in drug distribution, batch-level traceability, and accurate inventory control [47]. For instance, the integration of IoT and blockchain technologies has significantly improved the transparency and traceability of information in pharmaceutical logistics. This technology-driven visibility ensures full-process monitoring of drug movement, from production to the hands of patients, thus enhancing patient safety [48]. Consequently, data visibility confers strategic advantages to HSCs. Through the transparent and visualized presentation of data across procurement, inventory, and distribution processes, healthcare organizations can more rapidly identify potential shortages, excess inventories, and logistical bottlenecks, enabling more precise responses and resource optimization [49]. Agrawal et al. [50], through a Delphi study, confirmed that managers place a high value on visibility, recognizing its significant role in reducing supply risks and enhancing system flexibility and agility.

While existing literature has extensively emphasized the benefits of visibility, such as improved transparency, traceability, and collaboration, there remains a lack of clarity regarding the underlying mechanisms by which visibility contributes to enhanced information processing capabilities and ODO. Such a gap becomes most apparent in the context of HSCs, where the logical linkage between visibility and decision support remains underexplored.

2.2. Supply Chain Information System Agility

The supply chain ISA should not be limited to the mere speed of information transmission; rather, it should be understood as a system’s inherent capability to respond instantly, process at high frequency, and execute tasks rapidly [51]. Tarannum and Hossain [52] further emphasized that agility denotes a firm’s capacity to ensure instantaneous information transmission and processing across decision-making, coordination, and execution stages through system-based solutions during supply chain operations. For example, Real-Time Management Information Systems (RT-MIS) enable enterprises to meet high standards for zero-delay real-time management through efficient data synchronization and operational execution mechanisms.

This study defines ISA as the extent to which HSC information systems, under the deployment of digital technologies, possess the capabilities of rapid response, high-frequency processing, and instant feedback [53,54]. Specifically, this refers to the system’s ability to transmit instructions, execute tasks, and update information in real time when faced with situations such as drug shortages, sudden demand surges, or logistical delays. Such agility shortens decision-making cycles, improves response speed, and ensures the precise allocation and uninterrupted supply of critical medical resources in urgent scenarios [55].

However, current research has not empirically deconstructed agility into measurable dimensions such as real-time task execution, event-driven coordination efficiency, and dynamic system operability [56]. Therefore, building on the OIPT, the present research examines the effect of system-level agility on decision-making performance, specifically within the high-frequency, rapid-response, and closed-loop operational needs of HSCs.

2.3. Healthcare Supply Chain Operational Decision Optimization

Decision optimization is not only a critical pathway for improving service efficiency and reducing resource waste, but also a key manifestation of organizational capability restructuring and strategic advancement [57]. In the context of multiple coexisting constraints, such as time sensitivity, product risk, and regulatory compliance, decision-making within HSCs is increasingly shifting toward a governance model that integrates data-driven approaches with embedded organizational capabilities. Li et al. [58], from the perspective of information uncertainty, define decision optimization as a technical control process that aims to minimize resource waste and response delays under conditions of incomplete information. Establishing effective and smart decision-making systems rooted in organizational capabilities plays a critical role in managing the heightened complexity and continual disruptions of HSCs [59]. From a Resource-Based View, Colombari et al. [60] emphasize that digital technology capabilities, organizational agility, and data processing systems constitute the fundamental resource base driving intelligent decision-making. Similarly, Martínez-Peláez et al. [61] highlight from a data-driven perspective that behavioral coordination between supply and demand plays a crucial role in forming optimal decision pathways. However, most existing studies remain focused on technical functionalities or system-level optimizations, overlooking the relational mechanisms between DTCs, including compatibility, reliability, visibility, and decision-making processes. This gap limits the understanding of how digital technologies support dynamic decision-making by enhancing organizational information processing capabilities.

3. Hypotheses Development

3.1. Research Hypotheses

3.1.1. Digital Technology Characteristics and Operational Decision Optimization

Healthcare organizations are not only required to meet the demands of service safety and clinical professionalism but are also expected to respond swiftly to fluctuating demands, allocate resources precisely, and enhance patient satisfaction across multiple dimensions. Consequently, improving the efficiency and quality of HSC decision-making has become a critical issue [62]. With the increasing integration of emerging technologies including Artificial Intelligence (AI), the IoT applications, and big data analytics in the healthcare sector, the inherent characteristics of digital technologies have demonstrated their capacity to enhance organizational information processing capabilities, improve transparency, and increase process execution efficiency. These capabilities, in turn, facilitate more efficient, agile, and intelligent resource allocation and strategic decision-making [12,63]. However, from a mechanistic perspective, the specific pathways through which different technological attributes affect organizational decision performance remain underexplored.

From a process-oriented view, various DTCs influence organizational decision performance through distinct mechanisms. First, compatibility determines the degree of integration between new systems and existing technological platforms and operational processes [29]. Highly compatible systems reduce transformation barriers and organizational learning costs, improve data flow between systems, and support the smooth establishment of operational mechanisms during digital transitions. This enhances data sharing and resource coordination across the HSC, ultimately enabling more efficient decision systems [64,65]. Second, reliability refers to the stability and accuracy of a system during its operation. In HSCs, any data interruption or anomaly can result in stockouts, delivery failures, or even patient safety incidents. Therefore, a stable and efficient data processing system is a prerequisite for reliable risk assessment and strategic planning [37,66]. Third, visibility captures the organization’s real-time perception of supply chain node status, logistics flows, and inventory levels. By integrating information and presenting it through visualization tools, managers can more effectively identify operational bottlenecks and early warning signals, enabling multi-departmental coordination and managerial decisions informed by real-time information [49,67].

From the perspective of OIPT, HSCs characterized by dynamic complexity and uncertainty, require enhanced information processing capabilities to reduce the mismatch between information processing needs and organizational capacity [10]. Compatibility, reliability, and visibility collectively constitute a complementary technological foundation that strengthens an organization’s ability to execute productive and accurate operational decisions in the face of environmental dynamism and resource constraints [33]. Accordingly, our study introduces the hypotheses outlined below:

H1-1. 

Compatibility has a positive effect on operational decision optimization.

H1-2. 

Reliability has a positive effect on operational decision optimization.

H1-3. 

Visibility has a positive effect on operational decision optimization.

3.1.2. Digital Technology Characteristics and Supply Chain Information System Agility

According to the bibliometric analysis conducted by Seyedghorban et al. [22], data-driven supply chain management and supply chain ISA are among the most prominent topics shaping the future of digital supply chains. From the perspective of OIPT, data-driven mechanisms that enable information sharing, rapid trust-building, and inter-organizational collaboration are regarded as essential enablers of agility in supply chains [19]. For instance, the integration of the IoT into HSCs enhances data visibility and transparency by capturing real-time monitoring data and supporting automated, data-driven decisions [68,69]. Edge computing and cloud technologies further allow for instantaneous data aggregation, while AI enables forecasting analytics, and blockchain technology ensures the traceability, security, dependable information exchange [66,70]. The compatibility among these digital technologies and the integration across operational processes collectively enhance the integrity and agility of supply chain information systems [64]. Empirical evidence by Ramos et al. [71] also confirms that under highly uncertain and dynamic conditions, both internal and external data integration can significantly improve organizational flexibility, which in turn drives agility and enables high-performance supply chain operations.

In summary, DTCs including compatibility, reliability, and visibility, enhance an organization’s information processing capacity by improving data integration, ensuring stable transmission, and enabling greater information visibility. These capabilities collectively contribute to the development of ISA, which is defined by rapid responsiveness and efficient execution. In the complex and multi-stakeholder environment of HSCs, compatibility facilitates seamless integration across technical systems, reducing information silos and process discontinuities, and accelerating data flow. Reliability ensures system continuity and data trustworthiness, which are critical for decision accuracy and risk mitigation. Visibility, in turn, strengthens real-time awareness of operational states and enhances the organization’s ability to respond to disruptions. Drawing on OIPT, these DTCs reduce the gap between information processing requirements and organizational capacity, thereby providing a technological foundation for agile information systems. Accordingly, our study introduces the hypotheses outlined below:

H2-1. 

Compatibility has a positive effect on supply chain information system agility.

H2-2. 

Reliability has a positive effect on supply chain information system agility.

H2-3. 

Visibility has a positive effect on supply chain information system agility.

3.1.3. Healthcare Supply Chain Information System Agility and Operational Decision Optimization

The agility of supply chain information systems reflects an organization’s ability to respond swiftly, process information in real time, and provide effective feedback when facing external disruptions or demand fluctuations [62]. In the context of HSCs, the timeliness of pharmaceutical delivery, real-time inventory coordination, and emergency responsiveness are highly dependent on system performance speed and the efficiency of information synchronization [71]. Shashikumar [72] highlights that information systems with agile characteristics, enabled by predictive modeling and data analytics, can rapidly integrate multi-source data and generate forward-looking insights, thereby supporting optimal decision-making in pharmaceutical procurement, logistics scheduling, and emergency response. Similarly, Vanvactor [73] finds that data-driven agile systems significantly shorten the decision-making cycle of healthcare organizations, enhancing both intervention timeliness and service quality. Mandal [74] further reveals a significant positive relationship between high-frequency data processing and strategic adaptability in technology-oriented organizations. Especially in healthcare environments characterized by resource constraints and volatility, agile information systems play a pivotal role in capturing real-time signals, linking decision-making processes, and optimizing response flows, ultimately improving the quality of operational judgment and strategic execution [75]. Accordingly, our study introduces the hypotheses outlined below:

H3. 

Healthcare supply chain information system agility has a positive effect on operational decision optimization.

3.1.4. The Mediated Effects of Supply Chain Information System Agility

Within the framework of OIPT, DTCs enhance an organization’s information processing capacity, thereby narrowing the gap between information processing requirements and capabilities, and indirectly influencing ODO [76]. ISA serves as the core link in this mechanism, functioning as the conduit through which technological attributes are transformed into decision-making advantages. On one hand, characteristics such as compatibility, reliability, and visibility reinforce the efficiency of information acquisition, transmission, and integration from the perspectives of system integration, data credibility, and state awareness, respectively [77]. On the other hand, these improvements enable information systems to more rapidly detect environmental changes, formulate response strategies, and execute them efficiently, thereby facilitating rapid reactions to dynamic demands and unforeseen disruptions [78]. As such, ISA mediates the relationship between technological characteristics and ODO by converting potential technological benefits into tangible decision-making and action performance.

Specifically, compatibility ensures seamless data and interface integration between systems and across departmental processes, thereby reducing data conversion delays and process interruptions. This high level of integration allows information systems to more quickly and comprehensively consolidate cross-functional data, forming agile response capabilities [79]. In this process, agility acts as a mediator by transforming the efficient data flows enabled by system integration into rapid cross-departmental collaborative judgments and execution capabilities, thereby achieving more precise and efficient operational decisions in the face of external demand fluctuations or resource constraints [80]. Reliability ensures the stability and accuracy of information collection, transmission, and processing, minimizing the risk of decision-making delays caused by distorted or interrupted information. Based on this foundation, agile information systems can rapidly generate credible decision inputs at critical moments. Through this mediating mechanism, the stable data flows afforded by reliability are transformed into highly time-sensitive interventions and strategy adjustments [81,82]. Visibility provides full transparency regarding supply chain operating status, inventory dynamics, and logistics routes, enabling organizations to promptly identify risk points and operational bottlenecks. Building on this, ISA converts real-time situational awareness into flexible decision adjustments and efficient resource allocation plans [82,83,84].

In sum, compatibility through integration speed, reliability through data security, and visibility through state awareness, enhance ISA via three distinct information processing mechanisms. Agility shortens decision-making cycles in HSCs, improves the execution efficiency of strategies under uncertainty, and thereby achieves ODO. Accordingly, our study introduces the hypotheses outlined below:

H4-1. 

Information system agility mediates the relationship between compatibility and operational decision optimization.

H4-2. 

Information system agility mediates the relationship between reliability and operational decision optimization.

H4-3. 

Information system agility mediates the relationship between visibility and operational decision optimization.

3.2. Research Model

The research model of this study is developed based on the OIPT. According to this framework, organizations need to enhance their information processing capabilities by investing in vertical information systems and establishing horizontal relationship networks in order to achieve predetermined objectives [10]. In this study, vertical information systems are represented by DTCs. Specifically, compatibility corresponds to the information input stage of OIPT, emphasizing the degree of integration across systems and processes to ensure seamless data flows across platforms and departments. Reliability aligns with the information processing stage, underscoring the stability and accuracy of data transmission and handling, thereby preventing systemic risks caused by data distortion or interruptions. Visibility reflects the information output and environmental sensing stage, whereby end-to-end transparency and traceability enhance the organization’s capability to monitor external disruptions. Horizontal relationship networks, in turn, correspond to the cross-functional integration that enables digital technologies to foster ISA. Within HSCs, the efficiency of information acquisition, transmission, and processing directly determines the effectiveness of operational decision-making. Accordingly, this study conceptualizes DTCs including compatibility, reliability, and visibility, as the key independent variables that influence information processing capabilities. Through the mediating role of supply chain ISA, these characteristics contribute to the achievement of the dependent variable ODO. As illustrated in Figure 1, the proposed research model integrates the relationship pathways among DTCs, ISA, and ODO.

4. Methodology

SEM provides a robust analytical approach for simultaneously estimating measurement models and the structural relationships among latent constructs, while offering clear visualization for model validation. SEM typically evaluates two interrelated components: the measurement model and the structural path model [85]. Two dominant approaches exist in the SEM domain: covariance-based SEM (CB-SEM) and variance-based partial least squares SEM (PLS-SEM). CB-SEM is generally more appropriate when the research objective is theory testing and validation using factor-based models, whereas PLS-SEM is recommended for prediction and theory development with composite-based models [86]. Given that this study aims to validate an established theoretical framework and is supported by a sufficiently large sample size (n > 200), we employed CB-SEM implemented through SPSS 26.0 and AMOS 24.0, rather than PLS-SEM via SmartPLS 3.0 [87]. This methodological choice allows for a more rigorous evaluation of model fit indices and enhances the robustness of the hypothesized causal inferences. It is particularly suitable for HSC research, where stringent methodological standards are required to ensure reliability and validity of the findings [88].

To ensure the rigor of the research methodology, this study adopted a two-step analytical strategy using SPSS 26.0 and AMOS 24.0 [89]. In the first stage, Exploratory Factor Analysis (EFA) was conducted to identify the underlying factor structure and minimize model specification bias [90]. This was followed by Confirmatory Factor Analysis (CFA) to assess the reliability and validity of the measurement model, thereby ensuring the robustness of construct development [91]. Pearson correlation analysis was performed to examine the associations and distinctions among variables. Construct reliability was evaluated using Cronbach’s α and Composite Reliability (CR), while model fit was assessed through indices such as CFI, TLI, RMSEA, and SRMR to confirm the robustness of the measurement model [92,93]. Furthermore, discriminant validity and multicollinearity tests were conducted to ensure variable independence and model stability [94,95,96]. Building upon these validated foundations, the second stage tested the hypothesized causal relationships through CB-SEM. Path analysis was employed to examine the direct effects among variables, while the mediating effects were assessed using bootstrapping procedures with 5000 resamples [97]. Compared with other statistical approaches, SEM offers the advantage of simultaneously testing both measurement and structural models, thereby reducing estimation bias and providing a holistic view of the interrelationships among constructs. This analytical strategy not only ensures the robustness of the empirical results but also strengthens the theoretical persuasiveness of the proposed model.

4.1. Measurement

Based on the theoretical framework and validated hypotheses of this study, a structured questionnaire was designed using measurement items adapted from well-established and empirically validated scales in prior literature. Each latent construct in the proposed model, including DTCs (compatibility, reliability, visibility), supply chain ISA, and ODO, was measured using five items. The selected items were refined to align with the HSC context while ensuring construct validity and conceptual consistency. This study employed a five-point Likert scale to measure all constructs. Under this approach, respondents were asked to indicate their level of agreement with each item on a five-level continuum: 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. This scaling method allows for the systematic quantification of respondents’ attitudes, ensuring comparability across constructs and enhancing the reliability of the measurement model [98]. A detailed list of the measurement items and their sources is provided in

Table 1. Variable Measurement.

Variables		Items	Sources
Compatibility	C1	Our firm’s software is compatible with supply chain partners’ software.	[64]
	C2	Our supply chain partners’ information systems are technically compatible with those of our firm.
	C3	Technical capabilities of our firm and supply chain partners are compatible.
	C4	Our firm’s procedures are compatible with our supply chain partners’ business procedures.
	C5	Our firm’s operational processes are compatible with supply chain partners’ operational processes.
Reliability	R1	We believe our digitally transformed supply chain system is stable.	[66]
	R2	We believe our digitally transformed supply chain transmits data accurately.
	R3	We believe the information in our digitally transformed supply chain is reliable.
	R4	We believe the data in our digitally transformed supply chain is secure.
	R5	We believe our digitally transformed supply chain is trustworthy.
Visibility	V1	Our company shares data with our supply chain partners.	[99,67]
	V2	Our data exchanged with supply chain partners is traceable.
	V3	Our company exchanges information with supply chain partners.
	V4	Our company maintains information transparency with supply chain partners.
	V5	Our company ensures information symmetry with supply chain partners.
Information System Agility	ISA1	Our organization is able to quickly process information.	[71,67]
	ISA2	Our organization is able to analyze data in a timely and effective manner.
	ISA3	Our organization is able to transmit information rapidly across the supply chain.
	ISA4	Our organization is able to quickly generate action plans in response to new data.
	ISA5	Our organization is capable of swiftly receiving and integrating incoming information.
Operational Decision Optimization	ODO1	We make managerial decisions more efficiently and accurately with the support of digital technologies.	[78]
	ODO2	We identify and respond to risks more effectively by leveraging digital technologies.
	ODO3	We improve our strategic planning through the use of digital tools and data analytics.
	ODO4	We optimize our operational decision-making with the aid of digital technologies.
	ODO5	We make product development decisions that are increasingly driven by data and digital intelligence.

.

4.2. Demographics

This study targeted managers from organizations that constitute the Chinese HSC, including pharmaceutical companies, medical device manufacturers, pharmaceutical logistics enterprises, and online pharmaceutical retail platforms. These organizations are directly associated with the core links of the HSC, namely production, distribution, and end-user service. According to statistics from the National Medical Products Administration (NMPA), there are 10,239 registered pharmaceutical enterprises in China, encompassing state-owned, private, and multinational companies [100]. This study targeted managers from these organizations. A non-probability random sampling method, specifically convenience sampling with random distribution, was employed, which is widely applied in quantitative research [101]. Data were collected through an online questionnaire (www.wjx.cn), which was distributed and retrieved via email and social networking services.

Prior to the formal survey, a pre-testing was conducted to assess the adequacy of the questionnaire design. The pilot respondents were also managers involved in the Chinese HSC, and the same random sampling method was applied. The pre-testing lasted one week, during which 20 questionnaires were distributed and retrieved. Reliability and validity of the scale were examined through the KMO test and Cronbach’s α coefficients, which yielded a KMO value greater than 0.6 and α values above 0.8, indicating satisfactory internal consistency.

The formal data collection was conducted over a five-month period (7 October 2024–7 March 2025). A total of 381 responses were obtained. To mitigate potential bias introduced by convenience sampling and to ensure that the data met the assumptions of normal distribution for subsequent statistical analyses, this study performed an outlier elimination procedure on the formal survey data. The specific steps were as follows: first, all item responses from each participant were summed and averaged; the mean scores were then transformed into standardized Z-scores [102,103]. Using the standard normal distribution as a reference, observations with Z-values greater than +2 or less than –2 were identified as outliers, resulting in the exclusion of 21 extreme cases from the dataset. After excluding 21 invalid responses due to inconsistent data, 360 valid responses were retained for analysis, yielding a valid response rate of 94.48%. To ensure the adequacy of the sample size for CB-SEM, a post hoc power analysis was conducted using G*Power 3.1. With a sample size of 360, three predictors, a significance level of 0.05, and a medium effect size (f² = 0.15), the achieved power was 0.99, exceeding the recommended threshold of 0.80 [104]. This confirms that the sample size was sufficient for the application of CB-SEM.

As shown in

Table 2. Demographic Characteristics of the Respondents.

Variables	Category	Frequency	Ratio (%)
Gender	Male	160	44.4
	Female	200	55.6
Age	<35	249	69.2
	35~50	106	29.4
	>50	5	1.4
Education Background	Associate Degree	53	14.7
	Bachelor	224	62.2
	Master	54	15.0
	Doctor	29	8.1
Professional Experience	<5	196	54.4
	5~15	136	37.8
	>5	28	7.8
Annual Revenue of the Respondent’s Organization (CNY)	<40 million	197	54.7
	40~400 million	113	31.4
	>400 million	50	13.9
Ownership Type of the Organization	Domestic Enterprise	223	61.9
	Sino-Foreign Joint Venture	112	31.3
	Wholly Foreign-Owned Enterprise	25	6.9

, the respondents were predominantly female (55.6%) and relatively young, with 69.2% under the age of 35 and 29.4% between 35 and 50 years old. Most participants held a bachelor’s degree (62.2%) or above, indicating a well-educated sample. Regarding professional experience, 54.4% had less than five years of work experience, while 37.8% had five to fifteen years, aligning with the relatively young age distribution. In terms of organizational background, the majority of respondents worked in firms with annual revenue under 40 million CNY (54.7%), followed by 31.4% in medium-sized firms (40–400 million CNY), and 13.9% in large-scale enterprises (above 400 million CNY). Most organizations were domestic enterprises (61.9%), with the remainder comprising joint ventures (31.1%) and wholly foreign-owned firms (6.9%).

5. Data Analysis and Results

5.1. Exploratory Factor Analysis

To examine the construct validity and underlying structure of the measurement items, EFA was conducted using SPSS 26.0. According to Kaiser and Rice [105], the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is evaluated as follows: values greater than 0.90 are considered marvelous, between 0.80 and 0.90 are meritorious, between 0.70 and 0.80 are middling, between 0.60 and 0.70 are mediocre, between 0.50 and 0.60 are miserable, and values below 0.50 are deemed unacceptable. As shown in

Table 3. Exploratory Factor Analysis Results.

Variables	Codes	Factor Loading					Cronbach’s α
		1	2	3	4	5
Compatibility	C1	0.154	0.772	0.225	0.135	0.216	0.916
	C2	0.212	0.773	0.236	0.205	0.153
	C3	0.221	0.737	0.188	0.175	0.256
	C4	0.202	0.796	0.210	0.144	0.197
	C5	0.171	0.793	0.146	0.193	0.226
Reliability	R1	0.114	0.273	0.108	0.169	0.703	0.875
	R2	0.165	0.104	0.135	0.133	0.778
	R3	0.159	0.189	0.159	0.134	0.766
	R4	0.204	0.183	0.181	0.165	0.764
	R5	0.113	0.226	0.201	0.226	0.717
Visibility	V1	0.150	0.157	0.789	0.179	0.209	0.905
	V2	0.152	0.210	0.779	0.194	0.155
	V3	0.206	0.147	0.808	0.126	0.141
	V4	0.178	0.247	0.792	0.201	0.136
	V5	0.266	0.228	0.689	0.204	0.198
Information System Agility	ISA1	0.144	0.129	0.171	0.766	0.172	0.886
	ISA2	0.195	0.224	0.120	0.741	0.161
	ISA3	0.204	0.119	0.187	0.698	0.198
	ISA4	0.214	0.196	0.198	0.761	0.174
	ISA5	0.263	0.133	0.184	0.788	0.131
Operational Decision Optimization	ODO1	0.774	0.231	0.224	0.258	0.148	0.931
	ODO2	0.835	0.171	0.165	0.249	0.153
	ODO3	0.715	0.194	0.229	0.131	0.182
	ODO4	0.820	0.193	0.139	0.280	0.153
	ODO5	0.810	0.185	0.228	0.201	0.216
Eigen Value (Rotated)		3.850	3.738	3.673	3.547	3.447	-
Explained Variance (%)		15.398	14.953	14.693	14.189	13.787
Cumulative Variance (%)		15.398	30.351	45.045	59.234	73.021
KMO = 0.947, Bartlett = 6477.564, Sig = 0.000, df = 300.

, the KMO measure of sampling adequacy was 0.947, and Bartlett’s Test of Sphericity was significant (χ² = 6477.564, df = 300, p < 0.001), indicating the appropriateness of factor analysis. Principal component analysis with varimax rotation was employed. Based on the eigenvalue criterion (>1), five distinct factors were extracted, which together explained 73.021% of the total variance [106]. Each item exhibited a high factor loading (>0.60) on its corresponding construct and low cross-loadings on unrelated factors, indicating satisfactory convergent and discriminant validity [107]. The extracted factors aligned with the proposed constructs: compatibility, reliability, visibility, ISA, and ODO. Similarly, Cronbach’s alpha is generally interpreted as follows: α ≥ 0.90 indicates excellent internal consistency, 0.80–0.90 is good, 0.70–0.80 is acceptable, 0.60–0.70 is questionable, 0.50–0.60 is poor, and below 0.50 is unacceptable. In this study, all constructs demonstrated strong internal consistency, with Cronbach’s α coefficients ranging from 0.875 to 0.931, well above the recommended threshold of 0.70 [108]. To assess potential Common Method Bias (CMB), Harman’s single-factor test was applied. This test evaluates whether a single latent factor accounts for the majority of the variance in the dataset; if such dominance is observed, it suggests that common method variance may inflate the results [109]. In this study, the results of the unrotated exploratory factor analysis indicated that the first principal factor accounted for 45.202% of the variance, which is below the critical threshold of 50% [110]. This value confirms that no single factor dominated the variance structure, and thus common method bias is unlikely to pose a serious threat to the validity of the findings. Collectively, these results confirm the validity and reliability of the measurement model.

5.2. Confirmatory Factor Analysis

To assess the construct validity of the measurement model, CFA was conducted using AMOS 24.0. The Goodness of Fit Index (GFI) and Adjusted Goodness of Fit Index (AGFI) evaluate overall model fit, with thresholds of >0.90 and >0.80, respectively. The Comparative Fit Index (CFI) and Tucker–Lewis Index (TLI) compare the proposed model with a baseline, where values >0.90 indicate acceptable fit. The Root Mean Square Error of Approximation (RMSEA) measures approximation error (<0.08 acceptable, <0.05 good), while the Standardized Root Mean Square Residual (SRMR) captures residual differences (<0.08 acceptable) [86]. As shown in

Table 4. Confirmatory Factor Analysis Results.

Variables	Codes	Unstd.	SE	T-Value	p	Std.	CR	AVE
Compatibility	C1	1				0.811	0.917	0.690
	C2	1.007	0.055	18.328	***	0.837
	C3	1.018	0.058	17.660	***	0.815
	C4	1.050	0.056	18.685	***	0.848
	C5	0.889	0.048	18.423	***	0.841
Reliability	R1	1				0.727	0.877	0.589
	R2	1.068	0.080	13.322	***	0.740
	R3	1.125	0.081	13.881	***	0.782
	R4	1.074	0.074	14.554	***	0.812
	R5	0.923	0.066	14.037	***	0.773
Visibility	V1	1				0.810	0.909	0.667
	V2	0.966	0.055	17.580	***	0.820
	V3	0.971	0.055	17.601	***	0.810
	V4	0.811	0.044	18.605	***	0.858
	V5	0.827	0.050	16.456	***	0.783
Information System Agility	ISA1	1				0.756	0.889	0.615
	ISA2	1.023	0.070	14.668	***	0.769
	ISA3	0.926	0.067	13.750	***	0.722
	ISA4	1.087	0.069	15.863	***	0.832
	ISA5	0.937	0.058	16.272	***	0.837
Operational Decision Optimization	ODO1	1				0.850	0.932	0.732
	ODO2	1.114	0.048	23.193	***	0.911
	ODO3	0.801	0.050	16.155	***	0.723
	ODO4	1.099	0.048	23.033	***	0.902
	ODO5	1.069	0.049	21.877	***	0.880
CMIN = 395.759, df = 265, CMIN/df = 1.493, GFI = 0.919, AGFI = 0.900, NFI = 0.940, RFI = 0.933, IFI = 0.980, TLI = 0.977, CFI = 0.979, RMSEA = 0.037, SRMR = 0.034

Note: *** p < 0.001.

, the model exhibited a satisfactory overall fit to the data, χ² = 395.759, df = 265, χ²/df = 1.493 (<3), GFI = 0.919, AGFI = 0.900, NFI = 0.940, RFI = 0.933, IFI = 0.980, CFI = 0.979, RMSEA = 0.037, SRMR = 0.034, all of which fall within the recommended thresholds [111]. All standardized factor loadings were statistically significant (p < 0.001) and exceeded the minimum threshold of 0.70, ranging from 0.722 to 0.902, thereby indicating strong item reliability. CR values for all constructs ranged from 0.877 to 0.932, exceeding the accepted benchmark of 0.70 [112]. The average variance extracted (AVE) for each construct was also above the 0.50 threshold, ranging from 0.589 to 0.732, confirming acceptable convergent validity [113]. These results support the robustness and validity of the measurement model, providing a solid foundation for subsequent structural model analysis.

5.3. Correlation Analysis

To examine the basic relationships among the study constructs, descriptive statistics and bivariate Pearson correlations were calculated, as shown in

Table 5. Descriptive Statistics and Bivariate Correlations Results.

Variables	M	SD	VIF	C	R	V	ISA	ODO
Compatibility	3.113	0.652	1.772	0.831
Reliability	3.164	0.616	1.642	0.555 **	0.767
Visibility	3.162	0.756	1.663	0.551 **	0.487 **	0.817
Information System Agility	3.220	0.623	1.586	0.502 **	0.495 **	0.513 **	0.784
Operational Decision Optimization	3.940	0.932	-	0.542 **	0.486 **	0.538 **	0.575 **	0.856

Note: ** p < 0.01.

. The mean scores of the variables ranged from 3.113 to 3.940, while standard deviations ranged from 0.616 to 0.932, indicating moderate dispersion in respondents’ perceptions. All correlation coefficients were positive and statistically significant at the 0.01 level (p < 0.01), with values ranging from 0.486 to 0.575, indicating moderate inter-construct associations [94]. Discriminant validity was supported, as the square root of AVE for each construct (ranging from 0.767 to 0.856) exceeded its corresponding inter-construct correlations, satisfying the Fornell–Larcker criterion [95]. To further assess potential multicollinearity concerns, Variance Inflation Factor (VIF) values were examined. All VIF values ranged from 1.586 to 1.772, well below the commonly accepted threshold of 5.0 [96], indicating no serious multicollinearity issue among independent variables.

5.4. Path Analysis

The structural model demonstrates a good overall fit with the data, χ² = 395.759, df = 265, χ²/df = 1.493, GFI = 0.919, AGFI = 0.900, NFI = 0.940, RFI = 0.933, IFI = 0.980, TLI = 0.977, CFI = 0.979, RMSEA = 0.037, SRMR = 0.034, indicating that the proposed model is statistically acceptable [114]. Path analysis results are presented in

Table 6. Path Analysis Results.

Hypothesis	Path	Estimate	SE	CR	p	Results
H1-1	C → ODO	0.275	0.091	3.025	0.002	Supported
H1-2	R → ODO	0.173	0.101	1.715	0.086	Rejected
H1-3	V → ODO	0.206	0.070	2.956	0.003	Supported
H2-1	C → ISA	0.197	0.066	2.998	0.003	Supported
H2-2	R → ISA	0.278	0.073	3.794	***	Supported
H2-3	V → ISA	0.225	0.050	4.531	***	Supported
H3	ISA → ODO	0.539	0.095	5.650	***	Supported
CMIN = 395.759, df = 265, CMIN/df = 1.493, GFI = 0.919, AGFI = 0.900, NFI = 0.940, RFI = 0.933, IFI = 0.980, TLI = 0.977, CFI = 0.979, RMSEA = 0.037, SRMR = 0.034

Note: *** p < 0.001.

. Among the direct effects of DTCs on ODO, compatibility (β = 0.275, p = 0.002) and visibility (β = 0.206, p = 0.003) exert significant positive effects, supporting H1-1 and H1-3. However, the effect of reliability on ODO (β = 0.173, p = 0.086) was not statistically significant, leading to the rejection of H1-2. With respect to the effects on ISA, all three technological characteristics, including compatibility (β = 0.197, p = 0.003), reliability (β = 0.278, p < 0.001), and visibility (β = 0.225, p < 0.001), demonstrated significant positive influences, supporting H2-1, H2-2, and H2-3. Furthermore, ISA significantly affects ODO (β = 0.539, p < 0.001), confirming H3. Among all predictors, ISA emerged as the strongest determinant of ODO, underscoring its critical mediating role within the proposed model.

5.5. Test of Mediating Effect

To test the mediating role of ISA, we employed the bias-corrected bootstrapping method with 5000 resamples, as recommended by Tibbe and Montoya [97]. As shown in

Table 7. Mediating Effect Test Results.

Hypothesis	Path	Estimation	S.E.	Bias-Corrected 95% CI			Percentile 95%CI			Results
				Lover	Upper	p	Lover	Upper	p
-	Total Effect
	C → ODO	0.381	0.099	0.196	0.581	***	0.191	0.579	***	-
	R → ODO	0.323	0.102	0.121	0.524	0.001	0.126	0.528	0.001
	V → ODO	0.327	0.068	0.188	0.455	***	0.189	0.457	***
	Direct Effect
	C → ODO	0.275	0.098	0.088	0.473	0.004	0.082	0.463	0.005	-
	R → ODO	0.173	0.104	−0.04	0.371	0.109	−0.031	0.380	0.095
	V → ODO	0.206	0.068	0.070	0.336	0.002	0.070	0.336	0.002
	Indirect Effect
H4-1	C → ISA → ODO	0.106	0.040	0.043	0.200	***	0.039	0.194	***	Supported
H4-2	R → ISA → ODO	0.150	0.046	0.075	0.259	***	0.071	0.250	***	Supported
H4-3	V → ISA → ODO	0.121	0.034	0.064	0.197	***	0.063	0.194	***	Supported

Note: *** p < 0.001.

, the results confirmed that all total effects from the three DTCs, including compatibility, reliability, and visibility on ODO were statistically significant (β = 0.381, p < 0.001; β = 0.323, p = 0.001; β = 0.327, p < 0.001), thus satisfying the prerequisite for mediation testing. The indirect effects of compatibility, reliability, and visibility on ODO through ISA were all significant (β = 0.106, p < 0.001; β = 0.150, p < 0.001; β = 0.121, p < 0.001), with 95% bias-corrected confidence intervals that excluded zero, indicating the presence of statistically significant mediation. Specifically, for compatibility and visibility, the direct effects remained significant even after accounting for the mediating effect of ISA (β = 0.275, p = 0.005; β = 0.206, p = 0.002), indicating partial mediation. In contrast, for reliability, the direct effect became non-significant (β = 0.173, p = 0.095), while the indirect effect remained significant, suggesting a full mediation. These results suggest that ISA acts as a crucial mechanism through which DTCs influence decision optimization, highlighting the central role of agility in converting technical inputs into enhanced operational outcomes.

6. Discussion

This study applied structural equation modeling to empirically assess the causal relationships among DTCs (compatibility, reliability, and visibility), ISA, and ODO in the HSC context.

First, the path analysis results indicate that compatibility exerts a significant and positive effect on ODO, supporting Hypothesis H1-1. This finding is consistent with Rong and Liu [65] and confirms the critical role of compatibility in enhancing information processing capability and resource integration efficiency, while also underscoring its strategic value as a foundational technology for achieving high-quality operational decisions in HSCs. The positive impact of reliability on ODO, however, was not significant, and thus Hypothesis H1-2 was not supported. This result contrasts with the results of Skowron-Grabowska et al. [37] and suggests that reliability primarily ensures stability and accuracy in information processing. Such stability does not necessarily translate directly into decision optimization outcomes. Rather, it may require the involvement of mediating mechanisms, such as supply chain agility, integration capability, or collaborative information sharing to translate its potential benefits into improved decision-making and performance [70,115,116]. The mediation analysis in this study further corroborates that the influence of reliability on decision optimization is largely dependent on such transformation mechanisms. Visibility has a significant and positive impact on ODO, supporting Hypothesis H1-3. This finding is consistent with Baah et al. [67], confirming the pivotal role of visibility in strengthening HSC transparency and environmental sensing capability. It also demonstrates that by facilitating the rapid identification of operational bottlenecks and risk nodes, visibility provides an essential safeguard for enabling more precise and efficient operational decisions within HSCs.

Compatibility has a significant and positive effect on ISA, supporting Hypothesis H2-1. This finding is consistent with Rajaguru and Matanda [64] and indicates that compatibility, by facilitating seamless integration between systems and processes, effectively enhances the efficiency and speed of data flows across multiple supply chain stages. Reliability also has a significant and positive impact on ISA, supporting Hypothesis H2-2. In line with the findings of Joo and Han [66], reliability ensures the stability and accuracy of data transmission, thereby providing a solid foundation for rapid response and efficient system operation during critical periods. Visibility exerts a significant and positive influence on ISA, supporting Hypothesis H2-3. Consistent with Srinivasan and Swink [69], visibility enhances the timely perception of supply chain operational status and potential risk points, enabling the information system to adjust strategies more flexibly and coordinate resources effectively, thus improving overall agility.

Furthermore, ISA has a significant and positive effect on ODO, supporting Hypothesis H3. This result aligns with Shashikumar [72] and demonstrates that higher levels of agility can accelerate information processing and response speed, thereby improving both the accuracy and execution efficiency of operational decisions.

Secondly, the mediation analysis results indicate that ISA exerts a significant total mediation effect between compatibility and ODO, with both the direct and indirect effects being significant. Therefore, ISA plays a partial mediating role in this relationship, supporting Hypothesis H4-1. Consistent with Ma and Chang [79], compatibility not only directly promotes ODO but also enhances the timeliness and effectiveness of decision-making through improved system agility. The mediation results also show that ISA has a significant total mediation effect between reliability and ODO, with a significant direct effect but a non-significant indirect effect. Thus, ISA fully mediates the relationship, supporting Hypothesis H4-2. In line with Akhtar et al. [81], the influence of reliability on ODO primarily depends on its transformation through enhanced system agility, whereby its potential advantages are converted into tangible decision-making performance only when agility is improved. Moreover, ISA demonstrates a significant total mediation effect between visibility and ODO, with both direct and indirect effects being significant. Therefore, ISA serves as a partial mediator in this relationship, supporting Hypothesis H4-3. Consistent with Roy et al. [82], in the context of HSCs, the visibility afforded by digital technologies can, through enhanced system agility, translate rapid environmental perception into efficient strategy adjustments and resource allocation.

7. Conclusions

This study examined how DTCs (compatibility, reliability, and visibility) shape ODO in HSCs, with ISA serving as a key mediating mechanism. The empirical findings demonstrate that compatibility and visibility directly and indirectly enhance decision optimization, while reliability exerts its influence primarily through the full mediation of agility. These results not only validate the applicability of OIPT in the HSC but also highlight the critical role of agility in transforming technological potential into actionable decision-making advantages.

7.1. Theoretical Contribution

This study addresses the theoretical gap in applying OIPT to the digitalization of HSCs and contributes academic value to research in this domain.

First, this study substantially extends the applicability of the OIPT to the HSC domain and demonstrates its theoretical value in high-uncertainty, high-risk environments. OIPT posits that organizations should improve their data processing competence to bridge the divide between information needs and capacity [10]. This theoretical logic is particularly salient in the dynamic and complex context of HSCs, which not only face demand fluctuations, public health emergencies, and stringent regulatory requirements but also carry exceptionally high demands for timeliness and accuracy in operations. These characteristics render the sector heavily dependent on the efficiency of information processing and the speed of decision-making responses. Existing OIPT research has largely focused on manufacturing and general commercial supply chains, with limited attention to the highly dynamic HSC context. By incorporating DTCs (compatibility, reliability, and visibility) alongside ISA into the OIPT framework, this study systematically examines the mechanisms through which these technological features enhance ODO via information processing capabilities. The findings not only validate the explanatory power of OIPT in this domain but also enrich its theoretical connotation in the emerging research directions of digitalization and information systems.

Second, this study develops a novel structured path analysis framework, the chain of DTC → ISA → ODO. Specifically, it disaggregates the DTC of the HSC into three dimensions including compatibility, reliability, and visibility, revealing the differentiated pathways through which each characteristic enhances information processing efficiency and optimizes operational decision-making. The analysis further verifies how these characteristics, through technological integration and functional complementarity, drive the agility of supply chain information systems, thereby supporting decision optimization. This framework underscores the bridging role of ISA in transforming technological advantages into decision-making advantages and provides new theoretical and empirical support for the digital management mechanisms of HSCs.

7.2. Practical Implications

China’s pharmaceutical-centered HSC is currently at a critical stage of accelerated digitalization; however, challenges such as system fragmentation, insufficient collaboration, and uneven levels of technological maturity continue to constrain its high-quality development [117]. According to the empirical analysis, our study introduces concrete recommendations for practice.

First, from the perspective of technological deployment, the findings provide clear guidance on the prioritization and integration strategies for digital technology implementation in HSC enterprises. In China’s HSC, it is essential to formulate a phased and tiered roadmap for digital transformation, specifying the implementation pace for different types of enterprises in process reengineering, system deployment, and capability building, thereby enhancing overall structural decision-making efficiency [118]. Given the foundational role of technological compatibility in system deployment, it is critical to establish unified data standards and interface protocols to enable rapid cross-organizational and cross-regional data exchange and collaborative decision-making [119,120]. This approach can not only strengthen the overall resilience and responsiveness of the industry but also provide real-time and accurate decision support for resource allocation and policy formulation during public health emergencies [121]. Moreover, the deployment of digital technologies should consider their contribution to system visibility. For instance, priority should be given to platforms integrating 5G, IoT, and real-time visual analytics to achieve end-to-end, full-chain transparency, thereby enhancing risk perception and rapid response capabilities [122]. Such initiatives can significantly optimize operational decision-making efficiency and offer a viable pathway for developing standardized and replicable digital transparency management models in the healthcare sector [123,124]. Finally, embedding emerging technologies such as IoT, AI, and blockchain into scenarios including pharmaceutical traceability, anti-counterfeiting verification, and collaborative decision-making can substantially improve the security and stability of the supply chain [125]. Through coordinated advancement along these multiple pathways, China’s HSC is poised to achieve systematic enhancement and sustained optimization of operational decision-making within the broader context of digital transformation.

Second, from the perspective of capability enhancement, the study highlights the pivotal role of ISA in transforming technological advantages into decision-making advantages. Operationally, drawing on case studies of organizational resource interconnection and omnichannel integration in Ghana’s HSC, China should strengthen resource coordination and data sharing among hospitals, pharmaceutical companies, insurance providers, and logistics platforms to establish a diversified service linkage mechanism anchored in data [126]. Promoting cross-platform technological integration across industries is essential; for example, enterprises can integrate healthcare platforms (HIE, Hospital Information Systems, Personal Health Records), business management platforms (ERP, MES), supply chain collaboration platforms (Transportation Management Systems, Warehouse Management Systems), and data analytics platforms (big data and AI-based decision support systems) to improve information processing speed and system flexibility [18,127,128]. For instance, deeply integrating electronic Health Record Systems (EHR), pharmaceutical traceability systems, and logistics scheduling platforms can enhance cross-functional coordination capabilities, enabling the optimized allocation of healthcare resources and rapid linkage in emergency responses [129,130]. Such agility can help enterprises swiftly devise and efficiently execute viable response plans in the face of sudden changes in drug demand, supply disruptions, or strategic adjustments [131]. Strategically, it is recommended that industry regulatory bodies establish a digital maturity assessment and certification system for the HSC, enabling the quantification of enterprise capabilities in areas such as digital technology adoption, system integration, data collaboration, and agile responsiveness [132]. Moreover, this digital capability building should be closely aligned with supply chain sustainability objectives, ensuring the long-term stability and balance of resource utilization, economic efficiency, and public health goals by optimizing resource allocation, reducing operational waste, and mitigating risk exposure, thereby securing the sustained effectiveness of decision optimization [133,134].

Third, from a micro-operational perspective, this study demonstrates how DTC, particularly compatibility, reliability, and visibility, integrated through ISA, enable healthcare organizations to streamline decision-making processes and improve operational efficiency. From a resource utilization perspective, the model highlights how compatibility-driven system integration reduces duplication of effort, reliability ensures the stable use of scarce medical resources, and visibility minimizes mismatches in inventory and logistics, thereby conserving resources and reducing waste [135]. At the macro level, by strengthening responsiveness and coordination across hospitals, pharmaceutical firms, and logistics platforms, this supports long-term system resilience, improves equitable access to medical resources, and reduces the environmental footprint of healthcare operations [136]. These improvements contribute to building a sustainable healthcare system that supports long-term stability, environmental responsibility, and social value.

7.3. Limitations and Future Research

Although this study offers valuable theoretical and practical insights for the continuous optimization of HSCs, this study is subject to certain limitations that call for additional inquiry. First, the empirical evidence relies on cross-sectional data, which limits the ability to capture the dynamic evolution of relationships among variables over time and under changing environmental conditions. Future studies could adopt a longitudinal research design to more comprehensively validate and strengthen the robustness of the proposed theoretical model. Second, the scope of this research is confined to the HSC sector; applying the proposed framework to other types of supply chains would allow for further examination of its generalizability and explanatory power. Finally, the data sample is primarily drawn from organizations in China, and contextual factors may influence the external validity of the findings. Future research could extend the investigation to other countries and regions to enhance the applicability and international relevance of the results.