This study aims to fill a notable research gap by investigating the potential advantages of investing in big data analytics capability, particularly in the context of enhancing supply chain innovation and improving the environmental performance of hospitals. The underlying concept is that hospitals may utilize big data analytics to improve the efficiency and resilience of their supply chains, as well as increase their sustainability practices and decrease their environmental impact. The study utilizes the SOR paradigm53 to investigate the correlation between big data analytics capability, supply chain innovation, decision making quality, risk taking, and hospital environmental performance. This framework is employed to comprehend the influence of external stimuli (Stimulus: big data analytics capability) on the internal state of an organization (Organism: supply chain innovation, decision making quality, and risk taking), which ultimately shapes its responses (Response: environmental performance). The proposed model in this study was rigorously examined using SEM, an advanced statistical method meant to clarify the intricate network of connections, intermediary effects, and causal directions among variables.
The Cronbach’s α value ranges from 0.631 to 0.872, while the factor loading in the final model for each measurement variable ranges from 0.711 to 0.832. The AVE for each latent variable ranges from 0.501 to 0.615. Therefore, the reliability and validity of the study model have been confirmed. The results of the SEM analysis indicate that all the hypotheses proposed in the study were confirmed. This is consistent with the theoretical predictions and expectations.
The results of this study support this group of hypotheses by verifying a significant positive association between big data analytics capability and hospital environmental performance. It illustrates the pivotal contribution of data-based methodologies not only to enhanced operations but also to the overall enhancement of sustainable development in healthcare. This result is consistent with previous findings reported by Benzidia, Bentahar17, Benzidia, Makaoui12, and Batko and Ślęzak27. Hospitals that strategically invest in and effectively implement big data analytics tools and techniques generally experience notable improvements in their sustainability initiatives. The observed positive correlation clearly indicates that an increase in big data analytics capabilities directly translates into enhanced environmental outcomes. This improvement can be attributed to various factors, such as more efficient resource management, reduced waste achieved through optimized supply chain operations, and the integration of eco-friendly practices driven by insights gained from data analytics.
The second set of hypotheses (H2a–H2c) emphasizes the strategic role of big data analytics capability in enhancing internal processes and its impact on shaping supply chain innovation, decision-making quality, and risk-taking within hospitals. These are based on the observation that big data analytics enables healthcare organizations to handle large-scale and complex datasets, thereby providing them with the opportunity to achieve more creative, informed, and flexible responses to operational and strategic challenges. Through increased analytical capabilities, hospitals can more effectively transform their supply chains, utilize data to inform decisions, and take targeted risks that advance long-term sustainability objectives.
Findings that support the second group of research hypotheses suggest that big data analytics capability has a direct positive effect on innovative supply chain practices. This finding aligns with previous studies7,29,31, which also emphasize the importance of big data analytics in enhancing supply chain innovation. Novelty: The paper provides empirical evidence supporting and extending previous insights regarding the positive impact that organizations dedicated to enhancing their big data analytics capabilities (i.e., investments in technologies, tools, and skilled professionals to collect, manage, and interpret large datasets) have on their supply chain innovation. In addition, our findings further generalize the findings by presenting that the hospital sector can use big data to improve logistics and inventory management, realize more sustainable sourcing, and respond more effectively to dynamic market conditions30. Unlike previous studies, mainly in the context of manufacturing and retail, our research contributes by illustrating the impact of big data analytics capabilities on supply chain innovation in the healthcare sector, where efficiency and sustainability are becoming increasingly important. This hospital-based extension constitutes an original contribution to the literature on big data and supply chain management.
Hypothesis H2b of the study posits that the capability for big data analytics is positively associated with the quality of decision-making within hospitals. This investigation provides support for certain previous studies in general and healthcare/ or hospital studies32,34,36 which suggests that as companies enhance their ability to gather, process, and analyse large datasets, the quality of their decision-making processes improves. One possible reason is that big data analytics provides a wealth of information and insights, which can help decision-makers understand complex situations better, forecast future trends with more accuracy, and assess the potential outcomes of different decisions more effectively. This improvement can be attributed to the ability of big data analytics to reduce uncertainty and provide a solid empirical basis for decisions. By leveraging data-driven insights, organizations can minimize the risks associated with decision-making under uncertainty, leading to choices that are more aligned with strategic objectives and expected outcomes34,35.
Hypothesis H2c examines the effect of big data analytics capability on hospitals’ risk-taking tendencies, suggesting that this capability enhances their willingness of hospitals to experiment and take calculated risks. This hypothesized association is in line with previous studies such as those by Tipu and Fantazy21, Jalali, Palalić20, and Basile, Carbonara38, who stress that envelope-pushing environments may foster strategic risk behaviour. Yet, much of the literature has focused on firms in the manufacturing or commercial sector, and thus, little is known about this relationship in healthcare, a significant gap that the current study addresses. Hospitals that have embraced big data to a greater degree are not just freer with their risk- taking; they are also more capable of assessing, controlling and selectively acting on such risks based on predictive models and proof- based planning39. This enables them to innovate in treatment protocols, operational models, or technology implementation with reduced risk and uncertainty. For example, such analytics could help identify excess capacity or predict the impact of using new medical technologies37, and thus reduce the high levels of uncertainty that often impair innovation in the healthcare system. In doing so, we offer a critical empirical extension and analysis of this relationship within hospital settings, thereby providing unique and timely insights that contribute to the literature on digital transformation and organizational risk behaviour in complex, regulated contexts, where the role of big data remains largely underexplored.
The third group of hypotheses (H3a–H3c) examines how internal organizational capabilities; specifically, supply chain innovation, decision-making quality, and risk-taking; directly impact hospital environmental performance. These constructs represent critical levers through which hospitals can translate strategic intent into sustainable outcomes. Supply chain innovation enables greener logistics and resource efficiency; high-quality decision-making ensures that sustainability is factored into operational choices; and a willingness to take calculated risks facilitates the adoption of novel environmental practices and technologies. Together, these capabilities reflect the internal mechanisms that support an institution’s shift toward environmental sustainability.
The current study uniquely incorporates supply chain innovation (H3a), decision-making quality (H3b), and risk-taking (H3c) as a complementary set of analytic lenses, providing a more comprehensive understanding of how supply chain innovation, decision-making quality, and risk-taking collectively contribute to hospital environmental performance. Although such antecedents have previously been studied in isolation (e.g40,42,46, relatively little attention has been paid to their combined effects in a hospital context, particularly in the context of big data analytics capabilities. This study addresses a significant theoretical gap in the literature regarding the collaborative impact of connected managerial capabilities on achieving environmental outcomes in healthcare organizations. H3a examines the relationship between supply chain innovation and hospital environmental performance. Although studies like that of Fu, Yang40 and Alkhatib41 recognize that sustainability supply chain practices are essential, their applicability in hospitals is less understood. Our results indicate that hospitals can achieve significant environmental savings by integrating sustainable logistics, applying predictive analytics to inventory management, and aligning their suppliers with ecological criteria. In contrast to extant literature that primarily overlooks the healthcare sector, we develop a theoretical perspective where healthcare organizations are considered as active players in sustainable evolution and development, driven to impact upstream and downstream supply chain management practices. This is not only beneficial for the environment but also aligns with the broader social (public health) responsibility of the healthcare industry. By framing our results within this cross-sectoral conversation, we challenge the current siloed approach and call for an integrated model of environmental practice—one that is compatible with the complex and hybrid nature of hospitals and their capacity for environmental leadership.
This study demonstrates that the quality of decision-making has a significant influence on the environmental performance of hospitals, as supported by earlier research42,43. One of the limitations we found in the literature is the lack of attention to how the quality of decision-making and risk-taking, characterized by big data analytics, enhances sustainability performance in hospitals. Our results help to fill such a gulf, indicating that sound environmental decision-making in healthcare settings will hinge on making timely, evidence-based decisions that balance operational constraints against longer-term ecological consequences. This reinforces and builds upon prior research45,66 in this area, which demonstrates how hospitals that adopt sustainability assessments (e.g., environmentally preferable purchasing, sustainable supplier evaluations, investments in energy-saving technologies) achieve greater consistency between operational choices and environmental targets. Moreover, while previous research (e.g46, has established a link between organizational risk-taking and environmental innovation, few studies have explored this dynamic within hospitals. Our enabling factor, H3c, raises awareness that taking calculated risks is a prerequisite for environmental leadership in healthcare, particularly in embracing disruptive green technologies and novel waste management practices. These costly and uncertain efforts, however, can reap significant environmental rewards. With this conclusion situated within the broader literature on organisational sustainability, our work not only ratifies but also advances the debate by demonstrating how hospitals can be agents of environmental transformation, driven by open, data-enriched decision-making and courageous risk leadership. This fusion of empirical and theoretical dialogue directly addresses the previously identified voids and expands the academic conversation regarding sustainability within the healthcare industry.
The study’s findings indicate that supply chain innovation plays a crucial role as a partial mediator in the connection between several organizational aspects (such as big data analytics capability, decision-making quality, and risk-taking) and the environmental performance of hospitals. Consequently, the influence of these organizational characteristics on environmental performance is partially mediated by supply chain innovation. Put simply, these characteristics have a direct impact on environmental performance, but they also indirectly promote innovations in the supply chain.
This outcome aligns with the assertions of the study, confirming the crucial significance of supply chain innovation in improving environmental sustainability in hospitals. Supply chain innovation include strategies such as developing environmentally friendly procurement policies, optimizing logistics to decrease carbon emissions, and embracing circular economy concepts to minimize the generation of waste67. Supply chain innovation acts as a mediator, transforming the organization’s capability and strategic behaviours into concrete environmental gains.
The study’s findings emphasize a notable pathway by which the capability of big data analytics might improve the environmental performance of hospitals. The quality of decision-making and the willingness to take risks play crucial roles as mediators in this relationship. This result highlights the complex relationship between an hospital’s analytical abilities and its strategic actions, demonstrating how the former can lead to enhancements in sustainability through a two-step mediated process. At first, the capability of big data analytics enhances the quality of decision-making by offering thorough, precise, and timely information. The abundance of data aids hospitals in making well-informed decisions, enabling them to thoroughly assess the environmental consequences of their actions and select methods that are in line with their sustainability objectives12. Improved decision-making quality acts as the initial mediator, converting data into practical insights that can result in superior environmental results. According to the study, hospitals are more likely to take strategic risks in order to achieve their environmental performance targets as a result of using this enhanced decision-making process68. The second mediating stage is represented by the willingness to take risks, which is fostered by the confidence in their decision-making processes. Hospitals may choose to include novel, eco-friendly technologies or embrace inventive operating methods that, although potentially hazardous, hold the potential to substantially diminish their ecological impact69. Hence, engaging in risk-taking serves as a link between making well-informed choices and carrying out actions that have the potential to result in environmental enhancements.
Theoretic implications and applications
The theoretical implications of these findings are significant, as they enhance our comprehension of the complex interactions among big data analytics capability, decision-making quality, risk-taking, and environmental performance, specifically in the realm of hospital operations. This study contributes to the existing body of knowledge by presenting a sequential mediation model that illustrates how the abilities in big data analytics increase the quality of decision-making. This, in turn, promotes prudent risk-taking, resulting in enhanced environmental performance. This model emphasizes the significance of taking into account both the immediate and indirect impacts of big data analytics on sustainability results, indicating that the route to environmental enhancements is complex and requires a sequence of internal organizational changes. The results enhance the SOR theoretical framework by presenting empirical proof of how particular organizational capability (stimuli) impact internal processes (organism responses) that result in specific outcomes (responses). This offers a detailed comprehension of how technology-driven capability can influence organizational behaviour and sustainability efforts.
From an application perspective, these results have practical ramifications for hospital administrators and legislators who are striving to enhance environmental sustainability. Hospitals may improve their data analytics capability to not only enhance operational efficiency but also use it as a strategic tool for sustainability by acknowledging the importance of decision-making quality and risk-taking as mediators. This entails allocating resources towards the development of data analytics infrastructure and providing training to employees. It also involves cultivating a work environment that encourages effective decision-making and strategic risk-taking. Additionally, it requires incorporating sustainability factors into the decision-making procedures. These findings emphasize the need of legislators implementing policies that promote the adoption of big data analytics in healthcare facilities, with a specific focus on sustainability. This may be achieved by providing incentives for green technologies and investing in data-driven decision-making frameworks. In summary, the report offers a clear plan for using technology to improve environmental performance, directing both professionals and politicians towards more sustainable practices in the healthcare industry.
Policy implications
Applying the results of our study, we provide with five customized policy implications that can then be articulated for hospital administrators, health policy makers, and regulators:
Advocate for data-driven hospital environmental policies
Hospitals should be incentivized to incorporate the institutional capacity for big data analytics into their environmental strategy. Policies allow investment in digital infrastructure, which allows for real time monitoring of energy usage, waste management, and resource use. Hospitals can further harmonize their operations with their sustainability objectives by incorporating data analytics into environmental compliance and reporting requirements.
Enhance data literacy and long-term support for building capacity
Considering the importance of quality decision-making, decision-makers should commit themselves to training programs that improve hospitals’ employees’ skills in interpreting data related to sustainable activities. This encompasses tailored workshops, certifications, and continuing education with a focus on data analytics, green healthcare operations and evidence-based environmental planning.
Rewarding supply chain innovation in healthcare
To enhance the mediating role of supply chain innovation in environmental performance, the healthcare policies should develop procurement reforms, supplier assessment criteria and eco-innovative logistics trends as well. Government-friendly facilities, tax breaks, or funding grants could be offered to hospitals that implement sustainable supply chain changes brought on by big data.
Institutionalize risk-informed innovation policies
Our results validate risk-taking as an intermediate factor between big data capability and environmental performance. Lawmakers should therefore encourage systems that incentivize responsible innovation in new eco-technologies or business models applied within hospitals. This could mean regulatory sandboxes or innovation testbeds in the public health space.
Big data strategies, regional vision
In view of the results of our study, which focuses on Zhejiang Province, it is essential for policymakers to recognize that opportunities to drive big data roll-out must be context-specific. Local government entities can develop region-specific guidance for hospitals that considers the technological preparedness, regulatory readiness, and environmental needs of a region, making the policies more relevant and increasing the likelihood of successful implementation.
Limitations and future studies
A key limitation of our study is the focus on the hospital sector of only one province in China. Although the focus on the Zhejiang healthcare system represents a strength of our study, it also introduces limitations, as the findings may not be generalizable to different regions, nations, and industries. Hospitals operate within unique regulatory, cultural, and institutional environments that may not be applicable to other industries or geographies. Therefore, caution should be exercised when generalizing the results to other medical settings. This research is to be seen as exploratory, with a specific interpretative horizon and not as making any general claims for universal validity.
Furthermore, the self-reported nature of the data raises questions regarding response bias, particularly in areas such as environmental performance and organizational risk-taking, where the social desirability response set may influence answers. While actions were taken to maintain the respondent’s anonymity and minimize bias, this remains a methodological limitation.
Another limitation is the dynamic nature of big data technologies and sustainability standards. Given the recent development in these domains, the associations found in this study may, in turn, be time attenuated. Future work should explore more longitudinal designs to determine how these relationships evolve as technology advances and regulations change.
In addition, the quantitative design of the study, although appropriate for testing the model developed, may fail to address the more interactive and qualitative aspects of human decision-making and risk-taking in a hospital setting. Prospective research may use mixed approaches such as combining quantitative data with qualitative interviews or case study data to gain a richer understanding of the mechanisms underlying the associations. Cross-sector and multi-country comparisons are also proposed to confirm and generalize the findings.