The integration and development of the digital economy and the health and senior care industry has gradually become a focus of attention for academics and policy makers. While traditional research has mostly focused on the contradiction between supply and demand of senior care services or the single technical application of digital economy, the latest research has emphasized more on the systemic role of digital economy in promoting the high-quality development of the smart healthy aging industry (4). The smart health and aging industry refers to an emerging industrial cluster driven by next-generation information technologies such as the internet of things, big data, artificial intelligence, and cloud computing. Its primary aim serves the older adults requirements in areas such as assistance with daily activities, health management, medical rehabilitation, and mental and cultural well-being (26). At its essence, it represents the deep integration of traditional older adults care services with strategic emerging industries. The high-quality development of the smart healthy aging industry is not only reflected in the improvement of service efficiency, but also in its ability to achieve sustainable, inclusive and precise aging service provision (3). Therefore, more and more scholars have adopted industrial effectiveness as an indicator of the quality of the development of the senior care industry to reflect the degree of optimization of resource inputs and service outputs (15). The facilitating effect of the digital economy on the smart health and aging care industry has been verified in several countries and regions. For example, a study based on a panel data analysis of OECD countries found that the digital economy significantly improved the accessibility of senior care services, especially in the areas of telemedicine and smart care (27). Similarly, using Chinese provincial data, one study demonstrated that the digital economy is significantly and positively related to the efficiency of the smart health and aging care industry, but this relationship is significantly differentiated between eastern and central and western regions (15, 18). This regional disparity has also drawn attention from other scholars. For instance, some studies indicate that while digital technologies can enhance the distribution of aging care resources, weak digital infrastructure may further delay the development of the aging care industry in underdeveloped regions (28).

The impact of the digital economy on the development of the smart health and aging care industry can be explained from three key perspectives. First, the extensive deployment of digital technologies, including the Internet of Things, AI, and cloud computing, can enhance the intelligence of senior care services. For example, smart wearables enable continuous health surveillance for seniors, mitigating critical health incident risks (20, 21), and big data analysis can optimize the allocation of resources in senior care and medical institutions and reduce service redundancy (29). Second, the digital economy has given rise to new forms of business such as “Internet + aging care” and “shared aging care,” which have broken the time and space limitations of traditional aging care services. For example, online consultation platforms have made it possible for older people in remote areas to have access to high-quality medical resources, while the sharing economy model has increased the utilization rate of home beds for the older adults (30). Finally, the government’s digital governance capabilities and industrial policies are crucial to the development of smart healthy aging. For example, studies have pointed out that China’s smart healthy aging action plan effectively promotes the digital transformation of enterprises through financial subsidies and standardization, while the European Union’s digital health strategy promotes cross-border collaboration in older adults care services through data-sharing regulations (31). However, the role of the digital economy in advancing the smart health and aging care industry is not a linear relationship. Some scholars point out that significant disparities exist across regions in terms of digital infrastructure development, levels of digital economic advancement, and governmental digital governance capabilities (24). All of these factors may constrain the high-quality development of the digital economy-driven smart health and aging industry.

In summary, there are still obvious limitations in the theoretical perspectives of studies on the digital economy and the development of the smart health and aging industry. First, although scholars have identified key drivers such as digital infrastructure, policy support, and human capital (15, 24, 31), these studies have mostly focused on the common features of different regions while ignoring the key differences between cases. For example, why has the smart aging industry developed significantly faster in the Yangtze River Delta than in other regions? Why is it difficult for some central and western provinces to realize industrial upgrading despite the same policy support? These unanswered questions highlight the inadequacy of existing studies in providing systematic explanations, and relevant research findings are still characterized by fragmentation (19). Second, existing studies have paid insufficient attention to the dynamic adjustment mechanism of the synergistic development of the digital economy and the smart pension industry. This limitation makes it difficult for researchers to adequately explain the formation mechanism of the health digital divide (24), and to dissect rebound effects such as the mismatch between technology application and aging-appropriate demand (28). Much of this theoretical blind spot stems from the fact that most studies have focused excessively on the independent effects of a single factor while ignoring the interactions between system elements. Third, existing studies have relied heavily on linear regression models to estimate the net effect of specific variables (16, 18, 24). Although this approach can identify the key influencing factors, it is difficult to capture the complex causal relationship between the digital economy and the development of the senior care industry, especially the phenomenon of “different paths to the same destination” in different development paths. In addition, there are obvious limitations in the traditional measurement methods in dealing with the problems of multicollinearity and variable selection bias (32), which to a certain extent reduces the robustness of the research conclusions. Hence, the evolution of the tech-fueled smart health and aging sector is typically shaped by a multitude of variables, including technological advancements, organization and environment, the adoption of a group perspective may be more suitable for exploring the complex mechanisms between the two.

To elucidate the mechanism by which the digital economy fosters the advancement of the smart, healthy aging sector, this study adopts the TOE framework for analysis. The TOE framework, as a comprehensive analysis of technology adoption and innovation models, emphasizes the combined impact of various elements indicated from the three dimensions of technology, organization, and environment (25). Among them, technological conditions focus on fitness and benefits, organizational conditions contain internal characteristics such as scale and capacity, and environmental conditions involve external factors such as policies and markets. This framework is widely used in the field of economic management and has strong explanatory power when dissecting the interaction effects between its technology, organization and environment (33). Similarly, digital economy-driven high-quality development of the smart health and aging care industry also relies on the coupling and synergy of the three, which is highly compatible with the TOE theoretical framework. The logical basis for the development of smart health and aging care industry driven by digital economy is as follows:

To systematically analyze the dynamic causal mechanisms by which the digital economy drives high-quality development in the smart health and aging care industry, this study employs the Dynamic QCA method. Proposed by García-Castro and Ariño, this approach effectively integrates panel data with configuration comparison logic, overcoming the limitations of traditional QCA in analyzing temporal dimensions (53). Its implementation involves three primary steps: First, data calibration and configuration construction. Based on theoretical frameworks and data distribution, all condition variables and outcome variables from the provincial panel data (cases) spanning 2012–2022 are uniformly calibrated into fuzzy set scores. Second, dynamic analysis operations: the resulting “case-year” configuration data is imported into the R programming environment. By calculating three core metrics aggregate consistency, inter-group consistency, and intra-group consistency the robustness of configurations is systematically evaluated at the overall, cross-case, and temporal levels. Finally, configuration evolution trajectory identification: Based on the above analysis results, key configuration paths were identified through multi-period comparisons. Their dominant, complementary, or transitional evolution trajectories were extracted based on their emergence and succession across different time periods. Compared to traditional QCA, dynamic QCA employs a multi-level consistency analysis framework. This approach not only identifies equivalent paths that converge to the same outcome but also precisely quantifies the dynamic characteristics of these paths as they vary over time and across cases. Consequently, it reveals the causal complexity within the temporal dimension more profoundly. Based on the TOE framework, this study constructs a standard implementation flowchart for dynamic QCA, as shown in Figure 2.

Drawing on existing literature, the data were standardized in this study. Based on the sample and variable distribution characteristics, the internal direct calibration method of fsQCA was used to convert the raw observations into affiliation scores in the (0–1) interval (53). Three key anchor points were set for the specific calibration process: the 95th quartile (full affiliation), the 50th quartile (crossover point), and the 5th quartile (no affiliation at all) (53). In order to avoid omission of data at intersection point 0.5 in the truth table analysis, a fine-tuning treatment of 0.001 was applied to the 0.5 variable values during the preprocessing stage. The specific calibration results are shown in Table 2.

This study follows the principle of necessity test of the standard QCA method, whereby when the consistency coefficient of a conditional variable exceeds the critical threshold of 0.9, it can be determined that the variable constitutes a necessary condition for the outcome variable (59). Under the dynamic QCA analysis method, two key indicators, inter-group consistency adjustment distance and inter-group coverage, need to be additionally examined. Specifically: first, when the adjustment distance is less than 0.2, it indicates that the aggregated results have a high degree of accuracy, which can be directly used as the basis for the judgment of necessity; second, when the adjustment distance is more than 0.2, it is necessary to validate its necessity through a supplemental test (53). The results of the analysis of the necessity conditions for the high/low quality development of the smart health and aging care industry (as shown in Table 3), specifically, the aggregated consistency level of the six antecedent condition variables is less than 0.9, indicating that these variables are not the necessity conditions for the outcome variables. Moreover, by observing the inter-group consistency adjustment distance of the 12 types of the 6 conditional variables, it is found that the inter-group consistency adjustment distance of either the high/low quality development of the smart health and aging care industry is less than 0.2. Therefore, there is no necessary condition that constitutes the outcome variable. Taken together, none of the six single conditional variables is a necessary condition leading to the high/low quality development of the smart health and aging care industry. This means that the digital economy-driven development of the smart health and aging care industry is not the net utility of a single factor, but is subject to the complex linkage and synergistic matching of multiple factors.

Conditional grouping sufficiency analysis was further employed to reveal the effect of multivariate synergy on the outcome. Citing the research methodologies employed by earlier scholars (60), the consistency threshold condition is used to determine whether there is sufficiency between the condition and outcome variables. Specifically, the established analysis criteria include a consistency level for group path at 0.8, a threshold for PRI at 0.7, and a minimum case occurrence of 1. The ensemble solution is obtained through R language computation, and intermediate solution is the main solution, and parsimonious solution is the auxiliary solution for validation, which identifies the core and auxiliary conditions of the group path. Considering the asymmetry of the role of factors, the independent group state analysis of high/low quality development of the smart health and aging care industry is carried out respectively, and a total of 8 group state paths for the high-quality development of the smart health and aging care industry are obtained. The detailed outcomes are presented in Table 4.

To delve into the intricate progression and key features of the flourishing smart health and longevity sector, powered by the digital economy, this research employs a time-sensitive analytical approach, drawing inspiration from the methodologies of past studies (61). This study divides the timeline into two phases 2012–2016 and 2017-2022—based on China’s five-year planning cycles and the evolution of smart older adults care policies. This division is primarily based on the fact that China’s smart aging care industry began in 2012. It wasn’t until 2017 that three national ministries jointly issued the “Smart Health and Aging Care Industry Development Action Plan (2017–2020),” marking the introduction of the first national-level smart aging care industry plan. This signaled a shift in the industry’s development from a phase of macro-level advocacy and scattered pilot projects to a nationwide promotion phase, forming a policy milestone for dividing the stages. Additionally, examining the dynamic evolution of core variables reveals that during 2012–2016, the primary drivers were the construction of digital infrastructure and preliminary exploratory government policies. From 2017 to 2022, the focus shifted to the release of accumulated digital infrastructure and technological application capabilities. These synergized with organizational innovation, human capital, targeted government policies, and digital finance, collectively propelling the industry from scaled expansion toward a high-quality development paradigm. This division effectively captures the dynamic evolution of causal combinations driving the high-quality development of the smart health and aging care industry, ensuring the logical consistency and robustness of subsequent multi-period QCA comparative analysis. A standardized analysis process was used for each time period: first, the calibration standard was unified; second, the aggregated fsQCA method was used. The results of the necessity condition test show that the aggregated consistency coefficients of the six condition variables in the two time periods do not reach the critical threshold of 0.9, and none of them is a necessary condition to drive the high-quality development of the industry. Subsequently, frequency thresholds for both time periods were set to 1, while original consistency and PRI thresholds were established at 0.80 and 0.70, respectively, and the final enhanced intermediate and parsimonious solution grouping results for the two time periods were obtained. Detailed outcomes are presented in Table 5.

Table 5 reveals four classification methods are identified in the first time period and three grouping paths are identified in the second time period. The consistency coefficients of all individual grouping patterns and the overall consistency of the two time periods exceed the threshold judgment criterion of 0.75, and the overall coverage of the two time periods are 0.883 and 0.609, which are able to explain 88.3 and 60.9% of the observed cases, respectively. The multi-temporal evolutionary trajectory patterns were categorized into transitive, buffer-dominant, dominant, and hybrid trajectories (62). According to the evolution pattern of the digital economy-driven high-quality development of the smart health and aging care industry in the two time periods, it can be observed that the digital technology-driven and “digital technology-digital environment” dual-core-promoted grouping patterns have always appeared stably in the two time periods, which implies that the two grouping patterns have an important influence on the high-quality development of the smart health and aging care industry, and their evolution trajectories are the “dominant” trajectories. This means that both models have an important impact on the high-quality development of the smart health and aging care industry, and the trajectory of change is the “dominant trajectory.” On the other hand, the digital environment-led group appeared in the first time period, and also appeared in the second time period, which means that the trajectory of this group has turned, so it is a “turning trajectory.” In addition, multi-temporal comparative analysis can also be used to explore the evolutionary trajectory of individual variables. Based on the cross-temporal dynamic analysis of the six antecedent variables of the digital economy, it is found that the government’s digital policy continues to play a key driving role in promoting the high-quality development of the smart health and aging care industry in the grouping path.

To ensure the robustness of the panel QCA analysis results, this study employed multiple verification methods (63). First, the PRI threshold was adjusted by raising the consistency threshold from 0.70 to 0.75. The revised QCA analysis yielded an overall consistency of 0.893 and a coverage of 0.814. The core condition composition and high-configuration paths remained substantially unchanged after the PRI adjustment. Second, calibration anchor points were adjusted by modifying the quantile values for full membership, cross-membership, and full non-membership from (95, 50, 5%) to (85, 50, 15%). The overall consistency reached 0.881 with a coverage of 0.797. The reconfigured results remained largely consistent with the original configuration. Third, by dividing the research cycle into three phases for dynamic analysis and adjustment: 2012–2015, 2016–2019, and 2020–2022. The overall consistency values for each phase were 0.837, 0.815, and 0.823, respectively, with coverage rates of 0.373, 0.421, and 0.245. Despite the finer temporal segmentation, the core conditions and key pathways driving the high-configuration trajectory remained largely consistent across different time periods, with its evolutionary trajectory remaining clearly discernible. These systematic tests collectively demonstrate that the conclusions drawn in this study exhibit strong robustness to parameter settings and period divisions, yielding reliable results. Detailed outcomes are depicted in Table 6.

This study employs the TOE theoretical framework and dynamic QCA methodology to examine data of 30 Chinese provincial panels over the period 2012 to 2022. It examines the diverse pathways and temporal evolution patterns through which the digital economy drives high-quality development in the smart health and aging care industry, exploring these dynamics across three dimensions: technology, organization, and environment. The findings not only reveal the complex influence mechanisms of different configuration pathways on the high-quality development of the smart health and aging care industry but also identify the evolutionary trajectories of core driving factors across distinct developmental stages.

From a prerequisite analysis perspective, no single factor constitutes a necessary condition for the high-quality development of the smart health and aging care industry. The qualitative leap in this sector fundamentally stems from the synergistic interactions and systemic coupling among multiple elements, rather than being dominated by any single independent variable. This finding resonates with existing research. For instance, Liu et al., noted that in complex socioeconomic systems, aging care industry transformation and development are often driven by multi-level, multi-dimensional factors. Relying solely on technological investment or policy support is insufficient to achieve systemic quality breakthroughs (19). Therefore, examining the interactive relationships and overall configuration among different factors from a configuration perspective offers a more realistic and comprehensive theoretical explanation for understanding the high-quality development pathways of the smart health and aging care industry (40, 41).

Within the configuration pathway, the digitally technology-driven model (M1) highlights the synergistic effects of digital technology applications and digital infrastructure in driving the high-quality development of the smart health and aging care industry. This conclusion aligns with the findings of Ding et al., indicating that the competitiveness of smart aging care industry development depends on the depth of embedding and breadth of coverage of digital technologies and their infrastructure within the industrial system (16). Unlike the research by Liu and Xu, they study by confirms the crucial role of industrial chain innovation in the relationship between digital technology application and the development of the smart health and aging care industry (19). Digital technologies drive systematic innovation and development by integrating the upstream, midstream, and downstream segments of the health and aging care industrial chain (21, 34). This study further expands this perspective by focusing on how digital technology applications synergize with other key elements to collectively influence the high-quality development of the smart health and aging care industry. Concurrently, digital infrastructure serves as the foundational support, utilizing advanced information technologies including 5G and IoT to ensure the inter connectivity of industrial data and the efficient implementation of services (36, 38). Through these mechanisms, digital technology applications and digital infrastructure jointly form an integrated driving force propelling the sustainable development of the smart health and aging care industry.

The dual-core “digital technology-digital environment” driving model (M2, M3, M4) emphasizes the synergistic effects of digital technology applications, digital infrastructure, government digital policies, and digital inclusive finance. Existing research confirms that these elements significantly influence the development of the smart health and aging care industry (40, 41). For instance, Liu and Xu notes that the deep integration of smart terminals with health cloud platforms makes digital technology applications a key driver for the smart aging care industry (19). Hou and Li demonstrate that digital infrastructure—represented by high-speed networks and the Internet of Things—not only ensures the continuity and reliability of smart older adults care development but also builds foundational support for industrial innovation (20). Gomber et al., determined that areas with greater concentrations of digital inclusive finance are more likely to foster a virtuous industrial ecosystem through policy guidance, thereby validating the synergistic effects between digital policies and financial instruments (50). This study further reveals that synergies between digital technologies and digital environments are particularly pronounced in contexts where digital organizational capabilities are weak but policy direction is clear. Digital technologies provide the “hard power” for industrial upgrading, while policy and financial environments constitute the “soft power” for sustainable development. Consequently, even with relatively scarce organizational resources, regions can achieve leapfrog development in the aging care industry by building systematic digital ecosystems.

The digital environment-driven model (M5) emphasizes the synergistic effects of government digital policies and digital inclusive finance. Against the backdrop of weak industrial foundations and immature technology applications, governments guide resources toward the smart health and aging care sector through forward-looking policy planning (45, 47). Concurrently, digital inclusive finance effectively enhances the sector’s inclusiveness and sustainability by lowering service access barriers. This synergistic mechanism between digital policy and finance is supported by existing research. For instance, Yang note that robust data security and privacy protection regulations not only help regulate market order but also activate the industry’s endogenous momentum by enhancing user trust (52). In this process, digital inclusive finance provides indispensable financing support for small and micro enterprises to participate in market competition, thereby strengthening their market survival and development capabilities (50, 51). Thus, within this pathway, the synergy between government digital policies and digital inclusive finance not only fortifies the risk resilience of industrial systems but also injects sustained momentum toward achieving more inclusive long-term growth.

Consistent with existing research findings, this study further confirms the pivotal role of digital technologies in driving the development of the smart health and aging care industry. It highlights that, in the long term, the continuous iteration and integrated application of technologies are crucial for addressing the challenges of healthy aging and enhancing the quality of aging care services (16, 19, 21, 36). Moreover, this research underscores the twofold effect of environmental ambiguity on the progression of the intelligent health aging sector. On the one hand, as a vital driver of industrial growth, proactive government digital policies can accelerate the implementation and promotion of smart health industry solutions by improving digital infrastructure and fostering a favorable institutional environment (45, 46). On the other hand, the deepening development of digital inclusive finance can effectively alleviate financing constraints during the industry’s early stages and broaden service coverage (50). However, in environments with high uncertainty, fluctuations in digital inclusive finance may also “induce” some enterprises to over-rely on short-term funding support, thereby weakening the intrinsic momentum for the development of the smart health and aging care industry (52).

Beyond the aforementioned analysis, this study further reveals through multi-period comparative analysis that the digitally technology-driven configuration and the dual-core “digital technology-digital environment” configuration persist stably across both periods, exhibiting strong path dependency. Their evolutionary patterns align with the “dominant trajectory.” This indicates that sustained technological accumulation and digital ecosystem development provide fundamental support for the high-quality advancement of the smart health and aging care industry. In contrast, the digitally environment-led configuration, emerging in the first period, underwent structural adjustments and path renewal in the second period. Its transformation pattern exhibits a typical “turning-point trajectory,” reflecting that development paths driven by external policy environments require dynamic adaptation to changing macro-conditions. Furthermore, this research uncovers substantial spatial variation in the influence of digital economy catalysts. In eastern coastal regions, digital inclusive finance and digital technology applications exert particularly pronounced impacts on the smart health and aging care industry. This is closely linked to their higher economic levels, mature digital infrastructure, and talent aggregation effects. In contrast, central and western regions, constrained by resource endowments and institutional environments, exhibit relatively slower penetration of digital technologies and services. They rely more heavily on policy guidance and foundational environment construction to achieve localized breakthroughs. This finding aligns with regional innovation system theory, which posits that disparities in resource distribution and developmental stages across regions determine the diversity and specificity of their industrial development pathways (19, 23).

The theoretical significance of this study lies primarily in three aspects: First, it deepens and expands the theoretical implications of the TOE framework within the context of the smart health and aging care industry. While the traditional TOE framework systematically identifies the influence of technology, organization, and environment, it fails to clarify their synergistic logic within the dynamic evolution of specific industries. This study reveals that no single TOE element constitutes a necessary condition for high-quality industrial development. This finding strongly confirms that in the digital economy era, the smart health and aging care industry follows a complex causal logic characterized by “multiple concurrent paths” and “convergence toward a common goal.” This conclusion breaks the traditional analytical mindset of seeking a single core driver, emphasizing the nonlinear, synergistic interplay among technological application, organizational transformation, and institutional environment. It thereby enriches the TOE framework’s capacity to explain the dynamic mechanisms underlying the formation and development of emerging cross-sector industries.

Second, this study provides key empirical evidence from the smart health and aging care sector for understanding the relationship between digital governance ecosystems and high-quality industrial development. While existing research often highlights the importance of digital technology itself, it lacks detailed exploration of the macro-governance ecosystem models that support its implementation. The three high-quality pathways identified in this study: digital technology-driven, dual-core “digital technology-digital environment” driven, and digital environment-led along with two low-quality traps: the “digital technology-digital environment” constrained and the multidimensional “digital technology-digital organization-digital environment” constrained essentially reflect the differing efficacies of digital governance ecosystems. This demonstrates that a high-quality industrial digital ecosystem can be either strongly technology-driven or carefully environment-nurtured, providing a precise theoretical framework for constructing multi-stakeholder, multi-level collaborative governance models in practice.

Third, this study responds to management scholarship’s call for incorporating the “time” dimension into configurational analysis and demonstrates its methodological value. Static QCA analysis struggles to capture the dynamic evolution of driving pathways, risking theoretical stagnation. By adopting dynamic QCA, this study not only identifies driving pathways but also traces their evolutionary trajectories over time (e.g., “dominant trajectories” and “transitional trajectories”) and their heterogeneous spatial distributions across cross-sections. This methodological advancement enables research findings to reveal more complex dynamic coupling relationships between digital economic development and industrial evolution, significantly enhancing theoretical saturation and the robustness of conclusions. It provides a valuable template for subsequent studies engaged in diachronic, process-oriented configuration research. Second, this study provides crucial empirical evidence from the smart health and aging care sector for understanding the relationship between digital governance ecosystems and high-quality industrial development. Existing research often emphasizes the importance of digital technologies themselves while lacking detailed exploration of the macro-level governance ecosystem models that support their implementation. The three high-quality pathways identified in this study: digital technology-driven, dual-core “digital technology-digital environment” propulsion, and digital environment-led, alongside two low-quality traps: constrained “digital technology-digital environment” and multidimensionally restricted “digital technology-digital organization-digital environment,” essentially reflect varying levels of digital governance ecosystem efficacy. This demonstrates that a high-quality industrial digital ecosystem can be either strongly technology-driven or carefully environment-nurtured, thereby providing a precise theoretical framework for constructing multi-stakeholder, multi-level collaborative governance models in practice.

Additionally, this study’s results provide three practical applications. First, they provide a theoretical basis and practical framework for governments at all levels to formulate differentiated and targeted industrial promotion policies. This research reveals that digital technology and digital environment are core drivers of high-quality industrial development, yet their pathways exhibit significant regional heterogeneity. Consequently, policy creation must eschew a universal solution method. For eastern coastal provinces with robust digital infrastructure and dense technical talent pools, the focus should be on deepening digital technology applications and refining digital inclusive financial systems to foster a technology-driven development model. For central and western provinces, priority should be given to optimizing the digital policy environment and strengthening digital infrastructure to cultivate a “digital ecosystem” conducive to industrial incubation and growth, pursuing an environment-nurturing pathway.

Second, it identifies key strategic resource allocation directions and potential risks for various industry participants, including aging care service providers and technology companies. The study reveals that no single factor can independently drive development; enterprises must seek synergistic alignment between technology and the environment based on their regional ecosystem patterns. For instance, in the dual-core “digital technology-digital environment” driven pathway, enterprises should focus on deeply integrating technological R&D with local policy orientations and financial support. Simultaneously, the low-quality development pathways identified in the study serve as a warning to market entities. They suggest that enterprises should avoid blind investment under scenarios of dual constraints in “digital technology-digital environment” or lagging organizational innovation. Instead, they should proactively seek integration into regional collaborative ecosystems to circumvent multidimensional development traps.

Third, it provides a temporal perspective and early warning indicators for evaluating and adjusting industrial development strategies within dynamic processes. The “dominant trajectories” and “transition trajectories” identified in this study indicate that the driving models of the smart health and aging care industry evolve over time. Notably, the widespread decline in configuration consistency observed in 2020 suggests that major external shocks, such as public health events can inflict systemic impacts on the industry’s digital ecosystem. This necessitates that managers establish dynamic monitoring mechanisms to closely track the stability of key core conditions. When signs of diminishing effectiveness emerge in the dominant pathway’s driving effects, decision-makers should be able to gain timely insights. By referencing the experience of the “transition trajectory,” they can guide the industry to shift from a single technological or environmental pathway toward a more robust “dual-core propulsion” model, thereby enhancing the resilience and sustainability of industrial development.

This research has specific limitations. First, at the theoretical framework level, it analyzes the driving forces behind the smart health and aging care industry primarily from a supply-side perspective (technology, organization, environment) via the TOE framework. While the study validates the pivotal impact of elements like digital technology and its environment, the framework fails to systematically incorporate demand-side factors, such as the digital literacy of older adults and their heterogeneous service needs (64). This limitation primarily stems from the challenges in obtaining micro-level individual data in current research. Consequently, the study’s conclusions may emphasize depicting the “supply potential” of industrial development while failing to fully reveal the obstacles technology may encounter in actual implementation due to the “digital divide.” Second, regarding variable measurement, constrained by the availability and uniformity of macro-level data, proxy indicators for certain variables may not accurately or comprehensively capture their theoretical essence. For instance, the measurement of “digital organizational innovation” and “digital human capital” requires further refinement, which to some extent limits the possibility of more nuanced characterization of the digital economy’s impact mechanisms. Third, at the research data level, while the provincial panel data effectively captures macro trends, its granularity remains insufficient. It fails to reveal the heterogeneity and dynamic micro-level mechanisms within provinces, particularly between cities or at the community level, limiting the interpretive power and practical guidance of the findings at the micro level.

Given these limitations, future research may explore breakthroughs in multiple directions. First, regarding theoretical frameworks, subsequent studies could integrate established theoretical perspectives (such as dynamic capability theory and innovation ecosystem theory) or incorporate demand-side variables like digital literacy and usage willingness among older adult users. This would establish a comprehensive analytical framework balancing supply and demand, enabling more precise prediction and evaluation of the social benefits within the smart health and aging care industry. This would address the theoretical boundaries of the TOE framework, revealing more comprehensively the diverse pathways and potential “blind spot” factors driving high-quality development in the smart health and aging care industry from both supply and demand dimensions. Second, regarding variable measurement and data, future research should focus on developing more precise, multi-level indicator systems and advancing research data to the micro level. On the one hand, data collection should extend to the municipal level and even down to the enterprise/institutional level, utilizing more advanced econometric methods for validation. On the other hand, it is strongly recommended to incorporate qualitative research methods (such as multi-case comparisons, in-depth interviews, and participant observation) to gather first-hand textual and narrative data through in-depth investigations of policymakers, community managers, aging care service providers, and senior users. This strategy of combining “macro-level quantitative characterization” with “micro-level qualitative analysis” will help truly unravel the “black box” of the intrinsic role of the digital economy in driving the development of the smart health and aging care industry. It will enhance our insight into its intricate cause-and-effect processes and lay a robust conceptual groundwork for developing more nuanced and targeted implementation strategies.

This study employs the TOE framework and dynamic QCA methodology, employing panel data from 30 Chinese provinces for the period 2012–2022. It systematically examines the multiple pathways and dynamic evolution through which the digital economy drives high-quality development in the smart health and aging care industry from a configuration perspective. Findings reveal: First, necessity analysis indicates that none of the six digital economy variables covered by the TOE framework passed the necessity test. This suggests that both high-quality and low-quality development in the smart health and aging care industry stem not from a single factor but depend on the synergistic interaction of multiple elements. Second, five high-quality development pathways were identified, categorized into three models: digital technology-driven, dual-core “digital technology-digital environment” propulsion, and digital environment-led. Low-quality development pathways manifested as “digital technology-digital environment” constraint-type and multidimensional “digital technology-digital organization-digital environment” limitation-type. Third, temporal evolution revealed a significant decline in the consistency of high-quality configurations in 2020. spatially, provinces exhibited pronounced regional heterogeneity in configuration coverage. Finally, multi-period comparative analysis further revealed multiple evolutionary trajectories: the digital technology-driven and dual-core “digital technology-digital environment” models constitute the “dominant trajectory,” while the digital environment-led model represents a “transitional trajectory.” In summary, the digital economy’s driving force for high-quality development in the smart health and aging care industry follows a complex causal logic of “multiple concurrent paths” converging toward a common destination. This finding not only deepens theoretical understanding of industrial digitalization but also provides empirical evidence for implementing differentiated, dynamic industrial policies in the smart health and aging care sector.

This study proposes the following three differentiated policy recommendations: First, eastern regions should focus on technological leadership and environmental optimization to establish innovation hubs for the smart health and aging care industry. Eastern regions possess significant first-mover advantages in digital technology application and digital inclusive finance. Policies should drive two key initiatives: First, encourage the deep integration and model innovation of cutting-edge technologies like artificial intelligence, the Internet of Things, and big data in smart health and aging care scenarios such as home-based care, chronic disease management, and rehabilitation assistance. Support the development of a group of demonstrative, comprehensive benchmark industrial projects. Second, guide financial institutions and technology companies to collaboratively develop scenario-based, customized digital financial products such as older adults care insurance, smart wellness trusts, and rehabilitation wealth management. This will build an inclusive financial service ecosystem covering the entire life cycle, achieving a leap in capability from technology empowerment to model innovation, and from financial services to ecosystem construction.

Second, the central region should strengthen organizational transformation and efficiency enhancement to stimulate the intrinsic momentum of the smart health and aging care industry. While the central region has established a certain foundation in digital infrastructure, its overall development efficiency is constrained by insufficient digital organizational innovation capabilities. Policies should focus on bridging the “last mile” of digital transformation: by establishing dedicated subsidies for the digital transformation of smart health and aging care institutions, implementing star-rating evaluations and model selection initiatives, and encouraging various older adults care service providers to undertake organizational process reengineering and digital management upgrades. Support should be provided for community-based older adults care service centers and private institutions to adopt intelligent management platforms, deliver integrated online-offline health and older adults care services, and enhance data-driven operational management and resource allocation capabilities. This will effectively translate infrastructure advantages into service efficiency and organizational competitiveness.

Third, western regions should focus on addressing infrastructure gaps and cultivating talent to solidify the foundation for the smart health and aging care industry. Western provinces commonly face dual bottlenecks of inadequate digital infrastructure coverage and shortages in digital human capital. Policies should prioritize “strengthening foundations and cultivating talent”: On the one hand, implement a Western Digital Infrastructure Initiative to extend 5G networks and gigabit fiber-optic coverage to rural and remote areas. Leverage regional hub nodes to deploy centralized, green computing resources, providing stable physical support for smart health and aging care applications. On the other hand, launch the Silver-Haired Digital Talent Revitalization Program. Through targeted recruitment, tuition compensation, on-the-job training, and continuing education, systematically cultivate versatile professionals with both healthcare expertise and digital application skills, providing robust talent support for the industry’s sustainable development.