A Multi-Pathway Study on the Impact of the Business Environment on Digital Elderly Care Services in China

A Multi-Pathway Study on the Impact of the Business Environment on Digital Elderly Care Services in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Multi-Pathway Study on the Impact of the Business Environment on Digital Elderly Care Services in China Jianli Gao, Xiaoqing Zhang, Lejie Wang, Xiaoyan Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4235268/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract A high-quality business environment contributes to unleashing the innovative vitality of market, what kind of business environment can promote the the construction of digital elderly care services is an important issue worthy of attention. Utilizing spatial visualization methods, we unveil distinct regional heterogeneity in the provincial distribution of digital elderly care services. Yangtze River Delta and the Pearl River Delta leading the way. To understand this pattern, we engage the fuzzy-set qualitative analysis and examine the configuration of six equivalent pathways for the the business environment on digital elderly care services. In general, we find digital talent aggregation and government public expenditure playing a deterministic role in all configurations and offer recommendations for the innovative development of digital elderly care services under different digital resource endowments. Business Environment Elderly Care Services Digitalization Configurational Analysis Figures Figure 1 Figure 2 1. Background Population aging presents a pressing challenge for many countries around the world, and China is no exception. President Xi Jinping pointed out in the report of the 20th National Congress of the Communist Party of China that “actively addressing population aging, accelerating the construction of a new development pattern of digital China, and adhering to promoting high-quality development as the theme” .[1] As the world’s largest developing country, China is facing a monumental shift in its population pyramid structure, elevating the provision of elderly care on top of the agenda. According to the Seventh National Population Census, by the end of 2020, there were 264 million people aged 60 and above, accounting for 18.70% of the total population, of which 191 million people were aged 65 and above, accounting for 13.50% of the total population [2] . By the World criteria for aging, China has entered a critical stage, and its aging population is expected to peak in the next 10 to 20 years. Undoubtedly, this rapidly aging population trend has raised several challenges in China’s massive elderly care service market. The elderly care service market struggled to meet the surging demand for community-based home care. In part, this struggle stemmed from a small service radius created by the heterogeneous and dispersed living arrangements of the elderly and the high dependence of traditional elderly care on labor input (Wang & Du, 2018 ). Moreover, the lack of financial capacity for many elderly people severely constrained the realization of economies of scale in the elderly care market that left little incentives for new service providers to enter. As a result, the elderly care market not only faces a mismatch between the supply side and the demand side but also suffers from low efficiency in resource allocation, severely undermining the government’s active aging goals. This trend, coupled with the “becoming old before becoming rich” phenomenon has exerted a significant impact on social and economic development, urgently calls for a pathway suitable for China that requires a complete overhaul of the traditional elderly care paradigm. Promoting the comprehensive integration of the digital economy and the real economy has attracted policymakers’ attention in recent years. The digital economy, encompassing the Internet, big data, and artificial intelligence, has revolutionized resource allocation in various fields. Its exceptional capabilities in matching the buyers and sellers, improving information-processing time, and reducing selection and transaction costs, have enabled its ability to meet the heterogeneous needs of the elderly and provide the catalyst for transforming the elderly care market (Cai, 2021). For example, a digitized elderly care system can reduce the dependence on geographical proximity in the past by developing a seamless information network space that connects online and offline activities, aggregates data in time and space, and involves virtual and real interactions, to develop a high-quality elderly care industry (Legato et al., 2022 ). A high-quality business environment creates conditions for sustainable economic development and is a prerequisite for maintaining long-term competitiveness, which can enhance a country or region’s economic soft power and comprehensive competitiveness. In this study, addressing the requisite business environment support for the realization of digital elderly care services emerges as a pivotal concern. Digital elderly care services are a complex system engineering, the result of the interaction of complex elements. Exploring digital elderly care services requires considering the joint effects of multiple factors in multiple business environments. Against this backdrop, we employ the fuzzy-set qualitative comparative analysis (FSQCA) to handle multiple interactions under different configurations of the digitalized elderly care system (Du & Jia, 2017). In this study, we dissect the configurational paths of China’s digitized elderly care system across five dimensions: digital infrastructure, investment in digital technology research and development, digital talent, digital government service capabilities, and government public expenditure. Using data from 31 provinces, we find how digital technology drives the innovation development of China’s public elderly care system. 2. Theoretical Foundations 2.1 Research on Digital Elderly Care Services With the rise of the digital economy, both domestically and internationally, the application of digital tools like the Internet of Things (IoT), remote communication and sensing, and real-time data analysis has revolutionized the elderly care systems (Do et al., 2017 ). For example, Sweden’s ACTION model utilizes remote information processing technology to assist home care providers in meeting the needs of the elderly. Finland’s remote assistance model relies on the IoT to build a monitoring system, providing comprehensive security for the elderly (Deen, 2015 ). Germany’s AAL employs the environment-assisted living intelligent technology platform to enhance the independent living capabilities of the elderly (Chu et al., 2015). Turning to North America, in the United States follows, there is a tiered and differentiated service model to that accurately provides services based on the needs and economic conditions of the elderly (Pal et al., 2018 ). Canada’s SIPA (Systematic Interdisciplinary Palliative Care Approach) offers community-based comprehensive, integrated services (Maswadi et al., 2020 ). Unlike its European and North American counterparts, China emphasizes on the feasibility and necessity of digitalized elderly care systems (Zuo, 2014 ). For instance, its long-term goal has always been to establish smart digital platforms that connect the demand and supply for personalized elderly care (Liu, 2021 ). However, the lack of supporting digital infrastructure and elderly care data collection have limited the economies of scale in elderly care delivery, impeding the private sector’s willingness to invest in this market. Consequently, elderly care delivery has been dominated by inefficient and sometimes outdated government-purchased business models (Bian and Li, 2019 ; Li and Ding, 2019 ; Zhang, 2021 ). Recently, the dominance of these models has been called into questions by the application of digital tools that improves the efficiency and quality of elderly care delivery in areas like inclusive growth (Fan & Wu, 2022), product innovation (Xia & Wang, 2021 ), supply chain logistics (Liu et al., 2021), interactive supply and demand (Zhang, 2021 ; Zhao & Deng, 2021 ), and community-based elderly care (Chen et al., 2001). Since the literature on the potential benefits of digitized elderly care for the elderly in China remains sparse to date, we intend to close this gap by focusing on the innovation development of a digitized elderly care system. 2.2 Research on Business Environment The concept of the business environment originated from the investment environment and later expanded to other aspects of enterprise operations. The World Bank explicitly states that the business environment includes institutional environment, infrastructure conditions, geographical location, etc., and has been releasing annual reports on the business environment since 2003, stimulating countries to optimize their business environment. Drawing on research by Chinese scholars from different perspectives on the business environment (Li et al., 2019 ; Zhang, 2006 ), this study considers the business environment is a collection of external environments, including institutional environment, social environment, market environment and economic environment. The economic effects of the business environment can be examined from both macro and micro perspectives. From a macro perspective, scholars unanimously believe that continuous optimization of the business environment has a positive effect on improving economic quality. A good business environment helps solve the problem of resource misallocation (Zou and Lei, 2021 ) and enhances resource utilization efficiency (Wang et al., 2022), achieving market-oriented allocation of production factors and promoting economic growth (Huang and Wang, 2022 ). From a micro perspective, regarding how the business environment affects business operations, scholars have considered entrepreneurship and innovation aspects. A favorable business environment can stimulate entrepreneurial innovation and enthusiasm (Gong and Liu, 2014), eliminate the impact of rent-seeking behavior on business operations, break administrative monopolies, unleash market vitality, and stimulate business creativity (Xu and Cui, 2015 ). Some scholars have also verified the positive impact of single dimensions of the business environment, such as government service capabilities, on business operations. Additionally, a good business environment can lower market access barriers, promote business growth, broaden financing channels, alleviate financing constraints, and help businesses withstand operational risks (Yu and Liang, 2019). Existing research on the evaluation index system of the business environment mainly focuses on international organizations and research institutions, with countries or economic entities as the main evaluation objects. For example, the World Bank’s evaluation index of the business environment focuses on institutional construction norms, completeness, and degree of marketization. The IMD World Competitiveness Center in Lausanne, Switzerland evaluates from four aspects: economic performance, government efficiency, business efficiency, and infrastructure. The Economist Intelligence Unit mainly considers the degree of marketization of regional development. The “Global Cities Index” published by Kearney mainly evaluates from five dimensions: human capital, information exchange, business activities, cultural experiences, and political environment. Under the guidance of the National Development and Reform Commission of China, assessments of the business environment have been carried out across all 31 provinces, autonomous regions, and municipalities, and the “China Business Environment Report (2020) ” was released in 2020. On November 25, 2021, the State Council issued the “Opinions on Conducting Pilot Innovations in the Business Environment”, selecting six market entities to carry out pilot innovations in the business environment. Meanwhile, local governments and scholars are actively exploring various approaches. For instance, they are constructing evaluation index systems from multiple perspectives, such as workforce, government, finance, and market (Li et al., 2019 ). Additionally, they are proposing evaluation system based on provincial-level data with four dimensions like government affairs, law, humanities, and market (Zhang et al., 2020). Furthermore, there are proposals for evaluation index system focusing on the business environment of Chinese cities. These systems cover key urban agglomerations and include seven dimension like government affairs environment, human resources, innovation environment, legal environment, market environment, public services, and financial services (Li et al., 2021 ). 2.3 Selection of Evaluation Indicators for Variables In the indicator selection process, we utilized both literature analysis and expert discussions. Through literature analysis, we delved into the core concepts of “digitalized elderly care services” and “business environment” identifying indicators that closely align with our research focus. Additionally, expert discussions were integral in refining the indicator system, as we sought expert opinions to adjust and screen indicators at multiple levels, ensuring the suitability of our evaluation indicators for this study. This comprehensive approach ensured a robust selection of indicators that effectively capture the nuances of our research objectives. The expert panel comprised seven professors and corporate executives with expertise in the research field. The research methodology involved a blend of interviews and phone calls. From the literature review above, it can be seen that there are common dimensions for evaluating the business environment, which should include innovation environment, human resources, government service environment, economic environment, and financial services. When choosing indicators, it is necessary not only to consider findings from existing literature but also to pay close attention to the distinctive charactieristics of the research subject, including greater reliance on data, higher demand for efficiency, higher demand for precision, higher demand for innovation and greater reliance on government financial input. Due to the high degree of reliance on accurate and efficient data, it is necessary to have digital infrastructure as a basic guarantee. Moreover, given that society’s digital transformation remains in its nascent phase, ongoing investment in research and development is essential to foster continual innovation in digital technology, thereby enhancing its capacity to drive tangible economic growth. Consequently, prioritizing investment in digital technology research and development is essential. The traditional elderly care service industry is a labor-intensive industry. Although digital technology empowerment can reduce the reliance on labor input, the demand for digital talents is greatly increased. Therefore, the quantity of digital talents to a certain extent determines the speed and quality of social digital transformation, and the aggregation of talent serves as an indicator of the business environment. As elderly care services have a strong public service nature, excellent government service capabilities facilitate the integration and efficient utilization of data resources, which helps improve service efficiency and precision. Digitalized elderly care services require a large amount of government financial input or commissioning of social organizations in the form of government procurement to complete services, so there is a high requirement for local government’s public expenditure. In summary, this study believes that the evaluation of the business environment needed for digitalized elderly care services should focus on five dimensions: digital infrastructure construction, investment in digital technology research and development, aggregation of digital talents, capability of digital government services, and scale of government public expenditure. The level of digitalized elderly care services is measured by the total number of digitalized elderly care service institutions in each region, including smart elderly care service platforms, smart elderly care information service centers, and smart elderly care service companies, as well as the total number of smart elderly care demonstration bases, demonstration streets, and demonstration enterprises. 2.4 Theoretical Foundations 2.4.1 Construction of digital infrastructure and digitalized elderly care services Digital infrastructure constitutes the prerequisite for empowering digitalized elderly care through innovation development in new technologies, new heights, new fields, new formats, new models, and new governance (Guo Chaoxian et al., 2020). This involves advancing internet deployment to enhance the accessibility and availability of network infrastructure, as well as planning for facilities such as towers, pipelines, and base stations to facilitate infrastructure deployment. Additionally, it requires coordinating the construction of application facilities such as industrial internet, data centers, and smart computing platforms. These measures support enterprises in leveraging data for intelligence (Zuo Pengfei & Chen Jing, 2021). Moreover, the role of digital infrastructure in supoorting economic transformation and upgrading is becoming increasingly prominent (Li Wei, 2023). Encouraging the application of biometric recognition technologies such as facial and fingerprint recognition, deepening the application of online payment in government services, public services, and other fields can reduce transaction costs, improve transaction efficiency, and prevent financial risks (Yu Chaoyi et al., 2020). Digital infrastructure is the bottom-level technological foundation for the development of the digital economy, capable of unleashing the innovative effects of information technology (Chao Xiaojing, 2020 ), constituting the basic guarantee for the development of digitalized elderly care services. Therefore, the construction of digital infrastructure is the most important foundation for building a business environment under the new era background, and it is the fundamental guarantee for realizing the empowerment of digital technology for elderly care services. The overall level of informatization and the construction of digital infrastructure in a region can be measured by the number of computers used per hundred people and the number of mobile phones owned per hundred people[3] . 2.4.2 Investment in digital technology research and development and digitalized elderly care services In the era of the digital economy, the economic environment plays an important catalytic role in digital innovation (Wang Lijian & Di Xiaodong, 2022). The inherent adaptability of digital innovation allows innovation activities to organically evolve in respond to changes in the environment (Yang Zhongji & Qi Liangqun, 2021). The investment in scientific research and innovation can provide complementary resources for digital innovation, accelerate the transmission speed of digital innovation to terminal demands, and lead digitalized elderly care services to achieve high-quality development. With its unique network aggregation effect, digital technology breaks through traditional spatial and temporal limitations, further opening up the geographical boundaries of traditional elderly care services, helping to reduce information asymmetry, and improve the accuracy and efficiency of services (Ren Zhuanzhuan & Deng Feng, 2023). The development of digitalized elderly care services relies heavily on the support of digital technology. Continuous innovation and incremental investment in research and development of digital technology are the means and approaches to achieve digital transformation, requiring continuous innovation and research and development investment support (Sun Jiling et al., 2022). Through innovation in digital technology, it becomes possible to generate new products, services, or business models, thereby fostering competitiveness in the market (Ding Zhifan, 2020 ). Moreover, this investment ensures accurate and timely data collection and feedback, improves resource utilization, and encourages real-time interaction among stakeholders in the elderly care market. Consequently, this promotes the high-quality development of public elderly care services (Chen Hong & Feng Dayang, 2022). In summary, research and development investment is an important factor in guaranteeing continuous innovation and continuous improvement of digital technology, and it is one of the important dimensions of evaluating the business environment, measured by the investment in digital technology research and development funds in each region. 2.4.3 Support of digital talents and digitalized elderly care services The rapid development of the digital economy undoubtedly stems from the pivotal role played by digital technology (Xie Kang et al., 2020). However, digital talents, as active resources, largely determine the efficiency of the development and utilization of digital technology and are the core force behind the high-quality development of the local economy. The advancement of the human capital structure can enhance labor efficiency and promote technological innovation (Dai Kuizao et al., 2023 ). Especially in the process of industrial upgrading, with the gradual improvement of the business environment, the aggregation of talents and the advancement of human capital can significantly promote the high-quality development of the service industry (Yu Boyang & Cong Yi, 2021). Moreover, the higher the heterogeneity level of the human capital structure, the more conducive it is to innovation, and the more significant the economic effects (Li Mengna & Zhou Yunbo, 2022). Therefore, accelerating the improvement of the business environment, formulating policies and mechanisms to attract talents, and increasing the stock of digital talents have become the consensus of various regions in pursuing high-quality development of the digital economy (Yang Dong et al., 2021). Furthermore, the availability of digital talent largely determines the extent and efficiency of digitalized elderly care (Fan Hejun & Wu Ting, 2022). As an active resource, procuring digital talents has been a top priority for many policymakers wanting to expand digitalized elderly care in their jurisdictions. For example, many jurisdictions created a friendly environment to foster digital talents by guiding development programs, reforming incentive and remuneration structures, and identifying intellectual bottlenecks in digitalized elderly care (Wang Shiwei, 2022). In summary, the aggregation of digital talents is a necessary condition for promoting the digitization of elderly care services. It serves as a significant indicator of a favorable business environment and can be assessed through the workforce size within telecommunications, computer, and other digital technology sectors across different regions. 2.4.4 Capability of digital government services and digitalized elderly care services The government plays an active role in coordinating and regulating digital economic development, balancing digital resources, and enhancing the international competitiveness of the digital economy, which is a necessary approach to accelerating economic transformation. Traditional government service methods and processes suffer from problems such as “running back and forth for handling affairs” and “multiple steps in the documentation process”, resulting in deficiencies or mismatches in the government’s service functions (Xu Xiaolin et al., 2023). As such, the promotion of digitalized elderly care poses new challenges for digital government services, transforming their role from regulators to service providers. The application of digital technology can expand the coverage and efficiency of service resources, thereby improving the “efficiency dilemma” in the business environment (Shi Qingbo & Huang Qisong, 2023). Unlike Germany, Japan, and the United States, it is the Chinese government rather than the private sector that is likely to lead the digitalized elderly care strategy. They advocate for a holistic approach that fosters the creation of a robust digital ecosystem through synchronized efforts in cultivating digital technologies and talents across foundational, platform, and application domains. This, in turn, is seen as instrumental in further bolstering governmental service capabilities (Yang Yan, 2019). In addition, attention should be paid to regulatory innovation and actively studying relevant support policies that are compatible with and conform to the digital economy, creating a digital and service-oriented government. This helps coordinate digital resources, accelerate the integration of the digital economy with traditional industries (Xie Lijuan & Zhuang Yiqun, 2021), and bring about “digital dividends” for the business environment, enhancing the effectiveness of the business environment (Zhang Banghui et al., 2021). In essence, enhancing the capability of digital government services comprehensively signifies an essential aspect of enhancing the business environment. This enhancement is gauged across five dimensions: the effectiveness, maturity, completeness, coverage, and accuracy of online government services.[4] 2.4.5 Scale of government public expenditure and digitalized elderly care services Relying on government purchasing of elderly care services, actively nurturing market forces, introducing social references, building multi-party cooperation relationships, optimizing the elderly care service system, is an effective way to alleviate the current pressure on elderly care (Lu Xuanru & Zhang Xiaoyi, 2022). However, elderly care services belong to the quasi-public goods category, which is an important manifestation of the government’s people-oriented approach and has certain public welfare characteristics. It requires the government to support it with public finances, especially in terms of public space, facilities, and public service expenditures, as well as expenditures for special elderly groups (Song Huan et al., 2022). As a key player in the elderly care service system, government engagement goes beyond direct investment. Government procurement of elderly care services represents a proactive response and a significant approach to address the considerable pressures and practical needs surrounding elderly care issues. This strategy involves transferring certain elderly care service functions to third-party providers, fostering market development while meeting the demands of the aging population (Xu Xianmei, 2019). As early as 2014, the Ministry of Finance, the National Development and Reform Commission, the Ministry of Civil Affairs, and other departments jointly issued the “Notice on Doing a Good Job in the Government’s Purchase of Elderly Care Services”, which clearly proposed the mode and responsibilities of government purchases, marking a significant guiding policy. Empirically, regional public expenditure is often used as a measure for the effect of the macroeconomic environment on digitalized elderly care services. In summary, compared to other industries operating on a market-oriented basis, the digitalization of elderly care services requires more government financial support. Therefore, considering the specificity of this industry, this indicator can be measured using data on the scale of public financial support in a region. Traditional empirical research does not consider the interaction between independent variables, and the explanation of the dependent variable belongs to a linear relationship, based on the premise of causal symmetry. However, in practice, the factors affecting digitalized elderly care services are complex and diverse, and variables interact with each other and collaborate. Fuzzy set qualitative analysis method can explain the interaction between complex variables and is good at small-sample analysis, especially suitable for addressing the issue of causal asymmetry (Du Yunzhou & Jia Liangding, 2017). Therefore, based on examining the characteristics of different regional developments, this study conducts configuration analysis on the impact of the business environment on digitalized elderly care services in various regions, interprets their causal relationships from a holistic perspective, and provides theoretical basis for regions to enhance the level of digitalized elderly care services in a targeted manner. 3. Methods 3.1 Sample selection and data sources Limited by data availability, we explore the empowering factors of digitalized elderly care across the 31 provinces on mainland China due to the inconsistent data collection methods and standards in Taiwan, Hong Kong, and Macau compared to the mainland. We obtain the data on the number of digitalized elderly care institutions using the web scraping technology from Baidu Maps by including keywords like “smart elderly care service platform” “smart elderly care information service center” and “smart elderly care service company”. We source the data on “smart elderly care demonstration bases, streets and enterprises” from the Ministry of Civil Affairs and the data on digital infrastructure, digital technology investment, and digital talent support from the Digital Economy Database. Finally, we extract the data on digital government services from the 2021 Provincial Government and Key City Online Government Service Capability Survey and Assessment Report and the data on macroeconomic empowerment from the China Statistical Yearbook 2021. We finalized our data collection in March 2022. 3.2 Research methodology 3.2.1 Spatial visualization analysis and global Moran’s Index We employ ArcGIS 10.2 to perform the spatial visualization analysis on digitalized elderly care and its empowering factors in 31 Chinese provinces. To that end, we apply natural breaks to categorize these provinces into five levels and calculate the global Moran’s I statistics for each variable to ascertain if their provincial distribution is subject to non-random spatial clustering. Specifically, we obtain the global Moran’s I as follows: $$I＝\frac{\sum _{i=1}^{n}\sum _{j=1}^{n}{w}_{ij }({x}_{i}-\stackrel{-}{x})({x}_{j}-\stackrel{-}{x})}{{S}^{2}\sum _{i=1}^{n}\sum _{j=1}^{n}{w}_{ij}}, {S}^{2}=\frac{\sum _{i=1}^{n}{\left[\left({x}_{i}-\stackrel{-}{x}\right)\right]}^{2}}{n}$$ 1 where \({x}_{i}\) and \({x}_{j}\) ​ represent the variable of interest in province i and j , respectively \(. \stackrel{-}{x}\) and \(n\) is the mean and the total number of spatial elements, respecitvely. S 2 denotes the variance of the spatial element. \({w}_{ij}\) represents the province pair of the spatial weight matrix measured by the distance between i th and j th province. Mathematically, the value of the global Moran’s I must fall between − 1 and 1, with a value greater than zero indicating positive spatial autocorrelation and a value less than zero negative spatial autocorrelation. 3.2.2 FSQCA The QCA method was first proposed by Ragin in 1987, using configurational logic to analyze complex causal relationships among small samples. In the field of social science research, causal variables are often asymmetric and nonlinear, making the QCA method particularly suitable for analyzing the relationships between complex variables in social science. It has been widely applied in related studies in economics and management (Chen Xuelin et al., 2022 ; Zhang Xin et al., 2022; Zhao Yan et al., 2021). In this study, we have selected FSQCA to examine the empowering factors of digitalized elderly care in China on the following grounds. First, since our sample size contains only 31 provinces, it is best analyzed by tools developed for small sample sizes like FSQCA. And second, FSQCA can control for possible asymmetric and nonlinear causal effects between the empowering factors of digitalized elderly care; a distinct feature that is missing from conventional linear models. By considering the interactions among the factors through the lens of FSQCA, we can identify the driving force behind quality digitalized elderly care and provide policy recommendations on enhancing digitalized elderly care. 4. Spatial Differentiation Characteristics of China’s Digital Elderly Care Service Level 4.1 Spatial differentiation of digital elderly care service level We measure the provincial level of digitalized elderly care based on the total number of smart elderly care demonstration bases, streets, and enterprises selected by the Ministry of Civil Affairs and the number of digitalized elderly care platforms between 2017 and 2020. Based on ArcGIS 12 software, Fig. 1 depicts the spatial distribution of digitalized elderly care across 31 provinces, with darker colors representing provinces having a greater presence of digitalized elderly care. A glance at the figure reveals that more economically developed and densely populated areas like the Yangtze River Delta, the Pearl River Delta, the Bohai Rim, and the Chengdu-Chongqing region host more digitalized elderly care than elsewhere. At the provincial level, Zhejiang, Shandong, Sichuan, Shanghai, and Guangdong are among the top five regions with the greatest presence of digitalized elderly care, reaching a maximum of 80 and averaging 64.8. In contrast, Hainan, Qinghai, Tibet, Guizhou, Guangxi, and Xinjiang are among the bottom six regions with the lowest presence of digitalized elderly care, with an average of only 3.2. In part, we attribute this regional imbalance to a low level of economic development, population density, and digital talent in the western provinces. Overall, we observe noticeable inter-province differences in digitalized elderly care. 4.2 Spatial Distribution of Business Environment The spatial distribution of various dimensions of the business environment is shown in Fig. 2 . Figure 2 a provides the provincial level of digital infrastructure measured by the number of mobile phones and computers per 100 people. The national average of digital infrastructure is 74.77. While Beijing and Shanghai stand out with 131.99 and 114.16, respectively, most regions report a range between 56.33 and 93.29. These trends can be attributed to the central government’s strategic emphasis on the digital economy as a catalyst for high-quality and sustainable development. Adhering to this policy directive, many provincial governments have responded by prioritizing the construction of digital infrastructure that resulted in small provincial differences in digital infrastructure. Figure 2 b shows the provincial distribution of digital technology investment, with Guangdong leading the way. To put this in context, it reports a total investment of 1,271.18 billion yuan, or 2.28 times that of Jiangsu, the second highest ranked province, and 9.89 times the national average. As more economically advanced provinces, Jiangsu, Zhejiang, and Shandong, have also prioritized digital technology, with an annual investment of 557.32 billion yuan, 332.18 billion yuan, and 204.53 billion yuan, respectively. As a median province, Chongqing has invested a totel of 68.42 billion yuan in digital technology. Overall, these substantial provincial disparities largely reflect the pattern of economic development. Figure 2 c illustrates the concentration of digital talent across Chinese provinces. The top five provinces for the number of digital talents in China are Beijing, Guangdong Province, Shanghai, Jiangsu, and Zhejiang, with 922,900, 740,400, 448,000, 328,500, and 283,000 people, respectively. This is significantly higher than the national median of 85,500 people, and the average for the bottom five provinces is a mere 17,700 people. Moreover, the top 10 cities in China’s 100 most attractive cities for digital talents, in alphabetical order, include Beijing, Changsha, Chengdu, Guangzhou, Hangzhou, Nanjing, Ningbo, Shanghai, Shenzhen, and Suzhou. From the available data, digital talents remain concentrated in the Yangtze River Delta and the Pearl River Delta, which continue to attract digital talents from the Beijing-Tianjin-Hebei belt, the central-western region, and the northeastern region. Swimming against this trend is the Chengdu-Chongqing region, whose net outflows of digital talents appear to be stable. In general, these trends can be attributed to favorable policies and a more supportive environment for digital talents, especially in the IT, internet, communication, and electronics industries, highlighting substantial provincial differences in attracting and utilizing digital talents. Figure 2 d describes the digital government service capability indexies of each administrative region, measured by based on the effectiveness, maturity, completeness of service methods, coverage of service items, and accuracy of service guides. Eight provinces and regions, including Anhui, Beijing, Guangdong, Guizhou, Jiangsu, Shanghai, Sichuan, and Zhejiang have, Guangdong, Beijing, Jiangsu, Guizhou, Anhui, and Sichuan, recordedexhibit the very highest overall indices, exceeding 90, accounting for 25%. For the other 18 provinces, this index falls between a range of 80 and 90, accounting for 56%. The remaining six provinces and regions, with Jilin beingas the median province, have reported an index of 83.9. In general, tThe provincial differences in digital government services level among regions are not significant, indicating the effectiveness of the nationwide integrated national digital government service platform platform. It is expected that tThe continuous improvement in the public's public’s awareness and experience of government digital services platforms will continue to improve, contributeing to the rapid enhancement of government digital service capabilities. Figure 2 e describes the provincial macroeconomic environment measured by the total sum of public service expenditures, urban and rural community expenditures, and health expenditures related to elderly care services. The top five provinces are Guangdong, Jiangsu, Shandong, Henan, and Zhejiang, with an amount of 5,023.41 billion yuan, 4,059.05 billion yuan, 3,217.43 billion yuan, 3,210.75 billion yuan, and 2,912.07 billion yuan, respectively. There are 22 provinces fluctuate within the range of 800 billion yuan above or below the national mean of 1,840.90 billion yuan. The remaining four provinces have reported an average expenditure of only 476.07 billion yuan. The provincial distribution of macroeconomic environment for digitalized elderly care across exhibits a “spindle-shaped” pattern, with most regions reporting little variations. 4.3 Spatial agglomeration characteristics of digital elderly care services and business environment in various regions We calculate the global Moran’s I index to gauge the level of spatial autocorrelation for the empowering factors of digitalized elderly care and report their corresponding p-values and Z-values in Table 1 . In general, a positive global Moran’s I index indicates a stronger spatial autocorrelation and a value of zero for spatial randomness. Moreover, for spatial autocorrelation to be statistically significant, the global Moran’s I index must report a p-value less than or equal to 0.05 and a Z-value greater than or equal to 1.96. Our results show that, except digital government services, none of the empowering factors shows signs of spatial autocorrelation, ruling out traditional geographic factor analysis and factor interaction detection methods for analyzing the empowering factors of digitalized elderly care. Instead, we have selected FSQCA to explore concurrent conditions and configurations influencing digitalized elderly care in China. Table 1 The global Moran’s I test results for the level of digital elderly care services and influencing factors in various regions of China are presented. Variable Moran’s I P-value Z-value Digital elderly care service 0.113 0.040 1.896 Digital infrastructure (per 100 people) 0.004 0.237 0.494 Digital technology R&D investment (billion yuan) 0.081 0.02 2.324 Digital talents (per 10,000 People) 0.070 0.044 1.543 Digital government service capability index 0.315 0.001 4.425 Government public expenditure scale (billion Yuan) 0.194 0.013 2.794 Complex social phenomena reflect complex sets of relationships among multiple concurrent conditions and outcome variables. In different combinations of conditions, the logical factors between single conditions and results may change. In recent years, the configurational thinking and FSQCA methods based on different logics have been developed to provide more refined analyses of heterogeneity, concurrent conditions, and equivalent paths between cases, which can better explain complex phenomena in management fields. Therefore, this study adopts FSQCA, based on fuzzy set qualitative comparison, to explore the concurrent conditions and configurations of the level of digital elderly care service. 5. Configurational Analysis of Digitalized Elderly Care 5.1 Data calibration We calibrate the sample based on three-level anchors, including full membership, crossover points, and full non-membership, before using the FSQCA software to perform Boolean minimization operations on variable combinations to obtain results for the necessary condition analysis and the configuration analysis. 5.2 Necessary condition analysis The prerequisite for the configuration analysis is that the consistency of the data must meet the theoretical model’s requirements based on (Ragin, 2008 ): \(\text{C}\text{o}\text{n}\text{s}\text{i}\text{s}\text{t}\text{e}\text{n}\text{c}\text{y}\left({X}_{i} \le {Y}_{i}\right)=\sum \left(\underset{}{\text{min}}\left({X}_{i }{,Y}_{i}\right)/\sum {X}_{i}\right)\) where \({X}_{i}\) and \({Y}_{i}\) represent the membership score in the condition combination and the membership score of the outcome variable, respectively. \(n\) is the number of conditions. In theory, if the consistency index exceeds 0.8, then \({X}_{i}\) is considered a necessary condition for \({Y}_{i}\) . Table 2 reports the results of the necessary condition analysis for each empowering factor of digitalized elderly care. In general, it shows that the consistency of all empowering factor is below 0.9, indicating multiple pathways in shaping digitalized elderly care. With this in mind, it is necessary to further analyze the configuration of pathways for enhancing digitalized elderly care. Table 2 The necessary condition analysis, by empowering factor. Empowering factor Consistency Coverage Digital infrastructure 0.611 0.670 Digital technology R&D investment 0.709 0.959 Digital technology talent 0.745 0.920 Digital government service capability 0.799 0.774 Government public expenditure 0.771 0.810 5.3 Configuration Analysis We present the results of configuration analysis proposed by Ragin and Strand ( 2005 ). Specifically, if an empowering factor of digitalized elderly care appears in both the intermediate solution and the parsimonious solution, it is considered a core condition. However, if the empowering factor only appears in the intermediate solution, it is considered an edge condition. In FSQCA, there are three types of solutions: complex, parsimonious, and intermediate. The intermediate solutions, favored by researchers, incorporates “logical residuals” consistent with theoretical and empirical knowledge. We conduct FSQCA on 31 Chinese provinces by setting the criteria for entering the group to a frequency greater than one and consistency greater than 0.8. As a result, we uncover six configurations of empowering factors that can improve digitalized elderly care. Specifically, Table 3 shows that all configurations have a consistency value of 0.868 and a coverage value of 0.814, suggesting that these six pathways explain about 81.4% of provincial variations in digitalized elderly care. Table 3 Configuration analysis of the empowering factors of digitalized elderly care. Configuration 1 2 3 4 5 6 Digital infrastructure △ △ △ ○ ○ Digital technology R&D investment ○ ○ ○ △ △ Digital technology talent ● △ ● ● ● Digital government service capability △ ○ △ Government public expenditure ● ● ● ● △ Consistency 0.989 0.986 0.816 0.996 0.972 0.906 Coverage 0.494 0.629 0.442 0.406 0.457 0.262 Unique coverage 0.007 0.172 0.043 0.006 0.008 0.011 Solution consistency 0.868 Solution coverage 0.814 Note: ● indicates the presence of core conditions, ○ indicates the presence of marginal conditions, ▲ indicates the absence of core conditions, △ indicates the absence of marginal conditions, and blank spaces indicate that the condition is not relevant in this configuration. 5.3.1 Configuration 1: The public expenditure–research and development type Configuration 1 indicates that the core conditions for enhancing digitalized elderly care in a province include substantial public spending on health and urban community services, supplemented by digital technology investment. Consequently, we name this configuration the “public expenditure–research and development type” which explains 49.4% of digitalized elderly care in provinces like Shandong, Hubei, Sichuan, Shaanxi, Fujian, Hunan, and Jiangxi. 5.3.2 Configuration 2: The public expenditure–talent aggregation type Configuration 2 suggests that provinces with a favorable macroeconomic environment, a concentration of digital talent, and a focus on digital technology investment often report better digitalized elderly care. The core conditions in this configuration include macroeconomic environment and digital talent, with digital technology investment as an edge condition. Consequently, we name this configuration the “public expenditure–talent aggregation type” which explains 62.9% of the case regions, and 17.2% of regions can only be explained by this path. Areas conforming to this development path include Shanghai, Jiangsu, Zhejiang, Guangdong, Beijing, Anhui, Shandong, Hubei, Sichuan, Shaanxi, and Fujian. 5.3.3 Configuration 3: The public expenditure dominant type Configuration 3 indicates that even if a province has a low level of digital infrastructure, dispersed digital tech talent, and poor digital government service capability, it can still achieve quality digitalized elderly care, provided that there is public spending on general public services, health, and urban community services. In other words, macroeconomic environment serves as a core condition in this configuration and plays a dominant role when other empowering factors are weak. Logically, we name this configuration the “public expenditure dominant type” which explains 44.2% of digitalized elderly care in provinces like Hunan, Yunnan, and Guangxi provinces, with Yunnan and Guangxi provinces being explained only by this path. 5.3.4 Configuration 4: The technical service assurance type Configuration 4 indicates that a province with a concentration of digital talent, the necessary support from digital technology investment, and capable digital government services, exhibits better performance in digitalized eldecare. In this configuration, digital talent is a core condition, and digital technology investment and digital government services are edge conditions. These latter two factors act as safeguards for the utilization of digital talent and technology, collectively contributing to a high level of digitalized elderly care. Therefore, we name this configuration the “technology service assurance type,” which explains 40.6% of digitalized elderly care in provinces including Hubei, Sichuan, Shaanxi, Fujian, and Henan provinces, with Henan province being explained solely by this path. 5.3.5 Configuration 5: The comprehensive development type Configuration 5 suggests that a province with good digital infrastructure, a concentration of digital talent, and a focus on public spending on health and urban community development achieves a high level of digitalized elderly care. In this configuration, digital talent and macroeconomic environment are core conditions, and digital infrastructure is an edge condition. Since this configuration consists of both hard and soft empowering factors, we name it the “comprehensive development type.” It explains 45.7% of the case regions, including Fujian, Hubei, Sichuan, Shaanxi, and Hebei provinces, with Hebei province being explained solely by this path. 5.3.6 Configuration 6: The talent support–facility emphasis type Configuration 6 reveals that a province emphasizing digital talent and favorable digital infrastructure enhances digitalized elderly care. In this configuration, digital talent is a core condition, and digital infrastructure is an edge condition. We name this configuration the “talent support–facility emphasis type,” which explains 26.2% of the case regions, and the region conforming to this path is Liaoning province. Among these six configurations, digital technology talents and the scale of public expenditure are identified as core conditions, highlighting the importance of these two factors in enhancing the digitalization level of elderly care services in regions. The aggregation of digital talents is a key driving factor for regional innovation and development. Strengthening mechanisms to attract, develop, and incentivize digital talents, along with improving the education level of digital technology talents, is crucial for enhancing the digitalized elderly care service level in regions. Additionally, the scale of public expenditure, as another important core condition, underscores the role of governments in regulating and coordinating digital economic development. It also demonstrates that a favorable fiscal environment can enhance complementary resources for digital innovation, leading to high-quality development of digitalized elderly care services. 6. Discussion 6.1 Discussion In conclusion, this study employed spatial visualization methods to explore the spatial distribution characteristics of digital elderly care service platforms and five influencing factors across 31 provincial-level administrative regions in China. Utilizing a configuration perspective through fsQCA software, the study conducted a fuzzy set qualitative comparative analysis to explore the combination paths of factors influencing the digital elderly care service level in different regions. The key findings are as follows: Overall, there is significant spatial differentiation in the digital elderly care service platforms across the 31 provincial-level administrative regions in China. The level of digital elderly care services is higher in economically developed regions such as the Yangtze River Delta and the Pearl River Delta regions. This pattern closely correlates with the levels of regional economic development, population size and the concentration of digital technology talent. These regions are possessing a substantial economic advantage in terms of investment in digital tech R&D and public spending on health and urban community services. From the perspective of elderly care service demand, robust economic capacity can lead to higher demands for higher-quality services. This necessitates further improving the quality and efficiency of elderly care services in the context of the digital economy to meet the personalized and heterogeneous service needs of the elderly, driving government and societal efforts to explore paths for enhancing the digital technology-enabled elderly care service level. While none of the five influencing factors individually constitutes sufficient conditions for the high-level development of digital elderly care services, they can form six driving paths through configuration.The first pathway is “public expenditure-research and development investment type”, characterized by economic empowerment as the primary focus with research and development funding as a supplementary investment. The second pathway is “public expenditure-talent aggregation type”, characterized by economic empowerment and simultaneous support for talent, fostering dual forces for co-development. The third pathway is “public expenditure-dominated type”.The fourth pathway is “technical service Assuarance type, characterized by a primary focus on digital technology talent, supplemented by investments in research and development and the digitization of government services. The fifth pathway is “comprehensive development type”. Lastly, the sixth pathway is “talent support-gacility emphasis type”, characterized by a core emphasis on digital technology talent. These pathways are chosen based on the regions’ unique endowments of digital economic resources, providing different development templates for regions with diverse digital economic resource endowments. The six configuration paths for enhancing digital elderly care services reveal that digital tech talent and economic environment empowerment are core conditions, playing important roles in each configuration. Given the deep integration of digital technology with elderly care services, involving significant technological and management innovation, the regions in the Yangtze River Delta and the Pearl River Delta, relying on their accumulated economic advantages, industrial agglomeration advantages, attractive talent policies, and international perspectives, have advanced further in the integration of digital economy with the real economy. These regions have implemented a variety of measures in a coordinated manner, promoting a higher level of digital elderly care service. The conclusions drawn from the study indicate that the digital elderly care service level across the 31 provincial-level administrative regions in China is more influenced by digital tech talent and regional economic environment empowerment. In the future, with the rapid development of digital technology and its fast penetration into various real sectors, the overall level of digital elderly care services in each region is expected to see a qualitative improvement. 6.2 Policy Recommendations Based on the research findings, the following three policy recommendations are proposed. 6.2.1 Encourage More Social Forces to Join the Elderly Care Service Sector Given that public service investments in health and urban community development are the primary driving factors for the rapid development of digital elderly care services, especially in regions with limited local fiscal capacity, the government can provide subsidies, tax reductions, and other incentives to encourage more social forces to participate in elderly care services. A multi-source collaborative approach is a sustainable development strategy. 6.2.2 Enhance Training for Digital Tech Talent Digital tech talent is undoubtedly the most important strategic resource for the high-quality development of the digital economy. However, traditional education models are insufficient to meet the current demands of the rapidly developing digital economy. There is a need to redefine the profile of digital talent, change talent development models, innovate talent growth ecosystems, and bridge the supply and demand sides of digital talent. Otherwise, this could be a constraint factor hindering the rapid improvement of digital elderly care service level. 6.2.3 Promote the Coordinated Development of Industries Related to Elderly Care Services In the context of rapid aging of the Chinese population, achieving the goal of active and healthy aging is a complex systemic project. It requires the coordinated governance of multiple forces involved in elderly care, including civil affairs departments, fiscal and tax departments, land and resources departments, social security departments, medical service institutions, education and training institutions, and suppliers of elderly care products and services. These forces need to collaborate, integrate resources, and work together to build a grand strategy and framework for elderly care services. Abbreviations FSQCA: fuzzy-set qualitative comparative analysis IMD: Institute for Management Development Declarations Availability of data and materials Tha data and materials of this study are publicly available. In the paper, sections 4 and 5 belong to the results section. Additional information and supplementary information are not applicable. Acknowledgements We thank George Chen (Ph.D.) from the University of New England in Australia for his suggestions for revision. Funding This study gets support by the National Social Science Foundation of China to Jianli Gao under Grant number [21BRK010]. Author information Authors and Affiliations School of Business Administration, Shandong Technology and Business University, Yantai, People’s Republic of China Jianli Gao，Xiaoqing Zhang，Lejie Wang Business School, Shandong Normal University, Jinan, People’s Republic of China Xiaoyan Zhang Contributions Jianli Gao designed the framework of this paper and contributed to paper writing; Xiaoqing Zhang contributed to data collection; Lejie Wang contributed to data analysis; Xiaoyan Zhang participated in writing and paper revising. All authors reviewed the manuscript. Corresponding authors Correspondence to Jianli Gao ( [email protected] ) and Xiaoyan Zhang ( [email protected] ) Ethics declarations All methods were carried out in accordance with relevant guidelines and regulations. References C. C. Ragin. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago: University of Chicago Press. C. C. Ragin, S. I. Strand. (2005). Using Qualitative Comparative Analysis to Study Causal Order: Comment on Caren and Panofsky. Sociological Methods & Research, 36(4), 431-441. D. Pal, S. Funilkul, V. Vanijja, B. Papasratorn. (2018). Analyzing the Elderly Users’ Adoption of Smart-Home Service. IEEE Access, 6, 51238-51354. H. M. Do, M. Pham, W. Sheng, D. Yang, M. Liu. (2017). Rish: a robot-integrated smart home for elderly care. Robot Auton System, 26(18), 122-146. K. Maswadi, N. B. A. Ghani, S. B. Hamid. (2020). Systematic literature review of smart home monitoring technologies based on IoT for the elderly. IEEE Access, 8, 92244-92261. M. J. Deen. (2015). Information and Communications Technologies for Elderly Ubiquitous Healthcare in a Smart Home. Personal and Ubiquitous Computing, 19(3), 599-615. P. Legato, R. M. Mazza, G. Fortino. (2022). A multi-level simulation-based optimization framework for IoT-enabled elderly care systems. Simulation Modelling Practice and Theory, 114, 102420. DOI: 10.1016/j.simpat.2021.102420. Bian Shu, Li Linxian. (2019). Research on China’s multidimensional pension service system from the perspective of active aging. Journal of Liaoning University (Philosophy and Social Sciences Edition), 47(02), 83-91. Cai Yuezhou. (2021). National governance mechanism of digital economy: a perspective of data-driven technological innovation. Journal of Beijing Jiaotong University (Social Sciences Edition), 20(02), 39-49. DOI: 10.16797/j.cnki.11-5224/c.20210406.010. Chao Xiaojing. (2020). The path of promoting high-quality development in China with new digital infrastructure. Journal of Xi’an University of Finance and Economics, 33(02), 15-19. DOI: 10.19331/j.cnki.jxufe.2020.02.003. Chen Gong, Du Peng, Chen Yi. (2001). Problems and thoughts on “time saving”for the elderly. Population and Economics (06), 67-73. Chen Hong, Feng Dayang. (2022). Digital empowerment promotes the high-quality development of rural public services ideas and approaches. World Agriculture (02), 55-65. DOI: 10.13856/j.cn11-1097/s.2022.02.005. Chen Xuelin, Zhou Dongmei, Zhou Yang, Lu Ruoyu. (2022). Research on the impact of organizational identity of digital platform enterprises on the acquisition of financial resources: based on fuzzy set qualitative comparative analysis. Technoeconomics, 41(02), 167-177. Chu Shijing, Wang Meng, Yang Shenghui. (2015). International comparison and enlightenment of home-based elderly care services under typical welfare types. Population and Economics (04), 119-126. Dai Kuizao, Huang Zi, Wang Siman. (2023). Has the digital economy promoted the structural upgrading of China’s service industry? Quantitative Economics and Technoeconomics Research, 40(02), 90-112. DOI: 10.13653/j.cnki.jqte. 2023.02.002. Ding Zhifan. (2020). Research on the mechanism of digital economy driving high-quality economic development: a theoretical analysis framework. Modern Economic Exploration(01), 85-92. DOI: 10.13891/j.cnki.mer.2020.01.011. Du Yunzhou, Jia Liangding. (2017). Configuration perspective and qualitative comparative analysis (QCA): a new path for management research. Management World(06), 155-167. DOI: 10.19744/j.cnki.11-1235/f.2017.06.012. Fan Hejun, Wu Ting, He Sijin. (2022). How to optimize urban business environment through “Internet + government service” platform? —Based on the perspective of interactive governance. Management World, 38(10), 126-153. DOI: 10.19744/j.cnki.11-1235/f.2022.0149. Fan Hejun, Wu Ting. (2022). New digital infrastructure, digital capability and total factor productivity. Economic and Management Research, 43(01), 3-22. DOI: 10.13502/j.cnki.issn1000-7636.2022.01.001. Gong Weiping, Liu Yuezhong. (2014). Research on the business environment of small and medium-sized enterprises in Macao. Industry and Economy Review, 5(02), 121-131. DOI: 10.14007/j.cnki.cjpl.2014.02.010. Guo, Chaoxian, Wang, Jiaqi, Liu, Haorong. (2020). Research on the Path of Empowering China’s Economy with New Infrastructure for High-Quality Development. Journal of Beijing University of Technology (Social Sciences Edition), 20(06), 13-21. Huang, Manyu, Wang, Xiaoxing. (2022). Digital Economy, Resource Misallocation, and Total Factor Productivity of Enterprises. Macroeconomic Research, (12), 43-53. DOI: 10.16304/j.cnki.11-3952/f.2022.12.001. Li, Juan, Ding, Liangchao. (2019). Government-Social Organization Synergy from the Perspective of Government Purchase of Elderly Care Services. Theoretical Exploration, (02), 63-69. Li, Mengna, Zhou, Yunbo. (2022). Research on the Effect of Human Capital Structure on the Development of Digital Economy. Economic and Management Research, 43(01), 23-38. DOI: 10.13502/j.cnki.issn1000-7636.2022.01.002. Li, Wei. (2023). Theoretical Logic of Digital Economy Development, Strategic Deployment of Developed Countries, and China’s Response. China Soft Science, (05), 216-224. Li, Zhijun, Zhang, Shiguo, Li, Yifei, Shan, Dan. (2019). Evaluation of Urban Business Environment in China and Relevant Suggestions. Jiangsu Social Sciences, 02, 30-42. DOI: 10.13858/j.cnki.cn32-1312/c.2019.02.005. Li, Zhijun, Zhang, Shiguo, Niu, Zhiwei, Yuan, Wenrong, Liu, Qi. (2021). Theoretical Logic, Comparative Analysis, and Countermeasure Suggestions for Evaluating Urban Business Environment in China. Management World, 37(05), 98-112. DOI: 10.19744/j.cnki.11-1235/f.2021.0067. Liu, Yi. (2021). From Resource Network to Digital Map: Research on the Driving Mode of Community Elderly Care Service Platform. Electronic Government, (08), 40-51. Lv, Xuanru, Zhang, Xiaoyi. (2022). Research on the Diffusion of Policy of Government Purchase of Home-Based Elderly Care Services from the Perspective of Multiple Logics: An Event History Analysis Based on Provincial Data in China. Journal of Northeastern University (Social Science Edition), 24(04), 54-63. Ren, Zhuanzhuan, Deng, Feng. (2023). Digital Technology, Factor Structure Transformation, and High-Quality Economic Development. Soft Science, 37(01), 9-14. DOI: 10.13956/j.ss.1001-8409.2023.01.02. Shi, Qingbo, Huang, Qisong. (2023). Research on the Optimization of Business Environment Driven by Digital Government: Taking Guiyang as an Example. Think Tank Theory and Practice, 8(03), 71-80. DOI: 10.19318/j.cnki.issn.2096-1634.2023.03.08. Song, Huan, Sun, Meng, Sun, Tao. (2022). Community Home-Based Elderly Care Service Mechanism of Government Purchase, Institutional Supply, and Family Participation: Analysis Based on Evolutionary Game Theory and Typical Cases. Journal of Aging Science Research, 10(04), 28-41. Sun, Jiling, Liu, Shangjun, Suo, Haoyu, Chen, Gong. (2022). Theoretical Thinking on the Support of Technological Development for Elderly Care Services from the Perspective of Social Governance. Population and Development, 28(01), 50-58. Wang, Jun, Zhang, Yi, Ma, Xiao. (2022). Digital Economy, Resource Misallocation, and Total Factor Productivity. Finance and Trade Research, 33(11), 10-26. DOI: 10.19337/j.cnki.34-1093/f.2022.11.002. Wang, Lijian, Di, Xiaodong. (2022). Impact Study of Policy Combination on the Supply of Elderly Care Service Resources. Journal of Xi’an Jiaotong University (Social Sciences), 42(02), 112-121. Wang, Shiwei. (2022). Leading and Promoting National Digital Literacy and Skills Action with High-End Digital Talents. Library Tribune, 42(03), 11-13. Wang, Yongmei, Du, Peng. (2018). A Study on the Attitude of the Elderly towards Social Elderly Care Services: A Case Study of Six Districts in Beijing.Population Research, 42(06), 74-86. Xia, Jiechang, Wang, Pengfei. (2021). Role and Key Directions of Digital Economy Empowering High-Quality Development of Public Services. Jiangxi Social Sciences, 41(10), 38-47. Xie, Kang, Wu, Yao, Xiao, Jinghua. (2020). Adaptive Innovation Based on Big Data Collaboration Assets: Innovation Logic of Digital Economy (II). Journal of Beijing Jiaotong University (Social Sciences Edition), 19(02), 26-38. DOI: 10.16797/j.cnki.11-5224/c.2020.0005. Xie, Lijuan, Zhuang, Yiqun. (2021). China’s Circulation Reform and Government Functions in the Era of Digital Economy: Investigation Combined with Micro Mechanisms of Enterprises. Chinese Public Administration, (08), 55-61.DOI: 10.19735/j.issn.1006-0863.2021.08.05 Xu, Xianmei. (2019). Research on the Problem of Government Purchase of Home-Based Elderly Care Services in China. [PhD thesis], Central University of Finance and Economics. Xu, Xiaolin, Qiao, Yali, Mao, Zijun, Zou, Qi. (2023). Research on the Path of “Internet Plus Government Services” Promoting the Development of Digital Economy from the Perspective of “Streamlining Administration, Delegating Powers, and Improving Services”. Electronic Government, (01), 52-63. DOI: 10.16582/j.cnki.dzzw.2023.01.005. Xu, Yudong, Cui, Riming. (2015). Factors Influencing the Quantity-Based Development of Small and Medium-Sized Enterprises in Shandong and the Construction of Business Environment: Based on Panel Data of 17 Prefectures and Cities in Shandong from 2006 to 2013. East China Economic Management, 29(04), 34-39. Yang, Dong, Zhang, Yuting, Hu, Dengfeng. (2021). Research on the Empowerment Path of Digital Economy for High-Quality Development: Configuration Analysis Based on Core Cities of the Yangtze River Delta Integration. East China Economic Management, 35(10), 39-47. DOI: 10.19629/j.cnki.34-1014/f.210516004. Yang, Yan. (2019). Exploration of China’s Digital Economy Development Strategy from an International Comparative Perspective. Science and Technology Management Research, 39(19), 33-42. Yang, Zhongji, Qi, Liangqun. (2021). Review of Foreign Research on Digital Innovation and Enlightenment to the Digital Transformation of Chinese Manufacturing Enterprises. Scientific Management Research, 39(04), 120-124. DOI: 10.19445/j.cnki.15—1103/g3.2021.04.018. Yu, Bo, Cong, Yi. (2021). Digital Economy, Human Capital Dividend, and Upgrading of Industrial Structure. Finance and Economics Journal, 42(03), 124-131. DOI: 10.16339/j.cnkihdxbcjb.2021.03.017. Yu, Chaoyi, Wang, Xuebiao, Sun, Guanglin. (2020). Digital Finance and China’s Economic Growth Quality: Intrinsic Mechanism and Empirical Evidence. Exploration of Economic Issues, (07), 1-14. Zhang, Banghui, Wan, Qiulan, Wu, Jian. (2021). Analysis of the Optimization Effect of Online Government Service on Business Environment: Digital Dividend and Digital Divide. Chinese Public Administration, (04), 70-75. DOI: 10.19735/j.issn.1006-0863.2021.04.10. Zhang, Bo. (2006). International Comparison of Enterprise Business Environment Indicators and Countermeasures in China. Economic Observer, (10), 62-65. DOI: 10.16528/j.cnki.22-1054/f.2006.10.021. Zhang, Si Feng. (2021). Government Behavior and Market Mechanism in the Construction of China’s Elderly Care Service System. Social Security Review, 5(01), 129-145. Zhang, Xin, Xu, Yaoyu, Ma, Liang. (2022). Configuration Effect Study on Influencing Factors of Digital Transformation of Small and Medium-Sized Enterprises. Economic and Management Review, 38(01), 92-102. DOI: 10.13962/j.cnki.37-1486/f.2022.01.007. Zhao, Yan, Tan, Haibo, He, Mengshu. (2021). Factors Affecting the Internet Service Supply Capacity of Local Governments and Their Configuration: A Qualitative Comparative Analysis Based on 27 Provincial Cases. Electronic Government, (04), 68-78. DOI: 10.16582/j.cnki.dzzw.2021.04.007. Zhao, Yijun, Deng, Dasong. (2021). Construction of Intelligent Elderly Care Service Mode Driven by Artificial Intelligence. Journal of Jianghuai Forum, (02), 146-152. Zou, Wei, Lei, Hao. (2021). Improvement Effect of Business Environment on Resource Misallocation and Its Mechanism: Analysis Based on the Manufacturing Industry. Journal of Wuhan University (Philosophy & Social Sciences), 74(01), 121-139. DOI: 10.14086/j.cnki.wujss.2021.01.011. Zuo, Meiyun. (2014). Connotation, Model, and Opportunities of Smart Elderly Care. Chinese Public Security, (10), 48-50. Zuo, Pengfei, and Chen, Jing. (2021). Digital Economy and Economic Growth from the Perspective of High-Quality Development. Finance and Economics Research, (09), 19-27. DOI: 10.19654/j.cnki.cjwtyj.2021.09.003. Footnotes Xi Jinping: Hold High the Great Banner of Socialism with Chinese Characteristics and Strive for Unity in Fully Building a Modern Socialist Country—Report at the 20th National Congress of the Communist Party of China, The Central People’s Government of the People’s Republic of China, www.gov.cn . National Bureau of Statistics of China: Bulletin of the Seventh National Population Census Report, www.stats.gov.cn China Academy of Planning and Design: “China Regional Digital Development Index Report”, March 2021. China National Academy of Governance, E-Government Research Center: “Assessment Report on the Integrated Government Service Capability of Provincial Governments and Key Cities (2022) ”, September 2022. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4235268","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291753572,"identity":"2860e1cc-074f-49bd-8487-7234c15cf34a","order_by":0,"name":"Jianli Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIie3PMWvCQBjG8TcI53KY9Q0V8xVeKZRCh479GieCLhmyCg6Z4lLrZ+mWMeFAl+su6BCXuGRxEbOIl7q4eGZ0uP8QuCM/Hg7AZnvSSAB0WTsCdKL6LJoRzngKmDUldRxQNCT+l0zDfLrlHW+/2xwSCW47IKiS+6SvRoLEsuDsJXh9z5QE77skZ64MJAqIBJM1YZjFEmgdUMuJDWRRanLWxFsV/+TzEfFRrwxiTRDeriv4gBAWggY/mnD9lj815qiKMJubVhbDZf90lD1/ttqtJ8lHz50Nf/PKtJICo9sLXn/S+0CvRNDKTT/YbDabDS7siVJUlkrDjQAAAABJRU5ErkJggg==","orcid":"","institution":"Shandong Technology and Business University","correspondingAuthor":true,"prefix":"","firstName":"Jianli","middleName":"","lastName":"Gao","suffix":""},{"id":291753573,"identity":"a476fe92-9153-4df5-aaed-202082716e20","order_by":1,"name":"Xiaoqing Zhang","email":"","orcid":"","institution":"Shandong Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqing","middleName":"","lastName":"Zhang","suffix":""},{"id":291753574,"identity":"dc907a30-5af8-434c-ba8c-4710b12f49a6","order_by":2,"name":"Lejie Wang","email":"","orcid":"","institution":"Shandong Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Lejie","middleName":"","lastName":"Wang","suffix":""},{"id":291753575,"identity":"3168cc90-086d-40b7-a4a3-a2d6373dd17b","order_by":3,"name":"Xiaoyan Zhang","email":"","orcid":"","institution":"Shandong Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-04-08 09:12:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4235268/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4235268/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54817083,"identity":"c38b98b2-ba07-421f-8541-c9efe6e3789d","added_by":"auto","created_at":"2024-04-17 07:42:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":233330,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe spatial distribution of digitalized elderly care in China.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Based on the standard map production of the Ministry of Natural Resources according to the GS(2016)2923, with no modification to the base map boundaries.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4235268/v1/9f1e58bd566739833d9e3831.png"},{"id":54816667,"identity":"8fcac589-512d-4228-99c0-7b878fcf3be3","added_by":"auto","created_at":"2024-04-17 07:34:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1202076,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe spatial distribution of various dimensions of the business environment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Based on the standard map production of the Ministry of Natural Resources according to the GS(2016)2923, with no modification to the base map boundaries.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4235268/v1/1f6614d4f20a25cc768cad76.png"},{"id":64436526,"identity":"dcbd8571-7bac-4858-b596-562b9789db50","added_by":"auto","created_at":"2024-09-13 07:18:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3074070,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4235268/v1/aa9e3a72-29e8-455c-be05-2f196b87bfac.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multi-Pathway Study on the Impact of the Business Environment on Digital Elderly Care Services in China","fulltext":[{"header":"1. Background","content":"\u003cp\u003ePopulation aging presents a pressing challenge for many countries around the world, and China is no exception. President Xi Jinping pointed out in the report of the 20th National Congress of the Communist Party of China that \u0026ldquo;actively addressing population aging, accelerating the construction of a new development pattern of digital China, and adhering to promoting high-quality development as the theme\u0026rdquo; .[1]\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e\u003c/p\u003e \u003cp\u003eAs the world\u0026rsquo;s largest developing country, China is facing a monumental shift in its population pyramid structure, elevating the provision of elderly care on top of the agenda. According to the Seventh National Population Census, by the end of 2020, there were 264\u0026nbsp;million people aged 60 and above, accounting for 18.70% of the total population, of which 191\u0026nbsp;million people were aged 65 and above, accounting for 13.50% of the total population [2]\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e. By the World criteria for aging, China has entered a critical stage, and its aging population is expected to peak in the next 10 to 20 years.\u003c/p\u003e \u003cp\u003eUndoubtedly, this rapidly aging population trend has raised several challenges in China\u0026rsquo;s massive elderly care service market. The elderly care service market struggled to meet the surging demand for community-based home care. In part, this struggle stemmed from a small service radius created by the heterogeneous and dispersed living arrangements of the elderly and the high dependence of traditional elderly care on labor input (Wang \u0026amp; Du, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, the lack of financial capacity for many elderly people severely constrained the realization of economies of scale in the elderly care market that left little incentives for new service providers to enter. As a result, the elderly care market not only faces a mismatch between the supply side and the demand side but also suffers from low efficiency in resource allocation, severely undermining the government\u0026rsquo;s active aging goals. This trend, coupled with the \u0026ldquo;becoming old before becoming rich\u0026rdquo; phenomenon has exerted a significant impact on social and economic development, urgently calls for a pathway suitable for China that requires a complete overhaul of the traditional elderly care paradigm.\u003c/p\u003e \u003cp\u003ePromoting the comprehensive integration of the digital economy and the real economy has attracted policymakers\u0026rsquo; attention in recent years. The digital economy, encompassing the Internet, big data, and artificial intelligence, has revolutionized resource allocation in various fields. Its exceptional capabilities in matching the buyers and sellers, improving information-processing time, and reducing selection and transaction costs, have enabled its ability to meet the heterogeneous needs of the elderly and provide the catalyst for transforming the elderly care market (Cai, 2021). For example, a digitized elderly care system can reduce the dependence on geographical proximity in the past by developing a seamless information network space that connects online and offline activities, aggregates data in time and space, and involves virtual and real interactions, to develop a high-quality elderly care industry (Legato et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA high-quality business environment creates conditions for sustainable economic development and is a prerequisite for maintaining long-term competitiveness, which can enhance a country or region\u0026rsquo;s economic soft power and comprehensive competitiveness. In this study, addressing the requisite business environment support for the realization of digital elderly care services emerges as a pivotal concern. Digital elderly care services are a complex system engineering, the result of the interaction of complex elements. Exploring digital elderly care services requires considering the joint effects of multiple factors in multiple business environments. Against this backdrop, we employ the fuzzy-set qualitative comparative analysis (FSQCA) to handle multiple interactions under different configurations of the digitalized elderly care system (Du \u0026amp; Jia, 2017). In this study, we dissect the configurational paths of China\u0026rsquo;s digitized elderly care system across five dimensions: digital infrastructure, investment in digital technology research and development, digital talent, digital government service capabilities, and government public expenditure. Using data from 31 provinces, we find how digital technology drives the innovation development of China\u0026rsquo;s public elderly care system.\u003c/p\u003e"},{"header":"2. Theoretical Foundations","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research on Digital Elderly Care Services\u003c/h2\u003e \u003cp\u003eWith the rise of the digital economy, both domestically and internationally, the application of digital tools like the Internet of Things (IoT), remote communication and sensing, and real-time data analysis has revolutionized the elderly care systems (Do et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For example, Sweden\u0026rsquo;s ACTION model utilizes remote information processing technology to assist home care providers in meeting the needs of the elderly. Finland\u0026rsquo;s remote assistance model relies on the IoT to build a monitoring system, providing comprehensive security for the elderly (Deen, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Germany\u0026rsquo;s AAL employs the environment-assisted living intelligent technology platform to enhance the independent living capabilities of the elderly (Chu et al., 2015). Turning to North America, in the United States follows, there is a tiered and differentiated service model to that accurately provides services based on the needs and economic conditions of the elderly (Pal et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Canada\u0026rsquo;s SIPA (Systematic Interdisciplinary Palliative Care Approach) offers community-based comprehensive, integrated services (Maswadi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnlike its European and North American counterparts, China emphasizes on the feasibility and necessity of digitalized elderly care systems (Zuo, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For instance, its long-term goal has always been to establish smart digital platforms that connect the demand and supply for personalized elderly care (Liu, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the lack of supporting digital infrastructure and elderly care data collection have limited the economies of scale in elderly care delivery, impeding the private sector\u0026rsquo;s willingness to invest in this market. Consequently, elderly care delivery has been dominated by inefficient and sometimes outdated government-purchased business models (Bian and Li, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li and Ding, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recently, the dominance of these models has been called into questions by the application of digital tools that improves the efficiency and quality of elderly care delivery in areas like inclusive growth (Fan \u0026amp; Wu, 2022), product innovation (Xia \u0026amp; Wang, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), supply chain logistics (Liu et al., 2021), interactive supply and demand (Zhang, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhao \u0026amp; Deng, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and community-based elderly care (Chen et al., 2001). Since the literature on the potential benefits of digitized elderly care for the elderly in China remains sparse to date, we intend to close this gap by focusing on the innovation development of a digitized elderly care system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research on Business Environment\u003c/h2\u003e \u003cp\u003eThe concept of the business environment originated from the investment environment and later expanded to other aspects of enterprise operations. The World Bank explicitly states that the business environment includes institutional environment, infrastructure conditions, geographical location, etc., and has been releasing annual reports on the business environment since 2003, stimulating countries to optimize their business environment. Drawing on research by Chinese scholars from different perspectives on the business environment (Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), this study considers the business environment is a collection of external environments, including institutional environment, social environment, market environment and economic environment.\u003c/p\u003e \u003cp\u003eThe economic effects of the business environment can be examined from both macro and micro perspectives. From a macro perspective, scholars unanimously believe that continuous optimization of the business environment has a positive effect on improving economic quality. A good business environment helps solve the problem of resource misallocation (Zou and Lei, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and enhances resource utilization efficiency (Wang et al., 2022), achieving market-oriented allocation of production factors and promoting economic growth (Huang and Wang, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). From a micro perspective, regarding how the business environment affects business operations, scholars have considered entrepreneurship and innovation aspects. A favorable business environment can stimulate entrepreneurial innovation and enthusiasm (Gong and Liu, 2014), eliminate the impact of rent-seeking behavior on business operations, break administrative monopolies, unleash market vitality, and stimulate business creativity (Xu and Cui, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Some scholars have also verified the positive impact of single dimensions of the business environment, such as government service capabilities, on business operations. Additionally, a good business environment can lower market access barriers, promote business growth, broaden financing channels, alleviate financing constraints, and help businesses withstand operational risks (Yu and Liang, 2019).\u003c/p\u003e \u003cp\u003eExisting research on the evaluation index system of the business environment mainly focuses on international organizations and research institutions, with countries or economic entities as the main evaluation objects. For example, the World Bank\u0026rsquo;s evaluation index of the business environment focuses on institutional construction norms, completeness, and degree of marketization. The IMD World Competitiveness Center in Lausanne, Switzerland evaluates from four aspects: economic performance, government efficiency, business efficiency, and infrastructure. The Economist Intelligence Unit mainly considers the degree of marketization of regional development. The \u0026ldquo;Global Cities Index\u0026rdquo; published by Kearney mainly evaluates from five dimensions: human capital, information exchange, business activities, cultural experiences, and political environment. Under the guidance of the National Development and Reform Commission of China, assessments of the business environment have been carried out across all 31 provinces, autonomous regions, and municipalities, and the \u0026ldquo;China Business Environment Report (2020) \u0026rdquo; was released in 2020. On November 25, 2021, the State Council issued the \u0026ldquo;Opinions on Conducting Pilot Innovations in the Business Environment\u0026rdquo;, selecting six market entities to carry out pilot innovations in the business environment. Meanwhile, local governments and scholars are actively exploring various approaches. For instance, they are constructing evaluation index systems from multiple perspectives, such as workforce, government, finance, and market (Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, they are proposing evaluation system based on provincial-level data with four dimensions like government affairs, law, humanities, and market (Zhang et al., 2020). Furthermore, there are proposals for evaluation index system focusing on the business environment of Chinese cities. These systems cover key urban agglomerations and include seven dimension like government affairs environment, human resources, innovation environment, legal environment, market environment, public services, and financial services (Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Selection of Evaluation Indicators for Variables\u003c/h2\u003e \u003cp\u003eIn the indicator selection process, we utilized both literature analysis and expert discussions. Through literature analysis, we delved into the core concepts of \u0026ldquo;digitalized elderly care services\u0026rdquo; and \u0026ldquo;business environment\u0026rdquo; identifying indicators that closely align with our research focus. Additionally, expert discussions were integral in refining the indicator system, as we sought expert opinions to adjust and screen indicators at multiple levels, ensuring the suitability of our evaluation indicators for this study. This comprehensive approach ensured a robust selection of indicators that effectively capture the nuances of our research objectives. The expert panel comprised seven professors and corporate executives with expertise in the research field. The research methodology involved a blend of interviews and phone calls.\u003c/p\u003e \u003cp\u003eFrom the literature review above, it can be seen that there are common dimensions for evaluating the business environment, which should include innovation environment, human resources, government service environment, economic environment, and financial services. When choosing indicators, it is necessary not only to consider findings from existing literature but also to pay close attention to the distinctive charactieristics of the research subject, including greater reliance on data, higher demand for efficiency, higher demand for precision, higher demand for innovation and greater reliance on government financial input. Due to the high degree of reliance on accurate and efficient data, it is necessary to have digital infrastructure as a basic guarantee. Moreover, given that society\u0026rsquo;s digital transformation remains in its nascent phase, ongoing investment in research and development is essential to foster continual innovation in digital technology, thereby enhancing its capacity to drive tangible economic growth. Consequently, prioritizing investment in digital technology research and development is essential. The traditional elderly care service industry is a labor-intensive industry. Although digital technology empowerment can reduce the reliance on labor input, the demand for digital talents is greatly increased. Therefore, the quantity of digital talents to a certain extent determines the speed and quality of social digital transformation, and the aggregation of talent serves as an indicator of the business environment. As elderly care services have a strong public service nature, excellent government service capabilities facilitate the integration and efficient utilization of data resources, which helps improve service efficiency and precision. Digitalized elderly care services require a large amount of government financial input or commissioning of social organizations in the form of government procurement to complete services, so there is a high requirement for local government\u0026rsquo;s public expenditure.\u003c/p\u003e \u003cp\u003eIn summary, this study believes that the evaluation of the business environment needed for digitalized elderly care services should focus on five dimensions: digital infrastructure construction, investment in digital technology research and development, aggregation of digital talents, capability of digital government services, and scale of government public expenditure. The level of digitalized elderly care services is measured by the total number of digitalized elderly care service institutions in each region, including smart elderly care service platforms, smart elderly care information service centers, and smart elderly care service companies, as well as the total number of smart elderly care demonstration bases, demonstration streets, and demonstration enterprises.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Theoretical Foundations\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Construction of digital infrastructure and digitalized elderly care services\u003c/h2\u003e \u003cp\u003eDigital infrastructure constitutes the prerequisite for empowering digitalized elderly care through innovation development in new technologies, new heights, new fields, new formats, new models, and new governance (Guo Chaoxian et al., 2020). This involves advancing internet deployment to enhance the accessibility and availability of network infrastructure, as well as planning for facilities such as towers, pipelines, and base stations to facilitate infrastructure deployment. Additionally, it requires coordinating the construction of application facilities such as industrial internet, data centers, and smart computing platforms. These measures support enterprises in leveraging data for intelligence (Zuo Pengfei \u0026amp; Chen Jing, 2021). Moreover, the role of digital infrastructure in supoorting economic transformation and upgrading is becoming increasingly prominent (Li Wei, 2023). Encouraging the application of biometric recognition technologies such as facial and fingerprint recognition, deepening the application of online payment in government services, public services, and other fields can reduce transaction costs, improve transaction efficiency, and prevent financial risks (Yu Chaoyi et al., 2020).\u003c/p\u003e \u003cp\u003eDigital infrastructure is the bottom-level technological foundation for the development of the digital economy, capable of unleashing the innovative effects of information technology (Chao Xiaojing, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), constituting the basic guarantee for the development of digitalized elderly care services. Therefore, the construction of digital infrastructure is the most important foundation for building a business environment under the new era background, and it is the fundamental guarantee for realizing the empowerment of digital technology for elderly care services. The overall level of informatization and the construction of digital infrastructure in a region can be measured by the number of computers used per hundred people and the number of mobile phones owned per hundred people[3]\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Investment in digital technology research and development and digitalized elderly care services\u003c/h2\u003e \u003cp\u003eIn the era of the digital economy, the economic environment plays an important catalytic role in digital innovation (Wang Lijian \u0026amp; Di Xiaodong, 2022). The inherent adaptability of digital innovation allows innovation activities to organically evolve in respond to changes in the environment (Yang Zhongji \u0026amp; Qi Liangqun, 2021). The investment in scientific research and innovation can provide complementary resources for digital innovation, accelerate the transmission speed of digital innovation to terminal demands, and lead digitalized elderly care services to achieve high-quality development. With its unique network aggregation effect, digital technology breaks through traditional spatial and temporal limitations, further opening up the geographical boundaries of traditional elderly care services, helping to reduce information asymmetry, and improve the accuracy and efficiency of services (Ren Zhuanzhuan \u0026amp; Deng Feng, 2023). The development of digitalized elderly care services relies heavily on the support of digital technology. Continuous innovation and incremental investment in research and development of digital technology are the means and approaches to achieve digital transformation, requiring continuous innovation and research and development investment support (Sun Jiling et al., 2022).\u003c/p\u003e \u003cp\u003eThrough innovation in digital technology, it becomes possible to generate new products, services, or business models, thereby fostering competitiveness in the market (Ding Zhifan, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, this investment ensures accurate and timely data collection and feedback, improves resource utilization, and encourages real-time interaction among stakeholders in the elderly care market. Consequently, this promotes the high-quality development of public elderly care services (Chen Hong \u0026amp; Feng Dayang, 2022). In summary, research and development investment is an important factor in guaranteeing continuous innovation and continuous improvement of digital technology, and it is one of the important dimensions of evaluating the business environment, measured by the investment in digital technology research and development funds in each region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Support of digital talents and digitalized elderly care services\u003c/h2\u003e \u003cp\u003eThe rapid development of the digital economy undoubtedly stems from the pivotal role played by digital technology (Xie Kang et al., 2020). However, digital talents, as active resources, largely determine the efficiency of the development and utilization of digital technology and are the core force behind the high-quality development of the local economy. The advancement of the human capital structure can enhance labor efficiency and promote technological innovation (Dai Kuizao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Especially in the process of industrial upgrading, with the gradual improvement of the business environment, the aggregation of talents and the advancement of human capital can significantly promote the high-quality development of the service industry (Yu Boyang \u0026amp; Cong Yi, 2021). Moreover, the higher the heterogeneity level of the human capital structure, the more conducive it is to innovation, and the more significant the economic effects (Li Mengna \u0026amp; Zhou Yunbo, 2022). Therefore, accelerating the improvement of the business environment, formulating policies and mechanisms to attract talents, and increasing the stock of digital talents have become the consensus of various regions in pursuing high-quality development of the digital economy (Yang Dong et al., 2021).\u003c/p\u003e \u003cp\u003eFurthermore, the availability of digital talent largely determines the extent and efficiency of digitalized elderly care (Fan Hejun \u0026amp; Wu Ting, 2022). As an active resource, procuring digital talents has been a top priority for many policymakers wanting to expand digitalized elderly care in their jurisdictions. For example, many jurisdictions created a friendly environment to foster digital talents by guiding development programs, reforming incentive and remuneration structures, and identifying intellectual bottlenecks in digitalized elderly care (Wang Shiwei, 2022). In summary, the aggregation of digital talents is a necessary condition for promoting the digitization of elderly care services. It serves as a significant indicator of a favorable\u003c/p\u003e \u003cp\u003ebusiness environment and can be assessed through the workforce size within telecommunications, computer, and other digital technology sectors across different regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4 Capability of digital government services and digitalized elderly care services\u003c/h2\u003e \u003cp\u003eThe government plays an active role in coordinating and regulating digital economic development, balancing digital resources, and enhancing the international competitiveness of the digital economy, which is a necessary approach to accelerating economic transformation. Traditional government service methods and processes suffer from problems such as \u0026ldquo;running back and forth for handling affairs\u0026rdquo; and \u0026ldquo;multiple steps in the documentation process\u0026rdquo;, resulting in deficiencies or mismatches in the government\u0026rsquo;s service functions (Xu Xiaolin et al., 2023). As such, the promotion of digitalized elderly care poses new challenges for digital government services, transforming their role from regulators to service providers. The application of digital technology can expand the coverage and efficiency of service resources, thereby improving the \u0026ldquo;efficiency dilemma\u0026rdquo; in the business environment (Shi Qingbo \u0026amp; Huang Qisong, 2023).\u003c/p\u003e \u003cp\u003eUnlike Germany, Japan, and the United States, it is the Chinese government rather than the private sector that is likely to lead the digitalized elderly care strategy. They advocate for a holistic approach that fosters the creation of a robust digital ecosystem through synchronized efforts in cultivating digital technologies and talents across foundational, platform, and application domains. This, in turn, is seen as instrumental in further bolstering governmental service capabilities (Yang Yan, 2019). In addition, attention should be paid to regulatory innovation and actively studying relevant support policies that are compatible with and conform to the digital economy, creating a digital and service-oriented government. This helps coordinate digital resources, accelerate the integration of the digital economy with traditional industries (Xie Lijuan \u0026amp; Zhuang Yiqun, 2021), and bring about \u0026ldquo;digital dividends\u0026rdquo; for the business environment, enhancing the effectiveness of the business environment (Zhang Banghui et al., 2021). In essence, enhancing the capability of digital government services comprehensively signifies an essential aspect of enhancing the business environment. This enhancement is gauged across five dimensions: the effectiveness, maturity, completeness, coverage, and accuracy of online government services.[4]\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.5 Scale of government public expenditure and digitalized elderly care services\u003c/h2\u003e \u003cp\u003eRelying on government purchasing of elderly care services, actively nurturing market forces, introducing social references, building multi-party cooperation relationships, optimizing the elderly care service system, is an effective way to alleviate the current pressure on elderly care (Lu Xuanru \u0026amp; Zhang Xiaoyi, 2022). However, elderly care services belong to the quasi-public goods category, which is an important manifestation of the government\u0026rsquo;s people-oriented approach and has certain public welfare characteristics. It requires the government to support it with public finances, especially in terms of public space, facilities, and public service expenditures, as well as expenditures for special elderly groups (Song Huan et al., 2022).\u003c/p\u003e \u003cp\u003eAs a key player in the elderly care service system, government engagement goes beyond direct investment. Government procurement of elderly care services represents a proactive response and a significant approach to address the considerable pressures and practical needs surrounding elderly care issues. This strategy involves transferring certain elderly care service functions to third-party providers, fostering market development while meeting the demands of the aging population (Xu Xianmei, 2019). As early as 2014, the Ministry of Finance, the National Development and Reform Commission, the Ministry of Civil Affairs, and other departments jointly issued the \u0026ldquo;Notice on Doing a Good Job in the Government\u0026rsquo;s Purchase of Elderly Care Services\u0026rdquo;, which clearly proposed the mode and responsibilities of government purchases, marking a significant guiding policy. Empirically, regional public expenditure is often used as a measure for the effect of the macroeconomic environment on digitalized elderly care services.\u003c/p\u003e \u003cp\u003eIn summary, compared to other industries operating on a market-oriented basis, the digitalization of elderly care services requires more government financial support. Therefore, considering the specificity of this industry, this indicator can be measured using data on the scale of public financial support in a region. Traditional empirical research does not consider the interaction between independent variables, and the explanation of the dependent variable belongs to a linear relationship, based on the premise of causal symmetry. However, in practice, the factors affecting digitalized elderly care services are complex and diverse, and variables interact with each other and collaborate. Fuzzy set qualitative analysis method can explain the interaction between complex variables and is good at small-sample analysis, especially suitable for addressing the issue of causal asymmetry (Du Yunzhou \u0026amp; Jia Liangding, 2017). Therefore, based on examining the characteristics of different regional developments, this study conducts configuration analysis on the impact of the business environment on digitalized elderly care services in various regions, interprets their causal relationships from a holistic perspective, and provides theoretical basis for regions to enhance the level of digitalized elderly care services in a targeted manner.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample selection and data sources\u003c/h2\u003e \u003cp\u003eLimited by data availability, we explore the empowering factors of digitalized elderly care across the 31 provinces on mainland China due to the inconsistent data collection methods and standards in Taiwan, Hong Kong, and Macau compared to the mainland. We obtain the data on the number of digitalized elderly care institutions using the web scraping technology from Baidu Maps by including keywords like \u0026ldquo;smart elderly care service platform\u0026rdquo; \u0026ldquo;smart elderly care information service center\u0026rdquo; and \u0026ldquo;smart elderly care service company\u0026rdquo;. We source the data on \u0026ldquo;smart elderly care demonstration bases, streets and enterprises\u0026rdquo; from the Ministry of Civil Affairs and the data on digital infrastructure, digital technology investment, and digital talent support from the Digital Economy Database. Finally, we extract the data on digital government services from the 2021 Provincial Government and Key City Online Government Service Capability Survey and Assessment Report and the data on macroeconomic empowerment from the China Statistical Yearbook 2021. We finalized our data collection in March 2022.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Research methodology\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Spatial visualization analysis and global Moran\u0026rsquo;s Index\u003c/h2\u003e \u003cp\u003eWe employ ArcGIS 10.2 to perform the spatial visualization analysis on digitalized elderly care and its empowering factors in 31 Chinese provinces. To that end, we apply natural breaks to categorize these provinces into five levels and calculate the global Moran\u0026rsquo;s I statistics for each variable to ascertain if their provincial distribution is subject to non-random spatial clustering. Specifically, we obtain the global Moran\u0026rsquo;s I as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$I＝\\frac{\\sum _{i=1}^{n}\\sum _{j=1}^{n}{w}_{ij }({x}_{i}-\\stackrel{-}{x})({x}_{j}-\\stackrel{-}{x})}{{S}^{2}\\sum _{i=1}^{n}\\sum _{j=1}^{n}{w}_{ij}}, {S}^{2}=\\frac{\\sum _{i=1}^{n}{\\left[\\left({x}_{i}-\\stackrel{-}{x}\\right)\\right]}^{2}}{n}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{j}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e​\u003c/sub\u003e represent the variable of interest in province \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e, respectively\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(. \\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(n\\)\u003c/span\u003e\u003c/span\u003e is the mean and the total number of spatial elements, respecitvely. \u003cem\u003eS\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e denotes the variance of the spatial element. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{ij}\\)\u003c/span\u003e\u003c/span\u003e represents the province pair of the spatial weight matrix measured by the distance between \u003cem\u003ei\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003ej\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e province. Mathematically, the value of the global Moran\u0026rsquo;s \u003cem\u003eI\u003c/em\u003e must fall between \u0026minus;\u0026thinsp;1 and 1, with a value greater than zero indicating positive spatial autocorrelation and a value less than zero negative spatial autocorrelation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 FSQCA\u003c/h2\u003e \u003cp\u003eThe QCA method was first proposed by Ragin in 1987, using configurational logic to analyze complex causal relationships among small samples. In the field of social science research, causal variables are often asymmetric and nonlinear, making the QCA method particularly suitable for analyzing the relationships between complex variables in social science. It has been widely applied in related studies in economics and management (Chen Xuelin et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang Xin et al., 2022; Zhao Yan et al., 2021).\u003c/p\u003e \u003cp\u003eIn this study, we have selected FSQCA to examine the empowering factors of digitalized elderly care in China on the following grounds. First, since our sample size contains only 31 provinces, it is best analyzed by tools developed for small sample sizes like FSQCA. And second, FSQCA can control for possible asymmetric and nonlinear causal effects between the empowering factors of digitalized elderly care; a distinct feature that is missing from conventional linear models. By considering the interactions among the factors through the lens of FSQCA, we can identify the driving force behind quality digitalized elderly care and provide policy recommendations on enhancing digitalized elderly care.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Spatial Differentiation Characteristics of China’s Digital Elderly Care Service Level","content":"\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003e4.1 Spatial differentiation of digital elderly care service level\u003c/h2\u003e\n \u003cp\u003eWe measure the provincial level of digitalized elderly care based on the total number of smart elderly care demonstration bases, streets, and enterprises selected by the Ministry of Civil Affairs and the number of digitalized elderly care platforms between 2017 and 2020. Based on ArcGIS 12 software, Fig. \u003cspan\u003e1\u003c/span\u003e depicts the spatial distribution of digitalized elderly care across 31 provinces, with darker colors representing provinces having a greater presence of digitalized elderly care.\u003c/p\u003e\n \u003cp\u003eA glance at the figure reveals that more economically developed and densely populated areas like the Yangtze River Delta, the Pearl River Delta, the Bohai Rim, and the Chengdu-Chongqing region host more digitalized elderly care than elsewhere. At the provincial level, Zhejiang, Shandong, Sichuan, Shanghai, and Guangdong are among the top five regions with the greatest presence of digitalized elderly care, reaching a maximum of 80 and averaging 64.8. In contrast, Hainan, Qinghai, Tibet, Guizhou, Guangxi, and Xinjiang are among the bottom six regions with the lowest presence of digitalized elderly care, with an average of only 3.2. In part, we attribute this regional imbalance to a low level of economic development, population density, and digital talent in the western provinces. Overall, we observe noticeable inter-province differences in digitalized elderly care.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003e4.2 Spatial Distribution of Business Environment\u003c/h2\u003e\n \u003cp\u003eThe spatial distribution of various dimensions of the business environment is shown in Fig. \u003cspan\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan\u003e2\u003c/span\u003ea provides the provincial level of digital infrastructure measured by the number of mobile phones and computers per 100 people. The national average of digital infrastructure is 74.77. While Beijing and Shanghai stand out with 131.99 and 114.16, respectively, most regions report a range between 56.33 and 93.29. These trends can be attributed to the central government\u0026rsquo;s strategic emphasis on the digital economy as a catalyst for high-quality and sustainable development. Adhering to this policy directive, many provincial governments have responded by prioritizing the construction of digital infrastructure that resulted in small provincial differences in digital infrastructure.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan\u003e2\u003c/span\u003eb shows the provincial distribution of digital technology investment, with Guangdong leading the way. To put this in context, it reports a total investment of 1,271.18\u0026nbsp;billion yuan, or 2.28 times that of Jiangsu, the second highest ranked province, and 9.89 times the national average. As more economically advanced provinces, Jiangsu, Zhejiang, and Shandong, have also prioritized digital technology, with an annual investment of 557.32\u0026nbsp;billion yuan, 332.18\u0026nbsp;billion yuan, and 204.53\u0026nbsp;billion yuan, respectively. As a median province, Chongqing has invested a totel of 68.42\u0026nbsp;billion yuan in digital technology. Overall, these substantial provincial disparities largely reflect the pattern of economic development.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan\u003e2\u003c/span\u003ec illustrates the concentration of digital talent across Chinese provinces. The top five provinces for the number of digital talents in China are Beijing, Guangdong Province, Shanghai, Jiangsu, and Zhejiang, with 922,900, 740,400, 448,000, 328,500, and 283,000 people, respectively. This is significantly higher than the national median of 85,500 people, and the average for the bottom five provinces is a mere 17,700 people. Moreover, the top 10 cities in China\u0026rsquo;s 100 most attractive cities for digital talents, in alphabetical order, include Beijing, Changsha, Chengdu, Guangzhou, Hangzhou, Nanjing, Ningbo, Shanghai, Shenzhen, and Suzhou. From the available data, digital talents remain concentrated in the Yangtze River Delta and the Pearl River Delta, which continue to attract digital talents from the Beijing-Tianjin-Hebei belt, the central-western region, and the northeastern region. Swimming against this trend is the Chengdu-Chongqing region, whose net outflows of digital talents appear to be stable. In general, these trends can be attributed to favorable policies and a more supportive environment for digital talents, especially in the IT, internet, communication, and electronics industries, highlighting substantial provincial differences in attracting and utilizing digital talents.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan\u003e2\u003c/span\u003ed describes the digital government service capability indexies of each administrative region, measured by based on the effectiveness, maturity, completeness of service methods, coverage of service items, and accuracy of service guides. Eight provinces and regions, including Anhui, Beijing, Guangdong, Guizhou, Jiangsu, Shanghai, Sichuan, and Zhejiang have, Guangdong, Beijing, Jiangsu, Guizhou, Anhui, and Sichuan, recordedexhibit the very highest overall indices, exceeding 90, accounting for 25%. For the other 18 provinces, this index falls between a range of 80 and 90, accounting for 56%. The remaining six provinces and regions, with Jilin beingas the median province, have reported an index of 83.9. In general, tThe provincial differences in digital government services level among regions are not significant, indicating the effectiveness of the nationwide integrated national digital government service platform platform. It is expected that tThe continuous improvement in the public\u0026apos;s public\u0026rsquo;s awareness and experience of government digital services platforms will continue to improve, contributeing to the rapid enhancement of government digital service capabilities.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan\u003e2\u003c/span\u003ee describes the provincial macroeconomic environment measured by the total sum of public service expenditures, urban and rural community expenditures, and health expenditures related to elderly care services. The top five provinces are Guangdong, Jiangsu, Shandong, Henan, and Zhejiang, with an amount of 5,023.41\u0026nbsp;billion yuan, 4,059.05\u0026nbsp;billion yuan, 3,217.43\u0026nbsp;billion yuan, 3,210.75\u0026nbsp;billion yuan, and 2,912.07\u0026nbsp;billion yuan, respectively. There are 22 provinces fluctuate within the range of 800\u0026nbsp;billion yuan above or below the national mean of 1,840.90\u0026nbsp;billion yuan. The remaining four provinces have reported an average expenditure of only 476.07\u0026nbsp;billion yuan. The provincial distribution of macroeconomic environment for digitalized elderly care across exhibits a \u0026ldquo;spindle-shaped\u0026rdquo; pattern, with most regions reporting little variations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003e4.3 Spatial agglomeration characteristics of digital elderly care services and business environment in various regions\u003c/h2\u003e\n \u003cp\u003eWe calculate the global Moran\u0026rsquo;s I index to gauge the level of spatial autocorrelation for the empowering factors of digitalized elderly care and report their corresponding p-values and Z-values in Table \u003cspan\u003e1\u003c/span\u003e. In general, a positive global Moran\u0026rsquo;s I index indicates a stronger spatial autocorrelation and a value of zero for spatial randomness. Moreover, for spatial autocorrelation to be statistically significant, the global Moran\u0026rsquo;s I index must report a p-value less than or equal to 0.05 and a Z-value greater than or equal to 1.96. Our results show that, except digital government services, none of the empowering factors shows signs of spatial autocorrelation, ruling out traditional geographic factor analysis and factor interaction detection methods for analyzing the empowering factors of digitalized elderly care. Instead, we have selected FSQCA to explore concurrent conditions and configurations influencing digitalized elderly care in China.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe global Moran\u0026rsquo;s I test results for the level of digital elderly care services and influencing factors in various regions of China are presented.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMoran\u0026rsquo;s \u003cem\u003eI\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigital\u0026nbsp;elderly\u0026nbsp;care\u0026nbsp;service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigital\u0026nbsp;infrastructure\u0026nbsp;(per\u0026nbsp;100\u0026nbsp;people)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigital\u0026nbsp;technology\u0026nbsp;R\u0026amp;D\u0026nbsp;investment\u0026nbsp;(billion\u0026nbsp;yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigital\u0026nbsp;talents\u0026nbsp;(per 10,000\u0026nbsp;People)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigital\u0026nbsp;government\u0026nbsp;service\u0026nbsp;capability\u0026nbsp;index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGovernment\u0026nbsp;public\u0026nbsp;expenditure\u0026nbsp;scale\u0026nbsp;(billion\u0026nbsp;Yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eComplex social phenomena reflect complex sets of relationships among multiple concurrent conditions and outcome variables. In different combinations of conditions, the logical factors between single conditions and results may change. In recent years, the configurational thinking and FSQCA methods based on different logics have been developed to provide more refined analyses of heterogeneity, concurrent conditions, and equivalent paths between cases, which can better explain complex phenomena in management fields. Therefore, this study adopts FSQCA, based on fuzzy set qualitative comparison, to explore the concurrent conditions and configurations of the level of digital elderly care service.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Configurational Analysis of Digitalized Elderly Care","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Data calibration\u003c/h2\u003e \u003cp\u003eWe calibrate the sample based on three-level anchors, including full membership, crossover points, and full non-membership, before using the FSQCA software to perform Boolean minimization operations on variable combinations to obtain results for the necessary condition analysis and the configuration analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Necessary condition analysis\u003c/h2\u003e \u003cp\u003eThe prerequisite for the configuration analysis is that the consistency of the data must meet the theoretical model\u0026rsquo;s requirements based on (Ragin, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e):\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{C}\\text{o}\\text{n}\\text{s}\\text{i}\\text{s}\\text{t}\\text{e}\\text{n}\\text{c}\\text{y}\\left({X}_{i} \\le {Y}_{i}\\right)=\\sum \\left(\\underset{}{\\text{min}}\\left({X}_{i }{,Y}_{i}\\right)/\\sum {X}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{i}\\)\u003c/span\u003e\u003c/span\u003e represent the membership score in the condition combination and the membership score of the outcome variable, respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(n\\)\u003c/span\u003e\u003c/span\u003e is the number of conditions. In theory, if the consistency index exceeds 0.8, then \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{i}\\)\u003c/span\u003e\u003c/span\u003e is considered a necessary condition for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{i}\\)\u003c/span\u003e\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the results of the necessary condition analysis for each empowering factor of digitalized elderly care. In general, it shows that the consistency of all empowering factor is below 0.9, indicating multiple pathways in shaping digitalized elderly care. With this in mind, it is necessary to further analyze the configuration of pathways for enhancing digitalized elderly care.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe necessary condition analysis, by empowering factor.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmpowering factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital technology R\u0026amp;D investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital technology talent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital government service capability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment public expenditure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Configuration Analysis\u003c/h2\u003e \u003cp\u003eWe present the results of configuration analysis proposed by Ragin and Strand (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Specifically, if an empowering factor of digitalized elderly care appears in both the intermediate solution and the parsimonious solution, it is considered a core condition. However, if the empowering factor only appears in the intermediate solution, it is considered an edge condition. In FSQCA, there are three types of solutions: complex, parsimonious, and intermediate. The intermediate solutions, favored by researchers, incorporates \u0026ldquo;logical residuals\u0026rdquo; consistent with theoretical and empirical knowledge.\u003c/p\u003e \u003cp\u003eWe conduct FSQCA on 31 Chinese provinces by setting the criteria for entering the group to a frequency greater than one and consistency greater than 0.8. As a result, we uncover six configurations of empowering factors that can improve digitalized elderly care. Specifically, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that all configurations have a consistency value of 0.868 and a coverage value of 0.814, suggesting that these six pathways explain about 81.4% of provincial variations in digitalized elderly care.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfiguration analysis of the empowering factors of digitalized elderly care.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfiguration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e△\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e△\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e△\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital technology R\u0026amp;D investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e△\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e△\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital technology talent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e△\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital government service capability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e△\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e△\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment public expenditure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e△\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnique coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolution consistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolution coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: ● indicates the presence of core conditions, ○ indicates the presence of marginal conditions, ▲ indicates the absence of core conditions, △ indicates the absence of marginal conditions, and blank spaces indicate that the condition is not relevant in this configuration.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e5.3.1 Configuration 1: The public expenditure\u0026ndash;research and development type\u003c/h2\u003e \u003cp\u003eConfiguration 1 indicates that the core conditions for enhancing digitalized elderly care in a province include substantial public spending on health and urban community services, supplemented by digital technology investment. Consequently, we name this configuration the \u0026ldquo;public expenditure\u0026ndash;research and development type\u0026rdquo; which explains 49.4% of digitalized elderly care in provinces like Shandong, Hubei, Sichuan, Shaanxi, Fujian, Hunan, and Jiangxi.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e5.3.2 Configuration 2: The public expenditure\u0026ndash;talent aggregation type\u003c/h2\u003e \u003cp\u003eConfiguration 2 suggests that provinces with a favorable macroeconomic environment, a concentration of digital talent, and a focus on digital technology investment often report better digitalized elderly care. The core conditions in this configuration include macroeconomic environment and digital talent, with digital technology investment as an edge condition. Consequently, we name this configuration the \u0026ldquo;public expenditure\u0026ndash;talent aggregation type\u0026rdquo; which explains 62.9% of the case regions, and 17.2% of regions can only be explained by this path. Areas conforming to this development path include Shanghai, Jiangsu, Zhejiang, Guangdong, Beijing, Anhui, Shandong, Hubei, Sichuan, Shaanxi, and Fujian.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e5.3.3 Configuration 3: The public expenditure dominant type\u003c/h2\u003e \u003cp\u003eConfiguration 3 indicates that even if a province has a low level of digital infrastructure, dispersed digital tech talent, and poor digital government service capability, it can still achieve quality digitalized elderly care, provided that there is public spending on general public services, health, and urban community services. In other words, macroeconomic environment serves as a core condition in this configuration and plays a dominant role when other empowering factors are weak. Logically, we name this configuration the \u0026ldquo;public expenditure dominant type\u0026rdquo; which explains 44.2% of digitalized elderly care in provinces like Hunan, Yunnan, and Guangxi provinces, with Yunnan and Guangxi provinces being explained only by this path.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e5.3.4 Configuration 4: The technical service assurance type\u003c/h2\u003e \u003cp\u003eConfiguration 4 indicates that a province with a concentration of digital talent, the necessary support from digital technology investment, and capable digital government services, exhibits better performance in digitalized eldecare. In this configuration, digital talent is a core condition, and digital technology investment and digital government services are edge conditions. These latter two factors act as safeguards for the utilization of digital talent and technology, collectively contributing to a high level of digitalized elderly care. Therefore, we name this configuration the \u0026ldquo;technology service assurance type,\u0026rdquo; which explains 40.6% of digitalized elderly care in provinces including Hubei, Sichuan, Shaanxi, Fujian, and Henan provinces, with Henan province being explained solely by this path.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e5.3.5 Configuration 5: The comprehensive development type\u003c/h2\u003e \u003cp\u003eConfiguration 5 suggests that a province with good digital infrastructure, a concentration of digital talent, and a focus on public spending on health and urban community development achieves a high level of digitalized elderly care. In this configuration, digital talent and macroeconomic environment are core conditions, and digital infrastructure is an edge condition. Since this configuration consists of both hard and soft empowering factors, we name it the \u0026ldquo;comprehensive development type.\u0026rdquo; It explains 45.7% of the case regions, including Fujian, Hubei, Sichuan, Shaanxi, and Hebei provinces, with Hebei province being explained solely by this path.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e5.3.6 Configuration 6: The talent support\u0026ndash;facility emphasis type\u003c/h2\u003e \u003cp\u003eConfiguration 6 reveals that a province emphasizing digital talent and favorable digital infrastructure enhances digitalized elderly care. In this configuration, digital talent is a core condition, and digital infrastructure is an edge condition. We name this configuration the \u0026ldquo;talent support\u0026ndash;facility emphasis type,\u0026rdquo; which explains 26.2% of the case regions, and the region conforming to this path is Liaoning province.\u003c/p\u003e \u003cp\u003eAmong these six configurations, digital technology talents and the scale of public expenditure are identified as core conditions, highlighting the importance of these two factors in enhancing the digitalization level of elderly care services in regions. The aggregation of digital talents is a key driving factor for regional innovation and development. Strengthening mechanisms to attract, develop, and incentivize digital talents, along with improving the education level of digital technology talents, is crucial for enhancing the digitalized elderly care service level in regions. Additionally, the scale of public expenditure, as another important core condition, underscores the role of governments in regulating and coordinating digital economic development. It also demonstrates that a favorable fiscal environment can enhance complementary resources for digital innovation, leading to high-quality development of digitalized elderly care services.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"6. Discussion","content":"\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Discussion\u003c/h2\u003e \u003cp\u003eIn conclusion, this study employed spatial visualization methods to explore the spatial distribution characteristics of digital elderly care service platforms and five influencing factors across 31 provincial-level administrative regions in China. Utilizing a configuration perspective through fsQCA software, the study conducted a fuzzy set qualitative comparative analysis to explore the combination paths of factors influencing the digital elderly care service level in different regions. The key findings are as follows:\u003c/p\u003e \u003cp\u003eOverall, there is significant spatial differentiation in the digital elderly care service platforms across the 31 provincial-level administrative regions in China. The level of digital elderly care services is higher in economically developed regions such as the Yangtze River Delta and the Pearl River Delta regions. This pattern closely correlates with the levels of regional economic development, population size and the concentration of digital technology talent. These regions are possessing a substantial economic advantage in terms of investment in digital tech R\u0026amp;D and public spending on health and urban community services. From the perspective of elderly care service demand, robust economic capacity can lead to higher demands for higher-quality services. This necessitates further improving the quality and efficiency of elderly care services in the context of the digital economy to meet the personalized and heterogeneous service needs of the elderly, driving government and societal efforts to explore paths for enhancing the digital technology-enabled elderly care service level.\u003c/p\u003e \u003cp\u003eWhile none of the five influencing factors individually constitutes sufficient conditions for the high-level development of digital elderly care services, they can form six driving paths through configuration.The first pathway is \u0026ldquo;public expenditure-research and development investment type\u0026rdquo;, characterized by economic empowerment as the primary focus with research and development funding as a supplementary investment. The second pathway is \u0026ldquo;public expenditure-talent aggregation type\u0026rdquo;, characterized by economic empowerment and simultaneous support for talent, fostering dual forces for co-development. The third pathway is \u0026ldquo;public expenditure-dominated type\u0026rdquo;.The fourth pathway is \u0026ldquo;technical service Assuarance type, characterized by a primary focus on digital technology talent, supplemented by investments in research and development and the digitization of government services. The fifth pathway is \u0026ldquo;comprehensive development type\u0026rdquo;. Lastly, the sixth pathway is \u0026ldquo;talent support-gacility emphasis type\u0026rdquo;, characterized by a core emphasis on digital technology talent. These pathways are chosen based on the regions\u0026rsquo; unique endowments of digital economic resources, providing different development templates for regions with diverse digital economic resource endowments.\u003c/p\u003e \u003cp\u003eThe six configuration paths for enhancing digital elderly care services reveal that digital tech talent and economic environment empowerment are core conditions, playing important roles in each configuration. Given the deep integration of digital technology with elderly care services, involving significant technological and management innovation, the regions in the Yangtze River Delta and the Pearl River Delta, relying on their accumulated economic advantages, industrial agglomeration advantages, attractive talent policies, and international perspectives, have advanced further in the integration of digital economy with the real economy. These regions have implemented a variety of measures in a coordinated manner, promoting a higher level of digital elderly care service.\u003c/p\u003e \u003cp\u003eThe conclusions drawn from the study indicate that the digital elderly care service level across the 31 provincial-level administrative regions in China is more influenced by digital tech talent and regional economic environment empowerment. In the future, with the rapid development of digital technology and its fast penetration into various real sectors, the overall level of digital elderly care services in each region is expected to see a qualitative improvement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Policy Recommendations\u003c/h2\u003e \u003cp\u003eBased on the research findings, the following three policy recommendations are proposed.\u003c/p\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e6.2.1 Encourage More Social Forces to Join the Elderly Care Service Sector\u003c/h2\u003e \u003cp\u003eGiven that public service investments in health and urban community development are the primary driving factors for the rapid development of digital elderly care services, especially in regions with limited local fiscal capacity, the government can provide subsidies, tax reductions, and other incentives to encourage more social forces to participate in elderly care services. A multi-source collaborative approach is a sustainable development strategy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003e6.2.2 Enhance Training for Digital Tech Talent\u003c/h2\u003e \u003cp\u003eDigital tech talent is undoubtedly the most important strategic resource for the high-quality development of the digital economy. However, traditional education models are insufficient to meet the current demands of the rapidly developing digital economy. There is a need to redefine the profile of digital talent, change talent development models, innovate talent growth ecosystems, and bridge the supply and demand sides of digital talent. Otherwise, this could be a constraint factor hindering the rapid improvement of digital elderly care service level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e6.2.3 Promote the Coordinated Development of Industries Related to Elderly Care Services\u003c/h2\u003e \u003cp\u003eIn the context of rapid aging of the Chinese population, achieving the goal of active and healthy aging is a complex systemic project. It requires the coordinated governance of multiple forces involved in elderly care, including civil affairs departments, fiscal and tax departments, land and resources departments, social security departments, medical service institutions, education and training institutions, and suppliers of elderly care products and services. These forces need to collaborate, integrate resources, and work together to build a grand strategy and framework for elderly care services.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cem\u003eFSQCA:\u003c/em\u003e\u0026nbsp; fuzzy-set qualitative comparative analysis\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIMD:\u003c/em\u003e\u0026nbsp; Institute for Management Development\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTha data and materials of this study are publicly available.\u003c/p\u003e\n\u003cp\u003eIn the paper, sections 4 and 5 belong to the results section.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditional information and supplementary information are not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank George Chen (Ph.D.) from the University of New England in Australia for his suggestions for revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study gets support by the National Social Science Foundation of China to Jianli Gao under Grant number [21BRK010].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSchool of Business Administration, Shandong Technology and Business University, Yantai, People\u0026rsquo;s Republic of China\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJianli Gao，Xiaoqing Zhang，Lejie Wang\u003c/p\u003e\n\u003cp\u003eBusiness School, Shandong Normal University, Jinan, People\u0026rsquo;s Republic of China\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eXiaoyan Zhang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJianli Gao designed the framework of this paper and contributed to paper writing; Xiaoqing Zhang contributed to data collection; Lejie Wang contributed to data analysis; Xiaoyan Zhang participated in writing and paper revising. All authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Jianli Gao ([email protected]) and Xiaoyan Zhang ([email protected])\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eC. C. Ragin. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago: University of Chicago Press.\u003c/li\u003e\n\u003cli\u003eC. C. Ragin, S. I. Strand. (2005). Using Qualitative Comparative Analysis to Study Causal Order: Comment on Caren and Panofsky. Sociological Methods \u0026amp; Research, 36(4), 431-441.\u003c/li\u003e\n\u003cli\u003eD. Pal, S. Funilkul, V. Vanijja, B. Papasratorn. (2018). Analyzing the Elderly Users\u0026rsquo; Adoption of Smart-Home Service. IEEE Access, 6, 51238-51354.\u003c/li\u003e\n\u003cli\u003eH. M. Do, M. Pham, W. Sheng, D. Yang, M. Liu. (2017). Rish: a robot-integrated smart home for elderly care. Robot Auton System, 26(18), 122-146.\u003c/li\u003e\n\u003cli\u003eK. Maswadi, N. B. A. Ghani, S. B. Hamid. (2020). Systematic literature review of smart home monitoring technologies based on IoT for the elderly. IEEE Access, 8, 92244-92261.\u003c/li\u003e\n\u003cli\u003eM. J. Deen. (2015). Information and Communications Technologies for Elderly Ubiquitous Healthcare in a Smart Home. Personal and Ubiquitous Computing, 19(3), 599-615.\u003c/li\u003e\n\u003cli\u003eP. Legato, R. M. Mazza, G. Fortino. (2022). A multi-level simulation-based optimization framework for IoT-enabled elderly care systems. Simulation Modelling Practice and Theory, 114, 102420. DOI: 10.1016/j.simpat.2021.102420.\u003c/li\u003e\n\u003cli\u003eBian Shu, Li Linxian. (2019). Research on China\u0026rsquo;s multidimensional pension service system from the perspective of active aging. Journal of Liaoning University (Philosophy and Social Sciences Edition), 47(02), 83-91.\u003c/li\u003e\n\u003cli\u003eCai Yuezhou. (2021). National governance mechanism of digital economy: a perspective of data-driven technological innovation. Journal of Beijing Jiaotong University (Social Sciences Edition), 20(02), 39-49. DOI: 10.16797/j.cnki.11-5224/c.20210406.010.\u003c/li\u003e\n\u003cli\u003eChao Xiaojing. (2020). The path of promoting high-quality development in China with new digital infrastructure. Journal of Xi\u0026rsquo;an University of Finance and Economics, 33(02), 15-19. DOI: 10.19331/j.cnki.jxufe.2020.02.003.\u003c/li\u003e\n\u003cli\u003eChen Gong, Du Peng, Chen Yi. (2001). Problems and thoughts on \u0026ldquo;time saving\u0026rdquo;for the elderly. Population and Economics (06), 67-73.\u003c/li\u003e\n\u003cli\u003eChen Hong, Feng Dayang. (2022). Digital empowerment promotes the high-quality development of rural public services ideas and approaches. World Agriculture (02), 55-65. DOI: 10.13856/j.cn11-1097/s.2022.02.005.\u003c/li\u003e\n\u003cli\u003eChen Xuelin, Zhou Dongmei, Zhou Yang, Lu Ruoyu. (2022). Research on the impact of organizational identity of digital platform enterprises on the acquisition of financial resources: based on fuzzy set qualitative comparative analysis. Technoeconomics, 41(02), 167-177.\u003c/li\u003e\n\u003cli\u003eChu Shijing, Wang Meng, Yang Shenghui. (2015). International comparison and enlightenment of home-based elderly care services under typical welfare types. Population and Economics (04), 119-126.\u003c/li\u003e\n\u003cli\u003eDai Kuizao, Huang Zi, Wang Siman. (2023). Has the digital economy promoted the structural upgrading of China\u0026rsquo;s service industry? Quantitative Economics and Technoeconomics Research, 40(02), 90-112. DOI: 10.13653/j.cnki.jqte. 2023.02.002.\u003c/li\u003e\n\u003cli\u003eDing Zhifan. (2020). Research on the mechanism of digital economy driving high-quality economic development: a theoretical analysis framework. Modern Economic Exploration(01), 85-92. DOI: 10.13891/j.cnki.mer.2020.01.011.\u003c/li\u003e\n\u003cli\u003eDu Yunzhou, Jia Liangding. (2017). Configuration perspective and qualitative comparative analysis (QCA): a new path for management research. Management World(06), 155-167. DOI: 10.19744/j.cnki.11-1235/f.2017.06.012.\u003c/li\u003e\n\u003cli\u003eFan Hejun, Wu Ting, He Sijin. (2022). How to optimize urban business environment through \u0026ldquo;Internet + government service\u0026rdquo; platform? \u0026mdash;Based on the perspective of interactive governance. Management World, 38(10), 126-153. DOI: 10.19744/j.cnki.11-1235/f.2022.0149.\u003c/li\u003e\n\u003cli\u003eFan Hejun, Wu Ting. (2022). New digital infrastructure, digital capability and total factor productivity. Economic and Management Research, 43(01), 3-22. DOI: 10.13502/j.cnki.issn1000-7636.2022.01.001.\u003c/li\u003e\n\u003cli\u003eGong Weiping, Liu Yuezhong. (2014). Research on the business environment of small and medium-sized enterprises in Macao. Industry and Economy Review, 5(02), 121-131. DOI: 10.14007/j.cnki.cjpl.2014.02.010.\u003c/li\u003e\n\u003cli\u003eGuo, Chaoxian, Wang, Jiaqi, Liu, Haorong. (2020). Research on the Path of Empowering China\u0026rsquo;s Economy with New Infrastructure for High-Quality Development. Journal of Beijing University of Technology (Social Sciences Edition), 20(06), 13-21.\u003c/li\u003e\n\u003cli\u003eHuang, Manyu, Wang, Xiaoxing. (2022). Digital Economy, Resource Misallocation, and Total Factor Productivity of Enterprises. Macroeconomic Research, (12), 43-53. DOI: 10.16304/j.cnki.11-3952/f.2022.12.001.\u003c/li\u003e\n\u003cli\u003eLi, Juan, Ding, Liangchao. (2019). Government-Social Organization Synergy from the Perspective of Government Purchase of Elderly Care Services. Theoretical Exploration, (02), 63-69.\u003c/li\u003e\n\u003cli\u003eLi, Mengna, Zhou, Yunbo. (2022). Research on the Effect of Human Capital Structure on the Development of Digital Economy. Economic and Management Research, 43(01), 23-38. DOI: 10.13502/j.cnki.issn1000-7636.2022.01.002.\u003c/li\u003e\n\u003cli\u003eLi, Wei. (2023). Theoretical Logic of Digital Economy Development, Strategic Deployment of Developed Countries, and China\u0026rsquo;s Response. China Soft Science, (05), 216-224.\u003c/li\u003e\n\u003cli\u003eLi, Zhijun, Zhang, Shiguo, Li, Yifei, Shan, Dan. (2019). Evaluation of Urban Business Environment in China and Relevant Suggestions. Jiangsu Social Sciences, 02, 30-42. DOI: 10.13858/j.cnki.cn32-1312/c.2019.02.005.\u003c/li\u003e\n\u003cli\u003eLi, Zhijun, Zhang, Shiguo, Niu, Zhiwei, Yuan, Wenrong, Liu, Qi. (2021). Theoretical Logic, Comparative Analysis, and Countermeasure Suggestions for Evaluating Urban Business Environment in China. Management World, 37(05), 98-112. DOI: 10.19744/j.cnki.11-1235/f.2021.0067.\u003c/li\u003e\n\u003cli\u003eLiu, Yi. (2021). From Resource Network to Digital Map: Research on the Driving Mode of Community Elderly Care Service Platform. Electronic Government, (08), 40-51.\u003c/li\u003e\n\u003cli\u003eLv, Xuanru, Zhang, Xiaoyi. (2022). Research on the Diffusion of Policy of Government Purchase of Home-Based Elderly Care Services from the Perspective of Multiple Logics: An Event History Analysis Based on Provincial Data in China. Journal of Northeastern University (Social Science Edition), 24(04), 54-63.\u003c/li\u003e\n\u003cli\u003eRen, Zhuanzhuan, Deng, Feng. (2023). Digital Technology, Factor Structure Transformation, and High-Quality Economic Development. Soft Science, 37(01), 9-14. DOI: 10.13956/j.ss.1001-8409.2023.01.02.\u003c/li\u003e\n\u003cli\u003eShi, Qingbo, Huang, Qisong. (2023). Research on the Optimization of Business Environment Driven by Digital Government: Taking Guiyang as an Example. Think Tank Theory and Practice, 8(03), 71-80. DOI: 10.19318/j.cnki.issn.2096-1634.2023.03.08.\u003c/li\u003e\n\u003cli\u003eSong, Huan, Sun, Meng, Sun, Tao. (2022). Community Home-Based Elderly Care Service Mechanism of Government Purchase, Institutional Supply, and Family Participation: Analysis Based on Evolutionary Game Theory and Typical Cases. Journal of Aging Science Research, 10(04), 28-41.\u003c/li\u003e\n\u003cli\u003eSun, Jiling, Liu, Shangjun, Suo, Haoyu, Chen, Gong. (2022). Theoretical Thinking on the Support of Technological Development for Elderly Care Services from the Perspective of Social Governance. Population and Development, 28(01), 50-58.\u003c/li\u003e\n\u003cli\u003eWang, Jun, Zhang, Yi, Ma, Xiao. (2022). Digital Economy, Resource Misallocation, and Total Factor Productivity. Finance and Trade Research, 33(11), 10-26. DOI: 10.19337/j.cnki.34-1093/f.2022.11.002.\u003c/li\u003e\n\u003cli\u003eWang, Lijian, Di, Xiaodong. (2022). Impact Study of Policy Combination on the Supply of Elderly Care Service Resources. Journal of Xi\u0026rsquo;an Jiaotong University (Social Sciences), 42(02), 112-121.\u003c/li\u003e\n\u003cli\u003eWang, Shiwei. (2022). Leading and Promoting National Digital Literacy and Skills Action with High-End Digital Talents. Library Tribune, 42(03), 11-13.\u003c/li\u003e\n\u003cli\u003eWang, Yongmei, Du, Peng. (2018). A Study on the Attitude of the Elderly towards Social Elderly Care Services: A Case Study of Six Districts in Beijing.Population Research, 42(06), 74-86.\u003c/li\u003e\n\u003cli\u003eXia, Jiechang, Wang, Pengfei. (2021). Role and Key Directions of Digital Economy Empowering High-Quality Development of Public Services. Jiangxi Social Sciences, 41(10), 38-47.\u003c/li\u003e\n\u003cli\u003eXie, Kang, Wu, Yao, Xiao, Jinghua. (2020). Adaptive Innovation Based on Big Data Collaboration Assets: Innovation Logic of Digital Economy (II). Journal of Beijing Jiaotong University (Social Sciences Edition), 19(02), 26-38. DOI: 10.16797/j.cnki.11-5224/c.2020.0005.\u003c/li\u003e\n\u003cli\u003eXie, Lijuan, Zhuang, Yiqun. (2021). China\u0026rsquo;s Circulation Reform and Government Functions in the Era of Digital Economy: Investigation Combined with Micro Mechanisms of Enterprises. Chinese Public Administration, (08), 55-61.DOI: 10.19735/j.issn.1006-0863.2021.08.05\u003c/li\u003e\n\u003cli\u003eXu, Xianmei. (2019). Research on the Problem of Government Purchase of Home-Based Elderly Care Services in China. [PhD thesis], Central University of Finance and Economics.\u003c/li\u003e\n\u003cli\u003eXu, Xiaolin, Qiao, Yali, Mao, Zijun, Zou, Qi. (2023). Research on the Path of \u0026ldquo;Internet Plus Government Services\u0026rdquo; Promoting the Development of Digital Economy from the Perspective of \u0026ldquo;Streamlining Administration, Delegating Powers, and Improving Services\u0026rdquo;. Electronic Government, (01), 52-63. DOI: 10.16582/j.cnki.dzzw.2023.01.005.\u003c/li\u003e\n\u003cli\u003eXu, Yudong, Cui, Riming. (2015). Factors Influencing the Quantity-Based Development of Small and Medium-Sized Enterprises in Shandong and the Construction of Business Environment: Based on Panel Data of 17 Prefectures and Cities in Shandong from 2006 to 2013. East China Economic Management, 29(04), 34-39.\u003c/li\u003e\n\u003cli\u003eYang, Dong, Zhang, Yuting, Hu, Dengfeng. (2021). Research on the Empowerment Path of Digital Economy for High-Quality Development: Configuration Analysis Based on Core Cities of the Yangtze River Delta Integration. East China Economic Management, 35(10), 39-47. DOI: 10.19629/j.cnki.34-1014/f.210516004.\u003c/li\u003e\n\u003cli\u003eYang, Yan. (2019). Exploration of China\u0026rsquo;s Digital Economy Development Strategy from an International Comparative Perspective. Science and Technology Management Research, 39(19), 33-42.\u003c/li\u003e\n\u003cli\u003eYang, Zhongji, Qi, Liangqun. (2021). Review of Foreign Research on Digital Innovation and Enlightenment to the Digital Transformation of Chinese Manufacturing Enterprises. Scientific Management Research, 39(04), 120-124. DOI: 10.19445/j.cnki.15\u0026mdash;1103/g3.2021.04.018.\u003c/li\u003e\n\u003cli\u003eYu, Bo, Cong, Yi. (2021). Digital Economy, Human Capital Dividend, and Upgrading of Industrial Structure. Finance and Economics Journal, 42(03), 124-131. DOI: 10.16339/j.cnkihdxbcjb.2021.03.017.\u003c/li\u003e\n\u003cli\u003eYu, Chaoyi, Wang, Xuebiao, Sun, Guanglin. (2020). Digital Finance and China\u0026rsquo;s Economic Growth Quality: Intrinsic Mechanism and Empirical Evidence. Exploration of Economic Issues, (07), 1-14.\u003c/li\u003e\n\u003cli\u003eZhang, Banghui, Wan, Qiulan, Wu, Jian. (2021). Analysis of the Optimization Effect of Online Government Service on Business Environment: Digital Dividend and Digital Divide. Chinese Public Administration, (04), 70-75. DOI: 10.19735/j.issn.1006-0863.2021.04.10.\u003c/li\u003e\n\u003cli\u003eZhang, Bo. (2006). International Comparison of Enterprise Business Environment Indicators and Countermeasures in China. Economic Observer, (10), 62-65. DOI: 10.16528/j.cnki.22-1054/f.2006.10.021.\u003c/li\u003e\n\u003cli\u003eZhang, Si Feng. (2021). Government Behavior and Market Mechanism in the Construction of China\u0026rsquo;s Elderly Care Service System. Social Security Review, 5(01), 129-145.\u003c/li\u003e\n\u003cli\u003eZhang, Xin, Xu, Yaoyu, Ma, Liang. (2022). Configuration Effect Study on Influencing Factors of Digital Transformation of Small and Medium-Sized Enterprises. Economic and Management Review, 38(01), 92-102. DOI: 10.13962/j.cnki.37-1486/f.2022.01.007.\u003c/li\u003e\n\u003cli\u003eZhao, Yan, Tan, Haibo, He, Mengshu. (2021). Factors Affecting the Internet Service Supply Capacity of Local Governments and Their Configuration: A Qualitative Comparative Analysis Based on 27 Provincial Cases. Electronic Government, (04), 68-78. DOI: 10.16582/j.cnki.dzzw.2021.04.007.\u003c/li\u003e\n\u003cli\u003eZhao, Yijun, Deng, Dasong. (2021). Construction of Intelligent Elderly Care Service Mode Driven by Artificial Intelligence. Journal of Jianghuai Forum, (02), 146-152.\u003c/li\u003e\n\u003cli\u003eZou, Wei, Lei, Hao. (2021). Improvement Effect of Business Environment on Resource Misallocation and Its Mechanism: Analysis Based on the Manufacturing Industry. Journal of Wuhan University (Philosophy \u0026amp; Social Sciences), 74(01), 121-139. DOI: 10.14086/j.cnki.wujss.2021.01.011.\u003c/li\u003e\n\u003cli\u003eZuo, Meiyun. (2014). Connotation, Model, and Opportunities of Smart Elderly Care. Chinese Public Security, (10), 48-50.\u003c/li\u003e\n\u003cli\u003eZuo, Pengfei, and Chen, Jing. (2021). Digital Economy and Economic Growth from the Perspective of High-Quality Development. Finance and Economics Research, (09), 19-27. DOI: 10.19654/j.cnki.cjwtyj.2021.09.003.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Xi Jinping: Hold High the Great Banner of Socialism with Chinese Characteristics and Strive for Unity in Fully Building a Modern Socialist Country\u0026mdash;Report at the 20th National Congress of the Communist Party of China, The Central People\u0026rsquo;s Government of the People\u0026rsquo;s Republic of China, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003cspan\u003ewww.gov.cn\u003c/span\u003e\u003c/span\u003e\u003cspan address=\"http://www.gov.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e National Bureau of Statistics of China: Bulletin of the Seventh National Population Census Report, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003cspan\u003ewww.stats.gov.cn\u003c/span\u003e\u003c/span\u003e\u003cspan address=\"http://www.stats.gov.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e China Academy of Planning and Design: \u0026ldquo;China Regional Digital Development Index Report\u0026rdquo;, March 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e China National Academy of Governance, E-Government Research Center: \u0026ldquo;Assessment Report on the Integrated Government Service Capability of Provincial Governments and Key Cities (2022) \u0026rdquo;, September 2022.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Business Environment, Elderly Care Services, Digitalization, Configurational Analysis","lastPublishedDoi":"10.21203/rs.3.rs-4235268/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4235268/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA high-quality business environment contributes to unleashing the innovative vitality of market, what kind of business environment can promote the the construction of digital elderly care services is an important issue worthy of attention. Utilizing spatial visualization methods, we unveil distinct regional heterogeneity in the provincial distribution of digital elderly care services. Yangtze River Delta and the Pearl River Delta leading the way. To understand this pattern, we engage the fuzzy-set qualitative analysis and examine the configuration of six equivalent pathways for the the business environment on digital elderly care services. In general, we find digital talent aggregation and government public expenditure playing a deterministic role in all configurations and offer recommendations for the innovative development of digital elderly care services under different digital resource endowments.\u003c/p\u003e","manuscriptTitle":"A Multi-Pathway Study on the Impact of the Business Environment on Digital Elderly Care Services in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-17 07:34:11","doi":"10.21203/rs.3.rs-4235268/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d087ab2-d385-456a-ba26-dfd987acf833","owner":[],"postedDate":"April 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-13T07:10:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-17 07:34:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4235268","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4235268","identity":"rs-4235268","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}