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The application of big data resources and the development of medical collaborative networks (MCNs) boost each other. However, MCNs are often assumed to be exogenous. How big data resources affect the emergence, development, and evolution of endogenous MCNs has not been well explained. This study aimed to explore and understand the influence of the mechanism of a wide range of shared and private big data resources on the transaction efficiency of medical services to reveal the impact of big data resources on the emergence and development of endogenous MCNs. This study was conducted by administering a survey questionnaire to information technology staff and medical staff from 132 medical institutions in China. Data from information technology staff and medical staff were integrated. Structural equation modeling was used to test the direct impact of big data resources on transaction efficiency of medical services. For those big data resources that had no direct impact, we analyzed their indirect impact. Sharing of diagnosis and treatment data (β=.222; P=.03) and sharing of medical research data (β=.289; P=.04) at the network level (as big data itself) positively directly affected the transaction efficiency of medical services. Network protection of the external link systems (β=.271; P=.008) at the level of medical institutions (as big data technology) positively directly affected the transaction efficiency of medical services. Encryption security of web-based data (as big data technology) at the level of medical institutions, medical service capacity available for external use, real-time data of diagnosis and treatment services (as big data itself) at the level of medical institutions, and policies and regulations at the network level indirectly affected the transaction efficiency through network protection of the external link systems at the level of medical institutions. This study found that big data technology, big data itself, and policy at the network and organizational levels interact with, and influence, each other to form the transaction efficiency of medical services. On the basis of the theory of neoclassical economics, the study highlighted the implications of big data resources for the emergence and development of endogenous MCNs.

Background This study aims to examine how big data resources affect the recall of prior medical knowledge by healthcare professionals, and how this differs in environments with and without remote consultation platforms. Method This study investigated two distinct categories of medical institutions, namely 132 medical institutions with platforms, and 176 medical institutions without the platforms. Big data resources are categorized into two levels—medical institutional level and public level—and three types, namely data, technology, and services. The data are analyzed using SmartPLS2. Results (1) In both scenarios, shared big data resources at the public level have a significant direct impact on the recall of prior medical knowledge. However, there is a significant difference in the direct impact of big data resources at the institutional level in both scenarios. (2) In institutions with platforms, for the three big data resources (the medical big data assets and big data deployment technical capacity at the medical institutional level, and policies of medical big data at the public level) without direct impacts, there exist three indirect pathways. (3) In institutions without platforms, for the two big data resources (the service capability and big data technical capacity at the medical institutional level) without direct impacts, there exist three indirect pathways. Conclusions The different interactions between big data, technology, and services, as well as between different levels of big data resources, affect the way clinical doctors recall relevant medical knowledge. These interaction patterns vary between institutions with and without platforms. This study provides a reference for governments and institutions to design big data environments for improving clinical capabilities.

To foster the sustainable development of culture, particularly focusing on the preservation of cultural heritage, encompassing relics, intangible cultural heritage, and historical sites, China has launched a strategy for the digitalization of culture, with the goal of establishing a holistic national big data framework for cultural resources. To improve the efficiency of collaborative supply of cultural resource big data among various parties and to further advance the sustainable development of culture, this research has created a cooperative model that includes cultural institutions, a cultural resource big data service platform, and government participation. Our research findings, based on prospect theory and evolutionary game theory combined with Chinese practice, are presented below. (1) Various factors, including the coefficient of digital infrastructure empowerment, access charges for digital infrastructure, government penalties, and the probability of data leakage, have varying effects on the system in different states. (2) Once the industry has developed, the government can increase the impact of digital infrastructure empowerment to create stronger incentives, rather than relying solely on rewards or penalties. (3) When the value level of cultural resource big data is high, the benefit distribution coefficient does not affect the system evolution results. Finally, we offer practical insights for the government, cultural organizations, and cultural resource big data service platforms based on our research results. Our research offers Chinese insights for global cultural sustainable development.

Abstract This article aims to analyze the setting up of tangible resources and human big data skills, in the face of institutional pressures, in the big data analytics capability in Brazilian companies. Innovation influences the environment in which companies are inserted, increasing uncertainties, resulting in behavioral changes of social players. In response to individual efforts to rationally deal with uncertainties and constraints, organizational homogenization emerges. However, the institutional pressures that influence the setting up of specific resources are still not fully understood in the literature. The replication of the study by Dubey (2019b) is considered, seeing big data technology as an innovation that has caused changes in the social context, thus we seek to grasp the setting up of organizational big data resources in Brazilian companies to build BDA capability, due to institutional pressures. The study makes it possible to see how institutional pressures set up BDA capability, thus being able to provide means to investment allocation decisions in data technology or improve technical management skills in the business intelligence team. The study brought to light the environmental response, resulting from the technological innovation of big data, in Brazilian companies. This demonstrates that organizations adhering to big data technology select their resources in the face of various pressures, in order to build big data analytics capability. This research has a descriptive and quantitative nature, and its operationalization took place through a survey. The research population consists of Brazilian companies that use technology with a large volume of structured and/or unstructured data, to generate results and insights, which support decision making. The survey participants were employees of Brazilian companies that have positions related to building big data analytics capability, located through the LinkedIn platform. 136 valid responses were obtained. To test the hypotheses, the Structural Equation Modeling technique was used by means of the software Smartspls v. 3.2.3. This study contributes by bringing an understanding of organizational behavior in the face of institutional pressures (coercive, normative, and mimetic) when selecting tangible resources and human big data skills to build BDA capability, using Resource-Based Theory. It is observed that the setting up of BDA capability is influenced by tangible resources and human skills. Tangible resources are selected due to formal pressures, competitive conditions, and by imitating existing standards in the market. Meanwhile, the required human skills are impacted, through legitimation and professional networks of decision makers. Resumo O objetivo deste artigo é analisar a configuração dos recursos tangíveis e das habilidades humanas de big data, diante das pressões institucionais, na capacidade de análise de big data em empresas brasileiras. A inovação influencia o ambiente em que as empresas estão inseridas, aumentando as incertezas, resultando em modificações comportamentais dos atores sociais. Em resposta aos esforços individuais para lidar com as incertezas e restrições de forma racional emerge a homogeneização das organizações. No entanto, as pressões institucionais que influenciam a configuração de recursos específicos ainda não são totalmente entendidas pela literatura. Considera-se a replicação do estudo de Dubey (2019b), entendendo a tecnologia big data como uma inovação que tem causado mudanças no contexto social, assim, busca-se compreender a configuração dos recursos organizacionais de big data nas empresas brasileiras para o desenvolvimento da capacidade de ABD, devido às pressões institucionais. O estudo possibilita compreender como as pressões institucionais configuram a capacidade de ABD, podendo assim subsidiar decisões de alocação de investimento em tecnologia de dados ou aprimoramento de habilidades técnicas de gerenciais da equipe de business intelligence. O estudo trouxe a conhecimento a resposta ambiental, resultante da inovação tecnológica de big data, das empresas brasileiras. Isso demonstra que as organizações que aderiram a tecnologia big data selecionam seus recursos diante de diferentes pressões, a fim de desenvolver a capacidade de análise de big data. Esta pesquisa possui caráter descritivo e quantitativo e sua operacionalização ocorreu por uma survey. A população pesquisada consiste em empresas brasileiras que usam tecnologia com grande volume de dados estruturados e/ou não estruturados, para a geração de resultados e insights, que auxiliam na tomada de decisão. Os participantes da pesquisa foram colaboradores de empresas brasileiras que apresentem funções relacionadas ao desenvolvimento da capacidade de análise de big data, localizados por meio da plataforma LinkedIn. Foram obtidas 136 respostas válidas. Para testar as hipóteses se usou a técnica de Modelagem de Equações Estruturais empregando o software Smartspls v. 3.2.3. Este estudo contribui trazendo a compreensão do comportamento organizacional diante das pressões institucionais (coercitiva, normativa e mimética) na seleção dos recursos tangíveis e habilidades humanas de big data para o desenvolvimento da capacidade de ABD, fundamentado na Teoria Baseada em Recursos. Observa-se que a configuração da capacidade de ABD é influenciada por recursos tangíveis e habilidades humanas. Os recursos tangíveis são selecionados devido a pressões formais, condições competitivas e por imitação de padrões existentes no mercado. Enquanto, as habilidades humanas requeridas, são impactadas, por meio da legitimação e redes profissionais dos tomadores de decisão.

Purpose Today, big data (BD) is considered as a crucial investment for firms to stay competitive. However, the human resource (HR) function within small- and medium-sized enterprises (SMEs) has been slow to adopt this innovation. Drawing on the organizational learning theory (OLT), this study aims to propose that BD can improve HR functions, especially of SMEs, thereby yielding them a competitive edge. Design/methodology/approach This study analyzed unstructured data from 41 journal papers, based on which, a conceptual framework was developed. Further, this framework was validated with responses collected from 148 SMEs in India. Findings Bibliometric analysis and results of partial least squares techniques revealed that better BD quality is needed to improve HR practices, human resource service quality (HRSQ) and innovation competency of SMEs. Research limitations/implications This paper contributes to the extant literature by considering strategic management theories such as resource-based view and OLT to evaluate BDA’s effect on organizational functional practices such as HR and HRSQ. Practical implications In Indian SMEs, BD quality has a substantial effect on BD HR practices and HRSQ. However, these factors influence can constructively impact SMEs, if SMEs are open to organizational change, whereby they need to develop technical skills and competencies of the HR professionals. Originality/value Though BD research works have shown exponential growth in recent times, scholarly empirical research investigating BD’s impact upon human resource management (HRM) is scarce. The present study appraises extant literature on BD in HRM.

[Purpose / Significance] In the era of big data, this paper discusses the application direction of the new technology of block chain in university libraries, analyzes how this technology can solve the predicament of the current digital resource construction in university libraries, and promotes the construction of university library ecological system in the new era. [Method / Process] Through literature research, this paper analyzes the difficult problems in the construction of digital resources in university libraries at present, and discusses the feasibility of its application in the construction of digital resources in university libraries from the technical characteristics of block chains and application cases. [Results / Conclusions] Block chain technology has the advantages of decentralization, intelligent contract and asymmetric encryption, and can be applied to various stages in digital resource construction such as data collection, data processing, and data service, in the premise of ensuring data security and credibility to achieve user participation, improve service efficiency. Block chain technology can solve many difficult problems in the construction of digital resources and become a new driving force for university libraries.

The development of information technology expands the spatial and temporal scale and types of elements of the water resources information, making the water resources data show the characteristics of multi-source, heterogeneous, massive, and the traditional data processing method is difficult for fine processing and dynamic analysis. Combined with the \"4v\" characteristics of big data, we put forward a framework for processing water resources big data, to process and analyze modern water resources data for real-time and rapid, and discuss the related application. Based on the features of modern water resources data, this paper discusses the characteristics and application technology of big data, and briefly describes a framework for processing water resources big data and application.