Major public health incidents such as COVID-19 typically have characteristics of being sudden, uncertain, and hazardous. If a government can effectively accumulate big data from various sources and use appropriate analytical methods, it may quickly respond to achieve optimal public health decisions, thereby ameliorating negative impacts from a public health incident and more quickly restoring normality. Although there are many reports and studies examining how to use big data for epidemic prevention, there is still a lack of an effective review and framework of the application of big data in the fight against major public health incidents such as COVID-19, which would be a helpful reference for governments. This paper provides clear information on the characteristics of COVID-19, as well as key big data resources, big data for the visualization of pandemic prevention and control, close contact screening, online public opinion monitoring, virus host analysis, and pandemic forecast evaluation. A framework is provided as a multidimensional reference for the effective use of big data analytics technology to prevent and control epidemics (or pandemics). The challenges and suggestions with respect to applying big data for fighting COVID-19 are also discussed.

Here we show a heterogeneous energy landscape approach to describing the Kohlrausch-Williams-Watts (KWW) relaxation function. For a homogeneous dynamic process, the distribution of free energy landscape is first proposed, revealing the significance of rugged fluctuations. In view of the heterogeneous relaxation given in two dynamic phases and the transmission coefficient in a rate process, we obtain a general characteristic relaxation time distribution equation for the KWW function in a closed, analytic form. Analyses of numerical computation show excellent accuracy, both in time and frequency domains, in the convergent performance of the heterogeneous energy landscape expression and shunning the catastrophic truncations reported in the previous work. The stretched exponential β, closely associated to temperature and apparent correlation with one dynamic phase, reveals a threshold value of 1/2 defining different behavior of the probability density functions. Our work may contribute, for example, to in-depth comprehension of the dynamic mechanism of glass transition, which cannot be provided by existing approaches.

While researchers from many disciplines are increasingly interested in studying issues related to sustainability, few studies have presented a holistic view of sustainability from the perspectives of business and management. This bibliometric study quantitatively analyzed a big data set of 30 years of sustainability research (1990–2019), consisting of 37,322 publications and 1,199,398 cited references, visualizing major topics, dynamic evolution, and emerging development. The decade-by-decade in-depth analysis shows a clear shift from a nearly exclusive focus on economic growth and consumption to all three pillars of sustainability, i.e., economic growth, social development, and environmental protection. Highlighting the differences between United Nations’ Sustainable Development Goals and the popular research topics from academia, our analysis uncovers research gaps and suggests future research directions for sustainability researchers and practitioners.

Purpose: This study explores the factors influencing the value of enterprise social media (ESM) from the perspective of compatibility. Establishing a theoretical model based on compatibility theory, the authors examine the effects of two dimensions of compatibility and the mediating effects of employees' intrinsic motivations. ESM is an important tool that helps companies to enhance knowledge sharing and cross-department collaboration. Thus, it is important to understand factors that can facilitate the role of ESM in improving enterprise operating performance.Design/methodology/approach: The authors conducted a survey among 353 users of a leading ESM platform and empirically investigated how compatibility influences ESM value through employees' intrinsic motivations. Structural equation modeling (SEM) was applied to study the relationship among compatibility, employees' intrinsic motivations and ESM value.Findings: The empirical research results indicate that compatibility of self-interest with group interest influences the value of ESM, and intrinsic motivations toward collaboration and toward knowledge management partially mediate the effects of the two dimensions of compatibility on ESM value.Research limitations/implications: First, the empirical analysis relies on data from surveying employees of Chinese companies. Therefore, one direction for future research is to reexamine the model using data from other countries. Second, the effects of compatibility identified in the study may vary among different ESM platforms. In addition, the findings may change for organizations having different sizes.Practical implications: This finding suggests that managers should pay close attention to potential conflicts of interest when implementing ESM to enhance group communication and collaboration. This study also highlights the importance of compatibility of new working processes with experience in practice. In addition, intrinsic motivations towards both cooperation and knowledge management in ESM are important factors influencing the value creation of ESM. Therefore, to cultivate employees' intrinsic motivation, managers and organizations need to facilitate the formation of a collaborative atmosphere and habits of cooperative adoption.Originality/value: Although previous studies show that compatibility is a strong belief salient to technology acceptance and continuance usage behavior, the operational definition of compatibility developed by prior studies has generally been limited to the technology perspective and the individual level. However, the primary benefit of ESM is enabling online team collaboration and knowledge sharing across various departments. Thus, the level of compatibility between employees' self-interests and group interests may influence their intrinsic motivations toward ESM usage. From this perspective of individual–group interest conflicts, the authors propose a conceptual research model based on the theory of compatibility in innovation diffusion.

Purpose: Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.Design/methodology/approach: As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.Findings: The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.Practical implications: With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.Originality/value: To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

Background: Online review system contains multiple components, such as ratings, review text, product pictures, and video uploads, that could affect consumer loyalty. However, how the affordance of such components influences perceptions and behaviors of consumers remains unclear. We extend stimulus-organism-response (S-O-R) theory to the online review system. Specifically, we combine affordance theory and the technology acceptance model (TAM) to investigate the relations among the affordance of review systems, consumers’ perceived beliefs, and their loyalty. Methods: We surveyed 320 customers on their online shopping experiences in China. We tested our hypotheses using the partial least squares path structural equation modeling (PLS-SEM) method. We report the direct effect of affordances of review components on consumer loyalty and its indirect effects on consumer loyalty through perceived beliefs. Results: Our results show that integrity and social interaction affordance of review components have significant relations with perceived ease of use, perceived usefulness, and perceived enjoyment. Intelligent topic mining reveals a positive relation on perceived enjoyment. Operability has a positive relation with perceived ease of use and perceived usefulness. These three consumer-perceived beliefs can mediate, to different degrees, the relationship between affordance of review components and consumer loyalty. Conclusion: This research takes an innovative approach to offer insights into the relationships between IT affordances and consumer perceptions. We examine S-O-R theory through the lens of information technology and extend S-O-R theory by integrating IT affordances. Our research findings pave the way for businesses to design and implement more effective online review systems.