Digital Economy and Health: A Case Study of a Leading Enterprise's Value Mining Mode in the Global Big Health Market

Guan, Jialin; Xu, Huaxi; Wang, Yunfeng; Ma, Yaoguang; Wang, Yu; Gao, Rui; Yu, Kexuan

doi:10.3389/fpubh.2022.904186

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Historically, big tech companies have made substantial contributions to healthcare, while the impact of digital healthcare ventures has often been underappreciated and overlooked. Recently, these ventures have seen a significant surge in growth, capturing the attention of policymakers, investors, entrepreneurs, and healthcare professionals alike ( 1 , 2 ). The COVID-19 pandemic acted as a catalyst for this transformation, highlighting the essential role of digital innovations in public healthcare ( 3 , 4 ).…”

Section: Introductionmentioning

confidence: 99%

Coopetition, exploration and exploitation capabilities, and growth performance in digital healthcare ventures

Liu,

Bai,

Leng

et al. 2024

Front. Public Health

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IntroductionStudies focusing on coopetition and dynamic capabilities have expanded significantly over the past several decades. Coopetition strategy and dynamic capabilities are increasingly recognised as sources of sustained competitive advantage. The purpose of this paper is to provide a better understanding of the factors driving growth performance in digital healthcare ventures by examining the role of coopetition, exploration and exploitation capabilities, and environmental uncertainty. While numerous studies have examined the competitive advantage of coopetition, its specific contribution to the growth of ventures in the digital realm remains less explored. Clarifying the strategic role of coopetition in driving growth performance is critical for delineating the intricate relationship between coopetition and growth performance, particularly in the context of digital healthcare ventures. To fill in this research gap, this study uses coopetition theory and dynamic capabilities theory to look at how exploration and exploitation capabilities, as well as environmental uncertainty, affect the relationship between coopetition and growth performance in digital healthcare ventures.MethodsWe collected a total of 338 questionnaires from Chinese digital healthcare ventures between March 2023 and August 2023. We conducted data analysis using SPSS 26.0 and its macro-program PROCESS.ResultsOur results confirm that coopetition has a positive effect on growth performance in digital healthcare ventures. Furthermore, exploration and exploitation capabilities fully mediate the relationship between coopetition and growth performance. Moreover, environmental uncertainty significantly and distinctively moderates the impact of exploration and exploitation capabilities on growth performance.DiscussionThis study contributes to the existing literature by providing deeper insight into the relationship between coopetition and growth performance in digital healthcare ventures. It also offers important practical implications for public health improvement and socio-economic development.

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Section: Introductionmentioning

confidence: 99%

Coopetition, exploration and exploitation capabilities, and growth performance in digital healthcare ventures

Liu,

Bai,

Leng

et al. 2024

Front. Public Health

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Predicting the epidemiological trend of acute hemorrhagic conjunctivitis in China using Bayesian structural time-series model

Xu,

Fan,

Zhao

et al. 2024

Sci Rep

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This study aims to explore the application value of the Bayesian Time Structure Sequence (BSTS) model in estimating the acute hemorrhagic conjunctivitis (AHC) epidemics. The reported AHC cases spanning from January 2011 to October 2022 in China were collated. Utilizing R software, the BSTS and Autoregressive Integrated Moving Average (ARIMA) models were constructed using the data from January 2011 to December 2021. The prediction effect of both models was compared using the data from January to October 2022, and finally the AHC incidence from November 2022 to December 2023 was predicted. The results indicated that forecast errors under the BSTS model were lower than those under the ARIMA model. The actual AHC incidence in July 2022 from the ARIMA model deviated from the 95% confidence interval (CI) of the predicted value. However, the observed AHC incidence from the BSTS model fell within the 95% CI of the predicted value. Notably, the BSTS model predicted 26,474 new AHC cases in China from November 2022 to December 2023, exhibiting better prediction performance compared to the ARIMA model. This indicates that the BSTS model possesses a high application value for forecasting the epidemic trends of AHC, making it a valuable tool for disease surveillance and prevention strategies.

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Predicting the epidemiological trend of acute hemorrhagic conjunctivitis in China using Bayesian structural time–series model

Xu,

Fan,

Zhao

et al. 2024

Preprint

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This study aims to understand the epidemic characteristics of acute hemorrhagic conjunctivitis (AHC) in China and to explore the application value of the Bayesian Time Structure Sequence (BSTS) model. The reported data of AHC cases in China were collected from January 2011 to October 2022. R software was used to construct the BSTS and the Differential Autoregressive Integrated Moving Average (ARIMA) models based on the AHC incidence data from January 2011 to December 2021. The prediction effect of both models was compared by using the data from January to October 2022, and finally the incidence of AHC in China from November 2022 to December 2023 was predicted by the BSTS model. The actual value of AHC incidence in July 2022 under the ARIMA model was not within 95% CI of the predicted value, and these under the BSTS model was within 95% CI of the predicted value. 26,474 new AHC cases were predicted using the BSTS model in China from November 2022 to December 2023. The prediction performance of the BSTS model was better than that of the ARIMA model, and it has a had high application value for the prediction of AHC epidemic trends.

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Digital Economy and Health: A Case Study of a Leading Enterprise's Value Mining Mode in the Global Big Health Market

Cited by 3 publications

References 35 publications

Coopetition, exploration and exploitation capabilities, and growth performance in digital healthcare ventures

Coopetition, exploration and exploitation capabilities, and growth performance in digital healthcare ventures

Predicting the epidemiological trend of acute hemorrhagic conjunctivitis in China using Bayesian structural time-series model

Predicting the epidemiological trend of acute hemorrhagic conjunctivitis in China using Bayesian structural time–series model

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