Big data analytics (BDA) enhances knowledge and decision-making. Despite its importance, the connection between technical progress and political change is neglected in the administrative process. Most studies focus on e-government, e-governance, and how technology can improve existing operations of the bureaucracy. However, this article aims to explore the potential of BDA for public policy systems and provide a linkage for the transformation toward digital and smart governance using preferred reported items for systematic review and meta-analysis (PRISMA) approach to reveal the relevant documents and narrative review approach to interpret the application of BDA at each step of the public policy system. In addition, this study identifies several common public policy-related big data sources and techniques that could be used at the various stages of the public policy process. This study argues that BDA has the potential to be used for policy formulation in the four main phases—planning, design, service delivery, and evaluation. Most studies confirm its potential in the policy process for taxation, health, education, transportation, law, economy, and social system. This study reveals that it is also suitable for public policy execution stages, such as public supervision, public regulation, service delivery, and policy feedback. Previous studies have indicated that the application of BDA can transform traditional or manual governance systems into digital and smart governance. We contend that the policy cycle should be seen as a dynamic and iterative process characterized by continuous evolution. Though each step of transformation has its unique challenges in handling BDA and maintaining the Information and Communication Technology (ICT) infrastructure, it can ensure an accurate, prompt, and context-oriented public policy system. These insights provide a novel outlook on effectively managing the interplay between innovation and traditional approaches in the realm of public policy development.

It is a well-known fact that diabetic retinopathy (DR) is one of the most common causes of visual impairment between the ages of 25 and 74 around the globe. Diabetes is caused by persistently high blood glucose levels, which leads to blood vessel aggravations and vision loss. Early diagnosis can minimise the risk of proliferated diabetic retinopathy, which is the advanced level of this disease, and having higher risk of severe impairment. Therefore, it becomes important to classify DR stages. To this effect, this paper presents a weighted fusion deep learning network (WFDLN) to automatically extract features and classify DR stages from fundus scans. The proposed framework aims to treat the issue of low quality and identify retinopathy symptoms in fundus images. Two channels of fundus images, namely, the contrast-limited adaptive histogram equalization (CLAHE) fundus images and the contrast-enhanced canny edge detection (CECED) fundus images are processed by WFDLN. Fundus-related features of CLAHE images are extracted by fine-tuned Inception V3, whereas the features of CECED fundus images are extracted using fine-tuned VGG-16. Both channels’ outputs are merged in a weighted approach, and softmax classification is used to determine the final recognition result. Experimental results show that the proposed network can identify the DR stages with high accuracy. The proposed method tested on the Messidor dataset reports an accuracy level of 98.5%, sensitivity of 98.9%, and specificity of 98.0%, whereas on the Kaggle dataset, the proposed model reports an accuracy level of 98.0%, sensitivity of 98.7%, and specificity of 97.8%. Compared with other models, our proposed network achieves comparable performance.

Bigdata is a potential instrument to transform traditional governance into smart governance. There are a long debate and discussion on the application of big data for the transformation of traditional public administration to modern and smart public administration in the academician, researchers, and policymakers. This study aims to explore the suitability and applicability of big data for smart governance of public agencies. A systematic review of literature and meta-analysis method is employed with various levels of scales and indicators. Literature survey shows that a number of models have been developed to explain smart governance but systematic research on the suitability and applicability of big data for smart governance of public agencies is still lacking. This article argues that the application of big data for smart governance in the public sector can increase the efficiency of the public agencies fastest public service delivery, enhancing transparency, reducing public hassle and helping to the become a smart agency. This paper further argues that implementation of big data for smart governance has a significant role in timely, error-free, appropriate and cost-effective service delivery to citizens which leads to the sustainable economic development of a country. The findings suggest that every public-sector agency should be brought under smart governance which should be a fully promoted under big data technologies for easy access, transparent and accountable, and hassle-free public agencies.

The outbreak of COVID-19 has exerted an enormous impact on society, enterprises, and individuals. It has affected the work attitudes and psychology of employees to a certain extent and their job stress (JS) has also augmented accordingly, leading to increased turnover intention (TI). With the survey responses of 720 employees of small and medium enterprises (SMEs) in China as the sample, we studied the impact of COVID-19 related JS and TI with the moderating effect of perceived organizational support (POS). We utilized linear and multiple regression analysis using Windows SPSS 25. The research findings indicated that the JS caused by COVID-19 in the first affected region (Hubei) was significantly stronger than that in other regions (non-Hubei). JS had a significant positive relationship with employees’ TI, while POS had a significant negative connection with employees’ TI. We also identified that POS weakened the positive association between JS and employees’ TI. These findings are expected to be conducive to and conductive for the upcoming theoretical and empirical investigations as the founding guidelines, as well as for managers in formulating effective policies to curb JS, which would ultimately be helpful in reducing TI.