This paper presents an analysis of the literature concerning the impact of corporate sustainability on corporate financial performance. The relationship between corporate sustainable practices and financial performance has received growing attention in research, yet a consensus remains elusive. This paper identifies developing trends and the issues that hinder conclusive consensus on that relationship. We used content analysis to examine the literature and establish the current state of research. A total of 132 papers from top-tier journals are shortlisted. We find that 78% of publications report a positive relationship between corporate sustainability and financial performance. Variations in research methodology and measurement of variables lead to the divergent views on the relationship. Furthermore, literature is slowly replacing total sustainability with narrower corporate social responsibility (CSR), which is dominated by the social dimension of sustainability, while encompassing little to nothing of environmental and economic dimensions. Studies from developing countries remain scarce. More research is needed to facilitate convergence in the understanding of the relationship between corporate sustainable practices and financial performance.

Sustainability practices in a working environment represent superior quality performances, while risks remain to be a challenge. Our study’s primary purpose is to deploy the bibliometric method to analyze the related literature. Bibliometric parameters analyzed using the VOSviewer software were employed to identify citations relevant to sustainability and risk contexts’ critical themes. From 1990–2020, a reflection of 1233 documents appeared in Scopus on sustainability practices and risk management. We searched the current papers, authors, institutes, and keywords on VOSviewer. The bibliometric search provided us an understanding, which reflected that the collected works on literature of sustainability and risk factors, in general, is suggestively increasing. Mainly, in our report, we highlighted six major streams, related to topics such as the moral responsibilities and sustainability development, blockchain technology and minimization of risks, social sustainability and supply chain, environmental impacts, safety engineering and risk identification, and optimization and sustainability practices. The primary purpose of using streams was to cite the key authors and their contributions to the related literature. This bibliometric analysis was developed to obtain further understanding regarding the importance of sustainability to the individual, firms, and the entire economy. Moreover, the factors associated with risk also sought to be examined to prevent or at least minimize its negative impact. It was identified in this paper that sustainability remains an issue in the global perspective that has been challenging the individual and/or the organization’s point of views. Risk factors were also identified as inevitable; hence, everyone must be socially responsible to minimize the negative impact on the economy.

Currently, the Distributed Denial of Service (DDoS) attack has become rampant, and shows up in various shapes and patterns, therefore it is not easy to detect and solve with previous solutions. Classification algorithms have been used in many studies and have aimed to detect and solve the DDoS attack. DDoS attacks are performed easily by using the weaknesses of networks and by generating requests for services for software. Real-time detection of DDoS attacks is difficult to detect and mitigate, but this solution holds significant value as these attacks can cause big issues. This paper addresses the prediction of application layer DDoS attacks in real-time with different machine learning models. We applied the two machine learning approaches Random Forest (RF) and Multi-Layer Perceptron (MLP) through the Scikit ML library and big data framework Spark ML library for the detection of Denial of Service (DoS) attacks. In addition to the detection of DoS attacks, we optimized the performance of the models by minimizing the prediction time as compared with other existing approaches using big data framework (Spark ML). We achieved a mean accuracy of 99.5% of the models both with and without big data approaches. However, in training and testing time, the big data approach outperforms the non-big data approach due to that the Spark computations in memory are in a distributed manner. The minimum average training and testing time in minutes was 14.08 and 0.04, respectively. Using a big data tool (Apache Spark), the maximum intermediate training and testing time in minutes was 34.11 and 0.46, respectively, using a non-big data approach. We also achieved these results using the big data approach. We can detect an attack in real-time in few milliseconds.

Purpose: The purpose of this paper is to investigate the relation between a firm's cash conversion cycle and its profitability.Design/methodology/approach: The relation between the firm's cash conversion cycle and its profitability is examined using dynamic panel data analysis for a sample of Japanese firms for the period from 1990 to 2004. The analysis is applied at the levels of the full sample and divisions of the sample by industry and by size.Findings: A strong negative relation between the length of the firm's cash conversion cycle and its profitability is found in all of the authors’ study samples except for consumer goods companies and services companies.Originality/value: Traditional focus in corporate finance was on the long‐term financial decisions, particularly capital structure, dividends, and company valuation decisions. However, the recent trend in corporate finance is the focus on working capital management. Most of working capital management literature is based on the US experience. This study investigates the relation between the firm's cash conversion cycle and its profitability of Japanese firms where the organizational structure is totally different from that of the US firms; most of the Japanese firms are interconnected and related through corporate groups (keiretsu).

Through recent progress, the forms of modern supply chains have evolved into complex networks. The supply chain management systems face a variety of challenges. These include lack of visibility of the upstream party (Provider) to the downstream party (Client); lack of flexibility in the face of sudden variations in demand and control of operating costs; lack of reliance on safety stakeholders; ineffective management of supply chain risks. Blockchain (BC) is used in the supply chain to overcome the growing demands for items. The Internet of Things (IoT) is a profoundly encouraging innovation that can help companies observe, track, and monitor products, activities, and processes within their respective value chain networks. Research establishments and logical gatherings are ceaselessly attempting to answer IoT gadgets in supply chain management. This paper presents orderly writing on and reviewing of Blockchain-based IoT advances and their current usage. We discuss the smart devices used in this system and which device is the most appropriate in the supply chain. This paper also looks at future examination themes in blockchain-based IoT, referred to as the executive’s framework production network. The essential deliberate writing audit has been consolidated by surveying research articles circulated in highly reputable publications between 2016 and 2021. Lastly, current issues and challenges are present to provide researchers with promising future directions in IoT supply chain management systems.