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Identifying and managing supply chain risk is crucial for the competitiveness of a company. However, research focused on the risks of supply chain operations in Brazil is scarce. The purpose of this study is to analyze and assess the risk of cargo theft in the country. The methodology adopted is deductive and based on an analysis of historical data from January 2015 to November 2017, aiming to evaluate risk based on probability and impact. The findings unveil a scenario of criminality of transporting goods in Brazil, where the use of force, violence, and threats to steal goods is most likely to occur en route or when parked in key locations on the way to the distribution center. On the other hand, the higher impact cargo crimes are concentrated en route to the customer. This chapter provides a better understanding of the risks of transporting goods by road in Brazil and contributes to a more efficient supply chain design by identifying the risks and assessing the primary locations of the crimes along with their modi operandi and the period of the day during which the crime occurs.

Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

Predictive analytics is the science of decision-making that eliminates guesswork out of the decision-making process and applies proven scientific procedures to find right solutions. Predictive analytics provides ideas on the occurrences of future downtimes and rejections thereby aids in taking preventive actions before abnormalities occur. Considering these advantages, predictive analytics is adopted in various diverse fields such as health care, finance, education, marketing, automotive, etc. Predictive analytics tools can be used to predict various behaviors and patterns, thereby saving the time and money of its users. Many open-source predictive analysis tools namely R, scikit-learn, Konstanz Information Miner (KNIME), Orange, RapidMiner, Waikato Environment for Knowledge Analysis (WEKA), etc. are freely available for the users. This chapter aims to reveal the best accurate tools and techniques for the classification task that aid in decision-making. Our experimental results show that no specific tool provides the best results in all scenarios; rather it depends upon the datasets and the classifier.

Financial assurance rules, also known as financial responsibility or bonding requirements, foster cost internalization by requiring potential polluters to demonstrate the financial resources necessary to compensate for environmental damage that may arise in the future. Accordingly, assurance is an important complement to liability rules, restoration obligations, and other regulatory compliance requirements. The paper reviews the need for assurance, given the prevalence of abandoned environmental obligations, and assesses the implementation of assurance rules in the United States. From the standpoint of both legal effectiveness and economic efficiency, assurance rules can be improved. On the whole, however, cost recovery, deterrence, and enforcement are significantly improved by the presence of existing assurance regulations.

Considering the dearth of industry-specific empirical research exploring sustainability reporting in the context of developing countries, this chapter aims to critically examine the extent and the nature of sustainability information disclosure of environmentally polluting industries in India.

Data are collected from business responsibility reports (BRRs), sustainability reports, Corporate Social Responsibility (CSR) reports and integrated reports of all 57 energy and mining companies included in NIFTY500 Index at National Stock Exchange of India for the year 2017–2018 and 2018–2019. Content analysis is used to examine the sustainability disclosure practices and one-way analysis of variance (ANOVA) statistical analysis is performed to test the difference across various dimensions of sustainability reporting of companies.

The results indicate low environmental reporting of the key indicators by energy and mining companies in India. It is found that state-owned companies have better social reporting practices against private sector companies. The findings also indicate that Global reporting initiative (GRI) based reporting have better sustainability disclosure practices and companies reporting based on BRR lack quantitative information disclosure.

The findings of the present chapter have several implications for policymakers, investors, regulators and management of these high environmental and social impact companies in India. The findings which coincide with the key areas of sustainability disclosure can be used for improving sustainability disclosure practices by the various stakeholders.

This is one of the first studies to investigate the nature and extent of sustainability performance disclosure of the companies from polluting industries in India. This chapter also contributes to the existing sustainability reporting literature by providing evidence on industry-specific disclosure in the context of a developing country.

This chapter presents an overview of the Bitcoin and its impacts on the environment and economics from the viewpoint of carrying out a systematic analysis of the literature related to the environmental and economic effect of digital currency. It is aimed to summarize and critically examine the points of view regarding Bitcoin mining, considering its effects on global warming and the social environment, employing peer-reviewed data associated through literatures. As a result, this study provides the chance to analyze the set of knowledge regarding the effects of the Bitcoin mining procedure on the ecosystem in regard to energy use and CO₂ emissions regarding unit root tests and causality test based on nonlinear models. The results show that there exists a nonlinear causal relationship between statistics on Bitcoin mining and the CO₂ emissions. The results also imply that Bitcoin remains to be a tool utilized in the economic environment for a range of objectives despite high energy consumption and some negative environmental impact within the scope of renewable energy; hence, authorities would take Bitcoin mining impacts into account to reduce CO₂ emissions.

Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.

Numerous research questions in e-HRM research are directly related to the usage of diverse information systems by HR professionals, line managers, employees, and/or applicants. Since they are regularly based on Internet technologies, information systems in e-HRM automatically store detailed usage data in log files of web servers. Subsumed as “web mining,” such data are frequently used as inputs for innovative data analysis in e-commerce practice. Though also promising in empirical e-HRM research, web mining is neither discussed nor applied in this area at present. Our chapter therefore aims at a methodological evaluation of web mining as an e-HRM research approach. After introducing web mining as a possible approach in e-HRM research, we examine its applicability by discussing available data, feasible methods, coverable topics, and confirmable privacy. Subsequently, we classify the approach methodologically by examining major issues. Our evaluation reveals that “web mining” constitutes a promising additional research approach that enables research to answer numerous relevant questions related to the actual usage of information systems in e-HRM.