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Today, information systems and technology provides a wide set of tools for companies to increase the efficiency of their businesses. Although technology offers many benefits to businesses, it also brings risks as the information systems security breaches. Security breaches and their financial impact is a constant concern of the researchers and practitioners. This paper explores information systems breaches and their financial impacts on the publicly traded companies in different sectors.

After a comprehensive data collection process, data from 192 events are analyzed by employing Event Study Methodology and a comparison of the results between the four highly affected sectors (Consumer Goods, Technology, Financial and Communications) is presented. The abnormal returns on the prices of stocks after the events are calculated with the Market Model. Also, the results of the Market Adjusted Model and Mean Adjusted Model are presented to support the results.

While information systems security breaches have a significant negative impact on the Financials and the Technology sectors for all the event windows in the study ([−5, 0], [−5, 1], [−5, 5], and [−5, 10]), the significant negative impact is observed only on the [−5, 5] and [−5, 10] event windows for the Consumer Goods sector. No significant negative impact is observed in the Communications sector, in fact, the cumulative abnormal returns are positive for this sector.

The contribution of this paper to provide evidence about the financial impacts of the information systems breaches for businesses in different sectors. While there are studies that have previously focused on the information systems breaches and their financial impacts on businesses, to the best of our knowledge, this is the first study that compares this effect between the four highly impacted sectors. With a relatively larger sample size and broader event windows than the past studies in the literature, statistical evidence is provided to managers to justify their investments in information security and build preventive measures to secure the market value of their firms.

This study aims to propose a novel framework for barriers to circularity within cooperative supply chains. The barriers in the adoption and implementation of circular economy principles are examined within a framework.

Fuzzy best-worst method is used to calculate the weights of barriers and identify the prioritization of barriers to circularity within cooperative supply chain.

“Insufficient implementation of circular economy laws” was found as the most important barrier, followed by “Lack of information”, “Ineffective recycling policies”, “Lack of awareness for circular economy”, “Remanufacturing is a labor-intensive procedure”, “Inconsistent price policies in sources and products”, “Lack of environmental management system”, “Cost of implementation for green activities” and “Lack of R&D capability” barriers, respectively.

The number of participant professionals limits the conclusions of the study and reaching more general conclusions. A comprehensive research can be conducted by the participation of a greater number of professionals.

Several studies analyzed the barriers to circularity; however, to the best of the authors' knowledge, no study has been taken an approach for barriers to circularity for cooperatives or cooperative supply chains.

The main aim of this study is to understand the role of smart technologies and show the rankings of various smart technologies in collection and classification of electronic waste (e-waste).

This study presents a framework integrating the concepts of collection and classification mechanisms and smart technologies. The criteria set includes three main, which are economic, social and environmental criteria, including a total of 15 subcriteria. Smart technologies identified in this study were robotics, multiagent systems, autonomous tools, smart vehicles, data-driven technologies, Internet of things (IOT), cloud computing and big data analytics. The weights of all criteria were found using fuzzy analytic network process (ANP), and the scores of smart technologies which were useful for collection and classification of e-waste were calculated using fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR).

The most important criterion was found as collection cost, followed by pollution prevention and control, storage/holding cost and greenhouse gas emissions in collection and classification of e-waste. Autonomous tools were found as the best smart technology for collection and classification of e-waste, followed by robotics and smart vehicles.

The originality of the study is to propose a framework, which integrates the collection and classification of e-waste and smart technologies.

Performance assessment of layouts requires a systematic approach because of its multi-objective nature. The purpose of this paper is to propose a framework to the performance assessment of layout designs.

A layout performance assessment framework is proposed, grounded on a literature review. Then, the causal relationships and prioritization of the sub-criteria are analyzed by fuzzy Decision Making Trial and Evaluation Laboratory technique in an elevator and escalator-manufacturing firm.

An integrated holistic performance assessment framework, specifically, the 7 criteria, 19 sub-criteria and 112 measures, are studied in this model which represents causal relationships and prioritization of sub-criteria.

The proposed framework can be generalized, because an integrative framework can be used in future empirical studies to analyze performance of layout design. However, the causal relationships and prioritization among sub-criteria are analyzed based on the needs and capabilities of the individual company; therefore, the results of the causal relationships are company specific.

With this framework, the companies may assess their current layout’s performance, may analyze causal relationships and prioritization of sub-criteria.

There are very few models or frameworks regarding the performance assessment of layout designs. In this paper, a new conceptual holistic framework was proposed as three-dimensional hierarchy, which includes the main criteria, sub-criteria and the measures, respectively. Cost, flow, flexibility, surrounding environment, environment quality, time and characteristics are identified as the main criteria for the layout design performance assessment. In addition, cause-effect relationships, which will be the base for improvement of the performance, are found.

This study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive maintenance. Opportunities and challenges in developing anomaly detection algorithms for predictive maintenance and unexplored areas in this context are also discussed.

For conducting a systematic review on the state-of-the-art algorithms in fault detection for predictive maintenance, review papers from the years 2017–2021 available in the Scopus database were selected. A total of 93 papers were chosen. They are classified under electrical and electronics, civil and constructions, automobile, production and mechanical. In addition to this, the paper provides a detailed discussion of various fault-detection algorithms that can be categorised under supervised, semi-supervised, unsupervised learning and traditional statistical method along with an analysis of various forms of anomalies prevalent across different sectors of industry.

Based on the literature reviewed, seven propositions with a focus on the following areas are presented: need for a uniform framework while scaling the number of sensors; the need for identification of erroneous parameters; why there is a need for new algorithms based on unsupervised and semi-supervised learning; the importance of ensemble learning and data fusion algorithms; the necessity of automatic fault diagnostic systems; concerns about multiple fault detection; and cost-effective fault detection. These propositions shed light on the unsolved issues of predictive maintenance using fault detection algorithms. A novel architecture based on the methodologies and propositions gives more clarity for the reader to further explore in this area.

Papers for this study were selected from the Scopus database for predictive maintenance in the field of fault detection. Review papers published in this area deal only with methods used to detect anomalies, whereas this paper attempts to establish a link between different industrial domains and the methods used in each industry that uses fault detection for predictive maintenance.