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Reverse logistics services are designed to move goods from their point of consumption to an endpoint to capture value or properly dispose of products and materials. Artificial intelligence (AI)-based reverse logistics will help Micro, Small, and medium Enterprises (MSMEs) adequately recycle and reuse the materials in the firms. This research aims to measure the adoption of AI-based reverse logistics to improve circular economy (CE) performance.

In this study, we proposed ten hypotheses using the theory of natural resource-based view and technology, organizational and environmental framework. Data are collected from 363 Indian MSMEs as they are the backbone of the Indian economy, and there is a need for digital transformation in MSMEs. A structural equation modeling approach is applied to analyze and test the hypothesis.

Nine of the ten proposed hypotheses were accepted, and one was rejected. The results revealed that the relative advantage (RA), trust (TR), top management support (TMS), environmental regulations, industry dynamism (ID), compatibility, technology readiness and government support (GS) positively relate to AI-based reverse logistics adoption. AI-based reverse logistics indicated a positive relationship with CE performance. For mediation analysis, the results revealed that RA, TR, TMS and technological readiness are complementary mediation. Still, GS, ID, organizational flexibility, environmental uncertainty and technical capability have no mediation.

The study contributed to the CE performance and AI-based reverse logistics literature. The study will help managers understand the importance of AI-based reverse logistics for improving the performance of the CE in MSMEs. This study will help firms reduce their carbon footprint and achieve sustainable development goals.

Few studies focused on CE performance, but none measured the adoption of AI-based reverse logistics to enhance MSMEs’ CE performance.

This study aims to evaluate the presence of abnormal returns due to stock splits or reverse stock splits in the Brazilian capital market context.

The event study technique was used on data from 518 events that occurred in a 30-year period (1987–2016), comprising 167 stock splits and 351 reverse stock splits.

The results revealed the occurrence of abnormal returns around the time the shares began trading stock splits or reverse stock splits at a statistical significance level of 5%. The main conclusion is that stock split and reverse stock split operations represent opportunities for extraordinary gains and may serve as a reference for investment strategies in the Brazilian stock market.

This study innovates by including reverse stock splits, as the existing literature focuses on stock splits, and by testing two distinct “zero” dates that of the ordinary general meeting that approved the share alteration and the “ex” date of the alteration, when the shares were effectively traded, reverse split or split.

This study aims to present a methodology and a system to support the technical and managerial issues involved in anomaly detection within the reverse logistics process of an e-commerce company.

A case study approach is used to document the company’s experience, with interviews of key stakeholders and integration of obtained evidence with secondary data.

The paper presents an algorithm and a system to support a more efficient and smart management of reverse logistics based on a set of anticipatory actions, and continuous and automatic monitoring of returned goods. Improvements are described in terms of a number of key performance indicators.

The analysis and the developed system need further applications and validations in other organizational contexts. However, the research presents a roadmap and a research agenda for the reverse logistics transformation in Industry 4.0, by also providing new insights to design a multidimensional performance dashboard for reverse logistics.

The paper describes a replicable experience and provides checklists for implementing similar initiatives in the domain of reverse logistics, in the aim to increase the company’s performance along four key complementary dimensions, i.e. time savings, accuracy, completeness of data analysis and interpretation and cost efficiency.

The main novelty of the study stays in carrying out a classification of anomalies by type and product category, with related causes, and in proposing operational recommendations, including process monitoring and control indicators that can be included to design a reverse logistics performance dashboard.

Many firms are placing greater emphasis on managing returned product. Reverse logistics programs are being used to recover assets that would otherwise be lost. Research results are presented covering reverse logistics programs in the electronics industry, specifically among firms selling through catalogs. The paper looks at how reverse logistics performance and satisfaction with reverse logistics service are influenced by industry, firm size/sales volume, and internal or external assignment of responsibility for disposition. The results are mixed. Performance is significantly impacted by sales volume, while industry effects significantly impact satisfaction. Neither performance nor satisfaction was significantly influenced by location of responsibility for disposition.

In reliability studies, interests in discrete failure data came relatively late in comparison to its continuous analogue. Also, discrete failure data arise in several common situations. So, in this paper the authors try to study some reliability concepts such as reversed variance and reversed mean residual life functions based on discrete lifetime random variable.

Supposed T be a non‐negative discrete random variable, then based on reversed residual random variable T_k^*=(k−T|T≤k), some useful and applicable relations and bounds are achieved.

In this paper, the authors study the reversed variance residual life in discrete lifetime distributions, the results of which are not similar to the continuous case. Its relationship with reversed mean residual life and reversed residual coefficient of variation are obtained. Also, its monotonicity and the associated ageing classes of distributions are discussed. Some characterization results of the class of increasing reversed variance residual life, which is denoted by IRVR, are presented and the upper bound for reversed variance residual life under some conditions is obtained.

There are many situations where a continuous time is inappropriate for describing the lifetime of devices and other systems. For example, the lifetime of many devices in industry, such as switches and mechanical tools, depends essentially on the number of times that they are turned on and off or the number of shocks they receive. In such cases, the time to failure is often more appropriately represented by the number of times they are used before they fail, which is a discrete random variable.

All the results based on discrete reversed residual lifetime, such as the relationships among reversed mean, variance and coefficient of variation residual lifetime and also their monotonicity ageing classes, are new.

The Supreme Court’s decision in Federal Trade Commission v. Actavis, Inc. is a challenge to conventional antitrust analysis. Conventional civil antitrust cases are decided by a preponderance of the evidence. This means that conduct challenged under the rule of reason is only condemned if the conduct resulted in more competitive harm in the actual world than a world without the alleged violation. Under conventional analysis, the intent of the parties also plays only a supporting role in determining whether the conduct was anticompetitive. A holder of a valid patent has a right to exclude others practicing the patented technology. And, the patent holder is not assumed to have market power because it expended resources in maintaining exclusionary rights. Actavis creates doubts about these propositions in circumstances beyond the “reverse” payment settlement of a patent suit that may have delayed an alleged infringer market entry. This chapter explores whether applying Actavis logic to antitrust litigation can result in condemnation of practices where there is little chance of an anticompetitive effect, where the patent holder likely has a valid and infringed patent, where there is little reason to believe that the patent holder has market power, and where only one party, or no parties, to an agreement have an anticompetitive intent. This chapter also investigates whether Actavis creates new problems with standing analysis, damages calculations, and the balancing of efficiencies against anticompetitive effects. Nevertheless, the lower courts have begun to extend the logic of Actavis. This is apparent in the condemnation of no-Authorized-generic settlements.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

This chapter presents digital reverse mentoring as a novel kind of human and technological resource management intervention. It presents a case study of digital reverse mentoring at a large metal multinational. It highlights the various design elements of digital reverse mentoring that contribute towards achieving digital transformation and rebuilding of mindsets in the company. Through the case study the chapter also suggests that HRM needs to look beyond adoption of technological tools to actively participate in addressing the strategic concerns of digital transformation in a company.

Overconsumption of resources has become a global issue. To deal with resource depletion and mitigate these impending crises, the circular economy (CE) holds some promise. A wide range of performance measurements for CE have emerged over the years. However, with increasing complexity of supply chains, appropriate and potentially new performance measurements are needed for effective CE management. Blockchain is an innovative technology that may advance CE development. This chapter provides an overview of the potential linkages between blockchain technology and CE from sustainability perspectives – the specific focus will be on the performance measurement of reverse logistics activities. One of the main findings indicates that both blockchain and CE performance measurements – especially reverse logistics processes – are still evolving in both theory and practical developments. Future directions with a critical analysis including research and theoretical applications will conclude this chapter.