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Industrial Revolution 4.0 prompts organizations to adopt emerging technologies, and the healthcare industry is no different from them. Further, it is important to adopt new edge technologies to improve services and the well-being of patients. This research synthesizes the work most influenced by this technology and the trends and usage of Industry 4.0 technologies in the healthcare sector.

The study has used the Scopus and Web of Science databases to retrieve articles published in healthcare and Industry 4.0 for bibliometric analysis. Specifically, Bibliometrix (R-package) and VOSviewer were used to analyze data related to authors, sources, keywords and content analysis.

The study found increased research trends in Industry 4.0 and healthcare in recent years. The USA, India and China are top contributors in this field, showing research progress in developed and developing economies. Dwivedi Y and Kumar A. were top researchers in the field. The finding also reveals that predictive analytics, deep technology and sustainable development are emerging areas for healthcare where Industry 4.0 can play a crucial role.

Using Industry 4.0 technologies can help the company improve its services, operational efficiency and patient care.

The study explored the trends in the healthcare sector for using Industry 4.0 technologies through bibliometric analysis.

A novel phenomenon in product and service promotions, social coupons facilitate group buying at lower prices, bringing key benefits to customers, merchants and coupon aggregators. This study maps the evolution and innovations in social couponing, identifies knowledge gaps in the domain and sets the future research agenda.

Through a detailed systematic literature review and bibliometric analysis, this study maps the evolution of social coupons over time. The analysis examines social coupon research by studying research outputs by authors, institutes, countries and research themes. It also explores how the social couponing phenomenon has benefited the three key stakeholders: customers, merchants and coupon aggregators.

An innovation in couponing, social coupons are discount coupons that feature group buying, pre-purchase and daily deals. Based on the extensive review of extant literature, the study proposes a conceptual model for the social couponing process. The study also provides inputs for future research on social coupons and delineates their academic and managerial implications.

This study makes a pioneering endeavor to comprehensively map the knowledge structure of social coupons from multiple dimensions.

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.