Rapid growth in end-of-use and end-of-life products and their current unsustainable disposal methods are the cause of worries among the researchers and academicians across the globe. For that, remanufacturing is emerging as a viable option for sustainable production. Moreover, acceptance of remanufactured products by the consumers is highly essential for the success of the circular economy. Therefore, a study of the purchase intention of the consumers towards remanufactured products becomes inevitable. Here, our research uses meta-analysis to statistically synthesise and analyze the factors relevant to the purchase intention of the remanufactured products. Based on the published literature a total of ten studies are found suitable for the meta-analysis. The result shows that purchase intention of consumers is positively and strongly influenced by attitude and subjective norm, whereas it is moderately influenced by perceived green benefits and perceived behaviour control. In addition, meta-analysis supports the negative relationship between perceived risk and purchase intention. The findings also suggest that consumers who purchase remanufactured electrical and electronics products are less environmentally conscious than consumers who purchase remanufactured automotive products. Further, authors have discussed the managerial implications of the factors which influence the purchase intention of the consumers towards remanufactured products.

Purpose: Acceptance of remanufactured products by the consumers is highly essential for the success of closed loop supply chain and for achieving the goal of circular economy. However, the literature shows that consumers are reluctant to purchase remanufactured products. Therefore, the study of attitude and purchase intention (PI) of the consumers toward remanufactured products becomes inevitable for popularizing these products. The paper aims to discuss this issue.Design/methodology/approach: This research proposes a conceptual model to examine the critical factors influencing the PI of Indian consumers toward remanufactured products. Further, this model is empirically tested, using structural equation modeling technique, based on the data obtained from 1,534 respondents.Findings: The findings of this research suggest that PI of consumers is influenced by attitude, personal benefits, remanufactured product knowledge, risk perception, subjective norm and market strategy. However, perceived behavior control and green awareness have a non-significant impact on the PI of Indian consumers.Research limitations/implications: The proposed conceptual model is tested only against the data received from the students of Indian universities who possess electronic gadgets.Practical implications: The circular economy can be realized through remanufacturing if the attitude of consumers is shaped positively toward remanufactured products through the dissemination of comprehensive product information.Originality/value: This research is the first attempt to assess the PI of Indian consumers by developing and testing the conceptual model. Further, this research provides guidelines to remanufacturing firms for attracting the consumers toward the purchase of remanufactured products.

The incorporation of artificial intelligence (AI) into the healthcare system has been revolutionized, promising key advancements in diagnosis, treatment, patient care, administrative tasks, and operational efficiency. Using an in-depth analysis of the extensive amount of research on artificial intelligence and how it could help the medical industry, this study identified eleven barriers and challenges. Interpretive structural modeling (ISM) was used as a methodological approach to determine the relationship between the difficulties extracted and their dependency and driving powers. It resulted in a five-tiered model, with the introduction of innovative and new-generation tools topping the chart as the most dependent challenge. Similarly, Insufficient Data, Data Acquisition, Data Misuse, and Missing Compassion were the key drivers. Therefore, during the implementation of artificial intelligence in medicine, these challenges should be considered. Although artificial intelligence (AI) possesses the groundbreaking power to enhance patient care and operational efficiency in the healthcare sector, there are several key problems that must be addressed for implementation to be fruitful. The order of these challenges was ascertained through interpretive structural modeling, underlining the significance of innovation and data-related issues. Health systems can optimize AI’s benefits and enhance diagnosis, patient care, and overall hospital management by aggressively eliminating its deterrents.

Electronic waste (e-waste) in India is increasing rapidly which may cause severe health hazard and environment problems. Remanufacturing seems to be one of the method which may lead to sustainable production. The concept of remanufacturing is well recognized and practiced in the countries like UK,USA and Japan. In India it is still in nascent stage. This provide the opportunities for researchers to analyze the factors which may influence the remanufacturing in India. A hybrid model based on SWOT analysis and AHP is formed to identify the critical and most critical factors affecting remanufacturing of electrical and electronics products (EEP) in India after surveying the EEP companies and taking the inputs from experts.

Background: Our day-to-day commodities truly depend on the industrial sector, which is expanding at a rapid rate along with the growing population. The production of goods needs to be accurate and rapid. Thus, for the present research, we have incorporated machine-learning (ML) technology in the manufacturing sector (MS). Methods: Through an inclusive study, we identify 11 factors within the research background that could be seen as holding significance for machine learning in the manufacturing sector. An interpretive structural modeling (ISM) method is used, and inputs from experts are applied to establish the relationships. Results: The findings from the ISM model show the ‘order fulfillment factor as the long-term focus and the ‘market demand’ factor as the short-term focus. The results indicate the critical factors that impact the development of machine learning in the manufacturing sector. Conclusions: Our research contributes to the manufacturing sector which aims to incorporate machine learning. Using the ISM model, industries can directly point out their oddities and improve on them for better performance.