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Healthcare wastes (HCWs) present substantial environmental and societal risks, including infection and exposure to hazardous substances. The aim of this study is to present a new multi-criteria decision-making (MCDM) method, named the ELECTOR method, for selecting the best healthcare waste disposal method (HCWDM) based on a comprehensive list of criteria. The main research question of this study is: What is the prioritization of HCWDMs considering economic, environmental, technical and social criteria?

This research employs a novel hybrid MCDM method to evaluate and select suitable HCWDMs. Initially, a comprehensive set of criteria for assessing and prioritizing HCWDMs is established. Criteria weights are determined using the best-worst method. Subsequently, a hybrid MCDM method is introduced to rank the HCWDMs. Fuzzy numbers are applied to handle qualitative criteria uncertainties. The proposed method is applied to a real-world case study to prioritize HCWDMs.

A total of 24 criteria, including two novel criteria (“System process speed” and “System setup speed”), for evaluating and prioritizing the HCWDMs were identified from the literature review and case study analysis. The study showed that the key criteria influencing HCWDM selection were “Operation cost”, “Occupational hazards of human resources”, and “The impact of released substances on health”. Based on the results, the autoclave, encapsulation and hydroclave methods are identified as the most suitable HCWDMs for the studied case, respectively.

This study introduces a novel hybrid MCDM method tailored for HCWDM selection, enhancing the robustness of the decision-making. The inclusion of innovative criteria and the integration of fuzzy numbers to address qualitative ambiguities strengthen the originality of the findings. Specifically, introducing “System process speed” and “System setup speed” contributes to expanding the criteria landscape in HCWDM research.

This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.

The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.

Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.

While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.

This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

The rapid evolution of digital innovation has significantly revolutionized the business landscape for entrepreneurs. Embracing digital innovation is crucial for all stakeholders to achieve sustainable development goals (SDGs) and promote sustainability. However, there is little understanding of how entrepreneurial leadership in developing nations has proactively responded to the challenge of digital innovation. Based on Drucker’s productivity theory, this study examines the relationship between entrepreneurial leadership (EL), digital orientation (DO) and digital capability (DC) as predictors of digital innovation (DI). The proposed model aims to establish the causal connections between variables and elucidate the complex interplay between digital innovation and the resulting outcome of sustainable performance (SP).

Two research studies were carried out in the Chinese IT industry to assess the efficacy of the theoretical framework among IT workers. Study 1 utilized a three-week, two-week time-lagged design (N = 299), while Study 2 used a two-week, four-week survey design (N = 341). The study used Smart-PLS 4.0 for data analysis.

The results showed that entrepreneurial leadership significantly impacts employee digital orientation and digital capabilities, fostering digital innovation. Moreover, digital innovation has a significant impact on sustainable performance.

The study’s findings allow authors to contribute to the existing scholarship on employee digital orientation, digital capabilities, digital innovation and sustainable performance in an emerging economy.