Amidst rivalry and intricate value networks the relationship between Supply Chain Management (SCM) and Total Quality Management (TQM) has emerged as a fundamental element of contemporary operational strategies and is essential for securing long-term competitive benefits. This bibliometric analysis methodically charts the framework and research directions within this crucial overlap. Drawing on a collection of 371 publications from the Scopus database, this study offers an in-depth summary of the discipline's progression from 1994, to 2025. The study indicates a developed research area that has seen a significant rise in scholarly attention with the number of publications increasing more than threefold since 2017. Major contributions are regionally clustered with the USA, India and China standing out as the leading contributors. A thematic keyword map uncovers the fields framework: 'Total Quality Management' and 'Supply Chain Management' serve as central driving themes propelling the research. These are underpinned by fundamental themes, like 'sustainability'. The analysis also indicates an evolution in terminology, with older concepts like 'just in time' now appearing as declining themes, superseded by more integrated frameworks. This survey serves as a valuable resource for researchers and practitioners by providing a data-driven landscape of the field's foundational pillars, dominant topics, and future research trajectories.

This research paper investigates the integration of Explainable Artificial Intelligence (XAI) into Predictive Maintenance (PdM) systems, aiming to enhance transparency, interpretability, and reliability in industrial applications. The primary contribution is the introduction of the Explainability Parameters (XPA) framework, which offers a structured methodology for evaluating and applying XAI in PdM. The study systematically reviews recent advancements and challenges in the literature, categorising explanations into pre-modelling, in-modelling, and post-modelling processes. It presents and analyses significant case studies across various industrial sectors to illustrate the practical implications and hurdles of XAI methodologies. Key findings indicate that while XAI significantly improves the effectiveness and trustworthiness of PdM by clarifying model predictions, its implementation is hindered by the complexity of industrial data and the absence of standardised evaluation methods. The XPA framework addresses these challenges by providing tailored metrics for specific applications and advocating for a multi-phase approach to convert technical outputs into actionable maintenance recommendations. The originality of this paper lies in its comprehensive review and the establishment of rigorous standards for assessing XAI methodologies, thereby bridging the gap between theoretical frameworks and practical applications. By promoting adaptable XAI frameworks that cater to real-world industrial needs, this study fosters trust in automated decision-making processes. It enhances the overall understanding of XAI's role in PdM.

The paper explores how social influence and consumer engagement affect the purchase intention of complementary and alternative medicine. The study aimed to investigate consumers of alternative medicine within Indonesia. The study provides practical insights into enhancing marketing strategies and strengthening consumer engagement with complementary and alternative medicine (CAM) by understanding these factors. A quantitative approach was used, involving a sample of 580 complementary and alternative medicine consumers in Indonesia. Participants were selected using purposive sampling criteria, which included individuals aged 17 years and older who had consumed CAM at least twice in the month. Data analysis was conducted using SmartPLS4 as the analytical tool. These insights offer a deeper understanding of how consumers behave in relation to alternative medicine, which can assist healthcare professionals in formulating more effective, targeted approaches to encourage the adoption of alternative medicinal practices.

This paper presents a novel hybrid model that integrates predictive and optimization techniques to enhance the scheduling and management of electricity generation in large-scale power systems, with a focus on the variability of photovoltaic (PV) energy. By combining a long short-term memory (LSTM) neural network with an optimization framework, the model forecasts PV power generation over a one-month horizon using historical data, validated against actual production. The optimization component, built on a refined large-scale power system model, incorporates these predictions using a block representation approach to simulate diverse generation technologies, including natural gas, fossil fuel-based thermal units, hydroelectric, PV, nuclear, and wind power plants. This integrated approach addresses the stochastic nature of renewable sources, distinguishing it from prior studies that focus solely on prediction or optimization. The Argentine Interconnection System (SADI) serves as the case study, leveraging over a decade of time-series data to evaluate the model’s performance. Results demonstrate reliable prediction and scheduling capabilities, achieving a low prediction error of approximately 0.01% for key PV sources. Implemented in Python within the Spyder environment, with TensorFlow and Keras for LSTM predictions and PYOMO for optimization, the model offers a practical and effective solution for system operators to optimize resource allocation in renewable-heavy power systems.

The research aimed to examine the role of business incubators and accelerators in promoting innovation among Saudi entrepreneurs, in support of the Kingdom's Vision 2030 agenda of diversifying the economy and strengthening entrepreneurship. The study population consisted of male and female entrepreneurs in Saudi Arabia, and a random sample of 120 participants with varying educational levels and experiences was selected. The researcher used a quantitative approach based on a questionnaire whose validity and reliability were statistically verified. The results showed that most participants had previously joined incubators and accelerators, and emphasized their significant importance in developing ideas, building relationships, accelerating growth, and supporting projects with training and modern technologies. The research recommended the continuous development of these programs, facilitating government policies and procedures, and increasing entrepreneurs' awareness of the importance of innovation to achieve sustainable economic and social development.

The objectives of the study were to investigate 1) whether paternalistic leadership affects exploitative and exploratory innovation, and 2) whether intrinsic motivation and environmental dynamism moderate the direct effects of paternalistic leadership on exploitative and exploratory innovation. The study was conducted in Sri Lanka by taking a sample of respondents from the information technology sector. The results indicate a notable distinction between the factors driving exploitative and exploratory innovation. Both types of innovations are significantly affected by paternalistic leadership. However, it has a positive influence on exploitation while it has a negative influence on exploration. Intrinsic motivation significantly predicts only the exploratory innovation while environmental dynamism significantly predicts only the exploitative innovation. This divergence can be explained by the inherent differences in the nature of these two types of innovation. Overall, this research advances theoretical understanding and provides practical guidance.