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also indicates an evolution in terminology, with older concepts like 'just in time' now appearing as declining themes, superseded by more integrated frameworks.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.

This study aims to investigate the key drivers of supply chain complexity in the footwear industry business sector. After conducting extensive literature review and consultation with experts, complexity drivers in the context of the footwear business sector were identified. The content validity of the identified drivers was checked by 13 experts using content validity ratio (CVR), which were used as the basis to select 20 key Supply Chain complexity drivers. Using a multi-criteria decision-making DEMATEL approach the cause and effect drivers were investigated. The findings of the study investigated 12 cause and 8 effect drivers in the footwear sector. Among the cause drivers, four linkage drivers that have strong driving power and dependency were identified. This study is the first in the developing county footwear business sector using DEMATEL approach to identify the key supply chain complexity drivers and their interrelationship. The study identified complexity drivers in the context of the footwear business sector that were not explored before in the existing studies. The outcome of this study will help SC complexity decision-makers in that case sector to control and manage the cause and effect drivers and thus improve the efficiency and effectiveness of their SC performance.

In today's fiercely competitive retail landscape, implementing effective pricing strategies is critical not only for boosting sales but also for securing a larger market share and ensuring long-term business sustainability. The ability to capture a greater share of the market directly influences a retailer's positioning and competitive edge, making pricing decisions pivotal. This paper introduces a hybrid optimization model that strategically combines Everyday Low Pricing (EDLP) and High-Low Pricing (HL) strategies, designed to address the intricacies of dynamic retail markets. The model is initially formulated as a nonlinear optimization problem aimed at maximizing sales to increase market share, all while maintaining profitability within a predefined threshold to ensure the retailer does not incur losses. To enhance the model's practical applicability, particularly in small-scale scenarios, the nonlinear problem is transformed into a Mixed-Integer Programming (MIP) model, facilitating its solvability. However, as retail applications scale up, the computational complexity becomes more challenging, necessitating the use of the Grey Wolf Optimization (GWO) algorithm. The GWO algorithm effectively balances computational efficiency with solution quality, making it a robust approach for large-scale problems. A significant contribution of this research is the linearization of the model under conditions where the products designated for High-Low pricing (referred to as 'Golden' products) are predetermined by the retailer. This linearization simplifies the computational process, enabling the model to scale and be applied in large retail settings. Developed in collaboration with a major Iranian supermarket chain, the model leverages real-world data to optimize discount levels and timing across various product categories. Extensive numerical experiments demonstrate the model's effectiveness in increasing sales, thereby contributing to a larger market share while ensuring that profitability remains within acceptable bounds. By providing actionable insights and strategic recommendations, this research offers a practical, scalable solution for optimizing retail pricing strategies in a data-driven and competitive environment, ultimately supporting retailers in their quest to dominate the market.

Providing K-12 students with proper science, technology, engineering, and math (STEM) education is important to ensuring an innovative and prosperous economy. A highly skilled STEM workforce can lead to increased productivity and competitiveness, which can lead to a host of new ideas being researched and developed. STEM workers make added-value products, build bridges and roads, and conduct lifesaving medical research, among other important activities. The use of virtual reality (VR) technology for both education and workforce training has grown in recent years. VR technology can accelerate these processes at maximum efficacy and minimum costs and can have a significant impact on productivity gains, earnings, new jobs, innovation through research and development, and high-growth industries. This paper presents the development of a series of VR modules using the Unity game engine, the HTC VIVE Pro VR headset, and the Hi5 VR glove for the purposes of K-12 STEM education. Specifically, these developed modules have been designed to instruct K-12 students on topics related to motion and heat, with future goals to expand the modules to cover topics related to light, magnetism, electricity, radioactivity, sound, and waves. This paper will cover the methodology and design considerations that went into developing these modules, with a focus on how these modules relate to various learning strategies as well as with existing research on the use of VR in K-12 education.

This study aims to describe, analyze, and interpret the role of multi-set data consistency in regional development planning in Kebumen Regency, Central Java Province, Indonesia. This study uses qualitative research methods. The selection of informants used purposive and snow-ball sampling. Data collection uses in-depth interviews, observations, and documentation. Data analysis was carried out through open coding, axial coding, and selective coding procedures. The validity of the data is determined through an examination technique based on the criteria of credibility, transferability, dependability, and confirmability. The results of the study show: (1) Regional development planning policies, which regulate the process and stages of the preparation of regional long-term development plans/RPJPD, regional medium-term development plans/RPJMD, and regional development work plans/RKPD were developed in the process of preparing the three regional development planning documents of Kebumen Regency to meet local needs; (2) The process of preparing the 2005–2025 RPJPD, 2006–2010 RPJM, 2010–2015 RPJMD, and 2006 to 2014 RKPD uses a technocratic approach that emphasizes the importance of multi-sector data (3) Factors that affect the process of preparing and documenting regional development planning documents are compliance in the hierarchy of multi-sector existing data administration, the development of regulations for the implementation of regional development planning policies, the existence of existing multi-sector planning data documents national, provincial, and regional development documents related to development planning, as well as the use of technocratic approaches; (4) The 2010–2015 RPJMD is inconsistent with the 2010–2015 RPJPD.