World University of Bangladesh

Purpose This paper aims to provide a structured, practice-oriented and methodologically coherent guide to case study research within the qualitative inquiry tradition. The study seeks to clarify the defining characteristics, conceptual foundations, operational processes, strengths, limitations and analytical procedures of case study research while addressing common methodological misunderstandings faced by novice and early career researchers. The paper further aims to demonstrate how case study research can be systematically designed, managed and implemented to investigate complex, context-dependent and under-researched phenomena across disciplines. Design/methodology/approach The paper adopts a conceptual and methodological synthesis approach grounded in established qualitative and case study literature. Drawing from seminal and contemporary methodological scholarship, the article integrates theoretical explanations with practical guidance through a series of structured operational tables covering case study design, applications, data collection, thematic analysis, probing techniques, ethical management and implementation workflows. The paper also incorporates interpretive and thematic analytical perspectives to strengthen methodological transparency and analytical coherence in qualitative case study inquiry. Findings The paper demonstrates that case study research is a rigorous, context-sensitive and interdisciplinary methodology capable of generating rich, holistic and analytically meaningful insights into real-world phenomena. The study highlights that methodological rigour in case study research is strengthened through triangulation, reflexivity, multiple data sources, systematic thematic analysis and clearly bounded case design. The paper further identifies recurring methodological challenges, including limited generalisability, analytical complexity, resource intensity and ethical concerns, while presenting practical mitigation strategies to enhance credibility, transferability and transparency. The study also illustrates how structured workflows, probing techniques and thematic development processes can improve the execution and reporting of case study research. Research limitations/implications As a conceptual and methodological guide, this paper does not report empirical findings or test propositions through field-based investigation. This paper provides a structured framework for conducting case study research; future empirical applications across disciplines may help evaluate, refine and extend its practical and analytical utility. Practical implications The paper provides researchers, students, supervisors and practitioners with a comprehensive and accessible roadmap for designing, conducting and managing rigorous case study research. The structured frameworks, operational guidelines and analytical tools presented in this paper can support improved research design, data management, thematic analysis and methodological decision-making across diverse disciplinary, organisational and applied research contexts. Social implications By promoting methodological clarity, ethical awareness and analytical transparency, the paper contributes to improving the quality, credibility and societal relevance of qualitative research. The guide supports the development of contextually grounded knowledge that can inform organisational practices, strengthen policy development and enhance evidence-based decision-making across complex social and institutional settings. Originality/value The paper offers an original and highly structured synthesis of case study research by integrating conceptual foundations with applied methodological guidance in a single coherent framework. Unlike broad qualitative research overviews, the article focuses specifically on case study research and provides a systematic operational roadmap supported by integrative tables, practical examples and analytical guidance. The paper contributes value to both novice and experienced researchers seeking a rigorous, accessible and practically applicable reference for conducting qualitative case study research.

PurposeThe present study examines the influence of leadership, employee involvement and Green Lean Six Sigma (GLSS) on the quality improvement of patient safety in specialised hospitals. This study also investigates the mediating roles of GLSS and employee involvement in the relationship between leadership and the improvement in patient safety quality.Design/methodology/approachIn this study, a stratified random sampling technique was applied to collect data from 384 respondents of specialised hospitals in Bangladesh. SmartPLS 4 was employed to assess construct reliability and validity and to test mediation effects via bootstrapping. Furthermore, hypotheses were tested via partial least squares structural equation modelling (PLS-SEM).FindingsThe study's findings indicate that leadership, employee involvement and GLSS have a positive and significant influence on the quality of patient safety. Based on the results, employee involvement has the greatest impact on quality and patient safety improvement compared to leadership and GLSS. However, the research findings also indicate that leadership does not directly influence the quality improvement of patient safety but does so indirectly through the mediating effects of GLSS and employee involvement.Practical implicationsThis study provides important insights into how healthcare practitioners can enhance patient safety outcomes through effective leadership and operational activities. The results are informative regarding the management of specialised hospitals. The findings may serve as a reference point for the evaluation of GLSS and for the ecological sustainability of hospital management practices in developed and developing countries.Originality/valueThis study offers theoretical and practical contributions, and it is anticipated that the research findings will generate ideas to improve patient safety and quality in healthcare organisations.

The Additive Manufacturing (AM) has emerged as a pivotal technology in the realm of medical robotics, facilitating the creation of patient-specific robotic components, miniaturized mechanisms, and application-oriented system architectures. The paper presents a review of the significance of AM techniques, particularly stereolithography, selective laser sintering, and fused deposition modeling, in the development of medical robotic structures, end-effectors, actuation housings, surgical instruments, and rehabilitation devices. The range is covered by the description of how AM enables the design of anatomically conformal robotic interfaces, lightweight kinematic structures, and customized surgical guides that are integrated into robotic platforms. The paper evaluates the materials utilized in AM-based medical robotics, which include biocompatible polymers, metallic alloys, and composite materials, focusing on their mechanical performance, sterilizability, and clinical applicability. Through a critical review of recent literature, the paper discusses key technical challenges, such as dimensional accuracy in precision robotic assemblies, material behavior under repetitive robotic loading, and the constraints related to the regulatory approval of additively manufactured robotic components. The review highlights the importance of AM in advancing robot-assisted surgery and rehabilitation by enabling the development of task-specific designs tailored to the unique anatomy of individual patients and their clinical workflows. Finally, potential future research avenues are identified, including multi-material robotic fabrication, embedded sensing, and actuation through additive manufacturing, all of which leverage the capabilities of this innovative technology.

The automated diagnosis of lumbar spinal stenosis from magnetic resonance imaging (MRI) remains challenging due to anatomical complexity and variability in interpretation. This study introduces SpineNet-Fusion, a hybrid deep learning framework that combines EfficientNet-B3 and DenseNet-121 to capture both global and fine-grained features. The model is trained on a publicly available Kaggle dataset and evaluated using 5-fold cross-validation and external clinical data from Bangabandhu Sheikh Mujib Medical University (BSMMU). The experimental results demonstrate robust performance, achieving a mean cross-validation accuracy of 97.4% and an external validation accuracy of 92.9%. Grad-CAM visualization further enhances interpretability by highlighting clinically relevant regions. The framework demonstrates strong generalization capability and provides support for clinical decision-making in real-world healthcare settings.

Digital transformation of the manufacturing industry is the driver of industrial sustainability in an increasingly competitive and dynamic business environment. This paper explores the industry 4.0 (I4.0) transformation of fashion and textiles manufacturing through a systematic literature review and evaluates the technology adoption and strategies driving the industrial shift towards a digital future from the perspectives of upstream fashion manufacturers. A total of 74 articles published between 2014 and 2024 were identified for analysis, concentrating on fashion and textiles industry, I4.0, and manufacturing. The technological–organizational–environmental framework was utilized to guide data analysis and understand the barriers, drivers, and opportunities that influence the adoption and implementation of I4.0 strategies in fashion and textiles manufacturing. The findings inform a conceptual model for decision-making on I4.0 transformation in the fashion industry. The overall findings will assist fashion practitioners, who are at the early stage of adopting I4.0 or have already adopted it in small scale and seeking to expand it further, to identify the barriers, drivers, and opportunities within an intricate value chain network and take informed decision. Managers might use the conceptual model in strategic decision-making by selecting priority areas of I4.0 adoption, avoiding scattered efforts and financial investments in unclear directions, and minimizing implementation futility, costs, and risks of failure.