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Quality 4.0 is being presented as the new stage of quality development. However, its overlying concept and rationale are still hard to define. To better understand what different authors and studies advocate being Quality 4.0, a systematic literature review was undertaken on the topic. This paper presents the results of such review, providing some avenues for further research on quality management.

The documents for the systematic literature review have been searched on the Scopus database, using the search equation: [TITLE-ABS-KEY (“Quality 4.0”) OR TITLE-ABS-KEY (Quality Management” AND (“Industry 4.0” OR “Fourth Industr*” OR i4.0))]. Documents were filtered by language and by type. Of the 367 documents identified, 146 were submitted to exploratory content analysis.

The analyzed documents essentially provide theoretical discussions on what Quality 4.0 is or should be. Five categories have emerged from the content analysis undertaken: Industry 4.0 and the Rise of a New Approach to Quality; Motivations, Readiness Factors and Barriers to a Quality 4.0 Approach; Digital Quality Management Systems; Combination of Quality Tools and Lean Methodologies and Quality 4.0 Professionals.

It was hard to find studies reporting how quality is actually being managed in organizations that already operate in the Industry 4.0 paradigm. Answers could not be found to questions regarding actual practices, methodologies and tools being used in Quality 4.0 approaches. However, the research undertaken allowed to identify in the literature different ways of conceptualizing and analyzing Quality 4.0, opening up avenues for further research on quality management in the Industry 4.0 era.

This paper offers a broad look at how quality management is changing in response to the affirmation of the Industry 4.0 paradigm.

The study aims to explore the dimensions of Quality 4.0 adoption, prioritization of these dimensions and the influential dimensions and their causal relationships that can guide the smooth adoption of Quality 4.0 to boost organizational performance.

The Quality 4.0 dimensions are explored from the extant literature. The qualitative data were captured from 12 highly experienced experts from diverse industries and academia through structured interview questions and group discussions in multiple phases. The inputs obtained from the experts were analyzed using Fuzzy-Technique for Order of Preference by Similarity to Ideal Solution for dimension priority, and Fuzzy-Decision-Making Trial and Evaluation Laboratory was employed to reveal the influential relationship between them.

The analysis reveals that quality scalability, quality culture and quality conformance are investigated as primary drivers of Quality 4.0 adoption. Data-driven analytical thinking and customer centricity emerge as dynamic dimensions that act as quality deliverable ends. Integrating these methodologies provides a robust framework for understanding and managing Quality 4.0 complexities, offering actionable insights for prioritizing initiatives and addressing interdependencies to ensure successful adoption and implementation.

The practical implications guide industries in creating strategic action plans tailored to their needs and fostering a quality-focused culture. The study also offers valuable insights into government policies, promoting sustainability, efficiency and a circular economy.

The study’s novelty lies in its prioritization and examination of the most influential causes and effects within the Quality 4.0 dimensions. This approach highlights core drivers and critical factors, providing a comprehensive framework for successful implementation.

This study aims to provide a comprehensive, textometric analysis of the evolving Quality 4.0 (Q4.0) research, identifying key themes, emerging trends and critical research gaps in the field.

A total of 129 peer-reviewed articles on Q4.0 were analyzed using IRAMUTEQ software. The textometric approach employed includes lexicographic analysis, descending hierarchical classification, factorial correspondence analysis and similarity analysis to uncover underlying patterns in the literature.

Three major clusters emerged from the analysis of Q4.0 research: (1) the digital transformation of quality management practices, (2) technological enablers of Q4.0 and (3) organizational implications of Q4.0 adoption. Key themes identified include the integration of Industry 4.0 technologies (e.g. IoT, AI and big data) into traditional quality management systems, the role of artificial intelligence in quality control and the challenges organizations face during Q4.0 implementation. This research also conceptualizes a comprehensive framework that outlines a strategic roadmap for Q4.0 adoption and integration, including the identification of antecedents and consequences of Q4.0 implementation. Additionally, the study proposes a measurement scale for assessing the dimensions of Q4.0.

The research proposes valuable implications for practitioners and organizations seeking to implement Q4.0 strategies as well as for researchers in the field of digital transformation and quality management. The proposed conceptual framework serves as a practical guide for effectively navigating Q4.0 implementation in diverse organizational contexts.

This study presents an innovative approach by applying textometric analysis to the field of Q4.0, offering an objective, data-driven perspective on the existing literature. It identifies significant gaps in research and proposes future avenues for investigation. As one of the first studies to develop a conceptual framework for Q4.0, including its antecedents, consequences and a measurement scale, this research provides a foundational contribution to the growing body of knowledge on Q4.0.

This review paper aims to explore and investigate the Quality 4.0 current knowledge, emerging areas, and trends available in the literature and provide insights for future research directions. The bibliometric analysis determines the most prominent journals, authors, countries, articles, and themes. The Citation and PageRank analysis identifies the most influential and prestigious articles. The author's keyword analysis identifies the research theme, patterns, and trends within a particular area of research.

This study utilised the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analysis) declaration as a review protocol, and the data is retrieved accordingly. Therefore, 104 articles from Scopus and 28 from Web of Science were combined in R-Environment, and 25 duplicates were removed using RStudio. Finally, 107 papers were selected for further analysis. After the abstract level screening, the study reviewed 99 articles bibliographically published in peer-reviewed journals from prominent academic databases Scopus and WoS between 2011 to April 2023. We used the VOSviewer software tool for analysing bibliometric networks that allow the construction, visualisation, and exploration of maps based on any form of network data.

The review identified emerging themes: artificial intelligence, digitalization, sustainability, root cause analysis, topic modelling, and digital voice-of-customers. To establish the intellectual structure of the field and identify gaps, co-citation and content analysis were used. The content of 49 papers in the identified clusters was then carefully analysed. The four primary themes are the relationship of Quality 4.0 with Industry 4.0, the conceptualization of Quality 4.0, recommendations for the new Quality 4.0 model, and the impact of Quality 4.0. The findings provide an excellent foundation for future research in this field for policymakers, managers, practitioners, and academia.

This is the first systematic literature review-cum-bibliometric analysis on quality 4.0 that covers the field comprehensively. Based on the present review, the paper proposes six possible future research directions to investigate.

The aim of the study is to investigate the effects of perceived healthcare service quality (human aspects, technical aspects and tangible aspects) on satisfaction and guest loyalty in the hotel industry in the COVID-19 pandemic era.

A total of 130 guests in the hotel were selected purposively in Iraq. Data from self-administered questionnaires were analyzed through the VB-SEM statistical technique using Smart-PLS software towards testing the hypotheses.

The findings indicated that perceived service quality influences satisfaction and guest loyalty of guests in the hotel. This study reveals that human aspects, technical aspects and tangible aspects directly positively affect satisfaction and guest loyalty in the hotel industry.

This study highlights that perceived service quality (human aspects, technical aspects and tangible aspects) are vital and practical strategic tools that could be positioned to accelerate guest loyalty in the hotel industry. Furthermore, satisfaction mediates the relationship between human aspects, technical aspects, tangible aspects and guest loyalty.

The present research work reviews and maps the thematic evolution of the interface between human resource (HR) practices and service quality (SQ) over the last 33 years.

The authors employed systematic literature review (SLR), bibliometric analysis and visualization to comprehensively map 215 papers extracted from the Web of Science and Scopus databases. The present study also helps to document the research themes that evolved through co-occurrence networks and thematic maps.

The study identifies that HR practices are the central drivers for maintaining SQ in an organization and found that teamwork, empowerment, recruitment, selection, training and reward are key for improving the SQ. It concludes the impact of HR practices on SQ, develops the knowledge structure of human resource management (HRM) operations and SQ literature and organizes it under various dimensions as antecedents and outcomes. As its foremost input, the current study proposes human resource practices and service quality (HRPSQ) framework for comprehensive HR practices and SQ in an organization.

The study is unique as it map the journey of HR practices and SQ and proposes a framework for improved performance.

The objective of this work is twofold: firstly, to develop a model for assessing healthcare service quality (HSQ), and secondly, to evaluate the effectiveness of machine learning algorithms in predicting the quality of healthcare services.

In this study, a comprehensive literature review has been performed to identify key quality dimensions in the healthcare services domain. Delphi’s method has been used to confirm the criticality of these dimensions based on experts’ opinions and proposed a novel CIRMQUAL model. Factor analysis techniques have been used to further validate the CIRMQUAL model. Using the data collected through a questionnaire survey, a number of machine learning models have been developed to predict the customer satisfaction level based on the service quality (SQ) performance of a healthcare unit on different dimensions of the CIRMQUAL model.

The study developed a CIRMQUAL model with 14 dimensions (quality of care, safety and security, skill and conduct, staff attitude, tangibles, quality of the atmosphere, patient rights, follow-up, communication, cost of treatment, availability of resources, accessibility, waiting time and services), and these dimensions have been clubbed into four major dimensions, i.e. clinical quality, infrastructural quality, relationship and managerial quality. Furthermore, the application of machine learning algorithms has demonstrated significant accuracy in predicting SQ, highlighting its ability to improve healthcare services and the satisfaction level of patients.

Managers of healthcare units work hard to identify and address the pain points of the patients and improve the working of the healthcare units being managed by them. The availability of many scales with numerous dimensions adds to their confusion in selecting a suitable scale. The current work addresses this confusion and provides four clear areas for assessing the quality of healthcare units. By using this scale, managers can assess the quality of services provided by them, identify the dimensions of low performance, plan and take suitable corrective actions to improve the performance of their healthcare units.

A comprehensive SQ model, i.e. CIRMQUAL has been proposed as a new scale to assess SQ in healthcare units. The model has been developed after analyzing the dimensions used by many researchers available in the literature. This model can be used by future researchers to assess the SQ in healthcare units. Moreover, an attempt has been made to use artificial intelligence-based techniques for predicting customer satisfaction. Such attempts are in the initial stage for healthcare sector. Future researchers can take this concept forward and test the applicability of different machine learning techniques in different functional areas of healthcare.

Good health is of utmost importance for all human beings. In spite of the expenditure of substantial time and efforts by various stakeholders, the service delivery doesn’t match the expectations of patients. Many times, the service providers are not aware of this dissatisfaction and specific aspects of service delivery that need to be improved to reduce dissatisfaction. The model proposed will help the service providers in this regard and the service providers will be able to take focused steps. Such initiatives will definitely improve patient’s satisfaction and their social well-being.

This work is unique because it uses a novel technique to redefine the quality of services in healthcare by using a dual methodology. The research presents a model that includes various factors and it is specially developed to evaluate the quality of services in healthcare settings. This study advances the area’s progress by implementing computational tools for accurate evaluation of HSQ. The healthcare decision-makers may use this novel perspective to evaluate and enhance the quality of service.

Industry 4.0 or I4.0 has transformed the manufacturing landscape by integrating social and technical factors by means of the sociotechnical framework. However, the sociotechnical aspects of digitalization of total quality management (TQM 4.0), especially in small and medium enterprises (SMEs) remain largely unexplored. This groundbreaking research endeavors to delve into the pivotal role played by social (soft) and technical (hard) TQM 4.0 in driving I4.0 readiness among SMEs.

A research framework has been developed by harnessing the principles of Socio-technical systems (STS) theory. Data collection from a sample of 310 randomly selected SMEs manufacturing in Malaysia through an online survey approach. The collected data is then subjected to analysis using Partial Least Square-Structural Equation Modeling (PLS-SEM) through SmartPLS.

The study findings indicate that both hard and soft TQM 4.0 factors are vital to promoting I4.0 readiness (R² = 0.677) and actual implementation (R² = 0.216). Surprisingly, the findings highlight that customer-related construct has no impact on hard TQM 4.0 attributes. Furthermore, hard TQM 4.0 factors have played a partial mediating role on the relationship of soft TQM 4.0 and I4.0 attributes (20% = VAF = 80%).

This is a novel research as it explores the underexplored domain of sociotechnical aspects of TQM 4.0 within SMEs amid I4.0 transformation. The study distinctive contributes include revealing the pivotal role of both soft and hard TQM 4.0 factors in driving I4.0 readiness, emphasizing the primacy of people-related dimensions for successful implementation in manufacturing SMEs.

This study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.

This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.

The framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.

The framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.

To the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

This study investigates the challenges and opportunities that organisations face in implementing Quality 4.0 as an approach to quality management and investigate the current state of Quality 4.0 implementation.

This study uses a qualitative research methodology to interview senior managers from globally based manufacturing and service industries.

The study explicates that most organisations implemented Quality 4.0 to improve their flexibility, efficiency, transparency and productivity while focusing on improving service quality, customer satisfaction and reducing cost. In terms of sustainability of Quality 4.0 the key factors found were a consistent effort from the top management, continuous training to employees, building leadership quality and creating a habit of using Quality 4.0.

The findings of this study offer useful guidance to organisations desirous of implementing Quality 4.0. In addition, the findings have identified key sustainability factors, helping organisations ensure a successful implementation and long-term returns from Quality 4.0.

The findings of this study contribute to the body of knowledge related to Quality 4.0 and help organisations in their digital transformation journey. In addition, it is one of the first studies to investigate the key factors for Quality 4.0 sustainability.