The primary objective of this study is to examine the present literature of Total Quality Management (TQM) and its connection with industry 4.0. The author tried to understand what subject and problems might be deemed more relevant, referring to the so-called Quality 4.0, which is absent from the literature to open avenues for further research. To achieve the primary objective, the author reviewed the literature on TQM from the objective (soft) and subjective (hard) perspectives, followed by identifying the critical success factors and future research avenues for researchers. A total of 87 empirical studies from 1987–2019 were studied. The descriptive and thematic analysis identified the soft and hard total quality management practices. A quality tool, ‘Pareto Analysis’, was applied to soft and hard TQM practices. The comprehensive review identified 23 soft and 15 hard TQM practices in the manufacturing and services sectors. The findings revealed four soft dimensions for the effective implementation of Quality 4.0 in business: Top management commitment, customer focus, training and learning, and quality of big data and analysis. Furthermore, three hard dimensions revealed the effective implementation of Quality 4.0: process management, continuous improvement, and product/service design.

Purpose: Despite just eight years remaining to meet the sustainable development goals (SDG, 2030), the manufacturing industry faces numerous challenges for small and medium-sized enterprises (SMEs). Some notable challenges include integrating sustainability, circular economy (CE), and industry 4.0 (I4.0) technologies in a productive manner. However, there is a paucity of evidence available on the role of institutional pressures and organizational resources to promote I4.0 and sustainability. To fill this void, this study develops and tests a model based on institutional theory and resource-based view (RBV) theory while also taking company size into view as a moderating construct.Design/methodology/approach: To test the study hypotheses and validate the model, data were obtained through a survey from 228 randomly selected SMEs manufacturing in China. Structured equation modeling and multigroup analysis were used to analyze the data.Findings: The research findings indicate that institutional pressure has a positive effect on organizational resources (i.e., tangible and intangible), which are capable of orchestrating I4.0 readiness effectively. Also, I4.0 readiness has a positive effect on sustainable manufacturing practices and CE capabilities. Finally, firm size was revealed to be a significant moderator in driving overall integration.Practical implications: Based on the findings, practical implications and future research directions are discussed.Originality/value: Based on the institutional and RBV theories, this research shows how SMEs could be influenced by different stakeholders to acquire and develop their resources and capabilities to accelerate I4.0 readiness that further enhances sustainable practices.

In Industry 4.0, the adoption of new technology has played a major role in the transportation sector, especially in the electric vehicles (EVs) domain. Nevertheless, consumer attitudes towards EVs have been difficult to gauge but researchers have tried to solve this puzzle. The prior literature indicates that individual attitudes and technology factors are vital to understanding users’ adoption of EVs. Thus, the main aim is to meticulously investigate the unexplored realm of EV adoption within nations traditionally reliant on oil, exemplified by Saudia Arabia. By integrating the “task technology fit” (TTF) model and the “unified theory of acceptance and usage of technology” (UTAUT), this research develops and empirically validates the framework. A cross-section survey approach is adopted to collect 273 valid questionnaires from customers through convincing sampling. The empirical findings confirm that the integration of TTF and UTAUT positively promotes users’ adoption of EVs. Surprisingly, the direct effect of TTF on behavioral intentions is insignificant, but UTAUT constructs play a significant role in establishing a significant relationship. Moreover, the UTAUT social influence factor has no impact on the EVs adoption. This groundbreaking research offers a comprehensive and holistic methodology for unravelling the complexities of EV adoption, achieved through the harmonious integration of two well-regarded theoretical frameworks. The nascent of this research lies in the skilful blending of technological and behavioral factors in the transportation sector.

Industry 4.0 (I4.0) is a technological development in the manufacturing industry that has revolutionized Total Quality Management (TQM) practices. There has been scant empirical research on the multidimensional perspective of TQM. Thus, this study aims to empirically examine the effect of the multidimensional view of TQM (soft and hard) on I4.0 readiness in small and medium-sized (SMEs) manufacturing firms. Based on the sociotechnical systems (STS) theory, a framework has been developed and validated empirically through an online survey of 209 Malaysian SMEs manufacturing firms. Unlike the existing TQM studies that used structural equation modeling (SEM), a two-stage analysis was performed in this study. First, the SEM approach was used to determine which variable significantly affects I4.0 readiness. Second, the artificial neural network (ANN) technique was adopted to rank the relative influence of significant predictors obtained from SEM. The results show that the soft and hard TQM practices have supported the I4.0 readiness. Moreover, the results highlight that hard TQM practices have mediating role between soft TQM practices and I4.0 readiness. The ANN results affirmed that customer focus is considered an important TQM factor for I4.0 managerial readiness, advanced manufacturing technology for operational readiness and top management commitment for technology readiness. In a nutshell, the SEM-ANN approach uniquely contributes to the TQM and I4.0 literature. Finally, the findings can help managers to prioritize firms’ soft and hard quality practices that promote I4.0 implementation, especially in emerging economies.

Digitalization transforms the business environment, and organizations face challenges to make progress. The initial step to support organizations is to evaluate their readiness, resources, and capabilities for improvement. Drawing on the resource-based view (RBV) theory, the purpose of this study is to empirically analyze the effect of digitalization of Total Quality Management (TQM) or Quality 4.0 on industry 4.0 (I4.0) readiness and actual practices of I4.0 with firm size as a moderator. Based on data from 228 respondents, the study model was empirically validated in Malaysian small-medium enterprises. The results revealed that soft TQM (top management commitment (TMC) and human resource management (HR) and hard TQM practices; process management (PM) and quality information & analysis (QIA)) are positively associated with I4.0 readiness that further improves the actual practices of I4.0. Contrary to our expectations, education and training have no significant effect on I4.0 readiness. The results also show the mediating role of I4.0 readiness between TMC and QIA with the actual practice of I4.0. Furthermore, firm size significantly moderated the proposed relationships. The established framework helps managers to anticipate their management decisions’ effects on those results affecting I4.0.

Purpose: This study aims to holistically present a systematic literature review (SLR) triangulated with bibliometric analysis on environmental, social and governance (ESG) research to synthesize and comprehensively review its evolving journey and emerging research streams.Design/methodology/approach: Using R-studio software, this study carried out a retrospective quantitative bibliometric analysis through performance analysis, science mapping and network analysis, covering 261 documents published on ESG research between 2007 and 2022 in Scopus and Web of Science databases.Findings: Performance analysis depicts the trends in publications, impactful journals and influential publications, authors and countries, while science mapping incorporates co-words and thematic analysis. Likewise, co-occurrence analysis provided four different clusters, representing ESG research linkage to other management fields along with key insights from co-citation network analysis. Additionally, the theory–context–characteristics–methods (TCCM) framework has provided valuable results in terms of widely and emerging used theories, contexts, characteristics and methodologies in ESG research.Research limitations/implications: The findings of this study’s comprehensive bibliometric analysis combined with SLR uncovered a robust roadmap for further investigation in ESG research by identifying the inherent structure and evolution of research themes. This review has not only identified the prevalent gaps in determining priorities for future research but also provides insights which not previously been captured and evaluated on this topic.Originality/value: To the best of the author's knowledge, no study presents the TCCM framework in the context of bibliometric analysis of ESG research. Besides, a conceptual framework is developed that illustrates antecedents, mediators, moderators and outcomes of research on ESG practices and provides the concluded key takeaways and recommendations for potential authors intending to publish their research papers on ESG practices.