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The purpose of this study is to discuss the construction of a structural measurement model utilizing structural equation modelling (SEM) to confirm the link between Industry 4.0 technologies, sustainable manufacturing practices and organizational sustainable performance. Relationship among the paradigm has yet to be fully investigated, necessitating a more conceptual and empirical examination on what impact they have on organizational sustainable performance when used together.

Industry 4.0 and sustainable production practices aim to progress a company's business competitiveness, forming sustainable development that benefits manufacturing companies. The aim of the study is to analyze the relationship between constructs that lead to operational excellence in firms that use Industry 4.0 technologies and sustainable manufacturing techniques. Experts from diverse automotive industries, who are applying both Industry 4.0 and sustainable manufacturing practices, provided data for the study.

Statistical estimations (hypotheses) are created to substantiate the measurement model that has been developed. The structural model was analysed, and the findings were discussed. The statistical estimate is either approved or rejected based on the findings. According to the conclusions of this study, strong link exists between Industry 4.0 technologies and sustainable manufacturing practices that affect organizational sustainable performance environmentally, economically and socially.

The research was conducted in the framework of automobile component manufacturing companies in India. The outcomes of the study are practically feasible.

The authors' novel contribution is the construction of a structural model with Industry 4.0 technologies and sustainable manufacturing practices into account.

The purpose of this study is to examine the impact of sustainable supply chain management (SSCM) practices on both competitive advantage (CA) and organizational performance (OP) in the manufacturing sector in Ethiopia.

Data for the study were collected from a sample of 221 manufacturing companies operating in the four manufacturing groups/sectors in Ethiopia. In addition, data analysis was performed using the partial least squares method, which is a variance-based Structural Equation Modeling approach in the Smart-PLS software version (SmartPLS 4.0).

Based on the statistical analysis of the collected data, it demonstrates that SSCM has a significant and positive impact on both competitive advantage and organizational performance. Furthermore, statistical findings offer proof of the clear connection between competitive advantage and organizational performance. Moreover, competitive advantage indirectly mediates the relationship between SSCM and OP.

The primary limitation of this research is its reliance on a cross-sectional design. The generalizability of the findings obtained from the present study may be hindered. The variable under investigation in this research assessed organizational performance, a concept that is widely acknowledged to be extremely dynamic.

The study provides managers and researchers with valuable information on Sustainable Supply Chain Management strategies and how they influence competitive advantage and organizational performance in commercial and industrial environments.

This paper adds to the body of knowledge by providing new data and empirical insights into the relationship between SSCM practices and the performance of manufacturing companies in Ethiopia.

This study aims to demonstrate the influence of Industry 4.0 (I4.0) adoption on improving logistics capabilities to achieve remanufacturing operations and business logistics performance.

This research uses a deductive survey-based research approach. A structured questionnaire was used to collect data from managerial-level employees associated with logistics functions from 121 manufacturing firms. The partial least square method was used to conduct structural equation modeling to test the hypothesis empirically.

The analysis revealed that using I4.0 improves the ability to achieve higher levels of logistics capabilities (i.e. instrumented and intelligent logistics). Moreover, I4.0 integration with instrumented and intelligent logistics capabilities positively impacts business logistics optimization. On the other hand, intelligent logistics directly and positively impacts remanufacturing operational performance, whereas instrumented logistics does not reflect any significant influence on remanufacturing performance.

This study is one of the limited studies on the focus area and is an effort to fulfill an identified gap in applying innovative logistics capabilities using I4.0 technologies.

This research aims to explore the relationship between sustainable manufacturing practices (SMP) and financial performance (FP) by considering the mediating role of green product innovation (GPI) and the moderating effect of digital transformation (DT).

This study proposes a research model grounded in a practice-based view and a resource-based view and conducts empirical tests by using a sample of 244 manufacturing firms.

This study revealed that SMP influences GPI, and GPI mediates the SMP–FP link. In addition, findings demonstrated that DT strengthens the impact of SMP on GPI, and moderates the mediation impact of GPI on the relations between SMP and FP.

Although overwhelming environmental concerns cause SMP to be considered increasingly crucial, there is a dilemma regarding its impact on FP. Moreover, due to the strategic importance of DT, there is a lot of interest in its relationship with sustainability-related issues. Nevertheless, this association is still not clarified. This study addresses the research gaps, provides an extended understanding of how SMP affects FP and offers a novel insight that reveals the role of DT.

Buoyed by the increasing demand for improved productivity and environmentally conscious manufacturing, research in the area of lean production and green manufacturing has experienced significant growth since Dües et al. (2013). Taking the latter as the point of reference, a review of recent developments in the complementary and conflicting areas between lean production and green manufacturing that has been missing is presented.

A systematic search was done to identify articles on lean production and green manufacturing from Scopus, Web of Science and Google Scholar. The population-intervention-outcome format was used to develop and answer the research questions. ATLAS.ti 22 was used to analyse 141 qualifying papers and identify the research themes.

Lean production and green manufacturing have strong synergy, and when integrated, they tend to deliver superior organisational performance than their individual implementations. This is consistent with the pre-2013 results, and other areas of synergy and divergence were also identified.

The study considers only papers published in the manufacturing sector after Dües et al. (2013). A review of lean production and green manufacturing in integrated product-service systems may also be relevant, especially due to the continuing trend since its introduction.

Any new adopter of lean production should consider implementing it simultaneously with green manufacturing.

This study establishes the persistence of the pre-2013 patterns of synergy and divergence between lean production and green manufacturing, and identifies new considerations for their joint implementation.

Though business analytics capability continues to attract considerable industrial and scholarly attention, its holistic performance implications, especially in the post-COVID-19 period, have not been fully understood. Thus, there have been calls for a full understanding of the implications of BAC for achieving holistic, sustainable outcomes among firms. This study therefore examines the influence of BAC on the three dimensions of sustainable performance. We also proposed the mediating role of circular economy implementation.

We tested the proposed model using survey data from 246 managers of manufacturing firms in Ghana. Partial least squares structural equation modelling was employed to validate the model.

Our findings showed that BAC significantly enhances both sustainable performance and circular economy implementation. We also found a significant association between CEI and sustainable performance. We further found significant partial mediation of CEI in the BAC sustainable performance nexus.

Our study offers thoughtful insights for managers, policymakers and the academic community that firms should simultaneously implement circular models alongside building analytics competencies in the quest to achieve balanced performance outcomes.

To the best of our knowledge, our study is among the very few attempts to understand the mechanism that channels the benefits of BAC for a holistic, sustainable outcome.

By incorporating I4.0 technologies, the agri-food supply chain (AFSC) can become leaner, faster, more robust and greener. However, many challenges must be overcome to fully realise I4.0 in this context. Therefore, this paper aims to identify the challenges that hinder the adoption of I4.0 technologies on the development of the Lean, Agile, Resilient and Green (LARG) AFSC.

The approach adopted was to identify challenges addressed in the literature with expert opinion and Total Interpretive Structural Modelling (TISM) for adaptation. In addition, a Weighted Influence Non-linear Gauge Systems (WINGS) methodology has been developed that uses expert opinion to generate a power and influence matrix.

The results show that lack of commitment and understanding of top management (X12), lack of long term vision (X17) and lack of incentives and government support (15) are the most important challenges.

This study does not explore the effectiveness of the concluded challenges of I4.0 and their strategy to overcome them. Also, the authors relied on a limited sample size for this study, which might not cover the detailed challenges within LARG AFSC. Finally, this study lacks in future advancement of I4.0, which may further affect the challenges.

By mentioning the key challenges, this study empowers LARG AFSC organisations to build a targeted strategy for smoother I4.0 implementation.

Industry 4.0 challenges remain unexplored in LARG AFSC. This improved awareness equips managers to navigate better the potential issues and complexity that may arise when adopting I4.0 in the LARG AFSC.

The purpose of this study is to perform bibliometric analysis to systematically and comprehensively examine the current landscape of digital knowledge, integration and performance in the transformation of sustainability accounting, reporting and assurance.

This research uses a systematic literature review, following the Scientific Procedures and Rationales for Systematic Literature Review protocol and uses various bibliometric and performance analytical methods. These include annual scientific production analysis, journal analysis, keyword cooccurrence analysis, keyword clustering, knowledge gap analysis and future research direction identification to evaluate the existing literature thoroughly.

The analysis reveals significant insights into the transformative impact of digital technologies on sustainability practices. Annual scientific production and journal analyses highlight key contributors to the adoption of digital technologies in sustainability accounting, reporting and assurance. Keyword cooccurrence analyses have identified key themes in sustainability accounting, reporting and assurance, highlighting the transformative role of digital technologies such as artificial intelligence (AI), blockchain, Internet of Things (IoT) and big data. These technologies enhance corporate accountability, transparency and sustainability by automating processes and improving data accuracy. The integration of these technologies supports environmental, social and governance (ESG) reporting, circular economy initiatives and strategic decision-making, fostering economic, social and environmental sustainability. Cluster-by-coupling analyses delve into nine broader revealing that IoT improves ESG report accuracy, eXtensible Business Reporting Language structures ESG data and AI enhances life cycle assessments and reporting authenticity. In addition, digital transformation impacts environmental performance, big data optimizes resource use and edge computing improves eco-efficiency. Furthermore, this study identifies avenues for future research to advance the understanding and implementation of digital technology in sustainability accounting, reporting and assurance practices.

Academically, this research enriches the understanding of how digital technologies shape sustainability practices and identifies gaps in digital knowledge and integration. Practically, it provides actionable insights for organizations to improve sustainability reporting and performance by effectively leveraging these technologies. Policy-wise, the findings advocate for frameworks supporting the effective implementation of these technologies, ensuring alignment with global sustainability goals.

This study offers a detailed analysis of the performance and intellectual framework of research on implementing digital technology in sustainability accounting, reporting and assurance. It highlights the evolving research landscape and emphasizes the need for further investigation into how emerging technologies can be leveraged to achieve sustainability goals.

After the COVID-19 pandemic, companies from a variety of sectors began repurposing their product development and manufacturing activities. To be successful, repurposing requires a framework that illustrates Agile New Product Development (ANPD) and Industry 4.0 practices. The current study aims to focus on developing a framework that managers and decision-makers can use to successfully adopt ANPD-Industry 4.0 practices and decision-making activities.

Initially, a literature review is conducted to identify practices related to ANPD and Industry 4.0. Similarly, performance metrics are identified through a review of the literature. To compute the weights of the shortlisted practices, the Pythagorean fuzzy Analytical Hierarchy Process is used and the Pythagorean fuzzy Combined Compromise Solution (PFCoCoSo) method is used to rank the shortlisted performance metrics.

According to the findings, ANPD practices (ADP) are the most prominent among shortlisted practices. Following that are Technology Adoption Practices, Organizational Management Practices (OMP), Human Resource Management Practices and System Integration Practices. Customer requirement analysis, for example, is an ADP practice that has a significant impact on the successful repurposing of product development activities.

The identified practices can make a significant contribution during repurposing product development activities. Practices that promote sustainable product development, as well as the use of advanced technologies, will be beneficial in improving organizational performance. Managers can evaluate performance using performance metrics that have been prioritized.

After the COVID-19 pandemic, this could be the first of its kind to develop an RPD framework.