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The purpose of this paper is to contribute to the existing knowledge about the relationships between information technology (IT), lean manufacturing (LM), organizational environmental issues and business performance.

A questionnaire-based survey was conducted to collect data from 122 elite manufacturers, and the hypothesized relationships were tested using partial least squares structural equation modeling.

IT competence in LM acts as a lower-order organizational capability, and its business value should be recognized through the intermediate roles of LM effectiveness and environmental management capability. Findings recommend that the net benefits of LM are mainly materialized through waste and pollution reduction and simplified implementation of proactive environmental practices.

Among other limitations, relying on a rather small sample size and cross-sectional data of this research, and lack of generalizability of findings, tends to have certain limitations. An interesting direction for future research would be to extend this research by assessing interaction of other types of IT resources with LM and organizational environmental issues.

Both LM and proactive environmental management are information-intensive. Investment in both technological and human aspects of IT resource aimed at increasing the effectiveness of LM activities and proactive environmental practices is imperative for contemporary manufacturers.

This study introduces the IT capability of IT competence in LM and two organizational capabilities of LM effectiveness and environmental management capability. By doing so, the study highlights the significant role of organizational environmental issues in devising firms’ IT and advanced manufacturing technology investment strategies in LM context.

The purpose of this paper is to demonstrate how small manufacturing firms can leverage their Information Technology (IT) resources to develop the lean-digitized manufacturing system that offers sustained competitiveness in the Industry 4.0 era.

The study performs an in-depth five years case study of a manufacturing firm, and reports its journey from failure in the implementation of enterprise resource planning to its success in integrating IT-based technology trends of Industry 4.0 with the firm’s core capabilities and competencies while pursuing manufacturing digitization.

Industry 4.0 transition requires the organizational integration of many IT-based modern technologies and the digitization of entire value chains. However, Industry 4.0 transition for smaller manufacturers can begin with digitization of certain areas of operations in support of organizational core strategies. The development of lean-digitized manufacturing system is a viable business strategy for corporate survivability in the Industry 4.0 setting.

Although the implementation of lean-digitized manufacturing system is costly and challenging, this manufacturing strategy offers superior corporate competitiveness in the long run. Since this finding is rather limited to the present case study, assessing the business value of lean-digitized manufacturing system in a larger scale research context would be an interesting avenue for future research.

Industry 4.0 transition for typical manufacturers should commensurate with their organizational, operational and technical particularities. Digitization of certain operations and processes, when aligned with the firm’s core strategies, capabilities and procedures, can offer superior competitiveness even in Industry 4.0 era, meaning that the strategic plan for successful Industry 4.0 transition is idiosyncratic to each particular manufacturer.

Manufacturing digitization can have deep social implications as it alters inter- and intra-organizational relationships, causes unemployment among low-skilled workforce, and raises data security and privacy concerns. Manufacturers should take responsibility for their digitization process and steer it in a direction that simultaneously safeguards economic, social and environmental sustainability.

The strategic roadmap devised and employed by the case company for managing its digitization process can better reveal what manufacturing digitization, mandated by Industry 4.0, might require of typical manufacturers, and further enable them to better facilitate their digital transformation process.

This study aims to examine the adoption of Metaverse technology in the manufacturing industry and its potential impact on firms’ social and environmental sustainability performance.

Data were collected from 157 technology-based firms in the Malaysian high-tech manufacturing industry and analyzed using PLS-SEM to investigate the influence of social (i.e. social capital, open/innovative culture and empowerment) and technological factors (i.e. digitalization preparedness, integrability and strategic value) on Metaverse adoption and the moderating roles of digital trust and absorptive capacity.

Social and technological factors were found to significantly impact Metaverse adoption, with digital trust enhancing the influence of social factors. Absorptive capacity strengthens firms’ abilities to use social factors for adoption. However, digital trust does not significantly moderate the relationship between technological factors and adoption, nor does absorptive capacity impact this relationship. Finally, Metaverse adoption is shown to positively contribute to firms’ social sustainability, improving social well-being and equity, but it does not significantly impact environmental sustainability.

For practitioners, the study highlights the importance of fostering an organizational culture that supports digital trust and developing absorptive capacity as critical enablers of successful Metaverse adoption. Policy implications include the need for creating supportive policies that encourage digital transformation efforts aligned with sustainability goals.

Theoretically, this study integrates the Technology-Organization-Environment (TOE) framework, Human-Organization-Technology fit (HOT-fit) framework and Resource-Based View (RBV) to improve understanding of technology adoption and sustainability performance. From a managerial perspective, it highlights the importance of fostering digital trust and developing absorptive capacity as critical enablers of successful Metaverse adoption. Policy implications include the need for policies supporting digital transformation efforts aligned with sustainability goals.

The study seeks to understand the possible opportunities that Industry 5.0 might offer for various aspects of inclusive sustainability. The study aims to discuss existing perspectives on the classification of Industry 5.0 technologies and their underlying role in materializing the sustainability values of this agenda.

The study systematically reviewed Industry 5.0 literature based on the PRISMA protocol. The study further employed a detailed content-centric review of eligible documents and conducted evidence mapping to fulfill the research objectives.

The advancement of Industry 5.0 is currently underway, with noteworthy initial contributions enriching its knowledge base. Although a unanimous definition remains lacking, diverse viewpoints emerge concerning the recognition of fundamental technologies and the potential for yielding sustainable outcomes. The expected contribution of Industry 5.0 to sustainability varies significantly depending on the context and the nature of underlying technologies.

Industry 5.0 holds the potential for advancing sustainability at both the firm and supply chain levels. It is envisioned to contribute proportionately to the three sustainability dimensions. However, the current discourse primarily dwells in theoretical and conceptual domains, lacking empirical exploration of its practical implications.

This study comprehensively explores diverse perspectives on Industry 5.0 technologies and their potential contributions to economic, environmental and social sustainability. Despite its promise, the practical evidence supporting the effectiveness of Industry 5.0 remains limited. Certain conditions are necessary to realize the benefits of Industry 5.0 fully, yet the mechanisms behind these conditions require further investigation. In this regard, the study suggests several potential areas for future research.

The purpose of this study is to contribute to the existing knowledge about the value of post-implementation Enterprise Resource Planning (ERP) system at the firm level.

A questionnaire-based survey was conducted to collect data from 217 Malaysian firms that successfully implemented ERP system. Data analysis was conducted with partial least squares-structural equation modeling and partial least squares multi-group analysis techniques.

Higher ERP spending and greater ERP scope in the post-implementation stage were associated with higher performance gains.

Among other limitations, relying on a small sample size and cross-sectional data of this study and lack of generalizability of findings tend to have certain limitations. An interesting direction for future research would be to extend this study by conducting a multi-level analysis to understand how ERP spending and scope would affect the micro-level performance.

Non-financial performance gain is another valuable outcome of ERP implementation. The choice between in-house and off-the-shelf ERP systems will have dramatic impacts on the future profitability of firms. ERP risk management team and related practices during implementation phase result in a significantly higher financial gain in the post-implementation phase.

This study assesses the business value of ERP at the post-implementation phase while accounting for key contextual and managerial issues, a topic that has received little attention to date.

The purpose of this paper is to contribute to the existing knowledge about the relationships between advanced manufacturing technology (AMT), lean manufacturing (LM), agile manufacturing (AM), and business performance.

A questionnaire-based survey was performed to collect data from 189 Iranian automobile part manufacturers. Statistical analysis of hypothesized relationships was conducted via partial least squares structural equation modeling.

AMT significantly contributes to the development of both LM and AM. These manufacturing systems can co-exist in one system, and LM is a precursor to AM. LM contributes to operational performance whereas AM improves marketing performance and financial performance.

Relying on the cross-sectional data of this research, and lack of generalizability of findings are key limitations. An interesting direction for future research would be to empirically offer a hybrid lean-agile approach and further map the mechanism through which this hybrid approach can be achieved in practice.

Both LM and AM are information-intensive and highly supported by AMT. They contribute to different aspects of business performance. Pursuing both cost-leadership strategy and product mix flexibility is viable via hybridizing the lean and agile systems.

This study is among the first to address issues related to the lean-AM relationship among developing countries. This study is unique in the sense it shows the mechanism through which the value of AMT is truly transformed to performance improvement.

Considering the importance and the broad applications of the Fourth Industrial Revolution in various organizations and industries and enjoying the many benefits of this digital transformation framework, organizations need to measure their Industry 4.0 readiness as a starting point and take steps to achieve the strategic goals of Industry 4.0. This study aims to design a comprehensive and practical model that can determine Industry 4.0 readiness level, allowing organizations to implement and exploit technological constituents of this phenomenon.

A systematic literature review (SLR) methodology was used to evaluate and summarize a clear and comprehensive literature overview of Industry 4.0 readiness models and to certify the validity and transparency of the review process. After reviewing 71 articles and survey and then the consensus of Industry 4.0 experts, the 10 dimensions of the 4.0 Industry readiness model were finalized with their indicators having the most frequency in the published articles and models.

The application of the SLR to the development of the new Industry 4.0 readiness model which includes 10 dimensions and 37 indicators and can assess the Industry 4.0 readiness of firms and industries accurately and effectively.

An extensive review of the previous literature yielded the current Industry 4.0 readiness model. The comprehensiveness of this model leads to its wide application in different companies. Future research suggestions are presented at the end of the manuscript.

The concept of the Fourth Industrial Revolution and the application of its technologies are vague and complicated for many organizations and managers, while the need to implement the components and technologies of Industry 4.0 is essential to achieve organizational goals. The presented readiness model helps companies to measure their readiness to enter the Fourth Industrial Revolution and achieve long-term goals.

In this study, an attempt was made to examine the Industry 4.0 readiness models thoroughly and extensively and identify their different approaches. Finally, a comprehensive and multi-dimensional readiness model is presented to assess the position of organizations in order to enter Industry 4.0.

The present study offers a holistic but detailed understanding of the factors that might affect small and medium-sized enterprises (SMEs) adoption of Industry 4.0 technologies to empower smaller businesses to embrace Industry 4.0.

The study conducted a systematic review of the literature and drew on the technology-organization-environment framework to identify various technological, organizational and environmental determinants of Industry 4.0 technology adoption and their underlying components. The study applied the textual narrative synthesis to extract findings from the eligible articles and interpret them into the Industry 4.0 technology adoption roadmap.

Industry 4.0 is a vital strategic option to SMEs, enabling them to keep up with the digitalization race. SMEs significantly lag behind large organizations in benefiting from disruptive Industry 4.0 technologies. SMEs are still struggling with the initial adoption decisions regarding the digital transformation under Industry 4.0. Results identified various determinants that might explain this condition. The study developed a digitalization roadmap that describes the necessary conditions for facilitating SMEs’ digitalization under Industry 4.0.

Various technological, organizational and environmental factors might determine the current positioning of SMEs against Industry 4.0. These determinants can act as barriers or drivers depending on their properties. The roadmap describes determinants indispensable to promoting Industry 4.0 technology adoption among SMEs, such as knowledge competencies or value chain digitalization readiness.

Exclusively focusing on empirical research that reported applied insights into Industry 4.0 technology adoption, the study offers unique implications for promoting Industry 4.0 digital transformation among SMEs.

This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.

The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.

Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.

While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.

This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

The purpose of this study is to first investigate the efficiency of the most commonly used performance measures for minimizing the number of workstations (NWs) in approaches addressing simple assembly line balancing problem (SALBP) for both straight and U-shaped line, and second to provide a comparative evaluation of 20 constructive heuristics to find solutions to the SALBP-1.

A total of 200 problems are solved by 20 different constructive heuristics for both straight and U-shaped assembly line. Moreover, several comparisons have been made to evaluate the performance of constructive heuristics.

Minimizing the smoothness index is not necessarily equivalent to minimizing the NWs; therefore, it should not be used as the fitness function in approaches addressing the SALBP-1. Line efficiency and the idle time are indeed reliable performance measures for minimizing the NWs. The most promising heuristics for straight and U-shaped line configurations for SALBP-1 are also ranked and introduced.

Results are expected to help scholars and industrial practitioners to better design effective solution methods for having the most balanced assembly line. This study will further help with choosing the most proper heuristic with regard to the problem specifications and line configuration.

There is limited research assessing the efficiency of the common objectives for SALBP-1. This study is among the first to prove that minimizing the workload smoothness is not equivalent to minimizing the NWs in SALBP-1 studies. This work is also one of the first attempts for evaluating the constructive heuristics for both straight and U-shaped line configurations.