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To become a sustainable lean manufacturing (LM) system, an organization must be first distinctly cognizant of the terms “lean” and “sustainability” as they relate to the state of affairs of their particular industry and business. Next, the organization must identify and acquire the necessary qualities it needs to become sustainable in lean philosophy and its practices in the organization. The LM paradigm has been a top priority for many businesses; thus, this article is based on actual research done in Indian small and medium scale organizations to see how widely it is understood and implemented.

A framework was developed based on literature review and academic research. A preliminary analysis of a small number of small and medium-sized enterprises (SMEs) that, conceptually, summarizes and demonstrates the concerted efforts that a company may undertake to increase its leanness. This conceptual model was employed to create a questionnaire that was administered to survey the SMEs of India. The information gathered through this questionnaire was analyzed using the model developed by the researchers. Then fuzzy logic and systems approach were used to find out the effectiveness index (EI) of the organization.

The EI for system leanness at different organizational levels within an organization is determined using fuzzy logic and systems approach for 48 SMEs in different clusters. The average EI of the system was found to be 0.336 on a scale of 0–1 which indicates that the current state of lean implementation and its sustainability is very low and poor in Indian SMEs. This article outlines the key model components and describes how they were applied to analyze the data gathered from an industry study.

The research aims to make lean continuously sustainable by surfacing and eliminating the wastes in the Indian SMEs whenever it appears rather than using it as a cleaning tool. The present study was focused on India’s southern industrial areas and it was difficult to gather the information around the country due to its diverse industrial culture and geography. Hence, more research and the comparative study of the same that takes into account the various regions of the nation’s industrial lean behavior can be conducted.

The generalized sustainable lean framework analyzed using fuzzy logic and systems approach gives the current effectiveness of the leanness in SMEs of south India. This model can be effectively implemented in other areas of the nation to identify the scenario of lean and its sustainability and a final comprehensive model can be developed.

There is a dearth of comprehensive studies on the assessment of sustainability of the lean philosophy in Indian SMEs. With the help of combined fuzzy logic and systems approach, the model developed in this study evaluates the sustainability of the lean methodology using the EI used in SMEs by taking into account both the lean and sustainability factors as well as enablers like customer satisfaction, ethics, innovation and technology.

The paper focuses on bridging the existing literature gap on the role of leadership in influencing employee engagement considering the advancement in technologies. With this, the author explores how the three critical elements of service-based companies' business environment-artificial intelligence (AI) success, employee engagement, and leadership are interlinked and are valuable for raising the engagement level of employees.

A purposive sampling strategy was used to select the employees working in the respective companies. The survey was distributed to 150 senior management employees but responses were received from only 56 employees making the response rate 37.33%. Consequently, an empirical examination of these 56 senior management employees belonging to service-based companies based in Delhi NCR using a survey questionnaire was conducted.

The PLS-SEM (partial least squares structured equation modelling) revealed that AI has a positive role in affecting employee engagement levels and confirmed the mediation of leadership. The magnitude of the indirect effect was negative leading to a reduction in total effect magnitude; however, as the indirect effect model has a higher R square value, the inclusion of a mediating variable made the model more effective.

This study contributes to extending the existing knowledge of the academicians about the relationship theory of leadership, AI implementation in organizations, AI association with leadership and AI impact on employee engagement. The author extends the theoretical understanding by showing that more integration of AI-supported leadership could enable organizations to enhance employee experience and motivate them to be engaged. Despite its relevance, due to the limited sample size, focus on a specific geographic area (Delhi NCR) and the constraint of only using quantitative analysis, the findings open the scope for future research in the form of qualitative and longitudinal studies to identify AI-supported leadership roles.

The study findings are beneficial majorly for organizations to provide them with more in-depth information about the role of AI and leadership style in influencing employee engagement. The identified linkage enables the managers of the company to design more employee-tailored strategies for targeting their engagement level and enhancing the level of productivity of employees. Moreover, AI-supported leadership helps raise the productivity of employees by amplifying their intelligence without making technology a replacement for human resources and also reducing the turnover rate of employees due to the derivation of more satisfaction from existing jobs. Thus, given the economic benefit and societal benefits, the study is relevant.

The existing studies focused on the direct linkage between AI and employee engagement or including artificial intelligence as a mediating variable. The role of leadership is not evaluated. The leadership enables supporting the easy integration of AI in the organization; therefore, it has an important role in driving employee engagement. This study identifies the contribution of leadership in organizations by providing the means of enhancing employee satisfaction without hampering the social identity of the company due to the integration of AI.

The COVID-19 pandemic has had far-reaching effects on society and will continue to be a subject of study for researchers in the years to come. Businesses have implemented technologies that reduce reliance on physical currencies, such as e-commerce sites and contactless payments. This study aims to examine the users’ attitudes and behaviors toward mobile payments. The focus is on identifying the most effective techniques and approaches that businesses can use to encourage user adoption of mobile payments.

This study uses survey data from 396 active mobile payment users across the mid-west region of the USA to test the proposed hypothesis. The snowball sampling approach is used to sample the participants for the data collection. This study uses partial least squares structural equation modeling to test the ten hypotheses proposed in this study.

This study finds that organizational commitment and privacy customization can significantly overcome users’ protective attitudes toward mobile payments during the pandemic. In addition, providing users with privacy customization options can significantly encourage self-disclosure, which is crucial for transaction authentication and fraud detection.

Envisioned in the backdrop of the COVID pandemic, this is one of the earliest studies investigating the role of privacy customization, self-disclosure and organizational commitment on mobile payment adoption.

The aim of the paper is to rank and analyze the key strategies to sustainable finance adoption in the manufacturing sector using Fuzzy Delphi method (FDM), Interpretive Structural Modeling (ISM) and MICMAC (impact matrix cross-reference multiplication applied to a classification) analysis.

The study develops a novel framework to identify and analyze the mutual relationships among set of sustainable policies using extensive literature survey and experts opinion. Initially, the study found 14 strategies to implement sustainable finance with the help of vast literature. Then, the list of identified factors were screened through Fuzzy Delphi Method (FDM). Based on driving and dependence power, the final list of factors are divided into three categories.

The study findings reveal that “environmental rules and practices”, “financial incentives, tax reduction and subsidy”, have strongest driving power for promoting sustainable financial system in Pakistani manufacturing sector. Furthermore, “environmental awareness” and “long term vision” are found to be highly influenced by other corresponding elements in a system.

The ISM approach assists professionals, academics, and managers in identifying and ranking policies in implementing green business techniques. The hierarchical representation of ISM results provides a roadmap for decision-makers to navigate and prioritize factors effectively, facilitating the implementation of strategies that contribute to sustainable growth within organizations.

The study results provide interesting clues regarding green finance policies that provide the foundations, incentives, protections or other provisions that support the ecological conservancy’s mission. Specifically, the findings guide that government must offer research grants to private enterprises, research and development institutions, and universities to promote environmental protection and develop transformative technologies such as waste recycling, renewable energy, carbon capture, and power consumption.

The exploration of strategies for sustainable finance adoption with the help of mixed methodological approach and classification of these strategies on the basis of importance level is a new attempt in the field of manufacturing sector.

Libraries, as a peaceful arena for promoting knowledge and culture, are often challenged by various means, which are mostly linked to the material handling. However, literature shorts fall in either recognizing how material handling comes under threat or evaluating the influence of promising solutions on the library’s performance. Thus, this study aims to present a comprehensive understanding of material handling in the library through the identification of characteristics, key performance indicators (KPIs) and challenges of this driving factor.

This research benefits from a triangulation of qualitative methods, including systematic literature review, case study, in-depth semi-structured interview (SSI) and focus group meetings, to present a comprehensive understanding of material handling in the library.

The initial findings highlight the characteristics, i.e. incremental inventory, no packaging, open access order, etc., and the KPIs of material handling in the library: flexibility, productivity, service quality and cost. In addition, the challenges that limit the libraries’ performance are discovered based on field observation through five Nordic libraries (Norway and Finland), which are further studied for discovering the potential remedies. Corollaries advise that material handling challenges in the library are potentially tackled through three pillars, known as Triple E’s: employee, equipment and environment.

The performance of libraries is notably linked to the material handling processes and this agenda has received limited attention by academia. This study is among the initial research efforts in this context seeking to develop a comprehensive understanding of the characteristics, KPIs and challenges of material handling in the library and facilitate the improvement of library’s performance.

The study investigates the relationship between social presence dimensions and customer brand engagement (CBE) as well as the relation between customer brand engagement and purchase intention (PI) in the fashion retail metaverse with self-efficacy moderating between CBE and PI.

The data were gathered by conducting an online survey (n = 476) from young adults exposed to fashion retail metaverse platforms. The collected data were analyzed using structural equation modeling.

The findings discovered that social presence dimensions positively impact CBE, which substantially impacts the PI of young consumers in the fashion retail metaverse. The findings demonstrate that self-efficacy moderates the relationship between CBE and PI.

This study uses cross-sectional data in the fashion retail metaverse for young consumers. Future studies can use longitudinal data in the context of other industries and demographic profiles to assess changing customer behavior.

This study implies that customer experiences can be enriched through social presence dimensions, helping brands adapt their offers to create more engaging and rewarding customer interactions. It offers insights for brand managers aiming to augment the relationship between CBE and PI.

The study uniquely explores the relationship between social presence dimensions and CBE within the fashion retail metaverse. It examines self-efficacy as a moderator between CBE and PI, providing fresh insights into consumer behavior in the fashion retail metaverse.

The purpose of this study is to provide a holistic understanding of the factors that either promote or hinder the adoption of artificial intelligence (AI) in supply chain management (SCM) and operations management (OM). By segmenting the AI lifecycle and examining the interactions between critical success factors and critical failure factors, this study aims to offer predictive insights that can help in proactively managing these factors, ultimately reducing the risk of failure, and facilitating a smoother transition into AI-enabled SCM and OM.

This study develops a knowledge graph model of the AI lifecycle, divided into pre-development, deployment and post-development stages. The methodology combines a comprehensive literature review for ontology extraction and expert surveys to establish relationships among ontologies. Using exploratory factor analysis, composite reliability and average variance extracted ensures the validity of constructed dimensions. Pearson correlation analysis is applied to quantify the strength and significance of relationships between entities, providing metrics for labeling the edges in the resource description framework.

This study identifies 11 dimensions critical for AI integration in SCM and OM: (1) setting clear goals and standards; (2) ensuring accountable AI with leadership-driven strategies; (3) activating leadership to bridge expertise gaps; (4) gaining a competitive edge through expert partnerships and advanced IT infrastructure; (5) improving data quality through customer demand; (6) overcoming AI resistance via awareness of benefits; (7) linking domain knowledge to infrastructure robustness; (8) enhancing stakeholder engagement through effective communication; (9) strengthening AI robustness and change management via training and governance; (10) using key performance indicators-driven reviews for AI performance management; (11) ensuring AI accountability and copyright integrity through governance.

This study enhances decision-making by developing a knowledge graph model that segments the AI lifecycle into pre-development, deployment and post-development stages, introducing a novel approach in SCM and OM research. By incorporating a predictive element that uses knowledge graphs to anticipate outcomes from interactions between ontologies. These insights assist practitioners in making informed decisions about AI use, improving the overall quality of decisions in managing AI integration and ensuring a smoother transition into AI-enabled SCM and OM.

Green construction is increasingly vital in promoting sustainability within the construction industry. The development and promotion of green construction technologies are central to this endeavor. However, existing evaluations mainly target building components, construction projects or certain construction processes. There is a notable absence of research into the greenness of construction technologies. Assessing the greenness of construction technologies is crucial for streamlining resource utilization and reducing waste. To address this gap, this study aims to establish a Greenness of Construction Technologies (GCT) evaluation model using the method of analytic network process (ANP).

Green construction is increasingly vital in promoting sustainability within the construction industry. The development and promotion of green construction technologies are central to this endeavor. However, existing evaluations mainly target building components, construction projects or certain construction processes. There is a notable absence of research into the greenness of construction technologies. Assessing the greenness of construction technologies is crucial for streamlining resource utilization and reducing waste. To address this gap, this study establishes a GCT evaluation model using the method of ANP.

Among the four formwork technologies of plastic, steel, plywood and wooden formwork, the plastic formwork exhibits the best performance in terms of environmental friendliness, economic and social effects, while facing issues like material wastage and low static strength. The results align with practical observations which validates the model’s applicability.

This research contributes to the field by introducing the concept of greenness into construction technology evaluation for the first time. The establishment of the GCT evaluation model promotes the adoption of green construction technologies and advances sustainable practices in the construction industry.