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Digital supply chains require nascent technologies like blockchain and Internet of Things (IoT). There is a need to develop a roadmap for the implementation of these technologies, as they require a huge amount of resources and infrastructure. The purpose of this paper is to analyze the challenges of implementing blockchain-IoT integrated architecture in the green supply chain and develop strategies for the same.

After a thorough literature survey of Scopus-indexed journals and books, 37 barriers were identified, which were then brought down to 15 barriers after confirming with industry and academic experts using the Delphi method. Using the total interpretive structural modeling (TISM) method and cross-impact matrix multiplication applied to classification (MICMAC) analysis, the barriers were modeled, and finally, strategies were formulated using a concept map to handle the barriers in the blockchain-IoT integrated architecture for a green supply chain.

This paper presents the research on barriers that can be considered for incorporating blockchain and IoT in the green supply chain. It was found from the TISM model that environmental concerns are Level-1 barriers and need to be addressed by developing appropriate technology and allocating funds for the same. An integrated ecosystem with blockchain and IoT is developed.

The focus of this study was on the challenges of blockchain and IoT; hence, it is required to extend the research and find challenges for different industries and also analyze the criteria using other multi-criteria decision-making (MCDM) methods. Further research is required for the integration of blockchain-IoT with supply chain functions.

The transformation of a traditional supply chain into a green supply chain is possible with the integration of technologies. This research work and the strategies developed are useful to managers and practitioners working on technology implementation. Planning resources and addressing key barriers is possible with the concept maps and architecture developed.

Green supply chain management (SCM) is gaining importance in industry as well as the academic sector due to government Policies and norms worldwide for reducing emissions and encouraging environment-friendly production systems. Incorporating blockchain and IoT in a green supply chain will further digitize and increase transparency in supply chains.

We have done a categorization of all barriers based on the expert survey by academicians and industry experts from industries in India. The concept map helps in identifying possible solutions for the challenges and initiatives to be taken for the smooth integration of technologies in the green supply chain.

The imperative to conserve resources and minimize operational expenses has spurred a notable increase in the adoption of lean manufacturing within the context of the circular economy across diverse industries in recent years. However, a notable gap exists in the research landscape, particularly concerning the implementation of lean practices within the pharmaceutical industry to enhance circular economy performance. Addressing this void, this study endeavors to identify and prioritize the pivotal drivers influencing lean manufacturing within the pharmaceutical sector.

The outcome of this rigorous examination highlights that “Continuous Monitoring Process for Sustainable Lean Implementation,” “Management Involvement for Sustainable Implementation” and “Training and Education” emerge as the most consequential drivers. These factors are deemed crucial for augmenting circular economy performance, underscoring the significance of management engagement, training initiatives and a continuous monitoring process in fostering a closed-loop practice within the pharmaceutical industry.

The findings contribute valuable insights for decision-makers aiming to adopt lean practices within a circular economy framework. Specifically, by streamlining the process of developing a robust action plan tailored to the unique needs of the pharmaceutical sector, our study provides actionable guidance for enhancing overall sustainability in the manufacturing processes.

This study represents one of the initial efforts to systematically identify and assess the drivers to LM implementation within the pharmaceutical industry, contributing to the emerging body of knowledge in this area.

Since Industry 4.0 (I4.0) is a new idea in Bangladesh, this study supports I4.0 adoption. Companies struggle to implement I4.0 and fully profit from the fourth industrial revolution’s digital transformation due to its novelty. Although barriers to I4.0 adoption are thoroughly studied, the literature has hardly examined the many aspects that are crucial for I4.0 adoption in Bangladesh’s Ready-Made Garment (RMG) industry. So, the purpose of this study is to investigate the barriers of adopting I4.0 in relation to Bangladesh’s RMG industries to enhance the adoption of I4.0 by developing a framework. Ultimately, the goal of this research is to improve the adoption of I4.0 in Bangladesh.

Through a comprehensive analysis of the existing research, this paper aims to reveal the barriers that must be overcome for I4.0 to be adopted. For evaluating those barriers, a decision analysis framework based on the combination of Delphi technique and Decision-Making Trial and Evaluation Laboratory (DEMATEL) method has been developed. The use of DEMATEL has led to a ranking model of those barriers and a map of how the barriers are connected to each other.

The findings reveal that “I4.0 training”, “Lack of Motivation” and “Resistance to Change” are the most significant barriers for adopting Industry 4.0 in RMG sector of Bangladesh based on their prominence scores.

These findings will help the people who make decisions in the RMG industry of Bangladesh, such as company owners, managers and the executive body, come up with a plan for putting I4.0 practices into place successfully. The decision-making framework developed in this research can be utilized by the RMG industry of Bangladesh and other similar industries in developing countries to figure out how important each barrier is for them and how to get rid of them in order of importance.

As far as the authors are aware, there has not been a comprehensive study of the barriers inhibiting the adoption of I4.0 within the scope of Bangladeshi RMG industry. This work is the first to uncover these barriers and analyze them using the combination of Delphi technique and DEMATEL.

Green innovations (GI) is an emerging field that presents an opportunity to thrive in the competitive market. Nevertheless, in the field of green innovation, there is no clear and complete picture. To fill this gap the current study was conducted with the following objectives. (1) To identify existing knowledge on green innovation and offer bibliographic insights through a systematic literature review (SLR), (2) To comprehend the areas in which research is lacking within the territory of green innovation.

The SLR methodology was employed in this study, following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. A total of 381 articles published between 2015 and 2023 were extracted from Lens org. database for review. Additionally, a bibliometric analysis was conducted to fulfill the research objectives.

The findings revealed that the field of green innovation lacks sufficient scholarly attention, despite being an emerging area. As a result, several gaps have been identified, encompassing various aspects of green innovation. These gaps include areas such as green innovation behavior, green finance, barriers to green innovation, green product innovation, green technological innovation and more.

This study adds to the existing body of knowledge on green innovation by addressing identified knowledge gaps. In particular, this knowledge contributes to future researchers aiming to design and conduct studies that target these identified research gaps.

The purpose of this paper is to develop a software-library in the R programming language that implements the concepts of the interpretive coordinate, interpretive axis and interpretive plane. This allows for the automatic and reliable interpretation of results from the multiple correspondence analysis (MCA) as previously proposed and published. Consequently, the users can seamlessly apply these concepts to their data, both via R commands and a corresponding graphical interface.

Within the context of this study, and through extensive literature review, the advantages of developing software using the Shiny library were examined. This library allows for the development of full-stack applications for R users without the need for knowledge of the corresponding technologies required for the development of complex applications. Additionally, the structural components of a Shiny application were presented, leading ultimately to the proposed software application.

Software utilizing the Shiny library enables nonexpert developers to rapidly develop specialized applications, either to present or to assist in the understanding of objects or concepts that are scientifically intriguing and complex. Specifically, with this proposed application, the users can promptly and effectively apply the scientific concepts addressed in this study to their data. Additionally, they can dynamically generate charts and reports that are readily available for download and sharing.

The proposed package is an implementation of the fundamental concepts of the exploratory MCA method. In the next step, discoveries from the geometric data analysis will be added as features to provide more comprehensive information to the users.

The practical implications of this work include the dissemination of the method’s use to a broader audience. Additionally, the decision to implement it with open-source code will result in the integration of the package’s functions by other third-party user packages.

The proposed software introduces the initial implementation of concepts such as interpretive coordination, the interpretive axis and the interpretive plane. This package aims to broaden and simplify the application of these concepts to benefit stakeholders in scientific research. The software can be accessed for free in a code repository, the link to which is provided in the full text of the study.

Virtual goods consumption has risen dramatically in recent years. Recognizing the benefits of virtual goods in generating revenue for online game companies, marketers strive to understand the motives behind virtual goods purchases. We investigated the direct and indirect effects of functional, emotional, and social values through player satisfaction on purchase intention toward virtual goods among online players.

In total, we surveyed 332 online game players utilizing a structured questionnaire. We employed a multi-analytic approach combining partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) to examine the proposed relationships.

The findings show that all dimensions of value and player satisfaction significantly affect the intention to acquire virtual goods. However, social value does not exert a significant effect on player satisfaction. Moreover, we confirmed that player satisfaction mediates the relationships between functional value, emotional value, and purchase intention. Furthermore, NCA results indicated that all predictors in the model are necessary conditions of purchase intention for virtual goods.

These findings contribute to an enhanced understanding of purchase intentions among online game players from a symmetric (PLS-SEM) and asymmetric (NCA) perspective by proposing a multi-analytic approach.

The objective of this research is to investigate the barriers impacting the integration of Industry 5.0 (I5.0) in supply chain sustainability. By understanding these challenges, this study aims to provide valuable insights that can guide organizations in successfully implementing the transformative potential of I5.0. The ultimate aim is to improve operational efficiency and advocate for sustainable practices within supply chains.

Research has used industry expert interviews, a comprehensive literature review and the decision-making trial and evaluation laboratory approach for analysis. Industry expert interviews serve to capture first-hand insights from professionals well versed in the field, providing practical perspectives on the barriers to I5.0 adoption.

This study identifies technological challenges, organizational barriers, regulatory impediments and economic constraints as pivotal factors inhibiting the widespread adoption of I5.0 in supply chain sustainability.

This research serves as a foundation for future investigations into overcoming barriers to I5.0 adoption, guiding scholars and practitioners in refining strategies for successful implementation.

The findings offer practical insights for organizations aiming to adopt I5.0, informing decision-makers on key challenges and facilitating the development of targeted strategies to overcome them.

The social implications lie in fostering sustainable business practices through the adoption of I5.0, contributing to environmental responsibility and societal well-being.

This research contributes original insights from practitioners, policymakers and researchers in navigating the complex landscape of I5.0 adoption, ensuring meaningful contributions to both academia and industry.

Spiritual tourism is becoming a significant growth area of the Indian travel market, with more Indians opting to go on pilgrimage to popular religious cities. There are many spiritual destinations where some of this life's essences can be sought to enjoy harmony and peace. The study aims to prioritize motivators driving the intentions of the tourists to visit the spiritual destination.

The current study applied the analytical hierarchical process, a multi-criteria decision-making technique, on the sample of visitors from all the six spiritual destinations to rank the motivational factors that drive the intentions of the tourist to visit a spiritual destination.

The study's results postulated that spiritual fulfillment motives and destination atmosphere are the top prioritized motivations, while destination attributes and secular motives emerged as the least prioritized.

The research study provides valuable insights to the spiritual tourism industry stakeholders to target the tourists' highly prioritized motivations to augment the visits to a particular spiritual destination.

Previous research has explored the motivations and modeled their relationships with tourists' satisfaction and intentions. But, the present study has applied a multi-criteria decision-making technique to add value to the existing knowledge base.

This study examined the psychometric properties of intention to drop out and study satisfaction measures for first-year South African students. The factorial validity, item bias, measurement invariance and reliability were tested.

A cross-sectional design was used. For the study on intention to drop out, 1,820 first-year students participated, whilst 780 first-year students participated in the study on satisfaction with studies. Confirmatory factor analysis (CFA), differential item functioning (DIF), measurement invariance and internal consistency were used to test the scales.

A one-factor structure was confirmed for both scales. For the intention to drop out scale, Items 3 and 4 were identified with statistically significant item bias; however, these differences had no practical impact. Except for scalar invariance for language, sufficient measurement invariance was established. No problematic items were identified for the study satisfaction scale.

In essence, this study provides evidence of two short measures that are culturally sensitive that could be used as short and valid measures across contextual boundaries as practically valuable tools to measure intention to drop out and study satisfaction in diverse and multicultural contexts.

This study contributes to limited research on bias and invariance analyses for scales that can be used in interventions to identify students at risk of leaving the university and utilising psychometric analyses to ensure the applicability of these two scales in diverse and multicultural settings.

This study intend to investigate a theoretical model looking at how particular tourist emotions, such as “joy,” “love,” and “positive surprise,” might predict their behavior by looking at how satisfied they are with their whole experience when visiting spas, and to examine the relationship of emotional experience, destination image, satisfaction and intention to revisit for spa tourism.

A sample of 345 individuals who traveled to Alleppey as domestic tourists participated in the research study. A non-probability (purposive) sampling method in this study. The structural model was analyzed using Structural Equation modeling (SEM), and the path coefficients were examined to test the hypotheses.

The results supported the hypotheses, indicating that specific emotions, image of the destination, and satisfaction significantly impacted tourists' intentions to revisit Alleppey as a spa tourism destination. This study demonstrated that “emotions of joy, love, and positive surprise” have a considerable influence on the image of the destination and satisfaction. The findings reveal a substantial correlation between satisfaction and behavioral intention (“Intention to revisit”). The research suggests that a higher degree of satisfaction would encourage visitors to revisit the location.

The research suggests that a higher degree of satisfaction would encourage visitors to revisit the location. This research offers vital information for developing, planning, and putting into practice tourism policies in the spa tourism sector. This article focuses on domestic travelers who travel to Alleppey, so the conclusions may not be relevant to research utilizing foreign tourists.

According to the literature study, and to the authors` knowledge, only limited number of studies that look at spa tourism from a wellness perspective. Additionally, Alleppey is used in the study as the study’s setting, providing insight into the visitor experiences of this expanding spa tourism business. This study gives understanding about how emotional experience predicts behavioral intentions.