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The ability of a business to outperform its rivals is known as its competitive edge, and it presents special difficulties in the context of the “digital revolution,” or the fourth industrial revolution. To obtain a competitive edge in the startup operations 4.0 era, this study aims to examine the organizational, technological and competence-related challenges presented by Industry 4.0. It does this by concentrating on the tools, competencies, methods, approaches, tools and strategies that are crucial. Using the Total Interpretive Structural Modeling (TISM) technique, the goal is to find, analyze and classify enablers for startup operations 4.0.

A closed-ended questionnaire and planned interviews were used in the data collection process. In startup operations 4.0, the cross-impact matrix multiplication applied to classification method is used to rank and categorize competitive advantage factors, whereas the TISM technique is used to analyze how components interact.

The study highlights the critical significance of the “Internet of Things (IoT),” “information technologies,” “technological platforms,” “employee empowerment,” “augmented reality (AR)” and “operational technologies” in its identification of 12 enablers for startup operations 4.0.

The main focus of the study is on the variables that affect startup operations 4.0’s competitive advantage.

Academics and important stakeholders can better understand the factors influencing competitive advantage in startup operations 4.0 with the help of this research.

Large businesses have been profoundly impacted by Industry 4.0 principles; however, startup operations 4.0’s competitive advantage has not received as much attention. This paper offers a fresh take on the concept of competitive advantage in startup operations 4.0 research.

Organizations have increasingly been compelled to engage in ecological businesses in recent decades, necessitating identifying environmental practices contributing to enhanced sustainability. One of the main reasons for doing this research is to see how far down the path to green transformational leadership (GTFL) in Green Human Resource Management (GHRM) practices in the healthcare industry in Pakistan. Additionally, this research aims to analyze how this change affects the long-term success of businesses in sustainable performance (SP).

To identify factors related to the study variables, the research utilized master journals, as well as the Web of Science and Scopus databases. The ISM-DEMATEL (Interpretive Structural Modeling - Decision Making Trial and Evaluation Laboratory) technique was employed to establish a hierarchical model. This model facilitated the identification of cause-and-effect relationships among factors, which were further elucidated using the DEMATEL interrelationship diagram.

The analysis of the results indicates that Green Training (F4), Green Job Analysis (F1), Intellectual Stimulation (F10), and Green Product Innovation (F9) are the primary factors that have a significant impact on achieving Environmental Policies and Regulations (F13), and Subjective Environment Norms (F14) of SP factors.

The study is implemented in the healthcare industry of Pakistan, with a focus on practical and managerial aspects. It encourages managers to develop and adapt their human resources policies and environmental strategies. Implementing safety health standards is crucial to mitigate the detrimental effects on the environment. The research was carried out during the period of the pandemic. The scope of this study was restricted to the healthcare industry in Pakistan.

In order to improve SP, this study presents a unique strategy combining sustainability into decision-making procedures with the function of GTFL in GHRM. Implementing safety health standards is crucial to mitigate the detrimental effects on the environment.

This study aims to assess the factors influencing the effective implementation of extended producer responsibility (EPR) practices within the context of sustainable accounting goals and circular economy principles. It seeks to provide insights into the significance of EPR policies for manufacturing industries striving to enhance their environmental, social and governance performance while ensuring sustainable accounting of their business operations.

The methodology proposed in this article is based on the decision-making trial and evaluation laboratory (DEMATEL) technique. This approach formulates a structural framework for evaluating influential elements among critical recognized factors. By using DEMATEL, the study examines the interconnectedness between assessed factors through a cause-and-effect diagram, facilitating the integration of EPR into sustainable accounting practices.

The research findings reveal that the most impactful contributors to sustainable accounting practices of EPR within the framework of sustainable development goals and circular economy are producers, consumers, eco-design, public awareness and the support of local authorities. These findings underscore the importance of considering these factors in implementing EPR and advancing sustainable accounting practices.

This paper contributes to the literature by proposing a DEMATEL-based model for evaluating the factors affecting the implementation of EPR within the context of sustainable accounting goals and circular economy principles.

As the global financial ecosystem grapples with the complexities of modernization, blockchain technology emerges as a pivotal catalyst, offering the banking, financial services, and insurance (BFSI) industry unprecedented opportunities for secured digital transformation and enhanced customer trust. To gain a comprehensive understanding of blockchain technology adoption, this study aims to identify the factors and establish the contextual interrelationships among them.

The authors have identified the factors affecting blockchain technology adoption in BFSI industry through extensive literature review and experts’ interviews. After identification of factors, contextual relationship has been established based on experts’ opinion and total interpretive structural modeling (TISM) approach. Furthermore, factors are categorized into autonomous, dependent, linkage and driving variables using cross-impact matrix multiplication applied to classification analysis.

The TISM-based structural model is divided into eight different hierarchal levels in which Government support is placed on the lower most layer (level 8) which indicates that this is the most crucial factor in blockchain adoption. Further social influence and security are placed on seventh and sixth level in the hierarchy.

The results of this study will help the policymakers to direct the resources from the most crucial factor to other factors in the hierarchy as per their relevance. In essence, this study serves as a guiding compass, steering the course of blockchain technology adoption in the BFSI sector toward a more secure and digitally transformed future.

In the current landscape, blockchain technology remains in its nascent stage, leaving ample room for exploration and innovation. This study stands as the pioneering effort to comprehensively identify and establish the contextual relationships among the adoption factors of blockchain technology within BFSI industry. Through rigorous TISM analysis, this paper enriches the existing body of knowledge on blockchain technology adoption.

This study aims to develop cause-and-effect relationships among the critical success factors (CSFs) of fintech adoption and rank these CSFs based on their importance in the model.

To achieve the objectives of the study, the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) approach was used. The data was collected from 16 experts using a questionnaire.

The findings demonstrated the interrelationships among the CSFs. In total, 16 critical factors were recognized as causal factors, and the remaining eight were considered effect factors. The CSFs were ranked based on their importance in fintech adoption.

This study is novel as it investigates CSFs of fintech adoption using FDEMATEL, and it contributes to understanding the nature of these factors and how they affect fintech adoption. The findings propose a significant basis to deepen fintech adoption and deliver a clue to design a practical framework for fintech adoption.

The precise and sensitive methods for authentication and differentiation of natural and synthetic indigo dyes are required for assurance of textile safety and public health. This study aims to develop a fast and simple method to distinguish natural indigo from synthetic one.

A static headspace gas chromatography-mass spectrometry (GC-MS) method was developed for identification of natural and synthetic indigo samples. Natural indigo samples prepared from three different plants and synthetic indigo samples from three famous manufacturers in China, were involved in this study, along with some nonindigo blue samples (such as direct blue, active blue and neutral blue). The yarns and fabrics dyed with natural and synthetic indigo were also analyzed by the GC-MS method.

High levels of aniline (21.87%–71.59%) or N-methylaniline (25.26%–38.73%) were detected only in synthetic indigo samples (1 g) using the static headspace GC-MS method. The yarns and fabrics dyed with the synthetic indigo were also detected with residual aniline (0.47%–14.86%) or N-methylaniline (6.59%–40.93%).

The results clearly demonstrated that aniline or N-methylaniline can be used a diagnostic marker for distinguishing natural indigo from synthetic indigo. The proposed static headspace GC-MS method is a rapid, simple and convenient approach for differentiation of natural and synthetic indigo, as well as for the yarns and fabrics dyed with synthetic indigo.

Given the rising complexities around organizational resilience, this study identifies and explains the critical enablers of developing organizational resilience (OR). It offers logical reasoning into the interactions and interdependencies among the identified elements with the help of a hierarchical model of the antecedents of OR.

This paper deployed a mixed methodology research design. Firstly, critical enablers of OR are identified from the literature review. Second, contextual relationships and interactions between the enablers are examined using modified total interpretive structural modeling to derive a hierarchical model among the antecedents that characterize OR. Lastly, a survey study including industry experts is used to statistically verify the model links.

Developing resilience lies at the intersection of organization science and strategic management, involving the interaction of factors within an organization’s strategic behaviors, organizational practices, and people processes. The study identifies twelve antecedents of OR. The resultant interpretive hierarchical model helped decipher internal relationships among the antecedents. The proposed model helps determine how organizations move through different phases (before, during, and after) of turbulences and how organizational resilience helps overcome negative spirals.

This research is original and refreshing in its attempt to necessitate resilience as a processual characteristic needed to survive, thrive, and transform amidst business tensions. The hierarchical model of antecedents garners a better understanding of how their interactions and interdependencies help organizations enhance their capacity to adapt and build resilience in organizational systems and processes. It answers questions of “what,” “how,” and “why” relevant to theory building in OR.

Social entrepreneurship, leveraging economic activities to achieve social benefits, offers women the opportunity to freely and actively shape the contours of their work in meaningful ways. This study aims to examine how Chinese women use job crafting in social entrepreneurship to align their gender identity, forge meaningful work and new relationships and navigate mixed gender expectations.

The study is based on 19 in-depth interviews with young women engaging in social entrepreneurship in China. Using a grounded theory approach, the study explores how women craft their gender identity into the unconventional career path of creating their social venture, focusing on the creative combination of task, relational and cognitive crafting in shaping social entrepreneurship.

The findings suggest that women make social entrepreneurship meaningful by actively aligning their gender experiences to delineate a relational and cognitive causal path between their social enterprise, their identity as women and their moral values. By working for a larger social cause, women may cognitively reframe their gender identity to compromise financial performance for social impact.

Current studies on social entrepreneurship in China have yet to examine its development through a gender lens. This study uses job crafting to highlight the distinctive gender meaning-making process for Chinese women to enhance their work identity and to challenge normative gender expectations. The study shows that job crafting enables women to view their social ventures as a means of gender empowerment, helping them to reconcile the paradoxical pressures of normative gender expectations and scaling up their businesses.

This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.

The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.

DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”

This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.

Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.

The agri-food industry is experiencing a revolutionary shift due to the introduction of Industry 4.0 technologies to improve efficiency, transparency and sustainability. The importance of agri-food supply chains (AFSC) in promoting sustainability is expanding as the globe struggles with issues including resource scarcity, climate change and population growth. In order to better understand how Industry 4.0 might improve sustainability in a world that is changing quickly, this work aims to focus on identifying various sustainability assessment factors influencing AFSC to increase overall sustainability, minimize resource consumption, cut waste and streamline operations.

Important sustainability assessment factors are identified from the past academic literature and are then validated using the fuzzy-Delphi method. A method called decision-making trial and evaluation laboratory (DEMATEL) is used to examine and analyze structural models with complex causal linkages. The results are then validated using sensitivity analysis.

The factors that emerged as the highest ranked for evaluating the sustainability of Industry 4.0 in AFSC are market competitiveness, and knowledge and skill development, followed by resource efficiency. Industry 4.0 technologies are essential for increasing the marketability of agricultural products because of the major implications of market competitiveness. The significance of knowledge and skill development draws attention to Industry 4.0’s contribution to the promotion of chances for farmers and agricultural employees to increase their capability.

By outlining the nexus between Industry 4.0 technologies and sustainability, the study presents a comprehensive framework that would be relevant for researchers, policymakers and industry stakeholders who want to leverage Industry 4.0 technology to build more sustainable AFSC in the future. The study findings can help the farmers or producers make sensible choices that adhere to sustainability standards and guarantee long-term financial viability.

The originality of this work lies in the identification of sustainability assessment factors especially for AFSC in the era of digitalization which has not been discussed previously.