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The corona virus outbreak has affected the entrepreneurial ecosystem adversely. This in particular has impacted on females. This study aims to identify the factors affecting the entrepreneurial intention (EI) of females under post-COVID adverse conditions.

A total of 340 articles were screened applying the preferred reporting items for systematic reviews and meta-analyses method. The EI of individuals undertaking different professional courses from three top National Institute Ranking Framework (NIRF) government institutes were then analyzed. In the expert analysis, the nominal group technique (NGT) and analytic hierarchy process (AHP) were used to identify and rank the crucial factors. Subsequently, in the exploratory analysis, a 19-item questionnaire was framed. The data was analyzed using SmartPLS 3.

Resilience, entrepreneurial education, self-concept and self-efficacy, social influence and opportunity perception were identified as critical indicators. Resilience was identified as the most significant factor. The partial least square structural equation modeling (PLS-SEM) revealed that all the factors except social influence had significant effect on the EI of females.

This study focuses primarily on factors affecting females in India. As ecosystems and support vary by region and country, the authors suggest that this study be replicated in different regions/countries in the future.

The potential entrepreneurs can use this study's reference to identify the abilities they need. The government and academic institutions can have skill/training programs to enhance the effect of important factors identified in the study.

While there is growing research of entrepreneurship and entrepreneurial marketing post-pandemic, there are a lack of Indian studies and female entrepreneurship studies.

This study aims to identify and assess the factors challenging the integration of artificial intelligence (AI) technologies in healthcare workplaces.

The study utilized a mixed approach, that starts with a literature review, then developing and testing a questionnaire survey of the factors challenging the integration of AI technologies in healthcare workplaces. In total, 46 factors were identified and classified under 6 groups. These factors were assessed by four different stakeholder categories: facilities managers, medical staff, operational staff and patients/visitors. The evaluations gathered were examined to determine the relative importance index (RII), importance rating (IR) and ranking of each factor.

All 46 factors were assessed as “Very Important” through the overall assessment by the four stakeholder categories. The results indicated that the most important factors, across all groups, are “AI ability to learn from patient data”, “insufficient data privacy measures for patients”, “availability of technical support and maintenance services”, “physicians’ acceptance of AI in healthcare”, “reliability and uptime of AI systems” and “ability to reduce medical errors”.

Determining the importance ratings of the factors can lead to better resource allocation and the development of strategies to facilitate the adoption and implementation of these technologies, thus promoting the development of innovative solutions to improve healthcare practices.

This study contributes to the body of knowledge in the domain of technology adoption and implementation in the medical workplace, through improving stakeholders’ comprehension of the factors challenging the integration of AI technologies.

The purpose of the study is to examine how risk factors contribute to the occurrence of defects in a process. By analyzing these risk factors in relation to process quality, the study aims to help organizations prioritize their resources and efforts toward addressing the most significant risks. These challenges, integrated with the emerging concept of Quality 4.0, necessitate a comprehensive risk assessment technique.

Fuzzy logic integrated with an analytic network process is used in the process failure mode and effects analysis for conducting risk identification and assessment under uncertainty. Through a mathematical model, the linkage of risk with Six Sigma is established and, finally, a value–risk matrix is developed for illustrating and analysing risk impact on process quality.

A case study on fused filament fabrication demonstrates the proposed methodology’s applicability. The results show its effectiveness in assessing risk factors’ impact on Six Sigma metrics: defects per million opportunities/sigma level.

By integrating qualitative assessments and leveraging available data, this approach enables a more comprehensive understanding of risks and their utilization for an organization’s quality improvement initiatives.

This approach establishes a risk-centric Six Sigma assessment method in accordance with the requirement of ISO 9001:2015 and in the context of Quality 4.0.

Blockchain technology can potentially address the challenges of information storage, sharing and management and improve them further in an organization and sector as a whole. This study aims to investigate the effects of technology, organization and environment on the behavioral intention of employees to adopt blockchain in the Indian insurance sector and the mediating role of knowledge management practices.

A structured questionnaire was used to collect a sample size of 390 responses based on convenience sampling. Partial least square structural equation modeling was used to analyze the data.

The findings highlighted that organizational factors, followed by technological factors, significantly impact employees' behavioral intentions. The results established that the impact of environmental factors is insignificant on blockchain adoption intention. Knowledge management practices significantly mediate the relationship between organizational factors, technological factors and behavioral intention.

The results indicate that organizations must prioritize organizational factors (technological competence, top management support and financial readiness) and knowledge management practices (knowledge creation, sharing and retention) to positively impact employees' behavioral intentions and ensure successful and effective technology adoption.

Using the Technology-Organization-Environment framework, the study tests the conceptual model, showing the relationship between technological, organizational and environmental factors, behavioral intention and knowledge management practices. The role of knowledge management practices in technology adoption within organizations has been scarcely explored. This study adds significant and novel contributions in this area.

Under the rural revitalization, the effect of China's implementation of rural prefabricated housing is not obvious. Cost has become the biggest obstacle to its development. Therefore, it is necessary to study the factors influencing the cost of prefabricated buildings in villages and clarify the focus of cost control.

This paper focuses on the whole process of prefabricated housing construction in villages in China and uses grounded theory to identify and screen out 27 related factors that affect the construction cost of prefabricated buildings. A system dynamics model is used to dynamically analyze the influencing factors. The engineering examples in rural areas of southern Shaanxi are simulated. Finally, five key factors that influence cost are obtained. Based on this, cost control countermeasures are proposed for rural prefabricated housing in southern Shaanxi.

The results show that: the key factors affecting the cost of prefabricated buildings in villages include the selection of production methods, the degree of design standardization, the quality of construction personnel, the level of construction technology and the circulation cycle of molds. The cost of prefabricated housing in villages can be controlled through five aspects: mass production of components, design exchange and feasibility analysis, improvement of employee professionalism, strict selection of construction schemes and technologies and improvement of mold turnover rate.

The system dynamics model applied in this paper is based on the idealized state. The system boundary is narrow and has a certain subjectivity. It needs further detailed research to make it closer to the engineering practice. In addition, this paper applies the rural engineering example in southern Shaanxi to carry out a single case study, and the universality of the research results needs to be further tested. There are many village construction projects and building types, so the research results can be further enriched through large sample research.

Rural construction is an important step in the implementation of rural revitalization. Exploring the factors that affect the key costs of prefabricated buildings in villages and towns in view of the particularity of rural areas will help provide a reference for their cost control and help the rural development of prefabricated houses.

The research results of this paper can provide a reference for the development of prefabricated buildings in other rural revitalization areas.

Different from the traditional research on urban prefabricated buildings, this paper focuses on rural areas and explores the core factors affecting the cost of prefabricated buildings from the micro level. This study establishes a system dynamics model suitable for the cost control of prefabricated housing at the village level and provides methods for its cost control. Based on the identified key factors affecting costs, cost control measures were proposed for prefabricated housing tailored to the unique characteristics of villages.

This study aims to develop a model for readiness measurement and to study readiness levels for online testing of undergraduate students in Thailand’s distance education programs.

In total, 870 undergraduate students enrolled in the 2022 academic year of a Thai university were sampled for the study. The samples were divided into two groups: Group 1 comprised 432 students who underwent exploratory factor analysis (EFA) and Group 2 comprised 438 students who underwent second-order confirmatory factor analysis (CFA). Both were multi-stage random samples. Descriptive statistics, item-total correlations (ITCs), coefficient correlations, EFA and second-order CFA were used.

The readiness for the online testing model comprised 5 factors and 33 indicators. These included self-efficacy (SE) in utilizing technology (nine indicators), self-directed learning (SL) for readiness testing (six indicators), adequacy of technology (AT) for testing (five indicators), acceptance of online testing (AC) (seven indicators) and readiness training for testing (six indicators). The model was congruent with empirical data, and the survey results indicated that students were highly prepared at the “high” level.

This study disclosed several factors and indicators involved in the readiness for online testing. The university may use these findings in preparing its students for online testing for better achievement.

These findings may serve as a framework for the analysis of the readiness issues for online testing of undergraduate students and also offer guidance to the universities preparing to offer online testing.

The paper aims to clarify the relationship of various enablers of supply chain (SC) management like lean enablers, agile enablers and leagile enablers. It proposes modeling the enablers to find the most appropriate strategy or methodology for determining the lean enabled SC agility.

The paper proposed the fuzzy SWARA-WASPAS methodology for determining the role of lean in enabling the SC agility. Also in continuation the AHP methodology is applied to find the priority weightage and ranking of leagile enablers, and a comparative analysis is done to select the best approach among the above two methodologies so that it would be beneficial for all the stakeholders.

The paper provides the investigation and identification of 28 lean enablers, 11 agile factors which are highly responsible to affect any SC specially focusing of automobile sector. Apart from above 9 leagile enablers were also identified in the paper. Finally, the comparative analysis has been done in the results obtained by two methodologies – AHP & fuzzy SWARA-WASPAS – to determine the lean enabled SC agility, and also to which strategy should be adopted by the organizations as per the customized requirement of their SC.

The research limitation is that in future, there may be more number of lean, agile and leagile enablers which may be explored by different researchers in their findings, which may vary the output result accordingly. Though the research implications focus on having an advantage and impact on all aspects whether it is social, economic or commercial, there is a possibility of exploration of new and better decision-making tools and approaches in future. Also, the researchers are encouraged to test the proposed propositions further by taking case study of any automobile manufacturing organizations for the validation of the results.

The paper includes implications for the development of a powerful interrelationships between lean enablers, agile enablers and leagile enablers, which will help organization and the managers to take decisions regarding selection of best strategy appropriate to them to enhance their SC. This will also help new researchers of the field to take help of the research findings for exploring new and better optimization tools and techniques in future.

The findings of the research work will definitely help society, as the successful implementation of the lean, agile or leagile strategies in their SC system will leads to an increase in their efficiency and productivity, which will ultimately results in huge advantage to all the stakeholders directly or indirectly connected with the organization. The productivity dynamics cycle will also improve which will lead to more benefits to all in the market and achieving higher living index with better living standards.

This paper fulfills an identified need to study the various enablers of lean, agile and leagile SC, as well as their interrelationships. Also there is a need to understand the importance and effect of lean in enabling the SC agility.

This paper analyses and assesses the effect of firm-specific determinants (FSDs) on supply-chain performance (SCP) and export performance (EP). It examines SCP’s influence on EP and its mediating effect on the relationship between FSD and EP.

This paper develops a theoretical framework based on the resource-based view (RBV) and dynamic-capability theory to understand SCP’s role in the FSD-EP link while empirically validating using the Indian automobile industry segments (IAIS) data. The sample frame comprises all listed firms in IAIS between the financial year 2010-11 and 2021–22, with continuous data availability throughout the considered timeline. The paper employs factor analysis for dimension reduction, a panel-data-fixed-effect model to analyze the relationships, bootstrap to test the mediation effect and focus-group discussion for validating the results obtained through statistical analyses.

FSD directly influences SCP’s efficiency aspect and EP. Distribution efficiency and inventory efficiency characteristics of SCP directly impact EP and completely mediate the relationship between FSD and EP.

This study provides significant insights into how firms can increase EP by focusing on firm-specific and SCP-related factors. To improve EP, firms should concentrate on enhancing distribution and inventory efficiencies. Firms must focus on critical firm-level factors like age, size and raw-material import capability to increase their ability to solve SC-specific barriers and improve SCP, resulting in enhanced exports.

This study investigates the impacts of FSD on SCP and EP and examines the mediating effect of SCP on the relationship between FSD and EP. Such a mediating role of SCP has rarely been probed in the literature.

This study aims to investigate the influence of external factors identified by the Political, Economic, Social, Technological, Environmental and Legal (PESTEL) framework on corporate social responsibility (CSR) performance in Vietnamese construction firms.

The snowball sampling method was employed to gather 182 validated responses. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the research analyzed how these factors correlate with CSR practices under institutional theory.

Results indicated that social, economic, environmental, legal and technological factors positively impacted CSR performance. Among these, social factors had the most significant effect, followed sequentially by economic, environmental, legal and technological influences. Intriguingly, political factors demonstrated no significant association with CSR performance.

The strong impact of social factors confirms that societal norms and cultural values are critical in shaping corporate behavior in Vietnam. Firms can leverage this insight by intensifying their community engagement and social investment. Additionally, the negligible role of political factors in shaping CSR suggests that firms might not need to focus heavily on political engagement in Vietnam. However, firms should remain aware of legal changes as legal factors influence CSR outcomes.

Despite CSR’s growing importance, there remains a notable research gap regarding how external macro-environmental factors influence CSR performance, particularly within the construction industry. The findings emphasize the importance of aligning business strategies with socioeconomic and environmental aspects.

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.