This study aims to identify the relationship between social self-efficacy, social innovation, resilience, and proactive personality concerning university students’ behavioral intention to engage in social entrepreneurship, particularly in emerging economies, like Bangladesh. A structured questionnaire was utilized to collect quantitative data from 540 students in various disciplines of study as part of the study's quantitative research methodology using partial least squares-Structural Equation Modelling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA). The analysis reveals that proactive personality traits are associated with the social entrepreneurship intention (SEI) and that leadership orientation is also significant to SEI. The study also demonstrates that social entrepreneurial activities tend toward higher social self-efficacy and resilience, making it crucial to focus on such characteristics while facing social risk and bearing innovations. This study's novelty lies in its focus on the unique combination of psychological traits—social self-efficacy, social innovation, resilience, and proactive personality—and their impact on university students' intention to engage in social entrepreneurship in emerging economies. Additionally, the research emphasizes the importance of integrating leadership skills and social innovation into academic curricula and policy development to foster social entrepreneurship. Practical implications indicate that leadership skills and social innovation should be included in the curricula of educational institutions, and supportive policies should be developed to create available resources for prospective social entrepreneurs.

Organizational resilience is a key aspect for sustaining comparative benefit and performance amidst uncertainties such as pandemics, political volatility, and financial crises. Despite its significance, limited studies have explored the potential sufficient solutions to resilience-enabling constructs, especially in emerging economies. This research combines the Resource-Based View (RBV) and Transaction Cost Economics (TCE) to propose a theoretical framework for understanding and predicting organizational resilience. Using survey data from 348 respondents serving corporate industries in Bangladesh, we employ Necessary Condition Analysis (NCA) and fuzzy set Qualitative Comparative Analysis (fsQCA) to identify causal configurations to predict organizational resilience. The findings reveal five configurations that are sufficient for achieving high resilience and four configurations associated with low resilience, highlighting the nuanced interplay between resources, costs, and adaptability. Specifically, flexibility, response, recovery, benevolence, and commitment must need conditions for achieving organizational resilience in NCA analysis. In fsQCA analysis, flexibility and commitment are core conditions, whereas response and information sharing are peripheral conditions for achieving high organizational resilience. This study strengthens resilient strategies by demonstrating the supplementary contributions of RBV and TCE. This combination offers policymakers actionable insights to develop resilient strategies that enhance organizational adaptability and performance in turbulent times.

Background: The adoption of technology in the tourism industry has become a worldwide trend aimed at enhancing effectiveness and efficiency in this sector. Objectives: This study investigates the drivers influencing e-tourism adoption in Bangladesh by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model with the addition of digital literacy and vacation packages. Methodology: This research proposes and tests a conceptual model grounded in complexity and configuration theory. We employ a mixed-method analytical approach, integrating Fuzzy-Set Qualitative Comparative Analysis (fsQCA) as a complementary tool to Structural Equation Modelling (SEM). Empirical validation was conducted using data from 297 online tourists. Results: Structural Equation Modelling (SEM) revealed that e-tourism adoption intention was significantly influenced by vacation packages, effort expectancy, performance expectancy, facilitating conditions, and digital literacy, whereas social influence did not show a significant effect. Furthermore, Fuzzy-Set Qualitative Comparative Analysis (fsQCA) identified five sufficient configurations of these causal conditions that lead to high levels of e-tourism adoption. Conclusion: The findings confirm the primary drivers of e-tourism adoption in Bangladesh and identify five specific, high-adoption pathways. Practically, these results enable e-tourism service providers and policymakers to strategically allocate resources, optimise platform design, and implement targeted digital literacy initiatives to accelerate e-tourism adoption rates in the region. Unique Contribution: This research provides a significant methodological advancement by integrating SEM and fsQCA to move beyond testing net effects and establish a detailed, configurational understanding of e-tourism adoption. Theoretically, it extends the UTAUT model by identifying new context-specific causal conditions relevant to travellers in Bangladesh. Key Recommendation: It is suggested that online travel service providers, marketers, and digital platform executives utilise the detailed findings of this study. Specifically, they should consider developing and implementing strategies that focus on optimising the five sufficient causal configurations identified by fsQCA and prioritise investments in the factors (e.g., performance expectancy, digital literacy) that significantly influence e-tourism adoption among Bangladeshi travellers.