Mixed methods research has a prominent place in tourism and hospitality scholarship, yet adoption has advanced faster than methodological selectivity, which leaves many studies combining qualitative and quantitative strands without explaining why both strands were necessary, how the strands were related, or what integrated inference their combination alone made possible. Drawing on a critical review of 222 mixed methods studies published in leading tourism and hospitality journals, this article identifies dominant patterns, strengths and weaknesses, and underused possibilities. In response, the article offers a stage-based guide that links problem formulation, purpose selection, design fit, sampling bridges, strand roles, integration, divergence handling, reporting architecture, and contribution development. In doing so, the article advances a more demanding standard for mixed methods scholarship and argues that the next phase of tourism and hospitality research should shift from routine method combination toward integrated inferences that are conceptually sharper, methodologically clearer, and practically more useful.
Big Data Analytics (BDA) has become an important organizational capability for strengthening firm performance and sustaining competitive advantage. This study examines how Big Data Acceptance, Big Data Assimilation, and Big Data Routinization contribute to Organizational Performance with Big Data Analysis (OPBDA), and how the resulting organizational benefits, such as smarter management decisions, increased innovation and agility, and improved organizational efficiency, relate to sustainable competitive advantage. Survey data collected from 414 respondents in China were analyzed using IBM SPSS 23.0 and partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4. The findings show that acceptance, assimilation, and routinization of big data positively and significantly influence OPBDA. OPBDA, in turn, significantly improves management's decision-making and organizational efficiency, while its relationship with Increased Innovation and Agility is not statistically significant. The study highlights the importance of effectively assimilating and routinizing BDA within organizational processes to strengthen managerial decision-making, operational efficiency, and sustained competitive positioning.
The study tests a new approach to linking collective environmental identity (CEI) and high organizational identity (HOI) to environmental conservation through affective, normative, and continuance commitment. We used PLS-SEM version 4 to examine these connections using cross-sectional data from hotel workers across the US. The results show that collective environmental identity is linked to affective, normative, and continuance commitment, and that a strong organizational identity further strengthens these commitments. Affective, normative, and continuance commitment also shape the link between employees' environmental conservation and collective environmental identity. These results show how important it is to have dedicated workers who support sustainable practices. Companies, especially those in the rapidly growing US hotel industry, can use this framework to embed sustainability in their business plans by engaging and empowering employees. This study contributes to the hospitality literature by demonstrating how identity-based commitments lead to concrete actions that benefit the environment. This has not been examined in the context of hotel employees before.
Heavy metal (HM) toxicity is a major concern in aquaculture due to bioaccumulation, persistence, and toxicological nature of these pollutants. These substances enter the aquatic environment and pose a serious threat to species. Although traditional remediation methods are used to eliminate HMs, they have significant drawbacks, including low processing speed, operational challenges, high costs associated with nanoparticle production, and regulatory issues. To address these limitations, nano-bioremediation – the integration of nanoparticles with bioremediation – has emerged as a rapid, sustainable, and eco-friendly solution that mitigates HM effects. Nano-bioremediation enhances the effectiveness of HM removal by harnessing the combined properties of nanoparticles and microorganisms, reducing ecological concerns and economic costs. Furthermore, nano-bioremediation improves contaminant degradation, increases stability, and lessens environmental and aquaculture impacts. Biogenic nanoparticles are preferred due to their large-scale synthesis, scalability, rapid production, eco-friendliness, and absence of harmful contaminants. Overall, this review critically compares conventional techniques with nano-bioremediation, highlighting its advantages, applications, significance, and optimisation of nano-microbe interactions for the future.
The incorporation of artificial intelligence (AI) and machine learning (ML) into microalgal research is transforming biomass generation, biofuel synthesis, and wastewater remediation strategies. Sophisticated ML techniques, such as artificial neural networks (ANN), support vector machines (SVM), and genetic algorithms (GA), facilitate precise simulation and forecasting of highly intricate microalgal systems. Although constraints related to data accessibility and model scalability persist, ML-based methodologies are increasingly demonstrating their value in enhancing the sustainability and operational efficiency of microalgal processes. Simultaneously, technoeconomic analysis (TEA) has become an indispensable framework for assessing biorefinery viability through systematic evaluation of life-cycle environmental burdens. Recent progress in TEA methodologies has strengthened iterative design optimization, uncertainty quantification, and user accessibility via open-source computational platforms. Broader systems boundaries now account for policy mechanisms, performance during end-use phase, and international market dynamics, thereby reinforcing TEA’s contribution to sustainable bioeconomic advancement. Collectively, these computational and analytical innovations are expediting the deployment of scalable and economically feasible microalgal technologies. Highlights
