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.
This study develops a Pelican Optimization Algorithm (POA)-based electro-thermal design framework for a vector-controlled permanent-magnet synchronous motor (PMSM) variable-frequency drive (VFD). The proposed tuning procedure adjusts the PI gains of the speed, torque, and cooling loops while considering tracking error, semiconductor losses, junction temperature, and cooling effort. The coupled PMSM–inverter–thermal model is simulated in MATLAB/Simulink and tested in real time through HiL testing under the constant-speed and variable-speed conditions. In the constant-speed test, the speed overshoot was reduced from 14.5% to 0.8%, and the settling time was reduced from 0.40 s to 0.05 s. In the variable-speed test, the overshoot was reduced from 13.6% to 0.5%, and the settling time was reduced from 0.30 s to 0.02 s. The optimized cooling controller maintained a model-predicted inverter junction temperature of approximately 24.4–24.6 °C, while the calculated inverter electrical-conversion efficiency increased from approximately 84% to 92% under the adopted semiconductor-loss model. Out of 30 independent runs, the POA approach yielded the lowest average final optimal objective function value compared with the other metaheuristic approaches with an equal number of function evaluations. The HiL responses were very similar to the simulated electrical behavior, allowing the use of offline-optimized fixed-gain controllers in real time without online population-based optimization.
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.
