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This study examined the revisit intention of tourists in Muslim-friendly hotels in nexus with Halal-friendly hotel attributes (social environment, facilities, food and beverage, locals and staff, and services), corporate image, customer engagement, perceived value, and service encounter evaluation. It further examined the mediating role of corporate image, customer engagement and perceived value between Halal-friendly hotel attributes and service encounter evaluation.

Using a structured survey instrument, 390 valid responses were gathered from international tourists who visited Malaysia. The collected data were analyzed using Partial Least Squares – Structural Equation Modeling (PLS-SEM) to test the assumed relationships.

The research shows that corporate image (CI), customer engagement (CE), and perceived value (PV) are directly influenced by Halal-friendly hotel attributes (HFHA) and exert influence on service encounter evaluation (SEE). Findings also suggest the mediating effect of CI, CE, and PV between HFHA and SEE. SEE further positively and significantly enhances the revisit intention and positive word-of-mouth.

The findings of this study stipulate that hotel owners should ensure the presence of HFHA in their offers to retain their existing guests and ensure their positive communication towards potential visitors through developing a fascinating image, engagement, and lucrative value.

Grounded on the attribution theory, this study contributes to the Halal tourism literature by exploring the role of Halal-friendly hotel attributes in enhancing the hotel’s image, customer engagement, and perceived value to enhance positive service encounter evaluation and revisiting intention for the hotel.

This paper aims to explore the use and impact of social media, specifically Twitter (now X), in political mobilization in Pakistan. It focuses on the events followed by the no-confidence motion against Imran Khan as Pakistan’s prime minister in April 2022 and the protest campaign that ensued, facilitated through the strategic use of the Urdu hashtag #امپورٹڈ_حکومت_نامنظور (translated as “imported-government unacceptable”) on Twitter, both within and outside Pakistan.

Using Web scraping, data from Twitter was extracted and analyzed between 2022 and 2023. By probing into user account profiles and interactions with this hashtag, this paper investigates the claims surrounding the hashtag’s popularity, by identifying suspicious accounts and their contributions in the trending of the hashtag.

Findings suggest that the claim of the hashtag's unprecedented success was overhyped, further suggesting that the popularity and impact of the social media campaign were exaggerated. Despite high engagement rates, the study indicates a discrepancy between perceived influence and actual impact on public sentiment and political mobilization.

This paper contributes to the literature on social media’s role in political mobilization and agenda-setting in the Pakistani context. More generally, understanding hashtag dynamics and their impact on shaping public opinion, may be beneficial to academics and practitioners in better understanding the role of digital platforms in the politics.

With ambidextrous innovation (AI) gaining paramount importance in the manufacturing sectors of emerging markets, this research aim to explore how leadership and management support (LMS) amplify this type of innovation by leveraging knowledge sources (KS). The study further probes the knowledge management capability (KMC) as moderating effect between KS and AI.

Using the convenient random sampling technique of a sample of 340 professionals within Pakistan’s manufacturing realm, data was collated via a structured questionnaire. The subsequent analysis harnessed the power of the variance-based partial least squares structural equation modelling approach.

This research underscores the pivotal role of LMS in elevating both facets of AI i.e. exploitative innovation (ERI) and exploratory innovation (ERT). KS emerge as a vital intermediary factor that bridges LMS with both types of innovation. Notably, the potency of KS in driving AI is significantly boosted by an organization’s KMC.

This study fills existing gaps in contemporary research by offering a nuanced perspective on how LMS enrich an organization’s dual innovation spectrum via KS. It sheds light on the symbiotic interplay of leadership, knowledge flows and innovation in Pakistan’s burgeoning manufacturing sector.

Recent advancements in technology have led to the exploration of solar-based thermal radiation and nanotechnology in the field of fluid dynamics. Solar energy is captured through sunlight absorption, acting as the primary source of heat. Various solar technologies, such as solar water heating and photovoltaic cells, rely on solar energy for heat generation. This study focuses on investigating heat transfer mechanisms by utilizing a hybrid nanofluid within a parabolic trough solar collector (PTSC) to advance research in solar ship technology. The model incorporates multiple effects that are detailed in the formulation.

The mathematical model is transformed using suitable similarity transformations into a system of higher-order nonlinear differential equations. The model was solved by implementing a numerical procedure based on the Wavelets and Chebyshev wavelet method for simulating the outcome.

The velocity profile is reduced by Deborah's number and velocity slip parameter. The Ag-EG nanoparticles mixture demonstrates less smooth fluid flow compared to the significantly smoother fluid flow of the Ag-Fe3O4/EG hybrid nanofluids (HNFs). Additionally, the Ag-Ethylene Glycol nanofluids (NFs) exhibit higher radiative performance compared to the Ag-Fe3O4/Ethylene Glycol hybrid nanofluids (HNFs).

Additionally, the Oldroyd-B hybrid nanofluid demonstrates improved thermal conductivity compared to traditional fluids, making it suitable for use in cooling systems and energy applications in the maritime industry.

The originality of the study lies in the exploration of the thermal transport enhancement in sun-powered energy ships through the incorporation of silver-magnetite hybrid nanoparticles within the heat transfer fluid circulating in parabolic trough solar collectors. This particular aspect has not been thoroughly researched previously. The findings have been validated and provide a highly positive comparison with the research papers.

The primary purpose of this research is to identify and compare the multifractal behavior of different sectors during these crises and analyze their implications on market efficiency.

We used multifractal detrended fluctuation analysis (MF-DFA) to analyze stock returns from various sectors of the Moscow Stock Exchange (MOEX) in between two significant periods. The COVID-19 pandemic (January 1, 2020, to December 31, 2021) and the Russia–Ukraine conflict (RUC) (January 1, 2022, to June 30, 2023). This method witnesses multifractality in financial time series data and tests the persistency and efficiency levels of each sector to provide meaningful insights.

Results showcased persistent multifractal behavior across all sectors in between the COVID-19 pandemic and the RUC, spotting heightened arbitrage opportunities in the MOEX. The pandemic reported a greater speculative behavior, with the telecommunication and oil and gas sectors exhibiting reduced efficiency, recommending abnormal return potential. In contrast, financials and metals and mining sectors displayed increased efficiency, witnessing strong economic performance. Findings may enhance understanding of market dynamics during crises and provide strategic insights for the MOEX’s investors.

Understanding the multifractal properties and efficiency of different sectors during crisis periods is of paramount importance for investors and policymakers. The identified arbitrage opportunities and efficiency variations can aid investors in optimizing their investment strategies during such critical market conditions. Policymakers can also leverage these insights to implement measures that bolster economic stability and development during crisis periods.

This research contributes to the existing body of knowledge by providing a comprehensive analysis of multifractal properties and efficiency in the context of the MOEX during two major crises. The application of MF-DFA to sectoral stock returns during these events adds originality to the study. The findings offer valuable implications for practitioners, researchers and policymakers seeking to navigate financial markets during turbulent times and enhance overall market resilience.

Nowadays, it is hard to retain a knowledge monopoly since tacit knowledge has become essential for innovation and organizational effectiveness (ORP). This study analyzed the role of product innovation as a mediator in the relationship between the tacit knowledge management process (TKMP) and organizational performance. In addition, two moderating variables were examined: (1) Affective trust (AFT) between the tacit knowledge management process and product innovation relationship and (2) Task efficiency in product innovation and organizational performance (ORP) relationship.

Around 344 questionnaires were collected from various Chinese regions between February and April 2023 to conduct this study. The regression, mediation and moderation analyses on lower and higher-order data were evaluated using the SmartPLS approach.

The results validate that product innovation mediates the connection between managing tacit knowledge and the organization’s performance. Affective trust also plays a positive moderating role between tacit knowledge and product innovation. These results provide valuable theoretical and practical insights, substantiating various direct, indirect, mediate, and moderated effects hypotheses.

The scope of the study was restricted to manufacturing companies; however, further research may broaden the model’s scope to include other industries. Furthermore, future research should continue to explore the role of task efficiency in the innovation process and identify strategies for enhancing task efficiency in organizations.

The study establishes the significance of effectively managing tacit knowledge for fostering product innovation. Company managers and leaders can promote employee trust, enhancing innovation capabilities and overall organizational effectiveness.

This study, involving dual moderation, explores the connections between processes of managing tacit knowledge, product innovation and organizational performance. It addresses research gaps, enriching the understanding of managing tacit knowledge, leading to organizational innovation and performance improvements. The study also highlights how affective trust is vital in strengthening the connection between TKMP and product innovation.

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

Thermophoresis deposition of particles is a crucial stage in the spread of microparticles over temperature gradients and is significant for aerosol and electrical technologies. To track changes in mass deposition, the effect of particle thermophoresis is therefore seen in a mixed convective flow of Williamson hybrid nanofluids upon a stretching/shrinking sheet.

The PDEs are transformed into ordinary differential equations (ODEs) using the similarity technique and then the bvp4c solver is employed for the altered transformed equations. The main factors influencing the heat, mass and flow profiles are displayed graphically.

The findings imply that the larger effects of the thermophoretic parameter cause the mass transfer rate to drop for both solutions. In addition, the suggested hybrid nanoparticles significantly increase the heat transfer rate in both outcomes. Hybrid nanoparticles work well for producing the most energy possible. They are essential in causing the flow to accelerate at a high pace.

The consistent results of this analysis have the potential to boost the competence of thermal energy systems.

It has not yet been attempted to incorporate hybrid nanofluids and thermophoretic particle deposition impact across a vertical stretching/shrinking sheet subject to double-diffusive mixed convection flow in a Williamson model. The numerical method has been validated by comparing the generated numerical results with the published work.

The study aims to explore how Soret and Dufour diffusions, thermal radiation, joule heating and magnetohydrodynamics (MHD) affect the flow of hybrid nanofluid (Al₂O₃-SiO₂/water) over a porous medium using a mobile slender needle.

To streamline the analysis, the authors apply appropriate transformations to change the governing model of partial differential equations into a group of ordinary differential equations. Following this, the authors analyze the transformed equations using the homotopy analysis method within Mathematica software, leading to the derivation of analytical solutions. This study investigates how changing values for porous medium, MHD, Soret and Dufour numbers and thermal radiation influence concentration, temperature and velocity profiles. In addition, the research assesses the effects on local Sherwood number, skin friction and Nusselt number.

In this investigation, the authors explore the movement of a needle away from its origin ( ε > 0). As the magnetic and porous medium parameters increase, there is a correspondence decrease in the velocity profile. Simultaneously, an increase in the Dufour number and thermal radiation parameter yields to a higher temperature profile, whereas arise in the Soret number results in an enhanced concentration profile. Furthermore, growth in the magnetic field parameter is correlated with a reduction in skin friction, Nusselt and Sherwood numbers. In addition, an examination of the data reveals that an escalation in the thermal radiation parameter is associated with an elevation in the Nusselt number. Moreover, an elevation in the Dufour number results in an augmentation in the Nusselt number.

These results have practical applications across diverse fields, including heat transfer enhancement, energy conversion systems, advanced manufacturing and material processing.

This study is distinctive in its investigation of the flow of hybrid nanofluid (Al₂O₃-SiO₂/water) over a slender, moving needle. The analysis includes joule heating, MHD, porous medium, thermal radiation and considering the effects of Soret and Dufour.