bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data-driven approach to optimizing property management strategies: spatial modeling analytics of short-term rentals
Authors: Ndaguba Emeka Austin , Van Zyl Cina 
Published: October 2024

Purpose
This study aims to explore the impact of locational and seasonal factors on the financial performance of short-term rental properties in Margaret River, Western Australia. It seeks to address the gap in understanding how these factors influence key financial metrics such as average daily rate (ADR) and occupancy rates, providing insights for property managers, investors and policymakers.

Design/methodology/approach
The research uses a mixed-method approach, integrating advanced predictive modeling techniques, such as Random Forests and Gradient Boosting, with spatial clustering algorithms like density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify the clustering structure (OPTICS). The study analyzes a comprehensive data set of short-term rental properties between 2012 and 2019. It focuses on locational attributes, seasonal variations and financial outcomes.

Findings
The findings reveal that properties located near tourist attractions and amenities consistently achieve higher ADRs and occupancy rates, confirming the critical role of location in driving rental demand. Seasonal analysis indicates significant fluctuations in both ADR and occupancy rates, with peaks during high tourist seasons and troughs in off-peak periods. The study underscores the importance of dynamic pricing strategies to optimize revenue and sustain occupancy across different seasons. In addition, it highlights the influence of property features, such as the number of bedrooms and bathrooms, on ADR, while noting that larger properties do not necessarily achieve higher occupancy rates.

Research limitations/implications
Future research could expand the scope to include different locations and explore the long-term impacts of locational and seasonal factors on property performance.

Originality/value
This research contributes to the literature by integrating spatial analysis with advanced predictive modeling techniques to provide a nuanced understanding of how locational and seasonal factors impact financial performance in the short-term rental market. It offers a novel application of data analytics within the context of tourism and hospitality management, bridging theoretical frameworks with practical insights.
Predicting the link between employees' task performance and propensity to take charge: the role of family supportive supervision and LMX
Authors: Ahmed Ishfaq , Islam Talat , Afzal Rabia , Iqbal Imlak , Faheem Muhammad Asim 
Published: October 2024

PurposeThe authors examined how employee led exchange benefits the organizations. Specifically, the authors aim at investigating the mediating role of family supportive supervision between employee performance and taking charge behavior. The authors further examined leader-member exchange (LMX) as a boundary condition between employee performance and family supportive supervision.Design/methodology/approachThe authors collected data from 295 employees and their supervisors working in various public sector organizations of Pakistan on a convenience basis. Specifically, data on family supportive supervision and LMX was collected from employees; whereas, data on employee performance and taking charge was collected from their supervisors between June–September 2021.FindingsThe statistical analysis reveals that high-performing employees are reciprocated by the high family-supportive supervision which increases their work-life balance and they further reciprocate by showing a propensity to take charge. In addition, LMX is noted to strengthen the association between employees' performance and family supportive supervision.Practical implicationsThis study explains how managers can extend the stream of employees' performance by highlighting the role of family-supportive supervision and LMX. The managers through high LMX and provision of family-supportive supervision can boost the employees' outcomes from job performance to extra-role performance (i.e. taking charge).Originality/valueThis study adds value to the existing body of knowledge by considering performance as a predictor of various organizational-level consequences. Recent studies have considered the negative consequences of employees' performance, while the positive aspect has been called for an investigation.
Exploring the foundations of Islamic moral economy: a return to substantive morality
Authors: Yilmaz Isa 
Published: October 2024

Purpose
This paper aims to explore the general flaws in the theorization of Islamic economics, particularly its failure to incorporate substantive morality into its framework. Since a considerable number of Islamic economic studies remain within the bounds of instrumental morality, this paper seeks to propose an alternative understanding of Islamic economics by emphasizing the essential role of Islamic moral economy (IME) through substantive morality.

Design/methodology/approach
The study critically examines the theorization of Islamic economics through a discursive methodology. The adopted method and strategy allow for the discursive evaluation of conceptual developments within the field of modern Islamic economics. It proposes an authentic definition of IME by outlining its paradigmatic framework of political economy, value system, axiomatic approach and operational principles.

Findings
Islamic economics has been theorized in a reactive and reflexive manner, aiming to provide an alternative to the neoclassical hegemonic discourse. However, it has largely neglected its inherent moral dimension, focusing instead on instrumental morality and failed to embed substantive morality into its system. By introducing an alternative reading of Islamic economics, this study presents IME as a distinct economic model that centers substantive morality in its theorization.

Originality/value
This paper contributes to the existing literature by offering a novel perspective on Islamic economics that challenges the prevalent instrumental approach and emphasizes the importance of substantive morality. It provides a comprehensive framework for understanding IME as a distinct economic model, thereby enriching the discourse on Islamic economics with an authentic and morally grounded paradigm.
Developing a severe gust load spectrum for transport aircraft based on measured load data
Authors: Wei Kunyu , Li Bowen , He Xiaofan 
Published: October 2024

PurposeDeveloping severe load spectrum of transport aircraft structures is crucial for enhancing the fatigue damage correlation between full-scale fatigue testing results and operational service. The lack of consensus on severe spectrum development methods for transport aircraft has prompted the current research, resulting in a proposed approach for a severe gust load spectrum based on the acceleration cumulative exceedance surface.Design/methodology/approachThe measured load data were analyzed using a model based on the cumulative exceedance number surface to describe the variation in exceedance numbers. An improved sampling method based on multivariate Markov Chain Monte Carlo was employed to obtain the fleet fatigue damage distribution, enabling the determination of the severity of severe spectrum and the corresponding cumulative exceedance number surface, and a severe gust load spectrum was developed based on the surface.FindingsThe method that characterizes load spectrum variation using the cumulative exceedance surface minimizes the randomness of peak-trough pairs by incorporating the correlation of load spectrum peaks and troughs. This approach reduces the variation in fleet fatigue damage, thereby lowering the requirements for the severity of severe spectrum fatigue damage.Originality/valueThe proposed methodology extends from a one-dimensional curve to a two-dimensional surface, accounting for the correlation between peak and trough values to develop a severe spectrum. This approach more accurately describes the variation in acceleration cumulative exceedance numbers, directly benefiting fatigue damage calculation. This study provides valuable references for developing severe spectrum for transport aircraft.
Enhanced PINNs with augmented Lagrangian method and transfer learning for hydrodynamic lubrication analysis
Authors: Zhou Guangde , Zhan Menghao , Huang Dan , Lyu Xiaolong , Yan Kanghao 
Published: October 2024

Purpose
By seamlessly integrating physical laws, physics-informed neural networks (PINNs) have flexibly solved a wide variety of partial differential equations (PDEs). However, encoding PDEs and constraints as soft penalties in the loss function can cause gradient imbalances, leading to training and accuracy issues. This study aims to introduce the augmented Lagrangian method (ALM) and transfer learning to address these challenges and enhance the effectiveness of PINNs for hydrodynamic lubrication analysis.

Design/methodology/approach
The loss function was reformatted by ALM, adaptively adjusting the loss weights during training. Transfer learning was used to accelerate the convergence of PINNs under similar conditions. Additionally, the iterative process for load balancing was reframed as an inverse problem by extending film thickness as a trainable variable.

Findings
ALM-PINNs significantly reduced the maximum absolute boundary error by almost 80%. Transfer learning accelerated PINNs for solving the Reynolds equation, reducing training epochs by an order of magnitude. The iterative process for load balancing was effectively eliminated by extending the thickness as a trainable parameter, achieving a maximum percentage error of 2.31%. These outcomes demonstrated strong agreement with FDM results, analytical solutions and experimental data.

Originality/value
This study proposes a PINN-based approach for hydrodynamic lubrication analysis that significantly improves boundary accuracy and the training process. Additionally, it effectively replaces the load balancing procedure. This methodology demonstrates considerable potential for broader applications across various boundary value problems and iterative processes.

Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0277/
A biparametric analysis on the steady performance of bidirectional pumping hydrodynamic-static hybrid dry gas seal
Authors: Wu Hongxing , Mao Wenyuan , Jiao Feng , Deng Qiangguo , Sun Xuejian , Xu Hengjie 
Published: October 2024

Purpose
The purpose of this paper is to obtain the matching relationship between spiral groove, equalizing groove and operation parameters through the biparametric analysis for the bidirectional pumping hydrodynamic-static hybrid dry gas seal (BP-HHDGS).

Design/methodology/approach
The large eddy simulation (LES) model in Fluent is used to simulate the flow field of BP-HHDGS, and the biparameter variables method is chosen to analyze the effects of different parameters on the performance of BP-HHDGS.

Findings
BP-HHDGS has a greater opening force than hydrostatic dry gas seal (HDGS); the vortex is formed after lubricating gas is exhausted from the throttle. Increasing the depth of the equalizing groove and spiral groove has a synergistic enhancement effect on the opening force and leakage of BP-HHDGS. There is a matching relationship between spiral angle and rotational speed. The preferred parameter ranges in current conditions are found as follows: spiral angle αa = 15°–24°; groove-dam ratio λ = 0.4–0.7; equalizing groove depth hj > 35 µm; spiral groove depth hg = 5-10 µm.

Originality/value
The high starting capacity of HDGS is given to the hydrodynamic type seal, and thus the application promotion of HDGS in high-speed working condition is realized at the same time. This work also provides precise and quick theoretical guidance for the selection and design of hydrodynamic-static dry gas seal and further promotion.
Understanding SNS use reduction from the perspective of the cognitive-affective model
Authors: Nascimento Pedro , Oliveira Tiago , Neves Joana 
Published: October 2024

PurposeThis investigation delves into the elements influencing social networking sites (SNS) use reduction behavior through the lens of the cognitive-affective (CA) model to understand the driving forces behind the decline in SNS use.Design/methodology/approachFollowing the CA model, this research introduces a theoretical framework that integrates the emotions of regret and guilt along with the principles of cognitive dissonance theory. The proposed theoretical framework was subjected to empirical validation, utilizing 453 responses gathered from Instagram users.FindingsThe results suggest that the emotions of regret and guilt have a favorable impact on users’ intention to decrease their SNS usage, with cognitive dissonance exerting an indirect positive influence through these emotions. Additionally, further examination unveils that fear moderates the connection between users’ SNS addiction and the CA components.Research limitations/implicationsAdditional cognitive and affective responses may influence the intricate relation between SNS addiction and SNS use reduction intention.Originality/valueThis research contributes to the existing body of knowledge on the information system use lifecycle by examining shifts in user behavior, notably the transition from excessive use to the adoption of use reduction strategies. Furthermore, it sheds light on the intricate role of cognitive dissonance in elucidating the intention to reduce SNS use from the perspective of the CA model. Additionally, this study advances our current understanding of how the fear of negative consequences arising from excessive usage plays a role as a moderating factor in elucidating the underlying internal factors related to reducing SNS usage.
Enhancing healthcare operations: a systematic literature review on approaches for hospital facility layout planning
Authors: dos Santos Vinícius Carrijo , Siqueira Regiane Máximo , Godinho-Filho Moacir 
Published: October 2024

PurposeThe appropriate physical layout of hospital services can help resolve management problems by streamlining the work of medical teams, improving the flow of patients between specific areas and the medical support environment. Nevertheless, the academic literature lacks structured research into how the physical layout of hospitals might be improved. Our study aims to fill this research gap, providing information for researchers and professionals who intend to guide the hospital facility layout planning (HFLP) from the steps and prescribed approaches found in the literature.Design/methodology/approachThis study analyzes the current literature status and concerning approaches that support HFLP and identifies their strengths and weaknesses. The literature was classified using the following criteria: approaches for layout generation, approaches for layout evaluation and healthcare facility layout outcomes.FindingsThe hospital facility layout outcomes achieved for each phase served as a basis for identifying a list of strengths and weaknesses for the hospital layout facility generation and evaluation approaches. Readers can refer to this paper to identify the approach that best fits the desired goal and the HFLP step.Practical implicationsThis is a contribution to current studies into HFLP, and it provides guidelines for selecting the approach to be utilized based on the desired outcome.Originality/valueThe paper describes how to conduct an HFLP and lists the strengths and weaknesses of each approach. The research may be used as a strategy for determining which tool is most suited based on the practitioner's target purpose.
ChatGPT adoption risks and cognitive achievement among tourism and hospitality college students: from faculty member perspective
Authors: El-Akhras Heba Atef , Abd El-Wahab Marwa Ali , Saghier Elham Gharib , Selem Kareem M. 
Published: October 2024

PurposeDespite ChatGPT usage advantages to complete assignments as quickly as possible with detailed information, its adoption risks are crucial factors that may significantly influence student outcomes, like creative skills and achievement levels. Hence, this paper examines the potential effects of ChatGPT adoption risks on students’ cognitive achievement, underpinned by perceived risk theory (PRT). Further, this paper examines the mediating role of creative thinking levels among hospitality college students.Design/methodology/approachThis paper employed a quantitative approach to collect data. Using an online survey, simple random sampling was employed to ensure bias. Hence, 267 faculty members at tourism and hospitality colleges affiliated with Egyptian public universities responded, and their responses were tested using SmartPLS v.4.FindingsCreative thinking partially mediated five potential risks of students’ ChatGPT adoption effects on their cognitive achievement. More specifically, psychological risks of ChatGPT adoption among students are considered one of the most likely to reduce their levels of creative thinking and their ability to achieve achievement.Practical implicationsThis paper provides insights for policymakers aimed at enhancing higher education quality in tourism and hospitality colleges. Accordingly, this paper concludes by highlighting the importance of addressing ChatGPT adoption’s psychological risks, integrating strategies to promote creative thinking among students and fostering responsible artificial intelligence usage in higher education.Originality/valueThis paper underscores the critical role of faculty members in navigating and mitigating these risks, fostering a more conducive learning environment for responsible ChatGPT usage.
Modeling the occupancy rate and nights spent at tourism accommodations of Spain using autoregressive deep learning networks
Authors: Sürekçi Yamaçlı Dilek 
Published: October 2024

Purpose
The purpose of this paper is to autoregressively model the net occupancy rate of beds and bedrooms in hotels and similar accommodations and the nights spent at these accommodations of Spain for the period of 1990–2023 using monthly data.

Design/methodology/approach
The monthly occupancy rate of hotels and the total number of hotel nights data of Spain for the 1990M01–2023M09 range is considered. An autoregressive deep learning network is developed for the modeling of both metrics. Moreover, the results of the proposed autoregressive deep learning method are compared to those of a classical artificial neural network.

Findings
The actual occupancy rate, total night data and the deep learning model results are compared showing the accuracy of the developed model. Moreover, the R2, mean absolute error, root mean square error and mean absolute percentage error of the models are calculated further demonstrating the high performance of the developed model. The R2 values higher than 0.9 are achieved for both occupancy rate and total number of hotel nights data.

Practical implications
The modeling results given in this paper demonstrate that the previous values of the net occupancy rate and the total number of nights can be used as inputs of a deep learning network model by which accurate forecasts can be made for the future values of the occupancy rate and the total number of hotel nights. This modeling approach possesses importance from the practical viewpoint considering that the accurate planning and forecast of the net occupancy rate and the total number of nights affect the tourism income.

Originality/value
This study differs from existing literature by attempting to model the occupancy rate and the total number of hotel nights data autoregressively using deep learning networks.
Tracking Smart Beta indices during different market phases: A “smarter” option for passive investors?
Authors: C. Vijaya , M. Thenmozhi 
Published: October 2024

Purpose
This study aims to examine whether tracking Smart Beta (SB) indices during bullish, bearish and stagnant market phases is a better choice for passive investors compared to Cap-Weighted (CW) indices. As investors’ strategies differ with market movements, this study analyses how single-factor and multi-factor SB indices perform during different market phases, in relation to CW indices. It also attempts to determine which SB factors are more suitable for investors in these phases.

Design/methodology/approach
Using various return and risk indicators, this study analyses how SB indices perform vis-à-vis CW indices during bullish, bearish and stagnant phases. The authors also evaluate the upside and downside participation advantage of SB indices and assess their ability to capture upside returns and limit downside risk. The authors attempt to determine the cyclical or defensive nature of SB indices using Average Participation values.

Findings
This study found that SB indices outperform CW indices during the bearish and stagnant phases. Multi-factor SB indices have lower risk levels in all market phases, providing downside protection to risk-averse investors. Dividend, Low Volatility, Quality and multi-factor SB indices are defensive portfolios offering better payoffs during the down market phases, while Alpha, Beta, Equal Weight and Value SB indices provide higher payoffs during the up-market phases.

Originality/value
To the best of the authors’ knowledge, this is the first study that examines the performance of single-factor and multi-factor Indian SB indices in different market phases. It determines the suitability of various factors to passive investors during these phases and also identifies whether SB indices are cyclical or defensive.