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This study aims to further refine the model, explore the influence of cutting parameters on the machining process, and apply it to practical engineering to improve the efficiency and quality of titanium alloy machining.

This paper establishes a comprehensive thermo-mechanical fully coupled orthogonal cutting model. This paper aims to couple the modified Johnson–Cook constitutive model, damage model and contact model to construct a two-dimensional orthogonal cutting thermo-mechanical coupling model for high-speed cutting of Ti6Al4V. The model considers the evolution of microstructures such as plastic deformation, grain dislocation rearrangement, dynamic recrystallization, as well as stress softening and hardening occurring continuously in Ti6Al4V metal during high-speed cutting. Additionally, the model incorporates friction and contact between the tool and the workpiece. It can be used to predict parameters such as cutting process, cutting force, temperature distribution, stress and strain in titanium alloy machining. The study establishes the model and implements corresponding functions by writing Abaqus VUMAT and VFRICTION subroutines.

The use of different material constitutive models can significantly impact the prediction of the cutting process. Some models may more accurately describe the mechanical behavior of the material, thus providing more reliable prediction results, while other models may exhibit larger deviations. Compared to the Tanh model, the proposed model achieves a maximum improvement of 8.9% in the prediction of cutting force and a maximum improvement of 20.9% in the prediction of chip morphology parameters. Compared to experiments, the proposed model achieves a minimum prediction error of 2.8% for average cutting force and a minimum error of 0.57% for sawtooth parameters. This study provides a comprehensive theoretical foundation and practical guidance for orthogonal cutting of titanium alloys. The model not only helps engineers and researchers better understand various phenomena in the cutting process but also serves as an important reference for optimizing cutting processes.

The originality of this research is guaranteed, as it has not been previously published in any journal or publication.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0168/

This study aims to establish a thermally coupled two-dimensional orthogonal cutting model to further improve the modeling process for systematic evaluation of material damage, stiffness degradation, equivalent plastic strain and other material properties, along with cutting temperature distribution and cutting forces. This enhances modeling efficiency and accuracy.

A two-dimensional orthogonal cutting thermo-mechanical coupled finite element model is established in this study. The tanh material constitutive model is used to simulate the mechanical properties of the material. Velocity-dependent friction model between the workpiece and the tool is considered. Material characteristics such as material damage, stiffness degradation, equivalent plastic strain and temperature field during cutting are evaluated through computation. Contact pressure and shear stress on the tool surface are extracted for friction analysis.

Speed-dependent friction models predict cutting force errors as low as 8.6%. The prediction errors of various friction models increase with increasing cutting forces and depths of cut, and simulation results tend to be higher than experimental data.

The current research results provide insights into understanding and controlling tool-chip friction in metal cutting, offering practical recommendations for friction modeling and machining simulation work.

The originality of this research is guaranteed, as it has not been previously published in any journal or publication.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0162/

Job satisfaction is essential in understanding turnover intentions. Previous studies reveal that highly educated hospitality employees generally have lower levels of job satisfaction, indicating that the antecedents of job satisfaction may be different from hospitality managers and frontline employees. This study compared the different antecedents of job satisfaction for housekeeping managers and employees.

This study used a mixed-methods approach for a two-part study. The researchers recruited housekeeping managers for the exploratory survey. The results of open-end questions helped us build a custom dictionary for the text mining of comments from Glassdoor.com. Finally, a multilinear regression of themes from housekeeping employees’ ratings on Glassdoor.com was conducted to understand the antecedents of job satisfaction for housekeeping managers and employees.

The results of the exploratory survey indicated that the housekeeping department has an urgent need for organizational support and training. The text-mining revealed organizational support impacts both managers and frontline employees, while training impacts managers more than employees. Finally, the regression analysis showed compensation, business outlook, senior management, and career opportunity impacted both groups. However, work-life balance only influenced managers.

With a large number of employees at low salaries, housekeeping departments have a higher-than-average turnover rate for lodging. This study is among the first to compare the antecedents of managers’ and frontline employees’ job satisfaction in the housekeeping department, extending Social Exchange Theory. It provides suggestions for the housekeeping department to decrease turnover intentions.

Drawing upon conservation of resources (COR) theory, this study aims to examine whether benevolent leadership from top hotel leaders can foster employees' work engagement during COVID-19 via two valued career-related resources, namely organizational career management (OCM) and individual career management (ICM). This study also proposes that the importance of ICM as a resource diminishes when ICM plays a prominent role.

Survey data were collected from 600 employees in 20 hotels located in a major tourist destination in Thailand during COVID-19. The data were analyzed using latent moderated mediation structural equation modeling (SEM).

This study found that the relationship between hotel leaders' benevolent leadership and employees' work engagement was mediated by both OCM and ICM. Furthermore, as expected, this study found that the indirect effect of benevolent leadership via OCM was weaker when ICM was high.

This study sheds light on the importance of hotel leaders and career management activities in promoting employees' work engagement. Thus, despite concerns that investing in career management activities might lead employees to manage themselves out of the organization, the current findings indicate otherwise.

Based on the resource-gain perspective, this study contributes to the leadership and hospitality literature by being among the first to show that the influence of benevolent leadership on work engagement occurs through the simultaneous mediating roles of OCM and ICM. Moreover, this study contributes to the current debate about the interactive effects of OCM and ICM.

This study examines how leaders’ narcissistic rivalry (LNR) affects the in-role performance (IRP) and proactive customer service performance (PCSP) of employees in the hospitality industry. Specifically, this study investigates the mediating role of psychological distress and the moderating role of locus of control (LOC) in the aforementioned relationships.

This study administered a multi-wave, multi-source questionnaire survey with 323 employees working in 11 full-service hotels in China. Statistical analyses were performed using the PROCESS macro in SPSS 26 software and structural equation modeling using Mplus 8.3 software.

The authors' results suggest that LNR can negatively affect hospitality employees’ IRP and PCSP and that these relationships are mediated by psychological distress. Additionally, the impact of LNR on psychological distress can be lessened by internal LOC.

This study contributes to the literature on leader narcissism by investigating how LNR affects IRP and PCSP among hospitality employees. Drawing on conservation of resources theory, this study also identifies a novel mediating mechanism (psychological distress) connecting LNR to hospitality employees’ service outcomes. Furthermore, this study reveals the moderating role of LOC in the relationship between LNR and psychological distress.

Turnover intention (TOI) has become a severe issue in Saudi Arabia’s health-care system as health professionals leave their organizations. Saudi Arabia’s health-care professionals’ TOI affects the organizations and the patients’ human rights. Therefore, this study aims to assess the factors that affected Saudi Arabia’s health-care professionals’ TOI.

This study based its findings on quantitative cross-sectional data. This study’s respondents were health-care professionals working in Saudi Arabia’s public and private health-care institutions.

By using path analysis, this study’s findings reveal that, on the one hand, job stress (JS), psychological distress (PD) and perceived work exhaustion (PWE) have positive and significant effects on TOI. On the other hand, perceived organizational support (POS) is a positive and significant predictor of TOI.

This study’s findings will help the Saudi Arabian Ministry and policymakers develop policies to encourage health professionals’ perseverance through reducing their JS, PD and PWE and by enhancing POS for health-care staff. Moreover, by controlling the increasing turnover ratio among Saudi Arabia’s health-care professionals, this study’s findings assist in overcoming the violations of human rights.

This study’s findings empirically confirm the development of TOI through JS, PD and PWE among Saudi Arabia’s health-care professionals.

The recent meeting of G-20 world leaders, held in New Delhi, in 2023, highlighted that the physical effect of climate change has considerable macro-economic costs at the national and global levels and they have also pledged to accelerate the clean, sustainable and inclusive energy transition along a variety of pathways. Climate change could pose various emerging risks to the firm’s operational and financial activities, specifically for those which are belonging to the energy sector. Thus, this study aims to investigate the impact of climate risks on the financial performance of select energy companies from G-20 countries.

The study considered 48 energy companies from G-20 countries as the sample for the period of 2017 to 2021. To measure the climate change-related physical risks, the study has considered the ND-GAIN climate vulnerability score and the firm’s financial performance has been measured by return on assets, return on equity, return on capital used and price-to-book ratio. To examine the impact of climate risks on the financial performance of the sample companies, the authors have used pooled ordinary least squares (OLS) and fixed/random effect regression analysis and required data diagnosis tests are also performed.

The empirical results suggested that climate risks negatively impacted the financial performance of the sample companies. The market performances of the firms are also being impacted by the physical climate change. The results of panel data regression analysis also confirmed the robustness of the empirical results derived from the pooled OLS analysis suggesting that firms that operated in a less climate-risky country, financially performed better than the firms that operated in a more climate-risky country.

The paper has significant practical implications like it could be helpful for the policymakers, investors, suppliers, researchers and other stakeholders in developing deeper insights about the impact of climate risks on the energy sectors from an international perspective. This study may also help the policymakers in developing policies for the management of climate risk for the energy sector.

This study adds insights to the existing literature in the area of climate risks and firm’s financial performance. Moreover, this may be the first study that attempts to evaluate the impact of climate risks on the financial performance of select energy companies from the G-20’s perspective.

The competitiveness of destinations is crucial for tourism. In this context, the study aims to uncover customer satisfaction, experiences, feelings, and thoughts by conducting a comparative analysis of social media comments from various competitive tourism destinations.

Big data research was conducted to answer the research questions. The data was collected on a social media platform focusing on three destinations in the Mediterranean region. Three methods were employed to analyse the data: sentiment analysis, topic modelling, and named-entity recognition.

This study addressed traveller satisfaction levels. It identified the topics concerning each destination, examined the emotions expressed by travellers about these topics, explored the potential impact on future behaviour, and investigated the features of the destinations and satisfaction levels about these features. It also identified the prominent food and beverage names in destinations and explored tourists’ preferences regarding these foods and beverages.

The limitations of this study relate to the sample. The data used in this study was solely obtained from a single social media platform and focused on English-only comments. Further research that includes different social media platforms for hotel categories and considers reviews in local languages could capture a broader range of customer opinions and experiences.

Policymakers can gain insight into a destination’s position in the competitive landscape. This study has numerous implications for policymakers in the relevant destinations and managers in the design and implementation of services.

The findings of this study can have broader societal implications if considered and implemented by decision-makers and tourism businesses in the context of competitiveness.

The study’s originality lies in integrating multiple disciplines and comparing tourism destinations using big data. This study improves the understanding of competitiveness in three specific Mediterranean destinations. Previous research has focused on different contexts in these Mediterranean destinations. Therefore, the study fills this gap by focusing simultaneously on all three destinations in the context of competitiveness.