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The purpose of the research is to identify the various factors affecting bread wastage and bread consumption of consumers in Isparta, Turkey.

This study uses the categorical regression (CATREG) model to estimate the driving forces of bread waste and consumption. The study concentrates on data obtained from 384 respondents speaking for the same number of households placed in the province of Isparta in Turkey.

The results indicate that both models are statistically significant at the 1% level. Parents' profession, storage method, type of bread consumed, daily bread expenditure, monthly income and the idea of “bread is cheap” are the most important variables affecting bread wastage. Regarding bread consumption, parents' profession, fathers' education level, monthly income, inadequate control in bakeries, household size and the idea of “bread makes people fat” are found to play significant roles.

The results could be helpful to develop influential policies on healthy eating and aiming plans to reduce bread consumption to healthy levels and prevent bread waste.

This research contributes to knowledge regarding the underlying causes of the bread consumption and wastage of Turkish consumers in the light of the data analysis for Isparta province.

This paper contains unique and original understandings concerning bread consumption and wastage attitudes and determinants for consumers from Isparta, Turkey. The novel findings of this research have conduced to a better understanding of the key factors that affect bread consumption and waste. The paper also applies an econometric analysis using a CATREG model to analyse the factors influencing consumers' bread waste and consumption behaviour in Isparta province, Turkey.

The purpose of this paper was to investigate the effects of hydrochloric acid (HCl), hydrazine, methyl methacrylate, styrene and hexamethyldisiloxane by radio-frequency (rf) plasma graftings on surface properties of wool and denim fabrics.

During plasma treatments, processing time was varied under optimized plasma conditions (50 W, rf: 13.56 MHz). All fabrics were comprehensively investigated by means of scanning electron microscopy-energy dispersive X-ray spectroscopy and contact angle measurements.

The experimental data shows that the rf-plasma processing has important effect on the wettability properties of wool and denim fabrics. The results indicated that HCl plasma treatment significantly improves the hydrophilicity of wool and denim fabrics.

The research on wool and denim fabric treatment by plasma is original.

The aim of the research is to reveal information with regard to being a digital nomad as a family. Within this brief, the aim is to provide information regarding the reasons for choosing the digital nomad lifestyle of families, the female and male thoughts regarding the experience, destination selection factors and the difficulties and conveniences of digital nomadism for family aspects.

The data were obtained through a semi-structured interview questionnaire with a British digital nomad family, individually from the wife and husband, using a qualitative research design in the scope of the phenomenological research.

It was determined that having a better lifestyle, being willing to spend more time with family and traveling with them were the reasons for becoming a digital nomad. The opinions of the woman and man are in parallel. While the main features of being a digital nomad are freedom and flexibility, having time to spend with family is also important. Factors for choosing a destination as a nomad were access to nature, quality of life, cost of living, quality of the food, climate, proximity to other families who are nomads and interest in nonschool education. As a family, they meet their needs and wants, such as going to a local shop or staying in a rented house, but prefer to use restaurants and other facilities if their stay is short. Although having children motivates digital nomadism, it has been determined that families prefer to stay in a fixed place as nomads since it is not appropriate for children over a certain age to travel frequently.

The study provides detailed information regarding a family experience with digital nomadism through a case study.

This study aims to investigate the international portfolio diversification opportunities provided by Turkish sustainable firms to international socially responsible investors.

The Borsa Istanbul Sustainability Index (XUSRD) and FTSE4Good index family daily data for the period of 11/04/2014-12/31/2017 is used and the DCC-GARCH model is applied to explore the dynamic correlation linkages.

The results indicate that co-movements between XUSRD and FTSE4Good indices are time-varying and generally display a low level. While the highest average conditional correlation value was observed between XUSRD and Developed 100 index, the lowest one was between XUSRD and FTSE4Good Japan index.

Since XUSRD was launched on 11/04/2014, there is no available data before this date. Additionally, because the study includes indices from the USA to Japan, it is not possible to use high-frequency stock index data due to lack of overlapping time series.

This study contributes implications for investors of sustainability assets to improve their diversification. Especially, it is identified that the diversification opportunities provided by Turkish sustainable firms are largely possible for Japanese and Australian socially responsible investors. Additionally, this research has contributions for policymakers.

Although the conventional stock market indices are widely examined in terms of their time-variant relationship, there are only a few studies in the literature focusing on sustainability indices. Socially responsible investments (SRI) are emerging as a new trend, and these investments are also in need of international portfolio diversification. Therefore, this study is expected to fill a gap in the SRI literature.

Artificial intelligence is one of the most significant and active fields of study in the last few years. Artificial intelligence-derived robotic technologies known as chatbots are gaining interest from both academic and industry sectors. By analyzing the development and patterns of research on the chatbot phenomena within the tourism field, this study seeks to develop a theoretical framework for the interaction between chatbots and tourism.

The Web of Science (WoS) database’s 33 articles on chatbots related to travel and hospitality were examined between 2019 and 2024 using VOSviewer software for bibliometric and thematic content analysis.

Research on chatbots for tourism and hospitality appears to be in its early stages. The factors influencing tourists' intentions to use chatbots have been thoroughly researched; the attitudes, perceptions and behavioral intentions of destinations, travel agencies and restaurant patrons regarding chatbots were examined, and it was found that the quantitative research approach was dominant. In addition, the majority of the studies are based on a particular theory or model.

This is one of the first attempts to directly comprehend and depict the interconnected structures of studies on the interaction between chatbots and tourism through the use of network analysis. Furthermore, the study’s findings can offer academics a comprehensive viewpoint and a reference manual for more accurate assessment and oversight of the chatbot-tourism interaction. Regarding the lack of research on the topic and the fragmented structure of the studies that exist, it is imperative to provide both a comprehensive overview and a roadmap for future investigations into the usage of chatbots in the travel and hospitality sector.

This study aims to examine the relationship between digital transformation (DigiTr), innovation and human resources planning (HRP) in hotels to investigate the impact of DigiTr on innovations and HRP and to test the mediating impact of innovation on the DigiTr-HRP relationship.

The authors used a quantitative research method in this study, specifically by conducting a hybrid face-to-face and online survey to collect data from 462 human resources (HR) managers, department managers and HR professionals at four- and five-star hotels in Turkey. The structured questionnaire assessed DigiTr, innovations in business models, services and processes and quantitative and qualitative changes in HR. The authors used covariance-based structural equation modeling to test the hypotheses.

DigiTr affected both innovations and HR planning in hotels, and also the effect of innovations on HR planning. In addition, DigiTr and innovations increased qualitative changes in HR planning but reduced quantitative changes. Finally, innovations mediated the relationship between DigiTr and HR planning.

These findings indicate that employers and employees need to be aware of developments in employment in the tourism industry, as these can significantly impact HR planning via DigiTr and innovations.

This study differs from the existing literature by providing empirical evidence to fill the knowledge gap regarding how DigiTr and innovation affect HR planning.

本研究考察了酒店数字化转型、创新和人力资源规划之间的关系, 以调查数字化转型对创新和人力资源规划的影响, 并检验创新对数字化转型与人力资源规划关系的中介影响。

我们在这项研究中使用了定量研究方法, 特别是通过进行面对面和在线混合调查, 从土耳其四星级和五星级酒店的 462 名人力资源经理、部门经理和人力资源专业人士那里收集数据。结构化问卷评估了数字化转型、商业模式、服务和流程的创新, 以及人力资源的数量和质量变化。我们使用基于协方差的结构方程模型来检验假设。

数字化转型既影响了酒店的创新和人力资源规划, 也影响了创新对人力资源规划的影响。此外, 数字化转型和创新增加了人力资源规划的质变, 但减少了量变。最后, 创新在数字化转型和人力资源规划之间的关系起到了中介作用。

这些调查结果表明, 就业发展可以通过数字化转型和创新显着影响人力资源规划, 因此雇主和雇员需要了解旅游业关于就业的发展动向。

The main aim of the present research is to investigate the role of sense of calling (SOC) in the relationships between human resource practices (HRP), turnover intention (TI) and employee satisfaction (ES) in the hotel industry. Moreover, it also examines whether the relationships between these variables are different for city and resort hotel employees.

Quantitative research was employed to test the proposed hypotheses, and the survey technique was used to collect data. The participants of the research consist of 432 hotel employees who work in Antalya, Turkey. The hypotheses were tested using the partial least squares structural equation modeling (PLS–SEM) analyses.

The results of the analysis have shown that SOC significantly impacts the ES and TI, and HRP could be a new antecedent of SOC. On the other hand, while ES is positively affected by HRP, the SOC has a moderating role in the relationship between these variables.

Hotel authorities' understanding of the SOC’s role in employee attitudes and behavior would be a significant factor not only in retaining them but also in reducing employee turnover. They should consider increasing employees' SOC by focusing on HRP.

Besides being one of the few studies that have discussed SOC in the hospitality literature, the current study also contributes by examining the role of SOC in the relationships between HRP, ES and TI. It also adds value to the calling literature by revealing whether the relationship between these variables differs for city and resort hotel employees.

The purpose of this paper is to investigate the effect of a lubricant with a polytetrafluoroethylene (PTFE)‐based additive on the friction behaviour in a steadily loaded journal bearing using an experimental and artificial neural network approach.

The collected experimental data, such as pressure variations, are employed as training and testing data for artificial neural networks (ANNs). A feed forward back propagation algorithm is used to update the weight of the network during the training.

An artificial neural network predictor has superior performance for modelling journal bearing systems under different lubricant conditions.

A feed forward back propagation algorithm is used as a training algorithm for the proposed neural networks. Various training algorithms can be used to train the proposed network. Various lubricants and concentration ratio of the different additives can be investigated.

The simulation results suggest that the artificial neural predictor would be used as a predictor for possible experimental applications, especially different lubrication conditions on the modelling journal bearing system.

The paper discusses a new modelling scheme known as ANNs. A neural network predictor has been employed to analyze the effects of a lubricant with a PTFE‐based additive on the friction behaviour in a steadily loaded journal bearing under different operating conditions.

The purpose of this paper is to extend the results of Meca et al. (2004) depending on the grey information revealed by the individual firms.

The authors introduce cooperative grey games and focus on sharing ordering cost rule (SOC-rule) to distribute the joint cost.

In this study, the authors introduce a model, where inventory costs are assumed as grey numbers instead of crisp or stochastic ones studied in literature. At first, grey numbers and classical cooperative inventory games are recalled. Then, cooperative grey games are introduced and related results are given. Finally, an application is performed for three shotgun companies in Turkey.

It is an effective approach for theoretical analysis of systems with imprecise information and incomplete samples. Therefore, grey system theory, rather than the traditional probability theory and fuzzy set theory, is better suited to model the inventory problems by using cooperative game theory. To the best of the knowledge no study exists modeling inventory situations by using cooperative grey games. From this point of view this study is a pioneering work on a promising topic.

The purpose of this paper is to estimate different air–fuel ratio motor shaft speed and fuel flow rates under the performance parameters depending on the indices of combustion efficiency and exhaust emission of the engine, a turboprop multilayer feed forward artificial neural network model. For this purpose, emissions data obtained experimentally from a T56-A-15 turboprop engine under various loads were used.

The designed multilayer feed forward neural network models consist of two hidden layers. 75% of the experimental data used was allocated as training, 25% as test data and cross-referenced by the k-fold four value. Fuel flow, rotate per minute and air–fuel ratio data were used for the training of emission index input values on the designed models and EI_CO, EI_CO2, EI_NO2 and EI_UHC data were used on the output. In the system trained for combustion efficiency, EI_CO and EI_UHC data were used at the input and fuel combustion efficiency data at the output.

Mean square error, normalized mean square error, absolute mean error functions were used to evaluate the error obtained from the system as a result of the test. As a result of modeling the system, absolute mean error values were 0.1473 for CO, 0.0442 for CO₂, 0.0369 for UHC, 0.0028 for NO₂, success for all exhaust emission data was 0.0266 and 7.6165e-10 for combustion efficiency, respectively.

This study has been added to the literature T56-A-15 turboprop engine for the current machine learning methods to multilayer feed forward neural network methods, exhaust emission and combustion efficiency index value calculation.