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This paper aims to determine the knowledge and attitudes of the physicians regarding human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS), to emphasize that these patients exist and they will exist in the future and to raise awareness so as to prevent that their rights to treatment are revoked.

The survey was conducted via a link sent through an online system. Random physicians from 81 cities of the country were invited to the survey. The survey has 41 questions regarding knowledge and attitudes in total, including epidemiological information such as age, gender and title.

A total of 3,107 physicians has voluntarily participated in the study. In total, 2,195 (70.7%) are internal physicians and 912 (29.3%) are surgical physicians among the participant physicians. In total, 1,452 (46.7%) of the participants are specialist physicians, 608 (19.6%) of the participants are practising physician and the rest of it is physician assistants, academicians and dentists, respectively.

In this study, it has been found out that the physicians have a lack of knowledge on HIV/AIDS and they adopt a discriminatory attitude towards HIV-positive persons. HIV-positive patients who are exposed to discrimination and scared of being uncovered refrain from applying to hospitals for treatment, which puts public health into jeopardy due to the high viral load and these patients are faced with difficulties in coping with both medical and emotional load of the disease.

Building on previous historical works, this study aims to develop a framework to represent chambers as meta-organizations and present the case of Dersaadet Chamber of Commerce (DCC), based on this framework, during its emergence and evolution in the late 19th and early 20th centuries.

In the study, a historical narrative was constructed from primary and secondary data. To complement data collected from the archives a systematic content analysis was used to explore the discourse of the chamber within its serial magazine.

It was found that the first chamber of the Ottoman Empire, DCC, was established according to the public law model as an extension of the economic context and the guild order, and it was observed that it increasingly conformed to this model between 1882 and 1929.

In this study, chamber models are examined for the first time according to the designated features of meta-organizational forms, built on the historical work on chambers. The case of DCC suggested that it adopted a public law model and displayed much continuity, even when significant transitions were observed during the modernization process from Ottoman Empire to Turkish Republic.

This paper aims to find the numerical solution of planar and non-planar Burgers’ equation and analysis of the shock behave.

First, the authors discritize the time-dependent term using Crank–Nicholson finite difference approximation and use quasilinearization to linearize the nonlinear term then apply Scale-2 Haar wavelets for space integration. After applying this scheme on partial differential, the equation transforms into a system of algebraic equation. Then, the system of equation is solved using Gauss elimination method.

Present method is the extension of the method (Jiwari, 2012). The numerical solutions using Scale-2 Haar wavelets prove that the proposed method is reliable for planar and non-planar nonlinear Burgers’ equation and yields results better than other methods and compatible with the exact solutions.

The numerical results for non-planar Burgers’ equation are very sparse. In the present paper, the authors identify where the shock wave and discontinuity occur in planar and non-planar Burgers’' equation.

The paper aims to reveal the intellectual structure of studies on artificial intelligence (AI) in the fields of tourism and hospitality. Evaluations regarding the intellectual structure have been made based on co-author, co-word and citation.

The study is exploratory in nature. The study, using bibliometric analysis, provides a Web of Sciences (WOS) overview. The data has been obtained from the WOS database by coding as “artificial intelligence” and “tourism” and “hospitality.” VOSviewer program has been used to obtain and analyze the data.

The findings of the research show that studies on the use of AI in tourism and hospitality have become very popular, especially in the last 4 years. The authors of the study are working in the tourism and hospitality fields and have a high h-index. Generally, in current AI studies in tourism, topics such as robot, automation, ChatGPT, technology adoption and mechanical learning are studied. It has also been determined that topics related to the future of destinations and literature reviews are also discussed.

Although this paper examines all studies identified as a result of filtering, the analysis is limited to 195 studies. However, due to the widespread use of AI in tourism-related studies recently, bibliometric analysis has been made with extensive filtering. As studies on the subject become more widespread in the coming years, it would be useful to repeat similar studies by filtering with more specific quotas.

There are a few similar studies on the subject in the field. However, these studies need to be repeated at certain periods. This paper contributes to monitoring the literature of AI studies, which are new to use in tourism and hospitality, and to the formation of a theoretical framework on the subject.

This study aimed to discover the factors affecting employee performance by testing the relationship of change management, job satisfaction, organizational commitment and leadership style on employee performance in Indonesian sharia property companies.

The study population was all members of “Sharia Property Developer” (DPS) across Indonesia with criteria of having subordinates at least one person and is listed as a DPS member. The samples used were 71 people from the 200 members of DPS across Indonesia. The sampling method used was based on R² value and significance level with an 80% statistical strength. Data analysis was carried out using smartPLS software to test the relationship of change management, job satisfaction, organizational commitment and leadership style on employee performance.

The utilization of SEM in Smart PLS for change management with the ADKAR method had a negative value of 6.2% in affecting employee performance and 4.6% in affecting job satisfaction. Job satisfaction insignificantly affected employee performance by 7.5%. Leadership style and organizational commitment positively affected performance by 57.9% and 25.6%, respectively.

This study did not limit respondents’ education levels. Twenty percent of respondents were middle and high school graduates. Respondent’s position was mostly the highest leader in the company by 58%. Indicators in the ADKAR model did not implement the construct validity test since the researchers did not find precedent studies that discuss the indicators of the ADKAR model in detail.

Factors that positively and significantly affected employee performance can be used to plan employee performance of DPS member companies.

The company must create a program to produce meaning in working, shape leaders to have discipline by putting appropriate employees as leaders.

This study used change management, organizational commitment, job satisfaction and leadership style as exogenous variables, job satisfaction and leadership style as intervening variables. The study model modified the previous study regarding employee performance improvement because it utilized the change management with the ADKAR model. The study objects were sharia property companies, where the researchers did not find previous studies discussing employee performance in sharia property companies.

This study aims to explore whether Chat Generative Pre-training Transformer (ChatGPT) can produce quantitative data sets for researchers who could behave unethically through data fabrication.

A two-stage case study related to the field of tourism was conducted, and ChatGPT (v.3.5.) was asked to respond to the first questionnaire on behalf of 400 participants and the second on behalf of 800 participants. The artificial intelligence (AI)-generated data sets’ quality was statistically tested via descriptive statistics, correlation analysis, exploratory factor analysis, confirmatory factor analysis and Harman's single-factor test.

The results revealed that ChatGPT could respond to the questionnaires as the number of participants at the desired sample size level and could present the generated data sets in a table format ready for analysis. It was also observed that ChatGPT's responses were systematical, and it created a statistically ideal data set. However, it was noted that the data produced high correlations among the observed variables, the measurement model did not achieve sufficient goodness of fit and the issue of common method bias emerged. The conclusion reached is that ChatGPT does not or cannot yet generate data of suitable quality for advanced-level statistical analyses.

This study shows that ChatGPT can provide quantitative data to researchers attempting to fabricate data sets unethically. Therefore, it offers a new and significant argument to the ongoing debates about the unethical use of ChatGPT. Besides, a quantitative data set generated by AI was statistically examined for the first time in this study. The results proved that the data produced by ChatGPT is problematic in certain aspects, shedding light on several points that journal editors should consider during the editorial processes.

本研究旨在探讨ChatGPT是否能够为那些可能通过数据伪造行为不道德的研究人员生成定量数据集。

本研究进行了与旅游领域相关的两阶段案例研究, 并要求ChatGPT（v.3.5.）代表400名参与者回答第一个问卷, 以及代表800名参与者回答第二个问卷。通过描述统计、相关分析、探索性因子分析、验证性因子分析和哈曼的单因素测试对人工智能生成的数据集的质量进行了统计测试。

结果显示, ChatGPT能够按照所需的样本大小水平回答问卷, 并以表格格式呈现生成的数据集, 以便进行分析。还观察到ChatGPT的回答是系统性的, 并且它创建了一个在统计上理想的数据集。然而, 本研究注意到所产生的数据在观察变量之间存在较高的相关性, 测量模型未能达到足够的拟合度, 并出现了共同方法偏差的问题。本研究得出的结论是, ChatGPT目前不能生成适用于高级统计分析的数据, 或者说不适合这样做。

本研究表明, ChatGPT可以为试图不道德地伪造数据集的研究人员提供定量数据。因此, 它为关于ChatGPT不道德使用的持续争论提供了一个新而重要的论点。此外, 在本研究中首次对由人工智能生成的定量数据集进行了统计检验。结果表明, ChatGPT生成的数据在某些方面存在问题, 为期刊编辑在编辑过程中考虑的几个要点提供了启示。

Service robots are gradually becoming more anthropomorphic and intelligent. This research aims to investigate how anthropomorphic service robots with different levels of intelligence affect their human counterparts.

Two between-subject experimental studies were used to test whether different levels of service robot anthropomorphism with different levels of intelligence influence employees' morale and resistance to service robots.

Study 1 shows that the effect of service robot anthropomorphism (low vs. high) on employees' resistance and morale is mediated by perceived job-security threat. Study 2 validates this mediating effect and shows that it is moderated by the type of AI (mechanical vs. analytical). Specifically, when exposed to mechanical AI-powered service robots, employees exhibit a higher perceived job-security threat toward robots with a high (vs. low) degree of anthropomorphism. This moderating effect is not observed when employees are exposed to analytical AI-powered service robots. This moderated mediation effect is also found for the signing of a petition as the behavioral outcome.

Service firms considering the adoption of mechanical AI-powered service robots should choose a low (vs. high) anthropomorphic robot to reduce the sense of job-security threat felt by human employees, which subsequently increases their acceptance. However, if analytical AI-powered service robots with are to replace their human employees, the degree of anthropomorphism becomes irrelevant.

This is the first empirical study to explore how anthropomorphic service robots can influence human employees' evaluations and behaviors.

This study aims to investigate tourists’ attitudes and intentions regarding the usage of Chat Generative Pre-trained Transformer (ChatGPT) for accessing tourism information. Furthermore, by integrating the perceived risks associated with ChatGPT and the theory of planned behavior (TPB), this research examines the impact of three types of perceived risks, such as privacy risk, accuracy risk and overreliance risk, on tourists’ behavioral intention.

Data were gathered for this study by using two online survey platforms, thus resulting in a sample of 536 respondents. The online survey questionnaire assessed tourists’ perceived risks, attitude, subjective norm, perceived behavioral control, behavioral intention and demographic information related to their usage of ChatGPT.

The structural equation modeling analysis revealed that tourists express concerns about the associated risks of using ChatGPT to search for tourism information, specifically privacy risk, accuracy risk and overreliance risk. It was found that perceived risks significantly influence tourists’ attitude and intention toward the usage of ChatGPT, which is consistent with the hypotheses proposed in previous literature regarding tourists’ perceived risks of ChatGPT.

This work is a preliminary empirical study that assesses tourists’ behavioral intention toward the use of ChatGPT in the field of tourism. Previous research has remained at the hypothetical level, speculating about the impact of ChatGPT on the tourism industry. This study investigates the behavioral intention of tourists who have used ChatGPT to search for travel information. Furthermore, this study provides evidence based on the outcome of this research and offers theoretical foundations for the sustainable development of generative AI in the tourism domain. This study has limitations in that it primarily focused on exploring the risks associated with ChatGPT and did not extensively investigate its range of benefits.

First, to address privacy concerns that pose significant challenges for chatbots various measures, such as data encryption, secure storage and obtaining user consent, are crucial. Second, despite concerns and uncertainties, the introduction of ChatGPT holds promising prospects for the tourism industry. By offering personalized recommendations and enhancing operational efficiency, ChatGPT has the potential to revolutionize travel experiences. Finally, recognizing the potential of ChatGPT in enhancing customer service and operational efficiency is crucial for tourism enterprises.

Recognizing the potential of ChatGPT in enhancing customer service and operational efficiency is crucial for tourism enterprises. As their interest in adopting ChatGPT grows, increased investments and resources will be dedicated to developing and implementing ChatGPT solutions. This enhancement may involve creating customized ChatGPT solutions and actively engaging in training and development programs to empower employees in effectively using ChatGPT’s capabilities. Such initiatives can contribute to improved customer service and overall operations within the tourism industry.

This study integrates TPB with perceived risks in ChatGPT, thus providing empirical evidence. It highlights the importance of considering perceived risks in tourists’ intentions and contributes to the sustainable development of generative AI in tourism. As such, it provides valuable insights for practitioners and policymakers.

本研究旨在调查游客对使用ChatGPT获取旅游信息的态度和意向。此外, 通过将与ChatGPT相关的感知风险与计划行为理论（TPB）相结合, 本研究探讨了三种感知风险（隐私风险、准确性风险和过度依赖风险）对游客行为意向的影响。

本研究通过两个在线调查平台收集了536名受访者的数据。在线调查问卷评估了游客对ChatGPT使用的感知风险、态度、主观规范、感知行为控制、行为意向以及与其使用ChatGPT相关的人口统计信息。

结构方程建模分析显示, 游客对使用ChatGPT搜索旅游信息的相关风险表示关切, 特别是隐私风险、准确性风险和过度依赖风险。发现感知风险显著影响游客对使用ChatGPT的态度和意向, 与先前有关游客对ChatGPT感知风险的文献中提出的假设一致。

本研究将TPB与ChatGPT中的感知风险相结合, 提供了实证证据。它强调了在考虑游客意向时考虑感知风险的重要性, 并为旅游中生成AI的可持续发展提供了贡献。因此, 它为从业者和政策制定者提供了宝贵的见解。