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This paper aims to systematically explore the experiences of restaurant patrons with quick response (QR) code menus during the pandemic to extract insights pertaining to the acceptance of technology adoption in service settings for the post-COVID era.

COVID-19 has enormously impacted consumer perceptions toward technology adoption in restaurants. Identifying these impacts requires qualitative inquiries because qualitative techniques, unlike quantitative methods, enable researchers to seek insights into the phenomenon without a predetermined stance. This study applied a qualitative research method. The qualitative data was obtained through in-depth interviews and a focus group. Thematic analysis was conducted.

This study identified perceived attributes of QR code technology by patrons: positive attributes such as easy to learn, quick and contactless; and negative attributes such as effortful and reliance on a smartphone. Also, the results distinguished perceived advantages of utilization of QR codes as restaurants’ menu: advantages such as hygienic, environmentally friendly due to no printing and contactless; and disadvantages such as poor menu design and lack of interaction.

This research presents patrons’ views on QR code menu utilization at restaurants under the pandemic conditions. Contradictory responses on the same features of technology reveal a lack of menu design in the utilization of QR codes. Restaurant practitioners can extract insights about how to better use technology like QR codes, cost-effective and environmentally friendly, in their operations in the postpandemic era.

This study included both deductive and inductive approaches. An inductive approach that progressed from theory to data was applied while forming interview questions. A deductive approach was used to inform theory with the findings of the data. The value of research derives from differentiating technology attributes (QR codes) and utilization domain (restaurant menu) in the realm of acceptance of technology in the consumer context.

This paper reports the results of research that examines the interrelationships between efficacy of sustainability values (SV) and pro-sustainable behaviors of potential tourists. A partially mediated model is postulated and tested to help explain additional error variance in predicting consumers’ destination choice decisions in tourism, hence voiding a critical research gap. Coined as the “environmentally intellectualist behavior,” a new mediator variable is tested to explain additional error variance in human-value models.

The study is based on data collected from two representative samples of potential tourists from the USA and Canada. Data analyses include exploratory and confirmatory factor analyses that were used to examine the underlying domain structures of SV, followed by a predictive model using structural equation modeling.

The study findings suggest that values are salient factors that underlie pro-sustainable tourism and travel behavior. Moreover, the results confirm the existence of a higher-order sustainability construct. The study contributes original insights to the field by demonstrating that there are direct and indirect positive relationships between SV, environmental behaviors and decisions of consumers who take a pro-sustainable stance when traveling.

By modeling values as antecedents to attitudes and testing interrelationships between SV and the mediator variables coined as the environmentally intellectual behavior, the authors developed and tested a predictive model to explain destination- and product choice decisions. The model tested herein advances the value theory in two fundamental ways: first, this study demonstrates that SV can be modeled as higher-order factors. Second, values are antecedents to attitude and other variables, therefore must be included in consumer behavior models. Finally, the culture or origin of tourists matters when examining the impact of values on tourists’ choice decisions. Political actions and environmental attitudes can be modeled as mediators to explain additional error variance.

The objective of this study is to assess quantitatively how feasible blockchain is for various industries, such as logistics and supply chain, health, energy, finance, automotive, pharmaceutical and agriculture and food using a comprehensive list of indicators.

A decision aid was applied to the problem of identifying the feasibility of blockchain in Turkish industries. To this end, first, a set of indicators was identified. Then, the fuzzy AHP and fuzzy TOPSIS were utilized to assess the feasibility comparatively using the data gathered from a group of experts. Finally, a scenario analysis was conducted to ensure the consistency of our evaluation.

The findings of this study suggest that comparatively, logistics and supply chain, finance and health industries are the most feasible industries for blockchain. This study further suggests that blockchain is the least feasible for the automotive industry compared to the rest of the identified industries.

It is cumbersome to find out the respondents who have sufficient knowledge of both blockchain and the identified industries. Even if we took the utmost care in identifying the right respondents, we limited our search to the biggest industrial hubs of Turkey.

The findings of this research may help various decision-makers employed in governments, conglomerates, software and consulting firms and national research institutions make more informed decisions and allocate their resources more effectively.

To this date, the current studies have solely investigated possible research opportunities in blockchain and demonstrated several blockchain applications in stand-alone cases. To the best of our knowledge, however, no single study exists that evaluates the feasibility of blockchain comparatively and holistically among a group of industries using various indicators.

The objective of this study is to investigate the role of blockchain in reducing the impact of barriers to humanitarian supply chain management (HSCM) using a list of blockchain benefits.

A decision aid was used to explore the suitability of blockchain in humanitarian supply chains. To achieve that, first, a list of barriers to HSCM was identified. Then, the intuitionistic fuzzy decision-making trial and evaluation laboratory (IF–DEMATEL) method was utilized to determine the relationships and the level of interdependencies among the criteria. Finally, the intuitionistic fuzzyanalytic network process (IF–ANP) technique was employed, as it successfully handles dependencies among the criteria.

The findings of this study suggest that interorganizational barriers are the most suitable ones, the impacts of which blockchain may alleviate. This study further suggests that trust turned out to be the most significant benefit criterion for the analysis.

The readers should construe the findings of this study with caution since it was carried out using the data collected from the experts of a particular country. Moreover, the proposed decision aid contemplates a limited set of criteria to assess a possible role of blockchain in overcoming the barriers to HSCM.

The findings of this study can assist humanitarian supply chain managers to make more judicious assessments on whether they implement the blockchain in humanitarian supply chain operations. Specifically, this research may help decision makers to identify the certain barriers, the impact of which may be reduced by using the blockchain. The findings of this research will also help various decision makers make more rational decisions and allocate their resources more effectively.

To the best of authors’ knowledge, no single study exists to investigate the role of blockchain in reducing the impact of barriers to HSCM using an intuitionistic fuzzy multi-criteria decision-making approach.

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。

The rapid growth of artificial intelligence is disrupting various industries, including the tourism sector. This paper aims to outline the applications, benefits and risks of ChatGPT and large language models in general on tourism. It also aims to establish a research agenda for investigating the implications of these models in tourism.

Drawing on the available literature on ChatGPT, large language models and artificial intelligence, the paper identifies areas of application of ChatGPT for several tourism stakeholders. Potential benefits and risks are then considered.

ChatGPT and other similar models are likely to have a profound impact on several tourism processes. They will contribute to further streamline customer service in front-of-house operations and increase productivity and efficiency in back-of-house operations. Although negative consequences for human resources are expected, this technology mostly enhances tourism employees.

To the best of the authors’ knowledge, this is one of the first studies that explore the potential implications of ChatGPT in tourism and hospitality.

人工智能的快速发展正在颠覆包括旅游业在内的各个行业。 本文旨在概述ChatGPT和大型语言模型在旅游业中的应用、好处和风险。同时, 旨在建立一个研究议程, 以调查这些模型在旅游业中的影响。

本文借鉴了关于ChatGPT、大型语言模型和人工智能的现有文献, 确定了ChatGPT在几个旅游利益相关者中的应用范围, 然后考虑了潜在的好处和风险。

ChatGPT和其他类似的模型可能会对一些旅游过程产生深远的影响。它们将有助于进一步简化前台业务的客户服务, 并提高后台业务的生产力和效率。虽然对人力资源的负面影响是可以预见的, 但这项技术主要是增强旅游业的员工能力。

这是首批探索ChatGPT在旅游业和酒店业潜在影响的研究之一。

A partir de la bibliografía disponible sobre ChatGPT, grandes modelos lingüísticos e inteligencia artificial, este artículo identifica las posibles áreas de aplicación de ChatGPT y actores que se pueden beneficiar. De igual forma, se examinan los posibles beneficios y riesgos.

El rápido crecimiento de la inteligencia artificial está afectando diversas industrias, incluyendo la del turismo. Este artículo pretende esbozar las aplicaciones, ventajas y riesgos de ChatGPT, así como los grandes modelos lingüísticos, en turismo. También pretende establecer una agenda de investigación para estudiar las implicaciones de estos modelos en el turismo.

Es probable que ChatGPT y otros modelos similares tengan un profundo impacto en varios procesos turísticos, contribuyendo a racionalizar, aún más, el servicio al cliente en las operaciones de front-of-the-house y aumentando la productividad y eficiencia en el back-of-the-house. Aunque se prevén consecuencias negativas para los recursos humanos, esta tecnología servirá sobre todo para potenciarlos.

Éste es uno de los primeros estudios que exploran las implicaciones potenciales de ChatGPT en el turismo y la hostelería.

The tourism industry is undergoing a rapid transformation brought on by artificial intelligence (AI), which is offering a wide range of benefits to both businesses and their customers. Nevertheless, this technological advancement also poses a number of difficulties for which the sector will need to find solutions. This chapter investigates the effects that artificial intelligence (AI) has had on the tourism industry in Europe, with a particular focus on how the hospitality industry has reacted to the advent of this technology. Following an overview of the tourism and hospitality industries in Europe, this chapter begins with an introduction to artificial intelligence (AI) in the tourism industry. The section on the methodology describes the various approaches to research that were utilised in this study, and the section on the conclusion summarises the findings.

This study aims to investigate the integration of blockchain technology into the food supply chain within the restaurant industry. It focuses on how blockchain can be applied to enhance transparency and trust in tracking food sources, ultimately impacting customer satisfaction.

A service design workshop (Study 1) and three between-subjects experiments (Studies 2–4) were conducted.

Results indicate that blockchain adoption significantly improves traceability and trust in the food supply chain. This improvement in turn enhances customer satisfaction through perceived improvements in food safety, quality and naturalness. This study also notes that the effects of blockchain technology vary depending on the type of restaurant (casual or fine dining) and its location (tourist destinations or residential areas).

The findings offer practical insights for restaurant owners, technology developers and policymakers. Emphasizing the benefits of blockchain adoption, this study guides decision-making regarding technology investments for enhancing customer service and satisfaction in the hospitality sector.

This research contributes novel insights to the field of technology innovation in the hospitality industry. It extends the understanding of signaling theory by exploring how blockchain technology can serve as a tool for signal transmission in restaurant food supply chains.

The paper aims to evaluate the ways ChatGPT is going to disrupt tourism education and research.

This is a conceptual paper.

ChatGPT has the potential to revolutionize tourism education and research because it can do what students and researchers should do, namely, generate text (assignments and research papers). Universities will need to reevaluate their teaching and assessment strategies and incorporate generative language models in teaching. Publishers will need to be more receptive toward manuscripts that are partially generated by artificial intelligence. In the future, digital teachers and research assistants will take over many of the cognitive tasks of tourism educators and researchers.

To the authors’ best knowledge, this is one of the first academic papers that investigates the implications of ChatGPT to tourism education and research.

This study aimed to investigate the impact of ChatGPT on students’ self-efficacy, anxiety and motivation within blended learning environments.

A correlational quantitative method was employed using the data collected through an online questionnaire distributed randomly to 293 science and engineering students from 2 state universities in South Sulawesi, Indonesia. Measurement analysis was conducted to assess instrument validity, and structural equation modeling (SEM) was used to examine the relationships between the constructs.

The results indicate that self-efficacy and motivation significantly enhance student performance in blended learning, with the positive effect of self-efficacy moderated by ChatGPT. Although anxiety had a positive, though not significant, effect, the interaction between anxiety, motivation and ChatGPT still demonstrated a positive effect on learning performance.

This study highlights the moderating effect of ChatGPT on motivation, anxiety and performance, emphasizing the need to understand educational contexts and individual differences. The study was limited to a specific student population and a short duration of ChatGPT usage, potentially restricting generalizability. Future research should explore diverse educational settings, disciplines, long-term effects and qualitative methods to gain deeper insights.

This study provides valuable new insights into the use of AI technology in education from the psychological perspective of students and offers practical implications for improving future learning strategies.