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Generative Artificial Intelligence (Gen-AI) has provided new opportunities and challenges in using educational environments for students’ interaction and knowledge acquisition. Based on the expectation–confirmation theory, this paper aims to investigate the effect of different constructs associated with Gen-AI on engagement, satisfaction and word-of-mouth.

We collected data from 508 students in the UK using Qualtrics, a prominent online data collection platform. The conceptual framework was analysed through structural equation modelling.

The findings show that Gen-AI expectation formation and Gen-AI quality help to boost Gen-AI engagement. Further, we found that active engagement positively affects Gen-AI satisfaction and positive word of mouth. The mediating role of Gen-AI expectation confirmation between engagement and the two outcomes, satisfaction and positive word of mouth, was also confirmed. The moderating role of cognitive processing in the relationship between Gen-AI quality and engagement was found.

This paper extends the Expectation-Confirmation Theory on how Gen-AI can enhance students’ engagement and satisfaction. Suggestions for future research are derived to advance beyond the confines of the current study and to capture the development in the use of AI in education.

Robotic process automation (RPA) is an automation technology that replaces front-end information technology (IT) human workers with software that handles simple and repetitive tasks. Despite its importance, RPA research remains limited to topics related to the introduction of the technology itself due to its novelty. This study examines users’ resistance attitudes to understand their intentions to use RPA.

By integrating the theory of planned behavior (TPB) and user resistance theory, this paper develops a research model to explain users’ intentions to use RPA. We employ a structural equation model (SEM) to analyze an online survey of individuals’ (N = 309) intentions to use RPA.

The results show that subjective norms are the largest positive significant factor explaining the intention to use RPA, whereas user resistance is the next largest significant negative factor for the intention to use RPA. Additionally, dehumanization is found to have the most significant effect on user resistance.

This study has meaningful implications for researchers and practitioners in the prediction of hyperautomation acceptance, such as RPA.

This study aims to address the macroeconomic factors effect on the travel and leisure (T&L) industry throughout Europe within the context of the Russo-Ukrainian war that have started on 24 February 2022. Specifically, top tourist destinations are analysed, such as Spain, France, Italy and Portugal, as well as Europe in general.

This study adopts the panel regression approach based on the data that is provided on a daily basis, and it covers a period of nearly 14 months, starting on 24 February 2022 and ending on 15 April 2023.

The findings indicate that the European T&L sector is impacted by macroeconomic variables. Namely, the T&L sector is significantly impacted by interest rates, geopolitical risk, oil and gas, whereas inflation has a muted effect, indicating a comparatively lesser influence on the dynamics of the industry. This research contributes to existing literature by providing one of the first quantitative analyses of how macroeconomic factors impact the European T&L business in the context of a geopolitical conflict.

A study of the Russian–Ukrainian war may be limited by a number of research constraints. The continuing nature of the conflict, the lack of communication between the parties and potential political prejudice are some of these difficulties. Any research on the Russo-Ukrainian war should be done with these limits in mind.

Macroeconomic variables play a significant role on the T&L sector development; therefore, when designing resilience strategies, they need to be accounted for.

To the best of authors’ knowledge, this is one of the first studies to analyse how macroeconomic factors affected the European T&L business using a quantitative approach. The macroeconomic variables that were taken into account in this study included interest rates, inflation, oil and petrol prices, as well as the geopolitical risk index.

本研究探讨在 2022 年 2 月 24 日开始的俄乌战争对整个欧洲旅游和休闲 （T&L） 行业的宏观经济因素的影响。具体来说, 本文分析了著名旅游目的地, 例如西班牙, 法国, 义大利和葡萄牙, 以及整个欧洲。

研究采用基于每天提供的数据的面板回归方法, 涵盖近 14 个月, 从 2022 年 2 月 24 日开始, 到 2023 年 4 月 15 日结束。

我们的研究结果表明, 欧洲运输和物流行业受到宏观经济变数的影响。也就是说, 运输和物流行业受到利率、地缘政治风险、石油和天然气的严重影响, 而通货膨胀的影响则很温和。

对俄乌战争的研究可能受到许多研究限制的限制。冲突的持续性质、各方之间缺乏沟通以及潜在的政治偏见是其中一些困难。任何关于俄乌战争的研究都应该考虑到这些限制。

据我们所知, 这是首批使用定量方法分析宏观经济因素如何影响欧洲运输和物流业务的研究之一。本研究考虑的宏观经济变数包括利率、通货膨胀、石油和汽油价格, 以及地缘政治风险指数。

宏观经济变数在运输和物流行业发展中起着重要作用, 因此, 在设计弹性策略时, 需要考虑它们。

La presente investigación aborda el efecto de los factores macroeconómicos en la industria de viajes y ocio (VyO) en toda Europa en el contexto de la guerra ruso-ucraniana que comenzó el 24 de febrero de 2022. En concreto, se analizan los principales destinos turísticos, como España, Francia, Italia y Portugal, así como Europa en general.

El estudio adopta un enfoque de regresión de panel basado en datos diarios y cubre un período de casi 14 meses, del 24 de febrero de 2022 al 15 de abril de 2023.

Nuestros resultados indican que el sector europeo de VyO se ve afectado por variables macroeconómicas. En concreto, el sector se ve significativamente afectado por los tipos de interés, el riesgo geopolítico, el petróleo y el gas, mientras que la inflación tiene un efecto moderado.

Un estudio de la guerra ruso-ucraniana puede verse limitado por una serie de restricciones a la investigación. La persistencia del conflicto, la falta de comunicación entre las partes y los posibles prejuicios políticos son algunas de estas dificultades. Cualquier investigación sobre la guerra ruso-ucraniana debe hacerse teniendo en cuenta estos límites.

Las variables macroeconómicas desempeñan un papel importante en el desarrollo del sector de VyO, por lo que es necesario tenerlas en cuenta al diseñar estrategias de resiliencia.

Hasta donde sabemos, éste es uno de los primeros estudios que analiza cómo los factores macroeconómicos afectaron al negocio europeo de VyO utilizando un enfoque cuantitativo. Las variables macroeconómicas que se tuvieron en cuenta en este estudio fueron los tipos de interés, la inflación, los precios del petróleo y de la gasolina, así como el índice de riesgo geopolítico.

This study proposed an extended theory of planned behaviour (TPB), that is, considering that behavioural beliefs, normative beliefs and control beliefs (beliefs) will influence perceived service quality (PSQ) on food delivery services. PSQ (behavioural intention) will influence electronic word-of-mouth (EWOM) (behaviour). In addition, exogenous variables including information from online ratings and consumer groups will affect the strength of the relationship between received service quality and EWOM on food delivery service.

This study aimed to investigate the mediation (PSQ) and moderation (Online ratings and consumer groups) effects on the extended TPB for Taiwanese consumers (n = 823).

This study first found a positive relationship between different beliefs and PSQ (behavioural intention). In addition, there is a positive relationship between PSQ and EWOM. Online rating has a moderating effect between PSQ and EWOM. Consumer group has a moderating relationship between PSQ and EWOM.

This study first found that the three stages of beliefs-intention-behaviour for consumers on food delivery service are reciprocal with two paths, starting with offline-to-online in terms of generating the positive relationship between individual belies and PSQ. Next, it can generate positive power to return online with a behaviour of EWOM. In addition, online ratings can enhance and strengthen the positive effect between PSQ and EWOM.

Generative artificial intelligence (GenAI) can potentially improve supply chain management (SCM) processes across levels and verticals. However, despite its promise, the implementation of GenAI for SCM remains challenging, mainly due to the lack of knowledge regarding its key drivers. To address this gap, this study examines the factors driving GenAI implementation in an SCM environment and how these factors optimize SCM performance.

A thorough literature review was followed to identify the drivers. The resultant model from the drivers was validated using a quantitative study based on partial least squares structural equation modeling (PLS-SEM) that used responses from 315 expert respondents from the field of SCM.

The results confirmed the positive effect of performance expectancy, output quality and reliability, organizational innovativeness and management commitment to GenAI usage. Further, they showed that successful GenAI usage improved SCM performance through improved transparency, better decision-making, innovative design, robust development and responsiveness.

This study reports the potential drivers for the contemporary development of GenAI in SCM and highlights an action plan for GenAI’s optimal performance. The findings suggest that by increasing the rate of GenAI implementation, organizations can continuously improve their strategies and practices for better SCM performance.

This study establishes the first step toward empirically testing and validating a theoretical model for GenAI implementation and its effect on SCM performance.

With its continuous development and application in the hotel industry, artificial intelligence (AI) is gradually replacing many jobs traditionally performed by humans. This research aims to understand how this threat and opportunity of substitution affects hotel employees’ behavioral decision-making.

This study uses a structural equation model, ordinary least squares and bootstrapping method to analyze the data collected with a field study and a scenario experiment from star-hotels in Shanghai, Paris and Seoul.

The results discovered that employees’ AI awareness has a positive relationship with their work engagement and AI boycott through two paths. The promoting path involves recovery level, while the hindering path includes job insecurity. In addition, the estimates showed that AI awareness has a great indirect effect on work engagement or AI boycott when innovativeness as a job requirement is high.

The findings offer insights to help hotels optimize the relationship between AI and hotel human workers while providing valuable implications for addressing behavioral dilemmas faced by hotel employees in the era of AI.

By integrating the behavioral decision-making literature with the conservation of resources theory, the study focuses on the dual mechanisms – challenging and hindering – through which AI awareness influences hotel employees’ coping strategies.

This study aims to investigate the effects of economic policy uncertainty (EPU) on Italian hospitality sector. The investigation attempts to explain whether hotel performance drops when the perceived economic uncertainty increases in the period 2018–2022.

The study examines the impact of EPU on hotel performance in a sample of 661 Italian luxury hotels. To establish the relationship between EPU and hotel performance, we employ the generalized estimating equations (GEE) technique on 3,305 hotel-year observations.

The results show that EPU has a negative impact on hotel performance. More specifically, the analysis reveals that EPU is negatively and significantly related to the revenue per available room (REVPAR), average daily rate (ADR) and hotel occupancy (OCCR). We also look at the role of hotel brand chain affiliation and the moderating effect of conference space and hotel wellness services on the relationship between EPU and hotel performance.

Results provide new evidence for academics to critically evaluate the behavior of luxury hotels under uncertain economic conditions. The investigation offers valuable information also for government, tourism policymakers, tourist hotel owners, hoteliers and tourism managers in their decision-making.

This study provides strategic implications for practitioners and operators in hospitality industry to evaluate the factors ensuring hotel profitability in periods of EPU.

This paper provides interesting insights into the characteristics and practices of profitable hotels in Italy. Few econometric studies empirically explored the effects of EPU in the hospitality field so far and no prior study investigated this topic in the Italian hospitality sector. Therefore, this paper tries to close an important gap in the existing literature improving the understanding of EPU in the Italian hospitality industry.

Supply chains need to be made viable in this volatile and competitive market, which could be possible through digitalization. This study is an attempt to explore the role of Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business performance from the lens of natural resource-based view.

The study tests the proposed model using a covariance-based structural equation modelling and further investigates the ranking of each construct using the artificial neural networks approach in AMOS and SPSS respectively. A total of 234 respondents selected using purposive sampling aided in capturing the industry practices across supply chains in the UK. The full collinearity test was carried out to study the common method bias and the content validity was carried out using the item content validity index and scale content validity index. The convergent and discriminant validity of the constructs and mediation study was carried out in SPSS and AMOS V.23.

The results are overtly inferring the significant impact of Industry 4.0 practices on creating smart and ultimately sustainable supply chains. A partial relationship is established between Industry 4.0 and supply chain agility through a smart supply chain. This work empirically reinstates the combined significance of green practices, Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business value. The study also uses the ANN approach to determine the relative importance of each significant variable found in SEM analysis. ANN determines the ranking among the significant variables, i.e. supply chain resilience > green practices > Industry 4.0> smart supply chain > supply chain agility presented in descending order.

This study is a novel attempt to establish the role of digitalization in SCs for attaining sustainable business value, providing empirical support to the mediating role of supply chain agility, supply chain resilience and smart supply chain and manifests a significant integrated framework. This work reinforces the integrated model that combines all the constructs dealt with in silos so far in prior literature.

This chapter seeks to answer the question of how food tourists will be in the future based on technology and digitalisation. Therefore, two future food tourist scenarios are proposed: realistic and utopian. More specifically, considering the developing technology, from a realistic perspective, future food tourists are evaluated according to their experiences (virtual food experiences, personalised and hyper-personalised food experiences, interactive tech-based food experiences, and sensory food experiences), information sources and communication, tendencies (seeking transparency and traceability in the food supply chain and sustainability-oriented), and payments. However, a utopian future food tourist was also provided as the second future food tourist scenario. In this scenario, the dimensions of future food tourists include instantaneous food travel thanks to teleportation, brain–computer interface-based food experiences, lab-grown food experiences, and intergalactic food tourism. Since this is the first study providing future food tourist scenarios, it plays a guidance role for service providers and launches a scholarly debate in food tourism literature.

This study aims to investigate the relevance, accuracy, specificity and justification of investment recommendations of generative artificial intelligence (GenAI) chatbots for different investment capitals and countries (UK and Bulgaria).

A two-stage mixed methods approach was used. Prompts were queried into OpenAI’s ChatGPT, Microsoft Bing and Google Bard (now Gemini). Finance and investment practitioners and finance and investment lecturers assessed the chatbots’ recommendations through an online questionnaire using a five-point Likert scale. The Chi-squared test, Wilcoxon-signed ranks test, Mann–Whitney U test and Friedman test were used for data analysis to compare GenAIs’ recommendations for the UK and Bulgaria across different amounts of investment capital and to assess the consistency of the chatbots.

GenAI chatbots’ responses were found to perform medium-to-high in terms of relevance, accuracy, specificity and justification. For the UK sample, the amount of investment had a marginal effect but prompt timing had an interesting impact. Unlike the British sample, the GenAI application, prompt timing and investment amount did not significantly influence the Bulgarian respondents’ evaluations. While the mean responses of the British sample were slightly higher, these differences were not statistically significant, indicating that ChatGPT, Bing and Bard performed similarly in both the UK and Bulgaria.

The study assesses the relevance, accuracy, specificity and justification of GenAI chatbots’ investment recommendations for two different periods, investment amounts and countries.