This study explores the impact of artificial intelligence (AI) on customer perceptions and behavior in restaurants, airline companies, and hotel sectors within the hospitality industry of Iran. The primary objective is to analyze how AI affects customer trust, brand engagement, electronic word-of-mouth (eWOM), and tourists’ readiness to use AI technologies. Using a comparative analysis approach and surveys, this research tests hypotheses about the effects of artificial intelligence on various dimensions of customer interaction. The findings highlight significant relationships between the quality of artificial intelligence and customer engagement metrics, such as trust and brand loyalty, which are crucial for understanding and predicting customer behavior in response to technological advancements. This study lays the groundwork for theoretical assumptions about sustainability in these sectors in developing countries, providing a basis for future empirical research that could validate these assumptions and explore broader implications of AI integration in enhancing sustainable practices within the hospitality industry.

This study examines the impact of service quality on intention-to-revisit through variables such as emotion, satisfaction, eWOM and loyalty in the hotel industry from a cross-cultural perspective that goes beyond national boundaries. Four countries were chosen from two clusters, based on the GLOBE Project, the UK and the US from the Anglo cluster and Iran and India from the Southern Asia cluster. A total of 1418 questionnaires were received from guests of 122 independent hotels, and the data were analyzed using the partial square technique in SEM-PLS. The findings reveal that antecedents of intention to revisit follow both similar and different patterns of relationships because of the cultural dimensions of the countries. While positive emotions experienced by Iranians and Indians can make them satisfied customers, the satisfaction of US consumers and the British is derived from their evaluation of service quality. In addition, the intention of US consumers and the British to revisit the hotel is created through their loyalty, but satisfaction and eWOM can lead to Iranian and Indian customers’ revisit intention. This study contributes to the literature on the hospitality industry and the antecedents of intention-to-revisit by defining the distinction between countries within different clusters in the GLOBE Project and adds new knowledge to our understanding of culture.

Purpose: Gripped by the COVID-19 pandemic, the airline industry has faced many restrictions. Based on the determining factor of repurchase intention, this research paper addresses the roles of brand engagement (BE) and consumer trust during the pandemic.Design/methodology/approach: A number of 439 out of 500 questionnaires, which were distributed among the Iranian airlines' customers, were completed and returned (an 87% response rate). SmartPLS 3 was used to carry out the confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) in order to validate the research hypotheses.Findings: The results show that during the COVID-19 pandemic, BE had a significant effect on customer purchase intention. In addition, BE boosted customer trust and thereby increased repurchase intention. Perceived risks of COVID-19 moderated these relationships. The findings also show that airline customers are highly sensitive to various perceived risks and were especially so during the COVID-19 pandemic. These perceived risks have major impacts on customers' intention to reuse the services of airline companies. High levels of perceived risk decrease customer trust, BE and repurchase intention rates, and vice versa.Practical implications: This paper provides authorities and decision-makers of the air transport industry with several practical recommendations based on the research findings. As the results confirmed, there is a worthwhile relationship between BE and customer trust; regarding the both direct and indirect benefits of customer trust for for-profit firms and the huge importance of trust in creating competitive advantages, different airline companies need to implement various strategies to engage customers with their brand names, and thereby institutionalize trust in their customers' minds during the pandemic. Since customer trust was also found to improve repurchase intention rates significantly, authorities of the airline industry must take actions to reduce feelings of mistrust and risk among consumers and increase customer trust during COVID-19.Originality/value: This paper is the first to explore the effect of BE on airline customers' repurchase intentions during the COVID-19 pandemic. It has produced practical suggestions to employees and activists of the air transport industry regarding the use of BE as a tool to restore customer trust during and after the COVID-19 epidemic. This research focuses on the airline industry, which is particularly interesting given the industry's intensely competitive environment and the dramatic effect of COVID-19 on the industry.

Independent hotels are in a competitive marketplace for staff in which they face a high rate of employee turnover resulting in poor performance and low profit. This study examines the impact of ethical leadership on turnover intention through ethical climate, employee voice, employee empowerment and affective commitment using a cross-cultural approach in two contrasting GLOBE surveyed countries, Iran and the United Kingdom, based on Social Exchange Theory. A sample of 807 personnel of independent hotels in the two countries were surveyed. Findings show that ethical leadership can reduce turnover intention by different mechanisms through the cultural features and leadership dimensions of the two countries.

Purpose: In most countries, independent hotels are a major contributor to both national economy and the hospitality sector, but to the best of the authors’ knowledge, there is a lack of any study on brand co-creation (BCC) in independent hotels. This study aims to examine the interrelationship of moral judgement (MJ), self–brand connection, customer–brand engagement (CBE), electronic word-of-mouth (eWOM) and how BCC occurs in independent hotels and contrasts visitors’ perspectives between two different Global Leadership and Organizational Behavior Effectiveness clusters in independent hotels. Furthermore, this study also examines the moderating impact of privacy concern (PC) on the relationship between eWOM and brand co-creation. Design/methodology/approach: Data were collected from two groups of tourists, Iranian (290) and European (224), who visited and stayed in independent hotels in some major cities in Iran. The structural equation modelling using AMOS 22 was adopted to test the hypotheses. Findings: The study’s findings revealed that there are significant differences between two culturally different travellers in terms of selected variables, suggesting that MJ is a main antecedent of CBE, which leads to eWOM among Iranian travellers, which consequently leads to BCC. Moreover, the study’s data indicates that European travellers show a high level of PCs while staying in independent hotels in Iran, which prevents them from sharing and getting engaged in the social media, which could delay the BCC process especially in international level. Originality/value: This study contributes to literature by providing new insights on the differences in the concept of brand co-creation between two culturally different groups in the context of independent hotels, which require special attention of independent hotel managers.

In today’s competitive world, organizations are in a constant struggle to retain their current customers while attracting new customers through various methods. Customer churn is a major challenge in different industries and companies. Despite their initial successful attempts at attracting customers, organizations soon face the fact that their current customers may turn away toward their rivals. By identifying churn candidates, organizations will be able to guarantee their future success by revising their customer relationship management policy. Analyzing the data of the telecommunications industries, this study provided an effective early-churn-detection solution using modern techniques by stacking data mining algorithms. Research findings indicate that integrating support vector machines (SVMs) with the chi-square automatic interaction detection (CHAID) decision tree can yield the best outcome. The results show the proper accuracy of the proposed churn prediction solution. In addition, stacking contributed to improved customer churn detection results.