Service failure is bound to happen, but dissatisfaction has always been undermined by scholars previously. The present study investigates dissatisfaction factors in travel decision-making leading towards re-travel intention with the help of the expectancy disconfirmation theory (EDT) model. Moreover, complaint behavior mediates the relationship between dissatisfaction and re-travel intention concerning the factors involved in it. For the findings, 434 real-time responses were collected through an offline survey following the lifetime value (LTV) approach. Kaiser-Meyer-Olkin (KMO) and Bartlett's tests were conducted to test the sphericity of the variables one by one. Furthermore, structural equation modeling (SEM) was implied to test the measurement and structural models. All the hypotheses supported and accepted the proposed research questions. The findings reveal that the dissatisfaction factors, with their interaction terms (transaction-based and experience-based), help study the variable dissatisfaction and its multifaceted concept in travel and tourism literature. Moreover, complaint behavior is identified as a key mediating factor in the relationship between dissatisfaction and re-travel intention, suggesting that commuters' response to dissatisfaction, including complaints, shapes their future re-travel intentions. The theoretical implications of the study are substantial, as it advances our understanding of commuter behavior in the context of public transport travel and tourism. Practically, the findings offer actionable insights for public transport authorities and service providers. Understanding the specific dissatisfaction factors influencing re-travel intention allows for targeted interventions to improve service quality and customer experience. Recognizing the importance of effective complaint management systems can help organizations better address customer grievances and mitigate the negative effects of dissatisfaction on re-travel intention.

The incorporation of chatbots within the tourism and hospitality sectors signals a transformative era aimed at enhancing customer engagement, operational efficiency, and industry responsiveness. With their diverse capabilities spanning from personalized recommendations to seamless booking processes, chatbots revolutionize the way travelers interact with service providers. Powered by cutting-edge technologies like natural language processing and machine learning, chatbots facilitate seamless communication channels, offering timely assistance and valuable insights to travelers. In the hospitality domain, chatbots serve as indispensable virtual concierges, ensuring round-the-clock customer support, personalized recommendations, and streamlined reservation management. However, the integration of chatbots also presents challenges, including data security concerns and the need for continuous innovation. By navigating through these challenges, stakeholders can unlock the full potential of chatbot technology, paving the way for exceptional guest experiences and sustained industry innovation.

The actions are oriented to delight travelers where existing protocols fall short of solving problems in the travel and tourism industry, seeking the retention of commuters by offering travel quality. The present study investigates the travel decision-making process by exploring the nexus between travel quality and re-travel intentions (RTI). The study also analyzes the relational switching cost's (RSC) unique role as a sequential mediator in driving travel quality through the theoretical lens of expectancy disconfirmation theory. Furthermore, the study also explores the role of gender in explaining RTI. 434 real-time responses were collected following the lifetime value (LTV) approach. The structural equation modeling (SEM) was used to test the hypotheses. The study reveals that travel quality significantly influences commuter satisfaction. The results also suggest the indirect effect of RSC on commuter satisfaction (CS) and (RTI). Finally, it is suggested that RTI is higher among female commuters than male commuters. The fundamental contribution of this study is developing and testing the travel decision-making process by extending the EDT model. Additionally, the TRAVQUAL scale contributes to measuring perceived and expected travel quality. Commuter satisfaction and relational switching costs act as sequential mediators, while gender emerges as a moderator, emphasizing the greater significance of re-travel intentions among female commuters post-RSC. These insights contribute valuable perspectives for enhancing travel experiences and decision-making processes in the travel and tourism industry.

The introduction of geotagging has transformed the tourism industry, impacting environmental conservation, social engagement, and economic development. Through GPS-enabled devices, augmented reality applications, social media networks, and more, tourists and stakeholders can enhance experiences and support responsible tourism practices. Geotagging can contribute significantly to biodiversity conservation, sustainable management, and environmental education, fostering a deeper appreciation for nature. Sociologically, it can promote community connection, cultural preservation, and tourist safety. Economically, it can boost revenue generation, marketing, and infrastructure development. Challenges include privacy concerns, data security risks, and the digital divide, which need addressing through prioritizing data protection and closing the digital gap. Future research should focus on enhancing geotagging tools, exploring blockchain and AI, and ensuring ethical use of geotagged data, thus propelling the tourism industry towards sustainable growth and innovation.

Using the service encounter model and stimulus-organism-response (SOR) theory, this study examines the influence of border and destination attributes across the tourist journey that lead to a psychological state of dissatisfaction and, subsequently, impacts on future behavioral intentions. Based on a survey of 329 Kazakhstani tourists returning from Uzbekistan, the study employs structural equation modeling (SEM) to test a conceptual framework of direct and indirect effects. The results show that border infrastructure, border visitor management, and tourism infrastructure significantly increase dissatisfaction with travel experience, while service quality has no such effect. Furthermore, the results also show that dissatisfaction significantly decreases revisit intention and increases avoidance behavior and negative word-of-mouth (WOM), with avoidance exhibiting the most substantial effect. This article extends the existing literature by providing empirical evidence from the Central Asian context. It highlights the important, albeit under-researched role of the psychological state of dissatisfaction through which cross-border encounters shape tourist behavior. The findings offer valuable insights for policymakers and tourism operators aiming to enhance cross-border tourism experiences.

The purpose of this chapter is to explore the underlying behaviors and motives that insist on a commitment to sustainable practices among tourists. Also, this study aims to suggest suitable methods and techniques for encouraging green practices among visitors. This study will provide stakeholders in the tourist sector with insight into useful information for educated decisions. The primary objective of this chapter is, with the help of theory of planned behavior (TPB) enhance understanding of the factors influencing tourists' participation in green tourism and provide insights on how to encourage sustainable tourism practices among tourists. Request access from your librarian to read this chapter's full text.