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The purpose of this study is to investigate multichannel integration of hotels and online travel agencies (OTAs) and to compare consumer behavior between China and Indonesia in the context of online to offline (O2O) commerce. We examine how the services, brand and market share of OTAs influence behavioral intentions in both online and offline channels. SERVQUAL, theory of reasoned action and the halo effect are integrated to develop the research model.

To investigate Chinese and Indonesian customers' experiences and behavioral intention of OTAs and hotels, the customers who booked hotels using OTAs were invited to participate in the questionnaire survey. This study collected 336 and 305 data from China and Indonesia, respectively. A partial least squares structural equation modeling technique was used to test and compare the research hypotheses and model between China and Indonesia.

The results compare the similarities and differences of cross-country customer experiences and behavioral intentions of OTAs and hotels. The effect of website service quality on online satisfaction, the effect of online satisfaction on offline confirmation and the effects of offline confirmation and booking intention on patronage intention are significant and positive in both countries. Website service quality is positively associated with booking intention for Indonesia but not for China. The relationship between perceived size and booking intention is significant for China but not for Indonesia. The findings provide insights into the development of O2O commerce for global markets and multichannel strategies between OTAs and hotels.

With the development of O2O commerce, increasingly more hoteliers are opening up online and offline sales channels by cooperating with OTAs. Although several cross-country studies have investigated consumer behavior or behavioral intentions, behavioral settings are based on online or offline channels rather than the integration of both channels. Although some research has studied the integration or competition of OTAs and hotels, none of these studies have investigated the issues from the perspective of country comparison. This study provides the understanding of how customers in different countries and with different backgrounds react to the same e-commerce development, especially the cooperation of OTA and hotels.

The purpose of the paper is to examine the conditional role of webcare as a service failure recovery strategy on customers’ hotel booking intentions in presence of different levels of observed severity and review agreement. Furthermore, the study also examines the mechanism through which webcare can shape the adverse effect of observed severity and review agreement on hotel booking intentions.

A 2 (severity: high or low) × 2 (agreement: high or low) × 2 (webcare: webcare or no webcare) between-subject experimental design was conducted to collect responses. Analyses of variance and moderated mediation analysis were performed to analyze the study data.

Prospective customers who observed high severe service failure from a review reported less booking intention. Further, customers’ observed agreement strengthened these behaviors, i.e. customers who are exposed to high severe service failure in high-agreement condition reported low booking intention, as compared to those who exposed to low review agreement condition. Furthermore, results supported the fact that use of appropriate webcare plays a significant role to shape or mitigate the negative effect of severity and agreement on hotel booking intentions via perceived trust.

This is the first in its stream of studies that examined how webcare can be used to tackle the adverse effects of observed severity and agreement, so that perceived trust would be formed to create hotel booking intention.

The purpose of this paper is to expand and analyze deeply customer emotions, concretize the levels of positive or negative emotions with the aim of using machine learning methods, and build a model to identify customer emotions.

The study proposed a customer emotion detection model and data mining method based on the collected dataset, including 80,593 online reviews on agoda.com and booking.com from 2009 to 2022.

By discerning specific emotions expressed in customers' comments, emotion detection, which refers to the process of identifying users' emotional states, assumes a crucial role in evaluating the brand value of a product. The research capitalizes on the vast and diverse data sources available on hotel booking websites, which, despite their richness, remain largely unexplored and unanalyzed. The outcomes of the model, pertaining to the detection and classification of customer emotions based on ratings and reviews into four distinct emotional states, offer a means to address the challenge of determining customer satisfaction regarding their actual service experiences. These findings hold substantial value for businesses operating in this domain, as the findings facilitate the evaluation and formulation of improvement strategies within their business models. The experimental study reveals that the proposed model attains an exact match ratio, precision, and recall rates of up to 81%, 90% and 90%, respectively.

The study has yet to mine real-time data. Prediction results may be influenced because the amount of data collected from the web is insufficient and preprocessing is not completely suppressed. Furthermore, the model in the study was not tested using all algorithms and multi-label classifiers. Future research should build databases to mine data in real-time and collect more data and enhance the current model.

The study's results suggest that the emotion detection models can be applied to the real world to quickly analyze customer feedback. The proposed models enable the identification of customers' emotions, the discovery of customer demand, the enhancement of service, and the general customer experience. The established models can be used by many service sectors to learn more about customer satisfaction with the offered goods and services from customer reviews.

The research paper helps businesses in the hospitality area analyze customer emotions in each specific aspect to ensure customer satisfaction. In addition, managers can come up with appropriate strategies to bring better products and services to society and people. Subsequently, fostering the growth of the hotel tourism sector within the nation, thereby facilitating sustainable economic development on a national scale.

This study developed a customer emotions detection model for detecting and classifying customer ratings and reviews as 4 specific emotions: happy, angry, depressed and hopeful based on online booking hotel websites agoda.com and booking.com that contains 80,593 reviews in Vietnamese. The research results help businesses check and evaluate the quality of their services, thereby offering appropriate improvement strategies to increase customers' satisfaction and demand more effectively.

This study aims to contribute to the context of electronic trust (e-trust) research with an emphasis on the determination of tourists’ e-trust in hotel websites. This research used social exchange theory to elaborate how perceived attributes of a hotel website influence purchase behaviour of tourists. This area is often neglected because most studies focus on website users’ adoption or acceptance of other service industries. It is expected that trust-generating mechanisms have different impacts on the e-trust level in the hospitality services.

A comprehensive literature review identified the major antecedents and outcomes of e-trust. Data was collected from an online survey and 586 usable questionnaires were achieved. Structural equation modelling was used to examine the hypothesized model.

The results of this study demonstrated that the more tourists perceive influences positively, the more they are possibly to trust in hotel website. Findings revealed that perceptions about the attributes of hotel website, including security, privacy, usefulness, ease of use and compatibility are the main antecedents of trust in hotel website, which, in turn, lead to actual usage of the website for booking online.

This study is based on a large sample of tourists and broadens the understanding of e-trust in hotel websites by considering factors rarely discussed in prior tourism and hospitality literature.

In the tourism industry, immersive technologies become increasingly vital, amplifying traveler experiences and industry growth. By studying “e-booking” applications prevalent in hotels, this study aims to analyze the impact of integrating an anthropomorphic virtual agent (AVA) on user perceptions of humanness and service usage intent.

Two experiments were conducted to examine the effects of using an AVA and explain the psychological mechanism of how AVA’s attributes increase intention to use “e-booking” application.

The results highlight the positive influence of AVA on the intention to use. They illustrate the psychological mechanism of how AVA’s attributes (agency and emotionality) influence perceived humanness and intention to use. More specifically, the results indicate that perceived humanness mediated the effect of an AVA on intention to use.

Further research should delve into additional capabilities related to humanness.

This study provides useful insights for hotels’ managers about incorporating AVAs in digital services to enhance the perceived humanness of AVAs. The findings suggest that such efforts could yield benefits, especially when they involve conveying that AVAs possess agency and emotionality.

To the best of the author’s knowledge, this study is the first to investigate how AVA impacts hotel human–computer interaction. It examines agency and emotionality features on humanness perception and behavioral intent. It also guides successful digitalized hotel service development and design, expanding existing research on human–virtual agent digital services, which mainly focuses on superficial traits like face and gender.

This paper aims to explore the impact of metaverse technology on the hospitality and tourism industry. The introduction of metaverse technology has revolutionised the way the hospitality and tourism industry works. In the present study, the authors have investigated the role of social media marketing in the adoption of metaverse technology in hotel booking in India.

An extended technology acceptance model was proposed for an empirical investigation in the Indian context. Sample of 344 respondents was collected across India using a purposive sampling technique for the purpose of data analysis. The structural model analysis is used to analyse the data collected from the respondents using the SmartPLS software to check the structural and the measurement fit of the model.

The adoption intentions were largely influenced by the utility, attitude (ATT) and ease of use of the technology, and social media marketing plays a major role in influencing the perceived usefulness (PU) and ease of use (PEU). The study finds positive ATTs of the customers for using metaverse technology for booking their hotels. PU and PEU significantly influence the ATT of the consumer indicating the traveller’s perception of the usefulness and ease of metaverse technology influence their ATTs towards adoption.

Influence of metaverse technology is at a nascent stage in India specifically for hotel booking and tourism. The authors have used discriminant validity by using the criteria for both the square root of the average variance extracted and heterotrait–monotrait ratio tests, and the results suggest that the constructs in the research are distinct from other.

This study aims to examine the factors determining the discontinuance intentions of millennials to use hotel booking apps.

A quantitative technique was followed to collect the data from the tourists, and partial least square structural equation modeling (PLS-SEM) technique was adopted to validate the proposed model.

Significant predictors of discontinuance intentions of hotel booking apps are usage barrier, values barrier, risk barrier, lack of facilitating conditions and digital self-efficacy.

The results of this study provide useful insights for tourism stakeholders and app developers to understand in real terms the setbacks that might be a hindrance to the users of such apps for hotel booking.

Despite the increasing focus of scholars toward understanding the determinants of technology adoption, the present study has extended innovation resistance theory with three novel constructs: social dependency, lack of facilitating conditions and digital self-efficacy.

This study aimed to identify distinctive characteristics of hotel guests and understand their perception of guestroom technologies (GRTs) in hotels by utilizing the theory of consumer innovativeness and the social influence theory.

The data were collected from 268 hotel guests. The K-means clustering algorithm was employed to identify participants based on their views on the significance of technologies provided in guest rooms. A multivariate analysis of variance was applied to investigate if there were significant differences among clusters regarding social influence on hotel bookings, technology innovativeness and technology expertise and knowledge.

Two clusters were identified: technology compassionates and casual users of technology. Findings revealed that technology compassionates are more influenced by their friends when booking a hotel compared to casual users of technology. The ability to link up multiple wireless mobile devices, accessible outlets and mobile websites were the most critical GRTs for technology compassionates.

Technology should be considered a crucial part of the hotel guest experience. Hoteliers ought to continue investing in smart technologies to improve their guests' experiences. GRTs can reduce overhead staff costs while giving guests more control over their stay by utilizing everyday items like smartphones and offering them more power over their lodging experience.

This study advances the existing literature on GRTs by identifying which GRTs produce the most customer satisfaction. Moreover, this study explores the impact of social influence, innovativeness as a personality trait and having expert knowledge of technologies on preferences for GRTs.

This case is written for those people who are interested in entrepreneurship, and to generate discussions on the Lean Start-up methodology, as well as other topics related to entrepreneurship and innovation.

It can be used with senior undergraduates, MBAs, EMBAs and senior executives.

In August 2011, Mars Ren and Gene Deng created a technology-based venture, Shanghai Tianhailu Network Information Technology Ltd. Filled with passion, they aspired to be the first “factory outlet” in China' hotel booking industry. To achieve this goal, they developed the Hotelvp app for mobile users. After 6:00 p.m. every day, users could book accommodation online in hotels above three-star standard for that same night at significant discounts. Hotels also benefited because they could sell their unsold room nights at the last minute and improve their revenue management. Ren and Deng were convinced that this win-win idea would take off. Unfortunately, it failed to fully satisfy either the users or the hotels. In spite of the founders' passion and energy, it was still acquired by a much more powerful player in the online sector, JD.com, in early 2014. This case is designed to stimulate in-depth discussions based on the question: What are the key obstacles when launching a startup and how to overcome them?

Through class discussion, it aims at teaching the student how to define and practice a start-up idea successfully by following the “Lean Start-up” methodology and/or take advantage of a practical tool, discovery-driven planning. Specifically, this case intends to teach students how: To identify and define a good start-up idea; To take actions based on the idea/opportunity, iterate and modify it along the way to create new start-ups, and finally lead the new start-ups to grow and succeed; To understand the key concepts, frameworks and theoretical logics of Lean Start-up methodology, and apply it in practice.

Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

This study aims to provide a systematic review and meta-analysis to explore the strength of the path relationship in the proposed model for online travel booking service adoption. It also explores the moderating effect of national or regional economic development levels and cultural differences.

A meta-analysis was conducted on the factors correlated with users' intention to adopt online travel booking services (OTBS) and the moderating effects of economic development levels and culture based on 42 empirical studies.

Perceived usefulness, perceived ease of use, attitude and perceived behavioral control have a significant positive impact on adoption intention. By contrast, subjective norms have a significantly negative impact on adoption intention. Furthermore, the economic development level of a country or region significantly moderates the relationships between perceived usefulness and perceived behavior control, attitude and perceived behavior control, and subjective norms and adoption intention. At the same time, national or regional cultural differences significantly moderate the relationships between attitudes and adoption intention, perceived usefulness and perceived behavior control, and subjective norms and adoption intention.

This study was conducted to obtain a unified conclusion regarding the research field of online travel booking service adoption. Its content was original. The conclusion provides theoretical references for follow-up research and the development of targeted marketing programs for online travel-booking service providers.