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The world today is heavily controlled by the content available on the internet, where a one-star rating gain may work wonders for a company and a one-star rating decline can cause huge damage. Online booking platforms provide more freedom, privacy and contact with experienced travelers than physical hotel booking. The study identifies the factors shaping travelers' online hotel booking intention (OHBI).

We utilized structural equation modeling (SEM) to expand the horizons of the technology acceptance model (TAM) and stimulus-organism-response (SOR) framework in the hospitality sector. The results are based on the data collected from 705 travelers who made online hotel reservations.

The findings demonstrate that online reviews, hotel website quality and hotel website convenience quotient favorably shape prospective tourists' perceived trust, magnifying their inclination to book a hotel online. Website convenience quotient and trust partially mediate the association between the constructs. In addition, the linkage between perceived trust and OHBI is strengthened by promotional offers but weakened by perceived risk.

Our findings provide several important implications for hotel managers, prospective travelers, hotel owners, website developers, policymakers, hotel employees, the local community and competitors to expedite the growth of the Indian hotel industry.

The literature reveals that website convenience quotient, perceived trust and promotional offers have not received enough attention in the hospitality industry and warrant attention. Our study strives to broaden the scope of the TAM and SOR models to better understand these constructs in the backdrop of the Indian hospitality sector. The study also examines how promotional offers and perceived risk influence the linkages between the underlying constructs.

In the era of new technological revolution, seeking to survive and guarantee business sustainability in their digital internationalization, enterprises choose to become environmentally oriented. The need for new green business models has become evident in recent years, and enterprises offer green services in creative and eco-friendly ways. However, does the display of a green label on hotels' websites really promote the eco-conscious tourists' online booking intention? This study aims to examine the impact of the perceived label on the online sustainable hotel booking intention of the eco-conscious tourists, using the foundations of signal theory.

This study adopted a structural equation model to integrate several constructs with a sample of 349 validated responses.

The empirical results highlight, the importance of the green label perception on the eco-conscious tourists' booking intention of online sustainable hotel and the role that green trust and green perceived risk play as a mediating variable between the perception of the exposed label and the booking intention. Indeed, when booking a sustainable hotel online, the tourists may be sensitive to the exposure of a green label. Therefore, this signal decreases the perceived risk of unsustainability and ultimately increases the trust in hotel's sustainability.

The first limitation is related to the sample employed in this study. Given that most of the participants were residents of France, the results of this study may not be generalized to the entire population. Secondly, a range of other factors can affect the eco-conscious tourists' intentions to book online a hotel with green label, such as their attitude, social media influence, tourists' satisfaction, etc. Indeed, other variables and/or signals could be adopted to study online booking intention in the pandemic era.

In light of these results, theoretical and managerial implications are discussed. The findings make an important contribution to SMEs sustainability and internationalization by exploring new ties. This study considers how SMEs and specifically hotels start following green practices (e.g. adoption of an eco-label) relevant to their international environment where they operate and in response to global pressures. SMEs can survive better in the highly competitive global environment where they need to employ more green practices, however, managers should consider how green trust and green perceived risk can affect customer behavior. It also adds to the existing literature by dealing with customer perceptions about the green label of sustainable hotels and its subsequent effect on booking intention.

This study had shown the importance of the display of green label on the eco-conscious tourist's online booking intention.

This study aims to examine involvement and perceived usefulness (PU) as the drivers of loyalty for online booking websites, revealing findings of theoretical and practical relevance. The proposed conceptual model is drawn on elaboration likelihood model (ELM) along with the consideration of expectation confirmation model for loyalty development.

A two-stage structural equation modeling was applied to test the reliability and validity of the constructs and the strength of the hypothesized relationships.

Attitudinal loyalty (AL) is found to have a significant role, resulting in behavioral loyalty (BL) toward online booking. The results of this study supported the ELM framework, where involvement and PU resulted in BL through AL. PU and AL also served as mediating mechanisms underlying the impact of consumer involvement on BL.

This study applies the ELM to online ticketing mechanisms and nuances amid AL and BL with their drivers.

Although various booking platforms have been contributing to the dramatic growth of hotel industry, little research has been conducted to understand consumer psychological processes and behaviors in online hotel booking. To fill this gap, the current study examines the effect of switching barriers (switching cost and alternative attractiveness) on consumers' decision postponement and repurchase intention. Additionally, the moderating effect of time pressure in different phases of booking decision is investigated.

A total of 352 samples was collected through an online platform. Data analysis was conducted via Amos 23 (structural equation modeling) and SPSS 24 (descriptive analysis and PROCESS macro).

Results show that switching cost and alternative attractiveness are two significant drivers of decision postponement and repurchase intention. Meanwhile, time pressure only has a significant moderating effect on the relationship between switching cost and decision postponement.

The findings of this research reveal that hotel operations need to implement strategies to prevent customers' delayed booking decisions and overcome the influence of time pressure on customer decision-making.

These findings stress the importance of consumer perceptions of switching barriers and time span when making hotel reservations online. Hotel practitioners are encouraged to provide multiple human–computer interaction applications to attract novice consumers and increase their familiarity with booking process.

Engaging in webcare, i.e. responding to online reviews, can positively affect consumer attitudes, intentions and behavior. Research is often scarce or inconsistent regarding the effects of specific webcare strategies on business performance. Therefore, this study tests whether and how several webcare strategies affect hotel bookings.

We apply machine learning classifiers to secondary data (webcare messages) to classify webcare variables to be included in a regression analysis looking at the effect of these strategies on hotel bookings while controlling for possible confounds such as seasonality and hotel-specific effects.

The strategies that have a positive effect on bookings are directing reviewers to a private channel, being defensive, offering compensation and having managers sign the response. Webcare strategies to be avoided are apologies, merely asking for more information, inviting customers for another visit and adding informal non-verbal cues. Strategies that do not appear to affect future bookings are expressing gratitude, personalizing and having staff members (rather than managers) sign webcare.

These findings help managers optimize their webcare strategy for better business results and develop automated webcare.

We look into several commonly used and studied webcare strategies that affect actual business outcomes, being that most previous research studies are experimental or look into a very limited set of strategies.

The goal of this paper is to use the stimulus-organism-response (S-O-R) paradigm to understand how ineffective marketplace stimuli affect perceptions related to online travel package booking, which in turn cultivate various types of confusion, and how these confusions are channelled into behavioural dispositions of consumers, such as negative electronic word-of-mouth (eWOM). It also aims to investigate the moderating effects of gender and technology self-efficacy for the suggested framework.

A sample of 437 participants who had recently booked an online travel package, underwent an analysis using a survey study design. Structural equation modelling with multigroup analysis was used to evaluate the hypotheses and the moderation effect.

The findings suggest that inefficient market stimulus results in various forms of confusion, further contributing to negative eWOM. The results also imply that technology self-efficacy lessens the effect of various confusions on adverse eWOM, and gender is found to have a moderating effect on the relationships between ineffective marketplace stimuli, confusion and negative eWOM.

The research offers tourism and hospitality management advice on how to deal with inefficient marketplace stimulation to lessen confusion, which then reduces unfavourable eWOM. Additionally, the moderate impact of technology self-efficacy and gender established through the current study has important ramifications from a tourism managers' perspective.

This study develops and validates an empirical model, which will be utilised as a framework to fully understand consumer confusion brought on by ineffective marketplace stimulation, which causes adverse eWOM. The study also gives new perspectives on the moderating roles of gender and technology self-efficacy, which have received little attention in earlier studies.

This study develops and validates an innovative approach for extracting knowledge from online user reviews by integrating textual content and emojis. Recognizing the pivotal role emojis play in enhancing the expressiveness and emotional depth of digital communication, this study aims to address the significant gap in existing sentiment analysis models, which have largely overlooked the contribution of emojis in interpreting user preferences and sentiments. By constructing a comprehensive model that synergizes emotional and semantic information conveyed through emojis and text, this study seeks to provide a more nuanced understanding of user preferences, thereby enhancing the accuracy and depth of knowledge extraction from online reviews. The goal is to offer a robust framework that enables more effective and empathetic engagement with user-generated content on digital platforms, paving the way for improved service delivery, product development and customer satisfaction through informed insights into consumer behavior and sentiments.

This study uses a structured methodology to integrate and analyze text and emojis from online reviews for effective knowledge extraction, focusing on user preferences and sentiments. This methodology consists of four key stages. First, this study leverages high-frequency noun analysis to identify and extract product attributes mentioned in online user reviews. By focusing on nouns that appear frequently, the authors can systematically discern the primary features or aspects of products that users discuss, thereby providing a foundation for a more detailed sentiment and preference analysis. Second, a foundational sentiment dictionary is established that incorporates sentiment-bearing words, intensifiers and negation terms to analyze the textual part of the reviews. This dictionary is used to assign sentiment scores to phrases and sentences within reviews, allowing the quantification of textual sentiments based on the presence and combination of these predefined lexical items. Third, an emoticon sentiment dictionary is developed to address the emotional content conveyed through emojis. This dictionary categorizes emojis based on their associated sentiments, thus enabling the quantification of emotional expressions in reviews. The sentiment scores derived from the emojis are then integrated with those from the textual analysis. This integration considers the weights of text- and emoji-based emotions to compute a comprehensive attribute sentiment score that reflects a nuanced understanding of user sentiments and preferences. Finally, the authors conduct an empirical study to validate the effectiveness of the proposed methodology in mining user preferences from online reviews by applying the approach to a data set of online reviews and evaluating its ability to accurately identify product attributes and user sentiments. The validation process assessed the reliability and accuracy of the methodology in extracting meaningful insights from the complex interplay between text and emojis. This study offers a holistic and nuanced framework for knowledge extraction from online reviews, capturing both explicit and implicit sentiments expressed by users through text and emojis. By integrating these elements, this study seeks to provide a comprehensive understanding of user preferences, contributing to improved consumer insight and strategic decision-making for businesses and researchers.

The application of the proposed methodology for integrating emojis with text in online reviews yields significant findings that underscore the feasibility and value of extracting realistic user knowledge to gain insights from user-generated content. The analysis successfully captured consumer preferences, which are instrumental in informing service decisions and driving innovation. This achievement is largely attributed to the development and utilization of a comprehensive emotion-sentiment dictionary tailored to interpret the complex interplay between textual and emoji-based expressions in online reviews. By implementing a sentiment calculation model that intricately combines textual sentiment analysis with emoji sentiment analysis, this study was able to accurately determine the final attribute emotion for various product features discussed in the reviews. This model effectively characterized the emotional knowledge of online users and provided a nuanced understanding of their sentiments and preferences. The emotional knowledge extracted is not only quantifiable but also rich in context, offering deeper insights into consumer behavior and attitudes. Furthermore, a case analysis is conducted to rigorously test the validity of the proposed model in a real-world scenario. This practical examination revealed that the model is not only capable of accurately extracting and analyzing user preferences but is also adaptable to different contexts and product categories. The case analysis highlights the robustness and flexibility of the model, demonstrating its potential to enhance the precision of knowledge extraction processes significantly. Overall, the results confirm the effectiveness of the proposed approach in integrating text and emojis for comprehensive knowledge extraction from online reviews. The findings validate the model’s capability to offer actionable insights into consumer preferences, thereby supporting more informed and strategic decision-making by businesses. This study contributes to the broader field of sentiment analysis by showcasing the untapped potential of emojis as valuable indicators of user sentiments, opening new avenues for research and applications in digital marketing and consumer behavior analysis.

This study introduces a pioneering approach to extract knowledge from Web user interactions, notably through the integration of online reviews that incorporate both textual content and emoticons. This innovative methodology stands out because it holistically considers the dual channels of communication, text and emojis, to comprehensively mine Web user preferences. The key contribution of this study lies in its novel insights into the extraction of consumer preferences, advancing beyond traditional text-based analysis to embrace nuanced expressions conveyed through emoticons. The originality of this study is underpinned by its acknowledgment of emoticons as a significant and untapped source of sentiment and preference indicators in online reviews. By effectively merging emoticon analysis and emoji emotion scoring with textual sentiment analysis, this study enriches the understanding of Web user preferences and enhances the accuracy and depth of consumer preference insights. This dual-analysis approach represents a significant leap forward in sentiment analysis, setting a new standard for how digital communication can be leveraged to derive meaningful insights into consumer behavior. Furthermore, the results have practical implications to businesses and marketers. The insights gained from this integrated analytical approach offer a more granular and emotionally nuanced view of customer feedback, which can inform more effective marketing strategies, product development and customer service practices. By pioneering this comprehensive method of knowledge extraction, this study paves the way for future research and practice to interpret and respond more accurately to the complex landscape of online consumer expressions. This study’s originality and value lie in its innovative method of capturing and analyzing the rich tapestry of Web user communication, offering a ground-breaking perspective on consumer preference extraction that promises to enhance both academic research and practical applications in the digital era.

This study analyzes how small French retailers are adapting their front-office to the digitalization of their business environment.

The qualitative study focuses on dynamic capabilities of 27 independent French retailers, in a wide variety of sectors.

The digitalization of small retailers does not date from the pandemic health crisis. Small retailers are willing, agile and organized to make controlled progress, ranging from the visibility on social networks to online sales with its specific logistical constraints. Even if their presence on marketplaces is trickier to implement, it represents the culmination of the digitalization process, once their online store has been launched. The digital transformation of independent retailers should be less radical than for large retailers.

By distinguishing between the concepts of adaptive, absorptive and innovative capabilities, this research highlights strong differences between small retailers, that is SMEs, and larger companies. In terms of adaptive capabilities, it confirms that small retailers are not embracing digitalization as a fad, but because of real changes in the market, and particularly in demand. In contrast to large companies, small retailers drive it more around external objectives linked to their intimate knowledge of changing customer behavior (customer centricity). In terms of absorptive capabilities, the success or failure of digital transformation weighs directly on the entrepreneur's shoulders, but is less hampered by technological legacy. Despite interviews only conducted in the Paris region, it converges with professional studies carried out on a larger scale in France. Its widespread use is certainly easier in countries at the same stage of commercial development.

In terms of innovative capabilities, independent retailers need to focus on four key areas: reinventing the in-store experience; increasing visibility on social networks; creating an online store; being present in one or two marketplaces or creating a common platform with other local merchants.

This research is one of the first to analyze the digital transformation experienced by small structures. It draws on the concept of dynamic capabilities, well-suited to technologically and commercially dynamic markets. It puts into perspective studies carried out in other countries on less diversified types of shops. Unlike other studies examining the front office, it does not exclude stores and SEO in marketplaces.

This study aims to explore when and for what purpose is social media used by tourists in their decision-making; and to further interpret the nature of such usage.

An online questionnaire was administered to 530 Indians who qualified as recent tourists and social media users. Partial least square structural equation modeling (PLS-SEM) is used to analyse hypothesised relationships; the results helped us to unveil the tourist social media cycle which is a conceptual model of the results obtained.

PLS-SEM results indicate that social media is predominantly used in the pre-travel stage for evaluation purposes, during travel stage for purchase purposes and post-travel stage for post-purchase behaviour. Also, it is noted that social media use by a tourist is an ongoing process, and thus, a conceptual model in the form of tourist social media cycle is unveiled.

This study reveals the unique behaviour of Indian tourists with respect to using social media for travel decision-making, which establishes a different perspective of understanding and further strategizing social media’s use in tourism for a developing nation. Also, the tourist social media cycle stresses on the importance of social media as a digital repository where continuously the user-generated content is used by self (existing) or “others” (potential tourists).

To the best of the author’s knowledge, this is the first study that addresses the population and knowledge gap of understanding social media–based tourist behaviour providing a perspective of a developing nation, specifically India. Further, it unveils the nature of social media use by tourist in the form of a first ever tourist social media cycle.

China’s healthcare system is being burdened by the increasing prevalence of chronic diseases. Therefore, this study investigated the health service requirements of mobile health applications (mHealth apps) users in Hangzhou, China. This study aimed to propose suggestions and theoretical references to improve mHealth apps and promote their development, thereby meeting public medical and health needs and creating an efficient medical service system.

We constructed a model of health service demands using a literature review, network survey, and semi-structured interviews. We analyzed the demand attributes using the Kano model and Better-Worse index and obtained the priority ranking of demands.

The results revealed 25 demand elements in four dimensions: must-be (M), one-dimensional (O), attractive (A), and indifferent (I) requirements. The findings suggest that mHealth app developers can optimize health services by categorizing and managing health services, focusing on middle-aged users, enhancing the professionalism of health service providers, and improving the feedback mechanism.

Studies on mHealth apps user demands, particularly on health service needs, remain scarce. This study employed a mixed-methods approach, integrating both qualitative and quantitative research techniques, to establish a priority ranking of user health service needs for mHealth apps. The study offers recommendations and theoretical references to optimize and improve mHealth app services.

1. Construct a better health service requirements model for mHealth app users.

2. Obtain the prioritization of demand elements in the model.

3. Propose some management suggestions to improve mHealth apps.

Construct a better health service requirements model for mHealth app users.

Obtain the prioritization of demand elements in the model.

Propose some management suggestions to improve mHealth apps.