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Environmental issues have triggered the need for sustainable behavior around the globe. The tourism industry’s rapid growth also contributes to environmental degradation through natural resource depletion and excess water and energy utilization. Based on social identity theory, this study aims to assess the impact of environmental corporate social responsibility initiatives on green customers’ citizenship behavior. Furthermore, the study assesses the mediating effects of green trust, customer–company identification and green image.

This study is a quantitative approach, and purposive sampling technique was used to collect the data from the hotels’ customers from northern areas of Pakistan. This study used partial least square-structural equation modeling to analyze the data of 426 customers.

The study’s findings show that environmental corporate social responsibility initiatives significantly impact green customers’ citizenship behavior, green trust, customer–company identification and green corporate image. However, the relationship between green corporate image and green customers’ citizenship behavior was insignificant. Furthermore, the study’s results revealed that green trust and customer–company identification partially mediate between environmental corporate social responsibility initiatives and green customers’ citizenship behavior.

The findings suggest that hotels’ environmental corporate social responsibility initiatives improve green customer citizenship behavior, green trust and enhance customer–company identification. Therefore, hotel industry managers should consider reinforcing existing environmental corporate social responsibility initiatives and make further efforts to highlight the importance of such initiatives for environmental sustainability, which ultimately affects customers’ green customer citizenship behavior.

This research developed a novel framework to understand green customers’ citizenship behavior in the tourism industry. It extended the literature on environmental corporate social responsibility initiatives and green customers’ citizenship behavior. In addition, the research adds value by confirming the significant direct and mediating role of customer–company identification in tourism industry context.

This paper aims to estimate the price premium for a sea view on room rent in a Nordic context, i.e. where proximity to the sea is not valued for the presence of swimmable beaches and suntanning activities. The analysis also explores regional and seasonal variations in price premiums.

To do so, the study uses information from a Web search of room rents during winter and summer peak seasons. The investigation is based on hotels located along the St. Lawrence River in the Province of Quebec (Canada), where about 40 to 60 km separate both shores. A matching procedure and hedonic pricing models are used to identify the causal impact of a sea view on individual room rents.

Results suggest that the view price premium varies between 0% and 20%. It is relatively stable on the North Shore, but varies highly on the South Shore, where touristic activities are mainly operating in summertime. The estimation suggests a median local economic benefit of about $30.1M/year.

The analysis reveals that a hedonic pricing model might fail to identify causal effects, especially if it does not account for hotel characteristics. A multiple linear regression model does not ensure a causal interpretation if it neglects unobserved characteristics correlated with the view.

The paper proposes a matching identification procedure accounting for spatial confounding to retrieve the causal impact of the view of the sea on hotel room rents. A heterogeneity analysis suggests that view price premium on room rent can vary within seasons but mainly across regions, even for the same amenities.

This research explores differences in consumer behavior across generational cohorts, particularly focusing on customer loyalty. With Millennials becoming the largest generational cohort, it is crucial to understand loyalty variations, given that many loyalty programs were established during the Baby Boomer era. This study investigates two vital aspects for hotel companies aiming to enhance guest loyalty: antecedents to loyalty and loyalty program design.

In part 1, a loyalty model was tested using corporate social responsibility (CSR), personalization, brand identity, and trust as antecedents for customer loyalty in full-service hotels. The study developed models for the overall sample and each generational cohort. Part 2 explored generational preferences regarding commonly offered hotel loyalty program benefits.

The study revealed no significant differences across generational cohorts in the loyalty model. Antecedents had similar effects on loyalty creation across all three cohorts. In part 2, the four most desired benefits for all generations were upgrades, customized service, late check-out, and empathetic employees.

This research supports Millennials' loyalty to hotels and highlights the importance of benefits that offer immediate advantages during a stay, such as upgrades, late check-out, empathetic employees, and personalization. These findings emphasize the need for loyalty program designs that provide faster rewards and personalization options.

This study pioneers the examination of hotel customer loyalty models across three generations and evaluates loyalty benefits across these cohorts. The results hold significance for researchers and practitioners in the field.

This study aims to investigate value co-creation, its antecedents (i.e. customer delight and place identity) and the consequences (i.e. satisfaction and revisit intention) at eco-hotels concerning sustainable practices.

Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the data collected from 562 guests surveyed online who had stayed and contributed to sustainable practices through interaction and collaboration with eco-hotels.

The results revealed that customer delight and place identity are critical antecedents of value co-creation, significantly influencing guests' intention to revisit. Further, value co-creation significantly influences satisfaction and revisits intention. Satisfaction significantly influenced revisit intentions. Moreover, customer delight was the most critical factor affecting value co-creation, followed by the path between value co-creation and satisfaction. Finally, the results confirmed the mediating role of value co-creation and satisfaction.

This research can support hotel managers in comprehending the motivating factors and outcomes of value co-creation among guests, allowing efficient hotel strategies to be planned and implemented. Managers should prioritize customer delight and place identity to maintain guests' involvement in value co-creation, resulting in satisfaction and a willingness to return.

This study contributes to the literature by tackling the scarcity of research on the significance of value co-creation, its drivers and outcomes at eco-hotels concerning sustainable practices within an emerging market context.

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 addresses the gap in understanding text analytics within the service domain, focusing on new service development to provide insights into key research themes and trends in text analytics approaches to service development. It explores the benefits and challenges of implementing these approaches and identifies potential research opportunities for future service development. Importantly, this study offers insights to assist service providers to make data-driven decisions for developing new services and optimising existing ones.

This research introduces the hybrid thematic analysis with a systematic literature review (SLR-TA). It delves into the various aspects of text analytics in service development by analysing 124 research papers published from 2012 to 2023. This approach not only identifies key practical applications but also evaluates the benefits and difficulties of applying text analytics in this domain, thereby ensuring the reliability and validity of the findings.

The study highlights an increasing focus on text analytics within the service industry over the examined period. Using the SLR-TA approach, it identifies eight themes in previous studies and finds that “Service Quality” had the most research interest, comprising 42% of studies, while there was less emphasis on designing new services. The study categorises research into four types: Case, Concept, Tools and Implementation, with case studies comprising 68% of the total.

This study is groundbreaking in conducting a thorough and systematic analysis of a broad collection of articles. It provides a comprehensive view of text analytics approaches in the service sector, particularly in developing new services and service innovation. This study lays out distinct guidelines for future research and offers valuable insights to foster research recommendations.

The purpose of this research study is to improve sentiment analysis (SA) at the aspect level, which is accomplished through two independent goals of aspect term and opinion extraction and subsequent sentiment classification.

The proposed architecture uses neighborhood and dependency tree-based relations for target opinion extraction, a domain–ontology-based knowledge management system for aspect term extraction, and deep learning techniques for classification.

The authors use different deep learning architectures to test the proposed approach of both review and aspect levels. It is reported that Vanilla recurrent neural network has an accuracy of 83.22%, long short-term memory (LSTM) is 89.87% accurate, Bi-LSTM is 91.57% accurate, gated recurrent unit is 65.57% accurate and convolutional neural network is 82.33% accurate. For the aspect level analysis, ρ_aspect comes out to be 0.712 and Δ²_aspect is 0.384, indicating a marked improvement over previously reported results.

This study suggests a novel method for aspect-based SA that makes use of deep learning and domain ontologies. The use of domain ontologies allows for enhanced aspect identification, and the use of deep learning algorithms enhances the accuracy of the SA task.

Workforce insufficiencies can impair firms' competitiveness in the hospitality industry. The problem of finding suitably trained employees has been aggravated by changes in consumer preferences, and the development of advanced technologies has led to the ‘smartization’ of upscale hotels. The consequent updating of business models means that decisive indicators of worker competence and employability are different from those applied previously. Thus, the aim of this study is to develop an indicator framework for assessing workforce employability with consideration of competence with artificial intelligence (AI) applications.

The initial indicators for the framework are obtained based on an intensive review of the relevant literature and roundtable meetings with academics and practitioners. The Delphi method is used to collect the data, and a hybrid fuzzy approach, which combines the modified Z-number and modified trapezoidal fuzzy number set techniques, is applied to quantify the information originating from the experts’ judgments. The interquartile range approach is applied to optimize the validity of the indicators.

The assessment framework is applied to evaluate workforce employability at an upscale hotel from the perspective of hotel executives. The capability of the workforce for the adoption of advanced technologies, including familiarity with AI, are considered.

The contributions of this research involve the identification of an updated list of determinants for the evaluation of workforce employability for hotels in today’s world, highlighting the value of AI applications to help ameliorate labor shortage problems. The results should benefit practitioners, allowing them to improve the efficiency of their operations, services and management practices, leading to sustainability and competitiveness in the upscale hotel industry.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

This research seeks to better understand the impact of employee-customer identification on critical job outcomes such as customer orientation and commitment to service quality.

A sample of 316 hotel/motel employees was used for the study. Structural equation modeling was used to analyze the data.

Results show a positive relationship between ethical values person-organization fit and employee-customer identification indicating that when customer-contact service employees’ ethical values align with those of the organization, they identify with customers more closely. Results also suggest that when employees identify with customers they are likely to be more customer-oriented and committed to providing service quality.

We learn how the relationship between employee and organization impacts employee-customer identification. Furthermore, we better comprehend the impact of employee-customer identification on critical outcomes in the hospitality industry such as customer orientation and commitment to service quality.