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Young consumers are increasingly using online reviews and celebrity influence to make purchase decisions. The purpose of this study is to ascertain the influence of online customer reviews, celebrity influencer’s attractiveness, celebrity influencer’s credibility on female millennials’ purchase intention of beauty products.

To validate the research questions and hypotheses, data were obtained from young female consumers using an electronic self-administered survey questionnaire that was close ended. A total of 203 valid responses were obtained from which data were analysed by making use of structural equation modelling Mplus and the Statistical Package for the Social Sciences version 28.

The obtained results showed that the seven hypotheses of the study were positive. However, two hypotheses were negative, namely, celebrity influencer attractiveness did not have a significant influence on the attitude of consumers; and brand loyalty was not significantly correlating with young female consumers’ purchase intention of beauty products.

Given that millennials are known to be active users of social media and often consult online peer product reviews, marketers and practitioners of beauty industry should improve the effectiveness and usability of beauty influencers and online reviews to attract female millennial consumers.

This research contributes to understanding young female consumers’ attitudes towards purchasing beauty products, especially the combined influence of group influence (online reviews) and media influence (celebrity beauty influencers) on such attitudes.

Drawing on social exchange theory, this study clarifies the roles of authenticity, interactivity and involvement in predicting customer engagement (CE) and, ultimately, customer loyalty towards an online peer-to-peer accommodation platform. In addition, the study explores the effect of interactivity in increasing authenticity.

Data were collected through an online questionnaire of a sample of Italian tourists who had previously booked a service on Airbnb. The analyses were conducted by adopting partial least squares structural equation modelling.

The model has high power in predicting customer loyalty to an online peer-to-peer accommodation platform. Specifically, involvement is the primary predictor of CE and customer loyalty. Authenticity and interactivity also have a significant and positive effect both on CE and customer loyalty. In addition, CE partially mediates the relationship between authenticity, interactivity and involvement and customer loyalty. Finally, interactivity has a significant positive effect on authenticity.

The results encourage hospitality service providers to invest in the creation (and co-creation) of authentic experiences to increase CE and customer loyalty. Hospitality managers can also enhance CE by increasing involvement and interaction with customers through various touchpoints (online and offline) in different moments of the customer journey.

This study proposes an original model to predict customer loyalty to peer-to-peer hospitality platforms. The findings shed new light on the drivers of CE and provide empirical support for the mediating effect of CE. The study also contributes to the literature on authenticity by demonstrating the positive effect of interactivity on authenticity.

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.

The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.

The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.

The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.

The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.

The article investigates different types of strategies for managing user-generated content (UGC) and provides some insights into their implications.

A unique sample of Italian hotels with current and prospective customers in the digital environment is investigated. A taxonomy of user-provider interactions mediated by UGC is developed. A mixed approach was designed to meet the study aims. Firstly, an exploratory factor analysis was performed in order to illuminate different strategies of UGC and electronic word-of-mouth (E-WOM) management. Secondly, a cluster analysis was implemented in order to explain hoteliers' behavior toward users' contents.

The study results suggested the existence of three clusters, which reflected three different types of interactions between hotels and customers in the digital domain. Interestingly, most of Italian hotels were found to adopt a reductionist approach to UGC and E-WOM management, turning out to be ineffective to exploit them for the purpose of quality improvement and hospitality service excellence.

Hotels were found to be largely unaware of the importance of UGC and web-based communication with customers to improve their digital business strategy. Tailored management approaches are needed to realize the full potential of hotels' online content responsiveness for the purpose of value co-creation and service co-production.

This is one of the first studies investigating the strategic and management perspectives embraced by hotels to handle their interactions with customers in the digital arena.

The purpose of this study is to discover what attributes of casino buffet restaurants are the most important for customers’ willingness to pay (WTP).

Choice-based conjoint analysis was used in this study to test seven attributes: food, price/value, real price, service, atmosphere, the number of reviews and user-generated star ratings. Sawtooth Software was used to do the conjoint analysis, and a series of significance t-tests were run to determine the significance of each attribute on WTP with Statistical Package for the Social Sciences (SPSS).

Based on a survey of 483 respondents who had visited a buffet at a casino within the last two years, this study found that food is ranked as the most significant attribute of a casino buffet restaurant, followed by real price and service quality.

Theoretically, this work is the first to the authors’ knowledge to apply the antecedents of behavioral intention to willingness-to-pay for niche restaurants. Practically, the results of this study will help casino buffet operators as they re-open after COVID-19. Future studies could collect data in the post-pandemic environment and examine WTP at casino buffets in different geographic locations.

The purpose of this paper is to explore factors and their impacts influencing online grocery shopping intentions among customers in the post COVID-19 situation. Moreover, the study aims at evaluating the mediating roles of shopping habits during COVID-19 between perceived usefulness, perceived ease of use and post COVID-19 online grocery shopping intentions.

Based on a review of the literature and collection of 401 useable valid responses, the study was conducted through structured questionnaires applying personal interview technique. The subsequent analysis was conducted through partial least squares structural equation modeling (PLS-SEM) using Smart PLS 3.3.3.

The study findings revealed that perceived usefulness, perceived ease of use and shopping habits during COVID-19 have a significant influence on post COVID-19 online grocery shopping intentions. Also, the study has uncovered that perceived usefulness and perceived ease of use significantly influence shopping habits during COVID-19 among customers. Furthermore, the current study has revealed that hopping habit during COVID-19 significantly mediates the relationship between perceived usefulness, perceived ease of use and post COVID-19 online grocery shopping intentions.

The study findings have provided practical suggestions of developing and improving technological platforms to attract new customers for online grocery shopping. Further, the study suggests that online grocery retailers should apply adjusted pricing strategies using coupons and discount offers.

This paper investigates factors and its impacts on online grocery shopping intentions in post COVID-19 context. Therefore, the study uncovers the factors that add value to understanding customers' post COVID-19 online grocery shopping intentions by integrating perceived usefulness, perceived ease of use and shopping habits during COVID-19.

The purpose of this study is to investigate the impact of e-service quality and e-trust on customer e-satisfaction and, subsequently, on customer e-loyalty towards a website in the online shopping environment of Pakistan.

The research employed a quantitative approach and utilised structural equation modelling to investigate the relationship between e-service quality and e-trust on consumers’ e-satisfaction and e-loyalty. The data were collected from 250 individuals who actively use online shopping websites to purchase products in Pakistan.

The findings revealed that e-service quality and e-trust offered on e-commerce websites significantly impacted customer e-loyalty. However, it was found that both e-service quality and e-trust do not have a significant impact on customer e-satisfaction. In addition, the findings showed that customer e-satisfaction positively impacts e-loyalty.

Overall, these findings emphasise the importance of e-service quality, e-trust and customer e-satisfaction and their role in cultivating customer loyalty within the context of the online shopping environment in Pakistan.

This study contributes to the existing literature on online shopping in Pakistan by exploring the factors influencing consumer behaviour in this context. The findings add to the academic understanding of consumer behaviour and provide valuable insights for e-commerce businesses in Pakistan.

The objective of this research is to analyze how omnichannel consumer journey design (OCJD) influences the online customer experience (OCE) and e-satisfaction in consumers' multirooming behavior (searching for information in online and offline channels and purchasing the product online).

The problem-solving theory and experiential marketing perspective are the theoretical background that enables the establishment of five hypotheses. A survey is conducted on multiroomers who had purchased a product online, following an online and offline research journey.

The results showed that OCJD directly and indirectly (through online consumer experience) influences e-satisfaction. Females and younger individuals exhibited higher levels of e-satisfaction.

First, this research analyzes consumers' multichannel search strategies. Second, the consumer journey is incorporated into the study of multichannel retailing. Third, an emergent typology of cross-channel free-riding behavior is analyzed: multirooming.

The purpose of this study is to systematically review available state-of-the-art literature on comparative studies on Quick Commerce (Q-commerce) and E-commerce and their greenhouse gas (GHG) emissions.

The literature survey methodology is based on the funneling approach of Kitchenham (2004), where results are obtained according to inclusion and exclusion criteria. The literature review methodology used for this study covers the period from 2016 to 2022. The areas considered for the survey are operations, logistics and supply chain network design for the distribution of goods in e-business. After deciding on the criteria, a total of 140 articles were extracted from 9 journal articles that study e-commerce and environmental emissions.

The result of this study reveals that GHG emissions from both modes of shopping depend on various parameters such as speed of delivery, last-mile depot locations, logistics and vehicle efficiency, customers’ order patterns and average basket size. Furthermore, the findings also highlight the difference between Q-commerce and E-commerce supply chain networks.

This study only accounts for GHG emissions from logistics activities, but there are other sources of GHG emissions in the overall supply chain that are not taken into consideration. Supply chain/business analysts in Q-commerce companies might refer the findings from this study to measure GHG emissions from their operations.

This is the first study in the Q-commerce field that uses a structured approach to find relevant literature from the years 2016 to 2022 and focuses on GHG emission measurement.