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Building on Stimulus-Organism-Response theory, the current study examines the influence of pickup service quality in buy-online pickup in-store service (BOPIS) on users' perceived relationship investment with the mediating role of users' perceived experience quality and relationship proneness. This research also demonstrates the subsequent impact of BOPIS users' perceived relationship investment on their relationship performance indicators, like their cross-buying behaviors (breadth), frequency of their purchase (depth) and longevity of their relationship (length) with the store. The moderating role of BOPIS users' service experience consciousness in a few proposed relationships was tested.

The research is descriptive, quantitative and cross-sectional investigation. The study employed a purposive sampling technique. It was conducted using data collected using a validated self-administered questionnaire from 786 Indian omnichannel shoppers who have used BOPIS services in the past. The proposed conceptual model was tested using Partial Least Squares-Structural Equation Modeling.

The results indicate that BOPIS users' perceived experience quality and relationship proneness positively mediate pickup service quality and perceived relationship investment. The users' perceived relationship investment subsequently significantly positively impacts different dimensions of their relationship performance with the store (breadth, depth and length). Additionally, BOPIS users' service experience consciousness has a significant negative moderating effect on the direct relationship between pickup service quality and different dimensions of relationship performance.

The study is conducted in the Indian population, where omnichannel retailing is still nascent.

This study addresses the need to investigate the relationship performance indicators of BOPIS users, like their cross-buying behaviors(breadth), frequency of their purchase(depth) and longevity of their relationship(length) with the store. This study is the first to show that pickup service quality might explain the relationship performance of BOPIS users through their perceived experience quality, relationship proneness and relationship investments. The moderating role of BOPIS users' service experience consciousness in a few proposed relationships was also tested for the first time.

This paper aims to investigate the dynamic relationship between the investor sentiment and the return of various sectors in the Chinese stock market.

The wavelet coherence and wavelet phase angle approaches are used to study the lead–lag associations between sentiment index and stock returns in a time–frequency way. The multiscale linear and nonlinear Granger causality tests are performed to explore whether there is a causality between them.

The empirical results show that during normal period, investor sentiment index has a stronger relationship with stock returns of industrials, consumer discretionary, health care, utilities, real estate and financial sectors. In crisis period, investor sentiment has a significant positive relationship with all industry sectors. In the short term, there is bidirectional causality between investor sentiment and stock returns of all sectors. In the medium and long run, almost all sector stock returns Granger-cause the investors' sentiment index but investor sentiment does not Granger-cause all sectors, which is in contrast to the developed markets.

The interindustry impact of investment sentiment on the stock market can help construct arbitrage portfolio by investors who are interested in Chinese stock market.

This paper focuses on the industry sector differences of investor sentiment impact on the Chinese stock market. As far as the authors know, this is the first paper to explore the time–frequency relationship between sentiment index and industry stock returns in China using the time–frequency method based on wavelet coherence, which considers the heterogeneity of different types of investors' responses to various economic and financial events.

“Idol pilgrimage tour” is a popular trend among young Chinese fans who travel for idol-related purposes, engage in interactive events and have co-created experiences at destinations. With the growing market size of fan economy, fan tourists generate significant revenue for the local. However, many destinations have not fully utilised this opportunity, and there is a lack of research on this niche form of tourism. This research was undertaken to address this research gap.

This study adopted an idol worship–motivation–co-created experience–tour satisfaction–destination loyalty framework in the context of idol pilgrimage tours. In addition, this study investigated the direct influence of idol worship on the other four constructs. Data were collected from 354 Chinese fans who had such experience through online questionnaires. The partial least squares–structural equation modelling technique was used to examine the research model.

It was demonstrated that idol worship has a direct influence on motivation, co-created experience, satisfaction and loyalty and that there is a positive relationship between motivation, co-created experience, satisfaction and loyalty. The results advance the brand sacralisation literature by studying worship in a tourism context and contribute to interactive marketing literature by clarifying the interactive mechanism between relationships among the idol, fans and destinations. The study suggests some practical implications for destination management organisations attempting to target tourist fans.

This is the first study introducing the concept of the idol pilgrimage tour. Empirical results of this study reveal the underlying mechanism of how idols influence fans' travel-related psychology and behaviour.

This study employed the commitment–trust theory in social psychology and relationship marketing to explore female customers' perception of channel integration quality in omnichannel retailing and its influence on their relationship commitment to and trust in the relationship with retailers, and thus on their stickiness. Channel integration quality consists of two dimensions: channel service configuration (channel choice breadth and channel service transparency) and integrated interactions (content consistency, process consistency and perceived fluency).

The study was carried out via a questionnaire survey, to which 868 valid responses were collected. The partial least squares technique was used to test the hypotheses.

Channel service transparency and perceived fluency influence relationship commitment; content consistency, process consistency and perceived fluency all have significant effects on trust. Interestingly, although less influential than integrated interactions, channel service configuration is the foundation of channel integration quality, testifying to its significant role.

This study provides strong evidence on how channel integration quality affects customer stickiness. Moreover, this study replicates the finding of significant relationships among relationship commitment, trust and stickiness in omnichannel retailing.

This study reviews existing cryptocurrency research to provide answers to three puzzles in the literature. First, is cryptocurrency more like gold (i.e., a commodity) or should it be classified as a new financial asset? Second, can we apply our knowledge of the traditional capital market to the emerging cryptocurrency market? Third, what might be the future of cryptocurrency?

Bibliometric analysis is used to assess 2,098 finance-related cryptocurrency publications from the Web of Science (WoS) Core Collection database from January 2009 to April 2022. Three key research streams are identified, namely, (1) cryptocurrency features, (2) behaviour of the cryptocurrency market and (3) blockchain implications.

First, cryptocurrency should be viewed and regulated as a new asset class rather than a currency or a new commodity. While it can provide diversification benefits to the portfolio, cryptocurrency cannot work as a safe haven asset. Second, crypto markets are typically inefficient. Asset bubbles exist and are exacerbated by behavioural finance factors. Third, cryptocurrency demonstrates increasing potential as a medium of exchange and store of value.

Extant review papers primarily study one or two particular research topics, overlooking the interaction between topics. The few existing systematic literature reviews in this area typically have a narrow focus on trend identification. This study is the first study to provide a comprehensive review of all financial-related studies on cryptocurrency, synthesising the research findings from 2,098 publications to answer three cryptocurrency puzzles.

Sustainable tourism is becoming more popular all over the world. Eco-friendly (green) hotels are properties that are friendly to the environment and are becoming increasingly popular among green travellers. Electronic word-of-mouth is a technique of communicating with consumers in order to share their experiences, and it is a significant marketing tool for hotels. This paper aims to identify the main themes shared in online reviews by tourists visiting eco-friendly hotels, and which of these themes were associated with satisfaction and dissatisfaction ratings.

The research used qualitative content analyses to analyse 1,202 user-generated content of the top 10 hotels in UK shared by guests on an online platform.

The analyses revealed nine themes in descriptions of airline travel experiences. These are “hotel amenities”, “services”, “location”, “staff”, “eco” (eco-friendly activities), “value” and “recommend/revisit” (intentions). Negative comments are associated with the “bathroom”, “mattress”, “water”, “bed”, “price”, “shower”, “Wi-Fi” and “restaurant” concepts.

This study differs from previous research in which it aims to address a void in the literature on the shortcomings of research focused on finding the dominant themes expressed in online reviews by tourists visiting eco-friendly hotels, and it does so using data mining approach.

This study aims to predict customer churn in resort hotels by calculating the churn probability of repeat customers for future stays in the same hotel brand.

Based on the recency, frequency, monetary (RFM) paradigm, random forest and logistic regression supervised machine learning algorithms were used to predict churn behavior. The model with superior performance was used to detect potential churners and generate a priority matrix.

The random forest algorithm showed a higher prediction performance with an 80% accuracy rate. The most important variables were RFM-based, followed by hotel sector-specific variables such as market, season, accompaniers and booker. Some managerial strategies were proposed to retain future churners, clustered as “hesitant,” “economy,” “alternative seeker,” and “opportunity chaser” customer groups.

This study contributes to the theoretical understanding of customer behavior in the hospitality industry and provides valuable insight for hotel practitioners by demonstrating the methods that facilitate the identification of potential churners and their characteristics.

Most customer retention studies in hospitality either concentrate on the antecedents of retention or customers’ revisit intentions using traditional methods. Taking a unique place within the literature, this study conducts churn prediction analysis for repeat hotel customers by opening a new area for inquiry in hospitality studies.

The purpose of this study is to examine how robotic anthropomorphism and personalized design may affect consumers' reactions to brands after service failure.

This study conducted two studies based on cognitive appraisal theory and artificial intelligence device acceptance theory. Study 1 explored the mechanisms by which the type of anthropomorphic design of the service robot (humanoid robot/nonhumanoid robot) influenced revisit intention after service failure through a one-factor between-subjects design based on a restaurant dining scenario. Study 2 was based on a hotel check-in scenario and explored the moderating effect of robot personalization design on the above mechanisms through a 2 (anthropomorphic design: humanoid robot/nonhumanoid robot) × 2 (personalized design: self-name/no name) between-subjects design.

Study 1 shows that consumers have higher performance expectations for nonhumanoid robots, leading to a higher tolerance for service failure, which in turn generates higher revisit intentions. Study 2 shows that consumers' performance expectations are significantly enhanced after custom naming of humanoid robots, so the serial mediation mechanism for the effect of robot anthropomorphic design on revisit intention does not hold.

This study extends the research of artificial intelligence device acceptance theory in the field of service failure and exploratively proposes an intervention mechanism for the negative effects of the anthropomorphic design of service robots.

In this study, the authors analyze the impact of geopolitics risk on Sukuk, Islamic and composite stocks, oil and gold markets and portfolio diversification implications during the COVID-19 pandemic and Russia–Ukraine conflict period.

The study used a mix of wavelet-based approaches, including continuous wavelet transformation and discrete wavelet transformation. The analysis used data from the Geopolitical Risk index (GP{R), Dow Jones Sukuk index (SUKUK), Dow Jones Islamic index (DJII), Dow Jones composite index (DJCI), one of the top crude oil benchmarks which is based on the Europe (BRENT) (oil fields in the North Sea between the Shetland Island and Norway), and Global Gold Price Index (gold) from May 31, 2012, to June 13, 2022.

The results of the study indicate that during the COVID-19 and Russia–Ukraine conflict period geopolitical risk (GPR) was in the leading position, where BRENT confirmed the lagging relationship. On the other hand, during the COVID-19 pandemic period, SUKUK, DJII and DJCI are in the leading position, where GPR confirms the lagging position.

The present study is unique in three respects. First, the authors revisit the influence of GPR on global asset markets such as Islamic stocks, Islamic bonds, conventional stocks, oil and gold. Second, the authors use the wavelet power spectrum and coherence analysis to determine the level of reliance based on time and frequency features. Third, the authors conduct an empirical study that includes recent endogenous shocks generated by health crises such as the COVID-19 epidemic, as well as shocks caused by the geopolitical danger of a war between Russia and Ukraine.

1. We analyze the impact of geopolitics risk on Sukuk, Islamic and composite stocks, oil and gold markets and portfolio diversification implications during the COVID-19 pandemic and Russia–Ukraine conflict period.

2. The results of the wavelet-based approach show that Dow Jones composite and Islamic indexes have observed the highest mean return during the study period.

3. GPR and BRENT are estimated to have the highest amount of risk throughout the observation period.

4. Dow Jones Sukuk, Islamic and composite stock show similar trend of volatility during the COVID-19 pandemic period and comparatively gold observes lower variance during the COVID-19 pandemic and Russia–Ukraine conflict.

We analyze the impact of geopolitics risk on Sukuk, Islamic and composite stocks, oil and gold markets and portfolio diversification implications during the COVID-19 pandemic and Russia–Ukraine conflict period.

The results of the wavelet-based approach show that Dow Jones composite and Islamic indexes have observed the highest mean return during the study period.

GPR and BRENT are estimated to have the highest amount of risk throughout the observation period.

Dow Jones Sukuk, Islamic and composite stock show similar trend of volatility during the COVID-19 pandemic period and comparatively gold observes lower variance during the COVID-19 pandemic and Russia–Ukraine conflict.

This study aims to use big data analysis and sheds light on key hotel features that play a role in the revisit intention of customers. In addition, this study endeavors to highlight hotel features for different customer segments.

This study uses a machine learning method and analyzes around 100,000 reviews of customers of 100 selected hotels around the world where they had indicated on Trip Advisor their intention to return to a particular hotel. The important features of the hotels are then extracted in terms of the 7Ps of the marketing mix. This study has then segmented customers intending to revisit hotels, based on the similarities in their reviews.

In total, 71 important hotel features are extracted using text analysis of comments. The most important features are the room, staff, food and accessibility. Also, customers are segmented into 15 groups, and key hotel features important for each segment are highlighted.

In this research, the number of repetitions of words was used to identify key hotel features, whereas sentence-based analysis or group analysis of adjacent words can be used.

This study highlights key hotel features that are crucial for customers’ revisit intention and identifies related market segments that can support managers in better designing their strategies and allocating their resources.

By using text mining analysis, this study identifies and classifies important hotel features that are crucial for the revisit intention of customers based on the 7Ps. Methodologically, the authors suggest a comprehensive method to describe the revisit intention of hotel customers based on customer reviews.