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This study investigates the service quality dimensions of hotel resorts in renowned beach destinations of Thailand. It also explores the relationship between review text sentiment expressed in online platforms and the satisfaction ratings provided for those reviews.

The study employs a two-step analysis approach: first, supervised and unsupervised machine learning via support vector machine (SVM) and latent Dirichlet allocation (LDA) are used to identify service quality dimensions, and second, SmartPLS with PROCESS macro is applied to analyze the moderating roles of quality signals and reviewer’s experience on the relationship between sentiment and satisfaction rating. The dataset comprises 102,179 online reviews from TripAdvisor, focusing on 187 selected hotels rated from 3 to 5 stars.

Eight service quality dimensions were identified, including leisure activities, tangibles and surroundings, reliability, responsiveness, service process, food, empathy and ambience. The study underscores that the service process stands as the sole dimension exhibiting negative sentiment. Furthermore, the analysis revealed a robust positive association between sentiment of review texts and satisfaction, and reviewers’ experience and brand affiliation influenced the relationship between customer sentiment and satisfaction.

Hotel managers should focus efforts on maintaining tangible aspects while enhancing existing service quality level of other dimensions, particularly those related to intangible elements. Independent hotels might implement quality audit to ensure that service quality gaps are monitored.

This study contributes an examination of the moderating roles of quality signals and reviewer’s experience on the relationship between review sentiment and satisfaction rating in online reviews.

Despite the burgeoning popularity of virtual gifting in live streaming, research lacks an in-depth understanding of the drivers behind this behavior. Using para-social relationship (PSR), this study aims to capture viewers’ lively social feelings toward the streamer as the key factor leading to the purchase behavior of virtual gifts. It also aims to establish a theoretical link between PSR and viewers’ holistic experience in live streaming as captured by cognitive absorption and aims to investigates the role of technological features (i.e. viewer–streamer and viewer–viewer interactivity, streamer-level and viewer-level deep profiling and design aesthetics) in shaping viewers’ experience.

Based on 433 survey responses, this study employs a combination of structural equation modeling and neural networks to offer valuable insights into the relationships between the technological environment, viewer experience and viewer behavior.

Our results highlight the salience of PSR in promoting the purchase of virtual gifts through cognitive absorption and the importance of the technological environment in eliciting the viewer experience. This study sheds light on the development of PSR in a technological environment and its relationship with cognitive absorption.

By applying PSR to conceptualize viewers’ perceived connection with the streamer, this study extends the research on purchase behavior in the non-shopping context by providing an enlightened understanding of virtual gift purchase behavior in live streaming. Moreover, by theoretically linking PSR with cognitive absorption, virtual gift purchase and technological features of live streaming, it enriches the theory of PSR and bridges the gap between the design practice of supporting the IT infrastructure of live streaming and research.

This study explores features that impact consumers' purchase intention through artificial intelligence (AI), because it is believed that through artificial intelligence, consumers' intention to purchase grows significantly, especially in the retail sector, whereby retailers provide lucrative offers to motivate consumers. The study develops a theoretical framework based on media-richness theory to investigate the role of perceived anthropomorphism toward an intention to purchase products using AI.

The study is based on cross-sectional data through an online survey. The data have been analyzed using PLS-SEM and SPSS PROCESS macro.

The results show that consumers tend to demand anthropomorphized products to gain a better shopping experience and, therefore, demand features that attract and motivate them to purchase through artificial intelligence via mediating variables, such as perceived animacy and perceived intelligence. Moreover, trust in artificial intelligence moderates the relationship between perceived anthropomorphism and perceived animacy.

The study investigates and concludes with managerial and academic insights into consumer purchase intention through artificial intelligence in the retail and marketing sector.

The purpose of this research is to identify the most important attributes of metaverse influencers and examine their impact on customer engagement and social glue.

Three studies (one qualitative and two quantitative) were conducted to understand the phenomenon better. The qualitative study (Study 1) was conducted to identify the antecedents of the theoretical model, which was tested in Study 2 using the covariance-based structural equation modelling (CB-SEM) technique. Study 3 then divided the respondents based on the metaverse influencer attribute preferences.

Results of Study 1 revealed the six most influential attributes of metaverse influencers: physical attractiveness, social attractiveness, perceived credibility, metaverse-influencer fit, intimacy and attitude homophily. Further, Study 2 validated that attractiveness and perceived credibility enhance engagement. Also, the results revealed that intimacy, perceived credibility and homophily enhance social glue. Moreover, parasocial relationships mediate the association between intimacy, attitude homophily, perceived credibility and (engagement and social glue). The conditional indirect effect of physical attractiveness, social attractiveness and metaverse–influencer fit on (engagement and social glue) via parasocial relationships at different high and low levels of self-discrepancy was significant. Finally, Study 3 used latent class analysis to reveal different clusters of metaverse users.

This research enriches our understanding of metaverse influencers, contributing to the influencer marketing literature. It offers actionable insights for marketers by elucidating key influencer attributes, aiding in enhancing engagement and social glue.

The recent livestreaming commerce has magnified the role of influencer marketing, where the influencers are partnering with brands for product promotion. This study examines the impact of influencer attributes, interaction strategies and parasocial relationships on impulsive buying in livestreaming commerce.

A survey with 368 livestreaming commerce users was analyzed using the symmetric-thinking approach – partial least squares structural equation modeling (PLS-SEM) and asymmetric thinking approach – fuzzy set qualitative comparative analysis (fsQCA).

The results of PLS-SEM indicate that influencer trustworthiness, influencer interactivity and self-disclosure determine parasocial relationships, which in turn influence impulsive buying. The fsQCA finding returned three configurations with various combinations of the causal conditions (i.e. influencer attributes, interaction strategies, parasocial relationships, perceived fit uncertainty and perceived quality uncertainty) explaining the formation of impulsive buying.

These findings provide unique linear and nonlinear insights to explain the combinatory effects of influencer attributes, interaction strategies, parasocial relationships, perceived fit uncertainty and perceived quality uncertainty on impulsive buying in livestreaming commerce.

This paper aims to explore the current trends in corruption and investigate the characteristics of corporate gift policies and their role in preventing bribery.

This is a descriptive study based on primary data from a recent sample of Canadian companies’ codes of conduct and secondary data from recent corruption surveys published by non-governmental organisations.

This study shows that 25% of all private and public corruption cases generate financial damages of more than US$1m per case and that 50% of all investigated fraud cases are corruption cases (ACFE, 2022). Furthermore, the Western Europe and EU region is perceived as least corrupt, whereas Sub-Saharan Africa is perceived as the most corrupt region (Transparency International, 2022). However, bribery is fairly common in nine EU countries where 10% or more of public service users bribed public officials to influence their decisions (Transparency International, 2021). Results from primary data show that 9.3% of firms put a total ban on gifts given to governmental officials, whereas 35.2% require a superior’s approval and only 5.5% state a dollar limit for the gift. Results also show that not a single firm prohibits the giving of gifts to non-governmental stakeholders or the receiving of gifts from any type of stakeholder. This paper argues that gifts can bias the recipient’s judgement and improperly influence future business decisions based on the gift’s subjective value, nature and context.

This paper extends previous research by examining the characteristics of corporate gift policies. It also helps organisations improve their gift policies in an effort to reduce corruption.

It is the first paper to investigate the characteristics of corporate gift policies and their role in preventing corruption.

This paper aims to offer the first known synthesis of peer-reviewed literature on trade-based money laundering (TBML). Given the topic is in its nascent stage yet gaining prominence across scholarship and practice, this foundation is pertinent for future TBML research.

A systematic literature review was undertaken with a formulaic search string. Both qualitative (thematic) and quantitative (meta) analysis methods were used to illustrate the findings.

The systematic literature review, using qualitative and quantitative synthesis, led to a thematic categorization of extant TBML literature into four categories: TBML risk assessment, TBML detection, the role of professionals and understanding of TBML. Due to the limited number of studies, insights that can be drawn from the extant literature on the best way to combat TBML are also limited.

As the first systematic literature review on TBML, this study identified that the existing TBML literature has focused on increasing the understanding of the phenomenon in terms of its definition and mechanisms, detection, linkage with other crimes, such as organized crime and terrorism financing, and risk assessment frameworks. The originality of these findings lies in identifying areas future researchers might explore to broaden the academic literature.

This study investigates the effects of two types of self-disclosure by influencers (i.e. personal self-disclosure and professional self-disclosure) on followers’ parasocial relationships with them and online engagement with their content, which eventually affect followers’ purchase intentions.

This study collected data based on a cross-sectional survey of 823 social media users. Structural equation modeling analysis was used to test the overall structural model and the mediating roles of parasocial relationships and engagement.

This study reveals that influencers’ personal self-disclosure has a positive impact on followers’ parasocial relationships with them and online engagement with their content. Interestingly, the results indicate an inverted U-shaped relationship between influencers’ professional self-disclosure and followers’ parasocial relationships, as well as online engagement with the influencers’ content. Furthermore, followers’ parasocial relationships and engagement partially mediate the impact of influencers’ personal and professional self-disclosure on followers’ purchase intentions.

This study contributes to the literature by revealing the underlying mechanisms of the differential effects of influencers’ personal and professional self-disclosure on followers’ purchase intentions.

The findings will assist marketers in leveraging influencer-generated content to enhance influencer marketing effectiveness.

This research provides a better understanding of the potential linear and nonlinear effects of influencers’ self-disclosure on followers’ parasocial relationships and engagement in social media marketing.

The study aims to predict the drivers of green purchase in extended theory of planned behaviour (TPB) by investigating direct and indirect role of consumers' attitude toward green purchase along with moderating effects of green trust and willingness to pay on purchase behaviour in an emerging market.

A total of 417 useable responses were recorded from three different states of north India to resolve the issues related to demographic diversity in country by using a structured questionnaire. Structural equation modelling, mediation and moderation analyses were used to investigate the hypothesised relationships.

The research unveiled that environmental concern, subjective norm have significant direct and indirect influence on green purchase intention via attitude. Furthermore, this study also elucidates that green trust significantly moderates attitude–behaviour, and intention–behaviour relationships, whereas willingness to pay does not significantly moderate these relationships in proposed model.

This study provides interesting insights regarding consumers of emerging market toward green purchase. These insights are useful for marketers to design more focused strategies for enhancing the consumers' preferences for green products and promotion of pro-environmental behaviour in emerging markets.

The novel insights of this study are the mediating role of attitude in an emerging market in extended TPB model and exploration of the moderating role of green trust and willingness to pay premium to reduce the attitude–behaviour and intention–behaviour gaps for enhancing the consumers' preferences for green purchase.

Existing research has predominantly concentrated on examining the factors that impact consumer decisions through the lens of potential consumer motivations, neglecting the sentiment mechanisms that propel guest behavioral intentions. This study endeavors to systematically analyze the underlying mechanisms governing how negative reviews exert an influence on potential consumer decisions.

This paper constructs an “Aspect-based sentiment accumulation” index, a negative or positive affect load, reflecting the degree of consumer sentiment based on affect infusion model and aspect-based sentiment analysis. Initially, it verifies the causal relationship between aspect-based negative load and consumer decisions using ordinary least squares regression. Then, it analyzes the threshold effects of negative affect load on positive affect load and the threshold effects of positive affect load on negative affect load using a panel threshold regression model.

Aspect-based negative reviews significantly impact consumers’ decisions. Negative affect load and positive affect load exhibit threshold effects on each other, with threshold values varying according to the overall volume of reviews. As the total number of reviews increases, the impact of negative affect load diminishes. The threshold effects for positive affect load showed a predominantly U-shaped course of change. Hosts respond promptly and enthusiastically with detailed, lengthy text, which can aid in mitigating the impact of negative reviews.

The study extends the application of the affect infusion model and enriches the conditions for its theoretical scope. It addresses the research gap by focusing on the threshold effects of negative or positive review sentiment on decision-making in sharing accommodations.