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Online reviewers' identity information is an essential cue by which consumers judge reviews on ecommerce platforms. However, few studies have explored how prior anonymous reviews and focal reviews affect reviewers' preference for anonymity. The purpose of this paper is to investigate why reviewers seek anonymity in terms of prior anonymous reviews and focal reviews.

Based on restaurant reviews collected from meituan.com, one of the largest group-buying ecommerce platforms in China, this study employed logistic regression to examine how prior anonymous reviews and focal reviews are associated with reviewers' preference for anonymity.

Results show that the volume and sequence of prior anonymous review are positively associated with the likelihood of reviewers' preference for anonymity, whereas focal review valence is negatively correlated with this preference. Focal review length is positively correlated with reviewers' preference for anonymity but negatively moderates the roles of review valence and prior anonymous reviews on this preference.

This study expands the information disclosure literature by exploring determinants of user identity disclosure from a reviewer perspective. This research also offers a methodological contribution by employing a more accurate measure to calculate reviewers' preference for anonymity, enhancing the empirical results. Lastly, this work supplements the online review literature on how prior anonymous reviews and focal reviews are associated with reviewers' identity disclosure.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

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 purpose of the study is to analyse the impact of COVID-19 on the hospitality industry during the rise of worldwide pandemic crises using Twitter analysis. The study is based on 57,794 English-language tweets mined from Twitter from 1 April 2020 to 15 October 2020. Based on thematic and sentiment analysis, the study found that overall sentiments expressed on Twitter were negative. This chapter contributes to existing knowledge about the COVID-19 crisis and broadens the respondents’ understanding of the potential impacts of the crisis on the most vulnerable tourism and hospitality industry. This research emphasises the sustainable revival of the hospitality industry.

The environment in which users acquire medical and health information has changed dramatically, with online health communities (OHCs) emerging as an essential means for accessing health information. It is imperative to comprehend the factors that shape the users' compliance willingness (UCW) to health information in OHCs.

This study adopted the information adoption model (IAM) and theory of planned behavior (TPB) to investigate the influence of argument quality (AQ), source credibility (SC) and subjective norms (SN) on UCW while considering the two types of online health information – mature and emerging treatments. The authors conducted an explanatory-predictive study based on a 2 (treatment types: mature vs. emerging) * 2 (AQ: high vs. low) * 2 (SC: high vs. low) scenario-based experiment, using the partial least squares structural equation modeling (PLS-SEM).

SC positively influences AQ. AQ, SC and SN contribute to information usefulness (IU). These factors positively affect UCW through the mediation of IU. SN were found to improve UCW directly. Moreover, the moderating effect of SC on AQ and IU was more substantial for emerging treatments.

The research model integrates IAM and TPB, considering information types as an additional variable. The approach and findings provide a valuable explanation for UCW to health information in OHCs.

The aging population, a higher proportion of older adults (aged 65+), is considered a global and severe problem, while the information systems (IS) literature on detecting the relationship between the aging population and the development of electronic commerce (e-commerce) is limited and insufficient. Hence, the main objective of this paper is to examine whether an aging population can moderate the effect of infrastructure constructions on e-commerce sales and whether an aging population can affect e-commerce sales.

To investigate the relationship between the aging population and e-commerce sales, this study proposes two potential influential mechanisms: moderating the effects of infrastructure development on e-commerce sales and direct influence. Subsequently, a sample of 31 Chinese provinces from 2013 to 2019 is utilized to conduct regression analyses in order to examine these hypotheses.

The findings suggest that the development of urban transportation infrastructure and network constructions can significantly contribute to the enhancement of e-commerce sales, and the influence cannot be affected by aging population. Furthermore, it is noteworthy that an aging population can have a positive effect on e-commerce sales.

The findings can inform future infrastructure constructions by assessing the potential of infrastructure projects to boost e-commerce sales and examining whether this effect varies in an aging population context.

The findings substantiate the pivotal role of older adults in the e-commerce industry. Moreover, the obtained results establish a positive relationship between an aging population and e-commerce sales, thereby offering diverse perspectives on existing theories.

Knowledge transfer which refers to the communication of knowledge from a source so that it is learned and applied by a recipient has long been a challenge for knowledge management. The purpose of this study is to understand influencing factors of transactive memory system (TMS) and knowledge transfer.

Drawing on the theories of communication visibility, social distance and flow, this study develops a research model. Then, data are collected from users of the social media mobile App. Partial least squares-structural equation modeling (PLS-SEM) is employed to analyze data.

TMS is a valid second-order construct in the social media mobile app context, which is more reflected by credibility. Meanwhile, communication visibility and social distance each have positive effects on TMS which further has a positive effect on knowledge transfer. Flow has a positive effect on knowledge transfer.

Developers of the mobile App should carefully consider the role of information and communication technology (ICT) in supporting TMS and knowledge transfer. They should consider recommendation algorithm so that the benefit of communication visibility can be retained. They should design the feature to classify users based on similarity so as to stimulate users' feeling of close social distance. They should keep on improving features based on users' holistic experience.

This study incorporates the perspectives of communication visibility, social distance and flow to understand TMS and knowledge transfer, presenting a new lens for research.

The purpose of this paper is to identify the practices that owners of public profiles in social networking sites can leverage to actively build online reputation and to evaluate the impact of the adoption of such practices on the initial formation of trust toward these individuals when they are presented as new virtual work partners.

A theoretical model was developed and an experiment with 233 participants was utilized to assess the model using partial least squares structural equation modeling.

The results suggest that individuals can build their online reputations in public profiles of social networking sites via a series of practices of self-disclosure of information and that the adoption of these practices has significant effects on the initial formation of trust toward the profile owner in virtual work contexts. Categorization mechanisms such as stereotyping, unit grouping and reputation categorization have been found to contribute to the initial formation of trust, both from an affect and cognition-based perspectives.

Little is known about the information disclosure practices in public profiles of social networking sites that new work partners can adopt to facilitate the formation of trust between them before they start working together. This study has contributed to the existing body of literature by clarifying these practices and the relative importance of online reputation to the initial formation of trust during the outset of a new virtual work relationship.

The purpose of this study is to identify the process of virtual influencer stickiness in the age of influencer marketing, which has received little attention in the literature. This is essential because the research creates a theoretical model of follower loyalty/stickiness to virtual influencer techniques from the standpoint of influencer marketing, which has a substantial effect on the evolution of the global marketing world.

In 2022, 302 people who currently follow an Instafamous virtual influencer took part in an Instagram self-administered online survey.

The findings show that both expertise and trustworthiness have a positive and significant influence on parasocial interaction, which in turn has a significant influence on virtual engagement and stickiness.

This research will specifically assist international readers in understanding how to harness and increase the efficiency and efficacy of interactive marketing strategies and methods to engage and retain followers of Instafamous virtual influencer. Moreover, the findings will be beneficial to opinion leaders, brand managers, company investors, entrepreneurs and service designers.

1. The study pioneers a holistic virtual follower stickiness mechanism that comprises the role of source credibility, parasocial interaction, informational influence and virtual follower’s engagement and their interrelationship to each other.

2. This study is based on parasocial interaction theory and source credibility theory to understand the relationship between virtual followers and influencers stickiness process at social media platforms.

3. In addition, the study examined the subsequent effects of sources of credibility components on parasocial interaction; as well as, on virtual follower engagement and stickiness.

4. This study also categorized and examined the moderating effects exerted by the genres of informative influence of virtual influencer.

The study pioneers a holistic virtual follower stickiness mechanism that comprises the role of source credibility, parasocial interaction, informational influence and virtual follower’s engagement and their interrelationship to each other.

This study is based on parasocial interaction theory and source credibility theory to understand the relationship between virtual followers and influencers stickiness process at social media platforms.

In addition, the study examined the subsequent effects of sources of credibility components on parasocial interaction; as well as, on virtual follower engagement and stickiness.

This study also categorized and examined the moderating effects exerted by the genres of informative influence of virtual influencer.

This study aims to explore the emotional landscapes and spatial preferences of digital nomads, focusing on how sentiments expressed in destination reviews influence their mobility and destination choices.

Employing a lexicon-based sentiment analysis of social media comments and reviews, alongside advanced geographical information systems (GIS) mapping techniques, the study analyzes the emotional tones that digital nomads associate with various destinations worldwide.

The analysis reveals significant patterns of emotional sentiments, with trust and joy being predominant in preferred destinations. Spatial patterns identified through GIS mapping highlight the global distribution of these sentiments, underscoring the importance of emotional well-being in destination choice.

Insights from this study offer valuable guidance for Destination Management Organizations (DMOs) in strategic planning, enhancing destination appeal through targeted marketing strategies that resonate with the emotional preferences of digital nomads.

This research introduces a novel approach by integrating sentiment analysis with GIS to map the emotional and spatial dynamics of digital nomadism, contributing a new perspective to the literature on tourism and mobility.