Search results

In this study, STEM resources reviewed in Goodreads were investigated to determine their authorship, linguistic characteristics and impact. The analysis reveals gender disparity favoring titles with male authors.

This paper applies theoretical concepts of knowledge commons to understand how individuals leverage the affordances of the Goodreads platform to share their perceptions of STEM-related books.

The analysis reveals gender disparity favoring titles with male authors. Female-authored STEM publications represent popular science nonfiction and juvenile genres. Analysis of the scholarly impact of the reviewed titles revealed that Google Scholar provides broader and more diverse coverage than Web of Science. Linguistic analysis of the reviews revealed the relatively low aesthetic disposition of reviewers with an emphasis on embodied experiences that emerged from the reading.

This study contributes to the understanding of the impact of popular STEM resources as well as the influence of the language of user-generated reviews on production, consumption and discoverability of STEM titles.

This study examined the links between user-generated content (UGC), dissatisfied customers and second-hand luxury fashion brands. A central premise of luxury fashion brands is the perceived status and privilege of those who own such items. Despite their marketing logic emphasising exclusivity and rarity, they have broadened their reach by integrating new digital marketing practices that increase access to luxury brand-related information and create opportunities for consumers to purchase products through second-hand sellers.

Building on an inductive qualitative study of 59 millennials from three European countries (France, Italy and the UK) and by examining the mediating role of UGC and dissatisfied customers, this paper develops a conceptual framework of three clusters of second-hand luxury fashion goods customers: spiritual consumers, entrepreneurial recoverer consumers and carpe diem consumers.

The proposed SEC framework (spiritual consumers, entrepreneurial recoverer consumers, and carpe diem consumers) illustrates how the emerging themes interconnect with the identified consumers, revealing significant consumer actions and attitudes found in the second-hand luxury goods sector that influence the usage of UGC and its integration into service failure and recovery efforts

This study suggested that the perceptions of consumers seeking second-hand luxury fashion products differ from those who purchase new or never previously owned luxury fashion products. Overall, this research sets the stage for scholars to forge a path forward to enhance the understanding of this phenomenon and its implications for luxury fashion companies.

The purpose of this paper is to investigate the impact of design features in in-feed advertising on its effectiveness. Previous research on various forms of advertising has demonstrated that design features can influence advertising effectiveness. However, given the distinct presentation mode and content of in-feed advertising compared to traditional forms, it is crucial to examine whether the effects of design features differ for this type of advertising. Through two studies, we examined how five specific design features affect consumers' purchase intention within the context of in-feed advertising. The mediating role of perceived value and the moderating role of product involvement are also proved.

Using the methods of online survey and online experiment, the author conducted two empirical studies. In study 2, the authors adopted the orthogonal array design to simplify experimental grouping.

The findings demonstrate that akin to conventional Internet advertising, the informational content, credibility and entertainment value of in-feed advertising exert a positive influence on its efficacy. Notably, the interactive nature of in-feed advertising significantly enhances users' inclination toward making purchases. Conversely, any form of interference can detrimentally impact its utility.

The study demonstrates five design characteristics that may impact the effectiveness of in-feed advertising, expanding the relevant theories about in-feed advertising. At the same time, this study contributes to the understanding of consumer responses to advertising. However, the two studies in this paper are conducted within the framework of WeChat, a popular Chinese social media platform, with the participants consisting exclusively of Chinese users.

Considering the rapid development of in-feed advertising in terms of quantity, content and form, the author believes that the results of this paper can help advertisers in their design thinking. The moderating effect of product involvement can be applied to optimize personalized advertising delivery schemes.

This paper focuses on a practical problem, that is, how to improve the effectiveness of in-feed advertising by modifying advertising design features.

This study aims to analyze memes as valuable engagement and marketing communication tools in promoting over-the-top (OTT) platforms by monitoring users' sentiments and offering insightful information about their opinions by drawing themes from viral memes.

Content analysis of 1,230 user interactions was conducted using NVivo software on Instagram and Twitter pages from May 2022 to July 2023. Data were collected for sentiment analysis (consumer responses), and relevant themes (consumer interactions) were drawn which created the virality of memes.

Research findings reveal relevant themes, such as relatable, informative and interest-generating, that make memes go viral over social media. The sentiment analysis results showed that the intensity and strength of the positive comments were more substantial, contributing more to the virality of memes.

These findings provide themes for engaging content for OTT advertisers to boost brand recognition and engagement by strategically creating meme content and implementing better marketing communication.

This study uses factual data to offer new perspectives on viral meme propagation. It provides evidence that OTT marketers boost brand value and customer engagement through innovative customer-centric social media analysis.

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.

Against the background of the popularity of social media and heritage tourism, this study aims to focus on world heritage sites, proposing a method to examine and compare the digital spatial footprints left by tourists using geographic information systems.

By analyzing user-generated content from social media, this research explores how digital data shapes the destination image of WHS and the spatial relationships between the components of this destination image. Drawing on the cognitive-affective model (CAM), it investigates through an analysis of integrated data with more than 20,000 reviews and 2,000 photos.

The creativity of this research lies in the creation of a comprehensive method that combines text and image analytics with machine learning and GIS to examine spatial relationships within the CAM framework in a visual manner.

The results reveal tourists' perceptions, emotions, and attitudes towards George Town and Malacca in Malaysia, highlighting several key cognitive impressions, such as history, museums, churches, sea, and food, as well as the primary emotions expressed. Their distributions and relationships are also illustrated on maps.

Tourism practitioners, government officials, and residents can gain valuable insights from this study. The proposed methodology provides a valuable reference for future tourism studies and help to achieve a sustainable competitive advantage for other heritage destinations.

This study aims to identify which types of information quality influence purchase intentions the most in live streaming commerce and to examine the role of network size in this context.

We propose a model to investigate the correlation among the quality of different information in live streaming commerce, consumer trust, network size and purchase intention. An empirical analysis of 505 questionnaires was conducted by constructing a structural equation model.

The empirical findings indicate that information quality can directly enhance purchase intention and exert an indirect influence through the mediating factors of trust in products and streamers. Perceived network size positively moderates the relationship between information quality and trust in products. Of the five types of information, the quality of bullet-screen comments information is most important to consumers.

This study represents the first systematic analysis of how the quality of multiple types of information in live streaming commerce influences consumer trust and purchase intention, integrated within a unified framework. It uniquely introduces network size as a moderating variable, offering both theoretical insights and practical guidance for balancing information quality with network size in live streaming commerce environments.

As an emerging video comment feature, danmaku is gaining more traction and increasing user interaction, thereby altering user engagement. However, existing research seldom explores how the effectiveness of danmaku on user engagement varies over time. To address this research gap, this study proposes a comprehensive framework drawing on social presence theory and information overload theory. The framework aims to explain how the effectiveness of danmaku in increasing user engagement changes over shorter time intervals.

A research model was proposed and empirically tested using data collected from 1,019 movies via Bilibili.com, one of China’s most popular danmaku video platforms. A time-varying effect model (TVEM) was used to examine the proposed research model.

The study finds that the volume of danmaku and its valence exert a time-varying influence on user engagement. Notably, the study shows that danmaku volume plays a more substantial role in determining user engagement than danmaku valence.

This research offers theoretical insights into the dynamic impact of danmaku on user engagement. The innovative conceptualization and measurement of user engagement advance research on pseudo-synchronous communication engagement. Furthermore, this study offers practical guidelines for effectively managing danmaku comments on online video platforms.

The competitiveness of destinations is crucial for tourism. In this context, the study aims to uncover customer satisfaction, experiences, feelings, and thoughts by conducting a comparative analysis of social media comments from various competitive tourism destinations.

Big data research was conducted to answer the research questions. The data was collected on a social media platform focusing on three destinations in the Mediterranean region. Three methods were employed to analyse the data: sentiment analysis, topic modelling, and named-entity recognition.

This study addressed traveller satisfaction levels. It identified the topics concerning each destination, examined the emotions expressed by travellers about these topics, explored the potential impact on future behaviour, and investigated the features of the destinations and satisfaction levels about these features. It also identified the prominent food and beverage names in destinations and explored tourists’ preferences regarding these foods and beverages.

The limitations of this study relate to the sample. The data used in this study was solely obtained from a single social media platform and focused on English-only comments. Further research that includes different social media platforms for hotel categories and considers reviews in local languages could capture a broader range of customer opinions and experiences.

Policymakers can gain insight into a destination’s position in the competitive landscape. This study has numerous implications for policymakers in the relevant destinations and managers in the design and implementation of services.

The findings of this study can have broader societal implications if considered and implemented by decision-makers and tourism businesses in the context of competitiveness.

The study’s originality lies in integrating multiple disciplines and comparing tourism destinations using big data. This study improves the understanding of competitiveness in three specific Mediterranean destinations. Previous research has focused on different contexts in these Mediterranean destinations. Therefore, the study fills this gap by focusing simultaneously on all three destinations in the context of competitiveness.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.