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1 – 10 of 323Xinhua Guan, Zhenxing Nie, Catheryn Khoo, Wentao Zhou and Yaoqi Li
This study aims to explore the connection between travel content consumption in social networks and social comparison, envy as well as travel intention. It analyzes whether…
Abstract
Purpose
This study aims to explore the connection between travel content consumption in social networks and social comparison, envy as well as travel intention. It analyzes whether tourists’ travel intention is affected by travel content consumption in social networks, and more importantly, whether social comparison and envy play a mediating role in this process.
Design/methodology/approach
Data was collected through intercept in four popular tourist spots in Guangzhou and Zhuhai in South China. A self-administered questionnaire was used. A total of 400 participants were recruited, and 291 valid questionnaires were obtained. Bias-corrected nonparametric percentile bootstrap mediation variable test method was used to test hypotheses.
Findings
The study yielded three results. First, travel content consumption in the social networks positively influences travel intention. Second, travel content consumption in social networks indirectly affects travel intention through social comparison and envy. Third, the control variables, such as gender, age, education and income, mainly affect envy.
Originality/value
This study constructs a theoretical framework of stimulus–cognitive appraisal–emotion–behavioral responses. To the best of the authors’ knowledge, it is the first study to reveal that the internal psychological mechanism of travel content consumption affects travel intention. It also discloses that envy of seemingly negative emotions can encourage positive behaviors in certain situations.
Details
Keywords
- Social networks
- Content consumption
- Social comparison
- Envy
- Travel intention
- Cognitive appraisal theory of emotion
- Redes sociales
- consumo de contenido
- comparación social
- envidia
- intención de viaje
- teoría de evaluación cognitiva emocional
- 社交网络
- 内容消费
- 社会比较
- 嫉妒
- 旅游意向
- 情感认知评价理论
- Redes sociales
- Consumo de contenido
- Comparación social
- Envidia
- Intención de viaje
- Teoría de evaluación cognitiva emocional
Xiaodong Li, Zibing Liu, Yuan Chen and Ai Ren
Message stream advertising (MSA) has become an increasingly popular option for advertising on mobile social media. However, MSA is often avoided by consumers, and this avoidance…
Abstract
Purpose
Message stream advertising (MSA) has become an increasingly popular option for advertising on mobile social media. However, MSA is often avoided by consumers, and this avoidance deserves more research attention. The purpose of this study is therefore to identify the underlying mechanism and key variables that affect consumer avoidance of MSA in the context of mobile social media.
Design/methodology/approach
A face-to-face survey was administered to current mobile users of WeChat (N = 438). Structural equation modeling was conducted to test the relationships in the research model.
Findings
Results revealed that mobile consumers employ mechanical avoidance methods (i.e. zipping, muting and zapping) against MSA. The findings also demonstrated that advertising intrusiveness (stimulus) is directly linked to negative emotions, perceived entertainment and sense of control (organism), which, in turn, relate to MSA avoidance (response).
Originality/value
The study contributes to the MSA avoidance literature by using the stimulus-organism-response model to deepen the understanding of consumers' MSA avoidance on mobile social media, and it suggests important managerial implications for advertising practitioners and platform operators.
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Keywords
Yongqing Yang, Jianyue Xu, Lesley Pek Wee Land, Shuiqing Yang and Thomas Chesney
People's socializing behavior in social networking services (SNS) presents dramatically different features, forming differentiated online social interaction patterns (DOSIP) in…
Abstract
Purpose
People's socializing behavior in social networking services (SNS) presents dramatically different features, forming differentiated online social interaction patterns (DOSIP) in SNS. This study aims to explore the relationships between users' multidimensional psychological needs and multiple social interaction patterns in SNS.
Design/methodology/approach
Based on Maslow's hierarchy of needs and use and gratifications (U&G) theory, the authors develop the research model to examine the effects of psychological needs on DOSIP. A survey is used to collect the data of SNS users' social interaction. The authors adopt structural equation modeling–neural network (SEM-NN) integrated method to examine the research model.
Findings
Need to belong, need for self-esteem, need for social contact, need for emotional expression, need for cognition, and need for external-esteem have significant influences on both active and passive social interactions respectively.
Originality/value
Based on the categorization of DOSIP into six types in terms of the level of activity and disclosure of social interaction, the authors construct an integrated research model of multidimensional psychological needs to multiple social interaction patterns, and validate the antecedents of DOSIP from the perspective of psychological needs.
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Teresa Fernandes and Rodrigo Oliveira
Social media has become an inescapable part of our lives. However, recent research suggests that excessive use of social media may lead to fatigue and users’ disengagement. This…
Abstract
Purpose
Social media has become an inescapable part of our lives. However, recent research suggests that excessive use of social media may lead to fatigue and users’ disengagement. This study aims to examine which brand-related factors contribute to social media fatigue (SMF) and its subsequent role on driving lurking behaviors, particularly among young consumers.
Design/methodology/approach
Based on survey data from 282 young users of social media, a holistic model of brand-related drivers and outcomes of SMF was tested, emphasizing the contribution of brands’ social media presence to users’ disengagement.
Findings
Research shows that branded content overload and irrelevance, as well as branded ads intrusiveness significantly impact SMF, which in turn plays a mediating role between brand-related drivers and lurking behaviors. The authors further conclude that the impact of SMF on lurking is stronger for users who follow a larger set of brands.
Originality/value
The study contributes to social media research by addressing its “dark side” and empirically validating the role of brands’ social media presence in developing young users’ fatigue and disengagement. The study further adds to the scant literature on SMF, which was mostly developed outside the branding field. Research also provides valuable insights to brands on how to improve their social media performance.
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Keywords
Zheshi Bao and Yun Zhu
Online reviews derived from peer communications have been increasingly viewed as an important approach for consumers to gather pre-purchase information. This study aims to examine…
Abstract
Purpose
Online reviews derived from peer communications have been increasingly viewed as an important approach for consumers to gather pre-purchase information. This study aims to examine factors affecting online reviews adoption in social network communities and then indicates the underlying mechanism of this process based on an extended information adoption model (IAM).
Design/methodology/approach
Using the data collected from 242 users of a social network community via an online survey, the proposed model is empirically assessed by partial least squares-based structural equation model (PLS-SEM).
Findings
The results show that both perceived diagnosticity and perceived serendipity are drivers of online reviews adoption in social network communities. Meanwhile, community identification is not only an antecedent of diagnosticity and serendipity perceived by community members, but also motivates source credibility which, in turn, positively influences argument quality. Finally, the importance of argument quality and source credibility in reviews adoption process is also presented.
Originality/value
This study extends the IAM and enriches the literature regarding online reviews adoption. It deepens the understanding of serendipitous experiences and community identification in social networking context by addressing their important roles in the authors' extended IAM.
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Keywords
An increasing number of images are generated daily, and images are gradually becoming a search target. Content-based image retrieval (CBIR) is helpful for users to express their…
Abstract
Purpose
An increasing number of images are generated daily, and images are gradually becoming a search target. Content-based image retrieval (CBIR) is helpful for users to express their requirements using an image query. Nevertheless, determining whether the retrieval system can provide convenient operation and relevant retrieval results is challenging. A CBIR system based on deep learning features was proposed in this study to effectively search and navigate images in digital articles.
Design/methodology/approach
Convolutional neural networks (CNNs) were used as the feature extractors in the author's experiments. Using pretrained parameters, the training time and retrieval time were reduced. Different CNN features were extracted from the constructed image databases consisting of images taken from the National Palace Museum Journals Archive and were compared in the CBIR system.
Findings
DenseNet201 achieved the best performance, with a top-10 mAP of 89% and a query time of 0.14 s.
Practical implications
The CBIR homepage displayed image categories showing the content of the database and provided the default query images. After retrieval, the result showed the metadata of the retrieved images and links back to the original pages.
Originality/value
With the interface and retrieval demonstration, a novel image-based reading mode can be established via the CBIR and links to the original images and contextual descriptions.
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Keywords
Lai-Ying Leong, Teck Soon Hew, Keng-Boon Ooi, Nick Hajli and Garry Wei-Han Tan
Social commerce (SC) is a new genre in electronic commerce (e-commerce) that has great potential. This study proposes a new research framework to address deficiencies in existing…
Abstract
Purpose
Social commerce (SC) is a new genre in electronic commerce (e-commerce) that has great potential. This study proposes a new research framework to address deficiencies in existing social commerce research frameworks (e.g. the information model).
Design/methodology/approach
In the era of Industrial Revolution 4.0 technologies and new social commerce (s-commerce) models, the authors believe that there is an immediate need for a new research framework. The authors analysed the progress of the s-commerce paradigm between 2003 and 2023 by applying longitudinal science mapping. The authors then developed a research framework based on the themes in the strategic diagrams and evolution map.
Findings
From 2003 to 2010, studies on s-commerce mainly focused on social networking sites, virtual communities, social shopping and analytic approaches. From 2011 to 2015, it shifted to s-commerce, consumer behaviour, Web 2.0, artificial intelligence, social technologies, online shopping, user studies, data gathering methods, applications, service-based social commerce constructs, e-commerce and cognitive factors. Social commerce remained the primary research paradigm from 2017 to 2023.
Practical implications
The SC framework may be analogous to popular research frameworks such as technology-organisation-environment (T-O-E) and stimulus-organism-response (S-O-R). Based on this SC framework, researchers may gain a better understanding by determining the factors of the social, commercial, technological and behavioural dimensions.
Originality/value
The authors redefined s-commerce and developed an SC framework. Practical guidelines for the SC framework and an exemplary research model are presented. Overall, this study offers a new research agenda for the extant understanding of s-commerce, with the SC framework as the next frontier of the theoretical advancements and applications of s-commerce.
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Dandan He, Zhong Yao, Futao Zhao and Yue Wang
Retail investors are prone to be affected by information dissemination in social media with the rapid development of Web 2.0. The purpose of this study is to recognize the factors…
Abstract
Purpose
Retail investors are prone to be affected by information dissemination in social media with the rapid development of Web 2.0. The purpose of this study is to recognize the factors that may impact users' retweet behavior, namely information dissemination in the online financial community, through machine learning techniques.
Design/methodology/approach
This paper crawled data from the Chinese online financial community (Xueqiu.com) and extracted author-related, content-related, situation-related, stock-related and stock market-related features from the dataset. The best information dissemination prediction model based on these features was determined by evaluating five classifiers with various performance metrics, and the predictability of different feature groups was tested.
Findings
Five prevalent classifiers were evaluated with various performance metrics and the random forest classifier was proven to be the best retweet prediction model in the authors’ experiments. Moreover, the predictability of author-related, content-related and market-related features was illustrated to be relatively better than that of the other two feature groups. Several particularly important features, such as the author's followers and the rise and fall of the stock index, were recognized in this paper at last.
Originality/value
This study contributes to in-depth research on information dissemination in the financial domain. The findings of this study have important practical implications for government regulators to supervise public opinion in the financial market.
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Keywords
Fatema Kawaf, Annaleis Montgomery and Marius Thuemmler
The paper addresses the privacy–personalisation paradox in the post-GDPR-2018 era. As the regulation came in a bid to regulate the collection and use of personal data, its…
Abstract
Purpose
The paper addresses the privacy–personalisation paradox in the post-GDPR-2018 era. As the regulation came in a bid to regulate the collection and use of personal data, its implications remain underexplored. The research question is: How do consumers perceive the matter of personal data collection for the use of highly targeted and personalised ads post-GDPR-2018? The invasion of privacy vs the benefits of highly personalised digital marketing.
Design/methodology/approach
To address the research question, this qualitative study conducts semi-structured interviews with 14 individuals, consisting of average users and digital experts.
Findings
This paper reports on increasing consumer vulnerability post-GDPR-2018 due to increased awareness of personal data collection yet incessant lack of control, particularly regarding the repercussions of the digital footprint. The privacy paradox remains an issue except among experts, and personalisation remains necessary, yet critical challenges arise (e.g. filter bubbles and intrusion).
Practical implications
Policy implications include education, regulating consent platforms and encouraging consensual sharing of personal data.
Originality/value
While the privacy–personalisation paradox has been widely studied, the impact of GDPR-2018 has rarely been addressed in the literature. GDPR-2018 has seemingly had little impact on instilling a sense of security for consumers; if anything, this paper highlights greater concerns for privacy as users sign away their rights on consent forms to access websites, thus contributing novel insights to this area of research.
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Ashraf Maleki, Javad Abbaspour, Abdolrasoul Jowkar and Hajar Sotudeh
The main objective of the present study is to determine the role of citation-based metrics (PageRank and HITS’ authority and hub scores) and non-citation metrics (Goodreads…
Abstract
Purpose
The main objective of the present study is to determine the role of citation-based metrics (PageRank and HITS’ authority and hub scores) and non-citation metrics (Goodreads readers, reviews and ratings, textbook edition counts) in predicting educational ranks of textbooks.
Design/methodology/approach
The rankings of 1869 academic textbooks of various disciplines indexed in Scopus were extracted from the Open Syllabus Project (OSP) and compared with normalized counts of Scopus citations, scores of PageRank, authority and hub (HITS) in Scopus book-to-book citation network, Goodreads ratings and reviews, review sentiment scores and WorldCat book editions.
Findings
Prediction of the educational rank of scholarly syllabus books ranged from 32% in technology to 68% in philosophy, psychology and religion. WorldCat editions in social sciences, medicine and technology, Goodreads ratings in humanities, and book-citation-network authority scores in law and political science accounted for the strongest predictions of the educational score. Thus, each indicator of editions, Goodreads ratings, and book citation authority score alone can be used to show the rank of the academic textbooks, and if used in combination, they will help explain the educational uptake of books even better.
Originality/value
This is the first study examining the role of citation indicators, Goodreads readers, reviews and ratings in predicting the OSP rank of academic books.
Details