Impacts of Social Media Mediated Electronic Words of Mouth on Young Consumers’ Disposal of Fashion Apparel: A Review and Proposed Model

2015 ◽  
pp. 47-58
Author(s):  
Nadine Ka-Yan Ng ◽  
Pui-Sze Chow ◽  
Tsan-Ming Choi
Keyword(s):  
Social Media ◽  
Proposed Model ◽  
Young Consumers ◽  
Fashion Apparel

Does Social Media Engagement Moderate Brand Engagement and Brand Loyalty? Evidence from Young Consumers of Malaysian Modest Fashion Industry

2020 ◽  
Vol 24 (02) ◽  
pp. 2500-2508
Author(s):  
Zulkarnian Ahmad ◽  
Ami Suhana Menon ◽  
Cordelia Mason ◽  
Mohd Farid Shamsudin ◽  
Ilham Sentosa
Keyword(s):  
Social Media ◽  
Brand Loyalty ◽  
Fashion Industry ◽  
Brand Engagement ◽  
Social Media Engagement ◽  
Young Consumers

scholarly journals Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1332
Author(s):  
Hong Fan ◽  
Wu Du ◽  
Abdelghani Dahou ◽  
Ahmed A. Ewees ◽  
Dalia Yousri ◽  
...  
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Modern Society ◽  
User Generated Content ◽  
Real World Application ◽  
Proposed Model ◽  
Social Media Platforms ◽  
Public Dataset ◽  
The Uk ◽  
Harmful Side Effect

Social media has become an essential facet of modern society, wherein people share their opinions on a wide variety of topics. Social media is quickly becoming indispensable for a majority of people, and many cases of social media addiction have been documented. Social media platforms such as Twitter have demonstrated over the years the value they provide, such as connecting people from all over the world with different backgrounds. However, they have also shown harmful side effects that can have serious consequences. One such harmful side effect of social media is the immense toxicity that can be found in various discussions. The word toxic has become synonymous with online hate speech, internet trolling, and sometimes outrage culture. In this study, we build an efficient model to detect and classify toxicity in social media from user-generated content using the Bidirectional Encoder Representations from Transformers (BERT). The BERT pre-trained model and three of its variants has been fine-tuned on a well-known labeled toxic comment dataset, Kaggle public dataset (Toxic Comment Classification Challenge). Moreover, we test the proposed models with two datasets collected from Twitter from two different periods to detect toxicity in user-generated content (tweets) using hashtages belonging to the UK Brexit. The results showed that the proposed model can efficiently classify and analyze toxic tweets.

Topic-BERT: Detecting harmful information from social media

2021 ◽  
pp. 1-10
Author(s):  
Wang Gao ◽  
Hongtao Deng ◽  
Xun Zhu ◽  
Yuan Fang
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Topic Model ◽  
Classification Performance ◽  
Critical Research ◽  
Short Text ◽  
Additional Information ◽  
Proposed Model ◽  
Weight Calculation ◽  
Two Stages

Harmful information identification is a critical research topic in natural language processing. Existing approaches have been focused either on rule-based methods or harmful text identification of normal documents. In this paper, we propose a BERT-based model to identify harmful information from social media, called Topic-BERT. Firstly, Topic-BERT utilizes BERT to take additional information as input to alleviate the sparseness of short texts. The GPU-DMM topic model is used to capture hidden topics of short texts for attention weight calculation. Secondly, the proposed model divides harmful short text identification into two stages, and different granularity labels are identified by two similar sub-models. Finally, we conduct extensive experiments on a real-world social media dataset to evaluate our model. Experimental results demonstrate that our model can significantly improve the classification performance compared with baseline methods.

Predicting young customers’ intention to repurchase green plastic products: incorporating trust model into purchase intention model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dwi Suhartanto ◽  
Ani Kartikasari ◽  
Raditha Hapsari ◽  
Bambang Setio Budianto ◽  
Mukhamad Najib ◽  
...  
Keyword(s):  
Purchase Intention ◽  
Developed Countries ◽  
Trust Model ◽  
Repurchase Intention ◽  
Content Type ◽  
Repurchase Intentions ◽  
Proposed Model ◽  
Young Consumers ◽  
Green Trust ◽  
Plastic Products

Purpose This study aims to assess young customers’ repurchasing intentions toward green plastic products by incorporating green trust model into green purchase intention model. It also evaluates the role of gender moderation in the green repurchase intention formation model. Design/methodology/approach A total of 314 young consumers of green plastic products in Bandung, Indonesia were determined for this study. This study used variance-based partial least squares (PLS) to evaluate the proposed model and examine the hypothesized relationship, by means of SmartPLS 3. The construct validity and reliability were evaluated by testing the measurement model, while the proposed hypotheses were examined by testing the structural model. Findings The assessment of the proposed model using PLS reveals that the incorporation of green trust model increases the prediction strength of green repurchase intentions model on green plastic products. Further, this study shows that, in general, gender did not moderate the formation of green repurchase intentions. Research limitations/implications Besides broadening the green repurchase intention theory, this finding offers a direction for green plastic businesses to improve their capability and their marketing strategies. This study offers an important contribution in understanding young consumers’ intentions to buy green plastic products, although it has several drawbacks. In the future, to increase its generalization, this study can be replicated on young consumers in other developing and developed countries, and this model can also be tested in other segments. Originality/value To the best of the authors’ knowledge, there are no published studies that have tested the repurchase intention model for green plastic products, and none of the past studies have incorporated these models to explain repurchase intention toward green plastic products. Furthermore, the inclusion of gender roles in green repurchase intentions for green plastic products is important to be explored.

THE ROLE OF ONLINE CONSUMER REVIEW, SOCIAL MEDIA ADVERTISEMENT AND INFLUENCER ENDORSEMENT ON PURCHASE INTENTION OF FASHION APPAREL DURING COVID-19

2021 ◽  
Vol 14 (8) ◽  
pp. 17-33
Author(s):  
Aqilah Yaacob ◽  
Jen Ling Gan ◽  
Shamsuddin Yusuf
Keyword(s):  
Social Media ◽  
Purchase Intention ◽  
Quantitative Methods ◽  
Marketing Research ◽  
Social Media Marketing ◽  
Online Purchase ◽  
Consumer Review ◽  
Online Consumer ◽  
Fashion Apparel ◽  
Online Consumer Review

Recent marketing research focuses on social media marketing as an essential tool for companies to fully utilise particularly with the increase of online and home-based consumption during pandemic. In particular, the authors hypothesize that online consumer review, social media advertisement and influencers endorsement may affect online purchase intention. The investigation of the hypotheses utilizes a sample of 163 customers who shop for fashion apparel via online platforms during the pandemic. In order to assess the relationships between these variables, the current research used quantitative methods through an online self-administered questionnaire, in which the scale items were derived from existing literature. These results suggest that ‘Online Consumer Review’, ‘Social Media Advertisement’, and ‘Influencer Endorsement’ have a positive and significant correlation with online purchase intention of fashion apparel during pandemic (r = .25; r = .35; r = .48, respectively). The researcher deliberates the implications for marketing research and practice which include addressing the literature gap in understanding online purchase intention of fashion apparel during the pandemic and highlighting the importance of social media marketing for companies to survive in the 21st century of online-based consumption and consumer-oriented social media.

Sarcasm Detection Using Multi-Channel Attention Based BLSTM On News Headline

2020 ◽  
Author(s):  
Azika Syahputra Azwar ◽  
Suharjito
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Important Task ◽  
Hybrid Neural Network ◽  
Negative Opinion ◽  
Proposed Model ◽  
Short Memory ◽  
Literary Devices

Abstract Sarcasm is often used to express a negative opinion using positive or intensified positive words in social media. This intentional ambiguity makes sarcasm detection, an important task of sentiment analysis. Detecting a sarcastic tone in natural language hinders the performance of sentiment analysis tasks. The majority of the studies on automatic sarcasm detection emphasize on the use of lexical, syntactic, or pragmatic features that are often unequivocally expressed through figurative literary devices such as words, emoticons, and exclamation marks. In this paper, we introduce a multi-channel attention-based bidirectional long-short memory (MCAB-BLSTM) network to detect sarcastic headline on the news. Multi-channel attention-based bidirectional long-short memory (MCAB-BLSTM) proposed model was evaluated on the news headline dataset, and the results-compared to the CNN-LSTM and Hybrid Neural Network were excellent.

Telugu News Data Classification Using Machine Learning Approach

2022 ◽  
pp. 181-194
Author(s):  
Bala Krishna Priya G. ◽  
Jabeen Sultana ◽  
Usha Rani M.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Research Work ◽  
Learning Approach ◽  
Fake News ◽  
Learning Models ◽  
Machine Learning Classifiers ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Machine Learning Models

Mining Telugu news data and categorizing based on public sentiments is quite important since a lot of fake news emerged with rise of social media. Identifying whether news text is positive, negative, or neutral and later classifying the data in which areas they fall like business, editorial, entertainment, nation, and sports is included throughout this research work. This research work proposes an efficient model by adopting machine learning classifiers to perform classification on Telugu news data. The results obtained by various machine-learning models are compared, and an efficient model is found, and it is observed that the proposed model outperformed with reference to accuracy, precision, recall, and F1-score.

EWOM firestorm: young consumers and online community

2019 ◽  
Vol 21 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Sasithorn Suwandee ◽  
Jiraporn Surachartkumtonkun ◽  
Aurathai Lertwannawit
Keyword(s):  
Social Media ◽  
Online Community ◽  
Community Members ◽  
Content Type ◽  
Service Failures ◽  
Young Consumers ◽  
Mixed Factorial Design ◽  
Information Characteristics ◽  
High Level

Purpose This study aims to examine the influence of homophily in an online community and the effect of electronic word of mouth (eWOM) consensus on young consumers’ attitudes. Design/methodology/approach This study implemented an experimental research design using a two (low/high homophily) × two (low/high eWOM consensus) mixed factorial design. This study explores young consumers’ changes in brand attitude after encountering negative eWOM. Findings The results indicate that a high consensus of negative eWOM among online community members leads to significant changes in attitude, while a low consensus of negative eWOM does not produce such an effect. Negative eWOM from either high or low homophilous sources produces significant changes in attitude. There are significant attitude changes when a strong consensus of negative eWOM is received from a source with a high level of homophily. Research limitations/implications Service failures in offline service settings lead to the dissemination of negative eWOM on social media. To handle and prevent social media crises, researchers should understand online crises antecedents relating to information characteristics i.e. eWOM consensus and characteristics of online community members to evaluate the crises impact. Brands should monitor tone and dialogue of online community member on social media to remedy and diminish any damage done to their brand image from negative eWOM. Originality/value This study contributes to the application of social network theory by understanding the role of nodes on negative eWOM effect in social media.

Exploiting business networks in the age of social media: the use and integration of social media analytics in B2B marketing

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yun Wang ◽  
Michel Rod ◽  
Qi Deng ◽  
Shaobo Ji
Keyword(s):  
Social Media ◽  
Development Model ◽  
Organizational Level ◽  
Social Media Analytics ◽  
Business Networks ◽  
Organizational Capability ◽  
Content Type ◽  
B2b Marketing ◽  
Proposed Model ◽  
The Impact

Purpose Based on an organizational capability perspective, this paper aims to propose a development model for social media analytics (SMA) capability that can be applied to business-to-business (B2B) marketing, with the aim of facilitating the use and integration of SMA in B2B marketing and maximizing the benefits of business networks in the age of social media. Design/methodology/approach This is a critical interpretive synthesis of SMA publications collected from academic journals, business magazines and the SMA service industry. In addition, an inter-disciplinary approach was adopted by drawing upon both marketing and information systems literature. In total, 123 academic papers, 106 industry case studies and 141 magazine papers were identified and analyzed. The findings were synthesized and compiled to address the predefined research question. Findings An SMA capability development model is proposed. The proposed model consists of four inter-dependent levels (technological, operational, managed and strategic) that collectively transfer the technological capability of SMA to the dynamic organizational capability. Each level of SMA capability is detailed. SMA-in-B2B marketing is highlighted as a socio-technical phenomenon, in which a technological level SMA capability is emphasized as the foundation for developing organizational level SMA capabilities and organizational capabilities, in turn, supporting and managing SMA activities and practices (e.g. strategic planning, social and cultural changes, skills and resources, measurements and values). Practical implications The proposed research framework may have implications for the operational level SMA development and the investigations on the direct and/or indirect measurements to help firms see the impact of SMA on their business. Originality/value This study may have implications for the adoption, use, integration and management of SMA in B2B marketing. The proposed model is grounded on the integrated insights from academia and industry. It is particularly relevant to B2B firms that have engaged in or plan to engage in applying SMA to extract insights from their online networks and is relevant to B2B researchers who are interested in SMA, big data and information technology organization integration studies.

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