Analysis of consumer emotions about fashion brands: An exploratory study

Giri, Chandadevi; Harale, Nitin; Thomassey, Sébastien; Zeng, Xiaoyang

doi:10.1142/9789813273238_0195

Cited by 7 publications

(10 citation statements)

References 5 publications

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“…Fashion brands fit with individuals' personalities. Consumers tend to create emotional bonds with fashion brands (Giri et al, 2018), leading to participating in fashion brand communities and satisfy feelings of belongingness. Customers use the love for fashion brands as an expression of themselves to create a better image and generate self-confidence about their appearance in interaction with others.…”

Section: Fashion Brand Attachment (Fba) and Purchase Intentions (Pi) ...mentioning

confidence: 99%

The Role of Social Media Usage and Engagement on Purchase Intentions for Fashion Brands

Thanasi-Boçe

Zeqiri

Kurtishi-Kastrati

2022

International Journal of E-Services and Mobile Applications

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This study aimed to examine the impact of fashion brand attachment on customer’s purchase intention and the role of social media usage and engagement on this relationship. The data were collected through surveys distributed randomly to 404 social media users in Albania during April 2020. PLS-SEM was used to analyze the relationships between Fashion Brand Attachment (FBA), Social Media Usage (SMU), Social Brand Engagement (SBE), and Purchase Intentions (PI). SMU integrated the social media activity during the coronavirus pandemic, motives, and frequency use of various platforms. The overall model revealed a positive impact of FBA in SBE and PI, indicating that customers with a stronger attachment to fashion brands show a higher commitment to purchase and recommend them to others. The results highlighted that SMU does not contribute directly to generate committed buyers, but moderates the FBA-SBE relationship, indicating an indirect influence on the intentions to purchase fashion brands.

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Section: Fashion Brand Attachment (Fba) and Purchase Intentions (Pi) ...mentioning

confidence: 99%

The Role of Social Media Usage and Engagement on Purchase Intentions for Fashion Brands

Thanasi-Boçe

Zeqiri

Kurtishi-Kastrati

2022

International Journal of E-Services and Mobile Applications

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“…This study has emphasized that online consumer sentiments are the promising factor to forecast short-term demand at a product level. Consumer sentiments derived from social media platform such as twitter, are studied in [20] to gauge the current fashion market trend and to predict the consumer perception toward brands. Study in [21] provides a comprehensive discussion on the usefulness of social media data for the operational activities of the industries.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This study uses two data forms, first, Twitter data, which were collected using tweet API by creating tweet app to get authentication key [22]. Open source tool "Rstudio" and its package "TwitterR" were used to request API to Twitter using Oauth access [20]. Semantic keywords with hashtag "Brand name" were applied on tweet retrieval string.…”

Section: Data Collectionmentioning

confidence: 99%

“…Secondly, as only text is required for sentiment analysis, first attribute shown in the Table 1 was chosen. Text mining was applied to remove duplicates, whitespaces, hashtags, urls, stop words, retweet entities and numbers [20]. As a result, the total count of tweets reduced to 313, which is 37.35 % of the actual collected data.…”

Section: Twitter Data Preprocessingmentioning

confidence: 99%

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Exploitation of Social Network Data for Forecasting Garment Sales

Giri¹,

Thomassey²,

Zeng³

2019

IJCIS

Self Cite

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Growing use of social media such as Twitter, Instagram, Facebook, etc., by consumers leads to the vast repository of consumer generated data. Collecting and exploiting these data has been a great challenge for clothing industry. This paper aims to study the impact of Twitter on garment sales. In this direction, we have collected tweets and sales data for one of the popular apparel brands for 6 months from April 2018-September 2018. Lexicon Approach was used to classify Tweets by sentence using Naïve Bayes model applying enhanced version of Lexicon dictionary. Sentiments were extracted from consumer tweets, which was used to map the uncertainty in forecasting model. The results from this study indicate that there is a correlation between the apparel sales and consumer tweets for an apparel brand. "Social Media Based Forecasting (SMBF)" is designed which is a fuzzy time series forecasting model to forecast sales using historical sales data and social media data. SMBF was evaluated and its performance was compared with Exponential Forecasting (EF) model. SMBF model outperforms the EF model. The result from this study demonstrated that social media data helps to improve the forecasting of garment sales and this model could be easily integrated to any time series forecasting model.

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“…An additional impact of digitalization is noticed in consumer behavior in the F&A industry. The increase in awareness and advent of new offline and online mediums has changed the contemporary consumer's decision-making pattern, influenced by the various online and offline mediums [13]. It is, therefore, important to create digital platforms for efficient requirements elicitation and collection.…”

Section: Introductionmentioning

confidence: 99%

A Detailed Review of Artificial Intelligence Applied in the Fashion and Apparel Industry

et al. 2019

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The enormous impact of artificial intelligence has been realized in transforming the fashion and apparel industry in the past decades. However, the research in this domain is scattered and mainly focuses on one of the stages of the supply chain. Due to this, it is difficult to comprehend the work conducted in the distinct domain of the fashion and apparel industry. Therefore, this paper aims to study the impact and the significance of artificial intelligence in the fashion and apparel industry in the last decades throughout the supply chain. Following this objective, we performed a systematic literature review of research articles (journal and conference) associated with artificial intelligence in the fashion and apparel industry. Articles were retrieved from two popular databases ''Scopus'' and ''Web of Science'' and the article screening was completed in five phases resulting in 149 articles. This was followed by article categorization which was grounded on the proposed taxonomy and was completed in two steps. First, the research articles were categorized according to the artificial intelligence methods applied such as machine learning, expert systems, decision support system, optimization, and image recognition and computer vision. Second, the articles were categorized based on supply chain stages targeted such as design, fabric production, apparel production, and distribution. In addition, the supply chain stages were further classified based on business-to-business (B2B) and business-to-consumer (B2C) to give a broader outlook of the industry. As a result of the categorizations, research gaps were identified in the applications of AI techniques, at the supply chain stages and from a business (B2B/B2C) perspective. Based on these gaps, the future prospects of the AI in this domain are discussed. These can benefit the researchers in academics and industrial practitioners working in the domain of the fashion and apparel industry.INDEX TERMS Artificial intelligence, big data analytics, machine learning, expert systems, fashion and apparel industry.

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Analysis of consumer emotions about fashion brands: An exploratory study

Cited by 7 publications

References 5 publications

The Role of Social Media Usage and Engagement on Purchase Intentions for Fashion Brands

The Role of Social Media Usage and Engagement on Purchase Intentions for Fashion Brands

Exploitation of Social Network Data for Forecasting Garment Sales

A Detailed Review of Artificial Intelligence Applied in the Fashion and Apparel Industry

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