Decoding service brand image through user-generated images

Bakri, Marlini; Krisjanous, Jayne; James, Richard D.

doi:10.26686/wgtn.12763550.v1

Cited by 5 publications

(5 citation statements)

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“…Recent literature uncovered social media influence from various marketing perspectives: small retailers (Devereux, Grimmer, & Grimmer, 2020), content strategies (Kordzadeh & Young, 2020), brand promotions (Nikolinakou & Phua, 2020), consumer value co‐creation (Alalwan et al, 2019), WOM and purchase intention (Alalwan, 2018; Chu & Chen, 2019), communication and social marketing strategies (Appel, Grewal, Hadi, & Stephen, 2020; Li, Larimo, & Leonidou, 2020), consumer empowerment (Tajurahim, Abu Bakar, Md Jusoh, Ahmad, & Muhammad Arif, 2020), user‐generated images (Bakri, Krisjanous, & Richard, 2020), consumer behaviour (Sobhanifard & Sadatfarizani, 2018), and brand engagement and brand equity (Algharabat, Rana, Alalwan, Baabdullah, & Gupta, 2020). However, there is inadequate understanding regarding the social motivational causes and social influencers that can play a role in generating and exchanging user‐generated fashion brand‐related content.…”

Section: Introductionmentioning

confidence: 99%

Developing UGC social brand engagement model: Insights from diverse consumers

Naeem

Ozuem

2020

J of Consumer Behaviour

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Understanding the social influence of user‐generated content (UGC) to create social brand engagement (SBE) through social media has become a topic of major interest for both academics and marketers since the birth of online social networking. This study explores the social interaction of social media users (SMUs) involving brand‐related UGC, and the outcomes of this study show how SMUs' social interaction through UGC generates SBE in a fashion retail context. There is inadequate understanding regarding what the social motivational contexts of social actors are; these social motivational contexts can play a role in the social generation, exchange, and consumption of fashion brand‐related UGC. The present study intends to develop a UGC SBE model using the diverse insights of the customers of fashion brands. The present study opted for a social constructionist epistemological position using a qualitative research method. Semi‐structured interviews were conducted in the UK with 32 customers of fashion brands. Findings highlighted that social responsibility, sharing experience, staying connected and updated, and reward sharing are the major motivational factors that lead to the generation, exchange, and consumption of UGC that would create SBE. Also, the findings revealed the SMUs, as social actors, who played a significant role in generating SBE through their social interaction with UGC in everyday life: frequent SMUs, celebrities, opinion leaders, and product users. Most importantly, it was found that expert opinion influence as identification and crowd opinion were recognised as internalisation social influencing factors that generate SBE. This study proposes a holistic conceptualisation in a “UGC SBE model” by considering: (a) the identification and (b) the internalisation types of social influence of UGC with respect to fashion brand engagement.

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Section: Introductionmentioning

confidence: 99%

Developing UGC social brand engagement model: Insights from diverse consumers

Naeem

Ozuem

2020

J of Consumer Behaviour

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“…Currently, the most innovative line of research, and which is offering more advances in the field, deals with understanding satisfied and dissatisfied customers through the use of new technologies as text‐mining approach (Berezina et al, 2016), brand‐image (Bakri, Krisjanous, & Richard, 2020), or self‐service technologies (Fan, Wu, Miao, & Mattila, 2020; Sangle‐Ferriere & Voyer, 2019). The amount of accessible information in some sectors, such as the hospitality sector, enables future researchers to continue the advances on the main aspects that influence consumer dissatisfaction, as well as its consequences.…”

Section: Discussionmentioning

confidence: 99%

Changes and evolution in the intellectual structure of consumer dissatisfaction

Nebreda

Martín

González

2020

J of Consumer Behaviour

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Technological progress has encouraged dissatisfied consumers hostility to companies. This has notably caught the attention of researchers, which have developed numerous advances about dissatisfaction causes and consequences. A large volume of scientific knowledge has been produced, making it difficult to process the multiple lines of research generated. The aim of this work is to provide an integrated perspective of the scientific domain's research traditions on consumer dissatisfaction, showing the development, trends, and future lines of research. Using bibliometric methodology, based on co‐cites, we map the intellectual structure of current scholarship in this field. Results point out the major areas of research, the evolution, path of dissemination, and transformative discoveries. This study contributes to the development of the customer dissatisfaction literature by providing an integrated view of the field, facilitating quick understanding of their structure and identifying future lines of research.

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“…In this paper, a neural network based method of learning tree is used which enhances picture information storage efficiency ( Han et al., 2020 ). Here, the goal of authors is to present the notion of image word of mouth (IWOM) and a thorough framework for it, which defines UGI as visual articulations of service experiences that lead to consumer assessments of service brand image ( Bakri et al., 2020 ). The authors of this paper have said about the importance of generative adversarial networks and its various applications in the field of image segmentation.…”

Section: Related Workmentioning

confidence: 99%

Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging

Chauhan

Palivela²,

Tiwari

2021

International Journal of Information Management Data Insights

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It’s been more than a year that the entire world is fighting against Covid-19 pandemic. Starting from the Wuhan city in China, COVID-19 has conquered the entire world with its rapid progression. But seeking the importance towards the human situation, it has become essential to build such an automated model to diagnose COVID-19 within less computational time easily. As the disease has spread, there is not enough data to implement an accurate COVID-19 predicting model. But technology is a boon, which makes it possible. Effective techniques based on medical imaging using artificial intelligence have approached to assist humans in needful time. It has become very essential to detect COVID-19 in humans at an early stage to prevent it from becoming more infectious. The neural networks have shown promising results in medical imaging. In this research, a deep learning-based approach is used for image classification to detect COVID-19 using chest X-ray images (CXR). A CNN classifier have been used to classify the normal-healthy images from the COVID-19 images, using transfer learning. The concept of early stopping is used to enhance the accuracy of the proposed DenseNet model. The results of the system have been evaluated using accuracy, precision, recall and F1-score metrics. An automated comparative analysis among multiple optimizers, LR Scheduler and Loss Function is performed to get the highest accuracy suitable for the proposed system. The Adamax optimizer with Cross Entropy loss function and StepLR scheduler have outperformed with 98.45% accuracy for normal-healthy CXR images and 98.32% accuracy for COVID-19 images.

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Decoding service brand image through user-generated images

Cited by 5 publications

References 0 publications

Developing UGC social brand engagement model: Insights from diverse consumers

Developing UGC social brand engagement model: Insights from diverse consumers

Changes and evolution in the intellectual structure of consumer dissatisfaction

Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging

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