SLIIT Conference and Symposium Proceedings

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Open Access
Effects of Media Richness and Verbal Interactivity on Consumer Engagement
(Emerald Publishing, 2022-12-01) Wimanthika, K; Wisenthige, K; Rathnayake, K; Edirisooriya, D; Cadir, I; Abeysekara, A
Many brands are interested in their public profiles because they highlight the value of actively engaging in overall social media interactions in establishing their brand within and between consumers. As today's fashion customers have more choice and understanding, this provides a challenge to companies in terms of client loyalty. Fashion and clothing manufacturers are increasingly conscious of the link between brand recognition, fashionrelated conversations, and future purchasing behaviour. The emphasis of this study is on fashion brands' successful customer-engagement marketing techniques through Instagram. This study looks at the most up-to-date consumer engagement that takes place on Instagram with the variables of media richness and verbal interactivity. Basically, the entire research that has been carried out is based on Morgan table 384 and incorporates pages of 14 specific brands with more than 50k followers. The result was generated using multinominal logistic regression to analysis data. The findings show that, among the variables chosen, media richness has a greater influence than verbal interactivity. When examining each variable separately, it becomes clear that the media richness variable has a greater engagement rate for edited photos than for actual photos, carousels, and videos. The main objective of the study is to realise the increase of consumer engagement through the above-mentioned independent variables. Empirical studies were uncovered defining how modern marketing communications connect brands into customer perception, while claiming more investigation. In addition, this research article investigates the usage of Instagram for penetrating consumer interaction in the context of social media platforms.
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An Enhanced Virtual Fitting Room using Deep Neural Networks
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Ileperuma, I.C.S.; Gunathilake, H.M.Y.V.; Dilshan, K.P.A.P.; Nishali, S.A.D.S.; Gamage, A.I.; Priyadarshana, Y.H.P.P.
As the customer's experience in present fit-on rooms is considered as an essential part of the textile industry, these fit-on rooms play a huge role in the textile shops. It is quite an arduous method and generates problems like long queues, having to change clothes individually, privacy problems and wasting time. The proposed convolutional neural network-based Virtual Fit-on Room helps to prevent the above mentioned problems. This product contains a TV screen, two web cameras, and a PC. It captures the customer's body by using two web cameras and displays the customer's dressed body. The combination of CNN in Deep learning and AR processes the body detection and generates the customer's dressed object. The application uses the stereo vision concept to get body measurements. The system detects customer age, gender, face type, and skin tones which are used to recommend cloth styles to customers. Another requirement of this system is customizing styles according to the customer requirements and suggests different styles of clothes. The system achieved 99% accuracy when suggesting different styles using FFNN. Customers can choose clothes for another person who does not physically appear with the customer in the textile shop. The expected output delivers the most realistic dressed object to the customer which allows the efficient customizations for the textile products according to customer requirements. This product can highly influence the textile and fashion industry. Therefore, this product is suitable to compete with other applications in the industry.

SLIIT Conference and Symposium Proceedings

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