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Study on Factors Affecting Purchase Intention of Fashion Clothes Advertised on Social Media Platforms
(Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Bandara, S. M.G. S.; Nethmini, K. A. D.; Nanayakkara,P. G. S. A.; Ranasinghe, H. M. D. K.; Niroshani, S. A.
With the evolution of the digital world, social media has become a key player in the online fashion industry. This study on factors influencing purchase intention for fashion clothing through social media focused on how factors such as price, brand reputation, product quality, design variety, brand image, customer reviews, delivery time, return policy, and delivery quality impact purchasing decisions. A questionnaire featuring a five-point Likert scale was used to gather data. Using simple random sampling, 203 responses were collected within Sri Lanka. The importance of each factor was analysedby calculating the means from the Likert scale responses in MATLAB (R2018a). According to the Likert scale interpretation for a five-point scale, with (4.21–5.00) considered very important: price (4.66), product quality (4.79), delivery quality (4.59), variety of designs (4.23), customer reviews (4.43), delivery time (4.24), and return policy (4.28). Other factors, such as brand reputation (4.01) and brand image (3.63), fell within the important range (3.41–4.20). Overall satisfaction with online shopping (3.43) alsofalls within this range. Therefore, this study concludes that apparel businesses should focus their marketing strategies on these key factors via social media to improve customer engagement and increase sales.
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Profiling Gen Z: Influencing Online Purchase Intention
(IEEE, 2023-08-23) Wijerathne, W.D.S K.; Peter, S
With technology playing an ever-increasingly significant part in our everyday lives, the study focused on profiling Gen Z Internet behavior and identifying factors influencing their online purchase intentions. Responses from 253 participants were captured using a standardized questionnaire in order to profile the online shopping behavior of Gen Z. The results showed that Gen Z heavily relies on the Internet for social media, education, and video streaming but spends less time on online purchasing. Significantly, there was a significant gender gap in their online shopping behavior, with females showing a higher propensity to shop online. Perceived enjoyment and perceived ease of use were the most significant factors influencing the online purchase intention of Gen Z. In contrast, subjective norm, perceived benefits, and perceived trust were less significant. The findings emphasize the importance of understanding the unique habits and preferences of this market segment and developing strategies to target them effectively.
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A trilateral influence model for online shopping
(IEEE, 2017-01-27) Samaraweera, S. A. K. G; Gamage, N. G. H. P; Gallage, I. G; Gunathilaka, D. D. T. M; Fernando, N; Kasthurirathna, D
Application of social influence toward E-commerce has brought a significant benefit for the stakeholders. Consequently, it has enhanced the consumer satisfaction as well as spread of experiences. However, even with the collaboration of social influence there are some visible short comings potentially appearing in such systems. In fact, the contribution of social influence is still in an evolving state. The reliability of products is such recognized key issue that still appears in exiting social E-commerce systems. In this context we introduce a social influence model combined with a built in social network which further improves the customer reliability and satisfaction on available products. Thus, it can propagate reliable knowledge among community and optimize product recommendation process. The implemented model considers the personal preferences of respective consumers, their social influences in social network and external social influences to the system for the execution. Furthermore, it operates as a multi-agent system. The model has been validated by two sample data sets of consumers and products. As the results, majority have picked products suggested by combining external influences, internal social influences, and personal preferences. Therefore it has concluded that recommendation of products considering above three combinations is more effective.

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