Research Publications
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Publication Open Access How social media impact social entrepreneurial intentions: the serial mediation roles of risk propensity and entrepreneurial self-efficacy(Cogent OA, 2025-08-20) Gomes, C; Wisenthige, KAs societies around the globe experience various social problems with a rising population and an ever-changing political and economic landscape, scholars have been paying much attention to social entrepreneurship. Social entrepreneurship possesses the ability to address many social problems, especially in developing nations such as Sri Lanka. In this light, this study was carried out to find the impact that social media has on social entrepreneurial intentions among undergraduate students in Sri Lanka, while exploring the mediation effects of entrepreneurial self-efficacy and risk propensity. A sample of 252 students was taken from a Sri Lankan university, and a telephone-based survey was used to collect data. Partial least squares structural equation modelling was used to analyze data, using the SmartPLS4 software. The results from the analysis showed that social media significantly impacts social entrepreneurial intention, while entrepreneurial self-efficacy and risk propensity had a serial mediation effect on the impact. This study makes many novel contributions to social entrepreneurial intention research, as it explores how social media impacts social entrepreneurial intentions and the serial mediation effect of risk propensity and entrepreneurial self-efficacy in a single theoretical model. Policymakers and educational institutions are heavily encouraged to use social media platforms to diffuse social entrepreneurial concepts among undergraduate students. Finally, the study offers limitations and directions for future research.Publication Open Access Does social media information credibility influence social commerce purchase intention of skincare products? Evidence from Facebook(Public Library of Science, 2025-10-22) Ranjith, P; Nisansala, S; Jayasingha, N; Weerasekara, K; Wisenthige, K; Dayapathirana, NSocial commerce is transforming consumer purchasing behaviours by blending social media interactivity with e-commerce functionalities, and most purchases today are evidently facilitated through social media platforms with ease. Recognising the importance of credibility in skin-related purchases, this study aims to examine how social media information credibility factors, specifically source credibility and electronic word of mouth (e WOM) credibility, influence consumers’ purchase intentions for skincare products on Facebook, considering the mediating roles of trust in online communities and perceived privacy risk. Primary data were collected through a structured survey from 384 skincare purchasers who made their purchases via Facebook, and the model was tested using structural equation modelling (SEM). Further, the results reveal that source credibility, e WOM credibility, and trust in online communities positively influence social commerce purchase intention (SCPI), while perceived risk has a negative effect. Trust in online communities also reduces perceived risk and mediates the relationship between information credibility and purchase intention. Hence, these findings highlight the pivotal roles of trust and risk perceptions in shaping online consumer behaviour in the social commerce space, especially within the skincare market. The study emphasises the need for businesses to leverage credible information sources and build trustworthy online communities to enhance consumer confidence and engagement. Moreover, it contributes to the growing literature on social commerce by offering insights from an emerging market context, Sri Lanka, and suggests future research into broader dimensions of credibility and cultural comparisons to deepen the understanding of social commerce.Publication Open Access The Role of Social Media in shaping Customary International Law: Opportunities and Challenges(School of Law, Faculty of Humanities and Sciences, 2025-10-10) Ekanayake, L; Gunawardena, VThis article examines the potential and pitfalls of using social media, specifically platforms like Twitter, to identify and shape Customary International Law (CIL). Traditionally, CIL is established through consistent state practice and a legal conviction known as opinio juris. The global shift to digital communication offers a new opportunity: public statements by states and their officials on social media could potentially serve as contemporary evidence of this required legal conviction. However, the analysis concludes that the risks associated with social media currently outweigh its potential benefits for CIL formation. Several critical challenges cloud its utility. These include the difficulty in distinguishing between a state official's personal and institutional legal views and the serious risks of misinformation stemming from hacking, diplomatic catfishing, and other forms of digital manipulation. Furthermore, social media inherently introduces biases, particularly by favouring Western democracies that have high digital adoption. This can lead to the strategic manipulation of legal narratives online. Without the implementation of robust verification mechanisms, the role of social media in articulating CIL remains fraught with ambiguity and is too unreliable to be a primary source for international law.Publication Open Access Digital Marketing Strategies for University Student Recruitments in Sri Lanka(Faculty of Humanities and Sciences, SLIIT, 2024-12-04) Bandara, K. M. G. C.; Jayasuriya, N.A.; Jayathilaka, RThis study aims to investigate the effectiveness of digital marketing strategies in attracting students into the non-state higher education sector in Sri Lanka. Many students in Sri Lanka sit for Advanced Level Examination but a few of them enrol in the non-state higher education institutions due to the ineffectiveness of marketing strategies of these institutions. Hence, the objective of this study is to explore the effectiveness of digital marketing strategies for university selection, and the effectiveness of involvement excellence (which includes first impression, brand recognition, intuitive navigation, and application submission) arising from digital marketing strategies for university selection. This qualitative study utilised judgemental sampling to select marketing managers for conducting structured interviews as the data collection method. The collected data have been analysed through thematic analysis. Findings show the effectiveness of social media, optimised website design, and search engine optimisation for university selection and the effectiveness of involvement excellence consisting of first impression, brand recognition, intuitive navigation, and application submission experience for university selection.Publication Open Access Hashtag Generator and Content Authenticator(researchgate.net, 2018-01) Yapa Abeywardena, K; Ginige, A. R; Herath, N; Somarathne, H; Thennakoon, T. M. N. SIn the recent past, Online Marketing applications have been a focus of research. But still there are enormous challenges on the accuracy and authenticity of the content posted through social media. And if the social media business platforms are considered, majority of the users who try to add a market value to their own product face the problem of not getting enough attention from their target audience. The purpose of this research is to develop a safe and efficient trending hashtag generating application solution for social media business users which generates trending and relevant hashtags for user content in order to get a broad reach of target audience, automatically generates a meaningful caption to their relevant posts and guarantees the authenticity of the product at the same time. The user content is analyzed and filters the important keywords, generates a meaningful caption, suggest related trending keywords and generates trending hashtags to get the required reach for online marketers. Additionally, the marketing products’ content authentication is ensured. The application uses Natural Language Processing, Machine Learning, API technologies, Java and Python technologies. A unique database is assigned to users which contains rankings for each user. The target audience who engages in buying products get to know about the status of the sellers with respect to authenticity of the content. It is believed that the application provides a promising solution to existing audience reach problems of online marketers and buyers. The significance of this system is to help marketers and buyers to engage in online buying and selling with much effective, reliable and safer ways. This mitigate the vulnerability of bad social media marketing influences and helps to establish a safe and reliable online marketing practice to make both sellers and buyers happy. This paper provides a brief description on how to perform an organized online marketing discipline via the Trending Hashtag Generator & Image Authenticator application.Publication Embargo Sentiment classification of Sinhala content in social media(IEEE, 2020-09-24) Jayasuriya, P; Ekanayake, S; Munasinghe, R; Munasinghe, B; Weerasinghe, I; Thelijjagoda, SIn this study, we focus on the classification of Sinhala social media sentiments into positive and negative classes for a particular domain (sports). We have employed machine learning algorithms and lexicon-based sentiment classification methods. We also consider a hybrid approach by constructing an ensemble classifier in which we combine Machine Learning and Lexicon based methods. For individual methods, machine learning algorithms performed best in terms of accuracy. The ensemble classifier was able to improve performance further.Publication Embargo Sentiment classification of Sinhala content in social media(IEEE, 2020-09-24) Jayasuriya, P; Ekanayake, S; Munasinghe, R; Kumarasinghe, B; Weerasinghe, I; Thelijjagoda, SIn this study, we focus on the classification of Sinhala social media sentiments into positive and negative classes for a particular domain (sports). We have employed machine learning algorithms and lexicon-based sentiment classification methods. We also consider a hybrid approach by constructing an ensemble classifier in which we combine Machine Learning and Lexicon based methods. For individual methods, machine learning algorithms performed best in terms of accuracy. The ensemble classifier was able to improve performance further.