Browsing by Author "Karunarathna, C."

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Open Access
Adoption success of using Generative AI apps for the ECommerce Platforms in Sri Lanka
(ICSDB 2024 and SLIIT Business School, 2024-12-10) Dilshan, A.; Wijayanayake, J.; Asanka, D.; Karunarathna, C.
The digital landscape has witnessed the widespread influence of e-commerce, with the Information Technology industry embracing generative AI applications. This research aims to investigate the adoption success of existing e-commerce platforms in Sri Lanka in incorporating generative AI technologies. A systematic literature review using the PRISMA framework identified how generative AI is used in various industries, its Future Directions, Ethical Concerns, Security, and Privacy Considerations, and the most widely used and accepted models for understanding technology adoption. The Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) are the two most widely used in past research for the acceptance of technology. These two models and past literature were used to develop a conceptual framework. The variables in this research model were measured through questionnaires with five-point Likert scales and close-ended questions completed by the Software Engineering and Software development process-related employees in Sri Lanka. Data cleaning and demographic data analysis were conducted using IBM SPSS 21, and preliminary data analysis was performed using PLS-SEM (SmartPLS 4). The study found that generative AI apps are productive, effective, and capable of retaining users with a positive intention to use them in Ecommerce. High implementation costs negatively impact, and Low training and maintenance costs positively affect the intention of users to adopt generative AI apps. The factors such as innovativeness, perceived benefits, and level of attitudes, positively impact the overall adoption success. These findings are expected to guide Sri Lankan e-commerce platforms, aiding them in enhancing the successful adoption and seamless integration of generative AI apps. By aligning with the wisdom of TAM and its associated models, our research contributes to understanding the adoption success of Sri Lankan e-commerce platforms to embrace generative AI technologies.
Open Access
Antibacterial Activities of Lichen-associated Fungi in Mangrove Ecosystems in Sri Lanka as Potent Candidates for Novel Antibiotic Agents
(Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Happitiya, H.A.D.N.N; Nanayakkara, C. M.; Ariyawansa, K. G. S. U.; Ediriweera, S. S.; Wijayawardene, N. N.; Jayasinghe, R. P. P. K.; Dai, Don-Qin; Karunarathna, C.
Antimicrobial resistance is a global threat to humans, prompting an increasing interest in exploring and developing novel antimicrobial substances derived from diverse sources. Together with the emergence of new diseases the search for novel drug leads has intensified. Less explored microbial habitats have become prime targets in mining for novel antimicrobial molecules. Secondary metabolites synthesized by lichen-associated fungi are good potential targets in this regard. Hence, this study was carried out to explore the antibacterial potential of lichenassociated fungi in mangrove ecosystems by taking National Aquatic Resources Research and Development Agency (NARA) Regional Research Centre, Kalpitiya, Puttalam District, Sri Lanka as the study site. Lichen-associated fungi were isolated from collected lichens by plating out surface sterilized lichen thalli pieces. Antibacterial activities of the isolates were tested using two gram-positive bacteria: Staphylococcus aureus and Bacillus cereus and two gram-negative bacteria: Pseudomonas aeruginosa and Escherichia coli. In this study, 72 putative fungal isolates were primarily screened for their antibacterial activity using agar plug diffusion assay and ethyl acetate crude fungal extracts of nine fungal isolates with marked activity were secondarily screened using the well diffusion assay in triplicate. Isolate LIF 0803 identified as Trichosporon faecale showed the most outstanding antibacterial activities as 2.58 ± 0.29, 3.43 ± 0.05, 4.2 ± 0, 4.5 ± 0.14 cm of zone diameter at 100 mg/mL and 1.95 ± 0.59, 3.08 ± 0.13, 3.7 ± 0.12, 4.3 ± 0.19 cm of zone diameter at 50 mg/mL against P. aeruginosa, S. aureus, B. cereus, and E. coli. All nine fungal isolates showed promising antimicrobial activity against both gram positive and negative bacteria. Therefore, this study showed that lichen-associated fungi in mangrove ecosystems have potent antibacterial activities. Hence, bioassay guided fractionation of active compounds from lichen-associated fungi and structure elucidation are warranted.