Browsing by Author "Siriwardana, D"

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Fueling the future: unveiling what drives gig worker motivation and engagement in Sri Lanka’s corporate landscape
(Emerald Publishing, 2025-03-25) Perera, L; Jayasena, C; Hettiarachchi, N; Siriwardana, D; Wisenthige, K; Wickramaarachchi, C
Purpose: The gig economy has rapidly grown due to economic trends supporting flexible work and digital platforms, leading to increased demand for corporate gig workers. Although numerous studies have explored various aspects of the gig economy, research on the motivational and engagement factors of gig workers remains relatively rare. This study aims to investigate the factors that influence corporate gig workers’ motivation and engagement in the geographical context of Sri Lanka. Specifically, job autonomy, remuneration, social connection and technology and investigated here. Design/methodology/approach: A quantitative study, employing a deductive research approach, was conducted with data gathered through a survey designed using a five-point Likert scale questionnaire. Respondents were conveniently selected from Sri Lankan corporate gig workers. A total of 397 responses were obtained through a snowball sampling technique. The collected data were analyzed using partial least squares structural equation modeling, providing a robust framework for evaluating the hypothesized relationships. Findings: The findings revealed that job autonomy, remuneration, social connection and technology significantly influence corporate gig worker motivation, whereas motivation significantly influences the engagement of corporate gig workers in Sri Lanka. Research limitations/implications: This study faced common limitations. Due to challenges in identifying the framework for the population, a snowball sampling technique was employed. One key limitation is the study’s narrow focus on motivation factors within the Sri Lankan context, which may affect the generalizability of the findings. Additionally, the geographic focus and uneven sample distribution could limit the broader applicability of the conclusions. Future research should adopt a cross-cultural approach to explore the influence of social commerce adoption, enhancing the generalizability of the results. Practical implications: A comprehensive understanding of the factors that influence the corporate gig worker motivation and engagement is provided, facilitating, the decision-makers to gain insight to enhance worker motivation and engagement by adapting strategies. This can lead to higher productivity and job satisfaction among gig workers. Originality/value: Examination of the motivational and engagement factors specific to corporate gig workers in Sri Lanka, a context that has received limited attention in previous research. Also, it contributes to the existing literature by providing a deeper understanding of the gig economy and gig work, particularly in a non-Western setting.
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Project HyperAdapt: An Agent-Based Intelligent Sandbox Design to Deceive and Analyze Sophisticated Malware
(Institute of Electrical and Electronics Engineers Inc., 2025) Perera, S; Dias, S; Vithanage, V; Dilhara, A; Senarathne, A; Siriwardana, D; Liyanapathirana, C
Malware increasingly employs sophisticated evasion techniques to bypass sandbox-based analysis, rendering traditional detection methods ineffective. This research presents Project HyperAdapt: Agent-Based Intelligent Sandbox, a framework that integrates both offensive and defensive machine learning models to enhance malware detection, deception, and behavioral analysis. The offensive RL model generates evasive malware samples, challenging the sandbox, while the defensive models including hybrid evasion detection, GAN-based behavior simulation, and a dynamically adapting RL agent work collectively to improve sandbox resilience. By continuously learning from evasive malware behavior, the defensive RL agent adapts in real-time, strengthening detection capabilities. Experimental results demonstrate that this approach enhances sandbox effectiveness, ensuring long-term adaptability against evolving malware threats.