http://drr.vau.ac.lk/handle/123456789/980 Sun, 05 Apr 2026 18:46:38 GMT 2026-04-05T18:46:38Z http://drr.vau.ac.lk/handle/123456789/1407 Impact of GDP and Inflation’s on the Growth of Startups in the Northern Zone of India Maran, K.; Harikumar, P. Northern India has emerged as a dynamic hub for entrepreneurship, driven by a combination of geographical advantages, government initiatives, educational institutions, investor interest, and supportive infrastructure. This study examines the evolution and current status of the startup ecosystem in key states, including Rajasthan, Uttar Pradesh, Punjab, Delhi, and Haryana. Through empirical research and case studies, we analyze the critical factors influencing growth, the challenges faced by local businesses, and potential strategies for fostering a more robust entrepreneurial environment. By offering valuable insights into these dynamics, this research provides policymakers, investors, and stakeholders with actionable recommendations to strengthen support mechanisms and drive sustainable growth in the region’s start-up ecosystem. Fri, 01 Dec 2023 00:00:00 GMT http://drr.vau.ac.lk/handle/123456789/1407 2023-12-01T00:00:00Z http://drr.vau.ac.lk/handle/123456789/1406 Determinants of Green Entrepreneurship Intentions Among Management Undergraduates in Sri Lankan State Universities During COVID-19 Kumari, A.G.M.; Wijethunge, M.N.M Entrepreneurship has seen significant growth in recent years, with green entrepreneurship emerging as a key area of focus. While universities play a crucial role in fostering entrepreneurial knowledge and skills, research has highlighted gaps in university education regarding the necessary competencies for entrepreneurship. Additionally, universities contribute to business development through theoretical and practical academic training, adding value to their institutions. The COVID-19 pandemic drastically altered lives and businesses, causing 3.7 million deaths as of June 5, 2021. This study aims to identify the factors influencing green entrepreneurship intention (GEI) among undergraduates in Sri Lankan state universities during the pandemic. Specifically, it examines the impact of education development support, conceptual development support, and country support on GEI, mediated by entrepreneurial self-efficacy. The study utilizes primary data from a sample of 377 participants, with 397 responses collected. Data analysis was conducted using SPSS 20.0 and Smart PLS. Internal consistency was assessed through Cronbach’s alpha, followed by analyses of construct validity, discriminant validity, and model fit through average variance extracted (AVE). Findings indicate that entrepreneurial self-efficacy, education development support, institutional support, and country support positively influence GEI among undergraduates. While most existing studies focus on the relationship between COVID-19 and general entrepreneurial intentions, this research bridges a gap by addressing green entrepreneurship. The study provides valuable insights for policymakers, educators, and university administrators in fostering green entrepreneurship among students. Additionally, it serves as a foundation for future research in this field. Fri, 01 Dec 2023 00:00:00 GMT http://drr.vau.ac.lk/handle/123456789/1406 2023-12-01T00:00:00Z http://drr.vau.ac.lk/handle/123456789/1405 The Economics of Everyday Life: Unpacking Household Spending in Anuradhapura District, Sri Lanka Prasangika, K.D.S.; Herath, D.M.H.; Phillip, P.M.G. Household expenditure patterns are crucial to economic stability, shaping aggregate demand and overall welfare. This study investigates the key determinants of household consumption expenditure in the Anuradhapura District, Sri Lanka, using data from 100 purposively selected households. It examines the influence of demographic and socio-economic factors - such as income, family size, education level, household head’s age, gender, occupation, and geographic location - on spending behavior. Employing descriptive statistics, correlation analysis, and multiple linear regression models, the study identifies income as the most significant determinant of household expenditure. The regression results indicate that a one-unit increase in household income leads to a 5.61-unit rise in expenditure. Other factors, including family size, education level, geographic location, and land ownership, also exhibit significant positive relationships with household spending. Conversely, age has no significant effect, while gender influences specific spending patterns, with male-headed households tending to allocate more toward discretionary expenses. The model accounts for approximately 62.5% of the variation in household expenditure, reinforcing the strong impact of these determinants. Additionally, urban households exhibit higher spending levels than their rural counterparts, reflecting differences in service accessibility and cost structures. The findings underscore the importance of policy interventions to enhance household economic conditions. Key recommendations include increasing disposable income, improving educational access, fostering employment opportunities, and promoting agricultural productivity. Addressing land ownership disparities is also essential to reducing income inequality and strengthening economic resilience. This study offers valuable insights for policymakers seeking to improve household financial stability and drive sustainable economic growth in Sri Lanka. Fri, 01 Dec 2023 00:00:00 GMT http://drr.vau.ac.lk/handle/123456789/1405 2023-12-01T00:00:00Z http://drr.vau.ac.lk/handle/123456789/1404 Enhancing Customer Retention with Data-Driven Strategies: Machine Learning Insights from Sri Lanka’s Telecom Industry Himali, L.P.; Abeysekara, J.D. Customer retention is a crucial factor in ensuring a firm's long-term sustainability, particularly in highly competitive industries like telecommunications. A stable customer base mitigates risks associated with demand fluctuations and enhances business resilience. Research consistently shows that retaining existing customers is more cost-effective than acquiring new ones, a principle widely recognized across industries. This study explores customer retention prediction among university students in Sri Lanka’s telecommunications sector using machine learning regression algorithms. Various predictive models were evaluated to determine the most effective approach for forecasting retention, with the Decision Tree Regressor emerging as the top-performing model. Key factors influencing customer loyalty were also analysed, revealing trust as the most significant determinant, followed by perceived pricing and service provider choice. These insights offer telecom providers a data-driven foundation for improving retention strategies, particularly for university students. By leveraging predictive analytics, businesses can anticipate customer behaviour more effectively and implement targeted interventions to foster loyalty, ensuring sustainable growth in a competitive market. Fri, 01 Dec 2023 00:00:00 GMT http://drr.vau.ac.lk/handle/123456789/1404 2023-12-01T00:00:00Z