http://irgu.unigoa.ac.in/drs/handle/unigoa/1 2026-05-12T13:22:27Z 2026-05-12T13:22:27Z Fernandes, Ancy http://irgu.unigoa.ac.in/drs/handle/unigoa/7862 2026-05-05T05:44:22Z 2025-08-01T00:00:00Z

The impact of financial literacy on the behavioural biases of individual investors’ investment decisions Fernandes, Ancy

2025-08-01T00:00:00Z Naik, Srimati Yeshwant http://irgu.unigoa.ac.in/drs/handle/unigoa/7861 2026-05-05T05:46:15Z 2025-09-01T00:00:00Z

Impact of Human Resource Practices on Employees’ Performance Naik, Srimati Yeshwant

2025-09-01T00:00:00Z Sukthankar, S.V. Fernandes, R. Gaonkar, S.P. Shetye A. http://irgu.unigoa.ac.in/drs/handle/unigoa/7857 2026-04-30T06:10:42Z 2026-01-01T00:00:00Z

Unveiling the determinants of tourists' behavioural intention to adopt AI-powered chatbots for the hospitality and tourism industry: Revising the UTAUT2 model Sukthankar, S.V.; Fernandes, R.; Gaonkar, S.P.; Shetye A. Emerging technologies, such as artificial intelligence (AI), including chatbots, are now transforming the hospitality and tourism industry. Chatbot technology is an excellent tool for enhancing communication, boosting service delivery efficiency, reducing costs, and improving the tourist experience. Despite their potential benefits, the adoption of AI-powered chatbots in Goa's hospitality and tourism industry remains low, underscoring the need to identify the determinants influencing tourists' behavioural intention to adopt this technology and use behaviour. Therefore, this study examines the key determinants influencing tourists' behavioural intentions to adopt AI-powered chatbots in the hospitality and tourism industry. In addition, the study also examines the impact of tourists' behavioural intentions to adopt AI-powered chatbots on use behaviour. For this purpose, a revised UTAUT2 model is assessed by leveraging a quantitative research approach. Structured questionnaires were distributed to a total of 400 inbound and outbound tourists, of which 227 respondents who were aware of AI-powered chatbots were chosen as the respondents for this study based on purposive sampling. The collected data were analysed using Partial Least Squares-Structural Equation Modelling (PLS-SEM) in SmartPLS 4.0. The findings revealed that attitude, performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived enjoyment significantly influence tourists' behavioural intention to adopt AI-powered chatbots, whereas automation and habit do not significantly influence their behavioural intention to adopt AI-powered chatbots. This study has implications for tourism managers and policymakers in the tourism and hospitality industry, who can gain insights into the factors that can encourage tourists to adopt A-based facilities.

2026-01-01T00:00:00Z Gaonkar, S.P. Sukthankar, S.V. http://irgu.unigoa.ac.in/drs/handle/unigoa/7856 2026-04-30T06:08:39Z 2026-01-01T00:00:00Z

Assessing India's National Tourism Policy (2002-2025): An empirical analysis based on the Tourism-Led Growth Hypothesis (TLGH) approach Gaonkar, S.P.; Sukthankar, S.V. The National Tourism Policy (2002) has significantly strengthened India's tourism industry and economy by positioning tourism as a catalyst for economic development. Therefore, this study examines tourism's contribution to India's economic growth and its long-term relationship with key indicators, including gross domestic product (GDP), employment, foreign exchange earnings (FEE), and tourist growth, using the Tourism-Led Growth Hypothesis (TLGH) approach. Based on secondary data from 2002 to 2025, the research employs descriptive statistics, regression analysis, and Johansen cointegration tests to explore these relationships. Findings indicate that tourist growth significantly enhances GDP growth, employment and FEE. Moreover, a long-term equilibrium relationship exists between tourism and the analysed economic factors. These results align with the National Tourism Policy 2002's vision of leveraging tourism for national development. The study provides theoretical support for the TLGH by establishing a long-term relationship between tourism growth and key economic indicators, such as GDP, employment, and FEE. In practice, the findings guide policymakers in enhancing tourism strategies, investing in infrastructure, and promoting skill development to drive equitable job growth. The results also highlight opportunities for private investment and public-private partnerships, reinforcing tourism's role as a driver of sustainable economic growth in developing economies.

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