P. S. Venkateswaran

Tourism ethnocentrism has increasingly attracted scholarly attention in tourism and consumer behaviour research; however, existing studies remain theoretically fragmented and lack an integrated explanatory framework capable of connecting its antecedents, mechanisms, and behavioural outcomes. This conceptual study develops a comprehensive framework explaining how tourism ethnocentrism shapes destination choice, destination loyalty, and destination advocacy. The study adopts a systematic conceptual review approach by synthesizing peer-reviewed studies indexed in Scopus, Web of Science, and Google Scholar between 2010 and 2024. The framework integrates three major theoretical perspectives: consumer ethnocentrism theory, social identity theory (SIT), and the theory of planned behaviour (TPB).

This study examines the impact of artificial intelligence technologies in digital marketing storytelling to enhance brand engagement through personalized, dynamic content creation strategies. A descriptive research design was used along with purposive sampling to select 376 respondents from digital marketing consumers. The study analysed AI-driven storytelling frameworks to understand their application in creating adaptive marketing strategies across various digital media. AI-driven storytelling improves brand engagement by delivering personalized methods that adapt to individual consumer preferences. The technology enables real-time content optimization, resulting in higher engagement rates, improved customer retention, and enhanced brand loyalty. The effectiveness of AI-driven storytelling depends heavily on data quality, algorithm sophistication, and the balance between automation and human creativity.

This study examines the relationships among cognitive load, trust, and design characteristics in human–AI educational interactions within higher education settings. It investigates how the intensity of AI tool usage affects cognitive load, the moderating role of user trust, and the influence of design features on user experience. Data were collected from 354 higher education participants, who were assessed for their frequency of AI tool use, cognitive load, trust in AI systems, and perceptions of design features. Participants were grouped as high AI users (n = 137) and low AI users (n = 217). High AI users reported significantly greater usage intensity than low AI users, with a large effect size observed. Trust emerged as a key moderating variable. Among participants with lower trust levels, increased AI use was associated with higher cognitive load. However, no such correlation was found among those with high trust in the technology.

Consumer experience has become a new competitive battleground with the shift of marketing to digital channels. This research examines consumer experience in the age of digitalization through the lenses of personalization, interactivity and intelligent data driven strategies. Based on a survey with 234 respondents, PROCESS regression analysis was applied to test twelve hypotheses on direct, indirect, and mediating effects. The results has shown that personalization increases relevance and satisfaction, interactivity increases engagement, and intelligent data-driven strategies enables timely and adaptive experience delivery. Further, the mediation analysis indicated that perceived value and trust in technology are important mediation pathways connecting digital strategies to consumer experience. This research addresses this gap by providing empirical evidence on the synergistic role of these three dimensions, which is a recently suggested approach to be consumer-driven and data-driven towards digital marketing.

This research investigates the emerging trends and transformative potential of Artificial Intelligence (AI) in education, examining its impact on learning outcomes, teaching effectiveness, and institutional readiness. Through comprehensive analysis, the study reveals that AI-powered teacher support systems and curriculum integration emerge as primary drivers of enhanced student learning outcomes, while ethical considerations and privacy concerns significantly influence teaching effectiveness. The findings indicate that student engagement with AI-based tools is crucial for institutional readiness, though current approaches to teacher support systems may require reconceptualization. The research highlights the need for a balanced approach to AI integration, emphasizing the importance of human-centric implementation while maintaining technological innovation. The study provides evidence-based recommendations for educational institutions, policymakers, and practitioners, addressing aspects of infrastructure development, professional training, and ethical governance.

This paper examines the determinants of economic consequences in the gig economy through regression analyses of five key factors: platform design, regulatory environment, worker characteristics, market conditions, and technological infrastructure. Using convenience sampling, regression model explains 75.1% of the variance in economic outcomes, revealing a significant hierarchy of influence among these factors. Worker characteristics and market conditions emerged as the most potent predictors, challenging narratives that prioritize technological or regulatory factors. The findings suggest potential synergistic interactions among variables, where improvements in one area may enhance the effectiveness of others. The moderate impact of regulatory environment indicates that regulatory approaches should support worker capabilities and adapt to market realities rather than imposing universal solutions. This paper provides strategic directions for platform companies, policymakers, educational institutions, and workers to optimize economic outcomes in the evolving gig economy landscape.

This study examines AI-powered predictive analytics in digital marketing, analyzing customer behavior and personalization impacts. Through a quantitative analysis of 207 respondents, the research reveals a gap between AI tool adoption (30% regular usage) and their demonstrated effectiveness, with recommendation systems showing high consumer relevance despite low conversion rates. Statistical analysis demonstrates strong correlations between customer experience and digital shopping behavior, with interconnections between personalized content quality and prediction accuracy. The study identifies distinct clusters in user behavior patterns, highlighting the relationship between AI trust indicators and traditional shopping patterns. These findings provide valuable insights for organizations seeking to optimize their digital marketing strategies through AI implementation while addressing the current challenges in adoption and effectiveness.

The study aims to explore the relationship between the big five personality traits—extraversion, agreeableness, conscientiousness, openness, and neuroticism—and levels of smartphone addiction among sales executives. Data were collected from 286 sales executives using a structured questionnaire, focusing on personality traits and smartphone addiction levels. Pearson correlation and multiple regression analyses revealed that agreeableness is the strongest predictor of smartphone addiction, suggesting that individuals who are more cooperative and empathetic are more likely to develop addictive smartphone behaviours. Conscientiousness showed a marginally significant negative association, indicating that individuals with higher self-discipline and responsibility are less prone to smartphone addiction. Extraversion, openness, and neuroticism did not show statistically significant relationships. These insights are relevant in the sales sector, highlighting the importance of integrating psychological factors into workplace policies to address smartphone addiction effectively.

Dynamic consumer behaviours, evolving market trends, and a data-rich environment mark the contemporary marketing landscape. Leveraging intelligent systems allows marketers to not only analyze large volumes of data efficiently but also to uncover patterns and trends that would be challenging through conventional methods. This introduction sets the stage for an exploration into the challenges and benefits that marketers encounter as they strive to optimize intelligent systems, highlighting the need for a nuanced understanding of technological advancements, ethical considerations, and strategic adaptations. As elucidated in contemporary discourse, adapting intelligent systems to rapidly changing preferences and industry dynamics requires a proactive and agile approach. Addressing these challenges demands a holistic strategy that combines technological innovation with ethical considerations, regulatory compliance, and a keen understanding of the evolving marketing landscape.

Predictive analytics, leveraging machine learning and big data, has emerged as a transformative tool in business and consumer behavior forecasting. This study explores the application of predictive models to anticipate market trends, optimize decision-making, and enhance customer engagement. Integrating machine learning algorithms with big data enables businesses to analyze vast amounts of structured and unstructured data, uncovering patterns that drive actionable insights. Results highlight the synergistic relationships among capabilities, such as real-time data availability and data source utilization, while emphasizing the foundational role of robust technical infrastructure. Managerial implications underscore the need for a balanced development of capabilities and strategic integration of predictive systems into decision-making processes. This study contributes to the understanding of how organizations can effectively deploy predictive analytics to anticipate trends, optimize decision-making, and achieve competitive advantage.