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In an effort to position higher education institutions to survive in this fiercely competitive environment, the paper aims to identify the direct and indirect relationships between higher education institutional positioning and exogenous factors (student engagement, employability, technology adaptation, teaching quality, and moral values).

A cross-sectional data was collected from 1,015 students studying in the pre-final year of graduation or post-graduate course/program from various educational institutions that were shortlisted based on the Indian NAAC and NIRF rankings. Thereafter, robust assessment criteria of PLS-SEM were used for model assessment and computation of results.

The findings revealed that to develop the greatest platform for upcoming young talent, higher educational institutional positioning ought to be addressed as a priority, which in turn will result in better living standards for upcoming generations.

Framing strategies for urban students can never match those living in rural areas, as they are deprived of money due to their level of upbringing from childhood, which creates a high difference in the psychological mindset of students while choosing a career path.

The higher positioning of educational institutions clearly reflects the authentic learning environment, with professionalism leading to better student engagement with best industry practice.

Research novelty is highlighted as a more focused and streamlined approach to students’ career development and institution branding by reanalyzing and grouping various concepts of institutional positioning into a single model.

The purpose of the study was to explore the purchase intention of online consumers by proposing and validating a model supported by exhaustive reviews from top-rated journals, where digital technology, consumer privacy, consumer engagement and online advertising were the extracted constructs influencing consumer learning on digital platforms and finally evaluating the purchase intention of online consumers.

A questionnaire representing these constructs was then sent to the 470 respondents on LinkedIn, and then designations like business heads, managers and faculty from educational institutions were selected using a stratified sampling technique and, finally, PLS-SEM robust computation standards aided in research model assessment and validation.

Results predicted that the variance explained by individual independent constructs defines consumer privacy as a priority for companies, followed by online advertising, consumer engagement and digital technology while measuring the final purchase intent for online consumption. Also, with dynamism in consumer sentiments and a rapidly changing technological environment, the consumer’s digital behaviour may differ in the coming future in relation to their online purchase intent.

Current research anticipates that the final online purchase intent of consumers has been vividly covered by our independent constructs, but an unexplained R² of 31% still promotes prospects related to the existing research. Furthermore, India has a huge rural population that, with a lack of money, has a complex behavioural mindset due to religious issues.

It is important to note that in a real-time market, a better understanding of the duality of persuasive and smart technology and the evaluation of the performance of social media helps in deciding the final online consumer intent.

The need for digital transformation has become an essential necessity for companies while managing the expectations and needs of the fastest-growing online consumers.

This research paper investigates the efficacy of artificial intelligence (AI)-powered Chatbot's in enhancing user engagement and satisfaction within various contexts. As organizations increasingly deploy Chatbot's to interact with users, understanding their impact on user experience becomes imperative. The study employs Smart partial least squares (PLS) as a robust analytical tool to assess the multifaceted dimensions of AI-powered Chatbot interactions. The research focuses on user engagement, examining the Chatbot's ability to captivate users and sustain their interest throughout interactions. Additionally, user satisfaction is explored to gauge the overall effectiveness of the Chatbot in meeting user expectations. The proposed model integrates key variables, including the Chatbot's responsiveness, conversational abilities, and problem-solving capabilities, to comprehensively evaluate its impact. The methodology involves collecting data from diverse user groups to ensure a representative sample. Smart PLS, a powerful structural equation modeling technique, is employed to analyze the relationships between the identified variables. The findings are expected to contribute valuable insights into the factors influencing user engagement and satisfaction in the context of AI-powered Chatbots. The implications of this research extend beyond theoretical understanding, offering practical recommendations for organizations seeking to optimize their Chatbot implementations. By leveraging Smart PLS, this study aims to provide a nuanced understanding of the intricate dynamics involved in AI-powered Chatbot interactions, ultimately guiding the development and deployment of more effective and user-centric conversational agents.