Academic stress has become a concern among secondary students and higher education students in today’s educational environment. Various study habits and patterns like study session length, break frequency, structured study techniques, multitasking behaviours and mainly social media usage during studying time affects the overall well-being as there is massive usage of social media among students. This study investigates the relationship between the study habits and student stress among secondary school and higher education students in India and USA. Study is descriptive in nature, primary data is collected using a structured questionnaire, data were collected from respondents aged 14–25 years from India and USA. Findings reveal significant cross-country differences in study duration, structured scheduling, and stress levels. Multitasking and poor break management significantly increase stress and burnout risk. The moderating role of country highlights cultural variations in stress pathways.

This study examines cryptocurrency market behaviour during crash conditions and evaluates the extent to which structured risk management techniques and behavioural biases affected financial losses. The study employs a quantitative design that is bolstered by qualitative observations. A structured questionnaire was used to gather primary data from (111) retail cryptocurrency traders. In order to obtain a deeper understanding of behaviour, (12) active cryptocurrency market traders were interviewed for qualitative analysis. The relationships between behavioural biases, social media influence, risk management techniques, and financial losses were examined using one-way ANOVA and Pearson correlation analysis. The results show that emotional biases like panic selling, overconfidence, FOMO, and frequent short-term trading greatly increase financial losses. A majority of respondent groups’ trading decisions were found to be influenced by social media sentiment. Most significantly, correlation analysis confirms that disciplined strategies significantly lower the magnitude of losses during times of high volatility by showing a strong negative relationship between structured risk management practices and financial losses.

This study examines how artificial intelligence is transubstantiating fiscal requests and provides perceptivity for investors, fiscal institutions, controllers. It analyzes both the practical & structural goods of AI on request dynamics, contributing to the literature & supporting the development of nonsupervisory fabrics that encourage invention while icing investor protection & request security. The findings show that AI significantly improves decision- making speed, soothsaying delicacy, & functional effectiveness. AI plays a critical part in fraud discovery, threat identification, non-supervisory compliance, maintaining overall fiscal stability. still, the study highlights that algorithmic, high-frequency trading, while enhancing request liquidity, can increase short- term volatility during ages of request stress. It concludes that sustainable AI relinquishment requires strong governance, translucency, effective regulation to align invention with long- term fiscal stability

Startups have an important role in promoting innovations, employment and economic development. However, they have to overcome various challenges during their early growth stage. Thus, this study aims to explore the major key challenges faced by startups during their initial phase. The major challenges may include funding issues, competition in the market, managerial inexperience, regulatory hurdles and technological hurdles. This research is based on descriptive research and uses primary and secondary data. In this paper the findings of the study show that funding issues, lack of awareness among customers and operational inefficiency are the major hurdles for the growth of startups. Thus, this paper concludes that with good financial planning, guidance, support of the government and effective business planning, startups can overcome their challenges during their early growth phase.

In years startups have become a popular career choice for students. However, many students are still hesitant to start their businesses. One of the reasons for this is the fear of failure and the perceived risk of starting a business. Student’s views on startup failure are influenced by factors, including financial risk, lack of knowledge and experience social and family pressure and concerns about future career stability. These views play a role in shaping their entrepreneurial intentions and decision-making. This study aims to examine the students’ perceptions of startup failure and risk factors and how these perceptions affect their attitudes towards entrepreneurship. By identifying whether fear of failure and perceived risk act as barriers or motivators the research seeks to understand the mindset among students. The findings are expected to provide insights for academic institutions and policymakers in developing effective entrepreneurship education and support systems.

This study examines how emotional patterns and cognitive processes influence learning efficiency among Generation Z students and teachers within modern educational institutions. It proposes a Structured Equation framework integrating core emotional-cognitive learning constructs to understand their collective impact on academic outcomes. A descriptive and applied research design with a mixed-method approach was adopted using purposive sampling techniques. Data were collected from 130 management students and 20 teachers in the KDMC region through structured questionnaires and interviews. Statistical tools including correlation, paired t-test and Single ANOVA were applied for analysis. The study further introduces the conceptual ECF-WR model and proposes an AI-based application to enhance emotional regulation, engagement and overall academic performance.

As the global financial landscape is transitioning from a traditional ecosystem to digitally based transactions rapidly, AI models for scam detection have turned into an indispensable pillar for proactive defence. The opaqueness of automated decision-making has however caused the Human Trust conundrum that cannot be addressed by algorithmic models alone. To find out the levels of trust among the users, this paper involves comparison of consumer trust in human-in-the-loop systems versus that of completely independent systems. The mean trust score of AI systems of the survey was calculated using inferential statistics and a 5-point Likert scale, the measure of trust, and provided a mean of 3.04 and a standard deviation of 0.30, compared to the average score of a human-led systems at 2.97 and a standard deviation of 0.32. The t-test of paired samples gave a value of 0.5984, that is, there is no considerable difference between the levels of trust between the two interventions. Moreover, ANOVA tests confirmed that trust level do not differ based on age group. Chi-square provided a p-value of 0.0529, that is, there is a strong marginal tendency preferring age- based preference, although 53.45% of the participants chose instantaneous AI blocking as opposed to human verification (46.55%). The result of these findings is a situation where trust is balanced, where users perceive that the respective systems are equally reliable, despite the possibility that functional preferences for speed may be directed towards a gradual shift to automated solutions.