Indeed, with the growing number of digital platforms and products being offered on a daily basis, consumers' decision making has undergone change, presenting new psychological and behavioural issues. In this study we will examine emotional market friction in the context of consumer distrust, information overload, emotional uncertainty and decision paralysis in a high-choice economies perspective. This research examines the impact of having too many choices and a lack of information in the market on consumer confidence and buying behavior, as well as concerns for brand credibility. The suggested method of the study is the quantitative research method, which will include a structured questionnaire and 200 adult consumers as sample population. Descriptive statistics and Pearson correlation as well as multiple linear regression identified as major analytical tools, based on python based statistical analysis. The postulated conceptual model defines the psychological mediator if it is not a dependent variable in the information overload/Emotional uncertainty/Decisions paralysis framework presented in the study as an independent variable and consumer distrust as another independent variable while dependent variable is decision paralysis. The analytical framework illustrated, indicates positive relationships between the dimensions and shows how the emotion of uncertainty may affect a person's hesitation to make a purchase. By incorporating both informational, psychological and trust-related aspects in one concept of emotional market friction, the study adds to the current consumer behaviour research. It also underscores the significance of transparency in communications, marketplace and information credibility and the streamlined customer-buying processes to enhance consumer confidence and empower healthy buying choices.

Thanks to the development and rise of social media and brand communities online, there is an emergence of revolutionary approaches to the way companies build their relationships with customers and values of their brands. Influence of social interactions, information sharing, brand advocacy and engagement of communities online has become an important aspect of the concept of understanding the value of social currency. The main aim of the study, named “Social Currency Metrics: Quantifying Influence Capital in Digital Brand Communities” is to analyze the aspects of social currency and develop a model for measuring influence capital in a digital environment. The study is carried out with the use of analytical approach, quantitative orientation and secondary research, the literature reviewed. There are six dimensions identified that are social engagement, information sharing, brand advocacy, social identity, trust and credibility, social influence. The suggested Social Currency Index (SCI) would integrate these dimensions along with the following combined indicators and measures, as demonstrated by the scale which is “normalized” to scale. As seen, the analysis demonstrates how beyond the conventional indicators of engagement (Likes, Comments, Shares) there is a need for an integrated approach that takes into account not only the behavioral engagement but also the relational value. The steps outlined above will assist organizations in evaluating the impact of the community, figuring out the valuable people to engage with, and developing a strategy of data-driven digital marketing. In the future, the research findings could be used to empirically validate the model with surveys and analytics of engagement.

Women workers in India's informal economy find themselves in a paradox while their participation in these sectors has been growing in a variety of areas, the employment relationship in which they are embedded often lacks strong ties to contributory social insurance mechanisms, stable employment, paid vacations, maternity protections, pensions, or formal grievance mechanisms. This article seeks to gauge the extent of the problem being solved by the current welfare regime, particularly in the case of Andhra Pradesh. Five analytical activities are conducted which are a benchmarking of state-level labour market with the national-level, employment status composition analysis, e-Shram registration-stock growth, women-centred employment-guarantee participation analysis and scheme-linkage penetration analysis. There is a dismal situation regarding casual workforce in the state among the rural women as well as urban women also do not have a comparable regular-salaried share as compared to the respective all-India female scenario, but they have greater share as casual labour. It should be noted that while the number of e-Shram registration in Andhra Pradesh has increased from approximately 80.03 lakh in August 2023 to 88.81 lakh in July 2026, it does not mean that the benefits are realised in the same proportion. The large employment footprint of women in MGNREGS and e-Shram linkages at the national level show that food security and health programmes have decent enrolment levels, whereas pension, maternity and housing programmes have limited enrolment levels and livelihood programmes are even less.

Sustainable innovation has emerged as a critical strategic approach for startups seeking competitive advantage and long-term growth. This study examines the impact of sustainable innovation on startup performance and investment attraction. Using primary data collected from 50 startup founders and managers, the study applies correlation and regression analysis to determine the relationship between sustainable innovation practices, startup performance and investor interest. The findings indicate that sustainable innovation significantly enhances financial performance, brand reputation, customer acquisition and investment attractiveness. The study concludes that sustainability-driven innovation is not only an ethical imperative but also a strategic growth driver for startups.

Artificial Intelligence (AI) has increasingly become an important technological capability for startups seeking to improve innovation, operational efficiency, decision-making and market competitiveness. However, the extent to which AI adoption contributes to startup performance may depend on the availability of appropriate institutional and government support. This study examines the relationship between AI adoption and startup performance and investigates the moderating role of government startup schemes. Primary data were collected from 100 startup entrepreneurs using a structured questionnaire. The study considers AI adoption as the independent variable, startup performance as the dependent variable and government startup schemes as the moderating variable. Descriptive statistics, correlation analysis, regression analysis and moderation analysis were proposed to examine the relationships among the variables. The findings indicate a positive relationship between AI adoption and startup performance, suggesting that startups using AI more extensively may achieve better innovation, productivity, decision-making and competitive outcomes. Government startup schemes are expected to strengthen this relationship by providing financial assistance, incubation, training, mentoring and technological support. The study highlights the importance of integrating AI capabilities with effective government support mechanisms to enhance startup growth and sustainability.