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Might the impact of the financial stress on exchange markets be asymmetric and exposed to regime changes? Departing from the existing literature, highlighting that the domestic and foreign financial stress in terms of money market have substantial effects on exchange market, this paper aims to investigate the impacts of the bond yield spreads of three emerging countries (Mexico, Russia, and South Korea) on their exchange market pressure indices using monthly observations for the period 2010:01–2019:12. Additionally, the paper analyses the impact of bond yield spread of the US on the exchange market pressure indices of the three mentioned emerging countries. The authors hypothesized whether the negative and positive changes in the bond yield spreads have varying effects on exchange market pressure indices.

To address the research question, we measure the bond yield spread of the selected countries by using the interest rate spread between 10-year and 3-month treasury bills. At the same time, the exchange market pressure index is proxied by the index introduced by Desai et al. (2017). We base the empirical analysis on nonlinear vector autoregression (VAR) models and an asymmetric quantile-based approach.

The results of the impulse response functions indicate that increases/decreases in the bond yield spreads of Mexico, Russia and South Korea raise/lower their exchange market pressure, and the effects of shocks in the bond yield spreads of the US also lead to depreciation/appreciation pressures in the local currencies of the emerging countries. The quantile connectedness analysis, which allows for the role of regimes, reveals that the weights of the domestic and foreign bond yield spread in explaining variations of exchange market pressure indices are higher when exchange market pressure indices are not in a normal regime, indicating the role of extreme development conditions in the exchange market. The quantile regression model underlines that an increase in the domestic bond yield spread leads to a rise in its exchange market pressure index during all exchange market pressure periods in Mexico, and the relevant effects are valid during periods of high exchange market pressure in Russia. Our results also show that Russia differs from Mexico and South Korea in terms of the factors influencing the demand for domestic currency, and we have demonstrated the role of domestic macroeconomic and financial conditions in surpassing the effects of US financial stress. More specifically, the impacts of the domestic and foreign financial stress vary across regimes and are asymmetric.

This study enriches the literature on factors affecting the exchange market pressure of emerging countries. The results have significant economic implications for policymakers, indicating that the exchange market pressure index may trigger a financial crisis and economic recession.

The authors argue that privacy is integral to the well-being of consumers and an essential component in not only corporate social responsibility (CSR) but what they term uniquely as social media responsibility (SMR). A conceptual framework is proposed that delineates the privacy issues companies should pay attention to in artificial intelligence (AI)-fueled social media environments.

The authors review literature on privacy issues in social media and AI in the academic and practitioner literatures. Based on the review, arguments focus on the need for an SMR framework, proposing responsible use of consumer data that is attentive to consumers' privacy concerns.

Implications from the framework are a path forward for social media companies to treat consumer data more fairly in this new environment. The framework has implications for companies to reduce potential harms to consumers and consider addressing their power and responsibility. With social media and AI transforming consumer behavior so profoundly, there are a variety of short- and long-term social implications.

Since AI tools are becoming integral to social media company activities, this research addresses the changing responsibilities social media companies have in securing consumers' data and enabling consumers the agency to protect their privacy effectively. The authors propose an SMR framework based on CSR research and AI tools employed by social media companies.

The purpose of this paper is to investigate the effectiveness of TikTok, a popular short-form video marketing platform, on purchase intention for cosmetics among Generation Z consumers in Singapore.

A quantitative survey was conducted using a convenience sampling method, with a sample of 136 responses. This study examined the influence of various characteristics of TikTok influencers, such as trustworthiness, expertise, attractiveness and entertainment content, on purchase intention.

This study found that trustworthiness and expertise of influencers, as well as entertainment content, had a significant positive impact on purchase intention. In addition, this study also found that influencer attractiveness and brand anthropomorphism were also significant factors influencing purchase intention. Thise study highlights the importance of the entertainment value, which is in line with the nature of TikTok as a medium.

This study contributes to the limited literature on the effectiveness of TikTok on purchase intention in Singapore, specifically in the cosmetics industry.

Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.