This research evaluates the extent to which firms’ “green” bond disclosures create and convey a meaningful representation of their Environmental, Social, and Governance (“ESG”) commitments. Additionally, this research explores how investors distinguish between disclosures that represent genuine commitment to sustainability and those that may be indicative of “greenwashing,” and how such distinctions impact their assessment of an issuer’s credibility as well as the issuer’s performance subsequent to the issuance of a “green” bond. The methodology employed in this research employs a convergent mixed-methods approach that combines quantitative methods (Natural Language Processing (“NLP”), financial modeling, etc.) with qualitative methodologies (case studies, interviews). The NLP methodology employed in this research includes sentiment analysis, topic modeling, and ambiguity measurement in order to determine the tone, thematic content, and linguistic clarity of the disclosure texts. Subsequently, the results of the NLP methodologies are correlated with firm level outcomes using cross validated partial least squares regression (“PLS-R”), event study methodologies, and one way ANOVA to test for temporal and industrial variability. Finally, the results of the computational and financial methodologies are supplemented by qualitative case studies and interviews to provide context for the patterns identified in the computational and financial methodologies. In summary, the results of this research demonstrate that firms that communicate in a clear, balanced, and verifiable manner experience better market reaction and more favorable accounting results subsequent to the issuance of a “green” bond than do firms whose communications are vague, overly optimistic, or lacking in consistency. Conversely, the findings suggest that investors have become increasingly sensitive to potential “greenwashing” and therefore are less likely to respond favorably to communications characterized by the aforementioned characteristics.

Purpose: This study aims to investigate how economic policy uncertainty (EPU) influences the inventory levels of 6,150 companies in ten emerging economies, specifically Chile, Brazil, China, Colombia, Hong Kong, India, Mexico, Pakistan, Russia and Singapore.Design/methodology/approach: The data used in this study is of quarterly frequency from 2004Q1 to 2020Q4 collected from the COMPUSTAT global database. To estimate the coefficients, this study has employed a two-step GMM model.Findings: We have discovered new evidence indicating a curvilinear relationship, specifically an inverted U-shaped pattern, between economic policy uncertainty (EPU) and corporate inventory holdings. These findings remain resilient when subjected to various rigorous tests. Furthermore, we observe that firms with lower financial constraints are capable of increasing their inventory holdings to a significant extent in the presence of high economic policy uncertainty (EPU) than firms facing higher financial constraints.Originality/value: Our research adds to the expanding body of literature that explores the impact of economic policy uncertainty on firm-level decision-making. We provide fresh evidence regarding the correlation between economic policy uncertainty and inventory holding, specifically focusing on emerging economies worldwide. Furthermore, we make a valuable contribution to the existing literature by examining whether the association between economic policy uncertainty and inventory holdings is influenced by the extent of financial constraints.

This study explores how AI augments human productivity, creativity, and decision-making across sectors, positioning it as a tool to enhance human potential. Using a qualitative approach with case studies from healthcare, business, and creative industries—such as IBM Watson—it illustrates AI's practical applications. Central to the study are ethical concerns like data privacy, algorithmic bias, and fairness. Findings suggest that AI significantly enhances human capabilities while also raising important ethical issues. However, limitations exist in measuring AI's long-term impact on human agency and creativity due to rapid technological evolution, and the study does not address regional disparities in AI access. Despite these challenges, the research offers a unique perspective on AI-human collaboration, highlighting AI as a partner in innovation rather than a replacement for human intelligence, and calls for deeper regulatory focus to ensure fairness, privacy, and equitable use.

In the Industry 4.0 era, organizations face challenges in sustaining employee engagement while enhancing productivity through data-driven strategies. This study explores the link between employee engagement and performance, emphasizing the mediating role of HR analytical strategies. A quantitative approach was used, with data from 125 professionals across sectors. Regression, correlation, and collinearity diagnostics revealed that physical, emotional, and cognitive engagement significantly influence task and contextual performance. HR analytics emerged as a key mediator, strengthening this relationship. Furthermore, organizational factors like compatibility, relative advantage, and top management support positively impacted engagement-performance outcomes. The study underscores how HR analytics can transform engagement into a strategic lever for sustainable performance. Practical implications, limitations, and future research avenues are discussed.

This paper examines the connection between working capital management (WCM) and profitability in terms of innovation within Indian non-financial firms. Regression results indicate that there is a significant negative relationship between working capital management and profitability and that inefficient management of current assets and liabilities cost a lot. Since innovation resource-constrained firms exert pressure on cash flow and profitability, R&D spending augment this negative correlation. The analysis shows the relationship among the return on assets, the non-current assets, operating cash flow, sales growth and the fixed assets concerning the operations of Indian enterprises. The financial analysis suggests that, an increase in the sales, business size, and capital sufficiency increase the profitability, and high leverage, and tangible assets reduce profitability implying that excessive debt and rigid business structures are hazardous to banks. The results have shown that there has been an urgent research agenda in improving working capital management in the context of financial performance and in the development of perspective regarding innovation as a driver of growth and risk. The study could be used by managers and policymakers to balance the liquidity management and creation of new initiatives to achieve prolonged profitability.