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This study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during COVID-19. The authors empirically test the response of the capital market participants for B2B firms, resulting in herding behavior.

Using the event study approach based on the market model, the authors test the impact of supply chain disruptions and resultant herding behavior across six sectors and among different B2B firms. The authors used cumulative average abnormal returns (CAAR) and cross-sectional absolute deviation (CSAD) to examine the significance of herding behavior across sectors.

The event study results show a significant effect of COVID-19 due to supply chain disruptions across specific sectors. Herding was detected across the automotive and pharmaceutical sectors. The authors also provide evidence of sector-specific disruption impact and herding behavior based on the black swan event and social learning theory.

The authors examine the impact of COVID-19 on herding in the stock market of an emerging economy due to extreme market conditions. This is one of the first studies analyzing lockdown-driven supply chain disruptions and subsequent sector-specific herding behavior. Investors and regulators should take sector-specific responses that are sophisticated during extreme market conditions, such as a pandemic, and update their responses as the situation unfolds.

Financial and nonfinancial disclosures (sustainable accounting) are crucial in the annual financial reports of many firms. This study aims to explore the dynamic relationship between sustainability disclosure quality (SDQ) and financial performance (FP) within mandatory disclosure frameworks. SDQ is evaluated across six dimensions, encompassing both the quality and quantity of disclosures, aiming to understand their reciprocal influence.

Using the generalized method of moments (GMM), this research analyzes data from 2013 to 2019, focusing on 99 listed Indian firms within the S&P Bombay stock exchange (BSE) 500 index. The study uses rigorous measurement criteria to assess SDQ and uses statistical methods to unveil the causal link between SDQ and FP.

The results show a positive causal connection between SDQ and FP, where organizations with good FP make relatively higher disclosures across FP proxies than their counterparts. Additionally, the study investigates the impact of research and development (R&D) expenditure and dividend payments (DIVD) on SDQ. Notably, lower R&D spending is associated with higher quality SDs, and companies with superior SDQ exhibit increased DIVD.

The findings advocate for strengthened regulatory compliance, incentivized sustainable practices and heightened reporting standards for a transparent business environment and achieving the relevant United Nations Sustainable Development Goals.

This research contributes original insights by uncovering the intricate relationship between SDQ and FP, shedding light on the impact of R&D expenditure and DIVD on SDQ. These findings contribute to a nuanced understanding of the interplay between FP and sustainability reporting within the context of mandatory disclosure frameworks.

The purpose of this study is to conduct a thematic content analysis of facial emotion recognition (FER) research within the context of the hospitality and tourism industry. Through this analysis, the study aims to identify key themes, trends and implications of the utilization of FER technology in enhancing customer emotions and experiences within hospitality and tourism settings.

This is qualitative research that utilizes thematic content analysis. The research data were obtained from the Scopus database. A total of 45 articles (titles, abstracts and keywords) were coded into MAXQDA and VOSWiever programs for data analyses and mapping.

Based on the analyses, the predominant term used in titles was emotion, indicating its centrality in the research domain. Moreover, the most prevalent concepts in this field were emotion and experience. Notably, facial emotion recognition emerged as the most frequently utilized term within this context. Within the hospitality and tourism industry, FER was primarily employed within the travel sub-branch. Finally, the research culminated in the visualization of the theoretical framework and conceptual background, offering a comprehensive overview of the field.

There is a growing demand for using FER technology specifically within the hospitality and tourism industry context; therefore, growing scientific research has been conducted on this topic recently. By conducting a thematic content analysis, this study uncovered novel insights into the utilization of this technology to enhance customer emotions and experiences, thereby contributing to a deeper understanding of its potential implications and applications within the hospitality and tourism industry.