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Overconfidence Bias in Equity Market: a comparative analysis of market uncertainty and tranquil periods in asian economies
Get PDFThe current study aims to examine the phenomenon of overconfidence bias in Asian stock markets, encompassing both market stress and tranquil periods. Utilizing daily data spanning from January 1, 2013, to April 30, 2023, the study employed bivariate vector autoregression (VAR) models and impulse response functions. The findings of the VAR model yield several significant conclusions. First, within our sample period, a notable and substantial correlation between market return and volume seems more prominent in advanced and rapidly expanding emerging markets such as China. Further, investors are more confident in the advanced market during the turbulence caused by the Covid-19 lockdown. The findings indicate that throughout the Russia-Ukraine crisis, Chinese and Thai investors exhibited assertive, overconfident behaviour. The implications of overconfidence bias, which ranges from investor protection to economic stability, demonstrate the significance of understanding and addressing behavioural biases in financial decision-making. This study is one of the early attempts to examine the empirical evidence of overconfidence bias at a crosscountry level in the aftermath of the recent global crisis
Consumer Adoption of Frozen Food Products in Uttarakhand, India during COVID-19 Pandemic
Get PDFSafety and quality are of extreme importance along with abundance of time availability owing to work-from-home scenario. Consumers are now ready to experiment with the new types of food products. Authors have attempted to determine percentage of consumers with respect to adoption of frozen food products in this work. The study analyses socio-demographic characteristics and understand perception with respect to adoption of frozen food products. The findings of study indicate that of the respondents, 31% were categorised as early adopters, 23% as late adopters and 46% as non-adopters of frozen food products. Early adopters perceived frozen food products to be value for money, had trust on quality, safety and brand, and also found it tasty. The overall analysis leads to a better understanding of consumer adoption towards frozen food with special reference to quality and safety
Spatiotemporal Dynamics and Attribution Analysis of Multitemporal Runoff Patterns for Water Resources and Climate Security in Huaihe River Basin
No full textUnderstanding the spatiotemporal evolution and attribution of streamflow is critical for effective water resource management and climate security. This study conducts a comprehensive analysis of runoff dynamics across multiple temporal scales in the Huaihe River basin, which is a major hydrological region in China. By examining annual, interannual, and interdecadal trends, the research delineates tendencies, abrupt changes, and periodicity in runoff patterns. The double mass curve (DMC) method is applied to quantify runoff variations, incorporating influences of vegetation dynamics, anthropogenic water withdrawals, and climate change. The results reveal a highly uneven annual runoff distribution in the Huaihe River basin. Most hydrometric stations show statistically insignificant declining trends in annual, flood-season, and non-flood- season runoff, except for the Hengpaitou section. Significant mutation points are detected around 1990 and 2000 across all runoff series, along with periodic fluctuations featuring dominant about 30-year cycles. Attribution analysis indicates that human activities account for over 80% of the observed streamflow variations. In the upper basin, indirect anthropogenic factors (e.g., land use changes and vegetation dynamics) dominate, whereas direct human interventions have a stronger influence in the middle reaches. These findings enhance understanding of the interactions between natural processes and anthropogenic impacts on hydrology, providing a scientific basis for sustainable water resource management under climate change in the Huaihe River basin and similar regions
Stop the Hate, Spread the Hope: an ensemble model for hope speech detection in english and dravidian languages
Get PDFThe rise of social media has led to vast amounts of user-generated content, with emotions ranging from joy to anger. Negative comments often target individuals, communities, or brands, prompting successful efforts to detect harmful speech such as hate speech, cyberbullying, and abuse. Recently, another type of speech referred to as ‘Hope Speech’ has gained attention from the research community. Hope speech consists of positive affirmations or words of reassurance, encouragement, consolation or motivation offered to the affected individual/ community during the lean periods of life. However, there has been relatively less research focused on the detection of hope speech, more particularly in low-resource languages. This paper, therefore, attempts to develop an ensemble model for detecting hope speech in some low-resource languages. Data for four different languages, namely English, Kannada, Malayalam and Tamil are obtained and experimented with different deep learning-based models. An ensemble model is proposed to combine the advantages of the better performing models. Experimental results demonstrate the superior performance of the proposed Ensemble (LSTM, mBERT, XLM-RoBERTa) model compared to individual models based on data from all four languages (weighted average F1-score for English is 0.93; for Kannada is 0.74; for Malayalam is 0.82; and for Tamil is 0.60). Thus, the proposed ensemble model proves to be a suitable approach for hope speech detection in the given low resource languages
Integrating Chatbots Into Metaverse-Based Classroom
No full textThe integration of chatbots into Metaverse-based classrooms offers new opportunities to enhance learning experiences through AI-driven, interactive, and personalized interactions. As educational institutions increasingly explore virtual environments, chatbots can provide real-time assistance, facilitate collaboration, and support adaptive learning in immersive settings. This paper explores the synergy between chatbots and the Metaverse, highlighting their potential to revolutionize education by improving student engagement, accessibility, and inclusivity. However, challenges such as technological limitations, data privacy concerns, and the quality of chatbot interactions remain. The paper also discusses the future potential of chatbots in virtual classrooms, with advancements in AI, VR, and accessibility promising further innovations. By addressing existing challenges, chatbots in the Metaverse can become valuable tools, fostering more dynamic, inclusive, and personalized educational experiences
The Integration of AI With IoT for Personalized In-Store Experiences in the Experience Economy
No full textThis chapter explores the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in retail, collectively referred to as AIoT, and its transformative impact on in-store customer experiences. By combining AI's ability to personalize customer interactions with IoT's capacity to connect and optimize physical environments, retailers can enhance engagement, improve operational efficiency, and create seamless, data-driven shopping experiences. The chapter examines AIoT's role in driving personalized retail experiences, its impact on customer behavior, and the technological innovations shaping future trends. It also discusses challenges and ethical considerations, including data privacy, cybersecurity, and algorithmic fairness. Ultimately, AIoT is positioned to revolutionize retail in the Experience Economy, offering significant opportunities for innovation and competitive advantage. The chapter concludes with recommendations for retailers to leverage AIoT responsibly while fostering customer trust
The role and impact of digital VoC in achieving operational excellence, driving innovation and adapting to the dynamic landscape
No full textPurpose
The study aims to measure the role and impact of digital Voice of Customer (DVoC) in achieving operational excellence (OE), driving innovation (DI) and adapting to the dynamic landscape (ADL).
Design/methodology/approach
The four factors and their items were initially identified based on an experimental research approach by gathering data from 283 respondents in the northern areas of India. The study employed Smart-partial least square (PLS) version 4.0 with structural equation modeling to analyze the data.
Findings
The results indicate that DVoC plays a significant role in explaining the positive relationship with OE, DI and ADL in the research model. The results are also verified with the existing literature in the field.
Research limitations/implications
DVoC has emerged as a meeting point in the research sector because of technology advancements and increased global Internet use. The DVoC gives greater power to the customer’s voice than traditional survey and feedback methods since it is more dependable and provides information in real time.
Originality/value
The research topic of DVoC is still in its early stages, hence the current study is pertinent. Operational Excellence (OE), Driving Innovation (DI) and adapting to the dynamic landscape (ADL) are all becoming increasingly important in today’s competitive climate as a result of the market’s diverse developments. These are some of the possible motivations for carrying out such work, which contributes to the originality of the current study. To bring the current study’s original addition, a research model was constructed using factors identified in previous studies
Modeling Critical Factors of MNCs International Performance in the Context of Digitalization: an integrated M-TISM and MICMAC approach
No full textThe current trend of digitalization, internationalization, and digital technology are the drivers of enabling new business opportunities that motivate multinational corporations (MNCs) to expand internationally and leverage digitalization in the changing international business ecosystem for enhancing international performance (IP). Many scholars have identified the factors that impact IP of MNCs, but the focus on digitalization is limited. Therefore, using the digitalization lens, this study aims to explore the critical factors (CFs) of MNCs' IP. This study utilizes modified total interpretive structural modeling (m-TISM) and MICMAC analysis to develop a hierarchical model that outlines the relationship among identified CFs impacting IP of MNCs. A five level hierarchical model is developed. The findings present that risk acceptance, international orientation, digital capability, institutional voids, and cultural distance experienced by MNCs are the driving factors that impact IP of MNCs. Using the internationalization and dynamic capability theory, this study enhances the understanding of dynamics between identified CFs of MNCs' IP. The proposed hierarchical model provides a structural framework that helps practitioners and researchers understand the interrelation among CFs of MNCs' IP and digitalization in the rapidly evolving landscape of international business
A Study Of The Management of The Herders-Farmers Conflict-Induced Internal Displacement In Nigeria
Get PDFThe management of the herdsmen-farmers conflict-induced internal displacement in Nigeria was characterised by remote factors that affected the protection and assistance of internally displaced persons (IDPs). This qualitative study was conducted on 12 participants comprising humanitarian workers, IDPs, and lawmakers who were conversant with the persistent herdsmen farmers conflictinduced internal displacement. The study explored the inter-connected relationship among stakeholders within the humanitarian ecosystem. It found that though the herdsmen-farmers conflict led to mass displacement, remote factors such as unwillingness of government, data challenges, the undiplomatic approach of government in the management of the displacement failed to win support of other stakeholders and therefore, escalated the displacement crisis. The study concluded that while the conflict led to mass displacement of crop farmers, its poor management and poor interconnected relationship between stakeholders led to protracted displacement and lack of sustainable measures for the protection and assistance of the displaced persons. The paper recommends political will, collaboration and humanitarian diplomacy in managing conflict and assisting IDP
Teacher-AI Collaboration and the Future of the Educator's Role
No full textThis chapter explores the evolving role of educators in the context of increasing Artificial Intelligence (AI) integration in education. As AI transforms traditional teaching methods, educators will shift from knowledge dispensers to facilitators of personalized learning, higher-order thinking, and social emotional development. The collaboration between teachers and AI holds significant potential for enhancing learning experiences through personalized instruction, data-driven insights, and improved classroom efficiency. However, this transformation also presents practical, social, and ethical challenges, such as the digital divide, algorithmic biases, and data privacy concerns. To harness the full potential of AI in education, robust professional development programs for teachers, ethical AI design, and equitable access to technology are essential. This chapter outlines the opportunities and challenges of AI integration, emphasizing the critical role of educators in guiding students through an AI-enhanced learning environment