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ESG or financial METRICS? What Retail Investors Really Look for in Decision-making
With the increasing global emphasis on responsible investing, this study explores the tradeoff between ESG and traditional financial metrics in shaping the investment decisions of retail investors in India. A within-subject experimental design was employed at Christ University, India, involving an initial sample of 75 participants, with 55 completing all three experiment rounds. The sample respondents evaluated masked stock profiles across three rounds, where updated financial and ESG information on masked stock was provided at each round. The results indicate that though ESG metrics are getting attention among retail investors, financial metrics are still the main determining factor for investment. It was found that ROE (52 responses), 3-year CAGR Net Profit (36 responses), and P/E ratios (48 responses) are the most influencing factors to make investment decisions. Similarly, ESG factors (Governance, Environmental, and Sustainability scores) are also frequently mentioned, with 74 citations. Retail investors mainly consider profitability and view ESG as risk-mitigating or neutralizing factors. While evaluating the ESG factors, retailers mainly look at the firms environmental concerns, followed by governance and social factors. This result contrasts with the previous studies in this domain, where the literature emphasized governance factors more than environmental factors. These results highlight the integration of ESG elements, as retail investors remain with favorable returns and sacrifice sustainability. Further, this study spots the need for better and quantifiable ESG performance reports to consider alternative data comparable to financial data for better investment decisions. Suresh Gopal, Saravanakrishnan V., Elangovan N., 2025. -
ESG Narrative Quality in Green Bond Disclosures: Implications for Risk Perception, Transparency, and Market Trust
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 issuers credibility as well as the issuers 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. 2025 by the authors. -
ESG efficiency analysis in the IT industry: a DEA-based approach
Unlocking the power of sustainable growth, Environmental, Social, and Governance (ESG) principles are redefining the future of responsible investment and corporate excellence. ESG regulations ensure that organizations maintain sustainable development and improve non-monetary metrics, such as stakeholders engagement, customer satisfaction, market acceptability, societal ethics, and values. Higher ESG scores demonstrate commitment towards responsible business practices and indicate higher market value for companies, which are valid for all sectors, including IT. However, existing literature reveals that IT sector companies pay less attention to planning their operations to make them more sustainable. Therefore, IT firms must identify methods and practices to maintain high ESG scores to achieve sustainable growth. The current study leads the readers into a new area of ESG through the help of an advanced method, DEA. DEA (Data Envelopment Analysis) methodology has been used to identify the decision units relative efficiency scores and helps identify peers and followers based on ESG scores. The study reveals that among the selected IT firms using the output-oriented strategy, 56.25% experience increasing returns to scale, 18.75 per cent experience decreasing returns to scale, and the remaining 25.00 per cent report constant returns to scale. This indicates that most IT industry firms can generate greater output change in proportion to the input change. 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
ESG efficiency analysis in the IT industry: a DEA-based approach
Unlocking the power of sustainable growth, Environmental, Social, and Governance (ESG) principles are redefining the future of responsible investment and corporate excellence. ESG regulations ensure that organizations maintain sustainable development and improve non-monetary metrics, such as stakeholders engagement, customer satisfaction, market acceptability, societal ethics, and values. Higher ESG scores demonstrate commitment towards responsible business practices and indicate higher market value for companies, which are valid for all sectors, including IT. However, existing literature reveals that IT sector companies pay less attention to planning their operations to make them more sustainable. Therefore, IT firms must identify methods and practices to maintain high ESG scores to achieve sustainable growth. The current study leads the readers into a new area of ESG through the help of an advanced method, DEA. DEA (Data Envelopment Analysis) methodology has been used to identify the decision units relative efficiency scores and helps identify peers and followers based on ESG scores. The study reveals that among the selected IT firms using the output-oriented strategy, 56.25% experience increasing returns to scale, 18.75 per cent experience decreasing returns to scale, and the remaining 25.00 per cent report constant returns to scale. This indicates that most IT industry firms can generate greater output change in proportion to the input change. 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Escitalopram treatment ameliorates chronic immobilization stress-induced depressive behavior and cognitive deficits by modulating BDNF expression in the hippocampus
Major depressive disorder (MDD) affects 21% of the global population. Chronic exposure to stressful situations may affect the onset, progression, and biochemical alterations underlying MDD and associated cognitive impairments. Patients exhibiting MDD are mainly treated with several antidepressants; one is escitalopram, a selective serotonin reuptake inhibitor. However, whether or not it mitigates chronic stress-induced cognitive deficits is unknown. The present study exposed rats to chronic immobilization stress (CIS) 2 hours/day for 10 days. Then, escitalopram (5 mg and 10 mg/kg i.p.) was administered for 14 days and subjected to the elevated plus maze, open field test, forced swim test, sucrose preference test, and radial arm maze task. A different set of animals were used to assess the vascular endothelial growth factor (VEGF), glial fibrillary acidic protein (GFAP), and brain derived neurotrophic factor (BDNF) levels in the hippocampus, frontal cortex, and amygdale. Our data suggest that escitalopram significantly protected CIS-induced spatial learning and memory deficits, behavioral depression, and anxiety. Furthermore, escitalopram (10 mg/kg) shows a remarkable recovery of dentate gyrus and hippocampal atrophy. In addition, the restoration of molecular markers BDNF, VEGF, and GFAP expression is also implicated in the neuroprotective mechanisms of escitalopram. Our results suggested that esciatlorpam restores cognitive impairments in stressed rats by regulating neurotrophic factors and astrocytic markers. 2024 Shilpa Borehalli Mayegowda et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). All Rights Reserved. -
Escape velocity backed avalanche predictor neural evidence from nifty /
International Journal of Recent Technology And Engineering, Vol.8, Issue 4, pp.486-490, ISSN No: 2277-3878. -
Ergos: redefining storage infrastructure and market access for small farmers in India
Learning outcomes: After completion of the case study, students will be able to analyse the path of the entrepreneurship from idea generation to market development to scaling up business, examine the impact of start-ups like Ergos on Indias agriculture value chain, discuss the challenges faced by tech entrepreneurs in growing a business, identify problems solved by Grain Bank Model and evaluate digitisation of farmings custodial services such as warehousing, market linkages and loans. Case overview/synopsis: The case study discusses how founders of Ergos, India-based leading digital AgriTech start-up, Kishor Kumar Jha and Praveen Kumar, started one of the unique models in the AgriTech landscape in India. After noticing the grim condition of small and marginal farmers in Bihar, India. Kishor and Praveen decided to put their banking and corporate experience to use in the farming sector. Ergos aimed to empower farmers by providing them with a choice on when, how much quantity, and at what price they should sell their farm produce, thus maximising their income. As a result, Ergos launched the grain bank model, which provided farmers with doorstep access of end-to-end post-harvest supply chain solutions by leveraging a robust technology platform to ensure seamless service delivery. Ergos faced many challenges in its journey related to financing, marketing and distribution. Amidst these developments, it remained to be seen how Kishor and Praveen would be able to realise their goal to serve over two million farmers across India by 2025 and create a sustainable income for them through its GrainBank Platform. Complexity academic level: This case study was written for use in teaching graduate and postgraduate management courses in entrepreneurship and business strategy. Supplementary materials: Teaching notes are available for educators only. Subject code: CSS 3: Entrepreneurship 2024, Emerald Publishing Limited. -
Eradication of Global Hunger at UN Initiative: Holacracy Process Enriched byHuman Will and Virtue
The researchers have directed their attention to the UNs 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals (SDGs), with a specific focus on two critical objectives: hunger and poverty alleviation. While the UN has been vocal about eradicating hunger and poverty, the researchers believe that a fundamental shift in human perspective is needed. They propose a novel approach rooted in holacracy to revolutionize food production, distribution, and management. At the core of their proposal lies the ancient Indian principle, Vasudhaiva Kutumbakam, which translates to The World Is One Family. While it may seem utopian, the researchers see it as a reachable goal through holacracy. Their hypothesis centres on producing food for all and collectively utilizing it, transcending national boundaries and individual interests. The researchers advocate for a transformation in the way the UN operates by embracing holacracy as a practical social technology rather than a mere concept. Holacratic organizations, they argue, have the potential to remove barriers obstructing progress. The implementation of their vision begins with the UN functioning as a global nerve centre for data, with its 193 member nations acting as equal and interdependent contributors. This Centre would display the worldwide food landscape and foster a moral and ethical awakening, emphasizing the shared responsibility for all humanity. Real-time data on food availability, supply chains, and consumption would be accessible on a public website. Holacracy, they contend, should inspire individuals to prioritize love for humanity as a panacea. Power circles interconnect to collaboratively address issues. The UN could serve as a catalyst for this transformation. The knowledge nerve centre would provide critical data on arable land, water resources, and supply chain infrastructure to facilitate problem-solving at various levels. Timely responses and actions would be driven by the principles of holacracy and advanced digital technologies, addressing concerns hindering the achievement of UN goals. This data-driven approach, coupled with actionable plans, aims to eliminate food shortages and subsequently combat poverty and hunger worldwide. In conclusion, the researchers envision a future where holacracy and a shared sense of responsibility propel humanity towards ending hunger and poverty, with the UN playing a pivotal role as a catalyst for change and a provider of essential data and guidance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Era of Education 5.0: Disruptive Technologies in a Learner- Cantered Educational Landscape
The chapter focuses on concepts of Education 5.0 and its competence in shaping future learning environments. It emphasises on learners social and personal growth by improving quality of life standards with the help of current technologies and digitalisation. (Shabir Ahmad, 2023) To deliver humanised approach by the application of new technologies is the primary use of Education 5.0. However, usage of new age technology in education doesnt mean giving laptop and tablet to each and every child and the usage of digital mediums for teaching and learning. After covid- 19, digitisation becomes the integral part of our life, education is no exception for that. (Shabir Ahmad, 2023) Beyond digitalisation pandemic also remained us the importance of human hardships to social transformation with emotional intelligence driving technology as a tool. In short education 5.0. (SYDLE.com, 2023) referring to the significance of human, social and emotional abilities to enhance wellbeing of an individual by using technology advancement as a tool. 2025 by IGI Global Scientific Publishing. -
Equity by Design: Embedding DEI Into AI- Enhanced Marketing Tools
In a time when artificial intelligence redefines marketing practice pillars, the concern is not innovation but conscience- driven innovation. While AI tools promise unmatched precision in reaching customers, segmenting, and budgeting, they carry a silent danger of deepening existing disparities, if they were to be unleashed without caution. This article advocates for the practice of Equity by Design, and it insists that Diversity, Equity, and Inclusion (DEI) must be made a part of the actual design of AI- driven marketing systems. By borrowing cross- disciplinary insights from finance, organizational ethics, and digital strategy, the case is argued through illustrations of how equitable design cuts down on algorithmic bias, expands financial service access to marginalized communities, and enhances consumer trust in a more data- driven market. Beyond compliance or corporate social responsibility, embedding DEI in AI is a competitive strategy, attaching ethical obligation to long- term brand worth, sustainable growth, and global competitiveness in the digital economy. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Equity and inclusion in literacy policies: Case study on the impact of including assamese literature and excluding Koch Rajbongshi literature
This chapter examines the impact of Assam's literacy policies, which includes Assamese literature but excludes Koch Rajbongshi literature, on cultural representation, student engagement, and social equity. Through a case study approach, it reveals how this selective inclusion perpetuates cultural marginalization, affecting the self- identity and educational experiences of Koch Rajbongshi students. Theoretical insights from cultural hegemony, social justice in education, and critical literacy emphasize that inclusive curricula foster a cohesive and equitable society by valuing diverse cultural narratives. The chapter calls for policy reforms to integrate minority literature, advocating for an inclusive educational system that respects Assam's cultural diversity. These efforts are essential for promoting equity, enhancing student engagement, and fostering a sense of belonging. Aligning Assam's literacy policies with UNESCO principles promotes equity, cultural diversity, and preservation of Koch Rajbongshi literature. 2025, IGI Global Scientific Publishing. All rights reserved. -
Equity and inclusion in GenAI innovation: Exploring the challenges and strategies for ensuring equitable access to and benefits from GenAI-driven innovation
This chapter examines the critical need for equity and inclusion in the development and deployment of Generative Artificial Intelligence (GenAI) technologies. As GenAI rapidly transforms various sectors, from health care to education, its benefits are not evenly distributed, risking the exacerbation of existing social inequalities leading to a huge digital divide. The chapter explores theoretical frameworks like critical race theory (CRT) and intersectionality to understand how biases embedded in AI systems can perpetuate discrimination. It also highlights the role of open-source platforms and emerging AI initiatives in the Global South in democratizing access to these technologies. Through case studies of companies like Procter & Gamble and Microsoft, the chapter demonstrates both the potential of GenAI to drive innovation and the challenges of integrating AI ethically into global operations. The discussion underscores the importance of deliberate, inclusive strategies to ensure that AI serves as a force for social good, fostering global equity rather than deepening divides. 2025 V. Padmaja, P. Bhanumathi and Bishal Patangia. All rights reserved. -
Equitable and inclusive online learning: A framework for supporting students with disabilities
Online learning has become a widely adopted mode of education, particularly during the COVID-19 pandemic. In general, individuals with disabilities face challenges when using non-technology components for studying. This chapter proposes a framework for equitable and inclusive online learning practices that support students with disabilities. The framework is based on a review of current research and best practices for online learning and disability accommodations. The framework emphasizes a collaborative, student-centered approach to online learning that acknowledges the unique needs and experiences of students with disabilities. Depending on the disabilities, the framework is divided into two phases namely: Prevalent Learning, and Discrete Learning. The former comprised components: Accessibility, Accommodation, and Engagement, and later has components like Methodology, Evaluation. The framework proposed provides a roadmap for addressing the challenges faced by students with disabilities in online learning environments. 2023 by IGI Global. All rights reserved. -
Equalization of Finite-Alphabet MMSE for All-Digital Massive MU-MIMO mm-Wave Communication
For more than twenty years, growing the performance and efficiency of wireless communications systems using antenna arrays has been an active field of study. Wireless networks with multiple-input multiple-output are also part of the current norms and are implemented around the world. Access points or BSs with comparatively few antennas are used for standard MIMO systems, and the resulting increase in spectral efficiency was relatively modest. A Multiple-Input Multiple-Output platform's capacity is researched where the transmitter outputs are processed and quantified by a set of limit quantizes through an analogue linear combining network. The linear mixing weights and cutoff levels are chosen from with a collection of possible combinations as a function of the transmitted signal. Millimetre-wave networking requires optimum data transmission to various computers on same moment network in combination with large multi-user actually massive. In order to guarantee efficient data transmission, the heavy insertion loss of wave propagation at su ch a faster speed needs proper channel estimation. A new channel estimation algorithm called Beam space Channel Estimation is suggested. From a set of possible configurations, the capacity of a massive stream from which antennas signals are handled by an analog channel as a part of the channel matrix, linear mixture weights and frequency modulation levels are selected. Probable implementations of specific analogue receiver designs for the combined network model, such as smart antenna selection, sign antennas output thresholding or linear output processing. To demonstrate the effectiveness of BEACHES in service and have FPGA implementation results, we are developing VLSI architecture. Our results show that for large MU-MIMOs, mm-wave communications with hundreds of antennas, specially made denoising can be done at maximum bandwidth and in an equipment format. Published under licence by IOP Publishing Ltd. -
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Equality Versus Discretion in Imposing Death Penalty in The Criminal Justice System : A Comparative Analysis Between India, UK and USA
The criminal justice system has two phases, namely, pre-conviction and postconviction, which are based on some theories which have to be exercised by the four major organs of administration of the criminal justice system, namely police (investigation), prosecution, defence and judiciary as well as correctional institutions. For this purpose, every legal system permits this mechanism to exercise equality and discretion at various phases such that justice is served according to the procedure established by law as it is required. The attempts to maintain a balance between the two in the sphere of criminal justice had begun long ago, although not succeeded yet by various countries. In the United States, more equality is emphasised in the postconviction stage. It focuses on offence egalitarianism quotrather than quotoffender egalitarianismquot. In Europe, the position is almost contrary. In India, strict adherence specifically to neither equality nor discretion at any step cannot be traced out. However, when it comes to sentencing cruel and heinous crimes, almost all countries fix a definite punishment where there is a broad scope for judicial discretion, often ending up squeezing the discretion to attain the idealistic concept of equality. This Study aims to discuss and point out the merits and demerits of the said system with suggestions. -
Epileptic Seizure Prediction from EEG Signals Using DenseNet
Epilepsy is a disorder in which the normal electrical pattern in the brain is disrupted causing seizures or loss of consciousness. Seizure is harmful during various events like swimming or driving. The electroencephalogram (EEG) is the measurement of electrical activity received from the nerve cells of the cerebral cortex. Forthcoming seizures can be predicted from scalp EEG signal to improve the quality of life. The study proposes a method of automatic epileptic seizure prediction from raw EEG signal. The raw EEG signal is converted into EEG signal image for automatic extraction of features and classification of inter-ictal and pre-ictal state using Dense Convolutional Network (DenseNet). This classification process is carried out in a manner similar to the process followed by a medical practitioner without resorting to hand-crafted features. The public CHB-MIT EEG database is used for training, validation, and testing. An EEG signal for 1 second duration is taken as one sample. The accuracy for the classification of inter-ictal and pre-ictal state is achieved up to 94% by using 5-Fold cross validation. However, the accuracy is not up to the mark for the presence of common artifacts caused by eye-blinking and muscle activities during EEG recordings. Hence, a 30 seconds pool based technique is used for decision on correct state identification. The proposed pool based technique provides an average specificity of 95.87% and a false prediction rate of 0.0413/hour. It also provide average sensitivities of 100%, 97%, and 90% for the time slots 0 - 5 minutes, 5 - 10 minutes, and 10 - 15 minutes before the seizure event. 2019 IEEE. -
Epileptic seizure detection using EEG signals and multilayer perceptron learning algorithm
Purpose: Epileptic is a neurological chronic disorder that causes unprovoked, recurrent seizure. A seizure is a sudden rush of electrical activity in the brain. The central nervous system characterized by the loss of consciousness and convulsions. Epileptic is caused by abnormal electrical discharge that lead to uncountable movements, loss of consciousness and convulsions. 50-80 million people in the world are affected by this disorder. Now a days children and adults are affected the most and it has been medically treated. Sometimes it may lead to death and serious injuries. In this technology world the computerized detection is an enhanced solution to protect epileptic patients from dangers at the time of this seizure. Method: Perceptron learning algorithm is a supervised learning of binary classifiers and also it is a simple prototype of a biological neuron in artificial neural network. EEG is extensively documented for the diagnosing and assessing brain activates and related disorders. In this paper EEG signals are taken as dataset for epilepsy detection. The data is been represented based on three domains namely frequency, time and time-frequency applied by the chebysev filter for processing the signals. Result: Help the patients from dangers at the time of the seizure. Conclusion: The neurological diseases can be divided into two loss of consciousness and convulsions. In this technology world the seizure can be detected by computerized way like EEG and so on. This paper proposes an epileptic seizure detection using EEG (Electroencephalogram) and perceptron learning algorithm. 2020, IJSTR. -
Epileptic Seizure Detection Contribution in Healthcare Sustainability
This study describes a sustainable EEG data methodology. Classification using Discrete Wavelet Transform (DWT) for feature extraction, with the objective of reducing the computational efforts while keeping accurate neural signal analysis. DWT decomposes the EEG signal into timefrequency specific components which allows extraction of ten key wavelet features, including wavelet energy, entropy, maximal coefficients, zero-crossing counts, and dominant frequency. These features capture essential timefrequency features of EEG signals, providing a comprehensive yet computationally efficient representation. By streamlining feature extraction, this approach reduces data dimensionality and minimizes computational processing time, aligning with sustainable technology objectives. The resulting feature vectors serve as robust inputs for classification models, effectively supporting EEG data interpretation with reduced energy and less resource utilization. This study demonstrates that targeted feature extraction can achieve high classification performance in EEG analysis while adhering to principles of sustainability and resource efficiency. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Epilepsy Detection Using Supervised Learning Algorithms
In the current scenario, people are suffering and isolated by themselves by seizure detection and prediction in epilepsy. Also, it is highly essential that it needs to be identified through wearable devices. Researchers discussed this issue and outlined future developments in this field, suggesting that Machine Learning (ML) techniques could radically change how we diagnose and manage patients with epilepsy. However, as data availability has increased, Deep Learning (DL) techniques have become the most cutting-edge approach to adopt and use with wearable devices. On the other hand, large amounts of data are needed to train DL models, making overfitting problematic. DL models are created with open-source toolboxes and Python, allowing researchers to create automated systems and broaden computational accessibility. This work thoroughly overviews deep learning (DL) methods and neuroimaging modalities for automated epileptic seizure identification. It covers several MRI and EEG techniques for epileptic seizure diagnosis and treatment programmes designed to treat these seizures. The study also covers the difficulties in precise detection, the benefits and drawbacks of DL-based strategies, potential DL models and upcoming research in this area. 2024 IEEE.