Browse Items (16481 total)
Sort by:
-
Analysing the Impact of Perceived Risk, Trust and Past Purchase Satisfaction on Repurchase Intentions in Case of Online Grocery Shopping in India
The Indian online grocery market has been propelling since last few years. The size of online grocery market in 2020 was estimated as $2.9 billion and it is further anticipated to reach at the compound annual growth rate (CAGR) of 37.1% during 2021 to 2028. Companies such as Amazon, Flipkart grocery, BigBasket, Grofers and Jiomart have been coming up with new attractions for consumers such as providing timely no contact delivery, accepting various digital modes of payment and offering several discounts which have fascinated consumers towards buying their regular grocery from various online platforms. Corona virus has also fuelled up the safety concerns of people; due to which a large section of the citizens are working from home and are dependent on the online platform for various purposes including grocery shopping. This has provided several growth opportunities to the online grocery market. This research investigates about the purchase behaviour of customers towards online grocery shopping. The study aims to understand the purchase behaviour of e-grocery shoppers of India and to examine the association between satisfactions with online purchase, trust on online grocers, perceived risk and online repurchase intention of grocery items. The study uses primary data collected from 555 online grocery buyers. The findings of the study indicate that online customer satisfaction is a significant factor that influences repurchase intentions of online grocery shopping. Perceived risk negatively influence trust as well as repurchase intentions. Trust is found to be a mediating factor between shopping satisfaction and repurchase intentions. The study also builds and tests an online customer behavioural model with actual purchasing behaviour and identifies the continued presence of perceived risk, shopping satisfaction and trust in grocery e-retailing. 2023 IMI. -
Analysing the impact of oil prices, economic activity, and trade policy uncertainty on CO2 emissions in the US context: A wavelet approach
This study examines the simultaneous co-movements between oil prices, economic activity, trade policy uncertainty, and CO2 emissions in the United States using a series of wavelet methodologies. Unlike traditional approaches, the wavelet approach is appropriate for understanding the time-varying associations at different frequencies and is designed to efficiently handle the non-stationary nature of economic and environmental time series data. The empirical results highlight the potential of a leading relationship where economic activities and trade policy uncertainties drive CO2 emissions in the US during the period from January 1990 to January 2022. Contrarily, the link between oil prices and CO2 emissions is characterized by intricate dynamics, exhibiting both lagging and leading co-movements at different frequencies. Moreover, economic activities have a positive impact on CO2 emissions, while in the high quantile tails, trade policy uncertainty decreases CO2 emissions. This means economic activity is slowing down during the period of high trade policy uncertainty. Our findings highlight the necessity of specific policies that reconcile economic growth with environmental sustainability, manage the effect of oil price changes on CO2 emissions, and match trade policies with emission-minimizing goals. Based on the results, this research offers important implications for policymakers to ensure the equilibrium between economic activity and environmental management within the scope of sustainable development goals. 2025 International Association for Gondwana Research -
Analysing the Impact of CSR Spending by Big 4 Firms on their Financial Profitability
This study delves into this ongoing debate whether socially responsible companies perform better which leads to financial profit or instead have no impact. This study focuses on leading accounting companies i.e., PricewaterhouseCoopers (PwC), Deloitte, Ernst & Young (EY), and KPMG and whether CSR Spending impacts their financial profitability or goes unnoticed. Grenze Scientific Society, 2024. -
Analysing the Effectiveness of Solana Blockchain Platform and PoH Consensus Algorithm in Providing a Solution for Blockchain Scalability Problem
Solana started its journey in April 2018 and is now a public blockchain - based platform which aspires better scalability than other existing blockchains while providing security and decentralization. It backs the development of decentralized applications and smart contracts (DApps). The goal of the study is to confirm several of its characteristics, like its transaction throughput, or the pace in which legitimate transactions are committed to a Solana network block over the course of a one-second period (TPS). A secondary dataset that was gathered over the course of 60 days and made available on GitHub was utilized. Our data analysis findings demonstrate that the transaction throughput on an average is about 3006 TPS at a much lower transaction fees than the fees users pay for many other blockchains that facilitate the same operations, such as use of smart contracts and the development of DApps. The document explains the workings of the Solana blockchain, which, in the words of its creators, claims to address the scalability issue without compromising security and decentralization. Grenze Scientific Society, 2025. -
Analysing the Ascendant Trend of Veganism: A Comprehensive Study on the Shift towards Sustainable Dietary Choices
Background: Veganism has become a prominent social and culinary movement due to concerns about animal welfare, environmental sustainability, and ones own health. Vegans strive to consume only plant-based meals in order to lessen the suffering of animals, stop the environmental damage caused by the animal agriculture sector, and enhance their own health. Objective: This chapter aspires to understand various dynamics of consumer consciousness towards veganism through social media analysis (Twitter) and research opinions. Materials and Methods: This chapter used a qualitative approach and a three-part methodology. Firstly, a literature review examines the impact of veganism on human health, ethical needs and sustainable food choices. Secondly, the authors extracted tweets and analysed them using data visualisation software- NVivo with the essential parameters being themes, sentiment, world map, and word cloud. Results: Sentiment analysis explained consumer perception towards veganism as a storming blackball result of 36.1 present positive insights. Word map analysis describes veganism as a global phenomenon. The third part analysed the Scopus research data and identified food, diet and meat as major themes in veganism. The Scopus database sentimental analysis also re-emphasised the growing positive insights towards it. Conclusion: This study highlighted the significance of veganism as a sustainable dietary choice for addressing urgent global issues while promoting a thoughtful and compassionate approach to eating. It is also emerging as a powerful tool for positive change in preserving and promoting biodiversity. 2024 selection and editorial matter, Mourade Azrour, Jamal Mabrouki, Azidine Guezzaz, Sultan Ahmad, Shakir Khan and Said Benkirane; individual chapters, the contributors. -
Analysing grief on twitter: A study of digital expressions on Om Puri's death /
Funes Journal of Narratives And Social Sciences, Vol.2, pp. 136-152, ISSN No. 2532-6732. -
Analysing Gendered Microaggressions in Bengalurus IT Sector: A Sentiment-Based Inquiry
Gender microaggressions experienced by women in Bengalurus IT sector forms the subject of this paper employing sentiment analysis tools such as VADER and BERT to process survey responses, interview transcripts, and publicly available reviews of employees from the platform Glassdoor. A mixed-methods design is chosen so that computational sentimental analysis can be integrated along with qualitative insights such that patterns of emotional response and disparities across hierarchical levels can be identified. The results obtained highlight that sentiments vary significantly across organisational roles and underscores the significant psychological impact these microaggressions have on job satisfaction and career growth. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Analysing Enhanced EEG Based Brain Computer Interface for Motor Imagery Tasks Using Statistical Analysis
BCIs have emerged as a useful tool for helping people with neurological disorders like epilepsy, ALS, and cerebral palsy, who have severely limited communication, by analyzing EEG signals and turning them into actionable signals. This paper aims to investigate the application of BCIs in analyzing EEG signals and designing them to give meaningful signals to the users. Five healthy, right-handed university students (18-21 years) were selected for the study and were asked to spell the words mentally without vocalizing for four cognitive tasks; forward, stop, left, and right. At 100 Hz, EEG signals were sampled and pre-processed with a notch filter and feature extraction was done using DWT to extract important features and KNN was used for classification. All the subjects showed high accuracy with more than 90% and maximum accuracy of 95.89% was obtained by Subject S3. Standard deviations between 1.32 to 1.41, which indicate low variability in performance among all the subjects. These results showed that the DWT with KNN combination can be used for real-time BCI applications and can offer a reliable communication method for people with motor impairment and disabilities. 2025 IEEE. -
Analysing Employee Management Using Machine Learning Techniques and Solutions in Human Resource Management
In the contemporary landscape of Human Resource Management (HRM), organizations are increasingly turning to advanced technologies to streamline employee management processes. This study explores the integration of machine learning (ML) techniques as a transformative solution for optimizing HRM practices, with a specific focus on employee management. By leveraging the power of ML algorithms, this research aims to enhance decision-making, efficiency, and overall effectiveness in HRM. The study encompasses a comprehensive analysis of existing HRM challenges, such as talent acquisition, performance evaluation, and employee retention, and proposes ML-based solutions to address these issues. By applying natural language processing, pattern identification, and predictive analytics, businesses may learn a great deal about employee behavior, performance patterns, and possible areas for development. HR professionals are more equipped to make well-informed choices, customize employee experiences, and put proactive talent development initiatives into action thanks to this data-driven approach. Additionally, the study examines the moral issues and difficulties surrounding the use of ML in HRM, stressing the significance of openness, justice, and privacy. By understanding and mitigating these concerns, organizations can successfully harness the transformative potential of ML in employee management, fostering a more dynamic and adaptive HRM framework. The study's conclusions add to the growing body of knowledge on the relationship between technology and HRM and offer useful advice to businesses looking to use cutting-edge approaches to improve labor management procedures. 2024 IEEE. -
Analysing different appeals in Cadbury India commercials /
Appeals are an important aspect in the field of advertising that are used widely to promote or market a product by persuading its consumers. Television is one of the strongest medium that brings out the clarity of advertising appeals. This study aims to analyse the different appeals that are used by the brand Cadbury in India. -
Analysing Customer Profile, Expectations and Satisfaction with Airport Retail in Coimbatore, Insights into the Airport Environment and Decision Making Dynamics
Airport retailing has become a crucial income source and a fundamental aspect of improving traveler experiences. Nevertheless, the intricate relationship between traveler characteristics, anticipations, shopping environments, promotional tactics, purchasing choices, and contentment remains insufficiently examined. This research fills this knowledge gap by exploring the structural associations among six key factors: Customer Profile (CP), Customer Expectation (CE), Airport Retailing Environment (ARE), Retail Marketing Strategy (RMS), Customer Preference Decision (CPD), and Customer Satisfaction (CS) in the context of airport retail environments A descriptive study framework was implemented, concentrating on travelers participating in retail purchases at Coimbatore airport. Firsthand information was obtained from 203 participants using a structured survey. The collected information was assessed using Structural Equation Modeling (SEM) to determine the interconnections among these elements and their overall effect on passenger contentment. The findings reveal that Customer Expectation is significantly influenced by Customer Profile, while Airport Retailing Environment is directly shaped by Customer Expectation but not by Customer Profile. Retail Marketing Strategy is strongly impacted by Airport Retailing Environment but shows no significant relationship with Customer Expectation. These findings emphasize the significance of analyzing traveler behavior and tailoring retail strategies to enhance contentment in airport shopping environments. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Analysing Crypto Trends: Unveiling Ethereum and Bitcoin Price Forecasts Through Analytics-Driven Weighted Moving Averages
This research meticulously analyses the performance dynamics of two paramount cryptocurrencies, Bitcoin and Ethereum, over 2,682 observations. Preliminary findings indicate a near alignment in the mean returns of both assets, with Ethereum marginally outperforming Bitcoin. Interestingly, Ethereums superior returns are accompanied by heightened volatility, underlined by its more significant standard deviation. Both cryptocurrencies manifest negative skewness, hinting at a proclivity for negative returns, with Bitcoin showing a sharper skew. Their pronounced kurtosis values attest to the potential for extreme price swings. Regarding forecasting efficacy, the Weighted Moving Average (WMA) method emerges as superior for both assets, yielding the most accurate predictions. At the same time, the Exponential Moving Average (EMA) demonstrates the highest forecast errors. Further, the Relative Strength Index (RSI) evaluation suggests Ethereum may be oversold, alluding to potential investment opportunities. In contrast, Bitcoin, with its mid-range RSI, resides in a neutral zone devoid of clear market signals. The findings shed light on the nuanced performance and forecasting landscape of these leading cryptocurrencies, offering pivotal insights for potential investors. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Analysing Collaborative Contributions and Sentiments in the Quantum Computing Ecosystem
Quantum computing, a revolutionary paradigm leveraging the principles of quantum mechanics, has emerged as a transformative technology with the potential to solve complex problems at unparalleled speeds. Within the quantum computing ecosystem, companies and research institutes play pivotal roles in advancing hardware, algorithms, and applications. This research explores the transformative landscape of quantum computing, focusing on key contributors such as Google, IBM, D-Wave, Azure, Amazon, Intel, EeroQ, and IonQ. Through sentiment analysis, topic modelling, and thematic analysis, the study aims to comprehensively understand the current state and trends within the quantum computing ecosystem. The findings unveil an overall positive sentiment and identified topics ranging from cloud computing services to quantum computing advancements. Thematic analysis provides actionable insights, emphasizing collaboration within the ecosystem. Rooted in the analysis of secondary data from key companies' articles, the methodology establishes a robust framework for discerning contributions, collaborations, and strategic orientations in quantum computing. 2024 IEEE. -
Analyses of the Power Flow through Distributed Generator based on Unsynchronized Measurements
Based on measurements taken from the main substation and the connections between distributed generators and micro-grids that are not in sync, this study suggests a new way to look at the load flow of distributed generation. The conclusions are based on data from a distribution generatora's Load Flow Analysis that was not in sync. Distributed generation is what this approach is based on. Creating a strong communication system and using measurement data from the past are two ways to make this happen. This objective may be achieved with the use of previously gathered measurements. The time-tested backward-forward sweep method is the method of choice for analyzing power flow using unsynchronized data. This is the preferred approach. The angles of synchronization are likely to be unknowns that must be estimated. On a smart grid system with a large number of distributed generation and microgrids, a range of mathematical computations are conducted to verify the correctness of performance predictions produced by the suggested theory. The classic backward-forward sweep was shown to be the most effective method for analyzing power flow based on data that was not synchronized in many instances. This is the strategy that is presently being recommended. Because the angles of synchronization are presumed to be unknown, a mathematical equation must be devised to determine them. The Authors, published by EDP Sciences, 2024. -
Analogy of Social Entrepreneurship and Community Empowerment: An Inclusive Tourism Approach with Technological Intervention
Destinations overall development is ascertained by its conscious measures toward inclusive engagement with responsible business practices. The social enterprises functioning in the tourism system engage the disadvantaged communities responsibly for economic prosperity and community empowerment. Tourism social entrepreneurship is an innovative mechanism toward social upliftment by enhancing the benefits and contemplating through the core issues of host communities. This chapter focuses on case lets on communities, particularly those located in less-developed regions of India, who encounter various social problems. A sustainable community development through tourism leads economic growth in developing marginalized communities and economically empowers the low-income localities. Technological intervention for community empowerment and better engagement is the need of the hour. It is instrumental to understand the ideology of social entrepreneurial opportunities for community empowerment. Tourism relies on multiple industries and it is challenging in delivering desired community development goals. Adaption of techno-enabled system toward community development can build the entrepreneurship capacity of marginalized communities. Technological interventions for empowering the community toward rural development is the thrust dealt in the proposed chapter. Springer Nature Singapore Pte Ltd 2024. -
Analgesic and Anti-Inflammatory Potential of Indole Derivatives
Some indole analogues show a good analgesic activity but on the other hand, it has some serious side effects like gastric ulcer. Therefore, there is still a need to develop derivatives of non-steroidal anti-inflammatory drugs (NSAIDs) with fewer side effects. For this purpose, some indole derivatives were prepared with objectives to develop new derivatives with maximum efficacy and minimum side effects. 1-(1H-indol-1-yl)-2-(sstituephenoxy)-ethan-1-one derivatives (M1M4) were analyzed further by thin-layer chromatorgarphy (TLC), melting point, IR, and 1H-NMR. The synthesized compounds then underwent oral toxicity studies that include hematological, biochemical, and histopathological findings. The compound was then evaluated for invivo anti-inflammatory and analgesic activities on carrageenan-induced rat paw edema and acetic acid-induced writhing methods. As a result of the biological activities, promising results were obtained in the compound M2 (2-(2-aminophenoxy)-1-(1H-indol-1-yl)ethanone) and it was subjected to further studies. It was found that compound M2 was practically nontoxic, and no clinical abnormalities were found in hematology and biochemistry, correlated with histopathological observation. It also showed significant anti-inflammatory and analgesic activities at its oral high dose (400 mg/kg). The study suggested that compound M2 was found to have significant anti-inflammatory and analgesic activities. The possible mechanism of M2 might suggest being act as a central anti-nociceptive agent and peripheral inhibitor of painful inflammation. The possible mechanism of action of the compounds whose biological activity was evaluated was explained by molecular docking study against COX-1 and COX-2, and the most active compound M2 formed ?9.3 and ?8.3 binding energies against COX-1 and COX-2. In addition, molecular dynamics (MD) simulation of both M2s complexes with COX-1 and COX-2 was performed to examine the stability and behavior of the molecular docking pose, and the MM-PBSA binding free energies were measured as ?153.820 11.782 and ?172.604 9.591, respectively. Based on computational ADME studies, compounds comply with the limiting guidelines. 2022 Taylor & Francis Group, LLC. -
An updated review on advancement in fermentative production strategies for biobutanol using Clostridium spp.
A significant concern of our fuel-dependent era is the unceasing exhaustion of petroleum fuel supplies. In parallel to this, environmental issues such as the greenhouse effect, change in global climate, and increasing global temperature must be addressed on a priority basis. Biobutanol, which has fuel characteristics comparable to gasoline, has attracted global attention as a viable green fuel alternative among the many biofuel alternatives. Renewable biomass could be used for the sustainable production of biobutanol by the acetone-butanol-ethanol (ABE) pathway. Non-extinguishable resources, such as algal and lignocellulosic biomass, and starch are some of the most commonly used feedstock for fermentative production of biobutanol, and each has its particular set of advantages. Clostridium, a gram-positive endospore-forming bacterium that can produce a range of compounds, along with n-butanol is traditionally known for its biobutanol production capabilities. Clostridium fermentation produces biobased n-butanol through ABE fermentation. However, low butanol titer, a lack of suitable feedstock, and product inhibition are the primary difficulties in biobutanol synthesis. Critical issues that are essential for sustainable production of biobutanol include (i) developing high butanol titer producing strains utilizing genetic and metabolic engineering approaches, (ii) renewable biomass that could be used for biobutanol production at a larger scale, and (iii) addressing the limits of traditional batch fermentation by integrated bioprocessing technologies with effective product recovery procedures that have increased the efficiency of biobutanol synthesis. Our paper reviews the current progress in all three aspects of butanol production and presents recent data on current practices in fermentative biobutanol production technology. 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
An Understanding of Knowledge Management Perception and Implementation in Higher Education
Global Journal of Arts and Management, Vol. 2, No.3, pp 204-206, ISSN No. 2249-2658 -
An Predictive Deep Learning Model is used to Identify Human Tissue-Specific Regulatory Variations For Diabetes
A predictive deep learning model is designed to predict a target variable based on a set of input variables to diagnose the tissue base regulatory variants in the human islets. In this article, the identification on human tissue-specific regulatory variations for Diabetes using the Pima dataset converting data into images, and then the input variables may include genetic data, gene expression data, and the proposed model uses Pima Indian dataset with the attributes such as age, sex, and BMI to predict whether a person has Diabetes or not. And this dataset is incorporated a combination two layered ResNet18 + ResNet50 and SVM classifier. The results obtained are compared with KNN, Naive bayes, SVM Random Forest, Gradient descent and the accuracy achieved is 98%. 2023 IEEE. -
An overview of nanomaterial technologies in the management of wastewater treatment
Nanomaterials are the foundation stone of nanotechnology. It is a broad and trans disciplinary section of exploration. Its developmental commotion has been rising intensively across the world in the last few years. Applications of nanotechnology are abundant in various fields like medicines, electronics, machines, and so on. This paper describes one such application that is nanotechnology in wastewater treatment viz nanosorption, nanophotocatalysis and Nano membrane technology. The Nano compounds engaged in such treatments have been discussed here. As of now the world is in distress from the non-Availability of drinking water and even the very existence of drinking water in nature are becoming toxic due to the addition of chemicals and heavy metals by man-made and by natural happenings as well. The distinctive properties of nanomaterial such as surface area, competent to toil at required and even at low concentration and their potential have prodigious prospects to reform wastewater treatment. Carbon based nanomaterial, metal oxide nanomaterials and nanomebranes have been discussed at this juncture. There are several disputes in treating wastewater with nanomaterials such as insufficient information about the nanomaterial. Still researchers have a lack of knowledge about how these materials are travelling and the effect of nanomaterial on human health. Though the nanostructured catalytic membranes, Nano sorbents and nanophotocatalyst are the established methods to eliminate water pollutants from wastewater, they need more energy and additional investment. 2021 Elsevier Ltd. All rights reserved.