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Digital Content Marketing
Digital Content Marketing (DCM) is the strategic creation and distribution of relevant brand-related content on digital platforms, aiming to foster favorable brand engagement, trust, and relationships with current or potential customers. Despite its widespread use in practice, academic research on DCM is limited, creating a significant knowledge gap. This study addresses this gap by conducting an exploratory research synthesis of DCM literature from 2008 to 2024. The primary objective is to provide a comprehensive overview of pertinent studies and create a conceptual integration that elucidates DCM?s impact on marketing practices. Utilizing a targeted search on the Web of Science Database, 15 published papers were identified and analyzed based on specified search criteria. The findings contribute to a more holistic understanding of DCM?s role as a prevalent trend in contemporary marketing practices. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Unleashing the Potential: How Influencers Drive Digital Content Marketing
Digital content marketing has revolutionized the marketing landscape, and influencers have emerged as powerful drivers in this realm. Digital content marketing (DCM) is marketing information that is valuable for consumers, which is created, distributed, and managed in a virtual space to engage current and potential consumer bases. An influencer is an individual who influences a broad segment of the target audience. They shape audience attitudes through blogs, tweets, and videos using social media. This demonstrates the power of an individual where an influencer markets the product/services of a particular firm via digital platforms. DCM exhibits the power of the content where the content is marketed via digital platforms. The objective of this study is to explore the impact of influencers on facilitating digital content marketing. This study employs the exploratory research synthesis method to gain insights into the current body of research concerning influencers and digital content marketing. Additionally, a case example is employed to shed light on how influencers play a role in driving digital content marketing. 2025 selection and editorial matter, Subir Bandyopadhyay and Bikramjit Rishi. -
Herding Behaviour on Investment Decision: Mediating Effects of Risk Tolerance of Generation Z Investors
The study explores whether the financial risk tolerance capacity and herding behaviour affect the Generation Z investors investment decision. A structured questionnaire has been developed, and used with Five-point Likert scaling technique for measuring the constructs in the study. The data was collected through both offline and online mediums from Generation Z individual investors, and the authors have used PLS-SEM technique to examine the data. The study reveals as the herding behaviour influences investors investment decisions. Also, it states the association between herding behaviour and financial risk tolerance capacity be positively associated. In contrast, financial risk tolerance capacity is without any mediating value between herding and their investment decisions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Demystifying the Metaverse Era: The Enabling Technologies and Industry Use Cases
Metaverse can be called a 3D shared virtual space that is hyper realistic, immer-sive, instinctive, and interactive. Through metaverse, people try to visualize life in the manner that do not exist in the real world. The potential and promising digital technologies turn out to be a huge enabler of the metaverse dream. This chapter is to delineate the various versatile metaverse applications, implementation technol-ogies, and use cases (individual as well as industrial). 2025 Scrivener Publishing LLC. -
Artificial Intelligence (AI) for IT Energy Efficiency and Green AI for Environment Sustainability
This volume approaches Artificial Intelligence for sustainability from two angles. First, it looks at AI systems themselves, which consume a surprising amount of energy to function, and examines ways they can be made more efficient and sustainable. Secondly, it examines how AI can be used to create efficiencies and make buildings, power grids, the manufacturing sector, and more sustainable. The chapters here also provide a comprehensive but compact overview of the latest in AI technologies and how they work. This volume will be useful to AI engineers, data scientists, software developers, academics, researchers, and more. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Mudhr: Malicious URL detection using heuristic rules based approach
Technology advancement helps the people in numerous ways such as it supports business development, banking, education, entertainment etc. Especially time critical and money related activities, people are fully really on internet and web applications. It saves valuable time and money. Despite of the benefits, it also gives wide space for the attackers to focus more victims. Malicious URL based attacks are most common and more dangerous attacks now a day which steals the credentials and sensitive data from the victims and perform malicious activities in the victim's space. Phishing, Spamming, drive by download are the example of such attacks and are preformed through malicious URL. Plenty of approaches are available to detect the malicious URL. That are grouped under three categories such as Blacklist based, Heuristic based and Machine Learning based approaches. Among the three, heuristic approach is better than the blacklist approach in term of better generalizing the malicious URL and gives equally accurate prediction with machine learning approach. This paper presents recent works in the field of malicious URL detection and novel technique to detect malicious URL based on the most important features derived from URL. 2022 Author(s). -
Intrinsic betterness and reason based extrinsic preference towards social shopping: A study among college students in Bangalore
The main purpose of this study is to analyze the Intrinsic and Extrinsic factors and its impact on the social shopping of student community in the city of Bangalore. Bangalore is one of the most popular cities in the down south. This city attracts a mix of various cultures from various countries of the world. Thus, this city and population would be apt to study the social shopping pattern considering the Intrinsic and Extrinsic factors. The data was collected from the student community, perusing their college education in the city of Bangalore. Using convenient sampling method, a sample size of 225 was drawn from educational institutions in the city of Bangalore. The research work makes use of both first hand and second-hand data. The reliability of the data is acceptable as the Cronbach's alphas value is more than. 6. The drafted questionnaire was subjected to expert opinion before the data collection process. The study results make it clear that both intrinsic and extrinsic values motivate the consumers to get involved in the social shopping. But comparatively consumers are more influenced by those factors present in the external environment. It can be concluded by saying that, youngsters are quite smart before putting themselves into the purchase behavior, as they are in a group of friends, they get influenced by various experiences and comments shared by many. In a way social shopping is better, as too many minds generate ideas for a single purchase. 2018 Transilvanian Association for the Literarure and Culture of Romanian People (ASTRA). All rights reserved. -
Machine learning in smart agriculture
Agriculture is the cultivation of the soil, the growth of crops and the raising of livestock. Agriculture is critical to the economic development of a country. Farming generates nearly 58% of a country's primary income. Previously, cultivators had accepted conventional farming practices. Because these methods were imprecise, they produced less and took longer time. Precise farming boosts productivity by precisely determining which steps must be completed at what time. Precision farming entails forecasting the weather, analyzing soil, recommending crops for cultivation and calculating the amount of fertilizer and pesticides that must be used. Precise farming uses advanced technologies such as IoT, data mining, data analytics, and machine learning (ML) to collect data, train systems and predict outcomes. Precision farming employs technology to reduce manual labor and boost productivity. Farmers have recently faced several difficulties, such as crop failure due to insufficient rainfall, soil infertility and so on. The proposed work in determining the soil, managing crops and harvesting efficiently can solve the problems caused by environmental changes. It guides a person's farming strategy to produce better results through a proper prediction process. The goal of this research is to assist an individual in efficiently cultivating crops, resulting in high productivity at a low cost. It also assists in estimating the total cost of cultivation and forecasting the likely economic barriers. This would help a person plan activities prior to cultivation, resulting in an integrated farming solution. 2023 River Publishers. All rights reserved. -
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An efficient methodology for resolving uncertain spatial references in text documents
In recent decades, all the documents maintained by the industries are getting transformed into soft copies in either structured documents or as an e-copies. In text document processing, there is a number of ways available to extract the raw data. As the accuracy in finding the spatial data is crucial, this domain invites various research solutions that provide high accuracy. In this article, the Fuzzy Extraction, Resolving, and Clustering (FERC) architecture is proposed which uses fuzzy logic techniques to identify and cluster uncertain textual spatial reference. When the text corpus is queried with a spatial-keyword, FERC returns a set of relevant documents sorted in view of the fuzzy pertinence score. Any two documents may be compared in light of the spatial references that exist in them and their fuzzy similarity score is presented. This enables finding the degree to which the two documents speak about a specified location. The proposed architecture provides a better result set to the user, unlike a Boolean search where the document is either rated relevant or irrelevant. Copyright 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. -
Forecasting the Volatility of Indian Forex Market: An Evidence from GARCH Model
Forecasting the volatility of forex market will create more trading opportunities to investors, despite of ups and downs in the forex market. The present study attempted to examine how the volatility in the exchange rate between Indian rupee and selected four foreign currencies, such as US dollar, euro, Japanese yen and British pound, can influence the market return. The data, used in the present study, covered the daily price observation of four foreign currencies, for a period of 5 years, from 2019-2023. The GARCH (1, 1) (generalized autoregressive conditional hetero skedasticity) was used for develop the model for foreign exchange (FX) rates volatility. Mean equation model confirmed that the series had attained stationary and previous price did influence the current price. It was also supported by co-efficient values in the variance equation. The co-efficient value, in the variance equation, was around one, which showed that the forex market was efficient. Further, it was validated that the volatility shocks in forex market were quite persistent. The active investors in the market may use this opportunity immediately. The policy maker may correct this deviation through timely intervention in the currency market. 2024, Iquz Galaxy Publisher. All rights reserved. -
Study on testing the stationarity and co-integration among the sectoral indices of national stock exchange of India
The Stock Market is a market for the trading of company stocks. It is an organized market place where members of the organization gather to trade company stocks and other securities. An index is important to measure the performance of investments against a relevant market index. Sectoral indices serve as a benchmark for measuring the performance of the stocks or portfolios. This study explores stationarity and co-integration relationship among stock market returns and the eight important NSE sectoral indices for the period of January 2013 to December 2017. Sectoral Index series indicates the existence of co integration among the sectoral indices of NSE. Co integration exists in long run equilibrium and in short run they diverge from each other or they have disequilibrium. This study is useful to find out the determinant factors of the National Stock Exchange and led lag relationship among the Sectoral Indices in National Stock Exchange. IAEME Publication. -
A study on optimal portfolio construction with special reference to NSE CNX Nifty pharma index
Portfolio is a process of blending together the broad asset classes so as to obtain optimum return with minimum risk is called portfolio construction. In order to reduce the risk, investors need to diversify, spread their portfolio across a broad mix of assets. Diversifying the portfolio can help smooth out market ups and downs and returns from better performing assets help to offset those that arent performing so well. The present study has empirically examined the portfolio construction with special reference to NSE CNX Nifty Pharma Index. The study applied the Sharpe Single Index model to generate an efficient combination of securities from sample Pharma companies and has come up with a subsequent pattern. The study found that out the sample Pharma companies, Aurobindo Pharma Ltd attracted high risk while Glenmark Pharmaceuticals Ltd experienced the least risk, on the basis of return earned by the companies in the Pharma Index; Aurobindo Pharma Ltd has high return while Lupin Ltd has lowest return. Experimental results have demonstrated the feasible of the investment strategy, portfolio idea and electiveness of the combination assets on the investment strategy. IAEME Publication. -
The Future of the Gig Professionals: A Study Considering Gen Y, Gen C, and Gen Alpha
This article aims to analyze the reasons behind the gig economys growth considering the present and future. From few empirical pieces of evidence, it is clear that the gig economy took shape after the 2008 great economic recession, where unemployment rose in leaps and bounds along with voluntary job leaving. In recent times, the COVID-19 pandemic is one of the primary reasons for the forced rise of the gig economy. Strategically and practically speaking, from the economic point of view, the gig economy is dominated by Gen Y and Gen C and will be dominated by Gen Alpha soon. However, there are very fragile differences between Gen Y, C, and Gen Alpha; all three generations are lashed together in the name of tech-savvy and flexibility-seekers. Thus, this article justifies the influence of technology generations and the rise of the gig economy. There is less research work exclusively on Gen Y, C, and Alpha. Still, regarding the gig economy in light of the great economic recession and COVID-19, this new work highlights originality. Future researchers working on Gen Alpha and Gen C can explore the role of digital technologies in changing routine life and its impact on their personal life and career life. Technology has been a significant reason for the sprung-in gig jobs. However, it can also have adverse outcomes, which has scope to be explored. Thus, this research may be a catalyst for future research works in similar lines. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Novel PAPR Reduction in UFMC system for 5G Wireless Networks Using Precoding Algorithm
The Universal Filtered Multi-carrier (UFMC) system is promising alternative multicarrier modulation scheme for fifth generation (5G) cellular networks. UFMC systems offer many advantages such as larger spectral efficiency, robustness, lower latency and minimizing out of band emission. However, the most serious problem in the UFMC system is high peak to average power ratio (PAPR). This high peak signal is seriously harmed by the high power amplifier (HPA). Therefore, this research presents a novel Square Root raised Cosine function (SRC)-Precoding method introduced to reduction of PAPR. A performance analysis of various methods being examined upon in terms of CCDF of PAPR and the BER. The Simulation result shows that the proposed approach can effectively reduce the PAPR 6dB compared to standard UFMC. Moreover, the bit error rate (BER) study of the UFMC model indicates that the proposed approach significantly improves 15 dB compared with conventional UFMC systems. 2022 IEEE. -
Eccentric Graph of Join of Graphs
The eccentric graph Ge corresponding to a graph G is a derived graph with the same vertex set of G and two vertices in Ge are neighbours if one of them is the eccentric vertex of the other. Motivated by the studies on derived graphs and graph operations, in this article, the eccentric graph of the join of two graphs is analysed based on the variations in the radius. The notion of eccentric join of two graphs with at least one of them having radius 1, is introduced. The eccentric graph of eccentric join of graphs is also examined. Finally, the concept of r-eccentric join of graphs is also introduced. This study is analytical in nature, which involves deductive and logical reasoning. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Cyclic property of iterative eccentrication of a graph
The eccentric graph of a graph G, denoted by Ge, is a derived graph with the vertex set same as that of G and two vertices in Ge are adjacent if one of them is an eccentric vertex of the other. The process of constructing iterative eccentric graphs, denoted by Gek is called eccentrication. A graph G is said to be ?-cyclic(t,l) if G,Ge,Ge2,...,Gek,Gek+1,...,Gek+l are the only non-isomorphic graphs, and the graph Gek+l+1 is isomorphic to Gek. In this paper, we prove the existence of an ?-cycle for any simple graph. The importance of this result lies in the fact that the enumeration of eccentrication of a graph reduces to a finite problem. Furthermore, the enumeration of a corresponding sequence of graph parameters such as chromatic number, domination number, independence number, minimum and maximum degree, etc., reduces to a finite problem. 2023 World Scientific Publishing Company. -
Eccentric completion of a graph
The eccentric graph Ge of a graph G is a derived graph with the vertex set same as that of G and two vertices in Ge are adjacent if one of them is the eccentric vertex of the other. In this paper, the concepts of iterated eccentric graphs and eccentric completion of a graph are introduced and discussed. 2022 The authors. -
Mapping Road Traffic Injury in India: Causes, Prevention, Economic Impact, and Role of Public Health Governance
Road Traffic Injuries (RTIs) represent the main reason for fatalities globally and are acknowledged as a significant national health problem. RTIs affect the victims and also have a profound impact on the family and relations. The socio-economic conditions of families, and consequently society and the nation, are negatively influenced by these incidents. A significant number of deaths on the roads involve cyclists, motorcyclists, and pedestrians. The prevalence of RTIs is particularly high in African and other middle-income countries, while developed nations experience comparatively fewer incidents. Each year, RTIs result in approximately 1.2 million deaths worldwide (WHO, 2018), marking them as a primary, preventable cause of mortality. Global attention has shifted towards the critical need for road safety, particularly with the endorsement of the 2030 Agenda for Sustainable Development Goals (SDGs). Implementing robust legal enforcement could lead to behavioural changes among road users. India ranks among the highest in the world for road accident-related fatalities. Ad-hering to safety measures such as using helmets, wearing seat belts, maintaining appropriate speeds, and following traffic regulations would significantly reduce Road Traffic Injuries. Stakeholders in road safety must be made aware of the economic costs, Disability Adjusted Life Year (DALY), and human losses associated with RTIs and their repercussions. This paper aims to outline the existing RTI situation, its causes, preventive strategies, magnitude of economic burden, costs involved in RTI, catastrophic health expenditure, Return on Investment (RoI) in Trauma Care Systems, Financing mechanisms, Governance, and the Health sector's role in addressing RTIs. The findings indicate that road accidents are the predominant reason for mortality in India (with many incidents being underreported or undocumented), and the state must take a proactive approach to tackle this issue by fostering strong connections among various stakeholders, while the health sector should implement a multifaceted strategy to manage RTIs. Authors. -
AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection
The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT Sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic Systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain. 2024 IEEE.