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The Impact of Cost- Efficient Market Entry Strategies on the Growth and Sustainability of Digital Startups in Emerging Markets
In emerging markets, digital startups face numerous challenges related to market entry, including financial constraints, regulatory hurdles, and competitive pressures. Cost- efficient market entry strategies play a pivotal role in determining the growth trajectory and long- term sustainability of these startups. This chapter explores various cost- effective approaches such as bootstrapping, lean startup methodology, strategic partnerships, and digital- first go- to- market strategies. By examining realworld case studies and industry insights, this chapter highlights the impact of these strategies on startup growth and sustainability. Overall, this research contributes to the growing discourse on sustainable entrepreneurship by offering practical insights into scalable, cost- efficient business models. By identifying key enablers of success for digital startups in emerging markets, this study provides valuable recommendations for entrepreneurs, policymakers, and investors aiming to drive innovation and economic development in resource- constrained environments. 2026 by IGI Global Scientific Publishing. All rights reserved. -
The Impact of Flexible Work Policies on Employee Well-Being and Retention in Modern Organizations
Flexible work policies have become foundational to contemporary organizational strategy, particularly in light of the transformative shifts triggered by the COVID-19 pandemic. This chapter investigates the multifaceted impact of such policies on employee well-being and organizational retention. Drawing from an integrative lens, it incorporates four major theoretical frameworksJob Demands-Resources (JD-R), Self-Determination Theory (SDT), Conservation of Resources (COR), and Social Exchange Theory (SET)to provide a comprehensive understanding of how flexible work arrangements influence employee engagement, satisfaction, and organizational loyalty. In addition to synthesizing existing scholarship, this chapter contributes a novel cross-t heoretical model that bridges motivational psychology and organizational behavior. It also proposes a suite of policy evaluation tools that blend qualitative and quantitative metrics, enabling organizations to monitor and. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Exploring the Digital Economy: Regulatory Challenges and Implications for Customer Dynamics
In the setting up of new digital platforms such as FinTech and e- commerce in the world, the most important task that has brought businesses to open up their markets to customers is interaction between the function before and after sale. This chapter thus reflects on the factors that have created regulatory hurdles for the digital economy, along with the effects it has had on the business- consumer relationship. It tries to analyze how the rapid adoption of digital technologies will change the old patterns of market architecture by adding to the already apparent gap between regulation and industries under new design. It also tries to look at how these regulatory issues would be about consumer trust and loyalty, about consumer behavior, and how businesses can adopt a more pragmatic approach to such problems without compromising ethical principles and developing strong consumer relationships. This chapter will show provisions of regulations that would enable innovation to ensure consumer safety, thus giving an appropriate digital marketplace in which a balance is created. 2026 by IGI Global Scientific Publishing. All rights reserved. -
A Review of Geospatial Urban Growth Modelling with Applications
The study inspires to know about geospatial model applications used in cities growth. The model is used to represent objects, targets, people interaction, and prediction on the size of city growth. Batty has elaborated in his research on traditional types of data and a combination of GIS data visualization (Batty, 2005). The aim of this chapter to review the list of models that influence urban growth. The objective of the study is to specify the geo-temporal dimension with symbolic representation for types of spatial modelling. Preliminary study has been explained in Tables 15.1-15.2 with conceptual models, analysis models, visualization, or cartographic model. 2025 selection and editorial matter, Uday Chatterjee, Avishek Bhunia, Jyothi Gupta and Krishnendu Gupta; individual chapters, the contributors. -
Opportunities for women's rural entrepreneurship in deprived rural environments: Empowering the pathway for women entrepreneurs in rural environments
Empowering women entrepreneurs in rural areas is essential for encouraging gender equality. This study explores opportunities, challenges, and strategies to support rural women entrepreneurs, focusing on the role of digital transformation, social enterprises, and policy interventions. Women face issues such as gender barriers, and social constraints, yet they leverage agriculture, handicrafts, and service sectors to drive growth. Success stories show how women face financial inclusion and other challenges. Digital platforms help access the market, skill development, and financial transactions, bridge gaps and promote sustainability. Policies like Stree Shakti Yojana, Mahila Udyam Nidhi Scheme and other provide financial aid, train and foster entrepreneurs. Sustainable practices, including renewable energy and eco-friendly techniques, further enhance prospects. By integrating sustainable strategies, advancing digital literacy, and aligning with Sustainable Development Goals, rural women entrepreneurs can emerge as leaders, ensuring inclusive economic growth and lasting social impact. 2025, IGI Global Scientific Publishing. -
From Clicks to Insights: AI's Impact on India's E-Commerce SMEs
This chapter examines the transformative role of Artificial Intelligence (AI) in revolutionizing operational and strategic aspects of Indian e- commerce SMEs. It explores how AI- driven technologies enhance operational efficiency, streamline inventory management, and personalize customer experiences, enabling SMEs to compete effectively in a rapidly digitizing marketplace. By analyzing case studies, such as Sephora's virtual assistant and 1822 Denim's virtual fitting rooms, the chapter illustrates the tangible benefits of tailored AI solutions. Also addresses challenges to AI adoption, including financial constraints, data quality issues, and organizational resistance, while emphasizing the importance of ecosystem support and change management. Furthermore, the chapter highlights future trends in AI and their implications for SMEs striving for innovation and sustainability. By offering actionable insights and strategic frameworks, this chapter provides a comprehensive guide for SMEs and stakeholders aiming to harness AI for growth and resilience in India's e- commerce sector. 2026 by IGI Global Scientific Publishing. -
Converging Deep Learning and Cloud Computing: A Scalable and Efficient Approach for Modern AI Infrastructure
Deep learning has proven to be a powerful approach to solving challenging problems, ranging from natural language processing, speech recognition, to computer vision. The proposed model has capable of matching the expanding amount of facts as well as complexity of the algorithms is demands of deep researching methodologies. These demands cannot be met with traditional computing environments. This is why cloud computing technologies have developed, offering a scalable and affordable alternative for executing deep learning algorithms. Cloud computing platforms provide the resources necessary to run deep learning workloads including compute, storage, and networking. This means you no longer have to spend lots of money on expensive hardware and makes it easier for teams and researchers to deploy and train deep learning models faster. Cloud computing has ushered in a new era of deep learning developments by mobilizing the power of specialized hardware, notably GPUs, to accelerate the training and performance of deep learning models. 2025 IEEE. -
An Improved AI-Based Low Latency Data Transmission in 5G Communication Systems
This paper devised an advanced artificial intelligence (AI) solution for ultra-low latency data transmission in 5G networks. With increasing data rates and lower latency required in 5G networks, efficient methods for transmitting the maximum amount of data are necessary. We have developed an approach that uses AI algorithms so that data transmission can be done more optimally and help reduce latency, providing better overall performance. Our approach consists of several steps, in which we predict the traffic patterns using machine learning techniques in step 1 and allocate network resources accordingly. That helps reduce network congestion and speeds up data transmission. We also introduce deep learning algorithms to adjust the transmission parameters according to network conditions, reducing latency. We simulate our algorithm in 5G network scenarios to assess its performance. The comparison of the results shows that a very low latency was achieved for this design over the earlier methods. Our developed AI-based improved solution provides a potential key to low latency data transmission in 5G communication systems. Integrating AI methods makes the system not only perform better but also be able to adapt more easily when network conditions change. The next steps are to explore the improvements of algorithms and implement them practically in 5G networks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Unveiling Patterns, Visualizations, and Trends from Patient Diabetes Data
The important role that exploratory data analysis, or EDA, plays in the context of diabetes prediction is explored in this work. EDA is used as a key component of a multimodal strategy to identify unique characteristics linked to diabetes. EDA offers insights that aid in the creation of prediction models by sifting through the complex patterns present in the medical data. The focus is on using EDA to fully grasp the data landscape while also comprehending the distinct features of diabetes. This investigation is critical to accurately categorizing people into discrete risk groups and emphasizes the use of domain-specific knowledge in enhancing diabetes prediction techniques. The research suggests using specific EDA techniques to gain deep insights and lead proactive responses. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Optimizing Healthcare: Enhancing Disease Management with Recommendation Systems
This paper explores a data-driven disease recommendation system for medical professionals based on symptoms. The technology examines symptom patterns to recommend diseases from large datasets by utilizing collaborative filtering and data analytics. To provide individualized disease recommendations based on symptom severity, it goes through data preprocessing and uses techniques like collaborative filtering and cosine similarity. Even if the technology is promising, disease predictions might be strengthened. It seeks to support early disease prediction and offer patients and healthcare professionals individualized guidance. This system demonstrates the potential of technology in healthcare decision-making using a basic Tkinter application. More improvements are anticipated as a result of data-driven approach advancements, which will improve patient care and optimize healthcare procedures. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
EEG Signatures of Resilience Across Individuals With High and Low Anxiety
Background. Over the past decade, psychological resilience has become a key focus in psychological science. However, most research relies on self-report and psychosocial assessments to explore resilience across different populations and contexts. Methods. This two-phased study examined resilience using self-reported measures and EEG recordings. Phase 1 involved a cross-sectional analysis of resilience and anxiety in young adults using correlation and regression analysis. Phase 2 utilized a grouped experimental design with EEG resting-state recordings to compare high- and low-resilience individuals. EEG data were collected using a 64-channel Geodesic Sensor Net, NetAmps 400 Amplifiers, and NetStation Acquisition 5.0 Software. Spectral analysis was performed for group comparisons. Results. Significant EEG differences emerged between high- and low-resilience groups in the anterior midline, right frontal, right central, left parietal, and right parietal regions. Alpha band differences were predominantly frontal and right-sided, while beta band differences were posterior and left-sided. Conclusions. Results of the two phased study bridge the gap between psychosocial measures and electrophysiological measures in the study of resilience and anxiety. A conceptual model based on the findings is outlined to guide future research to investigate the mechanism between resilience and clinical presentations of anxiety and/or depression at the psychosocial and electrophysiological level. Copyright: 2025. Gupta and Reddy. -
Risk and Resilience in Human Emergencies: Pedagogical Directions from a Psychosocial and Neuropsychological Paradigm
This chapter will furnish an introductory sketch of theoretical perspectives and current empirical findings on risk and resilience in human emergencies. While risk is an inherent part of human emergencies, resilience, the ability of individuals and systems to maintain functioning levels post adversity and adapt is equally important. The goal will be to collate conceptual framework and evidence to provide evidence-informed practices and directions for pedagogy. We will review a wide range of theoretical expositions and focus them on the level to explore how risk and resilience influence and are influenced by the socio-political, environmental, and psychological experiences of learners. Practical examples and best practice recommendations for pedagogy and andragogy to reduce risk and develop resilience at the individual and collective levels will be discussed. We will propose a model to include psychological science in pedagogical experiences to improve conceptualisation, experience, analysis, and application of the teaching and learning process to cope with human emergencies. 2025 selection and editorial matter, Kennedy Andrew Thomas and Joseph Varghese Kureethara; individuals, the contributors. -
Advance Data Ingestion Framework - Integration, Processing, Transformation, and Loading
The research introduces a new concept known as the Advanced Data Ingestion Framework, which is aimed at enhancing the process of getting into stored information through some intelligent methods like data preprocessing, transformation and loading. By making use of Azure services, the platform considers distributed computing and parallel processing so that structured as well as unstructured data can be incorporated from various origins without any difficulty. To begin with, the proposed framework starts with setting up scalable Azure infrastructure and integrating SAP S4/HANA for secure and efficient data transfer purposes. Within Azure Data Factory the ingestion occurs while Delta Lake ensures proper housekeeping & integrity within the system. It includes creating Power BI dashboards which allow users to see patterns easily and make better decisions based on what they know or can learn. The study brings out the flaws of current data input solutions and emphasizes the urgent requirement for a highly scalable low latency system that can support real time data processing efficiently. It tests the framework under different performance environments showing that it can effectively manage modern data within it. Finally, there is discussion about future improvements such as incorporating more sophisticated analytics or ML models thereby strengthening the decisionmaking process based on available facts. 2025 IEEE. -
Building Partnerships and Networks for Collaborative Practice-Led Research Initiatives
This chapter delves into the strategic development of partnerships and networks aimed at enhancing collaborative practice-led research initiatives. It underscores the significance of these collaborations in fostering professional development across various disciplines. By analyzing successful models and theoretical frameworks, the chapter provides actionable insights for building and maintaining effective partnerships that drive innovation and professional growth. It also addresses critical challenges such as organizational culture differences, misaligned goals, and communication barriers, offering practical solutions for sustaining long-term collaborations and ensuring continuous improvement. The insights presented are grounded in rigorous academic research and aim to guide practitioners and researchers in creating impactful and sustainable collaborative networks. 2025 by IGI Global Scientific Publishing. -
The Impactful Learning-Empowering Education Beyond Classrooms
Todays education is a dynamic ecosystem influenced by emerging technologies, human skills & iterative learning. The idea of learning beyond classrooms is introduced in this chapter, fostering an influential learning environment that aids in the process of continuous development. Both motivated students and empowered teachers are essential to ensuring the achievement of the impactful learning process. This chapters section offers an analysis of how instructors motivation, co- creation & continual training are essential for maintaining innovation. Education must extend beyond academic excellence to prepare learners for life. A beyond- classrooms learning approach is not a pedagogy but a mindset that integrates the competencies & prepares students to thrive in challenging, real-world situations in addition to achieving academic success. This chapter aims at rethinking education as a concept that expands classrooms, transforms communities, and prepares learners for impactful futures. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Indian Wives of Incarcerated Men Tell Their Own Stories: An Intersectional Narrative Analysis of Disenfranchisement and Resilience
Objective: Guided by intersectional feminism and symbolic interactionism, the purpose of this study was to document the untold stories of women with incarcerated spouses in India. Background: When a family member is incarcerated, the task of emotionally and financially supporting the family often falls upon women, who are likely to be underresourced and overwhelmed. Women whose husbands are incarcerated in India are likely to possess multiple marginalized identities, increasing their vulnerability to intersecting forms of oppression. Empirical research is lacking on wives of incarcerated men in India, contributing to their invisibility in policy-making and programmatic interventions. Method: In-depth, semi-structured interviews were conducted with 14 wives of prison inmates who resided in or around the capital city of Delhi, all of whom either held a lower caste identity or a Muslim religious identity. Transcribed interviews were analyzed following the steps of narrative analysis. Results: Results illustrate the diversity of storied experiences of wives of incarcerated husbands in India. Participants' narratives represented three types of stories: Ambivalent but Hanging On, Unconditionally Devoted, and Independent and Disillusioned. Four overarching themes characterized women's experiences with spousal incarceration: gendered care work, being stigmatized and sexualized, staying in the marriage, and ceilings of aspiration. Conclusion: This study renders visible women on the margins of Indian society, illustrating how they make meaning of extraordinary life circumstances and persevere through dire hardship. 2025 The Author(s). Journal of Marriage and Family published by Wiley Periodicals LLC on behalf of National Council on Family Relations. -
Integrating k-Means++ with ARCANE: A Scalable Framework for Exact Cluster Unlearning
To address the demand for exact data removal in unsupervised clustering, a novel framework for exact machine unlearning is proposed that integrates the K-Means++ algorithm with ARCANE. This framework combines high-quality cluster initialization with targeted partitioning, allowing a more efficient method for removing data without the need for a naive retraining of the model. The proposed model is compared to a SISA-based approach against synthetic and Iris datasets. The ARCANE K-Means++ model demonstrated superior clustering quality, achieving a Silhouette Score of 0.841 to the baseline's performance of 0.263. ARCANE framework also demonstrated better speedup and predictable unlearning times for typical deletion requests than the SISA model. This is a strong, scalable, and provably-exact method for machine unlearning, providing a new and intuitive framework for developing privacy-preserving AI. 2025 IEEE. -
An Integrated Pythagorean Fuzzy Delphi-AHP Framework for Optimizing Foreign Direct Investment: Key Drivers for Success
Foreign Direct Investment (FDI) plays a pivotal role in global economic development, fostering cross-border collaborations and driving economic growth. Recognizing the significance of optimizing FDI drivers, this study employs a novel approach by integrating the Pythagorean Fuzzy Delphi (PFD) and Pythagorean Fuzzy Analytic Hierarchy Process (PFAHP). The Pythagorean Fuzzy Delphi methodology was used to identify and classify drivers into Technological, Political, Environmental, Social, and Cultural categories. Subsequently, the PFAHP was employed to rank these drivers. The top three prioritized drivers are: advocating for favorable foreign investment policies and trade agreements; implementing advanced cybersecurity measures to safeguard sensitive technology and data; and developing cutting-edge research and development facilities to foster innovation and attract technology-intensive investments. The study concludes by discussing how implementing these top-ranked drivers can significantly enhance FDI by creating a conducive environment for international investment, thereby contributing to economic prosperity and technological advancement. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Optimized Fake News Detection in Social Networks Using Boosting Algorithms andMachine Learning Classifiers
Rising incidence of fake news on social media has turned verifying information into an imperative issue; hence, fact-checking information is becoming an important task. The traditional machine learning-based models like Logistic Regression, Nae Bayes, Support Vector Machines, and Random Forest suffer from the high-dimensional textual data, and the model may not yield optimal results in fake news detection classification. This paper suggests a better detection framework incorporating Gradient Boosting, CatBoost, and AdaBoost, along with Multinomial Nae Bayes for comparative study. This research uses TF-IDF vectorization and advanced text preprocessing, such as stopword removal, tokenization, and feature engineering,are done for better classification accuracy. The research was carried out on public dataset, including the Fake Job Posting dataset of Kaggle, to ensure model flexibility. The findings show remarkable performance enhancement with CatBoost posting the best accuracy of 98.23% and an ROC-AUC score of 0.9739, surpassing traditional models. A statistical significance test (t-test) validates the improvements as significant. Results have shown that ensemble-based approaches perform well in handling imbalanced and high-dimensional text data, and they should be generalizable to real-world fake news detection tasks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Volatility Prediction in the Indian Share Market Using Sentiment Analysis
In addressing the challenge of accurate volatility prediction in the Indian share market, the study explores the performance of deep learning-based models using sentimentdriven features. A demo model was deployed using data from five major Nifty 50 stocks-RELIANCE, HDFCBANK, INFOSYS, ITC, and MARUTI-for the financial years 2020 to 2023. We compared the results of traditional ARIMA model and standalone LSTM and its hybrid variants: LSTM + CNN and LSTM+RNN. Sentiment scores were gathered from financial news using FinBERT and NLTK, and combined with stock price data to generate time-series features. While all models demonstrated promising results, the LSTM+RNN hybrid model consistently achieved the lowest MAE and RMSE, indicating improved learning of temporal dependencies. The standalone LSTM and LSTM+RNN models also showed positive results for Sharpe ratio and Maximum drawdown indicating strong economic significance of the models. The study emphasizes the potential of hybrid LSTM architectures in modeling market volatility driven by investor sentiment. Limitations included limited dataset size, exclusion of other volatility factors, and overfitting in early hybrid GARCH trials. Future work aims to expand data coverage, integrate hybrid GARCH models more effectively, and explore additional market indicators. This research highlights a scalable and effective approach for sentiment-informed volatility forecasting in financial domains. 2025 IEEE.