Browse Items (16481 total)

Analyzing the Consumer Buying Behavior by Adapting Artificial Intelligence (AI)

In any business consumers or customers are important part of the market, so it is necessary to attract more customers for increasing the profits. The current research in this area has demonstrated that artificial intelligence (AI) has a substantial impact on the end customer, contrary to the widespread notion that it has more of an impact on industry than other manufacturers. There are many studies on the various applications of AI in analyzing and visualizing the consumer behavior. Thus, it is been observed the behavior of consumer is not same for same businesses, it varies from consumer to consumer. In other respects, AI is changing how consumers act right now. In coming year's use of AI will become common where the human dependable businesses also get automated with time. 2024 IEEE.

Impact of Artificial Intelligence on the Social Media Marketing Strategies

The use of social media is increasing as the use of smartphones is increasing, various applications on smartphones are now becoming good platform for market. As the use of smartphones is increasing the use of different artificial intelligence (AI) technologies making the phones smarter. The social media is now one of the most globally crowded platforms with millions of users. Most of the businesses are now turning their marketing strategies by highlighting the digital marketing from various platforms. Thus, the focus of study is to find the increasing impact of AI on the social media related marketing strategies. Different studies highlight the different impacts of social media and marketing using different AI tools and platforms which makes customer to find the best product as per their choice. So, social media marketing has become simpler and more adaptable thanks to the development of artificial intelligence. 2024 IEEE.

Why small business owners get demotivated? Modeling unwillingness to grow using ISM approach

Purpose: Even after establishing their business successfully, many business owners get demotivated, and it leads to unwillingness to grow. This study aims to propose a comprehensive model that represents interrelationships among various personal factors affecting unwillingness to grow. Design/methodology/approach: The personal factors for unwillingness to grow were identified by extant literature, and expert interviews were conducted to establish the contextual relationships among these factors. The interrelationships among the filtered variables have been done using interpretive structural modeling (ISM) and MICMAC analysis was done to determine the importance of each factor in influencing unwillingness to grow. Findings: In total, 30 personal attributes were identified from previous literature, out of which 15 were selected for the final study. The result identifies 7 variables having a strong impact on unwillingness to grow. These attributes are absence of strong network, lack of vision, lack of proactiveness, reluctance to involve external consultants, absence of/small founding team, lack of ambition and improper attitude. Originality/value: The research attempts to create a bricolage of all the important personal factors affecting unwillingness to grow. Previous researches have used few attributes, but with the help of ISM, a graphical modeling technique, it became possible to draw interrelationship between 15 attributes. Further, with the help of MICMAC, the importance of each attribute was determined. 2024, Emerald Publishing Limited.

Cognitive Engagement Scale (CES) in an Online Environment: Construction and Validation

Researchers have demonstrated linkages between active engagement of students with learning material and greater learning gains. Cognitive engagement is a significant component of educational experience. Understanding the challenges associated with cognitive engagement and measuring cognitive engagement in a MOOC environment is challenging. It is the need of the hour with online learning being equivalent to classroom learning in todays dynamic academic environment. The present study aims to construct cognitive engagement scale (CES) to measure the cognitive engagement of learners who sign up for the massive open online courses (MOOC). The aim of this study is dual-fold: firstly, to conceptualize the cognitive dimension of learner engagement within MOOCs, and secondly, to construct a theoretically informed scale for assessing cognitive engagement in online environments. Study presents a detailed process of the scale development, which included item generation, item evaluation, pilot testing, testing psychometric properties of the scale, and scale refinement. The researchers crafted the initial questionnaire drawing from both existing literature and personal insights. Subject matter experts then validated the items within the questionnaire and ensured its reliability through a pilot study, where it was administered to a sample of 100 participants The final version of the scale captures the four dimensions of cognitive engagement: Passive receiving, active manipulating, constructive generating, and interactive dialoguing. The present study contributes to the growing literature on cognitive engagement and adds to the existing literature of MOOC engagement scale with focus on cognitive engagement exclusively. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Measuring the Impact of Chatbot Attributes in Enhancing Consumer Satisfaction and Brand Loyalty Among Centennials: PLS-SEM Analysis

PurposeThis study investigates the potential impact of consumers views about chatbots dynamic, behavioral, and cognitive features on their satisfaction and brand loyalty. Design/methodology/approachData were collected using a survey comprising questionnaires from a sample of Indian centennial customers. Purposive sampling was the technique utilized. After that, the data was analyzed using the partial least squares algorithm with the help of smartPLS for structured modelling. FindingsThe results demonstrated that chatbots affective, behavioral, and cognitive characteristics significantly impacted consumer satisfaction and enhanced brand loyalty. Practical implicationsChatbots can improve brand loyalty by taking into account the affective, behavioral, and cognitive traits of their e-agents. Originality/valueThis work aims to contribute and enhance the expanding corpus of research on chatbots effects on increasing brand love. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Execution of green supply chain management analysis /

Patent Number: 202111046133, Applicant: Dr. Abhinav Priyadarshi Tripathi.
Our invention Execution of Green Supply Chain Management Analysis is a expanded consideration given to the subject of green store network the board (GSCM) warrants the composition of this invention. The idea of GSCM is to coordinate natural thinking into production network the board (SCM). In that capacity, GSCM is significant in affecting the absolute climate effect of any associations engaged with store network exercises.

Medical waste treatment device /

Patent Number: 354500-001, Applicant: Ila Anand.

The Impact of Fintech and Financial Inclusion on SMEs Growth and Development

[No abstract available]

Note

The paradox of leadership: Gandhis transformation through inconsistencies

[No abstract available]

Book Chapter

Tackling Malnutrition Among Children in India: The Role of National Health Policies

Child malnutrition in India is a significant public health concern. While there has been progress in reducing stunting, wasting, and underweight rates among children, the prevalence of malnutrition remains high, especially with a recent increase in childhood anaemia. It is essential to implement effective national health policies and programmes to address this issue. Initiatives such as the Integrated Child Development Services (ICDS), the Mid-Day Meal Scheme (MDMS), and the National Health Policy 2017 play a pivotal role in enhancing nutritional and health outcomes for children. However, challenges such as inadequate infrastructure, socio-economic disparities, and a lack of parental education and awareness hinder the effectiveness of these programmes. Misconceptions about nutrition and child-feeding practices among mothers are also significant contributors to malnutrition. This chapter aims to explore the potential of national health policies in combating child malnutrition in India and addressing the role they play in improving childrens nutritional status. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Artificial Intelligence Based Recruitment Prediction and Sentiment Analysis for Enhanced HR Efficiency

In the present era of data-driven organizational environment, the practice of Human Resource Management (HRM) has become increasingly reliant on intelligent Decision-Support Systems (DSS). This study develops a multifaceted two-pipeline model of Predictive Modelling (PM) and Sentiment Analysis (SA) to enhance workforce analytics capabilities. A publicly available HRM analytic dataset is used to train supervised classification models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM), as well as an ensemble model that integrates these classifiers. These approaches use structured data to predict employee attrition based on features such as age, job role, experience, and job satisfaction. The unstructured textual data sources, including resumes and employee reviews, are handled using state-of-the-art Natural Language Processing (NLP) such as tokenization, Term Frequency-Inverse Document Frequency (TF-IDF), and Bidirectional Encoder Representations as Transformers (BERT)-based embeddings. The new Mathematically Modified Robustly Optimized BERT Pretraining (MM-RoBERTa) is proposed for extracting the PM and SA. All the models are evaluated using k-fold Cross-Validation (CV) and standard evaluation measures, namely Accuracy, F1-score, Area Under the Receiver Operating Characteristic Curve (AUC), and Mean Absolute Error (MAE). The ensemble model achieves a predictive accuracy of 91.3%, and MM-RoBERTa outperforms existing SA with an accuracy of 93.1 %. The combination of predictive and affective insights is of practical use in fine-tuning talent retention, empowering HRM professionals to make informed decisions based on objective performance indicators and subjective emotional states. 2025 The Authors.

A Machine Learning Approach to Consumer Behavior Analysis in Social Media-Influenced E-Book Markets

Social media has emerged as a dominant marketing channel, significantly influencing consumer purchase decisions. Despite extensive global research, little is known about region-specific dynamics in emerging markets such as India. This study addresses this gap by applying Random Forest and Gradient Boosting models to survey data from 386 respondents in the Delhi-NCR region to analyze e-book purchasing behavior. Data were preprocessed through encoding, normalization, and stratified traintest splitting (80:20), with reproducibility ensured via a fixed random seed. Model evaluation employed R, RMSE, and MAE metrics, alongside a paired-sample t-test. Results showed that Gradient Boosting (R = 0.82) outperformed Random Forest (R = 0.78; p = 0.038). Feature importance analysis revealed that behavioral variablespurchase intention, brand awareness, and social media engagementwere the strongest predictors, whereas demographic features contributed minimally. These findings emphasize the primacy of behavioral traits in social mediadriven e-book markets and provide evidence for designing region-specific digital marketing strategies in emerging economies. 2025, Interdisciplinary Publishing Academia. All rights reserved.

You have to budget within the money you have: Intersections of immigration, health, and food insecurity in the South Bronx

This study explored food insecurity in the Bronx using a community-based participatory approach, including 38 interviews with food pantry users, four interviews with social service administrators, and two focus groups with 12 pantry staff and providers in New York City. Using a phenomenological framework, we identified three key themes: (1) negative impact of immigration status on food insecurity, (2) competing financial demands and limited access to social service assistance; and (3) adverse effects on physical and mental health. Findings highlight the relationships between food insecurity and immigration status. The costs of securing legal residency and sending remittances home contributes to the financial strain and poor health outcomes among immigrant communities in the South Bronx. Immigrants faced additional barriers that exacerbated these vulnerabilities, including language barriers and limited access to social services. With rising food costs due to inflation and uncertainties regarding potential cuts to federal food assistance programs, addressing food insecurity in the Bronx has never been more critical. Ensuring equitable food access for all Bronxites requires developing trusted relationships with immigrant communities and strong community partnerships with local social service organizations. 2025 Urban Affairs Association.

Why small business owners get demotivated? Modeling unwillingness to grow using ISM approach

Purpose: Even after establishing their business successfully, many business owners get demotivated, and it leads to unwillingness to grow. This study aims to propose a comprehensive model that represents interrelationships among various personal factors affecting unwillingness to grow. Design/methodology/approach: The personal factors for unwillingness to grow were identified by extant literature, and expert interviews were conducted to establish the contextual relationships among these factors. The interrelationships among the filtered variables have been done using interpretive structural modeling (ISM) and MICMAC analysis was done to determine the importance of each factor in influencing unwillingness to grow. Findings: In total, 30 personal attributes were identified from previous literature, out of which 15 were selected for the final study. The result identifies 7 variables having a strong impact on unwillingness to grow. These attributes are absence of strong network, lack of vision, lack of proactiveness, reluctance to involve external consultants, absence of/small founding team, lack of ambition and improper attitude. Originality/value: The research attempts to create a bricolage of all the important personal factors affecting unwillingness to grow. Previous researches have used few attributes, but with the help of ISM, a graphical modeling technique, it became possible to draw interrelationship between 15 attributes. Further, with the help of MICMAC, the importance of each attribute was determined. 2024, Emerald Publishing Limited.

Desk organizer /

Patent Number: 350152-001, Applicant: Vaibhav Tripathi.

USB and wireless mouse with left thumb scroll /

Patent Number: 350151-001, Applicant: Sanjay Rastogi.