Research Publications
Browse peer-reviewed journal articles, conference papers, and scholarly publications. Use the filters below to refine results by type, author, year, or research area.
1.
Meenakshi, N.; Kumar, Shashi; Eshwari, B.; Jayashankar, J.; Chandrakhanthan, J.
In: vol. 555, pp. 303-312,, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 21984182 (ISSN), (0).
@inbook{346,
title = {Impact of Learning and Development on Talent Retention in the Digital Era (A Study with Reference to Private Sector Banks)},
author = {N. Meenakshi and Shashi Kumar and B. Eshwari and J. Jayashankar and J. Chandrakhanthan},
url = {https://link.springer.com/chapter/10.1007/978-3-031-67890-5_28},
doi = {10.1007/978-3-031-67890-5_28},
isbn = {21984182 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Studies in Systems, Decision and Control},
volume = {555},
pages = {303-312,},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {In the contemporary digital era, it is imperative for banks to prioritize skill enhancement and optimize human capital utilisation to effectively address customer concerns and deliver top- notch financial services through information technology. The stability of a country’s financial landscape, crucial for economic development, heavily relies on the banking sector. The growing demand for reskilling and upskilling in this knowledge-based service industry underscores the need to retain talent over the long term. This article underscores the importance of a robust learning and development strategy in the digitalized world and its direct impact on talent retention within private sector banks in Chennai. Employing a descriptive research design, the study utilised well-structured questionnaires to collect primary data from middle and operational-level banking staff across five Private sector banks in the city. The results, derived from linear multiple regression analysis, affirm that an effective learning and development program significantly influences talented employees, ultimately contributing to their retention in private sector banks. The study aims to raise awareness regarding the critical role of learning and development as a success factor in talent retention, particularly within the dynamic banking sector. Practically, the study recommends that, given the knowledge-intensive nature of the industry, banks should create conducive learning environments and enhance workforce skills to not only meet the challenges posed by the dynamic business environment but also to secure a competitive advantage.},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {inbook}
}
In the contemporary digital era, it is imperative for banks to prioritize skill enhancement and optimize human capital utilisation to effectively address customer concerns and deliver top- notch financial services through information technology. The stability of a country’s financial landscape, crucial for economic development, heavily relies on the banking sector. The growing demand for reskilling and upskilling in this knowledge-based service industry underscores the need to retain talent over the long term. This article underscores the importance of a robust learning and development strategy in the digitalized world and its direct impact on talent retention within private sector banks in Chennai. Employing a descriptive research design, the study utilised well-structured questionnaires to collect primary data from middle and operational-level banking staff across five Private sector banks in the city. The results, derived from linear multiple regression analysis, affirm that an effective learning and development program significantly influences talented employees, ultimately contributing to their retention in private sector banks. The study aims to raise awareness regarding the critical role of learning and development as a success factor in talent retention, particularly within the dynamic banking sector. Practically, the study recommends that, given the knowledge-intensive nature of the industry, banks should create conducive learning environments and enhance workforce skills to not only meet the challenges posed by the dynamic business environment but also to secure a competitive advantage.
2.
Samal, A.; Hari, Sri; Karpagavalli, G.; Reddy, Y. M.; Roopa, K.; Sastry, N. S. K.
Financial Risk Management in Global Supply Chains: Strategies for Resilience and Profitability Journal Article
In: Journal of Information Systems Engineering and Management, vol. 10, pp. 110-115,, 2025, ISBN: 24684376 (ISSN), (0).
@article{377,
title = {Financial Risk Management in Global Supply Chains: Strategies for Resilience and Profitability},
author = {A. Samal and Sri Hari and G. Karpagavalli and Y. M. Reddy and K. Roopa and N. S. K. Sastry},
doi = {10.52783/jisem.v10i3s.363},
isbn = {24684376 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Journal of Information Systems Engineering and Management},
volume = {10},
pages = {110-115,},
publisher = {IADITI - International Association for Digital Transformation and Technological Innovation},
abstract = {Supply chains of the global economy are wedded to financial risks such as exchange risk, political risk, risk of variation in regulation, and risk of economic cycles. Mitigating of these risks is very crucial so as to guarantee the organization’s stability and its ability of making profits. This research paper analyses the measures that organizations have taken towards buffer financial threats in international supply systems. It explores the risk identification, risk evaluation and risk mitigation measures; that include, financial risk management, supplier diversification, application of IT and jointly managed risk-bearing structures. Importantly, the study also includes the discussion of the use of innovations, including blockchain and predictive analytics, in increasing positive financial reporting and better predicting necessary decisions. Examples of supply chain risk management solutions are discussed through various company examples and best practices. The case study lends credence to managing the financial risks in supply chain planning and realistic, responsive approaches to perpetuating profit margin in the unstable global economy. To the best of the author’s knowledge, this paper adds value to existing literature by offering specific recommendations that can be implemented by practitioners and policymakers to enhance supply chain manageability and affordability.},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {article}
}
Supply chains of the global economy are wedded to financial risks such as exchange risk, political risk, risk of variation in regulation, and risk of economic cycles. Mitigating of these risks is very crucial so as to guarantee the organization’s stability and its ability of making profits. This research paper analyses the measures that organizations have taken towards buffer financial threats in international supply systems. It explores the risk identification, risk evaluation and risk mitigation measures; that include, financial risk management, supplier diversification, application of IT and jointly managed risk-bearing structures. Importantly, the study also includes the discussion of the use of innovations, including blockchain and predictive analytics, in increasing positive financial reporting and better predicting necessary decisions. Examples of supply chain risk management solutions are discussed through various company examples and best practices. The case study lends credence to managing the financial risks in supply chain planning and realistic, responsive approaches to perpetuating profit margin in the unstable global economy. To the best of the author’s knowledge, this paper adds value to existing literature by offering specific recommendations that can be implemented by practitioners and policymakers to enhance supply chain manageability and affordability.
3.
Golhar, D. G.; Jeewankar, A. N.; Dhanalakshmi, R. V.; Pillai, A. D.; Ravi, K. R.
Biodegradable fabrics Journal Article
In: Asian Textile Journal, vol. 34, pp. 39-42,, 2025, ISBN: 09713425 (ISSN), (0).
Tags: MBA
@article{425,
title = {Biodegradable fabrics},
author = {D. G. Golhar and A. N. Jeewankar and R. V. Dhanalakshmi and A. D. Pillai and K. R. Ravi},
isbn = {09713425 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Asian Textile Journal},
volume = {34},
pages = {39-42,},
publisher = {G P S Kwatra},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {article}
}
4.
Abirami, P.; Kokila, M. S.; Dhanalakshmi, R. V.; Girimallanavar, B.; Kumar, D. K.
Advancement in cotton textile industry Journal Article
In: Asian Textile Journal, vol. 34, pp. 49-52,, 2025, ISBN: 09713425 (ISSN), (0).
@article{429,
title = {Advancement in cotton textile industry},
author = {P. Abirami and M. S. Kokila and R. V. Dhanalakshmi and B. Girimallanavar and D. K. Kumar},
isbn = {09713425 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Asian Textile Journal},
volume = {34},
pages = {49-52,},
publisher = {G P S Kwatra},
abstract = {India’s textile industry is a dynamic blend of tradition, innovation, and sustainability. As we explore this vibrant sector, we see the mastery of its artisans alongside the industry’s remarkable resilience and adaptability. The evolution of India’s textile sector is a tale of constant progress, where the rich heritage of the past intertwines with the emerging trends of the future, forming a fabric that is both enduring and continually evolving.},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {article}
}
India’s textile industry is a dynamic blend of tradition, innovation, and sustainability. As we explore this vibrant sector, we see the mastery of its artisans alongside the industry’s remarkable resilience and adaptability. The evolution of India’s textile sector is a tale of constant progress, where the rich heritage of the past intertwines with the emerging trends of the future, forming a fabric that is both enduring and continually evolving.
5.
Prasanna, B. R.; Nanthini, M.; Sivaranjani, P.; Prabhakaran, K.
Exploring textile industry landscapes Insights from diverse analysis Journal Article
In: Asian Textile Journal, vol. 33, pp. 25-29,, 2024, ISBN: 09713425 (ISSN), (0).
Tags: MBA
@article{4,
title = {Exploring textile industry landscapes Insights from diverse analysis},
author = {B. R. Prasanna and M. Nanthini and P. Sivaranjani and K. Prabhakaran},
isbn = {09713425 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Asian Textile Journal},
volume = {33},
pages = {25-29,},
publisher = {G P S Kwatra},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {article}
}
6.
Harish, K. S.; Kotehal, P. U.; Sandesh, M. M.; Reddy, Y. M.; Roopa, K.; Lokesh, G. R.
Artificial Intelligence in Supply Chain Management: Trends and Implications Journal Article
In: Nanotechnology Perceptions, vol. 20, pp. 1113-1120,, 2024, ISBN: 16606795 (ISSN), (0).
@article{18,
title = {Artificial Intelligence in Supply Chain Management: Trends and Implications},
author = {K. S. Harish and P. U. Kotehal and M. M. Sandesh and Y. M. Reddy and K. Roopa and G. R. Lokesh},
doi = {10.62441/nano-ntp.v20iS7.91},
isbn = {16606795 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Nanotechnology Perceptions},
volume = {20},
pages = {1113-1120,},
publisher = {Collegium Basilea},
abstract = {Artificial Intelligence (AI) is revolutionizing supply chain management (SCM) by introducing advanced analytics, automation, and data-driven decision-making. This research paper explores the current trends in AI adoption within SCM, including the use of predictive analytics, machine learning, and autonomous systems to optimize logistics, inventory management, and demand forecasting. The study also examines the implications of these technologies on efficiency, cost reduction, and competitive advantage. While AI offers significant benefits, it also presents challenges such as data privacy concerns, the need for specialized skills, and potential disruptions to traditional supply chain roles. This paper aims to provide a comprehensive overview of AI's impact on SCM, offering insights into how businesses can strategically implement AI to enhance their supply chain operations while navigating the associated challenges. © 2024, Collegium Basilea. All rights reserved.},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {article}
}
Artificial Intelligence (AI) is revolutionizing supply chain management (SCM) by introducing advanced analytics, automation, and data-driven decision-making. This research paper explores the current trends in AI adoption within SCM, including the use of predictive analytics, machine learning, and autonomous systems to optimize logistics, inventory management, and demand forecasting. The study also examines the implications of these technologies on efficiency, cost reduction, and competitive advantage. While AI offers significant benefits, it also presents challenges such as data privacy concerns, the need for specialized skills, and potential disruptions to traditional supply chain roles. This paper aims to provide a comprehensive overview of AI's impact on SCM, offering insights into how businesses can strategically implement AI to enhance their supply chain operations while navigating the associated challenges. © 2024, Collegium Basilea. All rights reserved.
7.
Dhanalakshmi, R. V.; Prasanna, B. R.; Tiwari, R.; Pavanathil, R. J.; Kumar, K. S.; Mathiyarasan, M.
In: vol. 536, pp. 421-430,, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 21984182 (ISSN), (0).
@inbook{50,
title = {Predictive Analytics in Retail: Revealing the Strategic Impact of Advertising Channels on Sales Performance Through Python and Linear Regression Model},
author = {R. V. Dhanalakshmi and B. R. Prasanna and R. Tiwari and R. J. Pavanathil and K. S. Kumar and M. Mathiyarasan},
doi = {10.1007/978-3-031-63402-4_35},
isbn = {21984182 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Studies in Systems, Decision and Control},
volume = {536},
pages = {421-430,},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {This research paper presents a comprehensive sales prediction model tailored for the retail industry, specifically focusing on diverse products within this sector. Leveraging advanced machine learning techniques such as Linear Regression and Random Forest Regression, the model assesses the nuanced impact of various advertising channels, with a particular emphasis on television (including a few social media like YouTube advertisements and Mobile Applications like Disney + Hotstar, SunNxt, etc.), radio, and newspaper mediums. Notably, the findings emphasize the pivotal role of TV advertising in driving sales, offering strategic guidance for resource allocation and marketing strategies. This research contributes to the enhancement of decision-making processes within the retail industry, empowering stakeholders to optimize marketing approaches and navigate the dynamic landscape with confidence and precision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {inbook}
}
This research paper presents a comprehensive sales prediction model tailored for the retail industry, specifically focusing on diverse products within this sector. Leveraging advanced machine learning techniques such as Linear Regression and Random Forest Regression, the model assesses the nuanced impact of various advertising channels, with a particular emphasis on television (including a few social media like YouTube advertisements and Mobile Applications like Disney + Hotstar, SunNxt, etc.), radio, and newspaper mediums. Notably, the findings emphasize the pivotal role of TV advertising in driving sales, offering strategic guidance for resource allocation and marketing strategies. This research contributes to the enhancement of decision-making processes within the retail industry, empowering stakeholders to optimize marketing approaches and navigate the dynamic landscape with confidence and precision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
8.
Prasanna, B. R.; Dhanalakshmi, R. V.; Dasgupta, S.; Bharathi, Sivagnana; Chandrakhanthan, J.; Mathiyarasan, M.
Candidate Performance Prediction—A Detailed Analysis Using Predictive Analytics Workbench Book Chapter
In: vol. 536, pp. 431-438,, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 21984182 (ISSN), (0).
@inbook{68,
title = {Candidate Performance Prediction—A Detailed Analysis Using Predictive Analytics Workbench},
author = {B. R. Prasanna and R. V. Dhanalakshmi and S. Dasgupta and Sivagnana Bharathi and J. Chandrakhanthan and M. Mathiyarasan},
doi = {10.1007/978-3-031-63402-4_36},
isbn = {21984182 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Studies in Systems, Decision and Control},
volume = {536},
pages = {431-438,},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Predictive Analytics involves anticipating future outcomes by leveraging both historical and current data. Descriptive analytics plays a crucial role in this process, providing a comprehensive understanding of the current problem scenario and insights from past data. Predictive analytics employs various tools such as statistics, modeling techniques, and data mining, and utilizes models like decision trees, correlation, and regression. The sequential application of techniques encompasses Deep Learning, Artificial Intelligence (AI), and Machine Learning (ML). This predictive approach finds applications across diverse domains such as Finance, Human Resources (HR), Marketing, and Operations. This research specifically focuses on predicting employee performance before the hiring process based on interview scores, utilizing the Predictive Workbench. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {inbook}
}
Predictive Analytics involves anticipating future outcomes by leveraging both historical and current data. Descriptive analytics plays a crucial role in this process, providing a comprehensive understanding of the current problem scenario and insights from past data. Predictive analytics employs various tools such as statistics, modeling techniques, and data mining, and utilizes models like decision trees, correlation, and regression. The sequential application of techniques encompasses Deep Learning, Artificial Intelligence (AI), and Machine Learning (ML). This predictive approach finds applications across diverse domains such as Finance, Human Resources (HR), Marketing, and Operations. This research specifically focuses on predicting employee performance before the hiring process based on interview scores, utilizing the Predictive Workbench. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
9.
Jahnavi, M.; Santosh, K.; Reddy, N. N.; Sireesha, B.; Kumar, M. S.; Bala, B. K.
Optimizing Mutual Fund Portfolio Management through the Application of Advanced Soft Computing Techniques Proceedings
Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835036908-3 (ISBN), (0).
@proceedings{74,
title = {Optimizing Mutual Fund Portfolio Management through the Application of Advanced Soft Computing Techniques},
author = {M. Jahnavi and K. Santosh and N. N. Reddy and B. Sireesha and M. S. Kumar and B. K. Bala},
doi = {10.1109/ICEEICT61591.2024.10718611},
isbn = {979-835036908-3 (ISBN)},
year = {2024},
date = {2024-01-01},
journal = {2024 3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The effective management of mutual fund portfolios is paramount for investors seeking to optimize returns while minimizing risk. However, traditional optimization techniques often struggle to accurately forecast portfolio performance, leading to suboptimal investment decisions. To address this challenge, this paper offers a novel solution to this problem by combining the Quantum Neural Network (QNN) for performance prediction with the Quantum-Inspired Evolutionary Algorithm (QEA) for portfolio optimisation. Building a flexible and dynamic framework to improve portfolio management's accuracy and efficiency is the main goal. With the utilisation of quantum computing concepts, the QNN uses real-time market data to make more accurate performance predictions, while the QEA effectively searches the solution space to find the best possible portfolio configurations. The suggested integrated framework outperforms conventional approaches, as evidenced by empirical testing results, which show a mean Absolute Percentage Error (MAPE) of 5.45% versus 5.68% for Traditional approaches. This indicates enhanced decision-making ability and forecast accuracy made possible by the integrated method. One potential path towards transforming mutual fund portfolio management is the implementation of quantum-inspired strategies. This novel paradigm has the potential to transform portfolio management techniques by providing investors with improved risk-adjusted returns and optimized investment strategies under turbulent market situations. © 2024 IEEE.},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {proceedings}
}
The effective management of mutual fund portfolios is paramount for investors seeking to optimize returns while minimizing risk. However, traditional optimization techniques often struggle to accurately forecast portfolio performance, leading to suboptimal investment decisions. To address this challenge, this paper offers a novel solution to this problem by combining the Quantum Neural Network (QNN) for performance prediction with the Quantum-Inspired Evolutionary Algorithm (QEA) for portfolio optimisation. Building a flexible and dynamic framework to improve portfolio management's accuracy and efficiency is the main goal. With the utilisation of quantum computing concepts, the QNN uses real-time market data to make more accurate performance predictions, while the QEA effectively searches the solution space to find the best possible portfolio configurations. The suggested integrated framework outperforms conventional approaches, as evidenced by empirical testing results, which show a mean Absolute Percentage Error (MAPE) of 5.45% versus 5.68% for Traditional approaches. This indicates enhanced decision-making ability and forecast accuracy made possible by the integrated method. One potential path towards transforming mutual fund portfolio management is the implementation of quantum-inspired strategies. This novel paradigm has the potential to transform portfolio management techniques by providing investors with improved risk-adjusted returns and optimized investment strategies under turbulent market situations. © 2024 IEEE.
10.
Dhanalakshmi, R. V.; Aradhya, Guru; Meera, C.; Sireesha, B.
Fraud detection in textile trade using machine learning techniques Journal Article
In: Asian Textile Journal, vol. 33, pp. 35-38,, 2024, ISBN: 09713425 (ISSN), (0).
Tags: MBA
@article{77,
title = {Fraud detection in textile trade using machine learning techniques},
author = {R. V. Dhanalakshmi and Guru Aradhya and C. Meera and B. Sireesha},
isbn = {09713425 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Asian Textile Journal},
volume = {33},
pages = {35-38,},
publisher = {G P S Kwatra},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {article}
}
11.
Santosh, K.; Kumar, M. S.; Jahnavi, M.; Reddy, N. N.; Sireesha, B.; Bala, B. K.
Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835035306-8 (ISBN), (0).
@proceedings{80,
title = {Creating an Advanced Recommendation System Integrating Collaborative Filtering and Social Media Analytics for Enhanced Customer Engagement},
author = {K. Santosh and M. S. Kumar and M. Jahnavi and N. N. Reddy and B. Sireesha and B. K. Bala},
doi = {10.1109/ICCSP60870.2024.10544247},
isbn = {979-835035306-8 (ISBN)},
year = {2024},
date = {2024-01-01},
journal = {Proceedings of the 2024 10th International Conference on Communication and Signal Processing, ICCSP 2024},
pages = {1146-1151,},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The rapid expansion of online platforms necessitates sophisticated recommendation systems to enhance user engagement. Leveraging user preferences and social interactions, the system aims to provide dynamic and tailored recommendations. Traditional recommendation systems face challenges in accuracy and personalization. Collaborative filtering struggles with the cold start problem, and social-based approaches may overlook individual preferences. Addressing these drawbacks, this paper proposes a hybrid model that combines collaborative filtering and social media analytics. This paper introduces an advanced recommendation system seamlessly integrating collaborative filtering and social media analytics to deliver real-time personalized suggestions. The novelty lies in assigning appropriate weights to recommendations based on both collaborative filtering and social influence, offering a comprehensive and accurate approach to personalized suggestions. The methodology involves defining objectives, collecting and pre-processing data, implementing the hybrid recommendation system, incorporating personalization techniques, and implementing a real-time engine. Evaluation includes key metrics such as accuracy, precision as 75 %, recall as 80 %, and user engagement. A / B testing and continuous optimization based on user feedback contribute to a comprehensive assessment, showcasing the hybrid model's effectiveness. In conclusion, this paper presents an innovative hybrid recommendation system, addressing existing drawbacks through integrated collaborative filtering and social media analytics. © 2024 IEEE.},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {proceedings}
}
The rapid expansion of online platforms necessitates sophisticated recommendation systems to enhance user engagement. Leveraging user preferences and social interactions, the system aims to provide dynamic and tailored recommendations. Traditional recommendation systems face challenges in accuracy and personalization. Collaborative filtering struggles with the cold start problem, and social-based approaches may overlook individual preferences. Addressing these drawbacks, this paper proposes a hybrid model that combines collaborative filtering and social media analytics. This paper introduces an advanced recommendation system seamlessly integrating collaborative filtering and social media analytics to deliver real-time personalized suggestions. The novelty lies in assigning appropriate weights to recommendations based on both collaborative filtering and social influence, offering a comprehensive and accurate approach to personalized suggestions. The methodology involves defining objectives, collecting and pre-processing data, implementing the hybrid recommendation system, incorporating personalization techniques, and implementing a real-time engine. Evaluation includes key metrics such as accuracy, precision as 75 %, recall as 80 %, and user engagement. A / B testing and continuous optimization based on user feedback contribute to a comprehensive assessment, showcasing the hybrid model's effectiveness. In conclusion, this paper presents an innovative hybrid recommendation system, addressing existing drawbacks through integrated collaborative filtering and social media analytics. © 2024 IEEE.
12.
Philip, B.; Mathew, G. A.; Sebastian, R. T.; Eshwari, B.; Roopa, M. B.; Mathews, S. B.
In: vol. 535, pp. 357-368,, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 21984182 (ISSN), (0).
@inbook{94,
title = {Strategizing Talent Acquisition for Fostering Future Workforce Success: Addressing Millennial Turnover and Hiring Challenges in a Rapidly Evolving Business Landscape},
author = {B. Philip and G. A. Mathew and R. T. Sebastian and B. Eshwari and M. B. Roopa and S. B. Mathews},
doi = {10.1007/978-3-031-63569-4_32},
isbn = {21984182 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Studies in Systems, Decision and Control},
volume = {535},
pages = {357-368,},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Rapid technical breakthroughs and a competitive labor market characterize today's business prospects, emphasizing the need for successful recruitment and talent acquisition. Preparing for your company's future workforce entails evaluating who, when, why, and how you hire—all of which are mirrored in your talent acquisition strategy. If given the option, 49% of Millennial workers would quit their present employment within the next two years (2019 Deloitte Global Millennial Survey). Many businesses consider finding the right people with the proper abilities their most pressing hiring challenge. Companies that employ effective talent acquisition methods lead to a successful workforce. However, lousy hiring can significantly reduce company profitability and efficiency. This research paper aims to analyze the current talent acquisition practices and processes adopted by the company and to understand the various factors affecting talent acquisition practices. A proactive talent acquisition strategy, encompassing strategic workforce planning, employer branding, continuous talent pipelining, technological integration, skill development, collaboration with educational institutions, data-driven decision-making, and a commitment to diversity and inclusion, forms a comprehensive model for long-term sustainable success. By adopting and adapting these practices, organizations can build a resilient and adaptable workforce capable of navigating the challenges of an ever-evolving business landscape. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {inbook}
}
Rapid technical breakthroughs and a competitive labor market characterize today's business prospects, emphasizing the need for successful recruitment and talent acquisition. Preparing for your company's future workforce entails evaluating who, when, why, and how you hire—all of which are mirrored in your talent acquisition strategy. If given the option, 49% of Millennial workers would quit their present employment within the next two years (2019 Deloitte Global Millennial Survey). Many businesses consider finding the right people with the proper abilities their most pressing hiring challenge. Companies that employ effective talent acquisition methods lead to a successful workforce. However, lousy hiring can significantly reduce company profitability and efficiency. This research paper aims to analyze the current talent acquisition practices and processes adopted by the company and to understand the various factors affecting talent acquisition practices. A proactive talent acquisition strategy, encompassing strategic workforce planning, employer branding, continuous talent pipelining, technological integration, skill development, collaboration with educational institutions, data-driven decision-making, and a commitment to diversity and inclusion, forms a comprehensive model for long-term sustainable success. By adopting and adapting these practices, organizations can build a resilient and adaptable workforce capable of navigating the challenges of an ever-evolving business landscape. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
13.
Dhanalakshmi, R. V.; Padmaja, P.; Sireesha, B.; Kumar, Santosh
Textile industry Sustainable solutions and practices Journal Article
In: Asian Textile Journal, vol. 33, pp. 55-57,, 2024, ISBN: 09713425 (ISSN), (0).
Tags: MBA
@article{371,
title = {Textile industry Sustainable solutions and practices},
author = {R. V. Dhanalakshmi and P. Padmaja and B. Sireesha and Santosh Kumar},
isbn = {09713425 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Asian Textile Journal},
volume = {33},
pages = {55-57,},
publisher = {G P S Kwatra},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {article}
}
14.
Anuradha, A.; Shilpa, R.; Thirupathi, M.; Padmapriya, S.; Supramaniam, G.; Booshan, B.; Booshan, S.; Pol, N.; Chavadi, C. A.; Thangam, D.
Importance of Sustainable Marketing Initiatives for Supporting the Sustainable Development Goals Book Chapter
In: pp. 149-169,, IGI Global, 2023, ISBN: 978-166848683-2 (ISBN); 978-166848681-8 (ISBN), (2).
@inbook{24,
title = {Importance of Sustainable Marketing Initiatives for Supporting the Sustainable Development Goals},
author = {A. Anuradha and R. Shilpa and M. Thirupathi and S. Padmapriya and G. Supramaniam and B. Booshan and S. Booshan and N. Pol and C. A. Chavadi and D. Thangam},
doi = {10.4018/978-1-6684-8681-8.ch008},
isbn = {978-166848683-2 (ISBN); 978-166848681-8 (ISBN)},
year = {2023},
date = {2023-01-01},
journal = {Handbook of Research on Achieving Sustainable Development Goals With Sustainable Marketing},
pages = {149-169,},
publisher = {IGI Global},
abstract = {Businesses that engage in sustainable marketing can benefit both the world and their bottom line. Earlier, companies could satisfy many customers by simply providing low pricing and high-quality goods. However, people’s concern for the environment and other social concerns have grown, and so has their desire to support groups that share their beliefs. Because they often generate strong market returns and demonstrate durability during economic downturns, many investors want to support businesses that use sustainable business methods. Also, these businesses are more likely to comply with social and environmental laws. Several companies use sustainable marketing to succeed in today’s ethical and ecologically sensitive marketplace. Organizations must finance sustainability programs in order to practice sustainable marketing. But, it can also improve employee engagement, promote regulatory compliance, raise revenues, and build brand loyalty. © 2023 by IGI Global.},
note = {2},
keywords = {MBA},
pubstate = {published},
tppubtype = {inbook}
}
Businesses that engage in sustainable marketing can benefit both the world and their bottom line. Earlier, companies could satisfy many customers by simply providing low pricing and high-quality goods. However, people’s concern for the environment and other social concerns have grown, and so has their desire to support groups that share their beliefs. Because they often generate strong market returns and demonstrate durability during economic downturns, many investors want to support businesses that use sustainable business methods. Also, these businesses are more likely to comply with social and environmental laws. Several companies use sustainable marketing to succeed in today’s ethical and ecologically sensitive marketplace. Organizations must finance sustainability programs in order to practice sustainable marketing. But, it can also improve employee engagement, promote regulatory compliance, raise revenues, and build brand loyalty. © 2023 by IGI Global.
15.
Varsha, P. S.; Karan, A.
Descriptive analytics and data visualization in e-commerce Book Chapter
In: pp. 86-104,, Edward Elgar Publishing Ltd., 2023, ISBN: 978-180088855-5 (ISBN); 978-180088854-8 (ISBN), (1).
@inbook{93,
title = {Descriptive analytics and data visualization in e-commerce},
author = {P. S. Varsha and A. Karan},
doi = {10.4337/9781800888555.00010},
isbn = {978-180088855-5 (ISBN); 978-180088854-8 (ISBN)},
year = {2023},
date = {2023-01-01},
journal = {Handbook of Big Data Research Methods},
pages = {86-104,},
publisher = {Edward Elgar Publishing Ltd.},
abstract = {The main objective of this study is to know the impact and benefits of descriptive analytics in the e-commerce industry. The research has been carried out to explore all the opportunities and possibilities of descriptive analytics through data visualization in Flipkart to increase organizational performance. The research is based on primary data with descriptive analysis incorporated and quantitative study. The paper also explains the various possible ways to tackle the challenges and minimize the issues by using automation. The research outcome reveals that descriptive analytics helps e-commerce to make strategic decisions that contribute the growth and enriches customer satisfaction. Future researchers can examine the study about prescriptive and predictive analysis by using real-time cases how to sustain in pandemic situations by proposing models, increase in customer involvement, and organization success. © Editors and Contributors Severally 2023. All rights reserved.},
note = {1},
keywords = {MBA},
pubstate = {published},
tppubtype = {inbook}
}
The main objective of this study is to know the impact and benefits of descriptive analytics in the e-commerce industry. The research has been carried out to explore all the opportunities and possibilities of descriptive analytics through data visualization in Flipkart to increase organizational performance. The research is based on primary data with descriptive analysis incorporated and quantitative study. The paper also explains the various possible ways to tackle the challenges and minimize the issues by using automation. The research outcome reveals that descriptive analytics helps e-commerce to make strategic decisions that contribute the growth and enriches customer satisfaction. Future researchers can examine the study about prescriptive and predictive analysis by using real-time cases how to sustain in pandemic situations by proposing models, increase in customer involvement, and organization success. © Editors and Contributors Severally 2023. All rights reserved.
16.
Varsha, P. S.; Akter, S.; Kumar, A.; Gochhait, S.; Patagundi, B.
The Impact of Artificial Intelligence on Branding: A Bibliometric Analysis (1982-2019) Journal Article
In: Journal of Global Information Management, vol. 29, pp. 221-246,, 2021, ISBN: 10627375 (ISSN), (66).
@article{383,
title = {The Impact of Artificial Intelligence on Branding: A Bibliometric Analysis (1982-2019)},
author = {P. S. Varsha and S. Akter and A. Kumar and S. Gochhait and B. Patagundi},
url = {https://www.igi-global.com/gateway/article/278776},
doi = {10.4018/JGIM.20210701.oa10},
isbn = {10627375 (ISSN)},
year = {2021},
date = {2021-01-01},
journal = {Journal of Global Information Management},
volume = {29},
pages = {221-246,},
publisher = {IGI Global},
abstract = {Understanding the growth paths of artificial intelligence (AI) and its impact on branding is extremely pertinent of technology-driven marketing. This explorative research covers a complete bibliometric analysis of the impact of AI on branding. The sample for this research included all 117 articles from the period of 1982-2019 in the Scopus database. A bibliometric study was conducted using cooccurrence, citation analysis and co-citation analysis. The empirical analysis investigates the value propositions of AI on branding. The study revealed the nine clusters of co-occurrence: Social Media Analytics and Brand Equity; Neural Networks and Brand Choice; Chat Bots-Brand Intimacy; Twitter, Facebook, Instagram-Luxury Brands; Interactive Agent-Brand Love and User Choice; Algorithm Recommendations and E-Brand Experience; User-Generated Content-Brand Sustainability; Brand Intelligence Analytics; and Digital Innovations and Brand Excellence. The findings also identify four clusters of citation analysis Social Media Analysis and Brand Photos, Network Analysis and E-Commerce, Hybrid Simulating Modelling, and Real-Time Knowledge-Based Systems and four clusters of co-citation analysis: B2B Technology Brands, AI Fostered E-Brands, Information Cascades and Online Brand Ratings, and Voice Assistants-Brand Eureka Moments. Overall, the study presents the patterns of convergence and divergence of themes, narrowing to the specific topic, and multidisciplinary engagement in research, thus offering the recent insights in the field of AI on branding.},
note = {66},
keywords = {MBA},
pubstate = {published},
tppubtype = {article}
}
Understanding the growth paths of artificial intelligence (AI) and its impact on branding is extremely pertinent of technology-driven marketing. This explorative research covers a complete bibliometric analysis of the impact of AI on branding. The sample for this research included all 117 articles from the period of 1982-2019 in the Scopus database. A bibliometric study was conducted using cooccurrence, citation analysis and co-citation analysis. The empirical analysis investigates the value propositions of AI on branding. The study revealed the nine clusters of co-occurrence: Social Media Analytics and Brand Equity; Neural Networks and Brand Choice; Chat Bots-Brand Intimacy; Twitter, Facebook, Instagram-Luxury Brands; Interactive Agent-Brand Love and User Choice; Algorithm Recommendations and E-Brand Experience; User-Generated Content-Brand Sustainability; Brand Intelligence Analytics; and Digital Innovations and Brand Excellence. The findings also identify four clusters of citation analysis Social Media Analysis and Brand Photos, Network Analysis and E-Commerce, Hybrid Simulating Modelling, and Real-Time Knowledge-Based Systems and four clusters of co-citation analysis: B2B Technology Brands, AI Fostered E-Brands, Information Cascades and Online Brand Ratings, and Voice Assistants-Brand Eureka Moments. Overall, the study presents the patterns of convergence and divergence of themes, narrowing to the specific topic, and multidisciplinary engagement in research, thus offering the recent insights in the field of AI on branding.
