Call for Chapters: Harnessing Large Language Models for Enhanced Business Analytics

Editors

FABIO MARZULLO, Politécnica/UFRJ, Brazil

Call for Chapters

Proposals Submission Deadline: June 2, 2024
Full Chapters Due: September 15, 2024
Submission Date: September 15, 2024

Introduction

The book will delve into the transformative role of Large Language Models (LLMs) in Business Analytics. It aims to bridge the gap between cutting-edge AI research and practical business applications by showcasing how LLMs can analyze vast datasets, uncover insights, predict trends, and facilitate decision-making processes. The book will cover theoretical foundations, practical applications, case studies, and future directions of LLMs in business environments.

Objective

The objective of this publication is to make a significant impact on both academic and professional realms by providing a comprehensive resource on leveraging LLMs for business analytics. It will contribute to the academic discussion by presenting research findings, methodologies, and theoretical advancements. For practitioners, it offers actionable insights, best practices, and examples of successful integration of LLMs into business processes. Moreover, it aims to foster innovation, encourage the adoption of LLM technologies in diverse business sectors, and stimulate further research in this dynamic field.

Target Audience

Academic researchers and students in AI, machine learning, business analytics, and related fields seeking to understand the application of LLMs in business contexts. Business professionals and industry practitioners looking for ways to apply LLMs to solve real-world business problems, enhance decision-making, and gain competitive advantages. Policy makers and technology strategists interested in the implications of AI technologies on business models, operations, and regulatory environments.

Recommended Topics

1. Introduction to Large Language Models in Business Analytics The application of Large Language Models (LLMs) in business analytics is a thriving field, encompassing a wide array of strategies and technologies aimed at enhancing decision-making and operational efficiency. Examples include the evolution of AI in business analytics, detailing its historical context and progression, and examining the specific role of LLMs in modern business settings. 2. Foundations of LLMs The foundational technology behind LLMs is both broad and complex, incorporating various aspects of machine learning and artificial intelligence. This includes in-depth discussions on core technologies and architectures such as Transformer models, as well as insights into the training and development processes necessary for creating effective LLMs. 3. Overview of Business Analytics Business analytics encompasses a wide range of tools and concepts, each contributing to the nuanced examination of business data. Key concepts and tools, along with data management and governance, are crucial for understanding how LLMs can be effectively integrated into existing business processes. 4. Theoretical Underpinnings of LLMs The theoretical aspects of LLMs include advanced concepts in machine learning and natural language processing. Topics such as advanced NLP techniques and integration with other AI technologies illustrate the depth and variety of theoretical knowledge required to leverage LLMs in business. 5. Machine Learning Algorithms for Business Analytics Machine learning algorithms form the core of LLM applications in business analytics, with various approaches tailored to specific business needs. The use of supervised and unsupervised learning techniques, alongside reinforcement learning for decision-making, demonstrates the diverse applications of these algorithms in business contexts. 6. Natural Language Processing in the Business Context Natural language processing (NLP) in the business context covers a broad spectrum of applications, from operational improvements to enhancing customer interactions. Automating customer interactions and text analytics for market intelligence are prime examples of how NLP is applied within business settings. 7. Practical Applications of LLMs in Business The practical applications of LLMs in business are vast, affecting multiple facets of business operations. From automated report generation to real-time decision support systems, LLMs offer transformative potential for businesses seeking to enhance their analytical capabilities. 8. Enhancing Customer Service with Chatbots Chatbots enhance customer service by providing timely and interactive communication solutions. This topic covers case studies of successful chatbot implementations and strategies for designing chatbots for multilingual markets, showcasing the broad application of LLMs in enhancing customer service. 9. Market Analysis and Trend Prediction Market analysis and trend prediction using LLMs involve a variety of techniques and tools to interpret and predict market dynamics. Predictive analytics for consumer behavior and real-time market monitoring are key areas where LLMs provide substantial insights into market trends. 10. Risk Management and Fraud Detection The broad scope of LLM applications in risk management and fraud detection highlights their importance in maintaining financial security and operational integrity. LLMs are used extensively in detecting fraud patterns and anomalies and in building risk assessment models to predict and mitigate potential threats. 11. Case Studies Case studies in the use of LLMs in business sectors like finance, healthcare, and retail illustrate the diverse and impactful ways these models can be applied. Examples include enhancing user experience in e-commerce and managing healthcare through innovative AI applications. 12. Ethical Considerations and Future Directions Ethical considerations in the use of LLMs in business analytics span a wide range of issues from privacy to regulatory compliance. The challenges of privacy and data security, along with navigating AI regulations, are crucial for maintaining ethical standards in the use of LLMs. 13. Addressing Bias and Fairness in LLMs Addressing bias and ensuring fairness in LLM outputs are significant concerns that encompass a variety of mitigation techniques and impact assessments. Methods for reducing bias and evaluating the societal impacts of LLMs demonstrate the critical need for ethical AI practices. 14. The Future of LLMs in Business Analytics The future of LLMs in business analytics is a broad topic that explores emerging trends and scalability issues. It includes discussions on nascent technologies and methodologies that could influence LLM applications and how businesses can adapt these solutions as they grow.

Submission Procedure

Researchers and practitioners are invited to submit on or before June 2, 2024, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by June 16, 2024 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by September 15, 2024, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-anonymized review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Harnessing Large Language Models for Enhanced Business Analytics. All manuscripts are accepted based on a double-anonymized peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.



Publisher

This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2025.



Important Dates

June 2, 2024: Proposal Submission Deadline
June 16, 2024: Notification of Acceptance
September 15, 2024: Full Chapter Submission
November 17, 2024: Review Results Returned
December 29, 2024: Final Acceptance Notification
January 12, 2025: Final Chapter Submission



Inquiries

FABIO MARZULLO Politécnica/UFRJ fabio@marzullo.com.br

Classifications


Business and Management; Computer Science and Information Technology; Library and Information Science
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