Call for Chapters Proposals

“New Perspectives on Enterprise Decision-Making Applying Artificial Intelligence Techniques”


Overview

Artificial Intelligence (AI) techniques cover the automation of cognitive and physical tasks. These techniques help people perform tasks faster and better and make better decisions. It enables the automation of decision making without human intervention. AI techniques can enhance automation thus reducing intensive human labor and tedious tasks. There are many more ways in which Artificial Intelligence is making a difference for enterprises in marketing decision-making, Customer Relationship Management, Recommender Systems, Problem Solving, Opinion Mining, Augmented Analytics, to mention but a few.

In marketing, it is necessary to understand customer needs and desires and aligning products to those needs and desires. A handle on changing customer behavior is vital to make the best marketing decisions. AI simulation and modeling techniques provide reliable insight into the consumers’ persona. This will help predict consumers’ behavior. Through real-time data gathering, trend analysis and forecasting, an AI system can help businesses make insightful marketing decisions. Furthermore, organizations can identify a consumer’s lifetime value with the help of AI’s buyer persona modeling. It can help organizations manage multiple inputs. During a complex decision-making process, AI can efficiently manage and control different factors at the same point in time. It can source and process large amounts of data within minutes while providing valuable business-based insights.

While we humans face decision fatigue, algorithms do not have such limitations, which make AI-based decisions faster and better. Also, AI techniques has provided businesses invaluable insight about consumers, which helps them enhance their communication with the consumers. It also helps retailers predict product demand and respond to it quickly. To that

end, opinion mining helps businesses understand why people feel the way they feel. Most often a single customer’s concerns might be common among others. When sufficient opinions are gathered and analyzed correctly, the information gleaned will help organizations gauge and predict the concerns of the silent majority. AI has improved this mining process through automation, which is quicker and more reliable, helping organizations in making critical business decisions.

In e-commerce, an AI system learns a consumer’s preference based on ‘explicit’ or ‘implicit’ feedbacks. This kind of systems are called recommender systems. A recommender system can provide information helping the organization to reduce bounce rate and craft better customer- specific targeted content. Wise business decisions are made when business executives and decision- makers have reliable data and recommendations. AI not only improves the performance of both the individual members of the team but also the competitive edge of the business.

According to above, the main objective of this book is to collect and consolidate innovative and high- quality research contributions regarding to the implementation of conceptual frameworks, strategies, techniques, methodologies, informatics platforms and models about Enterprise Decision- Making Applying Artificial Intelligence Techniques.


Topic Coverage

Topics of interest in this book include, but are not restricted to:
- Expert systems for Optimization Process
- Application of Knowledge-Based Methods
- Software Tools for Knowledge-Based Systems construction
- Artificial Intelligence Applying In Supply Chain
- Artificial Intelligence and Block Chain Technology in Logistics and Supply Chain Management
- Inventory Control, Production Planning and Scheduling Applying Artificial Intelligence
- Decision-Support mechanisms
- Knowledge Acquisition & Representation
- Business Intelligence
- Knowledge-Based Implementation techniques and System Architectures
- Decision Support Systems
- Intelligent Systems for E-commerce and Electronic Business
- Internet of Things (Iot) in Logistics and Supply Chain Management
- Case-Based Reasoning
- Big Data and Data Mining in Logistics and Supply Chain Management
- Fintech o Financial Technology
- Context-aware/context-sensitive mobile applications
- Machine Learning-based Applications
- Neural Networks and Deep Learning Applications
- Sentiment Analysis and Opinion Mining in social media
- Natural Language Processing Techniques
- Simulation in Logistics and Supply Chain


Target audience

The target audience includes researchers, practitioners and (Masters/PhD) students. Therefore, chapters need to address both scientific and practical implications of the research.


Type of contributions and length

- Case studies: In-depth reports of Semantic Web implementations in an organization or business.
- Full research papers: Contributions about frameworks, strategies, techniques, methodologies, informatics platforms and models about Enterprise Decision-Making Applying Artificial Intelligence Techniques.
- Conceptual papers: Contributions that synthesize existing studies.

This type of contributions are typically 18 to 22 pages in length (excluding references) when applying the Springer formatting instructions. Contributions should be original and not be submitted elsewhere.


Submission Guidelines and Other Considerations

Chapters submitted must not have been previously published or be under consideration for publication in other journals, books, though they may represent significant extensions of prior work. All submitted chapters will undergo a rigorous peer-review process (with at least two reviewers) that will consider programmatic relevance, scientific quality, significance, originality, style and clarity.

The acceptance process will focus on chapters that address relevant contributions in the form of theoretical and experimental research and case studies applying new perspectives for Enterprise Decision-Making Applying Artificial Intelligence Techniques. Before submitting a chapter proposal, authors must carefully read over the Springer’s Author Guidelines.

Authors should submit their complete chapter via email to: julian.zapata@ceipa.edu.co , galor@ito- depi.edu.mx , csanchezr@ito-depi.edu.mx and jorge.garcia@uacj.mx . The subject of the email should be: “Chapter Submission: New Perspectives on Enterprise Decision-Making Applying Artificial Intelligence Techniques.


Publication Fees

There are not publication fees for accepted chapters.


Important Dates

- Submission deadline: October 1th, 2020
- Reviews and acceptance notification: October 15th, 2020
- Camera-Ready papers due: October 30th, 2020
- Publication (tentative): February 2021


Editors

Julian Andres Zapata-Cortes, PhD. CEIPA Business School, Colombia. Email: julian.zapata@ceipa.edu.co

Giner Alor-Hernández, Ph.D.
Instituto Tecnológico de Orizaba, México Email: galor@ito-depi.edu.mx

Cuauhtemoc Sanchez-Ramirez, Ph.D. Instituto Tecnológico de Orizaba, México. Email: csanchezr@ito-depi.edu.mx

Jorge Luis García-Alcaraz, Ph.D.
Universidad Autónoma de Ciudad Juárez, Mexico Email: jorge.garcia@uacj.mx