Books

Innovation Management – A Strategic Guide for Future-Driven Organizations

In today’s fast-paced world, innovation is no longer optional—it’s essential. This book provides a concise yet powerful exploration of key innovation concepts, including R&D, open vs. closed innovation, and the differences between radical and incremental innovation.

Designed for managers, entrepreneurs, students, and researchers, it offers practical tools and strategic insights to help organizations stay competitive and adaptable. From enhancing internal processes to fostering breakthrough ideas, this guide reveals how innovation can drive sustainable growth and long-term success.

Whether you aim to lead innovation within your company or gain a deeper academic understanding of the field, Innovation Management is your essential companion in navigating today’s dynamic business landscape.

Why This Book is Essential and Its Applications?

In today’s rapidly changing world, the ability to identify and analyze weak signals has become a crucial skill for strategic decision-making. This book serves as a valuable resource for managers, analysts, researchers, futurists, and anyone involved in strategy, innovation, and decision-making.

By reading this book, readers will learn how to detect early signs of change in business, technology, politics, and society and leverage them to anticipate future trends and gain a competitive advantage. Additionally, this book provides practical tools and methods for processing and analyzing weak signals, helping organizations navigate uncertainty and manage risks effectively.

Beyond managers and policymakers, this book is also beneficial for researchers and students in the fields of management and foresight, as it combines key concepts with practical examples and scientific approaches. Ultimately, individuals looking to enhance their analytical skills, creativity, and strategic thinking will find this book instrumental in recognizing hidden patterns and making more informed decisions. 

Absorptive capacity is a fundamental concept in management and innovation research, referring to an organization’s ability to identify, assimilate, and apply external knowledge to enhance performance and sustain competitive advantage. Since its introduction in 1989, numerous studies have examined its conceptual foundations, measurement approaches, antecedents, and consequences. This book presents a narrative review of key research in this field, synthesizing theoretical perspectives and empirical findings on absorptive capacity. This book highlights its determinants, including prior knowledge base, human capital, organizational structure, and network relationships, while also discussing its significant implications for innovation, financial performance, competitiveness, and organizational learning. By integrating insights from previous research, this review identifies gaps in the literature and suggests directions for future studies, particularly regarding the refinement of measurement models and the exploration of absorptive capacity’s role in different organizational contexts. Given the increasing complexity of business environments and rapid technological advancements, absorptive capacity remains a dynamic and interdisciplinary concept with ongoing relevance in strategic management and knowledge-based policy making. 

A Practical Guide for Researchers and Professionals

In a world where data drives decisions, mastering the tools of analysis is no longer optional—it’s essential. R, one of the most powerful open-source platforms for statistical computing and data visualization, is the tool of choice for data scientists, researchers, and analysts across disciplines. This book offers a hands-on, practical introduction to data analysis using R, designed for those who want to learn through doing.

Whether you're a graduate student, academic researcher, or data analyst in business, social sciences, education, or public policy, this book will guide you through the entire data analysis process—from importing and cleaning data to running complex statistical models. Step-by-step examples and clear, reproducible code make each concept easy to understand and apply, even for readers with no programming background.

Topics covered include descriptive and inferential statistics, regression analysis, mediation and moderation models, structural equation modeling (SEM), big data processing with data. Table, reliability and validity testing, and automated report generation using R Markdown. Each chapter is crafted to build your skills progressively and help you apply R in real-world research and decision-making contexts.

By the end of this book, you’ll have the confidence and ability to manage, analyze, and report data professionally using R. This guide is an indispensable resource for students, instructors, researchers, and professionals who want to harness the full potential of data in a modern analytic environment.

Knowledge, absorptive capacity, entrepreneurship, research and development, and innovation are prerequisites and keys to achieving the future. Only businesses that establish these capabilities in their business can compete in the future.