Marcin Lis – författare

The growing complexity, fluidity and instability of the environment as well as changing needs are challenges that both enterprises and higher education institutions must face. Higher education institutions understand that their key product, i.e. knowledge, is a value that can and should be offered to enterprises in a desirable form as a key to innovation and development as well as the basis of the necessary internal transformation to respond to requirements of our times. Attempts to explain the process of collaboration between higher education institutions and businesses based on an institutional perspective fail to capture the complexity of this process. The purpose of this book is to develop a model approach to managerial competencies that affect the innovativeness of enterprises and to identify internal and external key factors strengthening or limiting the impact of managerial competencies on the innovativeness of an enterprise including organizational structure, strategy, organizational culture and more. It will be of value to researchers, academics, and students in the fields of entrepreneurship, innovation, management, strategy, and will be particularly useful to organizations that are aware of their operating conditions in the knowledge-based economy and of the impact of the COVID-19 pandemics on the acceleration of the digital transformation of the contemporary world.The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons (CC-BY-NC-ND) 4.0 license.

The growing complexity, fluidity and instability of the environment as well as changing needs are challenges that both enterprises and higher education institutions must face. Higher education institutions understand that their key product, i.e. knowledge, is a value that can and should be offered to enterprises in a desirable form as a key to innovation and development as well as the basis of the necessary internal transformation to respond to requirements of our times. Attempts to explain the process of collaboration between higher education institutions and businesses based on an institutional perspective fail to capture the complexity of this process. The purpose of this book is to develop a model approach to managerial competencies that affect the innovativeness of enterprises and to identify internal and external key factors strengthening or limiting the impact of managerial competencies on the innovativeness of an enterprise including organizational structure, strategy, organizational culture and more. It will be of value to researchers, academics, and students in the fields of entrepreneurship, innovation, management, strategy, and will be particularly useful to organizations that are aware of their operating conditions in the knowledge-based economy and of the impact of the COVID-19 pandemics on the acceleration of the digital transformation of the contemporary world.The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons (CC-BY-NC-ND) 4.0 license.

AI in Social Sciences Research explains how artificial intelligence (AI) is reshaping social inquiry, from data collection and research design through analysis, interpretation, and theory-building. Treating AI as both a tool kit and a methodological shift, the volume offers a clear route into machine learning (ML), natural language processing (NLP), simulation, and generative AI (GAI) for social researchers while keeping ethics, bias, transparency, and interpretability in view.Designed for postgraduate students, early-career researchers, and instructors teaching digital and computational social science (CSS) methods, the book supports readers who want to use AI with methodological care, not just technical speed.

Data Management and Digital Infrastructure in Social Sciences shows how to move from raw digital data to usable evidence, with practical guidance on data literacy, documentation, platforms, and workflows that support transparency and reproducibility.Written for postgraduate students and early-career researchers in the social sciences and management, the volume also supports instructors and research support staff who need a grounded, course-ready guide to modern data practices, including links to evidence-based management and real-world research settings.

AI in Social Sciences Research explains how artificial intelligence (AI) is reshaping social inquiry, from data collection and research design through analysis, interpretation, and theory-building. Treating AI as both a tool kit and a methodological shift, the volume offers a clear route into machine learning (ML), natural language processing (NLP), simulation, and generative AI (GAI) for social researchers while keeping ethics, bias, transparency, and interpretability in view.Designed for postgraduate students, early-career researchers, and instructors teaching digital and computational social science (CSS) methods, the book supports readers who want to use AI with methodological care, not just technical speed.

Data Management and Digital Infrastructure in Social Sciences shows how to move from raw digital data to usable evidence, with practical guidance on data literacy, documentation, platforms, and workflows that support transparency and reproducibility.Written for postgraduate students and early-career researchers in the social sciences and management, the volume also supports instructors and research support staff who need a grounded, course-ready guide to modern data practices, including links to evidence-based management and real-world research settings.

Data Management and Digital Infrastructure in Social Sciences shows how to move from raw digital data to usable evidence, with practical guidance on data literacy, documentation, platforms, and workflows that support transparency and reproducibility.Written for postgraduate students and early-career researchers in the social sciences and management, the volume also supports instructors and research support staff who need a grounded, course-ready guide to modern data practices, including links to evidence-based management and real-world research settings.

Data Management and Digital Infrastructure in Social Sciences shows how to move from raw digital data to usable evidence, with practical guidance on data literacy, documentation, platforms, and workflows that support transparency and reproducibility.Written for postgraduate students and early-career researchers in the social sciences and management, the volume also supports instructors and research support staff who need a grounded, course-ready guide to modern data practices, including links to evidence-based management and real-world research settings.

AI in Social Sciences Research explains how artificial intelligence (AI) is reshaping social inquiry, from data collection and research design through analysis, interpretation, and theory-building. Treating AI as both a tool kit and a methodological shift, the volume offers a clear route into machine learning (ML), natural language processing (NLP), simulation, and generative AI (GAI) for social researchers while keeping ethics, bias, transparency, and interpretability in view.Designed for postgraduate students, early-career researchers, and instructors teaching digital and computational social science (CSS) methods, the book supports readers who want to use AI with methodological care, not just technical speed.