The future of innovation processes is anticipated to be more data-driven and empowered by the ubiquitous digitalization, increasing data accessibility and rapid advances in machine learning, artificial intelligence, and computing technologies. While the data-driven innovation (DDI) paradigm is emerging, it has yet been formally defined and theorized and often confused with several other data-related phenomena. This paper defines and crystalizes "data-driven innovation" as a formal innovation process paradigm, dissects its value creation, and distinguishes it from data-driven optimization (DDO), data-based innovation (DBI), and the traditional innovation processes that purely rely on human intelligence. With real-world examples and theoretical framing, I elucidate what DDI entails and how it addresses uncertainty and enhance creativity in the innovation process and present a process-based taxonomy of different data-driven innovation approaches. On this basis, I recommend the strategies and actions for innovators, companies, R&D organizations, and governments to enact data-driven innovation.
A new perspective on the prediction of the innovation performance: A data driven methodology to identify innovation indicators through a comparative study of Boston's neighborhoods
What hackers talk about when they talk about AI: Early-stage diffusion of a cybercrime innovation
International Centre for Comparative Criminology（国际比较犯罪学中心）；Université de Montréal（蒙特利尔大学）；Deakin Centre for Cyber Research and Innovation（迪肯网络研究与创新中心）；Deakin University（迪肯大学）；Flare Systems（Flare系统公司）
"You should see my partners' fingers": A Qualitative Study of Construction Artisans' Perspective on Technical Innovation
Human Inspired Technologies Research Centre, University of Padova（人类启发技术中心，帕多瓦大学）；Dept. of General Psychology, University of Padova（普通心理学系，帕多瓦大学）；IUAV University of Venice（威尼斯 IUAV 大学）
VETNET: Assembling Benchmark Datasets for AI-Driven Veterinary Innovation
Cornell University（康奈尔大学）；University of Padua（帕多瓦大学）；Vet Validate Ltd.（Vet Validate有限公司）；Mars Veterinary Health（玛氏宠物保健）；VetApp；Cornell Veterinary Biobank, College of Veterinary Medicine, Cornell University（康奈尔大学兽医学院康奈尔兽医生物样本库）；The University of Melbourne（墨尔本大学）；Foundation for Food & Agriculture Research（食品与农业研究基金会）；The Schwarzman Animal Medical Center（施瓦茨动物医疗中心）；University of Leeds（利兹大学）；Colorado State University（科罗拉多州立大学）；Center for Applied Mathematics, Cornell University（康奈尔大学应用数学中心）；Cornell Law School, Cornell University（康奈尔大学法学院）；University of Liverpool（利物浦大学）；Toronto Animal Cancer Centre（多伦多动物癌症中心）；ANI.ML Health Inc.（ANI.ML健康公司）；University of Saskatoon（萨斯喀彻温大学）
Data Want to be Free: An Innovation Resistance Theory Model for Identifying Barriers to Government Data Sharing
University of Tartu（塔尔图大学）；Umeå University（于默奥大学）；Delft University of Technology（代尔夫特理工大学）；The Swedish National Road and Transport Research Institute（瑞典国家道路交通研究所）；University of Priština - Kosovska Mitrovica（普里什蒂纳大学-科索沃米特罗维察分校）；University of the Aegean（爱琴海大学）
On the false election between regulation and innovation. Ideas for regulation through the responsible use of artificial intelligence in research and education.[Spanish version]
Instituto de Investigación en Inteligencia Artificial (IIIA-CSIC)（西班牙国家研究委员会人工智能研究所）；La Trobe University Law School（拉筹伯大学法学院）；Institut de Dret i Tecnologia de la Universitat Autònoma de Barcelona (IDT-UAB)（巴塞罗那自治大学法律与技术研究所）
Intelligent Computing Social Modeling and Methodological Innovations in Political Science in the Era of Large Language Models
Shanghai Academy of Social Sciences（上海社会科学院）；Nanjing University（南京大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai Institutes for International Studies（上海国际问题研究院）
