Innovation processes: from linear models to artificial intelligence

被引:0
|
作者
Szymanska, Elzbieta [1 ]
Berbel-Pineda, Juan Manuel [2 ]
机构
[1] Bialystok University of Technology, Wiejska 45A, Bialystok,15-351, Poland
[2] University in Sevilla, De Utrera 1, Sevilla,41013, Spain
关键词
D O I
10.2478/emj-2024-0021
中图分类号
学科分类号
摘要
This study aims to map scientific publications, intellectual structure and research trends in the development of innovation process models and to characterise and compare them. Specifically, to identify the innovation process models and their characteristics, comparative analysis of the models, and predict the direction of development. A hybrid method was used, which involved many years of in-depth literature monitoring and comparative analysis based on a set of parameters developed by the authors. The results made it possible to identify and classify 15 various theoretical models of the innovation process (from M1 — linear to M15 with the AI contribution) development through categorisation according to five main features: C1 — complexity, C2 — openness, C3 — the role of technology, C4 — the participation of the market/users, and C5 — the form of presentation. This study identifies, explores, analyses and summarises the main ideas of innovation processes by identifying their models and characterising those specifics that can ensure international standards of excellence. The study provides an objective view of the existing innovation process models and the relevant studies that can guide managers in their decision-making innovation processes. This study is a first attempt at unveiling the evolution of knowledge in the field of existing innovation processes and their characteristics and comparative analysis. The presented models of innovation processes should constitute an indication for practitioners who can choose a model to be used in the economic practice of their organisation. © 2024 E. Szymanska and J. M. Berbel-Pineda.
引用
收藏
页码:15 / 28
相关论文
共 50 条
  • [41] The Role Of Artificial Intelligence In Facilitating Military Innovation
    Ijebor, Colin
    JOURNAL OF MILITARY AND STRATEGIC STUDIES, 2019, 20 (01):
  • [42] Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition
    Jiang, Bin
    Ozkara, Burak B.
    Zhu, Guangming
    Boothroyd, Derek
    Allen, Jason W.
    Barboriak, Daniel P.
    Chang, Peter
    Chan, Cynthia
    Chaudhari, Ruchir
    Chen, Hui
    Chukus, Anjeza
    Ding, Victoria
    Douglas, David
    Filippi, Christopher G.
    Flanders, Adam E.
    Godwin, Ryan
    Hashmi, Syed
    Hess, Christopher
    Hsu, Kevin
    Lui, Yvonne W.
    Maldjian, Joseph A.
    Michel, Patrik
    Nalawade, Sahil S.
    Patel, Vishal
    Raghavan, Prashant
    Sair, Haris I.
    Tanabe, Jody
    Welker, Kirk
    Whitlow, Christopher T.
    Zaharchuk, Greg
    Wintermark, Max
    AMERICAN JOURNAL OF NEURORADIOLOGY, 2024, 45 (09) : 1276 - 1283
  • [43] GOD FROM THE MACHINE: Artificial Intelligence Models of Religious Cognition
    Drozdek, Adam
    PERSPECTIVES ON SCIENCE AND CHRISTIAN FAITH, 2007, 59 (01):
  • [44] God from the machine: Artificial intelligence models of religious cognition
    Upal, M. Afzal
    COGNITIVE SYSTEMS RESEARCH, 2008, 9 (03): : 232 - 235
  • [45] From Correlation to Imagination: Deep Generative Models for Artificial Intelligence
    Serra, Joan
    ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT, 2019, 319 : 4 - 4
  • [46] Towards a Better Conceptual Framework for Innovation Processes in Agriculture and Rural Development: From Linear Models to Systemic Approaches
    Knickel, Karlheinz
    Brunori, Gianluca
    Rand, Sigrid
    Proost, Jet
    JOURNAL OF AGRICULTURAL EDUCATION & EXTENSION, 2009, 15 (02): : 131 - 146
  • [47] Innovation of production scheduling and service models for cloud manufacturing of tourism equipment based on artificial intelligence
    Lu, Junli
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2024,
  • [48] Augmenting human innovation teams with artificial intelligence: Exploring transformer-based language models
    Bouschery, Sebastian G.
    Blazevic, Vera
    Piller, Frank T.
    JOURNAL OF PRODUCT INNOVATION MANAGEMENT, 2023, 40 (02) : 139 - 153
  • [49] Innovation of production scheduling and service models for cloud manufacturing of tourism equipment based on artificial intelligence
    Lu, JunLi
    International Journal of Advanced Manufacturing Technology, 2024,
  • [50] Reinforcement Learning from Algorithm Model to Industry Innovation Innovation: A Foundation Stone of Future Artificial Intelligence
    DONG Shaokang
    CHEN Jiarui
    LIU Yong
    BAO Tianyi
    GAO Yang
    ZTE Communications, 2019, 17 (03) : 31 - 41