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Systems
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Systems Approaches in Knowledge Management and Organizational Innovation
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Systems Approaches in Knowledge Management and Organizational Innovation

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1749

Special Issue Editor

Dr. Halil Zaim

E-Mail Website
Guest Editor
College of Business, Alfred University, 1 Saxon Drive, Alfred, NY 14802, USA
Interests: methodology; knowledge management; supply chain management; intellectual capital; implementation; human resource management

Special Issue Information

Dear Colleagues,

In today’s dynamic business landscape, knowledge management (KM) is a cornerstone of organizational innovation and long-term success. KM refers to a systematic, multidisciplinary approach to capturing, organizing, sharing, and leveraging knowledge assets within an organization. By enabling employees to access and utilize critical information, KM enhances decision-making, fosters innovation, boosts employee performance, and provides a competitive advantage.

Effective KM enables innovation by offering access to past knowledge, identifying gaps, and connecting diverse expertise across teams. It supports collaborative innovation through tools like wikis and forums, accelerates the innovation cycle, and fosters organizational learning by analyzing successes and failures. KM transforms knowledge into a dynamic resource that drives creativity and sustains a culture of continuous improvement, helping organizations to adapt and thrive in an ever-evolving market.

Systems approaches ensure that knowledge is effectively converted into actionable strategies by aligning KM efforts with organizational goals. They provide a structured framework for accessing past knowledge, addressing gaps, and fostering collaboration. Through streamlined workflows and organizational learning, systems approaches enable KM to drive creativity, problem-solving, and sustained innovation, helping organizations to thrive in competitive environments.

Dr. Halil Zaim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • knowledge management
  • innovation
  • systems approaches
  • supply chain management
  • knowledge management processes
  • knowledge generation
  • knowledge creation
  • knowledge sharing
  • knowledge utilization
  • research and development
  • organizational learning
  • intellectual capital
  • decision making

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

26 pages, 2119 KiB  
Article
Actor Network Model of the Construction Mechanism of a Technology Standardization Innovation Ecosystem—Haier Case Study
by Yu Yuan and Dengyun Ma
Systems 2025, 13(4), 285; https://doi.org/10.3390/systems13040285 - 12 Apr 2025
Viewed by 421
Abstract
As the competition of standards among enterprises turns to the competition among innovation ecosystems, how to construct the technology standardization innovation ecosystem (TSIE) is of great significance to enhance the competitiveness of enterprises and even industries. Based on the perspective of actor network [...] Read more.
As the competition of standards among enterprises turns to the competition among innovation ecosystems, how to construct the technology standardization innovation ecosystem (TSIE) is of great significance to enhance the competitiveness of enterprises and even industries. Based on the perspective of actor network theory (ANT) through the case study of Haier, this paper constructs an ANT model for the formation of a TSIE and tries to answer the following questions: how is the TSIE formed? how do the actors gather and what roles do they play in the formation process? and what role do technology standards play in the formation process? This research finds that the formation of the TSIE results from interactions among the actors of ANT over different periods. The focal actors play a crucial role; their roles change from the construction of their own actor network to the empowerment of the sub-actor network construction. Other actors evolve from being defined to defining roles themselves. Standards are also crucial throughout this process: initially, they recruit and coordinate the primary actors to form close relationships, and later they facilitate bidirectional regulation, enable standardization, and coordinate the formation and development of sub-ecosystems. This paper explores the evolution of TSIE through the lens of ANT, advancing its application within this context. It enriches the theoretical research on this subject and offers a theoretical foundation for large enterprise platforms to facilitate the transformation of TSIE. Full article
(This article belongs to the Special Issue Systems Approaches in Knowledge Management and Organizational Innovation)
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24 pages, 703 KiB  
Article
R&D Subsidies and Radical Innovation: Innovative Mindset and Competition Matter
by Xiaotong Huo, Shuyang Wang, Bowen Zheng and Xiaoyu Wu
Systems 2025, 13(4), 282; https://doi.org/10.3390/systems13040282 - 11 Apr 2025
Viewed by 470
Abstract
With the increasing focus on R&D (research and development) subsidies of various researchers, there is growing interest in how these subsidies affect radical innovation. Based on the limited attention paid to this area in the existing literature, this paper investigates the impact of [...] Read more.
With the increasing focus on R&D (research and development) subsidies of various researchers, there is growing interest in how these subsidies affect radical innovation. Based on the limited attention paid to this area in the existing literature, this paper investigates the impact of R&D subsidies on radical innovation. Using a sample of Chinese listed firms, we investigate how innovation orientation and competitive intensity moderate this relationship. By incorporating concepts from Path Dependence Theory, we propose that R&D subsidies alter firms’ assessment of the value and risk associated with investments in radical innovation, influencing their innovation strategies. Subsidies may increase the attractiveness of incremental innovations, which have lower volatility and faster returns, compared to radical innovations, which inherently involve higher risk and uncertainty. Based on the results of our analysis, we find that R&D subsidies negatively affect radical innovation, but firms with a stronger innovation orientation (which reflects their greater tolerance for risk) are less negatively affected. Conversely, an increase in the intensity of competition exacerbates the negative impact of subsidies because it induces firms to make safer incremental investments. The robustness analysis confirms that the main effects remain significant even when using alternative proxies for innovation. Our study sheds light on the mechanisms affecting the effectiveness of subsidies from the perspective of finance theory and highlights the conditions under which subsidies may unintentionally discourage radical innovation. Full article
(This article belongs to the Special Issue Systems Approaches in Knowledge Management and Organizational Innovation)
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21 pages, 1354 KiB  
Article
PAI-NET: Retrieval-Augmented Generation Patent Network Using Prior Art Information
by Kyung-Yul Lee and Juho Bai
Systems 2025, 13(4), 259; https://doi.org/10.3390/systems13040259 - 7 Apr 2025
Viewed by 565
Abstract
Similar patent document retrieval is an essential task that reduces the scope of patent claimants’ searches, and numerous studies have attempted to provide automated patent search services. Recently, Retrieval-Augmented Generation (RAG) based on generative language models has emerged as an excellent method for [...] Read more.
Similar patent document retrieval is an essential task that reduces the scope of patent claimants’ searches, and numerous studies have attempted to provide automated patent search services. Recently, Retrieval-Augmented Generation (RAG) based on generative language models has emerged as an excellent method for accessing and utilizing patent knowledge environments. RAG-based patent search services offer enhanced retrieval ranking performance as AI search services by providing document knowledge similar to queries. However, achieving optimal similarity-based document ranking in search services remains a challenging task, as search methods based on document similarity do not adequately address the characteristics of patent documents. Unlike general document retrieval, the similarity of patent documents must take into account prior art relationships. To address this issue, we propose PAI-NET, a deep neural network for computing patent document similarities by incorporating expert knowledge of prior art relationships. We demonstrate that our proposed method outperforms current state-of-the-art models in patent document classification tasks through semantic distance evaluation on the USPD and KPRIS datasets. PAI-NET presents similar document candidates, demonstrating a superior patent search performance improvement of 15% over state-of-the-art methods. Full article
(This article belongs to the Special Issue Systems Approaches in Knowledge Management and Organizational Innovation)
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