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Administrative Sciences
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Knowledge Management and Organizational Learning: Strategies for Continuous Improvement
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Knowledge Management and Organizational Learning: Strategies for Continuous Improvement

A special issue of Administrative Sciences (ISSN 2076-3387). This special issue belongs to the section "Organizational Behavior".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 2078

Special Issue Editor

Prof. Dr. Kuei-Hsien Niu

E-Mail Website
Guest Editor
Strategy and Entrepreneurship Department, College of Business, California State University-Sacramento, Sacramento, CA 95630, USA
Interests: knowledge management; organization learning; AI; trust; industrial agglomeration

Special Issue Information

Dear Colleagues,

In recent years, technology has led to the increased use of Artificial Intelligence (AI) in many areas, providing a way to improve efficiency and organizational performance. AI is widely considered a crucial tool in sectors such as marketing and manufacturing, but adverse effects must be considered for its effective integration into organizational operations. While there may be concerns regarding the applications of AI (e.g., job displacement), it is essential to acknowledge the potential opportunities AI can offer (Sato & Kameya, 2001). One promising application of AI is its ability to facilitate information and knowledge management because of its capability in analyzing big data more efficiently and effectively by using cognitive search technology. Cognitive search technology can help AI-driven knowledge management systems to obtain relevant data quickly and easily. This makes it possible to comprehend difficult and complicated queries more effectively and to produce more reliable results, as well as to detect early patterns that human practitioners may have otherwise missed (Bahoo et al., 2024).

AI has proven to enhance efficiency, problem-solving capabilities, and decision-making processes in crucial areas such as healthcare, finance, marketing, manufacturing, and more. That said, an in-depth analysis is needed because AI and other business developments, such as organizational sustainability, blockchain, and self-supervised e-learning, etc., do not function independently (Prikshat et al., 2023). The interconnected nature of AI with business aspects requires a comprehensive investigation.

This Special Issue aims to integrate AI and knowledge management, with a focus on enhancing knowledge creation, storage, retrieval, sharing, and utilization within organizations. The themes will involve leveraging AI technologies to address challenges and explore opportunities in the areas mentioned above, streamline decision-making process, promote organizational innovations, and facilitate continued improvements.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. AI integration with knowledge management processes (e.g., knowledge creation, retrieval and storing, sharing, and applications);
  2. The impact of AI-driven knowledge management on organizational competencies;
  3. AI and knowledge collaboration across organizations;
  4. Applications of AI in business operations and innovation;
  5. Roles of AI vs. humans in organizational knowledge management.

I look forward to receiving your contributions.

References

  1. (Sato & Kameya, 2001) Sato, T and Kameya, Y. 2001. Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research, 15: 391-454. 
  2. (Bahoo et al.,2024) Bahoo, S., Cucculelli, M., Goga, X. and Mondolo, J. 2024. Artificial Intelligence in Finance: A Comprehensive Review through Bibliometic and Content Analysis. SN Business and Economics, 4 (23): 1-46. 
  3. (Prikshat et al., 2023) Prikshat, V., Islam, M., Patel, P., Malik, A., Budhwar, P. and Gupta, S. 2023. AI-Augmented HRM: Literature Review and A proposed Multilevel Framework for Future Research. Technological Forecasting and Social Change, 193: 1-19.  

Prof. Dr. Kuei-Hsien Niu
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 250 words) can be sent to the Editorial Office for assessment.

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 double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Administrative Sciences 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 1600 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

  • artificial intelligence
  • knowledge management
  • innovation
  • organizational competence
  • knowledge creation
  • knowledge sharing
  • knowledge retrieval

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Published Papers (3 papers)

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Research

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17 pages, 622 KB  
Article
Conceptualizing Holistic Capital
by Mohammad Naushad and Sulphey Manakkattil MohammedIsmail
Adm. Sci. 2026, 16(4), 161; https://doi.org/10.3390/admsci16040161 - 24 Mar 2026
Viewed by 503
Abstract
Capital is classified as tangible and is used in the production process. It is a resource or collection of resources that can be accumulated or depleted, exchanged for other forms of capital, and unequally distributed. This study proposes holistic capital (HolC) as the [...] Read more.
Capital is classified as tangible and is used in the production process. It is a resource or collection of resources that can be accumulated or depleted, exchanged for other forms of capital, and unequally distributed. This study proposes holistic capital (HolC) as the synergistic value derived from the combined effects of multiple capitals, including human, behavioral, social, and spiritual capitals. Holistic capital is defined as the complex integration of human, behavioral, social, and spiritual resources that collectively enable individuals to function, thrive, and contribute meaningfully to their organizations and societies. It reflects a comprehensive spectrum that provides growth, transience, performance, thriving, and sustainability beyond customary financial or human capital models. Human capital theory, on which this proposed study is based, has a profound impact on multiple disciplines and is of deep interest to academicians and social scientists. Though the theory is a subject of severe criticism, it has easily survived and expanded its influence since its inception. Not surprisingly, a considerable number of criticisms have been made in response to this expansion. Based on this theory and to bridge gaps in the literature and present them systematically, the proposed study adopts a holistic approach. This review article examines theories across four dimensions: theoretical, methodological, empirical, and practical. In this manner, the proposed study intends to conceptualize a new capital—the holistic capital. Full article
(This article belongs to the Special Issue Knowledge Management and Organizational Learning: Strategies for Continuous Improvement)
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19 pages, 829 KB  
Article
Unpacking the Black Box: How Occupational Subculture and Sensemaking Drive Strategic Learning Capability
by Hanna Moon
Adm. Sci. 2026, 16(3), 147; https://doi.org/10.3390/admsci16030147 - 18 Mar 2026
Viewed by 463
Abstract
This study investigates the internal antecedents of Strategic Learning Capability (SLC) within volatile business environments. Specifically, it explores the tripartite relationship between occupational subculture, the cognitive process of sensemaking, and the multi-dimensional facets of SLC (external focus, strategic dialogue, engagement, etc.). The research [...] Read more.
This study investigates the internal antecedents of Strategic Learning Capability (SLC) within volatile business environments. Specifically, it explores the tripartite relationship between occupational subculture, the cognitive process of sensemaking, and the multi-dimensional facets of SLC (external focus, strategic dialogue, engagement, etc.). The research aims to bridge the empirical gap regarding how bottom-up subcultural values influence a firm’s capacity to pivot and execute new strategies. The research adopts a multi-dimensional framework of SLC, integrating theories of occupational context with sensemaking theory. By distinguishing between top-down organizational culture and bottom-up occupational subcultures, the study utilizes a conceptual (or empirical—adjust if you have specific data) model to examine how localized rules and practices within specific functions (e.g., R&D vs. Operations) lead to varied strategic outcomes through the generation of meaning. The paper proposes that sensemaking serves as a critical “bridge” or mediating mechanism that translates localized subcultural values into systemic innovative behaviors. While organizational culture sets the general tone, the findings suggest that the specific occupational environment determines the depth of strategic engagement and reflective responsiveness. The results indicate that SLC is not a monolithic construct but is lived and enacted differently across various occupational silos within the same firm. Unlike previous studies that focus on top-down leadership as the primary driver of culture, this research highlights the “bottom-up” influence of occupational subcultures on strategic agility. By introducing sensemaking as a pre-decisional activity that connects subcultural identity to Strategic Learning Capability, the study provides a more nuanced, multi-level understanding of organizational learning that accounts for internal diversity rather than assuming cultural homogeneity. Managers and OD practitioners are provided with a framework to identify subcultural barriers to learning. The study suggests that to enhance SLC, leaders must move beyond uniform cultural initiatives and instead facilitate sensemaking processes that align diverse occupational identities with the broader strategic vision. Full article
(This article belongs to the Special Issue Knowledge Management and Organizational Learning: Strategies for Continuous Improvement)
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Review

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26 pages, 609 KB  
Review
Generative Behavioral Explanation in Micro-Foundational HRM: A Functional Architecture for the Safety–CLB Recursive Mechanism
by Manabu Fujimoto
Adm. Sci. 2026, 16(2), 77; https://doi.org/10.3390/admsci16020077 - 4 Feb 2026
Viewed by 539
Abstract
Micro-foundational HRM has advanced our understanding of how employees perceive and respond to HR practices, yet explanations of how HR systems can generate and sustain coordinated action in day-to-day work remain underspecified. This article presents a theory-building integrative review that specifies a constrained, [...] Read more.
Micro-foundational HRM has advanced our understanding of how employees perceive and respond to HR practices, yet explanations of how HR systems can generate and sustain coordinated action in day-to-day work remain underspecified. This article presents a theory-building integrative review that specifies a constrained, generative mechanism grounded in observable interaction episodes. We propose a functional architecture that assigns constructs to distinct explanatory roles: enabling states (Role A), interaction episodes as the behavioral engine (Role B), and emergent coordination products (Role C). Psychological safety is positioned as an enabling condition that shifts the likelihood and quality of enactment, whereas collective leadership behavior (CLB) is defined as response-inclusive influence episodes (an influence attempt plus an observable response such as uptake, contestation, neglect, or sanction). We formalize a recursive safety–CLB cycle in which response patterns update subsequent safety and influence dispersion over time, which can yield divergent coordination trajectories even when HR conditions are broadly similar. The framework generates discriminant predictions about response profiles, dispersion versus centralization of influence, and temporal signatures, and it clarifies minimal design requirements for testing recursion with episode-level and intensive longitudinal evidence. We discuss implications for micro-foundational HRM, measurement alignment, and testable design-relevant implications for HR system design as an interaction-relevant cue environment. Full article
(This article belongs to the Special Issue Knowledge Management and Organizational Learning: Strategies for Continuous Improvement)
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