ABSTRACT Generative artificial intelligence (AI) now performs many explicit tasks in organizational knowledge creation, such as drafting, summarizing, and combining documents. This paper asks what this change means for the human agency on which knowledge creation depends. The paper starts from the position that AI does not possess tacit knowledge. Its concern is that AI can automate explicit tasks through which people have traditionally developed tacit knowing. The study uses a conceptual theory‐building design supported by a structured literature review. A Scopus search returned 192 records, and two inclusion criteria were applied. Together with three contributions found through citation tracking, a final set of 15 models of organizational knowledge creation with a defined role for AI was analyzed. The review shows that, despite assigning AI different functional roles, these models continue to depend on human actors for embodied tacit knowing. However, they rarely examine how AI use may change the way people form tacit knowing over time. Building on this gap and on empirical studies of automation and learning, the paper introduces the Human Agency Deficit. It is defined as the gap between the human agency an organization needs to evaluate, own, and extend its knowledge and the human agency that its AI‐supported work practices actually develop and use. The deficit is treated as a conditional risk, not an inevitable outcome. It is mapped across the SECI spiral as a reinforcing loop, and boundary conditions are specified under which AI works as a learning scaffold. The paper then proposes a three‐dimensional process framework with observable indicators, an illustrative application, and five testable propositions.
ABSTRACT In the era of digitalization, innovative activities have received increasing attention from scholars, analysis of the mechanisms by which AI information quality shapes knowledge quality, ultimately enhancing innovative outcomes, remains limited. Drawing on Knowledge‐Based View (KBV) theory and integrating Conservation of Resources (COR) theory, this study empirically examines how and through which mechanisms AI information quality enhances firms' innovation capabilities and employees' innovative performance. It also investigates the boundary condition of AI trust in the linkage between AI information quality and knowledge quality. The three‐wave, multi‐source dataset comprises 71 supervisors and 408 employees working in Pakistan's Information Technology (IT) sector. We analyzed the data using AMOS software and employed covariance‐based structural equation modeling (CB‐SEM). In addition, Hayes PROCESS was used for mediation analysis. Our findings showed that AI information quality shapes knowledge quality, and knowledge quality simultaneously fosters firms' innovation capabilities and employees' innovative performance. Data analysis further showed that knowledge quality partially mediates the relationships among AI information quality, innovation capabilities, and innovative performance. Moreover, AI trust serves as a crucial boundary condition, amplifying the resource‐gain pathway and attenuating the resource‐loss pathway. The study offers practical implications beyond the IT sector.
ABSTRACT This study examines how Digital Knowledge Conversion (DKC) operates in decentralized socio‐technical ecosystems by investigating crowdsourced knowledge interactions on Stack Overflow project platform through the lens of tacit–explicit knowledge practices and digital knowledge conversion processes. A longitudinal dataset of 10,874 interactions from Stack Overflow (2015–2026) was analyzed using Natural Language Processing (NLP) techniques combined with statistical correlation analysis to explore the micro‐mechanisms of digital knowledge practices in real‐world Information Systems (IS) and/or Information Technology (IT) project problem‐solving. The results show that Digital Socialization is the dominant driver of knowledge‐sharing success, significantly outperforming the volume of documentation. The study uncovers a “Precision Paradox” in which excessive explanatory verbosity in explicit documentation and technical knowledge integration practices leads to diminishing returns. It also reveals a shift toward utilitarian knowledge consumption, as internalization‐related signals demonstrate limited predictive power in high‐pressure technical environments. This research makes a theoretical contribution by empirically extending tacit–explicit knowledge perspectives within DKC to decentralized digital contexts and introducing the “Precision Paradox” as a novel phenomenon. Methodologically, it demonstrates the utility of computational approaches in knowledge management research. The findings provide project managers with a clear rationale to shift from traditional repository‐centric knowledge management toward validation‐centric strategies that emphasize real‐time peer validation. This transition can improve knowledge utilization, reduce problem‐resolution time, and strengthen organizational resilience as project management evolves toward the PM2030 era.
ABSTRACT The growing use of intelligent digital technologies is reshaping airport supply chains, but technology adoption alone does not explain why automated baggage handling initiatives succeed. knowledge‐driven supply chain digitalization (KSCD) is defined here as the organizational capability to use connected digital technologies and real‐time data to share information, coordinate decisions, and integrate baggage‐handling activities across airport supply‐chain actors. Fully Automated Baggage Handling System Success (FABHSS) is conceptualized as a multidimensional operational outcome reflected in efficiency, timely and reliable processing, routing accuracy and traceability, cross‐functional coordination, and overall support for baggage operations. Drawing on the Knowledge‐Based View and Dynamic Capability Theory (DCT), the study tested a moderated mediation model using survey data from 220 employees in airport operations, logistics, information technology, engineering, and baggage handling at major international airports in the United Arab Emirates. Covariance‐based structural equation modelling showed that KSCD positively influenced Knowledge Integration Capability (KIC; beta = 0.724, p < 0.001) and FABHSS (beta = 0.321, p < 0.001), while KIC positively influenced FABHSS (beta = 0.486, p < 0.001). Bootstrapping confirmed that KIC partially mediates the relationship between KSCD and FABHSS (indirect beta = 0.352, p < 0.001), and Organizational Digital Readiness strengthened the positive KSCD–KIC relationship (interaction beta = 0.184, p < 0.001). The findings show that automated baggage‐handling success depends on converting digitally generated information into integrated operational knowledge within a digitally ready organization.
ABSTRACT Do organizational characteristics that formally differentiate nongovernmental organizations (NGOs), including legal form, size, and mission scope, also translate into meaningful differences in their management practices? Institutional theory suggests that they may not. This study examines institutional isomorphism in nonprofits through the lens of knowledge management (KM), using survey data from 249 Indian NGOs of various types, sizes, and operational focuses, collected via the Knowledge Management Assessment Tool (KMAT). Drawing on institutional theory, we hypothesize that isomorphic pressures—coercive (donor requirements), normative (professionalization), and mimetic (peer imitation)—homogenize KM practices across formal organizational boundaries, whereas mission complexity may create operational differentiation. Our findings support the convergence prediction and provide evidence consistent with the role of mission complexity: (1) no statistically significant differences in KM practices are observed by legal form or organizational size; (2) multifocused NGOs score higher across all five KM dimensions, but no dimension level contrast remains statistically significant after false discovery rate (FDR) adjustment; and (3) KM practices across all sample NGOs remain at a moderate level, rather than the desired good or excellent benchmarks. These results suggest that formal organizational classifications are weak predictors of KM practice and that mission characteristics warrant further attention in third sector research and capacity‐building interventions.
