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on Knowledge Management and Knowledge Economy |
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Issue of 2026–08–17
six papers chosen by Laura Nicola-Gavrila, Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | mohammed khaouja (LRMD FEG Settat - Laboratoire de Recherche en Management et Développement - Faculté des Sciences Economiques et de Gestion, ERMOT - Laboratoire "Etudes et recherches en Management des Organisations et des Territoires" [Fez] - USMBA - Université Sidi Mohamed Ben Abdellah); Sanaa Dfouf; Kaoutar Errakha; Hanan Elharissi (FEG SETTAT - Faculté d’Économie et de Gestion de Settat); Fekkak Hamdi |
| Abstract: | University-industry technology transfer (UITT) is essential for converting academic research into commercial use, yet traditional strategies often fail to address the knowledge gap. Literature suggests that institutional inertia, communication barriers, and ineffective marketing strategies hinder the commercialization of technology. This study proposes a conceptual framework that incorporates AI-driven marketing to enhance knowledge dissemination, market identification, and stakeholder engagement within the technology transfer process. This systematic literature review amalgamates insights from UITT, AI marketing applications, and knowledge management systems. A qualitative analysis of peer-reviewed literature from 2017 to 2025 identifies trends, deficiencies, and emerging patterns, leading to an integrated framework that assesses technology transfer strategies and the implementation of AI marketing across diverse sectors, leveraging the Technology-Organization-Environment (TOE) model and the Unified Theory of Acceptance and Use of Technology (UTAUT). The investigation demonstrates that AI-enhanced marketing can significantly bolster UITT through five AI-enhanced marketing capabilities: precise client segmentation, predictive analytics of market trends, tailored communication, improved knowledge management, and streamlined digital outreach. This methodology fosters reciprocal knowledge exchanges, positioning AI as a facilitator between market insights and university research aims while refining technology presentations for industry stakeholders. Moreover, the study highlights critical concerns regarding data privacy, implementation expenses, technical complexities, and the necessary proficiency in AI and technology transfer. |
| Keywords: | research initiatives, Collaboration university-industry technology transfer AI-enhanced marketing innovation knowledge sharing economic growth strategic partnerships research initiatives entrepreneurial mindset, entrepreneurial mindset, Collaboration, strategic partnerships, economic growth, knowledge sharing, innovation, AI-enhanced marketing, technology transfer, university-industry |
| Date: | 2026–06–01 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05638557 |
| By: | Bouchra El Amrani (UAE - Abdelmalek Essaadi University [Tétouan] = Université Abdelmalek Essaadi [Tétouan]); Mohamed-Larbi Aribou |
| Abstract: | In many contexts, organizational knowledge creation emerges as a major strategic lever for sustaining firms' competitive advantage. However, its effectiveness remains strongly conditioned by the organizational context, and more specifically by communities of practice, which influence the processes of knowledge creation, sharing, and integration. This research adopts a qualitative methodology conducted across three companies in the automotive sector, based on 42 interviews, in order to highlight the key role of communities of practice in the creation of new knowledge. The findings show that community dynamics grounded in the mobilization of "soft" factors foster the emergence of new knowledge, whereas those primarily based on technical factors tend to focus on compliance with existing standards, thereby limiting knowledge creation processes. |
| Abstract: | Dans de nombreux contextes, la création de connaissances organisationnelles s'impose comme un levier stratégique majeur pour le maintien de l'avantage concurrentiel des organisations. Toutefois, son efficacité reste fortement conditionnée par le contexte organisationnel, et plus particulièrement par la communauté de pratique, qui influence les processus de création, de partage et d'intégration des connaissances. Cette recherche adopte une méthodologie qualitative menée auprès de trois entreprises du secteur automobile, à travers 42 entretiens réalisés, afin de mettre en évidence le rôle déterminant des communautés de pratique dans la création de nouvelles connaissances. Les résultats montrent que les dynamiques de communauté de pratique fondées sur la mobilisation de facteurs « soft » favorisent l'émergence de nouvelles connaissances, tandis que celles reposant principalement sur des facteurs techniques se limitent au respect des standards existants, freinant ainsi les processus de création de connaissances. |
| Keywords: | Communities of practice Knowledge creation Case study Automotive sector, Communauté de pratique Création des connaissances étude de cas secteur automobile Communities of practice Knowledge creation Case study Automotive sector www.africanscientificjournal.com |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05623460 |
| By: | Deka, Anubrata; Meerza, Syed Imran Ali; Yiannaka, Amalia |
| Keywords: | Food Consumption/Nutrition/Food Safety, Public Economics, Research and Development/Tech Change/Emerging Technologies |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:nbaece:404861 |
| By: | Gondauri, Davit; Mikautadze, Ekaterine |
| Abstract: | This study develops and empirically audits a cross-country panel-econometric framework for measuring how research productivity and academic intangible value become economically visible. Rather than treating academic output as a universal short-run GDP-growth multiplier, the study constructs a layered measurement architecture in which the Research Productivity Index (RPI), Academic Intangible Value Index (AIVI), and Academic Value Monetization Index (AVMI) distinguish research production, broader academic intangible value, and monetization-oriented academic capacity. Using an effective lagged estimation sample of 464 economy-year observations across 39 economies for 2011-2022, the analysis combines pooled OLS, fixed-effects and two-way fixed-effects models, lag structures, dynamic and directional checks, transmission-channel regressions, component-exclusion designs, threshold and heterogeneity tests, robustness checks, advanced econometric extensions, level-output models, and GDP-equivalent monetization algorithms. The results support a disciplined and conditional interpretation: the direct full-sample AVMI effect on GDP per capita growth is positive but statistically imprecise, while stronger evidence appears through observable monetization channels. AVMI is most clearly associated with high-technology export outcomes, and productivity growth provides a strong transmission link to GDP per capita growth. The study therefore reframes academic value as an auditable intangible economic asset whose monetization depends on innovation output, technology-market participation, absorptive capacity, productivity transmission, and country-specific regimes. By translating empirical evidence into gross and net monetized academic value, Academic Economic Value Added (AEVA), conversion ratios, counterfactual scenarios, and policy-dashboard diagnostics, the study contributes a reproducible framework for evaluating academic value without overstating causality or reducing knowledge systems to a single coefficient. |
| Keywords: | academic value monetization, research productivity, intangible academic value, knowledge economy, economic growth, high-technology exports, panel econometrics |
| JEL: | O32 O33 O47 C23 C43 I23 E22 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:341767 |
| By: | Seo, Gangcheol; Paudel, Krishna P.; Nelson, Kelly |
| Abstract: | This study examines long-run convergence in U.S. state-level agricultural total factor productivity (TFP) and investigates the role of patent-based technological knowledge in explaining persistent productivity differences across states. Using annual agricultural TFP data for 48 contiguous U.S. states from 1960 to 2015, we assess convergence dynamics through σ-convergence tests and the club convergence approach proposed by Phillips and Sul (2007). We then use a two-way fixed effects (TWFE) panel framework to examine whether patent-based knowledge stocks are associated with state-level agricultural TFP. Patent stocks are constructed for six agricultural technology subsectors under alternative assumptions regarding knowledge depreciation and lag structures. The results indicate that U.S. agricultural TFP does not converge toward a common steady state but instead exhibits multiple convergence clubs, suggesting persistent heterogeneity in long-run productivity paths across states. Patent-based knowledge accumulation also displays substantial sectoral heterogeneity. Plants, research tools, animal health, and machinery patent stocks are positively associated with agricultural TFP across most specifications, whereas fertilizer-related patent stocks are negatively associated. These patterns remain broadly robust across alternative constructions of the patent stock. Overall, the findings highlight the importance of technological heterogeneity in long-run agricultural productivity and suggest that accumulated patent-based technological knowledge is associated with agricultural productivity in distinct ways across innovation sectors. |
| Keywords: | Agricultural and Food Policy |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404391 |
| By: | Diebel, Christopher |
| Abstract: | Recent advancements in artificial intelligence (AI) have made human–AI collaboration increasingly relevant in organizations across various industries. AI-based agents are able to act autonomously, learn from experience, and often outperform humans when solving problems. Moreover, they can generate content indistinguishable from that of human experts and interact in a conversational manner. The advanced capabilities of AI-based agents make human–AI collaboration a promising way for organizations to increase their productivity and performance. Due to this potential, human–AI collaboration has already established itself in several industries and proven to be effective. In software development, AI-based agents can support developers in writing computer code. In financial services, AI-based agents are utilized to make market predictions, and in healthcare, AI-based agents can enhance diagnosis by analyzing patient data. Notably, human–AI collaborations can take multiple forms that differ across several facets, including how responsibilities are distributed between humans and AI-based agents, how the collaboration is invoked, or the quality of the AI-generated output. However, while human–AI collaboration can increase productivity, improve decision-making, or enhance overall performance, previous research has demonstrated that collaborating with AI-based agents can have significant effects on humans in various forms. It is therefore important to understand how the different facets of human–AI collaborations influence the humans involved, to fully realize the potential of such collaboration. Consequently, this thesis aims to investigate how various facets of human–AI collaboration, specifically the type of collaboration, the type of invocation to collaborate, and the quality of output of the AI-based agent within the collaboration (i.e., quality of AI output), can affect humans involved in or affected by the outcomes of such collaborations. To this end, this thesis aims in its first strand to investigate how the type of human–AI collaboration can affect the perceptions of humans involved in or affected by the outcomes of such collaborations. To do so, the first study of this thesis (Article A) investigates how the two types of collaboration, namely delegation- and ensemble-based human–AI collaboration, can affect employees’ perceptions of fairness and the trustworthiness of the human collaborator when employees are affected by decisions made in such collaborations. The findings of Article A demonstrate that, in the context of our study, employees perceive a decision-making process as less fair when the decision is made in a delegation-based manager–AI collaboration rather than solely by the human manager, leading to a lower perceived trustworthiness of the manager. Notably, the results revealed that this effect does not occur when the decision is made in an ensemble-based manager–AI collaboration instead. Building on these findings, the second study of this thesis (Article B) explicitly aims to examine how ensemble-based human–AI collaborations can affect the perceptions of the human collaborators. In particular, Article B investigates how ensembling a hiring decision made by a human resource manager with that of an AI-based agent to form the final decision, as in an ensemble-based human–AI collaboration, can affect the managers’ process satisfaction. The findings of Article B show that ensembling a human manager’s decision with an AI-based agent’s decision can negatively influence the manager’s satisfaction with the decision-making process. More importantly, the results revealed that a loss of competence-based self-esteem is a crucial explanatory factor for this effect. The second strand of this thesis aims to examine how the type of invocation to collaborate in a human–AI collaboration can affect the human collaborators’ perceptions and intentions. Therefore, the third and fourth studies of this thesis investigate how the two types of invocation to collaborate, AI-invoked versus human-invoked, can affect the human collaborator of a human–AI collaboration in this regard. That said, Article C reveals that, in work settings, AI-based agents offering to collaborate proactively (i.e., AI-invoked collaboration) rather than reactively, in response to a human request (i.e., human-invoked collaboration), can result in lower satisfaction with the AI-based agent. Furthermore, the article identified a higher loss of competence-based self-esteem as a mediator for this effect. However, the results indicate that these effects do not occur in humans with low levels of knowledge about AI (i.e., AI knowledge) but rather increase with rising AI knowledge. The fourth study of this thesis (Article D) continues, indicating that the invocation of a delegation-based human–AI collaboration by an AI-based agent rather than by the human collaborator can threaten their self-view, negatively affecting their intention to collaborate. In particular, the findings reveal that when an AI-based agent invokes a collaboration by offering to take over a task on its own initiative rather than upon the human’s request, it can lead to higher self-threat for the human collaborator and, in turn, reduce their willingness to accept the offer. Furthermore, the results indicate that a high level of perceived human control after delegation can mitigate these effects. The last strand of this thesis aims to examine how the quality of output provided by AI-based agents in human–AI collaboration can affect the human collaborators’ knowledge. In this vein, the fifth study (Article E) analyzes the context of software development with particular focus on how the quality of code produced by AI-based agents during human–AI collaboration affects developers’ knowledge of how to employ programming syntax and underlying conceptual principles to write computer code that addresses programming tasks (i.e., developers’ procedural knowledge). The findings of this study indicate that developers receiving assistance with higher (vs. lower) code quality from AI-based agents exhibit relatively lower procedural knowledge because they experience lower cognitive load (i.e., the amount of mental resources used in working memory to process information). Overall, the five articles of this thesis advance research on human–AI collaboration by conceptualizing and empirically examining the type of collaboration, the type of invocation, and the quality of AI output as three essential facets that can shape the perceptions, intentions, and knowledge of humans involved in or affected by the collaboration. In doing so, this thesis emphasizes the multidimensional nature of human–AI collaboration and shows how these facets can influence the humans involved, highlighting the need for future research to move beyond general or unidimensional perspectives to better understand and design human–AI collaborations. |
| Date: | 2026–03–13 |
| URL: | https://d.repec.org/n?u=RePEc:dar:wpaper:160616 |