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on Knowledge Management and Knowledge Economy |
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Issue of 2025–09–08
seven papers chosen by Laura Nicola-Gavrila, Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Joshua S. Gans |
| Abstract: | This paper develops a unified model of the cognitive division of labour in a knowledge economy. Building on recent frameworks for knowledge creation and decision making under uncertainty, it distinguishes between specialists, who engage in costly “on the spot” reasoning to generate tacit knowledge around a focal point, and generalists, who search for and interpolate existing knowledge but deliver answers subject to error. The model characterises how these two types of workers should be allocated across a continuum of questions, given the location of codified knowledge points and the distribution of problems. It shows that optimal task assignment depends on the cognitive process through which information is processed rather than on skill endowments or task complexity. When specialists operate around both knowledge points, their allocation is shaped by their absolute advantage over generalists, leading to non‐contiguous specialist domains interspersed with generalist regions. When specialists cluster around a single point, a natural boundary emerges between specialist and generalist domains that shifts but persists despite changes in question distribution. Extending the analysis to a two‐period setting, the paper identifies when specialists should sacrifice static efficiency to codify their tacit discoveries, creating bridges that allow generalists to operate more effectively in the future. These results provide a formal microfoundation for Babbage’s insights into the division of cognitive labour and offer predictions about how knowledge work responds to changes in the knowledge environment, the distribution of questions, and the patience of capital. |
| JEL: | D83 J24 L23 L25 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34145 |
| By: | Diessner, Sebastian; Durazzi, Niccolo; Filetti, Federico; Hope, David; Kleider, Hanna; Tonelli, Simone |
| Abstract: | How have advanced capitalist democracies transitioned from a Fordist to a post‐Fordist, knowledge‐based economy? And why have they followed seemingly similar policy trajectories despite different economic models and sectoral specializations? We develop the notion of skill‐biased policy change to answer these questions. Drawing on a distinction between valence and partisan issues in the transition to the knowledge economy, we highlight the partisan and business group politics underpinning different policy areas to argue that policies that create or mobilize high‐level skills attract relatively broader consensus across political parties and business groups than protective labor market policies targeted at the lower end of the skills distribution. The argument is illustrated through case studies of Germany, Sweden, and the UK—three countries that have transitioned to a knowledge‐based economy but that have done so by relying on markedly different sectoral specializations. |
| Keywords: | technology; skill‐biased policy change; knowledge economy; high skills; digital transition |
| JEL: | J1 |
| Date: | 2025–08–25 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:129273 |
| By: | Zhonghong Kuang; Yi Liu; Dong Wei |
| Abstract: | We study the optimal design of relational contracts that incentivize an expert to share specialized knowledge with a novice. While the expert fears that a more knowledgeable novice may later erode his future rents, a third-party principal is willing to allocate her resources to facilitate knowledge transfer. In the unique profit-maximizing contract between the principal and the expert, the expert is asked to train the novice as much as possible, for free, in the initial period; knowledge transfers then proceed gradually and perpetually, with the principal always compensating the expert for his future losses immediately upon verifying the training he provided; even in the long run, a complete knowledge transfer might not be attainable. We further extend our analysis to an overlapping-generation model, accounting for the retirement of experts and the career progression of novices. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.11018 |
| By: | Abhinav Arun; Fabrizio Dimino; Tejas Prakash Agarwal; Bhaskarjit Sarmah; Stefano Pasquali |
| Abstract: | The financial domain poses unique challenges for knowledge graph (KG) construction at scale due to the complexity and regulatory nature of financial documents. Despite the critical importance of structured financial knowledge, the field lacks large-scale, open-source datasets capturing rich semantic relationships from corporate disclosures. We introduce an open-source, large-scale financial knowledge graph dataset built from the latest annual SEC 10-K filings of all S and P 100 companies - a comprehensive resource designed to catalyze research in financial AI. We propose a robust and generalizable knowledge graph (KG) construction framework that integrates intelligent document parsing, table-aware chunking, and schema-guided iterative extraction with a reflection-driven feedback loop. Our system incorporates a comprehensive evaluation pipeline, combining rule-based checks, statistical validation, and LLM-as-a-Judge assessments to holistically measure extraction quality. We support three extraction modes - single-pass, multi-pass, and reflection-agent-based - allowing flexible trade-offs between efficiency, accuracy, and reliability based on user requirements. Empirical evaluations demonstrate that the reflection-agent-based mode consistently achieves the best balance, attaining a 64.8 percent compliance score against all rule-based policies (CheckRules) and outperforming baseline methods (single-pass and multi-pass) across key metrics such as precision, comprehensiveness, and relevance in LLM-guided evaluations. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.17906 |
| By: | Jacinthe Cloutier (Laval University, Québec); Hugo Chouinard (Laval University, Québec) |
| Abstract: | The realm of cryptoassets is highly complex and requires specific knowledge to avoid making risky decisions. This study aims to identify the determinants of both objective and subjective knowledge levels regarding the Bitcoin blockchain. Data were collected from the adult population of Quebec (Canada) in the fall of 2024 (n = 1, 078). The results of multiple linear regression analyses indicate that men, perceived risk, self-efficacy, and subjective knowledge level positively influence the objective knowledge level about the Bitcoin blockchain. Conversely, the subjective knowledge level about the Bitcoin blockchain is negatively influenced by household size, age, perceived compatibility, and the presence of facilitators, while it is positively influenced by attitude, self-efficacy, objective knowledge about the Bitcoin blockchain, and perceived knowledge of traditional investment. The findings are discussed in light of the educational needs in this complex domain, particularly among young consumers. |
| Keywords: | Cyptoassets; Objective knowledge; Subjective knowledge; Canadian consumers; Investment |
| JEL: | G11 O33 D83 |
| URL: | https://d.repec.org/n?u=RePEc:sek:iefpro:15116802 |
| By: | Dohse, Dirk; Fehrenbacher, Sophia |
| Abstract: | African innovators typically suffer from severe resource constraints and need to develop strategies to cope with these constraints. This paper focusses on external knowledge sourcing and, in particular, on the role of cooperation as a means to compensate for missing resources. Findings suggest that domestic inter-firm coop eration is of outstanding importance for firm-level innovation in Nigeria, whereas cooperation with other partners (research institutions, foreign firms, consultants, or the government) has no sizable impact on the innovative performance of Nigerian firms. Moreover, we show that it is in particular young firms and firms suffering from financial constraints that benefit from cooperation, whereas foreign-owned firms benefit less. Our findings contribute to a better understanding of the drivers of firm-level innovation in sub-Saharan Africa and have important implications for firm strategies and innovation policy |
| Keywords: | Resource-constrained innovation, Knowledge sourcing, Inter-firm cooperation, Coactive learning, Africa |
| JEL: | D22 L25 O32 O36 O55 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:ifwkie:323982 |
| By: | Carina Altreiter (Institute for Comprehensive Analysis of the Economy, Johannes Kepler University Linz, Austria); Theresa Hager (Institute for Comprehensive Analysis of the Economy, Johannes Kepler University Linz, Austria; Socio-Ecological Transformation Lab, Johannes Kepler University Linz, Austria); Stephan Puehringer (Institute for Comprehensive Analysis of the Economy, Johannes Kepler University Linz, Austria; Socio-Ecological Transformation Lab, Johannes Kepler University Linz, Austria) |
| Abstract: | Western societies face unprecedented interconnected crises—climate change, democratic instability, rising inequality, and growing science scepticism—that demand fundamental socio-ecological transformation within a compressed timeframe. However, current university systems, shaped by neoliberal restructuring and historically patriarchal logics, are structurally inadequate to support the critical research and societal engagement necessary for this transformation. This diminishes academia's relevance, exacerbating the post-truth crisis. We examine how competitive evaluation mechanisms, economic incentives and academic culture in contemporary academia hinder research conducive to socio-ecological transformations while perpetuating existing power structures and knowledge hierarchies. Through critical analysis of academic structures and knowledge production systems, drawing on existing literature across relevant fields, we build upon the concept of academic capitalism but extend it. We identify four interconnected pillars of what we term "capitalist academia": commodification of knowledge, publish-or-perish logic and competitization, social homogenization, and entrenched hierarchies. These structural features lead to a prioritization of individual competition over collaborative problem-solving, favor incremental research over transformative inquiry, and systematically exclude diverse perspectives essential for addressing complex socio-ecological challenges. Our analysis reveals how these structures of knowledge production not only fail to contribute meaningfully to societal transformation but actively contribute to reproducing the very systems that perpetuate ecological and social crises. We propose replacing these problematic pillars with four alternative principles—the "4Ds": Dialogue, Decommodification, Diversification, and Democratisation. This framework represents a pathway toward emancipatory academia that can meaningfully engage with socio-ecological transformation challenges while preserving scientific integrity. We also provide examples of existing initiatives and ideas where principles of the 4Ds are already in place, demonstrating practical pathways for reform and critically reflect on the adaptability of the current system of knowledge production. This research contributes to ongoing debates about academic reform while offering concrete directions for aligning higher education with sustainability imperatives and building the consensus needed for emancipatory socio-ecological transformation. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:ico:wpaper:164 |