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on Economics of Strategic Management |
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Issue of 2025–08–18
seven papers chosen by |
| By: | Agbleke, Divine Swerwzie |
| Abstract: | Abstract This study develops and tests an empirical model for quantifying Indigenous Knowledge within firms. Indigenous Knowledge is conceptualized as the cognitive and educational capacity embedded in an organization’s workforce—an intangible asset that has garnered increasing scholarly interest. The paper contributes to this discourse by proposing a measurable framework and examining its determinants. The stock of indigenous knowledge is operationalized through a weighted average of cumulative years of education, normalized by the number of employees with comparable qualifications. Using multiple regression analysis, the study evaluates the impact of training practices, organizational structures, and performance indicators on the average stock of indigenous knowledge across firms in the finance and insurance sectors. Findings reveal that access to formal documents by heads of departments, the nurturing of innovative thinking, and the implementation of effective—particularly standardized—training programs significantly enhance indigenous knowledge. Equally, an exclusive focus on productivity is associated with a reduction in knowledge stock. However, a significant interaction between productivity and incremental sales revenue suggests that the negative effects of productivity can be offset when accompanied by improved commercial performance. The study has both theoretical and practical implications. It advances the empirical measurement of indigenous knowledge in organizational contexts and provides actionable guidance for firms. Key recommendations include improving access to formal knowledge systems, integrating innovation into operational practices, designing standardized and effective training programs, and monitoring the interaction between organizational culture and structural mechanisms. Keywords: Indigenous Knowledge Knowledge Quantification Standardized Training Human Capital Firm Performance Knowledge Management |
| Date: | 2025–08–04 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:ex59m_v1 |
| By: | Katharina Brennig (Paderborn University); Christian Bartelheimer (Georg-August-Universität Göttingen); Bernd Löhr (Paderborn University); Daniel Beverungen (Paderborn University); Oliver Müller (Paderborn University) |
| Abstract: | Knowledge-intensive processes (KIPs) are complex, strategic core processes that drive organizational competitive advantage. These processes rely on explicit and tacit knowledge. While explicit knowledge can be codified and leveraged---often through technologies such as process mining---tacit knowledge remains embedded in individual process participants, limiting knowledge transfer and organizational learning. Process mining, a data-driven approach to analyze process data, works best for standard processes that are managed for consistency, costs, and time but is insufficiently equipped to enhance KIPs, which depend on dynamic, experience-based decision-making. We present findings from a 39-month Action Design Research (ADR) project to conceptualize a new class of IT artifacts that enable process mining for KIPs. This class of IT artifacts integrates richer process-related information, facilitating knowledge transfer by allowing participants to learn from similar process instances and engage in socialization. We propose five theory-ingrained design principles that guide the development of such systems and examine their role in fostering knowledge creation within organizations. Our research bridges critical gaps between business process management and knowledge management, offering theoretical and managerial insights. For practitioners, our findings provide a foundation for improving knowledge-intensive processes, ultimately upgrading strategic decision-making and organizational performance. |
| Keywords: | Organizational Knowledge Creation, Business Process Management, Process Mining, Knowledge-Intensive Processes, Action Design Research |
| JEL: | M15 D83 O33 L86 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:pdn:dispap:148 |
| By: | Searle, Nicola; Ganglmair, Bernhard; Borghi, Maurizio |
| Abstract: | A robust Research & Innovation (R&I) ecosystem is essential for progress, economic resilience, and addressing complex challenges. At the heart of this ecosystem, knowledge fuels innovation and further discovery. However, knowledge leakage (the loss of valuable information) can disrupt this cycle. This poses a challenge for what is known as Trusted Research & Innovation (TRI), a framework designed to strengthen research security, protect national interests, and build resilient research systems. Despite its significance, the challenges of TRI remain poorly understood. This report investigates knowledge leakage. It begins with an overview of the TRI context, focusing on policymaking, and then reviews the literature on knowledge leakage and related concepts. An exploratory data analysis examines novel empirical data to better understand the extent of knowledge leakage and how it impacts economic areas of defence, economic, and national security importance. The data analysis finds that industries deemed important for economic and national security (the UK's 'sensitive economic areas') have an 18% higher incidence of leakage than those that are not. |
| Keywords: | knowledge leakage, research security, theft of IP, economic security, national security, Trusted Research & Innovation |
| JEL: | F52 O25 O33 O34 O38 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:321868 |
| By: | Kelchtermans Stijn; Goni Navarro Luis; Lindholm Dahlstrand Asa; Mifsud Solange (European Commission - JRC); Zacharewicz Thomas |
| Abstract: | "The Regional Innovation Valleys (RIV) initiative is a flagship action under the New European Innovation Agenda (NEIA), designed to strengthen innovation ecosystems across the European Union by fostering interregional collaboration and accelerating the deployment of innovation. The initiative supports partnerships between regions with varying levels of innovation performance to address key EU challenges such as energy resilience, food security, digital transformation, healthcare, and circularity.A total of 146 regions have been designated as RIVs through three mechanisms: a Call for Expression of Interest (CEI), the European Innovation Ecosystems (EIE) and the Interregional Innovation Investments (I3). The political commitment for the initiative totals €170 million, with initial funding of €116 million distributed through EIE and I3 calls.This Gap Analysis aims to (i) identify the motivations and obstacles influencing regional participation in RIV calls, particularly CEI and EIE; and (ii) provide evidence-based recommendations to enhance future calls and stimulate more effective interregional collaboration.To address these objectives, the study employed a mixed-methods approach, combining qualitative data from 30 regions (via interviews and focus groups) with survey responses from 89 regions across the EU and Horizon Europe Associated Countries. The analysis revealed four critical areas influencing regional participation. These refer to co-funding requirements, collaboration patterns, the RIV label and thematic focus areas. Based on these findings, the report proposes several policy recommendations, including simplifying co-funding mechanisms, enhancing matchmaking and capacity-building efforts, providing clearer guidance on the RIV label’s value, and improving alignment and continuity of thematic areas. By addressing these gaps, the RIV initiative can more effectively fulfil its dual mission of boosting innovation and reducing regional disparities across Europe." |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc142545 |
| By: | Sillero Illanes Carmen (European Commission - JRC) |
| Abstract: | This brochure is one of the deliverables of the JRC Exploratory Research Activity, REGDUALOSA (Regions, Dual Use, Open Strategic Autonomy). Its purpose is to disseminate the recommendations gathered from the work with experts and territories, particularly from the three case studies developed in Podkarpackie (Poland), Andalusia (Spain) and Estonia. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc142596 |
| By: | Taheri Hosseinkhani, Nima (Auburn University) |
| Abstract: | Purpose: This study synthesizes and evaluates the empirical evidence on the transfer and diffusion of artificial intelligence (AI) by analyzing whether its implementation delivers productivity gains that consistently exceed those of previous general-purpose technologies (GPTs), such as information and communication technology (ICT) and electricity. It aims to clarify the magnitude, mechanisms, and contextual dependencies of AI's impact, framing the issue as a challenge in technology transfer from development to widespread economic application. Methodology: A systematic literature review was conducted following the PRISMA 2020 framework. The search utilized the Consensus academic search engine, covering sources like Semantic Scholar and PubMed, with 22 targeted queries across seven thematic groups. The process involved identifying 1, 100 papers, screening 630, assessing 491 for eligibility, and conducting a full-text analysis and narrative synthesis of the 50 most relevant studies. Methodologies of the included papers range from large-scale panel data regressions and randomized controlled trials to systematic reviews and macroeconomic analyses. Findings: The evidence consistently shows that AI implementation delivers measurable productivity gains at the firm and process levels across various sectors. Key mechanisms for this value capture include cost reduction, process automation, skill-biased labor enhancement, and innovation acceleration. For instance, specific applications like generative AI have been shown to reduce task completion time by 40% and improve output quality by 18%. However, the evidence that these gains consistently surpass those of earlier GPTs is nuanced, revealing lags and barriers characteristic of historical technology transfers. The diffusion of benefits is uneven, disproportionately favoring larger, digitally mature firms with higher absorptive capacity. At the macroeconomic level, AI's contribution to aggregate productivity growth remains limited, echoing the "productivity paradox" observed during the initial transfer of ICT and electricity. Implications: The findings suggest that while AI is a potent productivity driver, realizing its full economic potential is contingent on overcoming key barriers to technology transfer, including the need for complementary investments, organizational restructuring, and workforce upskilling. For policymakers and technology managers, this underscores the need for strategic initiatives that address expertise gaps and integration challenges, thereby fostering more inclusive and widespread technology diffusion and productivity growth. The historical parallels with previous GPTs suggest that the transformative impact of AI may materialize over a longer time horizon than currently anticipated, dependent on the efficiency of these transfer mechanisms. |
| Date: | 2025–07–22 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:hqp28_v2 |
| By: | Luisa Corrado (DEF and CEIS, Università di Roma "Tor Vergata"); Stefano Grassi (DEF and CEIS, Università di Roma "Tor Vergata"); Aldo Paolillo (Università di Roma "Tor Vergata") |
| Abstract: | Recent studies suggest that space activities generate significant economic benefits. This paper attempts to quantify these effects by modeling both business cycle and long-run effects driven by space sector activities. We develop a model in which technologies are shaped by both a dedicated R&D sector and spillovers from space-sector innovations. Using U.S. data from the 1960s to the present day, we analyze patent grants to distinguish between space and core sector technologies. By leveraging the network of patent citations, we further examine the evolving dependence between space and core technologies over time. Our findings highlight the positive impact of the aerospace sector on technological innovation and economic growth, particularly during the 1960s and 1970s. |
| Keywords: | Aerospace, Space Economy, Growth |
| JEL: | A1 C5 E00 O10 |
| Date: | 2025–08–07 |
| URL: | https://d.repec.org/n?u=RePEc:rtv:ceisrp:609 |