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on Information and Communication Technologies |
| By: | Kaitila, Ville |
| Abstract: | Abstract Labour productivity in service industries in Finland is lower than in peer countries on average. The difference in the capital-stock-to-hours-worked ratio is even greater. We analysed five service industries in Europe, the United States, and Japan in 1995–2023. Labour productivity is strongly and positively related to how large the capital stock is in relation to the labour input and how high the level of education of the workforce is. Tangible capital, i.e. information and communication technology and traditional fixed capital (buildings, machinery, and transport equipment), is important, but so is intangible capital, i.e. software, and research and development. The results highlight the importance of investments for productivity growth in service industries and support policy measures that encourage companies to make long-term capital investments. In addition, policy measures to increase the level of education and, among other things, to improve the functioning of competition and markets in Finland are important. |
| Keywords: | Service industries, Productivity, Capital intensity, ICT, R&D, Software and databases |
| JEL: | C23 O14 O30 O47 |
| Date: | 2025–05–21 |
| URL: | https://d.repec.org/n?u=RePEc:rif:briefs:159 |
| By: | Magaletti, Nicola; Nortarnicola, Valeria; Di Molfetta, Mauro; Mariani, Stefano; Leogrande, Angelo |
| Abstract: | This case study features Tecnomulipast, an SME from Southern Italy that specializes in machinery production for the food processing industry. The study is in fact centered on the company's digital transformation process, facilitated by investments in advanced production systems and innovation-driven managerial practices both facilitated by regional co-financing initiatives, including from Regione Puglia. At the center of it all is the integration between a new Industry 4.0-compliant laser welding system in the company's ERP system. Through Internet of Things (IoT) technologies, the system is inherently equipped to collect and transmit batch-level as well as real-time data, instantiating a cyber-physical system for advanced manufacturing. Easy to connect by standard interface (i.e. OPC-UA), the system is tied to an analytics data framework capable of working on structured data (e.g., KPIs, sensors' metrics) as well as on unstructured data (e.g., images), allowing for real-time monitoring, early anomaly signaling, and optimization of processes. Designed for scalability, the related technology architecture is future-proof to include artificial intelligence (AI) integration for augmenting decision-making with predictive and prescriptive analytics. Beyond the technological enhancement, however, the transformation was facilitated by an excellence managerial model that focuses on flexibility, data-driven governance, as well as on constant learning. Tecnomulipast's case offers an replicable template for SMEs—especially in low digital maturity areas—showing that targeted investment, innovation-driven management, and system-level integration might finally eliminate the gap between tech potential and operational performance in Industry 4.0 transitions. |
| Keywords: | Digital Transformation, Industry 4.0, Innovation Management, IoT in Manufacturing, Smart Manufacturing. |
| JEL: | D21 D22 D23 D24 |
| Date: | 2025–04–22 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:124519 |
| By: | West, Klaus W.; Hilpert, Ulrich; Sandulli, Francesco |
| Abstract: | The working paper uses the example of digitalization to showcase how European Works Councils can further develop their participation practices in companies and their various locations. There is both room and need for improvement in organizing decent digital work and in the use of information and consultation rights by European Works Councils. It is possible to resolve the contradiction between what is and what should be when European Works Councils improve their knowledge about digitalization practice with a digitalization checklist and specific questions. Targeted processing and presentation of this knowledge lets them enhance their position in relation to company management. |
| Keywords: | digital transformation, co-determination, workers' representatives, EWC |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:hbsfof:316438 |
| By: | Lasdun, Violet; Harou, Aurélie; Magomba, Chris; Guereña, Davíd |
| Abstract: | Information constraints rank high among barriers to agricultural technology adoption among small-scale farmers, particularly for complex bundles of complementary practices. Information communication technologies are emerging to extend the reach of agricultural training, with potential to deliver information through mobile and smartphones at little or no cost to farmers. In this study, we develop a low-cost digital extension platform that facilitates peer-to-peer learning through SMS-based chat groups on basic feature phones. Using a randomized controlled trial, we evaluate its effectiveness in promoting the adoption of beneficial agricultural practices compared to a one-way SMS extension program. We measure strong positive effects of treatment on adoption of practices discussed in the chat groups, increasing intercropping and organic fertilizer production by 11-18 and 15 percentage points, respectively, suggesting that a simple group discussion forum can be a powerful addition to digital extension initiatives. However, chat group participation declined over the course of the study, underscoring the challenges of designing technological interventions that sustain user engagement. |
| Keywords: | digital peer-to-peer farmer extension; information communication technology; peer learning; regenerative agriculture; Tanzania; technology adoption |
| JEL: | O12 O13 Q16 |
| Date: | 2025–09–30 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:127762 |
| By: | Lawrence, Alice |
| Abstract: | In an era marked by increasing global uncertainties from pandemics and geopolitical tensions to climate-related disruptions, supply chain resilience has emerged as a strategic imperative. This paper explores the transformative role of Artificial Intelligence (AI) in enhancing the adaptability, visibility, and responsiveness of modern supply chains. By delving into real-world case studies and recent advancements, we analyze how AI-powered tools such as predictive analytics, intelligent automation, and machine learning enable companies to anticipate disruptions, optimize logistics, and accelerate decision-making. Rather than merely reacting to crises, AI equips organizations with the foresight and agility needed to proactively manage risks and sustain operations. While the integration of AI is not without challenges including data privacy concerns, integration complexities, and workforce readiness, this study underscores its potential as a cornerstone technology for future-proofing global supply networks. Ultimately, we argue that AI is not just a tool for efficiency, but a critical enabler of resilience in a volatile world. Keywords: Supply Chain Resilience, Artificial Intelligence, Predictive Analytics, Risk Management, Intelligent Automation, Machine Learning, Logistics Optimization, Disruption Forecasting, Digital Transformation, Strategic Agility |
| Date: | 2024–07–11 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:4x2jz_v1 |
| By: | Junjie Chen; Takuro Yamashita |
| Abstract: | An information broker incentivizes consumers to share their information, while designing an information structure to shape the market segmentation. The information broker is a metaphor for an Internet platform that matches consumers with retailers. We are interested in a market with heterogeneous retailers and heterogeneous consumers. The optimal broking mechanism consists of a simple threshold-based structure where consumers with strong preferences are assigned to the efficient retailer while consumers with weaker preferences are assigned to the inefficient retailer stochastically. Our analysis suggests that the privacy protection policy may have a stronger impact on less competitive retail markets. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.19539 |