Deadline for manuscript submissions: 31 January 2023 | Viewed by 558
2. Hughes Hall, University of Cambridge, Cambridge CB1 2EW, UK
Interests: diffusion of ICT, digital divide; sustainable development goals; internet market structure; economics of networks; regional asymmetries; economic geography; R&D and spillovers competition over physical networks; competition over virtual networks; spillovers and externalities in innovation activities; social capital and trust in crowdfunding
Interests: entrepreneurship; regional economic development; regional labour market effects of automation
Interests: small and medium-sized enterprises that want to make the transition to new ways of doing business, ways where wellbeing and welfare are central. This requires new business models, new approaches, new value chains and new professionals
One of the key challenges identified by the International Telecommunication Union’s 2017 World Telecommunication Development Conference on “Accelerating Digital Innovation Ecosystems for Digital Transformation” is that “talent is unfulfilled, SMEs are struggling, and slow digital transformation of communities is affecting social conditions and achievement of national ambitions”.
The OECD (2015) defines data-driven innovation (DDI) as the significant improvement of existing/development of new products, organisational methods and markets emerging from analysis of data, including real-time analysis of big data and digital technology. For small businesses, DDI might consist of the meaningful interpretation, and usage for forecasting, of small internal datasets based, for example, on customer bookings, suppliers’ transactions and tax reports. Interestingly, moving along the ladder of sophistication in DDI, new emerging techniques allow smaller datasets to be analysed in more detail, through the utilisation of deep learning and connected techniques (Medium, 2018). These techniques provide avenues to implement DDI, without, however, substituting the value of simpler and predominant forms of DDI, based on data visualisation, extrapolation and relevant summary statistics coupled with elementary forecasting and regression methods. Furthermore, one key aspect of the evolving data revolution is the increasing availability of open data, allowing SMEs to achieve DDI through use of their own data matched with access to external datasets. Digital innovation can be defined as “a previously non-adopted digital modality to satisfy a specific need in a given context” (Giovannetti, 2017). This definition includes more traditional classifications, e.g., process, product or organisational innovations, or those between radical and incremental innovations and includes the diffusion and adoption of digital innovations in new contexts. As an example, digital product innovations may satisfy the need for an intermediate product or service within a production chain in a business-to-business (B2B) framework while, contextually, the same digital platform might be used to improve service variety for the end users in a business-to-consumer (B2C) setting. For example, due to the new COVID-19-related restrictions, a farm might need to bypass retailers and directly reach consumers though digital e-commerce platforms to sell its products, while using the same platform to procure essential intermediate products. Thus, farmers’ adoption of digital platforms serves both organisational and process innovations, both in relation to manage upstream intermediate inputs and downstream relations with final customers.
This Special Issue stems from the desire to analyse the empirical evidence and the impact of the digital tools developed by Futures By Design (FBD), an EU Interreg North Sea Region created to help SMEs in rural areas of the North Sea region to use data analysis to innovate, grow and increase productivity. SMEs are critical to regional economies and contribute considerably to regional employment; however, their capacity for success can be limited by insufficient access to data and the inability to analyse data to drive innovation and obtain improved results. Hence, overcoming digital competence barriers is a key enabler for the sustainability of otherwise useless digital infrastructures. We seek contributions analysing the impact on sustainability of the development of virtual transnational horizon-scanning and knowledge transfer (HSKT) hubs, to support sustainable SME growth, innovation and productivity. In addition to the contributions from our six regional studies, we invite submissions from scholars and projects focusing on related initiatives and programmes focussing on digital exclusions due to skills and networking barriers across the world.
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