Abstract
A major component of the Smart Specialisation (SS) agenda is the monitoring of progress towards the achievement of the goals and development of tools to support the activities of stakeholders in entrepreneurial discovery. However, there are capacity and management challenges for many countries in measuring the progress of SS. High-quality data is crucial for transforming SS indicators into useful tools for developing national mission-oriented innovation policies as well as facilitating a European level monitoring of SS progress. The paper explores potential interventions to improve the implementation of data policy for SS and indicates that Innovation Capability Maturity Model can not only ensure high-quality data, but also standardise the key processes of public management around SS. The adoption and mainstreaming of ICMM may aid in mission-oriented assessments and articulate pathways towards the maturity of SS data for improving SS cycle.
Highlights
Introduction government, academia, business, and even individuals need to Achieving Smart Specialisation (SS) goals requires the be enhanced in order to enable the delivery of high-quality effective development of big data transformations the data
It is imperative to ensure that all countries integration of new and traditional data to produce high-quality participating in SS have dynamic national statistics systems indicators that are comprehensive, timely, and actionable for that are in line with SS standards and benchmarks
From the experience of countries, which implement cycle facilitates a shift toward outcome-orientated support and SS, it is assumed that the speed needed for data adaptation is the improvement of evaluation frameworks that could funnel generally not fast enough to justify different trajectories from resources to the most promising entrepreneurial discoveries the SS cycle
Summary
The analysis of documents on SS implementation- human, technological and financial resources for the defined that the key issue for the potential of SS is implementation of an effective data monitoring in SS to identify high-quality indicators that will grasp processes may be improved if previous experiences are the entrepreneurial discovery process within the policy cycle. analyzed in detail, for example in case of Lithuania, Estonia, It shows that governance processes and aims fall into the same Poland. Once the SS external stakeholders to pinpoint challenged areas in a targeted cycle moves from the priorities phase to the policy mix phase, way and the possible discovery of a value addition resulting governments can adopt or shape indicators of dynamic data from potential sectors could make it easier to gather support according to consumer and provider needs. The constructed SS for certain priorities while making the rejection of others more data cycle (see Figure 1) shows that credibility and traceability likely. This is especially evident from how different countries remain essential, the use of technological solutions of have been using big data for budgeting of specializations as data production to investigate the acceptance of specific means this can increase entrepreneurial discovery in smart sectors and among NSIs and external stakeholders is useful.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
