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Methodology for efficient segmenting innovating SMEs based on lifecycles, represented sectors and regional characteristics

Periodic Reporting for period 2 - SMEthod (Methodology for efficient segmenting innovating SMEs based on lifecycles, represented sectors and regional characteristics)

Reporting period: 2019-03-15 to 2020-06-14

SMEthod addressed the need for delivering a better methodology of segmenting innovating SMEs for the purpose of innovation support. Improving existing methods is crucial for achieving more efficient outputs from innovation support instruments offered by intervention organizations and other institutions. The critical importance lies in creating higher socio-economic impact from the activity of innovative SMEs and matching support measures in a more appropriate way.
An emphasis has been put on both economic and societal dimensions. Innovation support agencies concluded that innovation and its impact is strongly associated with numerous features of firms, including organizational culture, business models, enterprise life cycle, and represented sectors. It is also argued that regional characteristics should be considered when awarding support measures in order to take into account the specific needs, expectations and problems of the societies and enterprises in various European regions.
SMEthod met the requirements by developing an improved methodology of segmenting SMEs as well as a decision support tool (DST) that aims to assist innovation agencies.
SMEthod’s key exploitable results are the methodology of segmentation and the Decision Support Tool (DST).
The methodology of SME segmentation for the purpose of granting support to enterprises is the major result of the project. The SMEthod holistic approach was developed based on a variety of studies conducted within the project, including the review of the national innovation support policies, evaluation of the efficacy of innovation supporting measures, research on SMEthod’s categories and criteria for segmenting and classifying SMEs, and studies on matching innovation support measures to segments. The segmentation analyses were carried out using extensive primary data gathered from businesses in project’s case study countries (Finland, Poland, Spain, and the UK).
The SMEthod concept takes into account relevant categories for the purpose of SME segmentation such as enterprise lifecycle, innovation cycle, sectors, regions, socio-cultural values, and other. For each category a set of variables was selected. SMEthod also sought for general patterns linking novelty of innovation, identified SME segments and support measures. Econometric analyses on the relationships between specific support instruments and positive results made it possible to make suggestions regarding linking segments and support measures to reach the desired objectives by an intervention organization.
Besides the universal approach towards segmentation procedure regional perspectives were also investigated in the project, as an important part of SMEthod has been enabling support organizations to also include in their grant allocation procedures the socio-cultural characteristics of particular regions.
On the other hand, the IT Decision Support Tool (DST) is an easy-to-use, multiuser and collaborative environment hosted on a web platform and available in a few languages. The tool was built around machine learning (ML), meaning that it uses historical data to train its prediction algorithm that can then be used to support decision-making process of innovation agencies. The DST is flexible, as it is able to accommodate diverse data and use cases, thereby service a broad exploitation model. It might be applied at the EU level as well as at regional levels.
The DST will be incorporated into LEIMINTE (Learing Impact Indicator Technology, This technology incorporates classification technology via ML and in particular neural network (NN) deep learning approaches. Classification allows to map the data collected by innovation stakeholders in certain classes or segments. Although the DST has been developed with ML/ NN classification in mind, it can be also used in more conventional statistical modeling.
Another project result is the Policy Briefing Document. Based on previous project results and a set of additional interviews with regional and national innovation support actors and policy makers from project’s case study countries, the document discusses the structural barriers faced by SME innovation support actors today and makes a set of recommendations to innovation support agencies and to policy makers on how to tackle these identified restraints. The document was prepared with the aim to be disseminated to policy makers at the end of the project via different channels (mailing, social media, press, information during events).
Project Partners created an exploitation and sustainability plan that discusses exploitation strategy and detailed activities to ensure that the results generated by SMEthod are used further on. Partners carried out market analysis and developed the SMEthod business model.
Essential exploitable results were disseminated among target groups using, inter alia, the communication channels and tools such as: project website ( social media, videos, project final conference, national seminars, brochure, newsletter, articles and other publications, conference presentations and networking activities. Importantly, project Partners initiated and have maintained good-natured and promising contacts with national agencies and their representatives.
The innovative character of SMEthod lies in the fact that the project identified, studied, and addressed a variety of indicators potentially influencing innovative entrepreneurship, such as enterprise lifecycle, innovation cycles, industry sectors, regional characteristics, organization and business culture, organizational and ownership determinants, internationalization and export orientation. Based on the new set of segmenting criteria, variables were determined and integrated into the holistic and original approach.
Moreover, due to econometric analyses of detailed primary data collected in the project as well as micro data obtained from Eurostat’s Community Innovation Survey, it was possible to examine and understand various innovation-related activities of SMEs, their performance, societal and environmental impact they create, and to propose more efficient targeting of innovation support measures to reach the desired objectives (innovative, economic, and/or environmental).
The SMEthod methodology also illustrates the SMEs pathways to innovation – the segmentation procedures can help support agencies better understand the complex patterns of innovation opportunities and activities in different segments of SMEs according to their age, size, business activity, industry sector, organisational features and other relevant aspects.
SMEthod’s DST novelty also lies in including numerous economic and social criteria. The DST will have a capacity to be personalized for each country and will be available in project partners languages. An important advantage is also the integrated, low cost environment to work with data, built indicators and machine learning based predictions.
It is expected that SMEthod will contribute to increasing the adequacy and efficiency of innovation support policies and particular measures applied by the innovation support agencies. From a broader perspective, it is expected that the SMEthod results will contribute to more innovative, competitive and sustainable European society and economy.