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Responsible and resilient design of technological change and innovation call for early stage engagement and experimentation with relevant stakeholders in each stage of a value chain, which extends to digitizing, financing to marketing actors, for sustainable, multiple value transitions of (energy) systems and transformation of societies. Policy interventions collapse into non-intervention if market and non-market returns on public funding do not involve explicit return on investment and impact targets towards expected socio-economic and socio-ecological outcomes. In this regard, measurement of the speed of transformation among varieties of capital (e.g. from financial capital to human capital; from social capital to natural capital, and back to financial capital) requires more real-time (e.g. complementarily to annual indicators) and contextual (e.g. I/O and process legitimacy of an intervention) measurement and indicators. This challenges traditional measures of socio-economic (e.g. employment effect) and ecological economics (e.g. resource efficiency) measures. Moving forward with novel social (e.g. work satisfaction) and ecological (e.g. occupational health in a livelihood) measures and indicators requires real-time, and contextual measurement, which benefit from both digital and social small data and big data, that help support not rendering the measurement efforts to non-measurement.

In this chapter, I argue that globalization of economic activities of multinational companies that have not been in majority owned by multiple nationalities but only employing multinational workforce, attracting multinational corporate board members and financial investors has created regulatory, economic and voluntary challenges for localization of political and economic globalism worldwide. Although national level political, administrative, juridical institutions opened up local national markets through privatization, deregulation, intellectual property rights, and free trade laws, regulations and agreements; the regulatory deficiencies in creating local economic incentives and voluntary initiatives through strategic relational corporate laws, and the foreign ownership of the core productive means, in contradiction, have served a globalized techno-nationalism. Firstly, hit by the financial crisis, the competition among anti-competitive state aid-backed technologies ascended in the world market and caused degeneration in the implementation of competition laws, which is extended to trade laws, and thus foreign policies, hence the discussions for the populist relocation of production, techno-nationally advancing artificial intelligence and big data applications. These incompetent competition and trade laws and regulations for techno-nationalist products and services pave the way for the generic wage stagnation, thickening the gender-based wage differentials, as well as the CO2-embedded trade imports for high income and CO2 embedded exports for middle-income countries in the midst of democracy versus development debates. Laws and regulations are still challenged by a dual political economic risk embedded in offers for localization of supply, demand, distribution and financial solutions. By proposing a network of regulatory intelligence against the rise of populism at city level, techno-nationalist artificial intelligence and big data strategies without humanities knowledge, growing climate catastrophe markets for multinational companies and countries as an outcome of ongoing climate crime, and stiffening gender-based wage gap and basic income debates, this chapter offers a meta-governance framework for network of regulatory intelligence and transformative laws and regulations for sharing financing, data, knowledge, and power within and among cities to create and enable novel economic incentives and voluntary initiatives for self-organization and determination for individuals, connected cities and communities, and global societal political economic sustainability.

The study “New technologies and digitisation: opportunities and challenges for the social economy and social enterprises” sets out to explore how the rising importance of the social economy in Europe is facing the challenges of digital transformation. It focuses on whether, why, how, and to what extent the integration of digital platforms and advanced technologies (i.e. Open-Source, Artificial Intelligence, Internet of Things, Big Data, Distributed Ledger Technologies) may affect the design and delivery of new/better social and societal impact by the social economy. This study shows that digital platforms and advanced technologies’ capabilities of automating and simplifying operations are opening up opportunities for the social economy in terms of increased reach and enhanced effectiveness. Digitalisation also underpins the creation of new and innovative social services and working conditions helping to tackle existing and emerging social and societal problems. Whilst these trends are set to continue in the near future, European Institutions, Member States policymakers and social entrepreneurs should commit to promote and support the digital transformation of the social economy and encourage the uptake, scale-up/scale-out and duplication of innovative digitally-enabled initiatives. Areas of intervention have been identified. Resources for Research and Innovation as well as traditional sources of revenues play an important role for the development and take-up of digital technologies for and by the social economy. Likewise, the development of entrepreneurial and digital skills of social economy actors are needed to foster the vision of the European digital social economy. Collaborations between various stakeholders in nurturing environments (social clusters, creative spaces or incubators) may effectively promote the modernisation of the social economy by strengthening and growing digital native cohorts of social economy organisations. Finally, in order to promote cross-Member State growth of the social economy, it is advisable to harmonise the legal forms and statutes regulating the various actors of the social economy. This way, digital platforms and advanced technologies may truly contribute to the establishment of the European digital social economy fit for the societal challenges of the 21st century.

The ongoing digital transformation is crucial for the social economy as it presents both enormous opportunities and challenges. Digital technologies, such as online platforms, software applications (apps), distributed ledger technologies, the Internet of Things (IoT), cloud computing and Artificial Intelligence (AI), offer a wide variety of tools to support the creation of social values and the development and further expansion of the social economy. Budget constraints, digital skills shortages and technological gaps are clear barriers for the social economy to uptake new technologies. We selected 26 social economy organisations using innovative digital technologies effectively across Greece, Italy, The Netherlands and the UK. Our objective is to understand how they are working through the digitalisation process. The study, based on primary information and insights collected via personal interviews, workshops and on relevant literature, sheds light on the key questions stated above. Here we present highlights of the study.

This paper develops the plan for the econometric estimations concerning the relationship between firm productivity and the specifics of the innovation process. The paper consists of three main parts. In the first, we review the relevant literature related to the productivity paradox and its causes. Specific attention will be paid to broad economic trends, in particular the higher importance of intangibles, the increasing importance of knowledge spillovers and servitization as drivers of the slowdown in productivity growth. In the second part, we introduce a plan for the econometric estimation strategy. Here we propose an extended Crépon-DuguetMairesse type of model (CDM), which enriches the original specification by the three influence factors of intangibles, spillovers, and servitization. This will allow testing the influence of these three factors on productivity at the level of the firm within a unified framework. In the third part, we build on the literature review in order to provide a detailed plan for the data collection procedure including a description of the variables to be collected and the source from which the variables are coming. It should be noted that we will rely partly on structured data (e.g. ORBIS), while many others variables will need to be generated from unstructured sources, in particular the webpages of firms. The use of unstructured data is a particular strength of our proposed data collection procedure because the use of such data is expected to offer novel insights. However, it implies additional risks in terms of data quality or missing data. Our data collection plan explores the maximum potential of variables that will ideally be made available for later econometric treatment. Whether indeed all variables will have sufficient quality to be used in the econometric estimations will be subject to the outcomes of the actual collection efforts.

Effective research and innovation (R&I) policy depends on the extent to which ideas, interests and institutional mechanisms for policy making work together rather than work against each other. In a political governance model for effective R&I policy in the EU-28, the separate influence of inter-ministerial coordination, regulatory impact assessment extended to sustainability checks, parliamentary committee surveillance, media attention and societal consultation is investigated. Interaction effects are investigated in a set-theoretic analysis for the econometrically best-fit model. Our results show that the societal consultation, policy-informed opposition and sector-informed informal policy coordination are necessary but not sufficient to bring about effectiveness to R&I policy. Their influence on effectiveness of R&I policy depends on the combination with either media attention or Regulatory Impact Analysis (RIA) extended to sustainability checks. We reached these results with the help of ordered logit estimations and fuzzy-set qualitative comparative analyses using 2011-2013 (SGI) data of Bertelsmann Stiftung and Lexis Nexis Academic.