Nowadays scholars widely recognize that know‐how, capabilities and knowledge needed to generate innovations often reside outside the firm, start‐ups are a valuable source, and collaborative networks are a fundamental strategy for innovation. This is true especially for the clean‐tech sector, which is characterized by the continuous search for innovative solutions and technological advancements. The purpose of the paper is to provide a methodological support for the screening of potential partners based on network analysis and, then, help firms to select them for collaboration and knowledge exchange. The methodology can be easily adopted by managers and executives to identify firms to monitor with greater attention for future investments. The analysis is on a dataset of 4,782 clean‐tech companies operating worldwide. Results highlight that energy companies looking for external sources could investigate their network of business proximity if they intend to specialize in a defined field and/or collaborate with similar partners, while they could explore their network of strategic proximity if they intend to diversify their businesses, that is cooperating and exchanging knowledge with firms with distant but complementary capabilities and resources.

Even though scholars' attention has been placed on Social Innovation (SI), little evidence has been provided with regards to which tools are actually used to address social needs and foster Social Innovation initiatives. The purpose of the article is twofold. Firstly, the article offers empirical recognition to SI by investigating, on a large-scale, social and innovative activities conducted by start-ups and small and medium-sized enterprises (SMEs) across the world between 2001 and 2014. Secondly, the article intends to capture SI core businesses and underlying complementarities between products, markets, and technologies and show in which way digital media and IT are essentially tracing innovation trajectories over a multitude of industries, leading the current industrial patterns of SI, and continually fostering its cross-industry nature.

The paper presents a new approach based on Self-Organizing Maps (SOM) and a new index called Relative Industrial Relevance (RIR) to discover, track and analyze spatial agglomeration of economic activities. By comparing patterns of local employment, this methodology shows how the local supply of human capital can explain the advantages generating spatial agglomerations. The reference case for this research is Italy, which has developed one of the most remarkable and studied example of spatial agglomerations, the Industrial Districts (IDs). IDs are traditionally identified by indexes which measure the physical concentration of firms belonging to a given industry, but are unable to seize the overall productive structure of the local economy. Employing the Italian Clothing Industry as test bed, the approach proposed in this paper identifies spatial agglomerations in terms of industry patterns and not of industry concentration. This methodology can offer a new basis to analyze the multiple pattern of local development.

The literature identifies hard budget constraints, stock market liquidity, political preferences and legal origins as the main determinants of full and partial privatization. Nonetheless, the institutional environment, understood as the functioning of a diverse set of de facto institutions and arrangements, is fundamental for privatization decisions and outcomes. A multi‐dimensional and cross‐country approach is proposed to shed some light on the institutional characteristics that typically are associated with different choices (partial sales ratio, percentage of private ownership) and privatization outcomes (closeness of sale prices to market levels, public vs private interest). The purpose of this paper is to learn lessons from country institutional profiles and provide local governments with a basic frame to understand their institutional settings and prioritize instrumental reforms for privatization. Results suggest that strategy and administrative burden are important determinants of privatization outcomes, and that government commitment and strategic approach to privatization is usually associated to partial privatizations.

In order to overcome the limitations of defining industrial specializations in digital industries through SIC codes, this paper suggests measuring the specializations and competences of these industries on the basis of the degree of digital technologies present in the products and services supplied. Metadata from CrunchBase are employed, as proxies of firms' specializations and competences which are defined as the fields of activity in which firms are involved. Applying a network analysis, these specializations and competences are linked to the recognition of emerging digital technologies and the strongest combinations of products and services. We tested the proposed methodology on London, a leading centre for the digital economy.