This paper builds on Qian and Acs (Small Business Economics, 40, 185–197, 2013) concept of entrepreneurial absorptive capacity and empirically investigates its role in the context of new knowledge intensive entrepreneurship. When it comes to the business formation phase and especially to the creation of knowledge intensive entrepreneurial ventures, knowledge inheritance of new firms seems to be more related to individual attributes. The knowledge intensity of these ventures is very much dependent on the founders’ capability to understand new knowledge, combine their knowledge with external knowledge sources, and exploit it in the context of opportunities they can recognise; that is, their absorptive capacity. This study provides new insights into the concept of absorptive capacity by studying it at the individual level enhancing the way of measuring it. Based on quantitative data from a large-scale survey, we conducted a partial least squares structural equation modelling (PLS-SEM) application, which verified the role of entrepreneurial absorptive capacity in triggering knowledge intensive entrepreneurship. Furthermore, the results imply a weaker but positive indirect effect of absorptive capacity to new venture performance. There is also a statistically significant negative influence of competition intensity and a positive influence of equity funding on the formation of knowledge intensive entrepreneurship. At the policy level this analysis reveals the importance of micro-determinants in fostering knowledge intensive entrepreneurial activities.

As digital technology has become an integral part of urban life’s daily operations, the urban landscape is constantly evolving with the needs of its society. This new reality has allowed municipalities to invest in technologies related to smart cities and to exert a greater influence on the national and local economy. In line with this, the paper aims to understand the mechanisms of planning and implementing a municipality’s strategy in Greece to exploit the smart city benefits and to foster economic development. It is important to identify the role of different factors including strategy during the planning and implementing phases of initiatives concerning the economy and innovation in a smart city. To achieve this, data were collected via a questionnaire and processed using the advanced statistical technique PLS-SEM. The main findings highlight the importance of planning initiatives aligned with the needs of the municipality and the business ecosystem. The existence of a smart city strategy has a catalytic effect on the final impact of the implemented initiatives on the urban ecosystem. A systematic analysis of the smart cities’ dynamics and the new state of the urban ecosystem can help the local actors focus on value creation and public service provision, fostering innovation and profitability.

The dataset was formulated through field research in the Greek Municipalities. The survey was undertaken with an online questionnaire (available in Greek) and the contact person was the Chief Digital Officer or a person with similar responsibilities. The final sample is 252 out of a total of 325 Municipalities. Each respondent had to answer to 26 questions, including sub-questions, of different types (1) 5-point Likert scale, (2) rating scale, (3) closed type “Yes/no”, (4) open-ended and (5) dropdown, and, depending on them, there are 3 types of variables (1) ordinal, (2) nominal, and (3) scale variables. The concepts included in the questionnaire are a) the level of integration and use of digital technologies, b) the difficulties and the challenges municipality authorities face when trying to implement a smart strategy and c) the level of diverse collaborations and partnerships which are necessary in order to develop a strategy. The data can be used to analyse the strategic capabilities of the Greek Municipalities under the three concepts evaluated in the questionnaire. The data's primary beneficiaries include researchers, public authorities, digital platforms entrepreneurs, smart city specialists and smart city entrepreneurs, as they can use the dataset to develop models against already known results, to identify factors important in an urban strategy and to further study the correlations between the different factors, design and draft an urban strategy on real data.