Search results

This work aims to explore the relationship between entrepreneurial ecosystems' (EEs) inputs and outcomes within a digital-technology-driven EE. Specifically, it focuses on how being part of an EE enhances digital technology adoption (DTA) and consequently facilitates EE outcomes.

This paper employs a single-case study approach, focusing on Italian EE. The data analysis is based on the researchers' direct observations and semi-structured interviews with the EE founders' teams and the top management of the small- and medium-sized enterprises (SMEs) operating therein. Given the novelty of the topic and the lack of a clear research framework of analysis, a qualitative method is well suited for studying digital-technology-driven EE, thus gaining rich data about the phenomenon in a real-life context.

The findings of the study reveal that when specific eco-inputs (financial, knowledge, social and institutional assets) are correctly exploited to enhance DTA, important outcomes, namely, SME competitiveness and new technology-based venture creation and development, are generated.

The paper contributes to a relatively unexplored topic in the existing literature on EEs and digital technology. Specifically, through the proposition of a conceptual model, it sheds light on the relationship among EE inputs, DTA and EE outcomes.

The aim of the study is to explore the genesis of entrepreneurial ecosystems (EE) and highlight the role played by intellectual capital (IC) in that process. Specifically, the paper adopts the collective intelligence approach, and the study shows how human capital (HC), structural capital (SC) and relational capital (RC) interact to create an entrepreneurial ecosystem.

The paper adopts a single case study of an Italian EE. The data analysis is based upon the collection of different sources of data: semi-structured interviews with representatives of each actor of the ecosystem; email correspondence; meetings report; a 24-months period of direct observation. Given the novelty of the topic, the qualitative method seems well suited for studying innovation-based EE since the method offers rich data about a phenomenon in real-life context.

The case is a top-down, innovation-based EE in which all main components of the IC play a crucial role from the initial stage. Findings show how the constant interchange between IC components occurs at two different levels: the micro and the meso level. HC and RC play major roles at both levels, whilst SC only occurs at a meso level, representing the environment in which the whole ecosystem takes place. Additionally, the use case, a new intangible asset integrating all three components of IC, emerged as one of the main outcomes of this innovation-based EE.

The paper contributes to a rather unexplored topic in the existing literature on EE and IC, namely the formation process of EE and the role played by IC within that process. Additionally, through the application of the collective intelligence approach, the authors shed light on the need to manage IC at both micro and meso level in the creation of an EE.