Raffaella Manzini

In a dynamic world, full of opportunity, the development of tools able to support companies in the decision-making process plays a crucial role. In this paper, a new decision support system is presented. The tool developed is an agent-based simulator, which helps managers and decision maker in formulating choices to improve firms’ performance in terms of innovation. Both economic and innovation performance are considered. The economic performance is evaluated using firms’ profit, whereas the innovation performance is evaluated using the Innovation Patent Index (IPI) based on patent data. In particular, the paper shows how the newly developed tool can be used by managers to investigate the impact of open and closed innovation on the economic and innovation performance of firms through the “what-if” analysis. The results show that the tool is a very timely, useful and friendly instrument to investigate open and closed innovation used to improve the economic and innovation performance of firms.

Patents are the most important tool to protect innovation and support the value creation process in early-stage financing as well as in Private Capital investments. This work studies the growing body of academic literature concerning the relations between Private Equity, Venture Capital and patents and formulates a theoretical framework and an agenda for future research. We review the specific body of research resulting from the entwining of corporate finance and innovation management. Through a bibliometric analysis of the literature, we adopt a quantitative approach to develop a content analysis on the 292 articles found. We present the open debate related to the improvement of the metrics to proxy the technological value of a company's innovation, especially in relation to startups and venture capital investments. We assess the key contributions, thematic clusters and emerging themes, formulating a theoretical framework grounded on the state of research. Additionally, we highlight the lack of integration of findings on signalling before the investment and the strategic use of Intellectual Property Protection Mechanisms assets by the investor to create value during the holding period. Finally, we outline future venues of research in the study of the relations between investments in innovative companies and positive socio-economic externalities.

Urban infrastructures and services-such as public transportation, innovation bodies and environmental services-are important drivers for the sustainable development of our society. How effectively citizens, institutions and enterprises interact, how quickly technological innovations are implemented and how carefully new policies are pursued, synergically determine development. In this work, data related to urban infrastructure features such as patents and recycled waste referred to 106 province areas in Italy are investigated over a period of twenty years (2001–2020). Scaling laws with exponents characterizing the above mentioned features are observed and adopted to scrutinize whether and how multiple interactions within a population have amplification effects on the recycling and innovation performance. The study shows that there is a multiplication effect of the population size on the innovation performance of territories, meaning that the dynamic interactions among the elements of the innovation eco-systems in a territory increase its innovation performance. We discuss how to use such approach and the related indexes for understanding metropolitan development policy.

A company's growth depends not only on its achievements but also on how it can recover from failures. The study of innovation failure and learning-from-failure has gained attention over the years. Described as a complex problem, the dynamic of learning occurs as a non-linear phenomenon. Therefore, this study develops an agent-based model to examine and investigate, as a complex system, the impact on firms' performance of two main possible strategies of learning-from-failure, i.e. (1) the leveraging of the own experience and (2) the use of external resources. The findings suggest that embracing a learning-from-failure strategy in the innovation process enhances the firms' performance. In addition, the innovation intensity of the sector influences the impact of the strategy chosen. Comparing the use of internal vs external resources, the former seems to be a better strategy for enhancing the company's performance.
•Learning from failure is a window of wisdom but it is an understudied phenomenon.•Learning from failure can be addressed by leveraging internal or external resources.•An ABM has been developed to examine learning from innovation failure.•Adopting a learning-from-failure strategy enhances firm performance.•The developed ABM support managers to learn from failures by means of simulations.

Purpose: The study aims to test the success of university-industry (U-I) collaboration in terms of innovation process efficiency. Then, this study explores the moderating role of a set of organizational routines in the U-I relationship, which can help in overcoming the issues undermining the collaboration success. Design/methodology/approach: The study is based on an international Open Innovation (OI) survey. The survey investigated the items to build the main variables of the conceptual framework, measured through seven-point Likert scales. Steps to ensure the reliability and validity of the variables were conducted. Then, hypotheses were tested with an ordinary least squares regression. Findings. Results show that the higher the collaboration intensity (depth) with universities, the higher the innovation process efficiency. Furthermore, organizational routines aimed at improving firms’ assimilation absorptive capacity further strengthen the positive effects of intensive collaboration on innovation process efficiency. Practical implications: Findings indicate that R&D managers should strive to build deep collaborations with universities to enhance process efficiency and invest in the quality of these relationships. Managers should create and maintain an internal environment that further enhances the positive effects of intensive collaboration on innovation process efficiency. Originality/value: The OI literature has not reached a shared view on the positive contribution of universities toward industrial firms’ innovation performance. The study adopts a process-efficiency view, rarely used by other OI studies usually focused on output indicators; this study unpacks, respectively, the role of the intensity of collaboration and the organizational routines, thus disclosing the benefit of U-I collaboration on innovation efficiency.

The aim of this study is to examine the implications of knowledge collaboration in open innovation and the role of digital technology therein. The paper draws on a survey of manufacturing firms from Italy, Sweden, France and Spain. The results show that knowledge collaboration with external partners has a significant effect on innovation novelty but not on innovation efficiency. In addition, the results show that digital technology moderates the relationship between knowledge collaboration and innovation efficiency but not the relationship between knowledge collaboration and innovation novelty. Overall, this study contributes to the growing discussion about the benefits of adopting digital technologies in open innovation processes by investigating whether and under which conditions knowledge collaboration is beneficial.

Firm performance is an important output that managers should control. Of the several perspectives taken to analyse performance within companies, innovation performance and economic performance are especially relevant. Joint patents are an important but overlooked strategy that firms can use to improve their performance. In this paper, an agent-based model and simulator (PABIM) is developed to investigate the impact of noncopatenting and copatenting strategies on economic and innovation performance. Economic performance is evaluated by observing turnover, whereas innovation performance is evaluated using the Innovation Patent Index (IPI). IPI is based on five patent features, each of them defined using machine learning algorithms. The results show that, depending on the innovation intensity of the sector, both noncopatenting and copatenting strategies can be effective to improve both the economic and innovation performance.