Francesco Fasano, Carlo Adornetto, Iliess Zahid, Maurizio La Rocca, Luigi Montaleone, Gianluigi Greco and Alfio Cariola
Our aim is to develop a highly precise corporate crisis prediction model that surpasses previous versions, rooted in the forefront of technological advancements.
Abstract
Purpose
Our aim is to develop a highly precise corporate crisis prediction model that surpasses previous versions, rooted in the forefront of technological advancements.
Design/methodology/approach
Artificial Intelligence (AI) for corporate default prediction with a novel approach based on a mix of techniques, enabling it to achieve a higher accuracy. We investigated models with sequence lengths that were both fixed and variable, and we chose the best variable sequence length model.
Findings
Our findings demonstrate that the artificial techniques implemented lead to very high accuracy in predicting business crises compared to previous research efforts, even those utilising long-time sequences or a high volume of observations.
Research limitations/implications
We highlight the key variables with a higher predictive power that need monitoring to prevent business crises. We also aim to open a new avenue of research that, starting from the use of these techniques and our results, can implement models incorporating non-accounting variables to prevent business crises.
Practical implications
We provide a model/tool that assesses a possible business crisis in advance through a monitoring and alert system. Policymakers can use our research’s output as a tool to combine with current credit-scoring systems and to assess the effectiveness of the new corporate crisis reforms that are upcoming in many European countries. The results of our research can be useful also to banks, public entities, and consulting firms that interact with companies and are interested in the evaluation of a firm’s financial health and stability.
Originality/value
Our innovative work leverages cutting-edge methodologies such as deep Recurrent Neural Networks and explainable AI. This choice is driven by the rapid evolution of AI techniques in practical application.
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Mariacarmela Passarelli, Giovanni Catello Landi, Alfio Cariola and Mauro Sciarelli
The paper aims to advance knowledge by investigating the main factors that impact on innovation through the co-development process between researchers and firms at the very early…
Abstract
Purpose
The paper aims to advance knowledge by investigating the main factors that impact on innovation through the co-development process between researchers and firms at the very early stage of proof of concept.
Design/methodology/approach
The authors developed an empirical analysis on the proof of concept network project, through a mixed empirical analysis. They explored the main factors that affect the enactment of the co-development process and tested the impact of such factors on the probability for partners to enact a co-development project and generate innovation.
Findings
From the quantitative analysis comes out that the trust of the research team into the potentiality of the technology, the commitment of researchers concerning the scalability of technology and the IP value issued by external experts have a positive impact on the probability to create a match among partners and generate innovation.
Research limitations/implications
Even if all the population of technologies (108) considered in the project implementation are analyzed, the development of the empirical analysis on a specific project within a single country represents a limitation. Future analysis will concentrate on a larger panel of proof of concept experience across Europe.
Practical implications
The success of a co-development process between researchers and companies at the embryonic phase of the technology considers the opportunity to exploit the technologies into real products for the market.
Originality/value
This is an empirical analysis of the first Italian proof of concept implementation that deeply investigates which critical factors can enable innovation by enacting a co-development process between researchers and small and medium-sized enterprises (SMEs).
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Valentina Cucino, Mariacarmela Passarelli, Alberto Di Minin and Alfio Cariola
This study focuses on the role of individuals in the innovation management process, by concentrating on leaders and associated behaviors. Specifically, Entrepreneurial Leadership…
Abstract
Purpose
This study focuses on the role of individuals in the innovation management process, by concentrating on leaders and associated behaviors. Specifically, Entrepreneurial Leadership (EL) represent one of the most important fields of innovation management that has become increasingly multifaceted and interdisciplinary with its evolution. Thus, the purpose of this study is to examine a newly emerging research trend with a new lens that is “neuroscience”.
Design/methodology/approach
This paper finds an evidence-based roadmap by reviewing the literature with a quantitative Bibliometric Analysis (BA) employing Co-Citation (Co-C) and bibliographic coupling analysis (BcA) to find linkages between the leadership and entrepreneurship literature and the neuroscience literature.
Findings
This study identifies five promising groups of research areas such as the organizational approach, the biological approach, the cognitive approach, the emotional approach and it identify five future research topics such as dynamic skills in innovation exploitation process, the human aspect of leadership, the building process of leadership, the biological perspective of leadership and the application of neuroscience in the ecosystem. Moreover, we find an evidence-based roadmap for stimulating focused EL within the broad topic of innovation management research, to move the field forward.
Originality/value
Although the past few years have observed the necessity of review studies on the subsets of biological factors, no reviews have sought to bring those different subsets together into a broader biological perspective. This study provides important indications on the interdisciplinary developments between the neuroscience aspects and EL, as a new emerging paradigm within the broad field of innovation management.