Special Issues 2019

"Skills and competences in maritime logistics: managerial and organizational emerging issues for human resources" - call for abstracts

È disponibile qui la Call for Abstracts finalizzata a selezionare i contributi che saranno ospitati nello Special Issue, la cui pubblicazione è prevista per settembre 2019.

Scadenza per gli abstracts: 15.2.2019 extended! 5.3.2019. Scadenza per i full papers: 15.5.2019. Abstracts e full papers saranno sottoposti a double blind review.

 

"Studying organizations: identity, pluralism and change" - call for papers

La proposta nasce con esplicito riferimento all'annuale Workshop dei Docenti e deglisStudiosi di Organizzazione Aziendale, a cui prioritariamente si rivolge.

È disponibile qui la Call for Papers finalizzata a selezionare i contributi che saranno ospitati nello Special Issue, la cui pubblicazione è prevista per maggio 2019.

Scadenza per i full papers: 15.4.2019. I full papers saranno sottoposti a double blind review.

Data analytics e intelligenza artificiale per l’analisi di bilancio. Performance e profili di business degli spin-off accademici

Saggi
Sul numero: 
Settore: 
Economia Aziendale
Abstract: 

This research applies neural networks – namely: Self-Organising Maps (SOMs) - to analyse a bunch of financial indicators drawn from the balance sheet of academic spin-offs. The goal of the work is twofold: first, it aims at processing financial data to extract knowledge about the still uncertain role and strategic profile of academic spin-offs; second, it aims at understating whether SOMs are able to support investigations on firms’ performance, and to decide strategic orientation thanks to the processing of financial indicators. After a deep literature review about both the application of SOMs to financial reporting data and the business profile of academic spin-offs, the paper carries on an empirical investigation on 810 Italian academic spin-offs, using their financial reporting data. The results show that SOMs succeed in extracting the main features of different academic spin-off archetypes that can be then explained via traditional financial analysis instruments.