Persistence in innovation is an important phenomenon with relevant socio-economic implications. Understanding how to foster a continuous innovation process means to nurture society’s growth and development. The literature on innovation persistence has tried to explain its origin through theories regarding the impact of past innovations, contingent factors, as well as firms’ intrinsic characteristics. This thesis aims at identifying the major influencing factors of persistence in a sample of companies that won the “R&D 100 Awards” competition. The panel data-set analysed contains information on the innovative behaviour of more than two thousand firms, in a period of fifty years. To address the research question, two methodologies are implemented. First, Transition Probability Matrixes are employed to measure the probability of switching from one state to the other. This analysis, provides weak evidence of persistence. Second, Logistic and Poisson Model are estimated to investigate the influence exerted by multiple factors - among which particular relevance was given to past innovative activity - on both the probability to innovate and on the expected count of innovations. Final results suggest that persistence can be explained by the influence of past innovation only to a partial extent. After a threshold number of innovations, the probability to innovate marginally decreases. A closer look at companies’ size and stability in the innovation ranking within the markets relates the findings to Schumpeterian pattern of innovation. This gave the opportunity to discuss further the long-lasting debate on the relationship between innovation and competition.
La Persistenza nell’innovazione è un fenomeno rilevante con significative implicazioni socio-economiche. Comprendere come favorire un processo continuo di innovazione significa contribuire alla crescita della società ed al suo sviluppo. La letteratura sulla persistenza nell’innovazione ha tentato di spiegare l’origine stessa della persistenza tramite teorie riguardanti l’impatto delle innovazioni passata, fattori contingenti e le caratteristiche intrinseche delle aziende. Questa tesi si propone di identificare i fattori che maggiormente influenzano la persistenza in un campione di compagnie che hanno vinto la competizione “R&D 100 Awards”. Il panel data-set analizzato contiene informazioni sul comportamento innovativo di più di duemila aziende, in un periodo di cinquant’anni. Per affrontare l’oggetto di ricerca, due metodi sono implementati. Per primo, sono utilizzate le Matrici di Transizione di Probabilità per misurare la probabilità di passare da uno stato all’altro. Quest’analisi, fornisce una debole evidenza di persistenza. Secondariamente, sono stimati il modello Logistico e di Poisson al fine di investigare l’influenza esercitata da molteplici fattori - tra i quali, è stata attribuita maggior rilevanza al ruolo dell’innovazione passata – sia sulla probabilità di innovare, sia sul numero atteso di innovazioni. I risultati finali suggeriscono che la persistenza può essere spiegata dall’influenza dell’innovazione passata solo in misura parziale. Raggiunto il numero soglia di innovazioni, la probabilità di innovare decresce marginalmente. Uno sguardo più attento alle dimensioni delle aziende e alla stabilità nella classifica di innovazione all’interno dei mercati, collega i risultati trovati ai regimi di innovazione Schumpeteriani. Ciò, ha permesso di approfondire il duraturo dibattito sul rapporto tra persistenza ed innovazione.
Does success breed success? Empirical evidence on persistence in innovation from a dataset of R&D Awards - Il successo alimenta il successo? Evidenza empirica sulla persistenza in innovazione da un dataset di R&D Awards
CASALI, ENRICA
2022/2023
Abstract
Persistence in innovation is an important phenomenon with relevant socio-economic implications. Understanding how to foster a continuous innovation process means to nurture society’s growth and development. The literature on innovation persistence has tried to explain its origin through theories regarding the impact of past innovations, contingent factors, as well as firms’ intrinsic characteristics. This thesis aims at identifying the major influencing factors of persistence in a sample of companies that won the “R&D 100 Awards” competition. The panel data-set analysed contains information on the innovative behaviour of more than two thousand firms, in a period of fifty years. To address the research question, two methodologies are implemented. First, Transition Probability Matrixes are employed to measure the probability of switching from one state to the other. This analysis, provides weak evidence of persistence. Second, Logistic and Poisson Model are estimated to investigate the influence exerted by multiple factors - among which particular relevance was given to past innovative activity - on both the probability to innovate and on the expected count of innovations. Final results suggest that persistence can be explained by the influence of past innovation only to a partial extent. After a threshold number of innovations, the probability to innovate marginally decreases. A closer look at companies’ size and stability in the innovation ranking within the markets relates the findings to Schumpeterian pattern of innovation. This gave the opportunity to discuss further the long-lasting debate on the relationship between innovation and competition.È consentito all'utente scaricare e condividere i documenti disponibili a testo pieno in UNITESI UNIPV nel rispetto della licenza Creative Commons del tipo CC BY NC ND.
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https://hdl.handle.net/20.500.14239/3578