Objective – FinTech is one of the fastest growing industry and, consequently, it has attracted scholars attention. However, the research on the topic is still at its embryonic stage and some unexplored areas can be found. For this reason, this thesis aims at filling the gaps found out in the FinTech literature. In particular, it has a twofold objective: on the one hand it explores the FinTech landscape to assess whether successful models are spreading, on the other hand it aims at identifying the direction FinTech start-ups development is taking. Methodology – In order to meet the objectives of this paper – once completed an exhaustive literature review based on more than 100 contributions – al data analysis has been carried out. The 191 European FinTech start-ups considered have been described according to the dimensions of a fourth-dimensional clustering model. Data have been collected from company websites, newspaper articles, founders pitch videos and other publicly available sources. Moreover, an historical development assessment has been carried out exploiting Wayback Machine, an online tool that allows users to access old versions of web pages. Results – A complete mapping of a sub-sample of all European FinTech start-ups according to four dimensions – i.e. reference market, business function, business (revenue) model and technology – has been produced. Moving from the assumption that the more profitable a business (or a model) is (or is expected to be), the more it attracts funds and players, the most popular markets, business functions, business models and technological enablers have been identified. Moreover, six archetypes – i.e. profiles of recurring models described along a series of dimensions – have been detected and characterized. Those archetypes represent approximately a third of the whole sample. Moreover, the start-ups historical development path has been mapped as well. Combining this information with players specialization level – i.e. specialist vs. conglomerate – it has been possible to identify the major trends. In particular, no player was born as a conglomerate and then re-focused becoming a specialist, while 63% of current conglomerates started as specialists. Research limitations – The main limit of the research lies in collected data validation since they have been gathered directly by authors from secondary sources. This issue could be addressed through self-certifying surveys filled out by involved start-ups.
Obiettivo – Il settore del FinTech è fortemente in crescita e, di conseguenza, ha attirato anche l’attenzione degli accademici. Tuttavia, gli studi sulla materia sono ancora nella loro fase embrionale e si possono trovare alcune zone inesplorate. Perciò, questa tesi ha la finalità di colmare le lacune identificate. In particolare, l’obiettivo è duplice: da un lato esplorare il panorama FinTech per verificare se alcuni modelli vincenti si stiano diffondendo e dall’altro identificare gli sviluppi futuri che il settore del FinTech potrebbe avere. Metodologia – Per conseguire gli obiettivi di questa tesi, una volta completata una revisione esaustiva della letteratura basata su più di 100 pubblicazioni, è stata eseguita un’analisi dei dati. Le 191 start-ups FinTech europee considerate, sono state categorizzate secondo le quattro dimensioni di un modello di classificazione multi-dimensionale. I dati sono stati raccolti dai siti web delle società, da articoli di giornale e da altre fonti pubblicamente accessibili. Inoltre, è stata svolta un’analisi dello sviluppo storico utilizzando Wayback Machine, uno strumento online che permette di accedere a vecchie versioni di pagine web. Risultati – E’ stata effettuata una mappatura completa del campione considerato secondo quattro dimensioni, che sono il mercato di riferimento, l’area di business, il business model e la tecnologia. In seguito, sono stati identificati i mercati, le aree di business, i business model e le tecnologie più popolari, partendo dal presupposto che più un business (o un modello) risulta essere (o si ritiene sarà) profittevole, più tende ad attrarre finanziamenti e nuovi entranti. Inoltre, sei archetipi (profili ricorrenti descritti secondo le dimensioni considerate) sono stati individuati e caratterizzati. Questi archetipi descrivono circa un terzo del campione. Infine, è stato mappato anche lo sviluppo storico delle start-up. Combinando questa informazione con il livello di specializzazione delle società (specialisti o conglomerati) è stato possibile identificare i trend principali. Nello specifico, nessun conglomerato ha ridotto la sua offerta nel corso degli anni evolvendo, poi, in uno specialista. Il contrario, invece, si è verificato: il 63% degli attuali conglomerati sono nati inizialmente come specialisti. Limiti del lavoro – Il limite principale di questa tesi sta nella validazione dei dati, dato che sono stati raccolti direttamente dagli autori da fonti secondarie. Questo problema potrebbe essere risolto attraverso questionari di auto-certificazione distribuiti alle start-up.
FinTech start-ups : successful models identification and historical development assessment
FERRARI, GIACOMO;FARFALETTI CASALI, PAOLO
2018/2019
Abstract
Objective – FinTech is one of the fastest growing industry and, consequently, it has attracted scholars attention. However, the research on the topic is still at its embryonic stage and some unexplored areas can be found. For this reason, this thesis aims at filling the gaps found out in the FinTech literature. In particular, it has a twofold objective: on the one hand it explores the FinTech landscape to assess whether successful models are spreading, on the other hand it aims at identifying the direction FinTech start-ups development is taking. Methodology – In order to meet the objectives of this paper – once completed an exhaustive literature review based on more than 100 contributions – al data analysis has been carried out. The 191 European FinTech start-ups considered have been described according to the dimensions of a fourth-dimensional clustering model. Data have been collected from company websites, newspaper articles, founders pitch videos and other publicly available sources. Moreover, an historical development assessment has been carried out exploiting Wayback Machine, an online tool that allows users to access old versions of web pages. Results – A complete mapping of a sub-sample of all European FinTech start-ups according to four dimensions – i.e. reference market, business function, business (revenue) model and technology – has been produced. Moving from the assumption that the more profitable a business (or a model) is (or is expected to be), the more it attracts funds and players, the most popular markets, business functions, business models and technological enablers have been identified. Moreover, six archetypes – i.e. profiles of recurring models described along a series of dimensions – have been detected and characterized. Those archetypes represent approximately a third of the whole sample. Moreover, the start-ups historical development path has been mapped as well. Combining this information with players specialization level – i.e. specialist vs. conglomerate – it has been possible to identify the major trends. In particular, no player was born as a conglomerate and then re-focused becoming a specialist, while 63% of current conglomerates started as specialists. Research limitations – The main limit of the research lies in collected data validation since they have been gathered directly by authors from secondary sources. This issue could be addressed through self-certifying surveys filled out by involved start-ups.| File | Dimensione | Formato | |
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2019_12_Farfaletti_Ferrari.pdf
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https://hdl.handle.net/10589/151733