Three essays on a longitudinal analysis of business start-ups using the Kauffman Firm Survey

This dissertation focused on the longitudinal analysis of business start-ups using three waves of data from the Kauffman Firm Survey. ^ The first essay used the data from years 2004-2008, and examined the simultaneous relationship between a firm's capital structure, human resource policies, and its impact on the level of innovation. The firm leverage was calculated as, debt divided by total financial resources. Index of employee well-being was determined by a set of nine dichotomous questions asked in the survey. A negative binomial fixed effects model was used to analyze the effect of employee well-being and leverage on the count data of patents and copyrights, which were used as a proxy for innovation. The paper demonstrated that employee well-being positively affects the firm's innovation, while a higher leverage ratio had a negative impact on the innovation. No significant relation was found between leverage and employee well-being.^ The second essay used the data from years 2004-2009, and inquired whether a higher entrepreneurial speed of learning is desirable, and whether there is a linkage between the speed of learning and growth rate of the firm. The change in the speed of learning was measured using a pooled OLS estimator in repeated cross-sections. There was evidence of a declining speed of learning over time, and it was concluded that a higher speed of learning is not necessarily a good thing, because speed of learning is contingent on the entrepreneur's initial knowledge, and the precision of the signals he receives from the market. Also, there was no reason to expect speed of learning to be related to the growth of the firm in one direction over another.^ The third essay used the data from years 2004-2010, and determined the timing of diversification activities by the business start-ups. It captured when a start-up diversified for the first time, and explored the association between an early diversification strategy adopted by a firm, and its survival rate. A semi-parametric Cox proportional hazard model was used to examine the survival pattern. The results demonstrated that firms diversifying at an early stage in their lives show a higher survival rate; however, this effect fades over time.^

NOW workshop in NY for older women on "How to start your own business"

General note: Title and date provided by Bettye Lane.

Il crowdfunding come forma alternativa ed innovativa al finanziamento di una start-up: il caso Look Ahead

Il seguente percorso di tesi si articola in due parti. Nella prima si andranno ad illustrare le varie possibili fonti di finanziamento alle quali uno o più imprenditori potranno rivolgersi nella creazione di una nuova impresa, con una attenzione particolare a quello che è il crowdfunding, o finanziamento collettivo, considerato una alternativa valida e innovativa alle forme tradizionali per raccogliere i capitali necessari. Nella seconda parte sarà presentata la startup italiana Look Ahead, di cui il sottoscritto ne rappresenta una delle menti, come esempio di una startup che ha scelto di usufruire di questo particolare tipo di finanziamento. In particolare, ne sarà ricostruito l’intero Business Plan redatto in sede accademica, in modo da mettere in evidenza le caratteristiche del prodotto offerto, il segmento di mercato servito, il modello di business e l’analisi finanziaria.

La diffusione delle start-up eco-innovative in Emilia-Romagna

Il mio elaborato ha come scopo quello di creare un quadro della situazione regionale dal punto di vista delle start-up eco-innovative. Per fare questo ho utilizzato un sito/piattaforma internet contenente il registro delle start-up innovative in Italia. A partire da tale supporto ho analizzato 700 start-up situate nella regione Emilia-Romagna. Tra queste, ho individuato 78 start-up eco-innovative. Lo studio vuole comprendere le tendenze e la diffusione di tali start-up, studiandone, nell' ordine, la distribuzione geografica, l’inizio delle attività, il codice Ateco, la tecnologia OECD di riferimento. Con questo studio si arriverà poi in definitiva a capire quanto l’ambito della Green Economy sia diffuso in Emilia-Romagna.

Start-up e green economy. Un’analisi delle regioni italiane

Questa tesi si pone l’obiettivo di fornire un quadro generale relativo allo stato della Green Economy in Italia, riservando particolare attenzione alle start-up innovative e all’eco-innovazione, per poi passare ad una analisi particolareggiata delle start-up innovative di Marche, Umbria ed Abruzzo attive nel campo dell’ economia verde. Nel realizzare questo approfondimento ho raccolto, catalogato e rielaborato informazioni presenti nella sezione speciale del Registro delle Imprese dedicata alle start-up innovative. Si è trattato di selezionare le imprese green dall’universo delle start-up innovative di Umbria, Marche ed Abruzzo raccogliendone le informazioni più significative (Nome, Provincia, inizio attività, codice Ateco, requisiti d’innovazione, eventuale alto valore tecnologico in ambito energetico, dimensione economica, caratteristiche della risorse umane e mission dell’impresa) creando un dataset tramite un foglio excel. Terminata la fase di raccolta e catalogazione di dati è cominciato il lavoro di analisi che ha condotto allo studio riportato nella tesi. Nel riportare e commentare le informazioni raccolte mi sono avvalso dell’ausilio di grafici.

Role of assessment components and recent adverse outcomes in risk estimation and prediction: use of the Short Term Assessment of Risk and Treatability (START) in an adult secure inpatient mental health service

The Short Term Assessment of Risk and Treatability is a structured judgement tool used to inform risk estimation for multiple adverse outcomes. In research, risk estimates outperform the tool's strength and vulnerability scales for violence prediction. Little is known about what its’component parts contribute to the assignment of risk estimates and how those estimates fare in prediction of non-violent adverse outcomes compared with the structured components. START assessment and outcomes data from a secure mental health service (N=84) was collected. Binomial and multinomial regression analyses determined the contribution of selected elements of the START structured domain and recent adverse risk events to risk estimates and outcomes prediction for violence, self-harm/suicidality, victimisation, and self-neglect. START vulnerabilities and lifetime history of violence, predicted the violence risk estimate; self-harm and victimisation estimates were predicted only by corresponding recent adverse events. Recent adverse events uniquely predicted all corresponding outcomes, with the exception of self-neglect which was predicted by the strength scale. Only for victimisation did the risk estimate outperform prediction based on the START components and recent adverse events. In the absence of recent corresponding risk behaviour, restrictions imposed on the basis of START-informed risk estimates could be unwarranted and may be unethical.

STOPP/START Criteria and Their Role as a Clinical Tool in Routine Practice

Screening Tool of Older Persons’ Prescriptions (STOPP)/Screening Tool to Alert to Right Treatment (START) criteria was first published in 2008, primarily as an alternative set of explicit criteria for potentially inappropriate medications (PIMs) to Beers criteria.

Re-evaluation of central America drug market where to start?

Atral is a Portuguese Pharmaceutical firm devoted to the production of finished drugs. Due to domestic market hurdles, Atral is now, more than ever, focused in the world. The Central America region seams alluring due to its context alignment with firm’s resources bundle. As Atral should approach one regional country at a time, the purpose of this thesis is to find out the most suitable country to approach now. Hence a tailored scoring model was applied, based on contexts analysis and importance of benchmarking indicators to both firm and industry. Upon analysis of the highest scored country, the most appropriate entry modes were assessed.

Valoración de una Start-up de tecnología

Este documento expone la importancia del ejercicio de valoración como herramienta para la construcción de las proyecciones y la planeación financiera de una empresa en etapa temprana, al tiempo que prueba el método de flujos de caja con tasa de descuento ajustada al riesgo, como la metodología más recomendada por expertos, en la valoración de nuevas empresas (startups), y valida la marginalidad de la información contable y financiera entre los emprendedores -- Busca soportar en un único documento, las decisiones de inversión de financieros, prestamistas y emprendedores, en atención a la subjetividad con que muchos inversionistas valoran desde su percepción, el potencial de crecimiento, la generación de flujos futuros y/o el posicionamiento estratégico de las startup -- Como ya se mencionó, el método de Descuento de Flujos de Caja (DFC) será la metodología aplicada y analizada -- Entre otras ventajas, porque al estar basado en la generación de flujos a partir de los activos fijos, no se expone a percepciones del mercado ni a criterios no trasladables, en caso de una valoración por comparables -- El lector podrá constatar, y de acuerdo con la bibliografía consultada, que la metodología de DFC, no sólo es la más apropiada para la valoración de una startup, sino que dadas las circunstancias en cuanto a disponibilidad de la información, es obligatorio clasificarla, dentro de los métodos más sofisticados -- Finalmente, entre otras conclusiones, se hace énfasis en que el ejercicio de la valoración debe centrarse en identificar el potencial que tiene la empresa de convertirse en una entidad generadora de valor -- En tal sentido, el análisis se debe focalizar en el plan estratégico, que se espera desarrollar a corto, mediano y largo plazo, y en las acciones para alcanzar las metas planteadas -- No siempre la situación de la empresa ha de estar acorde con lo proyectado, se pueden presentar variaciones en el comportamiento financiero, adicionalmente, la demanda de capital líquido e inversiones en activos generan, en la mayoría de los casos, déficit en los flujos de caja producto de las dificultades de los emprendedores para garantizar dicha demanda de recursos

Three Essays on a Longitudinal Analysis of Business Start-ups using the Kauffman Firm Survey

This dissertation focused on the longitudinal analysis of business start-ups using three waves of data from the Kauffman Firm Survey. The first essay used the data from years 2004-2008, and examined the simultaneous relationship between a firm’s capital structure, human resource policies, and its impact on the level of innovation. The firm leverage was calculated as, debt divided by total financial resources. Index of employee well-being was determined by a set of nine dichotomous questions asked in the survey. A negative binomial fixed effects model was used to analyze the effect of employee well-being and leverage on the count data of patents and copyrights, which were used as a proxy for innovation. The paper demonstrated that employee well-being positively affects the firm's innovation, while a higher leverage ratio had a negative impact on the innovation. No significant relation was found between leverage and employee well-being. The second essay used the data from years 2004-2009, and inquired whether a higher entrepreneurial speed of learning is desirable, and whether there is a linkage between the speed of learning and growth rate of the firm. The change in the speed of learning was measured using a pooled OLS estimator in repeated cross-sections. There was evidence of a declining speed of learning over time, and it was concluded that a higher speed of learning is not necessarily a good thing, because speed of learning is contingent on the entrepreneur's initial knowledge, and the precision of the signals he receives from the market. Also, there was no reason to expect speed of learning to be related to the growth of the firm in one direction over another. The third essay used the data from years 2004-2010, and determined the timing of diversification activities by the business start-ups. It captured when a start-up diversified for the first time, and explored the association between an early diversification strategy adopted by a firm, and its survival rate. A semi-parametric Cox proportional hazard model was used to examine the survival pattern. The results demonstrated that firms diversifying at an early stage in their lives show a higher survival rate; however, this effect fades over time.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.