Researching insights into data in entrepreneurship
As a Researcher in Entrepreneurship, I focus on uncovering the key drivers of startup success by leveraging cutting-edge technologies and methodologies. My work is driven by a passion for understanding the dynamics of innovation, business growth, and the factors that enable startups to thrive in competitive and ever-evolving markets. To achieve this, I apply a robust, data-driven approach, utilizing advanced techniques such as data mining, machine learning, deep learning, natural language processing and generative AI. These tools allow me to extract actionable insights from complex datasets and contribute to the development of entrepreneurial theory and practice. Specifically, I am active in the following research streams:
New venture
creation
Leveraging AI to study how new business ideas are transformed into innovative entrepreneurial ventures. The insights generated can provide actionable guidance, empowering tech-entrepreneurs to make informed decisions ultimately increasing their chances of success in competitive environments.
Data-driven
venture capital
Designing and developing machine learning models tailored to support equity investors in making data-driven decisions. By analyzing vast amounts of data, these models can support VCs in identifying promising investment opportunities, assessing risk, and predicting future performance of emerging companies..
Generative AI
for innovation
Investigating how large language models (LLMs), such as GPT and other generative AI models, can reshape research in entrepreneurship and innovation. These models offer groundbreaking opportunities for enhancing the analysis and comprehension of entrepreneurial phenomena.
Here you can find some of my publications, others are already on the way.
Emerging Business Model Archetypes in the Circular Economy: A Systematic Literature Review
Sustainable Development
In the circular economy, companies design their business models to align with circular principles and explore pathways for sustainable value creation. However, research on the circular economy business model (CEBM) is in its infancy, and existing business models remain incomplete and lack comprehensiveness, failing to encompass all emerging business models, and lack explicit criteria and scientific procedures. Additionally, terminological inconsistencies persist, leading to ambiguity in defining circular strategies and their interconnections. In response to this challenge, our study conducted a rigorous review of 106 scholarly articles on circular business models. Our primary objective was to enhance the understanding of CEBMs with an in-depth review of these models from literature and employ an integrated framework to craft our unique categorization of CEBMs by thoroughly exploring the research purposes, categorization models, and CEBM archetypes outlined in the existing body of literature. Our findings show that research within this field remains to be theoretical. First, the primary aim of studies focuses on developing conceptual frameworks and models, creating supporting tools and methods, and analyzing drivers, enablers, and challenges. Second, among the most referenced frameworks in literature are those developed by the Ellen MacArthur Foundation and Accenture, which serve as foundational tools for practitioners. Third, studies of CEBM concentrate on closing the resource loop of a product life cycle. Future research should prioritize overlooked circular strategies, propose robust classification models, and address gaps in product-centric and resource-efficient business practice.
2024
Journal article
Ferrati, F., Kim, P. H. & Muffatto, M.
Generative AI in Entrepreneurship Research: Principles and Practical Guidance for Intelligence Augmentation
Foundations and Trends® in Entrepreneurship
This article investigates the integration of generative artificial intelligence (AI) into the academic research process of entrepreneurship. Specifically, we explore using Large Language Models (LLMs) like ChatGPT in several research scenarios to support novice and established researchers. As a practical guide, we introduce researchers to prompt engineering – formulating instructions for the LLMs to generate a desired output. We classify different types of prompts, present various technical strategies, and suggest the design of an effective prompt formula. We illustrate the prompt engineering process with different examples for entrepreneurship research. To assist researchers in systematically integrating LLMs into their research process, we present the ‘‘4D-Framework,’’ which consists of four phases (Discover, Develop, Discuss, and Deliver). Each phase contains four functions accomplished through four prompts, resulting in 16 functions and 64 specific prompts. The initial stage, “Discover,” involves using LLMs for project initiation tasks such as topic selection and literature review, theory exploration, conceptual or empirical puzzles, and research question identification. During the ‘‘Develop’’ phase, the focus shifts to operational aspects, where LLMs assist in designing methods, executing qualitative and quantitative research, and generating programming code. The third phase, ‘‘Discuss,’’ focuses on using LLMs to analyze findings, evaluate their robustness and limitations, highlight the research contribution, and identify future research directions. Finally, the ‘‘Deliver’’ phase emphasizes using LLMs to draft the manuscript, craft the narrative, prepare for submission, and disseminate the findings. We describe the application of LLMs in entrepreneurship research from a human-centric perspective, emphasizing an Intelligence Augmentation (IA) perspective for harmonizing human intelligence with AI capabilities. Given the novelty and impact of LLMs in knowledge-based areas, we also address the ethical implications of using AI in academia. We urge scholars to incorporate AI and LLMs into their research responsibly. While showcasing their potential, we also address their current limitations. We empower scholars to adopt a dynamic, AI-enhanced research approach that emphasizes the potential to unlock new insights and enhance the integrity of academic research.
2024
Journal article
Bekele, N. A., Muffatto, M. & Ferrati, F.
Desirability of consumer internet of things products: how emerging businesses address consumer desires to improve user experiences.
International Journal of Technology Marketing
Developing desirable consumer IoT products becomes the challenge for emerging businesses. Lack of clear understanding about the functions and desirability of such products has led to a lower level of consumption than was expected. The purpose of this paper is to propose a value-based framework for product desirability, and to examine value propositions in terms of product value, product features, and user experiences by considering emerging businesses. Data from 982 companies was extracted from CrunchBase. Desired value factors, and product features companies seek the most to develop desirable products were identified. Functional value was offered more frequently than emotional value or social value. Safety, interactivity, and connectivity are the most significant features considered by companies. Companies should consider the emotional and social aspects alongside the focus given to functional aspects. The proposed framework, and the results obtained could be important for companies to develop desirable products addressing consumer preferences.
2023
Conference paper
Ferrati, F., Kim, P. H. & Muffatto, M.
Patterns of Successful Founding Team Composition and Funding Outcomes
18th European Conference on Innovation and Entrepreneurship (ECIE), Porto
When it comes to assessing a startup’s chance of success, equity investors apply a specific set of criteria to minimize risk. In their decision-making process, most venture capitalists (VCs) agree with giving priority to the team composition, hence the popular saying: “Always consider investing in a grade-A team with a grade-B idea. Never invest in a grade-B team with a grade-A idea.” In this paper, we explore the profile of technology-based startup teams that are most likely to secure a Series-A funding round from VCs. From a methodological point of view, we applied a strongly quantitative approach, integrating several data mining techniques according to a multidisciplinary perspective, between data science and entrepreneurship. As for the company information, we used Crunchbase as our primary source, considering a set of U.S.-based startups founded from 2000 to 2017. For each venture we algorithmically integrated team-related information from the founders’ public LinkedIn profiles. Overall, we analysed more than 2,100 teams, involving a total of about 4,600 founders. Each founders’ experience was analysed by considering their professional background. Overall, more than 29,000 work experiences have been taken into consideration. Statistical analysis was carried out on both individual founders and their team organization. Both founders and teams were evaluated in terms of heterogeneity of prior experience and similarity of co-founder profiles using the Gini coefficient and Jaccard index, respectively. Statistics are expressed according to the companies’ sector and their fundraising profile. In fact, the different sectors are mapped on a 4-quadrant chart to identify different combinations between founders’ profiles (specialists VS generalists) and teams characteristics (combining co-founder with similar or diverse background). Results reveal the impact of team similarity and variety in terms of prior working experience. The findings provide valuable insights for scholars dealing with tech-driven startups teams, aspiring entrepreneurs looking for co-founders and for VCs seeking to invest in promising startups.
2023
Conference paper
Woldeyes, T. D., Muffatto, M. & Ferrati, F.
A Value Proposition Analysis of Emerging Circular Economy Business Models in the WEEE Sector
18th European Conference on Innovation and Entrepreneurship (ECIE)
Due to the rapid economic growth, growing demand for high-tech products, and decreasing service life of products, global waste generation from the electrical and electronic equipment sector is increasing. From the environmental and economic perspective, the circular economy (CE) emphasizes e-waste prevention as it is one of the fastest-growing waste streams having both valuable and rare materials as well as toxic substances. It is common to manage electrical products at their end-of-life through circular practices however, knowledge and implementation of CE in the waste electrical and electronic equipment (WEEE) sector still need to be improved. End-of-life practices center on recycling, and the percentage of valuable resources recovered is low. There is a missing insight into the business opportunities for alternative end-of-life options such as reusing, repairing, and re-manufacturing that hold stakeholders from implementing circular strategies. To fill the gaps identified, we developed a research question for investigating and analyzing the services offered and the electrical products adaptable to CE business models (CEBMs) of young companies operating in the WEEE. This study aims to explore the business models (BMs) of circular practice-based business options such as buyers and sellers of used and refurbished electronic devices, information technology asset dispositions (ITADs) companies, and e-waste recyclers to enhance other researchers with a better understanding of business options toward end-of-life e-waste handling and emerging issues in this industry. We conduct a literature review on CEBMs in the WEEE and conduct a multiple-case analysis of 412 emerging circular companies in the WEEE selected from the Crunchbase database to explore their BMs. Key findings show that most young WEEE companies focus on IT and telecom equipment and consumer electronics. Emerging WEEE companies mostly involve asset management and e-waste Recycling service, followed by ITAD services, trade-in/buyback, and reselling of preowned and refurbished electrical devices service, and e-waste collection, recycling, and disposal service. These companies provide unique offerings such as information security, compliance, trustworthiness, convenience, quality, social responsibility, and charitable purpose. Studies in the future may explore other dimensions of these BMs to gain a comprehensive picture and support the design of CEBMs.
2023
Conference paper
Woldeyes, T. D., Muffatto, M. & Ferrati, F.
Analyzing Emerging Circular Economy Business Models in the E-waste Sector Through the Business Model Canvas
Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures
Annual e-waste (waste electrical and electronic equipment) generation globally is increasing, resulting in a significant waste stream because of its large quantity, potential negative impact on health and the environment, and the valuable materials it contains. In managing e-waste, minimizing pollution, and maximizing product value, the circular economy (CE) becomes an ideal solution. However, despite the potential environmental, social, and economic benefits the circular economy approach can bring to the WEEE industry, knowledge and implementation remain limited. There is a lack of understanding of business opportunities related to alternative end-of-life options, which hinders interested parties from implementing circular strategies. To address these gaps, our study identifies and analyzes the business model (BM) of young companies operating in the WEEE industry. An exploratory research design with an inductive approach was employed, and we collected data from 412 companies selected from the Crunchbase database. Using the business model canvas, we examine these business models in four key dimensions. These are value propositions (products and services offered), value delivery (delivery processes and customer segments), value creation (creation processes and circular operation forms), and value capture (revenue streams). Through the collection and analysis of data pertaining to companies in this industry, significant insights have emerged. Our findings show that about 50% of these companies engage in IT asset disposition (ITAD). Among the target customers of these ITAD companies, an overwhelming majority of 78% focus on serving the B2B market and government agencies. These companies specialize in office equipment and networking devices. However, 27% of the analyzed businesses specialize in trade-in, buyback, and reselling pre-owned electrical devices. These companies serve both B2B and B2C markets. The findings highlight a concerning trend: despite the alarming increase in global e-waste generation caused by the rising demand for high-tech products and their decreasing service life, the practice of reusing these products, especially from individual customers, is not adequately observed among young companies operating in the WEEE sector.
2022
Journal article
Muffatto, M., Raza, A., Ferrati, F. & Sheriff, M.
The role of third mission orientation and motivational characteristics in young scientists’ entrepreneurial intention
Industry and Higher Education
This study examines the relationship between the individual motivational characteristics of young scientists (i.e. PhD students and post-docs) and their entrepreneurial intention, exploring also the mediating role of their third mission orientation. For this purpose, the authors considered the knowledge spillover theory of entrepreneurship at the level of the individual and the Theory of Planned Behaviour. Having university scientists as the unit of analysis, they used structural equation modelling to survey a sample of 337 young scientists working in a major Italian university. The authors were able to empirically identify the importance of third mission orientation as a mediating variable between scientists’ motivational characteristics and their entrepreneurial intention. The entrepreneurial orientation is reinforced if scientists are also engaged in third mission activities. The findings offer valuable insights for policy makers and higher education managers to develop strategies that could enhance knowledge transfer activities and produce additional benefits for universities and societies.
2022
Conference paper
Woldeyes, T. D., Muffatto, M. & Ferrati, F.
Archetypes of Business Models for Circular Economy: A Classification Approach and Value Perspective
International Conference on Sustainable Design and Manufacturing
In the circular economy, companies design their business models based on the principles of the circular economy and try to identify new opportunities for value creation. Research on the circular economy business model (CEBM) is in its infancy: the terminologies used in the classification and the meanings and relationships between circular practices are unclear. In addition, very few studies examine the value analysis in each business model archetype of a circular economy. This study aims to categorize archetypes of business models for a circular economy and present the value analysis of each archetype from the literature. With the help of an integrated framework by Accenture (2014) and Van Renswoude (2015), we categorize 23 archetypes of business models for the circular economy into seven categories and analyze their value based on three dimensions value proposition, value creation, and delivery and value capture. Our results show that the product-service system models, product life extension models, and end-of-life resource recovery models are the most commonly discussed ones in the circular economy literature. Only a few articles discuss how value is created, delivered, and captured in each circular business model. Therefore, it is necessary to explore further the value delivery and value capture dimensions. Future studies could investigate the value analysis of business models for a circular economy from sector-specific perspectives. Keywords: circular economy, circular business models, circular economy business models, value proposition, value creation, value delivery, value capture.
2022
Conference paper
Woldeyes, T. D., Muffatto, M. & Ferrati, F.
Emerging business models for circular economy: a systematic literature review
European Conference on Innovation and Entrepreneurship
Business models for circular economy keep products alive and operating for as long as possible during their use and consumption. Business model (BM) innovation in the circular economy (CE) helps firms design their BMs based on CE principles and identify new value-creation opportunities. Since research on the design of circular business models (CBM) is at an early stage, there is a lack of unified terminologies, and the meanings and relationships between the various CBM archetypes are unclear. This study aims to review and analyze existing emerging BMs in CE from peer-reviewed journals and recommend future research directions. The systematic review of 76 research articles resulted in identifying 23 CBM archetypes. This study analyzes the CBM archetypes based on descriptions, examples, similarities in the terminologies, and the frequency of use in the literature. The review identifies the three widely adopted CBM classification frameworks, the ReSOLVE framework (Ellen MacArthur Foundation), Accenture’s CBM framework, and the CBM strategies by Bocken et al. Most of the studies in this review combine BMs derived from several classification approaches to categorize suitable BM archetypes for the CE, which illustrates the lack of comprehensive classifications. The key indings show that providing access to a product’s functionality, recycling materials that would otherwise be sent to landfills, and restoring the functionality of existing products (re-manufacturing/refurbishing) are the most discussed archetypes in CBM literature, and other types do not get adequate attention. Future studies should examine BMs that have not received enough attention and provide a comprehensive classification of CBMs that clarifies their meaning and relationships. With this study, we contribute to advancing the body of knowledge in CBM and help practitioners in developing new CBMs.
2021
Journal article
Ferrati, F. & Muffatto, M.
Entrepreneurial Finance: Emerging Approaches Using Machine Learning and Big Data
Foundations and Trends® in Entrepreneurship
For equity investors the identification of ventures that most likely will achieve the expected return on investment is an extremely complex task. To select early-stage companies, venture capitalists and business angels traditionally rely on a mix of assessment criteria and their own experience. However, given the high level of risk with new, innovative companies, the number of financially successful startups within an investment portfolio is generally very low. In this context of uncertainty, a data-driven approach to investment decision-making can provide more effective results. Specifically, the application of machine learning techniques can provide equity investors and scholars in entrepreneurial finance with new insights on patterns common to successful startups. This study presents a comprehensive overview of the applications of machine learning algorithms to the Crunchbase database. We highlight the main research goals that can be addressed and then we review all the variables and algorithms used for each goal. For each machine learning algorithm, we analyze the respective performance metrics to identify a baseline model. This study aims to be a reference for researchers and practitioners on the use of machine learning as an effective tool to support decision-making processes in equity investments.
2021
Journal article
Ferrati, F. & Muffatto, M.
Reviewing Equity Investors’ Funding Criteria: A Comprehensive Classification and Research Agenda
Venture Capital
Venture capitalists and angel investors usually apply a set of assessment criteria in order to evaluate all the key elements of entrepreneurial projects. However, since each investor considers a different set of criteria, previous researchers who analysed investors’ decision making, ended up analysing a variety of divergent aspects. In this paper, a systematic literature review on the assessment criteria applied by equity investors was carried out. The purpose of this study was to identify and classify all the criteria considered by previous researchers in order to determine whether some aspects were investigated more extensively than others and to understand the reasons for this type of approach. After screening the abstracts of 894 journal publications, 53 articles were selected for a detailed analysis. In total, 208 unique criteria were identified and were subsequently classified into 35 specific categories, 11 generic classes and 4 main domains of analysis. The high level of detail and granularity of this work is one of its added values and can provide a knowledge base for future researchers who intend to apply new methodologies for the analysis of investors’ decision-making. Starting from the results obtained so far, a new agenda for future research in this field is suggested to encourage a more data-driven approach leveraging data science techniques.
2021
Conference paper
Ferrati, F., Chen, H. & Muffatto, M.
A Deep Learning Model for Startups Evaluation Using Time Series Analysis
16th European Conference on Innovation and Entrepreneurship (ECIE)
In the field of entrepreneurial finance, both academic researchers and venture capital firms are exploring the use of datadriven approaches to the analysis of entrepreneurial projects. For example, using the data provided by Crunchbase, some researchers have developed machine learning models aimed at predicting the exit event of startup companies. However, these previous contributions have always looked at ventures as static entities over time, only considering the values assumed by the key variables at the time of data extraction. This paper aims to propose a new modelling approach, based on the analysis of the evolution of companies over time. The work considers a sample of 10,211 US-based companies, appropriately selected through a sequence of data processing activities. The rationale applied to reorganize the information and design a database ready to be used for a temporal analysis is described. In particular, each firm is modelled considering three different groups of features whose values change as the company evolve and therefore describe the key milestones achieved. In this regard, the number and amount of funding rounds over time, the number of investors involved and the number of patents obtained over the years are considered. To highlight the importance of the evolution of these variables over time, their statistical trends are reported within a 10-year time window from the companies’ foundation. Considering a binary classification problem aimed at predicting whether or not a startup exit event will occur, statistics are presented for the two groups of companies, those that have made an exit or not. Figures show how this approach makes it possible to achieve a greater level of detail on the characteristics of the companies, not otherwise obtainable without considering the time factor. The obtained dataset is then used to train a binary deep learning classifier designed to perform time series analysis. The results obtained confirm the effectiveness of the applied modelling strategy. The obtained model is in fact able to predict whether a company will make an exit within 10 years of its foundation with a recall equal to 93%.
2021
Conference paper
Ferrati, F. & Muffatto, M.
Startup Exits by Acquisition: A Cross Industry Analysis of Speed and Funding
16th European Conference on Innovation and Entrepreneurship (ECIE)
Being acquired by a larger company represents the final step in a startup life cycle and is often the ultimate objective of both founders and equity investors. In fact, the occurrence of an acquisition allows shareholders to transfer their equity stake to the acquiring company and thus realize a return on their initial investment, hopefully resulting in a capital gain. From an investor’s point of view, two important elements to estimate are the time needed to take a company to an exit and the capital it will require to reach that result. These two factors are related to the sector in which the venture operates. Since acquisitions represent the most frequent case of exit, this paper focuses on their analysis. A sample from Crunchbase with more than 17,000 U.S.-based tech-startups founded after the year 2000 and acquired before 2021 was analysed. Starting from the original 744 categories used by Crunchbase for company classification, 64 sectors were identified through a clustering process. For each sector, the following elements were calculated: the number of acquired companies, the average number of months it takes for companies to be acquired as well as the average amount of capital raised before their acquisition. By combining these analyses, it was then possible to create a matrix in which each sector has been positioned within four quadrants, considering the variables “acquisition speed” and “required capital”. Considering also the number of companies in each sector, the weight of each sector in terms of investments can be estimated. On the other hand, more than 10,000 acquiring companies involved in the considered exits were also analysed, highlighting that 74% of them just made one single acquisition. Top 15 acquirers were also identified and their behaviour in terms of speed of acquisition and funding raised by target companies was then investigated.
2021
Conference paper
Bekele, N., Muffatto, M. & Ferrati, F.
Value-based framework development for consumer internet of things (ciots): A design thinking approach
European Conference on Innovation and Entrepreneurship, ECIE
The use of Consumer Internet of Things (CIoTs) is increasing due to their ability to deliver services anytime, anywhere and through any medium. To get the most out of CIoTs, a comprehensive design that incorporates consumer desires and preference are essential. For this purpose, establishing effective frameworks and models that can be taken as input in CIoTs development and design are important. However, lack of such frameworks has been affecting the consumption experiences of users. The objective of this paper is to develop a value-based framework that can be used for comprehensive design of CIoTs. To develop the framework, we systematically reviewed and analyzed 72 published peer reviewed articles. As an approach, we used Design Thinking (DT) methodology specifically, the double diamond model to develop the framework. Accordingly, in the value creation processes, desired consumer value is taken as design input, then a product possesses value through design based on desires, and create the actual value during interaction (user experience). In the proposed framework, desired value dimensions (functional, emotional and social) have been broken down in to their respective measurable units. Quality/performance, value for money and easy-to-use are elements in functional value whereas hedonic, control and novelty are dimensions of emotional value. Image/status, trust and networking are considered as dimensions of social value. Then, seven basic CIoTs features are identified and the impact on user have been analyzed. These include connectivity, interactivity, intelligence, observability, compatibility, adaptability and safety. In the process, we can observe that one or more CIoTs features can be affected by similar desired values. This framework integrates consumer desires (from functional, emotional and social value perspective), CIoTs features (desire-driven) and user experience (actual interaction). CIoTs features are derived from user desires and are enablers for better user experiences. Developing such value-based frameworks will help designers and producers to incorporate consumer needs in the early stages of development and design.
2021
Conference paper
Bekele, N., Muffatto, M. & Ferrati, F.
Value-Based Frameworks in Consumer Internet of Things (CIoTs): A Systematic Literature Review
22nd European Conference on Knowledge Management (ECKM)
Consumers face an increasing number of smart-connected products in their day-to-day activities. These connected everyday products are called consumer Internet of Things (CIoTs). Even though consumer IoTs possessed a significant share in a global market, researchers in both academia and industry so far have basically focused on the technical aspect of implementation ignoring user desires and values. This makes some consumers to be still not positive enough to purchase IoT devices, and others even fear the introduction of additional technological complications to their lives. Hence, appropriately designing these products based on consumer preferences to uncover consumer value is essential. The purpose of this paper is to analyze the value-based frameworks published in peer-reviewed journals related to CIoTs and provide research agendas in the context of consumer value. For this purpose, 72 research articles have been systematically reviewed and analyzed. In the process, four groups of major models/frameworks are identified to be Technology Acceptance Model, Theory of Planned Behavior, Theory of Consumption Value-based frameworks, and Design-based frameworks. CIoTs features, consumer values, attitude, user experience, and user intentions are the main constructs identified using construct mapping in CIoTs studies. The available rameworks/models have mainly focused on measuring acceptance/adoption levels, user intentions, and attitudes towards using CIoTs. The analysis also showed a lack of comprehensive value-based frameworks to create better user experiences. Therefore, it is important to develop models/frameworks that can discover implicit needs and wants of users, and facilitate usercentered approaches in the design and development of CIoTs. Effective frameworks/models able to cope with evolving user needs and wants are in demand for future studies.
2020
Conference paper
Ferrati, F. & Muffatto, M.
Setting Crunchbase for Data Science: Preprocessing, Data Integration and Feature Engineering
3rd International Conference on Advanced Research Methods and Analytics (CARMA)
In order to support equity investors in their decision-making process, researchers are exploring the potential of machine learning algorithms to predict the financial success of startup ventures. In this context, a key role is played by the significance of the data used, which should reflect most of the variables considered by investors in their screening and evaluation activity. This paper provides a detailed description of the data management process that can be followed to obtain such a dataset. Using Crunchbase as the main data source, other databases have been integrated to enrich the information content and support the feature engineering process. Specifically, the following sources has been considered: USPTO PatentsView, Kauffman Indicators of Entrepreneurship, Academic Ranking of World Universities, CB Insights ranking of top-investors. The final dataset contains the profiles of 138,637 US-based ventures founded between 2000 and 2019. For each company the elements assessed by equity investors have been analyzed. Among others, the following specific areas were considered for each company: location, industry, founding team, intellectual property and funding round history. Data related to each area have been formalized in a series of features ready to be used in a machine learning context.
2020
Conference paper
Ferrati, F. & Muffatto, M.
Using Crunchbase for Research in Entrepreneurship: Data Content and Structure
19th European Conference on Research Methodology for Business and Management Studies (ECRM)
The large amount of business-related data available today allows researchers in entrepreneurship to explore new methodologies for data analysis. This paper aims to present an overview of the database provided by Crunchbase for research purposes. Founded in 2007, Crunchbase collects worldwide data on companies, investors, funding rounds and key people of the entrepreneurial ecosystem. As of May 2019, Crunchbase had collected records on 760,590 organizations (of which 708,558 companies), 121,509 investors of different types, 263,426 funding rounds, 890,429 people, 17,068 initial public offerings (IPO) and 89,959 acquisitions. The main purpose of this work is to give a detailed description of the Crunchbase database in order to highlight its potential and facilitate future researchers who intend to use this source of data. In order to achieve this goal, three main topics are covered. Since the database is provided in seventeen independent datasets, the linking logics have been reconstructed applying a reverse engineering approach. The relationships between the individual files have been identified and then summarized in an original diagram. For each dataset all the available variables are provided. Afterwards, in order to quantify the scope and coverage of the database, some key variables have been analysed, resulting in descriptive statistics for three areas of interest: companies, funding rounds and investors. Specifically, analysis is provided about the geographical distribution of companies, the number of companies per year of foundation and current operating status, the number of companies by amount and number of investments raised and as well as the number of investors by number and amount of investments made. Finally, some indications on the potential uses of Crunchbase for research in entrepreneurship are given. Considering the characteristics of the available variables we focused on the applications of machine learning algorithms for the analysis and modeling of equity investment processes.
2019
Conference paper
Ferrati, F. & Muffatto, M.
A Systematic Literature Review of the Assessment Criteria Applied by Equity Investors
14th European Conference on Innovation and Entrepreneurship (ECIE)
What assessment criteria are most widely used by equity investors during their funding decisions? In the context of the so-called picking winner’s problem, which aspect do they consider most? Is it the jockey (entrepreneurial team), the horse (product/service), the race-track (market) or the odds (financials) to make the difference? Despite the investment evaluation funnel being very selective, about 35% of the venture-backed firms actually fail and, considering a conservative estimate, an additional 20% doesn’t provide the expected return on investment. The data therefore indicate that the investment process has large room for improvement. This paper is a systematic literature review of the research about the assessment criteria used by equity investors (venture capital and angel investors) during their investment decision making process. The research is designed around three research questions. RQ.1: what are the criteria used by equity investors to support their decision-making process in venture funding? RQ.2: what are the investment criteria that have been most discussed in the literature? RQ.3: which aspects of the company are mostly assessed by investors? After screening the abstract of 894 unique journal publications, 53 articles were selected for a detailed analysis. The criteria mentioned in every study were registered and 208 distinct drivers were identified. The criteria were classified into 35 specific categories, 11 generic classes and 4 main domains of analysis (respectively related to the venture, the investor, the risks factors and the environment). The high detail and granularity of the analysis is one of the added values of this work compared with previous literature. The authors propose a new approach to research, based on the use of large databases on ventures funding (e.g. Crunchbase). By analysing data on thousands of actual investments, researchers could introduce a radical change of perspective in this field of research.
