1,721,053 research outputs found
Core Competencies dell’impresa e competenze individuali: processi strategici e operativi
No full textsistema delle imprese dell’Associazione Italiana di Ingegneria Gestionale (AiIG), Torino, Giugn
Reviewing equity investors’ funding criteria: a comprehensive classification and research agenda
No full textVenture capitalists and angel investors usually apply a set of assessment criteria to evaluate the key elements of entrepreneurial projects. However, since each investor considers different 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 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 is suggested to encourage a more data-driven approach leveraging data science techniques
Entrepreneurial finance: Emerging approaches using machine learning and big data
No full textFor 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
Value-based frameworks in consumer internet of things (Ciots): A systematic literature review
No full textInpacts of information technology on outbound logistics: a case in the automotive industry
No full textEmerging Business Model Archetypes in the Circular Economy: A Systematic Literature Review
Full text linkIn 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
Desirability of consumer internet of things products: how emerging businesses address consumer desires to improve user experiences
Full text linkDeveloping 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
Patterns of Successful Founding Team Composition and Funding Outcomes
No full textWhen 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
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