"This dissertation explores the learning of social entrepreneurs in accelerators. Building on Jarvis' (2010) existential theory of learning, it conceptualises entrepreneurial learning as a process in which purposeful individuals encounter and transform experiences of disjuncture. These experiences are embedded in both human and material contexts. Learning processes and outcomes are portrayed as phenomena that are influenced by social entrepreneurs' interaction with these environments. Accelerators are depicted as non-formal contexts of learning, of relatively short duration - in which the structure and content of education is progressively adapted to the requirements of the individual."

"While business accelerators remain understudied in the academic literature, there is growing interest in understanding how accelerators work and where they provide value to entrepreneurs. In this paper, we focus exactly on this question – we examine how mentorship and investor ties, two key aspects observed across accelerators in general lead to positive accelerator outcomes and through them, to longterm firm success outcomes for the start-ups participating in accelerators. Using the full cohort (n=105) of an international accelerator, we follow the progress of the startups during the accelerated period and continue to follow these startups for 15 months. We find that startups that participate more in mentorship events have higher likelihood of achieving short-term outcomes during the accelerator, such as the release of a prototype and generating revenue for the first time. Similarly, startups that develop more investor ties during the accelerator survive and raise capital at a higher rate. Finally, we find evidence that certain short-term accelerator outcomes also increase the chances of survival and investment. On the basis of these results, we provide practical implications for start-ups as well as managers of accelerator programs, in addition to theoretical contributions to entrepreneurship research."

"This book summarizes five years of learning from data collected as part of the Global Accelerator Learning Initiative. The authors present data describing impact-oriented ventures and accelerators that operate in both high-income countries and in emerging markets. Blending survey data with insights from sector experts, their various analyses shed light on the basic structure of accelerators, showing where they are having their most promising results.

Unlike previous studies, this book does not focus on a few high-profile accelerators (like TechStars and Y Combinator) and startups (like AirBnB and Uber). Instead, it compares a range of accelerator programs that target specific impact areas, challenging regions, and marginalized entrepreneurs. Therefore, it serves as a valuable tool for scholars, policymakers, and practitioners interested in the effectiveness of accelerator programs as tools that unleash the economic potential currently trapped in entrepreneurial dead spaces."

"Organizational sponsorship mediates the relationship between new organizations and their environments by creating a resource-munificent context intended to increase survival rates among those new organizations. Existing theories are prone to treat such resource munificence as the inverse of resource dependence, indicating that the application of new resources in an entrepreneurial context should always benefit new firms. These existing theories, however, often overlook heterogeneity in both types of applied resources as well as founding environmental conditions. By attending to these nuances, we reveal that resource munificence is not necessarily predictive of organizational survival. We find that resource munificence related to sponsorship can potentially decrease or increase survival rates among new organizations and that these effects are contingent on fit of resource type with its respective geographic-based founding density. These findings confirm the need for a more-nuanced theory of sponsorship that attends to the mechanisms and conditions by which resource munificence is likely to alter new organization survival rates."

"The puzzle for policy-makers, or others interested in a specific 'place' or region, is that this phenomenon - especially of 'innovation-driven entrepreneurship' - is not only highly concentrated but also seems to be characterized by a positive reinforcing cycle of growth, once IDEs reach a particular concentration (Audrestch & Feldman 2004). The systems-like behavior of these places has knock-on consequences, both for the regions in which it takes place, but also for those localities that have not crossed the threshold for accelerated growth (or at least not at the same rate). The logic of 'co-location', with growing networks of exchange and the consequent 'network effects,' means that the successful regions (and nations) may end up continually doing better, while those less successful ones get left further and further behind. As Audrestch & Feldman described, "geography has been found to provide a platform upon which new knowledge can be produced, harnessed and commercialized into innovations" (2004, p.31)."

MIT's study of these phenomena tries to address this puzzle, and provide advice and options for those who wish to optimize innovation-driven entrepreneurship in their specific regions, and who seek to build a vibrant innovation ecosystem in their locality. A key to MIT's approach is a Stakeholder Framework (which will be the subject of this Working Paper), but it is important to first place this in context."

"This report investigates the role of SGBs in economic growth and the key success factors of business networks for SGBs. It also spotlights the impact of two organizations - Enablis Senegal and CEED Moldova - on SGB growth. Finally, the report outlines implications for funders, ecosystem builders, SGB-support organizations and SGBs."

"Anecdotal evidence of successfully accelerated ventures has been followed by more rigorous studies by GALI and some emerging academic research. But as the evidence behind accelerator effectiveness expands, the question remains-at what cost? This methods brief first frames the various ways accelerators can think about value for money of their programs. Then, it explores one practical approach to calculating value for money. Finally, the brief summarizes similar evaluations conducted for other types of entrepreneur support programs. Accelerators and funders can use this guide to understand their options for assessing value for money and to consider how they could incorporate this concept into their data collection and program assessments."

"Key research objectives of this report were to evaluate the quantifiable value created by impact-focused incubator/accelerator programs and to design and pilot a framework that can be used to objectively compare and benchmark impact incubator/accelerator programs against each other. This analysis builds on ANDE's previous findings and was conceived as a means to evaluate how and where incubators/accelerators are creating tangible value. One of the initial goals of the study was to help programs develop quantifiable evidence they need to make a stronger case for charging incubees and investors for their services and the value they create."

"Spring Impact's new report, generously supported by the Argidius Foundation, shares recommendations on creating effective mentoring programs for micro, small, and medium sized enterprises (MSMEs). This report is designed to provide immediate support to practitioners, funders and other champions of mentoring by sharing essential good practices to strengthen or build effective programs."

"What is the effect of exposing motivated youth to firm management in practice? To answer this question, we place young professionals for one month in established firms to shadow middle managers. Using random assignment into program participation, we find positive average effects on wage employment, but no average effect on the likelihood of self-employment. Within the treatment group, we match individuals and firms in batches using a deferred-acceptance algorithm. We show how this allows us to identify heterogeneous treatment effects by firm and intern. We find striking heterogeneity in self-employment effects, but almost no heterogeneity in wage employment. Estimates of marginal treatment effects (MTE) are then used to simulate counterfactual mechanism design. We find that some assignment mechanisms substantially outperform random matching in generating employment and income effects. These results demonstrate the importance of treatment heterogeneity for the design of field experiments and the role of matching algorithms in intervention design."