1. Introduction and Motivation

Scale-ups have received an almost mythical status, following on from the rapid growth and phenomenal profits of a handful of extremely successful firms such as Facebook, LinkedIn, Tesla, AirBnB, Uber, and so on. Mystery surrounds these firms, in terms of the secrets of their extraordinary dynamics, as well as their true nature. Indeed, we are not fully aware of how common scale-ups actually are, because there is still no agreed-upon empirical definition that allows us to identify and count scale-ups in the business population.

Scale-ups are an elite few. Most firms do not grow at all, while a small number enjoy very high growth rates. These fast-growing firms have received considerable attention in the entrepreneurship literature (Coad et al., 2014; Demir et al., 2017), and are labeled gazelles, high-growth firms (HGFs), and high-impact firms. Other terms include unicorns (and decacorns), which are private ventures with a valuation of at least $1 billion (and $10 billion, respectively) (Kuratko et al., 2020). More recently, an emerging body of work focuses on scale-ups. While scale-ups are argued to be of great economic and theoretical relevance (Autio et al., 2018; Monaghan et al., 2020), there seems to be a lack of a common understanding of what constitutes a scale-up and how it differs from an HGF.

Is it worth adding a new category of rapid growth firms (i.e. scale-ups) to the lexicon? Amid the excitement about scale-ups, there is also much confusion, with different (and sometimes conflicting) ideas emerging from different sources. A number of recent publications from academic scholars (e.g. DeSantola and Gulati, 2017; Giustiziero et al., 2023) and international organizations (e.g. OECD, 2021; Vandresse et al., 2023) focus on scale-ups as an important new category of dynamic firms, emphasizing that scaling up is something distinct from growth, and that scale-ups are not just high-growth firms, while at the same time containing empirical analysis that defines scale-ups in exactly the same way as one defines high-growth firms (e.g. Belitski et al., 2023; OECD, 2021, p. 42). It seems somewhat incoherent to claim that scale-ups are different from HGFs, while at the same time defining scale-ups in terms of HGFs.

Another problem is that the literature contains various definitions of scale-ups, which is problematic for empirical measurement, knowledge accumulation, comparison across countries and over time; leading to problems in developing a generalizable theory (Lee and Kim, 2023; Vandresse et al., 2023). This book seeks to clarify the confusion in the area, taking the space granted to book-length publications to enter into the required detail to give a deeper and multifaceted understanding of the term scale-up, as well as a practical discussion regarding how scale-ups can be measured in empirical analysis.

The literature has identified several case studies of successful scale-ups (e.g. Uber [Pfotenhauer et al., 2022], PayPal, Facebook, Tesla, and LinkedIn [Hoffman and Yeh, 2018], and many more) that have been useful in introducing the phenomenon, showing that some firms grow by a process of “scaling up” that differs from normal growth. However, it is not clear whether these case studies are outliers or representative of average firms (Tippmann et al., 2023). For example, are all firms that attempt scaling-up successful, or are failure rates higher for scale-ups? Is a dead scale-up a scale-up, in the same sense that a dead lion is a lion?¹ If a scale-up were never profitable, should we still call it a scale-up?²

The questions above suggests that it is necessary to move to the next stage of empirical analysis of the phenomenon of scale-ups, i.e. from case studies and anecdotes to analysis of large sample representative panel datasets through “new large-sample quantitative studies” (DeSantola and Gulati, 2017, p. 656) that can investigate issues such as survivor bias and selection bias, as well as showing population-level proportions and tracking the development of scale-ups over time.

Hence, although the methodology of scale-up research has generally been a discussion of cases (Ries, 2011; Hoffman and Yeh, 2018; Kuratko et al., 2020; Reuber et al., 2021), there is growing interest in investigations of proportions of scale-ups in large samples (OECD, 2021). This book seeks to help the transition from the former to the latter. Now that the term “scale-up” is entering into empirical investigations and policy discussions, it is time for a precise definition. Academics need to move on from the way of thinking that might be caricatured as follows: “on the one hand this firm is a scale-up, and on the other hand it isn’t a scale-up.” Saying that a firm is “somewhat like a scale-up” is not helpful for the emerging body of empirical analysis that needs a binary variable to classify firms into scale-ups vs non-scale-ups. Policy often draws on such empirical analysis to consider questions such as the following: are scale-ups valuable? What are the characteristics of scale-ups? Are they financially constrained? How should policy help them? As such, this book addresses the need for a clear binary definition of what a scale-up is.

There is some debate about whether scaling-up occurs at the level of firms, or whether it can also occur at other levels, such as the levels of initiatives within firms (Tippmann et al., 2023) or at the product-level (e.g. the scaling-up of the iPhone, Hoffman and Yeh, 2018), or at the level of ecosystems that include firms (Tippmann et al., 2023). This book focuses on the firm level. To the extent that scale-up firms are hyperspecialized single-product firms (Giustiziero et al., 2023), the distinction between firm-level and product-level disappears. Our focus is on the firm-level because (as with HGFs) this is the level at which SME and entrepreneurship policy focuses (e.g. OECD, 2021).

Given the apparent confusion in this area, it seems worth having a book-length (rather than article-length) discussion of the matter. We draw upon various literatures (practitioners, policy literature from countries and international organizations, and of course academics) and various academic disciplines (entrepreneurship, international business, strategic management, economics) and various theoretical concepts (such as stages of growth models, fixed vs marginal costs, intangible capital, network externalities affecting consumer demand) to explain in what ways scale-ups are theoretically different from HGFs. We then discuss how a scale-up definition can be operationalized empirically as the literature matures, moving from anecdotes to large-sample analysis.

It would arguably not be possible to discuss the many facets in a journal article format. Journal articles must be concise, to the point that they must abandon descriptive approaches in the struggle to emphasize their novel contributions. (Furthermore, it is often faster to publish a first draft of a book manuscript than the first draft of a journal article.) Given that considerable confusion remains regarding what a scale-up is, and how scale-ups should be defined (both in terms of theoretical concepts and in terms of defining scale-ups in terms of the variables usually observed in standard datasets), there seems to be a genuine need for this short book.

Chapter 2 contextualizes the literature on scale-ups by discussing the related literature on High-Growth Firms (HGFs). Chapter 3 discusses stages of growth models, a strand of literature that, despite risking oversimplification, gives a theoretical model of the stages through which growth unfolds. Scaling up is presented as a relatively advanced stage in the development of a new venture after it has overcome hurdles related to product design, development, and product-market fit. Chapter 4 contains eight propositions about scale-ups, building up towards the theoretical definition in Chap. 5, and the empirical definition in Chap. 6. Given that many of the concepts and ideas used in the theoretical definition do not correspond closely to the variables found in standard datasets, there is a difference between our theoretical definition (Chap. 5) and our empirical definition (Chap. 6). Chapter 7 contains some preliminary explorations of measuring scale-ups using Swedish data. Finally, Chap. 8 concludes.