This chapter focuses on the demand-side factor of venture growth, examining network effects, multi-sided platforms, and go-to-market strategies. It introduces winner-take-all (WTA) markets and outlines key forces and strategies for competing in them. While not all markets have WTA features, they are particularly relevant to many fast-scaling startups, especially in the digital sector. The chapter then explores how ventures design effective go-to-market strategies through collaboration and partnerships, and concludes by showing how network orchestration capabilities can accelerate scaling and value creation.

This chapter examines how digitalization reshapes the nature of the MNE. It shows that digital MNEs operate through distinct value-creation logics based on platforms, data, and network effects, enabling rapid international expansion with comparatively limited physical assets abroad. Drawing on comparative analysis, the chapter contrasts traditional and digital MNEs in terms of internationalization motives, asset configuration, process organization, and governance structures. It introduces indicators such as the FDI lightness index and internet intensity to capture the asset-light and scalable nature of digital firms. At the same time, digital MNEs face new regulatory and political tensions, particularly regarding taxation, data sovereignty, and market dominance. The chapter develops typologies of digital MNEs and situates their rise within broader structural transformations of the global economy. It concludes that digitalization does not eliminate spatiality but reconfigures it, producing new patterns of concentration, dependency, and contestation in international business.

This chapter challenges the popular narrative that open-source software will democratize generative AI (GenAI) and disrupt the existing oligopoly dominated by companies like Google, Microsoft, Meta, and OpenAI. It argues that Big Tech routinely “weaponizes” open-source projects (e.g., Linux, Android, PyTorch) as a strategic tool to gain a competitive advantage over rivals, not for true democratization. The analysis shows that despite impressive percentage growth from startups, the absolute market value gains of tech giants have dwarfed those of new entrants. The enduring concentration of power stems from eight key levers favoring Big Tech: vast capital, exclusive proprietary datasets, massive access to compute (GPUs), network effects, control over top talent, market access agreements, a high-interest investment environment, and a lack of a viable business model to unseat their integrated ecosystems. Ultimately, the chapter concludes that open source in GenAI acts as a “fig leaf” for corporate dominance, as these massive companies control nearly every link in the GenAI supply chain – making radical openness unlikely to have a meaningful impact on the existing power structure.

This chapter delves into the implications of citizen participation in commercial SCS for their support of the state’s digital policies. Unlike the focus on general political trust in Chapter 5, the attention here now turns to assessing support for a specific digital policy. This chapter finds that citizens are highly supportive of state, compared to company, involvement in managing the SCS. It explores potential explanations such as media exposure, social interaction, and potential network effects. However, high levels of state support can only be fully understood once how people experience policy implementation on the ground is taken into account. When these experiences are mostly financial in nature, individuals are more likely to believe that the chances of their data being used for political purposes is low. Therefore, they become more supportive of the state’s involvement in the SCS overall. For most citizens, a primarily financially oriented SCS is acceptable, but its use as a political tool is not.

This chapter critiques private property on four grounds. First, private property pushes resources into the hands of those who have more at the expense of those who have less. This arises because wealthier people are willing to pay more for normal goods so they tend to bid successfully for them, and because their wealth allows them to hold out for a larger share of the gains from trade. Second, private property is, in fact, allocatively inefficient. This is because the use of willingness to pay as an allocational technology means that the allocation of property is partially driven by ability to pay rather than a purchaser’s greater productive efficiency. Private property is also allocatively inefficient due to the monopoly power that it places in the hands of owners. Third, the regressivity of private property creates a powerful propertied class that can come to dominate the political system. Finally, both the regressivity and the inefficiency of private property have become even more stark in the internet era because markets which already have strong network effects and are vulnerable to domination by monopolists exacerbate both features.

This chapter reviews and reconciles two theories of voting. Rational man models treat voters as actors seeking to maximize their material self-interest. Because their policy knowledge is limited, they often rely on ideology, trusted figures, and information picked up in the course of everyday activities to reach a decision. By comparison, survey evidence has shown that many voters are social, i.e. routinely adopt the opinions of those around them. The chapter concludes by describing various theoretical frameworks which unify these models. The combined models typically predict new behaviors in which initially small majorities are typically amplified; established opinions resist change for long periods; and very dramatic, once-in-a-generation events can abruptly reshuffle voters beliefs.

Information is valuable and the cost of disseminating information is tending to zero. But realizing the value of information by getting it to the right place at the right time requires sharing and exchanging it. Its societal value is unleashed by networks of communication. Communication networks can be small or large technical systems that connect different types of devices to one another. The power of modern communication networks is amplified by their capacity to expand in a decentralized fashion. In network markets, firms need to decide about the architectural openness of their products and how to design the interfaces that facilitate connections. This chapter is focused on such network strategies.

The economies of scope and scale of finance combined with the network effects of data and technology create benefits and pose risks toccompetition within financial markets. The same FinTech economies that reduce costs and increase efficiencies often contribute to decreasing competition in FinTech market segments. This market concentration is at odds with key objectives of financial regulation, including market efficiency, investor/client protection, and systemic risk prevention, and may require regulators to intervene.

The ability to organize is our most valuable social technology. Organizing affects an enterprise’s efficiency, effectiveness, and ability to adapt. Modern organizations operate in increasingly complex, dynamic environments, which puts a premium on adaptation. Compared to traditional organizations, modern organizations are flatter and more open to their environment. Their processes are more generative and interactive – actors themselves generate and coordinate solutions rather than follow hierarchically devised plans and directives. Modern organizations search outside their boundaries for resources wherever they may exist. They coproduce products and services with suppliers, customers, and partners. They collaborate, both internally and externally, to learn and become more capable. In this book, leading voices in the field of organization design articulate and exemplify how a combination of agile processes, artificial intelligence, and digital platforms powers adaptive, sustainable, and healthy organizations.

In addition to cybersecurity, the digitisation and datafication of finance at the centre of FinTech raise a range of data-related risks, in particular to data security and privacy (the focus of Chapter 17) and also from concentration and the emergence of new Systemically Important Financial Institutions (SIFIs) in the form of digital finance platforms, TechFins and BigTechs. These new entrants bring with them a range of regulatory challenges we analyse here.

Contrary to a common picture of relationships in a market economy, people often express communal and membership-seeking impulses via consumption choices, purchasing goods and services because other people are doing so as well. Shared identities are maintained and created in this way. Solidarity goods are goods whose value increases as the number of people enjoying them increases. Exclusivity goods are goods whose value decreases as the number of people enjoying them increases. Distinctions can be drawn among diverse value functions, capturing diverse relationships between the value of goods and the value of shared or unshared consumption. Though markets spontaneously produce solidarity goods, individuals sometimes have difficulty in producing such goods on their own, or in coordinating on choosing them. Here the law has a potential role. There are implications for trend setting, clubs, partnerships, national events, social cascades, and compliance without enforcement.

Describes the characteristics of digital platforms and the debates about the need for regulation of such platforms. Also sets out the policies that have been introduced in different jurisdictions

Discusses the regulatory principles for access pricing and for regulating multi-sided markets. Considers how access pricing affects investment, and the merits of vertical and horizontal separation

Discusses both normative accounts for regulation (Why should we regulate?) and alternative accounts that attempt to explain the existence of regulation (Why do we regulate?)

This final chapter does not cover any new principles; instead it presents case studies that have a huge global impact in terms of both managerial and government decision making. These case studies relate to: the role of big tech firms in the economy and the opportunities and threats that they present; the problems that the Covid-19 pandemic has posed for governments at the global level; and the problems that climate change is posing for both governments and firms, again at the global level. The last two cases involve geopolitical issues that go beyond the scope of the text, but it is important for managers to have a general appreciation of these issues in order to anticipate government policy and respond appropriately. The questions at the end of the case studies are intended to prompt students to utilize principles explained throughout the text to develop an understanding of the relevant issues and determine optimal courses of action.

Managerial economics provides a toolbox for solving problems that managers frequently face. It addresses issues relating to any aspect of decision making that ultimately affects the profit of a firm. Although the general methodology of managerial economics has not changed over the decades, there have been rapid and significant changes in the business environment in the last ten years or so, and three new themes have become increasingly important: digitization; behavioural aspects; and globalization. The first of these developments involves aspects of big data and advanced data analytics, the human-machine interface and the interconnectedness of electronic devices. The second relates to psychological aspects of decision making that cause both consumers and managers to engage in behaviour normally referred to as ‘irrational’. The third development is that improvements in technology relating to digitization have made the business world more interconnected. The text makes heavy use of recent case studies involving these three themes, for example on tech firms, Covid-19 and climate change, so students can see how the tools of managerial economics can be applied in real-life situations.

In this chapter, we seek to understand key economic consequences of network effects. First, in Section 3.1, we analyze the impacts that network effects have on the demand for participation on a platform. The main lesson we draw is that the interdependence between individual demands leads to unconventional aggregate demands; in particular, we show that a given price for accessing the platform may be compatible with several levels of participation. Next, in Section 3.2, we explore the pricing of access to a platform, which is made complex by the presence of network effects. Finally, in Section 3.3, we discuss other strategic decisions that platforms need to combine with pricing to manage network effects; in particular, a platform has to decide the extent to which its services are compatible with alternative services.

In this chapter, we analyze the economics behind the use of big data and, in particular, ratings, reviews, and recommendations that have become mainstream on digital platforms. We start in Section 2.1 by analyzing rating and review systems. These systems provide platform users with information about either products or their counterparties to a transaction. Of crucial importance is, of course, the informativeness of these systems, which depends on the users’ actions. We then turn, in Section 2.2, to recommender systems, which aim to reduce users’ search cost by pointing them towards transactions that may better match their tastes. Besides the ability of such systems to generate network effects, we also discuss their effects on the distribution of sales between “mass-market” and “niche” products. Finally, in Section 2.3, we complement the analysis of ratings and recommender systems by uncovering additional channels through which big data may generate network effects and other self-reinforcing processes on platform.