Abstract

Cities are at the forefront of addressing multifaceted global challenges, such as climate change, rapid urbanization, and globalization. Temporal phenomena, such as COVID-19 served as opportunities to rethink radical solutions that help strengthen our worldwide urban systems. With its impact not only having reshaped the physical landscapes of cities, but also urging a fundamental re-evaluation of policies and decision-making processes governing urban development, in addition to recognizing the need for collaborative approaches between cities to navigate such phenomena. Business Improvement Districts (BIDs) have emerged as a globally diffusing model of urban governance, yet the mechanisms through which they travel across cities and the factors shaping their adoption in different institutional and planning contexts remain insufficiently understood, particularly in the Middle East. This study investigates what explains the diffusion of BIDs as a strategic urban development plan and examines the mechanisms and key factors influencing their adoption in two contrasting urban contexts: King Abdullah Financial District (KAFD) in Riyadh, Saudi Arabia, and Amsterdam Zuidas in Netherlands. The study adopts a policy diffusion framework centred on three mechanisms—coercion, competition, and learning—and examines their interaction with external factors, including organizational and geographical influences, and internal political and socio-economic factors. A qualitative comparative case-study strategy using a “most-different” research design was employed, supported by targeted surveys and desk research. Twelve responses were analysed, comprising seven from Riyadh and five from Amsterdam, alongside governmental, project, and academic sources. Survey and documentary evidence were qualitatively coded and analysed using ATLAS.ti to identify relationships between diffusion mechanisms and BID adoption. The findings demonstrate that BID diffusion is not driven by a single mechanism but by interacting processes whose relative importance varies according to the receiving urban context. In Riyadh, where BID adoption remains at an early stage, coercion and economic competition are dominant, reflecting a centralized planning environment, strong political leadership, and ambitions for economic diversification and international competitiveness. In contrast, Amsterdam demonstrates a stronger reliance on learning, particularly lesson drawing and rational learning, supported by a more decentralized governance structure and the formalization of BIDs within the Dutch legal framework. Competition remains relevant in both contexts, although it operates through regional positioning in Riyadh and the strengthening of local economic competitiveness in Amsterdam. The comparative analysis further identifies organizational influence, geographical influence, political support, financial incentives, and socio-economic conditions as important factors shaping BID adoption. Policy entrepreneurs and politically influential actors play a particularly significant role, while the findings also indicate a relationship between governance and planning structures and the diffusion mechanism through which BIDs are adopted. The study concludes that urban-policy diffusion is a dynamic process that can operate both horizontally and vertically, with the dominant diffusion mechanism potentially changing as a policy moves from initial adoption towards institutionalization. Consequently, the transfer of global urban policies should not be understood as a process of direct replication, but rather as one of contextual adaptation shaped by local political, institutional, governance, and socio-economic conditions. Building on the comparative findings, the study concludes with a conceptual global analytical framework for examining the diffusion of urban policies from one city to another, integrating diffusion mechanisms with the contextual factors that influence policy adoption. While the framework demonstrates analytical utility in the Riyadh and Amsterdam cases, its application across additional cities and regions is recommended to further test and strengthen its robustness and adaptability.

Keywords

Global Urban Policies, Policy Diffusion, Diffusion Mechanisms, Adoption of Policies, Middle Eastern, European Contexts

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1. Introduction

1.1. Background

Cities are at the forefront of addressing multifaceted global challenges, such as climate change, rapid urbanization, and globalization. Temporal phenomena (COVID-19) served as opportunities to rethink radical solutions that could help strengthen our worldwide urban systems. With its impact not only having reshaped the physical landscapes of cities, but also urging a fundamental re-evaluation of policies and decision-making processes governing urban development, in addition to recognizing the need for collaborative approaches between cities to navigate phenomena.

In response to the aforementioned challenges, urban governance models emerged with the aim of encapsulating these exchanges of set ideas, policies, and programs to better urban development strategies between affected cities (Newmark, 2002). Among these models is the controversial Business Improvement Districts (BIDs) model, which has gained traction for its capacity to transform urban areas and promote local economic growth.

BIDs could be described as an innovative sub-municipal public administration across diverse geographical settings, including urban centers, suburbs, industrial areas, business parks, and residential neighbourhoods (Ruffin, 2010). This BID model has been globally diffused, originating from North America and has since been spreading throughout Europe, to the east (including South Africa) (Peyroux, 2012). Due to its expeditious spread, preliminary findings from an international BID survey demonstrated that there are about 348 organizations in Canada, over 100 in Europe (excluding the UK), around 240 in Japan, 80 in New Zealand, and 39 in South Africa (Gopal, 2003). The real challenge is tracing back what the possible facilitator in this incubation period was.

However, some scholars see BIDs as a new model that policy entrepreneurs have developed for dissemination (both within and across national boundaries), while others see that economic globalization and the ensuing escalation between cities contributed to transformative changes in urban governance and the diffusion of BIDs (Ruffin, 2010).

In academic discourse, BIDs are seen as a successful model aimed at enhancing public service delivery within geographically defined urban areas, cities, and suburbs. Complex systems are composed of strategically planned approaches dealing with their development and the policies they adopt. This is also highlighted by Amin and Graham, who rely on similar urban contexts can easily lead to generalization based on a few cities, while also ignoring the diverse nature of urban assets, as well as the implications of policy innovation elsewhere (Amin & Graham, 1997).

BIDs are a reality; they are spreading everywhere. The argument is whether we can measure its impact as a phenomenon while it is still in motion, especially in Europe, South Africa, and arguably in the Middle East, before getting to know what explains this diffusion.

1.2. Problem Statement

While discussed in the literature, numerous communities have been drawn to the BID concept, with several cities contemplating their own implementation (Mitchell, 2001). Although little focus has been given to the diffusional factors that have had an influence on the study (particularly in Europe and South Africa), this has also been highlighted by Peyroux (2012) who notes that, the prominence of North American BIDs needs a critical examination of how urban issues have been perceived, as well as the rationale behind the diffusion of BIDs beyond North America, especially in diverse urban contexts such as Europe, Middle East and South Africa. Arguably, the Middle East is currently witnessing this diffusion as seen in the ongoing large-scale urban development projects, such as the new administrative capital in Egypt and its financial-business district, and King Abdullah Financial District (KAFD) in Saudi Arabia.

There is no one unified definition of BIDs, and they may take other names in different cities, in many countries, they are considered as new model of municipal governance to secure private financial capital for improving the attractiveness of city centres (Peyroux, Pütz, & Glasze, 2012). Some scholars have been criticizing BIDs for significant issues related to democratic participation and unequal service of delivery (Morçöl et al., 2008). While other scholars perceive BIDs as innovative and effective tools for tackling urban issues such as security, crime, and economic barriers throughout private sector involvement (Valli & Hammami, 2021), the factors that explain the diffusion mechanisms of BIDs remain understudied.

This research seeks to fill this gap by exploring what factors influence the diffusion of the BIDs model as a strategic development plan. It examines the adoption of BIDs in different urban planning systems, by empirically taking the opportunity of the rapid uptake of BIDs in Riyadh and Amsterdam cities to study this phenomenon. The KAFD in Riyadh and the Zuidas Financial District in Amsterdam represent two distinct and contrasting approaches to urban planning and economic development, influenced by BIDs as a model of sub-municipal governance to secure private capital for improving the attractiveness of urban areas.

The literature suggests that diffusion mechanisms, such as coercion, competition, and learning, explain the policy spread and why policies that are adopted in one city are also adopted in another city (Kuhlmann, 2021). However, in today’s globalized world, where cities collaborate more to address the arising urban challenges, learning as a mechanism of diffusion becomes much more significant, yet has dominant effect on the adoption of policies in cities.

Furthermore, by focusing on two distinct urban environments, especially in the urban planning systems—Riyadh, Saudi Arabia, and Amsterdam, Netherlands—the study aims to conduct a comparative case study analysis to identify the key factors that influenced the adoption of BIDs, as well as the mechanisms through which BIDs interact with and shape strategic planning initiatives in divergent urban contexts. It will also delve into the underlying factors that motivated and led to the emergence of BIDs.

1.3. Research Objectives

The main objective of this research is to understand the diffusion mechanisms of BIDs, and potentially outline key factors that influence the adoption of BIDs as a strategic urban development plan in Amsterdam and Riyadh cities.

1) To explore the emergence of BIDs, and the reasons behind their spread from one country to another (Background discussion which demonstrates why BIDs have been transferred from one country to another, as well as identify pathways for diffusion patterns).

2) To study the mechanisms used to adopt and contextualize BIDs across distinct urban planning contexts (comparative case study analysis between Riyadh and Amsterdam, representing Europe and Middle East, also enriching the existing literature by addressing the documentation gap in these regions).

3) To identify key factors that influence the adoption of BIDs in the examined contexts.

1.4. Research Questions

Main Research Questions: What explains the diffusion of BIDs as a strategic development plan?

Sub-Research Questions:

• Which diffusion mechanisms are employed to adopt BIDs as a strategic development plan in both Riyadh and Amsterdam?

• How does the “most-different” design enhance the analysis of BIDs adoption?

• What are the key factors that influence the adoption of BIDs in both cities?

1.5. Significance of the Study

Indeed, Business Improvement District policies are spreading, with the increasing importance of BIDs in the governance of urban and metropolitan areas across North America, as well as various countries in Europe, Asia, and Africa, there has been a corresponding rise in academic interest in this phenomenon (Morçöl, Hoyt, Meek, & Zimmermann, 2017). However, within this extensive body of both literature and practice, the factors that explain the diffusion of BIDs remain relatively understudied. Hence, the study aims at both distinguishing between different diffusion mechanisms by examining the adoption of BIDs in Riyadh and Amsterdam, as well as enriching the existing literature by addressing the documentation gap in these regions, particularly the Middle Eastern.

2. Literature Review and Hypotheses

2.1. Introduction

As this study aims to understand the key factors that influence the adoption of BIDs as a strategic development plan in divergent urban contexts, it is crucial to begin with the spread of BID policy, Where and Why did the BID model originate and diffuse across nations? Therefore, this chapter begins by offering a comprehensive review of the diffusion of BIDs, their emergence, global spread and significance. Indeed to study the spread of a policy is a challenging task. However, in the academic literature there are many theories that help explain the spread of a phenomena such as BIDs policies. Theories such as policy transfer, and policy diffusion are among the most proliferating ones, as they seek to elucidate these processes (Kuhlmann, 2021). Hence, this chapter also aims to answer what is policy diffusions, giving a snapshot on the different stand of policy transfer, and policy diffusion research. After examining the spread of BIDs, policy diffusion mechanisms, and adoption concepts the chapter reviews models used to analyse policy diffusion and concludes with the conceptual framework, and hypothesis.

2.2. The Diffusion of BIDs

To facilitate the discussion on BID policy as a subject of diffusion, and answer the sub-questions where, and why did the model originate and diffuse across nations? This section offers a comprehensive review on the diffusion of BIDs innovation, by especially looking into three important aspects that led to this phenomena; its emergence, global spread, and significance. Most diffusion studies concern the process by which innovations spread from one unit, individual, or entity to another (Walker, 1969; Gray, 1973; Savage, 1985; Berry & Berry, 1990; Rogers, 1995). Innovation can be described as an idea or program new to an entity, even if it already exists and has been adopted by other entities, which in this case the BID policy (Newmark, 2002). Despite the fact that, BIDs are a growing phenomena that have been actively studied in literature by many scholars in fields, such as urban affairs, public policy, public administration, and law, it is quite complex in terms of its interpretation, as it presents a significant challenge in establishing a common definition (Morçöl & Zimmermann, 2017). On one hand, some scholars defined BIDs as financing tools for neighbourhood revitalization and are currently used by hundreds of municipalities not only throughout the United States, but among the world (Caruso & Weber, 2006). On the other hands, some see BIDs are self-assessment zones established and managed by property or business owners, with governmental authorization, to function within specific urban and suburban geographic areas (Morçöl, Hoyt, Meek, & Zimmermann, 2017). What is possible is to present generally agreed characteristics of BIDs:

• A mechanism that utilizes a levy through which relevant property or business owners voluntarily agree to collectively contribute for a set period to generate private funding for initiatives aimed at enhancing the appeal of a specified commercial area;

• A well-defined geographic scope for these activities; and

• A collaborative approach between public and private sector stakeholders (Grail et al., 2020).

Due to the study focus, BIDs will be described as an innovation in sub-municipal public administration, manifesting across diverse geographical settings, including urban centres, suburbs, industrial areas, business parks, and residential neighbourhoods (Ruffin, 2010).

The Emergence of BIDs

Where did the model originate? In fact, due to both the absence of a standardized naming convention (Hoyt, 2003: p. 4), and its rapid spread, as the BID program has been introduced to a growing number of cities, and towns around the world, it is difficult to historically analyze BIDs, as well to identify who came up with this concept (Ward, 2006). However, in regard to literature, it appears to have originated from two distinct, yet, parallel, developments in Canada and the US (Ward, 2006; Stein, Michel, Glasze, & Pütz, 2017). The first BID model emerged in the 1960s in Canada, followed by an extensive spread in the US in the 1990s (Stein, Michel, Glasze, & Pütz, 2017). Moreover, the largest numbers of BIDs exist in the United States and Canada: at least 400 in the United States as of 1999 and at least 300 in Canada as of 2007 (Morçöl, Hoyt, Meek, & Zimmermann, 2017).

Global Spread and Significance of BIDs

In fact, Business Improvement Districts have been widely discussed in both academic and practical discourses, and while they are still in motion (Guimarães & Cachinho, 2019). Figure 1 shows the number of publications on BIDs from 1994 to 2021 (Guimarães, 2021). According to Peyroux (2012), BIDs is a “traveling concept” that started to proliferate from North America to Africa, Europe, and arguably currently in the Middle East (Hoyt, 2004; Peyroux, 2012). This growth naturally led to an increase in the number of professionals, such as planners, managers, and analysts to focus and specialize in BIDs operations (Morçöl, Hoyt, Meek, & Zimmermann, 2017). But why?

Figure 1. Number of publications on the subject of business improvement districts, per year. Source: Guimarães (2021).

In literature, BIDs have been portrayed as an innovation, especially when it comes to approaching problem-solving and efficient service delivery. However, it is also criticized for neglecting the needs and voices of residential property owners (Ross & Levine, 2001: p. 245), fostering social segregation in cities (Lavery, 1995), causing issues with equal-citizen representation, and for its lack of accountability when it comes to elected governments and/or the public (Briffault, 1999). These and other concerns have led academic researchers to begin taking an interest in BIDs, such as Mallett (1993) and Coleman (2004), who were the first to argue that BIDs reflect a broader political-economic shift towards neoliberal and entrepreneurial strategies aimed at stimulating local economic development, rather than protecting the rights and social welfare of citizens (see Harvey, 1989). This, in particular, gave rise to BIDs advocates being able to discern that it was being praised for promoting “New Urbanism” in urban planning, which emphasizes a strong sense of place, pedestrian-friendly environments, and the public realm (Davies, 1997). They have also been recognized for having introduced a more focused and flexible governance structure that encourages people to live and shop in downtown areas (Birch, 2002; Levy, 2001; MacDonald, 1996). Despite these on-going debates, BIDs are still in motion. In fact, many countries (such as New Zealand, Germany, Netherlands, UK, and South Africa) have adopted BIDs, as it was presented “as solutions to the failures of past policies and practices of the state” (Grail et al., 2020).

In short, the BID phenomenon is both significantly spreading, and highly complex. As many researchers advocate for multidisciplinary perspectives to examine such a phenomenon (Morçöl, Hoyt, Meek, & Zimmermann, 2017).

2.3. Theoretical Framing

Theoretical Framework

The urban-policy phenomenon is significantly increasing and becoming a common practice in a world characterized by globalization and urbanization, where the public sector’s involvement and support in urban development is diminishing (Hoyt, 2006). Additionally, in public policy analysis, it is widely recognized that policies are not always created within isolated units, such as individual countries or international organizations, and even when they are, they often do not remain confined to these boundaries (Kuhlmann, 2021). A famous example of policy diffusion is the conditional cash transfer programs, which were initially developed and implemented in Brazil and Mexico in the 1990s, and since then have been adopted by various Latin American countries and beyond (Osorio Gonnet, 2019). In the literature, there are two main theories that discuss the spread of policies: policy transfer and policy diffusion theories. Both of them have their advantages and disadvantages, as there is no superior theory in terms of examining the spread of a policy (Newmark, 2002).

Although both theories aim at describing and explaining such phenomena, they have different approaches (Kuhlmann, 2021). On one hand policy transfer studies focus on what was transferred, who was involved in the transfer, and how transfer occurred. It thus emphasizes the crucial role of information and expertise, as well as the importance of actors and their choices (Kuhlmann, 2021). While policy diffusion research has a predictive ability to determine what factors, whether organizational, geographic, or internal determinants, will lead to program or policy adoption (Newmark, 2002). It focuses more on the structural aspects of such a process (Obinger et al., 2013).

The literature on diffusion studies is vast, yet expanding rapidly. Some scholars (e.g., Berry & Berry 1990; Berry & Baybeck, 2005) have conducted studies on the diffusion of policies in the past decades, while others were more interested in the process by which diffusion takes place, focusing on the factors that enable or hinder the process (Shipan & Volden, 2008). While these works have revealed substantial evidence that policies do diffuse, much less is known about the specific mechanisms that cause a policy to spread from one government to another. In other words, if a second government adopts a policy because of the fact that the first government adopted it, so what explains that? Hence, examining the specific mechanisms in which policy spread from one unit to another is crucial (Kuhlmann, 2021).

This study aims at understanding the diffusion mechanisms and the key factors that influence the adoption of BIDs in Riyadh and Amsterdam cities. Therefore, policy diffusion theory has been adopted as a comprehensive framework guides the spread of BIDs from one country to another.

This research focuses on three main diffusion mechanisms: coercion, competition, and learning. In order to analyse each mechanism individually, the study focuses on city level (Riyadh and Amsterdam) diffusion rather than regions, countries, or states.

2.4. What Is Policy Diffusion?

Policy Diffusion

Policy Diffusion is a widespread practice, it is a phenomenon that shapes everyday life of people around the world. For instance, the diffusion of free market policies, marriage equality marriage, protests during the Arab Spring, and national conservative movements, to name a few (Gilardi & Wasserfallen, 2019). According to Mintrom (1997) and Walker (1969), policy innovation occurs whenever a government, a national legislature, a state or a city adopts a new policy (Mintrom, 1997; Walker, 1969). Although the literature on policy diffusion is vast, and rapidly growing, most of the studies focus on the structural aspects in which this process takes place (Kuhlmann, 2021). One of the most important aspects to study, in order to better understand the spread of policies: is mechanisms. A cornerstone of diffusion literature is the distinction between learning, competition, and coercion, as mechanisms of diffusion (Kuhlmann, 2021). These mechanisms summarize the main forces of diffusion, as policymakers are influenced by a) pressure from international organizations or powerful countries (coercion), b) the policies of other units with which they compete for resources (competition), or c) the success or failure of policies elsewhere (learning) (Gilardi & Wasserfallen, 2019). In fact, the success of these mechanisms in terms of explaining why policies that are adopted in one unit are also adopted in another one is documented in literature, although they faced many critics especially, when it comes to their meanings, and the analytical distinction between them, as it is often unclear (Kuhlmann, 2021).

Another crucial dimension in the research on policy diffusion is the factors that enable or hinder the process. According to Berry and Berry (1990), policy diffusion theory is a process of interdependent policy making, and it involves three main factors that influence the spread of a policy; organizational diffusions which mainly deals with people (individuals or groups), who spread the policy. The second one is geographic or regional diffusion, focusing at assessing the geographical impact on the adoption of policy. The third one is the internal determinants such as political, economic, or social aspects (Newmark, 2002). These three main factors are then categorized into two main analytical determinants: external, such as the organizational, and geographical, and internal or domestic factors which are treaded as control variables, such as economic, social, and political (Gilardi & Wasserfallen, 2019). In fact, policy diffusion has been criticized, because it pays little attention to the policy content. However, the focus of the study is on the policy adoption process. In the upcoming sections, a more detailed elaboration on each mechanism will be provided, as well as how they have been studied. Furthermore, it will distinguish between the aforementioned diffusion mechanisms, and provide context for better understanding of how these determinants could influence the adoption process.

Diffusion Mechanisms

As mentioned in the previous section, in the research of policy diffusion, a typology of mechanisms consisting of coercion, competition, and learning has become dominant (Kuhlmann, 2021). But, prior to beginning studying these mechanisms, what is a mechanism? In fact, defining what is a mechanism is complex, even in the social sciences, defining a mechanism is highly contested. However, most scholars likely agree that mechanisms focus on the process occurring between causes and outcomes, as Beach and Pedersen (2019) explain, “Mechanisms are not causes, but are causal processes that are triggered by causes and that link them with outcomes in a productive relationship” (Kuhlmann, 2021). Moreover, mechanism-based approaches emphasize that an explanation should disentangle the fundamental elements of a causal process—elements that often remain unexplored—that lead to a particular outcome (Beach & Pedersen, 2019; Hedström & Ylikoski, 2010). Here, the study focuses more on how causal mechanisms can be examined in-depth to draw causal inferences in single case studies. It is crucial to mention that mechanisms do not provide information about the outcomes of these processes in terms of convergence or divergence. However, in practice, processes and outcomes are often conflated (Elkins & Simmons, 2005).

The first mechanism of diffusion that this study explores—coercion—suggests a power asymmetry between two units and involves direct or indirect pressure from one unit on another to adopt a specific policy (Kuhlmann, 2021). For instance, in a more interconnected and globalized world, countries can coerce one another through trade practices and economic sanctions (Shipan & Volden, 2008). As Stokes Berry and Berry explained, in such a process, “A is coerced into adopting a policy when a more powerful [...] B, takes action that increases A’s incentive to adopt or, in the extreme case, forces A to adopt” (Berry & Berry, 2018). In other words, policies are introduced because powerful countries or international organizations (e.g., World Bank or United Nations) enforce policy change, which is called conditionality. But coercion can also take other forms such as policy leadership. Policy leadership can occur when countries need to coordinate their tasks in a policy area; in such cases, unit B might coerce unit A to adopt a policy (Kuhlmann, 2021).

The second mechanism of diffusion—competition—assumes that individuals, businesses, and investors evaluate multiple countries or subnational units as potential sites for their residence and activities (Gilardi & Wasserfallen, 2019). In other words, policies in unit A are influenced by policies from unit B, as Unit A seeks to gain an economic advantage (Kuhlmann, 2021). Competition mechanisms often concentrate on areas where actors compete for markets, typically involving “investor-friendly policies, including privatization, deregulation, balanced budgets, low inflation, and strong property rights” (Marsh & Sharman, 2009). Additionally, Berry and Berry (2018) argue that competition has multiple types, the most important one is “spillover-induced competition” (Kuhlmann, 2021). In this process, policymakers consider the economic effects of adoption (or lack thereof) by other governments. If negative economic spillovers occur, where a government would be disadvantaged by adopting a policy that its neighbors do not, it will be less likely to adopt that policy. Conversely, if positive spillovers are present, such as those resulting from uniformity in infrastructure, governments will be more likely to adopt the policies of others (Shipan & Volden, 2008). An empirical study conducted by Boehmke and Witmer (2004) explored the state adoption of Indian gaming compacts, arguing that both learning and economic competition play crucial roles in explaining initial adoptions. However, for subsequent compacts, only economic competition is significant, as previous experience with a state’s own compacts eliminates the need to learn from the experiences of others (Boehmke & Witmer, 2004). Furthermore, competition has been criticized, as it is often understood to be a form of horizontal policy diffusion. Therefore, vertical interdependencies are not considered as competition (Kuhlmann, 2021).

The third and final mechanism of diffusion that this study explores—Learning—is a process that leads states to be referred to as “laboratories of democracy” (Shipan & Volden, 2008). The traditional concept of learning in diffusion research is based on the idea that policymakers make decisions by analyzing the consequences of policies implemented in other places (Gilardi & Wasserfallen, 2019). In fact, learning is considered the most common, yet important mechanism of diffusion. In the literature on policy diffusion, the mechanism of learning is distinguished by two main methods (Vagionaki & Trein, 2020). The first one is called “rational”, in which actors are believed to select policies after revising their beliefs about the impacts of these policies based on the experiences of others. This revision of prior beliefs is then used to guide subsequent actions (Kuhlmann, 2021). The second form of learning is described as “Lessons-learned”, as Berry and Baybeck (2005) highlight, “[w]hen confronted with a problem, decision makers simplify the task of finding a solution by choosing an alternative that has proven successful elsewhere”. In essence of this, learning involves evaluating whether a policy has been successful in other contexts before adopting it. Although the learning process might appear straightforward, with political and policy successes ideally being clear to decision-makers and researchers, the measurement of success can be complex. For instance, in the case of city-level anti-smoking policies, scholars note that the difficulty in measuring success often leads to the adoption of various shortcuts that align with established learning approaches (Shipan & Volden, 2008). Another important criticism is that learning does not guarantee that adopted policies are inherently “better”. New information that underpins learning can turn out to be incorrect or unsuitable for the context in which it is applied (Kuhlmann, 2021).

In fact, although the three mechanisms can be theoretically distinguished, they often interact in practice, and even conceptually overlap. In policy diffusion studies, the typology of coercion, competition, and learning mechanisms has been extensively debated in terms of to what extent they could reduce complexity and provide comparability (Maggetti & Gilardi, 2016). At the core of these debates is the crucial need to clearly distinguish between the diffusion mechanisms. However, some researchers suggest that a viable approach is to distinguish mechanisms by considering extra factors, as they contend that studies on diffusion mechanisms should recognize that the policies being diffused may possess distinct characteristics, which are essential for comprehending policy adoption (Makse & Volden, 2011). Hence, in the next section, the study will focus more on the previous models of policy diffusion, conceding additional factors for further development.

Models of Policy Diffusion

In the policy diffusion studies, one of the most recent, yet dominant model especially in the research on international relations, political economy, and federalism is the “stylized model” (Gilardi & Wasserfallen, 2019). In Table 1, a summary of its three core components is illustrated.

Table 1. A stylised model of policy diffusion.

The first component is the key actors of diffusion, referred to as governments or legislature. Secondly, the model posits that these governmental or legislative entities base their decisions on information sourced from external contexts. Thirdly, it is anticipated that these entities will systematically process this information; that is, policymakers will evaluate and analyse policy-relevant data obtained from clearly identified reference countries, cities, or states. However, this model of diffusion has a long-standing tradition in research on international relations and federalism, it is selective, as it doesn’t accommodate other factors that influence the spread of a policy (Gilardi & Wasserfallen, 2019).

Therefore, this study adopts other models to address the aforementioned challenge in terms of distinguishing the diffusion mechanisms both theoretically and empirically, as well as to provide a more comprehensive understanding of the key factors that influence policy adoption. The study considers both external and internal factors. External factors are categorized as organizational or geographical. On one hand, organizational diffusion involves individuals and groups who disseminate policies through interactions at meetings, conferences, and other networking events (Newmark, 2002). In that essence the assumption is that, states or other entities are more likely to adopt a particular policy when their officials interact with counterparts from states that have already implemented it. On the other hand, geographic or regional diffusion models aim to determine the impact of geography on the adoption of innovations. Berry and Berry, states that “geographic” or “regional diffusion models emphasize the influence of nearby states, assuming that states emulate their neighbours when confronted with policy problems” (Newmark, 2002).

Moreover, the internal factors or as referred to internal determinant model which analyses political, economic, and social characteristics to forecast potential innovators (Newmark, 2002). In the literature, innovators are typically characterized by indicators of wealth, such as surplus resources, income per capita, and expenditures (Walker, 1969; Gray, 1973; Rogers, 1995). Other internal characteristics have been identified, such as the level of interparty competition, the degree of legislative professionalism, and the proportion of the urban population (Newmark, 2002). However, these characteristics have a potential to explain the diffusion of polices, it has been criticised for its lack of examining the policy content, as the argument is that, a state is an innovator in one policy area does not necessarily indicate that it will be a leader in another (Newmark, 2002). This suggests that the factors driving innovation in one domain may not translate seamlessly to other domains, highlighting the complexity and specificity of policy innovation processes.

2.5. Conclusion, Hypothesis, and Conceptual Framework

Hypothesis

Aligned with both theoretical and empirical approaches to coercion, economic competition, and learning, the following hypothesis have been developed to further understand the diffusion of BIDs in both Riyadh, and Amsterdam.

Coercion Hypothesis: The likelihood of a city adopting a policy increases when there are instances of coercion, such as pressure from political leaders or more powerful entities are present. This likelihood is further enhanced when hegemonic ideas drive ideological shifts or establish policy norms that compel cities to conform these dominant frameworks.

Competition Diffusion Hypothesis: The likelihood of a city or subnational unit adopting an investor-friendly policy increases when positive economic spillovers from neighbouring units are expected, such as benefits from uniformity in infrastructure.

Learning Evaluation Hypothesis: The likelihood that a city will adopt a policy increases when there is evidence of its success in other cities.

Conceptual Framework

Figure 2. Conceptual framework, developed by author based on literature review.

Figure 2 shows the conceptual framework which is primarily influenced by Berry and Berry (1990) and Newmark’s (2002) work on policy diffusion and transfer studies. The main goal is to achieve the objective of the study which is; distinguishing between diffusion mechanisms, and identifying the key factors that influence the adoption of BIDs as a strategic development plan. This is approached through two main stages: The first one, is to understand the diffusion of BIDs throughout studying the motivations, and examining the diffusion mechanisms that led to this phenomenon. The second stage is to explore the factors as referred to as enablers that influenced the adoption of BIDs in different urban contexts, which in this case Riyadh, and Amsterdam cities. Within this stage the literature on policy innovation and diffusion studies will help to examine the external and internal factors that led to the adoption process to happen.

3. Research Design and Methodology

3.1. Introduction

This chapter focuses primarily on laying out both the research design and the methods of empirical research used in this study. According to Yin (2013), he explains that a research design can be described as “the logical sequence that connects the empirical data to a study’s initial research questions and ultimately, to its conclusion” (p. 20). Hence, this chapter starts by stating the operationalization table, followed by the explaining of the employed strategies, and methods.

3.2. Operationalization Table: Themes, Concepts, Variables & Indicators

The operationalization table translates the research questions into specific themes, concepts, variables, and indicators, providing a clear roadmap for data collection and analysis.

3.3. Research Strategy

The study employs two main strategies; a survey, and two comparative case studies. Due to the exploratory nature of this study which aims to understand the diffusion mechanisms, and outline the key factors that influence the adoption of BIDs in divergent urban contexts, a survey has been applied to measure the variables presented in the operationalization table. In fact, the survey as a data collection method has been utilized in many policy diffusion studies, as it enhances the generalization and validity of findings due to its flexibility and ability to gather a wide range of data (Van Thiel, 2014). The comparative case study is employed to both exemplify the experience of BID adoption, and provide a more profound understanding of the variables in conjunction with their interrelationships. By examining two cases in both Riyadh, and Amsterdam cities, the approach, itself, will aim at producing more generalizable knowledge about causal questions; which in this case are the key factors that influence the adoption of BIDs (Goodrick, 2014). Furthermore, case studies are particularly valuable when studying a contemporary phenomenon, such as the diffusion of BIDs within its real-life context (Yin, 2013).

3.4. Data Collection & Sampling

The study employs both primary and secondary data collection, with primary data being collected through the distribution of a four-page survey, while secondary data is obtained through a comprehensive desk research drawing from governmental documents, project’s documents, academic databases, and articles.

3.4.1. Survey

The four-page survey instrument contains both open-ended, and multiple-choice questions, and they are structured into two main thematic areas. They are the diffusion mechanisms, and factors influencing the adoption of BIDs, as shown below in Figure 3. For the sake of comparison between the selected two cities, both surveys are identical, expect some portions that required minor changes, such as referring to BIDs as “Business Improvement Districts” in Riyadh, and BID as “Business Improvement District” for Amsterdam. One of the first steps in survey design is to define the population being studied (Groves et al., 2011). Here the term “population” is used in the technical sense of the totality of the elements under study, where the “elements” are the units of analysis (Kalton, 2020). Due to the technical nature of the research questions, a highly targeted group of participants was identified, resulting in a relatively small sample size (n = 12). This included governmental officials, project managers specializing in BIDs, urban planners, and academics. The selection aimed to examine the diffusion mechanisms and the key factors influencing the adoption of BIDs. The participants list was developed using contact information obtained from previous studies and professionals within the author’s network. A total of 12 participants were initially targeted—6 from Riyadh and 6 from Amsterdam. To enhance the likelihood of participation, and ensure clarity of understanding, the language of the survey instrument has been modified for each city.

Figure 3. Survey instrument, sample questions. Source: Author (2024).

3.4.2. Desk Research

For the secondary data collection, the project’s documents, articles, and academic databases were utilized, encompassing both qualitative and quantitative data. The secondary data sources were available to the public, however, with regard to the case of Amsterdam, considerable difficulties arose concerning Amsterdam Zuidas due to the language barrier. However, this challenge was addressed by leveraging the author’s relationships with some of the project’s stakeholders and conducting a field visit to the Amsterdam Zuid information center. This approach facilitates a comprehensive understanding of both the recent developments in regard to the two cities and the current situation of two case studies.

3.4.3. Rationale for Selecting the Case Studies

Prior to discussing the rationale behind the selection of the case studies, it is crucial to revisit the main research question; What explains the diffusion of BIDs as a strategic development plan? In this case, I argue that in order to comprehensively understand a certain phenomenon, it is best to study it within divergent contexts, extremely different. This is also highlighted by Seawright and Gerring as they identify seven principal strategies for case selection based on case type: typical, diverse, extreme, deviant, influential, most similar, and most different (Campbell, 2003; Seawright & Gerring, 2008). Therefore, the selected strategies of “extreme” and “most different” are utilized to lay the groundwork for the analytical framework.

By selecting Riyadh and Amsterdam as case studies, the research will provide an in-depth analysis into the reasons behind the adoption of BIDs in different geographical locations, representing both the Middle Eastern and European regions, which also contributes to the research gap described by Peyroux (2012) that justifications for the BIDs model need to be explored in diverse urban contexts such as Europe and South Africa. Additionally, this study also focuses on exploring how global policies such as BIDs are being adopted and contextualized within different planning systems. Therefore, examining the adoption of BIDs within both extreme contexts is posited as crucial for achieving more comprehensive research outcomes, that will cover the entire spectrum of planning systems by empirically examining the centralized, and decentralized decision-making environments.

In practical terms, studying the adoption of BIDs in Riyadh, where its urban planning system is characterized by a top-down approach with centralized decision-making processes. In contrast, Amsterdam’s bottom-up approaches in planning, which highlight grassroots participation, community engagement, and a decentralized environment (Van Kempen & Woltjer, 2016)—will help policymakers, urban planners, and researchers gain a more nuanced understanding of the effective strategies for policy implementation in opposite planning systems, as well as will uncover unique patterns for policy adoption and integration. Furthermore, this will help devise strategies tailored to the specific socio-political and cultural contexts of other cities and also foster the exchange of knowledge and experiences across divergent urban contexts.

3.5. Data Analysis Methods & Limitations

While this study is primarily qualitative in nature, it also adopts a quantitative analysis approach, taking into consideration both the practical implications of the research and its primary goal, as this study aims not only to examine the diffusion mechanisms influencing the adoption of BIDs but also to identify the key factors driving or hindering this adoption. Hence, it ultimately adopts a pragmatic approach in which both methods qualitative and quantitative are integrating to enhance each other performance (Morgan, 2014). It integrates a quantitative method in a sequencing manner, in which it is used in a specific order to follow-up on a core method which is qualitative. Figure 4 below shows how the sequence model is designed in a follow-up contribution, more specifically a small quantitative study has been conducted to identify the key factors that influence the diffusion mechanisms of BIDs adoption.

Figure 4. Sequential contribution model influenced by Morgan (2014). Source: Author (2024).

For the survey’s multiple-choice questions, descriptive statistics were used to summarize and describe the data, such as calculating frequencies, percentages, and measures of central tendency (mean, median, mode) (Mordkoff & Castro, 2016). Moreover, a thematic analysis was employed to identify and analyse patterns (themes) within qualitative data collected from open-ended survey responses and secondary sources. This method helped uncover patterns that supported or challenged the study’s hypothesis, and provided deeper insights into the contextual factors influencing BID adoption (Braun & Clarke, 2006; Guest, MacQueen, & Namey, 2012). Initial coding of the data was performed to identify recurring ideas, and concepts, which were then grouped into broader themes presenting significant aspects of the study.

Additionally, the research adopts two case studies, a narrative case description was created to provide contextual information about the setting and establishment reasons, general organizational features, as well as the activities the organization undertakes regarding each project. Due to limited data availability, reference case descriptions were prepared using a similar but more condensed format, highlighting key programs or project areas to showcase a range of BID activities. As noted by Yin (2013), this descriptive approach aids the case study practitioner in identifying explanations for their case.

In short, both methods helped identify patterns, and relationships between the variables, so we could test the study, hypotheses, as well as identify the key factors.

3.6. Ethical Considerations

In conducting this study several ethical principals have been taken into consideration; informed consent, confidentiality and anonymity. All the participants involved in the survey were informed about the research purpose, their role, as well as how the data would be used. Consent was obtained before their participation to ensure their voluntary involvement, and clear understanding of the study’s objectives (Groves et al., 2011). Additionally, the study maintained strict confidentiality protocols to protect the participants’ identity. All the collected data were anonymized and stored securely (Van Thiel, 2014). Moreover, these ethical considerations align with the “Netherlands Code of Conduct for Research Integrity” (VSNU, NFU, KNAW, NOW, & VH, 2018).

4. Results, Analysis and Discussion

4.1. Introduction

This chapter presents the findings of the study, focusing on the diffusion of BIDs in the two selected contrasting urban contexts: KAFD in Riyadh, and Amsterdam Zuidas. The analysis begins by establishing the background and context of these case studies to understand the diffusion mechanism, and factors influencing the adoption of BIDs policies in these cities. The subsequent sections provide an in-depth examination of the data collected through surveys, case studies, and other secondary sources, aiming to answer the main research questions. The results are formatted to present various diffusion mechanisms employed in both cities and, finally, to subject the findings of external and internal driving & restraining forces to the adoption of BIDs. The chapter concludes by interpreting the results in light of the study’s hypothesis and offers a comparative analysis of the selected cases with the purpose of enhancing the comprehensiveness of the understanding of the diffusion of urban policies.

4.2. Data Analysis Approach

As mentioned in the previous chapter, in (Section 3.5.1), surveys were distributed in both cities. While the initial goal was to collect 6 responses from each city, a total of 12 survey responses were obtained: 7 from Riyadh, and 5 from Amsterdam. The availability of secondary data in Amsterdam (such as “Visie Zuidas 2016” document from Gemeente Amsterdam, and “BID act” from overheid.nl) eliminated the need to extend the data collection period.

The responses have been printed out to enhance the comprehension and the engagement of the mind with the concepts, as well as their relationships as shown in Figure 5. This helped to better develop a comprehensive understanding of the study’s variables. ATLAS.ti 23 is used to further analyze and examine the relationships with all of the responses having been collected and prepared into two main consolidated documents representing both case studies. After the initial coding of the data according to the indicators mentioned in the operationalization table, a broader group was then formed representing the study’s variables, such as “Policy Leadership”, and “Economic Competition”. Consequently, links started to emerge between the variables, which were then categorized into three main ones: Coercion, Competition, and Learning categories. Grouping the variables into these new categorizes has the beneficial effect of capturing variations between the three mechanisms. For instance, the coercion and competition mechanisms are closely intertwined, especially in the case of KAFD, where numerous instances of policy leadership represented by governmental leaders under Saudi Vision 2030 were evident. The overarching goal is to establish “Riyadh as a leading global financial hub” of by attracting business and residents.

The dependent variable in the three study’s hypothesis capture whether a city adopts BIDs by each of the three presented mechanisms with a code labelled “Adoption of BIDs” having been developed for each city to examine the relationships between this variable and others, in order to explore the study’s hypothesis. An advanced analysis utilizing networks and queries within ATLAS has been conducted to visualize these relationships, which will be thoroughly presented later in a form of diagrams, and co-occurrence table in this chapter. In fact, while qualitative methods do not provide statistical generalizability, they offer insights into the contextual and interpretative aspects of the hypothesis being explored (Creswell, 2013). Moreover, a discussion will facilitate the comparison between the two cities, ultimately concluding by identifying the key factors that influenced the adoption of BIDs.

Figure 5. Survey results, sample of manual analysis. Source: Author (2024).

4.3. Case Studies Establishing the Context

This section is primarily aimed at establishing the contextual framework for later examining the diffusion mechanism of BIDs in Riyadh, and Amsterdam. It provides a thorough understanding of the inception and proliferation of BIDs within the case studies of King Abdullah Financial District (KAFD) in Riyadh, and the Amsterdam Zuidas. This section offers a detailed background on the initiation of these case studies and identifies the entities responsible for their development, which is an important factor to test the study’s hypothesis. Given the focus of the research, it elucidates specifically on the initial stages, rather than the subsequent development processes, or the arising conflicts.

4.3.1. King Abdullah Financial District (KAFD), Riyadh

The King Abdullah Financial District (KAFD) Project which is shown in Figure 6 is situated in Riyadh, the capital city of Saudi Arabia which is the largest country in both land area and population among the Arabian Gulf countries in the Middle East, covering approximately 2.15 million square kilometres, with an estimated population around 37.5 million (United Nations, Population Division, 2024). KAFD is a groundbreaking urban-development project aimed at establishing a premier financial hub in the Middle East. Conceived under the vision of King Abdullah, the district is designed to elevate Riyadh’s economic stature and align with the Kingdom’s Vision 2030 goals, which focus on economic diversification and growth (Saudi Vision 2030).

Who Manages the Riyadh City Development?

To understand how BIDs started to diffuse, it is important to know who the key actors managing the development of the city are. The first actor is the Royal

Figure 6. King Abdullah Financial District in Riyadh. Source: PIF (2023).

Commission of Riyadh (RCRC), which was established by royal decree in 2019, replacing the former Al Riyadh Development Authority (ADA), which is responsible for developing and improving Riyadh City’s urban planning, economic activities, environmental management, and social and cultural development (RCRC, 2023). The second actor is the Ministry of Municipalities and Rural Affairs (MoMRAH), which oversees the development of urban and rural areas, including the management of roadway networks and infrastructure in Riyadh City (Ministry of Economy and Planning, 2010). The third and final actor is Riyadh Municipality, which was established in 1356 AH as a small entity with limited capabilities, tasks, and employees, with Mr. Hasan Bukhari serving as the first “Municipality Manager”. It aims to fulfil the aspirations of the municipal sector by achieving an advanced future model through excellence in service provision, ensuring beneficiary satisfaction, all while contributing to economic, social, and environmental sustainability (Riyadh Region Municipality, 2024). In short, the three entities form the formal institutions that are responsible of Riyadh city development, however, they are operating at different levels with a more hierarchical and centralized environment.

Development of KAFD & Diffusion of BIDs

As mentioned above, KAFD was introduced as part of a broader Saudi Vision 2030, a strategic plan aimed at diversifying the economy, and modernizing urban infrastructure and management (PIF, 2023). The Saudi Vision 2030 plan, launched in 2016, emphasized the need for economic diversification away from oil dependency and highlighted the development of key urban centres as catalysts for economic growth (Saudi Vision 2030, 2016). Hence, KAFD was conceived to attract international businesses, create jobs, and position Riyadh competitively on the global financial stage “A city-within-a-city” Riyadh among world’s top 10 city economies (PIF, 2023). The district spans 1.6 million square meters and includes office towers, residential buildings, hotels, and entertainment facilities. The development was driven and funded by the Public Investment Fund (PIF) of Saudi Arabia, with the goal of creating a world-class business environment, and later managed by KAFD Development & Management Company. In that essence, the only owner for KAFD is PIF as shown in Figure 7, which illustrates a centralized decision-making process in regard to the management, and development of the project.

Figure 7. Development of KAFD, main actors. Source: Author (2024).

Diffusion of BIDs in Riyadh

In Riyadh, there is no legal basis for BIDs as a concept. However, the diffusion of BIDs is intrinsically linked to the broader urban development strategies exemplified by KAFD. The argument here is that on one hand, there are no BIDs in Riyadh as it is still not legalized within its legal frameworks. This is also supported by one of the respondents, as he says “In my work, and for the organizations that I have worked for we have never explicitly adopted the BID model, even as loosely applied as KAFD (which is not really a BID, given that it was a development project)”. On the other hand, BIDs in Riyadh is being adopted as part of a strategy to modernize the city’s governance model and attract private investment, aligning with the broader objectives of Saudi Vision 2030. The Saudi government, through entities like RCRC and MoMRAH, has promoted the adoption of BIDs by easing regulations, streamlining licensing processes, and fostering public-private partnerships (RCRC, 2023; Ministry of Economy and Planning, 2010). This is also supported by one of the respondents, as he says “The interest in adopting (BIDs) model for (KAFD) was driven by the goal of creating a vibrant, self-sustaining community with superior infrastructure and services to attract businesses and residents. This initiative was part of Saudi Arabia’s Vision 2030 plan, aimed at economic diversification”.

In short BIDs in Riyadh are in the early stages of adoption, and only focusing on economic drivers. The on-going development of KAFD serves as a pilot project which could catalyze the wider implementation of BIDs across the city. The commitment of the Saudi government to this model, taken together with the likely success that KAFD enjoys, creates a belief that BIDs can be more widely incorporated into the urban development strategy of Riyadh.

4.3.2. Amsterdam Zuidas, Amsterdam

The Zuidas project is situated in Amsterdam, the capital city of Netherlands, located in Western Europe. Netherlands is known for its advanced infrastructure and high quality of life as shown in Figure 8, with an area of 41,543 square kilometres and a population of around 17.4 million (United Nations, Population Division, 2024). Amsterdam Zuidas is a prestigious international hub for knowledge, and business, recognized as one of the most significant office locations in Netherlands. The project also known as the “South Axis”, as it is strategically located between Schiphol Airport and the office parks of Amsterdam Bijlmer, along Amsterdam’s southern ring road as shown in Figure 8, so it connects Schiphol and Amsterdam Zuid-Oost (South-East). This positioning is crucial for its goal to become a prime international office location and a new urban centre (Majoor, 2008). In short, the development aims to enhance Amsterdam’s competitiveness in the global arena by creating a vibrant and sustainable urban business district.

Figure 8. Amsterdam Zuidas, Zuidas accessibility. Source: Zuidas.nl (2024).

Who Manages the Amsterdam City Development?

In the case of Amsterdam City, managing urban development is complex due to the nature of the Dutch three-tier system’s co-governance and consensus model (Majoor, 2008). Barlow states that, co-governance is the concept that the three tiers of government are highly interdependent and closely interconnected through governmental relations, this dynamic interaction among relatively independent units within the state system creates a flexible and fluid structure, capable of adapting to various problems and societal changes (Barlow, 1997: p. 260). In that essence, the national government dominates many political areas, and local authorities rely on it for 90% of their funding. Despite this, local authorities retain significant autonomy in policy implementation. Aside from that, there’s the National Spatial Planning Act, which demonstrates the procedural, specifying how spatial plans should be prepared and how formal decisions and appeals should be handled, but it does not provide specific content guidelines for these plans (Majoor, 2008). Moreover, the spatial planning system in Netherlands is highly decentralized, with significant power vested at the municipal level, supported by procedural guidelines from the National Spatial Planning Act and strategic direction from the National Spatial Planning Report.

In spite of these complexities, in Amsterdam Zuidas, the governance structure is characterized by Public-Private Partnerships (PPPs), facilitating shared investment in infrastructure and public spaces (Majoor, 2008). Within this structure, the primary actor is the Municipality of Amsterdam, which is responsible for the overall urban planning and infrastructure development in the city. This also involves setting strategic directions, and ensuring the development aligns with the broader goals of the city. It is noteworthy that the government (Amsterdam Municipality) owns the majority of the land within its jurisdiction and implements a land-lease system to maintain control over its properties, which generates a consistent revenue stream and affords the city government significant leverage in the large-scale development or redevelopment of specific areas, such as Zuidas project (Majoor, 2008).

Development of Amsterdam Zuidas & Diffusion of BIDs

The project is introduced to redevelop the southern banks of the River IJ into a new central business district exemplified the renewed focus on the economic potential of cities, it was among the first large-scale development proposals in Amsterdam in decades with a strong emphasis on private investment (Majoor, 2008). The initial phase of the Zuidas development involved consolidating and operationalizing a vision for a decentralized urban centre, marked by effective city-government and business collaboration and optimistic about the area’s economic and urban growth potential (Majoor, 2008). Inspired by successful American examples in cities such as Boston, Baltimore, San Francisco, and Seattle, plans were made to redevelop former harbor areas with large office spaces and waterfront promenades (Ploeger, 2004; Rooijendijk, 2006). The latest master plan for the area envisions the development of a vibrant urban canter, encompassing approximately 1.1 million square meters of office space, 1.1 million square meters of residential apartments, and 500,000 square meters of various facilities. This extensive project is projected to be completed over a period of approximately 30 years. As shown in Figure 9, in the Amsterdam Zuidas project, the Zuidas Company demonstrates a decentralized decision-making structure, with 60% of the shares held by the private sector and the remaining 40% distributed among various governmental entities (Majoor, 2008).

Diffusion of BIDs in Amsterdam

In Amsterdam, the diffusion of BIDs significantly differs from that in Riyadh, as BIDs have already been introduced, and formally legalized within the Dutch legal framework. Legislative research on BIDs in European countries reveals that there are three countries—namely the UK, Germany, and Netherlands—that have specific laws and regulations governing BIDs (Dhamo, Beleraj, & Kume, 2022). In Netherlands, BIDs was introduced as a concept by the Dutch government through an “experimental law” in 2009, known as “The Investments Districts” (BID) Act, which became permanent in 2015—in a response to both the need for financing the increasing number of district management initiatives, and the positive reception of BIDs in other countries (Berndsen, Doornbos, van Vliet, & Maas, 2012). Hence, the argument here is that; BIDs are already adopted, and legitimized within the Dutch legal framework, which has enabled their structured implementation across various parts of the country. In fact, Netherlands has already 302 BID organizations, with an average annual growth rate of 17% between 2015 and 2020, and Amsterdam is the leading city for BIDs, hosting 65 in total (Hagemans, van Hemert, Meerkerk, Risselada, & van Winden, 2020).

Figure 9. Development of Zuidas, main actors. Source: Majoor (2008).

BIDs in Amsterdam were introduced to address the growing need for sustainable financing solutions for districts management and development initiatives, and this was inspired by the successful experiences of other countries, particularly in the UK, and Germany, where BIDs had proven effectiveness (Berndsen, Doornbos, van Vliet, & Maas, 2012). In Amsterdam, the Zuidas project exemplifies an effort to develop a new internationally competitive office location and a dynamic urban center (Jacob Trip, 2007). Hence, Zuidas Coalition initially aimed to enhance the area’s economic value by engaging key investors and government stakeholders (Braun, 2023). Besides the introduction of BIDs, more especially after its legal basis, BIDs in Amsterdam are strategically aligned with the city’s broader objectives, such as promoting sustainable development, enhance governance structures, and fostering economic competitiveness. The Amsterdam municipality has been actively supporting BID models, particularly in areas where mixed-use developments offer significant potential for both social and economic benefits (Majoor, 2008; Van Kempen & Woltjer, 2016). This is also supported by the project’s vision, mentioned in the official document prepared by Amsterdam City Council “By 2030, Zuidas wishes to be among the Top Ten sustainable urban centres in Europe”.

In conclusion, BIDs adoption in Amsterdam has progressed beyond the experimental phases, since the formalization of the BID Act has become an integral part of urban management in Netherlands. Additionally, the ongoing development and implementation of BIDs reflect a strong commitment to leveraging this model as a tool for achieving broader urban visions, including sustainability, economic resilience, and active community participation (Dutch Ministry of Economic Affairs, 2015).

4.4. Study Findings

In this section the survey results will be presented in terms of the number of the respondents per city, motivations for adopting BIDs, factors that influence the adoption both internal and external, as well as diffusion mechanisms.

4.4.1. Survey Respondents

A previously mentioned, a total of 12 survey responses were collected; 7 from Riyadh city, and 5 from Amsterdam city as shown in Figure 10. The respondents are governmental officials, urban planners, and academics. While, it would have been better if the survey respondents were equal in both cities, but as mentioned before the availability of the secondary data in Amsterdam, eliminated the need for extending the data collection period. Additionally, the core method of the study is qualitative, so there was no need to focus on this matter.

Figure 10. Survey respondents. Source: Author (2024).

4.4.2. Diffusion Mechanisms Variables

In this section, more specific elaboration will be provided on how the hypotheses have been tested. The Coercion Diffusion Hypothesis holds that a city will be more likely to adopt a policy if there are instances of coercion, such as pressure from political leaders or more powerful entities, are present. To explore this hypothesis, a policy leadership code has been constructed to capture instances from political leaders or powerful entities. Additionally, the expectation is that the adoption increases if there are also dominant ideological trends or policy norms, such as those sustainable development, and smart cities which are frequently referenced in KAFD case. Consequently, the testing of the other hypothesis has followed the same structure, utilizing the variables associated with each category as outlined in the operationalization table. In fact, to enhance the accuracy of both exploring, and testing the hypothesis, future studies could benefit from adopting more quantitative approaches.

4.4.3. Coercion Diffusion

Influence of Hegemonic Ideas and Policy Leadership:

The diagram distinguishes between two major forces:

• Hegemonic Ideas (represented by the grey flows): These seem to be broad, influential concepts or strategies that shape the adoption of BIDs, likely through global norms or widespread practices.

• Policy Leadership (represented by the red flows): This likely represents the direct influence of leadership and governance on BID adoption, suggesting that strong leadership is a significant driver in some contexts.

In Figure 11, the sanky diagram demonstrates the qualitative coding to understand the relationships between the adoption of BIDs as a dependent variable with the policy leadership, and hegemonic ideas variables. In the adoption of BIDs in both cities there were instances of hegemonic ideas, such as sustainable development, and better governance structures, but in Riyadh, the instances of policy leadership were also evident.

Figure 11. Coercion diffusion: Relationships between adoption of BIDs, policy leadership, and hegemonic ideas. Source: Author (2024).

Box 1. Examples of quotations of relationships between adoption of BIDs, policy leadership, and hegemonic ideas.

4.4.4. Economic Competition Diffusion

Influence of Economic Competitiveness, and Spillover-Induced Competition:

The diagram distinguishes between two major forces:

• Economic Competitiveness (represented by the blue flows): This variable is developed to capture instances of a city’s overall ability to attract and sustain businesses, investments, and any other economic activities. It includes broad strategies, and policies that aimed at making the city more attractive to investors.

• Spillover-Induced Competition (represented by the purple flows): This variable reflects instances of externalities, such as economic growth, increased investment, or reduced costs—from neighbouring jurisdictions on the decision to adopt BIDs.

Figure 12. Economic competition diffusion: Relationships between adoption of BIDs, economic competitiveness, and spillover-induced competition. Source: Author (2024).

The diagram in Figure 12 demonstrates the relationships between the adoption of BIDs, Economic Competitiveness, and Spillover-Induced Competition in both cities. It shows that both factors have influenced the BIDs adoption, with instances, such as “business attraction policies” and “Financial Incentives” as well as “Enhancing Urban Infrastructure”. Although economic competitiveness is important in both cities, spillover-induced competition has much more significant influence in Riyadh rather than Amsterdam.

Box 2. Examples of quotations of relationships between adoption of BIDs, economic competitiveness, and spillover-induced competition.

4.4.5. Learning Evaluation Diffusion

Influence of Lesson-Drawing, and Rational-Learning:

The diagram distinguishes between two major forces:

• Lesson-Drawing (represented by the blue flows): This variable is developed to capture instances of cities adopt BIDs by learning from the experiences, best practices, and policies implemented in other cities.

• Rational-Learning (represented by the purple flows): This variable reflects instances which involves adopting BIDs based on internal evaluations, data-driven decision-making, and the consideration of local needs and conditions. In other words, this approach emphasizes more analytical, and evidence-based decision-making processes.

The diagram in Figure 13 demonstrates the relationships between the adoption of BIDs, Lesson-Drawing, and Rational-Learning in both cities. It shows that Lesson-Drawing factor has influenced the BIDs adoption, with instances, such as “Highlighting Successful Examples” and “Enhancing Urban Infrastructure”. However, rational-learning is evident in the case of Amsterdam.

Figure 13. Learning diffusion: Relationships between adoption of BIDs, lesson-drawing, and rational-learning. Source: Author (2024).

Box 3. Examples of quotations of relationships between adoption of BIDs, lesson-drawing, and rational-learning.

4.4.6. Factors That Influence the Adoption of BIDs in Both Cities

This study also aims at identifying the key factors that influence the adoption of BIDs in both cities. According to the operationalization table, these factors have been categorized into external, and internal factors. On one hand, internal factors include organizational influences, such as the roles of both organizations, and individuals, as well as geographical influence which is related to proximity and influence of BIDs in neighbouring cities, or regional trends in BIDs adoption. On the other hand, internal factors cover, political aspects, such as policy support or statements from leaders, and socio-economic aspects, such as if there were financial incentives provided for BIDs adoption.

External Factors

In analysing the organizational influence factor, Figure 14 shows that, while 12.5% were uncertain, and 25% reported no involvement of international organizations or policy entrepreneurs, the majority 62.5% confirmed some level of engagement by policy entrepreneurs, ranging from passive participation to actively driving the process. Furthermore, 37.5% of respondents perceived a “strong” influence from these factors, while another 37.5% indicates a “moderately” influence. Meanwhile, 25% see no influence at all, highlighting variability in perceptions of how organizational actors, including businesses and policy entrepreneurs, shape the adoption of BIDs.

Figure 14. Organizational influences on BIDs adoption: Roles of policy entrepreneurs and businesses. Source: Author (2024).

On the other hand, Figure 15 illustrates the influence of BIDs as a growing phenomenon in neighbouring cities. The first chart shows that 37.5% of respondents confirmed the presence of geographical influence on BIDs adoption, indicating that the spread of BIDs is recognized as a growing trend in nearby cities. In contrast, another 37.5% reported no such influence, reflecting a divided perception of how significant geographical proximity is to the adoption of BIDs. Furthermore, in the second chart, indicates varied perceptions; 37.5% of respondents viewed the geographical influence of neighbouring BIDs as “moderate”, suggesting slight impact, while another 37.5 felt there’s no such influence. In short, the data suggests a mixed but notable recognition of geographical influence on BID adoption, with 50% of respondents indicating varying degrees of influence (from “slight” to “very strong”) from the presence of BIDs in neighbouring cities. This indicates that geographical proximity and the visibility of successful BID implementations in nearby areas can contribute to local adoption decisions. It is also worth noting that; the 25% of respondents perceiving a “very strong” influence suggests that proximity to cities with established BIDs can create considerable pressure or motivation to adopt similar models. This may result from competitive pressures, economic benchmarking, or a desire to align with regional development trends.

Figure 15. Geographical influences on BIDs adoption, BIDs as a growing phenomenon neighboring city. Source: Author (2024).

Internal Factors

Regarding the internal factors, Figure 16 shows the political influence on BIDs adoption. While 12.5% of respondents reported “no” political influence, the remaining 87.5% indicated that there was political influence, suggesting various forms, and views on how the specific political drivers behind BIDs adoption. For instance, in Riyadh, 12.5% cited “special economic areas in KSA” as a political factor, reflecting the importance of specific governmental initiatives aimed at economic diversification and regional development, another 12.5% mentioned the Saudi Arabia’s Vision 2030, a national development plan that promotes urban and economic transformation, as significantly shaping BID adoption. In Amsterdam, 12.5% stated that the city aims to become a multi-centric area supported by all political parties, indicating political consensus towards fostering economic competitiveness and urban development through BIDs. In short, the data reveals that the political factors had significant influence on the adoption of BIDs in both cities, however, it is multifaceted, and perceived differently by stakeholders.

Figure 16. Political influence on BIDs adoption, policies, and statements from leaders. Source: Author (2024).

Furthermore, other socio-economic aspects have manifested themselves in the adoption of BIDs in both cities. For instance, Figure 17 shows that; 12.5% of respondents reported “no” financial support has been provided, and another 12.5% were uncertain. However, the remaining 75% acknowledged that there was financial support. The data indicates a broad spectrum of perceptions regarding this aspect, and while some respondents remain uncertain about it, the majority recognized general financial support mechanisms, and specific incentives. Additionally, it is worth mentioning that Riyadh as part of Saudi Arabia has been witnessing a significant progress in the Human Development Index (HDI), especially in education as it is advanced 5 positions from 40^th to 35^th out of 191 countries (Arab News, 2022). In contrast, Amsterdam as part of Netherlands, often ranks high in HDI within top 10 countries globally, due to its strong educational system, inclusive policies, and sustainable economic practices (OECD-Education GPS, 2023). This may explain why BIDs are legalized within the Dutch legal frameworks, supporting municipalities like Amsterdam in their urban development strategies. Such frameworks suggest positive outcomes in areas like education, and overall HDI. In conclusion, however there’s a radical transformation in Riyadh in regard to the socio-economic aspects, there’s still a huge gap between it and Amsterdam, as reflected in the differing adoption of BIDs in the two cities.

4.5. Interpretation of the Results

Coercion Diffusion

The sanky diagram in Figure 11 provides insights that align with the coercion diffusion hypothesis. It indicates that; in Amsterdam, the adoption of BIDs is predominantly influenced by “Hegemonic Ideas” that emphasize local economic development, sustainability principles, and the attraction of businesses and residents. It demonstrates the city’s strategy focus on aligning with global standards and norms rather than direct coercion by its political leaders. Additionally, the Zuidas vision mentioned previously further supports this proposition.

Figure 17. Financial incentives as a factor influencing BIDs adoption. Source: Author (2024).

While in Riyadh, the adoption of BIDs is driven by a combination of both “Policy Leadership” and “Hegemonic Ideas”. Ideas, such as “better governance structures”, and “Funding Mechanisms” that area associated with the adoption of BIDs, and “Economic Diversification” which has been pressured by leaders, highlighting the significant role of direct political influence and leadership. This suggests that coercion from local entities is crucial factor, supported by the alignment with global norms. Hence, Riyadh’s approach aligns well with the Coercion Hypothesis, where both internal political pressures and external ideological shifts enhance the likelihood of policy adoption. This proposition is further supported by the fact that the KAFD is solely owned by PIF, the government of Saudi Arabia (PIF, 2023).

In short, the Amsterdam case shows an aspirational adoption of what are more global (or at best regional hegemony) ideas that resonate with broader economic and sustainable development goals. However, in Riyadh, the adoption of BIDs is more heavily influenced by local policy leadership, reflecting a more centralized approach where government direction plays a key role.

Competition Diffusion

The diagram in Figure 12 supports the Competition Diffusion Hypothesis by showing that both Riyadh and Amsterdam adopt BIDs in ways that consider their regional economic contexts. On one hand, Amsterdam shows a primary focus on “Economic Competitiveness”, in which its strategy revolves around strengthening its internal economic environment, with less reliance on the competitive dynamics of neighbouring regions. This demonstrates the city’s ability to maintain a strong economic position independently, with minimal dependence on spillover benefits. However, instances of successful examples, such as Boston, Baltimore, San Francisco, and Seattle, plans were evident during the development of Zuidas (Ploeger, 2004; Rooijendijk, 2006).

On the other hand, Riyadh shows a stronger alignment with the hypothesis, in which both spillover effects and economic competitiveness were illustrated. The city’s emphasis on spillover effects suggests that Riyadh is strategically aligning its policies with those of surrounding areas to maximize regional economic benefits, and competitiveness. This proposition supported by respondents as they said “The policies of Dubai and Doha and the fierce competition between them encouraged Riyadh to join the race”.

The analysis shows that while both cities aim to enhance their economic attractiveness through the adoption of BIDs, Amsterdam focuses more on internal policies to maintain its competitiveness. However, Riyadh follows a more competitive approach in which it considers leveraging both internal competitiveness, and positive spillovers from neighbouring cities.

Learning Diffusion

In Figure 13, the diagram also shows that both cities have been influenced by lesson-drawing regarding their adoption for BIDs. This supports the Learning Evaluation Hypothesis. For instance, Amsterdam considers both “Lesson-Drawing” and “Rational-Learning” when it comes to the adoption of BIDs. This suggests a more tailored approach that selectively applies external lessons deem relevant and applicable to its context.

In Riyadh, the focus is solely on “Lesson-Drawing” and instances of BIDs success in other cities. Although also “Rational-Learning” is also present, it plays a secondary role. Riyadh appears more focused on leveraging external examples than on independently assessing its local context through data-driven approaches.

Moreover, the analysis shows that while both cities utilize external learning to some extent, Riyadh is more driven by the success of BIDs in other cities. In contrast, Amsterdam considers both internal rational evaluation, and integrating external lessons selectively into its context-specific policy framework, highlighting a stronger alignment with Learning Evaluation Hypothesis.

4.6. Discussion

In fact, through the efforts made in conceptualizing, and analysing policy diffusion mechanisms separately, the study reveals overlaps between variables within different diffusion mechanisms. This has been mentioned before in the literature, that even if the diffusion mechanisms can be conceptually distinguished, they often interact in practice (Kuhlmann, 2021). However, the argument here is different—in Amsterdam, BIDs have been formalized under the BID Act, reaching a stage where economic competition and learning diffusion mechanisms are closely interrelated. In contrast, in Riyadh, BIDs are still in their conceptual development phase, with a strong focus on coercion and economic competitiveness diffusion mechanisms—this reflects both horizontal and vertical diffusion processes in each city’s approach to BIDs.

On one hand, this might be due to the planning systems, in which Amsterdam more decentralized so it facilitates more governance approaches, such as stakeholder engagements and therefore more learning environments. This is further supported by the fact that it has higher ranks globally in HDI (OECD-Education GPS, 2023). In contrast, Riyadh is more centralized planning systems, and within its radical transformation in regard to its socio-economic aspects, which might explain why there is much more instances of coercion, and economic competitiveness rather than learning diffusion mechanisms (Arab News, 2022).

Table 2. Co-occurrence between diffusion mechanisms, and the adoption of BIDs in both Riyadh and Amsterdam. Source: Author (2024).

On the other hand, this could be explained by the fact that BIDs are still in the conceptualization phases in Riyadh, in which both coercion and competition are more evident temporarily, considering the temporal vertical diffusion. While, in Amsterdam, the BIDs are already legalized, so it facilitates more learning diffusion mechanisms. In fact, this has been discussed in the literature by Shipan and Volden (2008); they wanted to distinguish between the diffusion mechanisms both theoretically and empirically, therefore, they studied when each of these mechanism takes place, and why would one may affect some cities more than others (Shipan & Volden, 2008).

Furthermore, this study could benefit more from both employing more quantitative approaches, and considering the notion of time when studying the policy diffusion mechanisms. As the spread of policies might employ certain diffusion mechanisms temporarily, and then transfer to other ones. For instance, Table 2 above indicates that, in the case of Amsterdam, the instances of hegemonic ideas, economic competitiveness, and spillover-induced competition were evident. However, learning diffusion mechanism is yet dominating, supporting the vertical diffusion processes. In contrast, Riyadh shows instances of policy leadership, hegemonic ideas, both economic and spillover competitiveness, as well as also learning, although, competition diffusion mechanism is dominating the scene.

5. Conclusion

5.1. Introduction

This final chapter presents answers with far more clarification in regard to the aforementioned research questions, a summary of the study, and it ends up with recommendations for future research.

5.2. Answering the Main Research Questions

5.2.1. What Explains the Diffusion of BIDs as a Strategic Development Plan?

The diffusion of BIDs as a strategic development plan can be understood by examining the processes and pathways through which these policies, ideas, and practices spread from one city or region to another. Although this diffusion is often driven by various motivations—political, economic, and social—supported by the desire to enhance local economy, improve urban management, and foster sustainable urban development as demonstrated in both Riyadh and Amsterdam, the study hypothesizes that the diffusion of BIDs can be explained through three primary mechanisms: Coercion, Competition, and Learning. These mechanisms highlight different pathways through which policies, such as BIDs, have spread across cities globally, and are tested through qualitative analysis, examining their adoption in the contexts of Riyadh and Amsterdam.

Coercion Hypothesis: It holds that a city’s likelihood of adopting BIDs increases when instances of coercion, such as pressure from political leaders or more powerful entities, are present. This is supported by the study’s findings, particularly in the case of Riyadh, where the coercion mechanism plays a dominant role. In fact, the adoption of BIDs is heavily influenced by top-down approaches that align with Saudi Vision 2030, which emphasizes economic diversification and sustainable urban development (Saudi Vision 2030, 2016). Additionally, the data shows that policy leadership by local government officials is a significant driver, compelling the adoption of BIDs as part of a broader strategy to establish Riyadh as a global financial hub (PIF, 2023).

Competition Diffusion Hypothesis: It suggests that a city’s likelihood of adopting BIDs increases when there are positive spillover or competitive economic advantages to be gained. The study’s findings from both cities support this hypothesis, though it is expressed differently. In Riyadh, there are instances that demonstrate that BIDs are being adopted to enhance the city’s competitive standing within the Gulf region, aiming to rival cities, such as Doha and Qatar. This is evidenced on both country and city levels; as the Saudi’s vision 2030 aims to diversify its economy, and foster sustainable urban development (Saudi Vision 2030, 2016), and Riyadh’s aims at positioning itself among top 10 citys’ economies exemplified by KAFD (PIF, 2023). In Amsterdam, the focus is more on economic competitiveness, by strengthening its local economic environment with less emphasis on regional competition. The city aims to attract businesses and investors to support and enhance the city’s urban infrastructure, rather than concentrating on competition with other cities in the region.

Learning Evaluation Hypothesis: It holds that a city’s likelihood to adopt BIDs increases when there is evidence of their success in other cities. The study’s findings strongly support this hypothesis, particularly in the case of Amsterdam, where the adoption of BIDs was highly influenced by both empirical evidence and successful examples from other cities, such as the ones mentioned earlier. Figure 13 indicates that the city’s approach involves both “Rational-Learning” (selecting policies based on revised beliefs from others’ experiences) and “Lesson-drawing” (choosing successful alternatives from other contexts). In contrast, Riyadh demonstrates instances of lesson-drawing, suggesting that the learning mechanism is secondary to coercion and competition.

Additionally, the study findings suggest that the diffusion process can be both horizontal and vertical. The argument here is that the diffusion of policies could employ certain temporary mechanisms, and then transfer to another ones. For instance, the study reveals that the adoption of BIDs in Riyadh is at its early stages; this might explain that coercion and competition mechanisms are dominating there. In contrast, the adoption of BIDs in Amsterdam is legalized, which could explain the dominance of learning mechanism over the coercion and competition.

5.2.2. Which Diffusion Mechanisms Are Employed to Adopt BIDs as a Strategic Development Plan in Both Riyadh and Amsterdam?

Adoption of BIDs in Riyadh

Although the adoption of BIDs in Riyadh is in its early stages, there are instances of multiple diffusion mechanisms in place. The study suggests that the primary diffusion mechanism is coercion. The city uses a top-down, centralized approach to adopt BIDs, driven by political directives aligned with the Saudi Vision 2030. The government mandates BID implementation to support economic diversification and urban modernization, exemplified by KAFD (PIF, 2023). Additionally, the city also aims to compete with other cities within the region, such as the ones mentioned earlier, Dubai and Qatar, by adopting BIDs to create a favourable business environment that attracts foreign and enhances its regional economic position. Moreover, learning mechanism, such as learning from international models, is present, but limited.

Adoption of BIDs in Amsterdam

In Amsterdam, the study suggests that the adoption of BIDs heavily relies on learning mechanism, especially learning from other cities’ successful BID implementations. The city facilitates these practices to enhance its local economic development, urban management, and sustainability. For example, the Amsterdam Zuidas Project draws heavily on lessons from other cities to design a BID that emphasizes sustainability, stakeholder engagement, and better governance structures. Moreover, competition diffusion to maintain the city’s economic competitiveness is also a key factor in the adoption of BIDs in Amsterdam. The city uses BIDs to both maintain its status as a leading European business hub, particularly in high-value areas such as Zuidas and enhance its urban infrastructure by fostering a competitive business environment. This strategy helps attract international businesses, investments, and tourists, ensuring that Amsterdam remains economically vibrant and competitive within the European market (Dutch Ministry of Economic Affairs, 2015).

5.2.3. How Does the “Most-Different” Design Enhance the Analysis of BIDs Adoption?

According to this research design, it adopts “most-different” strategy to layout the analytical framework for examining the adoption of BIDs in Riyadh and Amsterdam. This strategy enhances the analysis in various ways; firstly, it helped identify key factors that influence the adoption of BIDs, and the diffusion mechanisms at play, by selecting two cities with extreme differences in regard to their environments of decision-making and urban planning approaches. Due to these present cities (the most contrasting cases), the study’s findings are considered the most significant and robust indicators of the factors influencing BIDs adoption. Secondly, it broadens the understanding of diffusion mechanisms by revealing how they operate in varied contexts. The study demonstrates that Riyadh reliance on coercion and competition is explained by its centralized urban planning approaches. In contrast, Amsterdam emphasizes more on learning and reflects its decentralized urban planning approaches. Finally, it directly addresses the aforementioned research gap by providing a comparative analysis of BIDs adoption in diverse urban contexts, where previous studies often focused more on homogenous or similar cases. This approach contributes new insights into the global diffusion of urban policies, particularly understanding the different mechanisms and the factors under which policies can thrive, advancing the field of urban policy and planning.

5.2.4. What Are the Key Factors That Influence the Adoption of BIDs in Both Cities?

Key Factors That Influence the Adoption of BIDs

The comparative analysis between the two cities, employing the “most-different” strategy in terms of their characteristics, helped shed light on the factors determining the adoption of BIDs. The research findings demonstrate that the adoption of BIDs in Riyadh and Amsterdam is shaped by a combination of both external and internal factors, which vary in their significance depending on each city’s unique context. However, the following are considered the most important ones, due to their significance.

External Factors

• Organizational Influence: The data reveals a spectrum of organizational influence, with a notable portion of respondents both strong and slight influence. The significant involvement of policy entrepreneurs (62.5% reported varying degrees of involvement) underscores their potential impact as facilitators, negotiators, or champions of BID adoption. Hence, the findings suggest that when organizations, particularly those with a vested interest in local economic development, engage actively, they can meaningfully shape BID policies and adoption processes.

• Geographical influence: While geographical influence was observed in both cities, the incidences revealed fluctuation in the perception of the factor. There is a moderate, yet significant acknowledgment of the role of geography on the BID uptake, whereby 50% of the respondents claimed different levels of influence ranging from slight to very strong influence from the existence of BIDs in other nearby cities. This suggests that the (Local adoption decisions) can be influenced by geography and the possibility of “seeing” other successful BID implementations around.

Internal Factors

• Political Support: Political support, such as policies or political statements, is another crucial internal factor influencing the adoption of BIDs. The data indicated that 87.5% of respondents confirmed that there was political influence in both cities. However, they are interpreted in different ways, depending on the respondent’s perspective or level of engagement with political discourse. This reflects the complex and varied nature of political influence on urban development policies, including BIDs, yet it is significant.

• Socio-Economic Aspects: Socio-economic conditions play a significant role in BIDs’ adoption in both cities. For instance, the data demonstrated that 75% of the respondents acknowledged that there were financial incentives, which indicates the importance of financial support’s influence on BIDs adoption. Additionally, BIDs have a long history, seen as a tool for urban revitalization

• Urban Planning Approaches and Governance Structures: Although this factor hasn’t been mentioned in the study’s operationalization, the study’s findings reveal strong relationships between the planning approaches, governance structures, and the diffusion mechanisms. It suggests that bottom-up planning approaches, which are associated with more decentralized decision-making processes, positively correlate with the learning as a diffusion mechanism.

5.3. Summary

Such findings have important normative implications. Although the model originated in Toronto, Canada, and rapidly expanded to the United States, New Zealand, South Africa, Serbia, Albania, Jamaica, the United Kingdom, and European countries like Netherlands and Germany, it is still in the process of diffusing to other regions, such as the Middle East, as exemplified by KAFD in Saudi Arabia. The diffusion of policies across cities involves different mechanisms, such as coercion, competition, and learning, yet this process occurs both horizontally and vertically. The argument here is that: in today’s globalized world, where cities tend to collaborate in order to address the arising urban challenges, learning mechanisms have become the ones that dominate the scene.

The study also suggests that while BIDs can be an effective tool for urban development strategies across various contexts, their success is highly dependent on their alignment with political landscape, governance structures, and socio-economic objectives. For policymakers, this implies that adopting global urban policies, such as BIDs, should involve careful consideration of local conditions to ensure such policies are adapted to fit the unique needs and characteristics of each city. Moreover, fostering greater collaboration between local governments, international organizations, and other stakeholders can enhance the effectiveness of the adoption of urban policies, such as BIDs, as a strategic development tool.

5.4. Recommendations for Further Research

The study proposes a conceptual framework to explain and analyze the global diffusion of urban policies, such as demonstrated in the case of examining BID policies. While the framework has proven effective in the selected contexts of Riyadh and Amsterdam, it requires further testing across different regions, such as Asia, North, and South America to validate its applicability more accurately. Expanding this research to include additional cities in different contexts and regions would help determine the framework’s robustness and adaptability to diverse urban environments. Additionally, as mentioned earlier, considering both the notion of time and more quantitative approaches could provide deeper insights into both the horizontal and vertical diffusion mechanisms. Such studies could contribute to a more comprehensive understanding of how global urban policies evolve, diffuse and adopt over time.

Acknowledgements

A special thanks to my supervisor, Professor Lasse Gerrits, for his valuable and fruitful guidance. I would also like to acknowledge the respondents for sharing their experiences and participating in the study. Their contributions have not only shaped the findings, but also deepened my understanding of global urban policy diffusion and the key factors influencing its adoption.

Additionally, I am grateful to the Dutch Organisation for Internationalisation in Education (Nuffic) for funding my master’s studies at IHS. I also extend my appreciation to all professors and instructors who contributed to my learning throughout the program. Finally, I would like to express my sincere gratitude to my family, friends, and my team at UN-Habitat for their influence during my studies.

Abbreviations

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

References