Complexities, Uncertainties, and Responses

aStockholm Centre for Organizational Research (Score), Sweden
bStockholm Environment Institute (SEI), Sweden

Obsessive Measurement Disorder or Pragmatic Bureaucracy?

ISBN: 978-1-80117-377-3, eISBN: 978-1-80117-374-2

Publication date: 29 February 2024


Alexius, S. and Vähämäki, J. (2024), "Complexities, Uncertainties, and Responses", Obsessive Measurement Disorder or Pragmatic Bureaucracy?, Emerald Publishing Limited, Leeds, pp. 13-30.



Emerald Publishing Limited

Copyright © 2024 Susanna Alexius and Janet Vähämäki. Published by Emerald Publishing Limited. This work is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this book (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at


This work is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this book (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at

Complex systems are characterized by three ideal-typical traits: (1) multiple interacting components, (2) fluid boundaries, and (3) unpredictable dynamics (Rutter et al., 2020). These characteristic conditions of a complex system bring about a number of uncertainties that in turn call for a response to this uncertainty. (We look more closely at these traits in relation to the development aid system below.) Uncertainty can, generally speaking, be defined as a situation where there is no “single and complete understanding of the system to be managed” (Raadgever et al., 2011). Previous literature (Milliken, 1987; Sicotte & Bourgault, 2008) has commonly differentiated between three types of uncertainty: uncertainty of state – What are we up against and how will it change?, uncertainty of response – What can we do about the uncertainty of state? What responses are available and which should we chose?, and uncertainty of effect – What will happen if X happens, or if we respond in certain way Y, or certain way Z?

Fig. 1 below shows the social phenomena of complexity, uncertainty, and uncertainty responses, as well as their relations and potential outcomes. A general takeaway from the illustration in the figure is that complexity gives rise to uncertainty (Howell et al., 2010), and uncertainty in turn calls for some kind of uncertainty response. However, as the double-ended arrow between uncertainty and uncertainty response also indicates, we should not necessarily assume that uncertainty responses will lead to certainty or uncertainty reduction. That is, it will sometimes be the case that uncertainty responses lead to further, typically unintended, uncertainty.

Fig. 1. 
Complexity-Uncertainty Model.

Fig. 1.

Complexity-Uncertainty Model.

Multiple Interacting Components

Looking at the conditions in the field of development aid, we find that the first characteristic trait of complex systems, that of multiple interacting components, is unquestionably fulfilled. As insightfully stated by Ramalingam (2013, p. 5):

Today, we are dealing with what has been called a “many-to-many” world of aid. There are more agencies using more money and more frameworks to deliver more projects in more countries with more partners employing more staff specializing in more disciplines. The relationships and interdependencies between existing and new organizations have increased and so have the pathways and channels through which aid resources can flow.

The theoretical complexity condition of multiple interacting components (here, organizations) is also exemplified in a quote from one of our informants from the Pan-African Network for Economic Analysis of Policies (PANAP). The informant explains the organization's role in the Pesticide Action Network (PAN) relating to the “Towards a non-toxic South-East Asia” program (financed by Sida and coordinated by the Swedish Chemicals Agency (KEMI):

So we’re an organization that basically does mostly advocacy and campaigning, and through our partners we support farmers and communities to do agroecology work. So we’re a network, a very dynamic network, constituted of 400 partners in the Asia-Pacific region. […] We’ve also been active in various advocacy platforms through the UN chemicals framework, the BRS conventions, the Basel, Rotterdam and Stockholm conventions, and through the FAO JMPM, which is the joint pesticide management.

There are not only large numbers of organizations involved in the interlinked vast, transnational systems of aid delivery. Among them, there are also many different kinds of organizations such as nongovernmental organizations (NGOs), international development organizations (e.g., UN bodies), national governments, private and public companies, research organizations, and philanthropic organizations. Considering the multitude of organizations involved, and despite the efforts of international relations research to grapple with it, we find it unfortunate that many studies on development aid management continue to embrace a seemingly rationalistic perspective. For example, in the previous literature, one commonly finds the development aid system and its relations described in terms of decision-making “chains,” or “channels” of linear “principal-agent” relationships (Dietrich, 2021; Gulrajani, 2015; Wallace et al., 2007). Many studies also write about “donor–recipient” relations in an overly simplified way, like when Swedlund (2017) concludes that “it takes two to tango the development dance” (see also Edgren 2003; Ferrin et al., 2008). The “postaid” literature has criticized this simplified view of donors and recipients, and uses instead terms like “providers” and “partners.” The typical view taken in the current aid narratives is, moreover, that effective aid operations should be undertaken in an “equator-less landscape of multistakeholder global partnerships” (Eyben & Savage, 2013, p. 457) populated by “old” public aid donors from the North and “new” actors such as aid donors from the South, from civil society, and the private sector (Gulrajani, 2022; Taggart, 2022).

As we discuss in more detail in Chapter 3, however, both the “tango-for-two” and the “chain or channel” metaphors, along with the idea of “multistakeholder partnerships,” are misleading, firstly, because most recipients (or “partners”) are donors too and most donors (or “providers”) are recipients too, and secondly because, as rightly described in the “post-aid” literature, it takes many more than two organizations for most aid operations to materialize. Here, we also note that, in addition to the numerous organizations involved in multistakeholder partnerships, we must not forget the influence of a large number of horizontal relations. Many of these are market relations between, on one hand, aid organizations and external service providers such as management consultants, auditors, and legal experts on the other.

Some researchers argue that we live in an audit society (Power, 1997), or a performance measurement society (Bowerman et al., 2000), where effectiveness is valued according to measurable outputs. On the supply side, we find a growing number of actors in the fields of auditing and control (Gustafsson & Tamm Hallström, 2013, 2014) as well as evaluators and consultants. In the field of development aid, they work, for example, to improve developing countries' monitoring and evaluation systems (Hoey, 2015). The growing institutional demands on control and reporting affect how development aid is organized, creating new conditions for aid projects and programs. Later in the book, in Chapter 6, we take a closer look at what some of these external service providers and experts are selling, and how the content and rituals of their market deals contribute to perceptions of certainty in the system, but also to the perception of uncertainty. Thus, to keep to a dance metaphor, we could say that development aid takes a full dance floor of partners and perhaps an intricate group folkdance with a lot of twists and turns all around the floor would be a more fitting comparison than a lonely tango for two. For the outsider, it is often difficult to tell who is dancing with whom and who is taking the lead since this changes continuously with the flow of the music.

Fluid Boundaries

If we zoom out and acknowledge the horizontal market relations and the full impact of the wider institutional field (politicians, the media, etc.), we find that the field of development aid is indeed a system with fluid boundaries, the second characteristic trait of complex systems (Rutter et al., 2020). In fact, the complex system of development aid resembles more a dynamic network than, as the popular metaphors used in the field would suggest, a set of static funding “chains” or “channels” (Alexius & Vähämäki, 2020). As will be discussed in more detail in Chapter 3, we argue that the conventional way of perceiving some organizations in the aid system as “donors,” others as “recipients,” while a third group are described as “intermediaries,” is misleading since this neither captures the nature and dynamics of the relationships nor their outcomes (Alexius & Vähämäki, 2020). Rather, in the case studies recounted in this book, we found that most organizations involved in development aid act as both donors and recipients, many of them switching back and forth between the two roles daily (see Fig. 2 below and Chapter 3). Based on our findings, we argue that the social roles and institutionalized behavioral scripts and norms tied to these roles are also key to understanding the dynamics of the aid system, including the various responses and outcomes seen in the system (Dietrich, 2021).

Fig. 2. 
Schematic Representation of the Dynamic Web, With Examples of Its Vertical and Horizontal Relations.

Fig. 2.

Schematic Representation of the Dynamic Web, With Examples of Its Vertical and Horizontal Relations.

For aid bureaucrats and their organizations, the many and diverse parties involved and fluid boundaries of the system obscure clarity and give rise to confusion as to who is involved, who does what, and what (if any) coordination is taking place. As one interviewee from a study on the mediatization of development aid expressed it (Grafström & Windell, 2019, p. 23):

Development aid is spread out around the world. It is allocated to numerous, thousands of actors, and it is exceedingly difficult to-, well, quite simply just to see how it’s done.

Conflicts of interest are also common in the aid field and may bring about other challenges, such as inertia in decision-making (Alexius & Furusten, 2023). The boundary-spanning, fluid nature of grand societal challenges may also bring about confusion, power struggles, and conflicts about interdependencies and the allocation of responsibility among the many parties involved (Alexius, 2017). Are there inefficient overlaps or troublesome responsibility gaps, for example?

In addition to the great multitude of organizations involved, most of them now face expectations from their increasingly pluralistic institutional environments to take on a growing number of additional tasks and responsibilities, resulting in a broader and more fragmented mission for all (Alexius, 2021; Bromley & Meyer, 2015; Pache & Santos, 2010; Thornton & Ocasio, 2008). Digitalization and IT security, environmental concerns, and human and animal rights are just a few examples on this growing list of expectations on today's modern organizations. Among the drivers of this development, we find globalization in general and the many “institutional actors” – often meta-organized interest organizations – that see it as their mission to formulate and package norms and to spread them to other organizations by way of “institutional products” such as standards and certifications, rankings, and prizes (Brunsson & Jacobsson, 2000; Furusten, 2023). In addition to this rapidly spinning machinery of norms (which is fueled just as much by market incentives and profits as by benevolence and dreams of a better world), it is also clear that we face a number of critical and urgent grand societal challenges, such as pandemics and climate change. Examples of grand challenges that are expected to be top of mind for today's aid bureaucrats and their organizations include aggravated global instability, hunger, extreme poverty, fragility, and geopolitical shifts, along with climate change and pandemic preparedness and mitigation.

The growing literature on how society might respond to grand societal challenges (Ferraro et al., 2015; George et al., 2016) reflects an increasing interest in various forms of logic- and boundary-spanning organizing and, not least, collaboration among organizations from different societal sectors (Alexius & Furusten 2019; Brès et al., 2019; Gümüsay et al., 2022; Mair & Rathert, 2021). A case in point is the popular, hopeful “Collaborate more!” slogan that appears in contemporary policy debates and studies on climate change. In following with the ambitious intentions of the 17 Sustainable Development Goals (SDGs) of UN Agenda 2030, and specifically Goal 17 on partnerships for sustainable development, the expressed ideal of “multistakeholder collaboration” is typically motivated with reference to the complex and uncertain nature of this grand societal challenge (Brammer et al., 2019; Christensen & Lægreid, 2007; Dietz et al., 2003; Ferraro et al., 2015; George et al., 2016; Steurer, 2011). What collaboration actually means in practice, however, is rarely problematized – it is more often assumed valuable a priori (Greenwood & Freeman, 2017; Schreyögg & Sydow, 2010).

At the interorganizational project level, uncertainties are commonly found to derive from changes in strategy or in the setup of internal or external partners (Zheng & de Carvalho, 2016; Lechler et al., 2012; Migilinskas & Ustinovicius, 2008). Along the same lines, Knobloch and Solomon (1999) identified four sources of what they call “relational uncertainty” (see list below). As organization scholars, we note that most of these uncertainties can be linked to fundamental elements of organizing – members, decision-making procedures, rules, monitoring, and sanctions – as explained below:

The sources of relational uncertainty according to Knobloch and Solomon (1999) and how they link to fundamental organizational elements:

  • How is the relationship defined? (In essence, uncertainties concerning who the members are and what decision-making procedures will be used among them.)

  • What are the goals, future plans, and commitments of the relationship?

  • What are the norms that apply to the relationship? (In essence, uncertainties about the applicable rules.)

  • What are the ways in which the relationship is evaluated? (In essence, uncertainties about monitoring and sanctions, such as costs and rewards, that apply to the relationship.)

Unpredictable Dynamics

As will be discussed in detail in Chapter 4, it is also the case in development aid that a fixed input seldom, if ever, has a fixed, predictable output (Rutter et al., 2020). Rather, in terms of unpredictable dynamics – the third characteristic trait of a complex system – there is tension, ambiguity, and paradox all-around concerning both the nature and the development of the kinds of profound societal problems aid organizations work to mitigate (ibid). As another informant from Grafström's study on the mediatization of aid organizations explains (Grafström & Windell, 2019, p. 31):

Development aid work is a very complex process, that is dependent on what the history is, what institutions there are, what social customs, what politics, and all kinds of other aspects. So, the belief that we can design projects so that they can move forward in a straight line… They don’t […] Things do go forward, but very haltingly, with a lot of backlash.

The unpredictable dynamics of how a complex system will develop in the future therefore calls decision-makers to exercise judgment and flexibility since wicked problems can be neither neatly solved nor easily implemented by rational planning (Pressman & Wildavsky, 1973/1984; Rittel & Webber, 1973). In fact, research on decision-making and reform has found that decision-making is rarely rational (Brunsson, 1985; March & Simon, 1958) and, even when ambitious decisions are made, it is often uncertain whether they will be implemented as intended (Brunsson, 1989; Czarniawska & Sevón, 1996; Meyer & Rowan, 1977; Røvik, 2000). As one of our informants, a project leader at the Food and Agriculture Organization of the United Nations (FAO) in Cambodia put it:

In this area in particular, you need to have a lot of flexibility, because you get a lot of things popping up unexpectedly.

Uncertainties of state generally occur when there is a failure to understand how components of a complex environment are changing. In the development aid system, some examples of externally derived uncertainties of state relate to political and economic stability (or instability) in certain sectors, regions, and countries. (According to recent figures, 72% of the world's populations −5.7 billion people live in autocracies by 2022 (V-Dem, 2023). Uncertainties of state can also include changes in local infrastructure and nature, including natural disasters of various types (Kolltveit et al., 2004). With the ultimate objective of reducing poverty and inequality in the world, balancing power relations is yet another key challenge that poses considerable variation and unpredictable dynamics for organizers. While the power of an organization in the donor role may seem obvious, as demonstrated by its resources and funding decisions, the power of aid organizations that operate closer to the ground typically lies in their domain-specific knowledge about the local context (Pomerantz, 2004). Hence, although organizations in the donor role are traditionally seen as more powerful than those in the recipient role, in practice, different types of power play out and combine differently at different times, often in unpredictable ways.

For the aid system as a whole and promises made in international commitments like the 2030 Agenda for Sustainable Development, an uncertainty of state lies in the question of whether funding for the agenda projects will actually materialize. Despite there being a collective funding commitment of 0.7% of contributing nations' gross domestic product (GDP), resources allocated to development aid are shrinking. In fact, according to the Organization for Economic Cooperation and Development (OECD), in 2021, aid organizations in the donor role paid out less than a half of the funds promised (OECD, 2023). For organizations in the recipient role, the unpredictability of whether aid funds will actually be delivered causes many difficulties and is a costly uncertainty since planning for projects and programs might be done in vain. A typical problem in development aid is moreover that, even when agreed, funds are often not paid out according to schedule. For example, an earlier OECD Development Assistance Committee (DAC) survey showed that, on average, only 45% of aid is delivered on schedule (OECD, 2009). For organizations in the recipient role, this uncertainty of state (Will the money arrive?) can also lead to uncertainty of response, i.e., uncertainty about what the appropriate or best available response can be. Typical questions spurred by uncertainty of response include the following: “Should we start implementing the project and count on the money coming in retroactively?,” “Should we try to secure funding from other sources?,” or “Should we just assume that the money will not arrive and plan for a phase out?”

As a third kind of uncertainty (in addition to the uncertainties of state and the uncertainty of response), uncertainties of effect generally arise in situations where predicting the effect of a future state is not possible. 1 In development aid, decision-makers not only face challenges imposed by social, cultural, and geographical distances but must also handle uncertainties of effect when aiming for results in the unpredictable future, often several decades down the road (Andrews et al., 2017). This means that they don't always know if the response to a problem has been effective or not. At both the organizational level and project level, uncertainties of effect may, for example, derive from organizational resistance, lack of continuity or persistence as the project unfolds (Hong Zheng & Monteiro de Carvalho, 2016; Lechler et al., 2012; Migilinskas & Ustinovicius, 2008). With regard to the situation of unpredictability in funding, an organization in the recipient role might wonder: If we begin to implement now, and money does not arrive as expected – what will happen to the project? Or: What will happen if we receive funding from another source? Will we need to pay that back if the initial funding comes through?

To sum up, under conditions of high complexity, uncontested facts that are trustworthy, definite, transferable, and predictable are elusive (Greenhalgh & Papoutsi, 2018), and decisions must often be made in an uncertain state where information is typically scarce, contested, and/or flawed in different ways (Wolpert & Rutter, 2018).

Responding to Uncertainty

Looking at the broader, cross-disciplinary literature on responses to uncertainty, the first takeaway is that uncertainty constitutes a powerful stressor (Greco & Roger, 2001). Although uncertainty responses may range from passive to more active and interactive strategies, we can also conclude that active strategies aimed at reducing uncertainty have by far been the dominant focus in previous literature (Berger, 1979; Berger & Bradac, 1982). It is hence crucial to note from the outset that uncertainty is seldom accepted in modern, Western decision-making contexts. 2 Even in highly complex settings such as that of development aid, uncertainty is – at least officially – collectively frowned upon and treated as a problem to be solved (Cyert & March, 1963; Meyer & Rowan, 1977; Pfeffer & Salancik, 2003).

On a basic level, uncertainty reduction centers on attempts to make sense of something, either proactively or retroactively (Berger & Calabrese, 1975). Uncertainty reduction also often relates to our ability to predict what will happen in the future, as well as to understand the relationship between inputs and outputs (Williams, 2005). For example: What is the best response with respect to enabling continued aid that promotes equality and human rights in a country where a fundamentalist religious regime has recently taken office? Or, in cases where corruption has already occurred on numerous occasions, should decision-makers continue with an approach that favors local ownership and capacity-building, or would a stricter response that emphasizes control be a better option?

Since rationality is such a widespread ideal and virtue, not least in secular societies, control gained through rule-following and rational decision-making procedures is simply comme il faut. For example, questions like “What is the likelihood that something unexpected will happen?” and “How much will it matter if it does?” are expected to be answered clearly and promptly, typically following administrative ceremonies where the probability and consequences of different unknown future scenarios and outcomes have been estimated and decided on (Zeng & de Carvalho, 2016).

As mentioned in the introduction to the chapter, the double-ended arrow between uncertainty and uncertainty responses in Fig. 1 reminds us to not assume a priori that all responses will have the intended effect. We simply cannot know for sure whether a response will decrease the uncertainty at hand, as is usually the intention. In fact, rather than decrease it, some responses may actually increase uncertainty instead.

A basic distinction between different uncertainty responses is that some are oriented to the cause or source of the uncertainty and others target its effects. 3 Most of the uncertainty-reducing responses identified in previous literature are approach-oriented responses, meaning that individuals (at times on behalf of organizations) try to address and reduce the uncertainties at hand by doing things. Examples of such “doings” include planning, suppression of competing activities to focus solely on the uncertainty, and seeking support from others.

People may also work on their emotions to feel differently about the uncertain situation at hand. Emotion-focused responses include positive reinterpretation, acceptance or denial, turning to religion, and seeking sympathy from others (Kåver, 2004; Vazard, 2022). It is also a general takeaway that putting our trust in someone or something helps to calm our minds, thereby reducing our perception of uncertainty. This undoubtedly applies not only in social interactions with people we know but also when we interact in large complex systems where we are strangers to one another (Gambetta, 1988). Thus, in uncertain settings, trust can serve as a valuable substitute for the much sought-after certainty, and consequently, trust-enhancing efforts are a common emotion-focused response to uncertainty (Alexius & Vähämäki, 2020).

That said, depending on the setting and its institutionalized expectations regarding behavior, responses that focus on emotion and trust may not be accepted as legitimate. From an early age, we are taught that there is a difference between feeling certain and being certain and, despite a growing body of research on the rational aspects and outcomes of emotions (Lodge & Taber, 2013; Oscarsson, 2022), in many modern contexts, feelings are still officially frowned upon as grounds for decision-making. The institutionalized expectation on organizations and individuals in our Western culture to attempt to respond to uncertainty with doings rather than emotions may contribute to explain why many trust- and emotion-focused strategies, including acceptance, remain relatively understudied in the governance and management literature. 4 As will be argued and demonstrated throughout the empirical chapters of this book, this seems also to hold true for development aid relations, where doing something differently to cope with uncertainty is clearly expected, and formal control is seen as superior to trust- and emotion-oriented responses.

Looking specifically at the development aid sector, Riddell (2007) argues that when asked whether aid leads to results, i.e., a question with a highly uncertain answer, the answers given by aid organizations in the donor role typically fall into one of three types of approaches: (1) attempts to convince the public that some aid does indeed work and produce results, (2) attempts to convince the public that steps are being taken to enhance the future impact of aid while trying to reduce the number of cases where aid does not or has not worked well in the past, or (3) attempts to nurture, extend, and deepen the support for aid, acknowledging that a significant part of aid is ineffective and openly sharing knowledge about its evident failures as well as successes. The latter approach – to admit that aid is complex and sometimes ineffective, and that some failure is inevitable – is a response that Riddell (2007, p. 115) claims “has been avoided almost entirely.” Again, this suggests that when it comes to the field of development aid, it seems difficult, if not impossible or unacceptable, to accept uncertainty. Rather, in this field, uncertainty must be acted upon.

Anticorruption measures taken in development aid make up a clear and typical example of how aid organizations and their bureaucrats respond actively to uncertainty. As an example, the anticorruption regulation of Sida, Sweden's public development aid agency, for example, states that corruption represents a serious hinder to development and is incompatible with the objective of development cooperation. When it comes to corruption, staff should “Never accept! Always act! Always inform!” (Sida, 2004). Here, corruption, in the sense of obtaining an improper gain, is viewed in broad terms, where gains may be of a financial or nonfinancial nature. The Sida regulation also states that the risks associated with interpersonal trust are often linked to proximity as they involve “people close to me, my workplace, my political party or my village” (Sida, 2004, p. 7). The norms are clear: organizations in the donor role are criticized for having had too culture-relative a view on corruption and for covering up mistakes while neglecting to take proper action. 5

Increased measures targeting corruption and nepotism are understandable when considering the consequences for an agency such as Sida in the wake of a corruption scandal like the one in the health sector in Zambia in 2009. In that case, the embezzlement scandal originated in the Zambian Ministry of Health and involved close to SEK 50 million (about 10% of which, i.e., SEK 5 million, came from Sweden) that disappeared over the period January 2008 to May 2009 (Sundström, 2022). Despite the fact that it was a whistleblower within the Zambian ministry itself who broke the scandal, and that it was Swedish aid that had supported establishment of the whistleblower system, the Swedish Minister for Development Aid published an opinion piece in an online news platform in Sweden in the aftermath of the scandal (Carlsson, 2009) in which she argued that this type of corruption could be happening in all aid projects, and that it was only a coincidence that it had been detected. The harsh critique of Sida's handling of the matter was accompanied by severe cuts to the agency's budget, along with other restrictions such as new recommendations for reducing interpersonal relations in aid management (Sundström, 2022).

Trust Transference From Impersonal Sources of Trust

How trust is created and maintained is commonly analyzed as a process taking place in-between two persons. In so-called interorganizational trust processes, it is true that there are two organizations center stage, yet, as discussed in Chapter 3, these organizations – the legal persons – are in turn represented by physical persons. In the aid field, it is typically an organization in the donor role that is the trustor who, assisted by its bureaucrats, makes decisions aimed at assessing the ability, benevolence, and integrity of an organization in the recipient role, the trustee (Mayer et al., 1995). As argued by Mollering (2006), although there are many similarities to interpersonal trust processes, there tend to be a range of additional and impersonal factors assessed in interorganizational trust processes. Our previous studies (Alexius & Vähämäki, 2020) suggest that the greater the distance (physical and cultural distance) between the parties, the more likely it is that there will be trust transference from impersonal sources of trust, such as bureaucratic procedures and routines, general management technologies, and organizational structures or processes. But why is this?

In the highly complex and uncertain world of development cooperation, it would be fair to assume that interpersonal trust is the “glue” that holds the complex relationships together (see also Eyben, 2010; McGillivray et al., 2012; Pomerantz, 2004; Swedlund, 2017). In all of our case studies (Alexius & Vähämäki, 2020), aid bureaucrats in the recipient role mentioned good personal relations with aid bureaucrats representing the organization in the donor role as a success factor for aid projects. Yet, in line with findings of Eyben (2010), we found that there is a tendency, particularly among aid bureaucrats that represent the organization in the donor role, at least officially, to downplay or hide these interpersonal relations and their role in governance. When asked how they cope with uncertainty, aid bureaucrats in a donor role seldom mention key individuals as sources of trust. We suggest that this hesitation is due to several factors. It is true that the large distances, many parties involved, long-term investments, different cultures, and complex dependencies that characterize the field make it difficult for aid organizations to demonstrate that the funding is useful (Korsgaard et al., 2015). But these are not the only factors. In addition, a specific fear of corruption and nepotism, and a general fear of media scandals related to the taxpayers' money being wasted, also present aid bureaucrats with a challenge: a great need for trust in a situation where the conditions for and acceptance of interpersonal trust are limited (Alexius & Vähämäki, 2020).

We suggest that the donor's hesitation both to mention and to actually lean on personal relations stems mainly from concerns related to legitimacy and, more specifically, to the dreaded extremes of pragmatism – the risk of scams such as corruption or nepotism (see Chapter 8). The institutionalized ideal has it that donor representatives must not be naïve and “over-trusting” (Laroche et al., 2019). Large sums of taxpayer money are in circulation, and high demands are placed on independence, feedback, and corruption control. Due to the high external pressure on the aid organizations and their professionals to ensure that money flows to the right hands – there is a shared fear of media scandals in the increasingly mediatized aid field (Grafström & Windell, 2019). As a whole, this helps to explain why openly visible instances of interpersonal trust may contribute to increased levels of uncertainty, rather than reducing it. Faced with challenges to interpersonal trust, it is hence not surprising to find that the bureaucrats, pragmatically, look beyond interpersonal trust for alternative sources of trust that they can tap into. In the following, we introduce a pair of key concepts in this regard – trust transference (Bachmann et al., 2015) and sources of trust – which help to clarify how trust can be transferred from impersonal sources (e.g., from credible third-party actors, management tools and technologies, and organizational structures and processes) with the aim of making a trustee more trustworthy.

The concept of trust transference was first established by Bachmann et al. (2015) to describe how trust can be transferred from a credible third party who acts as a “go-between” in a new relationship. When the trustor (e.g., an aid organization in the donor role) assesses the trustworthiness of a trustee (e.g., an aid organization in the recipient role), trust in the trustee expressed by another trustor may be transferred into the new relationship. This can occur, for example, when a donor's trustworthiness assessment of a new recipient is elevated by the recipient already having received financial support from other donors, or when respected consultancy firms are involved, indicating that others have already assessed and helped to “qualify” the potential recipient organization as trustworthy. In a similar fashion to that described by Bachmann et al. (2015), we found that aid bureaucrats use knowledge of potential recipients' previous and current trustful relations to third-party actors in their organizations' trust assessment decisions (see also Chapter 6). Previous relations with legitimate expert organizations such as management consultancies or auditing firms are commonly referred to in processes of trust transference (Busco et al., 2006). But our data also give us reason to broaden the use of the concept of trust transference to include a range of management technologies like quality standards and project management methods.

Fig. 3. 
Trust Transference From Impersonal Sources of Trust (Based on Alexius & Vähämäki, 2020).

Fig. 3.

Trust Transference From Impersonal Sources of Trust (Based on Alexius & Vähämäki, 2020).

Fig. 3 illustrates how the trust process is typically described (situation A), where the trustor assesses the trustworthiness of the trustee and whereby it places its trust on the trustee. Situation B describes a situation where the trustor places its trust on impersonal sources of trust which transfer trust to the trustee. In these cases, trust may, for example, be transferred from third-party assessments of the trustee organization or from generally accepted management technologies to enhance the trustee's trustworthiness. An example is due to illustrate how this may happen. Following the 9/11 terrorist attacks in the United States, new airport security regulations and procedures were added with the aim of reducing and controlling the source of uncertainty, and hence the risk of another terrorist attack occurring in the air. However, for ordinary travelers who stood in longer lines, removed their shoes, and packed their toiletries in see-through plastic bags – how did the additional measures affect their perception of uncertainty? Since it is fair to assume that most travelers do not have the knowledge or data required to determine whether or not the taking off of shoes or use of clear plastic bags actually affect the source of the uncertainty and reduces the risk of another attack, it largely comes down to a question of systems trust (Giddens, 1990). If travelers have trust in the complex airport system with its organizations and experts, it is likely that the extra security procedures will indeed lower the travelers' perceived uncertainty of a terrorist attack occurring.

Trusting in someone or something calms our minds and reduces our perception of uncertainty. In uncertain settings, trust can serve as a substitute for certainty. If, however, the said travelers removing their shoes etc. do not trust the airport system's organization and experts and wonder whether the extra security measures are really that efficient rather than just a waste time and money, then the perceived uncertainty levels will not be reduced. In fact, they may even rise. Summing up what we can learn from the airport example, in a complex and uncertain setting, the prevalence of trust is often key to whether or not uncertainty-reducing responses have an effect on perceptions of uncertainty. When a gap remains between what we wish we knew and what we actually do know and are able to predict and control, trust is commonly used to attempt to bridge this gap. And as will be elaborated on in several of the chapters to come, the means of the approach-based responses to uncertainty (management standards, measurements, etc.) can also be referred to in emotion-based responses as impersonal sources of trust. The nature of these social processes will be the focus of the upcoming chapters of this volume.


An extreme example here would be unpredictable uncertainties (also called “unknown unknowns”) whose influences on effects are not possible to even identify beforehand (Pich et al., 2002).


One exception being decision-makers whose very goal is to increase uncertainty (e.g., terrorist groups).


For instance, whereas some approaches to a risk of fire involve trying to eliminate the source of the risk (e.g., installing fire-proof materials), others involve reducing unfortunate consequences or the likelihood they will occur (e.g., installing fire alarms to minimize the consequences in the event of a fire).


In other strands of literature, this stance is discussed and portrayed as either problematic (in, e.g., sociological studies on socioeconomic inequality and oppression) or as a solution (in, e.g., literature on meditation and mindfulness).


Along the same lines, Hope (2001) argues that, in Africa, the main motivating and driving factors of new public management (NPM) reforms were bureaucratic corruption, dysfunctional governance systems, and fiscal crises.