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THE  GENERATIVE  MECHANISMS  OF  DIGITAL  INFRASTRUCTURE  EVOLUTION  

(Accepted for publication in the special issue of MIS Quarterly on “Critical Realism in Information Systems Research)

Ola Henfridsson Chalmers University of Technology & University of Oslo

E-mail: ola.henfridsson@me.com

Bendik Bygstad Norwegian School of IT & University of Oslo

E-mail: bendik.bygstad@nith.no

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THE  GENERATIVE  MECHANISMS  OF  DIGITAL  INFRASTRUCTURE  EVOLUTION  

 Acknowledgements We thank Carsten Sørensen, Olga Volkoff, the two anonymous reviewers, the associate editor, and the senior editors for constructive and insightful comments on earlier versions of this manuscript. Special thanks to Ole Hanseth for intellectual support in designing and crafting this research. We are also thankful for the feedback received when presenting our work to researchers in the global infrastructure group at Department of Informatics, University of Oslo. Finally, we thank Hrafnhildur Jonasdottir for assistance in coding the sample of digital infrastructure cases used in this research.

Author biographies Ola Henfridsson is a Professor of Applied Information Technology at Chalmers University of Technology, Sweden, and an Adjunct Professor at Department of Informatics, University of Oslo, Norway. His research interests include digital innovation and technology management. Professor Henfridsson’s research has been published in Information Systems Research, MIS Quarterly, and other journals in the information systems discipline. He is a Senior Editor of Journal of Information Technology and a Senior Editor emeritus of the MIS Quarterly. He also serves on the editorial boards of Information Technology and People and Journal of the Association for Information Systems. Bendik Bygstad is a sociologist who is currently a Professor at the Norwegian School of Information Technology, and Adjunct Professor at the University of Oslo. His main research interests are IT-based service innovation and the relationship of IS and organizational change. He is also interested in IS research methods, in particular the philosophical and methodological implications of Critical Realism. He has published articles in journals such as Information Systems Journal, Journal of Information Technology and International Journal of Project Management.      

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THE  GENERATIVE  MECHANISMS  OF  DIGITAL  INFRASTRUCTURE  EVOLUTION  

Abstract. The received literature on digital infrastructure offers powerful lenses for conceptualizing the increasingly inter-connected information system collectives found in contemporary organizations. However, little attention has been paid to the generative mechanisms of digital infrastructure, that is, the causal powers that explain how and why such infrastructure evolves over time. This is unfortunate, since more knowledge about what drives digital infrastructures would be highly valuable for managers and IT professionals confronted by the complexity of managing them. To this end, this paper adopts a critical realist view for developing a configurational perspective of infrastructure evolution. Our theorizing draws on a multi-method research design comprising an in-depth case study and a case survey. The in-depth case study at a Scandinavian airline distinguishes three key mechanisms of digital infrastructure evolution: adoption, innovation, and scaling. The case survey research of 41 cases of digital infrastructure then identifies and analyzes causal paths through which configurations of these mechanisms lead to successful evolution outcomes. Our study contributes to the infrastructure literature in two ways. First, we identify three generative mechanisms of digital infrastructure and how they contingently lead to evolution outcomes. Second, we use these mechanisms as a basis for developing a configurational perspective that advances current knowledge about why some digital infrastructures evolve successfully while others do not. In addition, the paper demonstrates and discusses the efficacy of critical realism as a philosophical tradition for developing substantive contributions in the field of information systems.

Keywords: Digital infrastructure, case study, case survey, critical realism, generative mechanism,

information infrastructure, multi-method, adoption, innovation, scaling.

INTRODUCTION  

No idea in our field is more enduring than the notion that the introduction of a new IT-based

information system improves the possibility of effectively managing an enterprise. Yet, it is becoming

increasingly recognized that the pervasive adoption and use of information technology in

contemporary organizations makes the relationship between information systems and organization

increasingly complex (Zammuto et al. 2007). As information systems become interconnected, most

organizations face the challenges of controlling an entire array of systems and technologies, typically

introduced over many years and for different purposes (Ciborra et al. 2000). As a result, the

effectiveness of the single system is largely conditioned by an installed base of extant socio-technical

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arrangements, making it useful to apply modes of inquiry that offer a conceptual basis for going

beyond the scope of the single system.

To understand this phenomenon, there is an emerging literature that has adopted the notion of

infrastructure as a way of conceptualizing interconnected system collectives (rather than stand-alone

information systems). In fact, the past 15 years or so have witnessed research on digital infrastructure1

covering different settings (e.g., health, telecom, natural resources, government, and manufacturing),

levels of analysis (e.g., group, organization, industry, and society), and technologies (e.g., standards,

platforms, and the Internet). Yet, as highlighted in Tilson et al.’s (2010) recent research commentary,

there is an urgent need to theorize the evolution of digital infrastructures as our “field’s attention

moves beyond administrative systems and individual tools” (p. 748). Such a need calls for approaches

with the ambition to explicate the inner workings of digital infrastructure. It also indicates the

usefulness of a philosophical tradition that would build the intellectual structure for such explanation.

Covering the two main philosophical traditions in IS research (Mingers 2004), typically referred

to as positivism and interpretivism2, extant infrastructure research displays slightly different foci. It

tends to be occupied with either situated contexts of practice, or directly observable managerial

aspects. Adhering to interpretivism, considerable attention has been paid to the evolution of digital

infrastructure as it plays out in the complex interdependencies between socio-technical elements (Braa

et al. 2007); networks of human and non-human actors (Hanseth and Monteiro 1997); and the

relationships between organized practices (Star and Ruhleder 1996). In studies underpinned by

positivist assumptions, the research has primarily dealt with strategic IT portfolio management and the

alignment of IT imperatives with business strategy (Broadbent and Weill 1997).

As an alternative intellectual structure for theorizing digital infrastructure, we propose critical

realism (Archer et al. 1998, Bhaskar 1997, Sayer 1992) for its emphasis on generative mechanisms

1 The received literature uses different concepts for capturing this phenomenon including information infrastructure, IT infrastructure, e-infrastructure, and so on. Following Tilson et al.’s (2010) call for infrastructure research, we use the term “digital infrastructure” throughout this paper. 2 We use the terms “interpretivism” and “positivism” to align our terminology with previous writings on philosophical traditions in the information systems literature (cf. Orlikowski and Baroudi 2001, Walsham 1995). However, it should be emphasized that both labels span over multiple philosophical strands. For instance, as suggested by one of our anonymous reviewers, interpretivism includes both idealists and realists, taking different ontological positions.

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(Bhaskar 1997, 1998). While there are a few studies identifying specific mechanisms of digital

infrastructure (see e.g., Bygstad 2010), little, if any, research has been geared towards developing a

comprehensive understanding of the range and contingencies of causal structures in its evolution.

Understanding these issues is important, since more knowledge about what drives digital

infrastructures would be highly valuable for managers and IT professionals confronted by the

complexity of managing them.

Our paper deals with the following research question: Which mechanisms contingently cause

digital infrastructure evolution? We address this research question using a multi-method research

design (Mingers 2001), recently proposed as an important principle for conducting critical realist case

study research (Wynn and Williams 2012). We first conducted in-depth case study research (Gerring

2007, George and Bennet 2005) at a Scandinavian airline to identify key mechanisms of digital

infrastructure. Then, we conducted case survey research (Larsson 1993) based on a sample of 41 cases

to analyze the causal paths through which these mechanisms are combined to produce successful

digital infrastructure evolution.

Our research makes a number of contributions. First, we identify three generative mechanisms

of digital infrastructure and how they contingently lead to evolution outcomes. Second, we use these

mechanisms as a basis for developing a configurational perspective that describes infrastructure

evolution as an outcome of multiple paths of interconnected contextual conditions and mechanisms (El

Sawy et al. 2010; Meyer et al. 1993; Pawson and Tilley 1997). This perspective advances current

knowledge about why some digital infrastructures evolve successfully while others do not. Lastly, the

paper demonstrates and discusses the efficacy of critical realism as a philosophical tradition for

making substantive contributions in the field of information systems.

RELATED  RESEARCH  AND  CONCEPTUAL  BASIS  

Digital infrastructure evolution can be broadly referred to as a gradual process by which a

digitally-enabled infrastructure changes into a more complex form. Viewing digital infrastructure as

the collection of technological and human components, networks, systems, and processes that

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contribute to the functioning of an information system (Braa et al. 2007; Tilson et al. 2010), this

evolutionary process entails both social and technical elements (Vaast and Walsham 2009). However,

reviewing the literature, it is clear that definitions vary, and, over the years, the concept itself has

largely served as the lowest common denominator for IS researchers who have shifted attention from

single organizations to organizational networks, and from systems to infrastructures (Ciborra et al.

2000).

Views  on  Digital  Infrastructure3  

There exists a relatively large volume of research that uses the notion of infrastructure without

necessarily dealing substantively with the phenomenon itself (Tilson et al. 2010). In such cases,

infrastructure is typically used as an independent variable to explain something else (see e.g.,

Bharadwaj 2000; Malhotra et al. 2005; Rai et al. 2006; Tanverdi et al. 2007), and it often refers to a

collection of information technologies and systems that jointly produce a desired outcome. For

instance, Malhotra et al.’s (2005) study of information sharing between actors in the supply chain

understands IT infrastructure as partner interface-directed information systems that “enable an

enterprise to process information collected from its supply chain partners as to create new knowledge”

(p. 156).

In prior literature where digital infrastructure is at the center of attention, four streams of

research can be distinguished (see Table 1). Three of these streams manifest interpretivist

3 Drawing on Webster and Watson (2002), we conducted a concept-centric literature review of research on digital infrastructure. First, our ambition was to include most, if not all, relevant articles published in AIS basket-of-eight IS journals, science and technology journals, and other journals (e.g., Information & Organization, Information Society, Information Technology & People, the CSCW journal) that were likely to publish infrastructure studies. In addition, we added articles from conference proceedings and books on an ad-hoc basis. We used the Citeseer ABI/Inform research database and the selected articles had the phrases “digital infrastructure”, “information infrastructure”, “information technology infrastructure”, “IT infrastructure”, “information systems infrastructure”, or “IS infrastructure” in the title, abstract, keyword, or body of the publication. From the large number of publications that met this criterion, we briefly read the papers to identify articles with digital infrastructure as an important object of study. We analyzed the selected publications to identify key concepts that were used to characterize the nature of digital infrastructure. These concepts were then clustered with the intention of identifying dominant research streams in the digital infrastructure literature. The identified research streams were then labeled complexity, network, relational, and strategic asset to reflect their main theoretical emphasis.

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assumptions, while the fourth one reflects positivist assumptions4. First, complexity models presume

that there is no single source of digital infrastructure evolution. Grounded in literature on complexity

(Holland 1995; Mol and Law 2002; Urry 2003), such models typically highlight the complexity of

digital infrastructures as a multitude of actors simultaneously enact their own goals. In other words,

infrastructure evolution is seen as the process by which heterogeneous and autonomous human, or

organizational, actors seek to use information technology in their adaptation to each other and their

external environments (Braa et al. 2007; Ciborra and Failla 2000; Hanseth et al. 2006). For instance,

Braa et al. (2007) advances the notion of flexible standardization as a key process for addressing the

complexity of accommodating both global needs of scalability of infrastructure standards and local

needs of sensitivity to contextual differences.

Second, network models assume that networks of human and technical elements drive digital

infrastructure evolution. This stream of research is typically grounded in some of the early writings of

actor-network theorists such as Callon (1986) and Latour (1987). It views infrastructure evolution as a

process by which multiple human actors translate and inscribe their interests into a technology,

creating an evolving network of human and non-human actors (Aanestad and Blegind Jensen 2011;

Hanseth and Monteiro 1997; Yoo et al. 2005). For instance, Hanseth and Monteiro (1997) note how

barriers to end-user involvement were inscribed by human actors involved in the integration of the

EDIFACT standard in Norwegian healthcare. These barriers, the authors claim, were the result of an

actor-network beyond any single stakeholder’s control.

Third, relational models premise that infrastructure should be appreciated through the

sensemaking of its users and stakeholders. This stream of literature has its intellectual basis in theories

on learning and work practices (Engeström 1990; Lave and Wenger 1992). As noted in Star and

Ruhleder’s (1996) seminal article, digital infrastructure is a relational property that becomes

4 Some readers may feel that typifying infrastructure research into families of philosophical traditions such as positivism and interpretivism faces the risk of simplifying research in a substantive area by enforcing grand thought structures on it and its representative scholars. Appreciating this risk, we value the possibility to surface philosophical assumptions and their consequences for theorizing. As suggested by Lee (2004), recognizing philosophical underpinnings “can lead to findings that would help the information systems research community do better information systems research” (p. 18). Also, it should be emphasized that in quite some cases the philosophical tradition is explicitly indicated in the methods section of individual articles and/or in their reference literature.

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meaningful as an element of organized activity. In this regard, infrastructure evolution is seen as a

process by which socio-technical relations emerge from information technology-mediated activities

meaningful in a given community-of-practice (Pipek and Wulf 2009; Star and Ruhleder 1996; Vaast

and Walsham 2009). For instance, Vaast and Walsham (2009) propose a perspective on trans-situated

learning and how such learning is “supported by the local universality of an information infrastructure

whose use becomes embedded with other infrastructures” (p. 547).

Finally, strategic asset models view digital infrastructure evolution as the process by which

managers initiate and implement changes in an organization’s portfolio of systems and tools for

increasing the alignment between its information technology resources and strategic imperatives. In

this regard, a strategic choice view (Beckert 1999; Child 1972; Child 1997) is implied, that is, political

action is given primacy in analyzing organizational responses to environmental contingencies. For

instance, Broadbent et al. (2009) explain how the creation of a certain level of IT infrastructure

capability is needed to successfully implementing business process redesign.

Table 1. Research Streams and Definition

Research Streams

Philosophical tradition

Foundational Literature Definition (of DI evolution)

Example References

Complexity Interpretivist Complexity theory • Holland (1995) • Mol and Law

(2002) • Urry (2003)

The process by which heterogeneous and autonomous human, or organizational, actors seek to use information technology in their adaptation to each other and their external environments.

Braa et al. (2007) Ciborra and Failla (2000) Hanseth et al.(2006)

Network Interpretivist Actor-network theory • Callon (1986) • Latour (1987)

The process by which multiple human actors translate and inscribe their interests into a technology, creating an evolving network of human and non-human actors.

Aanestad and Blegind Jensen (2011) Hanseth and Monteiro (1997) Yoo et al. (2005)

Relational Interpretivist Work practice and learning theory:

• Engeström (1990)

• Lave and Wenger (1992)

The process by which socio-technical relations emerge from IT-mediated activities that become meaningful in a given community-of-

Pipek and Wulf (2009) Star and Ruhleder (1996) Vaast and Walsham (2009)

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practice. Strategic Asset Positivist Strategic choice theory

• Beckert (1999) • Child (1972,

1997)

The process by which managers initiate and implement changes in an organization’s portfolio of systems and tools for increasing the alignment between its IT resources and strategic imperatives.

Broadbent and Weill (1997) Broadbent et al. (1999)

The  Promise  of  Critical  Realism  

In this research, we seek to understand which mechanisms contingently cause the evolution of

digital infrastructure. We argue that the explanatory power of such mechanisms has been masked in

prior research by the adoption of philosophical assumptions inattentive to structures operating beyond

(a) the rich texture of people’s meaning-making of the socio-technical world (interpretivist streams),

or (b) events directly observable in the empirical domain of infrastructures (the positivist stream).

Interpretivism invites attention to actors’ sensemaking and the generation of process accounts on

digital infrastructure evolution. In the relational stream of research (e.g., Pipek and Wulf 2009; Star

and Ruhleder 1996; Vaast and Walsham 2009), for instance, this attention to sensemaking has been

materialized as a focus on the patterned activity that results from situated actors’ interaction and

dealing with technology in their work settings. The positivist assumptions underpinning the strategic

assets stream of research (Broadbent and Weill 1997; Broadbent et al. 1999) imply attention to

characteristics of strategy and portfolio of systems that can be directly observed and measured. This

may lead to overly simplistic assumptions about the relationship between digital infrastructures and

business success. Although divided by two broad, but significantly different, sets of philosophical

assumptions, we argue that the extant literature on digital infrastructure generally tends to shy away

from causality. To pick up on Tilson et al.’s (2010) call for infrastructure theory, we should seek

explanations that take into account both the dynamic character of digital infrastructures and the

contingent causality characterizing their evolution.

We adopt critical realism as an intellectual structure for reconciling existing perspectives of

digital infrastructure. Critical realism has been increasingly recognized in our discipline as a promising

philosophical tradition to overcome objectivism-relativism chasms (Mingers 2004; Smith 2010;

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Volkoff et al. 2007). It combines a realist ontology with an interpretive epistemology (Archer et al.

1998); although our knowledge of the world is socially constructed and fallible, that world exists,

often independent of human beings (Mingers 2004). Contrary to judgmental relativism, critical realism

therefore holds that some theories approximate reality better than others, making methodological

approaches to assess knowledge claims meaningful. In addition, research should not only concern

recording of constant conjunctions of observable events (Bhaskar 1997; Mingers 2004), as suggested

in so-called covering law theories of causality grounded in Hume’s skeptic discussion of causality

(Elder-Vass 2010, Lee 2004).

Generative  Mechanisms  

Following Bhaskar (1997, 1998), we define generative mechanisms as causal structures that

generate observable events. In contrast to Humean causality, critical realists typically ascribe such

structures causal powers (Sayer 1992). Causality is contingent, that is, the outcome of a mechanism

depends on other mechanisms (Elder-Vass 2010; Sayer 1992). In this regard, “they act transfactually.

The event or events that they are the powers to instantiate may never actually be instantiated; the

powers may remain unactualised, yet these powers remain in existence” (Fleetwood 2009, p. 362-

363).5

A generative mechanism is “one of the processes in a concrete system that makes it what it is -

for example, metabolism in cells, interneuronal connections in brains, work in factories and offices,

research in laboratories, and litigation in courts of law” (Bunge 2004, p.182). In this vein, our research

question, about which mechanisms contingently cause the evolution of digital infrastructure, is

partially geared towards defining what constitutes a digital infrastructure. Previous literature reviews

suggest that this is a fundamental issue for furthering research in the area (see Bygstad 2008), not least

by directing attention to the underlying mechanisms that produce observable events.

5 There is an extensive literature on the nature and definition of mechanisms, both in the philosophy of social sciences (Demetriou 2009, Hedstrom 2008, Glennan 2009), and in the critical realist community (Fleetwood 2009, Mingers 2010, Fleetwood 2011). While we relate broadly to this literature, it is beyond the scope of this article to engage fully in this discourse.

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Drawing on Hedström and Swedberg’s (1998) work on mechanisms, we make three

assumptions about mechanisms of digital infrastructure and their generic structure (see Figure 1).

First, digital infrastructure mechanisms are self-reinforcing (Hanseth and Aanestad 2003). A self-

reinforcing mechanism recursively feeds on itself. Since the control of an infrastructure typically is

distributed across multiple actors, infrastructures are – for practical and economic reasons – difficult to

govern. It is partly relying on positive, or negative, feedback loops beyond single stakeholders’ control

(Hanseth and Braa 2000). The phenomenon of self-reinforcement is well-known in technology and

diffusion research (Katz and Shapiro 1985), and has been attributed a central role for understanding

organizational stability and change (Sydow et al. 2009).

Second, digital infrastructure mechanisms are composites. They interconnect three types of

mechanisms: situational mechanisms (macro-micro level), action-formation mechanisms (socio-

technical action), and transformational mechanisms (micro-macro level) (DeLanda 2006; Hedström

and Swedberg 1998). Macro-micro mechanisms explain how the infrastructure as a whole enables and

constrains its various components. For instance, the Internet as an infrastructure has proved to enable

unprecedented innovation possibilities for individual entrepreneurs, as long as they are following its

standard interfaces (Hanseth and Lyytinen 2010; Zittrain 2006). Action-formation mechanisms explain

“how a specific combination of individual desires, beliefs, and action opportunities generate a specific

action” (Hedström and Swedberg 1998, p. 23). Continuing our example, Internet-entrepreneurs in

Silicon Valley display new forms of learning in innovation path creation (Hagel et al. 2010). Micro-

macro mechanisms explain emergent behavior, that is, how different components interact in order to

produce an outcome at a macro level. Completing our example, new innovation path creation leads to

new services and products that reinforce the Internet as a basis for innovative activity.

Third, although most established work on mechanisms only addresses the social (Hedström and

Swedberg 1998; Merton 1967), it goes without saying that a necessary element in digital

infrastructures is technology. Technology plays an active role at both the structural level and the action

level (Volkoff et al. 2007), and the interaction between social and technical elements is the

constituting process of the mechanism.

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Figure 1: The Self-Reinforcing Socio-Technical Mechanism

A  Configurational  Perspective  

Mechanisms are characterized by contingent causality (Elder-Vass 2010, Sayer 1992). The

actualization (or lack of actualization) of the powers of a mechanism may lead to one outcome in a

particular context, and another in a different context (cf. Ragin 2008). This multi-finality (George and

Bennett 2005) largely depends on the existence of other mechanisms in the same context. Such

dependence also indicates that there are multiple causal paths through which a particular outcome can

occur, and suggests that investigations of digital infrastructure should seek to analyze how different

mechanisms are configured and triggered to produce successful outcomes.

A configurational perspective (Pawson and Tilley 1997; El Sawy et al. 2010, Fiss 2007) can be

seen as a viable approach to investigate the causal complexity associated with such equifinality6

(George and Bennett 2005). It allows analysis of possible configurations of mechanisms and relevant

context-variation to explain a particular outcome (Pawson and Tilley 2009). However, it should not be

considered as a covering law (cf. Elder-Vass 2010), but as a conjectural explanation, being the basis of

further refinement.

Using Pawson and Tilley’s (2009) Context-Mechanism-Outcome (CMO) scheme as a basis,

Figure 2 illustrates the starting-point for our configurational analysis, where we derived four possible

6 Equifinality refers to situations where different causal paths may lead to the same outcome (George and Bennett 2005; Fiss 2009; Rihoux and Ragin 2009). Multiple determination is a similar term used by Bhaskar (1977) (see also Elder-Vass 2010).

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configurations of relevant context-variation from the literature and suggested a hypothetical space of

2n configurations of mechanisms that together lead to the same outcome (success7). This

configurational perspective provides the basis for our analysis of the causal paths that explain how, in

certain contexts, a digital infrastructure mechanism (or a combination of mechanisms) may lead to

successful evolution. Our configurational perspective rests on two assumptions. First, we assume that

the key mechanisms of digital infrastructure interact, that is, a mechanism may produce different

outcomes, depending on its actualization in combination with other mechanisms. Second, we buy into

the received wisdom that infrastructures consist of both social and technical elements (Ciborra et al.

2000; Edwards et al. 2009; Vaast and Walsham 2009). Our literature review of contextual conditions

was therefore geared towards covering both types of elements in our configurational perspective. On

the social side, type of control stood out as an important contextual condition (e.g., Ciborra 2000,

Hanseth and Braa 2000, Hanseth et al. 1996, Pipek and Wulf 2009, Rolland and Monteiro 2002, Sahay

and Walsham 2006). In particular, the idea of decentralized control of digital infrastructures (Ciborra

et al. 2000) has been considered a strong alternative to prevailing centralized approaches (cf.

Broadbent and Weill 1997). On the technical side, architecture, in particular loosely-coupled

architectures, has come to the fore as an important condition for infrastructure evolution (Fabri 2010,

Aanestad and Blegind Jensen 2010). Based on the extant infrastructure literature, we therefore propose

that decentralized control and loosely-coupled architecture work as key contextual conditions of

digital infrastructure evolution.

Before presenting our empirical study, we also need to clarify the outcome dimension of our

configurational perspective. Ever since DeLone and McLean’s (1992; see also 2003) seminal article, it

has been generally agreed that IS success is a multi-dimensional construct involving measures such as

system quality, information quality, service quality, organizational impact, and user satisfaction.

However, defining success for digital infrastructures requires consideration of the fundamental

difference between traditional in-house information systems and infrastructures. While the former

typically involves a relatively well-defined evaluation context in terms of objectives and end-user

7 Of course, a similar space of configurations can be created for failures.

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group, the latter refers to open, evolving networks of interconnected systems having many

stakeholders, for whom success may be interpreted differently (Hanseth and Lyytinen

2010). Infrastructures often evolve into larger and more complex structures (such as the Internet),

without any predefined end state as they are continuously extended and typically operate outside the

control of a single stakeholder. It therefore makes sense to relate an infrastructure’s success to its role

in, and fit with, the environment it inhabits. Using a biological metaphor, success can then be seen as a

question of survival in a volatile business ecosystem. In this vein, we view infrastructure evolution

success as an outcome realized when (a) the infrastructure survives in a business ecosystem by filling

a relevant role over time, and (b) the infrastructure’s affordances cannot be escaped endogenously but

is only vulnerable to exogenous shocks (cf. Vergne and Durand 2010).

Figure 2: A Configurational Perspective on Digital Infrastructure

Given this theoretical background, we set out to develop a configurational perspective by using

a multi-method research design (Mingers 2001). We first conducted a 4-year in-depth case study

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(George and Bennett 2005; Gerring 2007) of an airline in order to identify mechanisms with the power

to cause digital infrastructure evolution. We then conducted a case survey (Larsson 1993) to analyze

the causal paths through which these mechanisms are combined to produce successful digital

infrastructure evolution. The next section outlines the in-depth case study.

THE  NORWEGIAN  CASE  STUDY  

Norwegian Corp is an international airline carrier that pioneered the low-price airline market in

Scandinavia. Enabled by the European deregulation of the airline industry, the firm’s strong growth

started in 2002 as it established a national network of destinations. Some nine years later, in 2011,

Norwegian operated a total of 303 routes to 118 destinations in Europe and the Middle East, and

carried 15.7 million passengers. The company had 2500 employees and revenues were $ 1.9 bn. Over

this nine-year period, Norwegian has successfully used information technology in new ways,

cultivating the digital infrastructure that was the focal point of our in-depth research.

Methods  

We selected the case for our in-depth research based on two criteria. First, long-term

involvement and access was deemed important, since studies of evolution benefit from rich

longitudinal data. Such data also resonates well with critical realism’s theorizing process, commonly

referred to as “retroduction”, that is, taking an empirical observation and hypothesizing a mechanism

that might explain that particular outcome (Danermark et al. 2002; Sayer 1992). Second, among cases

to which we had access, we used a so-called extreme technique of case selection by engaging in

intense data collection and analysis of a digital infrastructure perceived to be unusually successful.

Extreme cases correspond “to a case that is considered to be prototypical or paradigmatic of some

phenomenon of interest” (Gerring 2007, P. 101) and is useful for theory generation because extremes

or ideal types typically define theoretical concepts. Compared to a representative case selection

technique, we anticipated that the selection of the airline Norwegian would offer access to ideal types

“formed by the one-sided accentuation of one or more points of view” (Weber, 1949, p. 90). In this

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regard, the case study generated conceptual constructs that manifest theorizing through idealization

(Lopreato and Alston, 1970; Ohlsson and Lehtinen, 1997).

Data Collection: We did not begin our study at Norwegian with the intention of studying the

mechanisms of digital infrastructure evolution (cf. Plowman et al. 2007). It started as an inquiry into

service innovation in a fast-growing firm. As the study unfolded, however, we discovered how

Norwegian’s success in terms of becoming an important player on the Scandinavian aviation market

was intimately related to its evolving infrastructure. We therefore gradually appreciated the process as

a paradigmatic example of digital infrastructure success and reoriented the study to inquire into the

underlying generative mechanisms of this evolution.

We used three methods to collect our data: interviewing, participant observation, and document

analysis. First, we conducted 31 semi-structured interviews with a total of 19 respondents (we

interviewed 6 respondents more than once). Some interviews were tape-recorded and transcribed

verbatim, while others relied on field notes. We interviewed the CIO, two top managers, three

business line managers, two business analysts, two IT managers, two systems developers, two project

managers, one booking assistant, one consultant, one vendor representative, and two customer

representatives. Second, participant observation was another important source of data. In addition to

system demonstrations and observations of direct system use, we spent approximately 12 hours

observing 7 meetings related to Norwegian IT and strategy projects. In addition to these discrete

events, we conducted ongoing debriefings after meetings and numerous informal interviews with

Norwegian employees. Finally, our study included a significant volume of archival data, including

business plans, project plans, joint venture contracts, and IT architecture documents. We also had

access to 18.846 Facebook postings of how Norwegian communicated with their customers during the

European airspace “ash crisis” in 2010. Although many of the documents we collected were

confidential and could not be used directly in this research, the material served to confirm or

disconfirm interpretations made throughout the data analysis process.

Data Analysis: We used a four-step approach (see Table 2) for analyzing the data collected at

Norwegian. First, we used an open coding procedure to discover key events. While many events were

deemed important a priori (e.g., business decisions or contract agreements), others emerged from data

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analysis (e.g. the importance of the IT architecture and the use of Facebook). This coding procedure

helped us establish a timeline of key events that occurred over time (see Figure 3).

Then, in the second step, we identified the objects of the case (cf. Danermark et al. 2002). We

then used Hedström and Swedberg’s (1998) work on mechanisms for typifying the identified objects

as being potential elements in macro-micro, socio-technical action, or micro-macro mechanisms (cf.

DeLanda 2006). We visualized the objects of the Norwegian case as a data display (Miles and

Huberman 1994: see Table 3). The third step was retroduction, that is, the identification of key

mechanisms among candidate mechanisms. We started by analyzing the interplay of objects, in

particular the interplay between social and technical objects, which allowed for the identification of

socio-technical mechanisms. With the infrastructure as point of departure, we tried to identify macro-

micro mechanisms, that is, enabling socio-technical features of the digital infrastructure. Transcripts

from the cases were analyzed in order to identify these enabling conditions. These were tracked

through organizational behavior (socio-technical action) and the self-feeding outcome, that is, how the

emergent behavior at the micro-level increased the capabilities of the infrastructure (micro-macro). At

this point in the analysis we had identified six candidate mechanisms. They were detailed and

assessed through backwards chaining (Pettigrew 1985), going from outcomes to causes (mechanisms),

and through forward-chaining, going from causes to outcomes. In this way, we assessed the

explanatory power of each one of them, in relation to the empirical evidence, and finally arrived at the

three ones (Table 4) that consistently could explain the sequence of events over time.

Lastly, in the fourth step, we analyzed the three selected mechanisms to establish contextual

conditions and outcomes. Whether a mechanism is actualized or not is contingent (Pawson and Tilley

1997). In our analysis at Norwegian, the decentralized control and loosely-coupled architecture of the

digital infrastructure stood out as important conditions of the mechanisms observed. Given that these

empirical results resonated well with our initial assumptions grounded in prior literature, we decided

that mode of control and technical architecture would be important conditions investigated further in

the case survey.

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Table 2. In-Depth Data Analysis Steps   Tasks   Outputs  1.  Coding  key  events   a.  Identify  key  events  in  the  data  

material    b.  Establish  a  timeline  of  the  key  events  

   

A  chronology  of  key  events  of  the  case  (Figure  3)  

2.    Identification  and  typifying  components    

a. Identify  networks  of  social  and  technical  components  

b. Use  Hedström  and  Swedberg  (1998)  for  typifying  components  as  macro-­‐micro,  socio-­‐technical  action,  or  micro-­‐macro  

c. Display  components  and  related  data    

A  set  of  components  and  related  data  (Table  3)  

3.  Retroduction  of  mechanisms  

a.  Investigate  interplay  between  micro  and  macro  elements  to  explain  outcomes  

b.  Identify  and  analyze  candidate  mechanisms.  Assess  explanatory  power  of  each.  

c.  Define  mechanisms  and  develop  measures  to  be  used  in  the  case  survey  

 

Three  digital  infrastructure  mechanisms  (Adoption,  Innovation,  and  Scaling)  including  definitions  and  measures  (Table  4)  

4.  Establish  contexts  and  outcomes  of  mechanisms  

a.  Analyze  mechanisms  to  confirm  contextual  conditions  and  outcomes  

b.  Develop  contextual  typology  

A  typology  of  contextual  conditions  of  mechanisms  over  two  dimensions:  control  and  technical  architecture.      

Case  Findings  

In 2002, a virtually unknown airline, Norwegian Air Shuttle, decided to challenge the dominant

airline on the Scandinavian market, SAS. The deregulation of the European airline market paved the

way for Norwegian’s ambition to outperform the incumbent airline through a low-price strategy.

As a new entrant without legacy, Norwegian started with a IT solution only consisting of the

most necessary systems for making an airline work, such as a basic booking system and a simple back-

office solution. As the company expanded, however, it was soon realized that a professional approach

to IT was imperative. In less than a year, the unknown airline flew more than 300.000 passengers, and

given plans to grow internationally, the IT solution soon turned out ill-dimensioned.

Accordingly, Norwegian hired a CIO and two IT architects with extensive experience from the

airline business. Starting in 2003, this team took a strategic outlook on IT governance. The team

envisioned an IT infrastructure that would need to scale over time to be at par with Norwegian’s

growth ambitions. Based on his earlier experiences of battling legacy systems when pursuing change

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ambitions, the CIO recalled: “We knew exactly what we needed; a service oriented architecture,

enabling easy communication and reuse of components across different technologies. We settled for a

simple, but fast open-source enterprise service bus, which could be scaled up in order to handle

transaction growth. Since we had no legacy systems we could implement the solutions within months,

including the integration with the Amadeus system”.

The new architecture was mainly designed in 2004, and, as outlined below, it gradually

expanded into a large-scale infrastructure over the following years. Figure 3 provides a chronology of

this infrastructure evolution. Reflecting an entrepreneurial culture with little bureaucratic control, each

new service was typically conceptualized in a meeting with business developers and IT seniors, and

then developed and deployed during a short project. “You see”, commented a business director, “we

do not have ‘IT projects’ in Norwegian, only business projects, with a clear economic objective.” As

an example of the agile approach, usability issues were typically not seriously addressed in early

releases. However, after the launch of a new service, use patterns were systematically monitored, and

used as a basis for quick adjustments in an iterative fashion.

Figure 3: Chronology of Key Events

Internetworking with customers. Since travel agent services were considered too expensive

for a low-cost airline, Norwegian’s first challenge was to enable Internet-bookings. In 2004, the airline

launched its Internet portal to by-pass travel agents. This launch was enabled by a solution that

allowed customers to print their boarding cards at home, making the traditional paper ticket obsolete.

The printout tickets included a barcode, which was scanned at the gate. This strategy turned out

20

successful, not least because customers quickly adopted this new type of travel processing. In fact, in

2006, when the airline flew a total of 5.1 million passengers, 90% of all customer interaction,

including email and web marketing, on-line sales, booking, and check-in, was digital.

Capitalizing on the service-oriented architecture, the Internet portal gave birth to another

innovation, the so-called low-price calendar. The low-price calendar provided an overview of the

cheapest flights to any chosen destination. It helped customers adjusting their travel to dates when

traveling was good value for money. Addressing a problem perceived by customers in the low-price

segment, the new innovation was an outstanding success, substantially increasing the number of

bookings after its inception in 2005. The low-price calendar was later copied by many other airlines,

including Norwegian’s competitor SAS.

Branching out. In 2007, the company, somewhat surprisingly, decided to enter banking by

establishing a bank called Bank Norwegian. Motivating this boundary-crossing initiative, Norwegian’s

CEO, stated that: “Today we have one of the most visited web pages in Norway, with 2-3 million

visitors each month. We aim at coupling this traffic with bank services.” In addition to the large

volume of potential customers, this radical diversification could be traced to the architecture of digital

infrastructure, which allowed quick integration with its banking partner’s systems. As the Director of

Business Development commented: “We had established a very flexible IT architecture, and we

realized at the time that it would be possible to innovate new services on this. First we were just

brainstorming rather freely; how could a combination of brand and technology generate new

business?” The establishment of the Internet bank merely took 6 months, and, in 2008, it served

50.000 customers.

Stimulated by the success in banking, the company launched a telecom company, Call

Norwegian. Initially, Call Norwegian involved a mobile portal to enable easy airline booking, and to

offer airport Wi-Fi hotspots and GSM network services. The Mobile Portal Director commented: “We

focused on how to make money on new services, analyzing which services we should provide

ourselves, which we should buy and how they should be integrated. At the same time we are very

concerned about our architecture. A chief ambition is to maintain it as ‘clean’ as possible. We don’t

really go for cutting-edge solutions. Rather, we combine known and stable components in new ways.”

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Between 2009 and 2011, the mobile solution was further enhanced to include cell phone-enabled bar

code tickets, a wide-range GSM and mobile broadband services, and on-flight broadband services (as

the first airline in Europe). As the CIO commented: “We strongly believe that the mobile phone will

become our key customer platform”.

This belief proved relevant earlier than anticipated. In April 2010, a volcano at Iceland, the

Eyjafjallajøkull, erupted, and an enormous ash cloud covered shifting parts of Europe for about ten

days. Most of the North and Central European airspace was closed, and hundreds of thousands of air

passengers were grounded. Angry passengers started to contact airline and travel agent call centers,

which quickly collapsed. As an improvised response, Norwegian quickly established a large-scale

Facebook-enabled customer communications and problem-solving operation, which actually

addressed most of the problems. Facebook was not only used for information purposes, but also to

negotiate customer rerouting and new tickets. The team leader commented afterwards: “Frankly, we

do not know where this Facebook thing will take us, but we certainly realized that our customers

preferred this communication channel in this urgent situation.”

Looking back at this evolution, Norwegian was characterized by modularity in both

organization and technology. The business units of Norwegian were loosely connected, allowing for

modular innovation, while still being able to draw on business and technical resources of other units.

As an example, the IT architect of Norwegian strongly believed that successful infrastructure

evolution depended on balancing increasing variety on the one hand and modularization on the other

hand. If increasing variety (resulting from continuous innovation) was not balanced with continuous

modularization of the architecture, it would lead to chaos. Indeed, the IT architect reasoned that “my

first and top priority is protecting the integrity of the SOA structure, no matter how important a project

deadline is”.

Table 3. Events and Objects Overview Key events Objects Data

Establishing a service oriented architecture (SOA)

Enterprise Service Bus (ESB), booking systems, IT architects, data center

- CIO and two architects designed the SOA

- open source software for ESB - run at outsources data center

Internet bookings

Internet portal, booking systems, Amadeus, users

- Internet portal - integration with Amadeus, allowed for

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direct booking - user-printed tickets

Low-price calendar

SOA, business developers, IT staff

- SOA-enabled low-price calendar - new user pattern triggered

Digital customer communication dominating

SOA, marketing department, customers

- successful portal and email services - 85% of customer communication was

electronic in 2006 -

Bank Norwegian established

Business developers, SOA, banking systems

- the bank was innovated on experiences from the airline solutions

- SOA allowed for short time-to-market

Call Norwegian established

Mobile portal, SOA, vendors, GSM network

- the telecom services extended services to airline customers

- mobile phones is perceived as the most important user platform

Using Facebook during the “ash crises”

Facebook, customers, crisis team - fast response to the “ash crisis” in 2010 - Facebook used for problem solving

Mechanisms  at  Norwegian  

The remarkable success of Norwegian’s digital infrastructure was characterized by growth

along three dimensions: (a) services, such as the web portals, the low-price calendar, the bank and the

mobile services; (b) users, from a few thousands in 2002 to several millions in 2011; and, (c)

stakeholders, as Norwegian aligned a number of networks to its infrastructure over time. Our

fieldwork revealed at least two conditions that provided a powerful environment for digital

infrastructure evolution. First, the enabling service oriented architecture, which allowed the addition,

replacement, or change of components at relative ease. Second, the entrepreneurial and open culture

that supported an evolution that was not totally dependent on top management directives. While these

conditions triggered the evolution, our data analysis showed that there were three mechanisms behind

the successful evolution observed at Norwegian.

The innovation mechanism: As evidenced by the testimonies of our respondents, there existed

a profound optimism that Norwegian would be able to expand its business proposition beyond its

original scope. The malleability of the service-oriented architecture created a space of possibilities that

served as a melting pot for innovation. In fact, this space of possibilities spawned a pace of service

innovation that, at the time, was largely unheard of in the institutionalized airline industry.

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The infrastructure malleability fostered a creative process involving IT personnel, business

managers, and external vendors where they started recombining infrastructure resources such as

technical elements, business routines, vendors, and user groups. This recombination resulted in new

ideas for services, which in many cases materialized at Norwegian. These services were typically

designed in relatively short, intensive projects, and then launched. In the Norwegian case, it had to do

with patterns of assembling different components into new services (Figure 4).

We refer to this mechanism as the innovation mechanism, that is, a self-reinforcing process by

which new products and services are created as infrastructure malleability spawns recombination of

resources.

Figure 4: The Innovation Mechanism

The adoption mechanism

Norwegian managed to attract customers to purchase tickets online. Although the web-based

infrastructure initially was relatively unsophisticated, it was easy to use and relied on resources readily

available to customers. For instance, customers quickly embraced the laser printing of tickets,

available at their work or home office. Indeed, the growth was so strong that by 2006 – only two years

after the introduction of the services – a large majority of customers (85%) was online buyers of

tickets. This convinced top management that the Internet strategy was paying off, and that the airline

should further exploit the momentum of growth. Accordingly, with more users adopting the

infrastructure services more resources were allocated to improve and extend the infrastructure. For

instance, resources were spent on maintaining rapid response time in spite of increased website traffic,

which, in turn, allowed for more services offered.

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As the number of users of the digital infrastructure increased, revenues increased, which, in

turn, attracted more resources to the infrastructure for providing more and improved services. We refer

to this mechanism as the adoption mechanism (Figure 5), that is, a self-reinforcing process by which

more users adopt the infrastructure as more resources invested increase the usefulness of the

infrastructure.

Figure 5: The Adoption Mechanism

As indicated above, “new services” is the outcome of the innovation mechanisms and “more services

are offered” is the starting point of the adoption mechanism. It makes analytical sense to keep the two

mechanisms apart, rather than viewing them as parts of a single, more complex mechanism.

Essentially, the two mechanisms involve different sets of actors. While business and technology

developers drive the innovation mechanism, users drive the adoption mechanism.

The scaling mechanism

The infrastructure at Norwegian did not only succeed by expanding its services and user base. It

also increased its scope by including the partner solutions of its emerging network of stakeholders.

Norwegian’s business infrastructure was unusually open, which lowered the barriers for outside actors

to integrate with the airline’s infrastructure. For instance, early on, Norwegian standardized its central

business bus to achieve compatibility with the European Amadeus system, opening up an entire array

of possible partners. Furthermore, in 2005, Norwegian provided full access (through an application

programming interface referred to as the Norwegian Application Interface) to its services to agencies

and search engines. As a result, Norwegian started to attract partners such as travel agencies, other

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airlines, hotel chains, and car rentals. Partner solutions were integrated with the web portal, which, as

a result, increased the reach of the infrastructure.

We refer to this as the scaling mechanism (Figure 6), that is, a self-reinforcing process by which

an infrastructure expands its reach as it attracts new partners by creating incentives for collaboration.

Figure 6: The Scaling Mechanism

Table 4 summarizes the definitions of the mechanisms generated from our in-depth case study

research at Norwegian. The case survey, presented in the next section, uses these conceptualizations as

a starting-point for analyzing how mechanisms interact in infrastructure evolution. Looking back at the

Norwegian case study, there were clear indications that the three mechanisms interacted. The

innovation mechanisms interacted with the adoption mechanism, in the sense that it provided the new

services that attracted more users. At the same time, the adoption mechanisms provided the financial

resources necessary to maintain the innovation mechanism. We also observed that the scaling

mechanism increased the space of innovation by adding more elements to the infrastructure. This type

of contingences between mechanisms is explored in more depth in what follows.

Table 4. Mechanisms Mechanism Definition

Innovation

A self-reinforcing process by which new products and services are created as infrastructure malleability spawns recombination of resources

Adoption A self-reinforcing process by which more users adopt the infrastructure as more resources invested increase the usefulness of the infrastructure

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Scaling A self-reinforcing process by which an infrastructure expands its reach as it attracts new partners by offering incentives for collaboration

THE  CASE  SURVEY  

Our in-depth case study research at the unusually successful Norwegian company yielded three

mechanisms of digital infrastructure evolution. We chose the company as an extreme case, since

deriving ideal types is typically seen as a useful starting-point for theory generation (Gerring 2007).

However, to substantiate this theoretical inquiry, we decided to survey whether (1) these mechanisms

were activated and (2) resulted in a successful outcome, in other cases as well.

Taking a critical realist stance, we hypothesized the existence of equifinality in digital

infrastructure evolution. In other words, we anticipated that there would a set of different causal paths

by which the mechanisms could be combined to produce successful outcomes. To investigate these

issues further, we conducted a case survey (Larsson 1993) of 41 cases of infrastructure evolution

reported in the scholarly literature to analyze its space of configurations. Configurational thinking has

emerged as powerful way of studying “any multidimensional constellation of conceptually distinct

characteristics that commonly occur together” (Meyer et al. 1993, p. 1175). Among critical realists,

Pawson and Tilley’s (1997) thinking epitomizes the promise of configurational analysis, and as

outlined in the theoretical background above, and further elaborated in the describing the

methodological choices below, their work serve as a backdrop to our analysis.

Methods  

A case survey involves systematic collection and coding of case studies, where preference is

given to the case characteristics rather than the original authors’ analysis and conclusions (Yin and

Heald 1975). Bridging nomothetic surveys and ideographic case studies, the case survey method is an

inexpensive way of learning from many rich case studies (Larsson 1993) and is ideal for

configurational theorizing (Fiss 2007).

Case Selection and Data Collection: Similar to Rivard and Lapointe’s (2012) recent study of

IT implementers’ responses to user resistance, we (a) collected a large sample of digital infrastructure

studies from scholarly sources, (b) refined the initial sample using inclusion and exclusion criteria

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(Yin and Heald 1975), and (c) coded the cases using the definitions of the mechanisms identified in

the in-depth study.

The initial sample, including journal articles, peer-reviewed conference papers, book chapters,

and working papers, covered well over 60 cases. In our further investigation, the inclusion criteria

were: (a) that the case documented the evolution of a digital infrastructure; and (b) that the case

narrative was long enough to provide sufficiently rich data. The main exclusion criterion was that the

case was not rich enough to make it possible to determine whether mechanisms were actualized or not.

All in all, we included 41 cases in our sample (see Appendix). The research database included

cases from articles published in information systems journals (e.g., MIS Quarterly, Information

Systems Research, Journal of AIS, and Information & Organization), medicine (e.g., Methods of

Information in Medicine), development studies (e.g., Information Technology for Development), as

well as science and technology studies (e.g., Science, Technology & Human Values). It also included

cases from articles published in conference proceedings of, e.g., ICIS and IFIP. In addition, cases were

selected from book chapters and working papers. The cases covered a variety of settings including

banking, healthcare, natural resources, pharmaceutics, public sector, and telecom.

Coding: We used Pawson and Tilley’s (1997) configurational framework for designing a

coding scheme focusing on three elements: context, mechanisms, and outcome. First, we drew on our

in-depth case study and the literature review to distinguish technical architecture and organizational

control as two key contextual conditions. In the in-depth case study, we discovered that a loosely-

coupled architecture and decentralized control were important conditions at Norwegian for activating

the power of the identified mechanisms. Based on this insight, we returned to our database of related

literature and studies to (a) assess what other possible architectures were present in digital

infrastructure evolution and (b) assess what other modes of control were present. In the architecture

case, our assessment yielded several examples of infrastructures that were based on tight integration

(see e.g., Hanseth and Braa 2000). We decided to label this category tightly-coupled architecture. In

the case of control as contextual condition, previous studies strike a difference between top-down

implementation and bottom-up cultivation of infrastructures. We decided to label this distinction as

centralized versus decentralized control (cf. Kirsch 2004; Yoo et al. 2010).

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Then, the three mechanisms (and their definitions, see Table 4) generated from the in-depth

study were used as a basis for our coding of the studies included in the case survey. The coding of

mechanisms required careful assessment of whether the mechanism in question in fact was actualized.

For example, most (if not all) digital infrastructure projects have the ambition of user adoption, and

therefore involve management interventions designed to achieve this. However, the sheer presence of

management intervention to achieve user adoption was not considered sufficient to code the adoption

mechanism as actualized. Rather, this would require evidence that user adoption was self-reinforcing,

as described in Figure 1. As an example, consider that a management team may stimulate user

adoption (action-formation) through a commercial campaign in the hope that it would increase user

adoption of the infrastructure. However, if we, in this hypothetical example, could not verify that this

management intervention paid off in a self-reinforcing process that generated resources

(transformation) that maintained user adoption beyond the end of the campaign, we reasoned that the

mechanism was not actualized. Similarly, the other two mechanisms were coded positively only to the

extent that they were self-reinforcing. In this context, it should be emphasized that, in specific cases

where mechanisms were coded as unactualized, the claim of actual presence of such unactualized

mechanisms builds on an assumption of coherence across cases of infrastructure evolution.

Finally, we coded outcomes based on the relative success of the evolution process. Starting from

the assessment of the author(s), we critically examined if the infrastructure had reached a state where it

displayed a capacity to endure, and adapt to environmental changes. In particular, we interpreted if (a)

the infrastructure filled a relevant role in its business ecosystem, and (b) the extent to which the

infrastructure’s affordances were possible to escape for its stakeholders. If in doubt, we supported our

interpretations by disciplined imagination of possible scenarios through which individual stakeholders

would be able to reverse the infrastructure’s state of success, or failure.

Three people independently coded the 41 studies included in the sample. In addition to the two

authors of this paper, a Master student served as a coder. In case of disagreements, the coders reread

the case and discussed the coding until 100% agreement was achieved. The inter-reliability of the

coding was approximately 85% in the first round of independent coding.

29

Data Analysis: We first examined the relationship between the eight possible combinations of

the three mechanisms (Adoption, Innovation, and Scaling (AIS), AI, AS, IS, A, I, S, and none) and

their outcomes (Table 5). This revealed clearly that the absence of activated mechanisms was strongly

associated with a negative outcome, and that two configurations were highly successful. Second, in

order to understand the causal paths of the two successful configurations (AS and AIS), we conducted

a qualitative investigation of all cases associated with these configurations. The first step involved

comparative analysis between the cases in the same configuration. This analysis revealed how the

mechanisms interacted. The second step involved a comparison of cases across the two successful

configurations. This helped us understand why both AS and AIS produced a successful outcome.

Thirdly, we then analyzed contextual conditions and their relation to the two configurations. One

interesting observation was that the activation of mechanisms in one configuration (AS) appeared to

be less dependent on the two contextual conditions advocated in the literature than the other

configuration.

Table 5. Case Survey Data Analysis Steps   Tasks   Outputs  1.  Descriptive  statistics   a.  Examine  relationships  between  

combinations  of  mechanisms  and  outcomes  

b.  Identify  highly  successful  and  highly  unsuccessful  combinations  of  mechanisms    

Frequency  distribution  of  the  cases  across  combinations  of  the  three  identified  mechanisms  (Table  6)    

 2.  Qualitative  analysis     a.    Comparative  analysis  of  cases  within  the  two  successful  combinations  (AS  and  AIS)  

b.  Comparative  analysis  between  the  two  successful  configurations  

c.  Examination  of  alternative  explanations    

A  systematic  assessment  of  causal  relationships  in  the  successful  cases.    

3.  Analysis  of  contextual  conditions    

a.  Analyze  the  effect  of  de-­‐centralized  control  on  the  two  combinations  

b.  Analyze  the  effect  of  loosely-­‐coupled  architecture  on  the  two  combinations  

c.  Assess  explanatory  power  of  the  configurations  

Two  configurations  of  successful  infrastructure  evolution  (Figure  7)    Completion  of  two  successful  causal  paths  in  digital  infrastructure  evolution  

30

Case  Survey  Results  

Table 6 outlines the descriptive statistics of the 41 cases of infrastructure evolution included in

our sample. It shows the frequency distribution of the cases across mechanism configurations and

evolution outcome. Since the three mechanisms (Adoption (A), Innovation (I), Scaling (S)) can be

combined in 8 different ways (none, A, I, S, AI, AS, IS, AIS), there are 8 logically possible

configurations of which a particular case can be a member (see Appendix for coding details of each

case). Among the cases in the sample, the IS subset was the only mechanism configuration that

remained unactualized. Furthermore, we identified 11 cases (26.8%) where none of the three

mechanisms were actualized, all of which resulted in unsuccessful digital infrastructure evolution. At

the other end of the spectrum, we identified 12 cases (29.3%) as members of the AIS subset and 7

cases (17.1%) as members of the AS subset. All these 19 cases were coded as successes, and will be

the primary focus for our further analysis.

Table 6. Descriptive Statistics Mechanism combination

N (%) Unsuccessful infrastructure Successful infrastructure Total

NONE 11 (26.8%) 11 0 11 (100%) A 3 (7.3%) 2 1 3 (100%) I 4 (9.7%) 2 2 4 (100%) S 1 (2.4%) 1 0 1 (100%) AI 3 (7.3%) 1 2 3 (100%) AS 7 (17.1%) 0 7 7 (100%) IS 0 (0%) 0 0 0 (100%) AIS 12 (29.3%) 0 12 12 (100%) Total: 41 (100%) 17 (41.5%) 24 (58.5%)

Our in-depth study at Norwegian established three individual mechanisms: adoption,

innovation, and scaling. It also suggested that the actualization of all three of them in the same case

would effectively contribute to a successful outcome of digital infrastructure evolution8. The

descriptive statistics of our case survey supports this suggestion by indicating a strong correlation

between the AIS configuration and a successful outcome. Beyond this confirmation of the Norwegian

case study results, we can make at least two additional observations. First, the actualization of a single

8 As highlighted by one of the anonymous reviewers, this is not to suggest a closed system. Consistent with critical realism assumptions, there might always be other mechanisms “lurking around the corner”.

31

mechanism is insufficient for leading to a successful outcome. For instance, our case survey suggests

that the innovation mechanism alone is insufficient for a successful outcome. Although there were a

few exceptions, our results show that the innovation mechanism is contingent on the adoption and

scaling mechanisms. These findings resonate well with critical realism’s assumption of contingent

causality, suggesting that the outcome of a mechanism depends on other mechanisms (Elder-Vass

2010, Sayer 1992). Second, the results also suggest that the actualization of the innovation mechanism

is not a necessary condition for success if the adoption and scaling mechanisms are interacting in the

same evolution process. This is interesting, since our analysis of the Norwegian case suggested it as a

vital mechanism for adoption and scaling in the AIS configuration. This insight indicates that the

natures of the AIS and AS configurations are qualitatively different and warrant further attention.

In view of these initial insights of our case survey results, we further investigated how the

mechanisms interacted in the two successful configurations (AIS and AS). In the Norwegian in-depth

case study (as an example of the AIS configuration), we noted how the three mechanisms interacted.

In what follows, we investigated whether the same type of interaction existed in the 12 AIS cases

included in the survey case. We also investigated the 7 cases with the AS configuration to further

understand how the adoption and scaling mechanisms alone can interact to produce successful

infrastructure evolution. In doing so, we also turned to the contextual conditions of architecture and

mode of control for both configurations to understand the differences in causal paths. Manifesting our

configurational perspective, Figure 7 portrays the causal paths that we identified through our analysis

including the number of cases that followed each path.

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Figure 7: Configurations of Digital Infrastructure Evolution

Adoption-Innovation-Scaling: Our examination of the AIS cases (Case #6. The Internet; 10.

Environmental Health in the French Public Health Administration; 12. Local Danish Electronic Patient

Record initiative; 22. The SWIFT Network; 23. Criminal Case Management in Finland; 27.

Pharmaceutics; 29; Gateways Vs. Standards; 30. Internet IPv6; 32. Telecom; 33. Broadband Mobile

Services in South Korea; 39. US Petroleum Company; 41. and US Retail Company) 9 included in the

case survey revealed a similar interplay of mechanisms as we found in the Norwegian case study. The

innovation and adoption mechanisms fed on each other, which created fertile ground for the scaling

mechanism as combinatorial possibilities (innovation) increased and the provision of more users

(adoption) leveraged the scope of the infrastructure. In the Local Danish Patient Record initiative

9 See Appendix for sources and coding.

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(Case #12: Aanestad and Blegind Jensen 2011), as one of three examples10 summarized in Table 7, the

innovative way of using existing systems to achieve “Standardized Extraction of Patient Data” (SEP)

triggered adoption from county hospitals, which, in turn, created the resources for establishing a

national SEP project that could support the initiative’s scale and scope. Starting as a local initiative

supported by two county hospitals, the SEP infrastructure counted 4.3 million Danes as users some

nine years after its inception. Similar interaction between mechanisms existed in the seemingly quite

different SWIFT network case (Case #22: Scott and Zachariadis 2010), in which 68 banks in 11

countries joined forces to create an infrastructure for financial transactions that developed “from an

efficiency initiative driven by a closed ‘society’ of banks to a network innovation of world-class

standing” (p. 2). The widespread adoption by banks of SWIFT in the 1970s created incentives for

innovating the infrastructure further, where speed, costs, volume, security, and uniform formats were

benefits that were targeted in a series of improvements of the infrastructure over the years. The

financial infrastructure scaled considerably in terms of its coverage and scope, and is since long the

most comprehensive one in the global bank sector. Regarding the Criminal Case Management system

in Finland (Case #23: Fabri 2008), innovation in the civil procedure rules (e.g., elimination of original

signature requirement) boosted new civil case management applications. The new infrastructure was

rapidly adopted by debt collecting companies, while lawyers’ use progressed well but more slowly.

Stimulated by this success, the infrastructure increased its reach by expanding into criminal cases,

which usually is considered more difficult since more stakeholders are involved.

Table 7. Content Analysis of Adoption-Innovation-Scaling Configuration Contextual

conditions Mechanisms Outcome Reference

12. Local Danish Electronic Patients Records initiative: The SEP project in Denmark was a local initiative in 2000 to share patient data, but expanded into a national solution during a nine-

Architecture: Modularized, many systems involved Control: Distributed

Innovation: Started as a simple problem-solving initiative, using existing systems in new ways. This was cleverly expanded into a national patient database. Adoption: Starting with pilot in two counties, gradually expanded into a national solution. Patients were also allowed access.

“Our empirical material describes two Danish initiatives, where a national project failed to deliver interoperable Electronic Patient Record systems while a small, local solution grew and now offers a nation-wide solution for sharing patient record information.” (p.161).

Aanestad and Blegind Jensen, (2011)

10 Our selection of illustrative examples of the AIS configuration was done with emphasis on making sense to readers without too much contextual background.

34

year period. Scaling: First accessing one EPR and PAS, later including all such systems in Denmark into the solution.

22. International Financial Communication Network: SWIFT is an infrastructure for financial transactions, now used in 200 countries. Starting in 1971 as a standard, SWIFT went live in 1977 and expanded into a global infrastructure.

Architecture: SWIFT is a network, with a common standard implemented in a large number of financial systems. Control: Distributed, SWIFT is owned by member banks.

Innovation: Building on Telex solutions, financial actors and technology vendors designed and developed the network with continuous innovation. Packet switching was introduced in 1979, and SWIFT 2 was launched in 1983 Adoption: Initiated by 68 banks in 11 countries. As services expanded, even more banks and countries joined. Scaling: The expansion was characterized by negotiations and adaptations. Moving from a closed society, SWIFT became a global network.

SWIFT developed into one of the world’s key infra-structures, proving fast, seamless and secure exchange of financial transactions. “Over time, what began as a closed society (..) grew into an industry cooperative supporting an enthusiastic community of practice and transformed into an unexpected network phenomenon.” (p. 2).

Scott and Zachariadis (2010)

23. Criminal Case Management in Finland: The Criminal Case Management system in Finland was introduced in 1992, and developed into a national integrated infrastructure.

Architecture: Modular, expanded into service oriented architecture. Control: Centralized (but managed by representatives of user institutions)

Innovation: The Sakari solution helped transforming the whole legal criminal case process, and was extended with new services annually. Adoption: Courts, police, prosecutors and prisons were gradually enrolled as new services were integrated. Scaling: Linking into other structures was a key strategy.

Sakari was considered a success in Finland. “It is recognised that it has helped make criminal proceedings quicker and more accurate, () and the system has also helped to create a useful exchange of information and practices among the different organizations and actors involved” (p.123).

Fabri (2008)

In view of the similarities in interaction between the mechanisms in the AIS configuration

cases, we further explored the contextual conditions that actualized the AIS configuration. As shown

in Figure 7, and in line with the extant infrastructure literature, there was a high correlation between

loosely-coupled architecture and decentralized control and the AIS configuration. One plausible

explanation is that the innovation mechanism is a less predictable process than adoption and scaling,

and that the innovation mechanism is dependent on a “space of possibilities” (Davenport and Short

1992; Bygstad 2010) that centralized control and tightly-coupled IT architectures cannot trigger.

Adoption-Scaling: Compared to the AIS cases, the examination of the AS cases (Case #1:

Health IS in Developing Countries; 9. Legal IS in Austria; 15. Power Systems; 21. Health IS in India;

35

28. OS IVs. IP standards; 34. University Software; and 36. E-government in Germany)11 showed

significantly different results. The results suggested that as long as the adoption and scaling

mechanisms were actualized, the powers of the innovation mechanism did not have to be actualized. In

view of our analysis of the AIS configuration, where we noted that the innovation mechanism helped

actualizing the adoption mechanism, this result requires further analysis.

The Legal IS in Austria (Case #9: Koch and Bernoider 2008), as one example summarized in

Table 8, was a result of in-house development work carried out by the Federal Chancellery, sometimes

done with the support of external systems development firms. The system was initially adopted for

increasing efficiency. Over time, however, more users including the ministries and eventually citizens

were adopting the infrastructure. Once citizens came into the picture, a web-based version was

developed in parallel to the system used in-house to facilitate user access. The reach of the

infrastructure increased as citizens were provided comprehensive law information through this web-

based system. Later on, the infrastructure was further extended in reach by digitally supporting the

process for producing judicial material from inception to Internet publication. This coincided with the

Council of Ministers’ decision to adopt the infrastructure in all ministries. Similar interaction between

the adoption and scaling mechanisms could be observed in the Health in India (21: Sahay and

Walsham 2006) and e-Government in Germany (36: Pipek and Wulf 2009) cases.

Table 8. Content Analysis of Adoption-Scaling Configuration Contextual

conditions Mechanisms Outcome Reference

9. Legal IS in Austria: The LIS system is a legal repository and supports the workflow for law making in Austria. Developed since 1972.

Architecture: Tightly-coupled (first mainframe, then Internet version) Control: Centralized (managed in-house in ministry)

Adoption: First, only internal users, then professional users, and finally the public. Scaling: The Internet version was launched in 1996, and extended with eLaw access.

The LIS was used by 4.5 million users per month, and was considered successful, both in terms of internal efficiency and public access. It ensured support for the law process. It provided transparency and services to citizens.

Koch and Bernroider (2008)

21. Health IS in India: The design, development, and implementation of a Health Information System

Architecture: Tightly-coupled (but adaptable) Control: Centralized (through HISP project and state

Adoption: Starting with a simple solution and pilot users, more services were added, which increased the user base. Scaling: Starting with a pilot, the solution was scaled in two

The HISP solution was in full use in Andhra Pradesh. The infrastructure was scaled to support a whole state in a low-resource setting. Adoption, although challenging, was steadily increasing.

Sahay and Walsham (2006)

11 See Appendix for source and coding.

36

in Southern India. authorities)

steps, first to 46 health centers, then to the whole state.

36. e-Government in Germany: The case is a solution to support work processes connecting the state government located in the state's capital with the German Bundesrat.

Architecture: Tightly-coupled workflow system Control: Centralized, co-operation

Adoption: Comprehensive requirements elicitation process with users, started growing use and adaptation. Scaling: A solution that first was proprietary, and later was expanded to Internet technology. This facilitated the alignment of new stakeholders.

The solution was considered successful, with a consistent growth of services and users. Transition to Internet technology was problematic, but solved.

Pipek and Wulf (2009)

Given this interaction between adoption and scaling, which was qualitatively different from the

mechanisms interaction in the AIS cases, we turned to the contexts of the cases. As illustrated in

Figure 7, three (Legal IS in Austria; Health IS in India; and E-government in Germany; see Table 8)

out of the seven AS cases were characterized by centralized control and a tightly-coupled architecture.

This breaks with some of the seminal works in the infrastructure literature, which argue that loosely-

coupled architecture and distributed control are important elements in making evolution processes

successful. It appears that the adoption and scaling mechanisms reinforce each other in a way that

leads to successful evolution, and that central control and tightly-coupled architecture sometimes are

important conditions in making this happen. For instance, in the Health IS in India-case, centralized

control supported the scaling of the infrastructure beyond the pilot to align health centers. Increasing

the reach, in turn, the health IS-solution became more attractive to adopt. However, as a concluding

remark, it should be emphasized that our investigation of contextual conditions of the AS

configuration does not provide a clear-cut picture. Although being beyond the scope of this study, this

observation indicates that there exist other contextual conditions that interact with these mechanisms

and suggests an opportunity for further research.

37

DISCUSSION  

The adoption of critical realism has helped us to explore a number of issues that challenge the

way we think about digital infrastructure. In particular, we highlight the existence of three self-

reinforcing mechanisms (adoption, innovation, and scaling) that serve as causal powers in digital

infrastructure evolution. In addition, we develop a configurational perspective that suggests multiple

causal paths of such evolution. In what follows, we outline implications for infrastructure research and

discuss the usefulness of critical realism in information systems research. In addition, we highlight the

study limitations and future issues of research.

Implications  

Our work synthesizes and conceptualizes earlier insight on self-reinforcing mechanisms

(Grindley 1995; Katz and Shapiro 1985; Sydow et al. 2009), innovation (Arthur 2009; Schumpeter

1980), and scaling (Hanseth and Lyytinen 2010; Pollock et al. 2007; Sahay and Walsham 2006) as

generative mechanisms. This is useful, since the causal powers of each of the mechanisms have been

largely masked in prior research. Our critical realist conception of digital infrastructure recognizes that

mechanisms act transfactually (cf. Fleetwood 2009) and that the actualization of their powers is

contingent on other mechanisms. In this regard, our research indicates that successful digital

infrastructure evolution cannot be explained by merely attending to a single mechanism. Among the

41 cases included in our sample, there were only three cases where an individual mechanism coincided

with a successful outcome (see 18: Hanseth and Aanestad 2003, 31: Rolland and Monteiro 2002; 20:

Scott and Walsham 1998). In all other 21 successful cases, the outcome was traced to a configuration

of two or three mechanisms. Although there is accumulating evidence on what drives infrastructure

evolution in the literature, the generative mechanisms reported in this paper offer the analytical

distinctiveness needed not only to zoom in on specific aspects of infrastructure but also to zoom out to

understand its contingencies.

In addition, while the literature is full of testimonies that express the complex relationships

between elements that make up an infrastructure (Braa et al. 2007, Ciborra and Failla 2000, Ciborra et

al. 2000, Hanseth et al. 2006), there have been few, if any, attempts to formulate perspectives that

38

allow the simultaneous study of multiple causes. Concurring with its critical realist underpinnings, our

configurational perspective recognizes both the inherent complexity of infrastructures and the need of

an analytical lens for making sense of them. As an example, consider that we observed that successful

infrastructure evolution does not depend on the innovation mechanism as long as the powers of the

adoption and scaling mechanisms are actualized. Both the AS and AIS configurations lead to

successful evolution. Such equifinality is accommodated by our configurational perspective, which

allows investigation of specific causal paths as well as the multiplicity characterizing digital

infrastructure.

Moreover, previous work on digital infrastructure has suggested that centralized control is

detrimental to the outcome of the evolution process (Ciborra et al. 2000). Our research provides a less

polarized view, where we found significant empirical support for the prevalent stance in the case of the

AIS configuration, while our evidence for the AS configuration tells another story. The same applies

to architecture: while a loosely-coupled architecture is found to be a valuable trigger for the AIS

configuration, a tightly-coupled architecture does not impede the AS configuration from a successful

outcome. In other words, it seems that there is no specific relationship between mode of control and

architecture in successful cases of combining the adoption and scaling mechanisms.

We believe that these findings have important implications for practitioners who face the

challenge of managing the evolution of digital infrastructures. Understanding the difference between

the two configurations is seminal for the choice of management strategy. The AIS configuration

describes the causal path of the really ambitious undertakings, where interaction between the three

mechanisms is crucial, and where the innovation mechanism should be targeted as the driver. Typical

examples of such projects are the Norwegian (our in-depth case study) and the International Financial

Communication Network (22: Scott and Zachariadis 2010) cases. In such cases, managers should note

that the dynamics of the configuration requires a loosely-coupled architecture and decentralized

control in order to create the space of possibilities necessary for actualizing the innovation mechanism.

Also, tight scheduling is detrimental, because the configuration feeds on exploration until momentum

is established.

39

Our study also suggests that the AS configuration involves lower stakes than AIS because the

interplay between the adoption and scaling mechanisms is relatively straight-forward. It allows for a

wider choice of management interventions. Seeking to actualize this configuration, managers should

have confidence in traditional project management techniques, and observe that both loosely-coupled

and tightly-couples architectures may be effective.

The  Usefulness  of  Critical  Realism  in  Information  Systems  

The promises of critical realism as a philosophical tradition for information systems research

have been voiced for some time now (Mingers 2004, 2010, Smith 2010). However, to date, there is a

paucity of empirical research, underpinned by critical realist assumptions, that makes substantive

contributions to established streams of information systems research. While there exists a few

exceptions (Bygstad 2010; Smith 2010, Volkoff et al. 2007), current writings on critical realism in our

discipline mainly provide useful conceptual guidance. Our research is an early example to challenge

some of the conventional wisdom in the extant literature of a substantive area of information systems

research.

The delay between the initial recognition of a philosophical tradition and its wider adoption in

the community is understandable. As an applied discipline, information systems is largely motivated

by its members’ capacity to generate knowledge with a capacity to, directly or indirectly, contribute to

the corresponding community of practitioners (Lyytinen 1999). In this regard, recognition of new

developments and directions in the philosophy of science is a necessary but not sufficient step for

paving the way for a philosophical tradition in the discipline. For instance, it took some ten years until

the early writings of Boland, Orlikowski, and Walsham on interpretive research were appropriated in

the field as a broadly recognized basis for conducting high-quality research. One important element of

this consolidation was examples of empirical research that could demonstrate its value in generating

new knowledge deemed relevant by the community. In doing this, they then served as sources of

inspiration for research to come.

In a similar vein, we view this article as an intermediate step between the pioneering writings on

critical realism and its adoption in wider circles in the community. In particular, we view the research

40

design, combining the in-depth case study and case survey methods, and the adoption of

configurational thinking as a promising direction for leveraging qualities of critical realism in our

discipline. For instance, as the field is beginning to deal with complex phenomena such as digital

innovation (Yoo et al. 2010), digital infrastructure (Tilson et al. 2010), and platforms (Tiwana et al.

2010), conceptions of causality that recognizes contingency and multiple paths are needed (cf. El

Sawy et al. 2010). Our configurational perspective offers an approach to accommodate such a view on

causality that can be reused in other settings. As indicated in the implications section, we offer a

number of contributions to extant infrastructure literature that can be traced back to our adoption of

critical realism for information systems purposes.

Limitations  and  Issues  for  Future  Research  

Future studies could address several limitations in our work. First, as noted by Rivard and

Lapointe (2012), the use of secondary data in case survey research certainly introduces some limits to

what can inferred from the cases included in the sample. The cases were originally written for a

different purpose, meaning that it would be unrealistic to imagine that we would be able to create a

situated understanding of the particulars associated with each of the cases. However, our use of

inclusion and exclusion criteria for deciding which cases to include in the sample restricted our inquiry

to studies that reported sufficiently rich accounts of the case setting. In order to offer scholars of

infrastructure the opportunity to challenge our coding, we also included an overview of the coding in

the Appendix. This makes it possible for original contributors to assess our coding, and it enables use

of our data material in future research. Second, the use of the case survey method necessarily

quantifies qualitative data in a way that risks draining ideographic accounts of their richness. We

compensated this loss of situated understanding by considering 41 cases in the same study in a

relatively ideographic manner. Third, mechanisms are causal structures that generate observable

events, and we offer an understanding of three mechanisms, as well as their combinations and

contextual conditions, that lead to successful infrastructures. We acknowledge that the granularity of

our analysis of causality is relatively high level, suggesting the existence of nested causal paths in

digital infrastructure evolution left unaddressed in this study. Thus, we do not claim that we have

41

discovered all mechanisms relevant for infrastructure evolution. For instance, while identifying

mechanisms of successful evolution of digital infrastructures, we have not addressed negative self-

reinforcing mechanisms. One example of such a mechanism is described by Hanseth et al. (2007) as

reflexive standardization. Generally increasing the complexity of an infrastructure, this mechanism has

been confirmed in recent cases, such as the European eCustoms case (25: Henningsson and Henriksen

2011). Future research that adopts critical realism for studying negative self-reinforcing mechanisms

would be worthwhile. Finally, focusing on successful configurations, our research primarily deals

with the equifinality of infrastructure evolution. While our case survey findings also indicate multi-

finality, we did not further analyze why particular configurations result in different outcomes (e.g., A,

I, and AI). Our understanding of digital infrastructure would benefit from future research on this issue.

CONCLUSION  

This paper proposes an alternative understanding of digital infrastructure evolution, which

emphasizes the relevance of more closely examining its generative mechanisms. The paper details and

illustrates a critical realist approach to digital infrastructure where configurational thinking serves as a

vehicle for understanding the combinations of mechanisms that lead to successful evolution. We

suggest that this approach may serve as a foundation for informing our understanding, as well as

future studies, of digital infrastructure and its inner workings.

Our perspective provides insights into the contributions and limitations of previous

understandings of digital infrastructure. To date, the four streams of digital infrastructure research

identified in our literature review have paved the way for establishing an area of research that

recognizes the arrays of systems and technologies that confront today’s managers and CIOs, rather

than the conventional inquiry in information systems within the confines of the single system.

However, as our research shows, conventional wisdom in the area falls short when it comes to

articulating the multiple paths by which successful digital infrastructure evolution come about. In the

extant literature, there is a tendency to offer partial explanations, rather than focusing attention on the

complete set of key mechanisms and their interaction. This is problematic since it tends to inhibit a

42

comprehensive understanding of how and why digital infrastructures evolve the way they do across

cases.

In response, our critical realist approach accommodates both the interpretivist and positivist

assumptions manifested in the extant infrastructure literature. The plausibility of this accommodation

is demonstrated in the paper by offering theoretical implications that challenge some of the hegemony

in infrastructure studies. It may appear somewhat ironic that our perspective draws on the many of the

findings already available in the area, yet brings new light on established truths by introducing new

philosophical assumptions. We attribute this result to the power of a philosophical tradition, critical

realism, which offers a set of new pillars for approaching substantive areas in information systems.

We hope that our example will inspire others to adopt critical realism in their attempts to advance

areas of information systems research where the findings in prior literature need to be released from

some of their long-established convictions.

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APPENDIX

Contextual conditions Architecture: tightly-coupled (0), loosely-coupled (1) Control: centralized (0) decentralized (1) Mechanisms Adoption (A): Unactualized (0), actualized (1) Innovation (I): Unactualized (0), actualized (1) Scaling (S): Unactualized (0), actualized (1) Outcome: Unsuccessful (0), successful (1) Comb: Combination of mechanisms

No Case Contextl. conditions

Mechanisms Out-come

Comb Reference

Arc Con

A I S

1 Health Information Systems Project HISP: A successful standardization strategy in low-resource countries, based on flexible and simple solutions. Continuously from 1992-2007.

1 1 1 0 1 1 AS Braa, J., Hanseth, O., Heywood, A., Mohammed, W., and Shaw, V. 2007. "Developing Health Information Systems in Developing Countries: The Flexible Standards Strategy," MIS Quarterly 31:2, pp 381-402.

2 National Hospital: A case of increasing complexity of requirements, leading to paralysis.

0 0 0 0 0 0 - Hanseth, O., Jacucci, E., Grisot, M., and Aanestad, M. 2006. "Reflexive Standardization: Side Effects and Complexity in Standard Making." MIS Quarterly, 302, pp.563-581.

3 Norsk Hydro: A case of an expanding corporate standard in the 1990s, leading to broad adoption, but difficult to scale

0 0 1 1 0 1 AI Hanseth, O., and Braa, K. 2000. "Who's in Control: Designers, Managers or Technology? Infrastructures at Norsk Hydro", in C. Ciborra et al.( eds.), From Control to Drift. Oxford University Press, pp. 125-147.

4 IBM: An innovative CRM project, with scaling problems.

0 0 1 1 0 0 AI Ciborra, C., and Failla, A. 2000."Infrastructure as a Process: The Case of CRM at IBM", in C. Ciborra et al.( eds.), From Control to Drift. Oxford University Press.

5 EDI: An ambitious project in health, but failing to align a complex network of actors and technology.

1 1 0 0 0 0 - Monteiro, E. og Hanseth, O. 1995. “Social shaping of information infrastructure: on being specific about the technology.” In Orlikowski, Wanda J., Geoff Walsham, Matthew R. Jones and Janice I DeGross. Information Technology and Changes in Organizational Work. Chapman & Hall, 1995, pp.325 - 343.

6 Internet: Describes how the dynamics of bootstrapping and adaptation explains the success of the Internet.

1 1 1 1 1 1 AIS Hanseth, O., and Lyytinen, K. 2010. "Design theory for dynamic complexity in information infrastructures: the case of building internet." Journal of Information Technology, 251, pp.1-19.

7 Genome project: An ambitious scientific community project, which fails to establish a sustainable solution.

0 1 0 0 0 0 - Star, S. L., and Ruhleder, K. 1996. "Steps Toward an Ecology of Infrastructure: Design and Access for Large Information Spaces." Information Systems Research, 71, pp.111-134.

8 Statoil: An innovative project of knowledge management, which fails to trigger internal dynamics.

0 0 0 0 0 0 - Hepsø, V., Monteiro, E., and Rolland, K. 2009. " Ecologies of eInfrastructures." Journal of the AIS, 105, pp.430-446.

9

Legal systems: An expanding legal infrastructure in Austria, growing organically from

0 0 1 0 1 1 AS Koch, S. and Bernroider, E. 2008. “Aligning ICT and legal frameworks in Austria’s e-bureaucracy: from mainframe to the Inter-net.” In Contini and Lanzara eds. ICT and

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1972. Innovation in the Public Sector European Studies in the Making of E-Government. Palgrave Macmillan, pp. 147-173.

10 Environmental Health in the French Public Health Administration: Analyzes a successfully distributed network of practice, 2000 to 2005, supported by an emerging information infrastructure.

1 1 1 1 1 1 AIS Vaast, E. and Walsham, G. 2009 "Trans-situated learning: supporting a network of practice with an information infrastructure." Information Systems Research, 20(4), pp.547-564

11 French Rail: Aiming to transfer an airline booking system to a railway context. Fails because of “translation” problems.

0 0 0 0 0 0 - Mitev, N. 2000 "Toward Social Constructionist Understandings of IS Success and Failure: Introducing a New Computerized Reservation System," in proceedings of the International Conference of Information Systems, Brisbane, Australia, pp. 84-93.

12 Local Danish Electronic Patient Record initiative: A local initiative, which surprisingly develops and scales into a national Danish Electronic Patient Record solution.

1 1 1 1 1 1 AIS Margunn Aanestad, Tina Blegind Jensen. 2011. “Building nation-wide information infrastructures in healthcare through modular implementation strategies.” Journal of Strategic Information Systems. 20(2), pp.161-176.

13 Health: A national Danish Electronic Patient Record standardization initiative, which never gets off the ground.

0 0 0 0 0 0 - Margunn Aanestad, Tina Blegind Jensen. 2011. “Building nation-wide information infrastructures in healthcare through modular implementation strategies.” Journal of Strategic Information Systems. 20(2), pp.161-176.

14 GIS in India: An attempt to introduce GIS technology into an Indial local administration. Fails because of “translation” problems.

0 1 0 0 0 0 - Sahay, S. and Walsham, G. 1996. "Implementation of GIS in India: organizational issues and implications." International Journal of Geographical Information Systems, 10(4), pp.385-404

15 Power systems: En epic description of how the US electric grid and companies expanded as networks of power.

0 1 1 0 1 1 AS Hughes, T.P. 1987.“The evolution of large technical systems,” in The social construction of technological systems,( eds) W.E. Bijker, T. P. Hughes, and T. Pinch., MIT Press, Cambridge, MA.

16 Health Broadband Networks: A telemedicine solution at the National Hospital is successfully innovated and adopted by health personnel.

1 0 1 1 0 1 AI Hanseth, O., and Aanestad, M. 2003. "Design as Bootstrapping. Onthe Evolution of ICT Networks in Health care." Methods of Information in Medicine, 42, pp.385-391.

17 Health: An EDI initiative gets mired in standardization issues, and never comes off the ground.

1 1 0 0 0 0 - Hanseth, O., and Aanestad, M. 2003. "Design as Bootstrapping. Onthe Evolution of ICT Networks in Health care." Methods of Information in Medicine, 42, 385-391.

18 Telemedicine: A successful case of telemedicine in ambulances, but mainly as a pilot project.

0 0 1 0 0 1 A Hanseth, O., and Aanestad, M. 2003. "Design as Bootstrapping. Onthe Evolution of ICT Networks in Health care." Methods of Information in Medicine, 42, 385-391.

19 EDIFACT standard: A national standardization initiative fails because of technical and organizational complexity.

1 1 0 0 0 0 - Hanseth, O., and Monteiro, E. 1997. "Inscribing Behaviour in Information Infrastructure Standards." Accounting, Management and Information Systems, 7(4), pp. 183-211.

20 Banking: A study of an innovative decision support system, with limited adoption and scaling.

0 0 0 1 0 1 I Scott, S.V. and Walsham, G. 1998. “Shifting boundaries and new technologies: a case study in the UK banking sector”. ICIS, pp.177-187

21 Health IS: A successful adoption and scaling of a health information system in an Indian state.

0 0 1 0 1 1 AS Sahay, S. and Walsham, G. 2006 “Scaling of Health Information Systems in India: Challenges and Approaches”, Information Technology for Development, 12(3), 165-200.

22 The SWIFT Network: A successful standards innovation in early 1970s, and the gradual expansion into a global financial

1 1 1 1 1 1 AIS Scott, S., and Zachariadis, M. 2010. “A historical analysis of core financial services infrastructure: Society for Worldwide Interbank Financial Telecommunication (S.W.I.F.T.)”. Working Paper Series, No

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network. 182., London School of Economics and Political Science.

23 Law: A Criminal Case Management system in Finland was developed and expanded gradually from 1992 into a successful legal network.

1 0 1 1 1 1 AIS Fabri, M. 2008. “E-justice in Finland and in Italy: enabling versus constraining models”. In Contini and Lanzara (eds.) ICT and Innovation in the Public Sector European Studies in the Making of E-Government. Palgrave Macmillan.

24 Law: The Civil Trial Online project is aimed at improving the workflow between courts and lawyers in Italy, had almost no results after 6 years, due to growing complexity.

1 0 0 0 0 0 - Fabri, M. 2008. “E-justice in Finland and in Italy: enabling versus constraining models”. In Contini and Lanzara (eds.) ICT and Innovation in the Public Sector European Studies in the Making of E-Government. Palgrave Macmillan.

25 e-Customs in Europe: An ambitious EU project to integrate customs had some local successes, but has problems in scaling and adoption.

0 0 1 0 0 0 A Henningsson, S. and Henriksen, H. Z. 2011. “Inscription of behaviour and flexible interpretation in Information Infrastructures: The case of European e-Customs”. Journal of Strategic Information Systems Vol. 20 No. 4, pp. 355-372.

26 Health: The NHS summary care record project is characterized by a number of problems, and fails to establish a sustainable development.

0 0 0 0 0 0 - Greenhalgh, T., Stramer, K., Bratan, T., Byrne, E., Mohammad, Y., Russell, J., 2008. Introduction of shared electronic records: multi-site case study using diffusion of innovation theory. British Medical Journal 337, a1786 Clinical Research Ed.

27 Pharmaceutics: The evolution of an intranet, from corporate asset to local adaptation in a loosely coupled architecture.

1 1 1 1 1 1 AIS Ciborra, C.U. 2000b. “From Alignment to Loose Coupling: From MedNet to www.roche.com”. In C. Ciborra ed. From Control to Drift: The Dynamics of Corporate Information Infrastructures, Oxford: Oxford University Press, 193–212.

28 OSI vs. IP standards: Compares the development and adoption of the OSI and IP standards, explain-ing the success of IP as the successful balancing between flexibility and standardization

1 1 1 0 1 1 AS Hanseth, O., Monteiro, E. and Hatling, M. 1996. “Developing information infrastructure: The tension between standardization and flexibility”, 1996. Science, Technology and Human Values. Vol. 21 No. 4, pp. 407-426.

29 Gateways vs. standards: Analyzes a “standards war” in Scandinavia in the 1980s, concluding with the importance of gateways.

1 1 1 1 1 1 AIS Hanseth, O. 2001. “Gateways - just as important as standards. How the Internet won the ‘religious war’ about standards in Scandinavia.” Knowledge, Technology and Policy. Vol. 14 No. 3; 71-89. Special Issue on IT Compatibility.

30 Internet IPv6: The case investigates the efforts in the early 90s to address the IP address shortage. Aligning the various actor-networks and protecting the installed base proved successful.

1 1 1 1 1 1 AIS Monteiro, E.1998. “Scaling information infrastructure: the case of the next generation IP in Internet”. The Information Society. 143:229 - 245

31 Maritime Classification Company: Balancing local contexts and corporate standards, adoption was successful, but scaling problematic.

1 1 0 1 0 1 I Rolland, K. and Monteiro, E. 2002. “Balancing the Local and the Global in Infrastructural Information Systems”, The Information Society, vol. 18, nr. 2, s. 87 – 100.

32 Telecom: The case explores the balance between central control and local autonomy. Relinquishing control led to innovative and successful infrastructure.

1 1 1 1 1 1 AIS Nielsen, P. and Aanestad, M. 2006. “Control Devolution as Information Infrastructure Design Strategy: A case study of a content service platform for mobile phones in Norway”, Journal of Information Technology, 21, pages 185-194

33 Broadband Mobile Services in South Korea: The case explains the rapid diffusion of broadband mobile services in Korea.

1 1 1 1 1 1 AIS Yoo, Y, Lyytinen, K, Yang, H. 2005. “The role of standards in innovation and diffusion of broadband mobile services: The case of South Korea”, Journal of Strategic Information Systems, Vol 14, Issue 3.

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34 University software: SAP module/Uni module: The successful generification and adaptation of two university software packages.

0 1 1 0 1 1 AS Pollock, N, Williams, R. and D’Adderio, L. 2007. “Global Software and its Provenance. Generification Work in the Production of Organizational Software Packages”. Social Studies of Science.

35 Health: A case on an innovative regional health network in Greece, with adoption and scaling problems, because failing to take part in a power network.

0 1 0 1 0 0 I Constantinides, P. and Barrett, M. 2006. “Large-Scale ICT Innovation, Power, and Organizational Change: The Case of a Regional Health Information Network.” Journal of Applied Behavioral Science 2006; 42; 76.

36 eGovernment: A project aimed at improving workflow between a state and federal level, with sustainable growth.

0 0 1 0 1 1 AS Pipek and Wulf, 2009. “Infrastructuring: Toward an Integrated Perspective on the Design and Use of Information Technology.” JAIS, May.

37 Health: A large NHS project in the UK fails because of too technical focus and no clear adoption strategies.

0 0 0 1 0 0 I Greenhalgh, T. 2010. “Adoption, non-adoption, and abandonment of a personal electronic health record: case study of HealthSpace.” British Medical Journal.

38 US petroleum company CostCo: Infrastructure project with limited effect because of strong focus on cost reduction.

0 0 0 0 1 0 S Broadbent, M., Weill, P., and St.Clair, D. 1999. "The Implications of Information Technology Infrastructure for Business Process Redesign," MIS Quarterly 23:2, pp 159-182.

39 US petroleum company: Innovative infrastructure project with redesigned business processes and sustaining growth.

0 0 1 1 1 1 AIS Broadbent, M., Weill, P., and St.Clair, D. 1999. "The Implications of Information Technology Infrastructure for Business Process Redesign," MIS Quarterly 23:2, pp 159-182.

40 US retail company: Innovative project with adoption and scaling problems.

0 0 1 0 0 0 A Broadbent, M., Weill, P., and St.Clair, D. 1999. "The Implications of Information Technology Infrastructure for Business Process Redesign," MIS Quarterly 23:2, pp 159-182.

41 US retail company: Opportunity oriented project with redesigned processes and successful adoption and scaling.

0 0 1 1 1 1 AIS Broadbent, M., Weill, P., and St.Clair, D. 1999. "The Implications of Information Technology Infrastructure for Business Process Redesign," MIS Quarterly 23:2, pp 159-182.