A Dynamic Perspective on Collaborative Innovation for Smart City Development: The role of uncertainty, governance, and institutional logics

Abstract

Collaborative innovation is at the heart of smart city development, yet also notoriously challenging due to fundamental differences between public and private sector actors that need to collaborate, while dealing with high levels of uncertainty. Whereas existing practice-based work on collaborative innovation describes various relevant antecedents, barriers and success factors, this prior work potentially underestimates the true complexity of collaborative innovation initiatives. Therefore, scholars have increasingly called for a more dynamic, theoretical understanding of collaborative innovation. In response to these calls, our study draws on institutional theory to build a dynamic understanding of collaborative innovation for smart city development. Specifically, we conduct a longitudinal in-depth case study to develop a causal loop model, grounded in rich qualitative data, to capture and theorize the key behavioural patterns of a collaborative innovation initiative for smart city development. The model describes how the dynamic interplay between uncertainty, adherence to own institutional logics and governance complexity can both enable and undermine collaborative initiatives. We contribute by developing a dynamic theoretical perspective on collaborative innovation, one that promotes cross-fertilization at the intersection of the smart city theory, organization theory and collaborative innovation literature. Moreover, our findings highlight the important role of organization theory, specifically institutional logics, in explaining the collaborative dynamics of smart city development.

Full text

https://doi.org/10.1177/01708406231169422
Organization Studies
2023, Vol. 44(10) 1577 –1601
© The Author(s) 2023
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/01708406231169422
www.egosnet.org/os
A Dynamic Perspective on
Collaborative Innovation for Smart
City Development: The role of
uncertainty, governance, and
institutional logics
Sharon A. M. Dolmans*
Eindhoven University of Technology, Netherlands
Wouter P. L. van Galen*
Eindhoven University of Technology, Netherlands
EGEN (part of PNO Group)
Bob Walrave*
Eindhoven University of Technology, Netherlands
Elke den Ouden
Eindhoven University of Technology, Netherlands
Rianne Valkenburg
Eindhoven University of Technology, Netherlands
A. Georges L. Romme
Eindhoven University of Technology, Netherlands
Abstract
Collaborative innovation is at the heart of smart city development, yet also notoriously challenging due to
fundamental differences between public and private sector actors that need to collaborate, while dealing
with high levels of uncertainty. Whereas existing practice-based work on collaborative innovation describes
various relevant antecedents, barriers and success factors, this prior work potentially underestimates the
*These authors contributed equally.
Corresponding author:
Bob Walrave, Department of Industrial Engineering and Innovation Sciences, ITEM group, P.O. Box 513, Den Dolech 2,
Eindhoven, 5600 MB, Netherlands.
Email: b.walrave@tue.nl
1169422OSS0010.1177/01708406231169422Organization StudiesDolmans et al.
research-article2023
Article

1578 Organization Studies 44(10)
true complexity of collaborative innovation initiatives. Therefore, scholars have increasingly called for a
more dynamic, theoretical understanding of collaborative innovation. In response to these calls, our study
draws on institutional theory to build a dynamic understanding of collaborative innovation for smart city
development. Specifically, we conduct a longitudinal in-depth case study to develop a causal loop model,
grounded in rich qualitative data, to capture and theorize the key behavioural patterns of a collaborative
innovation initiative for smart city development. The model describes how the dynamic interplay between
uncertainty, adherence to own institutional logics and governance complexity can both enable and
undermine collaborative initiatives. We contribute by developing a dynamic theoretical perspective on
collaborative innovation, one that promotes cross-fertilization at the intersection of the smart city theory,
organization theory and collaborative innovation literature. Moreover, our findings highlight the important
role of organization theory, specifically institutional logics, in explaining the collaborative dynamics of smart
city development.
Keywords
collaborative governance, collaborative innovation, institutional logics, smart city development, system
dynamics, uncertainty
Introduction
Enabling smart city development is key in responding to today’s grand societal challenges that
demand the development of innovative solutions that generate value for our society (Appio, Lima,
& Paroutis, 2019; Mora, Deakin, & Reid, 2019a; Mora, Appio, Foss, Arellano-Gault, & Zhang,
2020). Developing such smart city solutions requires urban stakeholders – such as public, private
and civic actors – to join forces in creating and implementing innovations, supported by high-level
collaborative models such as double-, triple-, or quadruple-helix structures (Mora et al., 2019a;
Van Winden & Van den Buuse, 2017). At the project level, collaborative innovation arrangements
allow such a diversity of stakeholders to engage in consensus-driven decision-making processes to
enable innovation for smart city development (Ansell & Torfing, 2014; Sørensen & Torfing, 2011;
White & Burger, 2023).
Whereas various scholars have advocated collaborative innovation arrangements to achieve
desired collaborative outcomes (Ansell & Torfing, 2014; Mazzucato, 2013; Torfing, 2019), it
remains a highly challenging endeavour (Cinar, Trott, & Simms, 2019, 2021) in which actors fre-
quently opt out of collaborations long before the contracts end (Ashraf, Ahmadsimab, & Pinkse,
2017; Leiringer, 2006; Selsky & Parker, 2005). Key issues often arise from disparities in organiza-
tional settings and institutional logics (Thornton & Ocasio, 2008) such as differences in goals,
preferences and ways of organizing between the public and private sector actors involved (Mahoney,
McGahan, & Pitelis, 2009; Quélin, Kivleniece, & Lazzarini, 2017; Rangan, Samii, & Van
Wassenhove, 2006). Moreover, collaborative innovation projects, such as those for smart city
development, involve high levels of uncertainty in terms of goals, processes and outcomes, making
it even more challenging for a multitude of actors to coordinate and align on project strategy
(Hartley, Sørensen, & Torfing, 2013; Rindova & Courtney, 2020).
To better understand collaborative innovation projects – at the heart of smart city development –
scholars have focused on project characteristics, contextual conditions, barriers and best practices
(e.g. Torfing, 2016). While significant progress has been made by (implicitly) drawing on these more
linear (often practice-based) perspectives on collaborative innovation,1 theoretical explanations of the
endogenous and non-linear nature of temporal relationships in and around collaborative innovation
are largely absent. In other words, the extant literature primarily describes the role and influence of
various variables separately, rather than addressing their non-linear (e.g. cascading) interactions.

Dolmans et al. 1579
Meanwhile, several scholars have started to acknowledge the dynamic nature of collaborative
innovation, also in the context of smart city development and hence the need to consider it as a
dynamic phenomenon (e.g. Ansell & Gash, 2008; Cinar et al., 2019, 2021; Lombardi, Giordano,
Farouh, & Yousef, 2012). Here, Sørensen and Torfing (2011, p. 851) note that ‘innovation is a
complex, non-linear and iterative process’. As such, collaborative innovation for smart city devel-
opment can be best understood in terms of endogenous, feedback-driven processes, delays and
non-linear relationships. In this respect, deliberately going beyond the (implicit) assumption of
linearity may enable a better understanding of the behavioural processes and boundary conditions
of collaborative innovation (Wegrich, 2019), offering a deeper understanding of micro-level
dynamics that drive collaboration for smart city development. This insight forms the raison d’être
for our study that seeks to build a dynamic understanding of collaborative innovation initiatives for
smart city development, including the key causal mechanisms that drive these complex
initiatives.
This study draws on an in-depth, longitudinal case study of a collaborative innovation project
for smart city development. We develop a causal loop model (Sterman, 2000) grounded in rich
qualitative data to capture, formalize and theorize the key behavioural patterns of this project. Our
model describes how the dynamic interplay between uncertainty, adherence to own institutional
logics and governance complexity can enable as well as undermine collaborative innovation initia-
tives for smart city development. We contribute by developing a dynamic theoretical perspective
on collaborative innovation (Ansell & Gash, 2008; Cinar et al., 2019; Torfing, 2016) promoting
further cross-fertilization between smart city theory, organization theory and work on collaborative
innovation. Institutional theory serves as a steppingstone in explaining how and why collaborative
innovation partners vary in their adherence to institutional logics over time, which – our case
shows – greatly influences the collaborative dynamics. This dynamic adherence to own institu-
tional logics and the intricate interplay with uncertainty and governance complexity, goes above
and beyond the often-supposed juxtaposition between public and private sector logics, in explain-
ing collaborative innovation dynamics for smart city development. The theorized project-level
collaborative dynamics serve to better understand collaborative innovation activities that, at the
micro-level, form the basis for smart city development.
The next section provides a concise review of the literature on collaborative innovation for
smart city development, the role of institutional logics and complexity in this context and discusses
the need for a more dynamic perspective on collaborative innovation for smart city development.
Subsequently, we outline the research method adopted and present the main findings.
Theoretical Background
Collaborative innovation for smart city development
The development toward smart cities gives rise to complex challenges requiring an open, inclusive
and engaging collaborative environment in which public, private and civic stakeholders can co-
create innovative solutions (Appio et al., 2019; Mora et al., 2019a, 2020). Here, collaborative
innovation, as a distinct form of collaborative governance (Ansell & Gash, 2008), aims to bring
together various organizations, experiences, skills and professional outlooks to enhance innovation
power (Wegrich, 2019). Specifically, collaborative innovation-driven initiatives involve processes
of knowledge recombination (Schumpeter, 1942) which need to go beyond bureaucratic or hierar-
chical modes of innovation in favour of collaborative ones (Torfing, 2019).
The extant body of knowledge on collaborative innovation draws on a cross-disciplinary approach
by bringing innovation studies into the realm of collaborative governance (Sørensen & Torfing, 2011;

1580 Organization Studies 44(10)
Torfing, 2019). Collaborative innovation can be defined as ‘a governing arrangement where one or
more public organizations engage other state or non-state stakeholders in a collective, consensus-
oriented and deliberate decision-making process with the goal to design and implement new, creative
solutions to a current governance challenge’ Wegrich (2019, p. 12). Various studies demonstrate the
positive impact of such collaborative forms of governance on, for instance, public innovation (Ansell
& Torfing, 2014), innovative urban planning (Dente, Bobbio, & Spada, 2005) and even technological
innovation (Mazzucato, 2013). For example, by estimating a linear regression model, Torfing, Krogh
and Ejrnæs (2017) find that collaborative innovation significantly facilitated the combat of crime in
Copenhagen.2
However, while collaborative innovation initiatives are very promising for smart city develop-
ment, they are also highly challenging (Ansell & Gash, 2008; Sørensen & Torfing, 2011). In this
respect, Torfing (2019, p. 5) argues that ‘while collaboration thrives on the presence of a certain
similarity between the actors in terms of their background, education, values and opinions, innova-
tion flourishes when different experiences, views and ideas complement and disturb each other,
stimulating creative problem-solving’. Cinar et al. (2019) review the literature to identify a multi-
tude of barriers for collaborative innovation: organizational barriers (e.g. resistance or lack of sup-
port from specific actors), innovation characteristics related barriers (e.g. complexity related to
technological matters or procedures), contextual barriers (e.g. laws, regulations and policies) and
interaction-specific barriers (e.g. lack of shared understanding). In a follow-up study, Cinar et al.
(2021) provide, among others, further empirical support for the barriers identified and describe
tactics to overcome them. For instance, managers are advised to ‘recognize and overcome prob-
lems as soon as possible because they may evolve into more serious barriers during the process’
(Cinar et al., 2019, p. 284). Similarly, Torfing (2019, p. 7) describes, among others, the role of
institutional design and integrated leadership and concludes that: ‘the drivers of collaborative inno-
vation can be enhanced and the barriers partially overcome, if public leaders and managers assume
the role of “conveners”, “facilitators” and “catalysts”’. These roles imply that leaders need to
engage with a variety of stakeholders to combine arenas, overcome limitations in knowledge and
gain new leadership capabilities to be able to succeed in collaborative innovation.
Nevertheless, the inclusion of various (fundamentally different) perspectives in a collaborative
process is bound to lead to selective perception, where different actors perceive the same problem
differently depending on their background (Dearborn & Simon, 1958). Wegrich (2019, p. 17)
explains as follows:
All of these organizations sign up to the common leitmotif of the collaboration, but this commitment is
formal or superficial only. Under such conditions, there might be a real possibility that those different
stakeholder groups actually have very different understandings about this leitmotif, leading to
misunderstandings and conflicts during implementation. This can be especially true in the ‘fuzzy’ area of
innovation, where actors might have contested notions of its benefits.
In this respect, stakeholder diversity is also likely to challenge the orchestration of the collabo-
ration (Reypens, Lievens, & Blazevic, 2021). This observation signals the importance of an insti-
tutional logics perspective to understand the functioning of collaborative innovation initiatives
(Hartley et al., 2013).
Collaborative innovation, institutional logics and complexity
A collaborative setting that includes actors from various domains needs to effectively combine
different institutional logics (Bryson, Crosby, & Stone, 2006; Mair, Mayer, & Lutz, 2015;

Dolmans et al. 1581
Pansera, Marsh, Owen, Flores López, & De Alba Ulloa, 2023; Thornton & Ocasio, 2008).
Institutional theory serves to explain that situated actors operate within a so-called institutional
framework of rules, norms, knowledge and sedimented discourses (Thornton & Ocasio, 2008).
An institutional logic provides the comprehensive ‘rules of the game’ in any sphere of social and
economic life (Jay, 2013). These logics include, but are not limited to, guidelines for work prac-
tices, governance arrangements and ways of organizing, preferences and goals (Thornton &
Ocasio, 2008). They provide organizations the cognitive models, schemata and standard prac-
tices (Scott, 2003) which actors use as frames of reference to guide and give meaning to their
activities (Smets, Jarzabkowski, Burke, & Spee, 2015).
In the context of collaborating for innovation, the logics maintained by the various actors
involved are often incompatible, which causes trade-offs, conflicts and tensions (Bryson et al.,
2006; Mair et al., 2015). These collaborative settings are thus characterized by institutional com-
plexity (Greenwood, Raynard, Kodeih, Micelotta, & Lounsbury, 2011): private actors need to go
beyond their corporate logic of creating economic value by developing technological solutions that
also create societal value (Kivleniece & Quélin, 2012; Rangan et al., 2006; Venkataraman,
Vermeulen, Raaijmakers, & Mair, 2016). Public actors, on the other hand, can no longer exclu-
sively draw on their social logic, focusing on public goods and social welfare, as they need to give
private actors access to a broader set of resources (Mahoney et al., 2009). Moreover, the pursuit of
innovation implies that public managers, as elected politicians, need to engage in risk-taking
behaviour, which they tend to avoid because any failure might harm their reputation and autonomy
as well as attract career-ruining media attention (Sørensen & Torfing, 2011; Wegrich, 2019).
The simultaneous enactment of divergent logics might cause significant tensions, for instance
when there are conflicts of interest or when satisfying the institutional demands from one side
violates the demands or innovative input from others (Hartley et al., 2013; Wegrich, 2019). In this
respect, each of the organizations involved has distinct decision-making processes and legal
boundaries (Quélin et al., 2017; Seibel, 2015) – implying different value creation and coordination
mechanisms, governance structures and operational procedures – which potentially complicate and
destabilize the joint innovation activity, especially when dealing with uncertainty throughout the
process (O’Toole, 1997). Here, actors guided by different logics may respond differently, at differ-
ent moments in time, to innovation-related uncertainties, thereby influencing the collaborative
effort, for better or for worse (Bryson et al., 2006; Dearborn & Simon, 1958).
Collaborative innovation and uncertainty: The need for a dynamic perspective
Innovation processes are characterized by high levels of risk and uncertainty (Rindova & Courtney,
2020) and typically involve highly iterative processes of prototyping, experimentation and learning
by trial and error (Crosby, ‘t Hart, & Torfing, 2017). The actual innovation outcomes (e.g. the novel
product or service, the market potential) often remain unknown for a long time, may generate
unintended (negative) side-effects, or may eventually not live up to the expectations of those
involved – thereby changing the nature of collaborative settings over time (Ansell & Gash, 2008).
Accordingly, a growing number of scholars point at the need to move beyond a linear perspec-
tive toward a more dynamic understanding of collaborative innovation. Collaborative innovation,
also in the context of smart city development, is thus best understood as a ‘complex, non-linear and
iterative process’ (Sørensen & Torfing, 2011, p. 851) ‘through which a plurality of actors work
together’ (Torfing, 2016, p. 64). Here, Ansell and Gash (2008) had already concluded that such
collaborative processes involve non-linear feedback loops through which commitment, shared
understanding and other factors evolve over time. Correspondingly, Cinar et al. (2021) point at the
dynamic nature of collaborative innovation barriers by describing the variation of barriers across

1582 Organization Studies 44(10)
the innovation process as well as the interactions between those barriers. Moreover, as these col-
laborative barriers may grow in a self-reinforcing manner over time, scholars need to redirect their
attention from the organizational level to the level of collaborative systems and networks (Cinar
et al., 2019; Vangen, Hayes, & Cornforth, 2015). Finally, Wegrich (2019) calls for a deeper under-
standing of the mechanisms that are responsible for biases in collaborative innovation, thereby
pointing at the need to build a more complex understanding of collaborative innovation
behaviour.
Research Method
To build a more dynamic understanding of collaborative innovation for smart city development, we
conducted a longitudinal case study (Yin, 2017) of a specific collaborative initiative for smart city
development. A longitudinal case approach is instrumental in advancing theory, by gaining a deep
understanding of the mechanisms underlying the dynamics of collaborative innovation processes.
By drawing on various sources of data, our case study serves to identify the underlying mecha-
nisms and temporal feedbacks driving the dynamics of the collaborative initiative (Gioia, Corley,
& Hamilton, 2013; Sterman, 2000).
Case setting
Following the logic of theoretical sampling (Eisenhardt & Graebner, 2007), we selected a collabo-
rative innovation project for smart city development driven by technological innovation. This pro-
ject involved a large Dutch municipality engaging in smart city development (henceforth
SmartCity), two large companies BuildCo and (multinational company) TechCo and citizens. The
collaborative initiative aimed to develop a radical technological innovation, geared toward improv-
ing the quality of urban life by means of smart lighting solutions and related (interactive) services
– as such, we refer to this project as Light Up the Future (LUF). More specifically, LUF sought to
develop a smart city grid, consisting of a dense, city-wide Internet of Things (IoT) platform that
would enable the accessibility of real-time data (e.g. from traffic, air pollution and more) and the
development of smart city services. This platform would need to be developed in selected regions
of the city, so-called pilot sites, in which experiments together with citizens would take place. The
multi-stakeholder LUF project was inherently characterized by high levels of uncertainty with
respect to its processes and outcomes and therefore particularly suitable for this study.
Data and data collection
The investigated period ran from 2012 to 2019. From 2016 onwards, we started engaging with
LUF in real time, as longitudinal participant-observers, after SmartCity’s tender process resulted in
the inception of the LUF project. As participant-observers, we were given (almost) unlimited
access to various sources of data regarding the development and functioning of the collaboration
over time as well as relevant dynamics during the period 2012–2015 retrospectively. We relied on
three data sources to develop and triangulate findings (Eisenhardt, 1989; Yin, 2017): (1) semi-
structured interviews; (2) observations made during formal and informal project meetings and site
visits; and (3) archival data on the organizations involved and the LUF initiative. Table 1 summa-
rizes all data sources.
In total, 49 semi-structured interviews were conducted, recorded and transcribed. We started
interviewing key informants of SmartCity, BuildCo and TechCo (see Table 1) after the competitive

Dolmans et al. 1583
Table 1. Data sources and their uses in the analysis.
Source Data type Use in the analysis
Semi-structured
interviews
(primary data)
SmartCity
14 interviews with 9
informants
Informants: contractor, urban
planners (2), program director
build environment, project
manager, strategic information
advisor, project leader,
consultants (2)
Collect detailed
information on key
phenomena of interest
(e.g. uncertainty,
motivation, logics, etc.)
over period 2012–2019
to better understand
how the actors were
involved, interacted
and designed the
collaborative initiative.
Understand how the
actors perceived,
identified and addressed
collaborative dynamics
that unfolded.
BuildCo
23 interviews with
10 informants
Informants: innovation
managers (3), strategy and
business developer, manager
technological innovation, area
manager, board member,
business unit director, regional
director, project manager
TechCo
12 interviews with
10 informants
Informants: researcher, R&D
group manager, LivingLab
group manager, business
development manager (2x),
designer, communications
specialist, former CTO,
content manager, general
manager
Participatory
observation
Formal meetings (104), including periodic gatherings,
strategic sessions and innovation workshops (2016–
2019).
Formal meetings (8) with LUF’s team (incl.
representatives from all actors), during the final phases
of the project (2018–2019).
Formal meetings (2) with citizens from pilot sites (2019)
Site visits (2016–2019) at LUF, SmartCity, BuildCo,
TechCo.
Informal talks that took place before, during and after
site-visits, meetings and/or interviews.
Observe collaborative
patterns and gain insights
in the actors’ motivation,
values, interests and
governance structures.
Observe the interaction
between the actors.
Observe actors’
organization.
Learn the important
issues regarding the
collaboration.
Observe citizen
perspectives.
(Re)connect with
informants and
informally discuss
emerging developments
and our interpretation
thereof.
Archival data Public sources (2012–2019) including: public LUF website,
websites, public annual reports and policy documents of
SmartCity, BuildCo, TechCo, newspaper articles, press
releases, video fragments and social media content.
Non-public sources (2012–2019) including: LUF
meeting minutes (150, 2016–2019) detailing progress,
organization and activities, LUF internal reports,
documents and communication (2016–2019) and
internal documents from SmartCity, BuildCo and
TechCo.
Chronologically trace
key activities and
developments of LUF.
Gain contextual
understanding of
actors’ individual and
collaborative activities
and interests over time.
Triangulation.

1584 Organization Studies 44(10)
process ended – as it then became clear the LUF proposal had won the tender – and we continued
to do so until the project was terminated. The first set of interviews (27 in total) was conducted in
the period 2016–2018 and was also used to, retrospectively, trace developments during the pre-
project period 2012–2015. To minimize retrospective bias, we aimed to collect data about signifi-
cant events – which are easier to accurately recall (Chell, 2004) – from at least two informants or
data sources (e.g. interview data and archival data, or interview data and observation data). This to
ensure that any potential biases or memory lapses were offset by those of other informants or other
data sources (Golden, 1992; Huber & Power, 1985).
The interviewees were asked to elaborate on topics such as their background, their organiza-
tion’s interests, visions and goals, their motivation to engage in the collaborative innovation initia-
tive, the perceived innovation potential and associated uncertainty. A subsequent set of interviews
(22 in total) took place in 2019 and served to better understand how specific events, including the
formalization process, shaped the collaborative dynamics. These additional data also helped refine
and triangulate key findings from the first set of interviews. Whenever necessary, we asked inter-
viewees for additional information on specific events and relationships.
As participant-observers, we attended various types of meetings, such as the bi-weekly LUF
team meetings (2016–2018) in which LUF’s operations, strategies and progress were discussed
and various other strategic sessions, innovation workshops and meetings with citizens. These
efforts allowed us to observe collaborative dynamics and gain insights in the actors’ motivation,
values, interests and governance structures. We also conducted regular site visits, exposing us
directly to the interaction between (representatives of the) the three partners in LUF. As partici-
pant-observers we also engaged in many informal talks that typically took place before or after
meetings, site visits, or interviews. Such talks allowed us to (re)connect with informants and infor-
mally discuss emerging developments by asking informants how they were doing and inquiring
about the developments of LUF. Moreover, by regularly (informally) sharing our preliminary find-
ings with various actors, we were able to continually validate our findings.
Finally, we collected various types of archival data over the period 2012–2019. Archival data
includes public sources such as the public LUF website, (company) websites, annual reports and
public (policy) reports of SmartCity, BuildCo, TechCo and newspaper articles. Non-public sources
include LUF meeting minutes, LUF internal reports, documents and communication and internal
documents from SmartCity, BuildCo and TechCo. The archival data also served to chronologically
trace key activities and developments of LUF, obtain a contextual understanding of actors’ indi-
vidual and collaborative activities and interests and triangulate our findings.
Data analysis
To structure the longitudinal qualitative analysis, we adopted the widely used approach developed by
Gioia et al. (2013) (see also Reay, Zafar, Monteiro, & Glaser, 2019), followed by causal loop model-
ling to facilitate a dynamic interpretation of the collaborative innovation initiative over time (Sterman,
2000). Data analysis commenced shortly after the start of the data collection and we kept iterating
while collecting data in the field, which is critical in longitudinal inductive research (Gioia et al.,
2013; Langley, 1999). Specifically, we started with open coding to make sense of the primary data.
Our goal here was to capture key events and activities over time from the perspective of the inform-
ants, including those events and activities that relate to the collaborative dynamics in the context of
uncertainty. Throughout this research phase, we triangulated the emerging findings with archival data
and observations. To develop a manageable set of first-order concepts, key codes and concepts were
iteratively refined by actively comparing similarities and differences (Gioia et al., 2013). As the data
analysis progressed, we turned to a more theory-driven analysis to better understand the role of

Dolmans et al. 1585
uncertainty in the collaborative dynamics. To distill second-order themes and aggregate dimensions
(Gioia et al., 2013), our analysis pointed at specific collaborative matters, such as motivation and
(shared) innovation potential and specific patterns related to the adherence of institutional logics,
which emerged as the LUF project unfolded. The described coding procedures and analyses resulted
in the so-called data structure outlined in Figure 1. This figure denotes the transition from raw data to
concepts and themes, to make the data analysis as transparent and rigorous as possible (Aguinis &
Solarino, 2019; Gioia et al., 2013).3
Figure 1. Data structure.

1586 Organization Studies 44(10)
Notably, Figure 1 depicts a static data representation of a dynamic phenomenon. Whereas this
type of data structure elegantly demonstrates data aggregation, it cannot capture the (complex)
causal and endogenous relationships that exist among second-order themes and aggregate dimen-
sions (Dolmans, Walrave, Read, & Van Stijn, 2022; Gioia et al., 2013). Because the data indeed
pointed at the existence of complex dynamic patterns (e.g. positive and negative effects of uncer-
tainty, dynamic adherence to one’s own institutional logics), we subsequently drew on causal loop
modelling for further analysis. Causal loop models or diagrams (CLDs) originate from the system
dynamics literature (Sterman, 2000) and are widely used in management and organization studies
to understand and codify feedback-driven systems that generate complex behaviour (e.g. Dolmans
et al., 2022; Van Oorschot, Akkermans, Sengupta, & Van Wassenhove, 2013). More specifically, a
CLD captures all important relationships through a visual representation of key ‘variables’ and
shows they are interconnected. These variables include, but are not limited to, factors, things,
issues, actions, processes and feelings (Sterman, 2000). CLDs employ arrows to represent the
causal relationships between those variables – and can be either positive or negative – which, taken
together, create positive or negative feedback loops. We developed a CLD from the data structure
by translating the inferred second-order themes and aggregate dimensions to variables and feed-
back loops in the CLD, to capture the key mechanisms driving the dynamic complexity in LUF’s
collaborative processes.4
While analysing the data and codifying the key findings, we observed a so-called tipping point
(Walrave, 2016), that is, a fundamental change in the system’s behaviour which is triggered by a
particular event or process and manifests itself – in the LUF case – in self-reinforcing growth (epi-
sode 1) followed by self-escalating decline (episode 2). The next section structures the main find-
ings according to these two behavioural episodes in the development of the LUF project (Van de
Ven & Poole, 1995).
Findings
This section first describes the collaborative innovation case for smart city development and the
background of the actors involved, to provide a comprehensive contextual understanding of the
initiative. Subsequently, we present the CLD and describe the two episodes that characterize the
collaborative dynamics over time.
The LUF collaborative innovation initiative for smart city development
In its quest to develop a smarter municipality, our focal municipality SmartCity initiated a formal
public procurement (tender) process for innovative smart city solutions in 2012. Various consortia
proposed ideas and plans and the consortium of TechCo and BuildCo (in collaboration with
SmartCity) eventually ‘won’ the tender by the end of 2015. At this point, the three partners were
highly motivated to co-develop an innovative platform for lighting solutions in their Light up the
Future (LUF) project. In 2016, the three stakeholders formally engaged in the LUF collaborative
innovation project, according to their joint ambitions outlined in the tender proposal. Over time,
however, the innovative and open-ended nature of LUF, which initially drove the partners’ collabo-
rative ambitions, made way for a growing number of issues, discussions and frustrations, which
eventually came to dominate the collaborative effort. Finally, three years after the formal start of
the project (in 2019), the three partners jointly decided to terminate the initiative. Figure 2 provides
an overview of the key events unfolding over the course of the project. Moreover, Table 2 provides
background information on the LUF partners, such as their organizing logic, key interest, core
business and organizational goals.

Dolmans et al. 1587
Developing a dynamic perspective on collaborative innovation
In the following sections, the key dynamics that characterized the LUF collaborative innovation
initiative over time (2012–2019) are captured and theorized. We narrate the two main episodes and
ground the resulting CLD in our data. We start with episode 1, the pre-project phase from 2012 to
2016 (see Figure 2). This episode develops from SmartCity’s need for ‘Innovation for smart city
development’ and is characterized by growing motivation, captured by the virtuous ‘Collaborative
innovation motivation’ loop (Figure 3). Subsequently we continue with describing episode 2
(period 2016–2019), the project phase, characterized by declining motivation, captured by the
vicious ‘Collaborative innovation motivation’ loop (Figure 3). Here ‘Formal engagement’ by the
partners brings about a tipping point, where the balancing ‘Formalization’ loops turn the once vir-
tuous ‘Collaborative innovation motivation’ loop in a vicious one, which explains the project’s
demise. To facilitate the interpretation of the virtuous and vicious dynamics in the causal loop
model, we also characterize and contrast the state of the main variables during both episodes in
Figure 3 (i.e. before and after the tipping point).
Episode 1: the virtuous collaborative innovation motivation loop
As explained, episode 1 spans the pre-project phase (2012–2016, see Figure 2) and starts with
SmartCity’s aspiration for smart city development. SmartCity’s ambition to engage in innovation
for smart city development is therefore also the starting point of the CLD in Figure 3, as indicated
by the variable ‘Innovation for smart city development’. For some time, SmartCity had the ambi-
tion to improve the quality of urban life in the city by adopting innovative smart solutions.
Specifically, SmartCity sought to implement smart lighting solutions and related (interactive) ser-
vices by pioneering an innovative (IoT) platform that would also be open for innovation by third
parties. Here, SmartCity envisioned a prominent role for technological innovation:
[Technology] guides our future. . . It leads to breakthroughs [and] a smarter society. But technology can
only transform lives if it interacts with society. [. . .] We’ve both the ambition and talent to develop the
products and services to help solve the grand societal challenges. By co-creating with citizens and
combining digital technology with creativity, we enhance the quality of life of our citizens.
Figure 2. Timeline of the LUF collaborative innovation project.

1588 Organization Studies 44(10)
Table 2. Overview of the three LUF project partners.
SmartCity BuildCo TechCo
Organization Public organization Private organization Private organization
Key interest Public and social interest Commercial interest Commercial interest
Core business Serve the public interest,
also for users of the public
space
Construction,
improvement and
maintenance of public
infrastructure
Development and
commercialization of
technologies
Organization
goals
Monitor and safeguard
public interest now and in
the future
Improve the quality of life
in terms of prosperity and
welfare
Be a forerunner in terms of
innovation, technology and
knowledge development
Develop profitable
projects that match
customer needs and
contractual agreements
Explore and pursue
strategic opportunities
for future business
activities
Develop profitable
products, systems and
services that satisfies
(future) customer needs
Explore and pursue
strategic opportunities for
future business activities
Innovation
interest
Achieve local objectives
and policies
Foster economic
development and
development of innovation
policies
Maintain and increase
status as technology and
knowledge hotspot
Commercial interests,
maintain and increase
national market share
Develop knowledge
and exploit knowledge
nationally
Commercial interests,
maintain and increase global
market share
Develop and exploit
knowledge
Integrate existing solutions
to new projects and / or
smart city grids
Innovation
Vision
Cross-sector collaboration
is necessary to address
societal challenges
Economic scalability serves
as the basis for social
innovation and knowledge
development
Collaborative innovation
initiatives attract and fuel
entrepreneurial activities
and contribute to the
status as innovation,
technology and knowledge
hotspot
Societal challenges impact
future activities, policies
and government
Smart city grid encourages
new facilities and
interactive services that
enhance the quality of life
in public spaces
Business opportunity
and more (financial)
gains for innovation
related investments
through pilot project
overarching activities
Innovation management
will be part of contracts
in nearby future (i.e.
more human-centric)
Opportunity to support
existing customers
to address societal
challenges
Long-term economic
viability through
partnerships
Application of new
business models, that
represent future core
activities, processes and
business management
Business opportunity and
more (financial) gains for
R&D investments through
pilot project overarching
activities
Technology infrastructure
forms the basic
infrastructure for IoT
and digital solutions that
contribute to society (i.e.
more technology-driven)
and smart cities
Opportunity to shift from
selling hardware (i.e.
components) to selling
services, providing an IoT
platform and become a
key player in the smart city
market
Long-term economic
viability through partnerships
Application of new business
models, that represent
future core activities,
processes and business
management
(Continued)

Dolmans et al. 1589
Figure 3. Causal loop model of interplay between uncertainty, adherence to own institutional logics and
governance complexity driving collaborative innovation motivation.
SmartCity BuildCo TechCo
Innovation
strategy
Develop vision and
strategic (collaborative)
approaches on societal
themes (e.g. new services
in public space) that go
beyond mere pilot projects
Develop and facilitate novel
policy frameworks and
governmental mechanisms
Strategize for open
innovation and
partnerships
Invest in novel projects
that contribute
to knowledge
development, innovative
image and distinguishing
capacity
Strategize to gather new
insights in government
mechanisms
Strategize for open
innovation and partnerships
Invest in R&D (processes)
to stay at the forefront of
technological developments
Strategize to shift from
selling hardware to selling
services and providing an
IoT platform
Table 2. (Continued)

1590 Organization Studies 44(10)
Yet, SmartCity also realized that, given its ambitious smart city plans, it would need to involve
other stakeholders in the process by engaging in collaborative innovation: ‘The municipality
aspires [smart city development] but could not define this themselves. Instead, we need [private
parties] to do so. [. . .] Nowadays, we can’t address these kinds of challenges alone, we need each
other.’ Correspondingly, this need, or motivation, is reflected in Figure 3 by the variable ‘Motivation
for collaborative innovation’. Simultaneously, private actors BuildCo and TechCo also recognized
that collaborative innovation was key in the developing smart cities of the future. As TechCo
explained: ‘The future of smart cities is the sum of many parts. Success requires the collaboration
between large and small companies, governments, research institutes and above all citizens.’
Whereas SmartCity envisioned that technological innovation would spur societal value by improv-
ing the quality of urban life, BuildCo and TechCo saw significant commercial and economic poten-
tial in this opportunity to collaborate – and were eager to jointly explore collaboration with
SmartCity and develop a tender proposal. As BuildCo explained:
The collaboration offers us the opportunity to manage the process which is very interesting. [. . .] The
breakthrough is that it does not have to be our self-developed innovation, it can be an innovation from any
organization. In essence it’s about collaboration and collaboration is of course the new way of competing.
SmartCity, BuildCo and TechCo were thus highly motivated to explore collaborative innovation
for smart city development, however, they also realized that such joint innovation project would
inherently come with a high level of uncertainty – as indicated in Figure 3 by the variable
‘Uncertainty’. The open-ended nature of such collaborative innovation initiative meant working
with high levels of uncertainty in terms of processes and outcomes, as a representative of SmartCity
explained:
We want to create scope for innovation, both literally and figuratively speaking. [. . .] This requires a
process without having a clear definition of the outcome upfront. [. . .] After all, we don’t tender a clearly
specified service, instead we search for an outcome in the form of societal impact. A clear outcome [. . .]
is in this case indescribable.
The private partners were equally aware of the required level of uncertainty to enable innova-
tion, as highlighted by TechCo: ‘The challenge of the collaborative innovation process is that it’s
unclear what the actual [urban] needs are and what kind of [innovation] concepts fit these needs.’
In this respect, as detailed in SmartCity’s vision report (2016), working under uncertainty meant
that collaborative innovation required ‘collaborative experimentation [as] the path to our city’s
next stage of development.’
SmartCity, BuildCo and TechCo thus featured strong motivation for collaborative innovation
while acknowledging its high level of uncertainty to produce meaningful technological innovation.
Provided this setting, all three partners recognized they would need to forego some of their (poten-
tially conflicting) conventional ways of working and procedures (or so-called ‘own’ institutional
logics) to enable collaborative innovation. In other words, to make potential collaboration and
experimentation for innovation work, the individual actors would need to be less strict in adhering
to their own institutional logics, as indicated in Figure 3 by the variable ‘Adherence to own insti-
tutional logics’. Statements from a SmartCity vision report (2014–2016) illustrate this awareness:
The ambitions are high. [. . .] This needs a municipal organization that’s allowed to experiment. An
organization that isn’t being guided by precedent. [. . .] For us, this means stimulating and removing
obstacles. Instead of imposing legal requirements, companies, institutions and society in general, expect
us to decontrol, show the capacity to connect and speed up our responsiveness.

Dolmans et al. 1591
In view of the joint innovation ambitions, SmartCity thus deliberately chose not to adhere to
their traditional, outcome-based public procurement logic:
[Technology] is developing rapidly, this is the reason we no longer develop comprehensive master plans.
The latter would take far too long, regardless of how gladly SmartCity would like to use this method. [. . .]
It’s a special project because of the novel manner of collaboration.
Being highly motivated to join LUF, BuildCo and TechCo were also prepared to deviate
from their own institutional logics. Because internal innovation projects in BuildCo and TechCo
typically followed standardized processes and procedures, the boards of both companies needed
to approve this non-standard, open-ended collaborative innovation project, as BuildCo
explained:
We needed approval from the board of directors for this, which is not easy [. . .]. So, the fact that we were
able to make resources available was already an achievement. But we managed because we firmly believed
in this initiative [. . .]. [. . .] we [BuildCo and TechCo] had a strong driving force to [make this collaboration
succeed].
The partners’ joint ambitions for collaborative innovation, together with high levels of uncer-
tainty and less strict adherence to the partners’ own institutional logics, enabled the deliberate
development of a large, shared and unarticulated solution space for collaborative innovation –
corresponding with the variable ‘Shared solution space’ in Figure 3. Such a large, shared solu-
tion space held a vast number of opportunities for technological developments that could create
both social and economic value, thereby appealing to all three partners. Anything seemed pos-
sible. A SmartCity representative explained: ‘The [LUF initiative] has everything to do with
ambition, we don’t know where we’ll end up, but we do have an ambition to be a frontrunner,
adopt all that’s innovative and implement it in the public space.’ BuildCo and TechCo equally
appreciated the large solution space that would allow their organizations, as well as society, to
benefit from innovations to be developed, as BuildCo illustrates: ‘We aim to develop a [techno-
logical] system that’s applicable to our market, but also provides scope for local [social] needs
and new technological developments.’ The collaborative innovation initiative would thus allow
for joint experimentation and exploration of new markets, indicative of the large, shared solution
space. TechCo explained:
To create [social] value that goes beyond [our core technology], but also to venture into new markets and
to experiment [with new applications], SmartCity provides a project to leverage our efforts to enter new
markets. In these new markets we’ll not develop new applications [only by] ourselves, but always with
partners. [. . .] The initiative, from a collaborative-mindset, suits this perfectly.
As the ‘shared solution space’ was large and full of potential, yet remained rather unarticulated
(i.e. leaving the expected outcomes largely undefined), the different actors were able to easily
identify (with) the high innovation potential of the collaboration, represented by the variable
‘Innovation potential’ in Figure 3. That is, each actor was able to clearly envision the innovation
potential for their own organization (and stakeholders) within the shared solution space, because of
the many opportunities for generating both social and economic value. TechCo highlighted: ‘At
our department, we were pushing hard [for this project] [. . .] because [smart technology] is really
important for the future of our company.’ In a joint meeting, SmartCity also reflected on the col-
laborative innovation potential for the city and how it could improve the quality of life:

1592 Organization Studies 44(10)
If we combine the frontrunner position of SmartCity and the track record of [BuildCo and TechCo] in
innovation, we’re able to stretch boundaries and explore new opportunities from a shared vision. We can
lead the way in innovation for quality of life and make it a new standard through our combined networks.
This high innovation potential, in turn, further fuelled and heightened the ‘Motivation for col-
laborative innovation’, driving the virtuous nature of the ‘Collaborative innovation motivation’
loop in Figure 3. At the end of the first episode, the collaborative innovation project proposal LUF
was internally approved by all partners and submitted to be considered in SmartCity’s formal ten-
der procedure.
Episode 2: the vicious collaborative innovation motivation loop
The ambitious project proposal, submitted at the end of episode 1, resonated well with SmartCity’s
innovation needs and aspirations. So, at the start of episode 2 (project phase 2016–2019, see Figure 2)
SmartCity formally granted the LUF project:
The municipality already tentatively selected BuildCo and TechCo last year [. . .] [as they] distinguished
themselves in their approach to replacing outdated infrastructure with intelligent solutions, innovation and
collaboration with citizens, the business community, government and knowledge institutions. The first five
years, the consortium will work in five selected pilot areas. After this period, the aim is to also provide the
rest of the city with innovative applications. (LUF project website, 2016)
This milestone, represented by the variable ‘Formal engagement’ in Figure 3, allowed the three
actors to formalize their innovation ambitions. After signing the cooperation agreement, further
formalization commenced, requiring the actors to make the various aspects of the collaborative
initiative and its governance explicit. However, this formal engagement step, marking the start of
episode 2, also constitutes the tipping point where the ‘Collaborative innovation motivation’ loop
changes from a virtuous into a vicious one as the ‘Formalization loops’ unfold. Below we narrate
these formalization loops that characterize the LUF dynamics in episode 2, detailing how the intri-
cate interaction between ‘uncertainty’, ‘adherence to own institutional logics’ and ‘governance
complexity’ (as shown in Figure 3) ultimately leads to the project’s demise.
As part of the formalization process, the three partners now had to further define their collabora-
tive initiative in terms of investments, processes and outcomes, thereby leaving little room for
uncertainty, in sheer contrast with the earlier intended open-ended nature of the project. Not only
SmartCity’s administrative procedures called for this, both BuildCo and TechCo were highly
dependent on their top management’s willingness to make resources available, which also required
more explicitly defined goals and outcomes of the LUF project. TechCo explained: ‘For us, it’s
important to clearly define what we need to deliver, to be able to obtain commitment for the
resources required from our own organization.’ In this respect, the formalization process brought
about a dramatic reduction in ‘Uncertainty’ (Figure 3). Unfortunately, this meant that the three
partners had to make significant concessions to their initial approach of leaving things open-ended
and, as one of SmartCity’s consultants noted ‘the highly innovative project was squeezed in a
[highly complex] procedural format that was just not suitable for this initiative’. TechCo and
BuildCo equally recognized this effect of formalization, which TechCo described as follows:
The partners started to objectify each aspect [of the collaboration], but this was impossible. [LUF] was all
about innovation, notably about a novel way of collaborating, which meant abandoning functional
descriptions, such as: this is what we want to achieve and these are the conditions by which the collaboration
must comply.

Dolmans et al. 1593
BuildCo concurred as they realized that the project had moved from ‘a kind of an experiment to
[become] a contract piece’ that included, suddenly, targets and deadlines.
Whereas the formalization process drove a significant reduction of uncertainty, it also implied
the further internalization or local embedding of the LUF project within each of the three organiza-
tions involved. As LUF’s innovation processes and outcomes were made more explicit, other pro-
cedural matters came into play, such as legal, financial and governance issues. Consequently, more
and more employees from various internal departments at SmartCity, TechCo and BuildCo became
involved in LUF to make sure the project would align and comply with their own organization’s
rules, regulations and ways of working. SmartCity reflected on this development: ‘the plan also
needed to be discussed internally, creating friction and stagnation’. Hence, formal engagement thus
triggered an increasingly strong adherence to established ways of working – called ‘Adherence to
own institutional logics’, in Figure 3 – in each of these organizations. As increasingly more people
became involved, the three partners needed to navigate an increasingly complex setting. BuildCo
observed:
Things become much more complex. If you talk about communication, innovation, data-security, it
involves different [internal] departments who talk along. But all these separate departments have their own
interests, but nobody [i.e. people outside of pre-project core team] knows what [the initial ambition was]
we agreed upon.
Adherence to own institutional logics made the partners ‘interpret the contract in different
ways’, as observed by SmartCity, complicated the quest for a governance framework that aligned
with all actors’ needs and organizational constraints – resulting in highly complex governance
arrangements (denoted by ‘Governance complexity’, Figure 3). SmartCity concluded: ‘The gov-
ernance was too complex, we maybe made it too difficult for ourselves.’
The complex and challenging setting that arose – characterized by a lower level of uncertainty
in combination with a strong(er) adherence to own institutional logics and a high level of govern-
ance complexity – dramatically bounded the once large ‘Shared solution space’ (Figure 3). More
specifically, the ‘governance complexity’, that came about from an increased adherence to own
institutional logics, started to substantially limit the way forward by emphasizing procedures over
open-ended collaborative innovation. TechCo provided an example of such behaviour:
People [would] receive an email that says: But back then you wrote this. And if I look at page 60, article
6, we miss points B and C. [. . .] This is dramatic, it kills all the innovation. And all positive intentions.
BuildCo added: ‘The collaboration was captured in a contract preventing our ambitions to be
materialized. This because of the fact that one can’t force successful innovations and there was no
scope in the contract to switch to a back-up scenario.’
The now very bounded ‘shared solution space’ made reaching a shared understanding on the
innovation(s) extremely difficult, if not impossible – thereby compromising the ‘Innovation poten-
tial’ of the initiative. BuildCo reflected on the implication of this dynamic: ‘Our main aim was to
make a smart city and we ended up delivering processes and little reports. [. . .] So basically, we’re
back at square one.’ The decreased innovation potential also implied that the ‘Motivation for col-
laborative innovation’ started to deteriorate due to a lack in potential and progress. In fact, the once
virtuous ‘Collaborative innovation motivation’ loop now turned into a vicious one as a lack of
motivation drove a stronger ‘Adherence to own institutional logics’, further limiting the ‘Shared
solution space’, in turn undermining the ‘Innovation potential’, et cetera. TechCo characterized
these developments as ‘exhausting’ and resulting in a ‘loss of enthusiasm’. In a final reflection,

1594 Organization Studies 44(10)
TechCo emphasizes the importance to ‘give each other time and the opportunity to collaborate in
an appropriate manner. And this can’t be achieved through a traditional approach and by being
fully absorbed in your own world.’
Eventually, this turn of events made the partners decide to terminate project. They made the
following public announcement:
To our deep regret, we decided in mutual consultation to terminate the LUF initiative. Despite the boundless
commitment of the partners and the substantial investments – both in time and money – it has emerged that
the results do not equate with the hopes and expectations. (LUF project website, 2019)
Discussion and Conclusion
Our study responds to recent calls for developing more theoretical explanations of the endogenous
and non-linear nature of temporal relationships in and around collaborative innovation in the con-
text of smart city development (e.g. Mora et al., 2020; Torfing, 2016; Wegrich, 2019). We con-
ducted a longitudinal in-depth case study of a collaborative innovation initiative for smart city
development, drawing on institutional theory, to develop a causal loop model. This model offers a
theoretical explanation of how and why such collaborations may initially thrive on innovation
potential, yet ultimately fail as the result of endogenous interactions between uncertainty, adher-
ence to own institutional logics and governance complexity. Our findings have various important
implications.
A dynamic perspective on collaborative innovation for smart city development
First and foremost, our findings contribute to a more comprehensive theoretical understanding of
collaborative innovation initiatives for smart city development, specifically with respect to the
mechanisms that drive their dynamic complexity. Specifically, our model is one of the first to cap-
ture and theorize micro-level dynamics that characterize collaborative innovation efforts for smart
city development (Ansell & Gash, 2008; Cinar et al., 2019; Mora et al., 2019a; Mora, Deakin, &
Reid, 2019b; Torfing, 2016; Wegrich, 2019), by drawing on organization theory (Mora et al.,
2020). Below, we provide the main implications of our work.
Scholars studying smart city development have long recognized the importance of partnerships
and collaborations – notably through double-, triple- and quadruple-helix collaborative models
(Mora et al., 2019b). In this respect, Mora et al. (2019a, p. 76) report that ‘public and private sector
collaboration is the core engine behind the four smart city development strategies under investiga-
tion and the programme of activities’ that they investigated. While many argue for the importance
of including all relevant stakeholders – to enable knowledge sharing and collaboration across all
levels of society (e.g. Mora et al., 2019a, 2019b; Selada, 2017) – not much attention has been paid
to the more micro-level collaborative dynamics that arise from such complex collaborative ways
of working. This study shows that such collaborations, while beneficial, are also highly challeng-
ing and subject to intricate dynamics that need to be recognized and considered. Here, our study is
illustrative of the importance of considering such project-level dynamics, through a collaborative
innovation lens, to better understand both the enablers and barriers to smart city development, to
further enable the building of the cities of the future.
In particular, this study responds to those calling for a more dynamic and theoretical under-
standing of collaborative innovation (Ansell & Gash, 2008; Cinar et al., 2019; Torfing, 2016;
Wegrich, 2019). The causal loop model explains the rise and fall of a collaborative innovation
initiative for smart city development by capturing intricate, endogenous interactions between

Dolmans et al. 1595
uncertainty, adherence to institutional logics and governance complexity – elements that might
otherwise have been wrongly positioned as exogenous, contextual factors or as having either a
structurally positive or negative effect on the collaborative effort. Moreover, the topic of uncer-
tainty has remained largely unaddressed in studies of collaborative innovation (see O’Toole, 1997,
for a notable exception).5 Our study is the first to capture and theorize the pivotal role of uncer-
tainty in both spurring and frustrating collaborative innovation. Specifically, our findings demon-
strate how high levels of uncertainty may promote a virtually unlimited solution space as actors are
less bound to their own institutional logics – thereby generating a huge innovation potential. On the
other hand, formalization of collaborative processes may inadvertently limit innovation potential.
As formalization leaves little room for uncertainty in governance arrangements and contractual
agreements, actors increasingly adhere to their own institutional logics, thereby dramatically
reducing the solution space and the associated innovation potential. Here our findings on formali-
zation and innovation for smart city development connect to current debates on how city organiz-
ing and bureaucracy may influence smart city implementation in view of the translation of complex
or external ideas (Khodachek, Aleksandrov, Nazarova, Grossi, & Bourmistrov, 2023).
Moreover, our findings illustrate the important role that organization theory, here institutional
logics, can play in understanding smart city development better. In this respect, this study responds
to calls (Arellano-Gault, Demortain, Rouillard, & Thoenig, 2013; Mora et al., 2020) for drawing
on organizational theory to better understand key phenomena in smart city development. This
paper shows that the often-supposed juxtaposition between public and private sector logics (e.g. in
terms of their processes and goals) is more nuanced in this context, as our findings point to a more
dynamic influence of logics. Here, our model explains how and why actors, involved in innovation
projects, may veer away from their organization’s logics, given certain contextual conditions, to
follow such logics more closely as those conditions change. In similar vein, further research could
draw on other organizational theories, such as behavioural theory (Cyert & March, 1963) or the
attention-based view (Ocasio, 1997), to explain collaborative behaviour dynamics for smart city
development.
Finally, the model presented in this paper also serves to provide a better understanding of the
underlying mechanisms that drive the empirical observations made by others in the field of col-
laborative innovation, such as ‘trust deficits are self-reinforcing’ (O’Toole, 1997, p. 124) and
the creative phase of collaborative exchange, learning and idea generation is perceived as constructive,
rewarding and filled with positive energy, whereas the decision-making and implementation process is
experienced as uncertain, risky and complex and ridden with interest conflicts, antagonism and power
games. (Torfing, 2016, p. 182)
In this respect, our work provides a solid foundation for others to build on, for instance, by
extending the model to include the role of integrated leadership (Torfing, 2019); or by using the
model in Figure 3 to build a mathematical model that would allow running what-if experiments, for
example, to explore how to enable smart city development by preventing the vicious collaborative
innovation motivation loop from becoming dominant.
Practical contributions
Our findings have important implications for those involved in smart city development. Specifically,
it is important to recognize that the various actors involved in smart city development may behave
differently over the various stages of the collaboration. In this respect, our findings highlight that
conventional linear formalization approaches (i.e. contractual agreements that typically include

1596 Organization Studies 44(10)
measurable KPIs) are likely to fundamentally distort the collaborative effort – by exposing con-
flicting logics that bring about a dramatic reduction in the shared solution space and innovation
potential. As such, there is a strong need for novel collaborative mechanisms that facilitate better
collective innovation efforts. Here, collaborative smart city development projects might benefit
from legal forms that facilitate the uncertain and unpredictable process of innovation in urban set-
tings. Policymakers, typically initiating such projects, might consider more ‘flexible’ (mission-
driven) innovation approaches that allow for alternative scenarios and unexpected outcomes. This
could imply using soft performance indicators, not uncommon in innovation, that emphasize learn-
ings over tangible, fixed outcomes with associated deadlines. A real-option approach might be
particularly valuable by enabling a step-by-step approach to deal with uncertainty and risk (e.g.
Lint & Pennings, 2001).
In extension, our findings suggest that managers of a collaborative smart city project should
have substantial discretion and authority to resist major institutional pressures. Inspiration on how
to achieve this might be taken from the corporate entrepreneurship literature (e.g. Ireland, Covin,
& Kuratko, 2009), which recommends a certain amount of separation between the ongoing busi-
ness and (radical) innovation activities – to protect the latter exploratory activities from the short-
term driven exploitative goals of the former – by employing so-called cross-functional units. This
also creates an interesting opportunity for future work, by studying how such units can be enabled
and sustained through smarter policies.
Limitations and future research
This study draws on longitudinal, partially retrospective, data to model and theorize causal dynam-
ics relationships in the context of collaborative innovation for smart city development. Whereas
this type of data is highly useful in making sense of the temporal causal complexity in such set-
tings, it also has several limitations. Although appropriate measures were taken to minimize retro-
spective bias (as detailed under the heading ‘Data and data collection’), it remains a potential
limitation of our findings.
The single in-depth case study approach adopted in this paper specifically served the purpose of
theory building (Eisenhardt, 1989; Eisenhardt & Graebner, 2007). Whereas this approach may
limit the generalizability of the main findings to other empirical settings, the key dynamics
described in this study may well be transferable to other contexts. In this respect, we aimed to
provide a level of methodological transparency that enables empirical replication or further exten-
sion of our findings (Aguinis & Solarino, 2019). We therefore invite future work, also drawing on
other methods, to validate, refine and extend our findings, also in different collaborative innovation
contexts.
This study focused on the dynamics of the interaction between uncertainty, adherence to own
institutional logics and governance complexity in the context of collaborative innovation, rather
than analysing institutional logics per se. Our findings show how actors, depending on the situa-
tion, may choose to (not) comply to their established ways of working and governing, which con-
stitutes a key aspect of institutional logics, rather than a complete operationalization of the
phenomenon. With these findings in mind, future work may engage in more detailed studies of
logics or other related mechanisms that drive dynamic adherence to logics, to explore the complex
relation with collaborative innovation (Reay & Jones, 2016).
Finally, as studies of collaborative innovation (including our study) demonstrate the potential of
cross-field fertilization, future work might benefit from studying and cross-fertilizing with addi-
tional theoretical angles, such as: adaptive management (Allen, Fontaine, Pope, & Garmestani,
2011; Kallis, Kiparsky, & Norgaard, 2009) to better understand collaboration and networking

Dolmans et al. 1597
dynamics; cross-sector collaborations (Bryson et al., 2006) to incorporate knowledge on the effect
and management of conflict; value frames (Le Ber & Branzei, 2010) to better study the influence
of individual interpretations that guide action; political theory (Torfing, 2016) to research power
imbalances and associated dynamics; and institutional theory (Thornton & Ocasio, 2008) such as
the influence of institutional logics – as we did in this study.
In sum, by drawing on the diverse set of angles and discourses as part of organization theory,
one can elaborate on other dynamics such as those involving ideological values, relationships
between social and economic value, formal and informal structures and power dynamics, to better
understand the complexities arising from collaborative innovation for smart city development (see
Arellano-Gault et al., 2013).
Concluding remarks
Collaborative innovation by local governments, companies and citizens is at the heart of develop-
ing smart cities. Yet, it is also notoriously challenging as it requires integrating fundamentally dif-
ferent backgrounds and logics, while navigating high levels of uncertainty. By conducting an
in-depth longitudinal case study, our findings highlight the important role that organization theory
(and institutional logics in particular) can play in explaining collaborative dynamics for smart city
development. A dynamic (causal loop) model of a collaborative innovation initiative demonstrates
and theorizes the intricate role that uncertainty and institutional logics play in enabling and frus-
trating such shared efforts for smart city development.
Acknowledgements
We are very grateful for all the support of editor Renate Meyer and special issue guest editors Luca Mora,
Francesco Paolo Appio, Nicolai Foss, David Arellano-Gault and Xiaoling Zhang. Moreover, we thank the
three anonymous reviewers for their time and valuable feedback, which truly enabled us to keep on develop-
ing this work. Furthermore, we acknowledge Floor Piron for her contribution to the data collection. Finally,
we thank TU/e Lighthouse and all interviewed stakeholders of the studied collaboration.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
ORCID iDs
Sharon A. M. Dolmans https://orcid.org/0000-0002-3652-1057
Bob Walrave https://orcid.org/0000-0002-7349-3737
A. Georges L. Romme https://orcid.org/0000-0002-3997-1192
Notes
1. For instance, direct input-output relations, such as best practices (e.g. Cinar et al., 2021; Crosby et al.,
2017; Torfing, 2019), or the contextualization of specific phenomena as exogenous conditions such as
uncertainty in collaborative innovation (Sørensen & Torfing, 2011; Wegrich, 2019).
2. We refer to Torfing (2019) for an overview of empirical evidence regarding the effectiveness of collabo-
rative innovation.
3. A data appendix is available upon request.
4. In this respect, Torfing (2016, p. 102) also describes that ‘multi-actor collaboration can be characterized
as systems [. . .] and we can describe their operations in terms of the inputs they receive, their internal
processes, their resulting outputs and outcomes, and the positive and negative feedback that these pro-
cesses engender’.

1598 Organization Studies 44(10)
5. O’Toole (1997) describes that uncertainty brings along the need for risk assessment and management,
to keep network actors willing to work on innovation – in the context of the implementation of public
innovation in networked settings.
References
Aguinis, Herman, & Solarino, Angelo M. (2019). Transparency and replicability in qualitative research: The
case of interviews with elite informants. Strategic Management Journal, 40, 1291–1315.
Allen, Craig R., Fontaine, Joseph J., Pope, Kevin L., & Garmestani, Ahjond S. (2011). Adaptive management
for a turbulent future. Journal of Environmental Management, 92, 1339–1345.
Ansell, Chris, & Gash, Alison (2008). Collaborative governance in theory and practice. Journal of Public
Administration Research and Theory, 18, 543–571.
Ansell, Chris, & Torfing, Jacob (2014). Public innovation through collaboration and design. New York, NY:
Routledge.
Appio, Francesco P., Lima, Marcos, & Paroutis, Sotirios (2019). Understanding smart cities: Innovation eco-
systems, technological advancements, and societal challenges. Technological Forecasting and Social
Change, 142, 1–14.
Arellano-Gault, David, Demortain, David, Rouillard, Christian, & Thoenig, Jean-Claude (2013). Bringing
public organization and organizing back in. Organization Studies, 34, 145–167.
Ashraf, Naeem, Ahmadsimab, Alireza, & Pinkse, Jonatan (2017). From animosity to affinity: The interplay
of competing logics and interdependence in cross-sector partnerships. Journal of Management Studies,
54, 793–822.
Bryson, John M., Crosby, Barbara C., & Stone, Melissa M. (2006). The design and implementation of cross-
sector collaborations: Propositions from the literature. Public Administration Review, 66, 44–55.
Chell, Elizabeth (2004). Critical incident technique. In Catherine Cassell & Gillian Symon (Eds.), Essential
guide to qualitative methods in organizational research (pp. 45–60). Thousand Oaks, CA: SAGE.
Cinar, Emre, Trott, Paul, & Simms, Christopher (2019). A systematic review of barriers to public sector inno-
vation process. Public Management Review, 21, 264–290.
Cinar, Emre, Trott, Paul, & Simms, Christopher (2021). An international exploration of barriers and tactics in
the public sector innovation process. Public Management Review, 23, 326–353.
Crosby, Barbara C., ‘t Hart, Paul, & Torfing, Jacob (2017). Public value creation through collaborative inno-
vation. Public Management Review, 19, 655–669.
Cyert, Richard M., & March, James G. (1963). A behavioral theory of the firm. Englewood Cliffs, NJ: Prentice
Hall.
Dearborn, DeWitt C., & Simon, Herbert A. (1958). Selective perception: A note on the departmental identifi-
cations of executives. Sociometry, 21, 140–144.
Dente, Bruno, Bobbio, Luigi, & Spada, Alessandra (2005). Government or governance of urban innovation?
A tale of two cities. disP – The Planning Review, 41, 41–52.
Dolmans, Sharon A. M., Walrave, Bob, Read, Stuart, & Van Stijn, Niels (2022). Knowledge transfer to
industry: How academic researchers learn to become boundary spanners during academic engagement.
Journal of Technology Transfer, 47, 1422–1450.
Eisenhardt, Kathleen M. (1989). Building theories from case study research. Academy of Management
Review, 14, 532–550.
Eisenhardt, Kathleen M., & Graebner, Melissa E. (2007). Theory building from cases: Opportunities and chal-
lenges. Academy of Management Journal, 50, 25–32.
Gioia, Dennis A., Corley, Kevin G., & Hamilton, Aimee L. (2013). Seeking qualitative rigor in inductive
research: Notes on the Gioia methodology. Organizational Research Methods, 16, 15–31.
Golden, Brian R. (1992). The past is the past – or is it? The use of retrospective accounts as indicators of past
strategy. Academy of Management Journal, 35, 848–860.
Greenwood, Royston, Raynard, Mia, Kodeih, Farah, Micelotta, Evelyn R., & Lounsbury, Michael (2011).
Institutional complexity and organizational responses. Academy of Management Annals, 5, 317–371.
Hartley, Jean, Sørensen, Eva, & Torfing, Jacob (2013). Collaborative innovation: A viable alternative to
market competition and organizational entrepreneurship. Public Administration Review, 73, 821–830.

Dolmans et al. 1599
Huber, George P., & Power, Daniel J. (1985). Retrospective reports of strategic-level managers: Guidelines
for increasing their accuracy. Strategic Management Journal, 6, 171–180.
Ireland, R. Duane, Covin, Jeffrey G., & Kuratko, Donald F. (2009). Conceptualizing corporate entrepreneur-
ship strategy. Entrepreneurship Theory and Practice, 33, 19–46.
Jay, Jeffrey (2013). Navigating paradox as a mechanism of change and innovation in hybrid organizations.
Academy of Management Journal, 56, 137–159.
Kallis, Giorgos, Kiparsky, Michael, & Norgaard, Richard (2009). Collaborative governance and adaptive
management: Lessons from California’s CALFED water program. Environmental Science & Policy, 12,
631–643.
Khodachek, Igor, Aleksandrov, Evgenii, Nazarova, Nadezda, Grossi, Giuseppe, & Bourmistrov, Anatoli
(2023). Smartocracy: Context entanglement of the smart city idea and bureaucracy in Russia.
Organization Studies, 44, 1625–1647.
Kivleniece, Ilze, & Quélin, Bertrand V. (2012). Creating and capturing value in public-private ties: A private
actor’s perspective. Academy of Management Review, 37, 272–299.
Langley, Ann (1999). Strategies for theorizing from process data. Academy of Management Review, 24,
691–710.
Le Ber, Marlene J., & Branzei, Oana (2010). Value frame fusion in cross sector interactions. Journal of
Business Ethics, 94, 163–195.
Leiringer, Roine (2006). Technological innovation in PPPs: Incentives, opportunities and actions. Construction
Management and Economics, 24, 301–308.
Lint, Onno, & Pennings, Enrico (2001). An option approach to the new product development process: A case
study at Philips Electronics. R&D Management, 31, 163–172.
Lombardi, Patrizia, Giordano, Silvia, Farouh, Hend, & Yousef, Wael (2012). Modelling the smart city perfor-
mance. European Journal of Social Science Research, 25, 137–149.
Mahoney, Joseph T., McGahan, Anita M., & Pitelis, Christos N. (2009). Perspective – The interdependence
of private and public interests. Organization Science, 20, 1034–1052.
Mair, Johanna, Mayer, Judith, & Lutz, Eva (2015). Navigating institutional plurality: Organizational govern-
ance in hybrid organizations. Organization Studies, 36, 713–739.
Mazzucato, Mariana (2013). Financing innovation: Creative destruction vs. destructive creation. Industrial
and Corporate Change, 22, 851–867.
Mora, Luca, Appio, Francesco P., Foss, Nicolai J., Arellano-Gault, David, & Zhang, Xiaoling (2020).
Boosting urban sustainability through organizing collaborative ecosystems for smart city development.
Organization Studies, Call for Papers.
Mora, Luca, Deakin, Mark, & Reid, Alasdair (2019a). Strategic principles for smart city development: A
multiple case study analysis of European best practices. Technological Forecasting and Social Change,
142, 70–97.
Mora, Luca, Deakin, Mark, & Reid, Alasdair (2019b). Combining co-citation clustering and text-based analy-
sis to reveal the main development paths of smart cities. Technological Forecasting and Social Change,
142, 56–69.
Ocasio, William (1997). Towards an attention-based view of the firm. Strategic Management Journal, 18,
187–206.
O’Toole, Laurence J., Jr. (1997). Implementing public innovations in network settings. Administration &
Society, 29, 115–138.
Pansera, Mario, Marsh, Alex, Owen, Richard, Flores López, Jesús A., & De Alba Ulloa, Jessica L. (2023).
Exploring citizen participation in smart city development in Mexico City: An institutional logics
approach. Organization Studies, 44, 1679–1701.
Quélin, Bertrand V., Kivleniece, Ilze, & Lazzarini, Sergio (2017). Public-private collaboration, hybridity
and social value: Towards new theoretical perspectives. Journal of Management Studies, 54, 763–792.
Rangan, Subramanian, Samii, Ramina, & Van Wassenhove, Luk N. (2006). Constructive partnerships: When
alliances between private firms and public actors can enable creative strategies. Academy of Management
Review, 31, 738–751.
Reay, Trish, & Jones, Candace (2016). Qualitatively capturing institutional logics. Strategic Organization,
14, 441–454.

1600 Organization Studies 44(10)
Reay, Trish, Zafar, Asma, Monteiro, Pedro, & Glaser, Vern (2019). Presenting findings from qualitative
research: One size does not fit all. In Tammer B. Zilber, John M. Amis, & Johanna Mair (Eds.), The
production of managerial knowledge and organizational theory: New approaches to writing, producing
and consuming theory (pp. 201–216). Bingley, UK: Emerald Group Publishing.
Reypens, Charlotte, Lievens, Annouk, & Blazevic, Vera (2021). Hybrid orchestration in multi-stakeholder
innovation networks: Practices of mobilizing multiple, diverse stakeholders across organizational
boundaries. Organization Studies, 42, 61–83.
Rindova, Violina, & Courtney, Hugh (2020). To shape or adapt: Knowledge problems, epistemologies, and
strategic postures under Knightian uncertainty. Academy of Management Review, 45, 787–807.
Schumpeter, Joseph A. (1942). Capitalism, socialism and democracy. New York, NY: Harper & Brothers.
Scott, W. Richard (2003). Institutional carriers: Reviewing modes of transporting ideas over time and space
and considering their consequences. Industrial and Corporate Change, 12, 879–894.
Seibel, Wolfgang (2015). Studying hybrids: Sectors and mechanisms. Organization Studies, 36, 697–712.
Selada, Catarina (2017). Smart cities and the quadruple helix innovation systems conceptual framework: The
case of Portugal. In Sara De Oliveira Monteiro & Elias G. Carayannis (Eds.), The quadruple innovation
helix nexus (pp. 211–244). New York, NY: Palgrave Macmillan.
Selsky, John W., & Parker, Barbara (2005). Cross-sector partnerships to address social issues: Challenges to
theory and practice. Journal of Management, 31, 849–873.
Smets, Michael, Jarzabkowski, Paula, Burke, Gary T., & Spee, Paul (2015). Reinsurance trading in Lloyd’s of
London: Balancing conflicting-yet-complementary logics in practice. Academy of Management Journal,
58, 932–970.
Sørensen, Eva, & Torfing, Jacob (2011). Enhancing collaborative innovation in the public sector.
Administration & Society, 43, 842–868.
Sterman, John D. (2000). Business dynamics: Systems thinking and modeling for a complex world. Boston,
MA: McGraw Hill.
Thornton, Patricia H., & Ocasio, William (2008). Institutional logics. In Royston Greenwood, Christine
Oliver, Roy Suddaby, & Kerstin Sahlin-Andersson (Eds.), The SAGE handbook of organizational insti-
tutionalism (pp. 99–129). London: SAGE.
Torfing, Jacob (2016). Collaborative innovation in the public sector. Washington, DC: Georgetown
University Press.
Torfing, Jacob (2019). Collaborative innovation in the public sector: The argument. Public Management
Review, 21, 1–11.
Torfing, Jacob, Krogh, Andreas H., & Ejrnæs, Andres (2017). Samarbejdsdrevet Innovation i
Kriminalpræventive Indsatser: Slutrapport om Sammenhængene Mellem Samarbejde, Innovation og
Kriminalpræventiv Effekt og Måling Heraf. Roskilde Universitet.
Van de Ven, Andrew H., & Poole, Marshall S. (1995). Explaining development and change in organizations.
Academy of Management Review, 20, 510–540.
Van Oorschot, Kim E., Akkermans, Henk, Sengupta, Kishore, & Van Wassenhove, Luk N. (2013). Anatomy
of a decision trap in complex new product development projects. Academy of Management Journal, 56,
285–307.
Van Winden, Willem, & Van den Buuse, Daniel (2017). Smart city pilot projects: Exploring the dimensions
and conditions of scaling up. Journal of Urban Technology, 24, 51–72.
Vangen, Siv, Hayes, John P., & Cornforth, Chris (2015). Governing cross-sector, inter-organizational col-
laborations. Public Management Review, 17, 1237–1260.
Venkataraman, Hemalatha, Vermeulen, Patrick, Raaijmakers, Aafke, & Mair, Johanna (2016). Market meets
community: Institutional logics as strategic resources for development work. Organization Studies, 37,
709–733.
Walrave, Bob (2016). Determining intervention thresholds that change output behavior patterns. System
Dynamics Review, 32, 261–278.
Wegrich, Kai (2019). The blind spots of collaborative innovation. Public Management Review, 21, 12–20.
White, Leroy, & Burger, Katharina (2023). Understanding frameworking for smart and sustainable city devel-
opment: A configurational approach. Organization Studies, 44, 1603–1624.
Yin, Robert K. (2017). Case study research: Design and methods. Thousand Oaks, CA: SAGE.

Dolmans et al. 1601
Author biographies
Sharon Dolmans is assistant professor of entrepreneurship and technology commercialization at Eindhoven
University of Technology (TU/e). She holds a MSc in finance from Tilburg University, a premaster in econo-
metrics from Erasmus University Rotterdam and a PhD in technology commercialization from TU/e. Her
research revolves around collaborative technology commercialization and societal impact in public and pri-
vate contexts. Her research activities aim to enhance our understanding of these dynamically complex multi-
stakeholder processes, by drawing on insights from organization science, sociology, entrepreneurship, and
strategic management. Sharon’s work has appeared in journals such as Organization Studies, Journal of
Technology Transfer, Human Relations, Space Policy.
Wouter van Galen is innovation consultant at EGEN (part of PNO Group). He holds a MSc and PhD degree
in innovation management from the Eindhoven University of Technology. He is involved in practising and
studying collaborative innovation within the context of smart cities and renewable energy transition. As prac-
titioner he supports companies in achieving their sustainability ambitions in the European energy sector. As
visiting researcher (TU/e), he studies the management of innovation in organizations, public-private partner-
ships and innovation ecosystems. He has published in Energies and presented his work at various interna-
tional conferences.
Bob Walrave is associate professor of modelling innovation systems at the Eindhoven University of
Technology. He holds an MSc in industrial engineering (cum laude) and a PhD from the same university. His
main research interests are centred on strategic decision making in dynamically complex situations in the
context of innovation management. In particular, he is interested in the management of innovation processes
within and across public and private organizations. His work has been published in the Journal of Management
Studies, Research Policy, Renewable and Sustainable Energy Reviews, Industrial and Corporate Change,
Long Range Planning, and many other journals and book chapters.
Elke den Ouden is TU/e Fellow in ‘new business development in public private value networks’ in the
Industrial Engineering & Innovation Science department of the Eindhoven University of Technology (TU/e).
Her research focuses on smart lighting and smart cities. She holds a MSc in industrial design engineering from
the Delft University of Technology and a PhD in technology management from TU/e. She also acts as strate-
gic director of TU/e LightHouse, where she leads projects with municipalities and industry to support the
co-creation of visions and roadmaps for smart lighting and smart cities as part of the university’s valorisation
activities.
Rianne Valkenburg is TU/e Fellow in ‘design-driven innovation of technological solutions’ in the Industrial
Engineering & Innovation Science department of the Eindhoven University of Technology (TU/e). Her
research focuses on open innovation, ecosystem and networked innovation, co-creation and stakeholder
involvement in innovation and product or service development. She holds an MSc in industrial design engi-
neering from the Delft University of Technology and a PhD from the same university. She is founder of TU/e
LightHouse, where knowledge and expertise of the TU/e are brought to practice, especially in the field of
smart city development.
A. Georges L. Romme is professor of entrepreneurship and innovation at Eindhoven University of Technology.
He obtained a master’s degree in economics from Tilburg University and a doctoral degree in business admin-
istration from Maastricht University. He previously was on the staff of Tilburg University and Maastricht
University. His research focuses on design science, new organizational forms, technology entrepreneurship,
innovation ecosystems and related topics. His publications have appeared in Strategic Management Journal,
Organization Science, Organization Studies, and many other journals. For his pioneering work on design sci-
ence and circular forms of organizing, he received the Academy of Management’s Distinguished Scholar-
Practitioner award.