Co-creating Value from Open Data: From Incentivizing Developers to Inducing Co-creation in Open Data Innovation Ecosystems

Book Chapter for “Open Innovation and Innovation Ecosystem”

edited by Satish Nambisan

Authors: Brunswicker, Sabine; Majchrzak, Ann; Almirall, Esteve; Tee, Richard

Abstract

Open governmental data available via platforms like data.gov have earned a place in the innovation

agenda of governments and local authorities alike. To successfully make use of these sources,

governments around the world experiment with competitive virtual contests or challenges to ignite the

creativity of developers and hackers and motivate them to turn this data into novel digital applications.

However, such efforts don't seem to be sustainable. Applications developed in such contests regularly

fail to ignite the continuous use by the end users. We argue that governments need to adopt an

ecosystem perspective facilitating co-creation within the diverse open data innovation ecosystems of

developers, producers, and users in order to foster the generativity needed for continuous value

creation. However, various tensions among actors appear along the way. Taking a paradoxical view

towards ecosystem tensions, we propose a socio-technical infrastructure that supports ecosystem

generativity by addressing latent tensions in the ‘breeding zone’ of an open data innovation. The

infrastructure supports generative responses to these tensions in three ways: creating virtual trading

zones, supporting the duality of stable and dynamic roles, and providing technological affordances for

fluidity. This framework could set the stage for future research, encouraging system designers and

policymakers to foster co-creation in open data innovation ecosystems.

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1 INTRODUCTION

Governmental actors increasingly engage more purposively in different strategies and modes

of ‘governmental open innovation’ (Brunswicker & Johnson, 2015). They publically release

governmental data that was previously hidden from the public with the objective to fuel innovation. In

the US, since 2009 all governmental agencies have been instructed to publish governmental data that

was previously hidden from the public (Janssen, Charalabidis, & Zuiderwijk, 2012). Today, the online

portal data.gov provides access to several hundred thousand machine-readable datasets in areas such

as energy, health, and education (McDermott, 2010; Peled, 2011). To ensure that open data is

successfully turned into open data innovations – defined in this chapter as digital applications that are

novel and useful - governments increasingly make use of virtual innovation contests and online

crowdsourcing initiatives. These efforts invite a crowd of developers via an open call and ask them to

design novel open data applications. Such efforts emphasize competition among these creative

individuals and prevent any virtual interaction among the participants in the hopes to increase the

developer’s efforts (Almirall, Lee, & Majchrzak, 2014; Brunswicker & Johnson, 2015; Noveck, 2015).

However, there is first empirical evidence that the design of these efforts is inappropriate. They fail to

facilitate the creation of novel and useful applications that are finally adopted and continuously used

(Janssen et al., 2012; Lee, Almirall, & Wareham, 2015). In short, they don't foster generative value

creation so that efforts sustain itself (Wareham, Fox, & Giner, 2014; Yoo & Euchner, 2015; Zittrain,

2008). The Leafully case illustrates such lack of generativity. The software company created a free

app that allows citizens to analyze their own electricity usage behavior and was awarded with

$100,000 as the winner of the “Apps for Energy challenge”; yet, over a year later, Leafully has only a

few thousand users out of the 34 million potential residential users (Cooper, Han, & Wood, 2012).

Open data initiatives in Europe that followed the example of the Federal Government in the US show

similarly disappointing results (Almirall et al., 2014; Lee et al., 2015).

We argue that virtual innovation contests, and the digital information systems used to realize

them, are not sufficiently fostering generative value creation as their design encourages individualism,

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knowledge herding and short-term value appropriation of prize money or other immediate rewards.

They do not focus on continuous value creation throughout the lifecycle of an application - from its

inception to its operational use. In order to ensure continuous value creation, we need to move away

from asking the question of how to design socio-technical infrastructures that encourage developers to

build technically functioning applications. Rather, we need to focus on infrastructures that facilitate

the participation of a diverse innovation ecosystem of actors spanning the entire lifecycle of an open

data application - including developers, potential producers, complementary service providers, and the

users themselves - to share their perspective and jointly co-create innovative open data solutions (A.

Majchrzak & Malhotra, 2013). We use the concept of an innovation ecosystem to describe the

collection of actors (and not just firms), who jointly produce an offering, such as an open data service

but who’s actions and choices are independent (K.J. Boudreau & Hagiu, 2009).

Indeed, we postulate that innovation co-creation among the ecosystem actors is pivotal for

facilitating generative value creation. Unfortunately, innovation scholars have paid little attention to

this question. The academic literature on open data and governmental open innovation is scarce (Peled

2011, Kassen 2013). Recent contributions are focused on the technical challenges of open data, and

highlight the institutional barriers and the reluctance of agencies to adopt the Federal open data policy

and to share data (Peled 2011). The flourishing literature on innovation crowds is also lacking an

ecosystem focus as they usually assume that the sponsoring organization either uses the external ideas

or solution internally or uses the contest to launch a two-sided platform like Apple that thrive because

of competition and network effects (Kevin J. Boudreau & Lakhani, 2013). Thus, they provide little

explanations for how to facilitate co-creation among an open data innovation ecosystem. Only recently

do scholars articulate a need for an ecosystem focus in open data innovation (Brunswicker & Johnson,

2015; Lee et al., 2015). These contributions also point us to tensions that emerge when diverse actors

participate in the early stages of an open data innovation ecosystem. For example, many existing open

data innovation ecosystems are characterized by misaligned incentives. The use prize money in

competition usually triggers a short-term orientation of the developers rather than a long-term focus on

value capture (Lee et al., 2015). How to channel these tensions in way that the lead to co-creation and

generative value creation remains unanswered. Thus, we ask: What socio-technical infrastructure is

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required in order to channel the tensions in open data innovation ecosystems towards generative

value creation?

To answer this question we take a dynamic view towards co-creation in open data ecosystems

which explicitly considers the contradictions and tensions among the various ecosystem actors (Das &

Teng, 2000; Farjoun, 2010; Smith & Lewis, 2011; Wareham et al., 2014). Integrating the perspectives

of the diverse ecosystem actors is no simple matter as co-creation requires generative responses to a

three critical tensions among the ecosystem actors (1) Competition versus collaboration, (2) rigidity

versus flexibility, and (3) short-term versus long-term orientation. Such generative responses are

needed to ensure that the ecosystem becomes ‘generative’. To develop such generative responses, we

argue that a socio-technical infrastructure is needed that supports these generative responses in a

tripartite way: (1) Establishing virtual trading zones for knowledge sharing as well as knowledge

protection (2) Supporting both stable and emerging roles, and (3) Offering technological affordances

that support managing fluidity and the integration of short-term as well as long-term orientation.

To develop our argument, we first introduce a lifecycle-oriented perspective towards open

data innovation ecosystems. From there we enumerate and discuss the main tensions that arise in the

process and finally we approach the socio-technical infrastructure that encourages generative

responses to these tensions. We present three propositions that may shape future research in the field

of open data innovation ecosystems. We conclude by highlighting the broader implications of our

framework for research as well as innovation practice.

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2 FROM DEVELOPER CROWDS TOWARDS OPEN DATA INNOVATION

ECOSYSTEMS: A LIFECYLCE-ORIENTED PERSPECTIVE

We begin with a discussion of the particularities of open data innovation ecosystems and its

actors. In order to foster generative value creation, an open data innovation ecosystem requires the

ability of this system to sustain itself over time. In short, it also needs to be generative (Zittrain, 2008).

Thus, we propose a lifecycle-oriented view towards an open data innovation ecosystem that covers all

stages of an innovation process, from the inception to its continuous improvement. Second, we argue

that co-creation among different ecosystem actors is needed already in the early stages of an

ecosystem, in order to create the conditions for generative value creation (Faraj, Jarvenpaa, &

Majchrzak, 2011). Our framework is summarized in Figure 1. We first introduce the concept of a

lifecycle-oriented view towards open data innovations and generative value creation. Afterwards, we

turn to the open data innovation ecosystem and the actors that are involved along the open data

lifecycle. We then discuss the tensions that emerge when the diverse actors involved co-create value

jointly.

Figure 1: Framework of a Lifecycle Oriented Open Data Innovation Ecosystem

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2.1 A Lifecycle Oriented Perspective towards Open Data Innovation

Each open data service application has a lifecycle, from initial identification, through design,

development, deployment, and continuity, all of which are preceded by the opening of the data.

Moreover, each application consists of two aspects: the application that pulls the open data and

displays it to the user, and the service that is provided by displaying the data. For example, in the

Chicago application, called flu shots, the application uses data on the locations of flu shot clinics,

maps, and data on the cost of flu shots, and the service that is provided consists on helping the citizen

to find free flu shot providers in their local area which is made possible by typing in one’s zip code

and seeing a map of the free flu shot providers. Therefore, in developing an application that is likely to

promote value creation, the entire lifecycle needs to be considered both for the data aspect of the

application as well as the service portion of the application.

To consider the entire lifecycle in the co-creation of the application and service requires

integrating principles from concurrent engineering in new product development and design

engineering which assumes a parallel (rather than sequential) interaction among various product

lifecycle processes (Wheelwright & Clark, 1992; Yang, 2007) . According to this integrated problem

solving view, new products are designed in an integrated way considering all product lifecycle phases,

including the phases where the product is launched and used by customers. This approach causes the

developers to consider all elements of the product lifecycle from conception through disposal,

including quality, cost, schedule, and user requirements. Empirical studies on new product

development literature clearly suggest that concurrent engineering may improve development

performance in terms of time, costs, quality and the customer benefits (Valle & Vázquez-Bustelo,

2009).

In analogy to this product-centric lifecycle perspective, we argue that the open data design and

co-creation process of requires the concurrent and integrated design of the application lifecycle. In

order to ensure continued value creation for the user, the co-creation of open data applications requires

the design of the service processes and interactions in which the service is used and the consideration

of complementary services and products required to make sure that the user can benefit from the

services in which the application is embedded. This also requires the consideration of the specific

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nature of services. Following the service-dominant view, services are not “exchanged” but are co-

created by a variety of different service actors (Vargo & Lusch, 2007). Thus, when developing open

data applications, those involved in the design need to envisage future service scenarios in which the

application is used, design the service interactions and the various interrelationships with other

services, products, and data at various services touch points and other actors related to them.

Due to the intangible nature of services in which value “unfolds” during use, prototyping,

experimentation, and co-creation is pivotal. Trial-and-error learning and continuous co-creation

through real-world and virtual prototyping will significantly improve the value creation potential of

open data applications and their services (Brunswicker, Wrigley, & Bucolo, 2013; Thomke, 1998). A

human-centered approach may offer additional benefits as it goes beyond a functional perspective of

design and considers emotional and symbolic aspects of services innovation (Verganti, 2008).

Following this idea, a novel service should hold a meaning for the user in a social and even emotional

sense, which creates a reason for continuous use and value creation.

To sum up, a continuous co-creation perspective suggests that is not enough to design and test

an application. Open data co-creation needs to include the development of various services scenarios

and experimentation along the future service processes and interactions considering the social and

personal context of the service users.

2.2 The Open Data Innovation Ecosystem and its Actors

A lifecycle-oriented perspective requires the consideration of various actors of an innovation

ecosystem, each representative of different points in the application lifecycle. These actors include

those holding knowledge relevant after the app is launched and used, such as service needs,

opportunities of services in which applications can be integrated or complementary service needs. For

example, the application may provide the data indicating the length of wait times at different

Department of Motor Vehicle (DMV) facilities, but the service that the individual is actually interested

in is having his DMV needs quickly served by the DMV. Thus, in this case, the DMV becomes the

provider of a service that has been facilitated by an application on wait times. As such, since the DMV

is part of the application lifecycle, the DMV is an actor that needs to be involved in the early stages of

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an open data innovation ecosystem. In addition, we assume that actors with particular insights into

data standards, technology development needs, appropriate marketing channels, and service operation

mechanisms can significantly improve continuous value creation when included in the co-creation

process since they understand (and in some sense control) potential barriers to deployment and

widespread use. Thus, the ecosystem supporting the development, deployment, and widespread use of

an application needs to include the whole range of actors involved in the lifecycle.

The existing dialogue on open data is dominated by the emphasis of pre-competitive

innovation crowds - a form of an innovation ecosystem that plays an important role in the early stages

of an open data innovation lifecycle. Such a front-end innovation ecosystem brings together only

‘invention’ oriented actors such as technology savvy developers, scientists, and tinkers co-create

innovative ideas and test them (Kevin J. Boudreau & Lakhani, 2013). On other end, management

theory on strategy and operations highlights the importance of ecosystems in competitive market

situations. Such ecosystems describe an operational network of independent organizations,

technologies, consumers and products (Iansiti & Levien, 2004). Due to the interdependencies of

business activities among these actors and their role for competitive advantage, management literature

is concerned about how to influence assets and resources outside of their organizational boundaries

and how to manage value capture and appropriation of financial benefits. According to this view a

“focal” firm – a kind of a “hub” – needs to consider its interrelationship with suppliers, customers,

complementors, and competitors when making a competitive move as their own actions need to be

evaluated within the context of the overall “value constellations” and the performance of the overall

ecosystem (Adner & Kapoor, 2010; Almirall et al., 2014; Iansiti & Levien, 2004; Nambisan &

Sawhney, 2011),

In an innovation context, we argue that is time to consider not just the role of value capture

and how ecosystem actors will bargain over this, but also include an explicit consideration of how

value can be created in the first place. In particular, we take inspiration from an emerging line of

research on generativity (Yoo & Euchner, 2015; Zittrain, 2008), which emphasizes the importance of

allowing systems to create, develop and extend it beyond what could be anticipated a priori. Such

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generative systems allow for the creation of things that are difficult, if not impossible, to foresee when

the system is designed initially.

Because of that, we also need to extend the “front-end” view of innovation communities in

open innovation and open data towards a lifecycle-oriented innovation ecosystem. Such an innovation

ecosystem ensures that the ecosystem co-creation activities are implemented in an integrated learning

perspective and spans the overall co-creation lifecycle from the standardization of data toward the

continuous improvement of applications and services in which open data applications are used. This

innovation ecosystem consists of different independent actors who co-create and test applications as

well as services and evolve and grow an ecosystem of actors who independently play a role in

continued value creation from services in which open data applications are used including users,

services providers, or service integrators. We assume that these ecosystems do not necessarily imply a

“focal hub” structure. They are less centralized and emerging, include a variety of different actors, and

contain relationships that are also social and personal in nature. In the Data.gov context, such a

lifecycle-oriented innovation ecosystem spans actors related to all phases of the application lifecycle

including the operational phase such as open data service providers, application developers,

application platform providers (e.g. Apple), customers, consumers, complementary service providers,

service integrators, and distributors and marketing partners – just to name a few.

2.3 Tensions Emerging in the Open Data Innovation Ecosystem

Integrating this open data innovation ecosystem along the lifecycle is not straightforward. First is the

problem that the actors will have different preferences and interests. For example, a company selling

solar panels may only be interested in supporting the wider deployment of an electricity-monitoring

application if it can be integrated into existing monitoring devices, while a non-profit organization

encouraging less use of electricity may be interested in promoting the application only if the

organization’s message can be integrated into its use. Second, actors are geographically distributed

and thus not easily “brought together”. Further, studies on innovation co-creation among individuals in

online communities, a particular form of an ecosystem, suggest that there will be wealth of tensions

between actors that jointly co-create new knowledge virtually (Faraj et al., 2011). In an open data

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innovation ecosystem we assume even greater tensions due to greater diversity in terms of motives,

resources, knowledge, and also greater contradictions due to the co-existence of producer innovators,

who aim to create financial benefits from their investment into open data innovation, and user

innovators, who participate in open data innovation only for personal reasons (Lee et al., 2015).

In particular, Das and Teng’s (2000) work on tensions in strategic alliances provides a useful

perspective to consider different types of tensions that may emerge in open data innovation

ecosystems. Their paper focuses on the following tensions: (1) Cooperation versus competition, (2)

rigidity versus flexibility, and (3) Short term versus long-term orientation. We briefly describe each of

these tensions below:

2.3.1 Cooperation versus Competition

In simple words, the tensions between cooperation and competition describes the fact that

different actors that are involved in the open data ecosystem may compete in one area, but might also

cooperate in another one (Das & Teng, 2000). An example of this tension commonly arises between

units of local governments or established organizations, and entrepreneurial app developers. Local

governments and large established firms regularly collaborate with application developers in the

definition of social problem to be solved with a new open data innovation or new features for an

existing application. Through hackathons and competition, open data application developers are

regularly sharing their ideas and new features for open data applications with local governments.

However, when it comes to the question of value capture from those ideas and features, these actors

compete: Governments and established business favor the comprehensive applications that they have

developed themselves. Thus, they tend to integrate new features that appear in applications developed

by entrepreneurial developers rather than supporting the value appropriation for the entrepreneurial

developer. While there is certainly an advantage for the local government this situation prevents new

and existing developers to increase their market share and in many cases threatens their business (Lee

et al., 2015). Indeed, many open data hackathons spur this tension, in particular if they foster free

disclosure of ideas and prototypical open data applications. Developers and entrepreneurs are not

always eager to disclose their application. Thus, even if they are interested in collaborating with the

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local governments to define the problems and data sets needed to tackle the problem, there is some

sense of competition when it comes to the question of creating and capturing value from new

applications or new features developed by them.

2.3.2 Rigidity versus Flexibility

As Das and Teng (2000) note in the context of strategic alliances, firms need to find a balance between

being sufficiently flexible, while also maintaining some degree of rigidity. Likewise, we also expect

this tension to be relevant for the different actors operating in open data innovation ecosystems. An

example of this tension typically emerges as new needs arise such are the release of new open data

sets, the promotion of open data datasets, the organization of hackathons, or the need to manage

accelerators or co-creation exercises. Local governments have difficulties in responding to these new

demands for flexibility in a timely fashion because their lack of flexibility to adapt their routines.

These tensions have led to appointment of intermediary organizations to foster a better connection

between the open data available and a flexible set of potential problem areas for developers to work

on. An extreme example is Infoshare in the Helsinki area. Recognizing that open data could not be

effectively established taking only in consideration the administrative area of the City of Helsinki,

they united the five cities in the Helsinki region into a single project ‘Infoshare’, responsible for open

data in the whole region.

2.3.3 Short-term versus Long-term Orientation

A third but also very important tension among the various ecosystem actors results from the

short-term versus long-term orientation of the different actors (Das & Teng, 2000). For instance,

public policy makers are regularly focused on long-term policy objectives. Their orientation differs

from profit-oriented firms and entrepreneurial ventures active in open data. The latter are interested in

short-term or at least medium-term rewards. The differences in motivations among the application

developers also illustrate differences in the temporal orientation even within one group of actors.

When the motivations of the developer focus more on obtaining visibility for cool applications rather

than sustainable ones, their orientation is often short-term (Lee et al., 2015). However, it is also

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common that governments discontinue interactions with developers and entrepreneurs as soon as an

innovation contests has been completed. In short, we frequently encounter the situation that a short-

term focus hampers long-term ambitions.

3 MANAGING TENSIONS: A SOCIO-TECHNICAL INFRASTRUCTURE SUPPORTING

GENERATIVE RESPONSES

These three tensions can be handled in manners that are destructive to the innovation ecosystem or in a

dynamic manner that is highly generative (Faraj et al., 2011; Wareham et al., 2014). A generative

digital infrastructure is a socio-technical information systems that creates the ‘venue’ for co-creation

among the innovation ecosystem actors (Henfridsson & Bygstad, 2013; Nambisan & Nambisan, 2013;

Tilson, Lyytinen, & Sørensen, 2010). It does not simply offer functionalities, but also fosters

generative responses to the tensions outlined earlier. As shown in Figure 2, we present three

generative responses that are essential for an innovation ecosystem’s generativity: (1) Virtual trading

zones, (2) dynamic roles, (3) technological affordances for fluidity.

Figure 2: Tensions and Generative Responses

3.1.1 Generative Response 1: Virtual Trading Zone

Open data infrastructures were born as repositories of open data datasets, which were categorized and

displayed ready for download. However, very soon it was clear that in order to meet the objective of

open data innovation that spur continuous value creation, simply just opening datasets and making

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them available for download is inappropriate. The repositories needed to evolve towards a venue of

interactions and exchange of the diverse open data innovation ecosystem actor: policymakers,

developers, digital companies, SMEs, academics and hackers, just to name a few (Lee et al., 2015). As

highlighted above, these actors may be in collaborative and competitive relationships. To respond to

this contradictory logic, over the last years the ‘virtual’ repositories evolved into trading zones for

information and knowledge: They support exchange based on jointly agreed rules for exchange among

actors embedded in different geographic, social, and professional context (Kellogg, Orlikowski, &

Yates, 2006). The encounter of the different constituencies involved triggered change such as the

release of new datasets, new calls for proposals, public consultation, discussion forums, and so forth.

Over time, a functional repository evolved into a socio-technical generative infrastructure comprising

not only a website portal with functionalities such as search or filtering. The generative infrastructure

linked and connected the different actors through events, a range of social networking tools, blogs, and

more features and affordances that supported the exchange among the actors. Virtual open data trading

zones available on this infrastructure supported transformative dynamics that potentially increase

generativity. This evolution has been particularly visible in cities such as Amsterdam, Helsinki or

Barcelona and also New York. They regularly scheduled a large number of events promoting data sets

and data portals with the hope to mobilize both developers and entrepreneurs creating new offerings

and extending the use of existing ones. Concretely, up to 50 events per year took place in cities such as

Barcelona. In a similar manner, the tech meet-up group in New York City became famous as a point of

encounter of the tech community.

Open data infrastructures became therefore a place of encounter where collisions among the

actors facilitated new outcomes through information and knowledge exchange. We can distinguish six

types of ‘trading zones’ and generative outcomes created through exchange as a response to the

tension between competition and collaboration:

1. Open Source ‘Apps’. The creation of new open data applications result from exchange of

information among policymakers, citizens, and new open data intermediaries such as Code for

America that facilitate dialogue between different actors within a particular geographic region

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and also across local geographic boundaries. This has been the case of some of the better-

known applications, such as “Adopt a Hydrant”, a result of the collaboration between the City

of Boston and Code for America. After the launch of the application, application users shared

it widely with other actors, leading to further exchange and also translation of the application

to other context. While the original application was designed for hydrants it was later used

also for fire sirens. The team made the code available via github following the logic of open

source software. This triggered generative activities and the update of the app in other cities.

2. New Dataset through Online Consultation. Developers and entrepreneurs suggest and ask

for new datasets to distinguish themselves from other developers. Because of the active

presence of policymakers in virtual and physical discussion forums and consultation activities,

many new datasets became open. Opening new datasets triggered through online consultations

with developers and entrepreneurs has become common in cities such as Helsinki or

Amsterdam (Lee, Almirall and Wareham, 2015).

3. Online Social Reputation Triggering Team Formation. New open data infrastructures

provide visibility about contributions and contributors through online profiles of ‘fellows’ and

other social technologies. They allow to establish a reputation that becomes essential for team

(re)forming. They also function as a marketplace for hiring not only new team members but

also specific services such as user interface design. The visibility provided has been

instrumental in supporting team formation among different open data actors and roles. This

has been particularly the case of the US where members of Code for America have been

appointed to various positions in the local and federal government.

4. Online Community Forming. The terms civic innovator or open data developer are already

well established, in spite of being recent. Open data infrastructures provide also ways to create

boundaries, where actors belong or not, and these boundaries create the notion of a group

reinforcing cooperation and resolving to certain extent the tension between collaboration and

competition. The community of civic innovators forms across different platforms and social

media sites such as twitter, facebook, blogs, open data portals, and other virtual venues that

aim to bring together a community of civic innovators that follows a joined vision.

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Particularly interesting is the fact that policies or policy blueprints have been devised for the

creation of physical and virtual communities of open data innovators, examples of that can be

found in the work of Nesta in the U.K. with the DSI project or the Infoshare project in the

Helsinki region (comprising Helsinki, Espoo, Vantaa and Kauniainen).

The resolution of the tension between collaboration and competition through such generative trading

zones was essential. The way in which it is solved determines the inner working logic of an

ecosystem. An ecosystem geared to excessive competition will not be able to find common grounds.

A highly collaborative local ecosystem won’t be able to compete effectively. Today, we are still far

away from having found a generative solution to this tension because open data ecosystems are still in

a phase of exploration of potential alternatives (Lee, Almirall and Wareham, 2015). The lack of a

continuous flow of highly innovative solutions (applications) clearly indicates this fact. Therefore,

there is still a lot dynamics and adaption needed to support generativity.

Proposition 1: The better open data trading zones support the dialogue and exchange between

the diverse actors, the greater the generative value creation in the open data innovation

ecosystem.

3.1.2 Generative Response 2: Dynamic Roles

The different ecosystem actors can take different roles during the lifecycle of an open data innovation

(Ann Majchrzak, Faraj, Kane, & Azad, 2013; Nambisan & Nambisan, 2013). Prior literature on co-

creation communities shows, that there are different kinds of co-creation roles that have been

demonstrated to be critical to respond to the tension between rigidity and flexibility. We have

proposed a set of such roles specifically for the open data context (Table .).

Type of actor DescriptionPromoter Promotes standardized open data and informs participantsIdea seeders Creative individuals developing open data solutions and seeding the

creative solutions and open data applicationsGatekeeper Participants who can prevent or regulate the usage of open data in

open data applicationsLead users Future users of the applications and lead users who are early adopters

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of open data applicationsExtender Actors who extend the applications with another application or

technological solution or “bundle” it with other applications so that it improves the value when the application is used (e.g. automatic open data transmission)

Translator/coordinator Actor who translates the service value of the application throughout the various interaction points and aligns and coordinators different actors

Service designer Actors who have the ability to design future service scenarios and make future service touch points visible and tangible

Value constellation designer Actors who can describe potential value creation opportunities in the emerging operational service network

Table 2: Overview of Potential Emerging Roles throughout the Application Lifecycle

These roles are designed to provide the innovation ecosystem with resources that foster

generative solutions from the tension between rigidity and flexibility, rather than breakdowns. In an

online co-creating collective, generative resources are often those of expertise, energy/effort, data,

perspective-taking, creativity, systems-orientation, and dialogue moderation (Faraj et al., 2011). Thus,

roles that bring these resources into a discussion are needed, especially in an open innovation

ecosystem of diverse actors. For example, as shown in Table 2, the role of promoter can help to keep

the actors informed about the nature of the open data, offering new ideas on data usage that may not

have been initially envisioned. Or the role of extender may help the generative process by suggesting

solutions that provide greater potential for CVC even with applications that do not have a wide appeal.

In this area, the case of Yelp has been particularly interesting incorporating in its app the information

on health inspections coming from open data, not only by making it relevant and by creating

significant awareness around it but also spurring the development of other applications using the same

open data dataset (Lee et al., 2015). Research has indicated that actors in crowd-sourcing challenges

can be encouraged to take on these roles if the proper technology affordances are in place.

The digital socio-technical infrastructure supporting these roles serves as an environment from

which ecosystem actors can concurrently and asynchronously design, test, and deploy software

applications in a virtual space. Thus, it requires technological features that allow co-creation

participants to create “service scenarios”, to prototype services and to understand service experiences

through different kinds artefacts (such as narratives, movies, visualizations, charts, etc). To encourage

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the widest involvement of the most ecosystem actors as possible, the affordances will promote

dispersion of usage, so costly face-to-face meetings are avoided and intensity of participation can

range from a single post to continuous dialogue (A. Majchrzak & Malhotra, 2013)

Proposition 2: The greater the support for dynamic roles, the higher the generative value

creation in the open data innovation ecosystem.

3.1.3 Generative Response 3: Technological Affordances for Fluidity

Open data, like many other technologies before, went through the hype curve where many of the initial

expectations failed to materialize into realities (Brunswicker & Johnson, 2015). This failure

demonstrated the inability to overcome the tension between short-term excitement and the need for

long-term value creation.

A new phase started, this one dominated by events such as hackathons and application

competitions. Local, regional and national governments recognizing the limitations of open data

repositories focusing only on availability of datasets, engaged in a process of co-horting and actively

managing innovation ecosystems with the ambition to increase their generativity (Lee et al., 2015).

The objective of this new approach was to trigger the development of mobile applications or

web applications that could not only provide the best use of the existing datasets but also foster the

openness of new ones in a kind of virtuous circle.

Therefore, attention moved from the datasets to the activation of an innovation ecosystem

composed by developers, policymakers, startups, academics and existing companies. Actions and

events following this movement focused on the activation of a community of diverse actors, which has

the ability to sustain itself because of the existence of a common goal.

Key to this process is the notion of a socio-technological infrastructure that supports and

fosters fluidity in the open innovation community in terms of continuous flow of new proposals and

extensions/ reuses of existing ones, continuous participation, knowledge sharing and high levels of co-

creation (Almirall et al., 2014). All of them elements that could ensure that open data infrastructures

can successfully compete on innovation with new or enhanced proposals that can capture the

imagination of citizens.

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Elements conductive to fluidity and generativity Platform affordances Concrete tools

Flow of new proposals through increased generative capacity

Innovation and innovators monitoring

Increased competition on innovation through ranking proposals

Flow of extensions Increased external visibility Events, hackathons, presence in Media

High levels of interaction Innovators monitoring

Needs awareness

Presence of policymakers in the events.Participatory virtual channels Co-creation events.Concrete challenges from the administration.

Knowledge sharing Innovation awarenessInnovation monitoring

Disclosure of Best CasesEvents such a hackathons

Knowledge contextualization and ranking

Innovators monitoring Authoring tools, identification of actors and constituencies

Involvement of a diversity of actors Innovators awareness Co-creation exercisesEvents such as hackathons

High Innovative potential of offerings

Needs awareness Ability to request new datasets

Table 3: Elements of Fluidity, Affordances, and Tools for Affordances

The infrastructure need to yield technological affordances build and mobilize the two critical

assets (Brunswicker & Johnson, 2015): (1) Highly skilled developers, innovative policymakers and

motivated entrepreneurs, who aim to create new proposals with high impact and high societal

transformative capacity; (2) high quality datasets that could enrich existing applications and create

value for new ones. Socio-technological infrastructures need to mobilize these two assets by

transforming them into either new proposals or extensions to existing ones.

To accomplish this objective, platforms should mobilize their existing resources by increasing

the level of competition among its participants. Creating innovation awareness about the current

innovation activities is an important affordance. Through innovation awareness open data

infrastructures trigger the exploration of new proposals, particularly by actors with comparatively less

successful solutions (Lee et al., 2015). The affordance ‘innovation awareness’ translates can be

classified into three groups of affordances because they make use of different tools: innovation

monitoring, innovators monitoring and needs awareness.

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Figure 3: Affordances for Increased Fluidity

1. Innovation monitoring is facilitated through tools that provide visibility of the existing open

data solutions, distilling and highlighting the new ones. Tools such as hackathons, co-creation

events, accelerators or platform leaderboards foster this visibility bringing to participants a

sense of the ‘state’ of innovation (Faraj et al., 2011; Kane, Johnson, & Majchrzak, 2014; Lee

et al., 2015).

Visibility is not only important as a trigger for creative exploration of so called ‘greenfield’

open data applications (Hibbets, 2015). It is also an essential element for innovation because

of recombination and augmentation of existing solutions to new contexts.

2. However, visibility is not limited to novel solutions, that is, novel open data applications. It

also covers the innovators pursuing them. open data infrastructures afford visibility on meta-

knowledge through another affordability: innovators monitoring (Leonardi, 2014). The

capacity to monitor innovators allows not only a more informed evaluation of proposals and

solutions but also an opportunity to connect the actors. This connection could be established

among actors who compete– such as other innovators – or most commonly between different

categories of actors, such as policymakers and entrepreneurs that don’t compete among them

(Lee et al., 2015). Further, they might also connect different roles taken by the different actors.

For example, they might connect an idea seeder with an idea extender.

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3. Open data infrastructures not only provide awareness about the actual open data innovation

and on the innovators. They also create needs awareness. For example, such needs and

opportunities relate to administration, citizens or developers. In the case of the administration

or citizens these needs can be potentially addressed by new innovation solutions such as new

applications. However, in the case of developers, untapped opportunities and needs refer to

potential new datasets, that are potentially rich and allow very creative exploration. Though, in

any of the three cases, these opportunities lead to an increase in either the innovation output of

the infrastructure if they result in new open data innovations, or in its generative capacity if

they result on an increase on the stock of open datasets.

These three basic affordances are activated and mobilized through a variety of techniques borrowed

from different strands of practice. They range from techniques of ‘designers’ such as design thinking,

co-creation, participatory design, or living labs to ‘technology’ practices such as hackathons, contests,

and developer communities borrowed from the software development and open source software

(OSS).

The choice of practice has been in many cases dependent on the geographic location. Even

though hackathons were widely used globally, there is variability in how other instruments such as

Living Labs or co-creation were adopted. The later were popular in European Countries but are hardly

used outside of Europe.

Nevertheless, a key element of each practice (or tool) is the capacity to align several

affordances in a single practice. For example, hackathons may not only increase visibility of the

solutions, which in turn triggers dynamics and fluidity because of the interaction of developers and

policymaker. They may foster fluidity even better if they enact all dimensions of innovation

awareness. The continuous use of different practices and tools creates a virtuous circle conductive to

fluidity, hence having a multiplying effect not only on scope, that is the number of new open data

innovations created, but also in terms of the level quality. Overall, innovation awareness can increase

the capacities in terms of scope and innovation quality.

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Proposition 3: The more that an open data infrastructure affords innovation monitoring,

innovators monitoring and needs awareness, the larger, the more diverse, and consequently

more generative will be the open data ecosystem.

4 DISCUSSION AND IMPLICATIONS

Overall, our chapter provides several contributions to existing literature. In particular, we

draw on the emerging interest in generativity (Faraj et al., 2011; Wareham et al., 2014; Yoo &

Euchner, 2015; Zittrain, 2008) to understand how open data innovation ecosystems can be sustained

by overcoming tensions that emerge in these settings. Such a perspective is useful as it suggests that it

is important not just to alleviate or otherwise minimize tensions, but also to positively address them by

allowing the innovation ecosystem to create value beyond what each actor would be able to do

individually.

Our chapter builds on the literature on ecosystems that typically considers the management of

ecosystems from the perspective of a firm that takes on a central orchestration role (Nambisan &

Sawhney, 2011). Our chapter extends this view by considering how tensions in open data innovation

ecosystems are managed by the ecosystem as a whole; governmental actors may take the lead here.

However, this is not necessarily the case, and the cooperation and engagement of other actors is

precisely what is necessary for these tensions to be resolved.

We also draw on existing work on technology platforms (Gawer, 2011, 2014), which has

identified how firms can successfully build trust and manage long-term relations with complementors.

Developing such relationships is not straightforward, as these complementing firms need to contribute

to the platform in spite of facing significant risks that the platform owner may enter these

complementor markets (Gawer & Henderson, 2007). Our chapter highlights the generative responses

that can be utilized to successfully manage this tension between collaboration and competition (as well

as the short and long term health of the ecosystem).

Related work on business ecosystems (Iansiti & Levien, 2004) also emphasizes the need to

balance the way value is captured within an ecosystem. Keystone firms allow all ecosystem members

to capture value, as opposed to “dominators” that discourage ecosystem activity by preventing other

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firms from sustainably participating. Our view, which focuses on generativity to address tensions that

emerge, emphasizes the importance of understanding not just how value is captured, but also how it is

created.

Through this chapter we discussed the evolution and the underlying tensions in open data

platforms. This investigation is a point of departure in the realization that contests are not enough to

fulfill the objectives open data innovation. Through the years it has been a clear understanding that the

level of generativity supported by the open data infrastructure is a fundamental aspect governmental

open innovation. This realization marked the transition from a policy where the opening of new

datasets was the central element to one where the action revolved around measures for fostering the

creation of open data innovation ecosystems. Because of this transition is now even more important

than ever to look at the internal tensions that open data infrastructures may create. Through this

chapter we have discussed the three more relevant and how they can be translated into a positive

dynamic that fosters generativity.

The first one is the tension between collaboration and competition. This is probably the most

important tension given the diversity of actors, endowed with diverse incentives. This tension can be

translated into positive dynamics through virtual ‘trading zones’ where collaboration and exchange is

possible. These trading zones create boundaries and norms of exchange of a new social ‘class’ of civic

innovators. Even if these civic innovators have different backgrounds, and also skills, the terms and

norms of exchange support communication. On its turn, this class generates a discourse that serves as

a catalyzer of the open data innovation ecosystem. The second tension revolves around solving rigidity

versus flexibility. open data infrastructure address this tension through the creation of a diversity of

roles, some of them are stable while others are emerging in the moment.

The last tension relates to the contradiction of short-term versus long-term orientation. This

tension has been addressed technological affordances for innovation awareness. By allowing all

participants to be aware of the new needs, ideas, and solutions, and the meta-information associated

with them, they can asses the prospects of their own path and an adapt it accordingly. In turn, these

affordances create fluidity. Ultimately, this fluidity can also trigger co-creation among the actors

across boundaries and context.

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The generative responses to these three tensions moved the attention away from the open data

itself, to the mechanisms and dynamics that could activate co-creation among the ecosystem actors

throughout the lifecycle. This co-creation is needed to turn an application into one that is continuously

used. The dynamic process has the potential to reinforce itself if successful, in a high intensity virtuous

circle like the one that we have witnessed in the case of mobile applications.

Finally, it is important to note that these tensions to do not disappear but they evolve in degree

and also the lifecycle stage. The more applications are developed, the more the tensions shift to the

latter stages of the lifecycle: For example, as the market for a certain open data application matures

more and more developers enter the segment, triggering competition and crowding, which may

potentially decrease the value creation for the actors in the ecosystem. To take the current open data

infrastructures to the next level, all three dimensions: Trading zones, roles, and technological

affordances for fluidity need to evolve into global infrastructures. The duality of global and local open

data infrastructure will be essential to ensure generativity in global open data innovation ecosystems.

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