The Sustainable Value of Open Data

@Thorhildur JetzekCBS / KMD1|

The Sustainable Value of Open Government Data

Uncovering the Generative Mechanisms of Open Government Data through a Mixed Methods Approach

Þórhildur Hansdóttir Jetzek

Main supervisor: Niels Bjørn-AndersenSecond supervisor: Michel Avital

LIMAC PhD SchoolDepartment of IT Management

Copenhagen Business School


Why is Data Important

@Thorhildur JetzekCBS / KMD3 I

Stakeholders

Ruth Wisborg, supervisor (previously Morten Binderup)

Ole Jensen, steering committee

Nicolas Lemcke Horst, strategy and other employees as informants

Jens Krieger-Røyen, supervisor

Lars Frelle-Petersen, steering committee

Many informants in different agencies

Professor Niels Bjørn-Andersen, supervisor

Professor Michel Avital, supervisor


The Basic Data Program

”We are reworking our entire data infrastructure from the bottom up. And nobody else is doing that.” (Interview program leader Digitaliseringsstyrelsen, November 2013).

“The needs for property information are very different between user groups and that is reflected in how the data is presented and preserved in each register” (Interview, product owner, KMD, August 2012).


Research Questions

How is value generated from open data?

1. What are the main enabling factors for value generation through open data ?

2. What are the unique features of open data?3. What are the value generating mechanisms of open data ?4. How can we identify, conceptualize and measure the value

that is generated from open data?5. What are the key implementation strategies and business

models that can promote long term generation of value from open data?


Research Design

2. Unique Features of Open Data

3. Value Generating Mechanisms

4. Sustainable Value

5. Implementation strategies and Business ModelsIVVI

VIII

IIIV

IVII

1. Enabling FactorsIIIIIVII

IIVI

Paper based, iterative design (learning)Phenomenon based (von Krogh et al., 2012)Exploratory -> Explanatory (Bhattarcherjee, 2012)Engaged Scholarship (Van de Ven, 2007) Mixed Methods (Creswell, 2003, Bhattarcherjee, 2012)


Method

A mixed method approach is one in which the researcher collects, analyses, and integrates both quantitative and qualitative data in a study (Creswell 2003).

Qualitative part: Longitudinal case study A case study is defined as “an empirical inquiry that investigates a

contemporary phenomenon in depth and within its real-life context.” (Yin, 2009, 18).

Fits well within the CR philosophy (Tsang, 2014).

Quantitative part: SEM modelling approach, PLS method The PLS method is preferred when; a) the goal is to build rather than test theory

(Hair et al., 2011); b) the researcher will use latent variables and mediating variables (Henseler et al. 2009) and; c) when there is strong collinearity between independent variables (Wold et al. 1984)

Recently been argued that econometric methods are not in contrast with the assumptions of CR (Downward and Mearman, 2007; Zachariadis et al., 2013)


Focus

Methods

Supply side: Policy and dissemination

Demand side: Business models and

evaluationOpen data value generation

Theoretical (Models and Frameworks with use cases)

The Value of Open Government Data: A Strategic Analysis Framework

Paper I – pre-ICIS eGov SIG workshop

Innovation in the Open Data Ecosystem: Exploring the role of real options thinking and multi-sided platforms

Paper VII - Book chapter (forthcoming)

The Value Generating Mechanisms of Open Government Data

Paper II – ECIS

Generating Sustainable Value from Open Data in a Sharing Society

Paper V – IFIP

Empirical(Case study or

quantitative data analysis)

Managing Complexity across Multiple Dimensions of Open Government Data: The Case of the Danish Basic Data Program

Paper VIII – Government information Quarterly (forthcoming)

Data-Driven Innovation through Open Government Data

Paper IV - JTAER

Generating Value from Open Government Data

Paper III – ICIS

Driving Sustainable Value: A Conceptual Model of Open Data as a Resource

Paper VI – Not submitted

Paper Overview

Thorhildur JetzekCBS / KMD9|

Research Philosophy

Domain of Real Domain of Actual Domain of

Empirical

Mechanisms Causal relationships or

tendencies that explain

how events happen

Events Events result from

mechanisms which are

triggered (or not)

depending on the

context

Manifestations If events are observed

they exist in the

empirical domain

Hypothesize about underlying

powers

Look for empirical

traces

What kind of events

am I looking for?

Critical Realism (Bhaskar, 1975; 1978)

Adapted from Bhaskar, 1975 and Wynn and Williams, 2012


Topic of interest, purpose of study and research questions

Literature review and use cases Theoretical

propositions (I, II, V, VIII)Open, exploratory

interviews

Secondary data collection

Statistical modelling

PLS pilot study (III)PLS study (VII)

Quantitative Research

Theory development

Interviews and observations

Qualitative data analysis

Case study I (IV)Case study II (VI)

Qualitative Research

Four classes of events and causal mechanisms

Guides data collection

Conceptual model

Constructs

Meta inferences

Meta inferences

Roman letters refer to publications, I means Paper I etc.


The PhenomenonMy definition of open data: Data that are available online, free-of-charge and under an open access license, published in machine-readable formats, easily discoverable, accessible and conceptually coherent. Open data can be re-used without discrimination or limitation, linked to other data and streamed across systems.

Digital data as an Infrastructure resource (Frischmann, 2012; OECD, 2014): Infrastructure resources are shared means to many ends, which satisfy the following three criteria: 1) they are non-rivalrous2) social demand is driven primarily by downstream productive activities (capital good criteria) 3) the resource can be used as an input for a wide range of purposes (general purpose criteria).

Excludable Non-excludable

Rivalrous Private goods Common poolresources

Non-rivalrous Club goods Public goods

Public good features of open data: 1) non-excludable - one individual´s use of

the data will not exclude the use of another

2) non-rivalrous - one individual´s use will not reduce the amount available to another


Literature Review

The scientific literature says:

The experiences of hundreds of initiatives have uncovered a high level of complexity and there is still little or no evidence of value generation (Davies, 2013, van Huijboom and den Broek, 2011, Zuiderwijk and Janssen, 2014, Zuiderwijk et al., 2014).

Governments are struggling with practical issues like financing, data quality, conceptual, technical and organizational interoperability, motivation and a lack of skills and resources (Conradie & Choenni, 2014; Janssen et al., 2012; Martin et al., 2014; Zuiderwijk and Janssen 2014)

Generating monetary revenues as an open data intermediary (infomediary) is difficult (Janssen and Zuiderwijk, 2014)


Open Data Value Paradox

Stakeholders will not invest their time and money unless they perceive that doing so will generate value for themselves and others

Publishing only raw data can create too high a threshold for users, as they often do not have the time and capability to manipulate and process data (Janssen and Zuiderwijk, 2014)

Need for more investment

Need to motivate, show value

Value generation will not be visible unless we a) see more investment and b) learn to elaborate on the type of value that is being generated


Liquid Open Data

Liquidity – reflects ability to link and stream data across systems

Openness – reflects ability to use data outside of organizational boundaries

Liquid dataIlliquid data

Closed data

Open data

Liquid data: Ability to reuse data within organization

Illiquid closed (silo’ed) data are only used for a single purpose, not reusable

Illiquid (silo’ed) open data : Potential to access data from outside of organization but limited potential for automation or coupling of data

Liquid open data: Ability to access and connect data across boundaries

Combining internal and external data for improved insights

Internally shared data

Most data within organizations

Many open government data initiatives


Dimensions of Liquid Open Data

Dimension Affordance Explanation

Strategic AvailabilityData are open to all by default, or data are shared (with chosen groups), or data are closed (to all but data owner)

Economic AffordabilityData are free or charged for at maximum at marginal cost of reproduction

Legal ReusabilityData are published using open licenses, having no license is detrimental for reuse

Conceptual InteroperabilitySemantics and syntax are clear, use of data models and metadata and standard identifiers

Technical

Usability Data are of high quality, published in machine readable and standard formats and providing metadata

DiscoverabilityData are easily found through central portals or published with metadata or using linked data semantics

Accessibility Data are easily downloadable or ”query-able”

@Thorhildur JetzekCBS / KMD16 I

Sustainable Value

Individuals

Organizations

Society

Levels of analysis

Perspectives of value

Economic

Social Physical and psycological

Environmental

Meaningful life

Wellbeing

Livability of environment

Responsibility to natureBelongingHealth

Social responsibility

Responsibility to employees

Eco-footprintIncome

Generating profit

Wealth

Adapted from Den Ouden, 2012


Focus areas of ODIs:E

xp

loita

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n:

Go

od

go

ve

rna

nce

Exp

lora

tio

n:

Drivin

g c

ha

ng

e

Economic:

Market mechanisms

Social:

Information sharing mechanisms

∆ Transparency

∆ Civic engagement

∆ Efficiency

∆ Innovation


Input – Activity - Outcome

Sustainable Value

Creation of products, services and processes

Creation ofinformation

Open Data

ActivitiesResource - inputs Outcome

Mechanisms


Coleman´s Boat

Situational mechanisms

Individual capacity

Individual actionsAction-formation

mechanisms

Open dataEnabling factors

Sustainable value

Transformational mechanisms

Tested – macro level association

Theorized micro/macro relationships

Societal context Societal outcome

Indicators of use of open data


Assumptions about Human BehaviourAny theory comprises a set of assumptions from which empirical generalizations have been derived (Merton, 1949).

A2. I assume that access to relevant information can push the boundaries of our ability to choose rationally and therefore improve decision making and change behavior, which contributes to the generation of sustainable value

A1. I assume that individuals, given the motivation, opportunity and ability, are willing to pursue the generation of sustainable value, including economic, social and environmental value, for all stakeholders and future generations


Final Model

Basic requirements

Social responsibility of private sector

organizations

Cost of high-speed networks

Sustainable value

Extent of new digital products and services

Extent of shared digital content dissemination

Openness of data

Effectiveness of data and privacy protection

frameworks

Digital leadership of government

Ease of reaching a skilled workforce

Soft infrastructure

0.485***

0.282***

0.331***

0.094n.s.

-0.047n.s.

0.465***

-0.187***

-0.131***

0.243***

0.074n.s.

0.412***

0.211**

-0.344***

0.256***

World Bank´s Voice and accountability

governance indicator

0.290***


The Role of Intermediaries

Soft infrastructure

Sustainable value

Paying sideBuying and selling goods and services

Non-paying sideSharing relevant

content

Cost of high-speed networks

Openness of data

Societal level impact

(MSPs)

Intermediaries

Information sharing + market

mechanisms = Synergy

Effectiveness of data and privacy protection

frameworks

Ease of reaching a skilled workforce

Motivation

AbilityBasic requirements

Resource

Digital leadership of government

Opportunity

Societal level structures

MSPs = Multi Sided Platforms


Open Data Value Generation Cycle

E2: Collection and dissemination of

open data

E3: Use and transformation of

open data

E4: Value generation and

capture

E1: ODI strategy and implementation

M3: Value generating mechanisms

M2: Engagement mechanisms

M1: Governancemechanisms

M4: Evaluation mechanisms

Multiple stakeholders


Final thoughts

• ”It was the best of times. It was the worst of times.” • I have really enjoyed these last three years. I feel that I have

grown, as a person and as a professional.• By no means easy, but has given me a deep sense of

satisfaction.

• While my reviewers have more than once mentioned that I may be too ”positive” (and yes, two possible meanings, and both fit :), I have always believed in looking forward and focusing on the possibilities and how we can do and be better.

• While I truly believe the sheer momentum of the changes caused by the digital revolution will soon enforce a paradigm change, I can only hope we will manage to structure our societies so that this will be a positive, rather than a negative, change.


THANK YOU!

The Sustainable Value of Open Data

Data & Analytics

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