Marianne Fraefel
email: marianne.fraefel@bfh.chstephan.haller@bfh.chadrian.gschwend@bfh.ch

Stephan Haller

Adrian Gschwend

Big Data in the Public Sector. Linking Cities to Sensors

Keywords: Big Data, Internet of Things, Open Government Data, Linked Data, Public Sector, Smart City, Data Quality, Platform Federation

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The digitization of the economy and society becomes apparent in the many applications and devices that use and produce data. As businesses and individuals use available technological innovations to improve business and facilitate the demands of everyday life, governments around the world are struggling with how to best put these advancements to use in the public sector and create public value. The European Commission for example has acknowledged that "data has become an essential resource for economic growth, job creation and societal progress" [START_REF]European Commission: Building a European Data Economy[END_REF] and is working on a policy and a framework for the free flow of data to reap the potential benefits and address challenges both in the technical as well as in the societal and legal fields. One of the primary difficulties lies in the diversity and the speed of the technological developments: The deployment of sensors delivers a multitude of new data sets, but with sometimes unreliable data, big data and machine learning is deployed for data analysis, and linked data and open government data approaches are used to make data more accessible to a wider clientele. The societal challenges that digitization will bring about manifest themselves first in the metropolitan, urban environment; hence the so-called "smart city" is an ideal field for experimentation to better understand and learn about the opportunities and potential pitfalls. While the term "smart city" is certainly hyped and many different activities are carried out under this label, two points are interesting when looking at cities that are generally regarded as pioneers in the field, e.g., Amsterdam, Barcelona, or Vienna. They all use a private-publicpartnership (PPP) model in order to bring together actors from different sectors and with different expertise and interests [START_REF] Manville | Mapping Smart Cities in the EU[END_REF][3], and the establishment of a platform for information exchange and data access is seen as a key enabler for an effective implementation of a smart city programme [START_REF] Vega-Gorgojo | Case study reports on positive and negative externalities[END_REF].

With the main goal of developing such a platform for smart city innovation, we launched in 2016 the CPaaS.io project. 1 In this 30-month research and innovation action between Europe and Japan, data from various sources are made accessible via a cloud-based platform to application developers and service providers. Data sets from open government data portals and other administrative or publicly available data can be linked with Internet of Things (IoT) data, e.g., data from sensors deployed in the communal infrastructure or from sensors worn by participants in a city event. The project is also developing several application use cases in the domains of event management, water management, and public transportation and pilots these in partnering cities like Amsterdam, Sapporo and Tokyo. The aim of this paper is to discuss the particular challenges of implementing a smart city platform related to data management and in particular data quality management of various data sources, including IoT data in particular. The use of this type of data is relatively new to governments and irrespective of the usage context, the question of how to validate IoT data is still quite open. In order to be adopted, a city data infrastructure needs to provide information on data quality in form of metadata and, as will be shown, linked data provides several advantages in that respect. At the current stage of the project, we provide generic considerations on the named challenges, based on selected research in the field. Thus, we do not account for potentially differing requirements depending on e.g. the size or smart city maturity (cf. [START_REF] Manville | Mapping Smart Cities in the EU[END_REF]) of a city.

The remainder of this paper is structured as follows: in the following section we summarize the state of adoption of big data in the public sector, with a special focus on the context of smart cities and the usage of the Internet of Things. Section 3 provides an overview of two challenges that need to be addressed for the successful deployment of such a platform: improving data quality and facilitating platform federation. Section 4 then describes linked data as a solution approach to tackle these issues and describes the state of the art in the field, while section 5 goes into more detail how the solution is implemented in the context of the CPaaS.io project. Section 6 finally contains the conclusions and outlines future work.

2

Big Data in the Public Sector

Big Data Opportunities for the Public Sector and State of Adoption

Big data is about generating value through collecting and analysing information to extract knowledge and insight (cf. [5] [6]). Governments are increasingly aware that big data offers value potentials for the public sector [START_REF] Munné | Big Data in the Public Sector[END_REF]. As scholars point out however, implementation by now tends to be limited ( [START_REF] Munné | Big Data in the Public Sector[END_REF] [8]) or as Desouza & Jacob put it, there is "some tension between the promise of Big Data and reality" [START_REF] Desouza | Big Data in the Public Sector: Lessons for Practitioners and Scholars[END_REF]. Accordingly, big data in the public sector has only recently started to raise academic interest (cf. [START_REF] Gaardboe | Characteristics of business intelligence and big data in e-government: Preliminary findings[END_REF]), but is expected to gain more attention within big data research [START_REF] Akoka | Research on Big Data -A systematic mapping study, Computer Standards & Interfaces[END_REF]. A first set of studies and reports rather looks at the public sector as a data producer for big data applications in other sectors (e.g. [12] [13]). Governments generate and collect large amounts of data through their everyday operations and the public sector is thus one of the most data-intensive sectors. Since public sector sources comply with high quality standards, they are considered an essential resource for the data-driven economy, which is reflected in the many open government data (OGD) initiatives that seek to make this data available for re-use [START_REF] Carrara | Analytical Report 1: Digital Transformation and Open Data[END_REF]. A second stream of research focuses explicitly on governments as big data user. This work includes cross-case studies on existing cases of big data implementation ( [15] [16]), general considerations on the opportunities and challenges of big data adoption by the public sector (e.g. [17] [7] [18] [START_REF] Desouza | Big Data in the Public Sector: Lessons for Practitioners and Scholars[END_REF]), considerations on the preconditions for using big data [START_REF] Klievink | Big data in the public sector. Uncertainties and readiness[END_REF] and/or specific fields of application, such as policy-making (e.g. [START_REF] Höchtl | Big data in the Policy Cycle. Policy Decision Making in the Digital Era[END_REF] also [15] [16]).

Based on available research, potential benefits of big data adoption in the public sector can be categorized as follows [START_REF] Munné | Big Data in the Public Sector[END_REF]: A first set of opportunities relates to improving the knowledge base. As in other sectors, data analysis is used for generating new insights. Big data analytics can be applied to various domains of public administration (cf. [START_REF] Chen | Big Data for Digital Government: Opportunities, Challenges, and Strategies[END_REF] [17] [16] [START_REF] Barbero | Big Data Analytics for Policy Making[END_REF]) and holds promises to improve all stages of policy-making [START_REF] Poel | Data for Policy: A Study of big data and other innovative datadriven approaches for evidence-informed policymaking[END_REF]: Better and faster insights derived from big data analysis (e.g. through machine learning) may help to better react to unintended effects of a policy decision [START_REF] Höchtl | Big data in the Policy Cycle. Policy Decision Making in the Digital Era[END_REF]. It may help to earlier detect mistakes, frauds or security threats [START_REF] Eckert | Big Data -Ungehobene Schätze oder digitaler Alptraum[END_REF]. Policy-makers can also use big data technologies to conduct policy impact assessments or gain a better understanding of citizen interests and opinions through the analysis of new data sources, e.g. social media, helping them to prioritize policy issues ( [START_REF] Munné | Big Data in the Public Sector[END_REF] [15]).

A second set of opportunities relates to improvements in effectiveness. Data analysis may be used to tailor service provisioning towards the needs of different citizen groups, increasing their satisfaction [START_REF] Chen | Big Data for Digital Government: Opportunities, Challenges, and Strategies[END_REF]. Better insights can also contribute to solving social problems related to public transportation, healthcare provision or energy production [START_REF] Klievink | Big data in the public sector. Uncertainties and readiness[END_REF]. Provided as open data, the public sector may facilitate the innovation of products and services by third parties.

A third set of opportunities relates to improvements in efficiency. Big data can be used to achieve greater internal transparency and to improve data sharing across administrative organizations. Available estimates suggest that the public sector could generate considerable revenues through better exploitation of data ( [12] [13]). Leveraging new data sources may also positively impact data generation by public administrations, e.g., when producing official statistics [START_REF] Barbero | Big Data Analytics for Policy Making[END_REF].

Smart Cities as Big Data application Domain in the Public Sector

Depending on the application domain and the type of data generated, big data analysis in the public sector is closely related to the concept of smart cities [START_REF] Chen | Big Data for Digital Government: Opportunities, Challenges, and Strategies[END_REF] (for a defini-tion see [START_REF]ITU-T: Shaping smarter and more sustainable cities[END_REF]). Cities are considered as distinct domain, in which the use of ICT in general [START_REF] Castelnovo | Smart Cities Governance. The Need for a Holistic Approach to Assessing Urban Participatory Policy Making[END_REF] and big data in particular are expected to generate impact ( [START_REF] Hashem | The role of big data in smart city[END_REF] [17] [START_REF] Munné | Big Data in the Public Sector[END_REF] [24] [START_REF]OECD: Data-Driven Innovation: Big Data for Growth and Well-Being[END_REF]). Thought leaders in the field expect that innovative examples of big data usage are more likely to be found at the city or regional level, since it is easier to get policy makers involved in small-scale initiatives, in new forms of collaboration and data usage [START_REF] Poel | Data for Policy: A Study of big data and other innovative datadriven approaches for evidence-informed policymaking[END_REF]. Also, a mapping of smart cities in Europe reveals that there are more smaller than large smart cities, while larger cities have more resources and tend to be more ambitious in scope and more mature regarding implementation [START_REF] Manville | Mapping Smart Cities in the EU[END_REF]. As several authors stress, the Internet of Things is an important data source in the smart city context: "The public sector is increasingly characterized by applications that rely on sensor measurements of physical phenomena such as traffic volumes, environmental pollution, usage levels of waste containers, location of municipal vehicles, or detection of abnormal behaviour" [START_REF] Munné | Big Data in the Public Sector[END_REF]. The analysis of such IoT data sources in combination with other data has the potential to improve urban management and the quality of life of city inhabitants: "Data from different sources need to be integrated and analyzed for smart urban planning, smart transportation, smart sanitation, smart crime prevention, etc" [START_REF] Aggarwal | Opportunities and Challenges of Big Data in Public Sector[END_REF]. As Scuotto et al. point out however, "the relationship between IoT and smart cities is still largely unexplored" [START_REF] Scuotto | Internet of Things: Applications and Challenges in Smart Cities[END_REF], which requires more research, e.g. on typical technological challenges to be tackled (cf. [START_REF] Hashem | The role of big data in smart city[END_REF]).

Technological Challenges of Implementing a Smart City Platform

While governments are considered as catalysts for boosting a data-driven economy and growth through opening up their data, big data adoption in the public sector is also confronted with a range of constraints and challenges [START_REF] Munné | Big Data in the Public Sector[END_REF]. These are related to governance (e.g. agreements for integrating data sources across organizations, datadriven culture), implementation (e.g. organizational maturity in terms of IT facilities and data systems, required skills) and risk management (privacy, security) (cf. [START_REF] Chen | Big Data for Digital Government: Opportunities, Challenges, and Strategies[END_REF] [7] [START_REF] Aggarwal | Opportunities and Challenges of Big Data in Public Sector[END_REF] [19] [START_REF] Klievink | Big data in the public sector. Uncertainties and readiness[END_REF]). As Munné points out [START_REF] Munné | Big Data in the Public Sector[END_REF], it is important that the public sector gains "adoption momentum", moving from marketing around big data to real experience, to derive lessons learned on which applications are valuable and how to deploy them: "This requires the development of a standard set of big data solutions for the sector." The CPaaS.io project provides such a solution for the smart city context and supports experimentation and capability building. One of several challenges to be addressed relates to the federation of existing platforms. Another typical challenge relates to data management. A linked data approach is suited to address both the challenge of ensuring system interoperability as well as data interoperability (cf. [START_REF] Janssen | Interoperability in Big, Open, and Linked Data--Organizational Maturity, Capabilities, and Data Portfolios[END_REF]).

Federation of Smart City Platforms

To exploit the full potential of a big data strategy, it is not enough that cities just implement a big data platform on their own. Unfortunately, this approach is still com-mon today, leading to data silo solutions lacking interoperability (cf. [START_REF] Aggarwal | Opportunities and Challenges of Big Data in Public Sector[END_REF]). Cities though are not standalone entities, they are embedded in a region, in a country, and they often cooperate with other citiestoday also on a global scale. Cities thus need to strive for interoperability of their platforms and the possibility to federate instances: This will enable data analysis across regions from which all participating cities can profit, for example by better understanding traffic patterns or in order to provide better services to an increasingly mobile population. Standards can help to achieve this, but often are not enough, as the adoption especially of data standards on a global scale is slow due to historic, legal and cultural differences.

Governance and Management of Data

In the age of big data, datasets become increasingly "complex", which requires adequate capabilities for managing the data [START_REF] Desouza | Big Data in the Public Sector: Lessons for Practitioners and Scholars[END_REF]. With the growing need to integrate data from multiple sources, data quality management becomes both more important but also challenging [START_REF] Merino | A Data Quality in Use model for Big Data[END_REF]. In the context of developing city data infrastructures, data management and in particular the management of data quality, i.e., ensuring that data is fit for use and free of defects [START_REF] Fürber | Data Quality Management with Semantic Technologies[END_REF] are important aspects (cf. [30] [28]) and part of an organization's overall data governance [START_REF] Otto | Organizing Data Governance: Findings from the Telecommunications Industry and Consequences for Large Service Providers[END_REF] (see Fig. 1):

Fig. 1. Data governance and related concepts (adapted from [START_REF] Otto | Organizing Data Governance: Findings from the Telecommunications Industry and Consequences for Large Service Providers[END_REF])

Smart city platforms are used for making decisions and providing services based on the results of querying various datasets, which entails that applications need to be trusted and accepted and data quality plays a major role in that respect (cf. [START_REF] Zaveri | Quality Assessment for Linked Data: A Survey[END_REF] [33] [START_REF]European Commission: Commission Staff Working Document. Advancing the Internet of Things in Europe[END_REF]). For a city data infrastructure aimed at integrating IoT data, managing data quality is particularly crucial, as sensors are an inherently unreliable data source. Sensors can become decalibrated, delivering inaccurate data readings, or they can fail or lose connectivity completely. Resolution, sensitivity, timeliness and provenance are other factors affecting the validity of IoT data. As a requirement, data quality is well understood [START_REF] Becker | Big Data Usage[END_REF] and there are many methodologies to conduct data quality management (cf. [START_REF] Batini | Methodologies for Information Quality Assessment and Improvement[END_REF]) as well as models and frameworks for assessing the quality of specific types of data, such as linked data [START_REF] Zaveri | Quality Assessment for Linked Data: A Survey[END_REF], IoT data [START_REF] Karkouch | Data Quality in Internet of Things. A State-of-the-Art Survey[END_REF], open government data [START_REF] Vetrò | Open data quality measurement framework: Definition and application to Open Government Data[END_REF] or more generic big data [START_REF] Merino | A Data Quality in Use model for Big Data[END_REF]. What constitutes "good data quality" is however depending on the context of its use and thus very much application-dependent. A city data infrastructure aiming to support a multitude of possible applications must provide sufficient metadata about the data quality, while it is left to the application to decide if the data is good enough to be used. This requirement is also grasped by the emerging "Smart Data" paradigm, according to which successful big data implementation has "a clear meaning (semantics), measurable data quality, and security (including data privacy standards)" [START_REF] Becker | Big Data Usage[END_REF]. This entails making data more accessible through adding metadata for structuring and integration across separate data silos and for storing information on data quality as well as benefitting from already available open and linked data.

Linked Data as Solution Mechanism

In the context of big data, linked data is both a specific type of data source and an approach for facilitating data integration and re-usage through providing clear meaning. This is essential, since only through understanding the context sensitive meaning of data can one assess whether data can be combined to generate value [START_REF] Janssen | Interoperability in Big, Open, and Linked Data--Organizational Maturity, Capabilities, and Data Portfolios[END_REF]. As Shiri points out: "the formalized, structured and organized nature of linked data and its specific applications, such as linked controlled vocabularies and knowledge organization systems, have the potential to provide a solid semantic foundation for the classification, representation, visualization and the organized presentation of big data" [START_REF] W3c | Data on the Web Best Practices[END_REF].

As the cross-case study on big data adoption in policy-making shows [START_REF] Poel | Data for Policy: A Study of big data and other innovative datadriven approaches for evidence-informed policymaking[END_REF], public administrations use a variety of data sources from administrative data, official statistics, surveys, sensors and social media. The data used may be either open or restricted. These siloed data sources are typically accessed over platform-proprietary APIs. To gain new insight about the data it is vital to fuse it from these different sources. This can be done by transforming the data into a more generic form, which is more accessible and provides standardized APIs on top of it. Such a generic API needs to provide a common way to exchange information between these sources and help the API consumer to understand the semantics and the meaning of the information. The W3C semantic web and linked data technology stack [START_REF] Domingue | Big Data Analysis[END_REF] aims at solving these problems. The RDF data model provides well-known schemas and ontologies as lingua franca, HTTP as transport layer, URIs as decentralized identifiers and multilingualism in its core. This makes it the data model of choice for bridging between data silos (cf. [START_REF] Janssen | Interoperability in Big, Open, and Linked Data--Organizational Maturity, Capabilities, and Data Portfolios[END_REF]).

In the past few years a lot of effort went into publishing best practices. In 2016, W3C released the "Data on the Web Best Practices" recommendation [START_REF] W3c | Data on the Web Best Practices[END_REF]: After roughly 10 years of open data movement [START_REF] Shiri | Linked Data Meets Big Data: A Knowledge Organization Systems Perspective[END_REF], the document summarizes best practices and recommendations about how to publish open data, especially in the context of what needs to be taken into consideration to ensure that the published data is of maximum value for the public. Most of the recommendations are related to machine readability and discoverability of open data.

In the domain of schema and ontologies, several search engine giants launched schema.org [START_REF] Shiri | Linked Data Meets Big Data: A Knowledge Organization Systems Perspective[END_REF], an initiative to "create and support a common set of schemas for structured data markup on web pages" Meanwhile schema.org seems to use "a simple RDF-like graph data model" and exposes its schema as embedded RDF. Over the past years schema.org had a huge impact; many sites started to include structured information within their websites and the support of first RDFa 2 and later JSON-LD 3 made people use semantic web technologies without being really aware of it. This increases visibility and perception of the semantic web as a whole. Developments that are still work in progress revolve around constraint languages; examples of that are Shapes Contraint Language (SHACL) and Shape Expressions (ShEx). RDF is a graph data model and by design it is possible to express any relationship between a subject and an object. In real world applications, it is often necessary to define structural constraints and validate RDF instance data against those. This can be done with both of the languages.

In the domain of IoT related ontologies there are even more options available and under discussion. Several groups are pushing their own concepts and ontologies, among others: Spatial Data on the Web Working Group's SSN Ontology, IoT + schema.org, Web of Things (WoT) Interest Group and the EU H2020's FIESTA-IoT project. It is too early to tell yet which of the proposed constraint languages and IoT ontologies will see wider adoption in the next years.

Implementation in the CPaaS.io Project

The smart city platform as developed by the CPaaS.io project is based on a common reference architecture, but is for pragmatic reasonsmainly in order to have instances up and running quickly in the two main regions of the projectimplemented on top of existing frameworks (see Fig. 1).

Fig. 2. Simplified CPaaS.io implementation architecture

The implementation in Europe is based on FIWARE [START_REF] Hierro | FIWARE: An Open Standard Platform for Smart Cities[END_REF], and the one in Japan on the u2 architecture [START_REF] Sakamura | Open IoT Platform & IoT-Engine[END_REF]. The disadvantage of having two different platform implementations within the project is that data federation across instances becomes more challenging. However, in real life it cannot be expected that all cities will use the same platform implementation anyway, so the two implementations within the project serve as a test regarding the real-world viability of the platform. For example in the domain of public transportation, it is important that innovations developed in one city can easily be transferred to another city.

The CPaaS.io Platform aims at fusing data from different sources, in particular the FIWARE platform in Europe and the u2 platform in Japan. From a data consumer perspective, it should not matter where the data is stored, CPaaS.io will facilitate discovery and access to information in these data storages via generic APIs. For that reason, CPaaS.io will use linked data and RDF to facilitate integration of data from platforms like FIWARE and u2. Neither of the two platforms is currently supporting RDF and linked data out of the box. CPaaS.io will integrate a semantic layer that enables mapping existing data to RDF. This semantic integration layer can be implemented in different phases and levels.

Initially the semantic layer will simply expose metadata as linked data, using common vocabularies and best practices as described in [START_REF] W3c | Data on the Web Best Practices[END_REF]. This enables users to query information about available data within the FIWARE and u2 platform as linked data. Access to this metadata layer will be done by providing a SPARQL endpoint that can be queried.

In a second step, data residing in FIWARE or u2 is mapped to RDF by extending the respective data model of each platform. In the case of FIWARE, this can be done by using the new NGSIv2 data model that supports JSON-LD representations. By providing appropriate tools and user interfaces, FIWARE users can thus map existing data to RDF representations. The ucode data model of u2 is close to RDF as it stores information in a triple-like data model. The semantic integration layer only needs to map internal ucode IDs to publicly used and dereferencable URIs, preferably as HTTP URIs to allow linked data usage like it has already been done for the Tokyo Metro real time data system.

To be able to query this kind of data, a SPARQL endpoint will proxy requests to the platform. CPaaS.io will provide a virtual-graph feature similar to what RDF graph databases provide to access relational data, using W3C standards like R2RML. Users will thus be able to run SPARQL queries on data residing in FIWARE or u2. In a final step FIWARE and u2 will implement its own SPARQL endpoint.

Conclusions and Outlook

While the public sector can be considered one of the most data-intensive sectors, actual use of big data in this sector is still rather limited. With the increased deployment of Internet of Things technologies and the international competition for cities to become smart, however, this is likely going to change. A lot of experimentation is still going on in this area to understand both the technologies as well as the applications that create real public value. To reap the potential benefits, cities will need an open city data infrastructure, where third parties can access the relevant city data, including data coming from the Internet of Things, and provide additional services on top. The platform that the CPaaS.io project is developing could serve as the basis for such an infrastructure if the two crucial issues that we highlighted in this paper are addressed: the ability to federate platform instances, and data quality. Linked data can serve as a possible mechanism to address both. The semantics behind linked data allow combining differently structured data from technically different platforms. And linked data can be used to annotate data sets with quality parameters so that an application using that data can decide if the data quality is good enough for the intended purpose. It is thus a fruitful approach for reaching the "smart data" paradigm.

Using linked data requires adequate vocabularies both for data integration into CPaaS.io and for re-usage by use case applications as well as for data dimensions and measures, accounting for the different types of data used in the project. Standards for such vocabularies and for validating data are still emerging; at this point in time none of these is well accepted yet. In the further course of the project, we will have to define which of the emerging vocabulary standards are suitable for the project and its use cases, and where we need to define our own. Furthermore, we plan to validate the applicability and the value of linked data, as well as the platform as a whole, in realworld use case implementations in European and Japanese cities. Both, the relationship between IoT and smart cities and the adoption of big data in the public sector in general require more research based on real applications. The CPaaS.io project will contribute to gaining new insights in these emerging research fields.

2

  RDF in Attributes, see RDFa Primer: http://www.w3.org/TR/xhtml-rdfa-primer.3 JSON for Linking Data (JSON-LD) is a JSON based RDF serialization. See json-ld.org.

The abbreviation stands for "City Platform as a Service -Integrated and Open". See http://www.cpaas.io for further details about this Horizon

project.

Acknowledgement: This work is supported by the Horizon 2020 EUJ-02-2016 Research and Innovation Action CPaaS.io; EU Grant number 723076, NICT management number 18302.


Kristina Zumbusch
email: kristina.zumbusch@unisg.ch

Philippe Zimmermann

Emamdeen Fohim
email: emamdeen.fohim@unisg.ch

Philippe -3700-510x]

Zimmermann

Outcome Evaluation of StartBiz How a Governmental Online-Tool Can Quantitatively Assess Its Benefits for SME

Keywords: E-government Services, Outcome Evaluation, Start-up Companies

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The following paper presents the results of the outcome evaluation of StartBiz, an online tool for start-ups in Switzerland. StartBiz is provided by the State Secretariat for Economic Affairs (SECO) and allows start-ups to enroll with trade registers, VAT, social insurances and accident insurances without any additional fees directly via the internet. The intended benefit of StartBiz for its users was to offer them a single portal to complete various government transactions with different administrative offices. Using this portal, start-up companies need to undertake one login exclusively. In this way, they can use the same web-frontend and reuse their previously entered data. This procedure confirms with the "Once-Only" Principle Project (TOOP), which has been launched by the European Commission [1]. Besides start-up companies, other important stakeholders, which might benefit from StartBiz were assessed in a first phase: for instance, trustees or bankers. However, due to their low usage rate, their potential benefit was not explicitly considered within the presented analysis. Hence, the presented outcome evaluation was applied to learn about the benefits, generated for startup companies that have used StartBiz so far. At the same time, the evaluation was aimed at providing decision-makers in the SECO with strategic information for their future egovernmental activitiesthat is an envisaged expansion of StartBiz to an electronic One-Stop-Shop (OSS) for small and medium sized enterprises (SME). The objective of the OSS is to offer companies, in particular to SMEs, the possibility to undertake any public services fully online and through a single portal. To advance with the OSS effectively and to set up an optimal solution for Swiss companies, it was essential to generate know-how on the created benefits of StartBiz so far and on the mechanism behind these benefits. Therefore, the outcome evaluation had to combine summative as well as formative evaluation-elements.

The Rise and the Challenges of Outcome Evaluations

Public managers are under increasing pressure in order to report the outcomes and results of their programs, their activities as well as their investments. With both internal and external demands for information, public managers not only need to provide an accounting for expended resources and for provided services, but also have to report on performances and outcomes [START_REF] Schedler | Outcome-oriented Public Management -A Responsibility-based Approach to the New Public Management[END_REF]. The assessment of generated outcomes gains importance to justify public fund expenditure on the one hand, but also to optimize future projects on the other hand. Consequently, outcome evaluations show an inflationary implementation in many different policy fields and for many different policy tasks [START_REF] Van Wouter | Performance management in the public sector[END_REF]. Outcome evaluations assess the effectiveness of a program in producing change. They focus on difficult questions such as: "what happened to the target groups of a program?" and "how much of a difference the program made for them?" [START_REF] Gertler | Impact evaluation in practice[END_REF]. Thus, in any program the crucial questions are "what do you want to change?" and "how would you know if you have changed it?". These evaluation questions are not just bureaucratic requirements, but meanwhile the essence of a good project management [START_REF]European Commission: Guidance Document on Monitoring and Evaluation -Concepts and Recommendations. Evaluation Unit of the Directorate-General for Regional and Urban Policy[END_REF]. Outcome evaluation mobilizes scientific and statistical tools to follow up on these questions. The term 'outcome' refers in this context to all induced changes that can be causally attributed to a particular activity, as not all observed changes are categorically an intended and direct consequence of the corresponding activity [START_REF] Scherer | Wirkungsmessung als Strategieinstrument[END_REF]. That is why outcomes are defined by their causality with the interventions carried out. So-called 'Outcomemodels', or' 'impact-models', attempt to map these causalities on the basis of hypotheses in the form of outcome-or causal action chains, more complex circuits of activity, or also as partially highly complex networks of effects [START_REF] Scherer | Wirtschaftsförderung -Erfolg ist schwer zu messen[END_REF]. Thinking in outcome-models, such as in the mentioned outcome-chains, begins from the (policy) objectives over the taken (policy) measures to the generated (policy) outcomes or benefits.

In other words, the identified (policy) problems and their corresponding objectives determine the basis of the outcome-model. They build the reference framework for assessing the outcome in the sense of target achievements as outcome evaluations are undertaken when it is important to know whether and how well the objectives of a project or program were met [START_REF] Scherer | Wirkungsmessung als Strategieinstrument[END_REF]. On this basis, the model leads to the question whether the respective measures exist for the defined objectives as well as whether they are also used by the planned target groups accordingly. If measures are used by the target groups, intended benefits are generated [START_REF] Scherer | Wirkungsmessung als Strategieinstrument[END_REF]. Based on this framework, corresponding variables should be identified to measureas far as possiblethe output (i.e. the concrete measure as well as the usage of this measure) and the outcome (i.e. the causally justified effects) (see Fig. 1). Ideally, the long-term impact should be measured by adequate variables as well. However, the impact is often influenced by other factors, which is why its allocation to the initial objectives is often difficult to determine. Consequently, a strict focus on measuring the direct outcomes allows identifying causal outcomes, which can be directly related to the taken measures and their objectives. In this way, the target achievement of these identified outcomes can be assessed and the underlying mechanisms about success factors but also about inefficiencies or other shortcomings can be detected [START_REF] Scherer | Wirtschaftsförderung -Erfolg ist schwer zu messen[END_REF]. While outcome models also support the accuracy of outcome evaluations, some main challenges remain, that have to be met by all different outcome evaluations. These challenges had to be considered in the outcome evaluation of StartBiz as well [START_REF] Scherer | Wirkungsmessung als Strategieinstrument[END_REF]:

 The problem of causality: The central problem is the proof that certain changes (or conservative effects) are causally related to certain activities. For even if the desired condition, the intended effect occurs, other factors can be responsible for it.  The problem of detectability: Since the comparison with a development without intervention (zero variant) is not possible in start-up promotion and in many other areas, the proof of the effects is a challenge. Counterfactual analyses may offer a resilient approach.  The problem of time: Most effects occur in the medium to long term exclusively and can then no longer be attributed to a single, concrete activity. The long-term nature of the effects thus makes both the detectability and the causality more difficult.  The problem of operationalization: Which indicators can be used to reliably quantify outcomes? Do we have the necessary data and information? Actuality, quality, spatial perimeter and comparability of the data are only some of the problems that can become critical here. At the same time, the generic requirements for any kind of quantitative analysis, such as objectivity, reliability and validity, have to be guaranteed.  The problem of aggregation: The effects of individual activities / offers cannot be simply summed up due to their partly completely different characteristics. However, individual indicators cannot adequately reflect the overall target achievement of an offer such as StartBiz.

Each outcome evaluation is confronted with these problems and must take them into account. Various strategies have already been discussed how to optimally deal with these challenges. Though, each outcome evaluation has to find its specific solution, considering the specific conditions of its particular evaluation questions. For the outcome evaluation of StartBiz, these challenges emphasized the focus on (i) the causal detection of (immediate short-term) outcomes that can be directly attributed to the services offered by StartBiz, (ii) the measurement and operationalization of these outcomes and (iii) on possibilities of their aggregation [START_REF] Zumbusch | Nutzenbewertung des Online-Schalters StartBiz[END_REF].

An Outcome Evaluation for StartBiz -Methodology and Procedure

Outcome evaluations in the field of e-government face additional challenges [START_REF] Ward | Benefits Management -How to Increase the Business Value of your IT proejcts[END_REF]. E-Government has emerged as one of several innovative ways for delivering services to citizens and companies. It is providing governments with new opportunities for bringing services closer to (small and medium sized) companies in cost-effective, efficient, and transparent ways [START_REF]European Commission: Future-proofing eGovernment for a Digital Single Market -Final Insight Report[END_REF]. Also Switzerland intends to reduce, with electronic tools, the administrative burden for companies. Its e-government is based on the Federal Council's strategies "Information Society Switzerland" and "E-Government Switzerland" that are jointly pursued by the Confederation, the cantons, and the communes.

The first e-government strategy was adopted in 2007. The second and current strategy was signed at the end of 2015 [START_REF]Schweizerische Eidgenossenschaft, Konferenz der Kantonsregierungen, Schweizerischer Städteverband, Schweizerischer Gemeindeverband[END_REF]. Simplified electronic licensing, application and registration processes are seen as important for reducing red tape. Electronic services of the authorities are increasingly popular with the economy, not only with regards to digitalizing the processes, but also simplifying them and gearing them towards the customers. That is why the Federal Council considers e-government to be an important pillar of growth policy [START_REF]eEconomy in der Schweiz -Monitoring und Report[END_REF]. For this purpose, various initiatives were undertaken, amongst others also for the small and mediums sized companies in Switzerland. In this context, also StartBiz was created to facilitate the process of setting up a new company (www.startbiz.ch). Start-Biz offers both informational and transactional services. In the first part (without registration), companies can check their obligations depending on their individual characteristics such as their legal form, their number of employees, their planned turnover or their industry. In this way, StartBiz provides start-ups with relevant information before starting the official registration of the enterprise. This clarification of requirements also allows start-ups to test various scenarios for their enterprise and to find out, which requirements apply in each case.

Once this informational part is completed, StartBiz allows start-ups to create an account and to register their enterprises fully online with the major administrative offices in Switzerland, which are the trade registry, the VAT, the social insurances as well as the accident insurances. This service, free of charge for the start-ups, is available regardless of the country level and its specific requirements, which are given by the confederation and the cantons.

Unquestionably, an underlying assumption for offering administrative tasks as electronic services is, that these services create benefits for the users of the tool. General advantages and disadvantages of e-government services have already been subject to a wide range of scientific discussions. Usually, by analyzing the potential of e-government solutions, a distinction is made between general and specific benefit potentials. The general benefit potentials are independent of the specific stakeholders. They mainly consist of increased efficiency and effectiveness in the management of administrative procedures. This is reflected in time and financial savings, comfort gains, lower error rates, greater transparency of service provision and expansion of services [START_REF] Scheer | E-Government -Prozessoptimierung in der öffentlichen Verwaltung[END_REF]. The specific utility potentials, on the other hand, are exclusively accessible to the respective stakeholders. These specific benefit potentials were the focus of the present outcome evaluation of StartBiz.

The SECO has already analyzed the benefits of its e-governmental services some years ago. At this time, all e-governmental services were considered at once, so that specific information for certain services were difficult to segregate [START_REF] Summermatter | Evaluation von E-Government Angeboten[END_REF]. Therefore, the purpose of the present evaluation was to provide the SECO with specific insights concerning the generated benefit by each StartBiz service. What kind of benefit has Start-Biz generated for its users so far? Is an e-government tool like StartBiz able to cause significant positive outcome for the start-ups? How can these outcomes be assessed quantitatively and qualitatively to legitimize the corresponding public investments? And by which means might the outcomes even be strengthened? What kind of lessons learned can be transferred to the planned OSS?

All in all, the two main objectives of the present study were (i) to develop a theoretical model for analyzing the benefits of the online tool StartBiz and of future e-government services within the planned OSS, and (ii) to summarize the benefits created by StartBiz so far based on this model [START_REF] Zumbusch | Nutzenbewertung des Online-Schalters StartBiz[END_REF]. To allow resilient answers to the research questions mentioned, the methodology of the present outcome evaluation combined the model of theoretical based outcome-chains on the one hand and a counterfactual analysis on the other hand.

 The outcome-model is essential to assure the causality, to understand the mechanisms of creating the benefits. It is crucial to identify bottlenecks, inefficiencies and to emphasize potential improvements.  The counterfactual analysis shows simply what would have happened without the given intervention. A comparison group of start-ups, that have not used StartBiz so far, serves as an estimate for this counterfactual. The difference in outcomes between the StartBiz user group and the control group allows the quantification of outcome and impact. Hence, the strength of counterfactual approach lies in quantified estimates of impacts at the micro level: "how much has changed because of the use of StartBiz?".

Hence, the two approaches were used complementary [START_REF] Zumbusch | Nutzenbewertung des Online-Schalters StartBiz[END_REF]: counterfactual methods to quantitatively assess the outcome, theory-based methods to understand the underlying mechanisms, thus helping to identify the need and possibilities for optimizations. The methodology was based on various approaches: The evaluation was implemented between January 2016 and October 2016. After concerting the results in a workshop, the evaluation was finalized in autumn 2016. Additional measurements and quantifications of the benefits created by StartBiz as well as by the future OSS are planned to be undertaken every two years from now on.

The Developed Outcome-Model

The developed outcome-model allows the causal deduction of StartBiz-/OSS-benefits by considering specific groups of users, their use of the services offered and the outcome created. The outcome-model for StartBiz is based on the before outlined model of outcome-chains, although it emphasizes the link between the output (specific services of StartBiz and its different components) on the one hand, and the benefit generated by the use of the output on the part of the target groups on the other hand. The further elements of the outcome-chain (problem/need, objective, inputs etc.) are taken into account as well, but are primarily in the way of additional explanation factors. This means that the developed StartBiz outcome-model focused on the three parts (i) output (= specific services of StartBiz), (ii) their usage by the target groups and (iii) the directly attributed and short-term outcome for these target groups.

Fig. 2.

The developed Outcome-Model for StartBiz [START_REF] Zumbusch | Nutzenbewertung des Online-Schalters StartBiz[END_REF].

The reason to focus the evaluation on short-term outcomes (in particular savings in time and costs by StartBiz-users) was the following: intended long-term impacts of StartBiz (such as the increase of location quality due to efficient administration processes) could have been influenced by other external factors and would not be meaningful enough in order to assess the online tool. The focus on short-term outcomes, on the other hand, allows the identification of the direct generated benefits for each target group as well as for each of the StartBiz-services separately. In this way, the benefits of StartBiz can be assessed ex-post for the last five years. Due to the counterfactual analysis, the intensity of the benefit and quantification at an individual level of a single company was possible. In order to reach an aggregated scale of outcome based on individual-level information, an aggregated monetarization of the achieved benefits was calculated. This in turn permitted the benefits of StartBiz to be put in relation to the invested costs in the sense of a cost-benefit assessment.

In order to best monetarize the benefits of StartBiz, a methodology was used, which is based on two complementary central pillars and a willingness-to-pay approach as a relativizing third pillar. The following figure shows this threefold hedge of the utility montage of StartBiz.

 The first pillar is the personal assessment of time and cost savings thanks to StartBiz.

For this purpose, StartBiz users of the last 5 years were asked in the second survey, how they estimate their time savings (working days) and their additional cost savings (in CHF) thanks to the use of StartBiz. By means of the conversion of the working days into CHF (1 working day = 425 CHF), the calculation of the respective mean values and their summation, a first monetary benefit per company founder could be determined.  The second pillar of monetarization is based on the counterfactual analysis. It is a calculated difference between time and cost investments between the group of entrepreneurs who have used StartBiz and the group who make the administrative registrations without StartBiz independently or with other support services. By asking the two groups for their work (in working days) and the cost (in CHF) for the formal start-up process, the two groups were again able to calculate the mean values for the

Summarized Benefit of StartBiz

Outcome for each target group respective categories per founder group. The difference between the two aggregated mean values per group served as a second basis for the calculation of the monetary benefits per company founder.  The calculated sum from these two pillars was compared to the amount resulting from the willingness-to-pay approach (Pillar 3). In this case, the determination of the benefit of the StartBiz services from the user's viewpoint is classified by the recording of a maximum payment for the benefit achieved [START_REF] Sattler | Ein empirischer Vergleich von Instrumenten zur Erhebung von Zahlungsbereitschaften[END_REF]. For this purpose, the Start-Biz users were asked about their potential willingness to pay, which is what they would have been willing to pay for the use of StartBiz and the resulting benefits [START_REF] Völckner | Methoden zur Messung individueller Zahlungsbereitschaften -Ein Überblick zum State of the Art[END_REF][START_REF] Bhatia | Validity of willingness to pay -Hypothetical versus actual payment[END_REF]. The mean value calculated for this category does not flow into the benefit calculation, but serves as a comparison value to the supplementary interpretation. It shows how much the benefit of the user side is appreciated and perceived.

Outcome of StartBiz

Identification and Quantification of Generated Benefits

What kind of benefits did StartBiz-users derive from using StartBiz services? In principle, companies which had used StartBiz, were highly satisfied with the StartBiz offer. They mainly expected savings of time and money. These expectations were largely met. To quantify these benefits, the three pillars explained in chapter 1.3 were used, encompassing the following three approximations: (i) the personal assessments of time and cost savings by StartBiz-users; (ii) the calculated differences of time and cost requirements for the formal establishment of a new company, indicated by StartBiz-users on the one hand and those who did not use StartBiz on the other hand; and (iii) the willingness to pay, which expresses the value of benefits in terms of money as it is perceived by the StartBiz-users themselves. However, the quantification of the benefit generated over the last five years was only possible for sole proprietorships, since only for this user group a sample of sufficient size was available. The calculations show that the use of StartBiz reduces the time required for formally establishing a sole proprietorship by approximately two-thirds. In addition, the costs for the formal establishment of a sole proprietorship is cut down by half in case of using StartBiz compared to the procedure without StartBiz. Hence, Swiss sole proprietorships can save time and money by using StartBiz, which they can invest otherwise. Especially during a company's founding phase when resources are usually scare, their saving is important, since investments have to be carefully taken in order to succeed.

To make these benefits comparable and summable, a monetarization was undertaken. It has to be considered that this monetarization represents only a partial benefit, since only the two parameters (time, costs) can be assessed in monetary terms. Consequently, the calculated benefit in terms of money indicates only the order of magnitude. An exact indication in Swiss francs would represent a spurious accuracy that is not reliable.

Based on the three abovementioned approximations, time and cost savings for formally establishing a sole proprietorship by using StartBiz can be assessed with approximately CHF 2,200.-. With regard to 9'448 sole proprietorships in the years 2011 to 2015 using StartBiz, StartBiz has generated a total benefit of about CHF 21 million over these past five years. This results in an average benefit of CHF 4 million per year by sole proprietorships using StartBiz. Since 84% of the current StartBiz users are sole proprietorships, this amount already represents the largest share of the generated benefit [START_REF] Zumbusch | Nutzenbewertung des Online-Schalters StartBiz[END_REF]. Further calculations show that the benefit of StartBiz for companies with different legal forms seems to be at least as high as that for sole proprietorshipsa distinction between different types of legal forms was undertaken, because the number of possible service uses depends on the legal form of a company due to legal conditions. However, for all legal forms of start-up companies other than sole proprietorships, the sample sizes were not large enough. Yet, assuming a similar benefit like that realized by sole proprietorships, one can estimate a total benefit of almost CHF 25 million generated by the total of 11'293 companies (irrespective of their legal form) using StartBiz for formally establishing their company in the years 2011-2015. This results in a total benefit of almost CHF 5 million per year generated by the use of StartBiz services.

Considering furthermore all StartBiz-registrars, who used the online tool for information reasons exclusively, additional generated benefits by StartBiz could be identified too. Pursuant to the above mentioned procedure among StartBiz-registrars from the years 2011-2015, a generated benefit by StartBiz of additional CHF 800,000 could be approximated. These benefits are offset by costs for the development of StartBiz and its operation as well as personnel costs for its maintenance and care. Between 2010 and 2015 SECO has invested around CHF 440,000 per year. Thus, the benefits for start-ups exceed the public investments by far. If these costs are taken into account in the benefit assessment, the total benefit of StartBiz amounts CHF 5.36 million per year [START_REF] Zumbusch | Nutzenbewertung des Online-Schalters StartBiz[END_REF].

Conclusions and Implications for the Planned OSS

In summary, StartBiz exhibits a constant number of users per year who are very satisfied with the StartBiz offer and have a proven benefit from the use of StartBiz. Nevertheless, it should also be pointed out that StartBiz addresses a comparatively small group of users. This is due to the limitation of its services only to the formal establishment of companies out of all the various phases that a start-up company has to go through. In addition, one has to note a limited degree of brand awareness for StartBiz. The great potential to increase the target group is not yet fully exploited. In particular, there is no possibility to use StartBiz under mandate (potential use by third parties such as trustees, lawyers or consulting companies on behalf of founders). This aspect restricts the positioning of StartBiz as a «tool» and fuels the reservation of other institutions in the start-up community, who perceive StartBiz as a competitive offer. In this regard, a clear and active communication of StartBiz (as well as of the future OSS) as an online tool seems advisable, which intends to facilitate administrative obligations of companies. Such a communication could sharpen the profile, reduce perceptions of StartBiz as a competitive offer, and could address additional user groups. In this sense, it should be a matter of course, that the future OSS also enables the use of its services under mandate.

In order to be able to identify the specific benefits generated by the use of StartBiz / OSS, more information on individual target groups would be necessary. For this purpose, a standardized, automated feedback loop could be established in order to measure customer satisfaction of all users promptly after they have used a certain service either of StartBiz or of the future OSS. This feedback may provide essential information specifically for each target-group as well as important strategic knowledge for required adjustments and optimizations of the services offered. At the same time, this may help to document reliable and sufficiently robust feedback informationso that in the future, amongst other things, one will be able to evaluate the benefits of each StartBiz-/OSS service and each user group separately.

In general, for outcome evaluations of e-governmental offers we see, that benefit models can significantly help to identify and quantify the benefits generated for the specific target groups. At the same time, it has to be taken into account that quantified benefits alone only show one puzzle piece in the vast fields of outcomes. So it is of great importance that all discussions emphasizing monetary effects of e-governmental services always underline their embeddedness in a broader field of qualitative outcomes and benefits. Public investments in e-government can no longer be legitimized only by cost-benefit ratios. In many areas, e-governmental services have become a matter of course as the low willingness to pay shows clearly. In this context, it is also clear that e-government solutions are increasingly of interest to companies. Simple and fast administrative processes, as promoted through e-government solutions [START_REF] Lenk | Bürokratieabbau durch E-Government[END_REF], are gaining in importance as a location criterion. In a time of increasing location competition, in which many of the infrastructural location factors are now largely ubiquitous, such egovernment approaches can certainly make a significant contribution to keeping companies at the location or gaining a location [START_REF] Göbel | Kommunalverwaltung und Wirtschaftsförderung als Standortfaktor. Forschungsbeiträge zum Public Management[END_REF][START_REF] Westerfeld | IT -Entwicklung und Stand in der öffentlichen Verwaltung[END_REF]. Thus, the question, what kind of egovernmental services have already to be seen as a matter of course on the one hand, and what kind of respective services constitute an additional offer on the other hand, will gain importance during future outcome evaluations of e-governmental activities.

Fig. 1 .

 1 Fig. 1. Outcome Chains as the Basis of the Outcome Evaluation: own illustration, 2016.



  a series of in-depth interviews with a broad range of experts of the entrepreneurship ecosystem,  one online survey amongst all start-ups in Switzerland of the last 5 years (sufficient representative response of 500 start-ups) for information with regard to the comparison group and to the embeddedness of StartBiz in the ecosystem,  data analysis of anonymized user data of the StartBiz tool from the last 5 years,  another online survey amongst the StartBiz users of the last 5 years (sufficient representative response of 250 StartBiz users of the last 5 years).  two reflection workshops with the responsible decision-makers of the SECO.

  Since the relaunch of StartBiz in 2011 until the end of 2015, there was a total of 19'626 company registrations on StartBiz. The number of registrations has remained largely constant since the beginning with about 4,000 per year. Almost half of all registered companies (42%) did not actively use StartBiz to register with trade registers, VAT, social or accident insurances. They remained StartBiz registrars. The rest (58%) of all registered companies has actively made use of one or more services offered by StartBiz. Most of these users used StartBiz only once and for only one service (mainly for registering with the social insurances). Some used two services, only very few StartBiz-users made three or four registrations via StartBiz. The vast majority of StartBiz-users were sole proprietorships (84%). Referring to this legal form, almost a quarter of all new companies in Switzerland over the last years has formally been established by using StartBiz[18].

Fig. 3 .

 3 Fig. 3. Specified benefit model for StartBiz according to its user data 2011-2015 [8].

Fig. 4 .

 4 Fig. 4. Time and cost savings (in working days / WD or CHF) by formally establishing a sole proprietorship (SP) with StartBiz [8].

Fig. 5 .

 5 Fig. 5. Generated Benefits by StartBiz per Target Group and Year [8].

Concrete challenges / problems Corresponding objectives Usage of the output by the target groups Directly attributed effects (outcome and benefit) Not directly attributed and long-term effects (impact) Other factors of influence Input (organization and resources) Output (activities and measures)


Marius Rohde Johannessen
email: [marius.johannessen@usn.no

Lasse Berntzen
email: lasse.berntzen@usn.no

Ansgar Ødegård
email: ansgar.odegard]@usn.no

A review of the Norwegian plain language policy

Keywords: eGovernment, public sector renewal, plain language, institutional theory, policy

In this paper, we examine the policy documents that define the Norwegian policies on language use in the public sector, with an emphasis on how ICT is mentioned as a tool for creating a public sector language citizens find easy to understand. Norway and other countries have had a series of projects aimed at making the public sector use plain language in their communication with citizens. We present two example cases of successful plain language use and one less successful case, and discuss these cases using the lens of new institutional theory. We argue that the institutional context of change and user-centricity have had a major impact on the success of our example cases.

Introduction

Language use and language policies are matters of great public interest, as language can be an instrument of inclusion or exclusion, discriminate or include certain groups and act to reinforce or break up existing power structures [START_REF] Sonntag | State Traditions and Language Regimes: Conceptualizing Language Policy Choices, in State Traditions and Language regimes[END_REF]. The ways in which we use language can be seen as a constant ideological battle about discourse, social control and social structure [START_REF] Woolard | Language Ideology[END_REF]. Public sector, or bureaucratic, language, has emerged in its current form because of the bureaucratic logic of impersonality, rationality and objective, rule-based decisionmaking [START_REF] Sarangi | Language, Bureaucracy and Social Control[END_REF], and the result has often been a language system that is difficult for users of public services to interpret. Partly because of the need for precise formulations dictated by bureaucratic logic, but also because of professionals using the terminology specific to their professions.

From a democratic perspective, the use of complex language is a problem, as it denies citizens the opportunity to participate in policymaking and to influence decisionmaking. The representative democratic ideal is that every citizen has both the right and the opportunity to be heard by elected officials. The use of language may be a major barrier to democratic participation and citizen access to the public sector, and plain language is thus an important prerequisite for eGovernment and eDemocracy [START_REF] Lutz | Plain Language: An Important Basis of E-Democracy and Open Government in CEDEM 16 -Conference for E-democracy and Open Government[END_REF]. This has been discussed since the 1980's when several scholars began arguing for the use of "plain English" in the public sector [START_REF] Sarangi | Language, Bureaucracy and Social Control[END_REF], as bureaucratic language had become difficult to understand for ordinary citizens.

Plain language has emerged in recent years as an international topic 1 , and in Norway, the plain language project emerged in 2008 as part of the government's initiative to modernize the public sector [START_REF] Dahle | Klart vi kan! En evaluering av effektene av prosjektet "klart språk i staten[END_REF]. The project is grounded in several policy documents, and ICT plays a central role in this effort [START_REF]Digital agenda for Norge: IKT for en enklere hverdag og økt produktivitet, k.-o. moderniseringsdepartement[END_REF], with a clear user-centric perspective on how digital communication channels should function.

Our objective with this paper is to examine the relationship between policy, technology and institutional culture in the plain language project. We do this by analyzing policy documents addressing plain language, looking for explicit mentions of ICT in these documents, and by examining two example cases of successful plain language work: The Norwegian tax administration and the Norwegian Public Roads administration. We contrast these successful cases with the case of the welfare agency NAV, which has not been as successful [START_REF] Andreassen | Den store reformen: Da NAV ble til[END_REF]. We apply institutional theory as our lens in order to explain these different results. This approach addresses Axelsson et al's call for research on policy documents in a wider range of contexts [START_REF] Axelsson | In Search of ICT in Smart Cities -Policy Documents as Idea Carriers in Urban Development[END_REF].

The rest of the paper is structured as follows: Section 2 presents related research on language use and institutional theory. Section 3 outlines our research approach. In section 4 we present our findings from the analysis of policy documents and example cases, and we discuss these findings in light of institutional theory in section 5. Section 6 presents our conclusion and suggestions for further research.

2

Related research

In this section, we discuss previous research on plain language, provide a brief overview of the Norwegian efforts in this area, and situate plain language in the wider context of digitizing the public sector in order to make it more effective and efficient. Further, we provide a brief overview of institutional theory as our analytical lens.

Plain language from a user-centric perspective

Plain language is defined as "correct, clear and user-centered language in texts from government" [START_REF] Kvarenes | En praktisk veileder i klarspråksarbeid[END_REF] (authors' translation), and should involve organizing information so that the most important points come first, breaking complex information into understandable chunks, using simple language and defining technical terms and using the active voice [START_REF] Kandula | The relationship between health literacy and knowledge improvement after a multimedia type 2 diabetes education program[END_REF] Researchers have discussed plain language at least since the 1980's [START_REF] Sarangi | Language, Bureaucracy and Social Control[END_REF]. OECD countries have emphasized the use of plain language in government for long time, and 23 countries had implemented plain language strategies in the year 2000, with varying degrees of success. The OECD considers plain language as important for facilitating transparency and accountability in government [START_REF] Deighton-Smith | Regulatory Transparency in OECD Countries: Overview, Trends and Challenges[END_REF]. In the Nordic countries, Sweden has been the driving force of plain language, and the Swedish efforts to simplify government communication has been an inspiration for Norwegian policy-makers [12].

Plain language did not receive much attention in Norway until the government initiated the project "klarspråk" (plain language) in 2008. The objective of the project was to improve communication between citizens and government, and the project involved more than 60 government agencies at the national level [START_REF] Dahle | Klart vi kan! En evaluering av effektene av prosjektet "klart språk i staten[END_REF]. Evaluators [START_REF] Dahle | Klart vi kan! En evaluering av effektene av prosjektet "klart språk i staten[END_REF] considered the initial project successful, and it was renewed in 2013 as the project "Plain language in public administration". This recent project is a collaborative effort between the Norwegian Agency for Public Management and eGovernment (DIFI) and the Norwegian Language Council. DIFI has created an online course for plain language use, and DIFI in collaboration with the language council has set up the web site "klarsrpåk", which provides guidelines, case studies, examples of good communication, language games and quizzes, as well as a project guide for planning and executing plain language projects in government agencies [12].

In order to involve municipalities as well as national government, the municipal organization KS has become involved, and is currently offering plain language courses to municipalities and working on guidelines for plain language, which will be presented as an e-learning application when completed. They have also set up a plain language award that goes to the municipality that has been most active in promoting and using plain language in the past year [START_REF] Ks | Klart språk[END_REF].

There are several approaches to evaluating plain language. Readability indexes are algorithms that attempt to calculate the readability of a text [START_REF] Mccallum | Computer-based readability indexes[END_REF]. The two main index types are readability instruments aimed at assessing print and web-based information and word recognition and comprehension tests [START_REF] Friedman | A systematic review of readability and comprehension instruments used for print and web-based cancer information[END_REF]. These indexes measure for example character, word and sentence length to determine the complexity of a text [START_REF] Friedman | A systematic review of readability and comprehension instruments used for print and web-based cancer information[END_REF]. By paying attention to the number of words and syllables we use when writing, we can make our texts easier to understand [START_REF] Flesch | How to Write plain English -A guide for lawyers and consumers[END_REF]. However, a recent study indicated that readability indexes are not necessarily the most reliable tool for plain language work [START_REF] Dahlia | Reassessing the accuracy and use of readability formulae[END_REF]. Nonetheless, readability indexes remain one important part of the plain language toolbox, and there is ongoing research on the automation of text simplification, where readability indexes are applied along with synonym dictionaries to replace difficult words in sentences [START_REF] Shardlow | A survey of automated text simplification[END_REF]. The second approach is to apply writing techniques aimed at clarity. These techniques involve guidelines for the structuring of texts, choice of words, layout and more. There are several published guidelines, focusing on different areas of the writing process [START_REF]Professionalising plain language options paper[END_REF]. The third approach differs from the other two, in that the focus is on evaluating the result of a text; How well is it understood? Are readers able to act on the content? Visual representation and communication is seen as important in this approach, and usability testing is the preferred way of evaluating texts [START_REF]Professionalising plain language options paper[END_REF].

The Norwegian plain language project recommends that writers should emphasize the latter approach, but does recommend some use of guidelines and readability indexes as supplements to user evaluations [START_REF] Kvarenes | En praktisk veileder i klarspråksarbeid[END_REF]. However, both DIFI's online course and the "klarspråk" web site's writing tips rely heavily on checklists and examples of structure and writing styles. The project guide presents guidelines and examples of usability testing, recommending this for agencies who are working consistently on plain language.

Plain language is, in both national and municipal policies, placed in the context of modernization and digitization of the public sector, and mentioned as an essential aspect of a user-centric government. In the white paper "Digital Agenda for Norway" [START_REF]Digital agenda for Norge: IKT for en enklere hverdag og økt produktivitet, k.-o. moderniseringsdepartement[END_REF], the government outlines its policy for a cost-efficient, digitized public sector. One of the two key objectives of the white paper is to create a citizen-centric mindset in government. Public services should be presented as coordinated and complete, even if a service involves several agencies and levels of government. Information sharing is another key element of the policy. Services that are not designed form a user-centered perspective tend to have a much lower rate of adoption [START_REF] Bertot | The E-Government paradox: Better customer service doesn't necessarily cost less[END_REF]. Usability testing is essential in user-centric government [START_REF] Bertot | Using ICTs to create a culture of transparency: E-government and social media as openness and anti-corruption tools for societies[END_REF], hence the strong focus on testing in the Norwegian policy. In public sector projects, the user groups are many and diverse, and there can be very large differences in the objectives of citizens using the system and the government officials at the other end. This presents an additional challenge for user-centric government [START_REF] Axelsson | Exploring the importance of citizen participation and involvement in e-government projects: Practice, incentives, and organization[END_REF], and could also be seen as one of the reasons why the Norwegian plain language project downplays the importance of "simple" language. Certain user groups are both able to and require, communication to be precise and sometimes complex [START_REF] Kvarenes | En praktisk veileder i klarspråksarbeid[END_REF].Usability testing with selected target groups is thus the only approach that can facilitate these many and varied user groups.

Despite this strong policy focus on user-centricity, eGovernment projects have had a tendency to be focused around the service being delivered, and citizen needs have not been taken into account [START_REF] Anthopoulos | Applying participatory design and collaboration in digital public services for discovering and re-designing e-Government services[END_REF]. In the next section, we present institutional theory as a possible explanation for this.

Neo-institutional theory and organization identity theory

From a Neo-Institutional perspective, the concept of plain language might be considered one of many recipes for modernizing the organizational field of public sector organizations within the ideas of New Public Management, which might be characterized a global mega trend in modernizing the public sector organizations since the introduction in the 1980's [START_REF] Røvik | Trender og translasjoner : ideer som former det 21. århundrets organisasjon[END_REF].

Organizations adapt to what they believe society expect from them [START_REF] Boxenbaum | Isomorphism, diffusion and decoupling[END_REF] and organizational changes thus emerge as a result of isomorphic processes [START_REF] Dimaggio | The New institutionalism in organizational analysis[END_REF] not necessarily founded in instrumental and rational reasons alone. This leads to institutional isomorphism and similarity between organizations [START_REF] Dimaggio | The New institutionalism in organizational analysis[END_REF]. However, when the institutional environments are ambiguous and pluralistic, there is a tendency of decoupling action from formal structure in order to maintain organizational efficiency [START_REF] Boxenbaum | Isomorphism, diffusion and decoupling[END_REF].

As Meyer and Rowan [START_REF] Meyer | Institutionalized Organizations: Formal Structure as Myth and Ceremony[END_REF] suggest, organizations embrace the wider culture and values institutionalized and legitimated in the society. Hence, the introduction of plain language may be explained within the frames of modern values and organizational phenomena like citizen-centrism, consumer dialogue, impression management and organization image.

Despite the focus on legitimacy through ceremonial changes and the tendency of decoupling action from formal structure, the adoption and implementation of the concept of plain language might be characterized as organization identity work [START_REF] Røvik | Identitetsutvikling i moderne organisasjoner[END_REF].

Within a dynamic perspective on organization identity [START_REF] Hatch | The dynamics of organizational identity[END_REF], an ongoing and ever moving relation between culture and image is affecting organization identity -"where we come from" and "who we are becoming" as an organization. This tension between the roots, history and traditions of the organization and the future represented by the image is to a great extent occupied with aligning the organization to expectations from the environments and the society.

Focusing only on "who we are becoming" might lead to adoption of plain language as neither accepted by the employees nor implemented and used in accordance with the ideas of the concept. On the other hand, focusing only on culture, traditions and the past might cause organizations to become immune to impulses, demands and changes initiated in the external environments. This might explain resistance to change, and should be taken in consideration when adopting concepts like plain language.

Seemingly contradictory theoretical perspectives like neo-institutional theory and organization identity theory might be of crucial importance when explaining adoption and implementation of new concepts. Formation of identity and construction of legitimacy through isomorphic processes are two sides of the same coin [START_REF] Pedersen | In search of identity and legitimation: Bridging organizational culture and neoinstitutionalism[END_REF]. Thus, adopting and implementing plain language without involving and connecting with the culture, roots and traditions of the organization presumably will lead to ceremonial changes with no or little influence on the quality of dialogue with the citizens. In accordance with [START_REF] Ødegård | Organisasjoners tilpasning til radikale reformermellom tradisjon og fornyelse[END_REF], we suggest a multidimensional time perspective when adopting new concepts. In order to succeed we recommend paying attention to both the past traditions and at the same time focus on the future, including changing expectations in society.

Research approach

The purpose of this paper is to examine the relationship between policy, technology and institutional culture in the plain language project. Policy documents can carry ideas from high-level to concrete policy [START_REF] Axelsson | In Search of ICT in Smart Cities -Policy Documents as Idea Carriers in Urban Development[END_REF]. This paper addresses the call for research on policy documents in a wider range of contexts [START_REF] Axelsson | In Search of ICT in Smart Cities -Policy Documents as Idea Carriers in Urban Development[END_REF], by examining policy documents in the Norwegian Plain language project. The study was conducted using a qualitative, interpretive approach.

We have collected the policy documents that the Norwegian Language council report are central to the plain language projects: Two white papers outlining the government strategy on language and digitization2 , the government communication policy 3 , the egovernment policy 4 and the strategy for accessibility 5 . We also have e-mail interviews with representatives from DIFI and KS, where we asked about status and future plans for the plain language project. Data for the two example cases are from DIFI's evaluation of government organizations working with plain language.

A policy analysis process can focus on policy problems, performance, expected and observed outcomes, as well as the actions that a policy leads to [START_REF] Dunn | Public Policy Analysis[END_REF]. We focus our analysis on problems (understood as target audience, value propositions and social aspects of the policy) and expected outcomes and actions, especially involving communication and ICT. Actors, the acts performed by actors and their engagement with artefacts are typical characteristics of an interpretive approach to policy analysis in concrete cases [START_REF] Yanow | Accessing Local Knowledge, in Theories of Institutional Design : Deliberative Policy Analysis : Understanding Governance in the Network Society[END_REF]. Analysis of the documents have been conducted using discourse analysis [START_REF] Granath | The Smart City -how smart can 'IT' be? : Discourses on digitalisation in policy and planning of urban development[END_REF] We have chosen two example cases, the Norwegian tax administration and the Norwegian Public Roads administration to examine how policy flows from high-level objectives to practical implementation. We apply institutional theory as our lens in order to explain why these two projects were successful in translating policy into action.

Findings

Policy analysis

We have analyzed five policy documents, explicitly mentioning plain language: The «digital agenda» and «language policy» are white papers from government presented for discussion in parliament. The government communication policy presents the highlevel policy for communication at all levels of government, and is a framework that can be used for further planning. The government accessibility strategy outlines the strategy for including people with accessibility challenges in society, and the language and digitization policy outlines the plan for modernizing and renewing the public sector. Table 1 summarizes the problem areas, plain language and related ICT aspects of these policy documents.

The five policy documents deal with plain language from different perspectives. The language policy's purpose is to outline a policy for the continued use of Norwegian language in all levels of society. Here, plain language is addressed as important for citizens, but the policy also discusses the need for complexity and emphasizes language education. The policy only mentions ICT as a contextual factor: As a driver for the requirement of higher literacy skills and as a threat to small languages such as Norwegian.

The communication policy builds somewhat on the language policy, but the purpose is to facilitate communication between citizens and government. Information and inclusion in public matters is the focal point of the policy. Plain language is mentioned as being important in order to reach the objectives of openness, participation and inclusion. ICT receives little attention. The only mention if ICT is that the public sector needs to use the possibilities offered by new communication technologies.

The eGovernment policy is more explicit on the role of ICT, and is the first document where digitization and plain language is set in the context of a more efficient public sector. Digitization is seen as essential for service delivery and the inclusion of all citizens, and the policy is more explicit on which tools (digital mailbox, user-centric design, common core components and digital communication as standard) to implement.

The Accessibility strategy addresses the needs of disabled people. In 2014 reguluation was introduced to facilitate accessibility in digital communication, and this strategy outlines the process for an accessible public sector. The document states that plain language is essential for accessibility, especially for people with certain kinds of cognitive disabilities. ICT plays a large role in this, and the document outlines 14 detailed points for accessible ICT. The points discuss what to do, but the responsibility for how is delegated to DIFI.

The Digital agenda is the most recent policy, released in 2016. The ambitions of the digital agenda pull together a lot of the content from the previous policies, and present a vision of a user-centric government that talks in a way people can understand. This document is much more emphatic in stressing the point that government agencies can no longer act as silos, but need to work together to solve complex social problems. Together, the five policy documents present a clear vision for a user-centric government, where plain language is essential for inclusive and efficient communication.

Problem area (target audience, social aspects, values) Communication & plain language ICT/outcome aspects

Language policy (2008)

Create a common language policy across government to ensure consistency. Preserve the Norwegian language in a globalized world. Reveal hidden, language-based power structures. Points to socio-demographic differences in language skills. Acknowledges role of tradition in language use as barrier to plain language. Simplify bureaucratic language where possible, but some texts require precision and complexity. Improve language education. Information society increases necessity of mastering language. IT (Internet) a challenge for continued use of Norwegian language.

Communications policy (2009)

Inform and include citizens in policy-making and service creation Openness, participation, reaching everyone, coherence in communication across gov' agencies. Plain language important to reach everyone.

Exploit new technologies

eGovernment policy (2012)

Digitize the public sector to a) create a more effective and efficient public sector, and b) to improve service delivery and communication with citizens.

Example cases: Successful digitization and plain language

The Norwegian tax administration and the Norwegian Public Roads administration have both worked extensively with plain language in the past years, and both agencies report that plain language has led to measurable improvements.

The tax administration has been leading the way in digitizing the public sector, and the main driver is the change from defining themselves as a control and surveillance agency into a service agency whose purpose is to help citizens, organizations and businesses. At the same time, they are focused on becoming more effective and efficient, and are working to improve digital self-service solutions on their web site, which is constantly updated. Plain language is part of this change into a user-centric service organization. When changing something, they start by inviting user feedback via their "beta" blog. For example, their tax return simplification project received feedback from 11.000 users and was tested over several iterations. They combined workshops with employees, aimed at understanding the internal processes and regulation, with user testing and user feedback. This thorough understanding of the regulations and processes involved in tax deductions allowed the design team to create a front-end where users did not have to know the details in order to get the reporting right. The results have been positive. The commuter part of the project led to a 40 % decline in complaints on tax returns, a 200 % increase in site visits and a significant reduction in calls and emails about commuter tax deductions as users were able to use and understand the information on the web site.

The public roads administration ran a plain language project from 2011 to 2012. The project was run by their communication department, and included users from several of the other departments in the agency. After the project was completed, they implemented plain language as part of the everyday work processes in the organization. As with the tax administration, the public roads administration also has a holistic approach, seeing plain language as part of their overall drive to become a user-centric organization. They have redesign their web site emphasizing self-service in order to save resources and be more efficient. Frequently used services such as change of ownership forms for cars are now digitized and automated, making the process of buying and selling used cars much easier. They have also worked on changing the wording of standard letters, in order to make them easier to understand. Each of these letters are sent to a million users every year, so even a marginal increase in the public's ability to understand and act on a letter provides significant savings. The new letters were user-tested over two iterations. In the final test, users reported they spent significantly less time understanding the message and the actions they were required to take. Internally, the new letters led to a 40% reduction in calls from frustrated citizens who did not understand the content.

In contrast to these successful cases, we have the NAV reform, where three agencies (unemployment, social services, welfare) merged into the welfare agency NAV. Despite a user-centric focus, NAV is criticized for being removed from the users and for extensive use of bureaucratic language [START_REF] Lundberg | Uforutsigbare relasjoner: Brukererfaringer, NAV-reformen og levd liv[END_REF]. A major reason for this is said to be the merger itself, with massive challenges stemming from the merger of three different organizational cultures [START_REF] Andreassen | Den store reformen: Da NAV ble til[END_REF]. While the plain language policy development reads as a linear progression, the policies behind the NAV reform have suffered from several changes in direction both before and after the merger was initiated [START_REF] Andreassen | Den store reformen: Da NAV ble til[END_REF]. While NAV has been slowly improving, they still lag far behind colleagues in other agencies and users, in usability tests conducted by the first author, report that navigating the self-service web site can be both frustrating and difficult.

Discussion

Both the tax and public roads administrations report that organizational change was essential for their plain language success. While plain language initially was a separate project, it was later implemented as an integrated part of everyday work tasks and practices within the wider context of user-centricity and modernization through citizen selfservice. Employees are positive, as they see that this approach has benefits in the form of fewer phone calls and complaints and more time for other and more interesting work.

While these two examples show how plain language and digitization can be implemented, the e-mail interviews with DIFI and KS confirms that despite a decade of plain language work, a lot remains to be done. Municipalities have only recently begun working with plain language, and large agencies such as NAV still have a long way to go.

Organizational theory can help explain these differences. Organizations adapt to the wider societal context, but in pluralistic organizational environments decoupling can occur [START_REF] Boxenbaum | Isomorphism, diffusion and decoupling[END_REF][START_REF] Dimaggio | The New institutionalism in organizational analysis[END_REF]. The tax and public roads administrations have internalized the digitization and plain language policies, and are working towards becoming service-organizations with the "client" (citizen) in focus. They have done this by seeing plain language, modernization and digitization as parts of an overall strategy, and made sure that this strategy is made part of the organizational culture. They have embraced the values legitimated in society [START_REF] Meyer | Institutionalized Organizations: Formal Structure as Myth and Ceremony[END_REF], as communicated by the policy documents related to plain language. NAV on the other hand, has struggled with a huge reform, having to merge cultures with at times very different understandings. Evaluations of the reform [START_REF] Andreassen | Den store reformen: Da NAV ble til[END_REF] points to the problems stemming from this as well as the changes in the policies related to the reform as important for the current situation in the agency.

The organization identity tension between where we come from and who we are becoming [28] [29] is also handled differently. The tax and public roads administrations have managed to handle this tension. While they are focusing heavily on the future and implementing strategies that ca be seen as a clear break with the past, they remain anchored in the existing organizational culture, as exemplified in the workshops held with case handlers, aimed at understanding and building services around existing processes, but which also manages to appear as user-friendly and understandable to citizens. NAV's problem with merging different cultures appears to create a stronger tension, as employees struggle to find their place in a new organization. This makes it more difficult to cope with the expectations from policies on user-centricity and plain language. Management and policy has a strong focus on "who we are becoming", while employees seem more concerned with culture, change fatigue and finding their place.

Conclusion and future research

In this paper, we have examined the policy documents relevant for plain language work in Norway. The five policy documents we have analyzed reveal a gradually evolving policy, which begins with a pure language focus and evolves into a holistic and ambitious plan that sees plain language as an important part of creating a more efficient and user-centric public sector. Further, we have examined example cases to analyze how agencies translate the policy to action. Finally, we have applied organization theory to discuss the differences in results in our example cases, showing that policy implementation require organizations that are able to successfully handle the tension between past traditions and existing organizational culture, and future expectations and direction.

The main limitation with our study is that we have used secondary data, DIFI evaluations, in discussing the cases. While this is sufficient to provide an overall picture, future research should focus on in-depth observation of government agencies in order to verify our conclusions.

Further, we argue that there is a need for research into other aspects of plain language. We have discussed the organizational aspect of translating the plain language policy to action. Another issue is how the policies are interpreted and implemented. Plain language is easily seen as a text-only issue, involving readability of information. The policy documents discuss why and what should be done, but leave the how to the agencies implementing policy. The egovernment and digital agenda policies do mention briefly that language can also involve visualization of information, and we argue that while simplifying language is important, other possibilities to increase understanding of public sector information, mainly by using techniques of visualization, are equally important. Techniques such as flowcharts, timelines, map-based information, video and animation can play an important role in helping citizens understand information from government. There is evidence of this in the cases, as both agencies have redesigned their web sites to be visually oriented. The public roads agency have created a map-based solution for traffic monitoring and flow. The tax agency has redesigned several of their services as step-by-step guides relying heavily on visual and typographic elements. There are other examples as well, found in municipalities and other government agencies. The digital planning dialog 6 , implemented in several municipalities, is a map-based solution for municipal planning where visualization has replaced long written documents. Several municipalities have implemented video streaming of meetings, survey results are presented using visualization 7 and open data policies are being implemented. However, these remain scattered examples. We are still sorely lacking an updated policy where the concept of plain language also includes these aspects, and future research should examine how different forms of communication can complement each other in order to continue working towards user-centricity and plain language as tools for modernizing government.

Table 1 :

 1 Overview of policies

			1/3 find it difficult to un-	Government communica-
			derstand public communi-	tion to be digital (digital
			cation. Objective: All	first choice).
			communication from gov-	Digital mailbox
			ernment should follow	User-centricity
			plain language guidelines	Create common set of core
				components
	Accessi-	Create a society where eve-	Plain language important	14 detailed policies on
	bility	ryone is able to participate,	for accessibility	ICT/accessibility. Ad-
	strategy	also disabled people		dresses "what", but not
	(2015)			"how"
	Digital	ICT is rapidly changing soci-	Plain language increases	User-centricity.
	agenda	ety on all levels. We must	use of digital services, and	Coordination across gov-
	(2016)	use ICT to create a) a user-	ensures more people can	ernment departments.
		centric, effective and effi-	take part. Young adults no	Digital first choice.
		cient public sector and b) In-	not understand how to use	Digital skills in schools.
		novation, value and equal	current services.	Continue to build digital
		possibilities for participation		infrastructure (mobile, fi-
				bre)

See f.ex the plain language network: http://plainlanguagenetwork.org

st.meld 27 (http://ow.ly/8kLj308wr5q) &

(http://ow.ly/jvut308wraz) 3 http://ow.ly/h9Ku308wrlE 4 http://ow.ly/C7HB308wt16 5 http://ow.ly/LbOb308wrt6

http://ow.ly/eIeY308EeSC

www.bedrekommune.no