Silja Eckartz

Wout Hofman

Anne Fleur Van Veenstra

A Decision Model for Data Sharing

Keywords: Data Governance, Data-Driven Innovation, Public Service Innovation, Open Data, Decision Model

Data-driven innovation has great potential for the development of innovative services that not only have economic value, but that help to address societal challenges. Many of these challenges can only be addressed by data sharing of public and privately owned data. These public-private data sharing collaborations require data governance rules. Data governance can address many barriers, for example by deploying a decision model to guide choices regarding data sharing resulting in interventions supported by a data sharing platform. Based on a literature review of data governance and three use cases for data sharing in the logistics sector, we have developed a data sharing decision model from the perspective of a data provider. The decision model addresses technical as well as ownership, privacy, and economical barriers to sharing publicly and privately owned data and subsequently proposes interventions to address these barriers. We found that the decision model is useful for identifying and addressing data sharing barriers as it is applicable to amongst others privacy and commercial sensitive data.

Introduction

Data is often proclaimed to be the new oil -or the new gold -for innovation and economic growth [START_REF]Digital agenda: Turning government data into gold[END_REF]. 'Open' and 'big data' raise high expectations [START_REF] Manyika | Big data: the next frontier for innovatioin, competition, and productivity[END_REF]. Open data is the provisioning of data by government organizations for free in a re-useable format [START_REF] Janssen | The influence of the PSI directive on open government data: An overview of recent developments[END_REF]. Most literature takes a so-called push approach in which the data availability will contribute to public -and private sector -innovation [START_REF] Jaeger | Transparancy and technological change: ensuring qeual and sustained public access to government information[END_REF]. A law such as the Freedom Of Information act in the United States, and expectations of economic growth and innovation [START_REF]Digital agenda: Turning government data into gold[END_REF] are drivers for open data. Open data aims for organizations to become more transparent and thereby accountable to citizens [START_REF] Harrison | Creating Open Government Ecosystems: a research and development agenda[END_REF], to realize economic activity [START_REF] Endsley | Toward a theory of situation awareness in dynamic systems[END_REF] or to increase organizational efficiency and effectiveness by better decisions [START_REF] Zuiderwijk | Guest Editors' Introduction. Innovation through open data: a review of the state-of-the-art and an emerging research agenda[END_REF]. Big data is the processing of large, (un)structured and real-time data sets for a wide variety of purposes, including the objectives of open data. Both developments are expected to not only create new economic activity, but also to contribute in addressing societal issues and challenges, such as a decrease of CO2 emissions, or a decrease of the costs incurred for health services or social welfare. Jetzek et.al [START_REF] Jetzek | Generating Value from Open Government Data[END_REF] have constructed and validated a model for value generation by open government data, where they have defined value from an economical and social perspective. According this model, open data has only a marginal impact on innovation.

Societal challenges can no longer be addressed by public organizations alone, they are often based on a combination of public and privately owned data. However, data sharing by private organizations may pose other challenges as those posed to public organizations [START_REF] Jaeger | Transparancy and technological change: ensuring qeual and sustained public access to government information[END_REF] since private organizations have their competitive position to consider. Organizations often find the process of opening data cumbersome and many challenges and barriers occur [START_REF] Janssen | Benefits, Adoption Barriers and Myths of Open data and Open government[END_REF]. To support the value creation with data, this paper develops, based on literature and practice, an approach to identify barriers to data sharing from the perspective of the public and the private sector, and proposes interventions to overcome these challenges and barriers.

Based on a literature review of data governance and open data, we first identify barriers to data sharing. These barriers are subsequently validated and potential interventions to overcome these barriers are identified by looking at three use cases from the logistics sector, using an interpretivist methodology [START_REF] Klein | A set of principles for conducting and evaluating interpretitive field studies in information systems[END_REF]. Interpretivist research is "aimed at producing an understanding of the context of the information system, and the process whereby the information system influences and is influenced by the context" [START_REF] Walsham | Doing interpretive research[END_REF]. The use cases are investigated using on desk research and interviews with stakeholders.

In the next section, barriers to data sharing are identified from literature. Subsequently, we present three use cases of data sharing in the logistics sector that are used to validate the barriers and identify the barriers found in literature. Based on the literature and on these use cases, we present a decision model for data sharing. Subsequently, we discuss the model and present conclusions and recommendations for further research.

Literature review

This section identifies and analyzes potential barriers to data sharing from literature on data governance and open data.

The management of data is of raising importance for many organizations given the growing supply of structured as well as unstructured data [START_REF] Mingers | Combining IS research methods: towards a pluralist methodology[END_REF]. Data governance is an emerging discipline and comprises parts of IT governance [START_REF] Janssen | Open data and transformational government[END_REF]. Weill and Ross [START_REF] Barry | Barriers to open data release: a view from the top[END_REF] distinguish IT governance and IT management where governance refers to the decisions that must be made to ensure effective management and use of IT and to whom these decisions are makes, and management involves the actual decision making and implementation. According to Thomas [START_REF] Weill | IT Governance: how top performers manage IT decisions rights for superior results[END_REF], "data needs to be governed as it has neither will nor intent of its own. Tools and people shape the data and tell it where to go. Therefore, data governance is the governance of people and technology." Data governance covers aspects such as data quality, data management, metadata management, access rights, decision rights, accountability, and data policies. [START_REF] Weber | One size does not fit all -a contigency approach to data governance[END_REF] Data governance literature shows that barriers to data sharing differ when considering open data, which is a form of data sharing by public organizations with private organizations, and data sharing in between private organizations.

Literature on open data often takes a so-called push approach in which the data availability will contribute to public -and private sector -innovation [START_REF] Jaeger | Transparancy and technological change: ensuring qeual and sustained public access to government information[END_REF]. From a data governance perspective, the most dominant open data barriers are found to focus on data quality. Data quality is specified in more detail by Batini et al. [START_REF] Batini | Data Quality: concepts, methodology, and techniques[END_REF]. Domainspecific metadata describing the data origin, the data production date, data provenance, and for which applications the data can be used is of crucial importance. Data quality aspects that should be considered with respect to the entire data set are: accessibility, data format, semantics, conciseness, completeness, believability and reputation [START_REF] Knight | Developing a framework for assessing information quality on the World Wide Web[END_REF]. Data quality aspects that should be considered with respect to data elements are: validity, completeness, consistency, uniqueness, timeliness, accuracy and preciseness [START_REF] Nousak | A scorecard approach to improving data quality[END_REF]. Other technical barriers to re-using open data are the publishing of data in a format that is not machine readable, the lack of an Application Programming Interface (API), difficulties to processing data sets, the lack of a linking or combining functionality, and difficulties in configuring data transformation [START_REF] Zuiderwijk | Guest Editors' Introduction. Innovation through open data: a review of the state-of-the-art and an emerging research agenda[END_REF][START_REF] Janssen | Open data and transformational government[END_REF][START_REF] Barry | Barriers to open data release: a view from the top[END_REF].

Other authors take a broader perspective on open data. For example, Jetzek et al. [START_REF] Jetzek | Generating Value from Open Government Data[END_REF] constructed and validated a model for value generation by open government data, where they have defined value from an economic and social perspective. Besides technical barriers, Zuiderwijk et al. [START_REF] Jaeger | Transparancy and technological change: ensuring qeual and sustained public access to government information[END_REF], Janssen et al. [START_REF] Mcdonnell | Big Data Challenges and Opportunities[END_REF], and Barry et al. [START_REF] Bizer | Linked Data -The Story So Far[END_REF] analyze potential barriers to publishing open data according to various perspectives: political, social, economic, institutional, operational, and legal. Political barriers include a lack of support, a lack of attention and a lack of knowledge about open data. Among the social barriers are a lack of interaction with users, difficulty to measure impact, cultural differences and risks and liability with respect to providing low data quality. The lack of business models is a main economic barrier to open data. Institutional barriers include a lack of standards, a lack of an open data policy, an inability to handle user requirements and a lack of guidelines. Data fragmentation, a lack of services, a lack of metadata, changing or a lack of clear semantics, and a lack of information on data quality are among the operational barriers. And the legal barriers include licensing, policy differences, lack of (detailed) policy.

In settings where data is shared with or between private organizations, most barriers to data sharing are related to privacy or to competition regarding economically sensitive data. While many authors mention privacy issues, not all of them explicitly elaborate on specific privacy problems [START_REF] Batini | Data quality: concepts, methodologies, and techniques[END_REF]. Bizer et al. [START_REF]Rotterdam Rules[END_REF] elaborate the perspective of the user of data and especially the privacy issues related to combining several data sources. As long as data from several sources is viewed separately it might not involve any privacy issues but as soon as one data source is combined with another privacy threats might arise. In all settings it is important to have clear defined decision rights [START_REF] Barry | Barriers to open data release: a view from the top[END_REF]. These are often defined by general IT governance and ownership structures. We further see that there is limited tool support and competing licenses for data sets.

Based on literature on data governance and open data we identify five main categories of barriers to data sharing: technical, data quality, ownership, privacy and economic. We consider barriers like political, institutional, and lack of or missing business models identified in open data literature as drivers to data sharing, which are a prerequisite for analyzing data sets. Our overall model addresses these conditions, but they will not be part of the proposed decision model. Technical barriers include barriers related to re-use and precision and recall of data and/or their source [START_REF] Dalmolen | Improving sustainability throuhg intelligent cargo and adaptive decision making[END_REF], which can be improved by metadata as an intervention. Data quality is addressed by aspects related to individual data as well as to datasets.. Data ownership regards the question of who is allowed to use and determine re-use of data and who has decision rights. Data privacy is not mentioned as a barrier in open data literature; we can only assume that it is addressed in specifying data policies, but it is mentioned in literature on data governance. Economic barriers include interventions like billing and invoicing of data usage and address liability, which also relates to data quality. Barriers in open data literature rank liability as a social risk, but one could also consider liability from a commercial perspective among businesses.

Logistics use cases

This section presents three use cases in the logistics sector to validate the identified barriers as well as identifying interventions for overcoming these barriers. Logistics is considered as an application area for data sharing, since it is a fairly complex environment with many Small and Medium sized Enterprises (SMEs) and a limited number of globally operating large enterprises. Each of these enterprises is autonomous, but has to adhere to international trade agreements that also address data sharing like the Rotterdam Rules [START_REF] Esmeijer | Thriving and surviving in a data-driven society[END_REF] that specify a clear separation of concern between transport of cargo and activities like declarations and warehousing with commercial information. Furthermore, it is generally expected that data sharing can improve decision making [START_REF] Zuiderwijk | Guest Editors' Introduction. Innovation through open data: a review of the state-of-the-art and an emerging research agenda[END_REF] and contribute to efficiency and effectiveness of logistics [START_REF] Hofman | A global IT infrastructure improving container security by data completion[END_REF]. In this particular dynamic environment, the use cases considered cover i) infrastructure data provided by Rijkswaterstaat, ii) data sharing between two container terminals that serve as hubs in logistics networks and have to process large amounts of data obeying the aforementioned Rotterdam Rules, and iii) the prediction of turnaround times at one container terminal in the Rotterdam port that has to be able to load and discharge vessels and at the same time the arrival and departure of containers by road. These environments are all data intensive environments with real time data requirements. The first use case about Rijkswaterstaat is based upon a detailed web-analysis done by two researchers independently. Input for use cases two and three has been collected during two in-depth interviews with stakeholders from the terminals. Given the complex and data intensive nature of the three use cases we believe that the most important barriers have been identified and argue that these use cases are sufficient for a first validation of our decision model. In-depth case studies are needed to strengthen our validation and further develop and validate Step 4 of the decision model.

Use case 1: infrastructure and its usage

Rijkswaterstaat (RWS) is a Dutch governmental organization that is part of the Department of Infrastructure and Environment (I&M), RWS is responsible for the design, construction, management and maintenance of the main infrastructure facilities, such as highways, water systems and waterways (www.rijkswaterstaat.nl/en/about_us/). The organization is publishing a number of data sets as open data, such as data sets of normal and current water heights and a map containing the location of objects for roads and waterways like lighting objects, painting on the surface of roads, traffic signs, locks, and bridges. The data is not only published on a map at the RWS website, but also available via the Dutch national open data portal. The map in which all this data is available is compatible with international standards of the Open Geospatial Consortium (OGC) for the exchange of geographical data.

As the organization is a public organization, they feel the obligation to publish its data. However, not all RWS data is available as open data. Roughly, the data held by the organization can be divided into static data specifying the infrastructure, which is updated yearly or a few times a year, and real-time data like traffic congestions and waiting times at locks and bridges. While most static data is published, not all realtime data sets are made available as open data. One reason is that some notifications are published as push messages (events) rather than raw data because of their urgency. It supports so-called data driven actions [START_REF] Miller | Open data commons, a license for open data[END_REF] required to improve decision making in situational awareness [START_REF] Zuiderwijk | Guest Editors' Introduction. Innovation through open data: a review of the state-of-the-art and an emerging research agenda[END_REF]. Also, some of the data is duplicated. Furthermore, the organization protects some data sets that contain personal data. One example is the real-time location of barges. Often, the barge operators live on their barges and therefore a barge is considered as a 'house' in terms of privacy laws. To protect the privacy of the barge operators, these data are only published anonymized.

Use case 2: sharing data between two container terminals

Lots of logistics operations occur between container terminals in the Netherlands, one for instance in the Rotterdam port and another functioning as inland port. However, little information is shared between these terminals. This use case examines the potential of information sharing for two terminals to improve their planning, be able to use the capacity of barges better and increase transshipment volumes at terminals. Shared data includes bookings, timetables, available capacity, and vessel or barge movements.

The analyzed data set includes privacy and potentially commercial sensitive information. While the location of inland barges is currently published as open data, the destination of these barges is not published. This information can be privacy sensitive as some inland shippers live on their vessels (see the previous use case). Thus, an intervention to filter out privacy sensitive information needs to be in place. Next to privacy issues, another challenge is commercial sensitivity. Sharing data on barge capacities could decreases transport prices leading to lower profits for barge operators. Sharing booking data could unveil commercial relations between logistic partners, which is considered sensitive from a competitive perspective. Intervention mechanisms are required in filtering commercial sensitive data and new business models are necessary for sharing capacity data. Thus, it is a challenge to find an economic model for sharing data between two terminals. Two options can be considered: i) a terminal sells its data to its partners and ii) the terminals make bilateral agreements about mutual data exchange. Such agreements should also include service level agreements (SLAs) about data quality and technical formats. The terminals should further specify who is liable for the data.

Use case 3: prediction of turn around times at terminals based on open data

Time and place (location) are important aspects of logistics. Waiting times decrease the profit of carriers, since transport capacity cannot be utilized during waiting. In this respect, there is a carrier demand for predictable turn around times at drop off or pickup locations like terminals. Currently, these locations are still a 'black box' which operation is determined by its internal processes and the large number of trucks arriving and departing at its location. In the case of a terminal in the Rotterdam port, this often results in a queue at its gate. Carriers could respond to the demand for more information about turnaround times at a terminal, by collecting and sharing information about the location, the average speed the destination and activities at that destination of their trucks. This information could help to determine turn around times at different locations in order to help i) carriers to improve their planning and ii) terminals to better manage container drop off and pickup. Information could be collected using the estimated time of arrival determined by the route planner systems used in most trucks. Other information could be provided by on board units used by truck drivers to report their activities.

Sharing this information involves i) privacy issues (information about location and destination could disclose commercial relations of a carrier), ii) economic investments and collaboration structure to set up a data sharing platform (a business model needs to be thought of, e.g. subscription) and iii) technical aspects like data formats and semantics, as currently no standard format is used to describe the location of a truck.

A decision model for data sharing

This section introduces a decision model for organizations to decide if they want to open their data, based on the literature review and the use cases. Firstly, the overall decision model is presented and secondly, individual aspects are described with potential interventions.

The decision model

We found both data governance and open data literature to address a decision structure, albeit in a different way. Decision rights in data governance literature address the decision structure and decision processes [START_REF] Barry | Barriers to open data release: a view from the top[END_REF], while open data literature addresses data policies from different perspectives, e.g. political on international (for instance European Union perspective) and national level, and institutional, based on a decision structure that is taken for granted. Based on the insights gained from the literature review and use cases we structure the barriers to data sharing into a decision model (figure 1). It is structured into four steps: identification of the goal of data sharing (Step 1), identification of incentives for individual stakeholders (Step 2), identification of barriers, also referred to as constraints, to data sharing (Step 3) and the definition of the process to publish data (Step 4). Examples of goals (step 1) are for instance accountability to citizens and improved decisions (section 1). A business case and business model are examples of incentives for individual organizations (step 2). Our proposed decision model addresses detailed analysis of individual data sets (step 3), within the context of goals and incentives. This third step supports data analysis by five categories identified in literature: ownership, privacy, economic, data quality, and technical. Each of these categories contains detailed questions to support decisions regarding data sharing. The process to open date (Step 4) consists of a stepby-step guidelines covering technical aspects, such as data conversion, metadata requirements and URI strategies as well as organizational aspects around governance. A detailed description of this step is out of the scope of this paper.

Figure 1. Decision model for opening up data

The decision model works as follows. If a certain constraint to data sharing is present in a given situation, the next step is to analyze if the constraint can be overcome by an intervention. For example, when a privacy constraint occurs, anonymization by filtering or aggregation by combining a data set into a single record, are potential interventions. Interventions are usually of a technical nature, but also include organizational mechanisms. When no suitable intervention can be identified the data set cannot be shared. Hence the arrow shown on the right-hand side of figure 1. The next sections describe the five categories for analysis in more detail and introduce, where possible, interventions. The decision model should be applied both on a data set level as well as on individual data properties and even data values of a data set (see for instance [START_REF] Berners-Lee | Linked Data -four rules[END_REF]). It should be noted that the decision model that is presented in this section, often serves as an example rather than a definite set of issues that needs to be addressed. While the categories remain more or less the same, for every use case new issues can be added to the categories.

Ownership

Only the owner of the data can make a decision about whether to publish data or not. If more than one organization owns data, all involved organizations need to agree on opening up the data. If one or more of the data owners are negative about opening up the data one possible intervention would be to start a process that informs all skeptical data owners about the advantages of open data. Improving trust is another important intervention needed at this point in the process. Involvement and support from higher management is essential for improving trust and for establishing a culture that is positive about open data. Licensing could be an intervention describing particular rules imposed by data owners. Licensing could be under similar conditions as given for open source [29]. Licensing mechanisms reflect data policies of data owners. Security mechanisms based on identity mechanisms could serve as an intervention to share data only with trusted organizations or individuals.

A particular aspect of data ownership refers also to culture within an organization. In many cases, the data manager of a particular data set is unwilling to provide data to others, since he has no control on how his data will be used. Clear data policies (institutional perspective, [START_REF] Jaeger | Transparancy and technological change: ensuring qeual and sustained public access to government information[END_REF]) and decisions structures [START_REF] Barry | Barriers to open data release: a view from the top[END_REF] are a means to cater with these barriers.

Privacy

If a data set contains classified or privacy sensitive information that can be traced back to individual persons or companies this will constrain the data owner to publish the data. Legal constraints related to the privacy of data can also present a constraint to publishing data. Possible interventions to overcome privacy constraints are anonymization by filtering of sensitive information and aggregation of data, thus, only publishing a selection of data properties and values. Another option is the deployment of access control mechanisms combined with identity management that regulates data access. This intervention limits the openness of the data and is therefore especially applicable for company data with access restrictions and less for open data in general. In case interventions are not carried out successfully the data cannot be opened up.

Economic

Several elements related to the economic and business value aspects of a data set may hinder publication. Often data owners do not have a clear view on which data to publish as they are unaware of the potential of the different data sets. Since data is also considered to have a large economic value [START_REF]Digital agenda: Turning government data into gold[END_REF], data owners also expect to make profit with their data. It is difficult for individual data owners to assess the value of data sharing for collaboration (see the logistics cases). In the case that a data owner currently earns money by providing his data, this will constrain the publication of the data -at least openly. The data owner could consider sharing the data only among a few organizations, and assess if there are other ways to earn money with the data when it is published (even among a few organizations). This means that the data set may be opened up but is only available for a fee (Open data business model), which is one possible intervention. Monitoring, billing, and invoicing have to be supported as intervention mechanisms, potentially with different business models.

Economic sensitivity may constrain the data to be opened up. Economic sensitivity refers to the consequences for the business processes and even the business model of a company. Opening up commercial sensitive data, such as available transport capacity, may result in a decrease of the commercial rates offered by that company and thus its profitability. Yield management mechanisms need to be in place to ensure that companies do not experience negative financial consequences when opening up data but can benefit from improved insights in consumer behavior. Another constraint to open data may be that the actual costs of publishing the data might be too high for the data owner. Only if the business case for opening data is positive the data should actually be published. To address these economic constraints it could be considered, especially when multiple organizations collaborate in a network: i) to share the costs of opening up data, ii) to define a pricing structure for data set usage and iii) to set up a separate organization to govern and maintain the data and its usage. Each of these interventions requires monitoring functions of a data sharing platform.

Data Quality

A data provider is responsible for the quality of the data that is published. Poor data quality of a data set or a selection of data properties should prevent the data from being published. Data quality could also have impact on liability in case poor data leads to accidents, incidents or increased turn around times. A related issue is that data gathered in a specific context may not be useful in another context, even though the quality 'in itself' is good. If data quality is a constraint, one needs to dive into the data to analyze if the data quality can be improved on the constraining factors to still be able to open up the data. One example is to explore whether the data set can be extended with other data to improve completeness. If data quality is too low, or if the data is not applicable in any other situation, the data set should not be opened up. In any case metadata describing the data quality should be added [START_REF] Miller | Open data commons, a license for open data[END_REF], [START_REF] Barry | Barriers to open data release: a view from the top[END_REF]. To allow for re-use of data by others, as much context information as possible about data should be provided. Furthermore, social interaction with the data should be supported: data is often most used and most easy to interpret when a community can be built around the data platform where the data is published. Data visualization can play an important role in this.

Technical

The technical format of a data set may be a constraint to open the data [30]. If the data is unstructured it may be difficult to convert it into a machine-readable format relevant to a data user. The size of the data set, the existence of a semantic model, and identifiers are other technical issues that need to be considered. For this group of issues, many interventions are possible. Examples include: offering the data in a structured format; reusing existing vocabularies and ontologies; publish the data according to existing data standards. Most technical interventions should be accompanied by an economic intervention.

Discussion

Based on barriers that organizations can apply on individual data sets, data properties, and data values, the decision model presented in the previous section provides a number of interventions. The decision model can be applied on individual data sets in the context of goals like economic growth or improved decisions (section 1) and incentives like a business case for an individual organization [START_REF] Jaeger | Transparancy and technological change: ensuring qeual and sustained public access to government information[END_REF]. In the decision model, we have structured the issues in five categories that reflect a data owner's perspective, which encompasses the perspectives for open data [START_REF] Jaeger | Transparancy and technological change: ensuring qeual and sustained public access to government information[END_REF]. As literature of open data considers only government data, the decision model for data sharing also considers decision structures [START_REF] Barry | Barriers to open data release: a view from the top[END_REF], commercial sensitivity, and potential business models for data sharing amongst competitors derived from logistics use cases. The latter also refers to the institutional barrier of the inability to process data user demands, whereas in the private sector data is shared to the benefit of both a data provider and -user. Based on governance literature and the use cases, private and commercial sensitivity has been included in the decision model.

Different incentives may apply to individual organizations. On a high level, we found that a distinction can be made between legal and economic incentives. The legal incentives are centered around any regulatory measures that can be taken by the government to stimulate open data. Examples are compliance to safety or environmental regulations or compliance to directives regarding open data. The economic category includes incentives that lead to economic gains for the organizations that publish their data, such as efficiency gains, enlarging their customer base, or creating a competitive advantage. Reciprocity is another incentive as one organization is often more willing to publish data if this is matched by another organization opening their data. If neither economic nor legal incentives to publish data can be identified the process to open the data set is usually aborted as it is unlikely that a positive business case can be identified for data publication.

Analysis of data sets is not only to be done on the level of a data set, but particular data properties and data values need to be considered. It implies that data analysis requires a detailed knowledge of semantic models of a data set, including vocabularies. Analysis on a high level of detail is necessary as intervention mechanisms also have to deal with that level of detail, for instance by implementing access control at the level data properties and -values. Interventions that can be applied are for instance attribute based access control, but these require particular tool support for managing access control rules combined with identity management of partners. Although this complexity is not required in open (government) data, it will be required by the private sector and thus potentially impact public-private collaboration leading for instance to economic growth [START_REF]Digital agenda: Turning government data into gold[END_REF].

The decision model provides a number of intervention mechanisms. One intervention mechanism could potentially be used to address different barriers and an intervention mechanism can be viewed in the context of another mechanism. Security interventions can for instance be applied in the context of a data policy. An extensive mapping of interventions to barriers in different contexts needs more attention in future research.

Conclusion

This paper presents a decision model for sharing publicly or privately owned data based on both a literature review and three logistics use cases. The decision model contains details addressing individual barriers in each of the five categories identified. Since we have taken use cases for data sharing in the private sector, other barriers than the ones identified for open data are introduced in our decision model, e.g. commercial sensitivity. We have also introduced a number of interventions, whereas the implementation of these interventions in for instance a data sharing platform still needs to be elaborated. Our objective is to create Web based tools supporting the decision model, in which a data owner can select particular intervention mechanisms that are supported by, for instance, a data platform. Such tools need to cater with various details of data set analysis.

Data governance literature emphasizes the importance of a decision structure [START_REF] Barry | Barriers to open data release: a view from the top[END_REF] for data sharing in the private sector, since goals and incentives need to be clear to all stakeholders. Goals can be formulated at a macro level, e.g. (inter)national or regional, and incentives need to be formulated at organizational level, e.g. by a clear business case or a data sharing strategy supported by management. These goals and incentives are drivers for analyzing data sets as formulated by our proposed decision model. Lack of a clear decision structure, an implicit decision structure, or lack of goals and incentives for individual organizations might be a barrier to the uptake of economic growth and innovation. Organizing data use for instance from a situational awareness perspective [START_REF] Zuiderwijk | Guest Editors' Introduction. Innovation through open data: a review of the state-of-the-art and an emerging research agenda[END_REF] could be the basis to stimulate data sharing.

A final finding is that organizations can apply the decision model, without making a distinction to whether the data will be publically available without any restrictions (open data), or whether data will be shared in a closed community. The model introduces interventions like restricted access based on authorization and access control rules. Thus, the model can be a basis for data driven innovation for open and big data in the context of goals and incentives.


Catherine G Mkude
email: cmkude@uni-koblenz.de

Maria A Wimmer
email: wimmer@uni-koblenz.de

Strategic Aspects for Successful E-Government Systems Design: Insights from a Survey in Germany

Keywords: e-government strategy, e-government design and implementation, benefits, challenges, success factors

The maturity of e-government implementation in research and practice has developed tremendously over the years. Nevertheless, the challenges encountered and the overall growth of e-government in different countries varies; studies by organizations such as the UN and World Bank evidence these variations. To successfully implement e-government, governments are required to deepen their understanding of aspects such as benefits, challenges and success factors. Contributing to this knowledge and understanding, the paper investigates factors framing successful design and implementation of e-government systems. The paper presents and analyses the literature and results from an egovernment inquiry in Germany. The paper highlights important factors for successful implementation of e-government and also presents opinions on strategic aspects for e-government systems design with reference to Germany. It finally highlights the need for further research in the domain.

Introduction

Since their beginning, e-government research and practice have matured tremendously over the years, with governments opting to fully utilise the opportunities and benefits of implementing electronic government (e-government). Dynamic developments and resulting innovativeness of information and communication technologies (ICT) largely contribute to the way governments deliver their services to the public. With the increasing desire of realising good governance principles (see [START_REF]OECD: The Case for E-Government: Excerpts from the OECD Report "The E-Government Imperative[END_REF] for a definition of good governance principles) in e-government implementations, more and more countries transform their governments from traditional forms of paper-based and unconnected organisations to seamless and networked 'e-'governments. Through recognising the potentials of e-government to improve public service delivery and to achieve good governance principles, governments all over the world have initiated strategies to support e-government implementations. However, the pace at which governments mature in implementing e-government varies immensely. International studies such as an UN e-government survey [START_REF]E-Government for the People[END_REF] and a World Bank publication on ICT [START_REF]World Bank: The Little Data Book on Information and Communication Technology[END_REF] evidence such variations as most developed countries mature far ahead of developing countries in the up-take of e-government services. Therefore in addition to the enormous amount of available research, where principles for successful implementation of e-government systems are presented, it is imperative for further research to be conducted to determine the differences in approaches of implementing egovernment with country-specific challenges and success factors. This will complement practices in different countries and help understand how these can be transferred from one country to another. This paper aims to investigate strategic approaches and the understanding of egovernment benefits, challenges, and success factors, which are important to successful design and implementation of e-government strategies with reference to Germany. An empirical research conducted among experts attending an e-government event investigates strategic aspects for e-government systems design. In particular, we aimed at receiving feedback and obtaining further understanding of strategic aspects for e-government systems design such as challenges and success factors, which need further investigations and particular attention. Prior to empirical investigations, relevant e-government literature was studied to gain an understanding of different factors influencing e-government development and implementation.

The remainder of the paper is as follows: the next section provides insights into current literature identifying distinct factors influencing e-government development and implementation. In section 3, we detail the research methodology for the study. The results of the survey are presented in section 4. We then discuss the findings with respect to the literature review in section 5 and conclude with final remarks and future research in section 6.

E-government development and implementation: a literature review of strategies, benefits, challenges and success factors

Governments continue to initiate e-government strategies with the aim of successfully transforming public sectors into technology-savvy organisations in order to realise the benefits of ICT in public sectors. E-government strategies describe in detail the implementation of e-government by setting objectives, which are further elaborated into programs and subsequently in projects [START_REF] Mkude | Strategic Framework for Designing E-Government in Developing Countries[END_REF]. With the different and changing foci of egovernment strategies, attributed by factors such as political interests and financial capabilities among others, it is inevitable also to understand the different approaches adopted by governments to implement their strategies. A study of e-government implementation approaches by OECD unveils the importance of a centralised approach, but -even more importantly -a relationship between centralised approach and coordination with decentralised actors. According to this study, "the e-government planning process within the central government helps to establish and diffuse the vision and to translate it into goals and targets. Goals serve not only to provide a direction for action and achievement, they can also be used to prioritise and even advance action. Government-wide planning and the setting of objectives can also improve coordination between government organisations, serve to establish criteria for reconciling conflicting approaches and signal preferred approaches and shared resources for overcoming challenges" [START_REF]OECD: The Case for E-Government: Excerpts from the OECD Report "The E-Government Imperative[END_REF].

Literature studies further unveil that the on-going efforts of governments initiating e-government strategies imply that the benefits of ICT in the public sector are increas-ingly acknowledged. Besides other resulting benefits of e-government, ICT is more and more viewed as a tool for seamless and improved interrelations among government organisations, among governments and businesses, and among governments and citizens. Literature study of e-government benefits identified the following: improved efficiency and effectiveness in public processes [START_REF] Alshawi | Information Technology for Development -e-Government Initiatives in the[END_REF] [START_REF] Wimmer | Integrated Service Modelling for Online One-Stop Government[END_REF]; cost-effective access of public services [START_REF] Kumar | Factors for Successful E-Government Adoption: A Conceptual Framework[END_REF] [START_REF] Lee | Current Practices of Leading E-Government Countries[END_REF]; ease of access to information [START_REF] Bertot | Citizen-Centered E-Government Services: Benefits, Costs, and Research Needs[END_REF] [START_REF] Lee | Current Practices of Leading E-Government Countries[END_REF]; creation of a more knowledgeable society [START_REF] Bertot | Citizen-Centered E-Government Services: Benefits, Costs, and Research Needs[END_REF] [START_REF] Layne | Developing Fully Functional E-Government: A Four Stage Model[END_REF]; and reduced corruption [START_REF] Alshawi | Information Technology for Development -e-Government Initiatives in the[END_REF] [4] [START_REF] Ndou | E-government for developing countries: opportunities and challenges[END_REF] [27] [START_REF] Schuppan | E-Government in developing countries: Experiences from sub-Saharan Africa[END_REF], which is more reported in literature investigating benefits of e-government in developing countries.

In addition to investigating e-government benefits, the challenges encountered were studied. Many e-government implementation challenges are reported in literature, which are often classified as financial, economic, technological, social and cultural challenges [START_REF] Gilbert | Barriers and Benefits in the Adoption of E-Government[END_REF] [17] [START_REF] Ndou | E-government for developing countries: opportunities and challenges[END_REF]. In a recent study, we outlined thirteen e-government challenges from nine literature studies [START_REF] Mkude | Strategic Framework for Designing E-Government in Developing Countries[END_REF]. Similar to the different ways, in which governments realise e-government benefits as reported earlier, e-government challenges seem to vary in different countries, too. This implies that e-government challenges in respective countries and their ways of addressing these challenges need to be analysed.

Finally, factors contributing to successful implementation of e-government were investigated. Numerous studies have highlighted success factors (see e.g. [START_REF] Baguma | Factors for Success and Failure of e-Government Projects: The Case of e-Government Projects in Uganda[END_REF] [10] [START_REF] Gichoya | Factors affecting the successful implementation of ICT projects in government[END_REF] [15] [START_REF] Jaeger | E-Government around the World: Lessons, Challenges, and Future Directions[END_REF] [19] [START_REF] Mkude | Strategic Framework for Designing E-Government in Developing Countries[END_REF]). Considering the extent of investments and commitment required for successful and sustainable development of e-government endeavours, most scholars point out the importance of political support to e-government implementation [START_REF] Allen | E-Governance & Government on-Line in Canada: Partnerships, People & Prospects[END_REF] [4] [START_REF] Bhatnagar | E-Government: From Vision to Implementation -A Practical Guide With Case Studies[END_REF] [9] [12] [23] [24] [START_REF] Schuppan | E-Government in developing countries: Experiences from sub-Saharan Africa[END_REF]. The presence of legal and interoperability frameworks are also regarded as important factor, which should ensure seamless integration of processes and improved coordination among organisations [START_REF] Belachew | E-government initiatives in Ethiopia[END_REF] [START_REF] Gil-García | E-Government Success Factors: Mapping Practical Tools to Theoretical Foundations[END_REF] [24] [START_REF] Rashid | An Investigation into Critical Determinants of e-Government Implementation in the Context of a Developing Nation[END_REF].

Based on the literature review studying aspects influencing and contributing to successful development and implementation of e-government, a survey was developed to collect insights on strategic aspects for successful design and implementation of e-government systems. The next section introduces the research methodology, followed by summarizing the insights of the inquiry in Germany (section 4).

Research Methodology

Based on the research objective outlined and the review of literature, a qualitative research approach was used to study e-government development and implementation aspects from German practitioners and academicians along an e-government event.

The objective of the study was to gain an understanding of e-government aspects from the perspective of German experts as outlined in the introduction. In such an exploratory study according to Flick et al., qualitative research is suitable to provide the basis of gaining such understanding and describe a phenomenon from the point of view of participants [START_REF] Flick | What is Qualitative Research? An Introduction to the Field[END_REF]. Germany was used in this study because one key country selected in the overall research works is Germany. The research method selected for this study was survey using a paper-based questionnaire as data collection method. The questionnaire was designed to capture relevant information and understanding of different aspects identified in literature (cf. section 2) and to gain insights from experts. The questionnaire consisted of eleven questions, with most questions being open-ended, which were assembled in five thematic parts with links and relationships among questions. The first part investigated demographical information of respondents, the second part investigated e-government benefits and challenges, the third part investigated development aspects of e-government, the fourth part investigated egovernment supporting frames and the fifth part investigated recommendations for successful design and implementation of e-government systems. The questionnaires were distributed in person to respondents attending an e-government event in Koblenz, Germany. The attendees included e-government experts from academia, private and public sectors. Among 15 questionnaires distributed, 8 questionnaires were returned and used for analysis. Among the unreturned questionnaires, three respondents reported that they do not understand English well, one respondent reported that the questionnaire was too long and three respondents did not provide any feedback.

Qualitative data analysis was used to investigate insights from literature and from the answers to open-ended questions to determining similarities, differences and patterns in responses. The results of the inquiry are summarized in the next section.

Results and insights from the survey

The first part of the questionnaire aimed at investigating the background of responders necessary for analysing responses provided therein with respect to the influencing factors leading to the responses. Among 8 respondents, 5 respondents are ICT practitioners in the public sector with an experience in e-government ranging from 20 to 4 years. 3 respondents are academics with an experience in e-government ranging from 8 to 1.5 years. Next, respondents were asked concerning the benefits of e-government implementation presented in literature, with respect to Germany. Further, the level at which the benefits are realised in Germany should be assessed. All respondents indicated that they are aware of the benefits of e-government implementation and they rated benefits of e-government as shown in Table 1. The assessment indicates the average rates of the level of realisation in Germany. All but one assessments were rather homogeneous. Reduced corruption yielded distinct assessments, with 2 respondents rating reduced corruption as a fully realised benefit of implementing e-government, 1 respondent rated it as not realised and 1 respondent indicated that reduced corruption is "not among the most realised benefits of e-government".

The next part of questions investigated challenges encountered in developing egovernment in Germany and possible ways of addressing these challenges. The literature review pointed out that e-government challenges are numerous but yet dissimilar in different countries. Therefore, the questions aimed at investigating what challenges are encountered from a German perspective and what solutions would be applicable. Respondents indicated diverse strategic challenges as listed in Table 2 (each mentioned once), and proposed possible solutions to these challenges (except for one). The objective of the subsequent question was to investigate the development and implementation of e-government frameworks in Germany with the basis of literature presented in section 2. Main focus in this study was whether the framework is developed at national level or whether many e-government frameworks exist at different levels and for distinct purposes. Respondents were asked whether they are aware of the presence of an e-government development framework at national level. Five respondents answered 'Yes' and they mentioned the following six different frameworks:

(i) 'Data exchange standards' to ensure interoperability1 , (ii) DOMEA2 -an organisational concept for public administration work, especially workflow and archiving, (iii) 'IT Planungsrat'as a coordination framework involving the federal government and the 16 federal states in Germany to coordinate e-government works3 , (iv) V-Model XT of the Federal Government to ensure quality with a standard engineering approach in systems development4 , (v) SAGA -a standards and reference architecture guideline for e-government systems development in German public administrations 5 , and

(vi) E-government law -a newly introduced legal framework for the federal governments to implement e-government 6 .

Further investigations were conducted to study and assess these frameworks and to get additional insights. One respondent answered that 'there are many initiatives in federal states, cities etc. to standardisation' and another one stressed that no unique framework for e-government development exists at national level. The latter respondent also commented that the lack of such a unique framework resulted in uncontrolled and diverse (not interoperable) evolutions with isolated applications over the years. The five respondents aware of the frameworks were further asked to assess the adoption level of the frameworks among different government levels in Germany, and further to state the impact of the assessed adoption levelshowing results in Table 3.

Table 3: E-government development frameworks and their adoption level (N=5

; scale: 1=not adopted; 2=rather not adopted; 3=rather adopted; 4=fully adopted) Four frameworks were rated as rather adopted. DOMEA was rated as rather not adopted. The e-government law was approved and entered into force only in mid-2013; hence, no indication and evidence of adoption or even impact exists so far. Political support in the implementation of e-government is deemed in literature among most important factors to establish and sustain successful e-government systems. Therefore, respondents were asked to assess the existing political support in implementing e-government. Two respondents assessed the political support as fully supportive, five respondents assessed it as rather supportive and one had no response.

The response seems congruent with the introduction of CIOs or IT directors in federal, federal state and also larger local governments. The inauguration of the IT Planungsrat (cf. development frameworks above) also evidences political support for egovernment developments in Germany.

Following the assessment of political support, respondents were then asked to assess whether the existing legal framework to support e-government implementation is sufficient. Literature review presented earlier revealed the importance of legal frameworks not only to provide an environment for integrated processes, but also for coordination among government organisations at different levels. Since the e-government law was only approved and entered into force in mid-2013, the assessment of the eight experts in to adoption level was equally distributed among 'rather sufficient' and 'rather not sufficient'. The impact of the legal framework could not yet be assessed due to the time-span of the e-government law being in force being too short.

As discussed above and in section 2, given the maturity of e-government developments, it is imperative to acknowledge the political, economic, socio-cultural, technological, environmental and legal differences in individual countries. With this regard, the subsequent part of the questionnaire asked for an assessment of selected principles, from literature, in their contribution to ensure successful implementation of egovernment in Germany. Results are presented in Table 4.

In the following section, we discuss above findings with reference to presented literature in section 2. Presence of political commitment 4

Presence of committed leadership 4

Presence of financial sources 4

Collaboration among government departments/agencies etc. 4

Digital literacy among government employees 3

Use of government enterprise architecture 3

Digital literacy among users of e-government systems 3

Presence of digital inclusion 3

Prioritisation of projects 3

Integration of processes among government departments/agencies etc. 3

Organisational interoperability 3

Legal interoperability 3

Semantic interoperability 3

Technical interoperability 3

Performance of risks management 3

Performance of change management 3

Implementation of small and/or pilot projects strategy 3

Presence of legal and regulatory frameworks 3

Promote e-government awareness across country 3

Generic approaches for re-usability in other areas 3

Discussion of findings from survey and literature review

The results and insights from the review of literature generally reveal that the knowledge of e-government development and implementation is immense. However, there is still a need of investigating and embracing the different approaches and perceptions in different countries. This will enable researchers and practitioners to fully collaborate in creating innovative e-government solutions while also tackling challenges in ways that take into account the different settings of particular governments.

The OECD study argues that a government-wide planning and implementation of e-government leads to more coordination among government organisations [START_REF]OECD: The Case for E-Government: Excerpts from the OECD Report "The E-Government Imperative[END_REF]. The survey results are congruent with the observation that the lack of such a 'framework' leads to isolated and un-interoperable e-government initiatives in the long run.

E-government benefits such as more effective processes, accountability of public authorities, ease of access of information, and enhanced quality of public services are among many benefits reported in literature. According to the results, the extent of realising these benefits is diverse. Such diversity highlights the need of not only understanding the benefits of implementing e-government in general, but also of more research into the extent, at which countries realise these benefits. Studies such as [START_REF] Alshawi | Information Technology for Development -e-Government Initiatives in the[END_REF] [4] [START_REF] Ndou | E-government for developing countries: opportunities and challenges[END_REF] [27] [START_REF] Schuppan | E-Government in developing countries: Experiences from sub-Saharan Africa[END_REF] indicate that e-government contributes more to reduced corruption in developing countries. Results from the interrogation of German experts reveal a different view: On average, respondents view reduced corruption among other egovernment benefits realised in Germany. However, results also reveal that reduced corruption is not yet among the benefits fully realised. This implies lack of common understanding of how e-government has impacted the level of corruption in public sectors, in developed countries such as Germany.

All e-government challenges that were indicated by respondents are challenges that are also widely documented in literature. The challenges indicated such as lack of political commitment, limited financial resources, cultural change and lack of interest, interoperability and legal challenges presents political, economic, social, technological, environmental and legal challenges categorised in literature [START_REF] Gilbert | Barriers and Benefits in the Adoption of E-Government[END_REF] [17] [START_REF] Ndou | E-government for developing countries: opportunities and challenges[END_REF]. In addition to the challenges identified by respondents, the National E-Government Strategy of Germany points out the following challenges 7 :  Global competition to ensure that Germany remains an attractive place to do business  Ensuring Internet access, especially in rural areas affected by demographic change, growing shortages of skilled workers etc.  Paying attention to and participating in the organisation of international processes and standards, to ensure Germany is with growing European integration  Ensuring legal, organisational and technical modernisation to enable public administration to act and respond flexibly in technologically fast moving times  Ensuring that standards and norms, especially when it comes to security and data protection, can be met also in the future  Ensuring willingness to invest to promote innovation in the public sector, e.g. regarding service orientation and the capacity for innovation overall  Ensuring greater agility and flexibility in public administration to make it easier to deal with difficult-to-control risks (such as financial and economic crises).

Literature studies reveal that lack of evaluation and sustainability frameworks [START_REF] Kunstelj | Evaluating the progress of e-government development: A critical analysis[END_REF] and decline of citizen trust in e-government [5] [31] are among the significant challenges in e-government. However these challenges are not mentioned by respondents of our survey or the IT Planungsrat as important in the German context. Political commitment and legal frameworks have been regarded as among important factors for innovative and sustainable e-government developments. The importance of political support is evidenced in a study by Furuholt and Wahid [START_REF] Furuholt | E-government Challenges and the Role of Political Leadership in Indonesia: the Case of Sragen[END_REF]. The authors determine lack of political support as one of the main failure factors of an egovernment project in Indonesia. Similarly in a case of a developing country, Belachew suggests the need for Ethiopia to develop a suitable legal framework to ensure seamless implementation of e-government services [START_REF] Belachew | E-government initiatives in Ethiopia[END_REF]. The responses gathered along the study here are deemed not to be sufficiently expressive, so further investigations will need to be made to confirm the impressions collected so far.

Implementation of successful e-government projects rely largely on lessons learned from previous projects and established knowledge of success principles from practice and research. All principles of success assessed by respondents are regarded as signif- icant in e-government endeavours. On the one hand, the results from the survey confirm the success factors reported in literature. On other hand, as results highlight that principles such as presence of national e-government strategy, presence of political commitment and presence of financial sources are fully significant in Germany, literature informs that the same principles might not be fully significant in other countries. Likely, principles such as prioritisation of projects, digital literacy among users of egovernment (citizens, businesses and NGOs) and interoperability might as well be fully significant while less significant in other countries. Therefore it is of utmost importance to note that the specific political, economic, socio-cultural, technological, environmental and legal settings of a particular country highly determine the 'right' approach and solutions for successful implementation of e-government.

Conclusion

Many approaches for designing and implementing e-government are documented in literature and in practice. The maturity of e-government over the years is reflected in continuous and innovative solutions of using ICT in public sectors. E-government is not only viewed as the use of ICT in public sector but more broadly, and even more significantly, as a tool for an integrated and better government. As far as egovernment benefits are realised, also challenges are encountered and subsequently ways of tackling the challenges. In this study the researchers identified e-government challenges and solutions, which have long informed research and practice of the political, economic, socio-cultural, technological, environmental and legal factors, which highly influence the outcome and success of e-government implementation. Based on the analysis from the survey of experts and literature study, implementing and sustaining e-government systems requires a profound understanding of, first, the expected benefits of e-government. Therefore, the design of e-government systems should be geared towards achieving the perceived benefits of a respective country. Second, the particular e-government challenges encountered and the factors that influence the challenges have to be understood well at the country level. By investigating factors influencing the challenges, governments will be able to find solutions, which will work specifically in the country's settings. Third, the right approach of formulating and implementing e-government strategies at national level has to be identified and understood. Presence of a centralised strategy enhances coordination and collaboration of solutions at different levels of government. However, it is important that the strategy is adopted throughout the government at different levels for the purposes to be achieved. Fourth, the role of political support is highlighted in the study. Successful and sustainable e-government implementation requires profound political and leadership support at all governance levels. Fifth, formulation of legal frameworks supporting (and enforcing) implementation of e-government is also among the important factors for consideration when designing e-government systems. Further research of the aspects investigated in this study will be carried out in a wider scope to deepen the understanding of different contexts and, hence, to contribute in theoretical perspectives of e-government research and in practical cases of successfully designing and implementing e-government systems.

Table 1 : Realisation of e-government benefits (N = 8; scale of rating: 1= not realised … 4=fully realised)

 1

	E-government benefits	Average rating
	More effective processes	3
	Accountability of public authority	3
	Ease of access of information	3
	Creation of more knowledgeable society	3
	Enhanced quality of processes	3
	More efficient processes	2
	Cost-effective to the public authority	2
	Cost-effective to access government processes	2
	Transparent processes	2
	Integrated services among public authorities	2
	Reduced corruption	3

Table 2 : Challenges for developing e-government and possible ways to address them Challenges identified Possible ways to address the challenges

 2

	Low interoperability at country level and EU level	Use of standards
	Lack of legal frameworks and therefore restrictions	Introduce legal and regulatory frameworks sup-
	in implementing e-government across organisations	porting e-government
	Lack of user-friendly applications hence technolo-	Develop user-friendly applications and integrate
	gy barriers	users and usability tests during development
	Lack of awareness of the potential of e-government	Create and promote awareness of the benefits of e-
	and therefore lack of interest in implementing e-	government among citizens, politicians and gov-
	government	ernment employees
	Financial challenges	Coordinate efforts among agencies and ministries
		to reduce reinventing the wheel and promote
		create-once-use-frequently concept
	Cultural challenges	Promote political changes and discussions among
		government and societies of the purposes of e-
		government implementation
	Lack of concrete privacy laws	Enact and enforce international and national
		agreements of privacy protection
	Lack of structured public procurement platforms	Developing and enforcing use for tendering plat-
		forms across the government
	Lack of single point of contact to interact with the	Developing service directive
	government at all levels
	Challenges in implementing electronic signatures	Develop and support validation infrastructures for
		online interaction with the government
	Ineffective and inefficient public administration	Optimise internal administrative processes such as
	hence challenges in transforming to seamless and	document management and workflow systems
	one-stop-government
	Lack of cooperation among government organisa-	No response
	tions

E-government development framework Adoption level Impact of adoption level

	Data exchange standards	3	Minimised costs in public sector
	DOMEA	2	No response
	IT Planungsrat	3	Enhanced exchange of governmental services;
			Improved cooperation of the federal, federal state
			and local governments
	V-Model XT	3	No response
	SAGA	3	Availability and use of agreed and user-friendly
			technologies, methods and standards
	E-government law	-	-

Table 4 : Assessment of the principles to ensure successful e-government systems design (N=8; scale of assessment: 1=not significant.....4=fully significant) Principle to ensure successful design of e-government systems Average assessment

 4

	Presence of national e-government strategy	4

KoSIT -Koordinierungsstelle für IT-Standards, http://www.xoev.de/

Dokumentenmanagement und elektronische Archivierung im IT-gestützten Geschäftsgang (DOMEA), http://www.verwaltung-innovativ.de/DE/E_Government/ orgkonzept_everwaltung/orgkonzept_everwaltung_artikel.html

Introduced through German's constitutional law (GG) in art. 91c to foster collaboration in the development of public sector ICT, http://www.it-planungsrat.de/

http://www.cio.bund.de/Web/DE/Architekturen-und-Standards/V-Modell-XT-Bund/vmodellxt_bund_node.html

[START_REF] Bélanger | Trust and Risk in E-Government Adoption[END_REF] http://www.cio.bund.de/Web/DE/Architekturen-und-Standards/SAGA/saga_node.

html 6 http://www.bmi.bund.de/DE/Themen/IT-Netzpolitik/E-Government/E-Government-Gesetz/egovernment-gesetz_node.html