Yingying Gao

Marijn Janssen

Generating value from government data using AI: An exploratory study

Keywords: Open data, Open Government, Artificial intelligence, Internet of Things, Value creation, Smart cities

Open government data initiatives have gained popularity around the world. Artificial Intelligence (AI) has the potential to make better use of data. Combining the OGD and AI is crucial to generate more value from data. In this paper we investigate what kind of value was generated through AI and how. A context-input-process-output/outcome (CIPO) framework is developed to describe and compare three cases. The overview of cases shows the huge potential of AI, but it also suggests that AI is hardly used by the public to create value from open data. The objectives of the three cases are efficiency, innovation and crime prevention, whereas common open government objectives like transparency, accountability and participation are given less attention. By using AI, the risks of data privacy and arriving at biased or wrong conclusions become more prominent. With the rise of data collection from Internet of Things, complying with the 5-stars of Berners-Lee becomes more important. We recommend policy makers to stimulate AI projects contributing to the open government goals and ensure that open data meet the 5-star requirements.

Introduction

Open government data (OGD) refers to publishing public sector data in open and reusable formats without restriction or charge for their use by society [START_REF] Kalampokis | Open government data: A stage model[END_REF]. This movements have gained a great popularity across the world [START_REF] Saxena | National open data frames across Japan, The Netherlands and Saudi Arabia: role of culture[END_REF]. The public sectors generate and maintain a large amount of data [START_REF] Kucera | Benefits and Risks of Open Government Data[END_REF], and its ability to collect data with application of low-cost devices enabled by Internet of Things (IoT), resulted in an explosion of digital data. Nevertheless, data is useless unless it is used to generate benefits from it [START_REF] Janssen | Benefits, Adoption Barriers and Myths of Open Data and Open Government[END_REF]. Although policy-makers and smart cities could use these data, it needs to be opened and shared with the public in order to be able achieved the full potential of OGD. Artificial Intelligence (AI) techniques have been extensively used to support the decision making and problem solving in different industries for many years [START_REF] Androutsopoulou | Transforming the communication between citizens and government through AI-guided chatbots[END_REF][START_REF] Sun | Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare[END_REF][START_REF] Lima | Predicting and explaining corruption across countries: A machine learning approach[END_REF]. AI can create new insights from combined datasets. Furthermore, AI systems can acquire their intelligence by using data to understand how past problems were solved, and applying this learning for predictive purposes. With the advancement of open government data, it becomes possible to combine AI technologies and OGD to obtain more value from data.

Some articles focused on exploring the economic value of OGD [START_REF] Ahmadi Zeleti | Exploring the economic value of open government data[END_REF][START_REF] Bonina | New business models and the value of open data : definitions , challenges and opportunities[END_REF] and social, political value like transparency, innovation and so on [START_REF] Jetzek | Data-driven innovation through open government data[END_REF][START_REF] Lakomaa | Open data as a foundation for innovation: The enabling effect of free public sector information for entrepreneurs[END_REF]. But the use of AI for OGD remains scarce. There are some examples of trying to employ AI processing open data. For example, [START_REF] Piscopo | Predicting Sense of Community and Participation by Applying Machine Learning to Open Government Data[END_REF] concluded that the prediction model of community capacity using machine learning is more accurate than other models built using conventional statistics. [START_REF] Saltos | An Exploration of Crime Prediction Using Data Mining on Open Data[END_REF] used the open crime data to test that decision trees can be used to reliably predict crime frequency. [START_REF] Kouziokas | The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment[END_REF] forecasted high crime risk transportation areas in urban environment by application of AI to test the most efficient algorithm and optimal model. Most of studies combined different datasets and suggested to give an in-depth investigation of AI especially the application of AI in certain area such as public sector [START_REF] Sousa | How and where is artificial intelligence in the public sector going? A literature review and research agenda[END_REF]. These studies are beneficial for us to understand how open government data could drive AI. However, most of these studies are conducted from a technical perspective and do not focus on understanding value creation from OGD based on AI.

In this study, we investigated the following research question: what kind of value was generated through AI application and how? For this, we conducted an exploratory study on OGD innovation cases through AI. The whole process from collecting data to creating value are analyzed. AI technologies facilitate data analysis and specific value generated.

2

Research approach

To investigate the value created from OGD using AI, we employ comparative case study research. In such type of research, two or more cases are systematically compared through examination of a real-world phenomenon within its naturally occurring context [START_REF] Kaarbo | A practical guide to the comparative case study method in political psychology[END_REF]. In our work we follow the four steps suggested by Yin [START_REF] Yin | Case study research[END_REF]. (1) identify specific research question to determine that it is appropriate to use comparative case study. (2) select cases and design the case study. (3) collect case data and analyze the case evidence. (4) report case study. Firstly, our research aim is to investigate how and what kinds of value was generated through AI. The type of research questions most appropriate for case studies are "how" and "why" questions since they focus on the underlying process and the causal relationship [START_REF] Kaarbo | A practical guide to the comparative case study method in political psychology[END_REF]. Accordingly, we focus on the cases of using AI to explore how value is created form OGD using AI. For this we examine the whole process from collecting data to evaluate its impact.

Secondly, we identified three cases. The cases should be in the public domain, employ AI and data should be available. We searched the European data portal for finding suitable cases. This portal covers more than 500 cases of OGD application or ideas from EU. Yet the number of cases employing AI is limited. Furthermore, we want to cover a diversity of AI applications, as for example there are several cases using chatbots. Finally, we selected three cases which reflect the three phases of AI application. In phase 1, AI could use structured data to automate simple and repetitive processes. In phase 2, AI is able to adapt to and learn from changes in the automated process. In phase 3, AI provides new and innovative insights by being able to analyze and learn from previous actions [START_REF]Leveraging AI in the public sector with open data | European Data Portal[END_REF]. The first selected case is the AI parking which is a German application using real-time traffic data to help people find the parking space. And the second case is a chatbot application in Singapore. The last case is the crime prevention through artificial intelligence employed by Kent Police in UK.

For the third step, we construct a context-input-process-output/outcome framework in order to establish the logic of comparative study, which is discussed in detail in the next section. Finally, we analyze the cases according to the framework and report the results, which are presented in section 4.

Towards a comparison framework

This section presents a comparative framework for OGD innovation cases using AI. Firstly, we describe the context-input-process-output/outcome (CIPO) model. Subsequently, a series of elements are derived referring to the CIPO model in order to construct the comparative framework of analyzing the AI-based innovation case.

Context-Input-Process-Output/outcome (CIPO) Model

Systems theory offers an important insight to the role of the information systems or technology in this sequence from data to information to knowledge. Systems thinking especially soft systems thinking provides us a way to conceptualize the social process in a particular context [START_REF] Checkland | Systems thinking:Rethinking management information systems[END_REF].Services or products like applications could be created by exploring OGD in which data is transferred into results. Any transformation process can be viewed as an input-output system [START_REF] Starr | Evolving Concepts in Production Management[END_REF].This system was then developed to inputprocess-output model by other researchers and applied in project evaluation, team innovation management etc. [START_REF] Bushnell | Input, process, output: a model for evaluating training[END_REF][START_REF] Mathisen | The relationship between creative personality composition, innovative team climate, and team innovativeness: An inputprocess -output perspective[END_REF]. However, projects using government data or applied in public sector should be studied as a complex process because they are influenced by socio-political and other context factor [START_REF] Arfina Osman | Public policy making: theories and their implications in developing countries[END_REF]. In addition, a difference between output and outcome is often made in which output is the immediate result and outcome the long-term impact [START_REF] Zuiderwijk | Open data policies, their implementation and impact: A framework for comparison[END_REF]. In this work, we study both the short-term and long-term impact. Therefore, we used the context-input-process-output/outcome (CIPO) framework to better understand the OGD innovation cases, which has been used in the past to analyze OGD policies [START_REF] Zuiderwijk | Open data policies, their implementation and impact: A framework for comparison[END_REF].

Elements for Comparing Cases

In this section, we derive the elements for comparing the OGD innovation cases from literature, related reports and websites. The findings are sorted by four parts following the CIPO model, namely project context, project input, process and the output/outcome (see in Table 1). The positive effects were derived according to the benefits of open data [START_REF] Kucera | Benefits and Risks of Open Government Data[END_REF][START_REF] Janssen | Benefits, Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Sébastien | Open data: Barriers, risks and opportunities[END_REF] Risks and challenges

The negative effects (like bias, discrimination) [START_REF] Kucera | Benefits and Risks of Open Government Data[END_REF][START_REF] Sébastien | Open data: Barriers, risks and opportunities[END_REF][START_REF] Sieber | Civic open data at a crossroads: Dominant models and current challenges[END_REF] Project context. The background describes the situation at hand, including the places, stakeholders and their contribution. The project objectives are referring the aims to be achieved through the projects. These projects may be set up to address multiple societal challenges. The OGD goals emphasized by different countries and different level of governments vary. So, it is necessary to investigate the context of the project to be able to evaluate the outcome.

Project

Case analysis result

In this section three cases from Germany, Singapore and UK using AI to explore government data are compared. The cases are diverse in background, objectives, data used, AI tools and result.

Project context

There are many challenges faced by different stakeholders and their emphasized objectives also differ. In the first case, its main aim to lower the searching time for parking through prediction using AI. Whereas the latter two cases which are launched by governments, the main focus is on the social challenges like improving the service quality and decreasing the crime rate under the situation of reducing expenditure.

Project input

Type of data. In terms of data content, these three cases use traffic parking data, government Q & A data as well as the crime data separately. From view of data structure, unstructured text data is adopted in case 2 while structured data is collected in case 3 because there are five data points for each crime incident to generate predictions. The data source in case 1 is a mix of the structured location information and the unstructured picture information. This analysis shows the innovative potential of OGD.

In addition, we also evaluated the quality of data open. According to the 5-star model developed by Berners Lee, we give the three cases one star. Because in the case 1, part of data was shared with government. In the case 2, people might find question and answer and the service catalog on the Singapore government website, but the full Q&A data and the catalog data was not in an open and machine readable. In the case 3, the number and type of crime in Kent could be found in the website, but not all the data points needed by crime prediction are published due to data privacy.

Type of AI technologies.

There are several technologies namely neural network, natural language processing and machine learning adopted by these three cases. But it can be seen that algorithm models are a combination of scene-based knowledge and technology. For example, when conducting crime prediction, experts in related fields together created specific machine learning algorithm models based on the knowledge of criminal behavior. Similarly, in the application of parking and chatbots, developers construct various specific algorithm models for better applied in different fields.

Project process

We analyzed the process steps taken to transform input into outputs by looking at three steps. (1) Data collection. After collecting the original data, the necessary clear data points are extract from combined datasets. ( 2) Using AI to process data. Using AI technologies to construct models and process data. (3) Applied in the certain field. At last, the data exploration processes are turned into applications.

Output/Outcome

Output. Three cases yield three applications namely AIPARK, Ask Jamie, Predpol.

The first two applications are still under development and improvement. But for Predpol system, Kent Police decided to stop using it in 2018.

Outcomes of generated value. Previous literature summarized a variety of benefits of open data including social, political, economic and so on [START_REF] Kucera | Benefits and Risks of Open Government Data[END_REF][START_REF] Janssen | Benefits, Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Sébastien | Open data: Barriers, risks and opportunities[END_REF]. We derived the values generated by these cases according to the literature. For case 1, the main value is about economy since this team earned the investment and built a company in Germany. Moreover, the team also showed that this innovative service makes the time for searching to park three times shorter than the normal trip. For case 2, the chatbot improves service delivery, especially saving citizens' time for simple information consultation. The survey by government showed that 58% citizens considered it successful and 60% found the answers useful, which helped to improve the public satisfaction on government. On the operational level, it also saved the time of public servants and optimized the administrative process so that they focus on more complex tasks. In case 3, at four-month trial in Medway, street violence falls by 6%. It has a social and political value of improving the police service delivery.

Risks and Challenges.

In the first case, the main challenge is about the data update since the outdated data could give a wrong parking suggestion. This application could also be extended by applying number plates data. And it is a similar challenge about the recommendation accuracy in the second case. There are only about 60% users think it useful. For the prediction application in UK, there are several challenges. First issue people concern is lack of transparency of how predictive algorithms reach their decisions. Another concern is the software entrenching pre-existing discrimination since the program had "learned" racism and bias. The last major risk is the data privacy. The principle of AI is that the more data an algorithm receives, the better it will become at its task. The more datasets mean less privacy generally.

Discussion

The framework was found to be useful for understanding the cases as a high level. It helps to systematically analyze the cases and enable us to compare them in the same dimension, which provides a comprehensive picture of the OGD innovation using AI. However, each innovation is a complicated process that include much detail like data collection, data cleaning and so on. The framework cannot cover all the process. It focuses on the critical process in the innovation cases. In this section, we discuss the case analysis result according the framework.

Context-Collaboration between government and private company

Although these projects were launched by different stakeholders, the entire implementation process of the project was based on the cooperation between government and private companies. In the first case, the company conducted a data collection project in cooperation with the local municipality to validate the parking prediction model. In the second and third cases, the government provided data and then hired technology companies to produce solutions. Therefore, private companies are an indispensable intermediary to create more applications and value for society from OGD using AI [START_REF] Bakici | The role of public open innovation intermediaries in local government and the public sector[END_REF]. Future research about combining AI and OGD can focus on strengthening cooperation between government and enterprises.

Input-Improve the data quality and technology transparency

Improve the data quality. These three applications still provide relatively useful predictions although the datasets are rated as 1 star. Indeed, there are some AI mechanisms that can deal with low data quality. But AI applications are highly dependent on the data quality (accuracy, completeness, availability, timeliness) [START_REF] Al-Hakim | Information Quality Management: Theory and Applications[END_REF]. Whereas persons can spot easily mistakes in data, AI is not able to spot such mistakes which might result easily in incorrect findings [START_REF] Butler | When Google got flu wrong. US outbreak foxes a leading web-based method for tracking seasonal flu[END_REF]. Evaluating the information quality before using them with AI technology is therefore important. Furthermore, if data was formatted comply with the 5-stars model suggested by Berners lee [START_REF]Leveraging AI in the public sector with open data | European Data Portal[END_REF], then the use of OGD based on AI is relatively easy, as the data is structured and well-described and has a persistent URI to ensure a continuous stream of data. Furthermore, there is a need for semantic descriptions of open data to explore the use of OGD. This is a condition for exploration of OGD to finding new insights and adds to representational information quality.

Improve the transparency of AI technologies.

There is a wide variety of AI techniques and AI can be used to create various types of value. There seems to be no best AI techniques and which type of AI is used is dependent on the type of problem that is tackled. AI can be used to increase the efficiency (of parking) facilitate positive relationship with citizens (chatbots) and find anomalies (crime prevention). The use of AI might have negative effects like strengthening the discrimination since little information is given to understand how predictive algorithms reach their decisions, which need to be managed and clear accountabilities should be ensured. The need for guidance by a legal framework should be further explored.

Output/Outcome-Create more value while effectively preventing risks

Increase the users' take-up rate. In order to sustainably achieve the value from these project, it is necessary to increase the users' take-up rate [START_REF] Osman | A cognitive analytics management framework for the transformation of electronic government services from users' perspective to create sustainable shared values[END_REF]. No matter the AI parking or chatbots, they are still in the early stage of development. The number of users is limited. For the crime prediction software, Kent police has stop using it in 2018. So, developing the application is not the end. It is important to improve the service so that to increase the level of usage for sustainable value creation.

Objectives Value. OGD initiatives often focus on transparency, accountability, innovation for companies and participation [START_REF] Mcdermott | Building open government[END_REF]. In contrast, the objectives of the three cases were focused on efficiency, innovation and crime prevention. The objectives were often very specific and the value generated had limited contribution to the original OGD objectives. Also, some of AI projects were beneficial for governments instead of the public. This could be due to selection bias, but also that the public have limited capabilities to make use of AI. We recommend to stimulate AI projects for obtaining these general objectives of OGD initiatives.

The balance between privacy and utility. In the case of automatic parking, the application might be easily extended with vehicle number plate recognition or in the third case the names of criminals might be identified which might violate the privacy. The more data is released and available, there is a need for data privacy preserving techniques [START_REF] Li | On the tradeoff between privacy and utility in data publishing[END_REF]. There is a tradeoff between privacy and utility of OGD publishing [START_REF] Li | On the tradeoff between privacy and utility in data publishing[END_REF]. It might be a suggesting to release only the metadata including semantic descripting and only grant access once the purpose has become clear to avoid misuse. With the use of AI algorithm, the data collection and processing become less visible [START_REF] Janssen | The challenges and limits of big data algorithms in technocratic governance[END_REF]. As such, mechanisms for dealing with the risks and AI-governance becomes more important.

Conclusion and implication

This study developed a framework for comparing OGD innovation cases using AI that consists of the critical elements related to an AI project. The framework was useful to describe and compare the AI cases and draw the attention to the main value creation elements. This framework can be used to analyzed other cases, also outside the OGD or AI domain, which helps government to improve the data provide and adjust the objective.

AI is an area that can be used to create more value from OGD. Surprisingly, different types of value are created using AI than often what is aimed for by OGD initiatives.

The cases show the high potential, but also challenges which complicates attaining the value. The analysis of three cases shows that it is necessary to strengthen the collaboration between the company and government since companies might have data and the capacity to process data using AI. In addition, the quality of the data is a key aspect for creating value using AI. This becomes more important as AI is less aware of the shortcomings in data, whereas data analyst can take this into account. Following the 5-stars model when opening data can simplify the use of AI.

There is a wide variety of AI techniques that can be used to create various types of value like neural network, natural language processing and machine learning. But it is important to improve the algorithm transparency and accountability to assess if there is bias or discrimination. Furthermore, the objectives of the project and value created in three cases hardly contributed to the ideals of the open government movement like transparency, accountability, innovation and participation. This can be explained as the projects investigated are initiated by governments. As further recommendation for policy-makers, we suggest more experiments with AI for OGD focused on the OGD objectives to facilitate learning.

Table 1 .

 1 CIPO Framework elements for comparing cases

	Framework	Elements	Explanation
	Project context	Background	Country, stakeholders' overviews, who initiated
			the project, description of societal challenge and
			so on.
		Project objectives	What aims the stakeholders want to achieve
			through the project
	Project input	Type of data	Analyzing what kind of data was used and eval-
			uating the data quality according to the 5-star
			model developed by Berners Lee [25]
		Type of AI	Specific AI technologies or algorithm
	Process	Process	The process steps taken to transform inputs into
			outputs.
	Output/Out-	Output	What type of application it generated, like an
	come		app, new insight into the situations etc.
		Outcome -type of
		value generated

Table 2 .

 2 The 5-star open data taken from Berners Lee[START_REF] Berners-Lee | Linked data-Design issues[END_REF] It comprehensively evaluates the immediate result and the longterm outcome which includes the value and the risks & challenges. The value refers more to the benefits of the AI applications. Although it is hard to identify what exactly values can gain from OGD because of different datasets and various application, some scholars tried to classify the benefits expected from leveraging open government data such as economic value and social value so on[START_REF] Janssen | Benefits, Adoption Barriers and Myths of Open Data and Open Government[END_REF]. This classification provides us insight to identify the value of these projects.

	Star	Explanation
	★	make your stuff available on the Web (whatever format) under an open license
	★★	make it available as structured data (e.g. excel instead of image scan of a table)
	★★★	make it available in a non-proprietary open format (e.g. CSV in-stead of excel)
	★★★★ ★★★★★	use URIs to denote things, so that people can point at your stuff link your data to other data to provide context

Input. It mainly refers to the data and technology input since high quality data is the fuel and technologies are core part driving the innovation process with AI. It focuses on what kind of data is required to enable successful AI application and how the data is provided (batch or real time, using a file or API). We then evaluate the quality of data open according to the 5-star open data model developed by Berners Lee as shown in Table

2

[START_REF] Berners-Lee | Linked data-Design issues[END_REF]

. In addition, AI technology takes often like a black box and it is unknown what type of AI technologies is used to process data and how the data is explored.

Table 3 .

 3 Overview of each element of three cases

	Frame-	Ele-	Case 1-AI parking Case2-Chatbot	Case3-Crime pre-
	work	ments			vention
	Project	Back-	Germany project	Singapore project	UK project launched
	context	ground	launched by Bliq	launched by The Gov-	by Kent Police in
			company in which	ernment Technology	which there were
			it was challenging	Agency of Singapore	high levels of crime
			to find a parking	in which citizens	and people did not
			solution without	could not find an an-	feel safe.
			sensors	swer to their questions
				easily
		Project	Efficiency objec-	1.Efficiency objec-	Efficiency objective:
		objec-	tive: Lower time to	tive: improve service	fulfill an ever-ex-
		tive	find parking place	delivery and quickly	panding range of
			and reduce pollu-	answer questions.	tasks for policies un-
			tion through pre-	2.Social and political	der the situation of
			dicting the parking	objectives: make citi-	reducing expenditure
			situation without	zens' lives better and
			installing sensors	improve the public
			on a larger geo-	satisfaction
			graphic scale
	Project	Types	1. Data content	1. Data content	1. Data content
	Input	of data	-Parking traffic	-Unstructured Q &A	-Structured current
			data including	and dialogue data	and historical crime
			parking locations	-Service Catalog	data, including five
			etc. -Street-level im-	2. Data quality: ★ Since most of Q&A	data points for each incident.
			agery data about	and dialogue data is unstructured and need	2. Data quality: ★


Jonathan Crusoe
email: jonathan.crusoe@liu.se

Elisabeth Gebka
email: elisabeth.gebka@unamur.be

Karin Ahlin
email: karin.ahlin@miun.se

Open Government Data from the Perspective of Information Needs -A Tentative Conceptual Model

Keywords: Open Government Data, Information Seeker, Information Need, Conceptual Model

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

Introduction

In previous open government data (OGD) research, little seems to be known about the relationships between information seekers, data transformers, and publishers. This gap leaves it unclear why OGD would be integrated into the rest of society. This article presents a tentative conceptual model from which data transformers and publishers can understand and consider the information needs of information seekers. Information needs arise when people experience a gap in their knowledge [START_REF] Belkin | Interaction in information systems: A review of research from document retrieval to knowledge-based systems[END_REF]. These needs can be satisfied when people make sense out of data [START_REF] Hey | The data, information, knowledge, wisdom chain: the metaphorical link[END_REF]. One source of data is OGD which is shared by public organisations (publishers) for anyone to be freely reused [START_REF] Attard | A systematic review of open government data initiatives[END_REF]. OGD can be used directly or integrated into services and products (solutions) by data transformers for information seekers to satisfy their information needs [START_REF] Davies | Open data, democracy and public sector reform. A look at open government data use from data[END_REF]. Data transformers can, for example, be developers, journalists, or researchers [START_REF] Safarov | Utilization of open government data: A systematic literature review of types, conditions, effects and users[END_REF], while information seekers are people that seek information to satisfy their information needs.

Publishers, data transformers, and information seekers can experience problems. Publishers may lose income or data transformers may be unable to find data [START_REF] Zuiderwijk | Barriers and development directions for the publication and usage of open data: A socio-technical view[END_REF], while information seekers can have unexpressed or unrecognised information needs [START_REF] Erdelez | Information encountering: It's more than just bumping into information[END_REF]. Previous research tends to focus on the publisher and data transformer, while the information seeker is in the background or a type of data transformer [e.g., [START_REF] Dawes | Planning and designing open government data programs: An ecosystem approach[END_REF][START_REF] Lindman | Business roles in the emerging opendata ecosystem[END_REF][START_REF] Zuiderwijk | Innovation with open data: Essential elements of open data ecosystems[END_REF]. This placement needs to change since the public interest in the re-use of OGD has been declared a myth [START_REF] Hellberg | The story of the sixth myth of open data and open government[END_REF]. The myth may come from that current information needs are already satisfied by other solutions [START_REF] Nicholas | Assessing information needs in the age of the digital consumer[END_REF]. The satisfaction of information needs may be the event where data and information, through use, impact information seekers' lives to realize the promised benefits of OGD.

The purpose of this article is two-fold: [START_REF] Attard | A systematic review of open government data initiatives[END_REF] create an initial understanding of information needs in relation to OGD and (2) suggest possible considerations for data transformers and publishers, both presented in a conceptual model. A conceptual model is a set of assumptions that supports and informs the research and is a key part of the design [START_REF] Maxwell | Qualitative research design[END_REF]. The model can help data transformers and publishers understand how OGD and OGD solutions can be of value to information seekers. On the other hand, researchers can use the model as a basis for future research on how to increase the use of OGD or how OGD can have an impact on society. In this paper, we focus on everyday activity-based information needs, such as finding your way to work or keep up-to-date on your urban surroundings. The following research question guides the research: what may data transformers and publishers need to consider about information needs of information seekers in an everyday context?

This paper starts by presenting the background, then the research process, and the conceptual model followed by a discussion. This paper ends with a conclusion.

Background

This section explains the concept of information needs and information solutions.

Information Needs of Information Seeker

People experience information needs when they experience a gap in their knowledge [START_REF] Belkin | Interaction in information systems: A review of research from document retrieval to knowledge-based systems[END_REF], which can happen when they attempt to satisfy a primary need [START_REF] Wilson | On user studies and information needs[END_REF].

If the situation is unclear, confusing, or difficult to understand the information seeker might be unable to formulate questions (identify information needs) that can help them identify the right information [START_REF] Daft | Organizational information requirements, media richness and structural design[END_REF]. Information needs are determined by individual's social role, environment, and previous knowledge [START_REF] Wilson | On user studies and information needs[END_REF] and satisfying them can help people work effectively, solve a problem satisfactorily, or pursue a hobby [START_REF] Nicholas | Assessing information needs in the age of the digital consumer[END_REF]. Information seekers' information needs can be complex. For example, in a modern city environment the urban system is a vast system of systems where information seekers can have different roles based on what functions of the city they are using, such as a citizen, professional, or commuter [START_REF] Fernández-Güell | Incorporating a systemic and foresight approach into smart city initiatives: the case of spanish cities[END_REF]. In each role, they have different information needs. OGD can be socially valuable when understandable and usable by the intended people or communities. OGD should fit the preferences, level of expertise, and personal needs of the information seekers [START_REF] Cabitza | Making open data more personal through a social value perspective: a methodological approach[END_REF]. Visualizations are one important tool to achieve these requirements [START_REF] Barbosa Tavares | Investigating citizens' information needs through participative research: a pilot study in candangolândia, brazil[END_REF].

People can use information for: (1) fact-finding -getting hold of answers to specific questions, (2) staying aware -keeping up to date on something, (3) researching -investigating a new field in depth, (4) briefing -obtaining background understanding of an issue or topic, (5) stimulus -procuring ideas or stimuli, and (6) recreation -looking for interesting tidbits of information just for the fun of it [START_REF] Nicholas | Assessing information needs in the age of the digital consumer[END_REF]. At the same time, individuals, experiences, and situations are sources of information and knowledge, and information can be found in unexpected ways and places [START_REF] Erdelez | Information encountering: It's more than just bumping into information[END_REF]. In the digital world, information seekers are creative and able to choose from a large offer of information sources and even influence information solutions. The information providers have to consider them as clients to meet their needs properly, at the risk of seeing them choosing other solutions [START_REF] Nicholas | Assessing information needs in the age of the digital consumer[END_REF].

Information Solutions from Data Transformers and Publishers

An information solution is a product or service that supports the sense making of information from data or presents information or data to the information seeker. Here, we focus on digital solutions, which seem to be well-suited to satisfy information needs [START_REF] Madsen | Channel choice complications -exploring the multiplex nature of citizens' channel choices[END_REF]. In the case of OGD, a published dataset is considered an information solution. The publisher is assumed to be a public organisation that releases data. On the abstract level, the release process for data starts with collecting data, then preparing data, publishing data, maintaining data, engaging data transformers, and ends with collecting feedback [START_REF] Attard | A systematic review of open government data initiatives[END_REF][START_REF] Carrara | Open data goldbook for data managers and data holders[END_REF][START_REF] Zuiderwijk | Innovation with open data: Essential elements of open data ecosystems[END_REF]. The result of the process is OGD that can be used by data transformers or information seekers. Data transformers can transform the data into facts, information, interfaces, new datasets, or services [START_REF] Davies | Open data, democracy and public sector reform. A look at open government data use from data[END_REF], which can cover a wide variety of roles and business models for the data transformers [START_REF] Lindman | Business roles in the emerging opendata ecosystem[END_REF]. The solutions should be specifically fit to their intended purpose or function [START_REF] Nicholas | Assessing information needs in the age of the digital consumer[END_REF]. The products can, for example, be reports, visualisations, simulations, and news articles, while services can be a web service that notifies information seekers about interesting events [START_REF] Crusoe | Users' activities for using open government data-a process framework[END_REF]. On the other hand, information seekers can read some facts in a dataset and be satisfied [START_REF] Davies | Open data, democracy and public sector reform. A look at open government data use from data[END_REF].

Research Process

This research is using design science research (DSR) to build a tentative conceptual model that is a new artifact. The conceptual model is directed toward solving a practical problem. This research followed the steps of DSR [START_REF] Peffers | A design science research methodology for information systems research[END_REF]: (1) problem identification and motivation, (2) definition of the objectives for a solution, (3) design and development, (4) demonstration, (5) evaluation, and (6) communication. The steps 2 to 5 were done in iterations. A previous version of the model was communicated at the 17th Scandinavian Workshop on E-Government. Here, we want to note that this paper has a sibling paper also presented at the workshop and accepted as ongoing research to the EGOV-CeDEM-ePart 2020 conference, which developed the data collection workshop [see 13].

Problem Identification and Motivation

This research was initiated when reflecting on the possible sources of the noted lack of use [START_REF] Safarov | Utilization of open government data: A systematic literature review of types, conditions, effects and users[END_REF] to then wonder about the need for OGD solutions. This reflection prompted a traditional literature review to identify previous research [START_REF] Cronin | Undertaking a literature review: a stepby-step approach[END_REF]. This literature review is presented in the background section. Google scholar was used as the search engine, as it has a good coverage and recall and we used simple keywords, which the search engine has a prevision above average with [START_REF] Walters | Comparative recall and precision of simple and expert searches in google scholar and eight other databases[END_REF]. Examples of used keywords are "open data", "information needs", "open data information needs", and "citizens information needs".

The design problem of this research is how to conceptualise the connection between information seekers' information needs, data transformers' solutions, and publishers' OGD to gain insight into how OGD and OGD solutions could be valuable to information seekers. Also, to identify considerations to data transformers and publishers to ensure this value. The theoretical motivation of the problem continues on previous research on information needs and OGD by combining both. The practical motivation is a perceived need to increase the use of OGD, which is believed to lead to several benefits. An important aspect of the development of the model was to use an information seeker perspective, which takes into account the publisher's and data transformer's role for the solutions.

Iterations

The iterations consist of step 2 to 5 of DSR [START_REF] Peffers | A design science research methodology for information systems research[END_REF]. For the first iteration, the purpose was to explore information needs. Two of the three researchers observed an information-rich bus-hub in a medium-sized Swedish town, conducted six structured-interviews with available commuters, and one in-depth interview with a veteran commuter. They were asked about activities and needed information. The empirical material was discussed between the two researchers and the insights were then used to design a workshop method [START_REF] Gebka | Open data reuse and information needs satisfaction: a method to bridge the gap[END_REF], as observations and interviews gave insufficient results or were too time-consuming.

The workshop method. The workshop method [START_REF] Gebka | Open data reuse and information needs satisfaction: a method to bridge the gap[END_REF] was used one time in the second iteration and one time in the third iteration. For the participants of the workshops to recognise information needs, they needed to face a situation where they experience knowledge gaps but are not so unfamiliar they cannot formulate questions. To simulate this situation, a scenario was developed and used in a collaborative workshop, since participatory approach can help participants identify information needs [START_REF] Barbosa Tavares | Investigating citizens' information needs through participative research: a pilot study in candangolândia, brazil[END_REF]. The selected scenario was "moved into a new city but not settled" (which in the first iteration generated the richest data about activity-based information needs in an everyday context). The second iteration allowed the participants to freely suggest categories of activities and information needs in the scenario context. However, it slowed down the discussions and idea generation as their focus was on clarifying the situation. The third iteration consisted of pre-defined options of activity domains, selected from the occurrences of the second iteration based on perceived commonness. At both workshops, participants, alternately alone and in small teams of 2 to 4, wrote down activities with the needed information, possible existing solutions, and brainstormed new dream solutions on specific cards. Finally, the result was presented and discussed together. In total, two workshops were conducted: the first with nine Belgian researchers and the second with eleven Belgian students. The Belgian participants came from the University of Namur. The university is part of the city of Namur, which is the regional capital of Wallonia. The city has 110,939 inhabitants, a dominant service industry (e.g., commercial activities and the university), and publishes OGD. Table 1 presents a summary of the empirical material.

Analysis of the results. Cards from the groups were grouped, skimmed, and prioritised based on their size and clearness on information needs (smallest and clearest first). This approach was chosen as it allowed the conceptual model to grow slowly and steadily. One at a time, the card groups were discussed and analysed by two of the researchers conducting this study based on the concepts presented in the background section. This process involved writing the data from the cards to a memo where analytical notes were added through discussion and analysis. The conceptual model was constructed through constant comparison between the current group and the model, which allowed for the identification of the theoretical concepts in the empirical material. This process helped to demonstrate and evaluate the model in small steps. Once the two workshop iterations had been executed, the memos and model were analysed by one of the researchers conducting this study and condensed to one memo, which was further refined to a new memo representing a new model. The process was then presented and discussed with one of the researchers of this study. Afterwards, the model was conceptualised and studied to identify gaps. The model was then reviewed by the remaining two researchers conducting this study based on [START_REF] March | Design and natural science research on information technology[END_REF]: fidelity with real-world phenomena, completeness, level of detail, robustness, and internal consistency. Finally, the model was submitted to a Scandinavian e-government workshop and received final touches based on feedback and recommended research. Previous research has supported the development of the data collection, guided the analysis, and became a part of the conceptual model. The concepts and considerations were interpreted from previous research and the empirical material, where the first provided a skeleton, which the second added to.

A Tentative Conceptual Model

In this section, we present a tentative conceptual model (see Figure 1). As the concepts are presented in the text below, examples from group 1, group 3, and group 4 are given. These groups were selected based on the perceived clearness of information needs and completeness and size of their descriptions. The presentation will follow information seeker, then data transformer, and, finally, publisher.

Information Seeker

The model starts with the information seekers trying to satisfy a primary need, which is the basic incentive to move and undertake any activity. However, they encounter a perceivable gap in their knowledge to perform the desired activity [START_REF] Belkin | Interaction in information systems: A review of research from document retrieval to knowledge-based systems[END_REF][START_REF] Daft | Organizational information requirements, media richness and structural design[END_REF][START_REF] Wilson | On user studies and information needs[END_REF]. If the gap is filled, it can help them satisfy their primary need through the activity. The activity domain chosen by group 1 was to make new friends or socialise by participating in the local life (primary need of love and belonging), group 3 was to go to shops (physiological needs), and group 4 sought to eat and drink in the city (physiological needs and sense of belonging). However, in the given scenario (settling down in a new town), they lacked information.

The information seekers recognize their knowledge gap and an information need arises. They start to search for solutions that they can use to satisfy their information need. This need takes the form of unformulated questions (gaps) with specific requests for data [START_REF] Belkin | Interaction in information systems: A review of research from document retrieval to knowledge-based systems[END_REF][START_REF] Daft | Organizational information requirements, media richness and structural design[END_REF][START_REF] Nicholas | Assessing information needs in the age of the digital consumer[END_REF]. Group 1 explained that they wanted descriptions and accessibility of events and products, but also possible friends. The information should contain recommendations, a list of social activities and groups, points of interest, prices, profiles of people attending, and schedules. Group 3 sought information about the weather, fashion, street descriptions, and shops. Group 4 saw four ways to approach their primary needs. First, look for a bar. Second, food delivery services. Third, look for where to purchase groceries. Fourth, look for restaurants. They also wanted ratings with the four approaches. In common, the groups tended to omit important data, such as locations for weather and opening hours for businesses.

The expression of an information need coalesce (grow together to form one thing) with different factors, which should be considered by data transformers and publishers. The information seeker's previous knowledge about a topic, preferences, intended information use, social roles, environment, and level of expertise coalesce with the information need to become the expression of their information need [START_REF] Cabitza | Making open data more personal through a social value perspective: a methodological approach[END_REF][START_REF] Fernández-Güell | Incorporating a systemic and foresight approach into smart city initiatives: the case of spanish cities[END_REF][START_REF] Nicholas | Assessing information needs in the age of the digital consumer[END_REF][START_REF] Wilson | On user studies and information needs[END_REF]. These requirements should be met to enable good satisfaction of the primary need [START_REF] Nicholas | Assessing information needs in the age of the digital consumer[END_REF]. Group 1 explained that new friends should have similar interests or needs, while the activities should be similar to their interests, reoccurring, prioritised, and in a certain format (e.g., parties and pubs). Group 3 wanted to quickly reach their destination in fresh and comfortable condition. Group 4 wanted the services to be rated by other customers to gain more transparency and trust from the provided information.

As a result, it can be interpreted that the participants seek reliable and useful information. Both social groups (researchers and students) value the trans-parency carried by peer reviews and their community, since the information source is not always clearly identified in a final solution, whatever the data comes from (OGD or businesses). The usefulness perception of the information is constrained by the personal considerations (e.g. preferences, previous knowledge, social role). For instance, for the food and restaurants activities, both groups mentioned categories of information such as price, location, and available products or menus. However, what is really valued and will make the difference is the personal consideration about the information (what is "expensive" for the information seeker, what makes a restaurant "good"). This evaluation of the information is better supported by the community ratings than by the transparency feature of OGD.

Data Transformer

Data transformers develop solutions that they provide to information seekers. A solution provides information and data that can answer questions and fill knowledge gaps [START_REF] Crusoe | Users' activities for using open government data-a process framework[END_REF][START_REF] Davies | Open data, democracy and public sector reform. A look at open government data use from data[END_REF][START_REF] Nicholas | Assessing information needs in the age of the digital consumer[END_REF]. Group 1 saw two possible solutions to their information need. First, Tinder4Friends is an application that suggests activities where they can find friends based on the information seeker's profile. Second, Place2Be is a website that describes where to be every day and night based on the information seekers' profiles and interests. The locations can be shared on social media to organise the activity with their friends. Group 3 suggested two solutions: (1) Mode-Fashion is an online catalog that in real-time provides the latest fashion and (2) Streets is a pedestrian navigation application (using a routing algorithm) based on community contribution that helps the information seeker navigate to shops. The community adds data about the state of streets, parking, pollution, snow levels, and the number of steps on the way. Group 4 suggested that every bar or restaurant has a happy and a sad button that customers can press to rate the service. The ratings are then published on a website or in an open database.

The data transformers have some possible considerations. First, how the solutions are designed for its intended use [START_REF] Fernández-Güell | Incorporating a systemic and foresight approach into smart city initiatives: the case of spanish cities[END_REF][START_REF] Wilson | On user studies and information needs[END_REF]. Tinder4Friends and Place2Be help to brief the information seeker about people or events in an area. Mode-Fashion helps the information seeker to stay aware on a topic. Streets helps the information seeker to find facts (a route between two locations). On the other hand, the button with its website could allow the seeker to brief or find facts depending on the implementation. "Brief" if it allows the seeker to see all options together. "Find facts" if the seeker can only ask for one restaurant at a time. Second, the type of subject in the solution [START_REF] Nicholas | Assessing information needs in the age of the digital consumer[END_REF] can be based on topics (e.g., fashion), problems (e.g., navigation), or events (e.g., parties). Third, how the solution is integrated with other solutions (e.g., social media) and the information seeker communities (e.g., in the Streets, the community contributes with data, while in Place2Be users find each other through common events). Fourth, alternatives to the solution [START_REF] Nicholas | Assessing information needs in the age of the digital consumer[END_REF]. Tinder4Friends and Place2Be satisfy the same need with a slight variation which could make them compete if developed. On the other hand, Mode-Fashion and Streets together help to satisfy an information need, which means they collaborate.

In sum, the respondents expressed their demand for efficient information solutions that can satisfy several information needs at a time, in one place. They value the quality standards and functionalities they know from other solutions, but more than everything, they value the fact they can find all information at once, save time, energy, with trust.

Publisher

The conceptual model focuses on the information seeker, while giving some attention to the publisher, who provides data requested by solutions. Data sources can originate from information seekers, data transformers, publishers, or a mix of them. The data transformers and publishers have some possible considerations. First, the origin of the data [START_REF] Lindman | Business roles in the emerging opendata ecosystem[END_REF][START_REF] Nicholas | Assessing information needs in the age of the digital consumer[END_REF]. The data transformer can provide data through the solution, or the information seeker can add data to the solution. The data provided can either be OGD from publishers or data from other sources, such as shops or restaurants. Second, the privacy of the data. Added data can range from private data (not shared) to public data (shared with other information seekers). Third, the role of the data. Provided data can act as a base or skeleton for other data, while added data can help to evaluate (e.g., ratings of restaurants). Origin, privacy, and role of data can be mixed in one solution. Streets could use locations from publishers, state and pollution of streets from information seekers, and private location data from an information seeker to calculate a route. Third, the commonality of the information needs of a group, community, or society [START_REF] Nicholas | Assessing information needs in the age of the digital consumer[END_REF]. The solutions and related data have a window of possible uses. The dream solution Streets is for traveling or planning to travel, Mode-Fashion is for when the information seeker wants to refresh their knowledge about fashion, and Place2Be when he seeks something to do with friends.

In sum, what matters for the information seekers is how a given solution meets their information needs. For that, many sources might be required to provide relevant data and offer a comprehensive solution. The real value proposition of the solution depends on the match between the solution and its user's information needs. In the activity-based scenario of this study, the solution never depends on one single publisher, regardless the quality of its data.

Discussion

Information needs are complex and not easy to capture. One described example of this is that there is a tendency to omit important data (e.g., geographical data for weather). This behaviour could be a form of unrecognised information need [START_REF] Erdelez | Information encountering: It's more than just bumping into information[END_REF], but likely the data is perceived to be commonly understood by the participants. Following that the information seekers do not fully deal with the underlying datasets is that data transformers and publishers have to take care about information seekers' information needs. They have to match the information seekers and their information needs with their solutions and data [e.g., 7, 9]. Therefore, OGD does not follow a one-directional flow. Rather, two sides of need and data meet in one solution. The data transformer has to ensure that the two sides mix properly, while he has little ability to control both.

Implications

We have also identified some implications for practice and future research. The practical implications are directed to publishers and data transformers of OGD. The model has taught us that before publishers provide OGD or data transformers develop solutions, they should identify information needs in their society, study existing solutions, and come up with ways to go beyond them. Data transformers should consider the content of their solution to ensure it can be used to answer questions that can come from several knowledge gaps. They are also recommended to think about how data is mixed from different sources and what data will be provided or added for the perceived value it adds to the solution for the information seeker (e.g., trust, reliability). On the other hand, publishers should consider how their data can be combined with other data of similar type (e.g., environmental or transportation) or relatedness (e.g., roads and weather). Identifying information needs and the possible answers could help reveal the relatedness. The theoretical implications are aimed towards OGD and e-government researchers. OGD tended to be painted as a supply or demand issue [e.g., 10]. Our model expands on this idea as it focuses on information needs that arise when there is a gap in the knowledge of the individual, while information demands are requests for information that are believed to be needed [START_REF] Nicholas | Assessing information needs in the age of the digital consumer[END_REF]. Based on the tentative model, there are some suggestions. The information seeker's role in OGD can be considered in relation to information needs in society. Previous research about information needs may be able to help, support, and guide future OGD research.

Limitations and future work

The above model can be generalised to primary needs and information needs that are activity-based in an everyday context where it is easy to formulate questions. The scenario was general enough to be experienced beyond researchers and students in Belgium, but focused on the "fact-find", "brief", and "stay aware" use of solutions. Future research is needed about situations with researchbased, stimulation-based, and recreational-based information use. The literature review aimed at gaining insight into an issue, and the resulting background could be further developed with a systematic literature review, but we believe it has captured relevant research for this study. The current model is based on the perspective of the information seeker and should be extended with the perspective of publishers and data transformers. At the same time, it needs further testing in other contexts. Further research is needed about how to bridge the gap between publishers and information seekers through the development and use of solutions. The combination of information needs and benefits of OGD is one possible direction. The connection between providing and adding data in a solution is one interesting gap that can be explored further.

Conclusion

The contribution of this paper is two-fold: a tentative conceptual model and possible considerations (see Figure 1). The model is one way to understand how OGD can be integrated into society by satisfying information needs. The use of data and information in solutions can impact information seekers' lives to realize the benefits of OGD. However, information needs come in many shapes and forms, are complex, and not always fully expressed, which can make them challenging to capture. Data transformers and publishers can view the information in a solution as a data concoction brought together from different sources rather than a one-way data flow. The model presents the complexity of connecting the needs of information seekers with data provided by publishers through a solution changeable by data transformers.

Fig. 1 .

 1 Fig. 1. The tentative model of information needs, solutions, and sources.

Table 1 .

 1 Summary of the empirical material from the workshops

			Participant Background	Primary needs	Information needs
	Workshop 1	Group 1	Researchers in Information Management (1), Computer Science (2), and Business Informatics (1)	Move to the city Discover the city Make friends Find store	Public transport List: description and accessibility of events, and available products Points of interest Recommendations for finding friends
	Activities (Primary needs) open to suggestions	Group 2	Researchers in Education (1), Management and Creativity (1), Management (1)	Find friends Find services (doctors, sport, shops) Environment	Descriptions: facilities, location, prices, timetables, soft skills, and atmosphere of the places Like-minded people Peer recommendations
					Weather, fashion, streets
				Get dressed	Descriptions: facilities, location,
			Researchers in	Leisure, shopping,	prices, timetables, and attendees
		Group 3	Education and Management (1),	services, and schools	Local food, stereotypes
			Media Industry (1)	Local integration,	Best schools, doctors, and service providers
				safety, and health	Peer recommendations and tacit rules
					Like-minded people
	Workshop 2	Group 4	Students in Management (3)	Eat and Drink: restaurants, shops bars, and delivery services	Descriptions: facilities, location, prices and payment, timetables, delays, attendees, and occupation rate per hours Peer recommendations and trustful reviews
	Activities (Primary needs) from suggested categories	Group 5	Students in Informatics (1) Political sciences (1) Project Management (1) Unspecified (1)	Socialize and make friends	Places to go out Like-minded people Associations and activities on the campus
					Services and activities, carpooling with friends,
					and accommodation with friends
		Group 6	Students in Analytics and Digital business (4)	Socialize and make friends	Descriptions: location, schedules, prices, accessibility, and conditions
					Attendees in working groups, travels,
					music bands, and parties