Daniel Toll
email: daniel.toll@liu.se

Ida Lindgren
email: ida.lindgren@liu.se

Ulf Melin
email: ulf.melin@liu.se

Christian Ø Madsen

Artificial Intelligence in Swedish Policies: Values, benefits, considerations and risks

Keywords: Artificial intelligence, e-Government values, public sector, benefits, risks

Artificial intelligence (AI) is said to be the next big phase in digitalization. There is a global ongoing race to develop, implement and make use of AI in both the private and public sector. The many responsibilities of governments in this race are complicated and cut across a number of areas. Therefore, it is important that the use of AI supports these diverse aspects of governmental commitments and values. The aim of this paper is to analyze how AI is portrayed in Swedish policy documents and what values are attributed to the use of AI. We analyze Swedish policy documents and map benefits, considerations and risks with AI into different value ideals, based on an established e-government value framework. We conclude that there is a discrepancy in the policy level discourse on the use of AI between different value ideals. Our findings show that AI is strongly associated with improving efficiency and service quality in line with previous e-government policy studies. Interestingly, few benefits are highlighted concerning engagement of citizens in policy making. A more nuanced view on AI is needed for creating realistic expectations on how this technology can benefit society.

Introduction

Artificial Intelligence (AI) is currently discussed as an enabler for transforming the public sector; in fact, AI is described as a solution to most types of administrative challenges, regardless of industry or sector [START_REF] Cave | An AI Race for Strategic Advantage: Rhetoric and Risks[END_REF]. In recent years, AI has changed from being 'science fiction' to being developed and applied on a large scale and is quickly becoming ubiquitous. AI is also portrayed as the next big area of digitalization; some even call it a revolution [START_REF] Makridakis | The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms[END_REF]. AI is portrayed as a solution to many of the problems related to poor efficiency, lack of resources and competence experienced in the public sector. This echoes the praise of previous technological solutions in different waves of e-government [START_REF] Rowe | People and Chips. The Human Implications of Information Technology[END_REF][START_REF] Heeks | Analyzing e-government research: Perspectives, philosophies, theories, methods, and practice[END_REF][START_REF] Madsen | The Development in Leading e-Government Articles 2001-2010: Definitions, Perspectives, Scope, Research Philosophies, Methods and Recommendations: An Update of Heeks and Bailur[END_REF][START_REF] Chadwick | Interaction between States and Citizens in the Age of the Internet: "e-Government" in the United States, Britain, and the European Union[END_REF]. Consequently, there are great expectations on what AI can do for public sector organizations, citizens and the society at large, in terms of e.g. improving service quality, reducing lead time and making unbiased decisions in case handling [START_REF] Lindgren | Close encounters of the digital kind: A research agenda for the digitalization of public services[END_REF].

AI is often discussed as something 'new', and in terms of its application areas this is correct. However, since its birth in the 1950s, AI as a phenomenon has had an unstable trajectory consisting of AI winters and AI springs [START_REF] Natale | Imagining the thinking machine[END_REF]. During AI winters, funding, efforts and interest in AI have diminished dramatically. Such periods occur when the technology fails to meet the high expectations set by scholars and others. It appears that we now find ourselves in the midst of an AI spring. Currently, everyone is aboard; the tech industry, consultancy firms, media, and government. With the history of unmet expectations, it begs to question if AI will finally deliver values as promised, or if we will soon experience another AI winter. We currently see both utopian and dystopian accounts of AI; e.g., [START_REF] Gurkaynak | Stifling artificial intelligence: Human perils[END_REF] portray AI as the humankinds' best hope to prevent extinction, whereas others fear an Armageddon caused by AI [START_REF] Mccauley | AI armageddon and the three laws of robotics[END_REF]. As AI enters into the e-Government domain, it is likely to affect public sector organizations and the lives of citizens. The conflicting portraits of AI call for further research in the area. It is imperative that we scrutinize how AI comes into play in the government domain, whether the expected transformative potential is realized, and what the implications for policy making are [START_REF] Lindgren | Close encounters of the digital kind: A research agenda for the digitalization of public services[END_REF].

This paper aims to investigate how AI is portrayed in a set of policy documents for public sector organizations, and what value ideals are attributed to the use of AI. We depart from a case where the Swedish government asked a number of organizations to map the usefulness of AI for Swedish industry and society. The resulting documentation from this initiative sets the frame for the discourse on AI in the Swedish public sector. We contribute to e-government research and practice by identifying what values are attributed to the use of AI for public sector organizations, and relating these to previous discussions on technology in the e-government research field.

The paper is organized as follows; first, we present the policy documents in our analysis and their origin. Next, we explain our method and analytical strategy. Then, we present our findings of the analysis and discuss our findings in relation to the analytical framework by Rose et al. [START_REF] Rose | Managing e-Government: value positions and relationships[END_REF] and previous analyses of e-government policy and technology. We contribute to e-government research and practice by illustrating how AI is portrayed in Swedish policies, illustrating a need for a more nuanced understanding of the potentials of this technology for public sector organizations.

2

Theoretical framing e-Government research shows that IT-development and implementation in government organizations is difficult to plan and organize for and that IT often results in unexpected outcomes [START_REF] Hood | A government that worked better and cost less?: Evaluating three decades of reform and change in UK central Government[END_REF]. Why should the implementation of AI technologies be any different? One possible reason for the difficulties of managing e-Government initiatives is the multitude of public values that government organizations are designed to uphold [START_REF] Almarabeh | A General Framework for Research Proposals[END_REF]. Bannister and Connolly discuss how the use of new types of information and communication technology (ICT) may transform such public values [START_REF] Bannister | ICT, public values and transformative government: A framework and programme for research[END_REF]. Several scholars have created value typologies as analytical lenses for studies of ICT's transforming power [START_REF] Rutgers | Sorting Out Public Values? On the Contingency of Value Classification in Public Administration[END_REF][START_REF] Beck Jørgensen | Public Values Inventory[END_REF]. Rose et al. [START_REF] Rose | Managing e-Government: value positions and relationships[END_REF] have synthesized four value positions in e-Government management: professionalism, efficiency, service, and engagement (see Table 1). The professionalism ideal concerns legality, durability and infrastructure. The efficiency ideal concerns value for money, efficiency, productivity and automation. The service ideal concerns utility of the government for the citizen, accessibility and service quality. The engagement ideal concerns engaging with the citizen, democracy and participation. Much of the research on public values in e-Government is purely theoretical, therefore scholars have called for empirical research that puts these models to use [START_REF] Twizeyimana | The public value of E-Government -A literature review[END_REF].

Table 1. Four value ideals for e-Government management (adopted from [START_REF] Rose | Managing e-Government: value positions and relationships[END_REF], p.542).

Value ideal type Definition and representative values

Efficiency

Providing lean and efficient administration, which minimizes waste of public resources gathered from taxpayers. Representative values: Value for money, cost reduction, productivity and performance

Service

Maximizing the utility of government to civil society by providing services directed towards the public good. Representative values: Public service, citizen centricity, service level and quality.

Professionalism

Providing an independent, robust and consistent administration, governed by a rule system based on law, resulting in the public record, which is the basis for accountability. Representative values: Durability, equity, legality and accountability

Engagement

Engaging with civil society to facilitate policy development in accordance with liberal democratic principles; articulating the public good.

Representative values: Democracy, deliberation and participation.

Methodology

We seek to analyze how AI is portrayed in Swedish policy documents, and the values attributed to the use of AI. We have chosen 10 documents for our analysis, which are presented in Table 1. The policy documents chosen for analysis are all a result of the initiative by the Swedish government to map and investigate the role of AI in Sweden, led by VINNOVA. They form a generative and representative sample of the discourse on AI for the Swedish public sector and are published by the following organizations;

• VINNOVA -Sweden's innovation agency, under the Ministry of Enterprise and Innovation, acts as the government's expert authority regarding innovation policy. • Governo -a Swedish management consultancy firm, known for their close collaborations with public sector organizations e.g., VINNOVA.

• the Swedish Association of Local Authorities and Regions (SALAR) -a interest

organization working for municipalities and regions in Sweden. • Inera -an organization under SALAR focusing on healthcare.

• WASP -Wallenberg Artificial Intelligence, Autonomous Systems and Software

Program. A research initiative initiated and financed by the Wallenberg foundation.

• The Swedish government.

We treat the documents as policy documents in the sense that their content is likely to trickle down through the governmental structures in Sweden and constitute the foundation of policies in this area for both public sector and private sector organizations. There are similar reports that mention AI in the Swedish public sector, focusing more on digitalization and automation in general. We excluded these documents from our analysis, and focused instead on reports in which AI has a dominant role. We have a broad and inclusive treatment of AI in this paper and have not defined AI in a technical sense as to make limitations to a subset of specific AI technologies. The analysis instead focuses on the discourse regarding AI and as such encompasses a broad variety of AI technologies and definitions associated with the term 'artificial intelligence'. We have performed a qualitative content analysis [START_REF] Krippendorff | Content Analysis: An Introduction to its Methodology[END_REF]. The research presented in this paper is hence qualitative and interpretive [START_REF] Walsham | Interpretive case studies in IS research: Nature and method[END_REF], although we quantify the results as a part of exploring patterns and interpretations. As an analytical lens, we used the four value ideals presented by Rose et al. [START_REF] Rose | Managing e-Government: value positions and relationships[END_REF]. We combined these value ideals with an inductive and iterative approach for analyzing the documents. We find the model by Rose et al. fitting for several reasons. First, it synthesizes previous literature on public sector values. Second, the Scandinavian origin of the model corresponds well with the Swedish culture and welfare system. Third, the model itself reflects the expectations and responsibilities of Scandinavian government organizations. In this paper, we have no ambition to further develop this model, but instead apply it as-is.

The analysis was performed in the following steps. (1) Each document was read to identify statements describing the nature and use of AI for public sector organizations. In total 522 statements were identified. (2) Each statement was condensed by highlighting its main message, e.g., the statement "AI can contribute to shortening lead times for case handling" (Doc.#10, p. 4) was condensed to "Shortened lead times". (3) Each condensed statement was then coded in relation to the Rose et al.'s value set [START_REF] Rose | Managing e-Government: value positions and relationships[END_REF]. This coding was performed in an interpretive manner, seeking to find a match between the statements and the value ideals in the analytical framework. The condensed statement "Shortened lead times" was categorized as belonging to the "Efficiency" value ideal. (4) As the analysis progressed, it became evident from the empirical material that the statements could also be characterized along a different dimension, highlighting negative and positive outcomes of AI for the public sector. Thus, additional categories were formed inductively, including benefits, considerations and risks associated with use of AI (further described in findings). ( 5) We returned to the original statements and categorized each statement in relation to the inductively generated categories. For example, the statement "AI can contribute to shortening lead times for case handling" was categorized as a "Benefit" of AI. ( 6) Finally, we combined the two sets of categorizations for each statement, thereby integrating the theoretical and empirical dimensions.

Findings

In our analysis of how AI is portrayed in Swedish policy documents, we identified three categories in the empirical material. These inductively generated categories are benefits, considerations and risks associated with use of AI, as described in Table 3. Figure 1 presents the distribution of the 522 statements across the two dimensions. First, most statements concern benefits associated with AI (281 statements) or considerations for public organizations when using AI (190 statements). Notably, only 50 statements concern risks associated with AI. Related to the four value ideals, most statements fall into professionalism (228 statements) and efficiency (157 statements). The service ideal appears less frequently (98 statements), and the engagement ideal is the least frequent one (39 statements). Professionalism is therefore the most frequent value ideal, and occurs almost six times as frequently as the least frequent ideal (engagement). Fig. 1. represents the distribution of statements across the categories and value ideals. In order to further display the content of the discourse on AI in the Swedish public sector, we have extracted condensed statements that exemplify the topics according to categories and value ideals (Table 4). These topics are discussed in the next section.

Analysis and discussion

This paper aims to investigate how AI is portrayed in a set of Swedish policy documents, and what value ideals are attributed to the use of AI. We depart from a Swedish case in which the Swedish government asked a number of organizations to map the usefulness of AI for Swedish industry and society. In the research discourse on AI, we see a strong polarization, where some scholars describe AI as a necessity for creating and maintaining a functioning society [START_REF] Gurkaynak | Stifling artificial intelligence: Human perils[END_REF], and others see AI as a threat to the world we live in [START_REF] Mccauley | AI armageddon and the three laws of robotics[END_REF]. In the documents analyzed in this paper, we a strong tendency towards the former (positive) view on AI; AI is presented as a way to maintaining and improving the well-functioning Swedish welfare system.

The main finding from our analysis is that the benefits of AI are highlighted extensively, whereas the potential risks of AI are few. Relating these statements on AI in the Swedish public sector to the value ideals presented by [START_REF] Rose | Managing e-Government: value positions and relationships[END_REF], we see that most benefits relate to increased efficiency of public sector processes. It is interesting, that the discourse does not regard risks to efficiency as a result of AI; using AI to increase efficiency is solely seen as creating desirable and positive effects. The second most frequent type of benefits concern service quality; hence, AI is described as both a way to increase competitiveness, make profit and savings, but also to increase quality and effectiveness of public sector processes. The focus on benefits may be explained by the purpose and nature of the documents included in the analysis; they are a result of an initiative to map the usefulness of AI for Swedish industry and public sector organizations. Hence, the purpose of the document is to inspire organizations to adopt and implement AI technologies. Overall, the discourse on AI is in line with the general discourse on digitalization in the public sector, highlighting the positive impact of the technology. In particular, technologies are promoted as means for increased efficiency and effectiveness, e.g., [START_REF] Rowe | People and Chips. The Human Implications of Information Technology[END_REF][START_REF] Heeks | Analyzing e-government research: Perspectives, philosophies, theories, methods, and practice[END_REF][START_REF] Madsen | The Development in Leading e-Government Articles 2001-2010: Definitions, Perspectives, Scope, Research Philosophies, Methods and Recommendations: An Update of Heeks and Bailur[END_REF][START_REF] Chadwick | Interaction between States and Citizens in the Age of the Internet: "e-Government" in the United States, Britain, and the European Union[END_REF]. Consequently, our analysis confirms that the discourse on AI for the public sector is characterized by an optimistic outlook on AI, and that there are great expectations on what AI can do for public sector organizations, citizens and the society at large.

In spite of the purpose of inspire AI use in the public sector, some considerations and risks are mentioned in the policy documents. The considerations typically fall under the professionalism value ideal [START_REF] Rose | Managing e-Government: value positions and relationships[END_REF]. We believe this as a result of the particular context highlighted in the professionalism ideal; the functioning bureaucracy. AI challenges the focus on the internal stability of government (status quo), e.g. in terms of how AI may lead to job redundancy in the public sector and a need for new competences. AI also requires new and different digital infrastructures, and poses questions on how the legality of public administration can be upheld. For these reasons, it is perhaps not surprising that the risks with AI highlighted in the policy documents were related to the values related to the professionalism ideal, e.g., security, integrity, and misinformation. Concerning the risks of AI, there are only a few risks mentioned that concern engagement. The engagement ideal is about engaging with society, about citizen participation and democracy; the communication between the citizen and the government [START_REF] Rose | Managing e-Government: value positions and relationships[END_REF]. Engagement is relatively underrepresented in the AI discourse; AI is not presented as an enabler of engagement and democratic discussions. This is interesting, because the Swedish Digital Agenda explicitly mentions citizen engagement as a benefit of digitalization [20]. However, in the discourse on AI we have analyzed, the values related to engagement of citizens in policy making are notably absent. This finding corresponds to previous policy studies in the e-government field, which have found that the democratic ideals often are sidelined in favor of New Public Management ideals of increased efficiency and effectiveness [START_REF] Chadwick | Interaction between states and citizens in the age of the internet: "e-government" in the United States, Britain, and the European Union. Governance-an[END_REF][START_REF] Jaeger | The history of the future: Changes in Danish egovernment strategies 1994-2010[END_REF][START_REF] Persson | Value Positions in E-Government Strategies: Something Is (not) Changing in the State of Denmark[END_REF].

Based on the findings from our analysis, we see a likelihood that the discourse on AI is overly optimistic and resemble previous hypes on various uses of technologies in the public sector (cf. [START_REF] Rowe | People and Chips. The Human Implications of Information Technology[END_REF][START_REF] Natale | Imagining the thinking machine[END_REF]). Returning to the metaphor of AI winters and springs, it appears that we are indeed in the midst of an AI spring [START_REF] Natale | Imagining the thinking machine[END_REF]. A core issue for future research will be to investigate whether we will soon find ourselves in a new AI winter, or if the AI spring will turn into an AI summer; where the AI-technologies are widespread and meet the high expectations attributed to them. An interesting difference that we see, compared to previous AI springs, is that the interest for AI is now widely spread, and not just seen in academia. It also appears that AI technology is likely to become more generally applied. As AI becomes more mainstream the expectations on this particular technology is likely to evolve and become more nuanced; therefore, it is vital that the e-government research community continue to follow this development.

Conclusions, limitations and future research

In this paper, we performed a content analysis on 10 policy documents describing the usefulness of AI for public sector organizations and industry in Sweden. We applied the value ideals model presented by Rose et al. [START_REF] Rose | Managing e-Government: value positions and relationships[END_REF], combined with three inductively generated categories for coding value statements in the documents. We found that; • AI is described as an enabler for increased efficiency and effectiveness in the public sector. This reflects an optimistic view on AI, highlighting the benefits of AI for public sector organizations. • AI challenges the values related to professionalism, reflected in an emphasis on considerations and risks concerned with legality, security and integrity. • AI is not described as an enabler for citizen engagement in government. This is an interesting contrast to general national policies stating that digitalization should be used to increase citizen engagement. • The AI-discourse analyzed in this paper is in line with previous e-government research.

• A more nuanced view on AI is needed for creating realistic expectations on what this technology can do for society.

This paper has several limitations. First, the analytical model gives a simplified overview of the values guiding e-Government management. In the future, the findings presented here could be complemented with additional value conceptualizations or a modified version of the current analytical lens [START_REF] Rose | Managing e-Government: value positions and relationships[END_REF] with improved suitability for analyzing AI. A second limitation concerns that the particular discourse analyzed is taken from one national context at one point in time. Furthermore, the documents we have analyzed dealt with both industry and public sector organizations combined. Future research could add additional national contexts to the analysis and focus on the public sector context alone (but from multiple perspectives, e.g., from the viewpoint of trade-unions, citizens, and businesses). We also see potential for investigating the discourse on AI in a longitudinal manner and see if and how the policy documents come into practice. A third limitation concerns our interpretation of AI. AI encapsulates a variety of different technologies and we have not unpacked the meaning of AI here. Instead, we have treated AI in the same overarching manner as is found in the policy documents that we analyzed. As AI evolves, the meanings attributed to this concept are likely to become increasingly differentiated and hence more important to state explicitly.

Fig. 1 .

 1 Fig. 1. The distribution of categories and value ideals associated with the use of AI.

Table 2 .

 2 The policy documents in the analysis.

	Document (title, translated if originally in Swedish) Year Author organization Doc.ID #Pages
	Mapping and analysis of artificial intelligence and	2017 Sweden's Govern-	#1	3
	machine learning's capabilities and application in	ment Offices
	Swedish industry and society
	Artificial intelligence -possibilities for welfare	2017 SALAR	#2	17
	AI and automation of first line care	2017 Inera	#3	51
	Artificial intelligence in Swedish business and soci-	2018 VINNOVA	#4	188
	ety
	Artificial intelligence in the public sector	2018 Governo	#5	50
	Correct payments with the help of AI	2018 Governo	#6	33
	Automation of work	2018 SALAR	#7	36
	Decisions within 24 hours	2018 SALAR	#8	4
	Collecting ideas and identifying challenges for fu-	2018 WASP	#9	28
	ture AI research in Sweden
	National alignment for artificial intelligence	2018 Sweden's Govern-	#10	12
		ment Offices

Table 3 .

 3 The inductively generated categories, with examples.

	Category	Definition	Representative quotes
	Benefits	Desirable, positive	"The [AI] system makes the process more effec-
		effects or statements	tive and saves time for personnel." (Doc.#7, p.
		of how AI solutions	10)
		will affect society in	"High risk work environments do not need to be
		a positive way.	populated by people and strenuous jobs can be
			performed by automatons." (Doc.#4, p. 56)
	Considerations	Things that public	"This is an area that needs to be investigated and
		sector actors must	where it can become necessary to change laws
		carefully think	and regulations" (Doc.#7, p. 15)
		about and keep in	"Naturally, it has to be performed in a safe and
		mind when using	transparent way" (Doc.#5, p. 33)
		AI.
	Risks	Undesirable, nega-	"AI can involve new types of intelligent cyberat-
		tive effects or state-	tacks or manipulated data which can have seri-
		ments of how AI so-	ous consequences" (Doc.#10, p. 12)
		lutions will affect	"An example of such a risk could be decision
		society in a negative	support systems in the area of jurisdiction falling
		way.	into the hands of criminals, enabling them to find
			ways to avoid prosecution." (Doc.#7, p. 12)

Table 4 .

 4 Typical statements for each category and value ideal.

	Category	Benefits	Considerations	Risks
	Value ideal
	Efficiency	Efficiency	Costs	Economic damage
		Competitiveness	Resources
		Profits/Savings
		Automating processes
	Service	Service quality	Loss of jobs	Data quality
		Personalization	Service quality	Loss of jobs
		Accessibility
	Professionalism	Security	Competence	Security
		Sustainability	Infrastructure	Integrity
			Data availability	Misinformation
			Legality
	Engagement	Citizen interaction	Transparency	Transparency
			Trust	Trust
			Co-operation	Democracy


Boriana Rukanova
email: b.d.rukanova@tudelft.nl

Yao-Hua Tan
email: y.tan@tudelft.nl

Micha Slegt

Marcel Molenhuis

Ben Van Rijnsoever

Krunoslav Plecko

Bora Caglayan

Gavin Shorten

Value of Big Data Analytics for Customs Supervision in e-Commerce

Keywords: Data analytics, Big data, Value, Customs, Government supervision, Web data retrieval, e-Commerce

Big data and analytics have received a lot of attention in egovernment research over the last decade and practitioners and researchers are looking into the transformative power of this technology to create for example competitive advantage, and increase transparency. Recent research points out that while parties are aware of the transformative power of this technology, understanding the value that this technology can bring for their specific organizations still remains a challenge. Data analytics is in particular interesting to support supervision tasks of governments. Here we take the customs supervision as a typical example where data analytics is used to support government in its supervision role. The main question addressed in this paper is: How to understand the value of big data analytics for government supervision? To address this question this research builds upon a case study where big data analytics solutions are developed and piloted as part of the PROFILE EU-funded research project. We adapt and utilize a recently published integrated model of big data value realization of Günther et al. [5] as a conceptual lens to structure the case findings. As a result we develop a more detailed model for analyzing value of big data specifically in the context of customs as an example of a specific domain of government supervision. This research contributes to the eGovernment literature on articulating value from big data analytics, particularly focusing on the role of government supervision.

Introduction

Big data and data analytics have received a lot of attention over the last decade and practitioners and researchers are looking into the transformative power of this technology to create for example competitive advantage or increase transparency. Big data has been considered to be a breakthrough technological development which brings opportunities, as well as challenges (e.g. [START_REF] Chen | Business intelligence and analytics: from big data to big impact[END_REF][START_REF] Fichman | Digital innovation as a fundamental and powerful concept in the information systems curriculum[END_REF][START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF][START_REF] Sivarajah | Critical analysis of Big Data challenges and analytical methods[END_REF]). Big data is seen as "massive amount of digital data being collected from all sorts of sources, is too large, raw, or unstructured for analysis through conventional database techniques" [8, p. 78] and analytics can help to generate new insights.

Although the business sector has been leading in using big data governments are also actively exploring the opportunity to use big data to address public sector challenges [START_REF] Chen | Business intelligence and analytics: from big data to big impact[END_REF][START_REF] Kim | Big-data applications in the government sector[END_REF]. A growing body of eGovernment research on big data focuses on Big Open and Linked Government Data [START_REF] Bertot | Big data, open government and e-government: issues, policies and recommendations[END_REF][START_REF] Janssen | Driving public sector innovation using big and open linked data (BOLD)[END_REF][START_REF] Janssen | Big and open linked data (BOLD) in government: a challenge to transparency and privacy[END_REF][START_REF] Lnenicka | Developing a government enterprise architecture framework to support the requirements of big and open linked data with the use of cloud computing[END_REF]. Often in these studies the focus is on government providing public access to their data to allow for applying further analytics; for example to increase transparency, or to extract further value from government data. In this context the government is more in a role of a data provider, opening up their data for wider use. In other cases, government can be seen as a user of information from business and non-government organization in order to create public value [START_REF] Gil-Garcia | Towards a smart State? Inter-agency collaboration, information integration, and beyond[END_REF][START_REF] Susha | Data collaboratives as a new frontier of cross-sector partnerships in the age of open data: taxonomy development[END_REF]. Prior research [START_REF] Maciejewski | To do more, better, faster and more cheaply: using big data in public administration[END_REF] distinguishes three roles that a government can play which call for different use of big data and analytics in government. These roles are (a) public supervision, which deals with identification of irregularities (e.g. legal incompliance) and the respective responsive action; (b) public regulation, which focuses on regulating social activities and relations by using e.g. permits, prohibitions or orders; (c) public service delivery which focusses on providing services or products. In this study we focus on examining the use of big data and analytics by government in its supervision role where government is a user of big data to improve the supervision process.

As argued by some researchers [START_REF] Kim | Big-data applications in the government sector[END_REF] parties recognize that being able to create value from big data "represents a new form of competitive advantage" [8, p. 85]. However in a recent study [START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF] it is concluded that although big data has been considered to be a breakthrough technological development there is still limited understanding of how organizations translate its potential into actual social and economic value. The main question that we explore in this paper is related to understanding value of big data analytics for government in its supervision role or in other words: How to understand the value of big data analytics for government supervision?

To address this question we use the domain of customs, which is one example of a domain where government acts in its supervision role 1 . This research builds upon a case study in the customs domain where big data analytics solutions are developed and piloted as part of the PROFILE 2 EU-funded research project. The case studied in this paper concerns e-Commerce goods imported to The Netherlands. One of the issues with these streams is undervaluation, i.e. the declared value of the goods is lower than the actual value for which the goods are bought. Undervaluation is one of the examples of fiscal fraud. The effect of this undervaluation is loss of revenue from duties and taxes levied when goods are imported in the EU, translating in loss of revenue for the national and EU budgets. The pilot aims to investigate how contextual information could be collected, for example from e-Commerce websites in China and the US, and how this information can be used in the customs process to support targeting officers in their risk assessment on undervaluation. In our analysis we adapt the model of big data value realization of Günther et al. [START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF] and use it as a conceptual lens to structure the case findings. Although the actual case in this paper is undervaluation, the societal value of this data analytics innovation is much broader. The same customs data analytics solutions that work for identifying undervaluation can also be used to identify unsafe and dangerous goods, such as counterfeited medicines that do not cure. Hence, the acute need for customs to develop data analytics solutions are a significant contribution to making our society a safer and more secure world.

The remaining part of this paper is structured as follows. In Sect. 2 we present our conceptual framework, followed by our interpretative case methodology in Sect. 3. Section 4 presents our case findings and we end the paper with discussion and conclusions.

Conceptual Framework

As argued in literature [START_REF] Kim | Big-data applications in the government sector[END_REF] parties recognize that being able to create value from big data "represents a new form of competitive advantage" [8, p. 85]. However in a recent study, based on a thorough literature review investigating the issue of value realization from big data, Günther et al. [START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF] conclude that although big data has been considered to be a breakthrough technological development there is still limited understanding of how organizations translate its potential into actual social and economic value. To address this issue, Günther et al. [5, p. 202] propose that it is imperative for organizations to "continuously realign work practices, organizational models, and external stakeholders interest to realize value from big data". The authors [START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF] formulated a number of propositions and propose an Integrated model of big data value realization.

In their model Günther et al. [START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF] position social and economic value of big data in the middle and they propose that in order to address the value of big data, parties need to look at the interrelationships among three levels as follows: (a) work practice level; i.e. working with big data analytics in practice; (b) organizational level; i.e. developing organizational models; (c) supra-organizational level; i.e. dealing with stakeholders' interests. As directions for further research Günther et al. [START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF] call for further empirical research to examine the cross-level interactions and alignments, and that is what we do in this paper.

Before continuing further it is worth elaborating on the concept of value. Günther et al. [START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF] support the argument that the perception of value of big data for organizations depends on the strategic goals of the organizations for using big data. Günther et al. [START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF] give various examples of areas of social and economic value that organizations may pursue. Examples of social value include enhanced transparency, prevention of fraud, improved security, improved wellbeing through better healthcare and education. Examples of economic value include increase in profit, business growth, competitive advantage. In the case that we analyze later in this paper the focus is on improvements in fiscal fraud detection due to under valuation of the value of the imported goods from e-Commerce transactions. In the wider societal sense reduced fiscal fraud also means that more duties are collected to finance the EU and the national budgets which can then be distributed for services to citizens.

We consider the model of Günther et al. [START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF] as a useful conceptual model that could be instrumental also for understanding of value of data analytics in the context of government supervision as it positions value at the center and it brings a broader perspective on examining value by looking at the interactions among work practice, organizational, and supra-organizational levels. For the purpose of our analysis we adapted a simplified version of the model of Günther et al. [START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF] in order to keep the analysis manageable when applying it to the empirical case 3 .

In our adaptation (see Fig. 1) we chose to stick to the main elements of the Günther et al. [START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF] model, namely positioning the concept of value at the center and looking at the inter-relationships among work practice, organizational and supra-organizational levels. In our case we use the term value to refer both to social and economic value and we chose to use the terminology Value of Big Data Analytics as big data on its own is of little value and becomes valuable when analyzed or combined and analyzed to produce some new insights.

Method

To address our main question we used an interpretative case study method [START_REF] Walsham | Interpreting Information Systems in Organizations[END_REF]. Data collection took place in the period June 2018-January 2019. The data collection took part in the context of the PROFILE EU-funded research project, where the goal is through the use of four real-life demonstration projects (called Living Labs) conducted in different EU countries to explore the potential of data analytics for customs. The Living Labs research approach "takes a development view of innovation and studies novel technologies in complex real-world setting" [15, p.32] 4 . This study focusses on the Dutch Living Lab, where Dutch Customs, IBM and the university partners are working together to develop and test data analytic innovations for the import of e-Commerce goods to The Netherlands. The aim of the Dutch Living Lab is to explore whether data analytics solutions can help customs to detect fiscal risks related to undervaluation. The data analytics solution that is used in the Dutch Living Lab is developed by IBM and is a web retrieval tool using a contextualization engine, a piece of software that can search on e-commerce websites in China and the US, and analyze for example price information of a product. The purpose of this web retrieval tool is to provide Customs officers with additional online publicly available information to cross validate the price that is declared on the import declaration.

While our empirical insights were predominantly gained form our interactions with the Dutch Living Lab they were further informed by our broader involvement in the PROFILE project, where more data analytics pilots are conducted. As a result of these iterations we identified an initial list of issues and considerations related to the value of data analytics in the customs process. As a next step we used the adapted model (see Fig. 1) based on Günther et al. [START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF] as a lens to structure our findings. In this process we arrived at a number of observations as follows:

1. We needed a further detailing of the work practice level (the customs process), as the value of data analytics can vary depending on where in the process data analytics is used and in what part of the process performance improvement is desired. 2. We needed further detailing on the organizational level to better capture issues related to value of data analytics that we identified. In this further detailing we added Based on these findings we extended the adapted model (Fig. 1) and we arrived at our more detailed model for analyzing value of big data analytics in the customs process (see Fig. 2

below).

Case Findings

In this section we start by discussing the Work practice level by describing the e-Commerce customs process in The Netherlands and by explaining the big data analytics use in the Dutch Living Lab. We then demonstrate how our models (i.e. Fig. 2) is applied by using examples from the case. Before the goods are sent to the final customer an e-Commerce declaration needs to be submitted to the declaration system of Dutch Customs. The submission of the e-Customs declarations is normally not done by the seller or the buyer but by a logistics service provider or a trader that handles the declaration procedures when the goods enter The Netherlands.

Once the declarations are available in the declaration system an automated risk analysis is performed based on pre-defined risk rules software (marked as step 1 in Fig. 3). As a result a list is generated which marks declarations that are suggested to be risky, and hence selected for inspection. As a next step (marked as step 2 in Fig. 3) a targeting officer further analyses the declarations and the list of declarations that were automatically selected for inspection from the system and makes a final list of packages to be inspected. One important aspect in this process is that in making the final selection decision the targeting officer is limited in the number of declarations that can be selected for inspection. This maximum number is defined by customs policy. This maximum number is determined by the capacity limitations of the inspection team to carry out inspections. After the targeting officer makes the final choice the refined list of declarations that are selected is provided to the inspection team. Subsequently the inspection team inspects (marked as step 3 in Fig. 3) the packages and documents the inspection results. The goods that are inspected were either selected as they were suspicious or they were randomly selected. For the suspicious goods the outcome of the inspection can be either that something wrong was indeed found (hit), resulting in a true positive (TP) selection. In case nothing suspicious was found in goods that were considered suspicious we have a case of false positive (FP). One of the goals that would indicate improved targeting with use of data analytics is to find ways to reduce the false positive inspections or the cases where customs invests resources to inspect a package but at the end nothing was found. Reduction of false positive is important as it leads to unnecessary use of customs resources and delays the trade flows.

Inspections also take place based on random selection in order to check whether in the flow of goods that were not identified as suspicious by the risk rules there are also cases where something is wrong with the goods. The results of the inspections performed on the goods that are randomly selected can either be true negative (TN), meaning that the goods were not selected as suspicious and indeed they were not suspicious. The result however could be also false negative (FN), meaning that the goods were considered as not suspicious in the selection process but in reality something wrong was found during the inspection. The availability of false negatives means that goods which were suspicious were not identified in the selection process. Apart from reducing false positives selections, data analytics can be used to better reduce false negative cases which are not detected by the current system. In fact, an improved balance between false positive and false negative cases generated by the system may be achieved.

Data Analytics in the Customs Process

IBM developed a prototype that allows the user to input a description of any item from a customs declaration and the user is then presented with other potentially useful contextual information about this particular item. For example, in the first instance this could be information sourced from e-Commerce websites via API's but it is envisaged that this can be extended to include insights gained form previous declarations and inspections.

After inputting a search string, the user is presented with an analysis of other similar product descriptions related to the search query and a statistical analysis of attributes associated with that item (e.g. price) is performed on the data set that is retrieved from the e-commerce website via the API. The high-level architecture is presented in Fig. 4. The user interface allows the customs officer to be informed via a Customs Portal. The main components of the system highlighted in Fig. 4 are: • Server-side (back-end) component which is retrieving item data (e.g., price, weight) from e-Commerce websites, performing analytical part and providing API (application programming interface). • Client-side (front-end) component designed for user interaction and data visualization.

The proof-of-concept tool has a page with search bar functionality. Results are generated based on search term, visualized as box plot to display range of values together with some statistical metrics like min, max and median. Using the initial box plot results display, a user can choose to change the view to the tabular data format. Table view contains product description, price in currency retrieved from web data extraction component and additional column with price converted to Euro currency to show both values. In the pilot initial functionality was added where every row with product description is a hyperlink to the sources of information. The targeting officer can choose this option to get complete information about the product.

Illustration of How the Model for Analyzing Value of Big Data

Analytics in the Customs Supervision Process Can Be Applied

Figure 5 below uses a number of examples identified in our case to demonstrate how the model for analyzing value of big data analytics in the customs supervision process (Fig. 2) can be applied. One of the decisions with which Dutch Customs was confronted was where in the customs process to place data analytics (see Fig. 5a). There are different possibilities where to include data analytics in the customs process, namely: (1) at the beginning of the customs process on the full set of customs declarations; (2) in the middle of the customs process, where pre-selection of declarations has already been made via running automated risk rules software and a sub-set of the declarations is presented to a targeting officer for further risk analysis. In this case data analytics can be used as a support tool for the targeting officer; (3) at the end of the customs process where data analytics (e.g. on scans) can be used to support the inspection process. In the Dutch Living Lab a decision was made to deploy data analytics in the second step, i.e. to provide decision support to the targeting officer. As such the immediate value of the data analytics solutions would be (on a work practice level) for the targeting officer.

Examples of application of the model and dependencies between decision aspects

To that extent the tool can be considered as a decision support tool which has big data analytics as an important element. In this case big data analytics is required because large number of web pages on The Internet with unstructured data need to be searched and analyzed on price information. Taking a process improvement perspective however Dutch Customs also needed to decide how to measure the performance improvement brought by the data analytics on the customs process (See Fig. 5b). In the discussions with Dutch Customs three areas for measuring performance improvements were identified, i.e.: (a) to reduce the number of false positive inspections (i.e. reduce the number of boxes which are selected and there is nothing wrong); (b) to reduce the number of false negatives, meaning catching more illicit trade; (c) to handle large increase in volumes of declarations. In the Dutch Living Lab we found out that there is an inter-dependence between the decision on where to place data analytics in customs process and the decision on what aspects to focus when measuring performance improvement (see arrow 5ab in Fig. 5). Namely the choice that was made in the Dutch Case (i.e. to use data analytics in the second step of the customs process as a decision support tool for the targeting officer) allows to measure performance effects on the reduction on false positives and possibly also effects on handling large volumes. In discussions with performance measurements experts from Dutch Customs however it became clear that placing the data analytics later in the process (in our case at step 2, as a support tool for the targeting officer) makes it difficult to realize data analytics improvements related to the reduction of false negatives (i.e. catching more illicit trade), as a large number of the declarations are already pre-filtered earlier in the process, before data analytics is applied.

When deciding where in the process to deploy data analytics (in our case in step 2 of the process) a next question was: What kind of data to use for data analytics? In the Dutch case it was decided to use price data obtained from e-Commerce websites to cross-validate the price on customs declarations. The choice of this type of data and possible value that it can bring on a work practice level however triggered issues related to data access (see Fig. 5c, and arrow 5ac) at the supra-organizational and organizational level. On a supra-organizational level in the case we found out that in many cases e-Commerce platforms would not allow robots to crawl their websites. An alternative way to access the price information from e-Commerce platforms was identified, namely via APIs and there are defined terms and conditions of these e-Commerce platforms of how organizations can access this information via APIs. This triggers issues at organizational level, as Customs needs to decide whether to accept the terms and conditions and under what circumstances. This shows that even if using data analytics and external data could bring value at a work practice level, for making this possible issues at supra-organizational and organizational level need to be solved. If these are not solved, even if potentially the data analytic solution could bring value to the work practice level, due to the dependencies with the other two levels this value may not be possible to realize in this specific customs situation.

Another issue that emerged from the discussions was that data analytics improvements are constrained by the existing organizational models and policies (see Fig. 5d). In the Dutch case the available resources in customs put an upper limit to the maximum packages that can be selected for inspection per day, as there are limited resources of customs officers who could actually inspect the packages. This maximum number of inspections is therefore an upper bound and even if data analytics can make the targeting officers very effective in selecting only packages that lead to true positive and they may be able to identify a large number of packages where things are wrong, the effects that data analytics will have on the whole customs process are limited by the inspection capacity that is set as policy from the customs organization. This shows how policies on an organizational level put an upper bound of what is possible to achieve (and the value that this data analytics solution can bring) at work practice level in this specific organization.

Another interesting issue that we identified in the case (see also Fig. 5e) is related to what we call here the paradox of data analytics improvement. To take a simple example. Customs may be able to inspect 100 packages per day. With the current methods some of these packages will be false positive, others will be true positive. For the false positive packages the inspection officer spends limited time, as it is mostly opening the package and identifying that nothing is wrong. In case of true positives there is much more processing time per package, as the customs officer needs to start procedures and follow-up activities. If with data analytics the customs inspection process is improved, and the false positives are reduced, with the available resources customs will be able to carry out less inspections (i.e. less than 100), as it takes more time to process packages where there is something wrong. Thus increased efficiency due to data analytics can overload inspection capacities. The arrow 5de in Fig. 5 shows this complex interdependency, where the value of the data analytics can be bounded by the available organizational capacity but also vice versa, that improvements with data analytics could influence the organizational model and available capacity.

Discussion and Conclusions

The main question that this paper set to explore was: How to understand the value of big data analytics for government supervision? As Günther et al. [START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF] argue it is "imperative for organizations to continuously realign work practices, organizational models, and external stakeholders interest to realize value from big data" [5, p. 202]. Building on Günther et al. [START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF] and on a case study of using data analytics in the customs domain we developed a model for analyzing value of big data analytics in the customs supervision process and demonstrate how it can be applied. As our analysis demonstrates there is no simple answer on what is the value that data analytics can bring in a specific customs supervision process and benefits at one level have effects on other levels. It is the understanding of these complex interdependencies from multiple level perspective that allows us to reveal the multiple considerations and effects which give a more complete picture for decision makers and policy makers about what value of data analytics is for their specific organization. This study is limited to the customs domain and one case study. Further research looking at the use of data analytics in other cases from the customs domain as well as from other domains where government acts in its supervision role could help to determine the applicability of the model in other contexts and refine the model. This will allow for a more complete view on how to analyse value of data analytics in the context of government supervision. Further research can specifically focus also on elaborating the organizational and supra-organizational level in more detail. Follow-up research can focus on elaborating elements from our model which we only highlighted here but which we were not able to go in detail (e.g. outsourcing to other data analytics providers). Another issue is that while many businesses companies do have highly skilled personnel to perform data analytics, many customs organizations do not have such in-house capabilities. Further research can examine what are successful organizational models where customs have successfully been able to incorporate such capabilities in their organizations. Next to that the supraorganizational level also deserves further attention, especially in the customs domain the links to other customs administration, as well as the link to the EU as a supranational body would bring additional insights.

Fig. 1 .

 1 Fig. 1. Adapted model form Günther et al. [5]

  (a) outsourcing to other data analytics (DA) providers, (b) existing IT systems, (c) priorities, policies, capacity and other legal constrains. 3. We needed further detailing of the supra-organizational level by looking at (a) external data providers, (b) other customs; and supra-national (such as the EU).

Fig. 2 .

 2 Fig. 2. A model for analyzing value of big data analytics in the customs supervision process

4. 1 Figure 3

 13 Figure 3 below captures on a high level the e-Commerce customs process in The Netherlands. The bottom part of the figure illustrates an e-Commerce process, where a customer buys a product on the Internet. This could be via an e-Commerce platform (e.g. Alibaba) or directly via a e-Commerce website. The buyer pays for the goods via the e-Commerce platform or directly to the e-Commerce website. The seller on its turns sends the products to the customer.Before the goods are sent to the final customer an e-Commerce declaration needs to be submitted to the declaration system of Dutch Customs. The submission of the e-Customs declarations is normally not done by the seller or the buyer but by a logistics service provider or a trader that handles the declaration procedures when the goods enter The Netherlands.

Fig. 3 .

 3 Fig. 3. High-level overview of the e-Commerce context and the customs inspection process for e-Commerce declarations in The Netherlands

Fig. 4 .

 4 Fig. 4. High-level architecture of Customs Portal (dashboard)

Other domains where governments act in their supervision role include areas such as tax, and food safety.

https://www.profile-project.eu/.

In order to keep the model manageable we also did not include explicitly portability and interconnectivity which are part of the original Günther et al.[START_REF] Günther | Debating big data: a literature review on realizing value from big data[END_REF] model but they are implicitly taken into considerations.

For earlier application of the Living Lab approach in the context of IT innovation in international trade and customs see e.g.[START_REF] Rukanova | Beer living labintelligent data sharing[END_REF]. See also http://tiny.cc/8s564y for other examples of applications of Living Lab approach.

B. Rukanova et al.

Value of Big Data Analytics for Customs Supervision in e-Commerce

Acknowledgement. This research was partially funded by the PROFILE Project (nr. 786748), which is funded by the European Union's Horizon 2020 research and innovation program. Ideas and opinions expressed by the authors do not necessarily represent those of all partners. The authors would like to thank also the PROFILE consortium partners for the fruitful collaboration and discussions on the topic in the broader project context.


Christian Østergaard Madsen

Sara Hofmann
email: sara.hofmann@uia.no

Willem Pieterson
email: willem@pieterson.com

Channel Choice Complications

Keywords: Channel Choice, Mixed Methods, Public Services, e-Government, Multi-Channel Management, Channel Behavior, Udbetaling Danmark

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

Introduction

To reduce costs, governments seek to migrate interaction with citizens from traditional channels to digital self-service channels. Early e-government studies assumed this process would happen automatically with the adoption of digital channels [START_REF] Layne | Developing fully functional E-government: A four stage model[END_REF]. Recent studies, however, demonstrate that this is not always the case [START_REF] Madsen | How to Migrate Citizens Online and Reduce Traffic on Traditional Channels Through Multichannel Management: A Case Study of Cross-Organizational Collaboration Surrounding a Mandatory Self-Service Application[END_REF]. In Canada, the use of websites declined between 2008 and 2012 [START_REF] Reddick | Interactions with e-government, new digital media and traditional channel choices: citizen-initiated factors[END_REF]. The continued use of traditional channels also occurs in settings where digital self-service channels are widely adopted, for example in Denmark [4], a leader in e-government [START_REF]European Commission: Digital Economy and Society Index (DESI[END_REF]. The question is why this happens. While many multi-channel management scholars [START_REF] Ebbers | Electronic government: Rethinking channel management strategies[END_REF][START_REF] Madsen | Multichannel Management in the Public Sector -A Literature Review[END_REF] have argued that electronic channels will not replace traditional ones [START_REF] Pieterson | Citizens and Service Channels: Channel Choice and Channel Management Implications[END_REF], they do not explain why use of traditional channels remains so important. One possible explanation lies in the nature of existing research and theory on citizens' channel behaviors [START_REF] Madsen | Channel Choice: A Literature Review[END_REF] and how these channel behaviors are incorporated in models of multi-channel service delivery [START_REF] Madsen | Multichannel Management in the Public Sector -A Literature Review[END_REF]. First of all, most studies have focused on citizens' initial and singular choice of a single channel. Next, most studies have focused on discrete channel choices in clearly described situations. Lastly, most research is quantitative and revolves around building and testing hypothetical variance models based on surveys. This leads to two shortcomings of existing CC research:

1. By studying channel choices in isolation, studies ignore the possibility that channel choices can have a sequential or multiplex (see below) nature. For example, calling an organization after failing to find information online. 2. By asking to choose a single channel in hypothetical situations, studies ignore that channel choices and uses could happen in parallel. For example, a citizen could call while looking online to verify the correctness of the information.

To move the CC field further forwards, scholars have repeatedly called for new empirical studies from actual use settings, which directly explore citizens' use of multiple channels, reasons for the continued use of traditional channels, and the interactions between different channel types [START_REF] Reddick | Channel choice and public service delivery in Canada: Comparing e-government to traditional service delivery[END_REF][START_REF] Kernaghan | Changing channels: Managing channel integration and migration in public organizations[END_REF][START_REF] Ganapati | The Use of ICT for Open Government in U.S. Municipalities: Perceptions of Chief Administrative Officers[END_REF][START_REF] Lindgren | Close encounters of the digital kind: A research agenda for the digitalization of public services[END_REF]. The objective of our research is to address these gaps in the extant CC literature. Herein, we present the results of a sequential mixed method study, employing observations, contextual interviews, and a survey of over 10,000 calls [START_REF] Madsen | Translating Telephone Calls To Spreadsheets : Generating Knowledge on Citizen Multichannel Behavior in Collaboration With Caseworkers[END_REF] to gain insights in CC in a real-life setting.

Aim of the study and research questions.

This purpose of this study is twofold. First, we aim to explore and measure the types of inquiries which are made by citizens who have adopted digital self-service channels, but still contact public authorities through traditional channels. Second, we seek to understand why these inquiries occur and explore the interplay between different channels, for example when channels are used sequentially or in parallel as one overall service interaction. In order words, we try to understand the multiplex nature of real-life channel choices. To achieve these goals, we present rich empirical examples of actual interaction between citizens and public authorities to put the identified topics into context. The research questions we seek to answer are:

1. To what extent are channel choices multiplex? (RQ1) 2. What causes this multiplexity, i.e. why do citizens that have adopted electronic service channels still choose traditional service channels? (RQ2)

The rest of this paper is organized as follows. The next section presents a brief outline of the literature on channel choice. Section three presents the research setting and method used. Section four discusses the findings. Section five presents concluding remarks, limitations and offers suggestions for future research.

2

Channel choice research

Channel choice

Channel choice (CC) can be seen as the first step in what is called a process of channel behavior [START_REF] Willem | Citizen Behavior in a Multi-Channel Environment[END_REF]. CC refers to "an individual's specific decision to use a medium in a particular communication incident" [15, p.163] This is followed by the actual interaction (channel use) and an evaluation of that use. This evaluation can create experiences that might trigger future use. CC studies in the e-government literature seek to explain and understand citizens' choice of channels for interacting with government organizations and identify the factors that affect this choice and measure their influence.

Studies have typically considered CC as a multi-nominal selection where users at a singular point in time choose between several channels. For example, Pieterson [START_REF] Pieterson | Citizens and Service Channels: Channel Choice and Channel Management Implications[END_REF] asked people to choose one channel in a number of hypothetical situations. Similarly, Reddick and Anthopoulos [START_REF] Reddick | Interactions with e-government, new digital media and traditional channel choices: citizen-initiated factors[END_REF] asked people to indicate their channel preferences for a number of hypothetical situations (e.g. solving a problem). However, research shows that rather than being a one-time decision, CC is actually a more complex process in which channels are used sequentially or in parallel (cf. [START_REF] Madsen | How to Migrate Citizens Online and Reduce Traffic on Traditional Channels Through Multichannel Management: A Case Study of Cross-Organizational Collaboration Surrounding a Mandatory Self-Service Application[END_REF][START_REF] Reddick | Channel choice and public service delivery in Canada: Comparing e-government to traditional service delivery[END_REF][START_REF] Madsen | The efficiency of freedom. Single parents' domestication of mandatory e-government[END_REF]). Regarding the latter, for example, Madsen and Kraemmergaard [START_REF] Madsen | The efficiency of freedom. Single parents' domestication of mandatory e-government[END_REF] found that digital and traditional channels often are used simultaneously, very often this entails people calling while looking at a website. Regarding the former, Pieterson [START_REF] Pieterson | Channel Choice. Citizens' Channel Behavior and Public Service Channel Strategy[END_REF] found that citizens typically use multiple channels in one service interaction and often have a first, preferred, channel and move to a second channel when their first choice fails to yield a result. These complex channel usage patterns require government organizations to consider cross-channel integration to ensure that the data and information citizens receive are consistent across all used channels. Scholars have called for further research on channel sequencing [START_REF] Madsen | Multichannel Management in the Public Sector -A Literature Review[END_REF][START_REF] Reddick | Channel choice and public service delivery in Canada: Comparing e-government to traditional service delivery[END_REF] and suggest direct observations and more in-depth case studies to understand issues relevant to government channel integration [START_REF] Kernaghan | Changing channels: Managing channel integration and migration in public organizations[END_REF].

Furthermore, research on citizens' CC has mainly focused on predefined factors such as channel, task, personal, and situational characteristics (cf. [START_REF] Pieterson | Citizens and Service Channels: Channel Choice and Channel Management Implications[END_REF][START_REF] Ebbers | Facts and feelings: The role of rational and irrational factors in citizens' channel choices[END_REF]). For example, Ebbers et al. [START_REF] Ebbers | Facts and feelings: The role of rational and irrational factors in citizens' channel choices[END_REF] test a model in which they relate task and channel factors, habits and personal characteristics to CC. While this yields valuable insights about the importance of these variables, it ignores other potential variables (see e.g. [START_REF] Madsen | Channel Choice: A Literature Review[END_REF][START_REF] Pieterson | Channel choice determinants; an exploration of the factors that determine the choice of a service channel in citizen initiated contacts[END_REF] for overviews of possible channel choice determinants). This point of criticism is closely related to the majority of CC studies being quantitative in nature and relying on standardized surveys. In addition, many studies have been conducted in controlled and artificial settings, which may limit their explanatory power for actual use settings, cf. e.g. [START_REF] Ebbers | Facts and feelings: The role of rational and irrational factors in citizens' channel choices[END_REF]. However, given the integrated nature of channel choices, as mentioned above, more exploratory insight into citizens' actual behaviors is needed [START_REF] Pieterson | Citizens and Service Channels: Channel Choice and Channel Management Implications[END_REF], [START_REF] Ebbers | Facts and feelings: The role of rational and irrational factors in citizens' channel choices[END_REF] as well as case studies and interviews with citizens on their actual channel choices [START_REF] Schmidthuber | Browse or Brush? An Exploration of Citizen-Government Interaction in the Municipal Realm[END_REF]. Thus, instead of enquiring citizens about their preferences, CC research should rather focus on the process from the initial CC to continued channel use [START_REF] Ebbers | Impact of the digital divide on e-government: Expanding from channel choice to channel usage[END_REF], such as through direct observations [START_REF] Reddick | Interactions with e-government, new digital media and traditional channel choices: citizen-initiated factors[END_REF].

To sum up, the existing research on CC has helped us discover and understand the types of variables that impact singular channel choices and how these variables differ for different tasks, people and situations. However, these insights are inadequate to help us understand real-life CC behaviors, let alone providing prescriptive insights for governments' multi-channel strategies. Therefore, new theories and models are needed to capture the complex, multiplex, nature of real-life channel behaviors.

Channel multiplexity

In communication theory, 'media multiplexity' was originally used to describe communication patterns of distributed scholars, with those with stronger ties using more means of communication [START_REF] Haythornthwaite | Social networks and Internet connectivity effects[END_REF]. More generally, the concept has been used to describe instances where communication relations are maintained by multiple media [START_REF] Ledbetter | Do online communication attitudes mitigate the association between Facebook use and relational interdependence? An extension of media multiplexity theory[END_REF] and where media multiplexity is associated with a higher communication quality. This idea is similar to the notion of 'intermediality' [START_REF] Jensen | Intermediality[END_REF] which refers to a) communication through several discourses at once, b) the combination of separate material vehicles of representation and c) the interrelations among the media as institutions in society. This concept was born out of the observations that, as communication becomes more networked, media themselves become more fluid and interwoven. These ideas of multiplexity and intermediality contrast more traditional notions of media that try to isolate media and focus on the attributes of individual media [START_REF] Stephens | Discrete, sequential, and follow-up use of information and communication technology by experienced ICT users[END_REF] and see media choices as singular or "immediate incidents" [START_REF] Saunders | Temporal Sequences in Information Acquisition for Decision Making: A Focus on Source and Medium[END_REF]. The reality, of course, is more complicated.

Research on technology use has shown that media are often used sequentially in what is considered one use case [START_REF] Stephens | Discrete, sequential, and follow-up use of information and communication technology by experienced ICT users[END_REF] and media are seen as compliments that can be used to satisfy certain communication needs [START_REF] Dutta-Bergman | Complementarity in Consumption of News Types Across Traditional and New Media[END_REF].

We expect similar phenomena to happen in citizen-initiated contacts, where rather than perceiving and choosing channels as being discrete entities, citizens use a combination of channels sequentially or in parallel in what could be considered one service delivery contact. For example, a citizen might go online to a find the phone number of an organization and then call (sequential use) or a citizen could call an organization to confirm the validity of information shown online (parallel use). Thus, channel multiplexity refers to the interrelated (sequential and/or parallel) choice of channels within one overall service interaction and multiplexity appears as a fitting theoretical concept to help us understand real-life channel choices.

Research setting

The study presented here was undertaken in 2017 in the Danish public authority Udbetaling Danmark (UDK) (Disbursement Denmark). UDK was established in 2012 by the Danish government to achieve economic savings in the administration of public benefits. The first author undertook the empirical studies in UDK's Parental leave division in the city of Vordingborg. The division is one of three, which administers parental leave. It consists of 50 caseworkers and two managers across two sections. UDK follows the Danish e-government strategy whereby digital communication and digital self-service are mandatory for citizens and businesses. UDK's digital and channel strategies also seek to promote automation and digital self-service. The web portal borger.dk is the primary means of self-service for citizens, while written communication is handled via digital post, a national e-mail system. Citizens who need help can call UDK. Counter turn-ups are also possible, but rare due to the physical location of UDK's centers. Thus, the interaction primarily occurs online, centered around borger.dk and digital post, while the telephone call are supplementary.

The parental leave scheme. While most citizens perceive parental leave as a single event, the leave itself consists of a variety of different schemes, such as pregnancy leave, maternity leave, paternity leave and parental leave. Each of these schemes have different underlying legal rules, duration, and payment amounts. For reasons of simplicity, we will refer to these using the joint term parental leave, although the majority of the leave is spent by mothers. When the individual leave schemes matter, we will refer to this specifically. Parental leave benefits are often the main source of income for the person on leave. This distinguished parental leave benefits from other types of public benefits, such as housing benefits, which supplement other sources of income. Therefore, parental leave may create more communication from citizens than other public services.

Method

Our study follows an inductive research strategy [START_REF] Blaikie | Approaches to social inquiry[END_REF], informed by CC studies and 'media multiplexity' . Previous CC studies show, that people choose traditional channels when problems occur, while 'media multiplexity' explains that interaction often occur across multiple channels. We seek to explore the nature of problems in digital interaction, and their relationship to citizens' multiplex interaction with UDK. We apply mixed methods, which is a research approach combining qualitative and quantitative techniques. According to Creswell: "(…) this form of inquiry provides a more complete understanding of a research problem than either approach alone" [START_REF] Creswell | Research Design. Qualitative, Quantitative, and Mixed Methods Approaches[END_REF]. Mixed methods studies are conducted according to a pragmatic worldview, encouraging researchers to use the most suitable techniques for gaining insight into practice oriented, real-world problems [START_REF] Creswell | Research Design. Qualitative, Quantitative, and Mixed Methods Approaches[END_REF]. We collected the data for our study through an exploratory sequential mixed method, as "a three-phase procedure with the first phase as exploratory, the second as instrument development, and the third as administering the instrument to a sample of a population." [START_REF] Creswell | Research Design. Qualitative, Quantitative, and Mixed Methods Approaches[END_REF]. Madsen presents a detailed account of the instrument development [START_REF] Madsen | Translating Telephone Calls To Spreadsheets : Generating Knowledge on Citizen Multichannel Behavior in Collaboration With Caseworkers[END_REF].

Phase 1. Qualitative data collection

In March 2017, the first author co-listened to 50 calls made to the parental leave section, followed by brief contextual interviews with the callers [START_REF] Madsen | Translating Telephone Calls To Spreadsheets : Generating Knowledge on Citizen Multichannel Behavior in Collaboration With Caseworkers[END_REF]. By observing actual telephone conversations, we gained insight into channel choices as they occurred in a realworld setting. Moreover, this approach increased the validity of the findings and reduced retrospective sense making, where people's descriptions of their actions (what they say) and motivation are biased, and differ from their actual actions (what they do) [START_REF] Blomberg | Ethnographic field methods and their relation to design[END_REF][START_REF] Eisenhardt | Theory building from cases: Opportunities and challenges[END_REF]. The co-listening occurred on a citizen line and an employer line. The callers were citizens, their representatives, employers, municipal caseworkers, and doctors. Upon answering the telephone, the caseworker asked callers for their permission for the first author to co-listen to the call and interview them. The caseworkers informed callers that the co-listening was voluntary, anonymous, and would not influence their cases in any way. One in ten callers declined to participate; non-participants were mostly doctors and other professionals who said they were too busy.

The first author documented observations and interviews, covering the event which initiated the call, the problems mentioned by the caller, the caseworkers' actions, previous communication with UDK and other authorities, next steps, and final comments by callers. The conversations and interviews were not recorded verbatim, although certain utterances were noted by the first author. Caseworkers and management served as highly knowledgeable informants, whose perspectives contributed to ensuring the validity of the first author's own interpretation of the qualitative data [START_REF] Eisenhardt | Theory building from cases: Opportunities and challenges[END_REF].

Phase 2. Instrument development

In October 2017, the first author collaborated with UDK caseworkers and management to measure the frequency of the discovered topics. We developed a classification scheme to increase caseworkers' classification rates and the validity of the results. The caseworkers suggested additional topics and examples for the scheme and used their own terminology to describe each topic. Exercises and discussions further helped to create a joint understanding of the problems and categories. The caseworkers also helped to solve technical and practical issues surrounding call classification. The final classification scheme covers where in the parental leave process the caller is, and the main topic of the call. Due to limited space, we focus on the topics here.

Phase 3. Administration of survey to register telephone calls

The caseworkers registered 11,600 inbound calls from citizens during October and November 2017. Some calls did not lead to actual casework (wrong number etc.), therefore 10,106, or 95 % of the completed calls were classified according to topic. The classification system requires the caseworkers to classify each call according to exactly one stage and one topic. However, a conversation may cover several topics and underlying problems. UDK administers maternity leave at three different locations, which receive calls on a rotation basis. According to caseworkers and management, there is no difference in calls received at the different locations. Thus, the collected data should be representative of all calls made to UDK on parental leave.

Findings

This section contains the results of our study. We begin answering our first RQ, on the multiplexity of citizens' CC, based on the observations. Then, we turn to our second RQ, why citizens supplement digital interaction with traditional channel use. First, however, it is important to keep two aspects of the study in mind. The observations presented here occurred in a call-center. Thus, we only get insight into the service interactions where citizens call. We do not get insight into the majority of cases where citizens complete an interaction without calling. Second, digital self-service is mandatory in Denmark. In 2017, UDK's parental leave section received 768,000 visits to its website at borger.dk and 340,000 incoming telephone calls on the citizen lines. Thus, website visits still outnumber telephone calls by more than 2:1.

Citizens' multiplex channel choice

Of the 50 calls observed in the call center, 32 were made by citizens, 17 by employers and one by a doctor. The majority of callers had previously contacted UDK for the same topic using one or two channels, pointing to the sequential nature of many interactions. Additionally, 14 of the 32 citizens who called had previously received digital post from UDK, either as letters concerning their parental leave or specifications of upcoming leave payments. Excluding contacts with their own company, 18 of the 50 callers had either called other organizations or visited their website for the same service interaction. We cannot make statistical generalizations from 50 observations. However, the majority of the interaction we have observed involves several channels, and roughly a third involve other organizations. These results are supported by the aggregated channel traffic to UDK. To explain these findings, we must study this interaction in more detail. Next, we present the results of the call classification, and then proceed to our observations of the calls and contextual interviews with callers.

Description and examples of call topics

Table 1 presents the distribution of 10,100 calls received by UDK's parental leave section in October and November 2017 as classified by the caseworkers. As shown in Table 1, three topics, planning, status and monthly payment, account for over 70 % of all inbound calls. While the topics are presented as discreet units above, some calls revolved around several topics. In these cases, we asked the caseworker to classify the call according to the topic, which the call initially revolved around.

As we observed these interactions and interviewed callers, we gained insight into the various reasons which caused citizens to call. In many cases, the citizen had several problems, which needed to be solved. Contrary to previous studies, we found that the calls did not just revolve around accessing or understanding information. The caller may also need the caseworker to do something for them. This problem may generate additional channel traffic because such a request cannot be conducted by digital selfservice applications. To demonstrate the complexity of such underlying problems and the multiplexity of the interaction, we present two observed calls.

Example 1. Planning the transition between leave partners (see Figure 1) A mother's maternity leave is ending, and the father is about to take over. She calls for an understanding of the rules for distributing the leave between parents and the options for extending her leave. She has received letters from UDK and visited borger.dk unsuccessfully. The caseworker informs her of the rules, how to extend her leave, and on the importance of not having any gaps between leave stages. Further, the caseworker says that extension will affect her employer, so the caller needs to inform her employer. The caller asks the caseworker to "spell it out to her" (explain in detail), and for the specific dates for the extended leave. Next, the caller inquires about the economic consequences of shifting from maternity to paternity leave on the household income. Finally, the caller says she wants to extend the leave, but needs to discuss this with the father and with her employer first.

Fig. 1 The sequence of interactions in example 1

Here, there are two underlying problems. The caller needs specific information instructions for how to extend her leave. Moreover, she needs to understand the consequences of these actions. Her need does not concern the rules in general, but rather how they apply to her specifically and what their economic consequences are for her family. We also see that the caseworker informs the caller of a problem, which the caller is unaware of -not having gaps in the leave. Finally, we see that the telephone call is at the middle of a series of interactions across several channels and with other actors besides UDK.

Example 2. Status of a case and discrepancy in information (see Figure 2)

A mother calls because there is a discrepancy between the number of days left of her leave according to a letter from UDK, and information on borger.dk. She has previously called her employer and UDK and been to borger.dk. She has trouble "navigating the parental leave rules". She is logged in to borger.dk while calling. She asks, which of the parents is entitled to the remaining leave. The caseworker informs her of several requirements which the father must meet to be entitled to leave. Next, the caller seeks to confirm that the size of the benefit depends on the employer and the father's collective agreement. She asks for this information in writing so she can forward it to her HR department. She has been unable to find it at borger.dk. The caseworker explains that she cannot reveal information concerning the father without his permission. Finally, they agree that the caseworker will send letters to both parents. Again, the caller both needs information and to understand what it means to her, and what the economic consequences are. However, we also discover another problem, the information presented online does not correspond to the information received in a letter. Therefore, the caller first needs to know which information is correct, and then asks for evidence in the shape of a written letter, which she can forward to her employer. This is an example of an interaction which concern several actors: UDK, two parents, and at least one employer. Thus, the service interaction observed here is a long process occurring sequentially and simultaneously across three different channels: the website, the telephone and letters. Finally, we see that the call is initiated by an outbound letter and ends -for now -with another outbound letter.

Citizens' supplementation of digital interaction with traditional channels

To answer why citizens who have already adopted digital channels still supplement an interaction through traditional channels, we returned to our observations and interviews. We analyzed these according to the underlying problems and needs which appeared during the callers' interaction with UDK or were mentioned in the contextual interviews. We identified five different types of problems and grouped these problems in two overall categories. The five identified problems are presented in Table 2. Information related problems concern people's access to, and understanding of, information from the public authorities regarding public benefits. Information related problems are well-known known in the CC literature. They cover:

• Need for information. The caller knows what information they are seeking, but not necessarily where to access it. They may call simply because this is quick and convenient. The problems belonging to this group are well-suited for digitization, but at the time of the study, many of the requested features did not exist at the parental leave section of borger.dk. • Need for explanation. The caller needs an explanation for the information, its application and consequences to their specific case, or to be certain their interpretation is correct. Offering explanations to people's specific cases is difficult online as it requires customization and decision-making, not merely presenting information.

Action related problems cover situations, where the caller needs the caseworker to perform an action for them. These are harder to digitalize because of their more complex nature and need for the caseworkers to exercise discretion. They cover:

• Need for documentation. The caller needs documentation about their case to be sent, either to themselves or a third party. Unlike the need for information, it is not necessarily the information itself which matters. Rather the documentation is needed as evidence to begin, or of a completed, administrative process. • Need for negotiation. The caller seeks to persuade the caseworker to adopt their perspective or set act outside established procedures. An example could be to make an unscheduled payment or look at the caller's specific case, thereby speeding up processing times. This type of need appears to be very difficult to digitize as negotiation and persuasion requires a human partner. • Need for revision. The caller needs UDK to make changes to their case or update specific information, either submitted by themselves or by a third party. Such actions can and have been digitized in a public context. It is, however, important to have control mechanisms in place.

Conclusions & Discussion

The goal of this paper was to explore the potentially multiplex nature of channel choices by citizens when interacting with governments. RQ1 focused on the extent to which channel choices are multiplex. While not being a quantitative, confirmatory study, our research does indicate a large proportion of channel interactions to be multiplex (i.e. the choice of multiple channels for what can be considered one interaction). This provides an important, possible, explanation for the observation that the overall number of channel interactions are increasing in many countries and citizens continue to use traditional channels after adopting digital service channels.

Our second research question (RQ2) focused on the causes of this multiplexity. Our analysis identified two main groups of causes. The first is information related problems, which result from people not being able to find the needed information and thus choosing a second channel or from citizens needing additional explanation or closure about the correctness and/or validity of the information. While these problems may stem from flaws in the design of a channel (e.g. the degree to which information is retrievable), they could also result from a lack of richness [START_REF] Trevino | Making Connections: Complementary Influences on Communication Media Choices, Attitudes, and Use[END_REF] of the channel to convey the required message. Surprisingly, there were few inquiries regarding access to and use of the digital channels. This indicates that citizens have the necessary digital skills, but lack knowledge of public services to use self-service application. The second group of causes are action related problems, which trigger some kind of transaction that might not be done in another original channel (e.g. the need for documentation, negotiation or revision of data).

Limitations and Future Studies

The study presented here is exploratory and provides the first insights into real-life channel choice behaviors and their often complex and multi-facetted nature. However, it has some limitations. While co-listening to calls and interviewing the callers, we did not explicitly ask for the problems that caused them to call UDK nor their channel choices. Rather, we deduced the problems from the cases the callers described. Furthermore, our study focused on welfare services in Denmark, which represents a mature research setting where social trust is high. Although this setting differs from other countries with non-mandatory e-government use and less social trust, we assume that our findings are valid for different cultural contexts as well. More research on this topic is needed especially in three areas. The first is further explorations about the sequential and/or parallel nature of channel interactions to fully understand all combinations and their causes. The second is confirmatory research testing the extent to which these multiplex channel choices happen in real life. The third is research exploring ways to reduce multiplexity in order to improve channel efficiencies, for example through the design of channels, improved communication or channel marketing. Further, while we introduce the concept of channel multiplexity, this is by no means a fully fledged theory and more theoretical work is needed to fully understand the concept and embed it in the existing theories and extant CC research.

Fig 2 .

 2 Fig 2. The sequence of interactions in example 2.

Table 1 .

 1 Call topics to UDK's parental leave section

	Topic	Share Description
	Planning	33 % Planning the overall leave, individual stages and transition between leave
		partners. Includes requests for guidance and specific information, requests
		for explanation or confirmation of rules applicability and financial impact.
	Status	25 % Concern the status of an ongoing case and case handling time, prompts for
		faster casework, and callers presenting new or correct information to UDK
		regarding their case.
	Monthly	13 % Concern the monthly payment of parental leave benefits. Questions concern
	payment	when payments are made, the payment amount, and explanations and calcu-
		lations behind payments.
	Illness	10 % The mother is ill during pregnancy or maternity leave. Often concern third
		party actors (doctors), and documentation regarding illness.
	Salary stop	6 % Salary stop concerns the specific process of switching from receiving a reg-
		ular paycheck to receiving parental leave benefits. These calls often involve
		the callers' employer and documentation they have provided.
	Refund	4 % Concern instructions for how to refund benefits if too much money has been
		received, and special forms for this.
	Technical	2 % Requests for technical support on the use of digital self-service applications
	support	for submitting and receiving information, including the use of digital post.

Table 2 .

 2 The types of problems which cause incoming calls

	Category	Information related problems	Action related problems
	Problem	Need for infor-	Need for explana-	Need for docu-	Need for nego-	Need for
	type	mation	tion	mentation	tiation	revision
	Examples -Confirmation	-Of rules and	-Receipts	-Concerning	-To regis-
		for submitted	consequences	-Case files, to	interpretation	tered or sub-
		information	-For UDK deci-	self or third	of rules	mitted infor-
		-Guidelines for	sions and received	party	-For faster	mation
		action	payments		casework	-To case and
		-Status of case			-For faster	benefits re-
		-Payments			payment	ceived