• Ingen resultater fundet

5. Towards a Theory of Value Generation through Open Data

5.2 A Framework of Four Mechanisms

that these tensions might actually be resolved by maintaining a focus on all the seven dimensions of liquid open data, which was possible in this case due to a limited number of datasets included in the program. The infrastructural features of open data have resulted in some unexpected synergies across value generating mechanisms, reflecting the serendipitous value generation opportunities offered by open data.

Infrastructural resources are considered as shared means to many ends, which satisfy the following three criteria: 1) they are non-rivalrous, 2) social demand is driven primarily by downstream productive activities, 3) the resource can be used as an input for a wide range of purposes (general purpose criteria) (Frischmann, 2012).

Tension 4: Short term gains vs. long-term investment

While it is natural for any ODI to focus on low-hanging fruits, it is important not to do so at the expense of future, currently unknown applications of the data. Thus, it remains a challenge to find a balance between being openly publishing a large number of datasets and ensuring that continuing publication of these data is actually sustainable. The two case studies conducted as a part of this study revealed that users hesitate to engage with open data unless they are convinced of the sustainability of the data resource. The chosen strategy of the BDP was to finance their ODI upfront, by effectively transferring funds from organizations that are expected to benefit from the liquid open data to the data custodians, which are responsible for remodeling data and building the technical infrastructure. This ensured enough funding to reorganize data-collection and data modeling efforts across central and local government, which is a long-term investment. Simultaneously, individual data stewards had the means to publish their open data through new data services, making the data available for interested users, although not yet in a fully coherent manner.

These governance mechanisms are important for understanding how data can become open and liquid. Therefore, they fall beyond the scope of the conceptual model I present as the overall biggest contribution of this dissertation. The conceptual model focuses solely on the relationship between data that are already liquid and open to some degree or another, the mediating variable that are intended to reflect manifestations of underlying generative mechanisms, and the resulting impacts, conceptualized as sustainable value.

(added income or lesser costs), or through other more subjective indicators (a more meaningful life for instance). In order to gain this understanding, we must move beyond positing a simple statement of desired outcomes, which in turn, would imply knowledge of the underlying mechanisms. Alternatively, we should endeavor to open the black box of mechanisms that would explain how value is generated. Detecting and consequently recognizing these mechanisms would greatly improve our ability to understand the complex interactions that happen when value is generated through the use of open data.

Paper I discussed open data strategies and identified four leading categories of value drivers, which explicate the demands for governments to open up their data. Firstly, open data are considered vital for creating transparency and accountability (Meijer et al., 2014). A second driver is participatory governance: Open data are used as a tool to enable citizens to participate in decision-making processes in an informed and structured manner (Bartenberger and Grubmüller, 2014). Thirdly, many analysts view open data as a catalyst for creating new data-driven applications and services, in addition to new business models (Lindman et al., 2014; Zuiderwijk et al, 2014a).

Finally, open data correspondingly offer an important internal value for the public sector itself. Public sector organizations may obtain access to data held by other public authorities, or else they may design potentially improved and novel techniques to use their own data. This enables them to significantly improve the efficiency of public services, and enhance their internal understanding of their fundamental tasks and objectives (Halonen, 2012).

After writing Paper I, I proceeded to discover if these value drivers could be conceptualized as mechanisms. Using the method of retroduction, I initially examined previously identified mechanisms that would assist in explaining value generation (Wynn and Williams, 2012).

The one mechanism that economists relate most of their analysis to – their master mechanism, so to speak – is the market. (Hedström and Swedberg, 1998, p. 3).

A natural first choice was to consider market mechanisms. Market-type mechanisms are a broad concept. The Organization for Economic Co-operation and Development (OECD) has adopted a very comprehensive definition of market mechanisms as

“encompassing all arrangements where at least one significant characteristic of markets is present” (OECD, 2005, 131). The types of mechanisms that are classified as market mechanisms utilize the forces of demand and supply to determine prices and quantities of goods and services offered for sale in a free market. We may infer that the

transaction generates value for both parties, as they are at liberty to enter the transaction and, would do so only if they perceive value from the exchange. The value generated for the buyer is to fulfill his or her specific needs, while for the seller, the value generated is the monetary value received to actualize the transaction.

Governments in western societies are increasingly making use of the market mechanisms or market-like practices, the chief motive being the necessity for governments to attain increased value for money expended in their operations (Blöndal, 2005; Henriksen, 2006).

Mechanisms form a hierarchy (Hedström &Ylikoski, 2010, p. 52).

While a mechanism at one level presupposes or takes for granted the existence of certain entities with characteristic properties and activities, it is acknowledged that there are lower-level mechanisms, which are capable of explaining them comprehensively (Hedström & Ylikoski, 2010). I propose that markets do indeed play a major role in facilitating value generation through use of open data. This occurs primarily through two classes of lower level (micro to macro) mechanisms: efficiency mechanisms and innovation mechanisms, as identified both in open data literature and in the qualitative data collected for this study.

The significance of the efficient use of public resources for economic growth and stability, along with general welfare and security, has been brought to the forefront by numerous developments over the past decades (Afonso et al., 2010). Public sector organizations are capable of achieving efficiency by cutting processing costs, making strategic connections between and among government agencies, and creating empowerment (European Commission, 2006). The aim of efficiency is to improve resource allocation, in order to minimize waste and maximize the outcome value, given a fixed pool of resources. The markets are important for creating efficiency because their primary goal is to facilitate efficient use of resources. While public sector efficiency is widely cited as one of the potential benefits of open data, the literature review conducted in this study confirms a lack of empirical and theoretical research that extensively examines the core relationships between open data and public sector efficiency. Nevertheless, Paper VI suggests that openly sharing data reduces administration and transaction costs through lesser demands for manual data entry, reduction in IT infrastructure investment needs, and by providing faster and easier access to information. Moreover, liquid open data enable automation of cross-organizational processes and increased interoperability between organizations. All these factors work towards improvement in public sector efficiency, in the sense of getting more value for money.

In the particular case of the Danish BDP, I could soon conclude that the Ministry of Finance was mostly motivated by the potential of using open data to improve efficiency and reduce costs through less use of resources (refer to Paper VI for details).

Moreover, it was apparent that different public sector organizations subscribed to different value drivers. Alternatively, the Ministry of Business and Growth emphasized the potential impact from private stakeholders who would use the public data to design and market new products and services, spawning new companies and contributing to job creation. Drawing from Schumpeter’s economic theory, innovation is the source of value creation, resulting in novel combinations of resources, new production methods, as well as new products and services, which, in turn, lead to the transformation of markets and industries and thus contribute to increasing value. Numerous studies have confirmed the relationship between macro-level business innovations and economic value (commonly conceptualized as economic growth). The social impacts of innovations have, however, been much less discussed and analyzed, with the possible exception of Simon Kuznets (1974), who divided the economic and non-economic consequences of technological innovations.

The economic properties of open data suggest that data is an infrastructural resource, which in theory, is used by an unlimited number of users and for an unlimited number of purposes as an input to produce goods and services (OECD, 2014). Recent technological developments have provided firms with the ability to collect, manage and use different types of data in multiple ways to innovate, and subsequently create value (Koski, 2013). Overall, empirical studies suggest a positive impact from innovative use of data of approximately 5% to 10% on productivity growth, depending on a number of enabling and complementary factors (OECD, 2014). New digital products driven by the increasing use for and use of data include new technologies which help to manipulate large volumes of unstructured data (for example, Hadoop), as well as multitude of new data analytics and visualization tools (for example, Qlik and Tableau). Moreover, driven by increasing availability of data, we are now able to witness the rise of platforms that use government data to provide services, which among other things help people locate the fastest route, save energy or buy their dream house (refer to narratives and further analysis in Paper IV, Paper V and Paper VII).

I have categorized the two drivers of efficiency and innovation as lower level market mechanisms due to their strong economic focus and use of the markets to allocate resources. However, this was evidently not the case for the two other value drivers, transparency and civic engagement.

Sunlight is said to be the best of disinfectants; electric light the most efficient policeman. (Brandeis, 1914).

Transparency does not depend on commercial markets, although it can be beneficial to markets as transparency is expected to reduce information asymmetry. Most definitions of transparency recognize the extent to which an entity reveals relevant information about its own decision processes, procedures, functioning and performance.

Unfortunately, it is common to see the concepts of open data, open government and transparency used to convey similar ideas in the open data literature. Opening access to chosen public documents does not necessarily contribute to a transparent government (Gurstein, 2011; Relly et al., 2009; Yu & Robinson, 2012). A government can provide open data on politically neutral topics, even as it remains deeply opaque and unaccountable (Yu & Robinson, 2012). In this PhD study I tend to view transparency as a tool against information asymmetry, moral hazard and corruption, as it creates a more equal access to important information and helps shed light on otherwise hidden activities. The transparency mechanism only generates value if it reduces information asymmetry through information creation and sharing over networks. While the power of market mechanisms can be measured via currency, we must measure the power of transparency through the “informativeness” that is created, not by counting numbers of published datasets.

Open data is also widely believed to contribute to increased public participation and collaboration. Public participation allows citizens to contribute their ideas and expertise allowing governments to create policies that reflect constituent driven information from all reaches of society. By this definition, participation provides a wider breadth of citizens with a voice in government (Linders & Wilson, 2011).

Information is considered vital to democratic participation (Jaeger, 2007). Information matters “in the processes by which citizen preferences are formed and aggregated, in the behaviors of citizens and elites, in formal procedures of representation, in acts of governmental decision making, in the administration of laws and regulations, and in the mechanisms of accountability that freshen democracy and sustain its legitimacy.”

(Bimber, 2003, p. 11). Currently, citizen participation in public administration decision-making is entering a new phase, as many government agencies have utilized information-based applications to communicate with constituents in order to provide online services, often termed e-participation (Kim & Lee, 2012). The idea behind using these applications is to lower the barriers for those willing-but-unable, and to make participation more attractive to those able-but-unwilling (Axelsson et al., 2010).

However, the exact benefits of the so-called e-participation are still not fully understood (Andersen et al., 2007).

In this study, I prefer to utilize the terminology civic engagement to move beyond the traditional meaning of public participation in government decision making and address both the participatory and collaborative impacts of open data. The civic engagement mechanism is defined as a collective source of positive change that occurs when multiple stakeholders start sharing information across boundaries.

Civic engagement means working to make a difference in the civic life of our communities and developing the combination of knowledge, skills, values and motivation to make that difference. It means promoting the quality of life in a community, through both political and non-political processes. (Ehrlich, 2000, vi).

Neither the mechanism that explains how value is generated from open data via transparency, nor the mechanism that explains how value is generated via civic engagement, make explicit use of the markets. In fact the shift towards an economy centered on information, and the move to a networked Internet-based environment have caused significant attenuation of the limitations that market-based production places on the pursuit of value (Benkler, 2006). In general, I propose that the previously dominant role of the market mechanisms is slowly giving way to new types of value generation mechanisms, which have not been previously identified. Private sector companies are slowly shifting towards a use of mechanisms that generate value through network effects. These effects are most commonly realized when dissemination of free information attracts relevant stakeholders to digital platforms where market mechanisms can subsequently be enacted (see analysis in Paper VIII).

This new type or classification of mechanisms is termed the information sharing mechanisms.

The information sharing mechanisms create mutual gain for a network of parties through the simultaneous creation, dissemination and appropriation of information by many stakeholders. Unlike the bilateral market mechanisms, these types of mechanisms are many-to-many, indicating a value network rather than a value chain.

In such a network, the size of the networks and the interactions themselves are primary sources of value (Bowman, 2014; Viscusi et al. 2014). When an increasing amount of information is available free of charge online, and networks are used to share this free content, we have no currency-like ‘token’ that indicates how the users of the network value this information. Accordingly, it is very difficult to quantify the resulting value.

However we can assume that the same principle applies to the information sharing

mechanisms as the market mechanisms; the information content will only be created and disseminated, and the ‘transaction’ initiated, if the information creator perceives that doing so generates some kind of value. Correspondingly, the content will only be used if the user perceives a similar value.

Figure 9 illustrates a framework of four archetypes of value generating mechanisms.

Figure 9: A Framework of Four Value Generating Mechanisms

The framework in figure 9 highlights two principal types of mechanisms that facilitate how value is generated through open data: the information sharing mechanism type and the market mechanism type. Additionally, the figure emphasizes that for each of these mechanisms, value generation can happen either through exploitation of current resources or through exploration, where the focus is on driving change.