Contributions

money or new income, and thus contributes to the financial sustainability of the platform.

individual level tendencies to generalize at the macro level. I propose this is an important step towards facilitating a discussion about the societal level impact of open data. Naturally, theorizing at the macro level means we must sacrifice the richness and diversity of individual action, but instead we gain a high-level framework from which detailed individual or organizational level analysis can depart. As a third contribution, I have suggested two general and overarching value generating mechanisms, the information sharing mechanism and the market mechanism. Moving down one level of abstraction, I suggest two types of information sharing mechanisms: transparency mechanisms and civic engagement mechanisms as well as two types of market mechanisms: efficiency mechanisms and innovation mechanisms. Throughout the papers that form the backbone of this study my co-authors and I have introduced a number of in-depth use cases and case studies intended to illustrate these causal relationships, and to bring about a more nuanced understanding of how use of open data contributes to the generation of sustainable value through these two different mechanisms.

Moreover, the information sharing mechanism has thus far not been explicitly discussed or conceptualized as a value generating mechanism in the context of open data. Therein lies my biggest contribution. I have contributed to a vibrant academic discussion on how we can better understand and articulate the value generation process that happens through information sharing in network relationships with the aim of generating a midrange theory. Midrange theory starts with an empirical phenomenon and abstracts from it to create general statements that can be verified by empirical data (Gregor, 2006; Merton, 1949; 1968). While the proposed mechanisms are general, the context and boundary of the theory is clearly the phenomenon of open data. According to Gregor´s (2006) classification the proposed theory of open data value generation can be classified as level IV theory for explanation and prediction, including causal explanations (mechanisms), testable relationships and the ability to predict as PLS-structural equation modeling is well-suited for predictions. My hope is that in developing a midrange theory, I have made a theoretical contribution to the emerging field of open data.

The generation of sustainable value from open data does not happen in a vacuum, and there are many contextual factors at differing levels of analysis that play a significant role. These factors can help us explain why, or why not, the suggested value generating mechanisms are activated in different countries. As a fourth theoretical contribution, I have identified a preliminary conceptualization of four societal level enabling factors

that together form a societal level infrastructure I propose can stimulate value generation through open data.

The fifth contribution of this PhD research is the identification of the open data value paradox. The open data value paradox is both theoretically interesting and practically relevant. It is interesting to private sector businesses as it applies to a very central problem they are currently facing. This problem is a manifestation of consumer adaptation to free information access, delivered over mobile devices, the World Wide Web, and most recently though wearable technology. Moreover, it is relevant for governments, as they must make decisions on how to spend the limited funds they possess. In many Western societies, governments are using evaluation methods based on the new public management paradigm, which is rooted in a logic based or market like practice (Henriksen, 2006). This paradigm makes it difficult for many ODIs to survive unless new ways to evaluate E-Government initiatives are suggested. The emerging New Public Value paradigm (the concept of Public Value was discussed briefly in section 2.4) might be more sympathetic towards initiatives such as ODIs but still faces considerable challenges of governance and evaluation (Stoker, 2006). I posit that it is imperative that businesses and governments find a way to understand, articulate, document and evaluate the inherent worth of open data.

Just as I have used the insights from practitioners to help create relevant theory, I have used the theoretical lenses that I have counted as contributions of this PhD project for a number of practical contributions.

Firstly, by continuously engaging the BDP participants and exposing them to different conceptualizations and models, as well as discussing the practical relevance of the findings (which has admittedly revealed that some of them are more readily utilized than others), I have managed to infuse and test some of the theory on a real-world ODI.

This academic-practice exchange of knowledge has been fruitful to both partners, as suggested by the Engaged Scholarship paradigm (Van de Ven, 2007). Moreover, it has helped all involved gain a holistic view of the phenomenon of open data. The Basic Data case study (Paper VI) concentrates on the supplier side of open government data.

This case study has offered a unique insight into the real technical and governance related struggles of an ODI over almost four years. It also offers an insight into how open data can contribute to public sector efficiency and how the impacts of an open data program can be evaluated. I have attempted to transform the experiences of the BDP group into a number of practical guidelines that can be used by other similar initiatives and summarized the findings in a process model that shows the open data value lifecycle from the supplier point of view.

Secondly, the Opower case study (Paper IV) is focused on the demand side of open data. Unlike the other papers in this PhD research, it is positioned at the organizational level. The Opower case gave some interesting insights into the dimensions of data that are of interest to private sector users, such as data usability, discoverability and accessibility. Moreover, the study highlights that open data are for the most part combined with proprietary data when utilized by private business users. Open data are thus an important resource for multiple stakeholders, but represent in many cases only a fraction of the overall resources used. Therefore, I consider the proposed similarities between open data and infrastructural resources as being both powerful and accurate.

Drawing on this approach, open data compare in certain aspects to public roads; they provide the path to destination, yet are seldom viewed as the largest contributor to the journey. Accepting this similarity might subtly affect the private sector´s view on the importance of open data.

Thirdly, the conceptualization of liquid open data and sustainable value combined with the four value generating mechanisms can be of practical relevance. As previously mentioned, I am designing a methodology for evaluating individual datasets, and ODIs in general, using these constructs. Moreover, I am continuing to develop the evaluation methodology based on real option value. These efforts will be part of a future research agenda, paying specific attention to the design related implications of my findings.