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Policymakers and analysts are heavily promoting data marketplaces to foster data trading between companies. Existing business model literature covers individually owned, multilateral data marketplaces. However, these particular types of data marketplaces hardly reach commercial exploitation. This paper develops business model archetypes for the full array of data marketplace types, ranging from private to independent ownership and from a hierarchical to a market orientation. Through exploratory interviews and case analyses, we create a business model taxonomy. Patterns in our taxonomy reveal four business model archetypes. We find that privately-owned data marketplaces with hierarchical orientation apply the aggregating data marketplace archetype. Consortium-owned data marketplaces apply the archetypes of aggregating data marketplace with additional brokering service and consulting data marketplace. Independently owned data marketplaces with market orientation apply the facilitating data marketplace archetype. Our results provide a basis for configurational theory that explains the performance of data marketplace business models. Our results also provide a basis for specifying boundary conditions for theory on data marketplace business models, as, for instance, the importance of network effects differs strongly between the archetypes.

Final strategy and charter for the big data community. Deliverable D7.1.2 BYTE Project.

Translation of the abstract into Arabic. (PDF 244Â kb)

Interim strategy and charter for the big data community. Deliverable D7.1.1 BYTE Project.

The BYTE Vision. Deliverable D5.1 BYTE Project.

Proximity in various forms drives collaborations and innovation. The relationship betweenproximity and innovation has been studied extensively. Most of the studies have focused ondimensions of proximity exogenous to the individual, such as one’s personal characteristics orworking style. However, as we will show in this paper, personal proximity – close similarityin terms of personal traits, behavioural patterns, and the degree to which individuals enjoyeach other’s company – affects research collaborations. In particular, whether and to whatextent partners “click” can make or break a collaboration. We conduct a multiple-case studyof research collaborations of Dutch nanotechnology scientists. Our qualitative analysis revealsthe role of personal proximity, relative to other dimensions of proximity, in the formation ofresearch collaborations and in shaping their output.

Report on legal, economic, social, ethical and political issues. Deliverable D2.1 BYTE Project.

This paper introduces a categorization of simulation models. It provides an explicit overview of the steps that lead to a simulation model. We highlight the advantages and disadvantages of various simulation approaches by examining how they advocate different ways of constructing simulation models. To this end, it discusses a number of relevant methodological issues, such as how realistic simulation models are obtained and which kinds of inference can be used in a simulation approach. Finally, the paper presents a practical guide on how simulation should and can be conducted.

The Lisbon strategy for growth and jobs seeks to use knowledge and innovation in the context of the European Research Area (ERA). To build the ERA the European Union (EU) implements - amongst others - regional innovation policy. Ample scientific publications have investigated how innovation drives regional dynamics. Therefore, we assess the goals of European regional innovation policy in the light of the scientific findings, which we collected and condensed by bibliometrical analysis. The general goals of the Lisbon strategy to at the same time stimulate growth and achieve cohesion of economic activities across the EU is not in line with the finding that positive cumulative and self-reinforcing processes go hand in hand with the agglomeration of economic activities. However, the goals of the specific innovation policies for the regional level are mainly in line with the scientific findings.

This paper discusses how the results of simulation models can be made more reliable and the method of simulating therefore more widely applicable. We suggested to calibrate simulation models empirically and developed a methodology based on Critical Realism in order to so. We suggested combining the procedures of two strands of literature: the empirical underpinning of the assumptions (like in microsimulations) and the empirical check of the implications (like in Bayesian inference). Both these strands of literature are mainly concerned with predicting future developments. We, instead, aim to infer statements about causal relations and characteristics of a set of systems or dynamics, such as, e.g., the development of an industry, that have a general validity for this set of systems or dynamics. In other words, instead of deriving probabilistic predictions of the future and statements of the current situation and dynamics of one single system we developed a methodology to gain general statements about the features of systems and dynamics.

The research area of the economics of innovation and technological change (EITC) has flourished over the past decades. While it was a relatively marginal field of economics in the 1950s and 1960s, the field has now grown to become a major part of economic analysis. Because many of the early scholars in the field were rather critical about the standard tools of (neoclassical) economics, EITC has become a research area in which a curious mix of mainstream methodology and alternative approaches co-exists and co-evolves. The paper reports on a survey that was conducted among scholars in the field. We construct networks of scholars, based on weak or strong linkages. Strong linkages are defined as relations between co-workers, weak linkages as relations between people that meet in the circuit of conferences, workshops, etc., or just read each other’s work. We also explore how network linkages are related to opinions on the field, e.g., which are the important journals or important centers of activity.

Studies in higher education 44(7), 1268-1289 (2019). doi:10.1080/03075079.2018.1434617, Published by Carfax Publ. Co., Oxford [u.a.]