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The digital transformation changes the way how organizations exchange data in supply chains. Data traditionally shared, is enriched by detailed data sets captured by sensors in the production itself. In addition to the promised benefits, also new risks arise. Advanced data analytic approaches make it possible to extract knowledge from such data sets and thus increase the risk that competitive knowledge unintentionally spills over. From a knowledge management perspective, little attention is paid to such knowledge risks arising from data-centric collaborations. Hence, this paper has the goal to investigate knowledge risks in data-centric collaborations by conducting a structured literature review. Thereby, we focus on digital supply chains, as data-centric collaborations play a central role within them. Based on our review, we identify four characteristics of digital supply chains relevant for managing knowledge risks. Based on these characteristics, we present causes, risks and countermeasures from an organizational, technical and legal perspective.

When organizations create new knowledge and work practices as a reaction to challenges they face, they often have difficulty to adopt these new practices "on the ground". One of the reasons is that in these cases, individual informal learning and collective knowledge creation are often insufficiently connected. In this paper, we investigate knowledge practices that explain how new knowledge generated in the process of creating and adapting new practices is applied in work situations. We conducted 30 semi-structured interviews in five networks of organizations focusing on knowledge sharing in the German construction industry. Through a qualitative content analysis, we first identified five patterns of situations where individual and collective knowledge interact to implement new work practices. We detail these patterns with four knowledge maturation practices that explain how individuals contribute to collective knowledge development, and three scaffolding practices that explain how individual learning processes are facilitated through help seeking and guiding. Four practices of knowledge appropriation explain how knowledge is adapted and validated in concrete work situations. We combine scaffolding, maturation and appropriation practices into a model of knowledge appropriation that extends workplace learning research by offering a distinctive perspective on the practices that shape the interaction between knowledge creation and individual learning.

Social software has changed research and practice in the knowledge management field. Several current trends and research issues offer a new understanding of how social software has changed this research field, as well as how scholars in business and information systems engineering (BISE) should take up this emerging research field. The article offers a review of such trends and a framework for addressing the remaining issues.

The financial sector is characterized as knowledge intensive with knowledge as the key source of competitive advantage. The introduction of social media within the organizational environment has raised the number of knowledge risks that can lead to knowledge leakage and thus to a loss of competitive edge. The authors investigated knowledge risks arising from the use of social media within the financial sector. They interviewed twelve employees from ten different European financial institutions to identify strategies how financial institutions currently deal with knowledge risks. The authors identified three major knowledge risks induced by social media and it appears that financial institutions are skeptical towards social media adoption. However, competition forces financial institutions to adopt social media and to change their attitude. As a consequence, financial institutions need to find different strategies for the management of knowledge risks. The authors identified such strategies and they show which strategies link to the major knowledge risks.

Personalised e-Learning represents a major step-change from the one-size-fits-all approach of traditional learning platforms to a more customised and interactive provision of learning materials. Adaptive learning can support the learning process by tailoring learning materials to individual needs. However, this requires the initial preparation of content upfront, which is a laborious task -and organizations have to target their limited resources effectively. In order to guide the process of creating adaptive learning materials, the criteria for adaptation -or adaptation needs -have to be known. The aim of this paper is to identify these adaptation criteria, applying a mixed method procedure. First, thirty adaptive systems selected from the literature are investigated using a qualitative content analysis. Then, the resulting set of adaptation criteria is validated by experts in the form of a series of two online questionnaires. As a result, a set of 13 adaptation criteria representing different adaptation needs emerge. IntroductionPersonalised learning content has been shown to increase learner interest, comprehension and hence their learning success (Triantafillou, Pomportsis, Demetriadis, & Georgiadou, 2004). The personalisation of learning material in the form of a content adaptation tailored to the needs of the learner is frequently proposed as one of the ways by which the acceptance and efficiency of e-learning can be increased (Brusilovsky, 2003;Chen, Lee, & Chen, 2005;Cristea, 2004;Gkatzidou & Pearson, 2009). Furthermore, the rise of mobile learning increased both the potential and the demand for the adapted delivery of learning materials (Chen, Chang, & Yen, 2012). Research has been conducted on the technical realisation of adaptive e-learning, and led to the development of a number of research prototypes (e.g. Conejo, Guzmán, Millán, Trella, Pérez-De-La-Cruz, & Ríos, 2004;Kayama & Okamoto, 2001;Maier, Armstrong, Hall, & Ng, 2005). However, one major challenge remains, which is the creation of suitably prepared learning materials (Akbulut & Cardak, 2012;Cristea & Stewart, 2006b;Foss, Cristea, & Hendrix, 2010).The adaptive provision of learning materials involves the identification of content that is relevant to the learner (Bunt, Carenini, & Conati, 2007). To this effect, user preferences and context must be known and represented in a way that is appropriate to adaptive systems. Numerous approaches that attempt to categorise people according to differences in learning and cognitive styles are known (see Coffield, Moseley, Hall, & Ecclestone, 2004). Also many attempts focussing on dimensions representing the learner context (see Zimmermann, Specht, & Lorenz, 2005) can be found in the literature. Together, these categorisation approaches structure and facilitate the authoring of (peronalised) educational resources. In this way, authors first identify an adaptation need for a concrete type of learner, acting in a specific context (see Figure 1) and then the learning materials suited to this particular adapta...