Businesses increasingly attempt to learn more about their customers, suppliers, and operations by using millions of networked sensors integrated, for example, in mobile phones, cashier systems, automobiles, or weather stations. This development raises the question of how companies manage to cope with these ever-increasing amounts of data, referred to as Big Data. Consequently, the aim of this paper is to identify different Big Data strategies a company may implement and provide a set of organizational contingency factors that influence strategy choice. In order to do so, we reviewed existing literature in the fields of Big Data analytics, data warehousing, and business intelligence and synthesized our findings into a contingency matrix that may support practitioners in choosing a suitable Big Data approach. We find that while every strategy can be beneficial under certain corporate circumstances, the hybrid approacha combination of traditional relational database structures and MapReduce techniquesis the strategy most often valuable for companies pursuing Big Data analytics.

A large amount of the pollution of modern cities is caused by individual transportation. Hence, many road users suffer from stress, emissions and noise. Smart mobility services can help improving the situation by distributing traffic more consistently across different routes, times, and transportation modes. These services comprise two dimensions, a technical and a socio-technical. The latter addresses the road user's role as data and knowledge provider and stresses the road user's role in actively contributing to relieved traffic. As such, road users display one of the strongest levers to sustainably relieve traffic both in terms of knowledge providers and traffic actors. Using a systematic analysis of 28 publications, we show that existing SMob services show several challenges related to the involvement of road users. We call for more research on SMob services that account for long-term user involvement e.g. by positively influences road users' practices and routines.

Given the continued economic pressure on information technology (IT) organizations, the effective and efficient delivery of IT remains a crucial issue for IT executives in order to optimize their department's performance. Due to company specifics, however, an absolute assessment of IT organizations' performance has often proven difficult in the past. Consequently, many IT executives revert to comparative assessments such as IT benchmarking. IT benchmarking has been increasingly used to support IT management, also on a strategic level. While past research on such strategic IT benchmarking (SITBM) often focused on process models and optimal peer group composition, many practitioners repeatedly report problems with identifying or developing suitable methods for collecting the data needed for SITBM. We introduce a design theory for SITBM methods and illustrate how we derived the theory from an SITBM project over a period of ten years. During that time, the method from which we abstract our design theory was applied in 102 different companies. We contribute to practice and theory by not only reporting a field-tested method for SITBM, but also by providing a design theoretical basis on how to develop such a method as a blueprint for IT managers that want to set up an SITBM.

Information technology (IT) organizations use strategic IT benchmarking (SITBM) to revise IT strategies or perform internal marketing. Despite benchmarking's long tradition, many SITBM initiatives do not reveal the desired outcomes. The vast body of knowledge on benchmarking and IT management does not help overcome the challenges of successful SITBM. We therefore synthesize the existing research on IT management and benchmarking into a concept relationship map and derive future research avenues. We find that there is much literature on how to obtain SITBM results. However, research does not explain how these results are transformed into outcomes – essentially the problem many practitioners struggle with

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