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The ability to effectively conceptualize services plays a key role in service design. Yet, action-relevant insights for service designers on how this crucial phase of service design could be systematically pursued remain surprisingly limited. This paper explores "modular reuse", a frequently used principle to efficiently implement and modify service processes in downstream, i.e. implementation, activities as a possible underlying mechanism to inspire and facilitate the conceptualization phase. In a Design Science Research approach, we develop and formatively evaluate three design principles, which enable the purposeful reuse of existing service elements for the conceptualization of new services. This research contributes by 1) tapping into novel ways to improve the conceptualization phase of service design, proposing an underlying mechanism to increase its effectiveness; 2) providing initial design principles that serve as prescriptive guidelines in the creation of new methods and tools to leverage the idea of modular reuse for the conceptualization of new services.

Modern vehicles typically are equipped with assistance systems to support drivers in staying vigilant. To assess the driver state, such systems usually split characteristic vehicle signals into smaller segments which are subsequently fed into algorithms to identify irregularities in driver behavior. In this paper, we compare four different approaches for vehicle signal segmentation to predict driver impairment on a dataset from a drunk driving study (n=31). First, we evaluate two static approaches which segment vehicle signals based on fixed time and distance lengths. Intuitively, such approaches are straightforward to implement and provide segments with a specific frequency. Next, we analyze two dynamic approaches that segment vehicle signals based on pre-defined thresholds and well-defined maneuvers. Although more sophisticated to define, the more specific characteristics of driving situations can potentially improve a driver state prediction model. Finally, we train machine learning models for drunk driving detection on vehicle signals segmented by these four approaches. The maneuver-based approach detects impaired driving with a balanced accuracy of 68.73%, thereby outperforming timebased (67.20%), distance-based (65.66%), and threshold-based (61.53%) approaches in comparable settings. Therefore, our findings indicate that incorporating the driving context benefits the prediction of driver states. I. INTRODUCTIONIn recent years, with the advent of driver assistance systems, research in reliably predicting driver states has increased significantly. The aim is not only to enhance the comfort of drivers [1], but especially to improve road safety [2] by preventing accidents related to driver impairment. Advances in in-vehicle computing capacities and sensor technologies further support these endeavors. Besides camera-based technologies [3], the focus is on privacypreserving and non-intrusive approaches [4], [5]. As outlined in recent work, a large number of in-vehicle signals can be accessed via the controller area network (CAN-bus), which allows insights into the driving behavior [6], [7]. The research field around gathering driver insights from in-vehicle signals is extensive. It ranges from driver identification [4], [5], [8], over driving style recognition [9]-[11] to

In the last few years there has been increased political awareness of the urgency to ensure the continuing and long-term involvement of the mining industry in Greenland's economy. Formalisation of this came in 1990 when an ad hoc working group was convened by the Danish Ministry of Energy and the Chairman of the Greenland Home Rule Authority with the purpose of working out a draft for a new strategy for exploration and utilisation of mineral resources in Greenland. The report containing specific recommendations was released the same year, the text being made available in Danish, English and Greenlandic (MRA, 1990).