Today, customers can conveniently compare products and decide how to interact with companies. With customer centricity becoming an important success factor, companies must drive customer satisfaction not only through excellent products but also through customer-centric processes. As many companies face an abundance of action possibilities, fast-changing customer needs, and scarce resources, guidance regarding the customer-centric prioritization of process improvement projects is in high need. As existing approaches predominantly focus on process efficiency, we propose a decision model that accounts for the effects of process improvement on customer centricity in line with justificatory knowledge on value-based process decision-making, project portfolio selection, and the measurement of customer satisfaction. When building the decision model, we adopted the design science paradigm and used multi-criteria decision analysis as well as normative analytical modeling as research methods. We evaluated the model by discussing it with practitioners, by building a software prototype, and by applying it at a German insurance company. Overall, our research extends the prescriptive knowledge on process prioritization and customer process management.

Recognizing opportunities enabled by digital technology (DT) has become a competitive necessity in today’s digital world. However, opportunity recognition is a major challenge given the influence of DT, which not only disperses agency across various actors, but also blurs boundaries between customers, companies, products, and industries. As a result, traditional entrepreneurship knowledge needs to be rethought and the effects of DT on opportunity recognition need to be better understood. Drawing from opportunity recognition theory – as one of the central theories in the entrepreneurship domain – this study builds on a structured literature review to identify and explain three direct as well as three transitive effects of DT on opportunity recognition. These effects have been validated with real-world cases as well as interviews with academics and practitioners. In sum, this study contributes to descriptive and explanatory knowledge on the evolution from traditional to digital entrepreneurship. As a theory for explaining, the findings extend opportunity recognition theory by illuminating how and why DT influences opportunity recognition. This supports research and practice in investigating and managing opportunities more effectively.

To understand the complex interactions between landslide risk, public and private risk awareness, including land use practices and repair and mitigation measures in a complete manner, case histories were developed and analyzed using the example of the highway network of the Lower Saxon Uplands, NW Germany. The case histories utilize datasets extracted from the German landslide database that includes information of historical and current landslide impacts, elements at risk as well as land use practices and provide an overview of spatio-temporal changes in the exposure and vulnerability to landslide hazards over the past 250 years. For the developed case histories the recorded landslide events were categorized and classified at representative sites, according to landslide types, processes, and damages as well as applied repair and mitigation measures. In a further step, data of recent landslides are compared with historical and modern mitigation measures and are correlated with concepts of risk management. As a result, it is possible to identify some complex interactions between landslide hazard, hazard awareness and damage impact. The case histories show that especially since the last 20 years public risk awareness rose due to an apparent increase in landslide frequency and magnitude at some sites. Before the 1990s landslide mitigation measures were mainly low cost prevention measures such as the removal of loose rock and vegetation, rock blasting, catch barriers, and temporal or perpetual traffic lane closure. Recently there is a shift towards the implementation of expensive mitigation measures in order to minimize landslide occurrence. Local decision makers increasingly invest in expensive long-term stabilization projects like soil anchoring, rock nailing, and steel-reinforced concrete walls.

Abstract. A rockfall dataset for Germany is analysed with the objective of identifying the meteorological and hydrological (pre-)conditions that change the probability for such events in central Europe. The factors investigated in the analysis are precipitation amount and intensity, freeze–thaw cycles, and subsurface moisture. As there is no suitable observational dataset for all relevant subsurface moisture types (e.g. water in rock pores and cleft water) available, simulated soil moisture and a proxy for pore water are tested as substitutes. The potential triggering factors were analysed both for the day of the event and for the days leading up to it. A logistic regression model was built, which considers individual potential triggering factors and their interactions. It is found that the most important factor influencing rockfall probability in the research area is the precipitation amount at the day of the event, but the water content of the ground on that day and freeze–thaw cycles in the days prior to the event also influence the hazard probability. Comparing simulated soil moisture and the pore-water proxy as predictors for rockfall reveals that the proxy, calculated as accumulated precipitation minus potential evaporation, performs slightly better in the statistical model. Using the statistical model, the effects of meteorological conditions on rockfall probability in German low mountain ranges can be quantified. The model suggests that precipitation is most efficient when the pore-water content of the ground is high. An increase in daily precipitation from its local 50th percentile to its 90th percentile approximately doubles the probability for a rockfall event under median pore-water conditions. When the pore-water proxy is at its 95th percentile, the same increase in precipitation leads to a 4-fold increase in rockfall probability. The occurrence of a freeze–thaw cycle in the preceding days increases the rockfall hazard by about 50 %. The most critical combination can therefore be expected in winter and at the beginning of spring after a freeze–thaw transition, which is followed by a day with high precipitation amounts and takes place in a region preconditioned by a high level of subsurface moisture.