Organization have to deal with a plethora of IT security threats nowadays and to ensure smooth and uninterrupted business operations, firms are challenged to predict the volume of IT security vulnerabilities and to allocate resources for fixing them. This challenge requires decision makers to assess which system or software packages are prone to vulnerabilities, what impact exploits might have, and how many vulnerabilities can be expected to occur during a certain period of time. The academic literature has increasingly drawn attention to the need for predicting IT security vulnerabilities.However, only limited research has addressed the problem of forecasting IT security vulnerabilities based on time series that deal with the specific properties of IT security vulnerabilities, i.e., rareness of occurrence and high volatility. To address this shortcoming, we apply established methods which are capable of forecasting events characterized by rareness of occurrence and high volatility. Based on a dataset taken from the National Vulnerability Database (NVD), we use the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to measure the forecasting accuracy of single, double and triple exponential smoothing methodologies, Croston's method, ARIMA, and a neural network-based approach. We analyze the impact of the applied forecasting methodology on the prediction accuracy with regard to its robustness along the dimensions of the examined system and software packages "operating systems", "browsers" and "office solutions" and the applied metrics.To the best of our knowledge, this study is the first that analyzes the effect of prediction techniques and applies forecasting metrics that are suitable in this context. Our results show that the optimal forecasting methodology depends on the software or system package as some methods perform poorly in the context of IT security vulnerabilities, that absolute metrics can cover the actual prediction error precisely and that the prediction accuracy is robust within the two applied forecasting-error metrics.

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‘Digital’ ways of working and organising are undeniably changing the nature of work that is becoming ever more uncertain, unsettled, and fluid. These changes call into question our understanding of work and our conceptions of worker identity. In this paper, we ask the question: how is digital worker identity performed in such fluid and unsettled work settings? To explain digital worker identity performance, we investigated digital nomadism as an extremely fluid and unsettled case of digital work. We studied digital nomads, high-skilled professionals who use digital technologies to work remotely and lead a nomadic lifestyle, in a multi-sited ethnographic field study. Based on a process-relational perspective, our analysis reveals how the identity of digital nomads, their “becoming”, is performed as an ongoing process. This process unfolds along the flow of three lines of identity performance—journeying, workliving, and digital reassembling. The lines of identity performance, as products of our theorising, are proposed as a foundation for a process-relational theory of digital worker identity.