PurposeWhile there are several readiness assessments regarding digital transformation (DT) and Industry 4.0 in extant literature, this study aims to contribute to (a) a better understanding of digital readiness in supply chain (SC) aspects and (b) elaborate on differences between small and medium-sized enterprises (SMEs) and large enterprises.Design/methodology/approachThe study is based on 409 companies that participated in the Digital Readiness Check (DRC) in the region of Salzburg (Austria) and Bavaria (Germany) – an online assessment for self-evaluating the digital readiness of companies.FindingsThe study's results provide insights for the categories of strategy, employees, initiation of business transactions and SC. These are further differentiated for SMEs and large enterprises.Research limitations/implicationsThe study is limited to two regions in Austria and Germany, based on a self-evaluation of companies in a single point of time perspective. For future research, the results of this study should be expanded for different regions. Further, the results could be validated regarding external observations and measuring results at a later point of time.Practical implicationsThe DRC may help companies in benchmarking themselves and gaining a better understanding about categories that must be improved, especially regarding SC aspects of DT.Originality/valueThe DRC extends extant literature regarding the differentiation between SMEs and large enterprises as well as focussing on SC aspects of DT.

No abstract

This study investigates the perceived safety of passengers while being on board of a driverless shuttle without a steward present. The aim of the study is to draw conclusions on factors that influence and contribute to perceived safety of passengers in driverless shuttles. For this, four different test rides were conducted, representing aspects that might challenge passengers’ perceived safety once driverless shuttles become part of public transport: passengers had to ride the shuttle on their own (without a steward present), had to interact with another passenger, and had to react to two different unexpected technical difficulties. Passengers were then asked what had influenced their perceived safety and what would contribute to it. Results show that perceived safety of passengers was high across all different test rides. The most important factors influencing the perceived safety of passengers were the shuttle’s driving style and passengers’ trust in the technology. The driving style was increasingly less important as the passengers gained experience with the driverless shuttle. Readily available contact with someone in a control room would significantly contribute to an increase in perceived safety while riding a driverless shuttle. For researchers, as well as technicians in the field of autonomous driving, our findings could inform the design and set-up of driverless shuttles in order to increase perceived safety; for example, how to signal passengers that there is always the possibility of contact to someone in a control room. Reacting to these concerns and challenges will further help to foster acceptance of AVs in society. Future research should explore our findings in an even more natural setting, e.g., a controlled mixed traffic environment.

In this paper, we assess whether using non-linear dimension reduction techniques pays off for forecasting inflation in real-time. Several recent methods from the machine learning literature are adopted to map a large dimensional dataset into a lower dimensional set of latent factors. We model the relationship between inflation and these latent factors using state-of-the-art time-varying parameter (TVP) regressions with shrinkage priors. Using monthly real-time data for the US, our results suggest that adding such non-linearities yields forecasts that are on average highly competitive to ones obtained from methods using linear dimension reduction techniques. Zooming into model performance over time moreover reveals that controlling for non-linear relations in the data is of particular importance during recessionary episodes of the business cycle.