Die Digitalisierung ist kein neues Phänomen für den Mittelstand, wird diesen jedoch in naher Zukunft ganz grundsätzlich verändern: Galt der Digitalisierungsfokus der Unternehmen bislang dem Aufbau abgestimmter bzw. einheitlicher IT- und Kommunikationssysteme zur Unterstützung der zunehmend internationalen Geschäftstätigkeit mit weltweiten Produktions- und Vertriebsstandorten, konzentriert sich die Digitale Transformation nun sehr viel mehr auf die Gestaltung zukunftsfähiger Prozesse, die Entwicklung intelligenter Produkte und Dienstleistungen sowie den Aufbau neuer Geschäftsmodelle. Diese in Deutschland gerne mit dem Begriff "Industrie 4.0" bezeichnete Entwicklungsstufe der Digitalisierung (international wird eher von IoT oder IoE (Internet of Everything) gesprochen) zielt auf die konsequente Nutzung aller Möglichkeiten ab, die eine digital vernetzte Welt (von Menschen, Prozessen, Daten und Dingen) den Unternehmen prinzipiell bietet.

This study examines the effects of medium-range influencer Instagram postings compared to traditional print ads. Using cosmetic products as an example, the study examines how these alternative forms of advertising influence attitude components and consumers' propensity to buy. As a result, the advertising impact of the Instagram posting is lower than that of the classic print ad. The Instagram posting has its greatest effect where consumers already follow influencers (follower status). The situational variable (type of advertising) proves to be more powerful in explaining consumers' attitudes as personal variables. Among the latter, product involvement has more explanatory power than the general personality traits of the Big Five. To explain the propensity to buy, product involvement is even more meaningful than the type of advertising. Overall, this shows that the effect of situational and personal factors on dependent variables of consumer behaviour cannot be generalised, but that it is generally recommended to include both situational and personal determinants in the analysis.

Research on attitude has a long tradition in marketing. It has commonly been assumed that attitudes are relatively stable over time. Contrary to this hypothesis, this paper shows that merely confronting consumers with some cognitive criteria concerning brand evaluation can in fact change their rating in the short term. A second finding is that evaluations of sportswear brands become consistently more negative when subjected to the cognitive evaluation process. A third result shows that purchasing behaviour can be explained to a greater extent by emotions than by cognitive components of attitude.

The influence of the sales force on buyers in business-to-business market transactions is continuously waning. Particularly due to various new digital information channels buyers are in their decision-making processes only to a limited extent still dependent on the information provided by the sales persons. This development is putting sales forces increasingly under pressure as it is restricting their range of actions. In this conceptual paper it will be shown that regaining influence in the sales process will only be possible by the implementation of an integrated market intelligence system.

With the emergence of self-driving technology and the ever-increasing demand of bandwidth-hungry applications, providing the required latency, security and computational capabilities is becoming a challenging task. Although being evolving, traditional vehicular radio access technologies, namely WLAN/IEEE 802.11p and cellular networks cannot meet all the requirements of future Cooperative, Connected and Automated Mobility (CCAM). In addition, current vehicular architectures are not sufficiently flexible to support the highly heterogeneous landscape of emerging communication technologies, such as mmWave, Cellular Vehicle-to-Everything (C-V2X), and Visible Light Communication (VLC). To this aim, Multi-access Edge Computing (MEC) has been recently proposed to enhance the quality of passengers experience in delay-sensitive applications. In this paper, we discuss the in-premises features of MEC and the need of supporting technologies, such as Software Defined Networking (SDN) and Network Function Virtualization (NFV), to fulfil the requirements in terms of responsiveness, reliability and resiliency. The latter is of paramount importance for automated services, which are supposed to be always-on and always-available. We outline possible solutions for mobility-aware computation offloading, dynamic spectrum sharing, and interference mitigation. Also, by revealing MEC-inherent security vulnerabilities, we argue for the need of adequate security and privacy-preserving schemes in MEC-enabled vehicular architectures.

Since Critical Infrastructures (CIs) use systems and equipment that are separated by long distances, Supervisory Control And Data Acquisition (SCADA) systems are used to monitor their behaviour and to send commands remotely. For a long time, operator of CIs applied the air gap principle, a security strategy that physically isolates the control network from other communication channels. True isolation, however, is difficult nowadays due to the massive spread of connectivity: using open protocols and more connectivity opens new network attacks against CIs. To cope with this dilemma, sophisticated security measures are needed to address malicious intrusions, which are steadily increasing in number and variety. However, traditional Intrusion Detection Systems (IDSs) cannot detect attacks that are not already present in their databases. To this end, we assess in this paper Machine Learning (ML) techniques for anomaly detection in SCADA systems using a real data set collected from a gas pipeline system and provided by the Mississippi State University (MSU). The contribution of this paper is two-fold: 1) The evaluation of four techniques for missing data estimation and two techniques for data normalization, 2) The performances of Support Vector Machine (SVM), Random Forest (RF), Bidirectional Long Short Term Memory (BLSTM) are assessed in terms of accuracy, precision, recall and F1 score for intrusion detection. Two cases are differentiated: binary and categorical classifications. Our experiments reveal that RF and BLSTM detect intrusions effectively, with an F1 score of respectively > 99% and > 96%.