Open Access

The e-commerce market has been growing for years and this trend seems to be continuing. Online stores for clothing are very successful. It seems that hardly any company can afford not to have a digital presence. This goes hand in hand with the fact that the range of products on offer to customers is getting bigger and bigger. But it's not just the range that's getting bigger, it's also the effort customers have to make to find the right product. For this reason, many successful online stores are already relying on AI. In doing so, companies are creating opportunities for customers that an employee could hardly manage. Implemented on the website, AI can check inventory, update it in real time, predict trends and evaluate customer or user data and make suitable recommendations. This is important for the customer because with the huge choice available, for one thing, personalization is increasingly important and being presented with a relevant selection. A central question is whether the recommendations are trustworthy and whether they can be equated with a real salesperson advising the customer. After all, trust is relevant in longterm customer relationships in that it leads to loyalty and satisfaction, which in turn increases the intention to repurchase. The recommendation tools mentioned are also of particular interest for another reason. On the one hand, they help customers to get a relevant selection of the offer and thus to get faster to the desired one. On the other hand, they are relevant for companies not only because of customer satisfaction, but also because of the chance to reduce returns. The large online stores for clothing offer their customers very generous opportunities to return the goods free of charge. In doing so, the companies have responded to customer wishes, because hardly anything is more important to them when it comes to online shopping: free returns. In this way, customers have minimized the risk of having to keep goods that do not fit or please them. This thesis examines whether recommendation tools can help customers to better assess the sizes and properties of clothing, so that they receive more suitable clothing and do not even feel the need to order several sizes of the same item of clothing. It can therefore be assumed that trust in the recommendations of the AI tools reduces uncertainty, which in turn should reduce the intention to return goods. Another assumption to be tested is that of the perceived usefulness of the recommendation tools. As a prerequisite to get an assessment of these assumptions is the usage of the tools. Therefore, a survey was initiated in the DACH region to assess the extent to which usage influences the factors mentioned. It was found by means of a regression analysis that the frequency of online purchases, mediated by perceived usefulness, explains the influence on trust.

Die Präsenz einer Person unterteilt sich in analoge, digitale und virtuelle Präsenz

Herkömmliche Lernprozesse sind in der dynamischen Arbeitswelt nicht mehr zielführend. Für transformatives Lernen werden neue Formen der Zusammenarbeit, neue Kompetenzen und neue Lernformate benötigt. Der selbstgesteuerte Lernansatz wird dabei der neuen Arbeitswelt gerecht. Jedoch werden die Potenziale des selbstgesteuerten Lernens in Unternehmen noch wenig genutzt. Im Rahmen der klassischen Personalentwicklung werden meist Kurskataloge und Schulungsprogramme erstellt, die den Inhalt sowie den zeitlichen und örtlichen Rahmen vorgeben. Somit ist es das Ziel dieser Arbeit, vorhandene Chancen und Herausforderungen des selbstgesteuerten Lernansatzes in Unternehmen zu identifizieren sowie Möglichkeiten zur Gestaltung von Rahmenbedingungen zur aktiven Förderung des selbstgesteuerten Lernens durch die Personalentwicklung aufzuzeigen. Dazu wird der Begriff des selbstgesteuerten Lernens abgegrenzt, Modelle werden aufgezeigt und personenabhängige Faktoren für das selbstgesteuerte Lernen werden betrachtet. In einem weiteren Schritt werden die Perspektive der Personalentwicklung und deren Rolle für das selbstgesteuerte Lernen dargelegt. Mithilfe der Durchführung zweier Case Studies wird ein tiefergehendes Verständnis zum selbstgesteuerten Lernen in Unternehmen erzeugt und die forschungsleitenden Fragen werden beantwortet. Die Ergebnisse dieser Arbeit resultieren in Implikationen für die Personalentwicklung, die die zu bearbeitenden Handlungsfelder für optimale Rahmenbedingungen für das selbstgesteuerte Lernen von Mitarbeitenden in Unternehmen abbilden.