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In an oversaturated market, companies are required to use innovative and, above all, creative advertising methods to capture their customers’ attention, and thus differentiate themselves from rival businesses. To this end, companies have been increasingly relying on the use of humor, a phenomenon that remains highly subjective and is perceived differently by each individual. This master’s thesis, which was completed as part of the International Marketing and Sales program at the FH Vorarlberg, focuses on this phenomenon of humor as well as its impact on advertising perception. With the aid of three different theories, the term “humor” is defined. Furthermore, this study explains and researches the so-called vampire effect, wherein various factors (in this case humor) draw attention away from the actual advertising message. In addition, this thesis takes a closer look at involvement, as a person’s involvement or interest in a brand or product can influence brand and product recall and recognition. An online survey was conducted to determine whether the vampire effect caused by humor is able to influence brand and product recall. In other words, this concerns whether the viewer can still remember the brand and product afterward or whether the humor employed triggers the vampire effect. Furthermore, this thesis explored whether the vampire effect caused by humor is able to influence brand and product recognition. Recall is the retrieval of information from memory without direct cues, whereas recognition refers to the recognition of information when it is presented again. Furthermore, within this context, it was discovered that brand and product recall varies with low and high involvement viewers of the advertisement. In other words, this means that the strength of the vampire effect caused by humor changes depending on the strength of the viewer’s involvement. During the course of this research, it was further observed that the humor employed significantly affects the perception of the advertising message, thus confirming the existence of the vampire effect. This effect also influences both brand as well as product recall and recognition. In both cases, participants in the survey were less able to remember the product and brand in the humorous advertising. Furthermore, it was proven that people with low involvement in the advertised product group are more heavily affected by the vampire effect. As such, they are more likely to not remember the product or brand after seeing the advertisement.
Digital twin as enabler of business model innovation for infrastructure construction projects
(2023)
Emerging technologies and methods are becoming an important element of the construction industry. Digital Twins are used as a base to store data in BIM models and make use out of the data respectively make the data visible. The transparency in all phases of the lifecycle of building and infrastructure assets is crucial in order to get a more efficient lifecycle of planning, construction and maintenance. Whereas other industries increased performance in these phases by making use out of the data, construction industry is stuck in traditional methods and business models. In this paper we propose a concept that focuses on the digital production twin. The comparison of planning data with As-Is production data can empower a data driven continuous improvement process and support the decision making process of future innovations and suitable business models. This paper outlines the possibility to use the data stored in a digital twin with regards to the evaluation of possible business models.
Lack of transparency and traceability of products and their raw materials means that most products can only be thrown away or not properly recycled due to a lack of relevant data. This conflicts with the circular economy principles, which are demanded by several initiatives, including the European Union. The aim of this master thesis is to analyze this conflict and to propose a technical solution based on Distributed Ledger Technology that enables transparency and traceability of products and their materials. Therefore, the thesis addresses two central research questions: 1. How can traceability and transparency be enabled by integrating a DLT solution? 2. How would a prototype with the integration of smart contracts and DLT look like? To answer these questions, a blockchain solution is implemented using Hyperledger Fabric. The solution uses the immutability and decentralized nature of DLT to record and track the movement of products and their materials throughout their life cycle in the Circular Economy. Furthermore, with private data collections, confidentiality, and privacy are granted while ensuring transparency. The thesis contributes to the Circular Economy field by exploring the principles, models, and challenges of the Circular Economy and the circularity goals of a Digital Product Passport to develop a suitable technical solution. The chosen blockchain framework, Hyperledger Fabric, is presented, and its key components and features are highlighted. The thesis also delves into the design decisions and considerations behind the Digital Product Passport platform, explaining the architecture and transaction flow together with the prototype implementation and demonstration to showcase the functionality of the solution. Results and analysis provide insights into the challenges of the Circular Economy, sustainable resource management, and the Digital Product Passport, resulting in recommendations for future improvements and enhancements. Overall, this thesis offers a practical solution utilizing DLT to enable transparency and traceability in the Circular Economy, contributing to the realization of sustainable and efficient resource management practices to ultimately contribute to the set Circular Economy initiatives.
Programmable Logic Controller (PLC) modules are used in industrial settings to control and monitor various manufacturing processes. Detecting these modules can be helpful during installation and maintenance. However, the limited availability of real annotated images to train an object detector poses a challenge. This thesis aims to research object detection of these modules on real images by using synthetic data during training. The synthetic images are generated from CAD models and improved with Generative Adversarial Networks (GANs). The CAD models are rendered in different scenes, and perfectly annotated images are automatically saved. A technique called domain randomization is applied during rendering. It renders the modules in different poses with constantly changing backgrounds. As the CAD models do not visually resemble the real modules, it is necessary to improve the synthetic images. This project researches StarGAN and CycleGAN for the task of image-to-image translation. A GAN is trained with real and synthetic images and can then translate between these domains. YOLOv8 and Faster R-CNN are tested for object detection. The best mean Average Precision (mAP) is achieved when training with a synthetic dataset where 50% of the images were improved with StarGAN. When trained with YOLOv8 and evaluated on a real dataset, it achieves a mAP of 84.4%. Overall, the accuracy depends on the quality of the CAD models. Using a GAN improves the detection rate for all modules, but especially for unrealistic CAD models.
The advent of autonomous and self-driving cranes represents a significant advancement in industrial automation. One critical prerequisites for achieving this long-term goal is the accurate and reliable detection of tools guided by ropes in real-world environments. Since the tool is suspended by ropes, the tool pose cannot be controlled directly. This master’s thesis addresses the challenges of pose estimation for rope-guided tools using point cloud measurements. The proposed algorithm utilizes constraints imposed by the crane kinematics and information extracted during the segmentation process to efficiently infer the pose of the hook, therefore enabling the use of the pose for decision making in real-time critical applications. RANSAC (Random Sample and Consensus) is deployed in the segmentation process to extract geometric primitives from the point cloud which represent the ropes and distinctive parts of the tool. Since the point cloud is often to sparse for feature matching a bounding box is used to estimate the initial position of the tool. Two different methods are presented to improve the initial pose. A computationally expensive method with a high level of confidence, integrating the ICP (Iterative Closest Point) algorithm is used as a benchmark. A linear Kalman filter is used in the second method which is real-time capable. The benchmark is then used to evaluate the real-time capable approach. The core contributions of this research lie in the innovative utilization of bounding boxes for pose estimation. The findings and methodologies presented herein constitute an advancement towards the realization of autonomous and self-driving cranes.
In the era of digital transformation an evolution takes place. Following this, new perspectives concerning leadership are required, especially in virtual teams. Shared Leadership is a promising leadership form to meet the challenges in a virtual team setting. Particularly, studies show that shared leadership increases performance, team creativity and innovative behavior. Moreover, the responsibility is distributed among several, not one individual. Nevertheless, it is unclear, which skills are needed in shared leadership teams and how they could be trained. Therefore, we develop a conceptual framework to pave the way for an empirical inquiry of the skills for and the role of shared leadership. Moreover, we encourage the discussion, whether the current leadership development is still viable and offer practical implications to develop shared leadership.
Offline speech to text engine for delimited context in combination with an offline speech assistant
(2022)
The inatura museum in Dornbirn had planned an interactive speech assistant-like exhibit. The concept was that visitors could ask the exhibit several questions that they would like to ask a flower. Solution requirements regarding the functionalities were formulated, such as the capacity to run offline because of privacy reasons. Due to the similarity of the exhibit, open-source offline Speech To Text (STT) engines and speech assistants were examined. Proprietary cloud-based STT engines associated with the corresponding speech assistants were also researched. The aim behind this was to evaluate the hypothesis of whether an open-source offline STT engine can compete with a proprietary cloud-based STT engine. Additionally, a suitable STT engine or speech assistant would need to be evaluated. Furthermore, analysis regarding the adaption possibilities of the STT models took place. After the technical analysis, the decision in favour of the STT engines called "Vosk" was made. This analysis was followed by attempts to adapt the model of Vosk. Vosk was compared to proprietary cloud-based Google Cloud Speech to Text to evaluate the hypothesis. The comparison resulted in not much of a significant difference between Vosk and Google Cloud Speech to Text. Due to this result, a recommendation to use Vosk for the exhibit was given. Due to the lack of intent parsing functionality, two algorithms called "text matching algorithm" and "text and keyword matching algorithm" were implemented and tested. This test proved that the text and keyword matching algorithm performed better, with an average success rate of 83.93 %. Consequently, this algorithm was recommended for the intent parsing of the exhibit. In the end, potential adaption possibilities for the algorithms were given, such as using a different string matching library. Some improvements regarding the exhibit were also presented.
The demand for managing data across multiple domains for product creation is steadily increasing. Model-Driven Systems Engineering (MDSE) is a solution for this problem. With MDSE, domain-specific data is formalized inside a model with a custom language, for example, the Unified Modelling Language (UML). These models can be created with custom editors, and specialized domains can be integrated with extensions to UML, e.g., the Systems Modeling Language (SysML). The most dominant editor in the open-source sector is Eclipse Papyrus SysML 1.6 (Papyrus), an editor to create SysML diagrams for MDSE.
In the pursuit of creating a model and diagrams, the editor does not support the user appropriately or even hinders them. Therefore, paradigms from the diagram modelling and Human Computer Interaction (HCI) domains, as well as perceptual and design theory, are applied to create an editor prototype from scratch. The changes fall into the categories of hierarchy, aid in the diagram composition, and navigation. The prototype is compared with Papyrus in a user test to determine if the changes have the effect of improving usability.
The study involved 10 participants with different knowledge levels of UML, ranging from beginners to experts. Each participant was tested on a navigation and modelling task in both the newly created editor, named Modelling Studio, and Papyrus. The study was evaluated through a questionnaire and analysis of the diagrams produced by the tasks.
The findings are that Modelling Studio’s changes to the hierarchical elements improved their rating. Furthermore, aid for diagram composition could be reinforced by changes to the alignment helper tool and adjustments to the default arrow behaviour of a diagram. Lastly, model navigation adjustments improve a link’s visibility and rating of a specialized link (best practice). The introduction of breadcrumbs had limited success in bettering navigation usability. The prototype deployed a broad spectrum of changes that found improvement already, which can, however, be further improved and tested more thoroughly.
Projects, in which software products, services, systems and solutions are developed, all rely on the right requirements to be established. Software requirements are the expression of user wants or needs that have to be addressed, business objectives that have to be met, as well as capabilities and functionality that has to be developed. Meanwhile, practice shows that very often incorrect, unclear or incomplete requirements are established, which causes major problems for such projects. It could lead to budget overruns, missed deadlines and overall failure in worst-case scenarios.
The field of requirements engineering emerged as an answer to these shortcomings, aiming to systematize and streamline the process that
establishes requirements. Requirements elicitation is a key component of this process, and one of its starting points. The current thesis attempts to outline best practices in requirements elicitation, as well as what issues, obstacles and challenges are currently faced, and then present this through the lens of national culture. In this way its effects on the practice, if any, could be highlighted and studied further. The way this was achieved was by interviewing practitioners from two nations, which are shown to be
culturally different, and then comparing and contrasting the findings.
Meanwhile, the validity of those findings was enhanced by comparisons with existing literature.
Even though the findings were not compelling enough to form generalizations or concrete conclusions about the effects of national culture on requirements elicitation, these findings revealed patterns that could be worth exploring further. When it comes to requirements elicitation itself, it was observed to benefit from a structured and systematic approach, and be
most effective with one-on-one, instead of group interactions. The main pain points of the process stem from the complexity of communication, but are not always obvious. Practitioners are also advised to carefully plan the gathering of requirements, as the source may not have them readily available, and could even be unclear about what exactly is needed. Overall, this thesis research could be considered successful in its goal to shed a modicum of light on the issue at hand from a different, underexplored angle. By following a systematic and methodical approach, this research has also been made easier to expand or replicate.