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The fact that services have emerged a driving force and the fastest growing sector in international trade attracts researchers to follow the changes taking place in the service industry. This study extends the scientific discussion on internationalization of service firms. Unlike previous research that examined factors that influence a single firm’s decision to internationalize, I acknowledge the heterogeneity of services, and based on the results obtained from secondary analysis of primary qualitative data sets, answer the main research question how internationalization motives differ between people-processing services, possession-processing services, and information-based services. This research goes beyond identification of variation in internationalization motives and analyses the service characteristics that might be responsible for the differences. In addition, I assess the key trends in the service sector and predict the possible future internationalization motives that are likely to emerge from the current trends.
Findings of this study reveal two major issues. First, it is evident that reasons for internationalization differ among hotel, retail firms and Higher education institutions representing people-processing services, possession-processing services, and information-based services respectively. Second, a few motives are common across sub-sectors, however the significance of the motives vary from sub-sector to sub-sector. I conclude that the differences in underlying structures of the respective service sub-sectors is the fundamental cause for the variation in internationalization motives among service sub-sectors. Other factors such as distinctive characteristics of service, firm’s competitive strategies, income elasticity of demand, and life-cycle stage of the service sub-sector also contribute to the differences in internationalization motives.
This paper also presents three different factors, which are likely to emerge as significant factors that influence service firm internationalization decision in future. (1) Company’s urge to be socially responsible and the need to contribute towards the environmental well-being (2) The need to sell regional products and services to neighbouring nations and respond to consumers’ demand for sustainable consumerism (3) Decision to penetrate foreign markets facilitated by the low risks and low cost of internationalization.
In today’s world, companies feel the urge to disguise from competitors and to connect emotionally with consumers in order to foster a meaningful and long-lasting relationship. Simultaneously, stakeholders demand an increase of companies’ social responsibility. Cause-related marketing (CRM) is a marketing tool that addresses the change in societal values and the rising expectations from stakeholder groups. The increasing number of companies that choose to partner with a non-profit organization highlights that linking a charitable cause to the company's brand is an effective marketing tool. Authors illustrate that CRM, as a form of showing corporate social responsibility, will become even more important in the future. This master thesis examines the relationship between CRM, emotions, and culture. The research goal is to identify if CRM programs are effective in evoking emotions in consumers and if the cultural background of a consumer influences the evocation of certain emotions. The empirical findings outline that CRM programs are effective in evoking emotions. Other-focused emotions evoked by CRM programs are stronger expe-rienced by members of collectivistic countries than by members of individualistic countries. Likewise other-focused emotions evoked by CRM programs are stronger experienced by high interdependent selves than by low interdependent selves.
In this paper, low-loss Y-branch splitters up to 128 splitting ratio are designed, simulated, and optimized by using 2D beam propagation method in OptiBPM tool by Optiwave. For an optical waveguide, a silica-on-silicon material platform is used. The splitters were designed as a planar structure for a telecommunication operating wavelength of 1.55 m. According to the minimum insertion loss and minimum non-uniformity, the optimum length for each Y-branch is determined. The influence of the pre-defined S-Bend waveguide shapes (Arc, Cosine, Sine) and of the waveguide core size reduction on the splitter performance has been also studied. The obtained simulation results of all designed splitters with different S-Bend shape waveguides together with the different waveguide core sizes are discussed and compared with each other.
In this paper, we propose and simulate a new type of three-dimensional (3D) optical splitter based on multimode interference (MMI) for the wavelength of 1550 nm. The splitter was proposed on the square basis with the width of 20 x 20 µm2 using the IP-Dip polymer as a standard material for 3D laser lithography. We present the optical field distribution in the proposed MMI splitter and its integration possibility on optical fiber. The design is aimed to the possible fabrication process using the 3D laser lithography for forthcoming experiments.
Marketing automation
(2021)
The phenomenon of the increase of numerous widespread and common diseases, ranging from simple dental problems to obesity, high blood pressure or diabetes, is a concern which can be observed in various aspects of the entire world. Reasons for this is a combination of an unhealthy diet and insufficient physical activity. Consequently, governments across the globe are seeing the need to change consumer behaviour by introducing plain packaging regulations. From an entrepreneurial point of view, this would result in financial losses as well as various limitations in product design, branding and differentiation. Therefore, an empirical study with 232 respondents was conducted, which revealed that the use of images on packaging activates emotions in customers within a few seconds. This master thesis examines possible correlations and differences between the importance of emotionally loaded product packaging and consumer behaviour at the point of sale. The persuasive effect of images on purchase decisions is based on personal consumer experiences and validated processing in the brain which trigger specific emotions. Modern companies are able to communicate company values through emotions by using images correctly. The results of the conducted survey enable Diezano to successfully implement the communication strategy in the event of the introduction of plain packaging regulations. By using mountain graphics, Diezano conveys values such as home, regional, refreshment, tasty, memories of childhood and nostalgia, which the brand aims to convey to the market. Based on the result of this master thesis, it is concluded that the picture superiority effect is mostly underestimated, yet it is an indispensable medium for emotional differentiation and thus a company’s success.
With green cosmetics becoming widely used in Germany, this research would like to fill a research gap and investigate the impact of transatlantic transportation on the willingness of German customers to purchasing the product. With growing environmental awareness this information might be decisive for companies willing to expand internationally. They can take it into consideration when creating their international expansion strategy and deciding for the mode of entry.
Current research also explores the option of targeting the customers with marketing messages to share information about the low environmental impact of the transatlantic transport. It tests different marketing messages and analyses their impact on green purchase intention.
The term start-up has become indispensable when new ideas are to be turned into business ventures. The number of start-ups is continuously increasing, but at the same time, many new start-ups are marked by failure. The successful path from start-up to a stable business model is to be explored in more detail in this thesis. The focus here is on the various success factors and how decisive they are.
At present, start-ups are mainly focusing on the area of digitalisation, not only triggered by the Covid19 pandemic but also by the high demand for digital products and services. In the past, start-ups founded in Silicon Valley/USA have been incredibly successful. Nevertheless, currently many other countries offer decisive advantages for start-ups and are therefore more attractive than the Silicon Valley. In Europe, Spain stands out with a rapidly growing start-up scene, primarily in the metropolises of Madrid and Barcelona.
This Master's thesis aims to empirically investigate the interrelationships of the necessary success factors in the start-up scene for the country of Spain. The focus of the study here is on the digitalisation industry. Qualitative content analysis is used for closer examination and is carried out through expert interviews. Qualitative research allows a much deeper insight into the interviewees' personal experiences and examines non-measurable factors. The subsequent evaluation is intended to present correlations and provide new insights. In summary, a list of recommendations is generated to provide future founders with suitable suggestions and advice right from the start.
The purpose of an energy model is to predict the energy consumption of a real system and to use this information to address challenges such as rising energy costs, emission reduction or variable energy availability. Industrial robots account for an important share of electrical energy consumption in production, which makes the creating of energy models for industrial robots desirable. Currently, energy modeling methods for industrial robots are often based on physical modeling methods. However, due to the increased availability of data and improved computing capabilities, data-driven modeling methods are also increasingly used in areas such as modeling and system identification of dynamic systems. This work investigates the use of current data-driven modeling methods for the creation of energy models focusing on the energy consumption of industrial robots.
For this purpose, a robotic system is excited with various trajectories to obtain meaningful data about the system behavior. This data is used to train different artificial neural network (ANN) structures, where the structures used can be categorized into (i) Long Short Term Memory Neural Network (LSTM) with manual feature engineering, where meaningful features are extracted using deeper insights into the system under consideration, and (ii) LSTM with Convolutional layers for automatic feature extraction. The results show that models with automatic feature extraction are competitive with those using manually extracted features. In addition to the performance comparison, the learned filter kernels were further investigated, whereby similarities between the manually and automatically extracted features could be observed. Finally, to determine the usefulness of the derived models, the best-performing model was selected for demonstrating its performance on a real use case.