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The current master thesis makes an effort to investigate relationships between perceived service quality, membership satisfaction, and membership loyalty at “Gesellschaft für technische Kommunikation – tekom Deutschland e.V.”, applying adjusted SERVQUAL model. To attain the formulated objectives in scope of the current research, the “Satisfaction – Profit Chain” model is applied, consisting of “Attribute Performance”, represented by SERVQUAL service quality dimensions supposed to serve as antecedent of the second component of the chain “Membership Satisfaction”, and “Membership Loyalty” in order to investigate relations between these three. The findings of the study are supposed to serve as a basis for altering the existing CRM Strategy in order to eliminate current issues within the association, develop strategic marketing capabilities, and create value for members’ attraction and retention.
Digital Transformation is gradually changing the ways of operating the business. With the advancements and innovations in technology and changing customer preferences, it is essential to adapt to these changes. Digital transformation has the capability to impact nearly every line of business but one of the most significant impact is on Customer Experience. Embracing new technology and processes provides opportunity to create better experience for customers by focussing on automation, self-service, value, quality, customer expectations etc. Advanced systems or solutions that fulfil these requirements can be incorporated in the technology and process landscape of an organization who is supporting customers. This thesis aims at conceptually integrating the Customer Service and Retail Store Support processes at HUGO BOSS into the new Enterprise Service Management (ESM) tool which will in turn drive the digital transformation at HUGO BOSS. Moreover, the purpose of the study is to provide a recommendation if the new ESM tool can replace the existing Retail & Customer Care ticketing tool, thus enabling the Retail & Customer Care team and their respective processes to be fully onboarded and implemented in future into the new tool. This thesis is a qualitative research. At first, qualitative data about the existing ticketing tools used to provide customer service and retail store support is gathered through secondary data collection methods. Secondly, in-depth semi structured interviews with nine respondents from Retail & Customer Care team and IT Support teams were conducted to collect their feedback and analyse the benefits and drawbacks of these tools. Next, the thesis introduces the new ESM tool followed by its evaluation using Fit-Gap Analysis method. Further, the thesis includes the concept of ideal customer service and retail store support processes to be supported by the new tool using process flowcharts. In conclusion, the results of the thesis are presented based on which a future recommendation is provided.
The object of the present master thesis is to understand the environment of the sales channel of Global Projects, its opportunities and challenges for Hansgrohe SE in order to clearly formulate a practicable, medium-term strategy for the period of 2020-2023 for the referred sales channel. A mixed method approach was used in this thesis, using both quantitative and qualitative methods. Raw data such as the channel’s net sales during 2016-2019 were observed from the internal sales system in order to understand the relevance in the past of the different business segments within the sales channel of Global Projects. Also, different external sources such as databases and reports from different consulting firms and international institutions were analyzed in order to determine the sales potential of the different business segments for the period of 2020-2023. A PEST analysis was conducted in order to identify the changes and effects of the external macroenvironment on the company’s strategic position. And finally, a competitor analysis was also conducted in order to identify the strengths and weaknesses of the company’s main competitors and the areas where the company should aim to improve. All previous research and analysis was complemented with expert interviews that were conducted with experts from ten different subsidiaries of the company who are in charge or involved in the sales channel of Global Projects in their respective regions, who helped defining the sales potential of the existing business segments and relevance of new segments that should be considered in order to develop the channel’s strategy. The result of the study suggested that the residential segment will represent the biggest sales potential for the channel followed by the hospitality segment. While there are other segments such as marine, retirement homes and bathroom pods & modular buildings which are expected to gain relevance in the future in some specific regions. As result of all research and analysis conducted throughout this thesis, the strategy and plan for the sales channel of Global Projects for the period of 2020-2023 was designed in line with the company’s strategic position.
All the companies need to plan and budget for future. For planning they need sale forecasting so that accordingly they can manage their supply chain efficiently. Companies do have historical data which can be used for forecasting sale. However, the accuracy of the predictive model depends on the quality of data which is being fed to the model. Poor data quality may result in poor forecasting. Hence, there is need to work on data quality management and to formulate some generic approach for ensuring data quality. Besides, it is also required to detect abnormal sale from the past data, get the reason for those abnormal sale records and remove them from the data. Subsequently, cleaned data can be used to work on predictive modelling which will forecast sales with the most likelihood of near to accurate results. These historical data can be analyzed as a time series data by using as simple time series analysis as ARIMA or by using complicated neural network. Evaluation of these predictive models will help in making a decision of selecting a best fitted model for future forecasting. The thesis aims to work on data quality management of raw data and then analyze time series data to determine predictive model for forecasting. Besides, thesis also aims to understand how data is collected and how organization performs sales processes. This would not only facilitate in finding and bridging the gaps in the business processes but also in preparing the organization for the state-of-the-art technologies to enhance their business for future.
Life insurance penetration rate in Malaysia has been stagnant in the past few years although a few InsurTech companies set up in Malaysia recently. Prior researches on InsurTech fail to clarify the gap of the target customers’ and the insurance experts’ opinions on how to enhance the customer experience in online life insurance with the help of Artificial Intelligence (AI). To address this, a model is recommended based on the literature review on similar articles and survey results conducted on both target customers and insurance experts. The recommended model has four main components: official website by InsurTech companies collaborated with traditional life insurers, customer support, customer service and customer engagement.
Prostate segmentation is an essential part of brachytherapy treatment planning, in order to perform the procedure with required accuracy. Nowadays, segmentation of the prostate is still carried out manually during the planning steps, therefore it is a process that can be tedious, time-consuming and prone to inter-observer error. Much effort has been made in development of an computer-based algorithm that can perform prostate segmentation automatically, but only with appearance of deep learning methods, more promising algorithms emerged. So far, convolutional neural networks demonstrated excellent results in fully automatic prostate segmentation. Development of such an algorithm and training an efficient deep learning model is a challenging task, and requires a lot of optimizations. The objective of this study is development and evaluation of an algorithm for image processing based on deep learning methods that can perform fully automatic segmentation of the prostate gland in transrectal ultrasound images. Additionally, we made an overview of the development process, along with challenges and their solutions and demonstrated an algorithm implemented using Python and Tensorflow library, consisted of preprocessing, augmentation, training and validation, postprocessing and validation steps, which is able to successfully carry out fully automatic prostate segmentation with expert level of accuracy. Finally, we presented our implementation of fully convolutional neural network model and results that are encouraging to continue with model improvements and potential clinical application.
Entrepreneurship research faces a crossroads and a new approach is needed to better understand entrepreneurial behavior. Incorporating neuroscience to comprehend the entrepreneurial mindset seems promising. Nevertheless, the potential of neuroscience for entrepreneurship research is only slowly being realized. Based on an extensive literature review, this thesis examines the emerging role of neuroscience with respect to entrepreneurship. Referring to the model of the entrepreneurial process, this thesis investigates how entrepreneurs discover, exploit, and finally capture opportunities. In this context, explanations regarding trait, expertise, adaptation, and mindset of the entrepreneur are relevant for further examination. Moreover, decision-making in uncertain situations is analyzed. In this context, the dynamic interplay between the reflective and reflexive system is considered. Ultimately, this thesis provides recommendations for organizational innovation to enhance entrepreneurial
activity.
Over the past few decades, there has been an increasing amount of academic literature recognizing the significance of innovation systems. Entrepreneurship is an important component of an innovation system, contributing to the enhancement of regional as well as national innovation. The transfer of knowledge and technology between science and the economy has become particularly important to reinforce overall innovation performance. Today, universities and other institutions of higher education play a crucial role in the system of innovation and have evolved as active and highly relevant participants in the innovation system. Therefore, various supportive measures have been developed to increase the level of innovation at universities and to drive entrepreneurial activities. However, due to the ever-growing entrepreneurial support environment and the great variety of support programs,the distinction between support measures has become unclear. Consequently, the main objective of the present research work is to contribute to the overall understanding of supportive measures at German universities and other institutions of higher education.
Eight experts were interviewed to ensure the compilation of meaningful data. The research findings highlight the importance of a solid network of external experts as well as collaboration with other entrepreneurial institutions. Moreover, the research results indicated that an organizational structure with decentralized decision-making processes and a greater scope of actions enhances operational efficiency. While considering the indistinctness of different support programs and specific terms, although some significant differences were evaluated, overall, the results present a clear tendency toward a more cautious use of specialist terms, therefore substantiating the missing preciseness.
Financial technology, popularly known as Fintech, has disrupted and revolutionized the financial service sector. Today, institutions such as banks are adapting digital transformation with the help of technological devices. There is no doubt that Fintech has transformed the way we bank. Nevertheless, there has been a growing need of understanding the future of financial institution with a holistic approach. Regulatory and governmental support towards maximizing opportunity, minimizing risk, and integrating financial inclusion is needed to accelerate the economy and attain sustainable development.
The following thesis aims to study financial inclusion and how to achieve it in the Fintech industry. It comprises of four areas of influence; market, social, technology and regulatory while emphasizing on the economic development, social progress, uplifting digital finance and robust regulatory system in the globalized financial market. The research aims to close the gap among the regulatory, economic, technological and social aspects of Fintech and then develop a pathway to attain financial inclusion. In addition, the objective of the research is to provide a comprehensive strategic plan towards a prespecified future in finance. This was achieved with the help of normative scenario planning. The result was calculated using scenario planning software INKA 4. The result consisted of four distinct scenarios supporting the pathway to implement financial inclusion in Fintech sector by the year 2030.
Zombie companies are widely discussed ever since the ‘lost decade’ in Japan. The prolonged recession was experienced for almost two decades and in great deal attributed to the zombie companies. The Eurozone is currently in recession and is experiencing a growing incidence of zombie companies. If this trend is not stopped but encouraged by the negative interest rates, there is a possibility for a prolonged recession or even secular stagnation. This study aims to examine the reasons for the emergence and existence of zombie companies in the past. It discusses the implications zombie companies had on the aggregate macroeconomic indicators. In discusses how zombie companies should be treated and whether they must inevitably be foreclosed. To determine the severity of the problem, it examines the incidence of zombie companies in selected industries in the Euro periphery countries based on their interest coverage ratio (ICR). The results show that there is no significant incidence in the selected markets. It concludes whether currently, the zombie companies are a real threat to the economy of the Eurozone. Furthermore, it suggests ways how the problem of zombie companies should be prevented and treated.