The Digital Transformation of the News Media Business – Paid Content and Entrepreneurship in Digital Journalism

The digital transformation of the news business continues to agitate publishers. Concerned about declining sales in the print segment, legacy outlets, local news companies and freelance journalists alike search for ways to monetize digital journalism properly. At first glance, digital journalism and its monetisation as paid content seem a promising effort. The digitisation of the news business enabled distribution at a marginal cost of almost zero while giving journalists access to new research technologies and lowering the cost of entry for smaller companies. However, while digital journalism enjoys broad popularity and use, online news are gaining few paying customers. Furthermore, online news compete within a larger digital media complex, comprising movies, games, and social media. After 25 years of experimentation, the digital future of journalism is still heavily debated in media management. Concerning the reconstitution as a digital medium, this research examines conditions of success and obstacles for the digital news media business to be successful as a business venture. Therefore, the research question reads What factors enable the viability and entrepreneurial success of the news media business in light of the consequences of digital transformation? The overarching research question is considered from two angles: The first angle concerns the demand side by looking at the antecedents of the audience's willingness to pay for paid content. The second angle focuses on the supply side and therefore examines antecedents of success in the context of digital journalistic start-ups and founders. In four studies, this thesis develops an analysis of the online news business with a local focus on the German news market. For this purpose, a variety of methods ranging from qualitative work and literature review to empirical research employing path analysis and predictive analytics are applied. Theoretically, digital transformation, free mentality and other peculiarities of information goods inform the frame of this work. Thus, this research aims at contributing to a financially sustainable news media business

Quantitative image analysis of peripheral nerves in whiplash injury patients

The research in this thesis has examined the use of texture and shape analysis to characterise Magnetic Resonance (MR) images of peripheral nerves in order to provide a potential quantitative tool for better diagnosis and treatments. Texture and shape can be considered as inherent properties of all surfaces and have the potential to provide sensitive information which cannot be quantitatively perceived by human vision. Texture analysis has been successfully used in image classification of aerial and satellite imagery and the diagnosis and prognosis of several types of cancer. However, to date, it has never been used in investigating peripheral nerve damage. In this thesis, we study the application of texture and shape analysis to the peripheral nerves in the upper extremities of patients suffering from Whiplash Associated Disorders (WAD). Specifically, quantitative texture analysis was performed on MR images of the carpal tunnel which contains the median nerve. The median nerve was studied to identify differences in textural patterns. Texture methods such as: first order features; co-occurrence matrices; run-length matrices and autocorrelation function were applied and their performance was assessed. Texture analysis was also performed to investigate nerve damage in the MR images of the brachial plexus, both in controls and patients. Further, spatial domain shape metrics were used to quantify and study the morphological differences of the median nerve in controls and patients. This highlighted that some significant differences exist between groups and thus could potentially be reliably used in combination with clinical scale metrics to identify possible nerve damage. As MR images contain noise, locating the median nerve accurately to perform image analysis is very important. Therefore, we further investigated the application of an enhanced correlation filtering method that could be trained on images of the median nerve and then applied to detect the median nerve in test images. The Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter includes the expected distortions in the target in the construction of the filter reference function. The OT-MACH filter was tuned in a bandpass to maximize the correlation peak and thereby successfully locate the position of the median nerve in the carpal tunnel. This study has successfully demonstrated that texture and shape analysis can be used to investigate possible peripheral nerve damage. Further research is required using larger datasets to establish a quantitative image analysis tool to support clinical decision making and thereby improve patient care and treatment outcome