The politics of content prioritisation online governing prominence and discoverability on digital media platforms

This thesis examines the governing systems and industry practices shaping online content prioritisation processes on digital media platforms. Content prioritisation, and the relative prominence and discoverability of content, are investigated through a critical institutional lens as digital decision guidance processes that shape online choice architecture and influence users’ access to content online. This thesis thus shows how prioritisation is never neutral or static and cannot be explained solely by political economic or neoclassical economics approaches. Rather, prioritisation is dynamically shaped by the institutional environment and by the clash between existing media governance systems and those emerging for platform governance. As prioritisation processes influence how audiovisual media services are accessed online, posing questions about the public interest in such forms of intermediation is key. In that context, this research asks how content prioritisation is governed on digital media platforms, and what the elements of a public interest framework for these practices might be. To address these questions, I use a within case study comparative research design focused on the United Kingdom, collecting data by means of semi-structured interviews and document analysis. Through a thematic analysis, I then investigate how institutional arrangements influence both organisational strategies and interests, as well as the relationships among industry and policy actors involved, namely, platform organisations, pay-TV operators, technology manufacturers, content providers including public service media, and regulators. The results provide insights into the ‘black box’ of content prioritisation across three interconnected dimensions: technical, market, and regulatory. In each dimension, a battle between industry and policy actors emerges to influence prioritisation online. As the UK Government and regulator intend to develop new prominence rules, the dispute takes on a normative dimension and gives rise to contested visions of what audiovisual services should be prioritised to the final users, and which private- and public-interest-driven criteria are (or should) be used to determine that. Finally, the analysis shows why it is crucial to reflect on how the public interest is interpreted and operationalised as new prominence regulatory regimes emerge with a variety of sometimes contradictory implications for media pluralism, diversity and audience freedom of choice. The thesis therefore indicates the need for new institutional arrangements and a public interest-driven framework for prioritisation on digital media platforms. Such a framework conceives of public interest content standards as an institutional imperative for media and platform organisations and prompts regulators to develop new online content regulation that is appropriate to changing forms of digital intermediation and emerging audiovisual market conditions. While the empirical focus is on the UK, the implications of the research findings are also considered in the light of developments in the European Union and Council of Europe initiatives that bear on the future discoverability of public interest media services and related prominence regimes

Eye Tracking Support for Visual Analytics Systems

Visual analytics (VA) research provides helpful solutions for interactive visual data analysis when exploring large and complex datasets. Due to recent advances in eye tracking technology, promising opportunities arise to extend these traditional VA approaches. Therefore, we discuss foundations for eye tracking support in VA systems. We first review and discuss the structure and range of typical VA systems. Based on a widely used VA model, we present five comprehensive examples that cover a wide range of usage scenarios. Then, we demonstrate that the VA model can be used to systematically explore how concrete VA systems could be extended with eye tracking, to create supportive and adaptive analytics systems. This allows us to identify general research and application opportunities, and classify them into research themes. In a call for action, we map the road for future research to broaden the use of eye tracking and advance visual analytics

Do you really know your consumers? : analyzing the impact of consumer knowledge on use and failure evaluation of consumer electronics

The field of Consumer Electronics (CE) can be characterized by continuous technological innovation, fierce global competition, strong pressure on time-to-market, fast adoption cycles and increasingly complex business processes. In this context it is increasingly challenging for product designers and developers to provide products with unique features and excellent price / performance characteristics, as well as having to provide products that meet all the consumer’s expectations. From a business perspective, research has shown that the number of consumer complaints and even product returns is increasing for complex CE (Den Ouden, 2006). Further research on the causes of these complaints showed that almost half of the complaints were due to non-technical reasons. Therefore, more insight is needed into product quality and reliability from a consumer point of view. A literature review showed that quality and reliability methods that are currently used in product development insufficiently prevent the large variety of consumer complaints: the number of consumer complaints is rising while at the same time the root cause of these complaints is more difficult to retrace. Product failures need to be measured and analyzed from a consumer’s point of view since the traditional fault-complaint propagation model fails to capture all potential sources of consumer complaints. More insight is needed into the relation between the diversity of consumers and the propagation of product development faults to these "Consumer-Perceived Failures" (CPFs).A conceptual framework was developed to model the underlying factors related to the propagation of product development faults to consumer complaints from a consumer point of view. This framework is based on insights from human-computer interaction and consumer behavior literature and the results of an explorative experiment. Furthermore, the most commonly used consumer selection criteria for consumer tests based on demographics and/or product adoption related characteristics do not sufficiently cover differences in CPFs. The consumer characteristic "consumer knowledge" is hypothesized to have a strong impact on differences in the underlying variables of this framework. A review of relevant consumer models and consumer characteristics used in human-computer interaction and consumer behavior research shows that this construct relates to cognitive structures consumers have about a product’s functioning as well as cognitive processes needed to use a complex CE product. This dissertation therefore aimed to investigate the hypothesized effect of consumer knowledge on two important variables of the conceptual framework: product usage behavior and failure attribution. By using multiple surveys, two laboratory experiments and a web-based experiment, the following aspects of the conceptual framework were investigated in this dissertation: • How and to what extent consumers can be differentiated on knowledge of complex CE • The effect of consumer knowledge on differences in product usage behavior • The effect of consumer knowledge on differences in attribution of product failures The results of the surveys to differentiate consumers on knowledge (both core and supplemental domains) of innovative LCD televisions demonstrated the successful development and validation of measurements of both subjective and objective measurements of expertise and familiarity. It was concluded that the selection of consumer knowledge constructs as criterion for differentiating consumers for a consumer test depends on the target consumer group for a product (e.g. a very narrow homogeneous consumer group versus mass consumer markets), the type of product (e.g. passive versus active interaction) and the goal of the consumer test. The laboratory experiment which investigated the effect of subjective expertise and objective familiarity on product usage behavior showed that higher levels of subjective expertise on both the television and computer domain result in significantly better effectiveness and efficiency and less interaction problems when performing complex product related tasks. Next, the results also showed that differences in subjective expertise stronger relate to differences in product usage behavior than those in objective familiarity. The findings of this study help product developers and designers to better understand differences in product usage behavior when consumers encounter interaction problems and can therefore help the product designers and developers to take better design decisions.The results of both failure attribution experiments with simulated failure scenarios of picture quality failures in an LCD television showed that only objective expertise differences affect differences in consumer perception of product failures. However, although the failure attribution of consumers with higher levels of objective expertise has more dimensions and is more refined, higher levels of objective expertise on a product do not automatically result in attributions that are more in accordance with the real physical cause of the failure. This has important implications because currently used test methods often differentiate consumers only on previous experience (i.e. familiarity) with a product. The results of both studies also demonstrated that both failure cause and failure impact do not significantly affect how consumers attribute the failures. In total it can be concluded that, when evaluating the effect of consumer diversity on fault-complaint propagation, consumer knowledge can be used to differentiate product use and failure attribution for complex CE. However, it should be noted that especially for failure attribution this effect is not consistent across different types of failures. In addition, compared to objective and subjective familiarity and subjective expertise, objective expertise has the strongest impact. In the context of fast evolving complex CE, objective expertise measurements are becoming increasingly important because familiarity or subjective expertise measurements on the (technical) functioning of currently available products can quickly become "incorrect" or "incomplete" for the next generation of products. These insights can support product designers and developers to make the right design decisions to enhance consumer satisfaction