IP Traffic Statistics - A Markovian Approach

Data originating from non-voice sources is expected to play an increasingly important role in the next generation mobile communication services. To plan these networks, a detailed understanding of their traffic load is essential. Recent experimental studies have shown that network traffic originating from data applications can be self-similar, leading to a different queueing behavior than predicted by conventional traffic models. Heavy tailed probability distributions are appropriate for capturing this property, but including those random processes in a performance analysis makes it difficult and often impossible to find numerical results. In this thesis three related topics are addressed: It is shown that Markovian models with a large state space can be used to describe traffic which is self-similar over a large time scale, a Maximum Likelihood approach to fit parallel Erlang-k distributions directly to time series is developed, and the performance of a channel assignment procedure in a wireless communication network is evaluated using the above mentioned techniques to set up a Markovian model. Outcomes of the performance analysis are blocking probabilities and latency due to restrictions of the channel assignment procedure as well as estimations of the overall bandwidth that the system is required to offer in order to support a given number of users

Information Systems Research for the Next Generation: Child-Centricity in a Digital World

Traditionally, information systems (IS) research investigates socio-technical systems in organizations and the workplace. As IS have become an integral part of our daily lives, IS research nowadays also incorporates the private space. However, efforts to date have mostly focused on adults. Children, born into a digital world today, have been mostly left out. Yet our discipline not only has the potential to contribute to the adequate and child-friendly design of IS artifacts for children but can also help to further develop theories on children's behavior. For this to succeed, IS researchers need to adapt their approach to children. Ethical considerations should address children's vulnerability, the design of interventions should happen in close collaboration with children, research methods should be child-centered, and the specificities of children should be kept present in result analyses

Resolving the Misalignment between Consumer Privacy Concerns and Ubiquitous IS Design: The Case of Usage-based Insurance

Ubiquitous IS enables novel services and business models, yet require a careful balancing of consumer privacy concerns (PC) – induced by the provision of particular sensors and information types – with functional performance in order to maximize acceptance. For the exemplary case of Usage-based Insurance (UBI), this paper presents a design science approach to the mitigation of PC under parallel consideration of functional system performance. Based on long-term location trajectories from 1’600 vehicles, we assess the predictive power of emulated system designs that substitute location information, presumably the most privacy sensitive type of information in current UBI designs. We find that there are substantial grounds to challenge prevalent design paradigms in UBI and infer general insights from this example for IS researchers and IT professionals, who, when seeking to improve system privacy, often focus on privacy-enhancing technologies instead of considering the socio-technical context of ubiquitous IS

Reducing Environmental Impact in Procurement by Integrating Material Parameters in Information Systems: The Example of Apple Sourcing

Legislation, customer pressure, and energy costs are increasing the interest of enterprises in environmental performance indicators such as greenhouse gas emissions and energy usage. Currently, business users take decisions across the value chain, from product design to disposal, without the ability to compare the environmental impact of alternatives within their information systems, thus limiting the optimization potential. In this paper we consider procurement as an example business operation and show how capturing previously-unknown material parameters in the respective information system can significantly increase the achievable optimizations. We use apple procurement into the U.K. to illustrate the paper’s idea, and conduct Monte Carlo analysis to quantify the realizable impact reductions as each additional life cycle parameter is tracked. The results show that taking into account the production country alone achieves a decrease in energy consumption of around 1250MJ per ton of apples, equivalent to 28% reduction from the base case

The Behavioral Mechanisms Behind Feedback – A Preliminary Model for Quantifying Cause-Effect Relationships

The ubiquity of information technology makes it possible to provide individuals with personalized, context-specific, and timely feedback at low marginal cost. This allows for highly scalable behavioral interventions that can support behavior change even for frequent, habitual, and incidental behaviors. Despite the well-documented potential of feedback interventions in improving personal and societal outcomes, the associated theoretical insights – in particular for real-time feedback on everyday behaviors – are sparse. To this end, this research-in-progress paper proposes a conceptual model to formalize cause-effect relationships between feedback interventions, the antecedents of behavior, and resulting behavioral changes by integrating important concepts from feedback intervention theory and other behavioral theories. Moreover, this paper outlines the planned validation of the model and research hypotheses with an empirical field study. Ultimately, the results are expected to inform the development of (future) Information Systems for behavior change by holistically explaining behavioral mechanisms associated with feedback interventions

The effects of user identity and sanctions in online communities on real-world behavior

This paper describes a field study to investigate whether and to what extent individuals conserve more electricity if they have the opportunity to signal their behavior to others in the online community. Moreover, the study intends to reveal how positive social sanctions (e.g., publicly rewarding people who reduce their energy consumption) and negative social sanctions (e.g., publicly warning people who increase their consumption) cause individuals to alter both their energy demand and their time dedicated to the online community. We discuss related work on identity disclosure in online communities, on promoting sustainable behavior with information systems, and on economic theory explaining the effects of prosocial motives on behavior. The study will be conducted as field experiment using an energy efficiency portal developed by us and operated by an Austrian utility company that currently facilitates 9,899 active users out of which 1,400 will be randomly selected as study participants

Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting

Forecasting electricity demand plays a critical role in ensuring reliable and cost-efficient operation of the electricity supply. With the global transition to distributed renewable energy sources and the electrification of heating and transportation, accurate load forecasts become even more important. While numerous empirical studies and a handful of review articles exist, there is surprisingly little quantitative analysis of the literature, most notably none that identifies the impact of factors on forecasting performance across the entirety of empirical studies. In this article, we therefore present a Meta-Regression Analysis (MRA) that examines factors that influence the accuracy of short-term electricity load forecasts. We use data from 421 forecast models published in 59 studies. While the grid level (esp. individual vs. aggregated vs. system), the forecast granularity, and the algorithms used seem to have a significant impact on the MAPE, bibliometric data, dataset sizes, and prediction horizon show no significant effect. We found the LSTM approach and a combination of neural networks with other approaches to be the best forecasting methods. The results help practitioners and researchers to make meaningful model choices. Yet, this paper calls for further MRA in the field of load forecasting to close the blind spots in research and practice of load forecasting.Comment: 8 pages, 3 figures, 7 table

Kundenportale in der Energiebranche: Bestandsaufnahme und Entwicklungspotenziale

Zusammenfassung: Seit der Liberalisierung des Energiemarktes hat der Wettbewerb unter den Energieversorgern (EVU) zugenommen. Um neue Kunden zu gewinnen und bestehende zu halten, müssen diese daher neue Services und Produkte entwickeln, die den Bedürfnissen der Kunden in hohem Masse entsprechen. Ein Online-Kundenportal kann einen solchen Service darstellen, da es zukünftig als zentrale Schnittstelle zum Kunden fungieren wird, wenn der persönliche Kontakt aufgrund eines abnehmenden regionalen Fokus der EVU und dem Wegfall des Zähler-Ablesens vor Ort durch Smart Metering abnimmt. In diesem Beitrag werden die Kriterien, die für die Gestaltung eines solches Portals relevant sind, vorgestellt. Eine Marktübersicht zeigt auf, welche dieser Kriterien bereits umgesetzt werden und wo Verbesserungspotenzials bestehen. Ein spezieller Fokus liegt auf psychologische Konzepte, die den Kunden motivieren, das Portal einerseits regelmässig zu nutzen und andererseits Energie zu sparen. Es zeigt sich, dass gerade in Bezug auf Kriterien zur Veranschaulichung des Energieverbrauches sowie bei der Ausgestaltung von psychologischen Anreizen ein enormes Verbesserungspotenzial vorhanden is

Identifying individuals\u27 preferences using games: A field experiment in promoting sustainable energy consumption

We present a novel approach to identify individuals’ preferences in the context of sustainable energy consumption by letting them play a public good game (PGG). The study will be conducted using an energy-efficiency website developed by us and operated by an Austrian utility company that currently facilitates 9,929 users out of which 1,000 will be randomly selected as participants. Laboratory experiments on PGGs identified two types of cooperative behavior: free riders and conditional cooperators. While free riders tend to act unaffected by the behavior of others, conditional cooperators are stronger influenced by the perception of their peers. In our study, participants receive normative feedback on their consumption, and we investigate the moderating effects of the PGG’s results on changes in energy consumption. The findings will contribute to the design of information systems to promote environmental sustainability because their effectiveness is increased if the consumption feedback provided reflects the recipient’s preference

Green IS Design and Energy Conservation: An Empirical Investigation of Social Normative Feedback

A combination of information systems and socio-psychological concepts holds the potential to exert a positive influence on the energy consumption of individuals. In a field study among 9,929 users, we investigate whether descriptive and injunctive normative feedback lead to significant effects on energy demand if delivered by a website have the same effects as when delivered via personal interaction as in Schultz et al.’s (2007) study, and whether both feedback types have different effects on repeated voluntary system usage. We found a similar pattern with respect to energy consumption as did Schultz et al. (2007) based on personal interaction, and no difference with respect to frequency of system usage. The findings are important for the design of information systems that utilize feedback concepts to induce a sustainable energy usage among households. Ultimately, the results support utility companies and policy makers to cost-effectively meet regulatory saving targets