Jurisdiction and applicable law determine key contract parameters for dispute resolution in cross-border business transactions. Each sovereign state may define its own Private International Law (PIL) governing the state-specific set of connecting factors based on which own or foreign jurisdiction is established. The directly comparable approach with state-specific connecting factors is in place with respect to applicable law. This territorial approach to dispute resolution makes PIL a highly complex field of law.
A thorough assessment in previous research revealed that service providers and customers are confronted with a high level of jurisdictional risk and uncertainty when doing international electronic business in the Internet. This is mainly due to the fact that there is no harmonized set of rules that applies specifically to the (near-)global and (by design) border-less infrastructure of the Internet and, thus, to contracts concluded in the Internet. Therefore, a transition towards a single, internationally harmonized PIL for electronic business in the Internet is perceived as the dominant long-term strategy in order to foster certainty and trust in international electronic business. Such transition needs time as it involves a large number of stakeholders with diverging agendas, interests, and objectives.
Accordingly, the overall research question has been determined in identifying and understanding new strategy implications that may result for different strategy proposals and scenario assumptions from a long-term transition to a harmonized, electronic business-compatible PIL in an interconnected system of service providers, consumers, legislators, courts, lawyers, and lobbyists. System dynamics was chosen as an appropriate modeling approach for understanding this complex system and the implications emerging in it from the transition modeled.
System dynamics-based model building is a multi-step procedure. Important steps, such as the identification of relevant stakeholders as well as the modeling of causalities, have been achieved already in a joint effort between experts of University of Zurich and University of Ljubljana. In this context, the thesis at hand is motivated by providing the relevant data collection and analysis that will facilitate the achievement of the next modeling step, i.e., a mathematical model formulation.
This next step in modeling bases on the causality model developed previously. Per stakeholder, a number of key indicators has to be determined. An indicator is a model variable that a stakeholder can influence or by that a stakeholder is directly affected. A key indicator is perceived as an indicator that the respective stakeholder has primary interest in when considering the modeled transformation. For instance, consumers might be most interested in reducing the assumed level of jurisdictional risk, courts might care primarily about the number of disputes filed in court, and legislators might focus mainly on the (cost-)effectiveness of an awareness campaign promoting newly available instruments to consumers.
Once the underlying dynamic hypothesis — i.e., a scenario — has been determined (which was done already), then the set of key indicators has to be defined and reasoned for. Each indicator needs to be characterized with regard to the following dimensions: What unit applies to an indicator in question? How can a stakeholder change a considered indicator? Which would be a value (measured in the respective unit assigned) that a stakeholder would expect in a "hoped for" and in a "feared for" scenario? The last mentioned dimension implies that both scenario outcomes ("hoped for" and "feared for") as well as an overall scenario (reflecting the modeled transition) need to be specified.
Next, for all indicators covered, a so-called behavior-over-time graph has to be drawn by eliciting expert knowledge in consideration of a given scenario. This graph uses a time unit — embracing the complete modeling horizon — for its x-axis and the respective applicable unit for the indicator in question for its y-axis. The graph visualizes how an indicator is assumed to change value for the full modeling period. The resulting behavior-over-time information will form the basis for achieving the next step in the overall system dynamics modeling procedure. It will provide necessary information for implementing the model in executable manner in the used modeling tool, VenSim.
Behavior-over-time information can only reflect assumptions, since the modeled transformation has not happened yet. And it is exactly in taking these assumptions in the right way and based on a scientific method where this thesis sees its main challenges, work load, and goals. Examples of assumptions to be taken include, but are not limited to the following: The overall nature of a key indicator when expressed over time (e.g., would an indicator rather behavior in a discrete or continous way, would it be assumed to move step-wise or in a rather "smooth" way?), absolute and relative changes to be assumed from a change in the overall system modeled, and assumptions on drivers for change in general.
Supervisors: Prof. Dr. Burkhard Stiller
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