Notwithstanding the considerable investments in the last decade, highly praised concept of e-government has largely failed to achieve the expected results. Consumption of e-government services is far below government anticipations, while the expected benefits in terms of cost reduction and greater effectiveness of public administration are still in early stages. All these facts suggest that current evaluation of e-government policies and related decision-making is inadequate, whereas lacking formal procedures and reliable indicator models consequently results in poor quality planning and implementation of e-government policies. Paper presents an analysis of existing indicator models for evaluation of e-government policies, identifies characteristic evaluation aspects and evaluation levels and conceptualizes an integrated indicator model for evaluation of e-government policies. Analysis offers an insight into the current evaluation practice, enables detection of its deficiencies and provides a significant contribution to the development of applicable indicator models facilitating more evidencebased evaluation of e-government policies.
B.03 Paper at an international scientific conference
COBISS.SI-ID: 3943086Scientists use two forms of knowledge in the construction of explanatory models: generalized entities and processes that relate them; and constraints that specify acceptable combinations of these components. Previous research on inductive process modeling, which constructs models from knowledge and time-series data, has relied on handcrafted constraints. In this paper, we report an approach to discovering such constraints from a set of models that have been ranked according to their error on observations. Our approach adapts inductive techniques for supervised learning to identify process combinations that characterize accurate models. We evaluate the methodʼs ability to reconstruct known constraints and to generalize well to other modeling tasks in the same domain. Experiments with synthetic data indicate that the approach can successfully reconstruct known modeling constraints. Another study using natural data suggests that transferring constraints acquired from one modeling scenario to another within the same domain considerably reduces the amount of search for candidate model structures while retaining the most accurate ones.
B.03 Paper at an international scientific conference
COBISS.SI-ID: 4051118The majority of existing methodologies for evaluation of e-government policies is underdeveloped and partial, preventing comprehensive and objective evaluation. This situation consequently results in poor quality of planning and implementation process, while further diminishing positive effects and decreasing public consumption of e-government services. One of the most frequently overlooked aspects of e-government policies evaluation is the concept of public interest, which is not given sufficient attention within existing evaluation methodologies, reducing the legitimacy of policy making in the field. The significance of public interest is often elusively defined, while its dimensions are somehow rendered particularly within the financial benefits. Paper provides an analysis of more than 50 methodologies for evaluation of e-government policies, exploring the presence of public interest aspect within. Analysis offers an insight into the current evaluation practice enabling detection of its deficiencies as well as their mitigation, and could facilitate a significant contribution to more evidence-based evaluation of e-government policies.
B.03 Paper at an international scientific conference
COBISS.SI-ID: 4008878