L
Lea M. Wakolbinger
Researcher at University of Vienna
Publications - 11
Citations - 626
Lea M. Wakolbinger is an academic researcher from University of Vienna. The author has contributed to research in topics: Lottery & Informative advertising. The author has an hindex of 8, co-authored 11 publications receiving 546 citations.
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Journal ArticleDOI
Agent-based simulation of innovation diffusion: A review
TL;DR: The strengths and limitations of agent-based modeling in the context of innovation diffusion are critically examined, new insightsAgent-based models have provided are discussed, and promising opportunities for future research are outlined.
Journal ArticleDOI
The Effectiveness of Combining Online and Print Advertisements: Is the Whole Better than the Individual Parts?
TL;DR: In this paper, the authors analyzed the advertising effectiveness of print and online media as well as the impact of combining these two media forms on overall advertising effectiveness, and they found that both media forms feature the same advertising effectiveness.
Journal ArticleDOI
An agent-based simulation approach for the new product diffusion of a novel biomass fuel
TL;DR: In this paper, an agent-based simulation approach is introduced to support decision-makers in marketing activities to support the market introduction of innovative goods or services by furthering their diffusion and thus their success.
Proceedings ArticleDOI
An agent-based simulation model for the market diffusion of a second generation biofuel
TL;DR: An agent-based simulation model is developed that can provide potential investors with forecasts for the biofuel's market diffusion and presents simulation results.
Journal ArticleDOI
Identifying the determinants of foreign direct investment: a data-specific model selection approach
Erhard Reschenhofer,Michael Schilde,Eva M . Oberecker,Ellen Payr,Hasan T. Tandogan,Lea M. Wakolbinger +5 more
TL;DR: In this paper, the potential determinants of foreign direct investment were examined and new exact subset selection procedures, which are based on idealized assumptions, as well as their possibly more plausible empirical counterparts to select the optimal set of predictors.