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Rinke, Wolfram
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Official Name
Rinke, Wolfram
Alternative Name
Rinke, Wolfram C.
Main Affiliation
ÖGAI Österreichische Gesellschaft für Artificial Intelligence
AAWE American Association of Wine Economists
Akademische Titel
Dipl.-Ing.
Email
wolfram.rinke@fh-burgenland.at
ORCID
Scopus Author ID
57203619840
Status
staff
Research Outputs
Now showing 1 - 5 of 5
- PublicationConsumer preferences for certified wines in France: A comparison of sustainable labels(Firenze University Press, 2021)
; 89 1Scopus© Citations 9 - PublicationDie Differenzierung des Herkunftsterroirs von burgenländischen Leitweinen auf Basis deskriptiver Kostbewertungen - Teil 1: 'Blaufränkisch' und 'Zweigelt'(2014)
; ; 144 2400 - PublicationHave consumers escaped from COVID-19 restrictions by seeking variety? A Machine Learning approach analyzing wine purchase behavior in the United States(2023)
; Ho, Shuay-tsyrThe COVID-19 pandemic itself constitutes an environment for people to experience the potential loss of control and freedom due to social distancing measures and other government orders. Variety-seeking has been treated as a mechanism to regain a sense of self-control. Using Machine Learning model and household-level data with a focus on the wine market in the United States, this study showcases the changing variety-seeking behavior over the pandemic year of 2020, in which people’s perception of the status of restriction measures influences the degree of their use of variety-seeking behavior as a coping strategy. It is the shopping pattern and store environments that drive the behavioral responses in wine purchases to freedom-limited circumstances. Coupon use is associated with a lower variety-seeking tendency at the beginning of the stay-at-home order, but the variety level resumes when more time has passed in the restriction periods. Variety-seeking tendency increases with shopping frequency at the beginning of the social distancing measure but decreases to a level lower than all the non-restriction periods.10 1 - PublicationPricing Models for German Wine: Hedonic Regression vs. Machine LearningThis article examines whether there are different hedonic price models for different German wines by grape variety, and identifies influential factors that focus on weather variables and direct and indirect quality measures for wine prices. A log linear regression model is first applied only for Riesling, and then machine learning is used to find hedonic price models for Riesling, Silvaner, Pinot Blanc, and Pinot Noir. Machine learning exhibits slightly greater explanatory power, suggests adding additional variables, and allows for a more detailed interpretation of results. Gault&Millau points are shown to have a significant positive impact on German wine prices. The log linear approach suggests a huge effect of different quality categories on the wine prices for Riesling with the highest price premiums for Auslese and “Beerenauslese/Trockenbeerenauslese/Eiswein (Batbaice),” while the machine learning model shows, that additionally the alcohol level has a positive effect on wines in the quality categories “QbA,” “Kabinett,” and “Spätlese,” and a mostly negative one in the categories “Auslese” and “Batbaice.” Weather variables exert different affects per grape variety, but all grape varieties have problems coping with rising maximum temperatures in the winter and with rising minimum and maximum temperatures in the harvest season.
738 2Scopus© Citations 10 - PublicationAn Algorithm to Transform an Artificial Neural Network into its Open Equation Form and its Potential ApplicationsDuring the last decades artificial neural networks have evolved to an accepted and proven technology for modelling and function approximation. Different kinds of network architectures exist to support certain domains and applications in an efficient way. This paper assumes the traditional multilayer feedforward artificial neural network (ANN) architecture with one input layer, one or more fully interconnected hidden layers and one output layer. Each layer consists of several classic perceptron nodes using a differentiable transfer function like the logistics function. Very often it is useful to have an ANN model in an open equation form available, that allows a deeper analysis of the model and to do more complex experiments and simulations. The following paper presents an algorithm that makes it possible to transform an ANN into its open form equivalent, called process model architecture network or PMA network. It has been used as an integral part in several industrial control projects. A PMA network can be used for system simulation, scenario analyses or inverse model based control. An example application is discussed.
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