Rinke, Wolfram

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

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    Bachelor Software Engineering und vernetzte Systeme Praxisprojekte 2022
    (FH Burgenland, 2022)
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    Entwicklung eines Tools zur Organisierung von Curricula / Modernisierung einer bestehenden IoT-Plattform bei FIB / Inventarmanagement / Pegelhub - Die zentrale Datendrehscheibe für Pegeldaten
      225  227
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    Pricing Models for German Wine: Hedonic Regression vs. Machine Learning
    (Cambridge University Press, 2020)
    Niklas, Britta 
    ;
    This 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.
      2Scopus© Citations 17  779
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    Bachelor Software Engineering und vernetzte Systeme Praxisprojekte 2023
    (FH Burgenland, 2023)
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    Inventory Management / Pegelhub – Die zentrale Datendrehscheibe für Pegeldaten / Schulgong – Neuentwicklung des altbekannten Pausengongs / Digitalisierung der Auswertung des Wissenstests der Feuerwehrjugend / Einrichtung einer Zeitreihendatenbank mit Benutzeroberfläche bei der FB
      305  314
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    Bachelor Software Engineering und vernetzte Systeme Praxisprojekte 2025
    (2025)
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    gAmIcode / Elektroauto Lade-App / I NEED HELP / VoiceArt / Atlas der Klima-bezogenen-Gesundheitsrisiken / NEXUS / Pegelhub / Klima-Gesundheitsplanspiel Burgenland
      284  164
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    Bachelor Software Engineering und vernetzte Systeme Praxisprojekte 2024
    (2024)
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    My Digital Profile / Hausübungsplattform mit Gamification / Typecode Registry / Pegelhub / RePro3D / Dokumentenmanagement
      169  175
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      1Scopus© Citations 20  119
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    Have consumers escaped from COVID-19 restrictions by seeking variety? A Machine Learning approach analyzing wine purchase behavior in the United States
    (2023)
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    Ho, Shuay-tsyr
    The 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.
      1Scopus© Citations 1  32