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Analyse sensorischer E-Commerce-Elemente mittels Big-Data-Methoden und Künstlicher Intelligenz: Automatisierung sensorischer Bewertungen von E-Commerce- und Social-Media-Plattformen auf Basis des Online Sensory Marketing Index

Editor

Listed:
  • FOM Hochschule für Oekonomie & Management, ifid Institut für IT-Management & Digitalisierung

Author

Listed:
  • Hamacher, Kevin
  • Blach, Maike
  • Kozlik, Jan
  • Muster, Felix
  • Nöllenburg, Pascal-Philipp
  • Ohletz, Jan-Henrik
  • Franken, Gereon
  • Hernes, Dominic
  • Hinterding, Martin
  • Höveler, Patrick
  • Huppertz, Michael
  • Leppkes, Niklas
  • Lopez Rodriguez, Ariana
  • Maucy, Kevin
  • Petrov, Alexsandar
  • Schäfer, Dennis
  • Schneider, Robert
  • Spiegel, Bastian
  • Stecker, Rouven
  • Steinmann, Philipp
  • Tembrink, Christoph
  • Titze-Wolter, Philipp
  • Vishnyakova, Liudmila
  • Zimmermann, Jürgen
  • Buchkremer, Rüdiger

Abstract

Kundinnen und Kunden kommunizieren, kaufen und teilen Produkte und Dienstleistungen über digitale Plattformen, in denen technische Möglichkeiten zur Präsentation sensorischer Elemente im Normalfall auf visuelle, akustische und schriftliche Elemente begrenzt sind. Um das sensorische Online-Erlebnis zu verbessern, ist es erforderlich, Medientypen und Inhalte quantitativ zu bewerten und sensorisch zu optimieren. Basierend auf dem Online Sensory Marketing Index (OSMI), einem Schema zur sensorischen Bewertung von E-Commerce-Webseiten, präsentiert das Paper Ansätze und Ergebnisse zur Entwicklung von alternativen Verfahren zur automatischen Erfassung und Bewertung sensorischer Elemente wie Texte und Bilder. In diesem Beitrag werden zu diesem Zweck moderne Big-Data- und Künstliche-Intelligenz-Ansätze aus dem Bereich der Verarbeitung natürlicher Sprache, "Natural Language Processing" und "Natural Language Understanding", angewendet. Dazu werden Worteinbettungstechniken mit GloVe und Word2Vec sowie Transformer-Technologien wie der BERT-Algorithmus zur Untersuchung von über 200 Webseiten eingesetzt. Die Ergebnisse demonstrieren einen funktionalen und skalierbaren KI-Ansatz zur Bestimmung von Elementen aus dem Themenfeld des sensorischen Marketings für E-Commerce-Plattformen und Social Media von verschiedenen Branchen und Unternehmen. Darüber hinaus sind die neuen KI-Auswahl- und Bewertungsverfahren deutlich effizienter und mit nur geringen Leistungseinbußen im Vergleich zu einer manuellen Bewertung verbunden.

Suggested Citation

  • Hamacher, Kevin & Blach, Maike & Kozlik, Jan & Muster, Felix & Nöllenburg, Pascal-Philipp & Ohletz, Jan-Henrik & Franken, Gereon & Hernes, Dominic & Hinterding, Martin & Höveler, Patrick & Huppertz, M, 2023. "Analyse sensorischer E-Commerce-Elemente mittels Big-Data-Methoden und Künstlicher Intelligenz: Automatisierung sensorischer Bewertungen von E-Commerce- und Social-Media-Plattformen auf Basis des Onli," ifid Schriftenreihe: Beiträge zu IT-Management & Digitalisierung, FOM Hochschule für Oekonomie & Management, ifid Institut für IT-Management & Digitalisierung, volume 2, number 2 edited by FOM Hochschule für Oekonomie & Management, ifid Institut für IT-Management & Digitalisierung, July.
    • Handle: RePEc:zbw:fomiid:2
    as

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    References listed on IDEAS

    as
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