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Das Rezept für die perfekte Rezension?

Einflussfaktoren auf die Nützlichkeit von Online-Kundenrezensionen

The Recipe for the Perfect Review?

An Investigation into the Determinants of Review Helpfulness

WIRTSCHAFTSINFORMATIK volume 55, pages 135–146 (2013)Cite this article

Zusammenfassung

Internethändler bieten ihren Kunden vermehrt die Möglichkeit, Online-Rezensionen zu erstellen. Diese reduzieren die Suchkosten anderer Kunden und erhöhen deren Verweildauer im E-Shop. Mittlerweile sind jedoch so viele Rezensionen verfügbar, dass das Auffinden von Produktinformationen und die Einschätzung der Produktqualität schwierig geworden sind. Abhilfe sollte die Bewertung der Nützlichkeit der Rezensionen durch Leser schaffen. Dieser Mechanismus hat jedoch zwei kritische Schwachstellen. Zum einen bleiben viele Rezensionen unbewertet, sodass sie bei einer Sortierung nach der Nützlichkeit herausfallen. Zum anderen gibt es keine Anhaltspunkte für Rezensenten, wie eine nützliche Rezension aussehen sollte. Zur Ableitung von Einflussfaktoren auf die Nützlichkeit von Produktrezensionen wird das Modell von Wang und Strong zur kontextabhängigen Beurteilung von Datenqualität adaptiert. Eine empirische Analyse von 27.104 Kundenrezensionen auf Amazon.com über sechs Produktkategorien zeigt, dass die Nützlichkeit einer Rezension nicht nur von ihren eigenen Attributen abhängt, sondern auch von kontextuellen Faktoren, die sich aus der Gesamtheit aller verfügbaren Rezensionen ergeben. Rezensionen für Erfahrungs- und Suchgüter unterscheiden sich systematisch voneinander. Das vorgeschlagene Modell erlaubt die Berechnung vorläufiger Nützlichkeitswerte für unbewertete Rezensionen und bildet die Basis für einen Kundenleitfaden zur Erstellung nützlicherer Rezensionen.

Abstract

Online product reviews, originally intended to reduce consumers’ pre-purchase search and evaluation costs, have become so numerous that they are now themselves a source for information overload. To help consumers find high-quality reviews faster, review rankings based on consumers’ evaluations of their helpfulness were introduced. But many reviews are never evaluated and never ranked. Moreover, current helpfulness-based systems provide little or no advice to reviewers on how to write more helpful reviews. Average review quality and consumer search costs could be much improved if these issues were solved. This requires identifying the determinants of review helpfulness, which we carry out based on an adaption of Wang and Strong’s well-known data quality framework. Our empirical analysis shows that review helpfulness is influenced not only by single-review features but also by contextual factors expressing review value relative to all available reviews. Reviews for experiential goods differ systematically from reviews for utilitarian goods. Our findings, based on 27,104 reviews from Amazon.com across six product categories, form the basis for estimating preliminary helpfulness scores for unrated reviews and for developing interactive, personalized review writing support tools.

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Notes

  1. In „Amazon-ähnlichen“ Rezensionssystemen geben Rezensenten eine numerische Bewertung für das Produkt sowie eine frei formulierte Rezension (minimale Länge 20 Worte) ab. Das Produkt muss nicht anhand vorgeschriebener standardisierter Kriterien bewertet werden (z. B. TripAdvisor.com).

  2. Zwei Studien zur Nützlichkeit von Rezensionen (Jin und Liu 2010; Chen und Tseng 2011) benutzen Teile dieses Klassifikationsmodells, um Qualitätsmaße für Rezensionen abzuleiten.

  3. Das Originalmodell wurde im Kontext des Managements von Unternehmensdatenbanken entwickelt.

  4. Die Produktattribute wurden aus den Webseiten der Hersteller extrahiert.

  5. Die Regressionen wurden ebenfalls mit anderen Lesbarkeitsmaßen (Flesch-Kincaid Readibility Ease, Flesch-Kincaid Grade Level, Gunning Fog Index, Automated Readability Index und Coleman-Liau Index) durchgeführt. Es konnten keine nennenswerten Veränderungen der Ergebnisse festgestellt werden. Das SMOG-Maß wies die geringsten Werte für das Akaike-Informationskriterium auf.

  6. Die 10 nützlichsten und unnützesten Adjektive und Adverbien für jede Produktkategorie sind in Online-Anhang D aufgelistet.

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Authors and Affiliations

  1. Juniorprofessur für E-Commerce, Universität Passau, Innstr. 43, 94032, Passau, Deutschland

    Michael Scholz

  2. Lehrstuhl für Wirtschaftsinformatik II, Universität Passau, Innstr. 43, 94032, Passau, Deutschland

    Verena Dorner

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  1. Michael Scholz
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  2. Verena Dorner
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Correspondence to Verena Dorner.

Additional information

Angenommen nach zwei Überarbeitungen durch Prof. Dr. Spann.

This article is also available in English via http://www.springerlink.com and http://www.bise-journal.org: Scholz M, Dorner V (2013) The Recipe for the Perfect Review? An Investigation into the Determinants of Review Helpfulness. Bus Inf Syst Eng. doi: 10.1007/s12599-013-0259-3.

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Scholz, M., Dorner, V. Das Rezept für die perfekte Rezension?. Wirtschaftsinf 55, 135–146 (2013). https://doi.org/10.1007/s11576-013-0358-2

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Schlüsselwörter

  • Electronic Commerce
  • Produktrezensionen
  • Internethandel
  • Elektronische Mundpropaganda

Keywords

  • Electronic commerce
  • Product reviews
  • Internet retailing
  • Electronic word-of-mouth