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Influence of Images in Online Reviews for Search Goods on Helpfulness

Author

Listed:
  • Osterbrink Lars

    (Information Systems, University of Marburg, Universitätsstr. 24, Marburg, Hesse35032, Germany)

  • Alpar Paul

    (Information Systems, University of Marburg, Universitätsstr. 24, Marburg, Hesse35032, Germany)

  • Seher Alexander

    (Information Systems, University of Marburg, Universitätsstr. 24, Marburg, Hesse35032, Germany)

Abstract

Reviewing and rating are important features of many social media websites, but they are found on many e-commerce sites too. The combination of social interaction and e-commerce is sometimes referred to as social commerce to indicate that people are supporting each other in the process of buying goods and services. Rgeviews of other consumers have a significant effect on consumer choice because they are usually considered authentic and more trustworthy than information presented by a vendor. The collaborative effort of consumers helps to make the right purchase decision (or prevent from a wrong one). The effect of reviews has often been researched in terms of helpfulness as indicated by their readers. Images are an important factor of helpfulness in reviews of experience goods where personal tastes and use play an important role. We extend this research to search goods where objective characteristics seem to prevail. In addition, we analyze potential interaction with other variables. The empirical study is performed with regression analyses on 3,483 search good reviews from Amazon.com followed by a matched pair analysis of 186 review pairs. We find that images have a significant positive effect on helpfulness of reviews of search goods too. This is especially true in case of short and ambiguous reviews.

Suggested Citation

  • Osterbrink Lars & Alpar Paul & Seher Alexander, 2020. "Influence of Images in Online Reviews for Search Goods on Helpfulness," Review of Marketing Science, De Gruyter, vol. 18(1), pages 43-73, September.
  • Handle: RePEc:bpj:revmkt:v:18:y:2020:i:1:p:43-73:n:3
    DOI: 10.1515/roms-2019-0072
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    References listed on IDEAS

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    1. Ert, Eyal & Fleischer, Aliza & Magen, Nathan, 2016. "Trust and reputation in the sharing economy: The role of personal photos in Airbnb," Tourism Management, Elsevier, vol. 55(C), pages 62-73.
    2. Zhang, Jason Q. & Craciun, Georgiana & Shin, Dongwoo, 2010. "When does electronic word-of-mouth matter? A study of consumer product reviews," Journal of Business Research, Elsevier, vol. 63(12), pages 1336-1341, December.
    3. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type," Grenoble Ecole de Management (Post-Print) halshs-01923243, HAL.
    4. Andreas Munzel, 2016. "Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus," Post-Print hal-02423574, HAL.
    5. Munzel, Andreas, 2016. "Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus," Journal of Retailing and Consumer Services, Elsevier, vol. 32(C), pages 96-108.
    6. Wang, Fang & Menon, Kalyani & Ranaweera, Chatura, 2018. "Dynamic trends in online product ratings: A diagnostic utility explanation," Journal of Business Research, Elsevier, vol. 87(C), pages 80-89.
    7. Bin Guo & Shasha Zhou, 2017. "What makes population perception of review helpfulness: an information processing perspective," Electronic Commerce Research, Springer, vol. 17(4), pages 585-608, December.
    8. Andreas Munzel, 2016. "Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus," Post-Print halshs-01522497, HAL.
    9. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    10. Wang, Xia & Yu, Chunling & Wei, Yujie, 2012. "Social Media Peer Communication and Impacts on Purchase Intentions: A Consumer Socialization Framework," Journal of Interactive Marketing, Elsevier, vol. 26(4), pages 198-208.
    11. Xiang, Zheng & Du, Qianzhou & Ma, Yufeng & Fan, Weiguo, 2017. "A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism," Tourism Management, Elsevier, vol. 58(C), pages 51-65.
    12. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type," Post-Print halshs-01923243, HAL.
    13. Felbermayr, Armin & Nanopoulos, Alexandros, 2016. "The Role of Emotions for the Perceived Usefulness in Online Customer Reviews," Journal of Interactive Marketing, Elsevier, vol. 36(C), pages 60-76.
    14. Zhuang, Mengzhou & Cui, Geng & Peng, Ling, 2018. "Manufactured opinions: The effect of manipulating online product reviews," Journal of Business Research, Elsevier, vol. 87(C), pages 24-35.
    15. Pan, Yue & Zhang, Jason Q., 2011. "Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews," Journal of Retailing, Elsevier, vol. 87(4), pages 598-612.
    Full references (including those not matched with items on IDEAS)

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