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Longer online reviews are not necessarily better

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  • Fink, Lior
  • Rosenfeld, Liron
  • Ravid, Gilad

Abstract

Models of information processing have long suggested that people respond in a curvilinear manner to variation in information load and that information use may be restricted when available information is either scarce or abundant. Research on online product reviews, however, suggests that the relationship between the length of online reviews available to consumers and effectiveness measures is positive and linear. To explain this discrepancy, we argue that review length has a negative curvilinear (inverted-U-shaped) relationship with effectiveness and that such a relationship has seldom been observed in previous studies because those have analyzed data collected in low-constraint settings of information processing. The analysis of data about online reviews for free and paid apps, collected on two mobile app stores, provides consistent evidence in support of the hypothesized curvilinear relationship. The findings suggest that maximum cognitive load is experienced at lower review lengths for paid apps than for free apps and that the marginal utility for the majority of review length observations is positive or nonsignificant for free apps and negative for paid apps. These findings are consistent with product-related differences in information processing motivation. The study contributes to the ongoing debate on the ideal length of messages in electronic environments.

Suggested Citation

  • Fink, Lior & Rosenfeld, Liron & Ravid, Gilad, 2018. "Longer online reviews are not necessarily better," International Journal of Information Management, Elsevier, vol. 39(C), pages 30-37.
    • Handle: RePEc:eee:ininma:v:39:y:2018:i:c:p:30-37
    DOI: 10.1016/j.ijinfomgt.2017.11.002
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    2. Gang Chen & Lihua Huang & Shuaiyong Xiao & Chenghong Zhang & Huimin Zhao, 2024. "Attending to Customer Attention: A Novel Deep Learning Method for Leveraging Multimodal Online Reviews to Enhance Sales Prediction," Information Systems Research, INFORMS, vol. 35(2), pages 829-849, June.
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    4. Wang, Qiang & Zhang, Wen & Li, Jian & Ma, Zhenzhong, 2023. "Complements or confounders? A study of effects of target and non-target features on online fraudulent reviewer detection," Journal of Business Research, Elsevier, vol. 167(C).

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