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The effects of 3rd party consensus information on service expectations and online trust

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  • Benedicktus, Ray L.

Abstract

The marketing literature has recently explored a number of ways in which trust can be communicated by Internet retailers, including 3rd party consensus ratings. This paper explores the impact of consensus sequences over time and across high and low ranges, rather than the mere valence of ratings as presented in past research. Second, effects are compared across products with variant levels of risk. Two experiments investigate service quality inferences, expected satisfaction, and trust beliefs for online retailers as outcomes of 3rd party consensus information (i.e., agreement among a firm's past customers). Results indicate that online trust beliefs vary positively with consensus ratings and trust is higher when ratings trends increase rather than decrease. Service quality inferences and expected satisfaction are shown to mediate these relationships. More interestingly, results of study two suggest sequence direction becomes insignificant when ratings do not approach certain range limits (e.g., high, moderate, low cut-offs). Comparisons across products varying in risk show that consensus ratings are more important when consumers evaluate high risk products. Implications for both researchers and practitioners are offered.

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  • Benedicktus, Ray L., 2011. "The effects of 3rd party consensus information on service expectations and online trust," Journal of Business Research, Elsevier, vol. 64(8), pages 846-853, August.
    • Handle: RePEc:eee:jbrese:v:64:y:2011:i:8:p:846-853
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    2. Purnawirawan, Nathalia & Eisend, Martin & De Pelsmacker, Patrick & Dens, Nathalie, 2015. "A Meta-analytic Investigation of the Role of Valence in Online Reviews," Journal of Interactive Marketing, Elsevier, vol. 31(C), pages 17-27.
    3. Wenlong Liu & Rongrong Ji, 2018. "Examining the Role of Online Reviews in Chinese Online Group Buying Context: The Moderating Effect of Promotional Marketing," Social Sciences, MDPI, vol. 7(8), pages 1-17, August.
    4. Thaichon, Paramaporn & Lobo, Antonio & Prentice, Catherine & Quach, Thu Nguyen, 2014. "The development of service quality dimensions for internet service providers: Retaining customers of different usage patterns," Journal of Retailing and Consumer Services, Elsevier, vol. 21(6), pages 1047-1058.
    5. Filieri, Raffaele, 2015. "What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM," Journal of Business Research, Elsevier, vol. 68(6), pages 1261-1270.
    6. Könsgen, Raoul & Schaarschmidt, Mario & Ivens, Stefan & Munzel, Andreas, 2018. "Finding Meaning in Contradiction on Employee Review Sites — Effects of Discrepant Online Reviews on Job Application Intentions," Journal of Interactive Marketing, Elsevier, vol. 43(C), pages 165-177.
    7. Jiménez, Fernando R. & Mendoza, Norma A., 2013. "Too Popular to Ignore: The Influence of Online Reviews on Purchase Intentions of Search and Experience Products," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 226-235.
    8. Filieri, Raffaele & Lin, Zhibin & Pino, Giovanni & Alguezaui, Salma & Inversini, Alessandro, 2021. "The role of visual cues in eWOM on consumers’ behavioral intention and decisions," Journal of Business Research, Elsevier, vol. 135(C), pages 663-675.
    9. Griva, Anastasia, 2022. "“I can get no e-satisfaction†. What analytics say? Evidence using satisfaction data from e-commerce," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).

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