Product Brokering Efficiency as a mediator of Online Product Recommendation and Customer Loyalty

Felisa Lilian, Honey Wahyuni Sugiharto Elgeka, V Heru Hariyanto
(Submitted 17 February 2021)
(Published 24 December 2021)


Marketing strategies in e-commerce have a main goal, that is to pursue customer loyalty. Sociolla is an e-commerce company that sells cosmetic products and has a recommendation feature to make it easier for customers during the shopping process. These recommendations can trigger customer satisfaction and generate loyalty. The purpose of this study was to examine the correlation between online product recommendation and customer loyalty with product brokering efficiency as a mediator. 179 Sociolla customers were recruited in this study using convenience sampling. The data were analyzed using the SPSS-Process Hayes model 4. Results showed that perceived decision quality acts as a mediator in the relationship between enablers and customer loyalty (β = .20, [ .13; .27]). It can be concluded that recommendations that are comprehensive, clear, and meet the customer needs will make it easier for customers to make purchasing decisions, which ultimately leads the customers to form loyalty toward the products.


customer loyalty; online product recommendations; product brokering efficiency.

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DOI: 10.22146/jpsi.64138


Cenfetelli, RT, & Schwarz, A. (2011). Identifying and testing the inhibitors of technology usage intentions. Information Systems Research, 22 (4), 808–823.

Craig, AW, Loureiro, YK, Wood, S., & Vendemia, JMC (2012). Suspicious minds: Exploring neural processes during exposure to deceptive advertising. Journal of Marketing Research, 49 (3), 361–372.

Darke, PR, & Ritchie, RJB (2007). The defensive consumer: Advertising deception, defensive processing, and distrust. Journal of Marketing Research, 44 (1), 114–127.

Fang, YH (2012). Does online interactivity matter? Exploring the role of interactivity strategies in consumer decision making. Computers in Human Behavior, 28 (5), 1790–1804.

Graham, L. (2020). Advantages of shopping online at Sociolla. Retrieved from: on August 24, 2021

Hess, T., Fuller, M., & Campbell, D. (2009). Designing interfaces with social presence: Using vividness and extraversion to create social recommendation agents. Journal of the Association for Information Systems, 10 (12), 889–919.

Iprice. (2021). Indonesia e-commerce map. Retrieved from: on August 24, 2021

Jacoby, J., Speller, DE, & Kohn, CA (1974). Brand choice behavior as a function of information load. Journal of Marketing Research, 11 (1), 63.

Jiang, Z., & Benbasat, I. (2007a). Investigating the influence of the functional mechanisms of online product presentations. Information Systems Research, 18 (4), 454–470.

Jiang, Z., & Benbasat, I. (2007b). The effects of presentation formats and task complexity on online consumers' product understanding. MIS Quarterly: Management Information Systems, 31 (3), 475–500.

Lee, BK, & Lee, WN (2004). The effect of information overload on consumer choice quality in an online environment. Psychology and Marketing, 21 (3), 159–183.

Liang, TP, Lai, HJ, & Ku, YIC (2006). Personalized content recommendation and user satisfaction: Theoretical synthesis and empirical findings. Journal of Management Information Systems, 23 (3), 45–70.

Lim, KH, Benbasat, I., & Ward, LM (2000). The role of multimedia in changing first impression bias. Information Systems Research, 11 (2), 115–136.

Markopoulos, PM, & Clemons, EK (2013). Reducing buyers' uncertainty about taste-related product attributes. Journal of Management Information Systems, 30 (2), 269–299.

Mavlanova, T., Benbunan-Fich, R., & Lang, G. (2016). The role of external and internal signals in E-commerce. Decision Support Systems, 87, 59–68.

Olbrich, R., & Holsing, C. (2011). Modeling consumer purchasing behavior in social shopping communities with clickstream data. International Journal of Electronic Commerce, 16 (2), 15–40.

Park, DH, & Lee, J. (2008). eWOM overload and its effect on consumer behavioral intention depending on consumer involvement. Electronic Commerce Research and Applications, 7 (4), 386–398.

Pratminingsih, SA, Lipuringtyas, C., & Rimenta, T. (2013). Factors Influencing Customer Loyalty Toward Online Shopping. International Journal of Trade, Economics, and Finance, 4 (3), 104–110.

Ratchford, BT (2001). The economics of consumer knowledge. Journal of Consumer Research, 27 (4), 397–411.

Srinivasan, SS, Anderson, R., & Ponnavolu, K. (2002). Customer loyalty in e-commerce: An exploration of its antecedents and consequences. Journal of Retailing, 78 (1), 41–50.

Tam, KY, & Ho, SY (2005). Web personalization as a persuasion strategy: An elaboration likelihood model perspective. Information Systems Research, 16 (3), 271–291.

Tam, KY, & Ho, SY (2006). Understanding the impact of web personalization on user information processing and decision outcomes. MS Quarterly, 30 (4), 865–890.

Wang, W., Qiu, L., Kim, D., & Benbasat, I. (2016). Effects of rational and social appeals of online recommendation agents on cognition- and affect-based trust. Decision Support Systems, 86, 48–60.

Xu, J., Benbasat, I., & Cenfetelli, RT (2014). The nature and consequences of trade-off transparency in the context of recommendation agents. MIS Quarterly: Management Information Systems, 38 (2), 379–406.

Zhang, H., Zhao, L., & Gupta, S. (2018). The role of online product recommendations on customer decision-making and loyalty in social shopping communities. International Journal of Information Management, 38 (1), 150–166.

Zhang, T., Agarwal, R., & Jr., HCL (2011). The value of it-enabled retailer learning: Personalized product recommendations and customer store loyalty in electronic markets. MIS Quarterly, 35 (4), 859–881.


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