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Purpose

Online retailers often recommend complementary products alongside focal products, but little is known about how these recommendations influence consumer perceptions and purchase decisions. This research investigates the effects of complementary product diversity and message framing on purchase intentions for both the focal and the complementary products.

Design/methodology/approach

Four experiments were conducted to test the proposed hypotheses. Studies 1a and 1b examined how recommending a more diverse set of complementary products influences purchase intentions for both the focal and the complementary products. Study 2 investigated the mediating role of confidence in the judgment of the focal product, while Study 3 explored the moderating role of recommendation message framing in this relationship.

Findings

Recommending a more diverse set of complementary products increased purchase intentions for both the focal and the complementary products (Studies 1a and 1b). Confidence in the judgment of the focal product mediated this relationship (Study 2). Additionally, high-distance message framing for recommending a more diverse set of complementary products increased confidence and purchase intention for the focal product (Study 3).

Practical implications

Online retailers should consider diversity and high-distance message framing when developing recommendation algorithms for complementary products. This approach can enhance purchase intentions, increase cross-selling opportunities and boost sales and profitability.

Originality/value

This study is among the first to examine how the diversity of complementary product recommendations influences consumer decision-making in online retail. It contributes to research on complementary products, product bundles, message framing and construal theory by showing that recommending a more diverse set of complementary products increases purchase intentions for both the focal and the complementary products. These findings offer valuable insights for online retailers to optimize recommendation strategies and algorithms, thus increasing sales and profitability.

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