Nudging moods to induce unplanned purchases in imperfect mobile personalization contexts

Shuk Ying Ho, Kai H. Lim

    Research output: Contribution to journalArticlepeer-review

    57 Citations (Scopus)

    Abstract

    By tracking consumers' browsing and purchase history, web personalization generates taste-matched recommendations for each consumer to stimulate purchases. In addition to taste-matching, mobile personalization matches recommendations to a consumer's physiological need and current location. These two additional features, referred to as need-matching and location-matching, are believed to be enablers of unplanned purchases. However, mobile advertisers may not be able to generate recommendations that meet all personalization criteria. Hence, mobile recommendations may be imperfect. We examine two questions in relation to imperfect recommendations. First, how do we use a descriptor to promote such recommendations? Second, what personalization criterion should be downplayed to induce unplanned purchases? Drawing upon the theory of mood congruence, we theorize that the effect of imperfect recommendation on consumers' unplanned purchases depends on their mood. We conducted three field experiments to test our hypotheses. Our findings indicate that (1) consumers in positive moods are more likely to form an urge to buy than those in negative moods, and this difference is larger when the descriptor is partial than when it is complete (Experiment 1); (2) need-matching is more influential on urge to buy for consumers in negative moods than for those in positive moods (Experiment 2); and (3) for taste-and-need-matched recommendations, location-matching exerts a stronger effect on the urge to buy for consumers in negative moods than for those in positive moods (Experiment 3). We validated the relevance of our research findings to practice through interviews with senior executives in personalization solution providers. Pathways for enhancing practical impacts of this line of research are recommended.

    Original languageEnglish
    Pages (from-to)757-778
    Number of pages22
    JournalMIS Quarterly: Management Information Systems
    Volume42
    Issue number3
    DOIs
    Publication statusPublished - Sept 2018

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