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      How consumers react to woke advertising: methodological triangulation based on social media data and self-report data

      , ,
      Journal of Research in Interactive Marketing
      Emerald

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          Abstract

          Purpose

          Guided by a synthesis of social norms theory (SNT), the social identity model of deindividuation effects (SIDE) and information cascades theory (ICT), this study aims to unveil the mechanism underlying the role of social norms in shaping consumer responses to woke advertising in the algorithmic social media environment.

          Design/methodology/approach

          This paper analyzed 125,481 unique comments on a woke campaign, which represented the dynamic social norms condition in which the prominence of popularity information sets a social norm that can be passed on through a sequential commenting process. Also, this paper conducted an experiment with two conditions, namely, static social norms condition, representing a situation in which the prominence of popularity information sets a social norm through a non-sequential commenting process; without social norms condition, epitomizing the situation in which there is no popularity information that can set a social norm.

          Findings

          The results revealed that when evaluating a social media-based woke ad, depersonalized consumers in a dynamic social norms condition were more likely to be influenced by the prevailing norms than those in a static social norms condition were.

          Originality/value

          Through the lens of ICT, this research extends SNT and SIDE by detailing the procedure regarding how perceived social norms shape the formation of consumer opinions in a sequential fashion.

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          Most cited references49

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          The theory of planned behavior

          Icek Ajzen (1991)
          Organizational Behavior and Human Decision Processes, 50(2), 179-211
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            A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades

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              SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

              The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages - from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we reflect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.
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                Author and article information

                Journal
                Journal of Research in Interactive Marketing
                JRIM
                Emerald
                2040-7122
                2040-7122
                May 31 2021
                October 15 2021
                May 31 2021
                October 15 2021
                : 15
                : 4
                : 529-548
                Article
                10.1108/JRIM-09-2020-0185
                f803c79b-1b52-45f6-bbe8-6fdbf4bce524
                © 2021

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