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Cause-Related Marketing: A Qualitative and Quantitative Analysis on Pinkwashing

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Data Science

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Abstract

In recent years a growing number of companies have decided to demonstrate their sensitivity to the subject of health. Therefore cause-related marketing campaigns that involve a partnership between companies and associations engaged in scientific research have increased significantly. Considering a particular initiative of cause-related marketing connected to those pink marketing projects aimed to fund breast cancer research, this paper intend to test the effects of the phenomenon of pinkwashing (pink = rose + washing = wash), a term proposed by the American Breast Cancer Action Campaign “Think before you pink,” launched in 2002, on trust, on the perceived risk, and consumer confusion. Based on an analysis of the literature on a similar subject, i.e., greenwashing it has developed a qualitative and quantitative research to explore the effects of pinkwashing. The starting point has been a structured questionnaire. Through this a descriptive analysis also using a two step cluster analysis and a hypothesis development has been performed.

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Authors and Affiliations

  1. Dipartimento di Scienze Economiche Aziendali Matematiche e Statistiche, Università di Trieste, via dell’Università 1, 34100, Trieste, Italy

    Gabriella Schoier & Patrizia de Luca

Authors
  1. Gabriella Schoier
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  2. Patrizia de Luca
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Corresponding author

Correspondence to Gabriella Schoier .

Editor information

Editors and Affiliations

  1. Department of Political Sciences, University of Naples Federico II, Napoli, Italy

    Francesco Palumbo

  2. Department of Statistical Sciences Paolo Fortunati, Alma Mater Studiorum, University of Bologna, Bologna, Italy

    Angela Montanari

  3. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Maurizio Vichi

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Schoier, G., de Luca, P. (2017). Cause-Related Marketing: A Qualitative and Quantitative Analysis on Pinkwashing. In: Palumbo, F., Montanari, A., Vichi, M. (eds) Data Science . Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-55723-6_25

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