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Exploring Susceptibility Measures to Persuasion

  • Conference paper
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Persuasive Technology. Designing for Future Change (PERSUASIVE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12064))

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Abstract

There is increasing evidence that indicates how personalising persuasive strategies may increase the effectiveness of persuasive technologies and behaviour change interventions. This has led to a wide range of studies exploring self reported, perceived susceptibility to persuasion, which highlight the role of individual differences. Conducting such studies, while accounting for individual differences can be challenging, particularly where persuasive strategies may be considered similar due to their underlying components. In this paper, we present a study exploring perceived susceptibility to Cialdini’s principles of persuasion, with a focus on how we can distinguish perceived susceptibility measures between the most recently identified Unity principle and Social proof. This study was conducted using an online survey incorporating perceived susceptibility measures to all seven Cialdini principles and a measure of the actual effectiveness of seven corresponding persuasive strategies. Our results indicate that while we are able to distinguish perceived susceptibility measures between Unity and Social proof, together with Commitment, Scarcity and Reciprocity, we were unable to obtain these measures for Liking and Authority.

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Notes

  1. 1.

    This analysis was performed prior to investigating whether perceived susceptibility measures for each component of the PCA model in Table 4 corresponded with participants’ actual behaviour.

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Acknowledgements

This research is supported by the UKRI EPSRC award: EP/P011829/1.

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Correspondence to John Paul Vargheese .

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Vargheese, J.P., Collinson, M., Masthoff, J. (2020). Exploring Susceptibility Measures to Persuasion. In: Gram-Hansen, S., Jonasen, T., Midden, C. (eds) Persuasive Technology. Designing for Future Change. PERSUASIVE 2020. Lecture Notes in Computer Science(), vol 12064. Springer, Cham. https://doi.org/10.1007/978-3-030-45712-9_2

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  • DOI: https://doi.org/10.1007/978-3-030-45712-9_2

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