Comparing fundraising campaigns in healthcare using psychophysiological data: a network-based approach

Spyros Balafas, Clelia Serio, Riccardo Lolatto, Marco Mandolfo, Anna Maria Bianchi, Ernst Wit and Chiara Brombin ()
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Spyros Balafas: Universita Vita–Salute San Raffaele
Clelia Serio: Universita Vita–Salute San Raffaele
Riccardo Lolatto: IRCCS San Raffaele Scientific Institute
Marco Mandolfo: Politecnico di Milano
Anna Maria Bianchi: Politecnico di Milano
Ernst Wit: Università della Svizzera italiana
Chiara Brombin: Universita Vita–Salute San Raffaele

Statistical Methods & Applications, 2024, vol. 33, issue 5, No 6, 1403-1427

Abstract: Abstract Measuring the effectiveness of fundraising campaigns is crucial for improving communication strategies. This is particularly pertinent for healthcare campaigns aimed at raising awareness about sensitive health issues that require financial support for advancing research efforts. The present work assesses campaign effectiveness by examining brain activation evoked by different video stimuli. Within a multivariate statistical setting, we compare the physiological responses that are induced by four fundraising campaigns designed under different communication strategies. Specifically, we model attention-related electroencephalographic (EEG) signals using graphical models to estimate partial correlation networks associated with each video campaign. These networks are then compared in terms of structure and connectivity using resampling methods. The proposed approach is flexible, allowing for the analysis of induced physiological responses at both local and global levels. It accounts for the interrelationships among collected EEG data and participants’ heterogeneity, overcoming the need to derive composite scores as is commonly done in neuromarketing research areas. The networks derived from different campaigns exhibit significantly different structures and connectivity, indicating distinct cognitive and emotional responses induced by the videos. Given its generality, our proposed approach can be applied effectively in psychological and neuroscientific research fields whenever the physiological response to affective stimuli is of interest. Graphical abstract

Keywords: Electroencephalography; Gaussian graphical model; Networks; Time series (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10260-024-00761-1

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