Category: PUBLICATIONS

The existing literature examines VIs’ utilitarian, personal, and relational traits, but a gap exists in interface design understanding. This study employs avatar marketing and ambivalence theories to explore how VIs’ design elements (VI’s varying form realism and behavioral realism design) induce ambivalent emotions (eeriness and coolness) and impact VI marketing performance (follow and purchase intentions). Two experimental studies were conducted, using ANCOVA, multiple regression, and PROCESS macro analyses. In Study 1, utilizing contemporary VIs from the market, it was revealed that a VI exhibiting mid (vs. low) form realism triggered heightened perceptions of both eeriness and coolness, with these effects being magnified under the condition of high (vs. low) behavioral realism. In Study 2, using VIs generated through the StyleGAN AI technique, it was validated that those with high (vs. low) form realism were associated with decreased perceptions of eeriness and increased coolness, particularly when combined with high (vs. low) behavioral realism. Additionally, our findings emphasized the negative impact of eeriness and the positive influence of coolness on VI performance outcomes. In summary, our study reveals the significant role of VI interface designs in VI marketing performance, highlighting consumers’ mixed emotions as crucial mediators in this context. Additionally, our findings highlight the negative impact of eeriness and the positive influence of coolness on VI performance. Its primary contribution is uncovering VIs’ interface designs as significant factors in VI marketing performance, while recognizing consumers’ ambivalent emotions as vital mediators in this relationship.

Spectrogram and other time-frequency analysis methods transfer an audio file into an auditory image. When signal processing-based analysis and interpretation is performed on these auditory images instead of an audio signal, spectrographic analyses can identify interesting patterns that focus on very different aspects of the signal compared to an audio-based analysis. To facilitate an auditory image-based study, a quantitative analysis and interpretation framework is implemented for exploring the spectrographic images in multiple time and frequency scales and for automatically identifying image features that are relevant to human auditory perception. This analysis framework is applied to two social media datasets: (1) soundtracks from video commercials and “hit” music excerpts from social media platforms, and (2) soundtracks from television and film. Analysis results from social media are also compared with audience subjective evaluations to validate the perceptual relevance of the identified spectrographic patterns.