This study examines consumers' evaluations of product consumption values, purchase intentions and willingness to pay for fashion products designed using generative…
This study examines consumers' evaluations of product consumption values, purchase intentions and willingness to pay for fashion products designed using generative adversarial network (GAN), an artificial intelligence technology. This research investigates differences between consumers' evaluations of a GAN-generated product and a non-GAN-generated product and tests whether disclosing the use of GAN technology affects consumers' evaluations.
Sample products were developed as experimental stimuli using cycleGAN. Data were collected from 163 members of Generation Y. Participants were assigned to one of the three experimental conditions (i.e. non-GAN-generated images, GAN-generated images with disclosure and GAN-generated images without disclosure). Regression analysis and ANOVA were used to test the hypotheses.
Functional, social and epistemic consumption values positively affect willingness to pay in the GAN-generated products. Relative to non-GAN-generated products, willingness to pay is significantly higher for GAN-generated products. Moreover, evaluations of functional value, emotional value and willingness to pay are highest when GAN technology is used, but not disclosed.
This study evaluates the utility of GANs from consumers' perspective based on the perceived value of GAN-generated product designs. Findings have practical implications for firms that are considering using GANs to develop products for the retail fashion market.
The use of text‐based communications such as instant messaging or social media such as Twitter has been growing significantly as the use of mobile devices increases. Not…
The use of text‐based communications such as instant messaging or social media such as Twitter has been growing significantly as the use of mobile devices increases. Not only do people share information via mobile communication, there are significant implications for advertising and marketing. Due to display limitations, however, the message senders use various conventions in addition to the text‐based message to more clearly and richly express emotions. Since users use a range of expressions to convey these emotions, it would be very useful to verify the relationships between users' emotional expressions and receivers' perceptions of the expressions. The purpose of this paper is to propose an integrated model to examine the relationship between emotional expressions and the emotional intensity of the receivers.
The authors formulated a series of research hypotheses and tested them using empirical survey data. The research model used is based on regression analysis with dummy variables for statistical analyses.
First, emotional intensity had a closer relationship to user acceptance than was expected. Second, the use of exclamation marks and emotional messages are far less acceptable in negative messages. Third, the high formalisation group has a more positive emotional intensity in their basic expression.
The authors successfully determined that emotional expressions significantly affect the message receivers' emotional intensity and hence acceptance of the message.