How do shareholders respond to technologies hyped in general discourse, e.g., artificial intelligence (AI), if a common understanding is lacking and the technologies are still evolving? Do they respond primarily to substantive signals in technology announcements, such as AI capabilities, or do rhetorical signals also play a significant role? Adopting signalling theory as a theoretical lens, we conceptualise announcements of AI capabilities as substantive signals and linguistic elements in the announcements pertaining to organisational time horizon and risk-reward considerations as rhetorical signals. Departing from the typical focus on bijective relationships, we consider holistic, complex configurations of interdependent factors using the qualitative comparative analysis (QCA) methodology. Notably, announcements pertaining to AI capabilities are not necessarily associated with positive market reactions; in fact, when all three types of AI are included in announcements without explicit consideration of risks, shareholders react negatively. We find that shareholder response is based on joint evaluation of substantive and rhetorical signals, and that these signals interact in a complex way to produce positive and negative market reactions. These findings motivate several propositions for market reactions to IT announcements, providing implications for both theory and practice.

This study seeks to develop and test a theory-driven model of technology addiction within the context of smartphone use. Drawing on incentive-sensitisation theory, we proposed a nomological network that centres on a second-order factor of smartphone addiction from a psychological perspective. We empirically evaluated the proposed model against longitudinal data collected from 441 smartphone users. Our analysis from structural equation modelling indicates that our two-factor measurement of smartphone addiction (heightened desire and mobile social interaction) is a parsimonious alternative to the four-factor model. In addition, our findings suggest that addiction and its negative consequences differ in their effects on subsequent perceptions, such as perceived usefulness and perceived enjoyment. We also found that personal innovativeness in information technology and a perception of loneliness explain a large amount of variation in smartphone addiction. Overall, the present study significantly contributes to the information systems literature by offering a refined model of smartphone addiction that effectively describes pathological smartphone use while correcting critical biases in existing models.