There has been little research on how market disruptions affect customer-brand relationships and how firms can sustain brand loyalty when disruptions occur. Drawing from social identity theory and the brand loyalty literature, the authors propose a conceptual framework to examine these issues in a specific market disruption, namely, the introduction of a radically new brand. The framework focuses on the time-varying effects of customers' identification with and perceived value of the incumbent relative to the new brand on switching behavior. The authors divert from the conventional economic perspective of treating brand switching as functional utility maximization to propose that brand switching can also result from customers' social mobility between brand identities. The results from longitudinal data of 679 customers during the launch of the iPhone in Spain show that both relative customer-brand identification and relative perceived value of the incumbent inhibit switching behavior, but their effects vary over time. Relative customer-brand identification with the incumbent apparently exerts a stronger longitudinal restraint on switching behavior than relative perceived value of the incumbent. The study has important strategic implications for devising customer relationship strategies and brand investment.

Our findings suggest that BDNF levels may serve as a potential differential diagnostic biomarker for bipolar disorder in a patient's first depressive episode.

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Unlike sales data, data on intermediate stages of the purchase funnel (e.g., how many consumers have searched for information about a product before purchase) are much more difficult to acquire. Consequently, most advertising response models have focused directly on sales and ignored other purchase funnel activities. The authors demonstrate, in the context of the U.S. automotive market, how consumer online search volume data from Google Trends can be combined with sales data to decompose advertising's overall impact into two underlying components: its impacts on (1) generating consumer interest in prepurchase information search and (2) converting that interest into sales. The authors show that this decompositional approach, implemented through a novel state-space model that simultaneously examines sales and search volumes, offers important advantages over a benchmark model that considers sales data alone. First, the approach improves goodness-of-fit, both in and out of sample. Second, it improves diagnosticity by distinguishing advertising effectiveness in interest generation from its effectiveness in interest conversion. Third, the authors find that overall advertising elasticity can be biased if researchers consider only sales data.