IESE 商学院

The authors explore the differential impact of national-populist election victories on marketing effectiveness for foreign versus domestic brands. The authors hypothesize that following a national-populist election victory, both advertising and price promotion effectiveness decline for foreign relative to domestic brands. These predictions are supported using the 2016 USA presidential election to estimate the impact of marketing tools on product market shares. We observe that the relative change in advertising effectiveness is driven by a decrease in foreign brand effectiveness while the relative change in price promotion effectiveness is driven by an increase in domestic brand effectiveness. These findings are generalized in two experiments conducted in the context of household textiles and the 2024 French European Union election. This research provides marketers with insights on effectively deploying marketing tools in an era increasingly characterized by populism and polarizing elections. After a national-populist victory, the environment for foreign brands becomes more challenging.

Research on the interactive effects of multiple dimensions of fit often comes with incomplete theorizing and equivocal results. Drawing on social cognition theory and person–environment fit literature, we theorized and tested the interactive effects of multiple fits, which state that interactions would happen only between complementary fit and supplementary fit categories but not within fit categories. Results from a time-lagged design of 168 employees in Study 1 (with one complementary fit and two supplementary fits) largely supported our hypotheses. Specifically, PJ fit and PO fit enhanced each other to affect job satisfaction and organizational commitment. In addition, using 120 subordinate-supervisor pairs in Study 2 (with two complementary fits and one supplementary fit), we found that PO fit, demands–abilities fit, and needs–supplies fit interactively affected job performance and organizational citizenship behaviors. Our study contributes to person–environment fit theory in increasing its theoretical precision in specifying when interactive effects are likely and in what direction they may unfold.

Although hype is widely discussed in the popular discourse around entrepreneurship, it has received limited attention in the entrepreneurship literature. In this introduction to the special issue on hype and entrepreneurship, we propose that hype in entrepreneurial settings can be usefully explored by considering the stages of the hype cycle that can unfold in these settings. The first stage is hyping, where entrepreneurs frame ideas, products, business models, and ventures using language designed to set exciting future expectations despite (and even because of) the absence of technical validation or evidence of the economic feasibility of the venture. These efforts can trigger a state of hype, which refers to a rapidly intensifying collective vision of the future across audiences leading to an upswing. During the upswing, unrealistic expectations emerge, which, if left unmet, can lead to a downswing when there is a reversal of momentum and even the stigmatization of the hyped assets. Yet, there is always the possibility of a revival of entrepreneurial efforts around what had been hyped but became stigmatized. Noting that not all entrepreneurial phenomena necessarily traverse all stages of the cycle, we discuss some of the triggers and contingencies that can lead to shifts across the different stages of the hype cycle. We also discuss the challenges and opportunities that these stages offer entrepreneurs to the extent they unfold within and across fields. We then introduce the papers that comprise the special issue and conclude with suggestions for future research.

Just as groups of people form large-scale macro-cultures, close relationship partners construct their own microculture. Dyads invent their own language (the word “muddles” for homemade ceramics), stories (rescuing their cat), rituals (weekly backgammon), morality (conflicts require mutual apologies), identity (activists), and sense of reality (belief in UFOs). We contend that the emergence, development, and maintenance of this co-created microculture predicts key relational and personal outcomes. We introduce Relationship Microcultures Theory (RMT), which complements prevailing relationship theories by examining how particular words and behaviors are interpreted within the history of the specific relationship in which they acquired idiosyncratic meaning, shaping in turn the well-being of the relationship and the individuals involved. In our Microcultural Unit Model, we describe a meta-theory for understanding how particular dyadic interactions become microcultural units (e.g., inside jokes) by acquiring mutual, idiosyncratic, and referential meaning. In our Aggregate Microculture Model, we advance a theory of how more extensive microcultures—those comprising more units, used more frequently, and spanning more life domains—contribute to relational outcomes (e.g., relationship satisfaction and longevity) and personal outcomes (e.g., meaning in life and self-regulatory success). We also theorize that microcultural extensiveness can undermine relational and personal outcomes when mutuality, idiosyncrasy, and referentiality are low. By examining the dyad as a culture in miniature, RMT opens novel research directions for relationship science.

We propose a signature-based framework for the identification of stochastic volatility model classes from observed path data. By mapping volatility trajectories into a feature space via truncated path signatures and applying a gradient boosting classifier, we show that it is possible to distinguish between different classes of volatility dynamics without relying on parametric calibration. Through a series of numerical experiments, we demonstrate that the method achieves high classification accuracy across a range of settings, from structurally distinct models to cases involving rough volatility models with closely spaced Hurst parameters. We show that the method remains effective under parameter uncertainty, where each simulated path is drawn with randomly sampled model parameters, and provide a detailed analysis of the misclassification pattern between the Heston and Ornstein–Uhlenbeck models in terms of the volatility of volatility parameter. The results highlight that most of the relevant discriminative information is captured by the first four levels of the signature, while higher-order terms provide only marginal improvements. Overall, the findings support the view that stochastic volatility models can be effectively identified through the geometry of their sample paths.