During family firm succession, parent-incumbents are often caught up in a paradox of both empowering and dominating their child-successors. To understand this recurring phenomenon, we draw from socioemotional wealth literature and a philosophical account of the power-transfer paradox in ancient patriarchal monarchies to hypothesize that parent-incumbents tend to exert generational coercive control when their child-successors are seen as very unwilling and incapable or very willing and capable of taking over patriarchal family organizations. We test our hypotheses in three studies. In Study 1, we coded data from succession cases in Chinese patriarchal monarchies (403 BC to 959 AD) and found support for the predicted non-linear effects of successor-princes’ willingness (63 cases) and capability (80 cases) on their father-kings’ coercive control (persecuting or murdering the princes). In Study 2, based on survey data from parent–child dyads of 157 family firms in Taiwan and mainland China, again, we found U-shaped effects of child-successors’ willingness and capability on parent-incumbents’ coercive control (restraining successors’ power). Moreover, parent-incumbents’ highly narcissistic personality attenuated these U-shaped relationships because they tend to devalue their child-successors’ willingness and capability. In Study 3, we conducted a survey of 103 parent–child dyads in family firms in mainland China and found a U-shaped relationship between capability and coercive control only when incumbents’ roles in the family and at work were highly intertwined.

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We propose a distributionally robust model for the influence maximization problem. Unlike the classic independent cascade model (Kempe et al., 2003), this model's diffusion process is adversarially adapted to the choice of seed set. Hence, instead of optimizing under the assumption that all influence relationships in the network are independent, we seek a seed set whose expected influence under the worst correlation, i.e. the "worst-case, expected influence", is maximized. We show that this worst-case influence can be efficiently computed, and though the optimization is NP-hard, a (1 − 1/e) approximation guarantee holds. We also analyze the structure to the adversary's choice of diffusion process, and contrast with established models. Beyond the key computational advantages, we also highlight the extent to which the independence assumption may cost optimality, and provide insights from numerical experiments comparing the adversarial and independent cascade model.

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