Impact of family control/influence on stakeholders' perceptions of benevolence

This article proposes a model explaining how family control/influence in an organization affects individual stakeholders’ perceptions of benevolence. The model suggests two effects. First, based on socioemotional wealth research, we propose that family control/influence positively affects stakeholders’ perceptions of benevolence through the benevolent behavior that the organization shows toward its stakeholders. However, this effect can be negatively influenced if the family’s socioemotional wealth goals in terms of “Family control and influence” and/or “Renewal of family bonds to the firm through dynastic succession” are at risk. Second, we argue that family control/influence, to the extent that it is perceivable to the stakeholder, influences stakeholders’ perceptions of benevolence through categorization processes. However, the impact of perceivable family control/influence on stakeholders’ perceptions of benevolence is not straightforward but instead hinges on a set of individual-level contingency factors of the stakeholder, such as stakeholders’ family business in-group membership, stakeholders’ secondhand category information, and stakeholders’ firsthand category information.

What do brain network interactions tell about cognitive processes? How to seek causality?

Optimal adjustment of brain networks allows the biased processing of information in response to the demand of environments and is therefore prerequisite for adaptive behaviour. It is widely shown that a biased state of networks is associated with a particular cognitive process. However, those associations were identified by backward categorization of trials and cannot provide a causal association with cognitive processes. This problem still remains a big obstacle to advance the state of our field in particular human cognitive neuroscience. In my talk, I will present two approaches to address the causal relationships between brain network interactions and behaviour. Firstly, we combined connectivity analysis of fMRI data and a machine leaning method to predict inter-individual differences of behaviour and responsiveness to environmental demands. The connectivity-based classification approach outperforms local activation-based classification analysis, suggesting that interactions in brain networks carry information of instantaneous cognitive processes. Secondly, we have recently established a brand new method combining transcranial alternating current stimulation (tACS), transcranial magnetic stimulation (TMS), and EEG. We use the method to measure signal transmission between brain areas while introducing extrinsic oscillatory brain activity and to study causal association between oscillatory activity and behaviour. We show that phase-matched oscillatory activity creates the phase-dependent modulation of signal transmission between brain areas, while phase-shifted oscillatory activity blunts the phase-dependent modulation. The results suggest that phase coherence between brain areas plays a cardinal role in signal transmission in the brain networks. In sum, I argue that causal approaches will provide more concreate backbones to cognitive neuroscience.