Time to leave? The interaction of temporal focus and turnover intentions in explaining voluntary turnover behaviour

Although turnover intentions are the strongest predictors of voluntary turnover behaviours, many employees who express intentions to leave do not. To explain why some employees translate turnover intentions into behaviour and others do not, this study examines the moderating effect of temporal focus (i.e. the degree to which one thinks about the past, present and future) between turnover intentions and voluntary turnover, using the lens of theory of planned behaviour. Data were collected from 683 full-time employees in a range of organisations at three points in time. Results show that past temporal focus conditions the positive relationship between turnover intention and turnover, such that the relationship is stronger when past temporal focus is high. Future temporal focus has an opposite moderating effect, such that the relationship between turnover intention and turnover is weaker when future temporal focus is high. Results show no significant moderating effect of current temporal focus. Overall, temporal focus helps explain why some employees leave and others stay by conditioning the likelihood of translating turnover intentions into quitting. We speculate that a dominant, unspoken paradigm in turnover research is an assumption that individuals are current focused, yet our results suggest scholars should explicitly examine this assumption.     

Modeling the emergence of informational content by adaptive networks for temporal factorisation and criterial causation

Propagated activation of neurons through their network is an important process in the brain. Another crucial part of neural processing concerns adaptation over time of characteristics of this network such as connection strengths or excitability thresholds. This adaptation can be slow, as in learning from a multiple experiences, or it can be fast, as in memory formation. These adaptive network characteristics can be considered informational criteria for activation of a neuron. This then is viewed as a form of emergent information formation. Activation of neurons is determined by such information via a process termed criterial causation. In the current paper, the relationship of criterial causation with the principle of temporal factorisation for the dynamics of the world in general is explored. Temporal factorisation describes how the world represents information about its past in its present state, which then in turn determines the world’s future. In the paper, it is shown how these processes are analysed in more detail and modeled by (adaptive) network models.