We argue that although HR has a lot of tools and practices, it still lacks an overarching decision science that defines how organizations can obtain strategic success through their human resources. In order to support companies’ informed HRM decision-making, we recommend establishing a tradition of evidencebased HR practices. Evidence-based HR is a family of practices, combining research evidence with contextual information and individual judgment of HR professionals as essential sources of information. After having reviewed implications for HR practice from scholarly work, economic and societal trends
as well as business tools from other managerial domains, we discuss the potential of Talentship as an evidence-based decision science and as a first step towards a general way of thinking to support HR decisions. As such, we believe the present chapter provides a significant contribution to the insights of practitioners and scholars into the further development towards evidencebased HR.
The purpose of this paper is to model analysts’ forecasts. The paper differs from the previous research in that we do not focus on how accurate these predictions may be. Accuracy may indeed be an important quality but we argue instead that another equally important aspect of the analysts’ job is to predict and describe the impact of jump events. In effect, the analysts’ role is one of scenario prediction. Using a Bayesian-inspired generalised method of moments estimation procedure, we use this notion of scenario prediction combined with the structure of the Morgan Stanley analysts’ forecasting database to model normal (base), optimistic (bull) and pessimistic (bear) forecast scenarios for a set of reports from Asia (excluding Japan) for 2007–2008. Since the estimation procedure is unique to this paper, a rigorous derivation of the asymptotic properties of the resulting estimator is also provided.
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