HRM in dynamic environments: Exploitative,exploratory, and ambidextrous HR architectures

Abstract

The current economic crisis has brought to the fore the need for firms to deal with ambiguity and complexity. Hence, firms need a specific balance between exploration and exploitation in order to keep pace with varying and changing environmental conditions. Hitherto, there is limited research that has examined the nexus of HR architectures, ambidexterity, and environmental dynamics. In this conceptual paper we ask: How do HR architectures serve as a means of balancing exploitative and exploratory learning in different dynamic environments? We explain how exploratory, exploitative, and ambidextrous HR architectures with their embedded HRM systems on the business unit level enable organizations to meet different environmental requirements. Thus, firms in which heterogeneous demands for flexibility and for innovation co-exist need to develop internally differentiated HR architectures. In particular, we elucidate how critical the organization’s ability is to connect different HRM systems to create an ambidextrous HR architecture to find an appropriate balance between exploration and exploitation.     

Contagion of network products in small-world networks

We formulate a model in which agents embedded in an exogenous social network decide whether to adopt a new network product or not. In the theoretical part of the paper, we characterize the stochastically stable equilibria for complete networks and cycles. For an arbitrary network structure, we develop a novel graph decomposition method to characterize the set of recurrent communication states, which is a superset of stochastically stable equilibria of the adoption game presented in our model. In the simulation part, we study the contagion process of a network product in small-world networks that systematically represent social networks. We simulate a generalization of the Morris (Rev Econ Stud 67(1):57–78, 2000) Contagion model that can explain the chasm between early adopters and early majority. Our numerical analysis shows that the failure of a new network product is less likely in a highly cliquish network. In addition, the contagion process reaches to steady state faster in random networks than in highly cliquish networks. It turns out that marketers should work with mixed marketing strategies, which will result in a full contagion of a network product and faster contagion rates with a higher probability.

     

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