Intellectual Capital Architectures and Ambidextrous Learning: A Framework for Human Resource Management

Abstract

abstract Both researchers and managers are increasingly interested in how firms can pursue ambidextrous learning; that is, simultaneously exploring new knowledge domains while exploiting current ones. Ambidextrous learning is derived from intellectual capital architectures that underlie unique configurations of human, social, and organizational capital. We identified two distinctive architectures of intellectual capital that facilitate ambidextrous learning. Refined interpolation is an architecture comprised of specialist human capital supplemented by cooperative social capital, and complemented by organic organizational capital. Disciplined extrapolation is an architecture comprised of generalist human capital, supplemented by entrepreneurial social capital, and complemented by mechanistic organizational capital. As organization contexts to support these architectures, we have also identified two alternative HR configurations that facilitate ambidextrous learning. One HR configuration combines job or function‐based development, ILM‐based employee relations, and error embracing performance/control systems to support refined interpolation. The other HR configuration combines skill‐based development, market‐based employee relations, and error avoiding performance/control systems to support disciplined extrapolation. Our framework may provide valuable theoretical implications for HRM systems regarding the issues of internal fits and best configurations.