Explicit Knowledge Transfers in New Product Development

Introduction

In the digital economy, the need for managing data, information and knowledge is ever-increasing. Technological advances such as supply-chain management, customer relationship management (CRM) and radio frequency identification (RFID) aim to enhance firm performance by passing data packets throughout the organization. Sadly, data packets by themselves do not automatically translate into effective knowledge transfers. Therefore, a failure to craft the underpinnings of knowledge transfers from multiple organizational resources such as people, technologies and processes, leaves firms struggling with a plethora of data and a paucity of knowledge.

The area of interest here is technological new product development (NPD), where learning dominates the process as well as its outcome. Product development is an appropriate area of study for knowledge transfer since it represents one of the few formal activities performed by the firm facilitating guided, purposeful learning across the entire organization. Hitt, Ireland and Lee [2000] state two forms of learning: acquisitive and experimental. Acquisitive learning is the acquiring and internalizing of external knowledge outside the firm’s boundaries, mostly during the innovation or adoption stages. Occurring mainly in NPD’s early innovation phase, acquisitive learning eventually giving way to experimental learning in the latter stages of development and production ramp-up. In complementary fashion, experimental learning is the result of coordination and active experimentation by members who acquire new knowledge, distinctive to the organization, to build templates of best practices that would become standard operating procedures (SOPs) in the development and transfer stages.

Our study investigates the rich tapestry of knowledge transfers as experimental learning paves the way towards creating SOPs. It is established that development and production ramp-up require stronger adherence to leveraging explicit knowledge [Terwiesch & Böhn, 2001]. In studying agent-driven process reengineering of organization knowledge from discovery to application, Datta [2008] notes how explicit knowledge creation is a prelude to knowledge application; Datta and Acar [2010] delve deeper to unravel how routinizing knowledge creation in organizations requires codification of knowledge. In this paper, the scope of knowledge transfers is primarily focused on explicit knowledge transfers. Given that our area of investigation is on knowledge transfers during production rather than the ab initio knowledge discovery and creative synthesis, our emphasis is on the transference of explicit knowledge specifications into a streamlined production environment.

Effective NPD requires effective knowledge flows [Mentzas, Halaris & Kavadias, 2001]. Nonaka’s [1991] attribution of knowledge as a dynamic resource created by the processes of combination and externalization is augmented by Gold, Malhotra and Segars [2001] who finds that knowledge flows are “embedded within, available through, and derived from the network of relationships”. Together, knowledge transfer is a process where an organization recreates a complex, causally ambiguous set of routines across various development environments [Szulanski, 2000].