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Abstract
Inter-organizational power relations have long been considered to be balanced in innovation networks, which are viewed as loosely coupled systems. Some recent studies, however, show that innovation networks are asymmetric and hierarchical, and the power of network actors has become a significant but rarely addressed issue. As knowledge is the most important resource in the network, this paper introduces the concept of knowledge power by combining related research perspectives and conducting some fundamental research on it as follows: (1) knowledge power’s origins are analyzed by proposing the term “activated knowledge” and studying the path through which it is formed over multiple levels of the network; (2) a multilevel framework of characteristics of activated knowledge, which is considered the major determinant of knowledge power, is established, and suggestions are offered for how they impact knowledge power; and (3) a multilevel measurement model for knowledge power is built, and the above propositions are tested by mathematical inference. The purpose of this paper is not only to study knowledge power’s formation, determinants, and measurement but also to offer a comprehensive view, combining multiple network levels and multiple research perspectives, that should be useful to researchers conducting future studies in this field.
Highlights
This paper introduces the term “knowledge power” into the study of technology-innovation networks and defines it as the interorganizational power-dependence relation that is formed on the basis of organizational knowledge and is eventually manifested by organizational positions in a “power network.”1 It matches the multilevel feature of the innovation network and combines multiple research perspectives
That is because: (1) as defined above, activated knowledge refers to knowledge that is activated by organizational capabilities, embodied through technology-innovation activities or outcomes, and able to be sensed and identified by outsiders; (2) organizations in the same innovation network are usually located in one industry or homogeneous zones and interact frequently, which increases their number of mutual acquaintances; and (3) when an organization cannot achieve a technologyinnovation goal by itself, it will be willing to let its potential partners know its knowledge advantages, with the aim of attracting them to cooperate
The knowledge-based view (KBV) is a major perspective for studying innovation networks, as it captures nicely the feature of technology-innovation networks that knowledge is the key resource
Summary
After being activated by capabilities can an organization’s knowledge be applied and used in technology-innovation activities, embodied through technology-innovation processes and outcomes, and sensed and identified by other network actors Those who need but do not have the knowledge will be attracted to and develop a knowledge dependence on the organization (Howard et al, 2016). According to related studies (Emerson, 1962; Casciaro and Piskorski, 2005; Cho, 2020), power is a dependence relation, which in innovation networks means interdependence on one another’s knowledge This interdependence is rooted in organizations’ heterogeneous capabilities and knowledge at the actor level and is eventually manifested as a knowledge-power network at the network level through interactions among inter-organizational relations at the dyadic level. This paper holds that a multilevel framework for AKCs should be built to perform a better investigation of how knowledge determines power in innovation networks
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