مقاله انگلیسی رایگان در مورد آنالیز شبکه پیچیده برای مدیریت دانش و هوش سازمانی – اسپرینگر 2018

The scope and focus of knowledge management has changed multiple times over the last decades, each shift revealing new challenges to management science. Recent change of perspective drawing from systems thinking is suggesting that knowledge is created through interaction between people. Complex network analysis is a rigorous method that can be used for evaluation of interaction patterns between employees. The literature suggests that specific interaction patterns are related to increased knowledge flow, innovativeness, and performance. Aim of this paper is to provide an overview of various approaches utilizing the complex network analysis in organizations and present suggestions that might support managerial decision-making processes related to knowledge management and organizational intelligence.

Introduction

Organizational learning is one of the key topics covered by managerial theories as it is tightly connected to the performance and the ability of a company to success on the market. Since the 1970s, the ability to transfer information and knowledge has grown to be perceived as a crucial source of competitive advantage for companies in achieving success (Arrow 1974). As a consequence, knowledge management (and related topics) has been a subject of intensive scholarly interest focused on organizational learning as a cognitive process or as a function of behavioral change, adapting vision, goals, or decision rules of a company (Borgatti and Cross 2003; Caputo 2016; Del Guidice et al. 2016). During the last 40 years, multiple shifts of interest in knowledge management happened related to what is to be managed, controlled, or designed. Each of these shifts was connected to new challenges, perspectives, theories, and tools, whose purpose was to support and increase growth and performance of companies. Each of these shifts brought another layer of complexity into the organizational analysis, and also increased the scope of the analysis itself—from individual tasks through cooperation between people to organizations as a whole. Current computational performance and analytical tools allow us not only to analyze interaction within whole companies but also among networks of cooperating companies or online communities (virtual organizations) consisting of hundreds or thousands of people (Zanetti et al. 2012). Interaction between people creates structure and patterns. Understanding these patterns can be used for acquiring deeper insight about the nature of the cooperation, with possible implications for knowledge management and organizational intelligence, which is the aim of the current work. At the same time, organizations with hundreds of employees produce thousands of interactions every day. It is above the human cognitive capacity to fully conceive such amount of information, and, on the top of that, it is conceptually impossible to properly understand patterns of interaction without global perspective. While some patterns are obvious and part of a Bcommon sense,^ other patterns can be well hidden in the social fabric, yet important, valuable, and quite surprising—once revealed. We need to use a tool to obtain unbiased understanding of interaction patterns in an organization. The complex networks analysis is a suitable tool that allows to achieve such goal.