Conceptualizing Knowledge Friction

Abstract

This dissertation explores knowledge friction. The concept knowledge flow is defined by many researchers as the transfer of actionable information between individuals, groups and organizations. Knowledge Flow Theory (KFT) frames and offers tools for conceptualizing, analyzing, visualizing, and measuring knowledge flows. Recently, Nissen conceptualized explicitness-based resistance to knowledge transfer; he referred to it as knowledge friction. This study addresses additional factors that inhibit knowledge transfer through an empirical look at Defense Acquisition University student surveys. These factors are clarity, near and longer-term relevance, certification level, and experience. The data was analyzed through descriptive statistics, multiple-regression analysis models, and a Partial Least Squares Structural Equation Model. The key findings: Clarity and near-term and longer-term relevance are quantitatively the largest contributing knowledge friction factors. These new factors interact with, but are also additive, to explicitness. Also discovered: The certification level and experience factors independently contribute little directly, but do increase the impact of longer-term relevance on knowledge transfer. Near-term relevances impact is not affected by certification level and experience. These findings significantly contribute to KFT by extending and quantifying the factors that contribute to knowledge friction.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2021
Accession Number
AD1164479

Entities

People

  • Paul Shigley

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Application Software
  • Artificial Intelligence
  • Data Analysis
  • Data Mining
  • Data Science
  • Databases
  • Factor Analysis
  • Information Processing
  • Information Science
  • Information Systems
  • Knowledge Management
  • Management Personnel
  • Network Science
  • Psychology
  • Regression Analysis
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Students
  • Surveys

Readers

  • Artificial Intelligence
  • Organizational Process Management (OPM).
  • Systems Analysis and Design