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Mapping and Analyzing the Scientific Map of Intangible Assets Using Research Indexed in Scientific Databases

Document Type : Research Paper

Authors

1 Assistant Professor, Science and Technology Policy Dept, Shahid Beheshti University, Tehran, Iran

2 PhD Student in System Management, Tehran University, Tehran, Iran

3 Assistant Professor, Science and Technology Policy Dept,, Shahid Beheshti University, Tehran, Iran

Abstract

Scientometrics is one of the most important scales for evaluating scientific products that are used to describe scientific studies in terms of their growth, structure, and interactions. The present study was conducted using a scientometrics approach and using co-word analysis and social network analysis (SNA) to investigate relationships in the field of intangible assets. In this regard, research indexed in Scopus on the topic of "intangible assets" has been analyzed using software including vosviewer, Gephi, HistCite, Publish or Perish and NodeXL. Questions such as what subject areas are constituted and how these areas are related to each other have been addressed using methods such as word co-occurrence and social network analysis. The findings of the study show that the most frequently used topics and words are knowledge management and intellectual capital. Also, the most valuable subject areas were identified based on the maps drawn using the closeness and centrality indexes; value creation, value chain, social responsibility and trademark. With the advent of the knowledge-based economy era, a large portion of the organization's assets are of an intangible type, which confirms the recognition of and investment in these types of assets. Co-authorship analysis revealed that the co-authorship network is discrete and has low-density, with a total of 12,472 citations in all articles. By using the co-word map of intangible assets, researchers and especially policymakers can plan appropriately through the knowledge of the research and thematic status of intangible assets.

Keywords

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Knowledge Retrieval and Semantic Systems
Volume 7, Issue 25
January 2021
Pages 33-65
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