The Intellectual Structure of Digital Stewardship Using Google Scholar and Web of Science Database

• Main Authors: Chun-Min Lin, 林俊銘
• Other Authors: Fang-Ming Hsu
• Format: Others
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/95824918960244529670

博士 === 國立東華大學 === 企業管理學系 === 103 === Digital Stewardship (DS) is a critical issue in the last few decades. Digital assets include any artifacts in digital forms. Government agencies, companies and individuals possess massive digital assets that are rapidly growing in amount. These assets require appropriate and strategic management to ensure long-term accessibility. The difficulty of digital asset management lies mainly in the rapid changes of information technology (IT), yet related issues such as the administration systems and information techniques must be taken into consideration at the same time. In government agencies or organizations, digital assets that need to be preserved include Electronic Record (ER) and Digital Archive (DA) of various topics. Information containing in ER and DA is comprised of text and varied multimedia formats, and they may be preserved by different database systems and operating systems. Such diverse problems revolving around digital asset management have become significant issues for the academic research. To learn about the intellectual structure of specific academic fields is a quantitative method to recognize the research issues of that field. Bibliometrics provides several tools for analyzing the intellectual structure, and co-word analysis is among one of them. Existing digitalized academic databases have provided enormous data for co-word analysis. The most representative academic database nowadays has been Web of Science (WoS) provided by Thomson Reuters. Later on, Google Scholar (GS) database developed by Google provides a new technique for collecting academic research data. WoS and GS databases employ different judgment rules and academic data collection strategies. WoS employs expert judgment is more rigorous in data collection, while GS uses algorithm judgement and is more open and diverse in data collection. Several different bibliometric methods have been applied to collect literature required for co-word analysis among assorted academic databases. These methods include queries by keywords, analyses of research paper contents collected from major journals, or a mixture of both ways. In this study, four different methods were adopted to collect literature for analysis: (1) locating major DS journals in WoS, (2) querying with keywords in ER- and DS-related journals, (3) querying with keywords in WoS, and (4) querying with keywords in GS, respectively. The analysis of the pros and cons of these four methods can be used as a reference for future researchers using bibliometric measures. In addition, this study investigated the intellectual structure of digital asset management and its related fields. The outcomes of this study may provide a reference to digital asset management agencies and to academic research of digital information or record management as well. The results showed that the research topics related to digital asset management include Information System and Management; Knowledge, Archive and Library; Patient Care and Health Care; Government and Community's History; Standard and Framework; Algorithm; and Hospital and Medical Record. By means of the research topic classifications, researchers may identify research issues and themes in digital asset management by categories.