Journal of the Korean Society for Library and Information Science (한국문헌정보학회지)

Korean Society For Library And Information Science (한국문헌정보학회)
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A Study on the Mediating Effect of Motivation Factors between the Quality of Research Data Metadata and the Activation of Research Data Platform

연구데이터 메타데이터의 품질과 연구데이터플랫폼의 활성화의 관계에서 동기부여 요인의 매개효과 연구

  • 박성은 (한국과학기술정보연구원(KISTI) 연구데이터공유센터)
  • Received : 2023.07.20
  • Accepted : 2023.08.11
  • Published : 2023.08.31
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Abstract

This study focuses on the impact of research data metadata quality evaluation index on the revitalization of K-BDS, a research data platform in the bio field, and examines the mediating effect of motivation factors for utilizing the platform. The investigation employs a structural equation model analysis and bootstrap analysis to explore the interrelationships among the three variables. The findings demonstrate that researchers who prioritize the quality of metadata display higher motivation to use the research data platform, leading to an intention to activate the platform. The study also confirms the mediating effect of motivation factors. Moreover, a comprehensive understanding of the sub-factors within each variable is attained through regression analysis and Sobel test. The results highlight that enhancing searchability is crucial to activate research data sharing in the bio field, while improving discoverability is vital for research data reuse. Interestingly, the study reveals that citationability does not significantly impact platform activation. As a conclusion, to foster platform activation, it is imperative to provide systematic support by enhancing metadata quality. This improvement can not only increase trust in the platform but also institutionally solidify the benefits of citation.

본 연구는 바이오 분야 연구데이터플랫폼인 K-BDS를 대상으로, 연구데이터 메타데이터의 품질이 연구데이터플랫폼의 활성화에 미치는 영향 및 이 관계에서 연구데이터플랫폼 이용에 관한 동기부여 요인의 매개효과를 밝히고자 하였다. 먼저 세 변인 간 구조적 관계를 구조방정식모형, 부트스트랩을 통해 분석하였으며 분석 결과, 연구자가 메타데이터의 품질에 대해 중요하다고 생각할수록 연구데이터플랫폼 이용의 동기부여 정도, 그리고 플랫폼의 활성화 의도가 높아지는 것으로 나타났다. 또한 동기부여 요인의 매개효과도 확인되었다. 추가적으로 각 변인의 하위요인간의 세부적인 구조를 회귀분석과 Sobel test를 통해 파악하였다. 그 결과 바이오 분야의 연구데이터 공유의 활성화를 위해서는 검색가능성을, 연구데이터 재이용의 활성화를 위해서는 발견가능성을 높이는 것이 가장 효과적이며, 인용가능성은 플랫폼의 활성화에 영향을 미치지 않는 것으로 나타났다. 따라서 플랫폼을 활성화하기 위해서는 우선적으로 메타데이터 품질을 향상시킴으로써 시스템적인 지원을 충분히 하는 것이 중요하며, 이를 통해 플랫폼에 대한 신뢰를 높이고 인용에 대한 혜택을 제도적으로 정착시켜 갈 필요가 있다는 시사점을 얻을 수 있다.

Keywords

References

  1. Bak, ji won & Chang, Woo kwon (2022). A study on factors affecting the reuse of research data by academic researchers in the social sciences. Journal of the Korean Society for Information Management, 38(4), 199-230. 
  2. Kim, Eun-Jeong & Nam, TaeWoo (2012). Factor analysis of effects on research data collection. Journal of the Korean Society for Information Management, 29(2), 27-44.  https://doi.org/10.3743/KOSIM.2012.29.2.027
  3. Kim, Jihyun (2012). A study on university researchers' data management practices. Journal of Korean Library and Information Science Society, 43(3), 433-455. 
  4. Kim, Moon Jung & Kim, Seonghee (2015). A study on the factors affecting sharing of research data of science and technology researchers. Journal of the Korean Society for Library and Information Science, 49(2), 313-334.  https://doi.org/10.4275/KSLIS.2015.49.2.313
  5. Kim, Nayon & Chung, Eunkyung (2020). An investigation on data needs and data reuse behavior in the field of social sciences. Journal of the Korean Society for Information Management, 37(4), 1-26.  https://doi.org/10.3743/KOSIM.2020.37.4.001
  6. Kim, Sun (2022). An exploratory study of biotechnology scientists' research data sharing intention: the moderating effects of academic reputation. Journal of the Korean Society for Information Management, 39(1), 45-68. 
  7. Ko, Young-Man, Ahn, Hye-yeon, Kim, Hyun Soo, & Shim, Ji-Woo (2019). Study on a model for metadata quality and usability assessment of the national research data platform. Korea Institute of Science and Technology Information. 
  8. Lee, Sang-Hwan & Shim, Wonsik (2009). Sharing and utilization of scientific data (KISTI knowledge report vol.5), KISTI. Available: https://repository.kisti.re.kr/handle/10580/7608  https://repository.kisti.re.kr/handle/10580/7608
  9. Park, Jin Ho, Ko, Young-Man, & Kim, Hyun Soo (2019). A study on evaluation model for usability of research data service. Journal of the Korean Society for Information Management, 36(4), 129-159.  https://doi.org/10.3743/KOSIM.PUB.36.4.129001
  10. Park, Seong-Eun & Ko, Young Man (2022). A study on metadata interoperability between the national research data platform and the bio research data platform. Journal of the Korean Society for Information Management, 39(2), 159-202. 
  11. Song, Baek-hwa (2018). Research on the Sharing of Research Data in the Field of Chemistry. MA. thesis, Dept. of LIS., Graduate School of Chung-Ang University. 
  12. Yu, jongpil (2012). Structural Equation Model Concepts and Understanding. Seoul: Hannarae. 
  13. Andersen, D. L. ed. (2004). Digital scholarship in the tenure, promotion, and review process. NY: M.E. Sharpe, Inc. 
  14. Azeroual, O. & Schopfel, J. (2019). Quality issues of CRIS data: an exploratory investigation with universities from twelve countries. Publications, 7(1), 14. 
  15. Baron, R. M. & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173-1182.  https://doi.org/10.1037/0022-3514.51.6.1173
  16. Bollen, K. A. & Long, J. S. (1993). Testing Structural Equation Models, 154. CA: Sage. 
  17. Borgman, C. L. (2010). Research data: who will share what, with whom, when, and why? In Paper Presented at the Fifth China-North America Library Conference, 1-21. Beijing, National Library of China. 
  18. Committee on Ensuring the Utility and Integrity of Research Data in a Digital Age (2009). Ensuring the Integrity, Accessibility and Stewardship of Research Data in the Digital Age. Washington, D.C.: National Academy Press. 
  19. Cragin, M. H. & Shankar, K. (2006). Scientific data collections and distributed collective practice. Computer Supported Cooperative Work, 15(2-3), 185-204.  https://doi.org/10.1007/s10606-006-9018-z
  20. Faniel, I. M., Kriesberg, A., & Yakel, E. (2016). Social scientists' satisfaction with data reuse. Journal of the Association for Information Science and Technology, 67(6), 1404-1416. https://doi.org/10.1002/asi.23730 
  21. Goncalves, R. S. & Musen, M. A. (2019). The variable quality of metadata about biological samples used in biomedical experiments. Scientific Data, 6(1), 1-15. https://doi.org/10.1038/sdata.2019.21 
  22. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis. Upper Saddle River, NJ: Pearson Prentice Hall. 
  23. Hedstrom, M. & Niu, J. (2008). Incentives for Data Producers to Create "Archive-Ready" Data: Implications for Archives and Records Management. Society of american archivists - 2008 Research Forum. 
  24. Hsu, C. L. & Lin, J. C. C. (2008). Acceptance of blog usage: the roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45(1), 65-74.  https://doi.org/10.1016/j.im.2007.11.001
  25. Joo, S., Kim, S., & Kim, Y. (2017). An exploratory study of health scientists' data reuse behaviors: examining attitudinal, social, and resource factors. Aslib Journal of Information Management, 69(4), 389-407.  https://doi.org/10.1108/AJIM-12-2016-0201
  26. Kankanhalli, A., Tan, B. C. Y., & Wei, K. K. (2005). Contributing knowledge to electronic knowledge repositories: an empirical investigation. MIS Quarterly, 29(1), 113-143.  https://doi.org/10.2307/25148670
  27. Kim, B. & Han, I. (2009). The role of trust belief and its antecedents in a community-driven knowledge environment. Journal of the American Society for Information Science and Technology, 60(5), 1012-1026.  https://doi.org/10.1002/asi.21041
  28. Kim, J. (2007). Motivating and impeding factors affecting faculty contribution to institutional repositories. Journal of Digital Information, 8(2), 1-11. 
  29. Kim, Y. & Stanton, J. M. (2016). Institutional and individual factors affecting scientists' data-sharing behaviors: a multilevel analysis. Journal of the Association for Information Science and Technology, 67(4), 776-799. https://doi.org/10.1002/asi.23424
  30. Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling. New York: Guilford publications. 
  31. Marc, D. T., Beattie, J., Herasevich, V., Gatewood, L., & Zhang, R. (2017). Assessing Metadata Quality of a Federally Sponsored Health Data Repository. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016, 864-873. 
  32. Piwowar, H. A., Day, R. S., & Fridsma, D. B. (2007). Sharing detailed research data is associated with increased citation rate. PLoS ONE, 2(3), 1-5. 
  33. Rousidis, D., Garoufallou, E., Balatsoukas, P., & Sicilia, M. A. (2014). Metadata for Big Data: a preliminary investigation of metadata quality issues in research data repositories. Information Services and Use, 34(3-4), 279-286. 
  34. Sass, D. A. & Smith, P. L. (2006). The effects of parceling unidimensional scales on structural parameter estimates in structural equation modeling. Structural Equation Modeling, 13(4), 566-586.  https://doi.org/10.1207/s15328007sem1304_4
  35. Sterling, T. D. & Weinkam, J. J. (1990). Sharing scientific data. Communications of the ACM, 33(8), 112-119.  https://doi.org/10.1145/79173.79182
  36. Tenopir, C., Christian, L., Allard, S., & Borycz, J. (2018). Research data sharing: practices and attitudes of geophysicists. Earth and Space Science, 5, 891-902.  https://doi.org/10.1029/2018EA000461
  37. Wiig, K. M. (1997). Knowledge management: where did it come from and where will it go?. Expert Systems with Applications, 13(1), 1-14.  https://doi.org/10.1016/S0957-4174(97)00018-3
  38. Yoon, A. (2017). Data reusers' trust development. Journal of the Association for Information Science and Technology, 64(8), 946-956.  https://doi.org/10.1002/asi.23730
  39. Yoon, A. & Lee, Y. (2019). Factors of trust in data reuse. Online Information Review, 43(7), 1245-1262. https://doi.org/10.1108/OIR-01-2019-0014 
  40. Zimmerman, A. (2008). New knowledge from old data: the role of standards in the sharing and reuse of ecological data. Science, Technology, & Human Values, 33(5), 631-652. https://doi.org/10.1177/0162243907306704