An Empirical Study on Data Reuse Intention among Social Science Researchers in Chengdu, Sichuan Province, China
DOI:
https://doi.org/10.14456/abacodijournal.2023.26Keywords:
data reuse, information quality, service quality, social science researchersAbstract
This study investigates the influencing factors of social science researchers’ intention to reuse data in Chengdu, Sichuan Province, China. The conceptual framework includes information quality, service quality, subjective norms, data repository, perceived effort, and intention to reuse data. The researchers conducted a quantitative method to distribute the online questionnaire to 500 social science researchers. The sample techniques were purposive, quota, and convenience sampling. Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were used to analyze the data and examine the research hypotheses. The findings show that information quality, service quality, subjective norms, perceived usefulness, perceived ease of use, and attitudes to data reuse significantly impact social science researchers’ intention to reuse data. In contrast, data repository has no significant impact on social science researchers’ intention to reuse data influences. It can effectively support the cycle between data sharing and reuse in the humanities and social sciences to promote the formation of ecology and provide references and suggestions for data management in this field.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior (1st ed.). Prentice-Hall.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
Curty, R. G., Crowston, K., Specht, A., Grant, B. W., & Dalton, E. D. (2017). Attitudes and norms affecting scientists' data reuse. PLOS ONE, 12(12), 1-10. https://doi.org/10.1371/journal.pone.0189288
Curty, R. G., & Qin, J. (2014). Towards a model for research data reuse behavior: Towards a Model for Research Data Reuse Behavior. Proceedings of the American Society for Information Science and Technology, 51(1), 1-4. https://doi.org/10.1002/meet.2014.14505101072
Daniels, M., Faniel, I., Fear, K., & Yakel, E. (2012). Managing fixity and fluidity in data repositories. Proceedings of the 2012 iConference on – iConference’12 https://doi.org/10.1145/2132176.2132212
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Faniel, I. M., Barrera-Gomez, J., Kriesberg, A., & Yakel, E. (2013). A comparative study of data reuse among quantitative social scientists and archaeologists [Paper presentation]. iConference, iSchools, Fort Worth, Texus, USA.
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.23480
Fear, K., & Donaldson, D. R. (2012). Provenance and credibility in scientific data repositories. Archival Science, 12(3), 319-339. https://doi.org/10.1007/s10502-012-9172-7
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, L. R. (2006). Multivariant Data Analysis (1st ed.). Pearson International Edition.
Harper, L. M., & Kim, Y. (2018). Attitudinal, normative, and resource factors affecting psychologists' intentions to adopt an open data badge: An empirical analysis. International Journal of Information Management, 41, 23-32. https://doi.org/10.1016/j.ijinfomgt.2018.03.001
Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
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
Joo, Y. K., & Kim, Y. (2017). Engineering researchers' data reuse behaviours: a structural equation modelling approach. The Electronic Library, 35(6), 1141-1161. https://doi.org/10.1108/el-08-2016-0163
Kim, Y., & Stanton, J. M. (2016). Institutional and individual factors affecting scientists' data-sharing behaviors: A multilevel analysis: 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
Kim, Y., & Yoon, A. (2017). Scientists' data reuse behaviors: A multilevel analysis. Journal of the Association for Information Science and Technology, 68(12), 2709-2719. https://doi.org/10.1002/asi.23892
Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling. Guilford Press.
Law, M. (2006). Reduce, Reuse, Recycle: Issues in the Secondary Use of Research Data. IASSIST Quarterly, 29(1), 1-5. https://doi.org/10.29173/iq599
Lin, H.-F. (2007). Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic Commerce Research and Applications, 6(4), 433-442. https://doi.org/10.1016/j.elerap.2007.02.002
Lin, H.-F. (2015). The impact of company-dependent and company-independent information sources on organizational attractiveness perceptions. Journal of Management Development, 34(8), 941-959. https://doi.org/10.1108/jmd-12-2013-0161
Lv, X. J., & Peng, G. L. (2019). Report on the development of Philosophy and Social Sciences in Sichuan. Comprehensive evaluation of institutions, 1(2), 28-32.
Melero, R., & Navarro‐Molina, C. (2020). Researchers' attitudes and perceptions towards data sharing and data reuse in the field of food science and technology. Learned Publishing, 33(2), 163-179. https://doi.org/10.1002/leap.1287
Park, N., Roman, R., Lee, S., & Chung, J. E. (2009). User acceptance of a digital library system in developing countries: An application of the Technology Acceptance Model. International Journal of Information Management, 29(3), 196-209. https://doi.org/10.1016/j.ijinfomgt.2008.07.001
Rui-Hsin, K., & Lin, C.-T. (2018). The usage intention of e-learning for police education and training. Policing: An International Journal, 41(1), 98-112. https://doi.org/10.1108/pijpsm-10-2016-0157
Schermelleh-Engel, K., & Moosbrugger, H. (2003). Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research Online, 8, 23-74.
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M.A. Lange (Ed.), Leading - Edge Psychological Tests and Testing Research (pp. 27-50). Nova.
Tanasapsakul, W., & Vongurai, R. (2018). An investigation of the factors influencing consumers of different generation’s behavioral intention towards franchised Japanese restaurants in Bangkok, Thailand. AU-GSB E-JOURNAL, 11(2), 40-53.
Veeravalli, S., Venkatraman, V., & Hariharan, M. (2019). Why do people seek knowledge? Tracing factors that affect knowledge seeking intention. VINE Journal of Information and Knowledge Management Systems, 50(2), 271-290. https://doi.org/10.1108/vjikms-04-2019-0059
Wang, K., & Lin, C. L. (2012). The adoption of mobile value-added services: investigating the influence of IS quality and perceived playfulness. Managing Service Quality, 22(2), 184-208.
Yan, W., Deng, S., & Zhang, Y. (2016). Factors influencing the intention to use information service mashups: An empirical study of digital libraries in China. The Electronic Library, 34(4), 696-716. https://doi.org/10.1108/el-05-2015-0082
Yoon, A., & Kim, Y. (2017). Social scientists' data reuse behaviors: Exploring the roles of attitudinal beliefs, attitudes, norms, and data repositories. Library & Information Science Research, 39(3), 224-233. https://doi.org/10.1016/j.lisr.2017.07.008
Yoon, A., & Kim, Y. (2020). The role of data-reuse experience in biological scientists' data sharing: an empirical analysis. The Electronic Library, 38(1), 186-208. https://doi.org/10.1108/el-06-2019-0146
Zhou, T. (2011). Examining the critical success factors of mobile website adoption. Online Information Review, 35(4), 636-652. https://doi.org/10.1108/14684521111161972
Zimmerman, A. S. (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
