THE USER PERCEPTION OF SWSEUM (SEMANTIC WEB MUSEUM)

Lidya Amalia Rahmania, Ilham Mulya Putra Perdana, Yuh-Wen Chen

Abstract


The perception of the user is one of the key qualities to measure whether an institution has a good governance and provides good service towards the stakeholders. The development of the Semantic Web for Mpu Purwa Museum was to build a better version of the current website and to give a structure between the many kinds of the artefacts and manuscripts. Thus, it could improve the perception of the user of the website of Mpu Purwa Museum. This research aims to collect the feedbacks from the users about their perceptions on the new version of the website of Mpu Purwa Museum. This research is using a quantitative method and the questionnaire is based on the End-User Computing Satisfaction (EUCS). The instrument to assess the perception of the users about the semantic web of the Mpu Purwa Museum shows that all of the hypotheses are proven right.


Full Text:

PDF

References


Aggelidis, V. P., & Chatzoglou, P. D. (2012). Hospital information systems: Measuring end user computing satisfaction (EUCS). Journal of Biomedical Informatics, 45(3), 566–579. https://doi.org/10.1016/j.jbi.2012.02.009

Altman, N., & Krzywinski, M. (2017). Points of Significance: Interpreting P values. Nature Methods, 14(3), 213–215. https://go.gale.com/ps/i.do?p=AONE&sw=w&issn=15487091&v=2.1&it=r&id=GALE%7CA483616934&sid=googleScholar&linkaccess=fulltext

Cheah, J. H., Sarstedt, M., Ringle, C. M., Ramayah, T., & Ting, H. (2018). Convergent validity assessment of formatively measured constructs in PLS-SEM: On using single-item versus multi-item measures in redundancy analyses. International Journal of Contemporary Hospitality Management, 30(11), 3192–3210. https://doi.org/10.1108/IJCHM-10-2017-0649

Choshaly, S. H., & Mirabolghasemi, M. (2019). Using SEM-PLS to assess users satisfaction of library service quality: evidence from Malaysia. Library Management, 40(3–4), 240–250. https://doi.org/10.1108/LM-03-2018-0023

Christodoulides, G., Jevons, C., & Bonhomme, J. (2012). Memo to marketers: Quantitative evidence for change - how user-generated content really affects brands. Journal of Advertising Research, 52(1), 53–64. https://doi.org/10.2501/JAR-52-1-053-064

Daubs, M. S. (2020). The SAGE International Encyclopedia of Mass Media and Society. In The SAGE International Encyclopedia of Mass Media and Society (pp. 1826–1827). https://doi.org/10.4135/9781483375519

Doll, W. J., & Torkzadeh, G. (1988). The Measurement of End-User Computing Satisfaction. Source: MIS Quarterly, 1213512(2), 259–274. http://www.jstor.org/stable/248851%0Ahttp://www.jstor.org/page/info/about/policies/terms.jsp%0Ahttp://www.jstor.org

Dolma, S. (2017). Re: What is the difference between Composite Reliability and Internal reliability? https://www.researchgate.net/post/What_is_the_difference_between_Composite_Reliability_and_Internal_reliability/58e4c37c615e274f4f43beec/citation/download.

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Horrell, M., Reynolds, L., & Mcelhinney, A. (2020). Data Science in Heavy Industry and the Internet of Things. Harvard Data Science Review, 2, 1–14. https://doi.org/10.1162/99608f92.834c6595

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Kock, N., & Lynn, G. S. (2012). Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations. Journal of the Association for Information Systems, 13(7), 546–580. https://doi.org/http://dx.doi.org/10.17705/1jais.00302

Krishnan, K. (2020). Data discovery and connectivity. In Building Big Data Applications (pp. 199–212). https://doi.org/10.1016/b978-0-12-815746-6.00011-9

Li, H. (2021). An empirical research on the construction of a government website public satisfaction index model in China. Journal of Global Information Management, 29(5), 112–135. https://doi.org/10.4018/JGIM.20210901.oa7

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Treating Unobserved Heterogeneity in PLS-SEM: AMulti-method Approach. Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications, 1–414. https://doi.org/10.1007/978-3-319-64069-3

SmartPLS. (2021a). Model Fit. https://www.smartpls.com/documentation/algorithms-and-techniques/model-fit

SmartPLS. (2021b). When to Use PLS-SEM (and When Not). https://www.smartpls.com/documentation/choosing-pls-sem/more-insights-on-when-to-use-pls-sem-and-when-not

SmartPLS, F. (2017). Interpreting Path Coefficients. https://forum.smartpls.com/viewtopic.php?t=16088

Syahroni, F., Nurmandi, A., & Salahudin. (2022). How Does Local Government Use the Website and Twitters as Public Services Tools? A Case Study of Aceh Province Government, Indonesia. 127–135. https://doi.org/10.1007/978-981-16-1781-2_13

Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2

Tanaka, J. S., & Huba, G. J. (1985). A fit index for covariance structure models under arbitrary GLS estimation. British Journal of Mathematical and Statistical Psychology, 38(2), 197–201. https://doi.org/10.1111/j.2044-8317.1985.tb00834.x

Vidgen, B., & Yasseri, T. (2016). P-values: Misunderstood and misused. Frontiers in Physics, 4(MAR), 10–14. https://doi.org/10.3389/fphy.2016.00006


Refbacks

  • There are currently no refbacks.


ISSN: 2598-0653