Quantitative Economics and Management Studies
Vol. 5 No. 4 (2024), Pages 793-804 · 2024-08-03
Articles | 2024-08-03
Implementation of Design Science for Developing BI Dashboard Employee Engagement Survey
- Publication History
- Published online: August 3, 2024
- DOI
- https://doi.org/10.35877/454RI.qems2699
- Copyright
- Copyright (c) 2024 Haifa Amirasari, Mone Stepanus Andrias
Abstract
Organizations are transitioning to become data-driven across all business aspects, including human resource management. The most crucial data managed is from Employee Engagement Surveys (EES). Organizations require process improvements to analyze EES results more quickly and accurately, even at the subsidiary level. This study implements the design science method to develop an artifact in the form of a Business Intelligence (BI) dashboard. The EES BI dashboard is evaluated using the Technology Acceptance Model (TAM) to assess user acceptance within the organization. The results indicate that the EES BI dashboard is well-received, with users demonstrating a high intention to use it. Thus, it is concluded that design science can be implemented to produce artifacts that assist organizations.
Keywords
References (33)
- Aken, J. v., Chandrasekaran, A., & Halman, J. (2016). Conducting and publishing design science research: Inaugural essay of the design science department of the Journal of Operations Management. Journal of Operations Management, 47-48, 1-8.
- Andrias, M. S. (2023). Pendekatan design science: Metode penelitian alternatif bagi mahasiswa Magister Manajemen. Jurnal Manajemen dan Usahawan Indonesia, 46(2), 131-142.
- Brocke, J. v., Hevner, A., & Maedche, A. (2020a). Design Science Research. Cases. Springer.
- Brocke, J. v., Hevner, A., & Maedche, A. (2020b). Introduction to Design Science Research. In J. v. Brocke, A. Hevner, & A. Maedche (Eds.), Design Science Research. Cases (pp. 1-13). Springer.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319-340. https://doi.org/doi: 10.2307/249008
- Deloitte. (2017). Deloitte Global Human Capital Trends: Rewriting the rules for the digital age.
- Eckerson, W. (2011). Performance dashboards : measuring, monitoring, and managing your business. John Wiley & Sons.
- Franklin, A., Gantela, S., Shifarraw, S., Johnson, T. R., Robinson, D. J., King, B. R., Mehta, A. M., Maddow, C. L., Hoot, N. R., Nguyen, V., Rubio, A., Zhang, J., & Okafor, N. G. (2017). Dashboard visualizations: Supporting real-time throughput decision-making. Journal of Biomedical Informatics, 71, 211-221. https://doi.org/http://dx.doi.org/10.1016/j.jbi.2017.05.024
- Han, J. H., & Sa, H. J. (2022). Acceptance of and satisfaction with online educational classes through the technology acceptance model (TAM): the COVID‐19 situation in Korea. Asia Pacific Education Review, 23, 403-415. https://doi.org/https://doi.org/10.1007/s12564-021-09716-7
- Hansoti, B. (2010). Business Intelligence Dashboard in Decision Making. In C. o. T. T. a. Projects (Ed.), College of Technology Directed Projects. Indiana.
- Hevner, A., & Chatterjee, S. (2010). Design science research in Information Systems. Integrated Series in Information Systems, 22, 9-22. https://doi.org/10.1007/978-1-4419-5653-8_2
- Hevner, A. R. (2007). A three cycle view of design science research. Scandinavian Journal of Information Systems, 19(2).
- Hong, X., Zhang, M., & Liu, Q. (2021). Preschool teachers’ technology acceptance during the COVID-19: An adapted technology acceptance model. Frontiers in Psychology, 12. https://doi.org/https://doi.org/10.3389/fpsyg.2021.691492
- Kintz, M., Kochanowski, M., & Koetter, F. (2017). Creating user-specific business process monitoring dashboards with a model-driven approach. Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development 353-361. https://doi.org/10.5220/0006135203530361
- Lawson-Body, A., Lawson-Body, L., & Illia, A. (2023). Data visualization: Developing and validating dashboard measurement instruments. Journal of Computer Information Systems, 63(3). https://doi.org/https://doi.org/10.1080/08874417.2022.2073295
- Madsen, S., C., H., & Nielsen, J. (2020). Accelerated digital transformation: The case of the online university caused by Covid-19. AIS Electronic Library (AISeL), 11.
- Marangunic, N., & Granic, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Univ Access Inf Soc, 14. https://doi.org/10.1007/s10209-014-0348-1
- Matheus, R., Janssen, M., & Maheshwari, D. (2020). Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities. Government Information Quarterly, 37, 1-9. https://doi.org/https://doi.org/10.1016/j.giq.2018.01.006
- Nicolás-Agustín, Á., Jiménez-Jiménez, D., & Maeso-Fernandez, F. (2022). The role of human resource practices in the implementation of digital transformation. International Journal of Manpower, 43(2), 395-410.
- Pandza, K., & Thorpe, R. (2010). Management as design, but what kind of design? An appraisal of the design science analogy for management. British Journal of Management, 21, 171-186. https://doi.org/DOI: 10.1111/j.1467-8551.2008.00623.x
- Papakostas, C., Troussas, C., Krouska, A., & Sgouropoulou, C. (2023). Exploring users’ behavioral intention to adopt mobile augmented reality in education through an extended Technology Acceptance Model. International Journal OF Human-Computer Interaction, 39(6). https://doi.org/https://doi.org/10.1080/10447318.2022.2062551
- Schilirò, D. (2021). Digital transformation, COVID-19, and the future of work. Department of Economics, University of Messina.
- Sheldon, P. (2016). Facebook friend request: Applying the theory of reasoned action to student-teacher-relationships on Facebook. Journal of Broadcasting & Electronic Media, 60(2), 269–285. https://doi.org/https://doi.org/10.1080/08838151.2016.1164167
- Sultana, S., Akter, S., Kyriazis, E., & Wamba, S. F. (2021). Architecting and developing big Data-Driven Innovation (DDI)
- in the digital economy. Journal of Global Information Management, 29(3), 165-187.
- Team, R. C. (2020, 6 June). R: A language and environment for statistical computing. https://www.R-project.org/
- Tregua, M., Mele, C., Russo-Spena, T., Marzullo, M. L., & Carotenuto, A. (2021). Digital transformation in the era of COVID-19. AHFE, 97-105. https://doi.org/https://doi.org/10.1007/978-3-030-80840-2_10
- Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010). Does the technology acceptance model predict actual use? A systematic literature review. Information and Software Technology, 52, 463-479.
- Van Rossum, G., & Drake, F. L. (2009). Python 3 Reference Manual: (Python Documentation Manual Part 2). CreateSpace Independent Publishing Platform. https://books.google.co.id/books?id=KIybQQAACAAJ
- Wallace, L. G., & Sheetz, S. D. (2014). The adoption of software measures: A technology acceptance model (TAM) perspective. Information & Management, 51, 249-259. https://doi.org/http://dx.doi.org/10.1016/j.im.2013.12.003
- Zehir, C., Karaboğa, T., & Başar, D. (2020). The transformation of human resource management and its impact on overall business performance: Big data analytics and AI technologies in strategic HRM. In U. H. (ed.) (Ed.), Digital Business Strategies in Blockchain Ecosystems. Springer.
- Zhang, J., & Chen, Z. (2021). Exploring Human Resource Management Digital Transformation in the Digital Age. Journal of the Knowledge Economy. https://doi.org/https://doi.org/10.1007/s13132-023-01214-y
- Zingde, S., & Shroff, N. (2020). The role of dashboards in business decision making and Performance management Doctoral Research Conference in Management, India.
How to Cite
Amirasari, H., & Andrias, M. S. (2024). Implementation of Design Science for Developing BI Dashboard Employee Engagement Survey. Quantitative Economics and Management Studies, 5(4), 793–804. https://doi.org/10.35877/454RI.qems2699
