FLEKSIBILITAS REKONFIGURASI ATURAN KETENAGAKERJAAN SEBAGAI MODE HIBERNASI USAHA PT CAHAYA BUKIT PERUNGGU (CBP)
DOI:
https://doi.org/10.46576/bn.v9i1.8659Keywords:
business continuity, COVID-19, factor analysis, labour flexibility, manufacturing hibernationAbstract
The COVID-19 pandemic forced manufacturing firms to redesign labour rules and operational practices in order to maintain business continuity under severe demand and supply uncertainty. This study aims to formulate a flexible reconfiguration strategy for labour arrangements in factory operations and to test the business hibernation mode at PT CBP, a manufacturing firm producing lamp housings. The study used a quantitative approach with exploratory factor analysis. Data were collected through questionnaires distributed to 29 respondents consisting of internal employees and external stakeholders. Thirteen indicators were analysed using principal component analysis with varimax rotation in SPSS. The Kaiser-Meyer-Olkin value was 0.821 and Bartlett's test was significant at 0.000, indicating that the data were suitable for factor analysis. The results formed three components with a cumulative variance of 67.419%. The components represent digital-adaptive capability, business-continuity marketing capability, and operational efficiency with market protection. These findings indicate that business hibernation can be implemented through proportional working hours, proportional wages based on effective work, and flexible labour allocation according to order volume.
References
Donthu, N., & Gustafsson, A. (2020). Effects of COVID-19 on business and research. Journal of Business Research, 117, 284–289. https://doi.org/10.1016/j.jbusres.2020.06.008
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis.
Herbane, B. (2010). The evolution of business continuity management: A historical review of practices and drivers. Business History, 52(6), 978–1002. https://doi.org/10.1080/00076791.2010.511185
Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922
Kuckertz, A., Braendle, L., Gaudig, A., Hinderer, S., Morales Reyes, C. A., Prochotta, A., Steinbrink, K. M., & Berger, E. S. C. (2020). Startups in times of crisis: A rapid response to the COVID-19 pandemic. Journal of Business Venturing Insights, 13, e00169. https://doi.org/10.1016/j.jbvi.2020.e00169
Papadopoulos, T., Baltas, K. N., & Balta, M. E. (2020). The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice. International Journal of Information Management, 55, 102192. https://doi.org/10.1016/j.ijinfomgt.2020.102192
Sahebjamnia, N., Torabi, S. A., & Mansouri, S. A. (2015). Integrated business continuity and disaster recovery planning: Towards organizational resilience. European Journal of Operational Research, 242(1), 261–273. https://doi.org/10.1016/j.ejor.2014.09.055
Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2019). Using multivariate statistics (7th ed.). Pearson.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Wenzel, M., Stanske, S., & Lieberman, M. B. (2020). Strategic responses to crisis. Strategic Management Journal, 42(2), O16–O27. https://doi.org/10.1002/smj.3161
