Disorientasi Moral sebagai Tantangan Etika Bisnis dalam Transformasi Ekonomi Digital
DOI:
https://doi.org/10.35905/sipakainge.v3i7.14141Keywords:
Disorientasi Moral, Etika Bisnis Syariah,, Ekonomi Digital.Abstract
ABSTRAK
Transformasi ekonomi digital telah menciptakan peluang besar dalam dunia bisnis, namun juga menimbulkan tantangan etis yang signifikan, salah satunya adalah disorientasi moral. Fenomena ini menggambarkan situasi ketika pelaku bisnis kehilangan kejelasan dalam membedakan tindakan yang etis dan tidak etis akibat tekanan sistem digital yang cepat dan kompleks. Dalam konteks Indonesia, percepatan digitalisasi belum sepenuhnya diiringi dengan penguatan nilai moral dan regulasi yang adaptif, sehingga memunculkan risiko penyimpangan etika dalam praktik bisnis digital. Studi ini bertujuan untuk menganalisis disorientasi moral dalam ekonomi digital dan mengeksplorasi kontribusi prinsip-prinsip etika bisnis syariah sebagai pendekatan alternatif untuk mengatasi tantangan tersebut. Melalui studi literatur yang komprehensif, artikel ini menemukan bahwa integrasi nilai-nilai Islam dalam etika bisnis digital dapat menjadi fondasi penting bagi pembangunan ekosistem digital yang berkeadilan, transparan, dan bertanggung jawab secara sosial. Kajian ini memberikan kontribusi teoritis dalam memperkaya diskursus etika bisnis kontemporer serta implikasi praktis dalam perumusan kebijakan bisnis digital berbasis nilai.
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[Soliani, R. D., Rita, A., Terra, T., Soliani, R. D., Rita, A., & Terra, T. (2024). BRAZILIAN HIGHWAYS : MIXED METHOD ANALYSES AND BRAZILIAN HIGHWAYS : MIXED METHOD ANALYSES AND CRASH. Multimodal Transportation, 100173. https://doi.org/10.1016/j.multra.2024.100173
Stra, L., & Hirte, G. (2024). The causal impact of a business cycle shock on road crashes and its determinants – A synthetic control group analysis. 91(July), 108–119. https://doi.org/10.1016/j.jsr.2024.07.005
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Yadav, D. C., & Pal, S. (2020). Prediction of heart disease using feature selection and random forest ensemble method. International Journal of Pharmaceutical Research, 12(4), 56–66. https://doi.org/10.31838/ijpr/2020.12.04.013
T. W. A. Putra, A. Solechan, and B. Hartono, “Transformasi digital pada UMKM dalam meningkatkan daya saing pasar,” Jurnal Informatika Upgris, vol. 9, no. 1, pp. 7–12, 2023.
H. Zikri, “Transformasi Ekonomi Digital untuk Meningkatkan Produktivitas dan Daya Saing UMKM di Indonesia,” Glossary: Jurnal Ekonomi Syariah, vol. 2, no. 1, pp. 16–25, 2024.
Abdi, A., & Hern, S. O. (2024). Investigating the severity of single-vehicle truck crashes under different crash types using mixed logit models. Journal of Safety Research, 88(December 2023), 344–353. https://doi.org/10.1016/j.jsr.2023.12.001
Ajanovic, A., & Haas, R. (2021). Prospects and impediments for hydrogen and fuel cell vehicles in the transport sector. International Journal of Hydrogen Energy, 46(16), 10049–10058. https://doi.org/10.1016/j.ijhydene.2020.03.122
Atombo, C. (2024). Heliyon Examining drivers injury severity for manual and automatic transmission vehicles-involved crashes : Random parameter mixed logit model with heterogeneity in means and variances. Heliyon, 10(16), e36555. https://doi.org/10.1016/j.heliyon.2024.e36555
Bešinović, N. (2020). Resilience in railway transport systems: a literature review and research agenda. Transport Reviews, 40(4), 457–478. https://doi.org/10.1080/01441647.2020.1728419
Chang, Y. H., Li, C. Y., Lu, T. H., Artanti, K. D., & Hou, W. H. (2020). Risk of injury and mortality among driver victims involved in single-vehicle crashes in Taiwan: Comparisons between vehicle types. International Journal of Environmental Research and Public Health, 17(13), 1–9. https://doi.org/10.3390/ijerph17134687
Chen, Z., Wen, H., Zhu, Q., & Zhao, S. (2023). Severity Analysis of Multi-Truck Crashes on Mountain Freeways Using a Mixed Logit Model.
Crizzle, A. M., McLean, M., & Malkin, J. (2020). Risk factors for depressive symptoms in long-haul truck drivers. International Journal of Environmental Research and Public Health, 17(11). https://doi.org/10.3390/ijerph17113764
Dias, M. C., Pinto, D. C. G. A., & Silva, A. M. S. (2021). Plant flavonoids: Chemical characteristics and biological activity. Molecules, 26(17), 1–16. https://doi.org/10.3390/molecules26175377
Fernandes, A. A. T., Filho, D. B. F., da Rocha, E. C., & da Silva Nascimento, W. (2020). Read this paper if you want to learn logistic regression. Revista de Sociologia e Politica, 28(74), 1/1-19/19. https://doi.org/10.1590/1678-987320287406EN
Gupta, V. K., Gupta, A., Kumar, D., & Sardana, A. (2021). Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model. Big Data Mining and Analytics, 4(2), 116–123. https://doi.org/10.26599/BDMA.2020.9020016
Harris, J. K. (2021). Primer on binary logistic regression. Family Medicine and Community Health, 9, 1–7. https://doi.org/10.1136/fmch-2021-001290
Huhta, R., Hirvonen, K., & Partinen, M. (2021). Prevalence of sleep apnea and daytime sleepiness in professional truck drivers. Sleep Medicine, 81, 136–143. https://doi.org/10.1016/j.sleep.2021.02.023
Inkinen, T., & Hämäläinen, E. (2020). Reviewing truck logistics: Solutions for achieving low emission road freight transport. Sustainability (Switzerland), 12(17), 1–11. https://doi.org/10.3390/SU12176714
Koul, S. K. (2020). Scanning the Issue. IETE Journal of Research, 66(3), 287–289. https://doi.org/10.1080/03772063.2020.1767397
Lee, T., Liford, M., Turner, M., & Bush, A. (2022). Driver injuries in heavy vs . light and medium truck local crashes , 2010 – 2019. Journal of Safety Research, 83, 26–34. https://doi.org/10.1016/j.jsr.2022.08.001
Mbatchou, J., Barnard, L., Backman, J., Marcketta, A., Kosmicki, J. A., Ziyatdinov, A., Benner, C., O’Dushlaine, C., Barber, M., Boutkov, B., Habegger, L., Ferreira, M., Baras, A., Reid, J., Abecasis, G., Maxwell, E., & Marchini, J. (2021). Computationally efficient whole-genome regression for quantitative and binary traits. Nature Genetics, 53(7), 1097–1103. https://doi.org/10.1038/s41588-021-00870-7
Modeling injury severity of crashes involving trucks Capturing and exploring risk factors associated with land use and demographic in addition to crash, driver, and on-network characte.pdf. (n.d.).
Mollah, M. B., Zhao, J., Niyato, D., Guan, Y. L., Yuen, C., Sun, S., Lam, K.-Y., & Koh, L. H. (2020). Blockchain for the internet of vehicles towards intelligent transportation systems: A survey. IEEE Internet of Things Journal, 8(6), 4157–4185.
Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S., & Mosavi, A. (2020). Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; A Comparative Analysis. IEEE Access, 8, 150199–150212. https://doi.org/10.1109/ACCESS.2020.3015966
Nævestad, T. O., Blom, J., & Phillips, R. O. (2020). Safety culture, safety management and accident risk in trucking companies. Transportation Research Part F: Traffic Psychology and Behaviour, 73, 325–347. https://doi.org/10.1016/j.trf.2020.07.001
Okafor, S., Ko, E., & Jones, S. (2022). Heliyon Severity analysis of crashes involving in-state and out-of-state large truck drivers in Alabama : A random parameter multinomial logit model with heterogeneity in means and variances. 8(September). https://doi.org/10.1016/j.heliyon.2022.e11989
Sahin, E. K., Colkesen, I., & Kavzoglu, T. (2020). A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping. Geocarto International, 35(4), 341–363. https://doi.org/10.1080/10106049.2018.1516248
Sandi, H., Afni Yunita, N., Heikal, Mohd., Nur Ilham, R., & Sinta, I. (2021). Relationship Between Budget Participation, Job Characteristics, Emotional Intelligence and Work Motivation As Mediator Variables to Strengthening User Power Performance: An Emperical Evidence From Indonesia Government. Morfai Journal, 1(1), 36–48. https://doi.org/10.54443/morfai.v1i1.14
[Soliani, R. D., Rita, A., Terra, T., Soliani, R. D., Rita, A., & Terra, T. (2024). BRAZILIAN HIGHWAYS : MIXED METHOD ANALYSES AND BRAZILIAN HIGHWAYS : MIXED METHOD ANALYSES AND CRASH. Multimodal Transportation, 100173. https://doi.org/10.1016/j.multra.2024.100173
Stra, L., & Hirte, G. (2024). The causal impact of a business cycle shock on road crashes and its determinants – A synthetic control group analysis. 91(July), 108–119. https://doi.org/10.1016/j.jsr.2024.07.005
Wang, J., Parajuli, S., Cherry, C. R., Mcdonald, N. C., & Lyons, T. (2022). Transportation Research Interdisciplinary Perspectives Vulnerable road user safety and freight vehicles : A case study in North Carolina and Tennessee. Transportation Research Interdisciplinary Perspectives, 15(March), 100650. https://doi.org/10.1016/j.trip.2022.100650
Yadav, D. C., & Pal, S. (2020). Prediction of heart disease using feature selection and random forest ensemble method. International Journal of Pharmaceutical Research, 12(4), 56–66. https://doi.org/10.31838/ijpr/2020.12.04.013
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2025-06-23
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