Analysis of the Impact of Digital Transformation on Economic Productivity in the Manufacturing Industry Sector in Tangerang Regency
Abstract
This study analyzes the impact of digital transformation on economic productivity in the manufacturing industry sector in Tangerang Regency, involving 100 respondents from various sectors, including food and beverages, textiles, chemicals, as well as automotive and electronics. The questionnaire explores demographic aspects such as position, industry sector, and scale of the company. The results of the demographic analysis indicate that most respondents are operational and production managers, with 50% coming from medium-scale companies. Validity and reliability tests show that all questionnaire items are valid (p < 0.05) and that the Cronbach's Alpha coefficient is 0.76, indicating good consistency. Linear regression analysis reveals a significant positive relationship between the adoption of digital technology and productivity, with a regression coefficient of 0.65 (p < 0.01), meaning an increase of one unit in the adoption of digital technology is associated with a productivity increase of 65%. With an R-squared value of 0.72, these findings indicate that 72% of the variation in economic productivity can be explained by the adoption of digital technology. This research provides important insights into how the implementation of digital technology can enhance productivity in the manufacturing sector and recommends the importance of effective digital transformation strategies to improve competitiveness and performance in the era of Industry 4.0.
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References
Abdallah, Y. O. (2021). Towards managing digital transformation in manufacturing industry: Theoretical framework. Advances in Transdisciplinary Engineering, 15(Query date: 2024-12-06 00:26:32), 21–26. https://doi.org/10.3233/ATDE210006
Adom, P. K. (2021). The role of climate adaptation readiness in economic growth and climate change relationship: An analysis of the output/income and productivity/institution channels. Journal of Environmental Management, 293(Query date: 2024-12-06 07:25:11). https://doi.org/10.1016/j.jenvman.2021.112923
Anna, T. (2022). Model of State Support for The Digital Transformation of The Manufacturing Industry in Russian Regions. International Journal of Technology, 13(7), 1538–1547. https://doi.org/10.14716/ijtech.v13i7.6219
Bhatia, V. (2024). Intelligent Manufacturing in Aerospace: Integrating Industry 4.0 Technologies for Operational Excellence and Digital Transformation. Springer Series in Advanced Manufacturing, Query date: 2024-12-06 00:26:32, 389–434. https://doi.org/10.1007/978-3-031-68271-1_18
Birkel, H. (2024). Small- and Medium-Sized Companies Tackling the Digital Transformation of Supply Chain Processes: Insights from a Multiple Case Study in the German Manufacturing Industry. IEEE Transactions on Engineering Management, 71(Query date: 2024-12-06 00:26:32), 13711–13726. https://doi.org/10.1109/TEM.2022.3209131
Dohale, V. (2023). Manufacturing strategy 4.0: A framework to usher towards industry 4.0 implementation for digital transformation. Industrial Management and Data Systems, 123(1), 10–40. https://doi.org/10.1108/IMDS-12-2021-0790
Gong, Q. (2023). How Can the Development of Digital Economy Empower Green Transformation and Upgrading of the Manufacturing Industry?—A Quasi-Natural Experiment Based on the National Big Data Comprehensive Pilot Zone in China. Sustainability (Switzerland), 15(11). https://doi.org/10.3390/su15118577
Görçün, Ö. F. (2024). Evaluation of Industry 4.0 strategies for digital transformation in the automotive manufacturing industry using an integrated fuzzy decision-making model. Journal of Manufacturing Systems, 74(Query date: 2024-12-06 00:26:32), 922–948. https://doi.org/10.1016/j.jmsy.2024.05.005
Hu, T. (2021). Movable oil content evaluation of lacustrine organic-rich shales: Methods and a novel quantitative evaluation model. Earth-Science Reviews, 214(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.earscirev.2021.103545
Ji, H. (2021). Qualitative and quantitative recognition method of drug-producing chemicals based on SnO2 gas sensor with dynamic measurement and PCA weak separation. Sensors and Actuators B: Chemical, 348(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.snb.2021.130698
Jian, C. (2020). Quantitative PCR provides a simple and accessible method for quantitative microbiota profiling. PLoS ONE, 15(1). https://doi.org/10.1371/journal.pone.0227285
Karaduman, C. (2022). The effects of economic globalization and productivity on environmental quality: Evidence from newly industrialized countries. Environmental Science and Pollution Research, 29(1), 639–652. https://doi.org/10.1007/s11356-021-15717-1
K?rm?z?, M. (2022). Digital transformation maturity model development framework based on design science: Case studies in manufacturing industry. Journal of Manufacturing Technology Management, 33(7), 1319–1346. https://doi.org/10.1108/JMTM-11-2021-0476
Kryshtal, H. (2023). The Impact Of Industry 4.0 On The Digital Transformation Of Manufacturing Enterprises In Ukraine. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 2, 149–153. https://doi.org/10.33271/NVNGU/2023-2/149
Liu, M. (2023). The dynamic impact of energy productivity and economic growth on environmental sustainability in South European countries. Gondwana Research, 115(Query date: 2024-12-06 07:25:11), 116–127. https://doi.org/10.1016/j.gr.2022.11.012
Liu, Y. (2022). The synergy degree measurement and transformation path of China’s traditional manufacturing industry enabled by digital economy. Mathematical Biosciences and Engineering, 19(6), 5738–5753. https://doi.org/10.3934/mbe.2022268
Maestas, N. (2023). The Effect of Population Aging on Economic Growth, the Labor Force, and Productivity. American Economic Journal: Macroeconomics, 15(2), 306–332. https://doi.org/10.1257/mac.20190196
Matricano, D. (2022). The behavior of managers handling digital business transformations: Theoretical issues and preliminary evidence from firms in the manufacturing industry. International Journal of Entrepreneurial Behaviour and Research, 28(5), 1292–1309. https://doi.org/10.1108/IJEBR-01-2021-0077
Miao, Y. (2024). Effect of digital transformation on labor income share in manufacturing enterprises: Insights from technological innovation and industry–university–research collaborations. Kybernetes, 53(13), 24–46. https://doi.org/10.1108/K-08-2023-1414
Monizza, G. P. (2021). Mass customization as the convergent vision for the digital transformation of the manufacturing and the building industry. Rethinking Building Skins: Transformative Technologies and Research Trajectories, Query date: 2024-12-06 00:26:32, 453–474. https://doi.org/10.1016/B978-0-12-822477-9.00006-1
Murshed, M. (2022). The impacts of renewable energy, financial inclusivity, globalization, economic growth, and urbanization on carbon productivity: Evidence from net moderation and mediation effects of energy efficiency gains. Renewable Energy, 196(Query date: 2024-12-06 07:25:11), 824–838. https://doi.org/10.1016/j.renene.2022.07.012
Nauta, M. (2023). From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. ACM Computing Surveys, 55(13). https://doi.org/10.1145/3583558
Olsson, A. K. (2024). Management toward Industry 5.0: A co-workership approach on digital transformation for future innovative manufacturing. European Journal of Innovation Management, Query date: 2024-12-06 00:26:32. https://doi.org/10.1108/EJIM-09-2023-0833
Park, J. (2024). Techno-economic analysis of solar powered green hydrogen system based on multi-objective optimization of economics and productivity. Energy Conversion and Management, 299(Query date: 2024-12-06 07:25:11). https://doi.org/10.1016/j.enconman.2023.117823
Raihan, A. (2023). The dynamic nexus between economic growth, renewable energy use, urbanization, industrialization, tourism, agricultural productivity, forest area, and carbon dioxide emissions in the Philippines. Energy Nexus, 9(Query date: 2024-12-06 07:25:11). https://doi.org/10.1016/j.nexus.2023.100180
Romanova, O. (2022). Impact of Digital Transformation on Labor Productivity Growth in the Manufacturing Industry in Russia. Lecture Notes in Information Systems and Organisation, 54(Query date: 2024-12-06 00:26:32), 433–445. https://doi.org/10.1007/978-3-030-94617-3_30
Salminen, K. (2023). Sustainable Digital Transformation of Manufacturing Industry: Needs for Competences and Services Related to Industry 5.0 Technologies. PICMET 2023 - Portland International Conference on Management of Engineering and Technology: Managing Technology, Engineering and Manufacturing for a Sustainable World, Proceedings, Query date: 2024-12-06 00:26:32. https://doi.org/10.23919/PICMET59654.2023.10216871
Singh, B. J. (2024). Managing digital manufacturing transformation: Assessing the status-quo and future prospects in North Indian industries. Journal of Strategy and Management, Query date: 2024-12-06 00:26:32. https://doi.org/10.1108/JSMA-07-2023-0168
Singh, S. (2021). Modeling the effects of digital transformation in Indian manufacturing industry. Technology in Society, 67(Query date: 2024-12-06 00:26:32). https://doi.org/10.1016/j.techsoc.2021.101763
Tan, R. (2022). Transportation infrastructure, economic agglomeration and non-linearities of green total factor productivity growth in China: Evidence from partially linear functional coefficient model. Transport Policy, 129(Query date: 2024-12-06 07:25:11), 1–13. https://doi.org/10.1016/j.tranpol.2022.09.027
Tana, G. (2023). Digital Transformation: Moderating Supply Chain Concentration and Competitive Advantage in the Service-Oriented Manufacturing Industry. Systems, 11(10). https://doi.org/10.3390/systems11100486
Wang, H. (2023). Micro-perspective of listed companies in China: Digital development promotes the green transformation of the manufacturing industry. PLoS ONE, 18(10). https://doi.org/10.1371/journal.pone.0293474
Xi, Q. (2021). The impact of special economic zones on producer services productivity: Evidence from China. China Economic Review, 65(Query date: 2024-12-06 07:25:11). https://doi.org/10.1016/j.chieco.2020.101558
Yilmaz, M. A. (2020). Simultaneous quantitative screening of 53 phytochemicals in 33 species of medicinal and aromatic plants: A detailed, robust and comprehensive LC–MS/MS method validation. Industrial Crops and Products, 149(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.indcrop.2020.112347
Yin, S. (2022). Enhancing Digital Innovation for the Sustainable Transformation of Manufacturing Industry: A Pressure?State?Response System Framework to Perceptions of Digital Green Innovation and Its Performance for Green and Intelligent Manufacturing. Systems, 10(3). https://doi.org/10.3390/systems10030072
Ying, C. (2023). On the Digital Transformation of the Automobile Manufacturing Industry in the Chengdu-Chongqing Economic Circle: Mechanism of Action and Feasible Paths. Contemporary Social Sciences, 1, 17–43. https://doi.org/10.19873/j.cnki.2096-0212.2023.01.002
Zeng, S. (2022). Total Factor Productivity and High-Quality Economic Development: A Theoretical and Empirical Analysis of the Yangtze River Economic Belt, China. International Journal of Environmental Research and Public Health, 19(5). https://doi.org/10.3390/ijerph19052783
Zhang, C. (2024). Research on the impact of enterprise digital transformation on carbon emissions in the manufacturing industry. International Review of Economics and Finance, 92(Query date: 2024-12-06 00:26:32), 211–227. https://doi.org/10.1016/j.iref.2024.02.009
Zhang, L. (2024). The Influence of Digital Transformation on the Reconfigurability and Performance of Supply Chains: A Study of the Electronic, Machinery, and Home Appliance Manufacturing Industries in China. Sustainability (Switzerland), 16(7). https://doi.org/10.3390/su16072689
Zheng, X. (2023). Digital transformation, industrial structure change, and economic growth motivation: An empirical analysis based on manufacturing industry in Yangtze River Delta. PLoS ONE, 18(5). https://doi.org/10.1371/journal.pone.0284803
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