From Data Sciences to Decisions: How Analytics, Sensing, Process Integration, Human Resource Flexibility, and Technology Orientation Lift Manufacturing Operations Under a Data-Driven Shop-Floor Culture
DOI:
https://doi.org/10.31181/rme502Keywords:
Human Resource Flexibility, Operational Performance, Data-Driven Culture, Manufacturing Companies, Process Integration, Sensing CapabilityAbstract
Decision making process of the companies improved through analytical capability, sensing capability, process integration, human resource flexibility, and technology orientation which leads to enhanced operational performance. Therefore, research aimed to test the influence of analytical capability, sensing capability, process integration, human resource flexibility, and technology orientation on operational performance with the moderating influence of data-driven culture. Cross-sectional quantitative data were employed from 315 transportation and engineering employees. The depicted results show that analytical capability, sensing capability, process integration, human resource flexibility, and technology orientation each have a significant and positive impact on the operational performance. Moreover, the data-driven culture moderates positively to promote the transportation and engineering company’s operational performance. Study results emphasize the significance of cultivating a decision-making making data-driven culture in manufacturing settings to transform sensor data into actionable decisions, enhance operational excellence, and improve responsiveness to dynamic market conditions. The results contribute theoretically by extending the dynamic capability theory and practically by guiding managers to raise a strong data-driven environment that leverages analytics, sensing, and technology for superior operational performance that could support to better operational decisions.
References
Adepoju, A. H., Austin-Gabriel, B., Eweje, A., & Collins, A. (2022). Framework for automating multi-team workflows to maximize operational efficiency and minimize redundant data handling. IRE Journals, 5(9), 663-664. https://www.researchgate.net/profile/Adebusayo-Adepoju/publication/388734627_Framework_for_Automating_Multi-Team_Workflows_to_Maximize_Operational_Efficiency_and_Minimize_Redundant_Data_Handling/links/67a43ea9645ef274a47139d2/Framework-for-Automating-Multi-Team-Workflows-to-Maximize-Operational-Efficiency-and-Minimize-Redundant-Data-Handling.pdf
Adesina, A. A., Iyelolu, T. V., & Paul, P. O. (2024). Optimizing business processes with advanced analytics: techniques for efficiency and productivity improvement. World Journal of Advanced Research and Reviews, 22(3), 1917-1926. https://doi.org/10.30574/wjarr.2024.22.3.1960
Adiguzel, Z., Sonmez Cakir, F., & Karaaslan, N. (2025). Examination of the effects of technology orientation, technology innovation strategy and strategic orientation on information technology companies in technoparks. International Journal of Innovation Science. https://doi.org/10.1108/IJIS-03-2024-0059
Ahmad, J. (2024). Strategic Planning: Navigating Uncertainty in Business Management. Journal of Management & Social Science, 1(02), 33-46. https://rjmssjournal.com/index.php/7/article/view/15/20
Akter, S., Bandara, R. J., & Sajib, S. (2021). How to empower analytics capability to tackle emergency situations? International Journal of Operations & Production Management, 41(9), 1469-1494. https://doi.org/10.1108/IJOPM-11-2020-0805
Al-Qubaisi, S. S., & Ajmal, M. (2018). Determinants of operational efficiency in the oil and gas sector: A Balanced scorecards perspective. Benchmarking: An International Journal, 25(9), 3357-3385. https://doi.org/10.1108/BIJ-04-2017-0079
Al Bashar, M., Taher, A., & Johura, F. T. (2024). Utilizing Predictive Analytics for Enhanced Production Planning and Inventory Control in the US Manufacturing Sector. International Research Journal of Modernization in Engineering Technology and Science, 6, 8332-8339. https://d1wqtxts1xzle7.cloudfront.net/115572342/UTILIZING_PREDICTIVE_ANALYTICS_FOR_ENHANCED_PRODUCTION_PLANNING_AND_INVENTORY_CONTROL_IN_THE_US-libre.pdf?1717343420=&response-content-disposition=inline%3B+filename%3DUTILIZING_PREDICTIVE_ANALYTICS_FOR_ENHAN.pdf&Expires=1762411796&Signature=Fra82PQ93c5Yyx~7asB4wW5zR0Iwhi1oyyou8DX3zhG1P5NOVhPh23JreTUH9s-6PppM4TzmSWwr3-eGex5Gwu7HwqFEF-bGkyN~TrTN-~66CVxSrxagdn84U-DCwRp1PWWkF3yx6IbmgcWTCcDdGnYkfm26h6OJfWtUrLKsJrVibpR61JOKXwIBrzKzOCymwvK-oafdCrsDGXkqFgvNElDJD6N3GJnYJlEo2BZ4wyXZaeL2s~uTCzODC0OCE6eCCrHBpvECCqw7lISg~BvNC8o2xQ7hPmSyGkfJd7h~yTYXmJZWnROFZFHGuqJojmXuZLd7yXIpC-yjjzaahTttPw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
Al Najjar, A. S., & Qandeel, M. S. (2025). Operational strategy, capabilities, and successfully accomplishing business strategy. Journal of Applied Research in Technology & Engineering, 6(1), 1-11. https://doi.org/10.4995/jarte.2025.20708
Alaskar, T. H. (2023). Innovation capabilities as a mediator between business analytics and firm performance. Sustainability, 15(6), 5522. https://doi.org/10.3390/su15065522
Almazmomi, N., Ilmudeen, A., & Qaffas, A. A. (2022). The impact of business analytics capability on data-driven culture and exploration: achieving a competitive advantage. Benchmarking: An International Journal, 29(4), 1264-1283. https://doi.org/10.1108/BIJ-01-2021-0021
Alonge, E. O., Eyo-Udo, N. L., Ubanadu, B. C., Daraojimba, A. I., Balogun, E. D., & Ogunsola, K. O. (2023). Real-time data analytics for enhancing supply chain efficiency. Journal of Supply Chain Management and Analytics, 10(1), 49-60. https://www.allmultidisciplinaryjournal.com/uploads/archives/1744697208.pdf
Althabatah, A., Yaqot, M., Menezes, B., & Kerbache, L. (2023). Transformative procurement trends: Integrating industry 4.0 technologies for enhanced procurement processes. Logistics, 7(3), 63. https://doi.org/10.3390/logistics7030063
Alzoraiki, M., Milhem, M., Ateeq, A., Almeer, S., & Hussein, T. M. (2024). Strategic flexibility: an essential capability for innovation and sustainable performance in times of technological uncertainty. In Business Development via AI and Digitalization: Volume 1 (pp. 271-281). Springer. https://doi.org/10.1007/978-3-031-62102-4_22
Ansari, K., & Ghasemaghaei, M. (2023). Big data analytics capability and firm performance: meta-analysis. Journal of Computer Information Systems, 63(6), 1477-1494. https://doi.org/10.1080/08874417.2023.2170300
Anwar, U. A. A., Rahayu, A., Wibowo, L. A., Sultan, M. A., Aspiranti, T., Furqon, C., & Rani, A. M. (2025). Supply chain integration as the implementation of strategic management in improving business performance. Discover Sustainability, 6(1), 101. https://doi.org/10.1007/s43621-025-00867-w
Atieh, A. A., Abu Hussein, A., Al-Jaghoub, S., Alheet, A. F., & Attiany, M. (2025). The impact of digital technology, automation, and data integration on supply chain performance: exploring the moderating role of digital transformation. Logistics, 9(1), 11. https://doi.org/10.3390/logistics9010011
Atiningsih, S., & Izzaty, K. N. (2021). The effect firm size on company value with profitability as intervening variable and dividend policy as moderating variable. International Journal of Economics, Business and Accounting Research (IJEBAR), 5(4). https://doi.org/10.29040/ijebar.v5i4.3450
Balogun, E. D., Ogunsola, K. O., & Ogunmokun, A. S. (2025). An Integrated Data Engineering and Business Analytics Framework for Cross-Functional Collaboration And Strategic Value Creation. ResearchGate, March. https://doi.org/10.47191/etj/v10i03.34
Bamakan, S. M. H., Faregh, N., & ZareRavasan, A. (2021). Di-ANFIS: an integrated blockchain–IoT–big data-enabled framework for evaluating service supply chain performance. Journal of Computational Design and Engineering, 8(2), 676-690. https://doi.org/10.1093/jcde/qwab007
Benzidia, S., Makaoui, N., & Bentahar, O. (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological Forecasting and Social Change, 165, 120557. https://doi.org/10.1016/j.techfore.2020.120557
Bhatia, M. S. (2021). Green process innovation and operational performance: The role of proactive environment strategy, technological capabilities, and organizational learning. Business Strategy and the Environment, 30(7), 2845-2857. https://doi.org/10.1002/bse.2775
Carmeli, A., Sheaffer, Z., & Yitzack Halevi, M. (2009). Does participatory decision‐making in top management teams enhance decision effectiveness and firm performance? Personnel review, 38(6), 696-714. https://doi.org/10.1108/00483480910992283
Chatterjee, S., Chaudhuri, R., & Vrontis, D. (2024). Does data-driven culture impact innovation and performance of a firm? An empirical examination. Annals of Operations Research, 333(2), 601-626. https://doi.org/10.1007/s10479-020-03887-z
Chaudhuri, R., Chatterjee, S., Vrontis, D., & Thrassou, A. (2024). Adoption of robust business analytics for product innovation and organizational performance: the mediating role of organizational data-driven culture. Annals of Operations Research, 339(3), 1757-1791. https://doi.org/10.1007/s10479-021-04407-3
Chukwunweike, J., Abubakar, I., & Anang, A. N. (2024). Enhancing Industrial Efficiency through Automation: Leveraging PLC, Scada, HMI, Batch, and DCS Systems for Downtime Mitigation in Industrial Control Projects. https://www.researchgate.net/profile/Joseph-Chukwunweike/publication/384043730_Enhancing_Industrial_Efficiency_through_Automation_Leveraging_PLC_Scada_HMI_Batch_and_DCS_Systems_for_Downtime_Mitigation_in_Industrial_Control_Projects/links/66e5eb68dde50b3258745cb7/Enhancing-Industrial-Efficiency-through-Automation-Leveraging-PLC-Scada-HMI-Batch-and-DCS-Systems-for-Downtime-Mitigation-in-Industrial-Control-Projects.pdf
Constantiou, I. D., & Kallinikos, J. (2015). New games, new rules: big data and the changing context of strategy. Journal of Information Technology, 30(1), 44-57. https://doi.org/10.1057/jit.2014.17
Drachuk, Y., Serhieieva, O., Snitko, Y., Belyaeva, G., & Trushkina, N. (2021). Standardization of business processes in the transportation sector. https://dspace.luguniv.edu.ua/xmlui/handle/123456789/10346
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. https://doi.org/10.2307/3151312
Gade, K. R. (2021). Data-driven decision making in a complex world. Journal of computational innovation, 1(1). https://researchworkx.com/index.php/jci/article/view/2/2
Ganbold, O., Matsui, Y., & Rotaru, K. (2021). Effect of information technology-enabled supply chain integration on firm's operational performance. Journal of enterprise information management, 34(3), 948-989. https://doi.org/10.1108/JEIM-10-2019-0332
Gangwani, K. K., & Bhatia, M. S. (2024). The effect of market orientation and technology orientation on industry 4.0 technologies and market performance: role of innovation capability. Industrial marketing management, 118, 231-241. https://doi.org/10.1016/j.indmarman.2024.03.004
Gayed, S., & El Ebrashi, R. (2023). Fostering firm resilience through organizational ambidexterity capability and resource availability: amid the COVID-19 outbreak. International Journal of Organizational Analysis, 31(1), 253-275. https://doi.org/10.1108/IJOA-09-2021-2977
Ghanad, A. (2023). An overview of quantitative research methods. International journal of multidisciplinary research and analysis, 6(08), 3794-3803. https://doi.org/10.47191/ijmra/v6-i8-52
Giannopoulos, A. G., & Moschovou, T. P. (2023). Estimating the Value of Information Technology in the Productivity of the Transport Sector. Future Transportation, 3(2), 601-614. https://doi.org/10.3390/futuretransp3020035
Golzar, J., Noor, S., & Tajik, O. (2022). Convenience sampling. International Journal of Education & Language Studies, 1(2), 72-77. https://doi.org/10.22034/ijels.2022.162981
Guo, D., Li, M., Lyu, Z., Kang, K., Wu, W., Zhong, R. Y., & Huang, G. Q. (2021). Synchroperation in industry 4.0 manufacturing. International Journal of Production Economics, 238, 108171. https://doi.org/10.1016/j.ijpe.2021.108171
Haider, A. (2024). A Parametric Study on Design Variables for Solar Powered Long Endurance Unmanned Aerial Vehicle. Reports in Mechanical Engineering, 5(1), 1-12. https://doi.org/10.31181/rme313
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
Hakeem, A. O. (2023). EXPLORING THE ROLE OF DYNAMIC ENVIRONMENTS ON THE FIRMS’INNOVATION PERFORMANCE: MEDIATING ROLES OF TECHNOLOGY AND MARKET-SENSING CAPABILITY. International Journal of Innovation Management, 27(06), 2350025. https://doi.org/10.1142/S1363919623500251
Hettich, E., & Kreutzer, M. (2021). Strategy formation across organizational boundaries: An interorganizational process model. British Journal of Management, 32(1), 147-199. https://doi.org/10.1111/1467-8551.12473
Hurbean, L., Militaru, F., Munteanu, V. P., Danaiata, D., Fotache, D., & Muntean, M. (2024). Assessing the Influence of Business Intelligence and Analytics and Data-Driven Culture on Managerial Performance: Evidence from Romania. Systems, 13(1), 2. https://doi.org/10.3390/systems13010002
Irfan, M., Wang, M., & Akhtar, N. (2020). Enabling supply chain agility through process integration and supply flexibility: Evidence from the fashion industry. Asia Pacific Journal of Marketing and Logistics, 32(2), 519-547. https://doi.org/10.1108/APJML-03-2019-0122
Iyer, S. S. (2025). Data-Driven Decision Making: The Key to Future Health Care Business Success. https://doi.org/10.47191/rajar/v11i3.06
JADOUN, R. (2024). Tech-driven efficiency: Exploring the strategic influence of IT in operational optimization. https://www.researchgate.net/profile/Roopal-Gangwar/publication/384239906_Tech-Driven_efficiency_Exploring_the_strategic_influence_of_IT_in_operational_optimization/links/66f02a4e750edb3bea68597b/Tech-Driven-efficiency-Exploring-the-strategic-influence-of-IT-in-operational-optimization.pdf
Kamble, S. S., & Gunasekaran, A. (2020). Big data-driven supply chain performance measurement system: a review and framework for implementation. International Journal of Production Research, 58(1), 65-86. https://doi.org/10.1080/00207543.2019.1630770
Kanike, U. K. (2023). Factors disrupting supply chain management in manufacturing industries. Journal of Supply Chain Management Science, 4(1-2), 1-24. https://doi.org/10.18757/jscms.2023.6986
Karami, H. (2023). Decision-Making in Data-Intensive Environments and Its Impact on Organisational Design: Dynamic Capabilities Approach Manchester Metropolitan University]. https://e-space.mmu.ac.uk/633065/
Kenyon, G. N. (2025). Supply Chain Dimensions of Quality. In The Perception of Quality: Establishing a Competitive Advantage Through Quality, Value, and Perception (pp. 291-364). Springer. https://doi.org/10.1007/978-1-4471-7606-0_9
Kissimoto, K. O., Mattos, C. A., & Laurindo, F. J. (2023). The role of the organizational and operational dimensions in the open collaboration performance: a strategic alignment perspective. RAUSP Management Journal, 58(1), 22-38. https://doi.org/10.1108/RAUSP-12-2021-0244
Larabi, C. (2025). Linking intangible resources to predict firm performance through technology innovation and strategic flexibility: leveraging the resource-based view of the manufacturing firms. Journal of Strategy and Management. https://doi.org/10.1108/JSMA-08-2024-0204
Lepore, D., Vecciolini, C., Micozzi, A., & Spigarelli, F. (2023). Developing technological capabilities for Industry 4.0 adoption: An analysis of the role of inbound open innovation in small and medium‐sized enterprises. Creativity and Innovation Management, 32(2), 249-265. https://doi.org/10.1111/caim.12551
Li, Y., Wang, Y., Wang, L., & Xie, J. (2022). Investigating the effects of stakeholder collaboration strategies on risk prevention performance in a digital innovation ecosystem. Industrial Management & Data Systems, 122(9), 2045-2071. https://doi.org/10.1108/IMDS-12-2021-0805
Liu, G., Ma, L., Xue, Y., Han, L., Xi, R., Han, Z., Wang, H., Dong, J., Lou, M., & Jin, J. (2024). SensCAP: A systematic sensing capability performance metric for 6G ISAC. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2024.3430502
Manafe, M. W. N., Rizani, A., & Purnawati, E. (2024). Analysis of the influence of technical expertise, market orientation, social capital, and innovation on the performance of micro and small businesses in the technology sector. International Journal of Business, Law, and Education, 5(1), 983-995. https://doi.org/10.56442/ijble.v5i1.493
Manzoor, U., Baig, S. A., Hashim, M., Sami, A., Rehman, H.-U., & Sajjad, I. (2022). The effect of supply chain agility and lean practices on operational performance: a resource-based view and dynamic capabilities perspective. The TQM Journal, 34(5), 1273-1297. https://doi.org/10.1108/TQM-01-2021-0006
Martinez-Sanchez, A., Vicente-Oliva, S., & Pérez-Pérez, M. (2021). The strategy of human resource flexibility versus absorptive capacity of knowledge: an integrative framework in industrial firms. European Journal of Innovation Management, 24(2), 315-337. https://doi.org/10.1108/EJIM-10-2019-0314
Nassani, A. A., Grigorescu, A., Yousaf, Z., Condrea, E., Javed, A., & Haffar, M. (2023). Does technology orientation determine innovation performance through digital innovation? A glimpse of the electronic industry in the digital economy. Electronics, 12(8), 1854. https://doi.org/10.3390/electronics12081854
Nugroho, A., Prijadi, R., & Kusumastuti, R. D. (2022). Strategic orientations and firm performance: the role of information technology adoption capability. Journal of Strategy and Management, 15(4), 691-717. https://doi.org/10.1108/JSMA-06-2021-0133
Nyangoma, D., Adaga, E. M., Sam-Bulya, N. J., & Achumie, G. O. (2024). Operational excellence in SMEs: A conceptual framework for optimizing logistics and service delivery systems. Journal of Frontiers in Multidisciplinary Research, 5(1), 149-156. https://doi.org/10.54660/.IJFMR.2024.5.1.149-156
Obiki-Osafiele, A. N., Efunniyi, C. P., Abhulimen, A. O., Osundare, O. S., Agu, E. E., Adeniran, I. A., & OneAdvanced, U. (2024). Theoretical models for enhancing operational efficiency through technology in Nigerian businesses. International Journal of Applied Research in Social Sciences, 6(8), 1969-1989. https://doi.org/10.51594/ijarss.v6i8.1478
Olayinka, O. H. (2021). Big data integration and real-time analytics for enhancing operational efficiency and market responsiveness. Int J Sci Res Arch, 4(1), 280-296. https://doi.org/10.30574/ijsra.2021.4.1.0179
Östlund, M., & Gustafsson, E. (2024). Managing Data-Driven Decision-Making: Managerial Practices: A Qualitative Multiple Case Study about Managerial Practices when Utilizing Data-Driven Decisions. https://www.diva-portal.org/smash/record.jsf?pid=diva2:1862400
Oteri, O. J., Onukwulu, E. C., Igwe, A. N., Ewim, C. P.-M., Ibeh, A. I., & Sobowale, A. (2023). Cost optimization in logistics product management: strategies for operational efficiency and profitability. International Journal of Business and Management. Forthcoming. https://www.allmultidisciplinaryjournal.com/uploads/archives/20250215171124_MGE-2025-1-361.1.pdf
Paiva, S., Ahad, M. A., Tripathi, G., Feroz, N., & Casalino, G. (2021). Enabling technologies for urban smart mobility: Recent trends, opportunities and challenges. Sensors, 21(6), 2143. https://doi.org/10.3390/s21062143
Palaniappan, M. K. (2025). Supply Chain Analytics & Logistics Management. Academic Guru Publishing House. https://books.google.com.pk/books?hl=en&lr=&id=llGJEQAAQBAJ&oi=fnd&pg=PA1&dq=+Supply+Chain+Analytics+%26+Logistics+Management.+Academic+Guru+Publishing+House.+&ots=AvlvindB-6&sig=nXYRAHg2jINwL0VtHxvArOA9GWQ&redir_esc=y#v=onepage&q&f=false
Raj, A., Kumar, R. R., Narayanan, S., Kirca, A. H., & Jeyaraj, A. (2025). Analytics Capability, Supply Chain Capabilities, and Operational Performance: A Meta‐Analytic Investigation. Journal of Operations Management. https://doi.org/10.1002/joom.1376
Rehman, A. u., & Jajja, M. S. S. (2023). The interplay of integration, flexibility and coordination: a dynamic capability view to responding environmental uncertainty. International Journal of Operations & Production Management, 43(6), 916-946. https://doi.org/10.1108/IJOPM-11-2021-0708
Ridwan, I. B. (2025). Dynamic strategic foresight using predictive business analytics: Strategic modeling of competitive advantage in unstable market and innovation ecosystems. https://doi.org/10.30574/wjarr.2025.26.2.1730
Savitri, E., Dp, E., & Syahza, A. (2021). Can innovation mediate the effect of adaptability, entrepreneurial orientation on business performance. Management Science Letters, 11(8), 2301-2312. https://d1wqtxts1xzle7.cloudfront.net/81261051/msl_2021_54-libre.pdf?1645578063=&response-content-disposition=inline%3B+filename%3DCan_innovation_mediate_the_effect_of_ada.pdf&Expires=1762415134&Signature=fHki1jjnORvBjRgolOPn7Z3xltasM~8-MpZh957~XYX-YEk1W5OAsJA4MZTyk6hKfyiLKvgtSNq-N7BBgrcFoNnjD1B5ASG903klLaIQau~x7RDFTNZep-rvWv7GC5GG5IZX1j3qljQYZrly9b-j6EX1n1mbW7-4g7SxPtmkHbP7DHCe1k9NHqZt-2WO0FJ8ackIc-X5pjNKHXfXAI01ND4GkELHG3NuhEwymMLO2c4mXD77ifUub1sav9gCvmW9ZOTtgSWJrtLaIP9Unu57KLXJY5IoenO7I6rWNZeGmnXIudekzu4yiNb2zh3n20SmuWu9dGfjMDEhSMTvGgI3fw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
Siagian, H., Tarigan, Z. J. H., & Jie, F. (2021). Supply chain integration enables resilience, flexibility, and innovation to improve business performance in COVID-19 era. Sustainability, 13(9), 4669. https://doi.org/10.3390/su13094669
Singh, S., Rajest, S. S., Hadoussa, S., Obaid, A. J., & Regin, R. (2023). Data-driven decision making for long-term business success. IGI Global. https://books.google.com.pk/books?hl=en&lr=&id=69LrEAAAQBAJ&oi=fnd&pg=PR1&dq=Singh,+S.,+Rajest,+S.+S.,+Hadoussa,+S.,+Obaid,+A.+J.,+%26+Regin,+R.+(2023).+Data-driven+decision+making+for+long-term+business+success.+IGI+Global.+&ots=uKzQWWI68o&sig=Ga-jweWRDrkTi7ykWK_UicKEaeA&redir_esc=y#v=onepage&q=Singh%2C%20S.%2C%20Rajest%2C%20S.%20S.%2C%20Hadoussa%2C%20S.%2C%20Obaid%2C%20A.%20J.%2C%20%26%20Regin%2C%20R.%20(2023).%20Data-driven%20decision%20making%20for%20long-term%20business%20success.%20IGI%20Global.&f=false
Sitorus, C. F., & Meilani, Y. C. F. P. (2024). The Influence of Organizational Culture and Big Data Analytics on Employee Performance Mediated by Organizational Support at PT XYZ Operating in the Consumer Goods Sales Industry. Jurnal Info Sains: Informatika dan Sains, 14(04), 729-744. https://ejournal.seaninstitute.or.id/index.php/InfoSains/article/view/5869
Song, J., Lee, K.-B., Zhou, Z., Jia, L., Cegielski, C., & Shin, S. I. (2022). Enhancing supply chain sensing capability through social media: an environmental scanning perspective. Information Technology & People, 35(1), 367-391. https://doi.org/10.1108/ITP-11-2019-0609
Stank, T. P., Dohmen, A. E., Saunders, L. W., Merrick, J. R., & Goldsby, T. J. (2022). Applying agility to improve customer performance when supply and demand vary from core conditions. International Journal of Physical Distribution & Logistics Management, 52(8), 722-744. https://doi.org/10.1108/IJPDLM-07-2021-0298
Su, X., Zeng, W., Zheng, M., Jiang, X., Lin, W., & Xu, A. (2022). Big data analytics capabilities and organizational performance: the mediating effect of dual innovations. European Journal of Innovation Management, 25(4), 1142-1160. https://doi.org/10.1108/EJIM-10-2020-0431
Susha, I., Rukanova, B., Zuiderwijk, A., Gil-Garcia, J. R., & Hernandez, M. G. (2023). Achieving voluntary data sharing in cross sector partnerships: Three partnership models. Information and Organization, 33(1), 100448. https://doi.org/10.1016/j.infoandorg.2023.100448
Tafoya, E. I. V. (2025). Flexibility Quantification and Multi-Objective Decision Making Model in Supply Chain Management Texas A&M University-Kingsville]. https://www.proquest.com/openview/dc85c6b0932e011ce01aa01692aa53d4/1?pq-origsite=gscholar&cbl=18750&diss=y
Teixeira, A. L., Beuren, J., Hoepfner, L. A., Maracaja, M. T., Pereira, N., & Sampaio, P. (2023). Data-Driven Agility: A Case Study on Practical Use of Agile Concepts in Engineering. In Offshore Technology Conference Brasil (pp. D012S052R002). OTC. https://doi.org/10.4043/32887-MS
Thummala, V. R., & Saxena, S. (2024). Data-driven decision-making for enhanced program management. https://www.researchgate.net/profile/Venkata-Thummala/publication/389395379_427_Attribution-NonCommercial-NoDerivatives_40_International_CC_BY-NC-ND_40_Data-Driven_Decision-Making_for_Enhanced_Program_Management/links/67c0a5ae645ef274a496630b/427-Attribution-NonCommercial-NoDerivatives-40-International-CC-BY-NC-ND-40-Data-Driven-Decision-Making-for-Enhanced-Program-Management.pdf
Tseng, P.-H., & Liao, C.-H. (2015). Supply chain integration, information technology, market orientation and firm performance in container shipping firms. The International Journal of Logistics Management, 26(1), 82-106. https://doi.org/10.1108/IJLM-09-2012-0088
Turi, J. A., Khwaja, M. G., Tariq, F., & Hameed, A. (2023). The role of big data analytics and organizational agility in improving organizational performance of business processing organizations. Business Process Management Journal, 29(7), 2081-2106. https://doi.org/10.1108/BPMJ-01-2023-0058
Úbeda-García, M., Claver-Cortés, E., Marco-Lajara, B., & Zaragoza-Sáez, P. (2017). Human resource flexibility and performance in the hotel industry: The role of organizational ambidexterity. Personnel review, 46(4), 824-846. https://doi.org/10.1108/PR-12-2015-0315
Wolniak, R. (2024a). CONTINOUS IMPROVEMENT: LEVERAGING BUSINESS ANALYTICS IN INDUSTRY 4.0 SETTINGS. Scientific Papers of Silesian University of Technology. Organization & Management/Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie(203). https://doi.org/10.29119/1641-3466.2024.203.33
Wolniak, R. (2024b). Smart manufacturing–the utilization of business analytics in Industry 4.0 environments. Zeszyty Naukowe. Organizacja i Zarządzanie/Politechnika Śląska(195), 647-663. https://doi.org/10.29119/1641-3466.2024.195.40
Wong, D. T., & Ngai, E. W. (2025). The effects of analytics capability and sensing capability on operations performance: the moderating role of data-driven culture. Annals of Operations Research, 350(2), 781-816. https://doi.org/10.1007/s10479-023-05241-5
Wyciślak, S. (2024). Real-time visibility as a catalyst for operational enhancements. Logforum, 20(2). https://doi.org/10.17270/J.LOG.001010
Yang, Z., Wu, M., Sun, J., & Zhang, Y. (2024). Aligning redundancy and flexibility for supply chain resilience: a literature synthesis. Journal of risk research, 27(2), 313-335. https://doi.org/10.1080/13669877.2024.2328196
Yu, W., Zhao, G., Liu, Q., & Song, Y. (2021). Role of big data analytics capability in developing integrated hospital supply chains and operational flexibility: An organizational information processing theory perspective. Technological Forecasting and Social Change, 163, 120417. https://doi.org/10.1016/j.techfore.2020.120417
Zhou, D., Kohtamäki, M., Peng, X., & Kong, X. (2025). How digital orientation impacts service innovation in hotels: The role of digital capabilities and government support. Tourism Economics, 31(2), 221-244. https://doi.org/10.1177/13548166241255408
Zhou, J., Xu, T., Chiao, Y., & Fang, Y. (2024). Interorganizational systems and supply chain agility in uncertain environments: The mediation role of supply chain collaboration. Information systems research, 35(1), 184-202. https://doi.org/10.1287/isre.2023.1210
Zungu, Z., & Laryea, S. (2025). Leveraging Sensing as a Dynamic Capability: A Strategy-as-Practice Approach to Enhancing Organizational Resilience in Construction Firms. In CIB Conferences (Vol. 1, pp. 317). https://doi.org/10.7771/3067-4883.1784
Орлов, І. В. (2024). Role of document management system for business processes optimization. Проблеми теорії та методології бухгалтерського обліку, контролю і аналізу(2 (58)), 45-49. https://doi.org/10.26642/pbo-2024-2(58)-45-49
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Reports in Mechanical Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.