The Impact of Supply Chain Management Practices on Competitive Advantage: The Moderating Role of Big Data Analytics
DOI:
https://doi.org/10.26668/businessreview/2023.v8i3.679Keywords:
Supply Chain Management Practices, Competitive Advantage, Big Data Analytics, Manufacturing Firms, JordanAbstract
Purpose: The current study aims to investigate the moderating role of Big Data Analytics (BDA) on the relationship between supply chain management practices (SCMPs) and the competitive advantage (CA) in the Jordanian manufacturing firms.
Design/methodology/approach: Quantitative method was used to collect data from 156 Jordanian manufacturing companies. And hierarchical linear multiple regression using SPSS technique was used to test the study hypotheses.
Findings: The results show significant positive impact of SCMPs on CA. Specifically, a significant positive impact is found between information quality (IQ), and information sharing (ISh) on CA. However, strategic supplier partnership (SSP), and customer relationship management (CRM) had no impact on CA. However, the study found that BDA does not enhance the impact of SCMPs on CA.
Research, Practical & Social implications: This study provides an inventory of knowledge about the reality of BDA and its moderating role on the relationship between SCMPs and CA, which contributes to enriching the library in overall and Jordanian in specific in this subject.
Originality/value: This paper is one of the first papers in the Jordanian context to address the moderating effect of BDA between SCMPs and CA.
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References
Agha, S., Alrubaiee, L., & Jamhour, M. (2012). Effect of core competence on competitive advantage and organizational performance. International Journal of Business and Management, 7(1), 192–204.
Aguinis, H., & Pierce, C. A. (1998). Testing moderator variable hypotheses meta-analytically. Journal of Management, 24(5), 577–592.
Agus, A., & Hassan, Z. (2008). The strategic supplier partnership in a supply chain management with quality and business performance. International Journal of Business and Management Science, 1(2), 129–145.
Arredondo, C. R., & Alfaro Tanco, J. A. (2021). Supply Chain Management: some reflections to improve its influence in business strategy. Innovar, 31(81), 7-19.
Azevedo, F., & Reis, J. L. (2019). Big Data Analysis in Supply Chain Management in Portuguese SMEs “Leader Excellence.” Journal of Information Systems Engineering and Management, 4(3), em0096.
Awwad, M., Kulkarni, P., Bapna, R., & Marathe, A. (2018, September). Big data analytics in supply chain: a literature review. In Proceedings of the international conference on industrial engineering and operations management (Vol. 2018, pp. 418-25).
Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y. (2020). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling, 153, 104559.
Cai, J., Liu, X., Xiao, Z., & Liu, J. (2009). Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment. Decision Support Systems, 46(2), 512–521.
Chen, I. J., & Paulraj, A. (2004). Towards a theory of supply chain management: the constructs and measurements. Journal of Operations Management, 22(2), 119–150.
Chileshe, M. J., & Phiri, J. (2022). The Impact of Supply Chain Management Practices on Performance of Small and Medium Enterprises in Developing Countries: A Case of Agro-Dealers in Zambia. Open Journal of Business and Management, 10(2), 591-605.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences.
Darvazeh, S. S., Vanani, I. R., & Musolu, F. M. (2020). Big data analytics and its applications in supply chain management. New Trends in the Use of Artificial Intelligence for the Industry 4.0, 175.
Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge university press.
Golicic, S. L., & Smith, C. D. (2013). A meta‐analysis of environmentally sustainable supply chain management practices and firm performance. Journal of Supply Chain Management, 49(2), 78–95.
Heizer, J., & Render, B. (2011). Operations management flexible version. Pearson Higher Ed.
Iqbal, T. (2020). The effect of operations management practices on the competitive advantages of SMEs: A mediating role of supply chain management practices. Uncertain Supply Chain Management, 8(4), 649–662.
Jin, Yan, Vonderembse, M., Ragu-Nathan, T. S., & Smith, J. T. (2014). Exploring relationships among IT-enabled sharing capability, supply chain flexibility, and competitive performance. International Journal of Production Economics, 153, 24–34.
Jin, Yuran, & Ji, S. (2013). Partner choice of supply chain based on 3d printing and big data. Information Technology Journal, 12(22), 6822.
Kankaew, K., Yapanto, L., Waramontri, R., Arief, S., Hamsir, H., Sastrawati, N., & Espinoza-Maguiña, M. (2021). Supply chain management and logistic presentation: Mediation effect of competitive advantage. Uncertain Supply Chain Management, 9(2), 255–264.
Khaddam, A., Irtaimeh, H., & Bader, B. (2020). The effect of supply chain management on competitive advantage: The mediating role of information technology. Uncertain Supply Chain Management, 8(3), 547–562.
Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.
Koh, S. C. L., Demirbag, M., Bayraktar, E., Tatoglu, E., & Zaim, S. (2007). The impact of supply chain management practices on performance of SMEs. Industrial Management & Data Systems.
Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6(70), 1.
Lee, C. W., Kwon, I. G., & Severance, D. (2007). Relationship between supply chain performance and degree of linkage among supplier, internal integration, and customer. Supply Chain Management: An International Journal.
Li, S., & Lin, B. (2006). Accessing information sharing and information quality in supply chain management. Decision Support Systems, 42(3), 1641–1656.
Li, S., Ragu-Nathan, B., Ragu-Nathan, T. S., & Rao, S. S. (2006). The impact of supply chain management practices on competitive advantage and organizational performance. Omega, 34(2), 107–124.
Mentzer, J. T., Myers, M. B., & Stank, T. P. (2006). Handbook of global supply chain management. Sage Publications.
Naway, F., & Rahmat, A. (2019). The mediating role of technology and logistic integration in the relationship between supply chain capability and supply chain operational performance. Uncertain Supply Chain Management, 7(3), 553–566.
Nimeh, H. A., Abdallah, A. B., & Sweis, R. (2018). Lean supply chain management practices and performance: empirical evidence from manufacturing companies. International Journal of Supply Chain Management, 7(1), 1–15.
Oncioiu, I., Bunget, O. C., Türkeș, M. C., Căpușneanu, S., Topor, D. I., Tamaș, A. S., Rakoș, I.-S., & Hint, M. Ștefan. (2019). The impact of big data analytics on company performance in supply chain management. Sustainability, 11(18), 4864.
Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. Routledge.
Phan, A. C., Abdallah, A. B., & Matsui, Y. (2011). Quality management practices and competitive performance: Empirical evidence from Japanese manufacturing companies. International Journal of Production Economics, 133(2), 518–529.
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual review of psychology, 63, 539-569.
Prajogo, D., Oke, A., & Olhager, J. (2016). Supply chain processes: Linking supply logistics integration, supply performance, lean processes and competitive performance. International Journal of Operations & Production Management.
Riahi, Y., & Riahi, S. (2018). Big data and big data analytics: Concepts, types and technologies. International Journal of Research and Engineering, 5(9), 524–528.
Sadalia, I., Muharam, H., & Mulyana, A. (2021). Change of business environment: competitive advantage of the international market. Utopía y Praxis Latinoamericana, 26(3), 10-17.
Saleem, H., Li, Y., Ali, Z., Ayyoub, M., Wang, Y., & Mehreen, A. (2020). Big data use and its outcomes in supply chain context: the roles of information sharing and technological innovation. Journal of Enterprise Information Management.
Sandhu, M. A., Helo, P., & Kristianto, Y. (2013). Steel supply chain management by simulation modelling. Benchmarking: An International Journal.
Sekaran, U., & Bougie, R. (2019). Research Methods for Business: A Skill Building Approach. Jhon Wiley and Sons Ltd: United Kingdom.
Sposito, V. A., Hand, M. L., & Skarpness, B. (1983). On the efficiency of using the sample kurtosis in selecting optimal lpestimators. Communications in Statistics-simulation and Computation, 12(3), 265-272.
Stevenson, W. J. (2014). Operations management. 12th global edition. New York: McGraw Hill/Irwin.
Tamym, L., El Oaudghiri, M. D., Benyoucef, L., & Moh, A. N. S. (2020, November). Big Data for Supply Chain Management in Industry 4.0 Context: A Comprehensive Survey. In 13ème CONFERENCE INTERNATIONALE DE MODELISATION, OPTIMISATION ET SIMULATION (MOSIM2020), 12-14 Nov 2020, AGADIR, Maroc.
Tarafdar, M., & Qrunfleh, S. (2017). Agile supply chain strategy and supply chain performance: complementary roles of supply chain practices and information systems capability for agility. International Journal of Production Research, 55(4), 925–938.
Tsai, C.-W., Lai, C.-F., Chao, H.-C., & Vasilakos, A. V. (2015). Big data analytics: a survey. Journal of Big Data, 2(1), 1–32.
Vargas, J. R. C., Mantilla, C. E. M., & de Sousa Jabbour, A. B. L. (2018). Enablers of sustainable supply chain management and its effect on competitive advantage in the Colombian context. Resources, Conservation and Recycling, 139, 237–250.
Verma, J. P., Agrawal, S., Patel, B., & Patel, A. (2016). Big data analytics: challenges and applications for text, audio, video, and social media data”. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 5(1), 41–51.
Waghmare, M. P., & Mehta, M. B. (2014). Information Technology and Supply Chain Management Practices in Global Business Organizations–A Study. IBMRD’s Journal of Management & Research, 3(2), 107–112.
Weinberg, S. L., & Abramowitz, S. K. (2008). Statistics using SPSS: An integrative approach. Cambridge University Press.
Wu, L., Chuang, C.-H., & Hsu, C.-H. (2014). Information sharing and collaborative behaviors in enabling supply chain performance: A social exchange perspective. International Journal of Production Economics, 148, 122–132.
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