How do digital technologies improve supply chain resilience in the COVID-19 pandemic? Evidence from Chinese manufacturing firms

Abstract

Digital technologies (DTs) can assist businesses in coping with supply chain (SC) disruptions caused by unpredictability, such as pandemics. However, the current knowledge of the relationship between DTs and supply chain resilience (SCR) is insufficient. This study draws on information processing theory to develop a serial mediation model to address this deficiency. We analyze a sample set consisting of 264 Chinese manufacturers. The empirical results reveal that digital supply chain platforms (DSCPs), as well as supply chain traceability (SCT) and supply chain agility (SCA), fully mediate the favorable association between DTs and SCR. Specifically, the four significant indirect paths indicated that firms can improve SCR only if they use DTs to directly or indirectly improve SCT and SCA (through DSCPs). Our study contributes to the literature on resilience by examining the possible mechanism of mediation through which DTs influence SCR. The findings also offer essential insights for firms to modify their digital strategies and thrive in a turbulent environment.

Graphical abstract

Keywords

digital technologies / supply chain resilience / information processing theory / COVID-19 / China

References

[1] Accenture (2020). China Enterprise Digital Transformation Index 2020. Online Report

[2] Angelidou, S Lisboa, A C C Saridakis, C (2022). Expanding into new product lines in response to COVID-19: The interplay between firm age and performance aspirations. Industrial Marketing Management, 104: 167–181

[3] Ardolino, M Rapaccini, M Saccani, N Gaiardelli, P Crespi, G Ruggeri, C (2018). The role of digital technologies for the service transformation of industrial companies. International Journal of Production Research, 56( 6): 2116–2132

[4] Armstrong, J S Overton, T S (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14( 3): 396–402

[5] Aslam, H Blome, C Roscoe, S Azhar, T M (2018). Dynamic supply chain capabilities. International Journal of Operations & Production Management, 38( 12): 2266–2285

[6] Aslam, H Khan, A Q Rashid, K Rehman, S (2020). Achieving supply chain resilience: The role of supply chain ambidexterity and supply chain agility. Journal of Manufacturing Technology Management, 31( 6): 1185–1204

[7] Balakrishnan, A S Ramanathan, U (2021). The role of digital technologies in supply chain resilience for emerging markets’ automotive sector. Supply Chain Management, 26( 6): 654–671

[8] Bechini, A Cimino, M G C A Marcelloni, F Tomasi, A (2008). Patterns and technologies for enabling supply chain traceability through collaborative e-business. Information and Software Technology, 50( 4): 342–359

[9] Blome, C Schoenherr, T Rexhausen, D (2013). Antecedents and enablers of supply chain agility and its effect on performance: A dynamic capabilities perspective. International Journal of Production Research, 51( 4): 1295–1318

[10] Brandon-Jones, E Squire, B Autry, C W Petersen, K J (2014). A contingent resource-based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50( 3): 55–73

[11] Bruque, Cámara S Moyano, Fuentes J Maqueira, Marín J M (2015). Cloud computing, Web 2.0, and operational performance: The mediating role of supply chain integration. The International Journal of Logistics Management, 26( 3): 426–458

[12] Büyüközkan, G Göçer, F (2018). Digital Supply Chain: Literature review and a proposed framework for future research. Computers in Industry, 97: 157–177

[13] Cegielski, C G Allison Jones Farmer, L Wu, Y Hazen, B T (2012). Adoption of cloud computing technologies in supply chains. International Journal of Logistics Management, 23( 2): 184–211

[14] Centobelli, P Cerchione, R Vecchio, P D Oropallo, E Secundo, G (2021). Blockchain technology for bridging trust, traceability and transparency in circular supply chain. Information & Management, 59( 7): 103508

[15] Chiarini, A (2021). Industry 4.0 technologies in the manufacturing sector: Are we sure they are all relevant for environmental performance?. Business Strategy and the Environment, 30( 7): 3194–3207

[16] Christopher, M (2000). The agile supply chain: Competing in volatile markets. Industrial Marketing Management, 29( 1): 37–44

[17] Cousins, P D Lawson, B Petersen, K J Fugate, B (2019). Investigating green supply chain management practices and performance. International Journal of Operations & Production Management, 39( 5): 767–786

[18] DiNunzioJ (2020). Siemens & SAP deliver supply chain resiliency through digitalization

[19] Dolgui, A Ivanov, D Rozhkov, M (2020). Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain. International Journal of Production Research, 58( 5): 1285–1301

[20] El Baz, J Ruel, S (2021). Can supply chain risk management practices mitigate the disruption impacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era. International Journal of Production Economics, 233: 107972

[21] Fairbank, J F Labianca, G J Steensma, H K Metters, R (2006). Information processing design choices, strategy, and risk management performance. Journal of Management Information Systems, 23( 1): 293–319

[22] Faruquee, M Paulraj, A Irawan, C A (2021). Strategic supplier relationships and supply chain resilience: Is digital transformation that precludes trust beneficial?. International Journal of Operations & Production Management, 41( 7): 1192–1219

[23] FayR (2020). What can we learn from a year of intense digital dependence? Online Paper

[24] Fornell, C Larcker, D F (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18( 3): 382–388

[25] Fosso Wamba, S Akter, S Edwards, A Chopin, G Gnanzou, D (2015). How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165: 234–246

[26] Frank, A G Dalenogare, L S Ayala, N F (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210: 15–26

[27] Galbraith, J R (1974). Organization design: An information processing view. Interfaces, 4( 3): 28–36

[28] GillisA S (2022). What is SAP ERP?

[29] Gunasekaran, A Papadopoulos, T Dubey, R Fosso Wamba, S Childe, S J Hazen, B Akter, S (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70: 308–317

[30] HayesA F (2017). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. 2nd ed. New York, NY: Guilford Press

[31] Hosseini, S Ivanov, D Dolgui, A (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125: 285–307

[32] Hu, Y McNamara, P Piaskowska, D (2017). Project suspensions and failures in new product development: Returns for entrepreneurial firms in co-development alliances. Journal of Product Innovation Management, 34( 1): 35–59

[33] Inditex (2020). Inditex 2020 Annual Report. Online Report

[34] 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

[35] Ivanov, D Dolgui, A (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning and Control, 32( 9): 775–788

[36] Joshi, A D Gupta, S M (2019). Evaluation of design alternatives of end-of-life products using Internet of Things. International Journal of Production Economics, 208: 281–293

[37] Kim, M Chai, S (2017). The impact of supplier innovativeness, information sharing and strategic sourcing on improving supply chain agility: Global supply chain perspective. International Journal of Production Economics, 187: 42–52

[38] Lee, C K H (2017). A GA-based optimisation model for big data analytics supporting anticipatory shipping in Retail 4.0. International Journal of Production Research, 55( 2): 593–605

[39] Lee, I Lee, K (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58( 4): 431–440

[40] Lee, S M Rha, J S (2016). Ambidextrous supply chain as a dynamic capability: Building a resilient supply chain. Management Decision, 54( 1): 2–23

[41] Li, L (2022). Digital transformation and sustainable performance: The moderating role of market turbulence. Industrial Marketing Management, 104: 28–37

[42] LiLGongYWangZLiuS (2022a). Big data and big disaster: A mechanism of supply chain risk management in global logistics industry. International Journal of Operations & Production Management, in press, doi:10.1108/IJOPM-04-2022-0266

[43] Li, L Tong, Y Wei, L Yang, S (2022b). Digital technology-enabled dynamic capabilities and their impacts on firm performance: Evidence from the COVID-19 pandemic. Information & Management, 59( 8): 103689

[44] Li, L Wang, Z Ye, F Chen, L Zhan, Y (2022c). Digital technology deployment and firm resilience: Evidence from the COVID-19 pandemic. Industrial Marketing Management, 105: 190–199

[45] Li, L Ye, F Zhan, Y Kumar, A Schiavone, F Li, Y (2022d). Unraveling the performance puzzle of digitalization: Evidence from manufacturing firms. Journal of Business Research, 149: 54–64

[46] Li, Y Dai, J Cui, L (2020). The impact of digital technologies on economic and environmental performance in the context of Industry 4.0: A moderated mediation model. International Journal of Production Economics, 229: 107777

[47] Lindell, M K Whitney, D J (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86( 1): 114–121

[48] Manuel Maqueira, J Moyano-Fuentes, J Bruque, S (2019). Drivers and consequences of an innovative technology assimilation in the supply chain: Cloud computing and supply chain integration. International Journal of Production Research, 57( 7): 2083–2103

[49] Melville, N Ramirez, R (2008). Information technology innovation diffusion: An information requirements paradigm. Information Systems Journal, 18( 3): 247–273

[50] Mimecast (2021). State of Email Security. Cyber Security Report

[51] Najafi Tavani, S N Sharifi, H Ismail, H S (2014). A study of contingency relationships between supplier involvement, absorptive capacity and agile product innovation. International Journal of Operations & Production Management, 34( 1): 65–92

[52] Nguyen, T Zhou, L Spiegler, V Ieromonachou, P Lin, Y (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98: 254–264

[53] Ning, Y Yan, M Xu, S X Li, Y Li, L (2021). Shared parking acceptance under perceived network externality and risks: Theory and evidence. Transportation Research Part A: Policy and Practice, 150: 1–15

[54] Omar, I A Debe, M Jayaraman, R Salah, K Omar, M Arshad, J (2022). Blockchain-based supply chain traceability for COVID-19 personal protective equipment. Computers & Industrial Engineering, 167: 107995

[55] Pan, S Trentesaux, D McFarlane, D Montreuil, B Ballot, E Huang, G Q (2021). Digital interoperability in logistics and supply chain management: State-of-the-art and research avenues towards Physical Internet. Computers in Industry, 128: 103435

[56] Pettit, T J Fiksel, J Croxton, K L (2010). Ensuring supply chain resilience: Development of a conceptual framework. Journal of Business Logistics, 31( 1): 1–21

[57] Podsakoff, P M MacKenzie, S B Lee, J Podsakoff, N P (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88( 5): 879–903

[58] Premkumar, G Ramamurthy, K Saunders, C S (2005). Information processing view of organizations: An exploratory examination of fit in the context of interorganizational relationships. Journal of Management Information Systems, 22( 1): 257–294

[59] Sarkis, J (2020). Supply chain sustainability: Learning from the COVID-19 pandemic. International Journal of Operations & Production Management, 41( 1): 63–73

[60] Sawik, T (2022). Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study. Omega, 109: 102596

[61] Schniederjans, D G Hales, D N (2016). Cloud computing and its impact on economic and environmental performance: A transaction cost economics perspective. Decision Support Systems, 86: 73–82

[62] Scholten, K Stevenson, M van Donk, D P (2020). Dealing with the unpredictable: Supply chain resilience. International Journal of Operations & Production Management, 40( 1): 1–10

[63] Shekarian, M Reza Nooraie, S V Parast, M M (2020). An examination of the impact of flexibility and agility on mitigating supply chain disruptions. International Journal of Production Economics, 220: 107438

[64] ShermanE (2020). 94% of the Fortune 1000 are seeing coronavirus supply chain disruptions. Online Report

[65] Shou, Y Li, Y Park, Y W Kang, M (2017). The impact of product complexity and variety on supply chain integration. International Journal of Physical Distribution & Logistics Management, 47( 4): 297–317

[66] Siemens (2013). Siemens Digital Logistics. Twitter

[67] Skilton, P F Robinson, J L (2009). Traceability and normal accident theory: How does supply network complexity influence the traceability of adverse events?. Journal of Supply Chain Management, 45( 3): 40–53

[68] Subramanian, N Abdulrahman, M D (2017). Logistics and cloud computing service providers’ cooperation: A resilience perspective. Production Planning and Control, 28( 11–12): 919–928

[69] The State Council Information Office of PRC (2022). China accounts for 30% of global manufacturing output in 2021

[70] TianF (2018). An Information System for Food Safety Monitoring in Supply Chains Based on HACCP, Blockchain and Internet of Things. Dissertation for the Doctoral Degree. Vienna: Vienna University of Economics and Business (in Austria)

[71] Tietze, F Vimalnath, P Aristodemou, L Molloy, J (2022). Crisis-critical intellectual property: Findings from the COVID-19 pandemic. IEEE Transactions on Engineering Management, 69( 5): 2039–2056

[72] Tushman, M L Nadler, D A (1978). Information processing as an integrating concept in organizational design. Academy of Management Review, 3( 3): 613–624

[73] Wang, Y Hong, A Li, X Gao, J (2020). Marketing innovations during a global crisis: A study of China firms’ response to COVID-19. Journal of Business Research, 116: 214–220

[74] WangYPettitS (2016). E-Logistics: Managing Your Digital Supply Chains for Competitive Advantage. London: Kogan Page Ltd.

[75] Wong, C W Y Lirn, T Yang, C Shang, K (2020). Supply chain and external conditions under which supply chain resilience pays: An organizational information processing theorization. International Journal of Production Economics, 226: 107610

[76] Wowak, K D Craighead, C W Ketchen Jr, D J (2016). Tracing bad products in supply chains: The roles of temporality, supply chain permeation, and product information ambiguity. Journal of Business Logistics, 37( 2): 132–151

[77] Xue, L (2014). Governance–knowledge fit and strategic risk taking in supply chain digitization. Decision Support Systems, 62: 54–65

[78] Yang, Y Pan, S Ballot, E (2017). Mitigating supply chain disruptions through interconnected logistics services in the Physical Internet. International Journal of Production Research, 55( 14): 3970–3983

[79] Ye, F Liu, K Li, L Lai, K Zhan, Y Kumar, A (2022). Digital supply chain management in the COVID-19 crisis: An asset orchestration perspective. International Journal of Production Economics, 245: 108396

[80] Yu, J Subramanian, N Ning, K Edwards, D (2015). Product delivery service provider selection and customer satisfaction in the era of Internet of Things: A Chinese e-retailers’ perspective. International Journal of Production Economics, 159: 104–116

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