Digitalization for supply chain resilience and robustness: The roles of collaboration and formal contracts

Abstract

Black swan events such as the coronavirus (COVID-19) outbreak cause substantial supply chain disruption risks to modern companies. In today’s turbulent and complex business environment, supply chain resilience and robustness as two critical capabilities for firms to cope with disruptions have won substantial attention from both the academia and industry. Accordingly, this study intends to explore how digitalization helps build supply chain resilience and robustness. Adopting organizational information processing theory, it proposes the mediating effect of supply chain collaboration and the moderating effect of formal contracts. Using survey data of Chinese manufacturing firms, the study applied structural equation modelling to test the research model. Results show that digitalization has a direct effect on supply chain resilience, and supply chain collaboration can directly facilitate both resilience and robustness. Our study also indicates a complementary mediating effect of supply chain collaboration on the relationship between digitalization and supply chain resilience and an indirect-only mediation effect on the relationship between digitalization and supply chain robustness. Findings reveal the differential roles of digitalization as a technical factor and supply chain collaboration as an organizational factor in managing supply chain disruptions. Paradoxically, formal contracts enhance the relationship between digitalization and supply chain resilience but weaken the relationship between supply chain collaboration and supply chain resilience. The validation of moderating effects determines the boundary conditions of digitalization and supply chain collaboration and provides insights into governing supply chain partners’ behavior. Overall, this study enhances the understanding on how to build a resilient and robust supply chain.

Graphical abstract

Keywords

digitalization / supply chain / resilience / robustness / collaboration / formal contract

References

[1] Aben, T A E van der Valk, W Roehrich, J K Selviaridis, K (2021). Managing information asymmetry in public-private relationships undergoing a digital transformation: The role of contractual and relational governance. International Journal of Operations & Production Management, 41( 7): 1145–1191

[2] Accenture (2022). Supply chain disruption. Online Report

[3] Afraz, M F Bhatti, S H Ferraris, A Couturier, J (2021). The impact of supply chain innovation on competitive advantage in the construction industry: Evidence from a moderated multi-mediation model. Technological Forecasting and Social Change, 162: 120370

[4] Ambulkar, S Blackhurst, J Grawe, S (2015). Firm’s resilience to supply chain disruptions: Scale development and empirical examination. Journal of Operations Management, 33–34( 1): 111–122

[5] Anderson, J C Gerbing, D W (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103( 3): 411–423

[6] BagSDhamijaPLuthraSHuisinghD (2021). How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic. International Journal of Logistics Management, in press, doi:10.1108/IJLM-02-2021-0095

[7] Bahrami, M Shokouhyar, S (2022). The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: A dynamic capability view. Information Technology & People, 35( 5): 1621–1651

[8] Barratt, M (2004). Understanding the meaning of collaboration in the supply chain. Supply Chain Management, 9( 1): 30–42

[9] BelhadiAManiVKambleS SKhanS A RVermaS (2021). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: An empirical investigation. Annals of Operations Research, in press, doi:10.1007/s10479-021-03956-x

[10] Bensaou, M Venkatraman, N (1995). Configurations of interorganizational relationships: A comparison between US and Japanese automakers. Management Science, 41( 9): 1471–1492

[11] 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

[12] Cao, M Zhang, Q (2011). Supply chain collaboration: Impact on collaborative advantage and firm performance. Journal of Operations Management, 29( 3): 163–180

[13] Cao, Z Lumineau, F (2015). Revisiting the interplay between contractual and relational governance: A qualitative and meta-analytic investigation. Journal of Operations Management, 33–34( 1): 15–42

[14] ComreyA L (1973). A First Course in Factor Analysis. New York, NY: Academic Press

[15] Cuervo-Cazurra, A Inkpen, A Musacchio, A Ramaswamy, K (2014). Governments as owners: State-owned multinational companies. Journal of International Business Studies, 45( 8): 919–942

[16] DillmanD A (2011). Mail and Internet Surveys: The Tailored Design Method. 2nd ed. Hoboken, NJ: John Wiley & Sons

[17] Dubey, R Gunasekaran, A Bryde, D J Dwivedi, Y K Papadopoulos, T (2020). Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. International Journal of Production Research, 58( 11): 3381–3398

[18] Dubey, R Gunasekaran, A Childe, S J Fosso Wamba, S Roubaud, D Foropon, C (2021). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research, 59( 1): 110–128

[19] Durach, C F Wieland, A Machuca, J A D (2015). Antecedents and dimensions of supply chain robustness: A systematic literature review. International Journal of Physical Distribution & Logistics Management, 45( 1/2): 118–137

[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] Eller, R Alford, P Kallmunzer, A Peters, M (2020). Antecedents, consequences, and challenges of small and medium-sized enterprise digitalization. Journal of Business Research, 112: 119–127

[22] Fan, H Li, G Sun, H Cheng, T C E (2017). An information processing perspective on supply chain risk management: Antecedents, mechanism, and consequences. International Journal of Production Economics, 185: 63–75

[23] Favoretto, C Mendes, G H S Filho, M G Gouvea de Oliveira, M Ganga, G M D (2022). Digital transformation of business model in manufacturing companies: Challenges and research agenda. Journal of Business and Industrial Marketing, 37( 4): 748–767

[24] Fornell, C Larcker, D F (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18( 1): 39–50

[25] GalbraithJ R (1973). Designing Complex Organizations. Boston, MA: Addison-Wesley Longman Publishing Co., Inc.

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

[27] Gebhardt, M Kopyto, M Birkel, H Hartmann, E (2022). Industry 4.0 technologies as enablers of collaboration in circular supply chains: A systematic literature review. International Journal of Production Research, 60( 23): 6967–6995

[28] HairJ FHultG T MRingleC MSarstedtM (2016). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 2nd ed. Los Angeles, CA: SAGE Publications, Inc.

[29] 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

[30] Henseler, J Chin, W W (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling, 17( 1): 82–109

[31] Henseler, J Hubona, G Ray, P A (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116( 1): 2–20

[32] IftikharAPurvisLGiannoccaroIWangY (2022). The impact of supply chain complexities on supply chain resilience: The mediating effect of big data analytics. Production Planning and Control, in press, doi:10.1080/09537287.2022.2032450

[33] 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

[34] Ivanov, D Dolgui, A (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability, a position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58( 10): 2904–2915

[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] Ivanov, D Dolgui, A Sokolov, B (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57( 3): 829–846

[37] Jia, X Chowdhury, M Prayag, G Hossan Chowdhury, M M (2020). The role of social capital on proactive and reactive resilience of organizations post-disaster. International Journal of Disaster Risk Reduction, 48: 101614

[38] Juan, S Li, E Y Hung, W (2022). An integrated model of supply chain resilience and its impact on supply chain performance under disruption. International Journal of Logistics Management, 33( 1): 339–364

[39] Keller, J Burkhardt, P Lasch, R (2021). Informal governance in the digital transformation. International Journal of Operations & Production Management, 41( 7): 1060–1084

[40] Kessler, M Arlinghaus, J C Rosca, E Zimmermann, M (2022). Curse or blessing? Exploring risk factors of digital technologies in industrial operations. International Journal of Production Economics, 243: 108323

[41] Kock, N Hadaya, P (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and Gamma-exponential methods. Information Systems Journal, 28( 1): 227–261

[42] Lee, Y Cavusgil, S T (2006). Enhancing alliance performance: The effects of contractual-based versus relational-based governance. Journal of Business Research, 59( 8): 896–905

[43] 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

[44] Malhotra, D Murnighan, J K (2002). The effects of contracts on interpersonal trust. Administrative Science Quarterly, 47( 3): 534–559

[45] Michalski, M Montes-Botella, J Narasimhan, R (2018). The impact of asymmetry on performance in different collaboration and integration environments in supply chain management. Supply Chain Management, 23( 1): 33–49

[46] Nayal, K Raut, R D Yadav, V S Priyadarshinee, P Narkhede, B E (2022). The impact of sustainable development strategy on sustainable supply chain firm performance in the digital transformation era. Business Strategy and the Environment, 31( 3): 845–859

[47] Nitzl, C Roldan, J L Cepeda, G (2016). Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models. Industrial Management & Data Systems, 116( 9): 1849–1864

[48] Nunnally, J C (1978). Psychometric Theory. New York, NY: McGraw–Hill

[49] Peng, D X Lai, F (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30( 6): 467–480

[50] 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

[51] Podsakoff, P M Organ, D W (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12( 4): 531–544

[52] Poppo, L Zenger, T (2002). Do formal contracts and relational governance function as substitutes or complements?. Strategic Management Journal, 23( 8): 707–725

[53] 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

[54] Puranam, P Singh, H Zollo, M (2006). Organizing for innovation: Managing the coordination-autonomy dilemma in technology acquisitions. Academy of Management Journal, 49( 2): 263–280

[55] Reinartz, W Haenlein, M Henseler, J (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26( 4): 332–344

[56] Roßmann, B Canzaniello, A von, der Gracht H Hartmann, E (2018). The future and social impact of big data analytics in supply chain management: Results from a Delphi study. Technological Forecasting and Social Change, 130: 135–149

[57] RuelSEl BazJ (2021). Disaster readiness’ influence on the impact of supply chain resilience and robustness on firms’ financial performance: A COVID-19 empirical investigation. International Journal of Production Research, in press, doi:10.1080/00207543.2021.1962559

[58] Salganik, M J Heckathorn, D D (2004). Sampling and estimation in hidden populations using respondent-driven sampling. Sociological Methodology, 34( 1): 193–240

[59] Scholten, K Schilder, S (2015). The role of collaboration in supply chain resilience. Supply Chain Management, 20( 4): 471–484

[60] Shou, Y Zhao, X Dai, J Xu, D (2021). Matching traceability and supply chain coordination: Achieving operational innovation for superior performance. Transportation Research Part E: Logistics and Transportation Review, 145: 102181

[61] Son, B G Kim, H Hur, D Subramanian, N (2021). The dark side of supply chain digitalisation: Supplier-perceived digital capability asymmetry, buyer opportunism and governance. International Journal of Operations & Production Management, 41( 7): 1220–1247

[62] Song, S Shi, X Song, G Huq, F A (2021). Linking digitalization and human capital to shape supply chain integration in omni-channel retailing. Industrial Management & Data Systems, 121( 11): 2298–2317

[63] Srinivasan, R Swink, M (2018). An investigation of visibility and flexibility as complements to supply chain analytics: An organizational information processing theory perspective. Production and Operations Management, 27( 10): 1849–1867

[64] Sturm, S Hohenstein, N Birkel, H Kaiser, G Hartmann, E (2022). Empirical research on the relationships between demand- and supply-side risk management practices and their impact on business performance. Supply Chain Management, 27( 6): 742–761

[65] Um, K Oh, J (2020). The interplay of governance mechanisms in supply chain collaboration and performance in buyer-supplier dyads: Substitutes or complements. International Journal of Operations & Production Management, 40( 4): 415–438

[66] Wang, G Gunasekaran, A Ngai, E Papadopoulos, T (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176: 98–110

[67] White, S (2000). Competition, capabilities, and the make, buy, or ally decisions of Chinese state-owned firms. Academy of Management Journal, 43( 3): 324–341

[68] Williams, B D Roh, J Tokar, T Swink, M (2013). Leveraging supply chain visibility for responsiveness: The moderating role of internal integration. Journal of Operations Management, 31( 7–8): 543–554

[69] 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

[70] Xue, L Zhang, C Ling, H Zhao, X (2013). Risk mitigation in supply chain digitization: System modularity and information technology governance. Journal of Management Information Systems, 30( 1): 325–352

[71] Yang, L Huo, B F Tian, M Han, Z J (2021). The impact of digitalization and inter-organizational technological activities on supplier opportunism: The moderating role of relational ties. International Journal of Operations & Production Management, 41( 7): 1085–1118

[72] Zhao, X Lynch Jr, J G Chen, Q (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37( 2): 197–206

[73] Zhou, D Yan, T T Dai, W Q Feng, J Z (2021). Disentangling the interactions within and between servitization and digitalization strategies: A service-dominant logic. International Journal of Production Economics, 238: 108175

[74] Zouari, D Ruel, S Viale, L (2021). Does digitalising the supply chain contribute to its resilience?. International Journal of Physical Distribution & Logistics Management, 51( 2): 149–180

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