A Data-Driven Supply Chain: Marketing Data Sharing, Data Security, and Digital Technology Adoption to Predict Firm’s Resilience

Authors

DOI:

https://doi.org/10.21512/bbr.v14i1.9305

Keywords:

data-driven supply chain, marketing data sharing, Data Security, digital technology adoption, firm’s resilience

Abstract

Business automation has been driven recently with Technology 4.0 to manage the supply chain process and complexity. The secured data-driven supply chain is critical for business competitiveness. However, not all companies can manage, analyze, and interpret structured and unstructured data wisely. For record-keeping purposes, data are left unprotected and stored. Ideally, it should play a strategic role in decision-making and escalating business performance. The practices are inconsistent with the awareness of data security governance and proper usage of digital technologies. The research aimed to examine the data-driven supply chain that conceptualised marketing data sharing, data security, and digital technology adoption to predict a firm’s resilience. The research applied a quantitative approach. The survey was conducted on Malaysian manufacturing firms. The data were collected electronically and analysed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) 4.0. Around 375 companies participated in the survey. The results show the positive path links from predictors (marketing data sharing, digital technology adoption, and data security governance) and criteria. It also finds that data security and marketing data sharing have impacted digital technology adoption, leading to the supply chain’s resilience. The research has concluded that the secure sharing of the data-driven supply chain can improve a firm’s resilience. Manufacturing companies should make swift focus on data quality and utilize it wisely. The research concludes that empowering data analytics to understand customer preferences is necessary.

Dimensions

Plum Analytics

Author Biographies

Yudi Fernando, Bina Nusantara University

Management Department, BINUS Online Learning

Ridho Bramulya Ikhsan, Binus University

Management Department, BINUS Online Learning

References

AlGhamdi, S., Win, K. T., & Vlahu-Gjorgievska, E. (2020). Information security governance challenges and critical success factors: Systematic review. Computers & Security, 99, 1‒39. https://doi.org/10.1016/j.cose.2020.102030

Alzahrani, F. A., Ahmad, M., & Ansari, M. T. J. (2022). Towards design and development of security assessment framework for Internet of medical things. Applied Sciences, 12(16), 1‒20. https://doi.org/10.3390/app12168148

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. https://doi.org/10.1016/j.jom.2014.11.002

Bechtsis, D., Tsolakis, N., Iakovou, E., & Vlachos, D. (2022). Data-driven secure, resilient and sustainable supply chains: Gaps, opportunities, and a new generalised data sharing and data monetisation framework. International Journal of Production Research, 60(14), 4397‒4417. https://doi.org/10.1080/00207543.2021.1957506

Beninger, S., & Francis, J. N. P. (2022). Resources for business resilience in a COVID-19 world: A community-centric approach. Business Horizons, 65(2), 227‒238. https://doi.org/10.1016/j.bushor.2021.02.048

Bui, T. D., Tsai, F. M., Tseng, M. L., Tan, R. R., Yu, K. D. S., & Lim, M. K. (2021). Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis. Sustainable Production and Consumption, 26(April), 373‒410. https://doi.org/10.1016/j.spc.2020.09.017

Chavez, R., Yu, W., Jacobs, M. A., & Feng, M. (2017). Data-driven supply chains, manufacturing capability and customer satisfaction. Production Planning & Control, 28(11-12), 906‒918. https://doi.org/10.1080/09537287.2017.1336788

Duchek, S. (2020). Organizational resilience: A capability-based conceptualization. Business Research, 13, 215‒246. https://doi.org/10.1007/s40685-019-0085-7

Fernando, Y., Abideen, A. Z., & Shaharudin, M. S. (2020). The nexus of information sharing, technology capability and inventory efficiency. Journal of Global Operations and Strategic Sourcing, 33(4), 327‒351. https://doi.org/10.1108/JGOSS-02-2020-0011

Fernando, Y., Chidambaram, R. R. M., & Wahyuni-TD, I. S. (2018). The impact of big data analytics and data security practices on service supply chain performance. Benchmarking: An International Journal, 25(9), 4009‒4034. https://doi.org/10.1108/BIJ-07-2017-0194

Fernando, Y., Halili, M., Tseng, M. L., Tseng, J. W., & Lim, M. K. (2022). Sustainable social supply chain practices and firm social performance: Framework and empirical evidence. Sustainable Production and Consumption, 32(July), 160‒172. https://doi.org/10.1016/j.spc.2022.04.020

Fernando, Y., Ho, T. C. F., Algunaid, N., & Zailani, S. (2013). A study of relationship marketing in Malaysian banks: Does Guanxi influence small medium enterprise owners’ satisfaction? Journal of Relationship Marketing, 12(1), 22‒40. https://doi.org/10.1080/15332667.2013.763718

Fernando, Y., Tseng, M. L., Nur, G. M., Ikhsan, R. B., & Lim, M. K. (2022). Practising circular economy performance in Malaysia: Managing supply chain disruption and technological innovation capability under industry 4.0. International Journal of Logistics Research and Applications, 1‒24. https://doi.org/10.1080/13675567.2022.2107188

Fernando, Y., Tseng, M. L., Wahyuni-TD, I. S., De Sousa Jabbour, A. B. L., Chiappetta Jabbour, C. J., & Foropon, C. (2023). Cyber supply chain risk management and performance in industry 4.0 era: Information system security practices in Malaysia. Journal of Industrial and Production Engineering, 40(2), 102‒116. https://doi.org/10.1080/21681015.2022.2116495

Fernando, Y., Wahyuni-TD, I. S., Gui, A., Ikhsan, R. B., Mergeresa, F., & Ganesan, Y. (2022). A mixed-method study on the barriers of Industry 4.0 adoption in the Indonesian SMEs manufacturing supply chains. Journal of Science and Technology Policy Management, Ahead-of-Print. https://doi.org/10.1108/JSTPM-10-2021-0155

Flores, W. R., Antonsen, E., & Ekstedt, M. (2014). Information security knowledge sharing in organizations: Investigating the effect of behavioral information security governance and national culture. Computers & Security, 43(June), 90‒110. https://doi.org/10.1016/j.cose.2014.03.004

Gani, A. B. D., & Fernando, Y. (2021). The cybersecurity governance in changing the security psychology and security posture: Insights into e-procurement. International Journal of Procurement Management, 14(3), 308‒327.

Gani, A. B. D., Fernando, Y., Lan, S., Lim, M. K., & Tseng, M. L. (2022). Interplay between cyber supply chain risk management practices and cyber security performance. Industrial Management & Data Systems, Ahead-of-Print. https://doi.org/10.1108/IMDS-05-2022-0313

Ghanbarpour, T., & Gustafsson, A. (2022). How do Corporate Social Responsibility (CSR) and innovativeness increase financial gains? A customer perspective analysis. Journal of Business Research, 140(February), 471‒481. https://doi.org/10.1016/j.jbusres.2021.11.016

Ghobakhloo, M., & Ching, N. T. (2019). Adoption of digital technologies of smart manufacturing in SMEs. Journal of Industrial Information Integration, 16(December). https://doi.org/10.1016/j.jii.2019.100107

Hair Jr, J. F. (2021). Next-generation prediction metrics for composite-based PLS-SEM. Industrial Management & Data Systems, 121(1), 5‒11. https://doi.org/10.1108/IMDS-08-2020-0505

Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Sage Publications.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115‒135. https://doi.org/10.1007/s11747-014-0403-8

Heredia, J., Rubiños, C., Vega, W., Heredia, W., & Flores, A. (2022). New strategies to explain organizational resilience on the firms: A cross-countries configurations approach. Sustainability, 14(3), 1‒22. https://doi.org/10.3390/su14031612

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(April), 1‒14. https://doi.org/10.1016/j.tre.2020.101922

Kane, G. C. (2017). The evolutionary implications of social media for organizational knowledge management. Information and Organization, 27(1), 37‒46. https://doi.org/10.1016/j.infoandorg.2017.01.001

Karkošková, S. (2022). Data governance model to enhance data quality in financial institutions. Information Systems Management, 40(1), 90‒110. https://doi.org/10.1080/10580530.2022.2042628

Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (IJeC), 11(4), 1‒10. https://doi.org/10.4018/ijec.2015100101

Kock, N. (2020). Harman’s single factor test in PLS-SEM: Checking for common method bias. Data Analysis Perspectives Journal, 2(2), 1‒6.

Kumar, A., Singh, R. K., & Modgil, S. (2023). Influence of data-driven supply chain quality management on organizational performance: Evidences from retail industry. The TQM Journal, 35(1), 24‒50. https://doi.org/10.1108/TQM-06-2020-0146

Li, L., Wang, Z., Ye, F., Chen, L., & Zhan, Y. (2022). Digital technology deployment and firm resilience: Evidence from the COVID-19 pandemic. Industrial Marketing Management, 105(August), 190‒199. https://doi.org/10.1016/j.indmarman.2022.06.002

Liu, C. (2022). Risk prediction of digital transformation of manufacturing supply chain based on principal component analysis and backpropagation artificial neural network. Alexandria Engineering Journal, 61(1), 775‒784. https://doi.org/10.1016/j.aej.2021.06.010

Liu, W., Yan, X., Li, X., & Wei, W. (2020). The impacts of market size and data-driven marketing on the sales mode selection in an Internet platform based supply chain. Transportation Research Part E: Logistics and Transportation Review, 136(April), 1‒19. https://doi.org/10.1016/j.tre.2020.101914

Lyu, C., Zhang, F., Ji, J., Teo, T. S. H., Wang, T., & Liu, Z. (2022). Competitive intensity and new product development outcomes: The roles of knowledge integration and organizational unlearning. Journal of Business Research, 139(February), 121‒133. https://doi.org/10.1016/j.jbusres.2021.09.049

Mangla, S. K., Kazançoğlu, Y., Yıldızbaşı, A., Öztürk C., & Çalık, A. (2022). A conceptual framework for blockchain-based sustainable supply chain and evaluating implementation barriers: A case of the tea supply chain. Business Strategy and the Environment, 31(8), 3693‒3716. https://doi.org/10.1002/bse.3027

Mezgebe, T. T., Gebreslassie, M. G., Sibhato, H., & Bahta, S. T. (2023). Intelligent manufacturing eco-system: A post COVID-19 recovery and growth opportunity for manufacturing industry in Sub-Saharan countries Scientific African, 19(March), 1‒13. https://doi.org/10.1016/j.sciaf.2023.e01547

Niu, B., Dong, J., Dai, Z., & Liu, Y. (2022). Sales data sharing to improve product development efficiency in cross-border e-commerce. Electronic Commerce Research and Applications, 51(January–February). https://doi.org/10.1016/j.elerap.2021.101112

Nyadzayo, M. W., Casidy, R., & Mohan, M. (2022). Maximizing customer adoption outcomes in emerging industrial markets via supplier innovativeness and relationship quality. Journal of Business & Industrial Marketing, Ahead-of-Print. https://doi.org/10.1108/JBIM-03-2021-0156

Shen, S., Zhu, T., Wu, D., Wang, W., & Zhou, W. (2022). From distributed machine learning to federated learning: In the view of data privacy and security. Concurrency and Computation: Practice and Experience, 34(16), 1‒19. https://doi.org/10.1002/cpe.6002

Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322‒2347. https://doi.org/10.1108/EJM-02-2019-0189

Sun, J., Tekleab, A., Cheung, M., & Wu, W. P. (2022). The contingent roles of market turbulence and organizational innovativeness on the relationships among interfirm trust, formal contracts, interfirm knowledge sharing and firm performance. Journal of Knowledge Management, Ahead-of-Print. https://doi.org/10.1108/JKM-04-2022-0289

Sundarakani, B., Ajaykumar, A., & Gunasekaran, A. (2021). Big data driven supply chain design and applications for blockchain: An action research using case study approach. Omega, 102(July), 1‒19. https://doi.org/10.1016/j.omega.2021.102452

Van der Burg, S., Wiseman, L., & Krkeljas, J. (2021). Trust in farm data sharing: Reflections on the EU code of conduct for agricultural data sharing. Ethics and Information Technology, 23, 185‒198. https://doi.org/10.1007/s10676-020-09543-1

Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77‒84. https://doi.org/10.1111/jbl.12010

Wei, Y., O’Neill, H., Lee, R. P., & Zhou, N. (2013). The impact of innovative culture on individual employees: The moderating role of market information sharing. Journal of Product Innovation Management, 30(5), 1027‒1041. https://doi.org/10.1111/j.1540-5885.2012.01000.x

Weko, S., & Goldthau, A. (2022). Bridging the low-carbon technology gap? Assessing energy initiatives for the Global South. Energy Policy, 169(October), 1‒10. https://doi.org/10.1016/j.enpol.2022.113192

Downloads

Published

2023-02-07
Abstract 819  .
PDF downloaded 1118  .