Hostname: page-component-848d4c4894-pftt2 Total loading time: 0 Render date: 2024-05-19T10:00:51.829Z Has data issue: false hasContentIssue false

A comprehensive and bibliometric review on the blockchain-enabled IoT technology for designing a secure supply chain management system

Published online by Cambridge University Press:  23 September 2022

Yacheng Li*
Affiliation:
School of Economics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
Xiaoyan Zhu
Affiliation:
School of Architectural Engineering, Hunan Communication polytechnic, Changsha, Hunan, 410132, China
Mehdi Darbandi
Affiliation:
Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Gazimagusa, via Mersin 10, Turkey
*
Author for correspondence: Yacheng Li, E-mail: liyc3369@163.com, d201780955@hust.edu.cn

Abstract

Blockchain is a well-known prominent technology that has gotten a lot of interest beyond the financial industry, attracting researchers and practitioners from numerous businesses and fields. Specific uses of blockchain in supply chain management (SCM) are addressed in business practice. By combining two perspectives on blockchain in SCM, this study provides comprehensive knowledge in this field using a bibliometric approach. We will explore the worldwide research trend in related topic areas. By collecting data from the Web of Science, we collected 400 articles related to our research topic from 2016 until early 2021. We eliminated research in the form of technical reports, editorials, comments, and consultancy articles to maintain the quality of the data gathering. VOSviewer is used to create visualization maps based on text and bibliographic information. The examination uncovered helpful information, such as annual publishing and citation patterns, the top research topic, the top authors, and the most supporting funding organizations in this field.

Type
Research Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press in association with the Australian and New Zealand Academy of Management

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdel-Basset, M., Manogaran, G., & Mohamed, M. (2018). Internet of Things (IoT) and its impact on supply chain: A framework for building smart, secure and efficient systems. Future Generation Computer Systems, 86, 614628.CrossRefGoogle Scholar
Aich, S., Chakraborty, S., Sain, M., Lee, H. I., & Kim, H. C. (2019). A Review on Benefits of IoT Integrated Blockchain-based Supply Chain Management Implementations across Different Sectors with Case Study. In 2019 21st International Conference on Advanced Communication Technology (ICACT). IEEE, pp. 138–141.CrossRefGoogle Scholar
Al-Rakhami, M. S., & Al-Mashari, M. (2021). A blockchain-based trust model for the internet of things supply chain management. Sensors, 21(5), 1759.CrossRefGoogle ScholarPubMed
Aste, T., Tasca, P., & Di Matteo, T. (2017). Blockchain technologies: The foreseeable impact on society and industry. Computer, 50(9), 1828.CrossRefGoogle Scholar
Ayoko, O. B., Caputo, A., & Mendy, J. (2022). Management research contributions to the COVID-19: A bibliometric literature review and analysis of the contributions from the Journal of Management & Organization. Journal of Management & Organization, 27(6), 127.CrossRefGoogle Scholar
Batwa, A., & Norrman, A. (2021). Blockchain technology and trust in supply chain management: A literature review and research agenda. Operation and Supply Chain Management: An International Journal, 14(2), 203220.CrossRefGoogle Scholar
Berneis, M., Bartsch, D., & Winkler, H. (2021). Applications of blockchain technology in logistics and supply chain management – insights from a systematic literature review. Logistics, 5(3), 43.CrossRefGoogle Scholar
Bojnec, S., & Ferto, I. (2009). Impact of the internet on manufacturing trade. Journal of Computer Information Systems, 50, 124132.Google Scholar
Büyüközkan, G., & Göçer, F. (2018). Digital supply chain: Literature review and a proposed framework for future research. Computers in Industry, 97, 157177.CrossRefGoogle Scholar
Callon, M., Courtial, J.-P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics, 22(1), 155205.CrossRefGoogle Scholar
Cao, B., Zhang, J., Liu, X., Sun, Z., Cao, W., Nowak, R., M., & Lv, Z. (2022a). Edge–cloud resource scheduling in space–air–ground-integrated networks for internet of vehicles. IEEE Internet of Things Journal, 9(8), 57655772.CrossRefGoogle Scholar
Cao, K., Ding, H., Wang, B., Lv, L., Tian, J., Wei, Q., & Gong, F. (2022b). Enhancing physical layer security for IoT with non-orthogonal multiple access assisted semi-grant-free transmission. IEEE Internet of Things Journal.CrossRefGoogle Scholar
Chen, X., Liu, S., Zhu, W., & Li, Q. (2020). Transition to the Intelligent Services Ecosystem: Integration of Block Chain and Internet of Things in Supply Chain Management. in 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA).CrossRefGoogle Scholar
Christidis, K., & Devetsikiotis, M. (2016). Blockchains and smart contracts for the internet of things. IEEE Access, 4, 22922303.CrossRefGoogle Scholar
Christopher, M. (2012). Logistics and supply chain management. Pearson UK.Google Scholar
Dabbagh, M., Sookhak, M., & Safa, N. S. (2019). The evolution of blockchain: A bibliometric study. IEEE Access, 7, 1921219221.CrossRefGoogle Scholar
Doewes, R. I., Gharibian, G., Zaman, B. A., & Akhavan-Sigari, R. (2022). An updated systematic review on the effects of aerobic exercise on human blood lipid profile. Current Problems in Cardiology, 101108. https://doi.org/10.1016/j.cpcardiol.2022.101108Google ScholarPubMed
Dolgui, A., Ivanov, D., Potryasaev, S., Sokolov, B., Ivanova, M., & Werner, F. (2020). Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain. International Journal of Production Research, 58(7), 21842199.CrossRefGoogle Scholar
Duan, J., Zhang, C., Gong, Y., Brown, S., & Li, Z. (2020). A content-analysis-based literature review in blockchain adoption within food supply chain. International Journal of Environmental Research and Public Health, 17(5).CrossRefGoogle ScholarPubMed
Fatorachian, H., & Kazemi, H. (2018). A critical investigation of industry 4.0 in manufacturing: Theoretical operationalisation framework. Production Planning & Control, 29(8), 633644.CrossRefGoogle Scholar
Feng, T. (2016). An agri-food supply chain traceability system for China based on RFID & blockchain technology. in 2016 13th International Conference on Service Systems and Service Management (ICSSSM).CrossRefGoogle Scholar
Guo, Y.-M., Huang, Z.-L., Guo, J., Guo, X.-R., Li, H., Liu, M.-Y., Ezzeddine, S., & Nkeli, M. J. (2021). A bibliometric analysis and visualization of blockchain. Future Generation Computer Systems, 116, 316332.CrossRefGoogle Scholar
Hood, W. W., & Wilson, C. S. (2001). The literature of bibliometrics, scientometrics, and informetrics. Scientometrics, 52(2), 291314.CrossRefGoogle Scholar
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), 829846.CrossRefGoogle Scholar
Kamble, S., Gunasekaran, A., & Arha, H. (2019). Understanding the blockchain technology adoption in supply chains-Indian context. International Journal of Production Research, 57(7), 20092033.CrossRefGoogle Scholar
Khan, S. A. R., & Yu, Z. (2021). A Systematic Literature Review: Blockchain Technology and Organizational Theories in the Perspective of Supply Chain Management. in Journal of Physics: Conference Series. 2021. IOP Publishing.Google Scholar
Lei, W., Hui, Z., Xiang, L., Zelin, Z., Xu-Hui, X., & Evans, S. (2021). Optimal remanufacturing service resource allocation for generalized growth of retired mechanical products: Maximizing matching efficiency. IEEE Access, 9, 8965589674.CrossRefGoogle Scholar
Li, J., Xu, K., Chaudhuri, S., Yumer, E., Zhang, H., & Guibas, L. (2017). Grass: Generative recursive autoencoders for shape structures. ACM Transactions on Graphics (TOG), 36(4), 114.Google Scholar
Liu, S., Zhang, J., Niu, B., Liu, L., & He, X. (2022). A novel hybrid multi-criteria group decision-making approach with intuitionistic fuzzy sets to design reverse supply chains for COVID-19 medical waste recycling channels. Computers & Industrial Engineering, 108228.CrossRefGoogle ScholarPubMed
Lou, P., Liu, Q., Zhou, Z., & Wang, H. (2011). Agile Supply Chain Management over the Internet of Things. in 2011 International Conference on Management and Service Science.CrossRefGoogle Scholar
Min, H. (2019). Blockchain technology for enhancing supply chain resilience. Business Horizons, 62(1), 3545.CrossRefGoogle Scholar
Nakamoto, S. (2009). Bitcoin: A Peer-to-Peer Electronic Cash System. Cryptography Mailing list at. https://metzdowd.com.Google Scholar
Pal, K., & Yasar, A.-U.-H. (2020). Internet of things and blockchain technology in apparel manufacturing supply chain data management. Procedia Computer Science, 170, 450457.CrossRefGoogle Scholar
Pan, W.-T., Zhuang, M.-E., Zhou, Y.-Y., & Yang, J.-J. (2021). Research on sustainable development and efficiency of China's E-agriculture based on a data envelopment analysis-malmquist model. Technological Forecasting and Social Change, 162, 120298.CrossRefGoogle Scholar
Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 21172135.CrossRefGoogle Scholar
Sangeetha, A. S., Shunmugan, S., & Murugan, G. (2020). Blockchain for IoT Enabled Supply Chain Management - A Systematic Review. in 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC).CrossRefGoogle Scholar
Shwetha, A. N., & Prabodh, C. P. (2021). A Comprehensive Review of Blockchain-based Solutions in Food Supply Chain Management. in 2021 5th International Conference on Computing Methodologies and Communication (ICCMC).CrossRefGoogle Scholar
Sui, T., Marelli, D., Sun, X., & Fu, M. (2020). Multi-sensor state estimation over lossy channels using coded measurements. Automatica, 111, 108561.CrossRefGoogle Scholar
Torraco, R. J. (2016). Writing integrative literature reviews: Using the past and present to explore the future. Human Resource Development Review, 15(4), 404428.CrossRefGoogle Scholar
Toyoda, K., Mathiopoulos, P. T., Sasase, I., & Ohtsuki, T. (2017). A novel blockchain-based product ownership management system (POMS) for anti-counterfeits in the post supply chain. IEEE Access, 5, 1746517477.CrossRefGoogle Scholar
Treiblmaier, H. (2018). The impact of the blockchain on the supply chain: A theory-based research framework and a call for action. Supply Chain Management: An International Journal, 23(6), 545559.CrossRefGoogle Scholar
Vahdat, S. (2020). The role of IT-based technologies on the management of human resources in the COVID-19 era. Kybernetes.Google Scholar
Vahdat, S., & Shahidi, S. (2020). D-dimer levels in chronic kidney illness: A comprehensive and systematic literature review. Proceedings of the National Academy of Sciences, India Section B: Biological Sciences, 90(5), 911928.CrossRefGoogle Scholar
van Nunen, K., Li, J., Reniers, G., & Ponnet, K. (2018). Bibliometric analysis of safety culture research. Safety Science, 108, 248258.CrossRefGoogle Scholar
van Raan, A. (2005). For your citations only? Hot topics in bibliometric analysis. Measurement: Interdisciplinary Research Perspectives, 3(1), 5062.Google Scholar
Wang, Y., Singgih, M., Wang, J., & Rit, M. (2019). Making sense of blockchain technology: How will it transform supply chains? International Journal of Production Economics, 211, 221236.CrossRefGoogle Scholar
Wu, L., Yue, X., Jin, A., & Yen, D. C. (2016). Smart supply chain management: A review and implications for future research. The International Journal of Logistics Management, 27(2), 395417.CrossRefGoogle Scholar
Wu, X., Zheng, W., Chen, X., Zhao, Y., Yu, T., & Mu, D. (2021). Improving high-impact bug report prediction with combination of interactive machine learning and active learning. Information and Software Technology, 133, 106530.CrossRefGoogle Scholar
Wu, X., Zheng, W., Xia, X., & Lo, D. (2022). Data quality matters: A case study on data label correctness for security Bug report prediction. IEEE Transactions on Software Engineering, 48(7), 25412556.CrossRefGoogle Scholar
Yan, J., Jiao, H., Pu, W., Shi, C., Dai, J., & Liu, H. (2022). Radar sensor network resource allocation for fused target tracking: A brief review. Information Fusion.CrossRefGoogle Scholar
Yan, L., Yin-He, S., Qian, Y., Zhi-Yu, S., Chun-Zi, W., & Zi-Yun, L. (2021). Method of reaching consensus on probability of food safety based on the integration of finite credible data on block chain. IEEE Access, 9, 123764123776.CrossRefGoogle Scholar
Yang, D., Zhu, T., Wang, S., Wang, S., & Xiong, Z. (n.d.). LFRSNEt: A robust light field semantic segmentation network combining contextual and geometric features. Frontiers in Environmental Science, 1443.Google Scholar
Yang, W., Chen, X., Xiong, Z., Xu, Z., Liu, G., & Zhang, X. (2021). A privacy-preserving aggregation scheme based on negative survey for vehicle fuel consumption data. Information Sciences, 570, 526544.CrossRefGoogle Scholar
Zhang, C., Gong, Y., Brown, S., & Li, Z. (2019). A content-based literature review on the application of blockchain in food supply chain management, in the 26th EurOMA Conference. 2019: Helsinki, Finland. p. 10 pp.Google Scholar
Zhang, L., Zheng, H., Cai, G., Zhang, Z., Wang, X., & Koh, L. H. (2022a). Power-frequency oscillation suppression algorithm for AC microgrid with multiple virtual synchronous generators based on fuzzy inference system. IET Renewable Power Generation.CrossRefGoogle Scholar
Zhang, L., Gao, T., Cai, G., & Hai, K. L. (2022b). Research on electric vehicle charging safety warning model based on back propagation neural network optimized by improved gray wolf algorithm. Journal of Energy Storage, 49, 104092.CrossRefGoogle Scholar
Zheng, W., & Yin, L. (2022). Characterization inference based on joint-optimization of multi-layer semantics and deep fusion matching network. PeerJ Computer Science, 8, e908.CrossRefGoogle ScholarPubMed
Zheng, W., Xun, Y., Wu, X., Deng, Z., Chen, X., & Sui, Y. (2021). A comparative study of class rebalancing methods for security bug report classification. IEEE Transactions on Reliability, 70(4), 16581670.CrossRefGoogle Scholar
Zheng, W., Zhou, Y., Liu, S., Tian, J., Yang, B., & Yin, L. (2022a). A deep fusion matching network semantic reasoning model. Applied Sciences, 12(7), 3416.CrossRefGoogle Scholar
Zheng, W., Tian, X., Yang, B., Liu, S., Ding, Y., Tian, J., & Yin, L. (2022b). A few shot classification methods based on multiscale relational networks. Applied Sciences, 12(8), 4059.CrossRefGoogle Scholar
Zhong, L., Fang, Z., Liu, F., Yuan, B., Zhang, G., & Lu, J. (2021). Bridging the theoretical bound and deep algorithms for open set domain adaptation. IEEE Transactions on Neural Networks and Learning Systems.Google Scholar
Zhong, R. Y., Xu, C., Chen, C., & Huang, G. Q. (2017). Big data analytics for physical internet-based intelligent manufacturing shop floors. International Journal of Production Research, 55(9), 26102621.CrossRefGoogle Scholar