Factors affecting blockchain technology in routing-location of combined transportation in the Omnichannel distribution system

Mir Hosseini, Sayed Ali; Aghajani, Hassanali; Valipour Khatir, Mohammad

doi:10.22034/jvcbm.2024.427097.1253

Factors affecting blockchain technology in routing-location of combined transportation in the Omnichannel distribution system

Document Type : Original Article (Qualitative)

Authors

Sayed Ali Mir Hosseini ¹

Hassanali Aghajani ²

Mohammad Valipour Khatir ³

¹ PhD student in Industrial Management, Faculty of Economic and Administrative Sciences, University of Mazandaran, Babolsar, Iran

² Professor, Department of Management, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran

³ Assistant Professor, Faculty of Economic and Administrative Sciences, University of Mazandaran, Babolsar, Iran

https://doi.org/10.22034/jvcbm.2024.427097.1253

Abstract

Abstract
The aim of the present study is to determine the factors affecting blockchain technology in routing-location of combined transportation of the Channel Security Distribution System. The research method is applicable according to its purpose, and its method is qualitative, and of meta-synthesis type. By using a systematic review and meta-synthesis approach, the results and findings of previous researchers were analyzed and the effective factors were identified by performing the 7 steps of the Sandelowski and Barroso method. Out of 580 articles, 79 articles were selected based on the CASP method. In this context, in order to measure reliability and quality control, the transcript method was used; and its value was identified at the high agreement level for the identified indicators. Based on the coding performed in the ATLAS TI software, 8 categories and 51 primary codes were identified. The identified categories are: electronic network management, route management, environmental factors, transparency, trust management, technical infrastructure, information standardization and business intelligence; therefore, by combining these 8 criteria, all of which have specific resources and recurrence, it is possible to optimize transportation routing-location by combining blockchain and omnichannel distribution. The results show that today's companies communicate and interact with each other in a dynamic market that is undergoing fundamental change and transformation, which is created by the increasing speed at which digitalization and technological advances are advancing.
Introduction
Today, supply chains are experiencing intense competition due to increasing supply and decreasing demand (Awan et al, 2022). Each supply chain tries to attract more customers and increase its sales profits by creating a competitive advantage and reducing costs, which leads to a decrease in the final price. One of the important and significant issues in industries is transportation cost, which accounts for 30% of the total cost of a product (Gao et al, 2022). Considering this share, with a small reduction in transportation cost, the profit from product sales will increase significantly. Accordingly, many companies are seeking to improve their transportation systems (Abdulkader et al, 2018). In the supply chain, the distribution of a research product plays a key role to achieving higher profitability, because it has a direct impact on supplier costs and customer experiences (Cheng et al, 2021). As a result, companies in the same industry often use different distribution networks; one of the most widely used and useful distribution networks is cross-warehouse (Jin et al, 2020). Cross-warehouse is more applicable to supply chains that have many components, including suppliers and retailers. One of the issues involved in reducing transportation costs is research in the field of vehicle routing. In routing, vehicles seek the path that has the lowest cost while respecting all constraints (Dong et al, 2021).
The process of retail digitization has had a huge impact on the distribution and retail sectors of the supply chain, and has changed the structure of retail (Xu & Li, 2021). Although online commerce is expanding and mobile devices are playing an increasingly important role; physical stores still remain a key retail space. Customers with access to digital tools have more information and power to make their purchases; this describes the omnichannel shopper (Nilsson & Linn, 2019). The omnichannel shopper is always connected to the Internet through communication devices such as mobile phones or computers, and is aware of market changes and new products, finds the best deal and expects to receive each purchase at the desired time and place. The omnichannel approach is the logical evolutionary step after the multichannel approach and includes all shopping paths (Lazrag et al, 2020). In this approach, the consumer experience is the same in each channel and switching from one channel to another will not result in receiving new or different information. This coordination in the presentation of information makes omnichannel more sophisticated than the traditional multichannel approach. Omnichannel retailing has activated many businesses and given them the ability to capitalize on new opportunities. There are many different sales channels in the business process, but the term omni suggests that customers can shop through all channels and all information about the shopping process is ideally available in real time across all channels (Liu et al, 2021).
Therefore, this research seeks to answer the question: what is the trend of the effectiveness of blockchain technology for integrating hybrid transportation routing-location in an omnichannel distribution system?
Theoretical Framework
Omnichannel
Omnichannel marketing is an approach that provides customers with a fully integrated and seamless shopping experience from the point of contact to the end of the purchase process. This means that each channel works in conjunction with other channels to create a single message to introduce the brand or company (Maharjan & Honaoka, 2018).
Blockchain
Blockchain is an extended data structure that is replicated and shared among members of a network. Blockchain can allow individuals and companies to conduct instant transactions without any intermediaries in a network (Hu et al, 2019).
Impact of Blockchain on Omnichannel
By implementing blockchain technology, all partners involved in the network (e.g. retailers and customers) share the same verified information, which allows for the optimization of the omnichannel strategy and eliminates the need for trust and transparency among omnichannel parties. In fact, blockchain can be useful for implementing successful multi-channel strategies when supported by other digital technologies. Therefore, blockchain is becoming a very popular platform in digital technologies adopted by supply chains, while the associated competitive advantages clearly decrease with its maturity (Davis et al, 2020).
Valafar et al, (2023) investigated the identification of antecedents of blockchain-based digital marketing development from the perspective of marketing professionals in the aviation industry. The results of this study provide useful insights to managers and decision makers in the aviation industry so that they can strengthen and develop digital marketing in airlines by recognizing these factors.
Awan et al, (2022) investigated blockchain-based secure routing and trust management in wireless sensor networks and showed that the efficiency of the proposed model in terms of packet delivery ratio was high in simulation results.
Research Methodology
The research method is applicable in terms of its purpose, and its method is qualitative and meta-synthesis. By using a systematic review and meta-synthesis approach, the results and findings of previous researchers were analyzed and the effective factors were identified by performing the 7 steps of the Sandelowski and Barroso method.
Research Findings
Out of 580 articles, 79 articles were selected based on the CASP method. In this context, in order to measure reliability and quality control, the transcript method was used, the value of which was identified for the identified indicators at the excellent agreement level. Based on the coding performed in the ATLAS TI software, 8 categories and 51 initial codes were identified. The identified categories are: electronic network management, path management, environmental factors, transparency, trust management, technical infrastructure, information standardization and business information; Therefore, by combining these 8 criteria, all of which have specific sources and recurrences, it is possible to optimize transportation routing-location by combining blockchain and omnichannel distribution. The results show that today's companies communicate and interact with each other in a dynamic market that is undergoing fundamental change and transformation, which is created by the increasing speed at which digitalization and technological advancements are progressing.
Conclusion
The present study aimed to investigate the factors affecting blockchain technology in transportation routing-location of a combined omnichannel distribution system. The results of this study are consistent with the results of Valafar et al, (2023), Awan et al, (2022), GAO et al, (2022), Bhatia et al, (2020), Naclerio (2020), Bai & Sarkis (2019), Arias et al, (2018), Christopher (2016), Miller & Liberatore (2015), and De Giovanni (2019). Naclerio (2020) also showed that today's companies communicate and interact with each other in a dynamic market that is undergoing fundamental change and transformation, which is created by the increasing speed at which digitalization and technological advances are advancing; Therefore, by combining these 8 criteria, all of which have specific sources and repetitions, it is possible to optimize transportation routing-location by combining blockchain and omnichannel distribution.
According to the research results, the following suggestions were made:
It is suggested that transportation companies increase their readiness to adopt blockchain and omnichannel. This readiness will be possible through the development of partnerships, identification of players and increased interaction with them, monitoring technology and events in the global community for companies and similar fields, flexibility and adaptability in terms of law, regulation and structural adjustment, as well as increasing operational efficiency and identifying and cultivating talented and relevant talents.

Keywords

Blockchain technology

routing and location

omnichannel distribution

smart management

Subjects

business management

Abdulkader, M. M. S., & Gajpal, Y., & ElMekkawy, T. Y. (2018). Vehicle routing problem in omni-channel retailing distribution systems. International Journal of Production Economics, 196, 43-55. https://doi.org/10.1016/j.ijpe.2017.11.011

Agrawal, T., & Sharma, A., & Kumar, V. (2018). Blockchain-Based Secured Traceability System for Textile and Clothing Supply Chain, Artificial Intelligence for Fashion Industry in the Big Data Era, Springer Series in Fashion Business, 10(6): 197: 208 DOI:10.1007/978-981-13-0080-6_10

Akbari, M., & Morris B., & Michela L. (2018).Optimal locating of electric vehicle charging stations by application of genetic algorithm." Sustainability 10.4 (2018): 1076.

Arias, A., & Sanchez, J., & Granada, M. (2018). Integrated planning of electric vehicles routing and charging stations location considering transportation networks and power distribution systems. International Journal of Industrial Engineering Computations, 9(4), 535-550. DOI:10.5267/j.ijiec.2017.10.002

Arkin, E. M., & Carmi, P., & Katz, M. J., & Mitchell, J. S., & Segal, M. (2019). Locating battery charging stations to facilitate almost shortest paths. Discrete Applied Mathematics, 254, 10-16. DOI:10.4230/OASIcs.ATMOS.2014.25

Awan, S., & Javaid, N., & Ullah, S., & Khan, A.U., & Qamar, A.M., & Choi, J.G. (2022). Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks. Sensors, 22, 411. doi: 10.3390/s22020411.

Bai, C., & Sarkis, J. (2019, July). A Supply Chain Transparency and Sustainability Technology Appraisal Model for Blockchain Technology. In Academy of Management Proceedings (Vol. 2019, No. 1, p. 16069). Briarcliff Manor, NY 10510: Academy of Management. DOI:10.5465/AMBPP.2019.16069abstract

Bailur, R. P., & Rao, S., & Iyengar, D. (2020). Use of Blockchain Partnerships to Enable Transparency in Supply Chain Digitization. In The Oxford Handbook of Supply Chain Management. DOI:10.1093/oxfordhb/9780190066727.013.14

Baralla, G., & Ibba, S., & Marchesi, M., & Tonelli, R., & Missineo, S. (2018, August). A Blockchain Based System to Ensure Transparency and Reliability in Food Supply Chain. In European Conference on Parallel Processing (pp. 379-391). Springer, Cham. DOI:10.1007/978-3-030-10549-5_30

Bechtsis, D., & Tsolakis, N., & Bizakis, A., & Vlachos, D. (2019). “A Blockchain Framework for Containerized Food Supply Chains”. Computer Aided Chemical Engineering, l46: 1369-1374.

Bhatia, N., & Gülpınar, N., & Aydın, N. (2020). Dynamic production-pricing strategies for multi-generation products under uncertainty. International Journal of Production Economics, 230, 107851. DOI:10.1016/j.ijpe.2020.107851

Bian, J., & Zhao, X., & Liu, Y. (2020). Single vs. cross distribution channels with manufacturers’ dynamic tacit collusion. International Journal of Production Economics, 220, 107456. DOI:10.1016/j.ijpe.2019.07.029

CAI, Y. J., & Lo, C. K. (2020). Omni-channel management in the new retailing era: A systematic review and future research agenda. International Journal of Production Economics, 229, 107729.

Campelo, P., & Neves-Moreira, F., & Amorim, P., & Almada-Lobo, B. (2019). Consistent vehicle routing problem with service level agreements: A case study in the pharmaceutical distribution sector. European Journal of Operational Research, 273(1), 131-145. DOI:10.1016/j.ejor.2018.07.030

Chao, C., & Zhihui, T., & Baozhen, Y. (2019). Optimization of two-stage location–routing–inventory problem with time-windows in food distribution network. Annals of Operations Research, 273(1-2), 111-134. DOI:10.1007/s10479-017-2514-3

Cheng, R., & Duan, Y., & Zhang, J., & Ke, H. (2021). Impacts of store-brand introduction on a multiple-echelon supply chain. European Journal of Operational Research, 292(2), 652-662. https://doi.org/10.1016/j.ejor.2020.10.044

Christopher, M.C. (2016), Logistics and Supply Chain Management, Pearson.

Cintron, A., & Ravindran, A. R., & Ventura, J. A. (2010). Multi-criteria mathematical model for designing the distribution network of a consumer goods company. Computers & Industrial Engineering, 58(4), 584-593. doi: 10.1016/j.cie.2009.12.006.

Cole, R., & Stevenson, M., & & Aitken, J. (2019). Blockchain technology: implications for operations and supply chain management. Supply Chain Management: An International Journal, 24(4), 469-483.

Crosby, M., & Pattanayak, P., & Verma, S., & Kalyanaraman, V. (2016). Blockchain technology: Beyond bitcoin. Applied Innovation, 2(6-10), 71.

Cui, T. H., & Ghose, A., & Halaburda, H., & Iyengar, R., & Pauwels, K., & Sriram, S. ... & Venkataraman, S. (2021). Informational challenges in omnichannel marketing: remedies and future research. Journal of Marketing, 85(1), 103-120. doi: 10.1177/0022242920968810

Davoodi, M. (2019). k-Balanced Center Location problem: A new multi-objective facility location problem. Computers & Operations Research, 105, 68-84.

De Boissieu, E., & Kondrateva, G., & Baudier, P., & Ammi, C. (2021). The use of blockchain in the luxury industry: supply chains and the traceability of goods. Journal of Enterprise Information Management. DOI:10.1108/JEIM-11-2020-0471

De Giovanni, P. (2019). Digital supply chain through dynamic inventory and smart contracts, Mathematics, 7(12), 12-35.

Dirsehan, T., & Dirsehan, M. Ç. (2020). The Melody of Omnichannel Customer Experience Management (OCCEM). In Managing Customer Experiences in an Omnichannel World: Melody of Online and Offline Environments in the Customer Journey. Emerald Publishing Limited.

Dong, S., & Yang, L., & Shao, X., & Zhong, Y., & Li, Y., & Qiao, P. (2021). How can channel information strategy promote sales by combining ICT and blockchain? Evidence from the agricultural sector. Journal of Cleaner Production, 299, 126857. https://doi.org/10.1016/j.jclepro.2021.126857

Dukkanci, O., & Kara, B. Y., & Bektaş, T. (2019). The green location-routing problem. Computers & Operations Research, 105, 187-202. https://doi.org/10.1016/j.cor.2019.01.011

Fazayeli, S., & Eydi, A., & Kamalabadi, I. N. (2018). Location-routing problem in multimodal transportation network with time windows and fuzzy demands: Presenting a two-part genetic algorithm. Computers & Industrial Engineering, 119, 233-246. https://doi.org/10.1016/j.cie.2018.03.041

GAO, R., & Ma, Y., & Ralescu, D.A. (2022). Uncertain multilevel programming with application to omni-channel vehicle routing problem. J Ambient Intell Human Comput. DOI:10.1007/s12652-022-04419-2

Ghane, M., & Tavakkoli-Moghaddam, R. (2018). A stochastic optimization approach to a location-allocation problem of organ transplant centers. Journal of Optimization in Industrial Engineering, 11(1), 103-111.

Hendalianpour, A., & Fakhrabadi, M., & Sangari, M. S., & Razmi, J. (2020). A combined benders decomposition and lagrangian relaxation algorithm for optimizing a multi-product, multi-level omni-channel distribution system. Scientia Iranica.

Hendalianpour, A., & Fakhrabadi, M., & Zhang, X., & Feylizadeh, M. R., & Gheisari, M., & Liu, P., & Ashktorab, N. (2019). Hybrid model of ivfrn-bwm and robust goal programming in agile and flexible supply chain, a case study: automobile industry. IEEE Access, 7, 71481-71492.

Hong, J., & Lee, M., & Cheong, T., & Lee, H. C. (2019). Routing for an on-demand logistics service. Transportation Research Part C: Emerging Technologies. 10.1016/j.trc.2018.12.010

Hu, H., & Li, X., & Zhang, Y., & Shang, C., & Zhang, S. (2019). Multi-objective location-routing model for hazardous material logistics with traffic restriction constraint in inter-city roads. Computers & Industrial Engineering, 128, 861-876. DOI:10.1016/j.cie.2018.10.044

Hübner, A., & Wollenburg, J., & Holzapfel, A. (2016). Retail logistics in the transition from multi-channel to Omni-channel. International Journal of Physical Distribution & Logistics Management, 46(6/7), 562-583. DOI:10.1108/IJPDLM-08-2015

Ionescu, L. (2019). Big data, blockchain, and artificial intelligence in cloud-based accounting information systems, Analysis and Metaphysics, 18,. 44-49. DOI:10.1051/shsconf/20219205010

Jiang, Y., & Liu, L., & Lim, A. (2020). Optimal pricing decisions for an omni‐channel supply chain with retail service. International Transactions in Operational Research, 27(6), 2927-2948. https://doi.org/10.1111/itor.12784

Jin, D., & Caliskan-Demirag, O., & Chen, F. Y., & Huang, M. (2020). Omnichannel retailers’ return policy strategies in the presence of competition. International Journal of Production Economics, 225, 107595. DOI: 10.1016/j.ijpe.2019.107595.

Karadag, G. M., & Erdogmus, I. E. (2020). Digitization of Luxury Fashion by Building an Omnichannel Dream. In Managing Customer Experiences in an Omnichannel World: Melody of Online and Offline Environments in the Customer Journey. Emerald Publishing Limited. DOI:10.1108/978-1-80043-388-520201018

Karakatič, S., & Podgorelec, V. (2015). A survey of genetic algorithms for solving multi depot vehicle routing problem. Applied Soft Computing, 27, 519-532. https://doi.org/10.1016/j.asoc.2014.11.005

Kınay, Ö. B., & Kara, B. Y., & Saldanha-da-Gama, F., & Correia, I. (2018). Modeling the shelter site location problem using chance constraints: A case study for Istanbul. European Journal of Operational Research, 270(1), 132-145. DOI: 10.1016/j.ejor.2018.03.006.

Kouhizadeh, M., & Sarkis, J. (2018). Blockchain practices, potentials, and perspectives in greening supply chains. Sustainability, 10(10), 3652. DOI:10.3390/SU10103652

Lazrag, H., & Chehri, A., & Saadane, R., & Rahmani, M. D. (2020). Efficient and secure routing protocol based on Blockchain approach for wireless sensor networks. Concurrency and Computation: Practice and Experience. https://doi.org/10.1002/cpe.6144

Li, B., & Hernandez, I., & Milburn, A. B., & Ramirez-Marquez, J. E. (2018). Integrating uncertain user-generated demand data when locating facilities for disaster response commodity distribution. Socio-Economic Planning Sciences, 62, 84-103. DOI: 10.1016/j.seps.2017.09.003.

Li, G., & Zhang, T., & Tayi, G. K. (2020). Inroad into omni-channel retailing: Physical showroom deployment of an online retailer. European Journal of Operational Research, 283(2), 676-691. DOI: 10.1016/j.ejor.2019.11.032.

LI, M. L., & Hong, Y. A. N. G., & Xiong, G. U. O. (2018). Research on Location-Allocation Problem of Emergency Logistics Based on Supply Chain Collaboration. DEStech Transactions on Engineering and Technology Research, (icmeit). DOI:10.12783/dtetr/icmeit2018/23443

Liu, P., & Hendalianpour, A., & Hamzehlou, M., & Feylizadeh, M. R., & Razmi, J. (2021). Identify and rank the challenges of implementing sustainable Supply Chain Blockchain Technology using the Bayesian Best Worst Method. Technological and Economic Development of Economy, 27(3), 656-680. OI:10.3846/tede.2021.14421

Liu, Z., & Chen, J., & Diallo, C., & Venkatadri, U. (2021). Pricing and production decisions in a dual-channel closed-loop supply chain with (re) manufacturing. International Journal of Production Economics, 232, 107935. https://doi.org/10.1016/j.ijpe.2020.107935

Maharjan, R., & Hanaoka, S. (2018). A multi-actor multi-objective optimization approach for locating temporary logistics hubs during disaster response. Journal of Humanitarian Logistics and Supply Chain Management, 8(1), 2-21.

Mahmoudabadi, A. (2015). Developing a chaotic pattern of dynamic risk definition for solving hazardous material routing-locating problem. Journal of Loss Prevention in the Process Industries, 37, 1-10.

Mahmoudabadi, A., & Farokhi, R., & Fattahi, A. A. (2016). Developing a Tolerated Risk Model for Solving Routing-Locating Problem in Hazardous Material Management. Journal of Intelligent Transportation and Urban Planning, 4(1), 53-61.

Miller, T., & Liberatore, M. (2015). Outbound logistics: startegies, performance and profitability. Material Handling and Logistics,70(4), 18-23.

Mohri, S. S., & Akbarzadeh, M., & Matin, S. H. S. (2019). A Hybrid model for locating new emergency facilities to improve the coverage of the road crashes. Socio-Economic Planning Sciences. DOI: 10.1016/j.seps.2019.01.005

Momen, S., & Torabi, S. A. (2021). Omni-channel retailing: A data-driven distributionally robust approach for integrated fulfillment services under competition with traditional and online retailers. Computers & Industrial Engineering, 157, 107353. DOI:10.1016/j.cie.2021.107353

Naclerio, A. G. (2020). The combined impact of Blockchain and Omnichannel on Logistics and Last Mile delivery. Available at SSRN 3668019.

Nayak, G., & Dhaigude, A. S. (2019). A conceptual model of sustainable supply chain management in small and medium enterprises using blockchain technology. Cogent Economics & Finance, 1667184. https://doi.org/10.1080/23322039.2019.1667184

Nica, E., & Stan, C.I., & Luțan, A.G., & Oa, R.Ş. (2021). Internet of things-based real-time production logistics, sustainable industrial value creation, and artificial intelligence-driven big data analytics in cyber-physical smart manufacturing systems, Economics, Management, and Financial Markets, 16(1), 52-63. DOI:10.22381/emfm16120215

Nilsson, F., & Linn, R. (2019). The Effect of Blockchain Technology on Supply Chain Management: Examining Supply Chain Visibility by Conducting a Cross-Comparison.

Paul, J., & Agatz, N., & Spliet, R., & De Koster, R. (2019). Shared Capacity Routing Problem− An Omni-channel retail study. European Journal of Operational Research, 273(2), 731-739. https://doi.org/10.1016/j.ejor.2018.08.027

Popescu, G.H., & Petreanu, S., & Alexandru, B., & Corpodean, H. (2021). Internet of Things-based real-time production logistics, cyber-physical process monitoring systems, and industrial artificial intelligence in sustainable smart manufacturing, Journal of Self-Governance and Management Economics, 9(2), 52-62.

Queiroz, M. M., & Telles, R., & Bonilla, S. H. (2019). Blockchain and supply chain management integration: A systematic review of the literature. Supply Chain Management: An International Journal. https://doi.org/10.1108/SCM-03-2018-0143

Radhi, M., & Zhang, G. (2019). Optimal cross-channel return policy in dual-channel retailing systems. International Journal of Production Economics, 210, 184-198. https://doi.org/10.1016/j.ijpe.2019.01.014

Rashidi, E., & Parsafard, M., & Medal, H., & Li, X. (2016). Optimal traffic calming: A mixed-integer bi-level programming model for locating sidewalks and crosswalks in a multimodal transportation network to maximize pedestrians’ safety and network usability. Transportation research part E: logistics and transportation review, 91, 33-50. DOI: 10.1016/j.tre.2016.03.016.

Saberi, S., & Kouhizadeh, M., & Sarkis, J. (2019a). Blockchains and the supply chain: Findings from a broad study of practitioners. IEEE Engineering Management Review. DOI:10.1109/EMR.2019.2928264

Sabouhi, F., & Heydari, M., & Bozorgi-Amiri, A. (2016). Multi-objective routing and scheduling for relief distribution with split delivery in post-disaster response. Journal of Industrial and Systems Engineering, 9(3), 17-27.

Schmidt, C. G. & Wagner, S. M. (2019). Blockchain and supply chain relations: A transaction cost theory perspective. Journal of Purchasing and Supply Management, 25(4), 100552. https://doi.org/10.1016/j.pursup.2019.100552

Sharma, M. J., & Moon, I., & Bae, H. (2008). Analytic hierarchy process to assess and optimize distribution network. Applied Mathematics and Computation, 202(1), 256-265.

Shen, Y., & Willems, S. P., & Dai, Y. (2019). Channel selection and contracting in the presence of a retail platform. Production and Operations Management, 28(5), 1173-1185. DOI: 10.1111/poms.12977.

Son, Y., & Kwon, H. E., & Tayi, G. K., & Oh, W. (2020). Impact of customers' digital banking adoption on hidden defection: A combined analytical–empirical approach. Journal of Operations Management, 66(4), 418-440. DOI:10.1002/joom.1066

Soomro, Z. A., & Shah, M. H., & Thatcher, J. (2021). A framework for ID fraud prevention policies in E-tailing sector. Computers & Security, 109, 102403 https://doi.org/10.1016/j.cose.2021.102403

Tavakkoli-Moghaddam, R., & Abolghasem, A., & Mahmoudabadi, A. (2015). Comparison between Combined and Separate Approaches for Solving a Location-Routing Problem in Hazardous Materials Transportation. International Journal of Transportation Engineering, 3(1), 67-77.

Tong, T., & Dai, H., & Xiao, Q., & Yan, N. (2020). Will dynamic pricing outperform? Theoretical analysis and empirical evidence from O2O on-demand food service market. International Journal of Production Economics, 219, 375-385. https://doi.org/10.1016/j.ijpe.2019.07.010.

Valafar, A., & Maleki MinBashRazgah, M., & Zarei, A., & davood, F. (2023). Identifying and ranking the antecedents of digital ‎marketing development based on blockchain ‎technology ‎(case: aviation industry) ‎‎. Business Intelligence Management Studies, 12(46), 219-257. doi: 10.22054/ims.2023.73305.2316. (In Persian).

Wang, Y., & Jia, F., & Schoenherr, T., & Gong, Y., & Chen, L. (2020). Cross-border e-commerce firms as supply chain integrators: The management of three flows. Industrial Marketing Management, 89, 72-88. DOI:10.1016/j.indmarman.2019.09.004

Wu, J., & Zhao, C., & Yan, X., & Wang, L. (2020). An integrated randomized pricing strategy for omni-channel retailing. International Journal of Electronic Commerce, 24(3), 391-418. DOI:10.1080/10864415.2020.1767434

Wu, X. Y., & Fan, Z. P., & Cao, B. B. (2021). An analysis of strategies for adopting blockchain technology in the fresh product supply chain. International Journal of Production Research, 1-18. https://doi.org/10.1080/00207543.2021.1894497

Xu, M., & Li, X. (2021). The interplay between e-tailer information sharing and supplier cause marketing. International Journal of Production Research, 1-16.

Xu, X., & Zhang, M., & Dou, G., & Yu, Y. (2021). Coordination of a supply chain with an online platform considering green technology in the blockchain era. International Journal of Production Research, 1-18. DOI:10.1080/00207543.2021.1894367

Zhang, S., & Zhu, H., & Li, X., & Wang, Y. (2019). Omni-Channel Product Distribution Network Design by Using the Improved Particle Swarm Optimization Algorithm. Discrete Dynamics in Nature and Society, 2019. https://doi.org/10.1155/2019/1520213

Zhou, R., & Liao, Y., & Shen, W., & Yang, S. (2020). Channel selection and fulfillment service contracts in the presence of asymmetric service information. International journal of production economics, 222, 107504. DOI: 10.1016/j.ijpe.2019.09.025

Zhu, Q., & Kouhizadeh, M. (2019). Blockchain Technology, Supply Chain Information, and Strategic Product Deletion Management. IEEE Engineering Management Review, 47(1), 36-44. DOI:10.1109/EMR.2019.2898178