Education, Science, Technology, Innovation and Life
Open Access
Sign In

Prospects of Omni-Channel Marketing from the Perspective of Complex Networks: A CiteSpace Knowledge Graph Analysis

Download as PDF

DOI: 10.23977/ieim.2023.060917 | Downloads: 25 | Views: 350

Author(s)

Zhifang Zhan 1, Yuhe Huang 1, Jiawen Xiang 1, Lei Hou 2, Hui Wang 3

Affiliation(s)

1 School of Business Administration, Hunan University of Technology and Business, Changsha, Hunan, 410205, China
2 School of Business, Hunan International Economics University, Changsha, Hunan, 410205, China
3 School of Humanities and Management, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, China

Corresponding Author

Lei Hou

ABSTRACT

With the rapid development of internet technology and mobile devices, omni-channel marketing has become an important trend in modern marketing. This paper conducts a knowledge graph analysis on the research prospects of omni-channel marketing from the perspective of complex networks. This paper conducts big data analysis on hot spots and frontiers of complex network research using CiteSpace Visual analysis based on literatures from 2012 to 2022 in Web of Science. The results show that: (1) There is still considerable research prospect for complex network research related to omni-channel marketing, and the cooperation between different institutions is relatively close; (2) The hot topics of complex network research related to omni-channel marketing are: "Network", "Social structure", "Model", "Supply chain management"; (3) The clustering results of complex network research related to omni-channel marketing can be summarized into three aspects: social, complexity and optimization. This study has certain reference value for grasping the knowledge base, research hotspots of complex network and exploring the latest research frontiers of omni-channel marketing from the perspective of complex networks.

KEYWORDS

Complex Network, Omni-Channel Marketing, CiteSpace, Co-occurrence Map, Visual Analysis

CITE THIS PAPER

Zhifang Zhan, Yuhe Huang, Jiawen Xiang, Lei Hou, Hui Wang, Prospects of Omni-Channel Marketing from the Perspective of Complex Networks: A CiteSpace Knowledge Graph Analysis. Industrial Engineering and Innovation Management (2023) Vol. 6: 118-126. DOI: http://dx.doi.org/10.23977/ieim.2023.060917.

REFERENCES

[1] Sadilek M, Klimek P, Thurner S. Asocial balance—how your friends determine your enemies: understanding the co-evolution of friendship and enmity interactions in a virtual world. Journal of Computational Social Science, 2018, 1(1):227-239.
[2] Chiva R, A Grand í o, Alegre J. Adaptive and Generative Learning: Implications from Complexity Theories. International Journal of Management Reviews, 2010, 12(2):114-129.
[3] Junmin Kimi. Optimization of Tourism Industry Ecosystem Structure Upgrading based on Complex Network. Academic Journal of Environmental Biology. 2021, 2(3): 1-9.
[4] Kunz T H, Arnett E B, Cooper B M, et al. Assessing Impacts of Wind‐Energy Development on Nocturnally Active Birds and Bats: A Guidance Document. Journal of Wildlife Management, 2011, 71(8).
[5] Hofmann S G, Curtiss J, McNally R J. A complex network perspective on clinical science. Perspectives on Psychological Science, 2016, 11(5): 597-605.
[6] Shen L, Xiong B, Li W, et al. Visualizing collaboration characteristics and topic burst on international mobile health research: bibliometric analysis. JMIR mHealth and uHealth, 2018, 6(6): e9581.
[7] Chen S, Huang W, Cattani C, et al. Traffic dynamics on complex networks: a survey. Mathematical Problems in Engineering, 2012.
[8] Xu R, Mi C, Mierzwiak R, et al. Complex network construction of Internet finance risk. Physica A: Statistical Mechanics and its Applications, 2020, 540: 122930.
[9] Pagani G A, Aiello M. The power grid as a complex network: a survey. Physica A: Statistical Mechanics and its Applications, 2013, 392(11): 2688-2700.
[10] Ruela A S, Cabral R S, Aquino A L L, et al. Evolutionary design of wireless sensor networks based on complex networks//2009 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2009: 237-242.
[11] Hearnshaw E J S, Wilson M M J. A complex network approach to supply chain network theory. International Journal of Operations & Production Management, 2013.
[12] Mari S I, Lee Y H, Memon M S. Complex network theory-based approach for designing resilient supply chain networks. International Journal of Logistics Systems and Management, 2015, 21(3): 365-384.
[13] Ding L H, Sun B, Shi P. Empirical study of knowledge network based on complex network theory. Acta Phys Sin, 2019, 68: 15.
[14] Li N, Huang Q, Ge X, et al. A review of the research progress of social network structure. Complexity, 2021.
[15] Stocker R, Cornforth D, Bossomaier T R J. Network structures and agreement in social network simulations. Journal of Artificial societies and social simulation, 2002, 5(4).
[16] Puga-Gonzalez I, Sueur C. Emergence of complex social networks from spatial structure and rules of thumb: a modelling approach. Ecological Complexity, 2017, 31: 189-200.
[17] Sun Q, Gao X, Zhong W, et al. The stability of the international oil trade network from short- term and long-term perspectives. Physica A: Statistical Mechanics and its Applications, 2017, 482: 345-356.
[18] Chandra Y, Wilkinson I F. Firm internationalization from a network-centric complex-systems perspective. Journal of World Business, 2017, 52(5): 691-701.
[19] Zhu J, Wang Q, Yang J. Research of Wechat network information transmission based on the complex network [C]//2015 International Conference on Intelligent Systems Research and Mechatronics Engineering. Atlantis Press. 2015.
[20] Liu L, Qu B, Chen B, et al. Modelling of information diffusion on social networks with applications to WeChat. Physica A: Statistical Mechanics and its Applications, 2018, 496: 318- 329.
[21] Park J H, Lee C, Yoo C, et al. An analysis of the utilization of Facebook by local Korean governments for tourism development and the network of smart tourism ecosystem. International Journal of Information Management, 2016, 36(6): 1320-1327.
[22] Zhou S, Wang Y. Service ranking in service networks using parameters in complex networks: a comparative study. Cluster Computing, 2019, 22(2): 2921-2930.
[23] Ma F, Xue H, Yuen K F, et al. Assessing the vulnerability of logistics service supply chain based on complex network. Sustainability, 2020, 12(5): 1991.
[24] Lu Z, Zhang Y, Xu L. Quality control decision of government procurement of elderly care service based on multi‐index fusion of Pythagoras TOPSIS: Perspective of complex network. Managerial and Decision Economics, 2021.
[25] Pérez J M Q, Tancrez J S, Lange J C. Express shipment service network design with complex routes. Computers & Operations Research, 2020, 114: 104810.
[26] Sohrabi M K, Karimi F. A feature selection approach to detect spam in the Facebook social network. Arabian Journal for Science and Engineering, 2019, 43(2): 949-958.
[27] Al-Andoli M, Cheah W P, Tan S C. Deep learning-based community detection in complex networks with network partitioning and reduction of trainable parameters. Journal of Ambient Intelligence and Humanized Computing, 2021, 12(2): 2527-2545.
[28] Carneiro M G, Zhao L. Organizational data classification based on the importance concept of complex networks. IEEE transactions on neural networks and learning systems, 2017, 29(8): 3361-3373.
[29] Hilgetag C C, Goulas A. Is the brain really a small-world network? Brain Structure and Function, 2016, 221(4): 2361-2366.
[30] Xinchao S, Geng Y. Distributed community detection optimization algorithm for complex networks. Journal of Networks, 2014, 9(10): 2758.
[31] Gomez Portillo I J, Gleiser P M. An adaptive complex network model for brain functional networks. PloS one, 2009, 4(9): e6863.
[32] Cheng X, Scherpen J. Model reduction methods for complex network systems. arXiv preprint arXiv: 2012.02268, 2020.
[33] Mayer K. Objectifying social structures: Network visualization as means of social optimization. Theory & Psychology, 2012, 22(2): 162-178.
[34] Rossetti G, Cazabet R. Community discovery in dynamic networks: a survey. ACM computing surveys (CSUR), 2018, 51(2): 1-37.

Downloads: 10927
Visits: 266499

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.