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Applications of social network analysis in promoting circular economy: a literature review

    Huchang Liao   Affiliation
    ; Zheng Wu   Affiliation
    ; Fan Liu   Affiliation
    ; Chonghui Zhang   Affiliation

Abstract

Circular economy (CE) is a sustainable alternative to tackle global challenges like climate change, waste, and pollution. The relations, perceptions and behaviors of stakeholders in circular economic activities may form barriers that hinder the circular transition. The promotion of CE requires investigating the interactions and information flow between CE stakeholders from a network perspective. This study revisits the applications of social network analysis (SNA) in promoting CE. Related concepts of CE and the research contents of SNA are reviewed. A bibliometric analysis is conducted to provide a bird’s eye on the research status and trend. On this basis, we summarize the challenges of promoting CE and refine specific problems, around which we review the research status of network modeling methods and statistical measures, information diffusion models, mining methods of perceptions, and social influence analysis. This study outlines the pathways through which SNA contributes to promoting CE, such as through revealing the relational structure and characteristics of stakeholders, forming and changing perceptions of stakeholders, improving behaviors of stakeholders, and examining the development of CE. The lessons learned from the review and future prospects are extensively discussed in combination with the features of the information age from both theoretical and practical perspectives.

Keyword : circular economy, sustainable development, social network analysis, stakeholder analysis, information diffusion

How to Cite
Liao, H., Wu, Z., Liu, F., & Zhang, C. (2023). Applications of social network analysis in promoting circular economy: a literature review. Technological and Economic Development of Economy, 29(5), 1559–1586. https://doi.org/10.3846/tede.2023.20104
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References

Bai, Y., Xu, Y. C., & Jiao, J. L. (2022). Can corporate environmental management benefit from multirelationship social network? An improved maturity model and text mining based on the big data from Chinese enterprises. Environment Development and Sustainability, 24(4), 5783–5810. https://doi.org/10.1007/s10668-021-01683-8

Birkenberg, A., & Birner, R. (2018). The world’s first carbon neutral coffee: Lessons on certification and innovation from a pioneer case in Costa Rica. Journal of Cleaner Production, 189, 485–501. https://doi.org/10.1016/j.jclepro.2018.03.226

Blazquez, D., & Domenech, J. (2018). Web data mining for monitoring business export orientation. Technological and Economic Development of Economy, 24(2), 406–428. https://doi.org/10.3846/20294913.2016.1213193

Boulding, K. E. (1966). The economics of the coming spaceship earth. Resources for the Future Forum on Environmental Quality in A Growing Economy. https://doi.org/10.4324/9781315064147

Boumaiza, A., Abbar, S., Mohandes, N., & Sanfilippo, A. (2018). Modeling the impact of innovation diffusion on solar PV adoption in city neighborhoods. International Journal of Renewable Energy Research, 8(3).

Burmaoglu, S., Gungor, D. O., Kirbac, A., & Saritas, O. (2022). Future research avenues at the nexus of circular economy and digitalization. International Journal of Productivity and Performance Management. https://doi.org/10.1108/IJPPM-01-2021-0026

Caprioli, C., Bottero, M., & De Angelis, E. (2020). Supporting policy design for the diffusion of cleaner technologies: A spatial empirical agent-based model. ISPRS International Journal of GEO-information, 9(10), Article 581. https://doi.org/10.3390/ijgi9100581

Chang, B., Xu, T., Liu, Q., & Chen, E. H. (2018). Study on information diffusion analysis in social networks and its applications. International Journal of Automation and Computing, 15(4), 377–401. https://doi.org/10.1007/s11633-018-1124-0

Comte, A. (1853). The positive philosophy of Auguste Comte. (H. Martineau, Trans.). Cambridge University Press, Cambridge.

David-Barrett, T. (2023). Clustering drives cooperation on reputation networks, all else fixed. Royal Society Open Science, 10(4), Article 230046. https://doi.org/10.1098/rsos.230046

Debnath, R., Bardhan, R., Shah, D. U., Mohaddes, K., Ramage, M. H., Alvarez, R. M., & Sovacool, B. K. (2022). Social media enables people-centric climate action in the hard-to-decarbonise building sector. Scientific Reports, 12(1), Article 19017. https://doi.org/10.1038/s41598-022-23624-9

Doussoulin, J. P., & Bittencourt, M. (2022). How effective is the construction sector in promoting the circular economy in Brazil and France?: A waste input-output analysis. Structural Change and Economic Dynamics, 60, 47–58. https://doi.org/10.1016/j.strueco.2021.10.009

Doyle, L., Weidlich, I., & Di Maio, E. (2022). Developing insulating polymeric foams: Strategies and research needs from a circular economy perspective. Materials, 15(18), Article 6212. https://doi.org/10.3390/ma15186212

Tirkolaee, E. B., Goli, A., & Mirjalili, S. (2022). Circular economy application in designing sustainable medical waste management systems. Environmental Science and Pollution Research, 29(53), 79667–79668. https://doi.org/10.1007/s11356-022-20740-x

Enikolopov, R., Makarin, A., & Petrova, M. (2020). Social media and protest participation: Evidence from Russia. Econometrica, 88(4), 1479–1514. https://doi.org/10.3982/ECTA14281

Ernst, A., & Briegel, R. (2017). A dynamic and spatially explicit psychological model of the diffusion of green electricity across Germany. Journal of Environmental Psychology, 52, 183–193. https://doi.org/10.1016/j.jenvp.2016.12.003

Esposito, M., Tse, T., & Soufani, K. (2017). Is the circular economy a new fast-expanding market? Thunderbird International Business Review, 59(1), 9–14. https://doi.org/10.1002/tie.21764

Fan, R. G., Dong, L. L., Yang, W. G., & Sun, J. Q. (2017). Study on the optimal supervision strategy of government low-carbon subsidy and the corresponding efficiency and stability in the small-world network context. Journal of Cleaner Production, 168, 536–550. https://doi.org/10.1016/j.jclepro.2017.09.044

Fang, B. X., & Jia, Y. (2017). Online social network analysis. Publishing House of Electronics Industry, Beijing.

Feng, Z. J., Chen, Z. N., Cai, H. C., & Yang, Z. L. (2022). Evolution and influencing factors of the green development spatial association network in the Guangdong-Hong Kong-Macao greater bay area. Technological and Economic Development of Economy, 28(3), 716–742. https://doi.org/10.3846/tede.2022.16618

Freeman, L. C. (2004). The development of social network analysis: A study in the sociology of science. Empirical Press.

Ghinoi, S., Silvestri, F., & Steiner, B. (2020). The role of local stakeholders in disseminating knowledge for supporting the circular economy: A network analysis approach. Ecological Economics, 169, Article 106446. https://doi.org/10.1016/j.ecolecon.2019.106446

Goldenberg, J., Libai, B., & Muller, E. (2001). Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, 12(3), 211–223. https://doi.org/10.1023/A:1011122126881

Gong, P., Wang, L., Liu, X. L., & Wei, Y. G. (2022). The value of social media tool for monitoring and evaluating environment policy communication: A case study of the ‘Zero-waste City’ initiative in China. Energy Ecology and Environment, 7(6), 614–629. https://doi.org/10.1007/s40974-022-00251-8

Goli, A., & Mohammadi, H. (2022). Developing a sustainable operational management system using hybrid Shapley value and Multimoora method: Case study petrochemical supply chain. Environment, Development and Sustainability, 24(9), 10540–10569. https://doi.org/10.1007/s10668-021-01844-9

Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 83(6), 1420–1443. https://doi.org/10.1086/226707

He, P., Lovo, S., & Veronesi, M. (2022). Social networks and renewable energy technology adoption: Empirical evidence from biogas adoption in China. Energy Economics, 106, Article 105789. https://doi.org/10.1016/j.eneco.2021.105789

Hsu, M. F., Chang, T. M., & Lin, S. J. (2020). News-based soft information as a corporate competitive advantage. Technological and Economic Development of Economy, 26(1), 48–70. https://doi.org/10.3846/tede.2019.11328

Huber, P. (1802). Observations on several species of the genus Apis, known by the name of humble bees, and called Bombinatrices by Linneaus. Transactions of the Linnean Society of London, 6, 214–298. https://doi.org/10.1111/j.1096-3642.1802.tb00484.x

Jokar, E., Mosleh, M., & Kheyrandish, M. (2022). GWBM: An algorithm based on grey wolf optimization and balanced modularity for community discovery in social networks. Journal of Supercomputing, 78(5), 7354–7377. https://doi.org/10.1007/s11227-021-04174-9

Khalili, S., & Breyer, C. (2022). Review on 100% renewable energy system analyses-a bibliometric perspective. IEEE Access, 10, 125792–125834. https://doi.org/10.1109/ACCESS.2022.3221155

King, S., Lusher, D., Hopkins, J., & Simpson, G. W. (2020). Industrial symbiosis in Australia: The social relations of making contact in a matchmaking marketplace for SMEs. Journal of Cleaner Production, 270, Article 122146. https://doi.org/10.1016/j.jclepro.2020.122146

Kumar, P., & Sinha, A. (2021). Information diffusion modeling and analysis for socially interacting networks. Social Network Analysis and Mining, 11(1), 11. https://doi.org/10.1007/s13278-020-00719-7

Leipold, S., & Petit-Boix, A. (2018). The circular economy and the bio-based sector – Perspectives of European and German stakeholders. Journal of Cleaner Production, 201, 1125–1137. https://doi.org/10.1016/j.jclepro.2018.08.019

Lieder, M., Asif, M. A., & Rashid, A. (2017). Towards Circular Economy implementation: An agent-based simulation approach for business model changes. Autonomous Agents and Multi-agent Systems, 31(6), 1377–1402. https://doi.org/10.1007/s10458-017-9365-9

Li, F. Y., Cao, X., & Ou, R. (2021). A network-based evolutionary analysis of the diffusion of cleaner energy substitution in enterprises: The roles of PEST factors. Energy Policy, 156, Article 112385. https://doi.org/10.1016/j.enpol.2021.112385

Luo, Y. Y., Yang, Z., Liang, Y., Zhang, X. X., & Xiao, H. (2022). Exploring energy-saving refrigerators through online e-commerce reviews: An augmented mining model based on machine learning methods. Kybernetes, 51(9), 2768–2794. https://doi.org/10.1108/K-11-2020-0788

Mansoureh, N., Hossein, F. Z. M., & Susan, B. (2022). A multilayer general type-2 fuzzy community detection model in large-scale social networks. IEEE Transactions on Fuzzy Systems, 30(10), 4494–4503. https://doi.org/10.1109/TFUZZ.2022.3153745

Marques, L., & Manzanares, M. D. (2022). Towards social network metrics for supply network circularity. International Journal of Operations & Production Management. https://doi.org/10.1108/IJOPM-02-2022-0139

Mirzynska, A., Kosch, O., Schieg, M., Suhajda, K., & Szarucki, M. (2021). Exploring concomitant concepts in the discussion on the circular economy: A bibliometric analysis of Web of Science, Scopus and Twitter. Technological and Economic Development of Economy, 27(6), 1539–1562. https://doi.org/10.3846/tede.2021.15801

Moreno, J. L. (1934). Who shall survive? Nervous and Mental Disease Publishing Company.

Nadar, E., Kaya, B. E., & Guler, K. (2020). New-product diffusion in closed-loop supply chains. Manufacturing & Service Operations Management, 23(6), 1413–1430. https://doi.org/10.1287/msom.2019.0864

Niang, A., Torre, A., & Bourdin, S. (2022). Territorial governance and actors’ coordination in a local project of anaerobic digestion. A social network analysis. European Planning Studies, 30(7), 1251–1270. https://doi.org/10.1080/09654313.2021.1891208

Nobre, G. C., & Tavares, E. (2017). Scientific literature analysis on big data and internet of things applications on circular economy: A bibliometric study. Scientometrics, 111(1), 463–492. https://doi.org/10.1007/s11192-017-2281-6

Oliveira, M., & Gama, J. (2012). An overview of social network analysis. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, 2(2), 99–115. https://doi.org/10.1002/widm.1048

Ozkan-Ozen, Y. D., Kazancoglu, Y., & Mangla, S. K. (2020). Synchronized barriers for circular supply chains in industry 3.5/industry 4.0 transition for sustainable resource management. Resources Conservation and Recycling, 161, Article 104986. https://doi.org/10.1016/j.resconrec.2020.104986

Patwa, N., Sivarajah, U., Seetharaman, A., Sarkar, S., Maiti, K., & Hingorani, K. (2020). Towards a circular economy: An emerging economies context. Journal of Business Research, 122, 725–735. https://doi.org/10.1016/j.jbusres.2020.05.015

Pearce, D. W., & Turner, R. H. (1990). Economics of natural resources and the environment. Harvester Wheatsheaf.

Radcliffe-Brown, A. R. (1940). On social structure. Journal of the Royal Anthropological Institute of Great Britain and Ireland, 7, 1–12. https://doi.org/10.2307/2844197

Sabour, M. R., Alam, E., & Hatami, A. M. (2020). Global trends and status in landfilling research: A systematic analysis. Journal of Material Cycles and Waste Management, 22(3), 711–723. https://doi.org/10.1007/s10163-019-00968-5

Saranya, S., & Usha, G. (2023). A machine learning-based technique with intelligent wordnet lemmatize for Twitter sentiment analysis. Intelligent Automation and Soft Computing, 36(1), 339–352. https://doi.org/10.32604/iasc.2023.031987

Schraven, D., Bukvic, U., Di Maio, F., & Hertogh, M. (2019). Circular transition: Changes and responsibilities in the Dutch stony material supply chain. Resources Conservation and Recycling, 150, Article 104359. https://doi.org/10.1016/j.resconrec.2019.05.035

Shahidzadeh, M. H., & Shokouhyar, S. (2022). Shedding light on the reverse logistics’ decision-making: A social-media analytics study of the electronics industry in developing vs developed countries. International Journal of Sustainable Engineering, 15(1), 163–178. https://doi.org/10.1080/19397038.2022.2101706

Shashi, Centobelli, P., Cerchione, R., & Mittal, A. (2021). Managing sustainability in luxury industry to pursue circular economy strategies. Business Strategy and the Environment, 30(1), 432–462. https://doi.org/10.1002/bse.2630

Shen, K. W., Li, L., & Wang, J. Q. (2020). Circular economy model for recycling waste resources under government participation: A case study in industrial waste water circulation in China. Technological and Economic Development of Economy, 26(1), 21–47. https://doi.org/10.3846/tede.2019.11249

Stewart, R., & Niero, M. (2018). Circular economy in corporate sustainability strategies: A review of corporate sustainability reports in the fast-moving consumer goods sector. Business Strategy and the Environment, 27(7), 1005–1022. https://doi.org/10.1002/bse.2048

Suchek, N., Fernandes, C. I., Kraus, S., Filser, M., & Sjogren, H. (2021). Innovation and the circular economy: A systematic literature review. Business Strategy and the Environment, 30(8), 3686–3702. https://doi.org/10.1002/bse.2834

Sun, Y. L., Jia, J. S., Ju, M., & Chen, C. D. (2022). Spatiotemporal dynamics of direct carbon emission and policy implication of energy transition for China’s residential consumption sector by the methods of social network analysis and geographically weighted regression. Land, 11(7), Article 1039. https://doi.org/10.3390/land11071039

Sweet, T. M., & Adhikari, S. (2022). A hierarchical latent space network model for mediation. Network Science, 10(2), 113–130. https://doi.org/10.1017/nws.2022.12

Tabassum, S., Gama, J., Azevedo, P. J., Cordeiro, M., Martins, C., & Martins, A. (2022). Social network analytics and visualization: Dynamic topic-based influence analysis in evolving micro-blogs. Expert Systems. https://doi.org/10.1111/exsy.13195

Tian, D., Zhang, M., Zhao, A. P., Wang, B., Shi, J., & Feng, J. Y. (2021). Agent-based modeling and simulation of edible fungi growers? adoption behavior towards fungal chaff recycling technology. Agricultural Systems, 190, Article 103138. https://doi.org/10.1016/j.agsy.2021.103138

Van Vlasselaer, V., Eliassi-Rad, T., Akoglu, L., Snoeck, M., & Baesens, B. (2017). GOTCHA! Network-based fraud detection for social security fraud. Management Science, 63(9), 3090–3110. https://doi.org/10.1287/mnsc.2016.2489

Venegas, C., Sanchez-Alfonso, A. C., Vesga, F. J., Martin, A., Celis-Zambrano, C., & Mendez, M. G. (2022). Identification and evaluation of determining factors and actors in the management and use of biosolids through prospective analysis (micmac and mactor) and social networks. Sustainability, 14(11), Article 6840. https://doi.org/10.3390/su14116840

Verbong, G. & Geels, F. (2007). The ongoing energy transition: Lessons from a socio-technical, multi-level analysis of the Dutch electricity system (1960–2004). Energy Policy, 35(2), 1025–1037. https://doi.org/10.1016/j.enpol.2006.02.010

Watanabe, N. M., Kim, J., & Park, J. (2021). Social network analysis and domestic and international retailers: An investigation of social media networks of cosmetic brands. Journal of Retailing and Consumer Services, 58, Article 102301. https://doi.org/10.1016/j.jretconser.2020.102301

Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442. https://doi.org/10.1038/30918

Wu, D., Yang, R. X., & Shen, C. (2021). Sentiment word co-occurrence and knowledge pair feature extraction based LDA short text clustering algorithm. Journal of Intelligent Information Systems, 56(1), 1–23. https://doi.org/10.1007/s10844-020-00597-7

Wu, M. F., Long, R. Y., Chen, F. Y., Chen, H., Bai, Y., Cheng, K., & Huang, H. (2023). Spatio-temporal difference analysis in climate change topics and sentiment orientation: Based on LDA and BiLSTM model. Resources Conservation and Recycling, 188, 106697. https://doi.org/10.1016/j.resconrec.2022.106697

Wurster, S., & Reis, C. F. D. (2022). Priority products for sustainability information and recommendation software: Insights in the context of the EU’s action plan circular economy. Sustainability, 14(19), Article 11951. https://doi.org/10.3390/su141911951

Xu, H. L., Feng, L. Y., Wu, G., & Zhang, Q. (2021). Evolution of structural properties and its determinants of global waste paper trade network based on temporal exponential random graph models. Renewable & Sustainable Energy Reviews, 149, Article 111402. https://doi.org/10.1016/j.rser.2021.111402

Xu, J., & Qiang, Y. (2022). Analysing information diffusion in natural hazards using retweets-a case study of 2018 winter storm Diego. Annals of GIS, 28(2), 213–227. https://doi.org/10.1080/19475683.2021.1954086

Yan, J. Y., & Xu, M. (2021). Energy and circular economy in sustainability transitions. Resources Conservation and Recycling, 169, Article 105471. https://doi.org/10.1016/j.resconrec.2021.105471

Yoo, K., & Blumsack, S. (2018). The political complexity of regional electricity policy formation. Complexity, Article 3493492. https://doi.org/10.1155/2018/3493492

Yuille, A., Rothwell, S., Blake, L., Forber, K. J., Marshall, R., Rhodes, R., Waterton, C., & Withers, P. J. A. (2022). UK government policy and the transition to a circular nutrient economy. Sustainability, 14(6), Article 3310. https://doi.org/10.3390/su14063310

Zarrabeitia-Bilbao, E., Morales-i-Gras, J., Rio-Belver, R. M., & Garechana-Anacabe, G. (2022). Green energy: Identifying development trends in society using Twitter data mining to make strategic decisions. Profesional De La Informacion, 31(1), Article 310114. https://doi.org/10.3145/epi.2022.ene.14

Zheng, J. J., Ma, G., Wei, J., Wei, W. D., He, Y. J., Jiao, Y. Y., & Han, X. (2020). Evolutionary process of household waste separation behavior based on social networks. Resources Conservation and Recycling, 161, Article 105009. https://doi.org/10.1016/j.resconrec.2020.105009