Abstract
To effectively address environmental and social issues such as climate change and sustainability, it is necessary to build a conceptual model that allows stakeholders with different perceptions and values to reach a consensus and make decisions toward achieving the desired goals. The construction of conceptual models using participatory modeling methods involves exploring psychosocial factors that cause stakeholders to act, and physical variables such as people’s activities and environmental events. However, many of these processes rely heavily on qualitative data obtained from interviews. This results in a lack of modeling transparency and excessive complexity. Psychosocial factors also tend to be treated as exogenous variables. In this study, we propose a participatory modeling method that uses qualitative interview data and quantifiable keyword search data to explore the psychosocial factors that should be incorporated into the conceptual model. These psychosocial factors are key components of stakeholders’ mental models. This method identifies characteristic search queries by considering changes in the quantity and time series of search queries, and combines interview data and distinct search queries to create a causal loop diagram in collaboration with stakeholders. Incorporating psychosocial factors as endogenous variables into the conceptual model increases the model’s reliability and enables an understanding of the potential for complex nonlinear dynamics across social and environmental dimensions. We examined the applicability of this process using a case study that explored changes in eating habits and intervention points in Tokyo before and after the COVID-19 pandemic.
References
Argent, R. M., Sojda, R. S., Guipponi, C., McIntosh, B., Voinov, A. A., & Maier, H. R. (2016). Best practices for conceptual modelling in environmental planning and management. Environmental Modelling and Software, 80, 113–121. https://doi.org/10.1016/j.envsoft.2016.02.023
Asif, M., Inam, A., Adamowski, J., Shoaib, M., Tariq, H., Ahmad, S., Alizadeh, M. R., & Nazeer, A. (2023). Development of methods for the simplification of complex group built causal loop diagrams: A case study of the Rechna doab. Ecological Modelling, 476, 110192. https://doi.org/10.1016/j.ecolmodel.2022.110192
Bosch, O., & Nguyen, N. C. (2015). Systems thinking for everyone: The journey from theory to making an impact. Think2Impact.
Chang, C. C., & Huang, M. H. (2020). Antecedents predicting health information seeking: A systematic review and meta-analysis. International Journal of Information Management, 54, 102115. https://doi.org/10.1016/j.ijinfomgt.2020.102115
Chisty, M. A., Islam, M. A., Munia, A. T., Rahman, M. M., Rahman, N. N., & Mohima, M. (2021). Risk perception and information-seeking behavior during emergency: An exploratory study on COVID-19 pandemic in Bangladesh. International Journal of Disaster Risk Reduction, 65, 102580. https://doi.org/10.1016/j.ijdrr.2021.102580
Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88(s1), 2–9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
Downs, A. (1972). Up and down with ecology: The “issue-attention cycle”, Public Interest, 28(Summer).
Doyle, J. K., & Ford, D. N. (1998). Mental models concepts for system dynamics research. System Dynamics Review, 14(1), 3–29. https://doi.org/10.1002/(SICI)1099-1727(199821)14:1<3::AID-SDR140>3.0.CO;2-K
Elsawah, S., Pierce, S. A., Hamilton, S. H., van Delden, H., Haase, D., Elmahdi, A., & Jakeman, A. J. (2017). An overview of the system dynamics process for integrated modelling of socio-ecological systems: Lessons on good modelling practice from five case studies. Environmental Modelling and Software, 93, 127–145. https://doi.org/10.1016/j.envsoft.2017.03.001
Etienne, M., du Toit, D. R., & Pollard, S. (2011). ARDI: A co-construction method for participatory modeling in natural resources management. Ecology and Society, 16(1). https://doi.org/10.5751/ES-03748-160144
Food and Agriculture Organization of the United Nations (2019). The state of food and agriculture: Moving forward on food loss and waste reduction. https://www.fao.org/3/ca6030en/ca6030en.pdf
Forrester, J. W. (1961). Industrial dynamics. MIT Press.
Forrester, J. W. (1992). Policies, decisions and information sources for modeling. European Journal of Operational Research, 59(1), 42–63. https://doi.org/https://doi.org/10.1016/0377-2217(92)90006-U
Garcia, J. M. (2018). System dynamic modelling with Vensim. Independently published.
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–1014. https://doi.org/10.1038/nature07634
Gupta, H. V., Clark, M. P., Vrugt, J. A., Abramowitz, G., & Ye, M. (2012). Towards a comprehensive assessment of model structural adequacy. Water Resources Research, 48(8). https://doi.org/10.1029/2011WR011044
Holt, D., & Barkemeyer, R. (2012). Media coverage of sustainable development issues – attention cycles or punctuated equilibrium? Sustainable Development, 20(1), 1–17. https://doi.org/https://doi.org/10.1002/sd.460
Huang, J. H., Floyd, M. F., Tateosian, L. G., & Hipp, J. A. (2022). Exploring public values through Twitter data associated with urban parks pre- and post-COVID-19. Landscape and Urban Planning, 227. https://doi.org/10.1016/j.landurbplan.2022.104517
Iranmanesh, M., Ghobakhloo, M., Nilashi, M., Tseng, M. L., Senali, M. G., & Abbasi, G. A. (2022). Impacts of the COVID-19 pandemic on household food waste behaviour: A systematic review. Appetite, 176. https://doi.org/10.1016/j.appet.2022.106127
Jones, N. A., Ross, H., Lynam, T., Perez, P., & Leitch, A. (2011). Mental models: An interdisciplinary synthesis of theory and methods. Ecology and Society, 16(1). https://doi.org/10.5751/ES-03802-160146
Komaki, A., Kodaka, A., Nakamura, E., Ohno, Y., & Kohtake, N. (2021). System design canvas for identifying leverage points in complex systems: A case study of the agricultural system models, Cambodia. Proceedings of the Design Society, 1, 2901 – 2910. https://doi.org/10.1017/pds.2021.551
Kurian, S. J., Bhatti, A. U. R., Alvi, M. A., Ting, H. H., Storlie, C., Wilson, P. M., Shah, N. D., Liu, H., & Bydon, M. (2020). Correlations between COVID-19 cases and Google Trends data in the United States: A state-by-state analysis. Mayo Clinic Proceedings, 95(11), 2370–2381. https://doi.org/10.1016/j.mayocp.2020.08.022
Lamy, E., Viegas, C., Rocha, A., Lucas, M. R., Tavares, S., Capela e Silva, F., Guedes, D., Laureati, M., Zian, Z., Machado, A. S., Ellssel, P., Freyer, B., González-Rodrigo, E., Calzadilla, J., Majewski, E., Prazeres, I., Silva, V., Juračak, J., Platilová Vorlíčková, L., … Anzman-Frasca, S. (2022). Changes in food behavior during the first lockdown of COVID-19 pandemic: A multi-country study about changes in eating habits, motivations, and food-related behaviors. Food Quality and Preference, 99. https://doi.org/10.1016/j.foodqual.2022.104559
Luštický, M., & Štumpf, P. (2021). Leverage points of tourism destination competitiveness dynamics. Tourism Management Perspectives, 38. https://doi.org/10.1016/j.tmp.2021.100792
Marchionini, G. (2006). Exploratory search: From finding to understanding. Communications of the ACM, 49(4), 41–46. https://doi.org/10.1145/1121949.1121979
Mayer, L. A., Loa, K., Cwik, B., Tuana, N., Keller, K., Gonnerman, C., Parker, A. M., & Lempert, R. J. (2017). Understanding scientists’ computational modeling decisions about climate risk management strategies using values-informed mental models. Global Environmental Change, 42, 107–116. https://doi.org/10.1016/j.gloenvcha.2016.12.007
Ministry of Agriculture, Forestry and Fisheries (2021). Publication of the amount of food loss. https://www.maff.go.jp/j/press/shokuhin/recycle/211130.html (accessed 26 December 2022).
Ministry of Health, Labour and Welfare. (2019). National Health and Nutrition Survey. https://www.mhlw.go.jp/stf/newpage_14156.html (Accessed October 1, 2024)
Mirchi, A., Madani, K., Watkins, D., & Ahmad, S. (2012). Synthesis of system dynamics tools for holistic conceptualization of water resources problems. Water Resources Management, 26(9), 2421–2442. https://doi.org/10.1007/s11269-012-0024-2
Nomura, S., Tanoue, Y., Yoneoka, D., Gilmour, S., Kawashima, T., Eguchi, A., & Miyata, H. (2021). Mobility patterns in different age groups in Japan during the COVID-19 pandemic: A small area time series analysis through March 2021. Journal of Urban Health, 98(5), 635–641. https://doi.org/10.1007/s11524-021-00566-7
Pluchinotta, I., Pagano, A., Vilcan, T., Ahilan, S., Kapetas, L., Maskrey, S., Krivtsov, V., Thorne, C., & O’Donnell, E. (2021). A participatory system dynamics model to investigate sustainable urban water management in Ebbsfleet Garden City. Sustainable Cities and Society, 67. https://doi.org/10.1016/j.scs.2021.102709
Pluchinotta, I., Salvia, G., & Zimmermann, N. (2022). The importance of eliciting stakeholders’ system boundary perceptions for problem structuring and decision-making. European Journal of Operational Research, 302(1), 280–293. https://doi.org/10.1016/j.ejor.2021.12.029
Purwanto, A., Sušnik, J., Suryadi, F. X., & de Fraiture, C. (2019). Using group model building to develop a causal loop mapping of the water-energy-food security nexus in Karawang Regency, Indonesia. Journal of Cleaner Production, 240. https://doi.org/10.1016/j.jclepro.2019.118170
Qian, K., Javadi, F., & Hiramatsu, M. (2020). Influence of the COVID-19 pandemic on household food waste behavior in Japan. Sustainability (Switzerland), 12(23), 1–14. https://doi.org/10.3390/su12239942
Reed, M. S., Graves, A., Dandy, N., Posthumus, H., Hubacek, K., Morris, J., Prell, C., Quinn, C. H., & Stringer, L. C. (2009). Who’s in and why? A typology of stakeholder analysis methods for natural resource management. Journal of Environmental Management, 90(5), 1933–1949. https://doi.org/10.1016/j.jenvman.2009.01.001
Robinson, S. (2008). Conceptual modelling for simulation Part I: Definition and requirements. Journal of the Operational Research Society, 59(3), 278–290. https://doi.org/10.1057/palgrave.jors.2602368
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98. https://www.inderscience.com/offers.php?id=17590
Sarkar, S., Mitsui, M., Liu, J., & Shah, C. (2020). Implicit information need as explicit problems, help, and behavioral signals. Information Processing and Management, 57(2). https://doi.org/10.1016/j.ipm.2019.102069
Sato, M., Mizuyama, H., Ogawa, M., Matsumoto, K., & Soneda, H. (2023). Changes in Consumer Eating Behaviors and Food Consciousness Due to COVID-19. Transactions of Japan Society of Kansei Engineering, 22(2), 135–145. https://doi.org/10.5057/jjske.tjske-d-22-00067
Schaffernicht, M. F. (2017). Causal attributions of vineyard executives – A mental model study of vineyard management. Wine Economics and Policy, 6(2), 107–135. https://doi.org/10.1016/j.wep.2017.09.002
Scott, R. J., Cavana, R. Y., & Cameron, D. (2016). Recent evidence on the effectiveness of group model building. European Journal of Operational Research, 249(3), 908–918. https://doi.org/10.1016/j.ejor.2015.06.078
Smith, N., Georgiou, M., King, A. C., Tieges, Z., & Chastin, S. (2022). Factors influencing usage of urban blue spaces: A systems-based approach to identify leverage points. Health and Place, 73. https://doi.org/10.1016/j.healthplace.2021.102735
Statistics Bureau of Japan (2020). Survey of household economy. https://www.stat.go.jp/english/index.html (accessed 26 December 2022).
Stave, K. (2010). Participatory system dynamics modeling for sustainable environmental management: Observations from four cases. Sustainability, 2(9), 2762–2784. https://doi.org/10.3390/su2092762
Villamor, G. B., Griffith, D. L., Kliskey, A., & Alessa, L. (2019). Contrasting stakeholder and scientist conceptual models of food-energy-water systems: A case study in Magic Valley, Southern Idaho. Socio-Environmental Systems Modelling, 2, 16312. https://doi.org/10.18174/sesmo.2020a16312
Voinov, A., & Bousquet, F. (2010). Modelling with stakeholders. Environmental Modelling & Software, 25(11), 1268–1281. https://doi.org/10.1016/j.envsoft.2010.03.007
Voinov, A., Jenni, K., Gray, S., Kolagani, N., Glynn, P. D., Bommel, P., Prell, C., Zellner, M., Paolisso, M., Jordan, R., Sterling, E., Schmitt Olabisi, L., Giabbanelli, P. J., Sun, Z., Le Page, C., Elsawah, S., BenDor, T. K., Hubacek, K., Laursen, B. K., … Smajgl, A. (2018). Tools and methods in participatory modeling: Selecting the right tool for the job. Environmental Modelling and Software, 109, 232–255. https://doi.org/10.1016/j.envsoft.2018.08.028
Wilson, T. D. (1999). Models in information behaviour research. Journal of Documentation, 55(3), 249–270. https://doi.org/10.1108/EUM0000000007145.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2025 Akinori Komaki, Mizuho Sato, Madoka Nakajima, Naohiko Kohtake