Skip to main content

A System Dynamics-Based Simulation Model to Analyze Consumers’ Behavior Based on Participatory Systems Mapping – A “Last Mile” Perspective

  • Chapter
  • First Online:
Innovative Logistics Services and Sustainable Lifestyles

Abstract

The complexity of the term sustainability is encouraging both policy makers and industry, to expand their methodology of solving environmental, social, and economic issues. In the field of applied science, sustainability-related research is thematic and policy driven; therefore involving the widest possible range of stakeholders is of importance. High uncertainty problems and high-risk decisions such as sustainability-related topics are difficult to analyze and solve with conventional scientific approaches and tools. Accordingly, discrete, simple, and short-term systems regarding one specific problem are increasingly being replaced by dynamic, complex, long-term, real-time, interdisciplinary models. This peculiarity requires decision-makers to have a system thinking approach. Participatory systems mapping (PSM) is, in this context, a methodology in which a structured process is used to design cause-and-effect relationships between different factors and elements in a defined system. It provides a multi-perspectival understanding of problems and can help to formulate effective policies for complex sustainability issues. This will be represented, in a first instance, as a causal loop diagram (CLD) and, subsequently, as a stock and flow diagram (SFD) which is an equation-based system dynamics (SD) modeling technique. This will be of assistance in developing strategies and recommendations for the food industry, where consumers are creating a dynamic environment through quickly adapting their consumption habits which are currently characterized by a growing demand for sustainable food production. As a result, this increasing importance of local and organic food logistics networks has a direct impact on the last mile and its sustainability performance. Therefore, the present study intends to contribute to the understanding of the system dynamics in local food logistics networks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 119.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Armendáriz, V., Armenia, S., & Atzori, A. S. (2016). A systemic analysis of food supply and distribution systems in city-region systems - An examination of FAO’s policy guidelines towards sustainable agri-food systems. Agriculture, 6(4), 65.

    Google Scholar 

  • Bass, F. M. (1969). A new product growth for model consumer durables. Management Science, 15(5), 215–227.

    Google Scholar 

  • Binder, T., Vox, A., Belyazid, S., Haraldsson, H., & Svensson, M. (2004). Developing system dynamics models from causal loop diagrams. In Presented at the 22nd International Conference of the System Dynamics Society, Oxford, UK.

    Google Scholar 

  • Bogdanski, R. (2015). Nachhaltige Stadtlogistik durch Kurier-, Express- und Paketdienste. Berlin, Germany: Bundesverband Paket und Expresslogistik e.V.

    Google Scholar 

  • Bohlmann, J., Calantone, R., & Zhao, M. (2010). The effects of market network heterogeneity on innovation diffusion: An agent-based modeling approach. Journal of Product Innovation Management, 27(5), 741–760.

    Google Scholar 

  • Bone, P. F. (1995). Word-of-mouth effects on short-term and long-term product judgments. Journal of Business Research, 32(3), 213–223.

    Google Scholar 

  • Bowersox, D., & Daugherty, P. (1995). Logistics paradigms: The impact of information technology. Journal of Business Logistics, 16(1), 65–80.

    Google Scholar 

  • Chandrasekaran, D., & Tellis, G. J. (2015). A critical review of marketing research on diffusion of new products. In K. Malhotra (Ed.), Review of marketing research (pp. 39–80). Bingley, UK: Emerald Group Publishing Limited.

    Google Scholar 

  • Chatterjee, P. (2001). Online reviews: Do consumers use them? In M. C. Gilly & J. Meyers-Levy (Eds.), Advances in consumer research (Vol. 28, pp. 129–133). Valdosta, GA: Association for Consumer Research.

    Google Scholar 

  • Chatterjee, R., & Eliashberg, J. (1990). The innovation diffusion process in the heterogeneous population: A micromodelling approach. Management Science, 36(9), 1057–1079.

    Google Scholar 

  • Chen, Y., & Xie, J. (2005). Third-party product review and firm marketing strategy. Marketing Science, 24(2), 218–240.

    Google Scholar 

  • Chevalier, J., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345–354.

    Google Scholar 

  • Coppini, M., Rossignoli, C., Rossi, T., & Strozzi, F. (2010). Bullwhip effect and inventory oscillations analysis using the beer game model. International Journal of Production Research, 48, 3943–3956.

    Google Scholar 

  • Coyle, R. G. (1996). System dynamics modelling: A practical approach. London, UK: CRC Press.

    Google Scholar 

  • Daneshpour, H., & Takala, J. (2016). The key drivers of sustainability. In IEEE International Conference on Industrial Engineering and Engineering Management 2016-December, 7798069 (pp. 1205–1209).

    Google Scholar 

  • Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. (2007). Developing theory through simulation methods. Academy of Management Review, 32(2), 480–499.

    Google Scholar 

  • Dellarocas, C. (2003). The digitalization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407–1424.

    Google Scholar 

  • Disney, S. M., Potter, A. T., & Gardner, B. M. (2003). The impact of vendor management inventory on transport operations. Transportation Research Part E: Logistics and Transportation Review, 39, 363–380.

    Google Scholar 

  • Dowlatshahi, S. (2010). A cost-benefit analysis for the design and implementation of reverse logistics systems: Case studies approach. International Journal of Production Research, 48(5), 1361–1380.

    Google Scholar 

  • Esper, T. L., Jensen, T. D., Turnipseed, F. L., & Burton, S. (2003). The last mile: An examination of effects of online retail delivery strategies on consumers. Journal of Business Logistics, 24(2), 177–203.

    Google Scholar 

  • Esser, K., & Kurte, J. (2014). Wirtschaftliche Bedeutung der KEP-Branche - Die Kurier-, Express- und Paketbranche in Deutschland. Berlin: Studie im Bundesverband Paket und Expresslogistik e.V.

    Google Scholar 

  • Farag S. (2006). E-shopping and its interactions with in-store shopping. PhD Thesis, Urban and Regional research center Utrecht, Faculty of Geosciences, Utrecht University.

    Google Scholar 

  • Flynn, B. B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management, 28(1), 58–71.

    Google Scholar 

  • Forrester, J. W. (1968). Principles of systems. Cambridge, UK: MIT Press.

    Google Scholar 

  • Forrester, J. W. (1977). Industrial dynamics. Cambridge, UK: MIT Press.

    Google Scholar 

  • Forrester, J. W. (1992). Policies, decision and information sources for modeling. European Journal of Operational Research, 59, 42–63.

    Google Scholar 

  • Garcia, R. (2005). Uses of agent-based modeling in innovation/new product development research. Journal of Product Innovation and Management, 22(5), 380–398.

    Google Scholar 

  • Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545–560.

    Google Scholar 

  • Gruchmann, T., Schmidt, I., & Pyankova, V. (2016). How logistics services can facilitate sustainable lifestyles – An explorative study. In EurOMA conference Proceedings.

    Google Scholar 

  • Gudehus, T., & Kotzab, H. (2012). Task and aspects of modern logistics. In Comprehensive logistics. Berlin, Germany: Springer.

    Google Scholar 

  • Haraldsson, H. V., & Sverdrup, H. (2003). Finding simplicity in complexity in biogeochemical modelling. In J. Wainwright & M. Mulligan (Eds.), Environmental modelling: Finding simplicity in complexity (pp. 211–213). New York, NY: Wiley.

    Google Scholar 

  • Harrison-Walker, L. J. (2001). The measurement of word-of-mouth communication and an investigation of service quality and customer commitment as potential antecedents. Journal of Service Research, 4(1), 60–75.

    Google Scholar 

  • Helmke, C. (2005). Der Markt für Paket- und Expressdienste – Eine Studie zu Kundenzufriedenheit und Kundenbindung im Markt für Paket- und Expressdienste. PhD Thesis, Fachbereich Wirtschaftswissenschaften, Universität Kassel.

    Google Scholar 

  • Henschel, S. (2001). Standortfaktoren im elektronischen Einzelhandel. In Berichte des Arbeitskreises Geographische Handelsforschung (Vol. 10, pp. 23–25).

    Google Scholar 

  • Jackson, M. C. (2003). Systems thinking: Creative holism for managers. Chichester, UK: John Wiley & Sons Ltd.

    Google Scholar 

  • Kille, C., & Schwemmer, M. (2012). Die Top 100 der Logistik. Hamburg, Germany: DVV Media Group.

    Google Scholar 

  • Király, G., Köves, A., Pataki, G., & Kiss, G. (2016). Assessing the participatory potential of system mapping. Systems Research and Behavioral Science, 33(4), 496–514.

    Google Scholar 

  • Klaus, P., Kille, C., & Schwemmer, M. (2011). TOP 100 in European transport and logistics services (4th ed.). Hamburg, Germany: DVV Media Group.

    Google Scholar 

  • Kumar, S., & Nigmatullin, A. (2011). A system dynamics analysis of food supply chains – Case study with non-perishable products. Simulation Modelling Practice and Theory, 19, 2151–2168.

    Google Scholar 

  • Maloni, M. J., & Brown, M. E. (2006). Corporate social responsibility in the supply chain: An application in the food industry. Journal of Business Ethics, 68(1), 35–52.

    Google Scholar 

  • Mansfield, E. (1961). Technical change and the rate of imitation. Econometrica, 29(4), 741–766.

    Google Scholar 

  • Meadows, D., Meadows, D. L., & Randers, J. (1992). Beyond the limits: Global collapse or a sustainable future. London, UK: Earthscan.

    Google Scholar 

  • Melkonyan, A., Krumme, K., Gruchmann, T., & De La Torre, G. (2017). Sustainability assessment and climate change resilience in food production and supply. Energy Procedia, 123, 131–138.

    Google Scholar 

  • Minegishi, S., & Thiel, D. (2000). System dynamics modeling and simulation of a particular food supply chain. Simulation Practice and Theory, 8, 321–339.

    Google Scholar 

  • Morecroft, J. D. (1992). Executive knowledge, models and learning. European Journal of Operational Research, 59(1), 9–27.

    Google Scholar 

  • Murray, K. B., & Schlater, J. L. (1990). The impact of services versus goods on consumers’ assessment of perceived risk. Journal of the Academy of Marketing Science, 18(1), 51–65.

    Google Scholar 

  • Nicolò, D., & Jean-Vasile, A. (2016). Sustainable entrepreneurship and investments in the green economy (First ed.). Hershey, PA: IGI Global.

    Google Scholar 

  • Özbayrak, M., Papadopoulou, T. C., & Akgun, M. (2007). Systems dynamics modeling of a manufacturing supply chain system. Simulation Modelling Practice and Theory, 15, 1338–1355.

    Google Scholar 

  • Petermann, T. (2001). Innovationsbedingungen des E-Commerce – das Beispiel Produktion und Logistik. Büro für Technikfolgen - Abschätzung beim Deutschen Bundestag. Hintergrundpapier Nr. 6.

    Google Scholar 

  • Popp, M., & Rauh, J. (2003). Standortfragen im Zeitalter des E-Commerce. In D. Ducar & J. Rauh (Eds.), E-Commerce: Perspektiven für Forschung und Praxis (pp. 47–61). Passau, Germany: Geographische Handelsforschung.

    Google Scholar 

  • Punakivi, M., Yrjola, H., & Holmstrom, J. (2001). Solving the last mile issue - Reception box or delivery box. International Journal of Physical Distribution & Logistics Management, 31(6), 427–439.

    Google Scholar 

  • Radas, S. (2005). Diffusion models in marketing: How to incorporate the effect of external influence. Economic Trends and Economic Policy, 15, 30–51.

    Google Scholar 

  • Rahdari, A. H. (2017). Fostering responsible business: Evidence from leading corporate social responsibility and sustainability networks. In M. Camilleri (Ed.), CSR 2.0 and the New Era of Corporate Citizenship (pp. 309–330). Hershey, PA: IGI Global.

    Google Scholar 

  • Randers, J. (1980). In J. Randers (Ed.), Guidelines for model conceptualization, elements of the system dynamics method (pp. 117–139). Cambridge, UK: Productivity Press.

    Google Scholar 

  • Saad, N., Kadirkamanathan, V., & Bennett, S. (2003). A discrete-event simulation model for analysis of supply chain dynamics, Computers in Industry. Amsterdam, Netherlands: Elsevier Science.

    Google Scholar 

  • Salehi, F., Ryssel, L., Doll, D. (2011). Internationales Segment wächst stärker als Inlandsmarkt. A.T. Kearney-Studie untersucht europäischen Markt für Kurier-, Express- und Paketdienste. http://www.atkearney.de/content/veroeffentlichungen/whitepaper_detail.php/id/51719/practice/transportation

  • Sedlacko, M., Martinuzzi, A., Røpke, I., Videira, N., & Antunes, P. (2014). Participatory systems mapping for sustainable consumption: Discussion of a method promoting systemic insights. Ecological Economics, 106, 33–43.

    Google Scholar 

  • Statistik Austria (2018). Ergebnisse im Überblick: Privathaushalte 1985–2017. Retrieved February 26, 2018, from http://www.statistik.at/web_de/statistiken/menschen_und_gesellschaft/bevoelkerung/haushalte_familien_lebensformen/haushalte/index.html

  • Sterman, J. (2000). Business dynamics: Systems thinking and modeling for a complex world. Boston, MA: McGraw-Hill.

    Google Scholar 

  • Sterman, J. D. (2006). Learning from evidence in a complex world. American Journal of Public Health, 96(3), 505–514.

    Google Scholar 

  • Straube, F., & Pfohl, H. C. (2008). Trends und Strategien in der Logistik – Globale Netzwerke im Wandel. Bremen, Germany: DVV.

    Google Scholar 

  • Tako, A. A., & Robinson, S. (2012). The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decision Support Systems, 52(4), 802–815.

    Google Scholar 

  • Wang, W., Cheong, F. (2005). A Framework for the System Dynamics (SD) Modelling of the Mobile Commerce Market, Proceedings of the International Congress on Modelling and Simulation - Advances and Applications for Management and Decision Making (MODSIM 2005), Melbourne, Australia, 12–15 December 2005, Modelling and Simulation Society of Australia and New Zealand Inc. http://www.mssanz.org.au/modsim05/ 1787–1793.

  • Ward, J. C., & Reingen, P. H. (1990). Sociocognitive analysis of group decision making among consumers. Journal of Consumer Research, 17, 245–263.

    Google Scholar 

  • Wolstenholme, E. F. (1990). System enquiry. A system dynamics approach. Chichester, UK: John Wiley & Sons.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gustavo De La Torre .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

De La Torre, G., Gruchmann, T., Kamath, V., Melkonyan, A., Krumme, K. (2019). A System Dynamics-Based Simulation Model to Analyze Consumers’ Behavior Based on Participatory Systems Mapping – A “Last Mile” Perspective. In: Melkonyan, A., Krumme, K. (eds) Innovative Logistics Services and Sustainable Lifestyles. Springer, Cham. https://doi.org/10.1007/978-3-319-98467-4_8

Download citation

Publish with us

Policies and ethics