Skip to main content
SpringerLink
Log in

Modelling Customer Preference for Sustainability Information via Clustering Analysis

  • Chapter
  • First Online:
EcoDesign for Sustainable Products, Services and Social Systems II

  • 55 Accesses

Abstract

Individual purchasing behavior has substantial impact on the environment and our society. To encourage sustainable consumption, this paper explores the application of clustering analysis techniques for modelling customer preference for sustainability information. This study has analyzed sales data provided by a furniture company that covers a one-year period and 7602 customer accounts. The analysis focused on the purchases of office chairs. Clustering analysis was applied to build preference models of the customers. This study has identified 3 typical customer behavior signatures w.r.t. the sustainability categories used in a sustainability index. We have shown how these models can be used to predict new customers’ sustainability preferences. The stability of the proposed solutions has been studied by comparing the preference models generated on different product groups. The results can provide insights for designing sustainability communication strategies to attract potential customers.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
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

  • Arbin N, Suhaimi NS, Mokhtar NZ, Othman Z (2016) Comparative analysis between k-means and k-medoids for statistical clustering. In Proceedings—AIMS 2015, 3rd international conference on artificial intelligence, modelling and simulation

    Google Scholar 

  • Arora P, Deepali, Varshney S (2016) Analysis of K-means and K-medoids algorithm for big data. Proc Comput Sci 78:507–512

    Article  Google Scholar 

  • Chen D, Sain SL, Guo K (2012) Data mining for the online retail industry: a case study of RFM model-based customer segmentation using data mining. J Database Mark Cust Strateg Manag 19:197–208

    Article  Google Scholar 

  • Chicco G (2012) Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy 42(1):68–80

    Article  Google Scholar 

  • Cohen MA, Vandenbergh MP (2012) The potential role of carbon labeling in a green economy. Energy Econ 34:S53–S63

    Article  Google Scholar 

  • Dalal MA, Harale ND (2011) A survey on clustering in data mining. In International conference and workshop on emerging trends in technology 2011, ICWET 2011—conference proceedings

    Google Scholar 

  • Das H, Naik B, Behera HS (2020) An experimental analysis of machine learning classification algorithms on biomedical data. Lecture Notes Electrical Eng 602

    Google Scholar 

  • El Dehaibi N, Goodman ND, MacDonald EF (2019) Extracting customer perceptions of product sustainability from online reviews. J Mech Des 141(12)

    Google Scholar 

  • Faludi J et al (2020) A research roadmap for sustainable design methods and tools. Sustainability 12(19):8174

    Article  Google Scholar 

  • Flach P (2012) Machine learning: the art and science of algorithms that make sense of data. Cambridge University Press

    Book  Google Scholar 

  • Garima HG, Singh PK (2015) Clustering techniques in data mining: a comparison. In 2015 International conference on computing for sustainable global development, INDIACom 2015

    Google Scholar 

  • Hallstedt SI, Isaksson O, Rönnbäck AĂ– (2020) The need for new product development capabilities from digitalization, sustainability, and servitization trends. Sustainability 12

    Google Scholar 

  • Ho GTS, Ip WH, Lee CKM, Mou WL (2012) Customer grouping for better resources allocation using GA based clustering technique. Expert Syst Appl 39(2):1979–1987

    Article  Google Scholar 

  • Jiang Y, Zhang J (2014) Parallel K-Medoids clustering algorithm based on Hadoop. In Proceedings of the IEEE international conference on software engineering and service sciences, ICSESS

    Google Scholar 

  • Khan MS, Saengon P, Alganad AMN, Chongcharoen D, Farrukh M (2020) Consumer green behaviour: an approach towards environmental sustainability. Sustain Dev 28(5):1168–1180

    Article  Google Scholar 

  • Kwok SY, Hallstedt SI (2018) Towards strategic sustainable product development: challenges and opportunities for communicating sustainability in a value chain. In Proceedings of NordDesign: Design in the Era of Digitalization, NordDesign 2018

    Google Scholar 

  • Kwok SY, Harrison D, Qin S (2014) Designing an individualised eco information system: a conceptual framework. In 19th DMI: academic design management conference: design management in an era of disruption

    Google Scholar 

  • Kwok SY, Harrison D, Malizia A (2017) Designing individualisation of eco information: a conceptual framework and design toolkit. Int J Sustain Eng 7038(July):1–11

    Google Scholar 

  • Kwok SY, Hallstedt SI, Boeva V (2021) Understanding customer preference: outline of a new approach to prioritise sustainability product information. In Sustainable design and manufacturing 2020. Smart innovation, systems and technologies, Vol 200, Springer, Singapore, pp. 27–40.

    Google Scholar 

  • Liao SH, Chen YJ, Deng MY (2010) Mining customer knowledge for tourism new product development and customer relationship management. Expert Syst Appl 37(6):4212–4223

    Article  Google Scholar 

  • MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, pp. 281–297.

    Google Scholar 

  • Mancini P, Marchini A, Simeone M (2017) Which are the sustainable attributes affecting the real consumption behaviour? Consumer understanding and choices. Br Food J 119(8):1839–1853

    Article  Google Scholar 

  • Meise JN, Rudolph T, Kenning P, Phillips DM (2014) Feed them facts: value perceptions and consumer use of sustainability-related product information. J Retail Consum Serv 21(4):510–519

    Article  Google Scholar 

  • Saeed MA, Farooq A, Kersten W, Ben Abdelaziz SI (2019) Sustainable product purchase: does information about product sustainability on social media affect purchase behavior? Asian J Sustain Soc Responsib 4(1)

    Google Scholar 

  • Sangvikar B, Pawar A, Kolte A, Mainkar A, Sawant P (2019) How does green marketing influence consumers? The market trend examination towards environmentally sustainable products in emerging Indian cities. Int J Recent Technol Eng 8(3S):561–571

    Google Scholar 

  • Silva J, Varela N, LĂłpez LAB, Millán RHR (2019) Association rules extraction for customer segmentation in the SMES sector using the apriori algorithm. Proc Comput Sci 151:1207–1212

    Article  Google Scholar 

  • Silverman BW, Jones MC (1989) E. Fix and J.L. Hodges (1951): An important contribution to nonparametric discriminant analysis and density estimation: Commentary on Fix and Hodges (1951). Int Stat Rev/Rev Int Stat 57:233–238

    Article  Google Scholar 

  • Wei JT, Lee MC, Chen HK, Wu HH (2013) Customer relationship management in the hairdressing industry: an application of data mining techniques. Expert Syst Appl 40:7513–7518

    Article  Google Scholar 

  • Young W, Hwang K, Mcdonald S, Oates CJ (2010) Sustainable consumption: green consumer behaviour when purchasing products. Sustain Dev 31:20–31

    Article  Google Scholar 

Download references

Acknowledgments

Financial support from the Knowledge Foundation in Sweden is gratefully acknowledged. Sincere thanks to the industrial research partners.

Author information

Authors and Affiliations

  1. Department of Strategic Sustainable Development, Blekinge Institute of Technology, Karlskrona, Sweden

    Sze Yin Kwok

  2. Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden

    Vedasree Reddy Sapatapu, Apoorva Kothapally & Veselka Boeva

Authors
  1. Sze Yin Kwok
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vedasree Reddy Sapatapu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Apoorva Kothapally
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Veselka Boeva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sze Yin Kwok .

Editor information

Editors and Affiliations

  1. School of Creative Science and Engineering, Waseda University, Tokyo, Tokyo, Japan

    Shinichi Fukushige

  2. Graduate School of Engineering, Osaka University, Suita, Osaka, Osaka, Japan

    Hideki Kobayashi

  3. College of Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Shiga, Japan

    Eiji Yamasue

  4. Graduate School of Engineering, Osaka University, Suita, Osaka, Osaka, Japan

    Keishiro Hara

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kwok, S.Y., Sapatapu, V.R., Kothapally, A., Boeva, V. (2024). Modelling Customer Preference for Sustainability Information via Clustering Analysis. In: Fukushige, S., Kobayashi, H., Yamasue, E., Hara, K. (eds) EcoDesign for Sustainable Products, Services and Social Systems II. Springer, Singapore. https://doi.org/10.1007/978-981-99-3897-1_25

Download citation

Publish with us

Policies and ethics

Search

Navigation