Abstract
Lead users are of great significance in today’s customer-centric product design environment, who are viewed as pioneers of customer needs. In order to identify the potential lead users in mass consumer products like phones, automobiles, a biclustering-based identification methodology taking advantage of BCBimax algorithm is proposed. Consequently, this method is implemented into a smart phone maker in China, Xiaomi, which has set good examples in occupying large market share by employing lead users in its design procedure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hippel EV (1986) Lead users: a source of novel product concepts. Manage Sci 32(7):691–705
Luthje C, Herstatt C (2004) The lead user method: an outline of empirical findings and issues for future research. R & D Manage 34(5):553–568
Lilien GL, Hippel EV (2002) Performance assessment of the lead user idea-generation process for new product development. Manage Sci 48(8):1042–1059
Prahalad CK, Ramaswamy V (2004) Co-creation experiences: the next practice in value creation. J Interact Mark 18(3):5–14
Hoyer WD, Dorotic M, Krafft M et al (2010) Consumer cocreation in new product development. J Serv Res 13(3):283–296
Cheng Y, Church GM (2000) Biclustering of expression data. Intell Syst Mol Biol 93–103
Lazzeroni L, Owen A (2002) Plaid models for gene expression data. Stat Sin 12(1):61–86
Murali T, Kasif S (2003) Extracting conserved gene expression motifs from gene expression data. In: Pacific symposium on biocomputing, vol 8, No. 9, pp 77–88
Kluger Y, Basri R, Chang JT et al (2003) Spectral biclustering of microarray data: coclustering genes and conditions. Genome Res 13(4):703–716
Prelić A, Bleuler S, Zimmermann P et al (2006) A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9):1122–1129
de Castro PA, de França FO, Ferreira HM et al Applying biclustering to text mining: an immune-inspired approach. In: Artificial immune systems. Springer, Berlin, Heidelberg, 2007, pp 83–94
Dolnicar S, Kaiser S, Lazarevski K et al (2012) Biclustering: overcoming data dimensionality problems in market segmentation. J Travel Res 51(1):41–49
Inbarani HH, Thangavel K (2011) A robust biclustering approach for effective web personalization. Visual analytics and interactive technologies: data, text and web mining applications, pp 186–202
Huang Q (2011) A biclustering technique for mining trading rules in stock markets. In: Applied informatics and communication. Springer, Berlin, Heidelberg, pp 16–24
Symeonidis P, Nanopoulos A, Manolopoulos Y (2008) Providing justifications in recommender systems. IEEE Trans Syst Man Cybern—Part A Syst Hum 38(6):1262–1272
Acknowledgements
This work was supported by in part by the “Dawn” Program of Shanghai Education Commission China (Grant No. 15SG36).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Miao, Y., Zhang, H. (2017). A Biclustering-Based Lead User Identification Methodology Applied to Xiaomi. In: Li, X., Xu, X. (eds) Proceedings of the Fourth International Forum on Decision Sciences. Uncertainty and Operations Research. Springer, Singapore. https://doi.org/10.1007/978-981-10-2920-2_80
Download citation
DOI: https://doi.org/10.1007/978-981-10-2920-2_80
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2919-6
Online ISBN: 978-981-10-2920-2
eBook Packages: Business and ManagementBusiness and Management (R0)