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Modeling customer preferences using multidimensional network analysis in engineering design

  • Mingxian Wang (a1), Wei Chen (a1), Yun Huang (a2), Noshir S. Contractor (a2) and Yan Fu (a3)...

Motivated by overcoming the existing utility-based choice modeling approaches, we present a novel conceptual framework of multidimensional network analysis (MNA) for modeling customer preferences in supporting design decisions. In the proposed multidimensional customer–product network (MCPN), customer–product interactions are viewed as a socio-technical system where separate entities of ‘customers’ and ‘products’ are simultaneously modeled as two layers of a network, and multiple types of relations, such as consideration and purchase, product associations, and customer social interactions, are considered. We first introduce a unidimensional network where aggregated customer preferences and product similarities are analyzed to inform designers about the implied product competitions and market segments. We then extend the network to a multidimensional structure where customer social interactions are introduced for evaluating social influence on heterogeneous product preferences. Beyond the traditional descriptive analysis used in network analysis, we employ the exponential random graph model (ERGM) as a unified statistical inference framework to interpret complex preference decisions. Our approach broadens the traditional utility-based logit models by considering dependency among complex customer–product relations, including the similarity of associated products, ‘irrationality’ of customers induced by social influence, nested multichoice decisions, and correlated attributes of customers and products.

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S. Aral  & D. Walker 2011 Creating social contagion through viral product design: a randomized trial of peer influence in networks. Management Science 57, 16231639.

W. Chen , C. Hoyle  & H. J. Wassenaar 2013 Decision-based Design: Integrating Consumer Preferences into Engineering Design. Springer.

A. Clauset , M. E. J. Newman  & C. Moore 2004 Finding community structure in very large networks. Physical Review E 70, 066111.

B. Corominas-Murtra , J. Goñi , R. V. Solé  & C. Rodríguez-Caso 2013 On the origins of hierarchy in complex networks. Proceedings of the National Academy of Sciences 110, 1331613321.

S. J. Cranmer  & B. A. Desmarais 2011 Inferential network analysis with exponential random graph models. Political Analysis 19, 6686.

H. De Vries 1998 Finding a dominance order most consistent with a linear hierarchy: a new procedure and review. Animal Behaviour 55, 827843.

S. A. Delre , W. Jager  & M. A. Janssen 2007 Diffusion dynamics in small-world networks with heterogeneous consumers. Computational and Mathematical Organization Theory 13, 185202.

P. Dimaggio , E. Hargittai , W. R. Neuman  & J. P. Robinson 2001 Social implications of the Internet. Annual Review of Sociology 307336.

O. Frank  & D. Strauss 1986 Markov graphs. Journal of the American Statistical Association 81, 832842.

L. C. Freeman 1979 Centrality in social networks conceptual clarification. Social Networks 1, 215239.

B. D. Frischknecht , K. Whitefoot  & P. Y. Papalambros 2010 On the suitability of econometric demand models in design for market systems. Journal of Mechanical Design 132, 121007.

J. C. Gower 1971 A general coefficient of similarity and some of its properties. Biometrics 857871.

L. He , M. Wang , W. Chen  & G. Conzelmann 2014 Incorporating social impact on new product adoption in choice modeling: a case study in green vehicles. Transportation Research Part D: Transport and Environment 32, 421434.

C. Hoyle , W. Chen , N. Wang  & F. S. Koppelman 2010 Integrated Bayesian hierarchical choice modeling to capture heterogeneous consumer preferences in engineering design. Journal of Mechanical Design 132, 121010.

E. Karahanna , D. W. Straub  & N. L. Chervany 1999 Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly 183213.

G. Kossinets  & D. J. Watts 2006 Empirical analysis of an evolving social network. Science 311, 8890.

R. E. Kraut , R. E. Rice , C. Cool  & R. S. Fish 1998 Varieties of social influence: the role of utility and norms in the success of a new communication medium. Organization Science 9, 437453.

D. Lusher , J. Koskinen  & G. Robins 2012 Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press.

M. Mcpherson , L. Smith-Lovin  & J. M. Cook 2001 Birds of a feather: homophily in social networks. Annual Review of Sociology 27, 415444.

V. Narayan , V. R. Rao  & C. Saunders 2011 How peer influence affects attribute preferences: a Bayesian updating mechanism. Marketing Science 30, 368384.

O. Netzer , R. Feldman , J. Goldenberg  & M. Fresko 2012 Mine your own business: market-structure surveillance through text mining. Marketing Science 31, 521543.

M. E. Newman  & M. Girvan 2004 Finding and evaluating community structure in networks. Physical Review E 69, 026113.

G. Palla , I. Derényi , I. Farkas  & T. Vicsek 2005 Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814818.

R. Real  & J. M. Vargas 1996 The probabilistic basis of Jaccard’s index of similarity. Systematic Biology 45 (3), 380385.

R. E. Rice  & C. Aydin 1991 Attitudes toward new organizational technology: network proximity as a mechanism for social information processing. Administrative Science Quarterly 36 (2), 219244.

G. Robins , P. Pattison , Y. Kalish  & D. Lusher 2007 An introduction to exponential random graph $(\text{p}^{\ast })$ models for social networks. Social Networks 29, 173191.

C.-S. N. Shiau  & J. J. Michalek 2009 Should designers worry about market systems? Journal of Mechanical Design 131, 011011.

T. A. Snijders , P. E. Pattison , G. L. Robins  & M. S. Handcock 2006 New specifications for exponential random graph models. Sociological Methodology 36, 99153.

M. Sosa , J. Mihm  & T. Browning 2011 Degree distribution and quality in complex engineered systems. Journal of Mechanical Design 133, 101008.

M. E. Sosa , S. D. Eppinger  & C. M. Rowles 2007 A network approach to define modularity of components in complex products. Journal of Mechanical Design 129, 11181129.

P.-N. Tan , V. Kumar  & J. Srivastava 2004 Selecting the right objective measure for association analysis. Information Systems 29, 293313.

K. E. Train 2009 Discrete Choice Methods with Simulation. Cambridge University Press.

K. A. Urberg 1992 Locus of peer influence: social crowd and best friend. Journal of Youth and Adolescence 21, 439450.

M. Wang  & W. Chen 2015 A data-driven network analysis approach to predicting customer choice sets for choice modeling in engineering design. Journal of Mechanical Design 137, 071410.

M. Wang , W. Chen , Y. Fu  & Y. Yang 2015 Analyzing and predicting heterogeneous customer preferences in China’s auto market using choice modeling and network analysis. SAE International Journal of Materials and Manufacturing 8 (3), 668677.

P. Wang , G. Robins , P. Pattison  & E. Lazega 2013 Exponential random graph models for multilevel networks. Social Networks 35, 96115.

S. Wasserman  & K. Faust 1994 Social Network Analysis: Methods and Applications. Cambridge University Press.

D. J. Watts  & S. H. Strogatz 1998 Collective dynamics of ‘small-world’ networks. Nature 393, 440442.

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Design Science
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