A network-based approach to modeling and predicting product coconsideration relations.

*(English)*Zbl 1390.93132Summary: Understanding customer preferences in consideration decisions is critical to choice modeling in engineering design. While existing literature has shown that the exogenous effects (e.g., product and customer attributes) are deciding factors in customers’ consideration decisions, it is not clear how the endogenous effects (e.g., the intercompetition among products) would influence such decisions. This paper presents a network-based approach based on Exponential Random Graph Models to study customers’ consideration behaviors according to engineering design. Our proposed approach is capable of modeling the endogenous effects among products through various network structures (e.g., stars and triangles) besides the exogenous effects and predicting whether two products would be conisdered together. To assess the proposed model, we compare it against the dyadic network model that only considers exogenous effects. Using buyer survey data from the China automarket in 2013 and 2014, we evaluate the
goodness of fit and the predictive power of the two models. The results show that our model has a better fit and predictive accuracy than the dyadic network model. This underscores the importance of the endogenous effects on customers’ consideration decisions. The insights gained from this research help explain how endogenous effects interact with exogeous effects in affecting customers’ decision-making.

##### MSC:

93A30 | Mathematical modelling of systems (MSC2010) |

91B42 | Consumer behavior, demand theory |

05C80 | Random graphs (graph-theoretic aspects) |

##### Keywords:

customer preferences; network-based approach based; exponential random graph models; dyadic network model
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\textit{Z. Sha} et al., Complexity 2018, Article ID 2753638, 14 p. (2018; Zbl 1390.93132)

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##### References:

[1] | Robins, G.; Snijders, T.; Wang, P.; Handcock, M.; Pattison, P., Recent developments in exponential random graph (p*) models for social networks, Social Networks, 29, 2, 192-215, (2007) |

[2] | Wang, M.; Chen, W.; Huang, Y.; Contractor, N. S.; Fu, Y., A Multidimensional Network Approach for Modeling Customer-Product Relations in Engineering Design, Proceedings of the ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers |

[3] | Wang, P.; Robins, G.; Pattison, P.; Lazega, E., Exponential random graph models for multilevel networks, Social Networks, 35, 1, 96-115, (2013) |

[4] | Sosa, M. E.; Eppinger, S. D.; Rowles, C. M., A network approach to define modularity of components in complex products, Journal of Mechanical Design, 129, 11, 1118-1129, (2007) |

[5] | Sosa, M.; Mihm, J.; Browning, T., Degree distribution and quality in complex engineered systems, Journal of Mechanical Design, 133, 10, (2011) |

[6] | Byler, E., Cultivating the growth of complex systems using emergent behaviours of engineering processes, Proceedings of the in International conference on complex systems: control and modeling, Russian Academy of Sciences |

[7] | Contractor, N.; Monge, P. R.; Leonardi, P., Multidimensional networks and the dynamics of sociomateriality: Bringing technology inside the network, International Journal of Communication, 5, 682-720, (2011) |

[8] | Cormier, P.; Devendorf, E.; Lewis, K., Optimal process architectures for distributed design using a social network model, Proceedings of the ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2012 |

[9] | Chen, W.; Hoyle, C.; Wassenaar, H. J., Decision-based design: Integrating consumer preferences into engineering design, Decision-Based Design: Integrating Consumer Preferences into Engineering Design, 1-357, (2013) · Zbl 1401.90010 |

[10] | Michalek, J. J.; Feinberg, F. M.; Papalambros, P. Y., Linking marketing and engineering product design decisions via analytical target cascading, Journal of Product Innovation Management, 22, 1, 42-62, (2005) |

[11] | Sha, Z.; Moolchandani, K.; Panchal, J. H.; DeLaurentis, D. A., Modeling Airlines’ Decisions on City-Pair Route Selection Using Discrete Choice Models, Journal of Air Transportation, 24, 3, 63-73, (2016) |

[12] | Sha, Z.; Panchal, J. H., Estimating local decision-making behavior in complex evolutionary systems, Journal of Mechanical Design, 136, 6, (2014) |

[13] | Wang, M.; Chen, W., A data-driven network analysis approach to predicting customer choice sets for choice modeling in engineering design, Journal of Mechanical Design, 137, 7, (2015) |

[14] | Sha, Z.; Panchal, J. H., Estimating linking preferences and behaviors of autonomous systems in the internet using a discrete choice model, Proceedings of the 2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 |

[15] | Hauser, J.; Ding, M.; Gaskin, S. P., Non-compensatory (and compensatory) models of consideration-set decisions, Proceedings of the in 2009 Sawtooth Software Conference Proceedings |

[16] | Shocker, A. D.; Ben-Akiva, M.; Boccara, B.; Nedungadi, P., Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions, Marketing Letters, 2, 3, 181-197, (1991) |

[17] | Hauser, J. R.; Wernerfelt, B., An Evaluation Cost Model of Consideration Sets, Journal of Consumer Research, 16, 4, 393, (1990) |

[18] | Nedungadi, P., Recall and Consumer Consideration Sets: Influencing Choice without Altering Brand Evaluations, Journal of Consumer Research, 17, 3, 263, (1990) |

[19] | Srivastava, R. K.; Alpert, M. I.; Shocker, A. D., A Customer-Oriented Approach for Determining Market Structures, Journal of Marketing, 48, 2, 32, (1984) |

[20] | Frederick, S. |

[21] | Shao, W., Consumer Decision-Making: An Empirical Exploration of Multi-Phased Decision Processes, (2006), Griffith University Australia |

[22] | Yee, M.; Dahan, E.; Hauser, J. R.; Orlin, J., Greedoid-based noncompensatory inference, Marketing Science, 26, 4, 532-549, (2007) |

[23] | Hauser, J. R.; Toubia, O.; Evgeniou, T.; Befurt, R.; Dzyabura, D., Disjunctions of conjunctions, cognitive simplicity, and consideration sets, Journal of Marketing Research, 47, 3, 485-496, (2010) |

[24] | Gilbride, T. J.; Allenby, G. M., Estimating heterogeneous EBA and economic screening rule choice models, Marketing Science, 25, 5, 494-509, (2006) |

[25] | Andrews, R. L.; Srinivasan, T. C., Studying consideration effects in empirical choice models using scanner panel data, Journal of Marketing Research, 32, 1, 30, (1995) |

[26] | Chiang, J.; Chib, S.; Narasimhan, C., Markov chain Monte Carlo and models of consideration set and parameter heterogeneity, Journal of Econometrics, 89, 1-2, 223-248, (1998) · Zbl 0962.62115 |

[27] | Erdem, T.; Swait, J., Brand credibility, brand consideration, and choice, Journal of Consumer Research, 31, 1, 191-198, (2004) |

[28] | Swait, J., A non-compensatory choice model incorporating attribute cutoffs, Transportation Research Part B: Methodological, 35, 10, 903-928, (2001) |

[29] | Gilbride, T. J.; Allenby, G. M., A choice model with conjunctive, disjunctive, and compensatory screening rules, Marketing Science, 23, 3, 391-406, (2004) |

[30] | Boros, E.; Hammer, P. L.; Ibaraki, T.; Kogan, A.; Mayoraz, E.; Muchnik, I., An implementation of logical analysis of data, IEEE Transactions on Knowledge and Data Engineering, 12, 2, 292-306, (2000) |

[31] | Ding, M., An incentive-aligned mechanism for conjoint analysis, Journal of Marketing Research, 44, 2, 214-223, (2007) |

[32] | Sha, Z.; Saeger, V.; Wang, M.; Fu, Y.; Chen, W., Analyzing Customer Preference to Product Optional Features in Supporting Product Configuration, SAE International Journal of Materials and Manufacturing, 10, 3, (2017) |

[33] | Eliaz, K.; Spiegler, R., Consideration sets and competitive marketing, Review of Economic Studies, 78, 1, 235-262, (2011) · Zbl 1215.91053 |

[34] | Resnick, P.; Varian, H. R., Recommender systems, Communications of the ACM, 40, 3, 56-58, (1997) |

[35] | Adomavicius, G.; Tuzhilin, A., Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering, 17, 6, 734-749, (2005) |

[36] | Zhoua, T.; Kuscsik, Z.; Liu, J.; Medo, M.; Wakeling, J. R.; Zhang, Y., Solving the apparent diversity-accuracy dilemma of recommender systems, Proceedings of the National Acadamy of Sciences of the United States of America, 107, 10, 4511-4515, (2010) |

[37] | Yu, F.; Zeng, A.; Gillard, S.; Medo, M., Network-based recommendation algorithms: a review, Physica A: Statistical Mechanics and its Applications, 452, 192-208, (2016) |

[38] | Lü, L.; Medo, M.; Yeung, C. H.; Zhang, Y.; Zhang, Z.; Zhou, T., Recommender systems, Physics Reports, 519, 1, 1-49, (2012) |

[39] | Zhou, T.; Ren, J.; Medo, M.; Zhang, Y., Bipartite network projection and personal recommendation, Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 76, 4, (2007) |

[40] | Pazzani, M. J.; Billsus, D.; Brusilovsky, P.; Kobsa, A.; and.; Nejdl, W., Content-based recommendation systems, The Adaptive Web: Methods and Strategies of Web Personalization, 325-341, (2007), Berlin, Germany: Springer, Berlin, Germany |

[41] | Blei, D. M.; Ng, A. Y.; Jordan, M. I., Latent Dirichlet allocation, Journal of Machine Learning Research, 3, 4-5, 993-1022, (2003) · Zbl 1112.68379 |

[42] | Fiasconaro, A.; Tumminello, M.; Nicosia, V.; Latora, V.; Mantegna, R. N., Hybrid recommendation methods in complex networks, Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 92, 1, (2015) |

[43] | Fu, J. S., Modeling Customer Choice Preferences in Engineering Design Using Bipartite Network Analysis, Proceedings of the in 2017 ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, ASME |

[44] | Wang, M.; Sha, Z.; Huang, Y.; Contractor, N.; Fu, Y.; Chen, W., Forecasting technological impacts on customers’ co-consideration behaviors: A data-driven network analysis approach, Proceedings of the ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2016 |

[45] | Robins, G.; Pattison, P.; Kalish, Y.; Lusher, D., An introduction to exponential random graph (p*) models for social networks, Social Networks, 29, 2, 173-191, (2007) |

[46] | Shumate, M.; Palazzolo, E. T., Exponential random graph (p*) models as a method for social network analysis in communication research, Communication Methods and Measures, 4, 4, 341-371, (2010) |

[47] | Tufféry, S., Data Mining and Statistics for Decision Making, Data Mining and Statistics for Decision Making, (2011) · Zbl 1216.62005 |

[48] | Church, K. W.; Hanks, P., Word association norms, mutual information, and lexicography, Computational linguistics, 16, 1, 22-29, (1990) |

[49] | Frank, O.; Strauss, D., Markov graphs, Journal of the American Statistical Association, 81, 395, 832-842, (1986) · Zbl 0607.05057 |

[50] | Wasserman, S.; Pattison, P., Logit models and logistic regressions for social networks: I. An introduction to markov graphs and p, Psychometrika, 61, 3, 401-425, (1996) · Zbl 0866.92029 |

[51] | McPherson, M.; Smith-Lovin, L.; Cook, J. M., Birds of a feather: homophily in social networks, Annual Review of Sociology, 27, 415-444, (2001) |

[52] | Greenacre, M., Correspondence analysis in practice, (2017), CRC press · Zbl 1352.62003 |

[53] | Wang, M., A Network Approach for Understanding and Analyzing Product Co-Consideration Relations in Engineering Design, Proceedings of the DESIGN 2016 14th International Design Conference |

[54] | Hunter, D. R., Curved exponential family models for social networks, Social Networks, 29, 2, 216-230, (2007) |

[55] | Shore, J.; Lubin, B., Spectral goodness of fit for network models, Social Networks, 43, 16-27, (2015) |

[56] | Powers, D. M., Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation, (2011) |

[57] | Saito, T.; Rehmsmeier, M., The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets, PLoS ONE, 10, 3, (2015) |

[58] | Sha, Z., Modeling Product Co-Consideration Relations: A Comparative Study of Two Network Models, Proceedings of the 21st International Conference on Engineering Design, ICED17 |

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