A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations

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.

[1]  Wei Chen,et al.  A Data-Driven Network Analysis Approach to Predicting Customer Choice Sets for Choice Modeling in Engineering Design , 2015 .

[2]  Rick L. Andrews,et al.  Studying Consideration Effects in Empirical Choice Models Using Scanner Panel Data , 1995 .

[3]  Jeremy J. Michalek,et al.  Linking Marketing and Engineering Product Design Decisions via Analytical Target Cascading , 2005 .

[4]  R. Spiegler,et al.  Consideration Sets and Competitive Marketing , 2009 .

[5]  Peter R. Monge,et al.  Network Theory | Multidimensional Networks and the Dynamics of Sociomateriality: Bringing Technology Inside the Network , 2011 .

[6]  Tyson R. Browning,et al.  Degree Distribution and Quality in Complex Engineered Systems , 2011 .

[7]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[8]  J. Swait,et al.  Brand Credibility, Brand Consideration, and Choice , 2004 .

[9]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[10]  B. Wernerfelt,et al.  An Evaluation Cost Model of Consideration Sets , 1990 .

[11]  Daniel DeLaurentis,et al.  Modeling Airlines’ Decisions on City-Pair Route Selection Using Discrete Choice Models , 2016 .

[12]  Wei Chen,et al.  A NETWORK APPROACH FOR UNDERSTANDING AND ANALYZING PRODUCT CO-CONSIDERATION RELATIONS IN ENGINEERING DESIGN , 2016 .

[13]  Rama Chellappa,et al.  An electronic infrastructure for a virtual university , 1997, CACM.

[14]  Kenneth Ward Church,et al.  Word Association Norms, Mutual Information, and Lexicography , 1989, ACL.

[15]  Peng Wang,et al.  Recent developments in exponential random graph (p*) models for social networks , 2007, Soc. Networks.

[16]  Garry Robins,et al.  An introduction to exponential random graph (p*) models for social networks , 2007, Soc. Networks.

[17]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[18]  Yi-Cheng Zhang,et al.  Recommender Systems , 2012, ArXiv.

[19]  Allan D. Shocker,et al.  Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions , 1991 .

[20]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[21]  Rosario N. Mantegna,et al.  Hybrid recommendation methods in complex networks , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  P. Nedungadi.  Recall and Consumer Consideration Sets: Influencing Choice without Altering Brand Evaluations , 1990 .

[23]  Greg M. Allenby,et al.  Estimating Heterogeneous EBA and Economic Screening Rule Choice Models , 2006 .

[24]  Siddhartha Chib,et al.  Markov chain Monte Carlo and models of consideration set and parameter heterogeneity , 1998 .

[25]  Joffre Swait,et al.  A NON-COMPENSATORY CHOICE MODEL INCORPORATING ATTRIBUTE CUTOFFS , 2001 .

[26]  Steven D. Eppinger,et al.  A Network Approach to Define Modularity of Components in Complex Products , 2007 .

[27]  Stphane Tuffry,et al.  Data Mining and Statistics for Decision Making , 2011 .

[28]  M. Greenacre.  Correspondence analysis in practice , 1993 .

[29]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[30]  Yi-Cheng Zhang,et al.  Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Min Ding.  An Incentive-Aligned Mechanism for Conjoint Analysis , 2007 .

[32]  Edward T. Palazzolo,et al.  Exponential Random Graph (p*) Models as a Method for Social Network Analysis in Communication Research , 2010 .

[33]  Wei Chen,et al.  Modeling product co-consideration relations: A comparative study of two network models , 2017 .

[34]  Allan D. Shocker,et al.  A Customer-oriented Approach for Determining Market Structures , 1984 .

[35]  Min Ding,et al.  NON-COMPENSATORY (AND COMPENSATORY) MODELS OF CONSIDERATION-SET DECISIONS , 2009 .

[36]  Toshihide Ibaraki,et al.  An Implementation of Logical Analysis of Data , 2000, IEEE Trans. Knowl. Data Eng..

[37]  Peng Wang,et al.  Exponential random graph models for multilevel networks , 2013, Soc. Networks.

[38]  Wei Chen,et al.  Decision-Based Design: Integrating Consumer Preferences into Engineering Design , 2012 .

[39]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[40]  Jitesh H. Panchal,et al.  Estimating linking preferences and behaviors of autonomous systems in the Internet using a discrete choice model , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[41]  T. Evgeniou,et al.  Disjunctions of Conjunctions, Cognitive Simplicity, and Consideration Sets , 2010 .

[42]  Jitesh H. Panchal,et al.  Estimating Local Decision-Making Behavior in Complex Evolutionary Systems , 2014 .

[43]  Wei Chen,et al.  Forecasting Technological Impacts on Customers’ Co-Consideration Behaviors: A Data-Driven Network Analysis Approach , 2016, DAC 2016.

[44]  P. Pattison.  LOGIT MODELS AND LOGISTIC REGRESSIONS FOR SOCIAL NETWORKS: I. AN INTRODUCTION TO MARKOV GRAPHS AND p* STANLEY WASSERMAN UNIVERSITY OF ILLINOIS , 1996 .

[45]  Yan Fu,et al.  A Multidimensional Network Approach for Modeling Customer-Product Relations in Engineering Design , 2015 .

[46]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[47]  Wei Shao.  Consumer Decision-Making: An Empirical Exploration of Multi-Phased Decision Processes , 2007 .

[48]  Yi-Cheng Zhang,et al.  Solving the apparent diversity-accuracy dilemma of recommender systems , 2008, Proceedings of the National Academy of Sciences.

[49]  Kemper Lewis,et al.  Optimal Process Architectures for Distributed Design Using a Social Network Model , 2012, DAC 2012.

[50]  Fei Yu,et al.  Network-based recommendation algorithms: A review , 2015, Physica A: Statistical Mechanics and its Applications.

[51]  Wei Chen,et al.  Analyzing Customer Preference to Product Optional Features in Supporting Product Configuration , 2017 .

[52]  Takaya Saito,et al.  The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets , 2015, PloS one.

[53]  Greg M. Allenby,et al.  A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules , 2004 .

[54]  Ove Frank,et al.  http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained , 2007 .

[55]  Stéphane Tufféry,et al.  Data Mining and Statistics for Decision Making: Tufféry/Data Mining and Statistics for Decision Making , 2011 .

[56]  David R. Hunter,et al.  Curved exponential family models for social networks , 2007, Soc. Networks.

[57]  J. Orlin,et al.  Greedoid-Based Noncompensatory Inference , 2007 .