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Predicting product co-consideration and market competitions for technology-driven product design: a network-based approach

  • Mingxian Wang (a1), Zhenghui Sha (a2), Yun Huang (a3), Noshir Contractor (a3), Yan Fu (a1) and Wei Chen (a4)...
Abstract

We propose a data-driven network-based approach to understand the interactions among technologies, products, and customers. Specifically, the approach enables both a qualitative understanding and a quantitative assessment of the impact of technological changes on customers’ co-consideration behaviors (decision of cross-shopping) and as a consequence the product competitions. The uniqueness of the proposed approach is its capability of predicting complex co-consideration relations of products as a network where both descriptive analyses (e.g., network statistics and joint correspondence analysis) and predictive models (e.g., multiple regressions quadratic assignment procedure) are employed. The integrated network analysis approach features three advantages: (1) It provides an effective visual representation of the underlying market structures; (2) It facilitates the evaluation of the correlation between customers’ consideration preferences and product attributes as well as customer demographics; (3) It enables the prediction of market competitions in response to potential technological changes. This paper demonstrates the proposed network-based approach in a vehicle design context. We investigate the impacts of the fuel economy-boosting technologies and the turbocharged engine technology on individual automakers as well as the entire auto industry. The case study provides vehicle engineers with insights into the change of market competitions brought by technological developments and thereby supports attribute decision-making in vehicle design.

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Copyright
Distributed as Open Access under a CC-BY-NC-SA 4.0 license (http://creativecommons.org/licenses/by-nc-sa/4.0/)
Corresponding author
Email address for correspondence: weichen@northwestern.edu
References
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Altman, D. G. & Bland, J. M. Diagnostic tests. 1: Sensitivity and specificity. BMJ: British Medical Journal 308 (6943), 1552.
Benzécri, J.-P. L’analyse des données, vol. 2. p. 1973. Dunod, Paris.
Brynjolfsson, E. 1996 The contribution of information technology to consumer welfare. Information Systems Research 7 (3), 281300.
Cantillo, V. & de Dios Ortúzar, J. 2005 A semi-compensatory discrete choice model with explicit attribute thresholds of perception. Transportation Research Part B: Methodological 39 (7), 641657.
Clauset, A., Newman, M. E. & Moore, C. Finding community structure in very large networks. Physical Review E 70 (6), 066111 2004.
Clauset, A., Moore, C. & Newman, M. E. J. Hierarchical structure and the prediction of missing links in networks. Nature 453, 98.
Davis, F. D. Jr. 1986 A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology.
Davis, F. D., Bagozzi, R. P. & Warshaw, P. R. 1989 User acceptance of computer technology: a comparison of two theoretical models. Management Science 35 (8), 9821003.
Dekker, D., Krackhardt, D. & Snijders, T. A. 2007 Sensitivity of MRQAP tests to collinearity and autocorrelation conditions. Psychometrika 72 (4), 563581.
Dieckmann, A., Dippold, K. & Dietrich, H. 2009 Compensatory versus noncompensatory models for predicting consumer preferences. Judgment and Decision Making 4 (3), 200213.
Fiasconaro, A. et al. Hybrid recommendation methods in complex networks. Physical Review E 92 (1), 2015.
Fruchterman, T. M. & Reingold, E. M. 1991 Graph drawing by force-directed placement. Software: Practice and Experience 21 (11), 11291164.
Gaskin, S. et al. 2007 Two-stage models: Identifying non-compensatory heuristics for the consideration set then adaptive polyhedral methods within the consideration set. In Proceedings of the Sawtooth Software Conference.
Greenacre, M. 2007 Correspondence Analysis in Practice. CRC Press.
Greenacre, M. & Blasius, J. 2006 Multiple Correspondence Analysis and Related Methods. CRC Press.
Gilbride, T. J. & Allenby, G. M. 2004 A choice model with conjunctive, disjunctive, and compensatory screening rules. Marketing Science 23 (3), 391406.
Hauser, J. R. 2014 Consideration-set heuristics. Journal of Business Research 67 (8), 16881699.
Hauser, J. R. & Wernerfelt, B. 1990 An Evaluation Cost Model of Consideration Sets. Journal of Consumer Research 16 (4), 393408.
Hauser, J. R. et al. 2010 Disjunctions of conjunctions, cognitive simplicity, and consideration sets. Journal of Marketing Research 47 (3), 485496.
Heckman, J. J. 1976 The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. In Annals of Economic and Social Measurement, vol. 5, (4), pp. 475492. NBER.
Hitt, L. M. & Brynjolfsson, E. 1996 Productivity, business profitability, and consumer surplus: three different measures of information technology value. MIS Quarterly 121142.
Hsu, C.-L. & Lu, H.-P. 2004 Why do people play on-line games? An extended TAM with social influences and flow experience. Information and Management 41 (7), 853868.
Kim, H. K., Kim, J. K. & Chen, Q. Y. 2012 A product network analysis for extending the market basket analysis. Expert Systems with Applications 39 (8), 74037410.
Krackhardt, D. 1988 Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks 10 (4), 359381.
Krivitsky, P. N. & Handcock, M. S. 2014 A separable model for dynamic networks. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76 (1), 2946.
Lee, Y., Kozar, K. A. & Larsen, K. R. 2003 The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems 12 (1), 50.
Liben-Nowell, D. & Kleinberg, J. 2003 The link prediction problem for social networks. In Proceedings of the Twelfth International Conference on Information and Knowledge Management, pp. 556559. ACM, New Orleans, LA, USA.
Lusher, D., Koskinen, J. & Robins, G. 2012 Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press.
Malhotra, Y. & Galletta, D. F. 1999 Extending the technology acceptance model to account for social influence: Theoretical bases and empirical validation. In Systems Sciences, 1999. HICSS-32 Proceedings of the 32nd annual Hawaii international Conference on, IEEE.
McPherson, M., Smith-Lovin, L. & Cook, J. M. Birds of a feather: Homophily in social networks. Annual Review of Sociology 2001, 415444.
Monge, P. R. & Contractor, N. S. 2003 Theories of Communication Networks. Oxford University Press.
Morrow, W. R., Long, M. & MacDonald, E. F. Market-system design optimization with consider-then-choose models. Journal of Mechanical Design 136 (3), 031003 2014.
Mostafa, M. M. 2015 Knowledge discovery of hidden consumer purchase behaviour: a market basket analysis. International Journal of Data Analysis Techniques and Strategies 7 (4), 384405.
Newman, M. E. 2006 Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103 (23), 85778582.
Perozzi, B., Al-Rfou, R. & Skiena, S. 2014 DeepWalk: online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701710. ACM, New York, USA.
Raeder, T. & Chawla, N. V. 2011 Market basket analysis with networks. Social Network Analysis and Mining 1 (2), 97113.
Resnick, P. & Varian, H. R. 1997 Recommender systems. Communications of the ACM 40 (3), 5658.
Sha, Z. et al. 2017 Modeling product co-consideration relations: A comparative study of two network models. In International Conference on Engineering Design, ICED, Vancouver, Canada.
Shocker, A. D. et al. 1991 Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions. Marketing Letters 2 (3), 181197.
Thatcher, M. E. 2004 The impact of technology on product design, productivity, and profits: A duopoly model of price-quality competition. In System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference on, IEEE.
Thatcher, M. E. & Oliver, J. R. 2001 The impact of information technology on quality improvement, productivity, and profits: An analytical model of a monopolist. In System Sciences, 2001. Proceedings of the 34th Annual Hawaii International Conference on, IEEE.
Turbocharging, R. H. Ray Hall Turbo Calculators: http://www.turbofast.com.au/javacalc.html [cited 2015 11/15].
Train, K. E. 2009 Discrete Choice Methods with Simulation. Cambridge University Press.
Venkatesh, V. & Bala, H. 2008 Technology acceptance model 3 and a research agenda on interventions. Decision Sciences 39 (2), 273315.
Venkatesh, V. & Davis, F. D. 2000 A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science 46 (2), 186204.
Venkatesh, V. & Morris, M. G. 2000 Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly 115139.
Wang, M. et al. Modeling customer preferences using multidimensional network analysis in engineering design. Design Science 2, 2016.
Wassenaar, H. J. & Chen, W. 2003 An approach to decision-based design with discrete choice analysis for demand modeling. Journal of Mechanical Design 125 (3), 490497.
Wang, M. & Chen, W. 2015 A data-driven network analysis approach to predicting customer choice sets for choice modeling in engineering design. Journal of Mechanical Design 137 (7), 071410.
Wang, M. et al. 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, (2015-01-0468).
Yu, F. et al. 2016 Network-based recommendation algorithms: A review. Physica a-Statistical Mechanics and Its Applications 452, 192208.
Zhou, T. et al. Bipartite network projection and personal recommendation. Physical Review E 76 (4), 2007.
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