Product Aesthetic Design: A Machine Learning Augmentation

Published Online:https://doi.org/10.1287/mksc.2022.1429

References

  • Aaker DA, Keller KL (1990) Consumer evaluations of brand extensions. J. Marketing 54(1):27–41.CrossrefGoogle Scholar
  • Aubry M, Maturana D, Efros AA, Russell BC, Sivic J (2014) Seeing 3D chairs: Exemplar part-based 2D-3D alignment using a large data set of CAD models. Proc. IEEE Conf. on Comput. Vision and Pattern Recognition, (IEEE, Piscataway, NJ)3762–3769.Google Scholar
  • Berlyne DE (1971) Aesthetics and Psychobiology (Appleton-Century-Crofts, East Norwalk, CT).Google Scholar
  • Berthelot D, Schumm T, Metz L (2017) BEGAN: Boundary equilibrium generative adversarial networks. Preprint, submitted March 31, https://arxiv.org/abs/1703.10717.Google Scholar
  • Blei DM, Kucukelbir A, McAuliffe JD (2017) Variational inference: A review for statisticians. J. Amer. Statist. Assoc. 112(518):859–877.CrossrefGoogle Scholar
  • Bloch PH (1995) Seeking the ideal form: Product design and consumer response. J. Marketing 59(3):16.CrossrefGoogle Scholar
  • Blonigen BA, Knittel CR, Soderbery A (2013) Keeping It Fresh: Strategic Product Redesigns and Welfare (National Bureau of Economic Research, Cambridge, MA).CrossrefGoogle Scholar
  • Bouchard C, Aoussat A, Duchamp R (2006) Role of sketching in conceptual design of car styling. J. Desert Res. 5(1):116.CrossrefGoogle Scholar
  • Chakraborty I, Kim M, Sudhir K (2022) Attribute sentiment scoring with online text reviews: Accounting for language structure and missing attributes. J. Marketing Res. 59(3):600–622.CrossrefGoogle Scholar
  • Chan TH, Mihm J, Sosa ME (2018) On styles in product design: An analysis of U.S. design patents. Management Sci. 64(3):1230–1249.LinkGoogle Scholar
  • Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P (2016a) InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. Adv. Neural Inform. Processing Systems, 29.Google Scholar
  • Chen X, Kingma DP, Salimans T, Duan Y, Dhariwal P, Schulman J, Sutskever I, et al. (2016b) Variational lossy autoencoder. Preprint, submitted November 8, https://arxiv.org/abs/1611.02731.Google Scholar
  • Cho H, Hasija S, Sosa M (2015) How Important Is Design for the Automobile Value Chain? (Social Science Research Network, Rochester, NY).CrossrefGoogle Scholar
  • Clement J (2007) Visual influence on in-store buying decisions: An eye-track experiment on the visual influence of packaging design. J. Marketing Management 23(9–10):917–928.CrossrefGoogle Scholar
  • Coates D (2003) Watches Tell More Than Time: Product Design, Information, and the Quest for Elegance (McGraw-Hill, London).Google Scholar
  • Cooper RG (1990) Stage-gate systems: A new tool for managing new products. Bus. Horizons 33(3):44–54.CrossrefGoogle Scholar
  • Creusen MEH, Schoormans JPL (2005) The different roles of product appearance in consumer choice. J. Production Innovative Management 22(1):63–81.CrossrefGoogle Scholar
  • Crilly N, Moultrie J, Clarkson PJ (2004) Seeing things: Consumer response to the visual domain in product design. Design Stud. 25(6):547–577.CrossrefGoogle Scholar
  • Danneels E, Kleinschmidtb EJ (2001) Product innovativeness from the firm’s perspective: Its dimensions and their relation with project selection and performance. J. Production Innovative Management 18(6):357–373.CrossrefGoogle Scholar
  • Daviet R (2018) Inference with Hamiltonian sequential Monte Carlo simulators. Preprint, submitted December 19, https://doi.org/10.48550/arXiv.1812.07978.Google Scholar
  • Dew R, Ansari A, Toubia O (2022) Letting logos speak: Leveraging multiview representation learning for data-driven logo design. Marketing Sci. 41(2):401–425.LinkGoogle Scholar
  • Dilokthanakul N, Mediano PAM, Garnelo M, Lee MCH, Salimbeni H, Arulkumaran K, Shanahan M (2016) Deep unsupervised clustering with Gaussian mixture variational autoencoders. Preprint, submitted November 8, https://arxiv.org/abs/1611.02648.Google Scholar
  • Ding M, Hauser J, Dong S, Dzyabura D, Yang Z, Chenting S, Gaskin S (2011) Unstructured direct elicitation of decision rules. J. Marketing Res. 48(1):116–127.CrossrefGoogle Scholar
  • Dzyabura D, Hauser JR, El Kihal S, Ibragimov M (2018) Leveraging the power of images in predicting product return rates. Preprint, submitted July 27, https://dx.doi.org/10.2139/ssrn.3209307.Google Scholar
  • Feldman J, Zhang DJ, Liu X, Zhang N (2022) Customer choice models vs. machine learning: Finding optimal product displays on Alibaba. Oper. Res. 70(1):309–328.Google Scholar
  • Gabel S, Timoshenko A (2022) Product choice with large assortments: A scalable deep learning model. Management Sci. 68(3):1808–1827.LinkGoogle Scholar
  • Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, et al. (2014) Generative adversarial nets. Adv. Neural Inform. Processing Systems, 2672–2680.Google Scholar
  • Griffin A, Hauser JR (1992) Patterns of communication among marketing, engineering, and manufacturing: A comparison between two new product teams. Management Sci. 38(3):360–373.LinkGoogle Scholar
  • Gross I (1972) The creative aspects of advertising. Sloan Management Rev. 14(1):83–109.Google Scholar
  • Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of Wasserstein GANs. Adv. Neural Inform. Processing Systems, 30.Google Scholar
  • Hartley J (1996) Brands Through the Lens of Style (Quest and Associates).Google Scholar
  • Hauser DJ, Schwarz N (2015) It’s a trap! Instructional manipulation checks prompt systematic thinking on “tricky” tasks. SAGE Open. 5(2):2158244015584617.CrossrefGoogle Scholar
  • Hekkert P, Snelders D, Wieringen PC (2003) ‘Most advanced, yet acceptable’: Typicality and novelty as joint predictors of aesthetic preference in industrial design. British J. Psych. 94(1):111–124.CrossrefGoogle Scholar
  • Heljakka A, Solin A, Kannala J (2018) Pioneer networks: Progressively growing generative autoencoder. Preprint, submitted July 9, https://arxiv.org/abs/1807.03026.Google Scholar
  • Heljakka A, Solin A, Kannala J (2019) Toward photographic image manipulation with balanced growing of generative autoencoders. Preprint, submitted April 12, https://arxiv.org/abs/1904.06145.Google Scholar
  • Hertenstein JH, Platt MB, Veryzer RW (2005) The impact of industrial design effectiveness on corporate financial performance. J. Production Innovative Management 22(1):3–21.CrossrefGoogle Scholar
  • Higgins I, Matthey L, Pal A, Burgess C, Glorot X, Botvinick M, Mohamed S, et al. (2017) beta-VAE: Learning basic visual concepts with a constrained variational framework. International Conference on Learning Representations.Google Scholar
  • Higgins I, Amos D, Pfau D, Racaniere S, Matthey L, Rezende D, Lerchner A (2018) Towards a definition of disentangled representations. Preprint, submitted December 5, https://arxiv.org/abs/1812.02230.Google Scholar
  • Homburg C, Schwemmle M, Kuehnl C (2015) New product design: Concept, measurement, and consequences. J. Marketing 79(3):41–56.CrossrefGoogle Scholar
  • Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. Proc. IEEE/CVF Conf. on Comput. Vision and Pattern Recognition, (IEEE, Piscataway, NJ), 7132–7141.Google Scholar
  • Huang H, Li Z, He R, Sun Z, Tan T (2018) IntroVAE: Introspective variational autoencoders for photographic image synthesis. Adv. Neural Inform. Processing Systems 31(1):52–63.Google Scholar
  • Jang E, Gu S, Poole B (2016) Categorical reparameterization with Gumbel-Softmax. Preprint, submitted November 3, https://arxiv.org/abs/1611.01144.Google Scholar
  • Jindal RP, Sarangee KR, Echambadi R, Lee S (2016) Designed to succeed: Dimensions of product design and their impact on market share. J. Marketing 80(4):72–89.CrossrefGoogle Scholar
  • Jordan MI, Ghahramani Z, Jaakkola TS, Saul LK (1999) An introduction to variational methods for graphical models. Machine Learn. 37(2):183–233.CrossrefGoogle Scholar
  • Kang N, Ren Y, Feinberg FM, Papalambros PY (2019) Form + function: Optimizing aesthetic product design via adaptive, geometrized preference elicitation. Working paper, University of Michigan, Ann Arbor.Google Scholar
  • Kang WC, Fang C, Wang Z, McAuley J (2017) Visually-aware fashion recommendation and design with generative image models. Proc. IEEE Internat. Conf. on Data Mining (IEEE, Piscataway, NJ), 207–216.Google Scholar
  • Karjalainen TM, Snelders D (2010) Designing visual recognition for the brand. J. Production Innovative Management 27(1):6–22.CrossrefGoogle Scholar
  • Karras T, Aila T, Laine S, Lehtinen J (2017) Progressive growing of GANs for improved quality, stability, and variation. Preprint, submitted October 27, https://arxiv.org/abs/1710.10196.Google Scholar
  • Keller KL (2003) Brand synthesis: The multidimensionality of brand knowledge. J. Consumer Res. 29(4):595–600.CrossrefGoogle Scholar
  • Keng (2017) Semi-supervised learning with variational autoencoders. Accessed July 17, 2019, https://bit.ly/2O9RvF8.Google Scholar
  • Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. Proc. 3rd Internat. Conf. on Learn. Representations.Google Scholar
  • Kingma DP, Welling M (2013) Auto-encoding variational Bayes. Preprint, submitted December 20, https://arxiv.org/abs/1312.6114.Google Scholar
  • Kingma DP, Mohamed S, Rezende DJ, Welling M (2014) Semi-supervised learning with deep generative models. Adv. Neural Inform. Processing Systems, 3581–3589.Google Scholar
  • Kreuzbauer R, Malter AJ (2005) Embodied cognition and new product design: Changing product form to influence brand categorization. J. Production Innovative Management 22(2):165–176.CrossrefGoogle Scholar
  • Krippendorff K (2011) Computing Krippendorff’s alpha-reliability. Working paper, University of Pennnsylvania, Philadelphia. https://repository.upenn.edu/asc_papers/43.Google Scholar
  • Kulkarni TD, Whitney WF, Kohli P, Tenenbaum J (2015) Deep convolutional inverse graphics network. Adv. Neural Inform. Processing Systems, 28.Google Scholar
  • Landwehr JR, Labroo AA, Herrmann A (2011) Gut liking for the ordinary: Incorporating design fluency improves automobile sales forecasts. Marketing Sci. 30(3):416–429.LinkGoogle Scholar
  • Larsen ABL, Sønderby SK, Winther O (2015) Autoencoding beyond pixels using a learned similarity metric. Preprint, submitted December 31, https://arxiv.org/abs/1512.09300.Google Scholar
  • Liu L, Dzyabura D, Mizik N (2020) Visual listening in: Extracting brand image portrayed on social media. Marketing Sci. 39(4):669–686.LinkGoogle Scholar
  • Liu X, Lee D, Srinivasan K (2019) Large scale cross-category analysis of consumer review content on sales conversion leveraging deep learning. J. Marketing Res. 56(6):918–943.CrossrefGoogle Scholar
  • Liu Y, Li KJ, Chen H, Balachander S (2017) The effects of products’ aesthetic design on demand and marketing-mix effectiveness: The role of segment prototypicality and brand consistency. J. Marketing 81(1):83–102.CrossrefGoogle Scholar
  • Lopez C, Miller S, Tucker C (2019) Exploring biases between human and machine generated designs. J. Mechanical Design 141(2):021104.CrossrefGoogle Scholar
  • Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B (2015) Adversarial autoencoders. Preprint, submitted November 18, https://arxiv.org/abs/1511.05644.Google Scholar
  • Martindale C (1990) The Clockwork Muse: The Predictability of Artistic Change (Basic Books, New York).Google Scholar
  • Miyato T, Kataoka T, Koyama M, Yoshida Y (2018) Spectral normalization for generative adversarial networks. Preprint, submitted February 16, https://arxiv.org/abs/1802.05957.Google Scholar
  • Morren M, Paas LI (2020) Short and long instructional manipulation checks: What do they measure? Internat. J. Public Opinion Res. 32(4):790–800.CrossrefGoogle Scholar
  • Nobari A, Chen W, Ahmed F (2021) PcDGAN: A continuous conditional diverse generative adversarial network for inverse design. Proc. 27th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (ACM, New York), 606–616.Google Scholar
  • Noble CH, Kumar M (2010) Exploring the appeal of product design: A grounded, value-based model of key design elements and relationships. J. Production Innovative Management 27(5):640–657.CrossrefGoogle Scholar
  • Norman DA (2004) Emotional Design: Why We Love (or Hate) Everyday Things (Basic Books, New York).CrossrefGoogle Scholar
  • Oppenheimer DM, Meyvis T, Davidenko N (2009) Instructional manipulation checks: Detecting satisficing to increase statistical power. J. Experiment. Soc. Psych. 25:867–872.CrossrefGoogle Scholar
  • Orme B, Chrzan K (2017) Becoming an Expert in Conjoint Analysis: Choice Modelling for Pros (Sawtooth Software).Google Scholar
  • Orsborn S, Cagan J, Boatwright P (2009) Quantifying aesthetic form preference in a utility function. J. Mechanical Design 131(6):061001.CrossrefGoogle Scholar
  • Orth UR, Malkewitz K (2008) Holistic package design and consumer brand impressions. J. Marketing 72(3):64–81.CrossrefGoogle Scholar
  • Palazzolo M, Feinberg F (2015) Modeling consideration set substitution. Working paper, University of Michigan, Ann Arbor, MI.Google Scholar
  • Pan Y, Burnap A, Hartley J, Gonzalez R, Papalambros PY (2017) Deep design: Product aesthetics for heterogeneous markets. Proc. 23rd ACM SIGKDD Internat. Conf. on Knowledge Discovery and Data Mining (ACM, New York), 1961–1970.Google Scholar
  • Pauwels K, Silva-Risso J, Srinivasan S, Hanssens DM (2004) New products, sales promotions, and firm value: The case of the automobile industry. J. Marketing 68(4):142–156.CrossrefGoogle Scholar
  • Person O, Snelders D, Schoormans J (2016) Assessing the performance of styling activities: An interview study with industry professionals in style-sensitive companies. Design Stud. 42:33–55.CrossrefGoogle Scholar
  • Person O, Snelders D, Karjalainen TM, Schoormans J (2007) Complementing intuition: Insights on styling as a strategic tool. J. Marketing Management 23(9–10):901–916.CrossrefGoogle Scholar
  • Pfitzer S, Rudolph S (2007) Re-engineering exterior design: Generation of cars by means of a formal graph-based engineering design language. Proc. Internat. Conf. on Engrg. Design (Design Society).Google Scholar
  • Ranganath R, Tran D, Blei DM (2015) Hierarchical variational models. Preprint, submitted November 7, https://arxiv.org/abs/1511.02386.Google Scholar
  • Ranscombe C, Hicks B, Mullineux G, Singh B (2012) Visually decomposing vehicle images: Exploring the influence of different aesthetic features on consumer perception of brand. Design Stud. 33(4):319–341.CrossrefGoogle Scholar
  • Reid T, Gonzalez R, Papalambros PY (2010) Quantification of perceived environmental friendliness for vehicle silhouette design. J. Mechanical Design 132(10):101010.CrossrefGoogle Scholar
  • Reppel AE, Szmigin I, Gruber T (2006) The iPod phenomenon: Identifying a market leader’s secrets through qualitative marketing research. J. Product Brand Management 15(4):239–249.CrossrefGoogle Scholar
  • Rubera G (2015) Design innovativeness and product sales’ evolution. Marketing Sci. 34(1):98–115.LinkGoogle Scholar
  • Sbai O, Elhoseiny M, Bordes A, LeCun Y, Couprie C (2019) DesIGN: Design inspiration from generative networks. Leal-Taixé L, Roth S, eds. Computer Vision Workshops (Springer International Publishing, Cham, Switzerland), 37–44.CrossrefGoogle Scholar
  • Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Preprint, submitted September 4, https://arxiv.org/abs/1409.1556.Google Scholar
  • Sohn K, Lee H, Yan X (2015) Learning structured output representation using deep conditional generative models. Adv. Neural Inform. Processing Systems. Google Scholar
  • Timoshenko A, Hauser JR (2019) Identifying customer needs from user-generated content. Marketing Sci. 38(1):1–20.LinkGoogle Scholar
  • Toffoletto G (2013) The Strategic Value of Design: A Model Derived from the Existing Literature and Six Case Studies of Design Driven Organizations (Politecnico di Milano, Milan).Google Scholar
  • Toubia O, Netzer O (2017) Idea generation, creativity, and prototypicality. Marketing Sci. 36(1):1–20.LinkGoogle Scholar
  • Ulyanov D, Vedaldi A, Lempitsky V (2018) It takes (only) two: Adversarial generator-encoder networks. Proc. 32nd AAAI Conf. on Artificial Intelligence (AAAI Press, Palo Alto, CA).Google Scholar
  • Vlasic B (2011) Once upon a Car: The Fall and Resurrection of America’s Big Three Auto Makers: GM, Ford, and Chrysler (William Morrow, New York).Google Scholar
  • Wu J, Zhang C, Xue T, Freeman B, Tenenbaum J (2016) Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. Adv. Neural Inform. Processing Systems, 29.Google Scholar
  • Yoganarasimhan H (2017) Identifying the presence and cause of fashion cycles in data. J. Marketing Res. 54(1):5–26.CrossrefGoogle Scholar
  • Zhang R, Isola P, Efros AA, Shechtman E, Wang O (2018) The unreasonable effectiveness of deep features as a perceptual metric. Proc. IEEE Conf. on Comput. Vision and Pattern Recognition (IEEE, Piscataway, NJ), 586–595.Google Scholar
  • Zhang W, Yang Z, Jiang H, Nigam S, Yamakawa S, Furuhata T, Shimada K, et al. (2019) 3D shape synthesis for conceptual design and optimization using variational autoencoders. Preprint, submitted April 16, https://arxiv.org/abs/1904.07964.Google Scholar
  • Zhao J, Mathieu M, LeCun Y (2016) Energy-based generative adversarial network. Preprint, submitted September 11, https://arxiv.org/abs/1609.03126.Google Scholar
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