Product Aesthetic Design: A Machine Learning Augmentation
References
- (1990) Consumer evaluations of brand extensions. J. Marketing 54(1):27–41.Crossref, Google Scholar
- (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
- (1971) Aesthetics and Psychobiology (Appleton-Century-Crofts, East Norwalk, CT).Google Scholar
- (2017) BEGAN: Boundary equilibrium generative adversarial networks. Preprint, submitted March 31, https://arxiv.org/abs/1703.10717.Google Scholar
- (2017) Variational inference: A review for statisticians. J. Amer. Statist. Assoc. 112(518):859–877.Crossref, Google Scholar
- (1995) Seeking the ideal form: Product design and consumer response. J. Marketing 59(3):16.Crossref, Google Scholar
- (2013) Keeping It Fresh: Strategic Product Redesigns and Welfare (National Bureau of Economic Research, Cambridge, MA).Crossref, Google Scholar
- (2006) Role of sketching in conceptual design of car styling. J. Desert Res. 5(1):116.Crossref, Google Scholar
- (2022) Attribute sentiment scoring with online text reviews: Accounting for language structure and missing attributes. J. Marketing Res. 59(3):600–622.Crossref, Google Scholar
- (2018) On styles in product design: An analysis of U.S. design patents. Management Sci. 64(3):1230–1249.Link, Google Scholar
- (2016a) InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. Adv. Neural Inform. Processing Systems, 29.Google Scholar
- (2016b) Variational lossy autoencoder. Preprint, submitted November 8, https://arxiv.org/abs/1611.02731.Google Scholar
- (2015) How Important Is Design for the Automobile Value Chain? (Social Science Research Network, Rochester, NY).Crossref, Google Scholar
- (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.Crossref, Google Scholar
- (2003) Watches Tell More Than Time: Product Design, Information, and the Quest for Elegance (McGraw-Hill, London).Google Scholar
- (1990) Stage-gate systems: A new tool for managing new products. Bus. Horizons 33(3):44–54.Crossref, Google Scholar
- (2005) The different roles of product appearance in consumer choice. J. Production Innovative Management 22(1):63–81.Crossref, Google Scholar
- (2004) Seeing things: Consumer response to the visual domain in product design. Design Stud. 25(6):547–577.Crossref, Google Scholar
- (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.Crossref, Google 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
- (2022) Letting logos speak: Leveraging multiview representation learning for data-driven logo design. Marketing Sci. 41(2):401–425.Link, Google Scholar
- (2016) Deep unsupervised clustering with Gaussian mixture variational autoencoders. Preprint, submitted November 8, https://arxiv.org/abs/1611.02648.Google Scholar
- (2011) Unstructured direct elicitation of decision rules. J. Marketing Res. 48(1):116–127.Crossref, Google Scholar
- (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
- (2022) Product choice with large assortments: A scalable deep learning model. Management Sci. 68(3):1808–1827.Link, Google Scholar
- (2014) Generative adversarial nets. Adv. Neural Inform. Processing Systems, 2672–2680.Google Scholar
- (1992) Patterns of communication among marketing, engineering, and manufacturing: A comparison between two new product teams. Management Sci. 38(3):360–373.Link, Google Scholar
- (1972) The creative aspects of advertising. Sloan Management Rev. 14(1):83–109.Google Scholar
- (2017) Improved training of Wasserstein GANs. Adv. Neural Inform. Processing Systems, 30.Google Scholar
- (1996) Brands Through the Lens of Style (Quest and Associates).Google Scholar
- (2015) It’s a trap! Instructional manipulation checks prompt systematic thinking on “tricky” tasks. SAGE Open. 5(2):2158244015584617.Crossref, Google Scholar
- (2003) ‘Most advanced, yet acceptable’: Typicality and novelty as joint predictors of aesthetic preference in industrial design. British J. Psych. 94(1):111–124.Crossref, Google Scholar
- (2018) Pioneer networks: Progressively growing generative autoencoder. Preprint, submitted July 9, https://arxiv.org/abs/1807.03026.Google Scholar
- (2019) Toward photographic image manipulation with balanced growing of generative autoencoders. Preprint, submitted April 12, https://arxiv.org/abs/1904.06145.Google Scholar
- (2005) The impact of industrial design effectiveness on corporate financial performance. J. Production Innovative Management 22(1):3–21.Crossref, Google Scholar
- (2017) beta-VAE: Learning basic visual concepts with a constrained variational framework. International Conference on Learning Representations.Google Scholar
- (2018) Towards a definition of disentangled representations. Preprint, submitted December 5, https://arxiv.org/abs/1812.02230.Google Scholar
- (2015) New product design: Concept, measurement, and consequences. J. Marketing 79(3):41–56.Crossref, Google Scholar
- (2018) Squeeze-and-excitation networks. Proc. IEEE/CVF Conf. on Comput. Vision and Pattern Recognition, (IEEE, Piscataway, NJ), 7132–7141.Google Scholar
- (2018) IntroVAE: Introspective variational autoencoders for photographic image synthesis. Adv. Neural Inform. Processing Systems 31(1):52–63.Google Scholar
- (2016) Categorical reparameterization with Gumbel-Softmax. Preprint, submitted November 3, https://arxiv.org/abs/1611.01144.Google Scholar
- (2016) Designed to succeed: Dimensions of product design and their impact on market share. J. Marketing 80(4):72–89.Crossref, Google Scholar
- (1999) An introduction to variational methods for graphical models. Machine Learn. 37(2):183–233.Crossref, Google Scholar
- (2019) Form + function: Optimizing aesthetic product design via adaptive, geometrized preference elicitation. Working paper, University of Michigan, Ann Arbor.Google Scholar
- (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
- (2010) Designing visual recognition for the brand. J. Production Innovative Management 27(1):6–22.Crossref, Google Scholar
- (2017) Progressive growing of GANs for improved quality, stability, and variation. Preprint, submitted October 27, https://arxiv.org/abs/1710.10196.Google Scholar
- (2003) Brand synthesis: The multidimensionality of brand knowledge. J. Consumer Res. 29(4):595–600.Crossref, Google Scholar
- Keng (2017) Semi-supervised learning with variational autoencoders. Accessed July 17, 2019, https://bit.ly/2O9RvF8.Google Scholar
- (2015) Adam: A method for stochastic optimization. Proc. 3rd Internat. Conf. on Learn. Representations.Google Scholar
- (2013) Auto-encoding variational Bayes. Preprint, submitted December 20, https://arxiv.org/abs/1312.6114.Google Scholar
- (2014) Semi-supervised learning with deep generative models. Adv. Neural Inform. Processing Systems, 3581–3589.Google Scholar
- (2005) Embodied cognition and new product design: Changing product form to influence brand categorization. J. Production Innovative Management 22(2):165–176.Crossref, Google Scholar
- (2011) Computing Krippendorff’s alpha-reliability. Working paper, University of Pennnsylvania, Philadelphia. https://repository.upenn.edu/asc_papers/43.Google Scholar
- (2015) Deep convolutional inverse graphics network. Adv. Neural Inform. Processing Systems, 28.Google Scholar
- (2011) Gut liking for the ordinary: Incorporating design fluency improves automobile sales forecasts. Marketing Sci. 30(3):416–429.Link, Google Scholar
- (2015) Autoencoding beyond pixels using a learned similarity metric. Preprint, submitted December 31, https://arxiv.org/abs/1512.09300.Google Scholar
- (2020) Visual listening in: Extracting brand image portrayed on social media. Marketing Sci. 39(4):669–686.Link, Google Scholar
- (2019) Large scale cross-category analysis of consumer review content on sales conversion leveraging deep learning. J. Marketing Res. 56(6):918–943.Crossref, Google Scholar
- (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.Crossref, Google Scholar
- (2019) Exploring biases between human and machine generated designs. J. Mechanical Design 141(2):021104.Crossref, Google Scholar
- (2015) Adversarial autoencoders. Preprint, submitted November 18, https://arxiv.org/abs/1511.05644.Google Scholar
- (1990) The Clockwork Muse: The Predictability of Artistic Change (Basic Books, New York).Google Scholar
- (2018) Spectral normalization for generative adversarial networks. Preprint, submitted February 16, https://arxiv.org/abs/1802.05957.Google Scholar
- (2020) Short and long instructional manipulation checks: What do they measure? Internat. J. Public Opinion Res. 32(4):790–800.Crossref, Google Scholar
- (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
- (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.Crossref, Google Scholar
- (2004) Emotional Design: Why We Love (or Hate) Everyday Things (Basic Books, New York).Crossref, Google Scholar
- (2009) Instructional manipulation checks: Detecting satisficing to increase statistical power. J. Experiment. Soc. Psych. 25:867–872.Crossref, Google Scholar
- (2017) Becoming an Expert in Conjoint Analysis: Choice Modelling for Pros (Sawtooth Software).Google Scholar
- (2009) Quantifying aesthetic form preference in a utility function. J. Mechanical Design 131(6):061001.Crossref, Google Scholar
- (2008) Holistic package design and consumer brand impressions. J. Marketing 72(3):64–81.Crossref, Google Scholar
- (2015) Modeling consideration set substitution. Working paper, University of Michigan, Ann Arbor, MI.Google Scholar
- (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
- (2004) New products, sales promotions, and firm value: The case of the automobile industry. J. Marketing 68(4):142–156.Crossref, Google Scholar
- (2016) Assessing the performance of styling activities: An interview study with industry professionals in style-sensitive companies. Design Stud. 42:33–55.Crossref, Google Scholar
- (2007) Complementing intuition: Insights on styling as a strategic tool. J. Marketing Management 23(9–10):901–916.Crossref, Google Scholar
- (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
- (2015) Hierarchical variational models. Preprint, submitted November 7, https://arxiv.org/abs/1511.02386.Google Scholar
- (2012) Visually decomposing vehicle images: Exploring the influence of different aesthetic features on consumer perception of brand. Design Stud. 33(4):319–341.Crossref, Google Scholar
- (2010) Quantification of perceived environmental friendliness for vehicle silhouette design. J. Mechanical Design 132(10):101010.Crossref, Google Scholar
- (2006) The iPod phenomenon: Identifying a market leader’s secrets through qualitative marketing research. J. Product Brand Management 15(4):239–249.Crossref, Google Scholar
- (2015) Design innovativeness and product sales’ evolution. Marketing Sci. 34(1):98–115.Link, Google Scholar
- (2019) DesIGN: Design inspiration from generative networks. Leal-Taixé L, Roth S, eds. Computer Vision Workshops (Springer International Publishing, Cham, Switzerland), 37–44.Crossref, Google 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
- (2015) Learning structured output representation using deep conditional generative models. Adv. Neural Inform. Processing Systems. Google Scholar
- (2019) Identifying customer needs from user-generated content. Marketing Sci. 38(1):1–20.Link, Google Scholar
- (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
- (2017) Idea generation, creativity, and prototypicality. Marketing Sci. 36(1):1–20.Link, Google Scholar
- (2018) It takes (only) two: Adversarial generator-encoder networks. Proc. 32nd AAAI Conf. on Artificial Intelligence (AAAI Press, Palo Alto, CA).Google Scholar
- (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
- (2016) Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. Adv. Neural Inform. Processing Systems, 29.Google Scholar
- (2017) Identifying the presence and cause of fashion cycles in data. J. Marketing Res. 54(1):5–26.Crossref, Google 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
- (2019) 3D shape synthesis for conceptual design and optimization using variational autoencoders. Preprint, submitted April 16, https://arxiv.org/abs/1904.07964.Google Scholar
- (2016) Energy-based generative adversarial network. Preprint, submitted September 11, https://arxiv.org/abs/1609.03126.Google Scholar

