A Theoretical Framework For Understanding Aesthetic Experiences In Relation To Website Design And Utilitarian Outcomes

Online affective experiences and their relationships to utilitarian and non-utilitarian outcomes are of growing interest. This study investigates one affective experience – aesthetics – and its impact on online utilitarian values. As the concept of web aesthetic experience has not been well studied or understood, this study proposes a novel theoretical model connecting web aesthetic experiences to online utilitarian values. Based on the prior literature from other disciplines, the study treats the aesthetic experience as a complex and multi-dimensional construct, which contains three distinct sub-dimensions: aesthetic appraisal, aesthetic judgment, and aesthetic emotion. The research process will include making the selection of diverse level of aesthetic websites and a cross-over design experiment. The structural equation modelling technique will be employed to evaluate the relationships between the web aesthetic experience and utilitarian outcome. The expected contributions of this study are: (i) to construct a novel theoretical model based on the psychological and philosophical concepts of aesthetic experience and utilitarian value; (ii) to identify the multi-dimensional evidence of web aesthetic experiences; (iii) to provide theoretical and practical knowledge for predicting the utilitarian outcomes through online users’ aesthetic experiences. The study also points to the need for more research in this complex area

Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach

Aesthetics are critically important to market acceptance in many product categories. In the automotive industry in particular, an improved aesthetic design can boost sales by 30% or more. Firms invest heavily in designing and testing new product aesthetics. A single automotive "theme clinic" costs between \$100,000 and \$1,000,000, and hundreds are conducted annually. We use machine learning to augment human judgment when designing and testing new product aesthetics. The model combines a probabilistic variational autoencoder (VAE) and adversarial components from generative adversarial networks (GAN), along with modeling assumptions that address managerial requirements for firm adoption. We train our model with data from an automotive partner-7,000 images evaluated by targeted consumers and 180,000 high-quality unrated images. Our model predicts well the appeal of new aesthetic designs-38% improvement relative to a baseline and substantial improvement over both conventional machine learning models and pretrained deep learning models. New automotive designs are generated in a controllable manner for the design team to consider, which we also empirically verify are appealing to consumers. These results, combining human and machine inputs for practical managerial usage, suggest that machine learning offers significant opportunity to augment aesthetic design

Preference Modeling in Data-Driven Product Design: Application in Visual Aesthetics

Creating a form that is attractive to the intended market audience is one of the greatest challenges in product development given the subjective nature of preference and heterogeneous market segments with potentially different product preferences. Accordingly, product designers use a variety of qualitative and quantitative research tools to assess product preferences across market segments, such as design theme clinics, focus groups, customer surveys, and design reviews; however, these tools are still limited due to their dependence on subjective judgment, and being time and resource intensive. In this dissertation, we focus on a key research question: how can we understand and predict more reliably the preference for a future product in heterogeneous markets, so that this understanding can inform designers' decision-making? We present a number of data-driven approaches to model product preference. Instead of depending on any subjective judgment from human, the proposed preference models investigate the mathematical patterns behind users’ choice and behavior. This allows a more objective translation of customers' perception and preference into analytical relations that can inform design decision-making. Moreover, these models are scalable in that they have the capacity to analyze large-scale data and model customer heterogeneity accurately across market segments. In particular, we use feature representation as an intermediate step in our preference model, so that we can not only increase the predictive accuracy of the model but also capture in-depth insight into customers' preference. We tested our data-driven approaches with applications in visual aesthetics preference. Our results show that the proposed approaches can obtain an objective measurement of aesthetic perception and preference for a given market segment. This measurement enables designers to reliably evaluate and predict the aesthetic appeal of their designs. We also quantify the relative importance of aesthetic attributes when both aesthetic attributes and functional attributes are considered by customers. This quantification has great utility in helping product designers and executives in design reviews and selection of designs. Moreover, we visualize the possible factors affecting customers' perception of product aesthetics and how these factors differ across different market segments. Those visualizations are incredibly important to designers as they relate physical design details to psychological customer reactions. The main contribution of this dissertation is to present purely data-driven approaches that enable designers to quantify and interpret more reliably the product preference. Methodological contributions include using modern probabilistic approaches and feature learning algorithms to quantitatively model the design process involving product aesthetics. These novel approaches can not only increase the predictive accuracy but also capture insights to inform design decision-making.PHDDesign ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145987/1/yanxinp_1.pd

Aesthetics in the Adoption of Information and Communication Technology

Borrowing from Davis’ (1989) Technology Acceptance Model (TAM), Venkatesh and Morris (2000) TAM2 and Kant’s (1790) Theory of Aesthetics; we aim to expand on the contributions and frameworks provided by the literature by testing the nomological relationships between aesthetic judgment, user’s personality and the adoption of innovation in Information and Communication Technology (ICT). This study contributes an exploratory scale for the measurement of aesthetics in ICT. Survey data is utilized to explain perceived aesthetics, moderated by aesthetic centrality of a user, in addition to perceived usefulness, as dimensions of an ICT product that influence adoption intent. Preliminary results also show a weakening influence of social norms, non-significant ease of use indicators. We propose a shift in the paradigm of adoption of ICT innovation in which design, brand affinity and usefulness define the competitiveness of an ICT device in today’s market

Individual differences in aesthetic preferences for multi-sensorial stimulation

The aim of the current project was to investigate aesthetics in multi-sensorial stimulation and to explore individual differences in the process. We measured the aesthetics of Interactive Objects (IOs) which are three-dimensional objects with electronic components that exhibit an autonomous behaviour when handled: e.g., vibrating, playing a sound, or lighting-up. The Q-sorting procedure of Q-methodology was applied. Data were analysed by following the Qmulti protocol. The results suggested that overall participants preferred IOs that (i) vibrate, (ii) have rough surface texture, and (iii) are round. No particular preference emerged about the size of the IOs. When making aesthetic judgment, participants paid more attention to the behaviour variable of the IOs than the size, contour or surface texture. In addition, three clusters of participants were identified, suggesting that individual differences existed in the aesthetics of IOs. Without proper consideration of potential individual differences, aesthetic scholars may face the risk of having significant effects masked by individual differences. Only by paying attention to this issue can more meaningful findings be generated to contribute to the field of aesthetics

Aesthetic objects, aesthetic judgments and the crafting of organizational style in creative industries

In this article, we conceptually engage with style as central to creative industries. We specifically argue that style is crafted into being via an interplay between aesthetic judgments and “aesthetic objects.” We define aesthetic objects as temporary, material settlements fueled by a continual sense of dissatisfaction, eventually resolved through relational engagements. These remain under aesthetic inquiry throughout the process of crafting, until brought to particular close. We elaborate our theorizing with a non-traditional exemplar of the Bride Dress in the preparation of a 2009 Jean-Paul Gaultier’s fashion show. Our subsequent contribution is a richer conceptual understanding of style, with a material, aesthetic engagement at its center. In addition, in foregrounding under-explored features (i.e., aesthetic judgments, crafting of physical materials), and introducing new concepts (i.e., aesthetic objects), we outline promising openings for and significant connections with scholarship on creative or fluid industries, style, and organizational identity

Reviews: Vera L. Zolberg’s Constructing a Sociology of the Arts

Book review for Constructing a Sociology of the Arts, Vera L. Zolberg, Cambridge University Press, New York, 1990

Reviews: John Lang’s Creating Architectural Theory

Book review for Creating Architectural Theory, Jon Lang, van Nostrand Reinhold, New York, 1987