Designing the appearance of environmentally sustainable products

Abstract The study presented in this paper uses a mathematical model to measure the degree in which a product will be perceived as environmentally friendly from its physical attributes. A model based on genetic algorithms and neural networks was developed to predict the judgement of the users about environmental friendliness of different tables. Opinions of real users about a large set of tables were used to train the model. The results of the study suggest that, using this procedure in advanced stages of product design process, designers can determine the set of product's physical attributes that best convey the idea of “environmentally sustainable” to the customer. The analysis of the obtained model allows establishing how different product's attributes influence users' perception. From these results, the utilization of users' affective response models to design the appearance of environmentally sustainable products is discussed.

[1]  Shih-Chang Tseng,et al.  A framework identifying the gaps between customers' expectations and their perceptions in green products , 2013 .

[2]  V. Albino,et al.  Environmental strategies and green product development: an overview on sustainability‐driven companies , 2009 .

[3]  K. Peattie,et al.  Green marketing: legend, myth, farce or prophesy? , 2005 .

[4]  Jose C. Principe,et al.  Neural and adaptive systems : fundamentals through simulations , 2000 .

[5]  Myung Hwan Yun,et al.  Evaluation of product usability: development and validation of usability dimensions and design elements based on empirical models , 2000 .

[6]  Peter H. Bloch.  Seeking the Ideal Form: Product Design and Consumer Response , 1995 .

[7]  S. Rosenthal,et al.  Signaling the Green Sell: The Influence of Eco-Label Source, Argument Specificity, and Product Involvement on Consumer Trust , 2014 .

[8]  Mariëlle E. H. Creusen,et al.  The Different Roles of Product Appearance in Consumer Choice , 2005 .

[9]  Donald M. Waldman,et al.  (www.interscience.wiley.com) DOI: 10.1002/jae.984 LEARNING AND FATIGUE DURING CHOICE EXPERIMENTS: A COMPARISON OF ONLINE AND MAIL SURVEY MODES , 2022 .

[10]  Lance Hosey.  The Shape of Green: Aesthetics, Ecology, and Design , 2012 .

[11]  O. Kvasova,et al.  Antecedents and outcomes of consumer environmentally friendly attitudes and behaviour , 2010 .

[12]  Filippo Menczer,et al.  Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms , 2005, Manag. Sci..

[13]  Ming-Chuen Chuang,et al.  Expressing the expected product images in product design of micro-electronic products , 2001 .

[14]  Wei Yan,et al.  An in-process customer utility prediction system for product conceptualisation , 2008, Expert Syst. Appl..

[15]  Vanessa C. Burbano,et al.  The Drivers of Greenwashing , 2011 .

[16]  P. Clarkson,et al.  Seeing things: consumer response to the visual domain in product design , 2004 .

[17]  Chung-Hsing Yeh,et al.  Consumer-oriented product form design based on fuzzy logic: A case study of mobile phones , 2007 .

[18]  Shang H. Hsu,et al.  A semantic differential study of designers’ and users’ product form perception , 2000 .

[19]  P. Desmet,et al.  A Multilayered Model of Product Emotions , 2003 .

[20]  Deoki N. Saraf,et al.  Design of neural networks using genetic algorithm for on-line property estimation of crude fractionator products , 2006, Comput. Chem. Eng..

[21]  Andrew D. Gershoff,et al.  What Makes It Green? The Role of Centrality of Green Attributes in Evaluations of the Greenness of Products , 2015 .

[22]  Dick R. Wittink,et al.  Verbal versus realistic pictorial representations in conjoint analysis with design attributes , 1998 .

[23]  Sukumar Bandopadhyay,et al.  Comparing the predictive performance of neural networks with ordinary kriging in a bauxite deposit , 2005 .

[24]  Li Pheng Khoo,et al.  A strategy for acquiring customer requirement patterns using laddering technique and ART2 neural network , 2002, Adv. Eng. Informatics.

[25]  John Wilson,et al.  Doing Well by Doing Good: Volunteering and Occupational Achievement among American Women , 2003 .

[26]  M. Asgari,et al.  Price presentation effects on green purchase intentions , 2014 .

[27]  Gülay Hasdoǧan,et al.  The role of user models in product design for assessment of user needs , 1996 .

[28]  Sanjoy Ghose,et al.  Comparing the predictive performance of a neural network model with some traditional market response models , 1994 .

[29]  Makoto Watanabe,et al.  Aesthetic and Sustainability: The Aesthetic Attributes Promoting Product Sustainability , 2003 .

[30]  Chih-Chieh Yang,et al.  A support vector regression based prediction model of affective responses for product form design , 2010, Comput. Ind. Eng..

[31]  E. Dahan,et al.  The predictive power of internet-based product concept testing using visual depiction and animation , 2000 .

[32]  Jose Antonio Diego-Mas,et al.  Influence of the mode of graphical representation on the perception of product aesthetic and emotional features: An exploratory study , 2008 .

[33]  Warren S. Sarle,et al.  Stopped Training and Other Remedies for Overfitting , 1995 .

[34]  P. Lin,et al.  The influence factors on choice behavior regarding green products based on the theory of consumption values , 2012 .

[35]  Erkki Oja,et al.  Principal components, minor components, and linear neural networks , 1992, Neural Networks.

[36]  E. Rex,et al.  Beyond ecolabels: what green marketing can learn from conventional marketing , 2007 .

[37]  Michael G. Luchs,et al.  The Sustainability Liability: Potential Negative Effects of Ethicality on Product Preference , 2010 .

[38]  Shang H. Hsu,et al.  Perceptual factors underlying user preferences toward product form of mobile phones , 2001 .

[39]  Pontus Engelbrektsson,et al.  Effects of product experience and product representations in focus group interviews , 2002 .

[40]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[41]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[42]  Yukihiro Matsubara,et al.  An analysis of Kansei structure on shoes using self-organizing neural networks , 1997 .

[43]  I. Brace,et al.  Questionnaire Design: How to Plan, Structure and Write Survey Material for Effective Market Research , 2004 .

[44]  Lindsay McShane,et al.  Isolated Environmental Cues and Product Efficacy Penalties: The Color Green and Eco-labels , 2017 .

[45]  Peter E. Rossi,et al.  Determinants of Store-Level Price Elasticity , 1995 .

[46]  Markus Ahola,et al.  Shaping the face of environmentally sustainable products: image boards and early consumer involvement in ship interior design , 2014 .

[47]  Shih-Wen Hsiao,et al.  An image evaluation approach for parameter-based product form and color design , 2006, Comput. Aided Des..

[48]  P. Parker,et al.  Marketing universals: Consumers' use of brand name, price, physical appearance, and retailer , 1994 .

[49]  Greg M. Allenby,et al.  Using Extremes to Design Products and Segment Markets , 1995 .

[50]  M. G. Luchs,et al.  Product Choice and the Importance of Aesthetic Design Given the Emotion-laden Trade-off between Sustainability and Functional Performance , 2012 .

[51]  MariAnne Karlsson,et al.  Eliciting Customer Requirements. Product representations as mediating objects in focus group interviews. , 1998 .

[52]  S. Han,et al.  A systematic approach for coupling user satisfaction with product design , 2003, Ergonomics.

[53]  Pascal Schlich,et al.  Adequate number of consumers in a liking test. Insights from resampling in seven studies , 2014 .

[54]  Chung-Hsing Yeh,et al.  User-oriented design for the optimal combination on product design , 2006 .

[55]  Sung H. Han,et al.  A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design , 2004 .

[56]  Jan P.L. Schoormans,et al.  Enhancing concept test validity by using expert consumers , 1995 .

[57]  Li Pheng Khoo,et al.  An investigation into affective design using sorting technique and Kohonen self-organising map , 2006, Adv. Eng. Softw..

[58]  Shih-Wen Hsiao,et al.  Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design , 2005 .

[59]  Terence D. Sanger,et al.  Optimal unsupervised learning in a single-layer linear feedforward neural network , 1989, Neural Networks.

[60]  Jorge Alcaide-Marzal,et al.  Single users' affective responses models for product form design , 2016 .

[61]  Chung-Hsing Yeh,et al.  Form design of product image using grey relational analysis and neural network models , 2005, Comput. Oper. Res..

[62]  K. B. Monroe.  The Influence of Price on Product Perceptions and Product Choice , 1982 .