Affect Product Design Research Articles

Capturing the sustainability features that most affect consumer evaluations case: mobile phones

ABSTRACTA product’s sustainability affects its consumer evaluation. Sustainable supply chain management (SSCM) research emphasizes the customer role, but typically adopts the company view. On the other hand, when consumer choice is studied, sustainability is mostly evaluated in terms of one feature only, instead of the many facets of sustainability. This research gap has been filled in this study by broadening the view of SSCM to encompass how the consumer relates to different features of sustainability. SSCM literature is first used to construct a conceptual framework for consumer product evaluations and to provide a basis for focus group discussions aimed at identifying the different sustainability features. The case product (mobile phones) has high volumes and short lifecycles that affect environmental sustainability. In our focus group results, all framework themes were present with varying importance. The groups unanimously wished to extend the lifecycle of the phones, an observation affecting product design. A strong demand for information sharing related to supplier management and recycling was also discovered.

Product design: Impact of government policy and consumer preference on company profit and corporate social responsibility

A multi-objective optimization framework that considers the influence of government policy on product design is presented. The government policy affects product design in the form of financial and non-financial incentives, and regulations. The consumers affect product design by their purchase behavior that is in turn influenced by price, product quality, and the presence of competing products. The company responds to these influencing factors to design a product with as high a profit as possible while satisfying corporate social responsibility. Different models as well as rule-based methods – quality, consumer utility, product demand, product cost, capital budgeting, social indices, and government policy – are presented. A solar photovoltaic case study is used to illustrate the framework.

A product affective properties identification approach based on web mining in a crowdsourcing environment

Affective product design, which aims to satisfy customer feelings as an aspect of product quality, has attracted more and more research attention. However, conventional product design relies on surveys and user experiments to collect user evaluations, which leads to the issues that (i) consumers can only express their feelings on design attributes specified by assigners; (ii) abundant online consumer resources are neglected; and (iii) a lack of further prioritisation and re-construction of affective design properties. This study aims to develop a product affective properties identification approach. Crowdsourcing platforms have the advantage of obtaining large numbers of free consumer comments and have been utilised as data sources. Web mining and text mining are deployed to capture the crowdsourced product review resources. Then product design knowledge hierarchy is established to find design properties, while sentiment analysis was undertaken to identify affections. With the help of domain ontology to connect design properties and corresponding affections, product affective properties can be identified. Furthermore, the identified affective properties are prioritised, so as to assist in design improvement and support decision making. To illustrate the proposed approach, a pilot study on iPhone 7 was conducted, in which influential affective properties have been identified and ranked.

Application of exergy-based approach for implementing design for reuse: The case of microwave oven

Systematic reuse of parts is not often achievable in a sustainable way. The methods associated with the available disassembly technology rarely addresses non-destructive disassembly approach that favor reuse as an end-of-life (EoL) strategy. In this sense, this paper aims to propose a method able to identify best opportunities and thus, focus on the designers’ efforts during the early stages of product development, with major repercussions during the product EoL. To build the method, the Design Science Research Methodology (DSRM) is adopted. The method scope relies on material and energy flows measured in terms of exergy that ultimately depicts for energy efficiency, environmental impact, cost and technical efficiency. By means of a case study, findings challenge common sense by quantitatively showing how small subsystems with 3% of mass can hold nearly 200 times more embodied exergy than another with 50% of the mass and thus, may greatly affect product design and EoL results. Therefore, the adoption of the method may prove useful for establishing easy-to-use design practices that favor green engineering, circular economy and environmental policies. An exergy-based approach would unbiasedly drive Reuse EoL that facilitate Design for the Environment (DfE) as well as focus efforts on specific disassembly technologies.

Automation of Synthesis of Structures, Systems Engineering Strategies for Production

The article describes an automated system of support of decision-making structures in the synthesis strategies of industrial engineering systems, based on the method of structural and technological complexity of produced parts. This paper critically evaluates various tool management approaches, identifying the operational tradeoffs and analyzing the models developed to address management decisions involving tooling. The evidence is clear that a lack of attention to structured tool management has resulted in the poor performance of many manufacturing systems. Plant tooling systems affect product design options, machine loading, job batching, capacity scheduling, and real-time part routing decisions. With increasing automation in manufacturing systems, there is a growing need to integrate tool management more thoroughly into system design, planning and control.

Rough set and PSO-based ANFIS approaches to modeling customer satisfaction for affective product design

Facing fierce competition in marketplaces, companies try to determine the optimal settings of design attribute of new products from which the best customer satisfaction can be obtained. To determine the settings, customer satisfaction models relating affective responses of customers to design attributes have to be first developed. Adaptive neuro-fuzzy inference systems (ANFIS) was attempted in previous research and shown to be an effective approach to address the fuzziness of survey data and nonlinearity in modeling customer satisfaction for affective design. However, ANFIS is incapable of modeling the relationships that involve a number of inputs which may cause the failure of the training process of ANFIS and lead to the ‘out of memory’ error. To overcome the limitation, in this paper, rough set (RS) and particle swarm optimization (PSO) based-ANFIS approaches are proposed to model customer satisfaction for affective design and further improve the modeling accuracy. In the approaches, the RS theory is adopted to extract significant design attributes as the inputs of ANFIS and PSO is employed to determine the parameter settings of an ANFIS from which explicit customer satisfaction models with better modeling accuracy can be generated. A case study of affective design of mobile phones is used to illustrate the proposed approaches. The modeling results based on the proposed approaches are compared with those based on ANFIS, fuzzy least-squares regression (FLSR), fuzzy regression (FR), and genetic programming-based fuzzy regression (GP-FR). Results of the training and validation tests show that the proposed approaches perform better than the others in terms of training and validation errors.

Unified assembly- and disassembly-line model formulae

Purpose – There is a rich body of literature on sequencing assembly and on sequencing disassembly, but little that either fuses or contrasts the two, which may be valuable for long-range planning in the closed-loop supply chain and simply convenient in terms of consistency in nomenclature and mathematical formulations. The purpose of this paper is to concisely unify and summarize assembly and disassembly formulae – as well as to add new formulations for completeness – and then demonstrate the similarities and differences between assembly and disassembly. Design/methodology/approach – Along with several familiar assembly-line formulae which are adapted here for disassembly, five (two specific and three general) metrics and a comparative performance formula from disassembly-line balancing are proposed for use in assembly- and disassembly-line sequencing and balancing either directly, through generalization, or with some extension. The size of assembly and disassembly search spaces are also quantified and formulated. Three new metrics are then developed from each of the general metrics to demonstrate the process of using these general formulae as prototypes. Findings – The three new metrics along with several of the original metrics are selectively applied to a simple, notional case study product to be sequenced on an assembly line and then on a disassembly line. Using these analytical results, the inherent differences between assembly and disassembly, even for a seemingly trivial product, are illustrated. Originality/value – The research adds several new assembly/disassembly metrics, a case study, unifies the evaluation formulae that assembly and disassembly hold in common as well as structuring prototype formulae for flexibility in generating new evaluation criteria for both, and quantifies (using the case study) how assembly and disassembly – while certainly possessing similarities – also demonstrate measurable differences that can be expected to affect product design, planning, production, and end-of-life processing.

Affective Product Design Based on Random Forest and Association Rules

Affective design, which aims to design favorable products that meet the customers affective needs and address customers affective satisfaction, has gain increasing attention in modern industries. This paper proposed an affective design approach combing random forest regression and association rule mining, where random forest is adopted to reduce the dimension of design elements and association rules is used to map the affective need to design element. The efficiency of the method is demonstrated by an application of elevator design.

A guided search genetic algorithm using mined rules for optimal affective product design

Affective design is an important aspect of new product development, especially for consumer products, to achieve a competitive edge in the marketplace. It can help companies to develop new products that can better satisfy the emotional needs of customers. However, product designers usually encounter difficulties in determining the optimal settings of the design attributes for affective design. In this article, a novel guided search genetic algorithm (GA) approach is proposed to determine the optimal design attribute settings for affective design. The optimization model formulated based on the proposed approach applied constraints and guided search operators, which were formulated based on mined rules, to guide the GA search and to achieve desirable solutions. A case study on the affective design of mobile phones was conducted to illustrate the proposed approach and validate its effectiveness. Validation tests were conducted, and the results show that the guided search GA approach outperforms the GA approach without the guided search strategy in terms of GA convergence and computational time. In addition, the guided search optimization model is capable of improving GA to generate good solutions for affective design.

Affective Product Design Research Based on Fishing Culture of Northern China

Globalization has caused some cultures with little influence to become assimilated, which has gradually resulted in the disappearance of these traditional cultures. Northern Chinese fishing minority groups, such as the Hezhen people, represent this phenomenon. Affectively developing products based on Hezhen fishing culture can both help protect and develop this unique culture. In order to accomplish this goal, this study firstly collected cultural symbols from Northern Chinese fishing groups by compiling and researching the distinct features of these people’s religion, art, architecture, clothing, etc. Next an affective design method based on specific cultural symbols was presented through analyzing the relationship between culture and emotions. Finally, the design concepts of affective products that were integrated with Hezhen fishing culture features were proposed and a series of affective products were designed with the purpose of representing leisure furniture.

Swarm-Optimization-Based Affective Product Design Illustrated by a Mobile Phone Case-Study

This paper presents a new approach of user- oriented design for transforming users' perception into product elements design. An experimental study on mobile phones is conducted to examine how product form and product design parameters affect consumer's perception of a product. The concept of Kansei Engineering is used to extract the experimental samples as a data base for neural networks (NNs) with particle swarm optimization (PSO) analysis. The result of numerical analysis suggests that mobile phone makers need to focus on particular design parameters to attract specific target user groups, in addition to product forms. This paper demonstrates the advantage of using KE-PSO for determining the optimal combination of product design parameters. Based on the analysis, we can use KE-PSO to suggest customers' preferences for mobile phone design attributes that would be considered optimal by various user groups of all surveyed. They can be used for improvement and development of new future products.

A multi-objective genetic algorithm approach to rule mining for affective product design

A novel multi-objective genetic algorithm (GA)-based rule-mining method for affective product design is proposed to discover a set of rules relating design attributes with customer evaluation based on survey data. The proposed method can generate approximate rules to consider the ambiguity of customer assessments. The generated rules can be used to determine the lower and upper limits of the affective effect of design patterns. For a rule-mining problem, the proposed multi-objective GA approach could simultaneously consider the accuracy, comprehensibility, and definability of approximate rules. In addition, the proposed approach can deal with categorical attributes and quantitative attributes, and determine the interval of quantitative attributes. Categorical and quantitative attributes in affective product design should be considered because they are commonly used to define the design profile of a product. In this paper, a two-stage rule-mining approach is proposed to generate rules with a simple chromosome design in the first stage of rule mining. In the second stage of rule mining, entire rule sets are refined to determine solutions considering rule interaction. A case study on mobile phones is used to demonstrate and validate the performance of the proposed rule-mining method. The method can discover rule sets with good support and coverage rates from the survey data.

A Comparative Study of Ordinal Probit and Logistic Regression for Affective Product Design

Affective product design, which concentrates on customers’ affective responses and aspirations, is arousing increasing attention. In this paper, ordinal probit regression (OPR) is introduced into this field to discover mapping knowledge from design elements to customer affect, and a comparative study is always recommended between OPR and ordinal logistic regression (OLR) for available data to choose a better fitted model. The discovered mapping relations could facilitate the handling of affective information and assist the designer to make trade-off decisions. A case study of cell phone design was conducted. Four generic affective dimensions and six key product attributes of the cell phone were identified. OPR and OLR were applied successively to reveal the quantitative relations from design elements to customer affect. For the two models, five classes of indexes were compared. The findings show that OLR is superior to OPR to fit the collected data.

A Comparative Study of Ordinal Probit and Logistic Regression for Affective Product Design

Affective product design, which concentrates on customers’ affective responses and aspirations, is arousing increasing attention. In this paper, ordinal probit regression (OPR) is introduced into this field to discover mapping knowledge from design elements to customer affect, and a comparative study is always recommended between OPR and ordinal logistic regression (OLR) for available data to choose a better fitted model. The discovered mapping relations could facilitate the handling of affective information and assist the designer to make trade-off decisions. A case study of cell phone design was conducted. Four generic affective dimensions and six key product attributes of the cell phone were identified. OPR and OLR were applied successively to reveal the quantitative relations from design elements to customer affect. For the two models, five classes of indexes were compared. The findings show that OLR is superior to OPR to fit the collected data.

An intelligent fuzzy regression approach for affective product design that captures nonlinearity and fuzziness

Affective product design aims at incorporating customers’ affective needs into design variables of a new product so as to optimise customers’ affective satisfaction. Faced with fierce competition in marketplaces, companies try to determine the settings in order to maximise customers’ affective satisfaction with products. To achieve this, a set of customer survey data is required in order to develop a model which relates customers’ affective responses to the design variables of a new product. Customer survey data are usually fuzzy since human feeling is usually fuzzy, and the relationship between customers’ affective responses and design variables is usually nonlinear. However, previous research on modelling the relationship between affective response and design variables has not addressed the development of explicit models involving either nonlinearity or fuzziness. In this paper, an intelligent fuzzy regression approach is proposed to generate models which represent this nonlinear and fuzzy relationship between affective responses and design variables. In order to do this, we extend the existing work on fuzzy regression by first utilising an evolutionary algorithm to construct branches of a tree representing structures of a model where the nonlinearity of the model can be addressed. The fuzzy regression algorithm is then used to determine the fuzzy coefficients of the model. The models thus developed are explicit, and consist of fuzzy, nonlinear terms which relate affective responses to design variables. A case study of affective product design of mobile phones is used to illustrate the proposed method.

A problem-solving approach to product design using decision tree induction based on intuitionistic fuzzy

Customer complaint problem is a product design used to understand customer requirements. Furthermore, product design corresponding to customer requirement does not feel adequately solved for a cause of problem. The cause of the problem affecting product design is solved to prevent customer complaint from reoccurring. However, the problems by customer may have observation uncertainty and fuzzy. Fuzzy concept considers not only the degree of membership to an accept set, but also the degree of non-membership to a rejection set. Therefore, we present a new approach for problem solving using decision tree induction based on intuitionistic fuzzy sets in this paper. Under this approach, we first develop the problem formulation for the symptoms and causes of the problem based on intuitionistic fuzzy sets. Next, we identify the cause of the problem using intuitionistic fuzzy decision tree by the problem formulation. We then provide the approach to find the optimal cause of the problem for the consideration of product design. A numerical example is used to illustrate the approach applied for product design.

Incorporating affective customer needs for luxuriousness into product design attributes

AbstractIn a highly competitive market, customers' product affection is a critical factor to product success. However, understanding customers' affective needs is difficult to grasp; product design practitioners often misunderstand what customers really want. In this study we report our experience in developing and using an affective design framework that identified critical affective features customers have on products and are systematically incorporated into product design attributes. To identify key affective features such as luxuriousness, we utilized the Kansei engineering methodology. This approach consists of three steps: (1) selecting related affective features and product design attributes through a comprehensive literature survey, expert panel opinion, and focus group interviews; (2) conducting evaluation experiments; and (3) developing Kansei models using multivariate statistical analysis and analyzing critical product design attributes. To demonstrate applicability of the proposed affective design framework, 30 customers and 30 product design practitioners participated in an evaluation experiment for car crash pads, and 44 customers and 20 designers participated in an evaluation experiment for two interior room products (wallpapers and flooring materials). The evaluation experiments were conducted via systematically developed questionnaires consisting of a 7‐point semantic differential scale and a 100‐point magnitude estimation scale. The results of the experiments were analyzed using principal component regression and quantification theory type I method. Using the analyzed survey data, the relationship between luxuriousness and related affective features and product design attributes were identified. This relationship indicated that there was a significant difference in the perception of luxuriousness between customers and designers. Consequently, it is expected that the results of this study could provide a foundation for developing affective products. © 2009 Wiley Periodicals, Inc.

The marketing implications of affective product design

Emotions are compelling human experiences and product designers can take advantage of this by conceptualizing emotion-engendering products that sell well in the market. This study hypothesized that product attributes influence users’ emotions and that the relationship is moderated by the adherence of these product attributes to purchase criteria. It was further hypothesized that the emotional experience of the user influences purchase intention. A laboratory study was conducted to validate the hypotheses using mobile phones as test products. Sixty-two participants were asked to assess eight phones from a display of 10 phones and indicate their emotional experiences after assessment. Results suggest that some product attributes can cause intense emotional experience. The attributes relate to the phone's dimensions and the relationship between these dimensions. The study validated the notion of integrating affect in designing products that convey users’ personalities.

How Does Usability Research Improve 21St Century Product Design?

Usability research is dramatically affecting product design in the 21st century. Five user experience and research managers from cutting-edge Silicon Valley companies discuss the strategic relationship between user research and product design, organizational processes that succeed in helping user research and user experience design improve products, and what's different about user research and user experience design for Internet companies and Web sites.

AFFECTIVE AND COMMUNICATIONAL EFFECT IN PRODUCT DESIGN : A Case Study based on ALESSI Kettles

AFFECTIVE AND COMMUNICATIONAL EFFECT IN PRODUCT DESIGN : A Case Study based on ALESSI Kettles