Guest Editorial

J. Mech. Des. 2015;137(7):070301-070301-2. doi:10.1115/1.4030425.

Addressing user needs and preferences is critically important in developing innovative and successful engineered products and systems. The task is inherently challenging due to the heterogeneity of user needs and the difficulty of modeling human behavior and preferences. In addition, it remains a challenge to integrate user preferences with technical and economic requirements during the engineering design process. The past decade has seen a significant growth in user-focused design research that introduces principles from different domains, such as market research, economics, cognitive science, and social science. However, there is still a lack of integration of these methods, either qualitative or quantitative, for directly supporting engineering design decisions. It is therefore imperative to develop interdisciplinary design approaches to address “interface” issues among different domains and engineering design, considering market demand, usage context, social behavior, environmental impact assessments, and other factors.

Commentary by Dr. Valentin Fuster

Papers: User needs and preferences elicitation

J. Mech. Des. 2015;137(7):071401-071401-12. doi:10.1115/1.4030159.

Different from explicit customer needs that can be identified directly by analyzing raw data from the customers, latent customer needs are often implied in the semantics of use cases underlying customer needs information. Due to difficulties in understanding semantic implications associated with use cases, typical text mining-based methods can hardly identify latent customer needs, as opposite to keywords mining for explicit customer needs. This paper proposes a two-layer model for latent customer needs elicitation through use case reasoning. The first layer emphasizes sentiment analysis, aiming to identify explicit customer needs based on the product attributes and ordinary use cases extracted from online product reviews. Fuzzy support vector machines (SVMs) are developed to build sentiment prediction models based on a list of affective lexicons. The second layer is geared toward use case analogical reasoning, to identify implicit characteristics of latent customer needs by reasoning the semantic similarities and differences analogically between the ordinary and extraordinary use cases. Case-based reasoning (CBR) is utilized to perform case retrieval and case adaptation. A case study of Kindle Fire HD 7 in. tablet is developed to illustrate the potential and feasibility of the proposed method.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(7):071402-071402-11. doi:10.1115/1.4030049.

Lead users play a vital role in next generation product development, as they help designers discover relevant product feature preferences months or even years before they are desired by the general customer base. Existing design methodologies proposed to extract lead user preferences are typically constrained by temporal, geographic, size, and heterogeneity limitations. To mitigate these challenges, the authors of this work propose a set of mathematical models that mine social media networks for lead users and the product features that they express relating to specific products. The authors hypothesize that: (i) lead users are discoverable from large scale social media networks and (ii) product feature preferences, mined from lead user social media data, represent product features that do not currently exist in product offerings but will be desired in future product launches. An automated approach to lead user product feature identification is proposed to identify latent features (product features unknown to the public) from social media data. These latent features then serve as the key to discovering innovative users from the ever increasing pool of social media users. The authors collect 2.1 × 109 social media messages in the United States during a period of 31 months (from March 2011 to September 2013) in order to determine whether lead user preferences are discoverable and relevant to next generation cell phone designs.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(7):071403-071403-11. doi:10.1115/1.4030161.

Understanding user needs and preferences is increasingly recognized as a critical component of early stage product development. The large-scale needfinding methods in this series of studies attempt to overcome shortcomings with existing methods, particularly in environments with limited user access. The three studies evaluated three specific types of stimuli to help users describe higher quantities of needs. Users were trained on need statements and then asked to enter as many need statements and optional background stories as possible. One or more stimulus types were presented, including prompts (a type of thought exercise), shared needs, and shared context images. Topics used were general household areas including cooking, cleaning, and trip planning. The results show that users can articulate a large number of needs unaided, and users consistently increased need quantity after viewing a stimulus. A final study collected 1735 needs statements and 1246 stories from 402 individuals in 24 hr. Shared needs and images significantly increased need quantity over other types. User experience (and not expertise) was a significant factor for increasing quantity, but may not warrant exclusive use of high-experience users in practice.

Commentary by Dr. Valentin Fuster

Papers: Incorporating user needs into engineering design

J. Mech. Des. 2015;137(7):071404-071404-9. doi:10.1115/1.4030050.

User preferences in design process of branded products are addressed through several layers of mediation occurring at the interfaces between consumers, product designers, and engineering designers, whereby product designers act as proxies for consumers. Rather than interacting directly each group makes assumptions about the consumer needs and preferences, which are not explicitly communicated. This paper explains the mediation layers between design team and consumers through a literature based framework of branded product emotions. The mediation in the design team is explained through a case study of communication across disciplinary boundaries.

Topics: Design , Teams
Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(7):071405-071405-10. doi:10.1115/1.4030047.

While there is increasing interest in designing for the developing world, identifying appropriate design research methods for understanding user needs and preferences in these unfamiliar contexts is a major challenge. This paper demonstrates how to apply a variety of statistical techniques to an online design case study repository, Human-Centered Design (HCD) Connect, to discover what types of methods designers use for identifying user needs and preferences for developing-world problems. Specifically, it uncovers how the following factors correlate to method usage: application area (e.g., farming versus healthcare), affiliation of the person using the method (IDEO designer versus not), and stages of the user research process. It finds that designers systematically use certain types of methods for certain types of problems, and that certain methods complement each other in practice. When compared with non-IDEO users, professional designers at IDEO use fewer methods per case and focus on earlier stages of the process that involve data gathering. The results demonstrate the power of combining data-driven statistical techniques with design case studies to identify user research methods for different developing-world problems, as well as locating which research methods complement each other. It also highlights that professionals designing for developing-world contexts commit more time to earlier stage user research efforts, rather than in concept generation or delivery, to better understand differences in needs and design contexts.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(7):071406-071406-9. doi:10.1115/1.4030057.

This paper examines the “reverse innovation” of the leveraged freedom chair (LFC), a high-performance, low-cost, off-road wheelchair originally designed for developing countries. A needs study of 71 developed world wheelchair users was conducted through three different data collection efforts. These data were contrasted with studies of 125 developing world wheelchair users, who were shown to be lead users for their developed world counterparts. The GRIT freedom chair (GFC), the developed world version of the LFC, was designed based on results of the study. By recognizing developing country users as lead users, designers can reveal latent needs and create globally disruptive innovations.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(7):071407-071407-10. doi:10.1115/1.4030058.

Elicitation and development of product requirements are crucial aspects of front-end design and have significant impacts on future product success. This study sought to better understand how novice designers approach the development of product requirements during a front-end design task. Results showed that the stakeholder validity of participants' requirements and the level of tailoring of the requirements to the design context and stakeholders were highly correlated to the number of distinct information sources used and moderately correlated to participants' dependency on particular information sources. Furthermore, an in-depth exploration of participants' information gathering behavior during the design task elucidated specific strategies and processes that may explain why some participants were more successful than others.

Topics: Design
Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(7):071408-071408-13. doi:10.1115/1.4030181.

Gathering user feedback on provisional design concepts early in the design process has the potential to reduce time-to-market and create more satisfying products. Among the parameters that shape user response to a product, this paper investigates how design experts use sketches, physical prototypes, and computer-aided design (CAD) to generate and represent ideas, as well as how these tools are linked to design attributes and multiple measures of design quality. Eighteen expert designers individually addressed a 2 hr design task using only sketches, foam prototypes, or CAD. It was found that prototyped designs were generated more quickly than those created using sketches or CAD. Analysis of 406 crowdsourced responses to the resulting designs showed that those created as prototypes were perceived as more novel, more aesthetically pleasing, and more comfortable to use. It was also found that designs perceived as more novel tended to fare poorly on all other measured qualities.

Topics: Design
Commentary by Dr. Valentin Fuster

Papers: Choice-based preference modeling and design

J. Mech. Des. 2015;137(7):071409-071409-11. doi:10.1115/1.4030162.

Consumers' product purchase decisions typically involve comparing competing products' visual features and functional attributes. Companies strive for “product differentiation” (Liu et al., 2013, “Product Family Design Through Ontology-Based Faceted Component Analysis, Selection, and Optimization,” ASME J. Mech. Des., 135(8), p. 081007; Thevenot and Simpson, 2009, “A Product Dissection-Based Methodology to Benchmark Product Family Design Alternatives,” ASME J. Mech. Des., 131(4), p. 041002; Kota et al., 2000, “A Metric for Evaluating Design Commonality in Product Families,” ASME J. Mech. Des., 122(4), pp. 403–410; Orfi et al. 2011, “Harnessing Product Complexity: Step 1—Establishing Product Complexity Dimensions and Indicators,” Eng. Econ., 56(1), pp. 59–79; and Shooter et al. 2005, “Toward a Multi-Agent Information Management Infrastructure for Product Family Planning and Mass Customisation,” Int. J. Mass Customisation, 1(1), pp. 134–155), which makes consumers' product comparisons fruitful but also sometimes challenging. Psychologists who study decision-making have created models of choice such as the cancellation-and-focus (C&F) model. C&F explains and predicts how people decide between choice alternatives with both shared and unique attributes: The shared attributes are “canceled” (ignored) while the unique ones have greater weight in decisions. However, this behavior has only been tested with text descriptions of choice alternatives. To be useful to designers, C&F must be tested with product visuals. This study tests C&F under six conditions defined by: The representation mode (text-only, image-only, and image-with-text) and presentation (sequentially or side-by-side) of choice alternatives. For the products tested, C&F holds for only limited situations. Survey and eye-tracking data suggest different cognitive responses to shared text attributes versus shared image features: In text-only, an attribute's repetition cancels its importance in decisions, while in images, repetition of a feature reinforces its importance. Generally, product differences prove to attract more attention than commonalities, demonstrating product differentiation's importance in forming consumer preferences.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(7):071410-071410-11. doi:10.1115/1.4030160.

In this paper, we propose a data-driven network analysis based approach to predict individual choice sets for customer choice modeling in engineering design. We apply data analytics to mine existing data of customer choice sets, which is then used to predict choice sets for individual customers in a new choice modeling scenario where choice set information is lacking. Product association network is constructed to identify product communities based on existing data of customer choice sets, where links between products reflect the proximity or similarity of two products in customers' perceptual space. To account for customer heterogeneity, customers are classified into clusters (segments) based on their profile attributes and for each cluster the product consideration frequency is computed. For predicting choice sets in a new choice modeling scenario, a probabilistic sampling approach is proposed to integrate product associations, customer segments, and the link strengths in the product association network. In case studies, we first implement the approach using an example with simulated choice set data. The quality of predicted choice sets is examined by assessing the estimation bias of the developed choice model. We then demonstrate the proposed approach using actual survey data of vehicle choice, illustrating the benefits of improving a choice model through choice set prediction and the potential of using such choice models to support engineering design decisions. This research also highlights the benefits and potentials of using network techniques for understanding customer preferences in product design.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(7):071411-071411-9. doi:10.1115/1.4030178.

Consideration set formation using noncompensatory screening rules is a vital component of real purchasing decisions with decades of experimental validation. Marketers have recently developed statistical methods that can estimate quantitative choice models that include consideration set formation via noncompensatory screening rules. But is capturing consideration within models of choice important for design? This paper reports on a simulation study of a vehicle portfolio design when households screen over vehicle body style built to explore the importance of capturing consideration rules for optimal designers. We generate synthetic market share data, fit a variety of discrete choice models to the data, and then optimize design decisions using the estimated models. Model predictive power and design profitability relative to ideal profits are compared as the amount of market data available increases. We find that even when estimated compensatory models provide relatively good predictive accuracy, they can lead to suboptimal design decisions when the population uses consideration behavior; convergence of compensatory models to noncompensatory behavior is likely to require unrealistic amounts of data; and modeling heterogeneity in noncompensatory screening is more valuable than heterogeneity in compensatory tradeoffs. This supports the claim that designers should carefully identify consideration behaviors before optimizing product portfolios. We also find that higher model predictive power does not necessarily imply more profitable design decisions; different model forms can provide “descriptive” rather than “predictive” information that is useful for design.

Commentary by Dr. Valentin Fuster

Papers: Modeling user behaviors and activities adapted to use contexts

J. Mech. Des. 2015;137(7):071412-071412-5. doi:10.1115/1.4030202.

Occupants' behavior exerts a significant influence on the energy performance of residential buildings. Industrial energy simulation tools often account for occupants' as monolithic elements with standard averaged energy consumption profiles. Predictions yielded by these tools can thus deviate dramatically from reality. This paper proposes an activity-based model for forecasting energy and water consumption of households and discusses how such an occupant-focused model may integrate a user-focused design of residential buildings. A literature review is first presented followed by a brief recall of the proposed modeling methodology and a sample of simulation results. The possible integration of the proposed model into the design and energy management processes of residential buildings is then demonstrated through a number of use cases.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(7):071413-071413-10. doi:10.1115/1.4030048.

Today, it is difficult to integrate the use phase optimization of consumer products into eco-design methodologies. Current eco-design approaches are in fact mainly focused on improving the technological performance of products while it has been proven that users behavior plays an important role in the overall environmental performances of products. This paper deals with the need to address the notion of user experience and behavior in the design process of today's low-complexity consumer products in order to improve their environmental performance. The research protocol presented in this paper is a new eco-design approach in six steps that can be used by designers to support eco-design decisions and integrate user behavior parameters into design activities. The first step consists in the identification of critical environmental aspects in use and usage drifts potential of the product. Steps two, three, and four support designers in the analysis of the use phase for the selection of efficient design for sustainable behavior (DfSB) strategies to be implemented on the product. Finally, steps five and six aim to test the selected strategies with product-in-use observations. Life cycle assessment (LCA) approach is used for the evaluation of the environmental benefits of the strategies. To illustrate this work, a case study of a coffee maker is described together with the eco-design solutions chosen for this product. The solutions reflect strategies targeting DfSB.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(7):071414-071414-12. doi:10.1115/1.4030180.

Usefulness, from the utilitarianism perspective, is the ability of a product or service to improve the well-being of humans and to minimize their suffering in different situations. In the case of the widespread issue of falls among the elderly, designing an adapted solution to is not an obvious task. The latter requires quantifying various usage scenarios. The usage scenarios, or segments, associated with elderly falls must be investigated to ensure that newly designed products and services are likely to bring essential health, social, and economic values. Optimizing a design solution by considering the coverage of such usage segments extends the classical methods of design for market approaches. Starting from a disparate literature on elderly falls’ issue, we have first built a usage scenarios space. Next, the usefulness and the coverage ability of three design solutions are evaluated over a tessellation of usage segments. In addition, the developed usage simulator is used to assess the potential of non or poorly covered usage segments to deliver insightful information in order to truly be a need seeker in the front-end of innovation.

Commentary by Dr. Valentin Fuster

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