Priming Designers to Communicate Sustainability167.99.111.205/.../Priming-Designers-to-Communicate-Sustainability … · Priming Designers to Communicate Sustainability Priming is

Jinjuan She1

e-mail: [email protected]

Erin MacDonalde-mail: [email protected]

Department of Mechanical Engineering,

Iowa State University,

Ames, IA 50010

Priming Designersto Communicate SustainabilityPriming is a psychological experimental technique that uses an artifact, exposure, or ex-perience to stimulate cognitive accessibility of specific mental content. Design techniquesthat use priming stimuli have thus far focused on generating more features, novel fea-tures, and relevant features and addressing latent customer needs. This article presents adesign technique that uses priming specifically to help designers to communicate sustain-ability via design at an early stage in the design process. The authors have determinedthat sustainable products face a special challenge in the market because thoughtful sus-tainability features such as decreased energy usage, use of recycled materials, or manu-facturing considerations are sometimes “hidden” from the customer. As green marketingmessages are not always trusted, another approach is to communicate sustainability tothe customer through product features. We propose and test a new design technique thatuses psychological priming to help designers generate product features that communicatesustainability to the customer. The technique involves performing a sensory-and-sustain-ability-heightening activity before generating ideas for product features. We investigatepriming stimuli in the form of a questionnaire and a collage activity and compare thesetechniques along with other existing priming-based techniques to a control condition.The new technique is proven to be more effective in helping designers generate productfeatures that communicate sustainability, as judged by both experts and consumers.[DOI: 10.1115/1.4025488]

1 Introduction

Product design has a tremendous impact on global sustainabil-ity. Particularly, early design decisions such as material selection,structure, and function affect a product’s whole life cycle [1].Design engineers have devoted much effort to improving productsustainability with cradle-to-cradle solutions [2] and investigatingnovel design methods and tools to support such movement, forinstance: design guidelines and checklists [3,4], life cycle assess-ment (LCA), life-cycle design strategies wheel [5], the house ofecology [6], the function impact matrix [7], and conceptual toolsto resolve functionality–environmental contradictions [8]. Fordetailed reviews of sustainable design theory, see Refs. [1,9].

While design engineers have addressed technical sustainabilityissues in product design, the fruits of their labors have not metwith much success in the market [10–12], although consumer sur-vey research has revealed that customers prefer sustainable prod-ucts and are even willing to pay a premium in some cases [13,14].The success of sustainable products has suffered from customerindifference in the marketplace [15]. Some sustainable endeavorsin product design are not readily apparent to customers as theyevaluate products. For example, customers cannot tell on firstinvestigation of a product that wind energy was used to powermanufacturing facilities. They also cannot see the use of biode-gradable materials for internal parts. Design for remanufacturabil-ity [16–18] is useful at the end-of-life of a product but is againoften hidden within the product’s internal design. Integration ofhuman health assessment in material selections [19] is appreciatedby customers, but something about the product must motivate thecustomer to seek out this information about the product, becauseit cannot be learned in a cursory evaluation. In common purchasesituations, it is unlikely that the majority of customers are natu-rally thinking about sustainability issues, as documented in mar-keting studies that separate the market into active “green”consumers (a minority of customers) and other groups [20]. The

construction of preference [21] implies that the decision contextplays a crucial role in whether or not the customer prioritizes oreven considers sustainability during a purchase decision.

It is often difficult for customers (and other stakeholders) totrust, or even ascertain, the extent to which a firm’s products andprocesses are sustainable [11,22]. A number of firms communi-cate the sustainability of their products directly with product pack-aging details, including: sustainability logos, a statementcombined with imagery, and third party certification. Firms mayalso advertise sustainability efforts through their Website, socialmedia, and other forms of promotion [15]. However, firms facechallenges with product labeling and claims, such as limited andcompeting uses of physical space on a product’s packaging anddifficulty in language determination [15]. More importantly, on-product messaging may not be effective because “people are busy,and may not be paying attention” [15]. With the public’s growingconcerns of “greenwashing,” superficial environmentally friendlyactions with a greater purpose of profit [23], customers might alsonot trust the on-product messaging [24]. For these reasons, somefirms avoid on-product messaging altogether [15].

An outcome of this research is to improve the communicationof a product’s sustainability by imbuing this communication inthe product’s design, instead of relying on superficial or tradi-tional marketing messages. The definition of sustainability usedthroughout this article is similar to that used by the authors previ-ously in Ref. [25] and refers to the intentional and methodicaldesign and manufacture of a product to decrease environmentalimpact vs. its competitors, using a scientifically based environ-mental impact assessment tool. This study focuses on communi-cating reductions in environmental impact, as environmentalimpact is an important focus for engineers but difficult to commu-nicate to the customer; refer to examples in paragraph 2 of thissection. In previous related work, MacDonald et al. found, that fora product to be successful, it must not only have good engineeringdesign but also be correlated with customer perceptions of thedesign [26]. Addressing the customer–product relationshipincludes the fields of study of emotional design and productsemantics. In Ref. [27], sustainable product semantics are devel-oped; for example, people associate “metal,” “simple,” and“durable” with a sustainable product. Reid et al. [28] provide

1Corresponding author.Contributed by the Design Theory and Methodology Committee of ASME for

publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received November21, 2012; final manuscript received September 3, 2013; published online October 17,2013. Assoc. Editor: Jonathan Cagan.

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engineering methods that quantify sustainable preferences associ-ated with a product’s form. Srivastava and Shu [29] present anaffordance transfer method for redesigning products to encouragepro-environmental behaviors. Our recent work [25] identifiesseven cognitive concepts that are crucial to the success of sustain-able design and emphasizes the importance of designing to the de-cision rules (heuristics) customers use to purchase sustainableproducts. Industrial designers attempt to purposefully shapebehavior toward more sustainable practices by using eco-feedback, behavior steering, and persuasive technology [30] intheir early design concepts [31].

In this paper, we present and test a design technique that helpsdesigners generate sustainability trigger (ST) features, which areproduct features that trigger customers to think about sustainabil-ity, to advance the product’s ability to express its sustainability tothe customer. Note that ST features may not be sustainable them-selves. The underlying hypotheses, which will be carried intofuture research, are that ST features inspire or cue customers toinvestigate real product sustainability, which may not be obvious.We sometimes refer to this as “communicating sustainability” forthe purpose of brevity. The “help” that designers receive comes inthe form of a priming exercise.

Priming, a well-established concept in behavioral psychology,is used in the design community to augment mental activity by ex-posure to a stimulus. Design researchers have used priming duringconceptualization to generate more features [32], novel features[33,34], and relevant features [35] and to address latent customerneeds [32]; refer to Sec. 2.2 for a review of studies. The designtechnique tested in this paper uses priming to enhance the design-er’s ability to communicate sustainability in his or her productconcepts. The ability to communicate is related to perceptions(sight, sound, touch, smell, and taste). Therefore, we test primingstimuli that focus on sensory perceptions and sustainability. Wepropose that exposure to such stimuli will increase designers’ abil-ity to communicate sustainability.

In the following sections, a review of priming literature in psy-chology, marketing, and engineering design is presented, followedby the details of the empirical study. Sections 3 and 4 formulateresearch propositions and introduce the priming stimuli. Section 5describes experimental methods and measurement procedures.Results and discussion are contained in Secs. 6 and 7, respec-tively, and Sec. 8 provides conclusions and future work.

2 Background

2.1 Overview of Priming. Psychologists use the term“priming” to describe what occurs in the mind when exposure tosome stimulus increases the accessibility of associated informa-tion or processes for a later activity. A priming stimulus is an arti-fact, exposure, or experience that stimulates increased cognitiveaccessibility of mental content [36,37]. For example, it has beenvalidated that (1) people behave more competitively when seeinga leather briefcase placed on the desk in an office [38]; (2) peopleare more likely to work together in resource-management gamesafter reading cooperation-related words [39]; (3) people are ableto overcome automatic stereotypes and associations when primedto think creatively [40]; and (4) people give alternate solutionsmore consideration in problem solving when they experience ascenario in which an alternative outcome almost occurs [41]. Theeffects of priming can be salient and long-lasting, not only on sim-ple recognition tasks [42,43] but also on decision-making [37], be-havioral change [44], creative thinking [40], and problem solving[41].

Sensory priming, the activating of a set of perception orienta-tions via specific tasks, influences higher social cognitive process-ing. Ackerman et al. [45] report a series of studies demonstratinghow weight, texture, and hardness nonconsciously influence bothsocial impression formation and decision-making. Their resultsshow that heavy objects make job candidates appear more

important (such as evaluating a resume on a heavy clipboard),rough objects make social interactions appear more difficult (suchas rating social coordination after handling rough-feeling puzzles),and hard objects increase rigidity in negotiations (such as sittingin a hard chair while completing a negotiation task). Marketingresearchers have studied the crucial role of nondiagnostic hapticcues of a product, such as packaging or serving container, andhow these cues affect product evaluation [46]. For instance, waterseems to taste better from a firm bottle than from a flimsy bottle[46].

For a comprehensive review of priming studies in social psy-chology, see Ref. [36], which addresses priming of, for example,trait, expectancy, mindset, goal, behavior, and affect. Custers andAarts discuss goal priming in Science and analyze the mechanismfor how this may happen [44]. Section 2.2 discusses the applica-tions of priming, specifically (a) priming customers’ decisions and(b) designers’ ideation processes.

2.2 Applications of Priming

2.2.1 Priming Customers. The notion of bounded rationality[47] implies that customers often do not have well-defined prefer-ences for utility maximization. Instead, they construct preferencescase-by-case when prompted to make a decision [48]. As a result,preferences are remarkably pliable, sensitive to the way a choiceproblem is described or “framed” and to the mode of responseused to express the preference [21,48].

Mandel and Johnson demonstrate how visual priming stimulican influence product choice [37]. In their study, groups of cus-tomers went shopping for hypothetical products in online shop-ping environments that were identical except for the backgroundpictures and colors of the Web page. In an experimental task of asofa purchase, the authors find that a blue background with fluffyclouds increases the importance of comfort information in search-ing information and the likelihood that a more comfortable sofa isselected. In contrast, a green background with pennies increasesthe importance of price information.

Haubl and Murray [49] examine an “inclusion effect” in a con-trolled agent-assisted online shopping experiment. They varyattributes included in a recommendation agent to compare prod-ucts and find that everything else being equal, the attributes men-tioned by the recommendation agent are more important incustomers’ purchase decisions. Moreover, this inclusion effectcan persist and affect subsequent purchase decisions in differentshopping environments.

Unlike Mandel and Johnson [37] and Haubl and Murray [49],who investigated priming effect on choice, Nedungadi [50]focuses on the early stage in the customer-decision process. In amemory-based choice experiment, priming of a brand increasesthe probability of retrieving, considering, and choosing that brand,although the corresponding brand evaluation does not change.The author suggests that brands sometimes benefit from their ownadvertising and that of highly similar competitors as well, due to apriming effect—advertizing increases customer awareness of thecategory of products advertized.

2.2.2 Priming Designers. The designer’s frame-of-mind dur-ing the conceptual design process has an important effect on theoutcome of this process—see, for example, the issue of design fix-ation [51,52]. Designers use longstanding knowledge as well asrecently introduced information to generate new and creativeproducts. Similarly, they pull together experiences both old andnew. The incorporation of newly acquired information and newexperiences can be a source of priming in the design process, anda number of ideation techniques are based on the principle of pri-ming [53–55], including role-playing [56,57], questioning [58,59],and improvisation [59,60]—although not all references mentionpriming explicitly. In the random stimuli technique [55], designersare presented with a word, a picture, or a heuristic and areexpected to explore some useful and unusual associations [55,61].

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Role-playing prompts designers to act out scenarios, helping themfind insights into target customers that may be difficult to accessdirectly [57]. Question technique, a recognized way to induceimagination, can give designers conscious guidance to their think-ing by asking a series of questions about each step in the designproblem [58]. Improvisation activities, implemented in differentforms, are helpful in breaking design fixations. For example, agroup of people stand in a circle to tell a story; one person saysone word at a time until the story is completed [60]. This activityencourages a participant to be more responsive and appreciativeof others’ contributions and to “let go” of any particular narrativesforming in his or her head. Herring et al. [62] conduct a study thatunderstands and compares how designers use examples to supportthe creative design process—these examples may be considered apriming stimulus. Shneiderman recommends ways in which tech-nology can enhance creativity and encourages the development ofcreativity support tools through human–computer interaction;again, the technology likely plays the role of an implicit orexplicit priming stimulus [63]. Fu et al. [64] find that initialevents, such as exposure to a good or poor example in terms ofcost, manufacturing, sustainability, and resources, can affect teamconvergence and the quality of the final design solution producedby the team.

The empirical laboratory experiments discussed below [32–35]demonstrate that priming can be used to enhance the design pro-cess in ideation, namely conceptual priming, sensory priming,affect priming, and mindset priming. The first three—concept pri-ming [35], empathic lead user [32], and computational affectivepriming [33]—intentionally use a priming stimulus. In the Word-Tree method [34], priming is probably an influence rather than thefirst goal of the design method.

In a problem-solving study, subjects are asked to unscrambleeither neutral or hostile sentences for 12 min before a creativesketching task [35]. The results show that subjects who unscram-ble mildly hostile sentences prior to sketching include more hos-tile features, such as features of spikes and claws, in their novelcreations than subjects in the control conditions. When a designproblem can be solved in a variety of ways, the design results aremost likely in a direction associated with the concept that isprimed [36].

The empathic lead user technique leads to breakthrough productideas via pushing designers to interact with products underextreme circumstances. In the experiment conducted by Lin andSeepersad [32], subjects are asked to assemble a two-person tentin the dark and with oven mitts on their hands. The results indicatea significant increase in latent needs discovery with the empathiclead user technique vs. other design methods. The authors proposethat the heightened senses associated with a loss of sight and dex-terity guide perceptions and thinking. This priming stimulus isincluded as a condition in the experiment below in order to bench-mark the effectiveness of the proposed design technique.

Lewis et al. [33] study affective (mood) priming in design anddemonstrate that inducing positive affect using digitally embed-ded stimuli on a simple sketching application facilitates creativeperformance. Subjects in the positive affect condition, whichallows them to practice drawing with an image of a laughing babyas a background before they start, create more original sketcheswith more elaborations. This effect is attributed to the fact thatpositive affect enhances motivation, broadens attention, and leadsto an increase in the numbers of cognitive elements treated asrelated to the problem. This priming stimulus is also included as acondition in the experiment below in order to benchmark theeffectiveness of the proposed design technique.

The WordTree method proposed by Linsey et al. [34] is basedon keywords. Designers are tasked with describing the key func-tions or customer needs of a design problem as “problemdescriptors” and systematically representing them in a diagramknown as a WordTree. Then, potential analogies and analogousdomains are identified and applied in a group idea generation.When designers are working on the WordTree task, not only

specific contents but also relevant cognitive procedures becomeactivated. Effects on subsequent design tasks may then be drivenby these procedures themselves as well as specific content that isprimed [36].

3 Research Proposition

We propose that we can extend the design capabilities of engi-neers by heightening their sensory perceptions and sustainabilityconsiderations and, thus, improve their ability to communicatesustainability via product design.

Proposition. Exposing designers to a priming stimulus on sen-sory perceptions and sustainability will increase their ability togenerate design features that communicate sustainability to thecustomer.

The basis for this proposition comes from the literature: pri-ming has been demonstrated to provide a “perceptual readiness”[65]. Heightening perceptions via priming increases the cognitiveaccessibility of anything that may be related, which in turn drivesindividuals to discover the “blind spots” in thinking and design. Itis this heightening of perceptions that we wish to exploit in ourdesign technique.

The motivation for this proposition comes from the challengesof designing sustainable products, as detailed in Sec. 1, paragraph2. We want to create design features that communicate sustainabledesign efforts to the customer and help the designer simultane-ously consider technical- and customer-centric concerns.

The research proposition phrases the goal as generating designfeatures that “communicate sustainability to the customer.” In theexperiment detailed in Sec. 5, features are judged on their abilityto “trigger the customer to think about sustainability.” This spe-cific phrasing was chosen in conjunction with future research,which will use the best features generated by the subjects in thisexperiment to design products that trigger customer preference forsustainable products. In the proposition, communicating sustain-ability is used as a generalization of this phrase.

4 Priming Stimuli

4.1 Proposed Priming Stimuli. To test the propositionabove, an experiment was created in which subjects (designers)were exposed to a priming stimulus and then asked to design aproduct. Two stimuli on sensory perceptions and sustainabilitywere used in this experiment: a written questionnaire activity anda physical placement (collage) activity. Subjects were exposed toone or the other, not both. The stimuli involved a focal product, akitchen sponge. The selection of this product occurred in a pilotstudy. The kitchen sponge was selected for a number of reasons:(1) it is ubiquitous; (2) it engages all five senses during use—thereare smells, sounds, textures, appearances, feelings of hot and cold,and even the recollection of the taste of a good meal associatedwith a kitchen sponge; (3) the senses may be engaged in a positiveand negative way while cleaning dishes; and (4) there are spongeswith readily apparent sustainable features (natural fibers and dye-free) available on the market. Pretests revealed that some culturesuse a rag instead of a sponge to clean dishes; thus, the primingstimuli were worded to be inclusive of both cleaning implements.

The priming stimulus product (sponge) is different from theconcept generation product (toaster, described in Sec. 5.1) in orderto reduce potential design fixations [60] caused by working withthe same product in the two stages of the experiment. In the ques-tionnaire priming stimulus, subjects might have fixated on the fea-tures of the toaster they currently own. In the collage activity,subjects evaluate existing products and, therefore, may have fix-ated on the product’s features.

The questionnaire consists of an exercise in which subjectswrite about: (1) three examples of things that they have done toreduce their environmental impact and (2) the sponge or cloththey use at home to clean dishes, using in their description someor all of the five senses (sight, sound, touch, smell, and taste). The

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maximum time spent on the questionnaire is 10 min. The motiva-tion behind this priming stimulus is as follows: by actively think-ing about the answers, the associated mental content of perceivingsensory information and sustainability may be vividly aroused andbecome highly accessible in the ideation process. Based upon thefindings of the mechanism of priming effect [36], the mindset acti-vated in the questionnaire activity will continue to be active dur-ing a subsequent design task, without subjects necessarily beingaware of or intentionally choosing this mode of thought.

For the collage priming stimulus, subjects arrange pictures ofsponges on a white background. The exercise is based on thework of Guyton [27], who develops sustainable product semanticsand establishes a set of design recommendations for sustainableproduct designers, as previously discussed in Sec. 1, with collageactivities. To develop these recommendations, Guyton asked sub-jects to place images of sustainable products on a two-axis dia-gram. One axis tracked preference, from “like” to “dislike,” andthe other environmental impact, from “high impact” to “lowimpact.” In order to prime perceptions, we combine Guyton’saxis-placement activity with a sensory-description activity, inwhich product images are matched with 28 sensory words, such asbright, harsh, cold, smoky, and bland, as shown in Fig. 1. In theexercise, subjects: (1) place eight images of dish sponges on theaxes and (2) place the sensory descriptor terms around the prod-ucts. Subjects can use one term multiple times but must use all 28terms at least once. They can also use their own words to describethe products. The maximum time spent on the collage is 10 min.

Based on findings from the priming literature mentioned inSec. 2, it is likely that performing the collage exercise activatesspecific cognitive orientations and relevant cognitive procedures.

Effects on subsequent design tasks may then be driven by both theorientations and procedures. We anticipated that the collage pri-ming stimulus would be more effective than the questionnaire 1,as rich, frequent, and recent priming stimuli are often especiallyeffective [36], lead to more spreading activation [66], and requiremore extensive cognitive processing. Subjects in the collagegroup physically interacted with a variety of sponge images andsensory descriptors. They repeatedly made judgments about per-ceptions and preferences, which required deeper and more fre-quent cognitive processing.

4.2 Benchmark Priming Stimuli. Two established primingtechniques are included as experimental conditions to test if exist-ing techniques can lead to improved sustainability communica-tion. This finding would eliminate the usefulness of the proposedmethod; thus, it should be checked. Empathic lead user [32],described in Sec. 2.2, is adapted to the toaster design task by ask-ing subjects to toast two pieces of bread only using their righthand while wearing an oven mitt and a pair of scratched goggles(Fig. 2, left). Positive affective priming stimulus [33], describedin Sec. 2.2, is also included. Subjects are asked to draw anythingthey want on a sheet of paper with a laughing baby image asa background (Fig. 2, right), adapted from iPad approach ofLewis et al.

5 Method

5.1 Experiment Design Overview. A controlled experimentwas designed and conducted to test the priming effect on ideationunder four priming stimuli conditions: (A) questionnaire, (B) col-lage, (C) empathic lead user, and (D) positive affective, asdescribed above. A control condition of (E) no-priming stimulusis also included. After interacting with a priming stimulus (ornone in the control condition), subjects designed new features fora target product (design phase 1). Then, they evaluated the fea-tures they had generated for their ability to trigger thoughts of sus-tainability. Next, subjects specifically designed features to triggerthoughts of sustainability and were asked to concentrate on thefive senses (design phase 2). After that, subjects provided somedemographic and purchase information. Finally, subjects elabo-rated on their designs, counted the features they generated, anddescribed their thinking process. Figure 3 provides an overview ofthe experiment, and procedure details are provided in Sec. 5.2.

The research proposition of Sec. 3 is broken down into hypoth-eses that are tested using this experiment:

Hypothesis 1a. The questionnaire increases the ability of fea-tures to trigger thoughts of sustainability when comparedwith no priming stimulus.

Hypothesis 1b. The questionnaire increases the number of fea-tures generated that trigger thoughts of sustainability whencompared with no priming stimulus.

Hypothesis 2a. The collage increases the ability of features totrigger thoughts of sustainability when compared with nopriming stimulus.

Fig. 1 Demonstration of a collage output

Fig. 2 Empathic devices (left) and positive priming image, Lewis et al. [33] (right)



Hypothesis 2b. The collage increases the number of featuresgenerated that trigger thoughts of sustainability when com-pared with no priming stimulus.

Hypotheses 3 and 4 proposed benchmark conditions against theno priming stimulus condition, to test the necessity of thenew proposed technique. If these hypotheses are accepted,the new technique is not needed.

Hypothesis 3a. The empathic lead user increases the ability offeatures to trigger thoughts of sustainability when comparedwith no priming stimulus.

Hypothesis 3b. The empathic lead user increases the number offeatures generated that trigger thoughts of sustainabilitywhen compared with no priming stimulus.

Hypothesis 4a. The positive affective priming stimulusincreases the ability of features to trigger thoughts of sus-tainability when compared with no priming stimulus.

Hypothesis 4b. The positive affective priming stimulusincreases the number of features generated that triggerthoughts of sustainability when compared with no primingstimulus.

A bread toaster was selected as the product focus for ideation.This is a common product with a variety of opportunities toimprove its sustainability with respect to both engineering designand customer perception. A toaster is suitable for laboratoryexperiments involving students as it has low technical barriers todesigners and has been used in other studies on creativity andsustainability of products [67,68]. We recruited 50 students fromthe departments of Mechanical Engineering and Industrial Engi-neering at Iowa State University to participate in the experiment.They were compensated with $10, $15, or extra credit. Theexperiment was conducted in two sessions. The first one comparedthe proposed priming stimuli (questionnaire and collage) to thecontrol condition, as reported in Ref. [69]. In order to benchmarkthe effect of the proposed priming stimuli against existing techni-ques, two additional conditions (empathic lead user and positiveaffective priming stimuli) were added in the second session. Bothexpert designers and potential customers rated design solutions totest the hypotheses, as discussed in Sec. 5.5.

5.2 Procedure. Subjects were assigned randomly to one ofthe five conditions, with ten subjects per condition. Subjects werepartially informed that the purpose of this study was to investigatethe design process and understand how designers solve a designproblem. Because of the implicit nature of priming technique, theobjective of the experiment was not fully revealed to the subjects(IRB approved). All instructions throughout the survey were givenboth in writing and verbally. First, subjects performed one of thefour priming stimuli (or none for the control). The maximum timelimit for the priming activity was 10 min, and a subject couldmove on to the design problem when ready.

Next, in design phase 1, subjects generated new design featuresfor a next-generation toaster in 15 min. The task was phrased assuch: “Imagine that you are working as a product designer in adesign consulting firm and that you are asked to design a next-generation toaster. What new product features do you want to addinto your design? How do you want to improve existing features?”After completing the design exercise, subjects marked any fea-tures they had generated that could trigger customers’ sustainabil-ity considerations.

In design phase 2, subjects were directly instructed to generatesuch sustainable features for the same design problem in 12 min;the task was phrased as such: “Now please generate additionalfeatures that can trigger customers to think about sustainability forthis design problem. Feel free to think about solutions that involveany of the five senses.” The rationale behind design phase 2 is thatmany creativity design experiments explicitly ask subjects todesign as many creative solutions for a design problem as possibleand then measure how creative each solution is and how manycreative solutions subjects generated. This is a congruous task. Inboth design phases 1 and 2, designers were encouraged to sketchand/or write as many creative features as they could, number eachdifferent feature, and elaborate in writing, using labels anddescriptions if necessary. Subjects were encouraged to thinkaloud, and their solutions and words were documented with anelectronic audio-recording pen.

Subjects answered a short postexperimental survey, providinginformation on demographics, purchasing, sustainability knowl-edge, environmental control belief, associations with sustainabil-ity, and evaluation of performance. Then, subjects wereinterviewed about their design solutions. They were asked toreview their sketches/words and to verbally describe the featuresthey generated, how they moved from one feature to another, andtheir approaches to ideation [70]. This exercise served to explorethe subjects’ cognitive processes and ensure the documented dataaccurately reflected intent. Subjects also confirmed the numberingof features on their experimental materials. Subjects in the collagecondition were questioned about their rational for the collage task.Finally, subjects were debriefed on the purpose of the experiment.

5.3 Data Processing. Visual data, in the form of sketches andwriting, were reviewed. Verbal data from the recording pen weretranscribed to supplement the visual data. For those features withonly sketches or unclear descriptions, written explanations wereadded according to the designers’ audio record. Based on thequantity-counting rules adapted from Linsey et al. [71,72] (Table1), the experiment administrator checked the self-counts of fea-tures. Compiled ideas and scanned sketches were entered into aWeb survey so that judges could rate them.

5.4 Measures. Shah et al. [73,74] point out two basic criteriafor evaluating ideation methods: (1) how well does the methodexplore the design space and (2) how well does the methodexpand the design space. Based on these two criteria, they proposefour effectiveness measures: quality, quantity, novelty, and varietyof the ideas generated using that method. In some cases, it is

Fig. 3 An overview of the experiment

Table 1 Quantity counting rules summary [71]

1 The same feature (or component) being used in multipleplaces counts as one feature

2 Each feature counts as only a single feature even if it solvesmore than one function

3 New combinations of already-counted features are countedin a separate measure

4 Categories of ideas only count as ideas when no subordi-nates are given

5 Features count even if they are not needed or cause the sys-tems to not function

6 Features must be shown and not just implied



appropriate to use a subset of these metrics. For example, Yang[75] finds that quantity of ideas generated in early phase is signifi-cantly correlated with the quality of design outcome, in terms ofstudents’ design grade in her study, but does not investigate nov-elty or variety. Here, we define quality as specific to the hypothe-ses at-hand. Novelty was among the dimensions rated by judgesbut did not produce significant/interesting results. Considering thetarget of the technique investigated in this paper, three metrics areinvolved in the analysis:

• ST rating: the ability of a feature to trigger a customer’s sus-tainability considerations, as judged by experts and potentialcustomers. A rating of 1 corresponded to “strongly disagree,”and a rating of 5 corresponded to “strongly agree.”

• Measure A: the average ST rating of the features generatedper designer subject, averaged across judges’ ratings (expertand customer judge ratings are kept separate) and across fea-tures generated by a designer.

• Measure B: the count of effective features generated per de-signer. An effective feature is defined as having an averageST rating above “neutral” (any number greater than three);ratings averaged across judges, with expert and customer rat-ings averaged separately. Refer to Fig. 4 for examples ofeffective features.

5.5 Judging of Measures. Judging of ST ratings, and thusthe basis of measures A and B, were conducted by two groups:experts (designers) and novices (consumers). Design featuresfrom conditions A, B, and E were rated by two expert judges;hypotheses were tested and are also reported in Ref. [69]. Oncethe basic viability of the technique was proven with expert judges,additional analysis was undertaken: (1) testing the effectiveness asjudged by novice raters (potential customers) and (2) testing exist-ing priming techniques against the control condition, to verify theusefulness of the proposed new technique. Both of these analysesadd rigor and scrutiny to further substantiate claims of the techni-que’s effectiveness. Design features from conditions A, B, C, D,and E were rated by potential customers on Amazon Mechanical

Turk (AMT), to be referred to as “AMT judges.” AMT is an effi-cient platform for crowdsourcing user studies [76] due to the lowrecruitment costs and immediate access to hundreds of subjects.Expert judges rated all design features generated from the condi-tions A, B, and E, which took approximately 8 h, while AMTjudges rated a subset of 31 design features across all conditions,which took approximately 30 min. Rating correlation between thetwo judge types was 0.7 on shared conditions, as discussed in Sec.6.4. This good correlation indicates that there was no need to col-lect rating data from expert judges for the remaining two condi-tions (C and D), which was time-consuming and costly.

5.5.1 Shared Judging Context Across Expert and Novice.Both sets of judges saw the same format of stimuli, as close aspossible to the original submissions of the experimental subjectsand were blind to the hypotheses of the experiment. Both sets ofjudges saw the same examples of an effective ST feature and a“bad” ST feature for training purposes before beginning. Both setsof judges rated each feature on novelty, perceptibility, sustainabil-ity trigger, and feasibility. The dimensions other than sustainabil-ity trigger are not discussed as we did not find significantdifferences between the test conditions and the control. The fea-tures described by the experimental subjects using only text wereshown to the judges as only text. The features described withsketches/text were shown to judges as sketches/text. The featuresdescribed by subjects with only sketches had text added to thestimuli shown to the judges, taken from the audio record of theexperiment.

5.5.2 Differences in Judging Context. Expert judges rated thefeatures in a laboratory and the experiment administratoranswered any questions they had during rating. AMT judges ratedfeatures on their own computers without access to clarification.Expert judges saw the “raw” or original concept descriptions fromdesigners, while AMT judges saw some slight rephrasing of con-cept descriptions when the raw descriptions were deemed difficultfor a nondesigner to understand, as determined by the two authors

Fig. 4 Example effective features on ST, with average ratings from expert judges and/or AMT judges separately; rated from 1strongly disagree to 5 strongly agree that a feature can trigger customers to think about sustainability



and an independent editor. Within the AMT survey, for cases ofmultiple designers describing the same feature with differentphrasing, this feature was rephrased to a “universal phrasing” andonly appeared as one feature in order to streamline data collectionand processing, and avoid surveys that might ask multiple ques-tions about the same feature but with different phrasings (confus-ing to respondent). In addition, only expert judges rated featureson the dimension “relatedness to engineering design.” There wasalso a slight difference in rating task wording, as detailed below.

5.5.3 Expert Judging Details. The expert judges who ratedfeatures were two graduate students in Mechanical Engineeringwith different design backgrounds. The male judge is a first-yearPh.D. student with a sustainability-related background in classand research projects, which involve a wind turbine model design,automotive emissions regulation study, and magnetorheologicalelastomers categorization (for energy reclamation and reductionof bridge maintenance). The female judge is a second-year Mas-ter’s student with 5 yr of design experience in industry and 2 yrexperience as a teaching assistant for an engineering design classthat includes sustainability-related teaching material. Judges wereasked how likely a feature is to trigger customers to think aboutsustainability upon interacting with that feature. The definition oftriggering sustainability considerations was given as, “The featurecauses customers to think about reducing environmental impact.For example, a hand-powered toaster might cause them to thinkabout sustainability, while a digital control panel on a toastermight not cause them to think about sustainability.” The judgeswere told that they might see some similar design features duringevaluation and requested to try their best to be consistent in thewhole process. In order to calibrate their ratings, the judges ratedtwo example features and discussed the rationale for their ratingsuntil they agreed with each other, and the experiment administra-tor answered any questions they had. During this discussion, thejudges agreed that a sustainable trigger feature must be noticeablewhen users interact with the product and must be related to sus-tainability in some way, such as lower environmental impact. Thedegree to which the features trigger sustainability considerationsdepends on judges’ own subjective evaluations. Take “usingrecycled materials” as an example, this feature lowers environ-mental impact, but whether or not it will trigger customers tothink about sustainability is highly dependent on specific imple-mentation in the design, which was not always specified by thesubject. Thus, when a feature is described as “using recycledmaterials, such as aluminum,” it could be rated as “4” or “5”; butsimply “uses recycled materials” could be rated as “3” or “2.”Then, judges rated each feature on a five-point scale from 1(strongly disagree) to 5 (strongly agree) regarding the degree towhich the feature can trigger customers to think aboutsustainability.

To evaluate the reliability of the judges’ ratings, the Cronbach’salpha statistic was used. It is known as an internal consistencyestimate of agreement among raters of a performance test with apossible range from 0 to 1.00 [77]. A higher alpha value indicatesa higher reliability of measurements, and a value of 0.9 or aboveis used frequently as an indicator of good to excellent interraterreliability. Cronbach’s alpha was 0.84 on the initial judges’ ratingsand 0.9 on their ratings after discussion of disagreements, demon-strating that the rating task was well-understood and consistentlyperformed by the judges.

5.5.4 AMT Judging Details. The AMT judges were 174 AMTworkers located in the United States; the average respondent took14 min to complete the survey. They were compensated via Ama-zon with $1.50. These AMT judges come from more than 60 dif-ferent occupations; 51% are female; 78% have bought sustainableproducts in the past; and 88% have used toasters in the past 12months. Other demographic information of the AMT judges issummarized in Table 2. Three respondents were screened outbecause they were an engineering designer or product designer.

An additional 21 respondents were screened based on spending 9min or less on the survey task. Screening AMT respondents fortime spent on the survey task is a recommended approach toreduce noise due to respondents choosing random answers, seeRefs. [76,78]. The authors identified 10 min as the fastest timethat they personally could take the survey and still read all of thefeature descriptions. Analyzing the subject data, the distributionof time spent shows an obvious drop-off at 9 min. In the survey,ST was defined as: “The feature causes you to think about reduc-ing environmental impact. For example, a hand-powered toastermight cause you to think about sustainability, while a digital con-trol panel on a toaster might not cause you to think about sus-tainability.” To calibrate their ratings, the AMT judges firstanswered an example question about a feature that should likelybe rated “agree” and were then shown the rating and explanation.After that, they rated each feature on a five-point scale, from 1(strongly disagree) to 5 (strongly agree) that, “The feature makesme think about sustainability.”

6 Results

6.1 Overview. In total, 550 features were generated by 50subjects in both design phases. Analysis published in Ref. [69]found that designers did not ideate enough sustainability-triggering features in design phase 1 to test the hypotheses; thepriming stimuli had a testable effect only in design phase 2, whensubjects received explicit instructions to design features that trig-ger sustainability considerations. Thus, this paper analyzes fea-tures generated in design phase 2. However, the results of designphase 1 indicate that the collage priming stimulus resulted in alarger number of features generated (p< 0.05) [69]. An overviewof results is shown in Table 3.

Between-subjects analysis of variance (ANOVA) tested hypothe-ses using expert and AMT ratings. ANOVAs were conducted withthe priming stimulus condition as an independent variable andsubject-level measures A and B as dependent variables. MeasureA was obtained by averaging ST ratings across the judges (expertand AMT separately and never combined) and the design featuresgenerated per designer. Measure B was calculated by counting thenumber of effective features generated by a designer, as defined inSec. 5.4. All the data satisfy the assumptions for a standard ANOVA

except for small departures from normality of measure B as sug-gested by Shapiro-Wilk’s test [79]. Section 6.2 presents the resultsbased on expert judges’ ratings, followed by analysis of AMTjudges’ ratings in Sec. 6.3, and then a comparison of the two inSec. 6.4. Table 4 presents a summary of the hypothesis testingresults.

6.2 Expert Judge Results. The expert ratings show signifi-cant differences for both measures A and B across the three condi-tions and provide strong support for Hypotheses 1b, 2a, and 2b.Comparing with the control condition, as demonstrated in Fig. 5,designers from the collage condition generated features with sig-nificantly higher ratings on measure A than those from the controlcondition (3.5 vs. 2.5, p< 0.05). A similar trend is also observedfor the questionnaire, which leads to 11% increase, although it isonly significant at the 0.1 level (3.2 vs. 2.5, p< 0.1). For quantitymeasure (B), number of effective features, both the collage and

Table 2 Summary of demographic information of AMT judgesfrom AMT (N 5 150)

Age Education Income Knowledge of design

18–25 36% High school 10% < $25k 29% None 64%26–35 31% College 77% $25k–$50k 34% A little 25%36–45 17% Master 10% $50k–100k 23% Some 8%46–55 11% PhD 1% >$100k 11% Good amount 3%56þ 5% JD,MD 2% No answer 3% Expert 0%



the questionnaire contribute significantly (collage vs. control: 4.8vs. 1.3, p< 0.05; questionnaire vs. control: 2.9 vs. 1.3, p< 0.05).In Ref. [69], the authors presented further analysis of the expertratings for testing Hypotheses 1a and 2a, including the use of alinear mixed model (LMM) without averaging ratings acrossjudges or features generated by a designer (see Appendix). Theresults of the LMM (taking raw ST ratings as observations) aresimilar to ANOVA (taking measure A as observations) in supportingHypothesis 2a. Hypothesis 1a is tested to be more significant bythe LMM model vs. AVOVA (significant at 0.05 level vs. 0.1

level), i.e., designers from the questionnaire priming stimulus con-dition generated features with significantly higher ability to triggercustomers’ sustainability thoughts than those from the controlcondition.

6.3 AMT Judge Results. ANOVA reveals no significant effectof priming stimuli on measure A, i.e., Hypotheses 1a, 2a, 3a, and4a are not supported (the left of Fig. 6). However, there is a trendthat designers’ features from the affective, collage, and

Table 3 Overview of results

Design phase 1 Design phase 2 detailed metrics

ConditionNo.

subjects

AverageNo. featuresper subject

AverageNo. features

per subject (as presentedin AMT survey)

Averagefeature rating

per subject(measure A)

AverageNo. effective

features per subject(measure B)

Expert AMT Expert AMT

A. Questionnaire 10 6.2 5 3.1 3.6 2.9a 3.6B. Collage 10 8.2a 6.8a 3.5a 3.6 4.8a 4.9a

C. Empathic 10 5.8 4.2 — 3.12 — 2.1D. Affective 10 6.4 4.3 — 3.87 — 3.6E. Control 10 4.7 3.4 2.5 3.4 1.3 2.2

ap< 0.05, compared with the control condition.—-, Not rated.

Table 4 Summary of hypothesis testing

Hypothesis Sig. Level (experts) Sig. Level (AMT) Measures related Evidence presented in

1a. The questionnaire increases the ability of features totrigger thoughts of sustainability when compared with nopriming stimulus

p< 0.1 p> 0.1 Measure A Table 3, Figs. 5 and 6

1b. The questionnaire increases the number of featuresgenerated that trigger thoughts of sustainability whencompared with no priming stimulus

p< 0.05* p> 0.1 Measure B Table 3, Figs. 5 and 6

2a. The collage increases the ability of features to triggerthoughts of sustainability when compared with no primingstimulus

p< 0.05* p> 0.1 Measure A Table 3, Figs. 5 and 6

2b. The collage increases the number of features generatedthat trigger thoughts of sustainability when compared withno priming stimulus

p< 0.05* p< 0.05* Measure B Table 3, Figs. 5 and 6

3a. The empathic lead user increases the ability of featuresto trigger thoughts of sustainability when compared with nopriming stimulus

— p> 0.1 Measure A Table 3, Fig. 6

3b. The empathic lead user increases the number of featuresgenerated that trigger thoughts of sustainability whencompared with no priming stimulus

— p> 0.1 Measure B Table 3, Fig. 6

4a. The positive affective priming stimulus increases theability of features to trigger thoughts of sustainability whencompared with no priming stimulus

— p> 0.1 Measure A Table 3, Fig. 6

4b. The positive affective priming stimulus increases thenumber of features generated that trigger thoughts ofsustainability when compared with no priming stimulus

— p> 0.1 Measure B Table 3, Fig. 6

Fig. 5 Comparison of measures across priming conditions based on expert judges’ ratings(*p < 0.05, compared with the control condition); error bars show 6 1 standard error



questionnaire conditions have higher ST ratings relative to thecontrol condition. ANOVA testing of measure B (Fig. 6) signifi-cantly supports Hypothesis 2b that the collage priming stimulusincreases the number of effective features that trigger thoughts ofsustainability when compared with no-priming stimulus (collagevs. control: 4.9 vs. 2.2, p< 0.05), while Hypotheses 1b, 3b, and 4bare not supported. Furthermore, the trends on measure B suggestthat designers in the questionnaire and affective condition maygenerate more effective features than the empathic and controlconditions.

6.4 Comparison of Expert Judge and AMT Judge Ratings.Figure 7 shows the rating correlation between expert and AMTjudges for features in priming conditions A, B, and E, on a per-designer basis, with a Pearson’s correlation at 0.7. Experts have alower average rating across designers (mean: expert vs. AMT: 3.1vs. 3.6, p< 0.05). Table 5 shows the distribution of the ratings.For example, for features rated about 3 or lower, the majority ofthe expert ratings fall between 1 and 2, while most AMT ratingsare between 2 and 3.

6.5 Postexperimental Survey Results. A postexperimentalsurvey of all experimental subjects (not the judges) measured avariety of items: sustainability knowledge, design experience,design interest, self-perceived performance on the design task,evaluations on the ideation session, and environmental controlbelief [80]. ANOVAs show that no ratings on these items are signifi-cantly different across conditions. The mean values for items byconditions are reported in Table 6.

7 Discussion

This experiment demonstrated that exposing designers to a col-lage priming stimulus that causes them to think about sensory per-ceptions and sustainability helps them to generate more productfeatures that communicate sustainability to the customers, asjudged by experts and customers (AMT). Subjects interacted withimages of products and sensory words and repeatedly made judg-ments about sensory perceptions and preferences, which, intheory, stimulated them to think about the sustainability commu-nication of the product. Other existing priming techniques werenot effective at a statistically significant level, as rated by AMTjudges. The experiment supports the research proposition: primingcan be used to improve design ideation for successfully communi-cating sustainability.

The questionnaire was also successful but, to a lesser extent,with only the expert ratings supporting its significance on improv-ing the number of effective features generated. The goal of includ-ing the questionnaire was to test if the same effect as the collagecould be generated with less effort. Less effort was indeedexpended in the experiment, as subjects primed with the question-naire spent less time on the exercise (most of them used 5 to 6min) than those in the collage condition (most of them used 10min). But, the effect was less-strong in the questionnaire condi-tion. This follows the prediction of the “richer” the priming stimu-lus, the stronger the effect [36]. The questionnaire encompassesno visual stimuli or kinetic participations, which we term “lessstimulation.” It is possible that less stimulation might lead tofewer spreading activations and require less cognitive processing.Finding the biggest lift in ideation performance for the smallestamount of extra effort, or priming efficiency, remains a possiblegoal for future research.

The two benchmarks, the empathic lead user and positive affec-tive stimuli, did not show significant effect on designers for thehypotheses tested; thus, not all priming stimuli are equally effec-tive at generating ST features. This suggests that, in some cases,stimuli perform better when tailored to match a specific goal ofthe ideation process. It does not suggest that these other stimuliare less effective in general, see Sec. 2.2. These priming stimulimay be more effective than the collage for other design tasks. Thebenchmarks may have been more effective if not taken as literallyfrom the previous research and instead adapted. For example, thesmiling baby could have been wearing a cloth diaper. However, asthe goal was to test against baseline, the authors attempted toremain as true to the original stimuli as possible. Although theempathic lead user stimulus is also sensory related, it did not havethe same effect as the collage and questionnaire. A possible expla-nation is that the empathic stimulus pushed designers to exploreextremes and limitations of visual and tactile interactions, thus fo-cusing mainly on the user-product physical interaction.

An interesting finding is that ST ratings of design features cre-ated in the positive affective condition were the highest (on aver-age) of all conditions, although not significantly higher than thetwo test conditions. This trend suggests that the effect of positive

Fig. 6 Comparison of measures across the five priming conditions based on AMT judges’ rat-ings (*p < 0.05, compared with the control condition); error bars show 6 1 standard error

Fig. 7 Comparison of expert and AMT judges’ ratings, in con-ditions A, B, and E, (N 5 30) on measure A

Table 5 Distribution of the ratings from expert judges andAMT judges

Rating range [1,2] (2,3] (3,4] (4,5]Expert 28% 12% 30% 30%AMT 9% 21% 31% 39%



emotion is not negligible and could potentially be combined withother stimuli to improve results.

The ratings between experts and customers (AMT) were well-correlated at 0.7. In general, AMT judge ratings are not as polar-ized as the expert ratings (see Sec. 6.4). This lack of polarizationled to weaker trends in measures A and B across conditions, butthe main finding is still significant at the p< 0.05 level: the col-lage is tested to be effective in helping designers generate moreeffective features (measure B). The expert and customer featureratings in this study can serve as additional data in understandinghow design judgments correlate between experts and novices.Expert judges evaluated from the third person, focusing on thecustomer’s likelihood of thinking about sustainability whenencountering the feature. AMT judges used the first-person orself-report, evaluating the effectiveness of communication directlywith them. No actual purchase decisions were tested in thisexperiment. It is possible that the expert judges had a broaderunderstanding of sustainability than the novices (AMT judges),due to their qualifications; yet, this difference did not manifest inany noticeable way in the results, as correlations in ST judgmentbetween the two groups was high. As the judges were not asked toassess the actual sustainability of the designs, differences in tech-nical qualifications between the two groups should and did havelimited impact. The postexperimental survey showed no signifi-cant difference between conditions in subjects’ in design interest,design experience, and environmental control belief. This corrob-orates that the proposition that the effect on communicating sus-tainability was due to the priming stimuli. Above-average ratingson the evaluation of the ideation session and subjects’ perform-ance indicated that the design problem used in the study was wellunderstood, subjects had enough time to work on the ideation, andthey were satisfied with their performance.

8 Conclusions and Future Work

Results shown by this study are a promising indicator that pri-ming exercises can be targeted to increase designs for sustainabil-ity communication in ideation, beyond increasing the sheer

number and novelty of features. Specifically, priming designerswith heightened sensory perceptions in the form of a collage ac-tivity significantly increases the number of design features judgedto trigger sustainability considerations. The results merit the fur-ther investigation of priming as an effective yet underutilizeddesign technique. However, the study presented here is limited tothe design of one product, and all subjects were students. A gener-alized study of a variety of target products and subjects with dif-ferent levels of design experience would further test ourhypotheses; the effect of varying stimulus exposure time, or awider variety of sustainability-triggering conditions, would pro-vide additional insight. This study did not attempt to identify het-erogeneity in customer response to ST features, due, for example,to different forms of feature embodiment (descriptions vs. proto-types) or customer demographic information.

The work is differentiated from past priming studies in that theimprovement in feature design is geared in a new direction, stimu-lating designers to design features that communicate sustainabilityto the customer. Past priming studies have focused on improvingor heightening a designer’s key skills, such as uncovering newuser needs, moving past fixation, and improving the ability ofdesign features generated to satisfy engineering design goals.The proposed approach benefits ideation in a different area. Forexample, if a number of LCA engineers were primed with the col-lage activity as proposed in this work, they may not producehigher-quality LCA results from the products they designed.Instead, they might refocus their design activities on communicat-ing the sustainability of the products. The ideas that they generatemight actually be worse in terms of traditional quality metrics ofLCA.

The positive effect of priming on sustainability communicationpresents a number of research opportunities. Currently, theauthors are conducting new experiments to spot-check some ofthe highest-rated features generated in this experiment, confirmingthat they trigger customers to think about sustainability whilechoosing between toaster prototypes. The experiment investigateshow engineering design facilitates customer decision-makingregarding sustainability. If successful, the results will contribute

Table 6 Likert-scale postexperimental survey results show no significant difference on means across priming conditions

Priming conditions

Categories Survey items Questionnaire Collage Empathic Affective Control

Self-evaluation I am knowledgeable on sustainability.1¼ strongly disagree, 5¼ strongly agree

3.8 4.1 3.4 3.5 3.7

How experienced are you in design?1¼ inexperienced, 5¼ experienced

3.6 3.3 3.4 3.5 3.4

How interested are you in design? 1¼ not atall, 5¼ very interested

4.1 4.6 4.5 4.3 4.4

My performance on the design task was1¼ poor, 5¼ excellent

3.6 3.6 3.5 3.3 3.4

Evaluation of theideation session

How do you think about this ideageneration session?1¼Difficult, 5¼ easy 3.5 3.3 3.1 3.3 3.21¼Boring, 5¼ fun 3.7 4 4.4 3.9 3.91¼Confusing, 5¼ clear 4.2 3.9 4.3 3.9 4.11¼ I did not have enough time; 5¼ I hadenough time

4.2 3.7 4.4 4.4 3.9

Environmentalcontrol belief

There is nothing the average citizen can doto help stop environmental pollution.a

1¼ strongly disagree, 5¼ strongly agree

1.5 1.2 1.4 1.2 1.4

My involvement in environmental activitiestoday will help the environment for futuregenerations. 1¼ strongly disagree,5¼ strongly agree

4 3.9 3.8 4 3.8

I would not carpool (share cars with others)unless I was forced to. It is too inconven-ient.a 1¼ strongly disagree, 5¼ stronglyagree

1.9 1.8 1.9 1.9 1.6

aInverse items—lower ratings on these items mean stronger environmental control belief.



to bridging the gap between engineering and marketing regardingsustainable products.

As corroborated by other work, this research further suggeststhat priming is useful in design. We envision that priming exer-cises can be created to address specific challenges found in aca-demic design problems, such as customer-centered failureanalysis, form design, and teamwork challenges. It also suggestsapplications of targeted priming to industry, in which engineeringdesigners could “wear more hats” or act in a more interdiscipli-nary fashion with appropriate priming. In addition, a primingexercise might be used to address negative tendencies such asdesign fixation [51]. Preparing the technique for use by “real-world” designers presents other research challenges: in this study,the subjects were not intentionally informed of the purpose of theexercises. A designer in practice would certainly be cognizant ofthe role of the priming activity. Therefore, future work couldinvestigate how implicit/explicit knowledge of the purpose of pri-ming affects results.

Acknowledgment

We thank valuable discussions in experiment design with Dr.Tahira Reid at Purdue University and Dr. Seda Yilmaz at IowaState University. We appreciate the guidance on statistical analy-sis from Dr. Frederick Lorenz, Dr. Robert Stephenson, and Dr.Alicia Carriquiry at Iowa State University. This work was sup-ported by the Mack 2050 Challenge Fund and the Department ofMechanical Engineering at Iowa State University.

Appendix: Analysis of Expert Data Using LMM

We analyzed ST using a linear mixed regression model withsubjects as a random factor and conditions and judges as fixed fac-tors. The benefit of this approach is that the variances of thejudges and features generated by one subject are modeled as wellrather than hiding them in the average. For more details aboutmixed models, see Ref. [81]. The regression model is representedby

ST ¼ b0 þ b1C1 þ b2C2 þ b3Dþ b4Rþ b5 C1Rð Þ þ b6 C2Rð Þ þ e

(A1)

where

C1 C2½ � ¼1 0½ � If condition is questionnaire

0 1½ � If condition is collage

0 0½ � Otherwise ðcontrol conditionÞ

8><>: (A2)

R ¼1 If judge is judge 2

0 If judge is judge 1

((A3)

ST refers to judge’s rating; C1 and C2 are dummy variables toindicate the conditions, shown in Eq. (A2); D represents subjects(designers), which is modeled as a random factor as subjects arerandomly selected from the population; R is a dummy variablerepresenting the judges, defined in Eq. (A3); b0–b6 are regressioncoefficients; and e is the error term. The fixed effect estimates arelisted in Table 7. Our criterion for significance is a coefficientmagnitude of at least two standard errors (SEs) (i.e., absolute tvalues> 2). The degrees of freedom for t values are not knownexactly for a LMM [82]. Given the large number of observations(298) (in our analysis, the t-distribution has converged to thestandard normal distribution. In this case, the 2SE criterion isclose to the conventional two-tailed 0.05 level of significance[83]. The obtained t-statistics in Table 7 show that both contrast 1(questionnaire vs. control) and contrast 2 (collage vs. control)reach the 0.05 significance level in terms of ST ratings.

Hypotheses 1a and 2a are supported by LMM results, as b1 andb2 in Table 7 are statistically significant at the 0.05 significancelevel. The ANOVA (Sec. 6.2) and LMM analyses lead to verysimilar conclusions, with the main difference being that theANOVA model only supports Hypothesis 2a. This is likelybecause ANOVA takes mean rating for features generated by asubject and rated by two judges as an observation.

References[1] Ramani, K., Ramanujan, D., Bernstein, W. Z., Zhao, F., Sutherland, J.,

Handwerker, C., Choi, J. K., Kim, H., and Thurston, D., 2010, “IntegratedSustainable Life Cycle Design: A Review,” ASME J. Mech. Des., 132(9),p. 091004.

[2] William, M., and McDonough, W., 2002, Cradle to Cradle: Remaking the WayWe Make Things, North Point Press, New York, NY.

[3] Luttropp, C., and Lagerstedt, J., 2006, “EcoDesign and The Ten Golden Rules:Generic Advice for Merging Environmental Aspects into Product Devel-opment,” J. Cleaner Prod., 14(15–16), pp. 1396–1408.

[4] Telenko, C., and Seepersad, C. C., 2010, “A Methodology for Identifying Envi-ronmentally Conscious Guidelines for Product Design,” ASME J. Mech. Des.,132(9), p. 091009.

[5] Van Hemel, C. G., and Keldmann, K., 1996, “Applying DFX Experience inDesign for Environment,” Design for X: Concurrent Engineering Imperatives,G. Q. Huang, ed., Chapman & Hall, London, pp. 72–95.

[6] Halog, A., 2001, “Using Quality Function Deployment for Technique Selectionfor Optimum Environmental Performance Improvement,” J. Cleaner Prod., 9,pp. 387–394.

[7] Devanathan, S., Ramanujan, D., Bernstein, W. Z., Zhao, F., and Ramani, K.,2010, “Integration of Sustainability Into Early Design Through the FunctionImpact Matrix,” ASME J. Mech. Des., 132(8), p. 081004.

[8] Fitzgerald, D. P., Herrmann, J. W., and Schmidt, L. C., 2010, “A ConceptualDesign Tool for Resolving Conflicts Between Product Functionality and Envi-ronmental Impact,” ASME J. Mech. Des., 132(9), p. 091006.

[9] Bras, B., 1997, “Incorporating Environmental Issues in Product Designand Realization,” Ind. Environ., 20(1/2), pp. 7–13. Available at: http://www.scopus.com/record/display.url?eid=2-s2.0-0030622726&origin=inward&txGid=F2C54BBBDF00C4AEF49ECEB0EA9ECFAD.FZg2ODcJC9ArCe8WOZPvA%3a2

[10] Roberts, J. A., 1996, “Green Consumers in the 1990s: Profile and Implicationsfor Advertising,” J. Bus. Res., 36(3), pp. 217–231.

[11] MacDonald, E., 2008, “The Construction of Preference in Engineering Designand Implications for Green Products,” Ph.D. thesis, University of Michigan,Ann Arbor, MI.

[12] Olson, E., 2012, “It’s Not Easy Being Green: The Effects of Attribute Tradeoffson Green Product Preference and Choice,” J. Acad. Mark. Sci., 41(2), pp. 1–14.

[13] Rex, E., and Baumann, H., 2007, “Beyond Ecolabels: What Green MarketingCan Learn from Conventional Marketing,” J. Cleaner Prod., 15(6), pp. 567–576.

[14] Vestas, 2011, “Global Consumer Wind Study 2011,” Available at: http://www.ves-tas.com/files%2ffiler%2fen%2fpress_releases%2fbloombergvestas_june_2011%2f-global_consumer_wind_study_2011.pdf, retrieved August 30, 2013.

[15] Brannan, D., Heeter, J., and Bird, L., 2012, “Made With Renewable Energy:How and Why Companies Are Labeling Consumer Products,” National Renew-able Energy Lab, Report No. TP-6A20-53764.

[16] Jiang, Z. H., Shu, L. H., and Benhabib, B., 2000, “Reliability Analysis of Non-Constant-Size Part Populations in Design for Remanufacture,” ASME J. Mech.Des., 122(2), pp. 172–178.

[17] Kwak, M., Behdad, S., Zhao, Y., Kim, H., and Thurston, D., 2011, “E-WasteStream Analysis and Design Implications,” ASME J. Mech. Des., 133(10),p. 101003.

[18] Behdad, S., Williams, A. S., and Thurston, D., 2012, “End-of-Life DecisionMaking With Uncertain Product Return Quantity,” ASME J. Mech. Des.,134(10), p. 100902.

[19] Yuan, C. Y., and Dornfeld, D. A., 2010, “A Schematic Method for SustainableMaterial Selection of Toxic Chemicals in Design and Manufacturing,” ASMEJ. Mech. Des., 132(9), p. 091014.

[20] Rokka, J., and Uusitalo, L., 2008, “Preference for Green Packaging in Con-sumer Product Choices—Do Consumers Care?,” Int. J. Consum. Stud., 32(5),pp. 516–525.

Table 7 LMM estimates of fixed effects

Coefficient Estimate SE T

(Intercept) b0 2.60 0.29 9.10a

Contrast1 (questionnaire vs. control) b1 0.84 0.39 2.16a

Contrast 2 (collage vs. control) b2 1.22 0.38 3.22a

Judge 2 b3 �0.21 0.29 �0.72Questionnaire * judge 2 b4 �0.27 0.28 �0.71Collage * judge 2 b5 �0.33 0.36 �0.93

ap< 0.05.



http://dx.doi.org/10.1115/1.4002308

http://dx.doi.org/10.1016/j.jclepro.2005.11.022

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http://dx.doi.org/10.1016/S0959-6526(00)00080-9

http://dx.doi.org/10.1115/1.4001890

http://dx.doi.org/10.1115/1.4002144

http://www.scopus.com/record/display.url?eid=2-s2.0-0030622726&origin=inward&txGid=F2C54BBBDF00C4AEF49ECEB0EA9ECFAD.FZg2ODcJC9ArCe8WOZPvA%3a2





http://dx.doi.org/10.1016/0148-2963(95)00150-6

http://dx.doi.org/10.1007/s11747-012-0305-6

http://dx.doi.org/10.1016/j.jclepro.2006.05.013

http://www.vestas.com/files%2ffiler%2fen%2fpress_releases%2fbloombergvestas_june_2011%2fglobal_consumer_wind_study_2011.pdf



http://dx.doi.org/10.1115/1.533558

http://dx.doi.org/10.1115/1.533558

http://dx.doi.org/10.1115/1.4004118

http://dx.doi.org/10.1115/1.4007394

http://dx.doi.org/10.1115/1.4002199

http://dx.doi.org/10.1115/1.4002199

http://dx.doi.org/10.1111/j.1470-6431.2008.00710.x

[21] Slovic, P., 1995, “The Construction of Preference,” Am. Psychol., 50(5), pp.364–371.

[22] McDonald, S., and Oates, C. J., 2006, “Sustainability: Consumer Perceptionsand Marketing Strategies,” Bus. Strategy Environ., 15(3), pp. 157–170.

[23] Laufer, W. S., 2003, “Social Accountability and Corporate Greenwashing,” J.Bus. Ethics, 43(3), pp. 253–261.

[24] D’Souza, C., 2004, “Ecolabel Programmes: A Stakeholder (Consumer)Perspective,” Corp. Commun.: Int. J., 9(3), pp. 179–188.

[25] MacDonald, E., and She, J., 2012, “Seven Cognitive Concepts for SuccessfulSustainable Design,” International Design Engineering Technical Conferencesand Computers and Information in Engineering Conference, Aug. 12–15, Chi-cago, IL.

[26] MacDonald, E. F., Gonzalez, R., and Papalambros, P., 2009, “The Constructionof Preferences for Crux and Sentinel Product Attributes,” J. Eng. Design, 20(6),pp. 609–626.

[27] Guyton, A. A., 2006, “Developing Sustainable Product Semantics For Con-sumer Products—A Sustainable Designer’s Guide,” Master thesis, GeorgiaInstitute of Technology, Atlanta, GA.

[28] Reid, T. N., Gonzalez, R. D., and Papalambros, P. Y., 2010, “Quantification ofPerceived Environmental Friendliness for Vehicle Silhouette Design,” ASME J.Mech. Des., 132(10), p. 101010.

[29] Srivastava, J., and Shu, L. H., 2012, “Affordance and Environmentally SignificantBehavior,” International Design Engineering Technical Conferences and Com-puters and Information in Engineering Conference, Aug. 12–15, Chicago, IL.

[30] Fogg, B., 2003, Persuasive Technology: Using Computers to Change What WeThink and Do, Morgan Kaufmann, San Francisco, CA.

[31] Lilley, D., 2009, “Design for Sustainable Behaviour: Strategies andPerceptions,” Des. Stud., 30(6), pp. 704–720.

[32] Lin, J., and Seepersad, C. C., 2007, “Empathic Lead Users: The Effects ofExtraordinary User Experiences on Customer Needs Analysis and ProductRedesign,” International Design Engineering Technical Conferences and Com-puters and Information in Engineering Conference, Sep. 4–7, Las Vegas, NV,ASME, New York, NY, pp. 289–296.

[33] Lewis, S., Dontcheva, M., and Gerber, E., 2011, “Affective Computational Pri-ming and Creativity,” CHI: International Conference on Human Factors inComputing Systems, May 7–12, Vancouver, BC, Canada.

[34] Linsey, J. S., Markman, A. B., and Wood, K. L., 2012, “Design by Analogy: AStudy of the WordTree Method for Problem Re-Representation,” ASME J.Mech. Des., 134(4), 041009.

[35] Marsh, R. L., Bink, M. L., and Hicks, J. L., 1999, “Conceptual Priming in aGenerative Problem-solving Task,” Mem. Cognit., 27(2), pp. 355–363.

[36] Stapel, D. A., 2011, “Priming as Proxy: Understanding the Subjectivity ofSocial Life,” Cognitive Methods in Social Psychology, K. Klauer, A. Voss, andC. Stahl, eds., Guilford Press, New York, NY, pp. 148–183.

[37] Mandel, N., and Johnson, E. J., 2002, “When Web Pages Influence Choice:Effects of Visual Primes on Experts and Novices,” J. Consum. Res., 29(2), pp.235–245.

[38] Kay, A. C., Wheeler, S. C., Bargh, J. A., and Ross, L., 2004, “Material Priming:The Influence of Mundane Physical Objects on Situational Construal and Com-petitive Behavioral Choice,” Org. Behav. Hum. Decis. Process, 95(1), pp.83–96.

[39] Bargh, J. A., Gollwitzer, P. M., Lee-Chai, A. Y., Barndollar, K., and Tr€otschel,R., 2001, “The Automated Will: Nonconscious Activation and Pursuit of Be-havioral Goals,” J. Pers. Soc. Psychol., 81(6), pp. 1014–1027.

[40] Sassenberg, K., and Moskowitz, G. B., 2005, “Don’t Stereotype, Think Differ-ent! Overcoming Automatic Stereotype Activation by Mindset Priming,” J.Exp. Soc. Psychol., 41(5), pp. 506–514.

[41] Galinsky, A. D., and Moskowitz, G. B., 2000, “Counterfactuals as BehavioralPrimes: Priming the Simulation Heuristic and Consideration of Alternatives,” J.Exp. Soc. Psychol., 36(4), pp. 384–409.

[42] McKoon, G., and Ratcliff, R., 1995, “Conceptual Combinations and RelationalContexts in Free Association and in Priming in Lexical Decision and Naming,”Psychon. Bull. Rev., 2(4), pp. 527–533.

[43] Tulving, E., Schacter, D. L., and Stark, H. A., 1982, “Priming Effects in Word-fragment Completion Are Independent of Recognition Memory,” J. Exp. Psy-chol. Learn. Mem. Cogn., 8(4), pp. 336–342.

[44] Custers, R., and Aarts, H., 2010, “The Unconscious Will: How the Pursuit ofGoals Operates Outside of Conscious Awareness,” Science, 329(5987), pp.47–50.

[45] Ackerman, J., Nocera, C., and Bargh, J., 2010, “Incidental Haptic SensationsInfluence Social Judgments and Decisions,” Science, pp. 1712–1715.

[46] Krishna, A., and Morrin, M., 2008, “Does Touch Affect Taste? The PerceptualTransfer of Product Container Haptic Cues,” J. Consum. Res., 34(6), pp.807–818.

[47] Simon, H. A., 1956, “Rational Choice and The Structure of the Environment,”Psychol. Rev., 63(2), pp. 129–138.

[48] Payne, J. W., Bettman, J. R., and Johnson, E. J., 1992, “Behavioral DecisionResearch—A Constructive Processing Respective,” Annu. Rev. Psychol., 43,pp. 87–131.

[49] Haubl, G., and Murray, K. B., 2003, “Preference Construction and Persistencein Digital Marketplaces: The Role of ElectronicRecommendation Agents,” J.Consum Psychol., 13(1–2), pp. 75–91.

[50] Nedungadi, P., 1990, “Recall and Consumer Consideration Sets: InfluencingChoice Without Altering Brand Evaluations,” J. Consum. Res., 17(3), pp.263–276.

[51] Jansson, D. G., and Smith, S. M., 1991, “Design Fixation,” Des. Stud., 12(1),pp. 3–11.

[52] Linsey, J. S., Tseng, I., Fu, K., Cagan, J., Wood, K. L., and Schunn, C., 2010,“A Study of Design Fixation, Its Mitigation and Perception in EngineeringDesign Faculty,” ASME J. Mech. Des., 132(4), 041003.

[53] Higgins, J., 2005, 101 Creative Problem Solving Techniques: The Handbook ofNew Ideas for Business, New Management Publishing Company, Winter Park, FL.

[54] Rickards, T., 1974, Problem Solving Through Creative Analysis Gower Techni-cal Press, Essex, United Kingdom.

[55] VanGundy, A. B., 1988, Techniques of Structured Problem Solving, Van Nos-trand Reinhold, New York, NY.

[56] Moore, P., and Conn, C. P., 1985, Disguised: A True Story, Word Books,Waco, TX.

[57] Eden, H., Scharff, E., and Hornecker, E., 2002, “Multilevel Design and RolePlay: Experiences in Assessing Support for Neighborhood Participation inDesign,” Proceedings of the 4th Conference on Designing Interactive Systems:Processes, Practices, Methods, and Techniques, Jun. 25–28, London, England,pp. 387–392.

[58] Osborn, A. F., 1956, Applied Imagination: Principles and Procedures of Creati-veThinking, Scribner, New York, NY.

[59] d.school, “Bootcamp Bootleg,” Available at: http://dschool.stanford.edu/wp-content/uploads/2011/03/BootcampBootleg2010v2SLIM.pdf, retrieved August30, 2013.

[60] Gerber, L., 2008, “Developing an Idea by Throwing It Away,” Ambidextrous,(9), Available at: http://ambidextrousmag.org/issues/09/article.php?i9p41_43,retrieved August 30, 2013.

[61] Smith, G. F., 1998, “Idea-Generation Techniques: A Formulary of ActiveIngredient,” J. Creat. Behav., 32(2), pp. 107–133.

[62] Herring, S. R., Chang, C.-C., Krantzler, J., and Bailey, B. P., 2009, “GettingInspired!: Understanding How and Why Examples are Used in Creative DesignPractice,” CHI ’09 Proceedings of the 27th International Conference on HumanFactors in Computing Systems, Boston, MA.

[63] Shneiderman, B., 2000, “Creating Creativity: User Interfaces for SupportingInnovation,” ACM Trans. Comput.-Hum. Interact., 7(1), pp. 114–138.

[64] Fu, K., Cagan, J., and Kotovsky, K., 2010, “Design Team Convergence: TheInfluence of Example Solution Quality,” ASME J. Mech. Des., 132(11), 111005.

[65] Bruner, J. S., 1957, “On Perceptual Readiness,” Psychol. Rev., 64(2), pp.123–152.

[66] Bargh, J. A., and Chartrand, T. L., 2000, “The Mind in the Middle: A PracticalGuide to Priming and Automaticity Research,” Handbook of Research Methodsin Social and Personality Psychology, H. T. Reis, and C. M. Judd, eds., Cam-bridge University, New York, NY, pp. 253–285.

[67] Bohm, M. R., Haapala, K. R., Poppa, K., Stone, R. B., and Tumer, I. Y., 2010,“Integrating Life Cycle Assessment Into the Conceptual Phase of Design Usinga Design Repository,” ASME J. Mech. Des., 132(9), p. 091005.

[68] Kudrowitz, B. M., and Wallace, D. R., 2010, “Assessing the Quality of IdeasFrom Prolific, Early-Stage Product Ideation,” International Design EngineeringTechnical Conferences and Computers and Information in Engineering Confer-ence, Aug. 15–18, Montreal, Quebec, Canada, pp. 381–391.

[69] She, J., and MacDonald, E., 2012, “Priming Designers to Communicate Sus-tainability,” International Design Engineering Technical Conferences and Com-puters and Information in Engineering Conference, Aug. 12–15, Chicago, IL.

[70] Yilmaz, S., Daly, S. R., Seifert, C. M., and Gonzalez, R., 2011, “A Comparisonof Cognitive Heuristics Use between Engineers and Industrial Designers,” Des.Comput. Cogn., 10, pp. 3–22.

[71] Linsey, J. S., Green, M. G., Murphy, J. T., Wood, K. L., and Markman, A. B.,2005, “‘Collaborating To Success’: An Experimental Study of Group Idea Gen-eration Techniques,” International Design Engineering Technical Conferencesand Computers and Information in Engineering Conference, Sep. 24–28, LongBeach, CA, pp. 277–290.

[72] Linsey, J. S., Clauss, E. F., Kurtoglu, T., Murphy, J. T., Wood, K. L., and Mark-man, A. B., 2011, “An Experimental Study of Group Idea Generation Techni-ques: Understanding the Roles of Idea Representation and Viewing Methods,”ASME J. Mech. Des., 133(3), p. 031008.

[73] Shah, J. J., Smith, S. M., and Vargas-Hernandez, N., 2003, “Metrics for Meas-uring Ideation Effectiveness,” Des. Stud., 24(2), pp. 111–134.

[74] Shah, J. J., Kulkarni, S. V., and Vargas-Hernandez, N., 2000, “Evaluation ofIdea Generation Methods for Conceptual Design: Effectiveness Metrics andDesign of Experiments,” ASME J. Mech. Des., 122(4), pp. 377–384.

[75] Yang, M., 2009, “Observations on Concept Generation and Sketching in Engi-neering Design,” Res. Eng. Des., 20(1), pp. 1–11.

[76] Kittur, A., Chi, E. H., and Suh, B., 2008, “Crowdsourcing User Studies WithMechanical Turk,” CHI 2008, Apr. 5–10, Florence, Italy.

[77] Cronbach, L. J., and Shavelson, R. J., 2004, “My Current Thoughts on CoefficientAlpha and Successor Procedures,” Educ. Psychol. Meas., 64(3), pp. 391–418.

[78] Mason, W., and Suri, S., 2012, “Conducting Behavioral Research on Amazon’sMechanical Turk,” Behav. Res. Methods, 44(1), pp. 1–23.

[79] Kutner, M. H., Nachtsheim, C. J., Neter, J., and Li, W., 2005, Applied LinearStatistical Models, McGraw-Hill Irwin, Boston, MA.

[80] Stone, G., Barnes, J. H., and Montgomery, C., 1995, “Ecoscale: A Scale for theMeasurement of Environmentally Responsible Consumers,” Psychol. Market.,12(7), pp. 595–612.

[81] Gelman, A., and Hill, J., 2007, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University, New York, NY.

[82] Kliegl, R., Masson, M. E. J., and Richter, E. M., 2009, “A Linear Mixed ModelAnalysis of Masked Repetition Priming,” Visual Cogn., 18(5), pp. 655–681.

[83] Baayen, R. H., Davidson, D. J., and Bates, D. M., 2008, “Mixed-Effects Model-ing with Crossed Random Effects for Subjects and Items,” J. Mem. Lang.,59(4), pp. 390–412.



http://dx.doi.org/10.1037/0003-066X.50.5.364

http://dx.doi.org/10.1002/bse.524

http://dx.doi.org/10.1023/A:1022962719299

http://dx.doi.org/10.1023/A:1022962719299

http://dx.doi.org/10.1108/13563280410551105

http://dx.doi.org/10.1080/09544820802132428

http://dx.doi.org/10.1115/1.4002290

http://dx.doi.org/10.1115/1.4002290

http://dx.doi.org/10.1016/j.destud.2009.05.001

http://dx.doi.org/10.1115/1.4006145

http://dx.doi.org/10.1115/1.4006145

http://dx.doi.org/10.3758/BF03211419

http://dx.doi.org/10.1086/341573

http://dx.doi.org/10.1016/j.obhdp.2004.06.003

http://dx.doi.org/10.1037/0022-3514.81.6.1014

http://dx.doi.org/10.1016/j.jesp.2004.10.002

http://dx.doi.org/10.1016/j.jesp.2004.10.002

http://dx.doi.org/10.1006/jesp.1999.1409

http://dx.doi.org/10.1006/jesp.1999.1409

http://dx.doi.org/10.3758/BF03210988

http://dx.doi.org/10.1037/0278-7393.8.4.336

http://dx.doi.org/10.1037/0278-7393.8.4.336

http://dx.doi.org/10.1126/science.1188595

http://dx.doi.org/10.1086/523286

http://dx.doi.org/10.1037/h0042769

http://dx.doi.org/10.1146/annurev.ps.43.020192.000511

http://dx.doi.org/10.1086/208556

http://dx.doi.org/10.1016/0142-694X(91)90003-F

http://dx.doi.org/10.1115/1.4001110

http://dschool.stanford.edu/wp-content/uploads/2011/03/BootcampBootleg2010v2SLIM.pdf

http://dschool.stanford.edu/wp-content/uploads/2011/03/BootcampBootleg2010v2SLIM.pdf

http://ambidextrousmag.org/issues/09/article.php?i9p41_43

http://dx.doi.org/10.1002/j.2162-6057.1998.tb00810.x

http://dx.doi.org/10.1145/344949.345077

http://dx.doi.org/10.1115/1.4002202

http://dx.doi.org/10.1037/h0043805

http://dx.doi.org/10.1115/1.4002152

http://dx.doi.org/10.1115/1.4003498

http://dx.doi.org/10.1016/S0142-694X(02)00034-0

http://dx.doi.org/10.1115/1.1315592

http://dx.doi.org/10.1007/s00163-008-0055-0

http://dx.doi.org/10.1177/0013164404266386

http://dx.doi.org/10.3758/s13428-011-0124-6

http://dx.doi.org/10.1002/mar.4220120704

http://dx.doi.org/10.1080/13506280902986058

http://dx.doi.org/10.1016/j.jml.2007.12.005

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