SrivastavaShuDTM2015April20 - 5pm · IDETC/CIE 2015 August 2-5, 2015, Boston, Massachusetts, USA DETC2015-47625 CONSIDERING DIFFERENT MOTIVATIONS IN DESIGN FOR CONSUMER-BEHAVIOR CHANGE

1 Copyright © 2015 by ASME

Proceedings of the ASME 2015 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference

IDETC/CIE 2015 August 2-5, 2015, Boston, Massachusetts, USA

DETC2015-47625

CONSIDERING DIFFERENT MOTIVATIONS IN DESIGN FOR CONSUMER-BEHAVIOR CHANGE

Jayesh Srivastava, L.H. Shu* Department of Mechanical and Industrial Engineering, University of Toronto

5 King’s College Road, Toronto, Ontario, Canada, M5S 3G8

*Corresponding author: [email protected]

ABSTRACT Much existing work aims to understand how to change

human behavior through product-design interventions. Given the diversity of individuals and their motivations, solutions that address different motives are surprisingly rare. We aim to develop and validate a framework that clearly identifies and targets different types of behavioral motives in users. We present a behavior model comprising egoistic, sociocultural and altruistic motives, and apply the model to sustainable behavior. We confirmed the explanatory power of the behavior model by categorizing user comments about an international environmental agreement from multiple news sources.

We next developed concepts, each intended to target a single motive type, and elicited evaluations from online respondents who self-assessed their motivation type after evaluating the concepts. We present and discuss correlation results between motive types and preference for products that target these types for two iterations of the experiment. Deviations from our expected results are mainly due to unexpected perceptions, both positive and negative, of our concepts. Despite this, the main value of this work lies in the explicit consideration of a manageable number of different types of motives. A proposed design tool incorporates the three types of motives from the model with the different levels of persuasion others have proposed to change user behavior.

1. INTRODUCTION Our long-term goal is to develop products that enable

environmentally conscious consumer behavior. Much engineering effort has focused on improving the technical efficiency of products. Yet, how people use products is clearly relevant to resource consumption. In fact, the rebound effect describes the use of technically efficient products more frequently, for longer periods, and potentially more wastefully than their less efficient predecessors (Sorrell et al., 2009).

Since product design that aims to change behavior draws from diverse fields outside of engineering design, we begin with a review of relevant concepts from these fields. 1.1. Models of human behavior

Psychologists developed models of human behavior that strive to explain the determinants of behavior and the process that underlies changes in behavior. One of the earliest models, Social Cognitive Theory, describes behavior change as based on the interaction between personal factors, environmental factors and behavioral factors (Bandura, 1977). Changed behavior results from changes in one or more of these factors.

The most difficult of the three factors to understand are personal factors, i.e., individual characteristics, which are represented in many different ways. One approach is to differentiate users by the values they hold. Schwartz (1994) explains that human values have two dimensions. The first dimension describes a person’s level of selfishness between the extremes of self-enhancement and self-transcendence. The second dimension describes a person’s ability to appreciate new ideas between the extremes of conservatism and openness to change. There is no judgment associated with these parameters, i.e., one type is not “better” than the other. Instead, assessments inform how behavior change is likely to occur in a person.

A significant amount of behavior literature is based on the Theory of Planned Behavior (Ajzen, 1991), which describes behavior as the result of interactions between the attitudes of the individual, subjective norms and the amount of control the individual has over the target behavior. Attitudes refer to desires to act in a particular way. Subjective norms are internal rules the person has about what s/he considers appropriate behavior. The perceived amount of control a person has over a situation is also known as self-efficacy, a measure of how a person perceives his/her abilities and the situation.


1.2. Using models to change behavior Designers can use the above and other models to develop

methods to change behavior. For example, influencing a person’s values, attitudes, subjective norms, environmental factors, etc., can be used to change their behavior.

The Transtheoretical Model describes the process of behavior change as occurring in five stages: pre-contemplation, contemplation, preparation, action, and maintenance (Prochaska and DiClemente, 1984; Klein et al., 2011; Montazeri, 2013). While the Social Cognitive Theory and Theory of Planned Behavior aim to explain the what of behavior change, the Transtheoretical Model aims to explain the how or when. Incorporating the Transtheoretical Model, the Health-Belief Model is applied to disease prevention, including exercising and weight loss, overcoming alcohol/drug/tobacco addiction, and using safety equipment. The Health-Belief Model identifies as main behavioral determinants: perceived susceptibility, severity, benefits, barriers, motivation and cues for action (Janz and Becker, 1984; Klein et al., 2011).

Newer theories include Thaler and Sunstein’s (2008) concept of nudges, or small reminders that target people’s cognitive biases to make them choose a particular behavior.

1.3. Changing behavior through products To design products that change behavior, we first examined how the user-product interaction takes place.

Since affordances are potential uses that are perceived in an object (Norman, 1988; Maier and Fadel, 2003), modifying product affordances may change the degree to which a user can perform a behavior. Our past work aimed to influence the behavior of the user by changing product affordances (Srivastava and Shu, 2013b).

The Design with Intent framework defines seven categories of behavior patterns, called lenses, which correspond to ways that products influence user behavior (Lockton et al., 2010). Examples under each lens may be used for inspiration.

Finally, the Fogg Behavior Model (2009) breaks down user behavior into different types and advises the designer on how to effect behavior change for each type. Behavior types include: doing a familiar behavior once, stopping a behavior for a period of time, and starting/continuing a new behavior, etc. 1.4. Products that encourage sustainable behavior

The above frameworks are intended to describe and affect behavior in general. A subset of behavior that interests us in particular is called pro-environmental, environmentally significant, environmentally conscious, or sustainable behavior.

Stern (2000) developed a model expressly to describe environmentally significant behavior, which states that values, beliefs and norms lead to attitudes, which lead to behavior. Researchers interested in consumer-level behavior change have used this and similar models to devise interventions that encourage conservation and curtailment behaviors (Elias et al., 2007; Wever et al., 2008; Srivastava and Shu, 2013ab; Zachrisson and Boks, 2010; Wilson et al., 2013; Bockarjova and Steg, 2014; Davoudi et al., 2014).

2. DESIGNING FOR DIFFERENCES IN USERS 2.1. Current approach to design for behavior change

Even when models conceptualize behavior change differently, they tend to be applied in a similar way. Usually, a behavior-change intervention is first devised based on one or more behavior models. The intervention is then tested with participants to reveal insights. These insights could then be incorporated in future behavior-change interventions. This approach has produced many valuable insights about behavior, and has led to improved methods and interventions. 2.2. All users are not the same Based on experience from our past work, our premise is that different users will likely not respond the same way to a given intervention. Results of other researchers that support our premise include the following.

Withanage et al. (2014), in their study of encouraging energy-use behaviors related to cooking, found two types of users: 1) those who did not know the correct behavior to perform (i.e., whose behavior could possibly be corrected through more knowledge) and 2) those who had the right knowledge but would not act on it (and therefore need some other motivation). Telenko and Seepersad (2014) use the term “Human Factors” to describe the different requirements different users impose on products. They cite the example of a family car that must satisfy different needs of a household, such as commuting, transporting children, etc. Varying levels of income, knowledge and time availability also affect how users will interact with the car. Van Horn and Lewis (2014) also accounted for this variability in user types by simulating light, medium and heavy product users in their models.

In addition, different users will react differently to the same intervention. For example, Goucher-Lambert and Cagan (2014) found that the addition of environmental information to a product alters the way it is perceived by users. Different consumers also assess that environmental information differently. Gromet et al. (2013) found that American consumers’ political alignments affected their purchase intent for energy-efficient products. Participants in their study were given a choice to buy Compact Fluorescent Light (CFL) or incandescent bulbs. The participants generally agreed that the CFL bulbs were superior in function, lifetime savings and environmental benefit, even though they were three times as expensive as incandescent bulbs. Despite the knowledge of their functional superiority, a CFL bulb with an environmental label actually deterred moderate and conservative participants from choosing it over the incandescent bulb. These participants were much more likely to choose the CFL bulb when it carried a generic label. The environmental label directly conflicted with the conservative participants’ dislike of political investments in energy reduction. Thus, promoting a feature intended to increase adoption of a product may inadvertently drive away a subset of customers.


2.3. Current approach to address user differences There are two main ways through which researchers

address user-group differences. In the first, designers are advised to apply behavior-change theories on a case-by-case basis, creating a new solution for every new customer group. For example, Kok et al. (2011) showed how Intervention Mapping—originally used for health promotion initiatives—could be applied to reduce home energy consumption. They recognized that different groups will have different objectives, and recommended that interventions be tailored by executing the six-step Intervention Mapping process for each group.

Secondly, researchers aware of the inherent complexity of behavior models treat the behavior-change task as an optimization problem. Klein et al. (2011) developed a mobile-phone application (app) that prompts users to engage in healthy behaviors. Based on a model that combines the tenets of many behavior-change models, the app asks the user questions to determine values for each of its parameters. It then provides a series of prompts and tasks customized to the user’s profile.

The computational optimization approach is also commonly applied to compute the best combination of products to help companies maximize profits (Morrow et al., 2014). The modeling of heterogeneous consumer preferences itself is a complex problem. Zhao and Thurston (2013) showed how a mathematical model could be used to forecast consumer demand based on varying preferences for attributes. 2.4. Between generic and customized extremes

We sought to address differences between users without accounting for a prohibitively large number of variables, and found such an approach applied to agricultural behavior change. While studying how Australian farmers reacted to the government’s promotion of resource conservation practices, Pannell et al. (2006) and Farmar-Bowers and Lane (2006) found that the farmers had differing goals and motivations, and responded best to interventions consistent with their interests.

Greiner et al. (2007) therefore recommended the Australian government take an “incentive tool-box approach” to change farmer behavior. After categorizing farmers under three sources of motivation (financial, social, and environmental), they determined the types of incentives that matched these motivations. Finally, they recommended a combination of incentives to motivate as many of the farmers as possible. 3. A MODEL FOR USER MOTIVATIONS AND BEHAVIOR-CHANGE INTERVENTIONS

Our goal is to develop and validate an approach in product design for behavior change that addresses different motivations. We first aimed to define categories of motivations that would explain as many responses to behavior-change interventions as possible. At the same time, we did not want an unmanageably large number of categories.

We identified three sources of user motivation as egoistic, sociocultural and altruistic. These categories are based on the three types of motivations Greiner et al. (2007) found, but generalized to apply to a wide array of problems. We posit that

most product users could be characterized as principally persuaded by one type of motive while still having some influence from the other two motive types. Figure 1 graphically describes this model as a triangle with the three motive types as vertices. Users’ particular combinations of motivations can be represented by their placement inside the triangle.

3.1. Egoistic motives

Egoistic motives have to do with the user’s self-interest. Consistent with traditional design objectives, these include a desire for increased convenience, performance and/or reduced time, cost, effort and steps of operation. In studies of antecedents of behavior, Davoudi et al. (2014) identify one pole of a major dimension of human values as concern for oneself, termed self-enhancement by Schwartz (1994). Of the 6 drivers of product utility found in a study of consumer purchasing behavior, 3 (productivity, simplicity, and convenience) can be categorized as egoistic (Burton and Easingwood, 2006).

3.2. Sociocultural motives

Sociocultural motives describe the desire to be perceived by one’s family, peers and other social groups in a positive way. Social attitudes and norms are one of the determinants of behavior in Ajzen’s (1991) Theory of Planned Behavior. They have been shown to influence many behaviors, such as whether people waste food (Evans, 2011), dispose of old possessions (Phillips and Sego, 2011), adopt energy-saving home innovations (Christie, 2010), and reduce water consumption (Corral-Verdugo et al., 2002). These motives are also manifested as the social-risk and potential image enhancement users assess in new products (Burton and Easingwood, 2006).

In fact, humans are peculiar among primates in how much importance we place on social norms. Haun et al. (2014) found that humans may abandon a tried-and-tested way of doing a task, adopting another method they observe peers using, even when the new method offers no functional benefit. Other primates studied stayed with a method they already knew worked, despite the presence of a peer accomplishing the same task in a different way. The desire to fit into a group therefore appears to be a strong motivator in humans. Greiner et al. (2007) identified a subset of farmers who were most interested in maintaining a tradition of farming and being respected by their community and peers. Such farmers were observed to respond best to interventions that helped enhance their image. In other work, Peschiera et al. (2010) found dormitory students to be most successful in conserving energy when they received information about their usage relative to their specific peer group, rather than the average usage in the building.

Altruistic

Sociocultural Egoistic Figure 1: User-motivation model


3.3. Altruistic motives Altruistic motives describe the user’s concern for others

and the environment, and are well documented as an influence on behavior. Related to Schwartz’s (1994) values of self-transcendence, Greiner and Gregg (2011) found that many Australian farmers were motivated primarily by a desire to conserve the land and be good stewards. De Groot and Steg (2008) & Bockarjova and Steg (2014) studied environmentally related behavior change, and differentiated between specifically altruistic values (concern for others) and biospheric values (concern for the environment). As we are uncertain of this distinction’s benefit in non-environmental applications, we use the term altruistic to refer to both types of values for now.

4. MODEL EXPLORATION

To explore the explanatory power of the model, we aimed to assess whether it could describe the motivations of the public at large. We chose an issue that involves resource conservation, and used social-media postings to assess a large amount of available opinion information. Specifically, we reviewed opinions expressed by internet commenters to an agreement to reduce future CO2 emissions, signed in November 2014 between the Presidents of the United States and China (The White House, 2014). Briefly, the U.S. pledged to reduce emissions by 26%-28% below 2005 levels by 2025. China pledged that its CO2 emissions would peak around 2030, and that it would meet at least 20% of its energy needs through non-fossil fuel sources by 2030. Little else was detailed in the announcement. This ambiguity allowed for a variety of commenter interpretations of the agreement’s potential effects, which revealed their motives for approval or disapproval. 4.1. Methods

We aimed to avoid the pitfalls of working with social media in human-behavior studies (Ruths and Pfeffer, 2014) by minimizing corresponding limitations. To overcome the frequent lack of diversity in social-media communities, we reviewed comments from a variety of news sources, countries and formats. We also wanted to control for political orientation and demographic factors, e.g., age, ethnicity, and gender. Therefore, we drew upon the Pew Research Center’s State of the News Media report (2014), histories of political endorsements by news sources and past analyses of news sources (Gentzkow and Shapiro, 2010). We reviewed comments from the websites of newspapers, television news services, and digital news outlets. To include multiple countries, but avoid language translation, we reviewed comments on English-language websites based in the U.S., U.K., Canada, and internationally. In all, we captured opinions from some of the most widely read Internet news sources, including 4 of the top 5 (Alexa Internet Inc., 2015). Other news sources, e.g., FOXNEWS, were excluded, because their articles reporting the U.S.-China agreement were not open to comments. Finally, we chose news articles about the agreement rather than editorials. We used existing comment-rating systems to select comments with 10 or more ‘likes’ (for

systems with only positive feedback) or 0 or higher comment score (for systems with both positive and negative feedback). Also excluded were replies to users’ comments. Selected comments were manually searched for information that revealed reader motivation, which were sorted into one of our 3 motivation types, or categorized as “other”, detailed below. 4.2. Results

For all but 4 of the news sources, over half the comments could be categorized into one of our 3 motivation types.

Comments that focus on the short-term economic impacts of the agreement fit our definition of egoistic motive. Also categorized as egoistic were comments on economic impacts to individual countries, along with patriotic and nationalistic appeals, as they prioritized the well being of one’s own nation at the expense of others. Our definition of sociocultural motives appeared in comments that focused on how the agreement enhanced or degraded the personal image of individuals or their country or government. Finally, comments on effects of the agreement on the environment, future generations and other species on the planet, fit our definition of altruistic motives.

The comments that could not be categorized as described above did not include any motive-related information. Such comments included curt declarations of praise for one of the parties involved, emotional statements against perceived enemies, and opinions that did not offer any reasoning. Some comments expressed skepticism about anthropogenic climate change and a lack of faith in the agreement. Other comments offered clarification of points in the article, questions, jokes, and nebulous conspiracy theories. While we were open to identifying additional motivation categories, we concluded from this exercise that the 3 sources of motivation defined for our model appear sufficient for explaining people’s sentiments.

5. EXPERIMENTAL VALIDATION OF THE MODEL

We next aimed to empirically validate our behavior-change model and its ability to predict user responses to proposed concepts. To do so, we created concepts intended to target the 3 sources of motivation, and sought user responses to these concepts through an online survey. In behavioral research, a psychometric construct is an instrument (usually a survey) that assesses the type or category to which a person belongs. After formulating and refining a construct (Harrington, 2008), it is tested with users to determine whether the hypothesized model explains relationships in the collected data. We developed a survey consisting of product concepts and examined the following three hypotheses empirically:

H1: Concepts devised to appeal to the same motivation would be rated similarly by respondents

H2: Respondent self-assessment of level of egoistic, sociocultural, and altruistic motivation would relate to their rating of concepts intended to appeal to those motivations

H3: Respondents would rate concepts that matched their own dominant motivation type higher than others.


5.1. Survey Design Figure 2 summarizes two concepts per motivation type for

2 target behaviors: 1) using reusable over disposable shopping bags and 2) taking stairs instead of elevators in a small office building. Also included were 2 control concepts describing existing solutions not tailored for any particular motivation type. All concepts were described as similar in costs and safety.

Using a 4-point Likert scale, participants were asked to 1) rate how likely the concept would persuade them to perform the behavior, and 2) provide reasons for their ratings. Participants were allotted 10 minutes to complete the survey, which contained the concepts in randomized order. Participants next quantified the relevance of each motivation type to them by distributing 10 points over descriptions of the motivation types.

Concepts to increase reusable over disposable bag use Concepts to increase stair over elevator use

Con

trol

Fold Sac This bag can be folded, rolled up or scrunched together to make it compact and easy to carry.

Paystub-Reminder Program Paystubs carry a message reminding staff that the elevators are intended for use by the disabled and elderly staff only.

Concept 1 for motive type Concept 2 for motive type Concept 1 for motive type Concept 2 for motive type

Egoi

stic

Geckskin Strip Bag This bag has a strip of a novel material that sticks to hard surfaces. Users can stick the bags to walls / doors and the message on the bag will remind them to take it with them when they leave.

Self-Clean Sac This bag has a special texture that reduces solids or liquid sticking to the material. The material also makes the bag leak proof.

Fitness-Tracking Incentives Staff gets incentives to use fitness apps on their phones. The building staircases are official routes in these apps and employees would be able to track how the stairs help them meet their fitness goals.

GearVator The GearVator is a small, economical elevator that can carry up heavy and bulky items for office staff. This will free up staff to take the stairs.

Soci

ocul

tura

l

MyCause Bag This bag promotes one of several non-profit organizations as chosen by the user. The logo of the organization is prominently displayed on the bag.

Statement Satchel Every time this bag goes through a retail checkout, it automatically posts a congratulatory message on the one of the user’s social media accounts.

Fitness Fasttrack The stairs would be painted in a racetrack theme and staff would scan their ID badges to keep track of how often they took the stairs. The names of the most prolific stair users would be displayed on a public scoreboard as “winners.”

GlassVator The company would install tempered glass doors in the elevator to make it more obvious when staff were using it when it was not necessary.

Altr

uist

ic

Carbon-Capture Sac This bag is made of a material that absorbs CO2 when exposed to sunlight. Taking the bag outside causes it to absorb CO2 and render it inert.

TreePlanting Bag This bag has a barcode on the front panel. Every time the code is scanned in a retail checkout, money is donated towards the planting of trees in deforested areas.

Piezo Stepper Piezoelectric plates would be installed below the surface of each step. The energy collected from people using the stairs would be used for powering lighting and/or other office systems.

Wait-Reduction Tracker A sign next to the elevator would show the reduction in elevator wait time that has resulted from the elimination of wasteful elevator trips that week.

Figure 2: Concepts to increase reusable bag use (left) and to decrease elevator use (right).


One hundred respondents were recruited through Amazon Mechanical Turk. The survey was posted through a service specializing in academic-research-related Human Intelligence Tasks (HITs). The service generally has very good reviews on third party sites for timely and fair compensation (http://mturkdata.com - Mturk Data Consultants). The data was then transcribed and analyzed. 5.2. H1 – Relatedness of Concepts

Researchers who wish to test the existence of underlying behavioral traits (also called factors) often develop surveys where each question relates to a single factor. They then perform a Confirmatory Factor Analysis (CFA) to see if the questions that address the same factor cluster together. They also test whether the combination of all the behavioral factors explains the survey results generally (Harrington, 2008). We used a CFA to check whether the concepts intended to address the same motivation type would elicit similar ratings. 5.2.1. Method

We used the R software package to perform the CFA and defined a model with three latent variables to represent egoistic, sociocultural and altruistic motivations. Multiple fit indices were used to evaluate the model: comparative fit index (CFI), Tucker-Lewis or non-normed fit index (TLI or NNFI), and root mean square error of approximation (RMSEA). Based on the indices, the initial model showed a poor fit (CFI=0.63, TLI=0.52, RMSEA=0.10). We removed the worst performing concepts based on respondent comments from each factor and performed another analysis. These concepts (Statement Satchel, Fitness-Tracking Incentives and Wait-Reduction Tracker) are discussed in detail below. The revised model, shown in Table 1, showed an improved fit with the data (CFI=0.80, TLI=0.70, RMSEA=0.08).

Table 1: Confirmatory Factor Analysis (CFA) Factor 1 Factor 2 Factor 3

Geckskin Strip Bag 1.00 (-) Self-Clean Sac 0.73 (0.24)**

GearVator 0.86 (0.27)** MyCause Bag 1.00 (-)

GlassVator 1.58(0.86) ‡ FitnessFasttrack 1.99(1.08) ‡

Carbon-Capture Sac 1.00 (-) TreePlanting Bag 0.40(0.23) ‡

Piezo Stepper 0.80(0.23)** ‡ p< 0.1, * p< 0.05, ** p< 0.01, *** p< 0.001 5.2.2. Discussion

The fit indices suggested that our revised survey produced an acceptable 3-factor model. Reasons provided by respondents for their ratings were reviewed to determine why some concepts performed unexpectedly. These concepts would be revised for the next survey iteration.

The Statement Satchel, the lowest scoring concept (1.16/4), posts to social media when users shop with the bag. Respondents were incensed by possible privacy violations, did not wish their shopping habits broadcast, and felt this type of

information would clutter others’ social-media accounts. Rather than be the point of pride intended, most respondents felt this type of messaging would harm their privacy and reputation.

We developed this concept because posting on social media appears to be a successful strategy for existing mobile phone applications, e.g., those that post users’ exercise statistics. Similarly, users routinely allow music-streaming applications to inform others of the songs on their playlists. Shopping behavior is perhaps more private than exercise habits or music listening. Conversely, perhaps many users do not know that their apps are already broadcasting information about them. On the other hand, existing apps can be configured to post messages to a smaller group of individuals. The Statement Satchel concept did not specify this control, and was almost universally disliked by all respondents.

The Fitness-Tracking Incentives concept to increase stair use, on the other hand, was the highest rated (2.97/4). The concept provides incentives to use fitness phone apps; the building staircases become official app routes and users would be able to track how the stairs help them meet their fitness goals. The concept’s almost unanimous appeal could be because the best-known benefit of stair usage is physical fitness, which this concept explicitly supports.

The Wait-Reduction Tracker concept to reduce elevator use, was also one of the highest rated concepts (2.57/4). Our inability to discriminate its ratings is more difficult to explain. Even egoistically motivated respondents felt that public display of increased wait times for those needing the elevator more would trigger shame within them, and be effective in reducing their elevator use. Conversely, some altruistically motivated respondents questioned the concept’s feasibility, i.e., whether showing wait times as percentages was meaningful or possible to measure accurately. Doubts of the altruistically motivated respondents combined with praise of the egoistically motivated respondents made differences between them harder to discern.

5.3. H2 - Relationship between Respondents’ Self-Assessment of Motivation and Concept Ratings We performed two tests to see if there was a relationship between the egoistic, sociocultural and altruistic components of respondents’ motivations and the way they rated products intended to address one of those motivations. 5.3.1. Method The simplest way to check for a relationship was to study the correlations between respondents’ self-assessed scores of egoistic/sociocultural/altruistic motivations and their ratings of concepts intended to address these motivations.

Table 2 shows Pearson correlations between the self-ratings of each motivation type and normalized ratings for each concept. Ratings were normalized by subtracting each respondent’s rating of the control concept from that respondent’s rating of other concepts.


Table 2: Correlation between motive and concept ratings

Reusable-bag concepts Egoistic Rating

Socioc. Rating

Altruist. Rating

Ego-istic

Geckskin Bag 0.020 -0.065 0.024 Self-Clean Sac -0.088 -0.087 0.156

Socio-cult.

MyCause Bag -0.073 -0.020 0.095 Statement Satchel 0.051 -0.053 -0.021

Altru-istic

Carbon-Capture Sac -0.168‡ -0.020 0.199* TreePlanting Bag -0.254* -0.020 0.294*

Increase-stair-use concepts Egoistic Rating

Socioc. Rating

Altruist. Rating

Ego-istic

Fitness Tracking -0.080 0.163 -0.024 GearVator 0.039 0.031 -0.064

Socio-cult.

Fitness Fasttrack -0.117 0.295* -0.074 GlassVator -0.069 0.159 -0.033

Altru-istic

Piezo Stepper 0.038 0.037 -0.067 Wait-Reduction Tracker -0.014 0.055 -0.022

‡ p< 0.1, * p< 0.05, ** p< 0.01, *** p< 0.001 Table 3 summarizes the second way we studied this

relationship, using multiple linear regression. In this analysis, we examined whether the ratings for egoistic concepts could be used to predict the egoistic component of respondents’ motivation, ratings for sociocultural concepts predict the sociocultural motivation component and so on.

Table 3: Multiple Regression Analyses to Test Relationships between Concept Ratings and

Motivation-Type Assessments

β t R2 Adj. R2 df F

Model: Egoistic Rating 0.08 0.04 4, 95 2.02‡

GeckSkin Bag 0.06 0.36 Self-Clean Bag -0.30 -1.55

GearVator 0.03 0.19 Fitness Tracking -0.38 -2.20

Model: Socio-cultural Rating 0.10 0.06 4, 95 2.51*

Statement Satchel 0.07 0.27

MyCause bag 0.02 0.16 GlassVator -0.02 -0.20

Fitness Fasttrack 0.30 2.93** Model: Altruistic Rating 0.17 0.13 4, 95 4.72**

Carbon-Capture Bag 0.16 1.25

TreePlanting Bag 0.39 3.09** Piezo Stepper 0.05 0.36

Wait-Reduction Tracker 0.16 1.17

‡ p< 0.1, * p< 0.05, ** p< 0.01, *** p< 0.001 5.3.2. Discussion

The results were mixed. In terms of direct correlations, Table 2 shows that in 5 cases (bold text in grey cell), concept ratings correlated best with the types of motives they targeted. The statistically significant results (2 altruistic and 1 sociocultural concept) also correlated best with their intended-motive type. In the regression analysis, the strength of the

relationship between concepts and motivation components varied by motivation type. The egoistic concepts only explained about 8% of the variance in egoistic self-ratings, sociocultural concepts explained 10% of the variance in sociocultural self-ratings while altruistic concepts accounted for a much higher 17% of the variance in altruistic self-ratings. Only two of the concepts (Fitness Fasttrack and TreePlanting Bag) contributed significantly to their respective models.

Participant comments provided better support for correlations between concept ratings and motive types. Respondents with high-rated egoistic motives tended to easily understand the benefits of the GearVator concept, which transports items in a smaller elevator system to reduce passenger-elevator use. One respondent stated that wearing a heavy coat was a major deterrent for climbing stairs, which this concept addresses. Conversely, respondents with low-rated egoistic motives focused on the drawbacks, e.g., the energy wasted by an additional elevator system. Similarly, high-sociocultural motivated respondents identified the benefits of the Fitness Fasttrack concept, with one describing it as a “fun competition.” Less socioculturally motivated respondents were more likely to discuss pitfalls, e.g., reduced productivity in the company if staff became distracted by the competition. Finally, high-rated altruism motive respondents appreciated the TreePlanting bag concept, stating reasons such as “it allows me to see the impact I’m making.” Low-rated altruism motive respondents focused on concept limitations, i.e., one respondent was unimpressed with the concept, as it did nothing to alleviate the main problem of forgetting to take reusable bags along. Table 2 also shows that reusable-bag concepts all correlated best with altruistic motives, while the increase-stair-use concept correlations showed more differences. This may suggest that the appeal of reusable bags is generally tied to altruistic motives. Indeed, when asked how frequently they used reusable bags, respondents’ answers correlated with their altruistic motive ratings. We do not suggest that reusable bag concepts should focus on altruistic motives, but rather, designers of reusable bags should target other motive types. 5.4. H3 – Dominant motivation type and concept preference Finally, we categorized respondents by their dominant motivation type and determined whether respondents who were predominantly egoistic, sociocultural or altruistic rated the various concepts differently from one another. The differences among them were compared using an Analysis of Variance (ANOVA) technique, and summarized in Tables 7 and 8. The results were again mixed. Predominantly egoistical and sociocultural respondents were the highest raters for some of the concepts intended for them (e.g., Geckskin Bag for egoists, MyCause bag for socioculturals), but they did not rate other concepts intended for them higher than others. Altruistic respondents, on the other hand, did rate the concepts intended for them the highest in three out of four cases. All in all, we were motivated to revise the survey.


6. REFINING THE CONCEPT-SKETCH SURVEY Participant comments further revealed that respondents often became fixated on unexpected elements of certain concepts, which affected their evaluations. For example (as shown in Table 7), while the Geckskin bag was well liked by respondents with high egoistic motives, altruistic respondents rated it poorly. Some respondents believed the bag material had the texture of lizard-skin. Our reference to Geckskin was to convey the stickiness of the strips. Since the feel of the material was not an intended feature (whether positively or negatively received), we replaced the Geckskin bag with the Post-It bag featuring the same benefit, under the guise of an adhesive material developed by 3M, as shown in Figure 3.

We made several such modifications to the survey concepts. The Self-Clean Sac concept was similarly appealing to respondent types other than the intended egoists, who did not appreciate the concept’s benefits. These users did not consider getting a bag dirty a significant problem, as it was already easy to wash reusable bags. To better target egoistic motivations, we replaced the Self-Clean Sac with the Dura-Light Bag, which features a reflective strip to help shoppers be more visible to cars in parking lots and on roads. The bag, shown in Figure 3, is also made using a ripstop material that prevents tearing.

Original Concept Replacement concept

Geckskin Bag

Post-It Bag

Self-Clean Sac

Dura-Light Bag

Figure 3: Replacement egoistic concepts

Figure 4 shows how both sociocultural bag concepts were modified. The Statement Satchel concept was seen to violate privacy concerns by publicizing the shopping habits of users. In its place, we devised the Seeing Green Bag to highlight the user’s interest in environmentally conscious causes without revealing personal details. While respondents identifying as socioculturally motivated generally liked the MyCause bag, it was very similar to the new Seeing Green concept. In addition, participant comments revealed that affiliation with a charitable cause aligned with the interests of many altruistically motivated respondents. Therefore, the MyCause bag was replaced with the Design-First Bag, which allowed respondents to select a bag associated with a luxury-goods brand.


Statement Satchel Seeing Green Bag

MyCause Bag Design-First Bag Figure 4: Replacement sociocultural concepts Figure 5 shows the replacement of an increase-stair-use

concept that targeted altruistic motives, but was difficult to understand as presented. Specifically, for the Wait-Reduction Tracker concept, respondents struggled with the meaning of the wait-time percentage information proposed. Therefore, we replaced the wait-time display with the universal accessibility symbol that would light up when someone with a specialized key-card uses it to hail the elevator. This is intended to have the same effect of reminding that those with greater needs for the elevator have higher priority. Finally, we also revised some of the instructional wording to increase clarity to respondents.


Wait-Reduction Tracker Accessibility Reminder

Figure 5: Replacement altruistic concept. 6.1. Method

To test the next iteration of the survey, 101 new Amazon Mechanical Turk respondents were recruited through the same service. Respondents were identified by their Mechanical Turk ID to ensure that they could not participate in more than one trial of our experiment.

6.2. H1 – Relatedness of concepts We performed another Confirmatory Factor Analysis (CFA). Again, 3 underlying factors, or similar groups, were verified, as shown in Table 4. The model was revised by again removing the worst performing concepts (Post-It Bag, Fitness Fasttrack, Accessibility Reminder), discussed in detail below. The resulting CFA yielded improved fit over all previous iterations (CFI=0.88, TLI=0.82, RMSEA=0.06).


Table 4: Confirmatory Factor Analysis (CFA) – Trial 2 Factor 1 Factor 2 Factor 3 Dura-Light Bag 1.00 (-)

Fitness Tracking 0.72 (0.43)‡ GearVator 1.39 (0.53)**

Design-First Bag 1.00 (-) Seeing Green Bag 2.27(0.92)*

GlassVator 1.12(0.56)* TreePlanting Bag 1.00 (-)

Carbon-Capture Bag 1.16(0.31)*** Piezo Stepper 0.67(0.26)*

‡ p< 0.1, * p< 0.05, ** p< 0.01, *** p< 0.001 6.3. H2 - Relationship between Respondents’ Self-Assessment of Motivation and Concept Ratings

Table 5 shows Pearson correlations between the self-ratings of each motivation type and normalized ratings for each concept. Table 6 shows a multiple linear regression.

Table 5: Correlation between motive and concept ratings – Trial 2

Reusable-bag concepts Egoistic Socio-

cultural Altruis-

tic Ego-istic

Dura-Light Bag -0.006 -0.035 0.049 Post-It Bag -0.067 -0.023 0.115

Socio-cult.

Design-First Bag -0.101 0.160 -0.052 Seeing Green Bag -0.236* 0.179 0.104

Altru-istic

Carbon-Capture Sac -0.185‡ -0.030 0.280* TreePlanting Bag -0.132 -0.075 0.262*

Increase-stair-use concepts Egoistic

Socio-cultural

Altruis-tic

Ego-istic

Fitness Tracking 0.171 -0.079 -0.134 GearVator 0.032 -0.052 0.017

Socio-cult.

Fitness Fasttrack 0.246* -0.196‡ -0.098 GlassVator -0.046 0.100 -0.055

Altru-istic

Piezo Stepper 0.076 -0.242* 0.181‡ Accessibility Reminder 0.100 -0.161 0.055

‡ p< 0.1, * p< 0.05, ** p< 0.01, *** p< 0.001

Table 6: Multiple Regression Analyses to Test Relationships Between Concepts and Motivation

Type Ratings- Trial 2

β t R2 Adj. R2 df F

Model: Egoistic Rating 0.04 0.005 4, 96 1.12

Dura-Light Bag -0.07 -0.26 Post-It Bag -0.20 -0.94 GearVator -0.31 -1.63

Fitness Tracking 0.08 0.43 Model: Socio-cultural Rating 0.20 0.17 4, 96 6.18***

Design-First Bag 0.45 2.43* Seeing Green Bag 0.35 2.01*

GlassVator 0.28 2.01* Fitness Fasttrack -0.35 -2.37*

Model: Altruistic Rating 0.23 0.19 4, 96 7.01***

Carbon-Capture Bag 0.28 1.77‡ TreePlanting Bag 0.30 2.13*

Piezo Stepper 0.31 2.56* Accessibility Reminder 0.11 0.85

‡ p< 0.1, * p< 0.05, ** p< 0.01, *** p< 0.001

6.4. H3 – Dominant motivation type and concept preference Finally, Tables 7 and 8 summarize an Analysis of Variance (ANOVA) of the different preferences of predominantly egoistic, sociocultural and altruistic respondents. 6.5. Discussion

The three hypotheses were better supported by the revised trial-2 model. Table 4 shows that the concepts clustered together much better, with almost all remaining concepts making a statistically significant contribution. Table 5 shows that the correlations between motive and concept type were improved overall with 8 (bold font in grey cell) showing expected correlations. Table 6 shows an improved regression model for the sociocultural and altruistic concepts, each explaining 20% and 23% of the variance in the sociocultural and altruistic self-ratings respectively. Tables 7 and 8 show that in trial-2, apart from the egoistic and Fitness Fasttrack concepts discussed below, respondents with the targeted motive types rated corresponding concepts higher than those with other motive types (bold font in grey cell).

Our revised egoistic concepts had either unintended limitations or broader appeal than intended because respondents perceived additional unstated benefits. Table 7 shows that the Dura-Light Bag was generally well liked by all types of respondents, making it difficult to discern motive types. On the other hand, the Post-It Bag was generally disliked. Many respondents too literally transferred the adhesive properties of Post-It notes, and did not trust that the same adhesive strength would hold up even an empty bag. In hindsight, we also neglected to include the “reminder” message of the GeckSkin Bag; thus the Post-It Bag’s intended benefits were not apparent.

Table 7: Bag concept ratings (out of 4) for both trials

Egoistic Sociocultural Altruistic

Trial 1

Trial 2 Trial 1 Trial

2 Trial

1 Trial

2 Folding Sac

(Control) 2.43 2.62 2.50 2.76 2.33 2.33

Geckskin Bag (Egoistic) 2.06 - 2.00 - 1.86 -

Post-It Bag (Egoistic) - 1.97 2.18 2.17

Self-Clean Sac (Egoistic) 2.77 3.00 - 3.05

Dura-Light Bag (Egoistic) - 2.83 - 2.82 - 2.83

Statement Satchel (Socio.) 1.16 - 1.00 - 1.19 -

Design-First Bag (Sociocultural) 1.59 2.00 1.42

MyCause Bag (Sociocultural) 2.00 - 2.50 - 2.33 -

Seeing Green Bag (Socio.) - 1.88* - 2.65* - 1.92*

Carbon Capture (Altruistic) 2.45 2.83 2.50 3.18 2.71 3.58

TreePlanting Bag (Altruistic) 2.48* 2.59* 1.50* 2.88* 3.19* 3.42*

‡ p< 0.1, * p< 0.05, ** p< 0.01, *** p< 0.001


Table 8 shows that in trial-2, the GearVator concept failed to elicit expected responses from the egoistic respondents compared to the other motive types. Egoists identified concept pitfalls; they expected the product to be cumbersome to load and were concerned about potential theft. Also, the Fitness Tracking Incentives did not appeal uniquely to egoistics in both trials, as many sociocultural respondents also appreciated being able to improve their fitness. In hindsight, this is not surprising, as improved personal fitness usually has aesthetic effects, which may lead to more positive attention from others.

In trial-2, for both pairs of sociocultural concepts, all but one was significantly correlated with respondents’ self-assessments of their sociocultural motive. The single exception, the Fitness Fasttrack concept, had sociocultural pitfalls we had not anticipated. This concept was generally disliked because respondents worried about their performance being on display in front of their colleagues. Socioculturally motivated respondents were particularly concerned about performing poorly in front of their peers, and thus rated the concept poorly.

In trial-2, a single altruistic concept, the Accessibility Reminder, did not significantly correlate with self-assessments of altruism. Respondent comments revealed that the concept had broad appeal across different motivation types. Indeed, the Accessibility Reminder was one of the highest rated concepts by all motivation types. Egoistic respondents appreciated being able to know explicitly when they should not call the elevator. Sociocultural respondents worried about the shame associated with hailing the elevator when the accessibility sign was lit. Altruistic respondents tended to be happy to leave the use of the elevators to those who needed it most.

Table 8: Stair-use concept ratings (out of 4) for both trials

Egoistic Socio-cultural Altruistic

Trial 1

Trial 2

Trial 1

Trial 2

Trial 1

Trial 2

Paystub Reminder (Control) 1.83 1.00 1.90

Email Reminder (Control) 2.29 2.82 2.67

GearVator (Egoistic) 1.96 2.24 1.00 2.29 1.95 2.50

Fitness Tracking Incentives (Egoistic)

2.83‡ 3.02 4.00

‡ 3.12 3.24‡ 2.42

GlassVator (Sociocultural) 2.83 1.91* 2.50 2.76* 2.14 2.33*

Fitness Fasttrack (Sociocultural) 2.54 2.53 4.00 2.18 2.76 2.08

Piezo Stepper (Altruistic) 2.91 2.84 3.00 2.71 2.95 3.42

Wait-Reduction Tracker (Altruistic) 2.48 2.50 2.81

Accessibility Reminder (Altruistic) 2.79 2.94 3.00

‡ p< 0.1, * p< 0.05, ** p< 0.01, *** p< 0.001

Overall, we found it difficult to target the concepts towards only one motivation type. As with many such experiments, respondents often found unexpected ways to see unintended

benefits or pitfalls in the descriptions. Our inability to target a single motivation type is unlikely be an obstacle in practice, as our results suggest that the most successful (overall highly rated) concepts are those that address multiple user motives. However, our experiments confirm the importance of user testing, as many concepts were not received as expected. 7. A DESIGN TOOL FOR APPLYING OUR MODEL

We believe that the practical value of our work is more the explicit consideration of different types of motivation rather the ability to target a single type in a given concept. Therefore, we present a matrix-based design tool that incorporates our user-motivation model. Shown in Appendix A with concepts that encourage the use of the stairs over elevators, the rows and columns correspond to different strategies for behavior-change interventions. The three major rows of the matrix correspond to the three types of motives in our model. Below the category names are tips to explain how each motive can be met, e.g., egoistic motives can be addressed by reducing the number of steps or time required for the task. 7.1. Levels of user versus product control

The 7 columns of the matrix represent the range of user versus product control that concepts allow. This range is based on the work of several researchers, from levels of automation (Sheridan and Verplank, 1978) to work specific to sustainable behavior (Zachrisson and Boks, 2010). Concepts in the left-most columns offer information intended to persuade users to perform behavior change. Concepts in the right-most columns give the user no choice but to perform the proposed behavior.

We explicitly divided the range into columns to encourage designers to more fully explore each level of user control, and move beyond information- and feedback-based concepts. Despite their inherent limitations, information- and feedback-based solutions are prevalent in concepts proposed for behavior change. On the other hand, completing the left-most columns clarifies the underlying message for subsequent columns. 7.2. Positive versus negative interventions

Each type of behavior-change problem can be expressed as the curtailment of one behavior or the promotion of an alternative. The matrix cells are therefore divided in two, one half for concepts that punish a behavior and the other for concepts that reward its alternative. This division encourages designers to consider the problem from yet another different perspective. 7.3. Problem clarification through repeated ideation

The ultimate benefit of the tool may be in improving designer understanding of the problem by considering several different perspectives. Specifically, solutions are developed that address different user motivations, levels of user control and through positive and negative reinforcement. As a complete matrix corresponds to 37 concepts, designer understanding of the parameters and constraints of the problem may be improved through sheer repetition. The final concept will ideally combine


several parts of the matrix. Designers can also use the matrix to determine 1) the breadth of appeal for a concept and 2) the number of ways a concept will influence users. The most effective concepts will likely appeal to more than one type of user and use more than one strategy to persuade them.

8. CONCLUSIONS AND FUTURE WORK We sought to increase the efficacy of interventions for behavior change, and noticed that corresponding concepts often do not account for user variation. While we wanted to address differences in users, we also want to do this in a manageable way. Based on an extensive theoretical review, we present a model, which postulates that there are three major types of motives: egoistic, sociocultural and altruistic. We validated this model in two ways. First, we demonstrated that the model was able to describe news-article comments about an international environmental agreement. Next, we tested the model empirically by developing concepts, each intended to address single motive types. We asked respondents to evaluate these concepts online, and compared participant preferences with self-assessed motivation types. Deviations from our expected results are mainly due to unexpected perceptions, both positive and negative, of our concepts, which we discussed in detail. Despite this, we believe that the main value of our work lies in the explicit consideration of a manageable number of different types of motives when developing behavior-change concepts.

Therefore, we present a design tool that incorporates our model for user motivation. The tool enables designers to consider a behavior-change problem from different perspectives, including user motive, user versus product control, and positive versus negative reinforcement. The multiplicity of matrix cells encourages prolific idea generation. We are interested in continued validation and optimization of the behavior construct, and the use and improvement of the design tool. The first case involves testing how the construct relates to other existing psychometric instruments. The second case involves improving the tool, and evaluating the effectiveness of the tool on design concepts.

In addition to incorporating the effects of concept novelty and quality, which clearly affected concept evaluation, we intend to assess the tool’s effect on concept variety. The variety metric, defined by Shah et al. (2003) has been refined recently by Verhaegen et al. (2013) and others. A robust debate has also emerged about the use of new crowdsourcing methods to assess variety (Fuge et al., 2013; Burnap et al., 2015). We plan to apply the results of this and other research to improve and assess the effectiveness of our design tool. ACKNOWLEDGMENTS

The authors are grateful for the financial support of the Natural Sciences and Engineering Research Council of Canada. REFERENCES Alexa Internet Inc. (2015) Top Sites in: All Categories > News

http://www.alexa.com/topsites/category;0/Top/News Last accessed 23 January 2015.

Ajzen I (1991) The Theory of Planned Behaviour. Organization Behaviour and Human Decision Processes, 50/2:179-211.

Bandura A (1977) Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84/2:191–215.

Bockarjova M, Steg L (2014) Can Protection Motivation Theory predict pro-environmental behavior? Explaining the adoption of electric vehicles in the Netherlands. Global Environmental Change, 28:276-288.

Burnap A, Ren Y, Gerth R, Papazoglou G, Gonzalez R, Papalambros P (2015) When Crowdsourcing Fails: A Study of Expertise on Crowdsourced Design Evaluation. ASME Journal of Mechanical Design, 137/3:031101.

Burton J, Easingwood C, (2006) A positioning typology of consumers’ perceptions of the benefits offered by successful service brands. Journal of Retailing and Consumer Services, 13/5:301-316.

Christie LH (2010) Understanding New Zealand Homeowners Apparent Reluctance to Adopt Housing-Sustainability Innovations. PhD Dissertation, Victoria Univ. Wellington.

Corral-Verdugo, V., Frias-Armenta, M., Pérez-Urias, F., Orduña-Cabrera, V., Espinoza-Gallego, N. (2002) Residential water consumption, motivation for conserving water and the continuing tragedy of the commons. Environmental management, 30/4:527-535.

Davoudi S, Dilley L, Crawford J (2014) Energy consumption behaviour: rational or habitual? disP-The Planning Review, 50/3:11-19.

De Groot JIM, Steg L (2008) Value orientations to explain environmental attitudes and beliefs: How to measure egoistic, altruistic and biospheric value orientations. Environment and Behavior, 40/3: 330-354.

Elias E, Dekoninck E, Culley S (2007) The potential for domestic energy savings through assessing user behaviour and changes in design. Proc. 5th Int. Symp. Env. Cons. Design & Inv. Manufacturing, Dec. 10-13, Tokyo, Japan.

Evans D (2011) Beyond the throwaway society: ordinary domestic practice and a sociological approach to household food waste. Sociology, 46/1:41-56.

Farmar-Bowers Q, Lane R (2006) Understanding farmers decision systems that relate to land use School of Global Studies, Social Sciences and Planning, Report, RMIT University, Melbourne, Australia.

Fogg BJ (2009) A behavior model for persuasive design. Proc. Persuasive’09, April 26-29, Claremont, CA, USA.

Fuge M, Stroud J, Agogino A (2013) Automatically Inferring Metrics for Design Creativity. ASME IDETC/CIE, Aug. 4-7, Portland, OR, USA, DETC2013-12620.

Gentzkow M, Shapiro J (2010) Ideological Segregation Online and Offline. National Bureau of Economics Research Working Paper No. 15916, Chicago Booth.

Goucher-Lambert K, Cagan J (2014) The Impact of Sustainability on Consumer Preference Judgments of Product Attributes. ASME IDETC/CIE, Aug. 17-20, Buffalo, NY, USA, DETC2014-34739.

Greiner R, Lankester A, Patterson L (2007) Incentives to Enhance the Adoption of ‘Best Management Practices’ by


Landholders: Achieving Water Quality Improvements in the Burdekin River Catchment. Report, River Consulting, Townsville, Australia.

Greiner, R., Gregg, D. (2011) Farmers’ intrinsic motivations, barriers to the adoption of conservation practices and effectiveness of policy instruments: Empirical evidence from northern Australia. Land Use Policy 28/1:257–265.

Gromet DM, Kunreuther H, Larrick RP (2013) Political ideology affects energy-efficiency attitudes and choices. Proc. National Academy of Sciences, 110/23:9314-9319.

Harrington D (2008) Confirmatory factor analysis. Oxford University Press, New York, NY, USA.

Haun DB, Rekers Y, Tomasello M (2014) Children conform to the behavior of peers; other great apes stick with what they know. Psychological science, 25/12:2160–2167.

Janz N, Becker M (1984) The health belief model: A decade later. Health Education Quarterly, 11:1–47.

Klein M, Mogles N, Van Wissen A (2011) Why won’t you do what’s good for you? Using intelligent support for behavior change. Human Behavior Understanding, Lecture Notes in Computer Science, 7065:104-115.

Kok G, Lo SH, Peters GJY, Ruiter RA (2011) Changing energy-related behavior: An Intervention Mapping approach. Energy Policy, 39/9:5280-5286.

Lockton D, Harrison D, Stanton NA (2010) The Design with Intent Method: A design tool for influencing user behaviour. Applied Ergonomics, 41/3:382-392.

Maier JRA, Fadel GM (2003) Affordance-based methods for design. IDETC/CIE, Sept. 2-6, Chicago, DETC2003-48673.

Montazeri S (2013) Design for Behavior Change: The Role of Product Visual Aesthetics. PhD Dissertation, U. Michigan.

Morrow WR, Long M, MacDonald EF (2014) Market-System Design Optimization With Consider-Then-Choose Models. ASME J Mechanical Design, 136/3:031003.

Norman D (1988) The Design of Everyday Things. Basic Books, New York, NY, USA.

Pannell DJ, Marshall GR, Barr N, Curtis A, Vanclay F, Wilkinson R (2006) Understanding and promoting adoption of conservation technologies by rural landholders. Australian J Experimental Agriculture 46/11:1407–1424.

Peschiera G, Taylor JE, Siegel JA (2010) Response–relapse patterns of building occupant electricity consumption following exposure to personal, contextualized and occupant peer network utilization data. Energy and Buildings, 42/8:1329–1336.

Pew Research Center Journalism Project (2014) State of the News Media Report and Database Pew Research Center, http://www.journalism.org/packages/state-of-the-news-media-2014/. Last accessed 23 January 2015.

Phillips BJ, Sego T (2011) The role of identity in disposal Lessons from mothers’ disposal of children’s possessions. Marketing Theory, 11/4: 435-454.

Prochaska J, DiClemente C (1984) The Transtheoretical Approach: Crossing the Traditional Boundaries of change. Irwin, Homewood, IL, USA.

Ruths D, Pfeffer J (2014) Social media for large studies of behavior. Science, 346/6213:1063-1064.

Schwartz SH (1994) Are there universal aspects in the structure and contents of human values? J Social Issues, 50/4:19-45.

Shah JJ, Smith SM, Vargas-Hernandez N (2003) Metrics for measuring ideation effectiveness. Design Studies, 24/2:111-134.

Sheridan TB, Verplank WL (1978) Human and computer control of undersea teleoperators. Technical Report, MIT Man-Machine Systems Laboratory, Cambridge, MA, USA.

Sorrell S, Dimitropoulos J, Sommerville M (2009) Empirical estimates of the direct rebound effect: a review. Energy Policy, 37:1356-1371.

Srivastava J, Shu LH (2013a) Encouraging Resource-Conscious Behaviour Through Product Design: The Principle of Discretization. ASME J Mechanical Design, 135:061002.

Srivastava J, Shu LH (2013b) Affordances and Product Design to Support Environmentally Conscious Behavior. ASME Journal of Mechanical Design, 135:101006.

Srivastava J, Shu LH (2014) An ontology for unifying behavior-change literature. CIRP Annals, 63/1:173-176.

Stern PC (2000) Towards a Coherent Theory of Environmentally Significant Behaviour. J Social Issues, 56/3:407-424.

Telenko C, Seepersad C (2014) Scoping Usage Contexts and Scenarios in Eco-Design. ASME IDETC/CIE, Aug. 17-20, Buffalo, NY, USA, DETC2014-34846.

Thaler RH, Sunstein CR (2008) Nudge: Improving Decisions about Health, Wealth, and Happiness. Penguin Books, New York, NY, USA.

Van Horn D, Lewis, K (2014) The use of analytics in the design of sociotechnical products. Artificial Intelligence for Eng. Design, Analysis and Manufacturing. 29/1:65-81.

Verhaegen PA, Vandevenne D, Peeters J, Duflou JR (2013) Refinements to the variety metric for idea evaluation. Design Studies, 34/2:243–263.

Wever R, van Kuijk J, Boks C (2008) User-‐centred design for sustainable behaviour. Int. J Sustainable Engineer. 1/1:9-20.

The White House, Office of the Press Secretary (2014) U.S.-China Joint Announcement on Climate Change. The White House Statements and Releases. Press Release, http://www.whitehouse.gov/the-press-office/2014/11/11/us-china-joint-announcement-climate-change.

Wilson GT, Lilley D, Bhamra TA (2013) Design feedback interventions for household energy consumption reduction. Proc. ERSCP-EMSU, June 4-7, Istanbul, Turkey.

Withanage C, Ashok R, Hölttä-Otto K, Otto K (2014) Identifying and Categorizing Opportunities for Design for Sustainable User Behavior. ASME IDETC/CIE, Aug. 17-20, Buffalo, NY, USA, DETC2014-34798.

Zachrisson, J., Boks, C., (2010) When to apply different design for sustainable behaviour strategies? Proc. ERSCP-EMSU, Oct. 25–29, Delft, The Netherlands.

Zhao Y, Thurston D (2013) Maximizing Profits From End-of-Life and Initial Sales with Heterogeneous Demand. ASME Journal of Mechanical Design, 135/4:041001-10.


Appendix A: Behavior model matrix completed for reducing elevator use

INFORMATION General

Messages

FEEDBACK Data reflecting

user performance

ENABLING Generally makes

the behavior easier to do

ENCOURAGING Advises user on

which behavior to perform

GUIDING Recommends an

action to user through

messages/cues

STEERING Selects best

action and then uses cues and instructions to make user do

that action

FORCING/ AUTOMATION

User has no choice but to do action/product

does action without user

EGOISTIC MOTIVES

Minimize cost, time,

steps, failures;

Increase fun; Increase

efficiency;

A sign saying “Taking the stairs helps you stay fit”

Smartphone app that tells user calories burned for taking the stairs

Provide running shoes for free to employees

Employees who take the stairs get entered into a draw for prizes like coffee

An employee’s stair taking is considered positive during annual reviews

Employees are given a monetary bonus for each time they take stairs

Elevator access is restricted to

employees with specially issued

access cards

A sign saying “sitting for long hours is bad for your heath, take the stairs”

Smartphone app that tracks the amount of time the user is sedentary

Relocate meeting rooms to be as far as possible from the elevators

Hailing the elevator requires remembering and entering a ten-digit code

Elevator access has quotas based on an appraisal of their need for it

Employees must pay a small amount each time they use the elevators

SOCIO-CULTURAL

MOTIVES Increase prestige, sense of

belonging; Join trends;

Posters showing the CEO and other executives taking the stairs

You can share the number of times you use the stairs on social media

Put windows in the stairwells so that other employees can see you

Host a stair-climbing challenge every month with prizes

Post floor signs with employee names and show how many take stairs

Employees are responsible for their subordinates taking stairs

Poster saying “Don’t be a wasteful employee like this person”

The number of times you take the elevator is displayed on your badge

Put glass doors on the elevators so other employees can see you

The elevator needs a minimum of three people to operate

Have HR meet with employees who have been seen taking the elevator frequently

You need to get authorization from a superior to take the elevator

ALTRUISTIC MOTIVES

Minimize environmental

impact; Improve lives

of others;

Posters showing an elderly employee happily using the free elevator

A sign at the top of the stairs tells you how much CO2 you have saved every time

Put piezoelectric sensors in the stairs, harvest the energy for office needs

Allow employees to set targets to save a certain amount of CO2 per day

Donate $1 for every instance of an employee taking the stairs

Give each employee a money-raising goal by stair climbing

A sign saying “Unnecessary elevator usage increases our CO2 emissions”

The elevators tells you the grams of CO2 you are responsible for every use

Print the environmental hazards of elevator components and lubricants inside elevator

Lights in the building flicker momentarily when the elevator starts up

Donate $1 to a tobacco company for every instance of elevator use

Separate the elevator systems from the grid and power them with diesel fuel

Documents

SrivastavaShuDTM2015April20 - 5pm · IDETC/CIE 2015 August 2-5, 2015, Boston, Massachusetts, USA DETC2015-47625 CONSIDERING DIFFERENT MOTIVATIONS IN DESIGN FOR CONSUMER-BEHAVIOR CHANGE