Engaging the Human in the Design of Residential Energy Reduction Applications

June A. Flora

H-STAR Institute Stanford

University Stanford, CA

Anshuman Sahoo

Management Science &

Engineering Stanford

University Stanford, CA

Annie Scalmanini Mechanical Engineering

Stanford University

Stanford, CA

Alexandra Liptsey-Rahe

Civil & Environmental

Engineering Stanford

University Stanford, CA

Shaun Stehly Communication

Stanford University

Stanford, CA

Brian Wong Science,

Technology, and Society Stanford

University Stanford, CA

Banny Banerjee

Mechanical Engineering

(Design Group) Stanford

University Stanford, CA

Abstract - Most online energy conservation interfaces assume that information provision is sufficient to induce behavior change and energy use reductions. A gap between behavioral theory and field practice partly explains why interfaces have not achieved this goal. In this paper, we describe a research program on human centered interactive interface design that bridges this gap with consumer based investigation of two energy reduction interfaces: Kidogo and Powerbar. Kidogo allows users to donate savings from energy conservation to public goods. In the first study, which examines Kidogo components, we investigate how alternative beneficiaries help users to connect emotionally with saving energy. In our second study, comparing Kidogo and Powerbar interventions, we investigate the ability of affectively and cognitively framed interfaces to persuade individuals to perform conservation behaviors. The first study suggests that interfaces should use negative valence images to establish an emotional connection, and the second provides evidence that affectively framed interfaces promote willingness to perform conservation behaviors.

Keywords-online interface design; residential energy consumption reduction; energy feedback

I. INTRODUCTION

Climate change mitigation and adaptation are among the most critical challenges facing contemporary society, and reduction of energy consumption is a critical target for reducing global CO2 emissions [1]. Residential energy consumption itself contributes to approximately 20% of all U.S. CO2 emissions and 22% of the annual U.S. consumption [2]. Reduction in energy usage hinges on changes to patterns of human behavior. Yet decades of research have not advanced our ability to deliver energy consumption reduction beyond 7% in the short term and 1-3% over longer periods of time [3]. However, modeling and small scale studies indicate that the potential for energy use reduction with proximal or real-time higher resolution feedback is 15-20% [4].

Recently, there has been an increase in installations of smart meters and other high-resolution real time electricity

data monitoring, analysis and access (e.g., online, specifically through mobile applications and social networks). However, these advances are not matched by intuitive, engaging, effective, persistently used and scalable interfaces, even though numerous studies have demonstrated that technology can play a critical role in human engagement and decision-making regarding energy reduction [3, 5-7].

In this paper, we describe a program of research to develop three human centered online residential energy reduction interfaces based on three different motivational frames; these frames apply affective, cognitive and social engagement in energy reduction. The interfaces are built to reflect electricity data. Each interface has been developed for use in the Facebook environment to ensure an “ambient” presence. This program originated from qualitative interviews of average and high electricity consumers which suggested that homeowners do not understand drivers of electricity consumption and that their motivations to reduce energy are multi-faceted and varied [2].

The research program we describe here investigates how

interfaces can be designed using motivational frames to induce behavior change in energy usage patterns. A. Framework of data-based human centered interface

design for behavior change Our framework for human centered interface design

combines the program design processes of social marketing applied to the specialized product test needs of online interfaces [8]. We draw from the disciplines of product design, psychology, marketing, and communication. Our framework for the design of behaviorally informed residential energy reduction applications is shown in Fig. 1.

978-1-4673-1382-7/12/$31.00 ©2012 IEEE 338

Figure 1. Four step interface research program

Phase 1 and 2 of our design framework are illustrated in the

two studies described here. The first phase investigates the major theorized factors of an interface. For example, in our affective interface we manipulated four factors: valence of imagery, content of the image, savings amount, and locus of the donation (see Fig. 2). Outcome measures for manipulations included perceived image valence, hopefulness, credibility, donation amount, and motivation to help. For other factors, we measured their perceived utility.

Figure 2. The Kidogo interface

We incorporated the second stage of the framework in study 2, which examined the influence of interface on behavioral intentions and perceived self-efficacy.

II. STUDY ONE ON KIDOGO: “A PICTURE IS WORTH A

THOUSAND WORDS”

Message design research has identified two key components for conveying persuasive content to receivers: message valence, such as positive versus negative messages, and message source credibility. The primary motivational frame of Kidogo assumes that affective engagement with donation targets will drive energy conservation behaviors: a desire to donate is assumed to motivate continued electricity conservation. A. Related work

1) Positive versus Negative Valence Messages: Several studies suggest that negative feedback (especially social but also factual) leads to more conservation actions than positive feedback. This finding fits earlier research indicating that negative (social) events more strongly draw attention and are processed more intensely than positive events [9]. Other work has shown that negative messages and feedback are more memorable and are processed more easily [10-12]. Countering this view are results suggesting that negative images cannot convey the skills, goal setting and positive feedback required for behavior change [13-15].

2) Human and animal altruism targets: Charitable causes related to the environment can have at least three beneficiaries, the people affected, the animals endangered, and the physical environment. We examine both human and animal causes. Animals have become a metaphor for the need for climate reduction action and are of particular interest here [16].

III. METHODS

A. Research design To examine the role of message valence and beneficiary

type, we used a within subject design, examining image valence, beneficiary, location and the amount of savings accrued from saving electricity. Since the savings dimension captures information beyond the scope of choice over charities to display, we do not discuss these results here. Moreover, since our experimental design was unbalanced in both location and valence dimensions, we are limited in the range of hypothesis testing we can perform and limit our discussion to image valence and target beneficiary effects.  B. Procedures

This online study took most participants between 30 minutes and one hour. In this time, they saw and rated 36 interfaces, allowing us to capture measures on 18 causes with human beneficiaries and 18 with animal beneficiaries. We similarly tested 12 positive valence and 24 negative valence interfaces. Participants were emailed a link to the study. After participants opened the link, they watched a brief video framing the study as related to energy conservation. They were shown the Kidogo interface and each of the elements they would be asked to respond to: the image, the story, the organization, and the amount of money they would have saved in the focal interface. In Fig. 3, we show four example interfaces, depicting positive human, positive animal, negative human and negative animal causes.

Figure 3. Study 1 Interfaces

C. Measures

1) Arousal: We drew on Lang’s work on image rating and posed two questions to assess arousal [10]. The questions were preceded by the statement, “The next set of questions has

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to do with HOW YOU ACTUALLY FELT WHILE YOU LOOKED AT THE PICTURE on the website” (emphasis in original). The first asked participants if they felt “excited, frenzied, jittery, wide-awake and aroused and the other asked if they felt “relaxed, calm, sluggish, dull, sleepy and unaroused”. Both were measured on a 1-10 scale, with 1 being “not at all” and 10, “a lot”. We assessed each question separately.

TABLE I. ANOVA MAIN AND INTERACTION RESULTS

a. Text in significant cells identifies factor value with higher mean measure. 2) Source credibility: We measured two dimensions of credibility: trust and similarity [17]. These measures were single question measures on the 1 – 10 scale. These questions were examined separately and asked users to respond to: “The organization is trustworthy” and “The organization is supported by people like me.” 3) Hopefulness: We assessed hopefulness with a single question also measured on a 1-10 scale [9]. 4) Effectiveness of narrative: We measured the perceived effectiveness of the story with a single question, “I find the story useful.” This question used a 1-10 rating scale, similar to

those above. Since each prompt was accompanied by a narrative explaining exactly how a donation would be used by the recipient, our aim in asking this question was to examine whether all narratives were judged equally useful in making a decision to donate to a particular cause. 5) Motivation to help: We tested motivation to help with, “The web page motivates me to help”, using the same 1 – 10 scheme.  

IV. RESULTS

A. Demographics of participant pool The 67 participants in this study were students at a

California community college, and they received course credit upon completion. Of these, 70% were women, 49% were renters, 21% were married, and 87% were Facebook users. The median age was 21, and students under 18 were excluded. B. Dependent Measures

We used a repeated measures ANOVA in our analysis of all measures. A summary of the scores on all measures is provided in Table IV (see appendix). The results of the repeated measures ANOVAs are in Table I.  

Participants reported being more excited and aroused by animal images relative to humans. There was also a significant interaction between image valence and beneficiary, with positive animals rated as most arousing. Similar results obtained in our calm/not arousing question. Positive valence images were rated as more calming, as were animals.

Across all other measures, image valence was the only significant dimension. Negative valence images promoted both credibility questions: they were rated as significantly more trustworthy and promoted responses to our similarity question. In contrast, positive valence images were positively associated with hopefulness ratings. Negative valence images were rated as having more useful stories and increased motivation to donate. Fig. 4 illustrates the trend in measures.

Figure 4. Motivation to help and image valence (positive in blue vs. negative in red) and target beneficiary

Measurea Main effects Interaction

Excited/ aroused

Beneficiary Valence

F(1,63) = 11.14 p < .001 Animals

NS

F(3,63) = 4.30 p < .05 Positive animals

Calm/not aroused

Beneficiary Valence

 F(1,63) = 11.14

p < .01 Animals

F(1,63) = 41.56 p < .001 Positive

NS

Trustworthy Beneficiary Valence

  NS F(1,63) = 25.31

p < .001 Negative

NS

Supported by similar people

Beneficiary Valence

  NS F(1,63) = 30.07

p < .001 Negative

NS

Hopefulness Beneficiary Valence

  NS F(1,63) = 11.85

p < .01 Positive

NS

Story usefulness

Beneficiary Valence

  NS F(1,63) = 26.09

p < .01 Negative

NS

Motivation to help

Beneficiary Valence

  NS F(1,63) = 40.17

p < .001 Negative

NS

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V. DISCUSSION

Image valence is important on all theorized dimensions, but the positive versus negative import varies across measures. Negative valence images are rated as more credible, as providing a more useful story and greater motivation to change, corresponding to results [11] and [12]. However, positive valence images promoted feelings of hopefulness and calm. Positive images may be more conducive to enhanced perceived self-efficacy for behavior change [13, 18]. The tradeoffs of negative and positive imagery can be strategically used in a multi-faceted interface. For example, users may tire of continuous exposure to negative images of charitable causes. It could be that the combination of negative images to stimulate donation needs to be combined with positive image of outcomes for continued behavior change for donation. This question will be a subject of research in subsequent trials.

We also observed that images with animal beneficiaries amplified both excitement/arousal and calm/non-arousal, suggesting that animals may amplify either response. Since our primary concern for Kidogo is with motivation to donate, our ultimate interest is in the differential impact of valence. We make an inherent but untested assumption that motivation to donate is a driver to visit the application web site and, in turn, to reduce electricity consumption.  

VI. STUDY TWO COMPARING KIDOGO AND POWERBAR: “HEAD OR HEART”

Study two compared the influence of cognitive and affective interfaces on energy conservation behavior intentions and performance confidence. The primary proposition of the Kidogo application is that individual differences influence energy consumption. In this study we examined the moderating role of need for affect, i.e. the disposition to approach emotions in a user’s behavioral intentions [19] to perform in the short term and their perceived self-efficacy [13] to perform energy conservation behaviors in the long term.

Figure 5. Example stimuli in study 2

In study 2, we expanded upon study 1 in three ways: (1) we compared an affective application (donation to a charity) to a cognitive application (energy feedback displayed in graphs) [11-12]; see Fig. 5 for sample stimuli (2) increased external

validity by using a between subjects randomized design, and (3) used outcome measures that are indicative of energy reduction behaviors. We asked whether users would respond differently to a “cognitive” (graphs) or affective (donation to charities) interface based on their individual predisposition on the Need for Affect (NFA) scale.

Need for Affect has been proposed by [20] as the affective counterpart to the need for cognition. They define the NFA as the “general motivation of people to approach or avoid situations and activities that are emotion inducing for themselves and others” (p. 585). Only the approach subscale of this measure was used because previous research had shown that this subscale was found to be related to emotional experiences during media reception [20-21].  

VII. METHODS

We used a simple between subjects randomized study with two conditions: experience of a cognitive or of an affective interface. A. Measures 1) Manipulation checks: Participants were asked two questions to check the veracity of the condition: “The videos I just saw made me: ‘Really want to save electricity to help our world’ and ‘Really want to learn about ways to cut electricity use to save money.’” Questions were rated on a 1-10 scale, with 1 “not at all” and 10, “a lot”. 2) Moderator measure: Need for Affect: We use a Need for Affect questionnaire, a self-report measure comprised of both an approach and avoidance subscale [20]. NFA was measured using eight questions, from the need for approach affect. To reduce participant burden and stay within a time limit of thirty minutes, we asked 8 of the original 13 items (with a 7-point scale) to individual predisposition to approach emotions (e.g. “It is important to me to be in touch with my feelings,” “I approach situations in which I expect to experience strong emotions”). Our review of the analyses of original norm study suggested that these were the most effective questions. In our sample of participants, the internal consistency of the NFA measure (Cronbach’s α) was 0.79. 3) Energy behavior: We asked about 42 electricity conservation behaviors rated as low skill and very low cost actions. The actions were judged in three ways: the extent to which users currently performed the actions (on a 1-5 scale, with 1 coding all of the time and 5, not at all), the extent to which they intended to perform the behavior in the next week to save electricity (1-10 scale, with 1 coding do not intend to perform and 10, great intention to perform) and confidence to maintain performance of this action over the next six months (1-10 scale with 1, not at all confident and 10, extremely confident). We calculated intentions and confidence in two ways. For each user, we produced an index comprised of actions rated as already, almost always or always performed

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and an index of actions that were rated as performed with a lower frequency. Below, we provide results on lower frequency behaviors, as these are the ones we would seek to activate. B. Procedures

Participants were students at a California community college, and they received course credit for participating in research. Students signed up online, accepted the IRB consent form, and were then sent a link to either the affective or cognitive interface. Once they received the link, they signed in and completed the study. Most students took about thirty minutes to complete the study.

C. Stimulus materials

Once students logged onto the online study, they saw a video that described the stimulus materials and reasons to be concerned about energy consumption. In the affective condition, students saw four videos on charitable causes; we used YouTube clips from the International Rescue Committee (on a Burmese refuge from Thailand), International Rescue Committee (on women in Darfur), British Red Cross (on victims of the 2011 Japanese Earthquake), World Wildlife Federation (on adopting a tiger). They could click any or all 1.5 minute videos, and users were able to proceed to the remainder of the study without watching a single video. We were unable to verify the number of videos watched and are unable to control for compliance in our analysis. Participants were then asked to select the one cause they liked most. A thumbnail image of this video was shown at the top of each page of the subsequent outcome questions.

For the cognitive interface, participants watched a video

explaining four graphs and the information they conveyed about energy use. The graphs (see Fig. 5) were screen shots of those available to utility customers with smart meters in northern California; these meters allow the utility to collect and feedback hourly electricity consumption data. We provided four bar graphs. Three of these had a yellow line graph overlaid. In particular, the four graphs conveyed: (1) electricity use for one week in kWh, (2) electricity use for one day in kWh and, through the overlaid yellow line, cost of electricity in dollar amounts, (3) electricity use for one week in kWh and, through the overlaid yellow line, average energy use, and (4) electricity use for one month in kWh and, through the overlaid yellow line, average energy use. Note that the average energy use information conveys the average daily electricity consumption over one week and over one month, respectively. After viewing graphs, participants chose the one they liked best; this choice was then displayed on the top of the subsequent outcome questions.

VIII. RESULTS

A. Demographics of participant pool The participant pool was composed of 158 students from a

California community college, with a median age of 22 and

with 64% women, 49% renters, 21% married, and 85% with Facebook accounts.

B. Interface component selection and manipulation

checks For the affective interface, 13% of users selected the

Burmese refuge video, 39% the Darfur women, 32% the British Red Cross, and 16% the World Wildlife Federation. For the cognitive interface:

14% selected use for one week in kWh, 35% use for one day in kWh and cost of electricity in

dollar amounts, 14% use for one week in kWh and average energy

use, and 37% use for one month in kWh and average energy

use.

For the affective interface manipulation check, the mean score for desire to save electricity to save the world was 6.3 and to save money, 6.7; for the cognitive interface the mean ratings were 5.7 and 6.9, respectively. We recognize that the magnitude of monetary savings is likely an important driver of response to this question, and in newer work, we explore the desire to donate money as a function of the money saved on electricity consumption reduction.

C. Energy behavior outcomes

For these measures, each subject’s intention and confidence rankings were normalized by the number of energy behaviors he or she performed infrequently. This normalization yielded normalized intentions and confidences.

We first examined if the interface type influenced energy behavior intentions and confidence. As seen in Table II, ANOVA tests suggested that interface type significantly influences intention and confidence. In particular, the affective stimulus is more effective in driving intent and confidence.

TABLE II. MEAN NORMALIZED INTENTION AND CONFIDENCE ACROSS INTERFACE TYPES

Interface Type

Mean Normalized Measure

Intention Confidence

Affectve 6.21 5.90

Cognitve 5.16 5.06

Significance F (1, 152) = 10.75 p<.001

F (1, 152) = 7.07 p <.001

We next examined if the NFA independently affects

behavior change intentions and confidence. Low NFA types are defined as those with a NFA at least 1 standard deviation below the mean, and high NFA types are defined as those with a NFA at least 1 standard deviation above the mean [20]. As shown in Table III, ANOVA results suggest that NFA levels are associated with behavioral intentions and perceived self-efficacy, with low NFA types revealing significantly lower intentions and confidences than either mid or high NFA types.

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TABLE III. MEAN NORMALIZED INTENTION AND CONFIDENCE ACROSS NFA

NFA Level Mean Normalized Measure

Intention Confidence

Low 4.62 4.54

Medium 5.84 5.63

High 6.25 5.81

Significance F (2, 152) = 4.77 p<.01

F (2, 152) = 3.49 p <.05

Fig. 6 presents a box and whiskers plot of data on behavioral

intentions by interface type and NFA.

Figure 6. Behavioral intentions versus need for affect and interface type

Finally, we examined whether people who have a high NFA

have higher behavioral intentions and perceived self-efficacy when using the affective interface, but we did not find any significant interaction between NFA and interface type.

IX. DISCUSSION AND CONCLUSIONS

While we found significant main effects for interface type and NFA, we did not find a significant interaction between NFA and interface types. The main effects are interesting themselves and, to our knowledge, remain relatively unexplored in the study of residential energy consumption. For example, low NFA individuals have lower energy behavior intentions and confidence overall. In future work, we plan to examine the relationship between NFA and Need for Cognitive (NFC) [22], as well as the relationship of those dimensions to a more developed cognitive interface and a revised Kidogo interface. The lack of interaction effects could stem from our use of videos, which did not provide an ability to engage with the interface. Further, the affective stimulus had inherent cognitive components, and the affective

component may not have been sufficiently salient to be emotionally engaging. In future work, we may, for example, add audio to enhance the emotional quality. Nonetheless, the overall greater effectiveness of the affective interface suggests a role for affect-based interfaces for energy consumption feedback, a primary contribution of study two.

In conclusion, the results from study one suggests the

importance of negative valence images in motivating users to help, but we offer the caveat that positive valenc images are useful to promote behavior skills learning and perceived self-efficacy to change behavior. Study two results reinforce the importance of emotional engagement both as an individual attribute and as an interface feature.

Our next steps include testing the graphic interfaces with

smart meter electricity data and investigating the moderating roles of both NFA and NFC in affective and cognitive interfaces.

ACKNOWLEDGEMENTS

This research was funded by DOE: DE- AR0000018 to Board of Trustees of the Leland Stanford Junior University, titled Large-Scale Energy Reductions through Sensors, Feedback & Information Technology.

REFERENCES

[1] Pacala, S. and Socolow, R. (2004). Stabilization Wedges: Solving the Climate Problem for the Next 50 Years with Current Technologies. Science, 305: 968 – 972.

[2] Kazaa, N. (2010). Understanding the spectrum of residential energy consumption: a quantile regression approach. Energy Policy, 38 (11): 6574 – 6585.

[3] Osbaldiston, R and Schott, J.P. (2011). Environmental sustainability and

behavioral science: Meta-analysis of proenvironmental behavior experiments. Environment and Behavior, 1 – 43.

[4] Laitner, J.A., Ehrhardt-Martinez, K., McKinney, V. (2009). Examining

the Scale of the Behaviour Energy Efficiency Continuum. American Council for an Energy-Efficient Economy, Paper ID 1367. Presented at the European Council for an Energy Efficient Economy Conference, 6/1/09, Cote d’Azur, France. In Press.

[5] Fogg, B.J. (1999). Persuasive technologies. Communications of the

ACM, 42(5): 26 – 29.

[6] Lipkus, I. M., Samsa, G., Rimer, B. K. (2001). General performance on a numeracy scale among highly educated samples. Medical Decision Making, 21(1): 37 – 44.

[7] Midden, C.J.H, Kaiser, F.G., McCalley, L.T. (2007). Technology’s four

roles in understanding individual’s conservation of natural resources. Journal of Social Sciences, 63(1): 155 – 174.

[8] McKensie-Mohr, D. (2011). Fostering Sustainable Behavior: An

introduction to community-based social marketing. New Society Pub.

[9] Baumeister, R.F., Bratlavsky, E., Finkenauer, C., Vohs, K.D. (2001). Bad is stronger than good. Review of General Psychology, 5(4): 323 – 370.

343

[10] Lang, P.J., Bradley, M.M., Cuthbert, B.N. (1999). International affective picture system (IAPS): Technical manual and affective ratings. University of Florida, Center for Research in Psychophysiology, Gainsville.

[11] Lee, S. Y., Hwang, H. Hawkins, R., Pingree, S. (2008). Interplay of

negative emotion and health self- efficacy on the use of health information and its outcomes. Communication Research, 35(3): 358-381.

[12] Leiserowitz, A.A. (2007). Communicating the risks of global warming:

American risk perceptions, affective images and interpretive communities. In S. Moser & Dilling (Eds.), Creating a climate for change: Communication climate change and facilitating social change (pp 44-63). Cambridge, UK: Cambridge University Press.

[13] Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall

[14] Rogers, R.W. (1975). A protection motivation theory of fear appeals

and attitude change. Journal of Psychology, 91(1): 93 – 114.

[15] Witte, K. (1992). Putting the fear back into fear appeals: The extended parallel process model. Communication Monographs, 59(4): 329 – 349.

[16] Dillahunt, T., Becker, G. Mankoff, J., Kraut, R. (2010). Motivating

environmentally sustainable behavior change with a virtual polar bear. Pervasive 2008 Workshop on Pervasive Persuasive Technology and Environmental Sustainability, Sydney, Australia.

[17] Hovland, Carl I. and Weiss, Walter (1951). The Influence of Source

Credibility on Communication Effectiveness. Public Opinion Quarterly, 15(4): 635 – 650.

[18] Maibach, E. and Murphy, D.A. (1995). Self-efficacy in health

promotion research and practice: Conceptualization and measurement. Health Education Research, 10(1): 37 – 50.

[19] Ajzen, I. and Fishbein, M. (1980). Understanding attitudes and

predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall

[20] Maio, G. R. and Esses, V.M. (2001). The Need for Affect: Individual differences in the motivation to approach or avoid emotions. Journal of Personality, 69(4): 583 – 614.

[21] Appel, M. and Richter, T. (2010). Transportation and Need for Affect

in Narrative Persuasion: A Mediated Moderation Model. Media Psychology, 13(2): 101 – 135.

[22] Cacioppo, J. T., Petty, R.E., Feinstein, J.A., Jarvis, W.G.B. (1996).

Dispositional Differences in Cognitive Motivation: The Life and Times of Individuals Varying in Need for Cognition. Psychological Bulletin, 119(2): 197 – 253.

APPENDIX

TABLE IV. STUDY I MEASURE OUTCOMES

Measure Means (with standard deviations)

Valence

Excited/ aroused

Positive Negative Overall

Humans 2.39 (1.50) 2.31 (1.58) 2.34 (1.50)

Animals 2.77 (1.71) 2.41 (1.65) 2.53 (1.56)

Overall 2.58 (1.52) 2.36 (1.60) 2.43 (1.96)

Calm/not aroused Positive Negative Overall

Humans 3.35 (1.89) 2.75 (1.78) 2.95 (1.76)

Animals 3.68 (1.74) 2.88 (1.79) 3.15 (1.69)

Overall 3.51 (1.75) 2.81 (1.77) 3.05 (2.25)

Trustworthy Positive Negative Overall

Humans 4.83 (1.82) 5.29 (1.80) 5.14 (1.77)

Animals 5.02 (1.77) 5.35 (1.75) 5.24 (1.72)

Overall 4.93 (1.75) 5.32 (1.75) 5.19 (2.13)

Supported by similar people

Positive Negative Overall

Humans 4.60 (1.89) 5.11 (1.86) 2.34 (1.50)

Animals 4.67 (1.91) 5.13 (1.92) 2.53 (1.56)

Overall 4.63 (1.85) 5.12 (1.84) 4.96 (2.28)

Hopefulness Positive Negative Overall

Humans 3.95 (1.70) 3.46 (1.80) 3.63 (1.65)

Animals 3.96 (1.92) 3.32 (1.67) 3.53 (1.60)

Overall 3.95 (1.75) 3.39 (1.70) 3.58 (2.24)

Story usefulness Positive Negative Overall

Humans 5.03 (1.95) 5.57 (1.87) 5.39 (1.85)

Animals 5.03 (1.90) 5.42 (1.82) 5.29 (1.80)

Overall 5.03 (1.85) 5.49 (1.79) 5.34 (2.25)

Motivation to help Positive Negative Overall

Humans 4.82 (1.86) 5.53 (1.77) 2.34 (1.50)

Animals 4.65 (1.79) 5.41 (1.79) 2.53 (1.56)

Overall 4.74 (1.69) 5.45 (1.71) 5.23 (2.28)

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