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# The Social Perception of Robots Scale (SPRS):
Developing and Testing a Scale for Successful Interaction
Between Humans and Robots

Sarah Mandl<fnref n="1" />, Maximilian Bretschneider<fnref n="2" />,<fnref n="3" />, Frank Asbrock<fnref n="2" />, Bertolt Meyer<fnref n="3" />,
Anja Strobel<fnref n="1" />

Abstract.

Robots are increasingly populating social settings. Social robots should elicit positive associations to be accepted and integrated into daily lives. Even though social perception is multi-dimensional, available scales do not adequately picture this complexity in the perception of robots. To develop a new scale, we aggregated data on social perception of robots, initially operationalized as competence, sociability, morality, and anthropomorphism from four prior studies. An exploratory factor analysis on a random sample revealed three factors: “anthropomorphism”, “morality/sociability”, and “activity/cooperation”. To validate these results, we performed confirmatory factor analysis (CFA) on the remaining sample and tested for validity and reliability. Reliability was appropriate. We found significant correlations between age, gender, educational level, and factors of the scale. However, missing values interfered with confirmatory and validating analyses. Despite these issues, the scale contributes to future research on social perception of robots.

**Keywords:** SPRS-social perception of robots scale, social perception, robots, HRI, social robots, scale construction

# 1 Social Perception of Robots

Robots have become indispensable assets for simplifying workflows in industrial settings. Smooth integration of robots into the workplace as co-workers is a crucial success factor for the industry of the future [1]. Recently, areas of robotic application have broadened into many settings [2]. Of special interest are social settings, such as health care [3], where socially assistive robots are deployed as interaction partners

<footnote n="1">¹ Chemnitz University of Technology, Professorship of Personality Psychology and Assessment, Wilhelm-Raabe-Str. 43, 09120 Chemnitz, Germany</footnote>

<footnote n="2">²Chemnitz University of Technology, Professorship of Social Psychology, Wilhelm-Raabe-Str. 43, 09120 Chemnitz, Germany</footnote>

<footnote n="3">³Chemnitz University of Technology, Professorship of Work, Organizational, and Economic Psychology, Wilhelm-Raabe-Str. 43, 09120 Chemnitz
{sarah.mandl, maximilian.bretschneider, frank.asbrock, bertolt.meyer, anja.strobel}@psychologie.tu-chemnitz.de</footnote>
[4]. Human-Robot Interaction (HRI) is thus multifaceted: Successful HRI should include aspects of the robot (e.g., its design, especially in terms of anthropomorphism, its technical capabilities), the task it is applied in (e.g., military, social), the intended interaction (e.g., type of communication, proximity), and aspects of the user (e.g., affinity for technology interaction, age) [5-7]. With the implementation of robots as social interaction partners, questions of their perception as a precursor for their acceptance need to be evaluated more closely and will be the focus of this study [6]. Social perception of human beings has been investigated in-depth over the last decades [8-12]. Social perception spans two (warmth and competence; [8]) or three (sociability, competence, and morality; [10]) dimensions, respectively. Warmth and competence are the core dimensions of the Stereotype Content Model and convey information about intentions (warmth) and the ability to put these intentions into action (competence) [8]. Furthermore, warmth can be separated into two sub-dimensions: sociability and morality, which provide a deeper understanding of the broad warmth-dimension [10]. Sociability refers to cooperation and forming connections with others, whereas morality is connected to ethics and essential social values [10]. Perceptions of these dimensions form stereotypes and interpersonal impressions [8], which affect emotions and behavior toward interaction partners. Perception of robots is strongly influenced by design aspects such as perceived gender [13], size [14], or anthropomorphism [15]. Additionally, social characteristics should be taken into account [16-18]. Available assessment scales for perceptions of robots include dimensions such as warmth, competence, and discomfort [18], or anthropomorphism, likeability, perceived intelligence, animacy, and perceived safety [19]. These scales depict a multitude of dimensions. Nevertheless, we propose that some relevant aspects are missing to adequately picture the multidimensionality of social perception of robots. Given the relevance of morality for human social perception [10], we posit that a scale measuring social perception of robots also needs to include this concept.

Despite the availability of scales to assess (social) perception of robots [18, 19] or human beings [8–10], we thus propose a new short scale to assess social perception of robots that includes morality, sociability, competence, and anthropomorphism.

The layout of this paper is as follows: in Sect. 2 we summarize the aim of the present study and elaborate on possible associations relevant for the validation of the scale. In Sect. 3 we describe the method of data acquisition, stimuli and items. In Sect. 4, we report results of the exploratory and confirmatory factor analyses as well as reliability and validity of the scale. In Sect. 5, we integrate our findings with prior research, point out limitations, and give an outlook to application and future research directions.

## 2 The Social Perception of Robots Scale (SPRS)

We aim to develop a short scale for measuring social perceptions of robots that comprises sociability, competence, morality, and anthropomorphism that can be applied to different robots in diverse research settings. We build on findings from social psychology [8, 10] and human-robot interaction [18, 19] that position
sociability (the extent to which actors bond with others and hold good [warm] or bad [cold] intentions), competence (the degree to which actors are perceived as being able to put their intentions into action), and morality (the extent to which actors' actions are in line with what is socially acceptable in terms of values and societal rules of ethical conduct) at the core of social perception. We thus include these constructs in our scale on social perceptions of robots and also add anthropomorphism (the extent to which human-like capacities are attributed to the robot [20]).

Social perception of robots is linked to sociodemographic characteristics: Younger people have more positive attitudes toward new technologies [21], as have people with higher education [22]. People identifying as male also have a more positive attitude toward robots in contrast to people identifying as female [23–25]. Personality traits are also related to social perception of robots: Affinity for Technology Interaction (ATI; [26]), the tendency to actively engage with new technologies, is connected to a positive evaluation thereof [27, 28]. ATI is rooted in the construct Need for Cognition (NFC; [29]). NFC is the tendency of an individual to engage in and enjoy thinking. It is connected to the tendency to anthropomorphize [20]. People with higher NFC are less likely to anthropomorphize than people with lower NFC. Consequently, we explore the associations of sociodemographic and personality variables with social perception of robots. In order to provide a concise and valuable instrument, we will test reliability and validity of the newly developed scale.

# 3 Method

We preregistered the study on OSF (https://osf.io/ymuwr). We aggregated data from four studies (https://osf.io/qz2ca/, https://osf.io/xsn5a/, https://osf.io/ung87/) and reached a total sample size of *n* = 1532.

## 3.1 Participants

The four independent samples were acquired via Social Media, distribution channels of the University, and two online survey platforms, Prolific academic (www.prolific.co; [30]), and clickworker GmbH (www clickworker.de). The total sample consisted of *n* = 1532 participants. From the total sample we drew a random subsample of *n* = 500 as construction sample. The mean age of the sample was *M* = 34.48 (SD = 13.27) and consisted of 282 male, 214 female, and 4 non-binary participants. Countries of residence were Germany (*n* = 423), Austria (*n* = 26), and Switzerland (*n* = 12), with 39 participants residing in other countries. The sample was mostly highly educated with 45.6% of the participants having obtained a university degree (high school diploma: 31.4%, other degrees: 22.6%, no degree: 0.4%).

We used the remaining sample (*n* = 1032) as validation sample: Mean age was *M* = 33.83 (SD = 12.66). The sample consisted of 538 male, 480 female, and 14 non-binary participants. Countries of residence were Germany (*n* = 866), Austria (*n* = 70), and Switzerland (*n* = 14), with 81 participants residing in other countries. 45.1% of
the participants having obtained a university degree (high school diploma: 32%, other degrees: 22.5%, no degree: 0.4%).

## 3.2 Stimulus Material

The stimulus material consisted of three pictures of robots with different levels of anthropomorphism: An industrial robot, which does not possess any human-like qualities such as a face, a social robot, Pepper [31], which possesses a face and mouth with a form resembling a typical human body with head, body, and arms, and an android robot, a still image taken from the movie ROBOLOVE [32], which is almost indiscernible from a human being. Apart from the industrial robot, which was presented in a typical setting, the robots were presented against a neutral background. We presented the pictures in randomized order. In order to assess spontaneous associations participants were asked to rate how they perceive the robot, how they think the robot would act/think/react, even though the first impression might be wrong and revoked later. Below the pictures, we presented twenty-five adjectives on opposing ends of a semantic differential in randomized order, to be rated on a five-point Likert-scale. Additionally, we provided the option for participants to choose ‘does not apply to robots in general’ and ‘does not apply to this specific robot’ [33].

## 3.3 Items

Based on three different instruments [8, 19, 34], we initially composed items to cover the three main dimensions of social perception: competence, sociability, and morality, as well as anthropomorphism.

**Competence.** Three items covered the dimension of competence (e.g., *competent, able*) which were drawn from previous studies [8, 12, 35].

**Sociability.** A total of eight items covered the dimension of sociability, taken from three subscales: warmth [8, 12, 35], animacy [19], and likeability [19].

**Morality.** We adapted eight attributions that people high in Moral Identity possess of the German version of the Moral Identity Questionnaire [34] based on theoretical considerations (i.e., intelligibility and relevance), and chose corresponding antonyms.

**Anthropomorphism.** We covered the dimension of anthropomorphism by using five items from the Godspeed Questionnaire [19].

**Affinity for Technology Interaction.** We used the German version of the Affinity for Technology Interaction (ATI) Scale [26]. Nine items were rated on a six-point scale (anchored at ‘not true at all’ and ‘very true’) to indicate whether people tend to interact with technological systems (e.g., *I like to try out functions of new technical systems*) and averaged the into a scale.

**Need for Cognition.** In three studies, we used the German version of the Need for Cognition (NFC) scale [36], comprising sixteen items (e.g., *I consider finding new solutions to problems a fun activity*), to assess NFC. The items were rated on a seven-point scale, anchored at 1 = strong disagreement and 7 = strong agreement. We calculated a sum score.
**Sociodemographic Data.** We assessed age, gender, and educational level as sociodemographic data from the participants.

**3.4 Statistical Analysis**

We used R (Version 4.1.1) [37] and the Rpackages psych (Version 2.1.6), plyr (Version 1.8.6), openxlsx (Version 4.2.4), corrplot (Version 0.90), ggplot2 (Version 3.3.5), nFactors (Version 2.4.1), dplyr (Version 1.0.7), haven (Version 2.4.3), lattice (Version 0.20-44), and lavaan (Version 0.6-9) for all statistical analyses.

# 4 Results

We drew a random subsample of *n* = 500 as a construction sample for performing exploratory factor analysis (EFA). On the remaining sample (*n* = 1 032), we performed a confirmatory factor analysis as we explain below.

**4.1 Step 1: Exploratory Factor Analysis**

We performed an EFA with the construction sample to identify factors and factor loadings. EFA is a multivariate statistical method, commonly used for the development and validation of psychological theories and measurements [38].

**Results.** We tested the data for appropriateness for EFA beforehand. Bartlett's test of sphericity was significant $\chi^2$ (276) = 5146.331, *p* = .000, indicating that it was appropriate to use the factor analytic model on this set of data. The Kaiser-Meyer-Olkin measure of sampling adequacy indicated that the strength of the relationships among variables was high (KMO = .82); thus, it was acceptable to proceed with the analysis. We conducted an EFA, performed a Varimax rotation since we expected the factors to be uncorrelated and Weighted Least Squares (WLS) Estimator. To estimate the number of factors, we ran a parallel analysis [39]. Only items with factor loadings above .33 were included. Four factors were extracted. We rejected factor four with an eigenvalue of 1.21 and explaining only 5.03% of the variance in the data due to the small explanatory value and limited interpretability. Furthermore, we excluded items that showed factor loadings above .33 on more than one factor. One item did not fit within the context of the factor and was thus rejected due to theoretical considerations. We ran another EFA with the remaining set of 18 variables. The resulting three factors explained a total of 43% of the variance in the data (Table 1).
<caption>**Table 1**</caption>

Results from the Exploratory Factor Analysis of the 18 items derived

<table><thead><tr><th rowspan="2">Item</th><th colspan="3">Factor loading</th></tr><tr><th>1</th><th>2</th><th>3</th></tr></thead><tbody><tr><td colspan="4"><strong>Factor 1: anthropomorphism<sup>a</sup></strong></td></tr><tr><td>humanlike – machinelike</td><td>.75</td><td></td><td></td></tr><tr><td>warm – cold</td><td>.68</td><td></td><td></td></tr><tr><td>natural – artificial</td><td>.70</td><td></td><td></td></tr><tr><td>real – unreal</td><td>.63</td><td></td><td></td></tr><tr><td>organic – mechanic</td><td>.58</td><td></td><td></td></tr><tr><td>pleasant – unpleasant</td><td>.48</td><td></td><td></td></tr><tr><td>moves smoothly – moves rigidly</td><td>.47</td><td></td><td></td></tr><tr><td>has a consciousness – does not have a consciousness</td><td>.45</td><td></td><td></td></tr><tr><td colspan="4"><strong>Factor 2: morality/sociability<sup>b</sup></strong></td></tr><tr><td>ethical – unethical</td><td></td><td>.73</td><td></td></tr><tr><td>moral – immoral</td><td></td><td>.68</td><td></td></tr><tr><td>honest – dishonest</td><td></td><td>.64</td><td></td></tr><tr><td>good-natured – hostile</td><td></td><td>.67</td><td></td></tr><tr><td>likeable – unlikeable</td><td></td><td>.60</td><td></td></tr><tr><td>polite – impolite</td><td></td><td>.56</td><td></td></tr><tr><td colspan="4"><strong>Factor 3: activity/cooperation<sup>c</sup></strong></td></tr><tr><td>fair – unfair</td><td></td><td></td><td>.63</td></tr><tr><td>altruistic – self-serving</td><td></td><td></td><td>.59</td></tr><tr><td>diligent – lazy</td><td></td><td></td><td>.59</td></tr><tr><td>active – inert</td><td></td><td></td><td>.48</td></tr><tr><td>Percentage of Variance explained</td><td>17%</td><td>15%</td><td>11%</td></tr><tr><td>Eigenvalue</td><td>3.14</td><td>2.66</td><td>2.06</td></tr><tr><td>Cronbach's alpha</td><td>.81</td><td>.84</td><td>.68</td></tr></tbody></table>

Note. N = 500. The extraction method was Weighted Least Squares with an orthogonal (Varimax) rotation. Factor loadings below .33 are not shown. Observations with missing values were excluded listwise: <sup>a</sup> n = 338; <sup>b</sup> n = 219; <sup>c</sup> n = 285 (see Appendix, Table A).

Factors one and two showed a marginal positive correlation ($r_{f1,f2} = .11$), Factors one and three showed a marginal negative correlation ($r_{f1,f3} = -.15$). Factors two and three showed a small positive correlation ($r_{f2,f3} = .26$).

**Interpretation of the factors.** Factor one, *anthropomorphism*, depicts the feelings toward and morphological perception of robots. It is comparable to the social dimension of *anthropomorphism* [16]. Factor two, *morality/sociability*, reflects how robots are perceived in terms of their ‘social personality’, comparable to *morality* and *sociability* [16]. Factor three, *activity/cooperation*, is more heterogeneous and comprises attributions that are necessary for smooth interaction, e.g., in work settings.

**Hypotheses Derivation for Validity Analyses.** To test validity, we chose criteria that were examined in the context of HRI before and assess how these criteria, i.e., age, gender, education, ATI, and NFC, are related to the factors *anthropomorphism*, *morality/sociability*, and *activity/cooperation*. We posited the following hypotheses:
H1: Age is negatively correlated with factors two and three [21].

H2: People identifying as female have lower ratings of factors one, two, and three, than people identifying as male [23–25].

H3: Lower educational levels correlate with lower ratings of factors one, two, and three [22].

H4: Need for Cognition is negatively correlated with factor one [20].

H5: Affinity for Technology Interaction is positively correlated with factor three [27, 28].

H6: By employing the social dimensions from our prior study (https://osf.io/xsn5a/) [16], we expect the following correlations:

a) the social dimension of anthropomorphism is positively correlated with factor one.

b) The social dimension of sociability is positively correlated with factor two.

c) The social dimension of morality is positively correlated with factor two.

## 4.3 Step 2: Confirmatory Factor Analysis and Psychometric Properties

**Confirmatory Factor Analysis.** In order to confirm the factor structure, we performed a confirmatory factor analysis (CFA) on the validation sample (n = 1032). Observations with missing values were excluded listwise. From the total number of observations (n = 1032) only 32.4 % were used to perform the CFA due to missing values (n<sub>f1</sub> = 711, n<sub>f2</sub> = 436, n<sub>f3</sub> = 551). Hence, model fit indices showed that the data did not fit the model well (χ<sup>2</sup> (115) = 508.12, p = .000; RMSEA = .101; CFI = .796; TLI = .759; SRMR = .096)<sup>1</sup>. We performed additional exploratory factor analyses to identify a factor structure that would show a better fit. Due to the number of missing values (see Appendix, Table A) we did not find an alternative that would show a better fit and be interpretable in terms of content. Thus, we proceeded with the analyses despite the limitations.

**Reliability.** We analyzed the internal consistency of the three factors by calculating Cronbach's α [40]. Factors one and two showed a good internal consistency (α<sub>f1</sub> = .82, α<sub>f2</sub> = .85). Factor three showed a lower internal consistency (α = .64). At the background of the respective numbers of items and the contents they represent, all reliabilities of the factors can be seen as appropriate.

**Validity.** To validate the scale by means of construct validity, based on former findings and EFA, we formulated hypotheses and tested them by employing correlational designs (H1, H3-H6) and analysis of variance (ANOVA; H2).

**Results.** Age was not correlated with factors two and three, therefore H1 was rejected. People identifying as female gave significantly lower ratings on factor one than people identifying as male, but not on factors two and three. Therefore, H2 was partly confirmed. Educational levels were correlated with factor one, but not with factors two, or three. Thus, H3 was partly confirmed. Need for Cognition was not correlated with factor one, therefore, H4 was rejected. Affinity for Technology

<footnote n="1">1 RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index; TLI = Tucker-Lewis Index; SRMR = Standardized Root Mean Square Residual</footnote>
Interaction was not correlated with factor three. Hence, H5 was rejected. The social dimension of anthropomorphism showed a strong positive correlation with factor one (r = .96), thus confirming H6a. The social dimension of sociability was strongly correlated with factor two (r = .70), confirming H6b. The social dimension of morality was not correlated with factor three (r = .08), thus rejecting H6c. The overall results showed that we could only partly validate the SPRS by means of the aforementioned hypotheses. For the results and comparison with the EFA see Table 2.

<table><caption>**Table 2**</caption><thead><tr><th>Hypothesis</th><th>Variable</th><th>Factor</th><th>EFA<br>(n = 500)</th><th>CFA<br>(n = 1 032)</th></tr></thead><tbody><tr><td rowspan="2">H1</td><td rowspan="2">Age</td><td>2</td><td>r = -.27**</td><td>r = -.02</td></tr><tr><td>3</td><td>r = -.05</td><td>r = .04</td></tr><tr><td rowspan="3">H2</td><td rowspan="3">Gender</td><td>1</td><td>r = .18**</td><td>r = .16**</td></tr><tr><td>2</td><td>r = -.05</td><td>r = .05</td></tr><tr><td>3</td><td>r = -.11</td><td>r = -.05</td></tr><tr><td rowspan="3">H3</td><td rowspan="3">Educational level</td><td>1</td><td>r = -.11*</td><td>r = -.09*</td></tr><tr><td>2</td><td>r = -.01</td><td>r = -.02</td></tr><tr><td>3</td><td>r = .07</td><td>r = .01</td></tr><tr><td>H4</td><td>NFC<sup>a</sup></td><td>1</td><td>r = -.11</td><td>r = -.07</td></tr><tr><td>H5</td><td>ATI</td><td>3</td><td>r = .09</td><td>r = .06</td></tr><tr><td>H6a</td><td>Anthropomorphism</td><td>1</td><td>r = .96**</td><td>r = .96**</td></tr><tr><td>H6b</td><td>Sociability</td><td>2</td><td>r = .71**</td><td>r = .70**</td></tr><tr><td>H6c</td><td>Morality</td><td>2</td><td>r = .84**</td><td>r = .87**</td></tr></tbody></table>

*Note.* Gender was transformed to a dichotomous variable due to the low count of participants who identified as non-binary (Female = 1, Male = 2). NFC = Need for Cognition; ATI = Affinity for Technology Interaction. * indicates *p* < .05. ** indicates *p* < .01.

## 5 Conclusion

Social perception of robots is a new field of research, which will gain relevance over the following years. By constructing the SPRS, an 18-item questionnaire to assess social perception of robots in a short but comprehensive manner, we add to already available scales on robot perception [18, 19]. We complement these scales by introducing the rather overlooked dimension of perceived morality and sociability, which has a tremendous impact on the overall evaluation of robots as social partners. The construction of the SPRS should therefore be seen as an ongoing process. The EFA did not confirm the four-dimensional approach of social perception of robots which included competence, sociability, morality, and anthropomorphism. Instead, the EFA revealed three dimensions of social perception of robots: anthropomorphism, morality/sociability, and activity/cooperation. The first factor, anthropomorphism, refers to the morphological attributes and feelings toward the robot. The second factor, morality/sociability, depicts the ‘social personality’ of the robot, Factor three is more heterogenous and includes attributions necessary for cooperation, for example
within specific work settings. Even though the factor structure has not yet been fully
confirmed, we are confident that this can be achieved in subsequent studies. As can be
seen in Table A (Appendix), items especially from the factors morality/sociability and
activity/cooperation showed large portions of missing values. All the more interesting
are the good reliabilities, which suggest that the subscales do hold merit. Further
research on whether the factorial structure can be assumed is needed and should be
advanced soon. As for the sample, we need to point out that it is not without bias: For
one, our sample is, as is common in this area of research, highly educated with almost
half of it (45.6%) having obtained a university degree. Therefore, one limitation of
this study is the generalizability. Nonetheless, we suggest that in order to collect more
diverse data, the SPRS should be applied in manifold contexts. As we pointed out
earlier, we see the development of this scale as an ongoing process. Secondly, since
robots are still somewhat of an exception especially in social settings, we suspect a
hypothetical bias, that is, the way in which people are perceiving robots now, without
close contact, is not necessarily the way in which they will perceive robots if applied
[41]. Therefore, we suggest to apply the SPRS two-fold: Once before the
implementation of robots in social settings, and once after a settling-in period. With
this approach, pitfalls can be identified and potentially eradicated.

To our knowledge, the Social Perception of Robots Scale is the first of its kind to be
constructed by means of exploratory and confirmatory factor analysis and validated
with external criteria. We found indications that the construct validity left some things
to desire. By comparing the results of the CFA and EFA, results indicate that the
sample was not ideal: as we already mentioned, we suspect that missing values had a
limiting influence on our approaches to validate the SPRS. Nonetheless, we are
confident that future research will fare better at validating the psychometric properties
of the SPRS. By taking into account that no other scale was validated along external
criteria, we suggest that despite the limitations, the SPRS could serve as a valuable
tool complementing existing scales to assess robot perception [18, 19]. Especially in
the light of recently published works investigating perceived danger of robots [42]
and trust in robots [14] in relation to anthropomorphic design and Robot Acceptance
at Work [43], questions of perception, including social perception, need to be
considered. We suggest that in order to mitigate possible adverse reactions of users,
social perception of the robot in terms of anthropomorphism, sociability/morality, and
activity/cooperation should be considered by designers. We suggest applying the
SPRS when implementing robots in cooperative work and social settings to prescreen
their perception and subsequent acceptance by users who will be in contact with these
robots. Even though first steps toward successful integration of robots in cooperative
work and social settings were taken, we are confident that a focus on the perception of
social dimensions of robots is highly relevant and deserves more recognition.

**Acknowledgments.** The research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation – [Project-ID 416228727 – SFB 1410]).
# Appendix

## Table A

Percentage of missing values per item, sorted by factors

<table><thead><tr><th rowspan="2">Item</th><th colspan="2">missing values in %</th></tr><tr><th>Sample 1<br/>n = 500</th><th>Sample 2<br/>n = 1 032</th></tr></thead><tbody><tr><td>Factor one</td><td></td><td></td></tr><tr><td>warm – cold</td><td>6</td><td>7,3</td></tr><tr><td>natural – artificial</td><td>5,6</td><td>5,1</td></tr><tr><td>humanlike – machinelike</td><td>0,2</td><td>0,3</td></tr><tr><td>has a consciousness – does not have a consciousness</td><td>23,6</td><td>21,6</td></tr><tr><td>real – unreal</td><td>2,4</td><td>2,8</td></tr><tr><td>moves smoothly – moves rigidly</td><td>1,2</td><td>1,0</td></tr><tr><td>pleasant – unpleasant</td><td>6,2</td><td>7,0</td></tr><tr><td>organic – mechanic</td><td>7</td><td>6,2</td></tr><tr><td>Factor two</td><td></td><td></td></tr><tr><td>likeable – unlikeable</td><td>9,4</td><td>10,5</td></tr><tr><td>good-natured – hostile</td><td>23,4</td><td>26,3</td></tr><tr><td>polite – impolite</td><td>12,2</td><td>14,0</td></tr><tr><td>moral – immoral</td><td>44,6</td><td>47,4</td></tr><tr><td>honest – dishonest</td><td>32,6</td><td>31,8</td></tr><tr><td>ethical – unethical</td><td>41</td><td>42,5</td></tr><tr><td>Factor three</td><td></td><td></td></tr><tr><td>fair – unfair</td><td>32,8</td><td>34,8</td></tr><tr><td>diligent – lazy</td><td>15,4</td><td>21,5</td></tr><tr><td>altruistic – self-serving</td><td>25,2</td><td>28,4</td></tr><tr><td>active – inert</td><td>6,8</td><td>8,3</td></tr></tbody></table>
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Social Perception of Robots Scale (SPRS) – German Version<fnref n="2" />

Wie schätzen Sie den Roboter in Hinblick auf die angegebenen Merkmale ein? Wie handelt/denkt/wirkt der Roboter aus Ihrer Sicht?

<table><thead><tr><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td>wie ein Mensch</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>wie eine Maschine</td></tr><tr><td>warm</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>kalt</td></tr><tr><td>natürlich</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>künstlich</td></tr><tr><td>echt</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>unecht</td></tr><tr><td>organisch</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>mechanisch</td></tr><tr><td>angenehm</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>unangenehm</td></tr><tr><td>bewegt sich flüssig</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>bewegt sich ungelenk</td></tr><tr><td>hat ein Bewusstsein</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>hat kein Bewusstsein</td></tr><tr><td>ethisch</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>unethisch</td></tr><tr><td>moralisch</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>unmoralisch</td></tr><tr><td>ehrlich</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>unehrlich</td></tr><tr><td>gutmütig</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>feindselig</td></tr><tr><td>sympathisch</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>unsympathisch</td></tr><tr><td>höflich</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>unhöflich</td></tr><tr><td>fair</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>unfair</td></tr><tr><td>uneigennützig</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>eigennützig</td></tr><tr><td>fleißig</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>faul</td></tr><tr><td>aktiv</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>träge</td></tr></tbody></table>

<footnote n="2">² Die Items sollten randomisiert vorgegeben werden.</footnote>