Author(s) This pre-registration is currently anonymous to enable blind peer-review. It has 5 authors.
Pre-registered on 2022/06/13 02:40 (PT)
1) Have any data been collected for this study already? No, no data have been collected for this study yet.
2) What's the main question being asked or hypothesis being tested in this study? 1. Validation of a questionnaire to assess people's knowledge about robots.
2. Research question: How does knowledge, attitude and anthropomorphism relate to people's willingness to help a robot in need?
Hypothesis: (a) Influence of knowledge: Knowledge influences people's willingness to help, with high knowledge leading to participants being less willing to help; (b) Influence of attitude: Attitude towards robots influences people's willingness to help. Positive attitudes lead to people being more willing to help, while negative attitudes predict the opposite trend; (c) Influence of anthropomorphism: Participant's own opinion on how anthropomorphic robots are influences their willingness to help a robot in need, with high anthropomorphism scores leading to participants being more willing to help.
3) Describe the key dependent variable(s) specifying how they will be measured. Willingness to help: Participants read 16 scenarios about a robot that needs help. Each scenario is presented 2 times in randomized order accompanied by a picture of a robot (32 counterbalanced experimental trials in total). Participants decide whether they would help or not. Willingness to help will be measured as the proportion of help responses recorded with key presses.
4) How many and which conditions will participants be assigned to? The study has a 4 (domain: domain 1, domain 2, domain 3, domain 4) x 2 (robot: anthropomorphic, non-anthropomorphic) within-subjects design.
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. Robot Knowledge: Questionnaire (12 true, 13 false items). The scale was pre-registered under https://aspredicted.org/8HD_BZ5 and pre-tested.
Attitudes will be measured using the 3-factor measure, Robotic Social Attitude Scale (RoSAS) (Carpinella et al., 2017). Participants rate on a 9-point scale how closely they associate 18 different words with the category "robots".
Anthropomorphism will be measured as the participant's opinion on how anthropomorphic robots are. The Waytz instrument (Ruijten et al., 2019) will be used.
Willingness to help will be calculated as the proportion of help responses.
Hypothesis:
(a) Multiple linear regression models predicting willingness to help from knowledge while controlling for attitude, anthropomorphism score, gender (1= male; 2 = female), age, and education (dummy-coded).
(b) Multiple linear regression models predicting willingness to help from the attitudes while controlling for knowledge, anthropomorphism, gender (1= male; 2 = female), age, and education (dummy-coded).
(c) Multiple linear regression models predicting willingness to help from anthropomorphism while controlling for knowledge, attitude, gender (1= male; 2 = female), age, and education (dummy-coded).
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. Requirements for inclusion: fluent in German, at least 18 years old, and successful completion of attention check
7) How many observations will be collected or what will determine sample size? No need to justify decision, but be precise about exactly how the number will be determined. We are aiming to sample N = 500 valid cases, a representative sample (age and gender) for the German population
8) Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) 1.We investigate how the physical appearance of the robot (anthropomorphic vs non-anthropomorphic) influences people's willingness to help.
2.Multiple linear regression models predicting willingness to help calculated separately for anthropomorphic and non-anthropomorphic items, from knowledge while controlling for attitude, anthropomorphism, gender (1= male; 2 = female), age, and education (dummy-coded).
3.We investigate whether the willingness to help scores, calculated as domain specific, are statistically different.
4.Multiple linear regression models predicting willingness to help from knowledge while controlling for attitude, anthropomorphism score, gender (1= male; 2 = female), age, and education (dummy-coded), and scenarios as a random intercept.
5.We investigate the relationship between reported political orientation and its effect on willingness to help, as well as the other measured variables.
6.We investigate whether the complexity of the helping behavior described in the scenarios (telling someone the robot is defective vs pushing the robot) have any influence on willingness to help.
