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Add preregistrations, edit Background, questions on depth and width of the paper

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Angelica Lermann Henestrosa 2025-10-02 13:23:49 +02:00
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@ -32,18 +32,11 @@ This dataset allows for future research on psychological and behavioral dynamics
% Taken from LEK application
This project is a joint project from the human-computer interaction group at
IWM. There will be several preregistrations from group members focusing on their
the Leibniz-Institut für Wissensmedien in Tübingen (IWM). There are several preregistrations from group members focusing on their
subquestions. However, the overall aim is to examine how people change their
use, perception, and attitudes towards AI-tools like ChatGPT, Siri, or Alexa.
We target an US-American sample and plan a longitudinal study starting at the
end of August 2024 (six waves, roughly one year). We use a longitudinal design
to track changes over time and to get some hints on causality. We target an
American sample because Apple announced to release its new AI platform Apple
Intelligence in autumn 24 (in the US due to the stricter regulations in the EU)
and we expect that many people will be exposed to this AI on their Apple
devices. Longitudinal studies are more likely to find changes if there is a
potential change trigger (Zhao et al., 2024)
This study targets an US-American sample due to Apple announcing to release its new AI platform Apple Intelligence in autumn 2024 (in the US due to the stricter regulations in the EU) and we expect many people to be exposed to this AI on their Apple devices. Data collection started at the end of August 2024?? (six waves, roughly one year). By using a longitudinal design we were able to track changes over time and to get some hints on causality. Longitudinal studies are more likely to find changes if there is a potential change trigger (Zhao et al., 2024)
Central questions are whether predictors of technology acceptance as well as
technology use change over time, whether the perception of AI-Tools as tools vs.
@ -57,6 +50,7 @@ the moderating role of personality.
...
Longitudinal studies like this are needed to capture the evolving perceptions of opportunities and risks associated with AI, perceived capabilities of AI systems, attitudes toward AI, trust in AI, willingness to delegate tasks to AI, areas of application, and the interrelationships among these constructs over time (to be continued). To examine those changes and relationships, an American sample mainly consisting of AI users (specify) was invited to participate in this survey at two-month intervals between September 2024 and July 2025.
% Overview of Dataset
%
% * Provide a clear overview of the dataset
@ -69,7 +63,7 @@ Longitudinal studies like this are needed to capture the evolving perceptions of
* outlook on potential developments in other countries
* connection of actual use and stable psychological variables
% WP1 Teresa/Nico/Vanessa/Angelica
% WP1 Teresa/Nico/Vanessa/Angelica https://osf.io/58tqc
%
% WP2 Teresa
%
@ -79,7 +73,7 @@ Longitudinal studies like this are needed to capture the evolving perceptions of
%
% WP5 Büsra/Teresa https://aspredicted.org/kx5r-4pxq.pdf
%
% WP6 Angelica/Gerrit
% WP6 Angelica/Gerrit https://doi.org/10.17605/OSF.IO/JAUD4
%
% WP7 Mike ???
%
@ -165,6 +159,8 @@ We collected sociodemographic information, including, age, gender, educational l
\section{Data Records}
% * @Nora das könntest du vllt. noch ausführen?
Data records for each of the six waves are available in csv format at (tbd) together with the R/python scripts for data anonymization, data cleaning, and data preprocessing.
That is, firstly the data was anonymized by removing participants' Prolific IDs and unused variables, empty variables resulting from faulty questionnaire programming, and xy were removed. Thus (filename) represents the cleaned and anonymized raw data, including the single items of each measurement. Second, variable names were harmonized and scales were calculated, resulting an the preprocessed data set xy, ready for analyses across scales.
Moreover, a codebook explaining variable abbreviations and containing information about the waves in which the variable was collected (what else?) is available at (tbd).
@ -178,6 +174,13 @@ Moreover, a codebook explaining variable abbreviations and containing informatio
% * Include 1-2 tables or figures if necessary, but avoid summarizing data that
% can be generated from the dataset.
% * how should we report on the variables and scales:
% **item and scale level OR just scale level ?
% **link to Gerrits scale list: https://gitea.iwm-tuebingen.de/AG4/project_HMC_preprocessing/src/branch/main/results/database_api_reference.md ?
% **extra codebook or merge that information to Gerrits list?
% --> an overview about all variables, their calculation, their measurement format and ideally their M, SD, cronbachs alpha would be ideal!
\section{Technical Validation}
Wave 1 was conducted shortly before iOs 18?? was published. -> were there any other external events potentially influencing the survey?