'Benefits & trustworthiness in disclosure decisions' (AsPredicted #125,906)
Author(s) Miriam Gieselmann (IWM Tübingen) - m.gieselmann+prereg@mailbox.org Kai Sassenberg (Leibniz-Institut für Wissensmedien, Tübingen) - ksa@leibniz-psychology.org
Pre-registered on 2023/03/21 01:05 (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? H1: Participants share less information if the system provider is less (vs more) trustworthy.
H2: The negative effect of low provider trustworthiness will be weaker when participants perceive the system output as more qualitative.
3) Describe the key dependent variable(s) specifying how they will be measured. - Perceived output quality (4 items)
- Information disclosure (Number of information that participants are willing to share from a list of 20 items)
4) How many and which conditions will participants be assigned to? We will implement a one-factorial between-subjects design. Participants will be randomly assigned to one of the following conditions: (1) low provider trustworthiness, (2) high provider trustworthiness.
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. We will conduct a multiple regression analysis regressing information disclosure on perceived output quality, provider trustworthiness, and the interaction of these two factors. Evidence for H1 will be provided by a significant main effect of provider trustworthiness, evidence for H2 will be provided by a significant interaction.
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. We will include participants for analysis if they pass two attention checks and are fluent in German (the language the study is conducted in).
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 seek for a power of 80% and an alpha error of 0.05 for observing a minimum effect of f2 = 0.03 in a multiple regression with 3 predictors. Hence, we aim to collect N = 368 valid cases. To account for data exclusion based on the above-described criteria, we aim to collect N = 405 cases (i.e., oversampling of 10%).
8) Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) We will further assess:
- Privacy concerns (5 items)
- Intention to use (1 item)
- Perceived usefulness (1 item)
- Perceived ease of use (1 item)
- Manipulation check: trust in provider (6 items)
- Individual relevance of breakfast, internet recipes and cooking (1 item each)
