Validity and reliability of the scale internet users' information privacy concerns (iuipc)

78 citations

Abstract

Internet Users’ Information Privacy Concerns (IUIPC-10) is one of the most endorsed privacy concern scales. It is widely used in the evaluation of human factors of PETs and the investigation of the privacy paradox. Even though its predecessor Concern For Information Privacy (CFIP) has been evaluated independently and the instrument itself seen some scrutiny, we are still missing a dedicated confirmation of IUIPC-10, itself. We aim at closing this gap by systematically analyzing IUIPC’s construct validity and reliability. We obtained three mutually independent samples with a total of N = 1031 participants. We conducted a confirmatory factor analysis (CFA) on our main sample to assert the validity and reliability of IUIPC-10. Having found weaknesses, we proposed a respecified instrument IUIPC-8 with improved psychometric properties. Finally, we confirmed our findings on a validation sample. While we found sound foundations for content validity and could confirm the overall three-dimensionality of IUIPC-10, we observed evidence of biases in the question wording and found that IUIPC-10 consistently missed the mark in evaluations of construct validity and reliability, calling into question the unidimensionality of its sub-scales Awareness and Control. Our respecified scale IUIPC-8 offers a statistically significantly better model and outperforms IUIPC-10’s construct validity and reliability. The disconfirming evidence on IUIPC-10’s construct validity raises doubts how well it measures the latent variable Information Privacy Concern. The less than desired reliability could yield spurious and erratic results as well as attenuate relations with other latent variables, such as behavior. Thereby, the instrument could confound studies of human factors of PETs or the privacy paradox, in general.

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Peer Review & Critical Discussion

3 threads

Potential Selection Bias in 2023 Cohort

DSJDr. Sarah J.
Verified PhD Candidate
12 replies

The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.

2 hours ago

Non-naive Participants Issue

MCM. Chen (OpenAI)
Data Scientist
8 replies

I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.

5 hours ago

RLHF Applicability to This Study Design

PRWProf. R. Williams
Verified Researcher
15 replies

The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.

1 day ago

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