# Online Privacy Fatigue: A Scoping Review and Research Agenda
## 4 Discussion
### Methodology and Theory  
**Recommendation 1: Researchers should explicitly conceptualize the phenomenon of interest within the context of their chosen theoretical framework. ** This should be carried out for each latent variable, regardless of the study design. Quantitative research designs should be accompanied by a well-specified questionnaire that clearly conceptualizes each latent variable within the context of the chosen theoretical framework. It should also align the measurement items in the questionnaire (i.e., the questions) with the latent variables. Qualitative research designs should conceptualize the similarity of their model variables by aligning the interview guide questions with the variables within the chosen theoretical framework. Although it was possible to obtain a clear understanding of how the reviewed articles conceptualized online privacy fatigue, many of the latent variable measures in these articles were not conceptualized in enough detail. **Recommendation 2: Researchers should carefully consider how (and where) they collect their primary data. ** This applies to both quantitative and qualitative research and should not be solely determined by that which is deemed cost effective and convenient. For example, several of the review articles gathered primary data from university students. Although student populations are suitable for studies focused on student-specific privacy research, most of the articles reviewed were not thus focused. Researchers should, therefore, obtain representative samples that enable the generalizability of results [34; 35]. It was refreshing to see quite a few articles (\(n=5\)) that used crowdsourced survey platforms (such as Amazon Mechanical Turk) to enhance the demographic diversity of their samples. These platforms also enable researchers to set very specific sampling criteria while ensuring overall cost-effectiveness [36]. Some of these platforms (e.g., Prolific) could even be used to gather representative samples, which some claim surpass those that can be obtained via MTurk [37]. As such, researchers are encouraged to carefully consider their study designs before selecting a survey platform. This applies specifically to studies focused on the behavioral influence of individual differences, where MTurk participants (also called workers) have been found to be more introverted, have lower levels of self-esteem, and higher cognitive needs [38], all of which may introduce sampling bias. Additionally, several articles gathered primary data from social media platforms, of which most were Chinese based. Some of the more popular social media platforms were either not used or not explicitly specified. For example, only one article collected data exclusively from Facebook, with a clear absence of Instagram-focused studies. It must be conceded that several articles focused on social media in general, but such approaches make comparisons difficult and complicate platform-specific theorization, something that would benefit this field of study. **Recommendation 3:**_Researchers should make use of (or adapt) established theoretical frameworks best suited to the study of online privacy._ This is an important consideration given our view that the study of online privacy fatigue is immature. Several articles made use of CB-SEM, which is confirmatory in nature. However, even these articles failed to use an established theoretical framework. Failure to move from exploratory to confirmatory methods implies that privacy fatigue research will remain immature, unable to empirically substantiate theoretical matters such as what is _sufficient_ in terms of statistical accuracy. Future research could, for example, use _protection motivation theory_, which is well suited to some privacy fatigue research because of its focus on the role of threats and risks [39; 40; 41], especially when considering that threats and risks featured prominently in this review (Figure 6).