# Online Privacy Fatigue: A Scoping Review and Research Agenda
## 4 Discussion
### Methodology and Theory
Additionally, of all the articles reviewed, only two used a privacy-centric theoretical framework (see Figure 2), and despite privacy concerns also featuring prominently as an antecedent, none of the reviewed articles used (or adapted) established privacy frameworks focused on modeling privacy concerns. This is problematic as it does not advance the study of privacy fatigue (as influenced by privacy concerns) within the context of established (and more suitable) theories, such as _concern for information privacy (CFIP), internet users' information privacy concerns (UIIPC)_, and _antecedents-privacy concerns-outcomes (APCO)_[42]. The use of _APCO_ is particularly appealing, as it has shown promise in recent privacy research [43; 44] and has also been used to model the influence of individual differences in a privacy context [45; 46]. For health-related studies, the use of the _health information privacy concern (HIPC)_ scale is recommended, but researchers are cautioned to model it multidimensionally, as this is the norm [47; 48]. Such theoretical aspects were notably absent from review articles focused on mHealth or smart healthcare. The use of theoretical frameworks also applies to qualitative privacy fatigue research. Avoiding the use of theory limits formal theorizing and, in fact, only one qualitative article used a framework. Such forms of theoretical imbalance may yield weaknesses. Conversely, the use of a well-defined (and established) theoretical framework enhances qualitative findings [49]. **Recommendation 4:**_Quantitative researchers should make an informed decision when selecting either CB-SEM or PLS-SEM._ If, for example, the objective of a study is to achieve path estimate consistency, then a CB-SEM approach is preferable [50]. The same applies to confirmatory studies where theory testing takes place [50; 51]. This is important given the sparse use of theoretical frameworks in the articles reviewed. The use of CB-SEM is also recommended when the measurement philosophy is based on common variance [52]. It is worth noting that this aspect was not explicitly specified in any of the articles reviewed. Despite this, there may be instances where it is acceptable (and even preferable) to use PLS-SEM, one such example being the estimation of formative (as opposed to reflective) models [53]. Although the theoretical differences between these two types of model are beyond the scope of this review, privacy fatigue researchers may wish to read [PERSON] and [PERSON] [54] or [PERSON], [PERSON] et al. [55] and explicitly specify these aspects in the future. This analysis also revealed that the reviewed articles that used SEM failed to classify their models as either common factor or composite in nature. Such classification is a contentious matter, and although [PERSON] and [PERSON] [50] argue that PLS-SEM is unable to estimate common factor models (as CB-SEM is preferred for this), many researchers do so because they either avoid classification or are unaware of its significance. To address such classification problems, researchers should use a consistent form of the PLS algorithm (e.g., the PLSc option in SmartPLS), especially since PLSc can accommodate both common factor and composite models [56; 57]. In doing so, a failure to classify models would not adversely affect the results to the same extent as being ignorant in this regard. **Recommendation 5:**_Quantitative researchers should use larger samples and not use said sample sizes to determine which SEM technique to select._ If a smaller sample is desired, some form of sample size power analysis must be carried out. There is evidence to suggest that this advice was not adhered to in the articles reviewed, especially given the smaller sample sizes of the PLS-SEM articles reviewed (Figure 3). In fact, the use of PLS-SEM is plagued by several misconceptions when it comes to sampling and overall study design [12]. In short, PLS-SEM is not superior when it comes to estimating models based on small sample sizes [12].