## 5 Discussion  
Our results show differences compared to what we hypothesized based on theory as well as what we found in the literature. We will discuss each hypothesis following the numbering in Figure 2.  
**Negative privacy experiences do not affect privacy concerns** (effect size of 0.071). This finding is in contrast to the majority of findings from the literature (H1 not confirmed). **Risk-aversion of Pokemon Go players increases privacy concerns** (0.182), indicating that risk-averse players tend to be more concerned (H2 confirmed). We find no statistical significant effect for age on CFIP. This might be due to our restricted sample including only players between 18 and 35 years. We ran the model again with the remaining data set consisting only of players older than 35 years (N = 263). We find that age has a statistically significant positive effect on CFIP (0.254), indicating that **age only matters for older players**. Therefore, we cannot confirm H3a, but acknowledge that age might have an effect on CFIP for older players. **Female (0.106) and more experienced (0.105) players have higher concerns** confirming Hypotheses 3b and 3d. In contrast, **education does not affect privacy concerns** (\(-\)0.020). This result is not in line with prior literature. Thus, the results regarding the impact of education remain inconclusive and H3c cannot be confirmed. **CFIP plays no role in the privacy calculus for active players** Hypotheses 4 and 5, which reflect the privacy calculus towards USE, can only be partially confirmed. Hypothesis 4 cannot be confirmed. This result indicates that privacy concerns with respect to organizational practices do not play a role in the players use decision. One explanation is that players in our sample already decided that they play Pokemon Go (since we only asked active players). Thus, it might be possible that they already ran through the process of weighing up benefits and costs before they decided to download the application. From a research point of view, the limitation of asking only active users of a technology is hard to \begin{table}
\begin{tabular}{l l} \hline \hline
**Independent Var.** & **Path Coeff.** \\ \hline H1: VIC & 0.071 \\ H2: RP & 0.182 *** \\ H3a: AGE & 0.094 \\ H3b: GDR & 0.106 * \\ H3c: EDU & \(-\)0.020 \\ H3d: EXP & 0.105 * \\ H6: CBN & 0.159 ** \\ \hline \multicolumn{3}{l}{**(a)** Dependent Variable: CFIP} \\ \hline
**Independent Var.** & **Path Coeff.** \\ \hline H7: CBN & 0.343 *** \\ \hline
**(b)** Dependent Variable: PE \\ \hline
**Independent Var.** & **Path Coeff.** \\ \hline H4: CFIP & \(-\)0.013 \\ H5: PE & 0.254 *** \\ H8: CBN & \(-\)0.024 \\ \hline \multicolumn{3}{l}{**(c)** Dependent Variable: USE} \\ \hline
**Dependent Var.** & **Adj. R2** \\ \hline CFIP & 0.073 \\ PE & 0.116 \\ USE & 0.053 \\ \hline \multicolumn{3}{l}{**(d)** Adj. R2 Values} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the structural model. Three, two and one asterisks show statistical significance at the 0.1%, 1% and 5% significance level, respectively (VIC: Prior privacy victim experiences, RP: Risk propensity, GDR: Gender, EDU: Education, EXP: Smartphone experience, CBN: Childhood brand nostalgia, CFIP: Concerns for information privacy, PE: Perceived enjoyment, USE: Use behavior). address since it is nearly impossible to find users who did not download and play the game due to certain factors. However, this reference group would be needed to address this limitation properly. In contrast to H4, we can confirm the positive effect of perceived enjoyment on USE (H5), indicating that fun is a major driver of use behavior (effect size of 0.254). **Nostalgia positively affects privacy concerns and enjoyment** Hypothesis 6 and 7 represent the effect of CBN on the two variables of the privacy calculus (CFIP and PE). Our results indicate that only hypothesis 7 can be confirmed, i.e., CBN towards Pokemon Go positively influences the fun players have when playing the game (effect size of 0.343). Thus, CBN enhances the benefits in the privacy calculus.