# How Reputation Systems Change the Psychological Antecedents of Fairness in Virtual Environments
## 4 Discussion  
The present study aims at extending previous findings on the impact of reputational systems on people's pro-social behaviors (i.e., Fairness) within a social dilemma framework [21]. Driven by literature evidence, this work focuses on identifying which socio-demographic and psychological characteristics represent the antecedents of Fairness in an online bargaining game context, paying attention to the influence of reputation on pro-social behaviors. The game employed to investigate the socio-psychological antecedents of Fairness was played online, a peculiar environment that can promote local adhesion to the norm because of its features and the psychological processes it triggers [66]. Since reputation has been conceptualized as a way to manifest local norms [27], increased adhesion to reputation can be expected in this context. Consequently, including a reputational system in the online bargaining game should increase its capability of exerting social influence and favoring the onset of Fairness.  
Two experimental conditions were compared: reputation off and reputation on. In the first case, the participants played without the reputational system, while in the second one it was included. The difference between the two conditions lay in the players' mere awareness of the system's presence; in fact, it did not have any effect on them or on the game purposes, since they were playing as Proposers (while the system concerned the Observers). In this respect, it is essential to highlight that when reputation was involved in the game, it was not directly attributed to the players who were filling such roles; therefore, they should not have been under its influence while bargaining. Literature contributions show that when allocating resources, players tend to change their game conduct [67], behaving more or less fairly, consistently with the presence (or the absence) of the reputational system [21].  
The Proposers' Offers and Requests were exploratively analyzed along with their socio-demographic and psychological characteristics to identify the antecedents of Fairness (i.e., calculated subtracting the latter from the former).  
The descriptive statistics provide initial information about the participants' behavior in each experimental condition. On average, when the reputational system was not involved in the game, the players allocated less and requested more resources to their counterparts, behaving less fairly. At the same time, when playing within the reputational framework, they acted oppositely, being more generous, less demanding, and fairer. The descriptive statistics and the final model presented appear to be in line with previous research, which found high Fairness when the interactions occurred in a context characterized by reputation [21].  
From the inferential analysis, it is possible to assume which are the socio-psychological antecedents of Fairness, deepening the knowledge of the factors that affect Offer, Request, and Fairness itself depending on the experimental conditions.  
\begin{table}
\begin{tabular}{l l l l} \hline \multicolumn{4}{c}{**Predictors of Fairness**} \\ \hline
**Best Model** & **Akaike** & **F** & **df-1 (2)** \\ \hline Fairness Final Model & 22176.750 & \(11.163\)*** & \(8\) (3152) \\ \hline \multicolumn{4}{c}{_Fixed coefficients_} \\ \hline
**Factors** & \multicolumn{1}{c}{**Coefficient (\(\beta\))**} & **Student t** & \(p\).** \\ \hline Reputation Condition (Rep. ON) & \(1.42\) & \(2.79\) & \(0.005\) \\ Sense of Community & \(1.93\) & \(6.35\) & \(0.001\) \\ Self-Efficacy & \(-1.08\) & \(-3.55\) & \(0.001\) \\ Age & \(1.39\) & \(3.51\) & \(0.001\) \\ Closeness X Rep ON & \(1.30\) & \(3.14\) & \(0.002\) \\ Neuroticism X Rep ON & \(-0.64\) & \(-1.54\) & \(0.048\) \\ \hline \multicolumn{4}{c}{*** = \(p<0.001\); df = degrees of freedom; \(X\) = interaction between variables.} \\ \end{tabular}
\end{table}
Table 7: GLMM 4. Final Socio-demographic and Psychological predictors fairness across conditions.