Group recommender systems aim to suggest appropriate products/services to a group of users rather than individuals. These recommendations rely solely on determining group preferences, which is accomplished by an aggregation technique that combines individuals' preferences. A plethora of aggregation techniques of various types have been developed so far. However, they consider only one particular aspect of the provided ratings in aggregating (e.g., counts, rankings, high averages), which imposes some limitations in capturing group members' propensities. Besides, maximizing the number of satisfied members with the recommended items is as significant as producing items tailored to the individual users. Therefore, the ratings' distribution is an essential element for aggregation techniques to discover items on which the majority of the members provided a consensus. This study proposes two novel aggregation techniques by hybridizing additive utilitarian and approval voting methods to feature popular items on which group members provided a consensus. Experiments conducted on three real-world benchmark datasets demonstrate that the proposed hybridized techniques significantly outperform all traditional methods. For the first time in the literature, we offer to use entropy to analyze rating distributions and detect items on which group members have reached no or little consensus. Equipping the proposed hybridized type aggregation techniques with the entropy calculation, we end up with an ultimate enhanced aggregation technique, Agreement without Uncertainty, which was proven to be even better than the hybridized techniques and outperform two recent state-of-the-art techniques.