As in any small group relationship, networking requires commitment from organisations in terms of time and finances and development of a common goal or bond is an important relational characteristic that allows effective performance of the group.
Many researchers, around the world (2, 4, 8, 10, 13) and in Latin America (17– 19, 21, 22), have pointed out the many types of masculinity and its relational characteristic in the general population.
Within this context, newer, more sophisticated formulations have been proposed that consider both the absolute and relational characteristics of the stimuli in transposition studies.
However, Wertheimer understood that the perceived motion is an emergent experience, not present in the stimuli in isolation but dependent upon the relational characteristics of the stimuli.
However, most existing studies only include the type and features of a product without considering the relational characteristics thereof.
Findings show that some relational characteristics explain multifaceted collaborative behavior as predicted, while others behave in unexpected ways.
Second, while most scientific analysis data is not relational in nature, a key subset, including the inputs and outputs of scientific workflows, has relational characteristics.
By combining transaction and relational characteristics, we argue that three different management control patterns can be distinguished: a content-based control pattern, a consultation-based control pattern, and a context-based control pattern.
Recently, Capellari and De Stefano (2014), adopting an approach combining local and individual relational characteristics, showed that university-owned and university-invented patents could create rather different network structures.
These initiatives are important to examine because, if successful, they may be helpful for understanding relational characteristics in other cultural contexts particularly those using holistic and differential approaches in child welfare.
Given that the relational characteristics can provide much useful information during the recommendation process, we have designed an easily realizable recommendation system framework based on relational data by integrating the relational data in the domain ontology and applying a genetic algorithm to process the recommendation.
