Sunbelt XXXI International Network for Social Network ... - INSNA

Using Weak‐ties For Problem Solving: Methodical Knowledge Exchange In Social NetworksKonert, Johannes; Göbel, Stefan; Steinmetz, RalfOnline Social NetworksKnowledge Networks, Methodical Artifacts, Recommender, Learning Commmunities, Serious GamesFRI.AM1Difficulties of problem solving in a domain, where users have the factual knowledge, are mostly ascribed to missing methodical knowledge (and practice). Incontrast to the common approach to consult expert networks, we suggest a solution where users can find content for methodical approaches and bestpractices for the current problem at hand in (one of) their online social networks. To find and suggest this User‐generated Experience Content (UEC), wechoose an algorithmic approach taking into account parameters for optimization like level of trust, context of the searching user and quality of the found UEC.Here one hypothesis is that UEC from peers is easier to understand due to a shared social context and common sense between the users. We presume that thisresults in a lower frustration in problem solving, shortens the time needed and increases the learning effect for the person facing the problem. We evaluatethe use of this peer‐based UEC suggestion approach in several projects. First application is in the context of game‐based problems (challenges) in levels ofSerious Games connected to Online Social Networks like Facebook. We will discuss further projects and possible applications.Validating Trends – The Typology Of Relational Embeddedness Network Data SurveyHite, Julie M.; Waakee, Ingrid; Hite, Steven J.; Sudweeks, Richard; Walker, Timothy D.Collecting Network DataMeasures, Network Data, Multiplexity, Embeddedness, Validation Methods, Strong TiesTHURS.AM1The Typology of Relational Embeddedness Network Data Survey (TRENDS) measures the extent and type of relational embeddedness within dyadic ties. Byidentifying seven different types of relational embeddedness, based on Hite’s (2003) typology, TRENDS assumes that relational embeddednessis a multidimensionalconstruct rather than simply dichotomous. Measuring the variability of relational embeddedness across ties can better facilitate its use as adependent or independent construct in network research. After extensive piloting, we report on the validation of TRENDS at the dyadic level (for individual,independent dyadic ties) in the context of the external ties of faculty in higher education. Construct validity, addressed using confirmatory factor analyses anditem analysis, both supported and further informed Hite’s (2003) typology. Criterion‐referenced validity was supported through factor analyses of TRENDSitems and literature‐based survey items for the constructs of embeddedness and strong ties. Second, we report on a validation study of TRENDS at thenetwork level (the combination of non‐independent dyadic ties), in the context of a multiple‐department college network in higher education. Construct andcriterion‐referenced validity were again assessed. In addition, at this network level, issues of accounting for actor, alter, and dyadic non‐independence areaddressed. The results of these studies indicate that, at both the dyadic and network levels, TRENDS provided a valid instrument for the measuring the extentand type of relational embeddedness. This instrument can facilitate the study of relational embeddedness as either a dependent or independent construct.