Abstract
Dyadic interactions are an important aspects in service encounters. They may be observed in B2B distribution channels, professional services, buying centers, family decision making or WOM communications. The networks consist of dyadic bonds that form dense but weak ties among the actors.
The aim of the paper is to identify latent properties of dyadic interactions on mobile phone service market. Latent variable models in relational marketing often focus either on the effects of relations or treat the relationship dimensions as psychological constructs on individual-trait level.
We propose an approach based on Bayesian latent variable modeling of social networks with dyads as analytic units. This approach allows to model emergent and relational properties of actors’ interactions in dyads that are irreducible to individual latent traits or psychological constructs.
Several competing models are developed and compared using Bayesian structural equation models of dyadic data. Bayesian SEM helps to overcome the limitations of the more traditional solutions based on ML or WLS estimations. It is robust for small samples which are common in social network analysis, it can also be applied to non-normal data as well as non-linear relations between latent variables.
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Sagan, A., Kowalska-Musiał, M. (2009). Dyadic Interactions in Service Encounter: Bayesian SEM Approach. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_53
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DOI: https://doi.org/10.1007/978-3-642-01044-6_53
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