H
Horst Treiblmaier
Researcher at MODUL University Vienna
Publications - 176
Citations - 4355
Horst Treiblmaier is an academic researcher from MODUL University Vienna. The author has contributed to research in topics: Blockchain & Computer science. The author has an hindex of 23, co-authored 141 publications receiving 2375 citations. Previous affiliations of Horst Treiblmaier include Vienna University of Economics and Business & MODUL University Dubai.
Papers
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The impact of the blockchain on the supply chain: a theory-based research framework and a call for action
TL;DR: In this paper, the potential implications of the blockchain for supply chain management (SCM) are investigated using four established economic theories, namely, principal agent theory (PAT), transaction cost analysis (TCA), resource-based view (RBV), and network theory (NT).
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What Keeps the E-Banking Customer Loyal? A Multigroup Analysis of the Moderating Role of Consumer Characteristics on E-Loyalty in the Financial Service Industry.
Arne Floh,Horst Treiblmaier +1 more
TL;DR: In this paper, the importance of antecedents of online loyalty such as trust, quality of the Web site, QoS and overall satisfaction was investigated, and the moderating role of consumer characteristics (gender, age, involvement, perceived risk and technophobia) was supported by the data.
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Leveraging the Internet of Things and Blockchain Technology in Supply Chain Management
TL;DR: It is illustrated how the deployment of Blockchain technology in combination with IoT infrastructure can streamline and benefit modern supply chains and enhance value chain networks.
Journal Article
What keeps the e-banking customer loyal? a multigroup analysis of the moderating role of consumer characteristics on e-loyalty in the financial service industry
Arne Floh,Horst Treiblmaier +1 more
TL;DR: In this paper, the importance of antecedents of online loyalty such as trust, quality of the Web site, QoS and overall satisfaction was investigated, and the moderating role of consumer characteristics (gender, age, involvement, perceived risk and technophobia) was supported by the data.
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Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research
TL;DR: This work compared classical exploratory factor analysis with a robust counterpart which is less influenced by data outliers and data heterogeneities and revealed that robust exploratory factors analysis is more stable than the classical method.