Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/228942
Authors: 
Haas, Jonas
Seiter, Mischa
Year of Publication: 
2020
Citation: 
[Editor:] Kersten, Wolfgang [Editor:] Blecker, Thorsten [Editor:] Ringle, Christian M. [Title:] Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 29 [ISBN:] 978-3-7531-2346-2 [Year:] 2020 [Pages:] 875-909
Abstract: 
Purpose: Digital platforms provide logistics service providers (LSPs) the opportunity to increase their capacity utilization. Since there are a large number of reasonable alternatives, LSPs should be able to systematically assess different platforms. How-ever, there is little knowledge on specific dimensions for such an assessment. Thus, the objective of this paper is to identify important dimensions to assess digital plat-forms from the perspective of LSPs. Methodology: We conducted semi-structured interviews with LSPs and platform operators. Based on a qualitative content analysis we identify specific dimensions for assessment of digital platforms. Findings: We find four specific dimensions for assessing platform potential. First, matching mechanisms that facilitate transaction processes and reduce search costs. Second, gatekeeping mechanisms that assure the quality of platform actors and increase trust. Third, pricing mechanisms that affect direct costs, and fourth, factors that lead to lock-in-effects. Originality: There are a large number of studies on criteria to select business partners, e.g., suppliers. Although the number of platform users increases rapidly, and their disruptive potential is high, there is only little knowledge on platform-specific evaluation criteria. In this paper, we identify relevant platform-specific dimensions for the selection of suitable platforms as an extension of existing partner selection criteria.
Subjects: 
Logistics
Industry 4.0
Digitalization
Innovation
Supply Chain Management
Artificial Intelligence
Data Science
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-sa/4.0/
Document Type: 
Conference Paper

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