Coordinated Border Management Through Digital Trade Infrastructures and Trans-National Government Cooperation: The FloraHolland Case

Abstract

Part 4: Infrastructures; International audience; Digital infrastructures (DI) that support information exchange related to international trade processes (here referred to as Digital Trade Infrastructures (DTI)) have been seen as an instrument to help address the trade facilitation and security challenges. Data pipelines can be seen as an example of a DTI. Data pipelines are IT innovations that enable the timely provision of data captured at the source from different information systems available in the supply chain. Using the pipeline companies can share information with authorities and enjoy trade facilitation in return. The benefits of such data pipelines have been showcased in demonstrator settings. However, outside the controlled environment of demonstrator installations, the adoption and growth of these DTIs has been limited. The benefits based on purely implementing the data pipeline are limited. Combining data pipeline capability with Coordinated Border Management (CBM) has potential to articulate more clear benefits for stakeholders and push further investments and wider adoption. In this paper based on the FloraHolland trade lane related to exporting flowers from Kenya to the Netherlands we discuss a data pipeline/CBM innovation. Through the conceptual lens of DI (examining architectural, process and governance dimensions) we demonstrate the potential benefits of data pipeline/CBM innovation and the complex alignment processes between business and government actors needed for the further adoption. From a theoretical point of view we enhance the understanding regarding the governance dimension of such data pipeline/CBM innovations by identifying four type of alignments processes involving businesses and government actors nationally and internationally. As such the paper contributes to the body of research on DI and more specifically DTI. Form a point of view of practice, the insights from our analysis can be used to better understand other data pipeline/CBM innovation alignment processes in other domains as well.

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• http://resolver.tudelft.nl/uuid:390947fa-87f4-45c5-9442-657263105a2e,

http://dx.doi.org/10.1007/978-3-319-64677-0_20 under the license http://www.springer.com/tdm

• https://hal.inria.fr/hal-01702968,

https://hal.inria.fr/hal-01702968/document,

https://hal.inria.fr/hal-01702968/file/453552_1_En_20_Chapter.pdf

• https://link.springer.com/chapter/10.1007/978-3-319-64677-0_20,

https://hal.inria.fr/hal-01702968,

https://core.ac.uk/display/130240751,

https://dblp.uni-trier.de/db/conf/egov/egov2017.html#RukanovaHT17,

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https://academic.microsoft.com/#/detail/2745077247 under the license http://creativecommons.org/licenses/by/