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Inter-organizational Data Sharing Processes – an exploratory analysis of incentives and challenges

Malysh, Konstantin LU orcid ; Ahmed, Tanvir ; Linåker, Johan LU orcid and Runeson, Per LU orcid (2024)
Abstract
Businesses across different areas of interest are
increasingly depending on data, particularly for machine learning (ML) applications. To ensure data provisioning, interorganizational data sharing is proposed, e.g. in the form of data ecosystems. The aim of this study was to perform an exploratory investigation into the data sharing practices that exist
in business-to-business (B2B) and business-to-customers (B2C)
relations, in order to shape a knowledge foundation for future
research. We launched a qualitative survey, using interviews
as data collection method. We conducted and analyzed eleven
interviews with representatives from seven different companies
across several industries with the aim of finding key... (More)
Businesses across different areas of interest are
increasingly depending on data, particularly for machine learning (ML) applications. To ensure data provisioning, interorganizational data sharing is proposed, e.g. in the form of data ecosystems. The aim of this study was to perform an exploratory investigation into the data sharing practices that exist
in business-to-business (B2B) and business-to-customers (B2C)
relations, in order to shape a knowledge foundation for future
research. We launched a qualitative survey, using interviews
as data collection method. We conducted and analyzed eleven
interviews with representatives from seven different companies
across several industries with the aim of finding key practices,
differences and similarities between approaches, so we could
formulate the future research goals and questions. We grouped
the core findings of this study into three categories: organizational
aspects of data sharing, where we noticed the importance of data
sharing and data ownership as business driver; technical aspects
of data sharing, related to data types, formats, maintenance and
infrastructures; and challenges, with privacy being the highest
concern along with the data volumes and cost of data. (Less)
Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2024 50th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
project
B2B Data Sharing for Industry 4.0 Machine Learning
language
English
LU publication?
yes
id
748f715d-5ddc-4054-9670-4f42acdf3424
date added to LUP
2024-09-05 15:14:42
date last changed
2024-09-11 11:03:10
@inproceedings{748f715d-5ddc-4054-9670-4f42acdf3424,
  abstract     = {{Businesses across different areas of interest are<br/>increasingly depending on data, particularly for machine learning (ML) applications. To ensure data provisioning, interorganizational data sharing is proposed, e.g. in the form of data ecosystems. The aim of this study was to perform an exploratory investigation into the data sharing practices that exist<br/>in business-to-business (B2B) and business-to-customers (B2C)<br/>relations, in order to shape a knowledge foundation for future<br/>research. We launched a qualitative survey, using interviews<br/>as data collection method. We conducted and analyzed eleven<br/>interviews with representatives from seven different companies<br/>across several industries with the aim of finding key practices,<br/>differences and similarities between approaches, so we could<br/>formulate the future research goals and questions. We grouped<br/>the core findings of this study into three categories: organizational<br/>aspects of data sharing, where we noticed the importance of data<br/>sharing and data ownership as business driver; technical aspects<br/>of data sharing, related to data types, formats, maintenance and<br/>infrastructures; and challenges, with privacy being the highest<br/>concern along with the data volumes and cost of data.}},
  author       = {{Malysh, Konstantin and Ahmed, Tanvir and Linåker, Johan and Runeson, Per}},
  booktitle    = {{2024 50th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Inter-organizational Data Sharing Processes – an exploratory analysis of incentives and challenges}},
  url          = {{https://lup.lub.lu.se/search/files/194583080/802600a080.pdf}},
  year         = {{2024}},
}