Inter-Organizational Data Sharing Processes - An Exploratory Analysis of Incentives and Challenges
Malysh, Konstantin LU
; Ahmed, Tanvir
; Linåker, Johan
LU
and Runeson, Per
LU
(2024)
50th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2024
p.80-87
- Abstract
Businesses across different areas of interest are increasingly depending on data, particularly for machine learning (ML) applications. To ensure data provisioning, inter-organizational 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... (More)
Businesses across different areas of interest are increasingly depending on data, particularly for machine learning (ML) applications. To ensure data provisioning, inter-organizational 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)
- author
- Malysh, Konstantin
LU
; Ahmed, Tanvir
; Linåker, Johan
LU
and Runeson, Per
LU
- organization
- publishing date
- 2024
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- B2B and B2C practices, data engineering, Data sharing, empirical interview study, machine learning
- host publication
- Proceedings - 2024 50th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2024
- edition
- 2024
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 50th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2024
- conference location
- Paris, France
- conference dates
- 2024-08-28 - 2024-08-30
- external identifiers
-
- scopus:85218642074
- ISBN
- 9798350380262
- DOI
- 10.1109/SEAA64295.2024.00021
- 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
- 2025-04-09 13:10:26
@inproceedings{748f715d-5ddc-4054-9670-4f42acdf3424,
abstract = {{<p>Businesses across different areas of interest are increasingly depending on data, particularly for machine learning (ML) applications. To ensure data provisioning, inter-organizational 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.</p>}},
author = {{Malysh, Konstantin and Ahmed, Tanvir and Linåker, Johan and Runeson, Per}},
booktitle = {{Proceedings - 2024 50th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2024}},
isbn = {{9798350380262}},
keywords = {{B2B and B2C practices; data engineering; Data sharing; empirical interview study; machine learning}},
language = {{eng}},
pages = {{80--87}},
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}},
doi = {{10.1109/SEAA64295.2024.00021}},
year = {{2024}},
}