TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Meierhofer, Jürg
A1  - Kugler, Petra
A1  - Vogt, Helen
A1  - Dobler, Martin
A1  - Benedech, Rodolfo
A1  - Strittmatter, Marc
A1  - Treiterer, Manuel
T1  - Improving service value creation for manufacturing SMEs by overcoming data sharing hurdles in ecosystems
T2  - Spring Servitization Conference, SSC 2022: Achieving Net-Zero through Servitation. Florence, Italy, 9-10 May 2022
N2  - Purpose:  Although there is an apparent potential in using data for advanced services in manufacturing environments, SMEs are reluctant to share data with their ecosystem partners, which prevents them from leveraging this potential. Therefore, the purpose of this paper is to analyse the reasons behind these resistances. The argumentation paves the way for elaborating countermeasures that are adequate for the specific situation and the typical capabilities of SMEs.
Design/Methodology/Approach: The analysis is based on literature research and in-depth interviews with management representatives of 15 companies in manufacturing service ecosystems. Half of these are manufacturers and the other half technology or service providers for manufacturers. They are SMEs or partly larger companies operating in structures that are typical for SMEs.
Findings: Data sharing hurdles are investigated in the five dimensions, 1. quantifying the value of data, 2. willingness to share data and trust, 3. organizational culture and mindset, 4. legal aspects, and 5. security and privacy. The ability to quantify the value of data is a necessary but not sufficient precondition for data sharing, which must be enabled by adequate measures in the other four dimensions.
Originality/Value: The findings of this empirical study and the solution approach provide an SME-specific framework to analyze hurdles that must be overcome for sharing data in an ecosystem. 
Manufacturing SMEs can apply the framework to overcome the hurdles by specific insights and solution approaches. Furthermore, the analysis illustrates the future research direction of the project towards a comprehensive solution approach for data sharing in a manufacturing ecosystem.
KW  - smart service ecosystems
KW  - data-driven value creation
KW  - data sharing
KW  - data-driven organization and culture
KW  - data governance
Y1  - 2022
SN  - 978-1-85449-805-2
SB  - 978-1-85449-805-2
SP  - 86
EP  - 94
S1  - 9
PB  - Aston University
CY  - Birmingham
ER  -