TY - GEN
T1 - Knowledge Sharing in Digital Platform Ecosystems – A Textual Analysis of SAP’s Developer Community
AU - Kauschinger, Martin
AU - Schreieck, Maximilian
AU - Boehm, Markus
AU - Krcmar, Helmut
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Research on digital platform ecosystems is growing rapidly. While the relevance of third-party applications is commonly known, scholars have made only minor attempts to analyze knowledge sharing between platform owners and third-party developers. We find that third-party application development is a knowledge intensive task that requires knowledge to cross organizational boundaries. In this paper, we use computational analytic methods to analyze knowledge sharing in a digital platform ecosystem. We collected trace data about a third-party developer ecosystem with frequent knowledge exchange between the platform owner and third-party developers. We developed a web scraper and retrieved all 4866 pages of SAP’s developer community that were tagged ‘SAP Cloud Platform’. Next, we used text mining to render a topic model. Based on the latent dirichlet allocation algorithm, we extracted 25 topics that were frequently discussed in the community. We clustered the topics into the following six meta-topics: User Accounts and Authentication, Connectivity, Cloud Database, Specific Technologies, SAP Resources, and Installation. Platform owners can use our approach to (1) identify frequently discussed topics, (2) generate meta-knowledge in these topics and (3) use the meta-knowledge to improve their platform core and its boundary resources.
AB - Research on digital platform ecosystems is growing rapidly. While the relevance of third-party applications is commonly known, scholars have made only minor attempts to analyze knowledge sharing between platform owners and third-party developers. We find that third-party application development is a knowledge intensive task that requires knowledge to cross organizational boundaries. In this paper, we use computational analytic methods to analyze knowledge sharing in a digital platform ecosystem. We collected trace data about a third-party developer ecosystem with frequent knowledge exchange between the platform owner and third-party developers. We developed a web scraper and retrieved all 4866 pages of SAP’s developer community that were tagged ‘SAP Cloud Platform’. Next, we used text mining to render a topic model. Based on the latent dirichlet allocation algorithm, we extracted 25 topics that were frequently discussed in the community. We clustered the topics into the following six meta-topics: User Accounts and Authentication, Connectivity, Cloud Database, Specific Technologies, SAP Resources, and Installation. Platform owners can use our approach to (1) identify frequently discussed topics, (2) generate meta-knowledge in these topics and (3) use the meta-knowledge to improve their platform core and its boundary resources.
KW - Application development
KW - Enterprise software
KW - Knowledge sharing
KW - Platform ecosystem
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85118131877&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-86797-3_2
DO - 10.1007/978-3-030-86797-3_2
M3 - Conference contribution
AN - SCOPUS:85118131877
SN - 9783030867966
T3 - Lecture Notes in Information Systems and Organisation
SP - 21
EP - 39
BT - Innovation Through Information Systems - Volume II
A2 - Ahlemann, Frederik
A2 - Schütte, Reinhard
A2 - Stieglitz, Stefan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Conference on Business Information Systems Engineering, WI 2021
Y2 - 9 March 2021 through 11 March 2021
ER -