TY - JOUR
T1 - Expert retrieval based on local journals metadata to drive small-medium industries (SMI) collaboration for product innovation
AU - Al Hakim, Shidiq
AU - Sensuse, Dana Indra
AU - Budi, Indra
AU - Subroto, Imam Much Ibnu
AU - Siagian, Al Hafiz Akbar Maulana
N1 - Funding Information:
This study was supported by the PUTI Q2 grant “The Concept Model Development of Social Media Application for Knowledge sharing between Researchers and Small and Medium Industries in Indonesia” (NKB-1483/UN2.RST/HKP.05.00/2020). We would express our gratitude to the Faculty of Computer Science and the directorate of Research and Community Engagement, Universitas Indonesia.
Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.
PY - 2023/12
Y1 - 2023/12
N2 - Innovation in the small-medium industries (SMI) sector, which has limited human resources, requires collaboration with academic researchers. Thus, SMI needs an appropriate search tool to find a suitable researcher. Since the skill classification of researchers arranged in an academic environment is very formal and hierarchical, it tend to be challenging for laypeople to understand that skills classification. This study proposes a method to combine expert retrieval based on portfolio content with expert topic maps based on search keywords from user preferences using local journal publication sources indexed at Portal Garuda. This research evaluates the TFIDF-VSM method with the BM25 Okapi for expert retrieval and text rank with text network analysis (betweenness centrality value) for the construction of a map of the topic of its expertise. Furthermore, the prototype was tested with the System Usability Scale instrument to measure the level of usability. The combined use of the BM25 Okapi method and the text network analysis with betweenness centrality value shows a pretty good usability with a value of 73,489. The different backgrounds of academics and SMI practitioners require an appropriate search method approach. Therefore, combining expert retrieval with expert topic maps based on keywords becomes a solution that can help ordinary people (SMI practitioners) find expert researchers to support product innovation in their SMI.
AB - Innovation in the small-medium industries (SMI) sector, which has limited human resources, requires collaboration with academic researchers. Thus, SMI needs an appropriate search tool to find a suitable researcher. Since the skill classification of researchers arranged in an academic environment is very formal and hierarchical, it tend to be challenging for laypeople to understand that skills classification. This study proposes a method to combine expert retrieval based on portfolio content with expert topic maps based on search keywords from user preferences using local journal publication sources indexed at Portal Garuda. This research evaluates the TFIDF-VSM method with the BM25 Okapi for expert retrieval and text rank with text network analysis (betweenness centrality value) for the construction of a map of the topic of its expertise. Furthermore, the prototype was tested with the System Usability Scale instrument to measure the level of usability. The combined use of the BM25 Okapi method and the text network analysis with betweenness centrality value shows a pretty good usability with a value of 73,489. The different backgrounds of academics and SMI practitioners require an appropriate search method approach. Therefore, combining expert retrieval with expert topic maps based on keywords becomes a solution that can help ordinary people (SMI practitioners) find expert researchers to support product innovation in their SMI.
KW - BM25
KW - Expert finding
KW - Expert retrieval
KW - System usability Scale
KW - Text network analysis
KW - Topic map
UR - http://www.scopus.com/inward/record.url?scp=85152565369&partnerID=8YFLogxK
U2 - 10.1007/s13278-023-01044-5
DO - 10.1007/s13278-023-01044-5
M3 - Article
AN - SCOPUS:85152565369
SN - 1869-5450
VL - 13
JO - Social Network Analysis and Mining
JF - Social Network Analysis and Mining
IS - 1
M1 - 68
ER -
