Details
| Original language | English |
|---|---|
| Article number | 114344 |
| Journal | Decision Support Systems |
| Volume | 187 |
| Early online date | 1 Oct 2024 |
| Publication status | Published - Dec 2024 |
Abstract
Clustering for taxonomy-based archetype identification has become an established method in Information Systems (IS) research, aiding strategic decision-making across diverse research and business domains. However, the effectiveness of the approach depends critically on the compatibility of clustering methods and algorithms with the specific data characteristics. This study, based on a comprehensive review of 87 articles employing taxonomy-based clustering in IS research, reveals a notable mismatch between the chosen clustering algorithms and the nature of the data, particularly in the context of archetype development from taxonomy-based data. To address these methodological inconsistencies, we introduce a set of clustering guidelines tailored to the unique requirements of archetype development from taxonomy-based data. These guidelines are informed by a computational study involving seven identified datasets from the taxonomy-building literature, ensuring their practical applicability and scientific relevance. Our guidelines are designed to enhance the robustness and scientific validity of insights and decisions derived from taxonomy-based clustering. By improving the methodological rigor of clustering methods, our research addresses a critical mismatch in current practices and contributes to enhancing the quality of decision-making informed by taxonomy-based analysis in IS research.
Keywords
- Archetypes, Categorical data, Clustering, Guidelines, Taxonomies
ASJC Scopus subject areas
- Decision Sciences(all)
- Information Systems and Management
- Computer Science(all)
- Information Systems
- Psychology(all)
- Developmental and Educational Psychology
- Arts and Humanities(all)
- Arts and Humanities (miscellaneous)
- Business, Management and Accounting(all)
- Management Information Systems
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In: Decision Support Systems, Vol. 187, 114344, 12.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Reassessing Taxonomy-Based Data Clustering
T2 - Unveiling Insights and Guidelines for Application
AU - Heumann, M.
AU - Kraschewski, T.
AU - Werth, O.
AU - Breitner, M.H.
PY - 2024/12
Y1 - 2024/12
N2 - Clustering for taxonomy-based archetype identification has become an established method in Information Systems (IS) research, aiding strategic decision-making across diverse research and business domains. However, the effectiveness of the approach depends critically on the compatibility of clustering methods and algorithms with the specific data characteristics. This study, based on a comprehensive review of 87 articles employing taxonomy-based clustering in IS research, reveals a notable mismatch between the chosen clustering algorithms and the nature of the data, particularly in the context of archetype development from taxonomy-based data. To address these methodological inconsistencies, we introduce a set of clustering guidelines tailored to the unique requirements of archetype development from taxonomy-based data. These guidelines are informed by a computational study involving seven identified datasets from the taxonomy-building literature, ensuring their practical applicability and scientific relevance. Our guidelines are designed to enhance the robustness and scientific validity of insights and decisions derived from taxonomy-based clustering. By improving the methodological rigor of clustering methods, our research addresses a critical mismatch in current practices and contributes to enhancing the quality of decision-making informed by taxonomy-based analysis in IS research.
AB - Clustering for taxonomy-based archetype identification has become an established method in Information Systems (IS) research, aiding strategic decision-making across diverse research and business domains. However, the effectiveness of the approach depends critically on the compatibility of clustering methods and algorithms with the specific data characteristics. This study, based on a comprehensive review of 87 articles employing taxonomy-based clustering in IS research, reveals a notable mismatch between the chosen clustering algorithms and the nature of the data, particularly in the context of archetype development from taxonomy-based data. To address these methodological inconsistencies, we introduce a set of clustering guidelines tailored to the unique requirements of archetype development from taxonomy-based data. These guidelines are informed by a computational study involving seven identified datasets from the taxonomy-building literature, ensuring their practical applicability and scientific relevance. Our guidelines are designed to enhance the robustness and scientific validity of insights and decisions derived from taxonomy-based clustering. By improving the methodological rigor of clustering methods, our research addresses a critical mismatch in current practices and contributes to enhancing the quality of decision-making informed by taxonomy-based analysis in IS research.
KW - Archetypes
KW - Categorical data
KW - Clustering
KW - Guidelines
KW - Taxonomies
UR - http://www.scopus.com/inward/record.url?scp=85205433109&partnerID=8YFLogxK
U2 - 10.1016/j.dss.2024.114344
DO - 10.1016/j.dss.2024.114344
M3 - Article
VL - 187
JO - Decision Support Systems
JF - Decision Support Systems
SN - 0167-9236
M1 - 114344
ER -