PREFERENCE SIMILARITY NETWORK CLUSTERING CONSENSUS GROUP DECISION MAKING MODEL IN ANALYSING CONSUMERS’ REVIEWS AND SELECTING SAMPLES OF PRODUCT a
DOI:
https://doi.org/10.24191/mjoc.v5i2.10719Keywords:
Preference similarity, Social Network Analysis (SNA), Clustering algorithm, Consensus Group Decision Making (CGDM), Product reviews, Selection problemsAbstract
In recent years, the integration of notions from Social Network Analysis (SNA) into decision making context is rapidly increased. One of the feasible procedures is Preference Similarity Network Clustering Consensus Group Decision Making model, where it is capable to improve the effectiveness and efficiency of decision making process. We utilize this approach in analysing consumers’ reviews and selecting the best sample of laboratory products. This is the first effort of applying this model in real life situation. The referred approach is capable of measuring the similarity of consumers’ reviews, visualize their similarities in the form of network structure, partition them into subgroups, measure their group consensus level and select the best sample of product. The obtained results provide essential information to the laboratory, manufacturer or a company to improve the quality of product and further plan on the marketing strategy, advertisement and research development. Generally, this model can be used as an alternative tool in solving decision making problems, especially in analysing reviews and selection of alternatives.
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Copyright (c) 2020 Nur Syahera Ishak, Nor Hanimah Kamis
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