IndetermSoft Set for Digital Marketing Effectiveness Evaluation Driven by Big Data Based on Consumer Behavior

This study proposes the multi-criteria decision-making (MCDM) methodology for Digital Marketing Effectiveness Evaluation Driven by Big Data Based on Consumer Behavior. This study uses two MCDM methods such as the Entropy method to compute the criteria weights and the MARCOS method to rank the altern...

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Main Authors: Tao Shen, Chunmei Mao
Format: Article
Language:English
Published: University of New Mexico 2025-05-01
Series:Neutrosophic Sets and Systems
Subjects:
Online Access:https://fs.unm.edu/NSS/22ConsumerBehavior.pdf
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author Tao Shen
Chunmei Mao
author_facet Tao Shen
Chunmei Mao
author_sort Tao Shen
collection DOAJ
description This study proposes the multi-criteria decision-making (MCDM) methodology for Digital Marketing Effectiveness Evaluation Driven by Big Data Based on Consumer Behavior. This study uses two MCDM methods such as the Entropy method to compute the criteria weights and the MARCOS method to rank the alternatives. We used the IndetermSoft set to deal with indeterminacy with different values in the criteria. IndetermSoft is integrated with the MCDM approach. Three experts evaluated the criteria and alternatives. We use seven criteria and ten alternatives. We conducted a comparative analysis to show the effectiveness of the proposed approach. The results show the proposed approach is effective compared to other MCDM methods.
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institution Kabale University
issn 2331-6055
2331-608X
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publishDate 2025-05-01
publisher University of New Mexico
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series Neutrosophic Sets and Systems
spelling doaj-art-e7c2b5efddad40aebe7fc32f1a219acd2025-08-25T08:33:14ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2025-05-018235236910.5281/zenodo.14991740IndetermSoft Set for Digital Marketing Effectiveness Evaluation Driven by Big Data Based on Consumer BehaviorTao ShenChunmei MaoThis study proposes the multi-criteria decision-making (MCDM) methodology for Digital Marketing Effectiveness Evaluation Driven by Big Data Based on Consumer Behavior. This study uses two MCDM methods such as the Entropy method to compute the criteria weights and the MARCOS method to rank the alternatives. We used the IndetermSoft set to deal with indeterminacy with different values in the criteria. IndetermSoft is integrated with the MCDM approach. Three experts evaluated the criteria and alternatives. We use seven criteria and ten alternatives. We conducted a comparative analysis to show the effectiveness of the proposed approach. The results show the proposed approach is effective compared to other MCDM methods. https://fs.unm.edu/NSS/22ConsumerBehavior.pdfindetermsoft setdigital marketingbig dataconsumer behaviormcdm methodology
spellingShingle Tao Shen
Chunmei Mao
IndetermSoft Set for Digital Marketing Effectiveness Evaluation Driven by Big Data Based on Consumer Behavior
Neutrosophic Sets and Systems
indetermsoft set
digital marketing
big data
consumer behavior
mcdm methodology
title IndetermSoft Set for Digital Marketing Effectiveness Evaluation Driven by Big Data Based on Consumer Behavior
title_full IndetermSoft Set for Digital Marketing Effectiveness Evaluation Driven by Big Data Based on Consumer Behavior
title_fullStr IndetermSoft Set for Digital Marketing Effectiveness Evaluation Driven by Big Data Based on Consumer Behavior
title_full_unstemmed IndetermSoft Set for Digital Marketing Effectiveness Evaluation Driven by Big Data Based on Consumer Behavior
title_short IndetermSoft Set for Digital Marketing Effectiveness Evaluation Driven by Big Data Based on Consumer Behavior
title_sort indetermsoft set for digital marketing effectiveness evaluation driven by big data based on consumer behavior
topic indetermsoft set
digital marketing
big data
consumer behavior
mcdm methodology
url https://fs.unm.edu/NSS/22ConsumerBehavior.pdf
work_keys_str_mv AT taoshen indetermsoftsetfordigitalmarketingeffectivenessevaluationdrivenbybigdatabasedonconsumerbehavior
AT chunmeimao indetermsoftsetfordigitalmarketingeffectivenessevaluationdrivenbybigdatabasedonconsumerbehavior