Graphs of SHAP value by key predictors of media.

<div><p>The objective of this study is to identify the characteristics of users of AI speakers and predict potential consumers, with the aim of supporting effective advertising and marketing strategies in the fast-evolving media technology landscape. To do so, our analysis employs decisi...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Yunwoo Choi (20448432) (author)
مؤلفون آخرون: Changjun Lee (2219410) (author)
منشور في: 2024
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_version_ 1852024276684636160
author Yunwoo Choi (20448432)
author2 Changjun Lee (2219410)
author2_role author
author_facet Yunwoo Choi (20448432)
Changjun Lee (2219410)
author_role author
dc.creator.none.fl_str_mv Yunwoo Choi (20448432)
Changjun Lee (2219410)
dc.date.none.fl_str_mv 2024-12-18T18:39:50Z
dc.identifier.none.fl_str_mv 10.1371/journal.pone.0315540.g006
dc.relation.none.fl_str_mv https://figshare.com/articles/figure/Graphs_of_SHAP_value_by_key_predictors_of_media_/28056576
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Science Policy
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
varied programming content
support vector machines
social networking platforms
providing valuable insights
machine learning models
machine learning insights
higher internet usage
distinct lifestyle patterns
artificial neural networks
60 &# 8211
ai speaker user
predict potential consumers
likely future users
supporting effective advertising
final xgboost model
effective advertising
model reveals
ai speakers
advertising corporation
xlink ">
random forests
pioneering effort
marketing strategies
leisure activities
korea broadcasting
creating focused
better understanding
best among
922 ).
5g technology
2019 media
dc.title.none.fl_str_mv Graphs of SHAP value by key predictors of media.
dc.type.none.fl_str_mv Image
Figure
info:eu-repo/semantics/publishedVersion
image
description <div><p>The objective of this study is to identify the characteristics of users of AI speakers and predict potential consumers, with the aim of supporting effective advertising and marketing strategies in the fast-evolving media technology landscape. To do so, our analysis employs decision trees, random forests, support vector machines, artificial neural networks, and XGboost, which are typical machine learning techniques for classification and leverages the 2019 Media & Consumer Research survey data from the Korea Broadcasting and Advertising Corporation (N = 3,922). The final XGboost model, which performed the best among the other machine learning models, specifically forecasts individuals aged 45–50 and 60–65, who are active on social networking platforms and have a preference for varied programming content, as the most likely future users. Additionally, the model reveals their distinct lifestyle patterns, such as higher internet usage during weekdays and increased cable TV viewership on weekends, along with a better understanding of 5G technology. This pioneering effort in IoT consumer research employs advanced machine learning to not just predict, but intricately profile potential AI speaker consumers. It elucidates critical factors influencing technology uptake, including media consumption habits, attitudes, values, and leisure activities, providing valuable insights for creating focused and effective advertising and marketing strategies.</p></div>
eu_rights_str_mv openAccess
id Manara_fbc4c20aee5fd62b52ec99aca07680cb
identifier_str_mv 10.1371/journal.pone.0315540.g006
network_acronym_str Manara
network_name_str ManaraRepo
oai_identifier_str oai:figshare.com:article/28056576
publishDate 2024
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv CC BY 4.0
spelling Graphs of SHAP value by key predictors of media.Yunwoo Choi (20448432)Changjun Lee (2219410)Science PolicyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedvaried programming contentsupport vector machinessocial networking platformsproviding valuable insightsmachine learning modelsmachine learning insightshigher internet usagedistinct lifestyle patternsartificial neural networks60 &# 8211ai speaker userpredict potential consumerslikely future userssupporting effective advertisingfinal xgboost modeleffective advertisingmodel revealsai speakersadvertising corporationxlink ">random forestspioneering effortmarketing strategiesleisure activitieskorea broadcastingcreating focusedbetter understandingbest among922 ).5g technology2019 media<div><p>The objective of this study is to identify the characteristics of users of AI speakers and predict potential consumers, with the aim of supporting effective advertising and marketing strategies in the fast-evolving media technology landscape. To do so, our analysis employs decision trees, random forests, support vector machines, artificial neural networks, and XGboost, which are typical machine learning techniques for classification and leverages the 2019 Media & Consumer Research survey data from the Korea Broadcasting and Advertising Corporation (N = 3,922). The final XGboost model, which performed the best among the other machine learning models, specifically forecasts individuals aged 45–50 and 60–65, who are active on social networking platforms and have a preference for varied programming content, as the most likely future users. Additionally, the model reveals their distinct lifestyle patterns, such as higher internet usage during weekdays and increased cable TV viewership on weekends, along with a better understanding of 5G technology. This pioneering effort in IoT consumer research employs advanced machine learning to not just predict, but intricately profile potential AI speaker consumers. It elucidates critical factors influencing technology uptake, including media consumption habits, attitudes, values, and leisure activities, providing valuable insights for creating focused and effective advertising and marketing strategies.</p></div>2024-12-18T18:39:50ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0315540.g006https://figshare.com/articles/figure/Graphs_of_SHAP_value_by_key_predictors_of_media_/28056576CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/280565762024-12-18T18:39:50Z
spellingShingle Graphs of SHAP value by key predictors of media.
Yunwoo Choi (20448432)
Science Policy
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
varied programming content
support vector machines
social networking platforms
providing valuable insights
machine learning models
machine learning insights
higher internet usage
distinct lifestyle patterns
artificial neural networks
60 &# 8211
ai speaker user
predict potential consumers
likely future users
supporting effective advertising
final xgboost model
effective advertising
model reveals
ai speakers
advertising corporation
xlink ">
random forests
pioneering effort
marketing strategies
leisure activities
korea broadcasting
creating focused
better understanding
best among
922 ).
5g technology
2019 media
status_str publishedVersion
title Graphs of SHAP value by key predictors of media.
title_full Graphs of SHAP value by key predictors of media.
title_fullStr Graphs of SHAP value by key predictors of media.
title_full_unstemmed Graphs of SHAP value by key predictors of media.
title_short Graphs of SHAP value by key predictors of media.
title_sort Graphs of SHAP value by key predictors of media.
topic Science Policy
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
varied programming content
support vector machines
social networking platforms
providing valuable insights
machine learning models
machine learning insights
higher internet usage
distinct lifestyle patterns
artificial neural networks
60 &# 8211
ai speaker user
predict potential consumers
likely future users
supporting effective advertising
final xgboost model
effective advertising
model reveals
ai speakers
advertising corporation
xlink ">
random forests
pioneering effort
marketing strategies
leisure activities
korea broadcasting
creating focused
better understanding
best among
922 ).
5g technology
2019 media