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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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| منشور في: |
2024
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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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 |