[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119234-en":3,"doc-seo-119234-105":29,"detail-sidebar-cat-0-en-105":89},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":11},119234,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Leveraging Machine Learning for Wellbeing Research in Marketing: Enhancing Federal Nutrition Programs and Food Decision-Making","Machine learning enables advanced analysis of federal nutrition programs and food decision-making, extending research beyond traditional methodologies. Feature-based algorithms identify potential program issues and support more refined predictions of participation and behavior through large-scale data, rather than controlled lab environments. The work presents a case-study approach to predict participation in a major nutrition education program, with downstream implications for diet quality and food security. Insights are validated using qualitative research and surveys, informing marketing strategies and public health outcomes.","University of Nebraska at Kearney  \nOpenSPACES@UNK: Scholarship, Preservation, and Creative Endeavors  \nMountain Plains Business Conference  \nOct 4th, 11:00 AM-11:50 AM  \nLeveraging Machine Learning for Wellbeing Research in Marketing: Enhancing Federal Nutrition Programs and Food Decision-Making  \nRohini Daraboina  \n[rohini.daraboina@sdstate.edu](rohini.daraboina@sdstate.edu)  \nAndrea Leschewski  \nSouth Dakota State University  \nFollow this and additional works at: [https://openspaces.unk.edu/mpbc](https://openspaces.unk.edu/mpbc)  \n Part of the Marketing Commons  \nDaraboina, Rohini and Leschewski, Andrea, \"Leveraging Machine Learning for Wellbeing Research in Marketing: Enhancing Federal Nutrition Programs and Food Decision-Making\" (2024) . Mountain Plains Business Conference. 2.  \n[https://openspaces.unk.edu/mpbc/2024/marketing/2](https://openspaces.unk.edu/mpbc/2024/marketing/2)  \nThis Abstract is brought to you for free and open access by OpenSPACES@UNK: Scholarship, Preservation, and Creative Endeavors. It has been accepted for inclusion in Mountain Plains Business Conference by an authorized administrator of OpenSPACES@UNK: Scholarship, Preservation, and Creative Endeavors. For more information, [please contact weissell@unk.edu](please contact weissell@unk.edu).  \nTITLE: Leveraging Machine Learning for Wellbeing Research in Marketing: Enhancing Federal  \nNutrition Programs and Food Decision-Making  \nAUTHORS: Dr. Rohini Daraboina, Dr. Andrea Leschewski  \nTRACK: Marketing  \nLeveraging Machine Learning for Wellbeing Research in Marketing: Enhancing Federal  \nNutrition Programs and Food Decision-Making  \nAbstract  \nMachine learning offers innovative tools to enhance research on federal nutrition programs and food decision-making, moving beyond traditional methods. Algorithms extract key features to pinpoint potential program issues, allowing for more refined predictions about participation and behavior using large-scale data, unlike prior studies that typically rely on controlled lab settings. We propose a case study using machine learning to predict participation in a major nutrition education program, with implications for diet quality and food security. Validation of machine learning insights will involve qualitative research and surveys. This approach demonstrates the potential for connecting wellbeing research and marketing by offering deeper insights into participant behavior and program effectiveness, which can inform marketing strategies for promoting healthier food choices and improving public health outcomes.","cbCaiqUuGnvn7RAo","https://ap.wps.com/l/cbCaiqUuGnvn7RAo","pdf",138646,1,3,"English","en",105,"# Abstract\n## Machine learning for federal nutrition research\n## Predicting participation and behavior\n## Case study and validation approach\n## Implications for marketing and public health","[{\"question\":\"How does machine learning improve research on federal nutrition programs?\",\"answer\":\"Machine learning uses algorithms to extract key features, helping pinpoint potential program issues and enabling refined predictions of participation and behavior using large-scale data.\"},{\"question\":\"What case-study approach is proposed in the presentation?\",\"answer\":\"A machine learning case study predicts participation in a major nutrition education program, linking outcomes to diet quality and food security.\"},{\"question\":\"How will the machine learning insights be validated?\",\"answer\":\"Validation will involve qualitative research and surveys to corroborate and interpret the model-derived findings.\"}]","Leveraging Machine Learning for Wellbeing Research in Marketing: Enhancing Federal Nutrition Programs and Food Decision-Making | 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