[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127666-en":3,"doc-seo-127666-105":30,"detail-sidebar-cat-0-en-105":91},{"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":29},127666,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","More is Less? Design Free Sample Strategy via Field Experiment and Double-Debiased Machine Learning - SlideShare","Free sample strategy has attracted strong attention in digital content industries such as e-books, music, and videos, yet two persistent design questions remain: the optimal quantity of free samples and how to personalize their offering under different contextual conditions. Collaborating with a China-based online reading platform, the study runs a field experiment grounded in Construal Level Theory. Results reveal an inverted U-shaped effect of free sample quantity on purchase decisions, and positive moderation by book popularity and quality when free chapters are provided. The work further builds a personalized strategy using causal forest with double/debiased machine learning and derives managerial implications.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \nRising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies ICIS 2023  \nDigital Innovation, Transformation, and Entrepreneurship  \nDec 11th, 12:00 AM  \nMore is Less? Design Free Sample Strategy via Field Experiment and Double/Debiased Machine Learning  \nJIN LIU  \nUniversity of Science and Technology of China, [liujin07@mail.ustc.edu.cn](liujin07@mail.ustc.edu.cn)  \nHanbing Xue  \nuniversity of science and technology of China, [xuehb@mail.ustc.edu.cn](xuehb@mail.ustc.edu.cn)  \nyongjun li  \nUniversity of Science and Technology of China, [lionli@ustc.edu.cn](lionli@ustc.edu.cn)  \nFollow this and additional works at: [https://aisel.aisnet.org/icis2023](https://aisel.aisnet.org/icis2023)  \nRecommended Citation  \nLIU, JIN; Xue, Hanbing; and li, yongjun, \"More is Less? Design Free Sample Strategy via Field Experiment and Double/Debiased Machine Learning\" (2023) . Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies ICIS 2023. 13.  \n[https://aisel.aisnet.org/icis2023/diginnoventren/diginnoventren/13](https://aisel.aisnet.org/icis2023/diginnoventren/diginnoventren/13)  \nThis material is brought to you by the International Conference on Information Systems (ICIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies ICIS 2023 by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact](contact elibrary@aisnet.org)[ elibrary@aisnet.org](contact elibrary@aisnet.org).  \nMore is Less? Design Free Sample Strategy  \nMore is Less? Design Free Sample Strategy via Field Experiment and Double/Debiased  \nMachine Learning  \nShort Paper  \nJin Liu  \nSchool of Management, University of Science and Technology of China 96, JinZhai Road, Hefei, Anhui, China [liujin07@mail.ustc.edu.cn](liujin07@mail.ustc.edu.cn)  \nHanbing Xue  \nSchool of Management, University of Science and Technology of China 96, JinZhai Road, Hefei, Anhui, China [xuehb@mail.ustc.edu.cn](xuehb@mail.ustc.edu.cn)  \nYongjun Li  \nSchool of Management, University of Science and Technology of China  \n96, JinZhai Road, Hefei, Anhui, China  \n[lionli@ustc.edu.cn](lionli@ustc.edu.cn)  \nAbstract  \nFree sample strategy has attracted considerable interest among practitioners and academics, it has been widely adopted in digital content industries (e.g., e-books, music, and videos). There are two issues that have been the continuous concerning and constantly optimized focus. How many free samples should be taken? How to design a personalized free samples strategy considering the contexts? To better understand these issues, we collaborated with an online reading platform in China to design and conduct afield experiment based on Construal Level Theory (CLT). The results showed an inverted U-shaped relationship between free sample quantity and consumer purchase decisions and also suggested when free chapters were offered, book popularity and quality were also found to positively moderate consumers’purchase decisions. Moreover, by combining the causal forest (CF) technique and the double/debiased machine learning model (DML), we develop a personalized free sample strategy and provide managerial implications.  \nKeywords: Digital Content; Free samples; Field Experiment; Construal Level Theory; Double Machine Learning  \nIntroduction  \nFrom 2021 to 2025, the total revenue of digital content industries (e.g., e-books, movies, and music) is expected to show an annual growth rate of 11.25%, resulting in a projected market volume of US $12,251 million by 2025. Digital content industries present new opportunities. However, the quality and preference fit of digital content products with hedonic properties and high consumer heterogeneity can only be determined after consumers experi","cbCaidVa7nYJoamF","https://ap.wps.com/l/cbCaidVa7nYJoamF","pdf",535973,1,10,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Digital content markets and free sample strategy\n## Research gaps in optimal design\n# Study design and theoretical basis\n## Construal Level Theory (CLT)\n# Methodology and model development\n## Causal forest and double/debiased machine learning\n# Findings and managerial implications","[{\"question\":\"What are the two key issues the paper addresses about free samples?\",\"answer\":\"The study focuses on how many free samples to take and how to design a personalized free-sample strategy that accounts for contextual conditions.\"},{\"question\":\"What does the field experiment show about the relationship between free sample quantity and purchase decisions?\",\"answer\":\"It finds an inverted U-shaped relationship between free sample quantity and consumer purchase decisions.\"},{\"question\":\"How does the paper create a personalized free sample strategy?\",\"answer\":\"It combines causal forest with a double/debiased machine learning model to build a personalized strategy and produce managerial implications.\"}]","More is Less? 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