More is Less? Design Free Sample Strategy via Field Experiment and Double/Debiased Machine Learning
Free 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...
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| Format: | Text |
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AIS Electronic Library (AISeL)
2023
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| Online Access: | https://aisel.aisnet.org/icis2023/diginnoventren/diginnoventren/13 https://aisel.aisnet.org/context/icis2023/article/1239/viewcontent/2546_doc.pdf |
| Summary: | Free 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 a field experiment based on Construal Level Theory. 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. |
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