Digital and Mobile Commerce
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Paper Number
2537
Paper Type
Completed
Description
Firms strive to continuously improve their products in order to compete effectively in the market by adding novel features or by imitating their competitors. With the advent of social media, there is also the possibility of obtaining customer input on their desired features. Customers post reviews, which include suggestions for improvement. In the case of mobile apps, user feedback may include suggestions of novel features or features that are already present in competing apps. Leveraging the information on reviews and version release notes of iOS apps, we build a novel deep-learning algorithm based on transfer learning and named entity recognition techniques to identify four types of app features - developer initiated innovative, developer initiated imitative, user suggested innovative and user suggested imitative. Further, we evaluate their demand impact of these feature categories on the demand and analyze plausible mechanisms for these results.
Recommended Citation
Karanam, Aditya; Agarwal, Ashish; and Barua, Anitesh, "ML-Based Product Design: The Case of Mobile Apps" (2021). ICIS 2021 Proceedings. 12.
https://aisel.aisnet.org/icis2021/digital_commerce/digital_commerce/12
ML-Based Product Design: The Case of Mobile Apps
Firms strive to continuously improve their products in order to compete effectively in the market by adding novel features or by imitating their competitors. With the advent of social media, there is also the possibility of obtaining customer input on their desired features. Customers post reviews, which include suggestions for improvement. In the case of mobile apps, user feedback may include suggestions of novel features or features that are already present in competing apps. Leveraging the information on reviews and version release notes of iOS apps, we build a novel deep-learning algorithm based on transfer learning and named entity recognition techniques to identify four types of app features - developer initiated innovative, developer initiated imitative, user suggested innovative and user suggested imitative. Further, we evaluate their demand impact of these feature categories on the demand and analyze plausible mechanisms for these results.
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Comments
22-Digital