Paper Number

ICIS2025-1560

Paper Type

Short

Abstract

This study investigates the causal effects of transitioning from an automatic to a manual resource acquisition policy (RAP) on user behavior within the cloud computing context. Leveraging a natural experiment involving 19,761 users of a Chinese cloud computing service provider (CSP), we employ a regression discontinuity in time (RDiT) design to assess the impact of this policy change. Our results reveal that the manual RAP significantly augmented user engagement metrics. While the aggregate value of consumed services remained statistically unchanged, we observed a significant shift in user preference toward premium services over standard offerings. We further conduct post-hoc analysis, including adapted difference-in-differences (DID), survival analysis, and a hidden Markov model to explore the mechanisms driving user behavior change. This research provides important insights for CSPs seeking to optimize incentive mechanisms, demonstrating how judicious RAP design can bolster engagement and strategically channel users’ resource utilization towards higher-value services.

Comments

16-UserBehavior

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Dec 14th, 12:00 AM

Claiming vs. Automatic Rewards: Impact of Incentive Mechanism on Engagement and Consumption in Cloud Computing

This study investigates the causal effects of transitioning from an automatic to a manual resource acquisition policy (RAP) on user behavior within the cloud computing context. Leveraging a natural experiment involving 19,761 users of a Chinese cloud computing service provider (CSP), we employ a regression discontinuity in time (RDiT) design to assess the impact of this policy change. Our results reveal that the manual RAP significantly augmented user engagement metrics. While the aggregate value of consumed services remained statistically unchanged, we observed a significant shift in user preference toward premium services over standard offerings. We further conduct post-hoc analysis, including adapted difference-in-differences (DID), survival analysis, and a hidden Markov model to explore the mechanisms driving user behavior change. This research provides important insights for CSPs seeking to optimize incentive mechanisms, demonstrating how judicious RAP design can bolster engagement and strategically channel users’ resource utilization towards higher-value services.

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