Empirically Estimating Strategic Behavior for Hotel Standby Upgrade Programs

Yılmaz, Övünç; Ferguson, Mark; Pekgün, Pelin; Shang, Guangzhi

doi:10.2139/ssrn.3411087

Cited by 1 publication

(1 citation statement)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Strategic customer behavior is documented in OM settings such as waiting for a potential price drop of flight tickets (Li et al, 2014) and restraining from booking a premium hotel room to be targeted a standby upgrade offer (Yilmaz et al, 2019). It is reasonable to expect that when faced with system congestion and empowered by NYOP, Bitcoin users would strategically choose a TxFee in hope to achieve the desired confirmation speed.…”

Section: Framework‐based Analysismentioning

confidence: 99%

Need for speed, but how much does it cost? Unpacking the fee‐speed relationship in Bitcoin transactions

Shang

Ilk

Fan

2022

J of Ops Management

Self Cite

View full text Add to dashboard Cite

The growing popularity of blockchain‐based cryptocurrencies is driven by the flexibility in transaction fee offerings, among other factors. To achieve service‐level differentiation among their users, many cryptocurrencies allow users to “name your own price,” giving rise to a large variation in fee offerings and hence, variation in confirmation times. Yet, the time it takes a cryptocurrency transaction to be confirmed in the blockchain is not only affected by the fee offered, but also by the contemporaneous congestion level and the inherent randomness in the verification process. Although it is generally expected that higher fees lead to quicker confirmation, the uniqueness of the cryptocurrency setting adds important nuances to the fee‐speed relationship. Using Bitcoin—the original and most heavily used cryptocurrency by far—as our empirical context, we stylize the transaction confirmation processes, propose a theoretical framework that maps the causal path from fee to speed, and estimate this framework using Bitcoin transaction data under periods of high volatility. Our results show strong evidence for two characteristics of fee's impact: congestion dependence and tail shrinkage. Our finding that the speed acceleration effect of fee is particularly strong on the tail of the confirmation time distribution motivates a target service level approach to fee recommendation. To put this finding into practice, we develop an efficient computational procedure that helps Bitcoin users accurately estimate fees based on their confirmation delay preferences. We discuss the implications of our analyses on future cryptocurrency development and the long‐term adoption of this revolutionary technology.

show abstract

Section: Framework‐based Analysismentioning

confidence: 99%