Stability of Transaction Fees in Bitcoin: A Supply and Demand Perspective

Self 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.

Section: Source Of Endogeneitysupporting

confidence: 60%

Section: Instrumental Variablesmentioning

confidence: 99%

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

Shang

Ilk

Fan

2022

Self Cite

“…Therefore, for operational voting tasks, another type of protocol with a shorter voting duration and voters who have relevant expertise or experience with the proposal should be designed. Overall, the complexity of the blockchain ecosystem should be recognized not only by users of blockchain‐based platforms (Ilk et al, 2021; Shang & Ilk, 2020), but also by the managers of these platforms when designing platform governance mechanisms.…”

Section: Discussionmentioning

confidence: 99%

Task management in decentralized autonomous organization

Zhao

Lai

et al. 2022

In the emerging platform economy, blockchain technologies are reshaping the digital economy. Moreover, disintermediation and decentralization have broken new ground for platform organizations and management mechanisms and instigated the concept of a DAO (Decentralized Autonomous Organization).Recent literature on operations management has called for further research on governance issues related to DAOs. In response to this call, we explore the relationship between DAO management efforts and platform performance in this study. Specifically, we propose and theoretically articulate decentralized voting tasks in DAOs as a new form of organizing. Harnessing both online and on-chain data from seven sources, we empirically examine how voting task division, task allocation, reward distribution, and information provision affect platform performance in the context of MakerDAO (an Ethereum-based stablecoin issuance platform). Our findings reveal that strategic decisions arrived at through voting have a positive impact on platform operational performance under certain conditions, whereas operational decisions resulting from voting have a negative impact. Moreover, we elucidate the moderating effects of voting task execution characteristics on the relationship between completed decision tasks and operational performance. These findings have important implications from both theoretical and practical perspectives. We also share all the raw data we use to promote the development of blockchainrelated empirical research.

“…The latter two function as instruments in the Y regression and the X regression of the simultaneous equations model, respectively. See Ilk et al (2021) for an implementation example and discussion.…”

Section: Issuementioning

confidence: 99%

Empirical research methods department: Mission, learnings, and future plans

Shang

Rönkkö

2022

Self Cite

A recent trend in management journals is to involve specialized methods editors to check for the quality of an article's empirical execution (Antonakis et al., 2019;Bergh & Oswald, 2020;Hardwicke et al., 2019). The Journal of Operations Management (JOM) has been engaging methods specialists explicitly in the review process for about 5 years now. The initial transition into a matrix organization (Guide & Ketokivi, 2015), in which each article is assigned to a subject area editor (vertical) and, if necessary, also a review-team member specializing in a method or a theory (horizontal) was superseded by the creation of the Empirical Research Methods in Operations Management (now shortened to just Empirical Research Methods, ERM) department by the current editors-in-chief (Browning & de Treville, 2018). In this editorial, we give an overview of the current operations of the ERM department, present a collection of common method issues we have gathered, and discuss the strategic direction of the department.The ERM department serves our research community in two ways: (1) by publishing manuscripts about the use of ERM in OM and (2) by performing method checks for incoming manuscripts before they go to a topical department. Both roles align with the journal's evolution in recent years in terms of methodological rigor. We will address these two roles in turn.