To incentivize or not: Impact of blockchain-based cryptoeconomic tokens on human information sharing behavior

• URL: http://arxiv.org/abs/2206.03221v3
• Date: Thu, 23 Jun 2022 06:41:06 GMT
• Title: To incentivize or not: Impact of blockchain-based cryptoeconomic tokens on human information sharing behavior
• Authors: Mark Christopher Ballandies
• Abstract summary: This study applies the theory of self-determination to investigate the impact of such tokens on human behavior in an information-sharing scenario. Individuals obtain these tokens in exchange for their shared information. The study includes quality characteristics of the information, such as accuracy and contextualization.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cryptoeconomic incentives in the form of blockchain-based tokens are seen as an enabler of the sharing economy that could shift society towards greater sustainability. Nevertheless, knowledge of the impact of these tokens on human sharing behavior is still limited and this poses a challenge to the design of effective cryptoeconomic incentives. This study applies the theory of self-determination to investigate the impact of such tokens on human behavior in an information-sharing scenario. By utilizing an experimental methodology in the form of a randomized control trial with a 2x2 factorial design involving 132 participants, the effects of two token incentives on human information-sharing behavior are analyzed. Individuals obtain these tokens in exchange for their shared information. Based on the collected tokens, individuals receive a monetary payment and build reputation. Besides investigating the effect of these incentives on the quantity of shared information, the study includes quality characteristics of the information, such as accuracy and contextualization. The focus on quantity while excluding quality has been identified as a limitation in previous work. In addition to confirming previously known effects such as a crowding-out of intrinsic motivation by incentives, which also exists for blockchain-based tokens, the findings of this paper point to a hitherto unreported interaction effect between multiple tokens when applied simultaneously. The findings are critically discussed and put into the context of recent work and ethical considerations. The theory-based-empirical study is of interest to those investigating the effect of cryptoeconomic tokens or digital currencies on human behavior and supports the community in the design of effective personalized incentives for sharing economies.

Related papers

• Exploring User Acceptance of Blockchain-Based Student Certificate Sharing System: A Study on Non Fungible Token (NFT) Utilization [0.0]
  This paper builds upon the Technology Acceptance Model TAM to scrutinize the impact of perceived ease of use, perceived usability, and attitude towards the system on the intention to use. Results indicate that individual constructs notably affect the intention to use the system, and their collective impact is statistically significant.
  arXiv  Detail & Related papers  (2024-12-18T17:47:17Z)
• Assessing the Efficacy of Heuristic-Based Address Clustering for Bitcoin [17.496149687704847]
  Clustering serves as an initial step in most analytical studies. We introduce the textitclustering ratio, a metric designed to quantify the reduction in the number of entities achieved by a given. We extend our study to explore the temporal evolution of the clustering ratio for each entity.
  arXiv  Detail & Related papers  (2024-03-01T13:31:05Z)
• Airdrops: Giving Money Away Is Harder Than It Seems [8.078207096142723]
  We present the first comprehensive empirical study of nine major airdrops across and Layer-2 ecosystems.<n>Our analysis reveals that a substantial share of tokens--up to 66% in some cases--are rapidly sold, often in recipients' first post-claim transaction.<n>We propose actionable guidelines for designing more effective airdrop strategies.
  arXiv  Detail & Related papers  (2023-12-05T13:27:15Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• CO-ASnet :A Smart Contract Architecture Design based on Blockchain Technology with Active Sensor Networks [7.987594879787377]
  This paper uses an event study to investigate the phenomenon in which opinion leaders use initial coin offerings to exert influence. Results show that opinion leaders can use to influence the price of token assets with money and data traffic in their social network. Based on this phenomenon and the results of its impact, we use the ChainLink Oracle with Active Sensor Networks(CO-ASnet) to design a safe and applicable decentralized regulatory scheme.
  arXiv  Detail & Related papers  (2023-10-08T08:31:49Z)
• Flexible social inference facilitates targeted social learning when rewards are not observable [58.762004496858836]
  Groups coordinate more effectively when individuals are able to learn from others' successes. We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behavior.
  arXiv  Detail & Related papers  (2022-12-01T21:04:03Z)
• Conductance and Social Capital: Modeling and Empirically Measuring Online Social Influence [9.556358888163983]
  Social influence pervades our everyday lives and lays the foundation for complex social phenomena. Existing literature studying online social influence suffers from several drawbacks. This work bridges the gap and presents three contributions towards modeling and empirically quantifying online influence.
  arXiv  Detail & Related papers  (2021-10-25T01:05:49Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• Fragments of the Past: Curating Peer Support with Perpetrators of Domestic Violence [88.37416552778178]
  We report on a ten-month study where we worked with six support workers and eighteen perpetrators in the design and deployment of Fragments of the Past. We share how crafting digitally-augmented artefacts - 'fragments' - of experiences of desisting from violence can translate messages for motivation and rapport between peers. These insights provide the basis for practical considerations for future network design with challenging populations.
  arXiv  Detail & Related papers  (2021-07-09T22:57:43Z)
• Expertise and confidence explain how social influence evolves along intellective tasks [10.525352489242396]
  We study interpersonal influence in small groups of individuals who collectively execute a sequence of intellective tasks. We report empirical evidence on theories of transactive memory systems, social comparison, and confidences on the origins of social influence. We propose a cognitive dynamical model inspired by these theories to describe the process by which individuals adjust interpersonal influences over time.
  arXiv  Detail & Related papers  (2020-11-13T23:48:25Z)
• Efficiency in Digital Economies -- A Primer on Tokenomics [55.41644538483948]
  cryptographic tokens are a new digital paradigm that can facilitate the establishment of economic incentives in digital ecoystems. We show how certain principles and values that arise from the evolutionary process of digital cooperation can lead to a market economy characterized by economic efficiency of both individuals and the tokenized ecosystem as a whole.
  arXiv  Detail & Related papers  (2020-08-06T09:31:56Z)
• Individual Factors that Influence Effort and Contributions on Wikipedia [0.0]
  attitude, self-efficacy, and altruism influence effort and active contributions on Wikipedia. We propose a new conceptual model based on the theory of planned behavior.
  arXiv  Detail & Related papers  (2020-07-14T19:57:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.