DAOs' Business Value from an Open Systems Perspective: A Best-Fit Framework Synthesis
- URL: http://arxiv.org/abs/2406.12445v1
- Date: Tue, 18 Jun 2024 09:48:10 GMT
- Title: DAOs' Business Value from an Open Systems Perspective: A Best-Fit Framework Synthesis
- Authors: Lukas Küng, George M. Giaglis,
- Abstract summary: Decentralized autonomous organizations (DAOs) are emerging innovative organizational structures.
This research applies a systematic review of organizations' business applicability from an open systems perspective.
We present a new business framework comprising of four core business elements.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Decentralized autonomous organizations (DAOs) are emerging innovative organizational structures, enabling collective coordination, and reshaping digital collaboration. Despite the promising and transformative characteristics of DAOs, the potential technological advancements and the understanding of the business value that organizations derive from implementing DAO characteristics are limited. This research applies a systematic review of DAOs' business applicability from an open systems perspective following a best-fit framework methodology. Within our approach, combining both framework and thematic analysis, we discuss how the open business principles apply to DAOs and present a new DAO business framework comprising of four core business elements: i) token, ii) transactions, iii) value system and iv) strategy with their corresponding sub-characteristics. This paper offers a preliminary DAO business framework that enhances the understanding of DAOs' transformative potential and guides organizations in innovating more inclusive business models (BMs), while also providing a theoretical foundation for researchers to build upon.
Related papers
- DAOs of Collective Intelligence? Unraveling the Complexity of Blockchain Governance in Decentralized Autonomous Organizations [0.7499722271664144]
Decentralized autonomous organizations (DAOs) have transformed organizational structures by shifting from traditional control to decentralized control.
Despite managing significant funds and building global networks, DAOs face challenges like declining participation, increasing centralization, and inabilities to adapt to changing environments.
This paper explores complex systems and applies complexity science to explain their inefficiencies.
arXiv Detail & Related papers (2024-09-03T12:06:15Z) - Redefining Data-Centric Design: A New Approach with a Domain Model and Core Data Ontology for Computational Systems [2.872069347343959]
This paper presents an innovative data-centric paradigm for designing computational systems by introducing a new informatics domain model.
The proposed model moves away from the conventional node-centric framework and focuses on data-centric categorization, using a multimodal approach that incorporates objects, events, concepts, and actions.
arXiv Detail & Related papers (2024-09-01T22:34:12Z) - Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition [70.60872754129832]
First NeurIPS competition on unlearning sought to stimulate the development of novel algorithms.
Nearly 1,200 teams from across the world participated.
We analyze top solutions and delve into discussions on benchmarking unlearning.
arXiv Detail & Related papers (2024-06-13T12:58:00Z) - Serving Deep Learning Model in Relational Databases [70.53282490832189]
Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains.
We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks.
The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS)
arXiv Detail & Related papers (2023-10-07T06:01:35Z) - Universal Information Extraction as Unified Semantic Matching [54.19974454019611]
We decouple information extraction into two abilities, structuring and conceptualizing, which are shared by different tasks and schemas.
Based on this paradigm, we propose to universally model various IE tasks with Unified Semantic Matching framework.
In this way, USM can jointly encode schema and input text, uniformly extract substructures in parallel, and controllably decode target structures on demand.
arXiv Detail & Related papers (2023-01-09T11:51:31Z) - Towards a multi-stakeholder value-based assessment framework for
algorithmic systems [76.79703106646967]
We develop a value-based assessment framework that visualizes closeness and tensions between values.
We give guidelines on how to operationalize them, while opening up the evaluation and deliberation process to a wide range of stakeholders.
arXiv Detail & Related papers (2022-05-09T19:28:32Z) - Framework for disruptive AI/ML Innovation [0.0]
This framework enables C suite executive leaders to define a business plan and manage technological dependencies for building AI/ML Solutions.
The business plan represents the fundamentals of AI/ML Innovation and AI/ML Solutions.
This framework incorporates value chain, supply chain, and ecosystem strategies.
arXiv Detail & Related papers (2022-04-27T00:22:13Z) - Towards Digital Twin Oriented Modelling of Complex Networked Systems and
Their Dynamics: A Comprehensive Survey [11.18312489268624]
We propose a new framework to conceptually compare diverse existing modelling paradigms from different perspectives.
We also appraise how far the reviewed current state-of-the-art approaches are from the idealised DTs.
arXiv Detail & Related papers (2022-02-15T15:44:00Z) - Dynamic enterprise architecture capabilities and organizational
benefits: an empirical mediation study [0.0]
This study focuses on EA-based capabilities, using the dynamic capabilities view as a theoretical foundation.
It develops and tests a new research model that explains how dynamic enterprise architecture capabilities lead to organizational benefits.
arXiv Detail & Related papers (2021-05-18T10:07:31Z) - Investigating Bi-Level Optimization for Learning and Vision from a
Unified Perspective: A Survey and Beyond [114.39616146985001]
In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems contain a series of closely related subproblms.
In this paper, we first uniformly express these complex learning and vision problems from the perspective of Bi-Level Optimization (BLO)
Then we construct a value-function-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies.
arXiv Detail & Related papers (2021-01-27T16:20:23Z) - Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components.
There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed.
We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
arXiv Detail & Related papers (2020-07-13T20:30:26Z)
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.