Skip to content

Technology-mediated emergence of a competing logic in a mature field: The case of Bitcoin

Oct 2026 · Information and Organization
Management and Organizational Studies

Abstract

Emerging technologies, such as blockchain and artificial intelligence, are enabling new configurations of practices, roles, and meaning systems within mature fields that can disrupt institutionalized field arrangements and alter the centrality of institutional logics. Given the growing significance of this phenomenon, we aim to understand the constitutive role of technology's materiality in such processes. Through a historical processual analysis of Bitcoin, we propose a process model of disruptive institutional change, in which a challenger proto-institution emerges from the periphery and introduces a competing logic in a mature field. We identify four mechanisms underlying this process: inscription, field-anchoring, translation, and contestation. Our proposed framework highlights the dual role of the technology artifact: as a contra-institutional technology that carries a marginal conflicting logic into the mature field and, simultaneously, as a boundary object that remains interpretively flexible and affords agency to heterogeneous actors drawing on different institutional logics. We theorize that the marginal conflicting logic inscribed in the technology is instantiated and strengthened at the field level as diverse actors, drawing on different institutional logics, develop and adopt novel technology-based field practices. As a boundary object, the technology enables these distributed efforts by actors pursuing different goals. As a carrier of the marginal conflicting logic, it anchors the dispersed efforts to the strengthening of that logic and, consequently, toward its emergence as a competing logic in the field.

View source

Similar papers

#artificial intelligence Conference Open access Jun 2018

Gibson Env: Real-World Perception for Embodied Agents

This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.

F. Xia, Amir Zamir, Zhi-Yang He et al. · 1,069 citations · ⚡112
#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...

Masoud Mohseni, Artur Scherer, K. Johnson et al. · 121 citations · ⚡9

Related blog posts

Hugging Face Blog Nov 10, 2026

Decision Models in Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.