يواجه النشر العلمي الرقمي تحديات متراكمة تتمحور حول التحيز التحريري، وغموض عمليات مراجعة الأقران، وتركّز السلطة في يد قلة من البوابات الأكاديمية، حتى في ظل اعتماد منصات مفتوحة المصدر مثل نظام المجلات المفتوحة (Open Journal Systems - OJS) بإصداره 3.x. تُقدّم هذه الورقة النظرية إطاراً مفاهيمياً رائداً يستكشف إمكانية دمج نماذج حوكمة المنظمات المستقلة اللامركزية (Decentralized Autonomous Organizations - DAOs) داخل البنية المعمارية لنظام OJS 3.x، بهدف إعادة هندسة عمليات مراجعة الأقران واتخاذ القرارات التحريرية وفق منطق الشفافية الفائقة (Hyper-Transparency). يتأسس الإطار المقترح نظرياً على تكامل النظرية المؤسسية (Institutional Theory) ونظرية الوكالة (Agency Theory)، حيث تُوظَّف الأولى لتحليل الضغوط المعيارية والتقليدية والقسرية التي تُشكّل سلوك المجلات العلمية، فيما تُستخدم الثانية لتفسير مشكلات عدم تماثل المعلومات بين الأطراف الفاعلة (المحررون، المراجعون، المؤلفون، القراء). يقترح البحث ستة مكونات معمارية للإطار: العقود الذكية لإدارة سير العمل التحريري، رموز الحوكمة (Governance Tokens) لتوزيع حقوق التصويت، السجلات الموزعة (Distributed Ledgers) لتوثيق المراجعات، آليات الإجماع للقرارات النهائية، أنظمة السمعة المُرمَّزة (Tokenized Reputation Systems)، وبروتوكولات حل النزاعات الخوارزمية. تكشف المناقشة أن هذا الدمج يُعيد توزيع سلطة الحراسة الأكاديمية، ويُقلّل من تكاليف الوكالة، ويُعزّز الشرعية المؤسسية للمجلات، إلا أنه يُولّد توترات جديدة تتعلق بالإجماع الزائف، والتمويل غير المستدام، والتعقيد التقني. يُقدّم البحث آثاراً إدارية محورية لمسؤولي OJS، ويختتم بأجندة بحثية مستقبلية تُشجّع على التحقق التجريبي للإطار في سياقات نشر متنوعة.
The article is devoted to the study of mechanisms for managing reputational risks in decentralized autonomous organizations (Decentralized Autonomous Organizations – DAO) operating on the basis of blockchain technologies. The relevance of the research is determined by the rapid development of decentralized digital ecosystems, the spread of algorithmic governance models, and the need to ensure trust among participants in an environment where traditional institutional mechanisms of centralized control are absent. Under such conditions, the issue of reputational risk management becomes particularly important, as the level of trust directly affects the stability and sustainability of decentralized organizations. The aim of the study is to identify and substantiate mechanisms for managing reputational risks in decentralized autonomous organizations based on blockchain technologies, taking into account the specific features of their functioning and the principles of decentralized governance. The methodological basis of the research includes methods of systemic analysis, institutional approach, comparative analysis, and modeling. To identify the key factors shaping reputational risks, the study employs the analysis of contemporary scientific publications, generalization of DAO project practices, and examination of digital governance tools within blockchain ecosystems. As a result of the study, the main sources of reputational risks in decentralized autonomous organizations were systematized, including information asymmetry among participants, insufficient transparency of decision-making procedures, technical vulnerabilities of smart contracts, and potential manipulation of voting mechanisms. The key mechanisms for managing reputational risks in the DAO environment were generalized, including participant reputation evaluation systems, transparent decentralized voting mechanisms, smart contract auditing, and moderation tools for digital communities. Based on the conducted analysis, a conceptual model for managing reputational risks in DAO was developed, which provides for the integration of blockchain transparency tools, collective control mechanisms, and procedures for evaluating the reputational behavior of participants in digital ecosystems. The scientific novelty of the study lies in substantiating a conceptual approach to reputational risk management in decentralized autonomous organizations, which combines the capabilities of blockchain infrastructure with self-regulation mechanisms of decentralized digital communities. The practical significance of the obtained results lies in the possibility of their application by developers of DAO projects, blockchain platforms, and digital ecosystems for the development of reputational risk management systems, increasing the level of trust among participants, and ensuring the stable functioning of decentralized organizations.
The cryptocurrency market, known for its volatility and rapid growth, is influenced by various actors, including investors, speculators, and volunteers. Investors and speculators often seek short-term profits, sometimes disregarding the long-term fundamentals of projects, leading to significant market instability and reputational damage. Conversely, volunteers contribute consistently to the development, governance, and sustainability of crypto projects, focusing on long-term value creation. This paper examines the contrasting roles of these groups and provides real-world examples where speculators and investors have caused harm to the market, while volunteers have played a vital role in building and preserving value.
This paper aims to provide novel insights in the use of recent advances about non-fungible and Soulbound tokens, as they are a growing reality in the context of the DLT framework. In particular, this work discusses how these technological provisions can enable a renovated and strengthened role of the Internet of Everything concept in the complex processes of the Industry 5.0, where the social, societal, and technical dimensions merge into an irreducible applicative context. The potentialities of the approach are expressed by means of a simple but meaningful example on an industrial case study concerning the food supply chain.
Pod koniec 2021 roku szacowano, że 300 milionów osób na całym świecie posiadało jakąś formę kryptowaluty. Dwie największe dźwignie popularności kryptowalut to DeFi i NFT. Gdy na początku 2021 roku NFT, czyli niewymienialne tokeny, wzbudziły lawinę transakcji detalicznych, duże marki zaczęły zwracać na to uwagę. NFT dają możliwość zachowania cyfrowego IP i aktywowania społeczności internetowych w sposób, który nigdy wcześniej nie był osiągalny. Jednakże, jak dotąd, nie obserwuje się "web3-natywnego" podejścia do NFT ze strony dużych marek. Oznacza to, że żadna marka nie zmieniła swojej architektury Web 2.0 i nie zastąpiła jej całkowicie strukturą Web 3.0. Zamiast tego, globalne firmy przyjęły bardziej ostrożne podejście, udostępniając kolekcje NFT jako odrębne człony swojej oferty, a zarazem stymulując rozwój skupionych wokół nich społeczności. Tę fazę, którą można określić mianem Web 2.5, cechuje stopniowe wdrażanie nowych technologii, takich jak NFT, z korzyścią zarówno do samej lansującej je marki, jak i dla konsumentów. Producenci połączyli to, co najlepsze w Web 3.0, ze sprawdzonymi modelami rozwoju i promocji Web 2.0. Zasadniczo tym właśnie jest Web 2.5: stapianiem innowacyjnych technologii web3, m.in. NFT, z infrastrukturą Web 2.0 i tworzeniem środowiska, które zapewnia odbiorcom silne immersyjne doświadczenia kształtujące więź z marką. Procesy te przedstawiam w artykule na przykładzie marek z sektora mody i sztuki.
Federico Caviggioli, Lucio Lamberti, Paolo Landoni, Paolo Meola
Purpose Evidence from previous literature indicates that adopting a new innovative technology has a positive impact on a company’s business performance. Much less work has been carried out into examining whether a technology adoption has impact on corporate reputation. This paper aims to examine the latter topic in a context where social media is the channel used to share news about the introduction of a new technology. The empirical setting of the study consists of five retail companies located in the USA that decided to include Bitcoin as a payment platform. Design/methodology/approach Twitter data were used to measure how sharing news about the adoption of new technology could affect the reputation of the companies selected, keeping a clear distinction between the volume of data relating to social media responses and the sentiment expressed in the tweets. A panel vector autoregression model was used to incorporate series of data relating to news items, volume and sentiment. Findings The results show that the news about the adoption of a new technology has a positive impact on both the volume of tech-related tweets and the sentiment expressed in the tweets themselves, although the patterns of these two effects are different. The resulting impact decreases after a few days, both in volume and in sentiment. Research limitations/implications The analysis has limitations that future research could address by extending and diversifying the examined companies and the social media used as data sources. The research suggests that managers in medium-sized companies can leverage on the introduction of new technologies that have a direct impact on their customers and gain reputational benefits in terms of immediate visibility. Originality/value The research introduces an additional dimension of analysis to the current stream of corporate reputation. Although the literature has already covered the dynamics of response to events on Twitter, by focusing on the adoption of the new Bitcoin technology, the paper provides novel insights.
Open access
Digital Marketing and Social Media
Corporate Identity and Reputation
Consumer Behavior in Brand Consumption and Identification
Dan Freeman, Tim McWilliams, Sudip Bhattacharyya, Craig Hall · 5 authors
Trust is paramount for the effective operation of any monetary system. While the distributed architecture of blockchain technology on which cryptocurrencies operate has many benefits, the anonymity of users on the blockchain has provided criminal users an opportunity to hide both their identities and illicit activities. In this paper, we present a scoring mechanism for cryptocurrency users where the scores represent users’ trustworthiness as safe or risky transactors in the cryptocurrency community. In order to distinguish law-abiding users from potential threats in the Bitcoin marketplace, we analyze historical thefts to profile transactions, classify them into risky and non-risky categories using several machine learning techniques, and finally calculate a reputation score for every unique user based on their past association with any unlawful Bitcoin incident. The Support Vector Machine model based on two key attributes produces an accuracy of 86% and is considered the most applicable for our dataset. Our reputation score ranges from 0 to the total number of transactions by a given user where a higher score indicates greater trustworthiness in making Bitcoin transactions. This score helps to identify reputable users and, therefore, acts as a guideline for safe Bitcoin transactions. In the cryptocurrency marketplace, our self-attestation metric in the form of a reputation score offers a foundation for enhancing trust between transacting parties.