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This study aims to evaluate blockchain as an e-government governance model. It assesses its alignment with legal frameworks, emphasizing robustness against disruptions and adherence to existing laws.

The paper explores blockchain’s potential in e-government, focusing on legal, ethical and governance aspects. It conducts an in-depth analysis of blockchain’s integration into data governance, emphasizing legal compliance and resilient security protocols.

The study comprehensively evaluates blockchain’s implementation, covering privacy, interoperability, consensus mechanisms, scalability and regulatory alignment. It highlights governance’s critical role in ensuring legal compliance within blockchain paradigms.

Ethical and legal concerns arising from blockchain adoption remain unresolved. The study underscores how blockchain challenges its core principles of anonymity and decentralization in e-government settings.

The framework outlined offers potential for diverse technological environments, albeit raising ethical and legal queries. It emphasizes governance’s pivotal role in achieving legal compliance in blockchain adoption.

Blockchain’s impact on legal and ethical facets necessitates further exploration to align with its core principles while addressing governance in e-government settings.

This study presents a robust framework for assessing blockchain’s viability in e-government, emphasizing legal compliance, despite ethical and legal intricacies that challenge its fundamental principles.

The purpose of this paper is to investigate, from a thorough review of the literature, the role of metaverse-based quality 4.0 (MV-based Q4.0) in achieving manufacturing resilience (MFGRES). Based on a categorization of MV-based Q4.0 enabler technologies and MFGRES antecedents, the paper provides a conceptual framework depicting the relationship between both areas while exploring existing knowledge in current literature.

The paper is structured as a comprehensive systematic literature review (SLR) at the intersection of MV-based Q4.0 and MFGRES fields. From the Scopus database up to 2023, a final sample of 182 papers is selected based on the inclusion/exclusion criteria that shape the knowledge base of the research.

In light of the classification of reviewed papers, the findings show that artificial intelligence is especially well-suited to enhancing MFGRES. Transparency and flexibility are the resilience enablers that gain most from the implementation of MV-based Q4.0. Through analysis and synthesis of the literature, the study reveals the lack of an integrated approach combining both MV-based Q4.0 and MFGRES. This is particularly clear during disruptions.

This study has a significant impact on managers and businesses. It also advances knowledge of the importance of MV-based Q4.0 in achieving MFGRES and gaining its full rewards.

This paper makes significant recommendations for academics, particularly those who are interested in the metaverse concept within MFGRES. The study also helps managers by illuminating a key area to concentrate on for the improvement of MFGRES within their organizations. In light of this, future research directions are suggested.

The purpose of this study is to review the different facets associated with Industry 5.0 (I5.0) and propose a conceptual framework to boost the applicability of this novel technological cum social aspects within industrial organizations for improved organizational sustainability.

This research work adopted a bibliometric analysis that encapsulates a quantitative set of tools for bibliometric and bibliographic information. This study uses the database of Scopus to acquire data related to different facets of I5.0. The study implies a different spectrum of terms to reach the final corpus of 91 articles related to I5.0. Furthermore, a conceptual define, measure, analyze, improve and control (DMAIC)-based framework based on different literature findings is proposed and validated based on the input of experts from different parts of the world.

The results indicate that I5.0 is still in its infancy. The wider applicability of I5.0 demands comprehensive theoretical knowledge of different facets of this new paradigm and the development of a framework to adopt it on a larger scale. Organizations that are in the race to adopt I5.0 face major challenges related to the digitization of processes along with well-defined cyber-physical systems and the lack of a dedicated framework to execute I5.0. Furthermore, the result also suggests that manufacturing industries are more ready to adopt I5.0 practices as compared to service industries, which can be attributed to well-defined technological measures available in manufacturing settings.

To the best of the authors’ knowledge, this is one of the first studies that explore different know-how and challenges and provides a holistic view of I5.0 by providing a systematic adoption framework.

This chapter reviews some of the technical background relating to the designs of digital infrastructures for financial services. Such technical background could be useful to the understanding of the design choices that the central banks and pertinent regulators face when developing or promoting open digital infrastructures for financial services. First, the chapter reviews modularity and interoperability in the design of digital infrastructure. The chapter then examines three broad architectural models of open digital infrastructures: decentralized, centralized and distributed. The chapter ends with some lessons learnt from the different architectural models that have been implemented in practice.

“Web3” is a practice in participatory digital infrastructures through the ability to read, write, and control own digital assets. Web3 is hailed as the alternative to the failings of big tech, offering a participatory mode of digital organization and shared ownership of digital infrastructure through algorithmic governance. This paper offers an introductory playbook to researchers entering the field of Web3 by providing an analytical lens to approach the emergent field of Web3 as “infrastructuring.” It argues that Web3 can be understood as a collective, community exploration of “how to infrastructure.” Drawing on qualitative examples derived from digital ethnographic methods, the study reveals that play, politics, and prefiguration are fundamental qualities underpinning Web3’s vision of offering an “exit” from established institutional infrastructures. Therefore, a primary challenge Web3 faces in its governance experiments centers around the question of how to effectively build and manage infrastructure.

The retail landscape is dramatically changing due to a series of socio-economic and technological challenges, which can be faced through the adoption of smart technologies. Accordingly, a significant number of publications in this field have been produced, albeit with fragmented results. Therefore, this paper aims at both providing a clear and organised overview of the main smart technologies for physical retailing, in terms of application fields and expected impact, while identifying the major shortcomings and future research avenues.

The research conducts a systematic review of the literature concerning the assimilation of smart technologies within physical retail environments, resulting in the analysis of 103 papers published from 2005 to 2023. The review highlights (1) the main smart technologies employed in retail stores, (2) their application area and (3) the beneficiaries of their adoption. Accordingly, these three aspects are initially assessed independently and then examined in combination.

The analysis presents a comprehensive list of 16 key technologies (what) that can support a wide range of processes, spanning from back-end functions to front-end activities, also enabling the connection with online channels (where), catering several and different benefits (why) to both customers and retailers (who). Besides, the research points out many uncovered topics that could be addressed by the academic community.

To the best of the authors’ knowledge, the review is the first one in the literature offering a thorough and organised overview of the different available technologies for in-store application and their impact on physical retail processes.

Financial technologies form the heart of considerable disruptive innovation. Fintech is the emerging financial infrastructure for modern business. Big data are the feedstock for artificial intelligence (AI) that drives many fintech sectors – start-up finance, commodities and investment instrumentation, payment systems, currencies, exchange markets/trading platforms, market-failure response forensics, underwriting, syndication, risk assessment, advisory services, banking, financial intermediaries, transaction settlement, corporate disclosure, and decentralized finance. This chapter demonstrates how analyzing big data, largely processed through cloud computing, drives fintech innovations, scholarship, forensics, and public policy. Despite their apparent virtues, some fintech mechanisms can externalize various social costs: flawed designs, opacity/obscurity, social media (SM) influences, cyber(in)security, and other malfunctions. Fintech suffers regulatory lag, the delay following the introduction of novel fintechs and later assessment, development, and deployment of reliable regulatory mechanisms. Big data can improve fintech practices by balancing three key influences: (1) fintech incentives, (2) market failure forensics, and (3) developing balanced public policy resolutions to fintech challenges.

Web3’s raison d’être is decentralization. Quite problematically, however, few industry analysts can articulate what “decentralization” really entails; whether it differs at all from the notion of “distribution,” and how either construct can be measured with observable data to enable a meaningful analysis of the industry’s core promise. Instead, Web3 is akin to a decentralization theater in which archetypical characters, who resonate with the likes of Hamlet and Godot, enact decentralization based on fictitious narratives. After critically reassessing these narratives about decentralization, this paper offers a fresh perspective to evaluate, less theatrically and hopefully more rigorously, future claims about “being decentralized.” I argue that the crucial issue lurking behind the decentralization narrative is the dispersion of authority within blockchain platforms, which consists of two fundamental dimensions, namely the dispersion of information and of decision-making. The value proposition of Web3 will not be taken seriously until the industry can provide reliable indicators of authority dispersion and demonstrate that the latter affects strategic outcomes for blockchain platforms, including innovation, growth, and value creation.

The sharing economy, supported by digital platforms, efficiently matches the demand and supply of underused resources. Expanding globally and impacting different industries is offering a new path to sustainable resource consumption. The United Nations endorsed the sustainable development goals (SDGs), responding to a global call to end poverty, protect the environment and ensure that by 2030, everyone lives in peace and prosperity. Researchers have linked the sharing economy with SGDs, and extensive efforts have been exerted to quantify its impact.

Due to its evolving nature, especially in developing countries, there is a need for research to assess its promises and challenges from the SDG perspective. This research fills in this gap and contributes to the sharing economy studies by exploring its evolution within the framework of sustainable development goals, followed by an assessment of various methodologies for measuring the sharing economy and highlighting the significance of the web mining technique.

The proper implementation of this decentralized business model within a country is encouraging optimal resource use, lowering energy consumption and increasing long-term economic development. The sharing economy is a disruptive force that addresses the pillars of sustainability.

The research holds importance in addressing the achievement of the SDGs through the sharing economy, necessitating focus from policymakers and scholarly discourse on its merits. The transformative impact of the sharing economy poses questions about its encouragement or regulation, with the potential to disrupt established monopolies and possibly create new ones. Its successful implementation underscores the significance of effective data sharing and governance.

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.