Volume 1, Issue 1, 2026
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The digital transition is creating new opportunities and challenges for labour markets across the European Union, influencing employment patterns, productivity, labour costs, and working conditions. This study investigates how the four dimensions of the Digital Economy and Society Index (DESI), Human Capital, Connectivity, Integration of Digital Technology, and Digital Public Services, are associated with labour market performance in the 27 EU Member States over the period 2017–2022.
The empirical analysis combines DESI and Eurostat data and applies descriptive statistics, panel unit-root tests, Granger causality analysis, and Elastic Net regression modelling. The results suggest that connectivity is the most consistent driver of improved labour market outcomes, being associated with higher labour productivity and fewer long working hours.
The analysis further highlights substantial cross-country variation, indicating that the benefits of digital transformation depend not only on technological progress but also on institutional and economic conditions. These findings provide new evidence on the labour market implications of digitalisation and offer useful insights for policymakers pursuing the objectives of the European Digital Decade.
Copyright: © The Author(s) 2026. This article is distributed under the terms and conditions of the license Creative Commons Attribution (CC BY) license.
Article info: Received: 2 March, 2026; Revised: 9 April, 2026; Accepted: 12 May, 2026; Published: 17 May, 2026
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The identity-management challenge addressed in this study emerges after device enrolment, when an Internet of Things (IoT) device may continue to present valid credentials even though its software state, behavioural patterns, or operating conditions have changed. To address this limitation, the study proposes an integrated architecture that treats cryptographic identity and observed behaviour as complementary but distinct sources of trust evidence. A permissioned ledger anchors decentralised identifiers, credential states, policy versions, model attestations, and decision evidence, while behavioural analysis is performed close to the device and federated learning enables collaborative model development without centralising raw telemetry. At the authorisation stage, credential validity, behavioural risk, and request context are combined into a dynamic trust state, enabling graduated responses as evidence evolves. Low-level observation and temporary restrictions may be automated, whereas suspension is subject to review and permanent credential revocation requires human authorisation.
The study adopts a Design Science Research approach, treating the proposed architecture as an artefact evaluated through requirements-based assessment, threat-model analysis, and feasibility evidence derived from the underlying technological mechanisms. The evaluation identifies both design-level capabilities and unresolved dependencies related to secure key custody on legacy devices, federated coordination at scale, poisoning by credentialled participants, and regulatory conformity. The proposed architecture therefore extends decentralised identity management beyond static credential verification by integrating continuous behavioural assessment, auditable decision-making, and human oversight into a unified IoT trust-management framework.
Copyright: ©The Author(s) 2026. This article is distributed under the terms and conditions of the license Creative Commons Attribution (CC BY) license.
Article info: Received: 9 March, 2026 Revised: 30 March, 2026 Accepted: 19 May, 2026 Published: 22 May, 2026
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The Jetsons (1962) imagined a future characterized by automation, instant transactions, and technologies capable of anticipating and responding to human needs. Six decades later, advances in digital assets, artificial intelligence (AI), decentralized finance, programmable money, embedded finance, and next-generation payment technologies suggest that elements of this vision are increasingly becoming part of financial infrastructure. This study examines ten emerging financial technologies through an integrative and conceptually oriented review, focusing on their collective contribution to increasingly autonomous financial systems.
The analysis organizes these technologies across three interconnected dimensions: programmable financial infrastructure, intelligent and autonomous financial decision-making, and ambient financial interfaces. Their convergence may progressively automate financial decision-making and execution, reduce transactional friction, and reconfigure rather than eliminate financial intermediation, while generating challenges related to accountability, privacy, systemic risk, governance, and financial inclusion.
The study develops an integrative conceptual framework in which autonomous finance is understood as an emergent outcome of interactions among programmable, intelligent, and ambient financial capabilities, rather than as the consequence of any single technology. The framework highlights how their convergence may reshape financial systems and the institutional and governance conditions influencing this transition.
Copyright: © The Author(s) 2026. This article is distributed under the terms and conditions of the license Creative Commons Attribution (CC BY) license.
Article info: Received: 25 March, 2026; Revised: 2 May, 2026; Accepted: 22 May, 2026; Published: 27 May, 2026.
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Digital transformation can alter what a firm is capable of faster than customers, partners and other external audiences revise their understanding of the firm. This conceptual paper calls that difference the market-recognition gap. The paper integrates research on digital transformation, signalling theory, market orientation and dynamic capabilities with bounded practitioner observations from B2B technology branding. It argues that strategic positioning can function as a translation layer between operational capability and market recognition when it makes consequential differences easier to understand and connects claims to verifiable evidence.
The paper develops a four-part model of recognition failure: capability change, category inertia, signal ambiguity and proof separation. It then proposes three research propositions concerning the relationship between capability change, signal specificity and external recognition. The contribution is intentionally conceptual. It does not test causal effects or generalize from proprietary client data. Instead, it offers a vocabulary and research agenda for examining why technological transformation may remain commercially under-recognized even when operational capability has materially changed. The paper further introduces a diagnostic framework and a two-dimensional market-recognition matrix to distinguish under-recognized capability change from broader visibility problems. These tools are proposed for structured managerial assessment and future empirical validation, not as validated diagnostic measures.
Copyright: © The Author(s) 2026. This article is distributed under the terms and conditions of the license Creative Commons Attribution (CC BY) license
| Article info: Received: 2 May, 2026; Revised: 5 June, 2026; Accepted: 29 June, 2026; Published: 9 July, 2026.
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This study examines barriers to artificial intelligence (AI) and machine learning (ML) adoption among women entrepreneurs in Zimbabwe. Drawing on an integrated Technology–Organization–Environment (TOE) and Diffusion of Innovation (DOI) framework, the research employs a quantitative cross-sectional survey of 384 women entrepreneurs across urban and rural locations and multiple business sectors. Questionnaire data were analysed using descriptive statistics and multiple regression. The findings reveal significant negative associations between AI/ML adoption and three barrier domains: technological and digital skills limitations, financial constraints, and socio-cultural and institutional barriers. Financial constraints emerged as the strongest predictor (β = −0.389, p < .001), while the regression model explained 71.8% of the variation in adoption. Advanced applications, particularly predictive analytics and business-intelligence tools, exhibited relatively low adoption levels. The study highlights the interconnected nature of financial, technological and institutional barriers and underscores the importance of affordable financing, targeted AI skills development and supportive digital infrastructure. By integrating TOE and DOI perspectives, it contributes context-specific evidence to understanding AI/ML adoption among women entrepreneurs in resource-constrained economies.
Copyright: © The Author(s) 2026. This article is distributed under the terms and conditions of the license Creative Commons Attribution (CC BY) license.
Article info: Received: 10 July, 2026; Revised: 31 July, 2026; Accepted: 22 August, 2026; Published: 30 August, 2026.
