The growing need for sustainable energy practices necessitates technology-driven interventions that can effectively bridge the disparity between consumer intentions and actual behavior. This paper formulates and empirically substantiates an IoT-enabled digital nudge architecture designed to promote sustainable energy behavior. The architecture provides goal-setting, social comparison, feedback, and informational nudges across multiple digital channels, utilizing linked devices, data processing layers, and a rule-based nudge engine. An 815-responder survey was analyzed using structural equation modeling with partial least squares (SEM-PLS) to identify the drivers of sustainable energy behavior and explore technology readiness as a moderating factor. The results show that nudges utilizing the Internet of Things (IoT) significantly enhance the alignment between intention and behavior. Goal-setting and feedback mechanisms have the highest effects. The findings also demonstrate that being ready for new technology improves nudge response, highlighting the importance of user-centered system design. This paper presents a scalable infrastructure for integrating IoT into sustainability projects, as well as theoretical contributions to technology adoption and behavioral intervention research. The study enhances the dialogue on environmental technology by illustrating the implementation of digital nudges through IoT infrastructures to expedite progress toward the Sustainable Development Goals (SDGs).

This study seeks to deliver a comprehensive bibliometric analysis of blockchain research undertaken from 2013 to 2023. It seeks to map the field’s evolution, highlight emerging trends, and identify key contributors and thematic areas. Understanding these aspects is crucial for guiding future research and policymaking in the rapidly growing domain of blockchain technology. The study utilises a systematic bibliometric analysis approach and the Scopus database was used for collecting data. The analysis includes keyword trends, citation patterns, author productivity, and network analysis. The results reveal significant expansion and diversification in blockchain research, with 13,369 publications and 52,009 contributing authors. Key metrics include a total citation count of 266,483 and an average of 19.93 citations per paper, underscoring the field’s scholarly impact. The study identifies core research themes such as smart contracts, security, IoT integration, and emerging topics like AI and sustainability. The findings highlight the interdisciplinary nature of blockchain research and its global distribution, with notable contributions from China and the United States. The study’s reliance on selected databases and predefined keywords may exclude relevant grey literature and industry reports. Future research should integrate these sources for a more complete view. This study provides a panoramic view of blockchain research, offering valuable insights into its development, current state, and future directions. Identifying key trends, influential contributors, and emerging themes contributes to the academic discourse and supports strategic planning for researchers, practitioners, and policymakers engaged in blockchain technology.

This paper discusses digital capability and its potential translation into sustainable organizational performance through the integration of sustainable management accounting and high-quality managerial decision-making. Even though previous studies acknowledge the importance of digital technologies and sustainability practices, the current literature mostly analyzes them separately, and no empirical models explain how digital capability can be translated into sustainability outcomes through internal decision-making and accounting processes. To fill this gap, this paper constructs and proves a Digital-Enabled Sustainability Management Accounting (DSMA) framework based on Dynamic Capabilities Theory, the Knowledge-Based View, and the Technology–Organization–Environment framework. Based on Survey data from 667 respondents in the financial services industry and Partial Least Squares Structural Equation Modeling (PLS-SEM), the results indicate that digital capability can significantly contribute to sustainability performance by increasing accounting integration and decision quality. Although technological readiness enhances such relations, it does so with only a low degree of influence, meaning it has a supportive rather than a transformative effect. The research is valuable because it contributes to sustainability theory, provides a solid empirical database that is understudied, and has practical implications for organizations striving to implement digitally enabled sustainability initiatives.

The rapid adoption of immersive and adaptive digital technologies is redefining sustainability education, but the mechanisms by which these technologies support perceived sustainability outcomes remain unclear. This paper models the Digital Experiential Learning Ecosystem (DELE), including simulation, AR/VR, gamification, AI personalization, and collaborative digital platforms, as a higher-order construct. It discusses its role in Perceived Sustainability Outcomes through learning engagement. Basing the study on the Stimulus-Organism-Response framework, the study hypothesizes that the digital ecosystem design can be viewed as an environmental stimulus, engagement as the organismic processing state, and Perceived Sustainability Outcomes as the developmental response. The results, obtained using Partial Least Squares Structural Equation Modeling (PLS-SEM), indicate that DELE is positively associated with learning engagement and Perceived Sustainability Outcomes. Learning engagement is found to be the leading mechanism through which digital experiential environments are converted into perceived sustainability outcomes, but a smaller yet significant direct structural relationship also remains. These findings indicate that digital transformation within the education sector creates sustainable value not only through technological sophistication but also through carefully planned engagement-based learning environments that support systems thinking, applied problem-solving, and adaptive readiness to work in multifaceted environments. The research also advances the body of research on sustainability education by developing a model of digital learning as an integrated ecosystem and by explaining the psychological and structural processes of perceived sustainability outcomes.

Supply chain information systems increasingly need to support decisions, yet Internet of Things (IoT) and blockchain approaches remain largely data-centric. We propose a decision-centric system in which IoT data acquisition, an artificial intelligence decision engine and a blockchain layer form one closed loop: each daily routing plan is optimized jointly for cost, lateness and delivered demand, validated against shared partner constraints and recorded before execution. We evaluated it by simulation on the supply chain logistics problem dataset (41 real customers) over 30 paired seeds. A learned deep Q-network engine with decision-time rollout reduced cost per 1000 units delivered by 24.3% relative to a demand-aware heuristic and 28.0% relative to OR-Tools, deciding in 43 ms; seeding adaptive large neighborhood search (ALNS) with its plan gave the lowest cost, 28.0% below the heuristic and below ALNS alone in 29 of 30 seeds. Joint routing–demand optimization lowered cost per unit by 33–45% relative to a cost-only objective. Under hard time windows, a heterogeneous fleet and eleven other scenarios, rollout stayed 12–32% cheaper than the heuristic in all 130 paired runs, and seeded ALNS was cheapest in every setting. Evidence-based validation blocked every invalid plan with up to four of five validators faulty.