Search Results (129)

Mandatory Environmental, Social, and Governance (ESG) disclosure requirements—anchored in Corporate Sustainability Reporting Directive (CSRD), International Sustainability Standards Board (ISSB), and Task Force on Climate-related Financial Disclosures (TCFD)—have placed unprecedented demands on supply chain data quality and auditability. Blockchain technology, combining immutability, decentralised governance, and smart contract automation, has emerged as a candidate infrastructure for addressing verification deficits across multi-tier supply chains. To our knowledge, no prior systematic review has simultaneously examined the blockchain specifically for formal ESG transparency and sustainability reporting across all three ESG dimensions within the post-CSRD mandatory reporting landscape. This study presents a systematic literature review (PRISMA 2020). Scopus and Web of Science searches identified 1166 records (2016–2026); after deduplication, 761 unique records were screened, and after blinded screening (κ = 0.84), 96 studies were included. Five blockchain application typologies are identified (T1–T5), spanning provenance tracing, smart contract compliance, carbon accounting, supplier data aggregation, and ESG disclosure systems. A structural asymmetry is identified: governance is addressed in 96% of studies (77.1% under the strictest G-CONFIRMED recoding; 95.8% under the moderate interpretation, including borderline cases), the environmental pillar in 49%, and the social dimension in 21%, explained through institutional theory, with significant implications for CSRD and Corporate Sustainability Due Diligence Directive (CSDDD). Key barriers include scalability, interoperability, and the blockchain–GDPR (General Data Protection Regulation) tension. Three principal contributions are made: (i) a systematic typology of blockchain for ESG transparency; (ii) institutional-theory explanation of ESG dimension asymmetry; and (iii) a research agenda centred on AI–blockchain convergence and post-CSRD empirical studies. The review is limited to English-language peer-reviewed literature. Full article

This paper presents a theoretically grounded integration of deterministic Q-learning with relational game theory (QLRG) for efficiently identifying minimal winning coalitions in Online Social Networks (OSNs). We address the fundamental challenge that coalition formation is NP-hard under traditional approaches by leveraging structural properties of relational dependencies and Armstrong’s axioms to transform the problem into one solvable in polynomial time. Our framework reduces the state space from exponential O(2ⁿ) to O(n²) through a sufficient statistic representation based on coalition size, follower reach, and terminal status, while achieving O(n⁴) time complexity under deterministic, static, and sufficiently symmetric influence structures. The QLRG framework introduces three critical innovations: (1) a principled agent selection mechanism derived directly from the Q-function that eliminates heuristic weight tuning; (2) a formal Boost action defined through temporal closure operators that captures influence spread dynamics; and (3) a constrained MDP formulation that enforces relational consistency through action elimination rather than penalty terms. We prove that the Bellman optimality operator forms a contraction mapping, guaranteeing deterministic convergence to optimal policies with established rates of O(1/√k) for decreasing learning rates or linear convergence up to bias for constant rates. To bridge the gap between this idealized model and the asymmetry inherent in real OSNs, we further develop a cluster-based sufficient statistics approach. By partitioning the network into communities with bounded internal variation, we relax the global symmetry requirement while preserving polynomial state space complexity, and obtaining a single within-community swap changes the optimal Q-value by at most ${\displaystyle \frac{{\epsilon }_i}{1-\gamma }}$, which is a local Lipschitz continuity result. The implications of this are both theoretical and practical, and they form the bedrock for relaxing the global symmetry assumption in the QLRG framework. Empirical validation on synthetic networks satisfying the symmetry assumption demonstrates that QLRG consistently identifies minimal winning coalitions matching the optimal solutions found by exhaustive search, while operating with polynomial-time complexity. Unlike conventional approaches, our framework simultaneously satisfies four critical properties: deterministic convergence, policy optimality, minimal coalition identification, and computational tractability. The work bridges computational social science and operations research, providing a mathematically rigorous foundation for strategic decision-making in influencer marketing and coalition formation. While the framework requires symmetry assumptions that may only hold approximately in real-world OSNs, it establishes an idealized baseline for future extensions addressing stochasticity, dynamics, and partial observability. This research represents a paradigm shift from empirical improvements to theoretically grounded convergence guarantees for coalition formation problems, demonstrating how structural mathematical insights can transform intractable problems into efficiently solvable ones without sacrificing solution quality. Full article

This study develops and tests an association-based model explaining how consumers interpret AI-enabled personalization in fashion e-commerce and how these interpretations relate to behavioral intentions. Integrating perspectives from Social Exchange Theory, the Antecedents of Trust Model, Self-Determination Theory, Psychological Contract Breach Theory, and Surveillance Capitalism, we examine the joint associations of perceived personalization, transparency, data control, and privacy concerns with brand trust, perceived surveillance, privacy violation perceptions, and purchase intention. Using PLS-SEM with data from 664 online shoppers, we find that personalization, transparency, and data control are each positively associated with brand trust, while personalization and privacy concerns are positively associated with surveillance perceptions. Brand trust is negatively associated with both surveillance and privacy violation perceptions, and privacy violation is negatively associated with purchase intention. Data control is directly associated with lower surveillance perceptions, whereas transparency operates indirectly through brand trust. Mediation analysis reveals that surveillance is associated with lower purchase intention only indirectly through privacy violation (full mediation), identifying perceived privacy violation as the central psychological pathway in the personalization-privacy paradox. Multi-group analysis identifies segment-level variations by gender and education: personalization is a stronger trust cue for men, while transparency is a stronger trust cue for women; trust buffers violation more strongly for higher-educated consumers. The results highlight a trust-first personalization strategy in which relevance must be paired with meaningful transparency and data-control features to mitigate surveillance and violation appraisals, supporting positive consumer outcomes in fashion e-commerce. Full article

In this article, we study the homotopy aspects of intersection graphs of topological semigroups. We begin by defining the top intersection graph $T{G}_{\mathbb{X}}$ and investigating how the algebraic and topological properties of a topological semigroup are reflected in the global structure of this graph. In particular, we characterize when $T{G}_{\mathbb{X}}$ is totally disconnected, bipartite, or planar in terms of the order and factorization of the underlying semigroup. We then introduce the notions of $HTG$-semigroups, graphical homomorphisms, and graphical homotopy relations, thereby developing a graphical homotopy framework. Within this setting, we study $Gr$-homotopy equivalences, $Gr$-contractible spaces, and retraction phenomena, including $DGr$-retracts and homotopy extension properties. Finally, we introduce graphical total semigroups and equip the set of $Gr$-path homotopy classes $\left[{\mathbb{X}}_{pe}\right]$ with a natural $\Delta$-uniform topology. We show that this topology is compatible with the induced semigroup operation, yielding a topological semigroup structure. Overall, this work provides a unified algebraic, topological, and graph-theoretic perspective, and opens the door to further applications of homotopy theory in the study of intersection graphs of topological semigroups. Full article

Nigeria entered the 2020 COVID-19-related oil price downturn without the fiscal buffers that numerous resource-rich economies had built over time. Despite heavy dependence on petroleum revenues, the country has made limited use of stabilization tools such as structured hedging programs, sovereign savings mechanisms, or strategic reserves, leaving public finances exposed to external shocks. Drawing on political choice theory and the resource governance literature, this study examines how institutional conditions shaped crisis management during the 2020 oil price collapse and the COVID-19 pandemic. The study combines qualitative institutional analysis with a stochastic counterfactual simulation. It compares Nigeria’s policy approach with those of oil-producing countries including Mexico, Saudi Arabia, the United Arab Emirates, Angola, and Ghana, using data from the IMF, World Bank, Afreximbank, and peer-reviewed sources. The counterfactual simulation is calibrated to Nigeria’s 2019 federal budget oil benchmark of US $60 per barrel, with the IMF’s 2019 petroleum price assumption used as a robustness check. The model treats hedging as a form of partial fiscal insurance rather than full stabilization. Results suggest that hedging sufficient to offset 10%, 20%, and 30% of the shock would have improved 2020 GDP decline from −1.80% to approximately −1.62%, −1.44%, and −1.26%, respectively. The analysis identifies institutional gaps in Nigeria’s use of hedging, sovereign savings, and reserve infrastructure. The counterfactual results indicate that even modest oil hedging could have meaningfully softened the 2020 downturn, with the 20% scenario reducing GDP contraction by an estimated 0.36 percentage points. These findings suggest that governance constraints contributed materially to fiscal vulnerability. The study proposes a four-pillar framework centered on risk hedging, revenue savings, strategic investment, and institutional reform to strengthen fiscal stability and resilience to external shocks. Full article

In this work, we construct a homotopy theory for a class of intersection graphs arising from topological monoids. We introduce the M-intersection graph of a ${\tau }_e$-monoid, where the vertices correspond to proper ${\tau }_e$-submonoids and adjacency is defined by trivial intersection. Several structural properties of the graph, including total disconnectedness, bipartiteness and planarity, are investigated and shown to be closely related to the algebraic structure and decomposition of finite ${\tau }_e$-monoids. Based on this framework, we develop a graphical homotopy theory by introducing graphical ${\tau }_e$-monoids, graphical homomorphisms, and graphical homotopies. We study graphical homotopy equivalence, graphical contractibility, and path monoids, and examine retraction properties through graphical retracts, D-graphical retracts and graphical homotopy extension properties. Furthermore, we present an example of graphical comprehensive monoids and construct a $\theta$-homogeneous topology on the set of graphical path homotopy classes. We show that this topology is compatible with the induced monoid operation, yielding a well-behaved functorial topological monoid structure. Full article

We develop a categorical framework for modeling recursive uncertainty over preferences in decision theory. Classical models of ambiguity allow for uncertainty over outcomes or beliefs but usually rely on finite or exogenously truncated representations when agents face uncertainty about their own evaluative criteria. Given that such recursive preference formation generates an infinite hierarchy that may not stabilize at any finite level, we introduce a contractive von Neumann–Morgenstern utility functor on a category of compact metric spaces enriched over complete metric spaces, and establish the existence and uniqueness of its canonical fixed point. This fixed point is interpreted as a universal preference space that contains all levels of recursive ambiguity in a consistent and metrically stable form. We further extend the construction to multi-utility representations and discuss its relation to existing models of ambiguity and universal choice spaces. This framework offers a minimal unified representation of recursive preference structures. Full article

This work aims to introduce the concept of graphic rational contractions in the framework of extended $\mathcal{F}$-metric spaces and to establish fixed point theorems related to these mappings. In addition, we define and examine the class of interpolative Ćirić–Reich–Rus-type cyclic contractions in the same setting, deriving several new fixed point results that broaden existing theories. To illustrate the validity and originality of the obtained results, appropriate examples are presented. Furthermore, the developed theoretical results are applied to study the existence of solutions for fractional differential equations and nonlinear mixed Volterra–Fredholm integral equations, highlighting their effectiveness and practical importance. Full article

This paper studies the existence and uniqueness of periodic solutions to a class of nonlinear neutral matrix difference systems with multiple delays. The analysis is based on the construction of a suitable Green operator combined with fixed-point methods under exponential dichotomy assumptions. The existence of periodic solutions is established using Krasnoselskii’s fixed-point theorem, while uniqueness is demonstrated under a natural contraction condition via Banach’s principle. The results extend previous contributions on neutral difference systems and provide discrete analogues of related differential models. Examples are included to illustrate the applicability of the theory. Full article

The engawa, a threshold space in traditional Japanese architecture, has been widely cited as the archetypal manifestation of Kurokawa’s grey space theory. However, prevailing interpretations treat it as a static prototype, overlooking the transformation of its spatial mechanisms across history. The present study addresses this lacuna through a comparative case analysis of three representative teahouses. The following three styles are examined in this study: the sixteenth-century sōan style, the early seventeenth-century samurai style, and the early seventeenth-century shoin-zukuri style. The evolution of the engawa’s mediating function is traced through these three styles. An analytical framework comprising five dimensions—boundary permeability, sequential flow, material tactility, integration of natural elements, and visual transparency—is applied consistently across all cases. The analysis demonstrates a discernible evolutionary trajectory, commencing with an inwardly contracting spiritual threshold in Myōki-an, progressing to an outwardly differentiating social interface in ma, and culminating in a meticulously crafted aesthetic artefact in Mittan. The present findings demonstrate that the engawa is not a fixed spatial prototype but rather a dynamic mediator whose form adapts to shifting social, cultural, and spiritual demands. The study posits that the essence of intermediary space does not lie in any specific configuration, but rather in its capacity to mediate between opposing realms, including self and nature, individual and society, and function and beauty. This reinterpretation provides a theoretical foundation for contemporary architectural practice, proposing that designers should prioritize diagnosing the relational challenges that intermediary spaces are designed to address, as opposed to merely imitating historical forms. Full article

Background: Ethical challenges in public procurement are often addressed through compliance approaches that stress rule awareness. These perspectives, however, offer limited insight into how ethical intentions form before professional practice. This study develops and empirically validates the Procurement Literacy Capability Theory (PLCT), which conceptualises procurement literacy as a sequenced, interdependent set of capabilities that produce ethical readiness. Methods: Survey data were collected from 776 undergraduates in procurement-related programmes at four accredited Ghanaian universities. Structural equation modelling tested capability interdependence, sequencing, and behavioural translation. Mediation was examined via bootstrapped indirect effects, with sensitivity analysis using reduced structural models. Results: The findings support PLCT. Digital and E-Procurement Literacy predicts planning and decision-making capability, which then predicts supplier and contract management literacy. This literacy strongly influences Ethical Procurement Practice Literacy, the strongest predictor of Ethical Behavioural Intention. Legal and policy knowledge literacy has no direct effect on ethical intention but acts indirectly through ethical procurement practice capability. Models excluding ethical practice capability have much lower explanatory power. Conclusions: Ethical Behavioural Intention in procurement is shaped by sequenced capability development and applied ethical competence rather than rule awareness alone, confirming ethical practice literacy as the central behavioural mechanism within PLCT. Full article

This paper investigates the fundamental mechanisms of technological change in complex systems by analyzing how the evolution of embedded subsystems dictates the trajectory and sets the tempo of a host technology. Building on the theoretical framework of technological parasitism, the study conceptualizes host systems having a modular architecture—such as smartphones—as evolving through dynamic, coevolutionary interactions with their constituent subsystems. These relations gradually shift from parasitic reliance to mutualistic and ultimately symbiotic interactions. Central to this research is the concept of subsystems as pacemakers. Methodologically, this research employs a longitudinal, mixed-methods approach, combining an 18-year case study of the iPhone (2007–2025) with time-series regression and log–log hedonic pricing models. Key findings are: (a) Temporal precedence: Advances in subsystems (e.g., Bluetooth protocols) consistently precede host releases. The integration lag has contracted from three years to one, signaling an acceleration in symbiotic coupling and highlighting Bluetooth as a systemic pacemaker whose evolutionary tempo anticipates shifts in the wider smartphone architecture. (b) Differential evolutionary pressure in technological host systems: While camera resolution exhibited the highest exponential growth (+16.73%), it remained a secondary driver of systemic evolution. (c) Economic pacemakers: Hedonic analysis identifies battery life as the dominant evolutionary predictor (standardized beta = 0.77). With an elasticity of approximately 0.30, a 1% gain in battery performance correlates to a 0.3% increase in nominal price, whereas display and camera resolution exert significantly less influence on the system’s valuation and trajectory. These findings reveal that subsystems evolve—and exert influence—at different speeds and with different degrees of systemic leverage. Overall, the proposed theory shows that subsystem evolution functions as a leading indicator of forthcoming host–system transitions. By identifying which subsystems act as temporal pacemakers, this research contributes new design rules for forecasting technological generations and optimizing R&D strategies in complex, multi-component innovations. Hence, the study demonstrates that mastering complex innovation requires a granular understanding of the asynchronous rhythms between a host technology and its constitutive parts. Full article

“Cashback for positive reviews” is a common form of e-commerce manipulation that may undermine consumer trust and distort the market evaluation system. However, there is a lack of systematic research on how it influences consumers’ willingness to provide evaluations through psychological mechanisms. This study, based on the Stimulus–Organism–Response (S-O-R) framework, integrates the theories of psychological contract and cognitive dissonance. An empirical analysis based on 460 valid questionnaire responses was performed using SPSS and AMOS, yielding the following findings. (1) Negative emotions, including disappointment, anger, and regret, significantly triggered psychological contract breach, both transactional and relational. (2) Psychological contract breach reduced consumers’ willingness to provide positive reviews and lowered their store evaluation behavior, fully mediating the relationship between negative emotions and evaluation behavior. (3) Cognitive dissonance partially moderated the pathway from negative emotions through psychological contract breach to review behavior. This study elucidated the influence mechanism of negative emotions in “cashback for positive review” scenarios on consumers’ evaluation behavior, established a “merchant-user” online review relationship model, and provided practical and managerial implications for fostering mutually beneficial outcomes among platforms, merchants, and consumers. Full article

As generative AI (GAI) becomes increasingly embedded in higher education teaching, its influence on teachers’ instructional behaviors has shown complex and even contradictory patterns. Moving beyond the dominant single-path perspective that emphasizes technological empowerment, this study integrates Conservation of Resources theory and Social Exchange Theory to develop a dual-path framework explaining how GAI simultaneously enables and depletes teachers’ psychological resources. Using survey data from 436 university design teachers in mainland China, structural equation modeling and conditional process analysis were employed. The results indicate that GAI use enhances teaching self-efficacy and teaching-related well-being, thereby promoting innovative work behavior and reducing work withdrawal through a resource-enabling pathway. Conversely, GAI use also increases AI-related anxiety and teaching-related occupational stress, forming a resource-depleting pathway that suppresses innovation and intensifies withdrawal tendencies. Further analyses show that perceived organizational support strengthens the positive effects of GAI, whereas psychological contract breach amplifies its negative impacts. These findings extend research on teacher behavior in educational technology contexts and offer practical insights for fostering supportive environments and mitigating psychological costs during GAI integration. Full article

Vertical shafts are key channels for underground energy storage, mineral exploitation, and related engineering fields. Yet in deeply buried complex strata and high ground stress environments, traditional passive supports are prone to lining failure, while linear yield criteria cannot accurately characterize rock masses’ nonlinear mechanical behavior, limiting their use in shaft analysis. The core mechanical process of shaft construction aligns with the cavity contraction–expansion mechanism: excavation induces cavity unloading and contraction, causing shaft deformation and plastic zone expansion in surrounding rock; support enables cavity reverse expansion via preset shaft wall counter loads to actively control surrounding rock deformation. Based on this, this study integrates the Hoek–Brown nonlinear yield criterion, large-strain theory, and non-associated flow rules; couples cavity contraction–expansion semi-analytical solutions with the composite shaft wall mechanical model; and establishes a composite shaft wall–surrounding rock interaction analysis method. This research clarifies excavation-induced surrounding rock mechanical responses, reveals shaft wall counter loads’ regulatory effect on surrounding rock, and develops a systematic excavation support calculation workflow. Parameter analysis shows that increasing lining thickness is the most direct way to reduce inner wall tensile stress and improve safety; composite linings optimize stress distribution and enhance structural collaborative performance; and safety assessment confirms the lining inner wall as a structural weak zone. The proposed method and findings fill the gap in applying cavity contraction–expansion theory to shaft construction, providing reliable theoretical and practical guidance for deep shaft design, construction, and safety evaluation. Full article