Search Results (312)

This study examines the association between financial development and selected telecommunications infrastructure indicators in sub-Saharan Africa (SSA), focusing on mobile cellular subscriptions (MBS) and fixed telephone subscriptions (FTS). It also examines whether urbanization moderates these associations and explores the relationships of trade openness, final consumption expenditure, and economic growth with telecommunications infrastructure. The study is based on an unbalanced panel of 44 SSA countries with data for the period 1981–2021 and includes two-way fixed effects (TWFE) models as the baseline specification, Panel Fully Modified Ordinary Least Squares (FMOLS) to estimate long-run associations within the cointegrating framework, and TWFE models with Driscoll–Kraay standard errors as a robustness procedure. The results indicate that financial development is positively and significantly associated with both FTS and MBS across the main specifications, with a substantially stronger association for MBS. These results validate the hypotheses that financial development has a positive relationship with the fixed and mobile telecommunications infrastructure. The moderation analysis also shows that financial development is positively associated with MBS when the level of urbanization increases, while such a positive moderating relationship is not statistically significant for FTS. Trade openness is generally positively associated with telecommunications infrastructure, while the associations of final consumption expenditure and economic growth vary across the MBS and FTS specifications. Overall, the findings suggest that the association between financial development and telecommunications infrastructure is technology-specific and varies with the level of urbanization. Robustness tests with the balanced panel sample and the post-1990s subsample generally give similar results, but caution should be taken in comparing results across specifications because of the differences in samples. The findings provide evidence relevant to the telecommunications infrastructure dimension of the broader Sustainable Development Goal 9 agenda, but they should not be inferred as evidence of comprehensive SDG 9 progress or causal effects. Future studies should use more comprehensive digital infrastructure indicators, disaggregated data, and better identification strategies to mitigate potential reverse causality and other forms of endogeneity. Full article

This paper develops a stylized continuous-time framework for open-end fund investment in which state-dependent fund flows, passive benchmarking, and strategic manager–investor interaction are modeled jointly. The contribution is not a new Stackelberg solution concept; rather, it is the economic mechanism created by combining a scale-dependent value-added objective with endogenous subscription and redemption jumps. Fund flows are represented by marked compound Poisson processes, and the manager acts as leader while a representative investor chooses participation as a follower. Under bounded controls, bounded Lipschitz jump intensities, admissible jump-size distributions, and strict-concavity conditions, we establish positivity and moment bounds for the state process, provide sufficient conditions for the existence and uniqueness of a Markov feedback Stackelberg equilibrium, and state a jump-diffusion verification theorem. A transparent local feedback approximation and Monte Carlo robustness exercise illustrate how redemption pressure, subscription intensity, jump-size dispersion, benchmark volatility, and risk aversion affect active exposure and terminal fund wealth. The results show that stronger redemption pressure and benchmark-relative risk reduce active positions, whereas subscription incentives increase risk-taking only when expected value added compensates for flow-induced dilution. Within its stated assumptions, the model provides a tractable theoretical benchmark rather than an empirically validated structural model. Full article

Business model adjustment is a critical capability for microenterprises operating in import-dependent industries within emerging markets, and is increasingly recognised in the sustainable business model literature as a mechanism through which firms build economic resilience under resource constraints and volatile operating conditions. This paper examines how a specialty coffee microenterprise in Almaty, Kazakhstan, has adjusted its business model to create, deliver and capture value in a non-producing, landlocked economy characterised by rapid demand growth, currency volatility and high import dependency. The study answers two research questions regarding this case using Osterwalder and Pigneur’s business model canvas as the main analytical framework: (1) How did the case company set up its business model to create, deliver, and capture value? (2) In the context of Kazakhstani specialty coffee roasting, what possibilities and challenges influenced this business model? The analysis combines secondary market data with qualitative evidence from a semi-structured interview conducted in autumn 2025 with the founder of a nine-employee microenterprise that has evolved from a mobile coffee bar into a hybrid B2B–B2C roaster, café operator and e-commerce subscription service. Although Kazakhstan is not a coffee-producing country, its retail coffee market expanded from USD 326.71 million in 2019 to an estimated USD 554.5 million in 2025, with the fresh-coffee share rising from approximately 30% to 37%. The interview account describes a business model centred on locally roasted, traceable specialty coffee delivered fresh to a young, urban customer base, supported by educational and community-building activities. As reported by the founder, the enterprise faces structural challenges including exposure to international green-coffee price spikes—such as the record nominal highs of 354.32 US cents/lb reached in February 2025—currency-related cost volatility, logistical complexity across Eurasian transit routes and constrained access to growth-stage financing. Because the evidence base is a single founder interview combined with secondary market data, the paper does not independently verify the firm’s resilience, viability or financial outcomes. The findings are presented in the form of analytical generalisations—that is, generalisations relating to theoretical conclusions drawn from this specific case, rather than from a broader set of companies—which illustrate, based on the founder’s own account, how this micro-enterprise pursued a targeted, phased adaptation of its business model rather than a radical overhaul; the study does not independently verify resulting sustainability or resilience outcomes. As this is a case study, the present analysis does not allow us to determine the extent to which this model is representative of other micro-enterprises involved in coffee production, or of other non-manufacturing sectors in developing economies; the contribution of this study is empirical and contextual rather than theoretical: it extends the scope of business model analysis to under-researched geographical and institutional contexts and lays the groundwork for future comparative studies. Full article

This study examines whether the diffusion of financial technology expands or constrains private-sector credit across OECD economies, and whether regulatory quality conditions that relationship. Fintech diffusion is operationalized through a composite index of general digital-infrastructure indicators (internet use, mobile subscriptions, and ICT-service exports); this index proxies the broader digitalization environment rather than measuring payment-platform use, embedded credit, or digital lending directly. It conceptualizes digital payment ecosystems as complex financial systems in which technological diffusion, regulatory capacity, and credit dynamics co-evolve through feedback mechanisms. The policy discourse often assumes that digital financial inclusion automatically enhances resilience, yet evidence on credit expansion and systemic exposure remains contested. Using an unbalanced panel of 37 OECD economies from 2015 to 2024, comprising 356 observations, the analysis employs Driscoll–Kraay standard errors to address cross-sectional dependence within a common-slope two-way fixed-effects framework, along with panel quantile regression at the 10th, 50th, and 90th percentiles and Dumitrescu–Hurlin causality testing. Fintech diffusion is positively and statistically significantly associated with private-sector credit exposure, and this association is robust to a two-way fixed-effects specification. The quantile estimates show that the association is present at these selected points of the conditional distribution and strongest at its lower and upper tails, a pattern consistent with complex-adaptive-system dynamics in which effects vary across system states. Contrary to the negative-feedback expectation, the interaction between fintech diffusion and regulatory quality is positive, consistent with high-quality institutions enabling rather than restraining the translation of fintech into credit, though the observational design identifies this interaction rather than the mechanism producing it. The findings therefore shift the discussion from fintech as a stand-alone inclusion tool to fintech as a system-shaping force with implications for institutional resilience, macroprudential supervision, and systemic credit exposure. Because domestic credit to the private sector aggregates household and corporate lending, the policy implication below is framed at this aggregate level: digital payment infrastructure should be governed jointly with consumer protection, credit reporting, and financial-resilience mechanisms rather than promoted as a neutral technological upgrade. Full article

Customer churn is a critical challenge for subscription-based digital platforms, as customer activity patterns change dynamically over time. Traditional churn modeling methods fail to account for sequential dependencies across customer interaction histories. This paper presents Attn-ChurnNet, a novel attention-based Transformer architecture that effectively predicts customer churn by modeling sequential customer contact histories. The proposed methodology leverages multi-head self-attention with sinusoidal positional encodings and Pre-LN residual connections to highlight key interaction sequences and interpret the temporal dynamics of customer activity in subscription platforms. Experiments are conducted on the large-scale WSDM–KKBox Customer Churn Prediction dataset using a temporal train/validate/test split, incorporating transaction records, usage logs, and customer demographic information. Comprehensive comparison against established baselines—Logistic Regression (LR), Random Forest (RF), XGBoost, and Gated Recurrent Unit (GRU)—demonstrates that Attn-ChurnNet achieves a macro-averaged classification accuracy of 97.03% (±0.41%), precision of 95% (±0.5%), recall of 96% (±0.6%), F1-score of 95.50% (±0.5%), AUC of 0.98 (±0.004), Average Precision of 0.963, and log-loss of 0.15 (±0.007) under five-fold stratified cross-validation, outperforming all competing approaches with statistical significance ($p<0.01$, McNemar’s test). A comprehensive two-part ablation study (22 variants), calibration analysis (ECE = 0.031; $T_s^{*}$ = 1.08), attention entropy analysis with Jensen–Shannon divergence and two-sample t-test ($t=18.4$, $p<0.001$), Integrated Gradients attribution, conformal prediction (91.4% coverage, 88% singleton efficiency), precision–recall analysis, and computational complexity evaluation further validate the model’s design and production readiness. Full article

Freely accessible weather APIs are an attractive input for photovoltaic (PV) power prediction, yet how their reference-based error carries through—under temporal aggregation—into day-ahead PV energy is rarely quantified before model development. This study audits eight freely accessible weather-data services—seven forecast services and the Meteostat observational archive—and finds that, for the endpoints, subscription tiers, and collection period evaluated here, only two of the forecast services expose hourly global horizontal irradiance (GHI), while a third advertised irradiance field returns empty—itself a material result for municipal integrators. The two irradiance-capable services are propagated through a common physical PV model of the 548 kWp east–west rooftop plant being built on a university campus in Bratislava, Slovakia, against independent references (CAMS irradiance; NASA POWER temperature and wind). Throughout, “error” denotes discrepancy against the reference, not measurement truth. Over a common 62-day spring–summer window, the day-ahead daily-energy mean absolute error (MAE, relative to mean reference daily energy) is 10–11% for both sources; Open-Meteo’s winter-inclusive own window raises its value to about 13%. On the matched window, Open-Meteo has the lower hourly GHI RMSE (121 vs. 128 W·m⁻²), yet this advantage does not carry through to day-ahead energy—the point-estimate ordering even changes—and neither matched-window difference is statistically resolved. Because daily aggregation rewards low bias over low scatter, this ordering change is already present in daily GHI energy—before the PV conversion—so hourly irradiance accuracy alone does not determine day-ahead energy. Propagated input-data auditing is therefore an advisable step before ML-based PV forecasting and day-ahead scheduling of urban distributed PV. Full article

Digital capitalism is often described either as a clean break with the past or as a continuation of older markets. This article takes a third position: digital capitalism is a reorganisation of capitalist accumulation around platforms, data, attention and digital labour, not a departure from capitalism’s basic logic. The study uses a critical narrative review approach, drawing on Marxian value theory and recent work on platforms, datafication and surveillance. It is anchored by a curated set of publicly reported indicators from institutional and market sources, used as context rather than as a causal test. These show a platform environment that reaches most of humanity, highly concentrated advertising and cloud markets, platform labour as a global phenomenon, and a supposedly weightless economy resting on dense physical infrastructure. The article traces four contradictions: the commodification of unpaid user activity, the material basis of immaterial production, the concentration of market power, and the gap between participation and algorithmic control. The contribution is conceptual. It shows that media business models usually treated as separate, including advertising, subscriptions, creator monetisation, in-game spending and platform commissions, share one logic: user activity is captured as attention, measured as data and converted into revenue. New media therefore function as economic infrastructures for value extraction. Full article

MQTT is widely used in Internet of Things (IoT) systems because of its lightweight publish–subscribe architecture and efficient support for resource-constrained devices. Although wildcard subscriptions simplify topic management, their impact on broker performance, stability, and security under authenticated high-load conditions has not been comprehensively investigated. Existing studies typically evaluate routing performance, security mechanisms, or broker scalability independently, leaving a limited understanding of their combined effects. This paper presents a systematic experimental evaluation of wildcard subscription behavior in an authenticated MQTT v5 environment. A controlled testbed employing TLS-based authentication and role-based access control was used to compare exact-topic subscriptions with single-level (+) and multi-level (#) wildcard subscriptions under progressively increasing workloads. Performance was evaluated using end-to-end latency, CPU utilization, throughput, delivery success rate, broker stability, and authorization exposure. The experimental results demonstrate that increasing wildcard-subscription complexity significantly increases routing overhead, resulting in higher latency and CPU utilization while reducing throughput and broker service capacity. Multi-level wildcard subscriptions consistently exhibited the greatest performance degradation and reached broker saturation at lower workload levels than exact-topic subscriptions, demonstrating that wildcard density compresses the broker’s operational stability region. The experiments also show that broad wildcard-based access control policies increase the risk of authorization leakage when improperly configured. These findings demonstrate that wildcard-subscription complexity is a critical determinant of MQTT scalability, broker stability, and security, and provide practical guidance for designing efficient and secure IoT messaging infrastructures. Full article

Embedded Subscriber Identity Modules (eSIMs) enable consumer devices to install mobile subscriptions remotely under the GSMA SGP.22 standard for Remote SIM Provisioning (RSP). Because RSP sessions rely on classical elliptic-curve cryptography and eSIM profiles can remain active for 5 to 20 years, recorded provisioning traffic faces a concrete Harvest-Now–Decrypt-Later (HNDL) threat. Upgrading the network transport to post-quantum Transport Layer Security (TLS) is often assumed to be sufficient. However, SGP.22 exchanges the keys that protect the profile across the local host-to-chip interface, beneath the transport layer. This paper presents a systematic post-quantum cryptography (PQC) migration framework for consumer RSP. We model four configurations of the SGP.22 on-card key-agreement step and determine, under a quantum key-recovery adversary, which configurations resist HNDL and what resources they require. We combine symbolic verification in ProVerif with a device-grounded evaluation that pairs provisioning and memory observations from a sysmocom C2T research embedded Universal Integrated Circuit Card (eUICC) with strict-instruction-set PQC measurements on an STM32 Nucleo-F446RE development board with an ARM Cortex-M4F core. Among the configurations studied, hybrid classical and post-quantum key exchange is the minimum configuration that resists HNDL, whereas a fully post-quantum configuration also protects authentication against signature forgery. Under the tested platform and resource assumptions, volatile Random Access Memory (RAM), rather than computation, is the binding deployment constraint. We therefore propose a capability-negotiation mechanism that would match a migration configuration to the memory advertised by each card. Full article

Background/Objectives: Patient-facing artificial intelligence chatbots are increasingly used as informal digital health education tools. In orthodontics, eating and drinking advice may directly affect appliance integrity, oral hygiene, enamel demineralization, caries risk, and clear aligner use. This study compared the understandability, actionability, overall quality, and potential harmfulness of responses generated by free and paid versions of ChatGPT and Gemini to patient-oriented orthodontic dietary questions. Methods: This cross-sectional comparative observational study evaluated 160 responses generated from 40 Turkish patient-oriented orthodontic eating and drinking questions across five clinically relevant categories. Each question was submitted separately to ChatGPT free version, ChatGPT Plus, Gemini free version, and Gemini Pro on 1 May 2026, using newly opened independent chat sessions without prompt engineering, follow-up prompts, response regeneration, or manual editing. Responses were anonymized, randomly coded, and independently evaluated by two specialist dentists. PEMAT-P actionability was defined as the primary outcome. PEMAT-P understandability, Global Quality Score, and potentially harmful advice classification were secondary outcomes. Repeated-measures comparisons were performed using Friedman tests and Bonferroni-adjusted Wilcoxon signed-rank tests. Results: PEMAT-P understandability was high across all groups, with median scores of 100.0 in every group. Significant group differences were found for understandability, actionability, and Global Quality Score. ChatGPT Plus achieved the highest actionability score and Global Quality Score and produced no responses classified as potentially harmful. Potentially harmful responses were identified in ChatGPT free version, Gemini free version, and Gemini Pro. For the primary outcome, PEMAT-P actionability, the overall group difference was statistically significant with a small effect size (χ² = 20.527, p < 0.001, Kendall’s W = 0.171), while the largest effect was observed for GQS with a moderate effect size (χ² = 46.520, p < 0.001, Kendall’s W = 0.388). Conclusions: All chatbot groups generated highly understandable responses; however, actionability, overall quality, and safety varied across systems. ChatGPT Plus showed the strongest overall performance under the specific interface, subscription, language, and date conditions tested; however, this finding should be interpreted as a time-specific benchmark rather than evidence that paid chatbot systems are intrinsically safer or more clinically reliable. Structured evaluation, transparent reporting, digital health equity considerations, and professional oversight remain necessary before AI-generated orthodontic dietary advice can be integrated into routine patient education. Full article

This study examines whether recognized intangible assets carry information about bank performance in an emerging market, and whether their information value is conditioned by the maturity of macro digital infrastructure. Using a balanced panel of 28 Indonesian commercial banks over 2015–2024 (280 firm-year observations), we estimate two-way fixed-effects models with macro digital infrastructure, an economy-wide principal component index of internet penetration, mobile and broadband subscriptions, and electronic payment volume as a cross-layer moderator. Intangible investment intensity, proxied by the ratio of reported intangible assets to total assets, shows weak direct associations with performance; only the operating efficiency ratio displays a marginally significant short-run cost, consistent with transition-cost dynamics. The central result is conditional: the interaction between intangible intensity and macro digital maturity is strongly significant for operating efficiency (β = −2.587, p = 0.005), with the implied efficiency cost contracting by a model-implied 88 percent across the observed range of digital maturity (an estimate computed from the estimated coefficients over the observed sample variation, not a structural causal magnitude). Heterogeneity is pronounced across regulator-defined bank tiers (KBMI): the four largest banks realize positive profitability effects, whereas mid-tier banks bear transition costs. Results are robust to Driscoll–Kraay standard errors, system GMM, sub-sample splits, and outlier exclusion. The findings show that the information value of recognized intangibles in banking is state-contingent, extending the intangible-asset and digitalization literature to emerging-market banking. Full article

We develop a stochastic Stackelberg model of a subscription market with cancellation costs. A representative consumer chooses when to subscribe to and cancel a service as the utility derived from the subscription evolves according to a diffusion process, while the firm selects the subscription fee and cancellation cost to maximize its expected payoff. The consumer’s problem is equivalent to the classical real-options model of entry and exit under uncertainty with adjustment costs and exhibits a two-threshold policy with an inaction band and hysteresis. Unlike the standard formulation, in which the optimal thresholds are characterized implicitly through a system of nonlinear equations, we derive an explicit parametric solution in closed form. This solution reduces the firm’s optimization problem to a two-dimensional unconstrained problem and yields a detailed characterization of the optimal pricing policy. We show that the firm’s strategy exhibits three qualitatively distinct regimes depending on the initial utility level. For small utility levels, the optimal cancellation cost is zero. In an intermediate regime, the firm’s optimal policy induces the consumer to set the entry threshold equal to the initial utility level, resulting in immediate subscription. For sufficiently large utility levels, the firm induces permanent lock-in by setting a high cancellation cost and a low subscription fee: the consumer subscribes immediately and never subsequently unsubscribes. The transition between the latter two regimes is discontinuous and results from competition between two local maxima of the firm’s payoff function. We then extend the model to a heterogeneous population of consumers. The superposition of individual two-threshold subscription strategies generates a Preisach hysteresis operator describing the aggregate dependence of the firm’s revenue on the utility dynamics. The discontinuous regime transition persists under heterogeneity, demonstrating the robustness of the underlying mechanism. The Preisach representation predicts complex history dependence and long-term effects of temporary utility shocks. For a gamma distribution of consumer preferences, the firm’s expected payoff is obtained in closed form in terms of incomplete gamma functions. Full article

Industrial systems increasingly rely on MQTT-based message streaming to enable automated, data-driven production processes at the network edge. While semantic models such as the SSN/SOSA ontology enable machine-interpretable descriptions of observations and actuations, an explicit model of message transport is rarely considered. Consequently, MQTT-based communication remains opaque, particularly regarding information processing, hindering the semantic analysis of application-specific topic structures and the behavior of transport protocols. To close this gap, this work introduces the revised MQTT4SSN ontology as a key contribution, extending existing semantic models with protocol-aware representations of MQTT entities, control packets, and transport-level interactions. MQTT4SSN enables end-to-end semantic traceability, from sensor observations and actuator controls to the underlying message transmission within distributed systems. Building on this contribution, the MQTT2RDF integration framework incorporates MQTT4SSN as its core to capture live MQTT traffic and represent both payload meaning and transport-level provenance within an RDF knowledge graph. This work presents a novel approach for representing edge computing and information processing over MQTT, addressing two key challenges. First, the framework supports semantic interpretation of topic hierarchies and provides configurable mappings between MQTT topics, payload structures, and observation or actuation semantics. This approach facilitates the setup of edge computing systems and enables context-aware subscription management and structured data formatting, thereby improving interoperability between heterogeneous deployments. Second, transport-level provenance analytics provide a semantic basis for query-based detection, classification support, and diagnostic analysis of malformed or incomplete MQTT communication. The approach provides explainable, traceable information processing through transport provenance, which is essential for safety-critical industrial environments. The contributions are validated through an industrial use case from a production environment, demonstrating its applicability for system monitoring, troubleshooting, and semantic analytics of MQTT-based infrastructures. Full article

Petri nets represent a highly versatile mathematical formalism for modeling discrete event and hybrid systems. For the development of modern complex production processes for Industry 4.0, integrating these formal models with industrial communication standards is an appropriate and effective option. The main aim of the proposed article is to design a new web-based software tool for the modeling, simulation, and control of mechatronic systems with OPC Unified Architecture support. To accomplish this task, an original software solution called PetriLink is proposed. This platform leverages an intuitive graphical interface and significantly expands the formalism by combining hybrid Petri nets with Colored Petri Nets (CPN) data extensions and a reactive OPC UA subscription model. These new features greatly expand the area of systems that can be modeled and controlled, bridging the gap between theoretical academic tools and practical industrial automation. Furthermore, the structural flexibility of the implemented Petri net models enables the explicit representation of symmetric cyber-physical architectures, as well as the design of asymmetric, event-driven control strategies (e.g., using inhibitor and reset arcs) for enhanced system robustness. The platform was evaluated on a reference net of 5000 places and 2500 transitions, where an incremental dirty-flag evaluation mechanism keeps the per-step engine cost below 1 ms for sparse industrial markings and at about 350 µs for a moderate workload of one hundred concurrent tokens, yielding a speed-up of up to roughly three orders of magnitude over naive full re-evaluation and confirming consistent soft real-time behavior on commodity hardware. Offering a graphical environment for the design of discrete event and hybrid system control algorithms, it can be used for education, research and practice in cyber-physical systems (Industry 4.0). Full article

The paper presents the results of an empirical study comparing the operational and environmental effects of using industrial printing machines under a subscription-based model (PaaS) versus a traditional ownership model. The analysis covered two identical, high-performance Heidelberg Speedmaster XL106-8P machines operating in print production facilities with similar production profiles. A range of quantitative indicators were examined, including Overall Equipment Effectiveness (OEE), as well as parameters such as operating time, production cycles, and the amount of production waste over a 14-month period. The results indicate that the subscription model delivers benefits in terms of quality, stability, and reduced material losses, despite a lower production volume. Statistically significant differences in favor of the subscription model were recorded in OEE Speed, OEE 10,000, and waste indicators (Run Waste % and avg). The article demonstrates substantial, independent of scale effects, operational and environmental benefits of the subscription model in manufacturing industrial applications. Full article