Power constitutes a central yet often overlooked factor in organizational decision-making. In companies, it shapes not only formal decision processes but also informal structures and the distribution of influence within hierarchies. This study examines how structural changes in formal organizational hierarchies affect the distribution of power. Building on an entropy-based modeling framework, directed authority relations are translated into a cardinal measure of structural power. The analysis employs the expert system Shell-SPIRIT to derive power potentials from probabilistic representations of organizational structures. Using systematically varied organizational scenarios under ceteris paribus conditions, the study quantitatively substantiates three general theses on steering power through organizational design. The results demonstrate how intuitive mechanisms of power formation can be formally measured and compared, thereby providing a transparent analytical foundation for integrating structural power into organizational analysis and decision-making. • Introduce a cardinal-scale method for measuring relational power in decision-making. • Quantify power dynamics using an entropy-based data-driven evaluation model. • Examine how hierarchical shifts affect power distribution within dynamic organizations. • Present strategic insights for steering power potential in organizational structures. • Derive actionable theses for managing power in complex decision environments.

In international trade, selecting an Incoterm is a critical decision impacting costs and risks. Previous quantitative studies relied on traditional econometric models imposing strict linearity, limiting causal interpretation. This study addresses this gap by applying a causal inference framework to identify Incoterm choice drivers among Colombian exporters. The methodology combines (i) directed acyclic graphs to encode causal assumptions; (ii) nested, cross-validated random forests to capture complex, nonlinear relationships; and (iii) G-computation to estimate unbiased causal effects. Results show that transactional features dominate selection. Transitioning from bulk to containerized cargo increases the probability of selecting an E/F-group term-such as Ex Works (EXW), Free On Board (FOB), or Free Carrier (FCA), where the seller’s obligations end upon delivery to the buyer-by 13.4 percentage points. Additionally, shipment weight and value exhibit nonlinear threshold effects, where E/F terms become significantly more probable only after shipments surpass specific size thresholds-a pattern missed by prior linear models. Relational factors also matter; sharing a common language with the trade partner increases E/F-term probability by 6.5 percentage points. This framework provides managers with a tool to predict Incoterm choices and quantify the causal impact of altering shipment profiles, enabling better-aligned, lower-risk trade contracts. • Apply a causal framework to model trade term selection in real-world customs data. • Capture nonlinear effects of shipment size and value using advanced learning models. • Identify primary trade drivers through empirical analysis of firm-level transactions. • Estimate causal impacts of logistics choices with robust decision tools. • Support international trade planning with data-informed strategic insights.

As markets become increasingly digitalised, understanding the emotional and cognitive drivers of consumer behaviour is essential for developing responsive and targeted marketing strategies. This study contributes to the development of Customer Digital Twins (CDTs) by integrating neuromarketing techniques with social media analytics, positioning the work at the intersection of data science and behavioural psychology. The proposed framework combines electroencephalography (EEG) recordings with sentiment analysis of user-generated content to capture complementary dimensions of consumer emotion and decision making. An experimental design was implemented in which participants’ emotional responses were monitored via EEG during an online shopping task, followed by an ethical priming intervention and sentiment-based analysis of post-task responses. The study focuses on a fast-fashion e-commerce setting in which ethically framed information is introduced to examine how value-laden cues reshape cognitive and emotional states. The results show that EEG-derived indicators, including engagement, workload, and emotional valence, and text-based sentiment measures provide partially complementary views of consumer states, while also highlighting significant challenges in their direct integration. The joint use of these modalities offers richer descriptive insight into customer motivation and behaviour, but simple fusion strategies exhibit limited predictive power under real-world, asynchronous conditions. The findings identify both technical and ethical challenges associated with multimodal consumer modelling and establish a foundation for future work on temporally aligned and dynamically adaptive customer digital twin architectures. A proof-of-concept modelling exercise further explores the feasibility of using combined neuro-sentiment features to predict valence-related indices, serving as an initial diagnostic decision analytics layer rather than a fully realised digital twin. • Integrate brain signal data and social media text to construct ethical customer digital twins. • Model emotional persistence to enhance the analysis of customer decision behaviour. • Predict emotional states using fused brain and sentiment features. • Develop a reproducible analytics pipeline for customer digital twin construction. • Support ethical marketing decisions through emotion-aware customer modelling.

Multi-criteria decision-making (MCDM) is widely used in engineering to assist in the selection of the best alternative according to various criteria. Many MCDM methods rely on fixed weight assignments, limiting their ability to reflect uncertainties or variations in decision-maker preferences. This work shows a novel approach that integrates the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with conventional one-at-a-time Weight Sensitivity Analysis (WSA) and, for the first time, introduces the Full-Range Weight Sensitivity Analysis (FRWSA). FRWSA enumerates all admissible weight vectors on a discretized simplex and summarizes robustness as dominance frequency, the share of the simplex for which an alternative ranks first. A wastewater treatment plant (WWTP) case study evaluating four different configurations illustrates the approach. Using FRWSA with step h = 0.05 (10 million weight combinations), configuration A2/O-D dominates 82.80% of the weight simplex, with UCT at 12.17%, A2/O-S at 4.94%, and BARD at 0.09%. Comparing with Monte Carlo sampling, FRWSA provides a deterministic, variance-free baseline: MC-Dirichlet with α = 0 . 5 is nearly identical (JSD ≈ 0.001 bits), α = 1 . 0 is close but distinct (JSD ≈ 0.007-0.008 bits), and plain MC remains farther (JSD ≈ 0.043 bits) over 10 4 – 10 7 draws. The framework improves transparency via global coverage and boundary diagnostics and is method-agnostic (replicated with VIKOR in the SI). A reusable MATLAB implementation is provided to facilitate adoption. This integrated analysis supports robust and transparent engineering decisions wherever weight uncertainty matters. • Apply TOPSIS to compare fixed and variable weight methods in selecting the best alternative. • Integrate full-range weight sensitivity analysis into TOPSIS to improve ranking transparency and robustness. • Evaluate all weight combinations systematically using a discretized weight space. • Offer a reusable MATLAB tool for exhaustive sensitivity-driven decision analysis. • Demonstrate the robustness of one wastewater treatment option across varied scenarios.

This paper presents a novel methodological approach for the dynamic evaluation of innovative medical devices under consideration of external stochastic risks. We develop a model based on the Hamilton–Jacobi formalism with stochastic extensions that integrates both deterministic aspects of regulatory inertia and stochastic influences such as patent disputes and market dynamics. The theoretical innovation diffusion patterns are described by coupled stochastic differential equations, whose solution is approximated using numerical methods. Particularly, the coupling of Bass diffusion models with multi-stage decision processes enables a realistic representation of the complex interactions between risk activation and market penetration. The presented methodology bridges concepts from statistical physics, thermodynamics, and innovation economics, providing both theoretical insights into the transfer of physical methods to economic questions and practical decision aids for innovators in the medical technology sector. By introducing the Non-ergodic risk functional (NERF) — based on recent advances in ergodicity economics—we establish a mathematically rigorous framework for evaluating innovation trajectories under various non-ergodic risk scenarios based on literature derived parametrizations. • Develop a decision model for optimizing innovation under regulatory and patent uncertainty. • Quantify how regulatory delays and patent erosion affect innovation outcomes. • Apply physics-inspired analytics to improve health technology decision-making. • Demonstrate the strategic benefits of dynamic pathway selection in device development. • Offer a reproducible method for optimizing high-stakes innovation decisions.

This study examines the nexus between Google Trends’ collective interest in specific keywords related to technological advancements utilized in design and the stock performance of major companies in design-related sectors. Specifically, the paper examines causality patterns between Google Trends keywords and stock prices of design companies, also employing multi-fractal detrended cross-correlation analysis, to test for long-term relationships. According to the results, varying impacts across keywords on stock prices are identified, with non-fungible token (NFT) exhibiting the greatest influence, followed by three-dimensional (3D) printing and computer-aided design, virtual reality (VR) displays a noteworthy impact, while artificial intelligence (AI) design and generative design indicate the least impact. The results also reveal persistent long-term relationships between the examined variables, with rich multifractal behavior indicating complex relationships, mostly balanced. The findings are important for policymakers and managers, necessitating close monitoring, especially of NFT, and for design companies to align strategies for market movements. • Examine how online interest in design technologies influences stock performance in design-focused industries. • Identify NFTs and 3D printing as major drivers of stock movements in the design market. • Reveal multifractal patterns indicating persistent, complex links between trend data and stock values. • Recommend tracking emerging design technologies for timely decision-making in investment and policy. • Provide data-driven insights for anticipating stock behavior in response to evolving digital interest.