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Non-native fish invasions are important drivers of freshwater biodiversity and ecosystem function loss. In this study, we compared the trophic niches of four non-native (Rhinogobius giurinus, Hemiculter leucisculus, Hypomesus nipponensis, and Tachysurus fulvidraco) and one native fish species (Carassius auratus) from April 2022 to December 2023 in Erhai Lake, a typical plateau lake on the Yunnan–Guizhou Plateau, China. We analyzed δ¹³C and δ¹⁵N from 766 fish samples and calculated 103 SEA_b values across species, seasons, and lake regions. Stable isotope analyses revealed pronounced trophic niche differentiation between non-native and native fishes. Non-native species exhibited wider niche width (4.81 ± 0.48), lower overlap (24.43 ± 1.57), and higher within-group dispersion (2.69 ± 0.07), indicating greater trophic plasticity. In contrast, native fishes showed narrower niches (2.72 ± 0.32), higher overlap (37.32 ± 4.21), and lower plasticity (1.68 ± 0.08). Moreover, non-native and native fishes adopted contrasting trophic strategies: individual fitness increased with niche expansion in non-native fishes, whereas it declined in native fishes. Multiple linear analyses further indicated significant competitive effects of non-native fishes on native species’ niches, suggesting that niche expansion in native fishes represents a passive response to intensified competition rather than an adaptive strategy. Overall, the high trophic plasticity of non-native fishes and their asymmetric effects on native species imply a high risk of food web reorganization in Erhai Lake. These results provide guidance for the sustainable management of Erhai Lake, balancing invasive species control with native fish conservation. Our results underscored the importance of incorporating trophic interactions into invasion management and native fish conservation. Full article

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35 pages, 6265 KB

Open AccessArticle

Topological Progress Potential-Enhanced Continuous-Space Ant Colony Algorithm for Robot Path Planning

by Guikun Dong, Feixiong Zhao, Jiaxiong Zhuo, Lei Zhou, Qiaoling Liu and Xiangjun Yang

Sensors 2026, 26(4), 1264; https://doi.org/10.3390/s26041264 (registering DOI) - 14 Feb 2026

Abstract

To address the issues of traditional grid-based Ant Colony Optimization path planning in discretized continuous space—including limited direction freedom, lack of global topological guidance, and difficulty in balancing path smoothness and safety margin—a topological progress potential-enhanced continuous-space ant colony path planning algorithm (TPP-CSACO) is proposed. TPP-CSACO discards grid-based expansion; instead, a perception circle centered on each ant is defined, movement is executed via a sector-based perception framework with probabilistic direction selection, and band-shaped decaying pheromones are deposited along the path. By coupling the global topological progress potential derived from the simplified probabilistic roadmap (PRM) with pheromones, a dual-field guidance mechanism is established to prevent local congestion. Combined with the explicit safety constraints of the signed distance field (SDF), an adaptive step size strategy that integrates elastic step size and frustration-induced temperature rise is introduced to enhance obstacle avoidance and search stability. Results from repeated experiments on multiscale constrained maps (conducted against six typical algorithms and the traditional ACO) show that compared with ACO, TPP-CSACO reduces the path length by up to 50.6% in the same environment, while achieving faster convergence and maintaining good search diversity. Although the path length increases slightly (by a maximum of 5.9%) compared with the shortest heuristic algorithms, the maximum turning angle is reduced by 75% to 93%, and a 100% success rate and zero safety violations are realized. This indicates that TPP-CSACO has achieved a relatively stable balance among safety, smoothness, and global search capability. Full article

(This article belongs to the Section Sensors and Robotics)

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20 pages, 1278 KB

Open AccessArticle

Graph Neural Network-Guided TrapManager for Critical Path Identification and Decoy Deployment

by Rui Liu, Guangxia Xu and Zhenwei Hu

Mathematics 2026, 14(4), 683; https://doi.org/10.3390/math14040683 (registering DOI) - 14 Feb 2026

Abstract

Static honeypot deployment and one-shot attack-path analysis often become ineffective against adaptive adversaries because fixed decoy layouts are easy to fingerprint and risk estimates quickly go stale. This paper presents a unified, mathematically grounded TrapManager framework that couples graph representation learning with budget-constrained combinatorial optimization for dynamic cyber deception. We model attacker progression on vulnerability-based attack graphs and learn context-aware node embeddings using a Graph Attention Network (GAT) that fuses vulnerability-driven risk signals (e.g., CVSS-derived node scores) with structural features. The learned representations are used to estimate edge plausibility and rank candidate source–target routes at the path level. Given limited resources, we formulate pointTrap placement as a Mixed-Integer Programming (MIP) problem that maximizes the expected interception of high-risk paths while penalizing deployment cost under explicit budget constraints, including mandatory coverage of the top-ranked critical paths. To enable online adaptiveness, a pointTrap-triggered, event-driven feedback mechanism locally amplifies risk around alerted regions, updates path weights without retraining the GAT, and re-solves the MIP for rapid redeployment. Experiments on MulVAL-generated benchmark attack graphs and cross-domain transfer settings demonstrate fast convergence, strong discrimination between attack and non-attack edges, and early interception within a small number of hops even with minimal decoy budgets. Overall, the proposed framework provides a scalable and resource-efficient approach to closed-loop attack-path defense by integrating attention-based learning and integer optimization. Full article

(This article belongs to the Special Issue New Advances in Distributed Systems, Edge Intelligence, and Artificial Intelligence)

21 pages, 663 KB

Open AccessArticle

Evaluation of Propylene Glycol and Essential Oil Supplementation on Growth Performance, Feed Efficiency, Serum Biochemical Indices, Hematological Parameters, and the Expression of Antifreeze IV and Lipid Metabolism-Related Genes in Nile Tilapia

by Doaa R. Saleh, Abeer F. El-Nahas, Walaa S. H. Abd El Naby, Hadir A. Aly, Ehab El-Haroun and Shymaa A. Khatab

Animals 2026, 16(4), 615; https://doi.org/10.3390/ani16040615 (registering DOI) - 14 Feb 2026

Abstract

Aquaculture output, sustainability, and profitability can be enhanced by using functional feed additives. The effect of supplementation with two different dietary levels of propylene glycol (PG) and essential oils (EOs) was evaluated in Nile tilapia. A total of 150 juvenile fish were randomly allocated into five groups. Growth performance, feed utilization, serum biochemistry, hematology, and gene expression were assessed. PG supplementation significantly improved growth performance, feed conversion, protein efficiency, and energy utilization. Both additives significantly reduced cortisol and glucose levels and altered liver enzymes and lipid profiles. PG improved immunological indices, while hematological responses were dose-dependent; both EOs and PG increased hematocrit, mean corpuscular volume (MCV), and mean corpuscular hemoglobin (MCH). Moreover, the high PG dose significantly increased platelet counts, reduced hemoglobin (Hb), and elevated hematocrit. Additionally, EOs significantly upregulated antifreeze protein IV (AFPIV) and fat metabolism-related genes in a dose-dependent manner, indicating a potential role in lipid mobilization and stress tolerance. Expression analysis of the immunoglobulin H (IGMH) gene revealed a significant increase in PG-supplemented groups, suggesting its immunostimulatory potential. Overall, PG enhanced immunity and growth performance, while EOs promoted AFPIV and fat metabolism gene expression. Therefore, PG and EO supplementation could serve as an effective functional strategy to enhance O. niloticus growth, stress adaptation, and immune resilience. Full article

(This article belongs to the Section Aquatic Animals)

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14 pages, 1921 KB

Open AccessArticle

The Seasonal Dietary Shift and Niche Resilience of Yaks on the Qinghai–Tibetan Plateau

by Shuai Zheng, Yuning Ru, Mengyuan Xu, Yushou Ma, Yuan Ma and Na Guo

Animals 2026, 16(4), 613; https://doi.org/10.3390/ani16040613 (registering DOI) - 14 Feb 2026

Abstract

Understanding how herbivores adjust their foraging strategies to cope with seasonal resource fluctuations has been central to the nutritional ecology. Optimal Foraging Theory (OFT) predicts that generalists should broaden their dietary niche when high-quality resources are scarce, but empirical evidence in extreme environments remains poorly understood. We used trnL-P6 metabarcoding of fecal samples (n = 10/season) and a local reference library of 120 plant species to quantify diet composition and niche metrics of free-ranging yaks (Bos grunniens) on the Qinghai–Tibetan Plateau in June (summer) and October (autumn) 2024. Yaks shifted from a diverse, forb-dominated diet (e.g., Polygonaceae, Rosaceae) in summer to a specialized diet dominated by grasses in autumn. Although dietary richness and total niche width (TNW) decreased in autumn, phylogenetic diversity remained stable, indicating a strategic shift to distinct evolutionary lineages to ensure functional redundancy. Furthermore, food network analyses demonstrated a transformation from a flexible, modular foraging pattern in summer to a highly integrated, synchronized network in autumn. These findings suggest that under the distinct quality–quantity trade-off of high-altitude ecosystems, yaks adopt an energy-maximization strategy by minimizing search costs, aligning with the opportunity cost constraints of OFT, rather than randomly expanding their niche. This insight into selective foraging dynamics is critical for developing sustainable grazing practices that accommodate the natural adaptive behaviors of alpine herbivores. Full article

(This article belongs to the Special Issue Maximizing Fiber Utilization for Sustainable, Efficient Ruminant Production)

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16 pages, 434 KB

Open AccessArticle

Modern Speech Recognition for Romanian Language

by Remus-Dan Ungureanu and Mihai Dascalu

Appl. Sci. 2026, 16(4), 1928; https://doi.org/10.3390/app16041928 (registering DOI) - 14 Feb 2026

Abstract

Despite having approximately 24 million native speakers, Romanian remains a low-resource language for automatic speech recognition (ASR), with few accurate and publicly available systems. To address this gap, this study explores the challenges of adapting modern speech recognition models, such as wav2vec 2.0 and Conformer, to Romanian. Our investigation is a comprehensive analysis of the two models, their capabilities to adapt to Romanian data, and the performance of the trained models. The research also focuses on unique attributes of the Romanian language, data collection techniques, including weakly supervised learning, and processing methodologies. Building on the previously introduced Echo dataset of 378 h, we release CRoWL (Crawled Romanian Weakly Labeled), a weakly supervised dataset of 9000 h created via automatic transcription. We obtain strong results that, to the best of our knowledge, are competitive with or exceed publicly reported results for Romanian under comparable open evaluation settings, with Conformer attaining 3.01% WER on Echo + CRoWL and wav2vec 2.0 reaching 4.04% (Echo) and 4.17% (Echo + CRoWL). In addition to the datasets, we also release our most capable models as open source, along with their training plans, thereby providing a solid foundation for researchers interested in languages with limited representation. Full article

(This article belongs to the Special Issue Computational Linguistics: From Text to Speech Technologies, 2nd Edition)

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14 pages, 591 KB

Open AccessReview

Distinguishing Mood and Emotion: Implications for High-Performance Regulation

by Andrew M. Lane

Brain Sci. 2026, 16(2), 231; https://doi.org/10.3390/brainsci16020231 (registering DOI) - 14 Feb 2026

Abstract

Distinguishing mood from emotion has long posed challenges for psychology, with persistent definitional ambiguity limiting both theoretical precision and applied effectiveness. Our early work, identified duration and cause attribution as the most reliable markers differentiating short-lived, event-linked emotions from more diffuse, enduring moods. Researchers further advanced understanding by conceptualising emotions as feedback signals that support learning and adaptation, while the 4Rs model translated these insights into applied practice by embedding cause attribution within affect regulation. This paper integrates these conceptual, functional, and applied perspectives to demonstrate why accurate classification of affective states is a functional necessity in high-performance contexts. I propose that misclassifying moods and emotions may contribute to inefficient deployment of self-regulatory resources, whereas distinguishing states based on cause attribution may support more targeted and efficient regulation. Drawing on examples from sport, healthcare, performing arts, military operations, and corporate leadership, this paper synthesizes existing work to highlight the practical implications of the mood–emotion distinction for applied psychology. Full article

(This article belongs to the Special Issue Defining Emotion: A Collection of Current Models)

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32 pages, 4352 KB

Open AccessArticle

Probability Distribution Tree-Based Dishonest-Participant-Resistant Visual Secret Sharing Using Linearly Polarized Shares

by Shuvroo JadidAhabab and Laxmisha Rai

Algorithms 2026, 19(2), 153; https://doi.org/10.3390/a19020153 (registering DOI) - 14 Feb 2026

Abstract

With the rapid growth of data transmission and visual encryption technologies, Visual Secret Sharing (VSS) has become an important technique for image-based information protection. However, many existing VSS schemes remain vulnerable to dishonest participants who attempt to recover secret images through unauthorized stacking or manipulation of shares. To address this issue, this paper proposes a dishonest-participant-resistant VSS scheme based on linearly polarized shares and Probability Distribution Trees (PDTs). The proposed method embeds both secret and fake images into polarized shares, such that any unauthorized stacking of ordinary shares produces a visually plausible fake image or random noise, while only stacking that includes the master share under a predefined optical ordering reveals the true secret image. Binary image binarization and probability-guided polarization assignment are employed to improve computational efficiency and increase uncertainty against adaptive attacks. In addition to visual inspection and contrast analysis, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and visual information fidelity (VIF) are used as complementary metrics to distinguish authorized reconstructions from unauthorized and partial ones. Experimental results show that authorized reconstructions achieve high visual fidelity and perceptual recognizability, whereas unauthorized and partial reconstructions yield significantly degraded or misleading outputs, demonstrating effective suppression of information leakage and strong resistance against dishonest behavior. Consequently, the proposed scheme enhances security and practical usability compared with existing polarization-based VSS approaches. Full article

(This article belongs to the Special Issue Visual Attributes in Computer Vision Applications)

19 pages, 5211 KB

Open AccessArticle

Predictions of Wear Performances of AlSi7Mg0.6 Cast Aluminum Alloy Under Different Displacement and Applied Load

by Guoqing Gu, Yun Ma, Fei Du and Aiguo Zhao

Materials 2026, 19(4), 752; https://doi.org/10.3390/ma19040752 (registering DOI) - 14 Feb 2026

Abstract

AlSi7Mg0.6 aluminum alloy is widely adopted in many industrial fields due to its favorable mechanical properties and lightweight merits. In the catenary system of high-speed railways, AlSi7Mg0.6 aluminum alloy is adopted as the substrate of the positioning hook and positioning support, which exhibit abnormal wear in some railways. Thus, it is very important to reveal the underlying wear characteristics and discover the key factors involved. In this study, the influences of displacement (0.5 mm, 1.5 mm, and 3.0 mm) and applied load (20 N, 50 N, 100 N, and 200 N) on the wear performance of AlSi7Mg0.6 aluminum alloy are investigated experimentally and numerically. Wear experiments are time-consuming and costly, but the finite element method (FEM) can effectively solve this problem. A UMESHMOTION user-defined subroutine integrated with an ABAQUS Arbitrary Lagrangian–Eulerian (ALE) adaptive mesh technique was developed to simulate the wear evolution process of the aluminum alloy under varying displacements and applied loads. The results indicate that the wear evolution process of AlSi7Mg0.6 aluminum alloy can be effectively simulated using the UMESHMOTION subroutine. The maximum wear depth (MWD) from the FEM deviates from the experimental results by no more than 10%, and the deviation is smaller than the experimental values. The largest deviation occurs when the displacement is 3.0 mm and the applied load is 100 N, where the discrepancy reaches 7.53%. The wear volume (WV) obtained from the FEM shows a deviation of less than 20% compared to experimental results. For the case with a displacement of 0.5 mm, the numerical results underestimate the wear volume, while for the case with displacements of 1.5 mm and 3.0 mm, the numerical results overestimate the wear volume. The largest deviation in this case occurs for the case with a displacement of 3.0 mm and applied loading of 100 N, with a discrepancy of 16.33%. Full article

(This article belongs to the Special Issue Advances in Metal Coatings for Wear and Corrosion Applications (Second Edition))

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32 pages, 7852 KB

Open AccessArticle

Techno-Economic and Environmental Evaluation of Building Retrofit Strategies Toward NZEB Targets in Hot Climatic Contexts

by Mohanad M. Ibrahim, Micheal A. William, Aly M. Elharidi, Ahmed A. Hanafy and María José Suárez-López

Sustainability 2026, 18(4), 1991; https://doi.org/10.3390/su18041991 (registering DOI) - 14 Feb 2026

Abstract

In response to growing energy demands and climate pressure in hot regions, this study presents an integrated techno-economic and environmental assessment of building envelope retrofit strategies aimed at facilitating the transition of existing buildings toward Nearly Zero-Energy Building (NZEB) targets. Three advanced retrofit solutions—radiative coatings (RC), glazing-integrated photovoltaic (GIPV) systems, and solar green roofs—are evaluated using a validated building performance simulation framework across four representative climatic zones in Egypt. The results demonstrate that radiative coatings provide the most favorable economic performance, achieving return on investment (ROI) values between 12.37% and 21.72% and payback periods ranging from 3.5 to 6.2 years. Solar green roofs and GIPV systems deliver substantial reductions in annual electricity consumption and operational CO₂ emissions, with their performance strongly influenced by climatic conditions and cooling demand intensity. Solar green roofs achieve ROI values of 5.15–6.54% with payback periods of 11.7–14.9 years, while GIPV systems yield ROI values of 4.0–5.24% and payback periods between 14.6 and 17.1 years. Overall, the findings indicate that climate-adapted envelope retrofit strategies can significantly enhance building energy performance while providing measurable economic and environmental benefits. This study offers a robust, data-driven basis for retrofit prioritization and policy formulation in hot regions. Full article

(This article belongs to the Section Sustainable Engineering and Science)

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23 pages, 19310 KB

Open AccessArticle

Towards Robust Infrared Ship Detection via Hierarchical Frequency and Spatial Feature Attention

by Liqiong Chen, Guangrui Wu, Tong Wu, Zhaobing Qiu, Huanxian Liu, Shu Wang and Feng Huang

Remote Sens. 2026, 18(4), 605; https://doi.org/10.3390/rs18040605 (registering DOI) - 14 Feb 2026

Abstract

Spaceborne infrared ship detection holds critical strategic significance in both military and civilian domains. As a crucial data source for ship detection, infrared remote sensing imagery offers the advantages of all-weather detection and strong anti-interference capability. However, existing methods often overlook the detailed features of small ships and fail to effectively suppress interference, leading to missed detections and false alarms in complex backgrounds. To tackle this issue, this study proposes a hierarchical frequency- and spatial-feature attention network (HFS-Net) for fast and accurate ship detection in spaceborne infrared images. The main motivation is to aggregate frequency-spatial information for improved feature extraction, while devising novel hybrid attention-based structures to facilitate interaction among semantic information. Specifically, we design an adaptive frequency-spatial feature attention (AFSA) module to enrich the feature representation. In particular, AFSA integrates information from spatial and frequency domains and introduces channel attention to adaptively extract important features and edge details of ship targets. In addition, we propose an attention-based component-wise feature interaction (ACFI) module that combines multi-head self-attention to capture long-range feature dependencies and component-wise feature aggregation to further enhance the interaction of high-level semantic information. Extensive experiments demonstrate that HFS-Net achieves higher detection accuracy than several representative detectors in maritime infrared scenes with small ships and complex backgrounds, while maintaining real-time efficiency and moderate computational complexity. Full article

21 pages, 958 KB

Open AccessArticle

Driving Style Recognition for Commercial Vehicles Based on Multi-Scale Convolution and Channel Attention

by Xingfu Nie, Xiaojun Lin, Zun Li and Bo Ji

Appl. Sci. 2026, 16(4), 1925; https://doi.org/10.3390/app16041925 (registering DOI) - 14 Feb 2026

Abstract

Driving style recognition plays a crucial role in improving the operational safety, fuel efficiency, and intelligent control of commercial vehicles. Under real-world driving conditions, Controller Area Network (CAN) bus data from commercial vehicles simultaneously contain rapid transient variations induced by pedal and braking operations, as well as long-term behavioral trends reflecting driving habits, exhibiting pronounced multi-temporal characteristics. In addition, such data are typically affected by high noise levels, high dimensionality, and highly variable operating conditions, which makes it difficult for methods relying on single-scale features or handcrafted rules difficult to maintain robust and stable performance in complex scenarios. To address these challenges, this paper proposes a driving style classification network, termed the Multi-Scale Convolution and Efficient Channel Attention Network (MSCA-Net). By employing parallel convolutional branches with different temporal receptive fields, the proposed network is able to capture fast driver responses, local temporal dependencies, and long-term behavioral evolution, enabling unified modeling of cross-scale temporal patterns in driving behavior. Meanwhile, the Efficient Channel Attention mechanism adaptively emphasizes CAN signal channels that are highly relevant to driving style discrimination, thereby enhancing the discriminative capability and robustness of the learned feature representations. Experiments conducted on real-world multi-dimensional CAN time-series data collected from commercial vehicles demonstrate that the proposed MSCA-Net achieves improved classification performance in driving style recognition. Furthermore, the potential application of the recognized driving styles in adaptive Automated Manual Transmission shift strategy adjustment is discussed, providing a feasible engineering pathway toward behavior-aware intelligent control of commercial vehicle powertrains. Full article

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19 pages, 1562 KB

Open AccessArticle

Vox2Face: Speech-Driven Face Generation via Identity-Space Alignment and Diffusion Self-Consistency

by Qiming Ma, Yizhen Wang, Xiang Sun, Jiadi Liu, Gang Cheng, Jia Feng, Rong Wang and Fanliang Bu

Information 2026, 17(2), 200; https://doi.org/10.3390/info17020200 (registering DOI) - 14 Feb 2026

Abstract

Speech-driven face generation aims to synthesize a face image that matches a speaker’s identity from speech alone. However, existing methods typically trade identity fidelity for visual quality and rely on large end-to-end generators that are difficult to train and tune. We propose Vox2Face, a speech-driven face generation framework centered on an explicit identity space rather than direct speech-to-image mapping. A pretrained speaker encoder first extracts speech embeddings, which are distilled and metric-aligned to the ArcFace hyperspherical identity space, transforming cross-modal regression into a geometrically interpretable speech-to-identity alignment problem. On this unified identity representation, we reused an identity-conditioned diffusion model as the generative backbone and synthesized diverse, high-resolution faces in the Stable Diffusion latent space. To better exploit this prior, we introduce a discriminator-free diffusion self-consistency loss that treats denoising residuals as an implicit critique of speech-predicted identity embeddings and updates only the speech-to-identity mapping and lightweight LoRA adapters, encouraging speech-derived identities to lie on the high-probability identity manifold of the diffusion model. Experiments on the HQ-VoxCeleb dataset show that Vox2Face improves the ArcFace cosine similarity from 0.295 to 0.322, boosts R@10 retrieval accuracy from 29.8% to 32.1%, and raises the VGGFace Score from 18.82 to 23.21 over a strong diffusion baseline. These results indicate that aligning speech to a unified identity space and reusing a strong identity-conditioned diffusion prior is an effective method to jointly improve identity fidelity and visual quality. Full article

(This article belongs to the Section Artificial Intelligence)

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17 pages, 592 KB

Open AccessArticle

Performance Evaluation of a Ship Waste Heat-Driven Freshwater Production System Based on Rotary Dehumidification and Seawater Condensation

by Guanghai Yang, Defeng Ding, Ziwen Zhu, Guojie Zheng and Shilong Jiao

Processes 2026, 14(4), 666; https://doi.org/10.3390/pr14040666 (registering DOI) - 14 Feb 2026

Abstract

This study evaluates integrated shipboard freshwater production and fresh air pretreatment on a 20,000 TEU-class container vessel, addressing its freshwater demand and the inefficient recovery of exhaust waste heat from the main engine. The system integrates rotary dehumidification, seawater condensation, and water purification. A theoretical model was developed to evaluate the system performance, incorporating design, thermodynamic modeling, parameter optimization, and adaptability analyses under various operating conditions. The results indicate that under optimal conditions (seawater at 25 °C, outlet temperature difference of 2 °C), the single-stage system is predicted to produce approximately 1.45 m³ of freshwater per day, meeting 20.7% of the vessel’s freshwater requirement. The auxiliary electrical energy consumption, estimated based on standard engineering correlations, is 1–1.5 kWh/m³, representing a 70–80% reduction compared to conventional reverse osmosis systems (3–6 kWh/m³). The sensitivity coefficient for seawater temperature was −0.334, whereas that for output temperature was −0.167. A two-stage series configuration has the potential to further improve the demand satisfaction rate to 41–61%. Overall, the proposed system enables the cascade utilization of ship waste heat and functional integration of air pretreatment and freshwater production, offering a promising auxiliary engineering solution for energy conservation, emission reduction, and onboard freshwater self-sufficiency in marine applications. Full article

(This article belongs to the Special Issue Heat and Mass Transfer Optimization in Efficient Energy Conversion and Thermal Management)

30 pages, 1605 KB

Open AccessArticle

Digital Disruptive Innovation and Firm Performance Nexus: Role of Dynamic Managerial Competence, Innovative Work Practices and COVID-19

by Omar Al Farooque, Shoaib Raza and Ashfaq Ahmad Khan

J. Risk Financial Manag. 2026, 19(2), 149; https://doi.org/10.3390/jrfm19020149 (registering DOI) - 14 Feb 2026

Abstract

This study investigates, with a particular focus on understanding how digital change shapes firm performance in an emerging economy context, first, the impact of digital disruptive innovation, conceptualized as an external condition characterized by technological, market, and competitive turbulence on firm performance within tech-intensive service sector companies, and second, the mediating influence of management skills, proxied by dynamic core managerial competence, and the moderating influence of modern management practices, proxied by innovative work practices, on this relationship. It also examines the moderating effect of innovative work practices on the relationship between digital disruptive innovation and dynamic core management competence, and the impact of COVID-19 on the link between dynamic core management competence and firm performance. This study applies structural equation modelling (SEM) (AMOS 26.0 software) to explore several hypotheses testing for target relationships. The sample was collected via a Qualtrics online survey from 730 senior executives working in digital telecom and banking firms in Pakistan. The study findings show that digital disruptive innovation has a negative effect on service sector performance, and this negative impact is also mediated by dynamic core management competence, as heightened digital disruption tends to weaken managerial competence, which subsequently affects firm performance. While innovative work practices exhibit a positive association with performance, they also positively moderate the negative effect of digital disruptive innovation on performance and mitigate the negative impact of dynamic core management competence on performance. The analysis also reveals that the COVID-19 pandemic positively moderates the effect of dynamic core management competence on performance, indicating that managerial adaptability becomes particularly important when firms operate under crisis conditions. Overall, this study highlights the significance of these phenomena on firm performance in an emerging economy context and provides practical insights for managers and policymakers operating in digitally disrupted service sectors. Full article

(This article belongs to the Section Business and Entrepreneurship)

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