Search Results (3,891)

In this digital era, many companies are integrating new solutions involving Artificial Intelligence (AI)-based automation systems to optimize processes, reach higher efficiency, and help them with decision-making. While implementing these changes, various challenges may arise, including resistance to AI integration from employees. This study examines how employees’ perceived benefits, concerns, and trust regarding AI-driven algorithmic decision-making influence their comfort with AI-driven algorithmic decision-making in the workplace. This study employed a quantitative method by surveying employees in the Gulf Cooperation Council (GCC) and Lebanon with a final sample size of 388 participants. The results demonstrate that employees are more likely to feel comfortable with AI-driven algorithmic decision-making in the workplace if they believe AI will increase efficiency, promote fairness, and decrease errors. Unexpectedly, employee concerns were positively associated with comfort, suggesting an adaptive response to AI adoption. Lastly, comfort with AI-driven algorithmic decision-making is positively correlated with greater levels of trust in AI systems. These findings provide actionable guidance to organizations, underscoring the need to communicate clearly about AI’s role, address employees’ concerns through transparency and human oversight, and invest in training and reskilling initiatives that build trust and foster responsible, employee-centered adoption of AI. Full article

The widespread adoption of Electric Vehicles (EVs) is critically dependent on the deployment of efficient charging infrastructure. However, existing facility location models typically treat charging duration as an exogenous parameter, thereby neglecting the traveler’s autonomy to make trade-offs between service time and energy needs based on their Value of Time (VoT). This study addresses this theoretical gap by developing a heterogeneous network design model that endogenizes both charging mode selection and continuous charging duration decisions. A bi-objective optimization framework is formulated to minimize the weighted sum of infrastructure capital expenditure and users’ generalized travel costs. To ensure computational tractability for large-scale networks, an exact linearization technique is applied to reformulate the resulting Mixed-Integer Non-Linear Program (MINLP) into a Mixed-Integer Linear Program (MILP). Application of the model to the Hubei Province highway network reveals a convex Pareto frontier between investment and service quality, providing quantifiable guidance for budget allocation. Empirical results demonstrate that the marginal return on infrastructure investment diminishes rapidly. Specifically, a marginal budget increase from the minimum baseline yields disproportionately large reductions in system-wide dwell time, whereas capital allocation beyond a saturation point yields diminishing returns, offering negligible service gains. Furthermore, sensitivity analysis indicates an asymmetry in technological impact: while extended EV battery ranges significantly reduce user dwell times, they do not proportionally lower the capital required for the foundational infrastructure backbone. These findings suggest that robust infrastructure planning must be decoupled from anticipations of future battery breakthroughs and instead focus on optimizing facility heterogeneity to match evolving traffic flow densities. Full article

Background/Objectives: This study explored how attribute framing (lifestyle-focused vs. technology-focused product descriptions) and popularity cueing (presence or absence of a “best-seller” label) influenced purchase likelihood for a fictitious selection of hearing aids (HAs) among Canadian adults aged 40 years and above. The study further aimed to investigate whether the effects observed were unique to HAs or applicable to less-specialized consumer technology contexts. Method: A 2 × 2 × 2 mixed experimental design compared attribute framing and popularity cueing effects across HAs and notebook computers at three technology levels (entry-level, midrange, and premium). Participants (n = 122) provided ratings indicating their purchase likelihood for each product. Results: Attribute framing showed no significant influence on purchase decisions across technology levels. The presence of a popularity cue that the midrange HA was the best-seller negatively affected purchase likelihood for the entry-level HA, with higher purchase likelihood ratings observed when this cue was absent. Participants expressed stronger purchase likelihood for premium HAs compared to premium notebook computers. Notably, these two effects were not statistically significant following correction for multiple comparisons. Conclusions: Popularity cues for HAs may have inadvertent consequences for consumer perceptions of models with differing technology levels. Findings also suggest potentially greater willingness to invest in premium health-related technologies versus familiar consumer technology. Further research involving current HA users or candidates is needed to better understand these findings. Full article

Remote Indigenous communities experience persistent inequities in access to fresh and nutritious foods due to the fragility of perishable food supply chains (PFSCs). Disruptions across procurement, transportation, storage, retail, and limited local production restrict access to perishable foods, contributing to food insecurity and diet-related health risks. This scoping literature review synthesizes evidence from 84 peer-reviewed, grey, and unpublished sources across fourteen countries to map PFSC management (PFSCM) challenges affecting food access in remote Indigenous communities worldwide and to synthesize reported practices implemented to address these challenges. PFSCM challenges were identified across all supply chain levels, and five categories of reported practices emerged: PFSC redesign strategies, forecasting and decision-support models, technological innovations, collaboration and coordination mechanisms, and targeted investments. These findings informed the development of a multi-scalar conceptual framework comprising seven interconnected PFSCM clusters that organize how reported practices are associated with multiple food access dimensions, including quantity, affordability, quality, safety, variety, and cultural acceptability. This review contributes an integrative, system-oriented synthesis of PFSCM research and provides a conceptual basis to support future scholarly inquiry, comparative inquiry, and policy-relevant discussion of food access and health equity in remote Indigenous communities. Full article

Renewable energy investment evaluation continues to rely predominantly on techno-economic and environmental criteria, while equity-related considerations remain weakly embedded within formal decision-support frameworks. Although recent research increasingly acknowledges social impacts, spatial constraints, policy uncertainty, and financing structures, these dimensions are rarely integrated in a systematic and operational manner into investment appraisal. This paper addresses this gap by advancing an equity-oriented conceptual framework for renewable energy investment evaluation. Using an integrative literature review combined with thematic analysis, the study synthesises insights from techno-economic assessment, multi-criteria decision-making, energy justice scholarship, and equity-focused modelling studies. The analysis demonstrates that existing evaluation approaches inadequately capture distributional impacts, accessibility constraints, differentiated vulnerability, and equity-adjusted risk. In response, the proposed framework systematises these equity dimensions and embeds them directly into the core logic of investment evaluation alongside conventional criteria. By consolidating fragmented research insights into a coherent evaluative structure, the study contributes to the literature by clarifying how equity can be operationalised within renewable energy investment decision-making. The framework provides a foundation for future empirical applications and supports more socially responsive and analytically robust investment evaluation. Full article

Wood flooring manufacturers face complex challenges in dynamically allocating resources across multi-channel markets, characterized by channel conflicts, demand uncertainty, and long-term cumulative effects of decisions. Traditional static optimization or myopic approaches struggle to address these intertwined factors, particularly when critical market states like brand reputation and customer base cannot be precisely observed. This paper establishes a systematic and theoretically grounded online decision framework to tackle this problem. We first model the problem as a Partially Observable Stochastic Dynamic Game. The core innovation lies in introducing an unobservable market position vector as the central system state, whose evolution is jointly influenced by firm investments, inter-channel competition, and macroeconomic randomness. The model further captures production lead times, physical inventory dynamics, and saturation/cross-channel effects of marketing investments, constructing a high-fidelity dynamic system. To solve this complex model, we propose a hierarchical online learning and control algorithm named L-BAP (Lyapunov-based Bayesian Approximate Planning), which innovatively integrates three core modules. It employs particle filters for Bayesian inference to nonparametrically estimate latent market states online. Simultaneously, the algorithm constructs a Lyapunov optimization framework that transforms long-term discounted reward objectives into tractable single-period optimization problems through virtual debt queues, while ensuring stability of physical systems like inventory. Finally, the algorithm embeds a game-theoretic module to predict and respond to rational strategic reactions from each channel. We provide theoretical performance analysis, rigorously proving the mean-square boundedness of system queues and deriving the performance gap between long-term rewards and optimal policies under complete information. This bound clearly quantifies the trade-off between estimation accuracy (determined by particle count) and optimization parameters. Extensive simulations demonstrate that our L-BAP algorithm significantly outperforms several strong baselines—including myopic learning and decentralized reinforcement learning methods—across multiple dimensions: long-term profitability, inventory risk control, and customer service levels. Full article

This case study in Foz do Iguaçu, Brazil, addresses the urban problem of the degradation of road corridors used by public transport, affecting the accessibility, safety, and efficiency of urban mobility. To address this issue, a multi-criteria methodology based on MIVES (Integrated Value Model for Sustainable Assessments) was applied, combined with the AHP (Analytic Hierarchy Process) method, allowing the evaluation of 20 key urban roads using a hierarchical set of indicators linked to infrastructure, accessibility, and mobility. The assessment was operationalized through the Transport Infrastructure Condition Index (TICI), which yielded results ranging from 0.32 to 0.88, reflecting significant contrasts in the road’s upkeep and maintenance conditions. The lowest scores were associated with deficiencies in universal accessibility, cycling infrastructure, signage, and adaptations for people with reduced mobility, highlighting structural limitations in sustainability and urban inclusion. The model facilitates the prioritization of road interventions based on urgency and criticality, becoming a useful tool for guiding public investment decisions. Its comprehensive approach and replicability make it a valuable methodological alternative for other Latin American contexts, where pressure to improve urban services coexists with budgetary constraints, contributing to more efficient and sustainable strategic planning of public transportation. Full article

The development of energy systems toward a decarbonized economy is increasingly constrained not only by technological challenges, but also by deficiencies in the organization, coordination, and governability of sustainable financing. This study aims to substantiate an integrated conceptual model and a multi-level governance framework for the sustainable financing mechanism of energy system development under decarbonization, ensuring the alignment of financial instruments with transition strategies, performance indicators, and feedback mechanisms. The methodology combines a bibliometric analysis of Scopus-indexed journal publications with an examination of international statistical and analytical data produced by leading global organizations, complemented by systemic, institutional, and comparative analytical approaches. The bibliometric analysis was conducted in 2025 and covered peer-reviewed articles published during 2017–2025, while empirical financial indicators were synthesized for the most recent available period of 2022–2024 using comparable time-series data reported by international institutions. The results indicate that despite global energy investments reaching approximately $3 trillion in 2024—nearly $2 trillion of which was allocated to clean energy technologies—a persistent annual financing gap for climate change mitigation in the energy sector remains. Moreover, to remain consistent with the Net Zero trajectory, investments in clean energy must increase by approximately 1.7 times by 2030. The synthesis of contemporary research and empirical evidence reveals a predominance of studies focused on individual green and transition finance instruments, accompanied by persistent fragmentation between financial flows, governance structures, and measurable decarbonization outcomes. To address this gap, the paper proposes a conceptual model that interprets sustainable finance as a governed system rather than a collection of isolated instruments, together with a multi-level governance framework integrating strategic (policy), sectoral, and project-level decision-making with systems of key performance indicators, monitoring, and feedback. The findings demonstrate that the effectiveness of sustainable financing critically depends on the coherence between financial instruments, governance architectures, and decarbonization objectives, which ultimately determines the capacity to translate mobilized capital into tangible energy infrastructure modernization and measurable emissions reductions. The proposed approach provides a practical foundation for improving energy transition policies and investment strategies at both national and supranational levels. Full article

Agricultural robotics and artificial intelligence (AI) are becoming essential to building more sustainable, efficient, and resilient food systems. As climate change, food security pressures, and labour shortages intensify, the integration of intelligent technologies in agriculture has gained strategic importance. This systematic review provides a consolidated assessment of AI and robotics research in agriculture from 2000 to 2025, identifying major trends, methodological trajectories, and underexplored domains. A structured search was conducted in the Scopus database—which was selected for its broad coverage of engineering, computer science, and agricultural technology—and records were screened using predefined inclusion and exclusion criteria across title, abstract, keywords, and eligibility levels. The final dataset was analysed through descriptive statistics and science-mapping techniques (VOSviewer, SciMAT). Out of 4894 retrieved records, 3673 studies met the eligibility criteria and were included. As with all bibliometric reviews, the synthesis reflects the scope of indexed publications and available metadata, and potential selection bias was mitigated through a multi-stage screening workflow. The analysis revealed four dominant research themes: deep-learning-based perception, UAV-enabled remote sensing, data-driven decision systems, and precision agriculture. Several strategically relevant but underdeveloped areas also emerged, including soft manipulation, multimodal sensing, sim-to-real transfer, and adaptive autonomy. Geographical patterns highlight a strong concentration of research in China and India, reflecting agricultural scale and investment dynamics. Overall, the field appears technologically mature in perception and aerial sensing but remains limited in physical interaction, uncertainty-aware control, and long-term autonomous operation. These gaps indicate concrete opportunities for advancing next-generation AI-driven robotic systems in agriculture. Funding sources are reported in the full manuscript. Full article

Trust (or distrust) in artificial intelligence (AI) is a critical research topic, given AI’s pervasive integration across societal domains. Despite its significance, scholarly attention to process-based learned trust in AI remains limited. To address this gap, this study designed a virtual non-fungible token (NFT) investment task, featuring seven rounds of risk decision-making scenarios, to simulate an investment/trust game to explore participants’ multifaceted trust under the influence of different chatbots’ social role. The findings suggested the chatbot’s social role had a significant impact on participants’ trust behaviors and perceptions over time. Trust in the two chatbot types diverged until the system-induced failures occurred. The friend-like chatbot elicited a higher level of behavioral trust than the servant-like counterpart. During those trust-damaging moments, the friend-like chatbot proved more effective in mitigating trust erosion and facilitating trust repair, as evidenced by relatively stable investment behaviors. The findings reinforce the notion that friendship with AI can function as a relational buffer, softening the impact of trust violations and facilitating smoother trust recovery. Full article

Existing buildings consume a large portion of the total current energy production, especially in developing countries such as Egypt. Increasing energy demand, coupled with decreasing availability and increasing cost of conventional non-renewable energy resources, have encouraged a “building green” retrofit trend in order to maximize the energy performance of the built environment. This paper outlines the development of an Energy Retrofit Decision-Support System (ERDSS) for hot, arid climates that models building retrofit scenarios and determines the impact of each retrofit measure on the overall energy consumption of a proposed building retrofit program. The methodology combines building an energy simulation with a database-driven, budget-constrained optimization framework based on the Savings-to-Investment Ratio (SIR) to evaluate and prioritize retrofit measures. In addition, ERDSS determines the impact of each retrofit measure on the overall energy consumption of a proposed building retrofit program, ranks the retrofit measures according to their Savings-to-Investment Ratio (SIR) and uses optimization to develop a suggested retrofit program for a given budget. ERDSS is applied on a case study of an education building in New Cairo, Egypt, in order to illustrate the performance of the framework. Results show that savings for the commissioned retrofit, standard retrofits, and deep retrofits reached 15 percent, 35 percent, and 45 percent, respectively. Full article

In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives. This paper addresses constrained multi-objective optimization (MOO) with an application to portfolio optimization for minimizing risk and maximizing return. To this end, and to address existing gaps, we propose a novel reinforcement learning (RL)-guided non-dominated sorting genetic algorithm II (NSGA-II) enhanced with gray relational coefficients (GRC), termed RL-NSGA-II-GRC, which combines an RL agent controller and GRC-based selection to improve the convergence and diversity of the Pareto-optimal fronts. The agent adapts key evolutionary parameters online using population-level metrics of hypervolume, feasibility, and diversity, while the GRC-enhanced tournament operator ranks parents via a unified score simultaneously considering dominance rank, crowding distance, and geometric proximity to ideal reference. We evaluate the framework on the Kursawe and CONSTR benchmark problems and on a NASDAQ portfolio optimization application. On the benchmarks, RL-NSGA-II-GRC achieves convergence metric improvements of about 5.8% and 4.4% over the original NSGA-II, while preserving a well-distributed set of non-dominated solutions. In the portfolio application, the method produces a smooth and densely populated efficient frontier that supports the identification of the maximum Sharpe ratio portfolio (with annualized Sharpe ratio = 1.92), as well as utility-optimal portfolios for different risk-aversion levels. The main contributions of this work are three-fold: (1) we propose an RL-NSGA-II-GRC method that integrates an RL agent into the evolutionary framework to adaptively control key parameters using generational feedback; (2) we design a GRC-enhanced binary tournament selection operator that provides a comprehensive performance indicator to efficiently guide the search toward the Pareto-optimal front; (3) we demonstrate, on benchmark MOO problems and a NASDAQ portfolio case study, that the proposed method delivers improved convergence and well-populated efficient frontiers that support actionable investment insights. Full article

This study examines the impact of tax avoidance practices on investment efficiency in Egypt, with particular emphasis on the moderating role of earnings management by exploring whether these tactics reflect managerial opportunism or serve as a mechanism to ease financial constraints. We employ panel data regression to analyze a sample of 58 non-financial firms listed on the Egyptian Exchange (EGX) over the period 2017–2024, yielding 464 firm-year observations. Data are collected from official corporate websites, EGX, and Egypt for Information Dissemination (EGID). Grounded in agency theory, signaling theory, and pecking order theory, this study reveals how conflicts of interest and information asymmetry between managers and stakeholders lead to managerial opportunism. The findings show that tax avoidance undermines the investment efficiency in the Egyptian market. Earnings manipulation further intensified this effect due to the financial statements’ opacity. A closer examination reveals that earnings management exacerbates overinvestment by masking managerial decisions. Conversely, for financially constrained firms with a tendency to underinvest, tax avoidance and earnings management may contribute to improved efficiency by generating internal liquidity and alleviating external financing constraints. These results provide valuable insights for regulators, highlighting that policy should be directed against managerial opportunism and improving transparency, instead of focusing solely on curbing tax avoidance. From an investor perspective, they should closely monitor and understand the tax-planning strategies to ensure they enhance the firm’s value. Full article

In the rapidly evolving digital economy, the expansion of business-to-business e-commerce ecosystems has compelled traditional industries to integrate into digital supply chains to achieve sustainable development. Industrial e-commerce is no longer limited to online transactions but extends to the digital transformation of backend operations. Drawing upon the perspective of the digital business ecosystem, this study investigates how digital supply chain integration, manifested through digital transformation, impacts energy efficiency. By utilizing a panel fixed effects model and advanced text mining techniques on a dataset of 721 listed firms in the resource-intensive sectors of China spanning from 2011 to 2023, this research constructs a novel index to quantify corporate digital maturity based on semantic analysis. The empirical results demonstrate that digital transformation significantly enhances energy efficiency by facilitating optimized resource allocation and data-driven decision making required by modern digital markets. Mechanism analysis reveals that green innovation functions as a pivotal mediator that bridges the gap between digital investments and environmental performance. Furthermore, this relationship is found to be contingent upon corporate social responsibility strategies, ownership structures, and the scale of the firm. This study contributes to the electronic commerce literature by elucidating how traditional manufacturers can leverage digital technologies and green innovation to navigate the twin transition of digitalization and sustainability, offering theoretical implications for platform governance in industrial sectors. Full article

The northern coast of Mauritania presents a strategic opportunity for clean energy investment due to its remarkable potential for green hydrogen production through wind energy. To determine the best location for wind-based green hydrogen production, this paper established a Multi-Criteria Decision-Making framework (MCDM) that combines the Analytic Hierarchy Process (AHP) and Evidential Reasoning (ER) to assess five coastal sites: Nouakchott, Nouamghar, Tasiast, Boulanoir, and Nouadhibou. Four main criteria (i.e., economic, technical, environmental, and social) and twelve sub-criteria were taken into account in the assessment. To ensure reliability and contextual accuracy, the data used in this study were obtained from geographic databases, peer-reviewed literature, and structured expert questionnaires. The results indicate that site 5 (Nouadhibou) is the most suitable location for green hydrogen generation using wind energy. Sensitivity analysis confirms the robustness of the ranking results, validating the reliability of the proposed hybrid framework. The findings of this study provide critical, data-driven decision-support insights for investors and policymakers, guiding the strategic development of sustainable wind-based green hydrogen projects along Mauritania’s coastline. Full article