Company XYZ, a toy manufacturing company, is pursuing a 40% reduction in manual material handling labor by 2030 through the implementation of autonomous mobile robots (AMRs). This study applies an integrated Analytical Hierarchy Process (AHP) and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) approach to select the optimal AMR, supported by discrete-event simulation modeling in FlexSim to validate designs prior to investment. A cost-benefit analysis, including Benefit-Cost Ratio (BCR), Internal Rate of Return (IRR), and Payback Period, demonstrates the economic feasibility of the proposed solution. Simulation results suggest a configuration of three AMRs to meet cycle-time targets with a projected labor cost reduction of 47%. This work contributes a validated methodology for robot selection, system design, and investment decision-making in manufacturing environments.

Examining the role of recognition-based heuristics in sustainable investment decision-making using a structural equation modeling–artificial neural network-based approach

Purpose This study aims to examine the relationship between environmental, social and governance (ESG) initiatives, board gender diversity and stock price volatility (PVOL) among FTSE 100 firms from 2015 to 2023. Specifically, it investigates whether board gender diversity moderates the ESG volatility relationship, thereby enhancing financial stability. Design/methodology/approach A panel data analysis is conducted using ordinary least squares (OLS), two-stage least squares (2SLS) and generalized method of moments (GMM) regressions to account for potential endogeneity and strengthen the robustness of results. This study leverages instrumental variables (IVs) to control for omitted variable bias and reverse causality, ensuring reliable empirical insights. Findings The results show that stronger ESG performance significantly reduces volatility (PVOL), and this stabilizing effect is amplified in firms with more gender-diverse boards. These findings remain consistent when volatility is assessed using realized volatility (RLZDVOL), confirming that the results are not merely artifacts of volatility specification. This evidence underscores the complementary roles of ESG and gender diversity on boards in strengthening corporate stability. Practical implications The findings emphasize the strategic importance of ESG integration in corporate governance and risk management. They also highlight the role of gender-diverse leadership in promoting financial stability and enhancing the confidence of investors. These insights are valuable for corporate executives, policymakers and investors, advocating stronger ESG policies, improved board diversity mandates and more sustainable investment strategies. Originality/value This study fills a critical gap in the literature by linking ESG factors to stock price volatility and highlighting the moderating role of board gender diversity. This study provides new evidence of how board mechanisms can enhance ESG effectiveness and contribute to long-term market stability. This study offers novel insights into sustainable finance, corporate governance and investment decision-making.

The global stock market has experienced a profound transformation over the past few decades due to rapid advancements in Information Technology (IT). Traditional stock market systems, once dominated by manual trading floors, paper-based documentation, and delayed information dissemination, have been replaced by highly automated, technology-driven platforms. Information Technology now forms the backbone of modern stock market operations, enabling real-time trading, high-speed data processing, electronic clearing and settlement, and enhanced market surveillance. This research paper examines the role of Information Technology in shaping modern stock market practices, with particular emphasis on trading efficiency, transparency, investor participation, decision-making processes, and associated technological risks. The study adopts a descriptive and empirical research design to analyze how IT-enabled systems—such as electronic trading platforms, online brokerage services, mobile trading applications, algorithmic trading systems, and digital data analytics tools—have redefined stock market functioning. Primary data were collected through a structured questionnaire administered to 104 respondents, including individual investors, students, salaried employees, and businesspersons with exposure to stock market activities. The questionnaire was designed to assess awareness levels, usage patterns, perceived benefits, and perceived risks related to IT-based stock trading systems. Secondary data were sourced from academic journals, textbooks, stock exchange publications, and authoritative financial websites to establish a strong theoretical and conceptual foundation. The findings of the study indicate that awareness of Information Technology in the stock market is generally high among respondents; however, the depth of understanding varies considerably. Online trading platforms and mobile trading applications are perceived as the most important technological systems, highlighting the growing importance of accessibility and convenience in modern trading practices. A significant proportion of respondents acknowledge that IT has led to substantial improvements in trading speed and operational efficiency, allowing transactions to be executed within seconds. At the same time, a notable segment of investors perceives electronic trading systems as complex, indicating challenges related to digital literacy and platform usability. The study further reveals mixed perceptions regarding the role of real-time data and digital analytics in investment decision-making. While many respondents believe that technology-based tools support better and faster investment decisions, others associate continuous data flow with confusion, impulsive behavior, or increased risk. Awareness of algorithmic and automated trading exists at a basic level, but detailed understanding remains limited, suggesting a gap between technological advancement and investor knowledge. Cybersecurity threats, system failures, and technical errors emerge as the most significant risks associated with IT-driven stock market systems. The increasing dependence on digital infrastructure has heightened concerns regarding data security, system reliability, and market stability. Nevertheless, technological advancements have also strengthened regulatory oversight through automated surveillance systems, improved transparency, and faster settlement cycles. The study concludes that Information Technology has fundamentally reshaped modern stock market practices by enhancing efficiency, transparency, and inclusivity. However, the effective utilization of technology depends on continuous investor education, simplified system design, robust cyber security frameworks, and adaptive regulatory mechanisms. The paper contributes to existing literature by offering an integrated analysis of technological, behavioral, and operational dimensions of modern stock markets, particularly from the perspective of an emerging economy. It provides practical insights for policymakers, stock exchanges, brokerage firms, and investors seeking to balance technological innovation with market stability and investor protection.

Off-grid integrated energy systems offer a practical solution for remote regions lacking access to the main power grid; however, their planning and design are challenged by the inherent uncertainty of renewable energy resources. To address this issue, this paper proposes a stochastic optimization framework for off-grid integrated energy systems that explicitly accounts for wind speed and solar irradiance variability. Continuous probability distributions combined with Monte Carlo sampling are employed to generate stochastic scenarios, which are embedded into a bi-objective optimization model minimizing total system cost and pollutant emissions under power balance and device operational constraints. Unlike existing studies that primarily focus on cost–reliability trade-offs, this work introduces the Renewable Energy Penetration Rate (REPR) as a quantitative, planning-oriented indicator and systematically investigates its interactions with economic performance, pollutant emissions, and renewable uncertainty. The REPR is not only used to characterize renewable utilization levels, but also to support investment decision-making and the comparative assessment of Pareto-optimal configurations. A real-world off-grid service area is adopted as a case study. The results show that increasing the REPR leads to a significant reduction in carbon emissions while exhibiting a nonlinear impact on total system cost. Specifically, the proposed framework identifies a Pareto-optimal solution set in which the total system cost varies within 40–92 million ¥, carbon emissions are reduced by 86% compared with diesel-dominated configurations, and the REPR increases from 70% to 96.4% as renewable capacity expands. In addition, the analysis reveals that higher renewable volatility requires a larger stochastic sample size to ensure solution stability. These findings demonstrate that the proposed framework provides a more comprehensive and decision-relevant assessment of off-grid integrated energy systems under renewable uncertainty, thereby offering practical insights for low-carbon and economically viable system planning.

This study examines the role of behavioural finance in shaping investment decisions among individual investors. Traditional financial theories assume rational decision-making, but behavioural finance highlights psychological biases influencing investor behaviour. The research investigates common biases such as overconfidence, loss aversion, herding, anchoring, and mental accounting. Statistical tools including descriptive statistics, independent sample t-test, and one-way ANOVA were applied. The t-test results reveal a significant gender difference in behavioural bias levels. ANOVA findings indicate that financial literacy significantly affects behavioural bias. Investors with lower financial literacy demonstrate stronger psychological biases. Market volatility analysis shows that loss aversion dominates during crash periods. The findings confirm that demographic and knowledge-based factors influence investment decisions. The study concludes that improving financial literacy can reduce irrational investment behaviour. Key Words:  Behavioural Finance  Investment Decision-Making  Psychological Biases  Overconfidence Bias  Loss Aversion  Herding Behaviour  Anchoring Bias  Financial Literacy  Risk Perception

The use of Artificial Intelligence in investment decision-making and corporate financial management constitutes part of the digital transformation in corporate governance, which requires legal certainty regarding the liability of the Board of Directors. While the use of AI has the potential to enhance efficiency and accuracy in business analysis, it simultaneously gives rise to legal risks where decisions generated by AI result in losses to the company.This study aims to analyze directors’ legal liability for corporate financial losses arising from the use of Artificial Intelligence in investment decisions and to examine the application of the Business Judgment Rule and fiduciary duty as forms of legal protection. This research employs normative legal methods using statutory and conceptual approaches, relying on primary, secondary, and tertiary legal materials. The findings indicate that AI does not constitute a legal subject; therefore, liability remains with directors as final decision-makers. The use of AI expands the directors’ duty of care by requiring technological risk awareness and system oversight. Legal protection under the Business Judgment Rule applies when decisions are made rationally, based on adequate information, and free from conflicts of interest. Excessive reliance on AI without critical evaluation may be considered negligence. Accordingly, AI utilization in corporations necessitates stronger fiduciary duty standards and good corporate governance.

Investment decision-making in financial markets is increasingly recognized as a process shaped by both cognitive and neurobiological factors. While prior behavioral finance studies have extensively examined psychological biases, empirical evidence integrating neurofinance and financial literacy remains limited, particularly in emerging market contexts. This study investigates the influence of neurotransmitter-related traits on investment decisions, with financial literacy examined as a moderating mechanism. Using primary survey data from 412 retail investors in an emerging market, this study applies Partial Least Squares Structural Equation Modeling (PLS-SEM) to test a moderated structural model. Neurotransmitter traits are specified as a higher-order construct capturing reward sensitivity, emotional regulation, vigilance, and stress responsiveness. Financial literacy is modeled as a moderator using a two-stage interaction approach. The findings reveal that neurotransmitter traits exert a positive and statistically significant effect on investment decisions. Financial literacy also demonstrates a direct positive effect on investment decision-making. However, the moderation analysis indicates a negative and significant interaction effect, suggesting that higher levels of financial literacy attenuate the influence of neurobiological traits on investment decisions. This result supports the interpretation of financial literacy as a behavioral buffering mechanism, reducing reliance on instinctive or emotionally driven investment behavior. This study contributes to the behavioral finance and neurofinance literature by providing empirical evidence on the conditional role of financial literacy in shaping investment decisions within an emerging market. The findings offer practical implications for investor education policies and financial market development strategies aimed at fostering more informed and disciplined investment behavior.

Financial sentiment analysis leverages natural language processing techniques to quantitatively assess sentiment polarity and emotional tendencies in financial texts. Its practical application in investment decision-making and risk management faces two major challenges: the scarcity of high-quality labeled data due to expert annotation costs, and semantic drift caused by the continuous evolution of market language. To address these issues, this study proposes PLTA-FinBERT, a pseudo-label generation-based test-time adaptation framework that enables dynamic self-learning without requiring additional labeled data. The framework consists of two modules: a multi-perturbation pseudo-label generation mechanism that enhances label reliability through consistency voting and confidence-based filtering, and a test-time dynamic adaptation strategy that iteratively updates model parameters based on high-confidence pseudo-labels, allowing the model to continuously adapt to new linguistic patterns. PLTA-FinBERT achieves 0.8288 accuracy on the sentiment classification dataset of financial sentiment analysis, representing an absolute improvement of 2.37 percentage points over the benchmark. On the FiQA sentiment intensity prediction task, it obtains an R2 of 0.58, surpassing the previous state-of-the-art by 3 percentage points.

Abstract Capacity sizing and calculating cost savings for residential households in a rapidly evolving energy market, influenced by fluctuating electricity prices and changing government incentives, is a highly complex problem. The key challenges stem from multiple interacting factors, including retail electricity prices, the desired payback period, household size, applicable electricity schemes, and the capacity factor of the photovoltaic (PV) system. The nominal power output of the solar energy system is constrained by both the specifications and the number of installed inverters and PV panels. As solar generation is intermittent and non-dispatchable, it is inherently weather-dependent and often unable to align with the dynamic fluctuations in household electricity consumption. From a financial modelling perspective, the length of the accounting period directly determines the time resolution of the model, influencing both the accuracy of cash flow estimation and investment decision-making. The proposed two-level investment planning model is based on the process network synthesis approach. At the upper level of the process model, solar generation technologies, including inverters and solar panels, are technically and economically assessed. At the lower level, which represents the load consumption side, the periodical energy balances for production, storage, demand, and purchase are considered. In order to accurately evaluate the solar energy system, the model is developed with both a monthly framework and a detailed hourly framework. The time resolution allows the model to account for grid intake, electricity sold, and storage inventory conditions over the defined periods, ultimately providing the optimal sizing for a solar system equipped with battery storage. Case studies are conducted to investigate the effects of household size, extended payback periods, varying retail electricity prices, and grid reliability. These scenarios demonstrate the key parameters that significantly influence the economic feasibility and optimal sizing of the solar energy system, which are discussed in detail in this paper.

This study aims to examine the effect of Environmental, Social, and Governance (ESG) disclosure on stock prices of banking sector companies listed on the Indonesia Stock Exchange (IDX) during the 2021–2025 period. The study is grounded in signaling theory, which suggests that non-financial information can be used by companies to convey quality and future prospects to investors. A quantitative approach was employed using panel data regression analysis, with the sample selected through purposive sampling. The independent variables consist of environmental, social, and governance disclosure, while the dependent variable is stock price. The results indicate that ESG disclosure does not have a significant effect on stock prices in the banking sector, either partially or simultaneously. These findings suggest that ESG information has not yet demonstrated strong value relevance in investment decision-making within the banking industry, as investors tend to prioritize financial information over non-financial disclosures. Furthermore, ESG disclosure in Indonesian banking remains largely compliance-driven and relatively homogeneous across firms. This study provides implications for banking companies to enhance the quality and differentiation of ESG disclosure and serves as a reference for future research.

This study aims to analyze the effect of ESG Risk and dividend policy on stock prices, with Good Corporate Governance (GCG) as a moderating variable. The research adopts a quantitative approach using multiple linear regression analysis and Moderated Regression Analysis (MRA). The data are obtained from the annual reports of energy sector companies listed on the Indonesia Stock Exchange (IDX). Using purposive sampling, the study yields 33 observations from 11 companies over a three-year period. The results indicate that stock price movements of energy sector companies during the 2022–2024 period are more strongly driven by financial fundamental factors, particularly dividend policy and the implementation of GCG. Meanwhile, ESG risk does not yet play a significant role in investors’ assessment of stocks. Furthermore, GCG is not proven to moderate the effect of ESG Risk or dividend policy on stock prices, suggesting that sustainability aspects and corporate governance have not been fully integrated into investment decision-making in the Indonesian capital market.

Stockouts are a significant issue for direct-to-consumer product suppliers, as demand for these products is often challenging to predict. However, the literature offers only limited guidance for companies to tackle this problem. To address this gap, this paper develops a decision framework for assessing when fast-track production is a viable response to stockouts. The framework integrates context, symptoms, feasibility and business case viability to provide a structured process that links lost sales estimation, replenishment lead times and investment evaluation. A case study demonstrates the framework’s practical application and its value for managerial decision-making in capacity investment and design. Building on this, a profitability threshold model is introduced to identify the production line capacity that maximises benefit by balancing recovered sales against investment costs. The study contributes to production research by advancing knowledge on lost sales estimation, decision-making under uncertainty and differentiated supply chain strategies. It further shows how responsiveness can complement efficiency-oriented practices.

Technological innovation projects are notoriously difficult to value due to their high uncertainty and the coupling of multiple value dimensions. Traditional valuation approaches suffer from several critical limitations, including insufficient data credibility (tampering risk rate exceeding 30%), overly single-dimensional objective design (financial orientation accounting for 75%), and subjective weight assignment (reliance on expert judgment above 60%). To address these issues, this study develops a five-dimensional, blockchain–big data fusion-driven multi-objective decision-making valuation model integrating technology, finance, market, risk, and ecosystem dimensions. Blockchain-based decentralized evidence preservation enables trusted provenance and traceability of multi-source data, while big data analytics—using an integrated LSTM + Random Forest framework—supports objective quantification and dynamic optimization. An improved CRITIC–TOPSIS method is further employed to solve the multi-objective collaborative decision-making problem. Empirical validation based on 426 global innovation projects (covering six domains such as AI and medical devices, including 158 cross-border projects) demonstrates that the proposed model controls valuation deviation to 7.2% ± 1.3%, representing reductions of 74.8% and 68.4% compared with the traditional DCF method (28.6% ± 4.2%) and relative valuation (23.1% ± 3.8%), respectively. Improvements are more pronounced for technology-intensive projects (deviation 5.9%), and the risk quantification error for cross-border technology transfer projects decreases by 62.7%. Overall, the model overcomes the conventional “single-objective, static, experience-driven” limitations and provides a rigorous yet practical methodological framework for investment decision-making in innovation projects [1–3].

Abstract Nanotechnology offers transformative potential for the global food and agribusiness sector, yet its commercial adoption remains limited. This review examines nanomaterials from a managerial and strategic perspective, focusing on their implications for agribusiness firms, supply chain efficiency, investment decision-making, regulatory compliance, and consumer acceptance. While technical advancements in nano-enabled packaging, nutrient delivery, and sensing systems promise reduced food loss, extended shelf life, and enhanced product differentiation, significant barriers persist, including regulatory fragmentation, uncertain return-on-investment, consumer skepticism, and liability risks. Drawing on multidisciplinary evidence, we identify key strategic challenges and propose practical frameworks for agribusiness managers to evaluate, adopt, and govern nano-enabled innovations responsibly. The analysis highlights the critical need for coordinated industry–regulator–science collaboration to translate laboratory successes into viable commercial strategies within the global food system.

Shale gas, as a typical low-quality marginal hydrocarbon resource, faces persistently high drilling costs, which have become one of the main bottlenecks restricting its large-scale development. The Southern Sichuan region of China holds enormous shale gas reserves and is a strategically important area for achieving cost-effective large-scale development. However, as production capacity construction intensifies and the volume of investment and cost data increases, traditional data processing methods can no longer meet the timeliness and accuracy requirements for handling massive data. Accurate prediction of oil and gas drilling costs will help in making scientific decisions and evaluations. In this study, based on the costs and engineering parameters of settled wells in the Southern Sichuan Block N shale gas field, we established a Back-Propagation (BP) neural network model incorporating principal component analysis (PCA) to achieve accurate prediction of single-well drilling costs. Results show that: (1) PCA can effectively extract useful information from the shale gas drilling cost influence factors. Specifically, the number of fracturing stages, drilling duration, well depth, total proppant volume, horizontal section length, etc., are identified as key parameters affecting single-well drilling cost. (2) Using Matlab programming and a graphical user interface (GUI), we developed an integrated shale gas single-well cost prediction software system that combines data import, model training, cost prediction, and results export. The BP neural network model’s predictions achieved an average relative error of only -0.57\%, demonstrating convenience, practicality, and high accuracy. This system can provide a basis for investment decision-making in the Southern Sichuan shale gas block and has value for commercial application.

Within the framework of the carbon market mechanism, corporate investments to secure forestry carbon credits play a pivotal role in mobilizing social capital for ecological construction and realizing the value of ecosystem services. This study integrates information decision theory and Bayesian network analysis to simulate corporate investment decision-making for forestry carbon sequestration within China’s carbon market. Through this approach, we explore the decision-making mechanisms behind corporate investments in forestry carbon sequestration and conduct decision simulations. The findings reveal several key insights: (1) External factors, including tax incentives, consumer preference for low-carbon products, and societal environmental awareness, exert a significant impact on the valuation of forestry carbon sequestration investments. Internally, the challenge posed by technological costs in achieving emission reductions significantly influences the evaluation of forestry carbon sequestration investments. (2) Investment value judgments are shaped by the nature of the decision-making problem, which inherently involves a synergistic relationship. (3) Corporations recognize the importance of forestry carbon sequestration in reducing the costs of emission reduction, formulating low-carbon development plans, expanding investment opportunities, and enhancing the quality of forestry carbon sequestration. (4) The collective value judgment of corporates regarding forestry carbon sequestration in terms of cost reduction for emission reduction, low-carbon development planning, investment opportunity expansion, and corporate image enhancement significantly influences their investment decisions in forestry carbon sequestration. (5) Corporate investment decisions exhibit a strong preference for market-based pricing and risk-sharing mechanisms. Consequently, enhancing the carbon information disclosure system and the carbon market trading mechanism, as well as establishing price protection and income stabilization expectations for forestry carbon sequestration, can encourage corporates to make investments in this area. This not only aids in the green, low-carbon transformation of businesses but also addresses the challenge of positive externalities associated with forestry carbon sequestration through market-oriented solutions.

As Artificial Intelligence (AI) technology continues to advance, the global financial markets are undergoing profound changes. The Private Equity (PE) industry, as a vital component of financial markets, is also encountering unprecedented opportunities and challenges. This paper explores the specific applications of AI across the private equity investment process—including due diligence, investment decision-making, risk management, and post-investment operations. By analyzing representative domestic and international cases, the paper summarizes the transformation trends and value enhancement pathways AI brings to the industry. Finally, it discusses current challenges and proposes strategies and future outlooks. The research suggests that AI will be a key driving force in promoting digital transformation and refined operations in the PE industry.

This study compares the forecasting performance of a traditional econometric model (ARIMA) and artificial intelligence (AI)-based models, namely Multilayer Perceptron (MLP) and Extreme Gradient Boosting (XGBoost), in predicting the VN-Index during the period from 2015 to June 2025, which was characterized by heightened volatility in Vietnam’s stock market. Daily VN-Index closing prices were employed and divided into an 80% training set and a 20% testing set for out-of-sample evaluation. Forecast accuracy was assessed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The Diebold–Mariano test was further applied to examine the statistical significance of differences in predictive performance among the models. The results indicate that ARIMA produced the highest forecasting errors, reflecting its limitations in capturing nonlinear dynamics and market volatility. The MLP model significantly improved forecasting accuracy, while XGBoost achieved the lowest error values across all evaluation metrics, demonstrating superior performance in handling noisy and volatile financial time series. AI-based models, particularly XGBoost, outperform the traditional ARIMA model in forecasting the VN-Index during volatile periods. The findings provide useful insights for investors and financial analysts by highlighting the effectiveness of advanced machine learning models in improving short-term market forecasting and investment decision-making in emerging markets.

The project cycle of complex construction projects covers the whole process from project decision-making, design, bidding, construction, completion acceptance, and the initial stage of operation. Among them, the investment risk assessment of complex construction projects focuses on the early decision-making stage of the project, aiming to provide a basis for investment feasibility analysis. The investment risk of complex construction projects is highly nonlinear and uncertain, and the traditional risk assessment methods have limitations in model generalization ability and prediction accuracy. To improve the accuracy and reliability of quantitative risk assessment, this study proposed a novel investment risk assessment model based on the perspective of investors. Firstly, through literature research, a multi-dimensional comprehensive risk assessment index system covering policies and regulations, economic environment, technical management, construction safety, and financial cost was systematically identified and constructed. Subsequently, the Least Squares Support Vector Machine (LSSVM) was used to establish a nonlinear mapping relationship between risk indicators and final risk levels. Aiming at the problem that the parameter selection of the standard LSSVM model has a significant impact on the performance, this paper proposed an improved Firefly Algorithm (IFA) to automatically optimize the penalty factor and kernel function parameters of LSSVM, so as to overcome the blindness of artificial parameter selection and improve the convergence speed and generalization ability of the model. Compared with the classical Firefly Algorithm, IFA strengthens learning and adaptive strategies by adding depth. The conclusions are as follows. (1) Compared with the Backpropagation Neural Network (BPNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), this model showed higher prediction accuracy on the test set, and its accuracy was reduced by about 3%. (2) Compared with FA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), IFA had a stronger global retrieval ability. (3) The model could effectively fit the complex risk nonlinear relationship, and the risk assessment results were highly consistent with the actual situation. Therefore, the risk assessment model based on the improved LSSVM constructed in this study not only provides a more scientific and accurate quantitative tool for investment decision-making of construction projects, but also has important theoretical and practical significance for preventing and resolving significant investment risks.