This paper applies the WSR systems methodology to explore how the quality monitoring and evaluation system in regional elementary education management acts as a carrier of value orientation and promotes high-quality development through feedback control. Based on the static Wuli (physical), Shili (practical), and Renli (human) elements, a time factor is introduced by adding a feedback control mechanism between cycles, creating a continuous dynamic closed-loop model for improving education quality. This ensures alignment with the system’s value and overall goals. Using student development data from the regional quality monitoring platform, the study implements personnel and distribution reforms, breaking system equilibrium and aligning individual and organizational goals. Based on data from 21 high schools in J City, H Province (2008–2019), the difference-in-differences (DiD) method is used to analyze the system’s impact on education quality. The results show a significant positive effect, with conclusions remaining robust after stability tests. This study enriches WSR’s application in elementary education, fills a research gap on policy effects, and offers practical insights for education managers.

Enhancing industrial linkage is an effective way to optimize the industrial structure, improve the efficiency of industrial resource allocation, and prompt the high-quality development of the industrial economy; therefore, analysing industrial linkage is highly important. On the basis of the complex network perspective, this paper uses the input-output table of Henan Province from 2017 to explore the topology and association characteristics of the industrial network in Henan Province, and the results show that the industrial network in Henan Province has some small-world nature and scale-free network characteristics; however, it is not completely consistent with the stochastic network model and the BA model, and the stochastic block model is able to fit the data better; however, there are some difficulties in model estimation.

The level of intelligence in weapon systems and equipment will be one of the key factors determining the victory or defeat of future wars. Methods to incorporate the level of intelligence as an incentive factor into the pricing system of weapons and equipment were explored, which can motivate contractors to strive to improve the level of intelligence in weapons and equipment. Based on the core combat capabilities of weapons and equipment in the context of intelligent warfare, a comprehensive evaluation index system for equipment intelligence level is constructed, which includes 6 primary indicators and 18 secondary indicators. Taking the intelligence index as the intelligence level of equipment, the calculation method of intelligence index is given by using the closeness in TOPSIS. On this basis, the equipment intelligence index is included as the main incentive factor in the equipment incentive pricing model, forming an incentive pricing method based on the level of equipment intelligence. The feasibility of the method was verified through simulated data and compared with the pricing method that only considers cost incentives. The results indicate the method proposed in this paper can obtain differentiated prices according to the market environment, which is more flexible and applicable.

In recent years, extreme weather events and pest/disease issues have made the resilience of the Agri-food supply chain a focus of social concern. Enterprises typically adopt two primary strategies to enhance the supply chain’s resilience, namely maintaining high inventory levels and improving logistics timeliness. The former, particularly through the implementation of the safety stock strategy, appears more feasible in the short term but incurs significant costs, especially for Agri-food. Therefore, striking a balance between resilience and cost efficiency is essential. This paper proposes a system dynamics model to collaboratively optimize resilience and holding costs in a three-level Agri-food supply chain. Using demand fulfillment rate as a resilience indicator, six simulation scenarios with varying inventory and transportation time configurations are designed. The dynamic impacts of these factors on both costs and resilience are analyzed. Optimization is performed using the Powell hill climbing algorithm in Vensim^® DSS to adjust the safety stock strategy. Results show that: Reducing distributors’ transport time enhances resilience more, but at higher costs; increasing the inventory levels of retailers and distributors is more effective in improving resilience, though also accompanied by increased costs; Collaborative optimization among supply chain members can maximize both resilience and cost efficiency.

With the rapid advancement of the Internet of Things, the generation and sharing of massive data have become a significant trend. However, the pervasive free-riding behavior among stakeholders has adversely impacted data quality. To address this issue, this study employs tripartite evolutionary game theory to construct a decision-making model involving data providers, data brokers, and regulators. This model depicts the strategic choices of these three parties in data quality management: data providers and brokers may opt for proactive investment or passive free-riding, while regulators may choose between stringent oversight or routine inspections. By constructing replicator dynamics equations, employing Jacobian matrix analysis to examine equilibrium points, and combining numerical simulations with sensitivity analysis, this paper explores the evolution of stakeholder strategies and the impact of key parameters on system stability. Results indicate that free-riding behavior significantly compromises data quality. When the net benefit of active quality control falls below the free-riding payoff, the system converges toward a fully passive equilibrium point (0, 0). However, appropriately calibrated incentive and penalty mechanisms can effectively promote active behavior. This study provides a quantitative foundation for understanding multi-agent interactions in data sharing and offers actionable strategic insights for optimizing IoT data sharing mechanisms.

The development of the new energy vehicle (NEV) industry is pivotal in addressing China’s energy security challenges and restructuring its automotive sector. It is a critical measure for achieving China’s carbon neutrality goals. Accurately analyzing and forecasting NEV sales volumes carries significant implications for industry planning and policy formulation. This paper proposes an integrated modeling framework that combines variable selection techniques with the XGBoost algorithm to forecast NEV sales in China. We compare three distinct variable selection methods and evaluate their performance against commonly used benchmark forecasting models. The prediction performance is assessed using two metrics: root mean squared error (RMSE) and mean absolute percentage error (MAPE). Our empirical results demonstrate that the variable selection-XGBoost integrated model outperforms both univariate models and models that do not incorporate core factor extraction, showing superior accuracy in both in-sample and out-of-sample predictions. Among the variable selection-XGBoost models, the SSL-XGBoost model yields the best performance, followed by GLMNET-XGBoost and LARS-XGBoost. The SSL method selects the greatest number of core variables. Cross-validation results indicate that integrating XGBoost with variable selection significantly reduces prediction errors. The variable selection-XGBoost integrated model surpasses traditional models in terms of both accuracy and reliability.

Financial holding companies (FHCs) in China leverage equity control to enhance operational efficiency and synergies, yet excessive equity concentration often undermines these benefits. This study investigates the impact of equity structure —specifically concentration and balance — on the performance of 17 A-share listed Chinese FHCs from 2010 to 2022, using data from the CSMAR database. Empirical results reveal an inverted U-shaped relationship between equity concentration and performance, with moderate concentration optimizing decision-making efficiency, while excessive levels risk power abuse. Equity balance, however, negatively affects performance by fostering power struggles and delaying decisions. These findings underscore the need for a balanced equity structure in Chinese FHCs. Policy recommendations include listing parent companies to diversify equity, keeping subsidiaries unlisted with concentrated ownership for synergy, strengthening regulation, and encouraging small shareholder participation to enhance governance and stability.

This study investigates the ex-ante information on initial public offering (IPO) underpricing in Malaysia using multiple machine learning techniques. The paper analyzes a sample of 350 fixed-price IPOs from 2004 to 2021, applying five machine learning models: artificial neural networks, random forest, gradient boosting, extra trees, and linear regression. The results indicate that random forests demonstrates superior performance, with a test $R^{2}$ of 0.2292, a CV $R^{2}$ mean of 0.529, and a CV $R^{2}$standard deviation of 0.1293, indicating moderate but reliable predictive power suitable for noisy financial data, such as IPO underpricing, where feature importance insights are more valuable than precise predictions. To reconcile and aggregate different outcomes from these multiple models, we implemented a voting algorithm to identify robust and reliable feature ranking for ex-ante determinants of IPO underpricing. Among the features, investor demand and divergence of opinions consistently emerged as the top two influential predictors of IPO underpricing, highlighting the key role investor sentiment and information asymmetry play in determining IPO pricing. These findings offer insights to investors, issuers, and policymakers, enabling a deeper understanding and effective management of the ex-ante drivers of underpricing in fixed-price IPOs, which lack market-based price discovery.

Trajectory prediction (TP) is critical for enhancing flight safety and operational reliability in small to medium-sized private and corporate aircraft, which involve complex multiple inputs and multiple outputs. While existing TP methods primarily focus on extracting coupled features, they often neglect the independent features of individual outputs, leading to unsatisfactory learning performance. To address this limitation, this paper proposes a multitask learning-based TP method using bi-directional long short-term memory (Bi-LSTM), consisting of two key components: 1) a multi-source feature fusion part that automatically extracts and integrates coupled evolutionary features across flight modes, and 2) a multitask learning part that mines independent change characteristics of each output. Firstly, the trajectory sequences are categorized into short, medium, and long-period flight modes to better capture temporal dependencies. The coupled characteristics in every flight mode are automatically excavated and integrated via the Bi-LSTM and fully connected network in the multi-source feature fusion part. Secondly, the fusion output is sent into the multitask learning part and every task has a customized model to learn independent evolutionary features of each output. Experimental results on real-world flight trajectories demonstrate the superiority of the proposed method in both one-step and multi-step prediction scenarios, highlighting its ability to leverage both coupled and independent features of flight trajectories.

Taking three nighttime commercial streets known for their nighttime lighting, with high vitality and large flow of people as representatives, we investigated customers’ perception of lighting and their willingness to approach in the field, and empirically examined the mechanism of the influence of lighting ambience cues on customers’ willingness to approach in nighttime commercial streets by using structural equation modeling (SEM). The results show that task-oriented cues, aesthetic cues and social cues in the lighting atmosphere have a significant impact on customers’ approach intention. Customers’ practical, hedonic and connectedness perceptions play a mediating role, and hedonic perception is the most influential factor. The findings are of great practical significance for optimizing the lighting design of commercial streets, enhancing customer experience and stimulating the vitality of nighttime economy.

With the rapid advancement of artificial intelligence technology, hospitals are accelerating their transition into a new era of intelligent construction. To scientifically and accurately assess the level of intelligent services in hospitals, this study introduces the hesitant fuzzy multi-attribute decision-making theory and proposes a hesitant fuzzy VIKOR evaluation model based on entropy weights. First, an evaluation attribute system was established from three dimensions: pre-diagnosis, during diagnosis, and post-diagnosis, and uses the Delphi method to collect the scores of experts on each alternative under each evaluation attribute. Furthermore, since the attribute weights are unknown, in this paper, the hesitant fuzzy entropy weight method is used to construct an attribute weight determination model. Building on these advancements, a hesitant fuzzy VIKOR multi-attribute evaluation model based on entropy weight was further established. Finally, five large public hospitals were selected as the evaluation objects, and the proposed model was used to evaluate their intelligent service levels. Through the comparative analysis of different evaluation methods, the results verified the scientificity and validity of this model. This study offers a novel theoretical framework and practical tool for the comprehensive assessment of hospital intelligent service levels.

The development of cross-regional medical treatment has always been a people’s livelihood security issue in China, which is also a significant footnote of common prosperity in the healthcare security undertaking. Based on the PESCT model, this study constructs an evaluation index system for the development level of cross-regional medical treatment. Using the TOPSIS method and in terms of 31 provinces data, it objectively evaluates the development level of cross-regional medical treatment from a national perspective, explores the characteristics of regional differences. Moreover, the Moran index is applied to conduct an in-depth investigation of its spatial correlation as well as the characteristics of spatio-temporal evolution. An empirical analysis is also carried out to study the influence mechanism of various influencing factors on the development level. The study finds that there are significant differences in the development level among regions in China, and a spatial agglomeration effect has emerged during the period from 2018 to 2022. Policy systems, economic conditions, social development, health concepts, and transportation conditions exert varying degrees of influence on the high-quality development level. Finally, corresponding suggestions for improving the high-quality development level of cross-regional medical treatment in China are put forward from five dimensions.

The continuous growth in energy demand, shortage of fossil fuels, and global climate change have raised significant attention towards renewable energy. In this paper, firstly, a three-echelon biomass-to-bioenergy supply chain composed of a farmer, collection station and power generation enterprise is developed. Secondly, the optimal decisions for four scenarios are investigated, namely, a decentralized decision-making model, a collaborative decision-making model between the farmer and the collection station, a collaborative decision-making model between the collection station and the power generation enterprise, and a centralized decision-making model. Thirdly, the average tree solution method of cooperative game theory is used to allocate the supply chain profits. Finally, numerical analysis is conducted by taking one biomass energy company as an example to support the results. Our research finds that: 1) In a centralized decision-making scenario, the individual and overall revenues are maximized. 2) For the collection station, allying with the power generation enterprise is more beneficial than allying with the farmer. 3) For the power generation enterprise, forming an alliance with the collection station is greater than decision-making independently.

Information leakage often influences manufacturers’ live-streaming strategies for product sales, particularly regarding the choice between self-streaming and internet celebrity-commissioned live streaming. We construct live-streaming supply chain models to investigate manufacturers’ choices of streaming modes and examine how information leakage affects decision-making. We find that, in the absence of information leakage, the selection of the live-streaming mode is primarily driven by the intensity of competition. However, when information leakage is present, factors such as the internet celebrity’s fan effect and the extent of information leakage also play significant roles. Under conditions of low information leakage, manufacturers are more inclined to commission internet celebrities with a strong fan base, whereas in scenarios involving high information leakage, they tend to favour those with a weaker fan effect. These conclusions offer valuable insights for manufacturers seeking to make informed live-streaming decisions while considering the implications of information leakage.

Amidst the ongoing debate surrounding algorithmic price discrimination, the regulation of price discrimination in the platform economy has once again emerged as a prominent topic of discussion in various spheres. The central focus and challenge of this matter lies in understanding the welfare implications of price discrimination, specifically how it impacts firms, consumers, and society as a whole. By comparing discriminatory pricing with uniform pricing (without price discrimination), we can find the changing laws of firms’ profit, consumer surplus, and social welfare. Based on the complete information static game, we derive the analytical expressions for the profit increment, the consumer surplus increment, and the social welfare increment through a mathematical model. The study focuses on the third-degree price discrimination in two consumer groups of $m$ (any positive integer) firms. Additionally, we prove the characteristics of these increments, whether they are positive, negative, or zero. The research results show that algorithmic price discrimination is beneficial to firms but detrimental to consumers. Its impact on the whole society is conditional and depends on the sum of the marginal costs of the $m$ firms. The study examines this issue through the lens of four core values: consumer rights, social welfare, fair value, and competition protection. Based on the experiences of developed countries, regulatory countermeasures are proposed to address this issue, focusing on protecting personal information and civil rights.

This study empirically examines the impact of climate policy uncertainty on bank systemic risk and the underlying mechanisms, using unbalanced panel data from 36 banks over 2008–2022. The results indicate that climate policy uncertainty significantly reduces bank systemic risk. This effect operates through three key channels: increasing capital adequacy, reducing leverage, and decreasing lending to high-carbon industries. Furthermore, the risk-reducing impact of climate policy uncertainty is strengthened by greater carbon emission reductions. It is noteworthy that the effect of climate policy uncertainty on bank systemic risk follows a U-shaped pattern. The risk-reducing effect is particularly evident in larger, non-state-owned banks with strong profitability, effective risk management, and higher credit allocation to green sectors.

A novel location-queuing problem for blood collection facilities against MPHEs is studied in this paper. The decision variables to be determined include the opening plan of fixed blood collection rooms, the location of mobile blood collecting vehicles, and the number of service desks within facilities. This problem is formulated as a bi-objective multi-period integer nonlinear programming model, incorporating unique features that distinguish it from previous studies, such as pandemic risk, blood donation behavior, and the heterogeneity of blood collection facilities. The objectives are to minimize the total system cost and maximize donor satisfaction. To solve this problem, an improved multi-objective grey wolf optimization (IMOGWO) algorithm, which incorporates chaotic mapping and adaptive convergence factors, is proposed. Real data from Chongqing, China, is utilized to demonstrate the applicability of the model and the effectiveness of IMOGWO. Using evaluation metrics such as the C metric (CM), number of Pareto frontier (NPF), maximum spread (MS), spacing (SP), mean ideal distance (MID) and computation time (CPU time), numerical experiments demonstrate that the proposed IMOGWO outperforms non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective particle swarm optimization(MOPSO), multi-objective whale optimization (MOWOA), multi-objective chimp optimization (MOChOA), and multi-objective grey wolf optimization (MOGWO).

Under the dual carbon goal and the national energy security strategy, improving the ESG development level of China’s energy enterprises is undoubtedly an important issue at present. Based on relevant theories of ESG development, this study constructs a model of influencing factors of ESG development of energy enterprises in China on the basis of summarizing factors that will have an impact on ESG development of energy enterprises. Then, combined with 501 effective studies obtained by questionnaire survey, it analyzes the interrelationship and action mechanism among various influencing factors. The results show that government factors have the greatest impact on the ESG development level of China’s energy enterprises, followed by enterprises, investors, the third-party rating agencies and financial institutions. The results of this study will play a certain reference role in China’s energy green transformation, ensuring national energy security, and promoting the sustainable development of energy enterprises.

Stunting has a significant impact on children’s health and, in the long term, negatively affects productivity and GDP by 2–3%. Therefore, it is crucial to reduce stunting rates through regional mapping based on their capacity to address stunting and by evaluating relevant indicators. A multi-criteria decision making (MCDM) approach, utilizing principal component analysis (PCA) and Entropy for weighting, and MARCOS, COPRAS, and WASPAS for ranking, can be applied. The weighting results from PCA and Entropy indicate that access to drinking water (C11) and households receiving food assistance (C10) are the largest contributing factors, while the smallest contributors are poverty rate (C7) and Gini ratio (C6). Using PCA weights across all MCDM methods, DKI Jakarta (A11) emerges as the best-performing region, while Papua (A34) ranks the worst. When Entropy weights are applied, DKI Jakarta (A11) ranks first in MARCOS and WASPAS, while South Kalimantan (A22) ranks best in COPRAS. Papua (A34), however, remains the worst performer across all methods. This study concludes that the ranking results from PCA and Entropy weighting methods are identical, showing a strong correlation. This provides policymakers with confidence in assessing each province’s capacity to address stunting, highlighting that Papua (A34) demonstrates relatively poor performance in managing stunting.

The debate surrounding the relationship between financial structure and systemic financial risk has been ongoing. This controversy arises from a lack of consideration for national resource endowments, which serve as the foundation for the development of industrial structure. Insufficient research has been conducted on the interplay between financial structure, industrial structure, and systemic financial risk. In light of this, the primary objective of this study is to underscore the crucial role of industrial structure in elucidating the interaction between financial structure and systemic financial risk. The research findings highlight that financial structure indirectly influences systemic financial risk through its impact on industrial structure. There exists significant heterogeneity in the transmission effect of industrial structure, with a more pronounced effect observed in areas characterized by high levels of economic development and belonging to mature clusters. Furthermore, the transmission effect of industrial structure is influenced by efficient market and effective government. The improvement of the efficient market can promote the upgrading of the industrial structure while reducing the level of potential risk. Excessive government intervention will reduce the transmission effect of the industrial structure.

Generative AI technology, represented by ChatGPT, has sparked profound changes in a variety of fields, and undoubtedly generative AI will drive continuous reforms and innovation in the business and experience of the travel and hospitality sectors. This article mainly explores the application of generative AI in the tourism industry based on existing literature, analyzes the constraints that may exist when it is applied in the field of tourism and hospitality, and explores future directions and opportunities for combining tourism, hospitality, and generative AI. This paper hopes to inspire scholars and practitioners to apply generative AI to tourism services, marketing and management, to explore smarter, more humane and more convenient tourism development modes and new business models, achieving high-quality development of tourism.

With the gradual advancement of digital transformation in the tourism industry, exploiting implicit information of tourism demand for prediction has gradually become mainstream in this research field. Among these works, the research on unidirectional implicit information is well-developed, whereas studies on interactive implicit information are scarce. Therefore, to further enhance the performance of tourism forecasting, this study employs a LangChain-based interactive context sentiment analysis model in the realm of feature engineering. By incorporating the sentiment tendencies found in online tourism reviews into tourism demand forecasting research, the model’s inferential capabilities are significantly improved. In terms of model processing, a new tourism demand prediction fusion model, EMD-STGCN-GRU-LSTM-Transformer (abbreviated as EST-Net), has been developed to address the unique spatio-temporal characteristics and imbalance of tourism data, thereby enhancing the model’s ability to accurately extract spatio-temporal sequences. Additionally, the PCA method is utilized to aggregate multiple key indicators of sentiment attention and natural environmental factors, constructing a tourism prediction indicator system to further correct the overall framework bias.

As one of the three pillars of the tourism industry, hotel sales are influenced by a variety of factors. Particularly, with the exponential growth of the internet, user-generated images, text, and data presented by hotels impact hotel sales to varying degrees. This study attempts to explore how different factors affect hotel industry sales. Firstly, it examines review images, text data, and hotel base attribute data; secondly, it employs a deep learning-based approach to analyze the different types of data; finally, it uses random forest to calculate feature importance values and analyzes them based on different star ratings variance. The results show that image-text consistency influences all types of hotel sales. Furthermore, the consistency of image text also affects all hotel sales, and there are differences in the factors influencing sales across hotel types. The findings can be used to provide valuable advice to hotel managers in the sales field.

In the context of rapid globalization and technological innovation, this study introduces a novel evaluation standard system designed for the modern service industry. Utilizing the DPSIR (driving forces, pressures, states, impacts, responses) framework, this study categorizes pertinent characteristics to elucidate their roles within the service sector, enhancing understanding of the complex dynamics between environmental factors and human activities. Meanwhile, the application of the entropy weight method significantly reduces subjectivity, thereby improving the reliability and effectiveness of assessment outcomes by quantifying the informational contribution of various indicators. Furthermore, incorporating the technique for order preference by similarity to ideal solution (TOPSIS), analysis fosters a robust index system for assessing the developmental levels of China’s life service industry. This study’s innovative approach and its practical implications mark a significant leap in strategic decision-making and understanding of the modern service industry, offering a novel and adaptable tool for industry analysis.

Green production is an effective approach to achieve sustainable development. In this paper, the government determines the optimal subsidy policy under a finite budget, and then a manufacturer and a retailer play a Stackelberg game for selling green products. First, the case of subsidy for the manufacturer is discussed. It is shown that the government subsidy for per product generated by green production and the cost coefficient of the green production technology are positively correlated. Second, the case of subsidy for the retailer is discussed. By comparing the two cases, it proves that subsidy for the manufacturer generates a higher green level. Nevertheless, in some situations, subsidy for the retailer is optimal for the sales volume. Some numerical illustrations are designed to analyze the sensitivity of each subsidy policy with respect to the cost coefficient of the green production technology and the cost coefficient of the blockchain technology, and to examine the dominant region of each subsidy policy.

Recidivism among ex-offenders is a complicated socioeconomic issue that now significantly affects social security and stability. This article’s theoretical foundations are primarily life story theory and identity label theory. It also builds a conceptual model of the effects of reoffending on social stability and social security using structure equation modelling (SEM) and trajectory analysis techniques, based on data from 355 questionnaires in 10 Chinese provinces. There was an empirical test of the model. The study’s findings indicate that: 1) There is a strong negative association between social stability and social security and recidivism; 2) Income status, education level, legal awareness, prior prison experience, social recognition, and other factors are closely associated with the likelihood of reoffending; 3) Reoffending risk may significantly affect public safety through intervention crimes, such as those that immediately compromise public safety or morality.

Network intrusion detection plays a critical role in safeguarding network security; however, traditional detection methods often struggle with complex attacks and large-scale data. To address these challenges, we propose a novel network intrusion detection model named GCM-CSDNN, which integrates the group cloud model (GCM) with a depthwise separable convolutional neural network (CSDNN). The model introduces group cloud transformation to reduce data dimensionality and employs 3D channel fusion technology to enhance feature extraction capabilities, thereby improving both accuracy and computational efficiency. We conducted extensive experiments on multiple benchmark datasets — including UNSW-NB15, KDD99, WSN-DS, and WADI — which cover diverse network environments and attack types. Experimental results demonstrate that GCM-CSDNN significantly outperforms traditional machine learning models and deep learning models in terms of accuracy and F1-score, achieving 98.79% and98.81% respectively, and surpassing the next-best model, SSG-DCNN. Moreover, GCM-CSDNN exhibits excellent performance on high-dimensional and large-scale datasets, significantly reducing training and testing times while demonstrating strong robustness and generalization capabilities. These findings indicate that GCM-CSDNN can efficiently and accurately detect network intrusions, making it suitable for real-time network security environments requiring the processing of large volumes of data.

In the modern economy, startups are not only significant drivers of innovation and technological progress but also key players in addressing employment issues and promoting economic diversification. However, startups often face substantial operational risks and uncertainties in their early stages, especially regarding financing. To uncover the impact of different resource allocations and strategic choices on financing success, this study proposes a predictive method based on the latent Dirichlet allocation (LDA) topic model and deep neural networks through an in-depth analysis of startup financing cases. We systematically collected description text data from 2,000 startups and extracted text features from these descriptions using the LDA topic model. These features, combined with several traditional numerical indicators such as industry, product type, technology type, number of employees, and company size, were used to train a deep neural network to predict startup financing outcomes. The experimental results show that the prediction performance based on the LDA topic model is significantly better than that of traditional models relying solely on numerical data. This highlights the importance of text features in predicting the success of startup financing.

In the contemporary globalized business environment, organizations face intense competition and significant pressure to navigate uncertainties. Strategic decision-making, particularly in allocating scarce resources to innovation endeavors, is a crucial yet complex task for organizational leaders. This study addresses the gap in the existing literature by proposing a decision-making framework grounded in multi-criteria decision making (MCDM), specifically utilizing the analytic hierarchy process (AHP), to enhance strategic decision-making capabilities. The framework aims to improve resource allocation and organizational performance by integrating cognitive and affective factors influencing decision-makers. The analysis presented in this study has successfully computed the final rankings of the strategic alternatives, scanning ability, interpretation ability, and action ability within the organization. By integrating the weights assigned to each criterion and alternative, it was determined that scanning ability holds the highest value at 50.75%, followed by interpretation at 26.65%, and action at 22.58%. Additionally, the factors influencing these alternatives were ranked, with sentiment being the most significant at 0.3607, followed by emotion at 0.2123, attention at 0.2011, ideation at 0.1271, and memory at0.0986. This outcome highlights the significance of scanning ability and sentiment in strategic decision-making. This research contributes to the field by providing a model influencing strategic decision-making, offering valuable insights for managers and policymakers aiming to optimize resource allocation and drive sustainable growth.