Key Influencing Factors Research Articles

Surrogate-model-based low-carbon optimization method for tolerance allocation of mechanical components under bidirectional constraints

The low-carbon optimization of tolerance allocation involves reducing production costs and carbon emissions, while ensuring dimensional precision, making it a crucial pathway to achieving low-carbon manufacturing. However, the complex mapping mechanism between tolerance, cost, and carbon emissions makes it challenging to construct effective mechanistic models. This complexity arises from both the forward constraints of multiple-coupled design features of components and backward constraints on the differentiated processing conditions of an enterprise. Therefore, a low-carbon optimization method for tolerance allocation based on surrogate models is proposed. Firstly, the RReliefF algorithm is utilized to analyze the influence of design features and enterprise processing condition factors on the low-carbon performance of tolerance allocation, identifying key influencing factors. Secondly, a convolutional neural network (CNN)-based surrogate model is established to predict the low-carbon performance of tolerance allocation. Specifically, datasets of tolerance, manufacturing costs, and carbon emissions under different design feature constraints are constructed. To improve the training efficiency and accuracy of the prediction model, the heterogeneous data of design features are processed using the one-hot encoding technique. Subsequently, the mapping relationship between tolerance, cost, and carbon emissions is established based on the CNN. Finally, with the aim of minimizing costs and carbon emissions, a low-carbon optimization method for tolerance allocation is established based on the surrogate model and the product size chain. The convolutional neural network-Harris Hawk optimization algorithm (CNN-HHO) is utilized to generate tolerance allocation schemes that minimize both cost and carbon emissions while satisfying dimensional precision. Through a series of multi-peak test functions and practical case studies of the overrunning clutch, the results indicate that the proposed CNN-HHO algorithm demonstrates exceptional accuracy and stability in the validation on the test functions. Furthermore, the optimized overrunning clutch solution achieves approximately 2.61% cost reduction and 2.63% decrease in carbon emissions, providing further support for the effectiveness of the proposed method.

From Waste to Value: Fish Protein Hydrolysates as a Technological and Functional Ingredient in Human Nutrition.

Fish provides a low-caloric content, polyunsaturated fatty acids, many essential trace elements and is also a rich source of protein, ranging from 10% to 25%. Therefore, the production of FPH (fish protein hydrolysates) is of great interest, as the resulting products exhibit a variety of important bioactive and technological properties, making them potential ingredients for new functional foods and supplements. The aim of this review was to compile and analyze information on enzymatic hydrolysates, with particular emphasis on those derived from fish by-products, as a potential ingredient in human nutrition. Their nutritional characteristics, food safety aspects, bioactive properties, technological attributes, key influencing factors, and applications in food products were evaluated. The findings revealed that these properties are influenced by several factors, such as the raw material, enzymes used, degree of hydrolysis, and the molecular weight of the peptides, which need to be considered as a whole. In conclusion, the gathered information suggests that it is possible to obtain high-value products through enzymatic hydrolysis, even when using fish by-products. However, although numerous studies focused on FPH derived from fish muscle, research on by-products remains limited. Further investigation is needed to determine whether the behavior of FPH from by-products differs from that of muscle-derived FPH.

Association between Land Use and Urban Vitality in the Guangdong–Hong Kong–Macao Greater Bay Area: A Multiscale Study

Urban vitality, which indicates the development level of a city and the quality of life of its residents, is a complex subject in urban research due to its diverse assessment methods and intricate impact mechanisms. This study uses multisource data to evaluate the urban vitality of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) across social, economic, cultural, and environmental dimensions. It analyzes the spatial distribution characteristics of urban vitality and examines the relationships between urban vitality and land use at both regional and city scales. The results indicate that the urban vitality in the GBA generally exhibits a spatial distribution pattern of a high central density and a low peripheral spread, where built-up areas and cropland emerge as key influencing factors. Cities with different developmental backgrounds have unique relationships between land use and urban vitality. In high-vitality cities, the role of the built-up area diminishes, and natural ecosystems, such as wetlands, enhance vitality. In contrast, in low-vitality cities, built-up areas boost urban vitality, and agriculture-related land types exert a lower negative or even positive effect. This research contributes to the understanding of the spatial structures of urban vitality related to land use at different scales and offers insights for urban planners, builders, and development managers in formulating targeted urban vitality enhancement strategies at the regional collaborative and city levels.

A Novel Framework for Identifying Major Fishing Vessel Accidents and Their Key Influencing Factors

This research addresses the critical issue of major fishing vessel accidents, which traditionally suffer from a lack of focused analysis due to their rarity and the subjective nature of their classification. We propose an innovative methodology of Peaks Over Threshold to overcome subjectivity in accident classification. This approach ensures a more representative and accurate analysis of major accidents, distinguishing them from more common, less severe incidents. Employing a Bayesian network model, we further explore the most influential factors contributing to these major accidents. The key innovation lies in our novel approach to data handling and analysis, enabling us to uncover hidden patterns and causal relationships that traditional methods often overlook. The results show that the approach proposed in this study can effectively capture the key factors of major fishing vessel accidents. This study identifies accident type, vessel-related factors, and accident location as the key influential factors leading to major accidents. The findings from our research are intended to inform sustainable fisheries management practices, promoting interventions that aim to decrease the occurrence and impact of severe maritime accidents while balancing economic, safety, and sustainable development considerations.

Nonlinear seismic performance of offshore wind turbines on hybrid pile-bucket foundation in sand: Combined earthquake and wind-wave loads

Offshore wind turbines (OWTs) are gaining prominence worldwide, and the hybrid pile-bucket foundation, which combines a monopole and a bucket, has emerged as a noteworthy development. In this study, a 3-D numerical model for the 5-MW OWT was constructed utilizing the OpenSees platform. The dynamic characteristics of the sand was modeled with the PDMY02 constitutive model and the soil was discretized using brick u-p elements. To investigate the dynamic behavior of the OWT in an actual marine environment, the coupled model was subjected to dynamic loadings, encompassing waves, wind, and earthquake. Two seismic motions with different frequency components were considered, respectively. The study focused on exploring the impacts of key influencing factors on the OWT rotation, tower-top acceleration development and spatiotemporal distribution of excess pore water pressure ratio (EPWPR). These factors include dynamic load combinations, earthquake intensity, soil relative density, wind speed, angle between load directions, and pile length. It is revealed that the inclination angle of offshore wind turbines (OWTs) may exceed the allowable threshold under specific conditions of load combinations, seismic motion inputs, and seabed conditions. Thus, it is suggested to appropriately consider the effects of wind and wave actions in the seismic analysis of OWTS.

Influencing Factors of Spatial Ability for Architecture and Interior Design Students: A Fuzzy DEMATEL and Interpretive Structural Model

Spatial ability is not just a skill but a crucial element for architecture and interior design students, significantly impacting their proficiency in tasks involving 2D drawings, 3D components, and artistic expression. Despite extensive research in this area, a gap remains in the understanding of how to effectively cultivate spatial ability through educational interventions. This study, with its unique approach of identifying key influencing factors and their interrelationships within the fuzzy decision-making laboratory analysis method (Fuzzy-DEMATEL) and the interpretative structural model (ISM), fills this gap. The method visualizes cause-and-effect relationships within a structural model and captures the interdependencies between influencing factors. In a collaborative effort between nine universities in 2023–2024, 17 experts selected through purposeful sampling contributed to the development of a comprehensive list of potential influencing factors. After refinement through filtering, comparison with the existing literature, and expert consensus, seven influencing factors of spatial ability for architecture and interior design students from personal traits and STEAM disciplines were identified, which are sketching and hand drawing skills, mathematical skills, video game practice, descriptive geometry skills, augmented reality practice, spatial talk, and gesturing while talking. Sketching and hand drawing skills, mathematical skills, and video game practice come under cause factors of spatial ability, whereas the rest are effect factors. Proceeding with ISM analysis revealed that sketching and hand drawing skills and mathematical skills are located in the input layer and have a continuous impact on spatial ability. Descriptive geometry skills lie in the transition layer, which are considered as deep influencing factors, the rest of the factors lie in the effect layer. This study delves into the theoretical and practical implications of these findings, offering valuable insights for educational policy and practice.

Research on Gas Extraction Effect of High Gas Mines Based on Stereoscopic Cross Directional Drilling Technology

Based on the advantages of three-dimensional cross drilling technology and directional drilling technology, a gas extraction technology of "three-dimensional cross + directional" drilling is proposed. The advantages, applicability, and key influencing factors of this technology are analyzed, and on-site gas extraction experiments are conducted at the 2210 working face of the No. 2 coal seam in a certain coal mine. The research results show that after adopting the stereoscopic cross directional drilling technology and extraction technology, the gas extraction effect of the 2210 working face has been significantly improved compared to using ordinary extraction drilling, and the stereoscopic cross directional drilling technology can effectively increase the gas extraction volume and concentration. This technology has obvious advantages in improving gas extraction rate and ensuring operational safety, providing a certain reference for solving gas control problems in high gas mines.

Teacher collaboration and innovative learning spaces in New Zealand

ABSTRACT The changing architectural design of the spaces where schooling occurs can impact on the types of pedagogical practices that were once solely viewed within the traditional single teacher classroom. Innovative learning environments are being built where there can be multiple teachers with larger cohorts of students. This may provide more opportunities for collaboration amongst teachers which may enhance learning opportunities. This New Zealand study looks at the views of ten primary school teachers in innovative learning environments and the results from a national survey in regard to primary teachers’ and principals’ perceptions on teacher collaboration and innovative learning environments. Specifically, we highlight the views of principals and teachers situated within the New Zealand education system and the range of views they bring to this change to the types of differing structural spaces of learning and teaching. The findings suggest that a continuing challenge for teachers is the level of noise and the wide range of distractions within innovative learning environment. The attitudes and adaptability of teachers to adjust to change, and their opportunities for professional development to prepare for this change are key influential factors.

Decomposing high uncertainty in greenhouse gas mitigation pathways in emerging regions: An approach to evaluate risks toward carbon neutrality

In the context of ambitious greenhouse gas (GHG) mitigation strategies in emerging regions, addressing the prevailing uncertainty and its key influential factors is crucial. While uncertainty analysis of GHG mitigation pathways has been extensively explored, the systematic identification and quantification of pivotal factors has been notably absent. This study introduces a novel methodology that combines Quasi-Monte Carlo simulation with Sobol variance decomposition, which identifies key influential factors and traces the flow of uncertainties. Applied in Anhui Province, a rapidly developing region in China, our findings indicate an uncertain GHG emission proportion of 6.2 % by 2030, escalating to 68.6 % by 2060, with a 95 % confidence interval. This uncertainty results in a cumulative projection error of 2.1 billion tons of CO2e from 2020 to 2070. The per capita GDP factor emerges as the predominant influence, alongside the increasing impact of renewable energy factor and UHV import electricity factor. In our uncertainty flow analysis, the energy transformation sector is identified as the principal contributor to total uncertainty, driven significantly by economic and energy-related factors. These results underscore the critical need for policymakers in emerging regions to incorporate uncertainty decomposition analysis into their strategic planning to mitigate risks in GHG reduction efforts.

Machine Learning Prediction of a Battery’s Thermal-Related Health Factor in a Battery Electric Vehicle Using Real-World Driving Data

Electric vehicles (EVs) are alternatives to traditional combustion engine-powered vehicles. This work focuses on a thermal management system for battery EVs using liquid cooling and a machine learning (ML) model to predict their thermal-related health. Real-world data of EV operation, battery and cooling conditions were collected. Key influencing factors on the thermal-related health of batteries were identified. The ML model’s effectiveness was evaluated against experimental test data. The ML model proved effective in predicting and analyzing battery thermal health, suggesting its potential for use with the thermal management system.

Tailor-made ammonia nitrogen risk management with machine learning models for aquatic environments in the Mainland of China

Efficient management of pollutant risks in water bodies is crucial for public health and aquatic ecosystem sustainability. However, the toxicities of pollutants, such as ammonia nitrogen (NH3-N), are often affected by multiple water quality factors, including the pH and water temperature. Extensive spatial and temporal variability in these factors hinders tailor-made management of risk. This study used high-frequency monitoring data collected over 1 year to evaluate the long-term NH3-N risk in China’s aquatic ecosystems. High accuracy and interpretability were achieved by decomposing NH3-N risk into the contributions of key influencing factors using random forest models and Shapley Additive Explanations. Two distinct types of NH3-N risk hotspots were identified across 18 cities: 15 cities with high NH3-N concentrations and 3 cities with low environmental carrying capacity due to high pH levels or elevated water temperatures. For the former, rapid NH3-N abatement measures are necessary to bring NH3-N concentrations back below the environmental capacity. For the latter, it is recommended that NH3-N related industries are relocated to regions with high environmental capacities because fragile environments are not suitable for such industries. Importantly, this study investigated methods for attributing pollutant risks in the context of non-linear influencing factors, and the risk of NH3-N was predicted to increase by 6.1 % by the end of 2100 in the context of increasing temperatures under the SSP 2–4.5 scenario. The methodology is also adaptable and suitable for integration into global ecosystem risk management efforts to balance development and aquatic ecological sustainability.

Propagation Dynamics from Meteorological to Agricultural Drought in Northwestern China: Key Influencing Factors

Meteorological and agricultural droughts are inherently correlated, whereas the propagation mechanism between them remains unclear in Northwestern China. Investigating the linkages between these drought types and identifying the potential influencing factors is crucial for effective water resource management and drought mitigation. This study adopted the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSMI) to characterize the meteorological and agricultural droughts from 1960 to 2018. The propagation time between these droughts was detected using the Pearson correlation analysis, and the cross-wavelet transform and wavelet cross-correlation were utilized to describe their linkages across the time–frequency scales. The grey relational analysis was applied to explore the potential factors influencing the propagation time. The results revealed that the agricultural drought typically lagged behind the meteorological drought by an average of 6 months in Northwestern China, with distinct seasonal and regional characteristics. The shortest propagation time occurred in the summer (3 months), followed by the autumn (4 months), and the propagation time was longer in the winter (8 months) and spring (9 months). Additionally, the average propagation time was longer in the plateau climate zone (8 months) than in the southeastern climate zone (6 months) and the westerly climate zone (4 months). There was a multi-timescale response between the meteorological and agricultural droughts, with a relatively stable and significant positive correlation over long timescales, whereas the correlation was less clear over short timescales. The key factors influencing the propagation time were soil moisture, elevation, precipitation, and potential evapotranspiration. Furthermore, the wavelet cross-correlation between agricultural and meteorological droughts was relatively high, with a lag of 0 to 3 months; as the timescale increased, the fluctuation period of their cross-correlation also increased.

Different macrophytes and eutrophic gradients regulate microbial communities of phytoremediation for eutrophication pollution and carbon reduction

Phytoremediation on eutrophicated ecosystems has received growing research interest, and macrophytes and eutrophic gradients are considered as key influencing factors. This study used Vallisneria natans, Iris pseudacorus, and their mixed plantings in aquatic ecosystems (9 m × 2 m × 0.5 m, L × W × D) receiving increased eutrophic nitrogen and phosphorus gradients (4, 40, 80 mg/L of nitrogen and 0.4, 4, 8 mg/L of phosphorus), to unveil phytoremediation effects and corresponding microbial mechanisms. Different phytoremediations could effectively improve water quality with removal efficiencies of 57.4%–98.7% on ammonium, 45.7%–93.7% on total nitrogen, 46.7%–100% on available phosphorus, and 38.4%–95.2% on total phosphorus, respectively. Macrophytes and eutrophic gradients jointly affected diversities, compositions, structures, interactions, and functions of sedimental bacterial and archaeal communities. Microbial diversities increased with macrophytes presence and increased eutrophic gradients. Principal component analysis demonstrated that different macrophytes diverged microbial community structures, and increment of eutrophic gradients reduced structural dissimilarities. Dominant compositional communities, including Chloroflexi, Firmicutes, Proteobacteria, Anaerolineae, and Nitrososphaeria, were also keystone taxa of community co-occurrence networks in phytoremediations. Increased eutrophic gradients enhanced microbial cooperative interactions, whilst V. natans fostered bacterial interactions, and I. pseudacorus and mixed plantings strengthened archaeal interactions. Functional prediction indicated that bacterial “phosphate transferase” and archaeal “methanogenesis” were significantly reduced, whilst archaeal “nitrification”, “denitrification”, “comammox”, and “methane oxidation” were elevated by macrophytes and eutrophic gradients, indicating likely mechanisms for nutrient and GHGs metabolisms. This study extended our knowledge on microbial mechanisms of phytoremediations with different macrophytes treating eutrophicated waters of increased gradients, therefore providing references for further developing phytoremediation technologies.

Statistical studies of the characteristics and key influencing factors of sediment changes in the upper Yangtze River over the past 60 years

Sediments in the upper Yangtze River have significantly changed over the past 60 years. The characteristics and causes of these sediment changes must be determined in the upcoming phases of the comprehensive management of the Yangtze River Basin. Based on sediment observation data from 1960 to 2020, this study examined the trends and sudden changes in the sediments in the mainstream and its significant tributaries in the upper Yangtze River. The relative contributions of precipitation change and various human activities to sediment load changes are quantified. The relationships between sediment variations and the construction and large dam projects, soil and water conservation measures, and earthquake disasters are established. The results indicate that there was a significant decline in sediment load with main changes occurring around the 1985s and 2010s. Multiple double mass curves are used to quantify the relative contributions of precipitation change and human activities to sediment load changes. The relative contribution of dams to the decrease in sediment load ranges from 53 % to 74 %. Precipitation change serves a significant role in the observed changes, accounting for a range of 10 % to 27 %. Soil conservation measures take some time to show their impact on sediment reduction. These measures work by stabilizing the soil and improving hydrological conditions. On average, they contribute between 7 % and 20 % towards reducing sediment. In recent times, there has been a noticeable rise in sediment load in rivers like the Jialing and Min Rivers. This unexpected increase could potentially be linked to a combination of factors including earthquakes, heavy precipitation, and localized sediment scouring. The results of this study offer valuable insights into the variations in river sediment load, aiding in the improved management of water resources and sediment in large dams. These findings provide helpful insights for dam management.

THE ROLE OF DATA ANALYTICS IN MAKING MANAGEMENT DECISIONS BY THE LOGISTICS INTERMEDIARIES

Data analytics plays a crucial role in increasing the effectiveness of management decisions by the logistics of intermediaries. The aim of the article is to identify the extent to which the quality of data analytics affects the effectiveness of decision-making in the logistics intermediaries, in particular, the speed of delivery of the studied companies.The study employed regression and correlation analysis to identify key influencing factors in terms of data analytics on the effectiveness of management decisions of the logistics intermediaries. The significance of investment in the qualification of analysts (with a coefficient of -1.6754), analytical tools (with a coefficient of -1.2575), and integration of analytics in decision-making processes (with a coefficient of -3.2511) directly affect the reduction of delivery time.It is emphasized that each analytical project contributes to the reduction of delivery time by 0.48 hours. Correlation analysis confirmed the relationship between the efficiency of logistics and the level of qualification of analysts (-0.283617), investment in analytical tools (-0.257322), the number of analytical projects (-0.343792), the level of integration of analytics (-0.712058). The strongest correlation was observed for the integration of analytics in management decision-making.It is recommended to focus on the development of analytical competencies, increase of investment in tools, intensification of projects, and integration of analytics in strategic management. Further research is planned on the use of artificial intelligence to optimize management decisions in logistics as part of ensuring the company’s sustainable development.

Clinical Practice Ability and Related Factors Among Senior Undergraduate Nursing Students Post-Clinical Internship: A Latent Profile Analysis

The shortage of nurses and inadequate clinical practice abilities among senior undergraduate nursing students has raised significant concerns globally. The discrepancy between nursing education and practical skill requirements in hospitals is the primary contributor to this issue. Objectives: This study aimed to (1) explore potential classifications for assessing the clinical practice abilities of senior undergraduate nursing students in clinical practice, (2) identify distinct subgroups based on their clinical practice abilities, and (3) analyze the factors influencing these subgroups. Design: This was a cross-sectional study using latent profile analysis. Participants: A total of 336 fourth-year nursing students who had recently completed clinical practice were included in this study. Methods: Data were collected using a structured questionnaire that included sociodemographic factors, the Nursing Professional Attitude Questionnaire (NPAQ), and the Environmental Assessment Scale for Nursing Clinical Practice. The Clinical Ability Scale for Undergraduate Nursing Students (CCSUNS) measures clinical practice abilities. Latent profile analysis was used to identify subgroups of students based on their clinical practice abilities. Multinomial logistic regression analysis was used to analyze the factors influencing these subgroups. Results: Three profiles of clinical practice ability were identified: (1) "low level - guidance dependent" (51.79%), (2) "intermediate - response improvement" (33.85%), and (3) "high level - autonomous decision-making" (16.37%). Key influencing factors included gender, academic performance, professional nursing attitude, and the clinical practice environment. Conclusions: More than half of the senior nursing students demonstrated poor clinical practice abilities. Enhancing the clinical practice environment, increasing practical opportunities, fostering professional attitudes, and addressing gender equality are critical for improving clinical practice abilities. Future multicenter longitudinal studies are recommended to explore changes in clinical practice abilities over time and across different stages of clinical practice.

Calculation method of renewable maximum penetration considering frequency response of renewable energy

The calculation of maximum penetration is crucial for planning renewable energy integration and ensuring the safe operation of power systems. This paper proposes a method for calculating maximum penetration under kinetic energy constraints. In scenarios where renewable energy lacks additional frequency control, we construct a frequency response model to reveal the energy change mechanism during active disturbances. By converting the frequency constraint into a kinetic energy constraint, we derive an analytical expression for maximum penetration under these constraints. Key influencing factors were quantitatively analyzed, revealing nonlinear relationships between system capacity, frequency constraints, and maximum penetration, while also calculating the minimum operational generator capacity. When renewable energy participates in frequency control, its frequency regulation can significantly enhance maximum penetration. However, frequency oscillations caused by time delays in current source frequency control become the primary constraint. The proposed method and conclusions are validated using actual sending-end and receiving-end grid data, providing valuable guidance for grid operation planning.

Key influence factors of carbon emissions in Malaysian construction operations

The urgency to mitigate carbon emissions has gained significant societal attention as the consequences of climate change continue to unfold. There is a notable scarcity of studies conducted on carbon emissions for construction operations. Hence, the aim is to identify the key influence factor of carbon emissions to develop a carbon reduction framework for construction operations. A survey was conducted with 297 Malaysian construction stakeholders to analyze 18 factors. Descriptive and Rasch analysis were used. Results revealed the reliability and validity of the model, which could help to explore ways to reduce carbon emissions during the construction phase. The findings indicate that a gradual transformation process is necessary to expedite the transition towards a low-carbon construction. The research enhances the existing literature by emphasising the key factors for stakeholders who aim to efficiently manage carbon emissions during a construction project. There are different ideas on how to estimate and manage carbon emissions in construction projects, but there are no standardized ways. This study looks into the main factors that contribute to the generation of carbon emissions in construction, to identify ways to reduce them evaluating these factors from the perspective of different stakeholders.

Predicting Quality of Life in Parkinson's Disease: A Machine Learning Approach Employing Common Clinical Variables.

Background: Parkinson's Disease significantly impacts health-related quality of life, with the Parkinson's Disease Questionnaire-39 extensively used for its assessment. However, predicting such outcomes remains a challenge due to the subjective nature and variability in patient experiences. This study develops a machine learning model using accessible clinical data to enable predictions of life-quality outcomes in Parkinson's Disease and utilizes explainable machine learning techniques to identify key influencing factors, offering actionable insights for clinicians. Methods: Data from the Parkinson's Real-world Impact Assessment study (PRISM), involving 861 patients across six European countries, were analyzed. After excluding incomplete data, 627 complete observations were used for the analysis. An ensemble machine learning model was developed with a 90% training and 10% validation split. Results: The model demonstrated a Mean Absolute Error of 4.82, a Root Mean Squared Error of 8.09, and an R2 of 0.75 in the training set, indicating a strong model fit. In the validation set, the model achieved a Mean Absolute Error of 11.22, a Root Mean Squared Error of 13.99, and an R2 of 0.36, showcasing moderate variation. Key predictors such as age at diagnosis, patient's country, dementia, and patient's age were identified, providing insights into the model's decision-making process. Conclusions: This study presents a robust model capable of predicting the impact of Parkinson's Disease on patients' quality of life using common clinical variables. These results demonstrate the potential of machine learning to enhance clinical decision-making and patient care, suggesting directions for future research to improve model generalizability and applicability.

The antecedents of purchase intention of local brands: A systematic literature review

This study reviews and summarizes empirical findings on the determinants of local brand purchase intentions over the past five years. It aims to provide a comprehensive understanding of the literature for future researchers by using a systematic literature review approach to identify key influencing factors. A total of 34 empirical research articles from 2018 to 2022 were reviewed. The dominant theory applied was brand management, found in nine articles (26%), followed by social identity theory in four articles (11.8%), consumer behavior in three articles (8.9%), and country of origin in two articles (5.9%). Most studies focused on emerging or developing countries. The review identified six main theories used to explain local brand purchase intentions: brand management, consumer behavior, consumer culture positioning, country of origin, social identity, and the theory of reasoned action. The findings highlight a strong correlation between these theories and the likelihood of purchasing local brands, though results varied. Further research is needed to understand the complex interplay of these factors fully.