Explanatory Power Research Articles

The Influence of Digital Marketing, Product Quality, and Emotional Factors on Consumer Satisfaction

The rapid evolution of digital technology has reshaped marketing approaches, with digital marketing emerging as a cornerstone in connecting brands with consumers. Alongside digital marketing, product quality and emotional factors have gained prominence in shaping consumer satisfaction. This study investigates the interplay between digital marketing, product quality, and emotional factors influencing consumer satisfaction in Indonesian markets. The research aims to evaluate their individual and collective impacts to provide insights for strategic optimization. Using a quantitative approach with a descriptive correlational design, data were collected through an online questionnaire from January to March 2024. A purposive sampling method was employed, targeting 150 respondents across Indonesia who had engaged with brands utilizing digital marketing strategies. The variables were measured using a 5-point Likert scale, and data were analyzed through multiple linear regression using SPSS. The findings reveal significant positive contributions from all three variables. Digital marketing had a regression coefficient of 0.321 (p < 0.001), emphasizing the importance of personalized and interactive strategies. Product quality demonstrated the highest influence with a coefficient of 0.412 (p < 0.001), confirming its critical role in exceeding consumer expectations and fostering loyalty. Emotional factors contributed a coefficient of 0.275 (p = 0.001), underscoring the relevance of positive brand interactions in enhancing satisfaction. The simultaneous effect of these variables was validated through an F-statistic of 42.7 (p < 0.001), with an R² value of 0.72, indicating strong explanatory power. This research provides a holistic framework for understanding consumer satisfaction by integrating digital marketing, product quality, and emotional factors. It recommends prioritizing product quality, optimizing digital marketing strategies, and fostering emotional connections to achieve sustained consumer loyalty. Future studies should explore these dynamics across diverse industries and consumer segments to expand upon these findings.

The Influence of Financial Literacy and Digital Literacy on Mobile Banking Adoption

This research examines the influence of Financial Literacy and Digital Literacy on Mobile Banking Adoption in Jabodetabek. This research data was obtained using a questionnaire in the form of a Google form and distributed via the WhatsApp friendship network and other social media such as TikTok and Instagram. The respondent criteria for this research are bank customers who are over 20 years old, have a mobile banking application, and actively use the mobile banking application for daily transactions. A total of 210 answers have been filled in and all answers have met the requirements for processing. This research observes that there is a significant influence of Financial Literacy and Digital Literacy on Mobile Banking Adoption. The dependent variables, namely Perceived Ease of Use, Perceived Usefulness, and Relative Advantage, have high explanatory power of the model. The independent variables Financial Literacy and Digital Literacy have a strong influence on the three dependent variables of this research, showing that Financial Literacy and Digital Literacy have a direct or positive influence on the use of Mobile Banking.

Spatial Heterogeneity and Risk Factors of Dental Caries in 12-Year-Old Children in Shanxi Province, China

ObjectiveThis study aimed to explore the spatial heterogeneity and risk factors for dental caries in 12-year-old children in Shanxi province, China. MethodsThe data encompassed 3,721 participants from the two most recent oral health surveys conducted across 16 districts in Shanxi Province in 2015 and 2018. Eighteen specific variables were analyzed to examine the interplay between socioeconomic factors, medical resources and environmental conditions. The Geo-detector model was employed to assess the impacts and interactions of these ecological factors. ResultsSocioeconomic factors (Q = 0.30, P < 0.05) exhibited a more substantial impact compared to environmental (Q = 0.19, P < 0.05) and medical resource factors (Q = 0.25, P < 0.05). Notably, the urban population percentage (UPP) demonstrated the most significant explanatory power for the spatial heterogeneity in caries prevalence, as denoted by its highest q-value (q = 0.51, P < 0.05). Additionally, the spatial distribution's heterogeneity of caries was significantly affected by SO2 concentration (q = 0.39, P < 0.05) and water fluoride levels (q = 0.27, P < 0.05) among environmental factors. ConclusionThe prevalence of caries exhibited spatial heterogeneity, escalating from North to South in Shanxi Province, China, influenced by socioeconomic factors, medical resources, and environmental conditions to varying extents.

Investigating the determinants of Singaporean citizens' attitudes toward marine litter pollution control: A policy acceptance model

The problem of marine litter has caused significant threat to marine environment and human health, and has attracted wide attention. It is estimated that the weight of plastic waste in the oceans will exceed that of fish by 2050. Since a large part of marine debris originate from land-based domestic waste, developing relevant policies to manage the disposal of domestic garbage can effectively prevent and control marine litter pollution. Public attitudes toward relevant environmental policies will affect their implementation and final outcomes. However, there is little research on public attitudes toward environmental policies. Therefore, this study draws on the framework of Technology Acceptance Model (TAM) to explore the factors that affect public attitudes toward policy, the affect theory, trust theory and habit are integrated into the model. An online survey for Singaporean residents was conducted, 450 questionnaires were collected and 417 of which were used for data analysis. The results suggest that 13 of the 14 hypotheses presented in the model are accepted. Perceived ease of implementation (β = 0.365), perceived policy effectiveness (β = 0.341) and trust in government policy (β = 0.319) are the main factors that directly affect citizens' attitude toward environmental policy. Perceived policy effectiveness is positively affected by the perceived ease of implementation (β = 0.457), while trust in government policies is positively influenced by both perceived ease of implementation (β = 0.142) and perceived policy effectiveness (β = 0.373). The model showed good explanatory power, explaining 74.6 % of the variance in public attitude toward policy. In this study, a relatively complete model for predicting public acceptance of marine litter prevention policies is proposed for the first time. The model presented in this paper also has the potential to be applied to evaluate policies of various scenarios.

Source apportionment and driving factor identification for typical watersheds soil heavy metals of Tibetan Plateau based on receptor models and geodetector

The identification and quantification of soil heavy metal (HM) pollution sources and the identification of driving factors is a prerequisite of soil pollution control. In this paper, the Sabaochaqu Basin of the Tuotuo River, located in the Tibetan Plateau and the headwater of the Yangtze River, was selected as the study area. The soil pollution was evaluated using geochemical baseline, and the source apportionment of soil HMs was performed using absolute principal component score-multiple linear regression (APCS-MLR), edge analysis (UNMIX) and positive matrix decomposition (PMF). The driver of the source factor was identified with the geodetector method (GDM). The results of pollution evaluation showed that the HM pollution of soil in the study area was relatively light. By comparison, UNMIX model was considered to be the preferred model for soil HMs quantitative distribution in this study area, followed by PMF model. The UNMIX model results show that source 1 (U-S1) was dominated by As, with a contribution rate of 53.31%; source 2 (U-S2) was dominated by Cd and Zn, whose contribution rates are 50.35% and 46.60% respectively; source 3 (U-S3) was dominated by Pb, with a contribution rate of 45.58%; source 4 (U-S4) was dominated by Cr, Cu, Hg and Ni, with contribution rates of 60.58%, 60.07%, 51.58% and 56.45%, respectively. The GDM results showed that the main driving factors of U-S1 were distance from lake (explanatory power q = 0.328) and distance from wind channel (q = 0.168), which were defined as long-distance migration sources. The main driving factors of U-S2 were parent material type (q = 0.269) and distance from Tuotuo river (q = 0.213), which were defined as freeze-thaw sources. The main driving factors of U-S3 were distance from town (q = 0.255) and distance from county road (Yanya Line) (q = 0.221), which were defined as human activity sources. The main drivers of U-S4 were V (q = 0.346) and Sc (q = 0.323), which were defined as natural sources. The GDM results of the 3 models were generally consistent with the analytical results of similar types of sources, especially the results of PMF model and Unmix model can basically verify each other. The research results can provide important theoretical reference for the analysis of HM sources in the soil of high-cold and high-altitude regions.

Amachine learning technique embedded reference-dependent choice model for explanatory power improvement: Shifting of reference point as a key factor in vehicle purchase decision-making

Machine learning is a powerful tool with the potential to improve a choice model's ability to explain consumer behavior. Although the reference-dependent choice model, developed with an emphasis on real decision-making processes, has an advantage over general discrete choice models in terms of explanatory power and interpretability, there is still a lack of consensus on how the reference point should be set. Currently, the common practice is to design a reference point-based utility equation to make an arbitrary decision between past experience, the status quo, and future expectations as the reference point. However, as individual consumers may differ from researchers in how they set their reference points, the current method is rather limited for understanding consumer choice behavior. Therefore, this study proposes a new approach to more accurately understand consumer choice behavior by shifting individual reference points using ANNs (Artificial Neural Networks). The analysis results show that the model proposed in this study has better explanatory power than both the discrete choice model and the existing reference-dependent choice model. This implies that the reference point typically set by researchers approximates each individual's actual reference point through artificial neural networks. This study is significant in that it confirms the possibility that the current status, which showed the highest model fit among several reference price proxy candidates in vehicle choice, may not function as the actual reference price, while also proposing a foundational framework for identifying each consumer's true reference price. Furthermore, it can contribute to corporate strategies and government policy recommendations based on consumer preference analysis, where high explanatory power is required.

Optimizing multiple ecosystem services in Eucalyptus plantations: Dual effects of intercropping and restoration

Mixed plantations of Eucalyptus with native tree species can effectively improve forest ecosystem services. However, the long-term effects and ratios of Eucalyptus-native tree species mixture on plant diversity in different plant layer, multiple ecosystem services, multiservice and the underlying mechanisms remain unclear. To fill these gaps, a four-level intercropping gradient of Eucalyptus urophylla planted with eight native tree species (NS) was set up (i.e., 20%NS, 30%NS, 40%NS, 50%NS), monitored and compared with a monoculture E. urophylla plantation (EU) and a randomly mixed plantation (98%NS) in southern China. We calculated the multiple ecosystem services of the plantation and the multidimensional diversity (taxonomic diversity (TD), functional diversity (FD), and phylogenetic diversity (PD)) of different plant layers. The results showed that the long-term (13–15a) and short-term (1a) plantation communities were significantly different in multidimensional diversity in all three vertical plant layers. In the long-term, intercropping with more than 20% native trees significantly increased the multidimensional diversity in the tree layer, but not the shrub and herb layers, compared to the EU. Intercropping with 50% native trees significantly promoted biodiversity conservation service, ecosystem multiservice and synergistic relationships among ecosystem services over a decade. Both initial mixing ratio and naturally restored multidimensional diversity in shrub and herb layers influenced tree layer's multidimensional diversity, contributing to long-term multiple ecosystem services of mixed Eucalyptus-native tree species plantations. Our results highlighted the risk of applying short-term based diversity results in long-term strategy making for E. urophylla plantation, quantitatively explored the high explanatory power of plant functional and phylogenetic diversities on multiservice, and expanded understanding of the mechanisms of the triad, biodiversity-ecosystem service-ecosystem stability. To practice, intercropping 50% native trees was recommend for long-term optimizing multiservice restoration, including but not limited to biodiversity conservation, and the syngeneic development among ecosystem services of Eucalyptus plantations.

The effects of self-efficacy, a health-promoting lifestyle, and social support on resilience of patients receiving percutaneous coronary intervention: A descriptive survey study

Purpose: This study aimed to identify the impact of self-efficacy, a health-promoting lifestyle, and social support on, resilience of patients with percutaneous coronary intervention (PCI). Methods: The participants were 146 patients with PCI in a university hospital located in B Metropolitan City. Data were collected from August to October 2023 using a questionnaire on self-efficacy, health-promoting lifestyle, social support, and resilience. Data were analyzed using descriptive statistics, t-test, ANOVA, Pearson's correlation coefficient, and multiple regression. Results: The subjects’ average age was 65.73±10.82 years, and men accounted for 76.5%. Resilience showed a statistically significant positive correlation with self-efficacy(r=.82, p&lt;.001), health promoting lifestyle(r=.63, p&lt;.001) and social support (r=.70, p&lt;.001). Total explanatory power was 75% of the total variance in resilience (F=44.70, p&lt;.001). The related factors of resilience were self-efficacy (β=.55, p&lt;.001), social support (β=.27, p&lt;.001), health-promoting lifestyle(β=.17, p=.003), and LDL(β=.10, p=.026). Conclusion: These results should be used as basic data for the development of patient education protocols to improve the resilience of patients with PCI.

Development of Data-Driven Feature Value Map Toward High-Performance Material Exploration

Currently, over 100,000 data on inorganic compounds was reported in the Inorganic Crystal Structure Database (ICSD). Until now, exploration of functional materials has been experimentally carried out using intuition and experience from such huge amount of materials. This approach needs several trials and errors, resulting in consuming times. To overcome this issue, we introduce data-driven exploration system that is guided by crystal structure, composition, and their properties. Since functional performances are strongly dependent of material's properties, this approach should potentially lead to novel discoveries beyond the subjective perception of the researcher. Our research goal is development of a data-driven feature value map that is strongly correlated with a functional performance. To achieve this, we aimed to find contribution factor of material performance in X-ray diffraction (XRD) profiles and structural properties. We also aimed to understand the material exploration efficiency using the feature value map that was composed of these descriptors.Approximately 100 kinds of crystalline materials were grown and their functional performance was measured. The feature value map was depicted using XRD profiles of over 60,000 reference data and this experimental data. The feature values extracted from XRD profiles (XRD feature values) and structural properties (structural feature values) were used as the explanatory variable to depict the map using t-SNE. The contribution factors for material performance were analyzed by multivariate regression analysis.The generated feature value map only using XRD feature values exhibits that similar materials in crystal structure make clusters in the map, indicating good explanatory power for crystal structures. However, its regressive prediction accuracy for material performance show low coefficient of determination (COD) at 0.18. By adding some structural feature values, the COD improved up to 0.7. This result clearly indicates the XRD feature values are not major contributing factor and other factors are necessary for high predictive map construction. Under the updated map, we succeeded to find novel material with high functional performance. The detail will be reported in the presentation.AcknowledgementThis work was partly supported by Priority Project in Aichi Knowledge Hub Aichi, JST Green Technologies of Excellence, the Strategic Innovation Program (SIP) of the Cabinet Office, and joint research with a company.

To activate, or not to activate? Officers’ decisions to turn on body-worn cameras during different police services

PurposeThis study explores how various officer and event-level factors influence Milwaukee Police Department officers’ decision to activate their body-worn cameras (BWCs) across both community member-initiated services and officer-initiated activities.Design/methodology/approachAcross the 1,052 officers and 1,066,112 officer-events in the sample, we use descriptive and logistical regressions to assess differences in BWC activations across calls for service and officer-initiated activities.FindingsWe found similar activation rates between calls for service (41.5%) and officer-initiated activities (44.1%). However, our logistic regression analysis results suggest the explanatory power of the event and officer-level variables was substantially better in models examining officer-initiated activities. Among calls for service, officers were more likely to activate BWCs during calls involving crimes against persons compared to other crimes or non-criminal incidents. Activation was more frequent during traffic stops than other self-initiated activities. Activation increased when the event resulted in an advisement, citation, detention or arrest.Originality/valueThe success of police BWC programs hinges on whether officers activate their cameras when interacting with community members. Findings suggest that officers are more likely to activate their BWCs during activities that involve direct interactions with community members, especially in situations with a higher potential for volatility or serious criminal implications.

Reverse Inference: Decoding of Brain Activity and Cognitive Process

Cognitive neuroscientists rely on functional neuroimaging techniques and behavioral assays to investigate correlation between brain activation and cognitive processes. Researchers would also infer what cognitive process is engaged under a condition based on neuroimaging data. This is referred to as reverse inference or encoding paradigm and it has generated longstanding discussions among cognitive neuroscientists, data scientists and philosophers. The consensus is that it should be done with rigorous statistical methods and careful interpretations. Statistical methods and large collaborative databases and data processing tools have been developed to build classificatory or predictive models of reverse inference. However, these tools are not without pitfalls. The problem of cognitive process further complicates the problem since it is a latent variable and the wide discrepancy on the taxonomy and ontology of cognitive processes within the field. Online collaborative project has been set up to combat this problem, while others try to circumvent pre-established concepts by extracting latent variables from existing data or by focusing on the evolutionary prerequisite of cognition. A fundamental limitation of reverse inference is its dependency on correlational data, which lacks the explanatory power interventionist studies have. Models that seek to establish causal relation from time-series data can only provide weak inferences. Furthermore, the dynamic complexity of the brain network complicates the mechanism. This review provides an overview of reverse inference in cognitive neuroscience. It discusses advances in methods, limitations, and conceptual issues inherent within reverse inference, particularly addressing the challenges mentioned above.

From philosophy to science and back: Dennett and the relation of science to philosophy

ABSTRACT One of the characteristic aspects of Daniel Dennett’s thought is his belief in the explanatory power of science. For Dennett, in order to make progress with certain philosophical problems, e.g. questions about the nature of consciousness, philosophers need to engage with the findings of empirical science. In this paper, I will take up the question of the relation between science and philosophy and I will start by giving a general overview of Dennett’s view of this relation while also addressing the question of whether he can properly be said to embrace scientism. In the second part of this paper, I will argue that philosophical thinking is continuous with science and that, though at the level of theoretical frameworks the intension of the words “philosophy” and “science” are the same, there is a distinction that can be made in practice if we look at the extensions of the terms.

Variations and influencing factors of vegetation net primary productivity over 31 years in Wuyishan National Park, China

In this study, the Carnegie–Ames–Stanford approach model was used to estimate the net primary productivity (NPP) of Wuyishan National Park from 1990 to 2020. Variation in NPP in parks was examined, and the influencing factors were identified. NPP showed an increasing trend annually with climate change. The value (625.67 gC m−2 a−1) obtained in this study was higher than the regional value along similar latitudes and coastlines. Affected by the spatial distribution characteristics of vegetation, NPP in the study area exhibited significant spatial heterogeneity, with a distribution of higher values in the northwestern region and lower values in the southeastern region. However, the growth rates showed the opposite trend. The higher growth rate of NPP in the ecological restoration zone was attributed to the ecological conservation efforts undertaken in Wuyishan National Park. Among the various environmental factors, the vapor pressure deficit contributed substantially to NPP temporal distribution, accounting for 25.71%. Terrain factors, which are influenced by the spatial distribution characteristics of vegetation and environmental factors, are important variables that affect the spatial distribution of NPP within the study area. The highest vegetation NPP in Wuyishan National Park was observed in regions with an elevation of approximately 1000 m, slopes ranging from 20° to 40°, and an aspect of approximately 150°. Compared with slope and aspect, elevation provides a stronger explanatory power (R2 = 0.93) for the spatial distribution patterns of vegetation NPP in Wuyi Mountain National Park. This study suggests that Wuyishan National Park has considerable carbon sequestration potential. In the context of climate warming, the favorable aspects of climate warming should be considered, and ecological restoration measures tailored to local conditions should be implemented.Article title mismatch between manuscript and publisher provided information, so we have follow the manuscript. Kindly check and confirm.Variations and influencing factors of vegetation net primary productivity over 31 years in Wuyishan National Park, China

Which jobs make cities attractive to migrants? Explaining the changing distribution of migrants from their employment geography in transitional China

ABSTRACT While the role of regional disparities in employment opportunities has been emphasised in unbalanced migration processes, the differences in employment across various sectors have not been fully addressed. This study attempts to explain the changing distribution of migrants across Chinese cities in the last decade based on the employment structure among those migrants. We found that compared with employment opportunities, employment structure has a stronger explanatory power for changes in net migrants across cities. Specifically, the geography of manufacturing employment has significantly affected the changing distribution of migrants, alongside the burgeoning impact of employment in new industries, including high-tech manufacturing and modern service employment. In addition, the influence of employment in different sectors on the redistribution of migrants varies according to city size. Our findings highlight the importance of focusing on regional disparities in employment structure to better understand regional migration dynamics.

Revealing the synergistic spatial effects in soil heavy metal pollution with explainable machine learning models

The identification of factors that affect changes in the heavy metal content of soil is the basis for reducing or preventing soil heavy metal pollution. In this research, 16 environmental factors were selected, and the influences of soil heavy metal spatial distribution factors and the synergy amongst space factors were evaluated using a geographic detector (GD) and the extreme gradient boosting (XGBoost)-Shapley additive explanations (SHAP) model. Three heavy metal elements, namely, Cd, Cu and Pb, in the study region were examined. The following results were obtained. (1) XGBoost demonstrated high accuracy in predicting the spatial distributions of soil heavy metals, with each heavy metal having an R2 value of over 0.6. (2) Geological type map (Geomap) and enterprise density considerably affected the concentrations of Cd, Cu and Pb in soil in the GD and XGBoost-SHAP models. In addition, cross-detection revealed strong explanatory power when natural and human factors were combined. (3) Under the same geological background, the different trends of gross domestic product effects on heavy metals indicated that pollution control measures were effective in economically developed areas, and the economy and the environment could be balanced. Meanwhile, the interaction between the normalised difference vegetation index and enterprise density showed that vegetation could alleviate heavy metal pollution in the region. This study supports strategic decision-making, serving as a reference for the global management of soil heavy metal contamination, sustainable ecological development and assurance of people’s health and well-being.

Performance measurement of Brazil Federal University hospitals through principal component analysis: a public health care services’ panorama

Purpose This paper aims to contextualize the process of public hospital providing services, based on the measurement of the performance of Federal University Hospitals (HUFs) of Brazil, using the technique of multivariate statistics of principal component analysis. Design/methodology/approach This research presented a descriptive and quantitative character, as well as exploratory purpose and followed the inductive logic, being empirically structured in two stages, that is, the application of principal component analysis (PCA) in four healthcare performance dimensions; subsequently, the full reapplication of principal component analysis in the most highly correlated variables, in module, with the first three main components (PC1, PC2 and PC3). Findings From the principal component analysis, considering mainly the component I, with twice the explanatory power of the second (PC2) and third components (PC3), it was possible to evidence the efficient or inefficient behavior of the HUFs evaluated, through the production of medical residency, by specialty area. Finally, it was observed the formation of two groups composed of seven and eight hospitals, that is, Groups II and IV, which shows that these groups reflect similarities, with respect to the scores and importance of the variables for both hospitals’ groups. Research limitations/implications Among the main limitations, it was observed incomplete data for some HUFs, which made it impossible to search for information to explain and better contextualize certain aspects. More specifically, a limited number of hospitals with complete information was dealt with for 60% of SIMEC/REHUF performance indicators. Practical implications The use of PCA multivariate technique was of great contribution to the contextualization of the performance and productivity of homogeneous and autonomous units, represented by the hospitals. It was possible to generate a high quantity of information, to contribute with assumptions to complement the decision-making processes in these organizations. Social implications Development of public policies, with emphasis on hospitals linked to teaching centers represented by university hospitals. The projection of improvements in the reach of the efficiency of the services of assistance to the public health, from the qualified formation of professionals, both to academy, as to clinical practice. Originality/value The originality of this paper for the scenarios, Brazilian public health sector and academic area involved the application of a consolidated performance analysis technique, that is, PCA, obtaining a rich work in relation to the extensive exploitation of techniques to support decision-making processes. In addition, the sequence and the way in which the content, formed by object of study and techniques, has been organized, generating a particular scenario for the measurement of performance in hospital organizations.

An innovation potential and organizational performance: an integrative role of company’s dynamic capabilities

AbstractThe restaurant business plays a crucial role in the food economy and, like many industries, faces numerous challenges due to the rapid pace of change, driven by consumer trends and shifting demographics. In response to the pressure on businesses to adapt to these changing circumstances, new restaurant concepts, organizational structures and technologies have emerged in the hope of finding methods that are more agile during unpredictable times. The development of dynamic capabilities is a critical issue for both academics and practitioners, as it can enhance competitive advantages and improve organizational performance. The purpose of this paper is to examine the impact of dynamic capabilities on innovative potential and organizational performance. Through a literature review and the dynamic capability view, a theoretical model is proposed. This model was validated using the PLS-SEM technique, based on responses from 143 restaurants across eight of the largest Russian cities. The results indicate that dynamic capabilities related to the value proposition serve as a complete mediator in the relationship between a company's innovative potential and its performance in a highly volatile context. The proposed theoretical model is both unique and effective, demonstrating high explanatory power. This study enriches the existing literature on dynamic capabilities by addressing empirical research gaps and elucidating the mechanisms through which dynamic capabilities influence organizational performance.

Conservation behaviors of local communities towards mangrove forests in Iran

Mangrove forests are a sensitive and fragile ecosystem at risk of extinction and destruction due to excessive exploitation in recent years. Moreover, insufficient studies, programs, and practical policies to safeguard these ecosystems have exacerbated their degradation. In this context, the present research was conducted to find the factors that determine the willingness of Iranian rural households towards mangrove forest conservation. The extended protection motivation theory (PMT) by adding the variables of moral norms (MN) and knowledge was used as the study's theoretical framework. This research was conducted using the questionnaire survey method and with the help of structural equations (SEM). The research's statistical population comprised all rural communities within Minab city in southern Iran. The findings of the research showed that the initial PMT explains 0.54 % of the variance of behavioral intention of rural households in applying forest conservation behaviors. The primary constructs of the PMT encompass perceived severity, perceived vulnerability, self-efficacy, response efficacy, and response costs, each of which positively influences the behavioral intention of rural households. Notably, the incorporation of knowledge and MN variables enhances the model's explanatory power regarding the BI of rural households to forest conservation by 21.8 %. In the current research, knowledge was identified as the most important factor, so policymakers can promote safe behavior in the natural environment by emphasizing it and providing the basis for sustainable forest development.

Spatial–Temporal Evolution Pattern of Soil Erosion and Its Dominant Factors on the Loess Plateau from 2000 to 2020

Global changes have led to significant changes in soil erosion on the Loess Plateau. Soil erosion leads to the degradation of land resources and a decline in soil fertility, adversely affecting agricultural production and the socioeconomic situation. Therefore, revealing the spatiotemporal evolution patterns of soil erosion in the Loess Plateau region and investigating the influencing factors that contribute to soil erosion are crucial for its management and restoration. In this study, the RUSLE monthly model and the Geodetector model were utilized to reveal the spatiotemporal trends of soil erosion in the Loess Plateau from 2000 to 2020 and to determine the dominant influencing factors in different periods. The main results are as follows: (1) From 2000 to 2020, the soil erosion in the Loess Plateau initially weakened and then intensified, indicating that precipitation and precipitation intensity have different effects on surface soil. (2) From 2000 to 2015, the area experiencing slight and mild erosion increased. This is attributed to the increase in vegetation coverage in the Loess Plateau region, which has alleviated soil erosion in the area. (3) From 2000 to 2020, zones of severe soil erosion were mainly located in the cities of Yan’an and Yulin and their surrounding areas. The gravity center of soil erosion shifted northwestward from Yan’an City overall, indicating an improvement in the soil erosion conditions in the Yan’an area. (4) The predominant level of soil erosion across different land-use types was slight erosion, accounting for over 40%. This may be a result of forestry ecological projects that effectively reduce soil loss. (5) In slope zones of 0–5°, slight erosion accounted for the largest area proportion. As the slope increased, the area proportion of severe and extremely severe erosion also increased. This is attributed to the protective role of vegetation on soil in gentle slope areas. (6) From 2000 to 2020, vegetation was the dominant single factor influencing the spatiotemporal changes in soil erosion, while the interactions between vegetation and land use had the largest explanatory power, indicating that changes in land-use types partially affect variations in vegetation coverage. Our research findings could provide important data support for soil erosion control and eco-environment restoration in the Loess Plateau region.

Measuring The Information Content Of Accounting Profits In The Iraqi Banking Sector For Banks Listed On The Iraqi Stock Exchange

The study aims to measure the information content of the accounting profits of banks listed on the Iraqi Stock Exchange, as the sample included 10 banks for a period of 4 years (2016-2019). The study concluded that banks that have a high explanatory ability reflect greater stability in the stock price, which would reassure Investors and stakeholders, thus reducing dispersion and fluctuations in the stock price, while banks with weak explanatory power show higher volatility in the stock price, which indicates additional risks that increase the fluctuation in investor returns, and the influencing factors have a major role in enhancing or reducing the information content of profits. Which explains the discrepancy between banks, and the study recommended the need to enhance the quality of financial disclosure, especially banks with weak explanatory capacity, increase transparency, and work to enhance the informational content of profits, which in turn increases investors’ confidence in accounting information, as well as encouraging banks to strengthen their internal control systems, which helps in Reducing accounting errors and the accuracy of declared profits, thus stabilizing the share price. Strengthening the administrative capabilities of executive managers contributes significantly to enhancing the informational content of profits by allocating the company’s available resources efficiently and effectively.