Strong Influence Research Articles

Impact of participation in collective action on farmers’ decisions and waiting time to adopt soil and water conservation measures

Purpose This study aims to analyze the influence of farmers’ degree of participation in collective action on their adoption decisions and waiting time regarding soil and water conservation (SWC) measures. Design/methodology/approach The Probit model and Generalized Propensity Score Match method are used to assess the effect of the degree of participation in collective action on farmers’ adoption decisions and waiting time for implementing SWC measures. Findings The findings reveal that farmers’ engagement in collective action positively influences the decision-making process regarding terrace construction, water-saving irrigation and afforestation measures. However, it does not significantly impact the decision-making process for plastic film and ridge-furrow tillage practices. Notably, collective action has the strongest influence on farmers’ adoption decisions regarding water-saving irrigation technology, with a relatively smaller influence on the adoption of afforestation and terrace measures. Moreover, the results suggest that participating in collective action effectively reduces the waiting time for terrace construction and expedites the adoption of afforestation and water-saving irrigation technology. Specifically, collective action has a significantly negative effect on the waiting time for terrace construction, followed by water-saving irrigation technology and afforestation measures. Practical implications The results of this study underscore the significance of fostering mutual assistance and cooperation mechanisms among farmers, as they can pave the way for raising funds and labor, cultivating elite farmers, attracting skilled labor to rural areas, enhancing the adoption rate and expediting the implementation of terraces, water-saving irrigation technology and afforestation measures. Originality/value Drawing on an evaluation of farmers’ degree of participation in collective action, this paper investigates the effect of participation on their SWC adoption decisions and waiting times, thereby offering theoretical and practical insights into soil erosion control in the Loess Plateau.

Similarity Measures between Buying and Selling Prices of Various Exchange Rates with the inclusion of Multidimensionality and Multicollinearity

This paper introduces a new statistical model, the multidimensional measurement error model with multicollinearity, to study the relationship between buying and selling prices of foreign exchange rates of various currencies. Such a model is needed due to the possible rise in multidimensionality and multicollinearity issues that may occur due to the movement of the financial markets towards stock market integration of various countries since the occurrences of the financial crisis as well as the part result of globalization. As this integration involves the participation of various countries, it will affect the foreign exchange rate. Hence, the analyses are performed on seven currencies against the Malaysian Ringgit where four models’ performances are compared. From this research, it can be concluded that the proposed model comparatively performs better than the other models in representing the relationship of the stationary prices and performs as well as existing models towards non-stationary prices. It can also be seen that the Japanese Yen is the currency that has the strongest influence and closer similar trends towards other currencies while the Great British Pound showed otherwise.

The global development of value orientations, political support and democracy since the 1990s

This article analyzes the global development of democracy since the 1990s. The theoretical introduction is followed by a descriptive analysis of several indices of political support based on survey results for 89 countries that participated at least twice in the World Values Survey (WVS) or the European Values Study (EVS). The analysis focuses on emancipative value orientations measured by Welzel's Choice Index and several indicators of political support. The scores of these indicators are broken down by five regime types and seven world regions. The results confirm that the global decline of democratic quality since the mid-2000s has not been as dramatic as the pessimistic analyses of Freedom House and the V-Dem project have claimed. It has primarily taken place in a limited number of populous countries on which these publications have focused. When countries are treated equally, the quality of democracy has remained remarkably stable since the early 1990s. Emancipative values are globally on the rise, but their increase has been considerably higher in the established democracies. This supports the skeptical argument that they cannot be considered as the major cause of the third wave of democratization. The descriptive analysis is complemented by regression analyses for confidence in regime institutions and support for democracy as dependent variables. The evaluation of democratic regime performance is the strongest predictor of confidence in regime institutions. An intrinsic conception of democracy and the importance assigned to living in a democracy have the strongest influence on support for democracy. Emancipative value orientations have a minor influence on political support even in consolidated democracies. Finally, the analysis does not confirm the suspected relationship between the different levels of political support. Easton's theory of political support assumed that they mutually influence each other via a generalization of experiences or an overflow of values. Instead, it seems that confidence in regime institutions and support for democracy follow different cognitive logics.

Seasonal antigenic prediction of influenza A H3N2 using machine learning

Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine updates as well as for pandemic preparedness. Performing antigenic characterization of IAV on a global scale is confronted with high costs, animal availability, and other practical challenges. Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their hemagglutinin subunit 1 (HA1) sequences and associated metadata. Each season, the model learns an updated nonlinear mapping of genetic to antigenic changes using data from past seasons only. The model accurately distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change. Antigenic predictions produced by the model can aid influenza surveillance, public health management, and vaccine strain selection activities.

How are sociodemographic differences contributing to red light violation behavior? the underlying role of gender, age, driving experience, and income

ABSTRACT This paper presents the results of the first driver behavior questionnaire (DBQ) study on analyzing Indian drivers’ aberrant behavior for red light violations (RLVs) at signalized junctions. An ordered probit model is developed using the drivers’ socio-demographic variables. The model results indicate that age has the strongest influence on drivers’ RLV behavior, followed by driving experience and level of education. The level of perceived RLV appears to increase with higher driving frequency and two-wheeler ownership. This research is also the first to analyze the aberrant behavior differences among male and female drivers concerning their demographics and driving experiences. It is unique to note that at a certain combination of various demographics (e.g., driving experience, education level, and daily trip frequency), the men and women are becoming equal in their aberrant behavior while committing RLVs. This finding is an interesting input for developing driver training and education programs.

Characteristic Curves of Polar Fluids: (I) The Two-Center Lennard–Jones Plus Dipole Fluid

Brown’s characteristic curves of polar fluids were studied using molecular simulation and molecular-based equation of state. The focus was on elucidating the influence of dipole interactions and the molecule elongation on the characteristic curves. This was studied using the symmetric two-center Lennard–Jones plus point dipole (2CLJD) model fluid class. This model class has two parameters (using Lennard–Jones reduced units), namely the elongation and the dipole moment. These parameters were varied in the range relevant for real substance models that are based on the 2CLJD model class. In total, 43 model fluids were studied. Interestingly, the elongation is found to have a stronger influence on the characteristic curves compared to the dipole moment. Most importantly, the characteristic curve results for the 2CLJD fluid are fully conform with Brown’s postulates (which were originally derived for simple spherical dispersive fluids). The independent predictions from the computer experiments and the theory are found to be in reasonable agreement. From the molecular simulation results, an empirical correlation for the characteristic curves of the 2CLJD model as a function of the model parameters was developed and also applied for modeling real substances. Additionally, the intersection points of the Charles and Boyle curve with the vapor-liquid equilibrium binodal and spinodal, respectively, were studied.

Combined application of arbuscular mycorrhizal fungi and selenium fertilizer increased wheat biomass under cadmium stress and shapes rhizosphere soil microbial communities

BackgroundSelenium (Se) fertilizer and arbuscular mycorrhizal fungi (AMF) are known to modulate cadmium (Cd) toxicity in plants. However, the effects of their co-application on wheat growth and soil microbial communities in Cd-contaminated soil are unclear.ResultsA pot experiment inoculation with two types of AMF and the application of Se fertilizer under Cd stress in wheat showed that inoculation AMF alone or combined with Se fertilizer significantly increased wheat biomass. Se and AMF alone or in combination significantly reduced available Cd concentration in wheat and soil, especially in the Se combined with Ri treatment. High throughput sequencing of soil samples indicated that Se and AMF application had stronger influence on bacterial community compared to fungal community and the bacterial network seemed to have more complex interconnections than the fungal network, and finally shaped the formation of specific microflora to affect Cd availability.ConclusionThese results indicate that the application of Se and AMF, particularly in combination, could successfully decrease soil Cd availability and relieve the harm of Cd in wheat by modifying rhizosphere soil microbial communities.

Relating laser powder bed fusion process parameters to (micro)structure and to soft magnetic behaviour in a Fe-based bulk metallic glass

This work aims to establish fundamental processing-(micro)structure-property links in a commercial Fe-based Kuamet6B2 bulk metallic glass (BMG) processed by laser powder bed fusion (LPBF). With that purpose, amorphous powders were processed using a pulsed-wave system and a simple meander strategy. The laser power, the scan speed, and the hatch distance were varied over wide intervals within the conduction regime. The processability window leading to samples with good dimensional accuracy and mechanical stability was determined. Within this window, the manufactured samples were crystalline/amorphous composites and the crystalline regions were formed by equiaxed ultrafine and nanograins with random orientations. Processing parameters yielding the densest prints caused severe crystallization while, conversely, parameter sets allowing the material to retain a high amorphous fraction led to significant lack-of-fusion defects and residual cracking along directions perpendicular to crystalline/amorphous interfaces. Comparatively, for a fixed hatch distance, the scanning speed had a stronger effect than the laser power in the resulting amorphous fraction due to its stronger influence on the melt pool size and, in turn, on the corresponding HAZ volume. The saturation magnetization and the coercive field were inversely related to the amorphous fraction. This work allows to derive fundamental guidelines for the successful additive manufacturing of soft magnetic Fe-based bulk metallic glasses (BMGs) by laser powder bed fusion (LPBF).

Abstract PS10-09: Development of an absolute risk prediction model for premenopausal breast cancer in an international consortium

Abstract Risk prediction models that have been developed for overall breast cancer risk are based on a limited number of premenopausal cases in individual cohorts. As some risk factors differ in their associations with pre- versus postmenopausal breast cancer, a distinct risk prediction model is needed for premenopausal breast cancer. We developed a risk prediction model for premenopausal breast cancer using 779,601 participants and 9,665 incident cases from 18 prospective studies within the Premenopausal Breast Cancer Collaborative Group (PBCCG), across North America (N=9), Europe (N=6), Australia (N=1), and Asia (N=2). Data were split, within each cohort, into training (2/3) and testing (1/3) datasets. Individual risk was assessed in five-year intervals, using variables reported at the start of the time interval. Cox proportional hazards regression was used to model risk factors in a backwards-selection method, stratified by cohort: age at menarche, age at first birth, parity, breastfeeding (months), height (cm), BMI (kg/m2), BMI at age 18, recent weight change (kg), alcohol consumption (drinks/week), first-degree family history of breast cancer, and personal history of benign breast disease. To enable the use of information from all cohorts despite differences in missing variables by design, cohorts were grouped by available variables and risk models were fit for each group. In cohorts with incomplete data, model coefficients were adjusted based on the correlation of covariates with the missing variables in the complete case dataset. Coefficients were meta-analyzed with inverse variance weighting to obtain final coefficients. Discrimination was evaluated in the testing dataset by calculation of the c-index. Work is ongoing to calibrate the model based on five-year absolute risk using GLOBOCAN continent- and age-specific incidence rates to represent baseline risk. The final model included age at menarche, parity, height, BMI, BMI at age 18, first-degree family history of breast cancer, and history of benign breast disease. Young adulthood BMI and BMI (at start of the 5-year risk interval) were associated with a decreased risk (Hazard Ratio (HR) (95% confidence interval (CI)) per 5 kg/m2 = 0.87 (0.81-0.93) and 0.90 (0.86-0.95), respectively) as was parity (HR (95% CI) = 0.92 (0.90-0.94)). Height was associated with increased risk (HR (95% CI) per 10 cm = 1.14 (1.07-1.21)), while history of benign breast disease and family history were associated with larger increases in risk (HR (95% CI) = 1.64 (1.30-2.06) and 1.76 (1.63-1.90), respectively). Model discrimination was higher than that reported for women under 50 years in existing breast cancer risk prediction models considering clinical factors (AUC (95% CI) = 0.61 (0.59-0.62)). Calibration of absolute 5-year risk is ongoing. Several factors driving risk prediction of postmenopausal breast cancer have similar influence on risk of premenopausal breast cancer, while family history has a stronger influence on premenopausal breast cancer risk. Our model demonstrates acceptable discrimination and will enable individual 5-year absolute risk prediction for premenopausal breast cancer. Citation Format: Kristen Brantley, Michael Jones, Minouk Schoemaker, Hazel Nichols, Anthony Swerdlow, Robert MacInnis, Roger Milne, Tess Clenenden, Yu Chen, Xiao-Ou Shu, Wei Zheng, Woon-Puay Koh, Jian-Min Yuan, Cari Kitahara, Martha Linet, Dale Sandler, Bernard A Rosner, Peter Kraft, A. Heather Eliassen. Development of an absolute risk prediction model for premenopausal breast cancer in an international consortium [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-09.

Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data

BackgroundMachine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios.MethodsWe compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning.ResultsDepending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively.ConclusionsBy illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions.

Improving the detection of hypo-vascular liver metastases in multiphase contrast-enhanced CT with slice thickness less than 5 mm using DenseNet

IntroductionThinner slices are more susceptible in detecting small lesions but suffer from higher statistical fluctuation. This work aimed to reduce image noise in multiphase contrast-enhanced CT reconstructed with slice thickness thinner than the clinical setting (i.e., 5 mm) using convolutional neural network (CNN) for enabling better detection of hypo-vascular liver metastasis. MethodsA DenseNet model was used to generate noise map for multiphase CT reconstructed with slice thickness of 2.5 mm and 1.25 mm. Image denoising was conducted by subtracting the CNN-generated noise map from CT images with reduced photon flux due to thinner slice thickness. The performance of DenseNet was evaluated on CT scans of electron density phantoms and patients with hypovascular liver metastases less than 1.5 cm in terms of Hounsfield Unit (HU) variation, statistical fluctuation, and contrast-to-noise ratio (CNR). ResultsThe phantom study demonstrated that the CNN-based denoising method was able to reduce statistical fluctuation in CT images reconstructed with slice thickness of 2.5 mm and 1.25 mm without causing significant edge blurring or variation in HU values. With regards to patient study, it was found that the denoised 2.5-mm and 1.25-mm slices had higher CNR than the conventional 5-mm slices for hypo-vascular liver metastases in all 4 phases of multiphase CT. ConclusionOur results demonstrated that the detection of hypo-vascular liver metastases in multiphase contrast-enhanced CT with slice thickness less than 5 mm could be improved by using the CNN-based denoising method. Implications for practiceReconstruction slice thickness has a strong influence on the image quality of CT imaging. A CNN-based denoising method was used in this work to reduce the image noise in multiphase contrast-enhanced CT reconstructed with slice thickness less than 5 mm.

Spatiotemporal variations in eco-environmental quality and responses to drought and human activities in the middle reaches of the Yellow River basin, China from 1990 to 2022

The middle reaches of the Yellow River basin (MYRB) are among the regions most severely affected by soil erosion globally. It has always held a pivotal role in soil and water conservation and ecological restoration efforts in China. Nonetheless, in the face of recurrent drought occurrences and growing human intervention, there have been notable alterations in the eco-environmental quality (EEQ) within the MYRB. However, the influences of drought and human intervention on the EEQ of MYRB remain unclear. In this study, the remote sensing ecological index (RSEI) was applied to quantify the spatiotemporal changes in EEQ and the contributions of drought and land use type transitions to EEQ in the MYRB from 1990 to 2022. The results showed that the EEQ in the MYRB fluctuated significantly and exhibited a weak overall improvement trend over the past 33 years. The proportion of good and excellent grades for EEQ significantly improved, while the proportion of poor and fair grades significantly decreased, especially in the northern regions. The trend of changes in EEQ follows a phased pattern. During the periods of 1990–2002 and 2011–2022, an improving trend is observed, while the period of 2003–2010 shows no significant change in EEQ. Drought had the strongest influence on the EEQ from 2003 to 2010, followed by that from 1990 to 2002, and had a lesser impact in the period from 2011 to 2022. The EEQ was primarily positively influenced by spring, autumn and winter droughts and negatively affected by summer droughts, especially in arid grassland and unused land areas. The EEQ in the MYRB improved primarily during the initial and final phases of the ecological projects, with a lesser impact from drought. The increase in EEQ during the initial phases of ecological project implementation was less noticeable, and drought significantly affected EEQ during this period.

Bison and cattle grazing increase soil nitrogen cycling in a tallgrass prairie ecosystem

AbstractNitrogen (N) is a necessary element of soil fertility and a limiting nutrient in tallgrass prairie but grazers like bison and cattle can also recycle N. Bison and cattle impact the nitrogen (N) cycle by digesting forage that is consumed, and recycled back to the soil in a more available forms stimulating soil microbial N cycling activities. Yet we do not know how both grazers comparatively affect N cycling in tallgrass prairie. Thus, we investigated if bison and cattle had similar impacts on N cycling in annually burned tallgrass prairie relative to ungrazed conditions over a 3-year period (2020–2022) at the Konza Prairie Biological Station. We examined: soil pH, soil water content, mineralized N, nitrification potential, denitrification potential and extracellular enzyme assays. Interannual variability in precipitation controlled soil water and N cycling microbial activities but grazing effects had a stronger influence on N cycling. We found significant differences and increased soil pH, nitrification and denitrification potential and less N limitation in bison vs cattle grazed soils where bison grazed soils exhibited faster N cycling. Differences between the grazers may be attributed to the different management of bison and cattle as both can impact N cycling. Overall, these data provide some evidence that bison and cattle affect N cycling differently at this study site, and improve the ecological understanding of grazer impacts on N cycling dynamics within the tallgrass prairie ecosystem.

Health Motivation in the Influencer Era: Analyzing Entertainment, Personal, and Social Media Role Models

ABSTRACT This research explored how personal and entertainment role models motivate an individual’s health goals, and how following a role model on social media affects this process. A survey of 404 adults revealed that identifying a personal role model, such as a family member, friend, or healthcare professional, had a stronger influence on health motivation in comparison to identifying an entertainment role model. Additionally, our findings indicated that following any role model on social media, whether a personal acquaintance or a celebrity, enhanced motivation. Role model attributes that mediated these relationships included perceived similarity, and in some instances, positive and negative norm deviance. This study highlights the importance of leveraging role model relationships in health communication campaigns and developing authentic influencer-driven health initiatives that feature relatable entertainment figures. Additionally, this research supports the need for further examination of how a role model’s perceived positive or negative norm deviance influences motivational capability. We discuss implications for the motivational theory of role modeling, health promotion, and entertainment education.

The Impact of Ambivalence on Persuasion: A Preliminary Meta-Analysis

This study is the first meta-analysis on the relationship between ambivalence and persuasive outcomes in communication. The meta-analysis included 22 studies with 51 effect sizes based on 14,173 (68–1,604) participants. When all the persuasive outcomes were considered together, ambivalence had a significant but small negative overall impact, r = -0.134, 95% CI [-0.192, -0.076], k = 33, z = -4.471, p < 0.001. Specifically, ambivalence had a small influence on attitude and intention. Its impact on perception and behavior were less pronounced. There was significant heterogeneity across effect sizes in overall persuasion, as well as specific persuasive outcomes. The ambivalence targeting behavior had a stronger influence on attitude than did ambivalence targeting other objects. Methodology significantly moderated the magnitude connecting ambivalence and behavioral intention, studies using experiments yielded greater sizes compared to those using surveys. With respect to context, the influence of ambivalence on attitude was stronger in advertising and health communication, compared to political communication. Studies in advertising yielded significantly greater effect sizes on behavioral intention compared to those in health communication. This study suggests that the implication of ambivalence in communication is likely a complicated and nuanced matter. Limitations and future research have also been outlined.

Analysis of the Evolution of Water Quality in Tanks According to the Connections and Operation Mode: Application to the Most Suitable Design

The analysis and simulation of water quality in distribution networks is a complex issue of great concern today. The analysis of the evolution of water age as a simple indicator of water quality in the network is of great interest in both the design and operation phases. Understanding the factors that have the strongest influence on water quality is key to developing adequate strategies aimed at preserving it. This paper first analyses the factors with the biggest influence on the tank’s water age to support the selection of the most appropriate configuration from the point of view of water quality during design phases. Then, the main considerations when modelling tanks following the different mixing models considered in Epanet are presented. Also, real tank behaviour is characterised through field measurements and Epanet simulations in order to determine the best-fitting mixing model.

Net Radiation Drives Evapotranspiration Dynamics in a Bottomland Hardwood Forest in the Southeastern United States: Insights from Multi-Modeling Approaches

Evapotranspiration (ET) is a major component of the water budget in Bottomland Hardwood Forests (BHFs) and is driven by a complex intertwined suite of meteorological variables. The understanding of these interdependencies leading to seasonal variations in ET is crucial in better informing water resource management in the region. We used structural equation modeling and AIC modeling to analyze drivers of ET using Eddy covariance water flux data collected from a BHF located in the Russel Sage Wildlife Management Area (RSWMA). It consists of mature closed-canopy deciduous hardwood trees with an average canopy height of 27 m. A factor analysis was used to characterize the shared variance among drivers, and a path analysis was used to quantify the independent contributions of individual drivers. In our results, ET and net radiation (Rn) showed similar variability patterns with Vapor Pressure Deficit (VPD) and temperature in the spring, summer, and autumn seasons, while they differed in the winter season. The path analysis showed that Rn has the strongest influence on ET variations via direct and indirect pathways. In deciduous forests like BHFs, our results suggest that ET is more energy dependent during the growing season (spring and summer) and early non-growing season (autumn) and more temperature dependent during the winter season.

Моральная конформность при разных формах виртуального давления

<p>The article presents the results of a study aimed at investigating the influence of two forms of virtual group pressure (depersonalized and personalized) on the manifestation of moral conformity. The experimental material consisted of two types of moral scenarios, where participants were asked to assess the acceptability of certain behaviors. Depersonalized pressure was implemented by informing participants, filling out an online form, about the percentage of people responding in a certain way to the presented moral scenarios. As for the personalized form of pressure, a situation simulating interactions among individuals in instant messaging systems (messengers) was utilized. The study involved 112 participants, with 20 of them forming the pilot study sample and 92 — the main experimental sample. The results indicate that social pressure in the virtual environment leads to the emergence of a conforming response, with personalized pressure in real-time having a stronger influence than passive exposure to depersonalized data. The obtained results are explained within the framework of the biological theory of social pressure, which views conforming behavior as a strategy to optimize metabolic costs. The manifestation of a conforming response for both types of moral scenarios and the absence of gender differences in its expression suggest the potential universality of this strategy.</p>

Unravelling the relationship between English reading habits and individual, family and school factors: A Chinese perspective

AbstractIn English teaching, developing students' reading habits has been emphasized for its critical role in promoting overall core competencies. However, in practice, many teachers and parents are not informed of the effective approaches to developing good English reading habits, hindering the overall development of students' core competencies. In light of this, an empirical study was carried out to explore the developmental features of students' English reading habits and individual, family and school factors that affect its development. A questionnaire was adopted to collect quantitative data. A total of 2,130 primary and secondary school students from China participated in the study. The results indicated that their English reading habits varied with age and gender. In terms of age, secondary school students, especially middle school students, performed better than primary school students. In terms of gender, females performed better in reading frequency, reading time and ways of reading, while males had a larger reading volume. Reading habits were found to be impacted by individual, school and family factors. Among them, age had the strongest influence on the development of English reading habits. These findings shed light on effective ways to develop English reading habits, especially in foreign‐language‐learning contexts.

The Impact of Gamification on Slovenian Consumers’ Online Shopping

Gamification involves integrating game mechanics into non-game environments such as business intranets, online communities, websites, and learning management systems to boost participation. Its aim is to actively engage employees, customers, and other stakeholders, fostering collaboration, sharing, and interaction. Gamification is a relatively unfamiliar term in Slovenia. The objective of this study is to investigate the influence of gamification on Slovenian consumers, specifically how it affects the online shopping process and user engagement during purchases. To test the hypotheses, we used appropriate statistical tools: chi-square, Friedman, and Wilcoxon tests. The findings indicate that gamification’s strongest influence is not on the post-purchase evaluation phase but rather on the alternative evaluation phase. It is interesting that highly rated reviewers significantly influence product purchases in online stores, while consumers are unwilling to increase their spending on online purchases in exchange for gamification-related benefits.