Path Model Research Articles

Dietitians as innovators: a deductive-inductive qualitative analysis

BackgroundFrontline health professionals are well-placed to develop and implement beneficial innovations. Evidence supports the clinical and financial benefits of Registered Dietitian (RD)-led improvement initiatives, but we know little about how RDs perceive of innovation or of themselves as innovators. The objectives of the study were to gain an understanding of: 1) how RDs define innovation; 2) who RDs perceive as innovative; 3) whether RDs feel prepared to innovate, and; 4) to what extent work context impacts RDs’ capacity to innovate at work.MethodsAll RDs employed in Canada were eligible to participate. Semi-structured interviews were conducted and a deductive-inductive approach was applied to qualitative analysis. Specifically, Scott & Bruce’s (1994) Path Model of Individual Innovation in the Workplace was first applied as a coding structure.ResultsRespondents (n = 18) exhibited a pro-innovation disposition and a gendered perception of innovation. Few felt their preparatory education prepared them to be innovators in the workplace. All components of Scott & Bruce’s model were supported. Inductive codes were categorized into five themes, including: benefits, dietetics-specific, health care system, technology and individual characteristics.ConclusionsResearchers have previously raised concerns about gendered perceptions of innovation; our results support the legitimacy of these concerns. RDs reported entering the workforce unprepared to be innovative. While the applicability of Scott & Bruce’s model among RDs was confirmed, deficits in the model were noted beyond what would be expected due to context.

The influential factors of depression among caregivers of children with Cleft Lip and/or Palate: a path analysis based on diathesis-stress theoretical model

BackgroundDepression is the most common psychological phenomenon among caregivers of children with Cleft Lip and/or Palate and affects the quality of life, treatment satisfaction, children’s self-esteem and parent–child relationship. However, Existing studies have limited explanations for the mechanisms that lead to depression in caregivers of children with Cleft Lip and/or Palate. The present study aimed to identify factors influencing depression among caregivers of children with Cleft Lip and/or Palate and to provide a reference point and new ideas for future research.MethodsThis cross-sectional study investigated 248 caregivers at two Cleft Lip and/or Palate treatment centers in China, spanning from April 2019 to July 2020. The diathesis-stress model was used to construct the model, and data analysis was conducted using SPSS 26.0 and AMOS 25.0. Path analysis was employed to test the hypothetical model, and the fit of the model was evaluated using Chi-Square/degree of freedom, Goodness-of-Fit Index, Normed Fit Index, Relative Fit Index, Incremental Fit Index, Tacker-Lewis Index, Comparative Fit Index and Root Mean Square Error of Approximation.ResultsThe study found that the average level of depression among caregivers of children with Cleft Lip and/or Palate was 4.83 (4.79). Additionally, 43.5% of caregivers exhibited depressive symptoms. The fit of the modified path model was found to be satisfactory, with the following indices: Chi-Square/degree of freedom = 1.881, Goodness-of-Fit Index = 0.986, Normed Fit Index = 0.973, Relative Fit Index = 0.919, Incremental Fit Index = 0.987, Tucker-Lewis Index = 0.960, Comparative Fit Index = 0.987, and Root Mean Square Error of Approximation (RMSEA) = 0.060. The results indicated that parenting stress had the greatest direct impact on depression, followed by hope. Indirect effects on depression were observed for optimism, resilience, and coping, which were mediated through perceived social support and parenting stress.ConclusionsThis study suggests that depression in caregivers of children with Cleft Lip and/or Palate was leadingly influenced by parenting stress and hope. Strategies aimed at the key factors are expected to decrease depression.

Learning about heart failure from the news on a college campus: The effects of health attitude, healthy lifestyle, and health efficacy

ABSTRACT Literature on how people learn from health news is limited. While heart-related diseases top the list of 10 leading causes of death and major health expenditures in the U.S. literature on how people learn about heart-related illness is scarce. As a result, this study aimed to examine how people learn about heart failure from a news article, drawing on Bandura’s social cognitive theory (SCT) as the theoretical framework. Health news learning, the dependent measure, was assessed by a one-group posttest only design on the comprehension and recall of information about heart failure from the news story. Health attitude, healthy lifestyle, and health efficacy were the independent measures. Three hundred and fifty-six students and staff from a college in the northeast of the United States participated in this study. It was found that whether people think their health is important (health attitude), healthy lifestyle choices, and health efficacy were significant predictors of health news learning. A path model was constructed, and health efficacy emerged as an important mediator of the path model. SCT was found to be a valuable conceptual framework in assessing health news learning. Limitations for the study were discussed.

Examining the Impact of ict Use and Related Attitudes on Students’ Achievements in Science Using the pls-sem Model

Abstract Investigations were conducted into the relationships between science achievement and ict use in and out of the classroom, as well as the mediating effects of students’ interest in and perceptions of their ict proficiency. The primary focus of this study has been the association between ict and scientific achievement among Singaporean students as measured by the Programme for International Student Assessment (pisa) 2018. This study examined pisa 2018 data using partial least squares structural equation modeling (pls-sem) to evaluate the effect of ict use on students’ achievements in science. Complex cause-and-effect relationships in path models with latent variables can be estimated using the structural equation modeling method known as pls-sem. 6,676 15-year-old Singaporean students who participated in the 2018 Programme for International Student Assessment (pisa) were examined. Students’ interest in ict, their assessment of their ict competency, and their accomplishment in science were found to be correlated with how they used ict both inside and outside of the classroom. The findings also imply that the association between ict use and science accomplishment may be mediated by students’ perceived ict proficiency and interest. This research can contribute to advancing the theoretical knowledge of technology, learning, and the cultural environment, in addition to practical ict implementations in scientific classes that consider the variances in ict affects.

The Role of Guilt Feelings in the Development of the Burnout Process: The Influence on Psychosomatic Problems

Burnout is a psychological consequence of prolonged work-related stress. Previous studies have concluded that guilt feelings could explain the development of the burnout process and its relationship with other health disorders. The aim of this study was to evaluate the mediating role of guilt feelings in the relationship between burnout and psychosomatic problems. The sample comprised 714 Brazilian teachers (82.10% women). Burnout was assessed using the Spanish Burnout Inventory (SBI). The hypotheses were evaluated together using a path model to test the mediating role of guilt feelings in the development of burnout and its relationship with psychosomatic problems. Two models were constructed: the hypothesized model (i.e., indolence → guilt → psychosomatic problems) vs. the alternative model (i.e., indolence → psychosomatic problems → guilt). According to the results, the hypothesized model obtained a satisfactory fit to the data, whereas the alternative model’s fit was found to be inadequate. We concluded that the hypothesized model was a good representation of the relationship among burnout, guilt feelings and psychosomatic problems. We recommend taking into consideration feelings of guilt to improve the diagnosis of burnout.

Experiences of Acculturation Stress in First-Generation Immigrant Children: Examining the Relationship Among Acculturation Stress, Depression, and Family Dynamics

AbstractThroughout the years, immigration has been a significant movement in the USA and leads to the examination of acculturative stress. By employing the acculturation stress and vulnerability models, the present study aimed to examine the association among acculturation stress, self-esteem, depression, family cohesion, and familism in first-generation immigrant children. Longitudinal data from the Children of Immigrants Longitudinal Study (CILS) were used. The analytical sample (n = 5262) was predominantly female (51.1%) and between the ages of 12 and 18 (M = 14.23) years old. SPSS 27 and AMOS 27 were utilized to conduct a path analysis to examine the relationships among acculturation stress, self-esteem, depression, family cohesion, and familism. Our results showed good fit (χ2/df ratio = 11.49; CFI = 0.91; RMSEA = 0.05, CI [.04, .05]) for the full path model. Acculturation stress had a significant, negative association with depression (β = − 0.12, p < .001); a significant, negative association with self-esteem (β = − 0.12, p < .001); and a significant, positive association with family cohesion (β = 0.60, p < .01) and the familism index (β = 0.05, p < .05). Acculturation stress was found to be predictive of self-esteem, depression, and family cohesion for first-generation immigrant children between the ages of 12 and 18 years old. This study provides general implications for how mental health providers can understand the struggles and provide culturally competent therapeutic services for children and their families.

Biochar Influences Polyethylene Microplastic-Contaminated Soil Properties and Enzyme Activities

Microplastics (MPs) have emerged as a significant pollutant, threatening agricultural ecosystem sustainability and global food security. However, relatively few studies have investigated biochar remedial effects on plant growth and biochemical properties in soils contaminated with MPs. In polyethylene microplastic (PE-MP)-contaminated soil, we investigated corn stover biochar effects on soybean growth, soil nutrient content, enzyme activity, and microbial biomass and assessed its impact on soil microbial resource limitations. The addition of MPs inhibited soybean growth in various forms across four stages. Conversely, the addition of biochar to MPs improved soybean growth to some extent, where above-ground biomass increased by 5.82% after adding biochar to soils containing microplastics. In soil treated with MPs and biochar, nitrate nitrogen (N), available phosphorus (P), and available potassium (K) increased by 20.1, 27.4, and 57.2%, respectively, while available nitrogen significantly decreased to 128.3 mg kg−1 compared to the MP-only treatment. PE-MPs alone significantly reduced soil carbon (C), N, and P enzyme activities, as well as microbial biomass, with β-glucosidase, leucine aminopeptidase, and acid phosphatase activities decreasing by 29.9, 27.8, and 25.5%, respectively. Interestingly, biochar addition to MPs significantly alleviated these detrimental effects. Microbial biomass C, N, and P increased by up to 56.0, 22.5, and 96.6%, respectively, following biochar addition to soils containing MPs. Analysis of vector lengths, vector angles, and scatter plots indicated that the presence of MPs reduced soil N and P availability. Overall, while MPs inhibited soybean development, biochar addition alleviated this effect to some extent. Furthermore, partial least-squares path modeling revealed that MPs negatively affected soil chemical properties, microbial biomass, and enzyme activities, whereas biochar positively influenced soil enzyme activities.

Accuracy and Precision of Earthquake Location Programs: Insights from a Synthetic Controlled Experiment

Abstract Earthquake location programs employ diverse approaches to address the challenges posed by incomplete knowledge and simplified representation of complex Earth structures. Assessing their reliability in location and uncertainty characterization remains challenging as benchmark datasets with known event locations are rare, and usually confined to particular sources, such as quarry blasts. This study evaluates eight earthquake location methods (GrowClust, HypoDD, Hypoinverse, HypoSVI, NonLinLoc, NonLinLoc_SSST, VELEST, and XCORLOC) through a controlled synthetic computational experiment on 1000 clustered earthquakes based on the setting of the 2019 Ridgecrest, California, earthquake sequence. We construct a travel-time dataset using the fast-marching method and a 3D velocity model extracted from the Community Velocity Model, supplemented with a von Karman perturbation to represent small-scale heterogeneity, and including elevation effects. Picking errors, phase availability, and outliers are introduced to mimic difficulties encountered in seismic network monitoring. We compare the location results from eight programs applied to the same travel-time dataset and 1D velocity structure against the ground-truth locations. For this aftershock sequence, our results reveal the superior accuracy and precision of differential time-based location methods compared to single-event location methods. The results validate the significance of compensating for deviations from assumed 1D velocity structure either by path or site correction modeling or by cancellation of unmodeled structure using differential arrival times. We also evaluate the uncertainty quantification of each program and find that most of the programs underestimate the errors.

Structural equation modelling of the critical barriers influencing the adoption of green building concepts and technologies in Ghana

Purpose In Ghana, the adoption and application of green building concepts and technologies have not been fully explored. The study aimed to look into the key barriers and how they affect this. Design/methodology/approach Purposive and snowball sampling techniques were used to select a total of 292 construction industry stakeholders in Ghana who provided information via a questionnaire survey used for the data collection. Exploratory factor analysis and Partial least squares structural equation modelling (PLS-SEM) were used for computing the data analyses. Findings According to the study findings, the top five most critical barriers to the uptake of green building concepts and technologies (GBCs and Ts) in Ghana are: lack of government incentives/supports for implementing green building technologies (GBTs), lack of knowledge and awareness of GBTs and their benefits, lack of GBTs databases and information, Lack of green building (GB) expertise/skilled labour and Higher costs of GBTs. Principal Component Factor Analysis was used to further analyse the data, which allowed for the reduction of the 27 (27) factors to just four (4) underlying critical barriers: (1) government and knowledge-associated barriers, (2) technical barriers, (3) cost and finance barriers and (4) stakeholders’ attitude barriers. PLS-SEM techniques were used to analyse this collection of barriers, and the results showed that stakeholders’ attitude-associated barriers and cost and finance-related barriers have a significant negative influence on the uptake of GBCs and Ts in Ghana. This study’s findings have provided empirical evidence of the critical barriers to the uptake of GBCs and Ts from all stakeholders. Stakeholders desirous of implementing GBCs and Ts would work against the negative influences on the uptake of GBCs and TS. Originality/value Although there has been an abundance of research to examine the critical barriers to GB, however, the uniqueness of this study is nested in modelling the influence of the barriers on the adoption of GBCs and Ts using the PLS-SEM path modelling.

Enhancing Employee Outcomes Through Common Good Human Resource Management: Exploring the Role of Meaningfulness and Thriving

ABSTRACTIn the evolving landscape of human resource management (HRM), common good HRM (CG‐HRM) practices are increasingly recognized for their potential to align organizational goals with societal sustainability. Despite their importance, how CG‐HRM influences employee outcomes remain underexplored. Drawing on Conversation of Resources (COR) theory, this research explores the impact of CG‐HRM practices, conceptualized as critical organizational‐level resources, on employees' perception of meaningfulness of work. This perception significantly affects employee thriving, which in turn fosters innovative behavior. Employing a multi‐wave, multisource survey design, we collected data from 45 firms in China, involving 82 executives and 206 employees, to test our conceptual model. The findings from a multilevel path model reveal that organizational CG‐HRM practices contribute to employees' perceived meaningfulness of work, which promotes employee thriving and subsequently innovative behavior at the organizational level, while meaningfulness also contributes to thriving and, thus, innovative behavior at the employee level. Our research enriches the COR theory and contributes to the HRM, thriving, and innovation literature, offering insights for future research and practical implications for organizations striving to align their goals with societal and environmental sustainability while nurturing a thriving workforce.

Brain metabolic resilience in Alzheimer's disease: A predictor of cognitive decline and conversion to dementia

BackgroundBrain atrophy measured by structural imaging has been used to quantify resilience against neurodegeneration in Alzheimer's disease. Considering glucose hypometabolism is another marker of neurodegeneration, we quantified metabolic resilience (MR) based on Fluorodeoxyglucose positron emission tomography (FDG PET) and investigated its clinical implications. MethodsWe quantified the MR and other resilience metrics, including brain resilience (BR) and cognitive resilience (CR), using partial least squares path modeling from the ADNI database. A linear mixed-effects model and a Cox proportional hazards model were used to identify the impact of each resilience on longitudinal cognitive function and conversion to dementia, respectively. ResultsA total of 848 participants were included in this study. All resilience metrics (CR, BR, and MR) were associated with slower cognitive decline. Results from the ANOVA test, AIC and BIC values showed that the additional inclusion of MR improved the performances of the linear mixed effect models. In survival analysis, all resilience variables were negatively associated with the risk of conversion to dementia. In line with the results of the linear mixed effects models, the additional inclusion of MR into the models with different resilience variables increased the C-index. ConclusionRelative preservation of brain glucose metabolism is a valuable predictor of future cognitive decline and conversion to dementia, adding value to existing resilience metrics. While the utility of FDG PET in clinical settings is limited by cost and accessibility, it might have potential usefulness as a prognostic marker, especially in a context of resilience.

Effects of VR technical interaction and acceptance on rural cultural identity: The mediating role of embodied cognition and flow experience

With the development of digital media, an increasing number of new technologies are being used in cultural communication, and virtual reality (VR) technology for cultural communication has become a modern tool of transmission. To investigate the factors that influence rural cultural identity (RCI) through technical interaction (TI) and technology acceptance (TA) in VR in cultural communication, this study initially constructed a model of the influence path mediated by embodied cognition (EC) and flow experience (FE) based on compensatory media, technology acceptance, embodied cognition and flow experience theory. All participants had experienced some rural historical and cultural VR work and then completed a corresponding questionnaire. A total of 356 questionnaires were collected and analyzed using SPSS and SmartPLS software. The results indicated that the technological interaction and acceptance of VR influenced identification with rural culture through the mediating role of embodied cognition and flow experience. These findings provide a reference for strengthening audiences' embodied cognition and flow experience to promote their rural cultural identity. When VR technology is used to communicate culture, its technological interaction and acceptance can be improved to provide audiences with a better experience and perhaps influence the development of local tourism.

Research on factors influencing total carbon emissions of construction based on structural equation modeling: A case study from China

As a pillar industry in China, the construction sector plays a crucial role in advancing the country's carbon emission (CE) reduction initiatives. The factors influencing CE in the construction industry (CI) are numerous and complex. This paper analyzes the influencing factors of CE in the CI based on structural equation modeling. Firstly, by combining the relevant literature and actual situation of CE in the CI, 38 common influencing factors corresponding to the building, economic, social, technological and energy levels are initially selected, and the data corresponding to each influencing factor in each province and city in China for 10 years are collected and then processed without dimension. Secondlyd, 12 hypothetical relationships between building, economic, social, technological and energy and CE from the CI are established based on the actual situation, and the initial CE in the CI structural equation model is established after the data set is tested for normal distribution, reliability and validity. Thirdly, the model is evaluated for path significance and model fit, and the modified structural equation model of CE from the CI is obtained after the initial model is modified, and the modified model is evaluated again. Finally, based on the modified model, the influence degree and effect of CE influencing factors in the CI are analyzed. It is concluded that economy, technology, and energy are the main factors successively affecting the total CE of buildings.

Biochar application improves maize yield on the Loess Plateau of China by changing soil pore structure and enhancing root growth

Biochar application emerges as a valuable soil management strategy for enhancing crop yield; however, the mechanisms underlying the relationships between soil and plants remain unclear after biochar application. In this study, soil pore characteristics and maize yield were assessed in a five-year biochar-application experiment on the Loess Plateau of China, including four treatments: Control (no biochar), low-dose biochar application (LB, 3 t ha−1), moderate-dose biochar application (MB, 6 t ha−1), and high-dose biochar application (HB, 9 t ha−1). Root growth traits were evaluated by cultivating maize in intact soil cores collected from field conditions using X-ray computed tomography. Our findings indicate that, compared to the Control, the HB treatment enhanced macroporosity (> 0.1 mm in diameter), porosity of 0.1–0.5 mm pores, and saturated water content, while reducing macropore connectivity and penetration resistance. However, biochar application treatments did not alter the water retention characteristics from field capacity to permanent wilting point or the plant-available water content (PAWC). Furthermore, the mean angle of primary and seminal roots as well as the length and surface area of entire roots increased in the HB treatment, showing a positive correlation with the porosity of 0.1–0.5 mm pores. The mean diameter of primary and seminal roots, leaf fresh and dry weights, and maize yield also increased in the HB treatment compared to the Control. Partial least squares path modeling analysis indicated that biochar application rates positively impacted on root growth and plant productivity through an indirect influence of soil pore size distribution, with 0.1–0.5 mm pores being particularly crucial for facilitating deeper root penetration and root elongation. These findings demonstrate that biochar application primarily augmented 0.1–0.5 mm pores, rather than affecting smaller pores capable of retaining plant-available water or larger macropores, enhancing deeper rooting and root elongation, thus improving plant productivity and crop yield.

AI in STEM Education: The Relationship between Teacher Perceptions and ChatGPT Use

The rapid development of AI in the recent years has triggered numerous discussions in the education sector since it offers numerous opportunities, but also challenges. This study addresses one context in which AI might become a relevant tool for different purposes: German secondary education. This study therefore examines the extent to which STEM teachers in German secondary education assess the benefits but also the risks of ChatGPT and how this influences the future use of AI in an educational context. As part of a questionnaire study, 102 STEM teachers were investigated. The current and future use of ChatGPT and various teacher variables were recorded with questionnaires based on classifications of researchers as well as political institutions. Relationships between the variables were explored using a path model investigating hypotheses regarding the affect heuristic, perceived risks and benefits as well as effects on teaching quality. In general, AI is still used relatively rarely, but future usage expectations are high compared to current usage. Results further revealed that perceived competence and benefits of ChatGPT have a positive influence on the use and intention to use it. Perceived risks and concerns have no significant influence on the usefulness of ChatGPT in the classroom, indicating that STEM teachers use AI in the classroom despite potential concerns and perceived risks. Additionally, perceived benefits and risks are negatively associated with each other, indicating that teachers rely on an affect heuristic when judging the usefulness of AI technology in the classroom.

Structural equation modeling: exploring relationships of body mass index, waist circumference, and hypertension in elderly

Waist circumference (WC) measurements in the elderly are uncommon and erratic. Furthermore, its correlation to hypertension has received less attention in Indonesia. This study attempted to investigate direct and indirect risk factors for high blood pressure in the elderly using structural equation modeling (SEM). A cross-sectional study was conducted in Surakarta, Central Java, Indonesia. The 297 elderly aged ≥60 is eager to take part. The path model of factors associated with blood pressure was analyzed using Stata 13. Male (b=-0.43; 95% CI=-5.67 to 4.81; p=0.872), age (b=0.52; 95% CI=0.12 to 0.93; p=0.012), and WC (b=0.35; 95% CI=0.17 to 0.53; p<0.001) directly affected systolic blood pressure (SBP). Male (b=1.83; 95% CI=-1.17 to 4.82; p=0.231), age (b=-0.03; 95% CI=-0.26 to 0.20; p=0.790), BMI (b=0.40; 95% CI=-0.03 to 0.83; p=0.067), and WC (b=0.04; 95% CI=-0.09 to 0.16; p=0.571) directly affected diastolic blood pressure (DBP). Increasing age, body mass index (BMI), and waist circumference (WC) are the important variables that influence blood pressure in older people. This study supports the evidence that body composition and weight control are necessary to prevent and control blood pressure in the elderly.

Model-free active noise control using second-order simultaneous perturbation stochastic approximation algorithm for periodic noise

Model-free active noise control (ANC) using simultaneous perturbation stochastic approximation (SPSA) algorithm has recently attracted interest of researchers since it avoids the problem of secondary path modeling (SPM). However, the existing SPSA-based ANC methods are all based on the first-order SPSA algorithm and the convergence rate of these methods are significantly lower than that of the Filtered-x least mean square (FxLMS) algorithm. In order to enhance the convergence performance, this paper introduces the second-order SPSA algorithm into the ANC system. During the implementation of the second-order SPSA algorithm, a novel method of matrix positive definite transformation based on eigenvalue decomposition (EVD) is proposed. Convergence performance and tracking performance of ANC using the second-order SPSA algorithm are compared with ANC using the FxLMS algorithm and the standard SPSA through simulation and experimental verification. Simulation and experimental verification results show that ANC using the second-order SPSA algorithm can not only achieve the convergence rate comparable to that of ANC using the FxLMS algorithm, but also track the secondary path change.

Insight into the mechanism of alkyl polyglucose biosurfactants enhancing antibiotics resistance genes removal during biotransformation of waste activated sludge into medium-chain fatty acids

Enhancing the biodegradability of waste activated sludge (WAS) based on environmentally friendly biosurfactants alkyl polyglucose (APG) to promote the recovery of medium-chain fatty acids (MCFAs) is a promising strategy. However, the effect and underlying mechanism of APG on antibiotic resistance genes (ARGs) removal during MCFAs production by WAS anaerobic fermentation are still unclear. This study found that APG significantly enhanced the removal of ARGs during the anaerobic fermentation to produce MCFAs, and the maximum removal efficiency of total ARGs was 95.64 % in 0.3 g APG/g TSS group, which was 46.31 % higher than the control group. The increase in extracellular lactate dehydrogenase, 16S rDNA and ARGs indicated that APG strengthened the lysis of ARGs hosts, thus suppressing the vertical transfer of ARGs caused by host proliferation. Furthermore, APG facilitated the decline in the abundance of mobile gene elements and two-component system genes, which was beneficial to reduce the horizontal transfer of ARGs. Microbial community and network analysis revealed that APG enriched functional microorganism and reduced potential ARGs hosts. Finally, partial least-squares path model analysis further demonstrated that the effectiveness of APG in ARGs removal was related to the restriction of ARGs transfer and change of bacterial community.

Impact of financial and internet support on SME performance: Moderating effect of technology adoption during COVID-19 pandemic

Small and medium-sized enterprises (SMEs) are widely recognised as significant catalysts for economic growth at a worldwide level. The benefits that are frequently highlighted for their promotion, particularly in developing nations like Bangladesh, include their relatively high labor intensity, reliance on local technology and skills, contributions to the growth of entrepreneurialism and innovativeness, and industrial linkage expansion. The objective of this study is to examine the influence of financial and internet support on the performance of small and medium enterprises (SMEs) in Bangladesh during the COVID-19 pandemic while also considering the moderating effect of technology adoption. By going through the literature, the study identified a number of factors namely financial support, ICT support, technological support played a strong role on SME performance during COVID-19. The present study utilises Partial Least Square (PLS) path modelling, a variance-based technique within the framework of Structural Equation Modelling (SEM), to investigate the correlations. A well-structured questionnaire is prepared for collecting primary data from the SME owners or managers and finally 378 data have been collected from the different types of SME entrepreneurs through using purposive sampling technique. Eight hypotheses are tested and found seven positive relationships with moderating interaction of technology adoption between influential factors and SME performance (financial and non-financial).

Key factors driving soil diazotrophic community assembly and nitrogenase activity in Qinghai-Tibet alpine meadows

Biological nitrogen (N) fixation by soil diazotrophs is the primary source of nitrogen for natural grasslands, especially at high altitudes such as alpine meadow ecosystems. However, the mechanisms shaping the compositions and assembly processes of diazotrophic communities, as well as soil nitrogenase activity in alpine meadows remain poorly understood. In this study, 241 soil samples were collected from alpine meadows on the Qinghai-Tibet Plateau to investigate the distribution patterns and driving factors of diazotrophic communities and nitrogenase activities. Our results showed that soil N fixation potential across all samples ranged from 28.8 to 110 nmol C2H4 g-1h-1, and Nitrospirillum and Bradyrhizobium were the most dominant diazotrophic genera. The composition and diversity of soil diazotrophic communities were mainly influenced by soil pH, and to a lesser extent by aridity index and mean annual precipitation. The abundance of nifH gene decreased linearly with soil pH, whereas the ɑ-diversity increased linearly with soil total phosphorus. Null model analysis indicated that deterministic processes governed diazotrophic community assembly, and more acidic soil conditions led to more phylogenetically clustered diazotrophic communities. Partial least squares path modeling and linear regression analyses identified diazotrophic community composition, followed by climatic factors, nifH gene abundance and total phosphorus, as the dominant regulators of soil N fixation potential in alpine meadows. Unexpectedly, soil N fixation potential was not closely related to dominant diazotrophic genera, diversity, or assembly, but highly related to the less abundant diazotrophs. This study provides a novel insight into the ecological mechanisms shaping soil biological nitrogen fixation in alpine meadow ecosystems.