Association of high-sensitivity C-reactive protein and hematologic-inflammatory indices with risk of cardiovascular diseases: a population-based study with partial least squares structural equation modeling approach.

Partial least squares structural equation modeling is a simple approach that may be used to determine the factors associated with diseases. In the current study, we aimed to explore the most associated high-sensitivity C-reactive protein (hs-CRP) as well as hematologic-inflammatory indices for the risk of cardiovascular disease (CVD). A total of 7362 healthy (non-CVD) participants aged 35-65 years old from baseline investigation were evaluated in the Phase 2 follow-up. Of these, 1022 individuals were found to have CVDs in the second phase (10-year follow-up) of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort study. We used partial least squares structural equation modeling to develop a prediction model for association of CVD risk factors and hs-CRP as well as hematologic-inflammatory indices in the study population. According to the study, age had the most significant impact on the presence of CVD. Increasing in age by one unit raises the risk of CVD by 0.166. Also, serum hs-CRP was found to have the second-highest impact on CVD; increasing in age by one unit raises the risk of CVD by 0.042. The study also discovered a strong and significant correlation between red cell distribution width (RDW) and CVD. Moreover, the study found that several factors such as hemoglobin (HGB), neutrophil (NEUT), neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and platelet-to-lymphocyte ratio (PLR) have indirect effects on CVD that are mediated by hs-CRP while controlling for age, sex and social-economic factors. Generally, the results showed that age, hs-CRP, and RDW were the most important risk factors on CVD.

Factors influencing the complex problem-solving skills in reflective learning: results from partial least square structural equation modeling and fuzzy set qualitative comparative analysis.

BACKGROUND: The Organization for Economic Cooperation and Development emphasizes the importance of complex problem-solving (CPS) skills in the 21st century. CPS skills have been linked to academic performance, career development, and job competency training. Reflective learning, which includes journal writing, peer reflection, selfreflection, and group discussion, has been explored to improve critical thinking and problem-solving abilities. The development of various thinking modes and abilities, such as algorithmic thinking, creativity, and empathic concern, all affect problem-solving skills. However, there is a lack of an overall theory to relate variables to each other, which means that different theories need to be integrated to focus on how CPS skills can be effectively trained and improved. METHODS: Data from 136 medical students were analyzed using partial least square structural equation modeling (PLSSEM) and fuzzy set qualitative comparative analysis (fsQCA). A hypothesized model examining the associations between the CPS skills and influence factors was constructed. RESULTS: The evaluation of the structural model showed that some variables had significant influences on CPS skills, while others did not. After deleting the insignificant pathways, a structural model was built, which showed that mediating effects of empathic concern and critical thinking were observed, while personal distress only had a direct effect on CPS skills. The results of necessity showed that only cooperativity and creativity are necessary conditions for critical thinking. The fsQCA analysis provided clues for each different pathway to the result, with all consistency values being higher than 0.8, and most coverage values being between 0.240 and 0.839. The fsQCA confirmed the validity of the model and provided configurations that enhanced the CPS skills. CONCLUSIONS: This study provides evidence that reflective learning based on multi-dimensional empathy theory and 21 stcentury skills theory can improve CPS skills in medical students. These results have practical implications for learning and suggest that educators should consider incorporating reflective learning strategies that focus on empathy and 21 stcentury skills to enhance CPS skills in their curricula.

Factors Affecting User's Behavioral Intention and Use of a Mobile-Phone-Delivered Cognitive Behavioral Therapy for Insomnia: A Small-Scale UTAUT Analysis.

A mobile app could be a powerful medium for providing individual support for cognitive behavioral therapy (CBT), as well as facilitating therapy adherence. Little is known about factors that may explain the acceptance and uptake of such applications. This study, therefore, examines factors from an extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT2) model to explain variation between people's behavioral intention to use a CBT for insomnia (CBT-I) app and their use-behavior. The model includes eight aspects of behavioral intention: performance expectancy, effort expectancy, social influence, self-efficacy, trust, hedonic motivation, anxiety, and facilitating conditions, and investigates further the influence of the behavioral intention and facilitating conditions on app-usage behavior. Data were gathered from a field trial involving people (n = 89) with relatively mild insomnia using a CBT-I app. The analysis applied the Partial Least Squares-Structural Equation Modeling method. The results found that performance expectancy, effort expectancy, social influence, self-efficacy, trust, and facilitating conditions all explained part of the variation in behavioral intention, but not beyond the explanation provided by hedonic motivation, which accounted for R2 = 0.61. Both behavioral intention and facilitating conditions could explain the use-behavior (R2 = 0.32). We anticipate that the findings will help researchers and developers to focus on: (1) users' positive feelings about the app as this was an indicator of their acceptance of the mobile app and usage; and (2) the availability of resources and support as this also correlated with the technology use.

Digging into the roots: understanding direct and indirect drivers of ecosystem service trade-offs in coastal grasslands via plant functional traits.

Recent empirical and theoretical approaches have called for an understanding of the processes underpinning ecosystem service provision. Environmental gradients have shown effects on key plant functional traits that subsequently explain ecosystem properties of several systems. However, little is known concerning how associations between plant functional traits, including both below- and aboveground plant components, predict ecosystem properties and independently measured final ecosystem services. Here, we modeled (1) the responses of the leaf and plant economics spectrum, Plant size axis, and root growth to environmental gradients and (2) how associations between plant functional traits explain trade-offs and synergies between multiple ecosystem properties and final services. Forty-four plots were studied in a coastal marsh landscape of the German North Sea Coast. We used a partial least square structural equation model approach to test the hypothesized model. We found (1) a negative covariation between plant traits pertaining to a size axis and traits explaining both plant growth (roots and stems) and the leaf economics spectrum; (2) this trade-off responded significantly to the land use gradient and nutrient availability, which were both strongly driven by the groundwater gradient; (3) this trade-off explained an initial major trade-off between carbon stocks, at one extreme of the axis, and both the habitat value to conserve endangered plants and forage production for meat and dairy products at the other extreme. However, a secondary trade-off between nature conservation value and forage production, explained by a trade-off between leaf economics spectrum and plant growth in response to the land use intensity gradient, was also found.

Understanding the adoption of smartphone apps in dairy herd management.

The number of decision support tools available to farmers, including dairy herd management smartphone apps, has been steadily increasing. The existing literature does not cover topics concerning the adoption and use of herd management smartphone apps or which specific functions of such apps are perceived as most useful by dairy farmers. It is unclear whether technology adoption can only be explained by economic reasoning, because the beliefs about a technology also play a role in decision-making. Therefore, this study seeks to determine whether an extended technology acceptance model can explain adoption and use of herd management smartphone apps. Results about the adoption and use of dairy herd management smartphone apps are derived from an online survey conducted in 2018 with 280 German dairy farmers. To model farmers' frequency of use of herd management smartphone apps, we applied partial least squares structural equation modeling and an ordered logit model. Our results show that 93% of the dairy farmers in our sample use a smartphone and 61% already use a herd management smartphone app. Daily use is reported by 38% of the adopters. Dairy farmers rated functions related to the observation of animal health, reproduction management, and data gathering as most useful, which should be in focus by developers and providers for future development. The key attitudinal components of the technology acceptance model, namely perceived ease of use and perceived usefulness, both positively influence the intention to use such apps. This ultimately has a positive effect on the actual usage behavior. Besides other factors, dairy farmers' education and knowledge of herd management smartphone apps have a positive effect on perceived ease of use. Our model explains 33% of the variance in the actual usage behavior related to herd management smartphone apps. Because perceived ease of use and perceived usefulness positively influence the intention to use such apps and ultimately the actual usage behavior, developers and providers should highlight the benefits of using herd management smartphone apps and also keep the interface of the apps as simple as possible.

An in-depth discussion and illustration of partial least squares structural equation modeling in health care.

Partial least squares structural equation modeling (PLS-SEM) has become more popular across many disciplines including health care. However, articles in health care often fail to discuss the choice of PLS-SEM and robustness testing is not undertaken. This article presents the steps to be followed in a thorough PLS-SEM analysis, and includes a conceptual comparison of PLS-SEM with the more traditional covariance-based structural equation modeling (CB-SEM) to enable health care researchers and policy makers make appropriate choices. PLS-SEM allows for critical exploratory research to lay the groundwork for follow-up studies using methods with stricter assumptions. The PLS-SEM analysis is illustrated in the context of residential aged care networks combining low-level and high-level care. Based on the illustrative setting, low-level care does not make a significant contribution to the overall quality of care in residential aged care networks. The article provides key references from outside the health care literature that are often overlooked by health care articles. Choosing between PLS-SEM and CB-SEM should be based on data characteristics, sample size, the types and numbers of latent constructs modelled, and the nature of the underlying theory (exploratory versus advanced). PLS-SEM can become an indispensable tool for managers, policy makers and regulators in the health care sector.

Alexithymia, Ego-Dystonicity, and Obsessive-Compulsive Symptoms: A Path Modeling Analysis.

AIMS: This cross-sectional study aimed to test the path relations between alexithymia, ego-dystonicity, anxiety, depression and obsessive-compulsive (OC) symptoms in obsessive-compulsive disorder (OCD) and healthy individuals. METHODS: Fifty-eight patients with OCD (mean age 35.5 years) and 54 healthy participants (mean age 33.5 years) completed an assessment via a structured clinical interview. All of them completed the Toronto Alexithymia Scale (TAS-20), the Vancouver Obsessive-Compulsive Inventory (VOCI), the Self-Consistency and Congruence Scale (SCCS), the Self-Rating Anxiety Scale (SAS) and the Self-Rating Depression Scale (SDS). The data were analyzed using partial least squares structural equation modeling (PLS-SEM). RESULTS: In the OCD patients, alexithymia (a linear combination of difficulty identifying and describing emotions in the self) was associated with the OC symptoms either with or without the presence of ego-dystonicity (a profile of self-inconsistency and self-stereotype). In the heathy participants, alexithymia was associated with the OC symptoms only through ego-dystonic experiences. CONCLUSION: This study provides evidence that ego-dystonicity partially affects the association between alexithymia and obsessive-compulsions. Alexithymia and ego-dystonicity have a synergistic effect on the symptoms of OCD. Alexithymia in healthy participants associates to the OC symptoms only through ego-dystonicity. Targeting ego-dystonicity dimensions in psychotherapy would help improve the symptoms of OCD.

Hospital nurses' attitudes, negative perceptions, and negative acts regarding workplace bullying.

BACKGROUND: Workplace bullying is a prevalent problem in today's work places that has adverse effects on both bullying victims and organizations. To investigate the predictors of workplace bullying is an important task to prevent bullying victims of nurses in hospitals. OBJECTIVE: This study aims to explore the relationships among nurses' attitudes, negative perceptions, and negative acts regarding workplace bullying under the framework of the theory of planned behavior (TPB). METHODS: A total of 811 nurses from three hospitals in Taiwan were surveyed. Nurses' responses to the 201 items of 10 scales were calibrated using Rasch analysis and then subjected to path analysis with partial least-squares structural equation modeling (PLS-SEM). RESULTS: The instrumental attitude was significant predictors of nurses' negative perceptions to be bullied in the workplace. Instead, the other TPB components of subjective norm and perceived behavioral control were not effective predictors of nurses' negative acts regarding workplace bullying. CONCLUSIONS: The findings provided hospital nurse management with important implications for prevention of bullying, particularly to them who are tasked with providing safer and more productive workplaces to hospital nurses. Awareness of workplace bullying was recommended to other kinds of workplaces for further studies in future.

Armed Conflict, Substance Use and HIV: A Global Analysis.

Armed conflict is frequently assumed to be a contributor to the global HIV epidemic, but existing evidence is sparse. We examined the relationship between armed conflict between 2002 and 2008 and HIV disability life years (DALYs) in 2010 among WHO Member States. Using partial least squares analysis we also examined moderation of the armed conflict-HIV link by two susceptibility constructs (background risk, substance use) and one vulnerability mediator (numbers of refugees, people on ART, and total HIV spending). Background risk directly impacted HIV DALYs (p < 0.05), substance use moderated the conflict-HIV relationship (p < 0.01). The vulnerability construct mediated the conflict-HIV association (p < 0.01). Findings underscore the need to align HIV prevention/intervention efforts with pre-existing HIV burden and reduce the impact of natural disasters on the populace in conflict-affected states. Integration of substance prevention/harm reduction programs within national HIV responses, attention to most-at-risk populations and increased surveillance/treatment of drug resistant HIV and TB is warranted.

Instagram influencers attributes and parasocial relationship: A dataset from Qatar.

The dataset investigates how social media influencers' attributes affect followers' parasocial relationship. It also examines the mediating role of the parasocial relationship between the social media influencers' attributes and behavioral intentions. A snowballing sampling technique was used to target Instagram users in Qatar. 574 valid responses were analyzed using Partial least squares structural equation modeling (PLS-SEM). The data provides descriptive information about the essential Instagram influencers among users in Qatar. It also gives new insight into the influencers' characteristics that will impact consumer behavior the most. The dataset could be very helpful for brands and marketers in Qatar in choosing the most effective influencers. The dataset presents a real value for researchers examining social media consumers behavior specifically in GCC countries context or conducting cross-national comparative studies.

Examining the Moderating Role of Reasons in Masstige Luxury Buying Behavior.

This study proposes a new model derived from Behavioral Reasoning Theory (BRT) to examine the purchasing behavior of masstige jewelry consumers. The suggested model provides a more comprehensive understanding of the determinants of purchasing masstige products by considering values and reasons in addition to the global motives and intention. The study also examines, for the first time, the moderating role of reasons. It explores how reasons may strengthen or weaken the impact of perceived values on global motives. The proposed model was empirically tested using partial least squares-structural equation modeling (PLS-SEM) with survey data on the consumption of masstige jewelry brands in Türkiye. To provide a more complete understanding of the moderating role of reasons, submodels were constructed for different value-reason combinations. The results demonstrate notable differences in the goodness-of-fit measures between the moderated and non-moderated models. Specifically, reasons contributed to enhanced explanations of global motives and intention, both directly and indirectly. However, not all submodels yielded significant results in terms of the moderator effect. Thus, the empirical tests supported the hypotheses regarding the moderating effect only partially. Overall, the current findings further extend the existing frameworks and provide valuable insights into masstige jewelry purchasing behavior, which can be used by marketers to develop more effective strategies.

Greenspace pattern, meteorology and air pollutant in Taiwan: A multifaceted connection.

Air pollution is a global environmental concern that poses a significant threat to human health. Given the impact of urbanization and climate change, green planning is being encouraged to improve air quality. The study aims to examine the intricate relationships between greenspace pattern and outdoor air around 73 in-situ stations over Taiwan during the dry (November to April) and wet (May to December) seasons from 2015 to 2020. To achieve this, Partial Least Squares - Structural Equation Modeling was utilized to analyze the interactions among seven dimensions: greenspace - GS, gaseous pollutant - GP, particle pollutant - PP, O3 - OZONE, air temperature - TEMP, relative humidity - RH, and wind speed - WS. The GS involves seven landscape metrics: edge density, total edge, effective mesh size, largest patch area, percentage of landscape, total core area, and patch cohesion index. The results indicate that the GS has a stronger effect on the GP, whereas its effect on the PP is weaker during the dry season compared to the wet season. While its effect on the TEMP is weaker, it shows a slightly stronger effect on the RH during the dry season. Moreover, the GS mediates the air pollutant dimensions during the two seasons, with the RH acting as a primary mediator. The meteorological dimensions primarily have a greater influence on the air pollutant dimensions during the dry season than the wet season. Consequently, the GS explains 11.3 % more and 18.4 % less of the variances in the RH and TEMP during the dry season, respectively. Moreover, the GS and meteorological dimensions yield a seasonal difference in explained variance, with the highest value observed for the OZONE (R2 = 24.2 %), followed by the PP (R2 = 9.7 %) and GP (R2 = 7.7 %). Notably, seven landscape metrics serve as potential indicators for green strategies in urban planning to enhance outdoor air quality.

Exploring college students' resistance to mandatory use of sports apps: a psychological reactance theory perspective.

Introduction: To improve the physical fitness of college students, numerous Chinese universities have mandated students to use sports apps for running on campus. However, this has led to widespread resistance among students. Methods: To gain a deep understanding of the potential reasons for student resistance, we have developed a conceptual model based on psychological reactance theory. Specifically, we conducted a questionnaire survey involving 449 Chinese college students, using partial least squares structural equation modeling to test and analyze the research model and its related hypotheses. Results: Our results reveal that: (1) Psychological reactance poses a potential reason for students' resistance to mandatory use of sports apps, and it has a significant negative impact on students' attitudes and behavioral intentions. (2) Students' perceived threat to freedom and reactance proneness are two important antecedents of psychological reactance, which can account for 51% (R 2 = 0.51) of the variance in psychological reactance. Furthermore, the results indicate that students' reactance proneness has a positive impact on perceived threats to freedom. (3) The mandatory use of sports apps leads students to have two different conditions: mandated-acceptance and mandated-rejection, both of which have a positive impact on the antecedents of psychological reactance. Discussion: These findings provide insights into the psychological processes underlying students' resistance to mandatory use of sports apps, facilitating the application of sports apps in intervention measures that improve health and fitness. Furthermore, this study is the first to apply psychological reactance theory to mandatory exercise behavior, contributing to the reactance literature.

Ageing well with tech: Exploring the determinants of e-healthcare services adoption in an emerging economy.

The adoption of e-healthcare services is critical for improving healthcare accessibility and efficiency, particularly in regions with diverse populations, such as Malaysia. Although e-healthcare services offer numerous advantages, their adoption is considerably low and requires a thorough understanding of the key factors that influence their use. This study investigated the determinants and dynamics of e-healthcare adoption among adults over 40 years by extending the unified theory of acceptance and use of technology. We employed a quantitative research approach, specifically a cross-sectional design. Data were collected from 393 Malaysian respondents through a structured survey questionnaire, using convenience sampling. They were analyzed using partial least-squares-structural equation modeling. The findings revealed that performance expectancy, effort expectancy, social influence, and perceived product value significantly influenced individuals' intentions to use e-healthcare services. Meanwhile, perceived risk had an insignificant negative effect. Facilitating conditions significantly influenced individuals' intentions and actual usage of e-healthcare services. Furthermore, individuals' intentions to use e-healthcare services significantly affected their actual use of these services. Additionally, the intention to use e-healthcare services mediated the relationship between the factors and usage of e-healthcare services, except for perceived risk. Surprisingly, perceived service accuracy had no significant moderating effect on the relationship between individuals' intention to use and their actual use of e-healthcare services. This study offers valuable insights for educators, practitioners, and policymakers, enriching the scholarly discourse in this field. For education, integrating e-healthcare topics into curricula can enhance digital health literacy. In practice, healthcare providers should focus on improving user experience and addressing barriers to technology adoption. For policy making, developing supportive policies, and infrastructure to facilitate e-healthcare adoption is crucial to enhancing public health outcomes.

Unpacking public resistance to health Chatbots: a parallel mediation analysis.

Introduction: Despite the numerous potential benefits of health chatbots for personal health management, a substantial proportion of people oppose the use of such software applications. Building on the innovation resistance theory (IRT) and the prototype willingness model (PWM), this study investigated the functional barriers, psychological barriers, and negative prototype perception antecedents of individuals' resistance to health chatbots, as well as the rational and irrational psychological mechanisms underlying their linkages. Methods: Data from 398 participants were used to construct a partial least squares structural equation model (PLS-SEM). Results: Resistance intention mediated the relationship between functional barriers, psychological barriers, and resistance behavioral tendency, respectively. Furthermore, The relationship between negative prototype perceptions and resistance behavioral tendency was mediated by resistance intention and resistance willingness. Moreover, negative prototype perceptions were a more effective predictor of resistance behavioral tendency through resistance willingness than functional and psychological barriers. Discussion: By investigating the role of irrational factors in health chatbot resistance, this study expands the scope of the IRT to explain the psychological mechanisms underlying individuals' resistance to health chatbots. Interventions to address people's resistance to health chatbots are discussed.

Evaluation of a Computer-Aided Clinical Decision Support System for Point-of-Care Use in Low-Resource Primary Care Settings: Acceptability Evaluation Study.

BACKGROUND: A clinical decision support system (CDSS) based on the logic and philosophy of clinical pathways is critical for managing the quality of health care and for standardizing care processes. Using such a system at a point-of-care setting is becoming more frequent these days. However, in a low-resource setting (LRS), such systems are frequently overlooked. OBJECTIVE: The purpose of the study was to evaluate the user acceptance of a CDSS in LRSs. METHODS: The CDSS evaluation was carried out at the Jimma Health Center and the Jimma Higher Two Health Center, Jimma, Ethiopia. The evaluation was based on 22 parameters organized into 6 categories: ease of use, system quality, information quality, decision changes, process changes, and user acceptance. A Mann-Whitney U test was used to investigate whether the difference between the 2 health centers was significant (2-tailed, 95% CI; α=.05). Pearson correlation and partial least squares structural equation modeling (PLS-SEM) was used to identify the relationship and factors influencing the overall acceptance of the CDSS in an LRS. RESULTS: On the basis of 116 antenatal care, pregnant patient care, and postnatal care cases, 73 CDSS evaluation responses were recorded. We found that the 2 health centers did not differ significantly on 16 evaluation parameters. We did, however, detect a statistically significant difference in 6 parameters (P<.05). PLS-SEM results showed that the coefficient of determination, R2, of perceived user acceptance was 0.703. More precisely, the perceived ease of use (ß=.015, P=.91) and information quality (ß=.149, P=.25) had no positive effect on CDSS acceptance but, rather, on the system quality and perceived benefits of the CDSS, with P<.05 and ß=.321 and ß=.486, respectively. Furthermore, the perceived ease of use was influenced by information quality and system quality, with an R2 value of 0.479, indicating that the influence of information quality on the ease of use is significant but the influence of system quality on the ease of use is not, with ß=.678 (P<.05) and ß=.021(P=.89), respectively. Moreover, the influence of decision changes (ß=.374, P<.05) and process changes (ß=.749, P<.05) both was significant on perceived benefits (R2=0.983). CONCLUSIONS: This study concludes that users are more likely to accept and use a CDSS at the point of care when it is easy to grasp the perceived benefits and system quality in terms of health care professionals' needs. We believe that the CDSS acceptance model developed in this study reveals specific factors and variables that constitute a step toward the effective adoption and deployment of a CDSS in LRSs.

The effect of positive reinforcement of behavioral-based safety on safety participation in Philippine coal-fired power plant workers: a partial least squares structural equation modeling approach.

The power plant accident is considered one of the man-made disasters in many developing countries, including the Philippines. This study aimed to identify the factors influencing behavioral-based safety (BBS) of coal-fired power plant workers which subsequently led to their active safety participation (SPT). In total, 201 coal-fired power plant workers in the Philippines answered the questionnaire consisting of 60 items using a purposive sampling approach. Partial least squares structural equation modeling (PLS-SEM) revealed that personal measures (PM) had significant effects on safety incentives (SI), safety management system (SMS) and safety perception (SP). SP also had significant effect on BBS which subsequently led to SPT. Finally, SMS had significant effect on safety culture (SC) which subsequently led to safety communication (SCM), BBS and SPT. This study is the first to explore BBS in the Philippine coal-fired power plant, and can positively reinforce SPT of workers in the coal-fired power plant industry.

Urban tourist profiles during the pandemic in Taiwan: A multigroup analysis.

Participating in tourism activities in crowded areas such as cities during the COVID-19 pandemic represents a risk. This study examined the demographic and psychological features of Taiwanese domestic urban tourists during the pandemic in 2021. The theoretical framework was based on push-pull motivation, self-concordance, and push-pull-mooring theories. The 680 valid questionnaire responses indicated that the respondents were generally interested in domestic urban tourism despite the pandemic threat. Moreover, 187 respondents regarded themselves as urban tourism seekers. Their demographic features were consistent with the typical primary urban tourism market profile: they were young, highly educated, and employed in skilled occupations. In terms of psychological features, the push factors, representing the individuals' intrinsic urban tourism motivations, were more potent than the pull factors, representing a city's tourism opportunities, as motivational drivers for increasing seekers' urban tourism intention during the pandemic. The methodology and findings of this study strengthen the literature on urban tourism and pandemic recovery.

Antecedents and consequences of corporate reputation: A dataset.

Corporate reputation is crucial for maintaining and enhancing a company's competitiveness in the marketplace. To actively manage this important intangible asset, which significantly contributes to a company's value, managers need to understand the relationship between reputation and its antecedents and consequences. The dataset presented in this article stems from a conceptual replication of a seminal model of corporate reputation, its antecedents and effects on customer satisfaction and loyalty. Potential mediators and moderators in these relationships allow us to extend the original model in order to clarify the mechanism through which corporate reputation impacts satisfaction and loyalty. We document some of the model's main effects using partial least squares structural equation modeling (PLS-SEM).

Driving mechanism of water replenishment on DOM composition and eutrophic status changes of lake in arid and semi-arid regions of loess area.

Water replenishment can be a key factor in driving lake eutrophication status. In arid and semi-arid regions of China, water replenishment for a lake has been widely carried out for not only improving water environmental quality, but also maintaining ecological system function. However, it is still unclear in terms of mechanism by which water replenishment drives lake eutrophication status. In this study, fluorescence excitation-emission matrix spectroscopy (EEMs) combined with multiple statistical analysis models (including parallel factor analysis, correlation analysis, redundancy analysis, and partial least squares structural equation modeling) was utilized to reveal potential driving mechanism and causality between water replenishment, dissolved organic matter (DOM) fractions and eutrophic status of Lake Shahu in China. Based on variations of DOM fractions, fulvic-like substances could be accumulated during the replenishment period, while nutrients carried along the replenishment might conduce to increase microbial activities during the non-replenishment period. This should be contributed to an alteration of prominent component from fulvic-like substances to tyrosine-like substances during the replenishment period to non-replenishment period. According to partial least squares structural equation modeling, two potential indirect paths were finally revealed, i.e., water replenishment derived the eutrophic status of Lake Shahu: water replenishment → microbial activity → algae → eutrophication, and water replenishment → microbial activity → eutrophication. This supposed that the water replenishment should indirectly drive the algae and eutrophication of the lake by promoting the transformation of DOM fractions. In addition, natural conditions could indirectly contribute to the eutrophication of the lake through impacting the algae growth. These findings should be conducive to trace the alteration of DOM fractions in lakes by water replenishment and in recognizing potential driving mechanisms of water replenishment on eutrophication of lakes by changing DOM fractions. This could provide basic theoretical support for policymakers to regulate and treat the eutrophication of lakes.