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Structural Equation Modeling
Abstract
This article covers both the historical and modern developments in structural equation modeling. The material is divided into what can be referred to as the ‘first generation’ and the ‘second generation’ of structural equation modeling. Topics discussed under the first generation include the history of structural equation modeling estimation, testing, and assessment of assumptions. Topics covered in the second generation include multilevel structural equation modeling and latent variable growth curve modeling.
... SEM-based approaches have significant advantages over first-generation methods when applied correctly (Chin, 1998). It provides powerful means of hypothesis testing and theory generation (Kaplan, 2001). Using SEM method, multiple related equations can be solved simultaneously to determine parameter estimates. ...
... Taking into consideration the different aspects of the reality under study and abstract concepts or theoretical constructs, SEM enables complex, multidimensional, and more accurate analysis of empirical data (Tarka, 2018). In fact, the advances in SEM including the developments in multilevel structural equation modeling, growth curve modeling, and latent class applications look very promising (Kaplan, 2001) and thus without doubt SEM is an important and critical tool for statistical analysis. Nevertheless, as advised by Tarka (2018) if we want to continue using SEM, it is vital that we work on improving the practices of using this analytical approach. ...
Theethi (class) laan vee ennu morning ah! [Let’s
have it (the class) in the morning!]
We often talk about our Maldivian learners mixing
Dhivehi (L1, first language) and English (L2,
second language) in their everyday conversations.
Frankly, it has become a nightmare for those
strongly against codeswitching (mixing languages
in written or oral communication). However, even if
we like it or not, it is not only our students but
academics like us who have begun (conversations)
mixing English and Dhivehi in our everyday talks.
The above comment, made by one of our colleagues
(an FES lecturer) while we were writing this article,
shows that codeswitching has become a regular part
of our everyday oral communication. Is this because
we are equally competent enough in these two
languages (as bilinguals) that what matters is getting
the message across rather than the language used?
In this regard, mixing two languages might not be
an issue as long as our conversations are
linguistically or grammatically correct and
meaningful. If codeswitching is here to stay, why
not use it to our advantage? While codeswitching is
done unconsciously or without deliberate planning,
intentionally using another language (a word or a
phrase) could be critical in understanding our target
language. Such intentional use of another language
is where translanguaging comes in.
... As the previous section explored the direct effects of the two main variables of interest, we find supporting evidence on the directionality of misinformation and populist attitudes toward trust across several domains. Next, we test the SEM model, which relies on the simultaneous estimation of causes and effects that are theoretically associated with each other (Kaplan, 2000;Kline, 2011). This approach allows us to test an entire model as well as the effects of individual parameters on several dependent variables. ...
Understanding trust in experts and scientists is crucial, especially in testing the challenges posed by pre- and post-pandemic realities. Establishing trust in experts, scientists, and institutions is beset by challenges, exacerbated by widespread misbeliefs on various science-driven topics. This paper explores how misinformation, particularly in the context of populist politics that fosters anti-intellectualism, undermines trust in these authorities. Using observational data from Turkey, a context driven by strong polarization and populist politics, we demonstrate how populism increases the acceptance of misinformation, which, in turn, mediates the relationship between populism and trust, decreasing trust for experts, scientists, and institutions. The findings also reveal that the negative impact of frequent social media use on trust is mediated by the acceptance of misinformation. By presenting a comprehensive model linking science-related misinformation and populist attitudes to trust dynamics in a polarized environment, this study contributes to the literature on trust-building and science communication.
... This approach ensures that we accurately model the relationships we aim to explain. In addition, SEM assesses a model's validity through various goodness-of-fit measures which allows the researcher to test whether the proposed model accurately reflects the relationships among the different variables examined in the study (Kaplan, 2001). ...
Purpose
This study aims to examine auditors’ perceptions of the changes in audit processes, fees, salaries and trainings during the Lebanese multidimensional crisis. The aim is to understand how going concern assessments were affected by the distortions imposed by the crisis.
Design/methodology/approach
Data was collected through a survey distributed among auditors at Big 4 audit firms in Lebanon during the crisis. The questionnaire, inspired by prior desk study research, aims to empirically assess auditors’ attitudes toward the variances in audit operations during the crisis and their implications on going concern assessments. This study uses the structural equation modeling (SEM) technique to develop and assess the research model.
Findings
The findings reveal notable changes in audit processes, fees, salaries and training programs during the crisis. SEM results highlight the association between the crisis-driven changes in audit procedures, fees and salaries and the increased uncertainty in issuing going concern assessments.
Practical implications
This study provides recommendations for both the auditing industry and regulatory bodies to ensure audit firms are prepared to face crises that might disrupt their work. Recommendations include the initiation of crisis management training programs, investments in technology solutions, the establishment of a protocol in response to crisis and the adoption of flexible yet reliable audit procedure to accommodate for the challenges of the crisis.
Originality/value
The originality of this study emanates from its adoption of a novel survey to assess a conceptual model that has not been empirically tested in earlier studies. The model examines changes in audit operations during the Lebanese crisis and their implications on going concern assessments, a context that has received little attention in the literature.
... Although the correlation corroborates our hypotheses, the SEM is particularly useful when dealing with complex models that involve multiple latent (unobserved) variables and their relationships handling multicollinearity better than some other methods. Also, SEM allows researchers to test specific hypotheses about the relationships between variables and evaluate the fit of the proposed model to the data (Kaplan 2001). Regarding our main hypotheses, the model showed that the estimated covariance's between latent variables are statistically significant for GBA, GT, and GL (p < 0.001), but not for GA ( Figure 2). ...
... SEM integrates measurement models and structural models, allowing for validation of instruments and analysis of relationships while considering variances and covariances. It facilitates the examination of complex relationships among multiple variables and enables mediation analysis [28]. During CFA, we assessed convergent validity using criteria suggested by Hair et al [29] and Fornell and Larcker [30], calculating average variance extracted (AVE) and composite reliability. ...
Background
Loneliness is a significant issue among older Asian Americans, exacerbated by the COVID-19 pandemic. Older age, lower income, limited education, and immigrant status heighten loneliness risk. Information communication technologies (ICTs) have been associated with decreased loneliness among older adults. However, older Asian Americans are less likely to use ICTs, particularly if they are immigrants, have limited English proficiency, or are low income. The Technology Acceptance Model posits that perceived usefulness (PU), and perceived ease of use (PEOU) are key factors in predicting technology use.
Objective
This study aimed to examine associations between PU, PEOU, ICT use, and loneliness among low-income, older Asian Americans.
Methods
Cross-sectional survey data were gathered from predominately older Asian Americans in affordable senior housing (N=401). Using exploratory factor analysis and Horn parallel analysis, we examined 12 survey items to identify factors accounting for variance in ICT use. We deployed structural equation modeling to explore relationships among the latent factors and loneliness, adjusting for demographic and cognitive factors.
Results
Exploratory factor analysis and Horn parallel analysis revealed 3 factors that accounted for 56.48% (6.78/12) total variance. PEOU combined items from validated subscales of tech anxiety and comfort, accounting for a 28.44% (3.41/12) variance. ICT use combined years of technological experience, computer, tablet, and smartphone use frequency, accounting for 15.59% (1.87/12) variance. PU combined 2 items assessing the usefulness of technology for social connection and learning and accounted for a 12.44% (1.49/12) variance. The 3-factor structural equation modeling revealed reasonable fit indexes (χ2133=345.132; P<.001, chi-square minimum (CMIN)/df = 2595, comparative fit index (CFI)=0.93, Tucker-Lewis Index (TLI)=0.88). PEOU was positively associated with PU (β=.15; P=.01); PEOU and PU were positive predictors of ICT use (PEOU β=.26, P<.001; PU β=.18, P=.01); and ICT use was negatively associated with loneliness (β=–.28, P<.001). Demographic and health covariates also significantly influenced PU, PEOU, ICT use, and loneliness. English proficiency and education positively predicted PEOU (r=0.25, P<.001; r=0.26, P<.001) and ICT use (β=1.66, P=.03; β=.21, P<.001), while subjective cognitive decline and Asian ethnicity were positively associated with loneliness (β=.31, P<.001; β=.25, P<.001).
Conclusions
This study suggests that targeted interventions enhancing PU or PEOU could increase ICT acceptance and reduce loneliness among low-income Asian Americans. Findings also underscore the importance of considering limited English proficiency and subjective cognitive decline when designing interventions and in future research.
... Besides, it will help to predict what influence does the independent variable has over the dependent one. The main advantage of SEM analysis is the ability to conduct confirmatory factor analysis (CFA) and in regression analysis simultaneously which helps testing mediation or moderation relationships [61], [65]. The reason for using SEM in this study is that SEM has advantages over regression analysis: i) it is more powerful in controlling for measurement errors compared to regression analysis; ii) SEM can deal with different dependent and independent variables at the same time which regression analysis usually does not provide; iii) SEM provide more flexibility in analysis and provide more accurate results compared to regression analysis [66]. ...
With the emerge of COVID-19 pandemic in Hungary, Hungarian universities have decided to opt for online teaching methods even for foreign language courses. This sudden change has required a better understanding of students' behaviors in classes also to view the importance of their feedback to enhance teaching quality and teachers' effectiveness. The purpose of our study was to focus on students' feedback and its impact on their engagement in the context of online classes, by considering the mediating role of teaching effectiveness in that relationship. Structural equation modelling was used to examine our primary data, which has been collected from a distributed online questionnaire dedicated to 222 students enrolled in Hungarian language courses at MATE University. The findings reveal that students' feedback has a direct, significant, and positive effect on students' engagement in online classes and teaching effectiveness, which itself plays a mediating role in that relationship.
... As with most statistical analyses, SEM requires major assumptions to be satisfied. The major assumptions in SEM include multivariate normality, no systematic missing data, sufficiently large sample size, and correct model specification (Kaplan, 2001). Multivariate normality was tested using Henze-Zirkler's and the p-value was >.05, which indicates that the assumption is met. ...
Emotional empathy, mindfulness, and subjective well-being are essential to understanding human behavior and mental health among students. However, more research is needed to investigate how these constructs interplay within academic contexts. This study explored the hierarchical relationships between emotional empathy, mindfulness, and subjective well-being. The Multidimensional Emotional Empathy Scale (MDEES), The Kentucky Inventory of Mindfulness Skills (KIMS), and the Subjective Well-Being Scale (WeBs) were administered with a sample of postgraduate professional diplomas in teaching students attending Al Ain University in Abu Dhabi campus and Al Ain campus (n = 1545). The results showed that emotional empathy (positive sharing, suffering, feeling for others, and emotional contagion) positively affects physical and eudaimonic well-being. A negative correlation was found between financial and social well-being and other components of emotional empathy, such as emotional attention and responsive crying. Mindfulness significantly improves emotional empathy in components like describing, accepting without judgment, and observing. This study revealed that some components of mindfulness, such as observing and acting with awareness, decrease emotional empathy, such as suffering and feeling for others. Acting with the awareness component in mindfulness decreases positive sharing, responsive crying, and emotional contagion. Future research could explore these relationships further and examine potential cultural differences.
... Structural equation modeling (SEM) is utilized for the analytical measurement of relationships among latent and observed variables through a set of statistical techniques [66]. As per Kaplan [82], SEM can be characterized as a class of techniques that tries to address speculations about the means, differences, and variables of observed information in terms of fewer 'primary' boundaries characterized by a hidden estimated or calculated hypothetical model. As per Xiao [83], SEM is a general way to deal with analysis among observed and unobserved variables [84]. ...
The surge in popularity of fad diets has raised concerns about compromised health among individuals due to their beliefs and intentions regarding consumption. The aim of this study was to examine the prevalence of fad dieting among persons who are dieting and to determine the different factors influencing the inclination to adopt fad diets. Specifically, this study explored the ways in which individual openness to following fad diets, participation in diet trends, and characteristics may influence attitudes towards fad diet adoption. Data from 407 participants aged 18–34, collected via Google Forms, were analyzed using a high-ordered construct approach between the theory of planned behavior (TPB) and health belief model (HBM). Employing partial least squares structural equation modeling, significant results were obtained. The key findings revealed that knowledge about dieting, perceived benefits, and health motivation significantly influenced individuals’ intentions to adopt fad diets. Additionally, the study demonstrated significant impacts of health motivation on attitude and perceived behavioral control, subsequently affecting individuals’ intention to adopt dietary practices. Practical implications include the development of tailored health communication strategies for government agencies and informed decision-making support for individuals considering adopting fad diets. This research contributes valuable insights into the perception and psychological and social factors shaping dietary decisions, laying the groundwork for enhanced health education and intervention strategies. Furthermore, the study’s theoretical framework offers potential for extension and application to health-related food consumption behaviors across diverse cultural contexts.
... Confirmatory factor analysis (CFA) was used as the indicators are well specified according to related theories and knowledge (Thakkar, 2020). SEM's equation can be written as follows (Kaplan, 2001): ...
Tax compliance (TC) behaviour differences between the Romanian Millennials and “Zoomers” are investigated in this paper to identify the variances in TC behaviour between generations. A questionnaire was developed, and it was applied to 350 respondents. Online survey data were collected from May to July 2022. The influences on TC from various variables were considered using generalized linear models (GLM) and path analysis with Structural Equation Modelling (SEM). The findings show a positive impact from awareness, age, and tax morale, emphasizing the need for measures to increase awareness of the tax system and the level of citizens’ tax morale. These findings are crucial for policymakers as they can create appropriate programs to educate people about TC behaviour and cater them to different generations. Also, tax morale's sensitivities may be affected by these tailored programs. The current research presents fresh avenues for further investigations into generational variations and the impact of emotions on TC behaviour.
... The research methodology consists of structural equation modelling (SEM) and analyses the interaction between the features of the learning system and the willingness of the students to accept AI technology in order to transfer part of their tasks into a digitisation-based automated programme. This methodology belongs to multivariate statistics (Kaplan, 2001) and offers the possibility to model complex relationships between observed or manifest variables, in our case students' propensity to integrate AI technologies in the act of learning and latent variables, respectively, that are also called constructs represented by the features of the learning system, providing in-depth information of the correlations between them. ...
... Moreover, SEM was adopted for further data analysis, as it is an efficient method for multidimensional modeling and measuring inner relationships among variables, and it also aligns with the predictive and verification modeling goals of the study meets [57]. The SEM was evaluated using model fitting indices (χ 2 , df, χ 2 /df, GFI, CFI, TLI, RMSEA, RMR, and SRMR), modification indices (MIs), factor loading β (standardized regression coefficient), and t-value [39,54,[58][59][60][61]. ...
Apparel has the potential to influence the external expression of wearer’s emotional state and can even empower them, making patients’ hospital wearing a crucial factor in their emotional experience and medical treatment. This study aims to investigate the emotional factors that drive patients’ behavioral responses to hospital gowns using the pleasure–arousal–dominance (PAD) model. With the survey conduction and data analysis, the results identified that the color and silhouette of hospital gowns lead to the emotional experience of arousal, while the structure leads to the emotional experience of dominance, which in turn brings patients a high sense of pleasure and further affect their acceptance and willingness to continue wearing hospital gowns. Based on the results of the research, new hospital gowns were designed and validated, which further confirmed the relationship between the attributes of hospital gowns and emotions of patients. Thus, by extending the PAD model to the context of patients’ use of hospital gowns, this study provides designers with a basis for creating emotionally driven atmosphere factors in the development of hospital gowns for the Chinese market that improve acceptance and continuation of hospital gowns, making a valuable contribution to knowledge in this field.
... According to Akkucuk (2014), Composite Reliability (CR) examines the reliability and consistency of the measurement variables, whereas convergence is measured by using the Analytical Value Estimator (AVE) 73 . When a latent variable's coefficient of determination (CR) exceeds 0.6, it is eligible to hold 74,75 . however, convergence validity may be accepted if CR is greater than 0.6 and AVE is less than 0.5 76 . ...
Public service motivation (PSM) has been the topic of many public administration and management studies since 1990. In Vietnam, 2016 marked the beginning of PSM research with a great deal of consideration of the impact of PSM on civil servants' job satisfaction, performance, and organizational commitment. However, no controlled studies have investigated PSM's impact on public employee outcomes, especially in the context of Vietnamese higher education institutions (HEIs). This study aims to examine how underlying aspects of public service motivation (Self-Sacrifice, Commitment to the Public Interest, Attraction to Public Policy Making, and Compassion) affect both positive and negative employee outcome factors, including Work Effort; Organizational Citizenship Behavior, Turnover Intention, and Work-related Stress. The results of structural equation modeling (SEM) point out that four PSM sub-constructs have varying effects on employee outcomes, as evidenced by the data from a random sample of three hundred and thirteen lecturers and administrators from Vietnamese public universities. In particular, three sub-constructs of PSM, namely self-sacrifice, dedication to the public interest, and attractiveness to public policymaking favorably influence employees' efforts in their duties; whereas compassion has a negligible influence on work efforts. Four aspects of PSM positively affect work effort and organizational citizenship behavior but adversely influence work-related stress and intention to leave the organization. This research demonstrates that a greater degree of PSM in all sub-constructs is favorably connected with public employees' organizational citizenship behavior. However, PSM negatively correlates with job stress although this is only supported by the sub-construct of selflessness. The findings of this research have proposed several significant recommendations for the approaches taken to human resource management within public universities in Vietnam.
... The primary assumption for the structural model analysis is that it will fit the diagnostic tools of the measurement, like reliability and validity. For example, the multi-co-linearity test is used for the structural model essential regression fitness [173]. The structural model is shown in given below Fig. 3. ...
Artificial Intelligence (AI) has become essential to Electronic-Commerce technology over the past decades. Its fast growth has changed the way consumers do online shopping. Using the Technology Acceptance Model (TAM) as a theoretical framework, this research examines how AI can be made more effective and profitable in e-commerce and how entrepreneurs can make AI technology to assist in achieving their business goals. In this regard, an online survey was conducted from the online purchasers of e-commerce firms. The Partial Least Square (PLS) Smart was used to examine the data. The broadly used TAM was identified as an appropriate hypothetical model for studying the acceptance of AI technology in e-commerce. The findings of this study show that Subjective Norms positively impact Perceived Usefulness (PU) and Pursued Ease of Use (PEU), trust has a positive effect on PEU, and PEU positively impacts PU and attitudes toward use. Similarly, PU also has a positive effect on attitudes toward use and intention to use. Furthermore, the findings do not support the impact of Trust on PU and attitudes towards behavioural intention to use. Lastly, behavioural intention to use positively impacted the actual use of AI technology. This study adds theoretical and practical knowledge for adopting the TAM model in the E-commerce sector. It helps entrepreneurs to implement the TAM model in their business to use AI in a better and more appropriate way.
... Third, the multivariate relationship among driving aggression variables and intimate partner aggression variables was analysed through Structural Equation Modelling (SEM). SEM is a multivariate correlational analytic strategy which allows to assess complex relationships among different observational and latent variables (Kaplan, 2002). Research has suggested that typical SEM models should contain at least 200 cases or 5-10 cases per parameter to guarantee statistical power (Kline, 2011). ...
Aggressive behaviour is a common response in different contexts all around the world. General aggression theories, such as the frustration-aggression theory, try to explain this behaviour in any context. However, situational specificity could play a relevant role in this issue, so proneness to behave aggressively may depend more on the context than on a general root or personality trait. With the aim of shedding light in this field, the current research aimed to analyse the relationship between aggressive behaviour on the road and intimate relationships. A sample composed of 275 participants who had a driving license and lived with an intimate partner completed a set of self-reports regarding aggressive behaviour in both contexts. The results suggested a convergence in the way of expressing anger, except in the case of adaptive aggression. A SEM-based approach indicated that the measured aggressive variables fitted better in two highly correlated factors rather than a single one, suggesting the relevance of the situational specificity in the prediction of aggressive behaviour in both contexts. Practical implications regarding evaluation and intervention for aggression reduction are discussed, as well as the limitations of the current research.
... SEM is a class of methodologies that "seek to represent hypotheses regarding the means, the variances, and covariances of observed data in terms of a smaller number of 'structural parameters' defined by a hypothesized underlying conceptual or theoretical model". 40 SEM is a very powerful multivariate technique. It has advantages beyond the traditional regression and ANOVA analyses -it can test models with multiple dependent variables, model mediating variables, and handle difficult data (multi-level, non-normal, etc.). ...
Purpose
Despite much attention within the literature, the multiple risk factors associated with CVD mortality in Russia are still not fully understood. Drawing on the Health Belief Model as a theoretical framework, we aim to elicit socioeconomic and behavioral determinants of cardiovascular risks in Russian men and women.
Methods
Using the Know Your Heart project data, we utilize regression analysis and then structural equation modeling (latent class analysis and mediation analysis) to study the determinants of CVD risks.
Results
OLS and ordered logit regressions show that the key factors defining cardiovascular health behaviors in Russia are health-related actions to reduce the perceived threat of diseases (physical activity and GP visits), perceived barriers to behavioral change (financial constraints), and cues to action (awareness of the federal health check-up program). The latent class analysis further identifies three distinct groups of the population with different CVD risk levels. Over one-third of respondents belong to the “high CVD risk” class characterized by the highest share of smokers and alcohol abusers who evade contact with primary care and face financial constraints. In the mediation analysis, we find that employment mediates the relationship between physical activity and CVD risks: physically active individuals have a greater chance of employment, and employment further mitigates CVD risks. We also find an indication of the selection of the healthy into employment in the causal relationship between GP visits, having a job, and CVD risks.
Conclusion
A corresponding set of policy actions stem from these findings. These include reinforcing the change of perceptions of CVD risks and lowering barriers to health care; raising awareness of the free preventive check-up program in the “high CVD risk” group; making sports and exercise accessible to the elderly; and using off-putting labels on alcohol products as behavioral nudges among “physically active but drinking” males.
... These two matrices are then used to solve a simultaneous equation for x ∼ MVN(0, ), i.e., a hypothetical draw from covariance among traits (Kaplan, 2001): ...
Traits underlie organismal responses to their environment and are essential to predict community responses to environmental conditions under global change. Species differ in life‐history traits, morphometrics, diet type, reproductive characteristics and habitat utilization.
Trait associations are widely analysed using phylogenetic comparative methods (PCM) to account for correlations among related species. Similarly, traits are measured for some but not all species, and missing continuous traits (e.g. growth rate) can be imputed using ‘phylogenetic trait imputation’ (PTI), based on evolutionary relatedness and trait covariance. However, PTI has not been available for categorical traits, and estimating covariance among traits without ecological constraints risks inferring implausible evolutionary mechanisms.
Here, we extend previous PCM and PTI methods by (1) specifying covariance among traits as a structural equation model (SEM), and (2) incorporating associations among both continuous and categorical traits. Fitting a SEM replaces the covariance among traits with a set of linear path coefficients specifying potential evolutionary mechanisms. Estimated parameters then represent regression slopes (i.e. the average change in trait Y given an exogenous change in trait X) that can be used to calculate both direct effects (X impacts Y) and indirect effects (X impacts Z and Z impacts Y).
We demonstrate phylogenetic structural‐equation mixed‐trait imputation using 33 variables representing life history, reproductive, morphological, and behavioural traits for all >32,000 described fishes worldwide. SEM coefficients suggest that one degree Celsius increase in habitat is associated with an average 3.5% increase in natural mortality (including a 1.4% indirect impact that acts via temperature effects on the growth coefficient), and an average 3.0% decrease in fecundity (via indirect impacts on maximum age and length). Cross‐validation indicates that the model explains 54%–89% of variance for withheld measurements of continuous traits and has an area under the receiver‐operator‐characteristics curve of 0.86–0.99 for categorical traits.
We use imputed traits to classify all fishes into life‐history types, and confirm a phylogenetic signal in three dominant life‐history strategies in fishes. PTI using phylogenetic SEMs ensures that estimated parameters are interpretable as regression slopes, such that the inferred evolutionary relationships can be compared with long‐term evolutionary and rearing experiments.
... Therefore, the model includes three main elements: observed (exogenous) variables, latent variables, and potential relations between them. These elements are combined within: (1) The structural part of the SEM model, which links latent variables to each other via systems of simultaneous equations; and (2) The measurement part, which links latent variables to explanatory variables [83]. ...
The aim of this study was to investigate the relationship between the socio-demographic background, patterns of recreational activity, and their impact on mood regulation strategies used by urban green spaces (UGS) visitors in Poland. In our research approach, we collected data from 376 participants through an online survey. In the next step, we developed structural equation models: one general model and two additional models for men and women. We discovered that both socio-demographic characteristics, as well as the variety of visited green spaces impact people's mood regulation strategies. In our research approach, latent variable places that consists of different types of green spaces is the key concept that positively affect mood regulation strategies; visiting more places reduces the tendency to decrease mood and increases the tendency to increase mood. Moreover, we identified some important gender similarities and differences. There is causation between the types of leisure activity and the frequency of a leisure activity among men and women. However, in the case of women, the frequency of a leisure activity is positively associated with the tendency to increase mood; in the case of men, the association is negative. The research results provide a deeper insight into of the patterns of green leisure that shape the subjective well-being of urban green space visitors in Poland.
... "SEM is, without question, one of the most popular methodologies in the quantitative social sciences. Its popularity can be attributed to the sophistication of the underlying statistical theory, the potential for addressing important substantive questions, and the availability and simplicity of software dedicated to structural equation modeling" [61]. All variables listed in Table 2 show the questions and abbreviation of each variable and the definition for each. ...
Background
Most cycling behaviour studies have defined it using objective variables and focused on normal conditions.
Objective
This study applies latent class analysis to a sample of 375 survey respondents in Tehran, the Capital city of Iran, exploring the variables influencing cycling behaviour during pandemic covid-19.
Methods
We made a statistical comparison among the data obtained from the questionnaires and the statistical data of the 2016 census. A structural equation modeling (SEM) was developed.
Results
Fourteen indicators define three latent variables. Cycling behaviour is defined by these three latent factors and three indicators. This paper goes through each of the indicators and their impact on latent variables. The findings show that latent factors have a direct impact on cycling behaviour.
Conclusion
Structural equation modeling (SEM) is a great tool for defining cyclist behaviour analysis that shows the positive and negative influence of variables on cycling rate during a covid-19 pandemic. There are some limitations in the area of this study in developing countries discussed in the paper.
... Applying notation similar to the ones used by [32], we can specify the model parameter posterior distribution as p(Ω|y) where: ...
This study sought to establish the performance of Spatially Varying Coefficient (SVC) Bayesian Hierarchical models using Landsat-8, and Sentinel-2 derived auxiliary information in predicting plantation forest carbon (C) stock in the eastern highlands of Zimbabwe. The development and implementation of Zimbabwe’s land reform program undertaken in the year 2000 and the subsequent redistribution and resizing of large-scale land holdings are hypothesized to have created heterogeneity in aboveground forest biomass in plantation ecosystems. The Bayesian hierarchical framework, accommodating residual spatial dependence and non-stationarity of model predictors, was evaluated. Firstly, SVC models utilizing Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI), derived from Landsat-8 and Sentinel-2 data and 191 sampled C stock observations were constructed. The SVC models built for each of the two multispectral remote sensing data sets were assessed based on the goodness of fit criterion as well as the predictive performance using a 10-fold cross-validation technique. The introduction of spatial random effects in the form of Landsat-8 and Sentinel-2 derived covariates to the model intercept improved the model fit and predictive performance where residual spatial dependence was dominant. For the Landsat-8 C stock predictive model, the RMSPE for the non-spatial, Spatially Varying Intercept (SVI) and SVC models were 8 MgCha−1, 7.77 MgCha−1, and 6.42 MgCha−1 whilst it was 7.85 MgCha−1, 7.69 MgCha−1 and 6.23 MgCha−1 for the Sentinel-2 C stock predictive models, respectively. Overall, the Sentinel-2-based SVC model was preferred for predicting C stock in plantation forest ecosystems as its model provided marginally tighter credible intervals, [1.17–1.60] MgCha−1 when compared to the Landsat-8 based SVC model with 95% credible intervals of [1.13–1.62] Mg Cha−1. The built SVC models provided an understanding of the performance of the multispectral remote sensing derived predictors for modeling C stock and thus provided an essential foundation required for updating the current carbon forest plantation databases.
... Typical modeling efforts using the polynomial equations have focused on determining optimal parameter values (i.e., coefficients of pre-chosen terms) through data fit. However, this approach cannot ensure robust development of microbial inactivation models because inadequate representation of equations can lead to poor performance in data fit and prediction due to intrinsic structural error that cannot be compensated through parameter estimation (Kaplan, 2002). Moreover, empirical determination of governing terms often lacks expandability with increasing number of process variables, necessitating a more systematic, rational approach. ...
Prevention of the growth of harmful microorganisms in food products is an important requirement for ensuring food safety and quality. Mathematical models to predict the quantitative changes in microbial populations in food to the variations of environmental conditions are useful tools in this regard. While equations for microbial inactivation have typically been formulated based on polynomial functions, empirical choice of the model order and terms not only results in over- or underfitting, but also makes it difficult to identify key factors governing the target variable. To address this issue, we present a data-driven modeling pipeline that enables 1) automatic discovery of model equations through parsimonious selection of relevant terms from a pre-built library and 2) subsequent evaluation of the impacts of individual terms on the model output. Through case studies using literature data, we evaluated the effectiveness of our pipeline in predicting the D -value (i.e., the time taken to reduce microbial population to 10% of the initial level) as a function of multiple factors including temperature, pH, water activity, NaCl content, and phosphate level. In doing this, we determined basic functional forms of input and output variables based on their pre-known relationships, e.g., by accounting for the Arrhenius dependence of D -value on temperature. Incorporation of such theoretical knowledge into the pipeline improved model accuracy. Using the Akaike information criterion, we optimally determined hyperparameters that control a trade-off between model accuracy and sparsity. We found the literature models benchmarked in this study to be over- or under-determined and consequently proposed better structured and more accurate equations. The subsequent global sensitivity analysis allowed us to evaluate the context-dependent impacts of key factors on the D -value. The pipeline presented in this work is readily applicable to many other related non-linear systems without being limited to microbial inactivation datasets.
... "SEM is, without question, one of the most popular methodologies in the quantitative social sciences. Its popularity can be attributed to the sophistication of the underlying statistical theory, the potential for addressing important substantive questions, and the availability and simplicity of software dedicated to structural equation modeling" [61]. All variables listed in Table 2 show the questions and abbreviation of each variable and the definition for each. ...
Background
Most cycling behaviour studies have defined it using objective variables and focused on normal conditions.
Objective
This study applies latent class analysis to a sample of 375 survey respondents in Tehran, the Capital city of Iran, exploring the variables influencing cycling behaviour during pandemic covid-19.
Methods
We made a statistical comparison among the data obtained from the questionnaires and the statistical data of the 2016 census. A structural equation modeling (SEM) was developed.
Results
Fourteen indicators define three latent variables. Cycling behaviour is defined by these three latent factors and three indicators. This paper goes through each of the indicators and their impact on latent variables. The findings show that latent factors have a direct impact on cycling behaviour.
Conclusion
Structural equation modeling (SEM) is a great tool for defining cyclist behaviour analysis that shows the positive and negative influence of variables on cycling rate during a covid-19 pandemic. There are some limitations in the area of this study in developing countries discussed in the paper.
... This paper followed the SEM approach. SEM is, without a doubt, one of the most popular methodologies in quantitative research (Kaplan, 2001). It allows the measurement error to be calculated and addresses the error of predicting relationships. ...
Accounting information system (AIS) is becoming increasingly paramount for SMEs in order to boost decision-making effectiveness (DME). The issue is no longer whether SMEs use AIS or not, but rather about the extent of benefits and effectiveness that can be achieved from such use. This paper aims to assess how AIS success impacts DME among SMEs in less developed countries (LDCs). Specifically, this paper develops a study model and validates it with empirical evidence gathered from the Yemeni business environment. This paper is based on DeLone and McLean (D&M) model to establish the relationship between AIS success and DME among SMEs in Yemen, a less developed country. A questionnaire is built and sent to owners and managers of 323 Yemeni SMEs from different sectors. PLS-SEM software was employed to analyze and test the gathered data. The findings exhibit important positive links between the success of AIS and DME. In particular, the outcomes appear that AIS information quality, use, and user satisfaction positively influence decision-making effectiveness; system and information quality are significant precedents of AIS usage and satisfaction; AIS usage is positively impacted by satisfaction. However, service quality is showed insignificant towards AIS usage and user satisfaction.
Theoretically, this study reduces research gaps related to AIS success in SMEs from a management perspective (i.e., DME) and LDCs context. Practically, the study model can serve as a device for SMEs to assess and anticipate the success of implemented AIS applications. Moreover, the study results can help SMEs' managers and owners to gain a clear insight into the positive role of AIS success in boosting decision-making effectiveness in their businesses. On the other hand, the study results may motivate those who depend on conventional manual accounting methods to make the transition to AIS. Also, this study offers practical guidelines for the government on AIS success importance among SMEs and its role in economic development. This research is deemed one of the first to introduce empirical evidence on the influence/relationship of AIS success on/and DME among SMEs in LDCs, Yemeni case.
... We analysed the quantitative data according to PLS-SEM. SEM refers to a diverse set of mathematical models, computer algorithms, and statistical methods that fit networks of variables to data (Kaplan, 2007). In recent years SEM use has grown enormously. ...
The United Arab Emirates (UAE) is encountering a scarcity of water resources. It is counting on innovation management to alleviate the situation. In that context, there is a need for a managerial framework for this subject. Therefore, the aim of the current study is to build up an innovative managerial model. To establish this model, we applied a convergent, parallel, mixed-methods design. The study participants (n = 42) consisted mostly of leaders and experts working for the main water institutions. We analysed the quantitative method via partial least squares structural equation modelling (PLS-SEM), a SmartPLS software. Qualitative method procedures were conducted starting from coding, categorising, obtaining themes, and lastly, the establishment of grounded theory. We obtained two rigid inputs (quantitative and qualitative models) for the last phase (mixed-methods analysis). The quantitative findings revealed a significant and robust relationship (t value = 26.6, p = 0.000, coefficient = 0.888, R2 = 0.788). The qualitative findings also produced a steady grounded theory. Both quantitative and qualitative models were crossed according to the ‘convergence coding matrix’ and ‘triangulation analysis protocol’. Ultimately, we built a holistic framework named ‘the UAE water innovation model’, consisting of 12 components (meta-themes). This model should be adopted as the main guide for innovation management and strategy in water public sector institutions. Globally, this model could be a significant contribution, and it would be applicable to any country in the world with the same arid environment as the UAE.
... Para establecer los índices de ajustes del modelo se utilizan los valores de TLI (Tucker-Lewis Index), también conocido como NNFI (Non-Normed Fit Index) y de CFI (Comparative Fit Index), que toman valores entre 0 y 1, indicando un buen ajuste aquellos valores cercanos a 1 (Kenny, 2012). Por otra parte, el valor de RMSEA (Root Mean Square Error of Approximation) mide la diferencia absoluta entre la estructura de relaciones, entre el modelo teórico propuesto y los datos observados, teniendo en cuenta el número de estimadores y el tamaño muestral (Steiger, 1990); tomando valores entre 0 y 1, indicando un buen ajuste aquellos valores cercanos a 0. ...
El presente trabajo se aborda teniendo en cuenta los estudios que demuestran la vinculación que realizan los docentes en formación con la función lúdica o motivacional concedida a las TIC y a los recursos digitales, así como a partir de las investigaciones que exponen la asociación existente entre el enfoque de enseñanza centrado en el docente (ITTF) con un aprendizaje superficial y el enfoque centrado en el alumnado (CCSF) con un aprendizaje profundo. Sobre este enunciado, se plantea como objetivo general analizar los enfoques de enseñanza del futuro profesorado de historia en España y su relación con sus opiniones sobre el uso de recursos digitales en un aula. Para ello, se utilizó un diseño cuantitativo no experimental, con una escala Likert, en el que participaron 646 estudiantes del Máster de Formación del Profesorado de la especialidad de Geografía e Historia de 22 universidades nacionales diferentes, es decir, el 70% de los centros de Educación Superior que ofertan dicha titulación en España. Como conclusión general, el análisis estadístico realizado con los datos correspondientes a los enfoques del futuro profesorado de historia en España y su relación con sus opiniones sobre el uso de recursos digitales en un aula permite inferir que la validación y la fiabilidad de la herramienta aplicada es muy positiva; que existe una débil vinculación de los recursos digitales con los procesos educativos y; por último, que se destaca una relación significativa entre la aplicación de un enfoque de enseñanza concreto y una visión particular acerca del uso de los recursos tecnológicos.
... Continuous between-person variables (age, personality variables, and psychological well-being) were grand-mean centered.The convergence of the models and the quality of the posterior distributions were evaluated using the potential scale reduction factor (PSRF; Gelman & Rubin, 1992) as well as Bayesian posterior parameter trace plots and autocorrelation plots. The PSRF of all models was 1.000, suggesting convergence (Kaplan & Depaoli, 2012). In addition, the Bayesian plots provided evidence of acceptable posterior distributions. ...
Individuals high in neuroticism tend to experience greater negative affect when confronted with stressors. In the present study, four other personality traits (i.e., openness, conscientiousness, agreeableness, and extraversion) were included to examine their unique contribution to affective reactivity to stress. In addition, three domains of psychological well-being (positive relations with others, environmental mastery, and autonomy) were included to examine whether they mediate the associations between the traits and affective reactivity. Data from a daily diary study were used, collected over 8 days (N = 782). The results of Bayesian multilevel modeling showed that, of the Big Five traits, only neuroticism moderated the relationship between stressful events and experienced negative affect. In other words, among the traits, neuroticism was the only robust predictor of affective reactivity. However, when the three well-being variables were added, neuroticism was no longer a significant predictor. Environmental mastery weakened the association between stressors and negative affect, whereas autonomy reinforced this association. The results of a Bayesian multilevel moderation analysis confirmed that mastery and autonomy fully mediated the relationship between neuroticism and stressor-induced negative affect. An important implication of the study is that the negative influence of neuroticism on affective reactivity can be reduced by developing mastery and competence skills.
... We used structural equation modeling (SEM) within Mplus 8.7 to test multiple associations simultaneously while accounting for measurement error (Kaplan, 2001). We estimated confirmatory factor analyses (CFAs) to assess latent depressive symptoms, perceived burdensomeness, thwarted belongingness, and suicidal ideation variables. ...
This study examined associations between structural racism, anti-LGBTQ policies, and suicide risk among young sexual minority men (SMM). Participants were a 2017-2018 Internet-based U.S. national sample of 497 Black and 1536 White SMM (ages 16-25). Structural equation modeling tested associations from indicators of structural racism, anti-LGBTQ policies, and their interaction to suicide risk factors. For Black participants, structural racism and anti-LGBTQ policies were significantly positively associated with depressive symptoms, heavy drinking, perceived burdensomeness, thwarted belongingness, self-harm, and suicide attempt. There were significant interaction effects: Positive associations between structural racism and several outcomes were stronger for Black participants in high anti-LGBTQ policy states. Structural racism, anti-LGBTQ policies, and their interaction were not significantly associated with suicide risk for White SMM.
Objective
This study explores how we can improve the government’s research and technology for disasters and safety.
Methods
This study employs the Structural Equation Model (SEM) based on 268 experts’ perspectives.
Results
R&D performance exerts a directly significant impact on R&D achievement with the coefficient of 0.429. Second, while professionality and environment of R&D do not show a direct effect on achievement, they exhibit an indirect effect on it with the coefficient of 1.124 and 0.354, respectively. Third, R&D professionality exerts a significant impact on the R&D environment (0.964), and R&D environment has a positive effect on R&D performance (0.827).
Conclusion
Governments and policymakers should develop disaster and safety policies by understanding direct and indirect effects and the relationship of factors related to R&D for improving R&D achievement.
Mental health is a subject that has been extensively researched by mental health experts. It is essential for individuals to give priority to their mental well-being in order to live productive lives. Individuals may face unforeseeable circumstances, a range of emotions, such as feeling trapped, and situations that demand emotional self-regulation at any stage of their lives. An individual's mental health is contingent upon their capacity to uphold a heightened state of well-being despite encountering challenging circumstances. The current study aims to look into the role of difficulties in emotion regulation and entrapment in the relationship between intolerance of uncertainty and mental well-being. The relationships between these variables have never been investigated, and this study is the first to address them. The current study's data were collected from 427 volunteer participants, including 316 women and 111 men. The mediation analysis was conducted using Structural Equation Modeling (SEM). According to the findings, difficulties in emotion regulation and entrapment served as serial mediators between intolerance of uncertainty and mental well-being. According to the model, uncertainty intolerance predicts difficulties in emotion regulation and entrapment, while difficulties in emotion regulation and entrapment predict negative mental well-being. It could be argued that being tolerant in the face of uncertainty enables people to easily regulate their emotions and avoid feeling trapped, potentially leading to higher levels of mental well-being.
Background
Motivation is the inherent belief to guide students learning goals and behaviors to make continuous efforts and strengthen learning outcomes. Previous research reported the positive impacts of learning motivation on student success, but there have been limited efforts in systematically and structurally studying different types of motivations and their impacts on students’ success in engineering education. The current study contributes to the literature by systematically examining two important types of motivations and their influences on undergraduate engineering students in a theoretically grounded manner while using an advanced analytical approach.
Methods
The current study conducted a cross-sectional survey with undergraduate engineering students (n = 514) from 18 different schools across nine U.S. states. The survey assessed students’ self-report scores on six types of motivations to study developed based on formative research and the current literature and then collected students’ self-reported learning outcomes, current GPA, university satisfaction, engineering program satisfaction, and individual demographic factors. The data were then analyzed using structural equation modeling.
Results
The results showed that motivations related to family, personality, and academic expectations were consistently positively associated with all measured students’ success outcomes; motivations related to educators were associated with all four outcomes but student GPA; motivations related to course contents were associated with learning outcomes and student GPA; and motivations related to peers did not predict any of the four measured students’ success outcomes.
Discussion
We explain some of the unexpected results with further literature that examines engineering culture and ecology. We also make recommendations related to cognitive training, tailored engineering education, peer culture interventions, and family orientation programs.
The purpose of this study is to clarify the relationships among the business philosophy, business strategy and business outcome of rice flour-related corporations in Japan. Specifically, structural equation modeling (SEM) and cognitive map analysis are introduced to the results of a questionnaire survey. The following results are obtained from the empirical analysis. First, the management philosophy (Effective Altruism, and membership in the Rice Flour Association) of rice flour-related corporations influences their business strategies (potential head market, tail market, organizational learning and proposals from stakeholders) which induce innovation and determine business performance (current performance and future prospects for shared value creation). Secondly, the business performance reflects their expectations for the rice flour market, and influences the direction of market development. Therefore, a policy innovation that strengthens effective altruism and the creation of shared value through organizational learning of stakeholders in rice flour-related businesses is called for.
Colombia ha atendido los llamados internacionales en contra de las violencias como se estipula en los Objetivos de Desarrollo Sostenibles (ODS), en especial en metas tales como: 5.2, donde se propone la eliminación de todas las formas de violencia contra mujeres y niñas; la meta 16.1, la cual se dirige a la reducción de todas las formas de violencia y las tasas de mortalidad; y la meta 16.2, con la cual se plantea poner fin al maltrato, la explotación, la trata y todas las formas de violencia contra los niños y las niñas. Así, desde los ODS, se hace hincapié en la eliminación de las disparidades de género, la promoción de una cultura de paz y no violencia y la generación de entornos no violentos, inclusivos y eficaces para todos. ISBN: 978-628-7662-19-3
Background
As new migraine therapies emerge, it is crucial for measures to capture the complexities of health-related quality of life (HRQoL) improvement beyond improvements in monthly migraine day (MMD) reduction. Investigations into the correlations between MMD reduction, symptom management, and HRQoL are lacking, particularly those that focus on improvements in canonical symptoms and improvement in patient-identified most-bothersome symptoms (PI-MBS), in patients treated with eptinezumab. This exploratory analysis identified efficacy measures mediating the effect of eptinezumab on HRQoL improvements in patients with migraine.
Methods
Data from the DELIVER study of patients with 2–4 prior preventive migraine treatment failures (NCT04418765) were inputted to two structural equation models describing sources of HRQoL improvement via Migraine-Specific Quality-of-Life Questionnaire (MSQ) scores. A single latent variable was defined to represent HRQoL and describe the sources of HRQoL in DELIVER. One model included all migraine symptoms while the second model included the PI-MBS as the only migraine symptom. Mediating variables capturing different aspects of efficacy included MMDs, other canonical symptoms, and PI-MBS.
Results
In the first model, reductions in MMDs and other canonical symptoms accounted for 35% (standardized effect size [SES] − 0.11) and 25% (SES − 0.08) of HRQoL improvement, respectively, with 41% (SES − 0.13) of improvement comprising “direct treatment effect,” i.e., unexplained by mediators. In the second model, substantial HRQoL improvement with eptinezumab (86%; SES − 0.26) is due to MMD reduction (17%; SES − 0.05) and change in PI-MBS (69%; SES − 0.21).
Conclusions
Improvements in HRQoL experienced by patients treated with eptinezumab can be substantially explained by its effect on migraine frequency and PI-MBS. Therefore, in addition to MMD reduction, healthcare providers should discuss PI-MBS improvements, since this may impact HRQoL. Health technology policymakers should consider implications of these findings in economic evaluation, as they point to alternative measurement of quality-adjusted life years to capture fully treatment benefits in cost-utility analyses.
Trial registration
ClinicalTrials.gov (Identifier: NCT04418765; EudraCT (Identifier: 2019–004497-25; URL: https://www.clinicaltrialsregister.eu/ctr-search/search?query=2019-004497-25).
Graphical Abstract
This paper investigates the problem of learning a graphical model from incomplete spatio-temporal measurements. Our purpose is to analyze a time-varying graph signal represented by an incomplete data matrix, the rows and columns of which correspond to spatial and temporal features/measurements of the signal, respectively. In contrast to the conventional approaches which utilize either a directed or an undirected graphical model for data analysis, we propose a compound multi-relational model exploiting both directed and undirected structures. Our approach is based on statistical inference in which a spatio-temporal signal is considered as a random graph process to which we can apply maximum-a-posteriori estimation methods for model identification. We incorporate the Gaussian-Markov random field (GMRF) and the vector auto-regressive (VAR) models to capture both the (undirected) spatial correlations and the (directed) temporal dependencies. We propose an algorithm for joint estimation of the signal and the graphical models, from incomplete measurements. For this purpose, we formulate an optimization problem that we solve using the block successive upperbound minimization (BSUM) method. Our simulation results confirm the efficiency of the proposed method for signal recovery and graph learning.
Welcome to this comprehensive reference book, the result of extensive research into the fascinating
world of celebrity endorsements and their impact on consumer behavior. In an era where marketing
dynamics are constantly evolving, and consumer choices are increasingly influenced by various
factors, understanding the profound effects of celebrity endorsements has never been more crucial.
This reference book is the product of a rigorous research endeavor, which sought to advance both
theoretical knowledge and practical utility. It aims to bridge the gap between academic research and
real-world marketing decisions. The aspiration behind this work is to offer a comprehensive resource
that not only deepens our theoretical understanding but also provides a valuable tool for marketing
professionals to make more informed decisions.
Within these pages, you will find a wealth of insights, analyses, and practical guidance. We delve
into the intricate dimensions of celebrity endorsements, specifically focusing on their effects on
consumer ad perception, brand attitude, and purchase intention. Our journey begins with the creation
and validation of a novel celebrity endorsement scale, integrating previous research findings with the
fresh insights garnered from an exploratory study conducted within the scope of this research
project.
The development and validation of this scale is a significant milestone, as it offers a practical
framework for marketing managers to make informed choices when selecting celebrity endorsers.
This tool is comprised of five dimensions - attractiveness, trustworthiness, expertise, popularity, and
relevance - and provides marketing professionals with an invaluable resource to guide their decision�making process.
In addition to this, our work offers a roadmap for marketing managers, emphasizing which specific
dimensions they should prioritize when choosing celebrity endorsers. Our findings underscore the
preeminence of a celebrity's popularity, followed closely by attractiveness, trustworthiness,
relevance, and expertise. This knowledge is pivotal for decision-makers as they navigate the
complex landscape of celebrity endorsements.
Moreover, the newly validated five-dimensional celebrity endorsement scale empowers marketing
professionals to evaluate the effectiveness of various celebrity endorsements across these
dimensions. It aids in examining the impact of attractiveness, trustworthiness, expertise, popularity,
and relevance, providing clarity on which factors contribute most significantly to a particular
endorsement's success.
Our research also demonstrates the influential role of celebrity endorsements as a marketing tool.
When executed skillfully, these endorsements are shown to have a positive impact on consumer ad
perception, brand attitude, and purchase intention. This knowledge underscores the strategic value of
celebrity endorsements when integrated into a marketing strategy.
Popularity and attractiveness emerge as critical factors in this regard, offering marketers clear
guidance in their selection of celebrity endorsers. At the same time, the study highlights that
expertise, although valuable in building brand attitudes and ad perceptions, may not necessarily
translate into increased purchase intentions.
This reference book serves as a valuable resource for scholars, students, and marketing professionals
who aspire to deepen their comprehension of the impact of celebrity endorsements on consumer
behavior. Whether you are a dedicated academic researcher, an aspiring marketing professional, or a
seasoned manager, the content within these pages is intended to enrich your knowledge and inform
your strategic decisions.
As we explore the compelling world of celebrity endorsements, we invite you to immerse yourself in
the insights, tools, and practical guidance presented in this reference book. Our goal is not only to
inform but also to inspire further exploration of the dynamic marketing landscape and the pivotal
role of celebrity endorsements.
We express our sincere appreciation to all those who contributed to this research and this reference
book, and we eagerly anticipate the continued growth of knowledge and practice in the field of
marketing.
Welcome to the world of celebrity endorsements and their profound influence on consumer behavior
with hope you keep enjoying the outcome of the book as WOW Talk.
Ecological analyses typically involve many interacting variables. Ecologists often specify lagged interactions in community dynamics (i.e. vector‐autoregressive models) or simultaneous interactions (e.g. structural equation models), but there is less familiarity with dynamic structural equation models (DSEM) that can include any simultaneous or lagged effect in multivariate time‐series analysis.
We propose a novel approach to parameter estimation for DSEM, which involves constructing a Gaussian Markov random field (GMRF) representing simultaneous and lagged path coefficients, and then fitting this as a generalized linear mixed model to missing and/or non‐normal data. We provide a new R‐package dsem , which extends the ‘arrow interface’ from path analysis to represent user‐specified lags when constructing the GMRF. We also outline how the resulting nonseparable precision matrix can generalize existing separable models, for example, for time‐series and species interactions in a vector‐autoregressive model.
We first demonstrate dsem by simulating a two‐species vector‐autoregressive model based on wolf–moose interactions on Isle Royale. We show that DSEM has improved precision when data are missing relative to a conventional dynamic linear model. We then demonstrate DSEM via two contrasting case studies. The first identifies a trophic cascade where decreased sunflower starfish has increased urchin and decreased kelp densities, while sea otters have a simultaneous positive effect on kelp in the California Current from 1999 to 2018. The second estimates how declining sea ice has decreased cold‐water habitats, driving a decreased density for fall copepod predation and inhibiting early‐life survival for Alaska pollock from 1963 to 2023.
We conclude that DSEM can be fitted efficiently as a GLMM involving missing data, while allowing users to specify both simultaneous and lagged effects in a time‐series structural model. DSEM then allows conceptual models (developed with stakeholder input or from ecological expertise) to be fitted to incomplete time series and provides a simple interface for granular control over the number of estimated time‐series parameters. Finally, computational methods are sufficiently simple that DSEM can be embedded as component within larger (e.g. integrated population) models. We therefore recommend greater exploration and performance testing for DSEM relative to familiar time‐series forecasting methods.
The importance of Stakeholder Theory as an integral part of business analysis becomes evident as all Stakeholders will influence the business environment at some point and therefore its constant monitoring becomes crucial. Despite the importance of stakeholder analysis, it remains a major gap in organizations. To find solutions to this problem, the main objective of this investigation is related to the study of the impact of Intelligent Systems on Stakeholder Theory. To obtain answers to the research questions, a quantitative method was used, with an analysis of 168 online questionnaires. The results obtained allow us to demonstrate that the use of Intelligent Systems related to the Stakeholder Theory in organizations becomes relevant and that the perception and knowledge of individuals, influenced or not by the benefits and challenges that the implementation of Artificial Intelligence can entail, becomes crucial in the decision to implement these systems in their companies.
Phylogenetic comparative methods (PCMs) can be used to study evolutionary relationships and trade-offs among species traits. Analysts using PCM may want to (1) include latent variables, (2) estimate complex trait interdependencies, (3) predict missing trait values, (4) condition predicted traits upon phylogenetic correlations and (5) estimate relationships as slope parameters that can be compared with alternative regression methods. The Comprehensive R Archive Network (CRAN) includes well-documented software for phylogenetic linear models (phylolm), phylogenetic path analysis (phylopath), phylogenetic trait imputation (Rphylopars) and structural equation models (sem), but none of these can simultaneously accomplish all five analytical goals. We therefore introduce a new package phylosem for phylogenetic structural equation models (PSEM) and summarize features and interface. We also describe new analytical options, where users can specify any combination of Ornstein-Uhlenbeck, Pagel's-δ and Pagel's-λ transformations for species covariance. For the first time, we show that PSEM exactly reproduces estimates (and standard errors) for simplified cases that are feasible in sem, phylopath, phylolm and Rphylopars and demonstrate the approach by replicating a well-known case study involving trade-offs in plant energy budgets.
Abstract
We develop a new R-package phylosem that provides a simple interface for phylogenetic structural equation models. We identify and visualize five desirable features (coloured ellipses and labelled using matching coloured boxes), and note how four existing R-packages (grey boxes) each address different combinations of these five features. In this paper, we then outline how phylosem incorporates all five features.
Shared spaces and in particular pedestrians-cyclists shared infrastructures are commonly implemented in many modern cities. The co-existence of different categories of users creates several challenges that need to be addressed, while it is essential to identify which factors affect the quality of service (QOS) that both pedestrians and cyclists perceive when using such infrastructures. For this purpose, a theoretical hypothesis was formed and it was investigated through structural equation modeling (SEM), based on data from 5 shared infrastructures in the city of Thessaloniki, Greece. The results show that the qualitative attributes that are closely linked with the infrastructure have a great impact both on pedestrians’ and cyclists’ perceived QOS. More specifically, the pavement quality is essential for both road user categories, while cyclists consider also urban equipment, lighting and traffic signals to be very important. Moreover, users’ demographic characteristics and cyclists’ previous experience from shared infrastructures significantly affects their perceptions. Finally, cyclists’ behavior in shared infrastructures has an impact on the QOS that both pedestrians and cyclists perceive. The findings of this paper pave the way for a more holistic assessment of pedestrians-cyclists shared infrastructures and provide guidance in researchers and practitioners for designing and managing infrastructures that meet users’ needs.
In this chapter, we construct a hypothetical model of the mechanism of creating shared value (CSV) in urban agriculture based on the review of previous studies in Chap. 2 and the theoretical examination. A mixed methods approach to combine a quantitative analysis of structural equation modeling (SEM) and a qualitative analysis of trajectory equifinality modeling (TEM) is designed for hypothesis verification in this research. The advantage of the mixed methods approach is expected to elucidate the complex problems that cannot be clarified by either quantitative or qualitative methods alone. That is, the multi-functionality of urban agriculture and the social institutions influence the cognition and behavior of stakeholders in urban agriculture, and innovation in collective cognition through organizational learning among stakeholders will enhance social entrepreneurship and improve the sustainability of the city. And these effects will be feedbacked to the multi-functionality of urban agriculture, the social institutions, and the cognition and behavior of stakeholders.
The study aims to find the impact of some life circumstances on psychological and pan-demic-related problems during the COVID-19 pandemic. Using the European student’s union survey of 2020, the research has negative emotions as the primary variable of interest. Other analyzed variables are pandemic-related behaviors and home infrastructure. A total of 1100 Ecuadorian university students let us conclude that those with moderate levels of emotional issues and high family income profiles suffered less during the lockdown. Negative emotions and home infrastructure sometimes depend on demographic factors like gender or family income. The multiple regression analysis shows that pandemic-related behaviors are positively correlated with negative feelings, which is the opposite of home infrastructure, which is negatively related to negative emotions—the CFA and SEM help to confirm the validity and reliability test of the questionnaire. The results let us understand the current university students’ situation and the public-related policies to enhance by filling the research gap and facing the scarce related literature in Ecuador
This study explores long-term stability of creative self-concept variables, which have gained attention in the past decade, but lacked specific longitudinal investigation and strong analytical decisions. We conducted two higher-order confirmatory factor analyses based on latent state-trait theory to investigate the underlying latent structure of two constructs—creative personal identity and creative self-efficacy—across 7 years. Our results demonstrated a satisfying model fit with high standardized latent state and trait loadings. Both constructs showed greater trait than state influences, and more than half of the state variance at each time point was explained by interindividual trait differences, leading to the conclusion that creative self-concept variables are relatively stable yet malleable trait-like constructs.
In the context of organizational change, employees can have different reactions, where some of them accept and engage with it, and others completely refuse and resist it. Hence, companies should permanently settle for the best and introduce a fruitful leadership style along with a good change management strategy to ensure the company’s prolonged survival and prosperity. This investigation aims to examine the influence of transformational leadership on employees’ affective commitment and intention to support change. Both were chosen to constitute the main dimensions of employees’ reactions toward organizational change and highlight the importance of organizational trust as a mediator in that correlation. To confirm the hypotheses subtracted from the literature review, a quantitative study was managed by a survey devoted to 428 employees working for diverse service companies in Hungary and going through different change cases.Structural equation modeling (SEM) was then applied to hold out the favorable findings, which reveal that transformational leadership is a booster for employees’ intention to support change but not for affective commitment. It was also found that organizational trust firmly mediates the relationship between transformational leadership and employees’ reaction to change. This statement justifies that both transformational leadership and organizational trust are able to reduce change complexity and lead to its acceptance, openness, and support.
Supply chain risk management is considered a topic of increasing interest worldwide and its focus has evolved over time. The recent coronavirus pandemic (known as COVID-19) has forced business to handle a new global crisis and rapidly adapt to unexpected challenges. In an attempt to help companies counteract the pandemic risk, as well as to fuel the scientific discussion about this topic, this paper proposes a systematic literature review on risk management and disruptions in the supply chain focusing on quantitative models and paying a particular attention to highlight the potentials of the studies reviewed for being applied to counteract pandemic emergencies. An appropriate query was made on Scopus and returned, after a manual screening, a useful set of 99 papers that proposed models for supply chain risk management. The relevant aspects of pandemics risk management have been first identified and mapped; then, the studies reviewed have been analysed with the aim of evaluate their suitability of being applied to sanitary crises. In carrying out this review of the literature, the study moves from previous, more general, reviews about risk management and updates them, starting from the lines of research that have been covered in recent years and evaluating their consistency with future research directions emerging also as a consequence of the pandemic crisis. Gaps and limitations of the existing models are identified and future research directions for pandemics risk management are suggested.
The financial performance of an organization is a key indicator of organizational success. It is also important that employees should be equipped with relevant technical and updated knowledge and soft skills so they can provide an improved level of service quality. Employee training is a systematic way to improve employee performance or in other words service quality of a company. In a service-profit-oriented organization, service quality has a large impact on the profitability of an organization. This study aims to find the impact of employee training on the financial performance of banks. A conceptual framework is proposed to observe the linkage of employee training and financial performance of banks, while customer satisfaction and perceived service quality are being tested as mediating variables. The valid sample size was 287 and a random sampling technique is used. In order to test the model, covariance based structural equation modelling (SEM) has been used. Results revealed that all hypotheses have been accepted i.e. customer satisfaction and perceived service quality partially mediate the relation between employee training and financial performance of the organization.
Anumber of goodness-of-fit indices for the evaluation of multivariate structural models are expressed as functions of the noncentrality parameter in order to elucidate their mathematical properties and, in particular, to explain previous numerical findings. Most of the indices considered are shown to vary systematically with sample size. It is suggested that H. Akaike's (1974; see record
1989-17660-001) information criterion cannot be used for model selection in real applications and that there are problems attending the definition of parsimonious fit indices. A normed function of the noncentrality parameter is recommended as an unbiased absolute goodness-of-fit index, and the Tucker–Lewis (see record
1973-30255-001) index and a new unbiased counterpart of the Bentler–Bonett (see record
1981-06898-001) index are recommended for those investigators who might wish to evaluate fit relative to a null model. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
P. M. Bentler and D. G. Bonett (1980) argue that it is often useful to compare a hypothesized covariance structure model or set of models to a nested null model using fit coefficients and they propose both generic null models for a variety of cases and 2 new measures of fit
extends the work of Bentler and Bonett in 2 ways / 1st, we provide general analytic conditions for ascertaining whether their generic null models are nested under a substantive model of interest, an issue they do not address clearly and completely / 2nd, we show that the null models they propose are inappropriate in all but the purely exploratory case / in other cases, we argue that the comparison should be developed in terms of baseline models that reflect the state of prior theory and knowledge, unlike the null models of Bentler and Bonett (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Factor analysis, path analysis, structural equation modeling, and related multivariate statistical methods are based on maximum likelihood or generalized least squares estimation developed for covariance structure models (CSMs). Large-sample theory provides a chi-square goodness-of-fit test for comparing a model (M) against a general alternative M based on correlated variables. It is suggested that this comparison is insufficient for M evaluation. A general null M based on modified independence among variables is proposed as an additional reference point for the statistical and scientific evaluation of CSMs. Use of the null M in the context of a procedure that sequentially evaluates the statistical necessity of various sets of parameters places statistical methods in covariance structure analysis into a more complete framework. The concepts of ideal Ms and pseudo chi-square tests are introduced, and their roles in hypothesis testing are developed. The importance of supplementing statistical evaluation with incremental fit indices associated with the comparison of hierarchical Ms is also emphasized. Normed and nonnormed fit indices are developed and illustrated. (43 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Aitken's generalized least squares (GLS) principle, with the inverse of the observed variance-covariance matrix as a weight matrix, is applied to estimate the factor analysis model in the exploratory (unrestricted) case. It is shown that the GLS estimates are seale free and asymptotically efficient. The estimates are computed by a rapidly converging Newton-Raphson procedure. A new technique is used to deal with Heywood cases effectively.
Normed and nonnormed fit indexes are frequently used as adjuncts to chi-square statistics for evaluating the fit of a structural model. A drawback of existing indexes is that they estimate no known population parameters. A new coefficient is proposed to summarize the relative reduction in the noncentrality parameters of two nested models. Two estimators of the coefficient yield new normed (CFI) and nonnormed (FI) fit indexes. CFI avoids the underestimation of fit often noted in small samples for Bentler and Bonett's (1980) normed fit index (NFI). FI is a linear function of Bentler and Bonett's non-normed fit index (NNFI) that avoids the extreme underestimation and overestimation often found in NNFI. Asymptotically, CFI, FI, NFI, and a new index developed by Bollen are equivalent measures of comparative fit, whereas NNFI measures relative fit by comparing noncentrality per degree of freedom. All of the indexes are generalized to permit use of Wald and Lagrange multiplier statistics. An example illustrates the behavior of these indexes under conditions of correct specification and misspecification. The new fit indexes perform very well at all sample sizes.
A structural equation model is proposed with a generalized measurement part, allowing for dichotomous and ordered categorical variables (indicators) in addition to continuous ones. A computationally feasible three-stage estimator is proposed for any combination of observed variable types. This approach provides large-sample chi-square tests of fit and standard errors of estimates for situations not previously covered. Two multiple-indicator modeling examples are given. One is a simultaneous analysis of two groups with a structural equation model underlying skewed Likert variables. The second is a longitudinal model with a structural model for multivariate probit regressions.
A general latent variable model is given which includes the specification of a missing data mechanism. This framework allows for an elucidating discussion of existing general multivariate theory bearing on maximum likelihood estimation with missing data. Here, missing completely at random is not a prerequisite for unbiased estimation in large samples, as when using the traditional listwise or pairwise present data approaches. The theory is connected with old and new results in the area of selection and factorial invariance. It is pointed out that in many applications, maximum likelihood estimation with missing data may be carried out by existing structural equation modeling software, such as LISREL and LISCOMP. Several sets of artifical data are generated within the general model framework. The proposed estimator is compared to the two traditional ones and found superior.
Methods for obtaining tests of fit of structural models for covariance matrices and estimator standard errors which are asymptotically distribution free are derived. Modifications to standard normal theory tests and standard errors which make them applicable to the wider class of elliptical distributions are provided. A random sampling experiment to investigate some of the proposed methods is described.