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Previous mental health studies have shown higher levels of anxiety, stress, and depression symptoms among university music students. In general, some similar findings have been observed for Malaysian music university students. In diagnosing the complications of mental health, there is consensus that it is essential to develop and evaluate a model oriented toward mental health illness prevention and treatment. To date, a suitable pattern for estimating mental health in terms of anxiety, stress, and depression among music university students is lacking. To fill this gap, we collected the necessary data from 691 music and 871 general students who were students for one year. The introduced pattern includes socioeconomic status, fear of COVID-19, nomophobia, sleep quality, loneliness, and mental health. Our data analysis proved that the levels of anxiety, depression, and stress of music students were lower than those of general students. Unlike some previous studies, in this study, the fear of COVID-19 and nomophobia didn’t have the most significant impact on mental health. The most significant impacts were related to sleep quality and loneliness. These findings have the potential to inform health promotion and services in the music education system.

Bayesian Structural Equation Modeling (SEM-Bayesian) was applied across different research areas to model the correlation between manifest and latent variables. The primary purpose of this study is to introduce a new framework of complexity to adolescent obesity modeling based on adolescent lifestyle through the application of SEM-Bayesian. The introduced model was designed based on the relationships among several factors: household socioeconomic status, healthy food intake, unhealthy food intake, lifestyle, body mass index (BMI) and body fat. One of the main contributions of this study is from considering both BMI and body fat as dependent variables. To demonstrate the reliability of the model, especially in terms of its fitting and accuracy, real-time data were extracted and analyzed across 881 adolescents from secondary schools in Tehran, Iran. The output of this study may be helpful for researchers who are interested in adolescent obesity modeling based on the lifestyle and household socioeconomic status of adolescents.

In the present work, the Taguchi experimental design technique, an effective and elegant tool, is employed to design and optimize a system for improving the quality of the products as well as discovering significant factors and optimize the parameters for electrodeposition of graphene oxide, in order to achieve low charge transfer resistance from the minimum number of experiments. Reduced graphene oxide (rGO) was prepared via electrochemical process. Electrochemical Impedance Spectroscopy was employed to evaluate the effects of variables on charge transfer resistance. Graphene oxide concentration, pH, scan rate and number of cycles were chosen as important effective parameters. Charge transfer resistance was minimized by measuring the signal-to-noise (S/N) ratio of the factors. Analysis of variance (ANOVA) was also employed to monitor the parameter influence and optimize the process.

The emergency transition from physical to online learning during COVID-19 has affected university students in various aspects, especially their academic performance. It can be caused by many factors, such as individual, environmental and social factors. Therefore, this study aims to determine the impact of fear, stress, well-being, teacher and parents’ support (independent variables) on undergraduates’ academic performance (dependent variable) during the COVID-19 pandemic. A structured online questionnaire has been developed and administered to 400 undergraduates. A structural equation model that integrated all variables under investigation was built and statistically validated using AMOS. The results demonstrated that well-being, teacher emotional support and teacher academic support have the highest significant impact on the respondents’ academic performance. It can be concluded that teachers’ support is the most substantial influence in ensuring student learning sustainability during the COVID-19 pandemic.

Background Since the last decade, postpartum depression (PPD) has been recognized as a significant public health problem, and several factors have been linked to PPD. Mothers at risk are rarely undetected and underdiagnosed. Our study aims to determine the factors leading to symptoms of depression using Structural Equation Modeling (SEM) analysis. In this research, we introduced a new framework for postpartum depression modeling for women. Methods We structured the model of this research to take into consideration the Malaysian culture in particular. A total of 387 postpartum women have completed the questionnaire. The symptoms of postpartum depression were examined using the Edinburgh Postnatal Depression Scale (EPDS), and they act as a dependent variable in this research model. Results Four hundred fifty mothers were invited to participate in this research. 86% of the total distributed questionnaire received feedback. The majority of 79.6% of respondents were having depression symptoms. The highest coefficients of factor loading analysis obtained in every latent variable indicator were income (β = 0.77), screen time (β = 0.83), chips (β = 0.85), and anxiety (β = 0.88). Lifestyle, unhealthy food, and BMI variables were directly affected by the dependent variable. Based on the output, respondents with a high level of depression symptoms tended to consume more unhealthy food and had a high level of body mass indexes (BMI). The highest significant impact on depression level among postpartum women was unhealthy food consumption. Based on our model, the findings indicated that 76% of the variances stemmed from a variety of factors: socio-demographics, lifestyle, healthy food, unhealthy food, and BMI. The strength of the exogenous and endogenous variables in this research framework is strong. Conclusion The prevalence of postpartum women with depression symptoms in this study is considerably high. It is, therefore, imperative that postpartum women seek medical help to prevent postpartum depressive symptoms from worsening.

As postpartum obesity is becoming a global public health challenge, there is a need to apply postpartum obesity modeling to determine the indicators of postpartum obesity using an appropriate statistical technique. This research comprised two phases, namely: i) development of a previously created postpartum obesity modeling, ii) construction of a statistical comparison model and introduction of a better estimator for the research framework. The research model displayed the associations and interactions between the variables that were analyzed using the Structural Equation Modeling (SEM) method to determine the body mass index (BMI) levels related to postpartum obesity. The most significant correlations obtained were between BMI and other substantial variables in the SEM analysis. The research framework included two categories of data related to postpartum women: living in urban and rural areas in Iran. The SEM output with the Bayesian estimator was 81.1%, with variations in the postpartum women&rsquo, s BMI, which is related to their demographics, lifestyle, food intake, and mental health. Meanwhile, the variation based on SEM with partial least squares estimator was equal to 70.2%, and SEM with a maximum likelihood estimator was equal to 76.8%. On the other hand, the output of the root mean square error (RMSE), mean absolute error (MSE) and mean absolute percentage error (MPE) for the Bayesian estimator is lower than the maximum likelihood and partial least square estimators. Thus, the predicted values of the SEM with Bayesian estimator are closer to the observed value compared to maximum likelihood and partial least square. In conclusion, the higher values of R-square and lower values of MPE, RMSE, and MSE will produce better goodness of fit for SEM with Bayesian estimators.

This study proposes a group of indicators in the analysis of port performance. This paper presents a new model of analysis to evaluate how the port cargo openness, the productivity level, the cargo expansion, and the technological change adaptability can affect directly on the marginal productivity growth rate and performance of a port. This new model, The Ports Efficiency Performance Model (PEP-Model) is intended to offer policy makers and researchers an additional analytical tool to study the impact of dynamic changes such as change in international trade trends and the impact of technology or competition on port performance from a new perspective.

The way of information that researchers use for data analysis especially in terms of statistical modeling is crucial. Some of studies could be improved of data analysis based on choosing proper information and statistical methodology. This research includes two scenarios. The first scenario is about two major mistakes in previous studies related to Structural Equation Modeling (SEM). I have used engineering data for this part of study. The Second scenario is about application of Taguchi method in educational sectors. Taguchi method is a common statistical analysis in experimental design for engineering studies and this research introduce it for educational sectors.

Quantifiable levels of 3-chloropropane-1,2-diol (3-MCPD) and 1,3-dichloro-2-propanol (1,3-DCP) were found in domestically manufactured soy-based sauces. Selected commercial foods in the Malaysian market (n = 43) were analyzed for their 3-MCPD and 1,3-DCP contents using a validated gas chromatography–mass spectrometry technique. The 3-MCPD and 1,3-DCP contents of the analyzed food samples varied from not detectable levels to 0.1223 ± 0.0419 mg kg−1 and not detectable levels to 0.025 ± 0.0041 mg kg−1, respectively. High concentrations of 3-MCPD, exceeding Malaysia's maximum tolerable limit of 0.02 mg kg−1, were found in chicken seasoning cubes (mean = 0.0898 ± 0.0378 mg kg−1). Monte Carlo simulation-based health risk assessment revealed that 3-MCPD and 1,3-DCP intakes in the 50th, 95th, and 99th percentiles were lower than 4 µg kg−1 bw day−1, the limit recommended by JECFA in 2016. Hence, it was concluded that the exposure of Malaysian citizens to chloropropanols through soy sauce consumption does not present a health risk.

BACKGROUND The development of mHealth (mobile health) has changed people’s routines of improving self-management. Nonetheless, the effectiveness of the apps depends on the frequency of use. OBJECTIVE This paper introduces a new framework of fitness apps associated with obesity modeling by using structural equation modeling (SEM) and examining data of postpartum women through moderation analysis. Fitness mHealth apps are considered the moderator in the research framework. METHODS Online questionnaires were sent to Malaysian postpartum women within one year after pregnancy and 468 completed questionnaires were returned. The frequency of mHealth fitness app use was categorized into four groups: daily, weekly, rarely and never. Therefore, four models were considered for the final analysis. RESULTS According to the output, every model has a different structure in terms of significance and coefficients. The R-squared obtained for the “daily” model (0.82) is the highest, followed by the “weekly” (0.79), “rarely” (0.66) and “never use” (0.59) models. This infers that 82% of mental health variation is related to the demographics, lifestyle, healthy and unhealthy food intake and BMI of daily fitness app users. In addition, the impact of BMI on mental health in every model is significant with a negative coefficient, which implies that users with high BMI have less mental health issues. Meanwhile, the factor loading analysis indicates that the screen time (0.89) and physical activity (0.81) indicators are the two highest coefficients in the lifestyle variable. This result is subsequently equivalent to the daily and weekly fitness app users who choose to consume more healthy food and thus have a lower BMI. CONCLUSIONS This paper proves that Malaysian postpartum women who use fitness apps as part of their daily life are more disciplined and such applications have significant effects on obesity and mental health. However, for never or weekly users, the statistical analysis shows there is no proof of the effectiveness of self-management activities that affected on obesity and mental health. Therefore, the frequency of fitness app use acts as a moderator in postpartum obesity and mental health modeling.

In obesity modelling studies, researchers have been seeking to identify the effective indicators of obesity by using appropriate statistical or mathematical techniques. The main objective of the present study is addressed in three stages. First, a new framework for modelling obesity in university students is introduced. The second stage involves data analysis based on Bayesian Structural Equation Modelling (BSEM) for estimating the Body Mass Index (BMI) (representative of the obesity level) of students at three university levels: Bachelor, Master and PhD. In the third stage, the highest significant correlation is determined between the BMI and other variables in the research model that were found significant through the second phase. The data for this study were collected from students at selected Malaysian universities. The results indicate that unhealthy food intake (fast food and soft drinks), social media use and stress exhibit the highest weightage contributing to overweight and obesity issues for Malaysian university students.

The main objective of this paper is to bring a model for firm sustainability performance index by applying both classical and Bayesian structural equation modeling (parametric and semi-parametric modeling). Both techniques are considered to the research data collected based on a survey directed to the China, Taiwan, and Malaysia food manufacturing industry. For estimating firm sustainability performance index we consider three main indicators include knowledge management, organizational learning, and business strategy. Based on the both Bayesian and classical methodology, we confirmed that knowledge management and business strategy have significant impact on firm sustainability performance index.

Background: Pregnancy has become the main constituent for women to become overweight or obese during the postpartum phase. This could lead women to suffer from postpartum depression as well. Information technology (IT) has become more prevalent in the healthcare industry. It offers patients the opportunity to manage their health conditions via the use of several applications, one being the mHealth applications. Objective: The main purpose of this study is to experiment and understand the effects the mHealth applications (i.e., fitness and nutrition applications) have on the body mass index (BMI) and depression levels amongst postpartum women. Methods: Online questionnaires were sent to postpartum women within one year after their pregnancy, of which 819 completed questionnaires were returned. The frequency of the mHealth applications usage was categorized into daily, weekly, rarely and never streams. Therefore, the frequency of use of the mHealth applications for BMI and depression levels was analyzed based on the available statistical data. Descriptive statistics, ANOVA, and Dunnet tests were applied to analyze the experimental data. Results: Out of 819 respondents, 37.9% and 42.1% of them were overweight and obese, respectively. Almost 32.9% of the respondents were likely depressed, and 45.6% were at an increased risk. This study reports that only 23.4% and 28.6% of respondents never used the fitness and nutrition applications. The impact of the frequency of using the fitness applications on BMI and depression levels was obvious. This means that with the increased use of the fitness applications, there was also a significant effect in maintaining and decreasing the BMI and depression levels amongst Malaysians postpartum women. However, from the data of weekly and daily use of fitness applications, we found that the contribution toward the BMI and depression levels was high (p = 0.000). However, nutrition applications amongst the users were not significant within the main variables (p &gt, 0.05). From the Dunnet test, the significance of using the fitness applications within the depression levels started from daily usage, whereas for BMI, it started from weekly usage. Conclusion: The efficiency of the fitness applications toward the BMI and depression levels has been proven in this research work. While nutrition applications did not affect the BMI and depression levels, some of the respondents were still categorized as weekly and daily users. Thus, the improvements in BMI and depression levels are associated with the types of mHealth app that had been used.

Structural Equation Modeling (SEM) has been used extensively in sustainability studies to model relationships among latent and manifest variables. This paper provides a tutorial exposition of the SEM approach in food security studies and introduces a basic framework based on family food security and children’s environment sustainability. This framework includes family food security and three main concepts representing children’s environment, including children’s BMI, health, and school performance. A detailed description is provided of how SEM is applied in this type of study. The proposed model contains dependent, independent, mediator, and moderator variables. Three latent variables categorized include family food security, children’s health, and children’s school performance, and two manifest variables are children’s body mass index and children’s gender. The samples for this study involve 452 Chinese children aged 7–12. The data analysis outcome indicates that the introduced model is capable of estimating the impact of family food security on children’s environment. The results from this study confirm that the combination of children’s body mass index with children’s health acts as a strong mediator in the relationship between family food security and children’s school performance.

Learning is an intentional activity, with several factors affecting students’ intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods’ results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Structural equation modelling (SEM) is an estimation method that can handle a large number of exogenous and endogenous factors as well as unobserved (latent) variables that are specified as linear combinations of observed (measurement) factors. This paper presents a SEM procedure with latent variables for estimating the financial and non-financial performance in airline companies. The model includes independent, mediator and dependent latent variables. The sample for this research comprises 214 airline companies. The results indicate that the model is capable of estimating performance with respect to the economic situation and it has also been determined that two types of constructs affect performance. One type is the economic situation acting as an initial construct and the other is the internal operation acting as the mediator in the research model.

The main objective of this study is to apply moderation analysis in structural equation modeling (SEM) to compare management information system (MIS) and enterprise resource planning (ERP). This comparison study is implemented in the relationship among, organizational learning, innovation, and performance. 168 companies in food production factories were selected from Malaysia, China, and Taiwan. The results show that even the implementation cost between MIS and ERP is quite different, but there is no significant difference between two systems in their innovation models.

The current study mainly aimed at validating the model of End User Computing Satisfaction (EUCS) in Enterprise Resource Planning (ERP) system in food production companies. In the EUCS model a Logit model on the seven items was applied. The tool used for the study was a questionnaire. A total of 126 questionnaires were collected from the ones given to ERP end users from food manufacturing in Malaysia, Taiwan, and China.

The purpose of this research article is to give a conceptual model of innovation management in food production industry in east of Asia. The researchers create and propose a conceptual model rooted in past research on innovation management, performance and organization culture (OC). The data for the research were obtained by surveys given to 168 Chinese, Taiwanese and Malaysian companies. Structural equation modeling (SEM) methodology was used in this study. The results of the study show that in food production firms OC and organizational innovation (OI) have positively direct impacts on organizational performance (OP). These results have useful implications for the companies specially those involved in food production which tries to win competitions and also positively respond to the environmental changes through successful applications of innovation. Findings also stress that systems used and applied to stimulate and boost an organizational innovative culture would most probably make the processes of introducing, adopting, and diffusing innovation simpler which would by itself read to higher OP.

Building materials have become increasingly diverse and flexible regarding their shapes, colours and texture. These include the use of highly reflective materials for facade (e.g. glass or metal claddings) which becomes a popular concept for high-rise buildings. However, these reflective materials contribute to an increasing number of feedbacks on discomfort associated with reflected glare at the pedestrian’s level and the surrounding environment. This study aims to evaluate the impact of reflected glare from the reflective building facade on pedestrians’ visual comfort in Kuala Lumpur city area. The relationship between pedestrians and reflected glare issues is determined by applying Structural Equation Modelling (SEM) between male and female. Total of 500 questionnaires is distributed at four different locations. The six-modelled fitting test was tested and within an acceptable range. For both male and female models shows that age significantly affects sensitiveness toward reflected glare.

This paper is about to examine the relationship of organisational learning (OL) with knowledge management (KM) and their effect on organisational performance (OP) in food industries. The conceptual framework proposed in this research is a creative model that contributes to relevant theories of KM, OL, and OP literature. 168 companies in food industry were selected in China, Taiwan, Malaysia, and structural equation modelling (SEM) is applied to test the hypotheses in our research model. The research model includes three constructs including KM, OL and OP, and three measurement variables include firm age, size, and type. The results show that KM and OL have positive effect on OL and OL is a mediator in the relationship between KM and OP. Moreover, firm age, size, and type are moderators in the relationship among three constructs in the research model.

The main objective of this paper is to find out the impact of organizational learning (OL) and organizational innovation (OI) on organizational performance (OP) in Asia manufacturing food industries. This study explores those linkages using structural equation modelling (SEM) with data from 172 companies in food manufacturing companies was selected from Taiwan, China, and Malaysia. The research model includes three latent variables including OL, OI, and OP. The results showed that OL and OI have positive effect on OP.

The main purpose of this study to find out the relationship among transformational leadership (TL), organizational learning (OL), and organizational innovation (OI) in manufacturing food industry in east of Asia countries include China, Taiwan, and Malaysia. Structural equation modeling (SEM) was used to test the research hypotheses from168 manufacturing food companies. Our research model contains three constructs, and the results showed there is positive impact between constructs. Also, Organizational learning is a mediator in the linkage between transformational leadership and organizational innovation.

There are many factors which could influence the sustainability of airlines. The main purpose of this study is to introduce a framework for a financial sustainability index and model it based on structural equation modeling (SEM) with maximum likelihood and Bayesian predictors. The introduced framework includes economic performance, operational performance, cost performance, and financial performance. Based on both Bayesian SEM (Bayesian-SEM) and Classical SEM (Classical-SEM), it was found that economic performance with both operational performance and cost performance are significantly related to the financial performance index. The four mathematical indices employed are root mean square error, coefficient of determination, mean absolute error, and mean absolute percentage error to compare the efficiency of Bayesian-SEM and Classical-SEM in predicting the airline financial performance. The outputs confirmed that the framework with Bayesian prediction delivered a good fit with the data, although the framework predicted with a Classical-SEM approach did not prepare a well-fitting model. The reasons for this discrepancy between Classical and Bayesian predictions, as well as the potential advantages and caveats with the application of Bayesian approach in airline sustainability studies, are debated.

The main purpose of the current article is to introduce a framework of the complexity of childhood obesity based on the family environment. A conceptual model that quantifies the relationships and interactions among parental socioeconomic status, family food security level, child’s food intake and certain aspects of parental feeding behaviour is presented using the structural equation modeling (SEM) concept. Structural models are analysed in terms of the direct and indirect connections among latent and measurement variables that lead to the child weight indicator. To illustrate the accuracy, fit, reliability and validity of the introduced framework, real data collected from 630 families from Urumqi (Xinjiang, China) were considered. The framework includes two categories of data comprising the normal body mass index (BMI) range and obesity data. The comparison analysis between two models provides some evidence that in obesity modeling, obesity data must be extracted from the dataset and analysis must be done separately from the normal BMI range. This study may be helpful for researchers interested in childhood obesity modeling based on family environment.

Sustainability has gained as much importance as management in business. Sustainable pavement development as a business practice should involve making evaluations according to the triple bottom line in the pavement life-cycle. Despite the current approaches to evaluating the social as well as economic and environmental feasibility of pavement projects (involving highway and airport infrastructure), there has recently been a lack of consensus on a methodology to guarantee sustainability upon assessment and analysis during the pavement life-cycle. As sustainability is a complex issue, this study intends to further explore sustainability and elaborate on its meaning. The second step involves a general depiction of the major sustainability appraisal tools, namely cost-benefit analysis, life-cycle cost analysis, life-cycle assessment, multi-criteria decision-making, environmental impact assessment and social life-cycle assessment, and an explanation of their cons and pros. Subsequently, the article addresses the application of an organized methodology to highlight the main factors or concepts that should be applied in sustainable pavement development and, more specifically, in sustainable pavement management. In the final step, research recommendations toward sustainability are given. This study is aimed to assist decision-makers in pavement management to plan sustainability frameworks in accordance with probable boundaries and restrictions.

This paper focuses on the application of mathematical theories in the study of information system (IS) success factors. The main objective is to apply Delone and McLean’s IS success model for radio-frequency identification (RFID) sustainability in Malaysian university libraries. Two approaches are applied to estimate user satisfaction, such as the Bayesian and maximum likelihood estimation approaches. In order to identify the best approach, four mathematical indices are used, namely root mean squared error, absolute error, mean absolute percentage error, and the coefficient of determination. The results reveal that Bayesian estimation provides good fit to the data, unlike the model with the maximum likelihood estimator. This study addresses the causes for this difference between the two approaches, as well as the potential merits and shortcomings of the maximum likelihood approach. The current study presents a novel and practical modeling and prediction concept for researchers and experts in the field of computer science.

Through public health studies, specifically on child obesity modeling, research scholars have been attempting to identify the factors affecting obesity using suitable statistical techniques. In recent years, regression, structural equation modeling (SEM) and partial least squares (PLS) regression have been the most widely employed statistical modeling techniques in public health studies. The main objective of this study to apply the Taguchi method to introduce a new pattern rather than a model for analyzing the body mass index (BMI) of children as a representative of childhood obesity levels mainly related to social media use. The data analysis includes two main parts. The first part entails selecting significant indicators for the proposed framework by applying SEM for primary and high school students separately. The second part introduces the Taguchi method as a realistic and reliable approach to exploring which combination of significant variables leads to high obesity levels in children. AMOS software (IBM, Armonk, NY, USA) was applied in the first part of data analysis and MINITAB software (Minitab Inc., State College, PA, USA) was utilized for the Taguchi experimental analysis (second data analysis part). This study will help research scholars view the data and a pattern rather than a model, as a combination of different factor levels for target factor optimization.

With the rapid growth and use of modern architecture practices for high-rise buildings, highly reflective materials have been adopted extensively for aesthetical reasons. However, outdoor glare from highly reflective facades might cause thermal and visual problems towards the occupants of neighbouring buildings and outdoor dwellers, particularly pedestrians. In tropical countries, this negative impact can be greater due to the higher solar radiation received throughout the year. At the present, there are few building guidelines limiting outdoor glare, or daylight reflectance from a building facade. This study aims to introduce a framework for outdoor glare studies that focus on perceived glare from highly reflective facades by pedestrians in Singapore. The introduced framework includes age, glare time, glare duration, avoidance and sensitiveness. For this study, the survey is carried out with the application of Structural Equation Modelling (SEM). This paper is helpful for planners, designers, and engineers to estimate the sensitivity of pedestrians' discomfort glare and towards the creation of sustainable architecture in Singapore.

This paper bring a brief discussion about sample size, outlier, and normality tests which have been not considered in some previous studies related to Structural Equation Modelling technique. The intended audience is statisticians, mathematicians, or methodologists who either know about SEM or simple basic statistics especially in regression and linear/nonlinear modelling, and Ph.D. students in statistics, mathematics, management, psychology, and even computer science.

Estimating the performance of the organization has always been of interest to management teams and researchers. In this regard, some researchers focused on determining definitions and how to measure organizational performance. The main objective of this study is to overview of organizational performance index definition and performance measurement. The history of performance is classified into six different subcategories. Various ideas and opinions on each of the performance subcategories are discussed. This review paper is applicant for researcher and student to better to understand definition and modelling of performance in organizational studies.

Airline companies are classified in very special service providers that for directing & controlling, expert & capable personnel should be employed. Therefore, managers of such companies should have special characteristics for suitable & accurate decision making in special occasions. In this study, the impact of personal characteristics of Iranian airlines managers on the performance of such companies has been surveyed. This paper models performance as a function of five potential variables. Data were gathered from four major airlines in Iran and the period of study is from 1996 to 2010. It appears that strong Iran airlines should be increasing their financial performance with high management experience and non-management experience.

This paper is challenging the previous studies in economic modelling related to airline performance studies. This research methodology paper is applicant for researcher and student to better to understand modelling in organizational studies especially in airline industry.

The main purpose of this research is to illustrate that school size a moderator can effect on the relationship among principal’s leadership style (PLS), principal's decision making style (PDMS), and teacher job satisfaction (TJS). Moreover, in this article, some hypotheses have been proposed to verify the existing relations among TJS, PLS, and PDMS. Based on samples randomly chosen from primary, secondary and high schools in Chinese educational systems in China, the required data are gathered through a mail survey, and the proposed hypotheses are tested via moderation analysis and structural equation modeling.

The Iranian population has gone through drastic changes in the last two decades with population crisis manifested in a few manners. The Iranian population has tripped in the last 30 years and now stands at 75 million. Drastic growth and population explosion in its consequences impelled the countries and governments to seek solutions for forward plan. Various dynamism in Iranian population changes were considered for this study, and patterns in growth and development were the main focus. The study period was post revolutions in Iran, from 1978 onwards with the economic indicators are among the factors that could indirectly affect population growth rates. This paper analyzed the correlation between the variable of population growth and the main macroeconomic indicators such as Human Development Index (HDI), Gross Domestic Product (GDP), as well as Inflation Rate in Iran. In this study, fixed effect models are used for collecting panel data for a time period between 2000 and 2009 (ten years) which allows a different intercept term serving for each cross-sectional unit as an unobserved random variable which is potentially interrelated with the observed regressors. The methodology employed cross sectional data over time. The study result indicates that the inflation rate would be the main effective and significant factor on population growth and development. The resulted findings would be advantageous if they were used in decision-making tasks performed in governmental and economic sectors by the relevant specialists and managers.