Firm Sustainability Performance Index Modeling

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

Family Food Security and Children’s Environment: A Comprehensive Analysis with Structural Equation Modeling

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

Family Environment and Childhood Obesity: A New Framework with Structural Equation Modeling

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

Preliminary safety evaluation and biochemical efficacy of a carum carvi extract: Results from a randomized, triple-blind, and placebo-controlled clinical trial

Carum carvi L. (Apiaceae) is known as caraway, and its derivatives find wide medicinal use for health purposes, including for gastrointestinal problems and obesity. Since there is inconsistency among the reports on the safety of this plant in humans, this research was aimed at assessing the safety of a characterized caraway aqueous extract (CAE) in a randomized, triple-blind, placebo-controlled study. Seventy, overweight and obese, healthy women were randomly assigned into placebo (n = 35) and plant extract (n = 35) groups. Participants received either 30 ml/day of CAE or placebo. Subjects were examined at baseline and after 12 weeks for changes in heart rate, blood pressure, urine test, 25-item blood chemistries, and general health status. No significant changes of blood pressure, heart rate, urine specific gravity, and serum blood tests were observed between the two groups before and after treatment. However, in the complete blood count test, red blood cell levels were significantly (p < 0.01) increased, and platelet distribution width was significantly decreased after the dietary CAE treatment, as compared with placebo. No negative changes were observed in the general health status of the two groups. This preliminary study suggests that the oral intake of CAE appears to be without any adverse effects at a dosage of 30 ml daily for a period of 12 weeks. Copyright © 2014 John Wiley & Sons, Ltd

Testing students’ e-learning via Facebook through Bayesian structural equation modeling

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