Influence of sustainability in the positioning of the university brand: Study in universities in Medellín-Colombia

The implementation of university sustainability affects brand perceptions by conferring attributes related to commitment and reputation, especially by students. The proper management of sustainability is related to an improvement in the university brand perceived by its stakeholders, including students. For this reason, this research aims to analyse whether university sustainability positively affects brand positioning among university students. For this purpose, a theoretical and quantitative mixed-method investigation is proposed that allows the establishment of a structural model in which the relationships between the dimensions of university sustainability with the dimensions of brand positioning are evident. The empirical analysis found that some dimensions of university sustainability, such as the institutional framework, campus operations and some dimensions of the substantive functions, positively affect brand positioning in university students and generate commitment to the university from both the prestige and reputation of the brand.

Technology acceptance factors of e-commerce among young people: An integration of the technology acceptance model and theory of planned behavior

This study aims to examine the factors involved in the technological acceptance of e-commerce among young university students. The research is focused on the articulation between the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) by collecting secondary data and conducting a survey with 376 young university students, validated by using structural equations (SEM). The predominant factors and variables involved in the adoption of e-commerce by young university students in Medellín were identified. The results show that the attitude towards usage has a positive effect on usage intention because people consider a website to be useful and trust builds regarding usage intention, which will enable to make the transaction in an easier and safer way. In conclusion, perceived usefulness comes before attitude towards usage, and thus the main direct antecedent of the intention to use e-commerce is established.

Effect of lean manufacturing tools on sustainability: the case of Mexican maquiladoras

The Mexican maquiladora industry is applying Lean Manufacturing Tools (LMT) in its production lines; however, few studies have investigated its relationship with sustainability (social, economic, and environmental). This paper presents a second-order structural equation model (SEM) relating 8 LMT integrated into three independent latent variables: continuous improvement (Kaizen and Gemba), supporting tools (Andon, visual management, and Poka-yoke), and machinery and equipment (total productive maintenance, overall equipment effectiveness, and Jidoka) that are related to social, economic, and environmental sustainability as dependent variables. The model is validated with information obtained from 249 companies using partial least squares. Findings show that the application of LMT in the Mexican maquiladora industry avoids the generation of waste and reprocessing. Likewise, the improvement of production processes reduces the waste emitted into the environment and reduces energy consumption. Also, when companies have environmental programs, the work environment is safe, and labor relations are improved, increasing morale and the commitment to work for the company, gaining economic and ecological benefits.

The relationship of supportive roles with mental health and satisfaction with life in female household heads in Karaj, Iran: a structural equations model

Background

Female household heads are faced with more problems than men due to their multiple concurrent roles. The present study was conducted to determine the relationship of supportive roles with mental health and satisfaction with life in female household heads in Karaj, Iran using a structural equations model.

Methods

The present descriptive-analytical study was conducted on 286 eligible female household heads in Karaj, Iran, in 2020, who were selected by convenience sampling. Data were collected using Vaux’s Social Support, the perceived social support scale, the general health questionnaire (GHQ), and the satisfaction with life questionnaire plus a socio-demographic checklist, and were analyzed in SPSS-16 and Lisrel-8.8.

Results

The participants’ mean age was 43.1 ± 1.7 years. According to the path analysis results, satisfaction with life had the highest direct positive relationship with perceived social support (B = 0.33) and the highest indirect positive relationship with age (B = 0.13) and the highest direct and indirect positive relationship with education and social support (B = 0.13). Also, mental health had a direct negative relationship with satisfaction with life (B = -0.29), an indirect negative relationship with social support, and both a direct and indirect negative relationship with perceived support (B = -0.26).

Conclusion

Based on the results, supportive roles had a negative relationship with mental health; in other words, mental health problems increase as supportive roles decrease. They also had a positive relationship with satisfaction with life in female household heads.

Accordingly, given the status and role of women in the health of family members and the community and their greater vulnerability, further attention and support should be directed toward these women by the government and relevant organizations like establishment of counseling-support centers.

Modeling organizational intelligence, learning, forgetting and agility using structural equation model approaches in Shiraz University of Medical Sciences Hospitals

Objectives

Strengthening the organizational agility of the hospital can lead to decreased production costs and increased market share, better serving to patients' needs, introduction of new services and increased competitiveness; therefore, this study aimed to investigate the agility of Shiraz public teaching hospitals.

Results

The results showed that organizational intelligence had a positive and significant effect on organizational agility with a path coefficient of 0.172. Organizational forgetting and organizational learning also played a mediating role between organizational intelligence and organizational agility. This means that organizational intelligence had positive effect on organizational forgetting with path coefficient of 0.482, organizational forgetting on organizational learning with path coefficient of 0.40 and subsequently organizational learning on organizational agility with path factor of 0.07. Organizational forgetting also played a mediating role between organizational intelligence and organizational learning.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13104-021-05682-w.

An exploration of colorectal cancer incidence rates in North Dakota, USA, via structural equation modeling

PURPOSE

The state of North Dakota has one of the highest incidence rates for colorectal cancer in the USA. Its high incidence rate, coupled with a large variation in incidence rates among counties within the state, makes North Dakota a "natural laboratory" in which to investigate environmental clues to colorectal cancer. We conducted a hypothesis-generating study to explore potential determinants of colorectal cancer in North Dakota.

METHODS

We obtained county-specific incidence rates for North Dakota's 53 counties from the statewide cancer registry and corresponding data on county demographic, agricultural, and geophysical features from population-based sources. Candidate demographic/agricultural variables included median household income, population density, colorectal cancer screening rates, average farm size (in acres), and the percent of county fertilized. Geophysical variables included the uranium content of soil, residential radon levels, and source of drinking water (municipal or well water). Statistical analyses were performed via multivariate regression and structural equation modeling.

RESULTS

Colorectal cancer incidence rates across North Dakota counties varied 3-fold. The structural equation model identified a significant role for well water use (p < 0.05). This finding is consistent with studies that implicate well water in colorectal cancer.

CONCLUSIONS

Well water contains several agents, e.g., bacteria, disinfection by-products, and nitrates that are potent colorectal carcinogens. Studies of well water use and colorectal cancer risk at the individual level in North Dakota are warranted.

Structural equations modelling of drivers' speed selection using environmental, driver, and risk factors

Drivers' speed has significant implications on road users' safety in general, and particularly so if a crash occurs. This paper explores the influence of environmental and road characteristics, situational factors, and individual characteristics on drivers' observed speed selection in a simulator experiment. The paper presents a theoretical framework for drivers' speed selection, and applies structural equation modeling for the various factors examined. The simulator experiments collected data of 111 drivers driving in 4 different scenarios composed of 22 segments for each scenario. The dataset was analyzed in several resolutions: Driver level, Trip level, and Segment level. The three models revealed that gender, age, and driving frequency are all significant in determining drivers' perceptions and attitudes, which in turn influence speed selection. Situational factors such as traffic speed, enforcement, and time-saving-benefits are also related to speed selection, as well as infrastructure characteristics. These findings demonstrate that structural equations provide a flexible modeling tool able to concurrently analyze the variety of factors that relate to speed selection. As a result, Structural Equations Modeling provides more accurate and refined explanations for the combined effects of various factors on drivers' speed selection than previous research so far. These tools can be useful in developing speed management strategies to improve road safety.

A new statistical framework for genetic pleiotropic analysis of high dimensional phenotype data

BACKGROUND

The widely used genetic pleiotropic analyses of multiple phenotypes are often designed for examining the relationship between common variants and a few phenotypes. They are not suited for both high dimensional phenotypes and high dimensional genotype (next-generation sequencing) data. To overcome limitations of the traditional genetic pleiotropic analysis of multiple phenotypes, we develop sparse structural equation models (SEMs) as a general framework for a new paradigm of genetic analysis of multiple phenotypes. To incorporate both common and rare variants into the analysis, we extend the traditional multivariate SEMs to sparse functional SEMs. To deal with high dimensional phenotype and genotype data, we employ functional data analysis and the alternative direction methods of multiplier (ADMM) techniques to reduce data dimension and improve computational efficiency.

RESULTS

Using large scale simulations we showed that the proposed methods have higher power to detect true causal genetic pleiotropic structure than other existing methods. Simulations also demonstrate that the gene-based pleiotropic analysis has higher power than the single variant-based pleiotropic analysis. The proposed method is applied to exome sequence data from the NHLBI's Exome Sequencing Project (ESP) with 11 phenotypes, which identifies a network with 137 genes connected to 11 phenotypes and 341 edges. Among them, 114 genes showed pleiotropic genetic effects and 45 genes were reported to be associated with phenotypes in the analysis or other cardiovascular disease (CVD) related phenotypes in the literature.

CONCLUSIONS

Our proposed sparse functional SEMs can incorporate both common and rare variants into the analysis and the ADMM algorithm can efficiently solve the penalized SEMs. Using this model we can jointly infer genetic architecture and casual phenotype network structure, and decompose the genetic effect into direct, indirect and total effect. Using large scale simulations we showed that the proposed methods have higher power to detect true causal genetic pleiotropic structure than other existing methods.

Safety propensity index for signalized and unsignalized intersections: exploration and assessment

The objective of this study is to develop a safety propensity index (SPI) for both signalized and unsignalized intersections. Through the use of a structural equation modelling (SEM) approach safety is quantified in terms of multiple endogenous variables and related to various dimensions of exogenous variables. The singular valued SPI allows for identification of relationships between variables and lends itself well to a comparative analysis between models. The data provided by the Highway Safety Information System (HSIS) for the California transportation network was utilized for analysis. In total 22,422 collisions at unsignalized intersections and 20,215 collisions at signalized intersections (occurring between 2006 and 2010) were considered in the final models. The main benefits of the approach and the subsequent development of an SPI are (1) the identification of pertinent variables that effect safety at both intersection types, (2) the identification of similarities and differences at both types of intersections through model comparison, and (3) the quantification of safety in the form of an index such that a ranking system can be developed. If further developed, the adopted methodology may assist in safety related decision making and policy analysis.

Signed directed acyclic graphs for causal inference

Formal rules governing signed edges on causal directed acyclic graphs are described in this paper and it is shown how these rules can be useful in reasoning about causality. Specifically, the notions of a monotonic effect, a weak monotonic effect and a signed edge are introduced. Results are developed relating these monotonic effects and signed edges to the sign of the causal effect of an intervention in the presence of intermediate variables. The incorporation of signed edges into the directed acyclic graph causal framework furthermore allows for the development of rules governing the relationship between monotonic effects and the sign of the covariance between two variables. It is shown that when certain assumptions about monotonic effects can be made then these results can be used to draw conclusions about the presence of causal effects even when data is missing on confounding variables.