Risk talk—but not if it rocks the boat. perceived social risk acceptability and risk talk engagement in the Netherlands

Risk-benefit perceptions and public acceptance of Carbon Capture and Utilization

Fraud, particularly corruption, remains a significant challenge in Indonesia, permeating various sectors, including higher education. This study investigates how students' perceptions of risk, corruption, and corruption justification influence corrupt behavior in university settings. By examining these factors, the study aims to uncover the underlying mechanisms driving corrupt practices and offer strategies to combat corruption in academia. Using a survey method, data were collected from 208 Indonesian university students via structured questionnaires. The study employed multiple linear regression and Structural Equation Modeling (SEM) using Partial Least Square (PLS) analysis to assess relationships among variables. The results reveal that higher risk perception significantly reduces corrupt behavior, emphasizing the deterrent effect of perceived consequences. Conversely, heightened corruption perception and corruption justification perception increase corrupt tendencies, as they normalize unethical behavior and reduce moral accountability. The findings underscore the importance of fostering an anti-corruption culture within universities by addressing students' perceptions and ethical rationalizations. By promoting awareness of the risks and consequences of corruption, strengthening institutional integrity, and countering justification narratives, universities can mitigate corrupt practices. This study also highlights the nuanced relationship between risk perception and reporting behavior, showing that individuals are more likely to report corruption when they perceive high risks and minimal retaliation. Additionally, corruption perception shapes social norms, with heightened perceptions often leading to the normalization of corrupt practices. The justification for corruption significantly influences ethical decision-making, enabling students to rationalize unethical actions. These insights contribute to the formulation of anti-corruption policies and educational programs that emphasize academic integrity, aiming to build trust and reduce corruption in higher education. This study provides a valuable foundation for advancing institutional ethics and integrity in Indonesian universities.

A methodology for characterizing ground water quality of watersheds using hydrochemical data that mingle multiple linear regression and structural equation modeling is presented. The aim of this work is to analyze hydrochemical data in order to explore the compositional of phreatic aquifer groundwater samples and the origin of water mineralization, using mathematical method and modeling, in Maknassy Basin, central Tunisia). Principal component analysis is used to determine the sources of variation between parameters. These components show that the variations within the dataset are related to variation in sulfuric acid and bicarbonate, sodium and cloride, calcium and magnesium which are derived from water-rock interaction. Thus, an equation is explored for the sampled ground water. Using Amos software, the structural equation modeling allows, to test in simultaneous analysis the entire system of variables (sodium, magnesium, sulfat, bicarbonate, cloride, calcium), in order to determine the extent to which it is consistent with the data. For this purpose, it should investigate simultaneously the interactions between the different components of ground water and their relationship with total dissolved solids. The integrated result provides a method to characterize ground water quality using statistical analyses and modeling of hydrochemical data in Maknassy basin to explain the ground water chemistry origin.

This is an empirical and quantitative study of the validity of four kinds of distal explanatory factors in risk perception. In an initial study, personality constructs (Five Factor Model, Myers-Briggs Indicator of Jungian constructs and risk attitudes) were related to risk perception data (26 hazards). A relationship was found between emotional stability and risk perception, but none with Jungian constructs. One risk attitude dimension, 'Macho' risk willingness, was (negatively) related to demand for governmental risk mitigation. In a second study with a different sample, indices were constructed to measure the four World Views according to Cultural Theory (CT) as well as Group/Grid dimensions, New Age beliefs and the New Environmental Paradigm (NEP) dimensions of Dunlap et al . Risk perception data were obtained with regard to 37 hazards, both general and personal risk. The respondents were a large representative sample of the Swedish population. Only about 5% of the variance of perceived risk was accounted for by Cultural Theory dimensions, considerably more by New Age beliefs and one of the NEP scales (eco-crisis). In a third study, data from the five Nordic countries were used to analyse the relationships between CT dimensions and risk perception. Only weak relations were found. The results are discussed in relation to other current work on models of risk perception and the question of what should be considered 'strong' evidence for a theory.

BackgroundThe educational views of parents with autistic children directly impacts their children’s academic success. However, little research has been done on how the COVID-19 pandemic impacted parents’ academic and social views.AimThis study analyzes parents’ views of school success for their autistic children in the context of the COVID-19 pandemic and examines the relationships among pandemic stress, parental involvement, and parents’ views of school success for autistic children in mainland China.MethodsIn this study, 713 parents of autistic children completed measures assessing their pandemic stress, parental involvement, and views of school success; linear regression and structural equation modeling were used to analyze the data.ResultsParents’ views of school success were influenced by factors such as parents’ level of education, household income, parents’ gender, and children’s age. The effects of pandemic stress on views of school success for parents of autistic children are complex: physical and mental reaction has a negative direct effect on views of school success, a positive indirect effect mediated by parental involvement, and a net positive effect; risk perception and concern has a negative indirect effect; and both the direct and indirect effects of pragmatic hopefulness are positive. Education policymakers and practitioners need to seriously and carefully assess these results’ implications for modern, inclusive education.

In today’s world, energy consumption constitutes a topic on countries’ main agendas. In parallel with the military, technological and scientific developments associated with the increasing population, countries are generating policies that highlight energy sources that play a part in global competition. As with many innovations, factors such as the public’s knowledge levels regarding the innovations, social acceptance, attitudes, intentions and risk perceptions are seen to be directly related to the use of renewable energy. For this reason, the aim of this study was to test the relationships among the knowledge levels, risk perceptions and intentions of preservice teachers regarding renewable energy sources using structural equation model analysis. 642 preservice teachers studying in 3rd and 4th grades, and selected by convenience sampling, participated in the study. According to the results of the structural equation model analysis, the knowledge levels of the preservice teachers related to renewable energy sources negatively predicted their risk perceptions regarding renewable energy sources. Furthermore, while individuals’ risk perceptions negatively predicted some of the theory of planned behavior components related to renewable energy sources, the theory of planned behavior components related to attitude, subjective norms and perceived behavioral control positively predicted the intention to use renewable energy sources. These analyses related to the structural equation model findings are discussed in detail.

The aim of this research was to predict operating speed by considering geometric and roadside factors. Although most previous studies have employed linear regression modelling (LRM) to predict operating speed, this study recommends structural equation modelling (SEM) for the prediction of operating speed on rural multi-lane highways. In addition to geometric variables, LRM takes roadside variables into account. When employing SEM in this work, two latent variables were defined, namely ‘roadside effects’ and ‘geometric effects’. The first latent variable was the combination of land-use type, land-use density and number of accesses per segment, while the second was extracted from the segment length, highway grade, curvature, the presence of a guardrail and flat roadside slope, and the posted speed limits. The residual analysis and R2 values for LRM and SEM suggest that SEM demonstrates superior modelling performance compared with LRM. These results show the significant role of latent variables in predicting speed, which cannot be achieved through ordinary LRM.

Beauty and attractiveness: Implications for advertising, self-evaluation and product choice

Fetal growth is shaped by a complex interplay of parental traits, environmental exposures, nutritional intake, and genetic predispositions. In epidemiological research, birth weight is widely used as a proxy of impaired or favorable fetal growth; but it fails to provide a comprehensive measure, particularly if used alone. In a cohort of 538 mother-fetal pairs from the New York University Children's Health and Environment Study (NYU CHES), we utilized multiple linear regression and structural equation modeling (SEM) to assess the influence of various determinants-maternal characteristics, chemical exposures, and dietary factors-on fetal growth. To comprehensively evaluate fetal growth, we employed the concept of latent variable Favorable Fetal Growth Conditions (FFGC), together with three observed outcomes: birth weight, birth length, and gestational age. Maternal characteristics such as height, BMI, race/ethnicity, and maternal alcohol intake were significantly associated with birth weight, birth length, and gestational age in both the linear regression and with FFGC in the SEM. However, SEM additionally revealed significant relationships that were not detected by linear regression. Specifically, di(2-ethylhexyl) phthalate (DEHP) latent factor showed a negative association with the FFGC (β=-0.16, 95% confidence interval (CI)=-0.27, -0.04). The diet latent variable positively impacted FFGC (β=0.15, 95% CI=0.04, 0.25), whereas total calorie intake exhibited a negative effect (β=-0.13, 95% CI=-0.22, -0.05). The SEM provided a thorough understanding of the multifaceted pathways through which multiple factors of chemical mixtures, diet intakes, and maternal characteristics affected fetal development, uncovering nuanced associations that were not apparent in direct effects models. Our findings highlight the intricate interplay of maternal characteristics, chemical exposures, and dietary factors in shaping fetal growth.

Structural equation modeling has been widely used by academic researchers and was rarely seen in other organizations. However, since data analytics has got a foothold in a rapidly increasing number of public and private organizations all over the world, advanced analytics techniques such as structural equation modeling (SEM) for making critical decisions are needed. Although we see SEM being introduced into practice here and there, traditional approaches like regression are still in the majority, even when SEM could help giving more educated advice to business leaders. Part of the reason is that SEM is not easily available and is more complex as compared to using regression methodology. This study aims to show the benefits of using SEM to help practitioners in using data for decision‐making and for organizational development. We compare the application of SEM and multiple linear regression and explain the condition for using either approach when analyzing organizational behavioral research data. Our study is based on a set of such data collected at public and private organizations in Southeast Asia. The discussion focuses on the practical application of multiple linear regression and structural equation modeling as a valuable input for organizational development.

Impact of safety attitude, safety knowledge and safety leadership on chemical industry workers’ risk perception based on Structural Equation Modelling and System Dynamics

Objective: The main objective of this study is to model the structural relationships of the tendency toward aggression based on social acceptance with the mediation of childhood abuse experience. Methods and Materials: The research method is correlational, based on structural equation modeling. The statistical population of this study includes 309 neglected and orphaned children. Considering the number of latent variables in this study, a sample of 250 individuals was selected. The sampling method is stratified sampling. The data collection tools include the Aggression Questionnaire by Nielson et al. (2000), the Social Acceptance Questionnaire by Crowne and Marlowe (1960), and the Child Abuse Questionnaire (Self-report Scale) by Nourbakhsh (2012). Structural equation modeling was used for data analysis, and SPSS and LISREL software were employed for data processing. Findings: The results from the research data indicate a relationship between social acceptance and childhood abuse experience and the tendency toward aggression in neglected and orphaned children. There is also a relationship between childhood abuse experience and the tendency toward aggression in neglected and orphaned children. Furthermore, a relationship exists between social acceptance and the tendency toward aggression through the mediation of childhood abuse experience in neglected and orphaned children. Conclusion: Enhancing social acceptance may reduce aggressive behaviors and improve mental health outcomes, emphasizing the need for supportive interventions and targeted programs to address the unique challenges faced by these children.

While the share of bioenergy in the overall energy supply has increased over the last decade, its social acceptance is fragile, mainly due to concerns about negative sustainability impacts. In this paper, we will investigate to what extent the extension of bioenergy towards ‘smart’ or ‘cascaded’ biomass use enhances a project’s social acceptance. Smart use involves the prioritised use of biomass for food and materials. We adopt an explorative single case study approach to investigate issues of social acceptance. Our case is the Biobased Economy Park at Cuijk, in The Netherlands. The central element in this project is the revival of an existing but off-line biopower plant. For the power company involved, the integration of biopower into a broader smart use scheme, involving several new business partners, is a strategy to make the exploitation of the plant profitable again. For the data collection, we used interviews, as well as information provided by members of our expert panel, in addition to information collected from websites and provided at a bioeconomy event. The data was analysed by taking existing conceptual work on the social acceptance of renewable energy innovation as a guide. We found that issues of social acceptance changed rather than diminished when entrepreneurs extended a project’s focus from biopower to smart biomass use. This change can be observed in relation to all three conceptual categories: market acceptance, sociopolitical acceptance and community acceptance. We conclude that the extension from bioenergy towards smart biomass use does not necessarily enhance a project’s social acceptance. Compared to the social acceptance of renewable energy innovation, the social acceptance of smart biomass use is fuzzier, more open to recursive patterns and more dependent upon inter-firm trust. Importantly, embracing the principle of smart biomass use instigates the question of how biomass use can be optimised—either with or without purposes related to energy. We suggest further comparative case study research into the social acceptance dynamics of smart biomass use, for which we identify the following variables as relevant: the type of bioenergy, the sector that takes the initiative, the greenfield character of the project and the complexity of the smart use scheme.

Evaluation of self-perceived oral function among older adults using linear regression and structural equation modeling.

Spiritual needs of women with breast cancer: A structural equation model