Modeling the Socioeconomic Metabolism of End-of-Life Tires Using Structural Equations: A Brazilian Case Study

Socioeconomic metabolism (SEM) is the exchange of materials and energy between society and the environment involving the social, economic and environmental sectors. In this paper, a boundary was defined between the economic (consumption) and environmental (waste recovery) limits in a city of 300,000 inhabitants in relation to the circulation (generation, reuse and disposal) of end-of-life tires (ELTs). The objective was to elaborate a theoretical structural model to evaluate the socioeconomic metabolism of waste (SEMw) by means of technical constructs (direct material flows (DMF), reverse material flows (RMF), socioeconomic environment (SEF) and sociodemographic factors (SDF)). Structural Equation Modeling (SEMm) was performed using Partial Least Squares Structural Equation Modeling (SmartPLS) software. The results obtained from the hypotheses show the causal relationships between the technical and social constructs and suggest guidelines for supporting the planning and management of urban solid waste in the collection and final disposal of ELTs. The processed information also contributes to the analysis of the city’s socioeconomic scenarios in relation to the disposal of ELTs. One of the hypotheses tested (RMF have a direct effect on SEMw) shows the importance of managing ELTs through the correct final disposal of waste and recycling. SEMw was evaluated from the perception of the local society and it is concluded that it is possible to plan public policies to avoid the formation of waste inventory in the city.

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Teachers' digital technology access to motivation, skills and use: a structural equation modeling study

Aslib Journal of Information Management ◽

10.1108/ajim-11-2020-0382 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Akhmad Habibi ◽

Mohd Faiz Mohd Yaakob ◽

Amirul Mukminin ◽

Muhaimin Muhaimin ◽

Lantip Diat Prasojo ◽

...

Keyword(s):

Structural Equation Modeling ◽

Digital Technology ◽

Structural Equation ◽

Structural Model ◽

Teaching Experience ◽

Equation Modeling ◽

Technology Access ◽

School Location ◽

Content Type ◽

Instructional Use

PurposeThe current study aimed to develop and validate a scale to model factors affecting digital technology access for instructional use. The scale was mainly used to assess the structural model. Besides, tests of difference were addressed regarding digital technology access for instructional use based on gender, teaching experience and school location.Design/methodology/approachThe authors implemented a survey design in this study. A scale based on prior studies was developed, validated and piloted. The pilot study data were computed for an exploratory factor analysis. Further, partial least squares structural equation modeling (PLS-SEM) and t-test procedures were used for the main data analysis (n.2677). The authors also included the importance-performance map analysis to extend of the results of the PLS-SEM.FindingsThe findings of the study successfully assessed the validity and reliability of the scale. All hypothetical relationships in the structural model were positively significant. The t-test results show that teaching experience and school location were significantly different regarding instructional use access; however, an insignificant difference emerged based on gender.Practical implicationsFailure in technology integration is possible if policies have not been carefully prepared. Therefore, users' perception is an essential factor in determining technology integration, including access to digital technology.Originality/valueThis research has the potential to enhance the understanding of access to digital technology in the context of developing countries by the elaboration of the proposed model's instrument development and validation, path analysis assessment and difference test examination with a large sample size. Also, the current study emphasizes the importance of raising awareness about digital technology access that the model can facilitate a valid and reliable foundation for future researchers interested in conducting similar types of research.

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Development of a System Measurement Model of the Brazilian Hospital Accreditation System

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15112520 ◽

2018 ◽

Vol 15 (11) ◽

pp. 2520 ◽

Cited By ~ 2

Author(s):

João Corrêa ◽

João Turrioni ◽

Carlos Mello ◽

Ana Santos ◽

Carlos da Silva ◽

...

Keyword(s):

Structural Equation Modeling ◽

Structural Equation ◽

Structural Model ◽

Measurement Model ◽

Equation Modeling ◽

Scientific Software ◽

Health Organizations ◽

Hospital Accreditation ◽

Management Quality ◽

Culture Process

The purpose of this study is to develop and validate a measurement model that evaluates the Brazilian hospital accreditation methodology (ONA), based on a multivariate model using structural equation modeling (SEM). The information used to develop the model was obtained from a questionnaire sent to all organizations accredited by the ONA methodology. A model was built based on the data obtained and tested through a structural equation modeling (SEM) technique using the LISREL® software (Scientific Software International, Inc., Skokie, IL, USA). Four different tests were performed: Initial, calibrated, simulated, and cross-validation models. By analyzing and validating the proposed measurement model, it can be verified that the selected factors satisfy the required criteria for the development of a structural model. The results show that leadership action is one of the most important factors in the process of health services accredited by ONA. Although, leadership, staff management, quality management, organizational culture, process orientation, and safety are strongly linked to the development of health organizations, and directly influence the accreditation process.

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Risk Assessment of Ex-Post Transaction Cost in Construction Projects Using Structural Equation Modeling

Sustainability ◽

10.3390/su10114017 ◽

2018 ◽

Vol 10 (11) ◽

pp. 4017 ◽

Cited By ~ 5

Author(s):

Zaigham Ali ◽

Fangwei Zhu ◽

Shahid Hussain

Keyword(s):

Structural Equation Modeling ◽

Life Cycle ◽

Transaction Cost ◽

Life Cycle Cost ◽

Structural Equation ◽

Construction Projects ◽

Structural Model ◽

Equation Modeling ◽

Ex Post ◽

Industry Professionals

The transaction cost (TC) escalation is the pervasive problem in the construction industry, which is continuously a threat to maintaining the life cycle cost of projects. Researchers have described the reality of risk for economic transactions. This study has taken the risk as a phenomenon to explore its influence on ex-post TC in construction projects. A questionnaire survey was undertaken from industry professionals to assess the risk of ex-post TC escalation in public-sector construction projects. In total, 475 surveys were conducted in Pakistan and used in the analysis. The data were analyzed using structural equation modeling (SEM) and the measurement and structural model was validated to determine the influence of risk on ex-post TC. The final SEM results show that internal and external risk, including sub hypothesized risks, positively influence TC. The weight of relative importance shows technical risk (23.82%) and environmental risk (22.88%) as significant sub-contributors from internal and external sources, respectively. This study recommends substantial investment in human capacity development to reduce the deficiencies in the ex-ante phase of the projects that help to reduce the risk of ex-post TC escalation. It also suggests the adoption of strict policies on contingency claims, and recommends nontraditional ways of monitoring to overcome the risk of ex-post TC. This study’s results provide valuable information for industry professionals and practitioners to maintain life cycle costs as a contribution to sustainable construction.

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Challenges in Nonlinear Structural Equation Modeling

Methodology ◽

10.1027/1614-2241.3.3.100 ◽

2007 ◽

Vol 3 (3) ◽

pp. 100-114 ◽

Cited By ~ 47

Author(s):

Polina Dimitruk ◽

Karin Schermelleh-Engel ◽

Augustin Kelava ◽

Helfried Moosbrugger

Keyword(s):

Structural Equation Modeling ◽

Maximum Likelihood ◽

Structural Equation ◽

Nonlinear Effects ◽

Structural Equations ◽

Estimation Methods ◽

Equation Modeling ◽

Parameter Estimates ◽

Nonlinear Structural ◽

Nonlinear Structural Equation

Abstract. Challenges in evaluating nonlinear effects in multiple regression analyses include reliability, validity, multicollinearity, and dichotomization of continuous variables. While reliability and validity issues are solved by employing nonlinear structural equation modeling, multicollinearity remains a problem which may even be aggravated when using latent variable approaches. Further challenges of nonlinear latent analyses comprise the distribution of latent product terms, a problem especially relevant for approaches using maximum likelihood estimation methods based on multivariate normally distributed variables, and unbiased estimates of nonlinear effects under multicollinearity. The only methods that explicitly take the nonnormality of nonlinear latent models into account are latent moderated structural equations (LMS) and quasi-maximum likelihood (QML). In a small simulation study both methods yielded unbiased parameter estimates and correct estimates of standard errors for inferential statistics. The advantages and limitations of nonlinear structural equation modeling are discussed.

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Moral Disengagement: Mediator Between Moral Perfectionism and Machiavellian Behavior Among Undergraduates?

Psychological Reports ◽

10.1177/0033294120964067 ◽

2020 ◽

pp. 003329412096406

Author(s):

Abbas Abdollahi ◽

Fatemeh Hashemi ◽

Hamid Rezaeian Faraji ◽

Simin Hosseinian ◽

Kelly A. Allen

Keyword(s):

Structural Equation Modeling ◽

Undergraduate Students ◽

Structural Equation ◽

Moral Disengagement ◽

Structural Model ◽

Two Dimensions ◽

Equation Modeling ◽

Moral Standards ◽

The Relationship ◽

Moral Perfectionism

To better understand Machiavellian behavior among undergraduate students, the current study examines moral disengagement as a mediator in the relationship between Machiavellian behavior and two dimensions of moral perfectionism (concern over moral mistakes perfectionism and personal moral standards perfectionism). Participants were 210 undergraduate students (64% female) from three universities in Tehran, Iran, aged between 18 and 27 years old. Structural equation modeling revealed that Machiavellian behavior was negatively associated with personal moral standards perfectionism and positively associated with moral disengagement. The results of the structural model showed a non-significant relationship between concern over moral mistakes perfectionism and Machiavellian behavior. However, multi-model analysis provided evidence that moral disengagement partially mediated the relationship between personal moral standards perfectionism and Machiavellian behavior. The findings also showed that there was a relationship between concern over moral mistakes perfectionism and Machiavellian behavior through moral disengagement. The results suggest that while moral perfectionism is often accompanied with moral disengagement, Machiavellian behavior may be an explanation for individuals with these traits.

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Teacher Role Stress, Satisfaction, Commitment, and Intentions to Leave: A Structural Model

Psychological Reports ◽

10.2466/pr0.105.3.771-786 ◽

2009 ◽

Vol 105 (3) ◽

pp. 771-786 ◽

Cited By ~ 39

Author(s):

Sharon Conley ◽

Sukkyung You

Keyword(s):

Structural Equation Modeling ◽

Structural Equation ◽

Structural Model ◽

Coastal Region ◽

Role Stress ◽

Equation Modeling ◽

Role Overload ◽

Intentions To Leave ◽

Teachers Perceptions ◽

Confirmatory Factor

Structural equation modeling was used to assess the plausibility of a conceptual model specifying hypothesized linkages among teachers' perceptions of the role stresses of role ambiguity, role conflict, and role overload and commitment, satisfaction, and intentions to leave their employing school. 178 teachers in four high schools in a southern coastal region of California responded to survey questions designed to capture the above constructs. Confirmatory factor analysis was used to assess whether the role-stress items fit hypothesized constructs. Structural equation modeling results indicated that satisfaction and commitment are two mediators in the role stresses–intentions to leave relationship.

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9. Structural Equation Modeling with Robust Covariances

Sociological Methodology ◽

10.1111/0081-1750.00052 ◽

1998 ◽

Vol 28 (1) ◽

pp. 363-396 ◽

Cited By ~ 71

Author(s):

Ke-Hai Yuan ◽

Peter M. Bentler

Keyword(s):

Structural Equation Modeling ◽

Structural Equation ◽

Structural Model ◽

Likelihood Function ◽

Breakdown Point ◽

Individual Case ◽

Equation Modeling ◽

Parameter Estimates ◽

Sample Covariance ◽

Bounded Influence

Existing methods for structural equation modeling involve fitting the ordinary sample covariance matrix by a proposed structural model. Since a sample covariance is easily influenced by a few outlying cases, the standard practice of modeling sample covariances can lead to inefficient estimates as well as inflated fit indices. By giving a proper weight to each individual case, a robust covariance will have a bounded influence function as well as a nonzero breakdown point. These robust properties of the covariance estimators will be carried over to the parameter estimators in the structural model if a technically appropriate procedure is used. We study such a procedure in which robust covariances replace ordinary sample covariances in the context of the Wishart likelihood function. This procedure is easy to implement in practice. Statistical properties of this procedure are investigated. A fit index is given based on sampling from an elliptical distribution. An estimating equation approach is used to develop a variety of robust covariances, and consistent covariances of these robust estimators, needed for standard errors and test statistics, follow from this approach. Examples illustrate the inflated statistics and distorted parameter estimates obtained by using sample covariances when compared with those obtained by using robust covariances. The merits of each method and its relevance to specific types of data are discussed.

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The influence of service quality on satisfaction and behavioral intentions of football spectators: a study in Pernambuco football

Journal of Physical Education ◽

10.4025/jphyseduc.v32i1.3203 ◽

2020 ◽

Vol 32 (1) ◽

Author(s):

Marcos Antonio Barros Filho ◽

Carlos Augusto Mulatinho de Queiroz Pedroso ◽

Yves de Holanda Batista de Miranda ◽

José Pedro Sarmento ◽

Victor Henrique Rodrigues Silva ◽

...

Keyword(s):

Structural Equation Modeling ◽

Service Quality ◽

Behavioral Intentions ◽

Structural Equation ◽

Structural Model ◽

Positive Influence ◽

Equation Modeling ◽

Professional Football ◽

Quality Perception ◽

Game Players

The aim of this study was to examine the influence of service quality perception on spectators’ satisfaction and behavioral intentions in the Pernambuco Professional Football Championship of 2017 - Brazil. Data were collected on match days, using an adapted questionnaire, consisting of four variables. The sample was composed of spectators at the competition, divided into two moments (1st. n = 386 / 2nd. n = 620). The data were analyzed through two-step structural equation modeling in AMOS 24. After the refinement of the model, the results of the structural model [χ² (314) = 762.849 (p <.001) χ² / df = 2.42; GFI = .92; CFI = .95; RMSEA = .05] indicated that the players' performance positively influenced both satisfaction (β = .76, p <.001) and behavioral intentions (β = .33, p <.001). In turn, the crowd experience had a positive influence only on the spectators’ behavioral intentions (β = .38, p <.001). It is concluded that provision of service quality, positively evaluated by the spectators, especially in relation to the aspects of the game/players, is a way for the club to satisfy them and generate positive behavioral intentions.

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Structural equation models - PLS in engineering sciences: a brief guide for researchers through a case applied to the industry

Athenea ◽

10.47460/athenea.v2i4.17 ◽

2021 ◽

Vol 2 (4) ◽

pp. 5-18

Author(s):

Juan Enrique Villalva A.

Keyword(s):

Structural Equation Modeling ◽

Least Squares ◽

Partial Least Squares ◽

Structural Equation ◽

Structural Equation Models ◽

Structural Equations ◽

Partial Least Square ◽

Equation Modeling ◽

Engineering Sciences ◽

The Impact

Modeling using structural equations, is a second generation statistical data analysis technique, it has been positioned as the methodological options most used by researchers in various fields of science. The best known method is the covariance-based approach, but it presents some limitations for its application in certain cases. Another alternative method is based on the variance structure, through the analysis of partial least squares, which is an appropriate option when the research involves the use of latent variables (for example, composite indicators) prepared by the researcher, and where it is necessary to explain and predict complex models. This article presents a brief summary of the structural equation modeling technique, with an example on the relationship of constructs, sustainability and competitiveness in iron mining, and is intended to be a brief guide for future researchers in the engineering sciences. Keywords: Competitiveness, Structural equations, Iron mining, Sustainability. References [1]J. Hair, G. Hult, C. Ringle and M. Sarstedt. A Primer on Partial Least Square Structural Equation Modeling (PLS-SEM). California: United States. Sage, 2017. [2]H. Wold. Model Construction and Evaluation when Theoretical Knowledge Is Scarce: An Example of the Use of Partial Least Squares. Genève. Faculté des Sciences Économiques et Sociales, Université de Genève. 1979. [3]J. Henseler, G. Hubona & P. Ray. “Using PLS path modeling new technology research: updated guidelines”. Industrial Management & Data Systems, 116(1), 2-20. 2016. [4]G. Cepeda and Roldán J. “Aplicando en la Práctica la Técnica PLS en la Administración de Empresas”. Congreso de la ACEDE, Murcia, España, 2004. [5]D. Garson. Partial Least Squares. Regresión and Structural Equation Models. USA. Statistical Associates Publishing: 2016. [6]D. Barclay, C. Higgins & R. Thompson. “The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration”. Technology Studies. Special Issue on Research Methodology. (2:2), pp. 285-309. 1995. [7]J. Medina, N. Pedraza & M. Guerrero. “Modelado de Ecuaciones Estructurales. Un Enfoque de Partial Least Square Aplicado en las Ciencias Sociales y Administrativas”. XIV Congreso Internacional de la Academia de Ciencias Administrativas A.C. (ACACIA). EGADE – ITESM. Monterrey, México, 2010. [8]J. Medina & J. Chaparro. “The Impact of the Human Element in the Information Systems Quality for Decision Making and User Satisfaction”. Journal of Computer Information Systems. (48:2), pp. 44-52. 2008. [9]D. Leidner, S. Carlsson, J. Elam & M. Corrales. “Mexican and Swedish Managers’ Perceptions of the Impact of EIS on Organizational Intelligence, Decisión Making, and Structure”. Decision Science. (30:3), pp. 633-658. 1999.[10]W. Chin. “The partial least squares approach for structural equation modeling”. Chapter Ten, pp. 295-336 in Modern methods for business research. Edited by Macoulides, G. A., New Jersey: Lawrence Erlbaum Associates, 1998. [11]M. Höck & C. Ringle M. “Strategic networks in the software industry: An empirical analysis of the value continuum”. IFSAM VIIIth World Congress, Berlin 2006. [12]J. Henseler, Ch. Ringle & M. Sarstedt. Handbook of partial least squares: Concepts, methods and applications in marketing and related fields. Berlin: Springer, 2012. [13]S. Daskalakis & J. Mantas. “Evaluating the impact of a service-oriented framework for healthcare interoperability”. Studies in Health Technology and Informatics. pp. 285-290. 2008. [14]C. Fornell & D. Larcker: “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error”, Journal of Marketing Research, vol. 18, pp. 39-50. Februay 1981. [15]C. Fornell. A Second Generation of Multivariate Analysis: An Overview. Vol. 1. New York, U.S.A. Praeger Publishers: 1982. [16]R. Falk and N. Miller. A Primer for Soft Modeling. Ohio: The University of Akron. 1992. [17]M. Martínez. Aplicación de la técnica PLS-SEM en la gestión del conocimiento: un enfoque técnico práctico. Revista Iberoamericana para Investigación y el Desarrollo Educativo. Vol. 8, Núm. 16. 2018. [18]S. Geisser. “A predictive approach to the random effects model”. Biometrika, Vol. 61(1), pp. 101-107. 1974. [19]J. Cohen. Statistical power analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum, 1988. [20]GRI (2013). G4 Sustainability Reporting Guidelines. Global Reporting Initiative. Available: www.globalreporting.org

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The effect of managers’ power on employees’ entrepreneurship: an empirical study in the public offices of Iran

Brazilian Journal of Operations & Production Management ◽

10.14488/bjopm.2019.v16.n4.a7 ◽

2019 ◽

Vol 16 (4) ◽

pp. 617-626

Author(s):

Farhad Mahbobkhah

Keyword(s):

Structural Equation ◽

Structural Model ◽

Structural Equations ◽

Equation Modeling ◽

Statistical Population ◽

Five Dimensions ◽

Referent Power ◽

The Public ◽

Expert Power ◽

Legitimate Power

Objectives: The aim of this paper is to examine the effect of managers’ power on employees’ entrepreneurship in the public offices by using five dimensions of power (Coercive, expert, legitimate, referent, and reward) to give constructive suggestions for situation improvement through identifying powers affecting employees' entrepreneurship. Design/Methodology/Approach: Considering the research objective, it is an applied study, and regarding data collection and the data analysis method, it is a descriptive-correlational study which has analyzed data through structural equations modeling with partial least squares (PLS) approach. Data were collected using two questionnaires including managers’ power and employees’ entrepreneurship. A sample of 600 employees was selected from the public offices of Iran. Data were analyzed using structural equation modeling and Amos software. Results: The finding revealed that reward power, referent power, expert power and legitimate power had a positive and significant effect on employees’ entrepreneurship, but the coercive power had not significant effect on it. Limitations: Among limitations of the current research are the big size of the statistical population that made it difficult to collect data, as well as the existence of different cultures in the statistical population which could partly affect the results of the research. Practical implications: Research findings can be used to improve the entrepreneurship of employees in government organization. Originality/value: Authors confirm that the current research and its results are genuine and have been published nowhere so far. The proposed structural model in the current research can be used in government departments and improve the entrepreneurship status of employees in the organization.

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