scholarly journals The Impact of Eco-Innovation on Performance Through the Measurement of Financial Resources and Green Patents

2018 ◽  
Vol 33 (2) ◽  
pp. 285-310 ◽  
Author(s):  
Luz María Marín-Vinuesa ◽  
Sabina Scarpellini ◽  
Pilar Portillo-Tarragona ◽  
José M. Moneva
Keyword(s):  
Structural Equation Modeling ◽  
Financial Performance ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Positive Relationship ◽  
Financial Resources ◽  
Equation Modeling ◽  
Environmental Capability ◽  
The Impact

The main objective of this article is to contribute empirically to the understanding of the impact that eco-innovation has on firms’ financial performance within the framework of the resources-based view. Specifically, eco-innovation is measured by using eco-innovative activities and financial resources applied to eco-innovation to argue that the identification and measurement of certain resources of firms allow companies that are particularly active in investing in eco-innovation to be more competitive. Furthermore, the analysis attempts to ascertain whether firms that own green patents and other characteristics exhibit different level of financial performance than firms without registered green patents. The empirical partial least squares structural equation modeling results indicate a positive relationship between the investment of resources and the financial performance of eco-innovative firms. The effects of involving managers in eco-innovative processes as an environmental capability of firms are also tested.

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

Athenea ◽  
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

scholarly journals Evaluation of Preventive, Supportive and Awareness Building Measures among International Students in China in Response to COVID-19: A Structural Equation Modeling Approach

2020 ◽  
Author(s):  
Tanwne Sarker ◽  
Apurbo Sarkar ◽  
Md. Ghulam Rabbany ◽  
Milon Barmon ◽  
Rana Roy ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
International Students ◽  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Convergent Validity ◽  
Structural Equation ◽  
Equation Modeling ◽  
Local Authorities ◽  
The Impact

Abstract Background The Coronavirus Disease 2019 (COVID-19) with its high mortality, stigma and panic has compelled many cities and countries to complete lockdown. The worldwide student group is one of the most affected and vulnerable communities in this situation. Our current study aimed to assess the impact of the behavior change communication among international students in China in current COVID-19 crisis.Methods In this paper, we have utilized partial least squares-structural equation modeling (PLS-SEM) to understand the health behaviour changes of international students in China in response to novel Coronavirus outbreak. We mainly analyzed the relationship among the three selected latent variables (preventive, supportive and awareness building) based on a survey among the international students (n=467) in China in February 2020. We obtained their valuable responses regarding level of awareness, satisfaction and trust in authorities (i.e., government, local authorities and institutions) during this emergency period. Results We utilized 22 indicators in the conceptual framework model with the help of Smart PLS 2.0 version software. The lowest average variance extracted (AVE) for all the constructs of our paper exceeded the minimum accepted value of 0.5, representing the adequate convergent validity. Prediction of students’ satisfaction, the key outcome degree of the model, was nearly moderate, with an R2 = 0.507 whereas the prediction of trust in authorities was above substantial, with an R2 = 0.797. Therefore, our PLS-SEM model showed a strong and significant positive association between preventive and supportive measures taken for the study population and gaining trust, awareness and satisfaction in authorities. Conclusions Integrated partial least squares-structural equation modeling (PLS-SEM) can be a great way to measure the satisfaction and trust level of various population groups over government, local authorities, and institutions in public health emergency like COVID-19 crisis. We believe that our findings are important for travel and global health perspectives. Other countries can learn and take necessary initiatives for their international students and general public to halt this deadly epidemic with gaining their satisfaction and trust as well.

scholarly journals A THE USE OF PARTIAL LEAST SQUARES MODELING IN FINANCE BUSINESS PARTNERING RESEARCH

Kursor ◽  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Amin Tohari ◽  
Faisol Faisol ◽  
Aeri Rahmad
Keyword(s):  
Structural Equation Modeling ◽  
Financial Performance ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Accounting System ◽  
Managerial Accounting

Structural equation modeling (SEM) is a set of statistical techniques that allows testing a model that is built between one or more endogenous variables with one or more exogenous variables, where each endogenous and exogenous variable can be in the form of latent or a construct built from several variables of manifest or indicator.  There is Structural Equation Modeling (SEM) based on covariance and variance, known as Partial Least Square (PLS), SEM-PLS is a powerful and flexible analysis method. This research discusses about the application of SEM-PLS in the field of managerial accounting system, namely the application of non-financial performance’s role that delivers the sustainability of the company's financial performance. Based on the results obtained, it can be concluded that partial least squares can be used to model finance business partnering, and it is known that employee performance and internal process performance contribute to achieve the firm’s financial performance.

scholarly journals KAJIAN ANALISIS JALUR DENGAN STRUCTURAL EQUATION MODELING (SEM) SMART-PLS 3.0

2020 ◽  
Vol 3 (2) ◽  
pp. 28-48
Author(s):  
I Made Anom Arya Pering
Keyword(s):  
Structural Equation Modeling ◽  
Path Analysis ◽  
Organizational Performance ◽  
Least Squares ◽  
Partial Least Squares ◽  
Direct Effect ◽  
Structural Equation ◽  
Employee Performance ◽  
Equation Modeling ◽  
The Impact

Study on Path Analysis using Structural Equation Modeling (SEM) Smart Partial Least Squares (PLS) software version 3.0 with the aim of testing the Impact or Effect of Training on Employee Performance and Organizational Performance, whether it has a significant effect.The analysis results obtained are:First, the Effect of Employee Performance on Organizational Performance.The t-statistic value of 2.721 and the significance (t-table significance of 5% = 1.96) because the t-statistic value of 2.721 is greater () than the t-table of 1.96, Employee Performance has a "significant" effect on Organizational Performance.Second, the Effect of Training (Training) on Employee PerformanceThe t-statistic value of 2.688 significance (t-table significance of 5% = 1.96) because the t-statistic value of 2.688 is greater () than the t-table of 1.96, the Training has a "significant" effect on Employee Performance.Third, the Effect of Training (Training) on Organizational PerformanceThe t-statistic value of 0.338 significance (t-table significance of 5% = 1.96) because the t-statistic value of 0.338 is smaller () than t-table 1.96, the "insignificant" Training has a direct effect on Organizational Performance.

scholarly journals eWOM in C2C Platforms: Combining IAM and Customer Satisfaction to Examine the Impact on Purchase Intention

2021 ◽  
Vol 16 (5) ◽  
pp. 1612-1630
Author(s):  
Salvador Bueno ◽  
M. Dolores Gallego
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Purchase Intention ◽  
Structural Equation ◽  
Source Credibility ◽  
Positive Impact ◽  
Equation Modeling ◽  
Research Model ◽  
Argument Quality ◽  
The Impact

This study is focused on communications that come from consumer-to-consumer (C2C) ecommerce relationships. This topic is directly associated with the electronic word-of-mouth (eWOM) phenomenon. eWOM is related to the set of positive or negative opinions made by potential, actual, or former customers about a seller. The present study proposes a structural equation modeling with partial least squares (PLS-SEM) research model to analyze consumers’ opinions impact on attitude toward purchasing. This model is based on the Information Adoption Model (IAM) in combination with an ecommerce satisfaction perspective, comprising five constructs: (1) service quality, (2) ecommerce satisfaction, (3) argument quality, (4) source credibility and (5) purchase intention. The model was tested by applying the Smart Partial Least Squares (SmartPLS) software for which 116 effective data from customers of the Taobao C2C platform were used. The findings reveal that all of the defined relationships were supported, confirming the positive impact of all the proposed constructs on the purchase intention. In this respect, the findings suggest that C2C platforms should strengthen the analyzed connections to grow the business and to promote transactions. Finally, implications and limitations related to the explanatory capacity and the sample are identified.

Partial least squares structural equation modeling of chemistry attitude in introductory college chemistry

2018 ◽  
Vol 19 (4) ◽  
pp. 1270-1286 ◽  
Author(s):  
James Ross ◽  
Leslie Nuñez ◽  
Chinh Chu Lai
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
At Risk Students ◽  
Affective Domain ◽  
Equation Modeling ◽  
College Chemistry ◽  
The Subject ◽  
Introductory College

Students’ decisions to enter or persist in STEM courses is linked with their affective domain. The influence of factors impacting students’ affective domain in introductory college chemistry classes, such as attitude, is often overlooked by instructors, who instead focus on students’ mathematical abilities as sole predictors of academic achievement. The current academic barrier to enrollment in introductory college chemistry classes is typically a passing grade in a mathematics prerequisite class. However, mathematical ability is only a piece of the puzzle in predicting preparedness for college chemistry. Herein, students’ attitude toward the subject of chemistry was measured using the original Attitudes toward the Subject of Chemistry Inventory (ASCI). Partial least squares structural equation modeling (PLS-SEM) was used to chart and monitor the development of students’ attitude toward the subject of chemistry during an introductory college chemistry course. Results from PLS-SEM support a 3-factor (intellectual accessibility,emotional satisfaction, andinterestandutility) structure, which could signal the distinct cognitive, affective, and behavioral components of attitude, according to its theoretical tripartite framework. Evidence of a low-involvement hierarchy of attitude effect is also presented herein. This study provides a pathway for instructors to identify at-risk students, exhibiting low affective characteristics, early in a course so that academic interventions are feasible. The results presented here have implications for the design and implementation of teaching strategies geared toward optimizing student achievement in introductory college chemistry.

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