scholarly journals Target Oriented Relational Model Finding

2014 ◽  
pp. 17-31 ◽  
Author(s):  
Alcino Cunha ◽  
Nuno Macedo ◽  
Tiago Guimarães
Keyword(s):  
Relational Model ◽  
Model Finding

Towards a relational model of workplace aggression

2007 ◽  
Author(s):  
Aaron C. H. Schat ◽  
M. Sandy Hershcovis ◽  
E. Kevin Kelloway
Keyword(s):  
Workplace Aggression ◽  
Relational Model

scholarly journals Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Area ◽  
Relational Model ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Ground Sample ◽  
Sensing Data

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

scholarly journals How Can Agricultural Corporate Build Sustainable Competitive Advantage through Green Intellectual Capital? A New Environmental Management Approach to Green Agriculture

2021 ◽  
Vol 18 (15) ◽  
pp. 7900
Author(s):  
Chulin Pan ◽  
Yufeng Jiang ◽  
Mingliang Wang ◽  
Shuang Xu ◽  
Ming Xu ◽  
...  
Keyword(s):  
Environmental Management ◽  
Competitive Advantage ◽  
Intellectual Capital ◽  
Moderated Mediation ◽  
Mediation Effect ◽  
Relational Model ◽  
Management Approach ◽  
Sustainable Competitive Advantage ◽  
Green Innovation ◽  
Mediation Effects

Based on natural resource-based theory, this study constructed a relational model between green intellectual capital, green innovation, and an agricultural corporate sustainable competitive advantage. The samples included a total of 341 agricultural companies in China, and multiple regression methods are used for the analysis. The results showed that green product innovation and green process innovation had a mediation effect between green human capital, green structural capital, green relational capital, and the sustainable competitive advantage of agricultural corporate. Beyond the simple moderation effect, a new integrated moderated-mediation effect model was established. It was shown that environmental leadership, green organizational identification, and green dynamic capability had different moderated-mediation effects under different conditions. The study is expected to close the previous research gaps and insufficiency in agricultural corporate environmental management and green agricultural. The empirical results and conclusions bring enlightenment and meaningful theoretical guidance to managers, researchers, practitioners, and policy makers in the green and sustainable development of agricultural corporates. The new environmental management path can help agricultural corporates conduct green innovation effectively, adapt to the green agricultural products market, and achieve sustainable competitive advantage. Ultimately, this will help to accelerate the development of green agriculture.

The Group Engagement Model: Procedural Justice, Social Identity, and Cooperative Behavior

2003 ◽  
Vol 7 (4) ◽  
pp. 349-361 ◽  
Author(s):  
Tom R. Tyler ◽  
Steven L. Blader
Keyword(s):  
Social Identity ◽  
Procedural Justice ◽  
Cooperative Behavior ◽  
Relevant Information ◽  
Psychological Research ◽  
Relational Model ◽  
Mediation Hypothesis ◽  
Value Model ◽  
Group Engagement ◽  
Group Engagement Model

The group engagement model expands the insights of the group-value model of procedural justice and the relational model of authority into an explanation for why procedural justice shapes cooperation in groups, organizations, and societies. It hypothesizes that procedures are important because they shape people's social identity within groups, and social identity in turn influences attitudes, values, and behaviors. The model further hypothesizes that resource judgments exercise their influence indirectly by shaping social identity. This social identity mediation hypothesis explains why people focus on procedural justice, and in particular on procedural elements related to the quality of their interpersonal treatment, because those elements carry the most social identity-relevant information. In this article, we review several key insights of the group engagement model, relate these insights to important trends in psychological research on justice, and discuss implications of the model for the future of procedural justice research.

The relational model: In search for lean and mean CPS technology

2017 ◽  
Author(s):  
Andrea Bonci ◽  
Massimiliano Pirani ◽  
Aldo Franco Dragoni ◽  
Alessandro Cucchiarelli ◽  
Sauro Longhi
Keyword(s):  
Relational Model

scholarly journals Examination of Heterogeneous Societies

2016 ◽  
Vol 48 (1) ◽  
pp. 39-57 ◽  
Author(s):  
Fumiko Kano Glückstad ◽  
Mikkel N. Schmidt ◽  
Morten Mørup
Keyword(s):  
Cultural Context ◽  
Latent Class ◽  
Cultural Analysis ◽  
Cross Cultural ◽  
Relational Model ◽  
Response Patterns ◽  
World Value Survey ◽  
Applied Model ◽  
Similarities And Differences ◽  
Cultural Data

The recent development of data analytic tools rooted around the Multi-Group Latent Class Analysis (MGLCA) has enabled the examination of heterogeneous datasets in a cross-cultural context. Although the MGLCA is considered as an established and popular cross-cultural data analysis approach, the infinite relational model (IRM) is a new and disruptive type of unsupervised clustering approach that has been developed recently by cognitive psychologists and computer scientists. In this article, an extended version of the IRM coined the multinominal IRM—or mIRM in short—is applied to a cross-cultural analysis of survey data available from the World Value Survey organization. Specifically, the present work analyzes response patterns of the Portrait Value Questionnaire (PVQ) representing Schwartz’s 10 basic values of Japanese and Swedes. The applied model exposes heterogeneous structures of the two societies consisting of fine-grained response patterns expressed by the respective subpopulations and extracts latent typological structures contrasting and highlighting similarities and differences between these two societies. In the final section, we discuss similarities and differences identified between the MGLCA and the mIRM approaches, which indicate potential applications and contributions of the mIRM and the general IRM framework for future cross-cultural data analyses.

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