## 3 Econometric Analysis of the Behavioral Model
### Research Methodology  
The objective of this paper is to validate the conceptual model by quantitative econometric analysis of the data collected from the companies involved in green procurement in Romania. The study also analyzes the impact of the quantitative model, represented by the strategic vision, regulation and the level of competences, the integrated operational management, the social responsibility, and the degree of innovation on the competition. The data was collected with the help of a questionnaire which was addressed to the managers and employees of the companies that invested in green projects. The companies were selected proportionally to their size. According to [22], the sample volume was calculated by multiplying the number of indicators by a value between 5 and 10. Thus, the sample should be at least 23 (indicators, see Table 1) \(\times\) 5 = 115 respondents. Thus, data was collected from 150 questionnaires out of which 140 were valid. The answer variants for the questions in the questionnaire were either binary (No = 0, Yes = 1), or using a seven-point Likert scale, where the answer variants ranged from \"very little = 1\" to \"very much = 7\". The questionnaire has been pre-tested to ensure that the format, language, and order of the questions are appropriate. In the past decades, for the complex analysis between the latent and observed variables covariance-based structural equation modeling (CB-SEM) method was used. Moreover, by 2010 more scientific papers used CB-SEM instead of PLS-SEM in their analysis. In the past few years, the number of published papers using CB-SEM was overpassed by the number of papers using PLS-SEM [23]. In fact, PLS-SEM is now used in many research papers in statistics, including statistical distributions [24], biostatistics [25], financial statistics and econometrics [26], sample surveys [27], machine learning [28], and time series analysis [29]. Several edited volumes [30; 31], textbooks [32; 33], and scholarly journals [34; 35] illustrate PLS-SEM methodology or purpose extensions of it. The PLS-SEM method is widely used to research papers which re estimating complex models with several constructs, structural paths and indicator variables without imposing assumptions on the distribution of the data. Moreover, PLS-SEM is an approach of SEM based on causal-prediction estimations of the statistical models [36; 37]. Other authors [38; 39; 40] provide detailed discussions and arguments related to why and when to use PLS-SEM methodology. The PLS-SEM method was used by many researchers to estimate composite models with several constructs, structural paths, and indicator variables without imposing assumptions on the distribution of the data. Moreover, PLS-SEM is a causal-predictive approach to SEM that underlines prediction in estimating econometric models, whose structures are made to provide causal explanations [41; 42]. Hence, the technique overcomes the possible dichotomy between prediction and explanation, which is the foundation for building managerial implications [43]. Consequently, user-friendly statistical software is available that usually require little technical knowledge about the method, such as SmartPLS and  
Figure 1: Conceptual model of the theory of rational behavior. Source: ([PERSON], 1991) [21]. PLS-Graph [44; 45]. Other authors [46; 47] provide more detailed discussions and arguments on when to use and not to use PLS-SEM. The authors conclude that the best situations when to use PLS-SEM are when the path model includes one or more formatively measured constructs, when a small population restricts the sample size, when the structural model is complex and includes many constructs, when the research is based on secondary data, and finally, when distribution of the data has normality issues.