Hlias Plikas’s scientific contributions

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Publications (3)

Figure 1. Regression Results In Figure 1, we indicate the regression results of the model analysis using Least Squares method. Firstly, the R-squared factor is 0.560899 in number, above 0.5, meaning that the regression analysis approaches reality. Let's check Coefficient numbers. C(1) is the constant value as implied. Values C(2), and C(3) are signed positively, which means a positive relation exist between Technology-Monitoring, E-Procurement and E-Sales. Checking the Probabilities in Prob. section and the Confidents together, we can see that C(1), C(2), C(3) of the Coefficient results have Probability less than a=5%. This reflects econometric theory where numbers with probabilities less than a=5%, which means results with a 95% level of confidence, have statistical significance and impact in the dependent variable. In other words Technology-Monitoring and E- Procurement affect E-Sales in a statistically significant way [15]. We see that C(3) Coefficient is higher than C(2) Coefficient for the same level of Prob. and same level of confidence. That means that E-Procurement variable affects in much higher level E-Sales factor.  
Figure 2. Substituted Coefficients In Figure 2 we can see the substituted coefficients of our model implicated in our initial equation. Here we can easily observe that positive influence, plus signs in front of each variable of the independent variables to the dependent one.  
Figure 5. Satisfaction_E_Procurement and Satisfaction_Technology_Monitoring:  
Figure 6. Bar-Chart: Satisfaction_E_Procurement and Satisfaction_Technology_Monitoring From Figure 6 and the bar-chart, we can observe that the Satisfaction_E_Procurement is higher than Satisfaction_Technology_Monitoring Bar. That means, that E-Procurement affects E-Sales factor by 88% whereas Technology- Monitoring affects E-Sales by only 12%.  
Figure 7. Stack-Chart: Satisfaction_E_Procurement and Satisfaction_Technology_Monitoring. From Figure 7 and the Stach-Chart, we can observe that the Satisfaction_E_Procurement is higher than Satisfaction_Technology_Monitoring Bar. That means, that E-Procurement affects E-Sales factor by 88% whereas  

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Modeling the adoption of E- Procurement and Technology, Marketing and Social media bi- Products from Greek businesses and their effect on electronic sales
  • Article
  • Full-text available

July 2016

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247 Reads

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Hlias Plikas

Today's Greek businesses strive to overcome economic obstacles and increase their sales by constantly searching new ways of improvement and economic sustainability. The technology factor has infiltrated every aspect of our lives, making the electronic element a vital part of living. Business bubble adopts more and more technological ways of improvement with E-Procurement and Technology adoption being the most dominant. The purpose of this paper is to analyze this adoption of Technology and E-Procurement from Greek businesses and the effect of those factors in businesses' electronic sales through simulation modeling using a sample of 400 Greek businesses. The reason the paper was created, is to provide this optimal solution to all businesses seeking how to increase their e-sales through Technology Monitoring and E-Procurement adoption. Thorough research through the paper revealed that Technology Monitoring and E-Procurement can be used from Greek companies to successfully increase their e-sales effectively.

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Figure 1. Dynamic Simulation Model. 
Figure 2. Satisfaction Academic Conference Blog Page in conjunction with Satisfaction Community, Number of Event Directories, Sharing and Engagement and Profit. 
Figure 3. Bar-Chart: Company Resources in conjunction with Academic Conferences, Event directories, Sharing and Engagement and Community. 
Figure 4. Pie-Chart: Company Resources in conjunction with Profit. 
Figure 8. Time-Color_Chart: Profit 

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Academic conferences promotion process and social media. Modeling of the problem

April 2016

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161 Reads

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1 Citation

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Hlias Plikas

Social networks are now a crucial part of today's way of life. Academic conferences is another chapter, that give people the opportunity to explore new ideas and share them with the scientific world. Blending those two factors together in order to achieve a main purpose, could give a remarkable effect. The purpose of this paper is to analyze the promotion process of academic conferences through social media and use simulation models to modelize that analysis. The reason the paper was created, is to provide this optimal solution to all those seeking how to promote academic conferences effectively through social media. Thorough research through the paper revealed that social media, nowadays used by millions and millions of users can be successfully used to promote academic papers and with great appeal.

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Citations (1)

... In this study, a dynamic simulation modeling process via iThink 10.0.2 simulator of iSee Systems was used for estimating the proper way of distributing the resources of an enterprise for the construction of a webpage with the appropriate keywords. The utility of DMS process has already been implemented in other studies that related heavily with the coordination and promotion of scientific conferences for managing and distributing company's resources (Sakas et al. 2016;Plikas et al. 2015). For the construction of the model iThink software uses stocks and flows, convertors and connectors as a graphical user interface representation. ...

Reference:

Stuffing Keyword Regulation in Search Engine Optimization for Scientific Marketing Conferences
Academic conferences promotion process and social media. Modeling of the problem

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Hlias Plikas