An economic view of the innovation potential, the tendencies of smoking in the developed countries and the importance of marketing in this field

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Smoking, as one of the main causes, is a negative factor associated with many diseases. The primary objective of the research is to determine the effect of innovation on selected smoking indicators in a sample of countries of the Organization for Economic Co-operation and Development (OECD). Four variables enter the analytical processing, such as Global Innovation Index, Population ratio of daily smokers (age 15+), Daily smokers (age 15-24), and Tobacco consumption in grams per capita (age 15+). These variables were included in the research from 2011 to 2018. The simple linear regression – the Ordinary Least Squares (OLS) model – and correlation analysis – Spearman’s rank correlation – was used for statistical processing. The results show that the effect of innovation on the ratio of daily smokers over the age of 15 to the total population may be considered a highly significant relationship. The effect on the annual tobacco consumption per capita is the second most significant relationship, and the effect on the ratio of daily smokers over 15 and under 24 years to the total population is the least significant compared to the previous two cases. Correlation analysis shows similar outputs. All these relationships may be considered negative. It is possible to talk about the lost innovation potential associated with smoking, primarily in the productive part of the population. A higher level of smoking can be associated with a lower level of innovation. Also, innovation negatively affects the tendency to smoke. Therefore, public policies should promote a healthy lifestyle.

Acknowledgments
This research is funded by the RVO 2020 internal grant scheme of the Tomas Bata University in Zlín titled “Economic quantification of marketing processes aimed at increasing value for the patient in the process of construction of system in order to measure and to manage performance in healthcare facilities in the Czech Republic.”

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    • Figure 1. Relationship between GII and G_Tob
    • Figure 2. Relationship between GII and Smors_D%15-24
    • Figure 3. Relationship between GII and Smors_D%15
    • Table 1. Descriptive statistics of variables
    • Table 2. Homoscedasticity test – Breusch-Pagan statistic
    • Table 3. Regression analysis – model 1
    • Table 4. Regression analysis – model 2
    • Table 5. Regression analysis – model 3
    • Table 6. Association analysis
    • Conceptualization
      Martin Rigelsky, Viera Ivankova, Beata Gavurova
    • Data curation
      Martin Rigelsky
    • Investigation
      Martin Rigelsky, Viera Ivankova
    • Methodology
      Martin Rigelsky, Beata Gavurova
    • Software
      Martin Rigelsky
    • Visualization
      Martin Rigelsky
    • Writing – original draft
      Martin Rigelsky, Viera Ivankova, Beata Gavurova, Jaroslav Gonos
    • Writing – review & editing
      Martin Rigelsky, Viera Ivankova, Beata Gavurova, Jaroslav Gonos
    • Formal Analysis
      Viera Ivankova, Jaroslav Gonos
    • Resources
      Viera Ivankova, Jaroslav Gonos
    • Supervision
      Viera Ivankova, Beata Gavurova
    • Validation
      Viera Ivankova, Beata Gavurova, Jaroslav Gonos
    • Funding acquisition
      Beata Gavurova
    • Project administration
      Beata Gavurova