An economic view of the innovation potential, the tendencies of smoking in the developed countries and the importance of marketing in this field
-
Author(s)Martin RigelskyLink to ORCID Index: https://orcid.org/0000-0003-1427-4689
,
Viera IvankovaLink to ORCID Index: https://orcid.org/0000-0002-6164-1165
,
Beata GavurovaLink to ORCID Index: https://orcid.org/0000-0002-0606-879X
,
Jaroslav GonosLink to ORCID Index: https://orcid.org/0000-0002-5708-7982
-
DOIhttp://dx.doi.org/10.21511/im.16(2).2020.04
-
Article InfoVolume 16 2020, Issue #2, pp. 43-53
- 970 Views
-
195 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
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.”
- Keywords
-
JEL Classification (Paper profile tab)O32, I12, I15
-
References48
-
Tables6
-
Figures3
-
- 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
-
- Ahlan, A. R., & Ahmad, B. I. (2014). User Acceptance of health information technology (HIT) in developing countries: A conceptual model. Procedia Technology, 16, 1287-1296.
- Aksoy, A., Bilgic, A., Yen, S., & Urak, F. (2019). Determinants of Household Alcohol and Tobacco Expenditures in Turkey. Journal of Family and Economic Issues, 40(4), 609-622.
- Ali, R., Zhang, Z. Q., & Soomro, M. B. (2019). Smoking-Cessation Acceptance Via Mobile Health and Quick Response Code Technologies: Empirical Evidence of a Pilot Study from China and Pakistan. Current Psychology, 1-13.
- Arnson, Y., Shoenfeld, Y., & Amital, H. (2010). Effects of tobacco smoke on immunity, inflammation and autoimmunity. Journal of Autoimmunity, 34(3), J258-J265.
- Baskerville, N. B., Struik, L. L., Hammond, D., Guindon, G. E., Norman, C. D., Whittaker, R., … & Brown, K. (2015). Effect of a Mobile phone intervention on quitting smoking in a young adult population of smokers: Randomized controlled trial study protocol. JMIR Research Protocols, 4(1), e10.
- Berkowitz, C. M., Zullig, L. L., Koontz, B. F., & Smith, S. K. (2017). Prescribing an app? Oncology providers’ views on Mobile health apps for Cancer Care. JCO Clinical Cancer Informatics, 1, 1-7.
- BinDhim, N. F., McGeechan, K., & Trevena, L. (2014). Assessing the effect of an interactive decision-aid smartphone smoking cessation application (app) on quit rates: a double-blind automated randomised control trial protocol. BMJ Open, 4(7), e005371.
- Borrelli, B., Bartlett, Y. K., Tooley, E., Armitage, C. J., & Wearden, A. (2015). Prevalence and Frequency of mHealth and eHealth Use Among US and UK Smokers and Differences by Motivation to Quit. Journal of Medical Internet Research, 17(7), e164.
- Breusch, T. S., & Pagan, A. R. (1979). A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica, 47(5), 1287-1294.
- Buttigieg, S. C. (2019). Innovation Strategies and Health System Guiding Principles to Address Equity and Sustainability in Responsible Innovation in Health Comment on “What Health System Challenges Should Responsible Innovation in Health Address? Insights From an International Scoping Review.” International Journal of Health Policy and Management, 8(9), 570-572.
- Chaloupka, F. J. (2013). Maximizing the public health impact of alcohol and tobacco taxes. American Journal of Preventive Medicine, 44(5), 561-562.
- Cook, R. D., & Weisberg, S. (1982). Residuals and Influence in Regression. New York: Chapman and Hall.
- Critchley, J. A., & Capewell, S. (2003). Mortality risk reduction associated with smoking cessation in patients with coronary heart disease. A systematic review. JAMA-Journal of the American Medical Association, 290(1), 86-97.
- Danaher, P. J., Hardie, B. G. S., & Putsis, W. R. (2001). Marketing-mix variables and the diffusion of successive generations of a technological innovation. Journal of Marketing Research, 38(4), 501-514.
- Do, Y. K., & Bautista, M. A. (2015). Tobacco use and household expenditures on food, education, and healthcare in low- and middle-income countries: a multilevel analysis. BMC Public Health, 15, 1-11.
- Gavurová, B., Huculová, E., & Kováč, V. (2019). Quantification of relationship between cardiovascular diseases mortality rate and selected socio-economic indicators – comparative analysis of the Czech Republic and the Slovak Republic. Economics and Sociology, 12(2), 284-300.
- Gavurova, B., Vagasova, T., & Grof, M. (2017). Educational Attainment and Cardiovascular Disease Mortality in the Slovak Republic. Economics and Sociology, 10(1), 232-245.
- Global Innovation Index (GII). (2019). Global Innovation Index. Analysis.
- Grier, S., & Bryant, C. A. (2005). Social marketing in public health. Annual Review of Public Health, 26, 319-339.
- Hoeppner, B. B., Hoeppner, S. S., Seaboyer, L., Schick, M. R., Wu, G. W. Y., Bergman, B. G., & Kelly, J. F. (2016). How smart are smartphone apps for smoking cessation? A content analysis. Nicotine and Tobacco Research, 18(5), 1025-1031.
- Hu, F. B., Manson, J. E., Stampfer, M. J., Colditz, G., Liu, S., Solomon, C. G., & Willett, W. C. (2001). Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. New England Journal of Medicine, 345(11), 790-797.
- Iacoviello, B. M., Luo, S. X., Klein, D. B., Steinerman, J. R., Berger, A. G., Schork, N. J., & Silver, T. L. (2017). Clickotine, a personalized smartphone app for smoking cessation: Initial evaluation. JMIR mHealth and uHealth, 5(4), e56.
- Jha, P., Landsman, V., Rostron, B., Thun, M., Anderson, R. N., McAfee, T., & Peto, R. (2013). 21st-Century Hazards of Smoking and Benefits of Cessation in the United States. New England Journal of Medicine, 368(4), 341-350.
- Kallas, R., Li, J., & Petri, M. (2019). Association of African-American ethnicity and smoking status with total and individual damage index in systemic lupus erythematosus. Clinical Rheumatology, 39, 365-373.
- Max, W., Sung, H., Tucker, L., & Stark, B. (2011). The Cost of Smoking for California’s Hispanic Community. Nicotine & Tobacco Research, 13(4), 248-254.
- Mosa, A. S. M., Yoo, I., & Sheets, L. (2012). A systematic review of healthcare applications for smartphones. BMC Medical Informatics and Decision Making, 12(1), 67.
- Odermatt, R., & Stutzer, A. (2015). Smoking bans, cigarette prices and life satisfaction. Journal of Health Economics, 44, 176-194.
- OECD. (2016). OECD Factbook 2015–2016: Economic, Environmental and Social Statistics. Paris: OECD Publishing.
- OECD. (2019a). Non-Medical Determinants of Health.
- OECD. (2019b). OECD Health Statistics 2019. Definitions, Sources and Methods. Daily smokers.
- OECD. (2019c). OECD Health Statistics 2019 Definitions, Sources and Methods. Tobacco consumption in grams per capita.
- Palali, A., & van Ours, J. C. (2019). The impact of tobacco control policies on smoking initiation in eleven European countries. European Journal of Health Economics, 20(9), 1287-1301.
- Peiris, D., Wright, L., News, M., Rogers, K., Redfern, J., Chow, C., & Thomas, D. (2019). A smartphone app to assist smoking cessation amongst aboriginal Australians: Findings from a pilot randomised controlled trial (preprint). JMIR mHealth and uHealth, 7(4), 1-16.
- Pesce, G., Marcon, A., Calciano, L., Perret, J. L., Abramson, M. J., … & Accordini, S. (2019). Time and age trends in smoking cessation in Europe. PLoS ONE, 14(2), e0211976.
- Peto, R., Darby, S., Deo, H., Silcocks, P., Whitley, E., & Doll, R. (2000). Smoking, smoking cessation, and lung cancer in the UK since 1950: combination of national statistics with two case-control studies. British Medical Journal, 321(7257), 323-329.
- Reddy, K. S., Yadav, A., Arora, M., & Nazar, G. (2012). Integrating tobacco control into health and development agendas. Tobacco Control, 21(2), 281-286.
- Reychav, I., McHaney, R., Hirak, E., & Merker, B. (2019). Smoking cessation: Exploration of perceived technology-related information value. Health Informatics Journal, 25(4), 1244-1264.
- Rice, D., Hodgson, T., Sinsheimer, P., Browner, W., & Kopstein, A. (1986). The Economic Costs of the Health Effects of Smoking, 1984. The Milbank Quarterly, 64(4), 489-547.
- Stampfer, M. J., Hu, F. B., Manson, J. E., Rimm, E. B., & Willett, W. C. (2000). Primary prevention of coronary heart disease in women through diet and lifestyle. New England Journal of Medicine, 343(1), 16-22.
- Tabuchi, T., Fukui, K., & Gallus, S. (2019). Tobacco Price Increases and Population Interest in Smoking Cessation in Japan Between 2004 and 2016: A Google Trends Analysis. Nicotine & Tobacco Research, 21(4), 475-480.
- Thun, M. J., Carter, B. D., Feskanich, D., Freedman, N. D., Prentice, R., Lopez, A. D., Hartge, P., & Gapstur, S. M. (2013). 50-Year Trends in Smoking-Related Mortality in the United States. New England Journal of Medicine, 368(4), 351-364.
- Totskaya, Y. G., & Sadovoy, M. A. (2014). Management of Innovation in Health Care at the Regional Level. Bulletin of Siberian Medicine, 13(5), 184-194. (In Russian).
- Ubhi, H. K., Michie, S., Kotz, D., van Schayck, O. C. P., Selladurai, A., & West, R. (2015). Characterising smoking cessation smartphone applications in terms of behaviour change techniques, engagement and ease-of-use features. Translational Behavioral Medicine, 6(3), 410-417.
- Vineis, P., Alavanja, M., Buffler, P., Fontham, E., Franceschi, S., Gao, Y. T., Gupta, P. C., Hackshaw, A., Matos, E., Samet, J., Sitas, F., Smith, J., Stayner, L., Straif, K., Thun, M. J., Wichmann, H. E., Wu, A. H., Zaridze, D., Peto, R., & Doll, R. (2004). Tobacco and cancer: Recent epidemiological evidence. Journal of the National Cancer Institute, 96(2), 99-106.
- WHO. (2013). WHO Report on the Global Tobacco Epidemic, 2013: Enforcing bans on tobacco advertising, promotion and sponsorship. Geneva, Switzerland.
- WHO. (2019). Tobacco.
- Willi, C., Bodenmann, P., Ghali, W. A., Faris, P. D., & Cornuz, J. (2007). Active smoking and the risk of type 2 diabetes. A systematic review and meta-analysis. JAMA-Journal of the American Medical Association, 298(22), 2654-2664.
- Yanbaeva, D. G., Dentener, M. A., Creutzberg, E. C., Wesseling, G., & Wouters, E. F. (2007). Systemic effects of smoking. Chest Journal, 131(5), 1557-1566.
-
Non-financial factors affecting the operational performance of hospitality companies: Evidence from Vietnam
Trung Kien Phan ,
Thi Hong Thuy Nguyen ,
Thu Ha Dang ,
Van Thuan Tran ,
Kim Ngoc Le
doi: http://dx.doi.org/10.21511/ppm.19(4).2021.05
Problems and Perspectives in Management Volume 19, 2021 Issue #4 pp. 48-62 Views: 9097 Downloads: 1414 TO CITE АНОТАЦІЯNon-financial factors including quality of services, the flexibility of a company, utilization of resources, and market orientation are regarded as significant determinants that enhance the profitability-based performance of a service company or a hotel. The study investigated the interaction between these factors and hotel operating performance measured by the hospitality performance and results indicators. Data on 346 executives of Vietnam’s hospitality companies were collected. A structural equation modeling (SEM) method was utilized to examine the positive-direct and moderating effects of non-financial factors on hotel performance in terms of occupancy rate (OCR), average daily rate (ADR), and the revenue per available room (RevPAR). The findings showed that service quality ( = 0.118, p < 0.05), flexibility ( = 0.173, p < 0.05) and resource utilization (= 0.172, p < 0.05) positively affected the performance of Vietnam’s hospitality companies. Meanwhile, innovation showed no direct influence (p = 0.068) but an indirect impact on the performance through service quality ( = 0.311, p < 0.05). Market orientation did not impact the performance (p = 0.076) but it positively affected both innovation ( = 0.322, p < 0.05) and service quality ( = 0.146, p < 0.05). The study contributed to a theoretical enhancement of the current level of knowledge on the factors that affect the performance and developed a reliable scale for measuring the performance of hotels in Vietnam.
-
Exploring fast moving consumer goods (FMCG) small, medium and micro enterprises manufacturers’ need for innovation to achieve growth
Lawrence Mpele Lekhanya ,
Nze Grace Olajumoke ,
Dorasamy Nirmala
doi: http://dx.doi.org/10.21511/ee.08(2).2017.01
Environmental Economics Volume 8, 2017 Issue #2 pp. 8-16 Views: 5486 Downloads: 1160 TO CITE АНОТАЦІЯThe purpose of this paper is to investigate the problems of the sustainable development, to explore the level of innovation in the fast moving consumer goods (FMCG) manufacturing SMMEs sectors, which most affects on the state of the environment, to identify the causes of low innovation in the industry and to examine these factors influence on the effectiveness of SMMEs manufacturers innovation strategies, as well as to invent a new innovation strategic approach to overcome innovation problems in the economic growth of fast moving consumer goods SMMEs manufacturers. The study is aimed to determine the level of innovation and factors contributing to low innovation in fast moving consumer goods (FMCG) SMMEs manufacturers, which hinder their economic performance. Mixed approach of quantitative and qualitative questionnaire is used for primary data collection. Sample consists of 120 SMMEs. Statistical Package for Social Sciences (SPSS) (23.0) was employed for data analysis. The study results are presented with figures and diagrams. This study will be a useful tool for general public and relevant stakeholders in this sector.
-
Perceived health risk, online retail ethics, and consumer behavior within online shopping during the COVID-19 pandemic
Yuniarti Fihartini ,
Arief Helmi ,
Meydia Hassan ,
Yevis Marty Oesman
doi: http://dx.doi.org/10.21511/im.17(3).2021.02
Innovative Marketing Volume 17, 2021 Issue #3 pp. 17-29 Views: 4454 Downloads: 1707 TO CITE АНОТАЦІЯThe risk of virus contracting during the COVID-19 pandemic has changed consumer preference for online shopping to meet their daily needs than shopping in brick-and-mortar stores. Online shopping presents a different environment, atmosphere, and experience. The possibility of ethical violations is higher during online than face-to-face transactions. Therefore, this study was conducted to investigate the influence of perceived health risk and customer perception of online retail ethics on consumer online shopping behavior during the COVID-19 pandemic, involving seven variables, namely perceived health risk, security, privacy, non-deception, reliability fulfillment, service recovery, and online shopping behavior. The data were collected through an online survey by employing the purposive sampling technique to a consumer who has shopped online during the COVID-19 pandemic in Indonesia. 315 valid responses were obtained and analyzed through quantitative method using SEM-Amos. The results showed that perceived health risk and four variables of online retail ethics including security, privacy, reliability fulfillment, and service recovery affected online shopping behavior. Meanwhile, non-deception was found to have an insignificant effect. The coefficient value proved perceived health risk to be more dominant in influencing online shopping behavior than the variables of online retail ethics. Thus, consumers pay more concern for their health during online shopping. However, positive consumer perceptions of the behavior of online retail websites in providing services also can encourage consumers to shop online during this pandemic.
