Impact of Smart Economy on Smart Areas and Mediation Effect of National Economy
Abstract
:1. Introduction
2. Literature Review
3. Setting the Hypotheses
4. Materials and Methods
4.1. Data
- Smart Economy: turnover of SMEs due to e-commerce, employment in knowledge-intensive areas, ICT staff as percentage of total employment, ICT input in GDP, individual online consumption in the domestic market and in the EU countries, import of ICT products, and fixed assets as a percentage of GDP [16,17,18,19,20];
- Smart Environment: air emissions, resource productivity, expenditures on environment protection, hazardous waste, hazardous pollutions of different types, emissions of heat plants and producing industries, various air pollutants, and share of electricity from renewable sources [38,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57];
- Smart Society (combines the indicators of Smart Governance and the traditional indicators of sustainable society): female employment in technological industries, inequality in education, social protection expenditures, female graduates from the universities, gender inequality index, youth participation, availability of e-public services, indicators of risk of poverty (Gini and Quintile indices), expenditures on health system, youth inequality indicators, and decreased access to the internet due to the cost factor [40,41,61];
- “Mediator” variable contains the general indicators of economy development, such as GDP, GDP growth (to year 2010), energy consumption per capita, rate of unemployment, rate of self-employment, and rate of inflation (consumer price index) [61].
4.2. Model
4.3. Software
4.4. Research Process
5. Results
5.1. Testing the Quality of the Constructs
5.2. Hypotheses Testing
6. Discussion
Research Problem
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Vinod Kumar, T.M.; Dahiya, B. Smart Economy in Smart Cities; Springer: Singapore, 2017; pp. 3–76. [Google Scholar]
- Gavalas, D.; Nicopolitidis, P.; Kameas, A.; Goumopoulos, C.; Bellavista, P.; Lambrinos, L.; Guo, B. Smart cities: Recent trends, methodologies, and applications. Wirel. Commun. Mobile Comput. 2017, 2017, 7090963. [Google Scholar] [CrossRef] [Green Version]
- Kirimtat, A.; Krejcar, O.; Kertesz, A.; Tasgetiren, M.F. Future Trends and Current State of Smart City Concepts: A Survey; IEEE Access: Piscataway, NJ, USA, 2020; Volume 8, pp. 86448–86467. [Google Scholar]
- Ismagilova, E.; Hughes, L.; Dwivedi, Y.K.; Raman, K.R. Smart cities: Advances in research—An information systems perspective. Int. J. Inf. Manag. 2019, 47, 88–100. [Google Scholar] [CrossRef]
- Klein, C.; Kaefer, G. From smart homes to smart cities: Opportunities and challenges from an industrial perspective. Next Generation Teletraff and Wired/Wireless Advanced Networking. Proc. Lect. Notes Comput. Sci. 2008, 5174, 260. Available online: http://www.sciepub.com/reference/40900 (accessed on 7 October 2021).
- Marsa-Maestre, I.; Lopez-Carmona, M.A.; Velasco, J.R.; Navarro, A. Mobile agents for service personalization in smart environments. J. Netw. 2008, 3, 30–41. [Google Scholar] [CrossRef]
- Giffinger, R.; Fertner, C.; Kramar, H.; Kalasek, R.; PichlerMilanoviü, N.; Meijers, E. Smart Cities: Ranking of European Medium-Sized Cities; Centre of Regional Science (SRF), Vienna University of Technology: Vienna, Austria, 2007; Available online: http://www.smartcities.eu/download/smart_cities_final_report.pdf (accessed on 13 December 2021).
- Dirks, S.; Keeling, M.A. Vision of Smarter Cities: How Cities Can Lead the Way into a Prosperous and Sustainable Future; IBM Global Business Services: Somers, NY, USA, 2009; Available online: https://www.semanticscholar.org/paper/A-Vision-of-Smarter-Cities-%3A-How-Cities-Can-Lead-a-Kulesa/f0a85e8413c1238c6c705b3fd02c38cb67933254 (accessed on 22 December 2021).
- Kanter, R.M.; Litow, S.S. Informed and Interconnected: A Manifesto for Smarter Cities; Harvard Business School General Management Unit Working Paper; 2009; Available online: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1420236 (accessed on 22 December 2021).
- Mitchell, W.J. Smart City 2020. Metropolis, April 2006. Available online: http://www.metropolismag.com/story/20060320/smart-city-2020 (accessed on 7 August 2021).
- Nam, T.; Pardo, T.A. Conceptualizing smart city with dimensions of technology, people, and institutions. In Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, College Park, MD, USA, 12–15 June 2011; pp. 282–291. [Google Scholar]
- Shapiro, J.M. Smart cities: Quality of life, productivity, and the growth effects of human capital. Rev. Econ. Stat. 2006, 88, 324–335. [Google Scholar] [CrossRef]
- Hollands, R.G. Will the real smart city please stand up? Intelligent, progressive, or entrepreneurial? City 2008, 12, 303–320. [Google Scholar] [CrossRef]
- Caragliu, A.; Nijkamp, P. The impact of regional absorptive capacity on spatial knowledge spillovers: The cohen and levinthal model revisited. Appl. Econ. 2012, 44, 1363–1374. [Google Scholar] [CrossRef]
- Kim, K.; Jung, J.K.; Choi, J.Y. Impact of the smart city industry on the Korean national economy: Input-output analysis. Sustainability 2016, 8, 649. [Google Scholar] [CrossRef] [Green Version]
- Popova, Y. Economic Basis of Digital Banking Services Produced by FinTech Company in Smart City. J. Tour. Serv. 2021, 23, 86–104. [Google Scholar] [CrossRef]
- IGI Global. Publisher of Timely Knowledge. What Is Smart Economy. 2021. Available online: https://www.igi-global.com/dictionary/smart-city--smart-citizen--smart-economy/87778 (accessed on 13 August 2021).
- University of Alicante. Smart Economy. 2015. Available online: https://web.ua.es/en/smart/smart-economy-economia-inteligente.html (accessed on 7 October 2021).
- Apostol, D.; Balaceanu, C.; Constantinescu, E.M. Smart Economy Concept—Facts and Perspectives. International Conference European Perspective of Labor Market-Inovation, Expertness, Performance. 2015. Available online: http://www.ipe.ro/RePEc/WorkingPapers/wpconf141113.pdf (accessed on 28 November 2021).
- Kézai, P.K.; Fischer, S.; Lados, M. Smart Economy and Startup Enterprises in the Visegrád Countries—A Comparative Analysis Based on the Crunchbase Database. Smart Cities 2020, 3, 1477–1494. [Google Scholar] [CrossRef]
- Turečková, K.; Nevima, J. The Cost Benefit Analysis for the Concept of a Smart City: How to Measure the Efficiency of Smart Solutions? Sustainability 2020, 12, 2663. [Google Scholar] [CrossRef] [Green Version]
- Popova, Y. Economic or Financial Substantiation for Smart City Solutions: A Literature Study. Econ. Ann. XXI 2020, 83, 125–133. [Google Scholar] [CrossRef]
- Balaceanu, C.; Tilea, D.M.; Penu, D. Perspectives on Eco Economics. Circular Economy and Smart Economy. Acad. J. Econ. Stud. 2017, 3, 105–109. [Google Scholar]
- Barrett, C. Smart People, smart ideas and the right environment drive innovation. Res. Technol. Manag. 2017, 53, 40–43. [Google Scholar] [CrossRef]
- Bates, T.C.; Gupta, S. Smart groups of smart people: Evidence for IQ as the origin of collective intelligence in the performance of human groups. Intelligence 2017, 60, 46–56. [Google Scholar] [CrossRef] [Green Version]
- Gurashi, R. Smart people: The individual challenge of the fourth industrial revolution. In Smart Society. A Sociological Perspective on Smart Living; Iannone, R., Gurashi, R., Iannuzzi, I., de Ghantuz Cubbe, G., Sessa, M., Eds.; Routledge: London, UK, 2019; Chapter 3. [Google Scholar]
- Gurashi, R. The era of the smart people. How techno capitalism is changing the lifestyles of the individuals of the smart society. Storiadelmondo 2018, 86, 1–12. [Google Scholar]
- Kar, A.K.; Gupta, M.P.; Ilavarasan, P.V.; Dwivedi, Y.K. Advances in Smart Cities: Smarter People, Governance, and Solutions; CRC Press: Boca Raton, FL, USA, 2017. [Google Scholar]
- Pournaras, E.; Yadhunathan, S.; Diaconescu, A. Holarchic structures for decentralized deep learning: A performance analysis. Clust. Comput. 2020, 23, 219–240. [Google Scholar] [CrossRef] [Green Version]
- European Environment Agency. Towards Clean and Smart Mobility: Transportation and Environment in Europe; Publications Office of the European Union: Luxembourg, 2016.
- Chen, C.; Liu, J.; Li, Q.; Wang, Y.; Xiong, H.; Wu, S. Warehouse Site Selection for Online Retailers in Inter-Connected Warehouse Networks. IEEE Int. Conf. Data Min. ICDM 2017, 2017, 805–810. [Google Scholar] [CrossRef]
- Gavalas, D.; Konstantopoulos, C.; Pantziou, G. Design and management of vehicle-sharing systems: A survey of algorithmic approaches. Smart Cities Homes 2016, 3, 261–289. [Google Scholar] [CrossRef] [Green Version]
- Sarma, U.; Ganguly, S. Modelling and cost-benefit analysis of PEM fuel-cell-battery hybrid energy system for locomotive application. Technol. Smart-City Energy Secur. Power ICSESP 2018, 1–5. [Google Scholar] [CrossRef]
- Bataev, A.V.; Dedyukhina, N.; Nasrutdinov, M.N. Innovations in the Financial Sphere: Performance Evaluation of Introducing Service Robots with Artificial Intelligence. In Proceedings of the 9th International Conference on Industrial Technology and Management (ICITM), Oxford, UK, 11–13 February 2020; pp. 256–260. [Google Scholar] [CrossRef]
- Bibri, S. The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability. Sustain. Cities Soc. 2018, 38, 230–253. [Google Scholar] [CrossRef]
- Boeri, A.; Gianfrate, V.; Longo, D. Green buildings and design for adaptation: Strategies for renovation of the built environment. Int. J. Energy Prod. Manag. 2016, 1, 172–191. [Google Scholar] [CrossRef] [Green Version]
- Beccali, M.; Bonomolo, M.; Galatioto, A.; Pulvirenti, E. Smart lighting in a historic context: A case study. Int. J. Manag. Environ. Qual. 2017, 28, 282–298. [Google Scholar] [CrossRef]
- Ahvenniemi, H.; Häkkinen, T. Households’ potential to decrease their environmental impacts: A cost-efficiency analysis of carbon saving measures. Int. J. Energy Sect. Manag. 2020, 14, 193–212. [Google Scholar] [CrossRef]
- Silva, B.N.; Khan, M.; Han, K. Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustain. Cities Soc. 2018, 38, 697–713. [Google Scholar] [CrossRef]
- Vinod Kumar, T.M. (Ed.) E-Governance for Smart Cities; Springer: Singapore, 2015. [Google Scholar]
- Willke, H. Smart Governance: Governing the Global Knowledge Society; Campus Verlag: Frankfurt, Germany, 2007. [Google Scholar]
- European Environment Agency. Shaping the Future of Energy in Europe: Clean, Smart and Renewable; Publications Office of the European Union: Luxembourg, 2017.
- Popova, Y.; Gunare, M.; Popovs, S. Forest Stock as a Renewable Energy Resource within the Frameworks of the Concept of Sustainable Development in Latvia: Pros and Cons. Acta Oecon. Univ. Selye 2019, 8, 127–138. [Google Scholar]
- Li, X.; Chalvatzis, K.; Stephanides, P. Innovative Energy Islands: Life-Cycle Cost-Benefit Analysis for Battery Energy Storage. Sustainability 2018, 10, 3371. [Google Scholar] [CrossRef] [Green Version]
- Huang, M.; Fu, L.; Zhang, Y.; Gao, X. Optimal design of an island microgrid with considering scheduling optimization. In Proceedings of the 2017 International Smart Cities Conference (ISC2), Wuxi, China, 14–17 September 2017; pp. 1–6. [Google Scholar] [CrossRef]
- Bačeković, I.; Østergaard, P.A. A smart energy system approach vs a non-integrated renewable energy system approach to designing a future energy system in Zagreb. Energy 2018, 155, 824–837. [Google Scholar] [CrossRef]
- Abdullah, N.; Alwesabi, O.A.; Abdullah, R. IoT-Based Smart Waste Management System in a Smart City. In International Conference of Reliable Information and Communication Technology; Springer: Cham, Switzerland, 2018; pp. 364–371. [Google Scholar]
- European Commission. European Packaging Waste Management Systems; Main Report; European Commission: Brussels, Belgium, 2001. [Google Scholar]
- Mingaleva, Z.; Vukovic, N.; Volkova, I.; Salimova, T. Waste Management in Green and Smart Cities: A Case Study of Russia. Sustainability 2019, 12, 94. [Google Scholar] [CrossRef] [Green Version]
- Popova, Y.; Sproge, I. Decision-Making within Smart City: Waste Sorting. Sustainability 2021, 13, 10586. [Google Scholar] [CrossRef]
- Tambovceva, T.; Urbane, V.; Ievins, J. Innovations in Construction Waste Management: Case of Latvia. Mark. Manag. Innov. 2020, 3, 234–248. [Google Scholar] [CrossRef]
- Rodrigues Filho, B.A.; Gonçalves, R.F.; Pessôa, M.S.P. Measuring the impact of utility services for a Smart City infrastructure using an Input-Output approach. J. Phys. Conf. Ser 2018, 1065, 202003. [Google Scholar] [CrossRef] [Green Version]
- Tsegaye, S.; Gallagher, K.C.; Missimer, T.M. Coping with future change: Optimal design of flexible water distribution systems. Sustain. Cities Soc. 2020, 61, 102306. [Google Scholar] [CrossRef]
- Nguyen, H.P.; Le, P.Q.H.; Pham, V.V.; Nguyen, X.P.; Balasubramaniam, D.; Hoang, A.T. Application of the Internet of Things in 3E (efficiency, economy, and environment) factor-based energy management as smart and sustainable strategy. Energy Sources Part A Recovery Util. Environ. Eff. 2021, 1–23. [Google Scholar] [CrossRef]
- Khan, S.A.R.; Zhang, Y.; Anees, M.; Golpîra, H.; Lahmar, A.; Qianli, D. Green supply chain management, economic growth and environment: A GMM based evidence. J. Clean. Prod. 2018, 185, 588–599. [Google Scholar] [CrossRef]
- Schenkel, M.; Caniels, M.C.J.; Krikke, H.; Van Der Laan, E. Understanding value creation in closed loop supply chains—Past findings and future directions. J. Manuf. Syst. 2015, 37, 729–745. [Google Scholar] [CrossRef]
- Ivlev, V.Y.; Ivleva, M.L. Philosophical Foundations of the Concept of Green Economy. In Proceedings of the International Conference on Contemporary Education, Social Sciences and Ecological Studies (CESSES 2018), Moscow, Russia, 29–30 March 2018; Volume 283, pp. 869–873. [Google Scholar]
- Shaheen, K.; Zaman, K.; Batool, R.; Khurshid, M.A.; Aamir, A.; Shoukry, A.M.; Gani, S. Dynamic linkages between tourism, energy, environment, and economic growth: Evidence from top 10 tourism-induced countries. Environ. Sci. Pollut. Res. 2019, 26, 31273–31283. [Google Scholar] [CrossRef] [PubMed]
- Ahmed, Z.; Asghar, M.M.; Malik, M.N.; Nawaz, K. Moving towards a sustainable environment: The dynamic linkage between natural resources, human capital, urbanization, economic growth, and ecological footprint in China. Resour. Policy 2020, 67, 101677. [Google Scholar] [CrossRef]
- Yasmeen, H.; Wang, Y.; Zameer, H.; Solangi, Y.A. Decomposing factors affecting CO2 emissions in Pakistan: Insights from LMDI decomposition approach. Environ. Sci. Pollut. Res. 2019, 27, 3113–3123. [Google Scholar] [CrossRef]
- Popova, Y. Relations between wellbeing and transport infrastructure of the country. Procedia Eng. 2017, 178, 579–588. [Google Scholar] [CrossRef]
- Wong, K.K.K. Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Mark. Bull. 2013, 24, 1–32. [Google Scholar]
- Iqbal, A.; Latif, F.; Marimon, F.; Sahibzada, U.F.; Hussain, S. From knowledge management to organizational performance: Modelling the mediating role of innovation and intellectual capital in higher education. J. Enterp. Inf. Manag. 2019, 32, 36–59. [Google Scholar] [CrossRef]
- Coltman, T.; Devinney, T.M.; Midgley, D.F.; Venaik, S. Formative versus reflective measurement models: Two applications of formative measurement. J. Bus. Res. 2008, 61, 1250–1262. [Google Scholar] [CrossRef] [Green Version]
- Garson, D. Partial Least Squares (PLS-SEM): Regression & Structural Equation Models; Statistical Associates Publishing, North Carolina State University: Chapel Hill, NC, USA, 2016. [Google Scholar]
- Jarvis, C.B.; Mackenzie, S.B.; Podsakoff, P.M. A critical review of construct indicators and measurement model misspecification in marketing and consumer research. J. Consum. Res. 2003, 30, 199–218. [Google Scholar] [CrossRef]
- Rossiter, J.R. The C-OAR-SE procedure for scale development in marketing. Int. J. Res. Mark 2002, 19, 1–31. [Google Scholar] [CrossRef] [Green Version]
- Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage: Thousand Oaks, CA, USA, 2017. [Google Scholar]
- Hayes, A.F. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach; Guilford Press: New York, NY, USA, 2013. [Google Scholar]
- Chin, W.W.; Cheah, J.-H.; Liu, Y.; Ting, H.; Lim, X.-J.; Cham, T.H. Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research. Ind. Manag. Data Syst. 2020, 120, 2161–2209. [Google Scholar] [CrossRef]
- Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M.; Danks, N.P.; Ray, S. An Introduction to Structural Equation Modeling. In Partial Least Squares Structural Equation Modeling (PLS-SEM) Using, R. Classroom Companion: Business; Springer: Cham, Switzerland, 2021. [Google Scholar] [CrossRef]
- Shujahat, M.; Ali, B.; Nawaz, F.; Durst, S.; Kianto, A. Translating the impact of knowledge management into knowledge-based innovation: The neglected and mediating role of knowledge-worker satisfaction. Hum. Factors Ergon. Manuf. 2018, 28, 200–212. [Google Scholar] [CrossRef]
- Valaei, N.; Nikhashemi, S.R.; Javan, N. Organizational factors and process capabilities in a KM strategy: Toward a unified theory. J. Manag. Dev. 2017, 36, 560–580. [Google Scholar] [CrossRef] [Green Version]
- Wang, Z.; Sharma, P.N.; Cao, J. From knowledge sharing to firm performance: A predictive model comparison. J. Bus. Res. 2016, 69, 4650–4658. [Google Scholar] [CrossRef] [Green Version]
- Ringle, C.M.; Sarstedt, M.; Mitchell, R.; Gudergan, S.P. Partial least squares structural equation modeling in HRM research. J. Hum. Resour. Manag. Res. 2020, 31, 1617–1643. [Google Scholar] [CrossRef]
- Mateos-Aparicio, G. Partial least squares (PLS) methods: Origins, evolution, and application to social sciences. Commun. Stat. 2011, 40, 2305–2317. [Google Scholar] [CrossRef] [Green Version]
- Schlägel, C.; Sarstedt, M. Assessing the measurement invariance of the four-dimensional cultural intelligence scale across countries: A composite model approach. Eur. Manag. J. 2016, 34, 633–649. [Google Scholar] [CrossRef]
- Ali, F.; Rasoolimanesh, S.M.; Sarstedt, M.; Ringle, C.M.; Ryu, K. An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. Int. J. Contemp. Hosp. 2018, 30, 514–538. [Google Scholar] [CrossRef] [Green Version]
- Baluch, A.M.; Salge, T.O.; Piening, E.P. Untangling the relationship between HRM and hospital performance: The mediating role of attitudinal and behavioural HR outcomes. Int. Hum. Resour. Manag. J. 2013, 24, 3038–3061. [Google Scholar] [CrossRef]
- Banks, G.C.; Kepes, S. The influence of internal HRM activity fit on the dynamics within the “black box”. Hum. Resour. Manag. Rev. 2015, 25, 352–367. [Google Scholar] [CrossRef]
- Chowhan, J. Unpacking the black box: Understanding the relationship between strategy, HRM practices, innovation and organizational performance. Hum. Resour. Manag. J. 2016, 26, 112–133. [Google Scholar] [CrossRef]
- Fernandes, V. (Re)discovering the PLS approach in management science. Management 2012, 15, 101–123. [Google Scholar]
- Ringle, C.; Da Silva, D.; Bido, D. Structural equation modeling with the Smart PLS. Braz. J. Mark. 2015, 13. [Google Scholar]
- Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
- Sarstedt, M.; Ringle, C.M.; Hair, J.F. Partial Least Squares Structural Equation Modelling. In Handbook of Market Research; Homburg, C., Klarmann, M., Vomberg, A., Eds.; Springer: Berlin/Heidelberg, Germany, 2017. [Google Scholar]
- Rahi, S. Structural Equation Modeling Using SmartPLS. Independent Publishing Platform, 2017. Available online: https://www.researchgate.net/publication/328560499_Structural_Equation_Modeling_Using_SmartPLS (accessed on 12 January 2022).
- Henseler, J.; Ringle, C.M.; Sinkovics, R.R. The use of partial least squares path modeling in international marketing. In New Challenges to International Marketing (Advances in International Marketing, Vol. 20); Emerald Group Publishing Limited: Bingley, UK, 2009; pp. 277–319. [Google Scholar] [CrossRef] [Green Version]
- Sarstedt, M.; Mooi, E.A. A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics; Springer: Berlin/Heidelberg, Germany, 2014; Chapter 7. [Google Scholar]
- Chin, W.W. The partial least squares approach for structural equation modeling. In Modern Methods for Business Research; Macoulides, G.A., Ed.; Lawrence Erlbaum Associates: Mahwah, NJ, USA, 1998; pp. 295–336. [Google Scholar]
- Höck, M.; Ringle, C.M. Strategic Networks in the Software Industry: An Empirical Analysis of the Value Continuum; IFSAM VIIIth World Congress: Berlin, Germany, 2006. [Google Scholar]
- Daskalakis, S.; Mantas, J. Evaluating the impact of a service-oriented framework for healthcare interoperability. In eHealth Beyond the Horizon; Anderson, S.K., Klein, G.O., Schulz, S., Aarts, J., Mazzoleni, M.C., Eds.; IOS Press: Amsterdam, The Netherlands, 2008; pp. 285–290. [Google Scholar]
- Wong, K.K.K. Mastering Partial Least Squares Structural Equation Modeling (PLS_SEM) with Smartpls in 38 Hours; iUniverse: Bloomington, IN, USA, 2019. [Google Scholar]
- Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef] [Green Version]
- Albers, S. PLS and success factor studies in marketing. In Handbook of Partial Least Squares; Springer: Berlin/Heidelberg, Germany, 2010; pp. 409–425. [Google Scholar]
- Vinzi, V.E.; Chin, W.W.; Henseler, J.; Wang, H. Perspectives on partial least squares. In Handbook of Partial Least Squares; Springer: Berlin/Heidelberg, Germany, 2010; pp. 1–20. [Google Scholar]
- Sanmukhiya, C. A PLS–SEM Approach to the UTAUT Model: The Case of Mauritius. Ann. Soc. Sci. Manag. Stud. ASM 2020, 6, 8–16. [Google Scholar] [CrossRef]
- Cohen, J. Statistical Power Analysis for the Behavioral Sciences; Lawrence Erlbaum: Mahwah, NJ, USA, 1988. [Google Scholar]
- Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling; Sage: Thousand Oaks, CA, USA, 2014. [Google Scholar]
- Hair, J.F., Jr.; Sarstedt, M.; Hopkins, L.; Kuppelwieser, V.G. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. Eur. Bus. Rev. 2014, 26(2), 106–121. [Google Scholar] [CrossRef]
- Hussain, S.; Fangwei, Z.; Siddiqi, A.F.; Ali, Z.; Shabbir, M.S. Structural Equation Model for Evaluating Factors Affecting Quality of Social Infrastructure Projects. Sustainability 2018, 10, 1415. [Google Scholar] [CrossRef] [Green Version]
- Karim, R.; Latip, N.A.; Marzuki, A.; Haider, S.; Nelofar, M.; Muhammad, F. The Impact of 4Ps Marketing Mix in Tourism Development in the Mountain Areas: A Case Study. Int. J. Econ. Bus. Adm. IJEBA 2021, 9, 231–245. [Google Scholar] [CrossRef]
- Popova, Y.; Strelchonock, V. Interdependence of Factors within the Complex Evolutionary System “Social Model”. In Proceedings of the International Conference Transformation of Regional Economies: Sustainable Development and Competitiveness, Riga, Latvia, 9–11 May 2016. [Google Scholar]
- Balsalobre-Lorente, D.; Shahbaz, M.; Roubaud, D.; Farhani, S. How economic growth, renewable electricity and natural resources contribute to CO2 emissions? Energy Pol. 2018, 113, 356–367. [Google Scholar] [CrossRef] [Green Version]
- Ulucak, R.; Khan, S.U. Determinants of the ecological footprint: Role of renewable energy, natural resources, and urbanization. Sustain. Cities Soc. 2019, 54, 101996. [Google Scholar] [CrossRef]
- Zafar, M.W.; Zaidi, S.A.H.; Khan, N.R.; Mirza, F.M.; Hou, F.; Kirmani, S.A.A. The impact of natural resources, human capital, and foreign direct investment on the ecological footprint: The case of the United States. Resour. Pol. 2019, 63, 101428. [Google Scholar] [CrossRef]
- Ahmed, Z.; Wang, Z.; Mahmood, F.; Hafeez, M.; Ali, N. Does globalization increase the ecological footprint? Empirical evidence from Malaysia. Environ. Sci. Pollut. Res. 2019, 26, 18565–18582. [Google Scholar] [CrossRef] [PubMed]
- Charfeddine, L. The impact of energy consumption and economic development on ecological footprint and CO2 emissions: Evidence from a markov switching equilibrium correction model. Energy Econ. 2017, 65, 355–374. [Google Scholar] [CrossRef]
- Destek, M.A.; Ulucak, R.; Dogan, E. Analyzing the environmental Kuznets curve for the EU countries: The role of ecological footprint. Environ. Sci. Pollut. Res. 2018, 25, 29387–29396. [Google Scholar] [CrossRef]
- Baloch, M.A.; Mahmood, N.; Zhang, J.W. Effect of natural resources, renewable energy and economic development on CO2 emissions in BRICS countries. Sci. Total Environ. 2019, 678, 632–638. [Google Scholar] [CrossRef]
- Hassan, S.T.; Xia, E.; Khan, N.H.; Mohsin, S.; Shah, A. Economic growth, natural resources, and ecological footprints: Evidence from Pakistan. Environ. Sci. Pollut. Res. 2018, 26, 2929–2938. [Google Scholar] [CrossRef] [PubMed]
- Sarkodie, S.A. The invisible hand and EKC hypothesis: What are the drivers of environmental degradation and pollution in Africa? Environ. Sci. Pollut. Res. 2018, 25, 21993–22022. [Google Scholar] [CrossRef] [PubMed]
- Dean, R.T.; Dunsmuir, W. Dangers and uses of cross-correlation in analyzing time series in perception, performance, movement, and neuroscience: The importance of constructing transfer function autoregressive models. Behav. Res. Methods 2016, 48, 783–802. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Indicators Included in Construct | Loadings | Indicators Excluded from the Construct |
---|---|---|
Smart Economy | ||
turnover of SMEs due to e-commerce input of ICT industry in GDP employment in knowledge-intensive areas | 0.890 (0.000) 0.945 (0.000) 0.765 (0.000) | individual online consumption, import of ICT products, fixed assets as % of GDP, ICT staff as percentage of total employment |
Smart Mobility | ||
passenger transportation green gas emissions from transport share of renewable sources in fuel consumption transportation of goods | 0.799 (0.001) −0.654 (0.022) 0.922 (0.000) 0.962 (0.000) | deaths number on the roads, investments in road infrastructure, number of transport sharing companies |
Smart Living | ||
working hours consumption expenditures of households rate of urban population | −0.648 (0.018) 0.943 (0.000) 0.969 (0.000) | proportion of leisure and working hours, proportion of leisure and working hours, vulnerable employment, level of criminal activity |
Smart Environment | ||
resource productivity hazardous waste, share of electricity from renewable sources | 0.952 (0.000) −0.888 (0.000) 0.968 | emissions of heat plants and producing industries, hazardous pollutions, air emissions, expenditures on environment protection |
Smart Society | ||
female employment in technological industries inequality in education female graduates of the universities gender inequality index social protection expenditures | 0.914 (0.000) −0.757 (0.000) 0.802 (0.000) −0.816 (0.000) 0.929 (0.000) | gender inequality index, youth participation, availability of e-public services, indicators of risk of poverty (Gini and Quintile indices), expenditures on health system, youth inequality indicators, decreased access to the internet due to the cost factor |
Smart People | ||
adult learning people without higher education expenditures on education | 0.831 (0.000) −0.925 (0.000) 0.946 (0.000) | Human Development Index, schooling years, number of higher educational establishments, number of students, % of people using the Internet |
Mediator | ||
rate of unemployment concentration index energy consumption per capita | −0.689 (0.013) 0.925 (0.001) 0.727 (0.004) | GDP per capita, GDP growth (to year 2010), rate of self-employment, consumer price index, import if ICT products |
Variable | CR (>0.600) | ρA | AVE (>0.500) | R2 | R2 adj. |
---|---|---|---|---|---|
Mediator | 0.449 | 0.770 | 0.620 | 0.820 | 0.800 |
Smart Economy | 0.903 | 0.844 | 0.757 | ||
Smart Living | 0.681 | 0.916 | 0.709 | 0.931 | 0.913 |
Smart People | 0.564 | 0.913 | 0.813 | 0.949 | 0.936 |
Smart Society | 0.448 | 0.917 | 0.717 | 0.923 | 0.904 |
Smart Environment | 0.750 | 0.941 | 0.880 | 0.906 | 0.883 |
Smart Mobility | 0.780 | 0.983 | 0.710 | 0.679 | 0.598 |
Variable | f2 | Q2 |
---|---|---|
Mediator | 0.460 | |
Smart Living | 0.142 | 0.599 |
Smart People | 0. 103 | 0.716 |
Smart Society | 0.555 | 0.609 |
Smart Environment | 0.078 | 0.745 |
Smart Mobility | 0.067 | 0.363 |
Path | Original Sample | Sample Mean | STDEV | T-Statistics | p-Value |
---|---|---|---|---|---|
H1: Smart Economy → Smart People | 0.816 | 0.777 | 0.194 | 4.200 | 0.000 |
H2: Smart Economy → Smart Society | 0.497 | 0.500 | 0.269 | 1.848 | 0.033 |
H3: Smart Economy → Smart Environment | 0.766 | 0.756 | 0.260 | 2.943 | 0.002 |
H4: Smart Economy → Smart Mobility | 1.124 | 1.016 | 0.564 | 1.992 | 0.023 |
H5: Smart Economy → Smart Living | 0.754 | 0.725 | 0.278 | 2.714 | 0.004 |
Path | Path Coefficient (Direct Effect) | Specific Indirect Effect | T-Statistics (for Sp. Ind.Eff.) | p-Value (for Sp. Ind.Eff.) | Total Effect |
---|---|---|---|---|---|
SEc → Mediator → SP | 0.816 | 0.155 | 0.855 | 0.197 | 0.971 |
SEc → Mediator → SS | 0.497 | 0.442 | 1.775 | 0.033 | 0.938 |
SEc →Mediator → SL | 0.754 | 0.207 | 0.799 | 0.218 | 0.961 |
SEc → Mediator → SM | 1.124 | −0.314 | 0.596 | 0.276 | 0.811 |
SEc → Mediator → SE | 0.766 | 0.182 | 0.720 | 0.236 | 0.948 |
Variable | Original Sample | Sample Mean | Stand. Deviation | T-Statistics | p-Value |
---|---|---|---|---|---|
SEc → Mediator → SP | 0.155 | 0.189 | 0.182 | 0.855 | 0.197 |
SEc → Mediator → SS | 0.442 | 0.440 | 0.249 | 1.775 | 0.038 |
SEc →Mediator → SL | 0.207 | 0.231 | 0.266 | 0.799 | 0.218 |
SEc → Mediator → SM | −0.314 | −0.192 | 0.527 | 0.596 | 0.276 |
SEc → Mediator → SE | 0.182 | 0.191 | 0.253 | 0.720 | 0.236 |
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Popova, Y.; Popovs, S. Impact of Smart Economy on Smart Areas and Mediation Effect of National Economy. Sustainability 2022, 14, 2789. https://doi.org/10.3390/su14052789
Popova Y, Popovs S. Impact of Smart Economy on Smart Areas and Mediation Effect of National Economy. Sustainability. 2022; 14(5):2789. https://doi.org/10.3390/su14052789
Chicago/Turabian StylePopova, Yelena, and Sergejs Popovs. 2022. "Impact of Smart Economy on Smart Areas and Mediation Effect of National Economy" Sustainability 14, no. 5: 2789. https://doi.org/10.3390/su14052789
APA StylePopova, Y., & Popovs, S. (2022). Impact of Smart Economy on Smart Areas and Mediation Effect of National Economy. Sustainability, 14(5), 2789. https://doi.org/10.3390/su14052789