Special Issue on Dynamical Systems and Their Applications to Engineering, Economy and Health Sciences
Guest Editors: Juan L. G. Guirao, Elbaz I. Abouelmagd and Bruce A. Wade

Abstract

With the rapid development of tourism economics, personalized and diversified demand of tourists and the advancement of modern information technologies, the investment of scenic spots faces multitudinous uncertainties and emergencies and can be considered as a nonlinear dynamic system. Given this situation, the existing investment decision-making model (e.g. net present value, NPV) is difficult to estimate the realistic value of scenic spots, and new theory and decision-making model are needed in the area. Based on real option theory and option function, the paper establishes a nonlinear dynamic investment decision model of scenic spots with uncertainties and emergencies, and then, the model is verified with an example. The results show that with the impacts of emergencies, when the investment value is considered, the option value of the investment project in the scenic spot can be maximized, thus maximizing the total value of the investment project. The proposed nonlinear dynamic investment decision model proves that the favourable impact of emergencies on the investment of scenic spots increases the investment opportunity value of scenic projects, thus increasing the investment value of scenic projects, emphasizes more on the dynamic nature of investment and the favourable conditions in the face of crisis, and helps enterprises investing in scenic spots make full use of its uncertain environment and the favourable impact of emergencies to choose the optimal investment opportunity. Therefore, this study has both theoretical and practical significance.

Keywords:

References

1. G. I. Crouch , Destination competitiveness: An analysis of determinant attributes, J. Travel Res. 50(1) (2011) 27–45. Crossref, Web of Science, Google Scholar
2. S. Haugland, H. Ness, B. O. Gronseth and J. Aarstad , Development of tourism destinations: An integrated multilevel perspective, Ann. Tour. Res. 38(1) (2011) 268–290. Crossref, Web of Science, Google Scholar
3. Q. Jiang and G. Ke , Information sharing and bullwhip effect in smart destination network system, Ad Hoc Networks 87 (2018) 17–25. Crossref, Web of Science, Google Scholar
4. Q. Jiang, Z. Xin, Y. Li and S. Ma , Differential dynamic decision-making model for multi-stage investment of scenic area, Alex. Eng. J. 59(4) (2020) 2819–2826. Crossref, Web of Science, Google Scholar
5. M. S. Alam and S. R. Paramati , The dynamic role of tourism investment on tourism development and CO₂ emissions, Ann. Tour. Res. 66 (2017) 213–215. Crossref, Web of Science, Google Scholar
6. Y. F. Lai and K. Y. Li , A research on the methods of economics evaluation and analysis on the tourism investment project, Int. J. Manag. Sci. Eng. Res. 1 (2014) 7–10. Google Scholar
7. S. R. Paramati, M. S. Alam and C. K. M. Lau , The effect of tourism investment on tourism development and CO₂ emissions: Empirical evidence from the EU nations, J. Sustainable Tour. 26(7–9) (2018) 1587–1607. Crossref, Web of Science, Google Scholar
8. M. C. Banerjee and S. Gachot , A quantitative framework for assessing public investment in tourism — An application to Haiti, Tour. Manag. 51(12) (2015) 157–173. Crossref, Web of Science, Google Scholar
9. H. Li, G. Gozgor, C. K. M. Lau and S. R. Paramati , Does tourism investment improve the energy efficiency in transportation and residential sectors? Evidence from the OECD economies, Environ. Sci. Pollut. Res. 26(18) (2019) 18834–18845. Crossref, Web of Science, Google Scholar
10. X. Tang , The historical evolution of China’s tourism development policies (1949–2013) — A quantitative research approach, Tour. Manag. 58 (2017) 259–269. Crossref, Web of Science, Google Scholar
11. T. Li, J. Liu and H. Zhu , The international investment in theme parks: Spatial distribution and decision-making mechanism, an empirical study for China, Tour. Manag. 67 (2018) 342–350. Crossref, Web of Science, Google Scholar
12. S. Wu , Internet public informatioan text data mining and intelligence influence analysis for user intent understanding, J. Intell. Fuzzy Syst. 38 (2020) 487–494. Crossref, Web of Science, Google Scholar
13. L. Ritzenhofen and S. Spinler , Optimal design of feed-in-tariffs to stimulate renewable energy investments under regulatory uncertainty: A real options analysis, Energy Econ. 53 (2016) 76–89. Crossref, Web of Science, Google Scholar
14. G. Brent and L. Oliver , Uncertainty, investment, and managerial incentives, J. Monet. Econ. 69(1) (2015) 121–137. Google Scholar
15. C. Xue and S. Takashi , Random shock uncertainty and investment reversibility: Real option framework, Bull. Econ. Res. 70(2) (2018) 150–164. Crossref, Web of Science, Google Scholar
16. C. Haehl and S. Spinler , Capacity expansion under regulatory uncertainty: A real options-based study in international container shipping, Transport. Res. E-Log. 113(5) (2018) 75–93. Crossref, Web of Science, Google Scholar
17. R. Sainaghi and R. Baggio , Complexity traits and dynamics of tourism destinations, Tour. Manag. 63 (2017) 368–382. Crossref, Web of Science, Google Scholar
18. Q. J. Jiang and Y. Li , Demand forecasting and information platform in tourism, Open Phys. 15(1) (2017) 247–252. Crossref, Web of Science, Google Scholar
19. R. Andergassen, G. Candela and P. Figini , The management of tourism destinations: A policy game, Tour. Econ. 23(1) (2017) 49–65. Crossref, Web of Science, Google Scholar
20. S. Wu, Q. Zhang, W. Chen, J. Liu and L. Liiu , Research on trend prediction of internet user intention understanding and public intelligence mining based on fractional differential method, Chaos Solitons Fract. 128 (2019) 331–338. Crossref, Web of Science, Google Scholar
21. F. Black and M. Scholes , The pricing of options and corporate liabilities, J. Polit. Econ. 81(3) (1973) 637–654. Crossref, Web of Science, Google Scholar
22. H. Zhang, F. Liu and I. Turner , Numerical solution of the time fractional Black–Scholes model governing European options, Comput. Math. Appl. 71(9) (2016) 1772–1783. Crossref, Web of Science, Google Scholar
23. H. Kleinert and J. Korbel , Option pricing beyond Black–Scholes based on double-fractional diffusion, Physica A 449 (2016) 200–214. Crossref, Web of Science, Google Scholar
24. S. O. Edeki, O. O. Ugbebor and E. A. Owoloko , Analytical solution of the time-fractional order Black-Scholes model for stock option valuation on no dividend yield basis, IAENG Int. J. Appl. Math. 47(4) (2017) 1–10. Google Scholar
25. S. Wu, J. Liu and L. Liu , Modeling method of internet public information data mining based on probabilistic topic model, J. Supercomput. 75 (2019) 5882–5897. Crossref, Web of Science, Google Scholar
26. L. Santos, I. Soares, C. Mendes and P. Ferreira , Real options versus traditional methods to assess renewable energy projects, Renew. Energy 68 (2014) 58–594. Crossref, Web of Science, Google Scholar
27. R. G. McGrath , Falling forward: Real options reasoning and entrepreneurial failure, Acad. Manag. Rev. 24(1) (1999) 13–30. Crossref, Web of Science, Google Scholar
28. D. Song and Z. Yang , Contingent capital, real options and agency costs, Int. Rev. Finance 16(1) (2016) 3–40. Crossref, Web of Science, Google Scholar
29. P. K. Wesseh and B. Lin , Renewable energy technologies as beacon of cleaner production: A real options valuation analysis for Liberia, J. Clean. Prod. 90 (2015) 300–310. Crossref, Web of Science, Google Scholar
30. N. Sene and K. Abdelmalek , Nonlinear sub-diffusion and nonlinear sub-diffusion dispersion equations and their proposed solutions, Appl. Math. Nonlinear Sci. 5(1) (2020) 221–236. Crossref, Google Scholar
31. M. Dewasurendra and K. Vajravelu , On the method of inverse mapping for solutions of coupled systems of nonlinear differential equations arising in nanofluid flow, heat and mass transfer, Appl. Math. Nonlinear Sci. 3(1) (2018) 1–14. Crossref, Google Scholar
32. J. P. Ruiz-Fernández et al., Influence of seasonal factors in the earned value of construction, Appl. Math. Nonlinear Sci. 4(1) (2019) 21–34. Crossref, Google Scholar

Vol. 30, No. 02

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History

Received 8 December 2020

Accepted 7 July 2021

Published: February 26, 2022

Information

This is an Open Access article in the “Special Issue on Dynamical Systems and Their Applications to Engineering, Economy and Health Sciences”, edited by Juan L.G. Guirao (Technical University of Cartagena, Spain), Elbaz I. Abouelmagd (National Research Institute of Astronomy and Geophysics (NRIAG), Cairo, Egypt) & Bruce A. Wade (University of Louisiana at Lafayette, USA) published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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DYNAMIC NONLINEAR DIFFERENTIAL INVESTMENT DECISION MODEL FOR SCENIC SPOT SYSTEM WITH UNCERTAINTIES AND EMERGENCIES

Abstract

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