Abstract
This paper provides a methodology for finding the optimum deployment strategy for information and communication technologies business activities in the context of initial infrastructure projects, which support a number of future investment opportunities. It treats these opportunities as real options (ROs) and assumes that there is a competitive threat that can influence them negatively, or even worse, eliminate their value. We take into account that the competitors’ rate of arrival and erosion of competitiveness, during the waiting phase for the ROs to invest, follow a joint-diffusion process with the investments’ revenues. The proposed methodology is applied to a real-life broadband technology business activity showing how it can be formulated and solved.
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References
Angelou G and Economides A (2005). Flexible ICT investments analysis using real options. Int J Technol Pol Mngt 5(2): 146–166.
Angelou G and Economides A (2006a). ICAT investment appraisal using real options. 10th WSEAS International Conference on COMPUTERS, Athens, Greece.
Angelou G and Economides A (2006b). Broadband investments as growth options under competition threat. FITCE 45th Congress 2006, Athens, Greece.
Angelou G and Economides A (2006c). ICT investments as real options under competition threat. International Telecommunications Symposium, Fortaleza-Ceará-Brazil.
Benaroch M (2002). Managing information technology investment risk: A real options Perspective. J Mngt Infor Syst 19(2): 43–84.
Benaroch M and Kauffman R (2000). Justifying electronic banking network expansion using real options analysis. MIS Quart 24(2): 197–225.
Bhagat S (1999). Real options applications in the telecommunication industry. In: Alleman J. and Naom E. (eds). The New Investment Theory of Real Options and its Implication for Telecommunications Economics. Kluwer Academic Publishers: Boston, pp. 35–47.
Boston J and Pointon J (1999). Valuation under technological change. J Opl Res Soc 50(3): 255–261.
Elnegaard N (2002). How to incorporate the value of flexibility in broadband access network rollout investment projects. In: 41st European Telecommunications Congress (FITCE), Genoa, Italy.
Elnegaard N and Stordahl K (2002). Choosing the right timing of investment in xDSL rollouts: A real option approach ISSLS 2002. TONIC Publication: Seoul.
EURESCOM Project-901 (2000). Investment Analysis Modelling—Investment Analysis under Uncertainty. In Project Deliverable 2, Vol 4 http://www.EURESCOM.de/public/projects/P900-series/P901/, accessed July 2007.
Gamba A and Trigeorgis L (2002). A log-transformed binomial lattice extension for multi-dimensional option problems. Presented at the 5th Annual Conference on Real Options, Anderson School of Management, UCLA, Los-Angeles, 2002.
Iatropoulos A, Economides A and Angelou G (2004). Broadband investments analysis using real options methodology: A case study for Egnatia Odos SA. Commun Strateg 55(3rd quarter): 45–76.
International Telecoms Intelligence (ITI) Company (2005). Available reports: Market Intelligence Report, http://www.espicom.com/web3.nsf/structure/tel_bksmgreece?OpenDocument.
Kalhagen K and Elnegaard N (2002). Accessing broadband investment risk through option theory. Telektronikk Magazine 2/3: 51–62.
Kantor Capital (2005). Overview of status of the telecommunication market in Greece. http://www.kantor.gr/files/surveys/ExecSummary_Telecoms12_9_05.pdf.
Kester WC (1984). Today's Options for Tomorrow's Growth. Harvard Bus Rev 62(2) : 153–160.
Kumar R (1999). Understanding DSS value: an options perspective. Omega, Int J Mngt Sci 27: 295–304.
Mun J (2002). Real Options Analysis: Tools and Techniques for Valuing Strategic Investments and Decisions. Wiley Finance: New Jersey.
Panayi S and Trigeorgis L (1998). Multi-stage real options: The cases of information technology infrastructure and international bank expansion. Quart Rev Econ Fin 38: 675–692.
Roper-Lowe G and Sharp J (1990). The analytic hierarchy process and its application to an information technology decision. J Opl Res Soc 42(1): 49–59.
Taudes A, Feurstein M and Mild A (2000). Options analysis of software platform decisions: A case study. MIS Quart 24(2): 227–243.
Trigeorgis L (1996). Real Options: Managerial Flexibility and Strategy in Resource Allocation. The MIT Press: Cambridge, MA.
Trigeorgis L (1999). Real options: A primer. In: Alleman J. and Noam E. (eds). The New Investment Theory of Real Options and its Implication for Telecommunications Economics. Kluwer Academic Publishers: Boston, pp. 3–33.
Zhu K (1999). Strategic investment in information technologies: A real-options and game-theoretic approach. Doctoral Dissertation, Stanford University, CA.
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Appendix
In any of the strategies we consider that EO starts with the immediate investment of DF installation along the motorway. In alternative 1, EO lights the DF providing WL services immediately, while in alternatives 2 and 3 adopts a WaS period of 1 year and 2 years, respectively. In alternative 4, the immediate implementation of the aforementioned stages including the NS provision is considered, while from alternatives 5 to 9 different implementation times for WL and NS services provision are examined (Figure A1).
Alternative business deployment strategies for Egnatia Odos S.A.
Revenue uncertainty is taken from the literature from similar case studies (Elnegaard and Stordahl, 2002). We assume zero correlation between V and competitive parameters. Actually, in case of correlation 1, competitive parameters have linear relationships with V; however, as mentioned before a smaller correlation value can be applied in real-life cases under competitors’ asymmetries such us investment cost, initial infrastructure ownership and other physical resources availability. In particular, EO has a competitive advantage over the rest of its competitors due to the availability of physical resources along the motorway. In addition, we model uncertainty for the one-time cost at expiration date assuming that it is stochastic too with 
Waiting can give a decision maker more information about costs. Costs can change through the introduction of new technologies, changes in the regulatory environment, new partnership possibilities or the availability of grants to offset some of the development costs. However, sometimes it is not waiting but investing that reveals information about costs. For instance, a negative correlation value between investment revenue and cost indicates the inability to control the cost of the development project associating with lower revenues after the project/phase is completed. Finally, the annual risk-free interest rate is r
f=4%, while the discounted factor for the revenues is r=8% (Table A1).
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Angelou, G., Economides, A. A real options approach for prioritizing ICT business alternatives: a case study from broadband technology business field. J Oper Res Soc 59, 1340–1351 (2008). https://doi.org/10.1057/palgrave.jors.2602477
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DOI: https://doi.org/10.1057/palgrave.jors.2602477