Information and communication technology investments evaluation: a real options framework of analysis

Abstract

This PhD thesis is a summary and compilation of our central works in Information Communication Technologies (ICT) investments evaluation. Our aim has been to improve our understanding, as well as that of practitioners and the general academic, about how to value and select ICT investments. This topic is fundamental to many academic fields, including information systems, telecommunications, corporate finance and management science. We develop models close related to ICT industry characteristics to guide decision making in the management and evaluation of ICT business. The target is to find optimum deployment strategy and mitigate the risk for ICT investments and finally enhance their techno-economic performance. Viewing ICT investment projects as real options (ROs), we develop a number of models for evaluating ICT investments in the joint presence of uncertainty and competition. Our target is to analyze investments risks, goals and constrains, estimate the optimum deployment strategy and finally evaluate the overall ICT business. We adopt ROs to model flexibility of implementing ICT business and combine them with various decision analysis techniques, such as game theory (GT), goal programming (GP), fuzzy logic (FL), analytic hierarchy process (AHP) and SWOT analysis, for modelling specific ICT business characteristics in a holistic decision analysis perspective. ROs have appeared in ICT literature in the last two decades. The ROs methodology has the strength, that it can be applied directly in already prepared detailed cash flow statements found everywhere. The methodology does therefore not discard the “old” framework but augments it in order to capture new insight, which is of high importance in decision-making under high uncertainty. At the beginning, most of the ROs literature was focused on market conditions without existence of competition, which can influence negatively or even-more eliminate their values. The main assumption was that the firm has a monopoly power over a business opportunity which is treated by ROs. However, after ICT market deregulation business opportunities are shared among many potential investors. Particularly, if the number of potential investors (players or competitors) continues to grow, we approach to exogenous competition modeling. Also, when there is in the market a strong player, like the incumbent operator, while the rest of the competitors are 4 able of subtracting only a small amount of overall market value, exogenous competition is more convenient. On the other hand, the situation could be more efficiently characterized as an oligopoly and not as a perfect competition if the deregulation of telecommunications did not result in a many-players “wild race” but rather in an oligopoly market. In such a market there are only a few companies present, who know about each other’s activities and take into account the other competitors actions. Situations like that can be more efficiently modeled by GT under endogenous competition modeling. However, as the competition intensity increases dramatically and the players are usually so many the oligopoly models are becoming very complicated to be used in practice. In addition, the existing ROs models are strictly quantitative, while ICT investments experience tangible and intangible factors and the latter can be mainly treated by qualitative analysis. Morever, ROs analysis in itself brings to the “surface” a number of factors that cannot be quantified, at least easily, by existing ROs models and methodologies. Particularly, even though it may be difficult to precisely calculate the value of ROs, it is plausible that managers ascribe a higher value to a project with one or more embedded options than they would to the same project without any embedded options. Also, the competition modeling under ROs perspective may be a very difficult task that requires high-level of mathematics, while managers and practisians do not prefer to adopt this modeling. In this thesis, we enhance and in some cases simplify the quantitative analysis of the ROs introducing further qualitative option thinking. Our work suggests the management and business analysts, which adopt ROs, to recognize qualitatively the factors affecting the investment value and treat them in a ROs perspective. The results from our models may change the conclusions extracted by the typical ROs approach given by the literature. The ability to hold the option and delay the investment depends on the balance between a large number of criteria, which some of them can be treated qualitatively while some others quantitatively. The results of the thesis prove that the combination of quantitative and qualitative analysis, under a multicriteria perspective, could provide different conclusion for an ICT business concerning its optimum deployment strategy and its overall performance comparing to single quantitative analysis. 5 Finally, we apply the proposed models and methodologies in real case studies showing how they can be formulated and solved. The cases analyzed prove the usefulness and efficiency of the proposed models and analysis