Real Options and Strategic Investment Decisions: Can ... - HEC Paris

2010

Krychowski and Que´lin

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Real Options and Strategic Investment Decisions: Can They Be of Use to Scholars? by Charlotte Krychowski and Bertrand V. Que´lin

Executive Overview Real options (RO) analysis has been of growing interest to the academic community as a promising approach to supporting investment decisions under uncertainty. In this article we examine an applied investment decision in the telecommunications industry to highlight the main benefits associated with using real options. The paper then discusses the theoretical issues raised by real options. Specifically, we examine two research streams to explain how real options contributes to a theoretical understanding of strategic management, and to better understand the gap between theory and practice of real options. Finally, we lay out an agenda for future research.

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he combined effects of globalization, deregulation, and reduced technology cycles result in managers facing very volatile environments in their strategic investment decisions. In spite of this ever-increasing level of uncertainty, most corporations base their decisions on discounted cash flow (DCF)-based methods, such as the internal rate of return (IRR) or net present value (NPV), which are static in nature. Indeed, these methods assume that a decision is taken once and for all, without any possibility of modifying the characteristics of the investment project later on. As a result, in the last decade it has been widely argued that compared with a conventional financial analysis, real options (RO) provides a more appropriate framework. The term “real options,” coined by Myers (1977), corresponds to the application of financial options theory to investment decisions made by firms. A financial option is the right, but not the obligation, to buy (or sell) a stock (the “underlying asset”) at a fixed price (the “exercise price”)

We acknowledge the valuable comments from AMP associate editor Chung Ming Lau and two anonymous reviewers. We also thank Kevin Boudreau, Russ Coff, Rodolphe Durand, and Mu¨ge Ozman for their insights. Financial support and data access provided by Mobitel Company is also greatly acknowledged.

within or at the end of a fixed period (“maturity”). Firms establish options by making an initial investment, for example by performing a market test, creating a joint venture, developing a prototype, or purchasing an operating license (e.g., in the mining or telecommunications industries). If the economic prospects of the project turn out to be favorable, a firm may later decide to exercise the option—that is, to launch the new product, to purchase the remaining capital of the joint venture, to build a plant for the new technology, or to operate the acquired license. Conversely, if economic circumstances are unfavorable, it will abandon the option—that is, not make any subsequent investment. The main contribution of RO is to recognize that investment projects can evolve over time, and that this flexibility has value. Myers (1984) considered that RO is a powerful approach to reconcile strategic and financial analysis. Indeed, conventional DCF methods often lead to recommendations that conflict with strategic analysis because they fail to take into account the value of growth opportunities created by a project. The RO approach has been welcomed by academics with great enthusiasm (e.g., Bowman &

* Charlotte Krychowski ([email protected]) is Associate Professor of Strategic Management at Telecom & Management SudParis, France. Bertrand V. Que´lin ([email protected]) is Professor of Strategic Management and Industrial Organization at HEC, Paris. Copyright by the Academy of Management; all rights reserved. Contents may not be copied, e-mailed, posted to a listserv, or otherwise transmitted without the copyright holder’s express written permission. Users may print, download, or e-mail articles for individual use only.

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Academy of Management Perspectives

Hurry, 1993) and has led to a great deal of literature on the topic. Managerial applications of RO have been developed mainly in the petroleum industry (e.g., Kemna, 1993; Miller & Park, 2002), the electricity industry (e.g., Leslie & Michaels, 1997; Miller & Park, 2002; Tufano, 1996), and the pharmaceutical and biotech industries (e.g., Bowman & Moskowitz, 2001). However, RO has not lived up to the expectations it raised at the end of the 1990s. Because the analogy between financial options and real options is imperfect, RO raises implementation problems, and several surveys show that it is little used in practice: Whereas about 75% to 85% of firms use NPV for their investment decisions, only about 6% to 27% of them use RO1 analysis (Graham & Harvey, 2001; Rigby & Gillies, 2000; Ryan & Ryan, 2002). In this article, we use an actual decision in the telecommunications industry to illustrate the concept of real options and the potential benefits of the RO approach to improve strategic decision making. We then draw from the case to discuss the existing literature on real options and strategy, and to better understand the gap between theory and practice. Finally, we suggest avenues for future research. An Illustrative Case in the Mobile Telecommunications Industry n this section, we present an investment decision and show how RO helped determine optimal investment timing. We then set out the lessons learned from the case.

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The Investment Decision

“Mobitel,” a major telecommunications operator, had to decide whether to deploy a third-generation (3G) mobile telecommunications network. Like its main competitor, “Comptel,”2 Mobitel had acquired a 3G mobile telecommunications license at a time when people had high expectations of mobile Internet. However, it progressively 1

Differences in these survey findings might be due to differing interpretations of the term “real options.” For further details, see Triantis, 2005, p. 8. 2

These names are used to protect the anonymity of both operators.

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Table 1 Net Present Value of the Main Competitive Scenarios Facing Mobitel (in Monetary Units) Scenario 1: Early entry 2a: Late entry, preemption by Comptel 2b: Late entry, no preemption Average for scenario 2

Project Value Investment (S) Cost (K) NPV 1,386 1,078 1,227 1,152

1,280 1,120 1,120 1,120

106 ⴚ42 107 32

S ⴝ Present value of cash flows generated by the 3G network; K ⴝ present value of the investment cost to build the 3G network; NPV ⴝ S – K ⴝ net present value.

became apparent that 3G mobile telecommunication would not be the bonanza that operators had hoped for. The strategic decision was whether the network rollout should start immediately or the decision to deploy the 3G network should be deferred by one year. Mobitel’s top management was torn between the risk of investing massive sunk costs in a technology that would not be profitable and the risk of being preempted by its archrival, Comptel. In the case of immediate investment, Mobitel would be ahead of Comptel, which had announced that it would start its 3G network deployment in six months. Mobitel’s operations managers believed that the project would generate a positive NPV of 106 monetary units (MU) (see Scenario 1 in Table 1). However, if the investment were delayed by one year, Mobitel would lag six months behind Comptel, which would entail a long-lasting decrease in Mobitel’s market share. Moreover, such a delay would lead to the loss of high-end customers and therefore result in a significant reduction in the average revenue per unit (ARPU). As a result, the project’s NPV would become negative, falling to -42 MU (see Scenario 2a in Table 1). Alternatively, if Mobitel postponed its deployment by one year, Comptel could well do the same. In such a case, Comptel would let Mobitel test the waters and follow suit six months later. The NPV of the 3G network would then be 107 MU, nearly the same project value as that obtained if Mobitel launched the investment immediately (see Scenario 2b in Table 1). If we assume

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a 50% probability of preemption, the revised NPV of the late-entry scenario amounts to 32 MU. However, this value remains lower than the NPV of the early-entry scenario. The point raised by Mobitel’s finance team was that the project’s profitability was subject to a great deal of uncertainty concerning the level of demand. The decision took place at a time when the speed of penetration of the 3G technology was uncertain because the market was not yet mature. Similarly, the ARPU generated by a 3G network was very uncertain. In particular, data ARPU could remain low if consumers used mobile Internet applications only occasionally or used primarily low-bandwidth applications. A sensitivity analysis showed that, depending on the value given to these two sources of uncertainty, the NPV of the 3G project could vary greatly, between -600 and ⫹500 MU. Given the high uncertainty surrounding the success of the 3G technology and the irreversibility of the investment in a new network, the Mobitel finance managers were in favor of deferring the rollout decision. The RO Framework to Determine Optimal Entry Timing

Choosing the right market entry timing of a new technology is difficult because it involves making a trade-off between commitment and flexibility (Ghemawat, 1991). Past studies do not provide a decisive answer to this dilemma. For example, first-mover advantages may be counterbalanced by first-mover disadvantages (Lieberman & Montgomery, 1998). From an organizational learning perspective, it can be argued that firms investing early in promising technologies increase their absorptive capacity (Cohen & Levinthal, 1990) and are later much better positioned than competitors to take advantage of the new technology (e.g., 3G) once it becomes clearly established on the market. But again, this line of reasoning can be altered by several contextual factors, such as the intergenerational spillovers (Leiblein & Ziedonis, 2007), which were significant for the migration from 2G to 3G mobile telecommunication networks. Given this complexity and the high level of

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uncertainty, decisions on the early adoption of innovation may lead to mimetic processes, inducing firms to make similar choices (DiMaggio & Powell, 1983). In the Mobitel case, the uncertainty regarding the success of 3G clearly resulted in pressure being placed on the operational team to conform to the strategy announced by their main competitor. As such isomorphic behavior can have a negative impact on profitability (e.g., Barreto & Baden-Fuller, 2006), it is of particular interest to focus on a framework such as real options, which holds the promise of a more disciplined decision process. Financial option models provide analysts with two types of information at the same time: (a) the option value and (b) the most optimal time to exercise the option. Similarly, RO is useful both to evaluate an investment project and to determine the optimal investment timing. The decision faced by Mobitel can be analyzed with an options lens because the operator had some flexibility if it chose to postpone the network rollout by one year. Indeed, if at the later date the case for 3G has improved, the operator will deploy the 3G network. If, to the contrary, 3G does not appear to be profitable in one year, Mobitel could instead invest in the alternative technology EDGE, which is slightly less powerful but much less costly to deploy than 3G. In other words, Mobitel held an option to wait. Exercising the option meant deploying the 3G network. The underlying asset S corresponds to the cash flows generated by the network. The exercise price K corresponds to the investment cost of rolling out the new network. The time to maturity T is one year: Beyond this date, the operator would have to invest in order to address its network’s looming capacity shortage. As a consequence, the late-entry scenario can be valued as an option to defer. The option can be valued with the Black-Scholes formula, which is the standard model used to evaluate simple options (see Table 2). We thus come up with a revised project value of 139 MU in the case of late market entry. This value is higher than that of the early-entry scenario (106 MU), which suggests

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Table 2 Valuation of the Option to Defer With the Black-Scholes Formula Parameter of a Financial Option Value

Application to Mobitel’s Investment Project

Value in Monetary Units (MU)

Underlying asset (stock) price (S) Exercise price (K) Time to expiration (T) Risk-free rate interest (r) Volatility of the underlying asset (␴)

Cash flows generated by the 3G network Investment cost necessary to deploy a 3G network Period during which the investment can be postponed Risk-free interest rate Volatility of the cash flows generated by the 3G network

50% * 1,078 ⫹ 50% * 1,227 ⫽ 1,152 MU 1,120 MU 1 year 5% p.a. (based on interest rate of Treasury bills) 20% (estimated with Monte Carlo simulations on S)

Value of the option to defer (C): C ⴝ S * N(d1) ⴚ K eⴚrT * N(d2), where d1 ⫽ [ln S/K ⫹ (r ⫹ ␴2/2) * T]/␴ 冑T

d2 ⫽ d1 ⫺ ␴ 冑T N(.) ⫽ cumulative standard normal distribution function C ⴝ 139 MU

that, according to RO, it would have been preferable to postpone the investment decision. Lessons Learned From the Mobitel Case

The Mobitel case highlights three main benefits of RO analysis (see Table 3). An Informed Decision on Optimal Investment Timing

In the Mobitel case, RO analysis produced the opposite recommendation on investment timing (delay the investment decision) as the NPV did (invest now). The NPV rule is biased in favor of early market entry because it takes into account the risk of waiting (preemption) but not the rewards of waiting (reduced uncertainty). NPV does not capture the value of staying flexible, and hence offers little guidance on optimal market entry timing. In contrast, RO explicitly incorporates uncertainty and the possibility of modifying the investment decision based on the value taken by uncertain variables.

The outcome of the managerial decision making confirms that this flexibility has value: In spite of the recommendation of an early market entry produced by the NPV calculation, Mobitel’s senior management decided to postpone the rollout of the 3G network until there were more clear signs that the technology would be profitable. Eventually, the operator deployed 3G in the most densely populated areas and EDGE in the rest of the territory. Thus, Mobitel took full advantage of the flexibility offered by waiting. A Helpful Tool for Dialogue Among Decision Makers

Strategic decision making often involves many people and groups pursuing divergent interests (Astley, Axelsson, Butler, Hickson, & Wilson, 1982). At Mobitel, it was difficult to establish a dialogue between the business unit and the finance department on the 3G network rollout issue. Yet, a successful strategy emerges from decision processes that take into account different viewpoints (Eisenhardt,

Table 3 Summary of Benefits of Real Option Analysis in the Mobitel Case Valuation Communication Decision Process

Situation at Mobitel

Potential Benefits of RO

Network rollout delayed, although NPV analysis recommended immediate launch Disagreement between the business unit and the finance department ● Attention focused on the 3G project ● No real interest paid to alternative technical solutions

Informed decision on investment timing (ability to balance the risks and rewards of delaying market entry) Improved dialogue between the various project stakeholders ● Greater ability to abandon the project if signs of failure multiply ● Enhanced reactivity to launch an alternative project in case of failure

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1999). RO has the advantage of incorporating within one single approach the various concerns expressed by the stakeholders. The approach does not eliminate any uncertainty regarding the success of the new technology or the strategy followed by the main competitor. Nevertheless, it provides the management with a framework in which hypotheses can be discussed and their impact measured through a sensitivity analysis. A More Efficient Decision Process

Cognitive biases can prompt management to pursue an investment in spite of negative feedback (Kahneman, Slovik, & Tversky, 1982). In particular, the sunk cost effect refers to the tendency for decision makers to pursue projects in which they have made substantial prior investment. At Mobitel, huge sums had already been invested in the 3G license, and we observed that the analysis concentrated on the 3G scenario alone, as if there were no alternative solutions. This type of antifailure bias can be addressed through real options reasoning (McGrath, 1999). RO prompts top management to seriously consider alternative scenarios from the outset, and to stop projects when signs of failure multiply. In Mobitel’s case, RO suggested paying greater attention to alternative migration paths, such as deploying EDGE instead of 3G and then leapfrogging to 3.5G. If 3G were abandoned, Mobitel would then be better prepared to quickly redirect resources to these alternative technologies. Theoretical and Research Implications he Mobitel example highlights several ways in which RO can improve the strategic decision process under uncertainty. Has this been confirmed by the existing literature, and if so, why do more firms not use this framework? We will next review some of the major domains that have employed RO in strategic decisions. We organize the key findings and debates in the RO literature along two main perspectives: as an interpretative lens and as a decision framework.

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Real Options as an Interpretative Lens

As an interpretative lens, RO can help analyze firms’ investment choices and their performance. The contribution of RO to each of these domains will be examined in turn. Firms’ Investment Choices

One of the areas in which RO has proved to be a useful theoretical perspective as an interpretative lens is related to choices for market entry investments. Firms contemplating entry into a new market can defer investment until the uncertainty is reduced. The value of this option to defer increases with uncertainty, which implies that the greater the uncertainty, the more firms will tend to postpone investment in the new market. However, RO takes into consideration that even if the initial investments are not profitable, they will provide firms with new capabilities (e.g., knowledge of a developing country) that will enable them to seize future opportunities (Kogut & Kulatilaka, 2004; Vassolo, Anand, & Folta, 2004). This tension between the option to defer and the option to grow explains why the effect of uncertainty on market entry is nonmonotonic (Folta & O’Brien, 2004). Another type of investment choice that fits well with the real options logic is research and development (R&D) and, more generally, hightechnology investment decisions. Indeed, these decisions are taken in a context of a high level of uncertainty and can be managed in a flexible way because the investment process is sequenced in different phases. For example, the analysis of patents by firms active in the pharmaceutical industry shows that their investments in R&D are consistent with the RO logic (McGrath & Nerkar, 2004). It appears, though, that not all firms are equal in their ability to create and exploit these options. While Japanese high-technology venture capitalists implicitly follow a real options logic, whereby initial investments in high-tech research are followed by a full-scale investment when the benefits of the new technology appear realizable, their American counterparts seem to follow an “all or nothing” strategy (Hurry, Miller, & Bowman, 1992).

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The literature has also used RO to better understand the structuring of investment choices. There is now a vast amount of literature analyzing governance choices with an options lens, often in combination with transaction costs economics (TCE) or the resource-based view. More precisely, scholars have built on Kogut’s (1991) research demonstrating that firms investing in a joint venture (JV) acquire the option to buy the partner’s stake if the firm considers it profitable to expand its activity in this sector. As option value increases with the level of uncertainty, a high level of uncertainty will lead firms to choose JVs as a mode of market entry rather than wholly owned subsidiaries, which are much less flexible. Thus, under certain conditions, RO theory comes up with a prediction that is the opposite of that provided by the transaction costs theory. Indeed, TCE claims that in order to avoid opportunistic behavior from the partner, the wholly owned subsidiary is preferable to the JV in case of high uncertainty (Chi & Seth, 2009). However, it appears that only exogenous uncertainty on which the firm has no influence, such as macroeconomic conditions in a foreign country, can explain the establishment of JVs with a real options logic, whereas endogenous sources of uncertainty, such as cultural uncertainty, do not have the impact predicted by RO theory (Cuypers & Martin, 2010). Performance Outcomes of Real Options

RO has also been useful in examining the link between firms’ performance and the presence of options within those firms (Tong & Reuer, 2007b). One of the main issues that has been investigated is whether firms actually capture the value of real options, and if so, under what conditions. Kester (1984) measured the value of growth options as the difference between the total market value of a company’s equity and the capitalized value of its current earning stream. He found that growth options constitute well over half the market value of many companies’ equity. However, the proportion of the growth options’ value is much higher for firms operating in high-technology industries than for other

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firms. Using a more sophisticated way of measuring growth options’ value (with Stern Stewart’s Market Value Added database) on a larger sample, Tong, Reuer, and Peng (2008) broadly confirmed Kester’s conclusions. Breaking down the option value component in the value of firms and identifying which types of investments create option value is a challenging task. More specifically, researchers have attempted to analyze the impact of JVs and R&D investment on options value. The study by Tong et al. (2008) showed that not all international JVs (IJVs) create option value. For example, IJVs in emerging economies do not create growth option value, probably because of the high transaction costs associated with the management of this option. Similarly, Oriani and Sobrero (2008) analyzed the market value of R&D investments, which are affected by three intertwined real options: the option to grow, the option to defer (further R&D investment), and the option to switch (to another technology). While option theory claims that the option value increases with the level of uncertainty, there is no simple relationship between the market value of R&D and the level of uncertainty. Indeed, technological and market uncertainty have different effects on the three types of options created by R&D. The bottom line is that we can observe a U-shaped relationship between market uncertainty and the market value of R&D, and an inverted U-shaped relationship between technological uncertainty and the market value of R&D. Real options not only enable firms to capture the value of growth opportunities in case of favorable circumstances; they also limit the downside risks in case of unfavorable conditions. In practice though, the literature again highlights the complexity of the relationship between the presence of options and the downside risk supported by firms. For example, multinational enterprises, which have an option to switch activities between countries, should have a lower exposure to international economic changes, such as exchange rate fluctuations. Empirical studies show that the relationship between multinationality and downside risk is curvilinear, whereby risk first declines and

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then increases beyond a certain point of international expansion (Tong & Reuer, 2007a). Real Options as a Decision Framework

The second main stream of literature employing the perspective of RO concerns decision frameworks in which RO is employed to help firms evaluate and structure strategic investment opportunities under uncertainty. It highlights two main possible roles for RO in strategic decision making: real option valuation and real option reasoning. Real Option Valuation

In the economic and financial fields, scholars focus on real option valuation. The pioneering valuation models have shown that in a context of uncertainty RO may produce more appropriate capital budgeting recommendations than the NPV rule (e.g., Brennan & Schwartz, 1985; Majd & Pindyck, 1987; McDonald & Siegel, 1986). The literature now offers a large number of increasingly complex real option valuation models, some of which have also become more sophisticated with the integration of competitive forces. The first real option model in a duopoly was developed by Dixit and Pindyck (1994) and opened a growing literature stream combining real options and game theory (see Smit and Trigeogis, 2006, for a review of the literature on this subject). Overall, these models lack practical implementability (Triantis, 2005). They are mathematically opaque and usually rely on restrictive hypotheses, which may be barely compatible with real-life investments. However, some rare empirical studies suggest that valuations calculated with real options models are consistent with empirical data and with investment decisions made by firms: Quigg (1993) in the real estate sector, and Harchaoui and Lasserre (2001) as well as Moel and Tufano (2002) in the mining industry. Real Option Reasoning

In the strategic management field, the literature concentrates on RO as a way of thinking. Many researchers (e.g., Coff & Laverty, 2001; Miller & Waller, 2003; Sharp, 1991) are aware of the difficulties of translating financial option theory to

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real investment decisions (Kogut & Kulatilaka, 2004) and of valuing real options. Therefore, they believe that RO should instead be used as a rhetorical tool. There are several ways in which real option reasoning can help organizations better structure their investment decisions in a context of high uncertainty. First, RO can induce firms to undertake risky projects, as option value increases with volatility (McGrath, 1999). Second, RO recommends that investments be sequenced in several phases, in order to take advantage of upside risk without bearing the cost of downside risk (Leslie & Michaels, 1997). Last, RO prompts management to pilot projects in a proactive way. Firms tend to suffer from inertia in their management of projects (e.g., Busby & Pitts, 1997). One benefit of RO is to encourage management to preserve flexibility of choice and to modify the investment project according to economic circumstances (McGrath, Ferrier, & Mendelow, 2004). More recently, the literature has warned about the limits of RO. These include three main shortcomings: (a) the framework does not apply to all investment decisions, (b) it raises serious implementation issues, and (c) it does not take into account behavioral and organizational biases. Below we will examine each of these in more detail. Application Domain. Not all investment decisions can be framed as options. Four main conditions have to be fulfilled in order for a decision to be appropriate for real option logic: irreversibility, uncertainty, flexibility, and information revelation. In case of low degree of uncertainty and irreversibility, the NPV rule is more appropriate than RO (Adner & Levinthal, 2004). Flexibility means that when the option expires, the firm really has the possibility to choose among several alternatives. In the Mobitel case, an alternative to 3G was to invest in the EDGE technology. If there is no other viable alternative, the investment project is a “bet,” not an option (Copeland & Keenan, 1998). On the other hand, if the scope of opportunities is too wide (either from a technological or from a market perspective), the decision process is more characterized by path dependence than by the option logic. Whereas the RO ap-

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proach requires specifying ex ante the possible project scenarios, exploration activities are difficult to anticipate. The risk of using RO in such contexts is to abandon a project that does not fulfill the rigid RO criteria, even if this project introduces unanticipated promising possibilities (Adner & Levinthal, 2004). Finally, the condition of information revelation refers to the possibility of reducing uncertainty during the life of the option, either by observation or by investing in information acquisition. However, there are projects for which the value of the underlying asset cannot be known at exercise time—for example, in case of a disruptive technology, of strong network externalities, or of knowledge-based assets, whose value is particularly difficult to analyze (Woiceshyn & Falkenberg, 2008). As a result, managers may erroneously drop options that would lead to a competitive advantage or conversely invest in the wrong project (Coff & Laverty, 2001). Implementation Issues. The identification and the valuation of real options both raise difficulties. Whereas financial options are formalized by a contract, real options are “part and parcel of the business” (Myers, 1996, p. 100). As a consequence, it is difficult for managers to identify the latent “shadow options” within their firms (Bowman & Hurry, 1993). Valuing real options is also a challenging task. The option theory has developed a vast variety of option valuation models, which rely on a number of implicit hypotheses and can lead to different results (Borison, 2005). Managers—and even many academics— do not have the mathematical skills necessary to use real option valuation models comfortably and knowledgeably (Lander & Pinches, 1998). Even if a model seems simple in use, it is important to understand the hypotheses behind it to avoid erroneous conclusions, as was shown by Bowman and Moskowitz (2001) with the example of Merck. For more complex investment decisions, it is also necessary to adapt standard valuation models to the specificities of the investment project. Again, this requires mathematical skills that are often beyond the capabilities of corporate managers (Bowman & Moskowitz, 2001).

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Behavioral and Organizational Biases. RO rests on the assumption that managers will follow a strict optional discipline, from the project inception to its implementation or abandonment. The RO approach consists of starting a lot of projects, but also of ruthlessly abandoning options that do not live up to expectations (McGrath, 1999). However, unlike financial options, real options do not have a clear expiration date. As the project’s champions can claim that an apparently failing project will produce valuable opportunities in the future, escalation of commitment may occur, entailing the multiplication of losses (Adner & Levinthal, 2004; Janney & Dess, 2004). The question is then whether firms are capable of putting in place the appropriate organizational mechanisms to ensure an effective management of real options (McGrath, Ferrier, & Mendelow, 2004). Empirical Evidence

Empirical studies on the implementation of RO are still rare (on this subject see, e.g., Tong and Reuer, 2007b), and research remains relatively silent on how to concretely apply RO theory. Yet, a few case studies inspired by real investment decisions underline the benefits of RO for strategic decision making and illustrate the wide range of potential applications of RO. In capital-intensive industries such as the petroleum industry, which are comfortable with sophisticated capital budgeting decision tools, real options are evaluated with complex models, often in combination with decision analysis approaches (e.g., Chorn & Shokor, 2006; Smith & McCardle, 1999), in order to make decisions on exploration investment projects. In other industries, case studies demonstrate that RO can be particularly useful in determining the optimal investment timing—for example, for the market introduction of a new product in consumer electronics (Pennings & Lint, 2000), for the deployment of a new banking IT system (Benaroch & Kauffman, 1999), or for the development of residential housing (Rocha, Salles, & Garcia, 2007). In other instances, real options are used to evaluate an investment under uncertainty, such as the investment in a software platform (Taudes, Feurstein, & Mild, 2000), in environ-

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mental mining equipment (Cortazar, Schwartz, & Salinas, 1998), or in an R&D project (Pennings & Lint, 1997). Overall, existing empirical studies provide limited evidence of the benefits of RO in the resource allocation process. Indeed, they do not reflect the practice of firms, but are rather the result of pilot projects on the use of RO. In addition, case studies mainly focus on the valuation aspect of RO; they tend to overlook the benefits of real options reasoning and leave unexplored the cognitive and organizational difficulties in the implementation of RO. Future Research n spite of its surge in publications, RO theory remains a relatively young field of research, of which only a small portion speaks directly to managers. In the following section we suggest research directions that will help to further the use of RO theory.

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Exploiting the Descriptive Power of Real Options: The “Telescope Dilemma”

The main contribution of RO theory in the interpretative lens perspective is to shed new light on the consequences of uncertainty, which is one of the five key concepts structuring research in strategic management (Rumelt, Schendel, & Teece, 1994). A central proposition in RO theory is that the option value increases with the level of uncertainty, just as the value of a financial option increases with the volatility of the underlying asset. As a consequence, RO theory shows that firms can take advantage of uncertainty rather than trying to avoid it. The options lens thus provides new insights into issues such as the optimal governance mode and the link between uncertainty and the level of investments. In addition, RO theory contributes to a better understanding of the impact of firms’ decisions on their performance. Other theories may fail to demonstrate the link between certain decisions and firms’ performance, because they look at the aggregated value of the firm, whereas these decisions may have an impact only on the option value of the firm and not on the value of assets in place, or vice versa. To illustrate, Tong et al.

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(2008, p. 1024) explained that existing studies came up with contradictory results on the performance impact of diversifying versus nondiversifying JVs. The contradiction could be reconciled by taking into consideration that diversifying JVs has a positive impact on the growth option value of a firm, as opposed to its total value. Overall, the literature on real options has significantly contributed to a better understanding of the behavior and performance of firms. However, real options have mostly been used in a very metaphorical way, and existing empirical studies do not provide direct evidence that decisions made by firms were guided by RO logic (Reuer & Tong, 2007). Scholars studying RO face the same dilemma as astronomers, who have to make a choice between using small telescopes that can see a large portion of the universe but cannot detect weak signals and large telescopes that are much more powerful but have a narrow focus. In other words, many issues can be analyzed with an options lens, but when researchers apply this framework to broad and general issues they cannot exploit the “power” of the theory. Future research should attempt to analyze more focused issues. First, this would reduce the difficulty of analyzing decisions that involve several intertwined options. Second, conducting studies in specific settings would enable scholars to model the impact of the main variables that affect option value instead of testing the effect of broad variables that provide only indirect evidence of the options perspective. As outlined in the Mobitel case, the option’s value depends on five main parameters: S, K, r, ␴, T (see Table 2). The uncertainty parameter ␴, which is central to the options theory, should be studied in great detail, because the various sources of uncertainty have a different impact— or no impact at all— on an option’s value. Interesting studies could also be conducted by analyzing similar decisions that have a different time to maturity (T). Similarly, it would be interesting to study to what extent the difference between the underlying asset value (S) and the exercise price (K) affects an investment decision based on real options logic. For example, JVs offering the possibil-

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ity to purchase equity from a partner at a prespecified price should provide firms with a potentially higher payoff than JVs where the partner’s stake is purchased at fair market value. Another variable that is often overlooked in the real options literature is the dividend rate (␦)3. In finance, because the payment of the dividend reduces the underlying asset value, it may be optimal to exercise the option, even if the date to maturity has not been reached. In the corporate world, the dividend rate corresponds to the cost of keeping the option alive. Taking this parameter into consideration would enable researchers to better understand why it is not always optimal to “keep options open” and come up with less intuitive results. Developing the Normative Aspirations of Real Options Theory

Another critical contribution that has been highlighted in the existing literature is the normative benefits of RO. However, the limits of RO as a strategic decision tool have also been recognized. Future research will have to better understand if and to what extent aligning corporate decision processes with real option logic may affect competitive advantage. This can be addressed through two main questions: (a) what is the application domain of RO, and what type of RO analysis should be conducted depending on the project characteristics?, and (b) under what conditions can firms achieve a successful implementation of RO? These two questions and the future research needed to answer them are addressed below. Application Domain of RO Analysis

Corporate applications and academic research have concentrated on specific decisions in a limited number of industries. At the same time, the actual debate among scholars on the situations in which RO can be applied has been limited to very general and theoretical considerations. Research now needs to study in greater detail for which types of decisions RO analysis is beneficial. In so 3

This parameter does not appear in Table 2, because the standard Black-Scholes formula calculates the value of a European option on an underlying asset that does not pay any dividend.

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doing, it is important to keep in mind that there is no clear distinction between projects that fit well with the RO logic and those that do not. Rather, an interesting research avenue would be to determine which type of RO analysis should be applied, depending on the project’s characteristics and on the level of analysis (one project versus a portfolio of projects or the entire firm). The “telescope” metaphor is also applicable to the normative perspective of RO: At one end of the spectrum, RO can be applied in a formal and quantitative way in order to make decisions on focused investment projects for which the main sources of uncertainty can be modeled. At the other end of the spectrum, RO can also be applied to broad and complex issues involving interdependent projects and options and covering a long time horizon, but in this case only a rhetorical use of RO will be possible. As a matter of fact, Triantis (2005) reported that some firms use RO as a way of thinking, whereas others apply formal, quantitative RO valuation models. Future research should further develop the understanding of the uses and limits of RO in each of these different settings. The RO method employed in one setting may be inappropriate in another. Conditions for a Successful Implementation of RO

Concerning RO valuation, research needs to move from rigid financial models to flexible and simple heuristic methods that can be more easily implemented. There is some potential for simplifying the computational side of real options, in particular by using more intuitive simulationbased methods. Similarly, future research on RO will have to shift from a “financial” to a “strategic” perspective and pay much greater attention to the behavioral and organizational constraints in the use of RO analysis. Like game theory, RO theory is grounded on a hyper-rational vision of the decision process, and on the capacity of agents to model uncertainty and its consequences. This contrasts sharply with many theories used in strategic management, such as organizational learning theory and institutional theory, that assume bounded rationality and significant difficulties in modeling uncertainty. Future research will have to incorporate a per-

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spective of how managers make decisions in light of their options (Barnett, 2008). Of particular interest would be to study the organizational mechanisms that are necessary to capture the full benefits of the real options approach. These include organizational structure, incentives for management, and allocation of decision rights. In terms of methodology there is a clear need for more empirical research on the conditions for successful implementation of RO. In particular, conducting detailed, longitudinal case studies, as is common in the strategic investment decision literature (e.g., Bower, 1970), would greatly advance RO research. Whereas the RO literature has concentrated on the initial investment decision, the Mobitel case has shown that RO may contribute to improving the decision-making process during the entire life of the project. Conducting in-depth case studies would enable researchers to understand how RO can improve the strategic decision process from project inception to termination, and to test whether, and under what conditions, managers are capable of sticking to the optional discipline. However, this will be a challenging task, since few firms currently use RO analysis. Building Bridges Between the Various RO Research Streams

The RO literature is marked by the lack of connections among the various subfields, and establishing bridges between them would advance RO theory. In particular, connecting the descriptive and normative perspectives plus integrating the qualitative and quantitative approaches would be valuable for RO. Reconciling the Descriptive and Normative Perspectives

Research generally involves a balance between a descriptive and a normative perspective, and RO theory is no exception. Myers, the “inventor” of real options, actually used this concept both as an explanatory framework to better understand corporate borrowing behavior (Myers, 1977) and as a normative decision tool bridging corporate finance and strategy (Myers, 1984). The interpretative lens and decision framework streams have evolved independently, whereas they ought to en-

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rich each other: Research on how firms actually make decisions should be used to validate and refine hypotheses of RO valuation models, as is suggested by Cuypers and Martin (2010) for governance choices. Conversely, research on the conditions under which RO can be used as a decision framework by firms can help scholars analyze the performance implications of RO. It will be a challenge to reconcile the descriptive and normative perspectives, as they rest on seemingly contradictory hypotheses: The interpretative lens stream makes the implicit assumption that firms intuitively use RO. In contrast, the decision framework stream implicitly hypothesizes that only firms capable of acquiring real options and properly managing them will achieve superior performance. Nevertheless, the Mobitel example suggests that a formalized RO analysis can be beneficial, even if the RO logic is intuitively used by the management: On one hand, Mobitel management intuitively pursued an RO logic, as they decided to wait in spite of a positive NPV. On the other hand, a formal use of RO analysis would probably have enabled a much smoother, more disciplined, and more reactive decision process. Reconciling the Qualitative and Quantitative Approaches

Within the decision framework stream, there is again an apparent contradiction between the “real option valuation” and the “real option reasoning” approaches. The qualitative use of RO should not be understated. Indeed, empirical research has demonstrated that, in practice, the quantitative evaluation of a project plays only a limited role in strategic investment decisions, and that management gives an equal, if not greater, importance to strategic considerations emerging from informal processes (e.g., Arnold & Hatzopoulos, 2000; Butler, Davies, Pike, & Sharp, 1991). At the same time, if we do not attempt to evaluate real options, there is the risk that RO could be perceived as a tool used to justify launching projects whose NPV is negative, or keeping alive projects that are not succeeding. A purely rhetorical use of RO presents two additional disadvantages. First, relying on an intuitive estimation of option value can be dangerous, as past research has shown that managers’ intuition is not

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necessarily in line with the value produced by RO theory (Howell & Ja¨gle, 1997; Miller & Shapira, 2004). Second, flexibility usually comes at a cost. It is therefore often necessary to value the option in order to assess whether the benefit of flexibility exceeds its cost. The field experience at Mobitel suggests that we should go beyond the “quantitative versus qualitative” debate. Because a strategic investment decision is so complex, the result of the option valuation should be taken with circumspection. The option value per se is only one of several elements affecting the decision. Qualitative findings from RO can also play a key role in the decision-making process. Conversely, an RO that would be based only on qualitative analysis, without attempting to value the real option(s) embedded in the investment decision, would be limited to superficial conclusions. Qualitative findings from RO emerge only when a detailed, quantitative analysis is performed: This prompts management to be more explicit about key assumptions (e.g., the success of 3G), and to manage the investment project proactively. Conclusion verall, this article has shown not only the practical use of RO theory but also the current gaping holes in its use. In particular, we believe that scholars should attempt to establish linkages between the various streams of the RO literature: Future research would benefit from integrating the explanatory and the normative perspectives of RO theory as well as connecting the qualitative and quantitative approaches of RO. There is also a clear need for more empirical research that directly demonstrates the relevance of the RO perspective to explain firms’ behavior and identifies clearly the benefits of RO to the resource allocation process. It is hoped that this manuscript generates both discussion among academics and further use of this theory in future strategy research.

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