ABS MBA COMPANY PROJECT
The use of Real Options
in Strategic Investment
Decisions
The option value of investing in online learning
Peter Tempelman1-4-2015
report company project Supervisor: dr. J. Ligterink
Date of submission: April 1, 2015
Confidentiality restriction: the of the providers of the online services are anonymized
1 Executive Summary
When making strategic investment decisions, assumptions have to be made about the future. If these assumptions hold true, the investment will have a positive outcome. The future however does not always turn out to be as expected. Customer preferences, technology, markets etc. are subject to change, causing uncertainty.
This uncertainty about the future holds value if an organization is able to adapt these changes and take advantage of the opportunities (‘options’) that present themselves. When applying a real options framework, investors include the value of the upside potential of these opportunities in their investment decision, while avoiding the negative consequences, or the downside potential.
Higher education faces an uncertain future as a result of the emergence of online learning. Online learning has the potential to disrupt the business model of traditional ‘brick-and-mortar’ institutions. Although the specific impact of online learning is unknown, ‘keeping the options open’ with regard to investing in online learning seems appropriate.
This leads to the question how a real options approach can help higher education
institutions in making strategic decisions about investing in online learning? In this report I apply a real options approach to an investment project in which a small course is offered online. In this investment project, the real options are embedded in a decision tree. The decision tree explicates the link between the investment that has to be made today and the investments that have to be made in the future.
The findings of the decision tree analysis indicate that there can be a significant upside potential, provided the costs of an external provider match the size of the investment project.
2 Table of contents
I introduction
II Disruptive technologies in higher education
a Disruptive technologies
b Online learning as disruptive technology c Scenario’s for online learning
III Valuation methods
a NPV
b Real options in strategic decision making c Financial options
d Real options
e Conceptual problems when applying option theory to strategy f Characteristics of the design of strategic investments
IV Applying valuation methods to a strategic investment in online learning
a Case description
b The investment project c Methodology used d Data collection
e Decision trees analysis f Discussion
V Conclusions and recommendations
References Appendices
3 I Introduction
Every organization makes strategic investment decisions. If these investments perform as expected, they should provide the organization with a competitive advantage, generate above normal returns and safeguard continuity.
When making strategic investment decisions, managers have to decide which alternative to choose and which alternative to discard. Therefore, making strategic investments decisions is a matter of selecting the best alternative. Based on these decisions, scarce organizational resources are allocated (Bowman and Hurry, 1993, Anderson, 2000).
One of the techniques used by managers to aid their decision making is the net present value analysis, or NPV analysis. This valuation tool discounts expected future cash flows and results in one single figure, either positive or negative. If this single figure is positive, the investment is expected to generate value and the project should be undertaken. If the single figure is negative value is destroyed, and the investment should not be made. The appeal of using the NPV analysis lies in its simplicity. The outcome is unambiguous.
The NPV analysis however also has its limitations. It presumes that management can predict all future events and is able to value them correctly (Kogut and Kulatilaka, 2001). NPV analysis also does not include managerial flexibility: over the investment period, as
experience and knowledge builds, management may take actions that positively influence cash flows. The NPV analysis treats the investment as a single decision (Kogut and Kulatilaka, 2004). Any positive consequences of managerial action are not taken into account at the time of conducting the NPV analysis (Tamayo-Torres, Ruiz-Moreno and Verdú, 2010). Furthermore, the NPV analysis is biased in favor of early market entry because it does not take the rewards of waiting, i.e. the reduction of uncertainty, into account (Krychowski and Quélin, 2010).
The overarching limitation of the NPV analysis is therefore its static nature. What is missing is the valuation of the possibility that an investment can be adapted to changing
4
Bowman and Hurry 1993, Krychowski and Quélin 2010, Anderson, 2000). A valuation technique that does include the value of being able to adapt to changing environments, managerial flexibility and reduction of uncertainty is the ‘real options theory’ (hereinafter referred to as ‘real options’). When adding real options to an NPV analysis as input for
strategic investment decision making , the limitations of the NPV analysis can, to a significant degree, be overcome. Using real options in conjunction to NPV analysis may lead to
situations where highly promising investments with a negative NPV may not be rejected when balancing the NPV with the value of real options.
Real options finds their origin in the financial options. There are however a number of difficulties that make application of financial option valuation techniques to real options difficult. The first one is that parameters that are easily set for financial options cannot be applied in an identical fashion to valuation of real options. Second, when applying real options to value strategic investment, various managerial biases come into play (Adner and Levinthal, 2004, McGrath, Ferrier and Mendelow, 2004). These biases make the use financial options techniques troublesome. Third, not all strategic investment decisions are structured in a way that real options theory can be used (Kogut and Kulatilaka, 2004, Krychowski and Quélin, 2010, Anderson, 2000, Burger-Helmchen, 2007). And fourth, applying real options valuation techniques requires extensive mathematics This makes applying real options theory for all intends and purposes in real life impractical (Luerhman, 1998).
For a higher education organization such as the Amsterdam School of Real Estate (ASRE) making strategic investments in on-line education is allocating resources for uncertain futures. Online education has been around for some 20 years, and has become mainstream in the US higher education (Clinefelter and Aslanian, 2014). Online education meets the definition of a disruptive technology and should therefore be watched closely by higher education organizations (Christensen and Eyring, 2011). Carefully considering investments in online education therefore seems prudent. How should such an investment decision be made?
5
This situation leads to the research question in this company project:
‘How can a real options approach help educational organizations in making strategic decisions about investments in online learning? What lessons can be learned from this approach by the educational institution when structuring investment decisions in on-line education?’
In this paper I will describe the rationale to invest in online education, using Christensen’s concepts of disruptive innovation (Christensen, 1997), as the emergence of online learning can be considered a disruptive innovation for which educational institutions should prepare. Preparation requires investment in an uncertain future. Using real options to strategic investment, I will apply a real options approach to three strategic investment alternatives.
This paper is structured as follows. In chapter II I the concept of disruptive technologies is discussed and applied to online learning in higher education. Chapter III elaborates on valuation methods of strategic investments: the net present value analysis (NPV) and real options theory. The characteristics of both are described, as well as their applicability to an investment in online learning. Chapter IV describes an actual investment project in online learning. The options embedded in this project are described by means of a decision tree analysis. The last chapter, chapter V, draws conclusions from the case study an provides recommendations on the use of real options in strategic decision making processes.
6 II Disruptive technologies in higher education
IIa Disruptive technologies
The continuity of a company depends on its ability to retain its position in the market by adapting to changes in that same market. Some of these changes are technological changes (Christensen, 1997). Failure to do so may result in the demise of the company. A well-known example is the Eastman Kodak Company. Once a leader in photography, it almost went bankrupt in 2013 due to missing the emergence of digital photography, a technology it had invented itself in 1975 (Meesters, 2012).
Common to failure caused by market or technological changes is that decisions that lead to the demise are made by highly regarded managers. According to Christensen, this paradox is caused because management did everything right in that particular situation. These
managers did what was to be expected to increase returns: they paid attention to their most profitable customers, kept an eye on market trends and systematically invested in promising innovations. These actions however turned out to be the root cause of failure. This paradox is what Christensen refers to as the ‘innovators dilemma’ (Christensen, 1997). Management appears to do all the right things, when they should be taking counterintuitive actions.
These fatal management decisions are taken in the so called ‘failure framework’, a
combination of factors that causes management makes the wrong decisions (Christensen, 1997). The framework consists of three factors.
The first is the emergence of a disruptive technology, a technology that offers worse product performance for existing clients, and is therefore disregarded by management. This worse performance however offers a different and new value proposition that may not appeal to existing customers today, but does so to no new customer groups.
The second factor follows from the first: the pace of technological progress and market demand is not the same: the existing market is not ready for a new technology.
7
Third, disruptive technologies are typically adopted by customers that are least profitable to the company, as these customers do not value the more profitable existing technologies. They happen to be the customers an organization is least inclined to serve.
What most companies do very well, is the development of sustainable innovations, as opposed to disruptive innovations. Sustainable innovation improve existing products and services for existing customers in existing markets (Christensen, 1997).
The innovators dilemma can partially be dealt with by taking a number of insights into account:
1. A disruptive technology may not be in demand by customers today, but can be in high demand tomorrow. For disruptive technologies, customer demand is not leading. The drive to adopt a disruptive technology should come from the firm. 2. It is financially unattractive to invest in disruptive technologies as long as sustainable
and profitable technologies are still in existence. In addition, exploiting these sustainable and profitable technologies comes more natural to employees than an unknown and less profitable (disruptive) technology.
3. Disruptive technology should be considered a marketing challenge: The value proposition of the disruptive technology is typically not applicable to the existing client base, markets should be found elsewhere.
4. New markets require new capabilities, capabilities organizations likely do not have. 5. Achieving success with disruptive technologies is accompanied by failures. Analysis of
failure produces knowledge that can be applied to successfully implement a disruptive technology.
6. Small entrants in emerging markets of a disruptive technology find themselves protected from more established competitors because of their inherent agility and the conventional managerial wisdom of these competitors.
8
The concept of disruption can also be illustrated by the S-curve analogy.
Figure 1: S-curve
source: Robertson, 2013
As the application of a technology matures, so do the returns it generates. At some point in the life cycle, a new, disruptive, technology appears and challenges the existing technology. At first, both technologies can exist together. At some point however, due to changes in the market, changing customer needs, the disruptive technology causes the profitability of the existing technology to decline. The disruptive technology then becomes mainstream, until it is replaced by a new disruptive technology. The length of an S-curve can vary from months to decades (Christensen, 1997), but every S-curve eventually reaches a state of decline (Robertson, 2013).
IIb Online learning as disruptive technology
A disruptive technology in higher education that has unfolded in recent years is ‘online learning’. Starting as an offering by for-profit educational institutions, online learning improved by its own sustaining innovations to the point that the performance
enhancements (in technology, marketing, investment and managerial processes) exceed the most demanding customers’ needs (Christensen, 2011).
Online learning has many definitions. In this paper the following definition is used: Online learning is ‘…wide range of programs that use the internet to provide instructional materials
9
and facilitate interactions between teachers and students and in some cases among students as well’ (Bakia et al, 2012, p. v).
Online learning can be fully online, with all instruction taking place through the Internet, or online elements can be combined with face-to-face interactions. This combination is
referred to as ‘blended learning’.
Established higher education institutions are threatened by a technology that is happening beneath them. Online learning had served less profitable client groups that had limited access to traditional institutions and is now ready to challenge the traditional ‘brick and mortar’ institutions. The reason why higher education had been immune to disruption was a combination of the power of prestige in the marketplace of higher education organizations, as well the accreditation system. But also, technology had not sufficiently advanced until recent, and was not support by legislators (Christensen, 2011).
However, both the increased speed of internet as well as enhancements of online instruction technology have increased the quality of online learning. Online learning has entered the realm of web 3.0: it has become individualized (it adapts to the needs of the student), is has become data driven (it infers the ways a student learns best) and it is social (online learning connects students) (Christensen, 2012). Furthermore, the outcome of online learning appears to be as least as good as conventional instruction (Means et al, 2010).
At the same time, due to the economic crisis, traditional higher education institutions are forced to cut the costs of their programs. In an example of the cost advantage of an online US college degree over a face-to-face US college degree, the costs of an online program are less than half the costs of the costs of a degree from a public institution and less than 12% of the costs of a degree from an private institution (Christensen, 2012).
The lower costs of online education by itself however is not a reason to consider online education a disruptive technology. It has to offer a new value proposition, different from the existing dominant value proposition.
10
One of the possible scenarios for traditional intuitions is to combine the best face-to-face learning with the advantages of online learning. This could bring traditional higher education to a higher level by going beyond formal classroom instruction and more emphasis on informal learning which happens when students interact with each other (Christensen, 2012).
The quality of online learning is expected to further improve as more suppliers of online learning systems will start to compete (an incentive to improve) and a new job category of professional course designers is emerging, improving the quality, design and content of online learning (Christensen, 2012).
In the next paragraph possible consequences for higher education institutions are discussed should they introduce online learning.
IIc Scenario’s for online learning
Terwiesch and Ulrich posit that even if an institution of higher education does not actively adopt any activity regarding online learning, which they refer to as ‘super text’, incumbents will do so, forcing all other parties in the higher education market to follow suit, albeit as a follower, not as a first mover (Terwiesch and Ulrich, 2014).
Although Terwiesch and Ulrich focus on business schools, they state that the implications of online learning is applicable to higher education and corporate learning, as business schools can be considered a useful microcosm for studying the implication of online learning
(Terwiesch and Ulricht, 2014).
Characteristics of online learning are:
Content is authored by a recognized expert and delivered via video segments
Content is ‘chunked’, i.e. broken up in discrete portions which enable student specific
11
Online learning enables synchronous pacing – within limits set by the course
administrator each student can consume contents at his/her own pace
Assessment can be adapted to the learning objectives set by the course administrator
Students interact with the course administrator and each other, not necessarily with
the content author. (Terwiesch and Ulrich, 2014)
Due to the above characteristics, especially the semi synchronous pacing, online learning challenges the boundaries of professional life and class time, as students are able to schedule their learning around working hours.
The impact of online learning on how business schools are effected can be seen in the shift of the efficient frontier. The concept of the effective frontier depicts the tradeoff between student learning (quality) and factuality productivity (cost).
Online learning causes a shift of the effective frontier to increase either quality or cost, or both.
Figure 2: The shift of the effective frontier with online learning
source: Terwiesch and Ulrich, 2014
The consequences for business schools, and for other higher education organizations could be one of a combination of the following pathways.
12
Pathway one: Status Quo plus.
Online learning enables business schools to serve more students in new markets or provide more learning for existing students.
More students: Those students that did not consider enrolling in face-to-face education due to obstacles such as cost, distance or schedules may be inclined to enroll in online programs. In addition, it could also be a means to reach out to alumni.
More learning: The use of online learning enables faculty to develop more content or force students to prepare online for certain courses.
Pathway two: Offer the same services with less faculty.
According to Terwiesch and Ulrich online learning offers a cost advantage of 40%. Part of this cost advantage will probably be used to lower costs and possibly admission fees. At the same time, A-brand institutions will likely uphold their status by directing the freed up funds toward amenities and/or scholarly research. In this pathway classroom time can be used for discussion, group work and other experimental activities (such as debates or generative learning, also referred to as ‘flipping the classroom) (Terwiesch and Ulrich, 2014).
Pathway three: Unbundling of business school activities
Online learning enables education to be offered into chunks of mini courses that are delivered to students on demand (business school education as the ‘iTunes model’). A side effect of an on-demand model is that it will generate data which enables business schools to make use of predictive analytics and match the offering of courses to the demand of
students (Terwiesch and Ulrich, 2014).
Pathway three will do away with the prestige of having graduated from a renowned business school (credentialing), as a way to stand out from the crowd (Terwiesch and Ulrich, 2014).
Unfortunately, Terwiesch and Ulrich do not discuss the consequences that unbundling of business activities may have for two main other reasons to enroll in a business school: connecting into a social network and making career transitions. The unanswered question at this point is if online learning can also offer an equivalent or better social network and provide opportunities for career transition.
13
Some of the observations Terwiesch and Ulrich make on how business schools should respond to the advent of online learning fit well in the theory of disruptive technology. For example, they recommend higher education institution to experiment with online learning as if it needs to create new demand, rather than as a substitute current offerings. This recommendation can be linked to the finding of Christensen that disruptive technologies have a different value proposition for a different target group (Terwiesch and Ulrich, 2014).
To summarize, the literature suggests that higher education institutions are being
confronted with a disruptive technology that may force them, either actively or passively, to respond to incumbents. The Leage of European Research Universities (LERU), of which the University of Amsterdam is a member, recommends in an ‘advice paper’ ‘All research-intensive universities need to take a strategic approach to the provision of online education. No one will be able wholly to predict how this fast-moving environment will shift and
develop, but leading universities must be both proactive and responsive in relation to it.’ (Mapstone, Buitendijk and Wiberg, 2014, p.16).
Experimentation with online education is thus required. The future of online learning however is unclear. Yet management of higher education intuitions cannot afford to
postpone investment in online education much longer, for they may face the same future as the company referred to at the start of this chapter.
What decision making aids does the management of higher education institutions have when considering investing in online education? I will elaborate on this question in the next chapter.
14 III Valuation methods
As we have seen in chapter II, the world of online learning is fast-moving, it will shift and develop. Higher education organizations must be responsive to it. Due to the nature of this disruptive technology, this implies dealing with uncertainty. With the information available, higher education management must try to make predictions of the future to the best of their abilities. Based on these predictions, investment decisions have to be made today which consequences will become clear in the future.
In this chapter I compare two investment valuation techniques, the ‘NPV analysis’ and the ‘real option approach’ (hereinafter referred to as ‘real options’). Of the latter, I will
specifically focus on the use of real option as an aid for making strategic investment decisions.
IIIa NPV
‘When the value of the benefit exceeds the value of the costs, the decision will increase the market value of the firm’ (Berk and DeMarzo, 2011, p.55). From this ‘valuation principle’ it becomes clear that an investment should be funded if it creates more value than it costs (Putten and MacMillan, 2004, Freitas and Brandão, 2010).
To be able to make that decision in the present, the value of today’s and future costs and revenues must be expressed in the cash value of today. The total present values of all project cash flows is the net present value, or NPV (Berk and DeMarzo, 2011).
The benefit of using the NPV as a basis for a strategic investment decisions is its simplicity. Characteristics of the NPV are the following:
the NPV allows for comparison of projects with different lengths and cash flow
patterns;
The NPV allows for comparison of projects with different risks
15
the NPV enables separation of investment decisions from capital structure decisions
(NPV is independent of how it is funded)
the NPV is independent from preferences of owners of the project (for example, as to
when they need the money)
These characteristics hold true under perfect capital market assumptions (Berk and DeMarzo, 2011).
The NPV provides a ‘go’ or ‘no-go decision’. There are however a number of shortcomings for using NPV as the sole basis for making strategic investment decisions. The NPV assumes all cash flows in a project can be accurately estimated (Putten and MacMillan, 2004,
Copeland and Tufano, 2004, Kogut and Kulatilaka, 2001). The NPV is static by nature (Krychowski and Quélin, 2010, Luehrman, 1998), it does not recognize the importance of managerial flexibility (Tamayo-Torres, Ruiz-Moreno and Verdú, 2010, Freitas and Brandão, 2010, Angelou and Economides, 2007) and fails to address other, positive (organizational) effects, for instance how a project enhances the organizational knowledge (Anderson, 2000, Freitas and Brandão, 2010, Angelou and Economides, 2007). The NPV ‘has failed to capture the essence of strategic decision making, which invites the application of option theoretical perspectives in investment analysis’ (Myers 1984 in Anderson, 2000, p. 235).
As we have seen in chapter II there are many uncertainties about the way online education will manifest itself in higher education. Although the future may be uncertain, its direction can be influenced by management of higher education organizations. This poses a challenge for management in making investment decisions. How can an investment project be valued taking this uncertainty into account? Management may find itself in a situation where the NPV of an investment project is negative. The aforementioned valuation principle would suggest not to make the investment. On the other hand, the uncertainty may also bring possibilities that will yield unforeseen revenues which may lead to a profitable investment, despite the negative NPV. Frequently mentioned examples are R&D projects, where it is uncertain whether the investment in such research will generate revenues in the future (e.g. in Krychowski and Quélin, 2010, Kogut and Kulatilaka, 2001 and McGrath, Ferrier and
16
Mendelow, 2004). For these type of projects, option theory is preferred in the valuation of these projects.
IIIb Real options in strategic decision making
Uncertainty is inherent in business (Luehrman, 1998). Being able to exploit this uncertainty to the organization’s advantage may have value. To determine this value the real options approach can be used. Uncertainty in online learning projects is caused by the large number of intangible benefits and results and the fast development of online learning IT technology (Freitas and Brandão, 2010), the degree to which an organization can align its online learning efforts to its strategy, the actual demand for the online learning programs and the influence of competitors -preemptive moves (Angelou and Economides, 2007).
Before going into detail on the valuation of real options, I will discuss the concept of real options and the link to strategic decision making first.
There are many definitions on what strategic decision making is. In this paper, I define
strategic decision making as the process in which decisions are made to allocate resources to significant investments which future performance has a degree of uncertainty (e.g. in
Bowman and Hurry, 1993, Anderson, 2000, Adner and Levinthal, 2004, Burger-Helmchen, 2007).
Why would organizations allocate resources to investments when there is uncertainty about the future performance? One reason is that the success of an organization depends on its capability to develop and exploit new projects (Burger-Helmchen 2007). Investing in
uncertain projects today can generate the possibility, or option, of new markets in the future (Luehrman, 1998). Being able to innovate by investing in uncertain projects enables an organization to adapt to its environment and stay ahead of the competition (Tamayo-Torres, Ruiz-Moreno and Verdú, 2010).
17
Uncertainty requires organizations to ‘keep their options open’. In today’s dynamic environment being able to adapt to changes are key to survival. For higher education organizations this means that they must be able to adapt to disrupting technologies such as online learning. Therefore, in the strategic decision making process, this adaptability, or flexibility, must be incorporated. As mentioned above, the NPV analysis provides a ‘now or never’ answer for an investment decision. It is however highly unlikely that any organization will make a strategic investment decision at only one point in time, and expect that its assumptions about the future will play out exactly as expected (Luehrman, 1998). When estimating the value of an investment, organizations must value the possibility to make intermediary decisions as the project moves ahead in time.
The ability to respond to a changing environment may increase due to the learning effects within an organization. During each step of an investment project new knowledge is obtained and new capabilities are developed which allow the organization to more
appropriately act upon the situation. For this reason, uncertainty does not only constitute a risk, but also represents possible future profit (Anderson, 2000, Putten and MacMillan, 2004). When making strategic investment decisions, the value of an option that provides possible future profits should also be included in the valuation of the strategic investment. This is where real options may be of use in strategic decision making.
Real option theory finds its origins in in financial options. In real option literature, various definitions of real options are given. They all have a number of characteristics in common, mostly derived from the analogy with financial options: a real option is the right, but not the obligation, to take a specific decision (invest, defer, alter) on an underlying asset, for a predetermined price at or before a certain time (e.g. in Putten and MacMillan, 2004, Krychowski and Quélin, 2010, Anderson, 2000, Bowman and Hurry, 1998, Baduns, 2013). Others have described real options in more prosaic terms: A real option it making good use of an opportunity that brings value to the firm (Burger-Helmchen, 2007).
18
IIIc Financial options
There are two basic types of financial options: call and put options.
- An option that gives the holder of an option the right to buy an asset at a certain date for a certain price is a European call option.
- An option that gives the holder of an option the right to sell an asset at a certain date for a certain price is a European put option.
The ‘certain date’ in the above mentioned definitions is commonly referred to as ‘maturity date’. The ‘certain price’ is the ‘exercise price’ or ‘strike price’. It is important to note that any option is a right to buy or sell, not an obligation.
Holders of call options will exercise their option if the price of the asset on the maturity date is higher than the strike price. In this case, the option holder will buy the assets at a lower price than he or she would have to pay when buying the assets on the open market.
Example of a call option: Call option to buy 100 shares. Strike price: €100
Current stock price: €98
Price of an option to buy one share: €5 Initial investment 100 x €5 = €500
At maturity the stock price is €115. If the option is exercised, the option holder will have a gain of (€115 - €100) x 100 = €1.500. The net gain after deducting the initial investment is €1.500 - €500 = €1.000
source: Hull, 2011, p.206
The opposite applies to holders of put options. They expect that the price of the asset will decrease. If that situation occurs, the holder of a put option will be able to sell assets at a higher price than the market price at maturity and buy back those shares at the lower market price. The difference between market price and strike price minus the costs of the option will be the profit for the (put) option holder.
19
Example of a put option: Put option to sell 100 shares. Strike price: €70
Current stock price: €65
Price of an option to sell one share: €7 Initial investment 100 x €7 = €700
At maturity, the stock price is €55. The option holder can sell 100 shares at €70 and buy 100 shares on the open market at €55, a difference of €15 per share. The option holder will gain €15 x 100 = €1.500. The net gain after deducting the initial investment is €1.500 - €700 = €800
source: Hull, 2011, p.207
Financial options may be used to reduce (hedge) risks. By buying an option, the option holder insures himself from negative price movements and at the same time benefit from favorable price movements (Hull, 2012). The costs of this insurance is the cost of the price of the option.
The price of a single-period option can be determined by using the replicating portfolio technique. In the replicating portfolio technique, a similar portfolio of other securities is defined that has the exact same value as the option. According to the ‘law of one price’ (Berk and DeMarzo, 2011), this similar, or replicating, portfolio must have the same value in one period as the single-period option mentioned above. In this way, the price of an option can be determined. The replicating portfolio consists of other securities with known values: risk free bonds and underlying stock in the case of an option on a stock.
By using the replicating portfolio technique, the value of an (single-period) option is based on a portfolio of stocks and bonds. From this it follows that the price of an option is determined by:
1. The price of stocks and bonds in the replicating portfolio.
2. The risk free rate of one period.
3. The number of periods or time to maturity.
The prerequisites for pricing financial options make it clear that it is not possible to apply a replicating portfolio technique to valuing a real option. Because the underlying asset is not traded, there is no input from a market, hence no replicating portfolio can be constructed (Damodaran, 2008).
20
IIId Real options
In paragraph IIIb I elaborated on two basic types of financial options. Call options were the right to obtain an asset, put options were the right to sell an option. When applying option theory to real options, various types of options can be distinguished (Burger Helmchen, 2007).
The option to defer - The option to wait derives its value from reducing uncertainty granted by the ability to wait until more information has arrived. In the case of a strategic investment in online education, this option can be used to postpone an investment until such time where there is more certainty about the success of this investment. Factors influencing the reduction of uncertainty have to do with the maturity of online education (e.g. acceptance by students, advances in technology).
The option to abandon - The possibility of shutting down an unprofitable project. This option would be the possibility to stop investing in online education after an initial or trial investment. For online education this would mean that the investments are of a magnitude and structure that makes abandonment feasible: a relatively small investment and a ‘stand-alone’ structure. However, in most cases investments in online learning programs constitute unrecoverable cost or ‘sunk costs’ (Oslington, 2004). As there are no or very little assets to sell, the abandonment option is not a very feasible option for investments in online learning.
The option to grow - To create infrastructure and opportunities for future expansion. This option is closely connected with the option to switch and the option to contract/expand. An important option value of investment in online education comes from the ability of the investment to adequately respond to changes in demand and technology.
Closely related to the option to grow are the options to switch (the flexibility to change the nature of the input or output, or modus operandi) and the option to contract/expand (the capability to alter the capacity depending on market conditions - low/high demand, intensity of competition). Scalability of offering online learning represents an important option as the demand for online learning and the fluctuation of the demand is greatly unknown.
21
A fourth real option is the option to stage/compound option - To break up investment into incremental conditional steps. As the foreseeable future of online learning hinges on
assumptions about the demand for online learning, the option to break up the investment in discrete steps holds value as it facilitates decision making based on increasingly available information about online learning.
Financial options and real options can be valued using the same principles. A key difference between valuing financial options and real options however is that the cost to acquire the real asset (the investment in online learning) is a unique, project specific cost. There is no market for online learning projects, i.e. there is no exercise price. Both financial options and real options however hold value. The value of the real option can therefore be determined by comparing the expected profit of online learning with and without the real option (be it a defer, abandon, growth or compound option) (Berk and DeMarzo, 2011)
As mentioned in the previous paragraph, the time to maturity is a determining factor in financial option pricing. With real options, the duration of the period a manager has until he has to exercise the option can be compared with the time to maturity. The longer this period is, the more value the option will have as the uncertainty is greater than in case of a short time to maturity.
The stock price of the stocks in a replicating portfolio used to value a financial option is one of the determining factors of a financial option. Its real option equivalent is the present value of the cash flows that the investment in online learning will generate.
The analogy between financial options and real options in online learning is summarized in table I.
22
Table I: comparing financial options to real options
Financial option Real option in online learning
Exercise (strike) price Cost to acquire the asset (number of Euro’s to be invested in online learning), or amount of money spent for the investment
Stock price PV of future cash flows generated from online learning programs or PV of cash flows from investment.
Time to expiration Length of time of the option to invest in online learning programs or length of time during which the investment decision may be deferred.
Risk free rate The time value of money
Variance in the returns of the stocks Variance in or riskiness of the costs and revenues (assets) of the investment
source: Burger-Helmchen, 2007, Angelou and Economides, 2007
According to McGrath, Ferrier and Mendelow real option ‘reasoning’ in strategic decision
making can be viewed from different perspectives. When discussing the use of real options for strategic decision making, it is important to consider which perspective of real options is used, as each perspective serves a different purpose. McGrath et al derive four concepts from existing literature:
1. Option value is a component of the value of the firm, i.e. growth opportunities stemming from a bundle of resources and capabilities.
2. A specific investment proposal with option like properties – the value is related to the preservation of choices which enable the firm several future actions.
3. Choices that pertain to one or more proposals – the focus is here on decisions that managers may make, rather than on the asset about which the choice is made.
4. Options reasoning as a heuristic for strategy – options on the premise that a resource creates future potential to act in ways that could not have been foreseen at the time the investment was made: the resources generate choices (McGrath, Ferrier and Mendelow, 2004).
What these perspectives have in common is that a real option has a value because it enhances upside potential but also limits downside losses.
23
IIIe Conceptual Problems when applying option theory to strategy
Real options have originated from financial options. The use of option theory for strategic investment decisions was justified after it became clear that organizational resource
investments were analogous to options: a small prior investment (and associated learning) is a necessary precondition for launching a successful new major investment. This is a pattern of investment behavior similar to financial options (Bowman and Hurry, 1993).
There are however a number of issues that separate financial options from real options. The information to value financial options and knowing when to exercise the option is much more available than it is for real options. Financial options are traded frequently, and the information of these transactions is readily available to investors. Furthermore, the value of the underlying asset (typically the stock price) is also clear with financial options, unlike the one-of-a-kind real options (Copeland and Tufano, 2004) such as the value of an on line learning program. As Kogut and Kulatilaka note ‘… who has ever seen a futures market for innovations?’ (Kogut and Kulatilatka, 2004, p. 102).
Besides the availability of data, there are conceptual problems when using real options as guidance in making strategic decisions under uncertainty. First of all, not all rights to take a predetermined decision can be considered a real option. Adner and Levinthal argue that investment with real options characteristics enables management flexibility because it allows management to abandon an investment initiative should it not generate sufficient profit. Clear and unambiguous abandonment criteria are therefore required (Adner and Levinthal, 2004). However, unlike financial options, such clear criteria are not always available with real options. In these cases, the abandoning decision of a real option then becomes a matter of ‘act and see’, where ‘act’ means changing the abandoning criteria, instead of ‘wait and see’ as it is with financial options. With shifting criteria, the value of the original option decreases, if on can speak of an real option at all (McGrath, Ferrier and Mendelow, 2004).
Another reason why abandoning an option is difficult in case there are no clear abandoning criteria, are organizational factors. Adner and Levinthal refer to the difference between
24
‘holding the option’ and ‘being the option’ (Adner and Levinthal, 2004). People who ‘are the option’, i.e. working on a strategic investment projects that is up for evaluation, may have ulterior motives to continue the projects. They may find it hard to change projects (status quo bias) and there may be an escalation of commitment (not accepting sunk costs) (Bazerman and Moore, 2009).
Kogut and Kulatilaka state that a theoretical tool such as real options in strategic investment projects, is sensitive to context. In other words, can the real options approach be applied to situations of strategic decision making where organizational and behavioral biases are at play? They too have concerns about the extent to which decision makers will be able to foresee the decision environment they will face when the time comes to make decision about the investment in the future. Not only are there biases involved, circumstances may have changed that require redesigning the decision making process. Therefore, when applying the real options, these biases and circumstances should be taken into account. Organizations may use real option theory incorrectly, but as they go, they learn how to overcome this problem (Kogut and Kulatilaka, 2004).
One solution for this problem is to push the organizational decision making process closer to the market assumption of continual trading (as with financial options), by more frequent evaluation of the option value. The status quo bias – the unwillingness to kill a project- , an irrational barrier to change (Bazerman and Moore, 2011) is therewith mitigated because more frequent reviews enable timely corrections and thereby avoiding regret (Kogut and Kulatilaka, 2004).
Organizational and behavioral biases also apply when using the NPV. However, and NPV valuation is conducted one time only (the ‘now or never decision’), whereas the use of real options in a strategic decision making process requires making a number of subsequent decisions. Hence, the influence of these organizational factors has a potentially more profound effect on abandoning decisions.
According to McGrath, Ferrier and Mendelow, the value of real options for strategic decision making is the value of the strategic flexibility that real options provide. The discussion about
25
the importance of the abandonment decision takes place in the context of real options as a method for managers to make decisions rather than on the value of the asset about which the choice is made. These decisions are e.g. the option to defer, to abandon, to grow or to stage and investment. The value of having flexibility in investment decisions is not driven by rigid abandonment criteria, but stems from the learning effect. Allowing to act upon new knowledge by reallocating resources is what drives the value of being flexibility. Rigid abandonment criteria would decrease this flexibility, as it only has two outcomes: to abandon or continue with the initial investment (McGrath, Ferrier and Mendelow 2004),
A second element that drives the value of real options in strategic decision making is the definition of ‘time’. When sequencing an investment in several stages, the duration of the stages has to be determined. McGrath, Ferrier and Mendelow (2004) argue for the use of ‘strategic time’ vs. ‘clock/calendar time’. Rigid abandonment criteria about when to take a decision (‘maturity date’) would limit the use of real options. Some stages in a strategic investment project require more clock/calendar time as a result of previous choices and learning, other stages may require less. This variation in in clock/calendar time is referred to a ‘strategic time’. The notion of flexible, strategic time allows for the learning effects to occur and as such, are a useful feature of real options (McGrath, Ferrier and Mendelow, 2004). This is major difference from time in financial options, where the time to expiration is solely ‘clock’ time and cannot be stretched if desired by the option holder. The use of
strategic time however makes the valuation of a real option troublesome, as strategic time cannot be quantified.
From the above discussion, the following benefits of using real options in strategic decision making can be derived (McGrath, Ferrier and Mendelow, 2004). Real options:
Provide additional information for management when making an investment decision
by including the value of the strategic flexibility
Keep the costs down by limiting the investment in situations of high uncertainty, until
the uncertainty is solved
Stimulate to define staged investment projects
Enables the pursuit of projects with significant upside potential
26
IIIf Characteristics of the design of strategic investments
According to Anderson the assessment of risk in strategic decision making can benefit from real options. Uncertainty and environmental change (risk) are fundamental prerequisites for the emergence of new competitive responses. This requires analytical techniques to guide the strategic decision making process (Anderson, 2008).
The use of real options for valuation of investment projects depends on a number of
assumptions about the risk free rate, the future variance of the investment value, the length of the stages of the investment. In itself, the fact that assumptions have to be made mirrors the uncertainty in which organizations operate. Having real options available helps
organizations to consider new opportunistic development projects and reduces the chances of premature commitments to strategic investments. It also helps the discussion about strategic consequences of investments under uncertainty.
As far as the use of real options as an (exact) valuation tool, the following should be kept in mind: ‘no option valuation holds true ex post. It is the underlying ex ante discussion among managers that matter’ (Anderson, 2000, p. 252). Freitas and Brandão (2010) have voiced a similar limitation.
Based on the literature reviewed in this chapter, several characteristics emerge with regard to the use of real options in designing strategic investments. If investments carries one or more of these characteristics, using real options theory provides added value.
1. The investment project must be staged. The value of using real option lies in the reduction of uncertainty. Only those funds will be committed to a project that are required to obtain the ‘right’ to invest in a following stage (Putten and MacMillan, 2004).
2. The investment project should have abandonment criteria. The literature is not conclusive as to the rigidity of these abandonment criteria. Should they be explicit and fixed, or can they be flexible? The first position (in favor of ridged abandonment criteria), is motivated by financial option theory and seems obvious in that context. For the use with real options, the second position, flexible abandonment criteria,
27
make sense. If the point of using real options in strategic decision making is to benefit from acquiring knowledge during a stage in the investment project, this knowledge should be used for the benefit of the organization. This may require applying different abandonment criteria, however within certain boundaries: ‘Indeed, the challenges associated with abandoning projects can be greater than those associated with initiating them’ (Brunsson, 1982; Garud & Van de Ven,1992 in Adner and
Levinthal, 2004).
3. As an investment project progresses, knowledge is gained. An organization should structure the project in such a way that it can act to make the option more attractive (Adner and Levinthal, 2004).
4. Because the organization that holds the option does not have the exclusive right to exercise the option, deferral options cannot be kept alive indefinitely. Possible strike signals could be the arrival of a new opportunity and the threat of preemption (Bowman and Hurry, 1993, Copeland and Tufano, 2004).
5. The stages of the investment project should allow for strategic time to enable learning (McGrath, Ferrier and Mendelow, 2004).
28
IV
Applying valuation methods to a strategic investment in online learning
In this chapter I will apply a real options framework to a strategic investment project in online education. I will apply decision tree analysis to determine the options embedded in the project.
IVa Case description
The strategic investment decision in this case is an investment in a blended online learning program by the Amsterdam School of Real Estate (ASRE). The ASRE is a not-for profit foundation. It was established in 1989 by the national government (currently named Ministry of Infrastructure and the Environment), a university (the University of Amsterdam) and three industry trade organizations: (the association of institutional property investors in the Netherlands – ‘IVBN’, the Dutch association of brokers and real estate experts – ‘NVM’ and the Organization of project development organizations – ‘NEPROM’). These five parties have a seat on the ASRE board. Recently, the Association of Social Housing Corporations was added to the board (‘Aedes’). The day to day operation is delegated to two executives.
Organizational goals
The organizational goals are threefold:
- Conducting real estate related research
- Providing education on real estate related topics
- Bundling real estate related information and making this information available to the
public
Providing face-to-face education is the prime source of revenue. The ASRE offers two academically accredited master programs and a number of shorter courses, referred to as ‘Executive Education’. The master programs can be compared with MBA-type studies in terms of required work experience, study load (ECTS) and duration (two years part time).
29
Courses within Executive Education vary from short one-and-a-half day courses (continuous education) to six-day courses. All courses and master programs have a real estate related topic and are offered in a face-to-face format.
The market for real estate related education has suffered as a result of the economic crisis. Less students apply for ASRE programs, compared to the period before the crisis. In line with the concept of disruptive technology as discussed in chapter II, offering online education may be a possibility to offer a value proposition for a new group of customers that are presently not served by the ASRE. There is no known literature available on how to implement such a program in an ASRE type setting.
IVb The investment project
The investment project is an online program of a 12-hour course on Real Estate Calculation. Currently, this course is offered in three blocks of four hours face to face lectures with homework in-between. The course deals with real estate calculation problems. In this program, the face to face nature of this course would be changed into an blended course consisting of one face-to-face lecture and two online lectures.
IVc Methodology used
Information on investment in online learning in Dutch higher education was not publically available. Providers of knowledge and IT-systems that develop and facilitate online learning typically provide their services to larger companies that look for ways to reduce the costs of (mandatory) training, such as OHSA-related training, or provide online learning as a product to consumers. Although several higher education organizations in the Netherlands are offering MOOC’s (Massive Open Online Course) and SPOC’s (Small Private Online Courses), online learning is only slowly becoming part of their regular offerings.
30
To obtain information on how to implement blended online learning programs I selected three providers, based on their experience in providing online learning (per their own information).
IVd Data collection
The providers were requested to submit a proposal about how to approach a blended program ‘starting from scratch’ and quote an estimation of the costs involved.
They were given information on what was requested during in-person discussions. The meetings with providers A and B took two hours each. The discussion with provider C was one hour, as acquaintance with provider C had been made previously.
The main limiting factor in data collection is the unique character of this project: the
introduction of blended learning in a Dutch higher education setting. Both the ASRE and the representatives have limited experience. In all cases, further meetings are required in order to fully understand the intricacies involved in this investment project.
Provider A is a multinational company in ‘e-learning solutions’. They offer a full service for online learning, both consultancy, IT implementation as well as content. Their main customers are corporations, they do however also provide services to business schools.
Provider B is a Dutch consortium of consultancy and IT-providers that operate in a network. Their customers are also corporations, with limited experience in higher education.
Provider C originated in academia. It provides full service online / blended education for healthcare professionals. They are experienced in offering accredited on line higher education programs. Provider C offers course design and IT services.
31
The data acquired from the three aforementioned providers are mutually exclusive, as all online training providers offer different characteristics. These characteristics are summarized in table 2.
Table 2: Online training provider characteristics
Online Training provider
Characteristics
A + experience with business schools + can offer both IT and content
+ part of a large educational publishing company - not geared to small projects
- not flexible in its offering
B + primarily a technological solution + IT component well covered
- no experience with business schools
- don’t speak the Language of academic business schools C + experience in accredited programs
+ experience in academic programs + can offer IT platform and course design
- primarily experience in healthcare training and education - limited possibility for a ASRE specific look and feel
IVe Decision tree analysis
The real options framework used in this chapter is a decision tree analysis of the investment project in online learning. In this chapter I will compare decision tree analysis to models for valuing real options and explain why I use decision tree analysis. I will then describe the design of the decision tree and apply the collected data to this tree.
Decision tree analysis
Decision tree analysis describes the real options that are embedded in the investment in online learning. Decision trees help to understand how risk and decisions that are made in the future affect the cash flows of this investment. The benefit of using a decision tree is that they show the link between present day’s decisions and future decisions (Brealy, Myers and Allen, 2011).
32
In the decision tree used in this chapter, the option to grow (strategic option) is studied. The options to abandon and to defer are not applicable to this specific investment. As discussed in paragraph IIId the abandonment option does not apply in this case as there will unlikely be any saleable knowledge or service should ASRE decide to abandon the product.
Within this specific realm of investment projects, the option to defer is also not applicable. As increasingly more institutions are offering online education, the window of opportunity to obtain a competitive advantage is rapidly closing. Deferral of this investment would
effectively end any competitive advantage of having unique knowledge as well as an advantage in marketing.
The uncertainty about the way to implement online learning, about the market for such education and the absence of specific capabilities to offer online learning favors an investment that could be considered a strategic or growth option (Bowman and Hurry, 1993). It is a strategic option because it enables the ASRE to build a knowledge base and the capabilities required to offer online learning.
Decision tree analysis versus real option valuation models
The decision tree in this chapter shows several possible outcomes of future decisions and therewith the impact real (growth) options could have on the project cash flows. The actual valuation of the real options is not provided in the decision trees.
To value real options several models have been constructed. These options all consist of a number of parameters as given in Table 1 on page 22. Commonly used models are e.g. the Black & Scholes model, the Samuelson-Mckean formula or the binominal tree (Huisman, 2014).
The use of any of these available models to value the real options in this decision tree is difficult due to the design of the decision tree. Because of the number of phases involved there are options in each phase. As these real options are not actually exercised they build on each other: in each phase an option is taken on the option in the following phase (compound option). Furthermore, the probabilities assigned to the various outcomes and
33
the required investments vary per outcome due to the nature of the blended course. In addition to this, the value of the underlying asset, or the present value of future cash flows generated from online learning programs, does not show continuous process. As a result of this design, these values show price jumps. For these reasons, it is not possible to apply these common option valuation models.
However, since option valuation requires a strong dose of managerial judgment, the
resulting valuation may not be very precise (Brealy, Myers and Allen, 2011, Anderson, 2000). For managerial decision making, the use of decision trees to summarize options in an
investment project is already helpful. It brings the underlying strategy into the open by linking present day decision with future decisions.
For the remainder of this chapter, the decision tree as provided above will be used as the basis for formulating a possible strategy for the ASRE. The decision tree in this case is kept limited, as decision trees have a tendency to become very complex very quickly (Brealy, Myers and Allen, 2011).
A benefit of using decision tree analysis is that it enables to specify subjective chances and risks that are specific to the project. ASRE management may be considered to be in a
position to estimate these subjective project-chances and project-risks. What is not included in a decision tree analysis are market risks. These risks cannot be estimated by any
management, but should be based on market data (Vlek et al, 2011).
This is a fundamental difference between decision tree analysis and real option valuation. A decision tree describes the specific path of an investment project with the corresponding probabilities. Valuation of real options using a real option valuation model takes the
influence of market risks into account, which are expressed as the volatility of the underlying value of the investment. A higher volatility results in a higher value of the option, as this increases uncertainty (Vlek et al, 2011). The volatility of an investment in online learning, the market risk, is estimated to be 40% (Angelou & Economides, 2007, Brandão & Freitas, 2010). However, as mentioned, for managerial decision making, decision tree analysis is very helpful.
34
Decision tree analysis design
The decision tree of the introduction of online learning in this project is designed as follows.
Phase 1 (t0): an initial investment has to be made in order to be able to offer the blended course Real Estate Calculation. The cost of the investments consist of both the costs of the providers, costs of the lecturers as well as the internal cost by ASRE staff. Providers differ in their costs due to difference in estimation and pricing policy. In this phase, the program is offered as many times as the market is able to absorb the program. The decision tree lists four possible outcomes: the program performs above expectation, as expected, below expectation or fails all together. Based on these outcomes, the decision to invest a second time must be made. The probabilities of these outcomes are estimated based on discussion with ASRE management. These probabilities are subjective and specific to this investment project.
Phase 2 (phase 0 plus 1 year): In this phase, each possible outcome from phase 1 determines whether a second round of investment will be made. This additional investment is a
prerequisite to be able to offer the course in phase 2. Per outcome in phase 1 three possible outcome in phase 2 are expected: the program performs above expectation, as expected or below expectation. Of course, should the project fail in phase 1, no further investment will be made.
Each alternative yields a different revenue, whereas the costs stay largely the same. This is a key characteristic of an online learning program: once the program is developed and in place, additional students only cause a very limited increase in costs. Since the project at hand is a blended course, each time the program is offered to a group of students, there is an increase in face-to-face lectures, which increases the costs more than in case the program would have been exclusively an online program. Other factors that might increase the costs of an online program are the costs of additional administration (depending on the degree of automation of the enrolment process), user fees of ICT providers and program management. In theory an online program (i.e. not blended) can be offered to an infinite number of
35
In phase 3 (phase 0 plus 2 years): the success of the programs offered in phase 2 determine whether another round of investments will be made. However, per outcome in phase 2 only two possible outcomes in this phase are expected: the program performs above expectation or as expected.
Phase 4 spans the following 10 years. It is assumed that during those 10 years fixed annual investments are made and fixed revenues are generated.
The NPV’s of each outcome in each phase depend on the number of times the program is offered times the number of students. These assumptions are discussed with ASRE management. In this analysis the assumptions are as follows made:
- Each group consists of 15 students
- The cost of enrolment is €595 per student
The discount rate is 5%
The decision trees per provider are given below.
The ‘□’ symbol depicts a decision by the ASRE, the ‘•’ symbol depicts an outcome of that decision
36
Decision tree provider A:
Phase 1 (1st round investment) Phase 2 (2nd round investment) Phase 3 (3rd round investment) Phase 4 (10 rounds of annual investments) 0 □Fourth - 13th investment
1x = 1 group/yr □ = investment 0,2 ●Above expectation (6x) NPV € 0 € 393.808 revenue 2x = 2 groups/yr ● = result (learning experience) cash flow € 53.550 € -455.582investment
0,2 ●Above expectations (5x) □Third investment? NPV € 0
etc. cash flow € 44.625 Investment € -57.550 0,8 ●As expected (5x) NPV € 0 € 328.174 revenue NPV € 0 NPV(com) € 0 cash flow € 44.625 € -444.386investment NPV(com)=Combined NPV's Invest? No NPV € 0
0,3 ●Above expectation (5X) NPV € 0 € 328.174 revenue cash flow € 44.625 € -444.386investment NPV € 0
0,15 ●Above expectation (4x) □Second investment 0,5 ●As expected (4x) □Third investment 0,7 ●As expected (4x) NPV € 0 € 262.539 revenue cash flow € 35.700 Investment € -56.100 cash flow € 35.700 Investment € -56.100 cash flow € 35.700 € -433.189investment NPV € 0 NPV (com) € 0 NPV € 0 NPV(com) € 0 NPV € 0
Invest? No Invest? No
0,7 ●Above expectation (4x) NPV € 0 € 262.539 revenue cash flow € 35.700 € -433.189investment NPV € 0
0,3 ●Below expectations (3x) □Third investment 0,3 ●As expected (3x) NPV € 0 € 196.904 revenue cash flow € 26.775 Investment € -54.650 cash flow € 26.775 € -421.993investment NPV € 0 NPV(com) € 0 NPV € 0
Invest? No
0,2 ●Above expectation (5x) NPV € 0 € 328.174 revenue cash flow € 44.625 € -444.386investment NPV € 0
0,25 ●Above expectation (4x) □Third investment 0,8 ●As expected (4x) NPV € 0 € 262.539 revenue cash flow € 35.700 Investment € -56.100 cash flow € 35.700 € -433.189investment NPV € 0 NPV(com) € 0 NPV € 0
Invest? No
0,3 ●Above expectation (4x) NPV € 0 € 262.539 revenue cash flow € 35.700 € -433.189investment NPV € 0
0,6 ●As expected (3x) □Second investment 0,5 ●As expected (3x) □Third investment 0,7 ●As expected (3x) NPV € 0 € 196.904 revenue cash flow € 26.775 Invest? € -54.650 cash flow € 26.775 Investment € -54.650 cash flow € 26.775 € -421.993investment NPV € 0 NPV(com) € 0 NPV € 0 NPV(com) € 0 NPV € 0
Invest? No Invest? No
0,4 ●Above expectation (3x) NPV € 0 € 196.904 revenue cash flow € 26.775 € -421.993investment NPV € 0
0,25 ●Below expectations (2x) □Third investment 0,6 ●As expected (2x) NPV € 0 € 131.269 revenue cash flow € 17.850 Investment € -53.200 cash flow € 17.850 € -410.796investment NPV € 0 NPV(com) € 0 NPV € 0
Invest? No
□ Initial investment 0,3 ●Above expectation (4x) NPV € 0 € 262.539 revenue Investment -56.250 cash flow € 26.775 € -433.189investment NPV(com) € 0 NPV € 0
Invest? No 0,4 ●Above expectation (3x) □Third investment 0,7 ●As expected (3x) NPV € 0 € 196.904 revenue cash flow € 26.775 Invest € -54.650 cash flow € 26.775 € -421.993investment NPV € 0 NPV(com) € 0 NPV € 0
Invest? No
0,4 ●Above expectation (3x) NPV € 0 € 196.904 revenue cash flow € 26.775 € -421.993investment NPV € 0
0,15 ●Below expectations (1x) □Second investment 0,5 ●As expected (2x) □Third investment 0,6 ●As expected (2x) NPV € 0 € 131.269 revenue cash flow € 8.925 invest? € -51.750 cash flow € 17.850 Investment € -53.200 cash flow € 17.850 € -410.796investment NPV € 0 NPV(com) € 0 NPV € 0 NPV(com) € 0 NPV € 0
Invest? No
0,1 ●Below expectations (0x) Terminate € 0 0 ●Failure - terminate cash flow 0
