CHAPTER 10
CONTEMPORARY PERSPECTIVES
There are a number of contemporary issues that we have not really discussed despite the size of this book. This is only due in part to a lack of space. Indeed, many of these issues are such that the best economic concepts and practices are only just emerging and many ideas are still subject to substantive debates. Nevertheless, it is important to round out this book by outlining these contemporary issues, as well as current thinking on how best to address them.
We provide an overview of some of these issues in this brief concluding chapter. Among the subjects that we briefly discuss are the following:
- evolutionary economics;
- path dependence and network effects;
- intellectual capital;
- value of information;
- investing in humans.
Evolutionary economics is basically concerned with situations where the economics of a situation evolves over time generally in a complex and adaptive fashion. If we were to examine evolutionary economics here, we would be particularly concerned with guidelines for success in industries subject to these characteristics. We would be especially concerned with such information and knowledge network issues as compatibility, interconnection, and interoperability, and how the influences of these issues, as well as coordination of pricing and quality of service, lead to emergence of a network of networks. A relatively good introductory treatment of the basic features on complex adaptive systems is contained in Chapter 30 of the Handbook of Systems Engineering and Management (Sage and Rouse, 2009). Also highly relevant is Chapter 34 in this book on information and knowledge management.
Good examples of industries where these phenomena are prevalent are telecommunications, energy, and healthcare. Convergence in telecommunications among landlines, mobile lines, Internet, and television has resulted in rapidly evolving networks of supplier relationships, alliances and joint ventures, and mergers and acquisitions. Optimal pricing and quality of service have become quite complicated, with new approaches quickly becoming outdated by the evolving network of relationships. The emergence of Smart Grid in the energy industry has resulted in the rapid proliferation of new entrants with offerings in intelligent sensing and control of generation, transmission, distribution, and consumption of energy. This has made competitive analysis very difficult. Forecasting revenue for yet-to-exist market segments by, as yet, unknown suppliers and customers makes planning quite difficult. The healthcare industry is undergoing a slow and painful transformation from a federation of millions of entrepreneurs with no one in charge to an integrated delivery system to provide quality affordable care for everyone. The uncertainties associated with this transformation, in both magnitude and timing, make planning and economic analysis quite problematic and very needed. All three of these examples illustrate how evolving economies require creative application of the concepts, principles, methods, and tools presented in this book.
10.3 PATH DEPENDENCE AND NETWORK EFFECTS
An important aspect of economic systems is path dependence (Arthur, 1994). The essence of this phenomenon begins with a supposedly minor advantage or inconsequential head start in the marketplace for some technology, product, or standard. This minor advantage can have important and irreversible influences on the ultimate market allocation of resources even if market participants make voluntary decisions and attempt to maximize their individual benefits. Such a result is not plausible with classic economic models that assume that the maximization of individual gain leads to market optimization unless the market is imperfect due to the existence of such effects as monopolies. Path dependence is a failure of traditional market mechanisms and suggests that users are “locked” into a suboptimal product, even though they are aware of the situation and may know that there is a superior alternative.
This type of path lock-in is generally attributed to two underlying drivers: (1) network effects and (2) increasing returns of scale. Both of these drivers produce the same result, namely that the value of a product increases with the number of users. Network effects, or “network externalities,” occur because the value of a product for an individual consumer may increase with increased adoption of that product by other consumers. This, in turn, raises the potential value for additional users. An example is the telephone, which is only useful if at least one other person has one as well, and becomes increasingly beneficial as the number of potential users of the telephone increases.
Increasing returns of scale imply that the average cost of a product decreases as higher volumes are manufactured. This effect is a feature of many knowledge-based products where high initial development costs dominate low marginal production and distribution costs. Thus, the average cost per unit decreases as the sales volume increases and the producing company is able to continuously reduce the price of the product. The increasing returns to scale, associated with high initial development costs and the decreasing sales price, create barriers against market entry by new potential competitors, even though they may have a superior product. If there is no competition, this phenomenon results in increasing profits due to the lack of incentives to decrease prices.
The controversy in the late 1990s over the integration of the Microsoft Internet Explorer with the Windows operating system may be regarded as a potential example of path dependence, and appropriate models of this phenomenon can potentially be developed using complexity theory. These would allow exploration of whether network effects and increasing returns of scale can potentially reinforce the market dominance of an established but inferior product in the face of other superior products, or whether a given product is successful because its engineers have carefully and foresightedly integrated it with associated products such as to provide a seamless interface between several applications. To some extent, the more recent success of Google and social networking websites indicates how technology changes and market forces eventually emerge in such situations.
A major determinant of organizational abilities is the extent to which an organization possesses intellectual capital, or knowledge capital, such that it can create and use innovative ideas to produce productive results. The concept of intellectual capital has been defined in various ways (Rouse and Sage, 2009; Rouse, 2010). We would add communications to the formulation of Ulrich (1998) representing intellectual capital to yield
One could argue that other important terms, such as collaboration and courage, could be added to this equation.
Loosely structured organizations and the speed, flexibility, and discretion they engender in managing intellectual capital fundamentally affect knowledge management (Klein, 1998; Rouse and Sage, 2009). Knowledge workers are not captive, and hence know-how is not “owned” by the organization. Patents are of much less value, for example, as evidenced by the substantial decline in use of this mechanism. Instead, what matters is the ability to make sense of market and technology trends, quickly decide how to take advantage of these trends, and act faster than other players. Sustaining competitive advantage requires redefining market-driven value propositions and quickly leading in providing value in appropriate new ways. Accomplishing this in an increasingly information-rich environment is a major challenge, both for organizations experiencing these environments and for those who devise and provide systems engineering and management methods and tools for supporting these new ways of doing business. There is a major interaction involving knowledge work and intellectual capital, and the communications-driven information and knowledge revolution that suggests many and profound complex adaptive system like changes in the economy of this century (Shapiro and Varian, 1998; Kelly, 1998).
What are the likely returns on capabilities gained from human capital investments? Tangible assets and financial assets usually yield returns that are important elements of a company’s overall earnings. It is often the case, however, that earnings far exceed what might be expected from these “hard” assets. For example, companies in the software, biotechnology, and pharmaceutical industries typically have much higher earnings than companies with similar hard assets in the aerospace, appliance, and automobile industries, to name just a few. It can be argued that these higher earnings are due to greater human capital among software companies, etc. However, since human capital does not appear on financial statements, it is very difficult to identify and, better yet, project knowledge earnings.
Mintz (1998) summarizes a method developed by Baruch Lev for estimating what he terms knowledge capital—what could be argued to be a surrogate for human capital. The key, he argues, is to partition earnings into knowledge earnings and hard asset earnings. This is accomplished by first projecting normalized annual earnings from an average of three past years as well as estimates for three future years. Earnings from tangible and financial assets were calculated from reported asset values using industry averages of 7% and 4.5% for tangible and financial assets, respectively. Knowledge capital was then estimated by dividing knowledge earnings by a knowledge capital discount rate. Based on an analysis of several knowledge-intensive industries, 10.5% was used for this discount rate.
Using this approach to calculating knowledge capital, Mintz compares 20 pharmaceutical companies to 27 chemical companies. He determines, for example, a knowledge capital to book value ratio of 2.45 for pharmaceutical companies and 1.42 for chemical companies. Similarly the market value to book value ratio is 8.85 for pharmaceutical companies and 3.53 for chemical companies.
The key issue within this overall approach is being able to partition earnings. While earnings from financial assets should be readily identifiable, the distinction between tangible and knowledge assets is problematic. Further, using industry average return rates to attribute earnings to tangible assets does not allow for the significant possibility of tangible assets having little or no earnings potential. Finally, of course, simply attributing all earnings “leftover” to knowledge assets amounts to giving knowledge assets credit for everything that cannot be explained by traditional financial methods. Nevertheless, this approach does provide insights into an important aspect of human capital—human skills and knowledge.
Networking internally and externally provides information about your operations, your suppliers, and perhaps your competitors. A wide range of databases provide information on consumer characteristics, behaviors, and preferences. All things, it seems, are possible within the new economics of information and its strategic management (Evans and Wurster, 1997; Sveiby, 1997).
A central economic issue in the new information economy concerns how to attach economic value to information. Shapiro and Varian (1998) provide an excellent treatment of this topic. They argue, quite convincingly, that fundamental principles of economics still apply in the realm of networks and information.
The first principle is that the selling price of any product or service tends to the marginal cost of production and distribution. In competitive markets, the players will continually push marginal costs down—thus, prices tend to go down. For information products distributed over the Internet, the marginal costs are zero! Consequently, companies will tend to give their information products away—and customers will expect information products to be free.
This principle explains why so many Internet businesses have focused on making money via advertising. They trade free content for people’s willingness to put up with banners and blinkers proclaiming the wonders of everything from security software to simulated sex. As irritating as this can be, people have long demonstrated their willingness to be manipulated by such messages via television.
Their second principle is that differentiation can help one to escape the fate of having to give away information products. They suggest one do this by selling customers personalized products at personalized prices. This requires in-depth understanding of customers’ needs and values so that one knows what to put in the package and which things can command higher prices. For example, some customers value time much more than others.
Put simply, the idea is to sell roughly the same things to different people for different prices. Sounds great, but the network economy enables everyone to know the lowest price for anything. Shapiro and Varian suggest that one can avoid this with versioning. With a modular design, based on a common platform, one can create different versions of products tailored to the desires of different market segments. While one wants to avoid blunders, such as putting Cadillac badges on Chevrolets (Hanawalt and Rouse, 2010), this principle can help provide the differentiation one needs at costs that can be endured.
Another principle focuses on lock-in. The essence of lock-in is that customers’ future options are constrained by the choices they make now. Once a customer commits to particular information products, invests in gaining competence in using these products, and becomes dependent on the tailored information they provide, it will be expensive for them to change providers. The switching costs are likely to be too high. Such customers are locked in. An installed base of locked-in customers can be a company’s most valuable asset.
Yet another principle concerns network externalities and positive feedback. As noted earlier, the value of some information products, for instance telephones, is much greater if many people use these products. The more people in the network, the better. In this way, larger networks get larger—this is called positive feedback. For obvious reasons, therefore, one typically wants to grow the network of users of a company’s information products.
These principles are particularly relevent to current trends toward “open access” of publications in science, technology, and medicine (Beaudouin-Lafon, 2010). The notion is that the content of research publications that was funded by the federal government should be free to the public who paid the taxes to fund the research. This has the potential to completely undermine the business models of the publishers and professional societies in these areas.
The key, assuming that these providers follow Shapiro and Varian’s principles, is to provide value-added services that justify—from the consumer’s point of view—charging for content that is otherwise free. A good example of this is census data, which is also free. However, most people do not buy the data directly. Instead, they buy value-added tools that enable manipulating this data and, in the process, receive the data for free. In fact, most major publishers and professional societies are pursuing this strategy (IEEE, 2010).
Human capital and the value information are central aspects of the contemporary economy. How can we economically assess the value of investments in training and education, safety and health, and work productivity? There is a long and rich history, and many successes, associated with effective integration of human behavior and performance into complex systems such as aircraft, automobiles, factories, process plants, and, more recently, service systems. Human Systems Integration (HSI)—as well as human-centered design—is now a well-articulated and supported endeavor. We have accumulated much knowledge and the skills needed to enhance human abilities, overcome human limitations, and foster human acceptance (Rouse, 2007).
However, as with any engineering activity, there are costs associated with HSI or human-centered design. Most would argue that these costs are actually investments in increased performance, higher quality, and lower operating costs. The economics of HSI addresses the question of whether such investments are worth it (Rouse, 2010). In particular, what are the likely monetary returns on such investments and do these returns justify these investments?
Of course, nonmonetary returns are often also of interest. However, the focus here is solely on getting the economics right. Admittedly, the numbers are not all that counts. But, we need to count the numbers correctly. Then we can trade off economic attributes versus noneconomic attributes.
Understanding the economic attributes of HSI investments is not quite as straightforward as it may seem. First of all, there are several levels of costs. At the lowest level, there are the labor and material costs of the personnel who do HSI. Their efforts usually result in recommendations for improving the system of interest. These recommendations often involve second-level costs that are much larger than those associated with those doing HSI. At the third level, there are the costs associated with operating the system after the HSI-oriented recommendations have been implemented.
From an investment perspective, we would hope that the third-level costs are decreased by having incurred the first- and second-level costs. (Some HSI practitioners characterize these savings as “cost avoidance.”) These reductions represent returns on having made the lower-level investments. There may be additional returns associated with selling more units of a well-designed system, such as we have seen of late with Apple’s iPhone. This increased demand can lead to greater production efficiencies and thereby increase profits per unit, creating a third source of return on investment.
The investment situation just outlined is summarized in Fig. 10.1. There are time series of upstream costs—or investments—and then time series of downstream returns. Standard discounted cash flow analysis as discussed in Chapters 6 and 8 can be used to determine whether or not expected returns justify the proposed investments. However, it is not at all a straightforward effort (Rouse, 2010).
Figure 10.1. Investments and Returns for Human Systems Integration.
One problem is that it is difficult to estimate the upstream and downstream time series of investments and costs. Point estimates will not suffice, as there is much uncertainty. Thus, we need probability distributions, not just expected values. For all but the most sophisticated enterprises, this poses data collection problems. Quite simply, while most enterprises understand their overall costs as seen on their income statements, most cannot attribute these costs to particular activities such as operations and maintenance of the systems they operate.
There are also uncertainties associated with what recommendations will emerge, which ones will be chosen for implementation, and whether the actual operating environment of the system once deployed will encounter operational demands that take advantage of the enhanced system functionality that was recommended by the HSI personnel. Consequently, the decision to invest in HSI is really a multistage decision as discussed in Chapter 9. Traditional discounted cash flow analyses substantially underestimate the value of multistage investments. While we have the analytic machinery to address these types of investments, many decision makers find this level of uncertainty daunting.
Beyond these technical and practical difficulties, there is often an enormous behavioral and social difficulty associated with the simple fact that different people and organizations make the investments and then see the returns. The organization developing or procuring a system is usually quite remote from the organization gaining the returns, both spatially and temporally. For example, engineering and manufacturing may incur the costs while marketing and sales see the returns. Further, the costs may be incurred today while the returns are not seen until years from now.
This spatial and temporal separation is less difficult for highly integrated enterprises such as companies operating in the private sector. In contrast, for government agencies and companies operating in the public sector, there may be no one who “owns the future.” In these situations, investments are treated as costs. While these expenditures may yield assets that can provide future returns, government agencies—and Congress—have no balance sheet on which to tally the value of these assets. Thus, no value is explicitly attached to the future.
As formidable as this litany of difficulties may seem, we still make investments in training and education, health and safety, and performance enhancements. Culturally at least, we value a healthy, educated, productive, and competitive workforce. We have the right inclinations. However, we have not had the right data, methods, and tools to make stronger economic arguments for investing in people. Fortunately, this situation is rapidly improving due to both the increasing availability of data and the development of easily accessible and usable tools. Perhaps in the not too distant future, human capital will make it onto the balance sheet (Rouse, 2010).
The subject of economic systems analysis and assessment is a continually evolving one. The 11 challenges discussed in Chapter 34 of the Handbook of Systems Engineering and Management (Sage and Rouse, 2009) are generally as applicable here as they are in the broader area of systems engineering and systems management. These are systems modeling, emergent and complex adaptive phenomena, uncertainties and control, access and utilization of information and knowledge, information and knowledge requirements, information and knowledge support systems, inductive reasoning, learning organizations, planning and design, optimization versus agility, and measurement and evaluation. Thus, we see that economic systems analysis and assessment is one of the core and central subject areas of systems engineering and management.
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