MGMT 304. BRIEF FOR ASSIGNMENT 1: Critical Evaluation of a Strategic Management Concept

1. For the purpose of this assignment, each student must choose one of the following Strategic Management (SM) concepts:

a) Strategic Groups and Strategic Group Mappingb) Scenario Planningc) Best Cost Provider Strategy

2. Refer to the textbook and lecture notes to clarify the exact meaning of the chosen by you Concept. How do we (or your text) define the Concept? Please, note that using random Internet sources for the purpose of this initial task can potentially lead you in a very wrong direction, so avoid doing this. Instead, use the seminal literature on this topic to clarify and expand on the meaning of the concept. Seminal literature consists of books or academic articles published in high quality journals some time ago. These seminal publications have proposed and outlined the concept for a first time. For example, The Concept of the 5 Forces Analysis was proposed by Michael Porter in his article "How Competitive Forces Shape Strategy", Harvard Business Review, March/April 1979.

3. If unsure about the seminal literature related to the Concept, please consult with your Tutor. You need to locate and read these seminal articles and to present a more augmented, and thorough view of the Concept in your paper. This will involves some literature search. While reading the seminal literature, make sure you understand why the concept is an important part of SM and how it is related to the SM process, where does it fit within this process? To which other important SM concepts does the Concept relate and how? (200 words)

4. After clarifying the exact meaning and the origin of the Concept, search the electronic databases in ACU Electronic library (e.g. Business Source Complete, Emerald, ProQuest ABI/Inform, etc.) using your Concept as a search term. This will provide you with a large number of entries (articles). Choose several of these using the following selection criteria:

a. Quality of the journal that published the article. Only the best journals should be used, please check the list of recommended journals in your Unit Outline;

b. Date of publication: the article has to have been published in the last 4-5 years; if there are no suitable articles published recently (i.e. in the last 4-5 years), search literature published up to 8-9-12 and even more years ago.

c. The articles should be peer-reviewed, this means that low quality sources, such as trade journals, are totally unsuitable for the purposes of this assignment;

d. The nature of the article – use your judgement of whether or not the article is helpful in providing recent thinking and new insight about the concept, criticism of the concept, new linkages with other SM concepts, new views about its usefulness, or its new applications in SM.

All you need is 5-8 carefully selected articles of this type, to provide you with material, which you will then analyse, in order to identify how it relates to your concept and you will then synthesise the new insight on the concept, derived from these articles. If you aim to obtain a D/HD, then more articles (e.g.10-12) should be analysed and discussed.

5. Finally, you need to write your essay incorporating the new knowledge derived from your research. That is, you need to show how the original concept has evolved over time and to indicate clearly in what ways it has evolved, what new extensions, developments, applications and criticism of the concept have been proposed recently, synthesising all this into a very carefully composed essay. This will take you some time, as you will need to write a couple of drafts before you end up with a clear, interesting, insightful and useful discussion about the new developments in the Concept. (600-700 words).

6. To finish the paper, please provide a conclusion, which needs to summarise the main findings of your research and outline why the content of the paper is important for scholars of SM. (100 words).

7. Structure of the assignment:

a. Introductionb. Body of the essay

i. Clarify the meaning and origin of the concept (discerned from the seminal literature)

ii. Provide a discussion of recent developments in academic thought related to your chosen Concept.

c. Conclusiond. Type the wordcount e. Ref. list, arranging the references in alphabetical order; using Harvard

referencing style

ARTICLE 1Wanted: a new way of planning for the future

PETER SCHWARTZ is the man who has done most to popularise the notion of scenario planning over the past ten years. His book, "The Art of the Long View", brought an arcane technique for thinking about the future (largely developed by Royal Dutch/Shell in the 1970s) to a wider audience, both corporate and political. Now president of Global Business Network (GBN), an organisation dedicated to "collaborative exploration of the future", Mr Schwartz was due to chair a conference in New York on September 12th. Its subject? "The Big Surprises".

Because of the attacks on America the previous day, the conference and its mirror image (due to be held in London two days later) were cancelled. But they were rescheduled last week, and the attendance at both was 50% higher than was booked for the original events. Suddenly, many more people are interested in thinking about big future surprises.

Scenario planning (often known as the "Shell method") is the most popular way of doing this. It relies on imagining the future rather than extrapolating from the past. When the Hart-Rudman Commission on "American security in the 21st century" looked into recent futurist studies in 1999, it analysed 20 in detail. Of these, it found that five followed the Shell methodology very

closely, another six used a variation, and six more used "very simplified versions". In other words, Shell's approach has had the field much to itself.

Pick your future

Shell has been pioneering the technique for 30 years, and on October 3rd, in New York, it took the wraps off the latest refinement of its own scenarios up to the year 2050. It has narrowed its vision down to two scenarios: "Dynamics as Usual" and "The Spirit of the Coming Age". The first sees only a gradual shift from carbon fuels, through gas, to renewable energy. The second looks at the possibility that a technical revolution will create dynamics that are anything but usual.

Asked whether the attacks on New York called for any refinement of the scenarios, a company spokeswoman says: "We need a few more years before the events of September 11th can be factored in." Few organisations are so laid-back. But terrorist threats are nothing new to the oil majors. Shell works in Congo, for instance, and BP in Colombia. Energy companies routinely include security issues in their scenario planning.

The new thing that companies need to add, says Mr Schwartz, is the Muslim element--in particular, the extent to which Islamic fundamentalism might block the spread of western capitalism and democracy. At one extreme, he suggests that companies imagine a scenario with a civil war between reformers and fundamentalists in at least ten Muslim states at once.

But this is not, strictly speaking, the next new thing; it is what scenario planners should have been daring to dream ten years ago. Scenario planning has been consistently criticised for its failure to spot in advance the weak signals that are going to emerge from the surrounding noise and change all our lives. The Hart-Rudman Commission worried that the futurist studies which it examined had "overly common threads" (and Islam was not one of them). It said they concentrated on present concerns and rarely identified "other possibilities that produce startling emergent behaviour".

So where might the next big surprise lie? The conference on the subject threw up various suggestions, including the Colombianisation of Mexico, the positive economic effect of later retirement and the emergence of Europe as the world's superpower. But maybe companies will just come up with a brand new way of thinking about the future. That really would be a surprise.

ARTICLE 2https://onlinelibrary.wiley.com/doi/full/10.1002/ffo2.18

ARTICLE 3Traditional strategic planning methods can make businesses overestimate or underestimate uncertainty leading to ineffective strategies which will not make the firm more competitive and expose it to greater risks. Managers can benefit by understanding the four levels of uncertainty in irregular business environments. Identification of the four levels can help the business formulate strategies to cope with the different variables which may influence decisions in the future.

What makes for a good strategy in highly uncertain business environments? Some executives seek to shape the future with high-stakes bets. Eastman Kodak Company, for example, is spending $500 million per year to develop an array of digital photography products that it hopes will fundamentally change the way people create, store, and view pictures. Meanwhile, Hewlett-Packard Company is investing $50 million per year to pursue a rival vision centered around home-based photo

printers. The business press loves to hype such industry-shaping strategies because of their potential to create enormous wealth, but the sober reality is that most companies lack the industry position, assets, or appetite for risk necessary to make such strategies work.

More risk-averse executives hedge their bets by making a number of smaller investments. In pursuit of growth opportunities in emerging markets, for example, many consumer-product companies are forging limited operational or distribution alliances. But it's often difficult to determine if such limited investments truly reserve the right to play in these countries or just reserve the right to lose.

Alternatively, some executives favor investments in flexibility that allow their companies to adapt quickly as markets evolve. But the costs of establishing such flexibility can be high. Moreover, taking a wait-and-see strategy - postponing large investments until the future becomes clear - can create a window of opportunity for competitors.

How should executives facing great uncertainty decide whether to bet big, hedge, or wait and see? Chances are, traditional strategic-planning processes won't help much. The standard practice is to lay out a vision of future events precise enough to be captured in a discounted-cash-flow analysis. Of course, managers can discuss alternative scenarios and test how sensitive their forecasts are to changes in key variables, but the goal of such analysis is often to find the most likely outcome and create a strategy based on it. That approach serves companies well in relatively stable business environments. But when there is greater uncertainty about the future, it is at best marginally helpful and at worst downright dangerous.

One danger is that this traditional approach leads executives to view uncertainty in a binary way - to assume that the world is either certain, and therefore open to precise predictions about the future, or uncertain, and therefore completely unpredictable. Planning or capital-budgeting processes that require point forecasts force managers to bury underlying uncertainties in their cash flows. Such systems clearly push managers to underestimate uncertainty in order to make a compelling case for their strategy.

Underestimating uncertainty can lead to strategies that neither defend against the threats nor take advantage of the opportunities that higher levels of uncertainty may provide. In one of the most colossal underestimations in business history, Kenneth H. Olsen, then president of Digital Equipment Corporation, announced in 1977 that "there is no reason for any individual to have a computer in their home." The explosion in the personal computer market was not inevitable in 1977, but it was certainly within the range of possibilities that industry experts were discussing at the time.

At the other extreme, assuming that the world is entirely unpredictable can lead managers to abandon the analytical rigor of their traditional planning processes altogether and base their strategic decisions primarily on gut instinct. This "just do it" approach to strategy can cause executives to place misinformed bets on emerging products or markets that result in record write-offs. Those who took the plunge and invested in home banking in the early 1980s immediately rome to mind.

Risk-averse managers who think they are in very uncertain environments don't trust their gut instincts and suffer from decision paralysis. They avoid making critical strategic decisions about the products, markets, and technologies they should develop. They focus instead on reengineering, quality management, or internal cost-reduction programs. Although valuable, those programs are not substitutes for strategy.

Making systematically sound strategic decisions under uncertainty requires a different approach one that avoids this dangerous binary view. It is rare that managers know absolutely nothing of strategic importance, even in the most uncertain environments. In fact, they usually can identify a range of potential outcomes or even a discrete set of scenarios. This simple insight is extremely powerful because determining which strategy is best, and what process should be used to develop it, depend vitally on the level of uncertainty a company faces.

What follows, then, is a framework for determining the level of uncertainty surrounding strategic decisions and for tailoring strategy to that uncertainty. No approach can make the challenges of uncertainty go away, but this one offers practical guidance that will lead to more informed and confident strategic decisions.

Four Levels of Uncertainty

Even the most uncertain business environments contain a lot of strategically relevant information. First, it is often possible to identify clear trends, such as market demographics, that can help define potential demand for future products or services. Second, there is usually a host of factors that are currently unknown but that are in fact knowable - that could be known if the right analysis were done. Performance attributes for current technologies, elasticities of demand for certain stable categories of products, and competitors' capacity-expansion plans are variables that are often unknown, but not entirely unknowable.

The uncertainty that remains after the best possible analysis has been done is what we call residual uncertainty - for example, the outcome of an ongoing regulatory debate or the performance attributes of a technology still in development. But often, quite a bit can be known about even those residual uncertainties. In practice, we have found that the residual uncertainty facing most strategic-decision makers falls into one of four broad levels:

Level 1: A Clear-Enough Future. At level 1, managers can develop a single forecast of the future that is precise enough for strategy development. Although it will be inexact to the degree that all business environments are inherently uncertain, the forecast will be sufficiently narrow to point to a single strategic direction. In other words, at level 1, the residual uncertainty is irrelevant to making strategic decisions.

Consider a major airline trying to develop a strategic response to the entry of a low-cost, no-frills competitor into one of its hub airports. Should it respond with4 a low-cost service of its own? Should it cede the low-cost niche segments to the new entrant? Or should it compete aggressively on price and service in an attempt to drive the entrant out of the market?

To make that strategic decision, the airline's executives need market research on the size of different customer segments and the likely response of each segment to different combinations of pricing and service. They also need to know how much it costs the competitor to serve, and how much capacity the competitor has for, every route in question. Finally, the executives need to know the new entrant's competitive objectives to anticipate how it would respond to any strategic moves their airline might make. In today's U.S. airline industry, such information is either known already or is possible to know. It might not be easy to obtain - it might require new market research, for example - but it is inherently knowable. And once that information is known, residual uncertainty would be limited, and the incumbent airline would be able to build a confident business case around its strategy.

Level 2: Alternate Futures. At level 2, the future can be described as one of a few alternate outcomes, or discrete scenarios. Analysis cannot identify which outcome will occur, although it may help establish probabilities. Most important, some, if not all, elements of the strategy would change if the outcome were predictable.

Many businesses facing major regulatory or legislative change confront level 2 uncertainty. Consider U.S. long-distance telephone providers in late 1995, as they began developing strategies for entering local telephone markets. By late 1995, legislation that would fundamentally deregulate the industry was pending in Congress, and the broad form that new regulations would take was fairly clear to most industry observers. But whether or not the legislation was going to pass and how quickly it would be implemented in the event it did pass were uncertain. No amount of analysis would allow the long-distance carriers to predict those outcomes, and the correct course of action - for example, the timing of investments in network infrastructure - depended on which outcome occurred.

In another common level 2 situation, the value of a strategy depends mainly on competitors' strategies, and those cannot yet be observed or predicted. For example, in oligopoly markets, such as those for pulp and paper, chemicals, and basic raw materials, the primary uncertainty is often competitors' plans for expanding capacity: Will they build new plants or not? Economies of scale often dictate that any plant built would be quite large and would be likely to have a significant impact on industry prices and profitability. Therefore, any one company's decision to build a plant is often contingent on competitors' decisions. This is a classic level 2 situation: The possible outcomes are discrete and clear. It is difficult to predict which one will occur. And the best strategy depends on which one does occur.

Level 3: A Range of Futures. At level 3, a range of potential futures can be identified. That range is i defined by a limited number of key variables, but the actual outcome may lie anywhere along a continuum bounded by that range. There are no natural discrete scenarios. As in level 2, some, and possibly all, elements of the strategy would change if the outcome were predictable.

Companies in emerging industries or entering new geographic markets often face level 3 uncertainty. Consider a European consumer-goods company deciding whether to introduce its products to the Indian market. The best possible market research might identify only a broad range of potential customer-penetration rates - say, from 10% to 30% - and there would be no obvious scenarios within that range.

Such a broad range of estimates would be common when introducing completely new products and services to a market, and therefore determining the level of latent demand is very difficult. The company entering India would be likely to follow a very different and more aggressive entry strategy if it knew for certain that its customer penetration rates would be closer to 30% than to 10%.

Analogous problems exist for companies in fields driven by technological innovation, such as the semiconductor industry. When deciding whether to invest in a new technology, producers can often estimate only a broad range of potential cost and performance attributes for the technology, and the overall profitability of the investment depends on those attributes.

Level 4: True Ambiguity. At level 4, multiple dimensions of uncertainty interact to create an environment that is virtually impossible to predict. Unlike in level 3 situations, the range of potential outcomes cannot be identified, let alone scenarios within that range. It might not even be possible to identify, much less predict, all the relevant variables that will define the future.

Level 4 situations are quite rare, and they tend to migrate toward one of the other levels over time. Nevertheless, they do exist. Consider a telecommunications company deciding where and how to compete in the emerging consumer-multimedia market. It is confronting multiple uncertainties concerning technology, demand, and relationships between hardware and content providers, all of which may interact in ways so unpredictable that no plausible range of scenarios can be identified.

Companies considering making major entry investments in post-Communist Russia in 1992 faced level 4 uncertainty. They could not outline the potential laws or regulations that would govern property rights and transactions. That central uncertainty was compounded by additional uncertainty over the viability of supply chains and the demand for previously unavailable consumer goods and services. And shocks such as a political assassination or a currency default could have spun the whole system toward completely unforeseen outcomes.

Those examples illustrate how difficult strategic decisions can be at level 4, but they also underscore their transitory nature. Greater political and regulatory stability has turned decisions about whether to enter Russian markets into level 3 problems for the majority of industries today. Similarly, uncertainty about strategic decisions in the consumer multimedia market will migrate to level 3 or to level 2 as the industry begins to take shape over the next several years.

Tailoring Strategic Analysis to the Four Levels of Uncertainty

Our experience suggests that at least half of all strategy problems fall into levels 2 or 3, while most of the rest are level 1 problems. But executives who think about uncertainty in a binary way tend to treat all strategy problems as if they fell into either level 1 or level 4. And when those executives base their strategies on rigorous analysis, they are most likely to apply the same set of analytic tools regardless of the level of residual uncertainty they face. For example, they might attempt to use standard, quantitative market-research techniques to forecast demand for data traffic over wireless communications networks as far out as ten years from now.

But, in fact, a different kind of analysis should be done to identify and evaluate strategy options at each level of uncertainty. All strategy making begins with some form of situation analysis - that is, a picture of what the world will look like today and what is likely to happen in the future. Identifying the levels of uncertainty thus helps define the best such an analysis can do to describe each possible future an industry faces.

To help generate level 1's usefully precise prediction of the future, managers can use the standard strategy tool kit - market research, analyses of competitors' costs and capacity, value chain analysis, Michael Porter's five-forces framework, and so on. A discounted-cash-flow model that incorporates those predictions can then be used to determine the value of various alternative strategies. It's not surprising that most managers feel extremely comfortable in level 1 situations - these are the tools and frameworks taught in every leading business program in the United States.

Level 2 situations are a bit more complex. First, managers must develop a set of discrete scenarios based on their understanding of how the key residual uncertainties might play out - for example, whether deregulation occurs or not, a competitor builds a new plant or not. Each scenario may require a different valuation model - general industry structure and conduct will often be fundamentally different depending on which scenario occurs, so alternative valuations can't be handled by performing sensitivity analyses around a single baseline model. Getting information that helps establish the relative probabilities of the alternative outcomes should be a high priority.

After establishing an appropriate valuation model for each possible outcome and determining how probable each is likely to be, a classic decision-analysis framework can be used to evaluate the risks and returns inherent in alternative strategies. This process will identify the likely winners and losers in alternative scenarios, and perhaps more i important, it will help quantify what's at stake for companies that follow status quo strategies. Such an analysis is often the key to making the case for strategic change.

In level 2 situations, it is important not only to identify the different possible future outcomes but also to think through the likely paths the industry might take to reach those alternative futures. Will change occur in major steps at some particular point in time, following, for example, a regulatory ruling or a competitor's decision to enter the market? Or will change occur in a more evolutionary fashion, as often happens after a resolution of competing technology standards? This is vital information because it determines which market signals or trigger variables should be monitored closely. As events unfold and the relative probabilities of alternative scenarios change, it is likely that one's strategy will also need to be adapted to these changes.

At one level, the analysis in level 3 is very similar to that in level 2. A set of scenarios needs to be identified that describes alternative future outcomes I and analysis should focus on the trigger events signaling that the market is moving toward one or another scenario. Developing a meaningful set of scenarios, however, is less straightforward in level 3. Scenarios that describe the extreme points in the range of possible outcomes are often relatively easy to develop, but these rarely provide much concrete guidance for current strategic decisions. Since there are no other

natural discrete scenarios in level 3, deciding which possible outcomes should be fully developed into alternative scenarios is a real art. But there are a few general rules. First, develop only a limited number of alternative scenarios - the complexity of juggling more than four or five tends to hinder decision making. Second, avoid developing redundant scenarios that have no unique implications for strategic decision making; make sure each scenario offers a distinct picture of the industry's structure, conduct, and performance. Third, develop a set of scenarios that collectively account for the probable range of future outcomes and not necessarily the entire possible range.

Because it is impossible in level 3 to define a complete list of scenarios and related probabilities, it is impossible to calculate the expected value of different strategies. However, establishing the range of scenarios should allow managers to determine how robust their strategy is, identify likely winners and losers, and determine roughly the risk of following status quo strategies.

Situation analysis at level 4 is even more qualitative. Still, it is critical to avoid the urge to throw one's hands up and act purely on gut instinct. Instead, managers need to catalog systematically what they know and what is possible to know. Even if it is impossible to develop a meaningful set of probable, or even possible, outcomes in level 4 situations, managers can gain valuable strategic perspective. Usually, they can identify at least a subset of the variables that will determine how the market will evolve over time - for example, customer penetration rates or technology performance attributes. And they can identify favorable and unfavorable indicators of these variables that will let them track the market's evolution over time and adapt their strategy as new information becomes available.

Managers can also identify patterns indicating possible ways the market may evolve by studying how analogous markets developed in other level 4 situations, determining the key attributes of the winners and losers in those situations and identifying the strategies they employed. Finally, although it will be impossible to quantify the risks and returns of different strategies, managers should be able to identify what information they would have to believe about the future to justify the investments they are considering. Early market indicators and analogies from similar markets will help sort out whether such beliefs are realistic or not.

Uncertainty demands a more flexible approach to situation analysis. The old one-size-fits-all approach is simply inadequate. Over time, companies in most industries will face strategy problems that have varying levels of residual uncertainty, and it is vitally important that the strategic analysis be tailored to the level of uncertainty.

Postures and Moves

Before we can talk about the dynamics of formulating strategy at each level of uncertainty, we need to introduce a basic vocabulary for talking about strategy. First, there are three strategic postures a company can choose to take vis-a-vis uncertainty: shaping, adapting, or reserving the right to play. Second, there are three types of moves in the portfolio of actions that can be used to implement that strategy: big bets, options, and no-regrets moves.

Strategic Posture. Any good strategy requires a choice about strategic posture. Fundamentally, posture defines the intent of a strategy relative to the current and future state of an industry. Shapers aim to drive their industries toward a new structure of their own devising. Their strategies are about creating new opportunities in a market - either by shaking up relatively stable level 1 industries or by trying to control the direction of the market in industries with higher levels of uncertainty. Kodak, for example, through its investment in digital photography, is pursuing a shaping strategy in an effort to maintain its leadership position, as a new technology supersedes the one currently generating most of its earnings. Although its product technology is new, Kodak's strategy is still based on a traditional model in which the company provides digital cameras and film while photo-processing stores provide many of the photo-printing and storage functions for the consumer. Hewlett-Packard also seeks to be a shaper in this market, but it is pursuing a radically different model in which high-quality, low-cost photo printers shift photo processing from stores to the home.In contrast, adapters take the current industry structure and its future evolution as givens, and they react to the opportunities the market offers. In environments with little uncertainty, adapters choose a strategic positioning - that is, where and how to compete - in the current industry. At higher levels of uncertainty, their strategies are predicated on the ability to recognize and respond quickly to market developments. In the highly volatile telecommunications-service industry, for example, service resellers are adapters. They buy and resell the latest products and services offered by the major telecom providers, relying on pricing and effective execution rather than on product innovation as their source of competitive advantage.

The third strategic posture, reserving the right to play, is a special form of adapting. This posture is relevant only in levels 2 through 4; it involves making incremental investments today that put a company in a privileged position, through either superior information, cost structures, or relationships between customers and suppliers. That allows the company to wait until the environment becomes less uncertain before formulating a strategy. Many pharmaceutical companies are reserving the right to play in the market for gene therapy applications by acquiring or allying with small biotech firms that have relevant expertise. Providing privileged access to the latest industry developments, these are low-cost investments compared with building a proprietary, internal gene-therapy R&D program.

A Portfolio of Actions. A posture is not a complete strategy. It clarifies strategic intent but not the actions required to fulfill that intent. Three types of moves are especially relevant to implementing strategy under conditions of uncertainty: big bets, options, and no-regrets moves.

Big bets are large commitments, such as major capital investments or acquisitions, that will result in large payoffs in some scenarios and large losses in others. Not surprisingly, shaping strategies usually involve big bets, whereas adapting and reserving the right to play do not.Options are designed to secure the big payoffs of the best-case scenarios while minimizing losses in the worst-case scenarios. This asymmetric payoff structure makes them resemble financial options. Most options involve making modest initial investments that will allow companies to ramp up or scale back the investment later as the market evolves. Classic examples include conducting pilot trials before the

full-scale introduction of a new product, entering into limited joint ventures for distribution to minimize the risk of breaking into new markets, and licensing an alternative technology in case it proves to be superior to a current technology. Those reserving the right to play rely heavily on options, but shapers use them as well, either to shape an emerging but uncertain market as an early mover or to hedge their big bets.

Finally, no-regrets moves are just that - moves that will pay off no matter what happens. Managers often focus on obvious no-regrets moves like initiatives aimed at reducing costs, gathering competitive intelligence, or building skills. However, even in highly uncertain environments, strategic decisions like investing in capacity and entering certain markets can be no-regrets moves. Whether or not they put a name to them, most managers understand intuitively that no-regrets moves are an essential element of any strategy.

The choice of a strategic posture and an accompanying portfolio of actions sounds straightforward. But in practice, these decisions are highly dependent on the level of uncertainty facing a given business. Thus the four-level framework can help clarify the practical implications implicit in any choice of strategic posture and actions. The discussion that follows will demonstrate the different strategic challenges that each level of uncertainty poses and how the portfolio of actions may be applied.

Strategy in Level 1's Clear-Enough Future. In predictable business environments, most companies are adapters. Analysis is designed to predict an industry's future landscape, and strategy involves making positioning choices about where and how to compete. When the underlying analysis is sound, such strategies are by definition made up of a series of no-regrets moves.

Adapter strategies in level 1 situations are not necessarily incremental or boring. For example, Southwest Airlines Company's no-frills, point-to-point service is a highly innovative, value-creating adapter strategy, as was Gateway 2000's low-cost assembly and direct-mail distribution strategy when it entered the personal computer market in the late 1980s. In both cases, managers were able to identify unexploited opportunities in relatively low-uncertainty environments within the existing market structure. The best level 1 adapters create value through innovations in their products or services or through improvements in their business systems without otherwise fundamentally changing the industry.

It is also possible to be a shaper in level 1 situations, but that is risky and rare, since level 1 shapers increase the amount of residual uncertainty in an otherwise predictable market - for themselves and their competitors - in an attempt to fundamentally alter long-standing industry structures and conduct. Consider Federal Express Corporation's overnight-delivery strategy. When it entered the mail-and-package delivery industry, a stable level 1 situation, FedEx's strategy in effect created level 3 uncertainty for itself. That is, even though CEO Frederick W. Smith commissioned detailed consulting reports that confirmed the feasibility of his business concept, only a broad range of potential demand for overnight services could be identified at the time. For the industry incumbents like United Parcel Service, FedEx created level 2 uncertainty. FedEx's move raised two questions for

UPS: Will the overnight-delivery strategy succeed or not? and Will UPS have to offer a similar service to remain a viable competitor in the market?

Over time, the industry returned to level 1 stability, but with a fundamentally new structure. FedEx's bet paid off, forcing the rest of the industry 1 to adapt to the new demand for overnight services.

What portfolio of actions did it take to realize that strategy? Like most shaper strategies, even in level 1 situations, this one required some big bets. That said, it often makes sense to build options into a shaper strategy to hedge against bad bets. Smith might have hedged his bets by leasing existing cargo airplanes instead of purchasing and retrofitting his original fleet of Falcon "minifreighters," or he could have outsourced ground pickup and delivery services. Such moves would have limited the amount of capital he would have needed to sink into his new strategy and facilitated a graceful exit had his concept failed. However, that kind of insurance doesn't always come cheap. In FedEx's case, had Smith leased standard-size cargo planes, he would have come under the restrictive regulations of the Civil Aeronautics Board. And outsourcing local pickups and deliveries would have diluted FedEx's unique door-to-door value to customers. Thus Smith stuck mainly to big bets in implementing his strategy, which drove him to the brink of bankruptcy in his first two years of operation but ultimately reshaped an entire industry.Strategy in Level 2's Alternate Futures. If shapers in level 1 try to raise uncertainty, in levels 2 through 4 they try to lower uncertainty and create order out of chaos. In level 2, a shaping strategy is designed to increase the probability that a favored industry scenario will occur. A shaper in a capital-intensive industry like pulp and paper, for example, wants to prevent competitors from creating excess capacity that would destroy the industry's profitability. Consequently, shapers in such cases might commit their companies to building new capacity far in advance of an upturn in demand to preempt the competition, or they might consolidate the industry through mergers and acquisitions.

Consider the Microsoft Network (MSN). A few years ago, one could identify a discrete set of possible ways in which transactions would be conducted between networked computers. Either proprietary networks such as MSN would become the standard, or open networks like the Internet would prevail. Uncertainty in this situation was thus at level 2, even though other related strategy issues -such as determining the level of consumer demand for networked applications - were level 3 problems.

Microsoft could reasonably expect to shape the way markets for electronic commerce evolved if it created the proprietary MSN network. It would, in effect, be building a commerce hub that would link both suppliers and consumers through the MSN gateway. The strategy was a big bet: the development costs were significant and, more important, involved an enormously high level of industry exposure and attention. In effect, for Microsoft, it constituted a big credibility bet. Microsoft's activities in other areas - such as including one-button access to MSN from Windows95 - were designed to increase the probability that this shaping bet would pay off.

But even the best shapers must be prepared to adapt. In the battle between proprietary and open networks, certain trigger variables - growth in the number of Internet and MSN subscribers, for example, or the activity profiles of early MSN subscribers - could provide valuable insight into how the market was evolving. When it became clear that open networks would prevail, Microsoft refocused the MSN concept around the Internet. Microsoft's shift illustrates that choices of strategic posture are not carved in stone, and it underscores the value of maintaining strategic flexibility under uncertainty. Shaping strategies can fail, so the best companies supplement their shaping bets with options that allow them to change course quickly if necessary. Microsoft was able to do just that because it remained flexible by being willing to cut its losses, by building a cadre of engineers who had a wide range of general-programming and product-development skills, and by closely monitoring key trigger variables. In uncertain environments, it is a mistake to let strategies run on autopilot, remaining content to update them only through standard year-end strategy reviews.Because trigger variables are often relatively simple to monitor in level 2, it can be easy to adapt or reserve the right to play. For instance, companies that generate electricity - and others whose business depends on energy-intensive production processes - often face level 2 uncertainty in determining the relative cost of different fuel alternatives. Discrete scenarios can often be identified - for example, either natural gas or oil will be the low-cost fuel. Many companies thus choose an adapter strategy when building new plants: they construct flexible manufacturing processes that can switch easily between different fuels.

Chemical companies often choose to reserve the right to play when facing level 2 uncertainty in predicting the performance of a new technology. If the technology performs well, companies will have to employ it to remain competitive in the market. But if it does not fulfill its promise, incumbents can compete effectively with existing technologies. Most companies are reluctant to bet several hundred million dollars on building new capacity and retrofitting old plants around a new technology until it is proven. But if they don't make at least incremental investments in the short run, they risk falling too far behind competitors should the technology succeed. Thus many will purchase options to license the new technology within a specified time frame or begin retrofitting a proportion of existing capacity around the new technology. In either case, small, up-front commitments give the companies privileged positions, but not obligations, to ramp up or discontinue development of the new technology as its performance attributes become clearer over time.

Strategy in Level 3's Range of Futures. Shaping takes a different form in level 3. If at level 2, shapers are trying to make a discrete outcome occur, at level 3, they are trying to move the market in a general direction because they can identify only a range of possible outcomes. Consider the battle over standards for electronic cash transactions, currently a level 3 problem since one can define a range of potential products and services that fall between purely paper-based and purely electronic cash transactions, but it is unclear today whether there are any natural discrete scenarios within that range. Mondex International, a consortium of financial services providers and technology companies, is attempting to shape the future by establishing what it hopes will become universal electronic-cash standards. Its shaping posture is backed by big-bet investments in product development, infrastructure, and pilot experiments to speed customer acceptance.

In contrast, regional banks are mainly choosing adapter strategies. An adapter posture at uncertainty levels 3 or 4 is often achieved primarily through investments in organizational capabilities designed to keep options open. Because they must make and implement strategy choices in real time, adapters need quick access to the best market information and the most flexible organizational structures. Many regional banks, for example, have put in place steering committees focused on electronic payments, R&D projects, and competitive-intelligence systems so that they can constantly monitor developments in electronic payment technology and markets. In addition, many regional banks are making small investments in industry consortia as another way to monitor events. This adapter approach makes sense for most regional banks - they don't have the deep pockets and skills necessary to set standards for the electronic payment market, yet it is essential that they be able to offer the latest electronic services to their customers as such services become available.

Reserving the right to play is a common posture in level 3. Consider a telecommunications company trying to decide whether to make a $1 billion investment in broadband cable networks in the early 1990s. The decision hinged on level 3 uncertainties such as demand for interactive TV service. No amount of solid market research could precisely forecast consumer demand for services that didn't even exist yet. However, making incremental investments in broadband-network trials could provide useful information, and it would put the company in a privileged position to expand the business in the future should that prove attractive. By restructuring the broadband-investment decision from a big bet to a series of options, the company reserved the right to play in a potentially lucrative market without having to bet the farm or risk being preempted by a competitor.

Strategy in Level 4's True Ambiguity. Paradoxically, even though level 4 situations contain the greatest uncertainty, they may offer higher returns and involve lower risks for companies seeking to shape the market than situations in either level 2 or 3. Recall that level 4 situations are transitional by nature, often occurring after a major technological, macroeconomic, or legislative shock. Since no player necessarily knows the best strategy in these environments, the shaper's role is to provide a vision of an industry structure and standards that will coordinate the strategies of other players and drive the market toward a more stable and favorable outcome.

Mahathir bin Mohamad, Malaysia's prime minister, is trying to shape the future of the multimedia industry in the Asian Pacific Rim. This is truly a level 4 strategy problem at this point. Potential products are undefined, as are the players, the level of customer demand, and the technology standards, among other factors. The government is trying to create order out of this chaos by investing at least $15 billion to create a so-called Multimedia Super Corridor (MSC) in Malaysia. The MSC is a 750-square-kilometer zone south of Kuala Lumpur that will include state-of-the-art "smart" buildings for software companies, regional headquarters for multinational corporations, a "Multimedia University," a paperless government center called Putrajaya, and a new city called Cyberjaya. By leveraging incentives like a ten-year exemption from the tax on profits, the MSC has received commitments from more than 40 Malaysian and foreign companies so far, including such powerhouses as Intel, Microsoft, Nippon Telegraph and Telephone, Oracle, and Sun Microsystems. Mahathir's shaping strategy is predicated on the notion that the MSC will create a

web of relationships between content and hardware providers that will result in clear industry standards and a set of complementary multimedia products and services. Intel's Malaysia managing director, David B. Marsing, recognized Mahathir's shaping aspirations when he noted, "If you're an evolutionist, it's strange. They're [the Malaysian government] trying to intervene instead of letting it evolve."

Shapers need not make enormous bets as the Malaysian government is doing to be successful in level 3 or 4 situations, however. All that is required is the credibility to coordinate the strategies of different players around the preferred outcome. Netscape Communications Corporation, for example, didn't rely on deep pockets to shape Internet browser standards. Instead, it leveraged the credibility of its leadership team in the industry so that other industry players thought, "If these guys think this is the way to go, they must be right."

Reserving the right to play is common, but potentially dangerous, in level 4 situations. Oil companies believed they were reserving the right to compete in China by buying options to establish various beachheads there some 20 years ago. However, in such level 4 situations, it is extremely difficult to determine whether incremental investments are truly reserving the right to play or simply the right to lose. A few general rules apply. First, look for a high degree of leverage. If the choice of beachhead in China comes down to maintaining a small, but expensive, local operation or developing a limited joint venture with a local distributor, all else being equal, go for the low-cost option. Higher-cost options must be justified with explicit arguments for why they would put the company in a better position to ramp up over time. Second, don't get locked into one position through neglect. Options should be rigorously reevaluated whenever important uncertainties are clarified - at least every six months. Remember, level 4 situations are transitional, and most will quickly move toward levels 3 and 2.

The difficulty of managing options in level 4 situations often drives players toward adapter postures. As in level 3, an adapter posture in level 4 is frequently implemented by making investments in organizational capabilities. Most potential players in the multimedia industry are adopting that posture today but will soon be making bigger bets as the industry moves into level 3 and 2 uncertainty over time.

A New Approach to Uncertainty

At the heart of the traditional approach to strategy lies the assumption that by applying a set of powerful analytic tools, executives can predict the future of any business accurately enough to allow them to choose a clear strategic direction. In relatively stable businesses, that approach continues to work well. But it tends to break down when the environment is so uncertain that no amount of good analysis will allow them to predict the future.

Levels of uncertainty regularly confronting managers today are so high that they need a new way to think about strategy. The approach we've outlined will help executives avoid dangerous binary views of uncertainty. It offers a discipline for thinking rigorously and systematically about uncertainty. On one plane, it is a guide to judging which analytic tools can help in making decisions at various levels of uncertainty and which cannot. On a broader plane, our framework is a way to tackle

the most challenging decisions that executives have to make, offering a more complete and sophisticated understanding of the uncertainty they face and its implications for strategy.

ARTICLE 4https://hbr.org/1997/11/strategy-under-uncertainty

ARTICLE 5

This paper introduces the development of scenario planning for sustainable tourism, from the 1970s to the present day. It outlines the links between scenario planning and forecasting, its role as a business-planning tool generally, and its use in tourism for destination planning and advocacy. The prominent role of models is shown and the evolution from the first generation of forecast-focused models of the 1980s to the new generation of complex, integrative, hierarchical, dynamic and even adaptive models of today is discussed. The paper introduces a series of new scenario planning papers that cover a range of subjects including theorising scenario analysis, counterfactual scenario planning, green economy support systems, climate change scenarios (for ski and coastal tourism), destination environmental footprint scenario tools and transition management as a tool for scenario building.

Keywords: scenario planning, modeling, forecast

The background

Sustainable tourism began life as a reaction to the impacts of tourism's growth in the 1970s and 1980s. But while it began life as a reaction, it was from the very beginning a visionary, future-oriented paradigm, with a wish list of improvements and a series of unresearched but desired future social, economic and environmental conditions (Krippendorf, 1982Krippendorf, J. 1982. Towards new tourism policies – The importance of environmental and sociocultural factors. Tourism Management,, 3(3): 135–148.[Crossref] , [Google Scholar]; Lane, 2009Lane, B. 2009. “30 years of sustainable tourism: Drivers, progress, problems and the future”. In Sustainable tourism futures, Edited by: Gössling, S., Hall, C. M. and Weaver, D. 19–32. New York and London: Routledge. [Google Scholar]). Since those early days, the subject has become linked with a range of additional concepts, procedures and issues, while, to some extent, the practices of scenario development and planning have tended to become a minor activity. This special issue reopens the issue of scenario planning for sustainable tourism.

Many attribute the origins of formalized scenario planning to military organizations in the nineteenth and twentieth centuries, though undoubtedly less formalized approaches occurred long before. Scenario planning was adopted as a business-planning tool in the 1970s to better understand the consequences of extreme situations, such as the 1973 oil crisis, as well as the potential outcomes of different development pathways. The technique became particularly known when it was presented to a broad audience in 1982, when the Club of Rome presented “Limits to Growth”, a first global scenario of continued resource depletion and population growth, leading to a long-term decline in key resources such as food production and water supply, and drawing a bleak and much discussed picture of humanity's future. In 2012, the Club of Rome published its third post-“Limits to Growth” report: “2052: A Global Forecast for the Next Forty Years” (Randers, 2012Randers, J. 2012. 2052: A global forecast for the next forty years Retrieved 25 May, 2012, from:http://www.clubofrome.org/?p=703 [Google Scholar]), again with a rather dystopian view of the coming decades.

Scenario planning is inextricably linked to forecasting, which is a widely used and accepted approach for business and government decision-making in tourism. Outside of tourism, scenario development has received continuously growing attention and is now an integral part of preparing for uncertain futures across a range of major global issues, including energy (e.g. IEA, 2011Dubois, G., Ceron, J.P., Peeters, P. and Gössling, S. 2011. The future tourism mobility of the world population: Emission growth versus climate policy. Transportation Research Part A,, 45: 1031–1042.[Crossref] , [Google Scholar]), demography (e.g. UN, 2012UN. 2012. “United Nations Population Division Home Page, various pages”. Retrieved 25 May, 2012, from:www.un.org/esa/population. [Google Scholar]), economic development (e.g. IMF, 2012International Monetary Fund (IMF). 2012. World economic outlook April 2012. Growth resuming, dangers remain, Washington, DC: International Monetary Fund. [Google Scholar]), climate change (e.g. IPCC, 2007Intergovernmental Panel on Climate Change (IPCC). 2007. Climate change 2007: Synthesis report, Cambridge: Cambridge University Press.[Crossref] , [Google Scholar]), technology (e.g. Rockefeller Foundation, 2010Rockefeller Foundation. 2010. Scenarios for the future of technology and international development, New York: The Rockefeller Foundation. Retrieved 27 May, 2012, from:http://www.rockefellerfoundation.org/news/publications/scenarios-future-technology [Google Scholar]), biodiversity and ecosystem change (e.g. Hassan, Scholes, & Neville, 2005Hassan, I., Scholes, R. and Neville, A. 2005. “Millennium ecosystem assessment”. In Ecosystems and human well-being: Current state and trends: Findings of the condition and trends working group, Washington: Island Press. [Google Scholar]; Leadley et al., 2010Leadley, P., Pereira, H. M., Alkemade, R., Fernandez-Manjarrés, J. F., Proença, V. and Scharlemann, J. P.W. 2010. Biodiversity scenarios:

Projections of 21st century change in biodiversity and associated ecosystem services, Montreal, , Canada: Convention on Biological Diversity. Retrieved 27 May, 2012, from:http://www.diversitas-international.org/activities/research/biodiscovery/cbdts50en.pdf. [Google Scholar]), transportation and mobility (e.g. Schäfer & Victor, 2000Schäfer, A. and Victor, D. G. 2000. The future mobility of the world population. Transportation Research A,, 34: 171–205.[Crossref] , [Google Scholar]), agriculture and food production (e.g. Godfray et al., 2010Godfray, H. C.J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D. and Muir, J. F. 2010. Food security: The challenge of feeding 9 billion people. Science,, 327: 812–818.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) and water security (Cosgrove & Cosgrove, 2012Cosgrove, C. E. and Cosgrove, W. J. 2012. The dynamics of global water futures driving forces 2011–2050, Paris: United Nations Educational, Scientific and Cultural Organization. Retrieved 27 May, 2012, from:http://unesdoc.unesco.org/images/0021/002153/215377e.pdf. [Google Scholar]). Too often, the implications of these major scenarios exercises have not been interpreted for tourism futures. This remains an important task for the tourism research community.Scenario planning in tourism

In tourism, scenarios have been used for destination planning for at least 30 years, beginning with the advocacy of “alternative” tourism in the late 1970s and early 1980s (De Kadt, 1979De Kadt, E. 1979. Tourism: Passport to development?, Oxford:: Oxford University Press. [Google Scholar]; Krippendorf, 1982Krippendorf, J. 1982. Towards new tourism policies – The importance of environmental and sociocultural factors. Tourism Management,, 3(3): 135–148.[Crossref] , [Google Scholar]) and the emergence of “futures research” (Van Doorn, 1982Van Doorn, J. 1982. Can futures research contribute to tourism policy?. Tourism Management,, 3(3): 149–166.[Crossref] , [Google Scholar]). Getz (1986Getz, D. 1986. Models in tourism planning. Towards integration of theory and practice. Tourism Management,, 7(1): 21–32.[Crossref] , [Google Scholar]) noted that by the mid-1980s, a wide range of modelling approaches had emerged, which he divided into “theoretical” and “process models”, i.e. those designed to facilitate planning and managing processes, and those explaining the functioning of tourism systems. Most of these first-generation models had in common that they were used to forecast developments.

More recently, progress has been made to develop a much more diverse new generation of highly complex, integrative, hierarchical, dynamic and partially even adaptive models, which can be used for forecasting as well as backcasting, and which can combine qualitative and quantitative aspects of tourism development. Some of these models use innovative and complex

scenario-building techniques and have evolved into strategic, often online-based tools for planning. Areas that have been covered recently with regard to tourism include, for instance, transport and mobility developments (Dubois & Ceron, 2007Dubois, G. and Ceron, J. P. 2007. “How heavy will the burden be? Using scenario analysis to assess future tourism greenhouse gas emissions”. In Tourism and climate change mitigation. Methods, greenhouse gas reductions and policies, Edited by: Peeters, P. Vol. 6,, 189–207. Breda, , The Netherlands: NHTV. [Google Scholar]; Dubois, Ceron, Peeters, & Gössling, 2011Dubois, G., Ceron, J.P., Peeters, P. and Gössling, S. 2011. The future tourism mobility of the world population: Emission growth versus climate policy. Transportation Research Part A,, 45: 1031–1042.[Crossref] , [Google Scholar]), demographic and social change (Yeoman, Hsu, Smith, & Watson, 2010Yeoman, I., Hsu, C., Smith, K. and Watson, S. 2010. Tourism and demography, Oxford: Goodfellow Publishers. [Google Scholar]), greenhouse gas mitigation (e.g. REAP, 2009REAP (Resource and Energy Analysis Program). 2009. Stepping towards a low carbon future, Stockholm Environment Institute and Southwest Tourism. Retrieved 27 May, 2012, from:http://www.swtourism.org.uk/our-strategic-work/sustainability-work/reap-resource-and-energy-analysis-program/ [Google Scholar]) and terrorism (e.g. Gordon et al., 2009Gordon, P., Richardson, H. W., Moore, J. E. II, Park, J., Kim, S. and Pan, Q. 2009. Tourism and terrorism: The national and interregional economic impacts of attacks on major U.S. theme parks, Non-published Research Reports). Paper 21. Retrieved 27 May, 2012, from:http://research.create.usc.edu/nonpublished_reports/21 [Google Scholar]).

Recent attempts to forecast global tourism volumes and activities to 2030 now consider how scenario assumptions could change as a result of changes in some of the aforementioned factors (e.g. slower than expected economic recovery, transport costs higher/lower than central estimates [UNWTO, 2011UN World Tourism Organization (UNWTO). 2011. Tourism towards 2030: Global overview, Madrid: Author. Retrieved 27 May, 2012, from:http://pub.unwto.org/WebRoot/Store/Shops/Infoshop/4E98/07B6/A1D8/382D/5B35/C0A8/0164/3066/111014_TT_2030_global_overview_excerpt.pdf. [Google Scholar]]). Others focus more on what future tourists might be doing in 2030 and the implications for market segmentation and marketing (Yeoman, 2008Yeoman, I. 2008. Tomorrow's tourist, London: Routledge. [Google Scholar]; now followed by a scenario for 2050 [Yeoman, 2012Yeoman, I. 2012. 2050 – Tomorrow's tourism, Bristol: Channel View Publications. [Google Scholar]]). A range of regional and national projections attempt to develop more detailed interpretations of global trends. The UK's Forum for the Future (2009Forum for the Future. 2009. Tourism 2023: Four scenarios, a vision and a strategy for UK outbound travel and tourism Retrieved 27 May, 2012,

from:http://www.forumforthefuture.org/sites/default/files/images/Forum/Projects/Tourism2023/Tourism_2023_full_report_web_version.pdf [Google Scholar]), for example, developed scenarios to 2023, including “boom and burst”, “divided disquiet”, “price and privilege” and “carbon clampdown”, to understand the implications of climate change, population growth and shortages of oil and other resources for outbound tourism and specific market segments.

As the global tourism system is affected by, and has to respond to, an increasing number of challenges, including on-going uncertainty related to the financial and sovereign debt crisis, a new geography of global tourist flows from emerging economy, the new realities of mobility in world of rising energy prices and the push towards a low-carbon economy, threats of terrorism and political upheaval, demographic change and the consequences of climate and environmental change for destination attributes and attractiveness, creating tools to understand and prepare for the future becomes increasingly important. A concentrated and closer look at the current state of scenario planning and its use in tourism is thus timely. As the Club of Rome (2012Club of Rome. 2012. The world in 2052 Retrieved 27 May, 2012, from:http://www.clubofrome.org/?p=2107. [Google Scholar], no page) suggests: “Humanity finds itself at an evolutionary crossroad. The choice is between a perfect storm of progressively deepening crises and expanding perspectives of unprecedented opportunities”. This special issue presents a wide range of papers covering different areas, illustrating recent advances across a range of scenario applications. They provide insights for destination managers and politicians to engage in scenario planning for sustainable tourism.The special issue papers

Papers included in this special issue on “Scenario Planning for Sustainable Tourism” provide a broad overview of current applications for scenario planning for policy-making and destination planning, particularly for long-term planning and sustainability. In the first contribution to this special issue, Moriarty (2012Moriarty, J. P. 2012. Theorising scenario analysis to improve future perspective planning in tourism. Journal of Sustainable Tourism,, 20(6): 779–800.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) provides a comprehensive review of scenario analysis, along with a discussion of its history, applications and methodological issues. Moriarty offers important insights into improving the design and use of scenarios in tourism, and outlines criteria for doing so. These criteria are then applied to the recent scenarios exercise of the Tourism 2023 scenarios for UK outbound tourism (Forum for the Future, 2009Forum for the Future. 2009. Tourism 2023: Four scenarios, a vision and a strategy for UK outbound travel and tourism Retrieved 27 May, 2012, from:http://www.forumforthefuture.org/sites/default/files/images/Forum/

Projects/Tourism2023/Tourism_2023_full_report_web_version.pdf [Google Scholar]) concluding that their planning value may be limited.

In the following paper, McLennan, Duc Pham, Ruhanen, Ritchie, and Moyle (2012McLennan, C., Duc Pham, T., Ruhanen, L., Ritchie, B. W. and Moyle, B. 2012. Counter-factual scenario planning for long-range sustainable local-level tourism transformation. Journal of Sustainable Tourism,, 20(6): 801–822.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) discuss counterfactual scenario planning for long-range sustainable local level tourism transformation. Research involves a survey of residents in three local areas characterized by differing stages of economic development and tourism dependency. Results are used to determine the impacts of changes in the tourism system on economy, society and environment, based on counterfactual data. The research indicates that depending on the stage of development, impacts differ, providing insights into local level long-range planning and identifying complexities of relevance for the sustainable development of tourism.

In paper three, Law, De Lacy, McGrath, Whitelaw, Lipman, and Buckley (2012Law, A., De Lacy, T., McGrath, G. M., Whitelaw, P. A., Lipman, G. and Buckley, G. 2012. Towards a green economy decision support system for tourism destinations. Journal of Sustainable Tourism,, 20(6): 823–843.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) develop a green economy decision support system for tourism destinations. The system, which involves a multidisciplinary approach involving destination management and system dynamics aspects, is illustrated using a consultancy for Sharm El Sheikh, Egypt. The resulting dynamic model allows consideration of a range of parameters, and will be developed to include adjustable user interfaces focusing on simplified, user-friendly variables, for example, hotel energy usage can be transformed towards “green energy” use by assuming “moderate” or “total” green conversion within shorter or longer temporal frameworks. Outcomes will assist decision-makers to understand costs and timeframes associated with sustainable tourism transitions.

Steiger (2012Steiger, R. 2012. Scenarios for skiing tourism in Austria: Integrating demographics with an analysis of climate change. Journal of Sustainable Tourism,, 20(6): 867–882.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) in the issue's fourth paper, discusses scenarios for skiing tourism in Austria, with a focus on demographic changes in key European markets and the interface with recent climate variability and future climate change. Winter tourism, and in particular skiing, is the tourism sub-sector that has been the focus of climate change modelling and scenario planning efforts for over a decade. Scenarios have become more complex, addressing supply- and demand-side adaptation options, such as snowmaking and tourist substitution (holiday timing, destination choice, activities and holiday type). In this paper, the role of demographic change, including ageing

populations or population decline in source countries has been considered for the first time in ski scenarios, leading to the conclusion that in the first half of the twenty-first century, demographic change is likely to have a greater impact on skiing tourism than climate change, with the latter becoming a more dominant driving force towards the end of the century. This has important consequences for planning future activities, including the need to focus on greater diversification of product portfolios. This finding is also consistent with scenarios of nature-based tourism to Canada's national parks (Scott, Jones, & Konopek, 2008Scott, D., Jones, B. and Konopek, J. 2008. Exploring the impact of climate-induced environmental changes on future visitation to Canada's Rocky Mountain National Parks. Tourism Review International,, 12(1): 43–56.[Crossref] , [Google Scholar]).

In paper five, Whittlesea and Owen (2012Whittlesea, E. R. and Owen, R. 2012. Towards a low carbon future – The development and application of REAP Tourism, a destination footprint and scenario tool. Journal of Sustainable Tourism,, 20(6): 845–865.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) focus on the implementation of low-carbon destinations, using a destination footprint and scenario tool for South West England. Reducing greenhouse gases is a major challenge for all destinations, demanding an understanding of where emissions occur. The Resource and Energy Analysis Programme for Tourism can calculate direct and indirect supply chain emissions related to mobility, accommodation, services, attractions, activities and events, food and shopping. The model's analytical capabilities allow for comparison of various holiday forms and packages, and the strategic development of a low-carbon destination based on insights derived from these results.

Coastal tourism is considered one of the largest tourism segments globally. Yet, despite the massive and continuing investment in tourism properties and its major economic importance for many Small Island Developing States (SIDS) and coastal communities, there has been very little consideration of the near- or long-term implications of sea level rise (SLR) for tourism. Scott, Simpson, and Sim (2012Scott, D., Simpson, M. and Sim, R. 2012. The vulnerability of Caribbean coastal tourism to scenarios of climate change related sea level rise. Journal of Sustainable Tourism,, 20(6): 883–898.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) provide an overview of the limited literature on future SLR and coastal tourism and then use a geographic information system to assess the impacts of scenarios of SLR on major coastal tourism resorts in 19 Caribbean countries. They conclude that the differential vulnerability of coastlines and tourism infrastructure would transform the competitive position and sustainability of coastal tourism destinations in the region, with important and detrimental implications for property values, potential tourism revenues, insurance costs, destination marketing, tourism development planning (retrofits and new investment), coastal management and governance, as well as local and national

economies. This scenario tool was developed to utilize data so that it can be applied in other SIDS and developing nations worldwide.

Finally, Gössling, Hall, Ekström, Brudvik-Engeset, and Aall (2012Gössling, S., Hall, C. M., Ekström, F., Brudvik-Engeset, A. and Aall, C. 2012. Transition management: A tool for implementing sustainable tourism scenarios?. Journal of Sustainable Tourism,, 20(6): 899–916.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) discuss transition management as a theoretical framework for the implementation of sustainable tourism scenarios. Following a national tourism sustainability initiative by the Norwegian government, the implementation of sustainable tourism scenarios in a stakeholder-involvement process is discussed with regard to its potential to achieve significant systemic change. Results suggest that such processes can create legitimacy for governance – and that governance will be a necessary precondition for significant change in the tourism system.

ARTICLE 6Introduction

In this paper we unpack and examine the oft-repeated claim by many in the scenario planning community that a key performance indicator of scenarios effectiveness is plausibility. We explore what lies behind this claim and how it works in practice, as well as theoretically. The paper contributes to a more rigorous theoretical understanding of plausibility, its relations with and differences with probability, and how they have been and still are related. We suggest that the links between them have been more complex than has generally been accepted, and clarify how the distinction can be more helpfully drawn to enhance the effectiveness of scenario work.

The paper is organized as follows. First, we contrast retrospective and prospective sense-making and sense-giving. Then we analyze how the etymological history of the terms "probability" and "plausibility" has confused much of the ongoing debate, and claims and counter-claims on both. In the Appendix we illustrate this confusion by analyzing [43] Millett's (2009) treatment of probability in scenario planning. This is followed by an exploration of three cultural dynamics present throughout the etymological history. This exploration dissects a thirst for wishing to settle, one way or another, on one's preference for plausibility or probability and the certainties they afford: an urge to use either to diminish the anxiety that an undecided and undefined question - a mess ([1] Ackoff, 1981) - causes. This dissection picks apart scenario planning processes in finer resolution, to show that different authors summon plausibility and probability not indiscriminately, but to serve given purposes at different stages in the process. While preference for either plausibility or probability matters in any one stage, honoring a deeper dynamic which attends to the discomfort and avoiding probability or plausibility too early helps to deepen learning with scenarios ([12] de Geus, 1988; [7] Chermack and van der Merwe, 2003) and generate productive insight. We then relocate the contradistinctions [43] Millett (2009) made within a broader conceptual space that captures the central intents of scenario planning, whether they are so see anew or gently re-perceive ([74] Wack, 1984), redirect attention to what has been peripheral or in the background ([26] Haeckel, 2004; [52] Ramírez et al. forthcoming), imagine what has been ignored ([55] Rosenthal, 1966), evoke insight ([70] van der Heijden, 1996), correct bias ([83] Wright and Goodwin, 2009), surface assumptions ([92] Ramírez et al., 2008), test or develop new options ([81] Wilson, 2000), or build new social capital fast ([35] Lang, 2012). We conclude by proposing that the result of this broader reframing of the plausibility-probability

debate can render scenario work more robust and more relevant to current challenges to avoid considering probability and plausibility as a zero sum game.

The paper builds on work in prior workshops held at the University of Oxford ([48] Oxford Futures Forum, 2008) and at Arizona State University (CSPO, 2009)[1] . The paper makes two major contributions in unravelling the theoretical dilemmas posed by the compromised use of plausibility and probability. First, it critically examines what appears to be a widely accepted (and often un-reflexive) distinction between "plausibility" and "probability" in scenario work. These are considered to be fundamental either/or criteria undergirding important methodological choices that influential scholars have gone as far as considering them to define different schools in scenario planning ([5] Bradfield et al. , 2005). As the attention on scenario planning continues to grow ([91] Varum and Melo, 2010), understanding how plausibility and probability operate and relate to each other clarifies where these distinctions are valid, and where each can and cannot legitimately be used. Sorting out this distinction offers a secondary and supporting contribution related to the qualities of effective scenario work - notably in making expectations of what scenarios can do easier to define ex ante and to be met ex post . This second contribution shows why probability cannot be used as criterion for scenario work, or as a basis to select a given scenario as "most likely" (here we disagree with Millet), but that it can be used within scenario work in ways that are developmental and not regressive ([19] Freud, 1920).

We define scenarios as a small set of stories of future contextual conditions linked with the present, made for someone and fulfilling an explicit purpose. This differs from Glenn's ([24] Glenn and The Futures Group International, 2009) seminal definition in its triple focus on the context, users and purpose.

Sense-making: retrospective and prospective

Prospective sense-making is a seldom-explored element of organizing (except in leadership research) although it is increasingly important as business, political, financial and social systems become more complex, inter-related, uncertain and turbulent. The past is a reasonable guide when growth is stable and past and present trends can be extrapolated reliably to account for emergence. However when change is novel, unexpected, unforeseen, unique, and/or radical, perhaps involving also manifold unintended consequences; the future and the uncertainty it holds means organizations can no longer rely on past or unfolding trends. Making sense of uncertain futures becomes a key survival skill within organizational life in turbulent times ([53] Ramírez et al. , 2010).

For Weick, sensemaking in organizational theory describes how meaning is constructed from experience. He was explicit in his retrospective focus: "The basic idea of sensemaking is that reality is an ongoing accomplishment that emerges from efforts to create order and make retrospective sense of what occurs" ([75] Weick, 1993, p. 635). Critical reviews drew attention to the possibility of prospective sensemaking: "prospective sense making is aimed at creating meaningful opportunities for the future. In a loose sense, it is an attempt to structure the future by imagining some desirable (albeit ill-defined) state. It is a means of propelling ourselves forward--one that we conceptualize in the present but realize in the future." ([23] Gioia and Mehra, 1996, p. 1229). Choo and Bontis called this "sense-giving", which "articulates a collective vision for the organization" ([8] Choo and Bontis, 2002, p. 81) and attends to the importance of interpretation in the prospective -it is not about locating or finding sense, but offering sense to fill imperfect knowledge and lack of understanding gaps to reduce anxiety.

Scenario planning seeks to rigorously surface and analyze assumptions about the future context to develop strategies, and ([8] Choo and Bontis, 2002, p. 81) the plausibility camp holds is most relevant when forecasting from past data sets whose distribution and the trends they suggest are unreliable ([70] van der Heijden, 1996; [53] Ramírez et al. , 2010). [82] Wright (2005) first examined scenarios as a means of prospective sensemaking, warning this disrupts rational decision makers ([11] Davidson, 1982) in their espoused use of concrete data and facts.

Scenario planning can serve as a "time reckoning system" ([62] Selin, 2006, p. 196) where sense is made forward and backward, drawing in both retrospective and prospective sensemaking iteratively. It raises fundamental questions about the limits of knowledge, the constraints of ignorance, the power of imagination, representation and communication forms, how uncertainty is accounted for and addressed, and what timescapes (Adam, 1998) can be identified. In scenario planning, imagination and intuition are combined with rigorous analysis. It also attends to experience, professional and cultural biases, representational heuristics, and rationality limitations. Within the practice of scenario planning, such issues have often been bounded in debates over plausibility and probability.

Wilkinson suggested that "Attempts to clarify the general methodological confusion about scenario practices - practices which encompass probable, plausible or possible futures - is already evident" ([78] Wilkinson, 2009, p. 110). Yet, despite multiple clarification attempts, methodological confusion remains rampant, and in our view extends to the epistemological disagreements of what is subsumed to what. For example, [25] Godet (2000, p. 7) suggested that "The uncertainty of the future can be appraised through the number of possible scenarios within the field of probables", thus situating the possible within the probable. Yet for others, the position of the probable is within the possible, inversing the relationship:

The future is an ever widening cone of possibility extended forward from the present moment. There is no future waiting to happen. There are probabilities which are related to current moments for which we cannot have total information but the further ahead we project, the more of these are likely to disrupt dramatically any extension of our own current moment and our extensions of the past ([38] McMaster, 1996, p. 150, emphasis in the original).

That the topology of the future is assumed to be conical, typically depicted sideways, starting from a single point today and moving to a broader set of equi-possible future states (see [69] UK Government Office for Science, 2009, p. 14; [59] Schoemaker et al. , 2013, p. 818)), is puzzling. How can this assumption hold? Why has that specific topological representation taken up credence? To our knowledge no empirical evidence supporting this assumption has been developed. For all we know in some situations the future is tetrahedrical and in others it takes the form of a teddy bear.

In any case, work that contributes to unravelling the confusions Wilkinson alludes to, and particularly unpacking the unsatisfactory and unproductive distinctions between plausibility and probability, is the central task of this paper. In doing so, we begin to articulate some of the knowledge quality issues that we hope will make prospective sense-making more effective.

There are several possible entry points to unravel the contemporary debates about plausibility and probability in scenario planning. We begin by examining the etymology to better ascertain how the history of each of these terms affects their descriptions and relevance. This suggests that definitional issues undergrid cultural preferences for given epistemological preferences.

The chequered past: the etymological history of the plausibility-probability distinction

The etymology of plausibility and probability in English has three distinct periods - a confused one up to and including the sixteenth century; a transition period in the seventeenth century wherein probability becomes part of the then unfolding and distinct field of science, and a clearer distinction period as of the eighteenth century. In the first period (see Figure 1 [Figure omitted. See Article Image.]), both terms are confused as both derive from classical Latin, which largely confused them. Probability first appeared in English in 1387 and plausibility over 150 years later, in 1541. In the sixteenth century probability denoted "appearing" truthful and plausibility "seeming reasonable or probable". Both were perception, not fact. Yet already then probability is associated with "likelihood" whereas the possibility of abusing plausibility as "false appearance" - which as we show below developed over the centuries into a consummate art - had already begun. Plausibility from its very beginning lent itself to what today is called "spin", as it is etymologically linked with "applause", expressing publicly available forms of approbation as early

as 1542. Plausibility is also more attached to how a person is perceived than probability ever was.

The seventeenth century saw a transition for each word and for their relationship. As is shown in Figure 2 [Figure omitted. See Article Image.], the deceptive aspects of what plausibility can entail became further entrenched both for individuals and doctrines; whereas probability started to become somewhat scientific - though not yet mathematical - and related to observational rigor. It was at this time, and most notably thanks to [36] Locke (1690), that probability was established as inference when insufficient experiential evidence was available to discover what inquisitive men sought through strong judgement. In the seventeenth century probability thus became noble, whereas the credentials of plausibility remained suspect.

As per Figure 3 [Figure omitted. See Article Image.], as of the eighteenth century, each term became more distinct. Probability denoted something served by, and even defined by, mathematical calculus. Probability most importantly became a central aspect of statistics, which itself quickly was very widely applied from its agricultural origins, through to ammunitions testing and thence to all matters of productive activity and policy. Plausibility in this third period referred to a consideration of what is "probable" as that which is "seemingly reasonable", and continued in its original sense of providing "the appearance of believability and credibility". It is the not innocuous "appearance of" element in the definition of plausibility that lends it to be (ab-)used for specious spin, culminating in the practice developed by US administrations as of 1978 of institutionalized State lying which came to be known in the Oxford English Dictionary as "plausible deniability" ([47] Oxford English Dictionary , 2012). On the positive side, plausibility is considered a feature of credible cause-effect relations.

In short, the two words have been confused with each other for centuries, and even although they became much more distinct in the last 300 years, today plausibility often still deploys probability to define itself. Plausibility, and its connections with the pliable notion of what can be applauded, quite quickly became something which one could fashion to ends of appearing truthful when actual truth might be wanting. On the other hand, probability - and the likelihood it sought to express - first started to become "scientific" when it could start to be taken as an approximation of fact when evidence was lacking. Probability came to be considered much more scientific when it was possible to express this in "mathematical" - and thus numerical - terms. In so doing, the numbers that probability can deploy in its action have come to lend it a "noblesse" that plausibility never obtained. For Bayesians, who have become highly influential, probability is the degree of belief that is objective and rational, not simply the relative frequency of occurrence of an outcome in given conditions on which Bayesians found these beliefs. Probability has thus come to measure belief, whereas plausibility simply proposes it, and in the eyes of objective Bayesians, lends itself to subjective manipulation. As a study where numbers were deployed in a complex decision-making situation, with many stakeholders and objectives in co-existence, concluded: "the power of numbers lies in their ability to fill the strategic void created by pluralism" ([14] Denis et al. , 2006, p. 349). For those advocating plausibility, however, probability is nonsensical and irrelevant if the future possible situation faced is a unique one: they argue that neither the relevant data set nor distribution of it that probability can marshal is available in such circumstances, and calling upon it in such circumstances is fraudulent.

That the exploration of the relative merits and roles of probability and plausibility in organizational studies continues to this day, and has not been resolved, we think is due to the historical complex relations between both. Thus, while [40] Meckler and Baillie (2003, p. 276) proposed that the usefulness provided by scientific method was precisely the move from plausibility, Bosch argued that, in practice, plausibility is a better assessment of truth within a constructivist epistemology - for him a theory can be accepted as plausible when it is "in accordance with (practical) empirical findings; subjective/inter-subjective ideas, thoughts, and feelings; and the opinions of and cultural categories used by others" ([4] Bosch, 2010, p. 387). In the Appendix we review how these confusions apply to Millet's work and we suggest in it that he - like many of us working with the English language - is a prisoner of the confusions built into the language[2] .

Three cultures of scenario planning and their models of plausibility and probability

Here we propose that the etymological confusions reviewed above are apparent in three distinctive but related culture clashes in scenario work. Each clash manifests preferences for plausibility or probability in its own way. First we have the qualitative vs quantitative divide. While often related to disciplinary orientation, a tendency to privilege an either quantitative or qualitative approach is often related to institutional context. A second clash arises with one camp epistemologically wanting to approximate prediction, while the other believes scenarios are only of help in intrinsically uncertain situations that are unforecastable. The final clash concerns the roles of creativity and codified knowledge in alternative futures, rendering scenario planning as entailing either "art" or "science".

The etymological confusions manifested in cultural divides makes each side value the relative truth merits of numbers and narratives incompatibly. One worldview privileges a deductive, positivist and reductive approach that approximates reality truth claims through exclusion - as in laboratories and controlled experiments - and simplification - as in models with assumptions that can ideally be completely captured in formulae, statistics, and regressions. This is a scientific gaze and aims to be predictive. But this approach relies on facts, which are all in the past: even if positivist model assumptions take projections "for a fact" they are not yet, actually, factual. The approach is also of necessity fragmented:

[...] science has emphasized thinking about "origins" rather than "emergence", about "feedback" rather than "feed-forward", about "learning from the past" rather than "anticipating the future". The inevitable corollary of that tendency is the fragmentation of our world view that we now see as one of the main handicaps in our attempts to understand the full complexity of the processes going on around us... ([72] van der Leeuw et al. , 2011, p. 4).

With fragmentation, the privileging of one perspective implies back-grounding others. It has a focus on one model held to have predictive applications; it frames attention and discounts other means to understand and interpret change, which from the point-of-view of "hard" (quantifiable) science is taken as "soft" (non-quantified) and "less serious" regardless of how effective they can be, e.g. psychoanalysis.

Scenario planning has not been immune to this clash of cultures. Wilkinson playfully referred to this split as consisting, on the one hand, of:

"Homo-Deductivist", the formal-expertise focussed, evidence led, computer-modelling based, often probabilistic scenarios folks; and on the other side is "Homo-Constructivist", the qualitative - evidence led, intuitive causal logics, storytelling, plausible or possible but certainly not probable futures, scenario folks ([78] Wilkinson, 2009, pp. 110-111).

The two species hold incompatible conceptions of knowledge and uncertainty, as depicted graphically in the bifurcation slide she proposed, which we have adapted in Figure 4 [Figure omitted. See Article Image.].

Wilkinson's distinction contrasts two incompatible assumptions and objectives in dealing with uncertainty:

For those she labeled futurologists, uncertainty is seen as an enemy to be attacked, reduced, and ideally vanquished; perhaps mostly addressed through collecting all that one can know through objective means to reduce uncertainty as much as possible. The products this effort delivers are considered "objective" and thus true for anyone - like weather predictions.

Those she considered to be involved in futurizing accept irreducible limitations to knowing and instead appreciate and engage intrinsic uncertainty. Here the inquiry is for someone and with a purpose, not for anyone, and effectiveness criteria cannot include the form prediction understood as forecasting. The authors are in this camp.

Futurologists thus seek to "predict the future" while the other camp works to "appreciate plausible futures". The strong Western scientific quest for more accurate prediction has been strengthened by enhanced data collection and processing capabilities spawned by the information technology revolution. Critics of prediction highlight empirical evidence of prediction failures, incommensurability of human values, and posit the inherent unknowability of times yet to come which are not represented by present or past facts and their probability distributions ([57] Sarewitz et al. , 2000). While futurizers may use methods used by futurologists, their epistemological stance is radically different in its insistence on unpredictability, multiple futures, discontinuous change, transformative viewpoints that bring about new units of analysis and categories (e.g. "hippies", "yuppies", "tablets", "the Arab Spring") which were not available ex ante ; and empirical evidence of self-organized or autopoetic - and unpredictable - emergence ([39] Maturana and Varela, 1980).

These two incompatible cultures on how to most effectively engage uncertain future contexts are often confused with two other cultures: one which favors the quantitative and another which favors the qualitative. It is not mere methodological differences which distinguishes them, but deeper perspectives on the very nature of what is to be done and how to assess effectiveness. While many scenario planners acknowledge that there is room for both approaches; the salience of each; their mix, limits, and advantages; and - most important - which frames the other, define this debate. While both approaches can be used to predict, and both can be used to appreciate plausible possibilities, the use of quantitative methods is culturally more tied to probability and the use of qualitative tools more linked to plausibility. In the scenario planning community the clash is most centrally manifested in the difference between - on the one hand - what [5] Bradfield et al. (2005) called the "intuitive logics" school - preferring plausibility; and - on the other hand - the Battelle ([43] Millet, 2009) and [25] Godet (2000) approaches, which place factors in cross-impact tables and attribute probabilities to the scenarios these analyses yield. According to [50] Postma and Libel (2005) and [54] Ringland (1998), the "intuitive logics" school of scenarios is the most widely used. A distinctive characteristic of the intuitive school is that it explicitly considers probability to be irrelevant in scenario work and uses plausibility precisely because it considers scenarios are used in situations where probability cannot operate and forecasting is impossible.

In [43] Millet's (2009) thoughtful examination on why probabilities should be used with scenarios, he used a framework he developed six years earlier ([42] Millett, 2003) to survey a history of scenario planning centered on distinguishing between both approaches. The approach that uses probability in conjunction with scenario work includes Millett's own professional experience with Battelle - but he ignores another version of that school developed in France that has been taken up in "prospective" studies by the European Commission ([25] Godet, 2000). Probability works as follows:

[...] the Battelle approach to generating scenarios used as many as 24 descriptors (not just the primary two employed to create the structure, or two-by-two matrix, of intuitive scenarios). A descriptor was a trend, issue, or factor relevant to the topic question. Each descriptor had two to four alternative outcomes, or states, by a target year, and each state was assigned an a priori probability of occurrence. A matrix arrayed all descriptors and their states against all other descriptors and their states. Expert judgment was also used to assign cross-impact values to the cells of the matrix, and the algorithm then used the cross-impact values to adjust the a priori probabilities up to 1.0 (occur) or down to 0 (not occur). The software also organized the scenario clusters according to their frequency of occurring, so that there were resulting scenarios and most likely outcomes presented as a posteriori probabilities . [43] Millett (2009, p. 64, emphases added).

A slightly modified version of [42] Millett's, 2003 historical review of scenario planning, distinguishing both schools and outlining their links graphically is reproduced as Figure 5 [Figure omitted. See Article Image.][3] .

The intuitive logics school proposes that scenarios seeking to (re-)establish plausibility better explore possibility than scenarios seeking or using probability, which is impossible in Knightian uncertainty ([34] Knight, 1921; [51] Ramírez and Ravetz, 2011). Yet deriving plausibility from intuition is problematic: Whereas [32], [33] Klein (2002, 2004) celebrated the "power" of intuition, and [22] Gershon (1999) explored the important function of gut feelings in its physiology, [45] Myers (2002) examined how, if left unchecked, intuition all too often leads to misfortune. [31] Kahneman and Klein's (2009) review of what is known about intuition strongly suggests that it operates only in more stable, certain, and predictable environments than those in which scenario planning is relevant ([53] Ramírez et al. , 2010). [37] Loewenstein et al. (2001) show that affective and emotive experiences expressed as "gut reactions" are neural and in this sense are mindful, cognitive contributors to gauge potential futures. Through that perspective, intuition can be viewed as an amalgamation of tacit and explicit knowledge that taps ignorance and combines it with creativity and design ([27] Hatchuel et al. , 2004) in a sort of reflexive experience that lets go of a-priori categories ([51] Ramírez and Ravetz, 2011). Here attention to other sensory perceptions can play a significant role in understanding plausible and challenging possible futures. Plausibility (whether derived from intuition or gut feelings or not) can be mapped with systems diagram to articulate explicitly how the plausibility works causally ([70] van der Heijden, 1996; [73] Vennix, 1996). [74] Wack (1984) suggested that it is on this basis of comparing plausible imagination and causal logic through analysis that sense is made and the bases for decisions improved. Proponents of this approach underline that plausibility invites further inquiry whereas probability closes it down.

Above we noted that for plausibility-centered scenario planners, probability is meaningless in situations when one cannot consider data distributions from the past to be relevant for uncertain futures ([51] Ramírez and Ravetz, 2011). Furthermore, [90] Mezias and Starbuck (2003, p. 5) argue that even if such data of the future was somehow available, it does not appear to be used in practice. Their review of two decades of research on managerial perception concluded that "on average, managers' perceptions of their environments' stabilities do not correlate with 'objective' measures of those stabilities". While [90] Mezias and Starbuck (2003, p. 15) "assumed people need to perceive problems accurately in order to solve them, (they) eventually realized that most problem solving, possibly all problem solving, does not depend on accurate knowledge of current situations". Yet those in the camp that prefers to use probability with scenarios: consider that the probable contains the possible ([25] Godet, 2000); and/or have found through experience that thinking of scenarios as equally plausible might perhaps be desirable in principle but is impossible in practice ([43] Millett, 2009). Millett also argues that having to directly justify one's thinking in terms of probabilities forces finer articulation of what might otherwise be bucketed as intuition.

Despite the problems of separating plausibility from probability we examined earlier, the two best-selling books in the so-called intuitive school of scenario work, [60] Schwartz's (1991) "The art of the long view" and [70] van der Heijden's (1996) "Scenarios: the art of strategic conversation" do make that separation, and move the qualitative/quantitative distinction to reflect two other cultures - the arts and the sciences. Both books consider scenario work an art, not a science - which we take as their implying that plausibility is more pertinent than probability. [60] Schwartz (1991) considers scenarios as memories of the future (p. 32), stories (p. 37), or myths (p. 39) to which assigning probabilities should be avoided (p. 247). For [70] van der Heijden (1996) scenarios also are plausible "perception devices" that are distinct from "the conceptual activity of forecasting" associated with probability (p. 29). Bernard [13] de Jouvenel (1967) too considered engaging with the future to be an art. Another influential scenario practice, that of the consulting firm McKinsey, appears to also consider that:

In a highly uncertain environment, the advantages of scenario planning are clear: since no one base case can be regarded as probable, it's necessary to develop plans on the assumption that several different futures are possible ([15] Dye et al. , 2009, p. 1).

Yet in the same firm, Roxburgh believes that "the scenario that is highest in probability should always be identified, and that ought to become the base case" ([56] Roxburgh, 2009, p. 9),

"picking the scenario whose outcome seems most likely" ([56] Roxburgh, 2009, p. 5). How this can be done in the Knightian uncertainty where scenarios are of particular help remains unclear to us.

Using plausibility and probability in practice

Comparing the relative merits of actually deploying a probability-centered approach to scenarios with those of a plausibility-centered approach within a single scenarios situation is difficult for practical and methodological reasons that go well beyond the definitional issues surveyed in the first section, and the cultural ones surveyed in the second one.

Imagine attempting to conduct an experiment to establish which of the two is better. To do that, the experimental setting would have to take into account the purposes and skill of the intended scenario user(s), which would have to remain constant across both approaches; the skills of the scenario team, which would have to be equal in each approach (even if one team did both approaches, they might be more skilful at the one than the other approach); the timing, and quality of the assessment, which would need to be identical; the comparability of the scenario work's contextual conditions, which would have to remain constant; and the metrics or criteria of what is meant by "effective" so that the comparison made sense. This type of comparison is what laboratories were created for - they isolate experimental conditions so that they are comparable and replicable. In scenario work, which is customer- or user- centric and contingent on context, obtaining this type of constant and isolated conditions is either impossible or very hard indeed to obtain. To our knowledge no one has attempted to conduct such an experiment, which we find unsurprising.

Given this experimental limitation, to further tease apart methodological confusions and the tensions among scenario planning schools, we here attempt to delineate how in different stages in scenario planning plausibility and probability have been summoned. This comparison is not itself objective; each school has given rise to its own "method", made up of so many phases or stages or steps, and no single common method that each school would recognize as belonging to both exists. We have thus contrived a minimum list of steps each method might recognize as relevant:

setting purpose and expectations with the intended user(s) and understanding how the users might use and value the scenario set;

engagement design;

investigations;

scenario development; and

implications for action - sometimes including dissemination.

We suggest that these steps are not always linear - in many cases we have been involved in iterations occur and one must often revisit earlier decisions. Nor are these steps sequential: often some occur synchronically. Nor are they always as distinct from each other as we present them here: sometimes they overlap. But the distinction helps to see how each step can call upon plausibility and probability. We proceed in the order above.

Setting purpose and expectations with the intended user and understanding how the users might use and value the scenario set

Probability focused scenarios claim to offer enhanced views of the likelihood of a given future over alternatives. For the plausibility-focused, that purpose is impossible: for them, if one could establish probability about a given future it would be unnecessary to do scenarios. For the plausibility-focused, scenario planning is about manufacturing challenging, relevant future

possible contexts for a user and with an explicit use of the scenarios. This means that the expectations and criteria for success of engaging with scenarios depend entirely on which school one intends to use.

Engagement design

Engagement design sets the bounds of the inquiry and its temporal horizons, and defines the collaborative architecture and roles and relations as well as a calendar. Objectives and effectiveness criteria are translated into milestones, performance indicators, and go/no go "early closure" options. The amount and type of research and form of investigation (data collection, interviews, discovery journeys, workshops) is specified. Much of what happens in this phase influences whether the scenarios will be qualitative or quantitative, and/or based on probability or plausibility.

Investigations

If the focus is on probability, investigations would gravitate towards identifying what data is on hand, what factual evidence exists of unfolding trends, and what research can reveal about how these different units do and may interact. If the focus is on plausibility investigations would unearth explicit and implicit perceptions of stakeholders, decision biases used in the organization being served by the scenarios ([58] Schoemaker, 1993), what has been talked about within the firm in its Board Meetings or other relevant internal documents, as well as with external stakeholders ([70] van der Heijden, 1996), deep cultural myths held and grounding existing world views ([29] Inayatullah, 2002), areas of discomfort ([51] Ramírez and Ravetz, 2011) and ignorance ([27] Hatchuel et al. , 2004). In both cases the investigations seek to understand what is known about the future context in question; what is uncertain; what is predetermined; identifying remarkable views about what might happen that differ from each other; determining ongoing trends which might continue, crash, or bend in the scenario period; scoping out things that do not matter to the users and determining which aspects are most useful to them given the purposes and expectations that have been agreed.

Scenario development

Probability and plausibility matter a lot in this phase. The probabilistic approach uses cross-impact analyses, deriving probabilities of likely relations, impacts, and outcomes, often based on algorithms. Plausibility-focused scenarios can be produced inductively, where the scenarios are produced first iteratively and then compared; or in a deductive fashion, where the framework for comparing among them is developed first, then the scenarios.

Each treats "relevant information" about the future in its own distinct way. The probabilistic scenarios think of the future as calculable; plausibility considers calculability irrelevant. Above we surveyed the confusion in debates on what exactly is meant by "probable" and by "plausible" - regardless of how any one intervention sorts these out, there are costs involved in pursuing each approach..

Implications for action/dissemination

Scenario use and effectiveness also depends whether plausibility or probability has been central. In our experience - contrary to Millet's assessment referred to above, evoking plausibility invites users to further inquiry, whereas assessing probability tends to close inquiry. Iterative investigation with scenarios ([71] van der Heijden's (2008) work with Indian Agriculture, is an example) is thus well supported by plausibility-focused scenario work.

Probability-focused scenario work, focuses further work on the "likelier" scenario, whereas plausibility-focused work pursues all scenarios, which are fabricated to be taken as equally plausible. If each scenario is as plausible as the next, each and every scenario is worthy of attention and it is the scenario set, not the single scenario, and the comparisons among them

that yield value. The charge within plausibility-focused planning is to keep the ambiguity of the future alive so it can remain productive (see [18] Fox Keller, 2002, for an example of how ambiguity is productive in inquiry).

If the scenarios are not used exclusively by those that produced them, and/or need to be shared or disseminated, altogether different dynamics around plausibility and probability erupt. When scenario sets leave their site of generation (the scenario workshops, the expert's computers, the core production team, maybe the organization that spawned them) and travel to new locations (e.g. the policy milieu, the strategy process, or a public audience) the plausibility/probability debate is tied to the uptake of scenarios instead of defining how they are produced and /or negotiated. Professional cultures of the users, success criteria, legislative frameworks, norms, power structures, transparency regimes - all such organizational, social, psychological, political and cultural aspects in the user communities play a role on how valid and helpful each of plausibility and probability are considered to be.

Plausibility and probability as social defences against anxiety

Psychology plays an important role in every aspect of scenario work. Social psychologists have found that ambiguity and uncertainty are uncomfortable and can create anxiety ([28] Heath and Tversky, 1991; [6] Cao et al. , 2011). Organizational efforts and even designs attempt to address this anxiety by tailoring work so that only "small chunks" of stress are involved for every individual worker ([41] Menzies Lyth, 1960). This tendency to keep the "dis" out of "distress" ([63] Selye, 1974) and keep the "stress" productive; to fix onto the flux, to construct order out of chaos, and to stand on firm footing appears linked to a basic human need to reduce ambiguity to productive levels, to obtain sufficient certainty, and to deny excessively stressful evil ([9] Cohen, 2001). Seeking to end the exposure to stressful ambiguity through closure is made by pushing for greater plausibility and by enumerating probability. Whether through plausibility or probability, at heart is a desire to alleviate the discomfort and distress that ambiguity and uncertainty entail; and to push for comfort, or at least a level of stress that is generative and helpful - not paralyzing and unsettling.

But ambiguity can also be surprisingly fertile and generative. To keep it so, "transitional spaces" have been designed and created. In transitional spaces the possibility of inquiry-enabling ambiguity is kept safe, helping people to leave the every-day safety to which they are accustomed, enter new and unfamiliar spaces for a limited time, and then re-enter the every-day space having explored the alternative. Social psychologies have studied why it can be difficult to use the helpful complexities of a transitional space, and have designed ways to make the time and work in the transitional space productive ([3] Amado and Ambrose, 2001). The "gentle art of re-perceiving" that Pierre Wack called scenario planning, may be far from gentle if the transitional space careful scenario work hones is broken, and instead of gently re-perceiving, shock and awe are what is experienced, with high distress and little or very slow learning.

As [76] Weick (2001, p. 44) noted, when approaching non-routine tasks with densely interconnected networks (as is the case in scenario work) ambiguity can be amplified. Fox Keller's analysis of the history of biology over the last part of the twentieth century suggests that apparently illogical combinations when used as metaphor in science "become the locus of ambiguity" ([18] Fox Keller, 2002, pp. 158-159), performing the crucial generative roles that make science advance. Here ambiguity, like in good scenario work, is an asset to be cherished, not a liability to be avoided. Similar points have been made about "boundary objects" in science and technology studies - as words, phrases or artifacts that can hold multiple meanings in productive tension ([65] Star and Griesemer, 1989). [61] Selin (2003) argued scenarios can work as boundary objects, usefully containing tensions, contradictions and diverse value systems to enhance understanding.

We suggest that many of the unhelpful debates, struggles and clashes over plausibility and probability can also be understood as efforts to obscure a fundamental concern of how to best keep discomfort (with what is known, ignored, hoped and feared) productive. How then might

comparisons between plausibility and probability be better deployed? This is what we survey in the last section of this paper.

Discussion: safeguarding productive ambiguity in scenario planning

Resisting the urge to find a falsely certain, infertile, and unproductive steady ground in numerical or other accounting - or in overly plausible outcomes - is a key factor of the effective practice of scenario work. Rethinking plausibility and reframing the plausibility-probability debate offers an entry point for more robust assessments of these problematic issues and more effective potential approaches. If an important benefit of scenarios is what [30] Kahn (1962) called to help "think the unthinkable", and in so doing to surface implicit assumptions, test tacit knowledge, question preconceptions of the impossible and the possible, change views and minds; then staying with ambiguity and uncertainty that is productive, rather than seeking the seductive comfort of early but unproductive resolution is a key purpose of scenario work.

It is therefore important to explicitly and reflectively seek to bypass the infertile distinctions we have surveyed and to see how different aspects emerging from both sides of these divides can help to productively deal with discomfort, disagreement, uncommon knowledge, articulating ignorance and ambiguity to more effectively explore what possibilities the future might hold.

Considering the "knowledge problems" of the future helps here. The knowledge under consideration - subjected to plausibilistic and probabilistic judgments - is ethereal. As the future is temporally distanced and has not yet taken place, we cannot grasp it. Human beings have fought the alternative - that everything is up to fate. If it were; free will, choice, strategy and agency would be irrelevant. Humans and institutions do influence how the future is made - but not the broader contexts in which it is made. Entrepreneurs think they actually make a future every day through designs, materials, agendas, choices, movements and policies. Yet [2] Adam and Groves (2007) have described futures-in-the-making as "future presents" and [16] Emery (1977) suggested that it is "Futures we are in". Both imply futures are events and impacts underway in the present; yet what the future context holds (including the entrepreneur) may - or not - unfold as the entrepreneur expects.

Some latent futures can be and are observed, such as CO2 emissions whose impact lies in wait, ageing populations, and crumbling infrastructures crying for repair. [74] Wack (1984) called these "predetermined elements" whose effects are yet to be fully experienced. While his choice of terms removes agency perhaps too drastically, assessing impacts "if other things are taken to remain unchanged" with reference projections is of help - as the IPCC scenarios attest ([44] Moss et al. 2010). Nevertheless accounting for possible futures (futures in the making) provides no assurance of certainty or comprehensiveness: the IPCC scenarios did not factor in Chinese or Indian manufacturing growth and were thus overly optimistic.

The core of scenario planning is about seeing anew, noticing differently, finding new units of observation and terms to describe these ([46] Normann, 2001), thus exploring the limits of available knowledge and sparking more effective inquiry. Scenario planning is about becoming more uncomfortable with the world's ignorance and making it more productive ([51] Ramírez and Ravetz, 2011). Scenario building works well when it departs from the comfortable familiarity of established plausibility and/or the settling security of purported probabilities to venture into the discomfort of inquiring into the unknown. Our investigations suggest that assigning probabilities or settling into too much plausibility reduces interest into a lowest common denominator made up of commonly held assumptions, baseline expectations, "the usual suspect" categories, and simplistic preconceptions and extrapolations. This is unhelpful in addressing uncertainty.

[79] Wilkinson and Eidinow (2008) suggested that "problem-focused" scenarios might be more effective than "actor-centric" scenarios, proposing what they termed "reflexive interventionist or multi-agent based" scenarios for situations that manifest post-normal science ([20] Funtowicz and Ravetz, 1993) conditions. These are characterized by short decision windows, contested validity of facts, high stakes, and uncertain values. In such situations, specifying probability and

maximizing plausibility might not be the most helpful way to assess scenario effectiveness. Instead, we propose that the scenarios which might better help to guide action in post-normal uncertainty are those considered highly implausible and uncomfortable- those outside existing mental models and framings that contest their relevance and validity. Of course, a scenario that is considered totally implausible or improbable would be rejected as irrelevant, but a scenario that seeks to make discomfort and ignorance productive in further inquiry would be of greater help than one that is highly probable or very plausible. This is depicted in Figure 6 [Figure omitted. See Article Image.], drawing from Tony Hodgson (personal communication, 15 April 2008).

So an alternative criterion to establish scenario effectiveness - instead of likely or plausible or probable - would be the extent to which it causes productive discomfort. The scholarly treatment of this dates back to Coulson's 1985 Malinowski Award Address:

It is a common charge that the social sciences, including anthropology, are unable to produce results in the form of generalizable principles that can be applied to particular cases. In fact, this has not been our primary problem ... . Our problem arises rather from the fact that our research challenges what others want to believe; our problem lies in obtaining an audience that will listen when the information is not palatable (1985:193)... "what we have is uncomfortable knowledge, the kind of knowledge that challenges established clichés and puts in question accepted solutions, and so those who champion them" (Coulson, cited in [67] Stein, 1988, p. 195).

The extent to which a scenario causes discomfort - without of course being rejected outright - will help it as a reframing device, and be of actual cognitive help contributing to the utility of the process. [80] Wilkinson and Ramírez (2010) explored scenarios as conceptual reframing, [52] Ramírez et al. (forthcoming) explored how scenarios in so doing redirect managerial attention; in helping their users to "think the unthinkable" ([30] Kahn, 1962), discomfort would appear to be a useful criterion.

A second criterion to that of discomfort would be user ignorance or inattention ([51] Ramírez and Ravetz, 2011). These would help the intended user to attend to that which has not been explored or understood well, with the scenarios highlighting which aspects they might want to invest research and educational resources into better engage.

If discomfort and ignorance, and not imagined future impact and present perceived uncertainty, were to be criteria with which to select drivers that most need to be considered in building scenarios, one would go beyond the unhelpful aspects of the plausibility/probability debate that has for so long, and in such an insidious way, crept into our understanding of scenario choices. Of course, practitioners using these criteria would need to carefully check that the scenarios that emerge are of actual help to the specific users and adhere to the agreed purpose of the intervention. This would re-position plausibility not as a condition of a future, nor as a quality of each scenario or the scenario set, but as a quality criterion of a co-produced conversation ([80] Wilkinson and Ramírez, 2010) that scenarios orchestrate. The first author has tried this with a group of mining executives, re-positioning factors from an impact/uncertainty map into one of discomfort/ignorance, resulting in a very different set of high priority items for their top management to reconsider.

Conclusions

In this paper we have surveyed the history of the concepts of probability and plausibility, the problems of distinguishing them, the cultural stances that prefer the one over the other - often equating them with methodological preferences - and the difficulties that each approach encounters in practice. We have also suggested that it so far has been impossible to empirically settle how helpful the distinctions might be. We pinpointed how the tensions raised by plausibilitic and probabilistic approaches play out differently in five specific phases of scenario planning.

In our critique of the plausibility/probability debate we have also noticed that this distinction is but one divide amidst at least three culture clashes and orientations towards the future tied closely, if not fundamentally, with epistemological preferences and ontological worldviews. A given school of scenario planning may have oriented its preference in one fashion or another, but there is nothing deterministic about the choices schools have so far made - nor are they the only ones logically available, as the cube in Figure 7 [Figure omitted. See Article Image.] illustrates. Alternative combinations may offer equally or even more fruitful approaches. For instance, scenarios that mesh science and plausibility would lead from lab experiments underway to consider logical outcomes (see [44] Moss et al. , 2010). Scenarios that worked to approach probable scenarios in a qualitative fashion might limit and enumerate the likelihood of a discrete set of scenarios while employing rich ethnographic study to shed light on those probable scenarios. One could also design scenarios that take art seriously while from a quantitative approach, with some of the work in data visualization and other infographics for beautifully representing complexity as starting points, perhaps building on [68] Tufte's (2001) insights. To summarize - beyond and between the plausibility/probability divide there are other possible attribute configurations - only some of which are depicted in Figure 7 [Figure omitted. See Article Image.] - for rendering scenario sets productive in compelling, relevant and effective ways.

We have surveyed how studies in social psychology shed light on some tendencies to resist living with open-endedness and uncertainty, and instead lead us to seek closure prematurely and unhelpfully. Nevertheless, we have shown that despite the real difficulties offered by the plausibility - probability distinctions, the quest to distinguish them has persisted as an important characterization of the field of scenario work. Our analysis leads us to suggest that many forms of the distinctions involved, at least as they have too often been invoked, are no longer helpful or productive. We have suggested alternative criteria - discomfort and ignorance - that might be more helpful in engaging with the contemporary forms of uncertainty that scenarios work with.

While assigning a numerical likelihood for a scenario can be seductive and can ease anxiety, what any form of scenario work seeks is to enhance prospective sense-making. An uncomfortable pause, staying with ambiguity and delving into ignorance may be of more value than a decisive judgement in this regard.

Our analysis leads us to call for careful caution when invoking the merits and perils of plausibility and probability; for greater reflexivity, for recognizing that "not everything that counts can be counted" and not everything that is intuitive counts. We suggest that challenging the obscurity in the debates and clashes over plausibility and probability in scenario planning; noticing the attachments of one's own mindset and the commitments it can imply; opens up the possibility of enhanced, richer, and more productive prospective sense-making.