can futures research contribute to tourism policy.pdf

Can futures research contribute to tourism policy?.

Joseph W.M. van Doorn

Following a definition of the basic In the title of this paper an assumption is made which specialists in terms employed, the author examines the field of futures research now safely take for granted, but which, the interrelationships between plann- nevertheless, may raise legitimate doubts in the fields of tourism and ing, po l icymaking and forecasting. The main trends in futures research recreation. The assumption is that scientific research can be and in are described, and some forecasting fact has been extended from past and present to future events. methods and techniques With the first societal grouping man's concern was directed conventionally used in tourism towards the preservation of his possessions and the acquisition of studies are considered. Criteria are derived by which tourism policy- new ones, which both implied risk-taking and hence an interest in the makers could measure the usefulness outcome of the actions to be undertaken. Similar to what happened in of forecasts presented to them. the field of medicine, where the witchdoctor had to give way to the

anatomist and the physician, and where the art of healing became a Keywords: future studies; tourism + recrea- science, the futures researcher has had to do away with superstition tion; foreeasting methods and occultism, with oracles, prophets, astrologers and fortune

tellers. His tools are no longer the crystal ball, dice, entrails of animals, the stars or cards, but statistics, hard evidence and facts,

This article is an edited version of a paper and computers. given to the International Conference on The missing link between the ambiguous and paramountly Winter Recreation, Ottawa, Canada, 10-15 applicable predictions of the oracle and our present day 'failure- February 1981. The author wishes to thank Dr B. Otto Schneider (Associate Professor prone' forecasts is to be sought in the still pre-scientific approaches to in English and Linguistics, University insight into the future; especially into the future of whole societies, of Barcelona, Spain) for reading, comment- such as Thomas More's Utopia ( 1517), Bacon's New Atlantis ( 17th ing and criticizing this paper.

century), Condorcet 's idea of conditional probability (1978), and Gilfillan's thesis (early 20th century) with regard to the methodology of futures research. Joseph W.M. van Doorn is Associate

Professor in Planning and Futures Research The 1920s brought the breakthrough. In the field of methodology at the Twente University ofTechnology, the We have already mentioned Gilfillan. Neither should we Netherlands. He has a special interest in the underestimate Ossip Flechtheim's endeavours to make futurology relationships between futures research and policymaking, on a national governmental acceptable in academic circles, as the idea that science could level, in tourism. He can be contacted at contribute to a better knowledge of the future was then rejected by Stadhouderslaan 22, 3583 JJ Utrecht, the most scholars. The vast literature in the field available today gives Netherlands. live evidence of a change in attitude. By the same token, we can infer

from the existence and creation of scientific institutions dedicated to futures research that the legitimate claims of this new science have been recognized and met. ~

In the field of policymaking at a governmental or industrial level the futures scientist still encounters obstacles in the form of certain

1. Good introductions to the history deep-rooted views that tend to prevail despite all theoretical of futures research are: Edward advances: Cornish, The Study of the Future, Washington, DC, (World Future • The future cannot be known. Science as such has to rely on Society, 1977), and Jib Fowles, Handbook of Futures Research empirical facts, which are available from the past and the present, (London, Greenwood Press, 1978). but not from an as yet non-existent future.

0261-5177/82/030149-18503.00 © 1982 Butterworth & Co (Publishers) Ltd 149

Can futures research contribute to tourism policy?

• Insight into the future ispossible only by means ofnon-scientiJTc methods, such as the crystal ball, astrology, intuition, rules of thumb and hand-reading.

• The future can be known i f we view it ideologically as the strict realization o f ideas conceived in the past, eg the realization of an ideal state of society in conformity with definite political ideas. Regrettably though, this view generally lacks any adequate time perspective within which the potential forecasts about this state of society have to come true (and thus could be assessed).

The above notions, while they prevail, have an inhibiting effect on the futures researcher's genuine commitment. Although it is true that there are certain unpredictable events, such as natural disasters, and although tacit knowledge and political ideas have a timely bearing on futures research, eg in the Delphi method, the only way for scientists to overcome the problem of acceptance is by converting policy makers to their credo:

• The future can partially be known by way o f scientific methods and techniques.

Thus the assumption we implied initially is not only a challenge for the scientist, it also makes a promise to the user in the sense of partially deleting risk from any venture. So in this paper I play the role of the devil's advocate and try to make planners and users of forecasts, with regard to tourism and recreation policies, keep both their feet on the ground, by pointing out some caveats, complexities and promising possiblities in futures research. I begin by defining the basic terms such as tourism, policymaking, planning, futures research and forecasting. Then the relationships and crosslinks that exist between planning, policymaking and forecasting are explained. Further we demonstrate some trends in futures research and consider the forecasting methods and techniques conventionally used in tourism to convince policymakers. This author is not overoptimistic about the results of future-oriented research in the field of tourism, as only certain aspects seem to form the focus for scientific attention. Apart from this, short-term explorative tech- nologies are given preference over more speculative and normative methodologies, wherever forecasting is applied, without the claims or evidence of better results.

Finally, we summarize a few criteria that could be used by policymakers to measure the usefulness of the forecasts presented to them.

Definitions of tourism Since the 1930s when tourism adopted the distinctive features of a mass phenomenon, a considerable bulk of the literature has been devoted to tailoring a universal definition that would cater to multidisciplinary requirements.-' Synthesizing these attempts into a typology, we can differentiate four currents:

2. Just a few examples: C. Kaspar, in Revue de Tour&me. 1954, page 1. Basic definitions. 50; F. Ogilvie, The Tourist 2. Mono-disciplinary definitions. Movement (London, 1933); A. 3. Statistical definitions. Norval, The Tourist Industry (London, Pitman, 1936); W. 4. Systems analysis definitions. Hunziker and K. Krapf, Grundriss der allgemeinen Fremdenverkehrslehre In the first type, the basic definitions, two elements are constantly (Zfirich, 1942). present: a static one (the tourist stay) and a dynamic one (the journey

150 Tourism Management September 1982


to the destination area and the activities during the stay), while at the same time the non-commercial character of the overall activity will be emphasized. A fine example of this type of definition is Burkart and Medlik's: "Tourism denotes the temporary, short-term movement of people to destinations outside the places where they normally live and work, for other than business or vocational reasons, and their activities during the stay at these destinations". 3

The second type lays greater emphasis on the motives and needs underlying the temporary movement of people, bringing in concepts such as relaxation, health, recovery from previous stress, cultural and educational interests, self-realization, and so on. The order of priorities is determined by the socio-psychological, anthropological or economic bias of the source of these definitions. Cohen 4 made a major contribution to this category, not only being the first to demonstrate that the very useful term 'fuzzy set' was fully applicable to tourism, but mainly by subsequently applying the fuzzy set rules to generate a viable definition of the tourist, as we see below.

Fuzzy sets are classes where there are no clear-cut distinctions between membership and non-membership. Moreover a fuzzy set can be specified by breaking down the class concept into its constituent elements or dimensions (eg the duration of the 'tour'). Deleting all non-touristic elements from the set and validating a necessary and sufficient number of constituent dimensions will enhance the generative power and hence the realism of the set. Figure 1 offers a slightly elaborated synthesis of Cohen's conceptual tree.

The definition is generated by combining certain subdimensions of preference or choice that condition others to be excluded. The chain underlying Cohen's own definition is the following: 1 A/2A, B/ 3AJ4B/5A,B/6B/7A; "A tourist is a voluntary, temporary traveller, travelling in the expectation of pleasure from the novelty and change experienced on a relatively long and non-recurrent round trip"?

However powerful this tree diagram appears to be, we will inevitably run into difficulties if we attempt to extend it beyond its inherent limits to generate a definition of tourism on the same lines. We would commit the old fallacy of mistaking a whole for the sum and total of its elements. Tour-ism is much more complex than summing up to the n th tour-ist! We will have to tackle this problem below when trying to set up a breakdown of tourism, since Cohen's definition comes in handy in the category of the tourist as an individual.

Coming back to our typology, we look at the third type. Statistical definitions are mainly used by governmental and international bodies, such as the World Tourism Organization (WTO) and the Organization for Economic Co-operation and Development (OECD). They serve highly specific purposes, eg to count the number of incoming or outgoing tourists, their spending, the length

3. A.J. Burkart and S. Medlik, and purpose of their stay, etc. Yet an imaginative futures researcher Tourism, Past, Present and Future would not be reluctant to interpret statistical definitions largely as the (London, lrleinemann, 1974), page 311. operationalization of mainly economic (in general monodisciplinary) 4. E. Cohen, "Who is a tourist? A definitions. conceptual clarification", The Socio- Before we consider the fourth type of definition, the systems logical Review, 22, 1974, pages 527- analysis approach, I acknowledge that my own definition of tourism 555. 5. Ibid. is heavily indebted to this type, because it offers the widest range of

Tourism Management September 1982 15 1


[~imensions Subdimenstons B C

I Voluntariness Voluntary Forced by social Forced by coercEon norms"

2 Time of travel and stay- t d a y f " ' ' ~ - " - % ~ < 6 monttls " Permanent (nomad)

3 Direction Round trip~,....._. One way (emigrant)

4 Distance Short ~ . . . . . I ~ Mediu m Long"

Figure 1. A morphological 5 Recurrency Non-recurrent Repeated visit Recurrent (regular)

approach to the tourist role 6 General purpose instrumental~...~ Non E(irregular)instrumental- - ~ Rest(Sec°nd- home) / / ( health, education) (business) ( pleasu re)

Source: Adapted from Cohen, text z Specific purpose Novelty ant i - -Cont inu i ty and~" reference 4. chonge'A"f / / stability"

(_*_.) Additional subdimensions to, ~ " or deviations from Cohort's { TOURIST t original scheme.

possibilities with regard to the application of sophisticated data- processing and assessment techniques. As we see below, close links to futures research, especially with integrative forecasting, can be established within this system. Here tourism is viewed as a series of cross-links arranged over a grid, for example:

• links between tourists and the region they visit, 6 • links between the tourist and the service sector, ie the agencies

responsible for transport, accommodation, recreation, catering etc,

• links between tourists and the host society, • links between tourists, the service sector, the destination area and

the policymaking authorities.

Leiper's definition illustrates the systems analysis approach to tourism: "The elements of the system are tourists, generating regions, transit routes, destination regions, and a tourist industry. These five elements are arranged in spatial and functional connections". 7

Components of tourism Let us now try to break down the overall phenomenon of (mass) tourism into its major constituent parts. We ought to differentiate at least four:

6. A few studies are of practical interest here: K. Przeclawski, La 1. The tourist. Rencontre des Cultures (Varsovie, 2. The intermediate framework. 1976); L. Turner and J. Ash, The 3. The supply of tourist resources and facilities. Golden Hordes: International Tourism and the Pleasure Periphery, 4. The societal context of 1-3 (tourist, intermediate framework, (London, Constable, 1975); K.D. tourist supply). Hartmann, A uslandsreisen, dienen Urlaubsreisen die Vflkerversti~ndigung? (Starnborg, T h e tour i s t Studienkreis ftir Tourismus, 1974). The first category, the tourist, generates three sources of data for the 7. N. Leiper, "The framework of tourism", Annals of Tourism forecaster. First he will collect f ac tua l information about the tourist Research, VI (4), 1979. For the that is the least liable to influence from his stay, eg items such as age, systems analysis approach, see also, political preference, profession, socio-economic status etc. The c. Kaspar, "Neuere wissenschaft- tourist has brought these from his home country and will most liche Erkentnisse zum Fremdenverkerhrs-bzw. Tourismus probably take them back unchanged. However, correlations between Begriff", Revue de Tourisme, 2, these factual data and recreational behaviour are worth considering 1979; and J. Jafari, Editor's page, Annals ofTourismResearch, V, and can act as 'probability-generators' for future recreational 1977 (special). behaviour.



Then the researcher will approach a set ofbehavioural properties which either pre-exist in the form of habits or prejudices and are subject to transformation, or else, if absent, will be generated at the tourist destination. To mention a few, we can think of attitudes towards the destination country and its inhabitants, activity patterns, likes and dislikes, etc.

The third category is perhaps the most delicate to enter the forecaster's calculations. It includes certain types of adaptive behaviour in the form of decisions generated on the basis of the expected and/or new experience in the host country, eg choice of transport, accommodation, region of preference, activity schedule etc. It is up to the forecaster, who, with the planner, shares the ambitious aim to satisfy tourist needs and expectations (in the medium and the long term), to cast the delicate balance between which types of adaptive behaviour the tourist can be expected to develop, and which tourist-adaptive steps will have to be undertaken on the part of the host region. If we now, in the light of our analysis of the tourist, reconsider Cohen's definition, we find the factual, the behavioural and the adaptive aspects covered.

The intermediate f ramework The second category is vast and encompasses, apart from what is generally defined as the 'tourist industry', various semi-public and private organizations, such as consumer associations, sports federations, the boards of certain recreational facilities, camp sites, or wild life reserves. Here the researcher is interested in possible or desirable developments as a function of tourist demand, and is concerned to safeguard the flow between the tourist and the supply of tourist resources and facilities. 8

Supply o f tourist resources and facilities In this category, the supply of, the access to, and the appeal of tourist resources and facilities are the main concern of the forecaster, as well as the supply structure itself, eg the functional and spatial balance between attractions, accommodations and infra- and superstructure, He will have to find answers to questions such as: How many camp sites do we need in 1985? How many ski-lifts are necessary for a new winter resort area near Ottawa? He will also find that, in order to meet demands in this category, he will have to rely heavily on data from categories 1 and 2.

Societal context o f the tourist, the intermediate framework and the supply structure In this last category, an often neglected one, the forecaster's interest

8. Studies with regard to this aspect focuses on developments in certain sectors of society that may have of tourism can be found in: C. an influence on tourism. Gearing, W. Swart and T. Var, Those developments could be derived from studies on, among Planning for Tourism Development, Quantitative Approaches (London, others, socio-cultural changes, the energy supply-demand situation, Praeger, 1976). political affairs and technology. All those studies have to be 9. Leading magazines dea l ing undertaken to gain insight in those sectors that surround tourism. partially with the future developments of tourism and recreation, are: The results will be forecasts on, for example, tourist flow, multi- Journal of Leisure Research, Leisure access hotel reservation systems, the willingness to travel and Sciences. Annals of Tourism (recreation and holiday participation), air fares (as influenced by fuel Research, Tourism Management, Journal of Travel Research, Tourist prices), changing patterns of recreational activities as influenced by Review. an increase of free spending time?

Tourism Management September 1982 153


Tourism as a system of four basic categories We can now develop a definition of tourism by way of the four mentioned categories: "Tourism is the composition and the result of the interactional patterns formed by three set-constituent elements: the tourists, the intermediate framework, and the supply of tourist resources and facilities, all three &which are placed in, conditioned by, and have a bearing on a definite societal context". ~0 See Figure 2 for a graphic representation of this definition.

The only thing we still have to do is to substitute the word tourist according to Cohen's definition. However, this will be done with one c h a n g e - - i n s t e a d of pleasure being viewed as the main motive behind the "search for novelty and change" we use the expression, "satisfying physical, cultural and recreational needs". This has been done so as to include for example health- and cultural tourism, which are not necessarily concerned with pleasure and relaxation.

Both tourism and recreation could be defined by the arrangement of the same constituent element, and the same holds true for the substitution of tourist with recreationist. As a number of tourist facilities and attractions are shared equally by foreign tourists, domestic tourists and the resident population, (eg sport facilities will serve the different needs of these groups), it is thus sensible for the futures researcher to focus on the relationship between futures research and tourism in general, while treating the subject of the relationship between different types of recreation and forecasting

10. Based on a definition from the specifically. author in "Toerisme, begrip voor een begripsontwikkeling", Recreatievoorzieningen, 11 (11), Futures research, planning and policymaking: a triad 1980, and refined by B. Otto Schneider (see acknowledgment). Policies are the consequence of a decisionmaking process which has

I ( I I generar,~ { Tourist } Intermeaiate framework [ T ~ _ . c ; aes,,na~,on I I ) - )

Figure 2. Tourism as a s y s t e m of four basic categories: compilation of conceptual frameworks Source: Leiper, tex t re ference 7; Kaspar, text re ference 7; Przeclawski , text reference 6; J .W.M. van Doom, " 'Toer isme en t o e k o m s t o n d e r z o e k , een heer in een te krap jasje", Recreatievoorzieningen, 9, 1979; J .W.M. van Doom, "Burma en het se lec t ie f toer i sme" , Intermediair, 14 (43), Oc tober 1978.



the objective of modifying the present in view of the future. This is why decisionmaking bodies do need valid information about the future and the positive and negative impact of potential decisions. This information-gathering can only be done by careful research, research which will then support the policymaking processes.

Let us consider an example that is common practice in many resorts - - the building of a hotel. Such accommodation already has something to do with the future: the expected flow of tourists, the necessary bed-capacities, the market share etc. Building a hotel means talking about objectives, involves data-collection and processing, and concerns several government departments and sectors at a local, regional, or national level.

Policymaking should display cohesion between multi-sectoral aspects and should strive to achieve coherent and viable coordina- tion between multi-level objectives. For example, the spatial aspect shows up with the different Zoning Acts and infrastructural needs; the economic aspect is demonstrated in employment, foreign currency and multiplier effects. The sociocultural and socioecological aspects will make their claims in balancing income in favour of a depressed region, the training of hitherto unskilled workers for hotel jobs, and the ecological imbalances created by visitors in the environment.

Goals, objectives and targets are not merely the concern of the investor and/or the hotel owner/manager. The Office of Public Works, the Town Council, the police and the hotel chain - - all have their say in the whole. The objectives of each may differ widely. They may aim at the creation ofjobs, attracting congresses or a certain kind of public relations exercise for this community.

And who is to coordinate all those objectives, interest groups and powers? The planner. The systematic support of policymaking by research takes place in the planning process. Planning is the basis of policymaking. Thus futures research becomes a fundamental part of the planning process. This is shown in Figure 3 in which the terms 'anticipation' and 'design' both refer to futures research, tt

To conclude: in tourism, policies have to rely on a coherent set of economic, political, sociocultural and spatial objectives. These objectives have to be placed into a decision framework whose primary function is the achievement of aims with specified means in a certain period of time. Policymaking in tourism is not an exclusive task of government, but grows along the lines of cooperation with the policymaking tourist organizations (national tourist organizations, information offices, consumer associations) and the tourist industry (hotels, restaurants, tour operators, travel agencies); even pressure groups might have a say in the policymaking processes. For such a complex task, planning in tourism as a function of, and in accordance with the four categories we established in our definition, is needed.

Futures research and forecasting: methods and techniques So far I have been using these two terms as if they were self- explanatory to the non-specialist. We now see what they stand for and how they are related. Any scientific study of the future is futures

11. J.W.M. van Doom and F.A. van research. We agreed above that for any planning, decision or Vught, Planning (Assen, van Goreum, 1978). policymaking process to be effective, it ought to be supported by



L..._ AnalySIs ~ ~ I POI'Cy prepOrct,On$

DesEgn * ~ j ~ . . . . . ~ " Action t ~ P o t i c y decisions 1 Pohcy implementation

Policy evo~u3t;on ~ E,o,oo,io~

Figure 3. Planning, L policymaking and forecasting viewed as the three components of the decisionmaking process

research. Thus this research is prior to the decisions that will determine and shape the future. It processes different projections of the present into the future by means of forecasting techniques to elicit a variety of results and thus a variety of potential futures for assessment and optimization. Hence the term futures research.

Forecasting techniques The supporting methodology consists of what we call forecasting techniques. Below we concentrate on the use of forecasting methods and techniques that are suitable for the field of tourism. Having differentiated four categories in this field (tourist, intermediate framework, supply and societal context), we can safely presuppose that forecasting techniques are applicable to each.

Two methods of approach can be distinguished:

• The database is taken from the past and the present and gives rise to 'exploratory futures research'.

• The desired future itself constitutes the database, decisions being shaped by working b a c k w a r d s - 'the normative approach' to futures research. ~2

On the basis of this almost classical distinction we can distinguish four different forms of forecasting. They have in common that they elicit conditional probability statements, based on either of the rational models of analysis. This means that forecasts, irrespective of their form (or modality) are obtained systematically and are subject to control and testing.

In exploratory forecasting the scientist is concerned with the extrapolation of trends and the search for the logical development of alternative possibilities.

Apart from this form it is also scientifically sound to base one's forecasts on a blend of intuition, expertise, and generally accepted a s sumpt ions - speculative forecasting. Whereas in exploratory techniques it is hardly possible to include expectations about future policy decisions, speculative techniques offer the advantage to do so by means of method-implicit procedures.

Here it comes close to the ideas of normative forecasting, although in normative forecasting the scientist starts out explicitly with the

12. E. Jantsch, Technological formulation of norms and values that are to be valid in the future. The Forecasting (Paris, OECD, 1967). procedure involves constructing a series of consistent images of the



future and subsequently tracing the route of attainment of(access to) these images, t3

Matters become even more complex when dealing with integrative forecasting, since its procedural capacity covers all the techniques customary in the three preceding forms. The aim of these techniques is to set up consistent relational patterns among isolated forecasts to enhance the plausibility of pronouncements deriving from any other technique. Thanks to this procedurally-comprehensive approach, a functional accumulation of knowledge, time, types and sectors is achieved.

In Table 1 we offer a typological synthesis view over the four forms of forecasting and the methodological tools applied in each. In Figure 4 we relate the four types of forecasting techniques with the required database and the timescale.

Exploratory forecasting in tourism Time series analysis One of the most important data for recreation as well as for tourism is to know how many people are involved. Information about flows of recreationists or tourists are the inputs into policy decisions. It is unsurprising that the majority of forecasting studies so far in tourism are devoted to demand.

The local council wants to know how many recreationists will stay overnight during a particular season. This will serve as an indication about how many new budget-class hotels have to be built. The ski- manufacturer is interested in the number of people per nation that will be expected to go on a skiing holiday. The national government will include into their economic budgets prognoses of the prospective

13. J.W.M. van Doom and F.A. van receipts from tourism and recreation. So demand is exceedingly Vught, Forecasting (Assen, van Gorcum, 1978). important. Demand depends on a range of factors (or variables).

!

F'xp[oratory Speculative [ Normative 1 I . . . . , 1 . , 1

forecasting wi th t ime- and database



Table 1. Type of forecasting technique and methodological tools

Type description Methodological tools

Explorative Extrapolation of trends Trend setting - time series analysis

Search for logical alternative Regression analysis possibilities Gravity models

Historical analogy method Scenario writing Morphological analysis

Speculative Probability estimates of event Brainstorming occurrence Delbecq and Impasse

Implicit expectations in Delphi policy decisions

Normative Explicit description of desired Normative scenario writing future states and the routes Bayesian statistics that lead to them Pattern

Integrative Research into the implications Input-output models of options Cross-impact analysis

Establishment of relational Mapping patterns among hitherto

Source: v a n Doom and van Yught, isolated forecasts text see reference 13.

Several explorative forecasting techniques are used to foresee the developments with regard to demand in tourism. Those techniques differ, among other things, with respect to the number of factors that are taken into account. Time series analysis merely focuses on the historical developments of one variable so as to forecast its (near) future developments; linear regression models do the same for two variables, while the multi-regression models (like the gravity model) consider demand in relation to three or more variables.

Thus time series would start with just one variable: tourist arrivals, recreational receipts, or aircraft sales, etc. The forecaster can choose one of the various time series analysis techniques: Box- Jenkins, Census II, Leading Indicator etc. All these are used to break down, in one way or another, a time-series into seasonal, trend, cycle and random elements.

Now let us consider, by way of an example, how a Dutch forecaster, who is - - in 1980 - - interested in 1981 winter tourism development (we only take air charter development), handles the one variable time series analysis. First, he has to collect a data-series on which he can base his forecasts (see Table 2). '~ Second, as he is not sure about developments in 1980, he finds it safer to use two indicative numbers for the year 1980, an optimistic and a pessimistic one, let's say 155 000 and 175 000. N o w he uses these two figures to estimate the distribution over the winter months. He can do this with the × 11 method. Then he will attach both the new series obtained to the base one (1966 -197 9 ) in such a way that the effect of the new series is greater than the base series. Third, he wants to produce the 1981 forecast with upper and lower limits. As he has already two

14. Adapted from a reportfromthe alternative 1980 figures, he will use an exponential smoothing Ministry of Transport and Public Works, Department of Aviation technique for the lower limit and a linear curve-fitting technique for Affairs, Schiphol/The Hague, 1980. the upper limit, and obtain the matrix shown in Table 3. The example given has been worked Of course our Dutch friend could extend this exercise again to eive out for a real situation in the charter- market by Holland International, us monthly totals for winter 1981. But how can we assess his results? Travel Group, Rijswijk, 1980. With this simple one-variable example from time-series calcula-



Table 2. Data series on Dutch winter tourism development.

1966 1970 1972 1974 1975 1976 1977 1978 1979

16000 73920 89330 107400 140000 160000 182880 182800 164000

Table 3. Possible outcomes of multiple use of time series forecasting techniques a

Optimistic Pessimistic

Source: Holland International, text 1980 175 000 155 000 reference 14.

1981 Upper limit (linear curve fitting) 180000 169000

aThey could have used better techniques but the example is one often seen in Lower limit (exponential smoothing) 145 000 131 000 practice.

tions I wanted to emphasize that forecasts tend to become less accurate and less reliable the longer the time-period they range over. What should the tour operator, as a decisionmaker, do when he is presented with figures ranging from 131 000 to 180 000, about a year and a half ahead? Can he really base his policy on forecasts that oblige him to operate with a risk-margin of roughly 25 000 passengers? He has to make investments, buy accommodation and book plane-seats, indeed quite some time ahead!

This example thus gives us some indication about the limited timescale within which time series analysis and forecasting on an exploratory basis are useful; it alludes to the problems a forecaster faces using a one-variable technique; it points towards the implications in policymaking and the 'interfering factors' that derive from lack of data, irregularities and seasonality.

Although the techniques mentioned are widely used elsewhere in tourism planning, they do not seem to be of too much help to the tourism forecaster who focuses on the medium and the long term. Several authors like BarOn and Vanhove have mentioned this problem. 15

Regres s i on m o d e l s

It is easy to see, however, that the apparent inaccuracy of the forecast above is caused by the fact that here future behaviour is explained only and exclusively by way of processing the data through time. Even admitting that knowledge of past behaviour can but indicate the probabilistic structure of future behaviour, it should be clear that demand, or any other variable we are interested in, depends on a lot more factors or variables than simply time.

The second type of exploratory forecasting therefore considers 15. BarOn, "Forecasting, theory and tWO or more variables in correlation. A well known technique here is practice", Tenth Annual Conference Proceedings of the Travel and the linear regression model, used for a two-variable relationship, eg Tourism Research Association income and holiday participation, based on the least squares method. (1979); N. Vanhove, "Forecasting in For more than two-variable calculations, multi-variable regression tourism", Revue de Tourisme, 35 (3), models will be required. Vanhove recommends for this purpose the 1980. 16. Brian H. Archer, "Forecasting Artus model, while Archer presents the Askari model. ~6 As demand: quantitative and intuitive examples, or better, slight deviations from the multi-variable techniques", International Journal of Tourism Management, 1 (I), 1980; regression models, we could mention the so-called trip-generation and Vanhove, op cit, reference 15. models and gravity models:



A multivariable demand model is specified as a functional relationship between a dependent variable and one or more explanatory variables: the object of analysis is to discover the absolute and relative degrees of influence exerted by each of the explanatory variables on the dependent variable. A Gravity model, however, is expressed in a more rigid form: the nature of the relationships, especially those concerning distance [and travel costs] is more closely specified. '7

In the literature a long list of models is presented. To mention a few with the name of the author: Armstrong, Crampon and Lesceux (gravity), Gordon (trip generation), Jud (linear regression). ~s

In the Armstrong model the following variables were used to forecast tourist flows to several tourist destinations:

• size of the population of the tourist generating country, • income per capita (in each of the generating countries), • distance from generating country to tourist destination, • travel time, • special relation variable, eg a common language between a

generating and a destination country, • a parameter for the relative appeal of the destination countries.

Using his model outcome for comparison with actual data, the results are not very encouraging. Nor do they become more promising if we allow for the extenuating circumstances that the tourist arrival statistics originated in different countries, which accounts for

17. Archer, op cit, reference 16, incongruencies in the census methodology, or the fact that the page 9. number of countries compared differ slightly with Armstrong's study 18. C. Armstrong, "International tourism, coming or going: the (see Table 4). methodological problems of forecast- Although our criticism cannot be as severe as Vanhove's, when he ing", Futures, 4 (2), June 1972, pages speaks about trend extrapolation - - "Pure trend extrapolations and 115-125; L. Crampon, "Gravitational model approach to travel market projections based on alternative rates of growth are lacking any analysis", Journal of Marketing, 30, background to justify future evolution ''19 - - this model at least shows April 1966; D. Lesceux, La Demande Touristique en Mediterranee (Aix- the weakness of any extrapolation method: en-Provence, 1977); G. Jud and H. • useful in the short term, Joseph, "International demand for Latin American tourism", Growth • losing power very quickly in the medium term, and Change, Jan 1974. • practically useless in the long term. 19. Vanhove, op cit, reference 15, page 5. m comparison of several explorative forecasting techniques by 20. S. Makridakis and S. Makridakis and Wheelwright may illustrate this (see Table 5). -~° Wheelwright, (eds), Forecasting (Studies in Management Sciences, Vol 12, Amsterdam, North Holland Scenarios Publishers, 1979), pages 6-9. In the forecasting techniques, arithmetic and mathematics were used.

The variables were quantifiable and the results were forecasts about a certain point in time, or points in time. Nothing or little could be

Table 4. A comparison of forecasts and actual data.

(× 103) Used data Armstrong's Actual data 1967 forecast for 18

Countries generating OECD countries All countries countries (1975) 1975

Canada 15 858 25 368 13 375 13 660 Netherlands 1 627 2 269 2 399 2 819 Spain 13 130 22800 28726 30 122 Portugal 635 971 788 888

Source: Armstrong, text reference 18; Australia 160 409 378 496 and OECD, Tourism Policies in Japan 336 591 438 707 O-ECD Countries ( OECD, Paris, 1975 ).



Table 5. Comparison of some exploratory forecasting techniques as to their usefulness in short-, medium- and long-term assessment

Short-term Medium-term Long-term 0-3 months 3 months - 2 years 2 years and up

Exponential smoothing Fair to very good Poor to good Very poor

X - 1 1 Very good to excellent Good Very poor Box-Jenkins Very good to excellent Poor to good Very poor Historical

analogy Poor Good to fair Poor to fair Regression

analysis Good to very good Good to very good Poor to fair

said about qualitative variables, such as the influence of the policymaking process itself, and the changes of variables are subject to time. With respect to the latter one has to keep in mind that two conditions are fundamental in time series and regression-like models:

• the variables in the model used will remain unchanged in the future, • the relationships between the variables are constant.

This, however, may be true for the short term, but for the medium and long term it is not. For medium- and long-term forecasts to be of any practical value to the planner, we must adjust our techniques to handle a bundle of qualitative variables denoting the expected turning points in a policy framework along a timescale as a result and extension of quantitative data processing.

This is what scenarios are intended to be. In the classical sense scenarios are hypothetical sequences of events. They pretend to trace possible designs of the future and the routes that subsequently would lead towards them. In exploratory forecasting, scenario writing means moving along the scale from past-present to future, while in normative forecasting the procedure is more sophisticated: the forecaster moves from the future backwards-forwards to the desired state.

To date no normative scenario exists in tourism. Principally there is no great difference between the methods of exploratory scenario writing (elsewhere called a projective scenario) and normative scenario writing (also called a prospective scenario); although in a prospective scenario description of the desired state might cause considerable difficulties. Even if problems of context can be given an acceptable solution, there will still remain certain methodological problems, eg the treatment of consistency, plausibility, and the level of aggregation as a challenge and task for futures research.

It would be a fallacy to assume that the problems arising from projective scenario writing have been overcome. Due to the novelty of the technique (relatively speaking) and the difficulty in handling qualitative data with tools developed for quantitative data processing, to my knowledge only a few noteworthy studies exist. And these, at best, present a set of quantitative/qualitative trends from various fields that might constitute (or could be used as) input for scenario proper.

To quote only a few examples of these inputs or 'preparatory stage-scenarios', we could mention MacGregor's, Koster's and Kahn's contributions to the conference "Tourism in the Next



Decade" (Washington, DC, 1979). -'t Kosters, for example, offers forecasts about some seven tourism-related fields (economy, leisure time, population, nature, space, technology and science, politics). He then tries to correlate these forecasts in a systematic framework so as to produce a weighted estimate with regard to the consequences of the correlations for tourism developments in the Netherlands. Yet he has to recognize that the direct and indirect influence of developments in different fields, with their separate impact on tourism, "in the long run remain uncertain". 22

Kahn, the father of scenario writing, has less to contribute to the future of tourism. His forecasts are either vague or so trivial that one wonders for whose benefit they have been produced.:" Just one example in this respect: Kahn foresees that in 1989 the tourism growth rate will be double the economic growth rate. This, however, is not surprising. Figures from 1969-78 on world tourism prove that this has not only been the case for quite some time already, but has even been recognized in the literature: "Apparently international tourism grows at almost twice the rate of G N P growth". -'~ Thus the borderline between simple truism and genuine forecasting is

21. M. Kosters, "Holland and tourism in the next decade", Tourism difficult to identify. Planning and Development Issues, Better examples that overtly point in the direction of scenarios as edited by Hawkins, Shafer and we defined them, are found in Baron's paper presented at the tenth Rovelstad (Washington, DC, George Washington University, 1980);J.R. annual Tourism and Travel Research Association (TTRA) MacGregor, "Latin America: future conference in 1979. Here it must suffice to skim through just one of scenario forecasting for the tourism the examples to illustrate what kind of alternative assumptions are industry in some of its developing made and how they are arrived at (see Table 6). The assumptions nadons', Tourism Planning Develop- ment Issues, pages 429-443. made explicitly (eg the relative reduction of oil prices) and the 22. Kosters, op tit, reference 21, underlying, implicit but less obvious, assumptions (eg the balance of pages 51-52. 23. H. Kahn, Travel Trade News powers in the world remaining unchanged), are always the weakest Edition, section one, XC VI (6), points of the exercise. The assumptions can be considered a result of 1979. a time-serialization of the database, the alternatives being triggered 24. The Big Picture, 24, ASTA travel news, Travel "79-'80, World by means of factorization towards negative or positive develop- Trends and Markets. ments. But there is a lack of hypothetical grading or stepwise

Table 6. Scenarios based on alternative assumptions (Tourism to Thailand: scenarios 1975-1980).

Field Optimistic Intermediate Pessimistic

Political 1. International Improved d~tente As 1975 Increased tension,

local wars Economic 2. Prices of oil Relative reductions As 1975 Further relative and air transport (relatively) increases

Air transport 3. Fare structure More fare As 1975 Decreased availability

reductions, of charter and scheduled and promotional fares charter

Forecasts of visitors arrivals (10 3 )

1975 1 185 1 165 1 135 1978 1 895 1 575 1 375 1980 2 550 1 900 1 550

Actual number of arrivals: 1975 1 180000 1978 1 475 000

Source: BarOn, text reference 15. 1980 1 850000



progression towards one of the alternatives. Thus the scenario- writing business needs to be supported by more elaborate techniques that will enable the forecaster to improve his assumptions, to strengthen their predictive power, and to widen their scope to range over qualitative data.

For this purpose we turn to the second area of forecasting speculative forecasting.

Speculative forecasting This type puts a series of tools into the hands of the scientist that allows him to move towards a less quantifiable terrain and become more independent of the influence from his past-present database, thus enhancing the accuracy of his studies. We are now ready to abandon the somewhat casual assumptions made in the Thai example in Table 6 and take a look at some of these tools. In passing we mention a few names from the list of (already) conventional techniques, such as SIG (Subjective Integrated Group Processors), JAM (Judgment Aided Models) and GDST (Group Discussion Structuring Techniques). The common objective in all these procedures is the pooling of the expertise and skills of people proficient in highly unstructured fields (eg tourism). The most famous among these techniques and perhaps the one that has been subject to the most passionate discussion and criticism is the Delphi method.

The Delphi method In a Delphi study, a questionnaire dealing with a specific problem is presented to a group of experts in the field. They will answer questions, for example, about the probability and/or desirability of certain events occurring, eg the likelihood of a 100% computerized reservation system for the leading hotel chains and travel agencies in 1984.

Characteristic of Delphi studies is their striving towards consensus. In several written rounds t h e - most of the time, a n o n y m o u s - experts try to convince each other, by their argu- ments, that certain answers are more likely than others. The result for each statement is then a statistically aggregated collective answer on which consensus was reached. A Delphi example focusing on

25. A.E. Robinson, "A return to tourism might be found in the report on the results of the international Delphi", in "A decade of achievement", Tenth Annual Conference symposium, "Tourism and the Next Decade". 25 Proceedings of the TTRA (1979). The validity of this method may be questioned and subjected to 26. Harold Saekman, Delphi serious criticism. Several papers dealing with Delphi have pointed Critique (Toronto, Lexington Books, 1975); H. Linstone and M. Turoff OUt the weaknesses and limitations. For criticism reference is (eds), The Delphi-Method: generally made to Linstone and Turoff, although to my knowledge Techniques and Applications (Reading, Sackman's Delphi Critique is even more thorough in this respect. 26 MA, Addison-Wesley, 1975). 27. Delphi-SEER- Two Delphi studies relevant to tourism were undertaken in Expertenbefragung 1978-1980 fiber Austria and Switzerland, respectively. 27 But one of the first studies of F.remdenverkehrsntwicklung in this kind was carried out in Canada. Referring to the Canadian study, Osterreich (Wien, Institut t'fir Fremdenverkehrsteehnik, 1980); J. it is said in the Austrian report that: "This was the first study in the Krippendorf, EineDelphi-Umfrage world that tried to estimate the future of tourism in a very f~ber die zf~kf~nftige Entwicklung des comprehensive way. It was not the aim of this study to work out Toutqsmus in der Schweiz, (Bern, 1979). measures or recommendations". :s In the Canadian study special 28. See ibid, page 51. attention was given to social trends (income structure, leisure time,



the role of women, norms and values), as well as to trends in demand and environmental settings.

While in the forementioned Washington exercise emphasis was laid on the societal context (our fourth category of tourism), the main core of the European studies (Austria and Switzerland) is found in the second and third categories (intermediate framework and the supply of tourist resources and facilities). In 1983 a Delphi, aimed to embrace all four categories, is due to be conducted in the Nether- lands. That study will undoubtedly be indebted to the earlier studies.

Integrative forecasting As was mentioned above, normative forecasting studies are almost completely absent in tourism research. This can be attributed partly to the vagueness of the concept of tourism. If one does not know exactly what tourism stands for, how can one develop a policy strategy for it? Second, in various Western European countries tourism is not attached to one single ministry but to several. In Holland at least two important ones (Economic Affairs, and Social and Cultural Affairs) and at most five claim a certain competence in questions of tourism.

Last but not least the sector is denied political importance, notwithstanding its vital economic importance. Even in the U S A this is the case if we are to believe senator Daniel Inouye:

Of course, neither the Administration nor its predecessors would con- sciously and deliberately ignore or otherwise frustrate an industry that contributes so much to our social and economic welfare. The answer has been and remains that as a Government we do not understand the industry, its magnitude and the size of its contribution to the nation's social and economic goals. 29

This attitude can be held partly responsible for the nonexistence of normative forecasting.

Remembering what was said above, this form of forecasting contains integrated forecasts from various sectors, from different forecasting methods, preferably long-term studies. Yet in the course of my paper reference has been made to the poor feasibility of long- term studies in tourism. However, there are a few good studies that comply with some criteria that define integrative forecasting. Falani 's study 3° on forecasting part of the air traffic between 14 US cities through an input-output model has turned out to be so important for airport directors, airlines, pressure groups, catering-

29. See reference 24, page 39. and fuel-suppliers, that it could be characterized as integrative at 30. M.O. Falani, "Air traffic fore- least in a sense. casting: an input-output technique Taylor, Edgell and Baron 3~ similarly emphasize the need for approach", Regional Studies, 7, 1973. 31. BarOn, op cit, reference 15; G. integrating several techniques in one comprehensive method. The Taylor and M. Doctoroff, An combination preferred by them and mysel f is the triad "T ime Series- approach to an integrated forecasting Delphi-Scenar io writing", in that order. system for a national tourist office", in IUOTO, The Measurement of Tourism (British Tourist Authority, London, 1975); D. Edgell and R. R e s u l t s Seely, "Tourism policy: a two stage model forthedevelopmentofinter- Despite the growing file of reports on tourism forecasting, national tourism flow forecast surprisingly little attention is paid to the comparison of actual data estimates", paper presented to the with the corresponding forecasts; and this, despite the existence of a Symposium, "Tourism in the next considerable number of criteria to assess/evaluate these results. The decade", Washington, DC, 1979; see also reference 2 1. scope of my paper only allows for a brief mention of a few of these.



Particularly in short-term forecasting, one should be happy when forecasts materialize. That could be proof that the technique used was valid to some extent. Usually, validation tests of post-diction and proximate variety are used. The former tries to validate historical data from one period as a function of historical data from the preceding period. The latter applies a score to the success of short-term forecasts, when materialized, in view of the long-term forecasts (see Figure 5).

In this context we must emphasize the role played by criteria of plausibility (versus causation) and logical consistency (versus systems or societal consistency). But even the role, function and random behaviour of a researcher have an impact on the output. Other criteria to assess the usefulness of forecasting results could be the number of alternatives presented, the contextual stability of the forecast, the presentation itself and last, but not least, the costs.

So the policymaker in tourism, whatever his place is, in government, industry or elsewhere, always has to counterweigh different criteria against his own objectives and preferences - - he will have to ask questions like:

• What will the forecast cost me? • What is the term of application and what is the relative value of

this term? • What data are available? • How valid is the technique proposed? • Do I need alternatives or just one answer? • Are the results plausible? • Can I use this forecast to impress my electorate or can it be used

to manipulate investors, political leaders or the public? • Do I really need a forecaster? Or could I do it better myself2.

Doubtless in most cases policymakers need the forecaster, but they should not blindly depend on his rulings. Decisions ought to be the result of communication, perhaps even directly embedded in a communicative or negotiation-type of planning, before both parties will benefit from one another in an optimal way.

Conclusions: the bearing of forecasting on tourism In this paper we have presented a typology of tourism on the one hand, and a typology of futures research (ie forecasting) on the other.

Most studies, briefly mentioned or quoted, focused on the first category of both typologies, the tourist and exploratory forecasting, or were concerned with the supply category (large companies such as in the aviation industry have their own forecasting departments).

The intermediate framework proved to be less covered by research, due to its inherent dispersion and fracturing, with the exception perhaps of some big hotel chains or united travel organizations. In the last category we witnessed the first fruitful signs of combined fo recas t ing- scenario writing.

Figure 5. Post-diction and (d) proximate variety: validation tests in a time perspective (a) (b) Short ~ ~ ' ~ r ~ . . ~ . ~ f e r m i ( c ) Lonq t

Key: post-diction: a = forecast for b ~ ~ " ~"=1 ~ - - ~ ' ~ proximate variety: c in accordance I I I I I I w i t h d t_z t. , t o t r t z t 3 t 4

Tourism Management September 1 9 8 2 165

Can ftttures research contribute to tourism polic3'?

Several Delphi studies which together would cover all four tourism categories, at least could have an impact on tourism and recreation policy. Yet maximum benefit could be derived from this technique when used in combination with time series and scenario writing.

But, on the whole, forecasting in tourism has not yet received universal recognition as a vital aspect of planning and/or policymaking in tourism. The greater part of research in tourism refers to short-term exploratory forecasting and therefore will not substantiate the necessary support to strategic planning, while on the other hand the few 'pre-stage' scenarios presented to date move more along the lines of contingency planning than in comformity with scenarios proper.

Yet in a period of continuous economic recession it is of great importance to view, through crises and disasters, a more hopeful future. However, that means a type of planning and decisionmaking that bases itself on strategic planning (or management). This planning is not widespread among the various tourism bodies and organizations. But whenever one bases oneself on norms and values set by politicians and policymakers together with their natural partners in the tourism field, integrative forecasting is needed, since this type of forecasting combines various social, political and economic trends in a normative policy framework.

Recreation stands and falls with society and the forms of tourism developed in this context. It is determined by factors such as income level, leisure time, energy supply and prices, processes of individualization and social segregation, as well as by inflation, demographic structures and ecological imbalances such as p o l l u t i o n - and, not least, by the changes in all those factors or variables through time.

All those aspects and factors will grow in complexity and dynamism. Besides, there is an increasing interdependence of the elements that make up the tourism and recreation system. In the decade to come tourism is bound to remain a fuzzy set. Forecasting has proved to be valid to some extent in economics, technology, demographics and a few other fields. I have tried to demonstrate in the course of the paper that it is also a handsome tool for planning and policymaking in tourism.

It thus seems fair to declare forecasting to be one of the fatally neglected but predictably most vital parts of tourism and recreation policymaking in this decade.


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