Tourism Management 24 (2003) 475–487

Events in Indonesia: exploring the limits to formal tourism trendsforecasting methods in complex crisis situations1

Bruce Prideauxa,*, Eric Lawsb, Bill Faulknerc,w

aThe School of Tourism and Leisure Management, The University of Queensland, 11 Salisbury Road, Ipswich, Queensland 4305, AustraliabHospitality and Tourism Management, The Robert Gordon University, Aberdeen, Scotland, UK, AB15 4PH

cCentre for Tourism and Hotel Management Research, Griffith University, Gold Coast, Australia

Received 24 March 2001; accepted 14 November 2002

Abstract

The desire to know the future is as old as humanity. For the tourism industry the demand for accurate foretelling of the future

course of events is a task that consumes considerable energy and is of great significance to investors. This paper examines the issue of

forecasting by comparing forecasts of inbound tourism made prior to the political and economic crises that engulfed Indonesia from

1997 onwards with actual arrival figures. The paper finds that current methods of forecasting are not able to cope with unexpected

crises and other disasters and that alternative methods need to be examined including scenarios, political risk and application of

chaos theory. The paper outlines a framework for classifying shocks according to a scale of severity, probability, type of event, level

of certainty and suggested forecasting tools for each scale of shock.

r 2003 Elsevier Science Ltd. All rights reserved.

Keywords: Indonesia; Asian financial crisis; Forecasting; Chaos theory; Risk analysis; Scenarios

1. Introduction

International tourism flows are subject to disruptionby a range of events that may occur in the destinationitself, in competing destinations, origin markets, or theymay be remote from either. The consequences may beeither mild and relatively short term or have cata-strophic impacts on existing industry systems. Majordisruptions, also referred to as shocks, are felt in bothorigin and destination areas, affect both the public andprivate sectors and disrupt the travel plans of intendingtravelers. In recent years major disruptions that haveaffected international tourism flows include the Gulf

War, the Asian financial crisis (Office of NationalTourism, 1998) and more recently, the September 11,2001 terrorist attack on the US. Commenting ondisruptions suffered by the tourism industry, which heclassified as either crises or disasters, Faulkner (2001,p.136) noted that ‘‘relatively little systematic researchhas been carried out on disaster phenomena in tourism,the impacts of such events on the tourism industry andthe responses of industry and relevant governmentagencies to cope with these impacts’’. Further, Faulknerstated that because of disruptions of this nature researchis required to assist the tourism industry to recover fromevents that are usually not forecastable. In contrast,during normal or tranquil times forecasting has provedto be a useful planning tool and predictions of futuretourism activity and is widely used by governments andthe industry (Uysal & Crompton, 1985; Chu, 1998; Wittand Song, 2001). Forecasting generally uses a range ofanalytical techniques based on recent and current touristflows between origin markets and destinations as well asa range of economic factors to predict future trends.However, the difficulty of predicting future economicactivity, particularly in times of uncertainty, is an issuethat has long bedeviled forecasters.

*Corresponding author. Tel.: +61-7338-11-008; Fax: +61-7533-81-

1012.

E-mail addresses: b.prideaux@mailbox.uq.edu.au (B. Prideaux),

e.laws@rgu.ac.uk (E. Laws).1This paper is one of the last in which Bill Faulkner was involved

before his untimely death in early 2002. Despite the illness from which

he never recovered, Bill continued to advise us as this paper

progressed. We wish to acknowledge the profound influence of Bill’s

insights into key tourism issues and emerging tourism theory, and to

pay tribute to the pleasure of working with him.wDeceased.

0261-5177/03/$ - see front matter r 2003 Elsevier Science Ltd. All rights reserved.

doi:10.1016/S0261-5177(02)00115-2

This paper is concerned with various events that havethe potential to disrupt established flows, resulting insubsequent tourist activity which is very different fromthe trends forecast in either overall level of activity, thepattern of flows, or both. The paper explores strategiesthat may be employed to improve the effectiveness offorecasting in circumstances where there are few pre-existing indicators of factors that may adversely affectnational tourism flows at some point in the future. Recentevents in Indonesia are used as a case study to build thisdiscussion. The paper develops a conceptual frameworkthat synthesises the issues identified but does not attemptto develop a detailed alternative forecasting model; itspurpose is to suggest a direction that may offer analternative to supplement current forecasting methods.

2. A critique of forecasting techniques

Calantone, Benedetto, and Bofanic, (1987) distin-guished between four forms of forecasting. Exploratoryforecasting extrapolates past trends using regression andsimilar techniques and is based on assumptions aboutrelationships between variables. Normative forecastingincorporates discussion of the methods needed to attaina desired future outcome. Integrative forecasting relieson a variety of methods to determine the underlyingrelationships amongst a variety of forecasts, integratingthese to maximise convergence of results. Finally,speculative forecasting uses techniques such as Delphicforecasting or scenario writing and relies on thejudgements of experts. In this approach probabilitiesrepresent an assessment of the degree of uncertainty of aparticular occurrence in the future. Given the frequentreliance of these methods on past experience, which inturn requires both explicit and tacit assumptionsregarding the stability of relationships, the ability offorecasting to generate long-term results and accountfor unforseen events remains limited. Even short-termforecasting can only factor in known relationships thatappear as identifiable trends, and building on these givea picture of what may occur if change occurs alongpredictable lines. The assumptions are basically those ofequilibrium and stability, in contrast to the dynamiccomplexity of turbulent systems perspectives (Laws,Faulkner, & Moscardo, 1998).A number of researchers (Witt & Song, 2001; Turner

& Witt, 2001) have acknowledged the limitations ofcurrent forecasting techniques, particularly the difficul-ties posed by the inability to predict irregularities such assudden changes in consumer taste and demand. Toovercome these shortfalls researchers such as Witt, Sohnand Turner have sought to improve the capability ofestablished techniques. For example, Turner and Witt(2001) found that structured time series models incor-porating explanatory variables produced the most

accurate forecasts. The identification of relevant non-economic variables as determinants for future growth,and the modelling of their significance pose a high levelof difficulty for forecasters. Uysal (1983 cited in Crouch,1994) for example, noted that there were a number oflimitations confronting demand forecasting including:ignoring supply factors, the omission of non-economicfactors which may have long-term consequences and thepotential for the appropriateness of variables to change.To these, a range of other non-specified crises anddisasters including domestic and international politicalfactors, wars and insurrections, movements in theinternational economy, and natural disasters such asearthquakes, cyclones or hurricanes should be added.Although forecasters try to account for these situationsby using dummy variables to allow for the impact of‘one-off’ events such as the two ‘oil crises’ in the 1970s(Witt & Song, 2001), the problem of irregularitiescontinues to defy prediction.A more sophisticated approach utilizing time varying

parameters (TVP) regression to model structural changeis one solution to the problem of predictive failureencountered by causal tourism demand forecastingmodels (Witt & Song, 2001). While the TVP approachis able to simulate a range of shocks that may influencethe relationship between explanatory and dependentvariables it assumes that explanatory variables areexogenous. Where there is some doubt about thecreditability of this assumption the vector autogressive(VAR) modelling approach may be more appropriate(Witt & Song, 2001). In the VAR model all variables aretreated as endogenous.While newer forecasting methods including TVP and

VAR allow researchers to model the impact of disrup-tions they are still dependent on the parameters that areselected for testing. It is at this point in the forecastingprocess that a major problem can be identified. Littleconsideration has been given to identifying the type ofunexpected disruptions that should be incorporated intothe current orthodoxy of forecasting.Recognising the inability of current forecasting theory

to cope with the unexpected, Faulkner and Russell(2000) put forward an alternative view suggesting that,owing to ‘the certainty of the unexpected’, authoritiesneed to implement policies for coping with unexpecteddisruptions to tourism flows. The long-standing New-tonian paradigm of the relative stability of both internaland external environments of organisations is aninefficient theoretical basis for coping with change andcrises. Yet the assumption of change along Newtonianlines underlies much of the current theory of forecasting.History has many times revealed that the tide of humanevents leans more to the chaotic than the ordered. If thisproposition is accepted, the norm of history is changerather than equilibrium. Chaos theory talks of ‘trigger-ing events’ such as a crisis or disaster. These events need

B. Prideaux et al. / Tourism Management 24 (2003) 475–487476

not be regarded as only destructive because they maylead to new configurations or structures that are moreeffective than those that are replaced.A well-developed literature exemplified by journals

that include Risk Analysis, Risk Management, Disaster

Planning and Prevention and Emergency Planning Digest

have recognised that there are a large range of eventsthat cannot be predicted with any certainity and whichlie beyond the range of predictions that standardforecasting techniques can be expected to yield. Employ-ment of scenarios as the basis for predicting the impactof a range of disruptions is a widely accepted method ofplanning for crises and disasters including multipleenvironmental, economic and natural disasters. Thetourism literature has not begun to investigate the richrange of techniques developed in the risk managementliterature, yet this literature has the potential to yieldmodels, frameworks and theories that will assist tourismforecasters and planners to cope with a range ofdisasters and crises.One direction that should be considered is a synthesis

between risk specification, identification and manage-ment, and forecasting. In such a synthesis, forecasting,using current techniques, could be based on revisedvariables determined by forward looking scenarios orrisk analysis as an alternative to the current reliance onvariables based on historical relationships. The riskliterature has demonstrated the validity of scenariobuilding as the basis for risk management. Haimes,Kaplan and Lambert (2002, p. 383) for example statethat ‘‘y. It is clear that the first and most importantstep in a quantitative risk analysis (QRA) is identifyingthe set of risk scenarios. If the number of risk scenariosis large, then the second step must be to filter and rankthe scenarios according to their importance’’. Rankingof risk, where the level of probability of occurrence andthe degree of impact can be established, provides datathat can then be used as a basis for forecasting. Ofcourse the possible range of scenarios is large and thereis some need to rank risk scenarios by the probability oftheir occurrence on a scale that must start with highlyprobable through to improbable. It is also apparent thatranking must include the flexibility to adjust the scale ofprobability. Prior to the September 11 terrorist attackon the USA, an incident of this nature could bedescribed as a highly improbable risk but after theattack the level of risk moved to highly probable.Where then, does this leave the study of forecasting

future growth trends in tourism? Where change is slowand ordered, and therefore relatively predicable, fore-casting may yield a high degree of accuracy as claimedby forecasters. On the other hand, where events followthe normal course of history and exhibit a tendency tosudden, large-scale instability and unpredictability,forecasting looses its potency and an alternative formof prediction is required. Faulkner (2001) described a

variety of situations that could be classified as eithercrises or disasters, but which were in the main singleevents attributable that can be classified as eithermanagement failures (crises) or unpredictable cata-strophic change (disaster). Beyond individual crisis ordisaster events lie complex situations where manyfactors coalesce to impact on the harmony of thetourism industry. Rather than pursue a quest fordefinitional precision there is a need to examine complexsituations where numerous crises, disasters and politicalsystem failures act in unison to create one or moreshocks on the tourism industry. Conditions in Indonesiabetween 1996 and 2002 created such a situation where anumber of events had serious consequences for thatnation’s tourism industry.By being able to determine the magnitude of the

problem and identify its cause as either natural orhuman or a mix of the two, actions to minimise theimpact of crisis and disaster can be implemented.According to Faulkner (2001), a synthesis of thecharacteristics of disaster or crisis situations based onresearch by Fink (1986, p. 20), Keown-McMullan (1997,p. 9) and Weiner and Kahn (1972, p. 21) identified thefollowing key factors:

* A triggering event, which is so significant that itchallenges the existing structure, routine operationsor survival of the organisation. Trigger events mayinclude political crises, religious or ethnic tensions,economic decline and climate change;

* Characterised by ‘fluid, unstable, dynamic’ situations(Fink, 1986, p. 20).

* High threat, short decision time and an element ofsurprise and urgency;

* A perception of an inability to cope among thosedirectly affected; and

* A turning point, when decisive change, which mayhave both positive and negative connotations, isimminent. As Keown-McMullan (1997, p. 9) empha-sise, ‘even if the crisis is successfully managed, theorganisation will have undergone significant change.

Understanding the path of the events following ashock may contribute to a methodology of identifyingrisk situations and thereby assist in providing somewarning of when such events may occur, and howthey may evolve. Faulkner’s (2001) Tourism DisasterManagement Framework provided one example of anoperational model of this type. Once it can be admittedthat current techniques have limitations, particularly inthe areas of risk and uncertainty, the way is open to lookfor new methods that move beyond the Newtonianassumption of stability. Ignoring the possibility ofdisruptions in the terms stated in this discussion maylead to a prolonging of the event and exacerbation of itseffects as remedial action is considered in the heat of

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unexpected unfolding events, rather than in the calm ofprior contingency planning based on new forecastingmethods.A typical large-scale disruption precipitates complex

movements away from the previous relationships whichusually trend towards stability and equilibrium. Duringa situation of this nature multiple events and theirfollow-on affects may prolong the period of disequili-brium unless there is some mechanism that can assist tore-establish a new equilibrium situation. Chaos theory(Faulkner, 2001) provides an insightful paradigm for theinvestigation of changing complex situations wheremultiple influences impact on non-equilibrium systems.In these conditions of uncertainty, fruitful approaches tostrategy formulation need to incorporate contingenciesfor the unexpected. Chaos theory demonstrates thatthere are elements of system behaviour that areintrinsically unstable and not amenable to formalforecasting. If this is the case, a new approach toforecasting is required. Possible ways forward mayinclude political audits and risk analysis to develop asense of the possible patterns of events allowing these tobe factored into projections of future tourism activityusing a series of scenarios. The latter may involve theuse of a scenario building approach that may incorpo-rate elements of van der Heijden’s (1997) strategicconversion model, elements of the learning organisationapproach based on a structured participatory dialogue(Senge, 1990) or elements of risk management describedby Haimes et al. (2002). Which ever direction is taken,there are a number of factors that must be identified andfactored into considerations of the possible course ofevents in the future.

3. Factors that may influence tourism flows

Throughout recorded human history it has provedimpossible to predict the future, although manyhave tried. Faulkner (2001), for example, cites theimportance of oracles in classical Greece and notedtheir failures. What is known of the future is that thereare a number of circumstances that may exert influenceon the course of events in following years. Events thatdisrupt the tourism industry can be divided into threegroups:

3.1. Trends

Trends describe a range of possible future trends thatcan be identified in the present and which, unlessremedial action is taken, will cause some magnitude ofdisruption in the future. The degree of impact of thesetrends will depend on how Governments and industryrespond to these trends to mitigate the worst of thepossible range of outcomes. One example is the future

impact of low fertility rates in developed countries whichmay see increased tensions in the future between retireesand workers (Willmott & Graham, 2001). For example,the Japanese population is projected to decline by 17.9million between 2000 and 2050 while the number of 60plus persons will climb to 42% of the population(Sayan, 2002). Population changes of this magnitude arealso occurring in Korea, Italy and Spain and in thefuture will have a significant impact on tourism as thenational tax base falls but consumption of healthservices escalates.

3.2. Crises

Crises can be described as the possible but unexpectedresult of management failures that are concerned withthe future course of events set in motion by humanaction or inaction precipitating the event. Events of thistype include the Foot and Mouth outbreak on UKfarms in 2001, the Chernobyl disaster and the ExxonValdez oil tanker wreck. Examples of crises that mayoccur at some point in the future include:

* The impact of AIDS particularly in Sub-SaharanAfrica and potentially in the Indian subcontinent andthe Russian Federation (Quinn-Judge, 2001);

* An increase in militant religious fundamentalism;* Nuclear war in Asia;* Financial meltdowns including global recession; and* Terrorism employed to achieve political or religious

objectives.

3.3. Disasters

Disasters can be described as unpredictable cata-strophic change that can normally only be responded toafter the event, either by deploying contingency plansalready in place or through reactive response. Dursch-mied (2000) cites a number of examples from historywhere unexpected weather turned the tide of battleincluding the typhoon that saved Japan from a Mongolinvasion in 1281. More recent examples include theKobie earthquake, the 1997 El Nino climate effect andthe 2002 floods in Europe and China. Events of thisnature occur regularly but at undeterminable frequency,intensity and location. Examples of future disasters thatthe Tourism industry could begin to prepare for include:

* Natural disasters of all types including floods,droughts and earthquakes;

* Long-term natural climate change separate from thecurrent concern over human induced global warming;and

* A pandemic perhaps caused by a new strain of flu orother unknown disease.

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3.4. Change in the structure of government, social

organisation or economic structure

To these previous classes of disruptions may be addedother factors (Prideaux, 1999) that while neither adisaster nor a crisis, may precipitate significant changein the organisation of international tourism:

* Development of new trading blocks where nationsjoin together in regional political and economicunions such as the European Union (EU);

* The future direction of capitalism;* Demographic change in terms of ageing populations

in developed economies as well as growing popula-tions in many underdeveloped nations;

* A continuing search for political identity by ethnicand religious groups causing further fragmentation ina number of nations;

* Sacristy, particularly of farming lands, water, marineresources and non-renewable energy; and

* Environmentalism, particularly if global warmingcontinues.

Consideration of these trends, crises, disasters andchanges to the structure of the economy or system ofgovernment is the first step to developing some capacityto factor disruptions into tourism forecasting. Theconcept of trends does afford some degree of predic-ability allowing the tourism industry to developresponses prior to the impact of the identified trend.Disasters on the other hand, can generally only beresponded to after the event, either by deployingcontingency plans already in place or through reactiveresponse. Crises may offer some scope for predictionbased on the premise that after a particular type of crisishas passed, analysis of its causes should enable greaterpredictability of similar problems in the future. Theimpacts of single or multiple disruptions occurringsimultaneously or in a sequence may create unexpectedpolitical, social and/or economic conditions that causethe decline of some destinations, growth of newdestinations or radical disturbances to global tourismflows should also be considered.To date, forecasts of future patterns of tourism

growth have used a range of econometric demandmodels. However, it is apparent that there is a need todevelop new techniques that identify risk and events thatmay cause future disruptions. Where the likelihood ofdisruption is small, techniques of this type are notnormally required. Where history has identified areas ofpolitical disruption, use of these techniques, in conjunc-tion with existing forecasting tools, may be an appro-priate course for forecasting. For example, disruptionssometimes generate waring signs or are initiated bytriggering events. Identification of warning signs andtrigger events may extend warning periods and allow

forecasters to produce revised forecasts using standardTVP, VAR or similar models.

4. Role of government in the unexpected

There has been limited discussion about the mechan-isms that may be used to assist tourism cope with thecertainty of the unexpected, and a general criticism ofthe literature relating to tourism forecasting is thatlimited attention has been given to the impact ofunforseen political and economic crises on policydevelopment. Friedman (1999) suggested that thevulnerability of individual countries to shocks hasincreased exponentially as a consequence of the increasein inter-locking systems associated with globalisation,political alliances and modern communication technol-ogy. Examples of research include Clements andGeorgiou (1998), who examined the impact of politicalinstability on tourism flows in Cyprus, and Prideaux andKim (1999) who analysed the impact of the Asianfinancial crisis on bilateral tourism between Australiaand Korea. Henderson (1999) compared the impact ofthe crisis on Indonesia and Thailand finding thattourism is vulnerable to outside forces such as economicconditions and suggested that there is a need for aresponse strategy to cope with the unexpected. Furtherresearch of this nature can be expected from analysis ofthe impacts of September 11, 2001.Government responses to shocks are important and

will often affect the rate of recovery of the tourismindustry, however, we find little in the tourism literatureto assist governments to prepare for the unexpected, andcope with its impact. Aside from limited use of scenarioplanning, governments rely on forecasts to developbudgets, policies and plans in the absence of othermethods of predicting the future. Policy frameworksenacted by government provide the incentives as well asthe constraints around which destinations must work asthey seek to attract investment and encourage visitation.Hall (1994), for example, noted that there was a need totake into account the political context within whichtourism development occurred. In an analysis of barriersto US tourism to Africa, Brown (2000) noted thatpolitical risk, defined as risks that arise from the actionof governments or political forces and which interferewith or prevent foreign business transactions, candisrupt tourism flows. Risks of this nature may inhibitthe flow of international tourists for a number ofreasons including the unwillingness of foreign investorsto support or extend lending facilities, the inability ofintermediaries to undertake financial transactions andfor airlines to operate into an uncertain logisticsenvironment. Richter (1995, 1999) has published anumber of studies examining the impact of politicalevents, including episodes of violence, on national

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tourism industries. In a recent article Richter (1999)commented on the impact on the tourism industries ofthe Philippines, Pakistan and Sri Lanka caused bypolitical disruption. It is salient to compare theconsequences of political tensions in these countrieswith the recent situation in Indonesia. Comparisonswith the Philippines are particularly relevant, asIndonesia appears to have experienced the same generalcourse of events that occurred in the Philippines afterthe removal of Marcos from the Presidency in 1986.According to Richter (1999), risk assessment andpolitical audits may be useful tools to have in place toassist nations to recover quickly from political disasters.Where there is an element of corruption evident,planning and management may be less adaptableto external shocks because of rigidities that reduceresponse options.One common element that emerges from the preced-

ing discussion is the role of governments in inadvertentlycreating as well as managing unforseen events. In thecase of Indonesia and the Philippines during the politicalupheavals that heralded the demise of the Suharto andMarcos administrations, the government appeared to bethe cause of the crisis. The corrupt and undemocraticnature of the Marcos and Suharto regimes eventuallygenerated sufficient opposition that mass unrest even-tually unseated the incumbent governments. In otherinstances the government’s response to crisis may be acritical element in the manner in which national tourismindustries cope with declining tourism flows. If govern-ments withdraw support for tourism promotion ortourism development the impact may be exacerbated.Conversely, if governments assist the industry, as in thecase of Thailand during the Asian Financial Crisis(Henderson, 1999), the impacts may be minimised.Analysis of the reactions of governments through policymechanisms to such crises and the results of thoseactions appears to be important areas that warrantfurther research.To illustrate the issues outlined above, the paper

presents a case study describing the main causes ofdisruption to the inbound tourism sector of Indonesiaduring 1997–2002. The difficulty of forecasting in theface of unexpected and complex disruptions to tourismflows is illustrated by analysing the projections ofbilateral tourism flows between Indonesia and Australiadeveloped by the Tourism Forecasting Council (TFC)(1997b, 1998, 2000, 2001).

5. Case study—Indonesia

During the 10 year period 1987–1997, Indonesiaachieved a 475 per cent increase in inbound tourismwith arrivals climbing from 1,060,000 to 5,036,000. Asignificant feature of the growth during this period was a

shift from traditional European, US and Australianmarkets to intra-regional travel from Asia, a result ofthe high and sustained growth of Asian economies.WTO (1994) had estimated that the growth ratethroughout the 1990s would average between 13 and15 per cent per annum, a target that appeared achievableuntil a series of events disrupted the Indonesianeconomy from 1997 onwards. Between 1993 and 1997total arrivals grew by 152.4% while receipts rose from$US3.99 billion to $US5.44 billion (WTO, 1999). By1997 foreign exchange earnings from tourism accountedfor 10.2% of Indonesia’s exports (WTO 1999).

5.1. Factors effecting Indonesian tourism during

1997–2002

In the period 1997–2002 Indonesia experienced 10major shocks that received widespread internationalpublicity and resulted in sharply reduced activity in thetourism sector. Many of the factors listed stem frompressures that have existed in Indonesian society formany decades and which surfaced as a consequence ofadverse political and economic factors.I. Smoke haze. Negative media reporting of the

annual smoke haze resulting from illegal burning offorests in Sumatra and Borneo. The haze was particu-larly bad in 1997II. The Asian financial crisis. The crisis lead to a large

and rapid fall in the value of the Indonesian Rupiahresulting in a substantial increase in unemployment,business failure and increase in price of many importsincluding a number of staple food items.III. Political unrest. Associated with the fall of the

Suharto regime commenced in late 1997 political unrestreached a peak in May 1998. Much of the unrestemanated from students factions in Jakarta’s universi-ties who were pressing for democratic reforms.IV. Ethnic unrest. Commencing in 1997 and appar-

ently sparked by the rapid deterioration of the domesticeconomy and rising unemployment, many Chinesecommunities and businesses in Java were targeted byrioters. Ethnic unrest also flared in Kalimantan in 1999between the native Dayaks and the immigrant popula-tion of Madurese leaving 50,000 internally displacedMadurese.V. Religious unrest. Commencing with the attacks on

Chinese Christian communities in Java in 1997, sectar-ian unrest spread into a number of provinces includingAmbon. The trouble continued into 2000 in the MalukuIslands where there were frequent clashes betweenChristians and Muslims. In some instances religiousunrest was related to ethnic tensions.VI. Rebellion and political unrest. Separatist move-

ments have been active in Aceh, East Timor and IrianJayra for several decades and subject to vigoroussuppression by the Indonesian military. Suppression of

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the Fretilin pro independence movement in EastTimor has regularly featured in the Western mediaparticularly after a number of reported massacres duringthe 1990s.VII. East Timor. The failure of the Indonesian

military and police to control pro-Indonesian militiasactive in East Timor after the territory voted for self-government in August 1999 resulted in intervention by aUnited Nation’s mandated peace making force. De-monstrations against Australia’s leadership of the UNforce (Interfet) sent to secure East Timor wereprominently featured in the Australian media and werethe cause of strained diplomatic relations with Australiafrom 1999 onwards.VIII. The Wahid Administration. Elected to the

presidency as a compromise candidate in 1999 by thePeoples Consultative Assembly (MPR) President Wahidwas frequently criticised during 2000 for his inability toachieve economic stability and alleged softness oncorruption, particularly in relation to the Suharto familyand business cronies. President Wahid was subsequentlyreplaced by his deputy Megawati Soekarnoputri.IX. 1997 El Nino Effect. The El Nino climate shock of

1997–98 greatly weakened Indonesia’s agriculturalsector (Sachs, 2002) as drought reduced farm produc-tivity and drove up food prices. While not as obvious asthe previous factors the conditions that resulted fromthe impact of the EL Nino climate shock appeared to bea significant background factor to the ensuing unrest.X. October 12, 2002 Night Club Bombing in Bali. On

12 October 2002, an unidentified (at the time of writing)terrorist group exploded a large car bomb in a popularnight club district in Kuta Beach Bali killing anestimated 190 persons including approximately 88Australian and 30 British tourists. The impact on theIndonesian economy will be determined by the successof the Government in arresting the perpetrators,eliminating terrorist cells and convincing the Govern-ments of Indonesia’s major generating counties thatsecurity conditions have improved to the extent thatadverse travel advisory warnings can be lifted.Combined, the factors described above generated

considerable and continuing adverse publicity in theinternational media. Graphic images of rioting, killings,destruction of commercial districts in Java and imagesof mass air evacuations of expatriates from Jakarta inMay 1998 made the selling of pleasure travel toIndonesia a difficult task for marketers. The ongoingand multifaceted nature of adverse events has contrib-uted to a picture of a nation in crisis, at least to theoutside observer. Terrorism and religious fundamental-ism are evident in many of the recent events inIndonesia, particularly in East Timor (terrorism andnationalism), Bali (terrorism) and religious warfare inAmbon where militant Muslim fundamentalist groupsdeclared a Jihad on the island’s Christian population.

Arguably, all the factors listed with the exception ofevent nine, are the result of crises. However, givenIndonesia’s history of having to deal with rebellions indisaffected provinces that were either ethnically orreligiously different from the dominate Javanese cultureand power structure events six and seven may also beclassed as trends. These events were not factored intoforecasts of tourism growth, thus confirming the needfor a new approach to forecasting that moves beyondthe manipulation of known variables to identificationand incorporation into forecasting of other factors thatmay be country-specific. Unfortunately, incorporationof these factors is unlikely to produce the apparentprecision achieved by contemporary forecast models.

5.2. Impact of the Asian financial crisis

The Asian financial crisis exercised the first majorshock on the Indonesian economy and, arguably,provided the trigger for the subsequent politicaldifficulties that ultimately translated into reducedtourism flows (Prideaux, 1998). Flowing on from theAsian financial crisis was a series of events thatultimately culminated in the overthrow of PresidentSuharto. These events received widespread coverage inthe international media. The anti-Chinese sentiment ofmuch of the earlier rioting in 1997 and the May 1998riots was viewed with particular concern by many Asianmarkets and is the likely explanation for the decline ofinbound tourists from Hong Kong (47.3% in 1998) andTaiwan (30.4% in 1998) (Statistics Indonesia, 2000).Continuing political uncertainty, combined with poli-tical, religious and ethnic tensions during the latter partof 1997–1998 appear to have had a cumulative effectresulting in a substantial fall in inbound tourismparticularly during 1998. In comparison, other Asiannations affected by the Asian financial crisis quicklyrecovered and with a few exceptions (Singaporeexperienced 4.63% fewer arrivals in 1999 than 1996)recorded higher inbound tourism in 1999 than 1996, thelast full year prior to the onset of the Asian financialcrisis.The net result of these series of shocks to the

Indonesian tourism industry can be illustrated bycomparing actual arrival data with forecast arrivalfigures (Table 1). The difficulties of forecasting tourismflows in periods of uncertainty or where the unexpectedoccurs is clearly illustrated by comparing forecasts withactual arrivals and departures using bilateral tourismflows between Indonesia and Australia in the period1997–2000. The table clearly illustrates the difficultiesencountered by forecasters using standard forecastingtools during times of uncertainty and the impact thatunanticipated shocks can produce.Australian outbound tourists indicating that Indone-

sia was their main destination of travel grew rapidly

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from 158,000 in 1990 to peak at 350,000 in 1998 whenIndonesia was the second most popular destinationfor Australian outbound visitors. Prior to the adversereporting in the Australian media of Indonesia’shandling of the East Timor situation and the subsequentwidespread coverage of anti-Australian sentiment inIndonesia, Australian departures were forecast to be531,000 (TFC, 1999) but were actually 246,000 becauseof the sharp fall in the value of the Rupiah. Thesignificant differences in projections made during theperiod 1997–2000 are a reflection of the inability oftraditional forecasting techniques to account for theunexpected and therefore point to the need for theinclusion of additional forecasting tools includingscenario analysis.

5.3. Indonesian crisis—the limits of forecasting

Forecasts produced during the early part of the 1990sdid not include an allowance for political and financialdifficulties that were experienced later in the decade.This is not surprising as a capability of this nature isbeyond the scope of current forecasting methods.However, alternative approaches including scenariodevelopment, risk analysis and/or political analysis assuggested by Richter (1999) may have concluded thatIndonesia was entering the same type of domesticconditions encountered prior to the overthrow of theMarcos regime in the 1980s.While on the surface Indonesia appeared politically

stable throughout the early 1990s, a number of politicalpressures were beginning to build that eventuallyculminated in the collapse of President Suharto’s NewOrder when he resigned in 1997. The hallmarks of theNew Order period were tight control of the media, thecentralisation of power in the Presidency, and theineffective nature of the parliamentary process char-acterised by the Peoples Consultative Council which

meet infrequently and had 200 of its 700 membersappointed by the President. In the final years of the NewOrder, concerns developed over systemic corruption, thealleged wealth of the Suharto family and infringementsof human rights. Importantly, there was no apparentmechanism for the succession of the Presidency orindication that thought had been given to the democra-tisation of the parliamentary process. If these factorshad been considered in the light of conditions thatexisted a decade earlier in the lead up to the overthrowof President Marcos in the Philippines and the ensuingyears of unrest as pro Marcos forces sought to restorethe old order, the possibility of some form of internaldisruption in Indonesia during the late 1990s wouldhave at least appeared to merit consideration. Unfor-tunately, forecasters appear to have neglected suchconsiderations.There are a number of commercially available risk

assessment reports that may assist, the InternationalCountry Risk Guide (Sealey, 2000) published by thePRS Group being one example. Based on a compositerisk rating that included evaluation of quantitativeindicators of political risk, economic risk and financialrisk, the International Risk Guide published in May2000 listed Indonesia as a high-risk nation ranking 106out of 140 countries evaluated. In the equivalent periodin 1999 the International Risk Guide had rankedIndonesia at 130 indicating that increased stabilityhad somewhat reduced the magnitude of risk during1999–2000. In comparison, the risk rankings for some ofIndonesia’s major trading partners in May 2000 were:Singapore 1, Japan 13, Australia and Taiwan shared15th place, the USA 19 and Korea 27. In a 5 yearforecast of the most probable risk, Indonesia rated62 against a 100 point index, with 100 indicatingthe least risk. In comparison Singapore rated 78,Japan 85, Australia 88, Taiwan 80.5, Korea 78.5 andthe USA 80.5

Table 1

Comparison of forecasts and actual arrival data 1996–2002 (‘000) by tourism forecasting council

1996 1997 1998 1999 2000 2001 2002 2003

TCF forecast Indonesian arrivals to Australia (1997) 186 221 259 300 347 389 457

TCF forecast Indonesian arrivals to Australia (1998) 75 75 88 97 108 121

TCF forecast Indonesian arrivals to Australia (1999) 90 102 111 124 139

TCF forecast Indonesian arrivals to Australia (2000) 102 111 124 139

TCF forecast Indonesian arrivals to Australia (2001) 95 101 112

Actual Indonesian Arrivals in Australia 155 160 93 91 83 94

TCF forecast of Australian departures to Indonesia (1997) 1

TCF forecast of Australian departures to Indonesia (1998) 311 339 360 376 397 412 426

TCF forecast of Australian departures to Indonesia (1999) 360 376 397 412 426

TCF forecast of Australian departures to Indonesia (2000) 276 316 345 372

TCF forecast of Australian departures to Indonesia 2001 222 242 260

Actual Australian Arrivals to Indonesia 380 539 394 531 460

Source: Based on forecasts by TFC 1997a,b, 1998, 1999, 2000, 2001.

B. Prideaux et al. / Tourism Management 24 (2003) 475–487482

The discussion of the Indonesian crisis has illustratedthat while unexpected events are certain to occur,traditional forecasting methods are inadequate withoutthe incorporation of new forecasting techniques thatare sensitive to signals regarding potential crisesand the consideration of risk ranking that use a rangeof quantitative measures not usually employed incurrent forecasting methods. Indonesia proved not tohave the institutional resilience to weather the partiallyself-inflicted disaster in its tourism industry. In the caseof Indonesia, years of undemocratic government,cronyism and corruption have resulted in rigiditiesthat have made its industry less adaptable and un-able to respond creatively to new challenges. While notall countries face the same level of problems experiencedby Indonesia in the period 1997–2002, the impact ofevents such as the Asian financial crisis and the GulfWar may have significant and unanticipated impacts.Introducing new elements into forecasting, including awide range of scenarios based on political and economicrisk may enhance managers’ ability to plan for thefuture.

6. Modelling disruptions to tourism

A model of the factors influencing tourism flowsbetween an origin and a specific destination wasproposed by Laws (1995). The direction, frequencyand intensity of tourist flows are the cumulativeoutcome of several influences either creating pushconditions in the origin, or pulling visitors towards thedestination. The approach is similar to Dann (1977),who explains that push factors are those that provide theimpetus for individuals to travel, raising the question ofwhere to go. According to Dann (1977, p. 168) ‘Pullfactors are the destination specific attributes which tendto determine whether the traveller will go to A or to B.’The amount of free time and disposable income of the

population in tourist origin areas determine the overallvolume of demand for travel from that area, pushingtourists towards destinations, while the differences inclimate, culture and other attractions of the destinationpull visitors towards it. Over a period of time, the flowlinking one destination and one origin will stabilise,through familiarity and the institutions of tourismmarketing and tour operating. But three additionalgroups of factors can disrupt the established flow,suddenly and unpredictably with potentially severeconsequences for the destination. The groups of factorsare:

Inhibiting factors. Events in the country of origin,such as an economic depression, political uncertainty oradverse foreign exchange rates may inhibit the outflowof tourists to all destinations.

Diverting factors. Another disruption to establishedtravel patterns is the development of new destinations(or existing destinations setting their prices at moreaccessible levels). This has the effect of diverting existingtourist flows.

Repelling factors. The destination may experience anatural catastrophe or civil unrest, thus repellingincoming tourists from all countries of origin. At alower level of disruption, Governments have sometimesimposed stringent visa requirements for visitors fromparticular countries that are not viewed favourably forideological reasons.Inhibiting and diverting factors are common occur-

rences affecting the trends of tourism between estab-lished origin and destinations pairs, but repelling factorshave different characteristics, arising with apparentsuddenness, and producing rapid and significant dis-ruption to familiar patterns of tourist activity. Thetriggering event may itself be the result of quite complexfactors but of significance here, there is often acascading sequence of other events each of which hasconsequences for the tourism industry and the particulardestination. The events outlined in the foregoingdiscussion of the Indonesian crisis are summarised inTable 2. The strength of origin push factors wasreduced, particularly in Asian source markets, whilethe marketing tactics adopted by the Indonesianauthorities were not effective in overcoming theindividual and cumulative repellents to inward tourismin the short term, despite the benefits to tourists frombeyond the region of the weakened Indonesian currencyand improvements made to security for tourists in Javaand Bali. Overall, Indonesia experienced a series oftourism shocks as the Asian financial crisis triggeredfurther events within the country which have continuedto destabilise tourism flow and revenue patterns as thispaper was being completed.

Table 2

Changed influences on tourist flows to Indonesia, 1997–2002

Origin pushes Indonesia became more

competitive as the value of the

rupiah fell during the Asian

financial crisis

Destination pull factors Asian financial crisis results in

falling value of rupiah giving

tourists greater buying power

Repellents Smoke haze

Political unrest

Ethnic violence

Religious violence

Rebellions in East Timor and

Ache

Bali terrorist attack in 2002

Destination pull response

tactics

Lets go Indonesia

Discount holiday packages

B. Prideaux et al. / Tourism Management 24 (2003) 475–487 483

7. Disruptions and forecasting the future

History has consistently demonstrated a propensity tomove beyond the expected with unexpected shocks thatdisrupt the smooth and ordered unfolding of humanaffairs. Shocks must now be regarded as an integralfeature of the tourism system and while unforecastablein the short term should be factored into long-termexpectations. In these circumstances the unfolding of thefuture as a function of past relationships ceases, and anew dynamic occurs imposing a new set of relationshipsbetween the demand and supply of tourism services.Shocks have three major elements:

(I) the cause of the shock(II) the magnitude of the shock; and(III) a time element.

This is a significant observation for the tourismindustry and provides a basic framework for any post-shock analysis. The time element is significant butdifficult to quantify. The further we look into the futurethe more uncertain we can be about any relationship.For example, there is evidence to suggest that Californiacan expect a repeat of the 1906 earthquake thatdevastated San Francisco. The time of the next majorearthquake is uncertain; it could be in the very nearfuture or decades or even centuries away. What iscertain, however, is that when the next earthquakeoccurs there will be severe disruption to tourism inCalifornia.

Table 3 develops a framework for classifying shocksaccording to a scale of severity, probability, type ofevent, level of certainty and suggested forecastingtools for each scale of shock. Shocks are grouped intofour categories according to scale, itself a reflectionof the probability of these events occurring. In thetable, events caused by factors such as shifts in exchangerates and inflation are classified as S1 and S2 events,and their effects should be forecastable by existingmethods. S3 and S4 represent events which are moresignificant in their effects. S3 shocks are beyond therange of normal forecasting tools but could be in-cluded in forecasts using scenarios to identify themagnitude of the problem and then applying standardforecasting tools to quantify and examine the likelyrange of impacts based on a possibility spectrumthat commences with low and moves through mediumto a high level of possibility. S4 shocks are notanticipated and may be the result of a crisis or disaster.Employing a range of tools that are often neglectedby current forecasting techniques will allow at leastsome assessment of timing, magnitude, severity andcost to be made of S3 and S4 events. Tools that areavailable include risk assessment, historical research,scenarios and Delphi forecasting. Using methodsof this nature Prideaux (2002) examined the impact ofa range of emerging technologies on cybertourismfinding cause for alarm and indicating the need fordiscussion of the possible negative impacts of cybertour-ism on humanity likely to become apparent within 2–3decades.

Table 3

Classification of shocks

Scale Probability Example of event Forecasting tools Level of certainty

of forecast

S4 Not anticipated September 11 terrorist attack in

the USA, 1991 Gulf War, Asian

financial crisis

Scenarios, risk assessment, Delphi

forecasting and historical research

may be used to identify risks of this

nature and develop estimates of post-

shock travel demand and supply

conditions. At this point new

parameters are established allowing

employment of standard forecasting

techniques

Very low

S3 Unlikely but just

possible

Pre-existing conditions cause

major disruption ie earthquakes,

terrorist attacks, coups

Scenarios determine possible

boundaries of the impact of shock

allowing employment of standard

forecasting techniques to test

tourism responses for a range of

possible outcomes

Beyond current range

of acceptability

S2 The possible based on

worst-case scenario of

past trading conditions

Upper limit of variables

normally used in forecasting

used i.e. rapid rise in exchange

rates

Existing forecasting techniques with

allowance for sudden changes in

demand and supply conditions

Medium to low

S1 The expected based on

recent past trading

conditions

Within the range of expected

movements in exchange rates

and inflation

Standard forecasting methods High for near term,

lower in the medium

term

B. Prideaux et al. / Tourism Management 24 (2003) 475–487484

The September 11 terrorist attack was entirelyunexpected and had a significant impact on tourismand for this reason is classified as a S4 shock. However,in the wake of the attack, further major attacks are nowmore likely and it has now, unfortunately, become morelikely that other such shocks will occur. On the scaleillustrated in Table 3 further terrorist attacks on civiliantargets should now be classified as S3 or in some cases asS2. In the future, it is shocks on the probability scale ofS3 to S4 that hold the greatest potential for disruptingtourism.

8. A risk forecasting approach

From the research conducted for this paper it isapparent that there are unresolved difficulties faced byforecasters. While the future will never be able to bepredicted with a reasonable degree of certainty, exceptperhaps for the inevitability of taxes and eventualmortality, a sense of how current factors and tensionsmight influence the future may offer forecasters awindow to the future. The need for forecasting isapparent given the long gestation periods of capital-intensive tourism investments and the need for businessplanning. While risk assessment based on indices andrankings offers a further tool for reducing the un-certainty of future trends there remains a need fornumerical forecasts and it is in this area that furtherresearch is required. Future research should be directedto developing new forecasting paradigms that incorpo-rate political risk, economic risk and a deeper under-standing of the influences of history. In any futuremodelling inclusion of qualitative as well as traditionalquantitative methods must be considered. Perhaps theway forward lies in developing a new set of tools basedon risk assessment, probability of occurrence andscenario generation which can produce new sets ofvariables that can then be used as the basis forforecasting.One avenue of approach may be to attempt to bring

together the quantitative elements of forecasting plusless frequently used qualitative methods to produce aseries of scenarios each based on a range of possiblefutures. The Tourism Forecasting Council (1997b)adopted a scenario approach in 1997, when forecastsof inbound tourism to Australia were revised in the lightof the Asian financial crisis. In the TFC approach,scenarios were based on possible combinations ofinterest rate rises and currency fluctuations. This waslargely based on quantitative trends rather thanqualitative trends that might be employed with riskassessment or political audits as suggested by Richter(1999). A possible method of analysis may be to producea standard trend analysis based on the assumption thatpast relationships will carry on into the future. This

projection could then serve as a base line. Next, riskanalysis could be employed to identify any potentialnon-economic problems that may destabilise the econ-omy. These may include terrorism, racial and religiousfactors, landownership disputes, changes in the level oflawlessness and political factors, and the potential forsignificant earthquake or weather induced disruption tocite some familiar causes. Delphi techniques provide onemethod of assessing these types of risk and assigning tothem a weighting. Using standard econometric forecast-ing tools these factors could be assessed and revisedtrends generated as scenarios resulting in discontinuitiesto the base line. Finally, a similar process could beemployed to factor possible adverse political conditionsidentified via a political audit to produce a totalprobabilities forecast range.The result would be a series of scenarios each based

on a set of possible adverse or favourable outcomes inthe future with a weighted probability index. Whileconsiderably more complicated than current projectionsit would enable the users of the projections to have amore complete understanding of the range of probabil-ities in the future. While some consumers of forecastsmay find added difficulty in understanding the greaterdetail and reduced certainty of this type of forecasting, itwould give users enhanced information on which tomake informed long-run business decisions and imple-ment appropriate strategies. Ideally, however, the usersshould be intimately involved in the scenario generationand assessment process, rather than simply abdicatingthis responsibility to the technicians (Faulkner &Valerio, 1995). In developing methods for such engage-ment, the tourism sector would do well to draw onstrategic management practices adopted elsewhere and,in particular, on methods developed by the learningorganisation school (Senge, 1990) and the strategicconversion approach (van der Heijden, 1997). A cultureof ongoing environmental scanning, assessment, dialo-gue and mutual learning is more fundamental to aninformed and appropriate strategic stance than anuncritical reliance on current forecasting models.

9. Conclusion

The task faced by forecasters is enormous and mademore difficult by deficiencies in current techniques.The problem of forecasting can be demonstrated bythe following example. A forecaster, asked to developtravel projections between 1910 and 1920, would haverecognised some political tensions in the Balkans butwould not have been able to forecast the effect of theFirst World War. The forecasting problem illustrated bythis example remains unresolved in the present era.Understanding the impact that unexpected disrup-

tions may have on tourism flows is important for

B. Prideaux et al. / Tourism Management 24 (2003) 475–487 485

forecasters, planners, investors and operators. Incidentssuch as the Asian Financial Crisis (1997–1999), break-upof the USSR, coups in Fiji in 1987 and 2000 and theinvasion of Kuwait by Iraqi in 1990 illustrate the mannerin which seemingly unpredictable events can negateexpert forecasts. The existing suite of statistical andeconometric forecasting techniques is unable to deal withthis type of uncertainty, and crucially they lack the abilityto articulate this into the interface between forecastingand strategy. Further refinement of current methodswhich factor the unexpected into forecasting techniquesmay become possible if tourism researchers embrace newapproaches to viewing the future. In particular, there is aneed to use scenarios as a platform to predict the patternsand effects of events which occur during periods ofdisruption, perhaps brought on by multiple shocks.From the evidence presented in the case study

discussing recent events in Indonesia it is apparent thatin the future forecasting techniques should incorporaterecognition of the potential impacts of the underlyingpolitical, economic, social and cultural trends that affecteach nation as well as the region in which that nation issituated. A thorough understanding of national history,and from this, identification of potential risk factors, isessential if potential disruptions arising from thesefactors are to be incorporated into new tourismforecasting model.This paper has identified deficiencies in current

forecasting techniques and pointed the way towardsgreater understanding of the impact of unexpectedtourism shocks. Table 2 identified how unexpected eventsinfluence tourism flows while Table 3 classifies unexpectedshocks by probability, examples and suggested forecast-ing tools. The incorporation of scenarios, risk audits andthe need for a greater understanding of national history isobvious. One solution to the way forward will be toincorporate these considerations as an interface betweentrends to the present and forecasting of trends into thefuture. If the potential for these considerations to disrupttourism flows is small current techniques are entirelyappropriate. If there is potential for a large disruption theuse of scenarios as the interfaced between the present andprojections of the future may well be the way forward.There is an obvious challenge for forecasters to

incorporate these views into their work. Accepting thischallenge and developing new methods of forecastingusing wider parameters than currently employed haspotential to produce forecasting of greater accuracy inthe future.

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