Tourism Management Perspectives 8 (2013) 60–67

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Tourism Management Perspectives

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Case study

Diagnosing the impact of an event on hotel demand: The case of the BPoil spill

John C. Crotts a,⁎, Josef A. Mazanec b

a Department of Hospitality and Tourism Management, College of Charleston, Charleston, SC, USAb MODUL University, Vienna, Austria

⁎ Corresponding author. Tel.: +1 11 843 860 4323.E-mail address: crottsj@cofc.edu (J.C. Crotts).

2211-9736/$ – see front matter © 2013 Elsevier Ltd. All rihttp://dx.doi.org/10.1016/j.tmp.2013.07.002

a b s t r a c t

a r t i c l e i n f o

Article history:Received 5 July 2013Accepted 12 July 2013

Keywords:BP oil spillTourism disastersMeasuring losses

Assessing the impact of the BP oil spill on hotel demandwas the focus of this study.More specifically, this analysischronicles the demand trajectories of the two most impacted regions of the Gulf Coast by employing a pseudo-experiment ‘compromise design’ tomeasure the impact of an event on consumer demand involving a sector sub-ject to high seasonality and other disruptive influences. The results reveal that the volatility brought about by thisman-made disaster varied greatly and, though significant, paled in comparison to impacts caused by major hur-ricanes and recessions. In addition, this research highlights an important but neglected area of the tourismliterature – the objective measure of tourism losses due to a disaster – that serves as one of the initial steps toeconomic recovery for a community or business sector.

© 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Though tourism is one of the world's most economically importantindustries, it is also one of the most vulnerable to both natural andman-made disasters (Pforr, 2009). Assessing damages from lost con-sumer demand and income in order to determinewhat is directly attrib-utable to a disaster is one of the initial steps to economic recovery. It isonly through such assessments that the insurance industry and thecourts can judge the cost of making an entity whole again, be it a busi-ness, industry sector, or community.

The BP oil spill (a.k.a., Deepwater Horizon explosion, Gulf of Mexicooil spill) represents one recent example of aman-made disaster that sig-nificantly impacted a regional economy. Todate, the explosion and sink-ing of the Deepwater Horizon oil rig created the largest accidentalmarine oil spill in the petroleum industry's history. The oil spill thatbegan on April 20, 2010 flowed unchecked for 87 days, discharging anestimated 4.9 million barrels (210 million U.S. gallons; 780,000 m3) ofcrude oil, causing damage to marine and wildlife habitats and to theGulf's fishing- and tourism-related industries (United States CoastGuard, 2011). As of February 2013, criminal and civil settlements andpayments to a trust fund had cost the BP US$42.2 billion (Helman,2013). Although there was no impact on hotel infrastructure, consumerperceptions of risk changed patterns of demand (Armstrong & Ritchie,2007; Kozak, Crotts, & Law, 2007; Ritchie, Crotts, Zehrer, & Volsky,2013; Young & Montgomery, 1998). It is the loss of this demand that,

ghts reserved.

to date, has not been resolved in civil settlements that this studywill at-tempt to address.

Ritchie et al. (2013), in their assessment of the impact of this oil spill,identified three types of impacts on hotel and vacation rental demand atthe regional level. Regions such as Northeast Florida actually benefittedfrom the oil spill due to the displacement effect, where consumersshifted their vacation patterns away from the afflicted Gulf coast areasto beaches on the Atlantic coast. Other areas such as the Louisianacoast also benefitted, at least from a hotel sales perspective, due to thesurge of demand from clean-up crews and media deployed to thearea. However, once the clean-up phase had been concluded, demanddeclined to levels below the 2008–09 trends. The third category repre-sents coastal regions like southwest Floridawho received nooil depositson their beaches, but who experienced sharp decreases in their hoteldemand that corresponded to the oil spill. Such regions experiencedthe double negative impact of consumers shifting their vacations tolower risk regions and with no concomitant demand from clean-upcrews. Although this assessment illustrated the types of experiencesthese communities had from a hotel demand perspective, assessingbusiness loss from an event such as this requires investigating a muchlonger period to put the effect into perspective. Hotel demand even atthe regional level can fluctuate erratically from year to year, and to par-tial out and control for the effects of such variation requires longitudinaldata.

This study is an attempt tomake such a loss assessment by employinga pseudo-experiment purported by Kerlinger (1986) to compliment andextend the findings of Ritchie et al. (2013). Moreover, the results willplace into perspective the impact of the BP oil spill as compared toother events that have impacted the region and influenced hotel de-mand. Hurricanes Ivan and Katrina, the Great Recession, and the 9-11

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terrorist attack are a few examples of events that affected the region.Hence, this research puts forth an analytical process where demand-impacting events can be compared and contrasted in an impartial waythat enhances our understanding of the impact of disasters from an eco-nomic standpoint.

2. Background

The tourism industry's vulnerability to crises has generated a streamof research, ranging from crisis detection, preparedness, containmentand damage limitation, to recovery and subsequent learning(Paraskevas & Altinay, 2013). These research streams have been appliedto a number of natural disasters such as tsunamis and hurricanes(Rittichainuwat, 2013; Ryu, Bordelon, & Pearlman, 2013), earthquakes(Henderson, 2013; Yin & Walsh, 2010), wildfires (Sanders & Laing,2010), floods (Whitworth & May, 2006), and infectious disease (Gu &Wall, 2006). Equal study has also been applied to a wide range ofhuman-inflicted crises which include negligence (Hines, 2005), terror-ism (Blake, Sinclair, & Thea, 2003; Hitchcock & Putra, 2005;Paraskevas & Arendell, 2007), or crime (George, 2010; Johnny &Jordan, 2007). Arguably, the BP oil spill represents a case of criminalnegligence that not only caused the oil spill disaster itself, but alsosnowballed into a media relations problem that afflicted the tourismregion.

Missing from the tourism literature is research demonstratinghow to assess the economic loss brought about by either natural orhuman-caused disasters. The means of assessing economic damages(and benefits) is well developed in the economics literature. Mea-suring economic loss ranges from natural (e.g., soil erosion) toman-made disasters (e.g., the impact of governmental policies andmarket imperfections on national income, calculating economicdamages due to wrongful death and personal injury). We contendthat tourism researchers who focus on economic benefits should be-come knowledgeable in themethod(s) of assessing loss in the case ofdisasters. Only through accurate assessments of business loss can acommunity and industry sector seek the financial means to recoverfrom a demand-impacting event. We also believe that tourism man-agers should be knowledgeable about how to assess damages fromboth plaintiff and defendant perspectives so as to insure that assess-ments are conducted in an objective and accurate manner. Hence itis our hope that this research will be construed as a best practicethat others can build upon in this important but neglected area oftourism research.

Sectors of the tourism industry are uniquely poised tomake such as-sessments given the availability of longitudinal data needed for such ananalysis. Local option sales taxes applied uniquely to hotel room sales,restaurants, and attractions – when combined with the tax rate – canbe used to determine sales revenue at the local and regional levels(Litvin, Crotts, Blackwell, & Styles, 2006). In addition, when they areavailable, online reservation systems can also provide needed longitudi-nal data (Ritchie et al., 2013). Specific to the hotel industry is the avail-ability of hotel performance measures collected by Smith TravelResearch (STR). Each month STR collects performance data from over43,000 hotels representing more than 5.7 million rooms in the U.S.These data are provided by hotel chain headquarters, managementcompanies' owners, and directly from independent hotels that subse-quently use the STR reports to benchmark their performance againstthe competition on a daily, weekly and monthly basis. This has enableda comparison of hotel performance trends in these tourism businessesbefore, during and after the oil spill, as well as the ability to contrastthe event with others in an objective way.

3. Method

Assessing the impact of disastrous events on tourism demand withlongitudinal data requires comparative judgment. The setup resembles

the ‘compromise design’ (Kerlinger, 1986) of a pseudo-experimentwhere the experimenter has no control over the treatment. A numberof analytical decisions and processing steps were required to makesuch an assessment.

(i) A selected region was compared to a group that had been aggre-gated fromother neighboring regionswith similar destination at-tributes as a benchmark. In this case, the choice of region wasguided by the actual amount of oil spill deposits and environ-mental damage.

(ii) Given the strong seasonality of each region's monthly mea-sures, the time series measurement of total hotel demandwas transformed into percentage changes for the corre-sponding months. The series comprised more than 10 years,which allowed the analyst to sort out what might have beenunusual spikes or ordinary fluctuations in each region'shotel demand.

(iii) One-to-multi-region comparisons were based on the series ofpercentage changes. Subtracting the growth rates of the bench-mark region from the region under study more clearly empha-sized where the changes in the focal region were more positive(or less negative) or more negative (or less positive).

(iv) The deviations gained in (iii) were indicative of local specifics ofthe focal region. The persistent trends lasting for several monthswere more relevant than deviations observed for individualmonths. Attention, therefore, was on structural breaks and theirtime stamp. The trend break method introduced by Verbesselt,Hyndman, Newnham, and Culvenor (2010, 2011), Verbesselt,Zeileis, and Herold (2011), and Verbesselt, Hyndman, Zeileis,and Culvenor (2010)was employed for detecting abrupt changesand locating the breakpoints in the series. Originally developedfor satellite imagemonitoring, themethod is applicable to any se-ries composed of season and trend. The method is implementedin the bfast package of the R open source system (http://cran.r-project.org/).

Given the optimized break dates and the slope parameters of the lin-ear trend segments, onemay examine whether they coincide with spe-cific local events thatmade the focal region fall off the normal pattern ofdeviations from the benchmark.

In this study, coastal counties of southwest Florida and Louisianawere the ‘groups’ selected for the ex-post-facto experiment since“oil spill deposit” is the treatment factor, present in Louisiana coastbut absent in southwest Florida (Ritchie et al., 2013). Hotel demandin the benchmark region consisted of other coastal counties of westFlorida, Texas and Alabama, and functioned as a control variablecapturing the general Gulf trend on a higher level of aggregation.The underlying hypothesis posits that predicted and actual shore-line deposits of crude oil entailed a structural break, introducing asubsequent period of significant downward movement in hoteldemand.

4. Results

4.1. Southwest Florida

Fig. 1 contrasts the long-term development of hotel demand insouthwest Florida with the combined region made up of the coastalcounties of west Florida, Texas, and Alabama (‘OtherGulfCoast’). Thetwo series represent percentage changes between correspondingmonths, but the monitoring extends the 2009–2010 period observedby Ritchie et al. (2013) to 1999–2012. Investigating a much longerperiod considers various other demand-impacting events and thusputs a potential oil spill effect into perspective. As one must expect,the series for the more highly aggregated multi-state region exhibitsa smoother behavior where the single-region demand fluctuates er-ratically. The three vertical bars mark the oil spill period (April–July

Fig. 1. Percentage change of hotel demand by month 1999–2012: Southwest Florida vs. ‘OtherGulfCoast’ (comprising west Florida, Texas, and Alabama coastal counties).

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2010) and the months of the subsequent clean-up activities(August–December 2010). During this time slice, the OtherGulfCoastclearly out performed southwest Florida in terms of the relative in-creases in hotel demand.

A visual inspection of the bar charts presented by Ritchie et al.(2013) – despite an increase in 2010 over 2009 – revealed “a softeningof hotel demand during the months of the oil spill” in the regions “thathad the highest probability of shorelinedeposits…” (p. 7). In comparingsouthwest Florida with the OtherGulfCoast region, one may go one stepfurther by directly analyzing the difference in the relative changes of de-mand. The resulting series illustrates how southwest Florida deviatesfrom the development in the OtherGulfCoast region and thus highlightsthe local specifics. Still another processing step searches for

Fig. 2. Breakpoints and trend segments in the southwest F

developments that persist for severalmonths and examines the new se-ries with respect to breaks in its trend structure. Fig. 2 exhibits the re-sults gained with decomposition and break date optimizationmethodology suggested in Verbesselt, Hyndman, Newnham, et al.(2010) and Verbesselt, Hyndman, Zeileis, et al. (2010) (see http://bfast.R-Forge.R-project.org/). Again the oil spill and post-spill periodsare marked with vertical bars indicating a steep slump and subsequentupsurge of hotel demand changes in southwest Florida as it differs fromits benchmarking partner the OtherGulfCoast region.

Table 1 shows the exact break dates of abnormal changes and theslope parameter values for each (linear) trend segment. According tothese estimates the relative changes in southwest Florida hotel demandin early 2010 dropped faster than the OtherGulfCoast benchmark, but

lorida minus OtherGulfCoast percentage change series.

Table 1Optimal breakpoints and trend segment slope parameters in the southwest Florida minusOtherGulfCoast percentage change series.

Breakpoints with 95% confidence interval Trend segment Slope Sig.

Lower bound Breakpoint Upper bound

1999 (1)–2001 (9) .29 .762000 (3) 2001 (9) 2001 (11) 2001 (10)–2004 (7) 3.93 b .0012004 (6) 2004 (7) 2004 (8) 2004 (8)–2005 (10) −33.54 b .0012005 (9) 2005 (10) 2005 (11) 2005 (11)–2007 (2) 10.86 b .0012007 (1) 2007 (2) 2007 (10) 2007 (3)–2009 (1) −2.72 .032008 (12) 2009 (1) 2009 (8) 2009 (2)–2010 (3) 6.45 .012010 (2) 2010 (3) 2010 (4) 2010 (4)–2012 (1) 10.09 b .0012011 (11) 2012 (1) 2012 (4) 2012 (2)–2012 (12) 7.72 .45

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recovered quickly. By contrast, the downturn in 2004–2005 lasted lon-ger and was much more accentuated as a result of the first and sec-ond most active and costly hurricane seasons ever recorded in theGulf of Mexico. Monitoring an extended time span of more than a de-cade reveals that, as far as southwest Florida is concerned, the 2010oil spill cannot be considered a disastrous event that exceeded theboundaries of ordinary demand fluctuations. Particularly, it comesnowhere near the disruptive natural events of the major hurricanesthat passed by the region (e.g., 2004 Ivan, 2005 Katrina).

4.2. Louisiana coast

During both the oil spill and the immediate post-spill cleanupmonths, the Louisiana coastal region experienced higher relative de-mand growth than the OtherGulfCoast region (Fig. 3). The reverse sit-uation characterized the months before (2009) and after (2011) theoil spill time interval. For the Louisiana coast, the time after the oilspill disaster and clean up entailed a subsequent period of declinemore distinct than the receding growth rates for the OtherGulfCoast

Fig. 3. Percentage change of hotel demand by month 1999–2012: Louisiana coast vs.

benchmark region. A trend break analysis further clarifies the persis-tence of this development. It also demonstrates the relative magni-tude in the longitudinal behavior of demand changes specific to theLouisiana coast.

The trend break analysis of the differences in growth rates be-tween the Louisiana coast and the OtherGulfCoast region yieldsseven trend segments (Fig. 4). All but one of the local slope parame-ters are significant (Table 2). The segment with the strongest up-ward trend lasted from September 2009 until July 2010 whichincludes the oil spill months. Both the preceding and followingsegments both suffered from marked and persistent downwardtrends. The dip in 2008–2009 associated with the Great Recession(December 2007–June 2009) is more dramatic than the after-spillreduction and renders the consequences of the oil spill as rathermodest. The recession in 2008–2009 seems to have impacted theLouisiana coast more severely than the Deepwater Horizon explo-sion and oil spill the following year. However, there is a significantdownward movement which began after the clean-up crews leftand lasted until the middle of 2011. Louisiana was the closest land-fall to the oil spill and received the most oil spill damage. Whilehotel demand temporarily benefited from the clean-up crews andmedia presence, the reports on environmental damage apparentlyharmed the destination image thus generating medium-term nega-tive effects on hotel demand.

4.3. Southwest Florida vs. Louisiana coast

Fig. 5 portrays the contrasting development in the two focal regionsvia direct comparison. With the exception of 2004–2005, the percent-age changes along the Louisiana coast fluctuate more wildly, and fre-quently, in opposite directions. This divergence points to substitutionand displacement effects at work between the two regions. Fig. 6 ex-hibits the breaks in the trend structure of the two series. (Note that nobenchmarks, like in the preceding trend extractions, are needed heresince the same series would be subtracted from both comparison

OtherGulfCoast (comprising west Florida, Texas, and Alabama coastal counties).

Fig. 4. Breakpoints and trend segments in the Louisiana coast minus OtherGulfCoast percentage change series.

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partners.) Recurrent cyclical patterns in both series were removedwithharmonics before the trend coefficients were calculated. Tables 3 and 4show the break dates and slope parameters together with their confi-dence intervals and significance levels. Southwest Florida featuresseven trend segments where four have a highly significant slope coeffi-cient. Among them are the two segments separated by the breakpoint inMay 2010 and relevant for judging the oil spill consequences. Unlike thehigh growth situation in the Louisiana coast, Southwest Florida's up-ward trend was interrupted in May 2010 and restarted at a lowerlevel and with smaller increments. Though the interruption corre-sponds to the oil spill event, the region suffered no oil spill landings.However, the National Oceanic and Atmospheric Administration issueda 21–40% probability of shoreline deposits for the regionwhichwas suf-ficient to cause the shift in consumer demand (Ritchie et al., 2013).

Of particular importance is the dramatic development in 2004–2005which coincides with hurricanes Ivan and Katrina making landfall onthe Louisiana coast. While demand growth rates dropped sharply insouthwest Florida, the hurricanes left undisturbed demand along theLouisiana coast for the medium-term. The opposite pattern emergesfor the 2008–2009 economic recession that coincides with an

Table 2Optimal breakpoints and trend segment slope parameters in the Louisiana coast minusOtherGulfCoast percentage change series.

Breakpoints with 95% confidence interval Trend segment Slope Sig.

Lower bound Breakpoint Upper bound

1999 (1)–2004 (8) 1.54 b .012003 (11) 2004 (8) 2004 (9) 2004 (9)–2006 (10) 13.65 b .0012006 (9) 2006 (10) 2006 (12) 2006 (11)–2008 (8) 9.37 .082008 (5) 2008 (8) 2008 (9) 2008 (9)–2009 (8) −37.05 .022009 (7) 2009 (8) 2009 (9) 2009 (9)–2010 (7) 67.81 .0032010 (6) 2010 (7) 2010 (8) 2010 (8)–2011 (6) −26.19 .012011 (5) 2011 (6) 2011 (7) 2011 (7)–2012 (12) 23.30 b .001

unprecedented downward movement of hotel demand changes in theLouisiana coast region.

For the Louisiana coast all six trend segments are based on highlysignificant slope coefficients. During the oil spill months, the Louisianacoast maintained its steep upward trend, but experienced a dramaticbreakpoint in July 2010. The medium-term loss of demand momentumin the aftermath of the oil spill is clearly visible as is the reverse behaviorof the southwest Florida series. The results are not in contradictionwithwhat was hypothesized. Note, however, that claiming corroborationwould be bold as the causal format of the hypothesis is not conclusivelyconfirmed. One cannot definitely rule out that the breakpoint mighthave occurred at about the same time independently from the oil spillevent. By comparison, the 9-11 terrorist strike in the U.S. seems tohave provoked a short-term demand drop for southwest Florida. Forthe Louisiana coast the impact was not strong enough to disrupt along-term trend.

4.4. Attributing loss and gain

With secondary data alone, there is no way to arrive at deductivelycompelling inferences regarding the cause–effect relationships that gov-ern the behavior of demand changes in the turbulent times of a disaster,be itman-made or a natural disaster. However, onemay approach an as-sessment based on plausible reasoning. The diagnosis pursued so faremphasizes amedium-termperspective. It avoids comparisons betweenvery short periods. Observations for an individual or a few months aresubject to random disturbances concealing the systematic influences.A medium-term trend analysis detects more stable structures. Abreakpoint occurring within a disaster period delivers a strong argu-ment for construing a cause–effect relationship. In this vein, the July2010 breakpoint in the Louisiana coast series (Fig. 6, Table 4) plausiblyrelates to the Gulf oil spill event. There is good reason to assume thatthe demand erosion of the following year was kicked off by the spill.

An interpretation of the opposite effect ismuchmore speculative. Lossand gain appear to be asymmetric. Displacement phenomena are likely,

Fig. 5. Percentage change of hotel demand by month 1999–2012: Southwest Florida vs. Louisiana coastal counties.

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but destination switchers could not be uncovered with the data at hand.The secondary demand data do not allow this kind of differentiation. Inloss estimation, however, comparing a disaster-stricken presumed loserdestinationwith a non-afflicted potential winner is feasible. For example,tourists originally headed for the Louisiana coast, who, after July 2010,chose to visit southwest Florida instead, would add to a Louisiana loss.

Considering all the diagnostic findings, a sensible estimate of poten-tial hotel demand and revenue loss along the Louisiana coast would

Fig. 6. Breakpoints and trend segments in the southwest F

have to rely on lagged effects and include the trend segment betweenAugust 2010 and June 2011 (see Table 2 in comparison withOtherGulfCoast). As the Louisiana coast fared sowell during the core pe-riod of the oil spill from April to July 2010, economic damage cannot becredibly argued for thesemonths. Also, the temporary advantage of thisregion over the OtherGulfCoast and southwest Florida regions was notinduced by cutting prices. The data revealed that hotels along theLouisiana coast did not lower the average room rates to capture demand

lorida and Louisiana coast percentage change series.

Table 3Optimal breakpoints and trend segment slope parameters in the southwest Florida per-centage change series.

Breakpoints with 95% confidence interval Trend segment Slope Sig.

Lower bound Breakpoint Upper bound

1999 (1)–2001 (5) .54 .592000 (11) 2001 (5) 2001 (6) 2001 (6)–2004 (7) 4.43 b .0012004 (6) 2004 (7) 2004 (8) 2004 (8)–2005 (10) −42.48 b .0012005 (9) 2005 (10) 2005 (11) 2005 (11)–2008 (8) −2.01 .302008 (6) 2008 (8) 2008 (9) 2008 (9)–2010 (5) 13.24 b .0012010 (4) 2010 (5) 2010 (10) 2010 (6)–2012 (1) 7.97 b .0012011 (12) 2012 (1) 2012 (7) 2012 (2)–2012 (12) 1.50 .46

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during this period. Actual prices (RevPAR/room nights sold) duringApril–July 2010 exceeded the 2009 rates by 1.5% (April) to 10% (July).Prices decreased only later (June–October 2011) but failed to stop thedecline in demand changes.

5. Conclusions

Before summarizing the findings of this study, it is important tohighlight its narrow focus and limitations. First, this study focusedonly on the hotel sector in theGulf coast counties. Therewas no attemptto assess the financial impact of the oil spill on other sectors such as res-taurants, attractions, and the vacation rental industry; nor the environ-mental health of the region in which the tourism industry is based.Second, the amount of loss and gain of demand and revenue was notprecisely calculated, since we deemed that the extrapolations requiredwould be controversial and not beyond reproach. However, the resultsoffer an objective starting point to assessing the sector's business lossesthat both plaintiffs and defendants could reference in basing their futureclaim on. Moreover they advance our knowledge in the tourism fieldand open the door to avenues of exploration that are important to thetourism industry.

In what Ritchie et al. (2013) construed to be the two most impactedcoastal regions in the Gulf, the difference in the slope parameters of theassociated trend segments (7.97 for southwest Florida and −43.33 forLouisiana coasts; Tables 3 and 4) reveals unique impacts during the oilspill and clean-upperiods. In particular, hotel demand along the Louisianacoast increased during the oil spill disaster and clean-up periods but wascounteracted by the period of demand loss in 2011. On the other hand,hotels in southwest Florida suffered a significant decline in demand dur-ing the oil spill incidence but recovered in 2011.Moreover, by contrastingthese impacts with other demand-impacting events such as the 9-11 ter-rorist attack, hurricanes Ivan and Katrina, and the 2008–2009 Great Re-cession, the ramifications of the oil spill on hotel demand are placedinto relative context. Though the impact was significant, it comes no-where close to the impact of amajor recession or amajor hurricane pass-ing close by on a region's hotel demand.

The regional focus of this study offers what we believe to be is an ob-jective and useful starting point to reaching settlements in the courts

Table 4Optimal breakpoints and trend segment slope parameters in the Louisiana coast percent-age change series.

Breakpoints with 95% confidence interval Trend segment Slope Sig.

Lower bound Breakpoint Upper bound

1999 (1)–2006 10) 1.41 b .0012006 (7) 2006 (10) 2006 (11) 2006 (11)–2008 (8) 11.36 .0062008 (6) 2008 (8) 2008 (9) 2008 (9)–2009 (8) −45.49 .0052009 (7) 2009 (8) 2009 (9) 2009 (9)–2010 (7) 97.22 b .0012010 (6) 2010 (7) 2010 (8) 2010 (8)–2011 (7) −43.33 .0012011 (6) 2011 (7) 2011 (8) 2011 (8)–2012 (12) 17.19 b .001

and through mediation. Given that the amounts of damages calculatedby economists often vary substantially between claimants (Tinari,Cahill, & Grivoyannis, 2006), being able to calculate total losses fromsuch a regional basis with a method both parties can agree uponwould remove some of the messy nature of the process for both defen-dants and plaintiffs, as well as those attempting to rule on the cases(Ury, Brett, & Goldberg, 1993). However, the reader should not construefrom such a process than an average impact should be the basis ofawards or settlements. In this case, even though the oil spill was large,the regions described in this analysis are larger, meaning that somecommunities receivedmore damages from oil deposits on their beachesthan others.

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Josef A. Mazanec is a full-time professor of MUV and anemeritus professor of the Vienna University of Economicsand Business (WU). He was the head of the Institute for

67J.C. Crotts, J.A. Mazanec / Tourism Management Perspectives 8 (2013) 60–67

Young, W. B., & Montgomery, R. J. (1998). Crisis management and its impact on destina-tion marketing: A guide for convention and visitors bureaus. Journal of Convention &Exhibition Management, 1(1), 3–18.

John C. Crotts is a professor and former founding directorthen chair of the Department of Hospitality and TourismManagement in the School of Business at the College ofCharleston. Prior to this position, he lectured in theAdvancedBusiness Programme on tourism subjects at Otago Universi-ty, Dunedin, New Zealand and was director of the Centerfor Tourism Research and Development at the University ofFlorida. His research encompasses the areas of economic psy-chology, tourism marketing and sales strategy, and manage-ment of cooperative alliances.DepartmentofHospitality andTourismManagement, CollegeofCharleston, 66 George Street, Charleston, SC 29424-001, USA;www.cofc.edu; Tel: +011 843 953 6916; crottsj@cofc.edu

Tourism and Leisure Studies of WU (1981–2010) and avisiting scholar at the Sloan School of Management,MIT, in 1992. During 1997–2002 he served as the vice-rector for Research of the WU. In 1997–2000 he was thespeaker of the Joint Research Program on “AdaptiveModels and Systems in Economics and ManagementScience” (comprising researchers from WU, ViennaUniversity of Technology, and University of Vienna). Hisresearch interests are in tourism management, explana-tory models of consumer behavior, strategic marketing,multivariate methods, decision-support systems, and

management science applications in tourism and hospitality.MODUL University Vienna, Am Kahlenberg 1, A-1190 Vienna, Austria http://www.modul.ac.at/josef@mazanec.com http://raptor.mazanec.com:3000/