group package tour leader’s intrinsic risksgordonwang/indexfile/group... · exogenous risks,...

Annals of Tourism Research, Vol. 37, No. 1, pp. 154–179, 20100160-7383/$ - see front matter � 2009 Elsevier Ltd. All rights reserved.

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doi:10.1016/j.annals.2009.08.004www.elsevier.com/locate/atoures

GROUP PACKAGE TOURLEADER’S INTRINSIC RISKS

Kuo-Ching WangNational Taiwan Normal University, Taiwan

Po-Chen JaoHsi-Chen Chan

Chia-Hsun ChungChinese Culture University, Taiwan

Abstract: This paper explores the intrinsic risks and risk perception of Taiwanese tour leadersin terms of group package tour (GPT). Both qualitative interviews and quantitative surveys areemployed in the study. Based on in-depth interviews with 24 GPT leaders, the study identifiesthe comprehensive risk items. Moreover, 12 risk factors are extracted through questionnairesurveys with 310 GPT leaders. Three clusters regarding to risk sources are also categorized:exogenous risks, tourist-induced risks, and tour leader’s self-induced risks. Furthermore,the study compares risk perception of 12 factors by means of six itineraries. Finally, severalacademic and managerial implications about the GPT tour risk controls were outlined as well.Keywords: risk, group package tour (GPT), tour leader. � 2009 Elsevier Ltd. All rightsreserved.

INTRODUCTION

In recent years, there has been dramatic growth in outbound toursfrom Asian countries, fuelled by the region’s rapid economic growthand rising income levels (China National Tourism Administration,2007). The international tourism industry is now witnessing an increas-ing number of inbound tourists from Asia, such as Australia (Reisinger& Turner, 2002) and Guam (Iverson, 1997). Moreover, as a result ofeasing restrictions on outbound tours by China, the number of Chi-nese tourists is expected to increase rapidly in the future.

Asian and Chinese tourists normally take all-inclusive tour packagesas compared with Western tourists (Wong & Lau, 2001), especially forinternational trips (Hooper, 1995). In many Asian countries and areas,the group package tour (GPT), or in the language of Cohen’s (1972)organized mass tour, is one of the main modes of outbound tour(March, 2000; Wang, Hsieh, Yeh, & Tsai, 2004; Yamamoto & Gill,

Kuo-Ching Wang, Professor (Graduate Institute of Hospitality Management and Education,National Taiwan Normal University, Taipei, Taiwan. Email <[email protected]>), hisresearch interest is tourism marketing. Po-Chen Jao, Doctoral Student, his research interestsinclude tourism marketing and advertising. Hsi-Chen Chan, Doctoral Student, her researchinterests include tourism marketing and group package tour. Chia-Hsun Chung, GraduateStudent, his research interests include tourism marketing and group package tour.

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K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179 155

1999). For example, in Taiwan, according to government’s statisticaldata, outbound tourists had grown to 95.74 million from 1992 to2006. For sightseeing purposes, almost half of the tourists participatedin GPTs (Tourism Bureau, 2007). As another example in China, out-bound tourists had reached 34.52 million in 2006, indicating an in-crease of 11.3% from 2005 (31.02 million), and the number oftourists for outbound GPTs had increased from 6.79 million in 2005to 8.43 million in 2006, an increase of 21.68% (China National Tour-ism Administration, 2007).

Previous studies have indicated that service industries are highlydependent on ‘‘contact employees’’ who have a strong influence onthe service quality as perceived by the consumers (Parasuraman, Zei-thaml, & Berry, 1985; Vogt & Fesenmaier, 1995). In GPTs, usually, atravel agency assigns a tour leader to accompany the tour. Therefore,customer relationship is mediated almost entirely by a tour leader.Accordingly, the tour leader’s behavior will be the predominant factorinfluencing the customer’s perception on travel service quality (Wang,Hsieh, & Chen, 2002; Wang et al., 2004). Quiroga (1990) clearlypointed out that the function of the tour leader within the group isconsidered to be indispensable by the tourists themselves, and thequality of the tour leader can be a crucial variable in the tour; his orher presentation can make or break a tour.

In brief, GPTs are a very popular outbound tour mode in many Asiancountries and the tour leader plays an important role in GPTs (Quiroga,1990; Wang, Cheng, & Wu, 2002). However, prior risk studies examinedrisk primarily from the tourist’s perspective (Pinhey & Iverson, 1994;Roehl & Fesenmaier, 1992; Sonmez & Graefe, 1998a; Teng, 2005). Nev-ertheless, who should be responsible for the tourists’ safety is still a con-troversial issue (Robinson & Marlor, 1995). For GPT tourists, thetourists’ safety primarily is the tour leader’s responsibility. However,the important questions, such as ‘‘What risks might have occurred whilethe tour leader is leading the GPT ?’’ and ‘‘What’s the relationship between dif-ferent risks with different GPT itineraries?’’ have not yet been answered. As aresult, tour leaders’ experience upon risks is worthy to be discovered.

In practice, the possible risks that one might face during tours arethe priority needed to be considered while planning GPTs. If risk isviewed as possible loss (Teng, 2005), it is reasonable to assume that atour leader’s risks might generate some service failures and then thosefailures might entail certain tourist’s losses and finally decrease the ex-tent of the tourist’s perception of service quality in GPT. Therefore, itis imperative for travel agency managers and tour leaders to augmenttheir perception and understanding of intrinsic risks in GPT leadersin terms of risk control strategies, cost reduction, and service qualitycontrol in GPT (Tesh, 1981; Tsaur, Tzeng, & Wang, 1997).

INTRINSIC RISKS IN GPT LEADERS

Risk has become one of the most hotly debated issues in Westernsocieties today (Okrent & Pidgeon, 1998), and it has been successfully

156 K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179

incorporated decision-making theories in economics, finance, and thedecision sciences (Cho & Lee, 2006; Dowling & Staelin, 1994). Unser(2000) indicated that risk and its measurement are still fascinating top-ics for studies in decision making under risk. In the marketing disci-pline, the concepts of risk and perceived risk were first discussed inBauer’s (1960) ‘‘Consumer Behaviour as Risk Taking’’ research (Bett-man, 1973; Stone & Grønhaug, 1993). Since the introduction of theconcept of perceived risk by Bauer, much research has been carriedout by utilizing the concepts of risk and risk reduction processes inconsumer decision making (Bettman, 1973) and many studies havemeasured risk perception in a wide variety of contexts (Mitchell &Boustani, 1994).

In the tourism field, several studies have discussed risk analysis issues,for example, Tsaur et al.’s (1997) study on tourist risks in GPT. Theydefined the risk from ‘‘process of tour’’ and ‘‘destination’’ perspectivesand classified risk into seven evaluative aspects: transportation, law andorder, hygiene, accommodation, weather, sightseeing spot, and medi-cal support. In addition, many studies adopted five risk dimensionsidentified by Jacoby and Kaplan (1972) which were financial risk, per-formance risk, physical risk, social risk, and psychological risk (Cheron& Ritchie, 1982; Mitra, Reiss, & Capella, 1999). Some studies adoptedsix dimensions (Stone & Grønhaug, 1993; Stone & Mason, 1995), byincluding time risk as suggested by Roselius (1971). Moreover, severalstudies focused on a particular dimension, such as political instability(Seddighi, Nuttall, & Theocharous, 2001), terrorism (Sonmez & Gra-efe, 1998a, 1998b), health concerns (Carter, 1998; Lawton & Page,1997), crime (Pizam, Tarlow, & Bloom, 1997; Pizam, 1999), and satis-faction which first appeared in the study regarding perceived riskand leisure activities (Cheron & Ritchie, 1982).

Furthermore, Roehl and Fesenmaier (1992) used seven differenttypes of risks, namely equipment risk, financial risk, physical risk, psy-chological risk, satisfaction risk, social risk, and time risk, to measurethe risk perceptions of pleasure tourists’. Pinhey and Iverson (1994)once explored the safety concerns regarding typical vacation activitiesamong Japanese tourists to Guam. The authors categorized the evalu-ative aspects of tour safety concerns into seven items: the perceptionsof the described safety, sight-seeing safety, water sports safety, beachactivity safety, night life safety, in-car safety, and road safety. Althoughthese existing risk/safety studies provided useful information, they didnot take tour leaders’ risk perception into consideration. Besides, mostprevious investigations focused on perceived risk, yet this studyexplores tour leaders’ experience with risks that have actually beenrealized.

More specifically, according to Wang, Hsieh, and Huan (2000), tourscan be categorized into two major modes: GPTs and independent tours;this categorization is similar to Cohen’s (1972) typology of internationaltourists based on their preference for either familiarity or novelty whentraveling, namely, organized mass tourist and individual mass tourist/explorer/drifter. The tourists on GPTs are Cohen’s organized masstourists who prefer the greatest amount of familiarity and travels in


an ‘‘environmental bubble’’ of the familiar on a packaged tour. Leppand Gibson (2003) verified tourist role based on Cohen’s typologywas the most significant variable in relation to risk perception, withfamiliarity seekers being the most risk adverse. They indicated thatthe organized mass tourists who seeking familiarity are likely to viewalien environments as more risky than the other tourist roles. Besides,Lepp and Gibson (2003) specified that different types of tourists per-ceived risks differently: organized mass tourists perceived terrorism asa greater risk and concerned more on strange food and health risksthan the other types of tourists. As such, what organized mass touristsperceived as a risk is different to the other three types. Accordingly,tourist risk based on Wang et al.’s (2000) tour typology can be catego-rized into: independent tourists’ risk and group package tourists’ risk.

However, in Sonmez and Graefe (1998a) and Roehl and Fesenma-ier’s (1992) studies, they mainly focused on independent tourists’ per-ceptions of the types of risk present in tours. Meanwhile, in Roehl andFesenmaier’s (1992) study, all the trips were either in-state or out-of-state destinations and most of the respondents traveled with familymembers. However, the risk perceptions between the independentand group package tourists’ are rather different because of the tours’characteristics. Besides, both Sonmez and Graefe (1998a) and Roehland Fesenmaier’s (1992) risk components are too broad to measure;for example, for measuring physical risk component, the questionwas asked as follow: possibility that a trip to this destination will result inphysical danger, injury or sickness. For such question, it seems difficultto fully conceptualize what physical risk actually entails.

Furthermore, in Tsaur et al.’s (1997) study, only physical and equip-ment risks in GPTs were emphasized. In fact, several important GPTsectors which have been indicated in prior studies were overlooked,such as shopping and optional tour (Wang et al., 2000). These ne-glected sectors essentially entail certain important risks that a tour lea-der might encounter during the GPT. Moreover, in Pinhey andIverson’s (1994) study on safety concerns, only independent tourists’risks like how safe is it driving a rental car on Guam were focused on. Inaddition, several tour or GPT related risks such as: restaurant, hotel,coach, shopping, optional tour, etc., were not taken into consideration.Finally, in a recent tour risk perception study by Teng (2005), sevenrisk aspects developed by Tsaur et al. (1997) were employed to evaluatethe destination risks for Thailand, Malaysia, and Singapore. Althoughthe methodology and findings in this study are instructive, severalimportant GPT sectors were overlooked, such as shopping and op-tional tour (Wang et al., 2000). Moreover, since Teng’s study was essen-tially a duplication of Tsaur et al.’s (1997) tour risk study, itscontribution to the existing knowledge is not quite apparent.

Thus, it appears that in the relevant theories, the intrinsic risks per-ceived by GPT leaders have not been clearly identified. Besides, from apractical viewpoint, perceptions and understandings of risk are impor-tant factors influencing the conceptualization of risk control strategies(Tesh, 1981). Consequently, in order to complement the previousstudies (e.g., Teng, 2005; Tsaur et al., 1997) which merely discussed


the risk from the perception of tourists, the present study were primar-ily to (1) explore the intrinsic GPT leaders’ risks and introduce agrounded model of it, (2) examine the relationship between differentGPT leaders’ risks with different GPT itineraries, and (3) explore therisk categorizations of GPT leaders.

Study Methods

Unlike previous studies that mainly utilized quantitative approach(Roehl & Fesenmaier, 1992; Teng, 2005; Tsaur et al., 1997), this studyemployed both qualitative and quantitative methods, which is comple-mentary. Mixing both methods can help avoid the problem of a com-mon method variance in using only one method of measurementbecause the strengths of one method can counteract the weaknessesof another (Jick, 1979). Moreover, this is to enhance confidence inthe research result and provide a more comprehension of domain un-der investigation. Therefore, this study employed both qualitative andquantitative methods, from in-depth interviews to questionnaire sur-veys conducted on tour leaders. The qualitative approach was usedto gain a more comprehensive and in-depth understanding of theintrinsic risks in GPT leaders. Subsequently, to further examine therelationship between different risks with different GPT itinerariesand classify the risk categorizations of tour leaders, the quantitativemethod was used. Both qualitative interviews and quantitative surveyswere conducted in the language of Chinese.

Definition of Intrinsic Risk in GPT Leaders. Although risk concept wasvaried in keeping with diverse research purposes (Stone & Grønhaug,1993) and was not easy to operationalize (Klinke & Renn, 2001; Unser,2000), Klinke and Renn (2001) stated that all risk concepts have onecommonality: risk is often associated with the possibility that an unde-sirable state of reality may occur as a result of natural events or humanactivities. With respect to risk analysis, Steene (1999) once suggestedthat risk analysis denotes the systematic examination of a course ofevents for the purpose of identifying the incidents and phenomenathat can lead to undesired consequences, as well as the assessment ofthese consequences and the judgment of their probability. Thus,according to Steene, risk analysis has three main aspects: (1) identifica-tion of the sources of risks, (2) judgment of probability, and (3) anal-ysis of the consequences. Therefore, based on above information, theoperational definition of intrinsic risks in GPT leaders in this study is:any events or accidents that would cause possible loss while tour leader is leadingthe outbound GPT.

Qualitative Questions Development. The questions were developed intotwo parts. In the first part, the travel duration of an outbound GPT isnormally long and covers diverse dimensions, as suggested by Wanget al. (2000), the GPT was divided into discrete sectors. There aretwo advantages to this approach. One is that it can facilitate data


collection; moreover, the precise definition of GPT sectors is condu-cive to eliciting the tour leaders’ recollections. The other advantage isthat dividing the GPT into sectors can prevent some sectors frombeing overlooked. Accordingly, Wang et al.’s (2000) nine GPT sec-tors, namely, the pre-tour briefing, airport/plane, hotel, restaurant,coach, scenic-spot, shopping, optional tour, and others, were em-ployed in this study.

However, in Wang et al.’s study, they did not separate the sectorof airport from airplane; moreover, and they did not take differentdeparture and arrival airports into consideration. Since differentrisks might be encountered at airports (departure and arrival)and in the airplanes, to prevent the omission of important intrinsicrisks faced by tour leaders in outbound GPTs, the airport/planesector was further divided into five sectors: departure airport(home country), airplane (forth and back), arrival airport (destina-tion country), departure airport (destination country), and arrivalairport (home country). Consequently, a total of 13 sectors wereemployed to explore tour leaders’ experiences with risks that haveactually been realized in this study. The detailed questions werepresented in Figure 1; the following is an example of the hotel sec-tor in GPTs:

Q1: According to your personal experiences in leading GPTs, werethere any events or accidents happening that caused you losses whilestaying in the hotel ?

In the second part, Rundmo (2002) and Rundmo and Sjoberg(1998) once indicated that when thinking about a risk source or poten-tial hazard, people may be worried or feel unsafe. Thus, an affectivecomponent is involved in risk perception. For example, the affect ofworry may be evoked every time a person thinks about a risk source.Since the current study aims at constructing a comprehensive sourcestructure of intrinsic risks in outbound GPT leaders, therefore, Run-dmo and Sjoberg’s ideas of risk and risk perception were taken intoconsideration for developing questions in order to capture tour lead-ers’ perception of possible or future risks. The following questionwas framed by taking the hotel sector as an example; the complete ques-tions were also presented in Figure 1.

Q2: With the exception of things mentioned above, what might be theevents and accidents that you would least expect to happen while stay-ing in the hotel ?

Qualitative Data Collection. Since the study was exploratory in nature, itaimed at eliciting GPT leader’s viewpoints on the intrinsic risks intours. To achieve this, in-depth interviews were the most suitable ap-proach. According to Wester-Herber and Warg’s (2002) study, per-sonal experiences, age, gender, and regional differences influencethe individual’s risk perception. However, regional difference was ex-cluded in this study because Taiwan is fairly small in its territory. There-fore, before in-depth interviews, personal experiences, age, and gender

Figure 1. The Hierarchical Structure of Questions


were employed as criteria for selecting the appropriate participants inorder to enhance data quality and increase generalization. In total, 24tour leaders were conducted; the interviewees’ profile is presented inTable 1.

To enhance the validity of data analysis, data triangulation techniquewas employed for data collection (Jick, 1979). Decrop (1999) indicated

Table 1. Profile of Interviewees (n = 24)

Experience Gender/Age

Male Sample Size Female Sample Size

With Throughout Guide Experiences* Age 25�35 2 Age 25�35 2Age 36�45 2 Age 36�45 2Age 46�55 2 Age 46�55 2

Without Throughout Guide Experiences Age 25�35 2 Age 25�35 2Age 36�45 2 Age 36�45 2Age 46�55 2 Age 46�55 2

Total 12 12

* In Taiwan, China, etc., throughout guide represents that tour leader plays two roles at thesame time while leading the outbound GPTs, one role is the tour leader and the other is thelocal guide. Typically, throughout guide would be mostly found in the long-haul outboundGPTs, such as Europe, New Zealand, Australia, America, etc.


that triangulation means looking at the same phenomenon, orresearch question, from more than one source of data and this is usefulfor supporting the results. Information coming from different anglescan be used to corroborate, elaborate or illuminate the research prob-lem. It limits personal and methodological biases and enhances astudy’s generalizability. For this purpose, two experts and two scholarswere recruited for data collection. In total, 28 respondents participatedin the in-depth interviews. Each interview lasted approximately 1.5-2hours. All of the above respondents were interviewed with the ques-tions in Figure 1.

Member Checking. Before the data analysis, a member checking wasconducted to verify the credibility of the interview data (Decrop,1999). All the 28 transcripts were returned, among them, 14 transcrip-tions did not require further amendments, the other transcriptsrespectively indicated some typing-errors, new events/accidents werefound, and some events/accidents were revised.

Qualitative Data Analysis. The overall process of this part was dividedinto three parts: unit of analysis, category development and reliability,and category confirmation. Part one, as indicated by Holsti (1968) andKassarjian (1977), the first step in data analysis is to determine theappropriate unit of analysis. In this study, the basic units of analysiswere the intrinsic risks in GPT leaders’ risks which resulting fromevents or accidents. For instance, in the coach sector, an intervieweeresponded that

‘‘. . .when I [female tour leader] was leading a tour to Europe, thecoach driver asked me to sleep with him. I think sexual harassmentis truly one of the risks when female GPT leaders leading a tour. . ..’’


The above-mentioned example implies that the sexual harassmentfrom driver toward tour leaders has actually caused psychologicaland physical harm to them. Therefore, such an event would be codedinto a risk unit of analysis and called ‘‘sexual harassment from thedriver.’’

Then, two judges (A and B) (both had plenty working experiences ina travel agency) independently coded the transcriptions from the 26questions into 1,809 units of analysis (Q1 and Q2). Upon completingthe coding of the unit of analysis, the two judges compared their deci-sions and resolved disagreements by discussion with the researchers.Nevertheless, some of the above units were of doubtful relevance tothe study. In the viewpoint, judge A and B and the researchers con-ducted a screening procedure. In total, 194 units in Q1 were foundto be irrelevant and 398 units in Q2 were found to be identical to theunits in Q1: those units were ultimately eliminated. Finally, 1,217 unitsof analysis were obtained for further category development (see Table2).

Part two, category development and reliability, after the basic unit ofanalysis was established, the 1,217 units were divided into categories.The single classification concept for category development, recom-mended by Weber (1990), was employed. In an iterative process con-ducted by judge A and B, each of the units was read out, classified,re-read, and re-classified. Finally, 135 inferred categories emerged with-in the 13 GPT sectors (see Table 2), and each of these categories wasnamed. After the categorization process was complete, this study testedthe reliability of the categorization process.

According to Keaveney (1995), if the inter-judge and intra-judge lev-els of agreement reach .80, the categorization process can be regardedas reliable. This study introduced judge C, an Assistant Professor in theDepartment of Tourism Management who also had working experi-ence in travel agency, in order to conduct inter-judge reliability testing.And a time-lag of two weeks was employed for the intra-judge (A and B)reliability testing, as suggested by Davis and Cosenza (1993). Judge Ccategorized all of the 1,217 units into the categories created by judgesA and B and was encouraged to create new categories if appropriate(Keaveney, 1995; Wang et al., 2000). The result of the inter-judge reli-ability was .993 for judge C, and no new categories emerged. With re-spect to the intra-judge reliability which were all above .996 for bothjudge A and B.

Part three, category confirmation, in addition to the interviews con-ducted on the 24 tour leaders, two senior travel experts and two schol-ars were also interviewed to further category confirmation. In total, 441units of analysis emerged from two experts and two scholars. Judge Aand B then tried to categorize these 441 units into the 135 categorieswith the aim of developing new categories; however, no new categoriesemerged in this confirmation process. This result is consistent with De-crop’s (1999) view that triangulation consists of strengthening qualita-tive findings by showing that several independent sources converge onthem. Therefore, the categories in this study have content validity andno further interviews were necessary.

Table 2. The Intrinsic Risk Units of Analysis

OutboundGPTSectors

Q1a Q2

a Total

OriginalUnits

RemovedUnits

RemainedUnits

ObtainedCategories

OriginalUnits

RemovedUnits

RemainedUnits

ObtainedCategories

RemainedUnits

ObtainedCategories

(%)b

� Coach 174 8 166 17 56 51 5 2 171 19 14.1� Departure

airport(destination)

100 17 83 13 30 24 6 3 89 16 11.9

� Hotel 207 36 171 12 38 36 2 1 173 13 9.6� Scenic-spot 130 9 121 11 35 35 0 0 121 11 8.1� Airplane

(forth andback)

98 27 71 9 37 28 9 2 80 11 8.1

� Arrivalairport(destination)

137 10 127 10 32 32 0 0 127 10 7.4

� Departureairport(home)

134 41 93 10 38 38 0 0 93 10 7.4

� Shopping 86 9 77 8 42 41 1 1 78 9 6.7� Optional

tour75 5 70 9 28 28 0 0 70 9 6.7

� Arrivalairport(home)

67 2 65 9 20 20 0 0 65 9 6.7

� Restaurant 114 23 91 7 31 31 0 0 91 7 5.2� Pre-tour

briefing49 5 44 6 29 28 1 1 45 7 5.2

� Others 16 2 14 4 6 6 0 0 14 4 3.0

Total 1,387 194 1,193 125 422 398 24 10 1,217 135 100

a Q1: According to your personal experiences in leading GPTs, were there any events or accidents happening that caused you losses while. . .? Q 2: With theexception of things mentioned above, what might be the events and accidents that you would least expect to happen while. . .?; b % = individual category/total categories (135 categories).

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Grounded Model of Intrinsic Risks in GPT Leading. According to Table 2,which showed the number of categories for each GPT sector, the threemain risk sectors as perceived by tour leaders were ‘‘coach’’, ‘‘depar-ture airport/destination country’’, and ‘‘hotel’’ and the least-men-tioned sector was ‘‘others’’. Taking the most noteworthy findings in‘‘coach’’ as an example, this sector represents 14.1%, the largest sectorof all of risk categories (19/135).

In this sector, ‘‘poor vehicle conditions’’ was major concern oftour leaders (41/171, 24.0%). The chief source of risk mainly comesfrom vehicles being too old and lacking cleaning, microphone mal-functions, or air conditioning not cold enough. Second largest cate-gory was ‘‘property loss of the GPT tourists’’ (22/171, 12.9%).Moreover, six out of the 19 categories of risk were related to coachdriver, among them, risks mainly came from the ‘‘poor attitude ofthe driver’’ (20/171, 11.7%) and ‘‘unprofessional driver’’ (13/171,7.6%). The results revealed in Europe or USA, because of drivers’multi-national background, language barrier sometimes causingproblems in the interaction and cooperation with the tour leader,for instance,

‘‘I once led a group to Europe and got an Italian driver. His Englishwas so poor that we had to communicate with hand signs. This wouldaffect the whole group’s rhythm and mood.’’

In summary, because the coach is the most relied upon transporta-tion in a GPT tour, the driver is the tour leaders’ working partner withthe most frequent interactions. Therefore, if the driver’s attitude andprofessionalism is poor, it will not only affect the flow of the entire tripbut also deal a severe blow to the tour leader’s mood when leading thegroup.

Quantitative Questionnaire Development. On the basis of qualitativeresults, a questionnaire was developed in three parts. In the firstpart, two questions were designed to capture the tour leaders’ pro-fessional backgrounds. One question asked the tour leaders to iden-tify his/her most specialized itinerary from six GPT itineraries,namely, China, Thailand, Japan, USA, New Zealand/Australia, andEurope; these six GPT itineraries were selected either because theyare the most popular GPT itineraries in practice or they are amongthe top five destination countries for outbound GPTs in Taiwan(Tourism Bureau, 2007). Another question asked each respondentindicate the frequency of this GPT itinerary that he/she has beenleading. In the second part, 13 GPT sectors with 135 categorieswere rewritten to develop an original scale wherein each categorywas anchored with a five-point scale ranging from ‘‘extremelyimpossible’’ to ‘‘extremely possible’’. The following is an example of aquestion pertaining to ‘‘unprofessional driver’’ category in the ‘‘coach’’sector:

Q: According to your personal experiences in leading your most spe-cialized GPT, in the coach sector, the possibility of confronting anunprofessional driver is?


Except those 135 questions, two questions were designed as reversedstatement in order to eliminate potential biased. Moreover, an open-ended question like ‘‘Except for the above-mentioned risk features, if thereare any risk features that you think important, please specify and write themdown below’’ is also included in each sector to detect whether or notthe risk categories obtained from the qualitative analysis is comprehen-sive. In the third part, several questions were included to capture thetour leaders’ demographic profiles, such as gender, age, education,average monthly income, how many years for being GPT leader, andthe name of the travel agency that the tour leader works for.

Prior to the data collection, two doctoral and two EMBA studentswho presently work for a travel agency were invited to assess the con-tent and relevance of this questionnaire. In addition, 30 undergradu-ate students from the Department of Tourism Management werealso invited to evaluate the comprehension of the words and phrasesof items. Based on their comments, some revisions were made to im-prove the clarity of the items.

Quantitative Data Collection. The survey was conducted by different tra-vel agency managers who volunteered to collect the questionnaires inTaiwan. The data collection was carried out over a three-month period.In total, 650 questionnaires were distributed, 437 surveys were re-turned, and of those, 310 were useable for the purpose of analysis.From the years for being outbound tour leader and frequency of lead-ing the most specialized itinerary, the statistics of cross tabulation re-veals that 64.4% of the respondents have at least five years and aboveexperience as tour leaders and all of them have led a specializedGPT itinerary at least six to ten times and above. Besides Europe wasidentified as the most specialized GPT itinerary (21.6%), followed byThailand (19%), and China (18.1%). A total of 75 major travel agen-cies were surveyed. Among them, 19 were wholesale travel agencies,which constitute 23% of the total wholesale travel agencies (81) in Tai-wan (Tourism Bureau, 2007).

The Risk Measurement of Six GPT Itineraries and 13 Sectors. According toTable 3, China was viewed as the most risky destination, followed byThailand, USA, Europe, New Zealand/Australia, and Japan. Analysisof variance (ANOVA) was conducted to test whether there were signif-icant differences in the risk measurement of these six itineraries. Theresult reveals significant differences in these six itineraries. On the ba-sis of the homogeneous subsets test (Scheffe, alpha at .05 level), they canthen be categorized into four different groups as follows: China andThailand (a), USA and Europe (ab), New Zealand/Australia (bc),and Japan (c). With respect to the risk in different GPT sectors,‘‘pre-tour briefing’’ was ranked as the most risky sector and ‘‘departureairport (destination)’’ as the least risky sectors. Except ‘‘others’’ sector,12 of the 13 GPT sectors were found to have significant differences. Forthe homogeneous subsets test, most sectors were categorized into threeor four different groups. Interestingly, for three sectors (airplane/forth and back, arrival airport/home, and others) the heterogeneous

Table 3. Risk Measurement of Six GPT Itineraries

MostSpecializedGPTItineraries

13 GPT Sectors with 135 Categories

Pre-tourbriefing

Departureairport(home)

Airplane(forth/back)

Arrivalairport(destination)

Coach Scenic-spot

Restaurant Optionaltour

Hotel Shopping Departureairport(destination)

Arrivalairport(home)

Others Averagescore

Ranking

� China/561

3.37a* 2.86a 2.66a 2.68a 2.68a 2.82a 2.66a 2.66a 2.65a 2.76a 2.54a 2.50a 2.62a 2.71a (.34)2 1

� Thailand/59

3.23ab 2.71ab 2.69a 2.53ab 2.59a 2.61ab 2.49a 2.75a 2.60a 2.49ab 2.31ab 2.38a 2.76a 2.59a (.35) 2

� Japan/51

2.84b 2.38b 2.49a 2.10c 2.14c 2.30bc 2.10b 1.99c 2.12c 1.95c 2.01c 2.16a 2.51a 2.20c (.36) 6

� USA/43

3.38a 2.69ab 2.64a 2.60a 2.51ab 2.61ab 2.52a 2.59a 2.46ab 2.33b 2.37a 2.40a 2.76a 2.57ab (.38) 3

� New Zealand/Australia/34

3.20ab 2.45b 2.44a 2.24bc 2.24bc 2.27c 2.11b 2.21bc 2.19bc 2.24bc 2.05bc 2.19a 2.54a 2.29bc (.51) 5

� Europe/67

3.22ab 2.63ab 2.50a 2.54ab 2.57a 2.67a 2.49a 2.51ab 2.43abc 2.28bc 2.34ab 2.38a 2.55a 2.53ab (.46) 4

Averagescore

3.21 2.63 2.58 2.47 2.48 2.57 2.42 2.48 2.43 2.35 2.29 2.35 2.62 2.50(.67)2 (.60) (.49) (.54) (.52) (.53) (.56) (.61) (.51) (.55) (.46) (.50) (.63) (.43)

Ranking 1 2 4 8 6 5 10 7 9 11 13 12 3F 4.42 4.43 2.28 9.90 9.39 8.91 8.96 13.62 9.68 15.47 10.98 3.24 1.55 11.19p .001 .001 .046 .000 .000 .000 .000 .000 .000 .000 .000 .007 .172 .000

1 Number represents how many GPT leaders have identified this itinerary as his/her most specialized route; 2 Number in the parenthesis represents thestandard deviation; * Means with two (ab) or three (abc) superscripts represent at two or three different homogeneous subsets based on Scheffe tests, alpha at.05 level.

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subsets were not identified which represent these three sectors havesimilar level of risks on these six outbound itineraries.

Purify the Intrinsic Risks in GPT Leaders

Exploratory Factor Analysis. According to the suggestions by Churchill(1979) and Wang, Hsieh, Chou, and Lin (2007), an exploratory factoranalysis (EFA) was employed to develop a reduced and more parsimo-nious measurement of intrinsic risks in GPTs. This study used the rawscores of the original scale to conduct the EFA. The items of EFA havebeen eliminated from 135 to 38 items. As a result, the communality ofeach item exceeded .66 and all factor loadings were exceeded the re-quired value of .4 (del Bosque, 2008; Stergiou, Airey, & Riley, 2008).The 38 items for the GPT leaders’ perceived risk produced 12 factorswith an eigenvalue greater than 1.0. The reliability of each factor ex-ceeded .76. These factors explained 73.95% of the variance. The de-tailed factors and items presented in Table 4.

Cluster Analysis. This study employed 12 groups of factor scores de-rived from the EFA in the cluster analysis. The K-means clusteringmethod, a nonhierarchical algorithm (Hair, Anderson, Tatham, &Black, 1991), was used to determine the optimal number of clusterson the basis of these factors. As a result, the three-cluster solutionwas the most appropriate for the data of tour leaders’ perceived risks.Moreover, each cluster’s name was denominated in accordance withthe manifestation of factor means. The multivariate statistics showedsignificant differences between the three clusters (p < .001).

The results indicated that cluster one has significantly higher meanof factors (M = 3.4, mean of cluster one = 3.02) in ‘‘hijacking and planecrash’’, ‘‘optional tour and shopping’’, ‘‘document and property sto-len’’, ‘‘luggage lost’’, ‘‘driver problems’’, and ‘‘bribery and obstructionby customs officers’’ than the other clusters. These factors are relatedto the externals; therefore, this cluster was named as ‘‘exogenous risk’’cluster. Most of exogenous risks cannot be controlled during the tour.Cluster two had comparatively higher mean of factors (M = 3.01, meanof cluster 2 = 2.69) in ‘‘sexual harassments and accusation from tour-ists’’, ‘‘tourist’s compensation problems associated with damages andhotel expense’’, ‘‘tourist’s taxable and prohibited goods’’, and ‘‘tour-ist’s visa and passport expiration issues’’. These factors are related tothe tourists; as a result, cluster two was named as ‘‘tourist-induced risk’’cluster. Cluster three had comparatively higher mean of factor(M = 2.5, mean of cluster three = 2.2) in ‘‘change in itinerary and tip-ping problems’’ and ‘‘tour leader’s operating negligence’’, followedby the exogenous risk. These factors are related to the tour leaders;accordingly, cluster three was named as ‘‘tour leader’s self-induced risk’’cluster. Moreover, the results showed that exogenous risks cluster havethe highest explanation power (37%), followed by tourist-induced riskscluster (24%) and tour leader’s self-induced risks cluster (13%) (seeTable 5).

Table 4. Results of Factor Analysis of the Perceived Risk by Tour Leader

Perceived risk factors and items Factorloading

Varianceexplained(%)

Cronbachalpha

Communality

Optional tour and shopping 8.833 .788 .666overly priced products in sopping store .771forced optional tours by local tour guide .709fake products and flawedmerchandise in shopping store

.701

forced shopping .696GPT tourists complainover optional tour unworthiness

.548

optional tour not well arranged .422Tour leader’s operating negligence 7.616 .822 .643

temporary closure of the scenic-spot .787schedule delay leading tothe missing a visiting scenic-spot

.728

sudden change of the schedule leadingto missing the visiting scenic-spot

.674

temporary closure of the restaurant .581special meals not ordered .479

Driver problems 6.956 .857 .787poor attitude of the driver .812unprofessionalism of the driver .807poor physical strength of the driver .768

Sexual harassment and accusation from tourists 6.304 .763 .666actual product different from theanticipation of the GPT tourists sales

.748

tour leader wrongfully accusedby the GPT tourists

.708

sexual harassment by the tourists .699tourists with deficient traveling knowledge .603

Bribery and obstruction by customs officers 6.294 .861 .777customs officers asking for bribes inarrival airport/destination country

.841

customs officers asking for bribes indeparture airport/destination country

.781

hard time getting through customs .766Tourist’s compensation problems associated

with damages and hotel expenses6.239 .855 .770

problem of compensation to thedamaged facilities of hotel room

.845

problem over compensation toloss of objects in hotel room

.796

dispute over payment items .687Tourist’s taxable and prohibited goods 5.784 .814 .718

taxable goods overweight in arrivalairport/home country

.807

taxable goods overweigh in departureairport/destination country

.714

GPT tourists carrying prohibited goods .616


Table 4 (continued)Perceived risk factors and items Factor

loadingVarianceexplained(%)

Cronbachalpha

Communality

Change in itinerary and tipping problems 5.460 .781 .713not paying tips .779GPT tourists are not given full andcomprehensive informationduring the pre-tour briefing

.774

tourist bargain for tip issue .684Tourist’s visa and passport expiration issues 5.265 .906 .878

passport expires .864visa expires .862

Hijacking and plane crash 5.217 .951 .880hijack .938plane crash .927

Luggage lost and damaged 5.105 .863 .803luggage damaged .828luggage loss .812

Document and property stolen 4.881 .886 .854documents stolen .838property stolen .806

Total variance explained 73.95

Note: Each statement was measured on a five-point scale ranging from 1 = extremely impos-sible to 5 = extremely possible.


Perceived Risks Analysis

Analysis of Six GPT Itineraries and 13 Sectors. After the EFA, the newranking of the average scores of the tour leaders’ perceived risks insix GPT itineraries was found to be similar with the original ranking(see Table 6). The new risk-ranking of six GPT itineraries from higherto lower is China, Thailand, USA, Europe, New Zealand/Australia, andJapan. Moreover, in regarding to tour leaders’ perceived risks in 13 sec-tors, the results revealed the new ranking of average scores in every fac-tor dimension was apparently different before and after the items wereeliminated. However, in spite of five dimensions, which are airplane,arrival home airport, arrival destination airport, departure home air-port, and shopping; the others deviated all within three positions. Suchan outcome not only indicated the reliability of the new GPT risk mod-el but also provided a more parsimonious measurement which theitems were condensed from 135 to 38 items.

Post-Hoc Test of the Perceived Risk. Post-Hoc analysis was used to reportthe differences in 12 factors among six itineraries. Meanwhile, the sixGPT itineraries were adopted as the independent variables of the anal-ysis, and they corresponded with the dependent variables, that is, the12 perceived risk factors. In Table 7, the numbers in the parenthesisrepresent the mean difference between two itineraries; for example,M-J stands for the mean difference of China minus Japan in relation

Table 5. Results of Cluster Analysis of Perceived Risk

Factors Cluster 1 Cluster 2 Cluster 3 F valueExogenous

riskTourist-induced

riskTour leader’sself-induced

risk

1 Optional tour and shopping 3.42 2.65 2.38 96.08*

3 Driver problems 3.33 2.62 2.27 84.47*

5 Bribery and obstructionby customs officers

3.19 2.70 2.13 82.06*

10 Hijacking and plane crash 3.70 2.74 2.42 42.85*

11 Luggage lost and damaged 3.38 2.11 1.76 47.71*

12 Document andproperty stolen

3.40 2.50 2.34 87.64*

4 Sexual harassment andaccusation from tourists

2.88 2.79 2.09 67.68*

6 Tourist’s compensationproblemsassociated with damagesand hotel expense

3.16 3.07 2.39 66.72*

7 Tourist’s taxable andprohibited goods

2.75 2.91 1.89 46.23*

9 Tourist’s visa andpassport expiration issues

1.60 3.28 1.56 50.51*

2 Tour leader’soperating negligence

3.15 2.49 2.44 57.78*

8 Change in itinerary andtipping problems

2.38 2.43 2.58 79.97*

n = 68 n = 146 n = 127 lambda = .23(p < .001)

Explained variance 37% 24% 13%

* p values are significant at the .001 level.


to 12 factors. The results revealed that among these 12 factors, exceptfor ‘‘Sexual harassment and accusation from tourists’’, ‘‘Tourist’s taxable andprohibited goods’’, ‘‘Tourist’s visa and passport expiration issues’’, and‘‘Hijacking and plane crash’’, eight factors were found to have significantdifferences with regard to the GPT itineraries. The mean differencesand explanation of three interesting risk factors in GPT leaders’ onevery GPT itinerary are described in following:

Optional tour and shopping. With regard to this risk factor, apparently,tour leaders perceived higher risks in China than Japan (.87, p < .001),New Zealand/Australia (.59, p < .01), and Europe (.54, p < .01); andalso this risk factor is higher in Thailand than Japan (.84, p < .001),New Zealand/Australia (.55, p < .01), and Europe (.51, p < .01). As indi-cated by Wang et al. (2000, p. 185), optional tour and shopping are twoof the most important service features in GPTs. The risks are mainly

Table 6. Risk Measurement of Six GPT Itineraries

MostSpecializedGPTItineraries

38 Items for New 13 GPT Sectors

Pre-tourbriefing

Departureairport(home)

Airplane(forth/back)

Arrivalairport(destination)

Coach Scenic-spot

Restaurant Optionaltour

Hotel Shopping Departureairport(destination)

Arrivalairport(home)

Others Averagescore

Ranking3 NewRanking4

� China/591 3.02a* 2.78a 1.73a 2.42a 2.57a 2.89a 2.48a 2.90a 2.97a 3.08a 2.26a 2.77a 2.62a 2.65a (.34)2 1 1� Thailand/65 2.88ab 2.56a 1.97a 2.45a 2.40a 2.59ab 2.29ab 3.05a 2.76a 2.86ab 2.30a 2.61a 2.75a 2.57a (.35) 2 3� Japan/56 2.47b 2.31a 1.93a 1.72bc 2.12c 2.53bc 2.21ab 1.99c 2.25bc 2.06c 1.94abc 2.31a 2.49a 2.17c (.36) 6 6� USA/50 3.12a 2.62a 1.98a 2.02ab 2.48ab 2.60ab 2.36a 2.77a 2.43ab 2.54bc 2.14ab 2.70a 2.72a 2.49ab (.38) 3 2� New Zealand/Australia/37

2.92ab 2.40a 1.71a 1.57c 2.01bc 2.38c 2.02bc 2.32bc 2.28bc 2.48bc 1.87bc 2.44a 2.58a 2.54bc (.51) 5 4

� Europe/74 2.95ab 2.58a 1.66a 1.91b 2.89a 2.67a 2.44a 2.57ab 2.42ab 2.33bc 2.04ab 2.70a 2.52a 2.78ab (.46) 4 5

Average score 2.89 2.54 1.83 2.01 2.41 2.61 2.30 2.60 2.51 2.55 2.09 2.58 2.61 2.77(.98)2 (.81) (.83) (.79) (.82) (.72) (.79) (.90) (.82) (.91) (.72) (.77) (.81) (.76)

Ranking 1 2 4 8 6 5 10 7 9 11 13 12 3New Ranking 1 7 13 12 9 3 10 4 8 6 11 5 2F 4.60 2.54 1.95 17.25 12.71 3.91 3.07 17.82 10.04 16.51 5.36 4.59 1.59 7.89p .000 .028 .085 .000 .000 .002 .010 .000 .000 .000 .000 .000 .161 .000

1 Number represents how many GPT leaders have identified this itinerary as his/her most specialized route; 2 Number in the parenthesis represents thestandard deviation; 3 Number represents the order of tour leader’s perceived risk in the original 135 items; 4 Number represents the order of tour leader’sperceived risk in the 38 items; * Means with two (ab) or three (abc) superscripts represent at two or three different homogeneous subsets based on Scheffetests, alpha at .05 level.

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Table 7. Result of Post-Hoc Test of the Perceived Risk by Tour Leader

Factor Scheffe multiple range tests

M-J M-A M-N M-E T-J T-A T-N T-E J-A J-E A-E N-E

1 (.87) (.59) (.54) (.84) (.55) (.51) (-.54)*** ** ** *** ** ** **

2 (.49)***

3 (.46) (.56) (-.49) (-.77) (-.42) (-.88)* ** ** *** * ***

4

5 (.63) (.39) (.77) (.42) (.69) (.44) (.82) (.47)*** * *** ** *** ** *** **

6 (.72) (.55) (.69) (.56) (.51) (.48)*** ** *** ** ** *

7

8 (.57) (-.68) (-.47)** *** *

9

10

11 (.61) (-.53) (-.62)*** ** *

12 (.55)*

*p values are significant at the .05 level;**p values are significant at the .01 level;***p values aresignificant at the .001 level.Number in the parenthesis represents the mean difference.Itinerary:M = China, T = Thailand, J = Japan, A = USA, N = New Zealand and Australia, E = European.Factor: 1: Optional tour and shopping; 2: Tour leader’s operating negligence; 3: Driver prob-lems; 5: Bribery and obstruction by customs officers; 6: Tourist’s compensation problemsassociated with damages and hotel expense; 8: Change in itinerary and tipping problems; 11:Luggage lost and damaged; 12: Documents and property stolen. Blank column indicates ‘notsignificant’.


caused by local agents and local guides in destination. In practice, per-ceived risks such as forced shopping, deliberate stalling of tourists inthe stores for shopping; incorporate additional shopping spots/op-tional tour during the main tour are often observed in China andThailand.

Driver problems. In this factor, risks perceived in Europe is apparentlyhigher than other itineraries such as Thailand (.49, p < .01), Japan (.77,p < .001), USA (.42, p < .05), New Zealand/Australia (.88, p < .001).Europeans have a strong geographic conception and less desire tocommunicate in English. Accordingly, it sometimes creates the


misunderstandings and communication gaps between European driv-ers and tour leaders in the itinerary, thus impeding the tour’s progress.

Bribery and obstruction by customs officers. With regard to this factor, Chi-na, as expected, ranks higher than Japan (.63, p < .001), USA (.39,p < .05), New Zealand/Australia (.77, p < .001), and Europe (.42,p < .01). Thailand also evidently ranks higher than Japan (.69,p < .001), USA (.44, p < .01), New Zealand/Australia (.82, p < .001),and Europe (.47, p < .01). Although, nowadays this type of risk is sel-dom found in many destination countries/airports, while leading thetour, especially during the CIQ (custom, immigration, and quarantine)procedures, risks such as ‘‘bribery and obstruction by customs officers’’ arestill perceived by tour leaders in certain destination countries/airports.

CONCLUSION

This study outlines both qualitative and quantitative approaches toexplore GPT leaders’ perception of intrinsic risks. The results obtainednot only fill up the theoretical gap in studies on tour risks but also offerinsights into risk management strategies. The discussions are describedin detail below.

First, with respect to the risk/safety analysis regarding destinations,most of the prior studies involved the participation of undergraduatestudents (Hsu & Lin, 2005) and tourists (Lepp & Gibson, 2003; Pinhey& Iverson, 1994; Roehl & Fesenmaier, 1992; Sonmez & Graefe, 1998a;Teng, 2005; Tsaur et al., 1997), most of whom were first-time tourists(Hsu & Lin, 2005; Roehl & Fesenmaier, 1992). Unlike previous studies,this study involved the participation of 310 tour leaders from 75 majortravel agencies in Taiwan. Because of their considerable experience inleading outbound tours, the authors believe that the results can offer abetter understanding/generalization of GPT related risks for the desti-nation risk analysis.

Second, although prior risk/safety studies have briefly discussed riskfactors, they are less specific when addressing the tour leaders’ per-ceived risks. For example, Roehl and Fesenmaier (1992) identified se-ven types of travel risks as perceived by independent tourists. However,these types are too general to allow for a more specific understandingof the cause of every tour risk. This study excluded tour leaders’ workcharacteristics to nullify the results of the previous GPT studies, whichmerely consider the risk perception in GPTs from the perspectives oftourists and destinations. In addition, following Roehl and Fesenma-ier’s classification of risk perception by independent tourists, whichwas carried out on the basis of the mean scores of a three-factor load-ing, this study considered the interaction between GPT participantsand the environment to identify three risk clusters, namely, tour lead-ers’ self-induced risks, tourist-induced risks, and exogenous risks, afterconducting a cluster analysis. This would more accurately prove thatthe risk perception of tour leaders is generalized on the basis of theinteraction between tour leaders, tourists, and the environment.


Moreover, although Tsaur et al. (1997) and Teng (2005) utilized theseven risk aspects, they are inadequate to cover all aspects of GPT risks.This study takes into consideration the ‘‘process of tour’’ which is di-vided into before, within, and after the tour, for an analysis of risk percep-tion. Interestingly, some substantial and concrete phenomena werediscovered: before the tour, fewer participants and insufficient informationin pre-tour briefing, and tourist’s visa and passport expiration issues;within the tour, arguments to incorporate optional tours and shoppingin the main tour, document and property stolen, problem of goods thatare taxable and prohibited for tourists, bribery and obstruction by cus-toms officers, luggage lost and damaged; after the tour, sexual harassmentand accusations from tourists and so on. Thus, this study explores amore comprehensive intrinsic risks faced by GPT leaders and expandsthe foundation of tour-related risk perceptions.

Finally, unlike previous researches on Asian destinations, whose inves-tigations have been limited to the geographic aspects (Teng, 2005; Tsauret al., 1997), this study expands its investigation to include China, Thai-land, Japan, USA, New Zealand, Australia, and Europe; the territory isvast, encompassing Asia, America, Oceania, and Europe. Besides, thisstudy compares risk perception of 12 factors by means of six itineraries.The results indicated that tour leaders who work on Japan routes per-ceived less risk with regard to all risk factors than on any other routes.On the contrary, the China route performs worse in many aspects, fol-lowed by the USA and Thailand. On the China route, tour leaders per-ceived higher risk in the cluster of exogenous risk. This result suggeststhat the overall quality of China’s tour sector is waiting to be raised.

Besides, in terms of risk perception with regard to drivers, Europeranks higher than Thailand, Japan, and the USA. The bad attitude,unprofessional conduct, and physical condition of European bus driv-ers usually lead tour leaders to form negative opinions of the tour.According to the statistical report of the Ministry of Transportationand Communications (2007), accidents caused by long-haul shuttlebuses are increasing annually. Many studies have showed that accidentsin the USA (Demos, 1992; Mackie & Miller, 1978) and Europe (Hame-lin, 1987) have a fairly significant association with fatigue due to driv-ing for a considerably long period of time. Moreover, according tothe interviews with tour leaders, Europeans have a strong geographicconception and less desire to communicate in English, thereby leadingto misunderstandings and communication gaps between Europeandrivers and tour leaders in the itineraries and impeding the tours’ pro-gress. In sum, owing to the inclusion of additional regions in this study,its results are more generalized than those of the previous studies onthe perception of GPT risks.

Implications

Touring Standard Operating Procedure. By explaining these tour riskswith six itineraries, this study contributes to provide comprehensivetour risks as well as concrete incidents to help tour leaders understand


the possible difficulties in different areas. In addition, this study con-structs an evaluative chart for tour leaders’ risk factors on the basisof the theoretical implication of this study. After eliminating the origi-nal questionnaire items, the travel agency can develop a clear standardoperating procedure for tour leaders and facilitate the control of GPTrisks. Practically, during tour, a travel agent prepares a detailed itiner-ary for tour leaders, which usually includes flight numbers, detailedschedules, name and number of restaurants, souvenir shops, coachcompany, and local agency. In addition, the travel agent can designa complete and exhaustive standard operating procedure to be imple-mented during tours for managing risks in tour leaders.

Risk Categorizations of GPT Leaders. The results in the cluster analysisshowed that most of the loss in tour leaders’ touring process is dueto exogenous risk. Since most of exogenous risks are uncontrollable, thecoping strategy for such risks is ‘‘precaution’’. Precaution can be effec-tively exercised by constantly reminding the tour leaders of such exog-enous risks during or providing printed material to remind both thetour leaders and tourists of the same. Besides, a travel agent also canenhance tour leaders’ risk-management ability by ensuring periodicaltraining of phenomenon simulation in order to improve tour leaders’risk perception and reduce loss under uncertainty.

The second type of risk perception in GPT leaders is tourist-inducedrisk; tour leaders can control a certain extent of risks in this type. Forinstance, tour leaders can provide complete information about the tax-able and prohibited goods, expenditures in hotel, etc. in the pre-tourbriefing to avoid certain problems. Moreover, the legal rights and du-ties between travel agents/tour leaders and tourists should be clearlystated in the pre-tour briefing to prevent sexual harassment and accu-sations from tourists. As most of risks in this cluster can be controlled,the coping strategy for risk type is ‘‘education and rewards’’. This strategycan be effectively implemented in two ways: first, by continually educat-ing the tour leader of such tourist-induced risks during the tour, andsecond, by encouraging the group through rewards to overcome cer-tain likely risks in sectors (e.g., taxable and prohibited goods).

Finally, tour leader’s self-induced risks, which results from the tour lead-ers’ negligence when they fail to get completely acquainted with infor-mation before or during the tour, are extremely lower than the othertypes of risk. Meanwhile, the risk of ‘‘change in itinerary and tippingproblems’’ and ‘‘tour leader’s operating negligence’’ can be con-trolled by following the touring standard operating procedure for riskmanagement. Since most of the tour leader’s self-induced risks can becontrolled during the tour, the coping strategy for these risks is ‘‘train-ing and penalty’’. This strategy can be effectively implemented, first, byfamiliarizing tour leaders those self-induced risks through training aswell as through a printed standard operating procedure, and second,be clearly stipulating the penalty, in print, for the ineffective manage-ment of controllable risks, along with the above-mentioned standardoperating procedure.


Pre-Tour Briefing. The risks stemming from tourist-induced risks andtour leader’s self-induced risks can be prevented by tour leaders if theyclearly elicit thorough information in the pre-tour briefing. However,in practice, tourists and travel agents pay little attention to pre-tourbriefing; moreover, the timings of pre-tour briefing are inflexibleand inconvenient, as result of which the tourists’ have less desire to par-ticipate in them. Furthermore, the pre-tour briefing is usually held notby the tour leader but by some inexperienced operators and sales rep-resentatives. Under such circumstances, the lack of practical experi-ence and professional knowledge generally leads to a lag in thecommunication of tour information. If the standard operating proce-dure of pre-tour briefing and lag of information is left unchecked, itwill lead to a gap between the service delivery and external communi-cation; this is referred to as ‘‘gap four’’ by Parasuraman, Zeithaml, andBerry (1988). Certainly, this will manifest into a more severe risk inGPT leaders’ and the travel agents should undeniably focus on suchserious practical managerial problems.

Finally, the intrinsic risks in the GPT faced by the Taiwanese GPTleaders are unlikely to be unique. The authors believe that the intrin-sic risks found in this study are common to leading professions world-wide. Certainly, it is worthwhile for destination countries to pay closerattention to this situation, and the findings and ideas put forthby this comprehensive study could be generalized to the tourismindustry.

Acknowledgements—One year ago, after a car accident, our good friend Chung, Chia-Hsun (thefourth author of this paper) passed away. All the teammates are truly saddened by the loss ofour good friend. Chia-Hsun’s smile will be embedded in everyone’s mind and heart, and wedo believe now Chia-Hsun is living well in heaven and he will guard us all.

REFERENCES

Bauer, R. A. (1960). Consumer behavior as risk taking. In R. S. Hancock (Ed.),Dynamic marketing for a changing world (pp. 389–398). Chicago, Illinois:American Marketing Association.

Bettman, J. R. (1973). Perceived risk and its components: A model and empiricaltest. Journal of Marketing Research, 10(2), 184–190.

Carter, S. (1998). Tourists and traveler’s social construction of Africa and Asia asrisky locations. Tourism Management, 19(4), 349–358.

Cheron, E. J., & Ritchie, J. R. B. (1982). Leisure activities and perceived risk. Journalof Leisure Research, 14(2), 139–154.

China National Tourism Administration (2007). The year book in China tourism in2006. Beijing, China: China National Tourism Administration.

Cho, J., & Lee, J. (2006). An integrated model of risk and risk-reducing strategies.Journal of Business Research, 59(1), 112–120.

Churchill, G. A. Jr., (1979). A paradigm for developing better measures ofmarketing constructs. Journal of Marketing Research, 16(1), 64–73.

Cohen, E. (1972). Towards a sociology of international tourism. SociologicalResearch, 39(1), 164–182.

Davis, D., & Cosenza, R. M. (1993). Business research for decision making. California:Wadsworth Publishing.


Decrop, A. (1999). Triangulation in qualitative tourism research. Tourism Manage-ment, 20(1), 157–161.

del Bosque, I. R. (2008). Tourist satisfaction: A cognitive-affective model. Annals ofTourism Research, 35(2), 551–573.

Demos, E. (1992). Concern for safety: A potential problem in the tourist industry.Journal of Travel & Tourism Marketing, 1(1), 81–88.

Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of Consumer Research, 21(1), 119–134.

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1991). Multivariate dataanalysis with readings (3rd ed.). New York: Macmillan.

Hamelin, P. (1987). Lorry driver’s time habits in work and their involvement intraffic accidents. Ergonomics, 30(9), 1323–1333.

Holsti, O. R. (1968). Content analysis. In G. Lindzey & E. Aronson (Eds.), Thehandbook of social psychology: Research methods (2nd ed., pp. 596–692). ReadingMA: Addison-Wesley.

Hooper, P. (1995). Evaluation strategies for packaging travel. Journal of Travel &Tourism Marketing, 4(2), 65–82.

Hsu, T.-O., & Lin, L.-Z. (2005). Using fuzzy set theoretic techniques to analyzetravel risk: An empirical study. Tourism Management, 27(5), 968–981.

Iverson, T. J. (1997). Decision time: A comparison of Korean and Japanesetravelers. International Journal of Hospitality Management, 16(2), 209–219.

Jacoby, J., & Kaplan, L. B. (1972). The components of perceived risk. In M.Venkatesan (Ed.), Proceedings of third annual conference (pp. 382–393). Chicago:Association for Consumer Research.

Jick, T. D. (1979). Mixing qualitative and quantitative methods: Triangulation inaction. Administrative Science Quarterly, 24, 602–611.

Kassarjian, H. H. (1977). Content analysis in consumer research. Journal ofConsumer Research, 4(1), 8–18.

Keaveney, S. M. (1995). Customer switching behavior in service industries: Anexploratory study. Journal of Marketing, 59(2), 71–82.

Klinke, A., & Renn, O. (2001). Precautionary principle and discursive strategies:Classifying and managing risks. Journal of Risk Research, 4(2), 159–173.

Lawton, G., & Page, S. (1997). Evaluating travel agents’ provision of health adviceto travelers. Tourism Management, 18(2), 89–104.

Lepp, A., & Gibson, H. (2003). Tourist roles, perceived risk and internationaltourism. Annals of Tourism Research, 30(3), 606–624.

Mackie, R. R., & Miller, R. R. (1978). Effects of hours of service, regularity of schedulesand cargo loading on truck and bus drivers. Technical Report 1796-F. Coleta,California: Human Factors Research, Inc.

March, R. (2000). The Japanese travel life cycle. Journal of Travel & TourismMarketing, 9(1&2), 185–200.

Ministry of Transportation and Communications (2007). Statistical abstract oftransportation and communications. Retrieved January 12, 2008, from Ministry ofTransportation and Communication. Department of Statistics Web site:<http://www.motc.gov.tw/mocwebGIP/wSite/np?ctNode=199&mp=2>.

Mitchell, V.-W., & Boustani, P. (1994). A preliminary investigation into pre- andpost-purchase risk perception and reduction. European Journal of Marketing,28(1), 56–71.

Mitra, K., Reiss, M. C., & Capella, L. M. (1999). An examination of perceived risk,information search and behavioral intentions in search, experience andcredence services. Journal of Services Marketing, 13(3), 208.

Okrent, D., & Pidgeon, N. (1998). Risk perception versus risk analysis. ReliabilityEngineering and System Safety, 59(1), 1–4.

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model ofservice quality and its implications for future research. Journal of Marketing,49(4), 41–50.

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal ofRetailing, 64(1), 12–40.

Pinhey, T. K., & Iverson, T. J. (1994). Safety concerns of Japanese visitors to Guam.Journal of Travel & Tourism Marketing, 3(2), 87–94.

http://www.motc.gov.tw/mocwebGIP/wSite/np?ctNode=199&mp=2


Pizam, A. (1999). A comprehensive approach to classifying acts of crime andviolence at tourism destinations. Journal of Travel Research, 38(1), 5–12.

Pizam, A., Tarlow, P., & Bloom, J. (1997). Making tourists feel safe: Whoseresponsibility is it? Journal of Travel Research, 3(3), 23–28.

Quiroga, I. (1990). Characteristics of package tours in Europe. Annals of TourismResearch, 17(2), 185–207.

Reisinger, Y., & Turner, L. W. (2002). Cultural differences between Asian touristmarkets and Australian hosts, Part 1. Journal of Travel Research, 40(3), 295–319.

Robinson, M., & Marlor, R. (1995). Tourism and violence: Communicating therisk. In N. Evans & M. Robinson (Eds.), Issues in travel and tourism(pp. 115–138). Sunderland: Business Educations.

Roehl, W. S., & Fesenmaier, D. R. (1992). Risk perceptions and pleasure travel: Anexploratory analysis. Journal of Travel Research, 30(4), 17–26.

Roselius, T. (1971). Consumer rankings of risk reduction methods. Journal ofMarketing, 35(1), 56–61.

Rundmo, T. (2002). Associations between affect and risk perception. Journal of RiskResearch, 5(2), 119–135.

Rundmo, T., & Sjoberg, L. (1998). Risk perception by offshore oil personnelduring bad weather conditions. Risk Analysis, 18(1), 111–118.

Seddighi, H. R., Nuttall, M. W., & Theocharous, A. L. (2001). Does culturalbackground of tourists influence the destination choice? An empirical studywith special reference to political instability. Tourism Management, 22(2),181–191.

Sonmez, F. S., & Graefe, A. R. (1998a). Determining future travel behavior frompast travel experience and perceptions of risk and safety. Journal of TravelResearch, 37(2), 171–177.

Sonmez, F. S., & Graefe, A. R. (1998b). Influence of terrorism risk on foreigntourism decisions. Annals of Tourism Research, 25(1), 112–144.

Steene, A. (1999). Risk management within tourism and travel suggestions forresearch programs. Turizam, 47(1), 13–18.

Stergiou, D., Airey, D., & Riley, M. (2008). Making sense of tourism teaching.Annals of Tourism Research, 35(3), 631–649.

Stone, R. N., & Grønhaug, K. (1993). Perceived risk: Further considerations for themarketing discipline. European Journal of Marketing, 27(3), 39–50.

Stone, R. N., & Mason, J. B. (1995). Attitude and risk: Exploring the relationship.Psychology & Marketing, 12(2), 135–153.

Teng, W. (2005). Risks perceived by Mainland Chinese tourists towards SoutheastAsia destinations: A fuzzy logic model. Asia Pacific Journal of Tourism Research,10(1), 97–115.

Tesh, S. (1981). Disease causality and politics. Journal of Health Politics, Policy andLaw, 6(3), 369–390.

Tourism Bureau (2007). 2006 annual survey report on R. O. C. outbound travelers.Taipei: Tourism Bureau.

Tsaur, S.-H., Tzeng, G.-H., & Wang, K.-C. (1997). Evaluating tourist risks from fuzzyperspectives. Annals of Tourism Research, 24(4), 796–812.

Unser, M. (2000). Lower partial moments as measures of perceived risk: Anexperimental study. Journal of Economic Psychology, 21(3), 253–280.

Vogt, C. A., & Fesenmaier, D. R. (1995). Tourists and retailers’ perceptions ofservices. Annals of Tourism Research, 22(4), 763–780.

Wang, K.-C., Cheng, S.-C., & Wu, S.-Y. (2002). Factor of service perception amongflight attendant, group package tour leader and tourist. In K. Chon & A. Ogle(Eds.), Proceedings of first Asia Pacific forum for graduate students research in tourism(pp. 305–310). Macao: The Hong Kong Polytechnic University & Institute forTourism Studies.

Wang, K.-C., Hsieh, A.-T., & Chen, W.-Y. (2002). Is the tour leader an effectiveendorser for group package tour brochures? Tourism Management, 23(5),489–498.

Wang, K.-C., Hsieh, A.-T., & Huan, T.-C. (2000). Critical service features in grouppackage tour: An exploratory research. Tourism Management, 21(2), 177–189.


Wang, K.-C., Hsieh, A.-T., Chou, S.-H., & Lin, Y.-S. (2007). GPTCCC: Aninstrument for measuring group package tour service. Tourism Management,28(2), 361–376.

Wang, K.-C., Hsieh, A.-T., Yeh, Y.-C., & Tsai, C.-W. (2004). Who is the decision-maker: The parents or the child in group package tours?. Tourism Management,25(2), 183–194.

Weber, R. (1990). Basic content analysis. Newbury Park, California: Sage.Wester-Herber, M., & Warg, L.-E. (2002). Gender and regional differences in risk

perception: Results from implementing the Seveso Directive in Sweden.Journal of Risk Research, 5(1), 69–81.

Wong, S., & Lau, E. (2001). Understanding the behavior of Hong Kong Chinesetourists on group tour package. Journal of Travel Research, 40(1), 57–67.

Yamamoto, D., & Gill, A. (1999). Emerging trends in Japanese package tourism.Journal of Travel Research, 38(2), 134–143.

Submitted 14 February 2008. Resubmitted 7 April 2009. 8 Final Version 20 August 2009.Accepted 25 August 2009. Refereed anonymously. Coordinating Editor: Abraham Pizam

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