Annals of Tourism Research, Vol. 37, No. 1, pp. 154–179, 2010
0160-7383/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved.
Printed in Great Britain
www.elsevier.com/locate/atoures
doi:10.1016/j.annals.2009.08.004
GROUP PACKAGE TOUR
LEADER’S INTRINSIC RISKS
Kuo-Ching Wang
National Taiwan Normal University, Taiwan
Po-Chen Jao
Hsi-Chen Chan
Chia-Hsun Chung
Chinese Culture University, Taiwan
Abstract: This paper explores the intrinsic risks and risk perception of Taiwanese tour leaders
in terms of group package tour (GPT). Both qualitative interviews and quantitative surveys are
employed in the study. Based on in-depth interviews with 24 GPT leaders, the study identifies
the comprehensive risk items. Moreover, 12 risk factors are extracted through questionnaire
surveys 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, several
academic and managerial implications about the GPT tour risk controls were outlined as well.
Keywords: risk, group package tour (GPT), tour leader. Ó 2009 Elsevier Ltd. All rights
reserved.
INTRODUCTION
In recent years, there has been dramatic growth in outbound tours
from Asian countries, fuelled by the region’s rapid economic growth
and rising income levels (China National Tourism Administration,
2007). The international tourism industry is now witnessing an increasing number of inbound tourists from Asia, such as Australia (Reisinger
& Turner, 2002) and Guam (Iverson, 1997). Moreover, as a result of
easing restrictions on outbound tours by China, the number of Chinese tourists is expected to increase rapidly in the future.
Asian and Chinese tourists normally take all-inclusive tour packages
as compared with Western tourists (Wong & Lau, 2001), especially for
international 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 <gordonwang@ntnu.edu.tw>), his
research interest is tourism marketing. Po-Chen Jao, Doctoral Student, his research interests
include tourism marketing and advertising. Hsi-Chen Chan, Doctoral Student, her research
interests include tourism marketing and group package tour. Chia-Hsun Chung, Graduate
Student, his research interests include tourism marketing and group package tour.
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155
1999). For example, in Taiwan, according to government’s statistical
data, outbound tourists had grown to 95.74 million from 1992 to
2006. For sightseeing purposes, almost half of the tourists participated
in GPTs (Tourism Bureau, 2007). As another example in China, outbound tourists had reached 34.52 million in 2006, indicating an increase of 11.3% from 2005 (31.02 million), and the number of
tourists for outbound GPTs had increased from 6.79 million in 2005
to 8.43 million in 2006, an increase of 21.68% (China National Tourism Administration, 2007).
Previous studies have indicated that service industries are highly
dependent on ‘‘contact employees’’ who have a strong influence on
the service quality as perceived by the consumers (Parasuraman, Zeithaml, & Berry, 1985; Vogt & Fesenmaier, 1995). In GPTs, usually, a
travel 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 factor
influencing the customer’s perception on travel service quality (Wang,
Hsieh, & Chen, 2002; Wang et al., 2004). Quiroga (1990) clearly
pointed out that the function of the tour leader within the group is
considered to be indispensable by the tourists themselves, and the
quality of the tour leader can be a crucial variable in the tour; his or
her presentation can make or break a tour.
In brief, GPTs are a very popular outbound tour mode in many Asian
countries and the tour leader plays an important role in GPTs (Quiroga,
1990; Wang, Cheng, & Wu, 2002). However, prior risk studies examined
risk primarily from the tourist’s perspective (Pinhey & Iverson, 1994;
Roehl & Fesenmaier, 1992; Sönmez & Graefe, 1998a; Teng, 2005). Nevertheless, who should be responsible for the tourists’ safety is still a controversial issue (Robinson & Marlor, 1995). For GPT tourists, the
tourists’ safety primarily is the tour leader’s responsibility. However,
the important questions, such as ‘‘What risks might have occurred while
the tour leader is leading the GPT ?’’ and ‘‘What’s the relationship between different risks with different GPT itineraries?’’ have not yet been answered. As a
result, tour leaders’ experience upon risks is worthy to be discovered.
In practice, the possible risks that one might face during tours are
the priority needed to be considered while planning GPTs. If risk is
viewed as possible loss (Teng, 2005), it is reasonable to assume that a
tour leader’s risks might generate some service failures and then those
failures might entail certain tourist’s losses and finally decrease the extent of the tourist’s perception of service quality in GPT. Therefore, it
is imperative for travel agency managers and tour leaders to augment
their perception and understanding of intrinsic risks in GPT leaders
in terms of risk control strategies, cost reduction, and service quality
control in GPT (Tesh, 1981; Tsaur, Tzeng, & Wang, 1997).
INTRINSIC RISKS IN GPT LEADERS
Risk has become one of the most hotly debated issues in Western
societies today (Okrent & Pidgeon, 1998), and it has been successfully
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incorporated decision-making theories in economics, finance, and the
decision sciences (Cho & Lee, 2006; Dowling & Staelin, 1994). Unser
(2000) indicated that risk and its measurement are still fascinating topics for studies in decision making under risk. In the marketing discipline, the concepts of risk and perceived risk were first discussed in
Bauer’s (1960) ‘‘Consumer Behaviour as Risk Taking’’ research (Bettman, 1973; Stone & Grønhaug, 1993). Since the introduction of the
concept of perceived risk by Bauer, much research has been carried
out by utilizing the concepts of risk and risk reduction processes in
consumer decision making (Bettman, 1973) and many studies have
measured 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. They
defined the risk from ‘‘process of tour’’ and ‘‘destination’’ perspectives
and classified risk into seven evaluative aspects: transportation, law and
order, hygiene, accommodation, weather, sightseeing spot, and medical support. In addition, many studies adopted five risk dimensions
identified by Jacoby and Kaplan (1972) which were financial risk, performance risk, physical risk, social risk, and psychological risk (Cheron
& Ritchie, 1982; Mitra, Reiss, & Capella, 1999). Some studies adopted
six dimensions (Stone & Grønhaug, 1993; Stone & Mason, 1995), by
including time risk as suggested by Roselius (1971). Moreover, several
studies focused on a particular dimension, such as political instability
(Seddighi, Nuttall, & Theocharous, 2001), terrorism (Sönmez & Graefe, 1998a, 1998b), health concerns (Carter, 1998; Lawton & Page,
1997), crime (Pizam, Tarlow, & Bloom, 1997; Pizam, 1999), and satisfaction which first appeared in the study regarding perceived risk
and leisure activities (Cheron & Ritchie, 1982).
Furthermore, Roehl and Fesenmaier (1992) used seven different
types of risks, namely equipment risk, financial risk, physical risk, psychological risk, satisfaction risk, social risk, and time risk, to measure
the risk perceptions of pleasure tourists’. Pinhey and Iverson (1994)
once explored the safety concerns regarding typical vacation activities
among Japanese tourists to Guam. The authors categorized the evaluative aspects of tour safety concerns into seven items: the perceptions
of the described safety, sight-seeing safety, water sports safety, beach
activity safety, night life safety, in-car safety, and road safety. Although
these existing risk/safety studies provided useful information, they did
not take tour leaders’ risk perception into consideration. Besides, most
previous investigations focused on perceived risk, yet this study
explores tour leaders’ experience with risks that have actually been
realized.
More specifically, according to Wang, Hsieh, and Huan (2000), tours
can be categorized into two major modes: GPTs and independent tours;
this categorization is similar to Cohen’s (1972) typology of international
tourists based on their preference for either familiarity or novelty when
traveling, namely, organized mass tourist and individual mass tourist/
explorer/drifter. The tourists on GPTs are Cohen’s organized mass
tourists who prefer the greatest amount of familiarity and travels in
K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
157
an ‘‘environmental bubble’’ of the familiar on a packaged tour. Lepp
and Gibson (2003) verified tourist role based on Cohen’s typology
was the most significant variable in relation to risk perception, with
familiarity seekers being the most risk adverse. They indicated that
the organized mass tourists who seeking familiarity are likely to view
alien environments as more risky than the other tourist roles. Besides,
Lepp and Gibson (2003) specified that different types of tourists perceived risks differently: organized mass tourists perceived terrorism as
a greater risk and concerned more on strange food and health risks
than the other types of tourists. As such, what organized mass tourists
perceived as a risk is different to the other three types. Accordingly,
tourist risk based on Wang et al.’s (2000) tour typology can be categorized into: independent tourists’ risk and group package tourists’ risk.
However, in Sönmez and Graefe (1998a) and Roehl and Fesenmaier’s (1992) studies, they mainly focused on independent tourists’ perceptions of the types of risk present in tours. Meanwhile, in Roehl and
Fesenmaier’s (1992) study, all the trips were either in-state or out-ofstate destinations and most of the respondents traveled with family
members. However, the risk perceptions between the independent
and group package tourists’ are rather different because of the tours’
characteristics. Besides, both Sönmez and Graefe (1998a) and Roehl
and Fesenmaier’s (1992) risk components are too broad to measure;
for example, for measuring physical risk component, the question
was asked as follow: possibility that a trip to this destination will result in
physical danger, injury or sickness. For such question, it seems difficult
to fully conceptualize what physical risk actually entails.
Furthermore, in Tsaur et al.’s (1997) study, only physical and equipment risks in GPTs were emphasized. In fact, several important GPT
sectors which have been indicated in prior studies were overlooked,
such as shopping and optional tour (Wang et al., 2000). These neglected sectors essentially entail certain important risks that a tour leader might encounter during the GPT. Moreover, in Pinhey and
Iverson’s (1994) study on safety concerns, only independent tourists’
risks like how safe is it driving a rental car on Guam were focused on. In
addition, 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), seven
risk aspects developed by Tsaur et al. (1997) were employed to evaluate
the destination risks for Thailand, Malaysia, and Singapore. Although
the methodology and findings in this study are instructive, several
important GPT sectors were overlooked, such as shopping and optional tour (Wang et al., 2000). Moreover, since Teng’s study was essentially a duplication of Tsaur et al.’s (1997) tour risk study, its
contribution to the existing knowledge is not quite apparent.
Thus, it appears that in the relevant theories, the intrinsic risks perceived by GPT leaders have not been clearly identified. Besides, from a
practical viewpoint, perceptions and understandings of risk are important factors influencing the conceptualization of risk control strategies
(Tesh, 1981). Consequently, in order to complement the previous
studies (e.g., Teng, 2005; Tsaur et al., 1997) which merely discussed
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the risk from the perception of tourists, the present study were primarily to (1) explore the intrinsic GPT leaders’ risks and introduce a
grounded model of it, (2) examine the relationship between different
GPT leaders’ risks with different GPT itineraries, and (3) explore the
risk categorizations of GPT leaders.
Study Methods
Unlike previous studies that mainly utilized quantitative approach
(Roehl & Fesenmaier, 1992; Teng, 2005; Tsaur et al., 1997), this study
employed both qualitative and quantitative methods, which is complementary. Mixing both methods can help avoid the problem of a common method variance in using only one method of measurement
because the strengths of one method can counteract the weaknesses
of another (Jick, 1979). Moreover, this is to enhance confidence in
the research result and provide a more comprehension of domain under investigation. Therefore, this study employed both qualitative and
quantitative methods, from in-depth interviews to questionnaire surveys conducted on tour leaders. The qualitative approach was used
to gain a more comprehensive and in-depth understanding of the
intrinsic risks in GPT leaders. Subsequently, to further examine the
relationship between different risks with different GPT itineraries
and classify the risk categorizations of tour leaders, the quantitative
method was used. Both qualitative interviews and quantitative surveys
were conducted in the language of Chinese.
Definition of Intrinsic Risk in GPT Leaders. Although risk concept was
varied 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 one
commonality: risk is often associated with the possibility that an undesirable state of reality may occur as a result of natural events or human
activities. With respect to risk analysis, Steene (1999) once suggested
that risk analysis denotes the systematic examination of a course of
events for the purpose of identifying the incidents and phenomena
that can lead to undesired consequences, as well as the assessment of
these consequences and the judgment of their probability. Thus,
according to Steene, risk analysis has three main aspects: (1) identification of the sources of risks, (2) judgment of probability, and (3) analysis of the consequences. Therefore, based on above information, the
operational definition of intrinsic risks in GPT leaders in this study is:
any events or accidents that would cause possible loss while tour leader is leading
the outbound GPT.
Qualitative Questions Development. The questions were developed into
two parts. In the first part, the travel duration of an outbound GPT is
normally long and covers diverse dimensions, as suggested by Wang
et al. (2000), the GPT was divided into discrete sectors. There are
two advantages to this approach. One is that it can facilitate data
K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
159
collection; moreover, the precise definition of GPT sectors is conducive to eliciting the tour leaders’ recollections. The other advantage is
that dividing the GPT into sectors can prevent some sectors from
being overlooked. Accordingly, Wang et al.’s (2000) nine GPT sectors, namely, the pre-tour briefing, airport/plane, hotel, restaurant,
coach, scenic-spot, shopping, optional tour, and others, were employed in this study.
However, in Wang et al.’s study, they did not separate the sector
of airport from airplane; moreover, and they did not take different
departure and arrival airports into consideration. Since different
risks might be encountered at airports (departure and arrival)
and in the airplanes, to prevent the omission of important intrinsic
risks faced by tour leaders in outbound GPTs, the airport/plane
sector was further divided into five sectors: departure airport
(home country), airplane (forth and back), arrival airport (destination country), departure airport (destination country), and arrival
airport (home country). Consequently, a total of 13 sectors were
employed to explore tour leaders’ experiences with risks that have
actually been realized in this study. The detailed questions were
presented in Figure 1; the following is an example of the hotel sector in GPTs:
Q1: According to your personal experiences in leading GPTs, were
there any events or accidents happening that caused you losses while
staying in the hotel ?
In the second part, Rundmo (2002) and Rundmo and Sjöberg
(1998) once indicated that when thinking about a risk source or potential hazard, people may be worried or feel unsafe. Thus, an affective
component is involved in risk perception. For example, the affect of
worry may be evoked every time a person thinks about a risk source.
Since the current study aims at constructing a comprehensive source
structure of intrinsic risks in outbound GPT leaders, therefore, Rundmo and Sjöberg’s ideas of risk and risk perception were taken into
consideration for developing questions in order to capture tour leaders’ perception of possible or future risks. The following question
was framed by taking the hotel sector as an example; the complete questions were also presented in Figure 1.
Q2: With the exception of things mentioned above, what might be the
events and accidents that you would least expect to happen while staying in the hotel ?
Qualitative Data Collection. Since the study was exploratory in nature, it
aimed at eliciting GPT leader’s viewpoints on the intrinsic risks in
tours. To achieve this, in-depth interviews were the most suitable approach. According to Wester-Herber and Warg’s (2002) study, personal experiences, age, gender, and regional differences influence
the individual’s risk perception. However, regional difference was excluded in this study because Taiwan is fairly small in its territory. Therefore, before in-depth interviews, personal experiences, age, and gender
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Figure 1. The Hierarchical Structure of Questions
were employed as criteria for selecting the appropriate participants in
order to enhance data quality and increase generalization. In total, 24
tour leaders were conducted; the interviewees’ profile is presented in
Table 1.
To enhance the validity of data analysis, data triangulation technique
was employed for data collection (Jick, 1979). Decrop (1999) indicated
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Table 1. Profile of Interviewees (n = 24)
Experience
Gender/Age
Male
With Throughout Guide Experiences*
Sample Size Female
Sample Size
Age 2535
Age 3645
Age 4655
2
2
2
Age 2535
Age 3645
Age 4655
2
2
2
Without Throughout Guide Experiences Age 2535
Age 3645
Age 4655
2
2
2
Age 2535
Age 3645
Age 4655
2
2
2
Total
12
12
*
In Taiwan, China, etc., throughout guide represents that tour leader plays two roles at the
same time while leading the outbound GPTs, one role is the tour leader and the other is the
local guide. Typically, throughout guide would be mostly found in the long-haul outbound
GPTs, such as Europe, New Zealand, Australia, America, etc.
that triangulation means looking at the same phenomenon, or
research question, from more than one source of data and this is useful
for supporting the results. Information coming from different angles
can be used to corroborate, elaborate or illuminate the research problem. It limits personal and methodological biases and enhances a
study’s generalizability. For this purpose, two experts and two scholars
were recruited for data collection. In total, 28 respondents participated
in the in-depth interviews. Each interview lasted approximately 1.5-2
hours. All of the above respondents were interviewed with the questions in Figure 1.
Member Checking. Before the data analysis, a member checking was
conducted to verify the credibility of the interview data (Decrop,
1999). All the 28 transcripts were returned, among them, 14 transcriptions did not require further amendments, the other transcripts
respectively indicated some typing-errors, new events/accidents were
found, and some events/accidents were revised.
Qualitative Data Analysis. The overall process of this part was divided
into three parts: unit of analysis, category development and reliability,
and category confirmation. Part one, as indicated by Holsti (1968) and
Kassarjian (1977), the first step in data analysis is to determine the
appropriate unit of analysis. In this study, the basic units of analysis
were the intrinsic risks in GPT leaders’ risks which resulting from
events or accidents. For instance, in the coach sector, an interviewee
responded that
‘‘. . .when I [female tour leader] was leading a tour to Europe, the
coach driver asked me to sleep with him. I think sexual harassment
is truly one of the risks when female GPT leaders leading a tour. . ..’’
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The above-mentioned example implies that the sexual harassment
from driver toward tour leaders has actually caused psychological
and physical harm to them. Therefore, such an event would be coded
into a risk unit of analysis and called ‘‘sexual harassment from the
driver.’’
Then, two judges (A and B) (both had plenty working experiences in
a travel agency) independently coded the transcriptions from the 26
questions into 1,809 units of analysis (Q1 and Q2). Upon completing
the coding of the unit of analysis, the two judges compared their decisions and resolved disagreements by discussion with the researchers.
Nevertheless, some of the above units were of doubtful relevance to
the study. In the viewpoint, judge A and B and the researchers conducted a screening procedure. In total, 194 units in Q1 were found
to be irrelevant and 398 units in Q2 were found to be identical to the
units in Q1: those units were ultimately eliminated. Finally, 1,217 units
of analysis were obtained for further category development (see Table
2).
Part two, category development and reliability, after the basic unit of
analysis was established, the 1,217 units were divided into categories.
The single classification concept for category development, recommended by Weber (1990), was employed. In an iterative process conducted by judge A and B, each of the units was read out, classified,
re-read, and re-classified. Finally, 135 inferred categories emerged within the 13 GPT sectors (see Table 2), and each of these categories was
named. After the categorization process was complete, this study tested
the reliability of the categorization process.
According to Keaveney (1995), if the inter-judge and intra-judge levels of agreement reach .80, the categorization process can be regarded
as reliable. This study introduced judge C, an Assistant Professor in the
Department of Tourism Management who also had working experience 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 C
categorized all of the 1,217 units into the categories created by judges
A and B and was encouraged to create new categories if appropriate
(Keaveney, 1995; Wang et al., 2000). The result of the inter-judge reliability was .993 for judge C, and no new categories emerged. With respect to the intra-judge reliability which were all above .996 for both
judge A and B.
Part three, category confirmation, in addition to the interviews conducted on the 24 tour leaders, two senior travel experts and two scholars were also interviewed to further category confirmation. In total, 441
units of analysis emerged from two experts and two scholars. Judge A
and B then tried to categorize these 441 units into the 135 categories
with the aim of developing new categories; however, no new categories
emerged in this confirmation process. This result is consistent with Decrop’s (1999) view that triangulation consists of strengthening qualitative findings by showing that several independent sources converge on
them. Therefore, the categories in this study have content validity and
no further interviews were necessary.
Table 2. The Intrinsic Risk Units of Analysis
Outbound
GPT
Sectors
Total
Q2a
Total
Original
Units
Removed
Units
Remained
Units
Obtained
Categories
Original
Units
Removed
Units
Remained
Units
Obtained
Categories
Remained
Units
Obtained
Categories
(%)b
174
100
8
17
166
83
17
13
56
30
51
24
5
6
2
3
171
89
19
16
14.1
11.9
207
130
98
36
9
27
171
121
71
12
11
9
38
35
37
36
35
28
2
0
9
1
0
2
173
121
80
13
11
11
9.6
8.1
8.1
137
10
127
10
32
32
0
0
127
10
7.4
134
41
93
10
38
38
0
0
93
10
7.4
86
75
9
5
77
70
8
9
42
28
41
28
1
0
1
0
78
70
9
9
6.7
6.7
67
2
65
9
20
20
0
0
65
9
6.7
114
49
23
5
91
44
7
6
31
29
31
28
0
1
0
1
91
45
7
7
5.2
5.2
16
2
14
4
6
6
0
0
14
4
3.0
1,387
194
1,193
125
422
398
24
10
1,217
135
100
163
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 the
exception 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).
K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
Coach
Departure
airport
(destination)
Hotel
Scenic-spot
Airplane
(forth and
back)
Arrival
airport
(destination)
Departure
airport
(home)
Shopping
Optional
tour
Arrival
airport
(home)
Restaurant
Pre-tour
briefing
Others
Q1a
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K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
Grounded Model of Intrinsic Risks in GPT Leading. According to Table 2,
which showed the number of categories for each GPT sector, the three
main risk sectors as perceived by tour leaders were ‘‘coach’’, ‘‘departure airport/destination country’’, and ‘‘hotel’’ and the least-mentioned sector was ‘‘others’’. Taking the most noteworthy findings in
‘‘coach’’ as an example, this sector represents 14.1%, the largest sector
of all of risk categories (19/135).
In this sector, ‘‘poor vehicle conditions’’ was major concern of
tour leaders (41/171, 24.0%). The chief source of risk mainly comes
from vehicles being too old and lacking cleaning, microphone malfunctions, or air conditioning not cold enough. Second largest category was ‘‘property loss of the GPT tourists’’ (22/171, 12.9%).
Moreover, six out of the 19 categories of risk were related to coach
driver, among them, risks mainly came from the ‘‘poor attitude of
the 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 causing
problems in the interaction and cooperation with the tour leader,
for instance,
‘‘I once led a group to Europe and got an Italian driver. His English
was so poor that we had to communicate with hand signs. This would
affect the whole group’s rhythm and mood.’’
In summary, because the coach is the most relied upon transportation in a GPT tour, the driver is the tour leaders’ working partner with
the most frequent interactions. Therefore, if the driver’s attitude and
professionalism is poor, it will not only affect the flow of the entire trip
but also deal a severe blow to the tour leader’s mood when leading the
group.
Quantitative Questionnaire Development. On the basis of qualitative
results, a questionnaire was developed in three parts. In the first
part, two questions were designed to capture the tour leaders’ professional backgrounds. One question asked the tour leaders to identify his/her most specialized itinerary from six GPT itineraries,
namely, China, Thailand, Japan, USA, New Zealand/Australia, and
Europe; these six GPT itineraries were selected either because they
are the most popular GPT itineraries in practice or they are among
the top five destination countries for outbound GPTs in Taiwan
(Tourism Bureau, 2007). Another question asked each respondent
indicate the frequency of this GPT itinerary that he/she has been
leading. In the second part, 13 GPT sectors with 135 categories
were rewritten to develop an original scale wherein each category
was anchored with a five-point scale ranging from ‘‘extremely
impossible’’ to ‘‘extremely possible’’. The following is an example of a
question pertaining to ‘‘unprofessional driver’’ category in the ‘‘coach’’
sector:
Q: According to your personal experiences in leading your most specialized GPT, in the coach sector, the possibility of confronting an
unprofessional driver is?
K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
165
Except those 135 questions, two questions were designed as reversed
statement in order to eliminate potential biased. Moreover, an openended question like ‘‘Except for the above-mentioned risk features, if there
are any risk features that you think important, please specify and write them
down below’’ is also included in each sector to detect whether or not
the risk categories obtained from the qualitative analysis is comprehensive. In the third part, several questions were included to capture the
tour leaders’ demographic profiles, such as gender, age, education,
average monthly income, how many years for being GPT leader, and
the name of the travel agency that the tour leader works for.
Prior to the data collection, two doctoral and two EMBA students
who presently work for a travel agency were invited to assess the content and relevance of this questionnaire. In addition, 30 undergraduate students from the Department of Tourism Management were
also invited to evaluate the comprehension of the words and phrases
of items. Based on their comments, some revisions were made to improve the clarity of the items.
Quantitative Data Collection. The survey was conducted by different travel agency managers who volunteered to collect the questionnaires in
Taiwan. The data collection was carried out over a three-month period.
In total, 650 questionnaires were distributed, 437 surveys were returned, and of those, 310 were useable for the purpose of analysis.
From the years for being outbound tour leader and frequency of leading the most specialized itinerary, the statistics of cross tabulation reveals that 64.4% of the respondents have at least five years and above
experience as tour leaders and all of them have led a specialized
GPT itinerary at least six to ten times and above. Besides Europe was
identified as the most specialized GPT itinerary (21.6%), followed by
Thailand (19%), and China (18.1%). A total of 75 major travel agencies were surveyed. Among them, 19 were wholesale travel agencies,
which constitute 23% of the total wholesale travel agencies (81) in Taiwan (Tourism Bureau, 2007).
The Risk Measurement of Six GPT Itineraries and 13 Sectors. According to
Table 3, China was viewed as the most risky destination, followed by
Thailand, USA, Europe, New Zealand/Australia, and Japan. Analysis
of variance (ANOVA) was conducted to test whether there were significant differences in the risk measurement of these six itineraries. The
result reveals significant differences in these six itineraries. On the basis of the homogeneous subsets test (Scheffe, alpha at .05 level), they can
then be categorized into four different groups as follows: China and
Thailand (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 ‘‘departure
airport (destination)’’ as the least risky sectors. Except ‘‘others’’ sector,
12 of the 13 GPT sectors were found to have significant differences. For
the homogeneous subsets test, most sectors were categorized into three
or four different groups. Interestingly, for three sectors (airplane/
forth and back, arrival airport/home, and others) the heterogeneous
China/
561
Thailand/
59
Japan/
51
USA/
43
New Zealand/
Australia/34
Europe/
67
Average
score
Ranking
F
p
1
13 GPT Sectors with 135 Categories
Pre-tour
briefing
Departure
airport
(home)
Airplane
(forth/
back)
Arrival
airport
(destination)
Coach
Scenicspot
Restaurant
Optional
tour
Hotel
Shopping
Departure
airport
(destination)
Arrival
airport
(home)
Others
Average
score
Ranking
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
2.49
a
2.75
a
2.60
a
a
2.76
a
2.10
b
1.99
c
2.12
c
2.51
a
2.52
a
2.59
a
2.46
ab
2.76
a
2.11
b
2.21
bc
2.19
bc
2.54
a
2.49
a
2.51
ab
2.43
abc
2.38
2.55
a
2.35
(.50)
12
3.24
.007
2.62
(.63)
3
1.55
.172
2.71
ab
2.38
b
2.69
ab
2.45
b
3.22
2.63
ab
3.21
(.67)2
1
4.42
.001
2.63
(.60)
2
4.43
.001
3.23
ab
b
2.84
a
3.38
3.20
ab
ab
2.69
a
2.49
a
2.64
a
2.44
a
2.50
a
2.58
(.49)
4
2.28
.046
2.53
ab
2.10
c
2.60
a
2.24
bc
2.54
ab
2.47
(.54)
8
9.90
.000
2.59
a
2.14
c
2.51
ab
2.24
bc
2.57
a
2.48
(.52)
6
9.39
.000
2.61
ab
2.30
bc
2.61
ab
2.27
c
2.67
a
2.57
(.53)
5
8.91
.000
2.42
(.56)
10
8.96
.000
2.48
(.61)
7
13.62
.000
2.43
(.51)
9
9.68
.000
2.49
ab
1.95
c
2.33
b
2.24
bc
2.28
bc
2.35
(.55)
11
15.47
.000
2.31
ab
2.01
c
2.37
a
2.05
bc
2.34
ab
2.29
(.46)
13
10.98
.000
2.38
a
2.16
a
2.40
a
2.19
a
a
2
c
2.20 (.36)
6
2.57
ab
(.38)
3
2.29
bc
(.51)
5
2.53
ab
(.46)
4
2.59 (.35)
2.50
(.43)
11.19
.000
Number represents how many GPT leaders have identified this itinerary as his/her most specialized route; 2 Number in the parenthesis represents the
standard 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.
K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
Most
Specialized
GPT
Itineraries
166
Table 3. Risk Measurement of Six GPT Itineraries
K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
167
subsets were not identified which represent these three sectors have
similar 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 factor
analysis (EFA) was employed to develop a reduced and more parsimonious measurement of intrinsic risks in GPTs. This study used the raw
scores of the original scale to conduct the EFA. The items of EFA have
been eliminated from 135 to 38 items. As a result, the communality of
each item exceeded .66 and all factor loadings were exceeded the required value of .4 (del Bosque, 2008; Stergiou, Airey, & Riley, 2008).
The 38 items for the GPT leaders’ perceived risk produced 12 factors
with an eigenvalue greater than 1.0. The reliability of each factor exceeded .76. These factors explained 73.95% of the variance. The detailed factors and items presented in Table 4.
Cluster Analysis. This study employed 12 groups of factor scores derived from the EFA in the cluster analysis. The K-means clustering
method, a nonhierarchical algorithm (Hair, Anderson, Tatham, &
Black, 1991), was used to determine the optimal number of clusters
on the basis of these factors. As a result, the three-cluster solution
was the most appropriate for the data of tour leaders’ perceived risks.
Moreover, each cluster’s name was denominated in accordance with
the manifestation of factor means. The multivariate statistics showed
significant differences between the three clusters (p < .001).
The results indicated that cluster one has significantly higher mean
of factors (M = 3.4, mean of cluster one = 3.02) in ‘‘hijacking and plane
crash’’, ‘‘optional tour and shopping’’, ‘‘document and property stolen’’, ‘‘luggage lost’’, ‘‘driver problems’’, and ‘‘bribery and obstruction
by customs officers’’ than the other clusters. These factors are related
to 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, mean
of cluster 2 = 2.69) in ‘‘sexual harassments and accusation from tourists’’, ‘‘tourist’s compensation problems associated with damages and
hotel expense’’, ‘‘tourist’s taxable and prohibited goods’’, and ‘‘tourist’s visa and passport expiration issues’’. These factors are related to
the 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 tipping problems’’ and ‘‘tour leader’s operating negligence’’, followed
by 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 have
the highest explanation power (37%), followed by tourist-induced risks
cluster (24%) and tour leader’s self-induced risks cluster (13%) (see
Table 5).
168
K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
Table 4. Results of Factor Analysis of the Perceived Risk by Tour Leader
Perceived risk factors and items
Optional tour and shopping
overly priced products in sopping store
forced optional tours by local tour guide
fake products and flawed
merchandise in shopping store
forced shopping
GPT tourists complain
over optional tour unworthiness
optional tour not well arranged
Tour leader’s operating negligence
temporary closure of the scenic-spot
schedule delay leading to
the missing a visiting scenic-spot
sudden change of the schedule leading
to missing the visiting scenic-spot
temporary closure of the restaurant
special meals not ordered
Driver problems
poor attitude of the driver
unprofessionalism of the driver
poor physical strength of the driver
Sexual harassment and accusation from tourists
actual product different from the
anticipation of the GPT tourists sales
tour leader wrongfully accused
by the GPT tourists
sexual harassment by the tourists
tourists with deficient traveling knowledge
Bribery and obstruction by customs officers
customs officers asking for bribes in
arrival airport/destination country
customs officers asking for bribes in
departure airport/destination country
hard time getting through customs
Tourist’s compensation problems associated
with damages and hotel expenses
problem of compensation to the
damaged facilities of hotel room
problem over compensation to
loss of objects in hotel room
dispute over payment items
Tourist’s taxable and prohibited goods
taxable goods overweight in arrival
airport/home country
taxable goods overweigh in departure
airport/destination country
GPT tourists carrying prohibited goods
Factor Variance Cronbach Communality
loading explained alpha
(%)
8.833
.788
.666
7.616
.822
.643
6.956
.857
.787
6.304
.763
.666
6.294
.861
.777
6.239
.855
.770
5.784
.814
.718
.771
.709
.701
.696
.548
.422
.787
.728
.674
.581
.479
.812
.807
.768
.748
.708
.699
.603
.841
.781
.766
.845
.796
.687
.807
.714
.616
169
K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
Table 4 (continued)
Perceived risk factors and items
Change in itinerary and tipping problems
not paying tips
GPT tourists are not given full and
comprehensive information
during the pre-tour briefing
tourist bargain for tip issue
Tourist’s visa and passport expiration issues
passport expires
visa expires
Hijacking and plane crash
hijack
plane crash
Luggage lost and damaged
luggage damaged
luggage loss
Document and property stolen
documents stolen
property stolen
Total variance explained
Factor Variance Cronbach Communality
loading explained alpha
(%)
5.460
.781
.713
5.265
.906
.878
5.217
.951
.880
5.105
.863
.803
4.881
.886
.854
.779
.774
.684
.864
.862
.938
.927
.828
.812
.838
.806
73.95
Note: Each statement was measured on a five-point scale ranging from 1 = extremely impossible to 5 = extremely possible.
Perceived Risks Analysis
Analysis of Six GPT Itineraries and 13 Sectors. After the EFA, the new
ranking of the average scores of the tour leaders’ perceived risks in
six GPT itineraries was found to be similar with the original ranking
(see Table 6). The new risk-ranking of six GPT itineraries from higher
to lower is China, Thailand, USA, Europe, New Zealand/Australia, and
Japan. Moreover, in regarding to tour leaders’ perceived risks in 13 sectors, the results revealed the new ranking of average scores in every factor dimension was apparently different before and after the items were
eliminated. However, in spite of five dimensions, which are airplane,
arrival home airport, arrival destination airport, departure home airport, and shopping; the others deviated all within three positions. Such
an outcome not only indicated the reliability of the new GPT risk model but also provided a more parsimonious measurement which the
items were condensed from 135 to 38 items.
Post-Hoc Test of the Perceived Risk. Post-Hoc analysis was used to report
the differences in 12 factors among six itineraries. Meanwhile, the six
GPT itineraries were adopted as the independent variables of the analysis, and they corresponded with the dependent variables, that is, the
12 perceived risk factors. In Table 7, the numbers in the parenthesis
represent the mean difference between two itineraries; for example,
M-J stands for the mean difference of China minus Japan in relation
170
K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
Table 5. Results of Cluster Analysis of Perceived Risk
Factors
Cluster 1
Cluster 2
Cluster 3
F value
Exogenous Tourist-induced Tour leader’s
risk
risk
self-induced
risk
1 Optional tour and shopping
3 Driver problems
5 Bribery and obstruction
by customs officers
10 Hijacking and plane crash
11 Luggage lost and damaged
12 Document and
property stolen
4 Sexual harassment and
accusation from tourists
6 Tourist’s compensation
problems
associated with damages
and hotel expense
7 Tourist’s taxable and
prohibited goods
9 Tourist’s visa and
passport expiration issues
2 Tour leader’s
operating negligence
8 Change in itinerary and
tipping problems
3.42
3.33
3.19
2.65
2.62
2.70
2.38
2.27
2.13
96.08*
84.47*
82.06*
3.70
3.38
3.40
2.74
2.11
2.50
2.42
1.76
2.34
42.85*
47.71*
87.64*
2.88
2.79
2.09
67.68*
3.16
3.07
2.39
66.72*
2.75
2.91
1.89
46.23*
1.60
3.28
1.56
50.51*
3.15
2.49
2.44
57.78*
2.38
2.43
2.58
79.97*
n = 68
n = 146
n = 127
lambda = .23
(p < .001)
37%
24%
13%
Explained variance
*
p values are significant at the .001 level.
to 12 factors. The results revealed that among these 12 factors, except
for ‘‘Sexual harassment and accusation from tourists’’, ‘‘Tourist’s taxable and
prohibited goods’’, ‘‘Tourist’s visa and passport expiration issues’’, and
‘‘Hijacking and plane crash’’, eight factors were found to have significant
differences with regard to the GPT itineraries. The mean differences
and explanation of three interesting risk factors in GPT leaders’ on
every 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); and
also this risk factor is higher in Thailand than Japan (.84, p < .001),
New Zealand/Australia (.55, p < .01), and Europe (.51, p < .01). As indicated by Wang et al. (2000, p. 185), optional tour and shopping are two
of the most important service features in GPTs. The risks are mainly
Table 6. Risk Measurement of Six GPT Itineraries
38 Items for New 13 GPT Sectors
Pre-tour Departure Airplane Arrival
Coach Scenic- Restaurant Optional Hotel Shopping Departure
Arrival Others Average
briefing airport
(forth/ airport
spot
tour
airport
airport
score
(home)
back)
(destination)
(destination) (home)
Ranking3 New
Ranking4
China/591
Thailand/65
Japan/56
USA/50
New Zealand/
Australia/
37
Europe/74
3.02a*
2.88ab
2.47b
3.12a
2.92ab
2.78a
2.56a
2.31a
2.62a
2.40a
1.73a
1.97a
1.93a
1.98a
1.71a
2.42a
2.45a
1.72bc
2.02ab
1.57c
2.57a
2.40a
2.12c
2.48ab
2.01bc
2.89a
2.59ab
2.53bc
2.60ab
2.38c
2.48a
2.29ab
2.21ab
2.36a
2.02bc
2.90a
3.05a
1.99c
2.77a
2.32bc
2.97a
2.76a
2.25bc
2.43ab
2.28bc
2.95ab
2.58a
1.66a
1.91b
2.89a
2.67a
2.44a
2.57ab
Average score
2.89
(.98)2
1
1
4.60
.000
2.54
(.81)
2
7
2.54
.028
1.83
(.83)
4
13
1.95
.085
2.01
(.79)
8
12
17.25
.000
2.41
(.82)
6
9
12.71
.000
2.61
(.72)
5
3
3.91
.002
2.30
(.79)
10
10
3.07
.010
2.60
(.90)
7
4
17.82
.000
Ranking
New Ranking
F
p
1
3.08a
2.86ab
2.06c
2.54bc
2.48bc
2.26a
2.30a
1.94abc
2.14ab
1.87bc
2.77a
2.61a
2.31a
2.70a
2.44a
2.62a
2.75a
2.49a
2.72a
2.58a
2.65a (.34)2
2.57a (.35)
2.17c (.36)
2.49ab (.38)
2.54bc (.51)
1
2
6
3
5
1
3
6
2
4
2.42ab 2.33bc
2.04ab
2.70a
2.52a
2.78ab (.46) 4
5
2.51
(.82)
9
8
10.04
.000
2.09
(.72)
13
11
5.36
.000
2.58
(.77)
12
5
4.59
.000
2.61
(.81)
3
2
1.59
.161
2.77
(.76)
2.55
(.91)
11
6
16.51
.000
7.89
.000
Number represents how many GPT leaders have identified this itinerary as his/her most specialized route; 2 Number in the parenthesis represents the
standard 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’s
perceived risk in the 38 items; * Means with two (ab) or three (abc) superscripts represent at two or three different homogeneous subsets based on Scheffe
tests, alpha at .05 level.
K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
Most
Specialized
GPT
Itineraries
171
172
K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
Table 7. Result of Post-Hoc Test of the Perceived Risk by Tour Leader
Factor
Scheffe multiple range tests
M-J
1
(.87)
***
2
3
M-A
M-N
M-E
T-J
(.59) (.54) (.84)
**
**
***
T-A
T-N
T-E
(.55) (.51)
**
**
J-A
J-E
A-E
N-E
(-.54)
**
(.49)
***
(.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)
***
*
11
(.61)
***
(-.53) (-.62)
**
*
12
(.55)
*
9
10
*p
values are significant at the .05 level;**p values are significant at the .01 level;***p values are
significant 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 problems; 5: Bribery and obstruction by customs officers; 6: Tourist’s compensation problems
associated 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 ‘not
significant’.
caused by local agents and local guides in destination. In practice, perceived risks such as forced shopping, deliberate stalling of tourists in
the stores for shopping; incorporate additional shopping spots/optional tour during the main tour are often observed in China and
Thailand.
Driver problems. In this factor, risks perceived in Europe is apparently
higher 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 to
communicate in English. Accordingly, it sometimes creates the
K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
173
misunderstandings and communication gaps between European drivers and tour leaders in the itinerary, thus impeding the tour’s progress.
Bribery and obstruction by customs officers. With regard to this factor, China, 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 seldom found in many destination countries/airports, while leading the
tour, especially during the CIQ (custom, immigration, and quarantine)
procedures, risks such as ‘‘bribery and obstruction by customs officers’’ are
still perceived by tour leaders in certain destination countries/airports.
CONCLUSION
This study outlines both qualitative and quantitative approaches to
explore GPT leaders’ perception of intrinsic risks. The results obtained
not only fill up the theoretical gap in studies on tour risks but also offer
insights into risk management strategies. The discussions are described
in detail below.
First, with respect to the risk/safety analysis regarding destinations,
most of the prior studies involved the participation of undergraduate
students (Hsu & Lin, 2005) and tourists (Lepp & Gibson, 2003; Pinhey
& Iverson, 1994; Roehl & Fesenmaier, 1992; Sönmez & 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 major
travel agencies in Taiwan. Because of their considerable experience in
leading outbound tours, the authors believe that the results can offer a
better understanding/generalization of GPT related risks for the destination risk analysis.
Second, although prior risk/safety studies have briefly discussed risk
factors, they are less specific when addressing the tour leaders’ perceived risks. For example, Roehl and Fesenmaier (1992) identified seven types of travel risks as perceived by independent tourists. However,
these types are too general to allow for a more specific understanding
of the cause of every tour risk. This study excluded tour leaders’ work
characteristics to nullify the results of the previous GPT studies, which
merely consider the risk perception in GPTs from the perspectives of
tourists and destinations. In addition, following Roehl and Fesenmaier’s classification of risk perception by independent tourists, which
was carried out on the basis of the mean scores of a three-factor loading, this study considered the interaction between GPT participants
and the environment to identify three risk clusters, namely, tour leaders’ self-induced risks, tourist-induced risks, and exogenous risks, after
conducting a cluster analysis. This would more accurately prove that
the risk perception of tour leaders is generalized on the basis of the
interaction between tour leaders, tourists, and the environment.
174
K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
Moreover, although Tsaur et al. (1997) and Teng (2005) utilized the
seven risk aspects, they are inadequate to cover all aspects of GPT risks.
This study takes into consideration the ‘‘process of tour’’ which is divided into before, within, and after the tour, for an analysis of risk perception. Interestingly, some substantial and concrete phenomena were
discovered: before the tour, fewer participants and insufficient information
in pre-tour briefing, and tourist’s visa and passport expiration issues;
within the tour, arguments to incorporate optional tours and shopping
in the main tour, document and property stolen, problem of goods that
are taxable and prohibited for tourists, bribery and obstruction by customs officers, luggage lost and damaged; after the tour, sexual harassment
and accusations from tourists and so on. Thus, this study explores a
more comprehensive intrinsic risks faced by GPT leaders and expands
the foundation of tour-related risk perceptions.
Finally, unlike previous researches on Asian destinations, whose investigations have been limited to the geographic aspects (Teng, 2005; Tsaur
et al., 1997), this study expands its investigation to include China, Thailand, Japan, USA, New Zealand, Australia, and Europe; the territory is
vast, encompassing Asia, America, Oceania, and Europe. Besides, this
study compares risk perception of 12 factors by means of six itineraries.
The results indicated that tour leaders who work on Japan routes perceived less risk with regard to all risk factors than on any other routes.
On the contrary, the China route performs worse in many aspects, followed by the USA and Thailand. On the China route, tour leaders perceived higher risk in the cluster of exogenous risk. This result suggests
that the overall quality of China’s tour sector is waiting to be raised.
Besides, in terms of risk perception with regard to drivers, Europe
ranks higher than Thailand, Japan, and the USA. The bad attitude,
unprofessional conduct, and physical condition of European bus drivers usually lead tour leaders to form negative opinions of the tour.
According to the statistical report of the Ministry of Transportation
and Communications (2007), accidents caused by long-haul shuttle
buses are increasing annually. Many studies have showed that accidents
in the USA (Demos, 1992; Mackie & Miller, 1978) and Europe (Hamelin, 1987) have a fairly significant association with fatigue due to driving for a considerably long period of time. Moreover, according to
the interviews with tour leaders, Europeans have a strong geographic
conception and less desire to communicate in English, thereby leading
to misunderstandings and communication gaps between European
drivers and tour leaders in the itineraries and impeding the tours’ progress. In sum, owing to the inclusion of additional regions in this study,
its results are more generalized than those of the previous studies on
the perception of GPT risks.
Implications
Touring Standard Operating Procedure. By explaining these tour risks
with six itineraries, this study contributes to provide comprehensive
tour risks as well as concrete incidents to help tour leaders understand
K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
175
the possible difficulties in different areas. In addition, this study constructs an evaluative chart for tour leaders’ risk factors on the basis
of the theoretical implication of this study. After eliminating the original questionnaire items, the travel agency can develop a clear standard
operating procedure for tour leaders and facilitate the control of GPT
risks. Practically, during tour, a travel agent prepares a detailed itinerary for tour leaders, which usually includes flight numbers, detailed
schedules, name and number of restaurants, souvenir shops, coach
company, and local agency. In addition, the travel agent can design
a complete and exhaustive standard operating procedure to be implemented during tours for managing risks in tour leaders.
Risk Categorizations of GPT Leaders. The results in the cluster analysis
showed that most of the loss in tour leaders’ touring process is due
to exogenous risk. Since most of exogenous risks are uncontrollable, the
coping strategy for such risks is ‘‘precaution’’. Precaution can be effectively exercised by constantly reminding the tour leaders of such exogenous risks during or providing printed material to remind both the
tour leaders and tourists of the same. Besides, a travel agent also can
enhance tour leaders’ risk-management ability by ensuring periodical
training 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-induced
risk; tour leaders can control a certain extent of risks in this type. For
instance, tour leaders can provide complete information about the taxable and prohibited goods, expenditures in hotel, etc. in the pre-tour
briefing to avoid certain problems. Moreover, the legal rights and duties between travel agents/tour leaders and tourists should be clearly
stated in the pre-tour briefing to prevent sexual harassment and accusations from tourists. As most of risks in this cluster can be controlled,
the coping strategy for risk type is ‘‘education and rewards’’. This strategy
can be effectively implemented in two ways: first, by continually educating the tour leader of such tourist-induced risks during the tour, and
second, by encouraging the group through rewards to overcome certain likely risks in sectors (e.g., taxable and prohibited goods).
Finally, tour leader’s self-induced risks, which results from the tour leaders’ negligence when they fail to get completely acquainted with information before or during the tour, are extremely lower than the other
types of risk. Meanwhile, the risk of ‘‘change in itinerary and tipping
problems’’ and ‘‘tour leader’s operating negligence’’ can be controlled by following the touring standard operating procedure for risk
management. Since most of the tour leader’s self-induced risks can be
controlled during the tour, the coping strategy for these risks is ‘‘training and penalty’’. This strategy can be effectively implemented, first, by
familiarizing tour leaders those self-induced risks through training as
well as through a printed standard operating procedure, and second,
be clearly stipulating the penalty, in print, for the ineffective management of controllable risks, along with the above-mentioned standard
operating procedure.
176
K.-C. Wang et al. / Annals of Tourism Research 37 (2010) 154–179
Pre-Tour Briefing. The risks stemming from tourist-induced risks and
tour leader’s self-induced risks can be prevented by tour leaders if they
clearly elicit thorough information in the pre-tour briefing. However,
in practice, tourists and travel agents pay little attention to pre-tour
briefing; moreover, the timings of pre-tour briefing are inflexible
and inconvenient, as result of which the tourists’ have less desire to participate in them. Furthermore, the pre-tour briefing is usually held not
by the tour leader but by some inexperienced operators and sales representatives. Under such circumstances, the lack of practical experience and professional knowledge generally leads to a lag in the
communication of tour information. If the standard operating procedure of pre-tour briefing and lag of information is left unchecked, it
will lead to a gap between the service delivery and external communication; this is referred to as ‘‘gap four’’ by Parasuraman, Zeithaml, and
Berry (1988). Certainly, this will manifest into a more severe risk in
GPT leaders’ and the travel agents should undeniably focus on such
serious practical managerial problems.
Finally, the intrinsic risks in the GPT faced by the Taiwanese GPT
leaders are unlikely to be unique. The authors believe that the intrinsic risks found in this study are common to leading professions worldwide. Certainly, it is worthwhile for destination countries to pay closer
attention to this situation, and the findings and ideas put forth
by this comprehensive study could be generalized to the tourism
industry.
Acknowledgements—One year ago, after a car accident, our good friend Chung, Chia-Hsun (the
fourth author of this paper) passed away. All the teammates are truly saddened by the loss of
our good friend. Chia-Hsun’s smile will be embedded in everyone’s mind and heart, and we
do believe now Chia-Hsun is living well in heaven and he will guard us all.
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Submitted 14 February 2008. Resubmitted 7 April 2009. 8 Final Version 20 August 2009.
Accepted 25 August 2009. Refereed anonymously. Coordinating Editor: Abraham Pizam
Available online at www.sciencedirect.com