Psychology
2012. Vol.3, No.9, 722-728
Published Online September 2012 in SciRes (http://www.SciRP.org/journal/psych) http://dx.doi.org/10.4236/psych.2012.39109
722
Development of Word-of-Mouth Elasticity Measures for
Tourism Product Categories
Yimiao Chen1, Fangyi Liu2*, Li-Ming Ho2, Tom M. Y. Lin1
1Department of Business Administration, National Taiwan University of Science and Technology,
Taipei, Chinese Taipei
2Department of Marine Leisure Management, National Kaohsiung Marine University,
Kaohsiung, Chinese Taipei
Received June 21st, 2012; revised July 24th, 2012; accepted August 28th, 2012
This study examines the influence of word of mouth (WOM) on eight tourism products by applying eco-
nomic elasticity theory to understand the relationship between behavioral outcomes and target elements to
these behavioral outcomes (customer consumption). The elasticity model of economics was developed to
conceptualize WOM elasticity () and measure WOM effects on various products. The results of
WOM elasticity values show a significant difference between physical and service product categories. In
addition, all effective respondents in this research are highly sensitive to WOM relating to specific prod-
ucts. This shows that WOM is not only a key variable of tourism products but also validates that WOM
communications are meaningful for tourism customers. The authors contributes to tourism service and
WOM marketing effects compared by validating empirical research in eight different product categories
and providing empirical support with prior services marketing literature. Theoretical and practical impli-
cations and future research issues are discussed.
x
WOM
E
Keywords: Tourism Product Categories; Word-of-Mouth; Elasticity Logic; Purchase Drivers
Introduction
Word-of-mouth (WOM) is an important issue in marketing.
Numerous studies have demonstrated that WOM is an effective
marketing tool, for example, Lan et al. (2012) shows people
like to talk and spread information in their familiar group,
whether products are sold at street vendors, night markets, ge-
neral shops or internet auction sites, most of customers receive
in-formation from their very close acquaintances. Villanueva et
al. (2008) concluded that the lifetime value of customers ac-
quired through WOM is twice as great as that acquired through
traditional marketing tools and Trusov et al. (2009) pointed out
that WOM in website member acquisition is 30 times higher
than media appearances. Furthermore, we understand WOM
can help purchase decision making (Harrison-Walker, 2001;
Crane & Lynch, 1988), especially in innovative products (Dod-
son & Muller, 1978; Arndt, 1967); durable goods (Mahajan et
al., 1990; Kiel & Layton, 1981), and professional services (File
et al., 1994; Murray, 1991); thus, the WOM effects are vary
across products, markets (Ennew et al., 2000), and even brands.
The potential impact of WOM and its value to an organization
is thus considerable, although the dominant explanation focuses
on the role of WOM as a risk-reliever or as a risk reduction
strategy (Fang et al, 2011; Forsythe, 2003; Derbaix, 1983), the
comparison for WOM influence on different products may be
more difficult to identify. Therefore, this study will directly
combines economic elasticity theory and WOM research to
explore a comprehensive principle for not only quantifying
WOM effects but also mirroring the primary cause in various
WOM phenomena, with a one-size-fits-all standard of WOM
quantification in market research can better use this effective
marketing tool.
Price elasticity theory originates from economics, where the
sales of good X responds to a 1% change in price; that is,
xxxxx
(E is the price elasticity, P represents
product price and Q is the quantity of product sale). A number
of WOM studies have demonstrated that the sales of goods
respond to changes in WOM (Bansal & Voyer, 2000; Goges &
Mayzlin, 2004). Thus, WOM elasticity follows the same logic
to quantify the relation between WOM and the sales of goods,
that is, the extent of WOM effects.
EQPPQ 
For the purpose of examining the usage of WOM elasticity,
the authors found the power of word-of-mouth (recommenda-
tion by friends and family) is the most credible for leisure tra-
velers (Murphy et al., 2007; Alvarez & Asugman, 2006). For
tourists, this recommendation of a product/service from a friend
or family member is the most powerful information source
available. It is also the least expensive. In the same time, lot of
literatures emphasis the influence of travel blogs as a commu-
nications tool to travel information (as an electronic word-of-
mouth) about travel experience (Pan et al., 2007; Schmallegger
& Carson, 2008). And the continuing rise of the internet as a
WOM communications tool for travel and tourism presents
challenges for tourism enterprises. After leisure travelers obtain
travel information either online or offline, many still need the
validation of a travel professional, especially for more complex
travel. It is essential for travel marketers to realize the sale is
not consummated at time of booking or final deposit. Rather, it
is after the customer consumes the travel experience of others.
So, how to understand the real effect of the travel experience
WOM? Thus, the authors choose 8 product categories of tour-
*Corresponding author.

Y. M. CHEN ET AL.
ism industry to calculate the WOM elasticity values and com-
pare the WOM effects on these product categories, and hope
the results can generalized to other industries.
This study is organized as follows. The authors first devise a
WOM sales function to integrate two major variables: tourism
product sales and its WOM. Second, the authors use question-
naires to collect product purchasing information from experi-
enced consumers. Third, the authors investigate two types
(physical vs service) of products mentioned in the literature.
Finally, the authors calculate the WOM elasticity value of the 8
product categories. Finally, the authors present the results and
discuss the limitations and implications of how WOM and its
effects impact each product category.
Measuring Word-of-Mouth Influences
Elasticity The ory for Qu ant i fi cati o n and Comparison
To understand the concept of WOM elasticity as applied to
WOM marketing research; it is useful to briefly review previ-
ous marketing research within the elasticity framework. For
example, Assmus et al. (1984) examined the elasticity frame-
work and noted the absolute value of price elasticity is eight
times that of advertising elasticity. Olivera-Castro (2008) ap-
plied price elasticity theory to brand selection; and Trusov et al.
(2009) used the elasticity indicator to estimate that WOM is 20
times as effective as marketing events and 30 times as effective
as media appearances. In general, these studies followed the
elasticity theory to describe variables and compare their effect-
tiveness, and used the concept to integrate research themes and
the variables of demand, sale, market share, and so forth, to
discuss marketing effects. Moreover, some researchers add a
time variable to understand elasticity trends in determining
marketing strategies. Overall, academics support elasticity as a
very useful concept that can be applied to the study of WOM.
WOM Elasticity Logic
WOM behavior means the number of people told about a
product experience (Halstead & Droge, 1991). Many WOM
studies have found that a considerable proportion of consumers
seek product WOM from the Internet or friends before buying
goods (Bloch et al., 1986; Katona & Mueller, 1954). Many of
these studies have determined that positive WOM can increase
the probability of product purchase (Ardnt, 1967; Katz &
Lazarfeld, 1955), and negative WOM can negatively influence
purchasing decisions (Homburg et al., 2005; Mittal et al., 1998).
That’s means the product sales as a function of WOM as fol-
lowing:



xx xxx
SalesQf WOM , other factorfWOM
where Qx is the sales of product X, x is the WOM of
product X, and other factorx is other variables (assumed to be
fixed here) that can affect the product sales,
WOM
According to the WOM sales function above, the definition
of the WOM elasticity of product X is as follow:
x
xxx x
WOM
xx xx
QQQ WOM
EWOM WOMWOMQ



where Qx is the sales of product X, is the WOM of pro-
duct X,
x
WOM
xx
shows how QX changes as x
changes, and this expression for shows how the sales
of X responds to a 1% change in X’s WOM.
Research Hypotheses
As above, WOM elasticity value (WOM ) can directly meas-
ure and compare WOM effects, shows that the differences in
WOM effects are then related to differences in WOM elasticity.
Accordingly, WOM becomes an intermediate indicator inte-
grating all influenced factors except WOM to WOM sale func-
tion so that can help us better understand WOM effects (see
Figure 1). Thus, the first hypothesis in this study is concerned
with the WOM elasticity value in tourism industry, this ex-
pected a positive value will be observed for all categories we
will investigate. And second hypothesis is subjected to empiri-
cal examination of difference of WOM elasticity value between
the service and physical products; in here the authors expected
a larger value for service than physical products in this paper.
E
E
Methodology
To Get the WOM Elasticity Values
The economic theory logic is used to calculate the WOM
elasticity value; thus, we must assume the other conditions are
unchanged and the system is a closed economy (without foreign
purchases). Therefore, the consumption equal product sale, the
relation between a product’s variables of consumption (C) and
WOM is Equation (1).
Mathematical Notations


QCfWOM (1)
WOM
ΔQQ ΔCC
EΔWOM WOMΔWOM WOM  (2)

βε
CAWOMe (3)


lnClnA βln WOMε


(4)
WOM
dlnC
βE
dlnWOM

1 (5)
i1i1 i1
i2i2 i2
i
ipip ip
inin in
lnClnWOM ε
lnClnWOM ε
MM
αβ
lnClnWOM ε
MM
lnClnWOM ε
M
M

 

 

 

 
 

 

 

 

 

 

 
(6)
where Q is the product sales; C is the consumption of goods, an
independent variable in this study’s framework; and WOM is
the product WOM, a dependent variable. Given Equation (1),
1Setting ln A = , a constant, we differentiate Equation (4) by WOM as α
follows:
dlnC dαdlnWOM dε
β
dWOM dWOMdWOMdWOM
1dC 1
Þ0β0
CdWOM WOM
 

where WOM
1CdC dWOMdC C
βE
1 WOMdWOMWOM


ΔQΔWOM WOM
x
WOM
E

Y. M. CHEN ET AL.
Price
Product
Category
Income
Other
Factors
Purchasing
Situation
Preference
WOM
Advertise-
ment
Consump-
tion
Function
consumpti
Figure 1.
Mechanism of WOM influence. Original state: Consumption function is
influenced by many factors; Present state: Assuming other independent
variables are fixed. *The outer circle are independent variables, the
centre circle is dependent variable.
the definition of WOM elasticity (WOM ) is Equation (2), where
ΔQ is the change in the amount of sales, ΔC is the change in the
consumption, and ΔWOM is the change in WOM.
E
Finally, as in Equation (3), a basic function type is set as a
Cobb-Douglas functional form, which is very easy to manipu-
late and has been widely used in prior studies in various fields
(e.g., Costrell & Loury, 2004; Wirjanto, 2004); In Equation (3),
where e is an exponential, A and β are constants, and ε is a
random variable. We then take the natural logarithm (ln) to plus
on both sides of Equation (3) to obtain a simple linear regres-
sion equation as Equation (4).
In Equation (5), is the value of continuous WOM elastic-
ity (WOM ). When the samples are from the same distribution,
the logarithm of consumption of product categories and WOM
has a linear relation, as in Equation (6), an econometric model
specification.
β
E
In Equation (6), where


ip ip
EεWOM 0;




2
ip ip
Var εWOM σhomoskedasticity,
α and β are the estimated regression coefficients, ε represents
the error term, n is the number of effective samples, ip
is the WOM evaluation of consumer p of product i (equal to the
degree consumer p is influenced by the WOM of product i),
ip is the amount of product i consumed by consumer p, and
is the in product i’s category.
WOM
C
i
βWOM E
Samples and Data Collections
For the tourism this research concerned, the authors screen 8
different categories of physical product and service to enhance
our understanding of WOM effects among different products,
including transportation, accommodation, meals, guiding, sou-
venir, admission, travel agency, scenic spots as objectives for
this investigation. We subsequently designed an appropriate
questionnaire and deliver to 300 customers in tourism site (ho-
tel and restaurant) for two week, and then we successfully col-
lect travel experiences of 241 tourists who consumed all or
some of these product categories. The questionnaire example
for all product categories is as following: (a group of three
questions for one product; X is transportation or accommoda-
tion or meals or…).
 A) When you go traveling, have you ever bought product X
over because of its word of mouth? → If your answer is yes,
then continue to answer the next two questions.
 B) By what degree were you influenced by word of mouth
of product X? (Please evaluate your feeling about X’s
WOM, in the range 1 - 100).
 C) How much did you spend purchasing product X? (new
Taiwan dollars).
Since the objective measurement of WOM is complex, the
alternative of the extent to which WOM recipients were influ-
enced by WOM (Bansal & Voyer, 2000) is the standard of
WOM measurement (degree 1 - 100) in this questionnaire. Af-
ter two weeks data collected by random sampling, there are 241
effective samples for calculating the WOM elasticity values of
the 8 products in tourism.
For each product, a group of three questions was designed.
Because this questionnaire is measured differently from the
traditional scale, the past instrument is not suitable for testing
the validity of this questionnaire. When we focus on the single
independent factor WOM that is simply understand the goal of
this questionnaire. Every product must be regarded as a differ-
ent aspect from others in this study, and the three questions can
construct to an independent questionnaire that is not necessary
to test the validity. Maybe in the future, when an extended
questionnaire is developed and the expert validity could verify
to detect the new developing scale.
The authors sample randomly to raise the reliability, and
screened 19 effective responders to test-retest to observe the
correlation coefficient to observe the reliability for this ques-
tionnaire (the period of test-retest is interval of two weeks). The
results of test-retest method are in Table 1, fortunately, the
authors found the correlation coefficients are large enough to
rely on; thus we can rule out the matter of no response set and
external validation and expect to increase samples is not appa-
rent to rise contribution.
In this study, the theoretical parameter values of both re-
search variables (WOM and C) are unknown. In addition, the
findings of previous literature conclude in lack of consensus in
tourism product. Thus it is hard to test the representativeness of
samples by statistic method, but we collect data in tourism site
724

Y. M. CHEN ET AL.
Table 1.
The test-retest reliability results.
Product category Correlation coefficient
Total point of WOM
evaluation and
consumption
Transportation 0.93
Accommodation 0.85
Meals 0.87
Guiding 0.84
Travel agency 0.89
Total point of WOM
evaluation and
consumption
Admission 0.87
Souvenir 0.96
Scenic spots 0.90
Total 0.89
(hotel and restaurant) for suitable sample is acceptable and, the
WOM evaluation is a subjective variable and consumption is
depended on consumer’s purchasing pattern is already under-
standing, too.
Results
Following the mathematical notations in page 3, the authors
obtain results by simple linear regression method. Table 2 pre-
sents the results of data analysis for a closed economy, where β
is a direct measure of the WOM elasticity of the 8 product
categories. The results clearly show that the β value of all cate-
gories of goods is greater than zero, and in which findings of all
product categories are significant except scenic spots, then
results in WOM with a positive enhancement can promote
product consumption.
The WOM elasticity results for the product categories are
different, proving that WOM effects vary across products.
Therefore, WOM elasticity is a considerable source of influ-
ence that leads to different WOM effects. Furthermore, the
empirical analysis examined that 1% changes in WOM have a
positive percentage impact on the sales of goods, except scenic
spots.
Our results indicate that these goods are services have WOM
elasticity values ranging from 0.58 to 1.29. Murray (1991)
found that consumers use personal sources of information more
when looking for service providers than when looking for
physical goods, and the more expert the product knowledge, the
greater the WOM influence on consumer decision making
(Bansal & Voyer, 2000; Gilly et al., 1998; Rabaglietti et al,
2011), and the more lasting the effects (Berry, 1995). For most
of the product of tourism, experience goods (Smith & Swinyard,
1982) also have a large influence like the travel agency, guiding
or accommodation.
However, these goods are physical have lower WOM elastic-
ity values ranging from 0.21 to 0.64, even that the result of
scenic spots is not significant, for three possible reasons. First,
as the post research speaking that physical good with less
WOM influence; second, the consumption of these products is
not necessary in traveling; and third, the tourists’ evaluation of
scenic spots have great response difference.
The results means WOM wields considerable influence on
Table 2.
Results of the data analysis of the 8 product categories.
Attribute Product
category α β (EWOM) p value Significant (ρ < 0.1)
Transportation3.83 0.58 0.03 **
Accommodation4.24 0.78 0.06 *
Meals 2.53 0.75 0.05 **
Guiding 3.03 1.03 0.02 **
Service
Travel agency5.53 1.29 0.09 *
Admission 4.40 0.21 0.03 **
Souvenir 6.82 0.33 0.10 * Physical
Scenic spots 7.57 0.64 0.23
Average
of total 4.740.65 0.076 *
The sign of two stars (**) is ρ < 0.05 that represent the result is very significant,
and one stars (*) is 0.05 ≦ ρ < 0.1 that represent the result is significant.
consumer purchases in tourism product categories, and the
average evaluation of WOM for all product categories is 77.89
(highest score is 100, see Table 3), which reveals that the con-
sumer would purchase goods if he or she had a stronger feeling
about the WOM and ensures that WOM is an effective market-
ing tool in tourism. The power of WOM observed in this
study’s results echoes previous researchers who emphasized
that WOM can make the difference between product success
and failure (e.g., Godes & Mayzlin, 2004; Laczniak et al.,
2001).
Discussion
Theoretical Implications
The most important theoretical implication in this study is
the concept of WOM elasticity was designed to an intermediate
indicator to integrate all factors which may affect the product
sales, then used to calculate and compare the WOM influence
on product sales. Although prior research has greatly taken the
use of elasticity theory to increase our understanding of social
science, most studies have focused solely on single event
(product) appearance or on a variety of products.
Remarkably, we have no one-size-fits-all standard to quan-
tify WOM to estimate WOM elasticity value; however, we can
bypass WOM measurement by using each recipient's personal,
subjective assessment of WOM influence. Note that the WOM
recipients (consumers) are the purchase decision makers
(Sweeney, 2008) evaluating how WOM affects their decision
making and expectations of consumptions (Devlin et al., 2002;
Webster, 1991).
This scale of WOM elasticity limits the ability of academics
to copy with the complex of WOM variable (e.g. negative
WOM), and hope to develop improved technology (construct
the panel data base for long term) for future use. One implica-
tion from the findings is that although consumers can be put
into segmented classes according to their purchase expenditures
it may not translate into purchase quantity. However, the intro-
duction of the concept of economic elasticity as an implement
to provide a simple method to quantify and compare WOM
effects that found WOM elasticity values showed a statistical
difference and that it has a significant WOM influence on ser-

Y. M. CHEN ET AL.
726
Table 3.
Results of the data analysis of the 8 product categories (continued).
Attribute Product
category
Mean of WOM Evaluation
(0 < μ < 100)
standard deviation of WOM
Evaluation (σ) effective sample (n) Ratio of effective sample (n/241)
Transportation 80.48 18.81 207 86%
Accommodation 74.56 13.78 223 93%
Meals 77.12 19.73 181 75%
Guiding 90.07 15.64 122 51%
Service
Travel agency 84.72 14.83 173 72%
Admission 66.53 22.29 109 45%
Souvenir 71.29 16.25 138 57% Physical
Scenic spots 78.34 17.13 215 89%
Average of total 77.89 171 71%
vice purchase than physical product purchase in tourism; thus,
we can incorporate them into a variety of product categories of
tourism of marketing strategies.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Transportatio n
Accommodatio n
Meals
Guiding
Travel agency
Admis sion
Souvenir
Scenic spots
Product Category
Percentage of Responder
Managerial Implicati o ns
The present study illustrates the differences in WOM elastic-
ity impact on WOM influences. The purpose of this study is to
explore the WOM origins so that we can quantify the relation
between product sales and WOM, which offers an approach for
tourism managers to compare WOM effects with those of other
marketing tools. After product WOM spread, firms of tourism
relative should collect consumers’ WOM evaluations over time
to estimate the WOM variation to calculate the immediate,
short-term, and long-term WOM elasticity values of products of
tourism. Managers can consider product WOM elasticity to
develop marketing strategies and increase the effectiveness of
marketing resource allocation.
The results show that, a proportion (see Figure 2) of the re-
spondents are highly influenced by product WOM, which
means that consumers will consume the tourism product if they
feel good about its WOM. However, managers cannot object-
tively measure consumers’ feelings of WOM if a standard
measurement of consumer perception does not exist. This study
introduces the subjective measurement of WOM as a measure-
ment of utility and makes it easy to screen out the target con-
sumer group of tourism WOM marketing. Thus, firms should
include in their after-purchase surveys a questionnaire item for
WOM, under “the evaluation of WOM when you have con-
sumed,” in addition to the question on reason for purchase.
Efforts to study the WOM evaluation of consumers who are
easily influenced by WOM will effectively increase product
revenue. Furthermore, we even suggest applying WOM elastic-
ity in customer relations management to gauge WOM effects
on individual customers to analyze their buying patterns.
Figure 2.
Ratio of consumption responding to WOM.
First, we integrated the influenced factor on WOM to quan-
tify the relation between product sales and WOM. Second, we
examine the responses of 241 tourists for 8 product categories
were proved with different WOM influences on product sales
due to the different WOM elasticity of product. And the WOM
elasticity of services is much greater than physical products.
Third, we substitute the WOM recipients’ evaluations for
WOM communicators’ spread quantities (Brown et al., 2005;
Harrison-Walker, 2001) to measure WOM, turning an objective
variable into a subjective one, firms should find calculations of
WOM elasticity of product categories to be helpful in managing
the marketing resources for their products, then to establishing
a trend of WOM elasticity would be a powerful tool in the
management of WOM marketing on the products like travel
agency, guiding and accommodation.
Conclusion
The concept of WOM elasticity can be established to quan-
tify WOM influences, and the value of WOM elasticity can be
calculated by a subjective measure of WOM, the literature on
WOM elasticity definitions (Trusov et al., 2009) have been
receiving increased attention. This study increases our under-
standing of this potential issue for future research. The main
conclusions of this study are as follows.
Further Research
This study the concept of WOM elasticity had applied to the
tourism industry, otherwise, we must use indicator of WOM
elasticity to estimate the WOM effects on products of other
industries for generalization. In practice, we provide a simple
and useful method for measuring WOM variations without any

Y. M. CHEN ET AL.
quantitative units (number or length of time). WOM can be
subjectively measured on the premise that consumers under-
stand their own perceptions and preferences, so firms can esti-
mate product WOM elasticity directly by asking consumers
feelings about WOM. Moreover, it will be helpful to add a time
variable to analyze WOM effects in the short and long run for
further research.
Another issue is that WOM affects not only product catego-
ries but other factors, such as consumer loyalty and communi-
cation sites. In addition, the indicator of WOM elasticity can be
used to compare the effectiveness of WOM marketing activities
with other marketing tools (such as price promotion or adver-
tising), this is also a worthwhile issue for future study.
Finally, the concept of WOM elasticity can be extended to
quantify WOM influences among brands. Taylor (1997) found
that cross-brand WOM also affects consumer attitudes and
behavior (Libai et al., 2009). Therefore, we can apply cross-
price elasticity theory to constructing the concept of “cross-
WOM elasticity” for WOM among the different brands.
The present study illustrates the differences in WOM elastic-
ity impact on WOM influences. The purpose of this study is to
explore the WOM origins so that we can quantify the relation
between product sales and WOM, which offers an approach for
tourism managers to compare WOM effects with those of other
marketing tools.
Acknowledgements
We would like to thank the anonymous referees for their
valuable comments and suggestions. The authors can be con-
tacted at [e-mail] or
REFERENCES
Alvarez, M., & Asugman, G. (2006) Explorers versus planners: A study
of Turkish tourists. Annals of Tourism Research, 33, 319-338.
doi:10.1016/j.annals.2005.12.001
Arndt, J. (1967). Role of product-related conversations in the diffusion
of a new product. Journal of marketing Research, 4, 291-229.
doi:10.2307/3149462
Assmus, G., Farley, J. U., & Lehmann, D. R. (1984). How advertising
affects sales: Meta-analysis of econometric results. Journal of Mar-
keting Research, 21, 65-74. doi:10.2307/3151793
Bansal, H. S., & Voyer, P. A. (2000). Word-of-mouth processes within
a services purchase decision context. Journal of Service Research, 3,
166-177. doi:10.1177/109467050032005
Bayus, B. L. (1985). Word of mouth: The indirect effects of marketing
efforts. Journ al o f Advertising Research, 25, 31-39.
Berry, L. L. (1995). Relationship marketing of services—Growing
interest, emerging perspectives. Journal of the Academy of Market-
ing Science, 23, 236-245. doi:10.1177/009207039502300402
Bloch, P. H., Sherrell, D. L., & Ridgway, N. M. (1986). Consumer
search: An extended framework. Journal of Consumer Research, 13,
119-126. doi:10.1086/209052
Brown, T. J., Barry, T. E., Dacin, P. A., & Gunst, R. F. (2005). Spread-
ing the word: Investigating antecedents of consumers’ positive word-
of-mouth intentions and behaviors in a retailing context. Journal of
the Academy of Marketing Science, 33, 123-138.
doi:10.1177/0092070304268417
Christiansen, T., & Tax, S. S. (2000). Measuring word of mouth: The
questions of who and when? Journal of Marketing Communications,
6, 185-199. doi:10.1080/13527260050118676
Costrell, R. M., & Loury, G. C. (2004). Distribution of ability and
earnings in a hierarchical job assignment model. Journal of Political
Economy, 112, 1322-1363. doi:10.1086/424741
Crane, F. G., & Lynch, J. E. (1988). Consumer selection of physicians
and dentists: An examination of choice criteria and cue usage. Jour-
nal of Health Care Marketing, 8, 16-19.
Derbaix, C. (1983). Perceived risk and risk relievers: An empirical in-
vestigation. Journ al of Economic Psychology, 3, 19-38.
doi:10.1016/0167-4870(83)90056-9
Devlin, J. F., Gwynne, A. L., & Ennew, C. T. (2002). The antecedents
of service expectations. The Service Industries Journal, 22, 117-152.
doi:10.1080/714005102
Dodson, J. A., & Muller, E. (1978). Models of new product diffusion
through advertising and word-of-mouth. Management Science, 24,
1568-1578. doi:10.1287/mnsc.24.15.1568
Ennew, C., Bauerjee, A. K., & Li, D. (2000). Managing word of mouth
communication: Empirical evidence from India. International Jour-
nal of Bank Marketing, 18, 75-83. doi:10.1108/02652320010322985
Fang, C. H., Lin, T. M. Y., Liu, F., & Lin, Y. H. (2011). Product type
and word of mouth: A dyadic perspective, Journal of Research in
Interactive Marketing, 5, 189-202. doi:10.1108/17505931111187802
File, K. M., Cermak, D. S. P., & Prince, R. A. (1994). Word-of-mouth
effects in professional service buyer behaviour. The Service Indus-
tries Journal, 14, 301-314. doi:10.1080/02642069400000035
Forsythe, S. M. (2003). Consumer patronage and risk perceptions in
internet shopping. Journal o f Business Research, 56, 867-875.
doi:10.1016/S0148-2963(01)00273-9
Godes, D., & Mayzlin, D. (2004). Using online conversations to study
word-of-mouth communication. Marketing Science, 23, 545-560.
doi:10.1287/mksc.1040.0071
Halstead, D., & Droge, C. (1991) Consumer attitudes toward com-
plaining and the prediction of multiple complaint responses. Ad-
vances in Consumer Research, 18, 210-216.
Harrison-Walker, L. J. (2001). The measurement of word-of-mouth
communication and an investigation of service quality and customer
commitment as potential antecedents. Journal of Service Research, 4,
60-75. doi:10.1177/109467050141006
Homburg, C., & Furst, A. (2005). How organizational complaint han-
dling drives customer loyalty: An analysis of the mechanistic and the
organic approach. Journal of Marketing, 69, 95-114.
doi:10.1509/jmkg.69.3.95.66367
Kiel, G. C., & Layton, R. A. (1981). Dimensions of consumer informa-
tion seeking behavior. Journal of marketing Research, 18, 233-239.
doi:10.2307/3150957
Laczniak, R. N., Decarlo, T. E., & Ramaswami, S. N. (2001). Consum-
ers’ responses to negative word-of-mouth communication: An attri-
bution theory perspective. Journal of Consumer Psychology, 11, 57-
73. doi:10.1207/S15327663JCP1101_5
Lam, D., & Mizerski, D. (2005). The effects of locus of control on
word-of-mouth communication. Journal of Marketing Communica-
tion, 11, 215-228. doi:10.1080/1352726042000333180
Lan, M. Y., Liu, F. Y., Fang, C. H., & Lin, T. M. Y. (2012) Under-
standing word-of-mouth in counterfeiting, Psychology, 3, 289-295.
doi:10.4236/psych.2012.33041
Libai, B., Muller, E., & Peres, R. (2009). The role of within-brand and
cross-brand communications in competitive growth. Journal of Mar-
keting, 73, 19-34. doi:10.1509/jmkg.73.3.19
Mahajan, V., Muller, E., & Bass, F. M. (1990). New product diffusion
models in marketing: A review and directions for research. Journal
of Marketing, 54, 1-26. doi:10.2307/1252170
Mittal, V., Ross, W., & Baldasare, P. (1998). The asymmetric impact of
negative and positive attribute-level performance on overall satisfac-
tion and repurchase intentions. Journal of Marketing, 62, 33-47.
doi:10.2307/1251801
Murphy, L., Mascardo, G., & Benckendorff, P. (2007). Exploring
word-of-mouth influences on travel decisions: Friends and relatives
vs other travelers. International Journal of Consumer Studies, 31,
517-527. doi:10.1111/j.1470-6431.2007.00608.x
Murray, K. B. (1991). A test of services marketing theory: Consumer
information acquisition activities. J ourna l of M arket ing, 55, 10-25.
doi:10.2307/1252200
Oliveira-Castro, J. M., Foxall, G. R., & Schrezenmaier, T. C. (2005).
Patterns of consumer response to retail price differentials. The Ser-
vice Industries Journal, 25, 309-335.

Y. M. CHEN ET AL.
728
doi:10.1080/02642060500050392
Pan, B., MacLaurin, T., & Crotts, J. C. (2007). Travel blogs and the
implications for destination. Marketing Journal of Travel Research,
46, 35-45. doi:10.1177/0047287507302378
Rabaglietti, E., Liubicich, M. E., & Ciairano, S. (2011). Gender dif-
fer-ences in the relationships between physical activity and the psy-
cho-logical and physical self-reported condition of the elderly in a
resi-dential care facility. Psychology, 2, 35-41.
doi:10.4236/psych.2011.21006
Schmallegger, D., & Carson, D. (2008). Blogs in tourism: Changing
approaches to information exchange. Journal of Vacation Marketing,
14, 99-110. doi:10.1177/1356766707087519
Smith, R. E., & Swinyard, W. R. (1982). Information response models:
An integrated approach. Journal of Marketing, 46, 81-93.
doi:10.2307/1251162
Sweeney, J. C., Soutar, G. N., & Mazzarol, T. (2008). Factors influ-
encing word of mouth effectiveness: Receiver perspectives. Euro-
pean Journal of Marketing, 42, 344-364.
doi:10.1108/03090560810852977
Sui, J. J., & Baloglu, S. (2003). The role of emotional commitment in
relationship marketing: An empirical investigation of a loyalty model
for casinos. Journal of Hospitality & Tourism Re search, 27, 470-489.
Taylor, K. A. (1997). A regret theory approach to assessing consumer
satisfaction. Marketing Letters, 8, 229-238.
doi:10.1023/A:1007966621364
Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of word-of-
mouth versus traditional marketing: Findings from an Internet social
networking site. Journal of Ma rk eti ng, 73, 90-102.
doi:10.1509/jmkg.73.5.90
Villanueva, J., Yoo, S., & Hanssens, D. M. (2008). The impact of mar-
keting-induced vs word-of-mouth customer acquisition on customer
equity. Journal of mar ke ti ng Research, 45, 48-59.
doi:10.1509/jmkr.45.1.48
Webster, C. (1991). Influences upon consumer expectations of services.
The Journal of Services Mar ke t i ng , 5, 5-17.
doi:10.1108/08876049110035440
Wirjanto, T. S. (2004). Exploring consumption-based asset pricing
model with stochastic-trend forcing processes. Applied Economics,
36, 1591-1597. doi:10.1080/0003684042000217940