2012 TOPCO 崇越論文大賞
論文題目：
The Effect of Queuing up and
Conformity Tendency on Expected
Product Value
報名編號：_______D0115_______________
Abstract
This plan includes two studies to investigate the effect of queue
information on the product expected value under different queue
circumstances, and further explore that how consumers’ conformity tendency
moderate the effect. This plan predicts that under different queue
circumstances, consumer will evaluate product by different ways. (Study 1)
Under the circumstance of physically waiting at the service setting,
investigating that how the number of people ahead or behind influence the
product evaluation. (Study 2) Under the circumstance of waiting elsewhere,
investigating that how the total queue length influence the product evaluation.
Both of Study 1 and 2, add conformity tendency of consumer as moderating
variable and employ regression to analyze the data and examine the effects of
queue on product evaluation.
Key-words: queue, conformity tendency, product expected value.
CHAPTER 1 INTRODUCTION
“Standing in line” is a common phenomenon in our daily life, for example, when
taking the mass rapid transit (MRT), using an automatic teller machine (ATM), going to
the public restroom, or buying movie tickets (Koo & Fishbach, 2010; Zhou & Soman,
2003; Larson, 1987). Consumer waiting may happen before, during, and after the
purchase (Taylor, 1994). Since a great deal of literature suggests that waiting will reduce
service evaluation, standing in line has both economic and psychological cost (Koo &
Fishbach, 2010; Bateson & Hui, 1992; Larson, 1987; Osuna, 1985; Katz, Larson, &
Larson, 1991; Hui & Tse, 1996). Current literature on waiting in lines deals with its
negative effects on service experience and the possible means of reducing the negative
affective responses (Baker & Cameron, 1996; Houston, Bettencourt, & Wenger, 1999;
Hui & Tse, 1996; Katz et al., 1991; Taylor, 1994; Tom & Lucey, 1997).
In Taiwan, many popular brands, such as Zara, Uniqlo, and iPhone, attract crowds
of purchasers. This results in long queues. People regard waiting in line as the only way
to obtain popular products. Indeed, people often take the length of the queue as
indicative of the popularity of the product. Recent research has investigated how far the
length of the queue influences the popularity estimation of the product’s value.
Researchers have suggested that people in a queue gauge the value of the products in
terms of the number of people behind them rather than the number of people standing
ahead of them (Koo & Fishbach, 2010). In fact, studies suggest that more people
standing ahead of one can result in a more negative waiting experience (Hui & Tse,
1996). However, when the product is unfamiliar to the consumer, the length of the
queue appears as an index of the product’s value, thus encouraging them to join long
queues (Koo & Fishbach, 2010). For example, while choosing a restaurant unfamiliar to
them, people tend to prefer restaurants with more occupied seats to ones with less
occupied seats. People tend to assess the value of unfamiliar products by the number of
purchases and the number of people waiting to buy the products. The more a consumer
tends to conform, the more easily he or she will be influenced by the decisions of others.
Therefore, consumers with a high tendency toward conformity are more likely to be
attracted to join long queues. For customers with higher conformity tendency, the
expected value of the product is higher when the queue is longer. However, the bulk of
queue research has focused on the length of a queue, and has not taken the
characteristics of the consumer into account. This research investigates how the extent
of conformity influences the effect of queues on consumers’ product evaluation.
Although the queue may attract consumer to purchase the product, the long waiting
may cause people be annoying or frustrated. Hence, now many sellers conducted “take a
number” system to manage the queue and let consumer do not need to waiting on the
physical lines, who can go shopping on the other store until the seller call their number.
Therefore, the waiting time is less of an irritant. On the other hands, physically queuing
up at a store is vastly different. The relevant literature suggests that “filling” the time
(preoccupying oneself with other activities) while physically waiting in a queue does
not significantly mitigate the negative affective response triggered by the wait (Taylor,
1994; Hui & Tse, 1996). Therefore, there is a world of difference between physically
waiting on the service setting and waiting on and waiting elsewhere. However, current
literature has focused on the queue set physical stores, and has not investigated waiting
elsewhere.
In order to cover the above research gap, this study investigates the extent to which
queues influence the expected product value under two different queue situations, and
whether and how far conformity tendency plays a role in people’s assessment of the
product value in a queue situation.
CHAPTER 2 LITERATURE REVIEWS
Studies in operations research have investigated queue structure and management
with the goal of developing efficient policies (Gross & Harris, 1985; Newell, 1982).
This kind of queue research has been mostly based on mathematical models, seeking to
explore possible means of improving queue efficiency on operation management. In
recent years, marketing researchers have been interested in the experience of consumers
waiting in queue and have suggested that it is important for marketing management to
understand the consumers’ waiting experience in order to maximize customer
satisfaction (Taylor, 1994; Zhou & Soman, 2003; Hui & Tse, 1996).
Taylor (1994) argued that in order to understand the waiting experience, one must
understand what wait for service means. Taylor (1994) defined wait for service as “the
time from which a customer is ready to receive the service until the time the service
commences.” Some researchers have suggested that service waiting can be controlled in
two ways: operation management and customer perception management (Katz et al.,
1991). However, even the most efficient operation process might not be able to
eliminate the formation of queues. Thus, customer perception management with regard
to service waiting becomes important to marketing managers in reducing negative
reaction of customers. If service providers cannot reduce the actual waiting time, they
can try to influence the customer perception of waiting in order to make customers’
waiting experience more positive.
2.1 Types of Waiting
Researchers have distinguished and defined various types of waiting (Taylor, 1994).
Consumers can wait before, during, and after a purchase (Taylor, 1994). Depending on
when it is taking place, waiting can be classified as “pre-process”, “in-process”, and
“post-process” (Dube-Rioux, Schmitt, & Leclerc, 1988). For example, in a movie
situation, a “pre-process waiting” would occur during the purchase of movie tickets, an
“in-process waiting” would occur during sitting and waiting for the movie to start, and a
“post-process waiting” would occur when leaving the theater. Researchers have
suggested that pre-process waiting is more uncomfortable for consumers than in-process
waiting (Dube-Rioux et al., 1988). Some researchers have suggested that marketing
management should be concerned only with pre-process waiting (Venkateson &
Anderson, 1985). Taylor (1994) further classified pre-process waiting into three types:
“pre-schedule waiting”, “delay” or “post-schedule waiting”, and “queue waiting”.
“Pre-schedule waiting” occurs when a customer arrives too early for a scheduled event
and therefore has to wait until the scheduled time. “Delay” occurs when service
commences later than the scheduled time. For example, in a restaurant situation, a
customer arrives at 11:50 for a 12:00 lunch appointment. Therefore, he or she may have
to wait for 10minutes to be seated. This is pre-schedule waiting. However, if the
customer does not get to be seated until 12:15, then that 15 minute stretch would be
classified as “delay” or “post-schedule waiting.” Taylor (1994) explained the distinction
between pre-schedule waiting and delay simply in terms of whether the waiting occurs
before or after the scheduled time of commencement. “Queue waiting” occurs when
customers do not have a reservation or appointment and the service provider follows the
first-come-first-served principle. Consequently, the customers have to wait in queue in
order to get the service they desire (Taylor, 1994).
Depending on the location of waiting, waiting can also be classified as “physically
waiting at the service setting” and “waiting elsewhere” (Taylor, 1994). For example,
waiting at the post office to send a parcel is “physically waiting at the service setting,”
and waiting at home for a delivery is “waiting elsewhere.”
This research focuses on one type of waiting, the pre-process queue waiting, and
explores the difference between physically waiting at the service setting and waiting
elsewhere. This research examines whether the different locations of waiting influence
consumers’ product evaluation.
2.2 Number of People on Physically Waiting
Waiting for service is considered to make the customer frustrated, angry, and
anxious (Larson, 1987; Hui & Tse, 1996; Taylor, 1994; Zhou & Soman, 2003). Hui &
Tse (1996) have argued that the longer a person believes he or she has waited, the more
negatively does he or she evaluate the service. In contrast, the service evaluation of
customers will be more positive if the waiting time is shorter in their perception. In a
goal-based analysis, Koo and Fishbach (2010) have viewed waiting in line as means to a
goal. The number of people ahead of a consumer represents the effort the consumer
must make to attain the goal (Koo & Fishbach, 2010). The more people ahead of a
consumer, the more the effort required. It also means that consumers will have to wait
longer to receive the desired service, thereby resulting in greater negative affective
response. Taylor (1994) suggested that if consumers have greater negatively affective
responses, then the product evaluation would be lower. Hence, more people ahead of a
consumer in a queue will result in lower product evaluation.
In a long queue, waiting in line is not the only choice that consumers have.
Consumers can choose alternatives or decide to come back another time when the
service is more easily available (Zhou & Soman, 2003). There are several reasons for
consumers to quit, such as high opportunity cost of waiting, scarcity of time, availability
of acceptable alternatives, and negatively affective response. On the other hand,
researchers note that when there are a large number of people behind a consumer, he or
she will have less likelihood of quitting. There are several reasons for this. First, more
people in queue is likely to lead consumers to place higher value on the service or
product, making it worth waiting for (Koo & Fishbach, 2010; Zhou & Soman, 2003;
Cialdini, 1985). Second, a large number of people behind him or her is likely to make
the consumer decide that if he or she were to rejoin the queue later, receiving the service
or product will require more time and effort (Koo & Fishbach, 2010; Zhou & Soman,
2003). Third, more people behind him or her will reduce the consumer’s negatively
affective response on account of the social comparison with someone behind him or her
(Zhou & Soman, 2003). Social comparison is considered spontaneous, effortless, and
relatively automatic (Gibbons & Brunk, 1999; Zhou & Soman, 2003). Social
comparison often occurs when consumers are uncertain. Koo and Fishbach (2003)
argued that presence of people in line conveys information regarding the value of goal
attainment.
Hence, this study offers the following hypotheses:
H1a: In physically waiting queue at the service setting, more people behind lead
consumers to infer higher expected product value.
H1b: In physically waiting queue at the service setting, more people ahead lead
consumers to infer lower expected product value.
2.3 Conformity
Conformity research initially started from Majority Effect, which means that the
individual is likely to follow the majority even when the majority is wrong (Asch, 1951;
1952; 1955; 1956). Experimental research has shown that when someone has no
information about something, he or she is likely to observe others in that respect and
follow their decision even if the decision is not right (Banerjee, 1992). People live with
others, and are often influenced by others in their behavior (Lee & Park, 2008). This
social phenomenon is called “conformity.” Allen (1965) defined conformity as a
manifestation of social influence due to the opposition of other group members to an
individual’s views. Burnkrant and Cousineau (1975) defined conformity as the tendency
of opinions to establish a group norm as well as the tendency of individuals to comply
with the group norm.
Social psychologists and sociologists emphasize that conformity is a result of
group pressure on the individual, and that the latter is likely to change his or her
thoughts or decisions in order to conform to others (Kiesler & Kiesler, 1969). Research
suggests that conformity in consumer behavior can be defined as the phenomenon of the
individual consumer following the group’s example in evaluation of products,
purchasing intention, and purchasing behavior (Lascu & Zinkhan, 1999). For example,
in a bookstore situation, if people have not decided about the books they want to buy,
they are likely to purchase books on the bestseller list (Bikhchandani, Hirshleifer, &
Welch, 1998). In choosing or deciding what to buy, consumers are likely to attach
importance to the thoughts and reactions of others (Calder & Burnkrant, 1977; Lee &
Park, 2008).
Deutsch and Gerard (1955) classified conformity into two types: informative and
normative conformity motivations. Informative conformity was defined as the influence
to accept information from others to evince the truth to reality, and normative
conformity was defined as the influence to conform to the expectation of others. Since
consumers are always attracted to join in the queue by people standing in lines, they are
influence by others they don’t know not the peers. Hence, this study focuses on
informative conformity.
When consumers consult their friends and acquaintances before they purchase
products, they seek to receive support for their purchasing decision (Lee and Park,
2008). They feel more confident if others’ decisions agree with their own (Banerjee,
1992; Lee & Park, 2008). Hence, if there are a large number of people behind the
consumer, this will make the consumer more confident about standing in queue and
enhance his or her product evaluation. Consumers with a high informative conformity
tendency will be more easily influenced by others’ behavior than those with low
informative conformity tendency. Similarly, even if there is a large number of people
ahead of them, consumers with high conformity tendency will have less negatively
affective response, because those with high informative conformity believe the product
is good by the influence of majority.
Therefore, this research proposes the following hypotheses:
H2a: When there are a large number of people behind a consumer, a consumer
with high informative conformity tendency infer higher expected product
value than those with low conformity tendency.
H2b: When there are a large number of people ahead of a consumer, a consumer
with high informative conformity tendency infer higher expected product
value than those with low conformity tendency.
2.4 Waiting Elsewhere
In the context of waiting elsewhere, consumers do not need to physically wait in
line. During this waiting time, consumers can carry on with their schedule and activities,
and hence might not mind the waiting so much. Taylor (1994) has suggested that this
“filling the time” of wait with other activities and concerns can reduce the anger and
uncertainty felt due to waiting by reducing boredom, tension, and anxiety. Experimental
research has shown that filling consumers’ time will reduce anger that may be triggered
by waiting, and have higher evaluation of service than time unfilled (Taylor, 1994).
Hence, in waiting elsewhere, even with a large number of people ahead of the consumer,
he or she might have less negative affective response than the one who waits physically
at a store.
If the information regarding these products is ambiguous to consumers, consumers
need more information provided by others who have already experienced the products
(Lee & Park, 2008). Consumers might infer the expected product value by the length of
queue. Cialdini (1985) suggested that people use total queue length as social proof that
the product is worth waiting for under the assumption that only valuable products will
attract large numbers of customers. As consumers with high informative conformity
tendency are more easily influenced by others’ behavior than those with low informative
conformity tendency, this research predicts that consumers with high conformity
tendency will have more positive affective response to long queues than those with low
conformity tendency. And the more positive affective responses consumers have, they
infer higher expected product value (Taylor, 1994).
Hence, this research has following hypothesis:
H3: In waiting elsewhere context, the longer the queue the higher will be the
expected product value.
H4: In waiting elsewhere context, consumers with high informative conformity
tendency, as compared to those with low informative conformity tendency,
infer higher expected product value from long queues.
CHAPTER 3 METHODOLOGY
This study proposes that consumers might infer the expected product’s value under
different circumstances in different ways. Under the circumstance of physically waiting,
consumers assess the expected product value by the number of people behind or ahead.
However, under the circumstance of waiting elsewhere, consumers might infer the
expected product value by the total queue length. This research predicts that a large
number people behind will increase the product evaluation in the situation of a
physically waiting, while a higher total queue length will increase the product
evaluation in the situation of waiting elsewhere. In addition, the conformity tendency
will moderate the effect of queues on product evaluation. This research further predicts
that, in the context of physically waiting, consumers will infer lower evaluation of
product when the number of people ahead increases.
Two studies test these hypotheses. Study 1 examines the effect of the actual
number of people ahead and behind on product evaluation in a physically waiting
situation. Study 2 examines the effect of the total queue length on product evaluation in
a situation of waiting elsewhere. Both these studies also investigate the role of
conformity tendency of consumers as a moderating variable.
3.1 Study 1
3.1.1
The Conceptual Model
Study 1 examines how the number of people behind and the number of people
ahead of a consumer influences his or her expected product value. This study further
investigates the possible role of the conformity tendency of the consumer as a
moderating variable, and examines whether the conformity tendency of consumer will
moderate the relationship between the number of people behind or ahead of a consumer
and his or her product evaluation. Study 1 tests the hypotheses 1a, 1b, 2a, and 2b. Figure
1 presents the conceptual model of Study 1.
The number of
people behind
in the queue
The number of
people ahead in
the queue
H1a, H1b
The expected
product value
H2a, H2b
Informative
conformity
Figure 1 Conceptual Model of Study 1
3.1.2 Method
In order to make it realistic, the experiment is conducted in a natural situation. The
experimenters recruited customers waiting at the one-third and two-third positions of
the queue at a popular Pig’s blood cake vendor in Sanxia in order to estimate their
expected value and enjoyment of the food. Figure 2 the queue situation. An
experimenter asks the customers to complete a short survey, and another experimenter
record the actual number of people behind and ahead of them. The surveyed participants
are unaware of the survey purpose and are promised that their individual information
would remain anonymous.
Figure 2 Queue Situation
The questionnaire includes two parts. The first part contains measurements of
expected product value; the second part measures the conformity tendency of the
consumers. In order to measure the participants’ evaluation of food, all participants were
asked to rate their expected product value and enjoyment by using a Likert 7-point scale,
with 1 being “not at all”, and 7 being “very much.” Informative conformity tendency of
the consumer is measured by using the scale of informative conformity developed by
Clark and Goldsmith (2006). As a measure of conformity tendency, participants
indicated their behavior in their daily lives by using a Likert 7-point scale, with 1 being
“not agree”, and 7 being “completely agree.”
3.2 Study 2
3.2.1 The Conceptual Model
Study 2 examines how the total queue length influences customers’ expected
product value. This study also investigates the possible role of the conformity tendency
of consumers as a moderating variable and examines whether the conformity tendency
of consumer moderates the relationship between the total queue length and the expected
product value. Study 2 tests hypotheses 3 and 4. Figure 2 presents the conceptual model
of Study 2.
Total queue length:
2 (People ahead: many vs. few)
X
2 (People behind: many vs. few)
H3
The expected
product value
H4
Informative conformity
Figure 3 Conceptual Model of Study 1
3.2.2 Method
This study employed a 2 (people ahead: many vs. few) × 2 (people behind: many
vs. few) between-subjects design. This study will recruit participants online and
requested them to watch a scenario. This shows that one person saw a pig’s blood cake
vendor which many consumers around it waiting for buying the product. Then, she or he
is attracted by these people and take a number to purchase this product. During the
waiting time she or he does not need to physically stand in lines, she or he can walk to
another place. After few minutes, this person come back to check the waiting process
and learn how many people are still ahead of him or her and how much people have
purchased the same product after him or her. The participants in the study are randomly
assigned to the four conditions (2 × 2, mentioned above). After the participants finished
watching, the experimenters will ask each of them to imagine that he or she is the
purchaser shown in the video and evaluate the product shown in the scenario. After each
participant verifies the product condition, the experimenter asks him or her to complete
a questionnaire. This questionnaire is the same as the one used in Study 1. In order to
avoid the product preference will bother this study’s results, the questionnaire add a
scale about consumers’ preference about pig’s blood cake. All the participants are
unaware of the study purpose.
All participants’ responses were measured using a Likert-type scale, with 1
="strongly disagree," 4 = "neutral," and 7 = "strongly agree." The questionnaire includes
two parts. The first part contained measurements of expected product value; the second
part measured informative conformity tendency.
Expected product value. Expected product value is measured by consumers’
perception of product. The measurements include “Purchasing this product will make
me feel joy” and “This product is worth to wait”.
Informative conformity tendency. Conformity tendency of consumer is measured
by using the scale of informative conformity and normative conformity developed by
Clark and Goldsmith (2006). The measurements include “It is important that others like
the products and brand I buy” and “I often consult other people to help choose the best
alternative available from a product class”.
CHAPTER 4 RESULTS AND ANALYSIS
4.1 Data Distribution
A total of 59participants took part in Study 1, 67.8 % (40) of who are man and 32.2
% (19) of who are women (see table 4-1). The age range was skewed between 21 and 30.
Table 4-2 showed the age distribute of participants in Study 1.
Table 4-1 Participants Gender Distribute of Study 1
Gender
Participants
Percentage
Male
40
67.8%
Female
19
32.2%
Table 4-2 Participants Age Distribute of Study 1
Age
Participants
Percentage
Below 20
5
8.5%
21~25
15
25.4%
26~30
22
37.3%
31~35
9
15.3%
36~40
4
6.8%
Above 41
4
6.8%
The total of 150 participants took part in Study 2, 52.0% (78) of who are men and
48% (72) of who are women (see Table 4-3). The age range skewed toward younger
group. Table 4-4 showed the age distribute of Study 2.
Table 4-3 Participants Gender Distribute of Study 2
Gender
Participants
Percentage
Man
78
52.0%
Female
72
48.0%
Table 4-4 Participants Age Distribute of Study 2
Age
Participants
Percentage
Below 20
16
10.7%
21~25
101
67.3%
26~30
23
15.3%
31~35
5
3.4%
36~40
3
2.0%
Above 41
2
1.4%
4.2 Descriptive Statistics, Reliability and Validity
This study examines the descriptive statistics, reliability and validity by conducting
SPSS 17.0.Table 4-5 shows the descriptive statistics of Study 1, including mean and
standard deviation, and Table 4-6 shows the descriptive statistics of Study 2. Table 4-7
showed that all the reliability scores (Cronbach’s α) are above 0.7, and it indicate that
this scale have high reliability (Nunnally, 1978).
Table 4-5 Descriptive Statistics of Study 1
Variable
Mean
S.D.
The number of people ahead
13.61
6.37
The number of people behind
14.05
6.70
Informative conformity
5.36
0.98
Expected product value
5.51
0.86
Table 4-6 Descriptive Statistics of Study 2
Variable
Mean
S.D.
Total queue length
14.87
3.375
Informative conformity
5.32
0.99
Expected product value
4.55
0.97
Table 4-7 Scale Items and Reliability Coefficients
Variable
Cronbach’s α
Expected product value
0.925
Informative conformity
0.797
Confirmatory factor analysis (CFA) was conducted to assess the dimensionality,
reliability, and validity. Factor loading, composite reliabilities (CR), and average
variance extracted (AVE) examined the convergent validity (Fornell & Larcker, 1981).
Table 4-8 shows the measurement analysis results, including factor loading, composite
reliabilities (CR), and AVE in this study. The CR value was calculated by conducting
the procedures from Fornell and Larcker (1981). In this study, the CR values were 0.93
and 0.81 for one dependent variable and one moderating variable, and all of which
exceeded 0.7. The AVE values were 0.68 and 0.59 for one dependent variable and one
moderating variable, and all the value exceeded the threshold level 0.5 which suggested
by Bagozzi, Yi and Phillips (1991). The factor loading ranged from 0.66 to 0.87 are
greater than 0.6, indicating convergent validity (Hair, Black, Babin, Anderson, & Tathan,
2006).
Table 4-8 Confirmatory Factor Analysis
Constructs
Factor loading
CR
AVE
Expected product value
0.71~0.87
0.93
0.68
Informative conformity
0.66~0.87
0.81
0.59
The correlation of paired construct was compared with AVEs to assess the
discriminative validity (Hair, Anderson, Tatham & Black, 1998). Table 4-9 also shows
that the correlation of paired construct is significantly less than 1, and less than AVEs,
indicating the discriminative validity of the construct (Hair et al., 1998).
Table 4- 9 Correlations and Square Root of AVEs
Variables
Expected product value
Expected product value
0.82
Informative conformity
0.185**
Note: **p<0.01
Informative conformity
0.77
4.3 Hypothesis Testing
The linear regression was used to examine the relationship between the number of
people ahead or behind of participants and expected product value. Table 4-10 shows
the results of Study 1. The results indicated that the number of people ahead of
participants had a negative effect on expected product value, and was significant at 0.01
(b=-0.402, p<0.01), which provided support for Hypothesis 1a. The results also
indicated that the number of people behind of participants had a positive effect on
expected product value, and was significant at level 0.05 (b=0.268, p<0.05), which
provided support for Hypothesis 1b.
On the moderating effect, only the number of people ahead of participants *
informative conformity (AI) had a positive effect on expected product value, and was
significant at level 0.05 (b=0.259, p<0.05). The number of people behind of participants
* informative conformity (BI) had no effect on expected product value. The results
provided support for Hypothesis 2a, but not supported Hypothesis 2b. It means
informative conformity only moderate the relationship between the number of people
ahead of participants and expected product value.
Table 4- 10Regression Results of Hypothesis 1a, 1b, 2a, and 2b Testing
Variables
Model 1
Model 2
Model 3
The number of people ahead of participants
-0.375***
-0.382***
-0.402***
The number of people behind of participants
0.310***
0.267**
0.268**
Informative conformity
0.219**
0.218**
0.205*
AI
0.239*
0.259**
BI
0.136
0.144
Gender
0.173
Age
-0.038
R2
0.445
0.484
0.493
Adjust R2
0.414
0.436
0.423
F-value
14.673***
9.952***
7.080***
Note: ***p<0.01; **p<0.05; *p<0.1;
AI = The number of people ahead of participants * Informative conformity;
BI = The number of people behind of participants* Informative conformity.
Table 4-11 shows the results of Study 2. The results indicated that the total of
queue length had a positive effect on expected product value, and was significant at 0.05
(b=0.158, p<0.05), which provided support for Hypothesis 3.
On the moderating effect, the total queue length * informative conformity (TI) had
a positive effect on expected product value, and was significant at level 0.1 (b=0.157,
p<0.1). This result supported Hypothesis 4.
Table 4- 11Regression Results of Hypothesis 3 and 4 Testing
Variables
Model 1
Model 2
Model 3
Total queue length
0.157*
0.155*
0.158**
Informative conformity
0.124
0.168**
0.183**
0.219**
0.157*
TI
Product preference
0.314***
R2
0.042
0.079
0.175
Adjust R2
0.029
0.06
0.152
F-value
3.252
4.154
7.664
Note: ***p<0.01; **p<0.05; *p<0.1;
TI = Total queue length * Informative conformity.
CHAPTER 5 CONCLUSIONS
5-1 Findings
Study 1was aimed at examining the effect of queue information (the number of
people ahead of consumers, and the number of people behind of participants) on
expected product value, and added informative conformity into the study framework as
moderating variable. The results of Study 1 showed that the number of people ahead of
consumers had negative influence on expected product value. It means that more people
ahead of consumers, the lower expected product value will be in physically waiting at
the service setting. And the results also showed that the number of people behind of
consumers had positive effect on expected product value, which indicating that in
physically waiting at the service setting, more people behind of consumers, the higher
expected product value will be. This finding was consistent with previous research
results (Koo & Fishbach, 2010; Zhou & Soman, 2003; Cialdini, 1985).On the moderate
effect, informative conformity would weaken the negative effect of the number of ahead
of consumers on expected product value, but had no influence on the relationship
between the number of people behind of consumers and expected product value. When
the number of people ahead of consumers was many, consumers with higher
informative conformity would have higher product evaluation than those who with
lower informative conformity.
Study 2 was aimed at the relationship between total queue length and product
evaluation. In Study 2, it also added informative conformity into Study 2 framework as
moderating variable. The results showed that in waiting elsewhere the longer total
queue length, the higher product evaluation will be. On the moderate effect, informative
conformity would enhance the positive effect of the total queue length on expected
product value. When the total queue length was long, consumers with higher
informative conformity would have higher product evaluation than those who with
lower informative conformity.
5-2 Implications and Contributions
This research makes some implications for managing and designing a queue
structure to improve consumers’ product evaluation to service industry. According the
results of Study 1, the greater the number of people behind consumers in physically
waiting at the service setting queue, the greater the expected product value will be.
Hence, the marketer can try putting the consumers’ attention to the people behind them,
improving consumers’ product evaluation.
According the results of Study 2, the longer the total queue length in waiting
elsewhere, the greater the expected product value will be. Hence, the marketer can try
making consumers perceive that the total queue length is long, which enhancing the
positive product evaluation.
In addition to marketing management implications, this research has some
academic contributions. Previous queue research on marketing only discussed the
relationship between the queue information and product evaluation, and focus on the
waiting type of physically waiting at the service setting. Hence, to fill out the research
gaps, this research added the informative conformity as moderate variable to examine
that whether the informative conformity will moderate the relationship between queue
information and expected product value or not. In addition, this research also examined
that whether it had difference effects at different waiting types. The results showed that
under the different queue circumstances, the different queue information will influence
customers’ product evaluation.
5-3 Limitations and Future Study Directions
Although this research has some managerial implications and contribution for both
academic community and service industry, it still remains some limitations, and some
suggestions for future research. First, not only consumers’ conformity tendency could
be used as moderating variable, many factors could influence consumers’ product
evaluation, such as product involvement (Koo & Fishbach, 2010).
Second, Study 2 experimental design with scenario is another limitation. Study 2
asked the participants to imagine that they were the main character of the scenario, and
filled out the questionnaire to answer their own product evaluation and their behavior in
daily life. Hence, if the experiment can examine on physically setting, the results may
close to the real life.
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