The Effect of Queuing up and Conformity Tendency on Expected Product Value

Transcription

1 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

2 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.

3 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

4 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

5 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

6 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

7 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.

8 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

9 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 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 H2a, H2b Informative conformity The expected product value Figure 1 Conceptual Model of Study 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

10 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 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

11 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 H4 The expected product value Informative conformity Figure 3 Conceptual Model of Study 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

12 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. 4.1 Data Distribution CHAPTER 4 RESULTS AND ANALYSIS 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 % Female % Table 4-2 Participants Age Distribute of Study 1 Age Participants Percentage Below % 21~ % 26~ % 31~ % 36~ % Above % 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 % Female %

13 Table 4-4 Participants Age Distribute of Study 2 Age Participants Percentage Below % 21~ % 26~ % 31~ % 36~ % Above % 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 The number of people behind Informative conformity Expected product value Table 4-6 Descriptive Statistics of Study 2 Variable Mean S.D. Total queue length Informative conformity Expected product value

14 Table 4-7 Scale Items and Reliability Coefficients Variable Cronbach s α Expected product value Informative conformity 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~ Informative conformity 0.66~ 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 Informative conformity Expected product value 0.82 Informative conformity 0.185** 0.77 Note: **p<0.01

15 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 *** *** *** 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 Gender Age R Adjust R F-value *** 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

16 (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 ** 0.183** TI 0.219** 0.157* Product preference 0.314*** R Adjust R F-value 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

17 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.

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