8. Implications Recommendations Limitations Conclusion References. 13. Appendix Questionnaire 13.

Transcription

1 A STUDY OF QUEUE: CONSUMERS PURCHASE INTENTION TRADE OFF BETWEEN PREIVCIED PRODUCT QUALITY AND PERCEIVED SACRIFICE BY Wong Tsz Fung Marketing Option

2 Acknowledgement Table of Contents iii Abstract iv 1. Introduction 1 2. Research Objectives 2 3. Literature Review Definition of Queue Queuing as Sacrifice Looking for Extrinsic Cues Social Proof: the Positive Signal of Queue The Interplay between Perceived Product Quality and Perceived Sacrifice 7 4. Statement of Hypotheses 8

3 8. Implications Recommendations Limitations Conclusion References 13. Appendix 13.1 Questionnaire SPSS OUTPUT ii

4 Acknowledgement First, I would like to appreciate my supervisor, Dr. Alex S.L. Tsang of this Honors Project. Although he was very busy at that time, he was patient to give a lot of valuable guidance and advice to me. Without his kind support, this study could not be accomplished favorably. Besides, I appreciate for the valuable time of supportive subjects, and for assistance and support of my friends. iii

5 Abstract Most people do not like to queue up because it causes people feel time wasting and anxiety. Abundant mathematical and psychological studies had been conducted to solve this problem. However, in some situation people can choose whether to wait, for example to decide which travel agents to inquire. But, why some consumers are willing to join a queue when they have alternative chooses. I proposed social proof effect can increase the perceived product quality and then indirectly affects purchase intention of consumers. However, queue signals sacrifice to the consumers perception at the same time. So in this study, a comprehensive view of queue was considered. I used the purchase decision made on two tuck shops with different queue lengths as an example to examine the relationship between perceived product quality, perceived sacrifice, and purchase intention. The results showed that queue length should be decomposed into perceived product quality and perceived sacrifice in order to understand its relationship with purchase intention. Recommendations were given to retailers to benefit retailers in the long term; also, this study gave some implications and paved the way for the future research. iv

6 1. Introduction Queuing is a common phenomenon in the world, when everything is added up, an average person may spends as much as 30 minutes a day waiting in line, which translates to 20 months of waiting in an 80-year lifetime (Wielenga 1997). Hong Kong is a high density city; we can see many places are full of queues. People queue up to get in the train, bus and taxi; wait for using toilet; deposit money in bank; purchase film ticket and pay money in supermarket etc. Queuing is divided into two situations. The first situation is that people have little choice but queue, such as withdrawing money from ATM; or waiting to check in for a flight, and even waiting for an organ donation (Zhou and Soman, 2003). In the second situation, people can choose whether to wait or switch another service provider. This happen in cases such as waiting to play roller coaster in a theme park or queuing up to buy a concert ticket of a pop star. Most of the people do not like to queue up because they feel that queuing is boring, time wasting and caused physical and psychological discomfort. Not surprisingly, abundant studies had evaluated how queuing negatively affect customers perception and service evaluation (Larson 1987; Chebat and Filiatrault 1

7 1993; Hui and Tse 1996). However, why do people still decide to wait in the second situation even they are not necessary to wait? I suggest queue length can be used as an extrinsic cue to help consumers to estimate product quality and make purchase decision, especially when they have imperfect information about product quality. As the other people state their preference behaviorally by queuing; thus the perceived risk of a consumer could be eased by using the wisdom of crowds. As the model developed by Dodds and Monroe (1985) had been frequently used by researchers to examine the extrinsic cues such as price, brand name, store name and country of origin etc. (Zeithaml 1988; Dodds, Monroe, Grewal 1991; Sanjeev 2000; Sanjeev 2001). Therefore, I use this model to test the interplay between queue length, perceived product quality, perceived sacrifice and purchase intention in the situation which consumers can choose whether to wait. 2. Research Objectives 1. To find out how queue length affects customers perception on product quality and sacrifice 2. To understand consumer how perceived product quality and perceived sacrifice affect the purchase intention 2

8 Theoretically, the major contribution is that I suggest that queue length carries both negative (perceived sacrifice) and positive (perceived product quality) signals to consumers. Queue length must be decomposed into these two factors to understand the significant association between queue length and purchase intention. Practically, the competition among the retail industry is very keen; the findings of this study would give some suggestions to better manage queues in order to gain long-term benefits. Therefore, retailers can use queues as a non-paid promotion to draw people attention and encourage them to purchase in the first time. Moreover, action would be taken to reduce perceived sacrifice with an aim to stimulate purchase intention. 3. Literature review 3.1 Definition of Queue Queuing occur when demand exceeding the capacity of the delivery system (Houston, Bettencourt, Wenger 1998). For example, when customers arrive at a rate that exceeds a travel agent capacity to serve; they form a queue to wait for inquiry. 3

9 3.2 Queuing as Sacrifice In general cases, queues are a symptom of unresolved capacity management problems. Analyses and modeling of queues are well established in operation management. And it can be tracked back to 1917 (Lovelock and Wirtz 2004). And journals such as Operations Research and Naval Research Logistics Quarterly frequently contain contributions on this subject with over 1,000 papers. (Randolph 1991). On the other hand, marketers have investigated the psychology of waiting. The studies found that waiting for service is typically a negative consumer experience and causes unhappiness, frustration, and anxiety (Larson 1987). Also, time costs, physiological (fatigue), emotional (depression) all enter explicitly or implicitly into consumer s perception of sacrifice (Zeithaml 1988). Moreover, some studies had investigated relationships among perceived time spent in waiting lines, clients mood, and perceived service quality. The results are consumers who find the waiting time unacceptable have a significantly lower mood and lower perceived service quality (Chebat and Filiatrault 1993). And people renege depends on the time already spent waiting in the queue and on the number of people ahead and behind the consumer (Hui and Tse, 1996; Zhou and Soman, 2003). 4

10 3.3 Looking for Extrinsic Cues However, if a consumer is choosing some brands, products or retailers that they had not purchased before under imperfect information, perceived risk will be caused. He will involve risk in a sense that any action of them will produce consequences which he cannot anticipate with any approximating certainty, and some of those at least are likely to be unpleasant (Boris 2004). Therefore, consumers commonly use intrinsic and extrinsic cues to signal quality in order to reduce perceived risk. Intrinsic cues involve the physical composition of the product and cannot be changed without altering the nature of product itself and are consumed as the product is consumed. In contrast, extrinsic cues are product-related but not part of the physical product itself. They are by definition, outside the product (Olson 1977). As a queue is outside the product, it can be an extrinsic cue to indicate product quality. Sometime consumers rely on extrinsic cue attributes more that the intrinsic cue, when evaluation of intrinsic cue requires more effort and time than the consumer perceives is worthwhile or if the product is an experience goods (Sawyer, Worthing, and Sendak 1979). Experience goods; by definition, consumer do not know how much utility they can receive from the goods before consuming it. (Kim 1992). For example, consumers have difficulty to evaluate the product quality of spa before 5

11 purchase. 3.4 Social Proof: the Positive Signal of Queue Perceived quality can be defined as consumer s judgment about a product s overall excellent or superiority (Zeithaml 1988). When the queue occurs, it can trigger the attention of passersby and lead to the effect of Social proof. It presumes that if a lot of people are doing the same thing, they must know something we do not (Cialdini 1993). By definition, it occurs when it is optimal for an individual, having observed the action of those ahead of him, to follow the behavior of the preceding individual without to his own information (Bikhchandani 1992). Interestingly, the previous researches indicated as the size of stimulus crowd increased, a greater proportion of passersby adopted the crowd s behavior (Milgram 1969; Knowles 1976). The stimulus crowd in these studies functioned as a kind of non-paid promotion that something unusual was happening (Mann 1977). On the other hand, the more uncertain an individual is about the correctness of his judgment, the more susceptible he is to informational influences on his decision (Deutsch 1955). Moreover, Hanson studied the perceived popularity of software 6

12 programs on a large commercial online download system. Among the same type of program, a selected program was manipulated as the most popular program. Eventually, He found that subsequent additional downloads were induced by the manipulation. Some customers apparently are drawn to the market leaders, further increasing their lead (Hansson 1996). Asch (1952) studied that people rationally take into account of the information revealed by the others action. The subjects, who reacting to the conflict between their own decision and the rest of the group, feel anxious and under pressure, and a third of their response made the same error as the majority. Hence, people are willing to place an enormous amount of trust in the collective knowledge of the crowd and make decision in a time-saving way (Cialdini 1993: 163). So, consumers under insufficient information may have positive perceived product quality toward the product based on social proof when other people are using it. 3.5 The Interplay between Perceived Product Quality and Perceived Sacrifice Commonly, consumers purchase a product if it is good of value and meet their need. Perceived value can be defined as consumer s overall assessment of the utility of a product based on perception of what is received and what is given varies, so value represents a tradeoff if the salient give and get components (Zeithaml 1988). 7

13 From Kotler (2000) value is given by: Value = Benefits/Costs = Functional benefits + emotional benefits/ Monetary costs + time cost + energy costs + psychic costs In this study, the perceived product quality and perceived sacrifice represent what consumer gets and gives, which are similar with the traditional theory of value. For a queue, it signals two type of information to the consumer, a longer queue leads to higher perceived product quality and greater purchase intention. At the same time, it represents more time, physical and psychic cost to consumer, and then reduces the willingness to buy. When consumers judge on queue, the queue gives contrary signals to the consumers, thus the two forces may cancel off each other making queue length not a good predictor of purchase intention. It means that consumer cannot make the decision based on the queue length, however, consumer should evaluate what he gets and gives from the queue, if the quality perception outweighs the sacrifice perception, the consumer may properly buy the product, vice versa. 4. Statement of Hypotheses According to our discussions, the following hypotheses were developed: 8

14 H1: Queue length is NOT significantly related to purchase intention H2: There is a positive relationship between queue length and perceived product quality. H3: There is a positive relationship between queue length and perceived sacrifice. H4: There is a positive relationship between perceived product quality and purchase intention H5: There is a negative relationship between perceived sacrifice and purchase intention The framework of the hypotheses are shown in Figure 1 (Figure 1) H2 + Perceived Product Quality H4 + Queue length H1 not sign. Purchase intention + H3 Perceived Sacrifice - H5 9

15 5. Methodology 5.1 Scenario Development Scenario is commonly used in marketing research (Zhou and Soman, 2003); it allows us to have a high degree of control because it isolates the experiment in a carefully designed scenar

16 5.2 Treatment Variable In this study, I used six levels of queue length from low to extremely high (1, 3, 6, 9, 12, and 15) to test the effect in order to create a clear picture of the consumer s perception change. For the scale of queue length, individual interview with fifteen people were conducted to find out people standard of queue length from short to extremely long. I used extreme rating (1 and 15 queue length) because they may indicate the effect sharply. 5.3 Instrument Development The questionnaire was divided into two sections. The first section is a scenario. Subjects were required to read the scenario first. In the scenario, Subjects were asked to image that they were facing a purchase decision between two tuck shops in front of them, a shop with queue named Sam tuck shop and a shop without queue named Tom tuck shop, except the queue length, the other things of these two shop such as appearance, product and price were very similar. In the second section, subjects were required to answer several questions on Sam Tuck Shop with totally three structured independent variables with modifications based on pretest and a review of the prior research to measure the shop with queue 11

17 length. The first variable measured the subjects perception of food quality by asking five statements developed by Dodds (1991), Buchanan (1999) and Coyle (2001).Then the subjects were asked to evaluate their perceived sacrifice toward the queue length (Hui and Tse 1996; Sanjeev 2001). Eventually, I assessed their purchase intention as the last dependent measurement (Dodds, Monroe, Grewal 1991). All of their views would be measure by a seven-point Likert scale ( 1 = strong disagree and 7 = strongly disagree). Comparison questions were asked to strengthen the treatment effects. Finally, the last part of the second section was used for conducting basic demographic information of the subjects. A sample questionnaire was provided in Appendix. 5.4 Pretest A pretest was conducted by asking 30 students before actual data collection. The scenario was revised to ensure the subjects understood the scenario; also some descriptions were inserted to eliminate the extraneous factors. For example, one minute was needed to handle each consumer was added into the scenario, in order to make sure subjects perception of sacrifice were consistent. Also, the comparison questions were asked in questionnaire. 12

18 5.5 Data Collection I collected 32 subjects for each of the six queue-length levels of the scenarios with a total sample size of 192. The data were collected in the Hong Kong Baptist University within March Student sample was used because of their homogeneity; which is good for assessing internal validity (Calder, Philips, Tybout 1981). During the data collection, each subject was randomly assigned to one of the six scenarios. And the subjects were interviewed face-to face; in order to make sure they can able to understand the scenario before asking the questions. 6. Finding and Results 6.1 Demographic Information In this research, totally 192 questionnaires, six versions with each 32 subjects were collected during the period. Among the subjects, 133 (69.3%) were female and 59 (30.7%) were male; People from age group and 22-23, had 88 (45.8%) and 87 (45.3%) subjects respectively, dominated a larger percentage of the sample; On the other hand, a majority 110(57.3%) of the subjects came from year three, year two students had 49 (25.5%) subjects, and year one students had 29 (15.1%); For the personal monthly income, 53 (27.6%) subjects had $1,000 or below income was the largest group; 40(20.8%) had $1501- $2000 was the second largest group with; And 13

19 only 15 (7.8%) of the subjects had $4,000 or above monthly income. (Table 1) Table 1: Demographic Information of the Subjects Frequency Percent Gender Male % Female Total % Age % % % % 26 or above 1 0.5% Total % % Year % % 4 2 1% Other 2 1% Total % Income $1,000 or below % $1,001-$1, % $1,501-$2, % $2,001-$2, % $2,501-$3, % $3,001-$ % $3,501-$4, % $4,000 or above % Total % 6.2 Reliability Test Before analyzing the data, reliability was tested by using Cronbach s alpha 14

20 coefficients. The alphas of product quality, sacrifice, value and purchase intention are , and 0.94 respectively (Table 2). Table 2: Scales and Reliabilities Mean S.D. Alpha Perceived product quality Perceived sacrifice Purchase intention All scales are seven-point scales (1=lowest; 7=highest) 6.3 Hypotheses Testing No Relationship between Queue Length and Purchase Intention H1 stated that queue length is NOT significantly related to purchase intention. Along a 7-point measurement scale, we can see that the overall mean is 4.33 (Table 3). A linear regression was used to determine whether the relationship was present between the queue length (independent variable) and purchase intention (dependent variable). As shown in Table 7, the queue length (R 2 = 0.001, p > 0.10) is not correlated with the purchase intention. Thus H1 is supported Positive Relationship between Queue Length and Perceived Product Quality H2 stated that there is a positive relationship between queue length and perceived product quality. Along a 7-point measurement scale, we can see that the 15

21 overall mean is 4.86 (Table 4). A linear regression was used to determine whether the positive relationship was present between the queue length (independent variable) and perceived quality (dependent variable). As shown in Table 6 and Table 7, the queue length (R 2 = 0.095, p < 0.001, Pearson correlation = 0.308) are positively correlated with subjects perceived product quality. Thus, H2 is supported. Table 3: Mean of Purchase Intention Queue Mean S.D

22 6.3.3 Positive relationship between queue length and perceived sacrifice H3 stated that there is a positive relationship between queue length and perceived sacrifice. The reported mean of perceived sacrifice among different queue lengths is shown in table 5; we can see that the overall mean is Simple regression was used to check whether a positive correlation between queue length (independent variable) and perceived sacrifice (dependent variable) exits. As shown in Table 6 and Table 7, the queue length (R 2 = 0.318, p < 0.001, Pearson correlation = 0.564) are positively correlated with subjects perceived sacrifice. Thus, H3 is supported. Table 5: Mean of Perceived Sacrifice among Different Queue Lengths Queue Mean S.D total Rounded up to 2 decimal places Table 6: Correlations among Queue Length and Observed Variables Variables Queue length Perceived sacrifice 0.564** Perceived product quality 0.308** **Correlation is significant at 0.01 level (2-tailed) 17

23 Table 7: Summary of Regression Analysis between Queue Length (independent variable) with Observed Variables Dependent variable Purchase intention (H1) Perceived product Perceived sacrifice (H3) quality (H2) Beta(t) Beta(t) Beta(t) Constant (0.000***) (0.000***) (0.000***) Purchase intention (0.747) Perceived product quality (0.000***) Perceived sacrifice (0.000***) R *** P < Perceived product quality = (0.055) * perceived quality Perceived sacrifice = (0.171) * perceived sacrifice Perceived Product Quality and Perceived Sacrifice with Purchase Intention H4 stated that there is a positive relationship between perceived product quality and purchase intention. And H5 stated that there is a negative relationship between perceived sacrifice and purchase intention. First, the correlation was run to check whether perceived product quality and perceived sacrifice are highly correlated. The result in Table 8 shown that there are no relationship between them (Pearson correlation = 0.054). The assumption of nonexistence of multicollinearity among independent variables is fulfilled. Then a multiple regression was conducted to analysis the effect of perceived product quality and perceived sacrifice (independent variables) on purchase intention 18

24 (dependent variable). As shown in Table 9, perceived product quality (R 2 = 0.401, Adjusted R 2 = 0.395, p< 0.001, beta = 0.71) is positively correlated with purchase intention. On the other hand, perceived sacrifice (p< 0.001, beta = -0.34) is negatively correlated with purchase intention. Hence, H4 and H 5 are supported. Table8: Correlations among Perceived Product Quality and Perceived Sacrifice Variable Perceived product quality Perceived sacrifice Correlation is insignificant Table 9: Summary of Multiple Regression Analysis on Perceived Product Quality, Perceived Sacrifice with Purchase Intention Dependent variable Purchase intention Beta (t) Constant (0.000***) Perceived product quality (0.000***) Perceived sacrifice (0.000***) R square ***P < Purchase intention = (0.7061) * perceived product quality (0.34)* perceived sacrifice 7. Discussions I tested the direct and indirect relationship between four variables (queue length, perceived product quality, perceived sacrifice and purchase intention). The design of 19

25 the experiment allowed analyzing the relative impact of different variables in different queue lengths. Then, some interesting findings are found in the study. First of all, there is no linear relationship between queue length and purchase intention. However, the association between queue length and perceived product quality is positively significant. Not surprisingly, the association between queue length and perceived sacrifice is positively significant and matches with the previous studies (Larson 1987; Zhou and Soman, 2003). However, there is no correlation between perceived product quality and perceived sacrifice. But these two variables have different impact on purchase intention. And the results indicate there is a significant positive relationship between perceived product quality and purchase intention. In contrast, there is a significant negative relationship between perceived sacrifice and purchase intention. 8. Implications Marketers cannot understand the impact of queue length on purchase decision because the queue length gives contrary signals to them. Therefore, queue length should be decomposed into perceived product quality and sacrifice in order to understand its relationship with purchase intention. And perceived product quality and sacrifice can be viewed as value assessment of consumers, consumer should 20

26 evaluate what he gets and gives in making purchase decision. Perceived product quality and sacrifice work as factors similar to mediator, which transform the relationship between independent (queue length) and dependent (purchase intention) variables (Baron and Kenny, 1986). 9. Recommendations Practically, Price is often the first variable to be proposed for bringing demand supply into balance. For example, significant different pricing schemes are used to fill capacity in each time period to make some customers shift to off-peak period. Additionally, another ways such as changes in product design, distribution strategy and communication efforts can also play an important role (Lovelock and Wirtz 2004). However, in this study, I view the demand management in the other view. Particularly, queuing is not totally negative; when consumers have imperfect information toward the shop, it can be used as a non-paid promotion to create a good quality impression toward consumers perception and then indirectly influences consumers purchase intention. Therefore, some new or infamous retailers can keep the queue as a marketing strategy to spend positive word of mouth to attract potential customers. 21

27 Moreover, as this study shows that the queue length should be decomposed into two variables; they are perceived product quality and perceived sacrifice. So, the marketers can increase the value of customers by trying to decrease the perceived sacrifice as it is negatively affect the purchase intention. For example, a clock should be eliminated from the waiting area, and some videos, TV programs or soft music can be played to occupy their time in order to reduce their perceived duration; And physical comfortable waits such as controlling the temperature, protecting from rain and sunshine and providing seats to the consumers, would make the wait seems less burdensome (Chebat and Filiatrault 1993; Davis and Heineke 1994). Academically, this study takes a first step to explore the positive effect of queues, as an extrinsic cue that indicates product quality and addresses whether a consumer decides to join the queue. Additionally, this study suggested perceived product quality and perceived sacrifice may be mediators that may transform the relationship between queue length and consumer purchase intention. Within the queue method, it is possible for future researches to evaluate whether there are different between the several types of queuing, such as take-a-number-and-wait, designated queues to designated servers and single line to multiple servers systems. Also, the effect of queue can be examined with the other extrinsic cues, such as price, 22

28 brand name or store name. For example, as Scitovovszky (1945) observed that price can be both an indicator of the amount of sacrifice needed to purchase a product and an indicator of the level of quality. So the different components (high queue length with high price; low queue length with high price etc.) would be examined in order to know the behavior of consumer toward social proof and price prestige. 10. Limitations Regarding the time and cost problems, I set a number of hypothetical waiting scenarios with no actual waiting, although I tried to minimize the limitations by proposing a real example. Moreover, a strong test effect may exit because there was only a different between these shops during comparison scenario, it may affect the result. So, this is a study of how subjects would behave to a queue, not an actual decision of people made. An experiment with realistic tuck shops and queue length would be conducted in order to increase the validity and reduce the artificial feeling of the experiments. Also, a single example was not enough to represent the validity; more examples should be used to demonstrate the signification. Besides, separate regressions were used to examine the effect of queue length (independent variable) to perceive product quality and perceived sacrifice (dependent variable), I ignored the interaction effect between two dependent variables, structural equation modeling 23

29 should be used in the future study. Additionally, the number of queues was not set wide enough to demonstrate the change during different queue lengths, the addition of comprehensive queue length scale (0, 1, 2, 3, 4, 5.) would provide a stronger test for the relationship among different variables. Also, as the convenience sampling was used, bias may exit. If the random sampling was used, it would contribute a higher credibility of the results. 11. Conclusion Queuing is a common situation in Hong Kong, as it is an unavoidable and causes depression to people, thus the general studies in the past mainly focused on the negative side of queue. However, this study, apart from perceived sacrifice, also examined perceived product quality of queue based on the theory of social proof, proposed a comprehensive view on the queue aimed at understanding consumer perception between queue length and purchase intention. I found that the increase in queue length has no direct relationship with the purchase intention and it should be decomposed into perceived product quality and perceived sacrifice in order to understand its relationship with purchase intention. As the perceived product quality positively related and perceived sacrifice negatively related to purchase intention. So, 24

30 I recommend marketers can use the queue to attract consumers and decrease the perceived duration. Also several actions would be taken to reduce the consumer perceived duration of the waiting time. Academically, this study takes a first step to use queue length as an extrinsic cue of product, thus marketers could spend more effort in an in-depth assessment of queue length and other extrinsic cues effect to consumers. 25

31 12. References Asch S.E. (1951) Effect of the Group Pressure upon the Modification and Distortion of the Judgement, in H. Guetzkow (ED.) Groups, leadership, and men, Pittsburgh PA: Carnegie Bateson John E.G., Hui Michael K. (1992) The Ecological Validity of Photographic Slides and Videotape, Journal of Consumer Research: Sept 1992, 19, P Bikhchandani Sushil, Hirshleifer David, Welch Ivo (1992) A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades, The Journal of Political Economy: Oct , 5; P Boris Snoj,

32 Dodds William B., Monroe Kent B., Grewal Dhruv (1991) Effect of Price, Brand, and Store Information on Buyers Product Evaluations, Journal of Marketing Research: 1991, Aug: 28, 3 P Hansson Ward A., Putler Daniel S. (1996) Hits and Misses: Herd behavior and Online Product Value Popularity, Marketing Letter: 1996, 7, 4 P Houston Mark B, Betthencourt Lance A., Wenger Sutha (1998) The relationship between Waiting in a service Queue and Evaluation of Service Quality: A Field Theory Perspective, Psychology & Marketing: 1998, Dec 15, 8 Hui Michael K., Tse David K. (1996) What to tell consumers in waits of different lengths: An integrative model of service evaluation, Journal of Marketing Chicago: 1996, Apr. 60, 2; P Kim Jae-Cheol (1992) Experience Goods, Expectations and Pricing, Economic Record.: 1992, Mar. 68, 200, P.7-16 Knowles E. S., Bassett, R. L. (1976) Groups and crowds as social entities: Effect of activity size and member similarity on nonmembers, The journal of personality and social psychology: 1976, 34, 5, P Kotler, Philip (2000): Marketing management, N.J.: Prentice Hall, c2000 Larson Richard (1987), Perspectives on Queues: Social Justice and the Psychology of Queuing, Operations Research: 35, Nov/Dec, P Lovelock C., Wirtz J. (2004): Service Marketing: People, Technology, Strategy. 5th Edition. Prentice Hall, P Mann Leon (1977) The effect of stimulus queues on queue-joining behavior, The journal of personality and social psychology: 1977, 35, 6, P Malhotra Naresh K. (2004): Marketing Research an Applied Orientation 4 th edition Pearson Prentice Hall Milgram S., Bickman L., Berkowitz L. (1969) Note of the Drawing Power of Crowds of Different size, The journal of personality and social psychology: 1969, 13, P

33 Sanjeev Agarwal R., Kenneth Teas (2001) Perceived value: Mediating role of perceived risk, Journal of Marketing Theory and Practice, Statesboro: Fall, 2001, 9, 4, P.1-15 Sawyer Alan G., Worthing Parker M., Sendak Paul E. (1979) The Role of Laboratory Experiments to Test Marketing Strategies, Journal of Consumer Research: 1979, 43 Summer, P.60-7 Scitovszky Tibor (1945), Some Consequences of the Habit of Judging Quality by Price, Review of Economic studies: 1945, winter, 12, P Wielenga Dave (1997) Not so Fine Lines, Los Angeles Time November: 1997, P.28 Zeithaml Valarie A. (1988) Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence, Journal of Marketing: 1988, Jul, 52, 3, P.2-22 Zhou Rongrong, Soman Dilip (2003), Looking back: Exploring the psychology of queuing and the effect of the number of people behind, Journal of Consumer Research: 2003, Mar, 29, 4, P

34 13. Appendix: 13.1Questionnaire 問卷編號 N 你好, 我是香港浸會大學, 工商管理市場學系三年級學生, 現正進行一項有關於消費者行為的學術研究 希望閣下能花 5 分鐘時間回答以下問題, 提出寶貴意見, 而所有資料只供學術用途, 謝謝! 在回答問題之前, 請先將自己代入故事中 現在, 你身處於旺角的街道上並想購買你喜歡吃的小食 在你眼前只有 Tom 小食店及 Sam 小食店可供選擇 由於兩間小食店都是新開的, 因此你對它們都沒有任何認識 在你觀察之後, 它們的裝璜 設備 價錢及所提供的食物類型都十分接近的 但單憑觀察卻未能分別哪一店的食品質素較高 由於製作需時, 故每分鐘只可以處理一個客人 現時 Tom 店子前無人在排隊等候購買小食, 在 Sam 店子前則有 N(1,3,6,9,12,15) 個人在排隊等候購買小食 ( 大約需時 N(1,3,6,9,12,15) 分鐘 )

35 甲部 請就你對 Sam 小食店的意見圈上適當數字, 7" 表示非常同意, 1" 表示非常不同意, 4" 則表示中立 你會選擇於那間小食店購買食物? Sam 小食店 (N 人 ) Tom 小食店 (0 人 ) Sam 小食店與 Tom 小食店相比 非常不同意 中立 非常同意 1) 我認為 Sam 小食店有良好的食物質素 ) 我認為 Sam 小食店的食物是可靠的 ) 我認為 Sam 小食店的廚師有好的廚藝 ) 我認為 Sam 小食店的食物味道比較優勝 ) 我認為 Sam 小食店的用料是優勝的 Sam 小食店與 Tom 小食店相比 非常不同意 中立 非常同意 6) 我認為在 Sam 小食店排隊的長度是不可以接受的 ) 若在有 N 個人的 Sam 小食店排隊會使我感到煩躁 ) 若在 N 個人排隊的 Sam 小食店排隊購買食品, 我會失 去做其他事件的時間 9) 若選擇排在 N 個人排隊的 Sam 小食店排隊購買食品, 你會感到吃力 10) 我認為在 Sam 小食店排隊的時間是長的

36 Sam 小食店與 Tom 小食店相比 非常不同意 中立 非常同意 16) 我現在會光顧 Sam 小食店的可能性是高的 ) 我考慮光顧這間有 N 個人正在排隊的 Sam 小食店 ) 我現在樂意光顧 Sam 小食店 ) 我現在會在 Sam 小食店購買食物 ) 我現在會嘗試 Sam 小食店的小食 乙部 ( 個人資料 ) 性別 1) 男 2) 女 年齡 a)18-19 b) c) d) e) 26 以上 你是 一年級學生 二年級學生 三年級學生 四年級學生 其他, 請注明 你的個人月入大約有 少於 $1,000 $1,001 $1,500 $1,501 $2,000 $2,001 $2,500 $2,501 $3,000 $3,001 $3,500 $3,501 $4000 $4,000 以上

37 13. 2 SPSS OUTPUT The SPSS output of the regression between queue length and purchase intention Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1.023(a)

38 ANOVA(b) Model Sum of Squares df Mean Square