Testing a Theory of Crowding in the Service Environment

Michael K.M. Hui, Concordia University
John E.G. Bateson, The London Business School
ABSTRACT - Perceived crowding is important to the managers of retail service settings. With a fixed area of space (the physical capacity), service managers need to know the maximum customer density level their customers will tolerate (the effective or physical capacity). This paper presents an empirical test of the control theory of crowding which suggests that perceived control is an important intervening variable between physical density and perceived crowding. The results confirm the theory and show that perceived choice is associated with perceived control and hence can modulate the effect of densitY.
[ to cite ]:
Michael K.M. Hui and John E.G. Bateson (1990) ,"Testing a Theory of Crowding in the Service Environment", in NA - Advances in Consumer Research Volume 17, eds. Marvin E. Goldberg, Gerald Gorn, and Richard W. Pollay, Provo, UT : Association for Consumer Research, Pages: 866-873.

Advances in Consumer Research Volume 17, 1990      Pages 866-873

TESTING A THEORY OF CROWDING IN THE SERVICE ENVIRONMENT

Michael K.M. Hui, Concordia University

John E.G. Bateson, The London Business School

ABSTRACT -

Perceived crowding is important to the managers of retail service settings. With a fixed area of space (the physical capacity), service managers need to know the maximum customer density level their customers will tolerate (the effective or physical capacity). This paper presents an empirical test of the control theory of crowding which suggests that perceived control is an important intervening variable between physical density and perceived crowding. The results confirm the theory and show that perceived choice is associated with perceived control and hence can modulate the effect of densitY.

INTRODUCTION

Retail services can be thought of as services that are produced through interactions between customers and the contact personnel, environment and physical support provided by service firms (Eiglier and Langeard, 1977; Langeard, Bateson, Lovelock, and Eiglier 1981). For example, to deposit $10 into a bank account, a customer has to go to a branch of his/her bank (the physical environment), fill in a slip (the physical support) and give the money and the slip to a teller (the contact personnel). Obviously, in order to produce a service, customers must be present in a milieu, usually the retail outlet of a service firm so that these interactions can occur.

One important characteristic of retail services is that they are produced and consumed simultaneously and hence cannot be inventoried (Eiglier and Langeard 1977). Meanwhile, the demand of any retail services is rarely smooth over time. Since stocks cannot be used as a tool to buffer the fluctuation in demand, crowds are unavoidable for retail services. One of the major determinants of the capacity of a service firm is the total area of that part of the premises accessible to the customers. When the demand level approaches or exceeds the service firm's capacity limit, there will be a high density of customers, being served or waiting to be served, in its premises. A huge crowd of people or long queues in banks, subways, supermarkets and other common service organizations is no longer a strange feature to consumers in the 1980's. The pervasiveness of crowding in retail services makes it an issue which deserves research attention from consumer researchers.

Stokols (1972) has contended that there is a need to distinguish between two terms "density" and "crowding". Density is defined by Stokols as "the physical condition, in terms of spatial parameter" while crowding is a psychological experience characterized by stress. This paper draws upon the latest theoretical developments in environmental psychology which suggest the importance of perceived control as an intervening variable between density and crowding. The procedures and findings of an experimental study conducted to test the control theory of crowding in the context of service delivery are presented.

THEORETICAL DEVELOPMENT ON HUMAN CROWDING

Findings from both laboratory settings (see, for example, Paulus, Annis, Seta, Schkade, and Matthews 1976) and real-life settings (see, for example, Mackintosh, West, and Saegart 1975) have indicated that crowding does have deleterious effects on human response such- as interpersonal behavior and task performance. However, a number of other experimental studies have obtained the opposite findings. They have,shown that crowding can have no, or even positive, effects on human behavior (see, for example, Freedman 1975). For a review, please refer to Sundstrom (1978).

The equivocal experimental evidence have forced theorists to revise their conceptualization of human crowding. Stokols (1972) is one of the pioneers who has tried to clarify the conceptual confusions in the study of human crowding. As noted earlier, he suggests that "density" and "crowding" are two different concepts. Density is a function of the area and the number of people in a setting. By contrast, crowding refers to the stress experienced by the occupants of a setting when there is an "excessive" number of people there. The term "excessive" needs to be elaborated. At least three different theoretical models have been developed to specify the crucial antecedents of crowding and explain why a certain number of people are considered as excessive or not excessive in a setting. The three models are commonly known as the overload model, the behavioral constraint model, and the ecological model.

The Overload Model

The main argument of the overload model is that crowding results when one's mental ability is unable to deal with all the impinging environmental stimuli. Altman (1975) has argued that crowding is an outcome of one's failure to maintain a desired level of social interactions through means like personal space and territoriality.

The Behavioral Constraint Model

The development of the behavioral constraint model draws heavily from Brehm's (1966) reactance theory which states that there is a basic desire among human beings to maintain their behavioral freedom. According to the model, a certain amount of people will be evaluated as excessive (and hence result in the perception of crowding) for a setting when one's desired actions are restricted or made impossible because of the presence of other people. It is conceivable that pressure and norms imposed by other people can restrain one from performing some kinds of actions. The constraint can be physical and instrumental as well.

The Ecological Model

Manning theory has also been used to explain human crowding. According to the theory, an individual's behavior is determined by the supply and demand of social roles existing in a setting. An individual will perceive more crowding when a setting is "overmanned" (when the number of occupants of the setting exceeds that required for the normal functioning of the setting) than when it is "undermanned". Similar arguments have been proposed by a number of authors that the perception of crowding is a function of the supply and demand of resources available in a setting (Stockdale and Schopler 1976). Empirical support for the ecological model, however, is far sparser than for the other two models.

PERCEIVED CONTROL AS AN INTERVENING VARIABLE BETWEEN DENSITY AND PERCEIVED CROWDING

According to a number of authors (Baron and Rodin 1978; Schmidt and Keating 1979), however, all the three conceptual models of crowding (overload, behavioral constraint, and ecological) can be subsumed under the concept of control. A review of the concept of control is deemed necessary before its relationship with crowding is explored.

Three Types of Control

Control has been operationalized in various distinctive ways which have been grouped by Averill (1973) into three different categories: behavioral control, cognitive control, and decisional control. In Averill's own terms, behavioral control refers to the "availability of a response which may directly influence or modify the objective characteristics of an event" (p. 286). Cognitive control is "the processing of potentially threatening information in such a manner as to reduce the net long-term stress" (p. 293). Cognitive control does not require behavioral control, rather it refers to a sense of "feeling in control". This has been broken down into predictability and evaluation in the literature. Finally, decisional control is regarded as the extent of "choice in the selection of outcomes or goal" (p. 289).

Crowding and Control

Constraints on or interruption of behavior obviously reduce one's ability to deal effectively with the environment in the way one desires (behavioral control, as in Averill's terms). Moreover, the more the constraints, the smaller will be the number of options available to an individual in the selection of (a) situational goals, and (b) means to attain the goals (decisional control, as in Averill's terms).

When an individual's mental capacity is impaired by excessive stimulus, he/she is less likely to collect and evaluate useful environmental information. As a result, environmental events will look less predictable and effective cognitive or behavioral coping (behavioral and cognitive control, as in Averill's terms) will become less likely.

In an overmanned setting, any marginal occupants are likely to be ignored by the majority and hence there is less chance for them to affect the social and physical characteristics of setting (behavioral control). On the other hand, any existing member is important to an undermanned setting and there is greater opportunity for an individual to claim the setting's resources and manipulate the setting in the way he/she desires.

It is conceivable therefore that all the harmful effects caused by behavioral constraints, information overload, or overmanning can be explained by Averill's three types of control as Baron and Rodin (1978) have noted:

"It appears that when density variation have no direct implications for control as in certain of the studies involving manipulations of room size ..., variations in density do not produce differential stress effects. Thus there is a preliminary indication that the density-control may not only be a sufficient condition for stress instigation but a necessary condition." (p. 181).

In other words, perceived control is suggested to be a crucial intervening variable between density and perceived crowding.

SITUATIONAL AND PERSONAL DETERMINANTS OF PERCEIVED - CROWDING

Besides density, however, it has been shown that a large number of physical, social, and personal factors can also modify an individual's perceived control without any variation in environmental density. A logical conclusion from the argument is that for a fixed density level, perceived crowding can vary between situations and persons.

For example, architectural design of university dormitories has been found to be a key determinant of the residents' perception of crowding (Baum and Valins 1977). Other design variables such as partitioning (Desor 1972), signage (Wener and Kaminoff 1983), and even the shape (Desor 1972), colour (Baum and Davis 1976), and light level (Baum and Davis 1976) of a setting have also been demonstrated to affect human crowding.

From a social-situational perspective, empirical evidence has revealed that crowding is affected by the nature of activities within a setting-recreational versus work, interactive versus coactive, cooperative versus competitive (Cohen, Sladen, and Benett 1973; Stokols, Rall, Pinner, and Schopler 1973). Moreover, acquaintance, group membership, and some basic social structure developed within a group can also alleviate one's perception of crowding (Baum and Koman 1976).

FIGURE 1

THE CONTROL THEORY OF CROWDING

Finally, demographic and personal variables like sex (Baum and Koman 1976), locus-of-control (Schopler and Walton 1974), and desire for control (Burger, Oakman, and Bullard 1983) have also been shown to affect an individual's perception of crowding. - It is difficult, if not impossible, to give an exhaustive list of mediating variables for crowding. However, a review of those variables which have been shown to affect crowding suggests that most of the variables actually affect one's perception of control. For instance, social structure has been shown to reduce perceived crowding (Baum and Koman 1976). The finding can be interpreted by suggesting that social activities will become more organized and hence more predictable when a simple social structure or hierarchy is developed within a group of individuals. Therefore, the first hypothesis is formulated as follow:

[Hypothesis 1] At a certain density level, perceived crowding is a function of all the environmental, situational, and personal variables which influence an individual's perceived control in a setting.

THE CONTROL THEORY OF CROWDING

As noted earlier, perceived control is suggested to be a crucial intervening variable between density and perceived crowding. Nonetheless, within a setting, manipulation of physical density has been found to exert a significant main effect on perceived crowding (see for example, Langer and Saegart 1977). Alternatively, other things being equal, higher density is expected to lead to higher perception of crowding. As a result, the second hypothesis is formulated as follows:

[Hypothesis 2] Density is hypothesized to affect perceived crowding both directly and indirectly through perceived control.

Hypotheses 1 and 2 together can be represented by Figure 1 which is labelled hereafter as the "control theory of crowding". In the remaining parts of this paper, the methodological procedures and findings of a study conducted to test the theory are described.

AN EXPERIMENTAL STUDY

An experimental study was conducted to examine the validity of the control theory of crowding within the service setting: In essence, a 3 (high, medium and low density) x 2 (choice, no choice) x 2 (slide, video) factoral design was employed. The extent of choice available to an individual has been argued by Averill (1973) as a major situational determinant of perceived control. Moreover, customer choice has been suggested to be a crucial concept related to the strategic issue of service customization (Surprenant and Solomon 1987). Hence customer choice was selected as the situational variable (see Figure 1) to be manipulated in the study.

Customer Density Manipulation

Customer density was operationalized by slides and video tapes portraying three different numbers of customers in the ticket office of a major railway station in London, England. The employment of two different representative media (slide and video) to simulate the same service setting allowed the researchers to detect any response bias caused by either media. The slides and video were taken st the station on two weekdays. On the first day, a photo camera, with a 24 mm lens, was fixed at one corner of the ticket office. On average, one picture was taken every 4 to 5 minutes and a total of 35 slides were obtained. Since the slides were shot by the same camera from the same location, the only difference between the slides is the number of customers present in the ticket office. On the second day, twenty-five 90-second video sequences were taken by a video camera fixed at the identical spot where the photo camera was located.

The number of customers shown in each slide and video sequence was then counted. The slides which had the highest and lowest number of customers were selected to simulate the high and the low customer density environments. One more slide with a number of customers equal to the mid-point between the two extremes was selected to represent the medium density environment. Three video sequences which showed approximately the same numbers of customers as the three selected slides were also retained for the study.

The Choice Manipulation

Choice was operationalized through two different written scenarios, both of which described the service situation in the station:

[High choice]

It is ten o'clock on Saturday morning and Mr. Y is going by train to visit his friend. Tickets are sold in the railway station at both the ticket office and through an automatic ticket machine located next to the office. Mr. Y decides to buy the ticket from the ticket office. Of course, he can always change his mind and go for the machine. However, he sticks to his original decision throughout the process. The slide we are going to show you depicts the setting of the ticket office while Mr. Y is there.

[Low choice]

... However, the machine only sells normal fare tickets. Since Mr. Y wants a cheap day-return ticket, he can only buy it from the ticket office. The slide ... there.

The two situations described by the written scenarios can be regarded as the service analogues of the two treatment conditions employed in a series of studies conducted by Glass and Singer (1972). In the studies, a stressful stimulus (e.g. electric shock) was administered to the subjects. Half of the subjects, however, were given an option to avoid or escape from the stimulus but they were asked not to exercise the choice as long as they felt the stimulus was tolerable. As a result, no subject selected the escape or avoid option and hence all the subjects went through the same stressful experience. Nonetheless, the choice subjects did feel and behave better than the no-choice subjects.

A similar approach was adopted in the design of the two written scenarios. In the high choice situation, Mr. Y can avoid the potentially stressful setting (when there are a lot of people there) by using the ticket machine but he does not take the available option. However, unlike the manipulations employed by Glass and Singer (1972), there is nc guarantee in terms of the present manipulations that the alternative option (ticket machine) is a better one. Nonetheless, the mere existence of a similar option has been shown to increase an individual' perceived choice (Harvey and Johnston 1973) and hence is expected to elevate his/her perceived control as well.

Finally, Havlena and Holbrook (1986) has suggested that there are two advantages of using X hypothetical figure (Mr. Y) in a written scenario:

... (a) to provide a projective task and thereby to discourage social desirability effects, and (b) to avoid problems involving individual differences in-reactions to specific types of activities" (Havlena and Holbrook, 1986, p. 396.)

One hundred and twenty-three individuals, age between 17 and 57 were recruited from the streets of a coastal city in the south of England to participate as the subjects of this study. The subjects were divided into 24 groups of four to six people. Each group of subjects watched 1 slide or 1 video sequence, randomly assigned to each session. The slide was kept projecting on a screen while the video sequence was continuously displayed on a 21-inch monitor throughout the whole session. Before the subjects watched the slide or video sequence, they were asked to read one of the two the written scenarios. Half of the subjects within a session were given the high-choice scenario while the other half were given the low-choice scenario. The subjects then filled out an environmental rating form. They were asked to describe Mr. Y's feelings on the following scales:

(1) CTL1:

A 9-item semantic differential scale, developed from a combination the Mehrabian and Russell's (1974) Scale of Dominance and Glass and Singer's (1972) Scale of Helplessness. Both scales have been suggested to be actually measuring perceived control (Bateson and Hui 1987).

(2) CTL2:

A 4-item Likert-type scale adapted from the studies conducted by Newcomb and Harlow (1986) and Fleming, Baum and Weiss (1987), was used as a second measure of perceived control.

FIGURE 2

THE HYPOTHESIZED STRUCTURAL EQUATION MODEL

(3) CROWD1:

A 5-item semantic differential scale of crowding which is developed from the crowding literature. The items were: stuffy/not stuffy; cramped/uncramped; crowded/uncrowded; free to move/restricted and spacious/confined.

(4) CROWD2:

A Likert-type scale of crowding which includes three different items: (a) He would not feel crowded (negatively formulated); (b) He would feel that there is almost no space for him; and (c) He would feel that there are too many people in the setting.

To avoid response bias, the direction of half of the items was reversed for the two semantic differential scales. The items of the two scales were then mixed together and put in one section of the environmental rating form. Similarly, half of the items of the two Likert-type scales were formulated negatively. The items of the two scales were also mixed together and put in a separate section of the environmental rating form.

TABLE 1

STRUCTURAL EQUATION MODEL ESTIMATES

Findings

The 123 subjects produced 119 (60 for the slides and 59 for the video sequences) useful cases for analysis. Each subject's item scores were reversed when necessary. The scores were then summed to provide indices of perceived control and perceived crowding. The slide and video subjects were considered as two independent samples and the two sets of data were submitted to a multi-sample LISREL analysis (Joreskog and Sorbom 1984, Chapter 5) to test the model shown in Figure 2. Assuming both the slides and video sequences are valid simulations of the service setting, one will expect (a) the data obtained from either sample to support the structural equation model (Figure 2); and (b) the two sets of data will produce identical estimates for the parameters of the model. Accordingly, in the multisample LISREL analysis, the parameters were specified in a way to test the model as shown in Figure 2. Moreover, all the measurement and structural matrices were also specified as invariant between the two samples.

A negative variance was obtained for the error term of the semantic scale of perceived control (CTL1) indicated the finding that there is a need to respecify the model (Bagozzi and Yi 1988). One possible explanation for the unexpected finding is that the error terms of CTL1 and CROWD1 were correlated because both scales consist of semantic differential items and were contained in the same section of the environmental rating form used in the study. Hence the covaraince between the two error terms was set free in another multi-sample LISREL analysis. The respecified model produced a chi-square of 22.47 with 29 degrees of freedom. The probability level (p=.80) of the chi-square value indicated an excellent fit. Moreover, the t-values also indicated that all the causal path coefficients were significantly different from zero (Table 1).

In short, the findings strongly support the control theory of crowding (Figure 1). Both density and choice affect perceived control which in turn shows a strong effect on perceived crowding. The direct causal link between density and perceived crowding is also consistent with the existing findings in the crowding literature.

IMPLICATIONS

The relationship between perceived crowding and psychological stress is a reasonably well supported relationship. In the context of a retail service firm such a relationship is important since it suggests that capacity will be constrained by consumers' perception of crowding. What this study has shown is the importance of perceived control as an intervening variable between density and perceived crowding. This raises important managerial implications since the finding shows that the impact of density on control may be moderated by the presence of other environmental and situational factors, for instance, the extent of choice available to an individual. This implies that when such factors can be manipulated by management, customers' perceived control and perceived crowding will remain at an acceptable level even when customer density is high. In other words, effective capacity (i.e. the maximum customer density level one would tolerate) may be extended without an increase in physical capacity (e.g. the area of the service setting). For instance, at the Disneyland, information (an important component of cognitive control) is posted at various points of a line so that people know how long they have to wait. With the given information, visitors are expected to feel better despite the length of the line is unchanged.

Methodologically, the present findings also provide evidence to support the ecological validity of slide and video as environmental simulations (Mckechnie 1977). Since both sets of data (slide and video) support the same causal model and produce the same estimates for all the parameters, it can be tentatively concluded that the findings are not due to the artifacts of the two employed media. In other words, both media (slide and video) can adequately represent the service setting and the findings are expected to be generalizable to real life settings.

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