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the task and may also fail to motivate members sufficiently. Both problems typically increase with group size. With divisible tasks, for example, assignments given to members may not provide a good match between member skills and the subtasks they are to perform. In face-to-face groups where each member contributes orally (e.g., giving novel ideas on some topic), larger groups derive decreasing benefits as group size increases simply because only one person can talk at a time. The best-studied example of motivational losses involves additive tasks where individual performances are simultaneous and anonymous. For example, where group members each make some response on cue (e.g., pulling on a rope, shouting), individual contributions are smaller the larger the group, an effect that is termed ‘‘social loafing.’’

In addition to a task structure, most groups also have a reward structure—the arrangement of payoffs or rewards that motivate members to work on the task. The type and nature of the reward structure may be imposed by a third party (e.g., a supervisor, leader), be part of the group’s history, or be chosen by the group itself. In most experimental studies of task-oriented groups, the reward structure is cooperative: The payoff or reward is a result of the group’s efforts in meeting some criterion and is shared by all members (although not necessarily equally). The payoff may be intangible (e.g., the satisfaction of solving the problem or completing the product) or tangible (e.g., money or prizes). Reward structures can also be competitive, where rewards are distributed unequally to members based on relative individual performance. Cooperative and competitive contingencies are often compared with a third alternative, individual contingency, where a member receives a reward when he or she meets an individual performance criterion; this is the case in most work- for-pay groups in industry.

These reward structures are not equally appropriate for all tasks. In particular, cooperative rewards are effective across a range of tasks (Johnson et al. 1981; Qin-Zhining et al. 1995) and are uniquely appropriate when the task requires collaborative activities such as response coordination, task subdivision, or information sharing. Collaboration is rewarded with cooperative rewards, increasing the likelihood that the criterion for the group reward will be met. By contrast, competitive

rewards are effective only where task responses can be made independently by each person. With competitive rewards, blocking another’s responses, not collaboration, is likely to lead to winning. When competition is appropriate, though, it is often more cost-effective (more responses made per unit of reward), easier to implement, and capable of producing short-term performance rates that are higher than those of the other conditions (Schmitt 1987). Competitive motivation can also arise outside of the formal reward structure. Under cooperative conditions, members may work harder simply to be the best performer in the group.

A group member’s motivation to perform a task can vary greatly depending on reward structure, and group size affects important aspects of that structure. With a cooperative structure, all members are rewarded, but inequities may exist in the size of the rewards received (e.g., some people contributing to a task may get more money than others). In general, people working on tasks expect their rewards to be proportional to their contributions (Homans 1974; Walster et al. 1978). Thus, if person A and person B have similar task skills, A will be upset if A and B contributed equally to the task but A received a reward half the size of B’s. Person A will not be upset, however, if A made a contribution half the size of B’s to the task. People who are inequitably underpaid relative to their contributions often work less hard on future tasks and may choose to leave the group (Marwell and Schmitt 1975). In the 1990s professional sports revealed a number of cases where athletes earning millions of dollars per year refused to play for their teams (or played less energetically) because comparable performers on their own or other teams earned more. In cases where the total amount of the cooperative reward for completing a task is proportional to the number of group members (e.g., $50 for a five-member group and $100 for a ten-member group), the larger the group, the greater the potential for larger inequities, that is, several of the members receiving a large share of the reward. If large inequities are present, productivity gains are likely to be a decelerating function of group size (and member discontent may provoke a change in distribution).

In arranging for competition, the reward structure is defined by the unequal rewards distributed to winners and losers at the end of the contest. In

most contests the distribution is fixed in advance and is known to the competitors. As with inequitable cooperative structures, the variety of competitive structures depends on the number of competitors in the group, assuming that the total contest amount is proportional to the number of group members (i.e., the larger the group, the larger the contest amount). Two properties of the distribution are relevant. One is the proportion of competitors receiving rewards in each contest. At one extreme, only one competitor receives a reward; at the other extreme, all competitors are rewarded, but in varying amounts. With a single winner, the larger the size of the group, the larger the competitive reward. As has been shown with large lottery prizes, the larger the competitive reward, the greater the motivation of group members to compete, at least in a single contest. However, if there is a series of contests and a difference in competitive skills causes some members to lose continually, the lack of earnings will lead to their withdraw from the group, thus lowering group productivity (Schmitt 1998). Maximizing the proportion of competitors rewarded should encourage poorer performers to remain in contests regardless of group size. When more than one competitor is rewarded, variation can occur in a second property—reward differential or spread, that is, the difference between the highest and lowest reward amounts in each contest. Again, the larger the size of the group, the larger the total competitive reward, hence the larger the possible differential. Maximizing reward differential more highly motivates those who have a chance of winning but gives those who lose frequently less incentive to continue over a series of contests. In sum, group size is a factor when competitive reward structures specify that few are rewarded or have reward differentials that are extreme. Although increasing group size (and the reward pool) is likely to increase member motivation and productivity in initial contests, over a series of contests these gains are likely to be a decelerating function of group size, as those who earn little contribute less or quit.

With individual reward structures, the potential effects of group size are similar to those for cooperative structures. Again assuming that the total reward amount for the group is proportional to the number of members, the larger the group, the greater the size of the reward inequities that are possible in arranging the individual rewards.

With large inequities, productivity gains are likely to be a decelerating function of group size.

SOCIAL DILEMMAS

Several types of social dilemmas have been investigated. The best-known type is prisoners’ dilemma. Originally conceived as a two-person game, an n- party prisoners’ dilemma in which the number playing the game can be varied has been used to study the effects of group size (Komorita and Parks 1994). Each group member has two choices: to cooperate (C), which maximizes payoffs for the group as a whole, or to defect (D), which maximizes the individual’s own payoff. The actual payoffs for each member depend both on own and others’ choices. Payoffs for each choice increase with the proportion of members who make the C choice, but the D always produces the higher individual payoff. Finally, the payoff if everyone chooses C is greater than the payoff if everyone chooses D, the selfish choice. Each group member chooses repeatedly over a number of trials. This basic dilemma has numerous everyday counterparts, as when commuters each prefer to use a private automobile instead of a bus, but if each does so the resulting chaos leaves everyone dissatisfied.

Other types of social dilemmas involve a pool of sources to which the group members have access (Pruitt 1998). One is the commons dilemma, based on the Tragedy of the Commons (Hardin 1968), in which a village’s common land is overgrazed because of the selfish actions of the individual herdsmen. As studied experimentally, subjects take turns removing resources (e.g., money) from a pool that is replenished periodically based on the amount remaining. The pool can be productive indefinitely, provided that the subjects don’t destroy it by taking all the resources. Another is the public goods dilemma, in which the resource pool is built up through individual contributions, as when people contribute money to support public television or some charity. Here the temptation is for individuals to ‘‘free ride’’ and let others make the contribution. As studied experimentally, subjects take turns contributing resources to a pool that is later enhanced by the experimenter and then divided equally.

Studies have found that the larger the group in social dilemmas, the less the cooperation and the greater the selfish behavior, although there is

little change in groups larger than eight members (Pruitt 1998). Various explanations of this relation have been proposed (Komorita and Parks 1994; Pruitt 1998). One possibility is that any defection breaches the trust required for cooperation, and that if one person defects, others view cooperation as unlikely and follow suit. The possibility of one person defecting increases as the group grows larger. In addition, as the group grows larger, each selfish individual response becomes less identifiable and may be seen as less responsible for the lower group payoffs. By the same logic, a cooperative response may also be seen as contributing less to the group product as the group grows larger. Finally, any opportunities by the group to communicate the group’s cooperative interests or to sanction defectors are more difficult to carry out in larger groups.

CONTEXT

Any social interaction among group members must take place in some context, for example, people standing, seated at a table, at stations on an assembly line, or in separate offices linked by a computer network. Physical contexts are very important because they affect both the nature of the interaction among members and the ease with which various members can interact. The nature of interaction among group members is determined in part by the number and kind of stimuli presented by those who interact. Stimuli may be verbal in the form of oral or written communication, or they may be nonverbal—information transmitted without using language (e.g., facial expressions, gestures, posture, appearance, voice quality, rate and temporal patterning of speaking).

The context in which a group functions determines whether verbal and nonverbal stimuli are transmitted among group members. If the context includes face-to-face contact, speakers can deliver messages using both verbal and nonverbal stimuli. Nonverbal stimuli add elements that can be both enriching and distracting. For example, a speaker’s facial expressions and gestures can reveal the strength with which a position is held, the truthfulness of a message, or the degree of intimacy sought by the speaker. Aspects of the speaker’s demeanor or appearance can also distract listeners from paying close attention to the message.

People differ, however, in how they use such stimuli and hence the meaning given to a message. This variability is greatly reduced if the messages are written. In addition, the quality of interaction may differ, depending on the presence of the verbal and nonverbal stimuli. For example, people are more likely to harm others (e.g., deliver punishment or bad news) when communication is restricted and nonverbal behavior is absent compared with face-to-face contact (where people are more fully personalized). In general, restricted communication does not lead to the full development of interpersonal relations, and it minimizes cues that reveal differences in status, power, and prestige among group members. Hence, differences in interaction and influence among group members tend to be less extreme than those in face-to-face groups (McGrath, 1984). Restricted communication is more likely as groups become large and more formally organized.

Another aspect of context concerns the timing of information transmitted among group members. When people interact, verbal and nonverbal stimuli may be presented synchronously (i.e., immediately and in real time), or asynchronously (i.e., at the member’s own time, place, and pace). Face-to-face interaction, telephone conversations, and video conferences are synchronous. Letters, memos, videotapes, electronic mail, and answering machine messages are asynchronous. Synchronous interaction, whether in person or electronically mediated, constrains interaction because only one person normally talks at a time. With an increase in group size, a few members typically lead or dominate the conversation. Asynchronous interaction, by contrast, typically permits any number of messages to any or all members, and the fact that the messages are necessarily recorded means that they are normally available for comparison and review later.

Asynchronous communication has increased enormously in popularity in recent years as com- puter-mediated electronic mail has become available in all organizations and many households. Computer-mediated communication has unique facilitating features: the ability to link anyone who has a network connection and the opportunity to send messages instantaneously to any number of people at very low logistical and social cost. For

some tasks, the use of asychronous communication changes the effects of group size on productivity, compared with traditional face-to-face synchronous communication. For example, Valacich and associates (1995) investigated groups formed to generate new ideas, that is, to ‘‘brainstorm,’’ where different group members have the potential for generating different ideas. In face-to-face interaction, larger groups derived decreasing benefits as group size increased from five to ten members. In computer-mediated groups, where members communicated via typed messages, larger groups derived increasing benefits as group size increased. Computer-mediated groups appear to use the advantage of reviewing the ideas of others to avoid redundant ideas and build new ones. The study of computer-mediated groups with other tasks promises to reveal further distinctions between synchronous and asynchronous interaction (see Kiesler 1997 for discussion of a number of computerand Internet-related issues).

An intriguing and potentially distinguishing feature of computer-mediated compared with face- to-face groups is that group size itself may become difficult to define and detect (Sproull and Faraj 1997). People in face-to-face groups take up space, and their physical presence and nonverbal reactions can affect others’ behavior even if a group member says nothing. By contrast, the readers of computer-mediated messages are typically invisible, and speakers may have little notion of who has seen or read a message.

In both industry and education, computers have been used to create humanlike partners who interact with people in various ways. Kiesler and associates (1996) investigated subjects’ responses in a prisoners’ dilemma game where the ‘‘partner’’ was known by the subject to be either a person or computer based. In both conditions, the subject and partner discussed options on each trial, and the partner asked the subject for commitments. For both types of partners, discussion and agreements with the subjects facilitated cooperation, although the effect was stronger with the human partner. Thus, nonhuman partners can produce ‘‘social’’ responses in people provided that the partners make humanlike use of social stimuli and responses.

Much of the early research on context concerned constraints on who could communicate

with whom in small groups. These constraints determine the group’s communication network (for a summary, see Shaw 1981). For example, two contrasting networks in groups of three or more members are the circle, where each member can communicate with two adjacent members, and the wheel, where one member occupies a central position and others can communicate only with that central person (the ‘‘hub’’). Wheels and circles are examples of highly centralized and decentralized networks, respectively. Groups studied in such networks have typically been small (e.g., three to five members), and communication has usually occurred via written notes. Comparisons of group problem solving using various networks have found that when tasks are relatively simple and require that members collect their information, centralized networks are more efficient than decentralized ones (with fewer messages and errors). Where tasks are more complex and require that members perform additional operations on their information, decentralized networks are more efficient than centralized ones (Shaw 1981). With complex tasks the communication overload (termed ‘‘saturation’’) experienced by the member in the central position slows the attainment of a solution. Saturation is likely to be a problem in all networks as the group increases in size, and it should emerge more rapidly when interaction is synchronous.

Communication networks arrange the contacts among group members in a decisive manner, but even where members are in face-to-face contact, aspects of the setting can make interaction between some members more likely than others, thus producing networklike effects. Seating arrangement is an example. Groups discussing problems are frequently seated at tables. One of the earliest findings was that people tend to communicate with others across the table and facing them instead of with those seated alongside them (Steinzor 1950). As groups grow larger, people are frequently seated at rectangular tables. Studies have found that people at the table’s end positions tend to participate more, are seen a having more influence, and are more likely to be chosen as leaders than those on the sides (for a summary, see Shaw 1981). Networklike arrangements can also be created if group members are instructed to follow a particular communication pattern in a discussion (e.g., ‘‘Talk only with the leader.’’). Instructions can also be used to limit style of expression (e.g.,

‘‘Give ideas for solving the problem but don’t criticize others’ ideas.’’).

In conclusion, differences in group size pose both opportunities and problems for the members. Because the nature of the consequences depends on the type of group task, reward structure, context, and the skills of the group members, little can be said about a group if only its size is known. Under certain circumstances, however, group size is highly consequential for the group’s performance and stability. It should be noted that for groups that form in everyday life, unlike those created for experimental purposes, problems posed by size do not necessarily condemn a group to subpar performance. Everyday groups usually have both pasts and futures, and are often skillful in identifying problems and in making changes that help mitigate them.

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HAWTHORNE EFFECT

See Industrial Sociology; Quasi-Experimental

Research Designs.

HEALTH AND ILLNESS BEHAVIOR

Health and illness behaviors are associated with level of disability, quality of life, patterns of illness, and risk of death. It is tempting to view such health-related outcomes solely through the lenses provided by the biomedical sciences; however, the behaviors that importantly shape individuals’ experiences of sickness or wellness, and life or death, are more completely understood from a sociological perspective. The confluence of individuals’ life histories, their personality characteristics and social experiences, and their social positions influences health and illness behaviors and tells us much about how to enhance health and wellbeing, and mitigate disability and sickness. An examination of health and illness behaviors, therefore, has important public health implications.

HEALTH BEHAVIORS

Health behavior usually refers to preventive orientations and the positive steps people take to enhance their physical well-being and vitality. Traditionally, work in health behavior has focused on the use of preventive services such as immunizations, medical checkups, hypertension screening, and

prophylactic dentistry (Becker 1974). It also includes research on such behaviors as cigarette smoking, seat-belt use, medication adherence, substance abuse, nutritional practices, and exercise (Janz and Becker 1994).

The conventional approach to health behavior has been limited, focusing on the origins of particular behaviors damaging to health and strategies to modify them. The most widely used general model—the health belief model—conceptualizes preventive health action within a psychological cost-benefit analysis (Rosenstock 1974). The health belief model conceptualizes decisions to take positive health actions as motivated by perceived threat (either susceptibility to a particularly condition or perceptions that the condition is severe) and judgments about the barriers and benefits associated with specific changes in behavior. Behavior change is seen as following motives that are salient, in situations where people have conflicting motives, following those that are perceived as yielding valuable benefits. An important component of the model involves cues to action, since an activating stimulus often appears to be necessary in the initiation of a new behavioral sequence. Both internal (e.g., feelings of symptoms) and external (e.g., suggestions from doctors, peers, or the media) stimuli may act as cues motivating change. Over the years, this model has been expanded (Becker and Maiman 1983), but it serves more as an organizing framework for the study of preventive health behavior than as a successful predictive model. An analysis of studies that have used the health belief model to explain a variety of health

behaviors indicates that the predictive value of the model is, at best, modest; the average variance in health behaviors explained is approximately 20 percent (Harrison, Mullen and Green 1992).

A second commonly used model to explain health decisions is the theory of planned behavior, originally developed as the theory of reasoned action (Ajzen 1991; Ajzen and Fishbein 1977). Like the health belief model, the theory of planned behavior conceptualizes changes in behavior as products of the saliency of individuals’ beliefs about the potential costs and benefits associated with an outcome or action. The theory of planned behavior, however, places greater weight on persons’ intentions, arguing that behavior is centrally motivated by intentions that are shaped by normative beliefs, feelings of control, and judgments about the barriers and benefits associated with potential change. Again, however, the model has had only modest success in predicting an array of health behavior; the association between intentions and behavior is typically about .40 (Conner and Norman 1994).

There are many other models and theories proposed to predict health behaviors, and much has been written comparing the relative efficacy of each for predicting health behaviors (Conner and Norman 1994; Mullen et al. 1987; Weinstein 1993). It appears that efforts to develop a general theory are limited by the fact that behavior conducive to health derives from diverse and sometimes conflicting motives. Consistently, research indicates that health behavior, or a healthy lifestyle, is not a unitary construct (Johnson et al. 1998; Sobal et al. 1992). One study of health behaviors among a nationally representative sample of adult Americans examined the clustering of four health behaviors: diet quality, alcohol use, tobacco use, and physical exercise (Patterson et al. 1994). The results (based on data from the late 1980s) suggest that approximately 10 percent of Americans live a ‘‘healthy’’ lifestyle, defined by a good diet, low use of tobacco and alcohol, and engaging in regular physical exercise. In comparison, about 2 percent of the population practice unhealthy behavior on all four of these dimensions. Thus, most Americans fall somewhere between the two extremes, practicing some positive health behaviors while neglecting others. Research that has attempted to

establish specific clusters of related health behaviors has proved inconclusive. Some studies have identified many apparently interrelated clusters of behaviors—for example, smoking/drinking and exercise/diet—and others have identified few (cf.Johnson et al. 1998; Sobal et al. 1992). In short, the research indicates that there is no simple identifiable positive health orientation that can serve as a basis for promoting risk aversion and health maintenance.

The lack of such a general orientation results because most behaviors with important implications for health arise from motives not related to health and are significantly programmed into the daily patterns and institutional life of communities and families (Mechanic 1990). Health-protective behaviors that are consequences of accepted, everyday, conventional activities require neither conscious motivation nor special efforts to be sustained. The favorable health experience of Mormons, for example, is a product of their belief systems and patterns of activity reinforced by the way of life of this cultural community (Mechanic 1990). To the extent that health behaviors are more the result of habits than cognitive decisions, we might expect that past health behaviors are robust predictors of current health behaviors. Yet neither the theory of reasoned action nor the health belief model adequately incorporates past behavior in its model, an omission that might partially explain their lack of predictive power (Conner and Norman 1994).

Promoting health may be more a matter of changing culture and social structure than of modifying personal motives or intentions. Patterns of behavior that depend on sustained conscious motivation are less stable than those that are a natural consequence of the accepted norms and understandings within a community. Expectations not only affect the prevalence of varying behaviors but also establish constraints on the acquired behaviors of children and adolescents. Changes in the social constraints on smoking, and the growing unacceptability of smoking in varying social contexts, may have more significance than any program to change personal behavior for explaining the dramatic decline from about 42 percent of the U.S. adult population being current smokers in 1965 to 25 percent in 1995 (National Center for Health Statistics 1998).

Although there is no evidence for a unitary health orientation, some social factors, particularly socioeconomic status (SES), predict good outcomes across a wide range of health indicators (Bunker et al. 1989; Marmot 1998; Ross and Wu 1995). Occupational status, income, and education each reflect some part of SES, and all are associated with health behaviors, whether one is comparing the health behaviors of populations or of individuals. Generally, wealthy nations show the highest rates of preventative health practices, such as child immunization, routine dental care, and the use of mammography, compared with less wealthy nations. But the importance of SES may be indirect, through social conditions. Indeed, as Caldwell (1986, 1993) has argued, mothers’ educational attainment appears to be particularly important, influencing health outcomes net of its relationship to per capita income. Caldwell suggests that maternal education increases women’s autonomy, enhances their ability to interact efficaciously with available health services and technology (even when such technology is not advanced), and enables women to increasingly control their own health and that of family members.

Within nations, there are also important SES differences in health behaviors. Table 1 presents some examples of SES differences in the U.S. population; the behaviors presented are meant to illustrate the gap and are not an exhaustive list of possible health behaviors. As shown in Table 1, Americans with lower SES are more likely to engage in health-risk behaviors and less likely to engage in health-promoting behaviors. The precise ways in which SES affects these outcomes are not fully understood, but the consistent findings point to an explanation of health behaviors that goes beyond personal responsibility and free choice. Research that examines the impact of SES over the life course illustrates the fallacy of relying on ‘‘choice’’ explanations to account for SES differences in health behaviors. In a sample of Finnish men, Lynch and his colleagues (1997) examined the effects of SES during childhood (parents’ occupation), adolescence (education), and adulthood (occupation) on psychosocial characteristics, such as hostility and hopelessness, that are important to health and on health behaviors, including smoking and alcohol use. They found that lower SES in childhood and adolescence is associated with greater health-risk behaviors in adulthood, in addition

to greater feelings of hopelessness and hostility. Given that childhood SES is not a matter of choice, their findings support an explanation of SES differences in health behavior rooted in persistent structural disadvantages and the accompanying differential opportunities and constraints. Others have also demonstrated that higher SES provides not only obvious economic advantages and related opportunities but also enhanced personal autonomy, increased sense of control, and greater social participation (Marmot 1998; Ross and Wu 1995, 1996), all of which also influence health outcomes.

A variety of behaviors noxious to health (smoking, drug use, and drinking) develop or increase during adolescence and young adulthood. However, young people who have a good relationship with their parents and who are attuned to parentoriented values—as measured by school performance, attendance at religious services, and participation in meals with parents—do relatively well across a variety of health measures (Hansell and Mechanic 1990). In contrast, high engagement with peer-oriented social activities is associated with increases in behavior associated with health risk. In addition, children model their parents’ health behaviors, an effect that persists at least into young adulthood (Lau et al. 1990).

Although it is apparent that adolescence is a time of life where there are likely to be changes in health behaviors, we know very little about other stages of the life course or the life transitions that may be especially important (Prohaska and Clark 1997). Prohaska and his colleagues propose a ‘‘stages of change’’ model that recognizes important transitions in the life course as explanations for changes in health practices (Prohaska and Clark 1997; Prohaska et al. 1994). They argue that individuals go through a number of steps, from not thinking there is a need for change to maintaining the new behavior after change. Public health efforts, therefore, could benefit from understanding what motivates or hinders a person’s progression through the steps. For instance, life transitions such as motherhood may motivate progression toward positive health practices, while transitions such as death of a spouse may make it difficult to maintain health practices and, therefore, may explain deterioration of positive health behaviors among persons recently widowed. Moreover, according to this model, persons may be differently prepared to progress through the steps depending

on their stage in the life course; for example, older persons may be better prepared to contemplate the health risks of smoking or heavy alcohol use, while adolescents are not. The important point for our purposes is that understanding how to modify health practices requires appreciation of how stages in the life course and transitions may influence when people are willing or able to make changes.

ILLNESS BEHAVIOR

The study of illness behavior, in contrast to health behavior, is concerned with the way people monitor their bodies, define and interpret bodily indications, make decisions about needed treatment, and use informal and formal sources of care (Mechanic 1986, 1995). Like other behavior, illness behavior is learned through socialization in families and peer groups and through exposure to the mass media and education. There is great diversity of attitudes, beliefs, knowledge, and behavior, all of which affect the definitions of problematic symptoms, the meanings and causal attributions that

explain them, socially anticipated responses, and the definition of appropriate remedies and sources of care. Motivation and learning affect the initial recognition of symptoms, reactions to pain, the extent of stoicism and hypochondriasis, and the readiness to seek release from work, school, and other obligations and to seek help (Mechanic 1978).

Illness behavior begins prior to the use of services with the recognition of illness or sickness. While a complex array of variables might explain variations in interpretation of sickness, they can be summarized in ten general categories: (1) the visibility, recognizability, or perceptual salience of deviant signs and symptoms; (2) the extent to which the person perceives the symptoms as serious (that is, the person’s estimate of the present and future probabilities of danger; (3) the extent to which symptoms disrupt family, work, and other social activities; (4) the frequency of the appearance of deviant signs and symptoms, or their persistence, or their frequency of recurrence; (5) the tolerance threshold of those who are exposed