When the presence of others leads to enhanced performance What is an example of it?

2 Effects of the Presence of Others

Social facilitations have been observed in a number of species. Chickens peck at food more quickly when other chickens are pecking; rats press a bar faster in the presence of other rats; cockroaches run with greater speed when running alongside other cockroaches. To the biologically oriented psychologist (e.g., Clayton 1978), these demonstrations of animal social facilitation hold profound interest.

People can be affected in many ways by the presence of others. Physiologically inclined researchers report social facilitations of human heart rate, blood pressure, and electrodermal activity. If often the presence of others increases an individual's physiological arousal, sometimes affiliation with others can reduce high levels of arousal (Mullen et al. 1997). Psychological investigators have documented social facilitations of various behaviors (Kent 1994). In the presence of others, people eat large meals, express conventional judgments, and show a tendency to smile. Gender and cultural differences in social facilitation are sometimes observed. In the presence of others, males (but not females) display a heightened tolerance for pain. In the presence of others, Japanese (but not American) students conceal facial signs of distress.

Since Triplett's pioneering investigation, psychologists have reported hundreds of experiments on the social facilitation of human task performance. The evidence that they have amassed indicates that the term social facilitation may be a misnomer. If sometimes the presence of others facilitates a person's task performance, often it has the opposite effect. In the presence of others, people have trouble unscrambling anagrams; in the presence of others, people lack motor coordination; in the presence of others, people do poorly on memory tasks.

When is a person's task performance facilitated by the presence of others, and when it is impaired? Psychologists had been pondering this question for decades before they found a convincing answer. In 1965, social psychologist Robert Zajonc published an article in the prestigious journal Science. There, Zajonc contended that the impact of the presence of others on task performance depends on the complexity of the performer's task. The presence of others serves to facilitate the performance of simple tasks and to impair the performance of complex tasks, Zajonc claimed. Subsequent evidence verifies that the impact of the presence of others does, indeed, depend on task complexity. At the same time, it indicates that social impairments of complex performance are stronger than social facilitations of simple performance. People perform simple tasks more quickly in the presence of others. However, there is little evidence that others' presence has any effect on simple performance quality, a quantitative review of 241 ‘social facilitation’ studies concluded (Bond and Titus 1983).

2.3.1 Social facilitation

Social facilitation is the term used to describe the finding that the mere presence of other people enhances the predominant behavioral responses in that situation. Social facilitation of eating was first described in detail by John de Castro, who conducted a series of diary studies in which participants were asked to record what and how much they ate over 7 days, alongside information about where and with whom they ate. Data from these studies revealed that people ate much more food when they ate in company than when they ate alone (de Castro & Brewer, 1992; de Castro & de Castro, 1989). These findings were observed for meals consumed during weekends and weekdays, thus ruling out the possibility that the social facilitation of eating reflects an artifact that arises because people eat more, and are more likely to eat with others, during weekends (de Castro, 1991). Social facilitation has been observed consistently across different meal types, including breakfast, snacks, meals eaten at home, and meals eaten without alcohol (de Castro, 1991). Further analyses by de Castro also suggested that the amounts eaten increases as the number of diners increases, such that groups of twelve consumed, on average, 60% more than did groups of two. Indeed, de Castro concluded that social facilitation was the single most powerful influence on eating, and that “the number of people eating with the subject …is the best predictor of how much food an individual will consume” (Redd & de Castro, 1992).

The conclusions based on these diary studies have been corroborated by results obtained from studies examining social facilitation within laboratory and field settings. For example, Berry, Beatty, and Klesges (1985) found that participants ate much more ice cream in 3- or 4-person groups than when alone. Similarly, Klesges et al. (1984) found that people dining out in a restaurant ate more in groups than when eating alone. The weight of evidence from numerous studies employing different methodological approaches supports the suggestion that social facilitation of eating is a real phenomenon (see Herman, 2015 for a review).

There are some factors that moderate the extent to which social facilitation of eating is observed. Social facilitation of eating is more likely to occur when friends eat together than when strangers dine in a group (de Castro, 1994). In fact, when eating with strangers, people may eat less than they would if they were eating alone, possibly because they feel self-conscious about their choices (e.g., Hetherington, Anderson, Norton, & Newson, 2006; Péneau et al., 2009). In this situation, impression management concerns may override any effect of social facilitation: intake may be supressed to avoid appearing “greedy.” Similarly, people with obesity have been observed to eat less in a group than when dining alone, and it has been proposed that this is due to concerns about the stigma associated with appearing to eat excessively (Krantz, 1979). Recently, it has also been observed that people with higher BMI were more likely to consume high-energy snacks when alone, and were more likely to consume low-energy snacks in the presence of others eating (Schüz, Revell, Hills, Schüz, & Ferguson, 2017).

The gender composition of a group can also moderate social facilitation effects. Specifically, Brindal, Wilson, Mohr, and Wittert (2015) reported that males eating in mixed-sex larger groups ate more than those eating in mixed- or same-sex pairs (reflective of social facilitation). Conversely, females eating in mixed-sex larger groups did not eat more than those eating in pairs, and ate significantly less than those eating in same-sex larger groups. These findings have been attributed to concerns about the image portrayed to others, such that when in mixed-sexed groups, women may eat less in order to convey a feminine impression (Brindal et al., 2015; Pliner & Chaiken, 1990).

Whether people eat more in very large groups (e.g., in a crowd) has not been thoroughly investigated. The results of a recent series of studies suggest that eating in a crowded environment is associated with increased intake (Hock & Bagchi, 2017), although other work suggests that eating in a very large group of more than 50 people does not facilitate intake (Hirsch & Kramer, 1993). Further work is required to assess the influence of eating in a crowd on intake, and to determine the limits of social facilitation in terms of group size.

Several explanations have been forwarded to explain social facilitation of eating (Herman, 2015). One theory is that social meals last longer than do solo meals, due to social interaction, thus extending the opportunity for eating (de Castro, 1990). However, exactly why lingering over a meal causes people to eat more is unclear. It has been suggested that social interaction may distract people from monitoring how much they are eating, or their awareness of internal cues that might inhibit eating (e.g., fullness). Additionally, seeing others eating may automatically trigger eating based on the conditioning of appetite to the social context (Schüz, Bower, & Ferguson, 2015). In this manner, social facilitation of eating may become habitual, or part of the ritual of commensal meals.

Another possibility is that meals eaten alone are smaller than social meals because eating alone is not as enjoyable as eating with company. However, there is only indirect evidence in support of this assumption. de Castro (1990) found that people were generally happier when eating with others than when eating alone, but his analysis found that mood and the number of people present contributed independently to variance in intake. In a more recent study, Boothby et al. (2014) reported that participants’ ‘liking’ evaluations of a good-tasting chocolate were higher when in the presence of a co-eater. However, the researchers did not assess participants’ food intake. Overall, there is some evidence that social meals may be larger because eating with others is more enjoyable, and the presence of others may disrupt usual processes associated with satiety. However, there has yet to be a systematic investigation of the effects of social context on these aspects of appetite while controlling for the amount consumed.

Importantly, explanations for social facilitation of eating fail to address the fact that, in order to eat more during a social meal, more food must be available. It is possible that both social and lone eaters serve the same amount of food, but that lone eaters do not finish all of their portion. This seems unlikely because recent evidence suggests that people tend to serve themselves the amount of food they believe will make them feel comfortably full, and then eat all of that portion (Brunstrom & Shakeshaft, 2009). In other words, decisions about portion size are made before eating in the pre-meal planning stage (e.g., Fay et al., 2011). Therefore, one explanation of social facilitation of eating is that people plan to provide more food when they know they will be eating socially (Herman, 2015). For example, people might cook or order larger portion sizes (or a greater number of dishes), per person, for meals with others versus meals eaten alone. In support of this idea, Cavazza, Graziani, and Guidetti (2011) reported that the number of dishes ordered per person, within a restaurant setting, increased as a direct function of group size.

Although we know that social factors influence people's food intake, very little is known about the relationship between social eating and obesity. There is evidence that obesity spreads via social networks (Christakis & Fowler, 2007), and one plausible underlying mechanism is that eating in social groups promotes food intake. However, as most direct evidence for social facilitation of eating comes from laboratory studies, in which intake is measured at one eating occasion, it remains unclear whether social eating leads to cumulative increases in energy intake, and ultimately weight gain. There is evidence to suggest that the effect of merely providing large portion sizes to individuals, which increases food intake, is not compensated for by eating less at subsequent meals (Rolls, Roe, & Meengs, 2007). Hence, it is possible that socially-induced increases in food intake might not be offset by subsequent reductions in intake. In support of this suggestion, Hirsch and Kramer (1993) found that total daily caloric intake of soldiers increased as a function of the number of meals eaten socially.

The phenomenon of social facilitation has been observed for other consumer behaviors, including alcohol and coffee drinking (Geller et al., 1986; Sommer & Sommer, 1989) and shopping (Sommer et al., 1992). People have also been shown to attend more to visual marketing stimuli when they are viewed in the presence of others, versus when they are viewed alone (Pozharliev, Verbeke, Van Strien, & Bagozzi, 2015). Taken together, these data suggest that social facilitation of consumer behaviors is a robust phenomenon, and that lone consumption experiences are not the same as consumption experiences shared with others.

4 The Relationship Between Social Facilitation and Social Loafing

Both social loafing and social facilitation are concerned with the effects of group contexts on motivation and performance and yet social loafing suggests that this presence decreases performance whereas social facilitation suggests that this presence increases performance for some tasks but decreases performance for other tasks. Indeed, the very first empirical studies of group productivity by Ringelmann and Triplett documented these seemingly contradictory effects of groups.

The key to resolving this discrepancy appears to be in specifying whether the other people present are co-workers or coactors. In social loafing studies, groups are composed of co-workers who pool their efforts together. In social facilitation studies, however, groups are composed of coactors who work in one another's presence but do not pool their efforts. Therefore, while both types of studies explore individual motivation in social contexts, they examine two very distinct aspects of group life. Taken together, they suggest that the impact that other people have on an individual's effort depends on whether or not the individual is actually working with those others on a collective task. Collective tasks have potentially demotivating properties, whereas coactive tasks tend to increase drive, thereby enhancing performance on simple tasks but reducing it on complex tasks. In other words, social loafing research is targeted to collective group work, whereas social facilitation research is targeted to the mere presence of coactors or observers.

3.1 Activating or Impeding Individual Performance

Whether social inhibition or social facilitation occurs via the mere presence of other persons is a question of the complexity and newness of the task to be performed. If other persons are present, difficult or new tasks are carried out less satisfactorily, since these tasks will lead to uncertainty and fear of negative feedback (‘social inhibition’). Simple or well-trained tasks, on the other hand, are usually carried out better in the presence of other people, as the active subject can expect success and a positive judgment from his/her colleagues (‘social facilitation’).

In the case of social inhibition, no matter whether the task performed is difficult or not, loss of motivation is involved. For example, if the individual contributions are not perceptible in the groups' overall output, or their importance for group productivity is not clearly seen, social loafing occurs. When group members expect other people in their group to be ‘free-riders,’ they may be inclined to conclude that their own individual contribution might be too high compared with the contributions of other group members. Consequently, individual performance will be decreased since no one wants to be left to carry the can; this is called the ‘sucker effect.’ Additionally, if group members fear negative comments from others, or if they are not sure about the norms and standards of evaluation as a whole, they will also withhold optimal performance (for fear of a negative evaluation). These motivational impediments on optimum individual performance can be met by transparency of performance feedback, by conveying concrete individual as well as collective performance norms, by fostering intrinsic motivation (e.g., interesting and challenging tasks), by strengthening group cohesion in combination with a high achievement norm, and by pointing out how important each and every individual contribution is for the group's productivity (for an overview see Sheppard 1993).

On the other hand, when group cohesion is high and the group's task is highly important, one can observe that groups will increase individual performance via motivation gain. If the group members' capabilities do not differ very much, and if the success of the group as a whole is very important for all the members, very strong and competent persons often try to compensate for the weaknesses of their colleagues by trying even harder to do their best. The weaker group members then likewise try to increase their output, as they do not want to be the ‘losers’ bringing the whole team down. These gains in motivation are especially likely if group cohesion is high—that is in case of a very attractive group (cf. Brown 1993), in case of a high mutual sympathy (cf. Latané 1986), and if the group's task is highly important (cf. Williams and Karau 1991).

6.3 Physical Enclosure and Regulation of Interaction

Perceived privacy (selective control over access by others to oneself) in a workspace correlates with enclosure by walls or partitions. Privacy facilitates regulation of workers' interaction. Some research in offices found privacy correlated with job satisfaction among employees of similar rank.

Enclosure of workspaces is important to performance because it restricts accessibility, limits noise and visual distraction, reduces interruptions, and possibly limits overload. Enclosure may also limit social facilitation (motivating effect of the presence of others) and spontaneous conversation with coworkers located nearby. Research points to a complex relationship of enclosure with performance depending on task complexity and personality traits like ability to ‘screen’ social input (see Oldham et al. 1995).

In the 1970s and 1980s many organizations adopted ‘open-plan offices,’ with minimal enclosure of workspaces and few signs of rank. Reduced enclosure was expected to aid communication. Some evaluations of open-plan offices found no change in communication; some found increases only in casual conversation; others found declines in confidential conversation, friendship opportunity, and supervisor feedback. Privacy declined, usually along with satisfaction with the environment.

13.7.6.4 Eating alone or with others (social facilitation)

Eating is generally regarded as a social activity, but in many instances, people actually eat alone, or at least, not in the company of others. In the hospital, or at a home for the elderly, people may be surrounded by others in similar circumstances, but for various reasons, may eat alone. Often, in such settings, diners do not choose whom they eat with, or they eat with strangers. The links between number of people present and the amount consumed can be mediated by other factors such as gender of consumers, level of familiarity, social status differences, power and subordination (Feunekes, Graaf, & Staveren, 1995). Studies conducted to determine how eating alone or in the company of others (social facilitation), have shown how they can influence both the amount and type of food consumed, and the time spent in consuming that food.

Why social facilitation should be so important is far from clear, although it has been suggested that it might be either conscious or subconscious. It could, for example, be that the presence of others increases levels of arousal and drive; or provides cues as to appropriate or inappropriate behavior (Zajonc, 1965). Alternatively, it could be that when meals are eaten together, more food is provided, and individuals might be hungrier in the presence of others, the context (atmosphere) might be more sociable, the food might taste better, or the meal might last longer (Feunekes et al., 1995).

In one of the earliest studies, adult diners (294 male and 245 female) were observed at midday and in the evening, in a variety of both formal dining rooms and fast food operations (Klesges, Bartsch, Norwood, Kautzman, & Haugrud, 1984). The amount of energy consumed was found to be determined by three main effects: gender, where overall, males consumed more than females (835 kcal vs. 716 kcal); the type of restaurant, where greater amounts were consumed in fast food restaurants than more formal dining settings (842 kcal vs. 710 kcal); and the company of others, where more food was consumed when eating in groups rather than alone (828 kcal vs. 742 kcal). What is perhaps important from this early study is that although the type of group, single sex or mixed sexes, did not affect the energy consumed, there was a significant difference in consumption in fast food restaurants where mixed sex groups consumed significantly more than those in single sex groups. Furthermore, females ate less than males in larger groups, but ate similar amounts as the males when eating in smaller groups (Klesges et al., 1984).

Social facilitation is not confined to single or formal meals, but can be seen in a complete range of meals served. Strong positive and significant correlations have been shown between meal size, and the number of people present for all meals consumed during the breakfast, lunch, and dinner periods, when eaten in restaurants, at home, and elsewhere, consumed with or without alcohol, for snacks on their own, for meals on their own (de Castro, Brewer, Elmore, & Orozco, 1990). Eating with others has also been shown to influence the speed of food consumption (Rosenthal & McSweeney, 1979), and also, in part at least, and to increase the length of the meal, but not the rate of intake. This is independent of the subjective state of hunger, emotional states of elation and anxiety, and acts independent of the content of the stomach and premeal interval (de Castro, 1990).

Not only is the number of people present important, their relationship to the person consuming the meal is also important. In comparison with other eating companions, it has been shown, irrespective of the time of the day, that meals eaten with a spouse and family are larger and eaten faster, while meals eaten with friends were larger but of a longer duration. Male companions had a greater impact for females, but not for males (de Castro, 1993).

The importance and robustness of the claim that the number of people present influences meal intake has also been demonstrated as being linear, described as a power function, clearly illustrating the orderliness and lawfulness of the phenomenon. Using data from a number of studies, the strength of the relationship has been shown (de Castro & Brewer, 1992) and summarized in Table 13.9.

Table 13.9. The relationship between the number of people present and meal size and the amount consumed

Source: de Castro, J.M., & Brewer, E.M., (1992). The amount eaten in meals by humans is a power function of the number or people present. Physiology & Behavior, 51(1), 121–125.

The amount consumed, however classified, that is, snacks, breakfast, midday and evening, increases when consumed in the presence of others. It is far from clear as to exactly why consumption should increase, but more recent research suggests that it is a function of the amount of time spent eating (Bell & Pliner, 2003). Meals consumed with others in convivial surroundings last longer, and as a result, more is consumed.

7.4 Social context

Food and eating are entangled with our social lives, and often people eat together. The social context encompasses social relationships and cultural milieus within which defined groups of people function and interact. Individuals can often have multiple social environments simultaneously (e.g., family, colleagues, friends) that are dynamic and change over time (Barnett & Casper, 2001). People around us—our social context—may regulate, influence, or constrain our eating behavior. Several social influences on food intake are described in the literature, and also addressed more extensively in Chapter 2.

Social facilitation of eating refers to the phenomenon of increased food consumption when people eat together instead of eating alone. In 1989, the first study into the particular influence of the number of other people and food intake was explored by means of an uncontrolled food diary study. Over a one-week period, participants filled out a food diary, including the social conditions of the meal. De Castro and de Castro found that individuals eating together ate significantly more (on average 44%) than individuals eating alone (de Castro & de Castro, 1989). Various experimental studies followed in subsequent years, showing comparable results: intake during group dinners is higher than intake during solo dinners (Berry, Beatty, & Klesges, 1985; Edelman, Engell, Bronstein, & Hirsch, 1986). In particular, social facilitation is present when participants eat together with others (rather than in the presence of a noneating audience) (Hetherington, Anderson, Norton, & Newson, 2006; Salvy & Pliner, 2010), and when eating together with friends (rather than strangers) (de Castro, 1994). In practice, social facilitation has been implemented and tested as a strategy in retirement homes to encourage residents to increase their meal intake. In a Dutch intervention study in a nursing home, the effect of family-style meals (e.g., eating together with other residents) versus individual preplating services on food intake was tested over a six-month period. The study showed that the intervention group (family-style meal) significantly increased their intake (992 kJ; 95%CI = 504–1479) in comparison with the control group (preplated solo meal) (Nijs, de Graaf, Siebelink, et al., 2006). Several underlying explanations have been suggested for the social facilitation effect, although there is no definitive explanation. In a recent review, Herman (2015) outlines potential underlying mechanisms in detail, and proposes future directions for research to enhance our understanding of the social facilitation of eating (Herman, 2015).

Via social modeling of food choices, people adapt their food choice to that of the food choice of their companion. In modeling, people use others’ food choices as a guide for their own behavior. A recent study reviewing 69 modeling studies over 40 years, including > 5800 participants, indicated that the majority of the studies (92.8%) found significant modeling effects on food intake, irrespective of the social context, methodology, food consumed, or demographics of the studied group (Cruwys, Bevelander, & Hermans, 2015). For example, a study of social modeling effects on food purchase behavior in supermarkets examined whether food purchase behavior of teenage girls would adapt to the same-sex confederate peer that purchased either low-caloric, mid-caloric, or high-caloric food products. Results suggested that teenage girls who shopped with a peer who chose high-caloric food products purchased higher caloric food products compared with the girls who shopped with a teenage peer buying low-caloric foods (Bevelander, Anschü, & Engels, 2011). First, people “model” because they search for information of the appropriate or “correct” behavior. Second, individuals model because they want to affiliate with others and be liked (Cruwys et al., 2015).

Social norms are “implicit codes of conduct” or “perceived standards” that exist in a social group and provide a guide to appropriate action (Higgs, 2015). There are two types of social norms. Descriptive norms refer to individuals’ perceptions about how others around them behave (e.g., make food choices), so “what do others do?”. Injunctive norms refer to the perceived approval of food choice behavior and represent perceived moral rules of the peer group, so “what do other people accept?” (Cialdini, Reno, & Kallgren, 1990; Reno, Cialdini, & Kallgren, 1993). Previous studies have outlined the relation between social norms and food choices (Robinson, Thomas, Aveyard, & Higgs, 2014; Stok, de Vet, de Ridder, & de Wit, 2016). For example, in a lab and real-life study, information about how others behaved was controlled by using empty chocolate wrappers, indicating that others ate chocolates on a previous occasion. In the real-life study, customers of bakery lunchrooms participated. A transparent bowl with individually wrapped chocolates was placed on the food counter. In the experiment, a bowl with empty wrappers was placed beside the bowl of chocolates. In the control condition, this bowl was empty. Outcome measures were the number of chocolates consumed. The results indicated that the number of consumed chocolates was higher when it was indicated that previous customers had taken a chocolate (RR 2.10, 95%CI = 1.08–4.09). The follow-up study in a laboratory setting resulted in similar outcomes (Odds = 3.07, 95%CI = 1.09–8.60) (Prinsen, De Ridder, & De Vet, 2013).

These prior examples of social influences on food choices illustrate that individuals are not always fully aware of social influence. Yet, social influences do not always occur unnoticed, and social influences are also embedded in family or group structures and dynamics. This is nicely illustrated by a qualitative study examining the role wives played in shaping the eating behaviors of middle-aged and older urban African-American men. The men indicated that women played a dominant role in household food provision and decision-making, and agreed that their wives influenced what they ate at home more than their own preferences. Quoting from this study: “When we first got married at 21 and 20, my wife decided that we weren’t going to eat like our parents. She made that decision for me. I didn’t think about it.... She made the decision that we’re not going to do this, and I didn’t argue with her.” Or “All my life I’ve been influenced by either my mother or my wife as far as food choices. I really didn’t have any choice other than what she put in front of me at the table” (Allen, Griffith, & Gaines, 2013). Although the interviewed men perceived themselves to have little control over what they ate at home, they appreciated the care and concern of their wives. This example illustrates that people experience and recognize social control others have over their food choices.

2.3 Belonging, In Groups

Crowds turn into groups, in Turner's emergent norm theory, when people cue each other's behavior. Theoretically, extreme cases result in deindividuation (Diener, Zimbardo); the self lost in the group.

Less radically, people join groups because they are attracted to them, according to group cohesion theory (Hogg); solidarity transforms aggregates into groups (see Moreno'ssociometry). Networks of interaction processes (Bales) define the group structure along task, social, and activity dimensions. Groups valuing cohesion over task effectiveness suffer Janis's groupthink, faulty decision-making that results from conformity pressures. French and Raven's referent power and Kelman's identification both influence through desire to belong. Group-level influence processes depend on the strength, number, and immediacy of people attempting influence (Latané's social impact theory; Tanford and Penrod's social impact model). These theories elaborate Zajonc's social facilitation theory, which describes the presence of others facilitating an individual's dominant response, benefiting well-practiced skills and undermining less-developed performances.

In organizations, effective performance meets reviewers' standards, maintains working relationships, and satisfies individual needs, in Hackman's normative model of group effectiveness. Relatedly, Steiner describes process losses that endanger group productivity, when coordination or motivation fails. Social loafing diminishes motivation in groups (Latané), but social compensation (Williams and Karau) can enhance motivation in groups.

The predominantly positive effects of group belonging all pertain to own group (in-group). Nevertheless, in-group favoritism (rewarding one's own), not out-group deprivation, poisons intergroup relations. Tajfel's social identity theory describes how in-group favoritism stems from self-esteem and categorization, the latter expanded in Turner's self-categorization theory. These theories hold that categorization into in-group and out-group differs from other kinds of categorization (see Sect. 3) because the perceiver belongs to one of the categories: Mere categorization into groups promotes conflict. In contrast, Sherif's realistic group conflict theory blames intergroup hostility on competition over scarce resources. If single in-group belonging promotes intergroup conflict, multiple belonging diminishes it, in the theory of crossed categorization (Dovidio and Gaertner).

3 The Appropriability Problem

An important complication entails what has come to be called the appropriability problem. Not all of an innovation's incremental value accrues to the innovator as quasi-rents or producer's surplus. Some of the value cannot be appropriated by the innovator, but accrues instead to consumers or other producers. This happens for three main reasons. First, even when the innovator commercializes a new product under monopoly conditions, it is difficult to devise a pricing scheme that does not confer new consumers' surplus upon those who purchase the product. (Only in the implausible case of perfect price discrimination could the innovator appropriate all of the surplus from a product innovation.) Second, if multiple firms innovate more or less simultaneously or if imitation is swift, competition may drive the relevant product's price below the monopoly profit-maximizing level, increasing the amount of surplus accruing to consumers and/or competitors and reducing the amount appropriated by innovators. Third, the very fact that a successful innovation has been achieved conveys, or ‘spills over,’ valuable information to other economic actors (see Social Facilitation, Psychology of; Information, Economics of). They can study the innovator's technical solutions and use the insights gleaned thereby to come up with their own innovations, which may either improve upon the original innovation's characteristics or provide differentiated new features. Empirical studies (Griliches 1992) have documented extensive spill-overs whose value is not appropriated by innovators.

The most influential quantitative research on the appropriability problem was by Mansfield et al. (1977). For a sample of 17 commercialized innovations, they painstakingly estimated the quasi-rents realized by the original innovator (called the private return) and the surpluses spilling over to consumers, imitators, and subsequent technology users. Converting these estimates to rates of return on the innovator's R&D investment, they found the median private return on R&D investment (i.e., the innovator's return) to be 25 percent, while the median social return (counting in addition surpluses accruing to consumers, rivals, and others) was 56 percent.

The implications of the appropriability problem are illustrated in Fig. 4. The innovator's inability to appropriate all the surplus from their innovation in effect inserts a wedge between the stream of benefits appropriable by the innovator (the private benefits) and the total benefits realized by all actors in the economy (the social benefits). The private benefits function in Fig. 4 is the same as in Figs. 1 and 2; the social benefits function is shifted upward by 80 percent. Whereas the innovation first becomes profitable to a private firm 14 years from the initial vantage point, social benefits begin to exceed R&D cost after year 7. This does not mean that the innovation should be introduced in year 7. An all-knowing decision-maker seeking an ideal solution from the perspective of all participants in the economy would choose an introduction date that maximizes the discounted surplus of social benefits less R&D costs. That date will be the same as the private sector break-even date (14 years) when k=(a+r)/r, where k is the ratio of social to private benefits (shown by Mansfield et al. (1977) to have a median value of approximately 2.25), a is the rate of R&D cost decline, as above, and the market growth rate g is assumed to be zero. For a proof, see Scherer (1980). If appropriability is so low that k<(a+r)/r, the private break-even date comes later than the socially optimal innovation date; if appropriability is sufficiently high that k<(a+r)/r, private break-even may precede the socially optimal innovation date.

Three main classes of remedies have been devised to lessen the innovation market failures believed to come from insufficient appropriability of innovation benefits by innovators.

A solution of particularly long standing, dating back to Venice in the Middle Ages, is the patent system. The first person to make an invention (or under some US interpretations, reduce it to practice) is granted by the government a temporary monopoly on the commercial exploitation of that invention. Patents delay the incursion of imitators and hence help innovators appropriate a larger share of the benefits from their innovations. Surveys of R&D decision-makers (Levin et al. 1987) reveal that the effectiveness of patents in achieving this result varies widely across industries. One reason for this variability is the fact that in some product lines, technological innovators enjoy substantial timing and reputation advantages over imitators even when no patent protection can be secured (Robinson et al. 1994).

Second, insufficient appropriability has been viewed as a rationale for government subsidies to support research and development investments. The relevant policy instruments range from corporate income tax credits for incremental funds spent on R&D (implemented first by Canada in 1962) to the conduct of R&D by government agencies or, more commonly, the issuance by government of contracts reimbursing more or less fully the R&D costs incurred by private firms. The contracts approach suffers from moral hazard problems and possible errors in the choice of innovators, but it is virtually unavoidable when large R&D outlays must be risked to develop high-technology products such as guided missile systems for government use. The vast array of policies chosen by governments to foster innovation in their home markets has been studied by economists under the rubric, ‘national innovation systems’ (Nelson 1993).

Third, firms have sought to alleviate appropriability problems by entering into joint ventures to conduct R&D and perhaps also to market the innovations achieved thereby. The cooperative R&D approach offers an added advantage of minimizing what might otherwise be costly duplication of more or less identical R&D efforts. Concomitant disadvantages include the lessening of diversity among technical approaches, where diversity enhances the prospects of achieving a successful solution, and reducing the competitive pressures that compel firms to conduct their R&D programs aggressively (see, e.g., Klein 1977).

How much competition is desirable in the pursuit of innovations is a question on which much economic research has been done (see Reinganum 1989). There are conflicting tendencies. On the one hand, competition forces firms to be aggressive and imaginative in their R&D efforts, seizing innovation opportunities at the earliest feasible moment (e.g., at year 14 in Fig. 1). On the other hand, too much competition reduces appropriability and may lead to a market failure under which no firm can anticipate sufficient quasi-rents to make its R&D investments worthwhile. This tension has led to what is called the ‘inverted U’ hypothesis, holding that the polar extremes of no competition at all (e.g., a secure monopoly) and intense competition are less conducive to innovation than an intermediate degree of competition, e.g., under differentiated oligopoly (see, e.g., Baldwin and Scott 1987).