Adult and Teenage Use of Consumer, Business, and Entertainment Technology:

Potholes on the Information Superhighway?

Journal of Consumer Affairs

Summer 1995

Volume 29, Number 1, Pages 55-84

Larry D. Rosen, Ph.D.

Michelle M. Weil, Ph.D.



Adults and teenagers were surveyed to determine their use and ownership of 32 consumer, business and entertainment technology devices. Demographics, technology experience and "technophobia" were examined as potential discriminators between Confident Users, Hesitant Users, and Nonusers of each technological device. Results indicated that older, technophobic adults with little computer training and lower income, black and Hispanic, technophobic teenagers did not use most technological devices.

The home and office of the 1990s has become a showplace for advances in computerized technology. Embedded computer chips are found in nearly every kitchen appliance, business machine, and entertainment device. Many children's toys are computerized. We bank through automated teller machines, buy gasoline by "paying at the pump" with electronic funds transfers, program the VCR with a string of numbers from a remote control device, communicate instantly via fax machines, and leave digitized voice mail messages for those we cannot reach directly.

Not everyone, however, rushes out to purchase or use the newest technological device. Higgins and Shanklin (1992) examined the acceptance of high technology consumer products as a function of lifestyle characteristics and demographic indicators for residents of a large midwestern city. Their results showed that 22 percent could be classified as "consumer product innovators," another 65 percent as "early and late majority adopters," and 13 percent as "laggards" based on the number of high-tech consumer products they owned or used. Higgins and Shanklin found the significant discriminators between innovators, adopters, and laggards were: age, income, lifestyle, technical complexity fear, and obsolescence fear.

Ownership of a technological device does not necessarily guarantee its use. Piper (1990) reported a Link Resources study showing that one-third of the people who own video cassette recorders (VCRs) never record television shows while they are away from home. McKee (1992) corroborated these results with a study of 1,156 VCR owners showing more than one-half of them had problems using some of their machine's functions. Finally, a recent study by Dell Computer Corporation (1993) showed that one-fourth of American adults had never used a personal computer, programmed a VCR nor programmed their favorite stations on their car radio. The Dell survey reported that nearly one-fourth of the adults felt uncomfortable setting their digital alarm clocks!


Given the government and business emphases on developing technology available to all citizens, this study investigates whether some group or groups may be unable to navigate the information superhighway of the near future. Demographic characteristics and psychological reactions to technology are examined to determine why an adult (Study 1) or teenager (Study 2) might choose to use or not use a particular type of technology. A variety of technological devices are investigated, including personal computers, fax machines, computerized kitchen appliances, technological toys and games, and computerized entertainment devices.

Rosen, Sears and Weil (1987) investigated the impact of negative reactions to technology (termed "technophobia") and found technophobes tend to avoid computer interaction. Based on these results and Weil, Rosen and Wugalter's (1990) study of the etiology of technophobia, it is hypothesized that psychological reactions to technology will compel both adults and teenagers to avoid computers and other forms of technology.


Little empirical work has been performed to determine why some people choose to use technology while others do not. In nearly all studies, however, one or more psychological factors have been pinpointed as delineating between technology users and nonusers. For example, Ellen, Beardon, and Sharma (1991) found a person's perceived ability to use a product successfully (termed self-efficacy by Bandura (1977)) was related to his/her resistance to change to technological innovations. Another study found beliefs that underlie attitudes toward technology could explain behavior toward technology ( Pancer, George, and Gebotys, 1992).

Using Rogers' (1962) diffusion of innovation framework, Anderson and Ortinau (1988) examined differences between innovators and late adopters of in-home personal computers (PCs) and found attitudes toward future PC use, satisfaction with the PC, computer experience, and electronic product ownership were moderating variables between these two groups. Similarly, Bord and O'Connor (1990) found psychological constructs such as alienation and "anti-technology" feelings were important predictors in a study of college students' decisions to try or not try an irradiated food product. Although not empirical studies, Nelson and Kletke (1990) provided an extensive framework for adjustment to technological innovation that relied on the awareness of "stressors" that accompany the introduction of any new technology and Ram (1987) composed a model of innovation resistance that included innovation characteristics, consumer characteristics (psychological variables), and propagation mechanisms.

Not all researchers have found differences attributed to psychological variables. McQuarrie and Iwamoto (1990) found differences in attitudes toward computers could be explained by differences in exposure. Adults who were exposed both at home and work had more positive attitudes than adults who were exposed at only one location who, in turn, had more positive attitudes than adults who had no exposure to computers. McQuarrie (1989) also found postadoption attitudes toward a microcomputer were comprised of the quality of the computer itself plus the ability to communicate with other users about the machine. Using a different approach, Breakwell, Fife-Schaw, Lee, and Spencer (1986) concluded that "attitudes toward new technology have a differentiated, if weak, structure." (p. 44).

Finally, some researchers have claimed it is the technology itself that promotes the discomfort. Norman (1990) places the blame directly on the engineer:

Why do we put up with the frustrations of everyday objects, with objects that we can't figure out how to use ..... with washing machines and dryers that have become too confusing to use, with audio-stereo-television-video-cassette- recorders that claim in their advertisements to do everything, but that make it almost impossible to do anything? .... Poorly designed objects can be difficult and frustrating to use. They provide no clues - or sometimes false clues. They trap the user and thwart the normal process of interpretation and understanding. ..... The result is a world filled with frustration. (p. 1-2).

Norman's thoughts about poor "user interfaces" have been corroborated by an empirical study of the use of automated teller machines (Muter et al. 1993). They presented subjects with two types of banking machines, one with a "user friendly" interface that simply required cued numerical responses while the other (termed "user-hostile") required complicated key strokes and contained instructions that were difficult to read, presented meaningless and confusing information, and required alphabetic responses rather than numerical responses to queries. Muter et al. found no difference between subjects using the two machines in heart rate, but did find a pronounced difference in electrodermal skin conductance level (1993). The subjects using the user-hostile system had elevated skin conductance levels indicating their bodies were producing the neural patterns usually seen in human fight-or-flight responses.

Much has been said about gender differences in the utilization of computers. According to the latest figures from the U.S. National Center for Education Statistics (1990) across all grade levels through college, 44 percent of the boys and men use computers at school compared with 42 percent of the girls and women. Although this difference may not seem profound, the difference between men and women with one to four years of college is 5 percentage points (42 percent to 37 percent) and between men and women with more than five years of college is 12 percentage points (47 percent to 35 percent). Many empirical studies have shown that boys use computers more than girls. Lipinski et al. (1986) found that boys spent twice as much time working with computers than girls and Hess and Miura (1985) found that the ratio of boys to girls in summer computer camps was nearly 3:1 increasing from the primary grades through high school and college. In a statewide study of over 300,000 California sixth and twelfth graders, Felter (1985) found boys had considerably more computer exposure at school and at home. Many other studies have shown that women have less computer experience than men in universities (e.g., Popovich et al. 1987) and business settings (e.g., Gattiker, Gutek, & Berger 1988).

Few researchers have looked at other demographic differences in who does and does not use technology. Zeithaml and Gilly (1987) found that compared with adults under 65, elderly adults had not adopted automated banking machines but had adopted more passive technological advances such as electronic funds transfers. McKee (1992) reported families with children are more likely to know how to use their VCR than families without children.

Overall, empirical studies have examined why people use personal computers, VCRs, or banking machines and have not examined a range of psychological and demographic characteristics that might explain technological avoidance. Study 1 examines which types of technology adults use and investigates which psychological and demographic variables can best predict who will and who will not use a variety of technological devices.




Data were collected from a convenience sample of 781 adults (defined as over 18 years of age) in a wide variety of Southern California neighborhoods by 50 senior Psychology students. As is evident from the data presented in Table 1, the adult sample exhibited some demographic characteristics that matched the general population and others that did not. In general, the sample appeared to have more younger people and fewer older people than the national census data. The adult sample were also more educated but appeared to have about the same family income as the census population. More of the adult sample were married and had more children than the national average. Finally, percentages of Asian and black adults in the sample were greater than the national average; the percentage of whites was less. These deviations from the national census figures may restrict the generalizability of the results from this sample. However, any bias due to the younger, more highly educated sample is likely to skew the results toward increased use of technology.



Each subject completed a single two-page questionnaire that asked about utilization and ownership of consumer and business technology, demographic characteristics, and psychological reactions to computerized technology.

Technology Utilization. Each adult was asked to indicate how often they had used each of 32 consumer and business technologies. The rating scale for each form of technology was as follows:

I have used many of the functions

I have only used a few of the functions

I have never used this but plan to use it within 1 year

I have never used this but plan to use it within 5 years

I have never used this but plan to use it within 10 years

I have never used this and do not plan to use it.

The 32 forms of technology were selected in a pilot study of 560 adults to represent common technological devices found in the home, in the consumer's world, or in the business world. A list of these devices can be found in Table 2.

Technology Ownership. For 27 of the 32 forms of technology, the subjects were asked if they owned or did not own the item. The five items eliminated were computer voice mail, automated banking machine, computerized postal stamp machine, and video arcade game (which could not be "owned"), and digital car stereo (which was left off inadvertently).

Demographic Characteristics. As seen in Table 1, subjects were asked their gender, age, education, yearly family income, marital status, number of children, ethnic background, and computer training.

Psychological Reactions to Computerized Technology. Three questions were posed to ascertain psychological reactions to computerized technology. First, the subject was asked to rate his/her current attitude toward computerized technology as either very positive, positive, neutral, negative, or very negative. Second, the subject was asked to rate his/her current level of anxiety about using computerized technology on a scale that included five choices: very low, low, moderate, high, or very high. Finally, the subject was asked the following. "Suppose that someone gave you a new computerized gadget that did lots of things. As you are learning to use it which of the following do you think that you would think and/or feel." This was followed by a checklist of 15 positive feeling adjectives (e.g., calm, eager, triumphant) and 15 negative feeling adjectives (e.g., dumb, frustrated, overwhelmed) presented in alphabetical order. These three questions were designed to represent, in an abbreviated format, the three different dimensions of "technophobia" - - negative attitudes, anxiety, and negative cognitions (Rosen, Sears & Weil, 1987). A pilot study with 58 university students, corroborated the relationships among these three questions and Rosen et al.'s (1987) technophobia dimensions.


This section first presents the results of a factor analysis to determine common groupings to streamline further analyses among the 32 forms of computerized technology. Second, the percentage of adults who use and own each form of technology is discussed. Third, three measures of psychological reactions to technology are examined to determine if they can be combined to form a single unidimensional instrument. Finally, a discriminant function analysis is performed on each form of technology to determine which variables best discriminate between those adults who use and do not use that form of technology.

Factor Structure of Technology Items

The 32 forms of computerized technology were subjected to a principal factors factor analysis with varimax rotation. Before this analysis, responses to each form of technology were combined to provide more statistically usable scales into (1) have used many functions, (2) have used few functions or plan to use within one year and (3) plan to use within five or ten years or never plan to use. These groups were labeled Confident Users, Hesitant Users, and Nonusers, respectively. The Factor Analysis found five factors with eigenvalues above 1.00 accounting for 50 percent of the variance.

Factor 1 appears to capture popular consumer technology including home entertainment technology (VCR, CD Player, Cable TV, 35mm camera, car stereo), a home kitchen appliance (microwave oven), home office items (calculator, answer machine), personal items (watch, alarm clock), and banking machines. Factor 2 includes more high-priced consumer technology that might be found in the home (e.g., security system, computerized refrigerator, picture-in-a-picture TV, satellite dish) while Factor 3 incorporates technological devices that are associated with leisure or "play" times (games, music, radio-controlled vehicles). Factor 4 captures all of the newest technological gadgets found in the business world including voice mail, fax machines, cellular phones, personal computers, and pocket pagers. Finally, Factor 5 includes only the computerized postal machine. One item (computerized coffee maker) was not found on any factor. Based on its largest loading, it was placed in Factor 2.

Use and Ownership of Technology

The left-hand column of Table 2 displays the percentage of adult Confident Users while the second column tallies the percentage of adults who claimed ownership of each technological device. Several points are worth mentioning from the data displayed in Table 2. First, all forms of "Popular Consumer Technology" were, indeed, owned by the majority of the adult sample (ranging from 57 percent owning a cable box to 91 percent owning a digital alarm clock). In addition, in each case except one (cable TV box), between 14 percent and 22 percent of the sample owned the piece of popular consumer technological equipment, but had not used many of its functions. Perhaps this trend did not hold for the cable TV box because it has only one function that must be performed to obtain cable television transmission.

Second, the "High-Priced Consumer Technology" items were owned by between eight and 36 percent of the adult sample. In contrast to the popular consumer technology, most of the sample who owned a high-priced consumer appliance also used many of its functions. Third, less than one-third of the sample owned and used technological games and toys.

Fourth, business technology appeared to be used by a small proportion of adults. Less than one-third of the adults have used many functions of a fax machine, less than one-fourth have done likewise with voice mail and pocket pagers and fewer than one in six sampled adults had used many of the functions of a cellular telephone. The personal computer, hailed by many in the 1980s as the most crucial business tool of the 1990s was owned by nearly half the sample, but only used for multiple functions by one-third of the sample.

A nationwide study by the Times Mirror Center for the People and the Press ("Technology in the American Household" 1994) examined ownership of five devices in common with this study finding comparable figures for the VCR (85 percent compared with 90 percent in this study), cable TV (64 percent compared with 57 percent), satellite dish (eight percent; four percent) and camcorder (30 percent; 28 percent). The Times Mirror study results found markedly fewer American adults than the study sample owning fax machines (six percent compared with 16 percent in this study) and personal computers (31 percent compared with 48 percent). This suggests that the present study sample has more exposure to business computing technology than the general population. A recent report by Wolff, Rutten, and Bayers (1992) also suggests that this sample may be more technologically sophisticated than typical American adults. For example, while 90 percent of the adult sample owned a VCR, Wolff et al.'s figures show that 60 percent of all American homes have a VCR. Similar comparisons can be made for answer machines (this study: 67 percent; Wolff et al. 42 percent), fax machines (15 percent; five percent) and personal computers (48 percent; 26 percent).

Psychological Reactions to Technology

Psychological reactions to technology were measured by three different questionnaire items - current attitude toward technology, current level of anxiety about using technology and positive, and negative cognitions when faced with a "new computerized gadget." Each measure was transformed to a z-score to provide a common measurement basis. Results of a Factor Analysis indicated that these scores formed a single factor that accounted for 45 percent of the variance. Based on the factor scores (beta weights), the following equation was used to capture a single dimension of negative psychological reactions to technology (termed "technophobia").

Technophobia = (.194)(zAnxiety) + (.566)(zAttitude) + (.223)(zNegative Cognitions) +(-.129)(zPositive Cognitions).

Discriminant Function Analyses

Individual discriminant function analyses were performed to determine which variables (including demographics plus the composite technophobia measure -- termed Phobia in the tables) could best discriminate between Confident Users, Hesitant Users, and Nonusers for each of the 32 technological devices.

Tables 3 through 6 present the results of the discriminant function analyses for the four types of consumer technology - - popular consumer technology, high-priced consumer technology, technological games and toys, and business technology. Each discriminant function analysis was performed twice. First, the 11 potential discriminating variables were all entered stepwise. In Tables 3 through 6, for each device, all discriminators which had standardized canonical coefficients (beta weights) greater than .20 (ignoring the sign) are listed from highest to lowest. Any equation for which beta weights are listed was significant at the .001 level.

The second discriminant function analysis examined whether the technophobia variable still provided significant discriminating power even after all other variables (demographics and computer training) were entered into the equation first. If the technophobia variable was no longer significant when entered last, it is in brackets (e.g., [Phobia]).

Popular Consumer Technology. Table 3 displays the results of these analyses for the popular consumer technology. In all cases, only the results of the first discriminant function are presented in the table (the second, when significant, is discussed in the text). Several points are worth noting. First, in all cases, at least one discriminant function was significant. From the discriminant function group centroids (not displayed here), it is clear that this first function discriminated Confident Users from Hesitant Users and Nonusers. In two cases (VCR and digital watch) a second function was also significant, successfully discriminating Hesitant Users and Nonusers.

Second, the composite technophobia measure appeared as a discriminator for all 11 popular consumer technology devices. It was the most important for two (VCR, 35mm camera) and the second most important for another five. In eight of the nine cases where it was not the most important discriminator, the beta weight was at least half the size of the most important discriminator's beta weight. In all cases, technophobia was still a significant discriminator even after first entering all other variables into the equation.

Third, age appeared to be an important discriminating factor, appearing first for five devices and second for one. Fourth, ethnic variables appeared in the discriminant functions for all 11 types of technology showing the most discriminating power in three cases - cable TV box, pocket calculator, and microwave oven. Fifth, surprisingly, computer training did not appear to be a major discriminator of the use of popular consumer technology, appearing first once (digital watch), second once (car stereo), third once (microwave) and fourth once (VCR). Sixth, income seemed to be a powerful discriminating variable for more expensive consumer technology including VCRs, answer machines, cameras, cable TVs, car stereos, and CD players. Finally, gender and education did not appear to be major discriminators of the use of any popular consumer technology except 35mm cameras (gender and education) and digital watches (gender only).

High-Priced Consumer Technology. Table 4 lists the variables that comprised each discriminant function for 10 high-priced consumer technology devices. From a cursory examination, it is obvious that no single variable provided the major discriminatory power for these items. Ethnic background most often had the highest beta weight with black appearing four times and the other ethnic groups each appearing once.

The "phobia" measure was the most important discriminator for programmable thermostat and 35mm camera and was also found in all other discriminant functions. Two of the loadings for technophobia were near the largest (satellite dish and exercise equipment) while for others (security system, computerized refrigerator, picture-in-picture TV, computerized sprinklers) technophobia was no longer significant when it was entered last.

Unlike the finding for popular consumer technology, age did not appear to be as important a discriminator between those who use and do not use high-priced consumer technology. As seen in Table 4, it only appeared in five of the ten equations and was first only twice (exercise equipment and computerized refrigerator). In all cases, the first functions separated the Confident Users from the other two groups. In three cases - - refrigerator, picture-in-a-picture television and car alarm - - an additional function also significantly discriminated between Hesitant Users and Nonusers.

As might be expected, income also appeared to be an important discriminator of those who do and do not use these high-priced items. It was a top discriminator for programmable thermostats, camcorders, and exercise equipment.

Computer training appeared as a discriminator in seven of the ten equations; however, its beta weight was only more than half of the highest beta weight for four devices (thermostat, camcorder, sprinklers, and coffee maker). Finally, gender and education did not appear to be particularly important discriminators except for gender in discriminating users and non-users of picture-in-a-picture televisions and satellite dishes, all often seen as male domains.

Technological Toys and Games. Table 5 displays the data for discriminating those adults who did and did not use six different types of technological games and toys. In all cases a single function significantly discriminated Confident Users from Hesitant Users and Nonusers. As might be expected, age played an extremely important role in discriminating between game/toy players and nonplayers. For four of the five toys and games, it was the most important discriminator. Interestingly, the technophobia measure was the second most important discriminator for three of these toys and appeared in all five equations (even when entered into the equation last!). Unlike previous devices, gender appeared to play an important role in discriminating who played games and who did not. It appeared in all five equations and was at least half as important as the most important predictor for four. The only other variable which appeared in all equations was computer training. This measure was the most important discriminator for digital watch and a moderately important discriminator for computerized music instruments. In all other equations computer training's beta weight was above the cutoff but was less than half the importance of the best discriminator.

Business Technology. The first discriminant function results for the five business technology devices are displayed in Table 6. Two of these (voice mail and cellular phone) also showed a significant second discriminant function that discriminated between Hesitant Users and Nonusers.

It is clear from the data presented in Table 6 that several issues played an important role in determining the utilization of business technology. Computer training played a large role in the utilization of business technology except the cellular phone. Similarly, technophobia was an important discriminator in the use of voice mail, fax machines, and the personal computer and a lesser role in the other two business technology devices. Other characteristics that appeared to discriminate users and nonusers included ethnic background (particularly in the use of pocket pagers, cellular phones, and personal computers) and income (which appeared in all discriminant functions and as the best predictor of the use of the cellular telephone).


The data from Study 1 strongly suggest that a combination of an adult's psychological reactions to technology and certain demographic characteristics can discriminate between those who use and do not use consumer and business technology. Each technological device had its own unique set of variables that discriminated users and nonusers. Across all 32 types of technology (using a criterion beta weight of .20) "technophobia" was found as a discriminator for 31, followed by computer training (28), age (25), black (24) and income (23). When a more stringent criterion of a .40 beta weight was used, age, technophobia, and computer training appeared to be the most important discriminating characteristics. It is important to note that in most cases, technophobia remained an important discriminator even after the effects of all other variables had been taken into account. The importance of age, technophobia, and computer training are highlighted by the data presented in Figure 1. This figure shows that older adults and those with less computer training were more likely to have never used a personal computer. In addition, adults who had never used a personal computer had a significantly higher level of technophobia (mean = .34) than Hesitant Users (mean = .09) and Confident Users (mean = -.41).


The results from Study 1 demonstrated the clear impact of psychological variables on the use of consumer and business technology by adults. Although little data have been collected with children, several authors (e.g., Enochs, 1985/1986) have suggested that these adverse psychological reactions will simply disappear as the next generation (sometimes referred to as the "Nintendo Generation") grow up immersed in technology. However, in opposition to this viewpoint, DeSantis and Youniss (1991) demonstrated that older teenagers had more positive attitudes toward technology than the younger teenagers. Study 2 was designed to assess teenagers' use of technology and their attitudes toward technology.



Data were collected from a convenience sample of 439 teenagers (age 12-17) in the Southern California area, 51 percent male and 49 percent female. Unlike the adult sample, 32 percent were white, 31 percent Hispanic, 18 percent Asian, 15 percent black and 4 percent other. These teenagers were asked to indicate their family's income on the same scale as used in the adult study and the resultant distribution differed significantly from the data presented for the adults in Table 1 with 26 percent of the families earning over $65,000 and another 20 percent earning between $40,000 and $65,000 (compared with 22 percent and 29 percent for the adult sample). The largest number (28 percent) claimed their family earned between $25,000 and $40,000 (compared with 22 percent of the adults). Finally, 11 percent stated their family income was under $10,000 and 15 between $10,001 and $20,000 compared with 8 percent and 19 percent for the adult sample (c2 = 16.67, p = .002). Thus, the teenage sample had more families in the middle income range as well as more in the extreme high and low income categories. With these noted sample differences, comparisons to the adult sample and to the general population may be biased.


The same instruments from Study 1 were used in this study with the exception of the questions concerning ownership of technological devices. For these questions, teenagers were asked to indicate if either they or their parents owned each device.


Use and Ownership of Technology by Teenagers

As seen in Table 2, the teenage sample claimed slightly higher rates of ownership of popular consumer technology with the exception of the telephone answer machine. However, in many cases they showed less utilization of the same devices than adults. For example, only 16 percent of the teenage sample had used many of the functions of an ATM compared to 68 percent of the adult sample. Fewer teenagers had also used 35mm cameras, answer machines, pocket calculators, and alarm clocks.

Substantially more of the teenage sample claimed to own (or their parents owned) high-priced consumer technology devices. This was not surprising given the data showing more teenage sample families earning above $65,000 compared with the adult families. Again, however, several high-priced items were used less by the teenagers than by the adults.

As might be expected, teenagers owned and used more technological games and toys. Similar to the adults, however, large numbers of teenagers claimed to own technological toys and games but not use many of their functions. Finally, perhaps due to their affluent families, more teenagers claimed to own business technology (including personal computers). However, most likely due to their age, fewer teenagers than adults claimed to have used many of the functions of these same devices.

Discriminant Function Analyses

Tables 7 through 10 display the best discriminating variables for the teenage sample. The variables included gender; black, Hispanic, white and Asian ethnic background; family income; computer training; and the composite technophobia score. Again, as with the adult sample, the analyses with teenagers were performed twice, first with all variables entered in a stepwise procedure and, second, with all demographic variables and computer training entered first hierarchically followed by the technophobia score. When the technophobia variable appears in brackets this indicates it was not a significant discriminating variable when entered last.

As seen in Table 7, there were no significant discriminating variables for three types of popular consumer technology (VCR, programmable microwave oven, ATM). Among the remaining eight, income was the best discriminator for three (CD player, answer machine and 35mm camera) and was also a strong discriminator for three others (cable TV, car stereo, and calculator). In addition, the composite technophobia measure was the most important discriminator for three popular consumer items (alarm clock, digital watch, and calculator) and was an important discriminator for all others (except car stereo and 35mm camera where it appeared in the equation, but was no longer significant when entered last). Ethnic background also provided strong discriminating power, appearing at or near the top in beta weight for all items. Gender was a relatively unimportant discriminator between who did and did not use popular consumer technology except perhaps for alarm clock where it was fourth most important, but with a beta weight that was similar to those above it.

For the high-priced consumer technology (Table 8), five of the nine equations had income as the best discriminator; income also appeared in all other equations. Technophobia appeared in six equations but only in two equations did it have a beta weight that was at least half the size of the leading discriminator and in most it was no longer significant when entered after all other variables. Ethnic background was, again, an important determinant in who used high-priced consumer technology, appearing as the most important discriminator three times and at or near the top in all but one case. Again, gender was not an important discriminator.

Income was also an important discriminating variable for who used and did not use technological games and toys. As seen in Table 9, in four of the five items income was the best discriminating variable and for another it was second best. Gender also proved to be a significant discriminator of who played arcade games and used radio-controlled vehicles while technophobia was a secondary predictor of who used computerized musical instruments and pocket computers. Finally, ethnic background was again important in discriminating which teenagers used and did not use technological games and toys.

Finally, Table 10 presents the data for the use of business technology. In the four cases where a discriminant function was significant, ethnic background and family income appeared to have the most discriminating power. In two cases, fax machines and personal computers, the composite technophobia measure was an important discriminator.

In summary, those teenagers who used computerized technology could be discriminated from those who did not primarily by family income level, followed by ethnic background and, in some cases, level of technophobia. The impact of these results is easily demonstrated when viewing teenagers who have never used a personal computer. Figure 2 shows that as income increases teenagers are more likely to have used a personal computer and that substantially more black and Hispanic teenagers have never used a personal computer. In addition, those teens who had never used a personal computer had significantly more technophobia (mean = .35) than the Hesitant Users (mean = .14) or the Confident Users (mean = -.15).



Several conclusions are clear from these two studies of technological utilization. First, for adults, the major discriminating factors between who uses and does not use technology appeared to be age, technophobia, and computer training while for teenagers, family income was clearly the most important variable followed by ethnic background and technophobia. These results suggest that technophobia may be a problem for both teenagers and adults but may be a more serious problem for the latter. The implication of these results are even more striking when you consider that the adult sample was younger, more educated and had more business technology experience (particularly with personal computers) than the national average.

For adults, technophobia appeared as a major predictor of the use of nearly all types of technology. Those adults who were more technophobic used technology less than those who were comfortable with technology. It is clear that technophobia is a major problem for adults functioning in the highly technological American society. A recent study by Dell Computer Corporation (1993) corroborated these results showing that 55 percent of Americans suffer from technophobia and that this technophobia directly affects their use of all forms of computerized technology.

It appears that older adults, technophobes and adults with little computer training actively avoid technology. What are the ramifications of this behavior? These adults may be performing tasks with devices that are either obsolete or limited in their functioning. In many cases, this may not be a problem. Clearly, a computerized coffee maker has more functions than a percolator or boiling water poured over coffee grounds. By pushing a few buttons, the computerized coffee maker can grind the beans and have the coffee prepared at a prespecified time. This is surely a convenience, but, does it make better coffee? This is a topic better debated by coffee connoisseurs. In other cases, however, avoiding technology may prove problematic. A report by Krueger (1991) showed that workers who use computers on their jobs earn 10 percent to 15 percent more than those who do not even after holding education, income, occupation, and other characteristics constant. Another examination of the same data by Boozer, Krueger, and Wolkon (1991) found that minority workers were less likely to use computers on their jobs than white workers. The same study found that minority workers were much less likely to be exposed to computers in school or at home than white workers even after adjusting for family income. These studies suggest that avoiding computers can be very costly!

The fact age and computer training were also important predictors of which adults would use technology is difficult to interpret. Do older adults stay away from technology because it is foreign to them? Have they avoided learning about computers and technology because they are afraid or do they simply have fewer opportunities? Or, as Weil, Rosen and Wugalter (1990) suggest, are they uncomfortable with technology because they have either had uncomfortable experiences with technology or, perhaps, were introduced to technology by someone who was technophobic. A study by Rosen and Weil (1995) showed that 45 percent of the sampled elementary and secondary teachers were technophobic. Since many children learn about technology from their parents or from teachers they face a great risk of learning from technophobic role models.

Thus, the results of this study suggests that older, technophobic adults with little computer training will be unable (and unwilling) to participate in the information superhighway. These adults may already have lower paying jobs or may be the first to be displaced by technology. What can be done to provide equal opportunities for all consumers? First, those who are technophobic can apply straightforward, proven strategies to eliminate their technophobia. Two such programs have been described by Heinssen (1987) and Rosen, Sears and Weil (1993). Second, adults can learn how to operate a computer (or any other technological device). This is not as simple as it appears. Although technology has become more "user friendly" in the past decade, it may still be quite complex. Rosen and Weil offered the following guidelines for the introduction of any technology.

1. The person who is teaching computers (or any form of technology) must be comfortable with that technology since a technophobic teacher will pass these attitudes and feelings to the learner.

2. The person who is teaching technology must be calm, clear and very open to questions.

3. The teacher should walk the learner through the process of using a technological device first with the learner pushing the buttons. Then the teacher should supervise the learner doing the steps by him/herself.

4. The introduction of technology should be in a nonevaluative atmosphere.

5. It is important to learn about technology by "playing" with it. (1994, p. 4-5).

Although technophobia was the most important discriminator between which adults did and did not use technology, it was an important predictor for fewer than half the forms of technology for teenagers. Corroborating the results of Boozer, Krueger, and Wolkon (1991) and Krueger (1991), ethnic background and family income were found to be much more important in determining which teenagers used technology. Asian and white teenagers were twice as likely than black teenagers and nearly three times more likely than Hispanic teenagers to have used a personal computer. A similar pattern held for personal computer use as a function of family income - teenagers from higher income families were more than twice as likely to use a personal computer than those from lower income families. These results, plus the data presented by Boozer, Krueger, and Wolkon (1991) suggest that America has a generation of youthful "knows" and "know-nots" who are exposed to, and learn about technology related to their family income and their ethnic background. When these same teenagers become adults, they will likely be forced to accept employment that does not involves computers and will face lower paying job as their career alternative.

As seen in Table 2, teenagers did have more experience than adults using technological games and toys and some entertainment devices, but they lagged far behind adults in the use of business technology and many popular home consumer items. And, more important, more adults had used PCs than teenagers! Many popular writers have simply assumed that the "Nintendo Generation", with their experience playing computer games, would be comfortable with and embrace all forms of technology, including the personal computer. The results of this study present a different picture. While over half the teenagers had played arcade games and played with other technological games and toys, less than one-third had used multiple functions of a personal computer. The results of this study suggest that, perhaps, the skills (and comfort level) that teenagers acquire playing computer games does not generalize to using personal computers or other complex technology.

Learning to play computer games will not prepare teenagers for the information superhighway. They must learn to be comfortable with computers and all technology. The same suggestions that were offered for adults to gain this confidence are useful. Technophobic teenagers should take steps to eliminate those feelings and attitudes and all teenagers should be introduced to technology by calm, confident teachers. Unfortunately, Rosen and Weil (1995) found that most teenagers learn about computers in school from technophobic teachers which explains why technophobia levels have not decreased as dramatically as one might assume based on the proliferation of computer games. Further, based on the results of this study and others, teenage boys comprise the "Nintendo Generation" leaving girls behind in the development of technological skills, knowledge and interest.

In summary, it appears that for adults, the use of technological devices may be strongly related to age, psychological reactions to technology, and computer training. Older adults; or adults with little computer training; or adults who perceive technology with anxiety, negative cognitions, and negative attitudes will avoid even the most basic consumer or entertainment technology. Even if they own technological equipment, they will likely only use a limited range of functions and capabilities. Teenagers from the "Nintendo Generation" are not the technology consumers that one might imagine from reading the popular press. Although they do play computer games with a zeal unmatched by adults, they have not embraced other forms of technology as eagerly. More important, the teenagers who are not using technology appear as an "underclass" on the basis of their income, negative psychological reactions to technology, and/or ethnic background. This does not bode well for the next generation of adults who will need to be technologically sophisticated to partake in the information superhighway of the future. The results of these studies suggest that few adults and only a segment of the teenage population will be able to participate in future technological developments.



Anderson, Robert L. and David J. Ortinau (1988), "Exploring consumers' postadoption attitudes and use behaviors in monitoring the diffusion of a technology-based discontinuous innovation," Journal of Business Research, 17: 283-298.

Bandura, Albert. (1977), "Self-efficacy: Toward a unifying theory of behavioral change," Psychological Review, 84: 191-215.

Boozer, Michael A., Alan B.Krueger, & Shari Wolkon (1991), Race and school quality since Brown vs. Board of Education., Unpublished manuscript, Princeton University.

Bord, Robert J. and Robert E. O'Connor (1990), "RIsk communication, knowledge, and attitudes: Explaining reactions to a technology perceived as risky," Risk Analysis, 10(4): 499-506.

Breakwell, Glynis M., Chris Fife-Schaw, Terence Lee and Judith Spencer (1986), "Attitudes to new technology in relation to social beliefs and groups memberships: A preliminary investigation," Current Psychological Research & Reviews, 5(1): 34-47.

Dell Computer Corporation (1993, July 26), Fear of technology is phobia of the '90s; Computer habits, attitudes determine 'Techno-Type'. Press release available from Dell Computer Corporation, 9505 Arboretum Blvd. Austin, TX.)

DeSantis, James P. and James Youniss (1991), "Family contributions to adolescents' attitudes toward new technology," Journal of Adolescent Research, 6(4): 410-422.

Ellen, Pam Schroeder, William O. Beardon and Subhash Sharma (1991), "Resistance to technological innovations: An examination of the role of self-efficacy and performance satisfaction," Journal of the Academy of Marketing Sciences, 19(4): 297-307.

Enochs, Larry G. (1985/1986), "General attitudes of middle school students toward computers," Journal of Computers in Mathematics and Science Learning, 5(2): 56-57.

Felter, M. (1985), "Sex differences on the California Statewide Assessment of Computer Literacy," Sex Roles, 13: 181-191.

Gattiker, Uri E., Barbara A. Gutek, and Dale E. Berger (1988), "Office technology and employee attitudes," Social Science Computer Review, 6: 327-340.

Hess, Robert D. and Irene T. Miura (1985), "Gender differences in enrollment in computer camps and classes," Sex Roles, 13(3/4): 193-203.

Higgins, Susan and William L. Shanklin (1992), "Seeking mass market acceptance for high technology consumer products," Journal of Consumer Marketing, 9(1): 5-14.

Krueger, Alan .B. (1991), How computers have changed the wage structure: Evidence from microdata, 1984-1989, (Working paper No. 3858), Cambridge, MA: National Bureau of Economic Research.

Lipinski, Judith M., Robert E. Nida, Daniel D. Shade and J. Allen Watson (1986), "The effects of microcomputers on young children: An examination of free-play choices, sex differences and social interactions," Journal of Educational Computing Research, 2(2): 147-168.

McKee, Victoria (1992, August 4), "Video age" The Times, pp 14A.

McQuarrie, Edward F. (1989), "The impact of a discontinuous innovation: Outcomes experienced by owners of home computers," Computers in Human Behavior, 5: 227-240.

McQuarrie, Edward F. and Kichiro Iwamoto (1990), "Public opinion toward computers as a function of exposure," Social Science Computer Review, 8(2): 221-233.

Muter, Paul, John J. Furedy, Alex Vincent and Ted Pelcowitz (1993), "User-hostile systems and patterns of psychophysiological activity," Computers in Human Behavior, 9: 105-111.

Nelson, Debra L. and Marilyn G. Kletke (1990), "Individual adjustment during technological innovation: A research framework," Behaviour & Information Technology, 9(4): 257-271.

Norman, Donald A. (1990), The Design of Everyday Things, New York: Doubleday.

Piper, Jill Johnson (1990, March 23), "Machines can make life easier, but they add stress, too," Chicago Tribune, pp. 96.

Popovich, P.M., K.R. Hyde, T. Zakrajsek and C. Blumer (1987), "The development of the Attitudes Toward Computer Usage Scale," Educational and Psychological Measurement, 47: 262-269.

Ram, S. (1987), "A model of innovation resistance," Advances in Consumer Research, 14: 208-212.

Rogers, Everitt M. (1962), Diffusion of Innovations, New York: The Free Press.

Rosen, Larry D., Deborah C. Sears and Michelle M. Weil (1987), "Computerphobia" Behavior Research Methods, Instruments, and Computers, 19: 167-179.

Rosen, Larry D., Deborah C. Sears and Michelle M. Weil (1993). "Treating technophobia: A longitudinal evaluation of the Computerphobia Reduction Program" Computers in Human Behavior, 9: 27-50.

Rosen, Larry D. and Michelle M. Weil (1995), "Computer availability, computer experience and technophobia among public school teachers," Computers in Human Behavior, 9: 27-50.

Rosen, Larry D. and Michelle M. Weil (1994, March), "The psychological impact of technology" Computers and Society, 24: 3-10.

Times Mirror (1994, May 24), "Technology in the American household" Times Mirror Center for the People & The Press, Washington, DC.

U.S. National Center for Education Statistics (1990), Digest of Education Statistics.

Weil, Michelle M., Larry D. Rosen and Stuart Wugalter (1990), "The etiology of computerphobia," Computers in Human Behavior, 6: 361-379.

Wolff, M., P. Rutten and A. F. Bayers (1992), Where we stand, Bantam Books: New York.

Zeithaml, Valarie A. and Mary C. Gilly (1987), "Characteristics affecting the acceptance of retailing strategies: A comparison of elderly and nonelderly consumers," Journal of Retailing, 63(1): 49-68.


From The Journal of Consumer Affairs, Volume 29, No. 1 (Summer 1995): 55-84. Copyright 1995 by the American Council on Consumer Interests. For permission to use this material in any way that falls outside the purview of "fair use," please apply to The University of Wisconsin Press (