Older Consumers' Vulnerability to Bait-And-Switch

Anil Mathur, Hofstra University
George P. Moschis, Georgia State University
ABSTRACT - The traditional view among researchers and public policy makers has been that the elderly are the most disadvantaged and most frequently victimized by consumer fraud. Contrary to this view some studies have found that the incidence of crime against the elderly is actually less than that against younger people. Based on a national telephone survey, this study found that age-related differences in experience of a common consumer fraud (bait-and-switch) could be explained by one's shopping behavior and knowledge of such practices rather than age per se.
[ to cite ]:
Anil Mathur and George P. Moschis (1995) ,"Older Consumers' Vulnerability to Bait-And-Switch", in NA - Advances in Consumer Research Volume 22, eds. Frank R. Kardes and Mita Sujan, Provo, UT : Association for Consumer Research, Pages: 674-679.

Advances in Consumer Research Volume 22, 1995      Pages 674-679

OLDER CONSUMERS' VULNERABILITY TO BAIT-AND-SWITCH

Anil Mathur, Hofstra University

George P. Moschis, Georgia State University

[The authors wish to express their gratitude to the American Association for Retired Persons (AARP) for making the data for this study available to the Center for Mature Consumer Studies.]

ABSTRACT -

The traditional view among researchers and public policy makers has been that the elderly are the most disadvantaged and most frequently victimized by consumer fraud. Contrary to this view some studies have found that the incidence of crime against the elderly is actually less than that against younger people. Based on a national telephone survey, this study found that age-related differences in experience of a common consumer fraud (bait-and-switch) could be explained by one's shopping behavior and knowledge of such practices rather than age per se.

INTRODUCTION

With the increase in the number of older population, public policy makers, marketers, consumer researchers, and social scientists have started paying attention to the needs of this segment. While marketers have seen this trend as an increasing opportunity to market products and services to meet the needs of the elderly, consumer researchers have attempted to understand the consumption patterns of the elderly that make them different from other segments of the population. Social scientists and public policy makers have focused their attention on the specific needs of the elderly, particularly on needs due to declining health and ability to care for themselves. Some unscrupulous operators might have also seen this demographic trend as an opportunity to profit.

It is widely believed that the elderly are the most disadvantaged group. It is also believed that they are the most victimized group (Butler 1975; McGhee 1983; Moschis 1992). Officers responsible for law enforcement have often reported that the elderly are frequent victims of consumer fraud (e.g., Friedman 1992). However, hard reliable data on the types of consumer fraud and the incidence of elderly victimization is scarce. Most of the information in this area is based on anecdotal evidence or surveys (Moschis 1992). The areas where the elderly are most susceptible to being victimized include housing, healthcare, insurance, automotive, appliance repair, general merchandise, utilities, and marketing methods (McGhee 1983; Alston 1986). Although previous research has contributed to our understanding of the situations experienced by the elderly in the marketplace, it has not focused on explanations of various types of crime and consumer fraud.

The purpose of this research is to examine the vulnerability of the elderly to one of the most common form of consumer fraud: bait-and-switch. Based on a field survey, reasons are sought to explain age-related differences in elderlys' experience of bait-and-switch.

BACKGROUND

Alston (1986) defines marketplace crime as "a broad category of crime which occurs in the context of buying and selling of goods and services." (p. 48). Marketplace crime may take several forms and may be directed toward businesses, government, or individuals. All marketplace crime adversely affects consumers. Consumer fraud is specifically directed toward consumers and affects them directly. It covers "a wide variety of behaviors, tactics, and practices detrimental to consumers" (Edelhertz et al. 1977). From a legal perspective such a fraud involves: knowingly making a false statement, assertion, or suggestion; deliberately suppressing facts to mislead; or making a promise with no intention of performing it (Newman, Jester, and Articolo 1978).

"Bait-and-switch" is a very commonly used form of consumer fraud (e.g., Zaltman, Srivastava, and Deshpande 1978). For example, in a study of the mail-order video camcorder market, Easley et al. (1992) found that 71.1% of the dealers misrepresented themselves as authorized dealers of a particular brand. Also, among unauthorized dealers who carried other brands, 25% attempted to use bait-and-switch on the prospective customer. Bait-and-switch involves advertising an item at a very low price, but when the potential customer arrives or calls to purchase that item, the salesperson may say a number of things disparaging the advertised item in an attempt to persuade the customer to purchase a more expensive substitute. Despite the advertisement and the offer of sale, in these cases the seller does not intend to sell the low-priced item. It is very easy for a marketer to engage in bait-and-switch, while it may be very difficult to catch and prosecute such unscrupulous marketers. However, when caught, companies engaging in this form of fraud have been subjected to legal action (e.g., Meeks 1990; Minkin 1990). In comparison to other types of crime, cases of consumer fraud are almost always nonviolent, yet they are viewed as very serious by the victims because they could involve large sums of money and may have serious consequences on the victims (Glick and Newsom 1974; Midwest Research Institute 1977).

Several studies have reported that the elderly are especially vulnerable to fraud and crime (e.g., Kosberg 1985). Also, both researchers and practitioners have reported that elderly are often victimized by businesses (Friedman 1992; Goldsmith and Goldsmith 1975; Hahn 1976). For example, several individuals representing victims, law enforcement authorities, and public policy makers recently testified in front of the Senate Committee on Aging that older Americans are seen as easy prey by unscrupulous marketers (U.S. Senate 1992). Moreover, it has been suggested that of all the crimes committed against the elderly, consumer fraud is the most frequent type. Consumer fraud against the elderly also ranks at the top in terms of the cost to the elderly (Hahn 1976).

While social service agencies have highlighted the plight of the elderly and have focused on the crime and fraud against them, some other empirical research has shown that the rate of victimization due to crime is actually less for the elderly than for the younger age groups (Hofrichter 1982; Lindquist and Duke 1982; Mawby 1982; Midwest Research Institute 1977). Some other studies have found that the rate of victimization of the elderly is not necessarily greater than those for the younger age groups (McGuire and Edelhertz 1980). Is this because elderly experience crime less than younger people, or is it because the crime against the elderly is not reported by them as often to the authorities? Could this reflect the older persons' inability to recognize crime?

According to Friedman (1992) there are four main reasons for elderly being victimized by consumer fraud in general: a) accessibilityCelderly are easily found at home and in shopping malls; b) social isolationCi.e., a majority live alone and, therefore, may be eager to socialize with salespeople and strangers; c) declining physical and mental abilities Ci.e., reducing the elderly's ability to identify the fraudulent situation and to protect himself or herself from such a fraud; and d) favorable financial circumstances contributing to targeting by unscrupulous businesses or organizations. For example, if a person experiences bait-and-switch and the victim does not know that such an action is illegal, chances are that this incidence will go unrecorded. Poor health and lack of consumer knowledge may make an elderly victim unaware of such a fraud. While accessibility of the potential victims is accounted by the frequency of store visits and major purchases, social isolation may not have any direct relationship to this particular type of consumer fraud. Biophysical declines associated with aging might reduce one's ability to comprehend complex information, thereby making elderly more vulnerable to consumer fraud. The problem is more complicated because in many cases victims might not realize that they have been targets of such a fraud if they do not understand the complexity of price-product feature relationships. Moreover, if the victims do not know that practices like bait-and-switch are illegal they may not complain. While potential swindlers may not know in advance who is knowledgeable, it is expected that those who know that bait-and-switch is illegal will mentally make a note of such experience and complain when opportunities arise.

METHODOLOGY

Data collection

The study consisted of 1,305 telephone interviews conducted by Market Facts' National Telephone Center in Evanston Illinois in December 1990 for AARP, which, in-turn, made the data available to the authors. Trained and experienced interviewers were briefed and monitored throughout the course of interviewing. The sample for the survey was selected from Market Facts' weekly omnibus telephone survey, TeleNation. This TeleNation survey is conducted on non-holiday weekends using random digit dialing to obtain a national sample of 1,000 households. The data from each week, including telephone numbers and demographic information for each respondent, are entered into a cumulative data base. The existing TeleNation data base allows interviewing of specific demographic groups without the need for expensive screening. For the present study, a disproportionately large number of interviews were conducted with older respondents. Using the TeleNation data base, which contains respondents' birthdays, Market Facts was able to identify respondents meeting the desired age characteristics.

To facilitate comparison across age groups quotas were set for five age groups; 25-49 (300), 50-64 (302), 65-74 (301), 75-84 (202), and 85+ (200). Within each age group, interviews were conducted for males and females proportionate to their distribution in the population. Although initial sample contained younger respondents too, those under 55 years of age were dropped from the analysis since the focus of this study was on older consumers. Thus, the analysis was carried out on the remaining 917 respondents. Demographic profile of the sample is given in Table 1.

Questionnaire development included pretesting and minor modifications due to pretest results. To measure the knowledge about bait-and-switch and to see if the respondents had experienced this form of consumer fraud they were read the following scenario:

"You read an ad in the paper for a television set on sale for $300. When you arrive at the store, the salesperson tells you that the advertised set is not a very good one. He advises you to buy a $500 set instead. What is your opinion of this selling technique?"

The above scenario explicitly included salesperson's action in disparaging the advertised item, a key component of bait-and-switch. Although intention not to sell the advertised item is another key aspect of bait and switch, the word "intention" was specifically not included in the scenario because most often persuasive communication from the salesperson sounds as if it is in the best interest of the potential customer. Therefore, the question of intention does not arise. Moreover, even if intention were specifically included, the respondents would have given their perception of salesperson's intention. The present scenario relied more on objective and observable aspects of the bait-and-switch tactic.

After the description they were asked: "Do you think it is a fair business practice?" and "Do you think it is legal?" with three response categories "yes," "no," and "don't know." They were also asked: "Has anything like this ever happened to you where you wanted to buy a less expensive product and were advised to buy the more expensive one?" Two response categories were given: "yes" or "no."

In order to measure frequency of in-store purchasing, respondents were asked: "On an average, how many times a week do you go to a store to purchase products or services other than food or drug items?" Response categories were: never go to the store, less than once a week, 1-2, 3-4, 5 or more times a week (responses were read, if necessary). Food and drug items were specifically excluded for several reasons: first food and drug purchase are essential purchases and do not reflect discretionary purchases; and second, a large proportion of cases of bait-and-switch involve major purchases other than food and drug items.

In order to measure the number of major purchases made, respondents were asked: "How many major purchases costing over $300 did you make in the past six months? Please include products as well as services such as repairs to your car, home, etc." Finally, to measure the number of prescription drug purchased, the respondents were asked: "How many purchases of prescription drugs did you make in the past six months? This would be the actual number of prescriptions filled."

ANALYSIS AND RESULTS

The first stage of the analysis was carried out to examine age-related differences in experience of bait-and-switch as well as certain shopping-related behaviors (Table 2). Cross tabulation and c2 test were used to test for the relationship between age and shopping-related variables, as well as between age and bait-and-switch experience. Age-related declines in general purchases are evident in the data. For example, there is a decline in the number of major purchases and frequency of in-store purchases with age. While 48.6 percent respondents in the 55-64 age group reported making at least one major purchase in the past six months, only 31.0 percent of the 75+ respondents made such a major purchase (c2=24.56, p<.001). Also, while 55.9 percent respondents in the 55-64 age group report making in-store purchases once a week or more often, only 30.7 percent of the 75+ respondents made in-store purchases once a week or more often (c2=53.33, p<.001). However, there is an age-related increase in the purchase of prescription drugs, suggesting that the health condition declines with age. For example, while 71.7 percent of the 55-64 age group reported making one or more prescription drug purchases in the past six months, more than four-fifth (82.8%) of the 75+ respondents made one or more prescription drug purchases in the past six months (c2=15.89, p<.001).

Almost 94 percent of people in 55-64 age group report that bait-and-switch is an unfair business practice, while only 85.6 percent of those in 75+ age group feel it is unfair (c2=10.85, p<.01). Almost 47 percent of the respondents in the 55-64 age group believe that bait-and switch is legal, however, there were no age-related difference (c2=1.36,p=.506). The experience of bait-and-switch declines with age. While 53.1 percent of those in the 55-64 age group report having experienced bait-and-switch, only 20.3 percent of those in the 75+ age group report a similar experience (c2=68.55, p<.001).

TABLE 1

DEMOGRAPHIC PROFILE OF THE SAMPLE

Reported experience of bait-and-switch is also related to some other shopping behaviors. Table-3 shows the relationship between experiencing bait-and-switch and shopping-related variables. Experiencing bait-and-switch is related to the extent of shopping one engages in. For example, among those who had experienced bait-and-switch, 50.9 percent had made a major purchase in the past six months, compared with only 34.9 percent of those who had not experienced such a practice had made a major purchase (c2=20.99, p<.001). Similarly, 53.4 percent of those with such an experience go to stores to purchase something at least once a week, compared with 39.9 percent of those who had not experienced bait-and-switch (c2 =14.82, p<.001). There was no relationship between the health status of the respondents (as reflected in the purchase of prescription drugs) and experiencing bait-and-switch (c2=.72, p=.398). The question still remains to be answered: Is it that the elderly are victimized less or there is some other explanation?

To find answer to the above question, age-related differences in the reported experience of bait-and-switch was examined for those who had made at least one major purchase in the past six months. The lower portion of Table-2 shows the results of this analysis. When controlled for major purchases, the proportion of the 75+ age group reporting bait-and-switch increased, however, it remains significantly below that of younger people reporting such an experience (c2=28.76, p<.001). When in-store purchases and the knowledge were also controlled for, age-related differences in reported experience of bait-and-switch disappear (c2=.06, p=.972).

TABLE 2

RELATIONSHIPS BETWEEN AGE AND SELECTED VARIABLES RELATING TO BAIT-AND-SWITCH EXPERIENCE

Finally, multivatriate analysis was carried out to assess the relative importance of knowledge about bait-and-switch, shopping-related variables, and age in predicting bait-and-switch experience. Two multiple regression models were built and tested for the same. In the first model, all the variables were entered at the same time. In the second model, the interaction of age and shopping related behaviors were also included in addition to the variables included earlier. The results of these regression analysis are shown in Table 4. As shown in the table, both the regression models were significant (R2=.09, F=15.08, p<.01 and R2=.10, F=9.11, p<.01 respectively). When only shopping related variables, and knowledge variables were entered in the equation, age was found to be the most important predictor (b=-.24, t-value=-7.15, p<.01). Major purchases was the next important predictor (b=.11, t-value=3.37, p<.01), and in-store purchase was marginally significant (b=.06, t-value=1.94, p<.10).

When additional terms representing interaction with age were entered in the equation, age continued to be the most important predictor (b=-.33, t-value=-2.62, p<.01). However, the interaction of age and the knowledge it is legal or not turned out to be the next important predictor (b=.12, t-value=2.78, p<.01). Major purchases continued to be another significant predictor (b=.10, t-value=3.21, p<.01). These findings confirm earlier results that experiencing bait-and-switch is a function of shopping related behavior rather than age per se. However, significant interaction term suggests that when older people lack specific knowledge about the legality of such practices they become more vulnerable to fraud than relatively younger people or those who are more knowledgeable.

TABLE 3

RELATIONSHIPS BETWEEN SELECTED VARAIBLES AND EXPERIENCE OF BAIT-AND-SWITCH

TABLE 4

RESULTS OF MULTIPLE REGRESSION ANALYSIS

SUMMARY AND IMPLICATIONS

One of the main findings of this research is that experiencing consumer fraud like bait-and-switch is related to shopping behavior rather than age per se. Although figures of reported cases indicate that among elderly, 'old-old' individuals are victimized less, it may be because they do not shop as often as their relatively younger counterparts, or they do not make major purchases as often. If such reasons are factored in, the older elderly's risk of being victimized by consumer fraud is the same as that for the relatively younger elderly person. Another factor that emerges from these findings is that when old age is associated with lack of consumer information (for example, regarding legality of practices like bait-and-switch) older-old adults become increasingly vulnerable to such consumer fraud. Also, the impact of such victimization on the elderly may be different from that on the younger people. Because of special economic circumstances (fixed income, impact of lower interest rates and inflation), social conditions (limited interaction, even isolation to some degree), and declining psychological well-being (negative self-concept) experienced by the elderly, the impact of such a victimization may be greater on older than on younger victims.

However, some other special circumstances may prevail in the case of the elderly that should be considered. Of great concern to public policy makers and consumer educators is the possibility that many elderly victims may not know that they have been victimized. This finding has important implications for public policy makers and consumer educators. Since there is a strong possibility that elderly may not know specific frauds or their legal rights, government agencies should focus on consumer education. Wider publicity and exposure of the businesses engaging in such practices could also increase awareness. Public policy makers should also consider stricter laws and their enforcement.

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