PSYCHOGRAPHICS

 

 

 

 

 

 

 

 

 

 

By Arnie Witchel

 

Copyright 2003: Witchel & Associates

 

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Abstract

The demographic is a fixture in marketing research, but psychographics contribute to our demographic understanding by examining a wide range of factors that focus on the quantitative and psychological perspectives of why consumers behave the way they do. This paper examines the concept of psychographics, its origins, and the debate over reliability and validity. Two of the more accepted tools, VALS and RISC are examined in detail, and some of the current uses of psychographics are also reviewed.


The Evolution from Demographics to Psychographics

     The demographic profile is a fixture in marketing research; profiles are collected as a matter of routine in the belief that age, income, education and other measurable factors can indicate product or brand preference, media preference or preference about programming choices (Wells, 1975). Demographic information has severe limitations, however. Demographics are not homogenous blocks, and can lead to over simplification, stereotypes of demographics may be incorrect, and demographics do not really provide guidance in marketing messages, the consumer’s problems and needs or what their lifestyles or values are (Langer, 1985). The reason a person buys a particular product or brand, or has explicit media preferences go beyond how old the person is or how much money the person makes (Bainbridge, 1999). The Holy Grail of marketing is based on discovering that succinct difference of between what consumers do and why they do it (Booth, 1999). However, it wasn’t until the social upheavals of the 1960s, which shattered the mass-market approach, that methods of measuring the values and lifestyles of consumers were developed (Heath, 1995).   

     Demby (1994) claims to be the first person to make up the name psychographics in 1965, although he admits that the term was used as early as World War I to describe a method of classifying people by physical appearance, rather than demographics. It later evolved in the 1920s as a term used to classify people by attitudes. Heath (1995) notes that the term appeared in Grey Advertising’s publication Grey Matter in 1965, and that Haley was the first to publish the term. Demby’s own description uses the term to combine psychological, sociological and anthropological factors such as self-concept and lifestyle to segment markets by purchase decisions or media use; demographics are used as a check to see if psychographic segmentation improves on other segmentation methods (1994).

The Definition of Psychographics

     Heath (1995) notes that if ten marketers were asked to define psychographics, ten different answers would be received. Eckman, Kotsiopulos and Bickle (1997), using the construct developed by Tigert in the 1970s (Demby, 1994), assert that psychographics measures lifestyles that are evaluated through activities, interests and opinions, and that psychographics are more effective than demographics. Silverberg, Backman and Backman (1996) state that psychographics is a way of describing customers and charting new trends. Booth (1999) describes psychographics as the why of consumer behavior, attempting to ascertain the motivation behind consumer purchasing decisions. Wyner (1992) calls psychographic measures attempts to isolate personality types across product category boundaries. Wells (1975, p.197), in his award-winning article for the Journal of Marketing Research, notes that psychographics are something beyond demographics, but that the dimensions studied in the field encompass “a wide range of content, including activities, interests, opinions, needs, values, attitudes and personality traits.” However, his interpretation allows this wide range, when he defines psychographics operationally as “quantitative research intended to place consumers on psychological—as distinguished from demographic—dimensions” (Wells, 1975, p. 197). Hasson (1995) adds to this, positing that psychographics may be the ultimate phase of motivation research developed in the 1950s, using quantification, multi-dimensional analysis and graphic representation. The one thing most researchers seem to agree upon is that geodemographic tools, such as PRIZM or Donnelly Marketing’s ClusterPlus, that describe averaged demographic information about product usage by geography, do not fall into a psychographic definition (Heath, 1995). Although they fit the lifestyle definition portion of psychographic definition and have been used to examine lifestyle imagery and reference groups (Englis & Solomon, 1995), they do not fulfill the psychological dimension of the operational definition; however, they do offer one benefit when used in conjunction with psychographic information: They also identify where the consumer lives (Heath, 1995).

How Psychographic Measures Are Conducted

     The original psychographic studies were primitive by nature, using Q clustering programs that were computer driven, although computing power had its own limitations (Demby, 1994). The clustering program used is actually an inverse factor analysis, and is also called Q-factor analysis (Heath, 1995). Basically, it looks for correlations between pairs of respondents, and then clusters respondents who have high correlations to similar answers (Heath, 1995). Typical psychographic studies today show consumers a large list of statements and they are asked to indicate on a five to seven point Likert scale how well each statement describes their attitudes or lifestyles; factor analysis is then used to identify underlying factor loading patterns, upon which factor scores are computed, and either principal component analysis or varimax rotation of retained components is used to approximate a structure in which each variable correlates highly with only one factor (Wind, Rao & Green, 1991).

The Debate Over Psychographic Reliability and Validity

     While seemingly straightforward, there has been much debate about the reliability and validity of psychographic research in terms of individual items and scales, reliability of dependent variables, relationships, and structure.  Wells (1975) notes that the scales used most by psychographic researchers, published in literature reviews, indicate reliabilities from .70 to .90. Due to the instability of consumer choice, perhaps the most that can be expected from psychographic research is satisfactory reliability; however, if important decisions are made with psychographic information, cross-tabulations, regression or cross-validation against hold out samples are recommended (Wells, 1975).

     Equally problematic for psychographic measurement is validity, the degree to which the psychographic tool is measuring what it is supposed to be measuring (Isaac & Michael, 1995). While some measures of validity, such as construct validity, are handled in the same manner as reliability, using hold out samples (Wells, 1975), the predictive validity of psychographic tools is much more difficult to ascertain. Often these tools are measured against other psychological constructs to compare performance. SRI introduced VALS as a psychographic tool in 1978, and the instrument segmented consumers into nine groups based on their inner/outer orientation; it was the only commercially available psychographic tool to gain a large measure of acceptance  (Riche, 1989). Kahle, Beatty and Homer (1986) conducted a study that indicated Rokeach’s List of Values (LOV) was a better predictor of consumer behavior than the original Values And Lifestyles (VALS) assessment tool. However, in a follow-up replication study, Novack and MacEvoy (1990) found serious methodology flaws in Kahle, Beatty and Homer’s study. Kahle et al. believed that because VALS had demographics built-in, but LOV did not, they included demographics in the regression model for LOV, but not for VALS (1986). In their replication, Novack and MacEvoy (1990) ran the same experiment, with extensions that included adjusting each instrument for demographics, ignoring demographics, and using demographics solely against LOV. Their conclusion was that VALS may be preferred over LOV as a segmentation tool, and that LOV was significantly less predictive than even VALS alone. They also noted that these findings could not be generalized to VALS’ eventual successor, VALS2. Novack and MacEvoy’s approach makes sense, since the intent of psychographic measurements is to add a dimension to demographic measures (Langer, 1985).  Bainbridge (1999) agrees, noting that psychographics are an extension of demographics. Heath (1995) confirms that the purpose of psychographics is to measure demographic characteristics along with attitudes, opinions and interests.

     However, this was not the sole study finding some validity problems with psychographics in general, and the original VALS typology in particular. Lastovicka, Murry and Joachimsthaler (1990) measured the VALS typology and the Drinking-Driving (DD) typology quantitatively, using statistical modeling, and qualitatively, using judgmental coding of data collected from open ended and projective tasks. They used a multimethod-multitrait approach, and ANCOVA structures (LISREL) approach to examine the convergent and discriminant validity of each tool. They found less convergent and discriminant validity for VALS than for DD. Unfortunately, as they note, a major limitation of their study was that it was against a small sample of 100 18 to 24 year old men, and using a different sample against of the general U.S. population could produce differing results; additionally, there were logical subgroupings of VALS types other than those used in their research. They also noted that SRI, the owners of VALS typology were introducing VALS2, which might make their research findings mute.

     Wells (1975) points out three problems relating to the studies such as Lastovicka et al. (1990): Using psychographics to find relationships that should not have been expected and they fail to appear; When the finding is too abstract to be useful; When the measurement is so close to the behavior studied that the relationship is essentially redundant. However, popular and accepted psychographic measures do have these inherent dangers, too, which led to the downfall of the original VALS. Because VALS did not discriminate enough, the measurement, to some, was redundant. It contained one of the problems clustering can be prone to: Lack of discrimination among segments. “People would say, ‘If 40 percent are Belongers, why should we bother with the rest” (Riche, 1989)?  Not only did marketers complain that VALS was not actionable, the originator of VALS, Arnold Mitchell, made a research design error, trying to prove an assumption. Designing the study in an attempt to prove Maslow’s theory of motivation, Mitchell placed a preconceived truth into the design (the assumption was that people buy according to where they fall in a hierarchy of needs), rather than attempting to discover what the real truth behind consumer motivation was (Heath, 1995). Not all researchers were that disenchanted with the original VALS, although there was agreement that the Belongers group needed to be split (Winters, 1992). However, some of those who liked the original VALS also shared in Mitchell’s Maslow based approach to its theoretical underpinnings and complained that VALS2 had no theoretical approach (Winters, 1992).

VALS2 and RISC Ameriscan

     SRI totally redesigned the VALS typology to fix this error in 1989, and called the new product VALS2. Appendix A contains a visualization of the VALS2 framework.

VALS2 was positioned at the time as moving away from values and lifestyles because it was too fragmented and did not adequately predict consumer behavior, which was shifting; instead, VALS2 was positioned as being designed to reveal unchanging psychological stances (Riche, 1989). However, as recently as 1996, SRI acknowledges that the attitudes of consumers can change, if not their VALS type (Heath, 1996). In fact, VALS current literature positions VALS as a combination of self-orientation and resources, noting that resources are the psychological, physical, demographic and material means upon which people can draw; these resources increase from adolescence through middle age, but decrease with extreme old age or mental or physical deterioration (http://future.sri.com/VALS/vals.segs.shtml).

     Using a national sample of 2,500, SRI developed a 43-question assessment tool that measures resources, including demographic information and internal resources, such as confidence, energy and intelligence; in addition, as noted above, the tool recognizes that resources tend to accumulate through middle age, and then decline (Riche, 1989). VALS2 identifies eight attitude and lifestyle segments, arranged primarily by three different orientations to buying. However, at the top of the rectangle are actualizers. Actualizers make up just 8% of the overall U.S. population (Bearden, Ingram & LaForge, 2001). Actualizers may be described as that segment of the population who have high resources with a focus on principle and action, who are active, take-charge in terms of expression of taste, independence and character. Their demographic characteristics include a median age of 43 and median income of $58,000 (Mowen & Minor, 1998). In addition, they are successful and sophisticated, can indulge in self-orientations; 95 percent have some college (Evans & Berman, 1997).

     Other VALS2 categories include individuals who are oriented toward principles (fulfilleds and believers). A primary differentiator between the two groups is the resources available to them, which affects their approach to lifestyles and values (Mowen & Minor, 1998). Fulfilleds make up approximately 11% of the population, while believers represent 16%. Those who are oriented toward status (achievers and strivers) focus on status and are also segmented by resources, including a strong orientation toward money and achievement. Achievers and strivers each represent 13% of the general population. Action oriented individuals would include the categories of experiencers and makers. Experiencers tend to be younger and focus on action as a means of excitement, while makers focus on practical action. Again, a differentiator between the two is the abundance or lack of resources. Experiencers represent 12% of the population, while makers account for 13%. Finally, VALS2 identifies the strugglers. Poor, with little education, the strugglers have few resources and their focus is on living and surviving for the moment. They represent 14% of the general U.S. population (Mowen & Minor, 1998). 

     While SRI attempted to position VALS2 as representing a more entrenched psychological approach, with less emphasis on values and lifestyles, RISC (the International Research on Social Change) was incorporated in Switzerland in 1978 to monitor social change and trends in European countries, the United States, and Japan (Hasson, 1995). Like the reasoning behind VALS 2 and other psychographic tools, RISC decided to monitor social change due to the realization that demographics provide decreasingly discriminating markets or segmentation opportunities. Based a little more widely than psychological typography, RISC adopts the statistical and conceptual tools of psychographics, but tends to use a three dimensional approach to diagnose socio cultural trends, market dimensions and demographics (Hasson, 1995).  Depending on the brand and choice of study, demographics could only explain 8-10% of brand choice for a particular brand; psychographics, using typologies, from 25-35%; however, socio cultural trends could explain 35-45% (Hasson, 1995). Appendix B depicts the RISC socio cultural trends model. These socio cultural trends include balanced and autonomous (A), eager and dedicated (B1), daring hedonist (B2), belongings and values (C1), transitional (C2), petit bourgeois (C3), self-centered impulsive pleasurist (C4), rational traditionalist (D1), anomy and disconnection (D2), withdrawn and distressed (D3). In many ways, the descriptions mirror some of the lifestyles and values of VALS, and the pyramid similarly seems aligned with resources. However, unlike VALS2’s description of an entrenched psychological approach, RISC’s methodology assumes that people and countries’ self-concept moves around the trends in a more dynamic manner, according to environmental influences, including the economy and social mores.  Marshall Marketing uses this tool in the United States to help retailers and broadcasters to monitor and predict social trends for brands (www.mm-c.com/RISC/risc.htm).

Psychographic Variations

     Wells (cited in Heath, 1995) noted that while psychographic studies now come in an infinite number of variations, there are five general types of psychographic study: 1) A lifestyle profile that includes questions on product use, media use, and demographic information, as well as psychographic and lifestyle information. Researchers then look for the information that discriminates between groups of users and nonusers of products; 2) Product specific psychographic profiles identifies the target group of consumers first, and then uses psychographic product relevant dimensions to segment the users; 3) Personality traits as descriptors analyzes dependent variables (e.g. specific attitudes, opinions or interests) and then uses personality traits as independent variables are highly correlated to the dependent variable, which is then used to segment markets; 4) General lifestyle segmentation is used to define a typology. While it collects much of the same information as a lifestyle profile, it does not assume what the common traits are, but does attempt to identify significantly different groups or significantly homogenous lifestyle segments. 5) Product specific segmentation alters general psychographic or lifestyle questions and adapts them to product specific statements, which are then analyzed, using factor analysis, to support or negate a hypothesis regarding the product. Among these various uses, many psychographic studies have been and are being conducted to determine the psychographic values or lifestyles that aid the researcher in understanding the why behind what consumers do and their addition to basic demographic information.

Some Psychographic Applications

     While not purporting to be an exhaustive list of studies, it is interesting to note some of the research to which psychographic measurements have been applied, and their direct application to marketing.

     Ailawadi, Nesline & Gedenk (2001) used psychographics and demographics to identify value-conscious consumers and their perceptions of store brands versus national brands. They found that store brand use correlates with economic benefits and costs, and identified four specific market segments: deal focused customers, store brand focused customers, the use-alls (those who will go either way), and use-nones (those who don’t use either store brands or deals). Their study used a structural model and discovered that demographics do not affect consumer behavior directly, but are funneled through psychographics. This study demonstrated how manufacturers and retailers could avoid marketing their brands to the same segment in a consumer tug of war.

     Eckman, Kotsiuopulos & Bickle (1997) used psychographics to measure to examine the store patronage behavior of Hispanic versus non-Hispanic consumer and the role of store attributes. Previous psychographic studies on Hispanics have focused on sports, television viewing and religion.  It is difficult to describe Hispanics as a homogenous group because there are many national subcultures that make up the Hispanic culture (Eckman, Kotsiuopulos & Bickle, 1997). However, they found that Hispanic consumers are less likely to participate in cultural activities and seek advice; however, they were more likely to experiment and proeducate. Attributes that were important to Hispanic consumers included services, language, resource management, pricing, comfort and selection. While non-Hispanics purchased in family-owned stores and catalogues, Hispanics purchased in second-hand stores more often.

     McCarty and Shrum (1993) examined the role of personal values and demographics in predicting television-viewing behavior. Using Rokeach’s LOV and a structural equation analysis, their study found that values do relate to television viewing, but the relationship and amount of influence is complicated or sometimes reduced to non-significance when demographics are factored in. They concluded that because the interrelationships between values, behavior and demographics are so complex, that segmentation schemes should employ both demographics and values in their consideration. They also found differences not only in values, but also in the demographic information they measured (gender, age, income and education) and their effects on viewing.

     Lin (1999) furthered research in this area by examining the relations between perceived television use and online access motives, using uses and gratifications perspectives. However, she found a weak correlation between user motives for television exposure and potential online access. The study also noted that the online world, at least for now, tends to be supplementary to television; however, when full convergence is achieved, there may be a need for advertisers to adjust their approach to the online world. Lin also criticizes SRI’s VALS2 for its lack of attention to measuring online users’ predisposition to technology, something that iVALS attempts to correct.

     A number of studies have been conducted on psychographics and travel related behavior. Silverberg, Backman and Silverberg (1996) investigated the psychographics of nature-based travelers in the United States and their relationship with attitudes about the environment, travel behavior and demographics. Using factor analysis, they found that that travelers whose primary nature-based activity was viewing nature participated in nature-based activities differed from those whose trip was for educational purposes. They also found differences between campers and non-campers and social travelers and all other groups. Six variables were found to be significant predictors of campers versus non-campers: education, age, likelihood of taking a nature-based trip, conservationist attitude, consumptive attitude, and involvement in other nature based activities. Non-campers are more highly educated, have a consumptive attitude and more likely to take a nature-based trip. Campers, on the other hand, tended toward conservationism and a greater involvement in other nature related activities.

     Without calling it psychographics per se, Stephens (1991) conducted an interesting study linking cognitive age with consumer behavior, especially when used in conjunction with demographic age. She found that it provided important clues regarding attitudes toward purchasing and consuming, and could aid targeting decisions, creative executions and media selection.

     Ohanian (1990) used psychographics to construct and validate a scale to measure celebrity endorsers’ perceived trustworthiness, expertise and attractiveness. She found that source credibility, defined by these three factors directly impacted intent to purchase and recommended that researchers could use this tool to investigate the credibility of political candidates, that advertisers use the scale as part of effectiveness testing and that celebrity endorsers be calibrated against varying demographic and psychographic groups.

     Dychtwald and Zitter (1988) recommend that hospitals use psychographics as part of their basic strategic marketing plan, noting that this tool may especially be useful in targeting the elderly population. They identify three separate segments from that group: The vitally active, who are still involved in the world and continue to grow; the adapters who face significant real health problems, but have either overcome or accepted those problems; the overwhelmed, who are anxious about the future and unable to manage their problems.

     Hornick (1990) used psychographics to predict smoking intensity and found that it was more meaningful and valid than consumer time preference (which relates to the timing of an outcome and perceived payoffs/time tradeoffs) and demographic characteristics. He discovered that adding psychographic variables to demographic variables improved the explained variance by more than 73%.

     Wells (1975) noted that already as of that date, psychographics had contributed to an understanding of opinion leadership, retail shopping, private brand buying, consumer activism, store loyalty, differences between Canada and the United States, as well as differences between English and French speaking Canadians.

The Future of Psychographics

     With the continued interest in the psychological factors that drive consumers to purchase, it is doubtful that the interest in psychographics will lessen in the future. The running feud between quantitative advocates of research and the qualitative advocates has abated with the more stringent adoption of statistical methods, better use of computer based research and applications in qualitative design (Heath, 1996). In fact, if anything, the trend in research has shifted to the qualitative approach (Heath, 1996). Tools such as VALS2 are being adapted to provide varying types of consumer information. iVALS was constructed to focus on the attitudes, preferences, and behaviors of Internet users, addressing some of the concerns Lin (1999) expressed. Not only did early results reinforce the notion of a dual-tiered have/have not society, it found that half of the web users were Actualizers, that three out of four were men, and virtually all had gone to college (Heath, 1996). Not only are broadcasters and cable channels tailoring their content according to their viewers’ psychographic profiles (Heath, 1995), but marketers are attempting to determine what their brands look like in consumers’ eyes (Heath, 1996).

     Perhaps the biggest caveat, though, comes from Wells’ (1975) seminal article.  The tools may not be valid or reliable if searching for relationships that do not exist, if they are too abstract, or if the behavior studied is essentially redundant to other variables. Hasson (1995), in discussing socio cultural models, notes that many argue that models such as these are reductive and too systematic. He questions whether the problem is the systematic nature of the model or whether researchers fall too much in love with their model and reduce everything down to their system. He further notes that there are no universal tools; in fact, if research were in 100% agreement, it might be indicative of a totalitarian society, but when clients successfully use the information, it might not be a proof, but it is a reward (Hasson, 1995). The future use of tools such as psychographics will be determined by the validity and reliability of the instrument, the application’s usefulness, and its ability to predict consumer behavior, which might not serve as a prove of their universal accuracy, but will be rewarding to marketers that use the tool well.

 

References

Ailawadi, K.L., Neslin, S.A., & Gedenk, K. (2001). Pursuing the value-conscious consumer: Store brands versus national brand promotions. Journal of Marketing, 65, 71-89.

 

Bainbridge, H. (1999, June 1). Sales Channels: Beyond demographics. Wireless Review, 61-62.

 

Bearden, W.O., Ingram, T.N., & LaForge, R.W. (2001). Marketing: Principles and perspectives. New York: McGraw-Hill Primis.

 

Booth, E. (1999, June 3). Getting inside a shopper’s mind. Marketing, 33-34.

 

Demby, E.H. (1994). Psychographics revisited: The birth of a technique. Marketing Research, 6(2), 26(4).

 

Dychtwald, K., & Zitter, M. (1988). Developing a strategic marketing plan for hospitals. Healthcare Financial Management, 42 (9), 42-46.

 

Eckman, M., Kotsiopulos, A., & Bickle, M. C. (1997). Store patronage behavior of Hispanic versus non-Hispanic consumers: Comparative analyses of demographics, psychographics, store attributes, and information resources. Journal of Behavioral Sciences, 19 (1), 69 (15).

 

Englis, B.G., & Solomon, M.R. (1995). To be and not to be: Lifestyle imagery, reference groups, and the clustering of America. Journal of Advertising, 24 (1), 13-28.

 

Evans, J.R. & Berman, B. (1997). Marketing (7th ed.). Upper Saddle River, N.J.: Prentice Hall.

 

Hasson, L. (1995). Monitoring social change. Journal of the Market Research Society, 37 (1), 69-80.

 

Heath, R.P. (1996). The frontiers of psychographics. American Demographics, 18(7), 39(6).

 

Heath, R.P. (1995, November-December). Psychographics: Q’est-ce q c’est. Marketing Tools, 74(7).

 

Hornick, J. (1990). Time preference, psychographics, and smoking behavior. Journal of Health Care Marketing, 10 (1), 36+.

 

Isaac, S. & Michael, W.B. (1997). Handbook in research and evaluation (3rd ed.). San Diego: Educational and Testing Services.

 

Kahle, L.R., Beatty, S.E., & Homer, P. (1986). Alternative measurement approaches to consumer values: The list of values (lov) and values and lifestyles (vals). Journal of Consumer Research, 13 (3), 405 (5).

 

Langer, J. (1985). Using psychographics to understand demographic groups. Marketing Review, 40 (4), 11+.

 

Lastovicka, J.L., Murry, J.P., & Joachimsthaler, E.A. (1990). Evaluating the measurement validity of lifestyle typologies with qualitative measures and multiplicative factoring. Journal of Marketing Research, 27, 11-23.

 

Lin, C. (1999). Online-service adoption likelihood. Journal of Advertising Research, 39 (2), 79-89.

 

Marshall Marketing and Communications RISC Ameriscan. (n.d.). Retrieved August 2, 2001, from http://www.mm-c.com/RISC/risc.htm

 

McCarty, J.A., & Shrum, L.J. (1993). The role of personal values and demographics in predicting television viewing behavior: Implications for theory and application. Journal of Advertising, 22 (4), 77 (25).

 

Mowen, J.C. & Minor, M. (1998). Consumer behavior (5th ed.). Upper Saddle River, N.J.: Prentice Hall.

 

Novak, T.P., & MacEvoy, B. (1990). On comparing alternative segmentation schemes: The list of values (lov) and values and lifestyles (vals). Journal of Consumer Research, 17, 105-109.

 

Ohanian, R. (1990). Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness, and attractiveness. Journal of Advertising, 19 (3), 14-22.

 

Riche, M.F. (1989). Psychographics for the 1990s. American Demographics, 11 (7), 24-53.

 

Silverberg, K.E., Backman, S.J., & Backman, K.F. (1996). A preliminary investigation into the psychographics of nature-based travelers to the southeastern United States. Journal of Travel Research, 35 (2), 19-28.

 

Stephens, N. (1991). Cognitive age: A useful concept for advertising? Journal of Advertising, 20 (4), 37-48.

 

VALS: Consumer motivations. (n.d.). Retrieved July 31, 2001, from http://future.sri.com/VALS/vals.segs.shtml

 

Wells, W. D. (1975). Psychographics: A critical review. Journal of Marketing Research, 12, 196-213.

 

Wind, J., Rao, V.R., & Green, P.E. (1991). Behavioral methods. In T.S. Robertson and Kassarjian (Eds.). Handbook of consumer behavior (pp. 507-532). Englewood Cliffs, NJ: Prentice Hall.

 

Winters, L.C. (1992). International psychographics. Marketing Research, 4 (3), 48+.

Wyner, G. A. (1992). Segmentation design. Marketing Research, 4 (4), 38+.

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APPENDIX A

 

VALS2 TYPOLOGY

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

VALS segmentation system

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

APPENDIX B

 

RISC TYPOLOGY FOR SOCIO CULTURAL TRENDS