The dynamics of value segments: modeling framework and empirical illustration

Intern. J. of Research in Marketing 19 (2002) 267 – 285 www.elsevier.com/locate/ijresmar

The dynamics of value segments: modeling framework and empirical illustration Kristine Brangule-Vlagsma a,*,1, Rik G.M. Pieters b, Michel Wedel c,d a

Department of Marketing and Organization, Faculty of Economics, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands b Department of Marketing, Faculty of Economics, University of Tilburg, P.O. Box 90153, 5000 LE Tilburg, The Netherlands c Department of Marketing and Marketing Research, Faculty of Economics, University of Groningen, P.O. Box 800, 9700 AV Groningen, The Netherlands d Business School, University of Michigan, Ann Arbor, MI 48109-1234, USA

Abstract Value systems are central to understanding consumer behavior and they are an important basis for market segmentation. This study addresses changes in individual value systems across time. First, we conceptualize main ways in which value systems may change over time. Next, we extend Kamakura and Mazzon’s [J. Consum. Res. 18 (1991) 208] segmentation approach to accommodate these value system changes. We present a continuum of value segment change opportunities, with intermediate models in-between. We apply the models to a data set consisting of longitudinal (Rokeach Terminal Values) value measurements in a nationwide sample in the Netherlands. The results support the stability of value systems within segments, but reveal that subjects show substantial switching among segments in the 3 years of the study. We then discuss the implications of our modeling approach for value theory and segmentation practice. D 2002 Elsevier Science B.V. All rights reserved. Keywords: Value change; Market segmentation; Mixture logit model; Hidden Markov

1. Introduction Personal values are relatively distal but nevertheless powerful determinants of consumer behavior (Rokeach, 1973; Schwartz, 1992). Among others, they have been used to explain readiness for outgroup social contact (Sagiv & Schwartz, 1995), voting behavior (Rokeach, 1973), charity contributions *

Corresponding author. Tel.: +31-10-4082853; fax: +31-104089169. E-mail address: [email protected] (K. Brangule-Vlagsma). 1 The work on which this manuscript is based was carried out while the first author was employed at the University of Groningen.

(Manner & Miller, 1978), mass media usage (Rokeach & Ball-Rokeach, 1989), socially conscious behavior (Anderson & Cunningham, 1972), ecological behavior (Ellen, 1994; McCarty & Shrum, 1994), cigarette smoking (Grube, Weir, Getzlaf, & Rokeach, 1984), innovativeness (Steenkamp, ter Hofstede, & Wedel, 1999), purchase of organic foods (Grunert & Juhl, 1995), and existence of market segments (Kahle, Beauty, & Homer, 1986; Madrigal & Kahle, 1994; Novak & MacEvoy, 1990; ter Hofstede, Steenkamp, & Wedel, 1999). Values help to explain and understand consumer behavior because they play a central role in consumers’ cognitive structures and because of their supposed

0167-8116/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 8 11 6 ( 0 2 ) 0 0 0 7 9 - 4

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stability. This study addresses the important issue of change in individual value systems. The relative stability of values is one of its attractive features for market segmentation. However, we will show that even though values are relatively stable, value systems of individuals are subject to change. Thus, we distinguish between notions of a fixed and a stable value system. This distinction has important implications for market segmentation based on value systems, which is the topic of this article. We believe that value systems are relatively stable at the level of societies, but the value system of individual society members varies over time within the overall system. To examine this notion, we conceptualize specific types of value system change and extend Kamakura and Mazzon’s (1991) segmentation approach to accommodate them. We propose a modeling framework to capture dynamic value segmentation and apply it to a data set consisting of longitudinal value measurements in a nationwide sample in the Netherlands. We show that accounting for the dynamic nature of values leads to new insights that cannot be obtained otherwise. The results support the stability of value systems within segments, but reveal that consumers show substantial and systematic switching among segments in the 3 years of the study. We then discuss the implications of our findings for value theory and segmentation practice.

of existence is personally and socially worth striving for, such as family security, freedom, pleasure, and social recognition. In the RVS, consumers are presented with a set of value descriptions and asked to rank-order them in terms of their importance as guiding principles in their life (Rokeach, 1973). Recently, Schwartz (1990, 1992, 1994, 1996), Schwartz and Bilsky (1987, 1990), and Schwartz and Sagiv (1995) have revitalized the study of values. Schwartz and Bilsky (1987) view values as ‘‘(1) concepts or beliefs, that (2) pertain to desirable end states or behaviors, (3) that transcend specific situations, (4) guide selection or evaluation of behavior and events, and (5) are ordered by relative importance’’. Thus, in addition to describing formal features of values, Schwartz and Bilsky propose that the primary content of a value is the motivational concern that it expresses. They derive a universal typology by reasoning that the source and nature of values are closely linked with three types of universal human requirements: biologically based needs of the organism, social interaction requirements for interpersonal coordination, and social institutional demands for Table 1 Rokeach values in motivational domains Primary motivational type

Value

Secondary motivational type

Prosocial

a world at peace equality salvation (forgiveness)a true friendship –

security – security security

comfortable life happiness pleasure exciting life accomplishment social recognition a world of beauty mature love self-respect wisdom – freedom family security inner harmony national security

– – – self-direction self-direction – – – self-direction – – security – maturity –

2. Segmenting value systems across time 2.1. Value systems A value is an ‘‘enduring belief that a specific mode of conduct or end-state of existence is personally or socially preferable to an opposite or converse mode of conduct or end-state of existence’’ (Rokeach, 1968, 1973). Once acquired, values form a system in which each value is ordered in priority relative to other values. These value systems are generally assumed to be relatively stable over long periods of time. A dominant instrument to measure personal values is the Rokeach Value Survey (RVS), which consists of 18 instrumental values and 18 terminal values. Instrumental values relate to modes of conduct, such as ambition, independence, imagination, and responsibility. A terminal value is a belief that some end-state

Restrictive conformity Enjoyment

Achievement

Maturity

Power Self-direction Security

a In this study, the Dutch wording of the original value label ‘‘salvation’’ was ‘‘forgiveness’’.

Table 2 Selected value change studies Sample

Value instrument

Findings

Factors of change

Hoge and Bender (1974)

Three groups of Dartmouth College students (N = 150, 60, and 47) were studied between 1931 and 1956 and restudied as alumni between 1952 and 1969. Representative national samples of adult Americans in 1968 (N = 1409) and 1971 (N = 1430), and two-wave panel survey (N = 933) in 1974 and 1981.

Allport-Vernon Study of Values

Significant shifts in mean scores for all groups. Value change at any period was greater among students and young alumni than among older alumni.

Change explained by current historical experiences, as well as background and personality variables.

Rokeach Value Survey

Mean rankings remarkably stable, nevertheless, means of few values decreased dramatically in importance from 1968 to 1981.

World Value Survey; 12-choice value battery

Permanent shift from materialist to postmaterialist value orientation.

Change as a result of dissatisfactions originating from the simultaneous activation and frustration of values derived from different levels of human motivation. Change induced by television programs. Trend toward postmodernist results from generational replacement and is closely linked with prosperity.

Rokeach Value Survey (terminal values)

Significant interaction between value, treatment, and time resulting in significant increase in mean ranking of the target value.

European Value Study; 12-choice value battery

Values of immigrants were found to be similar to those of Swedes or between the Hungarian and Swedish population.

World Value Survey; 12-choice value battery

Value orientation change from authoritarian to libertarian set of attitudinal orientations.

Rokeach and Ball-Rokeach (1989)

Inglehart and Abramson (1994)

Grube et al. (1994)

Hamberg (1995)

Flanagan and Lee (2000)

The 1981 – 1983 surveys conducted representative national samples in 22 societies (the sample size ranged from 97 to 2303). The 1990 – 1991 surveys—from 40 societies (N ranges from 588 to 4147). 112 undergraduate students; experimental group and control group both 56.

Representative samples of Hungarian (829) and Swedish (772) adult population from European Value Study survey in 1990, and 510 Hungarian immigrants in Sweden. Part of World Value Survey includes Japanese and Korean samples (N = 1041 and 898, respectively, in 1981 and N = 883 and 1228 in 1991.

The change mechanism remains unclear; possible hypothesis includes (1) that value self-confrontation (S-C) increases self-dissatisfaction (S-D) and, as a result, leads to value change; (2) value S-C focuses attention on existing S-D; (3) increases the silence of specific values; (4) undermines denial of responsibility. The assimilation process and exposure to the new culture leads to the value change.

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Study

Value orientation change results from generational replacement, and is closely linked with the increasing need for self-actualization that is facilitated by economic prosperity.

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group welfare and survival (Schwartz & Bilsky, 1987). They derive eight motivational domains of values from these three universal human requirements. The eight domains are prosocial, restrictive conformity, enjoyment, achievement, maturity, self-direction, security, and power. Table 1 presents the classification of Rokeach values according to the eight motivational domains. 2.2. Determinants of value system change Before examining how value systems can change, we first look at why they could change. There are two broad determinants of change in value systems over time. First, since values are learned and shaped by individual experience, major endogenous forces such as becoming a parent, divorcing, entering or leaving the labor force, and changing jobs ought to affect an individual’s value system. There is empirical evidence for value changes due to the transition of young adults from school to work (van der Velde, Feij, & Taris, 1995), for an association between work experience and value change (Roberts, 1997), and for the effect of family members on the individual’s values (Caspi, Herbener, & Ozer, 1992; Rohan & Zanna, 1996). Research has also found age-related changes in values and intergenerational differences in values (Badger, Simpson-Craft, & Jensen, 1998; Burke, 1994; Carmichael & McGue, 1994; Costa & McCrae, 1994; Limoux & Suave, 1999; McConatha & Schnell, 1997; McIlveen & Gross, 1999; Musek, 1990; Pedersen, 1993; Penn, 1977; Prager, 1998). To the extent that endogenous forces influence some but not all members of a society, they should influence the value systems of specific consumers, and not of society as a whole. Specifically, one would expect consumers to move in a nonrandom way through the overall stable societal value system. Second, exogenous forces, such as cultural, technological, and economic shifts and shocks, as well as wars or natural disasters, may affect and change the value system of one or more entire segments within the population (Marks, 1997; Raboteg, Zuzul, & Kerestes, 1994; Saha, 1998). For instance, Inglehart (1977) found an overall increase in postmaterialistic values between 1970 and 1976 in the US, which he attributed to the economic prosperity of a relatively large proportion of society. These affluent groups had

attained a sense of economic and physical security that enabled them to give top priority to the belonging and intellectual-aesthetic needs. Similar patterns of value change have been found in advanced industrial, as well as developing, countries (Flanagan & Lee, 2000; Inglehart & Abramson, 1994). Andorka (1995) documents value changes in the 1980s in Hungary due to the turmoil of the transition to a market economy. To the extent that exogenous forces influence all members of a society, they lead to a change in the overall value system. In any period of time, endogenous and exogenous forces will jointly influence the overall societal and specific individuals’ value systems. Although most value research to date has focused on one or more specific values or on value systems at one point in time, an emerging stream of research is concerned with tracking value and value system changes over time. Selected value change studies are presented in Table 2. The studies reported in Table 2 are representative for other value change studies. Most often, mean changes in the importance of one or more values in a value system are examined (Hoge & Bender, 1974; Rokeach & Ball-Rokeach, 1989). Changes in the value system as a whole, as well as other changes besides mean importance change, are typically not studied. Moreover, value change studies are typically empirically driven and have paid less attention to theoretical underpinnings of how value systems can change and what the determinants of such value system changes are (Grube, Mayton, & Ball-Rokeach, 1994; Hamberg, 1995). Finally, no study to date has examined the implications of value change for segmentation. It is important to examine the implications of value system change for segmentation since values are assumed to be stable, and stability is an important reason for their prominent use as a segmentation basis. In the following section, we develop and test a dynamic segmentation model based on fundamental ways in which value systems can change over time.

3. A framework for dynamic value segmentation Early value segmentation studies made direct comparisons of values between a priori defined segments or groups (females versus males, parents versus chil-

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dren) (Vinson & Munson, 1976). Recently Kamakura and Mazzon (1991) demonstrated the usefulness of analyzing value systems at the level of segments identified post hoc. They developed a mixture model that identifies segments with distinct value systems within the population and classifies individuals according to them, which has led to new insights in several studies to date (Kamakura & Mazzon, 1991; Kamakura & Novak, 1992; Wedel, ter Hofstede, & Steenkamp, 1998). After introducing the model proposed by Kamakura and Mazzon (1991), we extend it to capture dynamic value segmentation. 3.1. The Kamakura– Mazzon (KM) model We first establish some notation. Let i = 1, . . ., I j = 1, . . ., J s = 1, . . ., S Rij

index subjects; index value descriptions; index segments; denote the observed rank of value j by subject i; denote the vector of observed rankings for subject i; denote the ‘‘true’’ utility of the value j occupying the rth rank position; denote the (1  J) vector of value utilities representing value system held by members of segment s; denote the relative size of segment s.

Ri uj(r) us

ps

271

nents eij(r) are assumed to be independent and identically distributed extreme value error terms, then Eq. (1) can be rewritten in rank-order logit form (McFadden, 1974): PðRi Þ ¼

Y

expðuijðrÞ Þ X : expðuijðr VÞ Þ r¼1; J

ð2Þ

r Vzr

This form, obtained through the rank-explosion rule, is useful to assess the aggregate value system held by the population. To identify distinct value systems within segments in the population, we assume that there exist S unobserved segments and that the value system held by members of a particular segment s is represented by the vector of value utilities us. Then, the conditional likelihood of the observed ranking Ri given that an individual i = 1, 2, . . ., I belongs to a segment s is PðRi j us Þ ¼

Y

expðuijðrÞs Þ X : expðuijðr VÞs Þ r¼1; J

ð3Þ

r Vzr

The unconditional likelihood of observed value ranking could then be expressed as: X ps PðRi j us Þ; ð4Þ PðRi Þ ¼ s¼1;S

Let uij be the true utility for subject i of value j, and eij be the stochastic part of the utility. Then, the relationship between an observed ranking of J value descriptions Ri and the individual’s unobservable value system can be expressed as a probability function:

PðRi Þ ¼ Pðuijð1Þ þ eijð1Þ > uijð2Þ þ eijð2Þ ; uijð2Þ þ eijð2Þ > uijð3Þ þ eijð3Þ ; . . . ; uijðrÞ þ eijðrÞ > uijðrþ1Þ þ eijðrþ1Þ ; . . . ; uijðJ 1Þ þ eijðJ 1Þ > uijðJ Þ þ eijðJ Þ Þ;

ð1Þ

where r represents the rank position of value j and subscript j(r) indicates the particular value description occupying rth rank position. If the stochastic compo-

where ps is the relative size of the segment s = 1, 2, . . ., S, or the prior probability for any individual to belong to segment s and us. Those probabilities are constrained to be positive and sum to 1. This model accounts for similarity of observed judgements among individuals in a segment at one given moment in time. In the following sections, we extend this model to account for changes in value systems. In general, what could the effect of time on value segments be? At one extreme of the spectrum, the value segments found at different points in time could be completely different. That is, a different value system could be observed with different segments, in size and orientation, and with consumers being part of different segments per time point. At the other extreme of the spectrum, the value segments found at different points in time could be completely the same. That is, one

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overall value system with segments of fixed orientation and size would describe the data best. Between these two extremes, different more interesting models are situated. In the next section, we specify the two extremes and a specific class of models in-between the two. The models with their key assumptions are summarized in Table 3. For the dynamic value segmentation, let:

t = 1, . . ., T

index time when judgment was observed; denote the vector of rankings observed for subject i at time t, and denote the vector of all such rankings across t.

Rit

Ri

3.2. Model 1: idiosyncratic value systems and segments per time: panta rei The philosopher Heraclitus argued that everything is constantly changing, from the smallest grain of sand to stars in the sky, hence panta rei. The first of the models that we propose is a direct extension of the KM model for several time-points. The model describes idiosyncratic value system segments for each time. In other words, it assumes that segments can be identified at each period in time, but that the specific value systems that distinguish the value segments over time are independent. Thus, one effectively identifies segments independently at each time point, without imposing any dependencies on the segments thus identified. The likelihood of the observed ranking Ri is:

PðRi Þ ¼

Y X t¼1;T s¼1;S

pst

Y

expðujðrÞst Þ X ; expðujðr VÞst Þ r¼1; J

ð5Þ

r Vzr

where pst is the relative size of the segment s = 1, 2, . . ., S, at time t = 1, 2, . . ., T, or the prior probability for any individual to belong to segment s at time t. The probabilities are constrained to be positive and sum to 1 for each measurement time. Once parameter values are obtained, each individual can be assigned to the segments at a particular time using a posterior

Bayesian update of the prior membership probabilities, pst: pst PðRit j ust Þ sist ¼ X ; ps Vt PðRit j us Vt Þ

ð6Þ

sV¼1;S

where sist is the posterior probability that individual i belongs to a segment s at time t. The model provided by Eqs. (5) and (6) is the KM model independently for T times. For each time, a separate segment size is estimated while the value rankings are specific to each time and each segment, and independent over times. This model is not parsimonious since Q = T(SJ  1) parameters need to be estimated. Both changes in segment proportions and the within-segment value structure are completely unconstrained over time. If this model would fit the data best and no major exogenous events occurred between value measurements, one would have to conclude that value systems are of limited usefulness as a segmentation basis due to their inherent instability. 3.3. Model 2: stable value system and fixed segments across time: hen ta panta Parmenides argued that ultimately reality is constant. What we believe to be a world of things and motion and change is just an illusion. Hence, hen ta panta, all things are one. The second model we consider restricts the segment-specific value-utility parameters ujst as well as the size of these segments pst to be constant over time. This model assumes that at each measurement time, the size, number, and composition of value-segments are exactly the same. It is the most parsimonious model since only Q = SJ  1 parameters need to be estimated, which is (T  1)(SJ  1) less than for model 1. Essentially, it is the KM model estimated for several time points. If this model would fit the data best, and major exogenous events occurred between value measurements or the time interval between successive value measurements would be sufficiently large, value system segmentation would be very attractive due to the stability of value systems and the segments. From a theoretical perspective, the results would cast doubt on the status of the value construct itself, which assumes stability, not rigidity, across time and the

Table 3 Modeling framework for dynamic value-segmentation Equation *

Estimated parameters

(1) Idiosyncratic segments at each time point. Panta rei

1

mixing probabilities pst

Restrictions X pst z0; pst ¼ 1; s ¼ 1; . . . ; S; t ¼ 1; . . . T

Number of parameters T(S  1)

s

(2) Time invariant finite-mixtures model. Hen ta panta

segment-specific value utilities uj(r)st mixing probabilities pst

1

uJ(r)st = const.; j = 1, . . ., J; s = 1, . . ., S; t = 1, . . ., T pstk ¼ pstl 8k; l; ps z0;

X

ps ¼ 1; s ¼ 1; . . . ; S

S1

s

(3) Time invariant value segments with flexible mixing probabilities.

(4) Time-heterogeneous latent Markov model

segment specific value utilities uj(r)st mixing probabilities pst

1

uJ(r)st = const.; bk, l uj(r)stk = uj(r)stl j = 1, . . ., J; s = 1, . . ., S; t = 1, . . ., T pst z0;

X

pst ¼ 1; s ¼ 1; . . . ; S; t ¼ 1; . . . T

( J  1)S T(S  1)

s

segment-specific value utilities uj(r)st mixing probabilities pst = 1

2

uJ(r)st = const.; bk, l uj(r)stk = uj(r)stl j = 1, . . ., J; s = 1, . . ., S; t = 1, . . ., T pst¼1 z0;

X

pst¼1 ¼ 1; s ¼ 1; . . . ; S; t ¼ 1

( J  1)S S1

s

(5) Time-homogeneous latent Markov model

segment-specific value utilities uj(r)st transition probabilities pstVjst  1 mixing probabilities pst = 1 segment-specific value utilities uj(r)st transition probabilities pstVjst  1

2

uJ(r)st = const.; bk, l uj(r)stk = uj(r)stl j = 1, . . ., J; s = 1, . . ., S; t = 1, . . ., T pstVjst1 z0;

X

pstVjst1 ¼ 1; s ¼ 1; . . . ; S; t ¼ 2; . . . ; T ; sV¼ 1; . . . ; S

T X S Y t¼1 s¼1

pst

pst¼1 z0;

X

(T  1)(S  1)S

pst¼1 ¼ 1; s ¼ 1; . . . ; S; t ¼ 1

S1

s

uJ(r)st = const.; bk, l uj(r)stk = uj(r)stl j = 1,. . .,J; s = 1,. . .,S; t = 1,. . ., T

( J  1)S

8k; l pst¼k V jst¼k1 ¼ pst¼l V jst¼l1 ; pstVjst1 z0;

(S  1)S

X

pstVjst1 ¼ 1; s ¼ 1; . . . ; S; t ¼ 2; . . . T ; sV¼ 1; . . . ; S

S J T Y S Y S J Y Y X expðujðrÞst Þ expðujðrÞst¼1 Þ Y expðujðrÞstVÞ X X X pst¼1 pstVjst1 ; Equation 2: PðRi Þ ¼ . expðu Þ expðu Þ expðujðr VÞstVÞ ðjðr VÞstÞ ðjðr VÞst¼1 Þ t¼2 s V¼1 s¼1 r¼1 s¼1 r¼1 r¼1

J Y

r Vzr

( J  1)S

sV

sV

* Equation 1: PðRi Þ ¼

T( J  1)S

r Vzr

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Model

r Vzr

273

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exogenous and endogenous events that occur in it. These first two models are the extremes of a continuum between which more interesting models, from a dynamic segmentation point-of-view, are located. These models are considered next. 3.4. Model 3: stable value system and moving consumers across time In this model, the segment-specific value-utility parameters ujst are constant over time, i.e., uijst = ujs in Eq. (5), but the size of these segments pst may change, allowing individuals to switch from one segment to another, or in other words, it tracks the individual switches in latent segment space. Moreover, we assume that the number of segments is the same over time in order to reduce the number of possible models to be estimated. Note from Eq. (6) that although for each segment the probabilities of observed rankings are invariant across time, posterior segment membership may change since the segment sizes change over time thus in a parsimonious way allowing for subjects to switch segments over time. This model accommodates value changes of (groups of) consumers because of endogenous forces, such as family life cycle events, which promote switches from consumers from one segment to the other. The underlying value system and segment structure remains the same. This model should fit well when no major exogenous events between measurements occur, which is more likely the case when the time interval between successive value measurements is short. The parsimony of the model is between models 1 and 2 since here, Q = S( J  1) + T(S  1) parameters need to be estimated. 3.5. Models 4 and 5: stable value system and timeheterogeneous and homogeneous latent Markov moving consumers between segments Model 3 leaves the switching probabilities of consumers between segments unrestricted, i.e., consumers are allowed to switch from any segment to any other between two consecutive value measurements, and switching can be at random. Models 4 and 5 assume that consumers switch between segments in ordered, nonrandom, ways. In both models, transition probabilities between consecutive value measure-

ments follow a first-order Markov process, as described below. The models are what have been called hidden Markov models (Aldous & Pemantle, 1996; Elliott, Aggoun, & Moore, 1995). By introducing transition probabilities from one segment to another between two consecutive value measurements, we extend our analysis to a dynamic context. We assume a first-order Markov model for the mixing probabilities. The likelihood of the observed ranking Ri is:

PðRi Þ ¼

S X s¼1

pst¼1

J Y

expðujðrÞst¼1 Þ X expðujðr VÞst¼1 Þ r¼1 r Vzr

T X S X S Y t¼2 sV¼1 s¼1

pstVjst1

J Y

expðujðrÞstVÞ X ; expðujðr VÞstVÞ r¼1

ð7Þ

r Vzr

where psVtjst  1 is the transition probability from segment s to segment sV between time t  1 and t. In total, Q = S( J  1)+(S  1)(ST  S + 1) parameters are estimated. Model 4 in Eq. (7) is time-heterogeneous since the transition probabilities may change over time. In model 5, the transition probabilities are time-homogeneous (see Table 3), in which case, Q = S( J  1)+(S  1)(S + 1) parameters have to be estimated. We conjecture that model 4 would fit the data best when some endogenous factors have a larger effect on the switching probabilities between time 1 and 2 than between time 2 and 3. This situation may occur, for instance, when a larger (or smaller) than usual generation of consumers makes an important life transition between two value measurements (Generation X marries, Baby Boomers retire). Model 5 fits best when such ‘‘grouped endogenous forces’’ are absent between consecutive value measurements. In general, the shorter the time interval between consecutive value measurements, the more likely it is that model 5 outperforms model 4. Note that models 4 and 5 assume that the number of segments is the same over time in order to reduce the number of possible models to be estimated. More specific models that fit specific situations can be readily derived from our framework by appropriately conditioning and restricting parameters.

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4. Empirical illustration 4.1. Data The proposed models are applied to a value survey that was conducted by the Dutch market research agency NIPO. The survey was administered to a panel of 1000 households that is representative of the Dutch adult population with respect to a series of demographic and socioeconomic variables. Three annual waves for 1993, 1994, and 1995 are available for this study. We confine our sample size to the 600 respondents that took part in all three waves of the survey. The Rokeach Value Survey (RVS) was used. Following previous work, we focus on the terminal values because they operate at a greater level of abstraction than do instrumental values and are more relevant to consumer behavior (Howard & Woodside, 1984; Kamakura & Novak, 1992; Pitts, Wong, & Whalen, 1991). Additional sociodemographic information (described later on) of the survey participants is available to profile the segments. 4.2. Model estimation and selection The models are estimated by maximizing the loglikelihood numerically using the BFGS Quasi-Newton method that is implemented in GAUSS (Aptech, 1995). The log-likelihoods are derived as the product of the log-ranking probabilities in (Eqs. (4), (5), and (7). In order to overcome problems of local optima, several randomly selected values of the parameters were used to start the algorithm. We report the results for those runs that yield the maximum of the likelihood function. There are two sets of identification restrictions required: the sum of the prior probabilities needs to be constrained to 1, and the utility of the Jth value is set to 0. Empirical identification of the models is investigated by inspecting the Hessian matrix of second derivatives of the likelihood, locally at the MLE (Bekker, Merckens, & Wansbeek, 1994). The Consistent Akaike’s Information Criterion (CAIC) statistic is used for model selection (Bozdogan, 1987). It is defined as CAIC =  2ln L + Qln (JN + 1). Based on the assumptions that model dimensionality is fixed as N ! l and that the true model is

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among the set of candidate models, CAIC indicates the true model with probability 1 asymptotically. CAIC and related dimension-consistent criteria have high probabilities of selecting the true model at large sample sizes (Burnham & Anderson, 1998; Rust, Simester, Brodie, & Nillikant, 1995), which is the case in our application. All five models were fitted to the longitudinal value ranking data for increasing numbers of segments. 4.3. Results 4.3.1. Model selection Table 4 contains the information statistics for model selection. For model 1, the CAIC statistic reaches a minimum when the number of segments is equal to 3. Model 2 performs better than models 1 and 3 in terms of CAIC for any number of segments estimated. The CAIC value is lowest for six segments indicating that this is the best solution. Clearly, the higher parsimony of this model results into a higher number of segments being designated as appropriate since the penalty component is considerably smaller. In other words, panta rei does not hold, and there is substantial stability of value systems over time. Also, for model 3, the CAIC value is lowest for six segments. Both models 4 and 5 perform better then the other models considered. This shows that consumers do not move randomly between segments from one value measurement to the other, but those switches between segments follow a first-order Markov process. Here, CAIC reaches a minimum for six-segment solution. The six-segment solution of model 5 yields the lowest values of CAIC across all models. Also, the entropy (Wedel & Kamakura, 1998) of E = 0.87, computed from the posterior segment membership Table 4 CAIC statistics for model selection Number of segments

CAIC information criterion Model 1

Model 2

Model 3

Model 4

Model 5

2 3 4 5 6 7

120869 120509a 120529 120975 121290 121800

120874 120145 119796 119639 119547a 119549

120868 120176 119847 119713 119630a 119646

120386 119462 118883 118705 118570a 118630

120371 119409 118773 118500 118380a 118393

a

Denotes the lowest CAIC.

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probabilities, indicates that segments are well separated. Therefore, model 5 with six segments was selected. Model 5 assumes that segment structure is stable across time, but that segment sizes may change. These changing segment sizes together with the transition probabilities capture the dynamics of value systems in a parsimonious way and allow subjects to switch from one segment to another over time. The transition probabilities are homogeneous across time.

Model 5 thus identifies six stable value system segments in the sample as a whole, but shows that subjects may switch segments, resulting in changing individual value systems. Below, we indicate that the six-segment solution of model 5 is not only parsimonious, but that it also has an appealing substantive interpretation. First, we profile the six segments. Next, we examine the transition probabilities between segments in detail.

Table 5 Value priorities by segment Overall rank

Segment A (17%)a ‘‘true friendship’’

Segment B (9%) ‘‘personal growth’’

Segment C (22%) ‘‘striking a balance’’

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

true friendship (2.35) wisdom (1.38) self-respect (0.97) social recognition (0.84) a world at peace (0.48) a comfortable life (0.38) mature love (0.12) freedom (0.03) accomplishment (0.03) an exciting life (  0.15) forgiveness (  0.29) a world of beauty (  0.52) happiness (  0.53) national security (  0.63) inner harmony (  0.69) equality (  1.1) family security (  1.27) pleasure (  1.39)

inner harmony (1.85) forgiveness (1.68) an exciting life (1.40) a world of beauty (1.17) mature love (1.15) self-respect (0.60) a world at peace (0.13) national security (0.06) accomplishment (  0.05) wisdom (  0.19) equality (  0.20) a comfortable life (  0.64) happiness (  0.68) true friendship (  0.68) pleasure (  1.04) freedom (  1.15) social recognition (  1.18) family security (  2.22)

a world at peace (1.93) pleasure (0.90) accomplishment (0.65) inner harmony (0.57) happiness (0.56) equality (0.47) mature love (0.40) freedom (0.31) family security (0.27) national security (0.01) true friendship (  0.03) social recognition (  0.15) wisdom (  0.26) a comfortable life (  0.45) an exciting life (  0.63) a world of beauty (  0.80) forgiveness (  1.75) self-respect (  2.00)

Overall rank

Segment D (17%) ‘‘security for all’’

Segment E (25%) ‘‘equality’’

Segment F (10%) ‘‘family focus’’

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

family security (0.86) mature love (0.79) a world at peace (0.68) national security (0.65) a comfortable life (0.49) freedom (0.30) pleasure (0.23) wisdom (0.20) an exciting life (0.15) self-respect (0.10) forgiveness (0.04) accomplishment (  0.01) true friendship (  0.07) social recognition (  0.48) equality (  0.57) a world of beauty (  0.87) inner harmony (  0.98) happiness (  1.52)

equality (2.98) a world of beauty (1.48) happiness (1.29) family security (1.22) self-respect (0.74) pleasure (0.69) national security (0.60) social recognition (0.53) forgiveness (0.43) freedom (0.43) a comfortable life (  0.11) wisdom (  0.55) inner harmony (  0.86) mature love (  0.88) true friendship (  1.07) an exciting life (  1.60) accomplishment (  2.56) a world at peace (  2.75)

accomplishment (1.95) family security (1.15) happiness (0.88) an exciting life (0.83) pleasure (0.60) social recognition (0.44) a comfortable life (0.32) inner harmony (0.10) freedom (0.08) forgiveness (  0.11) self-respect (  0.40) a world of beauty (  0.46) a world at peace (  0.47) true friendship (  0.49) wisdom (  0.58) national security (  0.69) mature love (  1.57) equality (  1.58)

a

Average percentage across three time points.

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4.3.2. Profiling the segments Table 5 reports the value priorities of each of the six value segments (double centered utility estimates, following Kamakura & Mazzon, 1991), and Table 6 reports their socioeconomic profile. To facilitate the interpretation, Fig. 1 presents the relative importance of values per segment in bar charts. Inspection of the value profiles (Table 5 and Fig. 1) shows that some segments have an outspoken value structure dominated by one or two values, notably segments A (true friendship) and E (equality), while others are more homogenous in their value structure. Below, each of the segments is described. Segment A (‘‘true friendship’’) comprises 17% of the sample and it places a high value on true friendship in particular. Mature love, self-respect, and social recognition are valued as well. Low values are found for pleasure, equality, accomplishment, and family security. This segment values personal social relationships much more than personal or larger-scale welfare. It also has the lowest average income (Table 6) and has a lower than average level of university training.

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Segment B (‘‘personal growth’’) is the smallest of all segments (9%), and places high value on inner harmony, forgiveness, exciting life, and world of beauty as well as mature love. Values such as family security and social recognition receive low utility. These consumers are involved in developing their own individuality and seek personal growth. This segment has the lowest age (together with segment E), the smallest family size, is predominantly male (77%), and has higher then average employment status (75%). Segment C (22%: ‘‘striking a balance’’) values in particular a world at peace, pleasure, inner harmony, and equality, while self-respect and forgiveness are very unimportant to them. It balances prosocial and enjoyment motivations, and tries to combine innerand outer-directed values. This segment has the highest age (together with segment F), containing more female consumers, with lower education than average. Segment D (17%: ‘‘security for all’’) values in particular family security, world at peace, national security, while happiness is very unimportant to them. This segment is the least outspoken in its value

Table 6 Sociodemographic profiles of segments

Age Annual gross income of the respondent’s household, Dutch guilders Household size Gender: proportion of females Employment: proportion of employed persons Education: proportion of persons with at least college education Municipality size: proportion living in the city with more than 50 000 inhabitants a

Segment A

Segment B

Segment C

Segment D

Segment E

Segment F

Total sample

47.97 49 434* * ,a

41.85 * ,a 64 534

51.82* * ,a 58 948

45.23 66 095

41.25* * ,a 65 767* * ,a

52.14* * ,a 50 722* * ,a

46.48 59 947

2.82 0.58

2.62 * ,a 0.23* * ,b

2.94 0.59 * ,b

2.36* * ,a 0.60 * ,b

3.36* * ,a 0.47

3.11 0.53

2.93 0.52

0.50

0.75* * ,b

0.46 * ,b

0.50

0.63* * ,b

0.34* * ,b

0.53

0.18 * ,b

0.33

0.17* * ,b

0.54* * ,b

0.22

0.14* * ,b

0.26

0.44

0.52

0.45

0.55* * ,b

0.42

0.26* * ,b

0.45

Based on t value. Based on Z approximation. * Differences from the sample average significant at p < 0.05. ** Differences from the sample average significant at p < 0.01. b

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Fig. 1. Value systems by segment.

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Fig. 1 (continued).

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Fig. 1 (continued).

K. Brangule-Vlagsma et al. / Intern. J. of Research in Marketing 19 (2002) 267–285 Table 7 Marginal segment sizes of model 5: time-homogeneous latent Markov model

Segment Segment Segment Segment Segment Segment

A B C D E F

Time 1

Time 2

Time 3

Stable parta

0.18 0.07 0.23 0.18 0.23 0.11

0.17 0.09 0.22 0.17 0.25 0.10

0.16 0.11 0.20 0.15 0.28 0.10

0.21 0.06 0.24 0.17 0.20 0.12

a The proportion of the sample that remains completely stable in value segment across all time periods was computed from the marginal segment sized at the first time period using corresponding transition probabilities from Table 8. In total, 0.82 of the sample remain completely stable in value segment.

structure, with many values being similar in importance. The security domain mostly motivates this segment. It has the smallest household size, a higher than average proportion of females (60%), the highest proportion of university graduates (54%), and the highest proportion of city dwellers (55%). Segment E (‘‘equality’’) is the largest segment (25%). It values equality most and predominantly. Accomplishment and world at peace are least important. Although dominated by the equality value, it is still the most enjoyment-oriented segment. It is the lowest in age (41.25 on average), has the highest average income, the largest family size (3.36 on average), and is more than average employed. Finally, segment F (10%: ‘‘family focus’’) appreciates values as a sense of accomplishment, family security, and happiness. Mature love, equality, national security, and world of beauty are unimportant for members of this segment. This is the oldest segment (52 years on average), with the lowest labor participation (34%), the lowest income (because of retirees), and most often living in smaller communities (only 26% lives in cities larger than 50 000 inhabitants). Clearly, some of the segments are more innerdirected, with a focus on the self or the relationship of the self with others, notably segments A (‘‘true friendship’’), B (‘‘personal growth’’) and F (‘‘family focus’’). On the other hand, segments E (‘‘equality’’) and D (‘‘security for all’’) are more altruistic, with a focus on others or the world at large. Segment C (‘‘striking a balance’’) is in-between, valuing a world at peace and pleasure, as well as inner harmony and equality.

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4.3.3. Switching between segments The marginal segment sizes at each measurement time are reported in Table 7. Table 8 contains switching probabilities for each segment. Probabilities to stay in the same segment are in the diagonal of Table 8. Note that a high 82% of the consumers are in the same segment in all three measurements. This indicates a substantial overall stability in segment membership and in the underlying value system, which is reassuring. Since the three value measurements were in three consecutive years (1993 –1995) and a stable value system was found (model 5 performed better than model 1), a large amount of instability would cast doubt on the value measurements per se. This is the first evidence of value system stability across time. Second, 18% of the consumers change segments, which illustrates value system dynamics as well. Clearly segments A, C, and F are the most stable, whereas segments B and E are the least. The size of segments B (‘‘personal growth’’) and E (‘‘family focus’’) increases over time, whereas the size of segments A (‘‘true friendship’’), C (‘‘striking a balance’’), and D (‘‘security for all’’) decreases. The transition between segments D (‘‘security for all’’) and E (‘‘equality’’) is more or less symmetric. Hence, it is almost as likely to move from D to E as from E to D, illustrating how consumers may switch between a more prosocial and a more security focus. In contrast, the transition between segments B and E is most likely in the direction from segment B (‘‘personal growth’’) to E (‘‘family focus’’). Specific Table 8 Switching probabilities From

To Segment Segment Segment Segment Segment Segment A B C D E F

Segment A Segment B Segment C Segment D Segment E Segment F

0.97

0.01

0.00

0.02

0.00

0.00

0.01

0.85

0.00

0.02

0.10

0.01

0.00

0.04

0.93

0.03

0.00

0.00

0.00

0.00

0.02

0.89

0.09

0.00

0.01

0.00

0.00

0.14

0.85

0.00

0.01

0.03

0.01

0.00

0.00

0.95

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other segment transitions can be observed in Tables 7 and 8.

5. Conclusions We proposed a framework for dynamic value segmentation and offered a first empirical illustration of its application. We proposed two major forces that may impact on value systems, and which may influence segmentation results based on value systems: exogenous and endogenous forces. We argue that value systems in society are relatively stable, when no major exogenous events occur, but that individuals may have unstable value systems due to endogenous forces such as aging, having a baby, changing jobs, and so forth. In such a case, a model should fit well that assumes stable value systems, yet unstable (but not erratic) consumers, that is, consumers who move between segments in a structured manner. The empirical illustration, which used a nationally representative sample of Dutch consumers across 3 years, supported this. To our knowledge, ours is the first study in which value systems are examined over time, and in which dynamic value segmentation is applied. The modeling approach we propose allows identification of the extent and the nature of change in the value systems of consumers in a robust and parsimonious way. Nevertheless, as the empirical illustration demonstrates, this approach gives substantial insights into the dynamics of value change. More importantly, the modeling approach can be easily extended to accommodate a variety of specific situations. For instance, if the number of segments is no longer restricted to be the same across time, it is possible to model the emergence or disappearance of entire value segments (‘‘hippies’’). This could be attractive in situations when new subcultures are surfacing as a result of social change, or else, if the model is fitted to panel data with new respondents entering or some respondents leaving the panel. However, such a model with emerging and vanishing segments seriously complicates model selection and is likely to be of limited use in the short time span of our empirical study. Similarly, by adding more restrictions, for instance, on transition probabilities, it is possible to analyze more specific relationships between different segments and value systems of these segments.

The results of the empirical illustration demonstrate that value systems of consumers change even within a 3-year period. Although 82% of consumers were stable in a single segment, there were clear differences in stability across segments as well. Members of the two segments B and E were significantly more likely to switch segments than the rest. Interestingly, these two segments were also the youngest. Perhaps these segments were still in the value formation life stages than the other segments, as a consequence of which they changed more explicitly over time, a conjecture which future research may test. Transitions between segments D and E were symmetric to some extent, while transitions between segments B and E were not (the B to E switch as most likely). Notably, the transition between segments D and E is between two outward or collectively oriented value systems, whereas the transition between B and E is from an inward or individually oriented value system to an outward or collectively oriented value system. This may explain the difference in the symmetry of the transitions. Given the significant difference in household size between segment B (2.6) and E (3.4), we reason that childbirth may have had an effect on this value change from a personal growth to a family focus value orientation. However, in the present study, the underlying mechanisms and reasons of this change pattern could not be extensively addressed. Future research may actually condition the segment transition probabilities of consumers on endogenous forces, such as life-cycle changes. This research may lead to new insights not only in value systems of consumers, but also in the role that values play in anticipation of, and as a consequence of, lifecycle changes. For instance, having children is a major life cycle event, which may be preceded by anticipatory value changes (e.g., increase in the importance of family security), as well as by responsive value changes (e.g., increase in the importance of achievement). To the extent that specific values are more adaptive (behaviorally and mentally) for major life-cycle changes, the consumers’ successful transition through the life cycle may depend at least partly on the successful transition through the segments of the overall value system. One of our main findings is that value systems of consumers change substantially even in a period as

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short as 3 years. This instability is most likely not as a consequence of measurement error since measurement error in the value judgements was accommodated in our model, being based on a stochastic representation of value systems that allows for random variation in rankings. Furthermore, not all consumers exhibit the same extent of change in the importance of their values. These variations in consumer’s value importance are captured by transition probabilities between segments, and range between values close to 0 and 0.14. The main diagonal of the transition matrix measures endurance of the value system of the corresponding segment and takes values between 0.85 and 0.97. Thus, some value segments are more stable than others are. Our study demonstrates that accounting for change in the consumer value system is possible and important. More research, however, is needed to understand the forces powering value change. A main attraction of value-based segmentation is the assumed endurance of value systems of respondents. For that reason, segmentation results can be used as input for long-term strategies or else reapplied across time when new strategies regarding segmented markets have to be developed. Our results are a first proof that, although a reasonable amount of segment switching was observed over a period as short as 3 years, the overall value system was still quite stable. On the other hand, instead of relegating the instability to measurement error, our modeling approach allows detailed inspections of how and why consumers switched from one segment to the other, which should help managers to fine-tune their marketing activities to those consumers. In that respect, dynamic value segmentation holds the promise of being the best of both worlds, panta rei and hen ta panta. That is, while the overall value system in society may often be very stable, only slowly moving over time, individual consumers may shift between latent value systems. Further extensions of the proposed modeling framework and available data may show more detailed dynamics of consumers’ value systems.

Acknowledgements The authors are grateful to Gerard Bartels of the Ministry of the Environment for allowing us to use the

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RVS data, and NIPO Amsterdam for collecting them. The authors also thank the two reviewers and Wagner A. Kamakura for their insightful comments.

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