Psychological Indicators of Innovation Adoption: Cross-Classification Based on Need for Cognition and Need for Change

JOURNAL OF CONSUMER PSYCHOLOGY, 12(1), 1-13 Copyright Q 2002, Lawrence Erlbaum Associates, Inc.

Psychological Indicators of Innovation Adoption: Cross-Classification Based on Need for Cognition and Need for Change Stacy L. Wood Moore School of Business University of South Carolina

Joffke Swait Department of Marketing University of Florida and Advanis Inc.

Predicting how and when consumers will switch from a current familiar brand to a new option is a matter of concern for every level of new product introduction-from brand extensions to "really new" discontinuousinnovations.In this article, we build on the innovativeness literatureby investigatingthe degree to which 2 consumer characteristics, the need for cognition (N), and the need for change (Nchaoge), help explain individuals' propensity to choose new innovations versus status quo options. We demonstratethat by separatingN,, andNcbgeandcross-classifying individuals based on these attributes, 4 unique patterns of change behavior emerge. A largescale choice study was conducted by surveying metropolitan residents about changes in telecommunication services (local, long distance, and cellular). We use a latent class model to uncover the segmentation structure in the choice data, using the constructs as concommitant variables in the segment classification portion of the econometric model. The results show that the predicted theoretical structure explains observed data and can be used to significantlyincrease the predictive power of models of change behavior.

Seldom will a £innencounter the situation of providing for an entirely new human need through its product or service. In fact, it can be argued that nearly all innovations face competition from substitutes that are either in the same or other product categories. For example, introducing a telephone with video and voice capabilities in the market suggests the following existing substitutes, among others: voice-only telephone, Internet-based video and voice transmission, and camcorder plus voice cassette. Although one of these options (Internet-based video and voice transmission) can be loosely included in the same product category, the others cannot. This is also true of new brands in existing product categories. Consider, for instance, the introduction of a new brand of shelf-stable concentrate orange juice. Not only will the new brand compete with other shelf-stable and fiozen orange juice concentrates, but also with the concenRequests for reprints should be sent to Stacy L. Wood, Moore School of Business, University of South Carolina, Columbia, SC 29208. E-mail: [email protected]

trates of other h i t juices, fiesh pasteurized juices, and fresh squeezed juices. Thus, firms usually face situations in which new product success depends on displacing incumbent means of satisfying the same basic need. Predicting when and how consumers change fiom a status quo alternative to a new option is, therefore, a matter ofmuch marketing concern for every level of innovation-fiom brand extensions to "really new" products.

THE CONSUMER INNOVATIVENESS CONSTRUCT In an attempt to predict which consumers are likely to adopt new products or services, consumer behavior researchers have endeavored to define and identify "innovativeness" as a personality construct (e.g., Hirschman, 1980; Manning, Bearden, & Madden, 1995; Midgley & Dowling, 1978; Venkatraman & Price, 1990). Technologically savvy markets encourage an ever-increasing rate of product evolution,

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WOOD AND SWAIT

which in turn demands improved ability to predict consurners' innovativetendencies across a diverse range ofbehaviors. This serves to focus attentionon the well-developed literature on "innovativeness;" however, general innovativeness scales have been inconsistent in their ability to predict change behavior (Steenkamp & Baumgartner, 1992). Although some research has focused on a domain-specific conceptualization of innovativeness (e.g., technological innovators may not be fashion innovators; Goldsmith & Hofacker, 1991), a long history of efforts has identified several components of general consumer innovativeness, including optimal stimulation level, variety-seeking, novelty-seeking, exploratory tendencies, information-seeking, and cognition (Goldsmith, 1983; Hirschman, 1980; Raju, 1980; Steenkamp & Baumgartner, 1992; Venkatraman & Price, 1990). Most studies describe the innovative consumer as a dynamic, curious, communicative, stimulation-seeking, venturesome, and cogmtive individual (e.g., Goldsmith, 1983; Raju, 1980). Thus, innovators have previously been predicted to be both comfortable with novelty or stimulation and prone to cognition. In other words, thinkers are always changers. This assumption may arise because cognition is a form of mental stimulation or because novelty often requires cognitive efforts (Cacioppo, Petty, Feinstein, & Jarvis, 1996). However, both introspection and recent innovativeness research suggest that it is unlikely that highly cognitive individuals (such as college professors) are never creatures of habit, wary of changes in their environment. Therefore, similar to Venkatraman and Price (1990), we separate elements of novelty-seeking and cognition. Furthermore, we propose that both elements influence an individual's general innovative tendency, but are not always positively correlated at an individual level. We distinguish between an individual's need for cognition and his or her need for change, then use these two constructs to cross-classify innovative behavior. In t h s research, we use evidence from a latent class segmentation model to support this modified conceptualization of consumer innovativeness. We then show how this revised innovativeness construct can be used to significantly increase the ability of choice models to estimate demand for new products.

THE USE OF LATENT CLASS ANALYSIS Latent structure models have been demonstrated, yet infrequently used, in the consumer behavior literature (e.g., Dillon, Madden, & Mulani, 1983; Gupta & Chintagunta, 1994; Rosbergen, Pieters, & Wedel, 1997),despite the proven ability of thls methodology for the classification of individuals based on behavioral or attitudinal similarities.The historical development of latent structure analysis evolved from the need to classify people based on similar personality characteristics and, more basically, to support the existence of theoretical personality constructs (Lazarsfeld & Henry, 1968).

Personality characteristics are, by their very nature, nebulous and difficult to define. Scale developers labor to create items that have both ecological validity and internal reliability. Therefore, in our efforts to define constructs, we ofien identify traits by typical behaviors. For example, cautious individuals save money for a rainy day or skeptical individuals read Consumer Reports. Latent class models identify segments in a population based on similarbehavior (or more generally, response) patterns. In this study, we use a large-scale conjoint choice task to provide behavioral data. In addition, measures of both Need for Cognition PC,,; Cacioppo & Petty, 1982) and Need for Change (Ncbg,; developed in this research) were collected and used to drive the identification of the segments through latent class analysis. These constructs are discussed in more detail in the following section. The estimation of the demand for a product is a recognized step in marketing existing and new products (Urban & Hauser, 1980). Experimental choice analysis (e.g., Louviere & Woodworth, 1983) is a fiequently used technique to estimate demand and conduct competitive analyses useful for planning new product launches. This technique involves presenting respondentswith one or more experimentally designedproduct profiles and recording their preferred alternative(s).Although choice models may predict consumer preferences for new product attributes or dimensions and, thus, predict demand, this estimate will not commonly take into account the general tendencies of consumers to change from a status quo to a new alternative (see Neelamegharn & Jain, 1999, for a marketing example ofhowpsychologicalvariables are included in choice models). We propose that the inclusion of change propensity behavior data will improve the accuracy of demand estimates and enhance marketers' understanding of the psychological basis for individual innovation adoption. Our results suggest that both the theoretical and measurement frameworks fit the observed data pattern well and can be used to significantly increase the predictive power of models of change behavior. In addition to hnding the predicted segments, we are also able to produce a parsimonious 11-item measurement scale of Ncogand Nchangethat can be used in other applications. Our contribution, then, is two-fold. First, we continue work in the relevant and theoretically fertile field of individual consumer innovation. Specifically,we use recent research in innovativeness to support the conceptual separationofNcog and Nchgecharacteristics and hypothesize four unique segments of change behavior in the consumer population. We test our hypotheses through econometric methods that have the power to falsify our predictions with empirical evidence. Second, this proposed b e w o r k is subsequently used to increase the predictivepower ofpreference models based on experimentally elicited choices. Such improvement is significant not just fiom the practical perspective of improved demand estimation, but also because the econometric model specification follows from substantive theoretical research in the consumer behavior tradition. In essence, this work repre-

PSYCHOLOGICALINDICATORS OF INNOVATION ADOPTION

sents an interdisciplinary effort between marketing science and consumer behavior researchers, such as called for by Winer (1999), who strongly encouraged marketing academics to engage in such partnerships to improve on external validity issues in theoretical consumer research. In the following section, we motivate our hypotheses and review the relevant literature. We then present the measurement and modeling methods as well as describe data collection procedures. Modeling results are then presented and discussed, whereon the article concludes with a synthesis of the research findings and discussion of their implications for hture research and managerial decision making.

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tion. Venkatraman, Marlino, Kardes, and Sklar (1990) found that consumers with high Ncogpreferred factual ads, whereas those with low NCogpreferred evaluative ads. Haughtvedt, Petty, and Cacioppo (1992) found that consumer attitudes toward a new product were predicted by cognitive responses for high Ncogparticipants, but not for low Ncogparticipants. Although trying a new product provides arousal and stimulation, it also requires cognitive activities to assess the benefits and costs of foregoing currently used alternatives and to learn new attributes or processes. Thus, the manner in which people change may be affected by their propensity toward engaging in thought.

HYPOTHESES DEVELOPMENT Nchange: The Need for Change The starting point for this work is that an individual's preference for a status quo alternative will be strongly affected, ceteris paribus, by two personal characteristics: (a) his or her propensity to think about trade-offs offered, and (b) his or her propensity to seek change for its own sake. Both of these characteristics have their roots in the psychological literature and have been shown to impact various attitudes and aspects of behavior. We introduce the idea that these characteristics need not be strongly related, although at first sight they might seem to be. In fact, it is the interactionbetween them that generates four consumer types that we predict will behave in a systematic and predictable fashion regarding choice between new and status-quo alternatives.

No,: The Need for Cognition Ncogis a well-established and oft-used individual difference construct investigated in over 100 empirical studies (Cacioppo et al., 1996). It is defined as the tendency for individuals to engage in and enjoy thinking per se (Cacioppo & Petty, 1982). Ncogis not conceptualized as a level of intellectual ability, but rather as a relative proclivity to process information (Cacioppo et al., 1996). It is easy to see how an individual's propensity for cognition might influence a wide range of behaviors. It has been found to impact use of heuristic versus systematic processing (Chalken, 1987), procrastination (Ferrari, 1992), source credibility effects (Priester & Petty, 1995), and effects of argument quality versus quantity (Haughtvedt & Petty, 1992). In addition to its frequent use in psychological domains, Ncoghas been applied in several consumer behavior studies. For example, Inman, McAlister, and Hoyer (1990) found that low NCogparticipants reacted to the mere presence of a promotional signal whether the price of the promoted product was actually reduced, whereas high Ncogparticipants only reacted if the signal was accompanied by a price cut. Batra and Stayman (1990) found that Ncogmoderates the influence of positive mood on advertising message evalua-

We define the Nchgeconstruct as the extent to which people view novelty and innovation as intrinsically valuable. It can also be described as a consumer's "comfort level" with change and all the risks and rewards associated with innovative behavior. Although there are a number of scales similar to our Nchg, construct, no single scale focuses solely on our basic definition. Thus, we turned to the broad literature encompassing innovativeness and related concepts, such as sensation-seeking and novelty-seeking behavior, to borrow those elements that most directly capture a consumer's basic propensity to change. Many of these scales draw heavily from the Optimal Stimulation Level (OSL) literature (Steenkamp & Baumgartner, 1992; Wahlers, Dunn, & Etzel, 1986; Wahlers & Etzel, 1990). OSL is the degree of sensory stimulation with which an individual is comfortable. If the environment does not provide sufficient stimulation, an individual will pursue behaviors or choices that enhance stimulation. If stimulation levels are too high, an individual will act to reduce them. Such attempts to manipulate stimulation levels are commonly observed as actions that depict seeking or avoidance of novelty or change (Zuckerman, 1979). Individual differences in OSLs have also been used to explain "inexplicable" switching behavior known as variety-seelung (McAlister & Pessemier, 1982). One operationalizationof OSL that touches directly on innovation is the Arousal Seeking Tendency (AST) scale (Mehrabian & Russell, 1974), measuring the degree to which people actively seek or avoid arousal due to novel, complex, or unpredictable settings. The AST measure is broadly conceived and consists of five different subfactors: arousal from (a) change, (b) unusual stimuli, (c) sensuality, (d) risk, and (e) new environments. In predicting switching or innovative behavior, some subfactors (namely,a, b, & e) are obviously more relevant than others. Similarly, the Sensation Seeking Scale (SSS; Zuckerman, 1979)measures the need for varied, novel, and complex sensations. Both of these measures provide the framework from which many scales of innovativeness originate. One such scale,the Exploratory Tendencies in Consumer

4

WOOD AND SWAlT

NEED FOR CHANGE

Behavior scale (Raju, 1980), specifically examines factors that underlie exploratorytendencies, including the following:

1. 2. 3. 4. 5. 6.

Repetitive behavior proneness. Innovativeness. Risk taking. Exploration through shopping. Interpersonal communication. Brand switching. 7. Information seelung.

The items measuring repetitive behavior proneness and innovativeness were more influential in developing the Nchange scale than were the shopping, communication, and information-seeking factors, many of which seem to correspond with a broader conceptualization of Nmg.

High

Low

TC segment

~Fsegment

Fc segment

TC segment

NEED FOR COGNITION Low

FIGURE 1 Four classifications of innovative behavior based on

Need for Cognition and Need for Change.

The Separation of Ncog and Nchange: A Basis for Segmentation of Change Behavior Initially, it may seem intuitively appealing that Ncog and Nchange are positively correlated. Individuals high in their need for cognition may also report higher OSLs because cognitive activity can in itself be stimulating. However, as we have defined them here, high Ncogdoes not necessarily indicate high Nchge. Goldsmith (1983) looked at psychograplics that correlate with new product adoption and found that several personality traits, including "venturesomeness," are strongly related to adoption. He also found that some early adopters, although not a significant majority, reported that they "seek more information before purchase than others." Thus, not all "change-seekers" are "thinkers." We wish to further substantiate the discriminant validity between these two constructs. Our contentionis that it becomes possible to predict change or innovativechoice behavior when cognition and stimulation are regarded as unique constructs. The separationof these two constructs has been proposed in the research on the Cognitive and Sensory Innovativeness Scale (Venkatraman & Price, 1990),which suggests that people might be cognitiveinnovators (e.g., enjoy solving new problems) or sensory innovators (e.g., enjoy stimulating new activities like skydiving). We buildon this idea by proposing thatnot only are there two types of innovativeness, but in addition, that all individuals have a basic propensity for both cognitiveactivityand sensory stimulation specifically fiom change. And furthermore, we posit that the considerationofboth traits may predict amore general innovative tendency for important change behaviors (such as information search or attribute weighting). As shown in Figure 1, we hypothesize the existence of four different types of change behavior groups, or segments: (a) TC-Thinkers and Changers, (b) lf-Thinkers But Not Changers, (c) T c - ~ h a n ~ e r s But Not Thinkers, and (d)

--

TC-Neither Thinkers Nor Changers. We conjecture that the following innovating behavior would be characteristic of each segment.

TC-Thinkers and Changers. Thinkers and Changers are those individuals who have a high propensity for both thought and innovation. Thus, they should score higher than average on measures of both Nmgand New. We predict that these individuals' comfort level with change is high, but only ifit is chosen in a controlledand rational manner. When considering a new product or product bundle, these individuals' choice behavior should be driven by a larger than average number of attributes because they are not reluctant to expend cognitive processing resources. Focal attributes should include those that are innovativeor substantiallydifferent h m current alternativesbecause Thinkersand Changers are interested in novelty. In addition, it may be that these individualshave a higher likelihood of change, but only in those situations that allow for thoughtfill consideration. For example, these individuals may refuse to switch long distance carriers when importuned in a telephone appeal, but may be open to an appeal by letter because the letter format allows for a clear layout of attributes and greater consideration time. %-~hinkers But Not Changers. This group consists of individuals who enjoy cognitive activity, yet appreciate the comfort of routine and habit. These individuals are those who score high on Ncog,but low on Nchange. They should not switch impulsively and should tend toward remaining with the status quo alternative more than the Thinkers and Changers group. Because they are thinkers, they are more likely to have analyzed the benefits of staying with their current product or service and may change only if offered substantive benefits (e.g., financial savings or new services). In

PSYCHOLOGICAL INDICATORS OF INNOVATION ADOPTION

considering a change, they should be more likely to consider a large number of attributes, but those attributes considered are more likely to be those with which they are familiar. Like other Thinkers, this segmentmay react more positively to new product appeals that allow time for thought (e.g., letter vs. phone appeal). However, unlike Thinkers & Changers, additional persuasive appeal may be garnered by reducing the perception of novelty or innovation.

Tc--Changers But Not Thinkers. These are individuals who thrive on the stimulation achieved through novelty, but avoid cognitive effort when possible. Thus, they score low on Ncog,but high on Nchange.These consumers are likely to be impulsive switchers. Changers But Not Thinkers should consider fewer attributes than Thinkers and should focus on novel attributes. They are likely to be attracted by the "buzzwords" of the product or service domain (e.g., digital cellular phones in the telecommunication industry) or obvious short-term rewards and savings. Unlike Thinkers, these individuals may respond more positively to telephone versus letter appeals because the telephone offer requires less cognitive effort to process (someone is on the line to explain "key" attributes) and the offer can be accepted and implemented immediately with a minimum of effort.

z- either Thinkers Nor Changers. These individuals are not prone to thought or change, and so are the least likely segment to innovate. This group consists of those who score low on both Ncogand N c m emeasures. Thus, h s group is likely to consider a few familiarattributes, or even no attributes at all, if change is not consideredbecause of its perceived negative effect on mental effort and stimulation levels. These hypothesized segment profiles essentially link individual differences to behavioral outcomes. Hence, a combination of preference elicitation method and appropriate model formulation will enable testing the existence of these segments and their hypothesized profiles. We detail how this study is conducted in the following section. RESEARCH METHODS Initial Scale Development Measures of NcOg and Ncbge were initially pretested on undergraduate participants at a large U.S. state school. The short form of the Ncog scale (Cacioppo & Petty, 1982) was tested, along with a collection of items from OSL-related scales, such as the Innovativeness Factors scale (Craig & Ginter, 1975), the SSS (Zuckerman 1979), the Exploratory Tendencies in Consumer Behavior scale (Raju 1980), and the Use Innovativeness scale (Price & Ridgeway, 1983). Factor analysis supported a two-factor solution of cognitive and

5

stimulation components. Fifty-four items were selected for inclusion in the survey. Due to space limitations, we do not present the specific pretest scale development results here.

Survey Design Current events in the telecommunications industry in a major metropolitan area provided an ideal environment for testing a consumer decisionto stay with status quo serviceproviders or to choose new service providers. In the future, any telecommunications company will be able to offer any telecommunications service, such as local, long-distance, and wireless service. For example, current local companies (e.g., Regional Bell Operating Companies) will be permitted to offer long-distance services, and current long-distance companies (e.g., AT&T, MCI) will be able to sell local services. This deregulated environment motivatedus to send surveys concerning possible telecommunications service changes to randomly selected households in this metropolitan area. Respondents were asked to consider five different providers oflong-distance,local,andcellular telephone service. Two of these providers were local service providers at the time of the study (each serving separate areas of the metropolitan region studied), two were nationally known long-distance providers, and one was mainly a cellular serviceprovider in the locale studied. The task given to respondents was to design the telecommunicationsbundle (local, long-distance, and cellular services) for their residences. This task is familiar to the vast majority of North Americans due to near universal telephone availability and the high mobility of the population: on each move, consumers must select providers for long-distance and cellular services (if desired). To date, selection of local provider has not been an option, but it is relatively well-known among the public that competition in this service will occur in the near future. Despite its familiarity, the selection of telecommunications provider(s) is sufficiently involving and complex that we anticipated differentiation among respondents along the hypothesized dimensions Ncogand Ncimnge. In addition, this domain offered both novel and familiar attributes within the same choice environment. Familiar services included local and long-distance service (and included fees) and novel services included cellular service and fees.' Also, providers might be familiar for one service (e.g., MCI offering long-distance service) and novel for another service (e.g., MCI offering local service). In any given scenario, services not currently offered by a provider might be available for consideration by the decision maker. For instance, a consumer might find his or her current local provider,blocked by legislation from doing so at the time of the survey, offering long-distance services,or alternatively, 'At the time ofthis research, cellularphones hadnot achieved their current level of market penetration and could be considered a relatively innovative service.

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WOOD AND SWAIT

TABLE 1

Experimental Design of Stimuli for Service Availability by Provider Service Availability Provider

Local

Bell South sprint AT&T MCI GTE

Long-Distance

Presence Presence or absence Presence or absence Presence or absence Presence or absence

Presence or absence Presence Presence Presence Presence or absence

find his or her long-distance provider now offering a competitive local service. Table 1 shows how service availability was varied for each provider: "Presence or absence" indicates that the availability of these specific providers varied across conjoint choice tasks, as defined by an experimental design; "Presence"a1one indicates the service is always offeredby the provider in all choice scenarios shown to respondents. (Such designs are called "availability designs;" see Lazari & Anderson, 1994.) If none of the offers fiom competitors seemed attractive to the respondent, he or she could simply indicate that they would stay with their currentprovidersineachcategory. The following 10 service attributes were systematically varied for each provider and service combination: 1. Local service flat rate: $10, $12, $14, and $16. 2. Long-distance service fee: $0.10 per rnin flat, $0.15 per rnin flat, $0.20 per rnin flat, $0.15 per min peak, $0.12 per rnin off-peak, $0.20 per rnin peak, and $0.16 per rnin off-peak. 3. Cellular monthly service charge: $20 and $40. 4. Cellular fiee min: 30 rnin and 60 min. 5. Cellular home air time charges: $0.45 per rnin and $0.65 per min. 6. Cellularroaming charges: same as home air time; $0.45 per rnin more than home air time. 7. Cellular "rounding" of charged air time: nearest min, no rounding (exact). 8. Cellular fiee off-peak min: weekends free, weekends not free. 9. Multiple service discount if two or more services chosen: 0%, 5%, lo%, and 20%. 10. Billing method: combined bill per provider; separate bills per service. The independent variation of these attributesby provider, and the nesting of the attributes within the presence or absence of services, make a full factorial design impractical. Accordingly, a random sample of 160 ofthe 4*x 5 x27 (= 10,240) possible conditions, blocked into 20 groups of eight scenarios? --

-

-

-

Cellular

Presence Presence or absence Presence Presence or absence Presence

was used in this study. Figure 2 shows an example choice set, eight of which were given to each respondent. Respondents were also asked to complete the N, and Nchange scales, as well as indicate their current providers for each service and certain attributes of their service providers. No incentives were offered to respondents, who were informed in the accompanying letter that the research was academic in nature. Respondents were not prerecruited. After the initial mailing of the survey, two postcard reminders were sent (the first sent approximately 10 days after mailing the survey, and the second 10 days after that). Respondents were able to ask questions about the survey via a toll-fiee number. Of the 2,880 surveys mailed out in the course of I month in two waves, 408 were completed and usable surveys, constituting a 14.7% response rate.

Econometric Model Formulation Suppose we have a population of consumers that belong to S unknown (latent) groups or segments. From the analyst's perspective, both the number of segments and an indwidual's membership in a class are unknown, hence need to be estimated. Each group is characterized by a different 1 x K taste vector ps,s = 1, ... ,S, corresponding to the K x 1 vector of attributes Xi, where i is a product. The attractiveness, or utility, of a product, conditional on the person being from class s, is given by,

where uil, is an error term. If we assume that the error terms are identically and independently distributed (ID) Gumbel within segment and across products, the within-segment choice probability is the familiar multinomial logit (MNL) model:

--

*Eachrespondent received eight different choice tasks, as a form of controlling the cognitive burden imposed on respondents. The 160 tasks were divided into 20 sets of eight tasks and respondents were randomly assigned to each block.

where Cis a set ofproducts among which choice is exercised.

PSYCHOMGICALINDICATORS OF INNOVATION ADOPTION

7

FIGURE 2 Example choice set.

We are unable to observe an individual's class when he or she is sampled. However, we know from theory, or a priori reasoning, that individual characteristics Z, enable us to predict class membership probabilistically. For reasons of tractability, we shall assume that this probabilistic assignment is accomplished through a polychotomous MNL model:

where Wsis the probability, an individual with characteristics Z, (e.g., his or her score on the Ncogand Nchgescales, or other demographics or attitudinal measures) is in segment s; 8 , s = 1, .. . , S, are parameter vectors that reflect the extent to which the characteristics affect the classification of the person into each segment. (Note that for identification purposes, one must set one of these parameter vectors to zero. We set 8 ~ 0 . ) On the basis of choice model (2) and classification model (3), the unconditional probability of observing an individual choose good i from set Cis simply calculated as

Returning our attention to this research, we define three quantities of interest in the classification mechanism: NcOg zn = Nch N C OX~Netz

-

(5)

As we explained previously, we expect both NcOgand N c h g to divide segment membership between these four groups: (a) But_Not TC-Thinkers and Changers, (b) *-Thinkers Changers, (c) Tc--Changers But Not Thinkers, and (d) TCNeither ThinkersNor Changers. In addition, it is possible that the interaction between these two individual characteristics will play an important role in the classification portion of the model. This will happen ifthe impact of one or the other construct on behavior is moderated by the other. Further elucidation of the basic model formulation can be found in the Appendix. In the following section, we have the following tasks before us: (a) establish how many segments seem to be present in the data (we predict four groups, previously discussed), and (b) confirm the predicted profiles (in terms of the predicted behaviors for each segments, as per prior discussion).

Data Analysis (4)

Final NW and scales. To facilitate model formulation and the future applicability of these scales, we

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WOOD AND SWAIT TABLE 2 Scale Reliability Analysis and Factor Analysis

a

Scale Items

Total Scale N,,, Subscale I would rather do something that requires little thought than something that is sure to challenge my thinking abilities. I try to anticipate and avoid situations where there is a likely chance I'll have to think in depth about something. I only think as hard as I have to. The idea of relying on thought to get my way to the top does not appeal to me. The notion of thinking abstractly is not appealing to me. Nchang,Subscale When I see a new or different brand on the shelf, I often pick it up just to see what it is like. I like introducing new brands and products to my friends. I enjoy taking chances in buying unfamiliar brands just to get some variety in my purchases. I often read the information on the packages of products just out of curiosity. I get bored with buying the same brands even if they are good. I shop around a lot for my clothes just to find out more about the latest styles.

Item to Subscale Correlation

Factor Loading: I *

Factor Loading: 2 *

.6464

.386

.778

,8571 ,8065

*Maximum Likelihood Extraction, Promax Rotation with Kaiser Normalization; coefficient = 0.5278.

TABLE 3 Fit Statistics for Five and Fewer Latent Classes S

Number of Parameters

LL

AIC

BIC

1 2 3 4 5

36 75 114 153 192

-7876.4 -7303.0 -7113.5 -7027.4 -7013.6

15824.8 14756.0 14455.0 14360.8 14411.2

15884.2 14879.7 14643.1 14613.2 14728.0

Note. LL = log likelihood at convergence; AIC =Akaike's Information Criterion; BIC = Bayes Information Criterion.

first reduced the 54-item battery to 11 items used in the h a l model estimation. An exploratory factor analysis of the short scales showed that, similar to the full battery, the two component solution still held (see Table 2). This solution was used to provide the elements of the Z vector (expression 5) for the classification model.

Choice model estimation results. The first task in developing a latent class model is to determine S, the number of segments or classes. For our study, the appropriate number of segments was assessed based on several criteria, due to the fact that any single goodness-of-fit index has limitations for these models. Several alternative measures have been suggested in the literature, but we make use of Akaike's Information Criterion (AIC) and the Bayes Information Criterion (BIC), both based on the log likelihood at convergence (LL), as the basis for selection of the number

of segments.3 The model with the smallest AIC, BIC, or both, is selected. We estimated models for up to five latent segments. In Table 3 it can be seen that S = 4 latent segmentsproduce the minimum AIC and BIC relative to the number of parameters and the sample size. (The fact that the two measures agree, which they need not have, makes us more confident in our selection of the S= 4 solution.) On the basis of these results, we proceed with the 4-segment solution (see Table 4). Note that this result is consistent with our prediction. As a validity check of the entire latent class approach, we divided the sample into four groups based on the median values of NCogand Nchange(as per Figure l), and within each cell fit a MNL model. This approach yields a log likelihood of -7755.5 with 144 parameters, which is significantly worse than the 4-segment solution value of -7027.4 with 153 parameters. (The estimation results for the a priori segments are omitted here, but are available from the authors on request.) This result strongly supports the use of the latent class econometric formulation to describe individual consumer differences, emphasizing the efficacy of the clas3 ~ = [-2 ~ (LLs+Ks)] C and BIC = [-2LLs+ Ks In(N)]as, where L b is the log likelihood at convergence and Ks is the number of free pameters, for a model with S latent segments. N is thenumber of observations. The principle of the Akaike's Information Criterion(AIC) is to optimizethe log likelihood ratio statisticdefined with respect to the null hypothesis of all parameters being equal to zero. Parsimony is also a factor in the AIC: A model with additional latent segments is penalized for the inclusion of extra parameters to be estimated. The Bayes Information Criterion (BIC) is similarly defined, but considers both sample size and the number of parameters.

PSYCHOLOGICAL INDICATORS OF INNOVATION ADOPTION

~ificationportion of the model (expression 3) and of the specific m e of the Ncog and Nchange constructs in the classification model (expression 5). Each segment was examined to determine if the members' scores on the Ncogand Nebgescales differed significantly and

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in the direction predicted by the Ncogand Nchge framework. The classificationfunction coefficients permit interpretation of the drivers of classification into the segments. Increases in Ncoglead to decreases in membership probabilities in segments 1 and 3, hence membership in segments 2 and 4 is more

TABLE 4 4-Segment Latent Class Model Parameters Parameter Estimates

Predicted segment Segment sues Classification function

Segment I

--

TC 19.8%

Segment 2

Segment 3 TC 15%

TC 45%

Ncog

Ncimge Ncog X Nchange

Utility function Constants: Bellsouth (local) Sprint (local) AT&T (local) GTE (local) Bellsouth (long-distance) Sprint (long-distance) AT&T (long-distance) GTE (long-distance) Bellsouth (cellular) Sprint (cellular) AT&T (cellular) GTE (cellular) Nynex (cellular) Flat rate nonbundled (linear) Flat rate nonbundled (quadratic) Flat rate discount bundled (linear) Flat rate discount bundled (quadratic) Peak fee nonbundled (linear) Peak fee nonbundled (quadratic) Off peak fee (linear) Peak fee discount bundled (linear) Peak fee discount bundled (quadratic) Off peak discount bundled (linear) Cellular monthly charge nonbundled (linear) Cellular monthly charge discount bundled (linear) Cellular fkee minutes nonbundled (linear) Cellular home fee nonbundled (linear) Cellular home fee discount bundled (linear) Cellular roam fee nonbundled (linear) Cellular roam fee discount bundled (linear) Cellular rounding Cellular fkee weekends Consolidated billing Current local provider Current long distance provider Current cellular provider Summary statistics Log likelihood (random choice) Log likelihood at convergence Number parameters AIC rho-squared Note. AIC = Akaike's Information Criterion. *Ninety-five percent sigmficance level.

-7027.4 153 0.369

Segment 4

n: 20.2%

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WOOD AND SWAIT

attributes as their high Ncogcounterparts on average, and the type of attribute consjdered was very different than the low Ncbge groups. The TC segment considered more novel attributes (such as cellular services and new service providers) than familiar ones. The TC segment, those with low N, and low Nchge,considered few attributes and focused mainly on the familiar providers in the region, namely BellSouth, Sprint, and AT&T. Although their curre$ providers significantly influenced all segments, the TC segment showed the strongest tendency to stay with their statw quo (particularly for the long distance service). A sumrnaq of change behaviors evidenced by each segment can be seer in Figure 3. Thus, the type of attributes that drive choice be. havior in each segment generally support the profiles pre. dicted by the Ncog to Ncbge framework. Thus, in broad strokes, each group predicted by the theo, retical framework seems represented in the choice behavioi model. However, we would like to note that the classificatio~ mechanism is probabilistic, not deterministic. This mean! that there is a nonzero probability that an individual belong! to every one ofthe segments. Although the segments can gen erally be interpreted in the manner stated earlier, there is sig nificant "noise" in the classification mechanism that may bc the result of M e r classification variables that have not bee1 included in the specification.

llkely for individualswithhigher values ofNcop.Similarly,increases in Neb, lead to significantly higher probabilities of membership for segments 2 and 3. The interactionofNcogand Nchmge was a significantpredictor for each segment (see Table 4), indicating that one or both of these constructs have a strong moderating impact on the other. In segments 1 and 3, increases in Nth, increase the negative impact of increases in N,, (this is the result of the significant and negative interaction term); in segment 2, the interaction leads to a decrease in the impact ofNcogas Ncbgeincreases. Thus, it appe_arsthat latent segment 1 corresponds to predicted segment TC, latent segment 2 resembles the predicted segmznt TC, latent segment 3 resembles the predicted segment TC, and latent segment 4 resembles the predicted segment TC. The utility function parameter estimates provide evidence of the segment characteristics predicted by the N,,, to Nchmgeh e w o r k . The TC segment scored high both on N,,, and N c h g eand their choice behaviors supported the categorization. This segment considered many attributes, especially novel attributes. The TC segment evidenced choice behavior in accord with their high Ncogto low Nchge categorization. This segment considered a greater number of choice attributes than the average low Ncogsegments, yet focused on familiar attributes. The TC segment, those with low Ncogbut high Nchge, did not consider as many choice

Need for Change

High (Changers) TC segment

+ Many attributes considered High ( ~ h i n k ~ ~(29 ) significant attributes)

+ Emphasis on innovative

services (55% of total attributes were novel)

Need for Cognition

FC

segment

Low (Not Changers)

TCsegment + M,,, attributes considered

(14 significant attributes)

+ Emphasis on non-

innovative services (29%of total attributes were novel) segment

+ Fewer attributes considered + Fewer attributes considered LOW (Not =hinkers)

(1 3 significant attributes

(7 significant attributes)

+ Emphasis on innovative

+ Emphasis on non-

services (54% of total attributes were novel)

innovative services (14% of total attributes were novel) + Most influenced by current provider (status quo)

FIGURE 3 Summary of decision drivers for segments.

4

PSYCHOLOGICAL INDICATORS OF INNOVATION ADOPTION

CONCLUSION Four general consumer segments were predicted by the Ncog and Ncbgemeasures. Hypotheses of specific change behaviors such as the number of attributes considered, the types of attributes considered, and the importance of attributes, were developed in this h e w o r k . The Ncogto Ncbge framework is supportedby latent segmentation and product choice model analysis. In addition, consideration of these consumer types significantly increased the predictive power of demand and choice estimation. Thus, not only does the model reasonably validate the theoretical framework, but the theoretical h e work also improves the accuracy of the choice model. Further investigation of these results should be two-pronged. One n e c e s s q direction is to study the consumer behavior implications of the Ncogto Nchge framework via experimentation. The results of this research suggest that choice behavior can be influenced by the consumer's basic regard for thought and change. Not only are these individual difference variables important covariates to consider in studies concerning change or innovation, but also, choice behavior may be differentially influenced by the manipulation of consumers' ability to process information and consumers' perception of choice novelty. For example, although Ncogis treated as an individual difference construct in this research, it is also possible that it can be viewed as the opportunity for cognition, a construct that can be influenced by marketing strategies or environmental characteristics(e.g., Garbarino & Edell, 1997; Johar, Jedidi, & Jacoby, 1997). Marketers can easily influence both cognitive processing opportunities and perceptions of innovation through advertising medium and content. For example, when opportunity for cognitive processing increases (perhaps through the choice of marketing medium), the probability of innovation adoption may increase for some consumers and decrease for others based on their inherent need for cognition. Similarly, innovation "hype," as communicated in advertising content, may differentially affect consumers based on their regard for novelty versus routine. These hypotheses are easily testable in experimental research and have strong potential impact for marketing academics and practitioners alike. Future experimentation on the cross-classification of need for cognition and need for change should focus on future elucidation of the ?ksegment and the segment. In this study, the groups that were high in both Ncogand Nchge or low-in both Ncog and Ncbde were most clearly represented. The TC segment and the TC segment, although easily identified, were not so clearly discriminated. This suggests (along with the interaction ofNcogandNchange) that the disproportionatecombination of these two personality characteristicsmay affect decision making or choice in more complex ways than can be captured in this work. A second direction for furtherresearch is the use oftheNcog and Nchange scales in other choice model studies. We believe that an understanding of respondents' tendency, ceteris pari-

11

bus, toward specific change behaviors will improve the effectiveness of a wide range of choice model applications.This is especially true in those choice tasks in which status quo alternatives are offered. As emphasized by Dhar (1997), a "no-choice" (i.e., "stay withthe status quo") alternative should be available in choicetasks whenever consumers have the ability to maintain the status quo in the equivalent real world environment. Because of "status quo" choices, most situations involve a decision to change, whether it is from Ivory soap to Zest soap or from mall shopping to Internet shopping. Thus, almost every consumer decision, whether it involves a "really new" continuous innovation or simply an opportunity to try a known brand offering for the first time, is affectedby the individual's capacity to change and handle innovation.

ACKNOWLEDGMENTS We thank the editor and reviewers for their thorough and helpful analysis of this research during the review process, Tom Madden and Terry Shimp for their helpful comments, and Karen Mills for excellent research assistance.

REFERENCES Batril, Rajeev, & Stayman, Douglas M. (1990). The role of mood in advertising effectiveness.Journal of Consumer Research, 17, 203-214. Cacioppo,John T., & Petty, Richard E. (1982). The need for cognition. Journal of Personality and Social Psychology, 4, 1 16-13 1. Cacioppo, JohnT., Petty, Richard E., Feinstein, Jeffrey A., & Jarvis, Brian G. (1996). Dispositional differences in cognitive motivation: The life and times of individuals varying in need for cognition.Psychological Bulletin, 119, 197-253. Chaiken, Shelley. (1987). The heuristic model of persuasion. In Mark P. Zanna, James M. Olson, & C. Peter Herman (Eds.), Social influence: The Ontario symposium (pp. 3-39). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Craig, C. Samuel, & Ginter, James L. (1975). An empirical test of a scale for innovativeness.In Mary Jane Schlinger(Ed.),Advances in consumer research, (pp. 555-562). Ann Arbor, MI: Association for Consumer Research. Dhar, Ravi. (1997). Consumer preference for a no-choice option. Journal of Consumer Research, 24, 21 5-23 1. Dillon, William R., Madden, Thomas J., & Mulani, Narendra. (1983). Scalingmodels for categorical variables: An applicationof latent structure models. Journal of Consumer Research, 10,209-224. Ferrari, J . R. (1992). Psychometric validation of two procrastinationinventories for adults: Arousal and avoidance measures. Journal of Psychopathology and Behavior Assessment, 14, 97-1 10. Garbarino, Ellen C., & Edell, Julie A. (1997). Cognitive effort, affect, and choice. Journal of Consumer Research, 24, 147-158. Goldsmith,Ronald E. (1983). Psychographics andnew product adoption:An explorato~ystudy. Perceptual and Motor Skills, 57, 107 1-1076. Goldsmith,Ronald E., & Hofacker, Charles E. (1991). Measuring consumer innovativeness. Journal of the Academy of Marketing Science, 19, 209-22 1. Gupta, Sachin, & Chintagunta, Pradeep K. (1994). On using demographic variables to detemine segment membership in logit mixture models. Journal of Marketing Research, 31, 128-1 36.

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Haughtvedt, Curt P,, & Petty, Richard E. (1992). Personalityand persuasion: Need for cognition moderates the persistance and resistance of attitude changes. Journal of Personality and Social Psychology, 63, 308-3 19. Haughtvedt, Curt P., Petty, Richard E., & Cacioppo, John T. ( 1992).Need for cognition and advertising: Understanding the role of personality variables in consumer behavior. Journal of Consumer Behavioz I, 239-260. Hirschman, Elizabeth C. (1980). Innovativeness,novelty seeking, and consumer change. Journal of Consumer Research, 7, 283-295. Inman, J. Jefiey, McAlister, Leigh, & Hoyer, Wayne D. (1990). Promotion signal: Proxy forapricecut. Journalof ConsumerResearch, 1 7 , 7 4 81. Johar, Gita Venkatramani, Jedidi, Kamel, & Jacoby, Jacob. (1997). A varying-parameter averaging model of on-line brand evaluations.Journal of Consumer Research, 24, 232-247. Lazari, Andreas, &Anderson, Donald. (1994). Designs of discrete choice set experiments for estimating both attribute and availability cross effects. Journal of Marketing Research, 31, 375-383. Lazarsfeld, Paul F., & Henry,Neil W. (1968). Latent structure analysis. Boston: Houghton Mifflin. Louviere, Jordan J., & Woodworth, George. (1983). Design and analysis of simulated consumer choice or allocation experiments: An approach based on aggregate data. Journal of Marketing Research, 20, 350-367. Manning, Kenneth C., Bearden, William O., & Madden, Thomas J. (1995). Consumer innovativeness and the adoption process. Journal of Consumer Psychology, 4, 329-345. McAlister, Leigh, & Pessemier, Edgar.(1982). Variety seeking behavior: An interdisciplinary review. Journal of Consumer Research, 9, 31 1-322. McFadden, Daniel. (1986). The choice theory approach to market research. Marketing Science, 5,275-297. Mehrabian,Albert, &Russell, James A. (1974). An approachto environmental psychology. Cambridge, MA: MIT Press. Midgley, DavidF., & Dowling, Grahame R. (1978). Innovativeness:Theconcept and measurement. Journal of Consumer Research, 4, 229-242. Neelamegham, Rarnya, & Jain, Dipak. (1999). Consumer choice process for experience goods: An econometric model and analysis.Journal ofMarketing Research, 36, 372-386. Price, Linda L., & Ridgeway, Nancy M. (1983). Development of a scale to measure use innovativeness. In Richard P. Bagoui & Alice M. Tybout (Eds.), Advances in consumerresearch, (pp. 679-684). Ann Arbor, MI: Association for Consumer Research. Priester, Joseph, &Petty, Richard E. (1995). Source amibutions and persuasion: Perceived honesty as a determinant of message scrutiny.Personality and Social Psychology Bulletin, 21,637-654. Raju, P. S. (1980). Optimum stimulationlevel: Its relationshipto personality, demographics, and exploratory behavior. Journal of Consumer Research, 7,272-282. Rosbergen, Edward, Pieters, Rik, & Wedel, Michel. (1997). Visual attention to advertising: A segment-level analysis. Journal of Consumer Research, 24, 305-3 14. Steenkamp,Jan Benedict E., & Baumgamer, Hans. (1992). The role ofoptimum stimulation level in exploratory consumer behavior. Journal of Consumer Research, 1 9 . 4 3 4 4 8 . Swait, Jofie. (1994). A stnrctural equation model of latent segmentation and product choice for cross-sectional, revealed preference choice data. Journal of Retailing and Consumer Services, 1, 77-89. Urban, Glen L. & Hauser, John R., (1980). Design and marketing of new products. Englewood Cliffs, NJ: Prentice-Hall. Venkatraman, Meera P., Marlino, Deborah, Kardes, Frank A., & Sklar, Kimberly B. (1990). Effects of individual difference variables on responses to factual and evaluative ads. In Marvin E. Goldberg, Gerald Gom, & Richard W. Pollay (Eds.), Advances in consumer research, (pp. 761-765). Provo, UT: Association for Consumer Research. Venkatraman, Meera P., & Price, Linda L. (1990). Differentiating between cognitive and sensory innovativeness:Concepts, measurement, and implications. Journal of Business Research, 20, 293-315.

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Accepted by Dawn Iacobucci.

APPENDIX The Latent Segment and Choice Problem In this appendix we discuss a general conceptual b e w o r k in which we simultaneously model the choice behavior of a population and identify its latent segments or classes. Swait (1994) discussed this model in greater detail. Suppose we observe the choices of a sample of N individuals fiom a population with S segments (these are unobservable, or latent; we know only that S is a number between 1 and the number of individuals in the population). We assume that each segment has (potentially) different utility functions, but that it is not possible to unequivocally classify each individualin the sample to his or her true latent segment. Thus, to accurately estimate the utility hctions we must both classifythe individuals and explain their choices simultaneously. It is of equal interest to examine both the utility functions and the characteristics of each segment. We can observe the choice of an individual in response to a specific set of competitive product offerings along with his or her sociodemographic and psychographic, or personality, characteristics. We are also able to characterizemarket conditions and constraints acting on the decision maker. We must therefore postulate mechanisms for segment classification and product choice to make the model operational. The mechanism whereby choice occurs is assumed to be as follows: 1. Personality characteristics (reflected by scale responses) are the basis for latent segment membership likelihood scores for an individual. 2. Through a latent segment classification mechanism, the memberslup likelihood scores, or functions, determine the latent segment to which an individual belongs. 3. The decision maker has preferences with respect to a set of product offerings in the market. These preferences are determined by the individual's perceptions of product attributes, his or her personal characteristicsand the latent class to which he or she belongs.

PSYCHOLUGICAL INDICATORS OF INNOVATION ADOPTION

4. These preferences are processed according to a decision protocol, which leads finally to the outcome of interest, the observed choice behavior.

This structural model is an adaptation of the general h e work presented by McFadden (1986), who discussed the incorporation of psychometric data in choice models. This W e w o r k makes it possible to consider both objective and attitudinal data in the estimation of models describing the choice behavior of individual decision makers. Note that the framework assumes that preferences are indirectly affected by attitudes and perceptions through the latent segment to which the individual belongs. The framework also assumes that, independent of the choice problem being analyzed, the individual is a member of a group of consumers that has certain tastes, preferences, and so forth. When we observe an individual's behavior, such as a product choice, we are able to make inferences about preferences and latent segment membership.

13

Thus, the operationalization of the model requires that we specify the following: 1. A segment membership likelihood fhction formation mechanism in terms of measured attitudes (as well as sociodemographic characteristics in some cases). 2. A segment classification mechanism that predicts latent segment membership on the basis of the membership likelihood functions. 3. A preference formation mechanism, which is a function of latent segment membership, perceptions of product attributes, and sociodemographic characteristics. 4. A choice mechanism based on product and personal characteristics (both objective and subjective measures), given latent segment membership. 5. A mechanism to join the model of latent segment membership and the model of choice conditional on segment membership, to permit prediction of the individual's choice behavior.