JOURNAL OF THE ACADEMY CONSUMER INNOVATIVENESS Im by Juan Pardo

JOURNAL OF THE ACADEMY 10.1177/0092070302238602 Im et al. / CONSUMER OF MARKETING INNOVATIVENESS SCIENCE

ARTICLE

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An Empirical Study of Innate Consumer Innovativeness, Personal Characteristics, and New-Product Adoption Behavior Subin Im San Francisco State University

Barry L. Bayus Charlotte H. Mason University of North Carolina at Chapel Hill

This article explores the relationships between innate consumer innovativeness, personal characteristics, and newproduct adoption behavior. To do this, the authors analyze cross-sectional data from a household panel using a structural equation modeling approach. They also test for potential moderating effects using a two-stage least square estimation procedure. They find that the personal characteristics of age and income are stronger predictors of newproduct ownership in the consumer electronics category than innate consumer innovativeness as a generalized personality trait. The authors also find that personal characteristics neither influence innate consumer innovativeness nor moderate the relationship between innate consumer innovativeness and new-product adoption behavior.

The consumer innovator plays a prominent role in the diffusion and ultimate adoption of new products. Therefore, it is not surprising that much research has sought to identify variables useful for segmenting consumers into innovators and later adopters. One major research stream relating personal characteristics to new-product adoption behavior suggests that consumer innovators tend to have Journal of the Academy of Marketing Science. Volume 31, No. 1, pages 61-73. DOI: 10.1177/0092070302238602 Copyright ÂŠ 2003 by Academy of Marketing Science.

higher levels of income and education, are younger, have greater social mobility and favorable attitudes toward risk, and have greater social participation and higher opinion leadership (see Dickerson and Gentry 1983; Gatignon and Robertson 1991; Rogers 1995; Uhl, Andrus, and Poulsen 1970). Another major research stream focuses on identifying consumer innovators based on innate consumer innovativeness, a generalized unobservable predisposition that can be applied across product classes (see Foxall 1988, 1995; Hirschman 1980; Midgley and Dowling 1978, 1993). In relating innate consumer innovativeness and new-product adoption behavior, there has been debate on whether such an innovative predisposition determines innovative adoption behavior (e.g., Foxall 1988, 1995; Foxall and Goldsmith 1988; Goldsmith, Freiden, and Eastman 1995; Manning, Bearden, and Madden 1995; Midgley and Dowling 1993). Linking the major streams of research, Midgley and Dowling (1978) proposed a contingency model of innovativeness in which individual predispositions interact with personal characteristics and social communication networks (i.e., sociodemographic variables such as age, education, and social participation) to account for new-product adoption behavior. Although this conceptual framework is appealing, there have been limited empirical studies that test this contingency framework (see the review in Mudd 1990).

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Two major limitations of previous efforts examining this type of model are apparent. First, although there have been indirect attempts to test a contingency model of personal characteristics, consumer innovativeness, and newproduct adoption behavior (Midgley and Dowling 1993; Venkatraman 1991), no studies have directly tested a complete model of the possible relationships between these constructs (including the potential moderating role of personal characteristics). Second, although recommended by Midgley and Dowling (1993), no studies to date have examined these links using structural equation modeling (SEM). This is particularly significant since multiple scale items generally measure the construct of innate consumer innovativeness. Consequently, we seek to enhance our understanding of the relationship between personal characteristics, innate consumer innovativeness and new-product adoption behavior. First, we focus on the main effects using a structural equation model approach to simultaneously explore the underlying relationships between these constructs (Anderson and Gerbing 1988). Second, we examine whether personal characteristics moderate the link between innate consumer innovativeness and new-product adoption behavior by applying a two-stage least squares regression estimation procedure (Bollen and Paxton 1998). The remainder of this article is organized as follows. The next section reviews the literature. Then, we discuss our data and measures before we present our results. In the last section, we discuss the implications of our empirical results and outline some directions for future research. LITERATURE REVIEW Consumer Innovativeness One important research stream has focused on newproduct adoption behavior or “actualized innovativeness,” that is, the actual acquisition of new information, ideas, and products (Hirschman 1980; Midgley and Dowling 1978). Using a behavioral perspective, studies in this stream define new-product adoption behavior based on “the degree to which an individual adopts innovations relatively earlier than other members in his or her social system” (Rogers and Shoemaker 1971). In empirical work, researchers have used various indirect measures of this behavior, including the number of products owned (e.g., Foxall 1988, 1995; Rogers 1995), ownership of a particular product (e.g., Dickerson and Gentry 1983; Labay and Kinnear 1981), purchase intentions (e.g., Holak and Lehmann 1990), and the relative time of adoption for a particular product (e.g., Midgley and Dowling 1993; Rogers and Shoemaker 1971).

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At the same time, another major research stream has focused on identifying consumer innovators based on innate consumer innovativeness, defined as a generalized unobservable predisposition toward innovations applicable across product classes. This concept has been called “innovative predisposition” (Midgley and Dowling 1993) or “innate innovativeness” (Hirschman 1980) and has been widely accepted in psychology to identify the innovative characteristics of individuals (e.g., Kirton 1976). Goldsmith and his colleagues (Goldsmith and Hofacker 1991; Goldsmith et al. 1995) consider this generalized personality trait as global innovativeness and distinguished it from domain-specific innovativeness that can be applied to a specific product category. Researchers in marketing have also focused on this generalized perspective of innovativeness, which segments consumer innovators on the basis of their individual personality and cognitive style, that is, their way of processing information and approach to problem solving, as distinct from cognitive level, ability, or complexity (Foxall 1988; Kirton 1976; Midgley and Dowling 1978). These concepts of innovativeness share a high level of abstraction as they reflect a generalized, abstract personality trait of innovativeness. For example, early research by Hurt, Joseph, and Cook (1977) views innovativeness as a generalized personality trait reflecting “a willingness to change.” Other researchers consider innate consumer innovativeness to be the openness of information processing, which is defined in terms of an individual’s receptivity to new experiences and novel stimuli (Goldsmith 1984; Leavitt and Walton 1975). Midgley and Dowling (1978) suggested that the concept of innovativeness involves communication independence, determined by the degree to which a consumer’s decision process is independent of others’ personal influence in the social system. Hirschman (1980) and Manning et al. (1995) equated an innovative trait with consumer novelty seeking, which is defined as an inherent desire to seek out novelty and creativity. More recently, Steenkamp, ter Hofstede, and Wedel (1999) viewed consumer innovativeness as “the predisposition to buy new and different products and brands rather than remain with previous choices and consumption patterns.” Personal characteristics (i.e., sociodemographics and psychographics) have also been widely used to profile consumer innovators. Many studies provide evidence that innovators can be characterized by variables such as income, age, education, social participation, and risktaking propensity (e.g., Dickerson and Gentry 1983; Gatignon and Robertson 1991; Rogers 1995; Steenkamp et al. 1999). Among the many possible variables that can be used, demographic information such as household income, education, and age has been most widely used to identify innovators due to the simplicity of data collection (e.g., Dickerson and Gentry 1983; Labay and Kinnear

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1981; Martinex, Polo, and Favian 1998; Midgley and Dowling 1993; Ostlund 1974; Summers 1971). The Relationship Between Innate Consumer Innovativeness, Personal Characteristics, and New-Product Adoption Behavior A review of the related literature on innovativeness highlights four possible links between innate consumer innovativeness, personal characteristics, and new-product adoption behavior that are of interest to our research (see Table 1). As illustrated in Figure 1, these links reflect the relationship between personal characteristics and newproduct adoption behavior (Path 1), between innate consumer innovativeness and new-product adoption behavior (Path 2), between personal characteristics and innate consumer innovativeness (Path 3), and a potential moderating effect of personal characteristics on the relationship between innate consumer innovativeness and new-product adoption behavior (Path 4). Path 1: Personal Characteristics— New-Product Adoption Behavior

Consistent with the first path in Figure 1, a number of empirical studies relate personal characteristics to newproduct adoption behavior (e.g., Dickerson and Gentry 1983; Labay and Kinnear 1981; Midgley and Dowling 1993; Ostlund 1974; Summers 1971; Venkatraman 1991). The usual variables in these studies include income, age, life cycle, and family size (e.g., Gatignon and Robertson 1991; Rogers 1995; Uhl et al. 1970). For example, Labay and Kinnear (1981) found that ownership of a home solar energy system is related to consumer age, income, education, and occupational status. Dickerson and Gentry (1983) found that ownership of home computers is related to demographics (e.g., age, income, and education) and psychographics (e.g., opinion leadership and informationseeking behavior). More recently, Martinex et al. (1998) found that income, age, and employment status significantly distinguish adopters from nonadopters in consumer electronic products. Although some research indicates that demographic effects are weak (e.g., Ostlund 1974), there is general agreement that consumer innovators tend to have higher levels of income and education, are younger, have greater social mobility and favorable attitudes toward risk, and have higher opinion leadership (see the reviews by Gatignon and Robertson 1991; Rogers 1995). Path 2: Innate Consumer Innovativeness—New-Product Adoption Behavior

In line with the second path in Figure 1, some empirical studies have emphasized the relationship between new-product adoption behavior and innate consumer innovativeness as a generalized predisposition (e.g.,

FIGURE 1 Model of Personal Characteristics, Innate Consumer Innovativeness, and New-Product Adoption Behavior Innate Consumer Innovativeness 3 2 Personal Characteristics

1 New-Product Adoption Behavior

Foxall 1988, 1995; Goldsmith et al. 1995; Manning et al. 1995; Midgley and Dowling 1978). Using data from the food industry, Foxall (1988) reported that innate consumer innovativeness measured by the Kirton AdaptionInnovation Inventory is not related to the number of new products owned (see also Foxall and Goldsmith 1988). In contrast, Midgley and Dowling’s (1993) study involving the fashion industry found that innovative characteristics as measured by Leavitt and Walton’s (1975) innovativeness scale are related to new-product adoption behavior in terms of the time of adoption and purchase intention. In a subsequent article, Foxall (1995) reported that innate consumer innovativeness and new-product adoption behavior are positively related in the software product category (in which consumer involvement is high), but not in the food product category (in which consumer involvement is low). Manning et al. (1995) found that innovativeness as consumer novelty seeking is related to the initial adoption stages represented by actualized novelty seeking and newproduct awareness, while innovativeness as communication independence is more related to the later adoption stages of new-product trial. More important, Goldsmith et al. (1995) found that a global measure of consumer innovativeness is weakly related to adoption behavior, while domain-specific innovativeness is strongly associated with adoption behavior of fashion and electronic innovations. They further found that the relationship between global consumer innovativeness and new-product adoption behavior is mediated by domain-specific innovativeness. Citrin, Sprott, Silverman, and Stem (2000) supported this conclusion with their findings that domain-specific innovativeness along with Internet usage directly influences consumers’ adoption behavior of Internet shopping. Another recent

TABLE 1 Review of Empirical Studies on Personal Characteristics, Consumer Innovativeness, and Innovative Adoption Behavior a

Study

Focus of Study

Summers (1971)

Path 1

Stepwise regression

Ostlund (1974)

Path 1

Discriminant analysis

Labay and Kinnear (1981)

Path 1

Dickerson and Gentry (1983)

Path 1

Martinex, Polo, and Favian (1998) Citrin, Sprott, Silverman, and Stem (2000) Foxall (1988)

Path 1

Multiple discriminant function analysis Factor analysis and discriminant analysis Stepwise logit analysis

Path 2

Multiple regression

Path 2

ANOVA

Foxall (1995)

Path 2

ANOVA

Goldsmith et al. (1995)

Path 2

Factor analysis and partial correlations

Manning, Bearden, and Madden (1995) Limayem, Khalifa, and Frini (2000) Venkatraman (1991)

Path 2

Paths 1, 2, 3, 4

ANOVA and structural equation modeling Partial least squares analysis Logistic regression

Midgley and Dowling (1993)

Paths 1, 2, 3, 4

Path 2

Analysis Method(s)

Cluster analysis, survival analysis, and log-linear model

Key Finding(s) New-product adoption behavior (i.e., the time of adoption) is significantly related to personal characteristics such as income and product involvement. Personal characteristics (i.e., age, income, and education) are weak predictors of new-product adoption behavior (time of adoption), while perceived innovation attributes (such as perceived risk and relative advantage) are strong predictors. New-product adoption behavior (i.e., ownership of solar energy systems) is influenced by personal characteristics such as age, education, income, life-cycle stage, and occupational status. New-product adoption behavior (i.e., ownership of home computers) is determined by personal characteristics that include demographics (e.g., age and income) and psychographics (e.g., opinion leadership and information-seeking behavior). Personal characteristics (e.g., income, age, and employment status) significantly distinguish adopters from nonadopters in consumer electronic products. Domain-specific innovativeness (measured by Goldsmith and Hofacker’s 1991 scale) along with Internet usage directly influences consumers’ adoption behavior of Internet shopping. No significant correlation exists between consumer innovativeness (measured by the Kirton Adaption-Innovation Inventory [KAI] score) and new-product adoption behavior (measured by the number of products purchased). The relationship between consumer innovativeness (measured by the KAI score) and new-product adoption behavior (measured by the number of products owned) is contingent on the level of involvement in the product category. Global consumer innovativeness across product categories has a weak correlation with new-product adoption behavior (i.e., the number of products owned), while domain-specific consumer innovativeness for a specific product category has a strong correlation with it. Inherent consumer novelty seeking is more related to the initial stages of the adoption process (actualized novelty seeking and awareness), while consumer independent judgment making is more related to the later stages (new-product trial). Internet shopping behavior is directly influenced by consumer innovativeness (measured by Hurt, Joseph, and Cook’s scale), and this link is mediated by consumers’ attitude and intentions. The relationship between personal characteristics (e.g., age, income, and occupation) and new-product adoption behavior depends on consumer innovativeness type (either sensory or cognitive) and product type (either PC or VCR). Personal characteristics reflected by demographics (e.g., age and education) and social activity (e.g., social participation) determine the difference of new-product adoption behavior between innovative communicators and others classified by consumer innovativeness.

a. See Figure 1: Path 1 = Personal Characteristics → New-Product Adoption Behavior; Path 2 = Innate Consumer Innovativeness → New-Product Adoption Behavior; Path 3 = Personal Characteristics → Innate Consumer Innovativeness; Path 4 = Moderating Effects of Personal Characteristics Between Innate Consumer Innovativeness and New-Product Adoption Behavior.

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study by Limayem, Khalifa, and Frini (2000) found that consumer innovativeness (measured by the innovativeness scale developed by Hurt et al. 1977) influences Internet shopping behavior both directly and indirectly through the consumer’s attitude and intentions. In sum, the empirical literature addressing the relationship between innate consumer innovativeness and new-product adoption behavior is inconsistent across product categories that exhibit different levels of involvement and specificity. Path 3: Personal Characteristics— Innate Consumer Innovativeness

Consistent with the third path in Figure 1, a few studies have related personal characteristics to innovative psychological traits. Midgley and Dowling (1993) found that innovative communicators, as classified by innate consumer innovativeness, tend to be younger, are less likely to be married, and have high social status. Baumgarten (1975) also found that innovative communicators exhibit different sociodemographics and lifestyles as reflected by age, social class, mass media readership, and social and sports activities. Venkatraman (1991) further found that consumer innovators exhibit different personal characteristics with respect to income, age, and occupation status. Goldsmith and his colleagues (Goldsmith and Goldsmith 1996; Goldsmith et al. 1995) found that personal characteristics reflected by demographic variables (e.g., age, sex, and income) have differential effects on domain-specific innovativeness applied to different product categories. More recently, Steenkamp et al. (1999) reported that age negatively influences consumer innovativeness, while income and level of education have no impact. Path 4: Moderating Effects

Finally, consistent with the fourth path in Figure 1, some research has explored the role of personal characteristics in moderating the relationship between innate consumer innovativeness and new-product adoption behavior. As suggested by Midgley and Dowling (1978), consumers with high innovative dispositions may not always adopt new products earlier than others due to moderating factors (e.g., Steenkamp et al. 1999). Midgley and Dowling (1993) found that personal characteristics play an important role in explaining the different adoption behaviors of “innovative communicators” and the other groups classified by Leavitt and Walton’s (1975) innovativeness scale. Midgely and Dowling (1993) concluded that compared with other groups, innovative communicators (e.g., those who are younger, more educated, and highly involved socially) exhibit innovative adoption behavior in terms of their time of adoption and purchase intentions. In a related study, Venkatraman (1991) found that the influence of personal characteristics (such as age, income, and occupation status) on new-product adoption behavior depends on innate consumer innovativeness (see also Venkatraman and Price 1990).

DATA AND RESEARCH METHOD Data were obtained from the Arkansas Household Research Panel, which is organized and maintained by the University of Arkansas. Households owning their home or condominium were mailed a four-page questionnaire including measures of innovative cognitive style, the ownership of various consumer electronic products, and demographics. A total of 457 questionnaires were distributed. After listwise1 deletion of households giving incomplete responses on the key variables, 296 complete and usable responses remained (representing a response rate of 65%). Table 2 reports descriptive statistics for innate consumer innovativeness, for new-product adoption behavior, and for the four personal characteristic variables used in this study. Personal Characteristics Following prior studies of consumer durable purchase behavior (e.g., Pickering 1981; Winer 1985), our demographic information corresponds to family life-cycle stage (i.e., age), ability (i.e., household income, education), and the moving history of the family (i.e., length of residence at the current address). Specific variables include household income (an 11-point scale in $10,000 increments), age of head of household (in years), education of head of household (in years), and length of residence at the current address (in years).2 These measures are similar to those used in other studies (e.g., Midgley and Dowling 1993; Rogers 1995). Generally speaking, Table 2 indicates that our sample is composed of stable, middle-income families. Innate Consumer Innovativeness In our study, innate consumer innovativeness is defined as an individual’s inherent innovative personality, predisposition, and cognitive style toward innovations that can be applied to consumption domains across product classes.3 To operationalize this construct, we use the Kirton Adaption-Innovation Inventory (KAI) (Kirton 1976) that intends to measure an individual’s generalized innovative predispositions that are equivalent to “global innovativeness” (Goldsmith and Hofacker 1991; Goldsmith et al. 1995). Although other scales have been proposed (e.g., Goldsmith and Hofacker 1991; Hurt et al. 1977; Jackson 1976; Leavitt and Walton 1975), this particular inventory of items has been extensively tested for reliability, content validity, generalizability, and factor structure in numerous contexts, including both general consumption situations and organizational decision-making situations (see Bagozzi and Foxall 1996; Foxall and Hackett 1992; Goldsmith 1984, 1986; Keller and Holland 1978, among others). Goldsmith (1984, 1986) reported that the

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TABLE 2 Means, Standard Deviations, and Correlations Innate Consumer Innovativeness (ICI)a

New-Product Adoption Behavior (NPAB)

Income

ICI NPAB Income LOR Education Age

1.00 .21** .14 .17** .13* –.05

1.00 .46** –.28** .19** –.40**

1.00 –.09 .28** –.26**

M SD

3.37 0.67

2.98 1.48

3.97 1.92

Length of Residence (LOR)

1.00 –.12** .38** 16.28 12.19

Education

Age

1.00 –.09

1.00

14.71 2.32

55.55 13.94

a. An 11-item 5-point scale is used. The average score of these 11 items is used for the reported descriptive statistics. * Significant at p < .05. ** Significant at p < .01.

KAI is highly correlated with other innovativeness scales (e.g., the Open Processing Scale by Leavitt and Walton 1975 and the Jackson Personality Inventory by Jackson 1976), thus exhibiting convergent validity with scales of the generalized innovative personality trait. The KAI consists of a battery of items asking participants to evaluate whether it is easy or hard to describe their self-images by each item on 5-point scales (1 = very easy to project respondent’s image to 5 = very hard to project respondent’s image). We follow Keller and Holland’s (1978) suggestion of using the “originality” subdimension within the full 32-item KAI, which provides an efficient and reliable measure of innovative cognitive style without complication from the other dimensions of “conformity” and “efficiency.” Goldsmith (1984, 1986) also supported the use of this subdimension as it has substantially higher convergent validity with other innovativeness measures. The 11 items are the following: often risks doing things differently (X1), has original ideas (X2), copes with several ideas at the same time (X3), proliferates ideas (X4), has fresh perspectives on old problems (X5), is stimulating (X6), will always think of something when stuck (X7), can stand out in disagreement against a group (X8), would sooner create than improve (X9), likes to vary set routines at a moment’s notice (X10), and needs the stimulation of frequent change (X11). All 11 items were coded so that a high score represents a high level of innovative predisposition. New-Product Adoption Behavior We define new-product adoption behavior as the degree to which an individual adopts innovations relatively earlier than other members in his or her social system (Rogers and Shoemaker 1971). Of the many different measures of newproduct adoption behavior that appear in the literature, we use the “cross-sectional” method based on the number of products owned in a specific category at the time of the survey (e.g., Foxall 1988, 1995; Midgley and Dowling

1978; Robertson and Myers 1969; Rogers 1995). Midgley and Dowling (1978) recommended this cross-sectional method as a practical measure of new-product adoption behavior since it engenders fewer problems of respondent recall, reliability, validity, and generalizability than a more direct measure of adoption timing. Other evidence supportive of this measure indicates that consumers whose average time to adoption is shorter tend to own more innovative products (e.g., Midgley and Dowling 1978; Robertson 1971). This cross-sectional measure captures new-product adoption behavior by using actual actions rather than intentions that may not reflect behavior. More important, measuring adoption behavior across a set of products rather than a single product controls for some of the intervening effects associated with any individual product, thus providing better reliability (Lastovicka and Joachimsthaler 1988). We study the consumer electronics product category as there is considerable variation in actual newproduct adoption behavior for products in this category. Following prior research (e.g., Foxall 1988, 1995; Goldsmith et al. 1995), our measure of new-product adoption behavior (i.e., actualized innovativeness) is operationalized as the number of products owned from the following set of 10 consumer electronics products: CD player, cellular car phone, personal computer, video game system, fax machine, home alarm system, camcorder, photo CD player, VCR, and camera. All of these products, except VCR and camera, were considered innovative at the time of the survey in the surveyed region. We intentionally included these two noninnovative products to provide a small check of data reliability. On the basis of selfreported ownership of these two noninnovative products, we excluded respondents who might systematically and inaccurately report no ownership of any consumer electronics. Twenty-three respondents who reported no ownership of any of these products were excluded, thus leaving 273 responses for the final analysis. This method avoids inadvertently excluding respondents who truthfully reported extremely noninnovative behavior but still

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reported the ownership of general consumer electronic products (such as VCR and camera). We also note that there is variability in this measure of new-product adoption behavior (σ = 1.48), with a mean of 2.98 products owned.

FIGURE 2 Estimation Results of the Main Effects Model (Standardized Coefficients)

ANALYSIS AND RESULTS Income

We empirically examine the links in Figure 1 in two phases. First, we explore the main effects among personal characteristics, innate consumer innovativeness, and newproduct adoption behavior simultaneously (Paths 1, 2, and 3 in Figure 1) using a structural equation model as recommended by Midgley and Dowling (1993). Second, we examine the moderating role of personal characteristics in explaining the link between innovative predispositions and behaviors (Path 4 in Figure 1) using a two-stage least squares estimation procedure (Bollen 1996; Bollen and Paxton 1998; Oczkowski and Farrell 1998). The traditional structural equation model cannot test nonlinear interaction effects due to the inadequacy of significance tests and fit statistics (Bollen 1989). Thus, as suggested by Bollen and Paxton (1998), we use a two-stage least squares (2SLS) estimation procedure to test for interaction and moderating effects using the SYSLIN procedure in the Statistical Analysis System (SAS). Main Effects To examine the direct links between the constructs, we follow Anderson and Gerbing’s (1988) two-step approach where estimation of a confirmatory measurement model precedes the simultaneous estimation of the measurement and structural submodels. First, coefficients of the indicators of innate consumer innovativeness are estimated for the confirmatory measurement model. The high Goodness-of-Fit Indexes (higher than 0.95) indicate a good fit. All 11 indicators load positively and significantly on innate consumer innovativeness (all p-values < .01) with relatively high squared multiple correlations (SMCs) (SMC is equivalent to R2; lowest SMCs = .54 for X4 and X5) and low error variances, thus confirming good convergent validity (Bagozzi and Yi 1988). Second, coefficients of the confirmatory measurement model and the structural model are estimated simultaneously. We compared the goodness of fit between two models with different measurement error specifications for the indicators of innate consumer innovativeness (ICI). The first model does not specify correlated measurement errors among the indicators of ICI, while the second model does as suggested by Taylor (1989).4 The chi-square difference test (χ2 = 131.63, df = 16, p < .01) indicates that the model with correlated measurement errors provides a significantly better fit. This is also confirmed by the fit

0.09 -0.10

Length of Residence

……….

X10

X11

Innate Consumer Innovativeness

0.11 -0.03 0.11*

0.34** Education

d10 d11

-0.09 0.04

Age

-0.27**

New-Product Adoption Behavior

* Significant at p < .05. ** Significant at p < .01.

indexes (Goodness of Fit Index [GFI] = .96, Adjusted Goodness of Fit Index [AGFI] = .93, Normed Fit Index [NFI] = .97, Incremental Fit Index [IFI]= .99, TuckerLewis Index [TLI] = .98, Comparative Fit Index [CFI] = .99, root mean square error of approximation [RMSEA] = 0.04) compared with those for the other model (NFI = .93, IFI = .91, TLI = .94, CFI = .95, RMSEA = .07). Thus, we focus on the model with correlated errors in the remainder of our discussion. This model is shown in Figure 2, and the MLE estimation results are reported in Table 3. The structural submodel specifying relationships between the major constructs, combined with the measurement submodel, provides a comprehensive assessment of construct validity of the total model in Figure 2. Additional analyses show that chi-square differences between unrestricted models (i.e., correlations are freely estimated) and restricted models (i.e., correlation = 1.0) are significant for independent and dependent constructs as well as for indicators of the independent and dependent constructs at the .05 level, thus confirming discriminant validity (Anderson and Gerbing 1988). Regarding confirmatory validity, all indicators of latent variables have positive and significant loadings with high SMC values, thus showing convergent validity as suggested by Bagozzi and Yi (1988). Since the chi-square test (χ2 = 244.83, df = 94, p < .01) indicates possible lack of overall fit, we follow the recommendations of Bagozzi and Phillips (1982) and Tanaka (1993) and use multiple fit indexes to assess goodness of fit.5 As noted above, all fit indexes exceed .93 (see Gerbing and Anderson 1992 for details about the various fit indexes). In addition, the RMSEA is recommended as a fit

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TABLE 3 Maximum Likelihood Estimation Results for Main Effects Model Innate Consumer Innovativeness Income Length of residence Education Age Innate consumer innovativeness

New-Product Adoption Behavior

0.06 (0.04) –0.01 (0.01) 0.06 (0.03) –0.01 (0.01) a

Squared multiple correlation (R2) Fit statistics (N = 273) χ2 (df = 78) = 113.20** Goodness-of-Fit Index (GFI) = .96 Normed Fit Index (NFI) = .97 Tucker-Lewis Index (TLI) = .98 Root mean square error of approximation (RMSEA) = .04 Adjusted Goodness-of-Fit Index (AGFI) = .93 Incremental Fit Index (IFI) = .99 Comparative Fit Index (CFI) = .99

.04

0.26 (0.04)** –0.01 (0.01) 0.03 (0.03) –0.03 (0.01)** 0.14 (0.06)* .31

NOTE: Standard errors are in parentheses. a. Although not reported here, all paths from innate consumer innovativeness to the 11 indicators in the measurement model are significant at p < .01. *Significant at p < .05 level. **Significant at p < .01 level.

index since it provides confidence intervals (CIs) that allow for testing the null hypothesis of no fit (Browne and Cudeck 1993; MacCallum, Browne, and Sugawara 1996). To conclude that there is a good fit, Browne and Cudeck (1993) suggested that the lower bound for a 90 percent confidence interval should be below .05 (when the prescribed significance level is .05) and the p-value for a close fit test should be higher than .05. In addition, the narrower the confidence intervals, the more powerful the close fit test is. For our model, the p-value for the close fit test equals .88 and .02 < CI < .05—indicating a good fit according to Browne and Cudeck’s (1993) criteria. Our main concern is with the estimation of the coefficients of the structural submodel (paths 1, 2, and 3 in Figure 1). First, we expect that personal characteristics reflected in the demographic variables influence newproduct adoption behavior (Path 1 in Figure 1). The estimation results relating demographic variables to newproduct adoption behavior (see Figure 2) indicate that the paths from income to new-product adoption behavior (γ = .26, p < .01) and from age to new-product adoption behavior (γ = < .03, p < .01) are significant at the .01 significance level, while the paths from length of residence and education to new-product adoption behavior are not significant.6 The signs of the estimated coefficients indicate that highincome and younger households tend to own more innovative products. Second, we expect that innate consumer innovativeness influences new-product adoption behavior (Path 2 in Figure 1). The estimation results show that this path coefficient is positive and significant at the .05 significance level (β = .14, p < .05), indicating that innate innovativeness positively influences new-product adoption behavior.

Third, we tested the impact of personal characteristics on innate consumer innovativeness. Our results show that none of these path coefficients are significant at the .05 level, suggesting that sociodemographic variables do not affect innovative predispositions. Finally, we compared the SMC values for the two endogenous variables of innate consumer innovativeness and new-product adoption behavior. The small SMC of .04 for innate consumer innovativeness indicates that the sociodemographic variables explain a very small fraction of the variance associated with innate consumer innovativeness. In contrast, the relatively high SMC for new-product adoption behavior (SMC = .31) reveals that a substantial amount of the variance in new-product adoption behavior is accounted for by sociodemographic variables and innate consumer innovativeness. To further assess the differential impact of personal characteristics and innate consumer innovativeness on new-product adoption behavior, we tested two additional models. The first includes only the paths from the sociodemographic variables to new-product adoption behavior (Path 1 in Figure 1), while the second includes only the path from innate consumer innovativeness to new-product adoption behavior (Path 2 in Figure 2). The SMC for the first model is .29, indicating that demographic variables explain substantial variance in newproduct adoption behavior, χ2(df = 54) = 2.42, p < .12. However, the SMC for the second model indicates that less than 5 percent of the variance in new-product adoption behavior is explained by innate consumer innovativeness, χ2 (df = 54) = 181.41, p < .01). Here again, the demographic variables of income and age explain more of the variance in new-product adoption behavior

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than innate innovativeness. Overall, we conclude that the link from innate consumer innovativeness to new-product adoption behavior is weak, although statistically significant. Moderating Effects In the second phase of our analysis, interaction effects were tested to explore the moderating role of personal characteristics in explaining the relationship between innate consumer innovativeness and new-product adoption behavior (Path 4 in Figure 1). For this analysis, we use a 2SLS estimation procedure (Bollen 1996; Bollen and Paxton 1998). This approach is preferred to other methods (e.g., regression with a product term as suggested by Aiken and West 1991 or product term indicant analysis as suggested by Kenny and Judd 1984) for the following reasons: (1) it provides consistent estimates, (2) it does not require an assumption of multivariate normality, (3) it has no convergence problems, (4) it isolates specification error and generates estimated asymptotic standard errors for significance tests, and (5) it is less demanding computationally without requiring a specialized structural equation package. See Bollen (1996), Bollen and Paxton (1998), and Jaccard, Turrisi, and Wan (1990) for a review of approaches for testing nonlinear terms. The significance test for this method is also considered more powerful than that of the product-term regression approach suggested by Aiken and West (1991). This method is appropriate for our study since the innate consumer innovativeness measure exhibits multivariate nonnormality (as reflected by multivariate kurtosis = 23.91, p < .01), which tends to inflate the test statistic in maximum likelihood (ML) estimation. Details of this method are in the appendix (see Bollen and Paxton 1998 for the SAS code). Each personal characteristic variable (i.e., income, length of residence, education, and age) was tested in a separate 2SLS procedure to examine whether it strengthened or weakened the relationship between innate consumer innovativeness and new-product adoption behavior. Coefficients for the interaction terms involving innate consumer innovativeness and demographics are all nonsignificant at the .05 level (βincome = .35, p < .12, βlength of residence = –.03, p < .60, βeducation = .54, p < .23, and βage = –.06, p < .07). Not surprisingly, all the R2 values from the two-stage least squares estimations are low (the highest R2 value is .11 for the interaction between Income and ICI). These nonsignificant moderating effects of personal characteristic variables were also confirmed by interactionterm regressions as suggested by Aiken and West (1991). Thus, we conclude that the personal characteristic variables we consider do not moderate the relationship between innate consumer innovativeness and new-product adoption behavior for our data.

DISCUSSION Our study empirically explores the contingency framework proposed by Midgley and Dowling (1978). We examine the relationship between innate consumer innovativeness, personal characteristics, and new-product adoption behavior of consumer electronic products, as well as the moderating effect of personal characteristics on the link between innate consumer innovativeness and newproduct adoption behavior. We find that income and age, in combination with innate consumer innovativeness, are related to the ownership of new consumer electronics products. These findings are generally consistent with previous studies using personal characteristics and predispositions to explain new-product adoption behavior (e.g., Dickerson and Gentry 1983; Labay and Kinnear 1981; Martinex et al. 1998; Ostlund 1974; Summers 1971). Consistent with previous studies arguing that innate innovativeness enhances new-product adoption behavior (e.g., Foxall 1995; Manning et al. 1995; Midgley and Dowling 1993), we find that the impact of innate consumer innovativeness on new-product adoption behavior is positive and significant. From the estimated coefficients, we conclude that those consumers who have a high income, are younger, and have innovative predispositions tend to adopt more new products. Finally, we find no support for a link between personal characteristics and generalized predispositions of innate consumer innovativeness. Thus, at least for the set of consumer electronics products we consider, our empirical results do not support Midgley and Dowling’s (1978) contingency model. Our results indicate that the link from innate consumer innovativeness to new-product adoption behavior is weak, even though it is statistically significant. This result is consistent with other research indicating that the relationship between personality and buyer behavior is, if significant, weak due to the general conceptual irrelevance between traits and behavior (e.g., Goldsmith and Hofacker 1991; Goldsmith et al. 1995; Kassarjian 1971; Lastovicka and Joachimsthaler 1988). Thus, our results support Goldsmith et al.’s (1995) argument that the relationship between innate consumer innovativeness as a generalized innovative predisposition and new-product adoption behavior is not prominent. Managerial Implications Our results suggest that personal characteristics such as income and age, rather than measures of innovative predispositions, will better help marketers segment consumers into innovators and later adopters. However, we must be cautious in generalizing this finding as consumer electronics products may be a category where personal characteristics such as income and age may play a more critical role.

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In addition, the nonsignificant relationship between innate consumer innovativeness and personal characteristics implies that sociodemographic variables should not be used to identify the innovative predispositions of consumers. More important, we find that sociodemographic variables do not moderate the relationship between innate consumer innovativeness and new-product adoption behavior. This nonsignificant moderating effect indicates that the strength of the link between innovative traits and behavior is insensitive to changes in personal characteristics. Contrary to the contingency framework suggested by Midgley and Dowling (1978), we find that changes in personal characteristics such as income and age neither facilitate nor impede the new-product adoption behavior of those consumers who have already developed generalized innovative predispositions. This nonsignificant moderating effect further implies that marketers need to understand that changes in sociodemographic characteristics are less likely to influence innovative adoption behavior of those consumers who already exhibit certain levels of innovative psychological predispositions. In sum, this study adds to our understanding of different ways to identify consumer innovators who contribute to the diffusion and adoption of new products by providing initial cash flow, by establishing entry barriers to competitors, and by promoting new products through word-ofmouth communication (e.g., Citrin et al. 2000; Limayem et al. 2000). If innovators can be identified a priori based on their personal characteristics and psychological traits, marketers can target the consumers who are critical to the eventual success of their new-product innovation. Limitations and Future Research Directions As with any study, the generalizability of our findings beyond our specific sample and product class may be limited. Our results are from the consumer electronics product category in which consumers tend to be highly involved in information search and purchase decision making due to the relatively high cost of adopting new products (e.g., Foxall 1995). Our study used the “originality” subdimension from the KAI to measure a generalized innovative predisposition. Among the dimensions of the KAI, the “originality” subdimension best represents a generalized innovative predisposition in a general consumption situation as evidenced by the high convergent validity with other innovativeness scales that assess the generalized innovative trait (Goldsmith 1984, 1986; Taylor 1989). Although this subdimension is considered to be a reliable and valid measure of generalized innovative predisposition, it may capture only one aspect of innovative predispositions, that is, that concerned with generating and creating new and original ideas.

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To measure adoption behavior, we used a crosssectional measure based on the number of products owned in one product category. Although this measure is widely considered to be a valid and reliable measure of innovative adoption behavior reflecting the pattern and breadth of actual adoption behavior (e.g., Foxall 1988; Midgley and Dowling 1978; Robertson 1971; Rogers 1995), it may not represent the depth and intensity of behavior in terms of product knowledge and awareness, purchase intention, and repeat purchase. In addition, studying a set of new products in a specific product category raises the specificity/generality issue as noted by Goldsmith et al. (1995). In our study, we presume that a generalized innovative predisposition influences new-product adoption behavior in the consumer electronics product category. However, our empirical finding of a significant but weak relationship between our measures of innate consumer innovativeness and new-product adoption behavior strongly supports the claim that personality traits at the abstract, general level are poor predictors of new-product adoption behavior at the concrete, specific level (e.g., Goldsmith and Hofacker 1991; Goldsmith et al. 1995; Lastovicka and Joachimsthaler 1988). Another inherent limitation of our study lies in relating personal characteristics to new-product adoption behavior. Although our findings are consistent with previous studies that “younger” and “richer” consumers are more likely to buy innovative new products (e.g., Dickerson and Gentry, 1983; Labay and Kinnear 1981; Martinex et al. 1998), we cannot really discern whether this is simply due to the fact that “younger” and “richer” people buy more products in general. To further research in this area, we outline several possible directions. First is to examine different product categories and services (e.g., consumer products such as food and household goods, nondurable products, and service products such as e-commerce and banking). In addition, samples with different demographic profiles and from various regions (including different countries) would help ascertain the generalizability of our findings. Second, future research might use different measures of innate consumer innovativeness. The measure of innate consumer innovativeness we used captures inherent innovative traits across product categories based on an individual’s generalized innovative cognitive style. Since the consistency of any relationship between innovative traits and behavior often depends on involvement in the product category, a “domain-specific innovativeness” measure that captures product-specific characteristics of consumer innovativeness could help validate our findings (e.g., see Goldsmith et al. 1995). Alternatively, consumer innovativeness can be measured by the Exploratory Buying Behavior Tendencies Scale that aims to measure domain-specific new-product acquisition and information seeking (see, e.g., Baumgartner and Steenkamp 1996).

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Third, future research could use different behavioral measures to capture new-product adoption behavior. While we used the number of new products owned from a particular product category, other behavioral measures such as product knowledge and awareness, product trial, purchase intention, repeat purchase, and the average time of adoption could provide alternate ways of validating our basic results. It would also be worthwhile to examine the ability of several different innate consumer innovativeness measures to explain these different measures of newproduct adoption behavior. Manning et al. (1995) took a step in this direction by relating different concepts of innate consumer innovativeness (i.e., novelty seeking and consumer independent judgment making) to stages of the new-product adoption process (i.e., actualized novelty seeking, new-product awareness, and new-product trial). Taking a further step in this direction, Limayem et al. (2000) showed that Internet shopping behavior is influenced both directly by consumer innovativeness (measured by the Innovativeness Scale developed by Hurt et al. 1977) and indirectly through consumers’ attitudes and intentions. Finally, variables other than demographics might be used to profile consumer innovators. Such information might include lifestyle and attitudinal variables, such as the VALS typologies and the geodemographic segment descriptors (e.g., PRIZM). Other possible variables include information sources used (word-of-mouth recommendations, advertising), product category involvement, opinion leadership, venturesomeness, cosmopolitanism, preference, and decision-making ability (see, e.g., Midgley and Dowling 1993).

APPENDIX Two-Stage Least Squares Estimation Procedure (Example for the Interaction Between Innate Consumer Innovativeness and Income) Following Bollen and Paxton (1998), we test for the potential moderating effects of personal characteristics using a two-stage least squares (2SLS) procedure. In this appendix, we illustrate this procedure for testing the interaction effect between innate consumer innovativeness (ICI) as a latent variable and income as an observed variable in explaining new-product adoption behavior (NPAB) as an observed dependent variable. We begin with a structural model (a simple single-equation model) that has NPAB as a dependent variable, Y1, observed without measurement error: Y1 = α1 + β1ICI + β2Income + β3ICI • Income + ζ1,

(1)

where α1 is the intercept term and ζ1 is a random disturbance term. The latent variable ICI is measured with 11 indicators such that

X1 = λ1ICI + δ1 = 1 • ICI + δ1

(2)

X2 = α2 + λ2ICI + δ2

(3)

... X11 = α11 + λ11ICI + δ11,

(12)

where α2-α11 are intercept terms for equations (3) thorough (12) and for the measurement errors, δ1-δ11, E(δj) = 0. Equation (2) indicates that ICI is scaled to have the same metric as X1 by setting the coefficient (λ1) to be 1 as a scaling indicator for ICI. Thus, the latent variable ICI can be expressed as ICI = X1 – δ1.

(13)

When the observed variables in equation (13) are substituted for the latent variable ICI in equation (1), the observed dependent variable NPAB can be expressed by X1 and income as follows: Y1 = α1 + β1(X1 – δ1) + β2Income + β3 (X1 – δ1)Income + ζ1.

(14)

Then, equation (14) can be expressed as a linear regression equation with only observed variables: Y1 = α1 + β1X1 + β3X1Income + u1,

(15)

where the disturbance term is u1 = –β1δ1 – β3δ1Income + ζ1. In the first stage of estimation, each right-hand-side observed variable in equation (15) is regressed on all individual and product term instrumental variables to form the predicted value of these observed variables in ordinary least squares (OLS) estimation. For this process, instrumental variables that are observed variables correlated with the variables in a product term (i.e., X1, income, and X1 × Income) but not correlated with the disturbance term (u1 in equation 15) are selected. We chose all indicators of ICI except for the scaling indicator of X1 (i.e., X2 through X11) and all demographic variables except for income (i.e., length of residence, education, and age) as instrumental variables. We also add all product terms between the selected indicators of ICI and demographic variables as instrumental variables, as recommended by Bollen and Paxton (1998). In the second stage of estimation, after the predicted values from the first stage replace the right-hand-side variables (i.e., X1, Income, and X1 × Income) from equation (15), the dependent variable of NPAB was regressed on these predicted values using OLS estimation. Through this two-stage process, we are able to perform significance tests using the estimated standard errors since the secondstage coefficient estimator is a consistent estimator with an asymptotic distribution (Bollen and Paxton 1998).

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ACKNOWLEDGMENTS We thank Ronald Goldsmith, Kenneth Manning, and Valarie Zeithaml for providing helpful comments on an earlier draft of this article. We also thank the anonymous reviewers for their helpful comments. The second author appreciates the financial support of the University of North Carolina Research Council. The usual disclaimer applies. Please direct all correspondence to the first author. NOTES 1. In our analysis, listwise deletion provides consistent parameter estimators without influencing the significance test for random missing values (Bollen 1989). The sample covariance matrix remains positive definite after the listwise deletion, and the reduction in sample size is not considered severe for our analysis. This process is necessary since some respondents systematically omitted a series of questions related to consumer innovativeness or demographics. 2. In our study, we do not consider binary demographic variables such as sex and marital status since the coefficients for the links between binary variables and other constructs cannot be directly estimated in structural equation modeling. 3. Our definition of innate consumer innovativeness is narrower than Hurt, Joseph, and Cook’s (1977) broad definition of innovativeness as “a willingness to change” in all domains of life. However, our definition of innate innovativeness is broader than domain-specific innovativeness for a specific product category (Goldsmith and Hofacker 1991; Goldsmith, Freiden, and Eastman 1995). 4. Taylor (1989) suggested that the originality subdimension of the Kirton Adaption-Innovation Inventory (KAI) can be divided into two subdimensions: (1) idea generation, represented by X1, X2, X3, X6, X7, X8, and X10 in our innate consumer innovativeness measure and (2) preference for change, represented by X4, X5, X9, and X11. The model with correlated measurement errors reflects this pattern. 5. Based on bootstrapping estimation using bias-corrected confidence intervals (with 200 replicated samples), we conclude that no significant bias exists for the coefficient estimates. This implies that maximum likelihood (ML) estimation of our data is generally robust against a violation of the multivariate normality assumption. 6. In addition, we found that the covariance between income and age is negative and significant (φ = –6.72, p < .05), the covariance between income and education is positive and significant (φ = 1.32, p < .05), and the covariance between the length of residence and age is positive and significant (φ = 65.97, p < .05). The covariance between income and length of residence, between length of residence and education, and between education and age are not significant at the .05 level.

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ABOUT THE AUTHORS Subin Im is currently an assistant professor of marketing at San Francisco State University. His primary scholarly interest includes the organizational aspects of innovation, new-product development for marketing strategy, the consumer side of the innovation adoption process, organizational learning in newproduct development, moral hazard and adverse selection model, and research methodology using multivariate statistical techniques. His current research projects include creativity in newproduct development, market orientation and innovation, consumer innovativeness, entrepreneurship and organizational learning in new-product development, the development of the creativity measure, the validation of the innovativeness measure, and the testing of nonlinear effects in structural equation modeling. He received his Ph.D. from the University of North Carolina at Chapel Hill. Subin worked in banking and semiconductor industries before he joined academia. Barry L. Bayus is a professor of marketing in the University of North Carolina’s (UNC) Kenan-Flagler Business School. Prior to joining the marketing faculty at UNC, Barry worked in both industry and academia. He has also served as an expert witness in patent infringement cases involving high-tech products. His teaching and research interests are in the areas of new-product design and development, marketing analysis and strategy, and technological change. His recent research is concerned with the creation and evolution of new markets and the historical evolution of products, as well as new-product development issues such as speed to market, product life-cycle management, new-product preannouncements, product proliferation, firm entry, and exit timing in dynamically changing markets. Charlotte H. Mason is an associate professor of marketing in the University of North Carolina’s Kenan-Flagler Business School, where she leads the MBA Customer and Product Management concentration. Her industry experience includes work for Procter & Gamble, Booz, Allen and Hamilton, as well as consulting projects. Her research focuses on the development and testing of marketing models and applications of multivariate statistics to marketing problems. She is currently investigating issues relating to the analysis and use of large customer databases as well as strategic issues surrounding customer portfolio management. Her research has been published in Marketing Science, the Journal of Marketing Research, the Journal of Consumer Research, Marketing Letters, the Journal of the Academy of Marketing Science, and the Journal of Business Research. She is on the review boards of the Journal of Marketing Research and the Journal of the Academy of Marketing Science and is coauthor (with William Perreault) of The Marketing Game!, a strategic marketing simulation.