international Journal of
Research in Marketing ELSEVIER
Intern. J. of Research in Marketing
13 (1996) 201-213
Construct validation of a measure of adaptive-innovative cognitive styles in consumption Richard P. Bagozzi a**, Gordon R. Foxall b ’ School of Business Administration. University of Michigan, Ann Arbor, MI 48109-1234, USA b Department of Commerce, University of Birmingham, Edgbaston. Birmingham, BI5 2Tir, England Received
15 December
1994; accepted 24 January
1996
Abstract
The validity of a three-factor model of Kirton’s Adaption-Innovation Inventory was examined by use of a confirmatory factor analysis. The following psychometric properties were established for both the full 32-item inventory and a 13-item abridgment: reliabilities of the measures of the three factors, convergent validity of measures, discriminant validity of measures across factors, discriminant validity between measures of the factors and measures of involvement, and concurrent validity. In addition, generalizability was demonstrated by comparing psychometric properties across two independent samples of adult consumers: 150 male computer owners and 151 female healthy food purchasers. Keywords:
Adaption-innovation;
Construct
validity; Innovativeness;
1. Introduction Innovativeness has been shown to be a key determinant of the adoption of new products in such areas as food consumption (e.g., Foxall and Haskins, 1986) and computer purchases (e.g., Foxall and Bhate, 1991). In this research, innovativeness is conceptualized as a cognitive style and measured with the Kirton Adaption-Innovation Inventory (KAI, Kirton, 1976). The KAI was developed to measure differences in
* Corresponding
[email protected].
author.
Tel:
0167-8116/96/$15.00 Copyright PI1 SOl67-8116(96)00010-9
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Cognitive
style
cognitive styles related to creativity, problem-solving, and decision making. The premise is that individuals can be placed on a personality continuum ranging from an extremely adaptive to an extremely innovative style. The adaptor has an orientation characterized by “doing things better”, whereas the innovator focuses upon “doing things differently” (Kirton, 1980). Adaptors tend, in the extreme, to be methodical, prudent, disciplined, conforming (especially to authority), timid in ideation, sensitive to people, risk averse, dogmatic, and even stodgy. Innovators, in contrast, tend to be impractical, unconventional in their thinking, undisciplined, irreverent toward consensual views, nonconforming, bold in ideation, insensitive to people, risk seeking, flexible, and even abrasive.
0 1996 Elsevier Science All rights reserved.
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In consumption contexts, virtually all studies to date using the KAI have treated adaption-innovation as a unidimensional construct (e.g., Foxall, 1994; Foxall and Bhate, 1991, 1993a,b; Foxall and Hackett, 1994; Foxall and Haskins, 1986). Research in nonconsumption contexts, however, has recently revealed that the KAI may, in fact, be multidimensional (e.g., Foxall and Hackett, 1992; Goldsmith, 1985; Hammond, 1986; Lowe and Taylor, 1986; Mulligan and Martin, 1980; Payne, 1987; Taylor, 1989a,b). A consensus is emerging in support of a tripartite representation of adaption-innovation with the KAI: (1) sufJiciency of originality, wherein adaptors are shown to present a few, typically implementable solutions to a problem, while innovators propose many, possibly impracticable solutions; (2) efficiency, wherein adaptors prefer to progress incrementally toward a defined goal, while innovators avoid painstaking attention to detail; and (3) rulegovernance, wherein adaptors prefer to restrict their behavior to the socially acceptable, while innovators flout convention, ignoring the rules or even inventing their own as they go. One purpose of the present study is to examine the validity of the three-dimensional representation of adaption-innovation for the consumption of new products. Previous research in nonconsumption contexts used an exploratory factor analysis to investigate the factor structure of the KAI. In our study, we use a confirmatory factor analysis and scrutinize the following psychometric properties of the scale: the reliabilities of the measures of the three components, the convergent validity of measures, the discriminant validity of measures across components, the discriminant validity between measures of the components and measures of another construct, and concurrent validity. We also examined generalizability of the three-factor model by comparing its psychometric properties across two independent samples of adults. Two senses of discriminant validity were investigated. Discriminant validity of measures across components was done to ascertain the distinctiveness of components. Each component was treated as a trait and its measures were examined and compared with the measures of the other components (see Section 2). To establish discriminant validity between measures of the KAI and measures of another construct
13 (1996) 201-213
(and concurrent validity), we investigated the relationship between the three-factor KAI and measures of involvement. Involvement can be thought of as the degree of personal relevance of a product in terms of one’s needs, values, or interests (e.g., Zaichkowsky, 1985). As a consequence, measures of innovativeness and measures of involvement should not be too highly correlated because they reflect different constructs. On the other hand, we expect involvement to be positively associated with sufficiency of originality and not necessarily related to efficiency and rule-governance. Involvement should be positively related to sufficiency of originality because it is a necessary condition in the generation of many solutions to a problem: adaptors proffer a small number of solutions and are less engaged in problem solving (i.e., they tend to react to problems with set, circumscribed patterns of behavior); innovators cultivate a multitude of solutions and are more invested and committed to problem solving (i.e., they tend to interact with problems in the sense of generating solutions, anticipating and challenging outcomes stemming from these solutions, and in general immersing themselves in the task at hand). Neither efficiency nor rule-governance is expected to relate to involvement because both styles have little if anything to do with effort or motivation, per se. Rather, they refer to the manner of decision making, as opposed to its intensity. The only research that could be found that related the KAI to involvementlike constructs is the recent study by Amabile et al. (1994). Amabile et al. (1994) found that scores on the KAI were positively associated with intrinsic motivation (r = 0.41) and negatively associated with extrinsic motivation ( r = - 0.18). We suspect that involvement shares emotive content with intrinsic and extrinsic motivation and thus should be correlated with that part of the KAI most closely measuring effort or engagement in creative activities (i.e., sufficiency of originality). A second purpose of the study is to corroborate the usefulness of an abridged version of the KAI. A 13-item abridgment of the KAI has been investigated before (e.g., Foxall and Hackett, 1992; Taylor, 1989a); but has been based upon exploratory, and not confirmatory, factor analyses. In addition, the shortened scale has not been studied in the consumption context.
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J. of Research in Marketing
2. Method
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food; oat-bran based cereal; wheatmeal crackers; meat-extract drink; sparkling spring water; low fat, low-calorie meals; high-fiber, bean-based meal; high-fiber, oat-bran bread; and low-fat, sunflower spread. The mean number of purchases of brands from this group was 2.64 (s.d. = 1.5 1).
2. I. Subjects and procedure One hundred and fifty adult male computer owners residing in southern England comprised Sample I. Respondents were selected by a random walk procedure, and in-home interviews were conducted on behalf of the investigators by a professional market research organization. As background information, the computer users exhibited a mean of 2.45 software applications (s.d. = 1.34), where wordprocessing had been tried by 7 l%, video games by 53%, programming by 41%, personal finance software by 24%, data analysis (including database management) by 16%, personal record software by 1l%, spreadsheets by 7%, design/CAD functions by 6%, graphics by 5%, and other business and educational functions by 10%. For Sample II, 151 adult female healthy food consumers were recruited as they left supermarkets in southeast England. To be counted in the sample, each consumer had to have purchased at least one of nine food brands which had been introduced to the market the preceding four months. These foods had been extensively promoted on the basis of their contribution to a healthier lifestyle and resided in the following product classes: muesli-based breakfast
2.2. Measures The Kirton Adaption-Innovation Inventory was used to measure cognitive styles indicative of an adaptive versus innovative orientation. The KAI is a paper and pencil questionnaire on which a respondent is asked how easy or difficult he or she would find it to maintain specific adaptive or innovative behaviours. Thirty-two such items comprise the inventory, including as examples: “a person who conforms”, “a person who is methodical and systematic”, and “a person who has fresh perspectives on old problems” (see Taylor, 1989a,b for a description of the scale and its abridgment). Responses are recorded on a continuous scale anchored by “very hard” and “very easy”, with “hard” and “easy” in between as general markers. The Personal Involvement Inventory (PII) was used to measure ego-involvement at the product level (i.e., the importance of the product to the individual). The PI1 is a IO-item, 7-point scale, on
041
Fig. 1. Confirmatory
203
factor analysis model for the three-factor
adaption-innovation
scale and involvement.
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which the respondent indicates the extent to which he or she finds a specified object or activity personally important-unimportant, boring-interesting, exciting-unexciting, etc. (Zaichkowsky, 1994; see also Zaichkowsky, 1985). 2.3. Confirmatory factor analysis model A useful way to approach construct validation is with the confirmatory factor analysis model (e.g., Steenkamp and Van Trijp, 1991). Fig. 1 presents the confirmatory factor analysis model investigated herein. In this model, four latent variables or factors are shown (see ri’s in ellipses), which correspond to sufficiency of originality, efficiency, rule-conformity, and involvement. The three latent variables corresponding to the components of the KAI are shown connected to two boxes each in Fig. 1. The boxes or indicators represent aggregations of items corresponding to the measurements of the components. For example, the first indicator of sufficiency of originality (i.e., DO,) consists of the sum of subject responses to half of the items for this component, while the second indicator (XSOj) consists of the sum of subject responses to the other half of items. Likewise for the other components of the KAI shown in Fig. 1 (E = efficiency, RG = rulegovernance); and I = involvement. The items assigned to the respective indicators are selected at random from those shown to load on a single factor under exploratory factor analyses, as found in previous research (e.g., Foxall and Hackett, 1992; Goldsmith, 1985; Hammond, 1986; Lowe and Taylor, 1986; Mulligan and Martin, 1980; Taylor, 1989a,b) and reverified with the data at hand herein. This approach to measurement has been termed the partial disaggregation model in the literature (e.g., Bagozzi and Heatherton, 1994) and has been used by researchers in ability and personality studies (e.g., Hull et al., 1991; Marsh and Hocevar, 1985). The partial disaggregation model represents a compromise between the most aggregative approach (i.e., the standard practice of summing responses to all items of the KAI) and the most disaggregative approach (i.e., treating each item as an individual indicator of its appropriate factor). The main drawback with the aggregative approach is that information is lost and the distinctiveness of components is obscured. The
main drawback of the most disaggregative approach is that it is very sensitive to measurement error (making it difficult to obtain satisfactory fits of models) and many parameters must be estimated (thereby requiring large samples to achieve appropriate ratios of sample size to parameter estimates). The partial disaggregation model overcomes both drawbacks. The primary drawback with the partial disaggregation model concerns the basis for aggregating items, which introduces an element of arbitrariness. The rule used herein was to perform an exploratory factor analysis to confirm whether the hypothesized items load on the appropriate factors and then form composites by summing items to form pairs of indicators for each component. For the latent involvement variable shown in Fig. 1, the 10 PI1 items were summed to provide a single indicator, following recommended practice (e.g., Zaichkowsky, 1985). Each KAI indicator in Fig. 1 has two arrows terminating into it. The arrows from latent variables to indicators stand for sources of variance in the indicators that are due to the individual components of the KAI; the six h,‘s adjacent to the arrows are factor loadings relating latent variables to indicators. The six short arrows with ai’s at their origins depict variation in the indicators due to error. Finally, the curved lines connecting pairs of factors stand for correlations between the indicated factors and are designated 4jjk. As a consequence of the estimation procedure use to infer values for the +,jk’~, the correlations are corrected for attenuation due to the unreliability in the measures. With the variance of the factors standardized to 1.00, it is possible to examine the +jjk’s and their standard errors and determine whether the factors are distinct (i.e., achieve discriminant validity). Alternatively, Chisquare difference tests can be used to compare models with #jjk’s free to models where 4jjk’s are fixed to unity. 2.4. Estimation of models and assessment of fit The confirmatory factor analysis model was estimated by use of LISRELS (Jdreskog and Sijrbom, 1993). One measure of model fit that was used is the likelihood ratio Chi-square statistic, which can be used to test the null hypothesis that the model in Fig. 1 reproduces the population covariance matrix of the
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observed variables. By convention, an acceptable model is one where the p-value is greater than or equal to 0.05. Reliance on the Chi-square test as the sole measure of fit is not recommended because of its dependence on sample size. For example, in large samples even trivial deviations of a hypothesized model from a true model can lead to rejection of the hypothesized model or, for very small samples, large deviations of a hypothesized model may go undetected. Therefore, it is desirable to examine other measures of fit not as sensitive to sample size. Another limitation with the Chi-square test is that it does not directly provide an indication of the degree of lit such as is available with indices normed from 0 to 1. An additional approach to the assessment of goodness-of-fit is to use an index that is based on the comparison of the fit of a hypothesized model to the fit of a baseline model, such as the null model of modified independence, where the latter assumes that all variables are uncorrelated (i.e., only error variances are estimated). Such an approach is termed an incremental fit index in that a hypothesized model is compared with a more restricted, nested model. The best known index in this regard is the comparative tit index (CFI) developed by Bentler (1990) (see also the relative noncentrality index of McDonald and Marsh, 1990): cFI=
(x~~-eJ-(x;-4-f) (x02-&?)
’
(1)
where x,’ and xf’ are for the null and focal models, respectively. The CFI is normed in the population and thus has values bounded by zero and 1. Equally important, the CFI provides an unbiased estimate of its corresponding population value, and therefore it should be independent of sample size. Monte Carlo studies show that the CFI performed well for sample sizes varying from 50 to 1,600, in the sense of producing unbiased estimates and estimates low in variability (Bentler, 1990). From an intuitive perspective, the CFI can be thought of as a measure of how much variation in measures is accounted for from a practical standpoint. A rough rule of thumb is that the CFI should be greater than or equal to 0.90, where values less than 0.90 suggest that significant amounts of vari-
205
ance remain to be explained and values greater than or equal to 0.90 imply that further relaxation of parameter constraints are not warranted and might lead to over fitting. The null model for the CFI recommended by researchers (e.g., Bentler, 1990) is the model hypothesizing that the variance-covariance matrix is any positive definite matrix (i.e., it is an unconstrained matrix). A reviewer proposed another null model that provides additional perspective. Namely, a second null model of interest is the one hypothesizing two latent constructs: one for adaption-innovation measured by six indicators and one for involvement measured by a composite of 10 items. To differentiate this comparative fit index from the standard CFI, we shall represent it as CFI’, where the I refers to the goodness-of-fit of the multidimensional model shown in Fig. 1 relative to the second null model discussed above. 2.5. Tests of hypotheses The overall goodnesses-of-fit (i.e., x2 and WI) provide information about the degree of correspondence between the model in Fig. 1 and the data. Further analyses are required to ascertain the degree of construct validation. An indication of the magnitude of convergence of measures within components of the KAI can be gained by examining the factor loadings. These should be high and significant. The square of the standardized factor loadings show the amount of variance in the respective measures due to the hypothesized component. The reliability of the composite of measures loading on a factor can be computed as
(Ch;)zvq Sj)
pc =(C/Q2var( tj) + cei ’
(2)
where Ai is the factor loading on the jth factor, sj, ei is the corresponding error variance, var( tjl is the variance of the jth factor, and the summations are over loadings and error variances for a particular factor. Discriminant validity among components of the KAI can be assessed by examining 4*r, &, and in Fig. 1. These correlations should be signifi432
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cantly less than 1.0; the smaller the correlation, the greater the discrimination. Concurrency between the components of the KAI and involvement can be determined by evaluating 4441,44421and & in Fig. 1. As developed above, we expect involvement to be significantly related only to sufficiency of originality. The maximum likelihood estimation procedure in LISREL assumes that the distribution of measurements are multivariate normal. We checked the distribution of each item by use of PRELIS and found that some individual items showed excessive skewness and kurtosis. However, all tests of hypotheses are based on the sums of items. The tests of kurtosis and skewness for the sums of items revealed no significant departures from normality, with one exception: the second indicator for rule-governance in Sample I showed slight negative kurtosis (k = - 2.16, p < 0.02). Correlation matrices were used as input for the confirmatory factor analyses of the individual samples, whereas covariance matrices were used for tests of generalizability and structured means (discussed hereafter). 2.6. Generalizability To the extent that the structure of the KAI and its relationship to involvement generalize, the validity of the scale will be enhanced. We investigated generalizability by comparing the psychometric properties of the KAI and its association with involvement across Samples I and II. For the structure of the KAI, we tested first whether the factor patterns are equal across the samples. The question addressed here is whether the same factors underlie the measures for the two samples. A failure to reject this hypothesis was followed by a test of the equality of factor loadings. Equal factor loadings imply that the correspondence between factors and indicators are the same across samples. Given equal factor loadings, it is meaningful to test for the invariance of error variances. A failure to reject this hypothesis and the hypothesis of equal factor loadings, suggests that the measures are equally reliable for the two samples. Next, given equal factor loadings and error variances across samples, we tested whether the variance-covariance matrixes for the KAI factors were invariant. Finally, we tested whether the covariance between
sufficiency of originality and involvement was the same for Samples I and II. 2.7. Structured means The tests of generalizability address the measurement properties and structure of the KAI, as well as the association between sufficiency of originality and involvement. It is also interesting to examine differences in mean levels of the dimensions of the KAI across samples. Differences in factor means were investigated for sufficiency of originality, efficiency, and rule-governance. The structured means procedure described in Jijreskog and S&born (1989) was used.
3. Results 3.1. Conjirmatory factor analyses
A summary of goodness-of-fit indices is provided in Table 1. Although the x*-tests for the CFAs are significant in 2 of 4 cases, the CFIs exceed the 0.90 criterion in every instance, thus pointing to satisfactory overall fits in the sense of comparing the threedimensional KAI to the most general case of no structure underlying the data. The relative CFI’s reveal large improvements in fit for the three dimensional KAI models over the unidimensional KAI models. Table 2 shows the parameter estimates for the CFAs of the full 32-item and abridged 13-item KAI for Sample I (home computer users) and Sample II (healthy food product users). For the 32-item KAI, the model in Fig. 1 fit well for Sample I ( x*(10) = 20.84, p P 0.02, CFI = 0.96) and Sample II ( x*(10) = 38.01, p P 0.00, CFI = 0.91). Likewise for the 13-item abridged KAI, the model fit well for Sample I (x*(10) = 16.46, p P 0.09, CFI = 0.96) and Sample II (x*(10) = 12.36, p z 0.26, CFI = 0.98). Inspection of Table 2 reveals that all factor loadings are significant and generally high. The correlations among the dimensions of the KAI are all positive and significant for the 32-item version in both samples. The correlations between SO and E are moderate, between SO and R are moderately high, and between E and R are high (in fact, E and R fail to
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207
Eb
’ b ’ d e
0.00 0.00 0.75CO.17) 0.70(0.16) 0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.54(0.13) O.Ss(O.18) 0.00
0.00 0.00 0.00 0.00 0.66tO.08) 0.93fO.08) 0.00
R’
Sufficiency of originality. Efficiency. Rule-governance. Involvement. Standard errors in parentheses.
0.88(0.15) 0.69(0.13) 0.00 0.00 0.00 0.00 0.00
Abridged (1 j-item) scale
0.92fO.08) = 0.00 0.83(0.08) 0.00 0.00 0.65(0.081 0.00 0.89(0.081 0.00 0.00 0.00 0.00 0.00 0.00
soa
Factor loadings
Full (32-item) scale
0.00 0.00 0.00 0.00 0.00 0.00 1.00
0.00 0.00 0.00 0.00 0.00 0.00 1.00
Id
1.00 O.M(O.lll 0.25fO.11) 0.19(0.091
1.00 0.2NO.09) 0.53(0.07) 0.16fO.08)
so
0.00 0.27fO.11) -O.ll(O.lO)
1.00 0.73(0.071 -0.08(0.09)
E
1.00 -0.11(0.09Il.O0
1.00
I
scale
0.72(0.11) 0.8d0.12) 0.00 0.00 0.00 0.00 0.00
0.86(0.09) 0.84Co.091 0.00 0.00 0.00 0.00 0.00
SO
0.00 0.00 0.73(0.131 0.5NO.l I) 0.00 0.00 0.00
0.00 0.00 O.XNO.08) 0.87(0X191 0.00 0.00 0.00
E
Factor loadings
0.00 0.00 0.00 0.00 0.78(0.16) 0.47fO.12) 0.00
0.00 0.00 0.00 0.00 0.57(0.08) 0.75(0.09) 0.00
R
0.00 0.00 0.00 0.00 0.00 0.00 1.00
0.00 0.00 0.00 0.00 0.00 0.00 1.00
I
Healthy food product users ( N = 15 1)
adaption-innovation
I .OO 0.04(0.091
R
versions of the three-factor
Factor intercorrelations
estimates for full and abridged
Home computer users ( N = 150)
Table 2 Key parameter
1.00 0.87(0.08) 0.04fO.09)
E
1.OO 0.18fO.10)
R
1.00
I
1.00 -0.20(0.111.00 -0.06(0.12)D.53(0.13) 1.00 -0.01(0.10)3.10(0.10) 1.00 0.36(0.081
1.00 0.29fO.10) 0.43(0.101 0.33(0.08)
so
Factor intercorrelations
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show discriminant validity in Sample II>. For the 13-item abridgment, the correlations among dimensions are uniformly lower than corresponding values for the full scale. Indeed, SO and E are not significantly correlated in either sample, SO and R are positively correlated at a low level in Sample I but not significantly correlated in Sample II, and E and R are positively correlated at a relatively low level in Sample I and at a moderately high level in Sample II. Further insight into the measurement properties of the measures of the separate dimensions of the KAI can be seen in the composite indicators of reliability. As presented in Table 3, the composite reliabilities are high for SO, E, and R on the 32-item scale for Sample I. Likewise, the composite reliabilities of SO and E are high on the 32-item scale for Sample II. The reliability for R on the 32-item scale for Sample II is low. The composite reliabilities for the 13-item abridgment are uniformly lower than for the full scale. For Sample I, the reliabilities of SO, E, and R are in the high 0.60s. For Sample II, the reliability is high for SO but low for E and R. The variance Table 3 Reliabilities
and variance extracted
for the components
209
extracted (VE) is greater than 50% in 9 of 12 instances (see Table 3). Relatively low values for variance extracted are found for rule governance on the 32-item scale for healthy food product users (VE = 0.44) and for efficiency (VE = 0.44) and rule-governance (VE = 0.43) on the 13-item scale, also for healthy food product users. As hypothesized, sufficiency of originality is positively and significantly correlated with involvement for both the full and abridged KAIs and in both samples. The correlation between SO and I is relatively low for home computer users (rjd, = 0.16 and C#Q,= 0.19 for the full and abridged KAI, respectively) and moderate for healthy food product users CC&,= 0.33 and r&i = 0.36 for the full and abridged KAI, respectively). Also as expected, involvement was not significantly correlated with E or R in either sample. 3.2. Generalizability of the KAI Table 4 summarizes the findings for the tests of generalizability of the 32-item KAI across Samples I
of the adaption-innovation
scales
Rehability Home computer
users ( N = 150)
Healthy food product users (N = 151)
Full (32-item) scale
so a
Eb
RC
SO
E
R
0.87
0.75
0.78
0.84
0.70
0.61
Abridged (13-item) scale so
E
R
SO
E
R
0.66
0.69
0.68
0.77
0.61
0.57
Variance extracted Home computer
users (N = 150)
Healthy food product users (N = 15 1)
Full (32-item) scale
so
E
R
so
E
R
0.77
0.61
0.65
0.72
0.55
0.44
Abridged (13-item) scale
so
E
R
so
E
R
0.63
0.53
0.53
0.62
0.44
0.43
a Sufficiency of originality. b Efficiency. ’ Rule-governance.
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Table 4 Tests of generalizability of the three-factor adaption-innovation scale (32-item version) Model
Goodness-of-fit
Tests of hypotheses
M, : Baseline M2: Factor loadings invariant M,: Factor loadings and error variances invariant M.,:Factor loadings, error variances, and factor covariances invariant
x2(12,N “= 150, NF b= 151)-44.10, p=O.OO,GFI=0.96 ,$(18,NC = 150, NF = 151) = 50.09, p = 0.00 ,$(24,NC = 150, NF = 151) = 75.08, p = 0.00
M, -M,, x;(6) = 5.99, p > 0.40 M, - M,, x;(6) = 24.99, p < 0.001
x2(27,NC = 150, NF = 151) = 83.85, p 3 0.00
M4 - M,, x;(3) = 8.77, p < 0.04
NAC
* Home computer users. b Healthy food product users. ’ Not applicable.
and II. It can be seen that the factor pattern and factor loadings are invariant across samples. However, the error variances and factor variances and covariances differ. Table 5 presents the results for the tests of generalizability of the 13-item abridged KAI. Here we find that the factor pattern, factor loadings, error variances, and variances and covariantes among factors are invariant across samples. 3.3. Structured means To examine the difference across samples in the mean levels of sufficiency of originality, efficiency, and rule-governance, a test of structured means was performed. Home computer users scored significantly higher on sufficiency of originality than healthy food product users under the 32-item KAI (AM = 0.49, t = 3.87) and the 13-item abridgment (AM = 0.55, t = 4.05). No significant differences were found across samples on efficiency for the 32-item KAI (AM = 0.02, t = 0.16) or the 13-item
abridgment (AM = 0.02, t = 0.15). Similarly, no significant differences across samples were found on rule-governance for the 32-item KAI (AM = 0.23, t = 1.77) or the 13-item KAI (AM = 0.05, t = 0.32). Fig. 2 summarizes the differences in factor means.
4. Discussion The findings provide strong support for a three-dimensional conceptualization of adaption-innovation styles as measured by the KAI. Three factors were found corresponding to sufficiency of originality, efficiency, and rule-governance. Convergent validity of measures within each factor was generally high, as was reliability. Discriminant validity was achieved both between pairs of factors and between the factors and involvement. Concurrent validity was attained for sufficiency of originality with involvement. Generalizability across the samples was established, too. Finally, except for a few differences noted below,
Table 5 Yests of generalizability of the three-factor adaption-innovation scale (13-item version) Tests of hypotheses
Model
Goodness-of-fit
M, Baseline
x2(12,N c ’= 150, NF ’ = 151) = 19.84, p = 0.07. GFI = 0.98 NA = x2(18,NC = 150, NF = 151) = 27.69, p E 0.07 M, - M,, x:(6) = 7.85, p > 0.20 M, - M2, x;(6) = 12.00, p > 0.05 ~‘(24.N~ = 150, NF = 151) = 38.69, p c 0.03
M,: Factor loadings invariant
M,: Factor loadings and error variances invariant M4: Factor loadings, error variances, ~‘(27.N~ = 150, NF = 151) = 41.18, p s 0.04 and factor covariances invariant a Home computer users. b Healthy food product users. ’ Not applicable.
M4 - M,, x;(3) = 2.49, p > 0.50
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Means
211
I
-.3
I I
Sufficiency of originality
I
Efficiency
I
Rulegovernance
Resealed Factor Means
I Sufficiency of originality Fig. 2. Profiles of structured
I Efficiency
I Rulegovernance
means. (Top) Full (32-item) scale. (Bottom) Abridged (13-item) scale.
the above results held for both the full 32-item KAI and the 13-item abridgment. A number of differences between the 13- and 32-item versions of the KAI should be noted. The reliabilities of measures of the three components are generally higher for the full scale than for the 13-item abridgment. This is to be expected, as the full scale incorporates 19 more items than the abridgment. The reliability of measures of rule-governance from the 32-item scale for healthy food product users was low ( p = 0.611, as were the reliabilities of measures of both efficiency ( p = 0.61) and rule-governance ( p = 0.57) from the 13-item scale for healthy food product users. The nine remaining reliabilities were all satisfactory (Table 2).
The degree of distinctiveness of the components of adaption-innovation also varied for the 32- and 13-item versions of the KAI. Generally, the components were more highly correlated for the full scale than for the abridgment. The 32-item scale demonstrates a relatively high amount of shared variance of items across the three components. The 13-item abridgment shows less shared variance in this sense, and indeed, sufficiency of originality was, in fact, essentially orthogonal to both efficiency and rulegovernance. Thus there is little to recommend using the sum of all items to indicate a unidimensional representation. If one desires to investigate the effect of total creativity on a criterion, then a second-order factor for the three KAI first-order factors could be
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used to predict the criterion. This would be consistent with the original intent of the scale (Kirton, 1976) and subsequent applications to date. However, this procedure will work well only when considerable shared variance exists across measures of all components. The findings reveal further that the three-factor representation of adaption-innovation is remarkably stable. For the 32-item KAI, the factor pattern and factor loadings were the same across samples. For the 13-item KAI, the factor pattern, factor loadings, error variances, and variances and covariances among factors were invariant across samples. These are impressive results, indeed, since the samples differed in gender, products purchased, and other unmeasured characteristics. A caveat that should be mentioned is the possibility of an innovation bias in the sample. Because each consumer in Sample II was selected on the basis of having purchased at least one of nine newly introduced healthy foods over a four-month period, it is likely that the sample has a positive predisposition for innovativeness. This would tend to increase the mean scores for the KAI in comparison to shoppers selected at random. Note, however, that the means for efficiency and rule-governance did not differ between participants in the two samples and that home computer users showed higher means on sufficiency of originality than healthy food product users. The likelihood of an innovation bias should not affect the tests of hypotheses on the measurement properties of the scales, as these are based on slope parameters and not means. A number of directions for future research can be suggested. Now that the psychometric properties and generalizability of the three components of adaption-innovation have been established, it would be interesting to discover the differential impact of the components on such aspects of consumption as new product trial, adoption, and satisfaction. Likewise, the components might influence decision making in different degrees. It would be informative, as well, to explore how sufficiency of originality, efficiency, and rule-governance develop and what variables influence them. The cross-cultural applicability of the three components deserves further study, too. Finally, a related topic concerns the conditions under which the components converge or diverge. When
can one expect the components to function independently (for example, as predictors of consumption)? When might they coalesce or even influence one another? The three-factor model of adaption-innovation studied herein and its confirmatory factor analysis representation permit one to investigate issues such as these.
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