10. Vandecasteele, B., & Geuens, M. (2010)

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Motivated Consumer Innovativeness: Concept, measurement, and validation

Bert Vandecasteele a, Maggie Geuens b,c,⁎a Lessius University College, Department of Business Studies, Korte Nieuwstraat 33, B-2000 Antwerpen, Belgiumb Ghent University, Faculty of Economics and Business Administration, Tweekerkenstraat 2, B-9000 Gent, Belgiumc Vlerick Leuven Gent Management School, Department of Marketing, Gent, Belgium

a b s t r a c ta r t i c l e i n f o

Article history:

First received in 8, July 2009 and was under

review for 3 and ½ months

Area Editor: Jacob Goldenberg

Keywords:

Consumer innovativeness

Motivation

Scale development

Scale validation

Existing consumer innovativeness scales ignore the multitude of motivation sources of buying innovations.

The objective of this paper is to incorporate different motivations into a multi-dimensional innovativeness

scale to better account for the consumer–product relationship. An extensive literature review and ve studies

(with about 2600 respondents in total) indicate that four types of motivation underlie consumer

innovativeness: functional, hedonic, social, and cognitive. The proposed 20-item four-dimensional Motivated

Consumer Innovativeness (MCI) scale proves to be reliable and internally valid and does not seem to suffer

from social desirability bias. Moreover, the results of the studies indicate the predictive validity of every MCI

dimension. This new scale proves to measure more than existing consumer innovativeness scales; the

different MCI dimensions predict innovative purchase intentions better than both traditional and recently

developed innovativeness scales, and they disprove the general consensus that older people are always

signicantly less innovative than younger people. This MCI scale can serve as a tool for future research on

ef ciently and effectively segmenting and targeting (motivated innovative) consumers.

© 2010 Elsevier B.V. All rights reserved.

1. Introduction

Since the early seventies, several researchers have tried to predict

consumers' innovative buying behavior (i.e., the purchase of innova-

tions or new products) using different scales intended to measure

innovativeness as a personality trait. However, most previous research

disregards the consumer–product relationship (Gatignon & Robertson,

1985; Goldsmith & Flynn, 1992; Subramanian & Mittelstaedt, 1991);

there are few to no consumers who buy every new product of which

they are aware. In addition, Ostlund (1974) states that it is not solely

personality traits that are relevant but also the consumers' product

perceptions. Therefore, to understand consumer innovativeness well,

we must consider the interaction between the consumer and the

productitself. As a rst attempt, Goldsmith and Hofacker (1991)launch

the idea of domain-specic innovativeness (i.e., innovativeness withina

specic product domain of interest). However, Roehrich, Valette-

Florence, and Ferrandi (2003)question its discriminant validity because

the scale resembles Laurent and Kapferer's (1985) product category

interest scale more strongly than an innovativeness scale (Roehrich,

1994). In addition, Goldsmith and Hofacker's (1991) scale is not a pure

personality scale because it is very product specic. Finally, Baumgartner

(2002, p. 287) argues that“personality is best understoodin terms of thegoals that people pursue in their lives […].”

Building on this previous research, we would like to extend the

measurement of product–consumer interactions in consumer inno-

vativeness, working beyond existing, mostly uni-dimensional scales

to construct a new consumer innovativeness scale that incorporates a

diversity of underlying goals and motivations associated with buying

an innovation. We base our research on Rogers (2003, p. 115), who

states that “[w]e should increase our understanding of the motiva-

tions for adopting an innovation. Such ‘why’ questions about adoption

have seldom been probed effectively.” Huffman, Ratneshwar, and

Mick (2000) are also convinced that motivational goals provide us

with more powerful explanations for consumer behavior.

The main objective of the current paper is thus to develop and

validate a multi-motivational consumer innovativeness scale based

on general motivation and value taxonomies. Most of the current

innovativeness scales ignore the different sources of motivation. An

innovativeness scale that is more balanced in addressing potential

purchase motivations will go beyond existing scales of this type,

addressing the differences between consumers in terms of not only

their level of innovativeness but also their type of innovativeness. Five

studies in total are carried out to develop and validate the scale.

Additionally, in accordance with the trend toward ultra-short scales

(Geuens, Weijters, & De Wulf,2009; Rammstedt & John, 2007), special

attention is paid to developing a scale that is short and easy to use.

This is especially important because consumer innovativeness is often

only one of several measures in a questionnaire.

Intern. J. of Research in Marketing 27 (2010) 308–318

⁎ Corresponding author. Ghent University, Faculty of Economics and Business

Administration, Tweekerkenstraat 2, B-9000 Gent, Belgium. Tel.: +32 9 264 3521.

E-mail addresses: [email protected] (B. Vandecasteele),

[email protected] (M. Geuens).

0167-8116/$ – see front matter © 2010 Elsevier B.V. All rights reserved.

doi:10.1016/j.ijresmar.2010.08.004

Contents lists available at ScienceDirect

Intern. J. of Research in Marketing

j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / i j r e s m a r

http://dx.doi.org/10.1016/j.ijresmar.2010.08.004http://dx.doi.org/10.1016/j.ijresmar.2010.08.004http://dx.doi.org/10.1016/j.ijresmar.2010.08.004mailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.ijresmar.2010.08.004http://www.sciencedirect.com/science/journal/01678116http://www.sciencedirect.com/science/journal/01678116http://dx.doi.org/10.1016/j.ijresmar.2010.08.004mailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.ijresmar.2010.08.004


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2. Phase 1: theoretical background

2.1. Consumer innovativeness

Rogers and Shoemaker (1971, p. 27) dene innovativeness as “the

degree to which an individual is relatively earlier in adopting new

ideas than the average member of his social system.” This denition

focuses on the level of innovativeness that is observable in behavior.

Marketing researchers refer to this type of innovativeness as realizedor actualized innovativeness. In the late 1970s, researchers started to

dene innovativeness as a personality trait. Midgley and Dowling

(1978) are the rst to point out that innovativeness is a hypothetical

construct and is by denition unobservable. They refer to it as innate

innovativeness and describe it as being situated on a higher, more

abstract level than realized innovativeness (Foxall, 1988, 1995;

Hirschman, 1980; Midgley & Dowling, 1993; Steenkamp, Hofstede,

& Wedel, 1999). Consumer innovativeness is part of that broader, more

general category of innate innovativeness and indicates innovative

consumer behavior. It can be conceptualized as “the tendency to buy

new products in a particular product category soon afterthey appear in

the market and relatively earlier than most other consumers in the

market segment” (Foxall, Goldsmith, & Brown, 1998, p. 41). Leavitt and

Walton (1975) are among the rst researchers to develop a self-report

measure of consumer innovativeness; others follow with different

kinds of scales (e.g., Baumgartner & Steenkamp, 1996; Goldsmith &

Hofacker, 1991; Hartman, Gehrt, & Watchravesringkan, 2004; Le

Louarn, 1997; Manning, Bearden, & Madden, 1995; Roehrich, 1994;

Tellis, Yin, & Bell, 2009; Venkatraman & Price, 1990). This innate

innovativeness should provide an explanatory basis for realized

innovativeness ( Joseph & Vyas, 1984; Midgley & Dowling, 1978).

However, the issue of predictive validity is still problematic (e.g., Im,

Bayus, & Mason, 2003); some researchers (e.g., Citrin,Sprott, Silverman,

& Stem, 2000) are not able to detect signicant results, and although

others do nd a positive relationship, this correlation in some cases

accounts for 10 percent of behavioral variance at best (e.g., Cotte &

Wood, 2004; Roehrich et al., 2003). Moreover, these researchers do not

conceptualize consumer innovativeness in the same way. Several

researchers acknowledge the importance of different motivations orsources of innovativeness (as Daghfous, Petrof, & Pons, 1999 observe as

well) but take into account two different types of motivations at most.

We hope that by including a wider spectrum of motivations, we will be

able to construct an innovativeness scale that performs better in terms

of both content validity and predictive validity. Toward this end, we

rst review the different motivational dimensions that have been

linked to consumer innovativeness in the past and describe how these

dimensions t into the categories of general goals, values, and

motivational frameworks.

2.2. Motivational dimensions in current innovativeness scales

First, many consumer innovativeness scales include a hedonic

dimension. Oneexample often used to measure innovativeness(Chesson,2002; Steenkamp et al., 1999) is Baumgartner and Steenkamp's (1996)

Exploratory Consumer Buying Behavior scale. One of their subscales is

Exploratory Acquisition of Products, which refers to buying innovations

intended to stimulate the senses. Venkatraman and Price (1990) also

include a Sensory Innovativeness dimension in their concept of

innovativeness, and Roehrich (1994) denes his Hedonic Innovativeness

dimension as the drive to adopt innovations for hedonic reasons, such as

to enjoy the newness of the product.

Working from a different perspective, Hirschman (1984) and

Venkatraman (1991) point to innovative consumers who are attracted

to functional or useful new products. Babin, Darden, and Grif n (1994)

and Voss, Spangenberg, and Grohmann (2003) propose a similar

distinction in emphasizing utilitarian reasons for buying products (as

opposed to hedonic or affective reasons).

Of course, products are not always purchased for their hedonic or

functionalvaluealone.Consumers also want to impress others and raise

their social status (Brown and Venkatesh, 2005; Foxall et al., 1998).

Thus, innovativeness researchers stress the importance of the social or

symbolic component of consumer innovativeness (Roehrich, 2004;

Rogers, 2003;Venkatraman, 1991). Arnould (1989) andFisher andPrice

(1992) observe that social rewards and social differentiation may both

stimulate new product adoption. Simonson and Nowlis (2000) state

that the possession of innovations is a socially accepted way of makingaunique impression. Consumers build a certain identity through the

possessionof thesevisible newproducts(Tian, Bearden,& Hunter,2001;

Tian & McKenzie, 2001).

Finally, Cognitive Innovativeness is a distinct dimension of innova-

tiveness in the scale by Venkatraman and Price (1990) and isdened as

“the desire for new experiences with the objective of stimulating the

mind” (p. 294). Baumgartner and Steenkamp's (1996) Exploratory

Information-Seeking is also dened as providing mental stimulation,

although it is focused on information-seeking rather thanon measuring

consumer innovativeness.

2.3. Conceptual positioning of each dimension within taxonomies of

general goals, values, and motivations

The four dimensions reected in the innovativeness literature also

correspond to more general theories of values, goals, and motivation

(cf., Table 1).

First, motivations are what energize and encourage goal-oriented

buying behavior (Rossiter & Percy, 1997), and these motivations are

activated by the goals that individuals pursue. Taking the taxonomy of

human goals of Ford and Nichols (1987) as a reference framework, we

can see that the hedonic dimension of consumer innovativeness ts

nicely with Ford and Nichols's affective goals, such as arousal and

happiness. Hedonically motivated innovative consumers buy innova-

tions because they want to be excited and experience feelings of joy and

satisfaction.Cognitiveinnovativenesscan be conceptualized as resulting

from Ford and Nichols's cognitive goals, including exploration,

understanding, and intellectual creativity. Cognitively motivated inno-

vativeconsumers want to expandtheirown cognitivelimits. Individualswho score high on social motivation to innovate want to reach goals

referred to as self-assertive social relationship goals by Ford and Nichols

(1987). Goalsthat express individuality, self-determination, superiority,

and resource acquisition are important for these individuals because

they allow them to feel unique, special, or free and they can compare

each other in terms of winning, status, or success. Individuals with this

orientation obtain approval from others by buying innovations. Finally,

functional innovativeness seems to result from certain of Ford and

Nichols' task goals (e.g., masteryand management, which enableone to

reach one's goals in a proper, more ef cient and qualitative way).

Functionally motivated innovative consumers buy innovations to

improve their performance or the organization of things, to increase

their productivity, and to avoid threatening circumstances.

Secondly, as far as values are concerned, the innovativenessdimensions also mesh with existing value taxonomies. Based on

Schwartz's (1992) generally accepted value taxonomy, hedonic inno-

vativeness can be conceptualized as stimulation because it focuses on

the stimulation of the senses. Next to stimulation, this hedonic

innovativeness dimension ts uniquely into the hedonism value, with

pleasure and joy as important elements. The social source of

innovativeness lies mainly in the goals related to power, with social

power and public image as important factors. Cognitive innovativeness

originates in the desire for stimulation (in this case, mental) and

achievement (e.g., being considered intelligent and capable). Unlikethe

three previous dimensions of innovativeness, functional innovativeness

is not specically related to any of Schwartz's value dimensions.

However, the functional dimension and the other three dimensions of

innovativeness do emerge in Sweeney and Soutar (2001) who

309B. Vandecasteele, M. Geuens / Intern. J. of Research in Marketing 27 (2010) 308 – 318


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distinguish between four main types of value related to consumption:

functional, emotional (hedonic), social, and epistemic value. Epistemic

value isdened as “the perceived utility […] to arouse curiosity,provide

novelty, and/or satisfy a desire for knowledge” (Sheth, Newman, &

Gross, 1991, p. 162) and thus recalls the cognitive dimension.

Finally, it is important to note that some motivation researchers

develop broad categories of consumer needs and motivations as well.

Rossiter and Percy (1997), for example, distinguish between negative

reinforcers (i.e., informational motives such as problem removal andproblem avoidance) and positive reinforcers (i.e., transformational

motives such as sensory gratication, intellectual stimulation,and social

approval). The former can be interpreted as functional motivations and

the latter as hedonic, cognitive, and social motivations. Additionally,

Vallerand (1997) acknowledges the existence of four types of motiva-

tions. Within his intrinsic motivation dimension, he highlights motiva-

tion toward accomplishments (similar to functional motivation in this

study), motivation to experience stimulation (similar to hedonic

motivation in this study), and motivation to know, which is related to

constructssuchas learning goalsand intellectualism (and resemblesour

cognitive motivation dimension). In addition to emphasizing these

intrinsic dimensions, he also mentions extrinsic motivations (which

resemble our social motivations).

2.4. Conclusion

Despite all of this evidence in favor of different motivational

dimensions, the number of motivations included in current consumer

innovativeness scales is limited to two at most (e.g., Roehrich, 1994;

Venkatraman & Price, 1990). It is surprising that hardly any innovative-

ness scale has been developed that includes a wider array of potential

consumer motives. Indeed, a multi-dimensional consumer innovative-

ness scale is useful; it may have more predictive power for innovative

buying behavior because it takes into account a more complete range of

motivations for innovativeness. Moreover, such a scale may help

marketing researchers and managers to identify and reach motivated

innovative consumers, garnering interest in an innovative product or

service more effectively and ef ciently. It may also play a part in new

product development and marketing communications. The mainobjective of the current research is to ll this gap, developing and

validate a multi-dimensional consumer innovativeness scale called

Motivated Consumer Innovativeness (MCI) that takes into account the

different motivations of innovative consumers. (1) Functionally Moti-

vatedConsumer Innovativeness (fMCI) refers to self-reported consumer

innovativeness motivated by the functional performance of innovations

and focuses on task management and accomplishment improvement;

(2) Hedonically Motivated Consumer Innovativeness (hMCI) is concep-

tualized as self-reported consumer innovativeness motivated by affective

or sensorystimulation andgratication;(3)Socially Motivated Consumer

Innovativeness (sMCI) is dened as self-reported consumer innovative-

ness motivated by the self-assertive social need for differentiation; and

(4)CognitivelyMotivated Consumer Innovativeness(cMCI)refersto self-

reported consumer innovativeness motivated by mental stimulation.

3. Phase 2: scale development and renement

To ensure that (1) all motivational dimensions of consumer

innovativeness are covered and (2) only relevant aspects are taken into

account, a review of the literature is combined with exploratoryresearch.

This process allows us to be condent that we are beginning with the

appropriate items. Then, in studies 1 to 3, the scale is tested and rened to

create a short, practical, and easy to use scale.

3.1. Item generation and content validation

A total set of 254 items is constructed. This item pool originates

from the (1) review of the literature on innovativeness (n=68), (2)

the different existing consumer innovativeness scales (n= 77), (3) in-

depth interviews in which we probed reasons to buy innovations

using a convenience sample of 37 consumers who had recently

bought anything from a list of 502 innovations (n=67), and (4) an

exploratory quantitative study requesting that 279 online respon-

dents indicate to what extent 135 human motives (Chulef, Read, &

Walsh, 2001) affected their purchase of innovative products (n=42).

The relevant motivational items all correspond to one of the four a

priori de

ned dimensions.

1

Thus, the importance of each of the fourdimensions is conrmed, and there are no indications that an

additional dimension should be taken into account.

As Hardesty and Bearden (2004) and Rossiter (2002) stress the

importance of expert judgments to correctly dene a construct, the

authors, ve experts (Marketing Department members), and six non-

student consumer judges critically evaluate all items. The judges are

asked to pay attention to content validity, representativeness, dimen-

sionality, comprehensibility, and unambiguousness. If two judges

encounter an issue in assigning an item to a particular dimension, or

when twoor more judgesdeeman item notto be valid or representative,

it is deleted. Some items are reworded to address the judges' comments.

This procedure yields 90 remainingitems, of which 24 are functional, 24

are hedonic, 22 are social, and 20 are cognitive. Examples of deleted

items are“Buyinginnovations canmakemy day” (no clear dimension),“I

love brand switching” (not necessarily connected with consumer

innovativeness), and “I love experimenting” (vague).

3.2. Study 1: pilot study

This quantitative pilot study is intended to assess some basic

psychometric properties of the 90-item MCI scale and to purify the

scale, limiting it to a more manageable number of items.

3.2.1. Respondents, procedure, and measures

We recruit 452 respondents (M age=36, SD =15; 54% women) for

an onlinesurvey via35 webforums. Thequestionnaire includes the90

MCI items, which are randomly rotated. To be able to establish

convergent validity, we include Roehrich's (1994) 11-item Hedonic

and Social Consumer Innovativeness scale for half of the respondents(alpha=.866 and .887, respectively). We expect a signicantly higher

correlation between the respective hedonic and social components of

Roehrich's (1994) scale and ours than between the other dimensions.

The Exploratory Acquisition of Products (10 items) variety-seeking

subscale developed by Baumgartner and Steenkamp (1996)

(alpha=.863) and the 12-item Extraversion scale developed by

Eysenck, Eysenck, and Barrett (1985) (alpha=.901) are added for the

other half of the respondents to establish discriminant validity. We

expect no correlation or a relatively low correlation to exist between

these two scales and the MCI scale because the constructs of variety-

seeking and extraversion are conceptually distinct (e.g., Weijters,

Geuens, & Roehrich, 2004). All items are measured using a ve-point

Likert scale (1=strongly disagree, 5=strongly agree). Finally, the

respondents answer socio-demographic questions.

3.2.2. MCI results

Principal component analysis (promax rotation) yields 14 factors

with eigenvalues exceeding 1. After this procedure and an identical

analysis focusing on the theorized four factors, only items that load

higherthan .50on their focal factorand nothigher than .30on another

in one of the analyses are retained (Hair, Anderson, Tatham, & Black,

1998). A second analysis of the 57 items that are left points to four

factors (as does the scree plot) and yields 43 items. The Cronbach's

alphas of the four dimensions (alphasMCI =.929; alphafMCI =.907;

alphahMCI =.928; alphacMCI = .902) are comfortably high. The four

1 The detailed results of these exploratory studies are available from the authors

upon request. Table 1 offers some examples of the results of these studies.


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factors account for57.7%of thetotal variance, and each factorexplains

at least 5.4% of the total variance, fullling the minimal requirements

presented by Netemeyer, Bearden, and Sharma (2003). All item-to-

total correlations exceed .50, and the inter-item correlations of each

dimension exceed .30.

On the basis of a conrmatory factor analysis (with SAS CALIS

procedure), we delete the items with factor loadings below .60 and

squared multiple correlations below .50. Three items that have squared

multiple correlations between .47 and .50 are retained because of theircontribution to the content of the scale (Rossiter, 2002). These are the

only scale items that employthe phrases “easierto use”and“convenient

size” (fMCI) and “desire” (hMCI). A conrmatory factor analysis of the

remaining 30 items indicates an acceptable overall t (TLI=.952,

CFI= .952, RMSEA= .047). Additionally, the factors are shown to

possess high internal validity and suf cient discriminant validity.

Composite reliability (CR) and average variance extracted (AVE) are

satisfactory for sMCI (CR=.93, AVE=.64), fMCI (CR=.89, AVE=.53),

hMCI (CR=.92, AVE= .55), and cMCI (CR= .91, AVE=.58). The

average variance extracted is always larger than the squared correla-

tions between the factors (cf., Fornell & Larcker, 1981), proving the

discriminant validity of the dimensions. Moreover, this four-factor

correlated model proves to be the best model (χ2 =731.1, df =399,

BIC=−1635.9);it issuperior to null(χ2 =7318.2, df =435), one-factor

(χ2 =2933.6, df = 405, BIC = 531.0), and four-factor uncorrelated

(χ2 =1220.2, df =405, BIC=−1182.4) models. We can conclude that

this MCI scale and its dimensions have good internal consistency.

3.2.3. Convergent and discriminant validity results

sMCI appears to be strongly correlated with Roehrich's social

dimension (r =.790), and hMCI is strongly correlated with Roehrich's

hedonic dimension (r =.727). As expected, weaker correlations

emerge between fMCI and Roehrich's innovativeness scale

(r =.509) and between cMCI and Roehrich's scale (r =.629). Further,

Exploratory Acquisition of Products is weakly but signicantly

correlated with all of MCI's subscales (r =between .166, p =.019 for

fMCI and .242, p =.001 for cMCI) except sMCI (r =.104, p =.152).

Finally, the MCI dimensions are never signicantly correlated with

Extraversion.

3.3. Study 2: re nement study

The main objective of this study is to conrm the results of the

pilot study and further rene the 30-item scale.


Students (n= 349, M age =21, SD=2; 63% women) from the

departments of Economics and Business Administration (36%) and

Political and Social Sciences (63%) at a Western European university

are recruited through the websites of their respective departments.

The online survey includes the 30 MCI items in addition to socio-

demographic questions (gender, age, and undergraduate study).

3.3.2. Results

A conrmatory factor analysis of the 30 MCI items offers results

that are similar to those of previous analyses. On the basis of the

conrmatory factor analysis results, we remove the items with low

factor loadings (ve items), relatively low SMC (three items), and

relatively high modication indices (two items), yielding 20 items.

Again, the t indices (TLI =.977, CFI= .980, RMSEA= .033) indicate a

good modelt. Thefour-factorcorrelated model (χ2 =224.7, df =164,

BIC=−735.6) outperforms the other models (χ2N452.4, df =190–

170, BICN−543.0). The nal scale consists of 20 items (cf., Table 2).

An overview of the performance of the nal 20-item MCI scale

across previous, current, and future studies can be found in Table 3.

These results conrm the internal consistency of the MCI dimensions,

and the conrmatory factor analysis t statistics exceed the

recommended criteria well. The four-factor correlated model always

outperforms the null model, a one-factor model, and a four-factor

uncorrelated model.

As further proof that reducing the MCI scale from 90 items to the

nal 20 items does not lead us to exclude an important part of the

construct, we correlate the dimensions of the 90-item MCI and 20-

item MCI from study 1. This results in correlations as high as .90 (for

fMCI and hMCI), .91 (for cMCI), and .94 (for sMCI).

3.4. Study 3: test –retest reliability and socially desirability bias

Because theMCI scale is a personality scale,the conceptsmeasured

should be stable over time. Moreover, the respondents should not

respond to the scale items in a socially desirable manner.

All respondents from the previous study who volunteered to

participate in future surveys are invited through e-mail to ll out a

second questionnaire. A total of 111students(32% response rate; time

lag 36–60 days; M age=21, SD =2; 67% women) take part in this

follow-up study. The retest questionnaire consists of the nal MCI

items and a shortened version of the Marlowe–Crowne Social

Desirability scale — the 11-item scale by Ballard (1992, alpha=.605).

Aconrmatory factor analysis of the retest data yields results similarto those of previous analyses (cf., Table 3). Test–retest correlations for

MCI's four dimensions range from .56 (fMCI), over .68 (sMCI) and .72

(hMCI) to .75 (cMCI). According to the social desirability tests, neither

dimension shows a signicant correlation with the Social Desirability

scale (r between −.16 for sMCI and .03 for hMCI).

4. Phase 3: MCI's predictive validity

An innovativeness scale must also predict innovative consumer

behavior in everyday life. In the MCI scale, there should be a unique

relationship between each motivation dimension and the buying

intentions or buying behavior of consumers seeking innovations that

satisfy these specic functional, hedonic, social, or cognitive needs

(predictive validity). Additionally, the MCI scale should predict

Table 2

20-item Motivated Consumer Innovativeness (MCI) scale.

Factor Item

Social I love to use innovations that impress others.

I like to own a new product that distinguishes me from others who do

not own this new product.

I prefer to try new products with which I can present myself to my

friends and neighbors.

I like to outdo others, and I prefer to do this by buying new products

which my friends do not have.I deliberately buy novelties that are visible to others and which

command respect from others.

Functional If a new time-saving product is launched, I will buy it right away.

If a new product gives me more comfort than my current product, I

would not hesitate to buy it.

If an innovation is more functional, then I usually buy it.

If I discover a new product in a more convenient size, I am very

inclined to buy this.

If a new product makes my work easier, then this new product is a

“must” for me.

Hedonic Using novelties gives me a sense of personal enjoyment.

It gives me a good feeling to acquire new products.

Innovations make my life exciting and stimulating.

Acquiring an innovation makes me happier.

The discovery of novelties makes me playful and cheerful.

Cognitive I mostly buy those innovations that satisfy my analytical mind.

I

nd innovations that need a lot of thinking intellectually challengingand therefore I buy them instantly.

I often buy new products that make me think logically.

I often buy innovative products that challenge the strengths and

weaknesses of my intellectual skills.

I am an intellectual thinker who buys new products because they set

my brain to work.

312 B. Vandecasteele, M. Geuens / Intern. J. of Research in Marketing 27 (2010) 308– 318


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general innovation buying behavior better than existing scales. Weverify that these requirements are met using ctitious innovations

(study 4) and a list of existing innovations selected from different

product categories (study 5).

4.1. Study 4: predictive validity study with ctitious innovations

4.1.1. Pretest

Four different non-existent innovation packs for mobile phones

are created, each representing one of the four motivation dimensions.

Table 4 shows the descriptions as presented to the respondents.

A pretest with 40 respondents (M age=29, SD=8; 45% women)

shows that each innovation scores signicantly higher on the

intended motivational dimension than on the other dimensions

(M cognitive motivation =5.8 versus M otherb4.7, F (3,37) =12.5, pb .001;M hedonic motivation =5.8 versus M otherb4.3, F (3,37) =61.6, pb .001;

M functional motivation =6.5 versus M otherb4.0, F (3,37)=79.0, pb .001;

and M social motivation =6.1 versus M otherb5.4, F (3,37)=67.5, pb .001).


Four hundred and seven members of an opt-in consumer panel

participatein the predictivevalidity survey. The questionnaire begins by

addressing general interest in mobile phones (Beatty & Talpade, 1994)

and mobile phone buying frequency. Next, a description of the four

innovative option packs is presented, followed by questions on

awareness (“Have you heard from this product before?” with three

answer choices “yes”/“no”/“unsure”; “unsure” is considered equivalent

to “no” following Manning et al., 1995). The questions that follow are

Table 3

Performance of 20-item MCI scale across ve samples.

Study 1 Study 2 Study 3 Study 4 Study 5

Participan ts General population Univ ersity students Unive rsity studen ts Re presentative populati on General po pulation

Sample size 452 349 111 329 826

Number of items 90 30 20 20 20

Likert 1–5 Likert 1–5 Likert 1–5 Likert 1–7 Likert 1–5

Scale mean MCI

- cMCI 2.47 2.43 2.43 3.81 2.54

- sMCI 1.96 2.05 2.01 2.74 1.98

- hMCI 2.74 3.22 3.19 4.27 2.86

- fMCI 3.01 2.89 2.89 4.37 3.25

Standard deviation MCI

- cMCI .76 .68 .72 1.02 .77

- sMCI .84 .79 .74 1.20 .84

- hMCI .86 .75 .73 .99 .87

- fMCI .77 .73 .76 1.01 .82

Internal consistency

- Lowest corrected item-total correlation (N.50)

- cMCI .65 .68 .69 .70 .68

- sMCI .58 .69 .61 .68 .75

- hMCI .67 .54 .57 .67 .67

- fMCI .61 .54 .65 .79 .62

- Average inter-item correlation (N.3)

- cMCI .58 .59 .63 .62 .59- sMCI .65 .63 .52 .73 .67

- hMCI .58 .46 .61 .59 .58

- fMCI .52 .44 .55 .58 .51

- Composite reliability (N.80)

- cMCI .88 .88 .90 .89 .88

- sMCI .91 .89 .85 .93 .91

- hMCI .87 .81 .88 .88 .87

- fMCI .85 .80 .86 .88 .84

- Average variance extracted (N.50)

- cMCI .60 .59 .63 .62 .59

- sMCI .67 .63 .52 .73 .67

- hMCI .58 .47 .60 .59 .58

- fMCI .54 .44 .55 .59 .51

Factor analyses

- Percentage of total variance explained with

four factors (N

50)

68.1 63.4 67.2 71.4 67.4

- Minimal explanation of each factor (N5) 7.2 7.9 7.7 6.8 6.2

- CFA t TLI (N.90) .972 .977 .934 .960 .980

- CFA t CFI (N.95) .976 .980 .943 .965 .982

- CFA t RMSEA (b.06) .041 .033 .059 .053 .034

- CFA lowest standardized loading MCI (N.60) .70 .61 .66 .71 .68

Comparison four-factor correlated model with… χ2 =265.0 χ2 =224.7 χ2 =228.1 χ2 =311.7 χ2 =320.1

(df = 164) BIC =−707.8 BIC =−735.6 BIC =−544.2 BIC =−634.3 BIC =−787.5

- Null model (df =190) χ2 =4344.5 χ2 =3214.1 χ2 =1311.4 χ2 =4350.1 χ2 =9311.8

- One-factor model (df =170) χ2 =1789.7 χ2 =1589.9 χ2 =651.6 χ2 =1706.8 χ2 =2797.5

BIC = 790.3 BIC = 594.6 BIC =−149.0 BIC = 726.2 BIC = 1655.7

- Four-factor uncorrelated model (df =170) χ2 =650.6 χ2 =452.4 χ2 =323.6 χ2 =737.7 χ2 =1452.8

BIC=−357.9 BIC =−543.0 BIC =−477.0 BIC =−242.9 BIC = 310.9



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related to product trials (“Have you bought this product before?” with

identical answer choices), attitude towards the product (using a seven-

point semantic differential: good–bad, favorable–unfavorable, pleas-

ant–unpleasant), and buying intention (“Next time I buy a new mobile

phone, I want this option pack included,” with answers to be provided

accordingto a seven-point disagree–agree Likert scale). Thesecondpartof the survey presents the 16-item Cognitive and Sensory Innovative-

ness scale by Venkatraman and Price (1990), the 10-item Global

Consumer Innovativeness scale by Tellis et al. (2009), and the 20-item

MCIscalewith an instruction check as an extra item intended to identify

participants who have not read the items carefully (Oppenheimer,

Meyvis, & Davidenko, 2009). Finally, the respondents score each

innovative option pack using a seven-point Likert scale with a

description of the four possible innovative motivations as a manipula-

tion check. The survey closes with the socio-demographic questions. Of

the 407 respondents, 78 (i.e., 19%) fail to follow instructions (this

percentage is in line with the results by Oppenheimer et al., 2009 who

report failure rates between 7% and 46%dependent on the respondents'

motivations). Three hundred twenty-nine usable questionnaires re-

main. The corresponding respondents are representative of the

population in terms of gender (51% women), age (18–25 years: 15%;

26–35 years: 27%; 36–45 years: 28%; 46–60 years: 29%),education(39%

had completed their higher education), professional status (49%

employed full-time), and family situation (40% living with a partner

and child(ren)).

4.1.3. Results

The innovation motivation manipulation is successful; all fourinnovation packs score signicantly higher on intended motivated

innovativeness than on the other motivations (M cognitive motivation=5.2

versus M otherb4.5; M hedonic motivation =5.0 versus M otherb4.2;

M functional motivation=5.9 versus M otherb4.2; M social motivation =5.1

versus M otherb4.6; all p b .001). Because no one could actually have

bought these made-up innovations, we delete those respondents who

think that they are familiar with (awareness between 1.5% and 4.3%) or

claim to have bought (trial between 0.6% and 1.5%) one of them.

Multiple regression analyses taking product attitudes (alpha's between

.952 and .973) and buying intentions as the dependent variables for the

four innovative mobile phones and the four MCI dimensions as the

independentvariables (controllingfor productcategory interest, buying

frequency, andscores forproduct attitudes andbuying intentions forthe

other innovative packs)indicate predictive validity in seven of the eight

cases. Table 5 shows that the only variable that is not predicted by its

predetermined dimension is the intention measure for the hedonic

innovation.

After conducting this predictive analysis, we compare the

predictive strength of the MCI scale with two existing innovativeness

scales: (1) the 16-item Cognitive and Sensory Innovativeness scale by

Venkatraman and Price (1990), a traditional innovativeness scale that

uses cognitive (alpha=.730) and sensory (alpha=.777) sub-dimen-

sions to measure “engaging in new experiences with the objective of

stimulating the senses [… and] the mind” (p. 293); and (2) the

recently developed 10-item Global Consumer Innovativeness scale by

Tellis et al. (2009), which measures the “propensity to adopt new

products” (p. 1) using three factors: openness for innovations

(including stimulus variation, opinion leadership, and risk-taking;

alpha= .617), change-seeking (including novelty-seeking, variety-seeking, and habituation, which has an inverse relationship to

innovativeness; alpha =.448), and Reluctance (including effort,

nostalgia, suspicion, and frugality, all of which also have inverse

relationships to innovativeness; alpha=.528). The nal innovative-

ness scaleis theMCI scale consisting of thefour predetermined factors

Table 4

Overview of the description of the four mobile phone options with different motivation

manipulations (study 4).

Dimension Description

Functional This option pack consists of the following options:

- No adapter needed with the “Wire Free” technology. Your mobile

phone battery is constantly charged by three alternative energy

sources: light energy with the built-in solar cells, motion energy from

your daily movements, and energy from radio waves in the air.

- With the “Destruction Proof ” option, your mobile phone is optimallyprotected with invisible bumpers that prevent it from falling in 1000

pieces when it hits the oor. Moreover, this mobile phone is self-

cleaning and completely waterproof, which makes this phone much

more durable than its predecessors.

Hedonic This option pack consists of the following options:

- The built-in “Happy Me” function positively stimulates you every

day. This mobile phone can feel the user's mood through sensors.

Based on this, you will get specially adapted amusing messages like

jokes and cartoons on your screen. With this option, you can spend

your day cheerful and good humored.

- Thanks to the “Rise & Shine” awakening programs, your mobile

phone awakes you in a contented way. You get up with a good feeling

as the light intensity of your phone gradually increases. Together with

a set of stimulating awakening sounds you will get up with a happy

feeling. This option will keep you cheerful and good humored during

the day.

Social This option pack consists of the following options:- With the “Be U(nique)” technique, you can make a deep impression

on your friends. This eye-catching mobile phone can endlessly adapt

itself depending on who is in your neighborhood. Your phone will

always be unique and different: the looks change in the presence of

others, so nobody will have the same model.

- With the “Shout Proud” option, you can show programmed

messages to your neighborhood with the message line on the back of

thismobile phone.Impress friends, acquaintances, family, or strangers

with unexpected and original messages.

Cognitive This option pack consists of the following options:

- Thankstothe “Point & Learn” option,you getto knoweverything about

objects you photograph with your mobile phone. For example, do you

want toknow more about a specic product,thenyouonlyhaveto take a

picture of the barcode or theadvertisement.You willinstantlyget more

information about the ingredients, product content, and history of the

brand. This option gives you also more information about monuments,

buildings, and other places, home and abroad.

- “Brain Train” stimulates your mind and trains your memory andbrain thanks to the added brain exercises: do IQ tests, concentration

and number exercises, discover the possibilities of your brain with

memory tests, and learn new languages with interactive language

exercises, wherever you are. With this mobile phone option, next to

improving your memory and brain functions, you can also optimize

your concentration.

Table 5

Unstandardized coef cients of regression analyses (standardized coef cients in

brackets) with attitude toward the product and buying intention scores for mobile

phone innovations as the dependent variables and the four MCI dimensions as the

independent variables (study 4).

Type of

innovation

Dependent

variable

fMCI hMCI sMCI cMCI

Functional Attitude .150

(.149)*

NS NS NS

Buying intention .344

(.314)***

NS NS NS

Hedonic Attitude NS .180

(.116)*

NS NS

Buying intention NS NS NS NS

Social Attitude NS NS .336

(.238)***

NS

Buying intention NS NS .260

(.201)***

NS

Cognitive Attitude NS NS −.205

(−.173)**

.238

(.171)**

Buying intention NS NS NS .242

(.175)**

NS= not signicant, *pb .05, **pb .01, ***pb .001, p-values two-sided; gures in bold

are the expected signicant coef cients.



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(with alphas between .875 and .930). Table 6 shows the explanatory

value of the MCI scale in comparison with that of the other two

innovativeness scales. Using Williams's t-test for non-independent

correlations based on Steiger (1980), we achieve results indicating

that MCI explains a signicantly larger part of the variance than the

other innovativeness scales in 16 of the 20 cases, whereas the results

for the other four cases are as expected (i.e., we achieve a higher R 2

with the MCI model than with the other models).

4.2. Study 5: predictive validity study with existing innovations

We select 96 innovations (a well-balanced mix of new FMCGs,

durables,and services)from a list of 502 innovations that we haddrawn

up during the exploratory research phase and after an extensive search

for “new” products/services conducted in several venues: (a) super-

markets and shops; (b) websites for national brands in a variety of

product categories; and (c) advertisements for these products and

services in magazines. Products or services are removed from the list of

502 innovations if they do not meet the conditions described in the

denitionof an innovation (e.g., products that areno longerperceived as

innovative because a majority of consumers use them), cannot be used

or purchased by everybody (e.g., innovations within the category of

female hygiene products and products specically used by individuals of a certain age), or arenot affordablefor everybody(e.g.,luxury products).

To determine which motivation each of the existing innovations

inspires, a pretest is conducted.

4.2.1. Pretest

The 96 innovations are randomly divided into three groups of 62

respondents (M age=32, SD=9; 44% women) in total. The respondents

are staff members of the Economics and Business Departments of two

Western European higher education institutions. They score each

product according to the four motivation dimensions (e.g., “One

would purchase this new product for functional reasons”) using a

seven-point Likert scale (1=totally disagree, 7=totally agree) based

on a denition of each motivation. The innovations that score

signicantly higher on one motivation dimension than the other three

dimensions (i.e., prototypical innovations) are selected for use in the

predictive validity survey. This includes seven functional, six hedonic,

two social, and one cognitive innovations.


Predictive validity is tested using online self-report surveys

completed by consumers who are recruited through an announce-

ment in Metro, a free Belgian newspaper. Five gift boxes worth€250 in

total are offered to respondents as an incentive. The recruitment

efforts result in 1101 completed surveys that take on average 20 min

to complete (M age =32, SD =12; 58% women).

The questionnaire refers to the 16 selected, existing innovations. A

description of each innovation is accompanied by a picture of the

product and a link to a website for the innovation. The respondents

answer a buying intention question for each innovation using an 11-

point scale indicating the chances, from 0 to 10, that the respondent

will buy the product within the next 12 months, making allowances

for external factors such as budget restraints and responsibility. Some

ller items are included next, followed by the 20-item MCI scale with

an instruction check as an extra item (Oppenheimer et al., 2009) andsome socio-demographic questions.

4.2.3. Results

First, the instructional manipulation item is analyzed to exclude

those respondents whodo notread theitems carefullyenough. Twenty-ve percent of all respondents are omitted as a result, leaving 826

respondents. A conrmatory factor analysis of the 20 items using the

four-factor correlated model shows t comparable to that achieved in

previous studies (cf., Table 3). The dependent variables of the multiple

regression analyses are mean intentions to buy the prototypical

functional, hedonic, social, and cognitive innovations. Taking the four

MCI dimensions as independentvariables, Table 7 shows thatall buying

intentions are predicted by the expected MCI dimensions. First, buying

intention for the prototypical functional products is predicted by justone dimension: fMCI. Secondly, buying intention for prototypical

hedonic products is predicted by hMCI but also by fMCI. Thirdly,

prototypical social product buying intention is only signicantly

explained by sMCI. Finally, as expected, buying intention for the

prototypical product offering cognitive innovation is predicted signif-

icantly by cMCI but not by any of the other three dimensions. These

results conrm the predictive validity of the MCI scale established in

study 4, not for nicely controlled ctitious innovations but rather for

multiple real-life innovations.

5. Innovativeness and socio-demographics: nuancing a former

consensus

Having developed and validated a new and more effective MCI scale,we can also disprove the general consensus (cf., Dickerson & Gentry,

1983; Manning et al., 1995; Steenkamp et al., 1999; Tellis et al., 2009;Uhl,

Andrus, & Poulsen, 1970; Venkatraman, 1991) that older people are

always signicantly less innovative than younger people. A Manova

with MCI's four dimensions as the dependent variables and gender

and age as the independent variables using a combination of the

datasets of studies 1, 4, and 5 shows that age is only signicant for

sMCI (F (4,1488) =21.11, pb .001), hMCI (F (4,1488) =12.99, pb .001),

and, to a lesser extent, cMCI (F (4,1488)=5.88, pb .001). However, this

is not the case for fMCI (F (4,1488)=1.67, p =.155); older people are as

functionally innovative as younger people. Gender also leads to

signicant differences. Men are signicantly more innovative than

Table 6

Explained variance (adjusted R 2) of MCI compared with the Cognitive/Sensory Innovativeness scale (the CSI, by Venkatraman & Price, 1990) and the Global Consumer

Innovativeness scale (the GCI, by Tellis et al., 2009) (study 4).

Type of innovation Dependent variable MCI (four dimensions) CSI (two dimensions) Williams's t-test MCI-CSI GCI (three dimensions) Williams's t-test MCI-GCI

Functional Attitude .078*** .023* 1.86 .039** 1.49

Buying intention .209*** .033* 4.14*** .129*** 2.04*

Hedonic Attitude .093*** .054*** 1.21 .026* 2.75**

Buying intention .203*** .041** 3.90*** .077*** 3.74***

Social Attitude .155*** .055*** 2.47* .048*** 3.48***

Buying intention .189*** .032* 3.86*** .087*** 2.89**

Cognitive Attitude .126*** .063*** 1.65 .047* 2.50*

Buying intention .185*** .041** 3.50*** .080*** 2.91**

All Attitude .180*** .066*** 2.70** .076*** 3.24**

Buying intention .294*** .059*** 5.05*** .159*** 3.41***

*pb

.05, **pb

.01, ***pb

.001, p-values two-sided; Williams's t-test is distributed as a Student's t , with df =n−

3.



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women because of social (F (1,1488)=22.46, pb .001), cognitive (F (1,1488)=

15.62, pb .001), and hedonic motivations (F (1,1488)=9.96, pb .001).

However, when it comes to functional innovativeness, women are

as innovative as men (F b1). Moreover, for cMCI and fMCI, a

signicant interaction effect between gender and age emerges.

Youngmen have higher cMCI than young women, butfromthe age of

46 onwards, men and women score equally on cMCI. fMCI increases

with age for women and decreases for men. Other socio-demograph-

ic characteristics such as education, income, or family situation do

not affect the MCI dimensions.

6. General discussion

The primary contribution of this research is that it validates a new

consumer innovativeness scale that takes into account multiple

motivations for buying innovations. This effort yields a four-

dimensional consumer innovativeness scale including hedonic,

functional, social, and cognitive dimensions. There are several reasons

why this new consumer innovativeness scale is useful. First, the ve

studies show repeatedly and in great detail that the dimensionality,

reliability, and convergent, discriminant, and predictive validity of the

MCI scale are satisfactory.Secondly, this study allows us to conclude that the MCI scale

measures more than the existing consumer innovativeness scales. The

MCI scale measures not just the intensity of consumer innovativeness

but also itsorigin. Additionally, theMCI scale, with itsfour dimensions

of purchase motives, maintains the middle ground between the

existing general innovativeness scales, which are unimpressive in

predicting innovative buying behavior, and the Domain-Specic

Innovativeness scale developed by Goldsmith and Hofacker (1991),

which shouldscorebetter in terms of predictive validity butis product

specic and thus not very practical. We prove that the MCI scale

performs better in terms of predicting innovative behavior than the

existing general innovativeness scales. Especially for more ground-

breaking innovations designed with the different motivations in

mind, the predictive power of thenew scale seems high, amountingto30% (cf., study 4). Finally, this study disproves the general consensus

that younger people are more innovative than older people; older

people are as innovative as younger people as far as functional

innovations are concerned. Most existing innovativeness scales focus

on hedonic and — to a lesser degree — social innovativeness. Because

older people are less interested in buying innovations purely for the

sake of fun, excitement, or status (i.e., based on hedonic and social

motivations), these scales do not capture the innovativeness of older

people.

6.1. Managerial implications

This research also has practical relevance for product and marketing

managers who are confronted with the dif culty of creating and

launching successful innovations. In general, this research may assist in

three managerial domains. (1) The four different innovativeness

motives may provide an interesting line of reasoning for innovation

developers and be a useful product development tool. For example,

when a certain consumer need associated with a specic motivation is

not yet satised by a product within a specic product category, it may

be helpful to develop a newproduct thatcan satisfythat particular need.

(2) Helping to identify (motivated) innovative consumers, we offer

different insight than the general consensus in literature, which hasindicated thatinnovative consumersare usuallymale and younger than

non-innovative consumers. The use of the MCI scale as a useful market

segmentation tool proves that as far as functional innovations are

concerned, older people and women are as innovative as younger

consumers or men. As a result, an innovation with a functional

advantage as its unique selling proposition may be targeted toward

older or female consumers as well. (3) Moreover, these differently

motivated innovative consumers should be targeted with effective

marketing communicationsthat emphasize and trigger boththe specic

motivation and innovativeness itself; marketers should take into

account the needs that their innovations satisfy or the relative

advantages that they confer when selecting the most effective

positioning, communication message, communication medium, and

communication vehicle. For example, communications to hedonic

innovative consumers should focus on the pleasure of acquiring the

innovation. These innovative consumers may be approached more

effectively with experiential marketing, fun, creative TV, or viral

advertisements, whereas an in-store product demonstration or an

informational TV commercial may be a more effective way to reach

functional or cognitive innovative consumers.

6.2. Research limitations and further research

One limitation of this study is that only one Western European

country was surveyed. Lynn and Gelb (1996) show that nationality,

even withinEurope, may inuence innovativeness scores. However, the

objectiveof this research wasto constructa multi-dimensional scale and

to compare different motivations for innovation rather than to

determine a general, worldwide standard for Motivated ConsumerInnovativeness. Therefore, even though the means may differ between

countries and cultures, it is very unlikely that the type of motivations

will be different because these dimensions are based on motivation,

goal, and value taxonomies that have been validated cross-culturally

(e.g., Schwartz, 1992).

Moreover, we would not recommend using MCI as a general

innovativeness scale by simply summing the scores for all 20 items

because the model is not conceptualized andhas not been validatedin

this way. If we were to use this method, a consumer who scored high

on only one of the four motivations would no longer be recognized as

an innovative person, even though he clearly would be based on our

conceptualization of MCI. A general consumer innovativeness scale

would measure the tendency to buy innovations because of the

newness of the products as such. This newness could be considered ameans to different ends, ends that we conceptualize in our four-factor

correlated model. Thus, when one wants a full prole of an innovative

consumer, all four dimensions are of importance. In this sense, the

MCI scale could also be considered a formative model using the four

motivation dimensions, which in turn are reexive constructs.

Additionally, although the results of our predictive validity studies

are satisfactory, there are some issues that require further research.

First, although the cognitive dimension of MCI performs as expected

for the ctitious innovations, it was dif cult to identify existing

innovations that primarily minister to the cognitive motivation. On

the list of 96 innovations, there was only one innovation that scored

highest on the cognitive dimension. This result seems inconsistent

with the discriminant validity of cMCI with respect to the other three

dimensions. However, we must remember that the MCI scale is a

Table 7

Unstandardized coef cients of regression analyses (standardized coef cients in

brackets) with the buying intentions for the existing innovations as the dependent

variable and the four MCI dimensions as independent variables (study 5).

Buying intention R 2 fMCI hMCI sMCI cMCI

- Functional

innovations

.080 .419 (.142)*** NS NS NS

- Hedonic

innovations

.040 .267 (.110)** .228 (.099)* NS NS

- Socialinnovations

.060 NS NS .297 (.101)* NS

- Cognitive

innovation

.036 NS NS NS .534 (.110)*

NS=not signicant, *pb .05, **pb .01, ***pb .001; p-values two-sided; gures in bold

are the expected signicant coef cients.



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personality scale; the products used in study 5 could also be

purchased for other (personal) reasons (cf., Gatignon & Robertson,

1985; Venkatraman & Price, 1990) than those indicated in the pretest,

which presents average motivations for purchasing these products. In

fact, all existing products, including the most functional, hedonic, and

social innovations,can be purchased because they stimulatethe mind,

for example. With this in mind, an interesting avenue for future

research could be to rate the specic reasons or motivations of each

respondent for each product questioned. Secondly, the predictivepower of the new MCI scale differs signicantly for study 4 and study

5. The two studies seem to differ in two important ways: (1) the type

of innovations, i.e., whether they are ctitious and specically

developed with one of the four dimensions in mind or already exist

and are better performing on one dimension of innovativeness

relative to the others; and (2) the extent to which the innovations

can be classied as breakthrough as opposed to incremental. The

majority of the Consumer Packaged Goods (CPGs) among the 96

innovations in the pretest and all of the CPGs included in the 16

innovations used in study 5 are classied as incremental by Product

Launch Analytics, a subscription-based database that tracks CPGs

(Sorescu & Spanjol, 2008). In contrast, the ctitious innovations used

in study 4 seem to be more groundbreaking.Because these differences

cannot be disentangled in the current studies, it would be interesting

to investigate whether predictive power with respect to existing

innovations increases if the innovations are more groundbreaking

and/or correspond better to the MCI dimensions. Thirdly, hMCI does

not uniquely predict the intention to buy real-life hedonic innova-

tions. If we analyze the hedonic products separately, purchase

intention for some of these products appears to be signicantly

higher for older respondents than younger ones (i.e., After, Douwe

Egberts Black, and new scratch cards). These results contrast with

those obtained using hMCI as a personality scale, in which younger

people score higher than older people. These hedonic products are

probably af liated with product categories that are less popular with

the younger generation (i.e., liquors, coffee, and lottery products) and

that do not relate to consumer innovativeness. In all likelihood,

interest in these categories also has a major inuence on consumers'

buying behavior. This leads us to stress the importance of productcharacteristics and other external factors such as opportunity and

capacity as important determinants of innovative buying behavior in

addition to consumer innovativeness (cf., Midgley & Dowling, 1978;

Ostlund, 1974). It also leads us to emphasize that every list of existing

products is limited because of the specicity of these products

(Vandecasteele & Geuens, 2009, p. 142).

Future studies might also consider the consequences of our results

for marketing communications toward differently motivated innova-

tive consumers. A targeted approach in which the medium, form, and

content of the message matches the motivation source of the

innovative consumer may signicantly increase communication

effectiveness.

Acknowledgments

The authors gratefully thank Hans Baumgartner, Teun De Rycker,

Kelly Geyskens, the editors, the area editor and two anonymous

reviewers for their constructive comments and useful suggestions

regarding an earlier version of the manuscript.

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