Development of a Scale to Measure Use Innovativeness

Linda L. Price, University of Pittsburgh
Nancy M. Ridgway, University of Texas at Austin
ABSTRACT - A scale designed to measure use innovativeness (variety seeking in product use) was developed. From an original pool of 70 items, the scale was reduced to 60 items based on expert judgment. The scale was further reduced to 44 items by a combination of three analyses: factor analysis, item-total correlation for the total scale, and item-total correlation for each of five subscales. The scale was then related to use innovative behavior with a hand calculator. Those subjects scoring high on the use innovativeness scale were found to exhibit significantly more innovative use patterns with their calculator.
[ to cite ]:
Linda L. Price and Nancy M. Ridgway (1983) ,"Development of a Scale to Measure Use Innovativeness", in NA - Advances in Consumer Research Volume 10, eds. Richard P. Bagozzi and Alice M. Tybout, Ann Abor, MI : Association for Consumer Research, Pages: 679-684.

Advances in Consumer Research Volume 10, 1983      Pages 679-684


Linda L. Price, University of Pittsburgh

Nancy M. Ridgway, University of Texas at Austin


A scale designed to measure use innovativeness (variety seeking in product use) was developed. From an original pool of 70 items, the scale was reduced to 60 items based on expert judgment. The scale was further reduced to 44 items by a combination of three analyses: factor analysis, item-total correlation for the total scale, and item-total correlation for each of five subscales. The scale was then related to use innovative behavior with a hand calculator. Those subjects scoring high on the use innovativeness scale were found to exhibit significantly more innovative use patterns with their calculator.


Considerable interest has recently been generated regarding those mechanisms which lead consumers to engage in variety seeking. Excellent review articles which illustrate consumer behavior perspectives on exploratory behavior are available (Venkatesan 1973, Faison 1977, Raju and Venkatesan 1980). Several probable sources of variety seeking co-exist (McAlister 1981, Pessemier 19&1), but one important source is the internal need for stimulation. The idea of a preferred level of stimulation and individual differences in that preference derives from a body of literature introduced in psychology first by Hebb (1955) and Leuba (1955). Since that time, variety seeking has become a major topic of research in the psychology literature (Dember and Earl 1957, Berlyne 1560, Fiske and Maddi 1961, Kish 1966). Although some differences between the various theories exist, the common thesis is simply that as stimulation (complexity, arousal, etc.) falls below the ideal level, an individual will attempt to produce more stimulating input (through behaviors such as exploration and novelty seeking). As stimulation increases past the ideal level, an individual will attempt to reduce or simplify input.


Exploration in the consumer context can be divided into three main types: exploratory purchase behavior, vicarious exploratory behavior and use innovativeness (or variety in product use). The first type, exploratory purchase behavior, is variety seeking that involves product purchase and manifests itself in two ways: innovating and brand switching (Mazis and Sweeney 1972, Mittelstaedt et al. 1976). The second type of exploratory behavior in the consumer context is vicarious exploratory behavior. This involves variety seeking by engaging in behaviors such as reading about, talking to others about, or shopping for new or unfamiliar products (hirschman 1980, Raju 1980).

A major focus of this study is to examine the constructs of the third major type of consumer exploratory behavior: use innovativeness. The term can be described by two levels of behavior. The first level is the use of a previously adopted product in a single novel way. An example of this would be the use of a plastic egg carton as a painting palette or the use of tin cans that previously held canned vegetables to hold nails in the workshop, rather than buying products specifically designed for these tasks. The second level of use innovativeness is using a currently owned product in a wide variety of ways. For example, a consumer may own a home computer and either use it only to play electronic games or use it to play games, keep personal financial records, do programming, interface with other computers and learn a foreign language. In contrast to vicarious and exploratory purchase behavior, use innovativeness is a product consumption behavior. Still, the use innovativeness phenomenon has important implications for consumer researchers. Old products may be given new life by redefining the type and number of uses for a product based on suggestions from consumers--i.e., Arm and Hammer baking soda. New products with opportunities for use in a variety of ways (home computers, microwave ovens, video cassette records) could be promoted in terms of those opportunities to groups identified as use innovators--i e., Atari is now emphasizing that one can do more with their system than just play games. Operating features, accessory equipment, educational programs and product instructions could be geared to emphasize the variety of ways in which a product might be used.

Use innovativeness was first introduced by Hirschman (1980). Price and Ridgway (1982) found use innovativeness to be separate from purchase and vicarious exploration. Although use innovativeness was found to be positively correlated with optimal stimulation level, it was not significantly correlated with either purchase or vicarious exploratory behavior. Although Raju (1980) developed a scale which tapped both purchase and vicarious exploration, there have been no attempts to measure use innovativeness in the consumer context. This study describes the construction of a scale to measure use innovativeness. It also reports on the relationship of the scale to innovative use behaviors with hand calculators.


In generating items for a scale to measure use innovativeness, two keys are important. First, the items should be relevant to the concept being measured and second, consideration of the factorial composition of the construct-should be made (Osgood, Suci and Tannenbaum 1957).

Use innovativeness is expected to be composed of at least five factors. These five factors evolved from personal interviews with a variety of consumers about their product use, as well as from results of exploratory research (Price and Ridgway 1982). First, creativity and curiosity are expected to be major components of the concept. Hirschman (1980) first theorized the importance of this factor in explaining use innovative behaviors. In order to reuse old products in new ways or think of multiple uses for a product, a consumer must have both the ability (creativity) and the incentive (curiosity) to do so.

Values which may provide an incentive for use innovativeness include the-desire for material simplicity and self-sufficiency. In particular, individuals with a desire for voluntary simplicity would be expected to recycle old products (Leonard-Barton 1981). The second factor, therefore, attempts to tap voluntary simplicity.

Even though a consumer may have the ability, incentive and values conducive to use innovative behavior, s/he may not engage in the behavior because of an off-setting aversion to the risks associated with use innovativeness. For example, a person may restrict their use of a product based on manufacturers' warnings, instruction manuals or salesperson directions rather than use a product in new or different ways. Results of a recent study (Price and Ridgway 1982) suggest that risk-taking may be an especially important facet of use innovativeness. The third factor, therefore, is risk preferences.

Two types of use innovative behaviors have been delineated: re-use of an old product (such as vegetable cans to hold nails) and multiple uses for a single product (such as using a home computer in a wide variety of ways). Preferences for these two types of use behavior may differ from between individual consumers but both are expected to be important components of the construct. Thus, the fourth factor is creative re-use and the fifth factor is preference for multiple use of a product (or multiple use potential), Items were designed to tap each of the five factors. (The final instrument is shown in Table 1).


Initial Item Selection

In order to achieve content validity, items were generated in accordance with the five factors discussed earlier. Preliminary work resulted in the construction of 65 items. An additional five items were taken from an 18-item voluntary simplicity scale (Leonard-Barton 1981). The 70-item pool was reduced to 60 items based on the judgment of several experts. All items were scaled according to a 7-point Likert-type format.

Data Collection

A questionnaire packet was administered to 370 student subjects from undergraduate classes representing a variety of curricula. The packet consisted of the 60-item use innovativeness scale (hereafter referred to as the U.I. scale), six specific behaviors regarding calculator use (for example, "How often do you try new things on your calculator?", and "How often do you use your calculator?"), and eight items representing self-report of general calculator use patterns. These eight items were measured on a 7-point Likert-type scale and included items like "I often turn on my calculator without a specific task in mint" and "I use my calculator mainly for simple arithmetic calculations." For the purposes of this study, subjects were pre-screened to insure that they owned a hand calculator. Twelve questionnaires were discarded because of incomplete data, leaving 358 usable questionnaires. This number of subjects was considered adequate to develop a 60-item scale (Nunnally 1978, Peterson 1982).


The objective of analyses was to produce an internally consistent instrument to measure preferences for use innovativeness which exhibited the underlying factorial composition advanced in this research. Toward this end, three analytical procedures were employed--factor analysis, item reliability analysis on each of the five subscales, and item reliability on the total scale. A combination of techniques were employed so that multiple criteria could be used for the selection of final scale items (Nunnally 1978, Malhotra 1981). Also, due to the multi-dimensional nature of the use innovativeness concept, reliability analysis on the total scale was not considered to be a sufficient criterion for item elimination (Peter 1979).

Factor Analysis

Factor analysis was performed on the original 60-item scale to summarize the data in terms of a set of under lying constructs, and to identify factors with high intraset correlations. Principal factoring without iteration, followed by varimax rotation was used to extract the factors (this approach was used because it is considered less likely to take advantage of chance than principal components analysis with iteration). Using the "elbow" rule whereby eigenvalues are plotted and a significant drop in eigenvalues is used as the cut-off point (Catrell 1966), five factors were rotated. Subjective analyses indicated that the five factors extracted reflected the original factorial composition hypothesized to a good extent. After examining the content of the items with the highest loading, the five factors may be interpreted as follows:

Factor 1: Creativity/Curiosity

Factor 2: Voluntary Simplicity and Creative Re-Use (voluntary simplicity items listed in Table 1 suggest the similarity between those items and the creative re-use items.

Factors 3 and 4: Risk Preferences Factor 5: Multiple-Use Potential

Only four items loaded high on factors different from what was expected. These items were candidates for reassignment or elimination. Only three items had no loadings over .30 (a total of 13 items loaded no higher than .40). These items were also considered for elimination. Items with high inter-set loadings were not necessarily candidates for elimination, since the subscales were expected to be correlated (See Table 2 for correlations of final sub-scales). The factor analysis confirmed that all the factors had some items with reasonably high loadings on the first factor--creativity/ curiosity. This suggests that creativity/curiosity may be a primary factor while Factors 2, 3, 4 and 5 may be secondary factors.

Item ReLiability for-Original Subscales

Based on .he original assignment of the 60 items to the five hypothesized factors, item-total correlations and Cronbach's coefficient alpha were computed for each of the subscales. Alphas for four of the original subscales were quite good (between .65 and .80). The alpha for multiple use potential was somewhat lower (.47). A total of 13 items had low item to total correlations on the subscales and were considered for reassignment or elimination. Four of these were the same items which loaded high on a factor other than the one expected and were therefore reassigned to other subscales. There was a high consistency between items with low item to total correlations on the subscales and low loadings on the factors.

Item Reliability for 60-Item Scale

Item-total correlations and alpha coefficients were computed for the total scale. While several factors were hypothesized, the researchers wanted to verify that all items were drawn from the domain of a single construct-use innovativeness. The alpha for the original 60-item scale was .89. The variables with low item-total correlations were quite consistent with those previously identified through factor analysis and examination of item-total correlations for the subscales.


The following criteria were used to select the final scale items from the initial set of 60 items:

--high loadings on the factor they represent

--high item-total correlations on the relevant subscale

--high item-total correlations on the total U.I. scale

Accordingly, four items were reassigned to a subscale other than the originally hypothesized one and 16 items (predominantly from the risk-taking and multiple use potential subscales) were eliminated. The reliability and validity of the final 44-item scale are assessed and reported in the following section.


After selection of the final U.I. scale items, factor analysis was performed to insure factorial stability. Employing the elbow rule again, this analysis indicated that a four factor solution was superior. Table 1 indicates the factor loadings for each of the subscales. Coefficient alpha for the total scale was .91. This can be considered very good, since even with a reduction in the number of items in the scale, coefficient alpha improved. (Coefficient alpha is positively correlated with number of scale items, Nunnally 1978). Alphas for each of the subscales are also reported in Table 1, and all are above Nunnally's (1978) criterion of acceptable internal consistency of .50 to .60.

Table 2 reports the correlation matrix of the summated subscales with each other and with the total U.I. scale.



As indicated, the highest correlations are between the creativity/curiosity subscale and the other scales.

The final stage of the scale development process concerns the initial validation of the U.I. scale. To assess its criterion validity, responses on the 44-item version of the U.I. scale were related to six specific behaviors regarding calculator use and eight items representing a self report of general calculator use patterns (described earlier).

One way analysis of variance between the six behaviors and subjects grouped by their score on the U.I. scale was performed. Subjects were divided into three groups representing the upper (Group 3), middle (Group 2) and lower (Group 1) tertiles of the distribution of U.I. scores. The mean U.I. score was 199 (median = 199), varying from a low of 112 to a high of 299. Results of the analysis of variance are reported in Table 3. Significant F values for five out of six behaviors were found. Individuals scoring higher on the U.I. scale were significantly different from the other two groups. These high U.I. individuals had a higher frequency of trying new things on their calculators, a higher frequency of using the calculator's memory and programming functions, spent more time using the calculator and had used their calculator more recently than the other two groups. In order to provide an estimate of the magnitude of the relationship, Hays ' omega square measure was calculated (Hays 1963). The variance explained is not very large, but it is consistent with results from other studies of trait-reported behavior relationships (Engel, Kollat and Blackwell 1978).



To examine the relationship between the use innovativeness scale and the eight items representing self reports of calculator use patterns, two types of analysis were performed analysis of variance and canonical correlation. First, summated scores of the eight use items were compared for the three groups of use innovators on the U.I. scale. (Prior to this, analysis of the eight use variable inter-item reliabilities yielded an alpha of .85). As seen in Table 3, the analysis of variance shows a significant relationship between use innovativeness and reported patterns of calculator use. All three groups are significantly different in their calculator usage patterns.

The second type of analysis was canonical correlation. Canonical correlation was performed on the set of items in the U.I. scale and the eight items on calculator use patterns. The results are reported in Table 4. Of the eight canonical correlations, two were significant beyond the .02 level. While the squared canonical R's are both strong (.45 and .25), the relationship between the sets is overstated since the canonical correlations are by definition maximal. .The Stewart and Love redundancy index was calculated to provide a summary measure of the average ability of the items in the U.I. scale to explain variation in the calculator use pattern items (Alpert and Peterson 1972). This substantially lowered the estimate of the shared variance, to a little over .09 for the two significant relationships.




To summarize, from the initial pool of 70 items designed to tap five specific components of use innovativeness, a 60-item set was administered to student subjects. Subjects also reported on six specific behaviors regarding their use of hand calculators and an eight-item Likert-type scale asking about general calculator use patterns. The 60-item scale was reduced to 44 items by use of three techniques: factor analysis, item-total correlations for the 60-item U.I. scale and item-total correlations for each of the five subscales. After dividing subjects into upper, middle and lower U.I. groups, the 44-item scale was then related to the six individual use behaviors and the eight-item general use patterns scale by performing analysis of variance. In every case, the group scoring highest on the U.I. scale engaged in significantly more innovative behaviors with a hand calculator than the other groups.


Because this research is preliminary, several limitations exist. These limitations, in turn, suggest implications for future research. First, what is believed to be a very important factor, multiple use potential, was not measured very well. The items on this subscale had the lowest item-total correlations (below .2) and the lowest subscale coefficient alpha (.56). Interestingly, however, only one item on the multiple use subscale loaded highly on another factor. Work is currently underway to develop scale items which better capture this factor. A second limitation is the fact that many items loaded high on their appropriate factor and high on the creativity/curiosity factor as well. This also suggests future research. It is possible, as suggested earlier, that one general factor and several second-order factors exist. These second order factors may be the two levels of use innovativeness outlined at the beginning of this paper: multiple use potential and creative re-use. The risk factor may moderate both factors. Results using a second order confirmatory factor analysis are reported elsewhere (Ridgway, Price and Anderson 1982). A third limitation exists because validation of the scale is not complete. Test-retest reliability, testing the scale on a variety of samples using a variety of product behaviors and correlation of the scale with other scales (such as scales that measure optimal stimulation level or creativity) need to be done. Behavioral validation in this study resulted in relatively low explained variance.

In the past, consumer researchers have focused primarily on purchase exploration. With the recent introduction of products with multiple use potential (specifically, video cassette recorders, home computers, etc.), and with many consumers interested in re- sing ex sin products, it is important to examine exploratory consumption behaviors as well.




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