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Beyond simple innovativeness: a hierarchical continuum and thinking and feeling processing modes.

The important role of innovative consumers in the diffusion of new products has led researchers to invest considerable effort in trying to understand the nature of innovativeness. A central feature of past research has been that scholars have viewed consumer innovativeness as a continuum ranging from personality to behavior, that is to say, from a global trait to a tendency to be innovative in a specific domain/product category, and to innovative consumption behavior such as being an early adopter (cf. Bartels & Reinders, 2011; Goldsmith, Freiden, & Eastman, 1995).

Despite contributions of prior researchers to understanding of consumer innovativeness, there are two areas where questions remain unanswered. One of these areas is the relationships among nonadjacent constructs on the continuum. Researchers have reported both positive relationships (e.g., Hirschman, 1980; Midgley & Dowling, 1978) and negative relationships (e.g., traits and purchasing behaviors; Foxall, 1994) between the nonadjacent facets in the continuum. One explanation for the ambivalent results was that, in past research, scholars did not specify the hierarchical sequence of global, domain-specific, and behavioral innovativeness (Goldsmith et al., 1995). In addition, the focus in prior studies was primarily on cognitive aspects of innovativeness and little attention was paid to its affective aspects (Hoffmann & Soyez, 2010). To date, only a few scholars have argued that innovativeness could relate to both a cognitive- and a sensory-based construct (e.g., Bartels & Reinders, 2011; Venkatraman & Price 1990). A broader view of innovativeness could be instrumental in identifying how the two modes differ in their relationship with other factors and how they initiate different psychological processes (Hirschman, 1984).

In addressing the above limitations, we had two objectives in the present study. First, based on extant research, we tested a broad model of consumer innovativeness construed as a multifaceted hierarchical construct ranging from global personality traits to innovative behaviors. Second, we incorporated thinking and feeling elements (Epstein, Pacini, Denes-Raj, & Heier, 1996) and tested a distinction between cognitive and sensory aspects of innovativeness.

Consumer Innovativeness: Conceptualizations and Measures

Consumer Innovativeness as a Global Trait

Consumer innovativeness as a cardinal personality trait (also termed innate innovativeness) refers to a willingness to try new things (Hirschman, 1980). From an operational perspective, several researchers have developed scales for assessing the trait (e.g., Hurt, Joseph, & Cook, 1977). Given the multiplicity of similar yet distinct definitions of innovativeness, in the present study we adopted a multimethod approach using scales representing different psychological underpinnings: an originality scale in the Kirton Adaption-Innovation Inventory (KAI; Kirton, 1976), the innovativeness scale developed by Hurt et al. (1977), and Baumgartner and Steenkamp's (1996) two-factor conceptualization of exploratory consumer buying behavior: exploratory information seeking (EIS), and exploratory acquisition of products (EAP).

Kirton's (1976) KAI consists of three subscales: the first focuses on originality (the degree of preference for generating ideas), the second explores conformity (conforming to social standards), and the third subscale looks at efficiency (thoroughness and attention to detail). Though all three facets are deemed necessary for innate innovativeness, originality has been proven to be an efficient measure of innovative cognitive style. Conformity and efficiency are often seen as precursors of originality (see e.g., Miron, Erez, & Naveh, 2004). Despite the KAI having been widely used, scholars have challenged its unidimensionality (e.g., Miron et al., 2004) and the psychometric properties of the originality subscale have also been criticized (e.g., Foxall & Hackett, 1992). The innovativeness scale developed by Hurt et al. (1977) is another measure that has been widely used to classify consumers into innovator categories. Both the scales developed by Kirton and by Hurt and colleagues make no specific references to the marketplace or product consumption. This high level of abstraction and their distance from the marketplace are at the core of the debate regarding the appropriateness of "purer" trait scales for assessing innovativeness and its manifestations (Hoffmann & Soyez, 2010).

Two relatively broad trait scales that focus on marketplace-specific innovativeness are Baumgartner and Steenkamp's (1996) EIS and EAP. The EIS assesses product-related information seeking and the EAP reflects the tendency to seek new products and enjoy sensory stimulation through variety seeking. By focusing on the marketplace, the EIS and EAP have a lower degree of abstraction than either the KAI (Kirton, 1976) or the scale developed by Hurt et al. (1977). Moreover, in the EIS and the EAP a distinction is drawn between cognitive information seeking and experiential product acquisition. Both scales have been found to correlate to innovativeness-related personality traits and behaviors (Baumgartner & Steenkamp, 1996).

Domain-Specific Innovativeness

Domain-specific innovativeness (DSI) refers to the tendency to learn about, and adopt, innovations within a narrow product category or a specific domain (Goldsmith & Hofacker, 1991) with no generalization occurring across all product categories (Gatignon & Robertson, 1985). Espousing DSI implies a conceptual distinction between cardinal and behavioral innovativeness (Midgley & Dowling, 1978). To date, findings in a number of studies have provided evidence that trait innovativeness and DSI are similar but distinct concepts (e.g., Hirunyawipada & Paswan, 2006). Goldsmith and Hofacker's (1991) DSI scale has been used in multiple product sectors.

Behavioral Innovativeness

Behavioral innovativeness (also termed actualized innovativeness) is construed as the degree to which individuals adopt a new technology in a relatively short period of time (Hirschman, 1980). Hirschman (1980) distinguished between two aspects of actualized innovativeness, which he labeled vicarious and adoptive innovativeness. Vicarious innovativeness (VI) refers to the acquisition of information regarding the product whereas adoptive innovativeness (AI) refers to its actual adoption. As such, the two constructs parallel EIS and EAP in terms of their emphasis on information seeking and experiential product acquisition. Based on Hirschman's categorization, Hartman, Gehrt, and Watchravesringkan (2004) developed scales to measure VI and AI.

Synthesis and Hypotheses

Based on our review of innovativeness literature, we proposed a model (Figure 1) in which we have expanded the scope of previous research by incorporating both a detailed view of the degree of abstraction across the continuum, and the cognitive versus the sensory aspects. To provide nomological support for the model, we included antecedents of consumer innovativeness at the leftmost cardinal level (Figure 1). These represent personality traits associated with innovativeness, but not innovativeness per se. We proposed that the cognitive mode would stem from need for cognition (NFC) and the sensory mode would stem from faith in intuition (FII). Following Miron et al. (2004), we viewed the efficiency and conformity subscales of the KAI as the precursors of the cognitive and sensory modes.

The Innovativeness Continuum

At the cardinal level, trait innovativeness characterizes individuals who appreciate unusual ideas and are intellectually curious. The distinction between cognitive and emotional aspects of the cardinal trait implies that it has two central constituents. In this study we viewed originality as the cognitive constituent of the trait (Miron et al., 2004) and proposed a new emotional constituent of the trait. Though in neither the KAI (Kirton, 1976) nor the scale developed by Hurt et al. (1977) is there any differentiation made between the two constituents, we posited a distinction between cognitive- and emotion-based trait innovativeness. Later in this paper we have elaborated on the psychological underpinnings and properties of this proposed constituent. The next stage was marketplace innovativeness. This stage, assessed by exploratory information seeking (EIS) and exploratory acquisition of products (EAP), stands between personality traits and product-specific innovativeness. Following in the continuum is the tendency for innovativeness within a specific domain (DSI). This tendency may be activated at different degrees, depending on the consumer's interest in a product domain (Bartels & Reinders, 2011). Finally, there are two types of behavioral innovativeness, vicarious (VI) and adoptive (AI) innovativeness (Hirschman, 1980).

The preceding proposed continuum suggests a hierarchical flow of innovativeness so that adjacent facets in the continuum are associated with one another and the intensity of these relationships may diminish when facets are farther away on the trait-behavior continuum. Although a hierarchical continuum has been suggested in prior studies (e.g., Bartels & Reinders, 2011; Hirschman, 1980), it has not been empirically examined as a whole. In line with our proposed continuum we hypothesized as follows:

Hypothesis 1: Consumer innovativeness forms a hierarchical continuum following a flow from personality trait to global trait, from global trait to marketplace innovativeness, from marketplace innovativeness to domain-specific innovativeness, and thence to behavioral innovativeness.

Cognitive and Sensory Modes of Innovativeness

In past research scholars have viewed innovativeness mainly as an individual's cognitive style. Innovativeness can be accompanied by cognitive and sensory modes stemming from product anticipation or experience (Venkatraman & Price, 1980). To date, a few scholars have differentiated between cognitive and sensory innovativeness, and have identified their association with innovative behavior and novelty seeking (e.g., Hirschman, 1984; Venkatraman & Price, 1980). The distinction between thinking and feeling aspects of innovativeness is consistent with recent views of information processing as controlled by two parallel, independent, and interactive systems; the rational and the experiential (Epstein et al., 1996). According to this view, the rational system is cognitive and analytic, and the experiential system is affective and intuitive. Evaluations and behavior are seen as a joint function of the two systems whereby the level of relative dominance of either system stems from personality and situation (Epstein et al., 1996). Thus, we posited that the two modes would constitute distinct constructs on the innovativeness continuum. At the trait level, we suggested a distinction between originality and emotion-based trait innovativeness. We reasoned that a similar cognitive/sensory distinction might exist between EIS and EAP, as well as between VI and AI. The above led us to propose the following:

Hypothesis 2: The thinking and the sensory modes will emerge as distinct facets among constructs across the hierarchical continuum of consumer innovativeness such that need for cognition, efficiency, originality, exploratory information seeking, and vicarious innovativeness will constitute the thinking mode, whereas faith in intuition, conformity, emotion-based trait innovativeness, exploratory acquisition of products, and adoptive innovativeness will constitute the sensory mode.



We conducted a survey based on two products: cellular phones and MP3 players. At the time of data collection, the two products were widely owned, innovative, and differed in terms of usage goal orientation: cellular phones primarily serving utilitarian purposes, and MP3s being used mainly for hedonic purposes. We selected these two products on the basis that examining the relationships individuals have with two products that differ in regard to goal orientation could enhance the generalizability of results, and unique relationships could be revealed that would be differentiated by the two products (cf., Hoffmann & Soyez, 2010).


The participants in the study were 255 undergraduate and graduate students at a midwestern U.S. university. The students responded anonymously and at their own pace. The survey was composed of four sections: (a) individual traits, (b) cellular phone-specific questions, (c) MP3-specific questions, and (d) demographics. Of the 255 participants, 193 of the students owned cellular phones and 112 owned MP3s; 86 of them owned, and answered questions for, both products (107 owned only cellular phones and 26 owned only MP3s); 36 participants who did not own either product responded to the core personality scales and proceeded to the demographics section.

Scale Screening and Development

In order to examine the psychometric properties of the constructs, identify poorly performing items, and purify the scales, we employed factor analyses and maximum likelihood confirmatory factor analyses. All scales had only one dominant factor with eigenvalue > 1 and all final scale items loaded above 0.47 in their factor. The resulting parsimonious scales adequately captured the original constructs and were shown to have improved psychometric properties and reliabilities. Item purification also enhances construct validity and allows for examination of the interrelationships and nomological properties of the constructs (Netemeyer, Bearden, & Sharma, 2003). Another goal of scale reduction was to create identical measures that would allow for between-product comparisons. All items were measured with 5-point scales ranging from 1 = very unlikely to 5 = very likely. Final items can be provided upon request to the authors.

Core personality. Need for cognition (NFC) was measured with 19 items from the scale developed by Cacioppo and Petty (1982); analysis revealed one dominant factor (eigenvalue = 5.6) and the final scale consisted of 11 items ([alpha] = .87, loadings > .48). Faith in intuition (FII) was measured with 12 items developed by Epstein et al. (1996); analysis revealed one factor (eigenvalue = 3.58) and the final scale consisted of six items ([alpha] = .78, loadings > .48). Conformity was measured with 12 items from the KAI (Kirton, 1976); analysis indicated one factor (eigenvalue = 3.6) and the final scale consisted of nine items ([alpha] = .77; loadings > .47). Efficiency was measured with seven items from the KAI (Kirton, 1976); the result showed one factor (eigenvalue = 2.9) and the final scale consisted of five items ([alpha] = .75; loadings > .52).

Global trait innovativeness. Two scales were employed to measure global trait innovativeness: the 13-item originality scale from the KAI (Kirton, 1976) and the 20-item innovativeness scale developed by Hurt et al. (1977). In Kirton's 13-item scale we identified one dominant factor (eigenvalue = 2.7) but the final solution showed that there were only three items that met the criteria for a reliable scale ([alpha] = .73). This result is consistent with previous evidence suggesting issues with the reliability of this scale (cf., Foxall & Hackett, 1992). In the 20-item innovativeness scale (Hurt et al., 1977) we identified two dominant factors (eigenvalues 4.5 and 3.1) and subsequent screenings revealed two reliable scales. One contained six positively worded items ([alpha] = .79; loadings > .50) and the second consisted of seven negatively worded items ([alpha] = .79; loadings > .49).

When we examined the first factor derived from the items from Hurt et al., the item content suggested to us that this factor reflected the typical aspects of being original and inventive in one's ideas (e.g., "I have original ideas" and "I seek out new ways to do things"). After further analyses and considering the high correlation (r = .51, p < .001, N = 255) between the six-item scale from Hurt et al. and the three-item scale from Kirton, we combined these nine items into one scale, and labeled this factor Originality ([alpha] = .83). As our scale was based on shared items from two scales, the emergence of this factor suggested to us that it reflected cognitive aspects as a trait. Goldsmith (2012) had also previously suggested the convergence of the Kirton and the Hurt et al. scales. The second factor in the innovativeness scale developed by Hurt et al. consists of items, representing an emotional reluctance to adopt new ideas, which may lead to individuals being laggards in adopting innovations (e.g., "I am suspicious of new inventions"; "I am reluctant about adopting new ways"). Consistent with our earlier discussion, we identified this factor as reflecting emotion-based trait innovativeness and we labeled it Laggardness.

Emergence of separate factors comprised of only negative items could be attributed to a "methods effect", that is, an artifactual language-driven division arising from the difficulty in processing negatively phrased items. We examined this newly found factor. To examine the viability of two distinct factors of Originality and Laggardness, we conducted additional maximum likelihood factor analyses of both positive and negative items. Comparison of the one- and two-factor factor analyses, including calculating comparative fit index (CFI), showed a significant improvement of the two-dimensional over the one-dimensional solution (one-factor model: [chi square] = 203.7, df = 89, CFI = 0.54; two-factor model: [chi square] = 109.1, df = 75, CFI = .86).

To rule out further the possibility of a language-driven construct, we examined the variabilities in the Originality and Laggardness items. As response bias arising from negatively phrased items should introduce variation in responses that has not occurred because of variation in the measured construct, we could expect that any response bias would create additional variation in the negatively phrased items but not the positively phrased items. Analysis of the standard deviations of items in the positive and negative factors did not reveal any differences. The medians of the standard deviations of positive and negative items were similar (0.87 and 0.90) and the t test of the differences between them was not significant, t(14) = 1.1. In sum, the results suggest the existence of two inversely related (r = -.18, p < .00, N = 255) yet distinct scales of trait innovativeness.

Marketplace innovativeness. Baumgartner and Steenkamp's (1996) 10-item Exploratory Acquisition of Product (EAP) Battery revealed one dominant factor (eigenvalue = 3.6) and our final scale consisted of seven items ([alpha] = .80 after appropriate reversals; loadings > .48). Baumgartner and Steenkamp's 10-item Exploratory Information Seeking (EIS) Scale also revealed one factor (eigenvalue = 4.3) and our final scale consisted of eight items ([alpha] = .85; loadings > .57).

Domain-specific innovativeness. Goldsmith and Hofacker's (1991) scale for cellular phones revealed that five items loaded >.77 on one factor (eigenvalue = 2.98, [alpha] = .83). The parallel analysis for MP3s similarly revealed that the same five items loaded > .76 into one factor (eigenvalue = 2.96, [alpha] = .83).

Behavioral innovativeness. Vicarious (VI) and adoptive innovativeness (AI) were measured using the scales developed by Hartman et al. (2004) with nine and four items, respectively. All nine items of VI for cellular phones revealed one dominant factor (eigenvalue = 5.0, [alpha] = .90, loadings > .49). Analysis of items for MP3s revealed an identical factor (eigenvalue = 5.5, [alpha] = .92, loadings > .65). Analysis of the four AI items for cellular phones revealed one factor (eigenvalue = 3.2, [alpha] = .92; loadings > .63). The parallel analysis for MP3s also revealed the same factor (eigenvalue = 3.1, [alpha] = .90, loadings > .51).

Manipulation check. In order to examine between-product structural invariance, we based our examination on the expectation that cellular phones would be primarily used for functional purposes whereas MP3s would serve more fun-related purposes. For cellular phones, we combined Novak, Hoffman, and Duhachek's (2003) two work-related items (r = .34) and two fun-related items (r = .76) into two scales, with the factors labeled Goal-Work-Cell and Goal-Fun-Cell; the parallel correlations for MP3s were r = .26 and r = .38 and the two scales were labeled Goal-Work-MP3 and Goal-Fun-MP3. For the 86 participants in our study who owned both products, the result for the Goal-Work-Cell scale was significantly higher than the result for the Goal-Work-MP3s ([M.sub.cell] = 3.27 vs. [M.sub.MP3] = 2.63; t = 4.4, df = 85, p <.00). For the Goal-Fun scales, participants perceived MP3s as more fun-oriented than cellular phones ([M.sub.cell]= 3.59 vs. [M.sub.MP3] = 4.17, t = 4.4, df = 85, p < .00).


All scales were created by averaging their respective items. Correlations of the screened scales are shown in Table 1. For cellular phones, Originality was negatively related to Laggardness, and had a weak relationship with DSI, VI, and AI. Similar patterns were found for MP3s. These correlations provide initial support for a hierarchical continuum. Originality and EIS, representing cognitive aspects, correlated negatively with the experiential Laggardness and EAP. This suggests that these cognitive and emotional facets may play different roles in explaining consumer innovative behaviors.

To test our hypotheses across the two products, we conducted path analyses using Mplus. In Figure 2 the results are presented, including the statistically significant relationships that were not only predicted in the proposed model but also recommended by modification indices. The fit indices of CFI and standardized root mean square residual (SRMR) suggested that the model fit the data well ([chi square] = 53.80, df = 35, p = .02, CFI = 0.94, and SRMR = 0.05 for cellular phones; [chi square] = 67.77, df = 33, p = .00, CFI = 0.83, and SRMR = 0.08 for MP3s).

As we predicted in Hypothesis 1, the results generally support a hierarchical continuum. However, although the four core personality trait factors (NFC, Efficiency, Conformity, and FII) influenced Originality and Laggardness for cellular phones, Conformity and FII did not play any role in the continuum for MP3 players. EAP influenced DSI for MP3s but not for cellular phones. EIS did not influence DSI for either product. Thus, although the hierarchy is manifested throughout the continuum, it seems to be partially fractured in the link between marketplace traits and DSI. Thus, Hypothesis 1 was partially supported.

In Hypothesis 2 we predicted the emergence of cognitive and sensory modes as distinct facets. For cellular phones, NFC and Efficiency influenced Originality and Originality influenced EIS, which, in turn, affected VI. Similarly, for MP3s, NFC and Efficiency affected Originality, which, in turn, influenced EIS, which, in turn, affected VI. However, although Conformity and FII both influenced Laggardness for cellular phones, these two factors did not affect Laggardness for MP3 players. Laggardness influenced EAP for both cellular phones and MP3s, but EAP did not influence AI for either product. Thus, according to our results, for both products the hypothesized sensory mode was fractured, especially between marketplace and behavioral innovativeness. Hence, the results partially support Hypothesis 2.


Overall, the results indicated that the consumer innovativeness consisted of a series of interrelated functional and experiential constructs that spanned from its broad elements to specific behaviors. These findings expand understanding of this multifaceted construct. Despite the general support for our hypotheses, it should be noted that innovativeness at the trait level did not have an especially strong relationship with product-specific innovativeness. Indeed, EAP and EIS both had a relatively weak relationship with DSI in that, for cellular phones, DSI was not affected by any traits, for both products, only EIS influenced VI, and EAP affected DSI for MP3s only. As EAP and EIS focus on the broader and more abstract marketplace, they are weakly related to innovativeness at its more specific behavioral levels (Goldsmith, 2012). Taken together, the results in our study reveal the inability of general traits to explain variance in specific innovative behaviors (Epstein, 1979). Our results are also in line with the contention of Hoffmann and Soyez (2010) that constructs at the same levels of abstraction are more highly interrelated with each other than are constructs that are not at the same level of abstraction.

The partial disruption shown in our results stands in contrast with the robust findings of the relationships among DSI, VI, and AI. In line with findings in previous studies that have shown strong ties among behavior-related constructs (Hartman et al., 2004) DSI was a precursor of both VI and AI. This result confirms that DSI, rather than trait innovativeness, has a higher power to predict consumers' innovativeness behavior. Moreover, although we did not predict this, the information-related VI was a precursor of, and mediator to, the purchase-related AI (cf., Hirschman, 1980). These results support Gilbert's (1991) argument that individuals tend to comprehend before adopting a proposition for confirmation.

An important set of results in our study was the emergence of the cognitive/ sensory differentiation, especially at the trait level. Our results revealed that trait-innovativeness consisted of two distinct constructs, originality and laggardness. In past research scholars have focused primarily on originality as a cognitive aspect. Laggardness represents emotion-based trait innovativeness, and captures the reluctance to adopt innovations. It reflects how much people tend to delay their exploration of innovative products. This finding is in line with the recent emphasis on the "other side" of innovativeness, that is, the reluctance to change (Deslandes & Flynn, 2000; Goldsmith, 2012). The cognitive/sensory differentiation at the marketplace trait level was also manifested in our study in the contrast between EIS and EAP. Nonetheless, it is important to note that the cognitive mode (i.e., Originality [right arrow] EIS [right arrow] VI) was intact but the sensory mode (i.e., Laggardness [right arrow] EAP [right arrow] AI) was, in part, fractured. Together, the results suggest that a view of consumer innovativeness based only on cognition-centric underpinnings limits understanding of its true nature, and this highlights the need to investigate cognitive/sensory facets.

We had not expected that the modification index in the MP3 model would support the positive influence of laggardness on VI. This result may stem from consumers' goal orientation (fun vs. work) when using MP3s. When it comes to products that are fun, impulsivity may have a stronger influence in the purchasing process because of the possible ties of impulsivity to innovativeness (Hirschman & Stern, 2001). Thus, a greater tendency to wait longer before adopting fun products, that is, a sign of lack of innovativeness, may lead to a greater need to collect information before considering buying the product. This pattern was not revealed for the more functional product of cellular phones, perhaps because their adoption is typically accompanied by cognitive deliberations. This explanation may also apply to the aforementioned fracture in the emotional mode, whereas the thinking mode was intact for MP3 players. Together, these findings suggest that product specificities may differentially influence the innovativeness continuum.

Limitations and Implications for Future Studies

Several limitations of this study must be kept in mind before generalizing from the findings. First, the results indicated that the hierarchical continuum was partially fractured between marketplace and domain-specific innovativeness. Scholars (e.g., Bartels & Reinders, 2011; Hoffmann & Soyez, 2010) argued that situational variables like media, product attributes, and product involvement, can intervene in the relationships between trait and behavioral innovativeness. In future studies, researchers need to examine the roles of these variables. Second, in this study we identified laggardness as a part of emotion-based trait innovativeness, but in future studies further development and examination is necessary to shed more light on this important construct, especially for products related to hedonic goals. Third, in our study we did not specify the different cognitive/sensory aspects at the level of domain-specific innovativeness (an up-to-date literature review did not indicate prior differentiation of these two facets). Identification of the two different aspects of DSI and of other elements, for example, openness to experience, are needed for further nomological validation of this multifaceted construct. Finally, the respondents in this study were a convenience sample. Thus, limitations regarding the sample should be considered in interpreting the results.


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Kyootai Lee

Sogang University

Haim Mano

University of Missouri-St. Louis

Kyootai Lee, Graduate School of Management of Technology, Sogang University; Haim Mano, Marketing Department, College of Business Administration, University of Missouri-St. Louis.

This work was supported by the Sogang University Research Grant of 2013 (201310011.01).

Correspondence concerning this article should be addressed to: Kyootai Lee, Associate Professor, Graduate School of Management of Technology, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 121-742, Republic of Korea. Email:

Table 1. Correlation Matrix of Screened Scales for Consumer

                             1       2       3       4       5

1     Need for Cognition            .30     .43    -.42    -.06
2     Faith in Intuition    .10             .41     .02     .17
3     Originality           .45     .29            -.07     .07
4     Laggardness          -.48     .07    -.22             .16
5     Conformity           -.16     .03    -.07     .27
6     Efficiency            .27     .25     .26     .05     .16
7     EAP                   .09    -012     .06    -.22    -.13
8     EIS                   .03    -.03     .14     .06     .10
9     DSI                   .18     .06     .04     .02    -.11
10    VI                   -.02    -.01     .04     .09    -.02
11    AI                   -.05    -.03     .04     .05    -.12
M     3.51                 3.71    3.63    3.38    2.80    3.57
SD    .62                   .54     .57     .61     .52     .68

        6       7       8       9      10      11       M      SD

1      .25     .01    -.09     .08    -.28    -.21    3.54    0.64
2      .39    -.21     .16    -.30    -.12    -.01    3.76    0.55
3      .31     .10     .24     .06     .04     .04    3.66    0.63
4      .04    -.19     .17     .04     .37     .29    3.42    0.58
5      .26    -.10     .16    -.03    -.04    -.11    2.85    0.53
6             -.02     .27    -.05    -.17    -.10    3.53    0.70
7      .02            -.17     .17     .04     .15    2.78    0.79
8      .16    -.09             .10     .23     .02    3.00    0.79
9     -.00     .08     .09             .34     .51    3.36    0.76
10    -.01    -.02     .19     .46             .61    2.63    0.75
11     .01    -.06     .15     .54     .65            2.50    0.91
M     2.78    2.63    3.42    2.68    2.42
SD     .72    3.05     .81     .78    1.03

Note, n = 193 for cellular phones, n = 112 for MP3 players.

The numbers below the diagonal are correlations for cellular phones,
and those above the diagonal are for MP3 players.

The absolute value of the coefficients greater than .14 are
significant for cellular phones and those greater than .19 are
significant for MP3 players.
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Article Details
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Author:Lee, Kyootai; Mano, Haim
Publication:Social Behavior and Personality: An International Journal
Article Type:Report
Geographic Code:1USA
Date:May 1, 2014
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