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Comparative Analysis of Purchase Intentions Toward Smart Clothing Between Korean and U.S. Consumers Eunju Ko, Heewon Sung and Hyelim Yun Clothing and Textiles Research Journal 2009 27: 259 originally published online 10 February 2009 DOI: 10.1177/0887302X08327086 The online version of this article can be found at: http://ctr.sagepub.com/content/27/4/259

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Comparative Analysis of Purchase Intentions Toward Smart Clothing Between Korean and U.S. Consumers

Clothing & Textiles Research Journal 27(4) 259–273 © 2009 International Textile & Apparel Association Reprints and permission: http://www. sagepub.com/journalsPermissions.nav DOI: 10.1177/0887302X08327086 http://ctrj.sagepub.com

Eunju Ko, Heewon Sung, and Hyelim Yun

Abstract To examine perceived risks and attributes of smart clothing and how they influence attitudes and purchase intentions toward smart clothing, this study compares 300 Korean and 311 American purchasers to find any differences in the innovation-decision process. Analysis of the data indicates that perceived attributes are identifiable as relative advantage/compatibility and complexity factors. The most significant predictor explaining product attitude and purchase intention across the two countries is relative advantage/compatibility, whereas complexity is only significant in predicting attitude toward smart clothing in the U.S. model. Perceived risks among Koreans are generated into psycho/social, economic, time loss, and performance. Americans present five factors, with psychological and social risk dimensions being separate. Psychological and/or social risk and economic risk take on significant roles in explaining relative advantage, whereas different dimensions of perceived risks are associated with complexity across the two nations. These findings imply different marketing strategies for each cultural group. Keywords innovation-decision process, purchase intention, perceived attributes, perceived risks, smart clothing

Global interest in smart clothing has risen rapidly in the 21st century. Ubiquitous environments demand digital lifestyles, and smart clothing represents state-of-the-art fashion. Also called wearable computers or digital clothing, smart clothing is defined as “garment-integrated devices which augment the functionality of clothing, or which impart information-processing functionality to a garment” (Dunne, Ashdown, & Smyth, 2005, p. 2). In smart clothing, science has combined with fashion to provide the most comfortable and most suitable conditions for wearers by monitoring and enhancing environmental changes or personal circumstances.

Computer engineering in the United States was at the forefront of developing smart clothing in the 1980s and has been in collaboration with professionals in the areas of electronic engineering and clothing and textiles since 1998 (G. Cho & Cho, 2007). In the first stage, technologybased integration of electronic devices around the body space was the major issue (Mann, 1996). After 1998, collaborative projects between the technology and fashion industries were on the rise, developing prototypes that Yonsei University, Seoul, Korea Gyeongsang National University, Jinju, Korea Yonsei University, Seoul, Korea

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Clothing & Textiles Research Journal 27(4)

concentrated on consumer-oriented design (Rantanen et al., 2000). For instance, the Industrial Clothing Design+ line jackets developed by Levi Strauss in collaboration with Philips Research Laboratory allows wearers to use a remote-controlled microphone embedded in the collar for mobile phones and Digital MP3 players (Schreiner, 2001). At the beginning of the century, a number of smart clothing items became commercially available, such as the E-Vest by Philips to monitor body reactions; smart shoes by Adidas that adjust the heel cushion to a runner’s size, pace, and fatigue level; and smart sunglasses by Oakley and GapKids’ sweatshirts embedded with FM radio (Maxwell, 2005; McCarthy, 2005). Smart clothing is now being developed for everyday life, with the market expanding into the military, health and medical care, business, and leisure industries (G. Cho & Cho, 2007). According to Just-style.com (2008), the performance apparel market is forecast to grow from $6.4 billion in 2008 to $7.6 billion by 2014. South Korea wants to account for more than 20% of that market. Its government fully supports research and development of smart clothing, with an eye toward becoming the major exporter of digital apparel (Physorg.com, 2006). Some Korean companies, such as Hyosung, Kolon, and Beaucre Merchandising, are actively involved in developing smart clothing. Hyosung and Kolon have conducted research on developing e-textiles or smart fabrics to adjust environmental conditions to the wearer’s body condition (G. Cho & Cho, 2007). Beaucre Merchandising has launched a smart jacket that uses MP3 with a remote control embedded on the sleeve (Kang, 2007). Previous studies in the United States and Korea have focused on the technological development of smart clothes (Dodson, 2003; J. S. Lee, 2002), the history and/or present status of developing digital clothing (Dunne et al., 2005; J. H. Lee, 2004), or the application of these devices to daily life, such as health

care apparel (H. Cho, Lee, Lee, & Lee, 2006; Huang & Hsu, 2005; Moon, Cho, Lee, & Jung, 2006) or business e-suits (Dunne, Toney, Ashdown, & Thomas, 2004; Toney, Mulley, Thomas, & Piekarski, 2002). However, little research has attempted to examine potential adopters’ perceptions or attitudes toward smart clothing in either country. Based on diffusion theory (Rogers, 1995), the study presented here focuses on the perceived attributes and risks of smart clothing and their influence on attitudes and purchase intention. According to Rogers (1995), an innovation is defined as “an idea, practice, or object that is perceived as new by an individual or other unit of adoption” (p. 11). Smart clothing can be considered an innovation because it is still at the introduction stage of the product life cycle. An individual who is deciding whether to adopt an innovative product follows the innovation-decision process, especially in dealing with the uncertainty involved (Rogers, 1995). Because of this, perceived risk has an influence on consumers’ decisions on whether to purchase smart clothing. This study also compares the innovationdecision process between specific international groups in Korea and the United States. The rationale for such a choice of sample is twofold. First, the two nations are actively involved in developing smart-wear products in the marketplace, but little research has been found to examine potential adopters’ innovation-decision process. Second, the countries have very different cultural backgrounds. The United States is representative of individualism, whereas Korea is characterized by collectivism (Jin & Sternquist, 2003); such a disparity obviously gives rise to different purchase-decision processes. Moreover, Korea is one of the most famous testing markets for new products. This is because of the number of early adopters there interested in product innovations as well as a strong Internet system through which new product information and responses spread incredibly fast (Y. Park, 2007).


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Literature Review Perceived Attributes of an Innovation Rogers (1995) maintains that the innovationdecision process consists of five steps: (a) to obtain knowledge about an innovation, (b) to form an attitude toward it, (c) to decide whether to adopt or reject, (d) to implement the innovation, and (e) to confirm this decision. At the second stage, an individual develops general perceptions of the innovation based on the messages received or interpretation of the information from the first stage and accordingly forms a favorable or unfavorable attitude toward the innovation. There are also five perceived attributes of an innovation. The first is relative advantage, defined as the degree to which an innovation is perceived as being better than the idea it supersedes. Relative advantages for this study include economic benefits (value for the money invested), social advantage (bestowing a certain image), or usefulness (functionality and comfort). The second perceived attribute is compatibility, defined as the degree to which the innovation is perceived as consistent with the existing values, needs, and past experiences of a potential adopter. Compatibility in this study is viewed as consistency with one’s existing wardrobe and appropriateness for one’s current needs and lifestyles. Relative advantage of an innovation and compatibility with current situations are critical to enhancing the probability of adoption (Littrell & Miller, 2001; Sung & Jeon, 2005; Sung & Slocum, 2004). The third attribute is complexity, which is the degree to which an innovation is perceived as relatively difficult to understand or use and negatively associated with the probability of adoption. Littrell and Miller (2001) argue that clothing complexity is high when additional knowledge of garment construction is required due to lack of familiarity with the garment. Smart clothes carry various digital devices, so it is necessary to understand the diverse functions of those devices if the wearer is to use the clothing effectively.

Fourth is triability, which is defined as the degree to which an innovation may be experimented with on a limited basis. Giving potential adopters the chance to see the clothing demonstrated or to try it on before actual adoption should help reduce uncertainty about the product. Triability indicates the availability of and/or convenience of trying out smart clothes in the local stores. Finally, observability is defined as the degree to which the results of an innovation are visible to others. With smart clothes, it indicates how far potential adopters are able to observe the use of the apparel. These five attributes are perceived according to the messages to which individuals are exposed, both selectively and subjectively, and influence the formation of a favorable or unfavorable attitude toward an innovation (Rogers, 1995). Y. Park and Chen (2007) examined attitudes and behavioral intention to adopt smartphones among medical doctors and nurses and found that perceived usefulness, perceived ease of use, and observability were significant predictors for attitudes, but triability was not. Perceived usefulness and attitude positively determined a behavioral intention to use the product. A few studies have examined Rogers’s attribution theory in the clothing and textile area. According to Littrell and Miller (2001), familiarity with overall garment features and compatibility with existing wardrobe were significant for predicting consumers’ acceptance and purchase intention of India-inspired garments, but complexity was not. Sung and Slocum (2004) investigated the intention to adopt UV-protective clothing among U.S. golfers and found that triability, compatibility, and relative advantage, in that order, were significantly and positively associated with the intention to adopt. When Sung and Jeon (2005) examined the adoptability of UVprotective golf wear among Korean consumers, compatibility, triability, and relative advantage, in that order, were significant predictors for purchase intention. Despite these various studies, however, there has been little discussion regarding


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innovation adoption as a cross-cultural study in the literature. Because Korea and the United States present such different cultural backgrounds, we assume that differences would exist in the innovation-decision processes between the two cultural groups. Hence, we developed the following hypotheses:

There will be a difference in the effects of attitude on purchase intention between Koreans and Americans (Hypothesis 3b).

Hypothesis 1: Perceived attributes (e.g., relative advantage, compatibility, triability, observability) have positive effects on attitude toward smart clothing (Hypothesis 1a), whereas complexity has a negative effect on attitude toward smart clothing (Hypothesis 1b). There will be differences in the effects of perceived attributes on attitude toward smart clothing between Koreans and Americans (Hypothesis 1c). Hypothesis 2: Perceived attributes (e.g., relative advantage, compatibility, triability, observability) have positive effects on purchase intention (Hypothesis 2a), whereas complexity has a negative effect on purchase intention (Hypothesis 2b). There will be differences in the effects of perceived attributes on purchase intention between Koreans and Americans (Hypothesis 2c). In the innovation-decision process, the main outcome of the second stage is to develop a favorable or unfavorable attitude toward the product, which leads to the individual’s action in adopting or rejecting it. However, attitude and behavior are not always consistent, so intention is employed as a significant predictor of behavior (Blackwell, Miniard, & Engel, 2001). One’s beliefs and feelings about a product create an attitude toward it, which influences purchase intention. Thus, we developed the following hypothesis: Hypothesis 3: Attitude toward smart clothing has a positive effect on purchase intention (Hypothesis 3a).

Perceived Risks According to Rogers (1995), the newer an innovation and the higher the uncertainty associated with this newness, the more an individual is dependent on the innovationdecision process. Although consumers may form a favorable attitude toward smart clothing because of an affirmative perception of innovative attributes, the high level of uncertainty would prevent them from adopting it. Uncertainty related to an innovation can be conceptualized by perceived risk, defined as the “consumer’s uncertainty about the potential positive and negative consequences” (Blackwell et al., 2001, p. 108). Perceived risk is usually measured as a multidimensional construct, including social risk, psychological risk, performance risk, financial risk, time loss risk, and physical risks (Chen & He, 2003; Hirunyawipada & Paswan, 2006; J. Park & Stoel, 2005). Psychological risk is defined as the potential anxiety or disappointment caused by postpurchase results. Social risk refers to one’s fear of a negative evaluation from one’s social group. Financial risk and time loss risk involve concerns over the loss of the money or time invested in buying the product. Performance risk refers to concerns about the failure of a product to function as expected. And physical risk has to do with the possibility of personal injury resulting from using the product. There is little discussion in the literature about the association between perceived risk and perceived attributes of an innovation. However, perceived risk would affect those perceived attributes and have a negative influence on consumer attitudes toward new products and the purchase intention (Chen & He, 2003; J. Park & Stoel, 2005). In the innovation-decision process, an individual is motivated to seek evaluation information to reduce uncertainty about the product’s consequences (Rogers, 1995). As a psychological


263

Ko et al. trait, perceived risk is presumed to prevent the individual from forming a positive perception of the innovation’s attributes, which in turn causes a negative influence on his attitude and purchase intention. In other words, a consumer who would perceive a high level of social risk regarding a wearable medical system would see it as less compatible because of its innovative characteristics, whereas one who perceives a low level of performance risk would see the product as being less complex. In this light, we developed the following hypotheses: Hypothesis 4: Perceived risks have negative effects on perceived attributes (e.g., relative advantage, compatibility, triability, observability) of smart clothing (Hypothesis 4a), whereas perceived risks have a positive effect on complexity (Hypothesis 4b). There will be a difference in the effects of perceived risks on perceived attributes between Koreans and Americans (Hypothesis 4c). Based on the innovation-decision process, we focused on the first three stages of the process. As prior conditions, we assumed that Koreans and Americans would have different social norms because of the disparate cultural backgrounds. The first step, the knowledge stage, is measured by awareness of the innovation, and we must examine how such knowledge is associated with the second stage. Hence, we propose the following: Hypothesis 5: Awareness of an innovation would influence the perceived attributes of smart clothing (Hypothesis 5a). There will be a difference in the effects of awareness of an innovation on the perceived attributes between Koreans and Americans (Hypothesis 5b). Figure 1 shows the research model of this study. The proposed model describes the relationships between perceived risks, product awareness, perceived attributes, attitudes toward smart clothing, and purchase intention.

H2

Perceived Attributes H4 Perceived Risks

H1

Product Attitudes

H3

Purchase Intention

H5 Product Awareness

Figure 1. A Research Model

Note: H1 = Hypothesis 1; H2 = Hypothesis 2; H3 = Hypothesis 3; H4 = Hypothesis 4; H5 = Hypothesis 5.

Research Method The instrument for this study consisted of four sections: (a) awareness of, attitudes toward (5 items), and purchase intention (5 items) toward three smart clothing products; (b) perceived attributes of smart clothing (13 items); (c) perceived risks (14 items); and (d) demographics. The questionnaire was developed in Korean based on previous studies (Chen & He, 2003; Rogers, 1995; Stone & Gronhaug, 1993; Sung & Jeon, 2005; Sung & Slocum, 2004) and then translated into English by using back-translation techniques. The first three sections were measured on a 5-point rating scale (1 = strongly disagree, 5 = strongly agree). Demographic information included nationality, age, gender, household income, and monthly clothing spending. In the first section, the questionnaire included the definition of smart clothing, color photos, and descriptions of three commercial products: iPod Nike+ shoes, Burton’s jacket (in collaboration with Motorola), and an outdoor jacket (manipulated by researchers). We defined smart clothing to respondents as “a new concept of carrying digital functions into clothes that can be used for everyday life. Various sensors, micro-mini communication equipment, and MP3 players providing entertainment functions are attached to the clothes.” For each product, brief information on price and function was provided, along with a color photo. Awareness of, purchase experience in, attitude toward, and purchase intention for each product were measured repeatedly. A pretest was conducted with a number of university students in each country to clarify


264


Table 1. Descriptive Statistics Between Korean and U.S. Respondents Variables

Total (N = 611) Frequency

%

Koreans (N = 300) Frequency

%

U.S. (N = 311) Frequency

%

Gender Male 325 53.2 181 60.3 144 46.3 Female 286 46.8 119 39.7 167 53.7 Product awareness Yes 282 46.2 178 59.3 104 33.4 329 53.8 122 40.7 207 66.6 No Purchase experience Yes 15 2.5 9 3.0 6 1.9 No 596 97.5 291 97.0 305 98.1 Age Consumption for apparel Family income Attitude Purchase intention

χ2 12.074*** 41.194** 0.731

M

SD

M

SD

M

SD

t

20.65 2.61 4.23 3.16 2.70

1.84 1.14 1.67 0.61 0.70

21.33 2.61 3.23 2.96 2.64

2.21 1.10 1.43 0.56 0.65

20.00 2.62 5.21 3.36 2.76

1.03 1.18 1.27 0.59 0.74

9.457*** –0.129 –17.515*** –8.433*** –2.219*

**p < .01. ***p < .001.

the contents of the questionnaire. The data were collected in the classes during fall semester 2006 across the two nations. Korean student samples were obtained from several universities located in Seoul, and a total of 305 data were obtained. U.S. samples were also obtained during class from major universities in the northeastern area, with a total of 321 data collected. For the final analysis, 611 useful questionnaires were included: 300 Korean and 311 U.S. respondents (after excluding the incomplete questionnaires). For the data analysis, descriptive statistics, chisquare tests, t tests, factor analysis, and multiple regression analysis with stepwise methods were used with the Statistical Package for the Social Sciences 13.0 program.

Results and Discussion Sample Description As demonstrated in Table 1, about 53.2% of the respondents were male, and the mean age was 20.7 (ranging from 18 to 28). About 46.2% reported that they were aware of smart clothing, though only 15 respondents had purchased it.

Significant differences between Korean and U.S. respondents were found in gender, product awareness, age, family income, attitudes, and purchase intention. Americans were younger and earned a higher household income. Only 33.4% of Americans were aware of smart clothing, whereas 59.3% of the Koreans knew about it. However, the American respondents presented more positive attitudes and purchase intention toward smart clothing than did the Koreans, although the mean score of purchase intention was below the midpoint.

Perceived Attribute Dimensions of Smart Clothing Principal component factor analysis with varimax rotation was conducted to identify perceived attribute dimensions of smart clothing. For each nation, three attribute factors were generated: relative advantage/compatibility, complexity, and observability/triability. Compatibility items were combined with relative advantage because respondents might have considered compatibility with their existing wardrobe or lifestyle as another aspect of relative advantage. Littrell and


265

Ko et al. Table 2. Factor Analysis of Perceived Attributes and Mean Difference Between Korean and U.S. Respondents Item

M (SD)

Factor Loading Korean

U.S.

Korean

U.S.

t

Relative advantage/compatibility .677 .694 2.75 (0.80) 2.93 (0.92) –2.62** This product would maximize the value for the money that I spend. Wearing this product would improve .784 .667 2.79 (0.93) 2.42 (0.91) 4.94*** my image among my friends. This product would be more functional / .645 .701 3.28 (0.99) 2.75 (0.96) 6.67*** comfortable than other clothes I have. This product would coordinate well with .682 .702 2.41 (0.79) 2.70 (0.94) –4.20*** the other clothes I have. This product would be more compatible .842 .791 2.76 (0.85) 2.72 (1.00) 0.42 with my current needs than clothes I already have. This product would be appropriate for .693 .764 2.78 (1.05) 3.01 (1.06) –2.61** my lifestyle. Eigenvalue 3.210 3.430 40.100 42.920 % of variance Cronbach’s α .820 .830 Complexity .820 .895 2.89 (1.05) 2.43 (1.03) 5.45*** It would be difficult to understand how this product works. It would be difficult to explain the advantages .851 .755 2.94 (0.98) 2.61 (0.97) 4.13*** of wearing this product to others. Eigenvalue 1.480 1.240 % of variance 18.480 15.500 Cronbach’s α .620 .590 **p < .01. ***p < .001.

Miller (2001) also combined relative advantage of typical garment attributes with compatibility (with existing values). Factor analysis generated triability and observability as one factor. Because smart clothing is in the introduction stage, respondents seem to perceive that its availability and triability in local stores refer to its visibility. However, as only 2.5% of respondents purchased smart clothing items, observability was rare and unusual at that time, which led to a low internal consistency of this variable for each nation. Hence, we excluded observability/ triability from further analysis. The cumulative variance explained by two attribute factors, relative advantage/ compatibility and complexity, was 58.57% for the Korean model and 58.43% for the U.S. model. Cronbach’s alpha coefficients ranged from

.59 to .83 (see Table 2). KMO value was .792 for Koreans and .827 for Americans, and Bartlett’s tests were also significant at the .05 level. When comparing the mean of each item between two groups, seven of eight items were significant. The Americans perceived a higher level of compatibility with existing wardrobe or lifestyle than did the Koreans. On the other hand, the Koreans cared more about social image than the Americans when wearing innovative clothing. The Americans were more likely to be self-centered, whereas the Koreans tended to consider the self as inseparable from others, supporting previous literature (Jin & Sternquist, 2003). In addition, the Koreans were more inclined to perceive that smart clothing was functional and comfortable but found it more difficult to understand its usage than the Americans.


266


Perceived Risk Dimensions As can be seen in Table 3, 14 items of perceived risk for each nation were analyzed separately by factor analysis using principal components with varimax rotation. Four factors were identified for the Korean sample—psych/social risk, economic risk, time loss risk, and performance risk— explaining 59.69% of the total variance (KMO value = .747, Bartlett’s tests p = .000). The Koreans seemed to perceive evaluation from social group as psychological risk. The U.S. sample generated five factors: psychological risk, social risk, economic risk, time loss risk, and performance risk. Five factors of perceived risk in the U.S. sample explained 71.82% of the total variance (KMO value = .733, Bartlett’s tests p = .000). Across two nations, performance risk included the fear of physical injury. Among the 14 risk items, Korean and U.S. respondents presented a high level of concern for time and economic loss and functional failure but a low level for social risk. Respondents in both countries perceived that wearing smart clothes would not hurt their images or reputations among the social system. Of the 14 items, t test analyses indicated that 8 were significantly different between the two groups. The Koreans tended to perceive high risks of matching smart wear with other clothes and loss of time to repair or exchange it than the Americans did. The Koreans were also more concerned about the functional ability, along with physical injury and environmental disruption, than their counterparts. Respondents perceived high levels of economic risks, and the U.S. respondents were relatively more sensitive to cost effectiveness than were the Koreans.

Hypothesis Testing Hypothesis 1. Regression analysis was conducted to test Hypothesis 1, which posited that perceived attribute dimensions were significant predictors for attitude toward smart clothing for each nation. Table 4 shows that

relative advantage/compatibility had a significant, positive effect on attitude in the Korean model. In the U.S. model, relative advantage/ compatibility and complexity were significant in explaining attitude. Complexity was negatively related to attitudes only in the U.S. model, indicating that a low complexity level of smart clothing would lead to a positive attitude toward it. Koreans presented a higher level of complexity than Americans, but complexity was not salient for explaining attitude. Complexity of cultural products did not have a significant impact on attitude in Littrell and Miller (2001), whereas complexity of smartphones (as opposed to perceived ease of use) was significant for Y. Park and Chen (2007). Here, this may imply that smart clothing is perceived as an advanced technology rather than typical clothing. Hypothesis 1 was thus partially supported. Hypothesis 2. To identify the effect of perceived attribute dimensions on purchase intention, regression analysis was used. Across the two nations, relative advantage/ compatibility had a significant, positive effect on purchase intention. Slight differences existed in the effects of perceived attributes on purchase intention between Koreans and Americans (R2 = .195 for Koreans and R2 = .203 for Americans). Findings are comparable with previous studies (Littrell & Miller, 2001; Sung & Jeon, 2005; Sung & Slocum, 2004). Hence, only Hypothesis 2a and Hypothesis 2c were supported. Hypothesis 3. To identify the relationship between product attitude and purchase intention, regression analysis was used, and attitude toward smart clothing was a very important predictor of purchase intention for both countries (see Table 4). For the Koreans, 69.3% of total variance was explained, whereas 53.9% was explained in the U.S. model. The impact of attitude on purchase intention was stronger in the Korean model than in the U.S. model. Thus, Hypothesis 3 was supported. Hypothesis 4. Perceived risk dimensions and awareness of innovation were used as independent variables in the stepwise regression model to identify their effects on


267


Korean

U.S.


M (SD) U.S.

Psychological riska These clothes would be hard to match with my other clothes. .671 .833 3.41 (0.87) 2.96 (0.91) These clothes would not fit my style. .730 .846 3.23 (0.91) 3.10 (1.01) I would get tired of these clothes easily. .651 .700 3.07 (0.94) 3.05 (0.91) Eigenvalue 2.180 % of variance 15.580 .787 Cronbach’s α Social riska Wearing these clothes might make others have an unfavorable .538 .880 2.50 (0.86) 2.63 (0.93) impression of me. .714 .902 2.32 (0.82) 2.33 (0.85) Wearing these clothes might cause me to lose my reputation. Eigenvalue 2.350 1.760 % of variance 16.770 12.550 .698 .857 Cronbach’s α Economic risk The price of these clothes is out of my price range. .777 .908 3.67 (0.73) 3.61 (1.06) These clothes are not practical to wear considering the price. .694 .869 3.44 (0.87) 3.62 (0.95) Eigenvalue 1.680 1.620 11.990 11.590 % of variance .507 .753 Cronbach’s α

Item

Table 3. Factor Analysis of Perceived Risk and Mean Difference Between Korean and U.S. Respondents

(continued)

0.76 –2.49*

–0.17

–1.79

6.28*** 1.64 0.34

t

268


Korean

U.S.


M (SD) U.S.

a. In the Korean model, psychological risk and social risk were identified as one variable. *p < .05. **p < .01. ***p < .001.

Time loss risk It would take a long time to repair these clothes if defects were found. .555 .731 4.07 (0.78) 3.86 (0.76) I might need to pay extra in order to exchange these clothes. .840 .820 3.53 (0.97) 3.39 (0.88) It would be very difficult and inconvenient to exchange these clothes. .735 .820 3.59 (0.93) 3.45 (0.90) Eigenvalue 1.760 2.060 % of variance 12.560 14.720 Cronbach’s α .684 .753 Performance risk .627 .518 3.61 (0.93) 3.41 (0.94) I am concerned that these clothes might not provide the functions I expect. These clothes might release harmful electron waves to cause physical risk. .855 .858 3.52 (1.04) 2.78 (1.12) These clothes do not guarantee physical safety so I’m concerned about .856 .859 3.53 (0.99) 2.71 (1.10) future potential risk related to them. The material used in these clothes might pollute the environment when .662 .766 3.60 (0.99) 2.73 (1.01) it is disposed. Eigenvalue 2.570 2.440 18.380 17.390 % of variance .785 .777 Cronbach’s α

Item

Table 3 (continued)

10.71***

2.66** 8.50*** 9.71***

3.39** 1.83 1.98*

t

269

Ko et al. Table 4. Regression Analysis of Perceived Attributes, Attitudes, and Purchase Intention Dependent Variable

Korea U.S. Independent Variables B β t B β

Relative Product attitudes advantage/ compatibility Complexity F R2 (adj. R2) Purchase intention Relative advantage/ compatibility Complexity F R2 (adj. R2) Purchase intention Attitudes F R2 (adj. R2)

.380 –.029 .424 –.032 .963

.451

8.69***

.303

–.044 –.85 39.58*** .210 (.205) .435 8.29***

–.106

–.042 –.81 36.00*** .195 (.190) .832 25.94*** 672.68*** .693 (.692)

–.090

.431

.361

t 6.71***

–.151 –2.82** 34.96*** .185 (.180) .410 7.71***

–.102 –1.91 39.13*** .203 (.197) .921 .734 19.00*** 361.02*** .539 (.537)

**p < .01. ***p < .001.

perceived attributes (see Table 5). In explaining relative advantage/compatibility, psycho/ social risk and performance risk were significant in the Korean model, whereas psychological risk and economic risk were significant in the U.S. model. With two risk variables, the U.S. regression model presented strong explanatory power for relative advantage/compatibility (R2 = .448). However, performance risk showed a positive effect on relative advantage/compatibility, thus partially supporting Hypothesis 4a. With complexity as the dependent variable, awareness (of smart clothing), psycho/ social risk, and economical risk (in that order) were significant for the Koreans, whereas awareness, psychological risk, performance risk, and time loss risk were significant for the Americans. Risk variables were positively related to complexity, supporting Hypothesis 4b that the lower the perceived risks, the less complex smart clothing is perceived to be across the two nations. However, associations between perceived risk dimensions and perceived attributes of an innovation differed across the two countries, thus supporting Hypothesis 4c.

Hypothesis 5. As can be seen in Table 5, awareness of smart clothing was a salient predictor for complexity. The more potential adopters are aware of smart clothing, the easier it is to understand its usage. However, there was no influence to explain relative advantage/compatibility, so Hypothesis 5a was partially supported. The impact of awareness of smart clothing was slightly different in the two national models, so Hypothesis 5b was supported as well. Table 6 provides the results of hypothesis testing with R2 and significance of F values. In summary, findings confirmed the conceptual model of the innovation-decision process. Perceived attributes of smart clothing were significant predictors for attitude and purchase intention, in support of Hypothesis 1 and Hypothesis 2. Purchase intention was largely influenced by attitudes toward smart clothing across the nations (Hypothesis 3). Perceived risk dimensions had significant, positive effects on relative advantage/compatibility but negative effects on complexity (Hypothesis 4). However, not all the dimensions of perceived risks were salient in predicting perceived attributes, and different


270


Table 5. Regression Analysis of Perceived Risk on Relative Advantage/Compatibility and Complexity

Korea

Dependent Variable Independent Variables Relative advantage/ compatibility Complexity

B

β

U.S. t

–.523 –.466 –8.90*** Psychological risk a Social riska Economic risk –.082 –.080 –1.50 Time loss risk –.078 –.082 –1.45 Performance risk .101 .117 2.11* Product awareness .058 .043 0.85 F 21.24*** .265 (.253) R2 (adj R2) 2.46* Psychological risk .203 a .140 Social risk Economic risk .171 .131 2.25* Time loss risk .114 .093 1.50 Performance risk .002 .002 0.04 Product awareness –.447 –.255 –4.66*** F 9.04*** .133 (.119) R2 (adj R2)

B –.545 –.025 –.149 –.023 .042 .002 .166 .070 .057 .137 .156 –.288

β

t

–.611 –12.16*** –.029 –0.60 –.191 –4.37*** –.023 –0.49 .049 1.06 .002 0.04 41.06*** .448 (.437) .155 2.52* .069 1.16 .061 1.15 .113 2.00* .150 2.68** –.161 –3.01** 10.51*** .172 (.156)

a. In the Korean model, psychological risk and social risk were identified as one variable. *p < .05. **p < .01. ***p < .001.

impacts of perceived risks were presented between the Korean and U.S. models. Aware ness of smart clothing had a significant, negative effect on perceived complexity (Hypothesis 5).

Conclusions and Implications With advances in digital technology and emphasis on user friendly design, smart clothing has begun to receive growing attention in the fashion industry as well as in computer engineering science. Digital wear is an innovation for a new generation of fashion and presumably the future garments for enhancing convenience in daily life. Thus, managers must consider the welfare of adopters, including their aesthetic needs, when designing smart clothing, though little is yet known about the potential adopters’ perceptions or attitudes toward it. The aim of this study was threefold: (a) to identify young consumers’ perceived risks, perceived attributes, attitudes, and purchase intention regarding smart clothing; (b) to examine the relationships between variables

based on Rogers’s (1995) innovation-decision process; and (c) to provide a comparative analysis between Korean and American samples. In this regard, five hypotheses, developed according to the conceptual model, were partially supported (see Table 6). In general, the two cultural groups confirmed the innovation-decision process. Purchase intention was largely positively influenced by attitude. Attitude toward and purchase intention toward smart clothing were positively affected by relative advantage/ compatibility, which was in turn positively influenced by psycho/social risk for Koreans and both psychological and economic risks for Americans. For this study, relative advantage/compatibility included social image, economic benefit, and compatibility with lifestyles and existing wardrobe. Findings indicated that reducing psychological risk by enhancing the usefulness and profitability of smart clothing would increase the positive perceptions of its relative advantage. Awareness of smart clothing had a significant, negative effect on perceptions of complexity, supporting the position of the


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Ko et al. Table 6. Results of Hypothesis Tests Hypothesis

Korea

U.S.

1a RA/C (+) → AT RA/C (+) → AT 1b CP (–) → AT KR vs. U.S. difference 1c 2a RA/C (+) → PI RA/C (+) → PI 2b 2c KR vs. U.S. difference 3a AT (+) → PI AT (+) → PI KR vs. U.S. difference 3b 4a P/S R(–), PF R(+) → RA/C Psy. R, ER (–) → RA/C P/S R, ER(+) → CP Psy. R, TR, PF R(+) → CP 4b 4c KR vs. U.S. difference PA (–) → CP 5a PA (–) → CP 5b KR vs. U.S. difference

Results Supported (p < .001) Only U.S. (p < .01) Supported Supported (p < .001) Not supported Supported Supported (p < .001) Supported Partially supported (p < .001) Supported (p < .001) Supported Partially supported (p < .001) Supported

Note: RA/C = relative advantage/compatibility; AT = attitude; CP = complexity; PI = purchase intention; P/S R = psycho/social risk; Psy. R = psychological risk; ER = economic risk; TR = time loss risk; PF R = performance risk; PA = product awareness; KR = Korea; U.S. = United States.

innovation-decision process that information obtained at the knowledge stage would offset the difficulties of understanding its usage. These findings imply that the smart clothing industry needs to scrutinize with enthusiastic devotion the decision process of adopting an innovation. However, Koreans and Americans differed slightly in the specific relationships between variables in our conceptual model. Psycho/ social risks were significantly associated with perceived attribute variables in the Korean model, whereas social risk had no effect in the U.S. model. This was probably due to the difference in the degree of social interconnectedness in a collectivistic versus individualistic society. Economic risk had a significant, negative impact on relative advantage/compatibility in the U.S. model. American consumers are more value conscious than Koreans (Jin & Sternquist, 2003), so they are more sensitive to economic loss, leading our American subjects to form negative perceptions of relative advantage of the smart clothing. Hence, smart-wear marketers need to consider value for money paid and reflect it in pricing strategy, especially for U.S. consumers. Perceived complexity was a significant, negative predictor for attitudes only for U.S.

respondents and was positively influenced by psychological, time loss, and performance risk as well as product awareness. Accordingly, smart clothing marketers in the United States could use this finding when planning communication strategy. Designing commercial messages by controlling perceived risk dimensions is worthwhile, thereby making smart clothes seem less complex and generating a favorable attitude toward the apparel. Although this study contributes to the field by providing perceptions of smart clothing adoption, the data are collected from a limited generation in limited locations. Moreover, more than half of the respondents were unaware of smart clothing. They responded based on the descriptions provided in the questionnaire without having actual experience with the product. The study should be replicated based on buyers who have developed attitudes toward smart clothing and the intention to repurchase. The findings do provide some guidelines to marketers in the smart-wear business as to how to increase positive attitudes and purchase intentions and how to approach customers across the two different national groups. Through these research findings, marketers can better serve potential consumers by understanding the innovation-decision process.


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Bios Eunju Ko is a Professor of Clothing & Textiles at Yonsei University, and the 2008 President of Korean Academy of Marketing Science. Her research interests include cross-cultural issues and luxury brand management and her research has been published in Journal of Business Research, International Marketing Review, Psychology & Marketing, Industrial Marketing Management, International Journal of Operation & Production Management, Journal of Global Academy of Marketing Science, and other journals. Heewon Sung is a Professor of Fashion Marketing in the Clothing and Textiles Department at Gyeongsang National University. She teaches fashion marketing, consumer behaviors, and fashion retailing, and conducts research focused on e-tailing as well as consumer adoption of innovative apparel. Hyelim Yun is a Master Graduate of Clothing & Textiles at Yonsei University.
