Identifying tween fashion consumers’ profile concerning fashion innovativeness, opinion leadership, internet use for apparel shopping, interest in online co-design involvement, and brand commitment

Fashion adoption theory

Fashion adoption theory (Sproles 1979) is employed to identify key characteristics of the tween fashion consumer groups. Fashion adoption is an individual decision-making process to adopt any given style (Forsythe et al. 1991). Fashion adoption theory explains consumers’ information seeking and decision-making process of fashion adoption, which includes awareness of fashion objects, interest, evaluation, identification of alternatives, decision, inventory of clothing, use, and obsolescence (Forsythe et al. 1991; Sproles 1979). According to this theory, the fashion adoption process is influenced by the adopter’s identity and psycho-social motivations. The adopter’s identity influencing the fashion adoption process includes age, sex, socioeconomic characteristics, and physical profile, such as body size/shape and appearance. The adopter’s psycho-social motivations include cognitive orientation toward dress (e.g. awareness, interest, knowledge, innovativeness, perceived risk, expectations, attitudes, and values), psychological identity (e.g. self-concept, personality, and individuality-conformity), and social influences on adopter (e.g. collective behavior, socialization, reference groups, social communications, and opinion leadership). This theory underscores the role of awareness of new fashion, which can be embodied by fashion innovativeness, and social influences, represented by opinion leadership, in the decision-making process of fashion adoption.

Sproles’ theory is utilized as the conceptual framework for the present study to explain how tween fashion consumers differ, depending on the two key psycho-social motivations, fashion innovativeness and opinion leadership, as well as how tweens’ fashion innovativeness and opinion leadership influence their fashion adoption process in online apparel shopping. Based on this fashion adoption theory, it was hypothesized tween consumers who exhibit more fashion innovativeness and opinion leadership may utilize more technology, especially Internet, for apparel shopping, browsing, and fashion information search; have a greater interest in online co-design involvement for new shopping experiences; and have stronger brand commitment toward their preferred fashion brands than those who retain less fashion innovativeness and opinion leadership.

Fashion innovativeness and opinion leadership of fashion change agents

Fashion innovativeness refers to one’s willingness to try a new fashion product earlier than other members of society (Goldsmith and Flynn 1992; Goldsmith 2000). Consumers’ fashion innovativeness tends to influence their fashion adoption process (Blackwell et al. 2001; Park et al. 2010). Fashion innovators with a high level of fashion innovativeness constitute a small segment of the overall consumer market, but they are a catalyst for mass adoption of products/brands (Goldsmith and Flynn 1992). Fashion innovators are apt to adopt a product sooner than other consumers (Beaudoin et al. 2003; Beaudoin and Lachance 2006) even though the purchase of these brands involves potential risk (Muzinich et al. 2003).

Opinion leadership refers to leadership by those who actively learn from and form opinions from the mass media and influence the decisions of others from the theory of two-step flow of communication (Katz and Lazarsfeld 1955). This theory explains people are not directly influenced by mass media but by opinion leaders who have more understanding of messages or contents from the media, explain and diffuse these to others. Opinion leaders are “individuals who are influential on the attitude and decision making of people in their social circle” (Brannon 2010, p. 407).

The fashion consumer groups retaining the FCAs characteristics—fashion innovativeness and opinion leadership—become involved in the diffusion process in which new innovative styles are adopted and then spread throughout society (Workman and Johnson 1993; Brannon 2010). The fashion consumer groups can be broken into four groups according to their role in adopting and diffusing new fashions: fashion innovators, fashion opinion leaders, innovative communicators, and fashion followers (Workman and Johnson 1993). Three of these four groups assist the advancement of new trends and styles: fashion innovators, fashion opinion leaders, and innovative communications. Collectively, these groups are considered fashion change agents (Workman and Kidd 2000).

FCAs who have more fashion innovativeness and opinion leadership tend to be more knowledgeable and interested in fashion, communicate, and spread new and innovative fashion trends within their social group (Brannon 2010; Cho and Workman 2015). FCAs tend to have a greater need for variety or change, enjoy shopping, and spend more money on fashion products as compared to non-FCAs (Workman 2010; Workman and Johnson 1993). FCAs are likely to value more hedonic and adventure shopping offering enjoyment and excitement (Goldsmith and Stith 1992). It is common for fashion opinion leaders to have a greater need for stimulation and seek a greater level of sensation and/or exciting experiences (Stanforth 1995; Studak and Workman 2004). Fashion innovators and opinion leaders also tend to use multi-channels for information search and apparel shopping, indicating more active use of information technology, social media, and so on (Cho and Workman 2011). Additionally, FCAs have relatively higher brand sensitivity (Beaudoin and Lachance 2006) and are more brand conscious (Workman and Cho 2012a, b), while demonstrating lower brand attachment, brand love, and brand trust than fashion followers (Workman et al. 2015).

Overall, FCAs with higher levels of fashion innovativeness and opinion leadership are different from fashion followers in fashion adoption and consumption. Based on the literature review of FCAs, it is hypothesized tween consumers can be segmented into FCAs and fashion followers in terms of fashion innovativeness and opinion leadership. It was assumed FCAs in the tween consumer group may lead the tween fashion market.

Internet use and interests in co-design involvement

In a media-saturated society, technology, especially the Internet, is a means through which tween consumers become exposed to fashion information (Boden 2006). The Internet creates a competitive market place by presenting more options and experiences for consumers (Kim and Kim 2004), and Internet apparel shopping continues to increase (Rueter 2012). According to prior research, FCAs with higher fashion innovativeness and opinion leadership tend to become involved in shopping apparel products by utilizing multi-channels, including online and non-store channels (Cho and Workman 2011). Online apparel shoppers tend to be more innovative and knowledgeable about the Internet and use the Internet more frequently for shopping than non-online apparel shoppers (Goldsmith and Goldsmith 2001; Cho and Workman 2015). Park et al. (2007) also found fashion innovativeness positively relates to online shopping for purchasing foreign fashion goods beyond national borders. They also found that Internet use—a domain-specific innovativeness, which determines consumer adoption of the Internet for shopping—moderates the positive relationship between fashion innovativeness and attitude toward online apparel shopping for purchasing foreign fashion goods. Goldsmith and Flynn (2005) also found that consumers’ fashion innovativeness was positively associated with shopping in stores, catalogs, and the Internet. In particular, heavy shoppers of apparel products were more fashion innovative and more innovative for online apparel shopping than lower and medium shoppers. However, they did not find a significant relationship between opinion seeking from others and multi-channel shopping.

Additionally, technology allows consumers to participate in the co-design process through mass customization, a process that involves the customer in product development operations (Choy and Loker 2004). Co-design is defined as “the process of mass customization that allows a customer to interact with a set number of choices to individualize the product style, fabric, color, and size” (Choy and Loker 2004, p. 82). Co-design as part of an exciting consumer experience was more likely used among individuals with a higher level of optimum stimulation level (OSL)—individuals’ preferred level of stimulation from environmental stimuli (Fiore et al. 2004). According to Steenkamp and Baumgartner (1992), individuals with higher OSLs are more likely to have innovative behaviors by trying out new brands as well as exhibit more variety-seeking in a product category than individuals with lower OSLs. The relationship between fashion innovativeness and co-design was not investigated, but it is probable that individuals with higher fashion innovativeness would be more likely to try co-design for an exciting shopping experience.

Based on the literature, it is hypothesized that tween consumers with high FCA characteristics would use the Internet more for apparel shopping because they may possess more Internet use, while acquiring more interest in the co-design involvement for more experiential shopping experiences. These linkages between tweens’ FCA characteristics, and Internet use for apparel shopping and interests in co-design involvement may explore a better understanding of tween fashion consumers’ individual differences in online shopping orientations.

Brand commitment and children’s wear market

Brand commitment is an emotional, psychological attachment to a brand within a product class from an attitudinal perspective, while brand loyalty is repeated purchase of a single brand over time from a behavioral perspective (Warrington and Shim 2000). Brand commitment and brand loyalty are positively related, but brand commitment is a stronger indicator of consumers’ brand choice behavior because brand-committed consumers are less likely to switch to an alternative brand than brand-loyal consumers when a preferred brand is absent (Warrington and Shim 2000).

Children are exposed to brands at a young age, ranging from food to clothing (Ji 2008). Brand awareness has been found to commence at an early age and grow stronger with time (Ross and Harradine 2004). While tweens have been characterized as having brand awareness, they do not necessarily display characteristics of brand loyalty (Grant and Stephen 2005). They understand their expectations for a product, and if they are not satisfied, they do not hesitate to consider the alternatives (Grant and Stephen 2005).

Previous studies show consumers with a higher level of fashion innovativeness tend to be more brand conscious (Workman and Cho 2012a, b) and more brand sensitive (Beaudoin and Lachance 2006). However, Workman et al. (2015) have found fashion change agents showed weaker brand attachment, brand love, and brand trust than fashion followers, indicating more brand-switching tendency among fashion change agents with higher levels of fashion innovativeness. Further investigation on these mixed results is needed to explore the relationships between the FCA characteristics and brand commitment. While the tweens market group has been identified and labeled, the literature available on tween consumers’ brand commitment is limited, particularly information on tweens as fashion-oriented consumers. Exploring characteristics of this fashion consumer group’s brand commitment would be beneficial to understand their attitudinal attachment or connection to brand. Thus, the following hypothesis investigating consumer-brand relationship is proposed.

Sample

The sample for this study was composed of tween-aged girls (ages 8–12) from the Midwest region because girls have demonstrated more interest in fashion and online apparel shopping than have boys (Hayta 2008). The focus of this study is narrowed from girls aged 7–14 to girls aged 8–12. This is due to concerns that the questionnaire content would be difficult for a 7-year-old to understand; additionally, while some studies define tweens as old as 14, other studies consider 13 and 14-year-olds to be teens as opposed to tweens. As accessing tween subjects was limited and difficult, the snowball sampling method was used to recruit more future subjects from existing study subjects’ acquaintances. Participants were recruited through the local Girl Scouts organization, local school districts, and community programs.

Survey instrument

Established scales from previous studies were utilized or modified to measure the key variables: fashion innovativeness, opinion leadership, Internet use for apparel shopping, interest in online co-design involvement, and brand commitment. Fashion innovativeness and fashion opinion leadership were assessed by the innovativeness and opinion leadership scale (Hirschman and Adcock 1978). This scale contains items that measure the level of trying new fashions and the level of influencing the types of clothing friends buy on a 5-point Likert-type scale with reported reliabilities ranging from .73 to .76 (Hirschman and Adcock 1978).

Internet use for apparel shopping was measured with four items adapted from the Park et al. (2007). study of fashion innovativeness. Adoption and use of the Internet for apparel shopping, browsing, and information search was evaluated to determine tweens’ Internet use. Its reported reliability was .76 (Park et al. 2007). Interest in online co-design involvement for customization was also assessed with three items on a 5-point Likert-type scale. These items were adapted from the Design Involvement Scale (Choy and Loker 2004), used in a study of mass customization of wedding gowns. The reliability for these items was not reported in their study. Finally, brand commitment was assessed with 7 selected items on a 5-point Likert-type scale originated from Mittal and Lee (1989). The reliability for this scale was reported above .75 (Mittal and Lee 1989). Four items were used to assess how tweens became aware of fashion brands and shop for their preferred fashion brands. Survey instruments were pre-tested with five children and their parents, and were revised to simplify language, based on their comments.

Data collection

The snowball method was utilized to select the sample. Friends and family were reached through email and asked to contact children’s groups. Schools near the university were also recruited. This recruitment process took 2 months. Initial contact was made to supervisors or to parents, who had been contacted by the supervisors, via e-mail or in person via the researchers’ personal networks; when further contact was necessary, in-person meetings were scheduled. Email invitation, personal invitations, and parental and minor consent forms were all approved by the Institutional Review Board (IRB) prior to data collection.

A link to the online survey was emailed with an explanation of the study, not directly to the girls but to an adult. The first question of the survey was parental permission. After the parent’s approval, daughters completed the remainder of the survey beginning with a minor consent form, which explained the procedures of the survey. For those who participated by completing a hard copy, the survey was distributed and then the children were given a brief description of the study. They then took the hard copy home to their parents to sign the first page of the survey (the parental permission form). After this was completed, the girls answered the remainder of the survey. The surveys were collected at a later date.

Data analysis

Data analysis consisted of descriptive analysis, factor analysis, reliability, correlation, cluster analysis, MANOVA, and multiple regression analyses.

Participants

A total of 210 questionnaires were distributed through email or in-person with 63 returned for a response rate of 30%. After deleting the missing data, a total of 53 responses were deemed acceptable for data analysis. Participants included 53 tween-aged girls, aged 8–12, with a mean age of 9.96 (SD = 1.20) years. Most participants were Caucasian American (67.3%), followed by African American (14.5%), Asian or Pacific Islander (14.5%), and others (3.6%).

Preliminary analysis

Using LISREL 8.72, a confirmatory factor analysis (CFA) with a maximum-likelihood estimation procedure was also conducted to examine how well the five hypothesized factors fit the observed data. A model composed of 21 observed variables and five latent variables was tested. A test of the hypothesized five-factor model showed a satisfactory fit to the data within acceptable fit parameters: χ² (199, N = 53) = 227.00, p = .08; RMSEA = .052; CFI = .95; NNFI = .94; IFI = .95. Results verified the five characteristics—fashion innovativeness, opinion leadership, Internet use for apparel shopping, interests in online co-design involvement, and brand commitment—represented the tweens’ consumer group’s fashion adoption and diffusion behaviors. Concerning a small sample size issue for the CFA analysis, there are various rules about a minimum sample size for CFA and SEM. For example, Bentler and Chou (1987) suggested a ratio of sample size to number for parameters as low as 5:1 would be sufficient when latent variables have many indicators. A widely accepted rule is a ratio of at least 10:1 (Hair et al. 2014). The larger ratio of sample size to number of parameters tends to have more proximity with population parameters and obtain more trustworthy, better estimates of parameters and Chi squared probabilities. However, recent studies indicate marginal sample sizes of 50 to 70 were associated with an acceptable Chi square, satisfactory fit, low Type-I error rates of 25%, satisfactory RMSEA values between .5 and .08, and stable model parameters in both CFA and SEM (Sideridis et al. 2014; Wolf et al. 2013). The small sample size for this study is not idealistic, but the CFA results are viable to explain the hypothesized factor structure.

Hypothesis testing through cluster analysis and MANOVA

Utilizing IBM SPSS Statistics 22, a hierarchical cluster analysis using Ward’s method was conducted to group the respondents into homogeneous sub-groups, based on their responses to fashion innovativeness and opinion leadership. The squared Euclidian distance was chosen as the distance measure. Two, three, and four clusters were computed on the fashion changing agent qualities, fashion innovativeness and opinion leadership, and the researchers selected a two-group solution, considering a diffusion of innovation curve and the fashion cycle. The tween consumers were divided into two groups—FCAs and fashion followers. Univariate and multivariate analyses were further conducted among the two groups to verify the cluster solution. According to the MANOVA, all two groups were significantly different on the five variables, F(5,47) = 21.12, p < .001, Wilks’ Lambda (Λ) = .31, partial eta squared (η²) = .69, supporting Hypothesis 1.

According to univariate analyses (ANOVA), there was a statistically significant difference among the two groups on fashion innovativeness, F(1,51) = 58.06, p < .001, η² = .53, and fashion opinion leadership, F(1,51) = 73.74, p < .001, η² = .59; Internet use for apparel shopping, F(1,51) = 13.81, p < .001, η² = .21; interests in online co-design involvement, F(1,51) = 6.60, p < .05, η² = .12; and brand commitment, F(1,51) = 19.54, p < .001, η² = .28.

Both MANOVA and ANOVA results supported the two groups were significantly different when considering all five variables together as well as individually. A larger sample size is desirable for MANOVA, but the minimum sample size requirement for each group is greater than the number of dependent variables included (Hair et al. 2014). So, the sample size for each group meets the minimum requirement. However, the small sample size may decrease statistical power. Although the statistical power of the results is limited in representing the tween population, the results detected differences between the tween fashion consumer groups and would offer preliminary understanding or guidance for future research on tween fashion consumers.

Hypothesis testing through multiple regressions

Hypothesis 3 was also partially supported. The two explanatory predictors explained 16% of the variance in interest in online co-design involvement, F(2,52) = 4.91, p < .05. Fashion innovativeness positively influenced interest in online co-design involvement (β = .34, t = 2.03, p < .05), but opinion leadership did not influence it (β = .08, t = .45, p > .05). Participants who possessed greater fashion innovativeness tended to have more interest in involving online co-design of customization for exciting, fun experiential shopping. However, opinion leadership did not influence interest in online co-design involvement.

Hypothesis 4 was not supported. The two predictors explained 37% of the variance in brand commitment, F(2,52) = 14.83, p < .001. Fashion innovativeness did not influence brand commitment (β = .15, t = .99, p > .05). Interestingly, fashion opinion leadership positively influenced brand commitment (β = .51, t = 3.45, p < .001). The results were interpreted that participants who possessed greater opinion leadership were more likely to show greater brand commitment toward their preferred brand than those who possessed less opinion leadership.

Limitations and recommendations for future research

Tweens are an interesting consumer group because they have very little income, if any, and so must rely on another person, typically a parent, to make purchases. Fashion has been seen to trickle up from teens and young adults (Johnson, 2017; Rousso 2012), but never from an age group so young. With their growing economic power and the percentage of FCAs in the group, perhaps tweens might have more influence in the fashion market. The tween population is difficult to reach due to their young age and need for parental permission to participate in research. This difficulty is indicated as a limitation and suggested this study be repeated with a larger sample of tween girls. Most the sample for this study came from the Midwest region. Future studies should include participants with more regional diversity, which could be also valuable.

Since this group may be showing high levels of FCA qualities, it is suggested to further explore their fashion adoption and diffusion behaviors and/or relationships among Internet use for apparel shopping, interest in online co-design involvement, and brand commitment, which critically influence the information search and decision-making process in apparel shopping. This could be achieved with exploratory focus groups or interviews. A focus group or an interview would be a good platform to better understand attitudes and behaviors toward fashion adoption and diffusion among this age group.

Additionally, this study suggests marketers and retailers for tweens’ apparel market to develop more fashion innovative, Internet-mediated, interactive, and brand-oriented strategies for tween fashion consumers. Investigating tween fashion consumers’ responses to new, innovative technologies or strategies, experiential shopping environments, and branding strategies would be also needed as a follow-up research to expand an understanding of tween consumers’ profile.