Effects on multi-channel choice and touch/non-touch preference in clothing shopping
Siwon Cho, Fashion Design and Merchandising Program, Southern Illinois University, Carbondale, Illinois, USA
Jane Workman, Fashion Design and Merchandising Program, Southern Illinois University, Carbondale, Illinois, USA
Purpose – This study aims to examine whether gender, fashion innovativeness and opinion leadership, and need for touch have effects on consumers' multi-channel choice and touch/non-touch shopping channel preference in clothing shopping.
Design/methodology/approach – A survey was conducted using a convenience sample of 123 male and 154 female US college students. Data were analyzed using PASW Statistics 18 and Analysis of Moment Structure (AMOS) 18.
Findings – Results showed that participants' multi-channel choice was influenced only by fashion innovativeness and opinion leadership such that consumers high in fashion innovativeness and opinion leadership tend to use more than one shopping channel. Touch channel preference was influenced by need for touch and multi-channel choice such that participants who had higher need for touch and used more than one channel for clothing shopping preferred local and non-local stores. Non-touch channel preference was influenced by fashion innovativeness and opinion leadership and multi-channel choice. Regardless of gender, those high in fashion innovativeness and opinion leadership who used more than one channel preferred TV retailers, catalogs, and online stores.
Research limitations/implications – Results cannot be generalized to the larger population of other consumer groups. Future research should include other population groups.
Originality/value – This study is the first to investigate the effects of consumers' gender, fashion innovativeness and opinion leadership, and need for touch on their multi-channel choice and touch/non-touch shopping channel preference in clothing shopping.
Clothing; Consumer behaviour; Multi-channel retailing; Fashion; Gender; Innovation.
Journal of Fashion Marketing and Management
Emerald Group Publishing Limited
In the past, consumers often obtained products and services from a single retail channel at all stages of their decision process. In the 1990s, physical store retailing and in-home buying were vigorous competitors (Engel et al., 1995). Recently, retailers have employed multi-channel retailing by combining different distribution channels (e.g. brick-and-mortar, TV, catalog, online) to deliver products and/or services (Poloian, 2009). Multi-channel retailing helps to retain current customers and attract new customers by providing information, products, services, and support through two or more synchronized channels (Rangaswamy and Van Bruggen, 2005). Consumers can enhance their flexibility and convenience when shopping by switching from one channel to another because retailers offer identical information about products across different channels, which work as one company in meeting their customers' needs. Consumers may use different channels at different stages in the purchase decision-making process, for example, using online stores to obtain information, but making purchases offline (Balasubramanian et al., 2005). Thus, multi-channel shoppers in this study refer to customers who use more than one channel (e.g. local store, non-local store, TV retailer, catalog, online store) to purchase products.
Delivering products and services through multi-channel retailing increases a retailer's competitiveness (Lee and Kim, 2008) because it provides alternatives that satisfy multi-channel shoppers' needs (Schröder and Zaharia, 2008). Therefore, multi-channel retailers require an in-depth understanding of their customers' characteristics and shopping behaviors, and how these influence the retailers' performance (Rangaswamy and Van Bruggen, 2005; Schröder and Zaharia, 2008). Indeed, “It is arguable that the ultimate survival of all retail establishments depends on providing outlet features that generate patronage among a significant segment of consumers” (Dawson et al., 1990, p. 409). According to Dawson et al. (1990), outlet features include distance, assortment, travel time, and consumer characteristics. In the current study, outlet features include consumer characteristics (i.e. gender, fashion innovativeness and opinion leadership, need for touch) as well as touch and non-touch capabilities of retail outlets. Researchers have examined:
- online and offline shopping behavior (Danaher et al., 2003; Shankar et al., 2003);
- perceptions of multi-channel retailers and perceptions of a single channel (e.g. satisfaction, loyalty) (Lee and Kim, 2008);
- customer movement among channels and how the different channels work together (Falk et al., 2007); and
- characteristics of multi-channel shoppers (Kumar and Venkatesan, 2005).
In order to maximize multi-channel shoppers' satisfaction and retail sales, it is critical to understand the characteristics of multi-channel shoppers affecting retail channel choice and preference.
The purpose of the study was to examine how gender, fashion innovativeness and opinion leadership, and need for touch affect consumers' multi-channel choice and touch/non-touch shopping channel preference in clothing shopping. We chose these variables to study because of their theoretical linkages to individual differences in the Consumer Decision Process Model (Blackwell et al., 2001). In addition, these variables are important motivational factors when consumers choose where to shop. This study extends current understanding of multi-channel consumer behavior and will help retailers better understand consumers' channel choice and preferences. Thus, retailers will be better able to develop strategies that align and evolve with customers' needs.
The Consumer Decision Process Model by Blackwell et al. (2001) describes consumers' decision-process behavior from need recognition to satisfaction after purchasing products. In the model, there are two categories influencing decision making: environmental influences and individual differences. Environmental influences include culture, social class, personal influence, family, and situation. Individual differences are:
- consumer resources;
- motivation and involvement;
- attitudes; and
- personality, values, and lifestyles.
These factors play important roles when consumers face issues prior to purchase: whether to buy, when to buy, what to buy, where to buy, and how to pay. However, the model places less emphasis on choosing the source of purchase (i.e. where to buy) and does not specify what individual differences might influence the consumer decision-making process for choice of retailers.
As today's consumers have greater options on where to buy, researchers have studied the relationship of individual differences with choosing source of purchase (e.g. Cho, 2008; Eastlick and Lotz, 1999; Goldsmith and Flynn, 2005; Limayem et al. 2000; Schoenbachler and Gordon, 2002; Seock and Chen-Yu, 2007). For example, Kanu et al. (2003) found significant differences among characteristics of three different types of shoppers: traditional shoppers (i.e. shoppers who purchased products from brick-and-mortar stores only), on-off “switch” shoppers (i.e. shoppers who liked to surf the Internet and collected online information, but preferred to shop offline), and online shoppers (i.e. shoppers who liked to surf the Internet, collected online information, and shopped online). Based on the results, traditional shoppers did not surf the Internet for comparative information, neither did they look for bargains over the Internet. Although they came from all different age groups, a larger proportion of traditional shoppers, was from the age group of 40 to 49. On-off shoppers enjoyed looking at advertisements, were frequent users of bookmarks, and used the same search engine on a regular basis. They were experienced in surfing and often looked for best deals. Demographically, on-off shoppers were likely to be single and in the age group of 15 to 24. Online shoppers were also in the age group of 15 to 24; however, compared to on-off shoppers, they were more likely to be married; loved banner advertisements and clicked on them often; looked for promotional offers, had good navigation expertise and had online purchase experience.
However, there is limited research on consumer behavior in multi-channel retail settings (e.g. Johnson et al., 2006; Lee and Kim, 2008; Telci, 2010). Previous studies examined how various factors such as consumers' geographic location, shopping orientation, information search, and product category influenced multi-channel shoppers' behavior. Further research is needed to describe the characteristics of multi-channel shoppers. Therefore, this study explores gender, fashion innovativeness and opinion leadership, and need for touch as individual differences.
Literature review and proposed model
Based on the model of Purchase Decision-Making Process and related literature, a conceptual framework and four hypotheses were developed (see Figure 1).
Gender is a social construct that is intertwined with virtually all aspects of human behavior. Previous studies on consumer behavior discuss how gender affects consumption. For example, Kolyesnikova et al. (2009) found gender differences influence how identity and product knowledge impact feelings of gratitude and obligation and how these constructs impact purchasing. These authors concluded that men and women tend to reciprocate for different reasons that may be significant in consumer situations.
Men and women often shop differently. Standard marketing wisdom, holds that 80 percent of all buying decisions, are made by women (Cleaver, 2004). Compared with men, women are more oriented toward “shopping for fun”, spend more time browsing, more mental energy researching available options, compile information from various sources in order to make an informed decision, and, in particular, buy more clothing (Beaudry, 1999; Cleaver, 2004; Falk and Campbell, 1997; Hensen and Jensen, 2009). In contrast, many men make purchase decisions by “stripping away extraneous information” (Cleaver, 2004, p. 19). Men tend to be “quick shoppers” who avoid shopping, but when they cannot avoid it, make purchases quickly in order not to extend the time spent shopping (Falk and Campbell, 1997; Hensen and Jensen, 2009).
Gender and fashion consumer group. Previous studies showed contradicting results regarding the relationship of gender with fashion consumer group (e.g. Johnson, 2008; Quigley and Notarantonio, 2009). Johnson (2008) found no significant relationship between gender and fashion innovativeness, but being a woman positively predicted fashion opinion leadership; further, fashion opinion seekers tended to be men. Workman and Studak (2006) reported that fashion change agents and women have a “want-based” approach to fashion problem recognition style, while fashion followers and men reflected a “need-based” approach. Kwon and Workman (1996) found that women scored higher on a fashion leadership scale than men. Quigley and Notarantonio (2009) found that women accounted for a larger percentage of fashion leaders than men. Women are more involved in fashion and clothing than men (O'Cass, 2004).
Gender and need for touch. Women scored higher than men on the Need For Touch (NFT) scale, both autotelic (touch for pleasure) and instrumental (touch for information) dimensions (Workman, 2010). Among women, there were no differences in scores on the autotelic and instrumental dimensions of the NFT scale, suggesting that women used touch equally for pleasure and for information about products. Conversely, men scored higher on the instrumental than the autotelic dimension, suggesting that men use touch to obtain information about products.
Gender and shopping channel choice. Gender differences exist in aspects of shopping channel choice. Female consumers prefer physical evaluation of products more than men. Fewer women shop online because of a lack of social interaction (Hasan, 2010), implying that women are more likely to use brick-and-mortar stores than men. However, based on ComScore and iMedia Connection, Macklin (2006) reported that the percentage of female customers was higher than male customers for ten leading web properties (e.g. 61 percent at JC Penney, 56 percent Target Corporation). Goldsmith and Flynn (2005) found that women were more likely than men to buy apparel from any of three channels: brick-and-mortar stores, internet, and catalogs. Consumers who bought more apparel via one channel also bought more apparel via the other two channels, those who buy more clothing will do so using all three channels, while women buy more apparel than men regardless of shopping channel. Goldsmith and Flynn (2005) concluded that consumers who buy more apparel seem to use various shopping channels and are motivated by involvement with clothing. These findings indicate that when shopping for clothing, female consumers choose more than one shopping channel for various motives and situations and may be more likely to do so than men.
It was expected that female participants would more likely be higher in fashion innovativeness and opinion leadership, have higher NFT and use more than one shopping channel; thus, the first hypothesis was developed as following:
H1. Gender will influence fashion innovativeness and opinion leadership (H1a), NFT (H1b), and multi-channel choice (H1c) in clothing shopping.
Fashion innovativeness and opinion leadership
Fashion consumer groups include fashion followers (those who are lower in fashion innovativeness and opinion leadership) and fashion change agents (those who are higher in fashion innovativeness and opinion leadership) (Workman and Freeburg, 2009). Fashion change agents are the driving force behind fashion change: they are the first to buy and wear new fashions (fashion innovators), they persuade others to buy and wear new fashions (fashion opinion leaders) or they carry out both roles (innovative communicators). Fashion followers trail behind other consumers and wait until a new style is at its highest point of acceptance before purchase.
Research shows that consumers high, and low in fashion innovativeness, and opinion leadership, differ in many consumer behaviors, for example, experiential shopping (i.e. social or recreational shopping). Experiential shopping is motivated by a desire for pleasure and sensory gratification rather than practical purposes such as obtaining information about, evaluating or purchasing a product (Peck and Childers, 2003). Compared with consumers who are low in fashion innovativeness and opinion leadership, those high in fashion innovativeness and opinion leadership engage more often in experiential shopping. For example, they go shopping more often, buy more new fashion items, spend more money on clothing, are more interested and involved in fashion and are more likely to purchase products impulsively (Beaudoin et al., 1998, 2000; Cho-Che and Kang, 1996; Darley and Johnson, 1993; Flynn et al., 2000; Goldsmith et al., 1991; Phau and Lo, 2004).
Fashion innovativeness and opinion leadership and need for touch. Consumers who scored higher on fashion innovativeness and opinion leadership had a greater NFT in both autotelic and instrumental dimensions than those who scored lower (Workman, 2010). Those high in fashion innovativeness and opinion leadership appear to use touch for both pleasure and information; while those low in fashion innovativeness and opinion leadership use touch to gain information about products.
Fashion innovativeness and opinion leadership and shopping channel choice. Characteristics of fashion consumers affect where they shop. For example, consumers who bought more apparel were more fashion innovative and technology savvy and they were more likely to be multi-channel shoppers (Goldsmith and Flynn, 2005). Clothing innovativeness, was found to be related to an increase in online shopping (Park and Jun, 2002). Although clothing innovators shopped more frequently via all channels, they were most strongly drawn to brick-and-mortar stores (Goldsmith and Flynn, 2005). Consumers who are less fashion innovative might be discouraged from using non-store channels for apparel purchase because they cannot examine the product before purchase (e.g. fabric hand); thus, offering the least information and feedback (Goldsmith and Flynn, 2005). Individual's clothing innovativeness is associated with greater levels of multi-channel shopping (Flynn et al., 1996) and shopping from non-store channels (Park and Jun, 2002). In this study, local and non-local brick-and-mortar stores are defined as touch channels, where consumers are able to examine the quality of clothing by touching before purchase. We included non-local stores as a touch channel because consumers living in an area with limited availability of stores for apparel shopping may be willing to travel to nearby cities where there are more stores and a greater variety of products. Non-touch channels are TV, catalog, and online that have a non-store format where consumers cannot touch clothing before making a purchase decision.
It was expected that participants high in fashion innovativeness and opinion leadership would have higher NFT. However, characteristics of participants high in fashion innovativeness and opinion leadership might lead to use of more than one shopping channel and to a preference for non-touch channels. The second hypothesis examined this possibility:
H2. Fashion innovativeness and opinion leadership will influence NFT (H2a), multi-channel choice (H2b), and non-touch channel preference (H2c).
Need for touch
Need for touch refers to preference for handling products before purchase (Peck and Childers, 2003). NFT encompasses two dimensions: autotelic and instrumental. Autotelic need for touch relies on subjective, psychological information and is noticeable in the pleasurable emotions (i.e. fun, sensory stimulation, enjoyment) resulting from touch and using touch as a means of seeking variety. Instrumental touch is goal-directed touch focused on objective, tangible properties of hardness, temperature, texture, or weight. Individuals high in need for instrumental touch use touch to answer questions during information search and during evaluation of products.
Need for touch and shopping channel choice. The need to touch products was negatively related to online purchasing, particularly for clothing products (Citrin et al., 2003). Internet purchase and need for touch, specifically instrumental need for touch, were negatively correlated (Peck and Childers, 2003). Lester et al. (2005) found that one reason participants had not purchased goods online was because they could not touch the products. Dissatisfaction with online purchases may result because touch, a critical means for evaluating products, is missing. When product information is imprecise, inadequate, or insufficient, as with many online purchases, then products are more likely to be returned (Quick, 1999).
Need for touch and preference for touch shopping channels. Preference for handling products before purchase affects consumers' retail channel choice (Peck and Childers, 2003). Levin et al. (2003) showed that high-touch products and low-touch products clearly affect consumers' channel preference in multi-channel retailing. That is, high-touch products such as clothing were more likely to be purchased through brick-and-mortar stores when compared to low-touch products like computer software.
Based on this notion, it was expected that participants who had higher NFT would choose touch shopping channels and the third hypothesis was generated:
H3. NFT will influence touch channel preference.
Consumer Electronics is the product category most often chosen by multi-channel shoppers, followed by Apparel/Accessories and Footwear, and Home Improvement/DIY and Appliances. Over 75 percent of multi-channel shoppers prefer the combination of “Online to Store”, followed by “Store to Online” (7 percent) for all product categories (IBM, 2008). The reason consumers use multi-channels varies by product, channel, and demographic. For example, consumers use online and physical stores when they purchase Consumer Electronics because of store experience, convenience, product availability, and price. About 50 percent of multi-channel shoppers switch retailers as they move among shopping channels due to price as their primary motivator, followed by convenience and product availability (IBM, 2008). Other studies have found that shoppers move among shopping channels because of trust of brand/product/web site rather than price (e.g. Hahn and Kim, 2009).
According to a recent survey by IBM Global Business Services (IBM, 2008), in the USA, the age group with the highest percentage of frequent multi-channel shoppers is 18-24; in the UK, it is 25-34. In 2004, 65 percent of US consumers were multi-channel shoppers (Kerner, 2004) and more than 50 percent of apparel shoppers used multi-channels (McKinsey Marketing Practice, 2000). Compared to store-only shoppers and catalog shoppers, multi-channel shoppers were the most time pressed, least satisfied with local offerings, and the least concerned with financial security while shopping (Johnson et al., 2006). They were also more likely to spend money, revisit stores, and repeat product purchases than single-channel shoppers (Kumar and Venkatesan, 2005). They were more fashion innovative/conscious consumers who collected information about price, promotion, styles/trends, and merchandise availability of apparel products and were more satisfied with using multiple shopping channels (Goldsmith and Flynn, 2005; Lee and Kim, 2008).
Several researchers investigated factors influencing multi-channel choice. Gupta et al. (2004) found a positive relationship between risk-taking propensity and multi-channel shopping levels. Schröder and Zaharia (2008) investigated the influence of shopping orientations on customer behavior in multi-channel shopping. Results showed that people who seek information from the Internet and make a purchase at a traditional store are less “independence oriented” and more “risk averse” than the online shoppers. Conversely, online shoppers are more “convenience oriented” and less “recreation oriented” than store shoppers. Thus, availability of multiple channels allows consumers to use different channels for different purposes (Rangaswamy and Van Bruggen, 2005).
Consumers' preferences for online and offline services differ for different products at different stages of the shopping experience (Levin et al., 2003). Consumers place great value on the ability to touch clothing; therefore, they may prefer brick-and-mortar stores when shopping for clothing. Catalog shopping was the primary mechanism that enabled point-of-purchase to shift away from brick-and-mortar stores to home; major catalog retailers made efforts to increase the confidence of consumers by detailed, accurate descriptions of products (Naimark, 1965). Similar to catalog shopping, TV shopping provides consumers the opportunity to experience convenience through reduced time and physical effort associated with information search, travel, and in-store shopping (Lim and Dubinsky, 2004).
Researchers found that a significant predictor of online shopping was previous experience with catalog or TV shopping from home (Eastlick and Lotz, 1999; Goldsmith and Flynn, 2005; Schoenbachler and Gordon, 2002). According to Cho (2008), there is a significant relationship between consumers' experience with catalog shopping and online shopping for clothing. Results showed that consumers who had more experience with catalog shopping had more experience with online shopping, implying that consumers who shop from catalogs also shop online. Eastlick and Lotz (1999) identified TV shopping as one antecedent of intention to shop online, suggesting that the earliest online buyers might have been users of TV shopping media. Results of these studies indicate that consumers may use multiple channels (e.g. TV, catalog, online) within a similar format (e.g. non-touch channel).
Touch/non-touch channel preference
Consumers' perceptions of transaction costs (i.e. time, effort, and pleasure associated with shopping) relate to their channel preferences (Reardon and McCorkle, 2002). In addition, the relative salience of favorable and unfavorable features when comparing multi-channels varies across products, consumers, and situations. Different channel attributes become more dominant for different product categories (Chiang et al., 2006).
Chiang and Dholakia (2003) defined “search goods” as those for which full information can be acquired prior to purchase (e.g. books) and “experience goods” as those which require direct experience (e.g. perfume). Similarly, Lynch et al. (2001) indicated “high-touch” products as those that the consumer evaluates for quality by touching or experience before purchase and “low-touch” products as those that are standardized and do not require inspection. For high touch products, whose portrayal online may differ in color and texture from the actual product, traditional brick-and-mortar stores are preferred because consumers are able to handle and inspect the product before buying (Levin et al., 2003; Balasubramanian et al., 2005). Low-touch products are more compatible with an online shopping context because of the importance placed on saving time.
It was expected that multi-channel shoppers would likely prefer channels that have similar attributes. For example, multi-channel shoppers will likely prefer a combination of touch channels (i.e. local and non-local stores) or a combination of non-touch channels (i.e. TV retailers, catalogs, and online stores), but not a combination of touch and non-touch channels. The fourth hypothesis explores this idea.
H4. Being a multi-channel shopper will influence preference for touch (H4a) shopping channels or non-touch (H4b) shopping channels.
Research method and participants
Among 18-24-year-olds 37 percent of men and 42.3 percent of women are in college (Fry, 2009). In the US, marketers refer to this age group as Generation Y (Paul, 2001). Gardyn (2002) estimated that, in 2002, college students had a purchasing power of $200 billion and average monthly discretionary spending of around $287. Thus, college students, whose spending power is substantial, are an appropriate sample for studying consumer behavior.
A total of 36 questions was developed by adapting previous instruments and by current researchers to measure the variables in the study and participants' demographic information. Three strategies have been used to measure consumer innovativeness: cross-sectional, self-report, and time-of-adoption (Goldsmith and Hofacker, 1991). Criticism of the cross-sectional, and time-of-adoption strategies, stem from theoretical, and methodological positions. Findings are not comparable across studies, generalizability is limited, and sample sizes are restricted because of time and cost. The self-report method provides results that are not only comparable across studies, but are reliable and valid (Uray and Dedeoglu, 1997), therefore, the self-report method was used for this study. Specifically, fashion innovativeness and opinion leadership was measured using Hirschman and Adcock's (1978) six-item measure (e.g. “How often are you willing to try new ideas about clothing fashions?”). The scale accompanying each item ranges from 0=do not know to 4=often. Hirschman and Adcock provide a procedure whereby participants can be divided into fashion followers and fashion change agents (fashion innovators, fashion opinion leaders, innovative communicators) based on their scores.
A 12-item scale from Peck and Childers (2003) was used to measure Need For Touch (e.g. “I feel more confident making a purchase after touching a product”). The NFT scale is “based on a preference for the extraction and utilization of information obtained through the haptic system” (Peck and Childers, 2003, p. 435). Peck and Childers developed the NFT scale and, in a series of seven studies, empirically assessed it for its psychometric properties (e.g. response bias, dimensionality, reliability, and construct, convergent, discriminant, and nomological validity). The scale demonstrated high reliability (0.95) and validity and relates to theoretically grounded assumptions. More details on development and testing of the NFT scale are available in Peck and Childers (2003). The scale accompanying each item ranges from −3 (strongly disagree) to +3 (strongly agree). Scores can range from −36 to +36; higher scores represent greater levels of Need For Touch.
Lastly, questions developed by the researchers were used to measure participants' multi-channel choice and touch/non-touch channel preference. Participants were asked to check all shopping channels they used for clothing shopping. Ten questions measured their channel preference (e.g. “For clothing shopping, I prefer catalogs”). The questions have face validity, that is, they clearly measure the construct under study. Reliability was acceptable – preference for touch channels=0.70; preference for non-touch channels=0.78.
Data collection and analysis
A survey was conducted using a convenience sample of US college students. Participants in the study were 123 male and 154 female undergraduate students. Most participants were between 19-22 years old (70.9 percent), Caucasian (70.4 percent), and not married (90.3 percent).
Data were analyzed using PASW Statistics 18 and by Analysis of Moment Structure (AMOS) 18. Structural equation modeling (SEM) was used to test and estimate the causal relationships proposed in the study. Factor analyses and Cronbach's alpha coefficient were used to examine the construct validity and reliability of the scale. ANOVA was used to compare gender groups and fashion consumer groups on Need For Touch.
Preliminary data analysis
Factor analyses and Cronbach's alpha coefficient were used to examine the construct validity and reliability of the scales. Factors with eigenvalues greater than 1.0 and factor loading of 0.50 suggested by Hair et al. (1998) were used as the criteria for retaining items. The measures of all constructs contained a single factor, indicating that the multiple items in each construct comprised only one dimension. The average of the items in each factor was calculated and used in hypothesis testing. Alpha coefficient of the measure of each construct was greater than 0.70 as suggested by Cronbach (1951), indicating all measures had high internal reliability (see Table I).
SEM analysis and hypotheses testing
Correlation matrices were examined to detect if multicollinearity existed (i.e. a high level of association between variables) because a model with highly correlated predictors may not give valid results about individual predictors, because some predictors are redundant with others. The correlations between the six constructs proposed in the model were equal to or smaller than 0.75 as suggested by Tsui et al. (1995), indicating no high multicollinearity.
SEM analysis with a maximum-likelihood estimation method was used to examine the proposed model and a hypothesized SEM was developed. The fit indexes indicated that the fit of the hypothesized SEM was acceptable [Chi-square/degree of freedom (CMIN/DF)=2.06, Goodness-of-fit index (GFI)=0.99, Adjusted-goodness-of-fit-index (AGFI)=0.95, Comparative-fit-index (CFI)=0.98, Incremental-fit-index (IFI)=0.98, Bentler-Bonett Normed-fit-index (NFI)=0.97, Root mean square error of approximation (RMSEA)=0.06]. However, the structural path from gender to multi-channel choice (H1c) appeared not significant at a level of significance of 0.05; thus, the parameter was removed and the fit of the model was re-analyzed. Results showed that all fit indices indicated that the hypothesized model fit the data very well according to the criteria suggested by Carmines and Mclver (1981), Hair et al. (1998) and Hu and Bentler (1995) (CMIN/DF=1.82, GFI=0.98, AGFI=0.95, IFI=0.98, CFI=0.98, NFI=0.97, RMSEA=0.06). Results showed that the p-values of all parameters were significantly different at a level of significance of 0.05. Figure 2 displays results of the causal model analysis, including standardized path coefficients (β) and squared multiple correlations (R 2) for each endogenous construct.
Results showed that gender influenced fashion innovativeness and opinion leadership (H1a: β=0.42, p < 0.001) and NFT (H1b: β=0.14, p < 0.05), but not multi-channel choice (H1c). Results indicated that female participants were higher in fashion innovativeness and opinion leadership and had higher NFT for clothing shopping than males.
ANOVA was conducted with gender as the independent variable and the total scale of Need For Touch, autotelic need for touch, and instrumental need for touch as dependent variables. ANOVA revealed a significant effect for gender on the total scale of need for touch, on autotelic need for touch, and on instrumental need for touch (see Table II). In all cases, women scored higher than men. Based on the SEM and ANOVA results, H1 was partially supported.
Fashion innovativeness and opinion leadership impacted NFT (H2a: β=0.42, p < 0.001), multi-channel choice (H2b: β=0.38, p < 0.001), and non-touch channel preference (H2c: β=0.13, p < 0.05). Results indicated that those high in fashion innovativeness and opinion leadership had higher NFT, were more likely to use multiple channels for clothing shopping, and preferred non-touch channels compared with those low in fashion innovativeness and opinion leadership.
ANOVA was conducted with fashion consumer group (fashion change agents, fashion followers) as the independent variable and the total scale of need for touch, autotelic need for touch, and instrumental need for touch as dependent variables. ANOVA revealed a significant effect for fashion consumer group on the total scale of need for touch, on autotelic need for touch, and on instrumental need for touch (see Table II). In all cases, fashion change agents scored higher than fashion followers. Based on the SEM and ANOVA results, H2 was supported.
NFT influenced touch channel choice (H3: β=0.21, p < 0.001). Not surprisingly, results indicated that participants who had higher NFT preferred touch shopping channels. Based on the results, H3 was supported.
Multi-channel choice influenced both touch (H4a: β=055, p < 0.001) and non-touch channel preferences (H4b: β=0.27, p < 0.001). Participants who chose more than one shopping channel for clothing preferred to use a combination of local and non-local stores (i.e. touch channels) or a combination of TV, catalog, and online stores (i.e. non-touch channels). Based on the results, H4 was supported.
Discussion and conclusions
This study investigated factors influencing consumers' multi-channel choice and touch/non-touch channel preference in clothing shopping. The current study included specific individual characteristics (e.g. gender, fashion innovativeness and opinion leadership, Need For Touch) that may have impacted consumers' choice of shopping channels.
Findings of this study indicated that gender is a relevant individual difference variable for fashion innovativeness and opinion leadership and NFT. Female consumers are more likely to be high in fashion innovativeness and opinion leadership and need more touch when shopping for clothing than male consumers. Women were higher in both autotelic and instrumental Need For Touch than men, implying that women use their sense of touch for both pleasure and to gather information about products. Further, fashion change agents were higher in NFT – both autotelic and instrumental – than fashion followers. These findings are consistent with Workman (2010), who found that women and fashion change agents scored higher on both autotelic and instrumental NFT than men and fashion followers.
Participants' multi-channel choice was influenced by fashion innovativeness and opinion leadership. Consumers high in fashion innovativeness and opinion leadership tend to use more than one type of shopping channel, while those low in fashion innovativeness and opinion leadership tend to use only one type of shopping channel. This finding is consistent with Flynn et al. (1996) who found that less innovative consumers, the opinion seekers, used brick-and-mortar stores more than other channels because they relied heavily on product information and feedback when purchasing clothing.
Gender was not a significant factor for multi-channel choice. Male and female consumers were equally likely to choose multiple shopping channels. This finding is consistent with Slack et al. (2008) who found gender had no significant effect on patterns of multiple channel use.
Participants' preference for touch or non-touch channels in clothing shopping was influenced by several variables. Touch channel preference was directly influenced by NFT and multi-channel choice such that participants who had higher NFT and used more than one channel for clothing shopping preferred shopping at local and non-local stores. This finding is consistent with Citrin et al. (2003) who found that NFT negatively impacts the purchase of products on-line, which provides visual, but not tactile, cues.
Gender and fashion innovativeness and opinion leadership influenced touch/non-touch channel preference indirectly via NFT and multi-channel choice. Women who are high in fashion innovativeness and opinion leadership need more opportunity to touch when shopping for clothing and, therefore, prefer to shop at touch shopping channels. Non-touch channel preference was directly influenced by fashion innovativeness and opinion leadership and multi-channel choice. Regardless of gender, those high in fashion innovativeness and opinion leadership who are multi-channel shoppers prefer to shop from TV retailers, catalogs, and online stores. These findings are consistent with Park and Jun (2002) who found that innovativeness for clothing was associated with catalog shopping and linked to an increase in online shopping.
Interestingly, multi-channel choice (β=0.55) appeared as a more influential factor than NFT (β=0.21) in participants' touch channel preferences. Unlike previous studies (e.g. Balasubramanian et al., 2005), the current study indicates that consumers may prefer shopping channels that have similar attributes when using more than one channel. Multi-channel shoppers may prefer to use a combination of touch channels (e.g. local and non-local stores) rather than combining touch and non-touch channels (e.g. local stores and online). This shopping behavior can be explained by shopping motives, that is, customers who use only one type of channel within a buying process, select the channel that best satisfies their shopping motives in each situation (Schröder and Zaharia, 2008). For example, consumers who are recreation-oriented, interested in social interaction and desire experiential shopping may choose to shop at brick-and-mortar stores. Thus, they are more likely to be multi-channel shoppers who use both local and non-local brick-and-mortar stores, that is, touch channels.
In addition, multi-channel choice had a stronger impact on touch channel preference (β=0.55) than non-touch channel preference (β=0.28), implying that participants tended to use local and non-local stores more than TV, catalog, or online stores for clothing shopping. This finding is not surprising considering clothing was the product category in this study. When making purchase decisions for clothing, consumers consider not only sensory or aesthetic features (e.g. texture), but also how the item will look on the body (Geissler and Zinkhan, 1998) and how appearance will vary when several items are worn together (McKinney, 2000). Therefore, consumers may be more likely to prefer touch channels when they purchase clothing from more than one shopping channel.
Implications and limitations
Understanding individual differences in consumers that may affect their retail channel choice will help retailers generate patronage among a target group of consumers. At the same time, retailers can maximize consumer satisfaction by providing features that appeal to consumer needs. Results of the study indicate that each type of shopping channel has strengths that appeal to particular customers, strengths that can be emphasized in communication with consumers. For example, in physical stores (i.e. local or non-local stores), freedom to touch and try on garments is key to appealing to customers with high NFT. It should be encouraging for brick-and-mortar retailers to know that their customers are willing to invest resources such as time, money, and energy in traveling to non-local stores in order to experience touch. In TV, catalog, and online stores, the emphasis can be on what appeals to consumers who are high in fashion innovativeness and opinion leadership, such as frequent updates with latest styles, availability of a variety of products, and ways to socially interact with retailers and other customers (e.g. comment on products).
The Consumer Decision Process Model by Blackwell et al. (2001) is a theoretical description of decision making by consumers from need recognition to post-purchase satisfaction. The results of squared multiple correlations (R 2) showed a relatively low percentage of variance in each endogenous construct (multi-channel choice, touch/non-touch channel preference) was explained by the linear combination of the predictor variables (gender, fashion innovativeness and opinion leadership, Need For Touch). This implies that consumers' multi-channel choice and touch/non-touch channel preference are influenced by a complex mix of environmental and individual difference variables.
Limitations and research implications
Participants in the study were undergraduate students age 19-22 at a US university located in the Midwest. Students as participants limit the ability to generalize the results to the larger population of other consumers. Results may differ for students at other universities or other age groups because of factors such as socio-cultural and socio-demographic differences and differential access to various stores. Because of these limitations, research using samples from different geographic locations and age groups is needed to provide further evidence to verify the findings of the study.
Other limitations include the specific measures used and the cross-sectional survey method, which prevents researchers from making causal statements. The effects of other, unmeasured variables could not be assessed. Future studies could avoid these limitations by using data from several countries, representative samples, and additional variables. Future research might examine environmental influences such as culture on multi-channel choice and touch/non-touch channel preference. Additional individual difference variables such as preference for experiential shopping might add to understanding consumers' choices for clothing shopping. In addition, other topics in the decision making process could be explored as related to Need for Touch such as satisfaction after purchase as reflected by returns.
Figure 1Proposed model and research hypotheses
Figure 2SEM analysis results of the proposed model
Table IMeasurement scale and Cronbach's α
Table IIANOVA results of gender and fashion group for total scores on need for touch, autotelic need for touch and instrumental need for touch
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About the authors
Siwon Cho, PhD, is an assistant professor of Fashion Design and Merchandising in the School of Architecture at Southern Illinois University. Her BS degree is from Southern Illinois University and her MS and PhD degrees are from Virginia Tech. Her research interests include consumer behavior and brand image.
Jane Workman, PhD, is a professor of Fashion Design and Merchandising in the School of Architecture at Southern Illinois University. Her BS and MS degrees are from Iowa State University and her PhD from Purdue University. Her research interests include fashion consumer behavior. Jane Workman is the corresponding author and can be contacted at: email@example.com