Recent brand relationship research has paid attention to brand love, brand credibility, and brand loyalty. In market and society, various collaborations and co-creations derived from brand relationships generate various social network markets and open business innovations. Brand relationships and collaborative forms heavily depend on risk taking or risk avoidance. However, few studies have examined how brand relationship is related to risk avoidance. The purpose of this study is to investigate the influence of risk avoidance and gender on brand trust, brand credibility, brand loyalty, and brand choice overload. We review relevant literature on brand relationship and risk avoidance and develop research hypotheses about brand relationship and risk. We find that consumers’ risk avoidance influences brand trust, brand credibility, brand loyalty, and brand choice overload. We suggest implications about how brand relationships can promote social network markets and open business innovations through social construction process.
Recent research on brand relationships such as brand love, brand credibility, brand loyalty and brand choice overload suggests several important aspects of global marketing. One is for an interaction between brand relationships and social network markets. Recent emerging creative markets in various market areas involve intensive collaborations and networks based on brand trust, love, and credibility. In market and society, collaborations and co-creations derived from brand relationships generate various social network markets and open business innovations (Potts, et al., 2008a; 2008b). Currently, creative collaborations emerge from public institutions to nonprofit organizations, to universities, to business companies through constructive relationships between providers and customers (Krishna, 2014; Kodama and Shibata, 2015). Networks change the picture of market and technology through knowledge diffusion and collective intelligence (Surowiecki, 2004; Yun et al., 2015). It is expected that emerging brand networks from current brand relationships can significantly influence current market power through new digital technologies in a global network market. Our findings about brand relationships and risk avoidance will contribute to exploring various relationships between brands and customers and the role of brand communities involved in open innovation of fashion industry.
A second aspect for global marketing is the new social marketing perspective beyond conventional benefit-maximized marketing. Brand relationship theory suggests that network-based marketing and collaboration itself can create reciprocal and sharing relationships between suppliers and consumers. Brand love, brand trust, and brand credibility generate various types of social relationships in a globalized network market. It is also noted that the formation and development of brand relationships mainly depend on risk and uncertainty embedded in the brand relationship. The impact of risk on brand relationships can generate or destroy the formation and diffusion of the relationship. Risk avoidance can consolidate or weaken the brand relationship. Risk propensity can influence consumers’ attitudes about various brand relationship aspects which leads to facilitating or constraining customer creation. However, little knowledge and evidence exists regarding under what circumstances the relationship emerges and prospers and regarding how it evolves across different markets and cultures. Little research has yet explored an empirical connection between brand relationship and risk.
A recent trend in marketing involves a change in focus from gaining to retaining customers (Peppers & Rogers, 2005). The change is motivated by profit resulting from loyal customers due to their increased purchases, willingness to pay regular prices, and positive word-of-mouth (Reichheld & Sasser, 1990). The change in focus has resulted in a new emphasis on relationship marketing, that is, “marketing with the conscious aim to develop and manage long-term and/or trusting relationships with customers” (Bennett, 1995).
The purpose of this study was to investigate the influence of risk avoidance and gender on brand trust, brand credibility, brand loyalty, and brand choice overload. We review relevant literature on brand relationship and risk avoidance and develop research hypotheses about brand relationship and risk. We investigate the nature of the relationship and suggest implications for social network market and open business innovations.
Relevant literature review
Overview on brand relationship and social network markets
Social network strongly influences marketing and consumer behavior through brand community, brand trust, and on- or off-line social interactions (Ballester-Delgado and Aleman-Munuera, 2001; Caceres and Paparoidamis, 2007; Muniz and O’Guinn, 2001; Schau, Muniz, and Arnould, 2009). Social network markets consist of brand on- or off-line communities and provide various brand related social activities. For instance, web-based social networks produce various types of brand communities, which can sway brand relationship such as brand trust, credibility, and loyalty. The degree of concrete or fragile brand relationship is increasingly dependent on social networks. In this sense, the nature of brand relationship from social network perspective essentially produces brand trust, brand credibility, and brand loyalty. Consumer-brand relationship theory (Fournier, 1998) suggests a sincere relationship between consumers and brands as being trustworthy or devoted partners in an interactive relationship. In other words, brand relationships formed through various social networks build brand trust, credibility, and loyalty. The inherent frame of brand relationships from social network marketing emphasizes both non-economic elements and risk avoidance, which also depends on personal characteristics such as gender and age. This generates several research issues on brand relationship and risk. First, brand relationships can provide functional, psychological, social and emotional benefits (Aaker, 1996, 2009a, 2009b; Keller, 1993; Park, Jaworski, & Maclnnis, 1986). These non-economic elements become increasingly important to emerging creative social markets such as on-line business and e-commerce in a globalized network economy. Failure to provide benefits as promised or implied by marketing entails risks for consumers as well as companies. As companies aspire to establish relationships with their customers, it becomes essential to understand the nature of consumer-brand relationships. Second, it is difficult to form an initial brand relationship due to risk from a high level of uncertainty. However, the relationship is concretely formed through accumulated credible interactions. The degree of risk avoidance or risk taking can influence the formation of a brand relationship. Third, the theory of brand personality suggests that gender, age, and other human traits can influence brand relationships such as brand trust and loyalty (Sung and Kim, 2010). In the following, we review key theoretical issues on risk avoidance, gender impact on risk behavior, and brand relationships such as trust and credibility.
Risk avoidance
“Risk-taking is the degree to which an individual reports not only being willing to try new products, activities, and situations, but welcomes the stimulation of the newness as well” (Bearden, Netemeyer, & Mobley, 1993). According to Sheth and Venkatesan’s (1968) risk-taking theory, consumers experience differing degrees of uncertainty in the purchase decision-making process. Consumers may attempt to reduce the risk by relying on some idea or person. For example, consumers might seek information to reduce risks by relying on brand names, brand images, or fashion leaders’ opinions. When consumers feel vulnerable to risk during product purchase, they may rely on a strong consumer-brand relationship (e.g., brand trust, brand loyalty, brand credibility) because they believe the brand relationship can reduce risk.
Consumers often find themselves in a situation of decision ambiguity (Muthukrishnan, 1995) when shopping for apparel. Degree of ambiguity depends on the amount, type, and reality of information (Ellsberg, 1961). Studies have characterized ambiguity as resulting from missing information that is relevant (e.g., Camerer & Weber, 1992; Heath & Tversky, 1991). To reduce ambiguity, individuals with higher risk avoidance may recall previous experiences and product knowledge. For instance, Cho and Workman (2014) found that participants who were tolerant of risk-taking (low risk avoidance) tended not to use their previous experience and knowledge but used marketer-dominated sources such as Internet, fashion magazines, and catalogs. These information sources are risky because their goal is to persuade consumers to purchase products. Participants with lower tolerance for risk-taking (high risk avoidance) used all sources of information more than those with a greater tolerance for risk-taking (low risk avoidance).
Gender and consumer behaviour
Despite the importance of gender differences in consumer behaviour, little is known about how men and women differ in risk avoidance or in brand relationship variables such as brand trust or brand credibility. Women tend to have greater tolerance for risk-taking than men in terms of willingness to try new or unusual products and enjoyment from the stimulation of newness (Cho & Workman, 2014). Women are more willing than men to adopt a fashion innovation earlier than other consumers—a risky consumer behavior (Workman & Cho, 2012) and are more likely than men to purchase products impulsively (Chen, 2001). Other research (that did not examine risk related to purchasing apparel) found that women (compared with men) are more open to uncertain and unstructured contexts (Maio & Esses, 2001; Washburn, Smith, & Taglialatela, 2005), are disinclined to take risks (e.g., Wagner, 2001) or that men and women did not differ in tendency for risk taking (e.g., Maxfield et al. 2010). Research has found that women score higher than men on brand sensitivity (Beaudoin & Lachance, 2006; Warrington & Shim, 2000) and brand consciousness (Workman & Lee, 2013).
Brand trust & brand credibility
Brand trust refers to a consumer’s confidence in a brand’s reliability and integrity (De Wulf, Odekerken-Schro¨der, & Iacobucci, 2001). Customer trust in a brand is an essential component of relationship marketing. Brand benefits can enhance customers’ trust and loyalty (Lee, Ha, & Widdows, 2011). Brand trust is one means to reduce uncertainty when customers feel vulnerable (Chaudhuri & Holbrook, 2001). Consumers who lack confidence in a brand are not likely to develop brand loyalty (De Wulf et al., 2001). Brand loyalty is linked with number and frequency of repeat purchases, quantity of the product purchased, and the price consumers are willing to pay (Chaudhuri & Holbrook, 2001). Brands that reliably provide a unique functional and emotional experience can encourage consumers’ brand loyalty (Lin, 2010) and brand trust (Lee & Widdows, 2011). Brand credibility refers to “the believability of the product information contained in a brand, which requires that consumers perceive that the brand has the ability (i.e., expertise) and willingness (i.e., trustworthiness) to continuously deliver what has been promised” (Erdem & Swait, 2004, p.192). Erdem and Swait (2004) found that brand credibility influenced consumers’ brand choice and consideration for purchasing the brand.
Brand credibility and user based open innovation
Recent research suggests that dynamic concurrent digital environments such as smart mobile devices, peer-to-peer web characteristics and open source movements have a considerable impact on brand power (e.g., brand credibility, brand trust) and open innovation (Lee & Lee, 2015). In particular, customers or users of smart mobile devices based on web tools and environments with a community business model can create numerous crowdsourcing companies (Della Corte et al., 2015; Han and Cho, 2015; Howe, 2008; Page, 2007; Surowiecki, 2004).
In highly competitive market environment, every brand in the fashion industry faces a credibility crisis due to a growing vigilant consumer base well-versed in vibrant collaborative digital web circumstances. This fashion crisis operates to push out old brands and pull in new ones through various innovative processes. Recent studies have addressed how open innovation can contribute to co-creating a new brand and consolidating a brand’s credibility in the fashion industry (Brabham, 2010; Nickell, 2010). One example is the ‘Threadless’ model used to form a Chicago web-based T-shirt company (Threadless.com). In 2000, Jake Nickell and Jacob DeHart founded an online community where customers submit their own T-shirt designs and select which designs to produce through evaluating all the designs submitted and exchanging ideas at their own social networking sites. This is an amazing success story involving open innovation (Brabham, 2010; Nickell, 2010). A flock of amateur designers rather than star designers, started up the internet-based T-shirt company. This is an example of a community-based crowdsourcing business model through social and collaborative networks in the apparel and accessories industry.1 Brabham (2010) describes the Threadless model as a good online crowdsourcing production model with crowd wisdom similar to iStockphoto.com and InnoCentive. Overall, user based open innovation can coproduce a new star brand and strengthen its credibility.
There is, however, little knowledge and empirical evidence about the relationship between open innovation and brand credibility. While there may be a simultaneous relationship between them, it is expected that sustainable open innovation can generate strong brand credibility through customer engagement and collaborative development. Brand credibility can result from customer based open innovation with various risks and challenges. The impact of open innovation on brand credibility mainly depends on risk characteristics embedded in its innovation process.
Both strong brand credibility and open innovation involve risk-taking, rather than risk-avoidance. The inherent relationship between brand credibility and open innovation is likely to evolve through risk taking. Little research yet exists on this emerging topic between brand credibility, risk, and open innovation. In this paper, we first attempt to explore how brand credibility is related to risk avoidance (or risk taking), which can lead to constraining (or facilitating) open innovation in fashion and clothing markets. Little research has yet touched on the complex relationships among brand credibility, risk attitude, and open innovation.
Brand credibility and social construction
A wide variety of variables such as fashion knowledge and fashion engagement influence fashion emergence. Product credibility within certain industries varies within different social, cultural, and institutional contexts (Berger and Luckmann, 1967; Fairhurst and Grant 2010; Williams and Edge, 1996). The credibility of a fashion brand also depends on unique characteristics within the fashion industry through a social construction process. For instance, the fashion industry faces the ‘Megaphon Effect’ (McQuarrie et al., 2013) from numerous fashion bloggers and fashion crowds as well as fashion leaders. In addition, a variety of fashion information sources from magazine writers, editors, designers, models, and fashion bloggers construct the nature of the fashion industry (Polegato and Wall, 2009). Brand credibility and brand reputation are socially formed from various interactions between and among fashion leaders and consumers. In particular, leading fashion companies are likely to easily accumulate their credibility and reputation through open social construction process with their customers and citizens.
Brand choice overload
In the consumer market a growing number of options have resulted in choice overload along with resultant consumer feelings of confusion and uncertainty (Schwartz, 2004). The amount and complexity of choices will at some point exceed the choice capacities of many consumers (Berg & Gornitzka, 2012). The vast and ever-changing stream of available products is a major challenge for consumers. Similar products are often sold at different prices in different stores, and prices do not necessarily indicate quality. Consumer choice includes dozens, perhaps even hundreds, of different brands. Consumers cope with choice overload in various ways, for example, by relying on trust as a means of reducing complexity (Luhmann, 1979). Another coping mechanism used by consumers is to reduce available alternatives by selecting only familiar, well-known brands–brands they believe they can trust.
Research focus and method
Research purpose
The perspective of social network suggests that brand relationships should include trust, credibility, and loyalty in brand marketing. It is likely that brand trust, brand credibility, brand loyalty, and brand choice overload are strongly related to perceived risks involved in purchase decisions. Brand relationships representing brand trust and brand credibility are likely to be associated with risk avoidance, which can be dependent on gender. However, there is little research to examine the links among these variables. Thus, it is meaningful to explore if men and women differ in their response to brands or in their subsequent consumer-brand relationships. Therefore, the purpose of the study was to investigate the influence of risk avoidance and gender on brand trust, brand credibility, brand loyalty, and brand choice overload (see Fig. 1). The following hypotheses were proposed:
Fig. 1
Research Framework
H1: Participants high (vs. low) in risk avoidance will differ in brand trust.
H2: Participants high (vs. low) in risk avoidance will differ in brand credibility.
H3: Participants high (vs. low) in risk avoidance will differ in brand loyalty.
H4: Participants high (vs. low) in risk avoidance will differ in brand choice overload.
H5: Women and men will differ in brand trust.
H6: Women and men will differ in brand credibility.
H7: Women and men will differ in brand loyalty.
H8: Women and men will differ in brand choice overload.
Research method
Survey participants
Participants in this study were U.S. university students. In the U.S., in 2014, there were 21.6 million college students, 58 % female and 42 % male, estimated to have a spending power of $545 billion with $163 billion of that being discretionary spending (College Explorer’14, 2015; Back to school statistics, 2015). University student consumers are interested in fashion; apparel shopping is one activity that ranks high with these Millennials (16–34 year olds) in enjoyment, knowledge, and overall spending (Barton, Koslow, Fromm, & Egan, 2012). The 2014 college market study reported that college students spend $18.6 billion dollars on apparel (the third highest category following food and automotive) along with $9.8 billion on personal care products, and $7.5 billion on cosmetics. Millenials use social media to communicate their preferences and influence others’ choices; Burger (2013) found 86 % of students used the social media site Facebook regularly with 34 % using it to stay up-to-date with brands. According to Allen (2014), many retailers connect with these tech-savvy, fashion-forward consumers through social media. Companies who emphasize relationship marketing with university students may increase the probability of brand loyalty among this group after graduation and entrance into the workforce. Therefore, male and female university students were considered an appropriate and important sample for an investigation of risk avoidance and brand variables.
Survey procedure
Data were collected in large lecture classes from US university students who took about 20 min to complete the questionnaire. Participants listed their favorite brand. They were asked to keep this brand in mind as they responded to statements regarding the measures of brand trust, brand credibility, brand loyalty, and brand choice overload. Participants circled a number on a 7-point scale (7 = strongly agree; 1 = strongly disagree) to indicate degree of agreement with each item.
Survey instruments
The questionnaire contained demographic items and measures of brand trust (Delgado-Ballester, Munuera-Aleman, & Yagiie-Guillent, 2006), brand credibility (Erdem & Swait, 2004), brand loyalty (Carroll & Ahuvia, 2006), brand choice overload (Shim, 1996), and risk avoidance (Raju, 1980). Items in each scale were summed to arrive at a score on each brand variable and risk avoidance.
Brand trust measurement
Delgado-Ballester et al’s (2006) brand trust scale consists of eight items. Brand trust reflects the confidence that consumers have in the reliability and intentions of a brand, especially in situations involving risk. Sample items include “This brand is a brand that meets my expectations.” and “I feel confident in this brand name.” Delgado-Ballester et al (2006) verified that the construct of the brand trust scale exceeded the desired level of 0.7 for scale reliability, and all items demonstrated adequate convergent validity. The brand trust scale is reliable and valid.
Brand credibility measurement
Erdem and Swait’s (2004) brand credibility scale consists of six items. Brand credibility reflects the degree to which a brand’s product information can be trusted and believed. This requires that a brand is perceived as trustworthy and knowledgeable by consumers. Sample items include “This brand delivers (or would deliver) what it promises.” and “Product claims from this brand are believable”. The reliability of the scale was verified by Erdem and Swait (2004).
Brand loyalty measurement
Carroll and Ahuvia’s (2006) four-item brand loyalty scale was developed based on previous research. The scale reflects the extent of consumers’ commitment to repurchase the brand. Sample items include “This is the only brand of this product I will buy.” and “When I go shopping, I don’t even notice competing brands.” Carroll and Ahuvia (2006) verified that the reliability of the scale was .90 (coefficient alpha).
Brand choice overload measurement
Shim’s (1996) scale consists of four items that represent the extent to which consumers experience information overload, meaning that they have too many good brands and stores from which they would like to purchase. Sample items include “There are so many brands to choose from that I often feel confused.” and “Sometimes it’s hard to choose which stores to shop.” Shim (1996) reported the reliability of the scale was acceptable.
Risk avoidance measurement
Raju’s (1980) risk taking scale consists of three items that measure a preference for taking (or avoiding) risks. Sample items include “I’m cautious in trying new/different products.” and “I would rather stick with a brand I usually buy than try something I’m not very sure of.” The reliability of the scale was verified by Raju (1980) as exceeding .80 (coefficient alpha).
Empirical analysis and results
Descriptive analysis
Our analysis provides descriptive statistics, Cronbach’s alpha reliability, and MANOVA/ANOVA. Cronbach’s alpha reliability for all measurements was acceptable ranging from 0.88 to 0.92. Participants were 221 (138 women, 81 men, 2 missing data) university students from approximately 50 different majors. Age ranged from 18 to 30 (mean age = 21.18). There were 120 Caucasians, 74 African American, 6 Asian/Asian Americans, 13 Hispanic/Latinos and 8 classified as other. The majority (n = 199) were single, 12 were married, and 10 were otherwise classified. Class level included 34 freshman, 50 sophomores, 50 juniors, 57 seniors, 22 graduate students, and 8 otherwise classified or missing data. Participants listed 75 different favorite fashion brands such as Adidas, Aeropostale, American Eagle, Buckle, Calvin Klein, Forever 21, H&M, Levi’s, Nike, Polo, and Under Armour. See Table 1 for descriptive statistics and reliability of each measure used in the questionnaire.
Table 1
Descriptive statistics and reliability: risk avoidance and brand variables
Scale
Mean
SD
Range
Reliability (Cronbach Alpha)
Risk avoidance (3 items)
12.60
4.62
3–21
0.81
Brand trust (8 items)
42.71
8.48
23–56
0.89
Brand credibility (6 items)
32.93
6.14
18–42
0.87
Brand loyalty (4 items)
13.58
7.16
4–28
0.91
Brand choice overload (4 items)
15.80
6.18
4–28
0.86
MANOVA/ANOVA analysis
To test the strength of the relationship between the brand variables and risk avoidance, Pearson’s correlation analysis was used. As a result, all brand variables were significantly correlated with risk avoidance: brand trust 0.192, p < 0.01; brand loyalty 0.385, p < 0.01; brand credibility 0.174, p < 0.05, and brand overchoice 0.467, p < 0.01.
As a preliminary analysis, ANOVA was conducted to determine if men and women differed in risk avoidance. ANOVA with gender as the independent variable and risk avoidance as the dependent variable was not significant, [F(1, 213) = 1.057, p < 0.305]. Men (M = 12.21) and women (M = 12.88) did not differ in risk avoidance. Scores on risk avoidance were split at the median of 13 to create two groups for the MANOVA/ANOVA analysis resulting in one group labeled high in risk avoidance (114 participants who scored greater than 13) and a second group labeled low in risk avoidance (107 participants who scored less than or equal to 13). MANOVA/ANOVA was conducted to test the hypotheses using risk avoidance (high, low) and gender as independent variables with brand trust, brand credibility, brand loyalty, and brand choice overload as the dependent variables. MANOVA revealed that risk avoidance [F(4, 206) = 12.73, p < 0.000] was significant for the dependent variables but gender was not significant [F(4, 206) = 0.946, p < 0.439] and the interaction between gender and risk avoidance was not significant [F(4, 206) = 0.916, p < 0.456]. ANOVA results showed that risk avoidance was significant for all four brand variables (see Table 2). Participants who scored high (vs. low) in risk avoidance scored higher on brand trust, brand credibility, brand loyalty, and brand choice overload. All hypotheses related to risk avoidance (H1-4) were supported.
Table 2
ANOVA results for brand variables by risk avoidance and gender
Scale
Mean Square
F-value
p-value
Brand trust
Risk avoidance
596.71
8.78
0.003
High
M = 44.14 (SD = 7.98)
Low
M = 41.17 (SD = 8.56)
Gender
103.24
1.52
0.219
Women
M = 42.25 (SD = 8.44)
Men
M = 43.47 (SD = 8.27)
Brand credibility
Risk avoidance
214.14
5.97
0.015
High
M = 33.80 (SD = 5.58)
Low
M = 32.13 (SD = 6.48)
Gender
133.13
3.77
0.055
Women
M = 32.43 (SD = 5.92)
Men
M = 33.95 (SD = 6.27)
Brand loyalty
Risk avoidance
1042.71
22.51
0.000
High
M = 15.83 (SD = 7.12)
Low
M = 11.34 (SD = 6.39)
Gender
2.06
0.045
0.833
Women
M = 13.68 (SD = 6.95)
Men
M = 13.57 (SD = 7.47)
Brand choice overload
Risk avoidance
1179.35
35.38
0.000
High
M = 17.99 (SD = 6.07)
Low
M = 13.40 (SD = 5.45)
Gender
3.76
0.11
0.737
Women
M = 15.77 (SD = 6.06)
Men
M = 15.71 (SD = 6.47)
Discussion & implications
Implication for risk avoidance and social aspects of brand relationship
Results of this study support the hypotheses that consumers’ risk avoidance affects brand trust, brand credibility, brand loyalty, and brand choice overload. When consumers wish to avoid risk during product purchase, they may rely on a strong consumer-brand relationship (e.g., brand trust, brand loyalty, brand credibility) because they believe the brand relationship can help them avoid risks inherent in product purchase (e.g., financial, social, quality). Consumers may rely on their own experiences with brands that they trust and can rely on to provide satisfaction. Well-established brand name advertising or images may reduce perceived risk if the claims have an established record of credibility. With so many brands on the market competing for consumers’ attention, it is not surprising that feelings of brand choice overload are higher among consumers who are higher in risk avoidance. Brand loyalty may increase when companies provide reliable brands that consumers can depend on for functional and emotional benefits (Lin, 2010).
Fashion firms or marketers may use these results to build stronger consumer-brand relationships. Product and/or brand memories and preferences are encoded in long-term memory during childhood, adolescence and early adulthood influencing future consumption preferences (Braun-La Tour et al. 2007). Thus, it is important that companies who produce products targeted at Millennials (16–34 year olds) emphasize relationship marketing if they hope to increase the probability of brand loyalty among this group in later adulthood.
Results of this study indicated no gender difference in the brand variables examined. Further, there was no interaction between risk avoidance and gender on the brand variables. Men and women responded similarly to brand trust, brand credibility, brand loyalty, and brand choice overload. Risk avoidance seems to be a characteristic of consumers that overrides other characteristics such as gender. Risks are inherent in almost all purchasing decisions and consumers become aware of these risks from their own experiences or from the experiences of others within their social networks. Further, perhaps characteristics of the sample (male and female university students) may explain the lack of significant effects for gender. A sample of older adults regarding risk avoidance and gender on brand variables might yield different results.
Risk avoidance and open innovation in fashion industry
Our results show that for the fashion market, the higher the level of risk avoidance, the higher the level of brand trust, brand credibility, and brand loyalty. Conversely, the lower the level of risk avoidance, the lower the level of brand trust, brand credibility, and brand loyalty. Therefore, new fashion brands may want to target early adopters of fashion (i.e., fashion innovators or fashion opinion leaders) who are known to be lower in risk avoidance. When the benefits of purchasing and using a new fashion brand rise with the number of consumers adopting and diffusing it, switching to an alternative brand may be unappealing because a new brand presents various uncertain risks. It is very common to face this type of path dependence from innovation (Liebowitz and Margolis, 1994). When a new fashion brand emerges, the lock-in effect on the current brand entails familiarity and safety from brand trust and credibility, which leads to inhibiting adoption of a new brand. Open innovation in the fashion industry generates both powerful network effects and high switching costs. Thus, an emergence of an open innovation in the fashion industry like the Threadless model may involve the lock-in effect and present a barrier to sustainable open innovation. Under this circumstance, risk avoidance prevails and a potential for open innovation of a new fashion brand can be weak.
Risk avoidance and social construction in fashion industry
Emergence of a new fashion brand is a representative case of the social construction process. The new fashion comes from the nexus of social construction within the fashion industry. Numerous fashion-related events such as fashion shows are basically social events, where fashion leaders and ordinary citizens talk about fashion and develop new fashion trends. Fashion leaders and bloggers create a new fashion brand through such social construction. These characteristics of social construction in the fashion industry can facilitate brand awareness, brand power, and brand loyalty. However, the dynamic process of social construction in the fashion industry can make customers sensitive to risk. This study suggests that customers who are more risk-avoidant believe that the current brand is more credible and trustworthy.
Brand relationship and social construction in creative network market
Brand relationship involves various potential social networks between suppliers and consumers as well as within collective consumer interactions. Brand love, brand trust, and brand credibility can contribute to promoting sustainable market innovation within an e-business context. For instance, brand trust and love can evolve from co-innovation and co-creation through customer engagement and customer networks. Sustainable customer creation comes from trustworthiness and empathy embedded into brand identity. Co-pricing decisions through e-participation in a customer service delivery system can provide various opportunities for sustainable relationships, building trust, loyalty, and reciprocal love between a supplier and customer (Della Corte et al., 2015). Next generation brands in a network economy may be called upon to create their own new brand models, platforms and applications through reciprocal brand relationships between customers and providers.
Another important implication from brand relationship research comes from the critics of conventional marketing on aggressive campaigns and advertising. Consumers’ motivations are multiple from self-interest, empathy, sharing experiences, to altruism (Cherrier and Murray, 2004). The economic model based on brand performance and profitability cannot generate sustainable brand relationships from engagement to reciprocity to co-creation. The sociological perspective of marketing within ubiquitous network and platform environments emphasizes brand trust and credibility for reciprocal relationships through sharing values and empathy (Cherrier and Murray, 2004).
Further research on brand relationship and social construction
First, further study is needed in the area of consumer-brand relationships including more variety of brand variables, for example, brand charisma, brand consciousness, brand equity, and self-expressive brand. Understanding the link between brand variables, risk avoidance, and word-of-mouth (e.g., customer reviews) would provide useful information for retailers and marketers in planning strategies for targeting this group of consumers. With the growth of Internet shopping, it is important to examine how risk avoidance and brand variables influence the willingness to purchase products online. It is necessary to explore various emerging forms of producer-consumer collaboration during virtual co-creation tasks (Füllera et al. 2009; Potts et al. 2008b). It would also be meaningful to explore variables related to consumer-brand relationships within and across cultural contexts (e.g., collectivist versus individualist cultures) from various brand communities (Cova, and Pace, 2015; De Burgh‐Woodman and Brace‐Govan, 2007).
Second, further research is needed to explore how brand relationships have been formed and evolved from collective wisdom (Surowiecki, 2004) and collaborative marketing between providers and consumers (Cova and Pace, 2015; Potts et al., 2008). Various open innovation cases from research based social labs to global R&D centers across countries (Krishna et al., 2012; Patra and Krishna, 2015), to innovations at public space design (Pancholi et al., 2015), to innovations between university and industry (Sutthijakra and Intarakumnerd, 2015), to industrial textile clusters (Gulrajani, 2006) can be applied to those at fashion industry. Open innovations from fashion industry can provide potential opportunities for fashion companies as a strong social institution to link between fashion, technology and society (See Krishna (2014) for the implication of social institution to consolidate a legitimate network between science and society).
Third, new digital technologies and open web environments can generate various opportunities to influence brand relationship. For example, RFID technology can stimulate consumers’ participation in brand distribution and various interactions between brand suppliers and consumers.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
(1)
Southern Illinois University
(2)
Korea Institute of Public Affairs of the Graduate School of Public Administration, Seoul National University
References
Aaker, D. (1996). Measuring brand equity across products and markets. California Management Review, 38(3), 102–120.View ArticleGoogle Scholar
Aaker, D. (2009a). Managing brand equity. New York: Simon and Schuster.Google Scholar
Aaker, D. (2009b). Beyond functional benefits. Marketing News, September 6, p. 23.Google Scholar
Allen, K. (2014). Demand for clothing, supplies, electronics drives school/college spending up. National Retail Federation. Retrieved July 2, 2015 from https://nrf.com/media//press- releases.
Back to school statistics. (2015). National Center for Education Statistics, US Department of Education. Retrieved December 12, 2015 from http://nces.ed.gov.
Ballester-Delgado, E., & Aleman-Munuera, J. (2001). Brand Trust in the Context of Consumer Loyalty. European Journal of Marketing, 35, 1238–1258.View ArticleGoogle Scholar
Barton, C., Koslow, L., Fromm, J., & Egan, C. (2012). Millennial passions. Bcg perspectives by Boston Consulting Group. Retrieved July 2, 2015 from www.bcg.com/documents/file121010.pdf.
Bearden, W., Netemeyer, R., & Mobley, M. (1993). Handbook of marketing scales. Newbury Park, CA: Sage.Google Scholar
Beaudoin, P., & Lachance, M. (2006). Determinants of adolescents’ brand sensitivity to clothing. Family & Consumer Sciences Research Journal, 34, 312–331.View ArticleGoogle Scholar
Bennett, P. D. (1995). Dictionary of marketing terms (p. 242). Chicago: American Marketing Association.Google Scholar
Berger, P.L., and Luckmann, T. 1967. The Social Construction of Reality: A Treatise in the Sociology of Knowledge. Garden City, New York: Anchor.Google Scholar
Brabham, D. C. (2010). Moving the Crowd at Threadless. Information, Communication & Society, 13(8), 1122–1145.View ArticleGoogle Scholar
Braun-La Tour, K. A., La Tour, M. S., & Zinkhan, G. M. (2007). Using childhood memories to gain insight into brand meaning. Journal of Marketing, 71(2), 45–60.View ArticleGoogle Scholar
Burger, A. (2013). 2013 U.S. College Explorer: Connecting with increasingly connected college students. Retrieved July 2, 2015 from http://www.telecompetitor.com/.
Caceres, R. C., & Paparoidamis, N. G. (2007). Service quality, relationship satisfaction, trust, commitment and business-to-business loyalty. European Journal of Marketing, 41(7/8), 836–867.View ArticleGoogle Scholar
Camerer, C., & Weber, M. (1992). Recent developments in modeling preferences: Uncertainty and ambiguity. Journal of Risk & Uncertainty, 5, 325–70.View ArticleGoogle Scholar
Carroll, B., & Ahuvia, A. (2006). Some antecedents and outcomes of brand love. Marketing Letters, 17, 79–89.View ArticleGoogle Scholar
Chaudhuri, A., & Holbrook, M. (2001). The chain of effects from brand trust and brand affect to brand performance: The role of brand loyalty. Journal of Marketing, 65, 81–93.View ArticleGoogle Scholar
Chen, M. H. (2001). The determinants and traits of impulsive buying behavior. Journal of Takming University, 17, 59–74.Google Scholar
Cherrier, H., & Murray, J. B. (2004). The Sociology of Consumption: The Hidden Facet of Marketing. Journal of Marketing Management, 20, 509–525.View ArticleGoogle Scholar
Cho, S., & Workman, J. E. (2014). Influences of gender, need for affect, and tolerance for risk-taking on use of information sources. Journal of Fashion Marketing and Management: An International Journal., 18(4), 465–482.View ArticleGoogle Scholar
Cova, B., & Pace, S. (2015). Marketing with working consumers: The case of a carmaker and its brand community. Organization, 22, 682–701.View ArticleGoogle Scholar
De Burgh‐Woodman, H., & Brace‐Govan, J. (2007). We do not live to buy: Why subcultures are different from brand communities and the meaning for marketing discourse. International Journal of Sociology and Social Policy, 27(5/6), 193–207.View ArticleGoogle Scholar
Delgado-Ballester, E., Munuera-Aleman, J., & Yagiie-Guillent, M. (2006). Development and validation of a brand trust scale. International Journal of Market Research, 45(1), 35–53.Google Scholar
Della Corte, V., Iavazzi, A., & D’Andrea, C. (2015). Customer involvement through social media: the cases of some telecommunication firms. Journal of Open Innovation: Technology, Market, and Complexity, 1, 10.View ArticleGoogle Scholar
De Wulf, K. D., Odekerken-Schroder, G., & Iacobucci, D. (2001). Investments in consumer relationships: A crosscountry and cross-industry comparison. Journal of Marketing, 65(4), 33–50.View ArticleGoogle Scholar
Ellsberg, D. (1961). Risk, ambiguity, and the savage axioms. The Quarterly Journal of Economics, 75(4), 643–69.View ArticleGoogle Scholar
Erdem, T., & Swait, T. (2004). Brand credibility, brand consideration, and choice. Journal of Consumer Research, 31(June), 191–199.View ArticleGoogle Scholar
Fairhurst, G. T., & Grant, D. (2010). The Social Construction of Leadership: A Sailing Guide. Management Communication Quarterly, 24(2), 171–210.View ArticleGoogle Scholar
Fournier, S. (1998). Consumers and their brands: Developing relationship theory in consumer research. Journal of Consumer Research, 24, 343–373.View ArticleGoogle Scholar
Füllera, J., Mühlbacherb, H., Matzlerb, K., & Jaweckic, G. (2009). Consumer Empowerment Through Internet-Based Co-creation. Journal of Management Information Systems, 26(3), 71–102.View ArticleGoogle Scholar
Han, J., & Cho, O. (2015). Platform business Eco-model evolution: case study on KakaoTalk in Korea. Journal of Open Innovation: Technology, Market, and Complexity, 1, 6 (8 September 2015).View ArticleGoogle Scholar
Heath, C., & Tversky, A. (1991). Preference and belief: Ambiguity and competence in choice under uncertainty. Journal of Risk and Uncertainty, 4, 4–28.View ArticleGoogle Scholar
Howe, J. (2008). Crowdsourcing: Why the Power of the Crowd is Driving the Future of Business. New York: Crown.Google Scholar
Keller, K. (1993). Conceptualizing, measuring, and managing consumer-based brand equity. Journal of Marketing, 57, 1–22.View ArticleGoogle Scholar
Kodama, F., & Shibata, T. (2015). Demand articulation in the open-innovation paradigm. Journal of Open Innovation: Technology, Market, and Complexity, 1, 2 (12 August 2015).View ArticleGoogle Scholar
Krishna, V. V. (2014). Changing Social Relations between Science and Society: Contemporary Challenges. Science Technology & Society, 19, 133–159.View ArticleGoogle Scholar
Krishna, V. V., Patra, S. K., Bhattacharya, A. (2012). Internationalisation of R&D and global nature of innovation: Emerging trends in India. Science, Technology, & Society, 17(2), 165–199.View ArticleGoogle Scholar
Lee, S., & Lee, K. (2015). Heterogeneous expectations leading to bubbles and crashes in asset markets: Tipping point, herding behavior and group effect in an agent-based model. Journal of Open Innovation: Technology, Market, and Complexity, 1, 12 (15 September 2015).View ArticleGoogle Scholar
Lee, S., Ha, S., & Widdows, R. (2011). Consumer responses to high-technology products: Product attributes, cognitions, and emotions. Journal of Business Research, 64(11), 1195–1200.View ArticleGoogle Scholar
Lin, L. Y. (2010). The relationship of consumer personality trait, brand personality, and brand loyalty: An empirical study of toys and video games buyers. Journal of Product & Brand Management, 19(1), 4–17.View ArticleGoogle Scholar
Liebowitz, S. J., and Margolis, S.E. (1994). Network Externality: An Uncommon Tragedy. Journal of Economic Perspectives, 8(2), 133-150.Google Scholar
Luhmann, N. (1979). Trust and power: Two works. Chichester, UK: John Wiley & Sons.Google Scholar
Maio, G. R., & Esses, V. M. (2001). The need for affect: Individual difference in the motivation to approach and avoid emotions. Journal of Personality, 69, 583–614.View ArticleGoogle Scholar
Maxfield, S., Shapiro, M., Gupta, V., & Hass, S. (2010). Gender and risk: Women, risk taking and risk aversion. Gender in Management, 25(7), 586–604.View ArticleGoogle Scholar
McQuarrie, E. F., Miller, J., & Phillips, B. J. (2013). The Megaphone Effect: Taste and Audience in Fashion Blogging. Journal of Consumer Research, 40(1), 136–158.View ArticleGoogle Scholar
Mohini Gulrajani, M. (2006). Technological Capabilities in Industrial Clusters: A Case Study of Textile Cluster in Northern India. Science Technology & Society, 11(1), 149–190.View ArticleGoogle Scholar
Muniz, A. M. & O'Guinn, T. C. (2001). Brand community. Journal of Consumer Research, 27(4), 412–432.View ArticleGoogle Scholar
Muthukrishnan, A. V. (1995). Decision ambiguity and incumbent brand advantage. Journal of Consumer Research, 22(June), 98–109.View ArticleGoogle Scholar
Nickell, J. (2010). Threadless: Ten Years of T-shirts from the World’s Most Inspiring Online Design Community. New York, NY: Abrams Image.Google Scholar
Page, S. E. (2007). The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton, NJ: Princeton University Press.Google Scholar
Pancholi, S., Yigitcanlar, T., & Guaralda, M. (2015). Public space design of knowledge and innovation spaces: learnings from Kelvin Grove Urban Village, Brisbane. Journal of Open Innovation: Technology, Market, and Complexity, 1, 13 (16 September 2015).View ArticleGoogle Scholar
Park, C. W., Jaworski, B. J., & Maclnnis, D. J. (1986). Strategic brand concept-image management. Journal of Marketing, 50, 135–145.View ArticleGoogle Scholar
Peppers, D. & Rogers, M. (2005). Return on customer: creating maximum value from your scarcest resource. New York: Currency Doubleday.Google Scholar
Patra, S., & Krishna, V. (2015). Globalization of R&D and open innovation: linkages of foreign R&D centers in India. Journal of Open Innovation: Technology, Market, and Complexity, 1, 7 (15 September 2015).View ArticleGoogle Scholar
Polegato, R., & Wall, M. (2009). Information Seeking by Fashion Opinion Leaders and Followers. Home Economics Research Journal, 8(5), 327–38.View ArticleGoogle Scholar
Potts, J., Cunningham, S., Hartley, J., and Ormerod, P. (2008a). Social network markets: A new definition of the creative industries. Journal of Cultural Economics, 32(3), 166–185.Google Scholar
Potts, J., Hartley, J., Banks, J., Burgess, J., Cobcroft, R., Cunningham, S., and Montgomery, L. (2008b). Special Issue: Managing Situated Creativity in Cultural Industries: Consumer Co‐creation and Situated Creativity. Industry and Innovation,15(5), 459-474.Google Scholar
Raju, P. S. (1980). Optimum stimulation level: Its relationship to personality, demographics, and exploratory behavior. Journal of Consumer Research, 7(3), 272–282.View ArticleGoogle Scholar
Reichheld, F. & Sasser, W. E. (1990). Zero defections: Quality comes to services. Harvard Business Review, 68 (September/October), 105–111.Google Scholar
Schau, H. J., Muniz Jr. A. M., & Arnould, E. J. (2009). How brand community practices create value. Journal of Marketing, 73(5), 30–51.View ArticleGoogle Scholar
Schwartz, B. (2004). The paradox of choice: Why more is less. New York/Ecco: Harper Collins.Google Scholar
Sheth, J., & Venkatesan, M. (1968). Risk reduction processes in repetitive consumer behavior. Journal of Marketing Research, 5(August), 307–310.View ArticleGoogle Scholar
Shim, S. (1996). Adolescent consumer decision-making styles: The consumer socialization perspective. Psychology & Marketing, 13(6), 547–569.View ArticleGoogle Scholar
Sung, Y., & Kim, J. (2010). Effects of Brand Personality on Brand Trust and Brand Affect. Psychology & Marketing, 27(7), 639–661.View ArticleGoogle Scholar
Surowiecki, J. (2004). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations. New York: Random House.Google Scholar
Sutthijakra, S., & Intarakumnerd, P. (2015). Role and Capabilities of Intermediaries in University–Industry Linkages: A Case of Hard Disk Drive Industry in Thailand. Science Technology & Society, 20(2), 182–203.View ArticleGoogle Scholar
Wagner, M. K. (2001). Behavioral characteristics related to substance abuse and risk-taking, sensation seeking, anxiety sensitivity and self-reinforcement. Addictive Behaviors, 26, 115–20.View ArticleGoogle Scholar
Warrington, P., & Shim, S. (2000). An empirical investigation of the relationship between product involvement and brand commitment. Psychology & Marketing, 17, 761–782.View ArticleGoogle Scholar
Washburn, D., Smith, J. D., & Taglialatela, L. A. (2005). Individual differences in metacognitive responsiveness: Cognitive and personality correlates. The Journal of General Psychology, 132(4), 446–61.View ArticleGoogle Scholar
Williams, R., & Edge, D. (1996). The Social Shaping of Technology. Research Policy, 25, 865–899.View ArticleGoogle Scholar
Workman, J. E., & Cho, S. (2012). Gender, fashion consumer groups, and shopping orientation. Family and Consumer Sciences Research Journal, 40(3), 267–283.View ArticleGoogle Scholar
Workman, J. E., & Lee, S.-H. (2013). Relationships among consumer vanity, gender, brand sensitivity, brand consciousness, and private self-consciousness. International Journal of Consumer Studies, 37, 206–213.View ArticleGoogle Scholar
Yun, J. H., Jeong, E. S., & Yang, J. H. (2015). Open innovation of knowledge cities. Journal of Open Innovation: Technology, Market, and Complexity, 1, 16.View ArticleGoogle Scholar
