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ISSN : 1226-0401(Print)
ISSN : 2383-6334(Online)
The Research Journal of the Costume Culture Vol.28 No.1 pp.45-61
DOI : https://doi.org/10.29049/rjcc.2020.28.1.45

# Measuring ‘Consumer Smartness’ for the fashion consumption environment

Soo-kyoung Ahn†
Associate Professor, Dept. of Clothing & Textile, Chonnam National University, Korea
Corresponding author (skahn@jnu.ac.kr)
December 12, 2019 January 17, 2020 January 20, 2020

## Abstract

As consumers have transformed into the influential entities in the recent consumption environment, it needs a new concept to describe their characteristics. Drawn on the notion of smart consumer experience, this study views the multiple traits of new consumers as consumer smartness. Therefore, elaborating the concept of consumer smartness, this study aims to develop its measurement and validate it by examining the relationship with external variables. Two online surveys were conducted by a professional survey company that had nationwide consumer panels. A total of 531 adult consumers who had purchased fashion goods online completed a self-administered questionnaires. A series of exploratory and confirmative factor analysis generated 21 measuring items with six underlying constructs of consumer smartness such as innovativeness, opinion leadership, self-disclosure, marketing literacy, dissatisfaction, and technology sophistication. In order to validate the measurement, this study conducted a Pearson’s correlation test and structural equation modeling analysis with consumer smartness and external constructs. The result shows that there was a significant positive relationship between consumer smartness and behavioral intentions online. In addition, consumer smartness influenced their shopping and sharing intention which supported the validity of new measurement of consumer smartness. This study provides a theoretical and empirical ground of understanding consumer smartness as new consumer characteristics in the changing environment of fashion retailing.

## 초록

Ministry of Education
National Research Foundation of Korea
2017S1A5A8019513

## I. Introduction

Consumers are changing as well as retail environments are transforming. The digital revolution has enabled consumers to access more information and choices, to connect with more people and organizations, and to have more influence on people and societies than ever before (Ahn, Ryou, & Jeon, 2019). For example, instead of relying on traditional advertisement, consumers are actively looking for inspiration by exploring other consumers’ social media profiles and browsing price comparison websites (Deloitte, 2014). They are more likely to have purchased products using high-tech devices such as a voice-activated assistant today than a year before (RIS, 2018). After purchase, consumers are sharing their experience with others not only by writing online reviews but also by being brand ambassadors utilizing diverse digital tools (Forbes, 2019). They are also becoming more involved in product development (Deloitte, 2014), and willingly revealing more about their personal lives expecting smart and personalized promotions from retailers (RIS, 2018). They are not just buyers or users anymore, but influential entities in the recent consumption environment. This change has led to the need for a new concept to describe new consumer characteristics.

## Ⅱ. Literature Review

### 2. Consumers with innovativeness

Furthermore, innovativeness is closely related to other traits of smart consumer such as market mavenship and opinion leadership (Eastman et al., 2014; Goldsmith et al., 2003; Grewal et al., 2000;Ruvio & Shoham, 2007). Therefore, innovativeness can be a key concept describing personal traits of smart consumers.

### 3. Consumers with opinion leadership

In general, opinion leadership is a person’s tendency to influence the attitudes or behaviors of others in a specific domain through interpersonal communication (Flynn et al., 1996; Grewal et al., 2000;King & Summers, 1970). Opinion leaders are considered to have dual roles as information sources and influentials (Weimann, Tustin, Vuuren, & Joubert, 2007). Because they have more experience, expert knowledge, information sources, and higher level of interest or involvement in a specific product category (Gnambs & Batinic, 2013;Flynn, Goldsmith, & Eastman, 1994;King & Summers, 1970;Lyons & Henderson, 2005; Weimann et al., 2007) than non-leaders, and are very likely to communicate with others by virtue of their involvement in the product category (Flynn et al., 1996). Meanwhile, Grewal et al. (2000) called these roles as the gate-keeping role since opinion leaders pass the information to the others in the social system and influence others’ attitudes or behavior in a desired way inferring they control the flow of information.

### 4. Consumers with self-disclosure tendency

There are 3.2 billion social media users worldwide which equates to about 42% of the population (Oberlo, 2019). The reasons for internet users worldwide to use social media are to share photos or videos (33%), opinion (29%), details of ‘what I’m doing in my daily life (20%)’ (Statista, 2019). Likewise, the usage rate of social media was up to 65.2% of internet users in Korea (KISA, 2019). They use social networking for sharing personal interests like hobbies or leisure (43.3%) and information, knowledge, or incident (32%), and recoding personal daily life (39.9%). These statistics indicate that a number of current consumers are likely to disclose themselves to others online.

Self-disclosure is the telling of the previously unknown (Joinson & Paine, 2012), and occurs when a person tells another person something about him or herself (Rosenfeld, 1979). As the consumer environment changes, self-disclosure receives attention in online communication relating to product reviews (Forman, Ghose, & Wiensenfeld, 2008;Ghose & Ipeirotis, 2011; Huang, 2014;Shin, Van Der Heide, Beyea, Dai, & Prchal, 2017), WOM (Sicilia, Delgado- Ballester, & Palazon, 2016), and SNS or blog behaviors (Cho, 2015;Jin & Noh, 2015;Lee, Im, & Taylor, 2008;Park, Jin, & Jin, 2011). Lee et al. (2008) argued that many consumers are voluntarily disclosing a various aspects of their personal lives in an online context such as blogs and personal Web space without explicit reciprocity, despite of the privacy concerns. However, many personal Web space may be often connected with others so that it can be a means of communication and relationship management with others in the forms of WOM, product review, and other online behaviors. The effect of self-disclosure online is controversial. For example, the individual’s level of self-disclosure did not increase their engagement in positive WOM about the brand, but had an impact on WOM when supported by other moderator (Sicilia et al., 2016). Self-disclosure did not moderate the effect of hotel review quality on product attitudes (Shin et al., 2017), while the reviews on Amazon.com that disclose identity-descriptive information about the reviewer were rated as more helpful than anonymous reviews (Forman et al., 2008). In spite of the mixed results, self-disclosure is still a key factor in developing relationship in online as it is in faceto- face (Kim & Dindia, 2011) and related to social interactions and relationship development (Sicilia et al., 2016). Specifically, a consumer’s attitudes and purchase decision is based on the information obtaining from fellow consumers (Forman et al., 2008), because consumers perceive the information is more reliable, credible and trustworthy than firm generated (Sicilia et al., 2016). Therefore, computer mediated communication (CMC) and general Internet based behavior can be characterized as containing high levels of disclosure (Joinson & Paine, 2012). Thus, self-disclosure will be one of key features of smart consumers who are both message recipients and senders and be associated with online behaviors such as shopping experience and information sharing.

### 6. Consumers with marketing literacy

Marketing literacy means that consumers are aware of the ideas, objectives and methods of marketing and advertising (Macdonald & Uncles, 2007) and capable of conducting sophisticated analysis, dissection and critique of marketing activity (Garnier & Macdonald, 2009). This term is generally used to explain savvy consumers. Savvy consumers are confident consumers who understands how to make smart purchases empowered by the competency across the array of practical skills and knowledge to respond to a changing environment (Macdonald & Uncles, 2007). Defining the notion of new consumers, Macdonald and Uncles (2007) identified six characteristics of consumer savvy including technology sophistication, interpersonal and online networking, marketing/advertising literacy, their self-efficacy, and expectations of firms. Marketing literacy can be found in market mavens who are called as competent information providers and advisors (Walsh et al., 2004). Due to their marketplace knowledge and expertise, they can help other consumers in advertising saturated markets (Walsh et al., 2004).

For the present consumers easily access to abundant information from diverse sources and experience products and services more than traditional consumers, they can be familiar with marketing terminology and identify the persuasive technique of advertising just like savvy consumers who see through and decode advertising ploy (Garnier & Macdonald, 2009; Nancarrow et al., 2011) and market mavens.

### 7. Consumers with technology sophistication

Being a core component of consumer savvy, technology sophistication refers to the high rates of adoption of new technologies and also the ability of consumers to handle multiple technologies through media multi-tasking (Macdonald & Uncles, 2007). The rapid dissemination of internet and high-tech devices enables consumers have more control over the access and use of information than ever before (Shankar, Inman, Mantrala, Kelley, & Rizley, 2011). For example, Millennials representing the current consumers are described as the first digital natives being enthusiastic about technological advances (Eastman et al., 2014). Their affinity for technology is reshaping the retail space (Goldman Sachs, 2019). Just like savvy consumers, they use technology not only for communicating online but improving the effectiveness of their consumption. As Kim et al. (2014) described, smart consumers utilize smart device or software that tailored to their specific consumer profile. In short, they live online and buy online (Goldman Sachs, 2019). Therefore, technology sophistication may be a noteworthy feature of smart consumers. Additionally, it may be related to consumers’ behavioral intension because it enhances consumers’ interaction with the firm and the marketplace (Macdonald & Uncles, 2007).

## Ⅲ. Research Methods

The study involved two phases of measurement development and validation employing two surveys. In the first phase of measurement development, a series of exploratory factor analyses and confirmatory factor analyses (CFA) using SPSS 23.0 and AMOS 23.0 were executed to identify the underlying dimensions of consumer smartness. In the second phase of validation, correlation analysis and a structural equation modeling (SEM) examined the relationships of consumer smartness and external constructs such as shopping and sharing intentions. Descriptive statistics were used to describe general demographic characteristics and online behaviors of the respondents.

### 1. Data collection

This study employed two surveys collecting data of 531 adult consumers who had purchased fashion goods online. The surveys were conducted by a professional online survey company that hold a nationwide panel of over one million consumers in Korea. According to the national research (KISA, 2019), fashion products such as apparel, shoes, and accessories were the most popular online shopping category (87.7%). It is consistent with the worldwide statistics that 57 percent of global internet users had purchased fashion-related products through the internet (Statista, 2018). Purchasing a lot of fashion goods may mean they are more likely to talk about or do something about it, such as interaction that smart consumers mainly involve in. Thus, fashion consumers can be suitable subjects for this study.

### 2. Measures

In the initial survey, a total of 222 consumers with an average age of 33.6 completed a self-administered questionnaire including questions about opinion leadership with 11 items (Flynn et al., 1996), innovativeness with 10 items (Agarwal & Prasad, 1998; Goldsmith et al., 2003;Goldsmith & Hofacker, 1991), consumer savvy and tech savvy with 23 items (Agarwal & Prasad, 1998;Macdonald & Uncles, 2007), market mavenship with 8 items (Goldsmith et al., 2003), self-disclosure with 6 items (Joinson, 2001;Shih, Hsu, Yen, & Lin, 2012;Yu, Kim, & Suh, 2005), lead user with 15 items (Franke et al., 2006; Hoffman et al., 2008;Schuhmacher & Kuester, 2012) and several demographic information. Data were analyzed by employing a series of principal component analyses with Varimax rotation to reduce dimensionality. Removing items with a low loading or communality, or cross-loaded items from the initial set of 73 items, analyses initially extracted eight constructs of consumer smartness with 36 items showing 75.552 of total explained variance.

The second survey (n=309) was executed with self-administered questionnaire with 36 items based on the result of the previous analyses using a six-point Likert scale ranging from strongly disagree. The questionnaire contained the additional questions about the respondents’ behavioral intention with seven items, online behavior, and demographic information. Specifically, behavioral intention included shopping and sharing intention for validation. To scrutinize the nomological validity of the measurement, this study tested the simple correlations between consumer smartness and external constructs that proved theoretically correlate, and examined a structure model containing these external constructs. According to the literature, consumer characteristics such as innovativeness, opinion leadership, lead-userness, and market mavenship influence individual consumer behavior such as purchase (Blake, Neuendorf, & Valdiserri, 2003; Eastman et al., 2014;Goldsmith & Desborde, 1991), or usage (Lassar et al., 2005), product evaluations, channel choice (Cho & Workman, 2011), adoption (Geissler & Edision, 2005;Schreier & Prügl, 2008), store/site visit (Blake et al., 2003;Shoham & Ruvio, 2008) and attitude (Park, Burns, & Rabolt, 2007;Stokburger-Sauer & Hoyer, 2009). They are also related to communication behavior such as sharing attitudes and knowledge (Goldsmith, Clark, & Goldsmith, 2008;Ruvio & Shoham, 2007), and searching information from others (Clark, Goldsmith, & Goldsmith, 2008;Ruvio & Shoham, 2007;Wiedmann, Walsh, & Mitchell, 2001). Communication is the most important reason to use internet (KISA, 2019). This consumer-to consumer communication is also associated with self-disclosure (Forman et al., 2008;Ghose & Ipeirotis, 2011; Huang, 2014; Lee et al., 2008; Shih et al., 2012; Yu et al., 2005). In conclusion, smart consumers are principle agents of transaction and interaction (Kim et al., 2014) so that shopping and sharing are their main activities. Thus, shopping and sharing intentions were used as external variables for validation.

### 3. Demographic and behavioral characteristics of the respondents

The average age of the respondents in the second survey was 34.5 years with a standard deviation of 8.0 years. The majority of respondents were male (n= 156, 50.5%), employed (n=228, 73.8%) and university graduates (n=196, 63.4%). They were urban residents of Seoul, Busan, Taegu, Incheon, Daejeon, Gwangju and Ulsan (n=192, 62.1%). Their monthly income level most ranged from $2,000 to$4,000 (n=115, 37.2%). Most of them spent between $100 and$250 on clothes monthly (n=144, 46.6%) and shopped for apparel goods via internet or mobile phones (n=229, 74.1%). Naver Shopping (n=75, 24.3%) and 11st (n=58, 18.1%) were the most frequently visited e-tailers or shopping platforms. The most popular SNSs were Facebook (n=256, 82.8%), Kakao story (n=209, 67.6%), and Instagram (n=202, 65.4%) respectively. With the exception of apparel goods, food (n=255, 82.5%) and books (n=159, 51.5%) were the most often purchased items online.

They searched information on apparel products including price information (n=354, 89.6%), read relevant comments on the reviews pages (n=316, 80.0%), wrote product reviews (n=200, 50.6%), and made an inquiry about product details. They were mainly engaged in writing e-mails (n=245, 79.3%), news searching (n=286, 92.6%), subscribing Web toons (n=198, 64.1%), and blogging (n=256, 82.8%) online. With regard to shopping, most of the respondents searched product information including price (n=300, 97.1%) by reading the product reviews at the shopping sites (n=296, 95.8%), visiting blogs or SNS (n=254, 82.2%), review sites (n=200, 64.7%), and brand sites (n=195, 63.1%). After purchase, 219 respondents (70.9%) wrote reviews and 125 respondents (40.5%) posted reviews with photos. They shared the user experiences through blogs, SNS (n=84, 27.2%), or messengers (n=149, 48.2%). They contacted retailers or brands to make suggestions about the products through online (n=71, 23.3%), took part in product idea challenges (n=43, 13.9%), and attempted group buying (n=55, 17.8%). They were shopping and sharing online with an average of 4.43 times a week, spending about one-hour and 35 minutes each time.

## Ⅳ. Results and Discussion

### 1. Identifying consumer smartness

In order to identify underlying dimensions of consumer smartness, principle component analyses with Varimax rotation were performed with the data of 309 from the second survey. Removing items with low factor loading or cross-loadings from the initial 36 items, they finally revealed the six underlying dimensions of consumer smartness with final 21 items that explained 77.495% of total variance. Six dimensions include innovativeness (38.495% of variance, α=.908) which means the tendency of actively searching or adopting a new product or brand as soon as it becomes available and opinion leadership (10.345% of variance, α=.889) which represents the influence on other consumers’ shopping behavior. Self-disclosure (9.080% of variance, α=.871) which refers to the tendency of providing personal information to others, and dissatisfaction (7.658% of variance, α= .876) in which consumers are dissatisfied with the ex- isting products or shopping systems and expect potential benefits were included too. Technology sophistication (7.149% of variance, α=.845) denotes proficiency in using shopping-related technologies such as devices and apps, and marketing literacy is the familiarity with techniques of marketing and advertising (4.768% of variance, α=.869).

Subsequently, CFA confirmed 21 indicators to measure six latent constructs (Table 1). The χ2 of 280.784 (df=174, p=.000; χ2/df=1.614), NFI of .935, CFI of .974, and RMSEA of .045 supported a good model fit. All items loaded significantly (t-value>1.96) on their corresponding constructs so that construct validity was supported. Composite reliabilities ranged from .694 to .909 and AVEs ranged from .632 to .716 supported convergent validity. Discriminant validity was attained by the result that the square root of AVEs were larger than the squared correlation between each pair of constructs (Table 2).

### 2. Validating measurement

For the validation of the measurement, this study firstly tested the simple correlations between consumer smartness and behavioral intentions and secondly examined the relationships of consumer smartness and external constructs.

The result of correlation analysis indicated that consumer smartness was significantly correlated with information sharing intention (Pearson’s r=.607, p<.01) and buying intention (Pearson’s r=.231, p<.01). For further validation, the measure a structural model containing six constructs of consumer smartness and two behavioral intention constructs was tested. The result of measurement model testing by employing CFA presented an acceptable fit of measures of constructs (χ2(319)=600.475, p=.000; χ2/df=1.882; NFI of .899; CFI of .949, and RMSEA of .054). Construct validity (t-value>1.96), composite reliabilities (.692 to .891) and AVEs (.636 to .715) for all the measures supported convergent validity. Comparing the squared root of AVEs and the squared correlation of each pair of constructs confirmed discriminant validity. Next, a SEM was conducted to test if consumer smartness affected shopping and sharing intention. The model fit with the χ2 of 621.837 (df=322, p=.000; χ2/df=1.931), NFI of .897, CFI of .947, and RMSEA of .055 was acceptable. The result presented that eight paths out of twelve ones were significant. Technology sophistication (β=.313***), and self-disclosure (β=.153*), had positive impact on shopping intention, while all dimensions of consumer smartness affected sharing intention. In detail, opinion leadership (β=.283***), marketing literacy (β=.274**), self-disclosure, (β=.204**), dissatisfaction (β=.248***), and technology sophistication (β=.167***), positively influenced sharing intention, whereas innovativeness (β=‒.236**), negatively influenced sharing intention (Table 3). Therefore, consumer smartness influenced behavioral intentions including shopping and sharing intention so that the validity of the newly developed consumer smartness measures with 21 items was confirmed.

## Ⅴ. Conclusions

A tremendous amount of technological advancement and economic opportunity have disrupted the way of living, communicating, and even shopping, and have empowered consumers to be smart. Today’s consumers are an entirely different breed compared to the consumers of yesteryear (Mallee Blue, 2018). They demand personalized service, expect consistent business interaction through Omni-channel, do almost everything with mobile devices, experience through virtual visualization before making a purchase and actively interact with brands and other consumers through social media.

Inspired by the model of the paradigm shift of consumer experience (Kim et al., 2014), this study assume that smart consumers may have multiple traits in order to play the extended roles in digital consumption environment. Thus, this study attempted to integrate the distinctive traits of the consumers who received the attention from academia and industry and to define the consumer smartness. Based on the initial pool of measurements drawn on innovativeness, consumer savvy and tech savvy, market mavenship, self-disclosure and lead user literature, this study developed final measurement for consumer smartness.

First, this study empirically extracted six dimensions with 21 measuring items of consumer smartness as innovativeness, opinion leadership, self-disclosure, dissatisfaction, technology sophistication, and marketing literacy. The result supports that consumer smartness is a comprehensive concept to explain smart consumers’ multiple roles in present retailing environment. Therefore, this study suggests that marketers and researchers not to look at the recent smart consumers fragmentally but look at them as a whole, because their behaviors and roles are organically related each other. Although the previous studies argued that opinion leaders, market mavens, savvy consumers, lead-users were different concepts, the results of exploratory factor analysis exhibited a lot of cross-loaded items in the integrated measures. It implies that these consumers might share similar characteristics. Therefore, this study suggests the revisit these concepts with today’s consumers considering their extended roles. Also, fashion firms need to pay attention to consumer smartness that is a wide-ranging trait explaining multiple behaviors in digital retailing context and devise a new approach to smart consumers. For example, marketers should consider consumer smartness as a segmentation criterion rather than a narrow-ranging trait bringing a specific behavior.

Second, this study validated the measurement of smart consumers by examining the relationships with internal and external variables. The result showed not only the measurement validity, but also interesting implications. Consumer smartness influenced behavioral intentions including shopping and sharing intention as expected. In particular, only two traits, self-disclosure and technology sophistication positively affected on shopping intention while other traits did not. Moreover, all traits significantly affected on sharing intention. It implies that sharing activities may be key activities rather than purchasing in today’s digital consumption environment. It may be because consumers spend more time and effort on information sharing than on just purchasing. They may enjoying the process of consuming not only purchase itself. It supports that the modern consumers consume mass media heavily (Shoham & Ruvio, 2008) and share their attitudes and knowledge with others (Eastman et al., 2014;Goldsmith & Clark, 2008).

Third, interestingly, self-disclosure and technology sophistications have significant impacts on both shopping and sharing intension. It proves that these may be the most essential qualities of modern consumers. That’s why online communication literature (Forman et al., 2008; Huang, 2014; Park et al., 2011; Shin et al., 2017; Sicilia et al., 2016) relating to product reviews, WOM, and SNS or blog behaviors paid attention to self-disclosure and market maven and savvy consumer literature (Garnier & Macdonald, 2009;Geissler & Edison, 2005;Macdonald & Uncles, 2007) considered technology sophistication as a key attribute. Therefore, this study suggest marketers provide an interactive setting to their website so that customers can exhibit themselves and communicate with others in order to increase traffics and amplify their engagement. For example, personalized spaces to offer dynamic communication between consumers on the brand’s website and events to encourage active participation.

The previous studies exploring the new influential consumers have been focusing on a particular trait only according to a consumer’s specific role in the marketplace. However, the current consumers play diverse roles at the same time, which implies they have multifaceted characteristics. Developing the measures of consumer smartness, this study emphasized the emergence of smart consumers and their multiple roles in the contemporary consumption environment. The result of this study provides practical suggestion for segmentation and targeting new consumers and hands-on communication strategies to attract and maintain them within a brand’s sphere. It also provides a theoretical foundation of understanding smart consumer characteristics and academic implication for consumers’ sharing activities and self-exposure online.

However, there are several limitations to be addressed. Although this study found the meaningful traits such as self-disclosure and technology sophistication, there were negative and insignificant paths when examining the relationships between six dimensions and external variables including shopping and sharing intentions. Future studies can be expected to test the relationship with other online activities and consider the possibility of second-order structure of consumer smartness.

## Table

CFA result of consumer smartness (n=309)

Correlation and φ2 of constructs

The result of the structural model testing

## Reference

1. Agarwal, R. , & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-224.
2. Ahn, S. , Ryou, E. , & Jeon, S. (2019). The relationship of consumer smartness to demographic and behavioral characteristics. Proceedings of 2019 Annual conference of the International Textile and Apparel Association, 76(1).
3. Barnes, S. , & Pressey, A. (2012). In search of the “Meta-Maven”: An examination of market maven behavior across real-life, web, and virtual world marketing channels. Psychology & Marketing, 29(3), 167-185.
4. Bartels, J. , & Reinders, M. (2011). Consumer innovativeness and its correlates: A propositional inventory for future research. Journal of Business Research, 64 (6) 601-609.
5. Blake, B. , Neuendorf, K. , & Valdiserri, C. (2003). Innovativeness and variety of Internet shopping. Internet Research, 13(3), 156-169.
6. Cho, S. , & Workman, J. (2011). Gender, fashion innovativeness and opinion leadership, and need for touch: Effects on multi-channel choice and touch/ non-touch preference in clothing shopping. Journal of Fashion Marketing and Management, 15(3), 363-382.
7. Cho, Y. (2015). The effect of self-disclosure on the intention to use of SNS in the digital convergence environment. Journal of Digital Convergence, 13(5), 139-150.
8. Cision. (2018, July 3). Influencers vs. opinion leaders: Whom to trust? Retrieved August 30, 2019, from https://www.cision.com/us/2018/07/influencers-vsopinion-leaders/
9. Clark, R. , Goldsmith, R. , & Goldsmith, E. (2008). Market mavenism and consumer self-confidence. Journal of Consumer Behaviour, 7(3), 239-248.
10. Deloitte. (2014). The growing power of consumers. The Deloitte Consumer Review, Retrieved July 2, 2019, from https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/consumer-business/consumer-review-8-the-growing-power-of-consumers.pdf
11. Eastman, J. , Iyer, R. , Liao-Troth, S. , Williams, D. , & Griffin, M. (2014). The role of involvement on Millennials’ mobile technology behaviors: The moderating impact of status consumption, innovation, and opinion leadership. Journal of Marketing Theory and Practice, 22(4), 455-470.
12. Ehrhardt, J. (2018, February 8). When to use a key opinion leader (KOL) vs. an influencer. InfluencerDB, Retrieved August 30, 2019 from https://blog.influencerdb.com/kol-vs-influencer/
13. Eisenberg, I. (2011). Lead-user research for breakthrough innovation. Research-Technology Management, 54 (1) 50-58.
14. Feick, L. , & Price, L. (1987). The market maven: A diffuser of marketplace information. Journal of Marketing, 51(1), 83-97.
15. Flynn, L. , Goldsmith, R. , & Eastman, J. (1994). The King and Summers opinion leadership scale: Revision and refinement. Journal of Business Research, 31(1), 55-64.
16. Flynn, L. , Goldsmith, R. , & Eastman, J. (1996). Opinion leaders and opinion seekers: Two new measurement scales. Journal of the Academy of Marketing Science, 24(2), 137-147.
17. Forbes. (2015). The new world of ‘the empowered consumer’. Retrieved July 10, 2019, from https://www.forbes.com/sites/stevenrosenbaum/2015/07/16/the-new-world-of-the-empowered-consumer/#6dc4f3194aab
18. Forbes. (2018). Forget the Millennials, the connected consumer is who you should be chasing. Retrieved July 10, 2019, from https://www.forbes.com/sites/shamahyder/2018/01/18/forget-the-millennials-theconnected-consumer-is-who-you-should-be-chasing/#7ae2f2654172
19. Forbes. (2019). 11 trends that will shape marketing in 2019. Retrieved July 1, 2019 from https://www.forbes.com/sites/forbesagencycouncil/2019/01/15/11-trends-that-will-shape-marketing-in-2019/#1d24980e36a6
20. Forman, C. , Ghose, A. , & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information System Research, 19(3), 243-396.
21. Franke, N. , Hippel, E. , & Schreier, M. (2006). Finding commercially attractive user innovations: A test of lead-user theory. The Journal of Product Innovation Management, 23(4), 301-315.
22. Füller, J. (2010). Refining virtual co-creation from a consumer perspective. California Management Review, 52(2), 98-122.
23. Garnier, M. , & Macdonald, E. (2009). The savvy French consumer: a cross-cultural replication. Journal of Marketing Management, 25(9-10), 965-986.
24. Geissler, G. , & Edison, S. (2005). Market mavens’ at titudes towards general technology: Implications for marketing communications. Journal of Marketing Communications, 11(2), 73-94.
25. Ghose, A. , & Ipeirotis, P. (2011). Estimating the help fulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23 (10), 1498-1512.
26. Gnambs, T. , & Batinic, B. (2013). The roots of interpersonal influence: A mediated moderation model for knowledge and traits as predictors of opinion leadership. Applied Psychology: An International Review, 62(4), 597-618.
27. Goldman Sachs. (2019). Millennials: Coming of age. Retrieved September 29, 2019, from https://www.goldmansachs.com/insights/archive/millennials/index.html
28. Goldsmith, R. , & Clark, R. (2008). An analysis of factors affecting fashion opinion leadership and fashion opinion seeking. Journal of Fashion Marketing and Management, 12(3), 308-322.
29. Goldsmith, R. , & Desborde, R. (1991). A validity study of a measure of opinion leadership. Journal of Business Research, 22(1), 11-19.
30. Goldsmith, R. , & Hofacker, C. (1991). Measuring consumer innovativeness. Journal of the Academy of Marketing Science, 19(3), 209-221.
31. Goldsmith, R. , Clark, R. , & Goldsmith, E. (2008). Extending the psychological profile of market mavenism. Journal of Consumer Behaviour, 5(5), 411-419.
32. Goldsmith, R. , Flynn, L. , & Goldsmith, E. (2003). Innovative consumers and market mavens. Journal of Marketing Theory and Practice, 11(4), 54-64.
33. Grewal, R. , Mehta, R. , & Kardes, F. (2000). The role of the social-identity function of attitudes in consumer innovativeness and opinion leadership. Journal of Economic Psychology, 21(3), 233-252.
34. Hienerth, C. , & Lettl, C. (2017). Understanding the nature and measurement of the lead user construct. Journal of Product Innovation Management, 34(1), 3-12.
35. Hirschman, E. (1980). Innovativeness, novelty seeking, and consumer creativity. Journal of Consumer Research, 7(3), 283-295.
36. Hoffman, D. , Kopalle, P. , & Novak, T. (2008). Identifying and using emergent consumers in developing radical innovations. ResearchGate, Retrieved February 1, 2019, from https://www.researchgate.net/publication/228810680_Identifying_and_using_emergent_consumers_in_developing_radical_innovations
37. Hoffman, D. , Kopalle, P. , & Novak, T. (2010). The “right” consumers for better concepts: Identifying consumers high in emergent nature to develop new product concepts. Journal of Marketing Research, 47(5), 854-865.
38. Huang, L. (2014). Trust in product review blogs: The influence of self-disclosure and popularity. Behaviour & Information Technology, 34(1), 33-44.
39. Jin, B. , & Noh, H. (2015). Self-disclosure tendency, Facebook activities, and relational usefulness: Sex differences in young adults. The Journal of the Korea Contents Association, 15(7), 449-459.
40. Joinson, A. (2001). Knowing me, knowing you: Reciprocal self-disclosure in internet-based surveys. Cyber Psychology & Behavior, 4(5), 587-591.
41. Joinson, A. , & Paine, C. (2012). Self-disclosure, Privacy and the Internet. In A. Joinson, K. McKenna, T. Postmes, & U. Reips (Eds.), Oxford Handbook of internet psychology (pp. 235-250). Oxford: Oxford University Press.
42. Kim, H. , Ahn, S. , & Forney, J. (2014). Shifting paradigms for fashion: From total to global to smart consumer experience. Fashion and Textiles, 1(15).
43. Kim, J. , & Dindia, K. (2011). Online self-disclosure: A review of research. In K. Wright, & L. Webb (Eds.), Computer-meditated communication in personal relationships (pp. 156-180). New York: Peter Lang Publishing.
44. King, C. , & Summers, J. (1970). Overlap of opinion leadership across consumer product categories. Journal of Marketing Research, 7(1), 43-50.
45. Korean Internet & Security Agency. (2019). 2018 Internet usage report. Retrieved August 1, 2019, from http://www.itfind.or.kr/publication/statistics/read.do?selectedId=02-008-190516-000001
46. Lassar, W. , Manolis, C. , & Lassar, S. (2005). The relationship between consumer innovativeness, personal characteristics, and online banking adoption. International Journal of Bank Marketing, 23(2), 176-199.
47. Lee, D. , Im, S. , & Taylor, C. (2008). Voluntary selfdisclosure of information on the internet: A multimethod study of the motivations and consequences of disclosing information on blogs. Psychology & Marketing, 25(7), 692-710.
48. Lüthje, C. , & Herstatt, C. (2004). The lead user method: An outline of empirical findings and issues for future research. R&D Management, 34(5), 553-568.
49. Lyons, B. , & Henderson, K. (2005). Opinion leadership in a computer‐mediated environment. Journal of Consumer Behaviour, 4(5), 319-329.
50. Macdonald, E. , & Uncles, M. (2007). Consumer savvy: Conceptualisation and measurement. Journal of Marketing Management, 23(5-6), 497-517.
51. Mallee Blue. (2018, December 21). How technology is empowering 21st century consumers. Mallee Blue Media, Retrieved September 15, 2019, from https://www.malleeblue.com/how-technology-is-empowering-21st-century-consumers/
52. Mayer, D. , & Harrison, N. (2019), August 13). As cus tomers begin to shop through voice assistants, wh at can brands do to stand out? Harvard Business Review, Retrieved August 15, 2019, from https://hbr.org/2019/08/as-customers-begin-to-shop-through-voice-assistants-what-can-brands-do-to-stand-out
53. Nancarrow, C. , Tinson, J. , & Brace, I. (2011). Profiling key purchase influencers: Those perceived as consumer savvy. Journal of Consumer Behavior, 10(2), 102-110.
54. Oberlo. (2019). 10 social media statistics you need to know in 2019. Retrieved September 1, 2019, from https://www.oberlo.com/blog/social-media-marketing-statistics
55. Park, H. , Burns, L. , & Rabolt, N. (2007). Fashion innovativeness, materialism, and attitude toward purc hasing foreign fashion goods online across national borders: The moderating effect of internet innovativeness. Journal of Fashion Marketing and Management,11(2), 201-214.
56. Park, N. , Jin, B. , & Jin, S. (2011). Effects of selfdisclosure on relational intimacy in Facebook. Computers in Human Behavior, 27(5), 1974-1983.
57. RIS. (2018). Retail 2025 shopper study: The future of retail is already here. Retrieved July 1, 2019, from https://risnews.com/retail-2025-shopper-study-future-retail-already-here
58. Roehrich, G. (2004). Consumer innovativeness concepts and measurement. Journal of Business Research, 57(6), 671-677.
59. Rogers, E. (1983). Diffusion of innovations. New York: Free Press.
60. Rosenbaum, S. (2015, July 16). The new world of the ‘empowered consumer.’ Forbes, Retrieved August 15, 2019, from https://www.forbes.com/sites/stevenrosenbaum/2015/07/16/the-new-world-of-the-empowered-consumer/#59d00e5f4aab
61. Rosenfeld, L. (1979). Self‐disclosure avoidance: Why I am afraid to tell you who I am. Communication Monographs, 46(1), 63-74.
62. Ruvio, A. , & Shoham, A. (2007). Innovativeness, exploratory behavior, market mavenship, and opinion leadership: An empirical examination in the Asian context. Psychology & Marketing, 24(8), 703-722.
63. Ryou, E. , & Ahn, S. (2018). Identifying consumer smartness. Proceedings of 2018 Spring Conference of the Korean Society of Fashion Business, 92-95.
64. Schreier, M. , & Prügl, R. (2008). Extending lead-user theory: Antecedents and consequences of consumers’ lead userness. The Journal of Product Inno vation Management, 25(4), 331-346.
65. Schuhmacher, M. , & Kuester, S. (2012). Identification of lead user characteristics driving the quality of service innovation ideas. Creativity and Innovation Management, 21(4), 427-442.
66. Schwartz, E. (2019, June 12). New survey finds rising trend in voice shopping. Voicebot.ai, Retrieved October 1, 2019, from https://voicebot.ai/2019/06/12/new-survey-finds-rising-trend-in-voice-shopping/
67. Shankar, V. , Inman, J. , Mantrala, M. , Kelley, E. , & Rizley, R. (2011). Innovations in shopper marketing: Current insights and future research issues. Journal of Retailing, 87S(1), S29-S42.
68. Shih, D. , Hsu, S. , Yen, D. , & Lin, C. (2012). Exploring the individual’s behavior on self-disclosure online. International Journal of Human-Computer Interaction, 28(10), 627-645.
69. Shin, S. , Van Der Heide, B. , Beyea, D. , Dai, Y. , & Prchal, B. (2017). Investigating moderating roles of goals, reviewer similarity, and self-disclosure on the effect of argument quality of online consumer reviews on attitude formation. Computers in Human Behavior, 76(1), 218-226.
70. Shoham, A. , & Ruvio, A. (2008). Opinion leaders and followers: A replication and extension. Psychology & Marketing, 25(3), 280-297.
71. Sicilia, M. , Delgado-Ballester, E. , & Palazon, M. (2016). The need to belong and self-disclosure in positive word-of mouth behaviours: The moderating effect of self-brand connection. Journal of Consumer Behaviour, 15(1), 60-71.
72. Song, S. , Cho, E. , & Kim, Y. (2017). Personality factors and flow affecting opinion leadership in social media. Personality and Individual Differences, 114, 16-23.
73. Statista. (2018). Share of internet users who have purchased selected products online in the past 12 months as of 2018. Retrieved January 10, 2020, from https://www.statista.com/statistics/276846/reach-of-top-online-retail-categories-worldwide/
74. Statista. (2019). Most popular reasons for internet users worldwide to use social media as of 4th quarter 2018. Retrieved September 1, 2019, from https://www.statista.com/statistics/715449/social-mediausage-reasons-worldwide/
75. Stokburger-Sauer, N. , & Hoyer, W. (2009). Consumer advisors revisited: What drives those with market mavenism and opinion leadership tendencies and why? Journal of Consumer Behaviour, 8(2-3), 100-115.
76. Urban, G. , & Hippel, E. (1988). Lead user analyses for the development of new industrial products. Management Science, 34(5), 555-677.
77. Von Hippel, E. (1986). New product ideas from ‘Lead Users’. Research-Technology Management, 32(3), 24-27.
78. Von Hippel, E. (2005). Democratizing innovation. Cambridge: The MIT Press.
79. Walsh, G. , Gwinner, K. , & Swanson, S. (2004). What makes mavens tick? Exploring the motives of market mavens’ initiation of information diffusion. Journal of Consumer Marketing, 21(2), 109-122.
80. Weimann, G. , Tustin, D. , Vuuren, D. , & Joubert, J. (2007). Looking for opinion leaders: traditional vs. modern measures in traditional societies. International Journal of Public Opinion Research, 19 (2), 173–190.
81. Wiedmann, K. , Walsh, G. , & Mitchell, V. (2001). The mannmaven: An agent for diffusing market information. Journal of Marketing Communications, 7(4), 195-212.
82. Yu, S. , Suh, M. , & Kim, S. (2005). A study of factors influencing on consumer-to-consumer communication on online shopping environment. Journal of Marketing Management Research, 10(2), 49-74.