Smart Consumers: A New Segment for Sustainable Digital Retailing in Korea

Abstract

Today’s smart consumers are intelligent consumers with multiple roles in the digital consumption environment. Consumer smartness refers to the multi-dimensional qualities that support various roles. Aiming to discover who the smart consumers are in the digital consumption context, this study classifies consumer segments based on consumer smartness and explores each segment’s profile in terms of demographic and behavioral characteristics. Using the data of 541 adult consumers, a clustering analysis generated four optimal clusters: Go-getters, Socialites, Realists, and Shopping-pococurante. Consumers with a higher level of consumer smartness were likely to have stronger shopping and sharing intentions, which indicates that smart consumers are active entities in the digital consumption context. This is the first attempt to segment today’s consumers carrying out multiple roles based on the concept of consumer smartness.

1. Introduction

In a tough retail environment with a deepening economic recession, the unexpected outbreak of a pandemic, and consumer changes, retailers have recently faced matters of survival. No one can avoid this matter, as seen in the bankruptcy cases of giant retailers such as Neiman Marcus and J.C. Penney. A possible way to maintain their sustainability might be to recognize and understand changes in consumers quickly and accurately, and to concentrate marketing resources on the right segment. For this reason, unique consumers who quickly adopt new things, lead opinions, have a thorough knowledge of the market, or provide potential solutions for companies have always received attention from researchers and practitioners, because these unique consumers are influential to other consumers and represent consumer needs. However, most studies on such unique consumers have limitations in keeping up with consumer changes with respect to understanding consumer roles and business applicability.

First, earlier studies viewed unique consumers as individuals with a particular role, and did not pay attention to the expanded multiple roles of today’s consumers; for example, opinion leaders as important sources of advice for other consumers [1,2,3,4], innovative consumers as adopters of new products [5,6,7], lead users as partners for co-creation [8,9,10], savvy consumers improving the effectiveness of online shopping [11,12], and market mavens diffusing marketplace information [13,14] in the online market environment.

However, driven by the Internet, new business models, and smart devices, today’s consumers not only have overcome information asymmetry, but also have more information in some cases, resulting in “reversal” information, in which consumers are perceived to have more knowledge than sellers [15]. Without looking to a fashion leader in a peer group, anyone can tell what is fashionable this season, since consumers have a fair chance to acquire information. New business models such as social network services, crowdsourcing, and open innovation platforms (e.g., Lego Ideas and MADE TalentLab) have helped individual consumers to be networked and engage in co-creation [16,17,18,19]. Smart devices and technologies have enabled consumers to display their shopping power 24/7/365 from everywhere, at their convenience. Consumers skillfully conduct research prior to buying across retailers, manage their orders, contribute to Word of Mouth, or conduct other transactions simply with mobile devices using m-wallets [20,21,22,23,24,25]. Correia named these individuals “Fluid consumers” who use mobile technologies to flow easily between different transactions at any time and any place [26]. In short, consumers are no longer merely passive recipients in the marketing exchange process [17], but active entities who have diverse roles and various statuses. For example, they search for information from a wide range of sources [27], share their reviews and experiences with others [28], engage in brands [29,30], and talk about and post products and brands when shopping, or even when not shopping [31,32,33]. Therefore, a new perspective to understand today’s consumers as a whole with various roles rather than a particular role is needed.

Second, previous studies have mainly taken a dichotomous approach to unique consumers (i.e., opinion leaders or non-leaders, or mavens or non-mavens), lacking further segmentation and consumer profiles [1,13,14,34]. Market segmentation keeps businesses closely in touch with their consumers, ensures more efficient resource allocation, and results in marketing programs attuned to consumer needs [35] and a competitive advantage within the segment [36]. Identifying consumer needs and developing the right offers to specific consumer segments is a critical part of the marketing process for retailers with limited resources and attention spans [37]. For this reason, new segmentation should be made continuously according to consumer changes.

Recently, Flynn and Goldsmith defined the super consumer as someone who is market oriented and spends more than other consumers do, with identifiable motivational characteristics, and developed a profile of the super consumer segment [38]. However, the criteria for clustering were not enough to explain today’s consumers, and resulted in two clusters, “supers” and “regulars.” Other studies that described today’s consumers as digital consumers proposed a “4Cs” framework of behaviors [39] and defined digital consumer culture [40]. However, they seem to be far from segmenting and profiling for sustainable retailing. Today’s consumers should not be simply classified by only one characteristic. They can be propagators who share their knowledge and experience or gurus who advise other consumers or companies to co-create value and knowledgeable shoppers at the same time. Performing diverse roles based on varied statuses might be accompanied by multiple traits as a backup. Therefore, accounting for consumers with multiple roles and traits, more elaborate variables for market segmentation and consumer identification are needed.

Without considering the changes of consumers and their needs, companies no longer provide offerings that have value for consumers. Thus, management in business will need to clearly understand the characteristics of consumer segments associated with consumer changes. Therefore, this study pays attention to smart consumers who might reflect the nature of today’s consumers. They are distinct from traditional consumers in their consumption and everyday life in terms of intelligence and multiple roles in the digital consumption environment [41,42]. Grounded in the theory of the paradigm shift of consumer experience [41], Ahn refined its six dimensional qualities that support various roles of smart consumers [43]. Although smart consumers and consumer smartness have been conceptually defined, there is no study that empirically confirms the identity of smart consumers or proves consumer smartness as an appropriate concept to understand consumer behaviors in the digital consumption environment. This study aims to empirically discover who the smart consumers are. In order to examine whether there are smart consumers who have all six traits and whether they are active entities in the digital retailing environment, this study classifies consumer segments based on consumer smartness. It also identifies the characteristics of each segment by examining several demographic and behavioral variables related to shopping and sharing.

2. Literature Reviews

2.1. Smart Consumers

Smart consumers are intelligent consumers with multiple roles in the digital consumption environment [41]. According to the theory of the paradigm shift of consumer experience [41], the role has changed from the total consumer experience phase, which was limited to being the buyer in transactions, to the global consumer experience phase, in which consumers interact with other consumers, companies, or the market environment without time and spatial restriction, and to the smart consumer experience. Those in the smart consumer experience phase are deeply engaged in co-creating, sharing, and transforming values as key actors in the digital retailing context [41]. In other words, today’s consumers who are active in various roles and with various statuses in the digital environment could be smart consumers.

Smart consumers are distinct from traditional consumers in their consumption and everyday life. First, embracing new business models and technologies, they adapt to a new consumption environment [17,18,44]. Second, they have direct or indirect experience as well as market information and knowledge that is enough to advise peer consumers or companies about dissatisfaction or improvement [18,45]. Third, disclosing themselves, networked consumers do a lot of things online with diverse identities [46]. Hive-minded consumers drive participatory culture, in which consumers reclusively share information about products, monitoring updates, gathering opinions, and ratings [17,47]. Fourth, smart consumers are active omni-shoppers who are willing to move seamlessly across channels, expecting a consistent and integrated service or experience [48]. Fifth, smart consumers are open to sharing information such as browsing history and social media activity with companies in order to receive benefits, including tailored offers and promotions [49]. Specifically, consumers grant permission for expected personal relevance, entertainment, and information control in interactive marketing activities [50].

2.2. Consumer Smartness

The significant difference between smart consumers and traditional unique consumers who have drawn attention in the market, such as opinion leaders, market mavens, and lead users, is that they have multiple identities. Ahn argued that smart consumers have various traits in playing these diverse roles, such as trend setters, culture creators, and brand advisors [43]. Correia also claimed that fluid consumers have multiple consumer identities, and align those identities with various activities in their lives [26]. These multiple traits are called consumer smartness. Consumer smartness comprises six constructs: opinion leadership, innovativeness, marketing literacy, self-disclosure, dissatisfaction, and technology sophistication [43].

Opinion leadership is the influence on other consumers’ shopping behavior [43]. While traditional opinion leaders communicate with a limited number of acquaintances and influence their purchases, online opinion leaders can encounter an unlimited global audience with more diverse social connections and weaker relationships [51,52]. Many studies agree that online opinion leadership involves eWOM delivered in the form of writing, video, pictures, emoticons, or emojis [51], and influences consumer behavioral intentions such as intention to interact in the account and follow posted fashion advice [1] as well as intention to purchase [2]. However, Zhang et al. found that only the participation of ordinary users can create broad coverage of a trend in online social media, while opinion leaders can start diffusion locally [53]. This implies that opinion leadership is a characteristic not only of opinion leaders, but also of most modern consumers. Thus, smart consumers are expected to have a characteristic influencing others’ opinions and behaviors, which refers to opinion leadership.

Innovativeness is the tendency to actively search or adopt a new product or brand as soon as it becomes available [43] in the shopping context. As it does offline, consumer innovativeness influences Internet exposure, the ease-of-use perception of a shopping channel, online shopping intention [54,55], visiting sites for product information [56], intention to use remote mobile payment [57,58], social networking service (SNS) usage patterns correlated with social capital [59], and even moderate consumer attitudes and intentions of online apparel customization [60], and Internet use and online product purchases [61] in the digital environment. Hence, smart consumers who adopt new technologies and adapt to a new shopping environment are supposed to have a high level of innovativeness.

Self-disclosure refers to the tendency to expose personal information to others [43]. In the online context, such as blogs or SNS, consumers exchange information and reveal their feelings and personal information in the form of self-disclosure [62,63]. Disclosing product evaluations or personal information in blogs increases readers’ cognitive or affective trust [62], and influences online social well-being, which then leads to a continuous intention to participate in SNS [64,65]. Therefore, smart consumers who actively interact with others by posting information and their daily life online may have a high level of self-disclosure.

Dissatisfaction is when consumers are dissatisfied with existing products or shopping systems and expect potential benefits [43]. Dissatisfaction has often been highlighted as an important characteristic of a new consumer group, called “lead users”, who are ahead of the trends and are innovative in obtaining solutions to meet their needs [9,66,67,68]. Since consumers who are dissatisfied with existing products and services feel a deep need for better solutions and innovation, companies can integrate innovative and knowledgeable customers into the new product development process to detect future needs and market trends early and procure newer product ideas [10]. Thus, smart consumers who willingly participate in value co-creation with companies, such as idea challenges or advice for improvement, may have a high level of dissatisfaction.

Technology sophistication denotes proficiency in using shopping-related technologies such as devices and apps [43]. Technology has become inseparable from shopping today. Technology and mobility enable fluid consumers to be ubiquitous [26], and omni-shoppers no longer have access to channels, but rather are always in it or in several at once [48]. According to the National Retail Federation, 89% of those who have tried in-app store navigation would try it again, along with 88%, 83%, and 82% of those who have used smart dressing rooms, virtual fit, and virtual reality, respectively, in the US [69]. Therefore, it is an essential quality for today’s consumers, and smart consumers can skillfully use technology for their shopping.

Marketing literacy is the familiarity with techniques of marketing and advertising [43]. While traditional consumers were victims of media/marketing [11], overcoming the information asymmetry, today’s consumers can read the intention of advertising like savvy consumers [70,71], and keep abreast of information about markets and shopping, like market mavens [72,73]. Thus, as smart consumers who are not only sensible buyers themselves but also information aggregators interacting with other consumers or companies, they must have a high level of marketing literacy.

In sum, the following research question is proposed:

RQ1. Do the clusters based on consumer smartness have different levels of opinion leadership, innovativeness, self-disclosure, dissatisfaction, technology sophistication, and marketing literacy?

2.3. Shopping Intention, Sharing Intention

Making purchases is the most essential consumer behavior, so numerous consumer studies have considered it as a final step of decision-making. Shopping, which is a relatively broader term, includes browsing, comparing, and buying, while purchasing refers to an actual exchange of products and money. This study focuses on shopping behavior, because shopping “just to browse” has shifted to being more popular online than in stores, where more consumers seek to buy certain items [74]. Given the statistics showing that global e-commerce sales amounted to USD 3.53 trillion in 2019 and are expected to grow to USD 6.54 trillion in 2022 [75], this shows that online shopping is one of the most common activities for today’s consumers. Today’s consumers also share their experience and knowledge and seek consumption advice through social media [76,77] or Web-based discussion boards [78]. Belk mentioned that information that was owned, bought, and sold before has been shared by social platforms such as Flickr, YouTube, and Facebook, and a new era of sharing has quickly been embraced by millions [79]. Consumer characteristics such as innovativeness or a market mavenship are closely related to information sharing intention on SNS [80,81,82]. Therefore, both sharing and shopping intention should be considered with consumer behavior in the digital context. The following are proposed:

RQ2. Do the clusters based on consumer smartness have different levels of shopping intention? Specifically, do clusters with higher smartness have greater shopping intention?

RQ3. Do the clusters based on consumer smartness have different levels of sharing intention? Specifically, do clusters with higher smartness have greater sharing intention?

2.4. Demographics and Online Behavior

Despite the inconsistency of earlier studies on unique consumers such as opinion leaders and market mavens, demographics are still provide essential variables in marketing and marketing research as segmentation criteria or descriptors. Market mavens are more likely to be female [34,83], to be older [13] or younger [84,85], to be in a low socioeconomic group [34] or have higher income [86], and to be low-educated [85], while no differences between mavens and non-mavens in terms of age, gender, household income, education, or occupation were found [87]. Likewise, opinion leaders are younger, better educated, and have higher income than non-leaders in tourism [88], but Chaney found no relationship between opinion leadership status and demographic variables [89].

However, behavioral differences are often found between contradictory statuses. Differences in Internet usage were found, such as period of time and frequency in longer sessions between opinion leaders and non-leaders [52]; differences in shopping and buying volume [90], expenditure, number of fashion items purchased, and types of shopping behavior [91] between market mavens and non-mavens; and differences in volume and quickness of adopting new products between lead users and non-lead users [92] were found. Therefore, two research questions are presented:

RQ4. Is there a demographic difference between clusters based on consumer smartness?

RQ5. Is there a behavioral difference between clusters based on consumer smartness?

3. Methodology

3.1. Data Collection and Samples

Data were collected from a sample of consumers 19 to 59 years of age by a professional online survey company that holds over 1.2 million nationwide panels in Korea. According to the Pew Research Center, South Korea had the highest smartphone penetration in the world at 95%, and mobile phone (not smartphone) use was 5% in 2018 [93]. This was confirmed again by the National Information Society Agency in 2019 [94]. Considering the increase in shopping via mobile devices, 72.9% of all retail e-commerce is expected to be generated via m-commerce in 2021 [95], so a study targeting the country that uses the most will be meaningful. Thus, South Korean data can be suitable for this study. Furthermore, since almost 100% of people ages 10 to 59 use the Internet in Korea [94], the survey was conducted for adults.

Consumers who had shopped for fashion goods online in the past month completed self-administered questionnaires in April 2018. Because fashion-related products were the most popular online shopping category in Korea in 2019 [94], as well as around the world [96]. Data of 541 samples were used for this study. With a mean age of 39.68 years, 48.4% of respondents were female (n = 262) and 51.6% were female (n = 279). The majority of respondents were employed (n = 390, 72.1%) and university graduates (n = 330, 61.0%), and their monthly income ranged from USD 4000 to USD 6000 ((n = 217, 40.1%). They were urban residents of metropolitan cities: Seoul, Busan, Taegu, Incheon, Daejeon, Gwangju, and Ulsan (n = 295, 54.5%). Most of them spent between USD 10 and USD 100 on clothes monthly (n = 185, 34.2%) and shopped for apparel via the Internet or mobile phones (n = 340, 62.8%). The most frequently visited e-tailers or shopping platforms were 11st (n = 103, 19.0%) and Naver Shopping (n = 101, 18.7%). The most popular SNSs were Facebook (n = 443, 81.9%), KakaoStory (n = 370, 68.4%), and Instagram (n = 304, 56.2%). The respondents conducted shopping-related behavior such as searching, posting, sharing, and participating 3.87 times a week (SD = 8.724) for 1.54 h each time (SD = 1.430) on average.

3.2. Measures

The survey questionnaire included questions about consumer smartness, behavioral intent, shopping behavior, and demographic information. Consumer smartness instruments with 21 items [43] were used to assess the level of the respondents’ consumer smartness (Appendix A). Behavioral intention comprised shopping intention with three items (“I will continue shopping online”, “I will buy things online in the future”, and “I will continue to search information for better shopping”) and sharing intention with four items (“I will continue to share shopping information with others”, “Regardless of the sharing method, I will share shopping information with others”, “I’ll recommend shopping sites to others based on my purchasing experience”, and “I will post my product experience to share with others”). These were measured on a six-point Likert scale. Regarding shopping behavior, the respondents were asked about their buying, sharing, posting, searching, and interacting experience (20 items).

3.3. Analysis

This study involved three steps of analysis using SPSS 24.0 and AMOS 23.0. First, confirmatory factor analysis (CFA) was executed to examine the validity and reliability of the measure of consumer smartness. Second, hierarchical cluster analysis and K-means clustering were employed to segment consumers. Third, a one-way ANOVA and chi-square test were performed to verify differences between clusters. Descriptive statistics were used to describe the characteristics of each cluster.

3.4. Validation of Measures

The principal component factor analysis showed six dimensions of consumer smartness with 80.080% of total variance explained: opinion leadership (40.221% of variance, Cronbach’s α = 0.913), self-disclosure (11.143% of variance, Cronbach’s α = 0.899), innovativeness (10.172% of variance, Cronbach’s α = 0.912), tech sophistication (7.680% of variance, Cronbach’s α = 0.890), dissatisfaction (5.844% of variance, Cronbach’s α = 0.895), and marketing literacy (5.018% of variance, Cronbach’s α = 0.867). CFA confirmed the constructs of the measures with a chi-square of 299.857 (df = 172, p = 0.000), normed chi-square of 1.743, NFI of 0.964, CFI of 0.984, and RMSEA of 0.037, showing a good model fit. All items loaded significantly (t-value > 1.96) on their corresponding latent constructs and all factor loadings were higher than 0.50 [97]. Composite reliability ranged from 0.726 to 0.902 (>0.70) and average variance extracted (AVE) values ranged from 0.688 to 0.744, exceeding 0.50 [98]. Therefore, convergent validity was attained. Discriminant validity was obtained by comparison showing that the interconstruct correlation did not exceed the square root of AVE.

4. Results

4.1. Clustering Consumers

Preliminarily, a hierarchical cluster analysis was performed to determine the number of clusters. A dendrogram showed that four or five cluster solutions were possible. In order to find an optimal K, a series of K-means clustering analyses was employed with three to five clusters. The silhouette coefficient of each case was calculated to validate the results. The coefficient varied between −1 and 1; a value close to 1 indicates that the instance is close to its cluster and is a part of the right cluster [99]. First, when the number of clusters was set to three, iterations failed to converge, but the average silhouette coefficient was 0.192 which was the highest. When five clusters were specified, convergence was achieved in the 14th iteration, but the average silhouette coefficient was 0.155, which was the lowest. With four clusters, the cluster centers were converged in the 18th iteration and the average silhouette coefficient was 0.186. Therefore, the four-cluster solution was confirmed to be optimal. Cluster 1 contained 203 cases, representing 39.2% of total cases, cluster 2 contained 137 (25.3%), cluster 3 contained 83 (15.3%), and cluster 4 contained 109 (20.1%).

4.2. Identifying Clusters

ANOVA was performed to examine whether there were significant differences in mean scores between the clusters in terms of consumer smartness (

Table 1. Means comparison of smartness across clusters.

Smartness	Cluster 1 (n = 212)	Cluster 2 (n = 137)	Cluster 3 (n = 83)	Cluster 4 (n = 109)	F	p
	Mean (SD)	Mean (SD)	Mean (SD)	Mean (SD)
Opinion leadership	3.619 (0.514)	4.293 (0.594)	2.361 (0.745)	2.544 (0.679)	259.616	0.000
	B	A	C	C
Self-disclosure	3.777 (0.629)	4.697 (0.586)	2.889 (0.796)	3.624 (0.744)	134.498	0.000
	B	A	C	B
Innovativeness	3.564 (0.523)	4.493 (0.573)	2.289 (0.820)	2.624 (0.735)	279.762	0.000
	B	A	D	C
Marketing literacy	3.418 (0.605)	4.436 (0.608)	2.502 (0.724)	3.630 (0.730)	158.951	0.000
	B	A	C	B
Dissatisfaction	3.236 (0.697)	3.844 (0.843)	2.815 (0.841)	3.685 (0.947)	35.319	0.000
	B	A	C	A
Technology sophistication	3.830 (0.656)	4.747 (0.616)	2.900 (0.814)	4.180 (0.683)	135.098	0.000
	C	A	D	B

Post hoc: Scheffé test.

). The six dimensions of consumer smartness were used as dependent variables. The results indicate that opinion leadership (F = 259.616, p = 0.000), self-disclosure tendency (F = 134.498, p = 0.000), innovativeness (F = 279.762, p = 0.000), marketing literacy (F = 158.951, p = 0.000), dissatisfaction (F = 35.319, p = 0.000), and technology sophistication (F = 135.098, p = 0.000) significantly differed across the four clusters (Table 1), which responds to RQ1. The clusters had based on consumer smartness have different levels of opinion leadership, innovativeness, self-disclosure, dissatisfaction, technology sophistication, and marketing literacy.

Next, Scheffé post hoc tests were conducted to examine the between-group differences among the variables. Cluster 2 had the highest levels of all variables, whereas cluster 3 had the lowest means of variables. Clusters 3 and 4 were not significantly different in the level of opinion leadership. There were no statistically significant differences in the level of self-disclosure and marketing literacy between clusters 1 and 4. The level of dissatisfaction of cluster 2 was not significantly different from that of cluster 4.

4.3. Comparing Clusters

In order to compare the clusters in terms of shopping behavior and demographic characteristics, ANOVA and chi-square tests were performed. First, comparing the means of behavioral intention across four clusters, ANOVA revealed that there were significant differences in shopping intention (F = 21.193, p = 0.000) and sharing intention (F = 92.795, p = 0.000), as shown in

Table 2. Means comparison of behavioral intention across clusters.

Intention	Cluster 1 (n = 212)	Cluster 2 (n = 137)	Cluster 3 (n = 83)	Cluster 4 (n = 109)	F	p
	Mean (SD)	Mean (SD)	Mean (SD)	Mean (SD)
Shopping intention	4.530 (0.663)	4.967 (0.624)	4.298 (0.735)	4.725 (0.638)	21.193	0.000
	BC	A	C	B
Sharing intention	3.685 (0.585)	4.466 (0.732)	2.954 (0.696)	3.528 (0.770)	92.757	0.000
	B	A	C	B

Post hoc: Scheffé test.

. Comparing smartness, cluster 2 had the highest shopping and sharing intention while cluster 3 had the lowest among the four clusters. Clusters 1 and 4 had no significant differences in the level of shopping and sharing intention (Table 2). In sum, the clusters based on consumer smartness had different levels of shopping and sharing intention. Specifically, clusters with higher smartness had greater shopping and sharing intentions. This result responds to RQ2 and RQ3.

Second, the chi-square test was conducted to examine the differences of demographic characteristics between clusters. As

Table 3. Demographic characteristics of clusters.

Demographic Characteristics	Total n = 541 (100%)	Cluster 1 n = 212 (39.2%)	Cluster 2 n = 137 (25.3%)	Cluster 3 n = 83 (15.3%)	Cluster 4 n = 109 (20.1%)	χ2	p
Age
20 to 29	123 (22.7)	51 (24.1)	35 (25.5)	15 (18.1)	22 (20.2)	5.286 (df = 9)	0.809
30 to 39	138 (25.5)	54 (25.5)	31 (22.6)	21 (25.3)	32 (29.4)
40 to 49	140 (25.9)	48 (22.6)	39 (28.5)	24 (28.9)	29 (26.6)
50 to 59	140 (25.9)	59 (27.8)	32 (23.4)	23 (27.7)	26 (23.9)
Gender
Male	262 (48.4)	98 (46.2)	62 (45.3)	43 (51.8)	59 (54.1)	2.761 (df = 3)	0.430
Female	279 (51.6)	114 (53.8)	75 (54.7)	40 (48.2)	50 (45.9)
Occupation
Employed	351 (64.9)	135 (63.7)	92 (67.2)	48 (57.8)	76 (69.7)	10.297 (df = 12)	0.590
Student	55 (10.2)	27 (12.7)	13 (9.5)	7 (8.4)	8 (7.3)
Homemaker	77 (14.2)	26 (12.3)	20 (14.6)	17 (20.5)	14 (12.8)
Self-employed	36 (6.7)	17 (8.0)	8 (5.8)	6 (7.2)	5 (4.6)
Unemployed	22 (4.1)	7 (3.3)	4 (2.9)	5 (6.0)	6 (5.5)
Monthly Income
Less than USD 2 thousand	20 (3.7)	7 (3.3)	2 (1.5)	6 (7.2)	5 (4.6)	29.167 (df = 12)	0.004
USD 2 to 4 thousand	135 (25.0)	57 (26.9)	20 (14.6)	28 (33.7)	30 (27.5)
USD 4 to 6 thousand	211 (39.0)	85 (40.1)	51 (37.2)	30 (36.1)	45 (41.3)
USD 6 to 8 thousand	104 (19.2)	41 (19.3)	36 (26.3)	9 (10.8)	18 (16.5)
Over USD 8 thousand	71 (13.1)	22 (10.4)	28 (20.4)	10 (12.0)	11 (10.1)
Education level
High school graduate	81 (15.0)	36 (17.0)	14 (10.2)	21 (25.3)	10 (9.2)	19.033 (df = 9)	0.019
College graduate	70 (12.9)	31 (14.6)	12 (8.8)	7 (8.4)	20 (18.3)
University graduate	330 (61.0)	125 (59.0)	93 (67.9)	46 (55.4)	66 (60.6)
Graduate or higher	60 (11.1)	20 (9.4)	18 (13.1)	9 (10.8)	13 (11.9)
Family size
1 person	54 (10.0)	20 (9.4)	14 (10.2)	8 (9.6)	12 (11.0)	7.078 (df = 12)	0.852
2 people	83 (15.3)	25 (11.8)	28 (20.4)	12 (14.5)	18 (16.5)
3 people	145 (26.8)	56 (26.4)	33 (24.1)	26 (31.3)	30 (27.5)
4 people	207 (38.3)	89 (42.0)	49 (35.8)	29 (34.9)	40 (36.7)
5 or more people	52 (9.6)	22 (10.4)	13 (9.5)	8 (9.6)	9 (8.3)

shows, there were significant differences of monthly income level (χ² = 29.167, df = 12, p = 0.004) and level of education (χ² = 19.033, df = 9, p = 0.019), while the differences of age, gender, occupation, and family size between clusters turned out to be not significant. Cluster 2 had the highest levels of monthly income and education, and cluster 3 the lowest. In sum, the clusters based on consumer smartness had demographic differences in terms of monthly income and education level. Therefore, this partially responds to RQ4.

Third, the chi-square test was conducted to compare differences of the clusters in terms of general shopping behavior (

Table 4. Shopping behavior of clusters.

Shopping Behavior	Total n = 541	Cluster 1 (n = 212)	Cluster 2 (n = 137)	Cluster 3 (n = 83)	Cluster 4 (n = 109)	χ2	p
Monthly expenditure on apparel
Less than USD 100	69 (12.8)	22 (10.4)	6 (4.4)	20 (24.1)	21 (19.3)	76.496 (df = 12)	0.000
USD 100 to 200	160 (29.6)	62 (29.2)	27 (19.7)	29 (34.9)	42 (38.5)
USD 200 to 300	145 (26.8)	66 (31.1)	30 (21.9)	26 (31.3)	23 (21.1)
USD 300 to 400	84 (15.5)	33 (15.6)	32 (23.3)	5 (6.0)	14 (12.8)
USD 400 or over	83 (15.3)	29 (13.7)	42 (30.7)	3 (3.6)	9 (8.3)
Regular shopping place for apparel
Internet/mobile	340 (62.8)	139 (65.6)	84 (61.3)	47 (56.6)	70 (64.2)	21.525 (df = 12)	0.045
Department stores	55 (10.2)	20 (9.4)	25 (18.2)	5 (6.0)	5 (4.6)
Discount stores	91 (16.8)	32 (15.1)	17 (12.4)	20 (24.1)	22 (20.2)
Independent stores	35 (62.8)	14 (6.6)	7 (5.1)	7 (8.4)	7 (6.4)
Other	20 (3.7)	7 (3.3)	4 (2.9)	4 (4.8)	5 (4.6)
Average number of weekly visits to online stores for apparel shopping
Less than 5 times	347 (64.1)	137 (64.6)	71 (51.8)	63 (75.9)	76 (69.7)	18.469 (df = 6)	0.005
5 to 8 times	147 (27.2)	61 (28.8)	47 (34.3)	16 (19.3)	23 (21.1)
Over 9 times	47 (8.7)	14 (6.6)	19 (13.9)	4 (4.8)	10 (9.2)
Length of stay at online stores per visit
Less than an hour	390 (72.1)	155 (73.1)	92 (67.2)	71 (85.5)	72 (66.1)	14.694 (df = 9)	0.100
Up to 2 h	88 (16.3)	35 (16.5)	22 (15.3)	8 (9.6)	23 (21.1)
Up to 3 h	30 (5.5)	9 (4.2)	11 (8.0)	3 (3.6)	7 (6.4)
Over 3 h	33 (6.1)	13 (6.1)	12 (8.8)	1 (1.2)	7 (6.4)
Search for information including price
Yes	537 (99.3)	209 (98.9)	136 (99.3)	83 (100.0)	109 (100.0)	2.749 (df = 3)	0.432
No	4 (0.7)	3 (1.1)	1 (0.7)	0 (0)	0 (0)
Read product reviews
Yes	499 (92.2)	197 (92.9)	130 (94.9)	73 (88.0)	99 (90.8)	3.919 (df = 3)	0.270
No	42 (7.8)	15 (7.1)	7 (5.1)	10 (12.0)	10 (9.2)
Visit brand sites to get information
Yes	317 (58.6)	130 (61.3)	92 (67.2)	33 (39.8)	62 (56.9)	17.055 (df = 3)	0.001
No	224 (41.4)	82 (38.7)	45 (32.8)	50 (60.2)	47 (43.1)
Ask store managers for more information
Yes	239 (44.2)	99 (46.7)	80 (58.4)	26 (31.3)	34 (31.2)	24.786 (df = 3)	0.000
No	302 (55.8)	113 (53.3)	57 (41.6)	57 (68.7)	75 (68.8)
Visit blogs/cafes to read reviews/information
Yes	449 (83.0)	180 (84.9)	124 (90.5)	57 (68.7)	88 (80.7)	18.486 (df = 3)	0.000
No	92 (17.2)	32 (15.1)	13 (9.5)	26 (31.9)	21 (19.3)
Visit professional review sites to get performance information
Yes	325 (60.1)	134 (63.2)	98 (71.5)	35 (42.2)	58 (53.2)	21.603 (df = 3)	0.000
No	216 (39.9)	78 (36.8)	39 (28.5)	48 (57.8)	51 (46.8)
Write product reviews
Yes	380 (70.2)	142 (67.0)	107 (78.1)	52 (62.7)	79 (72.5)	7.676 (df = 3)	0.053
No	161 (29.8)	70 (33.0)	30 (21.9)	31 (37.3)	30 (27.5)
Write product reviews with photos
Yes	198 (36.6)	70 (33.1)	65 (47.4)	24 (28.9)	39 (35.8)	10.206 (df = 3)	0.016
No	343 (63.4)	142 (66.9)	72 (52.6)	59 (71.1)	70 (64.2)
Share information through blogs/SNSs
Yes	167 (30.9)	58 (27.4)	76 (55.5)	9 (10.8)	24 (22.1)	59.691 (df = 3)	0.000
No	37.4 (69.1)	154 (72.6)	61 (44.5)	74 (89.2)	85 (77.9)
Share experiences through blogs/SNSs
Yes	160 (29.6)	64 (30.2)	65 (47.4)	6 (7.2)	25 (22.9)	43.250 (df = 3)	0.000
No	381 (70.4)	148 (69.8)	72 (52.6)	77 (92.8)	84 (77.1)
Share information through messaging/talking
Yes	247 (45.7)	98 (46.2)	94 (68.6)	15 (18.1)	40 (36.7)	58.107 (df = 3)	0.000
No	294 (54.3)	114 (53.8)	43 (31.4)	68 (81.9)	69 (63.3)
Sign up for free product trials
Yes	210 (38.8)	92 (43.4)	63 (46.0)	18 (21.7)	37 (33.9)	16.181 (df = 3)	0.001
No	331 (61.2)	120 (56.6)	74 (53.4)	65 (78.3)	72 (66.1)
Group buying
Yes	80 (14.8)	38 (17.9)	35 (25.5)	0 (0)	7 (6.4)	34.701 (df = 3)	0.000
No	461 (85.2)	174 (82.1)	102 (74.5)	83 (100.0)	102 (93.6)
Enter product idea challenges
Yes	48 (8.9)	22 (10.4)	21 (15.3)	2 (2.4)	3 (2.8)	16.994 (df = 3)	0.001
No	493 (91.1)	190 (89.6)	116 (84.7)	81 (97.6)	106 (97.2)
Advise stores on solutions for problems/improvement ideas
Yes	130 (24.0)	52 (24.5)	43 (31.4)	10 (12.0)	25 (22.9)	10.689 (df = 3)	0.014
No	411 (76.0)	160 (75.5)	94 (68.6)	73 (88.0)	84 (77.1)

). The results indicate that the respondents’ monthly expenditure on apparel (χ² = 76.496, df = 12, p = 0.000), regular shopping place for apparel (χ² = 21.525, df = 12, p = 0.045), and average number of weekly visits to the patronized online stores for apparel shopping (χ² = 18.469, df = 6, p = 0.005) significantly differed across the clusters, but there was no difference in how long they stayed at online stores per visit. Those in cluster 2 spent more money on apparel and more often visited online stores than those in other clusters. Although all clusters showed an almost equal tendency to shop mainly via the Internet/mobile, the second regular shopping places were different: department stores (18.2%) for cluster 2 and discount stores for the others.

Fourth, the results of the chi-square test show that the four clusters were significantly different in terms of acquiring shopping information (Table 4). Visiting the brand site (χ² = 17.055, df = 3, p = 0.001), asking the store manager for more information (χ² = 24.7866, df = 3, p = 0.000), visiting blogs to read reviews/information (χ² = 18.486, df = 3, p = 0.000), and visiting professional review sites (χ² = 21.603, df = 3, p = 0.000) were different across the clusters. However, there were no differences in the experience of searching for prices and reading reviews at retailer sites across clusters. The majority of respondents in clusters 1, 2, and 4 visited brand sites and professional review sites to obtain shopping information, while those in cluster 3 did not. More than 80% of those in clusters 1, 2, and 4 read product reviews or information posted on blogs/cafes. Cluster 2 was the only one with more than half of the people asking store managers for more information (n = 80, 58.4%).

Fifth, the chi-square test revealed significant differences across clusters in sharing experiences, except writing product reviews (Table 4). Writing product reviews with photos (χ² = 10.206, df = 3, p = 0.016), sharing product information through blogs/SNS (χ² = 59.691, df = 3, p = 0.000), sharing experiences through blogs/SNS (χ² = 43.250, df = 3, p = 0.000), and sharing product information through messenger/talks (χ² = 58.107, df = 3, p = 0.000) were different across the clusters. Cluster 1 had the most experience, while cluster 3 had the least.

Sixth, the results of the chi-square test show that the four clusters were significantly different with regard to consumer participation, such as signing up for free trials (χ² = 16.181, df = 3, p = 0.001), group buying (χ² = 34.701, df = 3, p = 0.000), entering product idea challenges (χ² = 16.994, df = 3, p = 0.001), and advising stores on solutions or improvement ideas (χ² = 10.689, df = 3, p = 0.014). The level of consumer participation of clusters 1 and 2 was relatively higher than that of the others (Table 4).

Seventh, in order to examine the differences in SNS use by clusters, a chi-square test was employed (

Table 5. Social networking behavior of clusters.

SNS Use	Total n = 541	Cluster 1 (n = 212)	Cluster 2 (n = 137)	Cluster 3 (n = 83)	Cluster 4 (n = 109)	χ2	p
Facebook
Yes	443 (81.9)	172 (81.1)	118 (86.1)	69 (83.1)	84 (77.1)	3.541 (df = 3)	0.315
No	98 (18.1)	40 (18.9)	19 (13.9)	14 (16.9)	25 (22.9)
Instagram
Yes	304 (56.2)	123 (58.0)	92 (67.2)	38 (45.8)	51 (46.8)	14.542 (df = 3)	0.002
No	237 (43.8)	89 (42.0)	45 (32.8)	45 (54.2)	58 (53.2)
Twitter
Yes	193 (35.7)	67 (31.6)	58 (42.3)	23 (27.7)	45 (41.3)	7.969 (df = 3)	0.047
No	348 (64.3)	145 (68.4)	79 (57.7)	60 (72.3)	64 (58.7)
KakaoStory
Yes	370 (68.4)	135 (63.7)	112 (81.8)	54 (65.1)	69 (63.3)	15.222 (df = 3)	0.002
No	171 (31.6)	77 (36.3)	25 (18.2)	29 (34.9)	40 (36.7)
Pinterest
Yes	44 (8.1)	16 (7.5)	13 (9.5)	3 (3.6)	12 (11.0)	3.909 (df = 3)	0.271
No	497 (91.9)	196 (92.5)	124 (90.5)	80 (96.4)	97 (89.0)
KakaoTalk
Yes	532 (98.3)	210 (99.1)	134 (97.8)	81 (97.6)	107 (98.2)	1.206 (df = 3)	0.752
No	9 (1.7)	2 (0.9)	3 (2.2)	2 (2.4)	2 (1.8)
Line
Yes	218 (40.3)	76 (35.8)	65 (47.4)	26 (31.3)	51 (46.8)	9.340 (df = 3)	0.025
No	323 (59.7)	136 (64.2)	72 (52.6)	57 (68.7)	58 (53.2)
WhatsApp
Yes	17 (3.1)	6 (2.8)	4 (2.9)	2 (0.4)	5 (0.9)	0.984 (df = 3)	0.805
No	524 (96.9)	206 (97.2)	133 (97.1)	81 (15.0)	104 (19.2)
WeChat
Yes	36 (6.7)	12 (5.7)	10 (7.3)	6 (7.2)	8 (7.3)	0.555 (df = 3)	0.907
No	505 (93.3)	200 (94.3)	127 (92.7)	77 (92.8)	101 (92.7)

). There were significant differences in Instagram (χ² = 14.542, df = 3, p = 0.002), Twitter (χ² = 7.696, df = 3, p = 0.047), KakaoStory (χ² = 15.222, df = 3, p = 0.002), and Line (χ² = 9.340, df = 3, p = 0.025) usage across clusters. However, the use of popular SNSs such as Facebook and KakaoTalk and relatively unfamiliar SNSs in Korea such as Pinterest, WhatsApp, and WeChat was not different between clusters. Cluster 2 used the most diverse SNSs. Cluster 1 showed a high level of Instagram use, while cluster 4 used Twitter and Line more. KakaoStory was the most popular SNS for all clusters. Therefore, this partially responds to RQ5, which questions behavioral differences between clusters based on consumer smartness.

4.4. Cluster Summary

On the basis of the previous results, profiles of the four clusters can be summarized as follows.

Go-getters: authentic smart consumers (cluster 2: n = 137, 25.3%). The consumers in this cluster had the highest level of smartness and behavioral intention, including shopping and sharing, among the four clusters. In detail, they showed opinion leadership and innovativeness when shopping for fashion products and were likely to disclose various aspects of their personal lives online. They were capable of handling various technologies related to shopping and were proficient in understanding marketing terminology and advertising intention with unfulfilled needs of current shopping. With an average age of 39.1 years, the majority of them were female (54.7%), highly educated (81% had a university degree or higher), employed, and earned more money than others. The respondents in this cluster had more small families consisting of one or two people (n = 42, 30.6%). They spent the most on clothing, shopped regularly via Internet/mobile and department stores rather than discount stores, visited online stores more frequently, and stayed there longer. When getting shopping information, they were active doers. More people visited brand sites, blogs, and review sites and asked store managers for additional information than those in the other clusters did. They were also vigorous in terms of sharing information. They shared information or experience through blogs, SNSs, or messages as well as wrote product reviews with photos. Interestingly, they used blogs and SNSs as sharing channels far more than those in the other clusters. They had more experience signing up for free product trials, entering idea challenges, suggesting solutions for retailers, and participating in group buying than others. In regard to social networking, the usage rates of Instagram and KakaoStory were prominently higher than in the other clusters, not to mention other SNSs such as Facebook, Twitter, and Line.

Socialites: sociable smart consumers (cluster 1: n = 212, 39.2%). This was the largest group of respondents. Socialites and Realists comprised intermediate groups between Go-getters and Shopping-pococurante with respect to consumer smartness. With similar characteristics in self-disclosure, marketing literacy, and sharing intention, both clusters also showed differences in many aspects. Socialites were more likely than Realists to be innovative and give leading opinions but less aware of current shopping problems and less familiar with the use of high-tech tools for shopping. The average age of this cluster was 39.6 years and there were more women (n = 114, 53.8%) than men. The majority were employed (63.7%), earned between USD 4 and 6 thousand monthly, and had large families with more than four people (52.4 %). Only 68.4% had a university degree or higher, which was the second lowest level across clusters. Although their shopping intention was relatively lower than Realists, they spent more money on apparel. More people regularly shopped at department stores and more frequently visited online stores than Realists did. They searched for shopping information more actively, shared their knowledge and experiences, and participated in marketing events more than Realists did. Facebook, Instagram, and KakaoStory were used by most of them.

Realists: pragmatic consumers (cluster 4: n = 109, 20.1%). Realists made up the middle group with Socialites in terms of consumer smartness. However, as opposed to Socialites, Realists were able to figure out marketers’ intent behind advertisements and were confident using technology for shopping. They also had needs that were unfulfilled by current products and services. Their shopping intention was higher but monthly expenditure on apparel was lower compared to Socialites. With an average age of 39.8 years, the majority were male (n = 59, 54.1), employed (n = 76, 69.7%), earned between USD 4 and 6 thousand monthly, and were highly educated. This cluster had the highest proportion of college degrees or higher (n = 99, 90.8%) among the four clusters. They mainly shopped for apparel online (n = 70, 64.2%) and in discount stores (n = 22, 20.2%), and shopped at department stores the least (n = 5, 4.65) among the four clusters. Compared to Socialites, they were less likely to visit online stores, but tended to stay longer. This cluster showed a similar pattern to others in searching for shopping information, but seemed to be the most passive in making inquiries to stores (n = 34, 31.2%). Their passive attitude appeared in sharing and interacting with other consumers or firms. They were more likely to write product reviews with photos but less likely to share these through SNSs, messengers, or blogs than Socialites, and they seemed to be indifferent to group buying, idea challenges, free trials, and giving advice to firms. They used Twitter and Line more than Socialites did. The interesting result about SNSs was that among the respondents, 27.2% of Pinterest users were Realists, although shopping is generally known as a priority activity for Pinterest users (Lipsman, 2019).

Shopping-pococurante: uninterested consumers (cluster 3: n = 83, 15.3%). Shopping-pococurante made up the smallest segment. They had the lowest scores in all dimensions of consumer smartness and showed the lowest level of shopping and sharing intentions across clusters. With the average age of 40.8 years, most indifferent shoppers were male (n = 43, 51.8%) and had a family with four or more members (n = 37, 44.5%). Their employment rate (n = 48, 57%) was the lowest among the four clusters, while more homemakers were in this cluster (n = 17, 20.5%). Their levels of monthly income and education were also the lowest. They spent the least on apparel and shopped more at discount stores than the other clusters. They visited online stores the least and left the soonest. These were the most passive consumers in information searching, experience sharing, and interacting with firms. Overall, they used SNSs less than other clusters. Facebook and KakaoTalk were the most popular SNSs for them.

5. Discussion

As retailing has evolved with technology, new channels, and new business models, consumers also have transformed by extending their activities and roles in the consumption dynamics. They are expected to be key consumers who are prosumers [100], product or service evaluators [101], information aggregators [102], brand supporters, and influencers at the same time [41,43]. They are different from the consumers that have previously received attention by researcher and marketers. Addressing the lack of consideration of multiple roles of a new consumer segment, this study aimed to discover who the smart consumers are in the digital consumption environment. Thus, this study classified consumer segments based on consumer smartness, consisting of six dimensions, and explored each segment’s profile in terms of demographic and behavioral characteristics. The results provide empirical support for the proposed new segment, smart consumers.

First, this study classified four consumer segments based on six dimensions of consumer smartness and summarized the profiles of each segment. Go-getters are authentic smart consumers, displaying the highest level of consumer smartness. Socialites are sociable smart consumers, accounting for the largest proportion of consumers. Realists are pragmatists who can read marketers’ intent and use technology for shopping Shopping-pococurante are indifferent to fashion shopping. There were significant differences across segments in terms of demographics (monthly income and education level), online behavioral intentions (shopping and sharing intention), shopping-related behaviors (monthly expenditure on fashion goods, shopping channels, searching, information sharing, and participation), and social network usage. This new segmentation is different from traditional typologies of unique consumers, such as adopter categories by Rogers with respect to consumer roles and the shopping environment. While adopter categories were based on the readiness to try out a new innovation or product, focusing on the role as adopters, smart consumer segmentation reflects their multiple roles in the digital retail environment. The result implies that the approach to the newly segmented consumers should be differentiated.

Contrary to previous studies that took a dichotomous approach to unique consumers [1,14,34,38], it is necessary to understand the characteristics of each segment and develop marketing strategies to meet their needs each by considering their levels of consumer smartness. For examples, given Go-getters’ activeness and influence, companies should concentrate their marketing resources on this segment [35]. Socialites, the largest segment, who seem to enjoy shopping, sharing, and interacting with others, but have relatively less dissatisfaction, may be the most attractive segment for marketers. Easy access to shopping platforms, diverse e-communities, and enjoyable events by sending links or QR codes are absolutely necessary for targeting Socialites [23,103]. In order to attract Realists, who read marketers’ intents, companies should tell the truth in advertising. Companies also need to provide enough information and special offers to help consumers stay longer in stores [104]. Marketers should focus on advertising or influencer-generated content that might interest Shopping-pococurante through the SNSs they often use [105].

Second, this study empirically proved that sub-dimensions of consumer smartness are appropriate for identifying smart consumers. For example, Go-getters showed the highest level in each sub-dimension of consumer smartness, whereas Shopping-pococurante had the lowest scores. Go-getters had the highest shopping and sharing intention and were most active online, but Shopping-pococurante were the least likely to shop and share and least active. Many studies have mentioned that the roles of opinion leadership, market mavenship, lead userness, and innovativeness were somewhat related [66,72,73,92,106], but dealt with them separately, stressing their differences. On the contrary, this study notes that today’s consumers have multiple traits that align with their multiple roles, as expected, and multi-dimensioned consumer smartness is a very useful criterion for today’s market segmentation.

6. Conclusions

The contributions of this study are twofold. First, this is the first study to explore smart consumers, extending the framework for understanding and approaching today’s consumers by providing a theoretical and empirical foundation to grasp smart consumers as a whole with various characteristics rather than partial characteristics, considering their multiple roles. Second, confirming the identity of smart consumers, this study proposes a new segmentation criterion and practical directions for targeting strategies in the digital consumption environment. The revealed characteristics of smart consumers will help marketers develop more effective segmenting and targeting strategies in order to involve smart consumers in their marketing territories.

Segmenting a market effectively and identifying characteristics of consumers from each segment correctly are critical parts of marketing that should precede the market choice for a successful business [36]. However, some limitations should be surmounted in the next step. First, despite South Korea’s high rate of technology penetration, the influence of the country’s unique consumption culture cannot be excluded. Future studies need to apply the smart consumer concept to other countries that might have a different culture of consumption or digital environment in order to reinforce the results. Second, this study focused on shopping for fashion products. How smart consumers appear in other product categories as opinion leaders or innovators needs to be investigated with various product categories. Third, this study covered shopping and sharing behaviors, but future study is expected to expand to more diverse behaviors such as value co-creation or evaluating behaviors that are mentioned as smart consumers do. Further investigations to understand smart consumers, for example, in terms of their personalities and values, should follow to develop more elaborate marketing strategies.

Correia suggests that retail will not disappear, but will be reborn into a new form with some of the current features it possesses [26]. What should be kept in mind is that smart consumers will be at the center.