Smart divide: Paradigm shift in digital divide in South Korea

Abstract

There is still much controversy about whether the broad dissemination of smart devices is closing or deepening the digital divide. However, it certainly is clear that the adoption of smart devices has fundamentally evolved the traditional digital divide. This research empirically identified the existence and factors making up the smart divide, which is the evolving digital divide generated by the use of smart devices. After analysing the effects of smart devices, it has identified that the smart divide is being generated among the traditional information-haves not only by external factors such as gender and economic status, but also by an internal factor which is the literacy of smart devices. The smart divide, instead of generating a dichotomic divide, rather is evolving a multilayered interdigitated segmentation within groups of people who own smart devices. In addition, it is a future-oriented divide because it is tied to the opportunities to get information through more information channels on smart devices.

Keywords

Digital divide digital literacy ICT smart device smart divide smart literacy

Introduction

The digital divide is traditionally defined as a gap between those who can get Internet access through information and communication technology (ICT) and those who cannot. People who own ICTs can get Internet access and have more opportunities to obtain more information. In contrast, people who do not own ICTs are likely to have more limited Internet access and therefore obtain less information. With the broad dissemination of smart devices such as smartphones and tablet PCs, however, more people now own ICTs than previously and so have Internet access that was hitherto restricted. Due to this change, some researchers have insisted that smart devices bridge the gap between traditional information-haves and have-nots and they thus can overcome the digital divide.

However, the digital divide itself is a complex social phenomenon and it may not be fully addressed by the adoption of devices. Rather, the dissemination of smart devices has started to redefine the digital divide, which is evolving into the smart divide. This new divide may happen within the same group of people who own ICTs because of the different levels of ability in utilizing the complicated informational functions of smart devices. In this context, the ability to use smart devices has become one of the factors generating a gap between members of society.

Although debates continue about whether smart devices make the digital divide wider or narrower, it is clear that smart devices have social influences and that they affect the information activities of people. From this perspective, the purpose of this research is to explore the existence of the smart divide and the factors that contribute to its generation. In order to verify the factors of the smart divide, this research focused on a specified group of university students with common characteristics in Seoul, South Korea. The reason for such a focus was the contention that the nascent smart divide may be generated within a group of people who own smart devices. By conducting a survey for the specified group, the smart divide generated by the use of smart devices will be empirically examined.

Digital divide

Concepts of the digital divide

The concept of the digital divide was used in the mid-1990s to describe the patterns of unequal access to information technology based on income, race, gender, age and geography (Mossberger et al., 2003: 1). The first generally accepted definition of the digital divide was provided by National Telecommunications & Information Adminis-tration (NTIA) in its first report in 1995, which defined it as the disconnection between the people who can get a new information technology and who cannot (NTIA, 1995:2). Since the first report of NTIA, many researchers have defined the digital divide from different perspectives. Most studies have focused on the differentials in Internet access and inequalities among different social groups segmented by socio-economic indicators, including gender, age, income, education level, etc. (Dijk, 2000; Loges and Jung, 2001; Howard et al., 2001). Although many indicators contribute to the generation of the digital divide, the major indicators of inequality in these studies were the possession of ICTs with Internet access and economic status that makes ICTs affordable for people.

With the shift from desktop computers to smart devices, however, the digital divide has evolved and become a complex concept generated by multiple social phenomena. ICT has been most often used to refer to desktop computers and their wired networks, but other digital equipment such as mobile devices are not ruled out by some users of the term (Kijk, 2008: 288). With the adoption of smart devices, the denotation of the digital divide has also changed from the difference between ICT users and non-users (a vertical divide) to the differences in the quality and intensity of use among ICT users (a horizontal divide) (Cho, 2004). The horizontal divide has become a new focus and leads to a new division among people. On the one side of the divide are groups of better-skilled users proficient in ICTs, and on the other are users with less skill.

However, this binary division into two groups is no longer applicable in the current information environment because of the rapid development and broad dissemination of a variety of ICTs (Hsieh, 2012). The traditional digital divide has evolved into a divide with multiple sides: an access divide, a skills divide and an economic opportunity divide (Mossberger et al., 2003: 2). Although these divides seem to be generated independently, they all revolve around the adoption of ICTs.

Evolution of the digital divide: Smart divide

Smart devices carry many advantages over desktop computers in that they are portable, they take advantage of wireless networks, and they have real-time access to the Web. With the broad dissemination of smart devices, more people are likely to own ICTs, especially in Western and Far-East Asian countries. Research by the PEW Research Center found that the use of the Internet on mobile devices in the US is on the rise. From 2009 to 2012, the percentage of adults who used mobile devices for Web browsing increased by 28% (Duggan and Smith, 2013). In South Korea, one of the most technologically advanced countries, one study showed that the Internet penetration rate with mobile devices for traditional information-have-nots in 2013 went up to 50.8% (National Information Society Agency, 2013). These studies indicate that mobile devices have been distributed without regard to gender, age, education level and economic status, so that the divide along the lines of adoption of devices is no longer such a divisive issue. In addition, the emergence of smart devices such as smartphones and tablet PCs has led to Web usage via those devices that is far more intense than that via older mobile devices. With more powerful informational functions equipping operating systems, smart devices allow people to access the Web without any time or space limitations through the wireless network environment.

Although the broad dissemination of smart devices has many positive implications in terms of physical access to the Web, it also implies another social consideration: digital literacy. Because of the saturation of smart devices, the ability to use them has more relevance than the adoption of the devices. In this context, the concept of the digital divide can be divided into two generations: the first and the second generation, which has shifted from quantitative adoption to qualitative digital literacy with regard to smart devices (Hargittai, 2002).

The adoption of ICTs was important in the first generation of the digital divide because they gave people Web access. In this stage, the digital divide refers to the polarization of people into two different groups: information-haves and have-nots. This polarization may be affected by the external indicators, including gender, age, economic status and education level. As ICTs have been more broadly disseminated, however, many people now own various smart devices connected to wireless network. At this stage, the first generation of digital divide no longer occupies a dominant position. Rather, literacy of the devices has more bearing in getting multiple channels to information (Correa, 2010). However, there may be intellectual differences in digital literacy among the users of the devices. This is where the second generation of the digital divide arises. It may mainly be affected by internal indicators such as the ability or skill in utilizing the complex informational functions of the devices. This is a qualitative divide.

From this perspective, a substantial number of research studies have empirically documented the disparities in the digital literacy of ICT users. Livingstone and Helsper (2010) insisted that more skilled users were likely to engage in more types of online activities with greater frequency and intensity than those with fewer skills. Other researchers also addressed the fact that digital skill has differentiated Internet users’ opportunities to enhance their digital literacy (Gui and Argentin, 2011: 977; Stern et al., 2009: 413).

However, the second generation of the digital divide is still affected by the adoption of the devices. According to statistics provided by PEW Research Center, the traditional information-have-nots with lower than $30,000 of annual income in the US show increased use of wireless networks over wired. In contrast, the traditional information-haves have increased their use of both wireless and wired networks (Zickuhr and Smith, 2012). This implies that the traditional information-haves own both desktop computers and smart devices and therefore have more opportunities to access information, whereas the have-nots may rely mostly on smart devices. From this perspective, the dissemination of smart devices may actually deepen the first generation of the digital divide. It also simultaneously generates the second generation of the digital divide, which is a qualitative divide depending on the adoption and the skill in using the devices. Because this evolved and expanded digital divide is influenced by the unique characteristics of smart devices, it can be termed a smart divide. The smart divide can be defined as a qualitative divide determined by the intellectual and skill gap, or smart literacy, within groups of smart device users themselves. It may be generated not only by unequal adoption of smart devices but also by an imbalance of smart literacy.

In this context, some researchers argue that it is necessary to evaluate the digital divide in terms of the effective adoption of the ICTs and their impact (Hilbert, 2011). The Greenlining Institute (2009) suggested that the use of ICTs mirrors the patterns of digital inequality rather than a simple digital divide.

However, there is little research that identifies the evolution of the digital divide or the existence of the smart divide. Most research has focused on the statistical rates of the dissemination of smart devices, especially smartphones. For this reason, this research investigated the evolution of the digital divide affected by the use of smart devices. It also empirically verifies whether the adoption of smart devices makes a difference in the information activities of people and whether there are any internal or external factors that affect the ability to use smart devices.

Methodology

The goal of this study was to explore the existence of a smart divide and the factors that affect its generation. In order to achieve this goal, the variables that affect the adoption of smart devices were identified and there was an analysis of how smart devices affect the digital divide. Statistical analysis was applied to verify the various aspects of the smart divide based on the data collected by a survey questionnaire.

This research mainly focuses on three issues that may affect the evolution of the digital divide. First, the adoption of ICTs, such as desktop computers and smart devices, was investigated. The correlations between the adoptions of these devices were also considered. Second, whether or not there were differences in the use of ICTs among the information-haves was considered. Third, the existence of the smart divide was investigated by identifying whether there were any differences in smart literacy among information-haves. By combining the results of these three issues, the existence of the smart divide and the factors that affect the generation of the smart divide were explored. Statistical analysis, including Pearson’s correlation analysis and multiple regression analysis, was applied in the verification of various aspects of the smart divide based on the data collected.

Data collection

Smart devices are relatively new tools that may require some degree of knowledge in order to be utilized to the full. Therefore, the ratio of people who have successfully adopted smart devices may be higher among younger people: digital natives. In addition, students who are currently enrolled in universities are expected to seek a broad range of information more actively, in comparison to general users. For these reasons, this research limited the major target of the questionnaires to undergraduate students who are currently enrolled in universities in Seoul, South Korea. It is appropriate to limit the range of the subjects because the smart divide may be generated within the same group of people who own smart devices.

A total of 291 questionnaires were distributed by hand from 7 March to 21 March 2014. Among the distributed questionnaires, 23 submissions were eliminated because they were either incomplete or illegible. A total of 268 questionnaires were used for the analysis.

Variables and data analysis

The questionnaire consists of three phases: adoption of smart devices, information activities on smart devices, and literacy in relation to the devices. The specifics making up each phase were set as dependent variables. These are internal indicators of the digital divide and include the adoption of smart devices, Internet access, the number and content of applications installed on smart devices, and the proficiency of using smart devices. In addition, traditional indicators of the digital divide such as gender, age, economic status and education level were also considered. These are external indicators of the digital divide and defined as independent variables. Since the subjects were undergraduate students aged 20 to 29, education level and age differences were not considered. In addition, an operational definition was used for the indicator of economic status, defined as the total annual income of the participant’s family unit because most students are economically dependent on their parents.

Once all of the questionnaires were collected, the Statistical Package for the Social Sciences (SPSS) 19.0 for Windows was used to analyse the collected data set. This research used the Pearson’s correlation method to investigate the relationships among variables. In addition, multiple regression analysis was used to examine which of the independent variables can help to predict the variance in the adoption of smart devices and in information activities performed using smart devices.

In terms of reliability, the internal consistency reliability in the collected data set showed an acceptable level of reliability (Cronbach’s alpha = .653).

Findings

Socio-demographic statistics

The participants in the questionnaire consisted of 166 (61.9%) males and 102 (38.1%) females. In regard to the economic status of the participants, the survey results indicated that economic status was comparatively evenly distributed (see Table 1).

Table 1.

Socio-demographic data of the participants.

Gender	Frequency (N)	Percentage (%)
Male	166	61.9
Female	102	38.1
Total	268	100.0
Annual Income ($)	Frequency (N)	Percentage (%)
Under 20,000	73	27.2
20,000–40,000	75	28.0
40,000–60,000	59	22.0
60,000–80,000	34	12.7
Above 80,000	25	9.3
Missing values	2	0.7
Total	268	100.0

Adoption of smart devices and Internet access

Most participants (N=267, 99.6%) owned smartphones with wireless network access capabilities. Only one participant owned a feature phone with wireless network access (N=1, 0.4%). Approximately half the participants (N=151, 56.3%) owned only one smart device, whereas 116 participants (43.3%) had adopted at least two smart devices, including tablet PCs and ebook readers (see Table 2). With regard to the adoption of ICTs, 265 participants (98.9%), including the feature phone user, adopted both desktop computers and smart devices. Only three participants (1.1%) owned only smart devices but not a desktop computer.

Table 2.

Number of smart devices.

Number of smart devices	Frequency (N)	Percentage (%)
1	151	56.3
2	70	26.1
3	30	11.2
4	11	4.1
5	5	1.9
Missing values	1	0.4
Total	268	100.0

With regard to Internet access, all participants (N=268) could access the Internet, regardless of whether they owned a desktop computer or a smart device. The participants who used the Internet on a regular basis reached 197 (73.5%), whereas 23 participants (8.6%) did not access the Internet often (see Table 3).

Table 3.

Internet access on a regular basis.

Internet access on a regular basis	Frequency (N)	Percentage (%)
Strongly disagree	4	1.5
Disagree	19	7.1
Neither agree nor disagree	48	17.9
Agree	53	19.8
Strongly agree	144	53.7
Missing values	0	0.0
Total	268	100.0

Based on these results, it is assumed that the adoption of ICTs and Internet access, which are the major indicators of the digital divide, have been addressed in this group by the broad dissemination of desktop computers and smart devices. In spite of the diversification of devices, however, there may be differences between traditional information-haves and have-nots in terms of the quantitative adoption of devices. In order to identify whether the digital divide still exists in the adoption of devices, Pearson’s Correlation analysis was conducted among the traditional indicators of the digital divide (see Table 4).

Table 4.

Correlations among gender, economic status, adoption of devices and Internet access.

Correlations
		Gender	Economic status	Number of devices	Internet access
Gender	Pearson Correlation	1	.012	.130^*	−.201^**
	Sig. (2-tailed)		.842	.034	.001
	N	268	266	267	268
Economic status	Pearson Correlation	.012	1	.126^*	.065
	Sig. (2-tailed)	.842		.041	.291
	N	266	266	265	266
Number of devices	Pearson Correlation	.130^*	.126^*	1	.328^**
	Sig. (2-tailed)	.034	.041		.000
	N	267	265	267	267
Internet access	Pearson Correlation	−.201^**	.065	.328^**	1
	Sig. (2-tailed)	.001	.291	.000
	N	268	266	267	268

Correlation is significant at the 0.05 level.

**.

Correlation is significant at the 0.01 level.

First, Table 4 shows that male students tended to have more devices than female students (r = .130). Therefore, gender still affects the adoption of devices. In contrast, the result shows a negative linear relationship between gender and the use of the Internet (r = -.201), which means that female students used the Internet more than their male counterparts. This result shows that gender still affects the adoption of devices and the use of the Internet, although the effects appear not to be along the same lines as the traditional digital divide.

Second, the results with regard to economic status indicated that family income also still affects the adoption of smart devices (r = .126). Economic status was positively correlated with the number of devices adopted. In contrast, there was no correlation between economic status and Internet access. This means that most people can get Internet access regardless of their economic status. Although Internet access, which is one of the major indicators of the digital divide, is addressed by the broad dissemination of smart devices, there is still a divide in adoption of devices that is affected by economic status.

Another result shows that the participants tended to access the Internet more often if they had more devices (r = .328). This is somewhat different from the traditional digital divide which is a dichotomic divide between information-haves and have-nots. The number of devices generates a different dimension of divide: multi-layered divides even within the same group who own ICTs and have Internet access.

Synthetically, the indicators of gender and economic status have effects on the digital divide in a different dimension. They now generate a different type of gap within the same group of information-haves: more device-haves and less device-haves. This is a new stage of the digital divide generated by the dissemination of smart devices.

Information activities with smart devices

The digital divide is addressed to a certain extent in this group because most people now own ICTs. It is a quantitative divide focusing on the number of devices and Internet access based on the adoption of ICTs. However, some indicators of the digital divide still have an impact in generating a different dimension of the digital divide.

People may need some degree of knowledge in utilizing the informational functions of smart devices. Depending on ability to utilize the devices, there might be a gap within the same group of people who have adopted smart devices, which is a qualitative digital divide. If an individual knows how to utilize various informational functions of smart device, s/he may have more opportunities to conduct more information activities. In this sense, the ability to utilize smart devices has more of a bearing on device adoption.

Considering information activities, the number of applications the participants used on their smart devices was investigated. Although the applications are often simple software, they can be a measure of the level of utilizing the devices and the degree of information activities on the devices. In order to verify the information activities, the types of applications installed on smart devices and how many applications the participants used often were analysed. By focusing on information activities, this research eliminated the applications related to games. The result is shown in Table 5.

Table 5.

Number of applications on smart devices.

Number of applications	Frequency (N)	Percentage (%)
1–3	36	13.4
4–6	46	17.2
7–9	23	8.6
10–12	77	28.7
More than 13	84	31.3
Missing values	2	0.7
Total	268	100.0

As shown in Table 5, 84 participants (31.3%) usually used more than 13 applications on their smart devices. Of the participants 77 (28.7%) responded that they often used 10 to 12 applications. In contrast, 36 participants (13.4%) often used one to three applications. This result shows that the participants were relatively evenly distributed from the perspective of the quantitative use of the applications on smart devices.

However, the number of applications does not clearly show their qualitative information activities. In order to investigate the specific activities on smart devices, the applications used most by participants was also analysed. The content of applications varies from the traditional functions such as Internet access and instant messenger services to smart device-specific functions, including mobile banking, Social Network Service (SNS), location services, entertainment and educational purposes (see Table 6).

Table 6.

Contents of applications on smart devices.

Contents of applications	Frequency (N)	Percentage (%)
Internet access	35	13.1
(Instant) messenger service	39	14.6
Mobile banking	10	3.7
Information (news, job, etc.)	58	21.6
Social network service	48	17.9
Location services	15	5.6
Podcasting	28	10.4
Product information (QR code)	12	4.5
Email	11	4.1
Other	12	4.5
Total	268	100.0

Among the participants 58 (21.6%) responded that they used smart devices to get various kinds of information, including news information. SNS (N=48, 17.9%), Internet access (N=35, 13.1%), instant messenger service (N=39, 14.6%) were also used a lot on smart devices. Besides these, mobile banking, including mobile billing and payment services (N=10, 3.7%), and location services such as navigation systems, map services and transportation information (N=15, 5.6%) were also used. Other applications included entertainment such as music, movie, photos, document-related activities and ebooks.

Although most participants used a variety of applications, there were differences between males and females in the applications they preferred. Females tended to use the Internet and SNS to communicate with other people, whereas males often used informational purpose contents, including news information, podcast, video-sharing websites and ebooks.

However, it may be possible that these results could be affected by other external indicators such as economic status. In order to verify whether other indicators affected the results, multiple regression analysis was conducted (see Table 7).

Table 7.

Effect of external factors on the number of applications.

Coefficients^a
Model		Unstandardized coefficients		Standardized coefficients	t	Sig.	Collinearity statistics
Model		B	Std. Error	Beta	t	Sig.	Tolerance	VIF
1	(Constant)	2.702	.311		8.688	.000
	Gender	.310	.179	.106	1.732	.085	1.000	1.000
	Income	.144	.068	.129	2.106	.036	1.000	1.000

Dependent variable: number of applications.

The result shows that gender did not significantly increase or decrease the number of applications (p-value = .085, p>.05). In contrast, economic status appeared to have had a strong positive effect on the number of applications used (p-value = .036, p<.05). Due to this, it is assumed that the higher the family income was, the more applications the participants used. In addition, with regard to the result in Table 4, people who owned more smart devices tended to utilize more functions of smart devices, which resulted in more information activities. Therefore, economic status is generating a different dimension of divide from the perspective of information activities on smart devices.

Digital literacy with smart devices: Smart divide

It may require some degree of knowledge in order to fully utilize the informational functions of smart devices due to their complicated characteristics. If one knows how to utilize its various functions, the device can provide multiple channels and more opportunities to get a broad range of information. This is where a more complex divide is generated by the different levels of smart literacy.

Smart literacy, which is the ability of how to utilize the informational functions of smart devices, does have more meaning than digital literacy because smart literacy may divide people into another two groups within the same group of the traditional information-haves: one is the group who are skilled in utilizing smart devices and the second is those who are not. From this perspective, this research identified whether there was any gap related to smart literacy within the group of people who owned smart devices. The analysis consisted of two phases: how proficiently people used smart devices and how they learnt to use them.

From the perspective of proficiency of utilizing smart devices, 149 participants (55.6%) responded that they were skilled users of smart devices and utilized most of the informational functions on their devices. In contrast, 97 participants (36.2%) felt that they were not proficient in utilizing the functions of smart devices (see Table 8).

Table 8.

Proficiency in utilizing smart devices.

Proficiency in utilizing smart devices	Frequency (N)	Percentage (%)
Strongly disagree	41	15.3
Disagree	56	20.9
Neither agree nor disagree	22	8.2
Agree	73	27.2
Strongly agree	76	28.4
Total	268	100.0

As shown in Table 8, smart device users were arranged into two groups based on proficiency: one could proficiently utilize various functions of smart devices and another could not. This result implies that there is a new dimension of divide generated by smart literacy.

Smart literacy may depend on the skills in utilizing various functions of smart devices. However, many smart device-specific applications are distributed every day with more powerful functionalities. Therefore, people need to quickly learn how to use these new state-of-the-art functions. From this perspective, how people learnt how to use the new informational functions of smart devices was analysed (see Table 9).

Table 9.

Self-education in the functions of smart devices.

	Frequency (N)	Percentage (%)
Strongly disagree	13	4.9
Disagree	48	17.9
Neither agree nor disagree	76	28.4
Agree	68	25.4
Strongly agree	60	22.4
Missing values	3	1.1
Total	268	100.0

The result showed that 128 participants (47.8%) were self-educated in using the new functions of smart devices. In contrast, 61 participants (22.8%) tended to focus on using the applications or functions that they were already familiar with. Further, some learnt how to use the new functions by referring to manuals or instructions.

As shown in Table 9, there might be a learning method polarization for users of smart devices. It is assumed that the autodidactic group can acquire more knowledge in a short period of time so that they can get more opportunities to get information.

Although self-education can be considered an indicator that affects smart literacy, there might be other indicators that also affect the literacy of smart devices. From this perspective, multiple regression analysis was conducted in order to identify whether external indicators of digital divide also affect the generation of differences in smart literacy (see Table 10).

Table 10.

Effect of external factors on smart literacy.

Coefficients^a
Model		Unstandardized coefficients		Standardized coefficients	t	Sig.	Collinearity statistics
Model		B	Std. error	Beta	t	Sig.	Tolerance	VIF
1	(Constant)	2.371	.379		6.255	.000
	Gender	.650	.221	.181	2.944	.004	1.000	1.000
	Economic status	.138	.084	.101	1.645	.101	1.000	1.000

Dependent variable: proficiency of smart devices.

As shown in Table 10, the unstandardized coefficient for gender is equal to .650, which indicates that males tended to be more proficient than females. This means that gender is correlated with differences in the level of proficiency (p-value = .004, p<.05). In contrast, economic status does not have significant meaning (p-value = .101, p>.05). From this result, it is identified that economic status does not affect the proficiency in using smart devices once people own them.

In regard to the level of self-education, the unstandardized coefficient is equal to -.571 (see Table 11), meaning that females tended to learn how to use the devices unaided (p-value = .028).

Table 11.

Effect of external factors on smart literacy.

Coefficients^a
Model		Unstandardized coefficients		Standardized coefficients	t	Sig.	Collinearity statistics
Model		B	Std. error	Beta	t	Sig.	Tolerance	VIF
1	(Constant)	3.995	.446		8.949	.000
	Gender	−.571	.256	−.176	−2.225	.028	.999	1.001
	Economic Status	−.061	.101	−.048	−.603	.547	.999	1.001

Dependent variable: self-education.

Associating these results with information activities (see Table 6), males used smart devices more for informational purposes so that they tended to learn each function in detail. Therefore, it may take more time for them to acquire skill in using the devices, but they become more proficient if they keep using the devices. In contrast, females tended to use the devices for entertainment and communication purpose such as SNS and instant messenger services, so they did not need to learn in-depth knowledge for utilizing the devices. However, they felt that they were not proficient in the informational functions of the devices. As a result, gender affects the literacy of smart devices, which is not a clear dichotomic divides, but rather an interdigitated segmentation of people.

Taken together, economic status is still an indicator in the generation of the gap in adopting smart devices. However, people can use various functions of the devices to satisfy their purpose regardless of their economic status once they adopt smart devices. In regard to gender, there is no clear distinction between males and females in adoption of smart devices. Instead, gender influences differences in smart literacy. Males often use the applications for informational purposes, so that they need to acquire relatively higher levels of skill in utilizing smart devices, which is connected to the higher levels of proficiency in using the devices. In contrast, females tend to use the devices more for communication purposes, which may not actually require in-depth knowledge in utilizing the corresponding functions. These differences may generate a gap in taking more opportunities to get information. From this perspective, smart literacy is not restricted to the current use of the devices, but is instead expanded to the potential information activities in the future.

Discussion and conclusion

The traditional digital divide has clearly polarized society into two groups: those who have ICTs and those who do not. The adoption of ICTs such as desktop computers was an important indicator in generating the digital divide. With the broad dissemination of smart devices, however, there is no clear border of digital divide. Rather, the traditional indicators of digital divide, such as gender and economic status, are interconnected and they facilitate the second generation of digital divide. At this stage, the digital divide has more of a bearing on how many opportunities to get information people have and how proficiently they use smart devices than on how much information they have. Therefore, the concept of the digital divide has shifted from the adoption of ICTs to literacy of smart devices. Smart literacy, which refers to the ability to use smart devices, is now generating the evolved digital divide, which might now be more appropriately termed the smart divide, as a result of the shift. The smart divide is generated not only by external indicators such as gender and economic status, but also by the internal indicator of smart literacy.

These new characteristics of the smart divide are identified within the same group of the traditional information-haves. External indicators such as economic status still affect the generation of the smart divide in the adoption of smart devices. Once people adopt the devices, however, economic status does not affect the divide. In contrast, internal indicators such as smart literacy are affecting the generation of the smart divide within the same group of people who adopt the devices.

The smart divide is not a dichotomic divide, but a multi-layered interdigitated segmentation within the group of people who own smart devices. Although the traditional digital divide is a quantitative divide determined by the adoption of ICTs, the smart divide is a qualitative divide determined by the intellectual ability to use smart devices. In addition, the traditional digital divide is based on the results of the polarization among members of society, whereas the smart divide is more like a future-oriented divide because it focuses on the opportunities to get information through more information channels on smart devices. From this perspective, people who have more information channels will be able to acquire more information.

The results of this research may not be generalized because of the geographically limited range of the social groups to South Korea. Even so, we are now experiencing a more complex and evolved digital divide that fractionizes people by their imbalanced opportunities to get information. If the social group used in this research is to be generalized to smart device users, not limited to undergraduate students, the gap is expected to be wider and deeper. Although most people can access the Web using various types of devices, there are still gaps in utilizing the devices and opportunities taken to get information. It is not just a social phenomenon, but a social problem. Therefore, effective approaches to close the smart divide may be contemplated. This would constitute an effort to provide a balanced and equal distribution of information.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This Research was supported by the Sookmyung Women’s University Research Grants (1-1403-0151).

Author biography

Seugnmin Lee is an assistant professor, Department of Library and Information Science, Sookmyung Women’s University at Seoul, South Korea. Lee has a PhD in Information Science from the School of Library and Information Science at Indiana University at Bloomington. His research interests include information organization, metadata, ontologies, information architecture, digital divide and social informatics.

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